Repository: ncullen93/pyBN
Branch: master
Commit: fe5c49d6656d
Files: 121
Total size: 2.7 MB

Directory structure:
gitextract_62n8x0uq/

├── .gitignore
├── License.txt
├── Readme.md
├── data/
│   ├── andes.bif
│   ├── asia.bif
│   ├── bde_test.bn
│   ├── cancer.bif
│   ├── cmu.bn
│   ├── earthquake.bn
│   ├── flags/
│   │   ├── README.txt
│   │   ├── flags-test.arff
│   │   ├── flags-train.arff
│   │   ├── flags.arff
│   │   └── flags.xml
│   ├── flags.txt
│   ├── gs_data.txt
│   ├── letter.csv
│   ├── lizards.csv
│   ├── munin.bif
│   ├── mushroom.csv
│   ├── scale.csv
│   ├── simple.bn
│   ├── student.bn
│   ├── test.bn
│   ├── urn.bif
│   └── win95pts.bif
├── examples/
│   ├── Drawing.ipynb
│   ├── FactorOperations.ipynb
│   ├── MAP Inference as Optimization.ipynb
│   ├── Marginal_Inference.ipynb
│   └── ReadWrite.ipynb
├── pyBN/
│   ├── __init__.py
│   ├── classes/
│   │   ├── __init__.py
│   │   ├── _tests/
│   │   │   ├── __init__.py
│   │   │   ├── test_bayesnet.py
│   │   │   └── test_factor.py
│   │   ├── bayesnet.py
│   │   ├── cliquetree.py
│   │   ├── clustergraph.py
│   │   ├── empiricaldistribution.py
│   │   ├── factor.py
│   │   └── factorization.py
│   ├── classification/
│   │   ├── __init__.py
│   │   ├── classification.py
│   │   └── feature_selection.py
│   ├── inference/
│   │   ├── __init__.py
│   │   ├── _tests/
│   │   │   ├── __init__.py
│   │   │   ├── test_map_exact.py
│   │   │   ├── test_marginal_approx.py
│   │   │   └── test_marginal_exact.py
│   │   ├── map_exact/
│   │   │   ├── __init__.py
│   │   │   ├── ilp_map.py
│   │   │   └── ve_map.py
│   │   ├── marginal_approx/
│   │   │   ├── __init__.py
│   │   │   ├── forward_sample.py
│   │   │   ├── gibbs_sample.py
│   │   │   ├── loopy_bp.py
│   │   │   └── lw_sample.py
│   │   └── marginal_exact/
│   │       ├── __init__.py
│   │       ├── exact_bp.py
│   │       └── ve_marginal.py
│   ├── io/
│   │   ├── __init__.py
│   │   ├── _tests/
│   │   │   ├── __init__.py
│   │   │   ├── test_reading.py
│   │   │   └── test_writing.py
│   │   ├── read.py
│   │   └── write.py
│   ├── learning/
│   │   ├── __init__.py
│   │   ├── parameter/
│   │   │   ├── __init__.py
│   │   │   ├── _tests/
│   │   │   │   └── __init__.py
│   │   │   ├── bayes.py
│   │   │   └── mle.py
│   │   └── structure/
│   │       ├── __init__.py
│   │       ├── _tests/
│   │       │   ├── __init__.py
│   │       │   ├── test_chow_liu.py
│   │       │   ├── test_grow_shrink.py
│   │       │   ├── test_orient_edges.py
│   │       │   └── test_pc.py
│   │       ├── constraint/
│   │       │   ├── __init__.py
│   │       │   ├── fast_iamb.py
│   │       │   ├── grow_shrink.py
│   │       │   ├── iamb.py
│   │       │   ├── lambda_iamb.py
│   │       │   └── path_condition.py
│   │       ├── exact/
│   │       │   ├── __init__.py
│   │       │   └── gobnilp.py
│   │       ├── hybrid/
│   │       │   ├── __init__.py
│   │       │   ├── mmhc.py
│   │       │   └── mmpc.py
│   │       ├── mdbn.py
│   │       ├── naive/
│   │       │   ├── TAN.py
│   │       │   ├── __init__.py
│   │       │   └── naive_bayes.py
│   │       ├── score/
│   │       │   ├── __init__.py
│   │       │   ├── bayes_scores.py
│   │       │   ├── hill_climbing.py
│   │       │   ├── info_scores.py
│   │       │   ├── random_restarts.py
│   │       │   └── tabu.py
│   │       └── tree/
│   │           ├── __init__.py
│   │           └── chow_liu.py
│   ├── plotting/
│   │   ├── __init__.py
│   │   └── plot.py
│   └── utils/
│       ├── __init__.py
│       ├── _tests/
│       │   ├── __init__.py
│       │   ├── test_independence_tests.py
│       │   ├── test_markov_blanket.py
│       │   ├── test_orient_edges.py
│       │   └── test_random_sample.py
│       ├── class_equivalence.py
│       ├── data.py
│       ├── discretize.py
│       ├── graph.py
│       ├── hybrid_distance.py
│       ├── independence_tests.py
│       ├── markov_blanket.py
│       ├── orient_edges.py
│       ├── parameter_distance.py
│       ├── random_sample.py
│       └── structure_distance.py
└── setup.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
*.DS_Store
*.pyc
.ipynb_checkpoints/
*.ipynb_checkpoints/
*.bkbn
.spyderworkspace
.spyderproject

# setup.py working directory
build
# sphinx build directory
doc/_build
# setup.py dist directory
dist
# Egg metadata
*.egg-info
.eggs
pyBN.egg-info

================================================
FILE: License.txt
================================================
The MIT License (MIT)

Copyright (c) 2016, Nicholas Cullen <ncullen.th@dartmouth.edu>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

================================================
FILE: Readme.md
================================================

NOTE: I wrote this code to go along with Daphne Koller's book and no longer
maintain the repository, although the code should be easily adaptable.

<h1>Bayesian Networks in Python</h1>

<h2>Overview</h2>
This module provides a convenient and intuitive interface for reading, writing, plotting, performing inference, parameter learning, structure learning, and classification over Discrete Bayesian Networks - along with some other utility functions. There seems to be a lack of many high-quality options for BNs in Python, so I hope this project will be a useful addition.

I am a graduate student in the Di2Ag laboratory at Dartmouth College, and would love to collaborate on this project with anyone who has an interest in graphical models - Send me an email at ncullen.th@dartmouth.edu. If you're a researcher or student and want to use this module, I am happy to give an overview of the code/functionality or answer any questions.

For an up-to-date list of issues, go to the "issues" tab in this repository. Below is an updated list of features, along with information on usage/examples:


<h2>Current features</h2>

<h4>Marginal Inference</h4>
- Exact Marginal Inference
	- Sum-Product Variable Elimination 
	- Clique Tree Message Passing
- Approximate Marginal Inference
	- Forward Sampling  
	- Likelihood Weighted Sampling
	- Gibbs (MCMC) Sampling
	- Loopy Belief Propagation

<h4>MAP/MPE Inference</h4>
- Exact MAP Inference
	- Max-Product Variable Elimination
	- Integer Linear Programming
- Approximate MAP Inference
	- LP Relaxation

<h4>Constraint-Based Structure Learning</h4>
- Algorithms
	- PC
	- Grow-Shrink
	- IAMB/Lambda-IAMB/Fast-IAMB
- Independence Tests
	- Marginal Mutual Information
	- Conditional Mutual Information
	- Pearson Chi-Square

<h4>Score-Based Structure Learning</h4>
- Algorithms
	- Greedy Hill Climbing
	- Tabu Search
	- Random Restarts
- Scoring Functions
	- BIC/AIC/MDL
	- BDe/BDeu/K2

<h4>Tree-Based Structure Learning</h4>
- Naive Bayes
- Tree-Augmented Naive Bayes
- Chow-Liu

<h4>Hybrid Structure Leanring</h4>
- MMPC
- MMHC

<h4>Exact Structure Learning</h4>
- GOBNILP Solver

<h4>Parameter Learning</h4>
- Maximum Likelihood Estimation
- Dirichlet-Multinomial Estimation

<h4>Classification</h4>
- Naive Bayes
- Tree-Augmented Naive Bayes
- General DAG

<h4>Multi-Dimensional Classification</h4>
- Empty/Tree/Polytree/Forest
- General DAG

<h4>Comparing Two Bayesian Networks</h4>
- Structure-Based Distance Metrics
	- Missing Edges
	- Extra Edges
	- Incorrect Edge Orientation
	- Hamming Distance
- Parameter-Based Distance Metrics
	- KL-Divergence and JS-Divergence
	- Manhattan and Euclidean
	- Hellinger
	- Minkowski

<h4>Utility Functionality</h4>
- Determine Class Equivalence
- Discretize continuous data 
- Orient a PDAG
- Generate random sample dataset from a BN
- Markov Blanket operations

I previously wrote a Python wrapper for the GOBNILP project - a state-of-the-art integer programming solver for Bayesian network structure learning that can find the EXACT Global Maximum of any score-based objective function. It also links to CPLEX for incredible speed.
The wrappers can be found in the "pyGOBN" project at www.github.com/ncullen93/pyGOBN. For an overview of GOBNILP or to see its
great benchmarks on even the most massive datasets, visit https://www.cs.york.ac.uk/aig/sw/gobnilp/.


<h2>Examples</h2>
This package includes a number of examples to help users get acquainted with the intuitive syntax and functionality of pyBN. For an updated list of examples, check out the collection of ipython notebooks in the "examples" folder located in the master directory.

Here is a list of current examples:
- ReadWrite : an introduction to reading (writing) BayesNet object from (to) files, along with an overview of the attributes and data structures inherit to BayesNet objects.
- Drawing : an introduction to the drawing/plotting capabilities of pyBN with both small and large Bayesian networks.
- FactorOperations : an introduction to the Factor class, an exploration of the numerous attributes belonging to a Factor in
pyBN, an overview of every Factor operation function at the users' hands, and a short discussion of what makes Factor operations
so fast and efficient in pyBN.

<h2>Usage</h2>
Getting up-and-running with this package is simple:

1. Click "Download ZIP" button towards the upper right corner of the page.
2. Unpack the ZIP file wherever you want on your local machine. You should now have a folder called "pyBN-master"
3. In your python terminal, change directories to be IN pyBN-master. Typing "ls" should show you "data", "examples" and "pyBN" folders. Stay in the "pyBN-master" directory for now!
4. In your python terminal, simply type "from pyBN import ". This will load all of the module's functions, classes, etc.
5. You are now free to use the package! Perhaps you want to start by creating a BayesNet object using "bn = BayesNet()" and so on.

<h4>Unit Tests</h4>
If you want to test the functionality to make sure it all works on your local machine, navigate to the pybn-master directory and run the following command from the normal command-line (NOT ipython console):
- python -m unittest discover



================================================
FILE: data/andes.bif
================================================
network unknown {
}
variable GOAL_2 {
  type discrete [ 2 ] { false, true };
}
variable SNode_3 {
  type discrete [ 2 ] { false, true };
}
variable SNode_4 {
  type discrete [ 2 ] { false, true };
}
variable SNode_5 {
  type discrete [ 2 ] { false, true };
}
variable SNode_6 {
  type discrete [ 2 ] { false, true };
}
variable SNode_7 {
  type discrete [ 2 ] { false, true };
}
variable DISPLACEM0 {
  type discrete [ 2 ] { false, true };
}
variable RApp1 {
  type discrete [ 2 ] { false, true };
}
variable GIVEN_1 {
  type discrete [ 2 ] { false, true };
}
variable RApp2 {
  type discrete [ 2 ] { false, true };
}
variable SNode_8 {
  type discrete [ 2 ] { false, true };
}
variable SNode_9 {
  type discrete [ 2 ] { false, true };
}
variable SNode_10 {
  type discrete [ 2 ] { false, true };
}
variable SNode_11 {
  type discrete [ 2 ] { false, true };
}
variable SNode_12 {
  type discrete [ 2 ] { false, true };
}
variable SNode_13 {
  type discrete [ 2 ] { false, true };
}
variable SNode_14 {
  type discrete [ 2 ] { false, true };
}
variable SNode_15 {
  type discrete [ 2 ] { false, true };
}
variable SNode_16 {
  type discrete [ 2 ] { false, true };
}
variable SNode_17 {
  type discrete [ 2 ] { false, true };
}
variable SNode_18 {
  type discrete [ 2 ] { false, true };
}
variable SNode_19 {
  type discrete [ 2 ] { false, true };
}
variable NEED1 {
  type discrete [ 2 ] { false, true };
}
variable SNode_20 {
  type discrete [ 2 ] { false, true };
}
variable GRAV2 {
  type discrete [ 2 ] { false, true };
}
variable SNode_21 {
  type discrete [ 2 ] { false, true };
}
variable VALUE3 {
  type discrete [ 2 ] { false, true };
}
variable SNode_24 {
  type discrete [ 2 ] { false, true };
}
variable SLIDING4 {
  type discrete [ 2 ] { false, true };
}
variable SNode_25 {
  type discrete [ 2 ] { false, true };
}
variable CONSTANT5 {
  type discrete [ 2 ] { false, true };
}
variable SNode_26 {
  type discrete [ 2 ] { false, true };
}
variable KNOWN6 {
  type discrete [ 2 ] { false, true };
}
variable VELOCITY7 {
  type discrete [ 2 ] { false, true };
}
variable SNode_47 {
  type discrete [ 2 ] { false, true };
}
variable RApp3 {
  type discrete [ 2 ] { false, true };
}
variable KNOWN8 {
  type discrete [ 2 ] { false, true };
}
variable RApp4 {
  type discrete [ 2 ] { false, true };
}
variable SNode_27 {
  type discrete [ 2 ] { false, true };
}
variable COMPO16 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_48 {
  type discrete [ 2 ] { false, true };
}
variable TRY12 {
  type discrete [ 2 ] { false, true };
}
variable TRY11 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_49 {
  type discrete [ 2 ] { false, true };
}
variable CHOOSE19 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_50 {
  type discrete [ 2 ] { false, true };
}
variable SYSTEM18 {
  type discrete [ 2 ] { false, true };
}
variable SNode_51 {
  type discrete [ 2 ] { false, true };
}
variable KINEMATI17 {
  type discrete [ 2 ] { false, true };
}
variable SNode_52 {
  type discrete [ 2 ] { false, true };
}
variable IDENTIFY10 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_53 {
  type discrete [ 2 ] { false, true };
}
variable IDENTIFY9 {
  type discrete [ 2 ] { false, true };
}
variable SNode_28 {
  type discrete [ 2 ] { false, true };
}
variable TRY13 {
  type discrete [ 2 ] { false, true };
}
variable TRY14 {
  type discrete [ 2 ] { false, true };
}
variable TRY15 {
  type discrete [ 2 ] { false, true };
}
variable VAR20 {
  type discrete [ 2 ] { false, true };
}
variable SNode_29 {
  type discrete [ 2 ] { false, true };
}
variable SNode_31 {
  type discrete [ 2 ] { false, true };
}
variable GIVEN21 {
  type discrete [ 2 ] { false, true };
}
variable SNode_33 {
  type discrete [ 2 ] { false, true };
}
variable SNode_34 {
  type discrete [ 2 ] { false, true };
}
variable VECTOR27 {
  type discrete [ 2 ] { false, true };
}
variable APPLY32 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_56 {
  type discrete [ 2 ] { false, true };
}
variable CHOOSE35 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_57 {
  type discrete [ 2 ] { false, true };
}
variable MAXIMIZE34 {
  type discrete [ 2 ] { false, true };
}
variable SNode_59 {
  type discrete [ 2 ] { false, true };
}
variable AXIS33 {
  type discrete [ 2 ] { false, true };
}
variable SNode_60 {
  type discrete [ 2 ] { false, true };
}
variable WRITE31 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_61 {
  type discrete [ 2 ] { false, true };
}
variable WRITE30 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_62 {
  type discrete [ 2 ] { false, true };
}
variable RESOLVE37 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_63 {
  type discrete [ 2 ] { false, true };
}
variable NEED36 {
  type discrete [ 2 ] { false, true };
}
variable SNode_64 {
  type discrete [ 2 ] { false, true };
}
variable SNode_41 {
  type discrete [ 2 ] { false, true };
}
variable SNode_42 {
  type discrete [ 2 ] { false, true };
}
variable IDENTIFY39 {
  type discrete [ 2 ] { false, true };
}
variable SNode_43 {
  type discrete [ 2 ] { false, true };
}
variable RESOLVE38 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_66 {
  type discrete [ 2 ] { false, true };
}
variable SNode_67 {
  type discrete [ 2 ] { false, true };
}
variable IDENTIFY41 {
  type discrete [ 2 ] { false, true };
}
variable SNode_54 {
  type discrete [ 2 ] { false, true };
}
variable RESOLVE40 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_69 {
  type discrete [ 2 ] { false, true };
}
variable SNode_70 {
  type discrete [ 2 ] { false, true };
}
variable IDENTIFY43 {
  type discrete [ 2 ] { false, true };
}
variable SNode_55 {
  type discrete [ 2 ] { false, true };
}
variable RESOLVE42 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_72 {
  type discrete [ 2 ] { false, true };
}
variable SNode_73 {
  type discrete [ 2 ] { false, true };
}
variable KINE29 {
  type discrete [ 2 ] { false, true };
}
variable SNode_74 {
  type discrete [ 2 ] { false, true };
}
variable VECTOR44 {
  type discrete [ 2 ] { false, true };
}
variable SNode_75 {
  type discrete [ 2 ] { false, true };
}
variable EQUATION28 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_79 {
  type discrete [ 2 ] { false, true };
}
variable RApp5 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_80 {
  type discrete [ 2 ] { false, true };
}
variable RApp6 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_81 {
  type discrete [ 2 ] { false, true };
}
variable TRY25 {
  type discrete [ 2 ] { false, true };
}
variable TRY24 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_83 {
  type discrete [ 2 ] { false, true };
}
variable CHOOSE47 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_84 {
  type discrete [ 2 ] { false, true };
}
variable SYSTEM46 {
  type discrete [ 2 ] { false, true };
}
variable SNode_86 {
  type discrete [ 2 ] { false, true };
}
variable NEWTONS45 {
  type discrete [ 2 ] { false, true };
}
variable SNode_156 {
  type discrete [ 2 ] { false, true };
}
variable DEFINE23 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_98 {
  type discrete [ 2 ] { false, true };
}
variable IDENTIFY22 {
  type discrete [ 2 ] { false, true };
}
variable SNode_37 {
  type discrete [ 2 ] { false, true };
}
variable TRY26 {
  type discrete [ 2 ] { false, true };
}
variable SNode_38 {
  type discrete [ 2 ] { false, true };
}
variable SNode_40 {
  type discrete [ 2 ] { false, true };
}
variable SNode_44 {
  type discrete [ 2 ] { false, true };
}
variable SNode_46 {
  type discrete [ 2 ] { false, true };
}
variable NULL48 {
  type discrete [ 2 ] { false, true };
}
variable SNode_65 {
  type discrete [ 2 ] { false, true };
}
variable SNode_68 {
  type discrete [ 2 ] { false, true };
}
variable SNode_71 {
  type discrete [ 2 ] { false, true };
}
variable FIND49 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_87 {
  type discrete [ 2 ] { false, true };
}
variable NORMAL50 {
  type discrete [ 2 ] { false, true };
}
variable SNode_88 {
  type discrete [ 2 ] { false, true };
}
variable STRAT_90 {
  type discrete [ 2 ] { SNode_92_1, SNode_91_2 };
}
variable NORMAL52 {
  type discrete [ 2 ] { false, true };
}
variable INCLINE51 {
  type discrete [ 2 ] { false, true };
}
variable SNode_91 {
  type discrete [ 2 ] { false, true };
}
variable HORIZ53 {
  type discrete [ 2 ] { false, true };
}
variable BUGGY54 {
  type discrete [ 2 ] { false, true };
}
variable SNode_92 {
  type discrete [ 2 ] { false, true };
}
variable IDENTIFY55 {
  type discrete [ 2 ] { false, true };
}
variable SNode_93 {
  type discrete [ 2 ] { false, true };
}
variable WEIGHT56 {
  type discrete [ 2 ] { false, true };
}
variable SNode_94 {
  type discrete [ 2 ] { false, true };
}
variable WEIGHT57 {
  type discrete [ 2 ] { false, true };
}
variable SNode_95 {
  type discrete [ 2 ] { false, true };
}
variable SNode_97 {
  type discrete [ 2 ] { false, true };
}
variable FIND58 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_99 {
  type discrete [ 2 ] { false, true };
}
variable IDENTIFY59 {
  type discrete [ 2 ] { false, true };
}
variable SNode_100 {
  type discrete [ 2 ] { false, true };
}
variable FORCE60 {
  type discrete [ 2 ] { false, true };
}
variable SNode_102 {
  type discrete [ 2 ] { false, true };
}
variable APPLY61 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_103 {
  type discrete [ 2 ] { false, true };
}
variable CHOOSE62 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_104 {
  type discrete [ 2 ] { false, true };
}
variable SNode_106 {
  type discrete [ 2 ] { false, true };
}
variable SNode_152 {
  type discrete [ 2 ] { false, true };
}
variable WRITE63 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_107 {
  type discrete [ 2 ] { false, true };
}
variable WRITE64 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_108 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_109 {
  type discrete [ 2 ] { false, true };
}
variable GOAL65 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_110 {
  type discrete [ 2 ] { false, true };
}
variable GOAL66 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_111 {
  type discrete [ 2 ] { false, true };
}
variable NEED67 {
  type discrete [ 2 ] { false, true };
}
variable RApp7 {
  type discrete [ 2 ] { false, true };
}
variable RApp8 {
  type discrete [ 2 ] { false, true };
}
variable SNode_112 {
  type discrete [ 2 ] { false, true };
}
variable GOAL68 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_113 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_114 {
  type discrete [ 2 ] { false, true };
}
variable SNode_115 {
  type discrete [ 2 ] { false, true };
}
variable VECTOR69 {
  type discrete [ 2 ] { false, true };
}
variable SNode_116 {
  type discrete [ 2 ] { false, true };
}
variable SNode_117 {
  type discrete [ 2 ] { false, true };
}
variable VECTOR70 {
  type discrete [ 2 ] { false, true };
}
variable SNode_118 {
  type discrete [ 2 ] { false, true };
}
variable EQUAL71 {
  type discrete [ 2 ] { false, true };
}
variable SNode_119 {
  type discrete [ 2 ] { false, true };
}
variable SNode_120 {
  type discrete [ 2 ] { false, true };
}
variable GOAL72 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_121 {
  type discrete [ 2 ] { false, true };
}
variable SNode_122 {
  type discrete [ 2 ] { false, true };
}
variable VECTOR73 {
  type discrete [ 2 ] { false, true };
}
variable SNode_123 {
  type discrete [ 2 ] { false, true };
}
variable NEWTONS74 {
  type discrete [ 2 ] { false, true };
}
variable SNode_124 {
  type discrete [ 2 ] { false, true };
}
variable SUM75 {
  type discrete [ 2 ] { false, true };
}
variable SNode_125 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_126 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_127 {
  type discrete [ 2 ] { false, true };
}
variable RApp9 {
  type discrete [ 2 ] { false, true };
}
variable RApp10 {
  type discrete [ 2 ] { false, true };
}
variable SNode_128 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_129 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_130 {
  type discrete [ 2 ] { false, true };
}
variable SNode_131 {
  type discrete [ 2 ] { false, true };
}
variable SNode_132 {
  type discrete [ 2 ] { false, true };
}
variable SNode_133 {
  type discrete [ 2 ] { false, true };
}
variable SNode_134 {
  type discrete [ 2 ] { false, true };
}
variable SNode_135 {
  type discrete [ 2 ] { false, true };
}
variable SNode_154 {
  type discrete [ 2 ] { false, true };
}
variable SNode_136 {
  type discrete [ 2 ] { false, true };
}
variable SNode_137 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_142 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_143 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_146 {
  type discrete [ 2 ] { false, true };
}
variable RApp11 {
  type discrete [ 2 ] { false, true };
}
variable RApp12 {
  type discrete [ 2 ] { false, true };
}
variable RApp13 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_147 {
  type discrete [ 2 ] { false, true };
}
variable TRY76 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_149 {
  type discrete [ 2 ] { false, true };
}
variable APPLY77 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_150 {
  type discrete [ 2 ] { false, true };
}
variable GRAV78 {
  type discrete [ 2 ] { false, true };
}
variable SNode_151 {
  type discrete [ 2 ] { false, true };
}
variable GOAL_153 {
  type discrete [ 2 ] { false, true };
}
variable SNode_155 {
  type discrete [ 2 ] { false, true };
}
probability ( GOAL_2 ) {
  table 0.02, 0.98;
}
probability ( SNode_3 ) {
  table 0.02, 0.98;
}
probability ( SNode_4 ) {
  table 0.02, 0.98;
}
probability ( SNode_5 ) {
  table 0.02, 0.98;
}
probability ( SNode_6 ) {
  table 0.02, 0.98;
}
probability ( SNode_7 ) {
  table 0.02, 0.98;
}
probability ( DISPLACEM0 ) {
  table 0.5, 0.5;
}
probability ( RApp1 | DISPLACEM0, SNode_3 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( GIVEN_1 ) {
  table 0.02, 0.98;
}
probability ( RApp2 | GIVEN_1 ) {
  (false) 1.0, 0.0;
  (true) 0.0001, 0.9999;
}
probability ( SNode_8 | RApp1, RApp2 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.0, 1.0;
  (false, true) 0.0, 1.0;
  (true, true) 0.0, 1.0;
}
probability ( SNode_9 ) {
  table 0.02, 0.98;
}
probability ( SNode_10 ) {
  table 0.02, 0.98;
}
probability ( SNode_11 ) {
  table 0.02, 0.98;
}
probability ( SNode_12 ) {
  table 0.02, 0.98;
}
probability ( SNode_13 ) {
  table 0.02, 0.98;
}
probability ( SNode_14 ) {
  table 0.02, 0.98;
}
probability ( SNode_15 ) {
  table 0.02, 0.98;
}
probability ( SNode_16 ) {
  table 0.02, 0.98;
}
probability ( SNode_17 ) {
  table 0.02, 0.98;
}
probability ( SNode_18 ) {
  table 0.02, 0.98;
}
probability ( SNode_19 ) {
  table 0.02, 0.98;
}
probability ( NEED1 ) {
  table 0.5, 0.5;
}
probability ( SNode_20 | SNode_16, NEED1 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( GRAV2 ) {
  table 0.5, 0.5;
}
probability ( SNode_21 | SNode_20, GRAV2 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( VALUE3 ) {
  table 0.5, 0.5;
}
probability ( SNode_24 | SNode_21, VALUE3 ) {
  (false, false) 0.9, 0.1;
  (true, false) 0.9, 0.1;
  (false, true) 0.9, 0.1;
  (true, true) 0.00009, 0.99991;
}
probability ( SLIDING4 ) {
  table 0.1, 0.9;
}
probability ( SNode_25 | SNode_15, SLIDING4 ) {
  (false, false) 0.9, 0.1;
  (true, false) 0.9, 0.1;
  (false, true) 0.9, 0.1;
  (true, true) 0.00009, 0.99991;
}
probability ( CONSTANT5 ) {
  table 0.5, 0.5;
}
probability ( SNode_26 | SNode_11, CONSTANT5 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( KNOWN6 ) {
  table 0.5, 0.5;
}
probability ( VELOCITY7 ) {
  table 0.5, 0.5;
}
probability ( SNode_47 | SNode_3, VELOCITY7 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( RApp3 | KNOWN6, SNode_26, SNode_47 ) {
  (false, false, false) 1.0, 0.0;
  (true, false, false) 1.0, 0.0;
  (false, true, false) 1.0, 0.0;
  (true, true, false) 1.0, 0.0;
  (false, false, true) 1.0, 0.0;
  (true, false, true) 1.0, 0.0;
  (false, true, true) 1.0, 0.0;
  (true, true, true) 0.0001, 0.9999;
}
probability ( KNOWN8 ) {
  table 0.5, 0.5;
}
probability ( RApp4 | KNOWN8, SNode_11 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( SNode_27 | RApp3, RApp4 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.0, 1.0;
  (false, true) 0.0, 1.0;
  (true, true) 0.0, 1.0;
}
probability ( COMPO16 ) {
  table 0.5, 0.5;
}
probability ( GOAL_48 | GOAL_2, COMPO16 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( TRY12 ) {
  table 0.7, 0.3;
}
probability ( TRY11 | TRY12 ) {
  (false) 0.8, 0.2;
  (true) 0.0, 1.0;
}
probability ( GOAL_49 | SNode_5, SNode_6, GOAL_48, TRY11 ) {
  (false, false, false, false) 0.8, 0.2;
  (true, false, false, false) 0.8, 0.2;
  (false, true, false, false) 0.8, 0.2;
  (true, true, false, false) 0.8, 0.2;
  (false, false, true, false) 0.8, 0.2;
  (true, false, true, false) 0.8, 0.2;
  (false, true, true, false) 0.8, 0.2;
  (true, true, true, false) 0.8, 0.2;
  (false, false, false, true) 0.8, 0.2;
  (true, false, false, true) 0.8, 0.2;
  (false, true, false, true) 0.8, 0.2;
  (true, true, false, true) 0.8, 0.2;
  (false, false, true, true) 0.8, 0.2;
  (true, false, true, true) 0.8, 0.2;
  (false, true, true, true) 0.8, 0.2;
  (true, true, true, true) 0.00008, 0.99992;
}
probability ( CHOOSE19 ) {
  table 0.5, 0.5;
}
probability ( GOAL_50 | GOAL_49, CHOOSE19 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SYSTEM18 ) {
  table 0.5, 0.5;
}
probability ( SNode_51 | SNode_17, GOAL_50, SYSTEM18 ) {
  (false, false, false) 0.9, 0.1;
  (true, false, false) 0.9, 0.1;
  (false, true, false) 0.9, 0.1;
  (true, true, false) 0.9, 0.1;
  (false, false, true) 0.9, 0.1;
  (true, false, true) 0.9, 0.1;
  (false, true, true) 0.9, 0.1;
  (true, true, true) 0.00009, 0.99991;
}
probability ( KINEMATI17 ) {
  table 0.5, 0.5;
}
probability ( SNode_52 | SNode_51, KINEMATI17 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( IDENTIFY10 ) {
  table 0.5, 0.5;
}
probability ( GOAL_53 | GOAL_49, SNode_52, IDENTIFY10 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( IDENTIFY9 ) {
  table 0.5, 0.5;
}
probability ( SNode_28 | SNode_27, GOAL_53, IDENTIFY9 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( TRY13 | TRY12 ) {
  (false) 0.8, 0.2;
  (true) 0.0, 1.0;
}
probability ( TRY14 | TRY12 ) {
  (false) 0.9, 0.1;
  (true) 0.0, 1.0;
}
probability ( TRY15 | TRY12 ) {
  (false) 0.9, 0.1;
  (true) 0.0, 1.0;
}
probability ( VAR20 ) {
  table 0.3, 0.7;
}
probability ( SNode_29 | SNode_28, VAR20 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_31 | SNode_29, VALUE3 ) {
  (false, false) 0.9, 0.1;
  (true, false) 0.9, 0.1;
  (false, true) 0.9, 0.1;
  (true, true) 0.00009, 0.99991;
}
probability ( GIVEN21 ) {
  table 0.1, 0.9;
}
probability ( SNode_33 | SNode_10, GIVEN21 ) {
  (false, false) 0.9, 0.1;
  (true, false) 0.9, 0.1;
  (false, true) 0.9, 0.1;
  (true, true) 0.00009, 0.99991;
}
probability ( SNode_34 | SNode_10, CONSTANT5 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( VECTOR27 ) {
  table 0.5, 0.5;
}
probability ( APPLY32 ) {
  table 0.5, 0.5;
}
probability ( GOAL_56 | GOAL_49, SNode_52, APPLY32 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( CHOOSE35 ) {
  table 0.5, 0.5;
}
probability ( GOAL_57 | GOAL_56, CHOOSE35 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( MAXIMIZE34 ) {
  table 0.5, 0.5;
}
probability ( SNode_59 | SNode_7, GOAL_57, MAXIMIZE34 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( AXIS33 ) {
  table 0.2, 0.8;
}
probability ( SNode_60 | SNode_59, AXIS33 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( WRITE31 ) {
  table 0.5, 0.5;
}
probability ( GOAL_61 | GOAL_56, SNode_60, WRITE31 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( WRITE30 ) {
  table 0.5, 0.5;
}
probability ( GOAL_62 | GOAL_61, WRITE30 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( RESOLVE37 ) {
  table 0.5, 0.5;
}
probability ( GOAL_63 | SNode_28, GOAL_62, RESOLVE37 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( NEED36 ) {
  table 0.5, 0.5;
}
probability ( SNode_64 | GOAL_63, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_41 | SNode_9, CONSTANT5 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_42 | SNode_8, SNode_41, KNOWN6 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( IDENTIFY39 ) {
  table 0.5, 0.5;
}
probability ( SNode_43 | SNode_42, GOAL_53, IDENTIFY39 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( RESOLVE38 ) {
  table 0.5, 0.5;
}
probability ( GOAL_66 | SNode_43, GOAL_62, RESOLVE38 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( SNode_67 | GOAL_66, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( IDENTIFY41 ) {
  table 0.5, 0.5;
}
probability ( SNode_54 | GOAL_53, IDENTIFY41 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( RESOLVE40 ) {
  table 0.5, 0.5;
}
probability ( GOAL_69 | SNode_54, GOAL_62, RESOLVE40 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( SNode_70 | GOAL_69, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( IDENTIFY43 ) {
  table 0.6, 0.4;
}
probability ( SNode_55 | SNode_34, GOAL_53, IDENTIFY43 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( RESOLVE42 ) {
  table 0.5, 0.5;
}
probability ( GOAL_72 | SNode_55, GOAL_62, RESOLVE42 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( SNode_73 | GOAL_72, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( KINE29 ) {
  table 0.5, 0.5;
}
probability ( SNode_74 | GOAL_62, SNode_64, SNode_67, SNode_70, SNode_73, KINE29 ) {
  (false, false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false, false) 0.9, 0.1;
  (true, true, false, false, false, false) 0.9, 0.1;
  (false, false, true, false, false, false) 0.9, 0.1;
  (true, false, true, false, false, false) 0.9, 0.1;
  (false, true, true, false, false, false) 0.9, 0.1;
  (true, true, true, false, false, false) 0.9, 0.1;
  (false, false, false, true, false, false) 0.9, 0.1;
  (true, false, false, true, false, false) 0.9, 0.1;
  (false, true, false, true, false, false) 0.9, 0.1;
  (true, true, false, true, false, false) 0.9, 0.1;
  (false, false, true, true, false, false) 0.9, 0.1;
  (true, false, true, true, false, false) 0.9, 0.1;
  (false, true, true, true, false, false) 0.9, 0.1;
  (true, true, true, true, false, false) 0.9, 0.1;
  (false, false, false, false, true, false) 0.9, 0.1;
  (true, false, false, false, true, false) 0.9, 0.1;
  (false, true, false, false, true, false) 0.9, 0.1;
  (true, true, false, false, true, false) 0.9, 0.1;
  (false, false, true, false, true, false) 0.9, 0.1;
  (true, false, true, false, true, false) 0.9, 0.1;
  (false, true, true, false, true, false) 0.9, 0.1;
  (true, true, true, false, true, false) 0.9, 0.1;
  (false, false, false, true, true, false) 0.9, 0.1;
  (true, false, false, true, true, false) 0.9, 0.1;
  (false, true, false, true, true, false) 0.9, 0.1;
  (true, true, false, true, true, false) 0.9, 0.1;
  (false, false, true, true, true, false) 0.9, 0.1;
  (true, false, true, true, true, false) 0.9, 0.1;
  (false, true, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, false, true) 0.9, 0.1;
  (true, true, false, false, false, true) 0.9, 0.1;
  (false, false, true, false, false, true) 0.9, 0.1;
  (true, false, true, false, false, true) 0.9, 0.1;
  (false, true, true, false, false, true) 0.9, 0.1;
  (true, true, true, false, false, true) 0.9, 0.1;
  (false, false, false, true, false, true) 0.9, 0.1;
  (true, false, false, true, false, true) 0.9, 0.1;
  (false, true, false, true, false, true) 0.9, 0.1;
  (true, true, false, true, false, true) 0.9, 0.1;
  (false, false, true, true, false, true) 0.9, 0.1;
  (true, false, true, true, false, true) 0.9, 0.1;
  (false, true, true, true, false, true) 0.9, 0.1;
  (true, true, true, true, false, true) 0.9, 0.1;
  (false, false, false, false, true, true) 0.9, 0.1;
  (true, false, false, false, true, true) 0.9, 0.1;
  (false, true, false, false, true, true) 0.9, 0.1;
  (true, true, false, false, true, true) 0.9, 0.1;
  (false, false, true, false, true, true) 0.9, 0.1;
  (true, false, true, false, true, true) 0.9, 0.1;
  (false, true, true, false, true, true) 0.9, 0.1;
  (true, true, true, false, true, true) 0.9, 0.1;
  (false, false, false, true, true, true) 0.9, 0.1;
  (true, false, false, true, true, true) 0.9, 0.1;
  (false, true, false, true, true, true) 0.9, 0.1;
  (true, true, false, true, true, true) 0.9, 0.1;
  (false, false, true, true, true, true) 0.9, 0.1;
  (true, false, true, true, true, true) 0.9, 0.1;
  (false, true, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true, true) 0.00009, 0.99991;
}
probability ( VECTOR44 ) {
  table 0.5, 0.5;
}
probability ( SNode_75 | SNode_4, GOAL_72, SNode_73, VECTOR44 ) {
  (false, false, false, false) 0.9, 0.1;
  (true, false, false, false) 0.9, 0.1;
  (false, true, false, false) 0.9, 0.1;
  (true, true, false, false) 0.9, 0.1;
  (false, false, true, false) 0.9, 0.1;
  (true, false, true, false) 0.9, 0.1;
  (false, true, true, false) 0.9, 0.1;
  (true, true, true, false) 0.9, 0.1;
  (false, false, false, true) 0.9, 0.1;
  (true, false, false, true) 0.9, 0.1;
  (false, true, false, true) 0.9, 0.1;
  (true, true, false, true) 0.9, 0.1;
  (false, false, true, true) 0.9, 0.1;
  (true, false, true, true) 0.9, 0.1;
  (false, true, true, true) 0.9, 0.1;
  (true, true, true, true) 0.00009, 0.99991;
}
probability ( EQUATION28 ) {
  table 0.6, 0.4;
}
probability ( GOAL_79 | SNode_74, SNode_75, EQUATION28 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( RApp5 | VECTOR27, GOAL_79 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( GOAL_80 | SNode_75, EQUATION28 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( RApp6 | COMPO16, GOAL_80 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( GOAL_81 | RApp5, RApp6 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.0, 1.0;
  (false, true) 0.0, 1.0;
  (true, true) 0.0, 1.0;
}
probability ( TRY25 ) {
  table 0.1, 0.9;
}
probability ( TRY24 | TRY25 ) {
  (false) 0.95, 0.05;
  (true) 0.0, 1.0;
}
probability ( GOAL_83 | GOAL_81, TRY24 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( CHOOSE47 ) {
  table 0.5, 0.5;
}
probability ( GOAL_84 | GOAL_83, CHOOSE47 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SYSTEM46 ) {
  table 0.5, 0.5;
}
probability ( SNode_86 | GOAL_84, SYSTEM46 ) {
  (false, false) 0.9, 0.1;
  (true, false) 0.9, 0.1;
  (false, true) 0.9, 0.1;
  (true, true) 0.00009, 0.99991;
}
probability ( NEWTONS45 ) {
  table 0.5, 0.5;
}
probability ( SNode_156 | SNode_86, NEWTONS45 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( DEFINE23 ) {
  table 0.5, 0.5;
}
probability ( GOAL_98 | GOAL_83, SNode_156, DEFINE23 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( IDENTIFY22 ) {
  table 0.5, 0.5;
}
probability ( SNode_37 | GOAL_98, IDENTIFY22 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( TRY26 | TRY25 ) {
  (false) 0.9, 0.1;
  (true) 0.0, 1.0;
}
probability ( SNode_38 | SNode_37, VAR20 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_40 | SNode_38, VALUE3 ) {
  (false, false) 0.9, 0.1;
  (true, false) 0.9, 0.1;
  (false, true) 0.9, 0.1;
  (true, true) 0.00009, 0.99991;
}
probability ( SNode_44 | SNode_43, VAR20 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_46 | SNode_44, VALUE3 ) {
  (false, false) 0.9, 0.1;
  (true, false) 0.9, 0.1;
  (false, true) 0.9, 0.1;
  (true, true) 0.00009, 0.99991;
}
probability ( NULL48 ) {
  table 0.5, 0.5;
}
probability ( SNode_65 | SNode_29, GOAL_63, SNode_64, NULL48 ) {
  (false, false, false, false) 0.9, 0.1;
  (true, false, false, false) 0.9, 0.1;
  (false, true, false, false) 0.9, 0.1;
  (true, true, false, false) 0.9, 0.1;
  (false, false, true, false) 0.9, 0.1;
  (true, false, true, false) 0.9, 0.1;
  (false, true, true, false) 0.9, 0.1;
  (true, true, true, false) 0.9, 0.1;
  (false, false, false, true) 0.9, 0.1;
  (true, false, false, true) 0.9, 0.1;
  (false, true, false, true) 0.9, 0.1;
  (true, true, false, true) 0.9, 0.1;
  (false, false, true, true) 0.9, 0.1;
  (true, false, true, true) 0.9, 0.1;
  (false, true, true, true) 0.9, 0.1;
  (true, true, true, true) 0.00009, 0.99991;
}
probability ( SNode_68 | GOAL_66, SNode_67, VECTOR44 ) {
  (false, false, false) 0.9, 0.1;
  (true, false, false) 0.9, 0.1;
  (false, true, false) 0.9, 0.1;
  (true, true, false) 0.9, 0.1;
  (false, false, true) 0.9, 0.1;
  (true, false, true) 0.9, 0.1;
  (false, true, true) 0.9, 0.1;
  (true, true, true) 0.00009, 0.99991;
}
probability ( SNode_71 | GOAL_69, SNode_70, VECTOR44 ) {
  (false, false, false) 0.9, 0.1;
  (true, false, false) 0.9, 0.1;
  (false, true, false) 0.9, 0.1;
  (true, true, false) 0.9, 0.1;
  (false, false, true) 0.9, 0.1;
  (true, false, true) 0.9, 0.1;
  (false, true, true) 0.9, 0.1;
  (true, true, true) 0.00009, 0.99991;
}
probability ( FIND49 ) {
  table 0.5, 0.5;
}
probability ( GOAL_87 | GOAL_83, SNode_156, FIND49 ) {
  (false, false, false) 0.7, 0.3;
  (true, false, false) 0.7, 0.3;
  (false, true, false) 0.7, 0.3;
  (true, true, false) 0.7, 0.3;
  (false, false, true) 0.7, 0.3;
  (true, false, true) 0.7, 0.3;
  (false, true, true) 0.7, 0.3;
  (true, true, true) 0.00007, 0.99993;
}
probability ( NORMAL50 ) {
  table 0.5, 0.5;
}
probability ( SNode_88 | SNode_25, GOAL_87, NORMAL50 ) {
  (false, false, false) 0.9, 0.1;
  (true, false, false) 0.9, 0.1;
  (false, true, false) 0.9, 0.1;
  (true, true, false) 0.9, 0.1;
  (false, false, true) 0.9, 0.1;
  (true, false, true) 0.9, 0.1;
  (false, true, true) 0.9, 0.1;
  (true, true, true) 0.00009, 0.99991;
}
probability ( STRAT_90 ) {
  table 0.5, 0.5;
}
probability ( NORMAL52 ) {
  table 0.646, 0.354;
}
probability ( INCLINE51 | NORMAL52 ) {
  (false) 1.0, 0.0;
  (true) 0.0, 1.0;
}
probability ( SNode_91 | SNode_88, SNode_12, SNode_13, STRAT_90, INCLINE51 ) {
  (false, false, false, SNode_92_1, false) 0.8, 0.2;
  (true, false, false, SNode_92_1, false) 0.8, 0.2;
  (false, true, false, SNode_92_1, false) 0.8, 0.2;
  (true, true, false, SNode_92_1, false) 0.8, 0.2;
  (false, false, true, SNode_92_1, false) 0.8, 0.2;
  (true, false, true, SNode_92_1, false) 0.8, 0.2;
  (false, true, true, SNode_92_1, false) 0.8, 0.2;
  (true, true, true, SNode_92_1, false) 0.8, 0.2;
  (false, false, false, SNode_91_2, false) 0.8, 0.2;
  (true, false, false, SNode_91_2, false) 0.8, 0.2;
  (false, true, false, SNode_91_2, false) 0.8, 0.2;
  (true, true, false, SNode_91_2, false) 0.8, 0.2;
  (false, false, true, SNode_91_2, false) 0.8, 0.2;
  (true, false, true, SNode_91_2, false) 0.8, 0.2;
  (false, true, true, SNode_91_2, false) 0.8, 0.2;
  (true, true, true, SNode_91_2, false) 0.8, 0.2;
  (false, false, false, SNode_92_1, true) 0.8, 0.2;
  (true, false, false, SNode_92_1, true) 0.8, 0.2;
  (false, true, false, SNode_92_1, true) 0.8, 0.2;
  (true, true, false, SNode_92_1, true) 0.8, 0.2;
  (false, false, true, SNode_92_1, true) 0.8, 0.2;
  (true, false, true, SNode_92_1, true) 0.8, 0.2;
  (false, true, true, SNode_92_1, true) 0.8, 0.2;
  (true, true, true, SNode_92_1, true) 0.8, 0.2;
  (false, false, false, SNode_91_2, true) 0.8, 0.2;
  (true, false, false, SNode_91_2, true) 0.8, 0.2;
  (false, true, false, SNode_91_2, true) 0.8, 0.2;
  (true, true, false, SNode_91_2, true) 0.8, 0.2;
  (false, false, true, SNode_91_2, true) 0.8, 0.2;
  (true, false, true, SNode_91_2, true) 0.8, 0.2;
  (false, true, true, SNode_91_2, true) 0.8, 0.2;
  (true, true, true, SNode_91_2, true) 0.00008, 0.99992;
}
probability ( HORIZ53 | NORMAL52 ) {
  (false) 0.8, 0.2;
  (true) 0.0, 1.0;
}
probability ( BUGGY54 | NORMAL52 ) {
  (false) 0.2, 0.8;
  (true) 1.0, 0.0;
}
probability ( SNode_92 | SNode_12, STRAT_90, BUGGY54 ) {
  (false, SNode_92_1, false) 0.8, 0.2;
  (true, SNode_92_1, false) 0.8, 0.2;
  (false, SNode_91_2, false) 0.8, 0.2;
  (true, SNode_91_2, false) 0.8, 0.2;
  (false, SNode_92_1, true) 0.8, 0.2;
  (true, SNode_92_1, true) 0.00008, 0.99992;
  (false, SNode_91_2, true) 0.8, 0.2;
  (true, SNode_91_2, true) 0.8, 0.2;
}
probability ( IDENTIFY55 ) {
  table 0.5, 0.5;
}
probability ( SNode_93 | GOAL_87, SNode_88, IDENTIFY55 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( WEIGHT56 ) {
  table 0.3, 0.7;
}
probability ( SNode_94 | SNode_16, SNode_33, GOAL_87, WEIGHT56 ) {
  (false, false, false, false) 0.9, 0.1;
  (true, false, false, false) 0.9, 0.1;
  (false, true, false, false) 0.9, 0.1;
  (true, true, false, false) 0.9, 0.1;
  (false, false, true, false) 0.9, 0.1;
  (true, false, true, false) 0.9, 0.1;
  (false, true, true, false) 0.9, 0.1;
  (true, true, true, false) 0.9, 0.1;
  (false, false, false, true) 0.9, 0.1;
  (true, false, false, true) 0.9, 0.1;
  (false, true, false, true) 0.9, 0.1;
  (true, true, false, true) 0.9, 0.1;
  (false, false, true, true) 0.9, 0.1;
  (true, false, true, true) 0.9, 0.1;
  (false, true, true, true) 0.9, 0.1;
  (true, true, true, true) 0.00009, 0.99991;
}
probability ( WEIGHT57 ) {
  table 0.3, 0.7;
}
probability ( SNode_95 | SNode_94, WEIGHT57 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_97 | GOAL_87, SNode_94, IDENTIFY55 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( FIND58 ) {
  table 0.7, 0.3;
}
probability ( GOAL_99 | GOAL_98, FIND58 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( IDENTIFY59 ) {
  table 0.5, 0.5;
}
probability ( SNode_100 | GOAL_98, IDENTIFY59 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( FORCE60 ) {
  table 0.2, 0.8;
}
probability ( SNode_102 | GOAL_87, SNode_88, SNode_94, FORCE60 ) {
  (false, false, false, false) 0.9, 0.1;
  (true, false, false, false) 0.9, 0.1;
  (false, true, false, false) 0.9, 0.1;
  (true, true, false, false) 0.9, 0.1;
  (false, false, true, false) 0.9, 0.1;
  (true, false, true, false) 0.9, 0.1;
  (false, true, true, false) 0.9, 0.1;
  (true, true, true, false) 0.9, 0.1;
  (false, false, false, true) 0.9, 0.1;
  (true, false, false, true) 0.9, 0.1;
  (false, true, false, true) 0.9, 0.1;
  (true, true, false, true) 0.9, 0.1;
  (false, false, true, true) 0.9, 0.1;
  (true, false, true, true) 0.9, 0.1;
  (false, true, true, true) 0.9, 0.1;
  (true, true, true, true) 0.00009, 0.99991;
}
probability ( APPLY61 ) {
  table 0.5, 0.5;
}
probability ( GOAL_103 | GOAL_83, SNode_102, APPLY61 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( CHOOSE62 ) {
  table 0.5, 0.5;
}
probability ( GOAL_104 | GOAL_103, CHOOSE62 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_106 | GOAL_104, MAXIMIZE34 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_152 | SNode_106, AXIS33 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( WRITE63 ) {
  table 0.5, 0.5;
}
probability ( GOAL_107 | GOAL_103, SNode_152, WRITE63 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( WRITE64 ) {
  table 0.5, 0.5;
}
probability ( GOAL_108 | GOAL_107, WRITE64 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( GOAL_109 | GOAL_107, WRITE64 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( GOAL65 ) {
  table 0.5, 0.5;
}
probability ( GOAL_110 | GOAL_109, GOAL65 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( GOAL66 ) {
  table 0.6, 0.4;
}
probability ( GOAL_111 | GOAL_109, GOAL66 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( NEED67 ) {
  table 0.5, 0.5;
}
probability ( RApp7 | NEED67, GOAL_109 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( RApp8 | NEED36, GOAL_111 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( SNode_112 | RApp7, RApp8 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.0, 1.0;
  (false, true) 0.0, 1.0;
  (true, true) 0.0, 1.0;
}
probability ( GOAL68 ) {
  table 0.5, 0.5;
}
probability ( GOAL_113 | GOAL_110, GOAL68 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( GOAL_114 | GOAL_110, GOAL68 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_115 | GOAL_114, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( VECTOR69 ) {
  table 0.7, 0.3;
}
probability ( SNode_116 | SNode_95, SNode_97, GOAL_114, SNode_115, VECTOR69 ) {
  (false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false) 0.9, 0.1;
  (true, true, false, false, false) 0.9, 0.1;
  (false, false, true, false, false) 0.9, 0.1;
  (true, false, true, false, false) 0.9, 0.1;
  (false, true, true, false, false) 0.9, 0.1;
  (true, true, true, false, false) 0.9, 0.1;
  (false, false, false, true, false) 0.9, 0.1;
  (true, false, false, true, false) 0.9, 0.1;
  (false, true, false, true, false) 0.9, 0.1;
  (true, true, false, true, false) 0.9, 0.1;
  (false, false, true, true, false) 0.9, 0.1;
  (true, false, true, true, false) 0.9, 0.1;
  (false, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, true) 0.9, 0.1;
  (true, true, false, false, true) 0.9, 0.1;
  (false, false, true, false, true) 0.9, 0.1;
  (true, false, true, false, true) 0.9, 0.1;
  (false, true, true, false, true) 0.9, 0.1;
  (true, true, true, false, true) 0.9, 0.1;
  (false, false, false, true, true) 0.9, 0.1;
  (true, false, false, true, true) 0.9, 0.1;
  (false, true, false, true, true) 0.9, 0.1;
  (true, true, false, true, true) 0.9, 0.1;
  (false, false, true, true, true) 0.9, 0.1;
  (true, false, true, true, true) 0.9, 0.1;
  (false, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true) 0.00009, 0.99991;
}
probability ( SNode_117 | GOAL_113, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( VECTOR70 ) {
  table 0.1, 0.9;
}
probability ( SNode_118 | SNode_91, GOAL_113, VECTOR70 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( EQUAL71 ) {
  table 0.4, 0.6;
}
probability ( SNode_119 | SNode_93, SNode_117, SNode_118, EQUAL71 ) {
  (false, false, false, false) 0.9, 0.1;
  (true, false, false, false) 0.9, 0.1;
  (false, true, false, false) 0.9, 0.1;
  (true, true, false, false) 0.9, 0.1;
  (false, false, true, false) 0.9, 0.1;
  (true, false, true, false) 0.9, 0.1;
  (false, true, true, false) 0.9, 0.1;
  (true, true, true, false) 0.9, 0.1;
  (false, false, false, true) 0.9, 0.1;
  (true, false, false, true) 0.9, 0.1;
  (false, true, false, true) 0.9, 0.1;
  (true, true, false, true) 0.9, 0.1;
  (false, false, true, true) 0.9, 0.1;
  (true, false, true, true) 0.9, 0.1;
  (false, true, true, true) 0.9, 0.1;
  (true, true, true, true) 0.00009, 0.99991;
}
probability ( SNode_120 | SNode_92, SNode_93, GOAL_113, SNode_117, VECTOR69 ) {
  (false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false) 0.9, 0.1;
  (true, true, false, false, false) 0.9, 0.1;
  (false, false, true, false, false) 0.9, 0.1;
  (true, false, true, false, false) 0.9, 0.1;
  (false, true, true, false, false) 0.9, 0.1;
  (true, true, true, false, false) 0.9, 0.1;
  (false, false, false, true, false) 0.9, 0.1;
  (true, false, false, true, false) 0.9, 0.1;
  (false, true, false, true, false) 0.9, 0.1;
  (true, true, false, true, false) 0.9, 0.1;
  (false, false, true, true, false) 0.9, 0.1;
  (true, false, true, true, false) 0.9, 0.1;
  (false, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, true) 0.9, 0.1;
  (true, true, false, false, true) 0.9, 0.1;
  (false, false, true, false, true) 0.9, 0.1;
  (true, false, true, false, true) 0.9, 0.1;
  (false, true, true, false, true) 0.9, 0.1;
  (true, true, true, false, true) 0.9, 0.1;
  (false, false, false, true, true) 0.9, 0.1;
  (true, false, false, true, true) 0.9, 0.1;
  (false, true, false, true, true) 0.9, 0.1;
  (true, true, false, true, true) 0.9, 0.1;
  (false, false, true, true, true) 0.9, 0.1;
  (true, false, true, true, true) 0.9, 0.1;
  (false, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true) 0.00009, 0.99991;
}
probability ( GOAL72 ) {
  table 0.5, 0.5;
}
probability ( GOAL_121 | GOAL_109, GOAL72 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_122 | GOAL_121, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( VECTOR73 ) {
  table 0.5, 0.5;
}
probability ( SNode_123 | SNode_4, SNode_100, GOAL_121, SNode_122, VECTOR73 ) {
  (false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false) 0.9, 0.1;
  (true, true, false, false, false) 0.9, 0.1;
  (false, false, true, false, false) 0.9, 0.1;
  (true, false, true, false, false) 0.9, 0.1;
  (false, true, true, false, false) 0.9, 0.1;
  (true, true, true, false, false) 0.9, 0.1;
  (false, false, false, true, false) 0.9, 0.1;
  (true, false, false, true, false) 0.9, 0.1;
  (false, true, false, true, false) 0.9, 0.1;
  (true, true, false, true, false) 0.9, 0.1;
  (false, false, true, true, false) 0.9, 0.1;
  (true, false, true, true, false) 0.9, 0.1;
  (false, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, true) 0.9, 0.1;
  (true, true, false, false, true) 0.9, 0.1;
  (false, false, true, false, true) 0.9, 0.1;
  (true, false, true, false, true) 0.9, 0.1;
  (false, true, true, false, true) 0.9, 0.1;
  (true, true, true, false, true) 0.9, 0.1;
  (false, false, false, true, true) 0.9, 0.1;
  (true, false, false, true, true) 0.9, 0.1;
  (false, true, false, true, true) 0.9, 0.1;
  (true, true, false, true, true) 0.9, 0.1;
  (false, false, true, true, true) 0.9, 0.1;
  (true, false, true, true, true) 0.9, 0.1;
  (false, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true) 0.00009, 0.99991;
}
probability ( NEWTONS74 ) {
  table 0.5, 0.5;
}
probability ( SNode_124 | SNode_37, GOAL_109, SNode_112, SNode_122, NEWTONS74 ) {
  (false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false) 0.9, 0.1;
  (true, true, false, false, false) 0.9, 0.1;
  (false, false, true, false, false) 0.9, 0.1;
  (true, false, true, false, false) 0.9, 0.1;
  (false, true, true, false, false) 0.9, 0.1;
  (true, true, true, false, false) 0.9, 0.1;
  (false, false, false, true, false) 0.9, 0.1;
  (true, false, false, true, false) 0.9, 0.1;
  (false, true, false, true, false) 0.9, 0.1;
  (true, true, false, true, false) 0.9, 0.1;
  (false, false, true, true, false) 0.9, 0.1;
  (true, false, true, true, false) 0.9, 0.1;
  (false, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, true) 0.9, 0.1;
  (true, true, false, false, true) 0.9, 0.1;
  (false, false, true, false, true) 0.9, 0.1;
  (true, false, true, false, true) 0.9, 0.1;
  (false, true, true, false, true) 0.9, 0.1;
  (true, true, true, false, true) 0.9, 0.1;
  (false, false, false, true, true) 0.9, 0.1;
  (true, false, false, true, true) 0.9, 0.1;
  (false, true, false, true, true) 0.9, 0.1;
  (true, true, false, true, true) 0.9, 0.1;
  (false, false, true, true, true) 0.9, 0.1;
  (true, false, true, true, true) 0.9, 0.1;
  (false, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true) 0.00009, 0.99991;
}
probability ( SUM75 ) {
  table 0.5, 0.5;
}
probability ( SNode_125 | GOAL_109, SNode_112, SUM75 ) {
  (false, false, false) 0.9, 0.1;
  (true, false, false) 0.9, 0.1;
  (false, true, false) 0.9, 0.1;
  (true, true, false) 0.9, 0.1;
  (false, false, true) 0.9, 0.1;
  (true, false, true) 0.9, 0.1;
  (false, true, true) 0.9, 0.1;
  (true, true, true) 0.00009, 0.99991;
}
probability ( GOAL_126 | GOAL_108, GOAL65 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( GOAL_127 | GOAL_108, GOAL66 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( RApp9 | NEED67, GOAL_108 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( RApp10 | NEED36, GOAL_127 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( SNode_128 | RApp9, RApp10 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.0, 1.0;
  (false, true) 0.0, 1.0;
  (true, true) 0.0, 1.0;
}
probability ( GOAL_129 | GOAL_126, GOAL68 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( GOAL_130 | GOAL_126, GOAL68 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_131 | GOAL_130, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_132 | SNode_95, SNode_97, GOAL_130, SNode_131, VECTOR69 ) {
  (false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false) 0.9, 0.1;
  (true, true, false, false, false) 0.9, 0.1;
  (false, false, true, false, false) 0.9, 0.1;
  (true, false, true, false, false) 0.9, 0.1;
  (false, true, true, false, false) 0.9, 0.1;
  (true, true, true, false, false) 0.9, 0.1;
  (false, false, false, true, false) 0.9, 0.1;
  (true, false, false, true, false) 0.9, 0.1;
  (false, true, false, true, false) 0.9, 0.1;
  (true, true, false, true, false) 0.9, 0.1;
  (false, false, true, true, false) 0.9, 0.1;
  (true, false, true, true, false) 0.9, 0.1;
  (false, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, true) 0.9, 0.1;
  (true, true, false, false, true) 0.9, 0.1;
  (false, false, true, false, true) 0.9, 0.1;
  (true, false, true, false, true) 0.9, 0.1;
  (false, true, true, false, true) 0.9, 0.1;
  (true, true, true, false, true) 0.9, 0.1;
  (false, false, false, true, true) 0.9, 0.1;
  (true, false, false, true, true) 0.9, 0.1;
  (false, true, false, true, true) 0.9, 0.1;
  (true, true, false, true, true) 0.9, 0.1;
  (false, false, true, true, true) 0.9, 0.1;
  (true, false, true, true, true) 0.9, 0.1;
  (false, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true) 0.00009, 0.99991;
}
probability ( SNode_133 | GOAL_129, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_134 | SNode_91, SNode_93, GOAL_129, SNode_133, VECTOR73 ) {
  (false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false) 0.9, 0.1;
  (true, true, false, false, false) 0.9, 0.1;
  (false, false, true, false, false) 0.9, 0.1;
  (true, false, true, false, false) 0.9, 0.1;
  (false, true, true, false, false) 0.9, 0.1;
  (true, true, true, false, false) 0.9, 0.1;
  (false, false, false, true, false) 0.9, 0.1;
  (true, false, false, true, false) 0.9, 0.1;
  (false, true, false, true, false) 0.9, 0.1;
  (true, true, false, true, false) 0.9, 0.1;
  (false, false, true, true, false) 0.9, 0.1;
  (true, false, true, true, false) 0.9, 0.1;
  (false, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, true) 0.9, 0.1;
  (true, true, false, false, true) 0.9, 0.1;
  (false, false, true, false, true) 0.9, 0.1;
  (true, false, true, false, true) 0.9, 0.1;
  (false, true, true, false, true) 0.9, 0.1;
  (true, true, true, false, true) 0.9, 0.1;
  (false, false, false, true, true) 0.9, 0.1;
  (true, false, false, true, true) 0.9, 0.1;
  (false, true, false, true, true) 0.9, 0.1;
  (true, true, false, true, true) 0.9, 0.1;
  (false, false, true, true, true) 0.9, 0.1;
  (true, false, true, true, true) 0.9, 0.1;
  (false, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true) 0.00009, 0.99991;
}
probability ( SNode_135 | SNode_92, SNode_93, GOAL_129, SNode_133, VECTOR69 ) {
  (false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false) 0.9, 0.1;
  (true, true, false, false, false) 0.9, 0.1;
  (false, false, true, false, false) 0.9, 0.1;
  (true, false, true, false, false) 0.9, 0.1;
  (false, true, true, false, false) 0.9, 0.1;
  (true, true, true, false, false) 0.9, 0.1;
  (false, false, false, true, false) 0.9, 0.1;
  (true, false, false, true, false) 0.9, 0.1;
  (false, true, false, true, false) 0.9, 0.1;
  (true, true, false, true, false) 0.9, 0.1;
  (false, false, true, true, false) 0.9, 0.1;
  (true, false, true, true, false) 0.9, 0.1;
  (false, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, true) 0.9, 0.1;
  (true, true, false, false, true) 0.9, 0.1;
  (false, false, true, false, true) 0.9, 0.1;
  (true, false, true, false, true) 0.9, 0.1;
  (false, true, true, false, true) 0.9, 0.1;
  (true, true, true, false, true) 0.9, 0.1;
  (false, false, false, true, true) 0.9, 0.1;
  (true, false, false, true, true) 0.9, 0.1;
  (false, true, false, true, true) 0.9, 0.1;
  (true, true, false, true, true) 0.9, 0.1;
  (false, false, true, true, true) 0.9, 0.1;
  (true, false, true, true, true) 0.9, 0.1;
  (false, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true) 0.00009, 0.99991;
}
probability ( SNode_154 | GOAL_121, NEED36 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_136 | SNode_37, GOAL_108, SNode_128, SNode_154, NEWTONS74 ) {
  (false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false) 0.9, 0.1;
  (true, true, false, false, false) 0.9, 0.1;
  (false, false, true, false, false) 0.9, 0.1;
  (true, false, true, false, false) 0.9, 0.1;
  (false, true, true, false, false) 0.9, 0.1;
  (true, true, true, false, false) 0.9, 0.1;
  (false, false, false, true, false) 0.9, 0.1;
  (true, false, false, true, false) 0.9, 0.1;
  (false, true, false, true, false) 0.9, 0.1;
  (true, true, false, true, false) 0.9, 0.1;
  (false, false, true, true, false) 0.9, 0.1;
  (true, false, true, true, false) 0.9, 0.1;
  (false, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, true) 0.9, 0.1;
  (true, true, false, false, true) 0.9, 0.1;
  (false, false, true, false, true) 0.9, 0.1;
  (true, false, true, false, true) 0.9, 0.1;
  (false, true, true, false, true) 0.9, 0.1;
  (true, true, true, false, true) 0.9, 0.1;
  (false, false, false, true, true) 0.9, 0.1;
  (true, false, false, true, true) 0.9, 0.1;
  (false, true, false, true, true) 0.9, 0.1;
  (true, true, false, true, true) 0.9, 0.1;
  (false, false, true, true, true) 0.9, 0.1;
  (true, false, true, true, true) 0.9, 0.1;
  (false, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true) 0.00009, 0.99991;
}
probability ( SNode_137 | GOAL_108, SNode_128, SUM75 ) {
  (false, false, false) 0.9, 0.1;
  (true, false, false) 0.9, 0.1;
  (false, true, false) 0.9, 0.1;
  (true, true, false) 0.9, 0.1;
  (false, false, true) 0.9, 0.1;
  (true, false, true) 0.9, 0.1;
  (false, true, true) 0.9, 0.1;
  (true, true, true) 0.00009, 0.99991;
}
probability ( GOAL_142 | SNode_116, SNode_125, EQUATION28 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( GOAL_143 | SNode_116, SNode_132, EQUATION28 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( GOAL_146 | SNode_132, SNode_137, EQUATION28 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.8, 0.2;
  (false, true, false) 0.8, 0.2;
  (true, true, false) 0.8, 0.2;
  (false, false, true) 0.8, 0.2;
  (true, false, true) 0.8, 0.2;
  (false, true, true) 0.8, 0.2;
  (true, true, true) 0.00008, 0.99992;
}
probability ( RApp11 | VECTOR27, GOAL_142 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( RApp12 | COMPO16, GOAL_143 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( RApp13 | VECTOR27, GOAL_146 ) {
  (false, false) 1.0, 0.0;
  (true, false) 1.0, 0.0;
  (false, true) 1.0, 0.0;
  (true, true) 0.0001, 0.9999;
}
probability ( GOAL_147 | RApp11, RApp12, RApp13 ) {
  (false, false, false) 0.8, 0.2;
  (true, false, false) 0.0, 1.0;
  (false, true, false) 0.0, 1.0;
  (true, true, false) 0.0, 1.0;
  (false, false, true) 0.0, 1.0;
  (true, false, true) 0.0, 1.0;
  (false, true, true) 0.0, 1.0;
  (true, true, true) 0.0, 1.0;
}
probability ( TRY76 ) {
  table 0.5, 0.5;
}
probability ( GOAL_149 | GOAL_147, TRY76 ) {
  (false, false) 0.7, 0.3;
  (true, false) 0.7, 0.3;
  (false, true) 0.7, 0.3;
  (true, true) 0.00007, 0.99993;
}
probability ( APPLY77 ) {
  table 0.5, 0.5;
}
probability ( GOAL_150 | SNode_20, SNode_37, GOAL_149, APPLY77 ) {
  (false, false, false, false) 0.8, 0.2;
  (true, false, false, false) 0.8, 0.2;
  (false, true, false, false) 0.8, 0.2;
  (true, true, false, false) 0.8, 0.2;
  (false, false, true, false) 0.8, 0.2;
  (true, false, true, false) 0.8, 0.2;
  (false, true, true, false) 0.8, 0.2;
  (true, true, true, false) 0.8, 0.2;
  (false, false, false, true) 0.8, 0.2;
  (true, false, false, true) 0.8, 0.2;
  (false, true, false, true) 0.8, 0.2;
  (true, true, false, true) 0.8, 0.2;
  (false, false, true, true) 0.8, 0.2;
  (true, false, true, true) 0.8, 0.2;
  (false, true, true, true) 0.8, 0.2;
  (true, true, true, true) 0.00008, 0.99992;
}
probability ( GRAV78 ) {
  table 0.5, 0.5;
}
probability ( SNode_151 | GOAL_150, GRAV78 ) {
  (false, false) 0.9, 0.1;
  (true, false) 0.9, 0.1;
  (false, true) 0.9, 0.1;
  (true, true) 0.00009, 0.99991;
}
probability ( GOAL_153 | GOAL_108, GOAL72 ) {
  (false, false) 0.8, 0.2;
  (true, false) 0.8, 0.2;
  (false, true) 0.8, 0.2;
  (true, true) 0.00008, 0.99992;
}
probability ( SNode_155 | SNode_4, SNode_100, GOAL_153, SNode_154, VECTOR44 ) {
  (false, false, false, false, false) 0.9, 0.1;
  (true, false, false, false, false) 0.9, 0.1;
  (false, true, false, false, false) 0.9, 0.1;
  (true, true, false, false, false) 0.9, 0.1;
  (false, false, true, false, false) 0.9, 0.1;
  (true, false, true, false, false) 0.9, 0.1;
  (false, true, true, false, false) 0.9, 0.1;
  (true, true, true, false, false) 0.9, 0.1;
  (false, false, false, true, false) 0.9, 0.1;
  (true, false, false, true, false) 0.9, 0.1;
  (false, true, false, true, false) 0.9, 0.1;
  (true, true, false, true, false) 0.9, 0.1;
  (false, false, true, true, false) 0.9, 0.1;
  (true, false, true, true, false) 0.9, 0.1;
  (false, true, true, true, false) 0.9, 0.1;
  (true, true, true, true, false) 0.9, 0.1;
  (false, false, false, false, true) 0.9, 0.1;
  (true, false, false, false, true) 0.9, 0.1;
  (false, true, false, false, true) 0.9, 0.1;
  (true, true, false, false, true) 0.9, 0.1;
  (false, false, true, false, true) 0.9, 0.1;
  (true, false, true, false, true) 0.9, 0.1;
  (false, true, true, false, true) 0.9, 0.1;
  (true, true, true, false, true) 0.9, 0.1;
  (false, false, false, true, true) 0.9, 0.1;
  (true, false, false, true, true) 0.9, 0.1;
  (false, true, false, true, true) 0.9, 0.1;
  (true, true, false, true, true) 0.9, 0.1;
  (false, false, true, true, true) 0.9, 0.1;
  (true, false, true, true, true) 0.9, 0.1;
  (false, true, true, true, true) 0.9, 0.1;
  (true, true, true, true, true) 0.00009, 0.99991;
}


================================================
FILE: data/asia.bif
================================================
network unknown {
}
variable asia {
  type discrete [ 2 ] { yes, no };
}
variable tub {
  type discrete [ 2 ] { yes, no };
}
variable smoke {
  type discrete [ 2 ] { yes, no };
}
variable lung {
  type discrete [ 2 ] { yes, no };
}
variable bronc {
  type discrete [ 2 ] { yes, no };
}
variable either {
  type discrete [ 2 ] { yes, no };
}
variable xray {
  type discrete [ 2 ] { yes, no };
}
variable dysp {
  type discrete [ 2 ] { yes, no };
}
probability ( asia ) {
  table 0.01, 0.99;
}
probability ( tub | asia ) {
  (yes) 0.05, 0.95;
  (no) 0.01, 0.99;
}
probability ( smoke ) {
  table 0.5, 0.5;
}
probability ( lung | smoke ) {
  (yes) 0.1, 0.9;
  (no) 0.01, 0.99;
}
probability ( bronc | smoke ) {
  (yes) 0.6, 0.4;
  (no) 0.3, 0.7;
}
probability ( either | lung, tub ) {
  (yes, yes) 1.0, 0.0;
  (no, yes) 1.0, 0.0;
  (yes, no) 1.0, 0.0;
  (no, no) 0.0, 1.0;
}
probability ( xray | either ) {
  (yes) 0.98, 0.02;
  (no) 0.05, 0.95;
}
probability ( dysp | bronc, either ) {
  (yes, yes) 0.9, 0.1;
  (no, yes) 0.7, 0.3;
  (yes, no) 0.8, 0.2;
  (no, no) 0.1, 0.9;
}


================================================
FILE: data/bde_test.bn
================================================
{
    "V": ["V1", "V2", "V3", "V4", "V5"],
    "E": {
        "V1" : [],
        "V2" : [],
        "V3" : ["V2","V4","V5"],
        "V4" : [],
        "V5" : []
    },
    "F": {
        "V1": {
            "values": ["No", "Yes"],
            "parents": [],
            "cpt": [0.296, 0.704]
        },

        "V2": {
            "values": ["No", "Yes"],
            "parents": ["V3"],
            "cpt": [0.667, 0.333,0.897,0.103]
        },

        "V3": {
            "values": ["No","Yes"],
            "parents": [],
            "cpt": [0.012, 0.988]
        },

        "V4": {
            "values": ["No","Yes"],
            "parents": ["V3"],
            "cpt": [0.75,0.25,0.182,0.818]
        },

        "V5": {
            "values": ["No", "Yes"],
            "parents": ["V3"],
            "cpt":  [0.75,0.25,0.31,0.689]
        }
    }
}

================================================
FILE: data/cancer.bif
================================================
network unknown {
}
variable Pollution {
  type discrete [ 2 ] { low, high };
}
variable Smoker {
  type discrete [ 2 ] { True, False };
}
variable Cancer {
  type discrete [ 2 ] { True, False };
}
variable Xray {
  type discrete [ 2 ] { positive, negative };
}
variable Dyspnoea {
  type discrete [ 2 ] { True, False };
}
probability ( Pollution ) {
  table 0.9, 0.1;
}
probability ( Smoker ) {
  table 0.3, 0.7;
}
probability ( Cancer | Pollution, Smoker ) {
  (low, True) 0.03, 0.97;
  (high, True) 0.05, 0.95;
  (low, False) 0.001, 0.999;
  (high, False) 0.02, 0.98;
}
probability ( Xray | Cancer ) {
  (True) 0.9, 0.1;
  (False) 0.2, 0.8;
}
probability ( Dyspnoea | Cancer ) {
  (True) 0.65, 0.35;
  (False) 0.3, 0.7;
}


================================================
FILE: data/cmu.bn
================================================
{
    "V": ["Burglary", "Earthquake", "Alarm", "JohnCalls", "MaryCalls"],
    "E": {
        "Burglary" : ["Alarm"],
        "Earthquake" : ["Alarm"],
        "Alarm" : [ "JohnCalls","MaryCalls"],
        "JohnCalls" : [],
        "MaryCalls" : []
    },
    "F": {
        "Burglary": {
            "values": ["No", "Yes"],
            "parents": [],
            "cpt": [0.999,0.001]
        },

        "Earthquake": {
            "values": ["No", "Yes"],
            "parents": [],
            "cpt": [0.998,0.002]
        },

        "Alarm": {
            "values": ["No","Yes"],
            "parents": ["Earthquake","Burglary"],
            "cpt": [0.999,0.001,0.71,0.29,0.06,0.94,0.05,0.95]
        },

        "JohnCalls": {
            "values": ["No","Yes"],
            "parents": ["Alarm"],
            "cpt": [0.95,0.05,0.1,0.9]
        },

        "MaryCalls": {
            "values": ["No", "Yes"],
            "parents": ["Alarm"],
            "cpt":  [0.99,0.01,0.3,0.7]
        }
    }
}

================================================
FILE: data/earthquake.bn
================================================
{
    "V": ["Burglary", "Earthquake", "Alarm", "JohnCalls", "MaryCalls"],
    "E": {
        "Burglary" : ["Alarm"],
        "Earthquake" : ["Alarm"],
        "Alarm" : [ "JohnCalls","MaryCalls"],
        "JohnCalls" : [],
        "MaryCalls" : []
    },
    "F": {
        "Burglary": {
            "values": ["No", "Yes"],
            "parents": [],
            "cpt": [0.99,0.01]
        },

        "Earthquake": {
            "values": ["No", "Yes"],
            "parents": [],
            "cpt": [0.98,0.02]
        },

        "Alarm": {
            "values": ["No","Yes"],
            "parents": ["Earthquake","Burglary"],
            "cpt": [0.999,0.001,0.71,0.29,0.06,0.94,0.05,0.95]
        },

        "JohnCalls": {
            "values": ["No","Yes"],
            "parents": ["Alarm"],
            "cpt": [0.95,0.05,0.1,0.9]
        },

        "MaryCalls": {
            "values": ["No", "Yes"],
            "parents": ["Alarm"],
            "cpt":  [0.99,0.01,0.3,0.7]
        }
    }
}

================================================
FILE: data/flags/README.txt
================================================
Flags Dataset
=============

This dataset contains data about nations and their national flags. The multi-label 
classification task is to predict the colors that appear on the flags.

Dataset characteristics:
------------------------

instances = 194 

attributes = 19 (9 nominal and 10 numeric)

labels = 7  (L = {red, green, blue, yellow, white, black, orange})

label cardinality = 3.39 

domain = images


Citation Request:
-----------------

Please cite the following: 

@inproceedings{gpf13,
 author = {Eduardo Corr\^{e}a Gon\c{c}alves and Alexandre Plastino and Alex A. Freitas},
 title = {A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains},
 booktitle = {Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence},
 series = {ICTAI'13},
 address = {Washington, D.C., USA},
 month = {November},
 pages = {469--476},
 year = {2013}
}


The original data can be found at the UCI repository 
(http://archive.ics.uci.edu/ml/machine-learning-databases/flags/). 




================================================
FILE: data/flags/flags-test.arff
================================================
@relation flags_ml

@attribute landmass {1,2,3,4,5,6}
@attribute zone {1,2,3,4}
@attribute area numeric
@attribute population numeric
@attribute language {1,2,3,4,5,6,7,8,9,10}
@attribute religion {0,1,2,3,4,5,6,7}
@attribute bars numeric
@attribute stripes numeric
@attribute colours numeric
@attribute circles numeric
@attribute crosses numeric
@attribute saltires numeric
@attribute quarters numeric
@attribute sunstars numeric
@attribute crescent {0,1}
@attribute triangle {0,1}
@attribute icon {0,1}
@attribute animate {0,1}
@attribute text {0,1}
@attribute red {0,1}
@attribute green {0,1}
@attribute blue {0,1}
@attribute yellow {0,1}
@attribute white {0,1}
@attribute black {0,1}
@attribute orange {0,1}

@data
4,1,1001,47,8,2,0,3,4,0,0,0,0,0,0,0,0,1,1,1,0,0,1,1,1,0
2,3,178,3,2,0,0,9,3,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1,0,0
2,4,76,2,2,0,0,0,3,0,0,0,4,2,0,0,0,0,0,1,0,1,0,1,0,0
6,2,0,0,1,1,0,0,5,0,1,1,1,9,0,0,0,0,0,1,0,1,1,1,0,0
5,1,47,1,10,3,0,0,4,4,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,1
1,4,0,0,1,1,3,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0
5,1,121,18,10,6,0,5,3,1,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,0
3,1,301,57,6,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
5,1,0,0,10,2,0,0,3,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0
4,1,2388,20,8,2,2,0,3,0,0,0,0,1,1,0,0,0,0,1,1,0,0,1,0,0
4,1,1267,5,3,2,0,3,3,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1
5,1,435,14,8,2,0,3,4,0,0,0,0,3,0,0,0,0,0,1,1,0,0,1,1,0
3,4,92,10,6,0,0,0,5,1,0,0,0,0,0,0,1,0,0,1,1,1,1,1,0,0
4,1,583,17,10,5,0,5,4,1,0,0,0,0,0,0,1,0,0,1,1,0,0,1,1,0
4,2,783,12,10,5,0,5,5,0,0,0,0,1,0,1,1,0,0,1,1,0,1,1,1,0
5,1,2150,9,8,2,0,0,2,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0
6,2,30,0,1,1,0,0,4,0,0,0,0,5,0,1,0,0,0,0,1,1,1,1,0,0
2,4,1139,28,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
5,1,6,0,10,2,0,0,4,0,0,0,0,0,0,1,1,1,1,1,0,0,1,1,1,0
4,2,28,4,10,5,0,0,3,1,0,1,0,3,0,0,0,0,0,1,1,0,0,1,0,0
4,2,2,1,1,4,0,4,4,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0
3,1,337,5,9,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
4,2,1221,29,6,1,0,3,5,0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,1
6,1,0,0,10,1,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0
1,4,0,0,1,1,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,0
1,4,0,0,1,1,0,0,6,1,0,0,0,10,0,0,0,1,0,1,1,1,1,1,1,0
3,1,237,22,6,6,3,0,7,0,0,0,0,2,0,0,1,1,1,1,1,1,1,1,0,1
6,3,4,0,3,0,0,3,5,1,0,0,0,1,0,0,1,0,0,1,0,1,1,1,1,0
4,1,113,3,3,5,0,0,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0
6,1,1,0,10,1,0,0,2,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,0,0
3,1,0,0,4,0,0,2,3,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0
2,4,215,1,1,4,0,0,5,0,0,0,0,0,0,1,0,0,0,1,1,0,1,1,1,0
1,4,0,0,1,1,0,1,5,0,0,0,0,1,0,1,0,0,0,1,0,1,1,1,1,0
4,4,239,14,1,5,0,3,4,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0
3,1,41,6,4,1,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
4,2,600,1,10,5,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0
6,3,0,0,1,1,0,0,4,1,1,1,1,15,0,0,0,0,0,1,0,1,0,1,0,0
5,1,11,0,8,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1
6,2,15,0,6,1,0,0,4,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,1,0
5,1,1,3,7,3,0,2,2,0,0,0,0,5,1,0,0,0,0,1,0,0,0,1,0,0
3,1,313,36,5,6,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
1,4,9976,24,1,1,2,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0
5,1,66,15,10,3,2,0,4,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0,1
3,1,0,0,6,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
3,1,0,0,6,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
1,4,0,0,1,1,0,0,7,0,2,1,1,0,0,0,1,1,0,1,1,1,1,1,1,0
4,1,22,0,3,2,0,0,4,0,0,0,0,1,0,1,0,0,0,1,1,1,0,1,0,0
4,2,1247,7,10,5,0,2,3,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0
5,1,781,45,9,2,0,0,2,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0
3,1,43,5,6,1,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
3,1,450,8,6,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0
4,1,623,2,10,5,1,0,5,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,0,0
1,4,9,3,2,0,0,5,3,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0
4,2,2,0,3,2,0,0,2,0,0,0,0,4,1,0,0,0,0,0,1,0,0,1,0,0
4,2,26,5,10,5,3,0,4,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,1,0
1,4,0,0,6,1,0,1,3,0,0,0,0,6,0,0,0,0,0,1,0,1,0,1,0,0
3,1,84,8,4,0,0,3,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
4,2,30,1,10,5,2,0,4,0,0,0,0,0,0,0,1,0,0,1,1,1,0,1,0,0
5,1,333,13,10,2,0,14,4,0,0,0,1,1,1,0,0,0,0,1,0,1,1,1,0,0
3,1,324,4,6,1,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
4,4,1240,7,3,2,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0
6,2,463,3,1,5,0,0,4,0,0,0,0,5,0,1,0,1,0,1,0,0,1,1,1,0
3,1,31,10,6,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0
1,4,128,3,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
2,4,5,1,1,1,0,0,3,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,1,0


================================================
FILE: data/flags/flags-train.arff
================================================
@relation flags_ml

@attribute landmass {1,2,3,4,5,6}
@attribute zone {1,2,3,4}
@attribute area numeric
@attribute population numeric
@attribute language {1,2,3,4,5,6,7,8,9,10}
@attribute religion {0,1,2,3,4,5,6,7}
@attribute bars numeric
@attribute stripes numeric
@attribute colours numeric
@attribute circles numeric
@attribute crosses numeric
@attribute saltires numeric
@attribute quarters numeric
@attribute sunstars numeric
@attribute crescent {0,1}
@attribute triangle {0,1}
@attribute icon {0,1}
@attribute animate {0,1}
@attribute text {0,1}
@attribute red {0,1}
@attribute green {0,1}
@attribute blue {0,1}
@attribute yellow {0,1}
@attribute white {0,1}
@attribute black {0,1}
@attribute orange {0,1}

@data
4,1,164,7,8,2,0,0,2,1,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0
4,4,111,1,10,5,0,11,3,0,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0
1,4,0,0,1,1,0,0,3,1,0,0,0,7,0,1,0,1,0,1,1,0,1,0,0,0
1,4,109,8,2,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
4,4,10,1,1,5,0,5,4,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0
1,4,2176,0,6,1,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
2,3,12,0,1,1,0,0,6,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,0
5,1,3268,684,6,4,0,3,4,1,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,1
4,4,1031,2,8,2,0,0,2,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0
2,3,8512,119,6,0,0,0,4,1,0,0,0,22,0,0,0,0,1,0,1,1,1,1,0,0
6,2,1,0,10,1,0,0,2,0,1,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0
3,4,70,3,1,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1
4,4,196,6,3,2,3,0,3,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0
1,4,0,0,1,1,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1
6,1,0,0,1,1,0,0,4,0,0,0,0,1,0,0,1,1,0,1,0,1,1,1,0,0
5,1,333,60,10,6,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0
4,1,1760,3,8,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
6,3,3,0,1,1,0,0,3,0,0,0,1,5,0,0,0,0,0,1,0,1,0,1,0,0
1,4,0,0,1,1,0,0,5,0,0,0,0,2,0,1,0,0,0,1,1,0,1,1,1,0
5,1,195,9,8,2,0,3,4,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,1,0
1,4,28,6,3,0,2,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0
3,1,249,61,4,1,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0
4,2,945,18,10,5,0,0,4,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,0
4,1,57,2,3,7,0,5,4,0,0,0,1,1,0,0,0,0,0,1,1,0,1,1,0,0
3,1,128,15,5,6,0,0,3,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0
3,4,505,38,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0
4,2,342,2,10,5,0,0,3,0,0,0,0,1,0,0,1,1,0,1,1,0,1,0,0,0
3,1,132,10,6,1,0,9,2,0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0
5,1,185,10,8,2,0,3,4,0,0,0,0,2,0,0,0,0,0,1,1,0,0,1,1,0
6,2,268,2,1,1,0,0,3,0,1,1,1,4,0,0,0,0,0,1,0,1,0,1,0,0
4,2,905,28,10,5,0,0,4,1,0,0,0,0,0,0,1,1,0,1,1,0,1,0,0,1
5,1,804,84,6,2,1,0,2,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0
4,2,753,6,10,5,3,0,4,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,1,1
2,4,91,0,3,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
3,1,547,54,3,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
4,4,4,0,6,0,1,2,5,0,0,0,0,1,0,0,0,1,0,1,1,0,1,0,1,1
1,4,9363,231,1,1,0,13,3,0,0,0,1,50,0,0,0,0,0,1,0,1,0,1,0,0
4,1,474,8,3,1,3,0,3,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0
5,1,514,49,10,3,0,5,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
1,4,0,0,1,1,0,0,6,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0
5,1,181,6,10,3,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0
4,4,246,6,3,2,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0
5,1,98,2,8,2,0,3,4,0,0,0,0,1,0,1,0,0,0,1,1,0,0,1,1,0
3,1,256,22,6,6,0,3,4,0,0,0,0,1,0,0,0,0,0,1,0,1,1,1,0,0
5,1,212,1,8,2,0,2,3,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0
3,4,103,0,6,1,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
4,1,637,5,10,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0
3,1,108,17,4,6,0,3,3,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0
4,2,268,1,10,5,0,3,3,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0
5,1,1566,2,10,6,3,0,3,2,0,0,0,1,1,1,1,0,0,1,0,1,1,0,0,0
1,4,0,0,1,1,0,0,6,0,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,1
4,4,323,7,3,5,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
5,1,372,118,9,7,0,0,2,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0
3,1,0,0,3,0,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
4,1,1284,4,3,5,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
4,1,925,56,10,2,3,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0
3,4,245,56,1,1,0,0,3,0,1,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0
3,4,1,0,6,1,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
2,3,757,11,2,0,0,2,3,0,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0
6,1,300,48,10,0,0,0,4,0,0,0,0,4,0,1,0,0,0,1,0,1,1,1,0,0
3,1,93,11,9,6,0,3,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
4,1,2506,20,8,2,0,3,4,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0
5,1,36,18,7,3,0,0,3,1,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0
5,1,99,39,10,7,0,0,4,1,0,0,0,0,0,0,1,0,0,1,0,1,0,1,1,0
4,4,72,3,1,5,0,3,3,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0
5,1,288,2,8,2,0,3,4,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,1,0
1,4,21,5,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
4,1,28,0,10,5,0,3,4,0,0,0,0,0,0,1,0,0,0,1,1,1,0,1,0,0
2,3,2777,28,2,0,0,3,3,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0
1,4,0,0,1,1,5,0,4,0,0,0,0,0,0,0,1,1,1,0,1,1,1,1,0,0
4,4,274,7,3,5,0,2,3,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0
3,1,41,14,6,1,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
2,3,2777,28,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
1,4,112,4,2,0,0,3,2,0,0,0,0,5,0,0,0,0,0,0,0,1,0,1,0,0
6,3,0,0,1,1,0,0,4,1,1,1,1,5,0,0,0,0,0,1,0,1,1,1,0,0
5,1,84,1,8,2,1,3,4,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0
4,3,0,0,1,1,0,0,7,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,0,1
6,2,1904,157,10,2,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
4,4,447,20,8,2,0,0,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0
4,4,36,1,6,5,1,2,4,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0
1,4,0,0,1,1,0,0,6,1,1,1,1,4,0,0,1,1,1,1,1,1,1,1,0,1
5,1,1,0,8,2,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
4,2,118,6,10,5,0,3,3,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,0
6,2,0,0,10,1,0,3,3,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0
6,2,7690,15,1,1,0,0,3,0,1,1,1,6,0,0,0,0,0,1,0,1,0,1,0,0
1,4,19,0,1,1,0,3,3,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0
2,3,1099,6,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0
1,4,115,10,2,6,0,5,3,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0
3,1,9,1,6,1,0,0,3,0,0,0,0,0,0,0,1,1,0,0,1,0,1,1,0,0
3,1,0,0,10,0,2,0,3,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0
1,4,23,0,1,1,0,2,8,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1
4,2,391,8,10,5,0,7,5,0,0,0,0,1,0,1,1,1,0,1,1,0,1,1,1,0
6,3,0,0,1,1,0,0,5,0,0,0,0,0,0,1,1,1,0,1,0,1,1,1,0,1
5,1,1648,39,6,2,0,3,3,0,0,0,0,0,0,0,1,0,1,1,1,0,0,1,0,0
6,2,18,1,1,1,0,0,7,0,2,1,1,0,0,0,1,1,0,1,1,1,1,1,0,1
2,3,407,3,2,0,0,3,6,1,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,0
4,2,587,9,10,1,1,2,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
5,1,140,16,10,4,0,0,3,0,0,0,0,2,1,0,0,0,0,0,0,1,0,1,0,1
1,4,1973,77,2,0,3,0,4,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1
1,4,49,6,2,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
5,1,10,3,8,2,0,2,4,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1
3,1,0,0,6,0,2,0,4,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,1,0
5,1,236,3,10,6,0,3,3,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
1,4,0,0,1,1,0,0,6,0,1,1,1,0,0,0,1,1,0,1,1,1,1,1,0,1
4,2,17,1,10,1,0,5,7,0,0,0,0,0,0,0,1,0,0,1,0,1,1,1,1,1
2,3,1285,14,2,0,3,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
4,1,0,0,6,0,0,3,4,0,0,0,0,2,0,1,0,0,0,1,1,0,1,0,1,0
1,4,0,0,1,1,0,0,6,1,1,1,1,0,0,0,1,1,0,1,1,1,1,1,1,0
5,1,143,90,6,2,0,0,2,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0
5,1,9561,1008,7,6,0,0,2,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0
2,3,284,8,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
1,4,11,2,1,1,0,0,3,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,1,0
5,1,22402,274,5,6,0,0,2,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0
2,4,912,15,2,0,0,3,7,0,0,0,0,7,0,0,1,1,0,1,1,1,1,1,1,1
5,1,678,35,10,3,0,0,3,0,0,0,1,14,0,0,1,1,0,1,0,1,0,1,0,0
6,1,0,0,1,1,0,0,7,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,1
3,4,0,0,1,1,0,1,3,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,0,0
1,4,51,2,2,0,0,5,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
3,1,29,3,6,6,0,0,3,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,1,0
3,1,3,0,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
5,1,1,5,7,3,0,0,6,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,1
5,1,18,2,8,2,0,3,4,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0
5,1,21,4,10,7,0,2,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0
2,4,63,0,6,1,0,5,4,0,0,0,0,1,0,0,0,0,0,1,1,0,1,1,0,0
4,1,236,13,10,5,0,6,5,1,0,0,0,0,0,0,0,1,0,1,0,0,1,1,1,0
3,1,111,9,5,6,0,3,5,0,0,0,0,1,0,0,1,1,0,1,1,1,1,1,0,0
4,2,0,0,1,1,0,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
5,1,648,16,10,2,0,3,5,0,0,0,0,1,0,0,1,0,0,1,1,0,1,1,1,0
4,1,1222,31,10,1,0,3,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0


================================================
FILE: data/flags/flags.arff
================================================
@relation flags_ml

@attribute landmass	{1, 2, 3, 4, 5, 6}
@attribute zone	{1, 2, 3, 4}
@attribute area	numeric
@attribute population numeric
@attribute language {1, 2, 3, 4, 5, 6, 7, 8, 9, 10} 
@attribute religion {0, 1, 2, 3, 4, 5, 6, 7}
@attribute bars     numeric
@attribute stripes  numeric
@attribute colours  numeric
@attribute circles  numeric
@attribute crosses  numeric
@attribute saltires numeric
@attribute quarters numeric
@attribute sunstars numeric
@attribute crescent {0, 1}
@attribute triangle {0, 1}
@attribute icon     {0, 1}
@attribute animate  {0, 1}
@attribute text     {0, 1}
@attribute red      {0, 1}
@attribute green    {0, 1}
@attribute blue     {0, 1}
@attribute yellow   {0, 1}
@attribute white    {0, 1}
@attribute black    {0, 1}
@attribute orange   {0, 1}

@data
5,1,648,16,10,2,0,3,5,0,0,0,0,1,0,0,1,0,0,1,1,0,1,1,1,0
3,1,29,3,6,6,0,0,3,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,1,0
4,1,2388,20,8,2,2,0,3,0,0,0,0,1,1,0,0,0,0,1,1,0,0,1,0,0
6,3,0,0,1,1,0,0,5,0,0,0,0,0,0,1,1,1,0,1,0,1,1,1,0,1
3,1,0,0,6,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
4,2,1247,7,10,5,0,2,3,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0
1,4,0,0,1,1,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1
1,4,0,0,1,1,0,1,5,0,0,0,0,1,0,1,0,0,0,1,0,1,1,1,1,0
2,3,2777,28,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
2,3,2777,28,2,0,0,3,3,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0
6,2,7690,15,1,1,0,0,3,0,1,1,1,6,0,0,0,0,0,1,0,1,0,1,0,0
3,1,84,8,4,0,0,3,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
1,4,19,0,1,1,0,3,3,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0
5,1,1,0,8,2,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
5,1,143,90,6,2,0,0,2,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0
1,4,0,0,1,1,3,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0
3,1,31,10,6,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0
1,4,23,0,1,1,0,2,8,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1
4,1,113,3,3,5,0,0,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0
1,4,0,0,1,1,0,0,6,1,1,1,1,0,0,0,1,1,0,1,1,1,1,1,1,0
5,1,47,1,10,3,0,0,4,4,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,1
2,3,1099,6,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0
4,2,600,1,10,5,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0
2,3,8512,119,6,0,0,0,4,1,0,0,0,22,0,0,0,0,1,0,1,1,1,1,0,0
1,4,0,0,1,1,0,0,6,0,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,1
5,1,6,0,10,2,0,0,4,0,0,0,0,0,0,1,1,1,1,1,0,0,1,1,1,0
3,1,111,9,5,6,0,3,5,0,0,0,0,1,0,0,1,1,0,1,1,1,1,1,0,0
4,4,274,7,3,5,0,2,3,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0
5,1,678,35,10,3,0,0,3,0,0,0,1,14,0,0,1,1,0,1,0,1,0,1,0,0
4,2,28,4,10,5,0,0,3,1,0,1,0,3,0,0,0,0,0,1,1,0,0,1,0,0
4,1,474,8,3,1,3,0,3,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0
1,4,9976,24,1,1,2,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0
4,4,4,0,6,0,1,2,5,0,0,0,0,1,0,0,0,1,0,1,1,0,1,0,1,1
1,4,0,0,1,1,0,0,6,1,1,1,1,4,0,0,1,1,1,1,1,1,1,1,0,1
4,1,623,2,10,5,1,0,5,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,0,0
4,1,1284,4,3,5,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
2,3,757,11,2,0,0,2,3,0,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0
5,1,9561,1008,7,6,0,0,2,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0
2,4,1139,28,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
4,2,2,0,3,2,0,0,2,0,0,0,0,4,1,0,0,0,0,0,1,0,0,1,0,0
4,2,342,2,10,5,0,0,3,0,0,0,0,1,0,0,1,1,0,1,1,0,1,0,0,0
6,3,0,0,1,1,0,0,4,1,1,1,1,15,0,0,0,0,0,1,0,1,0,1,0,0
1,4,51,2,2,0,0,5,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
1,4,115,10,2,6,0,5,3,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0
3,1,9,1,6,1,0,0,3,0,0,0,0,0,0,0,1,1,0,0,1,0,1,1,0,0
3,1,128,15,5,6,0,0,3,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0
3,1,43,5,6,1,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
4,1,22,0,3,2,0,0,4,0,0,0,0,1,0,1,0,0,0,1,1,1,0,1,0,0
1,4,0,0,1,1,0,0,6,1,0,0,0,10,0,0,0,1,0,1,1,1,1,1,1,0
1,4,49,6,2,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
2,3,284,8,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
4,1,1001,47,8,2,0,3,4,0,0,0,0,0,0,0,0,1,1,1,0,0,1,1,1,0
1,4,21,5,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
4,1,28,0,10,5,0,3,4,0,0,0,0,0,0,1,0,0,0,1,1,1,0,1,0,0
4,1,1222,31,10,1,0,3,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0
3,4,1,0,6,1,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
2,3,12,0,1,1,0,0,6,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,0
6,2,18,1,1,1,0,0,7,0,2,1,1,0,0,0,1,1,0,1,1,1,1,1,0,1
3,1,337,5,9,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
3,1,547,54,3,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
2,4,91,0,3,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
6,3,4,0,3,0,0,3,5,1,0,0,0,1,0,0,1,0,0,1,0,1,1,1,1,0
4,2,268,1,10,5,0,3,3,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0
4,4,10,1,1,5,0,5,4,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0
3,1,108,17,4,6,0,3,3,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0
3,1,249,61,4,1,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0
4,4,239,14,1,5,0,3,4,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0
3,4,0,0,1,1,0,1,3,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,0,0
3,1,132,10,6,1,0,9,2,0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0
1,4,2176,0,6,1,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
1,4,0,0,1,1,0,0,3,1,0,0,0,7,0,1,0,1,0,1,1,0,1,0,0,0
6,1,0,0,1,1,0,0,7,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,1
1,4,109,8,2,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
4,4,246,6,3,2,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0
4,4,36,1,6,5,1,2,4,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0
2,4,215,1,1,4,0,0,5,0,0,0,0,0,0,1,0,0,0,1,1,0,1,1,1,0
1,4,28,6,3,0,2,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0
1,4,112,4,2,0,0,3,2,0,0,0,0,5,0,0,0,0,0,0,0,1,0,1,0,0
5,1,1,5,7,3,0,0,6,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,1
3,1,93,11,9,6,0,3,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
3,4,103,0,6,1,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
5,1,3268,684,6,4,0,3,4,1,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,1
6,2,1904,157,10,2,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
5,1,1648,39,6,2,0,3,3,0,0,0,0,0,0,0,1,0,1,1,1,0,0,1,0,0
5,1,435,14,8,2,0,3,4,0,0,0,0,3,0,0,0,0,0,1,1,0,0,1,1,0
3,4,70,3,1,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1
5,1,21,4,10,7,0,2,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0
3,1,301,57,6,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
4,4,323,7,3,5,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
1,4,11,2,1,1,0,0,3,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,1,0
5,1,372,118,9,7,0,0,2,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0
5,1,98,2,8,2,0,3,4,0,0,0,0,1,0,1,0,0,0,1,1,0,0,1,1,0
5,1,181,6,10,3,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0
4,1,583,17,10,5,0,5,4,1,0,0,0,0,0,0,1,0,0,1,1,0,0,1,1,0
6,1,0,0,1,1,0,0,4,0,0,0,0,1,0,0,1,1,0,1,0,1,1,1,0,0
5,1,18,2,8,2,0,3,4,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0
5,1,236,3,10,6,0,3,3,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
5,1,10,3,8,2,0,2,4,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1
4,2,30,1,10,5,2,0,4,0,0,0,0,0,0,0,1,0,0,1,1,1,0,1,0,0
4,4,111,1,10,5,0,11,3,0,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0
4,1,1760,3,8,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
3,1,0,0,4,0,0,2,3,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0
3,1,3,0,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
4,2,587,9,10,1,1,2,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
4,2,118,6,10,5,0,3,3,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,0
5,1,333,13,10,2,0,14,4,0,0,0,1,1,1,0,0,0,0,1,0,1,1,1,0,0
5,1,0,0,10,2,0,0,3,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0
4,4,1240,7,3,2,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0
3,1,0,0,10,0,2,0,3,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0
6,1,0,0,10,1,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0
4,4,1031,2,8,2,0,0,2,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0
4,2,2,1,1,4,0,4,4,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0
1,4,1973,77,2,0,3,0,4,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1
6,1,1,0,10,1,0,0,2,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,0,0
3,1,0,0,3,0,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
5,1,1566,2,10,6,3,0,3,2,0,0,0,1,1,1,1,0,0,1,0,1,1,0,0,0
1,4,0,0,1,1,0,0,7,0,2,1,1,0,0,0,1,1,0,1,1,1,1,1,1,0
4,4,447,20,8,2,0,0,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0
4,2,783,12,10,5,0,5,5,0,0,0,0,1,0,1,1,0,0,1,1,0,1,1,1,0
6,2,0,0,10,1,0,3,3,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0
5,1,140,16,10,4,0,0,3,0,0,0,0,2,1,0,0,0,0,0,0,1,0,1,0,1
3,1,41,14,6,1,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
1,4,0,0,6,1,0,1,3,0,0,0,0,6,0,0,0,0,0,1,0,1,0,1,0,0
6,2,268,2,1,1,0,0,3,0,1,1,1,4,0,0,0,0,0,1,0,1,0,1,0,0
1,4,128,3,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
4,1,1267,5,3,2,0,3,3,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1
4,1,925,56,10,2,3,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0
6,3,0,0,1,1,0,0,4,1,1,1,1,5,0,0,0,0,0,1,0,1,1,1,0,0
5,1,121,18,10,6,0,5,3,1,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,0
5,1,195,9,8,2,0,3,4,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,1,0
3,1,324,4,6,1,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
5,1,212,1,8,2,0,2,3,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0
5,1,804,84,6,2,1,0,2,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0
2,4,76,2,2,0,0,0,3,0,0,0,4,2,0,0,0,0,0,1,0,1,0,1,0,0
6,2,463,3,1,5,0,0,4,0,0,0,0,5,0,1,0,1,0,1,0,0,1,1,1,0
2,3,407,3,2,0,0,3,6,1,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,0
2,3,1285,14,2,0,3,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
6,1,300,48,10,0,0,0,4,0,0,0,0,4,0,1,0,0,0,1,0,1,1,1,0,0
3,1,313,36,5,6,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
3,4,92,10,6,0,0,0,5,1,0,0,0,0,0,0,1,0,0,1,1,1,1,1,0,0
1,4,9,3,2,0,0,5,3,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0
5,1,11,0,8,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1
3,1,237,22,6,6,3,0,7,0,0,0,0,2,0,0,1,1,1,1,1,1,1,1,0,1
4,2,26,5,10,5,3,0,4,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,1,0
3,1,0,0,6,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
4,1,0,0,6,0,0,3,4,0,0,0,0,2,0,1,0,0,0,1,1,0,1,0,1,0
5,1,2150,9,8,2,0,0,2,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0
4,4,196,6,3,2,3,0,3,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0
4,2,0,0,1,1,0,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
4,4,72,3,1,5,0,3,3,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0
5,1,1,3,7,3,0,2,2,0,0,0,0,5,1,0,0,0,0,1,0,0,0,1,0,0
6,2,30,0,1,1,0,0,4,0,0,0,0,5,0,1,0,0,0,0,1,1,1,1,0,0
4,1,637,5,10,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0
4,2,1221,29,6,1,0,3,5,0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,1
5,1,99,39,10,7,0,0,4,1,0,0,0,0,0,0,1,0,0,1,0,1,0,1,1,0
5,1,288,2,8,2,0,3,4,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,1,0
3,4,505,38,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0
5,1,66,15,10,3,2,0,4,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0,1
4,3,0,0,1,1,0,0,7,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,0,1
1,4,0,0,1,1,0,0,5,0,0,0,0,2,0,1,0,0,0,1,1,0,1,1,1,0
1,4,0,0,1,1,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,0
1,4,0,0,1,1,5,0,4,0,0,0,0,0,0,0,1,1,1,0,1,1,1,1,0,0
4,1,2506,20,8,2,0,3,4,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0
2,4,63,0,6,1,0,5,4,0,0,0,0,1,0,0,0,0,0,1,1,0,1,1,0,0
4,2,17,1,10,1,0,5,7,0,0,0,0,0,0,0,1,0,0,1,0,1,1,1,1,1
3,1,450,8,6,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0
3,1,41,6,4,1,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
5,1,185,10,8,2,0,3,4,0,0,0,0,2,0,0,0,0,0,1,1,0,0,1,1,0
5,1,36,18,7,3,0,0,3,1,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0
4,2,945,18,10,5,0,0,4,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,0
5,1,514,49,10,3,0,5,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
4,1,57,2,3,7,0,5,4,0,0,0,1,1,0,0,0,0,0,1,1,0,1,1,0,0
6,2,1,0,10,1,0,0,2,0,1,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0
2,4,5,1,1,1,0,0,3,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,1,0
4,1,164,7,8,2,0,0,2,1,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0
5,1,781,45,9,2,0,0,2,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0
1,4,0,0,1,1,0,0,6,0,1,1,1,0,0,0,1,1,0,1,1,1,1,1,0,1
6,2,0,0,1,1,0,0,5,0,1,1,1,9,0,0,0,0,0,1,0,1,1,1,0,0
5,1,84,1,8,2,1,3,4,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0
4,1,236,13,10,5,0,6,5,1,0,0,0,0,0,0,0,1,0,1,0,0,1,1,1,0
3,4,245,56,1,1,0,0,3,0,1,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0
2,3,178,3,2,0,0,9,3,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1,0,0
1,4,0,0,1,1,0,0,6,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0
1,4,9363,231,1,1,0,13,3,0,0,0,1,50,0,0,0,0,0,1,0,1,0,1,0,0
5,1,22402,274,5,6,0,0,2,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0
6,2,15,0,6,1,0,0,4,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,1,0
3,1,0,0,6,0,2,0,4,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,1,0
2,4,912,15,2,0,0,3,7,0,0,0,0,7,0,0,1,1,0,1,1,1,1,1,1,1
5,1,333,60,10,6,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0
6,3,3,0,1,1,0,0,3,0,0,0,1,5,0,0,0,0,0,1,0,1,0,1,0,0
3,1,256,22,6,6,0,3,4,0,0,0,0,1,0,0,0,0,0,1,0,1,1,1,0,0
4,2,905,28,10,5,0,0,4,1,0,0,0,0,0,0,1,1,0,1,1,0,1,0,0,1
4,2,753,6,10,5,3,0,4,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,1,1
4,2,391,8,10,5,0,7,5,0,0,0,0,1,0,1,1,1,0,1,1,0,1,1,1,0

================================================
FILE: data/flags/flags.xml
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================================================
FILE: data/flags.txt
================================================
4,1,1001,47,8,2,0,3,4,0,0,0,0,0,0,0,0,1,1,1,0,0,1,1,1,0
2,3,178,3,2,0,0,9,3,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1,0,0
2,4,76,2,2,0,0,0,3,0,0,0,4,2,0,0,0,0,0,1,0,1,0,1,0,0
6,2,0,0,1,1,0,0,5,0,1,1,1,9,0,0,0,0,0,1,0,1,1,1,0,0
5,1,47,1,10,3,0,0,4,4,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,1
1,4,0,0,1,1,3,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0
5,1,121,18,10,6,0,5,3,1,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,0
3,1,301,57,6,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0
5,1,0,0,10,2,0,0,3,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0
4,1,2388,20,8,2,2,0,3,0,0,0,0,1,1,0,0,0,0,1,1,0,0,1,0,0
4,1,1267,5,3,2,0,3,3,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1
5,1,435,14,8,2,0,3,4,0,0,0,0,3,0,0,0,0,0,1,1,0,0,1,1,0
3,4,92,10,6,0,0,0,5,1,0,0,0,0,0,0,1,0,0,1,1,1,1,1,0,0
4,1,583,17,10,5,0,5,4,1,0,0,0,0,0,0,1,0,0,1,1,0,0,1,1,0
4,2,783,12,10,5,0,5,5,0,0,0,0,1,0,1,1,0,0,1,1,0,1,1,1,0
5,1,2150,9,8,2,0,0,2,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0
6,2,30,0,1,1,0,0,4,0,0,0,0,5,0,1,0,0,0,0,1,1,1,1,0,0
2,4,1139,28,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
5,1,6,0,10,2,0,0,4,0,0,0,0,0,0,1,1,1,1,1,0,0,1,1,1,0
4,2,28,4,10,5,0,0,3,1,0,1,0,3,0,0,0,0,0,1,1,0,0,1,0,0
4,2,2,1,1,4,0,4,4,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0
3,1,337,5,9,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
4,2,1221,29,6,1,0,3,5,0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,1
6,1,0,0,10,1,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0
1,4,0,0,1,1,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,0
1,4,0,0,1,1,0,0,6,1,0,0,0,10,0,0,0,1,0,1,1,1,1,1,1,0
3,1,237,22,6,6,3,0,7,0,0,0,0,2,0,0,1,1,1,1,1,1,1,1,0,1
6,3,4,0,3,0,0,3,5,1,0,0,0,1,0,0,1,0,0,1,0,1,1,1,1,0
4,1,113,3,3,5,0,0,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0
6,1,1,0,10,1,0,0,2,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,0,0
3,1,0,0,4,0,0,2,3,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0
2,4,215,1,1,4,0,0,5,0,0,0,0,0,0,1,0,0,0,1,1,0,1,1,1,0
1,4,0,0,1,1,0,1,5,0,0,0,0,1,0,1,0,0,0,1,0,1,1,1,1,0
4,4,239,14,1,5,0,3,4,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0
3,1,41,6,4,1,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
4,2,600,1,10,5,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0
6,3,0,0,1,1,0,0,4,1,1,1,1,15,0,0,0,0,0,1,0,1,0,1,0,0
5,1,11,0,8,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1
6,2,15,0,6,1,0,0,4,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,1,0
5,1,1,3,7,3,0,2,2,0,0,0,0,5,1,0,0,0,0,1,0,0,0,1,0,0
3,1,313,36,5,6,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
1,4,9976,24,1,1,2,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0
5,1,66,15,10,3,2,0,4,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0,1
3,1,0,0,6,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0
3,1,0,0,6,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
1,4,0,0,1,1,0,0,7,0,2,1,1,0,0,0,1,1,0,1,1,1,1,1,1,0
4,1,22,0,3,2,0,0,4,0,0,0,0,1,0,1,0,0,0,1,1,1,0,1,0,0
4,2,1247,7,10,5,0,2,3,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0
5,1,781,45,9,2,0,0,2,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0
3,1,43,5,6,1,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
3,1,450,8,6,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0
4,1,623,2,10,5,1,0,5,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,0,0
1,4,9,3,2,0,0,5,3,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0
4,2,2,0,3,2,0,0,2,0,0,0,0,4,1,0,0,0,0,0,1,0,0,1,0,0
4,2,26,5,10,5,3,0,4,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,1,0
1,4,0,0,6,1,0,1,3,0,0,0,0,6,0,0,0,0,0,1,0,1,0,1,0,0
3,1,84,8,4,0,0,3,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0
4,2,30,1,10,5,2,0,4,0,0,0,0,0,0,0,1,0,0,1,1,1,0,1,0,0
5,1,333,13,10,2,0,14,4,0,0,0,1,1,1,0,0,0,0,1,0,1,1,1,0,0
3,1,324,4,6,1,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0
4,4,1240,7,3,2,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0
6,2,463,3,1,5,0,0,4,0,0,0,0,5,0,1,0,1,0,1,0,0,1,1,1,0
3,1,31,10,6,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0
1,4,128,3,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0
2,4,5,1,1,1,0,0,3,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,1,0

================================================
FILE: data/gs_data.txt
================================================
1 1 2 2 1
2 1 2 2 2
1 1 2 2 2
1 1 2 2 2
1 2 2 1 2
2 1 2 2 2
2 1 2 2 2
2 1 2 2 2
1 1 2 2 1
2 1 2 1 2
1 1 2 1 2
2 1 2 2 2
2 2 2 2 1
2 1 2 2 1
1 1 2 2 1
2 1 2 2 1
2 1 2 2 2
2 1 2 1 2
2 1 2 2 2
2 1 2 2 1
1 1 2 1 2
2 1 2 2 2
2 2 2 1 2
2 1 2 2 2
2 2 2 2 1
1 1 2 2 1
2 1 2 2 2
2 1 2 2 1
2 1 2 2 2
2 1 2 2 2
2 1 2 2 1
1 1 2 2 1
2 1 2 2 2
1 1 2 2 2
2 1 2 2 2
2 1 2 2 1
2 1 2 2 2
2 2 2 2 2
2 1 2 2 2
2 1 2 2 1
2 1 2 2 2
1 1 2 2 2
2 1 2 2 2
2 1 2 2 2
1 1 2 2 2
1 1 2 2 2
1 1 2 1 2
2 2 2 2 2
2 1 2 2 2
2 1 2 2 2
2 2 1 1 1
2 1 2 1 2
2 1 2 2 1
2 1 2 2 2
2 1 2 1 2
2 1 2 2 2
2 1 2 2 1
2 1 2 2 2
2 2 2 2 2
1 1 2 2 2
2 1 2 2 2
2 1 2 2 1
2 1 2 2 2
2 1 2 2 1
2 1 2 2 2
2 1 2 2 1
1 1 2 2 1
2 1 2 2 1
1 2 2 2 1
1 1 2 2 2
2 1 2 2 1
1 1 2 2 1
1 1 2 2 1
1 1 2 2 2
2 1 2 2 2
2 1 2 1 2
1 1 2 2 1
2 1 2 2 1
2 1 2 2 1
1 1 2 1 2
2 1 2 1 1
1 1 2 2 2
2 1 2 2 2
1 1 2 2 2
2 1 2 2 2
2 2 2 2 1
1 1 2 2 2
2 2 2 2 2
2 1 2 1 1
1 2 2 2 2
1 1 2 2 1
2 1 2 2 2
2 1 2 2 2
1 1 2 2 2
2 1 2 2 2
2 1 2 2 2
2 1 2 1 2
1 1 2 2 1
1 1 2 2 2
1 1 2 2 2
1 1 2 2 2
2 1 2 2 2
2 1 2 2 1
2 1 2 2 2
1 1 2 2 1
1 1 2 2 2
1 1 2 2 2
1 1 2 2 2
2 1 2 2 2
2 1 2 2 1
2 1 2 2 1
2 1 2 1 1
2 2 2 2 2
2 1 2 2 2
1 1 2 2 2
2 1 2 2 2
1 1 2 2 1
2 1 2 2 1
1 1 2 2 2
1 1 2 2 2
2 2 2 1 1
2 1 2 2 2
2 1 2 1 1
2 1 2 2 2
2 1 2 2 1
2 1 2 1 1
2 1 2 1 2
2 2 2 2 2
2 1 2 1 2
2 1 2 2 2
2 1 2 2 2
2 2 2 1 1
2 1 2 2 1
2 1 2 2 2
2 1 2 2 2
2 1 2 2 1
2 1 2 2 1
2 1 2 2 2
2 1 2 2 2
2 1 2 2 2
2 1 2 2 2
2 1 2 2 2
2 1 2 2 2
2 1 2 2 1
2 1 2 2 2
1 1 2 2 2
2 1 2 2 2
2 1 2 2 1
2 1 2 2 1
2 1 2 2 1
2 1 2 2 1
2 1 2 2 2
2 1 2 2 2
2 1 2 2 2
2 1 2 2 2
2 1 2 1 2
2 1 2 2 2
2 1 2 1 2
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================================================
FILE: data/letter.csv
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T,2,8,3,5,1,8,13,0,6,6,10,8,0,8,0,8
I,5,12,3,7,2,10,5,5,4,13,3,9,2,8,4,10
D,4,11,6,8,6,10,6,2,6,10,3,7,3,7,3,9
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G,2,1,3,1,1,8,6,6,6,6,5,9,1,7,5,10
S,4,11,5,8,3,8,8,6,9,5,6,6,0,8,9,7
B,4,2,5,4,4,8,7,6,6,7,6,6,2,8,7,10
A,1,1,3,2,1,8,2,2,2,8,2,8,1,6,2,7
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X,4,9,5,6,5,7,6,3,5,6,6,9,2,8,8,8
H,3,3,4,1,2,8,7,5,6,7,6,8,5,8,3,7
L,2,3,2,4,1,0,1,5,6,0,0,6,0,8,0,8
H,3,5,5,4,3,7,8,3,6,10,6,8,3,8,3,8
E,2,3,3,2,2,7,7,5,7,7,6,8,2,8,5,10
Y,5,10,6,7,6,9,6,6,4,7,8,7,6,9,8,3
H,8,12,8,6,4,9,8,4,5,8,4,5,6,9,5,9
Q,5,10,5,5,4,9,6,5,6,10,6,7,4,8,9,9
Q,3,8,5,7,3,8,7,9,6,6,6,9,3,8,5,9
Q,4,6,6,4,5,8,5,6,4,6,7,10,4,7,5,9
J,2,8,3,6,1,13,3,8,4,13,3,11,1,6,0,8
J,4,7,5,5,2,8,9,1,7,14,5,6,0,8,1,8
D,4,5,5,4,3,7,7,7,7,7,6,5,2,8,3,7
R,2,6,4,4,3,6,7,5,5,6,5,7,3,7,5,8
M,4,5,7,4,4,6,6,3,4,9,9,10,7,5,2,8
A,3,7,5,5,3,12,2,3,2,10,2,9,2,6,3,8
N,3,5,4,4,2,7,8,5,4,7,7,7,6,9,2,5
M,6,10,9,7,8,3,7,4,5,10,11,10,7,9,3,7
P,8,14,7,8,4,5,10,6,3,12,5,4,4,10,4,8
C,6,12,5,7,4,8,5,4,3,9,9,11,4,9,8,10
P,6,10,8,8,7,8,5,7,5,7,6,6,3,9,8,9
N,2,3,3,1,1,5,9,3,3,10,8,8,4,8,0,8
W,3,4,4,3,2,9,10,3,2,5,9,7,6,11,0,8
V,3,8,5,6,6,8,7,4,1,8,7,8,5,10,4,7
V,6,9,9,8,10,8,7,5,5,7,6,8,8,9,9,3
M,6,7,9,5,7,4,7,3,5,10,10,11,8,6,3,7
E,4,8,5,6,4,7,7,4,8,11,8,9,2,9,5,7
N,6,11,8,8,9,5,8,3,4,8,8,9,7,9,5,4
Y,8,10,8,7,4,3,10,3,7,11,12,6,1,11,3,5
W,4,8,5,6,3,6,8,4,1,7,8,8,8,9,0,8
O,6,7,8,6,6,6,6,5,6,8,5,8,3,6,5,6
N,4,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8
H,4,8,5,6,5,7,10,8,5,8,5,6,3,6,7,11
O,4,7,5,5,3,8,7,8,5,10,6,8,3,8,3,8
N,4,8,5,6,4,7,7,9,4,6,4,6,3,7,3,8
H,4,9,5,6,2,7,6,15,1,7,7,8,3,8,0,8
U,7,11,8,9,4,3,9,6,7,11,11,10,3,9,2,6
Y,2,7,3,5,1,6,10,0,3,7,11,8,1,11,0,8
S,6,8,7,6,4,6,9,4,8,11,7,6,2,8,5,5
S,5,9,6,7,5,7,7,7,5,7,6,7,2,8,9,8
T,2,3,2,2,1,7,11,2,6,7,10,8,1,11,1,7
C,2,3,3,2,1,4,7,4,6,10,9,13,1,9,2,7
V,4,10,7,7,2,9,8,5,3,5,14,8,3,9,0,8
T,5,8,5,6,3,6,12,4,7,12,9,4,2,12,2,4
K,7,12,6,6,3,7,8,2,7,9,7,8,5,8,3,7
D,3,8,4,6,4,7,8,7,6,7,7,5,3,8,3,7
W,2,1,3,1,1,7,8,4,0,7,8,8,6,10,0,8
I,2,9,3,7,2,8,7,0,7,13,6,9,0,8,1,8
E,3,8,3,6,2,3,7,6,10,7,6,14,0,8,7,8
F,6,9,8,7,4,6,12,6,6,13,7,3,2,9,2,5
I,3,9,4,7,3,7,7,0,7,13,6,8,0,8,1,8
H,5,10,7,8,8,7,7,6,6,7,6,9,3,8,4,8
U,4,7,4,5,2,7,5,13,5,7,14,7,3,9,0,8
C,2,3,3,2,1,6,8,7,7,9,7,12,1,10,3,9
F,5,9,6,7,7,7,6,6,4,7,7,8,5,10,9,12
A,3,8,5,6,3,9,2,2,3,8,2,8,2,6,3,7
Z,3,7,5,5,3,7,8,2,9,11,7,7,1,8,6,7
X,3,7,5,5,2,10,5,2,8,10,1,7,3,7,3,10
N,3,3,5,2,2,5,10,3,4,10,8,7,5,8,1,8
R,6,9,5,4,3,10,6,5,5,10,2,8,6,6,4,9
B,3,3,3,4,3,7,7,5,5,7,6,6,5,8,5,10
B,5,9,7,7,7,8,8,3,6,10,5,6,3,7,6,8
U,5,6,5,4,2,5,8,6,8,9,8,8,3,9,2,5
J,4,4,6,5,4,8,9,4,5,7,6,9,3,7,8,8
F,3,7,4,5,2,1,11,4,4,11,11,8,0,8,2,7
M,6,8,9,6,6,11,6,2,5,9,3,6,9,7,2,9
H,1,1,2,1,1,7,7,12,1,7,6,8,3,8,0,8
A,2,1,4,2,1,8,1,2,2,7,2,8,2,5,2,7
O,5,10,4,5,3,6,6,6,2,9,6,9,5,9,4,8
E,1,0,1,1,1,4,7,5,8,7,6,13,0,8,6,10
U,3,3,3,1,1,5,8,5,7,10,9,8,3,10,2,6
A,3,7,5,5,3,10,4,1,2,8,3,9,2,4,2,7
X,4,8,5,6,3,5,8,1,8,10,9,9,2,9,3,6
S,5,8,7,6,4,8,8,4,9,11,3,8,2,6,4,9
J,1,3,2,2,1,10,7,1,5,11,4,8,0,7,0,7
K,5,11,8,8,5,6,7,2,7,10,7,10,4,8,4,8
I,2,6,2,4,1,9,5,0,6,13,5,9,0,8,1,8
P,7,11,10,8,6,6,11,3,6,13,6,3,0,10,3,8
T,2,1,2,1,0,8,15,1,4,6,10,8,0,8,0,8
C,1,1,2,2,1,6,8,6,6,9,7,12,1,9,3,10
H,4,9,4,7,4,7,8,13,1,7,5,8,3,8,0,8
A,2,4,4,3,2,10,2,2,2,9,2,9,2,7,3,9
L,6,11,9,8,11,7,7,3,6,6,7,11,6,11,9,7
T,3,6,4,4,3,7,12,4,6,7,11,8,2,12,1,7
A,2,8,4,6,2,12,2,4,3,11,2,10,3,6,3,9
V,3,9,5,7,3,8,9,4,1,6,12,8,2,10,0,8
U,5,7,6,5,5,8,8,8,5,6,7,9,4,8,4,5
I,2,9,2,6,3,7,7,0,7,7,6,8,0,8,2,8
Z,8,14,8,8,5,8,5,2,9,12,6,9,3,7,6,7
V,4,7,6,5,6,8,8,4,2,8,7,8,7,11,5,8
M,5,9,7,6,7,9,7,5,5,6,7,6,10,7,4,6
L,4,8,6,6,4,6,5,2,8,7,2,8,1,6,3,7
O,3,3,5,5,2,9,6,9,7,7,5,10,3,8,4,8
B,4,2,5,4,4,7,7,5,6,7,6,6,2,8,7,10
A,8,15,7,8,4,8,2,3,2,8,5,12,5,4,5,5
K,6,9,9,6,7,9,6,1,7,10,3,8,3,7,3,9
H,3,7,4,5,4,7,7,5,6,7,6,8,3,8,3,8
T,7,9,7,7,5,6,10,1,8,11,9,5,2,9,3,4
W,6,5,7,4,4,4,11,2,2,9,9,8,7,11,1,7
F,3,8,3,5,1,1,12,4,5,12,11,8,0,8,2,6
A,3,3,5,4,1,7,6,3,0,7,0,8,2,7,1,8
E,3,8,3,6,3,3,6,5,9,5,4,13,0,8,6,9
M,4,9,5,7,4,7,7,12,1,7,9,8,8,6,0,8
B,4,9,5,7,6,7,8,9,6,7,6,6,2,8,8,10
M,3,7,5,5,6,7,8,6,4,6,6,8,5,8,7,9
Y,6,9,6,7,4,5,8,0,8,8,9,5,2,11,4,4
V,4,9,6,7,3,6,12,3,4,7,12,9,3,10,1,8
S,2,8,3,6,3,8,8,7,6,7,4,6,2,7,8,8
C,2,1,2,2,1,6,8,7,7,8,7,12,1,9,4,10
H,4,9,6,6,8,7,7,6,3,7,6,7,8,7,9,9
U,5,8,5,6,4,8,6,11,4,8,13,7,3,9,0,8
P,6,10,6,6,4,7,10,5,2,11,5,4,4,11,5,7
Z,3,8,4,6,2,7,7,4,13,10,6,8,0,8,8,8
E,3,4,5,2,2,7,7,2,8,11,7,9,2,8,4,8
N,5,9,7,6,4,4,10,3,4,9,10,9,5,8,1,8
P,6,10,5,6,3,5,9,6,3,10,5,6,5,9,4,7
H,4,5,5,4,4,7,7,6,6,7,6,7,3,8,3,6
T,6,11,6,8,5,6,11,3,7,11,9,5,2,12,2,4
B,5,5,5,8,4,7,9,10,8,7,5,7,2,8,9,10
U,5,10,6,7,2,7,4,14,6,7,15,8,3,9,0,8
S,3,4,4,6,2,8,8,6,9,5,5,5,0,8,9,7
F,5,6,5,8,2,1,14,5,3,12,9,5,0,8,3,6
J,3,9,4,7,4,9,6,1,6,11,4,7,0,6,1,6
R,3,6,4,4,4,6,7,5,5,7,6,7,3,7,4,9
E,3,2,3,4,3,7,8,5,7,7,5,9,2,8,5,10
P,6,9,6,4,4,8,9,5,3,10,5,5,5,10,5,6
I,2,2,1,4,1,7,7,1,7,7,6,8,0,8,3,8
K,7,10,9,8,9,7,7,5,5,7,6,7,7,6,9,13
D,4,10,6,7,4,8,6,8,7,4,5,3,3,8,4,8
B,4,7,6,5,7,9,6,4,4,6,6,7,7,8,6,7
H,5,10,7,8,6,7,9,8,5,8,5,6,3,7,7,10
M,7,10,10,7,7,9,6,2,5,9,5,7,8,6,2,8
P,6,11,7,8,3,4,13,9,2,10,6,4,1,10,4,8
O,4,8,6,6,4,8,5,8,4,7,5,8,3,8,3,8
S,2,8,3,6,3,8,7,7,5,7,6,7,2,9,8,8
K,4,7,6,5,4,3,8,3,7,11,11,12,3,8,3,5
V,7,10,7,8,3,3,11,4,4,10,12,8,3,10,1,8
F,7,11,10,8,5,6,11,2,7,14,7,4,1,10,2,7
Y,3,4,5,6,1,6,10,3,2,9,13,8,1,11,0,8
D,4,6,6,4,3,11,6,3,8,11,3,6,3,8,3,9
Z,1,0,1,1,0,7,7,2,10,8,6,8,0,8,6,8
Q,3,5,3,6,3,7,8,6,3,8,8,9,3,8,5,7
S,8,14,8,8,4,9,4,4,4,13,5,8,3,10,3,8
B,5,7,7,5,6,8,6,5,6,9,6,7,3,9,6,10
K,8,13,9,7,5,6,7,3,6,10,8,10,6,10,4,7
V,3,10,5,7,2,5,8,5,3,9,14,8,3,10,0,8
B,4,7,4,5,3,6,7,9,7,7,6,7,2,8,9,10
Y,2,2,4,4,2,6,10,1,7,7,11,9,1,11,1,8
O,3,4,4,3,2,7,7,7,5,7,6,8,2,8,3,8
Z,6,10,8,7,5,8,6,3,10,12,4,9,1,7,6,9
L,2,3,3,2,1,6,4,1,8,7,2,10,0,7,2,8
J,1,3,2,2,1,10,6,2,6,12,4,9,0,7,1,7
Z,1,3,3,2,1,7,8,1,9,11,7,7,1,8,5,7
A,3,5,5,7,2,7,7,3,1,6,0,8,3,7,1,8
L,3,5,4,5,3,8,7,4,5,7,6,8,3,8,7,10
J,2,5,3,3,1,10,7,1,7,12,3,7,0,7,1,7
Q,5,6,6,8,5,8,7,6,3,7,7,12,3,8,6,8
N,1,3,2,1,1,7,8,5,3,7,7,7,4,8,1,6
Z,2,6,3,4,1,7,7,3,13,9,6,8,0,8,8,8
E,4,9,5,7,3,3,7,6,11,7,6,15,0,8,7,7
J,4,7,3,10,3,9,7,3,3,11,4,5,3,8,6,9
D,4,8,5,6,3,5,7,10,10,6,5,6,3,8,4,8
O,5,10,6,8,5,7,8,9,6,8,8,6,4,8,5,10
L,3,6,4,4,2,5,4,3,8,3,2,6,1,6,2,5
V,4,5,5,4,2,4,12,4,4,11,11,6,2,10,1,8
X,6,10,9,8,6,7,7,0,8,9,6,8,3,8,4,7
J,5,8,6,6,3,6,6,4,5,14,8,12,1,6,1,7
N,7,10,10,8,5,7,8,3,5,10,6,7,7,8,1,8
H,3,5,5,7,4,10,11,3,2,7,8,8,3,10,7,9
A,2,7,4,5,3,13,3,3,2,10,1,8,2,6,2,8
P,4,6,5,9,9,8,7,5,0,7,6,7,6,10,6,9
A,4,9,6,6,2,10,5,3,1,8,1,9,2,7,2,8
F,4,8,5,6,3,5,10,4,6,11,10,5,2,10,3,5
X,3,4,6,3,2,7,7,1,9,10,6,8,2,8,3,7
D,5,11,6,9,4,5,8,11,9,8,7,5,3,8,4,8
V,3,7,5,5,3,9,11,2,2,4,10,8,2,12,1,8
P,7,10,6,5,4,10,7,4,4,12,4,5,4,11,5,9
F,5,9,6,7,6,6,9,4,6,9,9,6,5,9,3,6
P,6,11,9,8,5,8,10,7,5,10,4,3,3,10,4,8
Y,4,10,6,8,2,9,11,0,4,6,11,8,0,10,0,8
W,3,3,4,4,2,5,8,4,1,7,9,8,7,10,0,8
P,6,9,8,7,5,6,11,7,3,11,5,3,2,10,4,8
J,5,6,7,7,6,7,8,4,5,7,7,8,4,6,8,6
E,4,2,4,4,3,8,7,6,10,6,5,9,2,8,6,8
S,4,8,5,6,3,7,5,6,9,5,6,10,0,9,9,8
E,3,7,3,5,2,3,8,6,11,7,5,14,0,8,7,8
B,5,9,7,7,6,7,9,5,6,10,6,6,3,8,7,8
I,1,9,1,7,1,7,7,0,9,7,6,8,0,8,3,8
S,6,8,7,6,4,9,6,4,6,10,3,8,2,8,5,10
S,3,8,4,6,3,8,6,8,6,7,8,9,2,10,9,8
C,3,7,4,5,2,5,8,6,7,6,8,13,1,8,4,10
M,8,10,11,8,9,9,6,2,5,9,4,7,11,9,3,8
P,9,10,7,5,3,6,11,6,2,11,5,5,4,10,4,8
R,2,4,4,3,3,7,8,4,4,9,5,8,2,6,3,10
U,3,8,5,6,6,8,7,4,4,6,7,8,7,9,5,6
U,6,6,6,4,3,4,8,5,8,11,11,9,3,9,2,6
D,3,8,4,6,2,5,7,10,8,7,7,5,3,8,4,8
U,2,1,3,1,1,7,5,11,5,7,14,8,3,10,0,8
J,2,5,3,4,2,10,7,2,6,11,3,7,1,6,2,7
Z,7,13,7,7,5,6,7,2,9,11,6,8,4,5,8,6
F,5,11,7,8,6,7,9,3,6,12,7,6,3,9,2,7
V,1,0,2,1,0,8,9,4,2,7,12,8,2,10,0,8
M,6,5,8,4,7,6,9,5,3,6,4,8,13,8,5,7
U,3,6,4,4,1,7,5,13,5,7,13,8,3,9,0,8
B,3,7,4,5,5,7,7,4,6,6,6,6,2,8,5,10
L,4,8,6,6,4,6,5,1,9,7,2,10,0,6,3,8
T,4,10,5,8,3,8,13,0,5,7,10,8,0,8,0,8
D,8,12,8,6,4,9,4,4,6,12,3,8,5,6,5,10
T,6,11,6,8,4,6,10,1,10,11,9,5,2,8,4,4
O,5,8,7,6,4,7,5,8,4,6,4,7,3,9,4,9
T,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8
U,4,3,5,2,2,5,8,5,7,10,10,9,3,9,2,6
D,2,4,4,2,2,9,6,4,6,11,4,6,2,8,3,8
R,4,8,5,5,3,5,11,8,3,7,4,8,3,7,6,11
G,7,10,7,7,5,5,7,7,6,9,8,10,2,9,5,9
D,2,1,2,2,1,6,7,8,7,6,6,6,2,8,3,8
S,3,8,4,6,4,7,7,7,5,7,6,8,2,8,8,8
S,7,10,9,8,10,9,5,5,4,9,6,9,6,9,12,11
M,6,10,9,8,8,7,7,2,4,9,7,8,8,6,2,8
M,4,8,6,6,8,9,6,2,2,8,4,8,7,7,2,5
D,6,7,8,6,6,5,7,6,7,7,4,7,3,6,5,6
Z,4,9,6,7,4,8,6,2,9,12,5,9,3,6,8,8
W,4,9,7,7,5,9,8,4,1,6,9,8,7,11,0,8
C,2,1,3,2,1,6,8,6,7,7,8,12,1,9,4,10
K,3,6,4,4,4,6,7,3,7,6,6,9,3,8,6,10
J,6,9,9,7,5,8,5,7,7,8,6,7,2,6,4,6
A,6,9,8,8,8,8,7,3,5,7,8,8,6,8,4,5
D,5,12,5,6,4,8,7,3,6,10,5,7,5,8,7,6
C,4,8,6,6,4,8,8,8,6,7,6,8,4,7,4,8
V,3,4,4,3,1,4,12,3,3,10,11,7,2,11,1,8
S,4,7,4,5,2,9,10,6,9,5,6,6,0,7,8,9
F,4,9,6,6,4,9,8,1,6,13,5,5,1,10,2,9
A,5,10,7,8,5,8,3,3,1,7,1,8,5,9,5,8
B,3,2,4,3,3,8,7,5,6,7,6,6,2,8,6,9
N,2,3,3,2,2,7,9,5,4,7,6,7,5,9,2,7
U,6,10,5,6,3,6,6,4,4,6,7,8,5,5,2,8
S,4,10,5,8,5,7,7,5,8,5,6,8,0,8,9,8
Q,2,5,3,6,3,8,8,7,2,5,7,10,3,9,5,9
F,4,8,4,5,1,1,13,5,5,12,11,7,0,8,2,6
F,4,9,6,6,5,5,10,4,6,10,10,6,2,10,3,6
X,5,10,8,8,4,8,7,4,10,6,6,8,4,6,8,9
A,3,7,5,5,3,11,3,2,2,8,2,9,3,4,2,8
B,4,9,4,6,3,6,7,9,7,7,6,7,2,8,9,10
O,5,9,6,7,5,7,7,8,7,7,6,7,2,8,3,8
J,2,6,3,4,1,14,1,6,5,14,2,11,0,7,0,8
H,4,8,5,5,2,7,5,15,1,7,9,8,3,8,0,8
M,6,9,7,4,3,6,9,5,5,4,4,10,8,7,2,8
I,3,7,4,5,2,7,7,0,7,13,6,8,0,8,1,7
W,6,10,6,8,6,2,10,2,3,10,9,8,7,11,2,6
N,3,6,4,4,3,6,7,8,5,7,5,7,3,7,3,8
A,1,3,2,2,1,10,3,2,1,9,2,9,1,6,1,8
S,2,3,4,1,1,7,8,2,6,10,5,7,1,8,4,8
P,1,0,1,0,0,5,10,6,1,9,6,5,0,9,2,8
D,6,9,8,8,8,7,6,5,7,7,5,9,6,5,9,4
M,7,9,10,7,6,5,6,4,5,11,10,11,11,5,4,8
T,4,8,5,6,5,8,11,3,6,6,11,8,3,12,1,7
L,3,6,4,4,2,5,5,1,9,6,2,11,0,8,3,7
X,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8
G,2,4,3,6,2,7,7,8,6,5,7,9,2,7,5,11
D,6,10,8,8,5,10,5,4,8,11,3,7,4,6,4,9
E,3,4,4,6,2,3,8,6,10,7,6,14,0,8,7,7
N,2,3,2,1,1,6,9,6,3,8,7,8,4,9,1,7
G,2,4,3,3,2,6,6,6,5,6,6,9,2,8,4,8
V,7,10,6,8,3,2,11,5,5,12,12,8,2,10,1,8
A,2,7,4,4,2,7,3,3,2,7,2,8,3,7,2,8
E,5,9,4,4,2,8,7,4,4,10,5,10,3,8,7,10
K,4,5,5,8,2,3,6,7,3,7,8,12,3,8,3,10
M,6,9,9,6,6,5,7,3,5,10,10,9,8,6,3,8
L,5,11,5,6,3,7,5,3,5,12,7,11,3,8,6,9
T,5,6,5,4,2,5,12,3,8,12,9,4,1,11,2,4
G,2,4,4,3,2,7,6,6,6,6,6,10,2,9,4,9
J,2,4,4,3,1,8,8,2,7,15,5,8,0,7,1,8
F,5,10,5,7,2,1,14,5,4,12,10,6,0,8,2,5
W,7,8,7,6,6,6,11,5,2,9,6,6,8,13,4,4
A,3,6,5,4,3,12,3,2,2,9,2,9,2,6,2,8
K,6,11,8,8,5,9,5,1,7,10,3,9,5,9,5,11
P,1,1,2,2,1,5,10,4,4,10,8,4,1,9,3,7
M,7,6,9,5,9,7,7,5,4,6,5,8,10,8,5,5
O,2,6,3,4,2,8,7,7,5,7,6,9,2,8,3,8
S,4,11,5,8,3,6,6,6,9,4,6,9,0,9,9,8
C,4,6,6,5,5,4,7,3,6,6,6,10,4,10,7,7
O,1,0,2,1,0,8,7,6,4,7,6,8,2,8,3,8
Z,2,1,3,2,1,7,7,5,9,6,6,9,1,8,7,8
X,4,9,6,7,6,9,7,3,6,7,4,5,6,6,9,8
W,4,4,5,3,3,6,11,5,2,8,7,6,6,11,2,6
B,4,9,4,6,5,6,6,8,6,6,6,7,2,8,7,9
C,6,11,6,8,4,4,8,5,6,12,10,11,2,10,3,7
F,2,4,3,3,2,5,10,3,5,10,9,5,1,10,3,6
I,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8
B,1,3,2,2,1,8,7,3,5,10,5,7,2,8,3,9
P,3,4,5,2,2,8,9,3,5,13,4,3,1,10,3,8
K,11,14,10,8,4,6,8,3,7,9,8,10,6,8,4,6
U,4,5,5,4,2,5,8,5,8,10,8,8,3,9,3,5
N,3,8,4,6,2,7,7,14,2,5,6,8,5,8,0,8
Y,4,11,6,8,1,7,11,2,3,8,12,8,1,11,0,8
I,3,5,5,6,4,8,8,4,4,7,6,8,3,7,8,8
Q,8,15,7,8,4,8,5,4,9,11,4,10,3,6,9,9
Q,4,5,4,7,4,9,7,6,2,8,6,11,3,9,6,8
Z,1,0,1,0,0,8,7,2,9,8,6,8,0,8,5,8
F,4,5,5,6,5,7,9,5,4,8,6,7,4,9,8,7
M,8,10,11,8,8,9,6,2,5,9,6,7,8,6,2,8
D,4,7,5,5,2,5,7,10,9,7,6,5,3,8,4,8
N,2,3,3,1,1,8,9,3,4,10,4,6,4,9,1,7
W,5,10,8,8,5,3,10,3,3,9,10,9,8,11,1,8
U,4,9,4,6,4,7,5,13,5,7,10,8,3,9,0,8
G,6,11,8,9,6,6,6,7,5,5,6,12,5,7,5,6
G,7,11,9,8,11,8,5,4,3,6,5,10,8,7,9,13
O,5,10,4,5,3,7,7,5,4,7,4,7,5,8,5,8
U,2,6,3,4,1,8,6,13,5,7,13,8,3,9,0,8
K,6,10,8,8,8,6,7,5,3,7,5,8,5,7,10,8
W,4,8,6,6,3,4,8,5,1,7,9,8,8,10,0,8
K,5,7,7,5,5,5,7,2,6,10,8,11,3,8,3,8
E,5,11,4,6,2,9,6,4,6,10,5,9,1,10,7,9
V,3,6,5,4,1,9,8,4,2,5,14,8,3,10,0,8
R,8,12,8,7,6,9,6,3,5,9,4,8,6,8,6,8
Z,4,11,5,8,5,9,6,6,10,7,5,6,2,7,8,8
S,4,10,6,7,4,7,8,3,7,10,5,7,2,6,5,8
N,3,6,5,4,5,7,8,3,3,8,7,8,5,9,5,4
X,5,11,8,8,4,5,8,2,8,10,11,9,3,9,4,6
I,3,6,4,4,2,5,7,3,7,7,6,12,0,8,4,9
Q,1,2,2,3,1,8,7,4,1,7,8,10,2,9,3,9
N,10,15,9,8,4,7,9,4,5,3,5,10,6,10,2,7
C,5,7,5,5,3,6,7,6,8,12,8,12,2,10,4,7
Q,6,9,5,4,3,10,4,4,7,11,3,10,3,7,7,11
N,7,10,9,8,7,7,7,8,6,7,5,6,4,8,4,8
K,6,9,8,8,8,7,7,2,4,7,3,9,7,4,9,10
S,8,12,8,7,4,5,10,4,5,14,8,6,2,9,3,7
E,2,3,3,2,1,6,8,2,8,11,8,8,1,8,4,7
D,4,8,6,6,8,9,7,5,6,7,5,6,5,6,8,7
X,4,4,5,5,1,7,7,4,4,7,6,8,3,8,4,8
C,1,0,2,1,0,7,7,6,7,7,6,13,0,8,4,10
J,5,9,7,7,3,9,5,4,6,15,4,10,1,6,0,7
B,5,7,7,5,5,7,6,6,7,8,7,6,3,9,8,7
D,5,9,5,7,5,5,7,9,7,6,5,5,2,8,3,8
R,7,10,7,5,5,7,6,3,5,8,4,9,6,8,6,7
Z,2,1,2,1,1,7,7,3,11,8,6,8,0,8,6,8
T,2,3,4,4,1,8,14,1,6,6,11,8,0,8,0,8
K,5,9,5,7,2,4,7,9,2,7,6,12,3,8,3,11
G,5,11,7,8,5,7,6,7,8,7,4,12,2,9,6,8
B,11,15,10,8,9,9,8,3,6,10,4,6,8,3,9,7
X,8,11,11,9,7,6,8,1,8,10,8,9,3,8,4,6
D,6,9,8,7,6,10,6,3,7,11,4,7,4,7,4,9
V,2,5,4,4,1,6,11,3,4,8,11,8,2,10,1,8
I,2,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8
Z,2,5,4,4,2,7,8,2,10,11,6,8,1,8,6,7
G,2,3,3,2,1,7,6,5,4,6,6,9,2,9,4,9
J,3,5,4,8,1,13,2,9,5,14,3,12,0,6,0,8
V,2,5,4,4,2,7,12,2,3,7,11,8,2,10,1,8
V,8,15,8,8,5,5,8,5,4,8,7,6,6,12,3,8
J,6,12,5,9,4,10,7,2,4,12,4,6,2,9,7,9
C,3,6,4,4,1,6,7,6,10,7,6,13,1,8,4,9
U,2,3,3,1,1,4,8,5,6,11,10,8,3,10,1,6
A,2,6,4,4,2,12,2,4,3,12,2,10,2,6,3,9
Y,3,10,5,7,1,10,10,3,2,5,13,8,2,11,0,8
T,3,7,5,5,3,7,11,1,8,7,11,8,1,10,1,8
M,5,8,7,6,6,8,7,2,4,9,5,7,7,6,2,8
V,5,8,5,6,3,4,11,2,3,8,10,7,3,12,1,7
S,4,8,5,6,3,6,8,4,6,10,9,8,2,9,5,4
M,5,6,6,8,4,7,7,12,2,7,9,8,9,6,0,8
Y,3,8,6,5,1,10,10,3,2,5,13,8,2,11,0,8
Z,9,9,6,12,6,6,9,4,2,12,7,7,3,8,13,6
M,3,8,4,6,3,8,6,12,1,5,9,8,7,6,0,8
M,3,7,4,5,4,7,5,10,0,7,8,8,6,5,0,8
J,3,8,3,6,2,13,3,5,4,13,2,10,0,7,0,8
K,7,13,7,7,4,8,7,2,6,10,7,9,6,11,4,8
B,3,3,4,4,3,7,7,6,5,7,6,6,2,8,6,10
K,4,10,6,8,7,7,7,5,4,7,5,7,4,6,9,14
Z,5,8,7,6,4,5,10,3,10,11,9,6,1,9,6,5
F,4,8,4,6,3,1,13,4,4,12,10,6,0,8,1,6
N,1,1,2,1,1,7,8,5,3,7,7,7,4,8,1,6
P,7,10,10,8,5,7,14,5,3,13,4,0,0,10,4,7
C,4,3,5,5,2,6,7,6,10,7,6,13,1,8,4,9
Z,4,8,6,6,3,9,5,3,10,11,4,9,1,7,6,9
S,2,3,4,2,1,8,7,2,6,10,6,8,1,9,5,8
W,4,6,7,4,4,9,11,2,3,5,9,8,7,11,1,8
P,2,3,4,2,2,7,9,4,4,11,4,4,1,9,3,8
L,4,8,4,6,1,0,0,6,6,0,1,4,0,8,0,8
U,3,4,4,6,2,8,5,14,5,7,13,8,3,9,0,8
J,3,10,4,7,1,13,2,9,4,14,4,12,1,6,0,8
J,2,4,3,6,1,12,2,9,4,13,6,13,1,6,0,8
Z,3,5,3,3,2,8,7,5,10,6,6,7,2,8,7,8
M,4,2,5,4,4,8,6,6,4,7,7,8,8,6,3,6
U,7,13,6,7,4,8,4,5,5,7,9,8,4,8,3,8
J,1,3,2,2,1,9,7,2,6,14,5,8,0,7,0,8
B,5,9,5,7,7,6,7,8,5,7,6,7,2,8,7,9
B,7,11,9,8,7,10,6,4,7,10,3,7,2,8,6,12
Q,8,14,8,8,4,9,4,4,7,11,4,10,3,7,8,11
N,3,4,5,3,2,7,9,2,5,10,6,6,5,9,1,7
D,3,7,4,5,3,9,7,5,7,10,4,5,3,8,3,8
B,1,0,1,0,0,7,7,6,4,7,6,7,1,8,5,9
E,4,6,6,4,4,7,8,2,7,11,7,9,3,8,4,9
E,3,3,5,2,2,6,8,3,8,11,8,9,2,9,4,7
N,6,11,8,8,5,7,9,2,5,9,6,7,5,9,1,8
T,2,4,4,6,1,7,14,0,6,8,11,8,0,8,0,8
Y,3,3,5,4,1,9,9,3,1,5,13,8,2,10,0,8
Q,7,9,7,10,8,8,8,6,2,7,7,11,4,10,8,6
U,9,15,8,8,5,5,5,4,4,7,8,10,7,5,3,9
H,3,3,4,4,2,7,10,14,2,7,3,8,3,8,0,8
J,2,10,3,8,1,14,3,7,5,14,1,10,0,7,0,8
D,1,4,3,3,2,10,6,3,5,10,3,6,2,8,2,9
G,2,4,2,2,1,6,7,5,5,9,7,10,2,8,4,9
V,5,9,7,8,9,7,7,5,4,7,6,8,7,9,7,8
U,2,2,3,3,2,7,9,5,7,7,9,9,3,9,1,8
U,7,11,9,8,7,5,9,5,7,6,9,10,3,9,1,8
Z,3,3,4,4,2,7,7,3,14,9,6,8,0,8,8,8
G,2,3,3,1,1,7,7,5,5,9,6,10,2,9,4,10
L,4,11,5,8,4,7,4,1,7,8,2,10,1,6,3,8
D,4,6,5,4,4,9,7,3,6,10,4,6,3,8,2,8
J,3,10,5,7,5,9,8,3,3,8,4,6,4,8,6,5
K,3,6,5,4,5,7,6,3,4,6,6,8,7,7,6,9
A,3,9,5,7,3,12,3,4,3,11,1,9,2,6,3,9
L,3,4,4,7,1,0,1,6,6,0,0,6,0,8,0,8
X,4,9,6,7,5,8,5,3,5,6,7,8,3,9,9,9
Q,5,9,5,5,3,11,5,3,6,9,4,7,3,9,6,11
F,3,8,3,6,2,2,12,4,3,12,10,6,0,8,2,7
W,2,3,4,2,2,7,10,2,2,6,9,8,5,11,0,7
K,6,9,9,6,6,5,7,1,7,10,8,10,3,8,4,7
P,4,9,4,6,4,4,12,7,1,10,6,4,1,10,3,8
V,6,9,5,5,2,8,11,4,5,7,10,5,4,10,2,6
L,7,15,6,8,3,10,2,3,5,13,4,12,2,8,5,10
A,3,8,5,5,1,6,6,3,1,6,0,8,2,6,1,7
B,3,3,3,4,3,6,7,9,6,7,6,7,2,8,8,10
S,6,9,5,5,2,8,4,4,5,9,2,8,3,6,5,8
I,6,11,4,6,2,10,6,5,5,12,3,6,3,8,5,10
B,1,0,1,0,1,7,8,6,4,7,6,7,1,8,6,9
F,4,8,6,6,5,4,10,1,2,10,8,7,5,10,2,4
I,3,4,4,5,3,7,8,4,5,7,8,7,2,9,8,9
M,5,8,7,6,7,7,6,5,5,7,7,10,11,6,2,8
K,2,1,3,2,2,5,7,4,7,7,6,11,3,8,4,9
C,2,3,2,2,1,6,8,6,7,8,7,12,1,9,3,10
H,4,7,6,5,4,9,8,3,7,10,4,7,3,8,3,9
S,3,2,4,4,3,8,7,7,5,7,6,7,2,8,9,8
B,4,5,5,7,4,7,7,10,7,7,6,8,2,8,9,9
O,2,3,2,1,1,8,7,6,4,9,6,8,2,8,3,8
M,9,11,12,8,8,4,6,4,5,10,11,11,11,6,4,8
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
W,7,9,10,8,11,6,6,6,5,5,5,8,11,11,9,12
U,4,6,4,4,1,8,5,12,5,7,15,8,3,9,0,8
Y,4,5,5,4,2,4,11,2,7,11,10,6,1,11,2,5
A,3,9,6,7,3,11,2,3,3,10,2,9,2,6,3,8
P,2,3,4,2,2,7,10,3,4,12,4,3,1,10,2,8
B,4,7,6,5,5,7,8,5,5,9,6,6,3,7,6,7
O,4,7,5,5,3,8,6,8,5,10,6,9,3,8,3,8
Y,5,7,5,5,2,3,10,3,7,11,12,7,2,11,3,5
I,4,5,6,5,4,8,9,4,5,7,6,9,3,8,8,7
R,3,9,4,6,3,5,10,9,3,7,4,8,3,7,6,11
N,6,8,9,6,5,10,8,3,6,10,2,4,5,9,1,7
B,3,7,3,5,4,6,7,7,5,7,6,7,2,8,7,10
Q,9,15,8,8,4,9,3,4,7,11,4,10,3,8,8,11
H,6,11,8,8,6,9,7,6,7,7,6,8,6,8,4,7
J,2,4,3,3,1,11,6,2,7,11,3,7,0,7,1,8
W,4,4,5,6,3,7,8,4,1,7,8,8,8,10,0,8
Y,5,11,6,8,2,3,11,3,8,13,11,5,0,10,2,4
F,4,9,5,7,3,4,11,3,7,11,10,6,1,10,3,5
A,4,9,6,6,2,7,4,3,2,7,1,8,3,7,3,8
X,1,0,2,0,0,7,7,4,4,7,6,8,2,8,3,8
Z,5,10,7,8,5,7,8,2,9,12,7,8,1,9,6,7
H,3,7,4,4,2,7,9,14,2,7,3,8,3,8,0,8
K,6,11,9,8,8,7,6,1,6,10,5,9,7,7,6,9
J,4,7,5,8,5,8,8,4,5,7,6,8,3,8,9,8
N,2,4,4,3,2,6,9,2,4,10,7,7,5,8,1,8
W,5,9,7,7,7,7,5,6,2,7,7,8,7,7,6,12
U,4,6,5,4,2,6,8,7,9,9,10,8,3,9,1,8
Y,2,6,4,4,1,9,10,3,2,5,13,8,2,11,0,8
Q,4,7,4,9,4,7,7,6,3,8,9,10,3,8,6,8
V,3,3,4,2,1,4,12,4,3,11,11,6,2,11,1,8
D,6,13,6,7,5,11,5,4,6,10,4,7,5,9,6,10
V,3,8,4,6,2,7,9,4,2,8,12,8,2,10,0,8
R,4,8,5,6,3,5,12,8,4,8,3,9,3,7,6,11
L,1,0,1,1,0,2,2,5,5,1,1,6,0,8,0,8
V,4,10,7,8,3,8,12,3,4,5,11,8,3,10,1,8
F,5,10,7,8,5,6,10,2,6,13,7,5,1,10,2,7
B,5,8,7,7,8,7,8,5,5,8,6,8,7,8,9,6
R,3,4,4,5,2,6,10,9,4,7,4,8,3,7,5,10
J,3,6,4,4,3,9,6,6,5,8,6,7,2,8,4,6
V,3,7,5,5,1,7,8,4,2,7,14,8,3,10,0,8
Y,9,9,8,13,5,9,10,2,4,5,11,5,4,10,7,8
M,8,9,11,7,8,10,6,2,5,9,4,7,8,7,2,8
X,3,2,4,3,2,7,7,3,9,6,6,8,2,8,6,8
S,3,9,4,7,2,7,7,6,8,5,6,7,0,8,9,7
K,2,3,4,2,2,8,7,1,6,10,5,8,3,8,2,8
T,3,7,5,5,3,6,11,1,7,8,11,9,1,11,1,8
H,3,4,6,3,3,7,8,3,6,10,6,8,3,8,3,7
L,4,9,4,7,2,0,2,4,6,1,0,8,0,8,0,8
J,3,8,4,6,2,10,6,2,8,12,3,7,0,6,2,6
D,2,1,2,1,1,5,7,8,7,6,6,6,2,8,3,8
B,2,1,3,2,2,8,7,5,5,7,6,6,2,8,5,9
N,6,9,9,7,5,10,8,3,6,10,2,4,5,9,1,7
R,6,10,9,8,11,6,7,4,4,6,6,9,8,9,9,7
M,13,15,13,8,7,3,8,6,6,4,2,13,9,11,2,8
Z,3,6,4,4,2,7,7,4,14,9,6,8,0,8,8,8
R,6,9,8,7,9,6,7,4,5,7,6,8,6,9,7,5
K,5,11,6,8,2,3,7,8,2,7,6,11,4,8,2,11
W,5,9,8,7,7,7,12,2,2,6,8,8,7,12,1,8
P,3,6,4,4,2,7,10,4,4,12,5,3,1,10,3,8
E,3,8,4,6,4,6,7,5,8,7,5,10,3,8,5,9
E,7,12,5,6,4,8,7,4,4,11,5,9,3,9,8,11
Y,3,3,4,4,1,10,13,3,3,3,11,9,0,10,0,8
J,3,10,4,7,3,11,5,2,6,11,2,7,0,7,1,7
L,3,7,4,5,2,4,5,2,10,3,1,8,0,7,2,5
I,2,5,1,3,1,7,7,1,7,7,6,8,0,8,3,8
I,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8
M,6,10,7,6,4,13,2,4,3,12,1,9,5,5,1,9
V,2,5,4,3,2,7,12,2,2,7,11,9,2,10,1,8
P,7,10,9,8,6,7,10,4,4,12,5,3,1,10,3,8
H,5,10,7,8,7,7,7,6,7,7,6,8,6,8,4,8
R,9,13,7,8,5,8,6,5,5,9,5,9,6,6,7,11
X,4,9,6,7,5,8,7,3,5,6,7,6,4,11,10,8
P,2,5,3,7,6,8,7,4,1,7,6,7,5,8,4,8
V,4,10,5,7,3,7,9,4,2,7,13,8,2,10,0,8
N,3,6,3,4,3,8,7,11,1,6,6,8,5,9,0,6
O,3,1,4,3,2,8,7,7,5,7,6,8,2,8,3,8
G,6,11,7,8,5,5,7,6,6,9,8,10,2,8,5,9
T,4,5,5,4,2,5,12,3,7,11,10,4,1,11,1,5
R,8,12,7,6,6,6,7,3,5,7,5,9,6,9,6,6
W,4,3,5,5,3,8,8,4,1,6,8,8,8,9,0,8
U,6,9,6,5,3,7,5,5,5,7,8,8,5,7,2,8
L,6,12,6,6,4,8,3,3,4,12,8,12,3,10,5,11
S,7,12,7,6,3,10,4,5,4,13,5,8,2,9,2,7
Q,4,9,5,11,7,9,8,8,2,5,7,10,3,8,6,10
M,3,1,4,3,3,7,6,6,4,6,7,7,8,6,2,7
F,2,2,3,3,2,5,10,4,5,10,9,5,1,10,3,6
V,5,8,5,6,2,2,11,5,4,12,12,8,2,10,1,8
V,5,7,5,5,2,3,11,4,3,10,12,8,2,10,1,8
F,4,8,4,6,3,1,13,4,3,12,10,7,0,8,1,6
M,4,11,6,8,6,6,6,6,5,7,7,10,8,5,2,9
I,3,8,4,6,2,7,7,0,6,13,6,8,0,8,1,8
P,3,7,3,4,2,4,12,8,2,10,6,4,1,10,4,8
U,4,2,5,3,2,6,8,5,8,7,10,9,3,9,1,8
B,3,4,3,3,3,7,7,5,5,7,6,6,2,8,6,9
Q,4,10,6,9,6,8,7,8,5,6,4,8,3,8,4,7
H,5,8,5,6,4,7,8,13,1,7,5,8,3,8,0,8
X,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8
M,4,7,6,5,6,9,7,5,5,6,7,5,8,6,2,6
J,3,7,4,5,1,7,7,3,7,15,6,10,0,7,1,7
V,6,12,5,7,3,7,10,6,4,9,9,5,5,12,3,8
B,2,3,2,2,2,7,7,5,5,6,6,5,2,8,6,8
T,5,8,5,6,3,4,13,4,6,12,10,4,1,11,1,5
U,8,10,9,8,5,4,7,6,8,10,10,9,3,9,2,5
D,4,7,6,5,4,7,8,5,6,10,6,4,3,8,4,8
Y,4,9,6,7,2,7,12,1,3,7,11,8,0,10,0,8
O,2,1,2,2,1,8,7,7,4,7,6,8,2,8,3,8
O,2,0,2,1,1,7,6,7,5,7,6,8,2,8,3,8
K,4,4,4,6,2,4,7,8,1,7,6,11,3,8,2,11
W,8,11,8,6,5,9,8,4,5,6,9,6,11,8,3,6
A,5,11,7,8,4,8,3,2,3,7,1,8,2,7,3,7
O,8,12,6,6,4,5,9,6,4,9,8,9,5,10,5,8
S,5,9,6,8,7,9,7,5,6,7,6,7,6,8,10,12
T,5,7,5,5,3,6,11,3,7,11,9,5,2,12,3,4
S,2,7,3,5,3,8,8,7,5,7,5,6,2,8,8,8
X,3,4,5,3,2,7,7,1,8,10,6,8,2,8,3,7
T,2,9,4,6,1,5,14,1,6,9,11,7,0,8,0,8
U,5,9,6,7,5,6,6,8,5,7,6,10,5,8,7,3
Y,5,6,5,4,3,5,9,1,7,9,10,6,2,11,3,5
V,3,3,4,2,1,4,12,3,3,10,11,7,2,11,1,7
D,2,4,4,3,2,9,7,4,6,10,4,6,2,8,3,8
H,5,10,8,8,7,7,8,7,7,7,5,9,3,8,3,8
B,4,8,6,6,8,9,7,4,4,6,7,7,7,10,7,6
S,5,11,6,8,3,7,8,4,8,11,7,7,2,8,5,6
O,2,2,3,3,2,8,7,7,4,7,6,8,2,8,3,8
O,6,11,7,9,3,7,8,9,8,7,7,6,3,8,4,8
J,2,6,2,4,1,12,2,9,3,13,5,13,1,6,0,8
P,3,4,4,6,6,8,9,4,0,8,7,7,5,9,5,7
O,3,7,5,5,5,8,6,5,1,7,6,8,7,8,3,8
I,3,7,4,5,2,7,7,0,7,13,6,8,0,8,1,7
Z,4,9,6,6,6,7,7,3,8,8,6,9,1,8,11,7
W,3,6,5,4,7,9,7,4,2,7,6,8,8,11,1,6
F,2,4,3,5,1,1,13,4,4,12,11,7,0,8,2,6
H,5,7,8,5,6,8,7,3,6,10,6,8,3,8,3,8
D,2,4,3,3,2,9,6,3,5,10,4,6,2,8,2,8
B,2,1,2,2,1,7,7,8,5,6,6,7,2,8,7,10
K,0,0,1,0,0,4,7,5,3,7,6,10,3,8,2,11
T,4,10,6,8,3,5,11,1,9,9,12,8,0,10,1,7
N,3,8,3,6,2,7,7,13,2,5,6,8,5,8,0,8
F,2,6,2,4,1,1,11,3,6,11,11,9,0,8,1,6
A,3,8,5,5,2,7,4,3,1,7,1,8,3,7,2,8
V,8,11,7,8,5,4,11,1,3,8,10,8,5,12,1,7
F,9,13,7,7,3,4,13,3,5,13,7,3,2,7,5,4
Y,2,1,3,1,0,7,10,3,1,7,13,8,1,11,0,8
R,4,7,6,5,4,9,8,3,6,10,3,6,3,6,4,10
Q,5,9,5,11,7,8,7,6,2,8,7,10,3,8,5,8
M,6,9,9,7,6,10,5,3,5,9,3,6,8,6,2,9
P,3,5,3,3,2,4,10,3,5,10,8,4,0,10,3,7
K,3,7,4,5,3,6,7,4,7,6,6,9,3,8,5,9
Y,6,6,8,9,8,10,12,6,4,6,7,6,5,10,7,4
D,4,6,6,6,5,6,7,4,7,6,4,7,3,7,5,6
R,4,3,5,5,3,6,9,10,5,7,5,8,2,8,5,10
B,4,7,6,5,5,10,7,3,6,10,4,6,2,8,4,10
T,4,8,6,6,4,6,7,7,7,8,9,8,3,10,5,9
W,6,10,8,8,4,7,7,4,2,7,8,8,9,9,0,8
K,5,9,7,7,8,7,9,5,5,8,5,7,7,6,8,12
G,10,15,9,8,5,8,4,5,4,9,4,5,4,7,5,8
P,3,7,3,5,2,4,12,8,2,10,6,4,1,10,3,8
C,1,0,1,1,0,6,7,6,8,7,6,14,0,8,4,10
S,5,11,7,8,4,10,6,4,8,11,3,8,2,8,5,11
J,5,9,6,7,4,7,4,5,4,14,8,14,1,6,1,6
P,5,11,8,8,6,7,11,7,3,10,5,3,3,11,4,8
X,3,2,4,4,2,7,7,3,9,6,6,8,2,8,6,8
L,8,13,8,8,5,9,3,3,3,11,6,11,4,8,7,9
B,4,5,4,8,4,6,8,10,7,7,5,7,2,8,9,10
E,3,5,6,4,3,8,7,2,8,11,5,8,2,8,5,10
H,3,4,4,6,2,7,9,15,2,7,4,8,3,8,0,8
T,3,4,4,3,2,6,11,3,7,11,9,4,2,11,3,4
P,2,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8
S,2,3,4,2,2,6,8,2,6,10,8,8,1,8,5,6
N,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8
P,5,4,5,6,3,4,12,9,3,10,6,4,1,10,4,8
D,3,5,5,4,3,9,7,5,7,9,4,5,3,8,3,8
T,2,7,3,4,1,6,14,0,6,8,11,8,0,8,0,8
B,2,4,4,2,2,9,7,3,5,11,5,7,2,7,4,9
Y,3,7,5,5,2,7,10,2,2,7,12,8,1,11,0,8
J,3,11,4,8,2,15,3,4,5,13,0,8,0,7,0,8
A,3,9,6,7,4,11,3,2,2,9,2,9,3,4,3,8
J,3,7,5,5,2,11,6,2,7,14,3,8,0,8,0,8
S,2,3,3,4,1,9,10,5,9,5,6,5,0,7,8,7
E,4,8,4,6,2,3,7,6,11,7,6,15,0,8,7,7
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
H,5,9,7,6,7,8,8,6,7,7,6,9,6,8,4,7
G,4,5,5,8,2,7,6,8,8,6,5,10,2,8,6,11
Z,2,2,3,3,2,7,8,5,9,6,6,9,1,9,7,8
Y,4,6,6,4,3,9,10,1,7,3,11,8,1,11,1,9
H,3,7,4,5,5,7,5,4,4,6,5,8,4,8,5,7
B,4,8,6,6,7,8,8,7,6,7,5,6,2,7,7,10
A,4,9,6,7,4,10,3,2,2,8,2,10,4,4,3,8
B,6,10,6,6,5,10,6,3,5,9,5,8,6,8,7,9
M,6,10,8,8,7,9,6,6,5,6,7,6,8,6,2,6
P,4,9,4,6,2,5,10,9,4,9,6,5,2,10,4,8
W,5,11,8,8,6,4,11,2,3,8,9,9,9,11,2,7
S,2,4,4,2,1,6,8,2,7,11,7,8,1,8,4,6
J,3,10,4,8,1,13,2,9,4,14,5,13,1,6,0,8
N,8,14,9,8,5,7,7,2,4,12,4,8,6,8,0,7
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
P,5,8,7,11,11,8,9,4,1,8,7,6,8,10,6,8
G,2,3,3,2,1,6,7,5,5,9,7,10,2,8,4,10
E,6,8,8,6,5,4,9,4,9,11,10,9,2,9,4,5
Q,6,8,7,10,6,8,7,7,4,8,7,10,3,8,6,8
R,3,8,4,6,2,5,11,8,4,7,4,9,3,7,6,11
K,2,3,2,2,2,5,7,4,7,6,6,10,3,8,4,10
Y,4,5,6,6,6,9,9,6,4,7,7,7,5,11,6,4
P,4,5,6,6,6,6,9,4,2,8,7,6,6,10,4,6
G,6,9,6,4,3,7,3,5,2,6,5,4,4,8,5,7
G,4,7,6,5,6,7,7,6,2,7,6,11,4,9,7,9
P,7,9,8,7,7,6,7,5,5,7,6,9,5,9,7,10
L,7,15,7,8,5,9,3,3,4,12,6,11,3,8,6,9
J,1,2,2,3,1,10,6,2,6,12,4,9,0,7,1,7
Y,3,10,5,8,2,8,10,1,3,6,12,8,1,11,0,8
D,6,11,8,8,8,8,8,6,6,9,5,4,6,9,5,10
M,6,10,9,8,8,8,7,2,4,9,7,8,8,6,2,8
G,2,1,2,2,1,8,6,6,6,6,5,10,1,7,5,10
U,4,9,5,6,4,6,9,8,5,3,7,12,4,8,5,6
I,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8
I,4,11,5,8,3,8,6,0,7,13,6,9,1,7,3,8
B,6,11,6,8,5,6,6,9,7,6,6,7,2,8,10,10
Q,1,2,2,3,2,7,7,5,2,8,8,9,2,9,4,9
J,2,7,3,5,1,13,2,9,4,14,4,12,1,6,0,8
B,4,11,5,8,4,6,7,9,7,7,6,7,2,8,9,10
A,3,5,5,5,4,9,8,3,4,6,8,7,4,10,4,5
S,3,4,5,2,2,8,8,2,7,10,6,7,1,8,5,7
U,4,4,4,6,2,8,5,14,5,6,13,8,3,9,0,8
E,2,3,4,2,2,7,7,2,7,11,7,9,2,8,4,8
R,3,8,3,6,3,6,10,7,4,6,3,10,2,6,5,11
L,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8
N,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8
B,6,9,9,8,10,7,8,5,5,7,7,8,7,9,9,6
K,6,9,8,6,9,7,6,3,5,6,6,9,8,6,7,7
X,5,8,6,7,7,6,8,2,5,7,6,9,4,7,8,7
P,6,9,5,4,2,5,11,5,3,13,5,4,4,9,3,8
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
G,8,14,7,8,5,8,6,4,3,9,6,8,4,9,8,8
A,3,10,6,8,4,12,2,3,3,10,2,9,2,6,4,8
K,4,4,6,3,3,8,6,1,7,10,4,9,4,7,4,9
B,5,10,7,8,7,8,6,7,7,6,7,6,2,8,8,11
Y,3,8,5,6,3,5,9,1,6,8,12,10,1,11,2,8
N,2,4,4,3,2,5,10,3,3,10,8,8,5,8,1,7
Y,2,4,4,6,1,5,10,3,2,8,13,8,2,11,0,8
O,9,12,6,7,3,8,7,6,5,9,4,7,5,9,5,9
E,6,11,9,9,7,6,8,2,8,10,7,9,3,8,5,7
U,6,10,6,8,4,3,9,5,6,11,11,9,3,9,1,7
U,7,8,8,6,3,4,8,5,8,10,10,9,3,9,2,6
X,3,5,6,4,3,9,7,1,9,10,4,7,2,8,3,8
D,1,0,1,0,0,6,7,7,5,7,6,6,2,8,3,8
D,2,1,3,2,2,8,7,7,7,7,7,4,2,8,3,6
S,4,5,6,4,3,7,7,2,8,11,5,7,1,8,5,8
K,4,5,7,4,4,5,7,2,8,10,9,10,3,8,4,6
I,2,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
F,4,5,5,5,5,7,8,4,5,7,5,7,4,9,8,7
A,1,1,3,2,1,6,2,2,1,6,2,7,1,6,2,6
R,3,6,5,4,5,6,7,3,3,7,6,9,5,9,7,5
P,3,2,4,3,3,6,9,4,4,9,7,4,1,10,4,7
Y,2,7,4,5,2,8,10,1,6,5,11,8,1,11,1,8
V,4,9,6,7,3,9,11,2,4,4,11,8,2,10,1,8
M,5,11,6,8,4,7,7,12,2,7,9,8,9,6,0,8
K,4,9,6,6,5,6,6,3,7,6,6,9,3,8,5,10
F,4,8,7,6,7,10,6,1,5,9,6,6,5,10,4,6
X,7,10,7,6,4,9,6,2,7,11,4,7,4,9,4,9
H,4,9,6,6,7,7,7,6,6,7,6,9,3,8,3,8
L,4,9,5,7,1,0,0,6,6,0,1,5,0,8,0,8
H,6,9,8,6,7,11,6,3,6,10,3,7,3,7,3,10
J,5,11,7,8,8,10,7,4,4,8,4,5,4,7,6,5
R,2,3,3,2,2,6,7,5,5,7,6,7,2,7,4,9
G,1,1,2,1,1,7,7,5,5,6,6,10,2,9,3,9
F,5,9,8,6,7,4,10,1,4,10,7,6,7,10,3,4
D,6,9,6,4,3,12,2,4,5,12,2,9,4,7,2,10
K,9,13,9,7,4,4,9,3,7,10,9,11,5,7,3,6
L,3,9,5,6,3,5,4,2,8,6,1,10,0,6,3,7
P,5,4,5,6,3,5,9,10,5,8,5,5,2,10,4,8
A,2,3,3,2,1,8,2,2,1,6,2,8,2,7,2,7
I,6,10,8,7,5,7,6,2,8,7,6,8,0,9,4,8
X,3,7,5,5,2,9,7,1,8,10,4,7,3,8,3,8
M,3,6,5,4,5,12,5,3,2,9,4,8,5,6,2,6
R,4,2,4,3,3,7,8,5,5,6,5,6,3,7,4,8
Y,1,0,1,0,0,7,10,3,1,7,12,8,1,11,0,8
I,1,3,2,1,0,7,7,1,7,13,6,8,0,8,0,7
T,5,11,6,8,8,7,8,4,5,6,7,9,7,7,8,7
F,3,9,4,6,2,1,12,5,4,11,10,7,0,8,3,6
R,5,10,8,8,5,11,7,3,6,11,1,6,4,5,4,10
Z,9,11,6,15,6,5,11,3,3,12,8,6,3,9,11,5
K,4,8,6,6,3,7,7,1,7,10,6,9,3,8,4,8
R,6,9,6,5,4,9,8,4,6,9,2,6,5,5,5,7
E,4,8,5,6,3,3,9,6,12,7,5,14,0,8,7,7
Y,10,12,8,7,5,6,8,4,5,10,7,4,3,9,5,4
M,6,10,7,8,4,7,7,13,2,8,9,8,9,6,0,8
G,8,15,7,8,6,8,5,4,3,8,6,8,4,9,8,8
X,4,6,6,4,2,7,7,2,8,11,6,8,3,8,3,7
Z,3,10,4,7,3,7,8,3,11,9,6,8,0,8,7,8
T,3,8,4,5,1,8,15,1,6,6,11,9,0,8,0,8
A,3,3,5,4,1,9,3,3,2,8,2,9,3,6,2,8
A,2,4,4,5,1,8,6,3,1,7,0,8,2,6,1,8
P,3,5,5,4,3,8,9,3,4,12,4,4,2,8,3,8
H,4,8,4,5,2,7,8,15,1,7,5,8,3,8,0,8
T,5,10,7,8,7,7,6,7,7,6,8,9,4,9,8,6
M,7,10,9,8,8,9,6,2,5,9,5,7,9,8,2,8
K,4,9,6,7,7,6,6,3,4,6,5,9,8,6,8,8
T,1,1,2,1,0,7,14,1,4,7,11,8,0,8,0,8
J,2,7,3,5,2,8,7,2,5,11,5,8,3,8,2,6
I,2,8,3,6,2,9,6,0,7,13,5,9,0,8,1,8
J,1,5,2,3,1,10,6,2,6,11,3,8,0,7,1,7
C,4,8,5,6,2,5,9,8,8,13,9,6,2,10,2,6
Y,3,3,4,2,1,4,11,2,7,11,10,5,1,11,2,5
L,7,15,7,8,5,9,3,4,4,12,7,11,4,8,7,9
B,2,4,3,2,2,8,7,3,5,9,6,6,2,8,4,8
P,6,10,8,8,6,8,10,5,4,11,4,3,1,10,3,8
Q,4,10,5,9,6,8,7,8,5,6,3,8,3,9,4,8
D,3,6,4,4,5,9,7,5,4,7,5,7,4,7,6,5
E,5,11,4,6,3,9,6,3,5,11,4,8,3,9,7,11
D,5,7,6,6,5,6,7,6,7,7,5,7,4,7,5,5
P,6,11,8,8,8,7,5,6,5,7,6,9,6,9,8,9
O,4,7,6,5,4,7,7,8,4,7,6,8,3,8,3,8
H,4,7,6,5,5,8,6,6,7,7,7,6,3,9,3,7
N,7,11,10,8,6,12,7,3,5,10,0,3,7,11,2,9
A,3,6,5,4,2,9,2,2,2,7,1,8,2,7,3,7
T,4,8,6,6,5,7,7,7,7,5,9,10,4,8,7,6
C,1,1,2,1,1,6,8,6,6,8,7,12,1,9,3,10
L,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8
P,2,7,4,5,2,6,11,5,4,10,8,3,1,10,4,6
P,4,9,5,7,4,5,10,4,5,10,9,4,4,10,4,7
J,3,5,4,7,1,12,2,9,4,14,5,13,1,6,0,8
D,3,1,4,2,2,7,7,6,7,6,5,5,2,8,3,7
I,0,3,0,1,0,7,7,1,7,7,6,8,0,8,2,8
H,2,3,4,2,2,8,7,3,5,10,6,8,3,8,2,8
T,9,11,9,8,7,7,10,2,9,11,9,5,4,11,5,5
Y,7,9,7,7,4,3,10,2,7,11,11,7,1,11,2,5
C,3,6,4,4,2,6,7,5,6,11,8,12,1,9,3,9
S,4,5,6,4,6,8,8,4,5,7,7,8,4,9,9,10
L,5,11,5,8,2,0,0,6,6,0,1,5,0,8,0,8
N,3,2,4,4,3,7,9,5,5,7,6,6,5,9,2,6
G,5,7,6,5,4,5,7,6,5,9,7,10,2,9,4,10
Q,7,9,8,11,9,8,7,6,2,7,7,12,6,9,9,6
I,1,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8
V,4,8,6,6,3,7,11,2,3,5,11,9,2,10,1,8
R,4,9,5,7,4,9,8,4,6,9,3,7,3,7,4,11
N,1,3,3,1,1,9,8,3,4,10,3,5,4,8,1,8
C,6,14,5,8,4,7,8,4,3,9,8,10,4,9,9,11
T,4,7,5,5,4,7,8,7,7,7,8,9,3,10,5,8
R,4,9,5,7,5,9,7,3,5,9,3,8,3,6,3,11
Z,3,8,4,6,2,7,7,4,14,9,6,8,0,8,8,8
B,2,2,3,3,2,7,7,5,5,6,6,6,2,8,5,9
T,2,2,3,3,2,7,11,2,7,7,11,8,1,11,1,8
R,1,0,1,0,0,6,8,6,3,7,5,7,2,7,4,11
J,2,3,4,2,1,9,6,3,5,14,6,10,0,7,0,8
M,5,10,5,8,4,7,7,12,2,7,9,8,8,6,0,8
X,9,13,9,7,5,4,9,3,8,12,10,9,4,9,4,5
S,5,10,6,8,4,9,6,5,8,11,2,8,2,7,5,11
S,3,6,4,4,2,8,7,5,8,5,6,8,0,8,9,8
G,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10
B,9,14,7,8,4,9,6,6,6,11,4,9,6,7,7,10
M,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8
S,3,6,4,4,2,6,5,5,9,5,6,10,0,9,9,8
M,3,7,4,5,3,7,6,11,1,8,9,8,7,6,0,8
L,2,6,4,4,1,5,4,1,9,7,1,9,0,7,2,7
G,3,6,4,4,3,7,6,6,6,6,6,10,2,9,4,8
C,7,10,5,5,2,6,10,6,7,12,8,8,2,9,5,8
S,5,8,7,6,4,5,8,4,6,10,9,8,2,8,5,4
C,4,10,5,8,2,5,7,7,10,7,5,14,1,9,4,9
R,2,4,3,3,2,7,7,5,5,6,5,6,2,7,4,8
G,6,11,8,8,9,8,7,6,2,7,6,11,5,8,9,7
C,2,4,3,3,2,6,8,7,8,8,7,13,1,9,4,10
I,3,8,4,6,2,7,8,0,8,14,6,6,0,8,1,7
Y,8,11,8,8,4,2,11,3,6,12,12,7,1,11,2,5
C,3,6,4,4,4,8,6,5,2,7,7,10,6,9,4,7
Y,4,11,6,8,3,10,10,1,3,6,12,8,1,11,0,8
K,4,2,5,3,3,5,7,4,8,7,6,11,3,8,5,9
I,7,8,8,9,8,7,8,4,6,6,8,7,4,9,10,9
E,5,9,7,7,6,8,7,6,3,7,6,10,5,9,9,8
O,2,5,3,4,2,7,7,8,5,7,5,8,2,8,3,8
M,5,7,7,5,6,4,7,3,4,10,10,10,6,6,2,6
Q,4,8,5,9,5,8,7,7,2,8,7,12,3,9,6,8
Z,3,9,4,6,3,7,7,6,11,6,6,8,1,8,8,8
H,3,8,5,6,6,8,6,4,5,6,6,8,6,7,6,7
Z,8,8,6,12,5,8,5,5,4,11,6,8,3,9,11,7
H,4,9,6,7,7,7,7,5,6,7,6,8,3,8,3,8
O,5,9,6,7,4,7,8,8,6,7,8,8,3,7,4,9
U,5,9,5,6,4,7,5,14,5,7,11,8,3,9,0,8
W,6,9,6,7,7,4,9,2,3,9,8,8,7,11,2,7
L,3,8,3,6,1,0,1,5,6,0,0,6,0,8,0,8
J,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8
L,5,10,7,8,5,5,5,1,9,6,2,10,3,7,4,5
O,5,8,7,7,6,7,5,5,5,9,5,10,5,5,7,5
K,6,11,8,8,7,6,6,4,7,6,6,11,5,7,8,10
J,4,6,6,7,5,8,9,5,5,7,7,9,3,7,8,6
K,3,6,4,4,4,7,9,4,4,7,5,8,4,7,6,9
T,3,7,4,5,2,7,11,3,7,10,9,4,2,11,3,5
G,4,10,6,8,7,7,9,6,3,5,5,11,5,7,8,7
M,5,5,6,7,4,7,7,12,2,7,9,8,9,6,0,8
J,2,10,3,7,1,15,2,6,6,14,0,9,0,7,0,8
V,3,4,5,2,1,3,13,4,2,11,11,7,2,10,1,7
T,6,8,6,6,3,6,11,2,9,12,9,4,1,11,3,4
Y,2,1,4,2,1,6,10,2,7,8,11,8,1,11,2,7
C,3,3,4,5,1,5,7,6,9,6,6,12,1,8,4,8
A,3,4,5,6,2,8,5,3,1,7,1,8,2,7,2,8
R,8,12,6,6,4,10,6,6,5,11,2,8,7,6,5,10
M,5,9,8,7,7,12,6,2,4,9,2,6,8,6,2,8
U,9,10,9,8,3,4,9,6,9,13,12,9,3,9,1,7
Q,6,8,6,9,8,8,7,6,3,8,8,10,3,8,6,8
X,3,6,5,4,3,8,7,4,9,6,6,8,3,8,6,8
C,4,7,5,5,5,6,7,4,4,7,7,10,6,9,3,8
Y,4,10,6,7,1,6,10,3,2,8,13,8,1,11,0,8
L,3,6,4,4,2,4,4,4,9,2,1,7,0,7,1,6
M,4,8,7,6,9,9,5,2,2,8,4,8,10,6,2,6
S,1,0,1,0,0,8,7,3,5,5,6,7,0,8,6,8
I,2,7,2,5,2,7,7,0,7,7,6,8,0,8,3,8
J,6,7,4,11,3,6,9,3,4,13,5,5,3,8,6,9
E,10,15,7,8,5,7,7,4,5,10,6,8,3,9,8,9
M,5,9,8,7,10,7,6,4,2,7,4,8,13,6,3,7
L,3,4,3,6,1,0,1,5,6,0,0,7,0,8,0,8
T,3,8,5,6,3,7,12,4,6,8,11,7,2,12,1,7
X,5,11,6,8,2,7,7,5,4,7,6,8,3,8,4,8
X,5,11,6,8,2,7,7,5,4,7,6,8,3,8,4,8
V,8,11,8,8,3,2,11,6,5,13,12,8,3,10,1,8
D,2,4,4,2,2,9,6,4,6,10,4,6,2,8,2,9
X,8,15,8,9,4,8,8,2,8,9,7,8,4,12,4,7
S,3,2,4,4,3,8,7,7,5,7,7,8,2,9,9,8
B,1,3,2,1,1,8,7,2,5,10,5,7,1,8,3,9
E,1,3,3,2,2,6,8,2,7,11,7,9,2,8,4,9
S,5,11,6,8,3,9,10,6,10,5,6,5,0,7,9,8
K,5,8,8,6,5,3,8,2,6,10,11,11,3,7,3,5
P,4,8,6,12,10,8,9,5,0,8,6,7,5,11,6,6
Z,2,2,3,4,2,7,7,5,9,6,6,8,2,8,7,8
R,3,5,6,3,4,8,8,3,5,9,4,7,3,6,3,11
S,2,3,3,2,1,9,7,2,6,10,5,7,1,9,5,9
F,2,1,3,2,1,5,11,4,5,10,9,5,1,9,3,6
L,4,8,5,6,3,4,3,5,9,1,1,5,1,6,1,5
N,5,9,6,7,4,8,6,9,6,4,4,4,5,7,3,7
N,6,7,8,5,4,10,7,3,6,10,1,4,6,10,2,8
N,1,0,1,1,0,7,7,10,0,6,6,8,4,8,0,8
D,4,8,6,6,4,8,7,6,7,10,5,5,3,8,3,8
M,6,10,7,8,4,8,7,13,2,6,9,8,9,6,0,8
Y,5,10,7,8,6,8,6,7,5,5,8,8,3,8,10,6
R,9,13,7,8,5,7,7,5,5,9,5,9,7,5,7,11
D,6,9,8,8,8,6,8,5,7,5,3,6,5,9,8,5
E,3,6,3,4,2,3,8,6,10,7,6,15,0,8,7,7
R,2,0,2,1,1,7,11,8,2,7,5,8,2,7,4,10
M,4,7,6,5,5,6,6,2,4,9,8,9,7,5,2,7
I,6,10,8,8,5,9,5,2,6,6,7,6,0,10,4,7
F,4,9,5,6,3,2,11,5,6,11,10,9,0,8,2,6
P,5,10,7,8,7,6,9,3,7,9,8,5,4,10,4,7
S,6,10,7,8,4,6,8,4,8,11,8,7,2,8,5,5
X,3,5,4,4,4,5,8,2,4,8,7,9,2,7,7,8
T,2,7,3,4,1,7,14,0,6,7,11,8,0,8,0,8
R,5,10,5,8,7,6,8,8,4,7,5,7,3,8,6,12
O,2,3,2,1,1,8,7,6,4,9,6,8,2,8,3,8
A,4,9,6,7,3,11,2,4,3,10,2,9,3,7,3,8
P,5,8,8,6,3,7,11,3,7,14,5,2,0,9,3,8
X,5,8,6,7,6,7,7,2,5,7,6,8,3,10,8,7
M,3,1,4,2,1,8,7,11,1,7,9,8,7,6,0,8
U,3,5,5,4,4,8,6,4,4,6,6,8,4,10,1,7
B,2,6,3,4,3,6,7,7,5,7,6,7,2,8,6,9
Z,3,8,4,6,3,7,7,6,10,6,6,8,1,8,8,8
G,5,8,6,6,4,6,7,5,4,9,8,9,2,7,5,9
P,4,9,5,7,5,5,8,6,4,8,7,9,3,8,7,10
S,6,10,9,8,11,6,7,3,2,7,5,6,3,8,11,4
M,7,8,9,7,10,5,8,5,4,6,5,8,13,7,6,9
E,5,5,5,8,3,3,8,6,12,7,5,15,0,8,7,6
K,5,11,7,8,7,9,6,1,6,10,3,8,7,7,6,11
T,3,4,5,6,1,7,15,1,5,7,11,8,0,8,0,8
A,7,15,5,8,4,10,3,3,2,8,4,11,5,5,4,8
S,2,6,3,4,3,7,6,7,5,7,7,8,2,9,8,7
R,4,8,4,6,4,6,8,9,4,7,5,8,2,7,5,11
D,3,8,4,6,6,10,7,4,5,7,6,6,4,6,9,6
H,6,10,9,8,8,7,7,3,6,10,6,8,3,8,3,7
G,5,9,4,4,3,9,3,5,2,9,7,10,4,9,6,8
E,6,10,8,8,7,9,6,2,7,11,4,9,5,7,6,11
O,5,10,5,8,5,8,6,8,4,9,5,8,3,8,3,8
J,6,10,8,8,4,5,9,3,6,15,7,9,2,7,2,7
C,5,8,7,7,6,5,7,3,4,7,6,11,4,10,7,10
I,1,3,2,2,0,7,7,1,7,13,6,9,0,8,1,8
D,4,6,5,5,4,6,6,6,7,7,6,8,4,6,5,5
F,1,0,1,1,0,3,11,4,3,11,9,7,0,8,2,8
U,2,3,3,2,1,4,8,4,6,11,10,9,3,9,1,7
Q,2,3,2,3,2,8,8,5,2,8,7,10,2,9,3,8
Q,3,7,5,5,4,8,4,7,4,6,5,7,3,8,3,9
W,6,10,9,7,7,4,11,2,2,8,9,9,7,12,1,8
R,4,6,5,4,4,6,7,4,6,7,6,6,6,7,4,8
J,1,3,3,2,1,8,6,3,5,14,6,11,1,7,0,7
Z,6,10,8,8,5,7,8,3,10,12,8,7,2,8,7,6
C,4,9,5,8,6,5,7,3,4,7,6,11,4,10,7,10
Q,2,3,3,3,2,8,7,6,3,6,6,9,2,8,5,9
Q,2,2,2,2,1,7,8,4,2,8,7,9,2,9,3,9
T,2,7,4,4,1,8,15,1,5,6,11,8,0,8,0,8
E,3,5,3,4,3,7,7,5,7,7,6,9,2,8,5,10
E,3,5,3,3,3,7,7,5,7,7,6,9,2,8,5,10
P,4,7,5,5,2,6,14,5,3,12,5,1,0,10,3,7
F,2,3,4,2,1,6,11,2,5,13,6,4,1,10,1,8
C,4,4,5,6,2,6,6,7,11,8,5,12,1,9,4,9
S,1,3,3,2,1,7,9,3,7,10,7,6,1,8,4,6
S,3,6,4,4,3,7,6,7,5,7,7,9,2,9,8,7
N,3,2,4,4,3,7,8,5,5,7,7,6,5,10,2,5
T,6,8,6,6,3,7,11,3,8,11,9,4,2,12,3,4
A,1,3,2,2,1,9,3,2,1,8,2,9,1,6,1,7
X,3,2,4,3,2,8,7,3,9,6,6,8,2,8,6,8
W,5,7,7,5,3,4,8,5,1,7,9,8,8,10,0,8
V,3,6,5,4,5,8,5,5,2,7,7,7,4,9,4,6
F,3,6,4,4,2,6,11,2,6,14,6,4,1,10,2,8
D,2,2,3,3,3,7,7,6,6,7,6,5,2,8,3,7
Q,4,9,5,8,3,9,8,8,6,5,8,9,3,8,5,9
H,1,1,2,1,1,7,7,12,1,7,6,8,3,8,0,8
O,4,6,6,5,4,6,6,5,5,8,4,8,3,7,5,6
V,4,6,4,4,2,5,11,3,4,9,11,7,2,10,1,8
T,3,8,5,6,1,7,15,1,5,7,11,8,0,8,0,8
U,3,6,4,4,1,7,4,14,5,7,13,8,3,9,0,8
Y,2,7,3,5,2,5,10,2,2,8,12,8,1,11,0,8
G,1,0,2,0,1,8,7,5,5,6,6,9,1,7,5,10
H,4,7,6,10,7,8,9,5,1,8,6,6,5,11,10,6
U,2,1,2,1,1,8,5,11,4,6,13,8,3,10,0,8
I,3,10,4,8,2,7,7,0,8,13,6,8,0,8,1,8
L,4,10,5,8,3,0,2,4,6,1,0,8,0,8,0,8
P,4,7,5,5,4,8,9,4,4,11,5,5,3,10,3,7
L,4,10,6,7,8,7,7,3,5,6,7,10,6,10,7,4
N,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8
W,7,10,7,8,8,5,10,3,3,9,7,7,8,11,3,6
F,5,10,5,7,3,0,13,3,3,11,9,6,0,8,2,6
A,6,14,6,8,4,12,2,3,2,12,4,11,4,4,4,11
G,5,10,6,8,5,7,7,7,6,6,5,9,2,8,6,11
Y,4,7,6,5,2,9,11,1,7,3,11,8,1,11,2,9
L,5,10,7,7,3,6,4,1,10,8,2,11,0,7,3,7
M,4,1,5,3,3,8,6,6,5,6,7,8,8,5,2,7
O,2,4,3,2,1,8,7,7,5,7,6,8,2,8,3,8
G,4,7,5,5,4,6,7,6,4,5,7,9,2,7,4,9
Y,3,5,4,4,2,4,10,1,8,10,10,6,1,10,3,4
M,3,3,5,2,3,9,6,3,4,9,5,7,6,5,1,8
A,3,6,5,4,3,11,2,2,2,9,2,9,2,6,3,8
P,4,8,4,5,2,3,14,8,1,11,7,3,1,10,4,8
U,4,8,4,6,3,7,6,12,4,7,12,8,3,9,0,8
F,2,3,4,2,1,6,10,3,5,13,6,5,1,9,2,7
A,3,6,5,4,1,7,5,3,1,6,1,8,2,7,2,7
P,1,3,2,1,1,5,10,3,4,10,8,5,0,9,3,7
H,3,4,4,5,2,7,9,15,2,7,3,8,3,8,0,8
A,4,10,7,7,5,7,5,2,3,5,2,6,3,7,4,4
F,4,9,5,6,2,1,15,5,3,12,9,4,0,8,2,6
G,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10
X,11,14,10,8,4,8,7,2,10,9,6,8,4,8,4,8
S,9,15,8,9,4,11,2,2,5,10,2,9,3,7,4,12
K,5,8,7,6,5,5,7,1,7,10,8,10,3,8,4,7
D,4,4,5,6,3,5,6,10,9,5,5,5,3,8,4,8
J,4,5,5,6,4,9,8,5,4,6,5,8,3,8,9,8
T,7,9,7,7,5,6,12,5,6,11,9,4,3,13,3,4
P,3,3,5,2,2,7,10,3,4,12,4,3,1,9,3,8
K,4,8,6,6,5,5,8,1,6,9,7,9,3,8,3,8
A,2,7,4,5,2,12,2,4,3,11,2,9,2,6,3,9
A,3,4,6,6,2,9,3,3,3,8,2,9,3,6,3,9
C,4,7,5,5,3,6,7,6,9,8,4,10,1,10,4,10
M,2,3,4,2,2,6,6,3,3,9,8,9,5,5,1,8
V,3,4,4,3,1,4,12,4,3,10,11,7,2,10,1,8
M,5,8,7,6,8,7,7,7,5,7,5,8,7,9,8,6
J,2,6,2,4,1,15,3,3,5,12,1,8,0,8,0,8
O,3,7,4,5,2,7,8,8,7,7,7,8,3,8,4,8
S,5,10,7,8,5,10,6,4,6,10,3,7,2,8,5,10
N,3,5,4,7,3,7,7,14,2,5,6,8,6,8,0,8
L,5,11,6,9,4,4,4,1,9,6,1,10,0,6,3,6
V,2,5,4,4,2,6,12,2,3,8,11,8,2,10,1,8
H,3,3,3,4,2,7,8,14,1,7,5,8,3,8,0,8
Z,3,5,4,7,2,7,7,4,14,10,6,8,0,8,8,8
J,2,7,3,5,1,13,2,8,4,13,4,12,1,6,0,8
K,11,15,10,8,4,7,8,3,8,9,7,8,6,8,4,7
X,6,8,9,6,4,6,8,2,9,10,9,8,3,8,4,7
C,7,10,7,7,4,6,7,6,8,13,8,12,3,11,5,5
D,3,6,3,4,3,5,7,8,6,7,7,6,2,8,3,8
E,4,10,4,8,5,2,8,5,9,7,7,14,0,8,6,9
M,5,9,8,7,6,5,7,3,4,10,9,9,8,6,3,8
A,7,13,7,7,4,12,3,6,2,12,2,10,5,3,4,10
M,6,9,8,6,8,7,7,6,5,6,7,9,8,6,2,8
C,3,7,4,5,2,5,8,6,7,7,6,12,1,9,4,10
J,3,10,4,8,1,12,2,10,4,14,4,13,1,6,0,8
J,2,5,4,3,2,9,5,4,5,14,6,11,1,6,0,7
L,4,10,4,8,1,0,1,6,6,0,0,6,0,8,0,8
Q,3,4,4,5,4,8,8,7,2,5,7,9,3,8,5,9
M,4,3,4,4,3,7,7,12,1,7,9,8,8,6,0,8
Z,2,3,4,2,2,7,8,2,9,12,6,8,1,9,5,8
Q,6,7,8,6,6,7,4,4,5,7,4,9,5,5,6,7
N,5,6,6,6,6,7,7,4,3,7,5,7,6,9,5,5
V,2,3,3,1,1,4,12,3,2,10,11,7,2,11,1,7
B,3,6,4,4,3,8,6,6,7,6,6,6,2,8,7,10
K,5,7,8,5,5,9,5,1,6,9,3,8,4,7,4,10
Q,6,12,6,6,4,11,4,4,6,12,3,9,3,9,7,12
A,3,7,5,5,3,11,2,2,2,9,2,9,3,6,3,9
R,4,8,6,6,4,7,8,6,6,6,5,8,4,6,7,9
I,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8
M,6,8,9,6,9,10,6,3,3,9,4,7,9,8,3,6
H,6,11,6,8,3,7,6,15,0,7,7,8,3,8,0,8
D,4,6,6,4,4,7,7,6,5,7,6,6,4,8,3,7
D,2,4,4,3,2,8,7,5,6,9,5,5,2,8,3,7
H,3,4,4,3,3,7,8,5,7,7,6,8,6,8,4,8
R,3,3,4,5,2,5,11,8,4,7,3,9,3,7,6,11
Z,4,7,5,5,3,7,8,2,9,11,7,8,1,8,6,7
Q,4,7,5,6,3,7,6,8,6,6,7,7,3,8,5,9
B,4,9,5,6,4,6,7,8,7,7,6,7,2,8,9,10
G,2,4,3,3,2,6,7,5,5,9,7,10,2,8,4,9
B,1,0,2,1,1,7,8,7,5,7,6,7,1,8,6,8
C,4,5,5,7,2,5,7,7,10,7,6,13,1,8,4,8
K,8,15,8,8,5,3,8,4,6,10,11,11,5,11,4,7
C,8,13,5,8,2,8,6,7,7,12,5,9,2,10,5,9
W,4,7,5,5,5,7,7,6,2,7,8,8,5,8,3,9
Q,3,7,4,6,2,8,6,8,6,6,4,8,3,8,4,8
U,6,10,6,5,3,7,6,5,4,6,7,7,5,6,3,6
X,5,8,8,6,5,10,6,1,8,10,2,7,4,9,4,10
D,4,2,5,3,3,7,7,6,7,6,6,5,5,8,3,7
F,6,7,8,8,8,7,9,4,5,7,7,6,5,9,9,9
Z,5,9,7,6,5,7,8,2,9,12,6,8,1,9,6,8
K,7,9,10,7,7,2,9,2,7,10,11,11,5,7,4,4
S,2,3,3,2,2,8,7,6,4,7,6,8,2,8,9,8
O,6,11,7,9,5,8,7,9,6,7,5,10,4,8,5,5
X,10,12,9,7,4,8,7,2,9,9,6,8,4,11,4,8
N,3,7,4,5,3,7,7,12,1,6,6,8,5,8,0,8
Q,3,4,4,5,3,7,8,5,2,8,9,10,2,9,5,9
J,6,11,7,8,3,9,5,3,7,15,4,10,0,6,1,7
J,3,9,4,6,2,9,5,3,6,14,4,9,0,7,1,7
H,6,9,8,7,7,7,7,7,5,6,5,7,3,7,7,10
O,4,6,5,4,2,7,7,8,8,7,6,8,3,8,4,8
B,4,6,6,4,5,9,8,3,6,7,6,8,6,8,6,9
P,1,0,2,0,0,5,10,7,2,9,6,5,1,9,3,8
N,6,10,9,8,6,11,8,3,5,10,1,4,7,11,2,8
E,2,4,2,3,2,7,7,5,7,7,6,8,2,8,5,10
I,7,13,6,7,3,12,5,2,6,12,3,6,2,10,4,12
L,1,3,3,2,1,6,4,1,7,7,2,10,0,7,2,8
H,4,8,4,5,2,7,7,15,1,7,6,8,3,8,0,8
H,3,5,5,3,3,8,6,3,5,10,6,9,3,7,3,8
M,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8
H,6,11,6,6,3,8,8,3,4,9,6,7,6,9,5,9
X,1,0,1,0,0,8,7,3,5,7,6,8,2,8,3,7
U,9,10,7,5,3,5,3,5,5,3,7,7,5,8,2,7
I,5,11,6,8,4,9,6,0,8,13,5,9,0,8,1,9
X,6,10,9,7,5,7,7,0,8,10,6,8,3,8,3,8
C,2,6,3,4,2,7,8,8,7,10,6,11,2,11,4,9
B,2,3,2,2,2,7,7,5,5,7,6,6,2,8,5,9
D,2,3,3,2,2,7,7,6,7,7,6,4,2,8,3,7
V,3,2,6,4,2,7,12,2,3,6,11,9,4,12,2,7
K,4,9,5,6,5,6,5,4,4,6,6,9,3,6,8,10
K,4,8,6,6,7,7,7,3,4,6,6,8,7,7,7,7
B,4,11,6,8,8,8,8,6,6,7,6,5,2,8,6,9
Q,4,9,4,4,2,11,4,4,5,12,3,8,2,7,6,12
T,6,9,6,7,3,5,11,2,10,12,9,4,0,10,3,4
H,3,4,6,3,3,8,7,3,6,10,5,8,3,8,3,7
L,5,11,7,8,5,5,3,6,8,2,2,4,1,6,1,5
W,4,11,7,8,8,10,11,2,2,5,8,7,9,12,2,8
R,5,9,7,7,6,7,8,5,7,7,5,6,3,7,5,8
Y,2,7,4,5,2,5,9,1,6,9,12,9,1,11,2,7
A,3,3,5,4,1,8,6,3,1,7,1,8,2,7,1,8
U,8,10,9,8,6,3,9,5,8,10,10,10,3,9,2,6
Z,3,8,4,6,4,7,8,5,9,7,6,9,1,9,7,7
D,5,8,7,6,6,8,8,6,6,9,6,4,6,10,5,7
J,6,11,8,9,3,8,7,3,7,15,5,10,1,6,1,7
X,4,9,6,7,6,7,8,2,6,7,7,9,4,10,6,7
S,4,5,6,4,5,8,7,5,5,7,6,8,5,8,9,11
O,8,15,5,8,3,7,9,6,6,9,6,6,4,10,5,9
V,3,5,6,4,2,6,13,3,3,8,11,8,3,10,1,8
C,5,8,7,6,6,6,5,4,4,9,6,12,6,8,4,9
H,3,5,5,7,4,8,12,3,2,8,8,7,3,10,7,6
R,2,3,4,2,2,8,7,3,4,9,4,6,2,7,3,9
M,6,8,9,7,10,6,8,5,3,6,5,8,13,9,5,8
T,3,10,5,8,5,7,11,4,5,7,11,8,3,12,1,8
Y,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8
Y,2,4,3,3,1,3,12,3,6,12,10,5,1,11,2,5
J,2,6,3,4,1,13,2,8,4,14,4,12,0,7,0,8
F,2,7,3,5,1,1,12,4,4,12,10,7,0,8,2,6
J,5,9,6,7,3,9,5,4,6,15,5,11,1,6,0,7
C,5,11,6,9,3,4,9,7,8,13,11,12,2,9,3,7
G,1,0,2,0,1,8,6,5,5,6,5,9,1,8,5,10
Q,6,7,8,6,6,7,4,5,6,7,4,8,5,4,6,7
P,3,8,4,6,2,3,14,8,1,11,7,3,1,10,4,8
Q,2,2,3,2,2,8,7,6,4,6,5,9,2,9,3,9
D,4,9,6,7,9,8,8,5,4,7,6,5,6,8,8,5
V,7,12,6,6,3,9,10,5,4,6,10,6,5,12,3,7
G,5,9,4,4,3,7,6,3,3,8,6,8,4,9,8,6
Y,2,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8
L,3,6,3,4,1,0,1,6,6,0,1,5,0,8,0,8
N,5,11,5,9,6,7,7,13,1,6,6,8,5,9,0,7
V,4,6,5,4,3,9,11,3,1,4,10,9,3,11,4,9
C,2,1,2,2,0,6,7,6,9,7,6,15,0,8,4,10
I,1,4,2,3,1,7,7,1,8,13,6,8,0,8,1,8
Y,2,6,3,4,0,6,10,2,2,8,12,8,1,10,0,8
O,4,7,5,5,3,7,7,8,5,8,7,10,3,8,3,7
M,6,7,9,6,9,5,9,5,3,6,4,8,13,6,5,8
E,4,10,5,8,3,3,7,6,11,7,6,15,0,8,7,6
I,1,5,2,3,1,7,7,0,7,13,6,8,0,8,1,8
C,5,7,5,5,3,3,8,5,7,11,10,13,2,9,3,7
E,4,7,6,5,4,5,8,5,8,12,10,9,3,8,5,5
I,7,12,5,6,2,10,5,6,4,13,3,8,3,8,5,10
E,2,4,4,3,2,7,7,2,7,11,6,8,2,8,4,9
X,1,0,1,0,0,8,7,3,4,7,6,8,2,8,4,8
X,4,2,6,4,3,7,7,3,10,6,6,8,2,8,6,8
G,3,5,4,4,2,6,7,5,5,9,7,10,2,8,4,9
E,3,6,4,4,3,7,7,6,9,7,7,9,3,8,6,8
J,3,7,4,5,2,9,4,5,4,14,6,12,0,6,1,7
D,3,5,4,4,2,8,7,7,7,6,6,3,2,8,3,7
S,4,10,5,7,3,6,8,5,8,11,8,7,2,9,5,5
Z,4,9,6,6,4,8,7,2,9,11,5,9,1,8,6,8
W,7,10,10,8,14,9,7,5,2,7,7,8,13,10,4,6
M,4,3,4,5,3,7,7,12,1,7,9,8,8,6,0,8
L,4,8,5,6,3,3,4,2,8,2,1,8,0,7,1,6
R,3,9,3,6,4,5,9,8,3,7,5,8,2,7,5,11
H,4,7,6,5,4,7,8,3,6,10,7,8,3,8,3,8
D,3,7,3,5,3,6,7,9,7,6,5,6,2,8,3,7
S,2,4,4,3,2,7,7,2,7,10,6,8,1,9,5,8
Y,3,10,5,7,1,8,11,1,3,6,12,8,1,11,0,8
N,3,3,4,4,2,7,7,14,2,5,6,8,6,8,0,8
Y,4,4,5,6,7,8,8,3,2,7,8,9,4,11,7,7
R,3,9,4,6,4,6,8,8,4,7,5,8,2,7,5,11
D,2,4,4,3,2,8,7,4,6,10,5,6,2,8,3,8
O,3,4,4,6,2,7,6,9,6,6,5,6,3,8,4,8
G,3,7,4,5,2,7,6,8,7,6,6,8,2,8,6,11
H,9,15,10,8,7,8,7,3,5,10,4,7,7,6,6,7
Y,6,10,8,8,7,9,7,7,4,6,9,7,3,9,9,4
M,7,10,9,8,8,8,6,6,5,6,8,8,9,6,2,7
P,9,10,7,5,3,6,12,6,4,13,5,3,4,10,4,8
H,3,4,5,6,4,9,12,3,2,8,8,7,3,10,6,7
V,6,9,5,4,2,7,10,7,3,9,9,5,5,12,3,9
B,5,10,7,7,6,8,8,6,8,7,6,6,6,8,6,10
X,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8
O,3,3,4,2,2,7,8,7,5,7,7,8,2,8,3,8
K,4,11,5,8,2,3,7,8,2,7,5,11,3,8,2,11
G,1,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10
T,3,7,5,5,3,7,11,1,8,7,11,8,1,10,1,8
K,2,4,3,2,2,5,7,4,6,7,6,11,3,8,4,9
H,3,7,4,4,2,7,8,14,1,7,6,8,3,8,0,8
G,3,7,5,5,5,7,9,6,3,6,6,10,3,7,7,8
B,4,4,5,6,4,6,6,9,7,6,6,7,2,8,9,9
V,4,9,6,7,2,7,8,4,3,7,14,8,3,9,0,8
T,4,9,6,8,6,6,8,4,8,8,7,8,3,9,8,7
N,5,5,6,7,3,7,7,15,2,4,6,8,6,8,0,8
U,3,3,3,2,1,5,8,5,7,9,8,8,3,9,2,5
Z,4,6,6,4,3,6,9,3,9,11,8,6,1,9,6,6
K,1,0,1,0,0,5,8,7,0,7,6,10,3,8,2,11
H,8,13,8,7,4,10,8,4,6,9,2,5,6,7,4,9
I,1,3,1,2,1,7,7,1,7,7,6,8,0,8,2,8
A,2,1,3,2,1,8,2,2,1,7,2,8,2,7,2,7
H,4,8,6,6,8,7,6,5,3,6,6,8,7,7,9,9
G,4,8,5,6,2,8,6,8,8,7,5,12,2,8,5,10
O,8,13,6,8,3,7,7,5,5,8,4,7,5,9,5,8
E,1,1,2,2,1,4,7,5,8,7,6,13,0,8,6,9
X,4,8,7,6,4,4,8,2,8,10,12,10,3,8,4,5
J,4,10,5,7,3,8,6,3,6,14,4,9,0,6,1,7
O,2,4,3,3,2,7,7,6,4,9,7,7,2,8,3,8
X,1,1,2,2,1,7,7,3,8,6,6,8,2,8,5,8
V,4,4,6,7,1,8,8,4,3,7,14,8,3,9,0,8
C,5,10,5,8,3,6,8,7,7,13,7,9,2,11,3,7
M,5,5,6,4,4,8,6,6,5,6,7,7,10,6,4,6
Q,4,6,5,8,5,7,9,5,3,8,9,9,4,10,6,7
K,3,1,4,2,2,6,7,4,7,7,6,10,3,8,4,9
W,2,0,2,1,1,7,8,3,0,7,8,8,6,9,0,8
R,2,3,3,2,2,9,6,3,4,10,4,7,2,7,3,10
G,3,9,5,6,3,6,7,7,6,5,7,9,2,7,4,9
S,7,10,8,8,5,7,7,4,6,9,8,9,2,11,5,6
O,4,6,6,4,3,7,6,8,5,8,5,11,4,8,4,7
Y,3,10,5,7,3,7,10,2,6,6,12,9,2,11,2,8
P,5,4,5,6,3,4,13,8,2,10,6,3,1,10,4,8
T,3,4,3,3,1,5,12,3,6,11,9,5,1,11,1,5
R,4,6,6,4,4,9,7,4,5,9,3,7,3,7,4,11
H,6,10,6,6,4,5,8,3,4,10,9,9,6,10,5,8
E,6,9,8,7,7,8,6,6,2,7,6,9,4,8,8,10
B,3,7,5,5,3,9,7,4,6,10,5,6,2,8,6,9
F,3,4,3,3,2,5,11,3,6,11,9,5,1,10,3,6
N,5,10,5,7,3,7,7,15,2,4,6,8,6,8,0,8
H,3,4,3,5,2,7,7,14,1,7,6,8,3,8,0,8
H,5,9,8,7,5,6,8,3,6,10,8,9,3,8,3,7
L,3,7,4,5,2,6,4,2,8,7,2,10,0,7,2,8
Q,5,7,7,11,10,9,10,5,0,5,7,10,6,13,6,12
I,1,3,2,2,0,7,8,1,6,13,6,7,0,8,0,7
F,3,9,4,6,2,1,13,5,4,12,10,7,0,8,2,6
D,3,6,5,4,3,9,7,4,5,10,4,5,3,8,2,8
B,5,9,4,5,3,7,7,5,5,10,6,9,5,7,7,9
D,2,4,4,3,2,8,7,4,6,9,5,5,2,8,3,7
A,1,3,2,2,1,10,2,2,1,9,2,9,1,6,2,8
W,4,8,7,6,5,7,10,2,3,7,9,8,8,11,1,8
D,7,11,9,8,8,7,8,7,6,8,7,6,7,7,3,7
D,3,5,4,4,3,7,7,7,6,7,6,5,2,8,3,7
T,3,6,4,5,4,6,7,3,8,8,7,10,3,7,7,6
W,5,6,5,4,4,4,10,2,3,9,9,7,7,11,2,6
E,3,2,3,3,3,7,7,5,7,7,5,9,2,8,5,10
Z,3,9,5,7,5,8,6,3,7,7,6,8,1,7,11,9
Y,3,8,5,6,2,5,10,3,2,8,12,8,1,11,0,8
U,6,9,8,7,10,9,8,4,5,5,8,8,10,7,8,6
J,6,8,8,9,7,8,9,5,5,7,7,9,4,6,9,6
E,8,15,6,8,5,8,6,5,5,11,5,10,3,8,7,11
W,6,5,7,4,3,3,10,3,3,10,10,8,7,10,1,7
Y,6,8,8,10,10,10,10,6,3,6,7,7,6,11,8,3
I,1,7,0,5,1,7,7,5,3,7,6,8,0,8,0,8
J,3,9,4,7,3,10,6,0,8,11,3,6,0,7,1,7
I,1,4,2,3,0,7,8,0,7,13,6,8,0,8,0,8
P,8,15,7,8,4,6,9,6,4,13,5,5,4,10,4,8
N,4,9,6,6,4,8,9,9,6,8,5,5,3,7,3,7
H,3,9,4,6,2,7,8,15,1,7,4,8,3,8,0,8
P,11,13,8,7,4,6,12,6,5,14,6,2,4,10,4,8
Y,6,9,6,7,4,5,9,0,8,8,9,5,1,11,4,5
E,3,7,3,5,2,3,7,6,11,7,7,15,0,8,6,7
S,4,6,5,6,6,8,8,5,4,7,7,8,5,11,9,10
T,4,9,5,7,5,7,11,4,6,7,11,8,2,12,1,7
M,6,11,7,8,7,8,6,11,1,7,8,8,9,4,1,8
L,2,6,2,4,0,0,2,5,6,0,0,7,0,8,0,8
V,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8
B,4,11,6,8,8,7,7,6,5,7,6,6,3,8,6,10
V,2,3,2,1,1,5,11,2,2,9,10,7,1,11,1,7
S,4,9,5,7,3,8,6,6,9,5,6,8,0,9,9,8
Q,6,13,5,7,3,8,4,4,7,10,4,9,3,8,8,10
S,3,4,5,3,2,8,7,2,7,10,5,8,1,9,5,8
O,5,9,6,7,5,7,8,8,4,6,7,9,3,7,4,7
E,1,0,1,1,1,5,7,5,8,7,6,12,0,8,6,9
A,2,6,4,4,1,8,5,3,1,7,1,8,2,7,2,8
T,3,3,4,2,1,5,11,3,7,11,9,4,1,10,2,5
G,2,1,3,2,2,6,7,6,6,6,6,10,2,9,3,9
R,4,11,6,8,6,6,7,6,6,6,5,8,3,6,6,9
H,4,7,4,4,2,7,10,14,2,7,3,8,3,8,0,8
M,8,9,11,7,9,6,8,3,4,9,9,9,9,8,3,8
D,4,7,6,5,3,9,6,4,8,11,4,6,3,8,3,9
X,3,7,5,5,4,7,7,3,8,5,6,8,2,8,6,8
C,6,11,6,8,3,4,8,6,8,12,10,13,1,9,3,7
S,3,7,4,5,3,8,8,5,7,5,6,8,0,8,8,8
P,5,10,7,8,6,7,10,5,4,12,5,3,1,10,3,8
P,4,8,6,6,4,6,9,7,6,9,7,4,2,10,4,6
J,0,0,1,0,0,11,4,5,3,12,4,10,0,7,0,8
R,5,11,7,8,6,6,8,5,6,6,5,7,3,6,6,9
E,7,11,4,6,2,8,7,5,8,10,6,9,2,10,7,9
J,2,10,3,7,3,8,6,2,4,11,5,10,1,6,2,6
Q,2,3,3,5,3,8,8,6,1,5,7,10,3,9,5,10
V,7,12,5,6,3,8,10,5,5,7,10,5,5,12,3,7
O,2,6,3,4,2,7,7,6,3,7,6,8,3,8,2,8
G,2,4,3,3,2,6,7,5,4,9,7,10,2,9,4,10
P,5,7,6,5,5,6,9,6,4,8,7,9,2,9,7,10
I,0,3,0,2,0,9,7,2,6,7,6,7,0,8,1,7
G,3,2,5,3,3,7,7,6,6,6,7,9,2,8,4,8
D,3,4,4,3,2,7,7,7,7,7,6,5,2,8,3,7
U,6,9,8,6,6,7,8,8,6,5,7,11,4,8,5,6
D,5,8,7,7,6,6,7,5,7,7,5,7,4,7,6,5
A,1,0,2,0,0,8,4,2,0,7,2,8,1,6,1,8
U,1,0,1,0,0,7,7,10,4,7,12,8,3,10,0,8
V,3,4,5,3,2,8,12,2,3,6,11,9,2,10,1,8
D,5,9,6,7,3,6,8,11,10,8,7,6,3,8,4,8
A,3,6,5,4,2,11,2,4,2,10,2,10,2,7,3,8
U,7,11,6,6,4,6,6,5,5,7,8,9,5,8,3,9
M,4,7,4,5,3,7,7,12,1,7,9,8,8,6,0,8
X,7,10,7,5,4,6,9,3,7,11,10,8,4,14,4,6
P,5,11,7,8,5,8,8,1,6,13,6,5,1,9,3,9
Z,4,5,6,8,5,11,4,2,5,8,3,6,1,8,6,9
J,6,8,7,9,7,8,7,4,6,6,6,7,4,9,10,9
X,7,15,8,8,5,10,5,3,8,11,2,8,4,5,4,9
G,4,8,5,6,3,7,7,7,7,11,6,11,2,9,4,9
C,2,2,3,4,2,6,8,7,7,8,8,13,1,9,4,10
R,4,5,7,4,6,7,7,3,4,7,5,8,6,9,5,7
B,3,7,5,5,5,7,8,7,6,7,5,7,2,7,6,10
T,3,5,4,4,3,7,11,3,7,7,11,8,2,11,1,8
O,3,7,4,5,2,7,6,8,8,7,4,8,3,8,4,8
G,5,8,7,7,7,7,8,6,3,7,7,9,7,10,8,8
N,6,12,7,6,3,7,7,2,3,12,5,8,5,8,0,7
K,3,4,5,3,3,6,8,1,7,10,7,10,3,8,2,7
N,5,9,7,6,4,7,9,6,5,7,6,7,7,9,3,7
S,3,4,3,3,2,8,7,7,5,7,6,8,2,9,9,8
K,4,2,5,3,3,5,7,4,8,7,6,11,3,8,5,9
A,4,12,7,8,3,7,4,3,2,6,1,8,3,7,3,7
I,3,10,5,7,2,7,7,0,9,14,6,8,0,8,1,8
I,1,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8
J,5,6,6,7,5,8,9,4,5,7,6,8,3,8,8,8
O,3,4,4,3,2,8,6,7,4,9,5,8,2,8,3,8
C,3,4,4,6,1,5,6,7,9,6,5,13,1,8,4,9
L,2,5,4,4,2,7,4,1,8,8,2,10,0,7,2,8
T,3,7,4,5,2,6,12,2,7,8,11,8,1,11,1,7
K,5,10,7,7,6,6,7,4,7,6,6,9,7,8,5,9
P,6,9,8,7,6,5,14,6,2,12,6,2,1,10,3,7
U,6,7,7,5,4,4,8,5,7,9,8,9,3,9,3,5
P,4,10,6,7,6,7,6,7,4,7,6,8,3,8,7,10
T,6,13,5,7,2,6,9,3,7,13,6,6,2,8,4,4
C,3,2,4,4,2,5,8,7,7,9,8,13,1,9,4,10
N,2,3,3,2,2,7,9,6,4,8,6,7,4,8,1,7
H,4,2,5,3,4,7,7,6,6,7,6,8,3,8,3,9
K,5,11,6,8,2,4,5,9,2,7,7,12,4,7,3,11
F,4,8,6,9,8,7,9,4,4,8,6,7,4,9,8,8
C,4,7,5,5,2,7,7,6,7,12,7,11,2,10,3,8
F,5,11,6,8,6,6,9,4,7,10,9,6,2,9,4,6
L,4,9,5,4,3,9,4,3,4,12,7,11,3,10,4,10
Y,4,7,4,5,2,4,9,2,6,10,11,6,1,11,2,5
S,7,11,9,8,11,8,6,5,3,8,6,8,5,7,11,8
U,3,8,5,6,3,6,8,8,9,9,9,8,3,9,1,8
N,1,1,2,2,1,7,7,11,1,5,6,8,4,8,0,8
Z,4,8,5,6,3,8,6,2,10,11,4,9,2,8,6,9
O,2,1,3,2,2,8,7,7,4,7,6,8,2,8,3,8
P,6,9,5,5,2,7,9,5,3,12,4,5,4,9,4,8
J,3,10,4,8,2,14,3,5,5,13,1,9,0,7,0,8
O,4,9,6,7,3,8,7,9,8,7,5,8,3,8,4,8
Z,2,4,4,2,2,7,7,2,9,12,6,8,1,8,5,8
X,4,4,6,3,3,7,6,1,9,11,7,9,3,7,3,7
H,5,10,7,8,8,7,7,6,7,7,6,9,3,8,4,8
X,5,9,6,6,1,7,7,5,4,7,6,8,3,8,4,8
H,5,6,7,4,5,5,9,3,6,10,9,9,3,9,3,6
K,3,7,3,5,2,5,7,7,2,6,5,11,3,8,2,11
D,5,11,6,8,7,10,6,3,6,10,3,6,3,8,3,9
T,4,9,5,7,4,9,11,3,7,5,11,8,2,12,1,8
A,4,11,6,8,4,8,3,2,3,6,1,8,2,7,3,7
H,4,5,5,3,3,7,7,6,6,7,6,8,3,8,3,8
Q,2,3,3,3,2,8,9,5,2,5,8,10,2,9,5,9
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
P,8,11,7,6,4,8,9,4,4,12,4,4,4,10,5,7
F,6,10,8,8,9,7,6,6,4,8,7,8,4,10,8,11
K,1,0,1,0,0,4,6,5,1,7,6,10,2,7,1,10
H,1,0,2,0,0,7,8,11,1,7,6,8,2,8,0,8
E,3,6,3,4,2,3,8,6,10,7,5,14,0,8,7,7
A,2,4,4,3,2,9,2,2,1,8,2,9,2,7,2,8
C,7,11,8,8,5,4,8,6,7,12,9,11,2,11,3,7
H,7,12,8,6,5,11,6,3,5,10,4,6,5,9,4,8
I,1,10,0,8,1,7,7,5,3,7,6,8,0,8,0,8
J,1,7,2,5,1,9,6,2,7,11,4,8,0,7,1,6
Y,9,9,7,13,5,7,11,2,3,9,10,5,4,10,6,8
G,3,5,4,3,2,6,7,5,5,9,7,10,2,8,4,9
X,5,10,6,7,2,7,7,5,4,7,6,8,3,8,4,8
T,2,6,4,4,1,8,15,1,5,6,11,8,0,8,0,8
D,3,7,5,5,4,9,7,3,5,10,4,6,3,8,2,8
Q,5,8,5,9,5,8,6,7,2,8,7,12,3,8,6,7
Y,3,8,5,6,1,6,9,3,1,8,13,8,2,11,0,8
O,4,7,4,5,4,7,8,7,4,9,7,8,3,8,3,8
B,5,9,7,7,5,8,8,4,7,10,5,5,2,8,6,10
E,3,5,5,3,2,6,7,1,8,11,6,9,2,8,4,8
Z,3,3,4,5,2,7,7,4,14,9,6,8,0,8,8,8
X,7,15,8,8,5,11,7,2,8,11,4,6,3,11,4,9
G,2,2,4,3,2,6,7,6,6,7,6,10,2,8,4,9
M,6,10,8,8,10,7,6,7,4,6,5,8,8,8,8,9
U,4,7,5,5,2,7,4,14,5,7,14,8,3,9,0,8
X,4,9,7,6,4,7,7,0,8,9,6,8,2,8,3,7
W,3,2,4,3,3,8,11,2,2,6,8,8,6,11,0,8
M,2,3,4,2,2,8,6,3,4,9,6,8,6,5,1,7
T,3,4,4,3,2,5,12,3,7,11,9,4,1,11,2,5
B,2,3,4,1,2,8,7,2,5,10,5,6,1,8,4,9
J,1,6,2,4,1,14,2,6,5,13,1,9,0,7,0,8
I,1,3,2,2,1,7,8,1,7,13,6,7,0,8,1,7
L,2,6,3,4,2,3,4,3,8,2,1,8,0,7,1,6
F,3,9,5,6,3,4,11,2,6,11,10,6,1,10,3,5
L,3,7,4,5,3,5,5,2,8,7,2,9,1,7,3,7
N,4,9,4,6,4,8,7,12,1,6,6,8,5,9,0,8
J,3,7,4,5,2,9,6,1,6,11,4,7,0,7,1,7
E,4,6,6,4,4,6,8,3,7,11,8,9,3,9,4,7
B,3,5,5,3,3,9,7,3,6,10,5,6,2,8,5,10
G,1,0,2,0,0,8,6,5,4,6,5,9,1,8,5,10
Y,3,9,6,6,1,5,11,3,2,10,12,7,1,11,0,8
H,2,3,4,2,2,6,8,2,5,10,7,8,3,8,2,7
B,6,11,8,8,7,9,7,4,7,10,4,6,3,8,6,10
Y,7,9,5,13,4,5,11,2,4,11,10,6,4,11,6,7
A,3,9,5,6,3,9,3,2,3,7,1,8,2,6,2,7
R,1,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10
R,3,4,3,5,2,5,11,7,4,7,3,9,3,7,6,11
H,7,9,7,4,3,8,8,4,5,8,5,7,6,7,4,8
A,5,8,8,7,7,8,7,2,5,7,8,8,5,10,3,6
D,2,6,3,4,3,8,7,5,6,6,5,4,3,8,3,7
P,4,7,6,5,5,6,7,7,4,7,6,8,3,10,7,9
A,2,4,3,2,1,8,2,2,2,8,2,8,2,6,2,7
E,5,9,7,6,5,7,8,1,8,11,6,9,3,8,4,9
Y,2,3,4,4,0,7,10,2,2,8,12,8,1,11,0,8
V,6,8,6,6,2,4,12,4,4,10,12,7,3,9,1,8
Q,6,7,8,11,8,10,12,6,0,4,7,11,6,15,5,8
F,2,1,2,1,1,5,10,4,4,10,9,5,1,10,2,7
G,5,8,6,6,6,7,7,6,2,6,6,10,4,8,7,7
N,3,5,5,3,2,6,9,2,4,10,7,7,5,8,1,8
Q,3,4,4,5,3,8,8,5,2,8,7,10,2,9,4,8
X,2,5,4,3,2,7,7,3,9,6,6,8,2,8,6,8
K,4,5,7,4,4,6,7,2,7,10,7,10,3,8,3,8
I,1,3,2,1,0,7,7,1,6,13,6,8,0,8,0,8
V,4,7,5,5,3,9,12,2,3,4,10,9,3,12,2,8
K,5,9,7,8,8,8,8,2,5,8,3,8,5,4,4,10
V,7,12,7,7,4,7,8,4,4,7,7,6,6,12,2,9
L,3,6,5,4,5,8,8,3,5,5,7,9,6,11,6,5
W,7,10,7,8,7,4,11,2,2,9,8,7,7,12,2,6
D,5,8,8,6,6,8,8,5,6,10,6,5,5,9,4,10
U,2,6,4,4,3,6,9,4,5,6,9,9,3,9,0,8
S,6,11,8,8,5,8,7,3,6,10,5,7,2,8,5,8
D,1,0,1,0,0,6,7,7,5,7,6,6,2,8,3,8
V,9,14,7,8,3,7,11,5,6,10,10,5,4,11,3,8
Z,3,8,5,6,3,6,9,2,9,11,8,7,2,9,6,7
E,4,7,6,5,3,7,8,3,9,11,7,8,2,8,5,7
Y,4,10,6,8,2,5,11,1,4,9,11,8,0,10,0,8
T,2,8,3,6,2,7,13,0,5,7,10,8,0,8,0,8
M,4,6,6,4,4,10,6,7,5,6,7,4,8,5,2,5
L,2,1,2,3,1,4,4,5,7,2,2,5,1,7,1,6
S,2,1,2,2,1,8,7,6,4,8,5,8,2,8,8,8
J,1,3,3,2,1,8,6,3,6,14,5,10,0,7,0,7
B,2,2,3,3,2,8,7,5,5,6,6,5,2,8,7,9
Z,7,10,5,14,5,5,11,3,3,12,8,6,3,8,12,5
S,4,8,5,6,3,10,7,4,8,11,5,7,2,9,5,9
A,7,12,6,6,3,9,0,3,2,9,4,12,3,5,4,6
R,3,6,3,4,3,6,9,7,3,7,5,8,2,7,5,11
Y,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8
U,5,6,5,4,3,4,8,5,7,9,8,9,3,9,3,5
Y,5,8,5,6,2,3,13,4,6,13,11,4,1,11,2,5
Z,5,11,7,8,8,6,7,3,9,7,6,10,1,8,11,5
X,1,0,1,0,0,7,7,3,4,7,6,8,2,8,3,8
R,7,12,7,6,5,11,5,3,5,10,3,8,6,8,6,10
S,4,8,5,6,3,7,7,4,8,11,4,8,2,7,5,8
Q,4,7,5,6,3,8,5,8,7,7,4,9,3,8,4,8
D,2,5,4,4,3,8,7,5,6,9,5,5,3,8,4,8
C,1,3,2,1,1,5,9,4,6,11,8,10,1,9,2,8
P,6,9,5,4,2,7,9,6,3,13,5,4,4,9,4,7
G,7,15,6,8,5,8,7,4,4,8,6,7,4,9,9,8
Z,3,4,5,6,4,9,7,5,6,8,3,7,3,6,6,8
N,4,8,6,6,4,5,9,2,3,9,8,8,5,8,1,7
M,5,9,6,7,4,8,7,13,2,6,9,8,8,6,0,8
A,3,6,4,4,2,8,2,2,2,7,2,8,2,6,3,7
N,4,7,6,5,4,8,8,5,6,7,6,4,7,9,4,6
Z,2,4,4,6,3,11,5,3,4,9,3,7,2,7,6,8
T,4,8,6,6,5,6,8,7,7,8,7,8,3,10,6,9
I,3,6,4,4,2,7,7,0,7,13,6,8,0,8,1,7
N,3,3,5,2,2,7,9,3,4,10,6,6,5,9,1,7
D,4,6,5,5,5,7,7,5,6,7,5,9,4,6,6,5
X,4,11,6,8,6,6,7,2,7,7,7,10,6,5,8,7
Z,1,1,2,2,1,7,8,5,8,6,6,9,1,8,7,7
N,4,8,6,6,5,8,8,5,5,6,6,5,6,10,2,5
Q,3,4,4,5,3,8,7,6,3,8,8,10,3,8,5,8
Q,5,7,5,9,5,7,9,4,3,7,9,11,3,9,6,8
A,1,0,2,1,0,8,4,2,0,7,2,8,2,7,1,8
G,3,5,4,4,2,5,7,5,5,9,7,10,2,8,4,9
W,2,4,4,2,2,8,10,2,2,6,9,8,6,11,0,8
B,5,8,7,6,6,8,6,6,6,9,7,7,3,8,7,8
Q,3,4,4,5,3,8,8,6,2,5,7,10,3,9,5,10
O,2,2,3,3,2,7,7,8,4,7,6,8,2,8,3,8
N,3,7,4,5,3,8,8,6,4,7,6,6,5,9,1,6
I,2,8,2,6,2,7,7,0,7,7,6,8,0,8,3,8
N,4,5,5,4,3,7,8,6,5,7,6,6,6,9,2,5
U,4,5,5,3,2,3,8,4,6,11,11,9,3,9,1,6
Z,4,7,6,5,5,8,8,5,3,6,4,6,4,8,8,3
K,4,10,5,8,4,3,6,7,4,7,7,12,3,8,3,11
C,4,6,4,4,2,3,9,5,8,10,10,12,1,8,3,7
N,4,5,7,4,3,6,10,3,5,10,8,7,5,8,1,7
K,3,5,5,4,3,6,7,1,7,10,7,10,3,8,3,8
C,4,7,5,5,2,4,9,6,8,12,10,12,1,9,3,7
R,4,6,5,4,3,10,7,3,6,10,3,6,3,7,4,10
O,6,9,6,7,4,8,6,8,5,10,6,10,3,8,4,6
Q,1,0,2,1,1,8,7,6,4,6,6,8,2,8,3,8
I,1,4,2,3,1,7,8,0,6,13,6,8,0,8,0,8
W,4,10,6,7,4,11,8,5,2,7,9,8,8,10,0,8
X,2,1,3,2,2,8,7,3,8,6,6,8,2,8,5,8
T,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8
R,4,6,4,4,2,6,12,8,3,7,3,9,2,7,5,11
J,3,6,5,4,4,8,8,4,3,8,4,7,4,9,5,4
K,5,9,8,7,5,8,6,1,7,10,3,9,5,6,6,10
J,3,9,4,7,1,12,2,9,4,13,7,13,1,6,0,8
E,3,5,5,4,3,7,7,2,7,11,6,8,2,9,5,8
A,1,0,2,0,0,7,4,2,0,7,2,8,1,6,1,8
C,5,7,5,5,3,5,8,6,8,12,9,13,1,9,3,7
X,3,6,4,4,2,7,7,4,4,7,6,8,2,8,4,8
L,2,7,3,5,2,5,5,3,8,3,2,7,0,6,2,6
S,3,5,5,3,2,7,7,3,7,11,5,8,1,8,5,8
N,2,1,2,2,1,7,7,12,1,5,6,8,5,8,0,8
F,3,6,4,4,3,5,10,2,6,10,9,6,1,10,3,6
S,6,9,8,7,5,6,7,4,7,10,10,9,2,9,5,5
K,3,6,4,4,3,6,7,5,7,7,6,10,3,8,5,9
O,5,9,6,6,8,7,8,5,1,7,7,8,8,9,6,10
J,5,10,7,8,5,9,4,5,6,8,5,6,2,8,4,6
T,3,4,4,2,1,5,11,2,7,11,9,5,1,10,2,5
H,5,8,7,6,6,4,9,3,5,10,9,9,3,8,3,5
Z,2,0,2,1,1,7,7,3,11,8,6,8,0,8,7,8
X,7,15,8,8,5,6,8,2,8,11,7,8,4,9,4,6
F,3,5,4,7,2,0,14,4,4,12,11,6,0,8,2,6
D,6,11,8,8,8,10,7,4,6,9,3,6,3,8,3,8
C,3,10,5,8,4,5,8,7,6,9,8,14,2,9,4,10
B,3,7,3,5,3,6,8,9,6,6,5,7,2,8,8,9
C,4,9,6,6,7,7,7,4,3,6,7,10,8,10,6,6
H,5,10,5,8,5,8,7,13,1,7,7,8,3,8,0,8
T,3,3,3,2,1,5,12,3,5,11,9,4,2,11,1,5
J,2,7,3,5,2,9,6,1,6,11,4,8,0,7,1,7
S,2,3,2,1,1,8,8,6,5,7,5,7,2,8,8,8
M,3,2,4,3,3,8,6,5,3,7,7,9,9,5,1,8
H,2,3,4,1,2,8,7,3,6,10,5,7,3,8,2,7
N,4,7,4,5,3,8,8,12,1,6,6,7,5,8,0,9
K,3,6,5,4,5,6,7,3,6,6,5,8,3,8,4,9
A,2,5,4,3,2,10,2,3,2,10,2,9,2,6,2,8
D,1,1,2,2,1,6,7,8,6,6,6,6,2,8,3,8
P,4,6,6,4,3,8,10,3,5,13,4,3,1,10,3,9
I,1,4,3,3,1,7,7,1,7,14,6,8,0,8,1,8
J,6,11,5,8,4,9,8,2,3,12,5,6,2,9,7,9
J,2,10,3,8,2,12,3,7,3,12,5,11,1,6,0,8
F,3,8,3,6,1,1,13,5,3,12,10,6,0,8,2,6
K,6,7,8,5,5,7,8,2,7,10,5,9,4,7,3,7
W,4,9,6,6,3,4,8,5,1,7,9,8,8,10,0,8
V,4,5,7,8,2,7,8,4,3,7,14,8,3,9,0,8
I,1,1,1,1,0,7,7,2,8,7,6,8,0,8,3,8
O,2,3,3,2,2,7,7,7,5,7,6,8,2,8,3,8
P,4,7,6,5,3,7,11,5,4,12,4,1,1,10,3,8
W,4,4,6,6,3,8,8,4,1,7,8,8,8,9,0,8
U,4,8,5,6,5,7,6,9,5,7,6,9,4,9,6,4
O,3,8,4,6,3,7,7,8,4,10,6,8,3,8,3,7
U,5,9,7,6,4,4,8,7,9,9,10,11,3,9,1,7
I,3,9,4,6,2,7,8,0,8,14,6,6,0,8,1,7
X,2,3,3,2,1,7,7,1,8,10,6,8,2,8,2,7
J,2,8,3,6,2,9,6,3,6,11,4,9,1,6,2,6
B,4,2,5,4,4,8,7,5,6,6,5,5,2,8,7,10
Q,2,3,3,4,2,7,9,4,1,7,8,10,2,9,4,8
G,4,7,4,5,3,7,6,6,6,10,6,12,2,10,4,9
Q,7,8,7,9,7,8,7,6,2,7,7,11,5,9,7,7
J,7,11,9,8,6,9,4,5,6,8,5,6,2,7,5,6
D,8,12,8,6,4,11,3,3,6,10,2,8,4,7,3,12
U,4,10,6,7,9,10,8,4,4,5,8,8,9,8,8,6
P,4,10,5,8,4,6,10,6,5,9,7,3,2,10,4,7
X,4,8,5,6,1,7,7,4,4,7,6,8,3,8,4,8
B,3,6,4,4,4,8,7,5,6,7,6,6,2,8,6,9
G,4,8,5,6,3,7,7,8,6,7,6,12,3,8,4,9
W,3,4,5,6,3,6,8,4,1,7,8,8,8,10,0,8
G,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,9
A,4,9,5,6,4,7,5,3,0,6,1,8,2,7,1,7
R,4,8,6,6,6,6,7,5,6,7,6,7,3,7,5,9
B,2,4,3,2,2,10,7,2,6,10,4,6,2,8,4,10
X,8,11,7,6,3,7,7,2,9,9,7,9,4,11,4,7
N,3,4,5,3,2,6,9,3,4,11,8,8,5,7,1,8
K,7,10,10,7,8,8,6,1,7,9,5,9,5,7,5,8
B,4,8,6,6,6,7,8,6,4,7,5,6,4,7,6,6
E,3,7,4,5,3,4,9,2,8,10,8,10,2,8,4,5
E,4,8,4,6,2,3,6,6,11,7,7,15,0,8,7,7
W,4,6,6,4,5,7,11,2,2,7,8,8,6,11,1,8
A,3,9,5,7,3,13,3,3,2,11,1,8,2,6,3,9
U,3,5,4,4,2,6,8,6,7,7,9,9,3,9,1,8
M,3,4,5,2,3,8,6,6,4,7,7,9,9,5,2,8
J,5,9,7,7,3,9,6,3,7,15,5,10,1,6,1,7
C,5,4,6,6,2,6,7,7,11,7,6,14,1,8,4,9
M,3,6,4,4,4,7,6,6,4,6,7,8,7,5,2,7
P,4,7,6,5,3,8,9,3,5,13,5,4,1,10,2,8
I,1,5,1,3,1,7,7,1,7,7,6,8,0,8,3,8
D,5,10,7,7,6,11,6,2,6,11,3,7,3,8,3,9
E,5,11,7,8,5,5,9,4,9,11,9,8,3,8,5,5
S,4,7,5,5,3,10,6,4,8,11,3,8,2,8,5,10
I,3,8,6,6,5,8,8,4,5,9,6,4,5,10,4,6
W,8,13,8,7,5,4,8,1,3,8,9,8,9,11,2,5
C,3,7,4,5,2,3,8,5,8,9,9,13,1,8,3,7
F,4,9,4,6,2,1,13,5,3,12,10,6,0,8,2,6
C,3,7,4,5,2,3,9,6,7,11,11,11,1,8,2,6
Z,4,8,5,6,3,7,7,3,12,9,6,8,0,8,8,8
F,5,10,7,8,5,8,9,2,6,13,6,5,2,10,2,8
R,7,11,10,8,11,5,8,3,5,6,5,10,8,9,9,6
A,5,10,8,8,5,11,3,1,2,8,2,9,4,6,3,8
V,5,9,5,7,4,3,11,2,3,9,11,8,2,11,1,8
Z,4,5,5,8,2,7,7,4,14,9,6,8,0,8,8,8
I,1,3,2,2,1,7,8,0,6,13,6,7,0,8,0,7
O,10,13,7,8,4,7,7,5,5,8,4,7,5,9,6,8
D,1,0,1,0,0,6,7,6,4,7,6,6,2,8,2,8
M,3,4,4,2,3,7,6,6,4,6,7,8,6,6,2,8
S,6,12,6,6,3,7,8,3,6,13,6,7,2,8,3,7
E,6,10,9,8,6,4,9,4,9,12,10,9,3,8,5,5
D,6,11,9,8,8,7,8,6,6,10,7,5,7,9,6,11
I,2,5,4,4,1,7,7,0,7,13,6,8,0,8,1,8
Q,5,5,7,8,4,8,9,7,6,6,8,9,3,7,6,10
M,5,5,6,8,4,7,7,12,2,7,9,8,9,6,0,8
P,4,7,5,5,3,6,10,7,3,10,5,4,2,10,3,8
N,6,10,9,8,5,8,8,3,5,10,5,6,6,8,2,7
F,5,5,5,7,2,1,14,5,4,12,10,6,0,8,2,5
P,6,10,8,8,5,6,12,5,3,12,5,2,1,11,3,8
T,5,10,6,8,4,7,9,0,8,11,9,6,1,10,3,4
U,2,6,3,4,1,8,5,13,5,6,13,8,3,9,0,8
O,4,10,6,8,3,8,8,9,8,6,7,10,3,8,4,8
O,5,9,6,6,5,7,7,7,4,9,6,10,4,8,4,7
S,5,10,7,7,4,9,6,3,7,10,6,9,2,10,5,9
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
B,4,10,5,8,6,8,7,7,6,7,6,6,2,8,7,10
N,3,2,3,3,2,7,8,5,4,7,6,7,4,8,1,7
X,3,7,5,5,3,8,7,4,9,6,6,8,3,8,7,9
Q,3,6,4,6,2,8,6,8,6,6,6,8,3,8,4,8
P,2,1,2,2,1,5,10,4,4,9,7,4,1,10,3,7
L,5,10,7,8,4,7,3,2,9,7,1,9,1,6,3,7
W,3,5,5,4,4,9,11,3,2,5,9,7,7,11,1,8
N,3,7,5,5,3,6,9,6,5,8,7,8,5,8,1,7
O,4,9,5,6,4,8,7,8,5,7,7,7,3,8,3,8
V,7,11,9,8,6,5,12,3,2,9,10,7,5,10,6,8
G,4,8,6,6,3,6,6,6,7,7,5,9,3,10,4,8
F,3,4,3,5,1,1,14,5,3,12,9,5,0,8,2,6
L,2,4,4,3,2,6,4,1,8,8,2,11,0,7,2,8
B,4,5,5,8,4,6,7,10,7,7,6,7,2,8,9,9
I,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
X,4,4,5,6,1,7,7,5,4,7,6,8,3,8,4,8
D,4,8,5,6,8,9,7,5,5,7,6,6,4,7,7,6
I,1,8,2,6,2,7,7,0,8,7,6,8,0,8,3,8
D,2,7,4,5,4,7,7,4,6,7,6,6,3,8,2,7
C,6,11,7,8,4,5,8,8,9,9,9,13,2,9,4,9
G,4,6,5,4,3,6,7,6,6,9,8,9,2,8,4,9
V,1,1,2,1,0,7,9,4,2,7,13,8,2,10,0,8
T,5,5,5,4,2,5,12,3,7,12,9,4,1,11,1,5
K,5,7,7,5,4,4,8,2,7,10,10,11,3,8,4,6
C,7,11,7,8,4,5,7,6,8,12,9,14,2,10,4,7
Z,4,5,5,7,2,7,7,4,15,9,6,8,0,8,8,8
W,6,10,6,8,7,4,10,3,2,9,7,8,8,12,3,5
E,4,9,4,7,3,3,8,6,12,7,5,15,0,8,7,7
B,7,12,6,6,4,8,6,4,5,10,5,8,6,7,7,10
B,4,7,6,5,4,10,6,3,7,10,4,7,3,8,5,11
K,2,1,3,3,2,6,7,4,7,7,6,10,3,8,5,9
M,2,3,4,1,2,8,6,3,4,9,6,7,6,5,1,7
H,4,4,6,6,5,9,9,3,1,7,7,8,4,9,8,8
C,6,8,8,7,7,5,7,4,5,7,6,11,4,9,8,9
V,7,12,7,6,4,9,8,4,5,8,8,5,6,10,3,6
F,4,10,6,8,3,4,13,6,5,12,10,3,2,10,2,4
T,3,8,4,6,2,9,13,0,5,6,10,8,0,8,0,8
Y,5,10,7,8,3,7,10,1,3,7,12,8,1,11,0,8
F,3,7,3,4,1,2,14,5,2,12,9,4,0,8,3,6
X,2,3,3,2,2,8,7,3,8,6,6,6,2,8,6,8
D,5,8,7,6,6,8,7,5,6,9,4,5,3,8,4,9
R,2,0,2,1,1,6,10,7,2,7,5,7,2,7,4,9
Y,1,3,2,2,1,6,11,1,7,8,11,9,1,11,2,8
X,4,8,6,6,4,7,7,3,9,5,6,8,3,8,7,8
O,4,6,4,4,4,6,9,7,4,9,8,8,3,8,3,7
C,8,12,5,7,3,5,10,6,8,11,8,8,1,8,6,8
H,3,3,4,4,2,7,7,14,1,7,6,8,3,8,0,8
A,3,9,5,7,3,9,3,2,2,8,1,8,2,6,3,7
X,6,10,9,8,4,5,8,2,9,11,12,10,4,7,4,5
R,5,8,8,7,8,8,8,3,3,7,5,8,7,8,6,4
T,5,7,6,6,6,5,8,4,8,9,7,10,3,7,7,5
C,5,4,6,7,2,7,6,7,11,9,5,13,1,9,4,8
D,8,14,8,8,4,11,4,3,6,10,2,7,5,7,4,12
N,1,3,3,2,1,5,9,3,3,10,8,8,4,8,0,7
A,4,6,6,5,5,8,8,3,5,7,8,7,5,10,4,6
A,3,8,5,6,3,7,5,3,0,7,1,8,2,7,1,8
A,3,11,6,8,4,12,2,2,2,9,2,9,2,6,3,8
B,4,9,6,7,5,8,7,6,6,9,6,6,3,8,7,8
V,1,0,2,0,0,8,9,3,2,6,12,8,2,10,0,8
D,5,5,6,7,3,5,7,10,10,6,6,6,3,8,4,8
X,2,3,3,2,2,7,7,3,9,6,6,8,2,8,5,8
D,6,11,6,6,3,10,4,3,6,9,3,7,4,7,4,11
J,2,5,4,4,1,9,6,2,7,14,5,10,1,6,1,7
H,5,11,5,8,3,7,9,15,2,7,3,8,3,8,0,8
E,2,3,4,2,2,7,7,2,7,11,7,9,2,8,4,8
I,3,9,4,6,3,8,7,0,7,13,6,8,0,8,1,8
U,7,9,6,5,3,4,4,5,5,4,7,8,5,7,2,7
C,7,10,9,8,6,7,7,8,7,6,7,8,5,6,5,9
I,7,14,5,8,2,9,7,6,4,13,3,7,3,7,6,11
T,4,6,5,6,5,5,8,4,7,8,8,9,3,8,7,6
A,2,4,3,3,1,10,2,3,2,10,2,9,2,6,2,8
K,8,13,8,8,4,8,7,3,6,9,7,8,6,11,4,7
W,5,8,8,6,3,7,7,5,2,7,8,8,9,9,0,8
Q,4,7,5,9,6,8,6,8,2,5,6,9,3,8,5,10
T,5,10,6,8,4,5,11,1,9,8,11,9,1,10,1,7
Z,1,0,2,1,0,7,7,2,11,8,6,8,0,8,6,8
B,5,4,5,6,4,7,9,9,8,7,5,7,2,8,9,9
P,4,6,6,4,3,7,10,3,4,13,6,3,1,10,3,9
V,6,11,6,6,3,5,9,4,3,10,8,5,5,12,2,7
P,2,1,2,1,1,5,11,7,2,9,6,4,1,9,3,8
H,3,6,4,4,2,7,8,14,1,7,5,8,3,8,0,8
X,7,10,10,8,6,9,6,1,8,10,3,7,3,8,3,9
P,4,9,6,6,4,5,10,6,5,10,8,3,1,10,4,6
C,0,0,1,0,0,6,7,5,7,7,6,14,0,8,3,10
G,3,4,4,3,2,7,6,6,6,6,6,10,2,9,4,9
G,3,5,4,4,3,6,7,5,5,9,7,10,2,8,4,10
X,9,11,8,6,3,7,7,2,9,9,6,8,4,9,4,8
Z,5,8,6,6,4,7,8,2,10,12,7,6,1,7,6,6
W,6,11,9,8,7,5,9,5,1,7,9,8,8,11,0,7
I,2,8,3,6,1,9,7,0,8,14,5,8,0,8,1,8
Z,2,4,4,3,2,8,7,2,9,12,6,7,1,8,5,8
C,2,6,3,4,2,6,8,8,8,9,8,13,1,9,4,10
W,3,7,6,5,4,5,10,2,3,8,9,9,7,11,1,8
G,4,7,5,5,3,6,7,7,7,10,7,9,2,10,4,9
X,3,3,4,4,1,7,7,4,4,7,6,8,3,8,4,8
R,3,7,3,5,2,6,9,9,5,7,5,8,3,8,5,10
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
N,5,9,8,7,5,10,7,3,5,10,2,5,6,9,1,7
W,8,10,8,5,4,1,9,3,3,11,12,9,8,10,1,6
K,7,10,10,8,6,4,9,2,7,10,9,10,5,6,4,5
U,4,7,4,5,3,7,6,12,4,7,12,8,3,9,0,8
B,8,12,6,6,4,7,7,5,6,10,5,8,6,6,7,10
H,4,5,6,4,4,7,7,3,6,10,6,8,3,8,3,8
E,4,7,5,5,4,10,6,2,7,11,4,8,4,9,5,12
C,3,7,5,5,5,5,7,3,4,7,6,10,5,10,3,8
Q,3,6,4,8,4,8,11,4,3,5,9,11,2,10,5,8
P,6,11,8,8,5,5,11,7,4,11,7,2,1,10,4,6
L,3,7,3,5,1,0,1,5,6,0,0,6,0,8,0,8
E,0,0,1,0,0,5,7,5,6,7,6,12,0,8,6,10
K,1,1,2,1,1,6,7,4,7,7,6,11,3,8,4,9
R,4,4,4,6,3,5,11,8,4,7,2,9,3,7,6,11
O,1,0,1,0,0,7,6,6,3,7,6,7,2,8,3,8
K,4,9,6,7,4,5,7,5,7,6,6,10,4,8,5,9
V,5,6,5,4,2,3,12,4,4,10,12,7,2,10,1,7
F,5,8,7,6,4,7,9,3,6,13,6,5,2,9,3,7
T,2,4,3,3,1,7,11,2,7,6,11,8,1,10,1,8
S,2,6,3,4,2,7,6,5,8,4,6,8,0,9,8,8
Q,3,7,4,6,3,8,7,7,4,6,6,9,2,8,4,9
V,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8
U,3,6,4,4,2,7,4,14,5,7,12,8,3,9,0,8
P,1,0,2,0,0,5,11,6,1,9,6,5,1,9,3,8
J,1,1,2,3,1,11,6,2,6,12,3,7,0,7,1,8
S,6,9,7,6,4,8,8,4,10,11,4,7,2,6,5,9
M,10,15,12,9,6,10,2,3,2,10,3,9,8,1,1,8
T,3,9,4,7,4,7,11,3,6,7,11,9,2,12,1,8
U,5,6,6,4,3,3,9,5,7,11,11,9,3,9,2,6
F,3,7,3,4,1,1,13,5,4,12,10,7,0,8,2,6
X,3,5,6,3,3,7,7,1,8,10,6,8,2,8,3,8
L,7,11,6,6,3,10,2,4,4,12,5,13,2,7,5,9
X,4,6,5,5,5,8,8,2,4,7,6,8,3,8,7,7
F,7,12,6,6,3,8,8,3,7,12,4,5,2,8,7,7
J,7,8,4,12,4,7,9,3,4,13,4,5,3,8,7,10
T,5,9,5,6,3,5,12,3,8,12,9,4,1,11,2,4
Y,3,9,5,6,2,9,11,1,3,5,11,8,1,10,0,8
A,3,8,5,5,2,7,5,3,1,6,1,8,2,7,2,7
F,4,7,6,5,3,5,11,3,5,13,8,5,1,10,2,7
I,2,9,2,7,3,7,7,0,7,7,6,8,0,8,3,8
U,3,2,4,4,2,6,8,6,7,6,9,9,3,9,1,7
I,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8
E,4,5,6,4,5,6,8,4,4,8,7,9,4,11,8,10
H,8,10,11,8,6,7,8,3,7,10,8,8,6,8,5,7
M,4,2,6,3,4,8,6,6,5,6,7,8,8,7,3,6
N,3,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8
S,3,8,4,6,2,7,6,6,10,5,7,10,0,9,9,8
L,10,15,8,8,4,9,2,4,5,12,4,13,2,7,6,8
F,3,5,5,6,4,6,9,3,4,8,7,7,4,10,8,9
H,5,9,8,7,7,8,8,5,7,7,6,7,6,8,4,7
W,5,6,7,5,8,6,7,6,5,6,5,8,8,10,8,10
N,4,7,6,5,3,11,6,4,5,10,1,5,5,9,1,7
H,7,9,10,7,6,5,8,4,7,10,9,10,3,8,3,6
I,2,8,4,6,4,10,6,1,4,8,5,5,3,8,5,7
D,3,5,5,4,3,9,6,4,7,10,4,6,2,8,3,9
D,4,11,5,8,5,5,7,10,7,6,5,6,3,8,3,8
Q,4,7,5,7,4,8,7,7,5,6,4,9,2,8,3,8
J,6,7,8,9,7,8,8,4,6,7,6,7,4,8,8,8
V,3,8,5,6,3,6,11,2,3,7,11,9,2,10,1,8
J,2,7,3,5,1,9,6,3,7,12,4,9,0,7,2,6
C,2,4,3,3,2,6,8,7,7,8,7,13,1,9,4,10
O,7,11,7,8,5,8,7,8,5,10,6,8,3,8,3,8
J,7,10,5,8,4,8,10,3,3,13,4,5,2,9,7,9
F,5,6,6,7,6,7,9,6,6,7,6,9,5,8,8,6
T,6,10,6,7,5,5,11,3,7,11,10,5,2,12,2,4
X,3,6,4,4,3,8,7,3,8,6,6,8,2,8,6,8
G,6,10,7,8,4,6,7,7,8,10,7,10,2,10,5,9
S,3,9,4,6,3,8,7,8,7,7,7,9,2,10,9,8
O,2,4,2,3,2,8,7,6,3,9,6,8,2,8,2,8
E,5,8,7,6,4,7,7,3,8,12,7,9,3,8,5,8
R,4,4,4,5,2,5,10,8,4,7,4,8,3,7,6,11
Y,4,7,6,5,3,9,10,1,7,3,11,8,1,11,2,9
B,3,6,5,4,4,7,8,5,5,9,6,6,3,8,7,8
T,3,11,4,8,1,5,14,1,6,9,11,7,0,8,0,8
F,4,8,5,6,4,4,11,6,5,11,10,5,2,9,2,5
Q,6,9,7,11,8,7,9,5,3,8,10,10,5,8,8,10
R,5,7,8,6,8,9,7,4,4,8,5,7,8,10,7,5
E,3,9,4,6,2,3,7,6,11,7,6,15,0,8,7,7
V,3,5,5,4,2,4,12,3,3,9,11,8,2,11,0,8
O,4,8,5,6,2,8,6,8,7,7,5,8,3,8,4,8
W,12,14,12,8,5,2,9,3,2,10,12,8,9,10,1,6
K,4,2,5,3,3,6,7,4,8,7,6,10,6,8,5,9
A,3,9,6,6,3,11,3,2,2,9,2,9,3,5,3,8
U,5,7,6,5,3,4,8,6,8,10,10,9,3,9,2,5
X,4,10,6,7,6,7,7,2,6,7,6,7,4,7,6,7
W,6,9,9,7,7,4,10,2,3,8,9,9,10,9,1,8
M,7,10,10,8,9,11,6,2,5,9,4,6,10,7,2,8
L,3,8,4,6,1,0,0,6,6,0,1,5,0,8,0,8
Q,4,9,6,8,3,9,8,8,6,5,8,9,3,8,5,9
H,6,9,6,4,3,9,6,4,4,9,7,8,6,12,4,9
N,1,0,1,0,0,7,7,9,0,6,6,8,3,8,0,8
N,4,8,6,6,3,5,9,3,4,10,9,9,5,8,1,7
Q,2,2,3,3,2,8,8,6,1,5,6,9,2,9,4,10
E,5,8,7,6,6,6,8,3,7,11,9,9,3,8,5,6
K,2,6,3,4,2,3,7,6,2,7,6,10,3,8,2,11
G,3,6,4,4,4,7,8,5,3,6,6,10,3,8,6,8
I,2,8,3,6,1,7,7,0,8,14,6,9,0,8,1,8
C,4,9,6,7,4,5,8,6,6,8,8,15,3,10,4,9
E,2,4,2,3,2,7,7,5,6,7,6,8,2,8,5,11
G,2,1,3,2,1,6,7,5,5,6,6,9,2,9,4,8
B,3,5,5,4,4,9,6,3,6,10,5,7,2,8,5,10
T,4,10,6,8,4,6,12,3,7,7,12,8,2,12,1,7
P,2,3,4,2,2,8,8,3,3,12,4,5,1,9,2,8
J,4,9,6,7,3,6,7,3,5,15,6,10,1,6,1,7
M,2,3,3,1,2,6,6,6,4,7,7,10,6,5,1,9
U,4,3,4,2,2,5,8,5,7,10,9,9,3,9,2,6
L,7,10,9,8,7,6,7,7,7,6,6,8,6,8,5,10
N,5,7,7,5,3,5,9,3,5,10,9,9,6,7,1,7
W,5,10,8,8,13,9,7,5,2,7,7,8,10,10,3,5
I,7,12,5,6,2,10,5,6,4,13,3,9,3,8,4,9
V,10,15,10,8,5,6,8,4,4,8,8,5,6,12,4,6
Z,6,9,8,7,4,9,6,3,10,11,4,9,1,7,6,8
M,5,5,6,8,4,8,7,12,2,6,9,8,9,6,0,8
E,5,5,5,7,3,3,8,6,12,7,5,15,0,8,7,7
A,2,2,4,3,2,8,2,2,2,8,2,8,2,6,3,7
M,4,7,8,5,8,10,6,3,3,9,4,7,10,5,3,6
D,1,0,2,0,0,6,7,7,6,7,6,6,2,8,3,8
Z,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8
N,8,11,11,8,6,12,7,3,6,10,0,3,7,11,2,8
J,2,5,5,3,1,8,6,3,7,15,6,10,1,6,1,7
Y,6,11,9,8,8,8,4,6,5,8,7,8,6,8,8,3
X,4,9,6,7,4,8,7,4,9,6,6,6,3,8,7,7
B,2,3,4,2,2,8,7,3,5,10,5,6,2,8,4,9
A,3,11,5,8,4,12,3,3,3,10,1,9,2,6,3,8
T,5,7,6,5,6,6,8,4,6,7,6,10,5,8,5,7
H,3,2,4,4,3,7,7,5,6,7,6,8,6,8,4,8
H,4,9,5,6,2,7,6,15,1,7,8,8,3,8,0,8
H,3,7,3,4,2,7,6,14,1,7,7,8,3,8,0,8
Y,4,5,5,7,5,10,11,5,4,5,8,8,5,10,9,5
V,4,6,4,4,2,3,11,4,3,10,12,7,2,10,1,8
K,5,11,6,8,2,4,6,9,2,7,7,12,4,8,3,11
X,3,7,5,5,2,9,7,1,8,10,4,7,3,8,3,8
Q,5,8,6,7,3,9,8,8,6,5,8,9,3,7,5,9
S,5,8,6,6,4,8,8,4,8,10,4,6,2,7,5,8
C,4,9,5,6,3,4,8,5,6,12,10,12,2,9,2,7
B,1,0,2,1,1,7,7,7,5,6,6,7,1,8,7,9
B,5,11,7,8,7,8,7,7,7,6,6,5,2,8,8,10
H,5,9,7,6,4,7,6,3,7,10,8,9,3,8,3,7
X,3,5,5,4,3,7,7,3,9,6,6,8,3,8,6,8
H,3,7,4,4,2,7,7,14,1,7,6,8,3,8,0,8
Z,3,8,5,6,3,8,7,2,9,11,5,9,1,8,6,8
S,7,11,7,6,3,7,6,3,4,13,8,8,3,8,4,7
M,5,2,6,4,4,9,6,7,4,6,7,5,10,5,3,5
V,1,0,1,0,0,8,10,3,1,7,12,8,2,11,0,8
W,4,7,6,5,3,9,8,5,1,7,8,8,8,9,0,8
F,4,8,6,6,4,7,9,2,6,13,6,5,2,10,2,8
O,5,9,5,7,4,7,7,8,5,9,7,10,3,8,4,6
T,4,9,5,7,3,6,11,2,8,8,12,8,1,11,1,7
O,4,10,5,8,3,9,7,9,7,7,6,10,3,8,4,8
B,5,7,7,5,5,8,6,6,7,9,7,6,3,8,8,7
T,4,8,5,6,4,6,7,7,6,8,10,8,3,9,5,9
X,3,7,4,5,2,7,7,4,4,7,6,7,2,8,4,8
C,9,12,7,7,5,7,9,4,5,9,8,9,4,9,8,11
N,6,5,6,8,3,7,7,15,2,4,6,8,6,8,0,8
X,6,10,9,8,5,7,8,1,8,10,6,8,3,8,4,8
Q,5,7,6,5,5,8,6,7,4,4,8,10,6,6,7,10
V,2,1,4,1,1,7,12,3,2,6,11,9,2,11,0,7
D,2,3,4,2,2,9,6,4,6,10,4,6,2,8,3,8
K,5,9,8,7,4,4,9,2,7,10,10,11,3,8,3,6
A,3,11,6,8,2,8,6,3,1,7,0,8,3,7,2,8
V,3,6,4,4,2,2,11,4,3,11,12,8,2,10,1,8
H,4,8,5,6,5,8,7,5,6,7,6,8,6,8,3,7
D,1,0,1,0,0,6,7,7,5,7,6,6,2,8,2,8
G,3,8,4,6,2,7,6,8,7,6,5,11,1,8,5,11
J,2,5,3,8,1,12,3,10,4,13,5,13,1,6,0,8
C,2,5,3,4,2,6,8,7,7,8,8,13,1,9,4,10
G,5,10,6,8,3,7,6,8,8,6,6,8,2,8,6,11
K,5,5,5,8,2,3,6,8,3,7,7,11,4,8,3,11
V,3,9,5,6,1,7,8,4,2,7,14,8,3,9,0,8
C,3,4,4,3,2,4,8,4,6,11,10,12,1,9,2,7
M,5,10,6,8,4,8,7,12,2,6,9,8,8,6,0,8
F,5,8,7,6,7,7,8,1,4,10,6,6,5,10,3,5
Y,2,3,3,4,0,8,11,1,3,5,11,8,0,10,0,8
C,3,5,4,3,2,4,8,5,8,11,9,13,1,9,3,7
Q,6,8,9,12,14,8,5,6,1,6,6,9,10,10,8,14
H,9,15,8,8,5,6,7,5,4,8,10,9,7,11,5,9
U,9,11,8,6,4,9,6,6,7,3,9,7,5,9,3,5
D,5,10,6,8,6,7,7,7,7,7,6,5,3,8,3,7
H,5,10,7,8,10,8,9,5,3,6,7,7,7,9,9,8
O,6,10,8,8,10,8,9,6,1,7,7,8,9,8,5,9
W,3,7,5,5,7,9,5,5,2,7,6,8,5,10,2,5
S,4,7,6,5,6,8,9,4,4,9,5,7,4,9,10,9
U,2,3,3,2,1,5,8,4,7,10,8,8,3,9,2,6
Y,6,11,6,8,3,4,10,2,7,10,11,6,1,11,3,4
N,3,8,4,6,2,7,7,14,2,5,6,8,6,8,0,8
N,7,12,6,6,3,5,9,4,5,4,4,11,5,9,2,7
M,4,2,5,4,4,8,6,6,4,7,7,8,7,5,2,7
E,2,7,4,5,4,7,8,3,5,5,7,10,3,11,7,8
I,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7
Z,2,3,2,2,2,7,7,5,9,6,6,8,1,8,6,8
B,2,4,2,3,2,7,7,5,5,6,6,6,2,8,5,9
B,7,11,9,8,7,9,6,4,7,10,5,6,3,8,6,10
G,6,10,8,8,8,6,6,6,3,6,6,10,5,8,8,7
P,4,9,5,6,2,3,14,7,1,11,6,3,0,10,4,8
P,3,4,5,6,7,8,5,5,2,7,6,7,6,9,6,9
U,3,5,5,3,2,7,9,6,7,7,10,9,3,9,1,8
K,8,10,11,8,5,4,8,4,9,12,12,11,3,8,4,5
V,6,10,5,5,2,8,10,6,3,7,9,5,6,13,3,7
C,9,14,7,8,5,7,6,4,4,9,8,11,4,9,9,11
W,3,8,5,6,5,6,11,2,2,7,8,8,6,11,1,8
V,4,7,6,5,4,8,11,2,2,6,10,8,4,10,4,9
C,5,5,6,8,2,6,7,7,10,8,6,15,1,9,4,9
E,2,3,3,2,1,7,7,2,7,11,7,9,1,8,3,8
O,4,9,5,7,2,7,7,9,8,7,5,8,3,8,4,8
O,1,1,2,1,1,7,7,6,4,7,6,8,2,8,2,8
E,4,7,6,5,4,6,8,3,7,11,8,8,3,8,4,6
C,2,1,2,1,1,6,7,6,9,7,6,13,0,8,4,9
O,3,5,4,4,2,8,6,7,4,9,5,8,2,8,3,8
A,3,8,6,6,4,8,5,2,4,6,2,6,2,6,2,6
N,4,10,4,7,3,7,7,14,2,4,6,8,6,8,0,8
Z,3,9,4,7,3,7,8,3,11,8,6,8,0,8,7,7
A,6,9,6,5,3,13,0,5,1,12,3,11,2,3,2,10
S,4,5,5,5,6,7,7,5,4,7,7,7,5,8,10,10
I,3,10,6,8,7,10,6,2,5,8,4,5,3,8,5,7
F,6,10,8,7,6,6,10,2,6,13,7,6,2,10,2,7
L,3,7,4,5,2,4,5,1,9,7,2,11,0,8,2,7
V,7,8,9,7,10,8,7,4,5,7,6,8,8,10,9,4
G,5,11,6,8,8,8,4,4,3,7,5,11,6,8,6,13
G,5,9,4,5,2,7,4,4,2,7,4,5,4,7,5,8
L,5,10,7,8,4,7,3,2,9,7,1,9,1,6,3,7
P,3,6,4,4,2,5,12,7,3,12,6,3,1,10,4,7
A,3,8,5,6,3,11,2,3,3,10,2,9,2,6,3,8
P,3,7,5,10,8,7,12,4,1,9,8,5,4,11,4,8
C,5,8,6,6,2,6,7,7,10,6,6,15,1,8,4,9
M,4,2,5,4,4,8,6,6,4,7,7,8,7,5,2,8
U,2,1,2,2,1,7,9,5,5,6,9,9,3,9,1,8
Y,5,6,5,4,3,5,9,1,7,9,10,6,1,11,3,5
D,7,10,9,8,7,7,7,5,6,7,6,7,7,8,3,8
Y,9,9,7,13,5,7,8,4,3,6,11,5,5,10,6,6
Y,4,5,5,4,2,4,11,2,7,11,10,6,1,11,2,5
T,4,9,4,4,2,5,10,2,6,11,8,6,2,9,4,3
W,4,9,5,6,6,7,6,6,2,7,7,8,6,8,4,8
B,5,11,8,8,11,9,7,4,4,6,7,7,8,8,8,7
E,3,5,6,4,3,6,8,2,9,11,8,8,2,8,4,6
X,3,9,5,7,4,8,7,3,8,5,6,8,3,8,6,8
S,2,7,3,5,3,8,7,7,5,7,7,8,2,9,8,8
U,5,5,6,4,3,4,8,5,8,10,8,9,3,9,2,6
T,4,6,5,4,4,7,7,7,7,7,6,8,3,10,6,8
U,3,4,4,3,2,5,8,5,7,9,8,8,3,9,2,5
V,3,8,5,6,2,5,11,3,4,9,12,9,3,10,1,9
J,4,6,5,4,2,9,7,0,7,13,5,7,0,8,0,8
B,5,10,5,5,5,7,8,3,5,9,6,7,6,5,7,8
H,1,0,1,0,0,7,8,10,1,7,6,8,2,8,0,8
D,6,11,8,8,7,7,7,6,6,7,7,7,7,7,3,7
S,3,3,4,4,2,7,7,5,8,5,6,8,0,8,8,8
L,1,4,2,3,1,7,4,1,7,7,2,10,0,7,2,8
Q,6,8,7,10,6,8,6,7,4,8,7,11,4,9,7,7
H,3,3,5,2,2,9,6,3,6,10,4,8,3,8,3,9
M,4,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8
C,1,0,1,1,0,7,7,5,7,7,6,14,0,8,4,10
R,3,2,4,4,3,7,8,5,5,6,5,6,3,7,4,8
G,1,0,2,1,0,8,6,6,4,6,5,9,1,8,5,10
A,2,6,5,4,3,6,5,1,3,5,2,7,2,6,3,4
O,4,7,4,5,3,9,6,7,5,10,4,10,3,8,3,8
I,1,6,3,4,3,9,6,3,4,7,6,5,3,8,5,5
V,4,8,4,6,2,3,11,4,4,11,12,8,2,10,1,8
V,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8
A,5,12,5,6,3,14,0,4,2,12,3,11,2,4,2,10
K,3,2,4,4,3,5,7,3,7,6,6,10,6,8,4,9
V,3,5,4,3,1,4,12,4,4,11,11,7,2,11,1,8
C,7,10,7,7,4,4,10,7,8,12,10,9,2,10,3,6
L,4,6,5,4,3,6,6,7,6,6,6,8,2,8,4,10
H,2,1,2,1,2,8,8,6,6,7,6,8,3,8,3,8
U,5,6,6,4,3,4,9,5,7,11,11,9,3,9,2,7
N,3,5,4,3,3,7,9,5,5,7,6,6,5,9,2,6
D,4,10,6,8,4,8,7,7,7,10,5,4,3,7,5,9
Q,3,3,4,4,3,8,7,6,2,8,7,9,2,9,4,8
X,3,5,5,3,2,8,7,3,9,6,6,9,3,7,7,9
N,4,11,5,8,5,7,7,13,1,6,6,8,6,9,1,6
N,1,0,1,0,0,7,7,10,0,5,6,8,4,8,0,8
L,3,9,5,7,7,7,8,3,5,6,6,10,6,11,7,5
F,4,11,5,8,4,4,11,4,6,11,10,6,2,10,3,5
N,5,9,5,5,2,5,8,4,6,4,4,9,4,7,2,8
I,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
Y,5,8,5,6,3,5,9,1,8,9,9,5,1,11,3,4
Z,2,4,4,3,2,7,8,2,9,12,7,7,1,8,5,6
N,5,11,7,8,6,7,7,8,5,7,5,6,3,7,3,8
N,4,7,5,5,4,6,7,8,5,7,5,7,3,7,3,8
P,4,10,4,7,2,4,10,10,3,9,6,4,2,10,4,8
K,12,15,11,9,5,6,8,3,7,9,9,10,6,10,4,6
A,1,0,2,1,0,7,4,2,0,7,2,8,2,7,1,8
K,5,10,6,7,2,4,7,9,2,7,6,12,3,8,3,11
G,7,11,8,8,5,6,6,7,8,10,7,11,2,10,5,9
K,4,5,7,4,4,6,7,1,7,10,7,10,3,8,3,8
A,3,10,6,7,4,11,3,2,2,9,2,9,3,7,3,9
T,2,1,3,2,1,6,12,2,7,8,11,8,1,10,1,7
Q,4,9,5,8,3,8,6,9,7,6,5,9,3,8,4,8
Y,3,4,5,6,1,7,12,2,3,8,12,8,1,10,0,8
N,3,7,5,5,3,7,9,6,5,7,6,7,5,9,2,6
L,2,6,2,4,1,0,1,5,6,0,0,7,0,8,0,8
E,7,13,5,7,3,6,8,6,8,10,6,10,1,8,8,8
J,1,3,2,5,1,12,3,9,3,13,5,13,1,6,0,8
Z,2,1,2,2,1,7,7,3,11,9,6,8,0,8,7,8
G,1,0,1,0,0,8,7,5,4,6,5,9,1,8,4,10
U,5,9,6,7,6,8,8,8,5,5,7,9,3,8,4,6
C,3,2,4,4,2,6,8,7,8,9,8,13,1,9,4,10
G,8,12,6,6,5,8,8,4,3,9,5,6,4,9,9,8
W,6,10,6,8,6,2,11,2,2,10,9,7,6,11,2,6
J,4,9,6,6,4,7,6,4,5,8,6,7,5,6,4,6
L,4,11,6,8,8,7,8,3,5,6,6,10,6,11,7,5
R,5,10,7,8,6,6,8,5,6,6,5,8,3,6,6,9
F,2,4,4,3,2,7,9,1,7,13,5,5,1,9,2,8
Y,6,12,5,6,3,4,9,4,2,10,9,6,3,9,3,4
Y,3,5,5,4,2,7,10,1,7,7,11,8,1,11,2,8
H,2,1,3,3,2,6,7,5,6,7,6,8,3,8,3,8
X,3,6,4,4,3,7,8,3,8,6,6,8,2,8,6,8
R,5,11,7,8,10,6,7,3,4,6,6,9,7,10,8,6
P,3,6,5,4,3,5,12,6,3,10,7,2,1,10,3,6
U,3,3,3,5,1,7,5,13,5,7,14,8,3,9,0,8
C,2,0,2,1,1,6,7,6,10,7,6,14,0,8,4,9
R,1,0,2,1,1,6,9,7,3,7,5,8,2,7,4,11
T,8,15,7,9,3,6,10,3,8,13,6,6,2,8,5,4
Y,6,8,8,11,12,8,8,4,2,7,8,9,4,12,9,8
O,4,7,4,5,3,7,7,8,6,9,7,10,3,8,3,7
J,0,0,1,0,0,12,4,6,3,12,4,11,0,7,0,8
R,3,4,5,3,3,9,7,3,5,11,3,6,2,7,3,9
Z,1,3,3,2,1,8,7,2,9,11,6,8,1,8,5,7
X,3,11,4,8,4,7,7,4,4,7,6,8,3,8,4,8
U,2,3,3,2,1,5,8,5,6,10,9,9,3,9,2,6
C,5,11,6,8,5,5,8,8,7,10,8,13,2,11,4,10
V,5,11,7,8,8,8,5,5,3,8,7,9,9,9,6,11
C,4,7,5,5,2,6,7,6,10,6,6,12,1,7,4,8
M,8,10,11,7,7,5,6,4,5,11,10,11,11,5,4,7
I,1,1,1,1,1,7,7,2,7,7,6,8,0,8,2,8
U,1,0,2,1,0,8,6,11,4,7,12,8,3,10,0,8
L,5,11,7,8,9,8,7,3,5,6,7,9,7,10,7,6
J,1,3,1,2,0,10,6,2,5,11,4,9,0,7,1,7
F,5,8,7,6,3,4,12,3,6,14,8,4,1,10,1,7
G,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,9
G,4,10,6,8,7,8,6,5,2,6,6,10,7,8,5,10
A,2,1,3,1,1,7,4,2,0,7,2,8,2,6,2,8
Q,2,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8
B,4,8,6,6,6,8,7,6,5,9,7,6,3,8,7,8
P,1,0,1,0,0,5,10,7,1,9,6,5,1,9,2,8
C,3,8,4,6,3,5,8,6,6,9,8,14,1,9,3,10
E,2,4,4,3,2,6,8,2,8,11,7,9,2,8,4,8
M,10,13,12,7,6,10,2,3,2,10,2,9,8,1,2,8
L,2,6,2,4,1,0,2,5,6,0,0,7,0,8,0,8
D,4,11,6,8,7,8,7,5,7,7,6,6,6,8,3,7
B,2,6,3,4,3,6,7,7,5,7,6,7,2,8,6,9
E,4,9,6,7,5,5,9,3,7,11,9,9,3,9,4,6
Q,6,15,6,8,4,7,5,5,8,9,5,8,3,7,9,9
M,7,6,9,5,9,7,8,5,4,6,5,8,13,7,5,8
G,4,6,6,6,6,7,7,5,4,7,7,8,7,10,6,8
U,3,6,5,4,4,8,8,8,5,6,7,9,3,8,4,6
X,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8
N,8,10,10,9,11,7,7,5,4,7,6,7,8,9,6,5
W,5,6,5,4,4,3,10,2,3,10,9,8,6,11,2,6
I,0,1,1,2,1,7,7,1,6,7,6,8,0,8,2,8
F,5,5,5,7,2,1,15,5,3,12,9,4,0,8,3,6
E,5,10,5,8,5,2,7,5,9,7,6,14,0,8,6,8
Y,4,10,6,7,1,8,10,2,2,6,13,8,1,11,0,8
B,4,8,6,6,5,10,6,3,6,10,4,7,3,8,5,11
D,4,8,5,6,4,6,7,8,7,6,5,4,3,8,4,8
O,2,1,2,2,1,8,7,7,5,7,6,8,2,8,3,8
H,3,3,5,2,2,7,8,3,5,10,7,8,3,8,3,7
Q,4,5,5,5,2,8,5,8,8,6,4,8,3,8,4,8
Y,4,8,4,6,2,3,12,4,5,12,12,6,1,11,2,6
B,3,8,4,6,5,8,7,8,5,7,6,6,3,8,7,10
O,4,9,5,7,3,6,9,7,5,10,8,7,3,8,3,8
P,3,4,5,3,3,7,10,6,3,11,4,3,1,10,3,8
E,1,0,1,0,0,5,8,5,7,7,6,12,0,8,6,10
O,3,7,4,5,2,8,8,8,6,6,7,9,3,8,4,8
V,3,8,5,6,1,9,8,4,3,6,14,8,3,9,0,8
Q,6,9,6,10,8,8,5,7,4,9,5,8,4,8,5,7
K,4,8,5,6,7,8,7,3,4,6,6,8,6,8,7,5
T,6,10,6,7,4,6,12,4,7,11,9,4,2,13,4,4
C,5,9,6,7,3,4,8,6,9,12,9,12,1,9,3,7
U,4,5,5,3,2,6,8,6,7,7,10,10,3,9,1,8
N,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
C,3,2,4,3,2,6,8,7,7,9,8,12,2,10,3,9
K,7,10,9,8,5,4,8,3,8,11,11,11,4,6,5,5
H,6,11,8,8,8,6,6,8,4,6,4,8,4,6,8,11
P,6,10,5,5,3,4,12,6,1,12,6,4,3,10,4,8
P,1,0,2,0,0,5,11,6,1,9,6,5,1,9,3,8
U,3,3,4,2,1,5,8,5,7,11,10,8,3,9,2,6
C,5,9,5,7,4,4,8,5,6,11,9,14,2,9,3,7
U,5,4,6,3,2,4,8,5,8,10,9,9,3,9,2,6
E,2,3,2,1,1,7,7,5,7,7,6,8,2,8,5,10
V,10,15,9,8,5,6,9,4,4,9,8,5,5,12,3,8
J,2,6,4,4,3,10,5,2,4,7,5,5,3,8,4,6
Y,5,9,5,6,3,3,10,2,7,11,11,6,1,11,2,5
B,4,6,5,4,4,8,9,3,5,10,5,5,3,8,5,8
F,4,9,5,6,2,1,12,5,5,12,10,8,0,8,2,6
L,8,12,8,6,4,8,4,4,5,12,8,11,3,9,6,9
S,4,9,6,7,3,9,7,4,8,12,4,7,2,8,5,9
O,2,6,3,4,3,7,8,7,5,7,9,8,2,9,3,7
U,6,7,7,5,3,4,10,6,7,12,11,8,3,9,2,7
R,3,7,4,5,2,5,10,8,3,7,5,8,2,8,6,11
T,3,6,4,4,3,7,8,7,6,7,7,8,3,10,5,9
Z,1,3,2,1,1,7,7,5,8,6,6,8,1,8,6,8
O,5,6,6,9,3,8,7,9,8,7,6,9,3,8,4,8
G,7,11,9,9,12,8,7,4,3,6,5,9,8,7,9,14
K,6,9,9,8,9,6,7,2,4,7,4,9,7,5,10,10
Y,8,10,6,15,5,4,9,3,1,9,10,5,4,10,7,5
G,6,9,5,4,3,8,3,4,3,7,3,5,4,7,4,9
H,4,5,7,4,4,7,8,3,6,10,6,8,3,8,3,8
J,3,9,4,7,1,12,2,10,4,14,5,13,1,6,0,8
T,3,5,4,4,2,6,10,1,8,11,9,5,1,9,3,4
W,5,9,7,7,3,8,8,4,2,7,8,8,8,9,0,8
B,2,4,4,3,3,8,7,2,5,10,5,6,2,8,4,9
U,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8
F,1,3,3,2,1,5,11,3,4,13,7,5,1,9,1,7
R,4,10,6,8,6,7,7,4,6,6,5,7,3,7,5,9
U,3,9,4,7,2,7,5,15,5,7,12,8,3,9,0,8
M,8,9,12,8,12,7,7,4,4,6,5,8,14,9,6,9
P,3,4,5,2,2,7,11,4,3,12,4,2,1,9,2,9
I,2,11,2,8,4,7,7,0,6,7,6,8,0,9,3,8
J,3,5,5,6,4,8,9,4,5,7,6,8,3,8,8,9
P,6,9,8,7,7,6,6,8,4,7,6,8,4,9,8,11
U,4,8,6,6,6,10,6,4,5,6,7,6,5,8,4,6
I,1,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8
P,4,7,6,5,3,7,12,4,5,14,5,1,0,10,3,8
H,3,3,5,2,3,7,7,2,6,10,6,8,3,8,3,7
V,4,2,6,4,2,7,13,3,4,8,12,8,3,10,2,8
N,6,9,9,7,5,7,9,2,5,10,6,6,6,9,1,8
G,6,11,7,8,4,7,6,7,7,11,6,13,3,9,5,8
W,4,2,6,4,4,9,11,2,2,5,9,7,9,11,2,7
S,5,11,6,8,6,8,8,5,8,5,5,8,0,8,9,8
J,1,3,3,2,1,9,4,4,4,13,7,12,1,7,0,7
B,3,7,4,5,3,6,6,9,7,6,6,7,2,8,9,9
V,3,10,5,8,2,8,8,4,3,6,14,8,3,9,0,8
E,4,9,4,6,4,3,6,5,9,6,7,13,0,8,7,9
Q,5,6,5,8,5,8,4,6,5,10,5,10,4,7,6,7
G,6,10,8,7,5,7,7,7,6,5,7,9,3,8,4,8
X,3,6,5,5,5,9,6,2,6,8,6,7,3,10,8,6
D,3,4,4,3,3,7,7,6,7,7,6,5,2,8,3,7
G,5,9,6,6,7,9,6,5,2,6,6,10,6,7,5,11
X,3,5,6,3,2,5,8,2,9,11,10,9,2,9,3,6
F,6,10,9,7,5,5,12,4,5,13,8,4,2,10,2,6
W,3,7,5,5,3,11,8,5,1,6,9,8,7,10,0,8
Q,4,9,5,8,5,8,8,7,4,5,9,9,2,9,4,9
Z,4,8,5,6,5,7,7,5,10,7,6,8,1,8,7,7
W,6,9,8,6,7,7,7,6,3,8,8,7,7,9,5,10
F,2,5,3,4,2,6,9,4,5,10,9,6,2,10,3,6
Y,6,9,6,4,3,7,7,4,4,9,8,6,4,10,4,4
I,1,9,0,7,1,7,7,5,3,7,6,8,0,8,0,8
U,2,3,3,2,1,5,8,4,5,10,8,9,3,10,1,7
A,4,11,7,8,6,7,5,1,4,5,2,6,5,6,6,6
U,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8
D,1,3,2,2,1,9,6,4,4,10,3,5,2,8,2,8
S,6,12,6,6,3,10,4,4,3,12,6,9,3,10,2,8
U,1,3,2,1,1,9,8,6,5,5,9,7,3,10,0,8
D,3,9,5,6,4,6,7,9,6,6,6,5,3,8,3,8
I,2,6,3,4,1,7,7,1,8,14,6,8,0,8,1,8
O,5,9,4,4,3,6,8,6,4,9,8,9,4,9,4,8
V,3,7,5,5,1,8,8,4,2,6,14,8,3,10,0,8
P,5,9,7,7,6,9,8,4,5,11,4,4,2,9,4,8
T,2,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8
S,3,7,4,5,3,8,8,7,6,7,6,8,2,8,9,8
G,3,6,4,4,2,8,6,7,7,6,6,9,1,7,6,11
O,4,11,5,8,3,7,7,9,7,7,6,8,3,8,4,8
X,5,9,8,6,4,10,6,2,8,10,2,7,3,8,3,9
G,5,9,7,7,7,7,9,6,3,5,5,10,4,8,7,8
U,6,9,6,7,4,5,7,7,8,8,6,9,3,9,3,4
F,4,9,5,6,2,1,11,5,7,12,11,9,0,8,2,6
Q,5,8,6,10,6,9,5,7,4,9,5,11,4,8,6,7
M,1,0,2,0,1,7,6,9,0,7,8,8,6,6,0,8
B,7,13,7,7,6,10,6,4,5,9,5,8,6,8,8,9
C,5,8,6,6,2,3,10,5,8,12,11,10,1,8,2,6
K,7,9,10,8,10,9,7,3,4,9,2,8,8,4,8,12
R,1,1,2,1,1,6,9,8,4,7,5,8,2,7,4,11
A,6,11,8,9,8,7,7,8,4,8,5,8,4,8,10,1
Q,6,7,6,9,7,8,5,7,4,8,6,7,6,8,6,9
K,4,10,5,7,2,3,8,8,2,7,5,11,3,8,3,10
I,1,6,1,4,1,7,7,0,7,7,6,9,0,8,3,8
V,5,6,5,4,3,4,12,1,2,8,10,7,3,11,1,7
F,2,1,3,2,1,5,11,3,5,11,9,5,1,10,3,6
O,4,8,5,6,4,7,8,8,5,10,7,8,3,8,3,8
A,2,7,4,5,3,12,3,2,2,9,2,9,3,7,3,9
N,6,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8
C,2,1,2,2,1,6,8,7,7,9,7,12,1,10,4,10
W,1,0,1,0,0,7,8,3,0,7,8,8,3,9,0,8
M,5,7,6,5,7,8,8,6,5,7,6,8,7,9,7,6
Q,5,9,6,11,7,7,9,4,3,8,10,10,4,10,8,8
C,3,8,4,6,3,5,7,6,7,7,5,11,1,8,4,10
A,7,9,10,8,8,6,8,2,4,7,7,9,8,6,4,8
Y,3,5,5,8,1,6,10,2,2,7,13,8,2,11,0,8
I,1,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8
B,5,9,7,8,9,7,7,5,4,7,6,8,6,9,8,7
G,4,7,4,5,2,6,7,6,6,10,8,9,2,9,4,9
L,4,9,4,5,2,9,3,4,5,12,4,13,2,6,5,9
I,2,8,3,6,2,8,7,0,7,13,6,8,0,8,1,8
Y,8,10,7,5,4,8,6,4,6,9,6,5,4,10,4,6
K,3,6,5,4,5,8,6,4,4,7,6,8,6,7,6,8
X,5,11,9,9,7,6,8,2,6,7,6,9,6,5,8,7
V,8,10,7,8,5,3,12,2,3,9,11,8,7,11,2,6
S,8,11,9,8,6,7,8,3,6,10,5,7,4,6,6,8
K,3,7,5,5,5,5,6,3,4,6,5,9,5,7,8,7
A,3,8,5,5,2,7,5,3,1,7,1,8,2,7,2,7
S,9,13,8,7,4,9,4,4,3,13,8,10,4,9,3,9
D,2,4,4,3,2,8,7,5,6,9,5,5,2,8,3,7
Q,6,9,8,7,6,8,4,9,5,7,4,8,3,8,4,8
N,10,13,9,7,4,4,9,4,7,3,3,11,5,8,2,7
Z,5,7,7,9,6,10,5,4,4,8,2,6,2,7,7,8
A,4,9,7,7,4,8,2,2,3,6,1,7,2,7,3,7
B,4,6,5,4,4,8,7,5,6,9,6,6,2,8,6,9
U,5,8,5,6,2,7,4,14,5,7,14,8,3,9,0,8
S,3,5,6,3,2,6,8,2,7,10,7,8,1,9,5,6
P,3,1,4,2,2,5,10,4,4,10,8,4,1,9,4,7
C,3,2,4,4,2,6,8,7,7,8,7,13,1,9,4,10
Q,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8
Q,5,6,7,9,8,8,11,4,3,5,8,11,5,13,7,11
E,3,8,5,6,4,4,8,5,8,11,10,9,2,9,4,5
D,2,4,4,3,2,9,6,4,6,10,4,5,2,8,3,8
G,4,2,5,4,3,7,6,6,6,6,6,9,2,8,4,8
V,2,1,4,2,1,6,13,3,2,8,11,8,2,10,1,8
A,4,10,7,7,2,7,5,3,1,7,1,8,3,7,2,8
A,2,8,4,6,2,12,3,3,2,10,1,9,2,6,2,8
R,3,2,4,4,3,7,7,5,5,6,5,7,3,6,6,9
S,3,5,4,4,2,8,7,3,7,10,6,8,1,9,5,8
Q,5,9,5,4,3,8,5,4,8,10,4,9,3,7,8,10
Z,6,9,9,7,6,8,6,2,9,11,5,9,3,5,7,8
C,7,10,8,8,5,3,7,5,7,11,10,14,2,9,3,7
F,6,12,5,6,3,4,12,3,5,12,7,4,2,7,5,5
B,4,10,5,8,7,7,8,9,6,7,6,7,2,7,8,10
R,3,7,3,5,3,5,9,7,3,7,5,8,2,7,4,11
W,8,10,9,8,6,1,10,3,3,11,11,9,7,10,1,7
K,6,10,8,7,6,6,6,1,7,10,6,10,4,8,4,8
P,2,4,4,3,2,8,8,3,5,12,4,4,1,10,2,8
O,3,6,4,4,3,7,8,7,4,9,7,8,3,8,2,8
J,5,8,4,6,3,8,7,2,4,11,6,8,2,10,6,11
A,6,9,5,4,2,11,3,3,1,9,4,11,4,5,4,9
P,3,7,5,5,3,9,8,2,5,12,4,5,1,10,2,9
N,10,13,12,8,5,4,9,3,4,13,10,9,6,8,0,7
M,3,2,5,3,3,9,6,6,4,6,7,6,7,5,2,6
P,3,7,5,11,8,8,9,5,0,8,6,6,5,11,6,9
Z,3,7,5,5,3,6,8,2,9,11,7,7,1,9,6,7
S,4,6,5,4,3,9,8,3,7,10,4,7,2,7,4,9
X,1,0,1,0,0,8,7,3,4,7,6,8,2,8,4,8
P,4,8,4,5,2,5,10,9,5,9,6,5,2,10,4,8
C,6,11,6,8,4,5,8,7,8,12,9,12,2,10,4,6
P,5,8,7,6,5,9,8,2,6,13,5,5,2,9,3,9
D,3,7,3,5,2,5,6,10,7,5,4,5,3,8,3,8
Z,2,4,5,3,2,7,8,2,9,12,7,7,1,8,5,6
Y,7,6,6,9,4,6,8,4,2,7,10,5,4,10,5,6
L,1,3,3,1,1,6,4,1,7,7,2,9,0,7,2,8
K,4,5,5,4,3,5,7,4,8,7,6,10,6,8,5,9
T,6,9,6,6,4,7,11,3,9,12,9,4,2,12,3,4
R,2,1,2,1,1,6,10,7,2,7,5,8,2,7,4,10
T,4,11,5,8,2,5,15,1,6,9,11,7,0,8,0,8
A,8,14,8,8,6,9,4,5,4,10,6,12,9,2,7,11
H,5,10,5,7,3,7,5,15,1,7,8,8,3,8,0,8
Z,5,7,6,5,4,8,5,2,9,11,4,10,2,8,6,10
G,4,4,6,6,2,8,5,8,9,6,5,10,2,8,5,10
U,3,1,4,1,1,7,9,5,6,7,10,9,3,10,1,8
B,4,8,6,6,4,11,6,3,7,11,3,7,2,8,5,11
L,7,13,6,7,3,7,4,3,6,11,4,13,2,7,6,8
V,7,9,9,8,10,7,6,5,4,7,6,7,7,10,8,11
B,2,5,4,3,3,8,7,3,6,9,5,7,2,8,5,10
Q,1,1,2,2,2,8,7,5,2,5,6,9,2,9,4,10
G,6,11,7,8,5,6,7,7,7,10,8,10,2,8,5,9
D,4,8,6,6,5,7,8,8,6,9,7,4,4,9,3,7
K,7,10,7,5,3,7,6,3,6,9,8,9,6,11,3,7
Y,4,5,5,7,7,9,6,6,3,7,7,8,7,8,6,4
W,4,8,6,6,3,6,8,4,1,7,8,8,8,9,0,8
Y,4,4,5,6,5,9,9,5,3,7,7,7,5,10,6,4
H,4,4,4,5,2,7,6,15,1,7,8,8,3,8,0,8
S,7,10,5,5,2,9,2,3,4,9,2,9,3,6,5,10
Q,8,15,8,8,5,11,4,5,7,12,3,10,3,7,8,12
D,6,5,6,8,3,5,7,10,10,7,6,5,3,8,4,8
G,3,8,5,6,4,6,6,6,6,7,5,11,2,10,4,9
F,4,11,4,8,3,1,11,4,5,11,11,9,0,8,2,6
U,2,3,3,2,1,5,8,5,6,10,9,8,3,9,2,6
S,2,5,2,4,2,8,8,7,5,8,5,7,2,8,8,8
A,3,9,5,7,3,11,3,2,2,9,2,9,3,5,3,8
C,2,2,3,3,2,6,8,7,8,8,7,13,1,9,4,10
I,3,9,5,7,3,7,7,0,7,13,6,8,0,8,1,8
I,1,1,1,2,1,7,7,1,6,7,6,8,0,8,2,8
H,3,3,5,2,2,7,8,3,6,10,7,7,3,8,3,8
Y,3,5,5,8,6,7,10,3,3,7,8,9,4,10,9,5
W,4,8,7,6,9,9,8,5,2,7,7,7,12,10,4,6
P,3,7,5,11,9,9,8,4,1,8,6,7,7,9,5,6
F,6,11,6,8,2,1,12,5,6,12,11,8,0,8,2,5
J,0,0,1,1,0,12,4,6,3,12,5,11,0,7,0,8
U,2,4,3,3,2,5,8,5,6,10,9,9,3,9,2,6
H,8,11,12,9,10,6,7,3,7,10,8,9,5,8,5,7
R,3,5,4,4,3,7,7,4,5,6,5,6,3,7,4,8
C,3,8,4,6,3,5,7,6,8,5,6,12,1,7,4,10
R,4,9,6,7,6,6,7,5,6,7,6,6,3,7,5,9
B,4,8,6,6,8,8,8,4,3,6,7,7,6,11,8,9
M,6,9,10,7,11,10,5,3,2,9,4,8,11,6,4,7
O,4,8,5,6,4,7,7,7,6,7,5,8,2,8,3,8
C,6,11,7,8,5,5,8,7,6,8,8,15,4,9,6,6
E,3,4,5,3,2,6,8,2,8,11,8,9,2,8,4,7
Z,1,4,2,3,1,8,7,5,8,6,6,8,1,8,7,7
M,6,11,8,8,8,8,6,5,5,6,7,8,8,6,2,7
E,3,7,3,5,2,3,7,6,11,7,7,15,0,8,7,7
U,4,10,5,8,4,8,6,12,4,7,13,7,3,8,0,9
R,3,8,3,6,4,5,9,7,3,7,5,8,2,7,5,11
D,4,11,4,8,3,5,7,11,8,7,6,5,3,8,4,8
F,5,7,6,5,6,7,8,6,4,8,6,8,3,10,8,11
C,7,11,8,8,5,5,7,5,7,12,9,13,4,11,5,5
L,5,10,6,8,4,7,4,2,8,7,1,8,1,6,3,7
F,5,11,6,8,7,6,9,6,4,8,6,8,3,10,8,11
S,2,4,4,3,2,9,6,3,7,10,4,8,1,9,5,9
C,6,9,8,8,8,5,7,3,5,7,6,11,4,11,8,8
U,4,6,4,4,3,7,5,12,4,7,12,8,3,9,0,8
P,6,13,6,7,4,8,9,4,3,12,4,4,4,11,5,6
H,3,6,4,4,2,7,7,14,0,7,6,8,3,8,0,8
H,3,5,6,3,3,7,7,3,6,10,6,8,3,8,3,7
H,4,5,5,4,4,8,7,6,6,7,6,8,3,8,3,7
V,4,7,4,5,2,2,11,3,3,11,11,8,2,11,0,8
J,4,11,5,8,4,8,5,3,7,12,4,10,1,6,2,6
Z,2,1,3,3,2,7,7,5,9,6,6,8,1,8,7,8
M,4,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8
I,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8
S,7,11,7,6,3,9,5,4,4,13,6,8,2,9,3,8
J,1,4,2,3,1,10,7,1,7,11,4,7,0,7,1,7
W,10,15,10,8,5,4,9,2,2,8,10,8,9,12,1,6
N,6,9,9,7,5,9,8,2,5,10,2,4,7,10,2,7
I,1,1,1,3,1,7,7,1,6,7,6,8,0,8,2,8
M,5,6,7,4,6,7,6,5,5,8,6,11,10,5,2,9
E,3,8,4,6,3,7,8,6,10,6,4,9,3,8,6,8
G,4,7,5,5,3,7,7,6,7,10,7,11,2,9,4,9
P,2,3,3,1,1,5,9,4,4,9,8,5,1,9,3,7
R,5,11,7,8,6,10,7,3,5,10,3,7,3,7,4,10
Z,2,3,3,2,2,7,7,5,9,6,6,8,1,8,7,8
O,2,4,3,3,2,7,7,6,4,9,6,8,2,8,2,8
W,4,8,6,6,6,5,11,2,2,7,8,8,7,11,1,8
A,2,4,3,3,1,11,3,3,2,11,2,9,2,6,2,9
J,0,0,1,0,0,12,4,5,3,12,5,11,0,7,0,8
T,3,5,4,4,3,9,12,4,5,5,11,9,2,12,1,8
X,2,1,3,3,2,7,7,3,8,6,6,8,2,8,6,8
J,2,7,3,5,1,14,2,6,5,14,2,11,0,7,0,8
D,2,2,3,3,2,7,7,6,6,7,6,5,2,8,3,7
J,2,9,3,6,2,10,6,1,7,11,3,6,0,7,1,7
X,3,8,5,6,3,8,7,4,8,6,6,8,3,8,6,8
J,1,7,2,5,2,10,6,1,5,11,4,8,0,6,1,7
W,4,4,6,3,3,8,11,3,2,6,9,8,8,12,1,7
A,3,9,5,7,3,11,4,2,2,8,2,9,3,5,3,8
R,3,5,5,4,3,9,7,3,5,10,4,6,3,7,4,10
V,6,11,6,8,4,3,11,1,2,9,10,8,4,11,1,7
T,8,11,7,6,3,7,9,4,8,13,6,8,2,7,4,6
V,5,8,5,6,2,3,11,3,4,10,11,7,2,10,1,8
B,1,0,1,0,1,7,7,6,4,6,6,7,1,8,5,9
G,2,1,3,2,2,7,7,5,5,7,6,10,3,7,4,9
B,4,9,6,6,5,9,6,4,6,10,5,7,3,7,6,9
N,5,9,5,7,3,7,7,15,2,4,6,8,6,8,0,8
K,2,3,3,2,2,6,7,4,7,6,6,10,3,8,4,9
U,3,6,4,4,1,7,4,13,5,7,13,8,3,9,0,8
L,3,7,4,5,3,5,4,5,7,2,2,5,1,6,1,5
O,2,4,3,3,2,8,7,6,3,9,6,8,2,8,2,8
W,5,5,7,8,4,9,8,4,2,7,8,8,9,9,0,8
H,4,11,6,8,7,7,7,5,6,7,6,9,6,8,3,8
B,8,12,6,6,4,9,5,4,5,10,5,9,5,7,7,10
V,5,8,5,6,3,3,12,3,3,10,11,8,2,10,1,8
D,4,6,6,4,5,9,6,4,5,10,3,6,3,7,3,8
Q,3,5,4,8,7,8,7,5,0,6,6,9,7,10,6,12
G,7,12,6,7,3,10,4,4,4,10,3,6,4,7,5,9
D,4,7,5,5,3,6,7,10,10,6,5,6,3,8,4,8
G,6,10,8,8,6,6,6,6,6,5,6,11,2,9,4,8
L,1,4,2,3,1,6,4,2,7,7,2,9,0,7,2,8
D,6,9,6,5,3,8,6,5,5,10,4,6,4,7,6,10
H,1,0,2,0,0,7,7,11,1,7,6,8,3,8,0,8
V,3,3,5,4,1,8,8,4,2,6,14,8,3,10,0,8
T,3,9,5,7,3,7,12,3,7,7,12,8,1,12,1,8
W,4,8,7,6,4,7,10,2,3,7,9,8,8,11,1,8
I,1,5,3,4,1,7,7,0,7,13,6,8,0,8,1,8
M,3,6,4,4,4,9,6,6,4,6,7,5,7,5,2,6
E,3,8,4,6,5,6,7,3,4,6,7,10,4,11,8,7
U,5,5,6,7,2,7,4,14,6,7,15,8,3,9,0,8
A,3,5,5,3,2,10,2,2,2,10,2,9,2,6,2,7
M,11,15,11,8,6,9,11,6,4,4,6,9,10,12,2,7
M,3,4,5,3,3,8,6,6,4,7,7,8,7,5,2,7
A,3,9,5,7,3,10,2,2,2,8,1,8,2,7,3,8
L,4,8,6,7,6,7,6,5,5,7,7,7,3,9,8,11
R,4,8,5,6,4,7,9,6,5,7,6,8,3,7,6,9
U,6,11,7,8,6,8,5,13,5,6,12,8,3,9,0,8
E,0,0,1,0,0,5,7,5,6,7,6,12,0,8,5,11
E,4,7,6,6,5,5,9,3,5,8,7,9,4,10,7,7
P,7,15,7,8,5,8,9,4,4,12,5,4,4,11,6,6
U,3,7,4,5,3,8,5,12,4,7,11,8,3,9,0,8
N,5,10,7,8,4,3,9,4,4,10,11,10,5,7,1,8
K,3,7,5,5,5,8,7,4,4,7,6,7,4,6,8,13
X,5,5,6,8,2,7,7,5,4,7,6,8,3,8,4,8
O,1,0,1,0,0,7,7,6,3,7,6,8,2,8,3,8
D,6,5,6,8,3,5,7,10,9,7,6,5,3,8,4,8
J,3,7,4,5,1,8,6,3,7,15,5,10,0,7,1,7
E,5,10,7,8,4,5,9,3,9,11,9,9,2,9,5,5
D,3,6,3,4,2,6,8,10,8,7,7,6,3,8,4,8
Q,3,3,4,5,3,8,8,7,2,5,7,9,2,9,5,9
D,9,15,8,8,6,8,6,3,7,10,5,6,6,7,9,6
O,3,4,4,3,2,7,7,7,4,9,7,9,2,8,3,8
Z,8,8,6,11,5,7,7,4,3,11,6,8,3,9,11,6
R,5,6,6,8,4,5,12,8,3,7,3,9,3,7,7,11
R,4,7,5,5,4,11,7,2,5,11,3,7,3,7,3,11
E,5,10,5,8,3,3,8,6,12,7,5,15,0,8,7,7
Y,7,10,7,7,3,2,11,5,6,13,13,7,2,11,2,6
I,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
W,4,7,6,5,3,4,8,5,1,7,9,8,8,10,0,8
K,4,10,6,7,6,6,7,3,7,6,5,8,3,8,5,9
F,2,6,3,4,3,6,10,3,5,10,9,5,2,10,3,6
B,5,5,5,7,4,6,7,9,6,7,6,6,2,8,9,10
D,3,7,4,5,3,6,7,8,6,7,6,4,3,8,3,7
Q,5,8,6,9,6,8,7,7,4,9,8,9,4,9,7,9
U,8,10,9,8,4,3,8,6,8,10,10,9,3,9,2,5
N,7,10,10,7,4,6,8,3,5,10,8,8,6,7,1,7
C,2,3,2,2,1,4,9,5,6,12,9,10,1,9,2,7
A,5,9,9,7,6,5,5,2,4,3,2,7,6,7,7,2
B,2,2,3,3,2,8,7,5,5,6,5,5,2,8,6,9
C,9,14,6,8,3,7,8,7,7,11,6,7,2,9,6,8
T,3,5,5,6,1,8,15,1,5,7,11,8,0,8,0,8
Y,2,4,4,5,1,8,10,2,2,6,12,8,1,11,0,8
Q,4,6,6,5,6,8,4,4,4,7,4,10,5,6,7,7
G,6,11,5,6,3,11,4,3,4,10,2,7,4,7,4,9
G,5,11,7,8,5,6,6,7,7,6,5,10,2,9,4,8
A,3,8,6,5,2,6,5,3,0,6,1,8,2,7,2,7
L,4,10,6,8,3,7,4,0,9,9,2,10,0,7,3,8
L,3,6,4,5,4,8,6,5,5,7,7,9,3,8,7,10
Y,9,12,8,7,5,6,6,5,4,9,9,6,5,11,4,6
D,1,1,2,1,1,6,7,8,6,6,6,6,2,8,3,8
D,5,10,8,8,5,9,8,5,8,10,5,4,4,7,5,9
A,3,9,5,7,4,9,3,2,2,7,2,8,2,6,3,6
P,7,15,7,8,5,6,11,3,5,13,6,3,3,9,5,5
D,4,10,5,7,3,5,7,10,9,6,6,5,3,8,4,8
O,4,8,5,6,4,8,6,8,4,7,4,8,3,8,3,8
U,5,6,5,4,4,5,8,5,6,9,7,10,3,9,3,6
S,4,7,5,5,3,5,9,3,6,10,7,6,3,7,4,5
E,7,10,9,7,7,5,9,2,8,11,8,9,3,8,5,6
U,5,8,6,6,5,8,6,8,5,7,6,9,3,8,4,6
C,5,9,6,7,4,4,8,7,8,7,8,14,2,8,4,10
P,5,11,6,8,3,5,9,11,6,8,6,5,2,10,4,8
U,2,0,2,1,0,7,5,11,4,7,13,8,3,10,0,8
B,3,6,4,4,4,9,6,3,5,10,4,7,2,8,4,10
D,6,9,8,6,5,10,6,4,7,10,4,6,5,9,5,8
Y,4,6,6,8,1,7,11,2,3,7,12,8,1,11,0,8
A,3,7,5,5,3,8,2,2,2,7,1,8,2,7,3,7
G,7,12,6,6,4,7,6,4,3,9,6,8,4,9,9,8
O,6,11,7,8,6,7,8,8,7,7,6,6,4,7,5,10
H,4,3,5,4,4,6,7,6,6,7,6,10,3,8,3,8
Y,3,6,5,8,1,10,11,2,4,4,12,8,0,10,0,8
K,3,2,4,4,3,5,7,4,7,7,6,11,3,8,5,9
B,5,10,5,7,7,6,7,8,5,7,6,7,2,8,7,10
M,2,3,4,1,2,5,7,4,3,10,10,10,4,7,1,6
C,3,7,5,6,5,5,8,3,5,7,7,11,3,9,6,10
Q,4,7,6,6,5,6,4,4,5,6,4,7,3,5,6,6
S,6,9,8,8,9,8,8,4,5,7,7,7,5,10,10,10
I,4,10,6,8,4,5,6,2,6,7,7,12,0,9,4,9
R,7,11,9,8,6,9,8,4,7,9,3,7,3,6,5,11
D,8,11,8,6,4,9,6,5,6,12,4,8,6,6,6,10
X,5,9,8,6,4,5,8,2,8,11,11,9,3,9,4,6
Q,3,5,4,5,3,8,7,7,4,6,6,9,2,8,4,9
L,3,5,4,4,2,4,4,4,8,2,1,6,0,7,1,6
R,3,7,5,5,5,8,7,7,5,8,6,7,3,8,5,9
H,9,13,9,7,5,10,6,4,5,9,5,5,7,10,5,9
K,4,5,5,7,2,4,7,9,2,7,5,11,4,8,2,11
I,1,9,2,6,2,7,7,0,6,7,6,8,0,8,2,8
A,2,6,5,4,3,5,5,2,2,4,2,7,2,6,3,3
D,4,6,6,4,4,7,7,4,6,7,6,8,6,8,3,7
G,3,7,4,5,2,8,7,8,7,6,6,9,2,7,5,11
Y,3,9,5,6,1,8,10,3,2,6,13,8,2,11,0,8
A,3,9,6,7,4,13,3,3,3,11,1,9,2,6,2,9
U,7,10,6,5,3,6,6,4,5,4,8,7,5,8,2,6
I,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8
Q,2,1,2,1,1,8,7,6,4,6,6,8,2,8,3,8
J,1,3,2,2,0,9,6,3,6,14,6,10,0,7,0,8
N,6,9,9,8,8,7,9,4,4,7,4,7,9,7,6,6
A,2,3,3,2,1,10,2,2,1,8,2,9,1,6,1,8
O,4,5,5,8,2,8,7,9,7,7,5,8,3,8,4,8
K,4,7,6,5,3,6,6,2,7,10,7,10,3,8,4,8
M,4,8,5,6,5,7,5,10,0,7,8,8,8,5,2,10
L,5,11,5,8,3,0,1,5,6,0,0,7,0,8,0,8
E,5,7,6,6,6,5,7,4,3,7,6,8,5,11,9,8
G,4,7,5,5,5,9,5,5,2,7,6,10,5,10,3,9
H,3,5,5,4,3,9,6,3,6,10,4,8,3,8,3,9
S,4,9,5,6,6,9,7,5,3,8,6,8,4,8,10,9
T,6,7,6,5,3,7,11,3,7,11,8,4,4,10,4,4
B,3,6,4,4,3,6,6,8,6,6,6,7,2,8,9,10
A,2,7,4,5,3,11,3,2,2,9,2,9,2,6,3,8
W,5,8,7,6,3,5,8,5,1,7,8,8,8,9,0,8
M,6,9,9,6,10,10,3,3,2,9,4,8,11,5,3,7
T,5,10,6,7,5,8,11,3,7,6,11,8,2,12,1,8
O,4,8,6,6,2,6,7,8,8,7,6,7,3,8,4,8
N,6,9,5,4,2,9,11,5,4,4,6,10,5,11,3,7
M,4,5,7,3,4,7,6,3,4,9,7,8,7,5,2,8
U,7,9,7,7,5,3,8,5,7,10,9,9,3,9,2,6
H,3,5,4,3,3,8,7,6,6,7,6,6,3,8,3,6
N,6,10,9,7,5,6,10,5,4,8,7,9,7,7,2,8
J,7,12,5,9,5,8,9,2,4,11,6,7,2,9,8,9
I,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8
O,3,8,4,6,2,7,8,9,7,7,7,8,3,8,4,8
B,3,8,5,6,5,9,7,4,5,10,5,7,2,8,5,9
Q,4,6,6,9,9,9,7,6,1,7,6,9,10,9,5,7
K,5,11,6,8,2,3,7,8,2,7,6,11,4,8,2,11
Z,7,9,7,5,4,7,7,2,9,12,6,8,3,7,6,6
R,2,3,3,2,2,8,8,3,4,9,5,7,2,7,3,9
B,3,4,5,3,3,7,8,3,5,10,6,6,2,8,5,9
V,7,9,7,7,4,3,12,2,3,9,11,8,4,12,1,7
R,6,12,6,7,5,10,6,3,5,10,4,8,6,8,6,10
Z,2,1,2,2,1,7,7,3,11,9,6,8,0,8,6,8
F,4,9,6,6,3,6,11,3,6,13,7,4,1,10,2,7
S,4,8,5,6,2,7,7,6,9,5,6,8,0,8,9,7
T,2,7,3,4,1,10,14,1,6,4,11,9,0,8,0,8
B,3,6,5,4,6,8,6,5,4,7,7,8,7,9,7,8
P,5,6,7,4,3,9,8,4,6,12,4,5,2,9,4,9
Y,7,10,7,7,3,4,10,2,8,11,11,6,1,10,3,3
A,5,6,7,5,5,8,8,3,4,7,8,8,5,9,3,6
V,5,6,5,4,2,3,12,5,4,11,11,7,3,10,1,8
P,3,9,5,7,5,6,9,4,5,9,8,4,1,10,4,7
C,7,11,5,6,2,7,7,7,7,12,6,9,2,9,5,9
H,4,8,6,6,7,8,7,4,3,6,6,7,7,8,7,6
A,3,4,5,3,2,10,2,2,2,8,2,9,4,5,2,9
A,3,6,6,4,4,8,5,2,4,7,2,6,2,6,4,6
L,5,10,5,5,3,8,5,3,5,12,7,11,2,9,6,9
J,3,8,4,6,2,8,7,1,8,12,5,8,0,6,2,6
R,8,12,6,6,4,11,5,5,5,11,2,8,6,6,6,10
T,2,6,3,4,1,7,13,0,5,7,10,8,0,8,0,8
M,5,10,6,8,4,7,7,12,2,8,9,8,8,6,0,8
W,7,7,7,5,6,4,11,2,2,10,9,7,7,11,2,6
Y,5,7,7,10,10,8,8,4,2,7,8,9,4,11,9,8
O,3,10,5,8,4,8,8,8,6,6,6,11,3,8,4,7
H,8,10,12,8,7,7,8,3,7,10,5,7,3,8,3,8
R,4,9,5,7,5,7,7,5,6,6,5,7,3,7,5,8
B,6,11,7,8,8,8,7,5,5,7,5,7,4,8,6,8
N,6,8,9,6,5,4,10,3,4,10,10,8,5,8,1,8
S,2,4,2,2,1,8,8,6,5,7,5,7,2,8,8,8
J,3,10,5,8,5,8,7,2,5,11,5,9,5,6,3,4
L,2,7,3,5,1,0,2,3,5,1,0,8,0,8,0,8
B,9,14,7,8,5,6,8,5,7,10,6,8,6,6,7,9
A,3,9,5,7,3,11,3,3,4,11,2,9,2,6,3,8
D,3,7,5,5,4,7,7,7,4,6,5,6,3,8,3,7
J,7,10,5,8,4,7,10,3,2,12,5,5,2,9,8,9
X,3,5,6,4,3,6,7,1,9,11,9,9,3,8,3,7
U,2,0,3,1,1,7,5,11,5,7,14,8,3,10,0,8
Q,1,0,2,1,0,8,7,7,3,6,6,9,2,8,3,8
N,2,1,3,2,2,7,8,5,4,7,6,6,4,8,1,6
Y,4,7,5,5,5,9,6,5,4,7,8,7,3,9,8,4
Y,7,6,6,9,3,6,12,2,3,10,10,6,4,11,5,7
M,5,11,8,8,8,9,7,5,5,6,7,5,10,8,3,5
D,4,8,4,6,2,5,7,10,9,6,6,5,3,8,4,8
H,4,7,6,9,5,10,11,3,2,7,8,8,3,11,7,10
C,7,10,8,8,5,5,8,6,7,12,9,12,3,11,4,6
Q,1,2,2,2,1,8,8,4,2,8,7,9,2,9,3,9
A,2,3,4,4,2,8,3,1,2,7,2,8,2,6,2,7
T,2,7,4,5,2,8,12,4,6,6,11,8,2,12,1,8
J,6,8,8,9,7,8,8,4,7,6,7,7,4,11,11,9
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
F,9,13,8,7,6,7,9,3,5,11,6,6,5,8,8,7
P,6,9,8,7,7,7,7,5,5,7,6,9,6,8,7,10
U,4,6,5,4,3,6,7,5,6,8,6,8,3,9,3,6
X,6,11,8,9,7,8,6,3,5,6,7,8,3,9,9,9
P,4,5,4,3,3,5,10,5,4,10,8,4,1,10,4,7
G,3,8,4,6,3,6,6,6,6,7,5,11,3,10,4,8
W,3,5,5,3,3,5,11,2,2,8,9,9,6,10,0,8
H,2,3,4,2,2,7,7,2,5,10,5,8,3,7,2,8
Z,3,8,4,6,2,7,7,3,13,10,6,8,0,8,8,8
S,2,1,2,2,1,8,8,6,5,8,6,7,2,8,8,8
S,7,12,7,7,4,10,3,4,3,13,6,10,3,9,3,9
M,5,10,5,8,4,7,7,12,2,7,9,8,8,6,0,8
G,4,8,5,6,3,5,6,5,6,6,6,9,2,8,4,7
R,6,11,6,8,4,5,11,9,3,7,4,8,3,8,6,11
R,9,11,7,6,4,10,6,6,5,11,2,8,6,6,5,10
I,1,6,0,4,1,7,7,5,3,7,6,8,0,8,0,8
U,6,9,8,8,7,7,7,5,4,5,7,8,7,7,2,6
O,5,5,6,7,3,7,7,9,8,7,5,7,3,8,4,8
B,9,12,8,7,7,7,8,4,5,9,6,7,8,5,9,7
N,3,7,5,5,3,4,10,3,3,10,10,9,5,8,1,7
J,4,9,6,7,3,6,9,2,6,14,6,7,1,7,1,7
A,3,10,6,7,2,7,3,3,3,7,1,8,3,6,3,7
P,3,2,4,3,2,5,10,4,5,10,8,3,1,10,4,7
Y,2,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8
Y,5,5,7,8,8,8,8,7,3,7,7,8,6,10,6,4
N,6,7,8,5,4,9,7,3,5,10,2,5,5,9,1,7
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
F,5,10,5,6,2,6,10,3,5,13,6,5,2,8,5,6
B,4,6,7,6,6,7,10,6,6,8,7,8,5,9,7,9
H,6,11,8,8,7,10,7,3,6,10,3,7,6,8,5,9
Q,5,9,5,4,3,12,3,3,4,10,3,8,3,9,5,12
L,4,10,6,8,3,3,4,3,10,2,0,7,0,7,1,5
E,4,8,5,6,5,7,7,5,8,7,7,9,3,8,6,9
N,4,11,4,8,5,8,7,12,1,6,6,7,5,8,0,9
R,4,7,6,5,4,9,7,4,6,9,4,7,3,7,5,11
G,4,9,5,7,4,5,7,6,5,9,8,10,2,8,4,9
L,5,12,5,7,3,10,2,3,4,12,4,11,2,8,4,10
S,8,11,9,9,6,5,8,3,6,10,8,8,3,8,5,5
Y,7,9,6,5,3,4,7,5,3,9,11,6,4,10,3,4
E,2,0,2,1,1,5,7,5,8,7,6,12,0,8,7,9
T,5,7,6,5,5,6,7,7,6,7,6,8,3,9,6,10
J,2,5,5,4,2,10,5,4,5,14,5,11,1,6,1,7
F,3,8,3,6,1,1,14,5,3,12,10,5,0,8,2,6
Z,4,8,5,6,2,7,7,4,14,9,6,8,0,8,8,8
V,6,9,6,4,2,5,12,5,2,11,7,4,3,11,2,7
A,2,3,3,1,1,8,2,2,1,7,2,8,2,6,2,7
I,2,2,1,4,1,7,7,1,8,7,6,8,0,8,3,8
V,2,7,4,5,2,5,11,3,3,9,11,9,2,11,1,8
V,2,7,4,5,2,8,11,2,3,5,10,8,2,11,1,8
B,2,4,3,3,3,7,8,5,5,7,6,6,2,8,6,9
C,7,9,7,7,4,4,7,5,7,11,9,14,3,10,4,7
U,7,15,7,8,5,8,5,5,5,6,6,7,5,7,3,6
Z,2,7,3,5,2,7,7,3,12,8,6,8,0,8,7,8
R,10,13,8,7,5,7,7,5,5,9,4,9,7,6,7,11
H,8,11,11,9,7,8,6,3,6,10,5,8,3,8,3,8
Q,4,5,4,6,4,8,5,6,3,9,6,9,3,8,4,8
Q,3,5,4,6,3,8,10,5,2,6,9,12,2,10,6,8
Y,5,8,5,6,3,5,8,0,8,9,9,6,1,10,3,4
A,5,10,7,8,4,9,2,3,3,8,1,8,2,7,4,8
B,4,6,5,4,3,8,8,4,6,9,6,5,2,8,7,7
J,2,4,4,3,1,9,5,3,7,15,6,12,0,7,0,8
E,2,3,4,1,2,7,7,2,7,11,7,9,1,9,4,8
K,4,5,5,4,3,4,7,5,8,7,6,13,3,8,5,9
Q,3,7,4,9,5,8,7,8,3,5,6,9,3,8,6,10
R,3,8,4,6,4,5,8,7,5,6,4,9,3,6,5,8
W,8,14,8,8,6,4,8,1,3,8,9,8,10,11,2,6
G,5,8,7,6,4,6,7,6,6,6,6,9,4,7,4,8
T,3,5,4,3,2,5,12,3,7,11,9,5,1,11,2,5
L,2,5,2,3,1,4,3,3,7,2,2,6,0,7,0,6
G,3,4,4,6,2,7,5,7,7,5,5,10,1,8,6,11
K,3,6,4,4,1,4,8,8,1,7,6,11,3,8,3,11
Z,4,9,6,7,6,7,9,3,8,7,6,8,2,10,13,6
E,4,9,6,7,6,8,9,7,2,6,6,11,4,7,8,9
S,4,10,6,7,4,9,6,3,7,10,6,8,2,9,4,9
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
M,6,11,9,8,10,7,10,6,4,7,7,8,6,8,6,8
X,3,4,4,3,2,7,7,3,9,6,6,10,2,8,6,7
C,3,9,4,6,3,5,8,8,7,10,8,12,2,11,4,10
T,4,10,6,8,8,7,7,4,6,7,7,9,5,9,5,7
A,2,6,4,4,1,7,5,3,1,6,0,8,2,7,1,7
B,5,9,7,6,9,8,6,5,4,7,7,8,7,9,7,9
K,4,7,6,5,4,3,8,2,6,10,9,11,3,8,2,6
K,3,4,6,2,3,7,7,2,7,10,6,9,4,8,4,7
T,7,12,6,7,4,9,8,3,7,11,5,6,2,8,6,7
W,11,14,12,8,7,8,9,3,4,6,9,6,11,8,2,6
R,3,7,5,5,3,9,8,6,6,8,5,8,3,7,6,10
M,6,8,9,6,10,6,8,4,3,7,4,8,14,3,4,7
U,4,9,5,7,4,8,6,13,4,7,11,8,3,9,0,8
M,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8
F,5,11,5,8,4,1,13,5,4,12,10,6,0,8,2,6
L,3,7,5,5,3,7,4,1,7,8,2,9,1,6,2,8
I,1,6,1,4,1,7,7,1,6,7,6,8,0,8,2,8
D,7,11,9,8,8,6,7,7,5,7,7,6,4,7,4,8
Y,6,8,6,6,2,2,12,4,6,13,12,6,1,11,1,6
U,3,4,4,6,2,8,5,14,5,6,13,8,3,9,0,8
Z,6,10,9,8,7,10,9,6,5,7,6,8,4,9,10,7
U,7,11,8,8,4,4,8,7,8,10,9,9,3,9,3,5
D,3,7,4,5,3,10,6,3,7,11,3,6,3,8,3,9
L,9,14,9,8,5,7,4,4,5,12,8,12,4,9,6,10
I,1,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8
O,4,9,5,7,4,7,6,8,4,7,5,9,3,8,3,8
D,5,11,7,8,7,9,7,4,5,10,4,5,3,8,3,8
D,2,1,3,3,2,7,7,6,6,7,6,5,2,8,2,7
N,10,12,8,6,4,9,11,6,5,3,5,10,6,9,2,7
B,6,9,9,8,11,7,7,5,4,8,6,8,7,9,8,7
X,1,0,1,0,0,7,7,3,4,7,6,8,2,8,3,8
Q,2,2,3,2,2,7,9,4,2,7,8,10,2,9,4,9
P,5,10,6,8,6,5,10,3,7,10,9,6,4,10,4,7
E,6,10,8,8,7,5,9,4,8,12,10,8,3,8,5,4
Y,4,7,6,10,9,9,7,6,3,7,7,8,7,10,6,4
M,4,9,6,7,6,8,6,6,4,7,7,8,8,5,2,7
M,4,10,5,8,4,7,7,12,2,7,9,8,8,6,0,8
B,1,0,1,0,0,7,7,6,4,7,6,7,1,8,5,9
L,4,6,5,6,4,7,7,4,5,7,6,8,2,9,7,10
N,6,8,8,6,4,10,8,3,6,10,2,4,5,9,1,7
W,2,1,3,2,2,5,10,3,2,8,9,9,5,11,0,8
L,3,9,4,6,2,7,4,0,9,9,2,11,0,7,2,8
V,9,11,7,6,4,9,9,6,5,6,10,5,6,13,3,7
M,5,10,6,5,3,10,2,1,3,9,3,9,5,4,1,9
Q,7,12,6,6,4,10,5,4,7,11,4,8,3,8,8,11
V,4,6,6,6,6,7,6,5,4,6,5,8,7,9,7,9
C,7,10,7,8,5,5,7,5,7,12,8,13,4,9,5,6
X,6,13,7,7,4,9,7,2,7,11,4,6,4,10,4,8
D,2,1,3,2,2,7,7,6,6,7,6,5,2,8,3,7
P,9,13,8,7,4,8,7,5,5,11,3,6,5,9,4,8
E,2,5,4,3,2,7,7,2,8,11,7,9,2,9,4,8
A,7,9,9,8,8,5,8,3,5,7,8,10,8,8,4,8
L,3,7,5,5,2,6,3,2,8,7,2,10,0,7,2,8
P,2,5,4,4,2,7,11,4,3,12,4,2,1,10,3,8
S,3,8,4,6,3,8,7,8,6,8,6,7,2,8,9,8
I,1,4,1,3,0,8,7,0,7,13,6,8,0,8,0,8
U,5,9,6,8,7,7,6,4,4,6,7,8,7,8,2,7
T,7,10,9,7,7,6,7,7,7,8,10,8,3,9,6,8
S,4,7,4,5,2,7,6,6,9,6,7,11,0,9,9,8
B,3,7,5,5,6,8,6,5,3,7,6,8,6,10,8,7
M,6,5,6,8,4,7,7,13,2,7,9,8,9,6,0,8
F,6,9,9,7,6,7,9,3,6,12,7,6,3,9,3,7
C,4,8,5,6,3,5,8,6,6,8,8,15,3,10,4,10
D,2,4,4,3,2,9,6,4,6,10,4,6,2,8,3,8
X,3,9,5,6,4,7,7,3,8,5,6,8,2,8,6,8
J,3,8,4,6,3,8,7,1,7,11,5,8,0,6,1,6
R,2,3,4,2,2,8,7,4,5,9,4,7,2,7,4,10
E,1,3,2,1,2,7,7,5,6,7,6,8,2,8,5,10
Q,4,8,5,10,5,10,11,6,2,3,8,12,3,10,6,10
L,2,5,3,3,1,4,4,4,7,2,2,6,1,7,1,6
E,4,5,4,4,4,7,7,5,8,7,7,9,2,8,6,9
Y,7,6,5,9,4,7,11,2,3,8,10,5,3,9,5,7
L,2,3,2,5,1,0,1,5,6,0,0,7,0,8,0,8
F,2,4,2,2,1,6,10,4,5,10,9,4,1,10,3,6
X,4,8,5,6,3,8,7,4,4,7,6,8,3,8,4,8
T,4,6,4,4,2,5,12,4,5,11,9,4,2,11,1,5
K,4,10,4,7,2,4,8,9,2,6,3,11,4,8,2,11
B,5,10,5,8,7,7,8,8,6,8,6,7,2,8,8,9
E,4,7,6,5,4,9,6,2,8,11,5,9,3,8,5,10
R,3,5,6,4,4,9,7,3,5,9,4,6,3,7,4,10
P,3,6,4,9,8,8,9,5,0,8,7,6,5,10,3,7
X,6,11,7,6,4,7,8,2,7,11,7,7,4,10,4,6
K,6,10,9,8,6,5,8,2,7,10,8,10,3,8,3,8
E,4,9,6,6,6,7,7,6,3,7,6,11,4,8,8,9
Y,3,9,5,6,1,6,12,2,3,9,12,8,1,10,0,8
E,4,5,4,8,3,3,8,6,11,7,5,14,0,8,7,7
A,2,4,4,5,2,7,4,3,1,7,1,8,2,7,2,8
X,4,7,7,5,3,10,6,1,8,10,3,7,2,8,3,9
A,4,7,5,5,3,8,2,2,2,6,2,8,2,6,3,6
Z,6,12,6,6,4,6,7,2,9,11,7,8,3,7,6,6
H,4,3,4,4,2,7,7,14,0,7,6,8,3,8,0,8
A,4,6,6,5,5,8,8,2,4,7,8,8,5,8,4,6
Q,6,10,6,5,4,9,6,4,6,10,5,8,3,8,9,9
O,4,6,4,4,3,9,6,7,5,10,4,10,3,8,4,7
M,2,1,3,2,2,7,6,6,4,6,7,8,6,5,1,8
F,4,5,4,8,2,1,12,5,4,11,10,7,0,8,3,6
A,4,6,5,6,5,7,9,2,4,7,7,9,7,7,3,8
D,5,11,5,6,4,6,8,5,6,9,6,7,5,10,6,5
H,5,11,6,8,3,7,9,15,1,7,3,8,3,8,0,8
O,2,5,3,4,2,7,7,8,5,7,6,8,2,8,3,8
A,5,7,6,5,6,8,7,7,4,7,6,8,2,8,8,3
T,7,15,6,8,4,9,7,3,7,12,5,7,3,9,5,6
L,2,7,4,5,5,7,8,2,4,6,7,10,5,11,6,5
F,3,8,4,6,2,1,14,5,3,12,10,5,0,8,2,6
V,4,4,6,6,1,10,8,5,3,5,14,8,3,9,0,8
P,2,3,4,2,2,7,9,3,4,12,5,4,1,10,2,8
T,4,7,5,5,2,6,11,2,8,11,9,5,1,11,3,4
R,5,10,5,5,4,7,7,3,4,7,5,9,5,8,6,7
J,1,7,2,5,1,13,3,8,4,13,4,12,1,6,0,8
L,2,3,2,5,1,0,1,5,6,0,0,7,0,8,0,8
E,5,10,7,8,6,4,9,4,7,12,10,8,3,9,4,5
Z,4,10,5,7,4,7,7,3,12,9,6,8,0,8,8,8
V,3,5,6,7,2,6,8,4,3,7,14,8,3,9,0,8
I,1,10,2,8,2,7,7,0,8,7,6,8,0,8,3,8
E,3,10,4,7,2,3,7,6,11,7,6,15,0,8,7,7
K,4,9,5,6,5,6,7,3,6,6,6,9,3,8,5,10
O,4,9,5,7,4,7,7,8,6,7,6,6,2,8,3,8
P,2,3,3,2,1,7,10,3,4,12,5,3,0,9,2,8
N,3,5,3,3,2,7,8,5,5,7,6,6,5,10,2,5
F,4,7,6,5,4,9,8,2,5,13,5,6,2,9,2,9
X,4,5,5,7,2,7,7,5,4,7,6,8,3,8,4,8
E,4,9,4,6,2,3,7,6,11,7,6,15,0,8,7,7
D,2,5,4,4,3,9,6,4,6,10,4,6,2,8,3,8
Z,2,8,3,6,3,7,8,3,11,8,6,8,0,8,7,8
S,4,6,5,4,2,8,8,4,8,11,8,7,2,10,5,6
V,2,4,4,3,1,7,12,2,2,7,11,9,2,10,1,8
G,3,5,4,4,2,6,6,5,5,9,7,11,2,9,4,10
O,3,5,4,3,2,7,7,7,4,9,6,7,2,8,3,7
P,1,1,2,1,1,5,11,7,2,10,6,4,1,9,3,8
G,4,6,5,4,3,7,6,5,6,11,6,12,2,10,3,10
V,6,8,6,6,3,3,12,3,3,9,11,8,3,11,1,7
E,2,5,4,3,2,7,8,2,8,11,7,9,2,9,4,8
C,2,1,3,2,1,6,8,7,7,8,7,13,1,9,4,10
O,2,4,3,3,2,7,7,8,5,7,6,8,2,8,3,8
G,4,8,5,6,3,6,6,7,6,10,7,12,2,9,4,9
U,2,0,2,1,0,7,5,10,4,7,12,8,3,10,0,8
J,4,8,6,6,2,7,6,4,6,15,7,11,1,6,1,7
C,3,6,5,5,4,5,6,3,4,7,6,11,4,10,7,8
E,3,5,5,4,3,5,8,3,8,11,9,10,2,8,4,6
Q,3,6,4,5,3,8,8,6,4,6,9,8,3,8,5,9
S,4,8,5,6,3,9,7,4,8,11,7,7,2,10,5,8
I,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7
T,3,10,5,7,1,5,15,1,6,9,11,7,0,8,0,8
C,5,10,6,8,2,6,7,7,11,6,6,13,1,8,4,8
X,5,11,8,8,7,7,7,3,8,5,7,10,3,8,6,8
A,3,9,4,6,3,9,5,2,0,8,1,8,2,6,1,8
A,4,7,5,5,5,7,7,6,3,7,6,8,2,7,8,3
W,3,2,5,4,3,7,11,2,2,7,9,8,7,11,0,8
G,3,3,5,5,2,8,6,7,8,6,5,10,1,8,6,11
Y,6,11,9,8,5,6,9,1,7,6,12,9,2,11,2,8
P,3,7,4,5,3,4,12,7,1,10,6,4,1,10,3,8
G,2,4,3,3,2,6,7,5,5,10,7,10,2,9,4,9
G,3,2,4,4,2,6,6,5,6,6,6,9,2,9,3,8
H,4,7,4,5,3,7,7,12,0,7,7,7,3,8,0,8
O,1,0,1,1,0,7,6,6,3,7,6,8,2,8,3,8
T,2,5,3,3,2,8,12,3,6,6,11,7,2,11,1,7
L,2,6,4,4,2,6,4,3,8,6,2,7,1,6,2,7
X,5,8,8,6,4,6,8,1,8,10,9,9,2,9,3,7
O,2,3,3,1,1,8,7,7,5,7,6,8,2,8,3,8
A,4,9,4,4,3,10,3,4,2,10,5,11,5,4,4,10
G,4,7,4,5,3,6,7,5,5,9,7,12,2,9,4,10
J,3,9,5,7,5,9,9,3,3,8,4,6,4,8,5,5
J,3,11,4,8,5,7,7,2,5,11,5,9,4,7,2,6
T,3,3,4,2,1,5,11,3,7,11,9,5,1,11,2,5
J,4,8,5,6,3,7,5,5,4,14,8,14,1,6,1,7
M,6,11,9,8,9,9,7,5,5,6,7,5,11,8,4,6
M,4,2,5,3,4,9,6,6,4,6,7,6,9,6,3,6
H,3,6,4,4,2,7,6,14,1,7,7,8,3,8,0,8
N,2,1,2,1,1,7,7,12,1,5,6,8,5,8,0,8
D,1,0,1,1,0,5,7,7,5,6,6,6,2,8,3,8
X,7,13,6,7,3,8,7,2,8,9,6,8,4,8,4,8
Z,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8
T,3,5,4,7,1,9,14,0,6,5,11,8,0,8,0,8
B,5,8,8,7,9,8,6,5,5,7,6,7,8,11,9,6
P,8,15,8,8,5,9,8,4,4,11,4,5,5,11,6,7
I,1,8,0,6,1,7,7,5,3,7,6,8,0,8,0,8
Z,3,5,4,4,3,7,7,5,10,6,6,8,2,8,7,8
G,2,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10
N,5,11,6,8,6,8,8,13,1,6,6,8,6,8,1,10
R,3,6,4,4,4,6,10,7,3,7,4,8,2,6,5,11
Z,3,6,6,4,3,7,7,2,10,12,6,8,1,8,6,8
J,3,8,5,6,5,9,7,3,3,8,4,5,4,8,6,5
S,2,4,4,3,2,7,7,2,7,10,5,7,1,8,5,8
B,2,7,3,5,2,6,6,9,7,6,7,7,2,8,8,10
Z,5,8,7,6,5,8,7,2,9,12,6,7,1,7,6,7
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
V,5,5,6,4,2,4,12,3,3,10,11,7,2,10,1,8
I,0,1,1,2,0,7,7,2,6,7,6,8,0,8,2,8
O,4,8,6,6,3,8,6,8,8,7,5,8,3,8,4,8
Q,2,5,3,6,4,8,7,8,3,6,6,10,2,9,4,9
P,7,11,10,9,8,7,11,6,4,11,5,3,2,11,4,8
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
N,4,11,5,8,3,7,7,15,2,4,6,8,6,8,0,8
F,2,7,3,5,2,1,11,3,5,11,11,9,0,8,2,7
Z,8,10,6,14,5,6,11,3,3,12,7,6,3,9,13,5
W,4,7,6,5,4,6,11,2,3,7,9,9,8,11,1,8
I,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
C,8,15,5,8,3,7,7,8,8,12,7,10,2,9,5,9
C,3,3,4,4,1,6,7,7,9,7,6,13,1,9,4,9
E,5,9,7,7,8,8,7,3,5,6,7,9,5,10,8,8
O,4,5,6,4,4,6,6,5,5,8,4,7,3,7,4,7
D,5,7,6,6,6,6,7,5,6,6,4,7,4,7,6,5
V,6,8,6,6,3,3,12,3,3,10,11,8,2,10,1,8
E,2,1,3,2,2,7,7,5,7,7,6,8,2,8,5,10
R,3,9,4,7,4,6,9,8,3,7,5,8,2,7,5,11
Z,2,3,3,2,2,7,7,5,9,6,6,9,1,8,7,8
J,4,5,6,6,5,8,9,4,5,7,6,8,3,8,8,7
X,4,9,6,7,6,9,7,2,6,7,5,6,3,8,6,8
E,3,9,5,7,4,7,7,6,9,6,4,9,2,8,6,9
E,4,7,5,5,3,7,7,6,10,7,7,9,3,8,6,8
F,9,14,8,8,6,9,8,3,5,12,4,5,5,7,8,9
A,1,1,2,1,1,7,3,2,1,6,2,8,1,6,1,7
T,10,14,8,8,4,7,8,3,10,13,6,6,2,9,5,5
V,2,3,3,2,1,4,12,3,3,10,11,7,2,11,0,8
H,4,7,6,5,5,7,7,2,6,10,6,8,3,8,3,8
Q,6,10,6,5,3,12,3,3,7,11,2,8,2,9,5,13
Y,6,9,5,4,2,5,9,4,4,11,9,6,4,10,4,4
F,3,5,5,3,2,6,10,2,6,13,7,5,1,9,2,7
I,1,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8
N,2,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
U,4,9,4,4,3,6,6,5,4,7,8,8,4,7,3,8
V,4,6,4,4,2,2,12,3,3,11,11,8,2,11,1,8
N,6,7,8,5,4,7,9,2,5,10,5,6,5,9,1,7
H,3,7,4,5,3,8,7,7,6,7,6,7,3,8,4,7
C,6,12,5,6,4,8,6,4,3,9,8,11,4,9,7,11
M,6,8,10,6,7,4,7,3,5,11,10,11,9,8,4,7
R,1,0,1,1,1,6,9,7,3,7,5,8,2,7,4,10
F,3,3,5,2,2,6,11,2,5,13,7,4,1,9,1,7
K,8,11,11,8,7,9,7,2,7,10,3,8,3,8,3,9
Z,5,10,6,8,3,7,7,4,15,9,6,8,0,8,9,8
Y,1,1,2,2,1,7,11,1,6,7,11,8,1,11,1,8
Y,5,7,6,9,9,8,8,7,3,7,7,8,9,11,6,4
W,3,7,5,5,3,6,8,4,1,7,8,8,8,9,0,8
Q,2,0,2,1,1,8,7,7,5,6,6,9,2,8,3,8
R,4,6,6,6,6,6,8,4,3,6,5,9,7,6,7,8
P,6,11,9,8,6,6,12,4,4,13,6,3,1,10,3,8
S,7,11,8,8,6,5,9,3,7,10,6,6,3,7,5,5
K,4,8,6,6,4,6,8,4,7,6,5,10,3,8,4,9
T,10,14,8,8,3,4,12,3,8,12,8,4,2,9,3,3
D,6,11,6,8,6,5,7,10,7,5,4,6,4,10,5,9
O,5,10,5,8,4,8,7,8,5,10,6,8,3,8,3,8
Z,1,0,2,1,0,7,7,3,11,8,6,8,0,8,6,8
R,6,9,9,8,9,7,7,4,4,7,5,8,8,7,7,9
A,6,11,8,8,8,8,9,8,5,6,6,8,3,7,8,4
O,6,7,8,6,6,7,5,4,5,8,5,11,5,6,7,6
N,3,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8
S,2,1,3,2,2,8,8,6,5,7,6,7,2,8,9,8
Z,9,10,6,14,6,6,10,4,2,12,7,6,3,9,14,6
Z,2,7,3,5,2,7,8,3,11,8,6,8,0,8,7,7
X,3,8,5,6,4,7,7,3,8,6,6,8,3,8,6,7
H,5,5,5,7,3,7,8,15,1,7,4,8,3,8,0,8
G,4,9,6,7,7,7,9,6,3,6,6,10,4,8,7,7
Y,3,5,4,7,6,7,9,3,2,7,7,8,3,11,7,6
I,1,4,3,2,1,7,7,1,8,14,6,8,0,8,1,8
N,8,10,7,6,3,7,10,5,5,4,5,10,6,11,3,7
P,4,9,4,6,4,5,10,8,2,9,6,5,1,10,3,8
P,4,7,5,5,5,5,9,6,4,8,7,9,2,9,7,10
Y,6,9,5,4,3,6,8,3,4,10,8,5,3,10,4,4
N,3,7,4,5,3,5,9,6,4,8,7,9,5,8,1,7
L,5,11,6,8,4,10,3,2,7,9,2,9,4,5,5,9
E,6,8,8,7,8,5,10,4,5,8,7,9,4,10,9,7
S,5,9,6,7,5,8,8,7,5,7,5,7,2,7,9,8
B,3,5,4,4,3,8,7,6,6,6,5,6,2,8,6,10
D,2,3,4,1,2,9,6,4,6,10,4,6,2,8,3,8
V,4,4,5,3,1,4,12,4,4,11,11,6,2,10,1,8
A,4,5,7,7,2,7,6,3,0,7,0,8,3,7,1,8
I,3,9,4,6,2,5,8,0,8,14,7,8,0,8,1,7
Q,5,6,5,8,5,8,8,5,2,8,8,10,3,9,6,8
C,2,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10
R,3,5,5,4,3,8,7,3,5,10,4,6,3,7,3,9
K,9,12,9,6,5,8,7,3,6,10,7,9,6,12,4,9
G,3,4,4,3,2,6,6,5,5,7,6,9,3,7,4,8
H,8,12,7,6,4,9,9,4,7,8,2,5,6,6,4,8
Y,4,5,5,8,7,10,8,5,3,7,7,7,6,10,8,4
L,4,9,5,7,3,6,4,4,8,5,2,6,1,6,2,6
V,3,6,5,4,3,8,11,3,2,5,10,9,3,10,3,10
J,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8
L,2,5,4,3,2,7,4,1,8,8,2,10,0,7,3,8
L,3,8,4,6,2,4,3,6,7,1,1,5,1,6,1,6
E,6,11,8,8,6,4,9,2,8,10,8,9,3,8,5,5
Y,5,6,6,9,6,10,12,5,4,6,7,7,6,11,8,6
J,1,2,2,3,1,10,6,3,5,12,4,9,1,6,1,7
A,2,8,4,5,2,6,5,3,1,6,1,8,2,7,2,7
K,2,1,2,1,0,5,7,8,1,7,6,11,3,8,2,11
R,4,7,7,6,6,7,7,3,3,7,5,8,6,8,5,7
Y,7,9,7,7,3,4,11,2,8,12,11,5,1,10,2,4
U,4,4,5,3,2,5,8,5,8,10,8,8,3,9,2,5
C,5,4,6,7,2,5,7,7,11,7,5,12,1,9,4,8
O,5,8,5,6,4,7,7,8,5,10,7,8,3,8,4,8
F,5,7,7,8,7,7,10,5,5,8,7,9,5,9,7,5
P,2,1,3,2,2,5,9,5,4,9,7,4,1,10,3,7
D,3,6,4,4,3,7,8,8,7,8,7,3,3,8,3,7
B,2,2,2,3,2,7,7,6,5,7,6,6,2,8,5,10
X,5,9,8,6,4,6,8,1,8,10,9,9,2,9,3,7
H,3,5,5,3,3,6,7,6,6,7,6,9,3,8,3,8
I,2,5,4,4,2,7,7,0,8,13,6,8,0,8,1,8
U,3,6,3,4,1,7,5,13,5,7,13,8,3,9,0,8
U,5,5,6,8,2,8,5,14,5,6,15,8,3,9,0,8
M,4,3,5,4,4,8,6,6,4,6,7,8,9,5,3,6
R,4,2,4,3,3,7,8,5,5,6,5,6,3,7,4,8
C,1,3,2,1,1,5,9,4,6,11,9,11,1,9,2,8
C,4,9,5,6,4,5,8,7,6,9,8,14,2,10,4,10
B,3,7,5,5,4,7,8,7,6,7,5,6,2,8,7,10
O,7,9,9,8,6,5,7,6,8,9,7,8,4,5,6,5
L,2,4,3,3,1,5,4,4,7,2,2,5,1,7,1,6
G,2,1,4,2,2,7,7,5,5,7,6,9,3,7,4,9
B,3,5,5,4,5,7,7,4,4,7,6,8,6,9,8,6
P,6,7,8,9,8,7,9,2,3,7,9,6,6,10,6,4
F,2,4,3,2,2,5,10,4,5,10,9,5,1,10,3,6
S,4,4,5,6,3,7,6,5,9,5,6,8,0,9,9,8
X,3,6,5,4,3,7,7,3,8,6,6,9,3,8,6,8
F,4,9,6,7,4,5,11,4,6,11,10,5,2,10,3,5
T,2,3,3,1,1,5,12,3,5,11,9,5,1,10,1,5
O,6,10,4,5,2,8,7,6,5,9,4,7,5,9,5,9
V,4,7,6,5,7,7,8,4,1,8,7,8,5,10,5,8
X,2,2,3,3,2,8,7,3,8,6,6,7,2,8,5,7
U,5,7,6,5,4,5,7,5,7,9,7,9,3,9,3,6
N,4,3,4,5,2,7,7,14,2,5,6,8,6,8,0,8
M,4,9,5,6,5,6,6,6,4,7,7,11,7,5,2,10
N,4,6,6,4,3,7,8,3,4,10,6,7,5,8,0,7
R,3,9,5,6,4,6,7,6,6,6,6,6,3,8,6,8
B,4,6,6,5,6,8,8,5,5,7,6,8,6,8,8,3
W,4,7,7,5,5,5,11,2,2,8,9,8,7,12,1,8
Z,1,0,1,1,0,7,7,2,9,9,6,8,0,8,6,8
P,7,14,7,8,5,8,9,4,4,12,5,4,4,11,6,6
B,6,8,9,7,9,7,8,5,5,8,6,8,7,7,9,6
O,3,2,4,3,2,7,7,7,5,7,7,7,2,8,3,7
Y,4,7,6,5,3,8,10,1,6,4,11,9,2,11,2,8
D,2,3,3,1,2,9,6,3,6,10,4,6,2,8,2,9
K,2,6,3,4,1,4,8,8,2,7,4,11,3,8,2,10
A,5,11,8,8,5,11,2,3,3,9,2,9,5,7,4,9
C,1,0,2,0,0,7,7,6,8,7,6,13,0,8,4,10
Z,4,10,5,8,5,8,7,6,10,7,5,7,1,7,8,8
A,3,6,5,5,4,9,7,3,5,7,8,6,4,10,3,5
I,5,11,6,9,4,9,5,2,5,6,7,5,1,10,4,7
H,6,9,6,5,4,8,7,3,4,10,5,7,6,9,5,7
D,3,2,4,4,3,7,7,7,7,7,6,5,2,8,3,7
B,3,6,4,4,4,6,7,6,3,6,5,7,2,8,6,6
B,4,6,6,4,6,9,9,4,4,5,7,7,7,12,6,8
M,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8
A,1,3,2,1,1,9,3,2,1,8,2,9,1,6,0,8
L,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8
M,4,7,7,5,7,9,5,2,1,8,4,8,10,5,2,6
B,6,10,6,8,5,6,6,9,8,6,6,7,2,8,10,10
F,2,3,3,4,1,1,14,4,3,12,9,5,0,8,2,6
L,4,3,4,5,1,1,0,6,6,0,1,4,0,8,0,8
A,3,7,5,5,3,11,2,3,2,10,2,9,2,6,3,8
Q,2,2,2,3,2,8,8,5,2,7,8,10,2,9,4,8
Y,3,7,5,5,2,7,10,1,2,7,12,8,1,11,0,8
X,3,7,5,5,2,6,9,1,8,10,8,8,2,8,3,7
T,1,1,2,1,0,7,14,1,4,7,10,8,0,8,0,8
Q,2,4,3,4,3,8,8,6,3,8,7,9,2,9,4,9
N,6,11,9,9,7,7,7,8,5,7,5,6,3,7,3,8
D,3,6,5,5,5,7,7,4,6,7,4,8,4,7,6,5
M,5,6,8,4,4,9,6,3,5,9,5,7,8,6,2,8
Q,4,6,5,7,4,8,6,6,4,9,6,10,3,8,5,8
F,3,9,4,7,3,0,11,3,4,11,11,8,0,8,2,7
V,3,5,6,4,2,6,12,2,3,8,11,8,2,10,1,9
V,4,9,7,7,2,9,8,5,3,6,14,8,3,9,0,8
O,4,6,5,4,5,8,5,5,2,7,5,8,8,8,3,9
E,6,8,8,6,5,7,8,2,10,11,6,9,2,8,4,8
H,3,7,4,5,3,7,7,12,1,7,6,8,3,8,0,8
S,2,7,3,5,3,8,7,7,6,7,8,9,2,10,8,8
I,3,9,5,7,6,10,8,2,5,9,5,4,3,9,6,5
X,2,7,4,5,2,7,7,4,8,6,6,8,3,8,6,8
U,2,0,2,1,1,8,5,11,5,7,13,8,3,10,0,8
X,3,5,4,3,2,7,7,3,9,6,6,7,3,8,6,7
T,3,5,4,3,2,9,12,3,7,5,11,9,1,11,1,8
N,2,4,4,3,2,6,8,2,4,10,7,8,4,8,0,8
Q,4,7,6,6,5,6,4,4,4,5,3,7,3,6,5,6
A,2,4,4,3,2,9,2,2,2,8,2,9,1,6,1,7
O,7,12,5,6,3,5,8,6,4,10,7,9,5,10,5,8
V,7,9,7,7,3,3,11,2,4,9,11,7,5,11,1,6
R,4,7,5,5,3,6,10,9,4,6,4,8,2,8,5,10
R,3,7,5,5,4,7,8,5,6,8,5,9,3,6,5,11
V,8,14,7,8,3,9,10,4,6,6,11,6,4,11,3,6
F,2,1,2,2,1,5,10,4,5,10,9,5,1,10,3,7
Y,3,6,5,9,8,8,6,4,1,7,8,9,7,10,8,8
X,4,6,6,4,3,8,7,2,8,10,5,8,3,8,4,8
M,5,6,7,5,7,7,9,4,4,7,5,7,9,8,5,5
Y,2,1,3,1,0,7,10,3,1,7,13,8,1,11,0,8
X,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8
U,4,6,6,5,5,7,8,4,3,6,6,8,4,7,1,7
G,6,11,5,6,4,8,5,4,3,9,6,9,4,9,8,8
S,3,7,4,5,2,7,8,4,8,11,8,8,2,10,5,7
F,6,10,10,8,9,8,8,1,6,10,6,5,5,10,4,6
L,2,4,3,2,1,6,4,1,8,7,2,10,0,7,2,8
S,5,9,8,7,9,7,4,3,3,7,5,7,4,8,11,6
M,5,8,7,6,5,11,5,2,4,9,3,6,7,6,2,8
U,4,9,4,7,2,7,5,14,5,7,14,8,3,9,0,8
A,2,1,3,3,1,7,2,1,2,6,2,7,2,5,2,8
S,3,7,5,5,6,7,5,3,3,6,5,7,3,8,9,3
J,4,9,5,7,3,7,9,2,6,14,5,7,0,6,1,7
X,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8
J,3,7,5,5,4,8,9,4,3,8,5,6,6,9,6,4
T,4,7,5,5,5,6,8,3,5,7,6,9,5,9,4,8
E,3,6,4,4,4,8,7,6,3,7,6,10,3,8,7,9
T,4,7,6,5,6,5,8,4,6,7,6,10,5,8,5,7
L,6,11,6,6,3,8,4,3,5,12,7,12,3,8,6,9
F,3,5,5,3,2,6,10,2,6,13,7,5,1,10,2,7
R,5,9,7,8,8,7,8,3,4,7,5,8,7,8,6,8
D,3,6,4,4,4,7,7,4,6,7,6,6,3,8,2,7
M,5,9,6,4,3,7,3,3,2,7,4,10,6,3,2,8
A,1,3,2,2,1,10,2,3,1,9,2,9,2,6,1,7
Q,4,9,5,10,7,8,6,8,3,6,5,9,3,8,6,10
M,4,4,5,6,3,7,7,12,1,7,9,8,8,6,0,8
W,2,1,4,2,2,7,11,2,2,7,9,8,5,11,0,8
M,4,4,7,3,4,7,6,3,4,9,8,9,7,5,2,8
Q,4,6,4,7,4,7,9,5,3,7,9,11,3,9,7,8
B,3,8,4,6,4,7,7,7,6,7,6,6,2,8,7,9
U,2,1,2,1,1,8,5,11,5,7,13,8,3,10,0,8
I,1,5,1,4,1,7,7,1,7,7,6,9,0,8,3,8
N,6,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8
I,1,2,1,3,1,7,7,1,7,7,6,8,0,8,3,8
U,6,11,9,8,5,6,8,6,8,7,9,10,3,9,1,8
C,6,11,6,8,4,2,9,5,7,11,11,13,1,8,2,7
W,8,9,8,6,6,2,10,2,3,10,10,9,7,10,2,7
Z,3,5,5,6,3,10,3,2,6,7,3,6,1,8,7,7
U,6,8,6,6,3,4,8,6,8,10,10,9,3,9,2,5
W,3,7,5,5,4,7,10,2,3,6,9,8,7,11,1,8
U,5,8,6,6,3,4,9,5,7,12,11,9,3,9,2,7
M,2,1,3,1,2,7,6,6,4,6,7,7,7,6,2,7
B,5,11,8,8,10,9,5,5,5,7,7,7,7,9,8,9
W,4,8,6,6,3,9,8,4,1,7,8,8,8,9,0,8
Z,2,2,3,3,2,7,7,5,9,6,6,8,2,8,7,8
T,3,1,4,3,2,7,12,4,6,7,11,8,2,11,1,8
U,2,5,3,4,2,7,8,7,8,8,9,7,3,9,1,8
T,1,0,2,0,0,7,14,1,4,7,10,8,0,8,0,8
Z,4,9,5,7,5,8,7,5,9,7,5,7,1,7,7,8
E,6,10,5,5,3,7,8,4,4,10,6,8,3,9,9,9
S,2,6,3,4,2,8,8,7,6,7,5,8,2,8,9,8
X,4,9,5,6,1,7,7,4,4,7,6,8,3,8,4,8
S,4,8,5,6,3,7,6,6,10,5,6,10,0,9,9,8
V,5,10,5,7,3,4,11,2,3,9,11,7,2,10,1,8
A,7,12,7,7,4,12,1,4,1,11,4,12,5,4,4,11
B,6,10,8,8,9,8,7,5,6,7,6,6,2,8,6,10
J,4,8,3,12,3,8,7,3,3,11,4,5,3,8,7,10
Z,2,1,3,2,2,7,7,5,9,6,6,8,1,8,7,8
L,5,9,6,6,5,6,7,7,5,6,6,9,6,8,5,11
A,3,11,5,8,4,12,3,2,2,9,2,9,2,6,2,8
H,5,10,5,8,5,7,8,13,1,7,5,8,3,8,0,8
S,4,7,5,5,3,8,7,3,6,9,6,7,2,8,5,8
Y,2,5,4,3,2,7,10,1,7,7,11,8,2,11,2,8
C,2,2,3,4,2,6,8,8,7,9,8,12,1,10,4,10
K,4,8,4,6,2,3,6,8,3,7,7,11,4,8,3,10
C,5,9,6,8,7,6,7,5,5,6,7,12,6,9,8,10
Y,1,1,3,2,1,8,11,1,6,6,10,8,1,11,1,8
Y,3,6,5,4,1,6,10,3,2,8,13,8,2,11,0,8
P,4,8,4,5,2,3,14,7,1,11,7,3,0,10,4,8
D,3,4,5,3,3,9,6,4,6,10,4,6,2,8,3,8
Q,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8
T,4,4,4,3,2,5,12,3,7,12,9,4,1,11,1,5
S,3,9,4,7,2,9,9,6,10,5,5,5,0,7,9,7
O,3,5,4,4,3,8,7,7,4,9,6,8,2,8,3,7
V,6,8,6,6,3,3,12,4,4,10,12,7,2,10,1,8
O,5,11,5,8,6,8,6,8,4,9,4,8,3,8,3,8
K,3,4,4,3,3,6,7,4,7,7,6,11,3,8,5,10
F,5,8,7,6,5,7,9,2,5,13,6,5,2,10,2,8
T,3,7,4,5,3,7,11,1,8,7,11,8,0,10,1,8
T,3,4,4,3,1,5,12,2,7,11,9,5,0,10,1,5
P,3,4,5,3,2,7,10,3,4,12,4,2,1,10,2,8
L,4,10,6,8,4,5,5,1,9,6,2,10,1,7,3,7
H,5,8,7,6,5,5,8,3,6,10,8,9,3,8,3,6
C,2,5,3,3,2,6,8,7,7,9,7,12,2,9,4,10
T,4,10,6,7,5,9,11,3,7,5,11,8,2,12,1,8
W,4,7,6,5,8,9,6,5,2,7,6,8,10,10,2,7
D,5,11,7,8,5,8,8,6,7,10,6,4,4,7,5,10
S,5,10,6,8,4,10,7,4,6,10,3,7,3,9,5,11
C,1,0,2,1,0,6,7,6,8,7,6,14,0,8,4,10
F,4,9,5,6,2,1,14,5,3,12,9,4,0,8,3,6
X,3,6,5,4,4,7,7,3,5,6,6,9,2,8,8,8
N,4,7,6,5,3,4,9,4,4,10,10,9,5,8,1,7
C,6,9,8,7,5,7,7,8,6,8,5,11,4,9,4,8
W,6,9,8,7,4,11,8,5,2,6,9,8,8,9,0,8
R,6,11,8,8,7,9,7,3,6,10,3,7,3,6,3,11
Q,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8
H,2,1,3,2,2,7,8,6,6,7,6,9,3,8,3,8
Y,2,7,3,4,0,7,11,1,3,8,12,8,1,11,0,8
J,2,3,2,5,1,12,3,9,3,13,5,12,1,6,0,8
P,6,6,8,8,8,6,11,2,3,8,9,6,5,10,5,6
M,3,6,4,4,4,7,5,10,0,6,8,8,6,5,0,7
J,2,3,3,5,1,13,3,8,4,13,4,11,1,6,0,8
Y,5,10,8,8,5,8,10,1,7,4,11,8,1,11,2,8
A,1,3,3,2,1,8,3,2,2,7,2,8,2,6,2,7
S,5,10,6,7,4,6,8,5,8,11,10,7,2,10,5,5
N,5,4,5,7,3,7,7,15,2,4,6,8,6,8,0,8
I,2,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
I,1,1,1,3,0,7,7,1,8,7,6,8,0,8,3,8
A,6,11,6,6,4,10,2,4,2,11,5,12,5,4,6,10
V,5,8,7,6,5,7,12,2,1,7,10,8,5,11,6,8
C,4,7,5,5,2,4,9,6,8,13,11,10,1,9,2,6
C,5,8,5,6,3,6,7,5,6,12,8,13,3,9,4,7
G,3,7,5,5,5,8,9,6,3,6,6,9,4,8,6,7
W,6,10,6,7,7,4,10,2,3,9,8,8,7,11,2,7
W,6,8,9,7,11,9,7,5,5,7,5,8,12,10,9,4
C,3,7,4,5,2,6,8,8,8,9,7,12,1,10,4,9
Y,4,10,6,8,3,7,10,1,7,7,12,8,1,11,2,8
L,4,7,5,5,6,6,7,3,6,7,7,11,6,11,6,5
K,2,1,2,1,1,6,7,4,7,6,6,10,3,8,4,9
F,1,3,3,2,1,7,9,2,4,13,6,6,1,9,1,8
S,2,3,2,2,2,8,7,6,5,7,7,8,2,9,8,8
I,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8
R,5,9,7,7,6,6,8,6,6,6,5,8,3,6,6,9
Y,3,5,5,8,7,8,7,4,2,6,8,9,4,11,7,8
W,4,7,6,5,4,7,8,4,0,6,9,8,7,12,0,8
N,6,7,8,5,3,8,8,3,5,10,5,6,6,9,1,7
I,1,6,1,4,1,7,7,0,7,7,6,8,0,8,2,8
X,3,7,4,5,3,8,7,2,5,6,6,7,2,8,8,9
Z,6,10,8,7,5,6,9,3,10,12,9,7,1,9,6,6
U,5,10,7,7,5,6,8,8,5,5,6,12,5,9,7,3
H,4,11,6,8,10,8,9,5,3,6,7,7,8,8,10,8
U,5,8,5,6,2,7,4,14,5,7,14,8,3,9,0,8
F,2,7,4,5,2,5,11,2,7,10,9,5,1,10,3,5
K,4,9,5,7,2,3,7,8,2,7,6,12,3,8,3,11
V,2,6,4,4,2,9,12,2,3,4,10,9,2,11,1,9
H,2,3,3,1,2,7,8,6,6,7,6,9,3,8,3,8
G,2,1,3,1,1,8,6,6,6,6,5,9,1,7,6,10
Z,2,7,4,5,4,8,8,2,7,7,7,7,0,8,8,9
L,6,11,8,8,10,7,7,3,6,6,7,11,6,11,7,5
S,3,4,5,3,2,8,8,2,8,11,5,6,1,9,4,8
F,4,9,4,7,3,1,12,4,4,11,10,7,0,8,2,7
Z,2,4,3,2,1,8,7,2,9,11,5,9,1,9,5,8
P,7,10,7,5,4,7,10,3,6,13,5,4,3,10,5,6
G,5,11,5,8,5,5,7,5,4,9,9,9,2,8,5,9
E,6,9,8,7,7,8,5,7,3,8,6,11,5,8,8,10
U,9,11,9,8,7,4,8,5,8,9,7,9,6,8,4,3
D,4,5,4,8,3,5,7,11,8,7,7,5,3,8,4,8
E,5,9,7,6,4,7,7,2,9,11,6,9,2,8,4,8
I,1,8,2,6,2,7,7,0,7,7,6,8,0,8,2,8
W,9,12,9,6,5,4,8,2,4,8,10,8,10,10,2,5
K,1,0,2,1,0,5,8,8,1,7,6,11,3,8,2,11
R,5,6,7,5,7,6,7,4,3,6,5,9,7,9,6,10
H,11,13,10,7,5,8,9,4,6,9,5,7,6,8,5,9
I,2,8,3,6,1,7,7,0,7,13,6,9,0,8,1,8
C,5,5,6,7,2,5,7,7,11,7,6,12,1,8,4,9
J,1,1,2,2,1,11,7,1,5,11,4,7,0,7,0,8
N,3,6,4,4,3,6,9,6,4,7,6,8,5,9,1,7
P,7,10,6,5,4,6,12,5,3,12,6,3,3,11,5,6
D,2,5,4,3,3,9,6,3,6,10,4,7,3,7,3,8
T,3,7,5,5,3,7,12,3,7,8,11,7,2,11,1,7
F,2,2,3,3,2,5,11,4,5,11,9,5,1,10,3,6
K,1,0,1,0,0,5,7,7,0,7,6,10,2,8,2,11
W,8,11,7,6,4,1,9,3,2,11,12,9,7,10,0,6
H,2,1,3,2,2,6,7,6,5,7,6,8,3,8,3,8
I,5,8,6,9,6,7,8,5,6,7,7,7,3,9,9,9
Y,3,9,5,6,1,7,12,1,3,7,12,8,1,10,0,8
H,3,3,5,2,3,7,7,3,6,10,6,8,3,8,3,7
F,7,13,6,7,3,7,9,3,6,12,5,5,2,8,6,6
V,7,11,7,8,4,2,12,2,3,9,11,8,2,10,1,7
O,4,9,4,7,4,7,8,7,4,9,7,8,3,8,3,7
C,4,9,5,6,3,5,7,6,8,5,7,12,1,7,4,9
E,3,9,5,6,5,6,8,3,6,6,7,11,4,11,8,8
H,8,10,8,5,5,8,8,3,5,10,3,7,6,5,4,7
E,3,7,4,5,3,3,7,5,9,7,6,14,0,8,6,9
B,3,7,4,5,3,7,6,9,7,6,7,7,2,9,8,9
N,8,11,11,8,5,9,7,3,6,10,3,5,6,8,2,7
P,2,4,4,3,2,7,11,5,2,11,5,3,1,10,2,9
N,2,3,4,2,1,7,9,3,4,10,7,7,5,8,1,8
U,4,4,5,3,2,4,8,5,8,10,10,9,3,9,2,6
O,6,11,7,8,3,8,8,8,9,6,7,9,3,8,4,8
C,3,4,4,3,2,6,8,7,7,9,8,12,2,10,3,9
F,2,2,3,3,2,5,10,4,5,10,9,5,1,10,3,6
Q,2,1,2,1,1,8,7,7,4,6,6,8,2,8,3,8
F,2,6,3,4,2,4,11,5,5,11,10,6,2,10,2,6
T,3,10,5,7,1,8,14,0,6,6,11,8,0,8,0,8
I,0,1,1,2,1,7,7,1,6,7,6,8,0,8,2,8
R,3,6,5,4,5,7,8,3,4,6,6,9,6,8,7,6
T,3,8,5,6,3,6,12,4,7,9,12,7,2,12,1,7
X,3,7,4,5,4,6,6,3,5,6,6,10,2,8,8,7
A,2,4,4,2,2,11,2,2,1,9,2,9,2,7,2,9
L,3,9,5,6,6,7,8,3,6,5,7,10,5,12,6,5
C,5,8,6,6,2,6,6,7,11,8,5,12,1,9,4,8
R,4,7,6,5,4,10,7,3,7,11,2,6,4,5,3,9
P,6,6,8,9,8,7,9,3,2,8,8,6,8,11,6,5
G,4,7,5,5,2,7,6,7,9,6,5,10,1,8,6,11
X,5,11,7,8,5,4,8,1,8,9,10,10,2,9,3,5
M,4,10,5,8,4,8,7,12,2,6,9,8,8,6,0,8
S,5,9,6,7,3,8,7,4,8,11,6,7,2,9,5,8
L,6,10,7,8,6,6,6,7,6,6,5,8,5,7,4,10
L,2,5,4,4,2,7,4,1,7,9,2,10,0,7,2,8
Y,3,10,5,7,1,7,10,1,3,7,12,8,1,11,0,8
U,3,3,4,2,1,4,8,4,6,11,11,9,3,9,0,7
F,2,5,4,4,2,6,10,2,6,13,7,6,1,9,2,7
G,2,3,3,2,2,7,7,5,5,9,7,10,2,8,4,9
Q,4,7,6,7,3,8,9,8,5,6,7,9,3,8,5,9
V,8,9,10,8,12,7,6,5,5,7,6,8,7,10,6,9
H,3,4,6,2,3,9,6,3,5,10,5,7,3,8,3,7
K,4,9,6,7,8,7,9,3,5,5,6,8,8,9,8,7
T,2,7,4,5,1,9,14,1,6,5,11,9,0,8,0,8
L,5,11,4,6,2,5,5,3,7,9,4,12,2,6,5,7
M,3,5,6,4,4,7,7,2,4,9,7,8,7,5,1,8
O,4,9,5,7,4,8,7,8,4,7,6,4,4,8,4,8
H,4,7,4,5,4,7,9,12,1,7,4,8,3,7,0,8
U,2,0,2,1,0,8,5,11,4,7,13,8,3,10,0,8
R,4,2,4,3,3,7,8,5,6,6,5,7,3,7,4,8
B,7,10,9,8,7,9,7,4,7,10,5,6,2,8,6,10
D,3,6,4,4,3,8,7,4,5,10,5,5,3,8,3,8
L,3,10,3,8,1,0,1,5,6,0,0,7,0,8,0,8
V,3,4,4,3,1,4,12,3,3,10,11,7,2,11,1,8
E,1,0,1,1,1,5,7,5,8,7,6,12,0,8,6,9
V,4,11,6,8,4,6,11,2,3,7,11,9,2,10,1,9
U,4,6,5,4,3,6,8,6,7,6,9,9,3,9,1,8
O,3,6,5,4,3,7,6,8,6,6,4,7,3,8,3,8
V,10,13,8,7,4,8,10,5,6,6,10,5,5,12,4,7
D,2,3,3,1,2,7,7,6,6,7,6,5,2,8,3,7
H,8,10,11,7,8,7,7,2,6,10,6,9,5,7,4,7
R,5,10,7,8,7,8,8,4,6,8,4,8,4,5,5,11
J,5,6,3,9,3,8,8,3,3,12,5,5,3,8,6,10
H,3,3,4,4,2,7,9,14,1,7,4,8,3,8,0,8
J,6,11,8,8,4,8,9,1,7,14,4,6,2,8,2,7
H,4,3,4,4,2,7,6,14,1,7,8,8,3,8,0,8
Q,2,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8
C,3,7,4,5,3,6,8,7,7,10,8,13,2,10,4,10
H,3,4,4,6,4,8,7,4,0,7,6,7,3,9,8,5
S,5,7,6,5,3,8,8,4,9,11,4,7,2,6,5,9
M,3,7,3,5,3,8,7,11,1,6,9,8,7,6,0,8
Q,2,2,3,3,2,8,8,5,2,5,7,10,2,9,5,10
M,7,9,10,7,6,4,6,4,5,11,11,11,9,3,4,6
C,4,4,5,7,2,7,6,7,10,9,5,13,1,10,4,9
F,3,8,3,6,1,1,12,4,5,12,11,8,0,8,2,6
Y,5,9,5,6,3,3,11,3,7,12,11,6,1,10,2,5
V,6,8,6,6,4,3,12,2,3,9,11,8,4,12,1,7
X,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8
L,2,5,4,4,2,6,4,1,8,8,2,10,0,7,2,8
C,2,3,2,2,1,4,8,4,6,11,10,11,1,9,2,7
T,2,8,4,6,2,6,11,3,7,8,11,7,2,11,1,7
C,9,13,6,8,3,6,9,7,7,11,7,6,2,9,6,8
G,5,9,6,7,3,8,6,8,9,6,6,10,1,8,6,10
K,1,0,1,1,0,5,7,7,1,7,6,11,2,8,2,11
S,9,14,7,8,4,8,3,4,5,8,1,7,3,6,6,9
Y,7,9,7,7,4,5,8,2,8,8,10,5,5,11,6,3
Q,3,3,4,4,3,8,7,6,3,8,6,9,2,9,3,9
F,2,3,4,2,1,6,10,3,5,13,7,5,1,9,1,7
W,4,5,4,3,3,3,10,2,2,10,10,8,5,11,1,7
T,4,11,6,8,6,9,11,3,7,5,12,8,2,12,1,9
U,2,6,3,4,1,8,6,12,5,7,13,8,3,9,0,8
I,1,5,1,4,1,7,7,1,7,7,6,8,0,8,2,8
G,5,10,6,7,3,7,5,8,10,7,5,12,1,9,5,10
G,3,5,4,7,3,7,7,8,7,7,7,7,2,7,6,11
L,7,15,6,8,3,8,4,3,6,11,4,12,2,7,6,9
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
R,3,4,5,3,3,8,7,3,5,9,4,7,3,7,4,10
X,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8
K,2,1,3,2,2,5,7,4,7,7,6,11,3,8,4,9
T,5,5,5,4,2,5,12,2,7,11,9,4,1,11,2,4
P,6,9,5,5,3,6,11,6,2,11,5,4,4,10,4,8
N,11,14,9,8,4,8,11,5,5,3,6,10,6,10,3,7
K,4,5,7,3,4,7,6,2,7,10,6,10,4,8,4,8
Q,4,6,6,9,3,7,6,8,6,5,7,8,3,8,5,9
M,5,7,7,5,6,7,7,3,4,9,8,9,7,5,2,8
G,6,11,7,9,5,6,7,8,7,6,6,13,3,8,5,7
J,7,10,4,14,4,7,9,3,4,13,5,5,3,8,7,10
I,4,7,6,8,5,8,8,5,6,7,7,7,4,8,9,9
B,3,6,5,4,5,8,8,6,6,7,6,6,2,8,6,9
S,6,10,8,8,4,9,7,4,8,11,7,8,2,10,5,7
L,2,3,3,2,1,6,4,2,8,8,2,10,0,7,2,8
O,3,7,4,5,3,9,5,7,5,10,4,9,3,8,3,8
I,1,3,2,2,0,7,7,1,7,13,6,8,0,8,0,7
K,3,2,4,3,2,5,7,4,8,7,6,11,3,8,5,9
Z,1,1,2,1,0,8,7,2,10,9,6,8,0,8,6,8
O,6,9,6,7,5,8,6,8,4,9,5,8,4,7,4,9
G,5,5,6,8,3,7,6,8,8,6,6,10,2,8,6,11
L,3,7,4,5,3,6,5,7,6,6,6,9,2,8,4,10
C,3,5,4,4,2,6,7,7,8,8,8,13,1,9,4,10
X,6,10,7,5,3,5,9,2,8,11,8,8,4,9,3,6
F,1,1,1,1,0,2,12,4,3,11,9,6,0,8,2,7
I,8,13,7,7,4,9,7,3,6,12,4,5,3,8,6,11
Z,2,7,3,5,2,8,6,5,10,7,6,7,1,7,8,8
Z,7,11,9,8,6,8,7,3,10,12,6,8,2,7,6,8
I,7,15,6,8,4,9,7,3,6,13,3,5,3,8,6,10
F,2,7,3,4,1,1,13,5,3,12,10,6,0,8,3,7
X,4,5,5,7,2,7,7,5,4,7,6,8,3,8,4,8
G,5,10,6,8,5,5,7,6,6,10,8,11,2,9,4,10
I,1,3,1,2,1,8,7,1,7,7,6,7,0,8,2,7
I,3,5,4,6,4,7,8,5,5,6,8,7,3,10,8,8
K,3,5,5,4,4,6,7,3,3,6,4,9,4,6,7,8
J,3,5,4,7,1,13,2,8,5,14,3,12,0,6,0,8
I,1,5,2,3,1,7,7,0,7,13,6,8,0,8,0,8
P,3,5,5,4,2,7,12,4,3,12,4,1,1,10,3,8
M,5,9,6,7,4,7,7,12,2,7,9,8,9,6,0,8
Z,3,9,4,6,2,7,7,4,14,9,6,8,0,8,8,8
J,5,11,4,8,3,9,7,2,4,12,5,7,2,9,7,9
O,2,5,3,3,2,8,7,7,4,9,5,8,2,8,3,8
G,4,8,5,6,2,7,6,7,9,6,6,9,2,8,6,11
E,3,8,3,6,2,3,7,6,9,7,6,14,0,8,7,8
L,6,11,6,6,3,6,6,5,5,12,9,11,3,10,6,9
P,3,6,3,4,2,3,12,6,1,11,7,4,0,9,3,8
V,4,5,6,3,2,4,13,3,3,10,11,7,3,10,1,7
V,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8
S,3,5,3,4,2,8,7,7,5,8,5,7,2,9,9,8
F,3,8,5,6,2,3,13,6,3,13,9,4,2,10,2,5
L,3,6,4,6,4,8,6,5,5,6,7,8,3,8,8,11
P,5,8,7,6,6,7,6,6,4,7,6,9,5,8,7,10
C,4,8,5,6,3,5,8,8,8,8,8,14,1,9,4,9
V,8,10,8,8,5,3,12,3,3,10,11,8,3,9,2,6
R,4,7,6,5,6,6,8,3,4,6,6,9,6,10,7,5
U,5,9,4,5,3,7,7,4,5,7,7,8,5,6,3,7
T,6,11,6,8,4,3,13,4,6,12,10,4,1,11,1,4
P,5,6,5,8,3,4,12,9,2,10,6,4,1,10,4,8
E,4,8,6,6,5,8,6,6,3,7,6,10,4,8,8,9
V,6,10,6,8,4,2,11,2,3,10,11,8,2,10,1,8
I,3,10,4,7,2,7,8,0,8,13,6,7,0,8,1,7
S,3,5,3,4,2,8,7,7,5,8,6,7,2,9,9,8
O,5,10,5,8,4,8,7,8,5,10,5,8,3,8,3,8
Q,7,9,8,11,7,8,7,6,3,8,7,11,5,8,8,6
A,6,12,6,6,3,13,1,5,2,12,2,10,2,3,2,10
P,3,9,4,7,3,5,11,9,2,10,6,4,1,10,3,7
H,6,11,6,8,3,7,7,15,0,7,6,8,3,8,0,8
N,4,6,6,5,5,7,7,4,4,6,5,8,6,8,4,6
B,6,10,6,8,5,6,6,9,8,6,6,7,2,8,10,10
W,5,8,8,6,11,7,7,7,2,7,6,8,15,12,4,11
H,6,8,9,6,7,6,9,3,6,10,8,8,3,8,4,6
C,1,3,2,2,1,6,8,7,7,8,7,13,1,9,4,10
O,2,1,2,1,1,8,7,7,6,7,6,8,2,8,3,8
W,6,9,9,8,11,10,7,5,5,7,5,8,10,11,9,4
H,3,3,4,2,2,7,8,6,6,7,6,8,3,8,3,7
T,4,5,5,8,2,9,15,1,6,5,11,9,0,8,0,8
H,4,6,6,4,5,7,8,5,6,7,5,8,3,8,3,8
G,4,5,5,8,2,7,6,8,8,6,5,11,2,8,6,11
Y,7,15,6,8,4,5,8,4,3,10,9,6,4,10,4,4
Z,5,9,7,7,5,6,9,3,9,12,8,6,3,10,8,6
D,4,7,5,5,5,7,8,6,6,8,7,5,3,8,3,7
Z,4,8,5,6,5,8,7,3,8,8,6,7,1,8,10,9
R,2,4,4,3,2,8,7,3,4,9,4,6,3,7,3,9
M,6,8,9,6,7,8,7,2,5,9,6,8,9,6,2,8
J,4,9,4,7,3,8,9,2,3,13,5,5,2,9,7,9
L,4,8,5,6,4,3,4,4,7,2,0,8,0,6,1,6
I,1,5,0,8,0,7,7,4,4,7,6,8,0,8,0,8
R,2,1,2,2,2,6,7,5,5,6,5,6,2,7,4,8
L,5,9,6,7,5,5,6,5,6,6,5,9,8,7,4,10
V,3,7,5,5,3,7,12,2,3,6,11,9,2,11,1,8
J,4,7,5,5,2,8,6,3,7,15,5,10,0,7,1,7
R,6,9,6,5,5,7,7,3,4,7,5,9,5,8,6,7
D,4,8,5,6,4,7,7,8,7,7,6,5,3,8,3,7
X,3,5,4,5,4,6,7,2,5,8,7,10,2,10,7,8
E,5,9,6,7,6,6,7,6,9,8,7,10,3,8,6,8
D,6,10,6,5,3,8,6,5,6,12,4,7,5,7,5,9
L,2,6,3,4,1,0,1,5,6,0,0,6,0,8,0,8
U,6,7,7,5,3,4,9,5,8,11,11,9,3,9,2,7
S,5,9,6,7,3,8,7,4,8,11,7,8,2,10,5,8
Q,6,8,6,9,7,8,9,6,2,6,8,11,3,8,6,8
K,5,9,8,6,6,6,7,1,6,9,6,9,3,8,3,8
F,4,5,4,8,2,1,12,5,5,11,11,8,0,8,2,6
H,3,2,5,3,3,7,7,6,6,7,6,8,3,8,3,8
G,3,7,4,5,2,8,7,8,8,6,6,11,1,8,5,10
N,6,9,8,8,8,7,7,5,4,7,5,8,7,9,5,7
X,4,8,6,6,4,8,8,3,9,5,5,6,3,9,7,8
V,1,0,2,0,0,7,9,3,1,7,11,8,2,11,0,8
G,5,9,4,4,3,7,8,4,3,8,6,7,3,10,9,7
Z,7,15,7,9,5,11,3,4,6,12,3,10,3,7,7,11
H,2,3,4,1,2,7,8,3,5,10,7,7,3,9,2,7
Z,7,11,9,8,7,7,7,2,9,12,6,9,2,9,6,8
F,7,13,6,8,3,6,9,3,6,12,5,5,2,9,6,6
T,5,9,5,4,2,7,8,2,7,11,7,8,2,9,4,5
I,4,10,7,8,8,10,8,1,6,9,5,5,3,8,7,6
X,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8
E,3,4,5,3,2,7,7,2,8,11,7,9,2,8,4,8
C,6,11,7,8,4,6,7,10,9,10,7,11,2,12,4,9
T,5,8,6,6,4,4,12,3,6,12,10,5,1,11,1,5
C,2,2,3,4,2,6,8,7,7,8,8,13,1,9,4,10
M,4,4,6,3,3,10,6,3,4,9,4,7,7,6,2,8
G,4,3,5,5,2,8,5,7,9,7,4,11,1,9,5,10
K,7,9,6,5,3,6,8,3,7,9,8,9,5,8,3,7
K,4,10,5,7,2,3,6,8,3,7,7,11,4,8,2,11
E,2,1,3,2,2,7,7,5,7,7,6,8,2,8,5,10
N,5,9,8,7,5,7,9,2,4,9,5,6,5,9,1,7
Z,2,4,5,3,2,7,7,2,9,12,6,8,1,8,5,8
M,4,3,5,5,3,8,7,12,1,6,9,8,8,6,0,8
I,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
I,1,7,1,5,2,8,7,0,7,7,6,7,0,8,2,7
Y,6,9,6,4,3,6,6,4,4,9,9,6,4,10,3,5
H,4,10,5,7,3,7,7,15,1,7,6,8,3,8,0,8
F,5,9,4,5,2,7,8,2,7,10,6,6,2,10,5,7
W,3,3,4,1,2,5,11,3,2,9,8,7,6,11,1,6
O,4,6,6,6,4,7,5,4,4,8,3,7,3,7,4,8
B,3,4,4,3,3,7,7,5,5,7,6,6,2,8,6,9
X,7,13,8,7,5,8,7,2,8,11,4,7,4,9,4,7
R,4,5,4,8,3,5,11,8,4,7,4,9,3,7,7,11
O,5,9,6,7,5,8,6,8,4,6,5,5,4,8,4,8
I,3,7,4,5,2,8,6,2,7,7,6,7,0,9,4,7
Y,3,7,5,5,4,8,6,6,5,5,8,8,2,8,9,5
V,2,6,4,4,4,8,7,4,2,8,7,8,5,10,4,7
L,3,2,3,3,2,4,4,5,7,2,1,6,1,7,1,6
F,5,9,4,4,2,6,10,3,5,12,6,5,2,8,5,6
O,3,5,4,7,2,7,7,9,6,7,6,8,3,8,4,8
V,2,3,4,4,1,7,8,4,2,7,13,8,3,10,0,8
U,1,0,2,1,0,7,6,10,4,7,12,8,3,10,0,8
H,5,10,6,8,8,8,8,6,6,7,6,6,6,8,3,8
T,5,9,5,7,3,5,12,4,6,12,9,4,2,12,2,4
L,3,3,3,4,1,1,0,6,6,0,1,5,0,8,0,8
D,8,14,7,8,5,7,6,3,7,10,5,7,6,7,8,5
Z,4,6,5,4,4,9,9,5,4,7,5,7,3,8,9,5
B,3,7,5,5,4,9,8,3,5,9,5,6,2,8,5,9
E,5,7,7,5,5,7,8,1,8,11,6,9,3,8,4,9
O,3,3,4,2,2,8,7,7,5,7,6,7,2,8,3,7
F,4,9,4,4,2,6,10,2,5,11,7,5,2,9,5,5
C,2,3,3,1,1,5,8,4,6,11,9,11,1,9,2,8
J,5,8,7,6,5,8,6,9,6,7,7,8,4,5,6,3
C,5,9,6,6,3,5,9,7,8,5,8,14,2,7,5,8
G,3,5,5,4,5,7,8,5,2,7,7,8,6,11,7,8
V,3,4,4,3,1,4,12,3,3,10,11,7,2,11,1,8
J,1,1,1,1,0,12,3,6,4,13,4,11,0,7,0,8
C,6,9,8,7,8,6,6,4,5,7,6,10,6,9,4,8
S,4,8,6,6,3,9,6,4,8,11,4,8,2,8,5,10
T,3,6,4,4,3,6,7,7,6,7,6,7,3,9,5,9
C,6,8,6,6,3,5,7,6,8,11,8,14,2,10,4,7
W,2,3,3,1,2,8,11,2,1,6,9,8,5,11,0,8
B,2,6,3,4,4,6,6,7,5,6,6,7,2,9,7,10
J,5,9,6,7,5,8,7,6,5,8,7,7,3,6,4,6
O,5,9,6,7,4,8,6,8,5,10,5,9,3,8,3,8
O,6,11,5,6,3,5,8,6,4,10,8,9,5,10,4,8
D,2,3,3,1,1,8,7,4,5,10,4,5,2,8,2,8
A,1,3,2,1,1,9,3,1,1,8,3,9,1,6,1,8
D,5,6,7,5,5,6,7,5,7,7,4,7,3,7,5,6
J,7,9,5,13,4,6,9,3,3,12,6,5,3,8,8,8
Y,6,9,8,7,7,9,7,6,5,5,9,7,3,8,9,5
D,4,7,4,5,2,5,6,10,9,5,5,6,3,8,4,8
N,5,6,7,4,3,6,8,3,5,11,8,8,5,8,1,8
Y,2,1,3,2,1,9,10,1,6,5,11,7,1,11,2,8
W,6,9,6,4,3,1,10,3,3,12,11,9,6,11,0,6
C,4,8,5,6,3,5,7,6,9,6,7,13,1,7,4,9
L,2,3,3,2,1,7,4,1,8,8,2,10,0,7,2,8
C,3,5,4,5,4,5,7,3,4,7,6,11,4,9,7,9
A,3,5,5,3,2,8,2,2,2,8,2,8,2,6,3,7
F,3,3,4,4,1,1,15,5,3,12,9,4,0,8,2,6
C,4,6,5,5,5,5,6,3,5,7,6,11,4,10,6,10
P,3,7,3,5,2,3,14,7,2,12,7,3,0,10,4,8
Q,9,14,8,8,5,9,3,4,7,11,4,10,3,7,8,11
E,2,4,4,3,2,7,8,2,7,11,7,8,2,8,4,8
M,4,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8
M,4,8,6,6,7,7,8,6,4,7,5,8,6,9,7,7
G,4,7,5,5,5,8,5,4,3,7,6,10,6,8,4,10
H,4,7,6,5,5,7,8,5,5,7,6,8,6,7,5,9
H,5,8,8,6,5,7,7,3,6,10,6,8,3,8,3,7
I,2,7,3,5,2,7,7,0,7,13,6,8,0,8,1,8
O,4,5,6,7,3,8,7,9,8,7,6,8,3,8,4,8
S,7,12,6,7,3,9,3,4,4,9,2,8,3,6,5,9
Q,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8
B,3,7,3,5,4,7,7,8,5,7,6,7,2,8,7,9
W,6,9,8,7,8,8,4,7,3,7,6,7,8,7,6,3
Z,2,7,3,5,3,7,8,5,8,7,6,10,1,9,7,7
O,4,7,6,5,6,8,10,5,2,7,7,8,7,9,4,9
S,3,5,5,4,2,7,7,3,7,11,6,8,1,9,5,7
Q,1,2,2,2,1,8,7,6,2,6,6,9,2,9,3,10
U,5,10,7,7,7,8,9,7,6,6,8,9,6,7,4,8
H,2,1,2,2,2,7,8,5,5,7,6,8,3,8,2,8
T,5,6,5,4,3,4,11,1,7,11,9,6,1,10,2,5
L,5,11,6,8,4,3,4,3,9,2,0,8,0,7,1,5
C,4,8,6,6,5,5,6,4,5,8,6,11,6,9,4,8
C,5,9,5,7,3,5,8,6,8,12,9,12,2,10,3,7
H,1,0,2,0,0,7,8,11,1,7,5,8,3,8,0,8
X,3,5,6,3,2,9,6,1,8,11,4,8,2,8,3,9
H,6,9,9,7,7,8,6,2,6,10,6,9,3,8,4,7
E,3,7,3,5,2,3,7,6,10,7,6,15,0,8,7,7
A,2,7,4,5,2,12,4,4,3,11,1,8,2,6,2,8
F,4,5,5,8,2,1,14,5,4,12,10,5,0,8,2,5
X,6,9,6,4,3,8,7,2,8,9,6,8,4,9,4,8
B,8,13,6,7,4,9,6,5,5,11,4,9,6,6,7,10
V,5,9,8,6,8,7,7,4,2,8,8,8,5,10,4,8
P,5,9,5,4,3,10,8,4,4,12,4,5,4,10,5,9
D,5,5,6,7,3,5,8,10,9,7,7,5,3,8,4,8
S,3,4,5,3,2,9,6,2,7,10,4,7,1,9,5,10
H,6,7,9,9,8,9,12,3,2,8,7,6,4,11,8,6
H,4,8,5,5,2,7,5,15,1,7,8,8,3,8,0,8
H,7,9,10,7,8,10,6,3,6,10,3,8,5,7,5,9
U,7,10,6,5,4,8,6,4,5,7,7,7,6,7,4,6
R,5,7,6,5,5,9,9,6,3,7,4,6,5,7,8,8
N,2,2,3,3,2,7,8,5,4,7,6,6,5,8,1,6
P,3,4,5,6,6,8,9,5,0,8,6,6,5,9,5,8
C,3,8,4,6,2,6,7,7,10,6,6,14,1,8,4,9
B,3,5,5,4,4,9,6,3,6,11,4,7,4,7,5,9
Z,4,10,6,7,4,8,7,2,10,11,6,8,1,7,6,8
E,5,9,7,8,7,5,8,4,3,8,6,9,5,11,10,9
E,6,9,8,7,5,9,7,2,9,11,4,9,2,8,5,10
W,7,9,7,4,3,6,10,1,3,8,10,7,7,12,1,7
H,4,9,4,7,4,7,7,13,1,7,6,8,3,8,0,8
L,5,10,5,5,3,10,3,3,3,11,6,10,3,10,4,10
O,7,14,5,8,4,5,6,7,4,10,6,9,6,9,5,7
G,5,9,7,8,8,7,6,6,4,8,7,9,10,8,10,7
Z,3,5,5,8,3,11,5,3,5,10,2,7,2,7,5,11
O,4,11,5,8,5,8,7,8,6,7,6,8,3,8,3,8
S,4,9,5,7,3,9,8,6,9,5,6,8,0,8,9,8
J,4,9,6,7,4,8,5,3,5,8,6,8,4,6,4,6
M,5,11,6,8,4,9,7,13,2,6,9,8,8,6,0,8
H,5,11,5,8,5,7,8,14,1,7,5,8,3,8,0,8
T,2,3,3,2,1,7,12,2,7,7,11,8,1,11,1,8
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
P,3,4,4,6,2,3,14,7,2,11,7,3,0,10,4,8
X,2,4,3,3,2,8,7,3,8,6,6,6,2,8,5,8
Y,3,5,5,4,2,4,10,1,8,10,10,5,3,11,5,3
O,5,10,5,8,5,7,7,8,4,9,6,8,3,8,3,8
Z,3,5,5,4,2,7,7,2,10,12,6,8,1,8,6,8
U,5,8,7,7,6,7,7,4,5,6,7,8,7,8,2,7
X,3,4,4,3,2,8,7,3,9,6,6,6,2,8,6,8
R,3,6,5,4,5,7,7,3,4,7,6,8,6,9,6,6
D,3,8,5,6,7,9,8,4,4,7,6,6,4,7,7,6
L,2,7,3,5,2,4,4,6,7,2,2,5,1,6,1,6
P,5,5,7,8,8,7,6,4,3,7,7,7,7,11,5,6
E,3,5,4,4,3,7,8,5,8,7,5,9,2,8,6,9
G,5,10,6,8,8,9,6,5,2,7,6,10,8,8,5,10
X,7,10,10,7,5,5,9,2,9,11,12,9,3,9,4,5
A,2,6,4,4,2,11,3,3,2,10,1,9,2,6,2,8
U,4,9,5,7,4,7,6,11,4,6,13,8,3,9,0,8
X,6,11,9,8,5,10,7,1,8,10,2,7,3,8,4,9
G,3,7,5,5,5,8,5,4,3,7,6,10,6,8,4,10
V,2,2,4,3,2,7,12,2,3,7,11,9,2,10,1,8
T,3,7,4,5,3,7,12,3,7,7,11,8,2,11,1,8
K,5,9,5,5,2,4,8,4,6,9,11,11,5,9,3,6
F,6,10,8,7,6,7,9,3,6,12,7,6,3,9,2,7
I,4,7,5,5,3,7,8,2,7,7,6,9,0,8,4,8
N,8,10,11,7,5,11,7,3,6,11,0,3,6,9,2,8
U,3,6,4,5,4,6,7,5,4,6,5,9,4,8,1,8
O,4,11,5,8,5,8,7,8,4,7,6,5,4,8,3,8
P,5,10,7,8,6,5,10,4,5,11,9,4,1,10,4,7
U,4,7,4,5,1,8,5,13,5,7,14,8,3,9,0,8
T,7,10,7,8,5,7,11,4,7,11,8,4,3,12,3,4
B,8,10,7,5,5,9,7,4,5,9,4,7,6,7,8,9
F,3,8,5,6,4,4,11,2,6,11,9,6,1,10,3,6
L,2,6,2,4,1,0,1,5,6,0,0,6,0,8,0,8
F,2,1,3,3,2,5,11,4,5,11,9,5,1,10,3,6
D,5,11,7,8,5,8,7,9,8,7,6,2,4,8,5,9
T,8,10,8,7,6,6,11,4,6,11,9,5,3,12,2,4
C,3,2,4,3,2,6,8,7,7,9,8,13,1,9,4,10
S,7,9,9,8,9,10,8,4,7,7,7,8,5,9,9,11
U,6,10,6,5,4,8,5,5,5,6,8,7,4,9,3,7
V,8,14,7,8,4,9,10,6,4,7,10,5,6,13,4,7
U,6,9,8,7,10,9,7,4,5,5,8,7,11,9,6,6
M,3,1,4,2,1,7,7,11,1,7,9,8,7,6,0,8
J,5,6,3,9,3,9,7,3,3,11,4,5,3,8,6,9
J,1,1,2,2,0,10,6,3,5,12,4,9,1,7,1,7
X,6,9,9,6,4,5,8,2,8,11,11,9,3,9,4,5
I,1,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8
D,6,11,8,8,7,7,6,7,5,5,5,6,7,9,3,7
B,4,6,6,4,7,8,7,5,2,6,7,8,5,10,9,8
D,3,6,5,6,4,7,7,5,6,7,5,7,4,7,6,5
E,2,1,2,2,1,4,7,5,8,7,6,13,0,8,7,9
P,4,10,6,8,6,7,7,8,4,8,6,7,3,9,7,9
Z,3,5,4,7,2,7,7,4,14,10,6,8,0,8,8,8
G,3,8,5,6,4,6,6,6,5,5,7,10,2,8,4,9
L,5,9,5,5,3,7,5,3,6,11,6,11,2,7,6,8
J,4,11,5,9,3,14,3,5,4,13,2,10,0,7,0,8
W,5,11,8,8,12,10,8,4,2,6,7,8,11,10,4,5
I,6,11,7,8,5,7,8,1,8,7,6,8,0,7,4,8
E,3,2,4,3,3,7,8,5,8,7,6,9,2,8,6,9
I,1,3,1,2,1,7,7,1,7,7,6,8,0,8,2,8
Y,4,5,5,4,2,4,10,2,8,11,10,5,3,10,4,3
C,5,6,5,4,2,5,9,6,8,12,9,10,2,9,3,7
V,3,8,5,6,1,6,8,4,3,8,13,8,3,10,0,8
P,4,8,6,6,5,7,5,6,5,7,6,8,3,8,4,8
R,12,15,9,8,5,9,6,6,5,10,2,8,7,6,6,11
U,2,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8
A,1,0,2,0,0,7,4,2,0,7,2,8,1,7,1,8
M,5,8,7,6,6,4,7,3,4,10,10,10,5,6,2,6
K,6,8,9,6,4,6,8,2,7,11,6,8,3,8,4,7
T,3,9,4,6,3,10,10,1,8,5,11,8,1,10,1,8
Q,4,5,4,6,4,7,8,6,3,9,9,9,3,9,6,7
X,4,5,5,7,2,7,7,5,4,7,6,8,3,8,4,8
J,4,9,6,7,2,10,5,4,7,15,3,9,0,7,0,8
X,2,1,3,1,1,8,7,4,8,6,6,7,2,8,5,8
F,2,6,4,4,3,5,10,3,5,10,9,6,2,10,3,6
S,4,10,5,8,3,8,7,6,9,4,6,8,0,8,9,8
V,8,10,8,8,3,2,11,6,5,13,12,8,3,10,1,8
X,3,4,5,3,2,9,7,2,8,10,3,7,2,8,3,9
M,11,13,11,7,6,11,11,7,3,4,7,9,10,13,3,6
T,7,10,9,8,7,6,7,7,8,7,8,9,4,10,7,7
S,1,0,1,1,0,8,7,4,6,5,6,7,0,8,7,8
C,4,8,5,6,2,5,7,7,10,7,5,12,1,9,4,9
I,4,5,6,5,5,8,8,4,5,7,6,7,4,8,8,9
P,5,9,5,7,5,4,11,9,2,10,6,4,1,10,3,8
D,4,8,6,6,5,7,5,8,5,5,5,5,3,9,3,8
C,5,9,6,6,5,7,8,8,6,6,6,11,4,8,4,8
T,2,3,2,1,1,5,12,2,5,11,9,5,1,10,1,5
D,5,7,6,5,5,7,7,4,6,7,7,9,6,7,3,7
P,5,11,7,8,5,5,12,5,5,11,8,2,1,10,4,6
G,3,7,5,5,6,7,6,4,3,6,5,10,6,8,6,10
J,1,3,3,1,1,8,8,2,5,14,5,8,1,7,0,8
W,4,4,5,3,3,4,11,3,2,9,9,8,6,11,1,7
Y,4,8,6,6,3,7,10,1,7,5,11,8,1,11,2,8
U,1,0,2,0,0,7,6,10,4,7,13,8,2,10,0,8
Y,3,4,5,5,1,6,11,2,2,9,12,8,1,10,0,8
W,4,2,5,4,3,6,10,2,3,7,9,8,7,11,0,8
F,6,12,6,7,4,8,8,3,4,10,5,5,3,9,7,8
U,3,6,3,4,2,8,6,12,4,8,11,8,3,9,0,8
C,3,7,5,6,5,5,8,3,5,7,6,11,3,9,7,9
S,5,7,6,5,3,6,9,3,8,11,5,7,2,6,5,8
W,5,4,6,3,3,5,11,3,2,9,8,7,7,12,2,6
T,2,7,3,5,1,9,14,0,6,5,11,8,0,8,0,8
S,5,10,8,7,9,5,10,3,4,8,7,6,3,8,10,1
W,3,1,3,2,1,8,8,4,0,7,8,8,7,10,0,8
I,3,7,4,5,1,7,9,0,7,14,6,6,0,9,2,7
C,3,10,4,8,3,5,7,6,8,7,5,12,1,8,4,10
Z,2,1,3,3,2,7,8,5,9,6,6,9,1,9,7,8
A,7,13,6,8,4,8,2,3,2,8,4,12,5,5,4,7
S,4,9,6,7,4,10,6,5,6,10,2,7,2,7,4,11
F,6,8,8,6,7,8,7,5,4,7,6,8,5,11,9,11
I,2,9,3,7,2,7,7,0,7,13,6,8,0,8,1,8
G,5,10,7,7,8,7,4,5,3,8,6,11,7,7,10,6
Q,6,7,8,10,8,10,13,5,2,3,8,12,5,15,4,10
O,5,10,7,8,5,7,8,9,6,7,7,8,3,8,4,7
V,2,6,3,4,1,7,9,3,1,7,12,8,2,11,0,8
O,5,11,7,8,5,8,5,9,5,6,6,4,5,7,5,9
S,5,11,6,8,6,8,7,8,5,7,6,7,2,8,9,8
D,4,10,5,8,7,7,7,6,6,7,6,5,6,8,3,7
U,2,3,3,2,1,7,8,6,6,7,9,8,3,10,1,8
T,5,8,7,6,7,7,8,4,6,7,6,9,5,8,5,7
U,4,7,5,5,3,7,9,6,7,5,10,9,3,9,1,8
A,4,9,7,7,5,6,5,2,3,4,1,6,5,7,5,4
S,4,10,5,8,5,8,8,5,9,5,5,6,1,6,9,6
J,5,10,7,8,3,8,8,1,8,14,4,6,1,9,1,8
W,4,8,5,6,3,4,8,5,1,7,8,8,8,10,0,8
M,4,7,7,5,8,7,7,3,2,7,5,8,8,5,2,7
E,6,11,4,6,2,6,8,4,6,10,6,9,2,9,8,9
Y,4,5,5,3,2,4,10,2,8,11,10,5,1,11,3,4
X,4,6,6,4,3,10,7,1,8,10,2,6,3,8,3,9
X,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8
K,3,9,4,6,2,3,7,7,2,7,5,11,3,8,2,10
U,7,10,6,5,3,6,4,5,5,4,8,8,5,10,2,7
D,1,3,3,2,2,8,7,4,5,9,4,6,2,8,2,8
M,3,7,4,5,3,7,7,11,1,7,9,8,8,5,0,8
T,3,3,3,2,1,5,13,4,5,11,8,4,2,11,1,5
K,5,6,7,4,4,4,7,2,7,10,9,11,3,8,3,6
O,4,6,6,4,3,7,5,7,5,8,4,10,3,7,3,8
P,2,3,3,2,1,5,11,3,4,10,8,3,0,9,3,6
Q,7,8,7,10,6,7,8,6,4,8,9,10,4,8,8,8
Y,3,8,6,6,3,9,10,1,6,4,11,8,1,11,2,9
S,4,5,5,7,3,8,8,6,9,5,6,7,0,8,9,8
X,4,7,6,5,3,5,8,1,8,10,9,9,2,9,3,6
M,4,4,6,3,4,5,7,3,4,10,10,10,7,7,2,8
C,3,2,4,4,2,6,8,8,8,9,8,12,2,10,4,9
J,1,4,2,2,1,8,7,2,5,14,6,9,1,7,0,7
L,5,11,6,8,2,0,0,7,6,0,1,4,0,8,0,8
F,4,8,6,6,5,7,8,6,4,8,6,8,3,10,8,10
X,3,5,5,3,3,8,7,3,10,6,6,8,4,7,7,9
E,2,6,3,4,2,3,8,5,8,7,6,13,0,8,6,10
D,3,8,3,6,2,5,7,10,8,7,6,5,3,8,4,8
W,4,6,6,4,5,7,7,6,2,8,8,8,6,7,4,8
L,2,4,3,6,1,0,1,5,6,0,0,7,0,8,0,8
U,4,4,5,3,2,5,8,5,8,10,9,9,3,9,2,6
H,4,7,6,5,4,8,8,3,6,10,5,7,3,8,3,7
K,2,3,3,1,1,4,8,2,6,10,10,10,2,8,2,6
W,9,10,9,5,3,3,10,2,2,10,11,8,8,12,0,7
W,3,1,5,2,2,8,11,3,2,6,9,8,6,11,1,7
R,4,7,4,4,2,5,11,8,3,7,4,8,3,7,6,11
Q,4,6,5,8,3,8,7,8,6,6,8,8,3,7,6,10
S,4,9,5,7,3,8,8,6,9,5,6,6,0,8,9,7
U,9,10,9,8,5,4,7,6,9,9,8,9,6,10,4,2
O,5,11,6,8,6,8,7,9,4,7,6,7,3,8,3,8
A,3,7,5,5,4,8,5,1,3,7,2,6,2,6,4,5
N,6,11,6,8,6,7,7,13,1,6,6,8,5,8,0,8
D,5,9,7,7,5,7,6,8,5,6,5,4,5,8,5,9
F,5,11,6,8,2,1,15,5,3,12,9,4,0,8,2,5
T,4,6,4,4,2,6,12,3,6,11,9,4,2,11,2,5
H,6,10,6,8,6,7,6,13,2,7,8,8,3,9,0,8
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
O,2,5,3,4,2,8,7,7,4,9,6,8,2,8,2,8
Y,6,8,9,12,12,7,9,3,3,7,8,9,7,13,12,5
C,10,15,6,8,3,7,8,6,7,12,6,7,2,9,5,8
N,3,3,5,2,2,6,10,3,4,10,8,7,5,8,0,8
Z,6,7,5,11,4,9,6,4,3,11,6,8,3,9,11,7
P,4,6,6,4,3,6,13,7,2,11,5,2,1,10,4,7
V,9,13,8,7,4,6,10,4,4,8,8,5,5,11,3,6
M,4,10,5,8,6,7,6,10,1,7,8,8,7,5,0,8
R,4,6,6,4,5,7,7,3,4,6,6,8,6,9,6,6
B,6,9,8,6,8,7,9,6,5,7,5,6,4,7,6,8
Z,5,8,7,6,4,7,8,2,10,12,7,9,1,9,6,8
Y,4,9,4,4,2,7,7,4,4,9,9,5,3,11,3,4
X,4,6,6,4,3,10,7,1,8,10,2,6,3,8,3,10
L,2,6,4,4,2,4,5,1,8,6,2,10,0,7,2,7
L,3,8,4,6,2,0,2,4,6,1,0,7,0,8,0,8
X,3,10,6,7,6,8,7,2,6,7,6,8,3,8,6,8
S,4,10,6,8,7,7,6,3,2,7,5,6,3,7,14,4
S,4,5,6,5,6,8,7,5,5,7,6,7,5,8,9,11
O,4,8,5,6,4,7,8,8,4,7,7,8,3,8,3,8
E,4,7,6,5,5,6,8,3,7,11,8,9,3,8,4,7
Y,1,1,2,1,0,8,9,2,2,7,12,8,1,10,0,8
O,9,14,6,8,4,4,9,6,5,10,8,9,5,10,5,7
G,7,10,6,6,4,8,7,4,3,9,5,6,4,9,8,8
A,3,4,5,3,2,8,2,2,2,8,2,8,2,7,3,7
D,4,9,5,6,4,8,7,6,6,10,5,5,3,8,3,8
Y,4,10,6,7,3,7,10,1,8,6,12,8,1,11,2,8
T,2,6,4,4,2,6,12,3,7,8,11,8,1,11,1,7
V,3,5,4,3,2,4,12,2,3,9,11,7,3,10,1,7
G,4,7,5,5,2,7,6,7,9,6,5,10,1,8,6,11
N,5,9,5,7,3,7,7,15,2,4,6,8,6,8,0,8
G,6,9,8,7,10,8,7,4,3,6,5,9,8,7,9,13
E,6,11,8,8,7,10,6,1,8,11,4,8,4,7,5,11
D,4,5,5,4,4,6,7,5,7,6,4,7,3,8,5,6
Q,2,1,3,2,1,8,7,7,5,6,6,8,3,8,4,8
C,5,11,6,8,5,7,8,8,6,5,6,12,4,7,4,7
W,4,10,6,8,8,7,7,6,3,8,8,7,6,8,4,9
O,4,6,6,5,4,7,6,4,4,7,3,7,3,7,4,8
I,3,11,5,8,6,10,6,1,4,9,4,4,3,8,5,8
W,3,7,5,5,5,7,7,6,2,7,8,8,5,8,3,8
P,4,7,6,5,4,6,12,5,3,12,5,1,1,10,3,8
E,2,7,3,5,2,3,7,6,10,7,6,14,0,8,7,8
V,4,7,4,5,3,3,11,2,2,9,11,8,2,11,1,8
D,8,13,8,7,4,9,5,4,6,11,3,7,5,6,6,11
N,2,2,3,3,2,7,8,5,4,7,7,6,5,9,2,5
H,5,7,7,5,5,6,7,6,7,7,6,8,6,8,3,8
W,4,9,6,7,3,8,8,4,1,6,8,8,8,10,0,8
A,5,12,5,6,3,12,2,4,2,11,3,11,4,4,4,11
N,7,13,7,7,3,6,9,4,5,4,5,11,6,11,2,7
A,2,1,4,2,1,8,2,2,1,7,2,8,2,6,3,6
Z,4,8,5,6,5,8,11,6,5,6,5,7,3,9,6,4
N,5,7,8,5,3,7,8,3,5,10,6,7,6,8,1,7
Z,2,4,4,3,2,8,6,2,9,11,5,9,1,8,5,9
V,3,9,5,7,1,7,8,4,2,7,14,8,3,9,0,8
B,8,13,8,7,7,8,8,4,5,9,5,7,8,4,9,7
M,3,4,4,3,3,7,6,6,4,7,7,10,6,5,1,9
R,6,11,9,8,6,9,9,4,7,8,3,8,3,6,5,11
B,6,11,4,6,3,10,5,6,5,11,3,9,6,7,6,10
E,1,3,3,1,1,6,7,2,6,10,7,10,1,8,4,9
K,2,1,3,2,2,7,7,4,6,6,6,9,3,8,5,10
E,2,3,3,5,2,3,8,6,10,7,6,14,0,8,7,8
X,4,9,7,6,4,7,7,4,10,6,6,8,3,8,7,8
Z,3,7,5,5,2,9,5,3,9,11,4,9,2,7,6,9
A,3,7,5,4,2,8,3,3,3,7,2,8,3,6,3,8
F,5,7,6,8,7,6,11,4,4,8,7,6,4,9,7,7
O,4,8,5,6,4,8,7,8,6,7,7,10,3,7,4,7
M,5,9,6,5,3,14,2,4,3,12,1,9,5,4,0,9
O,3,5,4,3,3,8,7,7,5,7,6,8,2,8,3,8
K,4,8,6,6,5,5,6,4,7,6,6,10,3,8,5,10
Q,3,5,4,6,4,9,8,7,2,4,7,11,3,9,6,10
I,5,7,6,8,6,7,9,4,5,8,7,7,4,8,9,10
U,4,5,4,4,2,4,8,5,7,10,9,9,3,9,2,6
G,5,10,6,7,3,7,5,8,9,6,5,10,1,8,6,11
X,1,0,2,1,0,7,7,4,5,7,6,8,2,8,4,8
A,2,5,3,3,2,11,3,2,2,9,2,9,2,6,2,8
T,1,6,2,4,1,7,13,0,4,7,10,8,0,8,0,8
J,2,6,4,4,3,9,7,2,3,8,4,6,4,8,6,5
A,3,5,5,4,2,10,2,2,2,8,2,9,3,5,2,8
P,1,0,1,0,0,5,11,6,1,9,6,5,0,9,3,8
K,4,8,4,6,2,3,6,9,2,7,7,11,3,8,2,11
Y,2,8,3,6,1,9,10,1,3,6,11,8,1,11,0,8
W,5,11,8,8,4,8,8,5,2,7,8,8,9,9,0,8
K,4,7,6,5,5,5,7,4,7,6,6,11,3,8,5,9
W,4,2,6,4,4,8,11,3,2,6,9,8,8,12,1,7
M,11,11,11,6,5,6,10,5,5,4,4,11,10,12,2,7
W,8,10,11,9,14,7,7,5,5,6,5,8,11,9,10,9
F,8,11,6,6,3,9,6,3,8,11,4,6,2,10,5,9
K,4,7,5,5,5,6,8,5,3,7,5,8,4,6,7,10
T,4,8,5,6,4,6,11,3,7,8,11,8,2,12,1,7
U,3,7,5,5,4,8,7,7,5,6,7,9,5,8,3,7
U,3,8,4,6,3,8,6,12,4,7,11,8,3,9,0,8
I,1,6,2,4,1,7,7,0,7,14,6,8,0,8,1,8
D,5,8,5,6,5,6,7,9,8,6,5,6,2,8,3,7
L,1,0,1,1,0,2,2,5,5,1,1,6,0,8,0,8
A,4,9,6,7,4,12,2,3,2,10,2,9,2,7,3,8
I,3,10,4,7,2,7,7,0,8,14,6,8,0,8,1,8
V,4,6,4,4,2,4,12,1,2,9,10,7,2,11,0,8
Z,4,8,5,6,5,8,10,5,4,6,5,6,3,8,8,3
H,4,9,4,6,2,7,8,15,1,7,5,8,3,8,0,8
C,2,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10
Y,1,0,2,0,0,7,9,3,1,7,12,8,1,11,0,8
H,6,10,6,6,3,7,8,3,5,9,5,7,6,7,5,8
F,1,3,3,2,1,5,11,3,4,12,7,5,1,9,1,7
A,3,2,5,4,2,10,2,2,2,9,1,8,2,6,3,8
B,5,7,6,5,6,8,7,4,4,7,5,7,6,8,5,8
T,7,9,7,7,4,5,10,1,8,11,9,6,0,9,2,4
G,10,14,8,8,4,11,4,4,5,11,3,8,5,7,5,10
E,6,9,8,7,6,5,8,3,8,11,9,9,3,9,4,6
Y,1,0,2,0,0,7,10,1,3,7,12,8,1,11,0,8
X,9,15,10,9,6,8,8,2,7,11,6,6,5,12,4,8
Y,3,4,5,6,6,7,8,4,1,6,8,9,4,11,7,7
V,3,4,5,6,1,9,8,4,3,6,14,8,3,10,0,8
M,3,8,4,6,3,7,6,11,1,7,9,8,8,6,0,8
X,3,8,5,6,3,7,7,4,9,6,6,8,3,8,6,8
H,4,10,6,7,10,8,8,5,2,6,6,7,8,9,10,8
S,5,5,6,8,4,8,9,6,10,5,6,6,0,7,9,7
Y,3,6,4,4,2,10,11,2,2,5,12,8,1,11,0,8
R,4,8,6,6,7,6,7,3,4,6,6,9,7,10,7,5
G,4,7,5,5,3,6,7,6,7,10,7,11,2,9,4,9
B,3,2,4,4,4,7,7,5,6,7,6,6,2,8,6,10
L,3,10,3,8,1,0,1,5,6,0,0,7,0,8,0,8
T,4,11,5,8,3,7,14,0,5,7,10,8,0,8,0,8
S,6,12,5,6,2,10,2,1,5,9,1,8,2,8,4,11
I,1,3,1,2,1,8,7,1,7,7,6,7,0,8,2,7
R,4,6,6,4,3,10,8,3,7,11,1,6,3,7,3,10
E,3,9,4,7,4,7,7,7,9,8,8,10,3,8,6,7
O,7,13,5,7,4,6,6,6,3,10,7,9,5,9,5,8
Y,7,11,7,8,5,4,9,1,7,9,10,6,2,12,4,3
X,4,4,6,3,3,5,8,2,9,11,10,9,3,7,3,6
O,4,8,6,6,5,7,7,8,4,7,5,8,3,8,3,8
V,3,7,5,5,5,8,5,4,1,7,7,8,5,9,3,7
H,3,7,4,5,2,7,8,14,1,7,6,8,3,8,0,8
C,8,12,5,6,2,7,8,7,7,11,7,9,2,9,5,9
P,7,10,6,5,3,5,11,5,2,12,5,4,4,9,4,8
R,7,11,10,8,7,11,6,3,6,11,2,6,6,7,5,10
A,2,1,3,1,0,7,4,2,0,7,2,8,2,7,1,8
P,2,6,2,4,2,3,13,5,1,11,7,4,0,9,2,7
R,3,8,3,6,2,6,10,9,4,7,4,8,3,7,5,10
Y,3,3,4,2,1,4,10,2,7,10,10,5,2,12,3,4
T,3,8,4,6,1,8,15,1,6,6,11,9,0,8,0,8
Q,5,10,7,9,3,8,7,8,7,6,7,9,3,8,5,9
V,3,2,5,3,2,7,12,2,3,7,11,9,2,10,1,8
J,6,10,9,8,6,9,5,6,5,8,6,7,3,7,4,6
N,5,9,7,7,4,8,7,3,5,10,5,6,6,8,1,7
M,4,5,6,4,4,8,6,2,4,9,6,8,7,5,2,8
V,7,9,7,7,3,3,11,5,5,12,12,7,3,10,1,8
W,4,4,6,6,3,4,8,5,1,7,8,8,8,9,0,8
E,1,0,1,1,0,5,7,5,7,7,6,12,0,8,6,10
M,4,2,5,3,4,8,6,6,4,7,7,9,9,5,2,8
S,3,5,3,4,2,8,7,7,5,8,5,7,2,8,9,8
H,5,8,8,6,5,5,9,4,6,10,10,9,4,9,4,6
J,4,9,6,7,3,7,7,3,6,15,6,11,1,6,1,7
T,6,9,6,7,2,5,13,3,9,12,9,3,0,10,2,4
J,3,6,4,4,2,9,4,5,4,14,7,13,1,6,0,7
B,6,9,8,7,6,9,7,3,7,11,4,6,2,8,6,10
J,2,8,3,6,1,11,5,2,9,12,2,8,0,7,1,8
S,2,4,4,3,2,9,6,2,7,11,5,8,1,9,4,9
K,3,9,4,6,3,4,7,7,3,7,6,12,3,8,3,11
L,4,9,6,6,3,7,3,2,8,7,2,8,1,6,2,7
N,4,4,4,6,2,7,7,14,2,4,6,8,6,8,0,8
F,5,7,7,8,7,7,8,4,6,7,6,6,4,9,9,9
N,6,10,8,8,6,7,9,5,5,7,6,6,7,7,3,7
V,1,0,2,1,0,8,9,3,2,7,12,8,2,10,0,8
U,3,2,4,4,2,8,8,6,7,5,9,8,3,9,1,8
P,5,7,6,10,11,8,6,6,1,7,6,7,9,8,6,8
O,3,7,4,5,3,6,8,7,5,9,8,8,3,8,3,8
I,4,10,5,7,3,7,7,0,7,13,6,8,0,8,1,8
J,3,7,5,5,2,9,5,5,5,15,6,11,0,6,1,7
M,3,7,4,5,3,7,7,11,1,7,9,8,8,5,0,8
P,8,9,7,4,3,6,11,6,2,10,4,5,4,9,4,8
Q,5,10,6,9,6,8,8,7,4,5,10,9,3,8,5,9
X,6,9,9,7,5,4,8,2,8,10,12,11,3,8,3,5
Z,3,8,4,6,3,9,6,5,10,7,5,6,1,7,8,8
M,1,0,2,0,1,7,6,9,0,7,8,8,6,6,0,8
G,5,11,6,8,7,7,8,6,2,6,6,10,4,8,7,7
T,4,5,5,7,2,5,15,1,6,9,11,7,0,8,0,8
I,6,12,4,7,3,11,3,4,5,12,2,8,3,8,4,10
I,4,10,5,8,4,6,8,0,7,13,7,8,0,8,1,7
L,5,10,5,5,3,5,7,3,7,12,8,11,2,8,6,7
J,2,6,4,4,3,9,7,3,3,8,5,6,4,8,6,5
J,4,9,6,7,6,9,9,4,3,8,4,6,4,8,5,4
P,3,3,4,5,2,4,11,9,2,10,6,4,1,10,4,8
M,5,5,7,4,4,7,6,6,6,6,7,7,9,7,3,6
I,3,11,4,8,3,9,7,0,7,13,5,8,0,8,1,8
U,3,1,4,3,2,6,9,5,7,7,10,9,3,9,1,8
B,2,3,4,2,2,8,8,3,5,10,5,6,3,7,5,8
U,5,10,6,8,6,9,6,6,6,7,6,8,9,8,4,7
F,3,8,3,5,1,1,13,4,4,12,11,6,0,8,2,6
F,5,9,5,5,4,5,12,3,3,11,7,4,4,10,7,5
L,5,10,7,8,4,7,3,1,9,9,2,11,0,7,3,9
Q,6,10,8,9,4,8,6,8,8,6,5,8,3,8,5,8
X,2,0,2,1,0,7,7,6,2,7,6,8,3,8,3,8
P,2,3,3,2,2,5,9,4,4,9,7,4,1,10,3,7
Z,2,1,2,2,1,7,7,5,9,6,6,8,1,8,6,8
B,7,10,9,8,6,11,5,3,7,11,3,7,5,7,7,12
E,6,9,8,8,8,6,9,4,4,8,7,10,5,11,8,10
E,3,2,3,3,3,7,7,5,7,7,5,9,2,8,5,10
F,6,9,8,6,5,6,11,3,5,13,7,4,2,10,2,7
V,3,6,5,4,1,7,8,4,2,7,13,8,3,10,0,8
M,4,11,5,8,4,7,7,12,2,7,9,8,8,6,0,8
F,4,10,7,8,8,9,6,1,5,9,6,6,6,11,5,6
K,4,10,6,8,6,7,6,5,4,6,6,8,4,6,10,11
X,1,1,2,1,1,7,7,3,7,6,6,8,2,8,5,8
Y,5,8,5,6,2,4,10,1,7,10,10,6,1,11,3,3
L,4,8,5,6,2,5,3,1,9,6,1,10,0,7,2,7
T,5,8,5,6,3,5,11,2,8,12,10,5,1,10,2,4
Y,9,13,8,7,4,6,7,5,4,10,9,5,4,10,4,5
S,8,15,6,9,3,11,3,4,5,11,2,9,4,6,5,11
G,3,8,4,6,2,8,5,8,9,8,4,12,1,9,5,10
B,6,10,8,7,6,10,7,3,7,11,3,6,2,8,5,11
H,4,5,5,4,4,8,7,6,6,7,6,8,3,8,4,7
J,3,6,5,4,2,7,6,3,6,15,6,11,1,6,0,8
V,4,5,7,8,2,7,8,4,3,7,14,8,3,9,0,8
V,3,6,5,4,1,8,8,4,2,6,14,8,3,9,0,8
Y,4,7,6,5,3,5,9,1,7,8,12,9,1,11,2,7
Z,3,9,5,7,5,8,6,2,7,8,6,8,1,7,11,10
Q,2,4,3,5,3,8,7,6,2,8,7,9,2,8,4,9
F,7,11,6,6,3,7,8,3,6,12,5,6,2,8,6,6
Y,3,7,5,4,1,7,10,3,2,7,13,8,2,11,0,8
T,5,6,7,6,6,7,9,5,8,7,7,7,3,11,7,7
E,4,9,5,7,6,6,7,5,8,7,6,10,3,8,6,9
Q,3,5,4,6,4,8,7,7,3,8,6,9,2,9,3,8
Z,3,5,6,4,3,8,7,2,10,12,6,9,1,8,6,8
U,12,14,10,8,4,6,5,4,7,3,9,6,6,7,2,7
X,2,3,4,1,2,7,7,4,9,7,6,8,2,8,6,7
D,8,14,7,8,5,6,7,5,6,9,6,6,5,8,7,4
I,5,10,4,6,2,9,7,3,6,13,4,5,2,9,4,10
C,4,9,5,6,3,4,8,6,6,11,10,12,2,10,3,7
N,6,9,5,5,2,4,8,4,6,4,4,11,5,9,2,7
R,5,9,8,6,9,8,7,4,4,6,7,8,7,9,8,6
U,4,5,5,4,2,4,8,5,8,10,9,9,3,9,2,6
X,3,6,5,4,4,8,6,2,5,6,6,7,2,9,8,9
L,7,11,9,9,7,9,7,8,6,6,6,7,6,7,9,15
B,7,11,9,8,9,8,8,4,6,7,6,7,7,7,7,9
F,3,10,4,8,3,1,12,3,4,12,11,8,0,8,2,6
G,5,11,6,8,4,6,7,7,7,10,7,11,2,9,4,9
K,4,5,6,5,5,9,5,2,4,8,4,8,4,6,6,11
J,3,9,5,7,3,5,7,3,5,15,8,11,1,6,1,7
X,3,8,5,6,4,8,7,3,8,5,6,8,2,8,6,8
R,1,0,1,0,0,6,8,7,3,7,5,7,2,7,4,11
R,2,3,3,2,2,6,8,4,5,7,5,7,2,7,4,9
R,3,8,5,6,5,8,8,7,4,8,5,7,4,7,6,11
R,5,9,7,6,6,6,8,5,6,7,5,8,3,7,5,9
J,1,3,2,2,1,8,6,3,4,14,6,10,1,7,0,7
Z,4,7,4,5,3,7,7,6,11,6,6,8,1,8,8,8
A,4,9,7,6,4,8,3,1,2,5,2,8,3,5,3,7
V,2,1,3,2,1,9,12,2,2,5,10,9,2,11,0,8
G,6,9,6,7,5,5,6,6,5,9,8,11,2,9,4,10
K,7,12,7,7,4,11,6,4,6,11,2,6,5,8,4,10
I,3,6,4,4,2,7,7,0,7,13,6,8,0,8,1,8
N,6,9,8,7,9,7,7,3,5,8,6,7,6,9,7,5
X,5,8,7,6,3,8,7,2,8,10,4,7,3,8,4,8
N,4,4,4,6,2,7,7,14,2,4,6,8,6,8,0,8
E,5,10,7,8,6,7,7,6,9,7,6,9,6,8,6,9
J,3,7,5,5,2,8,5,5,5,15,7,12,1,6,1,6
W,5,11,7,8,4,7,7,5,2,7,8,8,9,9,0,8
Z,4,8,6,6,3,7,7,2,10,12,6,7,1,7,6,7
D,7,12,7,6,4,12,2,4,5,12,1,9,4,7,2,11
J,4,5,6,5,5,8,8,4,6,7,6,8,3,9,8,8
D,5,8,7,6,5,10,5,2,7,11,3,7,4,7,3,9
J,4,11,6,9,5,7,7,1,6,11,5,9,1,6,2,5
Z,1,0,1,1,0,7,7,2,10,9,6,8,0,8,6,8
W,6,10,8,8,4,4,8,5,2,7,9,8,9,9,0,8
B,5,11,5,8,7,6,7,8,5,6,6,7,2,8,8,9
S,4,5,4,4,3,8,8,7,5,7,6,7,2,9,9,8
L,4,9,4,7,1,0,0,6,6,0,1,5,0,8,0,8
C,5,11,6,8,3,4,7,7,11,7,7,12,1,7,4,8
E,4,6,5,5,5,6,8,4,3,8,7,9,4,10,8,11
J,5,8,3,12,3,9,6,2,4,9,5,6,3,9,5,10
E,5,9,8,7,5,10,6,2,8,11,4,9,3,8,5,11
M,7,11,9,8,11,7,8,6,4,7,6,8,8,10,9,11
W,7,10,7,8,7,4,11,2,2,9,8,7,7,12,2,6
W,6,8,8,7,10,7,7,5,5,6,5,8,10,8,8,10
X,5,10,7,8,5,8,7,3,9,5,6,9,5,7,9,8
W,4,4,5,3,3,4,11,3,2,9,9,7,6,12,1,6
J,2,7,2,5,1,15,3,3,5,12,1,8,0,8,0,8
D,2,3,4,2,2,9,6,4,6,10,4,6,2,8,3,8
R,3,7,4,5,4,8,8,7,2,7,4,6,4,6,8,8
H,4,7,6,5,5,7,9,7,5,8,5,7,3,7,5,10
E,2,1,3,3,2,7,7,5,7,7,6,9,2,8,5,10
C,4,7,5,5,2,4,9,6,8,12,10,11,1,9,3,7
T,2,3,3,1,1,7,11,2,6,7,11,8,1,10,1,7
O,4,7,5,5,3,7,6,8,5,7,5,8,3,8,3,8
M,2,1,3,2,1,7,6,10,1,7,9,8,7,6,0,8
F,3,11,4,8,2,1,13,5,4,12,10,7,0,8,3,6
L,3,9,5,7,3,5,5,2,8,6,1,10,0,6,3,7
S,4,6,5,4,5,8,9,4,4,8,5,6,3,9,9,7
Y,1,0,2,0,0,8,10,3,1,6,12,8,1,11,0,8
R,4,5,5,7,3,5,10,9,4,7,4,8,3,7,6,11
F,2,4,3,2,1,4,11,4,4,13,8,5,1,9,1,7
Z,5,11,6,8,5,6,8,6,11,7,7,10,1,9,8,8
Y,3,2,5,4,2,6,10,1,8,8,12,8,1,11,3,7
J,5,9,7,7,4,6,7,3,6,15,7,11,1,6,2,6
D,6,11,9,8,8,8,8,5,6,10,6,5,4,8,4,9
T,2,1,3,2,1,8,12,3,6,7,11,8,2,11,1,8
M,3,5,4,4,4,8,6,6,4,7,7,8,7,5,2,7
Q,3,5,4,8,2,8,7,9,5,6,7,9,3,8,5,9
O,1,0,2,0,0,7,7,6,4,7,6,8,2,8,3,8
B,4,11,5,8,4,6,8,10,7,7,5,7,2,8,9,9
D,2,4,4,3,2,9,6,4,6,10,4,6,2,8,3,8
U,4,9,5,6,4,7,6,12,4,6,12,8,3,9,0,8
Z,3,9,4,7,5,9,9,3,7,7,7,5,0,9,12,9
C,4,7,4,5,3,5,8,5,6,11,9,13,2,9,3,7
N,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
F,6,11,8,8,8,6,10,6,5,9,6,9,3,10,8,10
J,5,10,6,8,3,6,9,3,6,15,7,8,2,8,2,8
C,5,11,6,8,3,5,8,8,8,8,8,14,2,9,4,9
P,2,3,2,2,1,5,9,3,4,9,8,5,1,9,2,7
L,7,10,9,7,7,6,6,9,5,6,6,9,3,7,6,13
I,4,5,6,5,4,8,9,4,6,7,7,8,3,8,8,8
M,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8
S,4,6,5,4,3,7,8,3,7,10,4,7,2,6,4,8
W,6,7,6,5,6,7,10,4,2,8,6,6,8,12,4,5
Z,5,6,4,8,3,8,6,5,5,10,6,7,3,9,9,9
C,1,3,2,2,1,6,8,6,7,7,8,12,1,9,4,10
J,5,7,7,8,6,8,9,4,5,7,6,7,3,8,9,8
F,2,4,3,3,2,7,9,2,5,13,6,6,2,11,2,7
S,4,7,6,5,3,9,7,4,8,11,3,8,2,8,5,11
Y,3,5,4,7,6,8,8,3,1,7,7,9,4,11,6,7
T,3,4,4,2,1,5,13,3,6,12,9,3,1,10,1,5
T,4,9,4,4,1,6,10,2,7,12,7,5,1,10,3,5
N,4,2,5,4,3,7,9,5,5,7,6,6,7,8,3,7
Y,5,6,6,8,9,9,8,6,3,7,7,8,6,10,7,4
C,3,3,4,2,1,4,8,4,7,10,9,12,1,9,2,7
G,6,9,5,5,4,8,4,5,2,9,6,9,4,8,7,8
F,8,15,7,8,5,8,10,3,5,12,5,4,4,9,7,7
A,3,7,4,5,3,9,4,3,1,8,1,8,2,6,1,8
Q,3,4,4,5,3,8,7,5,3,8,8,9,3,8,4,9
U,4,7,6,5,7,7,7,4,4,6,7,8,10,9,5,8
G,3,4,4,3,2,6,7,5,5,9,7,9,2,9,4,9
Q,3,4,4,6,2,8,7,8,6,6,7,8,3,8,5,9
K,5,9,6,5,3,3,9,3,6,9,8,11,4,7,3,6
C,1,3,2,2,1,6,7,7,6,8,7,13,1,9,3,10
O,4,5,5,8,3,8,8,9,7,6,7,9,3,8,4,8
C,5,11,6,9,4,6,8,9,7,10,7,11,2,11,4,9
W,4,6,6,4,3,9,8,4,1,7,8,8,8,9,0,8
Z,4,10,5,7,5,7,8,5,9,7,6,9,1,9,7,7
U,3,1,4,3,2,6,8,6,7,7,9,9,3,9,1,8
R,3,7,4,5,4,6,9,8,3,7,5,8,2,7,5,11
C,3,2,4,4,2,6,8,8,8,9,8,12,2,10,4,10
C,2,1,3,2,1,6,8,7,8,8,8,13,1,9,4,10
P,6,11,6,8,3,4,13,9,2,10,6,4,1,10,4,8
N,4,6,7,4,6,7,8,3,4,8,7,7,6,9,5,4
W,3,3,4,2,2,4,11,3,2,9,9,7,6,11,1,7
U,6,8,7,6,5,4,8,5,7,9,8,10,5,8,3,4
K,6,9,9,7,10,7,7,4,4,6,6,9,8,8,8,8
D,4,8,5,6,4,7,7,5,5,7,6,7,4,8,3,7
K,1,0,1,0,0,4,6,6,1,7,6,10,3,7,1,10
M,5,6,8,4,5,7,6,2,5,9,7,8,8,6,2,8
I,3,11,4,9,2,6,8,0,7,13,7,8,0,8,1,7
W,4,8,6,6,5,8,8,4,1,6,9,8,7,11,0,8
Z,3,5,3,3,2,8,7,5,10,6,6,7,2,8,7,8
B,3,7,3,5,4,6,7,8,5,7,6,7,2,8,7,10
G,9,14,7,8,5,8,7,5,4,9,5,6,4,9,8,8
Z,5,5,7,8,3,7,7,4,15,9,6,8,0,8,9,8
F,4,11,5,8,2,1,11,5,7,12,12,10,0,8,2,6
T,2,5,3,4,2,7,12,3,6,7,11,8,2,11,1,8
G,4,3,5,4,3,6,6,5,6,6,6,8,2,8,4,8
J,3,7,4,5,2,9,6,1,6,13,4,8,0,7,0,8
A,8,12,8,6,4,11,0,4,3,12,6,14,4,5,5,11
S,3,10,4,8,5,8,8,8,5,7,5,8,2,8,8,8
N,5,6,7,4,3,7,8,3,5,10,6,7,5,8,1,7
J,2,9,3,6,3,11,5,2,6,11,2,7,0,7,1,7
K,6,11,9,8,8,3,8,1,6,9,9,11,3,8,3,6
U,4,6,6,6,5,7,6,5,4,6,6,8,4,8,1,7
S,4,7,6,5,7,6,6,3,2,8,6,7,3,7,11,2
Y,4,7,6,10,10,7,5,4,3,8,8,9,9,10,8,8
E,2,1,2,1,2,7,7,5,6,7,6,8,2,8,5,10
O,5,11,5,8,5,8,7,8,4,9,6,6,4,9,4,8
V,3,9,5,7,3,7,11,2,3,6,11,9,2,10,1,8
W,7,10,9,7,6,4,8,5,1,7,9,8,8,10,0,8
A,3,7,5,5,3,10,2,2,2,9,2,8,2,6,4,8
I,2,6,4,4,2,7,7,0,8,13,6,8,0,8,1,7
Z,2,2,2,3,2,7,8,5,9,6,6,9,1,9,7,8
O,4,7,4,5,3,8,6,6,3,10,5,10,3,8,2,8
P,4,9,6,7,4,6,10,5,6,10,8,3,1,10,4,7
X,2,1,4,2,2,7,7,3,9,6,6,8,2,8,5,8
M,5,10,8,7,10,12,4,3,1,9,4,9,10,6,3,8
R,1,1,2,1,1,6,9,7,3,7,5,8,2,7,4,11
M,4,7,7,5,9,8,7,3,3,8,4,7,10,5,2,6
Z,2,5,3,3,3,8,7,5,9,6,6,7,2,8,7,8
N,5,5,5,7,3,7,7,15,2,4,6,8,6,8,0,8
M,5,10,6,8,4,8,7,13,2,6,9,8,8,6,0,8
L,3,6,4,4,2,5,3,5,8,2,2,4,1,6,1,5
M,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8
N,4,8,6,6,4,8,6,9,5,4,4,5,4,6,3,8
W,9,10,8,7,6,5,10,3,3,9,8,7,10,11,4,5
Y,4,9,5,6,1,7,10,3,2,7,13,8,1,11,0,8
N,3,6,5,4,3,4,9,3,3,10,10,9,5,8,0,8
J,3,8,4,6,2,9,5,4,5,15,6,12,1,6,1,7
L,4,11,6,8,3,4,4,1,10,6,1,11,0,7,3,6
C,7,11,8,8,4,2,10,5,8,11,11,11,1,8,2,6
A,4,9,6,7,4,7,5,3,0,6,1,8,2,7,1,7
F,2,3,2,1,1,5,11,3,5,11,9,5,1,9,3,6
T,2,7,4,5,1,7,14,0,5,7,10,8,0,8,0,8
N,2,3,4,2,2,8,9,3,4,10,4,5,5,9,1,7
I,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
H,3,2,4,4,3,8,8,6,6,7,6,8,3,8,3,9
A,3,7,5,6,4,6,8,2,4,7,7,9,5,9,3,7
R,2,3,3,5,2,6,9,9,4,7,5,7,2,8,5,10
H,5,8,7,6,7,6,7,6,6,7,6,10,3,8,3,9
E,4,8,6,6,4,6,8,4,9,11,9,9,2,9,5,5
Y,3,5,4,3,2,4,10,1,7,10,10,5,2,11,3,4
L,2,6,3,4,1,0,1,5,6,0,0,6,0,8,0,8
J,4,7,5,5,2,9,5,3,7,15,4,9,0,7,0,8
A,1,1,2,1,0,7,4,2,0,7,2,8,2,6,1,8
U,4,5,5,4,3,4,8,5,7,10,9,9,3,9,2,6
Y,2,7,4,4,1,9,10,2,3,6,12,8,1,11,0,8
I,7,12,5,6,2,9,6,5,5,13,3,8,3,7,5,10
O,6,9,8,8,7,7,5,4,5,9,3,7,4,8,6,9
T,4,5,5,3,2,6,11,2,8,11,9,4,1,11,3,4
K,4,8,6,6,4,4,7,6,7,6,6,13,4,7,6,10
Q,3,6,5,5,3,8,7,7,5,6,6,8,2,8,4,9
P,2,1,2,1,1,5,11,7,1,9,6,4,1,9,3,8
P,5,8,8,6,4,6,12,7,2,11,5,2,1,10,4,9
U,4,4,4,6,2,8,5,14,5,6,13,8,3,9,0,8
V,5,9,5,6,3,2,11,2,3,10,11,8,2,11,1,8
O,2,3,2,1,1,8,7,6,4,9,6,8,2,8,3,8
L,3,2,4,3,2,4,4,5,7,2,2,5,1,6,1,6
W,6,6,6,4,4,7,11,5,2,8,6,6,7,12,2,6
I,6,15,4,8,3,11,5,5,4,13,3,8,3,8,5,9
N,4,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8
V,6,9,5,4,2,6,11,6,3,11,9,4,4,12,3,9
K,4,8,6,6,4,5,7,4,8,7,6,10,4,8,5,9
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
I,0,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
H,2,6,3,4,1,7,8,14,1,7,5,8,3,8,0,8
B,7,9,6,4,5,9,7,3,5,9,4,7,7,5,7,8
D,8,12,7,6,5,8,6,3,7,10,5,7,6,7,8,6
A,6,14,6,8,5,11,3,4,2,11,3,11,5,3,4,11
R,7,10,9,8,11,7,7,3,5,6,6,9,7,9,7,6
P,3,2,3,3,2,5,11,5,4,10,7,2,1,10,4,6
L,5,11,7,8,5,9,3,1,8,9,2,10,3,6,5,10
L,1,0,1,1,0,2,2,5,5,1,1,6,0,8,0,8
E,2,4,4,3,2,6,7,2,7,11,7,9,2,8,4,8
M,3,4,4,3,3,8,6,6,4,6,7,8,7,5,2,7
J,2,6,4,4,1,10,6,2,7,15,4,7,0,7,0,8
I,4,9,5,7,3,9,6,0,7,13,5,9,0,8,1,8
H,7,9,10,6,6,6,7,3,7,10,8,9,3,8,3,7
V,3,8,5,6,1,8,8,4,3,6,14,8,3,10,0,8
D,3,5,4,3,2,8,7,7,8,6,6,3,2,8,3,7
D,10,15,9,8,5,7,6,5,6,9,4,5,5,8,6,9
K,5,6,7,4,5,6,7,1,6,10,6,10,4,8,4,8
D,5,8,8,6,5,8,8,7,8,10,5,4,3,8,5,8
L,4,10,5,8,7,8,8,3,5,5,7,9,5,11,9,10
Y,9,15,8,8,4,7,8,4,4,10,7,5,4,9,5,3
D,2,1,2,2,1,6,7,9,6,6,6,6,2,8,3,8
V,6,11,9,8,10,7,5,5,3,8,8,9,9,9,6,8
X,3,4,6,3,3,6,9,1,8,10,9,8,2,8,3,6
N,2,3,4,2,1,5,9,3,3,10,8,8,5,8,0,7
J,1,5,3,4,1,9,6,3,6,14,5,10,0,7,0,7
P,4,5,6,7,7,6,8,5,3,7,7,6,7,12,6,9
W,5,9,7,7,7,10,11,2,2,6,8,7,9,13,2,8
V,4,9,7,7,3,5,12,3,4,8,12,9,2,10,1,8
F,4,7,6,5,6,7,8,5,4,8,6,9,4,11,9,11
K,8,10,7,5,3,8,7,3,7,9,7,7,6,10,4,8
E,3,7,4,5,3,7,7,6,8,7,6,9,3,8,6,8
T,3,11,5,8,1,6,14,0,6,8,11,8,0,8,0,8
O,5,9,5,6,4,7,7,8,5,10,6,8,3,8,3,8
K,7,9,10,7,6,2,8,3,8,11,12,12,5,6,5,3
R,5,8,7,6,6,8,8,5,5,9,5,8,3,8,5,11
T,4,8,6,6,5,6,7,6,6,7,9,9,3,10,5,8
I,2,8,2,6,2,7,7,0,8,7,6,8,0,8,3,8
D,1,3,2,2,1,9,6,3,4,10,4,6,2,8,2,8
K,4,4,5,6,2,3,7,8,2,7,6,11,3,8,2,11
J,1,6,2,4,1,11,3,9,3,13,7,13,1,6,0,8
U,4,4,5,2,2,4,8,5,7,10,9,9,3,9,2,6
H,2,4,4,2,2,7,8,3,5,10,6,7,3,8,2,8
P,7,11,9,8,6,8,9,2,6,14,6,5,3,10,4,9
K,3,4,6,3,3,6,7,2,7,10,7,10,3,8,3,7
F,3,7,3,5,2,1,12,4,4,11,10,8,0,8,2,6
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
K,4,8,5,6,2,4,8,9,1,7,6,11,3,8,3,11
Q,1,0,1,1,0,8,6,7,5,6,6,9,2,7,3,9
Q,3,4,4,5,3,8,8,6,2,8,7,9,2,9,3,9
L,4,11,5,8,3,9,2,2,7,9,2,10,1,5,3,9
I,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8
T,7,13,6,7,4,8,8,3,7,11,5,6,2,8,6,7
A,4,8,6,6,3,13,3,4,3,11,1,8,2,6,2,9
G,5,10,6,7,4,6,7,7,7,10,7,11,2,9,5,9
O,6,9,8,7,5,7,6,8,4,7,5,8,3,8,3,8
P,2,2,3,3,2,5,10,4,4,9,8,4,1,10,3,7
N,4,10,6,8,4,8,8,6,5,6,6,6,6,9,2,5
T,3,1,4,2,1,7,11,2,7,7,11,8,1,10,1,8
V,5,9,6,7,4,8,11,3,2,5,10,9,5,11,5,8
H,5,6,7,4,4,10,7,4,6,10,2,7,3,8,3,9
G,1,0,1,0,0,7,6,6,5,6,5,9,1,7,4,10
P,4,9,4,7,4,5,10,9,3,9,6,5,1,10,3,8
D,3,6,5,4,3,9,7,5,7,10,5,4,3,8,3,8
K,5,6,7,5,6,7,7,2,4,7,3,9,6,4,8,10
Z,1,0,1,0,0,7,7,2,9,8,6,8,0,8,5,8
X,4,6,5,4,3,7,8,1,8,10,7,8,3,8,3,7
K,7,9,9,8,9,10,6,3,5,10,2,8,8,6,7,12
G,4,7,5,5,5,8,7,6,2,7,6,11,4,8,7,9
J,4,9,5,7,3,9,6,2,6,14,4,8,0,7,0,8
Q,9,15,8,8,5,9,4,5,9,11,3,11,3,6,8,11
M,5,5,8,4,4,10,5,3,5,9,3,7,10,7,3,9
Q,1,2,2,3,2,8,7,6,2,5,6,10,2,9,4,9
C,6,9,6,7,3,3,8,5,7,12,10,12,2,9,2,7
Y,7,11,6,6,3,5,9,4,3,11,10,6,4,10,3,3
F,3,10,4,7,2,0,13,4,4,12,11,7,0,8,2,6
U,4,10,4,7,2,7,5,15,5,7,13,8,3,9,0,8
T,6,9,5,4,2,5,11,3,6,13,7,5,2,8,3,4
I,6,7,8,8,7,9,8,5,5,7,5,9,3,8,9,7
F,4,8,4,6,3,2,12,5,6,11,10,8,0,8,2,7
L,2,3,2,2,1,4,4,4,7,2,2,6,0,7,1,6
L,3,7,5,6,4,8,5,5,4,6,7,8,3,8,8,11
G,4,6,6,6,6,8,9,6,3,6,6,9,7,11,6,9
H,2,4,3,3,3,7,7,5,5,7,6,8,3,8,2,8
K,8,13,8,7,4,9,7,3,7,9,3,6,5,8,4,8
C,2,5,3,3,2,6,8,7,8,8,7,13,1,9,4,10
V,2,7,4,5,1,9,8,4,2,6,13,8,3,10,0,8
Q,2,2,2,3,2,8,9,5,2,8,8,10,2,9,4,9
Y,1,0,2,0,0,8,10,3,1,6,12,8,1,11,0,8
O,2,1,3,2,2,7,8,7,5,7,7,8,2,8,3,8
Y,8,14,6,8,4,7,8,4,4,9,8,5,4,10,4,4
X,5,11,7,8,7,8,7,3,5,6,7,6,4,10,11,9
A,1,0,2,0,0,8,4,2,0,7,2,8,1,6,1,8
P,6,9,9,7,4,9,9,3,7,14,4,3,2,9,4,9
L,2,2,3,4,1,3,4,3,8,2,1,7,0,7,1,6
V,9,12,7,7,3,7,9,6,4,8,9,5,6,13,4,9
F,2,5,3,4,2,5,11,3,5,11,9,5,1,10,3,6
E,3,8,5,6,5,7,7,3,6,7,7,10,4,10,8,8
Z,5,12,6,6,4,9,5,3,8,12,4,9,3,9,5,11
U,3,3,4,1,1,4,9,5,6,11,10,9,3,10,2,7
J,2,5,3,4,1,11,6,2,6,11,3,8,1,6,1,7
F,2,4,4,3,2,6,10,2,6,13,7,5,1,10,1,7
F,4,6,6,4,5,10,7,1,5,9,5,6,4,10,4,7
Z,1,3,2,2,1,7,7,5,8,6,6,8,2,8,7,8
W,3,4,4,3,3,6,10,4,2,8,7,7,6,11,1,6
J,1,0,1,1,0,12,3,5,3,12,5,11,0,7,0,8
P,4,9,5,6,3,5,10,10,5,9,6,5,2,10,4,8
L,0,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8
C,5,11,6,8,2,5,7,7,10,7,6,14,1,8,4,9
A,5,8,7,6,6,8,9,7,5,6,6,8,3,7,7,4
X,6,9,8,8,8,8,7,2,5,7,6,7,4,9,8,8
Q,6,9,7,11,8,9,10,6,4,3,9,12,4,9,9,14
A,3,2,6,4,2,10,2,2,2,9,1,8,2,6,2,8
W,4,4,5,2,3,7,11,3,2,6,9,8,7,11,0,8
K,4,9,5,6,2,3,8,8,2,7,5,11,4,8,3,10
F,5,9,7,7,8,10,6,2,4,9,5,6,6,9,5,7
E,4,8,4,6,3,3,9,6,12,7,5,14,0,8,7,7
H,7,10,10,8,9,7,8,3,6,10,6,7,3,8,3,8
G,8,11,7,6,4,7,6,6,5,9,5,6,4,7,5,7
Y,5,6,6,8,8,9,7,6,3,7,8,8,7,10,6,5
T,2,3,3,4,1,7,14,0,6,7,11,8,0,8,0,8
J,2,8,3,6,1,12,2,9,4,14,6,13,1,6,0,8
T,8,11,8,8,4,4,12,2,8,13,10,5,0,10,2,4
V,4,10,5,8,3,5,11,3,3,9,12,9,2,10,1,8
A,5,6,7,5,6,6,6,3,5,7,8,10,8,11,3,8
V,5,9,4,4,2,9,9,5,4,5,10,6,4,11,3,5
M,3,7,5,5,5,9,6,6,4,6,7,6,7,5,2,6
U,5,10,6,8,5,6,8,8,5,5,6,12,5,9,7,3
D,5,10,7,8,6,6,7,8,7,7,7,5,4,7,4,9
Q,6,9,6,10,6,8,6,7,4,9,6,10,4,10,7,6
X,4,6,6,4,3,7,7,1,8,10,7,9,3,8,3,7
H,3,9,4,7,4,8,9,13,2,7,4,8,3,8,0,8
V,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8
G,4,6,4,4,3,6,7,5,6,9,8,10,2,9,4,9
F,3,4,3,6,1,0,11,4,7,12,12,9,0,8,2,6
H,6,7,9,10,8,7,11,5,1,8,7,5,4,12,10,4
U,6,10,6,6,4,7,6,5,5,7,8,9,5,8,3,8
W,4,4,5,2,3,4,11,3,2,10,9,8,6,11,1,6
H,4,10,4,7,2,7,7,15,1,7,7,8,3,8,0,8
M,6,10,8,8,7,6,7,3,5,9,9,9,8,6,2,7
U,10,14,9,8,5,6,6,5,5,6,8,9,7,7,4,9
E,3,3,3,5,2,3,9,6,11,7,5,14,0,8,7,8
Z,6,6,5,9,4,7,8,5,3,11,7,7,2,9,9,7
U,2,5,4,4,3,7,7,4,3,6,7,8,3,8,1,7
U,3,6,4,4,1,7,5,13,5,7,14,8,3,9,0,8
Y,3,6,5,4,2,10,11,2,7,3,11,8,1,11,1,9
T,1,0,1,0,0,7,14,1,4,7,10,8,0,8,0,8
D,6,11,6,8,4,6,7,11,11,6,5,6,3,8,4,8
I,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8
M,2,0,3,1,1,7,6,10,0,7,9,8,7,6,0,8
I,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8
I,3,10,4,8,2,7,7,0,8,13,6,8,0,8,1,7
E,2,3,3,2,1,6,8,1,7,11,6,9,2,8,3,8
Q,8,13,8,7,5,12,4,3,6,10,3,7,3,9,6,13
W,2,0,2,0,1,7,8,4,0,7,8,8,6,9,0,8
H,7,9,10,7,8,6,8,2,6,10,7,8,5,9,4,8
I,2,5,3,3,1,7,8,1,8,14,6,7,0,8,1,7
Q,4,6,5,9,6,8,13,4,3,4,8,12,4,14,7,13
D,3,6,5,4,3,7,7,8,7,7,6,5,3,8,3,7
Q,6,10,8,8,7,7,4,8,4,6,7,10,5,7,7,9
C,3,8,4,6,2,6,8,8,7,10,8,13,2,10,4,10
V,5,7,5,5,2,3,11,4,3,10,12,7,2,10,1,8
D,3,6,4,4,3,5,8,8,7,8,7,6,2,8,3,8
J,4,10,6,7,6,10,8,3,4,8,3,6,4,8,7,7
N,3,6,4,4,3,7,7,12,1,7,6,8,5,9,0,8
V,3,3,4,2,1,4,12,3,2,10,11,7,2,11,1,8
U,5,7,5,5,3,3,9,5,6,11,11,9,3,9,1,6
S,2,6,3,4,2,8,7,7,7,7,7,9,2,10,9,8
A,3,7,5,5,3,11,2,3,2,10,2,9,3,7,3,9
W,7,7,9,6,10,7,7,5,5,7,6,8,9,9,9,8
F,4,11,6,8,4,5,11,4,6,11,10,5,2,10,3,5
N,2,1,2,1,1,7,7,11,1,5,6,8,4,8,0,8
N,2,4,3,3,2,7,8,5,4,7,6,6,4,8,1,6
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
P,4,6,5,4,4,6,7,6,4,7,6,8,2,9,7,10
T,2,7,3,4,1,7,14,0,6,7,11,8,0,8,0,8
D,6,9,9,8,8,6,7,5,7,7,4,7,4,6,6,5
R,5,8,7,6,7,8,6,7,3,8,6,8,6,6,6,11
E,5,9,5,7,3,3,8,6,11,7,6,15,0,8,7,7
Z,7,10,9,8,6,7,8,2,9,12,7,7,1,8,6,8
F,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8
P,4,7,5,5,3,7,10,6,4,11,5,4,1,10,3,8
T,7,15,6,8,3,6,9,3,8,13,6,6,2,8,5,4
P,3,10,4,8,4,3,12,7,2,11,8,4,0,10,3,8
K,3,7,4,5,1,3,7,7,2,7,7,11,3,8,3,10
E,3,6,4,4,2,5,9,4,8,12,9,8,2,9,5,5
W,10,11,10,8,8,2,11,2,3,10,11,9,7,10,2,6
Z,4,9,4,7,2,7,7,4,14,9,6,8,0,8,8,8
B,4,5,5,8,4,6,9,9,7,8,5,6,2,8,9,10
R,3,7,4,5,4,7,9,6,5,8,5,9,3,6,5,10
Y,9,8,7,12,5,8,5,5,5,5,12,6,5,10,4,7
Z,5,10,6,8,5,7,8,3,12,9,6,8,0,8,8,7
Z,2,5,4,4,2,7,8,2,10,11,6,7,1,8,6,7
I,2,10,2,8,3,7,7,0,7,7,6,8,0,8,3,8
Z,4,8,5,6,5,8,8,3,7,7,6,7,1,9,10,8
L,2,3,3,2,1,5,4,5,8,3,2,6,1,7,1,6
E,3,5,3,4,3,7,8,5,8,7,5,9,2,8,6,9
K,3,9,4,7,2,3,7,7,3,7,7,11,3,8,3,10
G,3,7,4,5,3,6,6,5,4,6,6,9,2,9,4,8
P,4,9,6,6,3,9,9,2,6,14,5,4,1,10,3,10
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
P,7,10,7,5,4,8,9,4,3,12,5,4,4,11,5,6
K,6,11,8,8,5,6,7,2,7,10,7,10,3,8,3,8
V,4,8,6,6,1,6,8,5,3,8,14,8,3,9,0,8
T,3,4,3,3,1,5,13,3,5,11,9,3,1,11,1,5
Q,2,2,3,4,3,8,8,6,2,5,7,9,3,9,5,9
R,5,8,7,6,5,10,6,3,5,10,3,7,3,7,4,10
P,6,7,8,10,9,7,9,4,2,7,8,7,6,11,6,5
N,7,12,7,6,3,8,10,5,4,4,6,9,6,10,3,6
B,4,9,5,6,5,7,8,7,4,7,5,6,4,7,6,6
N,3,5,5,4,2,7,8,3,5,10,6,7,5,8,1,7
R,5,11,6,8,7,6,8,9,5,6,5,8,2,8,8,9
C,5,5,6,8,2,6,7,7,10,7,6,15,1,8,4,9
M,4,7,4,5,3,7,7,12,1,7,9,8,8,6,0,8
H,5,8,8,6,5,6,8,4,7,10,9,9,4,9,4,6
Q,4,8,5,8,3,8,7,8,6,6,7,8,3,8,5,9
Q,3,5,5,8,3,7,5,9,6,6,4,8,3,8,4,8
S,4,7,5,5,4,7,7,5,8,5,6,9,0,9,9,8
J,4,9,5,7,2,10,6,2,8,14,3,8,0,7,0,8
R,5,10,5,8,6,6,9,9,4,6,5,7,3,8,5,11
F,5,8,6,10,8,7,9,5,5,7,6,7,4,9,9,7
P,1,0,1,0,0,5,11,6,1,9,6,5,0,9,3,8
I,1,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8
I,3,11,6,8,7,11,6,1,6,8,4,5,3,8,5,9
S,2,7,3,5,3,8,8,8,6,8,4,6,2,6,8,8
Q,5,6,6,8,5,8,6,8,4,5,6,9,3,8,6,10
V,7,11,6,6,3,9,8,6,4,7,10,7,7,12,3,8
A,3,8,6,6,3,11,2,2,2,9,2,9,3,7,3,9
Q,4,5,5,8,3,8,7,8,6,5,6,8,3,8,5,9
J,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8
U,2,0,2,1,0,8,5,11,4,7,13,8,3,10,0,8
G,4,7,6,5,5,7,8,5,3,6,6,11,4,8,7,8
H,4,2,5,4,4,8,7,6,6,7,6,8,3,8,3,9
K,5,11,7,8,8,5,7,5,4,7,5,8,4,6,10,8
B,3,4,5,3,3,8,7,2,6,11,5,7,2,8,4,9
O,6,10,9,8,10,8,7,5,1,7,6,8,9,9,7,11
B,3,5,5,3,3,8,7,2,6,10,5,7,2,8,4,10
H,3,6,4,4,3,7,6,12,1,7,7,8,3,8,0,8
H,5,11,8,8,9,8,8,6,7,7,5,8,3,8,4,7
J,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8
K,3,6,4,4,3,5,7,4,8,6,6,11,3,8,6,9
E,3,8,5,6,6,5,7,3,7,8,7,12,4,9,8,8
R,10,11,8,6,4,10,5,5,5,10,3,8,6,6,6,11
F,4,9,5,7,4,4,11,4,6,11,10,6,2,10,3,5
I,4,6,5,7,5,8,9,4,5,8,7,8,4,7,8,7
B,4,10,6,7,7,8,6,4,4,6,5,7,4,9,5,8
Q,4,5,5,7,5,9,11,6,2,3,7,12,3,10,5,10
G,4,9,5,7,2,7,6,8,9,7,5,11,1,8,5,10
A,5,10,7,8,5,8,4,3,0,7,1,8,2,7,4,8
B,2,3,3,2,2,8,7,3,5,10,6,7,2,8,4,9
Y,2,1,3,1,0,7,10,3,1,7,12,8,1,11,0,8
A,3,5,5,3,2,11,2,2,2,9,2,9,2,6,2,8
V,8,11,8,6,4,7,10,4,5,8,9,6,5,11,3,7
Z,6,10,9,7,5,5,10,4,10,11,10,6,1,9,6,5
W,11,12,10,6,4,4,10,2,3,9,11,8,9,12,1,6
H,5,6,7,4,4,7,7,3,7,10,7,9,3,8,3,7
H,4,5,5,4,4,7,7,6,6,7,6,8,3,8,3,7
G,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,9
Z,4,6,5,4,4,9,9,5,4,6,5,7,3,8,8,4
N,5,11,6,9,3,7,7,15,2,4,6,8,6,8,0,8
S,5,9,6,6,4,6,7,4,7,10,10,9,2,9,5,5
B,7,15,7,8,6,10,6,4,5,10,4,7,7,6,8,9
Q,5,7,6,6,5,9,3,4,4,8,3,10,3,6,6,7
Z,5,11,7,8,5,7,7,3,13,9,6,8,0,8,8,8
Q,2,3,2,3,2,8,8,5,1,8,7,9,2,9,4,8
M,6,10,7,8,7,8,5,11,0,7,9,8,10,5,3,7
G,7,11,7,8,5,7,6,7,7,10,7,13,3,8,5,7
Y,4,6,6,8,8,9,6,6,3,7,8,8,6,10,6,4
A,2,7,4,5,2,8,3,2,2,7,1,8,2,6,2,7
J,2,6,2,4,1,13,2,7,4,13,4,12,0,7,0,8
J,2,8,2,6,1,15,3,4,5,12,1,8,0,7,0,8
E,8,12,5,6,3,8,6,5,7,11,6,9,2,10,8,9
D,5,11,7,8,8,7,7,4,8,6,6,6,3,8,3,7
Z,4,7,4,5,3,7,7,5,11,7,6,9,1,8,8,8
N,2,2,3,4,2,7,8,5,4,7,7,7,5,9,2,6
O,4,8,5,6,4,7,7,7,4,10,6,8,3,8,3,8
E,2,1,2,1,1,4,8,5,8,7,6,13,0,8,7,9
G,2,2,3,3,2,7,6,6,5,6,6,10,2,9,4,9
O,8,9,5,5,3,7,9,6,5,9,5,6,4,9,5,8
G,4,7,4,5,3,6,7,6,7,9,8,9,2,10,4,9
T,7,9,6,5,2,8,8,4,8,13,5,7,2,8,5,6
Y,5,8,5,6,4,4,9,1,6,9,10,7,3,11,4,4
G,4,7,6,5,3,5,5,5,7,6,5,9,2,10,4,7
T,6,8,6,6,4,6,12,4,6,11,9,4,3,12,3,4
A,1,1,2,1,0,7,4,2,0,6,2,8,2,7,1,8
O,5,11,6,8,9,8,10,5,2,7,7,8,8,9,5,10
A,3,9,5,7,4,12,2,3,3,10,2,9,3,8,4,9
M,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8
O,4,7,4,5,3,7,7,7,5,10,6,8,3,8,3,8
T,3,4,4,3,2,5,12,3,6,11,9,4,1,11,1,5
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
K,6,9,7,4,3,5,8,3,5,9,9,10,5,10,3,6
D,4,8,6,6,5,8,8,5,6,10,5,4,3,8,4,8
X,5,9,8,7,7,6,7,3,6,8,8,11,6,6,8,6
B,6,9,8,7,6,9,6,4,7,9,5,7,3,8,6,10
F,7,12,6,6,3,6,8,2,7,10,7,6,2,10,5,6
A,5,11,7,9,5,8,3,3,1,7,2,8,5,8,5,10
D,4,9,4,4,3,7,8,4,7,10,5,6,4,8,5,6
C,6,10,6,7,3,4,8,6,8,12,10,12,1,9,3,7
E,4,7,6,5,5,8,7,6,3,7,6,10,4,8,8,9
E,3,9,4,7,2,3,8,6,11,7,5,15,0,8,7,7
B,4,4,5,6,4,6,8,9,7,7,5,7,2,8,9,9
Y,5,10,5,7,3,3,10,2,8,11,11,6,0,10,2,4
D,2,5,4,3,3,9,6,4,6,10,4,6,2,8,3,8
E,4,6,5,4,5,7,9,5,4,6,6,9,4,7,7,7
N,10,14,12,8,5,12,5,3,4,13,1,6,6,7,0,8
V,8,10,8,8,4,2,13,5,4,11,12,7,3,9,2,7
Q,4,8,5,9,5,8,9,6,2,6,8,12,3,10,6,8
Y,3,7,5,5,2,4,9,1,7,10,12,9,1,10,2,7
W,12,15,11,8,5,6,10,2,3,7,10,7,10,12,1,6
U,8,9,9,7,6,4,7,5,8,9,7,9,6,8,4,3
M,3,1,3,1,1,8,6,11,0,7,9,8,7,6,0,8
Z,3,7,4,5,2,7,7,4,13,9,6,8,0,8,8,8
E,3,6,5,4,3,5,9,5,8,11,10,8,3,8,4,4
W,3,1,4,2,1,7,8,4,0,7,8,8,7,9,0,8
O,5,9,4,4,2,9,7,5,4,9,4,8,4,9,5,8
T,5,10,7,7,6,6,7,7,7,7,6,8,4,11,7,8
X,3,7,5,5,3,5,8,1,7,10,10,9,2,9,3,6
F,5,9,7,6,4,10,7,2,6,13,4,6,6,9,5,9
L,1,4,3,3,1,6,5,2,9,7,2,10,0,7,3,7
M,5,2,7,4,5,8,6,6,5,6,7,7,10,6,3,6
P,8,9,6,4,3,8,8,5,4,12,3,6,5,9,4,8
W,6,10,7,5,4,7,8,3,4,6,9,6,9,9,2,6
E,3,9,4,7,4,6,7,7,9,8,8,10,3,8,6,8
U,6,8,7,6,3,3,9,5,8,11,11,9,3,9,2,6
I,3,7,5,5,5,9,7,2,4,8,5,5,3,9,5,6
H,5,7,8,5,4,8,7,3,6,10,4,7,3,8,3,8
E,4,11,5,8,5,2,8,5,10,7,5,14,0,8,6,9
D,4,9,5,7,3,5,7,10,9,6,5,6,3,8,4,8
Q,1,2,2,3,2,8,7,6,2,6,6,9,2,9,3,10
R,6,11,6,8,4,5,13,8,4,7,3,9,3,7,7,11
M,2,1,2,1,1,8,6,10,0,6,9,8,6,6,0,8
B,5,10,8,8,11,8,7,5,3,6,7,7,7,10,9,9
M,2,4,3,3,3,8,6,6,4,6,7,6,6,6,2,6
Y,7,8,7,6,3,2,12,4,6,13,11,5,1,11,1,5
G,5,9,4,4,3,8,6,5,2,9,6,8,3,10,7,8
Y,2,3,3,2,1,4,10,2,7,10,10,5,1,11,2,4
U,4,4,5,3,2,4,8,5,7,11,10,9,3,9,1,7
O,1,0,2,0,0,7,7,6,4,7,6,8,2,8,3,8
K,4,7,6,5,4,6,7,1,6,10,7,10,3,8,3,8
X,2,2,4,3,2,8,7,3,9,6,6,8,3,8,6,8
X,2,1,3,3,2,7,8,3,8,6,6,7,2,8,6,7
C,2,1,2,2,1,6,8,7,6,9,7,11,2,10,4,10
U,6,10,7,7,4,3,8,5,7,10,9,9,3,9,2,6
X,4,8,7,6,6,8,7,2,6,7,6,7,5,7,7,8
N,5,5,6,7,3,7,7,15,2,4,6,8,6,8,0,8
M,4,7,5,5,6,7,7,7,4,6,5,8,6,9,7,8
D,3,6,5,4,4,7,7,6,6,6,5,5,3,8,3,7
E,4,7,6,5,4,5,9,2,8,11,7,9,3,9,5,7
R,4,8,5,6,4,8,8,5,7,6,4,7,3,6,5,8
A,4,8,6,6,4,9,5,3,0,8,1,8,2,7,1,8
U,5,10,6,8,4,5,8,7,7,8,10,10,3,9,1,8
Z,4,4,5,7,2,7,7,4,15,9,6,8,0,8,8,8
I,2,10,3,7,2,9,7,0,7,13,5,8,0,8,1,8
H,2,1,3,1,2,6,7,5,6,7,6,8,5,8,3,8
F,5,7,6,8,7,7,9,4,4,7,6,7,4,9,9,8
K,5,10,5,8,4,4,7,7,3,7,6,12,3,8,3,11
R,4,7,4,5,4,6,9,8,3,7,5,8,2,7,5,11
P,2,3,4,2,2,7,10,3,4,12,4,3,1,10,2,8
H,8,11,12,8,10,9,6,3,7,10,5,8,6,8,5,8
Q,6,6,8,6,6,8,3,4,5,7,3,9,5,5,6,8
G,7,10,6,6,3,7,6,6,5,10,5,7,4,7,5,7
D,6,7,8,6,8,7,6,5,6,7,5,9,5,6,8,3
B,6,10,8,8,9,8,6,5,6,9,5,7,4,9,7,10
F,4,9,4,6,2,1,14,5,3,12,9,4,0,8,3,6
U,3,7,4,5,3,7,5,12,4,7,11,8,3,9,0,8
K,3,4,6,3,3,7,7,1,6,10,5,9,4,7,4,8
R,3,7,4,4,2,5,11,8,3,7,3,9,3,7,5,11
X,5,9,7,8,8,8,7,2,4,8,6,8,4,10,8,6
W,3,6,5,4,3,7,10,2,2,7,9,8,6,11,0,8
N,4,7,6,5,4,7,9,6,5,7,6,6,5,9,1,6
W,1,0,2,0,1,8,8,4,0,7,8,8,5,10,0,8
K,5,8,7,6,5,4,7,2,6,10,10,11,3,8,3,6
E,2,3,4,2,2,7,7,2,7,11,6,9,2,8,4,8
E,2,3,3,2,2,7,7,5,7,7,6,8,2,8,5,10
A,3,8,6,6,3,12,2,2,2,10,2,9,2,6,2,8
R,2,1,3,2,2,7,8,5,5,7,5,6,2,7,4,8
S,2,3,4,2,1,9,6,3,7,10,4,8,1,8,5,10
P,6,10,5,5,2,6,10,6,5,14,5,5,3,9,4,8
F,1,1,2,1,0,3,12,4,3,11,9,6,0,8,2,7
G,5,7,5,5,4,6,8,5,5,9,8,8,2,8,4,9
O,4,7,5,5,4,7,7,7,4,8,5,10,4,8,3,7
X,5,8,7,7,7,9,8,3,5,8,5,6,3,6,8,8
B,5,11,7,8,8,7,6,8,6,6,6,6,3,8,8,11
Z,1,0,1,1,0,7,7,2,10,8,6,8,0,8,6,8
M,3,3,3,4,2,8,6,11,1,6,9,8,7,6,0,8
N,1,3,3,2,1,6,8,3,3,10,7,8,4,8,0,7
B,3,2,4,4,3,7,7,5,5,6,6,5,2,8,6,10
V,4,7,6,6,7,6,7,5,4,7,6,8,6,9,7,10
J,4,11,5,8,3,11,5,4,7,12,2,9,1,6,2,6
D,5,10,7,8,7,9,7,6,6,8,5,5,5,9,4,10
V,5,8,5,6,2,4,12,4,4,10,12,7,3,10,1,8
G,6,11,6,8,5,5,6,5,5,9,8,11,2,9,4,9
G,7,14,6,8,5,7,7,5,4,9,5,6,4,9,9,8
Q,3,6,4,7,4,9,8,7,2,5,7,10,3,9,5,9
C,4,5,5,5,4,6,8,3,5,6,6,11,4,9,7,9
F,9,14,8,8,6,7,11,3,4,12,6,4,6,8,9,5
D,6,11,9,8,8,8,8,5,6,10,6,5,6,9,6,11
H,6,9,8,7,7,6,7,5,5,7,6,8,6,7,7,11
M,5,8,8,6,5,3,7,4,5,11,12,11,6,9,3,7
L,3,2,4,3,2,5,4,5,7,2,2,5,1,7,1,6
S,5,9,7,8,8,9,8,4,5,7,6,8,5,10,9,11
P,5,11,5,8,3,5,10,10,4,9,6,5,2,10,4,8
Y,7,10,6,5,4,7,6,4,4,9,8,5,3,10,4,4
S,5,7,6,5,4,9,7,4,7,10,3,7,2,7,5,10
Q,3,5,4,6,4,9,10,6,2,4,7,11,2,9,5,10
T,2,4,3,5,1,5,14,1,6,9,11,7,0,8,0,8
Y,4,11,7,8,4,7,10,1,7,6,12,9,1,11,2,8
M,3,7,4,5,3,8,7,11,1,6,9,8,8,6,0,8
O,4,8,5,6,2,7,7,8,7,7,6,8,3,8,4,8
X,4,9,6,7,3,6,8,1,8,10,9,8,3,8,4,7
N,6,11,8,8,8,5,9,3,4,9,8,9,9,8,7,3
B,4,8,6,6,6,8,7,4,5,9,5,6,3,8,6,9
E,2,3,4,2,2,8,6,1,7,11,5,9,2,8,4,10
H,3,7,4,5,2,7,9,14,2,7,3,8,3,8,0,8
X,5,9,8,7,4,5,8,2,8,11,11,9,3,9,4,6
K,4,8,4,6,2,3,7,8,2,7,6,11,4,8,2,11
O,4,9,5,7,5,8,6,7,3,10,5,9,3,8,3,7
Y,4,6,6,9,7,9,10,6,3,7,7,7,6,11,6,5
C,6,10,7,8,4,6,8,7,8,13,8,10,2,11,3,7
W,6,9,8,6,7,7,7,6,3,5,8,9,11,8,6,4
U,5,5,6,8,2,8,4,14,6,6,14,8,3,9,0,8
F,6,10,8,7,8,8,8,6,4,8,6,7,5,11,9,11
C,1,0,1,1,0,7,7,5,7,7,6,13,0,8,4,10
O,5,9,6,7,5,8,7,8,7,7,6,8,3,8,4,8
X,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8
E,4,6,6,4,5,7,9,5,3,6,6,8,4,8,6,7
U,5,8,8,6,4,7,8,6,9,5,9,8,3,9,1,8
Q,4,7,5,7,3,8,7,8,6,6,7,9,3,8,5,9
R,5,11,7,8,6,7,7,4,8,7,6,6,3,8,5,8
P,6,12,6,7,4,6,10,3,5,13,6,3,3,10,5,6
S,3,9,4,7,4,8,8,5,7,5,5,6,0,8,8,8
Z,2,4,5,3,2,7,8,2,9,11,7,6,1,8,5,7
K,7,12,7,7,4,5,8,3,5,10,9,11,5,9,3,7
R,6,9,8,7,8,9,5,7,4,7,5,7,6,7,8,9
P,5,10,8,8,5,8,9,5,5,12,4,3,2,10,4,8
D,3,5,4,7,2,5,6,11,7,5,5,5,3,8,4,8
D,7,9,9,8,9,6,7,5,7,6,3,7,5,10,9,5
Q,2,2,2,2,1,7,8,5,2,7,8,10,2,9,4,8
L,4,11,6,9,5,8,4,0,8,9,3,10,2,5,3,9
N,6,10,8,7,5,8,9,2,5,9,4,6,5,9,1,7
V,3,6,5,4,2,7,12,3,4,8,12,8,3,10,1,8
A,1,1,2,1,0,7,4,2,0,7,2,8,2,7,1,8
S,6,10,5,5,2,8,4,5,4,9,3,8,4,6,5,9
R,6,10,6,8,4,6,10,10,5,7,5,8,3,8,6,10
D,5,10,7,8,7,7,8,7,5,8,7,7,7,8,3,7
T,4,10,6,8,5,6,7,8,7,8,8,7,3,10,6,10
V,4,6,5,5,6,8,7,5,4,7,6,9,6,8,8,4
E,6,11,8,8,5,4,10,4,9,11,10,9,2,8,5,4
C,4,10,6,8,4,4,9,5,6,5,7,14,4,8,5,7
W,6,10,9,8,8,5,12,2,2,8,8,9,9,14,2,8
V,1,3,2,1,1,6,12,2,3,8,11,8,2,11,0,8
H,2,7,3,4,2,7,6,14,2,7,8,8,3,8,0,8
J,2,8,3,6,1,13,3,9,4,13,4,12,1,6,0,8
B,4,7,4,5,3,6,7,9,7,7,6,7,2,8,8,9
N,5,8,8,6,3,3,9,4,4,11,11,10,5,8,1,7
I,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8
L,5,11,5,6,3,7,4,3,5,11,7,12,3,7,6,8
Z,5,8,7,10,7,11,5,5,4,9,3,8,3,6,6,9
K,10,12,10,7,4,9,8,4,8,9,1,5,5,6,3,9
Z,3,6,4,4,3,7,8,5,10,7,7,9,1,9,7,8
N,6,7,8,5,4,11,7,3,6,10,1,4,5,9,1,7
I,2,6,3,4,1,7,7,0,7,13,6,8,0,8,1,8
M,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8
Z,3,7,4,5,2,7,7,4,14,9,6,8,0,8,8,8
X,10,12,9,6,4,10,6,3,8,10,4,7,4,12,4,9
O,4,9,3,5,3,6,8,6,4,9,7,9,5,9,4,8
T,2,4,4,6,1,7,14,0,6,7,11,8,0,8,0,8
Z,4,7,6,5,3,7,7,2,10,12,6,8,1,9,6,8
P,5,10,7,8,6,6,6,6,4,7,6,9,5,8,7,10
M,7,11,8,8,4,7,7,13,2,7,10,8,9,6,0,8
I,1,10,0,8,1,7,7,5,3,7,6,8,0,8,0,8
Y,2,3,2,2,1,4,11,3,5,11,10,5,1,11,2,6
G,6,6,7,8,3,8,5,8,9,6,5,10,2,8,6,11
N,5,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8
N,3,5,6,4,3,9,8,3,5,10,3,5,5,9,1,8
D,5,11,6,8,4,5,7,10,10,6,5,6,3,8,4,8
D,4,7,4,5,2,5,7,10,8,7,6,5,3,8,4,8
T,2,9,4,6,1,7,14,0,6,7,11,8,0,8,0,8
L,2,5,4,3,2,7,3,2,8,8,2,9,1,7,3,8
G,3,6,4,4,3,6,7,5,5,10,8,10,2,9,4,9
O,5,8,7,6,8,8,6,5,2,7,5,8,9,9,5,11
R,5,10,5,7,3,5,10,9,4,7,4,8,3,7,6,11
S,6,13,5,7,3,10,4,5,5,10,3,10,4,5,4,10
X,3,1,4,2,2,7,7,3,9,6,6,8,3,8,6,8
L,1,3,2,1,1,7,3,1,6,9,3,10,0,7,2,9
W,6,6,6,4,4,2,11,2,2,10,10,8,6,11,1,7
J,0,0,1,1,0,12,4,5,3,12,4,10,0,7,0,8
F,3,7,5,5,2,5,12,4,6,12,9,4,1,10,3,4
F,2,5,3,4,2,5,10,4,5,10,9,5,2,10,3,6
C,2,4,3,3,1,6,8,7,8,8,7,14,1,9,4,10
C,2,7,3,5,2,5,8,7,7,8,8,13,2,9,4,10
U,5,9,5,7,2,7,4,14,5,7,14,8,3,9,0,8
V,8,11,7,8,4,3,11,3,4,10,12,8,3,10,1,7
L,2,5,4,4,2,6,4,1,8,8,2,10,0,7,2,8
X,2,3,3,2,2,7,8,3,8,6,6,7,2,8,5,7
V,4,6,6,5,6,7,7,4,4,7,6,8,6,10,7,7
G,7,8,9,7,10,7,6,5,4,7,7,9,8,10,9,7
D,6,10,6,5,4,9,6,3,7,10,4,7,5,7,8,7
L,4,9,4,7,2,0,2,4,6,1,0,8,0,8,0,8
P,1,0,1,1,0,5,10,6,1,9,6,4,1,9,2,8
X,3,4,5,3,2,9,6,1,8,10,4,7,2,8,3,8
C,8,12,6,6,2,5,11,5,8,11,8,9,1,7,5,8
M,6,10,7,8,4,7,7,13,2,7,9,8,9,6,0,8
Y,3,6,4,4,1,5,11,2,2,8,12,8,1,11,0,8
R,6,11,9,8,9,6,8,5,6,6,5,8,3,7,5,9
A,3,8,5,5,2,9,6,3,1,8,0,8,2,7,1,8
Q,4,3,5,5,4,8,8,6,2,5,7,10,3,9,6,9
M,3,3,4,1,2,8,6,6,4,6,7,8,6,5,2,7
U,5,5,6,3,3,4,8,5,8,10,9,9,3,9,2,6
T,6,9,6,6,5,6,11,4,6,11,9,5,3,12,2,4
J,2,8,3,6,1,13,2,8,4,13,4,12,1,6,0,8
M,4,7,6,5,5,9,7,2,4,9,6,7,7,5,2,7
O,4,11,5,8,3,8,8,9,7,7,8,8,3,8,4,8
U,5,9,6,7,6,8,6,8,5,7,6,9,3,8,4,6
E,7,10,9,8,8,7,4,6,4,7,6,9,5,9,9,8
N,4,5,6,5,5,7,8,5,4,7,5,7,6,9,5,3
L,3,7,4,5,2,6,4,1,9,8,2,11,0,7,2,8
Y,4,9,6,7,3,8,10,1,8,5,12,8,1,11,2,8
Z,5,9,7,7,5,9,6,2,9,11,4,9,3,7,7,9
W,9,11,9,6,5,5,8,2,4,7,9,7,10,10,2,5
K,2,3,4,1,1,4,9,2,6,10,9,10,3,8,2,7
Q,1,0,1,0,0,8,7,6,3,6,6,8,2,8,3,8
C,2,3,3,4,1,5,8,6,8,7,7,12,1,7,4,9
R,2,3,2,2,2,6,8,4,4,7,5,7,2,7,3,8
T,3,4,3,3,1,5,11,3,7,11,9,5,1,11,2,5
Y,3,5,4,4,2,4,11,2,7,11,10,6,1,11,2,5
Z,3,5,5,4,2,8,7,2,10,12,5,9,1,8,6,9
Z,3,4,4,6,2,7,7,4,14,9,6,8,0,8,8,8
M,4,3,4,4,2,7,7,12,1,7,9,8,8,6,0,8
I,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7
O,4,10,5,8,5,8,8,8,5,6,8,9,3,8,3,7
E,2,1,3,2,2,7,7,5,7,7,6,8,2,8,5,10
Y,5,9,5,4,3,6,6,5,4,9,10,6,3,9,2,5
E,2,3,4,2,2,7,7,1,8,11,6,9,1,8,4,8
Q,7,10,6,5,3,10,4,4,7,12,3,11,3,7,7,11
R,2,2,3,4,3,7,7,5,6,7,6,7,3,7,5,8
T,7,10,7,8,5,5,11,3,6,11,10,5,2,12,2,4
E,4,9,6,7,6,7,7,5,8,7,7,9,3,8,5,9
V,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8
U,3,4,4,3,2,4,8,5,6,11,10,9,3,9,1,7
T,4,7,5,5,4,7,7,7,6,6,7,9,3,10,6,7
G,3,6,5,5,5,7,8,5,2,7,7,8,6,11,7,7
D,3,7,4,5,2,5,7,10,8,7,6,5,3,8,4,8
W,4,4,5,3,2,4,10,3,2,9,9,7,6,11,1,7
N,5,5,5,8,3,7,7,15,2,4,6,8,6,8,0,8
M,4,4,7,3,4,7,5,3,4,10,8,9,7,5,2,8
R,5,7,7,5,4,8,9,4,6,8,4,8,3,6,5,11
Y,4,10,7,8,3,4,10,2,8,10,12,9,1,10,2,7
T,5,7,5,5,3,7,11,3,8,11,9,4,2,12,3,5
M,8,12,10,7,5,9,4,3,2,10,2,9,10,1,1,8
R,4,9,4,6,4,6,10,8,3,7,4,8,2,7,5,11
Y,3,7,4,5,4,9,8,6,4,5,9,7,2,8,7,5
J,3,6,5,4,1,7,8,2,8,15,6,8,0,7,0,7
T,2,3,3,2,1,5,12,3,6,11,9,5,1,10,1,5
P,2,2,3,4,2,5,10,4,4,10,8,4,1,10,3,7
D,5,5,6,5,5,7,7,4,6,7,4,8,4,7,5,6
K,4,10,6,8,6,5,7,4,7,6,6,11,3,8,6,9
V,5,10,7,8,4,9,9,4,2,5,13,8,4,9,1,7
V,5,10,5,8,3,3,11,5,4,12,12,8,2,10,1,8
V,5,9,5,6,3,2,11,4,4,11,12,8,2,10,1,7
P,5,11,6,8,3,4,12,9,2,10,6,4,1,10,4,8
X,4,7,6,5,3,6,8,1,8,10,8,9,3,8,3,7
B,7,12,6,6,6,8,8,4,5,9,6,7,6,7,8,7
C,3,5,5,3,2,4,8,5,7,11,10,12,1,9,2,7
P,5,8,7,6,5,8,9,5,4,11,4,4,2,10,3,8
M,7,9,10,7,7,3,7,4,5,10,11,11,10,6,4,7
S,5,9,7,6,8,7,8,5,3,8,5,8,4,8,10,7
H,5,11,6,8,3,7,7,15,1,7,7,8,3,8,0,8
J,2,7,3,5,1,12,2,9,4,13,5,13,1,6,0,8
N,4,7,5,5,4,8,8,13,1,6,6,7,6,8,1,10
S,4,8,6,6,4,9,7,3,6,10,5,8,2,9,5,9
B,4,9,4,7,6,6,8,8,5,7,5,7,2,8,7,10
R,5,7,6,5,6,8,9,7,3,8,4,6,5,7,7,9
T,3,5,4,7,1,7,14,0,6,7,11,8,0,8,0,8
J,2,7,3,5,3,9,7,1,5,10,4,7,0,7,1,6
I,2,11,2,8,2,8,7,0,8,7,6,7,0,8,3,7
A,2,4,4,3,2,8,2,2,2,7,2,8,2,6,2,7
E,4,8,5,6,4,6,8,2,8,11,7,9,2,8,5,7
S,9,15,8,9,4,11,2,4,5,11,2,10,3,6,5,12
Y,3,7,4,5,4,9,5,6,5,7,8,8,3,9,7,4
T,3,9,4,6,2,9,13,0,6,6,10,8,0,8,0,8
S,1,0,2,0,0,8,8,4,6,5,6,7,0,8,7,8
V,2,3,4,4,1,8,8,4,2,6,13,8,3,10,0,8
E,3,5,6,3,3,7,6,2,8,11,6,10,2,8,4,9
H,3,5,5,6,4,11,5,3,2,8,4,9,4,8,5,11
O,2,1,3,2,1,7,7,7,6,7,6,8,2,8,3,8
V,5,6,7,6,7,7,8,6,5,7,6,7,6,9,7,10
T,3,4,4,3,2,6,10,2,8,11,9,5,1,10,3,4
Z,6,6,5,9,4,7,8,4,2,11,7,7,3,9,10,6
C,3,4,4,3,1,5,9,5,7,12,9,11,1,10,3,7
O,5,10,6,8,5,7,8,8,5,10,8,8,3,8,3,8
V,2,6,4,4,1,8,8,4,2,6,13,8,3,10,0,8
P,6,11,8,8,5,7,11,6,4,12,5,2,1,11,4,9
U,6,11,6,6,3,6,5,4,5,6,8,8,5,7,2,8
Y,2,3,2,2,1,4,11,2,6,11,10,5,1,10,1,5
R,4,10,5,7,3,5,10,9,4,7,4,8,3,7,6,11
V,5,7,7,6,7,8,6,5,5,7,6,8,7,10,6,7
S,2,7,3,5,2,8,7,7,7,7,7,8,2,9,9,8
N,7,9,8,4,3,9,6,4,4,13,2,7,6,8,0,7
B,2,5,4,4,3,8,8,3,5,10,6,6,3,7,5,9
F,2,3,4,2,2,7,9,2,6,13,6,5,2,9,2,7
T,3,2,4,3,2,7,12,2,7,7,11,8,1,11,1,7
B,2,4,3,3,2,8,7,3,5,9,6,6,2,8,5,9
K,5,8,8,7,7,7,7,2,4,7,3,8,6,4,8,11
F,6,11,5,6,3,7,9,2,6,11,6,5,2,10,5,6
T,3,9,5,6,3,7,12,2,8,7,12,8,1,11,1,8
P,3,5,6,4,3,7,10,4,4,12,4,3,1,10,3,8
T,5,11,7,8,6,7,11,2,7,7,11,8,2,12,1,8
O,2,1,2,2,1,8,7,7,4,7,6,8,2,8,3,8
F,3,5,3,4,2,5,10,4,5,10,9,5,1,10,3,7
R,3,7,3,5,3,6,10,8,4,7,3,9,2,6,4,10
J,2,9,4,6,4,10,7,3,3,9,3,6,2,7,7,7
W,9,13,9,7,5,5,9,2,2,7,10,8,10,12,1,6
X,4,8,7,6,3,7,7,1,8,10,7,9,3,8,3,7
T,3,7,4,5,2,6,14,1,5,8,10,7,0,8,0,8
F,3,11,4,8,3,1,13,4,4,12,10,7,0,8,2,6
N,8,15,7,8,4,7,9,4,7,4,4,9,5,8,2,8
C,3,6,4,4,2,4,9,6,7,12,9,10,2,10,3,7
Z,3,8,4,6,2,7,7,4,14,9,6,8,0,8,8,8
B,4,8,5,6,4,6,6,8,7,6,6,7,2,8,9,10
P,6,9,9,7,6,6,13,7,2,12,5,2,1,11,4,8
R,4,8,6,6,4,8,7,6,6,9,5,7,3,8,5,11
F,2,4,4,3,2,7,9,2,6,13,6,6,1,9,2,8
K,6,11,6,8,3,4,9,9,2,7,3,11,4,8,2,11
O,4,8,5,6,3,7,6,8,5,7,5,8,3,8,3,8
V,6,10,6,7,4,4,12,1,2,8,10,7,6,12,2,7
E,4,7,6,5,4,7,8,2,7,11,6,9,3,8,4,9
D,5,11,6,8,5,9,7,5,7,10,4,5,3,8,3,8
L,9,15,8,8,4,9,2,4,5,12,4,13,2,7,6,8
R,4,8,6,6,5,6,8,5,6,6,5,7,3,7,5,8
W,5,7,5,5,5,5,9,3,3,9,7,7,6,11,3,6
B,2,3,4,2,2,9,7,2,5,10,5,6,2,8,4,9
C,6,12,4,6,2,5,10,5,8,11,8,9,2,8,5,8
L,4,10,4,8,1,0,0,6,6,0,0,5,0,8,0,8
P,3,5,4,4,3,5,10,4,4,10,8,4,1,10,3,7
V,5,10,7,9,8,7,7,6,4,7,6,8,7,10,8,11
Y,4,4,6,6,1,7,10,3,2,7,13,8,2,11,0,8
S,1,1,2,1,0,8,7,4,7,5,6,7,0,8,7,8
L,6,14,6,8,4,8,4,3,4,12,8,11,4,9,6,10
B,6,10,9,7,7,8,7,5,6,9,5,6,3,7,7,10
R,6,11,6,6,5,7,8,3,6,8,3,8,6,6,6,7
Z,2,2,3,3,2,7,7,5,9,6,6,8,1,8,7,8
C,4,9,5,7,4,6,8,6,8,7,6,13,1,8,4,9
Z,8,14,8,8,5,7,7,2,9,11,7,9,4,7,7,5
M,7,11,10,8,13,11,5,3,3,9,4,8,11,8,4,8
H,5,7,7,5,4,5,8,4,6,10,9,9,3,8,3,7
T,5,10,7,8,6,6,7,7,7,8,9,8,4,9,7,7
T,8,14,7,8,4,5,11,2,7,12,8,6,3,8,5,3
G,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,9
O,1,0,2,1,0,8,7,7,4,7,6,8,2,8,3,8
H,3,4,6,3,3,7,7,3,6,10,6,8,3,8,3,7
P,3,5,4,8,7,8,7,5,1,7,6,7,6,9,5,9
K,7,9,9,7,7,6,7,1,7,9,6,10,4,8,4,8
Q,6,8,6,9,6,8,7,7,4,8,7,10,3,8,6,8
X,4,6,5,5,5,7,9,2,6,8,6,8,3,6,6,7
Z,4,6,6,9,4,12,3,3,6,10,2,8,1,7,5,11
L,4,8,5,6,2,3,1,7,9,0,1,3,0,7,1,5
X,9,13,10,7,5,7,7,2,9,11,5,8,4,7,4,7
Q,6,11,6,6,4,11,3,4,5,12,3,9,3,9,7,12
W,4,5,6,3,4,6,11,2,2,7,9,8,8,11,1,8
J,3,6,4,4,2,6,8,2,5,14,7,9,1,6,0,7
A,2,3,3,1,1,8,2,2,1,7,2,8,2,7,2,7
P,1,0,2,0,0,5,11,7,1,9,6,4,1,9,3,8
M,2,3,4,1,2,5,6,3,4,10,10,10,4,7,1,7
T,4,11,6,8,2,8,15,1,6,7,11,8,0,8,0,8
F,3,2,4,3,2,6,10,4,5,10,9,4,2,10,3,6
Y,9,15,8,8,5,5,7,4,3,9,9,6,4,9,4,4
J,2,6,2,4,1,13,2,8,4,13,4,12,0,7,0,8
P,4,8,6,6,4,9,7,2,5,13,4,5,1,10,3,10
D,5,5,5,7,3,6,7,10,10,7,7,6,3,8,4,8
M,5,7,7,6,8,5,7,5,4,6,5,8,11,7,5,9
F,3,4,5,3,2,6,10,3,6,13,7,5,1,10,2,7
W,3,1,5,3,3,7,11,3,2,6,9,8,7,11,0,8
U,3,3,4,2,2,6,8,5,7,6,9,9,3,10,1,7
J,4,8,6,6,2,9,6,2,8,15,4,8,0,7,0,8
P,5,5,7,7,8,8,7,4,2,7,8,7,7,12,5,7
Z,5,10,6,8,3,7,7,4,15,9,6,8,0,8,8,8
C,5,9,6,7,7,5,6,3,4,7,6,11,6,9,3,8
E,3,2,4,3,3,7,7,5,8,7,5,8,2,8,6,9
K,4,7,5,5,4,6,6,1,6,10,7,10,3,8,3,8
U,5,11,7,8,5,4,9,6,7,9,11,10,3,9,1,8
B,7,11,7,6,5,9,7,3,5,10,5,7,6,7,7,8
X,3,4,4,6,1,7,7,4,4,7,6,8,3,8,4,8
Z,4,11,4,9,4,6,8,6,10,7,7,9,2,9,8,8
O,2,3,3,2,1,7,7,7,4,7,6,8,2,8,2,8
X,5,5,6,7,2,7,7,5,4,7,6,8,3,8,4,8
R,5,10,6,8,7,8,5,7,3,8,6,8,6,5,6,10
Y,2,3,4,4,1,8,10,2,2,6,13,8,2,11,0,8
I,3,10,4,8,3,7,9,0,7,13,6,7,0,9,2,7
V,5,11,7,8,9,8,5,5,2,8,7,8,6,8,5,8
J,5,11,7,8,8,9,7,4,4,8,4,6,4,7,6,4
C,5,10,5,7,3,3,8,5,7,11,10,13,1,9,3,8
N,3,5,4,7,3,7,7,14,2,5,6,8,6,8,0,8
P,3,8,4,5,2,5,10,10,4,8,6,5,2,10,4,8
O,2,5,3,3,2,7,7,8,4,7,6,8,2,8,3,8
O,3,7,4,5,3,7,8,7,4,8,7,10,3,8,3,9
A,2,6,4,4,2,11,3,3,2,10,1,9,2,6,2,8
R,3,6,4,4,4,7,8,4,6,6,5,7,3,7,5,8
W,4,2,6,4,4,8,11,2,2,6,9,8,9,13,1,7
D,4,6,5,4,3,8,8,7,8,9,5,3,3,8,4,6
T,3,8,4,6,2,8,14,0,5,6,10,8,0,8,0,8
N,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8
H,4,8,6,10,7,9,10,3,1,8,7,6,3,10,8,7
D,3,7,5,5,4,8,7,3,5,10,5,6,3,8,3,8
R,5,9,7,7,6,10,6,3,5,10,3,7,4,6,4,10
B,7,10,6,6,4,8,5,4,5,10,6,9,5,7,7,10
A,5,8,8,7,7,7,7,2,5,7,9,10,5,11,3,7
A,2,1,3,1,1,7,4,2,0,7,1,8,3,6,1,8
T,4,10,5,8,4,5,12,4,7,9,12,7,2,12,1,7
O,9,13,6,7,4,5,8,6,4,10,8,9,5,10,5,8
J,5,8,6,6,3,8,8,1,6,14,5,7,1,6,1,8
Y,4,4,6,6,6,9,10,5,4,6,7,7,5,10,7,5
L,6,15,6,8,4,7,5,3,5,11,7,11,3,8,6,9
Q,4,6,5,7,5,8,5,7,3,6,5,9,3,8,5,9
B,5,7,7,5,6,7,9,4,6,10,6,6,3,7,6,8
R,5,8,7,7,7,7,8,3,3,7,5,7,6,8,6,6
E,4,8,4,6,2,3,6,6,11,7,7,15,0,8,7,7
M,7,10,10,8,6,10,5,2,5,9,4,7,8,6,2,9
Q,4,9,5,10,5,7,7,6,3,8,9,10,3,8,7,9
U,4,5,4,8,2,7,4,15,6,7,12,8,3,9,0,8
E,6,9,8,8,8,7,9,5,6,6,8,11,8,9,9,8
N,4,6,6,4,5,4,10,2,4,8,7,9,6,7,4,5
G,5,8,6,6,3,7,7,7,8,10,6,11,2,10,4,9
D,2,4,4,3,2,9,7,4,6,10,4,6,2,8,3,8
U,8,9,9,7,4,3,8,6,8,10,11,10,3,9,2,5
A,5,11,9,8,7,10,5,1,5,9,1,5,3,7,4,8
P,4,10,5,8,5,4,10,4,5,11,9,5,1,10,3,7
L,5,11,6,8,5,3,4,3,8,2,0,8,0,7,1,5
P,4,11,5,8,3,4,12,9,2,10,6,4,1,10,4,8
L,2,2,3,4,2,4,4,5,7,2,2,5,1,6,1,6
B,3,6,5,4,4,7,8,4,5,9,6,5,2,8,6,6
E,3,4,4,6,2,3,8,6,10,7,6,14,0,8,7,7
C,3,8,4,6,3,6,8,6,5,9,8,14,1,9,3,11
H,4,5,6,3,3,8,8,3,6,10,6,8,3,8,3,8
L,3,7,3,5,1,0,1,6,6,0,1,6,0,8,0,8
T,5,7,7,6,6,5,8,3,8,7,7,9,3,7,7,5
O,2,2,3,3,2,7,7,7,4,7,6,8,2,8,3,8
D,7,11,6,6,5,8,8,4,7,10,5,6,6,6,7,6
K,4,6,6,4,4,3,8,2,6,10,10,11,3,8,3,6
B,3,2,4,3,3,7,7,5,6,6,6,6,2,8,7,10
F,2,1,2,2,1,5,11,3,5,11,9,5,1,10,3,6
Y,7,8,6,11,5,7,6,5,5,6,11,6,4,11,3,7
R,8,10,6,6,4,6,8,5,5,9,6,9,7,5,7,11
Y,7,9,7,7,4,3,10,2,7,11,11,6,1,11,2,5
K,5,9,7,7,7,6,6,3,7,6,6,9,7,8,5,9
M,3,3,5,2,3,8,6,6,4,7,7,8,7,5,2,7
Y,3,8,5,5,1,7,10,3,2,7,13,8,2,11,0,8
G,3,5,4,7,2,7,7,8,7,5,6,10,2,7,5,11
K,5,4,5,6,2,4,8,9,1,7,6,11,3,8,3,11
J,1,3,2,2,1,8,5,4,4,13,7,12,1,7,0,8
X,4,11,6,8,4,7,7,4,9,6,6,8,3,8,7,7
W,6,10,6,8,6,1,10,2,3,10,10,9,6,10,1,7
A,4,9,6,6,2,8,4,3,1,7,1,8,3,7,2,8
V,9,15,8,8,4,5,10,4,4,9,9,6,4,11,2,8
H,3,1,4,2,3,7,7,6,6,7,6,8,3,8,4,8
I,7,14,6,8,4,8,9,3,7,14,4,5,2,8,5,9
B,3,5,5,4,4,8,8,3,6,10,5,5,3,7,6,9
D,4,8,6,6,4,8,8,6,7,9,5,4,3,7,4,9
M,6,11,9,9,10,9,7,5,5,6,7,6,10,9,3,6
G,3,5,4,4,2,6,7,6,7,7,5,10,2,9,4,9
H,2,1,3,2,2,8,7,6,5,7,6,8,3,8,3,8
D,2,3,3,2,2,9,6,4,6,10,5,6,2,8,2,8
G,3,4,5,7,2,8,7,9,7,5,6,10,2,7,5,10
M,3,8,4,6,3,7,7,11,1,7,9,8,8,5,0,8
G,6,11,8,9,9,9,7,5,2,6,6,10,8,8,5,10
A,2,8,4,6,3,11,3,3,3,10,2,9,2,6,3,8
O,5,9,6,6,3,7,7,8,8,7,6,8,3,8,4,8
Y,3,5,4,6,5,8,10,6,4,7,7,6,4,10,6,3
D,2,5,3,3,3,7,7,6,6,7,6,5,2,8,2,7
J,6,11,5,8,4,5,13,3,3,13,6,4,2,8,6,6
C,2,5,3,4,2,6,8,7,7,9,8,13,1,9,4,10
M,4,10,5,8,6,7,5,11,1,7,9,8,9,5,2,8
T,3,4,5,6,1,7,15,1,6,7,11,8,0,8,0,8
L,5,11,7,8,4,7,3,2,8,7,2,8,1,6,3,7
J,1,6,2,4,1,10,6,1,7,11,3,7,0,7,1,7
D,4,9,5,6,8,8,10,5,4,8,6,5,6,10,9,6
Z,5,7,7,5,6,9,5,5,4,7,5,7,3,7,10,5
R,4,6,5,4,5,8,7,6,3,8,5,7,4,7,7,10
V,5,10,5,7,4,4,11,1,2,8,10,7,3,10,1,8
H,5,8,7,6,7,8,7,6,7,7,6,7,3,8,4,7
K,9,14,9,8,6,3,8,4,6,10,11,12,5,8,4,6
O,6,11,9,8,11,8,6,6,1,7,6,8,11,8,6,8
M,7,9,9,7,7,6,6,3,5,10,9,9,10,7,3,8
R,4,4,5,6,3,5,13,8,4,8,3,9,3,7,7,11
X,5,10,6,8,4,7,7,4,4,7,6,7,3,8,4,8
O,9,15,6,9,5,4,8,6,4,10,8,9,5,10,5,8
L,5,7,6,5,4,6,6,7,7,5,6,10,2,8,4,10
Z,4,7,6,5,4,9,6,2,8,11,4,10,2,7,6,10
F,4,7,6,5,4,5,11,3,5,13,7,5,1,10,2,7
Q,5,5,7,4,5,8,5,5,4,7,4,10,5,6,6,8
C,8,12,6,6,5,7,7,4,4,9,8,10,4,9,8,10
G,3,7,4,5,3,6,5,5,6,6,6,9,2,9,3,7
V,4,5,6,3,2,4,12,3,3,10,11,7,2,10,1,8
I,6,9,8,7,4,6,7,2,7,7,6,11,0,8,4,8
C,4,5,6,8,2,6,8,7,10,5,7,13,1,7,4,9
D,4,8,5,6,4,7,6,7,8,6,5,5,3,8,3,7
X,5,11,6,8,2,7,7,5,4,7,6,8,3,8,4,8
E,5,11,5,8,3,3,7,6,11,7,6,14,0,8,8,7
E,1,1,2,2,1,4,7,5,8,7,6,13,0,8,7,9
S,4,10,5,7,5,8,9,8,6,7,4,6,2,6,9,8
V,5,9,7,7,3,9,12,3,4,4,11,9,3,10,1,8
D,2,6,4,4,3,9,6,4,6,10,4,6,3,8,3,8
L,4,10,4,8,1,0,1,5,6,0,0,6,0,8,0,8
D,3,8,5,6,7,9,8,4,5,7,6,6,5,6,8,6
U,3,5,5,5,4,8,7,4,4,6,7,7,4,8,1,6
I,2,6,3,4,1,7,7,0,7,13,6,8,0,8,1,8
V,6,7,8,6,9,8,6,5,5,7,6,8,8,10,7,4
F,8,14,7,8,5,10,6,3,5,10,4,6,4,8,7,10
M,5,7,8,5,7,8,7,2,4,9,6,7,7,5,2,7
I,4,10,6,8,4,7,7,0,8,13,6,8,0,8,1,8
W,4,5,5,4,3,4,10,3,2,9,9,7,6,11,1,6
I,4,5,5,6,5,7,10,4,5,8,7,8,4,7,6,6
H,8,10,8,5,5,8,7,3,5,10,5,7,6,9,4,8
A,4,10,6,7,2,7,5,3,1,7,0,8,3,7,2,8
N,6,8,9,7,9,6,7,4,4,6,5,8,8,9,5,8
P,6,9,9,7,4,7,10,2,7,14,5,3,3,8,4,8
V,4,5,6,8,2,7,8,4,3,7,14,8,3,9,0,8
R,7,9,10,8,11,7,7,4,4,7,5,8,7,8,6,6
R,4,8,5,6,5,8,8,7,5,8,5,7,7,8,6,11
I,4,11,6,8,3,7,7,0,8,14,6,8,0,8,1,8
E,3,5,5,4,3,8,7,2,7,11,5,8,2,9,5,10
F,5,9,5,6,2,1,12,5,6,12,11,9,0,8,2,6
G,4,5,5,4,3,6,7,5,5,9,8,9,2,8,5,9
O,5,9,6,7,5,8,6,8,6,7,5,8,3,8,4,7
A,4,8,6,6,6,8,8,7,4,6,6,8,3,7,7,4
S,4,8,4,6,4,8,8,5,7,5,5,7,0,8,8,8
J,2,5,3,8,1,12,3,10,3,13,6,13,1,6,0,8
E,3,2,3,3,3,7,7,5,7,7,6,9,2,8,5,10
Q,6,14,5,8,5,9,6,4,6,11,5,7,4,8,9,10
F,3,8,4,6,3,3,12,4,5,12,10,5,1,10,3,5
X,4,8,6,6,4,8,7,1,8,10,4,7,3,8,3,8
X,3,3,4,4,1,7,7,4,4,7,6,8,3,8,4,8
G,5,9,4,4,3,7,8,3,3,8,6,6,3,9,8,7
U,3,5,4,3,2,6,8,6,7,7,10,9,3,9,0,8
Z,2,7,4,5,3,8,7,2,6,7,6,7,0,9,8,8
U,6,8,7,6,3,3,9,6,7,11,11,9,3,9,1,6
Q,4,7,5,8,5,8,6,7,5,9,6,9,3,8,5,7
V,3,5,5,4,2,4,12,3,3,9,11,7,2,10,1,8
K,3,4,4,3,2,5,7,4,7,7,6,11,3,8,5,9
U,5,9,7,6,9,7,6,4,4,7,7,8,10,8,6,6
C,6,10,6,8,4,5,8,6,8,12,9,13,2,9,4,7
N,4,7,4,5,3,8,7,12,1,6,6,8,5,8,0,9
H,4,4,6,3,3,7,6,3,6,10,5,9,4,6,4,7
Y,6,10,6,5,3,6,8,4,3,10,8,5,3,10,4,4
G,2,1,4,2,2,7,7,6,6,6,6,10,2,9,4,9
T,4,6,6,6,5,5,8,4,8,8,8,9,3,9,7,6
B,5,9,8,7,7,9,7,3,7,11,4,7,3,7,5,9
B,3,5,6,4,4,9,6,3,6,10,5,7,2,8,5,9
V,5,9,5,7,4,3,11,2,3,9,11,8,3,12,1,7
Z,4,8,5,6,2,7,7,4,14,9,6,8,0,8,8,8
X,3,9,5,6,4,7,7,3,8,5,6,8,2,8,6,7
S,7,11,8,8,6,6,7,3,6,10,7,8,3,7,5,6
V,7,9,6,7,3,4,11,3,4,9,11,7,3,10,1,7
R,5,9,6,6,7,8,8,7,3,8,4,6,5,7,8,9
U,2,1,3,2,1,6,8,6,6,6,9,9,3,9,0,8
J,4,8,3,12,3,9,7,3,3,11,5,5,3,8,6,10
Q,5,9,6,11,6,8,6,7,4,9,7,10,3,8,6,8
V,11,14,8,8,4,9,10,6,5,6,10,6,6,13,3,6
S,4,6,5,4,3,8,7,3,6,10,6,8,2,9,5,8
S,3,8,4,6,2,7,7,6,9,5,7,10,0,8,9,8
C,6,11,5,6,3,7,8,4,3,8,8,10,4,8,7,11
Q,4,6,6,8,8,9,9,5,0,6,7,10,6,12,5,10
H,1,0,1,0,0,7,7,10,2,7,6,8,2,8,0,8
M,5,11,7,8,9,8,9,7,4,7,7,8,8,8,9,5
V,3,7,4,5,2,8,9,3,1,7,12,8,2,10,0,8
S,5,11,6,8,5,7,8,3,7,10,4,6,2,6,5,8
E,4,10,4,8,4,3,8,5,9,7,6,14,0,8,6,8
G,5,11,5,8,4,5,6,7,6,7,7,11,3,7,5,8
T,2,1,3,2,0,7,15,1,4,7,11,8,0,8,0,8
F,4,9,4,6,2,1,14,5,3,12,9,5,0,8,2,6
J,5,11,7,8,4,8,6,3,6,15,4,9,0,7,0,7
P,4,9,5,6,4,5,11,8,2,9,5,4,1,10,3,7
D,3,7,5,5,3,10,6,4,7,10,3,6,3,8,3,9
L,3,3,4,5,1,1,0,6,6,0,1,5,0,8,0,8
G,5,10,6,7,5,6,6,6,6,10,7,13,3,9,5,9
Q,2,2,3,3,2,8,7,6,3,6,6,9,2,9,3,10
L,5,10,5,8,3,0,1,5,6,0,0,7,0,8,0,8
T,6,9,6,7,3,4,14,5,6,12,9,3,1,11,2,4
F,1,3,3,1,1,6,10,3,4,13,7,5,1,9,1,7
N,5,7,7,5,4,4,10,3,4,10,10,9,5,8,1,8
X,2,3,4,2,2,8,7,1,7,10,5,8,2,8,2,8
I,0,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8
C,4,9,3,4,2,6,8,4,3,9,8,10,3,9,8,9
L,4,10,6,7,4,8,4,0,8,10,3,10,0,7,3,9
A,3,7,6,5,3,11,2,3,2,10,2,9,2,6,3,9
T,2,8,3,5,1,8,13,0,6,6,11,8,0,8,0,8
N,4,7,6,5,4,5,9,3,4,10,9,8,5,8,1,8
V,3,1,5,3,1,7,12,2,3,7,11,9,2,10,1,8
L,2,3,2,2,1,4,3,4,8,2,2,5,0,7,1,6
H,5,9,8,7,7,5,9,3,6,10,9,9,3,9,4,6
X,8,12,9,6,5,8,7,2,7,11,4,7,5,10,4,8
S,4,8,5,6,5,8,6,7,5,6,8,9,3,9,9,8
Z,3,7,4,5,3,6,8,5,9,7,7,9,1,9,7,7
Y,5,8,7,11,11,8,8,4,2,7,8,9,4,11,8,8
Y,2,2,3,3,1,7,10,1,6,7,11,9,1,11,1,8
K,6,11,9,8,7,7,7,4,7,6,5,8,7,8,5,9
E,3,3,3,4,2,3,7,6,10,7,6,14,0,8,7,7
F,3,5,5,3,2,6,12,3,5,13,7,4,1,10,2,6
K,5,9,7,7,9,8,7,3,4,6,7,8,10,8,8,7
H,3,7,3,4,2,7,9,14,3,7,3,8,3,8,0,8
O,3,6,5,4,3,8,8,8,4,7,6,7,3,8,3,8
R,5,10,8,8,9,6,7,3,4,7,6,9,8,10,8,5
C,2,5,3,3,2,6,8,7,7,8,8,13,1,9,4,10
H,7,10,10,8,6,8,7,3,7,10,5,8,3,8,3,8
X,6,9,8,8,9,6,8,2,5,7,7,10,4,5,8,7
E,4,10,5,8,7,8,7,3,5,6,7,10,4,9,8,8
A,3,6,5,8,2,7,5,3,1,7,0,8,3,7,2,8
Z,3,5,5,4,3,7,8,2,9,12,6,8,1,9,5,7
Y,5,7,6,5,3,4,9,1,8,10,10,6,1,10,3,4
A,2,1,4,2,1,7,2,2,1,6,2,8,2,7,2,7
A,3,9,6,6,4,11,3,1,2,8,2,9,2,6,2,7
J,2,7,4,5,2,7,8,2,5,14,5,7,0,6,1,7
C,4,5,5,3,2,4,8,4,8,11,9,12,1,9,3,7
Q,6,8,6,9,6,7,7,7,4,9,9,9,4,8,7,9
F,5,8,7,6,4,8,9,3,6,13,5,5,2,9,2,7
U,5,10,6,8,4,3,9,5,7,11,10,9,3,9,2,7
S,5,11,6,8,6,8,8,7,5,7,5,7,2,8,9,8
E,3,9,4,6,4,3,8,5,9,7,7,14,0,8,6,9
I,1,3,1,1,1,7,7,2,7,7,6,8,0,8,2,8
E,4,9,6,7,5,7,7,2,7,11,7,9,3,8,4,8
F,3,5,4,4,3,5,10,3,5,10,9,5,1,10,3,6
U,3,2,4,4,2,6,8,6,6,7,9,9,3,9,1,8
O,2,2,3,3,2,7,7,7,4,7,6,8,2,8,3,8
H,10,12,9,7,5,7,7,5,5,8,9,8,7,11,5,9
X,2,1,3,2,2,7,7,3,9,6,6,8,2,8,5,8
Q,3,5,4,6,4,8,7,5,2,8,7,10,3,9,5,8
L,4,9,5,7,3,3,4,3,9,2,0,7,0,7,1,5
N,5,9,7,6,4,6,9,6,5,8,7,8,5,8,1,7
S,4,10,5,8,5,8,9,8,6,7,4,5,2,6,8,8
T,4,8,5,6,3,7,11,3,8,11,9,4,2,11,3,5
E,5,9,8,7,8,6,7,3,7,5,6,11,4,10,11,8
T,2,7,4,5,2,6,12,3,7,8,11,7,1,11,1,7
J,2,7,2,5,1,12,2,9,4,13,5,12,1,6,0,8
L,1,3,2,1,0,6,4,1,7,8,2,10,0,7,2,9
X,5,10,6,7,2,7,7,5,4,7,6,8,3,8,4,8
O,4,6,5,5,4,8,5,4,4,9,4,10,3,7,5,7
P,3,5,5,7,6,8,11,3,1,8,8,6,6,10,4,6
U,4,9,5,7,2,7,5,15,5,7,13,8,3,9,0,8
U,7,11,9,8,8,7,6,9,6,7,6,9,6,8,7,2
N,4,6,6,4,3,7,8,3,5,10,6,7,5,8,1,7
Y,9,12,8,6,5,7,5,4,5,9,9,5,4,10,4,5
M,4,2,5,4,4,8,6,6,4,6,7,8,8,6,2,7
P,3,10,4,8,4,4,12,7,2,10,7,4,1,10,3,8
A,2,3,4,2,1,6,2,2,2,5,2,8,2,6,2,6
K,3,7,5,5,6,8,6,3,4,6,6,8,6,8,6,8
S,3,4,5,3,2,9,6,3,8,11,4,7,1,9,4,9
G,4,7,5,5,2,8,6,7,7,6,6,9,2,8,6,11
B,4,10,5,7,4,6,8,9,7,7,5,7,2,8,9,9
U,4,4,5,6,2,7,4,14,5,7,14,8,3,9,0,8
Q,4,9,6,7,6,8,5,7,4,6,6,8,3,7,5,9
Z,5,6,4,9,3,6,9,5,3,12,7,7,2,9,10,6
N,6,9,8,7,4,6,8,3,4,10,8,8,5,8,1,7
R,5,5,6,8,4,6,9,10,5,6,5,8,3,8,6,11
V,2,3,3,2,1,7,12,2,2,6,10,9,2,11,0,8
M,5,9,5,6,6,7,5,11,0,7,8,8,7,5,1,9
L,3,4,4,6,1,0,0,6,6,0,1,5,0,8,0,8
N,4,5,4,3,3,7,8,5,5,7,7,6,6,9,3,6
E,4,9,6,7,5,9,6,2,7,11,5,9,3,7,6,10
J,4,6,5,4,2,8,6,4,7,15,6,10,1,6,1,7
V,4,11,5,8,4,6,9,4,1,8,12,8,3,9,1,8
A,2,7,4,4,1,8,5,3,1,7,0,8,2,7,2,8
P,1,1,2,1,1,5,11,7,2,10,6,4,1,9,3,8
Y,9,11,9,8,6,5,8,0,9,8,9,5,4,13,7,3
L,4,5,5,4,2,4,4,5,8,2,2,5,1,6,1,5
Q,7,8,7,10,7,8,5,7,5,9,6,9,3,8,6,7
U,5,11,5,8,4,7,5,12,4,7,13,8,3,9,0,7
D,4,9,5,7,3,5,7,10,9,6,5,5,3,8,4,8
B,4,11,5,8,4,6,8,9,8,7,5,7,3,8,9,10
N,3,6,3,4,3,8,7,11,1,6,6,8,5,9,0,6
L,4,9,5,8,6,7,6,5,4,7,7,8,2,9,7,10
U,5,9,5,6,2,8,4,14,6,7,14,8,3,9,0,8
V,4,10,5,8,3,6,9,4,1,8,12,8,4,9,1,8
P,6,11,9,8,7,9,8,2,5,13,5,5,3,9,4,9
E,6,8,8,6,6,7,7,2,8,11,5,9,3,8,4,9
A,4,8,5,6,3,7,5,2,0,7,1,8,2,7,1,8
Y,7,9,7,6,3,4,10,2,8,10,11,5,2,13,4,3
W,5,9,8,6,6,7,10,2,3,7,9,8,8,11,1,8
K,8,13,8,7,4,9,7,3,7,9,3,6,5,8,4,8
D,5,9,7,8,7,6,7,6,7,7,6,9,5,5,7,4
P,0,0,1,0,0,5,10,6,1,9,6,5,1,9,2,8
C,2,2,3,3,2,6,8,7,7,8,7,13,1,9,4,10
Y,3,7,5,4,1,8,11,2,2,5,12,8,1,11,0,8
J,2,5,3,3,1,9,6,3,6,12,4,9,1,6,1,7
C,6,11,8,9,6,7,7,8,6,6,6,13,6,8,5,7
Q,5,9,5,10,6,8,6,7,5,9,6,9,3,8,6,8
D,3,5,5,4,3,9,6,4,7,9,4,6,2,8,3,8
I,5,12,4,6,2,11,5,3,6,12,3,7,2,9,4,11
G,4,7,5,5,3,5,8,5,5,9,9,8,2,8,4,9
M,6,9,8,7,8,7,7,5,5,6,7,8,9,7,3,7
A,4,9,6,6,2,8,3,3,3,7,2,8,3,6,3,8
I,2,8,2,6,2,7,7,0,7,7,6,8,0,8,3,8
G,6,7,8,6,7,7,8,5,3,7,7,8,9,10,9,7
Q,4,7,5,9,5,9,7,7,3,5,7,10,3,8,7,10
D,4,8,4,6,4,6,7,9,7,7,7,6,2,8,3,8
D,3,8,5,6,7,10,8,4,5,7,6,6,4,7,7,4
D,4,9,6,7,6,7,7,8,6,6,5,5,3,9,4,8
T,3,3,3,2,1,6,11,2,7,11,9,5,1,10,2,5
Z,2,5,5,3,2,7,8,2,9,12,6,8,1,9,5,8
D,3,9,4,6,3,6,7,11,9,7,6,6,3,8,4,8
M,6,10,9,8,7,10,6,2,5,9,4,7,8,6,2,8
Z,2,1,3,2,2,7,7,5,8,6,6,9,2,8,7,8
G,5,8,6,6,3,5,7,6,6,10,8,10,2,8,5,9
D,3,2,4,3,2,7,7,6,7,6,6,5,2,8,3,7
K,5,10,7,8,6,10,6,1,6,10,3,8,4,9,4,11
Z,6,10,8,8,6,8,7,2,9,12,5,8,1,8,6,8
C,6,10,6,8,4,4,7,6,6,13,10,12,2,11,3,7
K,6,11,9,8,7,6,6,1,6,9,6,10,5,7,5,8
O,4,9,5,6,4,8,6,7,3,10,4,8,3,8,3,7
M,5,5,7,4,5,8,6,6,5,7,7,8,8,6,2,8
R,4,5,4,6,3,5,11,8,4,7,3,9,3,7,6,11
U,4,10,6,7,4,5,8,7,7,8,10,10,3,9,1,8
Q,7,13,6,7,4,10,3,4,7,10,4,9,3,9,6,13
O,4,9,6,7,4,8,6,9,3,6,5,7,3,8,4,9
U,3,5,4,3,2,5,8,4,7,10,8,9,3,9,2,6
H,3,7,5,5,4,6,7,6,4,6,5,8,3,7,6,11
A,2,3,4,4,1,7,5,3,1,6,1,8,2,7,2,7
W,5,10,7,8,4,11,8,5,2,6,9,8,8,10,0,8
F,2,3,4,2,1,5,11,3,5,13,7,4,1,9,1,7
U,5,5,6,3,2,4,8,5,8,10,10,9,3,9,2,5
U,1,0,1,0,0,7,6,10,4,7,11,8,3,10,0,8
J,5,9,6,7,3,7,7,3,6,15,6,10,1,6,1,7
S,3,3,4,5,2,8,6,5,9,5,6,8,0,9,9,8
W,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8
Q,2,2,3,2,2,8,8,5,2,8,7,9,2,9,3,8
A,3,9,5,7,3,13,2,4,4,12,1,9,3,6,3,10
U,6,7,7,5,4,3,8,5,7,10,9,10,3,9,2,5
G,3,3,4,5,2,8,7,8,7,6,6,10,2,7,5,11
Q,4,7,5,6,3,8,7,8,6,6,7,9,3,8,5,9
B,6,11,8,8,8,9,7,3,6,9,4,6,3,8,5,9
E,5,8,7,6,4,5,9,4,10,12,10,8,2,8,5,3
E,5,7,7,5,4,6,7,4,9,12,8,9,2,8,5,7
M,6,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8
E,4,9,4,7,2,3,7,6,11,7,7,15,0,8,7,7
I,4,11,6,8,3,7,7,0,8,14,6,8,0,8,1,8
D,2,4,4,3,2,9,7,4,6,10,4,6,2,8,3,8
W,4,5,5,4,3,7,11,3,2,6,9,8,7,11,0,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
F,8,12,7,6,4,9,7,4,7,12,4,6,2,8,8,8
T,4,5,6,4,5,8,9,5,7,7,7,8,3,10,7,7
G,7,11,8,8,6,6,6,7,7,9,8,11,2,11,5,9
H,7,11,8,6,5,6,9,3,5,10,6,8,6,7,5,7
H,4,10,6,8,7,8,7,5,6,7,6,6,3,8,4,7
F,5,6,6,7,6,6,12,3,5,9,7,6,4,9,5,6
N,4,10,5,8,3,7,7,14,2,4,6,8,6,8,0,8
C,4,7,4,5,2,5,9,6,7,12,9,9,2,10,2,7
X,4,5,6,3,3,7,7,1,9,10,6,8,2,8,3,7
P,3,7,4,5,3,5,11,5,4,11,8,4,1,10,4,7
N,3,8,4,6,2,7,7,14,2,5,6,8,6,8,0,8
D,4,8,5,6,5,8,8,6,5,9,5,5,3,8,3,7
U,6,11,8,8,8,9,7,8,5,6,7,9,6,8,4,6
P,6,6,8,8,9,7,9,3,3,8,8,6,6,11,6,5
B,0,0,1,0,0,6,7,6,4,7,6,8,1,8,5,10
X,4,7,7,5,3,5,9,2,8,10,10,8,3,8,4,6
T,3,5,4,7,1,10,14,1,6,4,11,9,0,8,0,8
O,3,10,5,8,5,8,6,8,4,6,5,6,3,8,3,7
Z,4,7,5,5,4,9,9,6,4,7,5,8,3,9,10,7
T,1,1,2,1,0,7,14,1,5,7,11,8,0,8,0,8
W,3,4,4,2,2,6,11,3,2,8,7,7,6,11,1,6
T,7,9,6,4,2,4,12,3,7,13,8,4,2,8,3,4
T,2,6,3,4,1,7,12,0,5,7,10,8,0,8,0,8
A,5,11,5,6,4,9,4,5,3,10,6,12,7,4,6,10
R,5,10,6,8,6,5,8,6,6,6,5,9,4,8,6,10
R,1,0,1,0,0,6,9,7,3,7,5,8,2,7,3,10
T,3,2,4,4,3,7,11,3,6,7,11,8,2,11,1,8
L,1,0,2,1,0,2,1,6,5,0,2,5,0,8,0,8
T,4,8,5,6,3,7,10,1,8,11,9,5,1,10,3,5
Y,2,3,3,5,0,7,10,1,3,7,12,8,1,11,0,8
T,5,11,5,8,3,5,11,2,8,12,10,4,1,11,2,4
S,2,6,3,4,1,6,5,5,8,5,6,11,0,9,8,8
N,6,8,8,6,4,9,9,3,5,10,3,5,5,9,1,7
F,4,7,6,5,5,5,9,2,4,10,8,7,5,10,3,4
M,3,1,4,3,3,8,6,6,4,7,7,8,7,6,2,7
H,9,11,12,8,8,5,7,4,7,10,9,10,3,8,4,6
D,6,9,6,5,3,9,5,4,5,12,3,8,5,7,5,10
J,4,9,6,6,5,9,8,3,4,8,4,6,4,8,5,4
O,4,7,5,5,2,8,7,8,7,7,6,9,3,8,4,8
P,2,4,4,3,2,8,9,3,4,12,4,3,1,10,3,8
C,3,9,5,7,3,4,8,7,7,9,9,14,2,9,4,9
B,3,5,5,3,3,9,7,3,6,10,5,6,2,8,5,9
T,2,1,3,3,1,7,12,3,6,7,11,8,2,11,1,8
F,4,4,4,6,2,1,13,5,4,12,10,7,0,8,2,6
J,1,3,2,4,0,14,3,6,4,13,2,10,0,7,0,8
L,3,7,5,5,3,7,3,2,7,8,2,9,2,5,3,8
E,3,5,6,3,3,7,7,2,8,11,6,9,2,8,4,8
S,4,7,5,5,6,8,8,4,3,8,5,7,3,8,10,8
P,7,12,6,6,3,8,9,7,5,14,4,4,4,10,4,7
A,2,6,4,4,2,8,2,2,2,7,2,8,2,6,3,6
F,5,10,7,8,4,5,13,5,4,13,8,3,2,10,2,5
H,6,9,6,4,3,8,8,4,4,8,6,6,6,10,4,8
S,5,11,7,8,4,8,9,6,10,5,6,6,0,8,9,7
B,3,7,4,5,3,6,7,8,6,7,6,6,2,8,9,10
G,7,11,8,9,5,7,6,7,7,12,6,12,3,11,5,8
Z,2,6,3,4,1,7,7,3,13,9,6,8,0,8,8,8
S,2,1,2,2,1,8,7,6,5,7,6,7,2,8,8,8
W,3,3,5,4,2,9,8,4,1,7,8,8,8,9,0,8
Z,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8
A,4,11,6,8,5,7,3,1,2,5,2,7,4,6,4,7
R,7,9,10,8,10,8,8,4,4,8,4,7,8,6,7,6
U,3,8,4,6,2,8,6,13,5,6,13,8,3,9,0,8
O,5,9,6,7,3,8,8,9,8,6,8,9,3,8,4,8
I,5,10,6,8,4,6,8,0,7,13,7,8,0,8,1,7
Z,3,5,5,7,4,9,4,3,5,7,3,6,3,8,7,6
V,6,11,5,6,3,9,10,5,4,7,10,5,4,11,3,6
R,6,10,8,8,6,8,8,5,7,8,3,9,4,5,5,12
N,7,9,9,8,9,8,7,5,4,7,5,7,7,9,6,5
C,4,7,5,5,2,6,8,6,10,6,7,12,1,7,4,8
F,3,8,6,6,6,11,6,1,5,9,5,6,4,10,4,7
A,3,8,6,6,4,10,5,1,4,8,2,6,2,7,4,8
X,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8
A,3,5,5,5,4,9,8,2,4,7,7,7,4,8,4,5
P,3,4,4,5,2,4,13,8,2,11,6,3,1,10,4,8
W,6,9,6,6,6,3,11,2,3,10,10,8,6,11,2,7
V,4,10,6,8,4,6,11,2,3,7,11,9,2,10,1,9
Q,3,5,3,6,4,8,9,5,1,6,7,11,2,9,5,9
B,5,10,5,8,4,6,8,9,7,7,6,7,2,8,9,10
C,5,10,6,8,2,6,7,7,11,7,6,14,1,8,4,9
L,4,8,4,6,2,0,1,5,5,0,1,6,0,8,0,8
S,5,10,6,7,3,7,7,6,9,5,6,8,0,8,9,7
V,2,2,4,3,1,7,12,2,3,7,11,9,2,10,1,8
F,2,4,4,2,1,6,10,2,5,13,7,5,1,9,1,7
E,4,9,4,6,2,3,8,6,10,7,6,14,0,8,7,7
T,6,7,6,5,3,7,10,2,10,11,9,4,1,11,4,5
Q,6,7,8,6,7,8,4,5,5,7,4,10,4,5,7,7
Z,1,3,3,2,1,7,8,2,9,11,6,8,1,8,5,7
K,1,0,1,0,0,5,7,7,1,7,6,11,2,8,2,11
M,3,4,6,3,3,7,6,3,4,9,7,8,7,5,1,8
E,1,3,2,2,2,7,7,5,6,7,6,8,2,8,5,10
U,6,9,8,8,7,7,6,5,4,6,7,8,7,8,2,7
D,3,8,3,6,2,5,7,10,8,6,6,5,3,8,4,8
E,4,6,4,8,3,3,7,6,11,7,6,15,0,8,7,7
W,9,11,9,9,7,7,11,4,3,8,6,6,11,13,4,4
W,5,11,7,8,9,8,7,6,2,7,8,8,6,8,4,8
H,4,4,5,3,4,6,7,6,6,7,6,10,3,8,3,9
Q,3,4,4,5,4,8,7,7,3,6,6,9,2,8,5,9
S,6,10,8,8,4,8,7,4,8,11,5,7,2,8,5,8
G,3,2,5,4,3,6,6,6,6,6,6,9,2,9,4,9
Z,4,10,5,7,4,6,8,6,10,7,7,10,1,9,8,8
A,8,12,6,6,3,10,1,2,2,10,4,12,3,5,4,8
R,4,9,5,6,3,5,12,8,4,7,3,9,3,7,6,11
F,6,11,8,8,8,7,6,5,5,7,6,8,6,10,8,12
Y,0,0,1,0,0,7,10,1,3,7,11,8,1,11,0,8
L,4,10,6,7,7,6,7,3,6,7,7,11,5,11,6,5
Z,5,8,6,6,5,9,11,6,6,6,5,9,3,8,8,5
B,3,7,5,5,5,8,7,5,6,6,6,6,2,8,5,9
V,3,3,4,2,1,3,12,4,3,11,11,7,2,11,1,8
E,2,4,4,3,2,7,7,2,7,11,6,8,2,8,5,9
E,7,13,5,7,4,9,5,4,5,11,4,9,3,9,7,11
Y,3,4,4,3,1,4,11,2,7,11,10,5,1,11,2,5
R,8,10,6,5,3,10,5,5,5,10,3,9,6,6,6,10
P,1,1,2,1,1,5,11,7,1,9,6,4,1,9,3,8
O,5,8,6,6,8,8,6,5,2,7,6,8,8,9,4,10
Y,4,5,5,4,2,3,11,3,6,12,11,6,1,11,2,5
Z,6,10,8,8,4,6,9,3,10,11,9,5,2,8,7,5
H,3,8,5,6,6,8,7,4,2,6,6,7,6,8,8,7
Y,7,10,7,8,4,4,10,2,8,10,11,6,2,10,4,3
T,6,9,6,6,3,7,10,2,9,11,9,4,3,9,4,4
P,2,4,3,2,2,5,10,4,4,9,8,5,3,10,3,7
G,2,2,4,3,2,6,6,6,6,7,6,11,2,9,4,9
W,7,8,7,6,6,4,12,3,2,9,8,7,6,12,2,6
X,3,2,5,4,3,8,7,3,9,6,6,8,3,8,6,8
D,5,9,8,6,5,10,6,5,8,10,3,5,3,8,4,9
K,4,5,5,7,2,4,8,8,2,7,4,11,3,8,2,11
J,3,9,5,6,3,7,7,3,6,15,5,9,1,7,1,7
H,5,5,6,7,3,7,6,15,0,7,7,8,3,8,0,8
T,2,5,4,7,1,5,14,1,6,9,11,7,0,8,0,8
K,7,9,7,5,4,6,8,2,6,10,4,9,5,6,3,8
X,4,8,6,6,3,5,8,2,8,10,10,9,3,8,3,6
P,2,1,2,1,1,5,10,4,4,10,8,4,0,9,3,7
S,3,6,4,4,3,8,7,7,5,7,6,8,2,8,9,8
U,6,10,7,8,4,4,8,6,8,10,9,9,3,9,2,5
S,3,8,4,6,2,8,7,5,9,5,6,7,0,8,9,8
M,7,8,10,6,7,11,6,2,5,9,3,6,9,8,2,9
Q,4,7,4,8,5,8,7,6,4,8,7,9,3,8,6,8
D,6,14,6,8,5,8,6,4,6,9,5,7,5,9,7,6
C,1,3,2,2,1,5,8,4,5,12,8,10,1,10,2,8
N,4,2,5,4,3,7,8,5,5,7,7,7,5,9,2,6
X,5,9,7,7,4,6,8,1,8,10,8,9,3,8,3,6
P,7,10,9,8,5,9,8,3,7,13,3,3,2,10,4,9
K,2,3,4,1,2,6,8,1,6,10,6,9,3,8,2,8
T,3,6,4,4,2,9,11,2,9,5,11,8,1,10,1,8
D,6,7,8,6,6,5,7,6,8,7,6,7,4,7,5,5
U,8,10,8,8,5,3,8,5,7,10,10,9,3,9,2,6
T,3,5,5,3,2,8,12,3,7,6,11,7,2,11,1,7
I,1,10,2,8,3,7,7,0,7,7,6,8,0,8,3,8
D,3,9,5,6,5,7,8,7,5,8,7,5,3,8,3,7
T,2,4,3,5,1,7,14,0,6,7,11,8,0,8,0,8
M,3,4,5,3,3,9,6,3,4,9,5,7,6,5,1,8
H,3,8,4,5,2,7,8,15,1,7,5,8,3,8,0,8
Z,5,11,7,8,5,8,7,2,9,12,6,7,1,7,6,7
H,8,10,11,8,7,9,7,3,6,10,4,7,3,8,4,8
I,1,5,3,4,1,7,7,0,8,13,6,8,0,8,1,7
A,4,10,5,7,4,9,4,3,1,8,2,8,2,7,2,8
G,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,9
L,3,7,4,5,2,5,4,1,9,7,2,10,0,7,2,7
O,3,5,4,3,2,8,7,8,5,7,7,8,2,8,3,8
G,6,10,8,8,9,7,7,6,2,7,6,11,7,9,10,7
W,3,6,4,4,3,9,10,2,2,6,9,8,6,11,1,8
J,2,9,3,7,2,10,7,0,7,10,4,6,0,7,1,7
N,10,12,8,6,4,4,8,4,6,3,2,12,6,11,2,7
F,3,7,3,5,2,1,11,3,4,11,11,8,0,8,1,7
S,3,2,3,3,2,8,8,6,5,7,6,7,2,8,9,8
G,4,10,6,7,3,7,5,7,10,5,4,10,1,9,6,11
H,5,9,7,7,5,9,7,3,6,10,5,7,3,8,3,9
B,5,9,7,6,7,8,7,4,4,7,6,7,4,8,6,8
W,5,7,7,5,4,5,8,5,1,8,10,9,8,11,0,8
U,4,5,5,4,2,4,8,5,7,10,9,9,3,9,2,5
S,8,15,6,9,3,8,4,4,5,8,2,8,4,6,6,9
L,5,9,5,5,3,10,4,3,3,12,7,11,3,10,4,10
W,4,9,6,6,3,6,8,4,2,7,8,8,8,10,0,8
N,7,9,6,4,3,4,9,4,7,3,3,11,5,9,2,8
O,2,1,2,2,1,7,7,7,4,7,6,8,2,8,2,7
O,7,11,9,8,10,7,10,5,3,8,7,7,11,11,7,10
L,7,14,7,8,4,6,5,4,5,12,9,11,3,9,7,8
S,3,7,4,5,5,8,8,5,3,8,5,8,4,9,10,6
B,6,9,9,7,6,10,6,3,7,11,3,7,6,8,7,11
H,2,6,2,4,1,7,8,14,1,7,5,8,3,8,0,8
G,3,1,4,2,2,7,7,6,6,6,6,9,2,9,4,9
L,2,1,3,2,1,4,3,5,6,2,3,4,1,7,0,6
Q,1,2,2,3,1,8,6,6,3,8,6,9,2,9,3,8
M,4,5,5,7,4,8,7,12,2,6,9,8,8,6,0,8
Z,2,4,3,3,1,8,6,2,9,11,5,8,1,8,5,8
U,5,11,6,8,5,6,9,5,6,6,9,9,3,8,1,8
V,3,5,4,4,5,8,8,5,4,7,6,8,6,8,7,4
T,1,0,1,1,0,8,13,1,5,6,10,8,0,8,0,8
T,4,10,6,7,7,7,8,5,5,6,7,9,6,8,8,6
K,1,1,2,1,1,6,7,4,7,6,6,10,3,8,4,9
O,2,4,3,3,2,7,7,7,4,9,6,8,2,8,3,8
K,6,11,9,8,6,7,6,2,7,10,5,9,4,7,5,9
U,3,6,5,4,3,6,9,5,7,7,9,9,3,9,1,8
L,3,7,5,5,2,5,4,2,10,6,1,9,0,7,3,6
R,10,15,8,8,5,9,7,7,5,10,2,9,7,5,6,10
M,6,7,9,5,6,9,6,2,5,9,5,7,8,4,2,8
A,2,2,4,3,2,6,2,2,2,5,2,8,2,6,2,6
H,12,15,11,8,6,8,7,4,5,9,7,7,7,10,5,9
J,5,9,6,7,3,10,6,1,7,14,3,7,0,7,0,8
H,8,13,8,8,5,6,9,4,5,9,8,9,6,8,6,8
M,7,11,11,8,8,7,6,3,5,9,8,9,8,6,2,8
F,4,10,4,7,2,1,12,5,5,12,11,8,0,8,2,6
S,4,8,5,6,4,7,9,7,7,8,4,6,2,7,9,9
Y,8,7,6,10,4,9,7,6,6,4,10,7,5,10,3,7
M,5,6,8,4,5,8,6,2,5,9,6,8,7,5,2,8
O,2,5,3,4,2,8,7,7,4,9,4,8,2,8,2,8
G,2,1,2,2,1,7,7,6,5,6,6,9,2,9,4,9
L,1,4,3,3,1,6,5,1,6,8,3,10,1,7,2,9
G,4,5,5,4,3,6,6,6,6,6,6,9,2,9,4,8
X,2,3,3,4,1,7,7,4,4,7,6,8,3,8,4,8
A,2,7,3,5,2,7,4,2,0,7,2,8,1,6,1,8
S,4,6,5,8,2,8,7,6,9,4,6,7,0,8,9,8
T,4,8,5,6,4,6,11,3,6,8,11,8,2,12,1,7
T,9,13,8,7,4,8,6,4,10,13,5,7,2,9,6,6
X,7,11,10,8,5,10,7,2,9,11,1,6,4,9,4,10
C,2,4,3,3,1,4,9,4,7,11,9,12,1,9,2,7
Z,4,6,5,4,3,7,8,2,9,11,8,6,1,8,6,6
Z,4,11,5,9,6,7,7,5,10,7,5,9,3,10,9,8
T,2,7,4,4,1,9,14,1,6,5,11,9,0,8,0,8
V,3,2,5,4,2,6,12,3,4,8,12,8,3,10,1,8
X,3,4,6,3,2,9,6,2,8,10,3,7,2,7,3,9
L,3,3,3,5,1,0,1,6,6,0,1,5,0,8,0,8
R,4,9,4,6,3,6,11,9,3,7,2,9,3,7,5,10
H,7,11,10,8,8,9,7,3,6,10,4,7,6,8,5,8
Q,5,8,7,9,7,9,7,7,2,5,8,9,5,9,7,11
P,8,12,7,6,3,7,9,6,4,13,4,5,4,10,4,8
J,2,7,4,5,2,7,7,3,5,15,6,9,1,6,1,7
V,6,11,5,6,3,8,10,5,5,7,10,5,5,12,3,7
Q,3,3,4,5,4,8,7,6,2,5,7,9,3,9,5,10
S,4,5,6,4,5,10,8,5,5,6,8,9,4,11,6,10
Y,3,5,5,7,1,9,10,3,2,6,13,8,2,11,0,8
L,3,10,4,7,4,3,4,4,7,2,0,8,0,6,1,6
L,5,11,6,8,4,3,4,4,9,2,0,7,0,7,1,5
A,5,8,7,7,6,9,7,3,4,7,7,6,5,10,4,6
X,8,13,8,7,5,7,6,2,8,11,4,8,4,5,4,7
F,4,11,4,8,2,0,13,5,4,12,11,6,0,8,2,5
M,4,10,5,8,6,7,5,11,1,7,9,8,8,6,1,9
I,2,9,3,7,3,8,7,0,7,7,6,8,0,8,3,7
H,3,4,6,3,3,8,8,3,6,10,6,7,3,8,3,8
L,6,9,8,8,7,7,7,4,6,7,7,8,3,8,9,10
E,2,1,3,3,2,7,7,5,7,7,6,9,2,8,5,10
V,4,10,6,7,2,9,8,4,3,6,14,8,3,10,0,8
G,4,9,5,6,2,7,6,8,8,6,5,10,2,8,5,11
P,4,7,6,9,7,6,8,5,2,8,7,6,9,12,7,8
G,5,9,7,8,8,7,9,5,2,7,7,8,6,11,7,9
A,3,8,5,6,3,5,4,3,0,5,2,7,2,7,2,7
S,5,10,6,8,3,8,8,6,10,5,6,6,0,8,9,7
X,11,15,10,8,5,11,6,3,9,10,4,7,4,11,4,11
I,2,9,3,6,2,6,8,0,7,14,7,8,0,8,1,7
D,3,6,5,4,3,7,7,7,8,6,5,4,3,8,3,7
L,2,2,3,4,2,4,4,4,7,2,1,6,0,7,1,6
W,4,3,4,2,2,4,11,3,2,9,9,7,6,11,1,6
A,3,2,6,4,3,8,1,2,2,7,2,8,2,7,3,7
P,5,9,6,7,5,7,9,5,6,9,8,4,5,10,5,7
A,4,6,6,6,5,7,8,2,4,7,7,9,5,7,3,7
K,9,13,9,7,5,10,6,2,6,11,4,7,6,11,3,9
N,3,7,5,5,3,5,10,6,4,7,7,9,5,9,1,8
X,4,4,5,6,1,7,7,4,4,7,6,8,3,8,4,8
T,5,10,7,8,6,7,7,7,6,5,10,10,5,6,9,7
R,5,10,6,8,4,5,11,8,3,7,4,8,3,8,6,11
P,6,10,9,8,5,7,12,7,3,11,4,2,2,11,4,8
L,2,5,4,4,2,7,3,1,8,9,2,10,0,7,2,8
X,5,9,8,7,5,4,8,1,8,10,11,10,3,9,3,5
W,7,11,9,8,10,8,7,6,3,6,8,8,6,8,5,7
P,5,11,8,8,6,8,9,3,5,13,5,4,4,10,4,8
B,3,7,4,5,3,6,7,8,6,7,6,7,2,8,9,10
C,2,1,3,2,1,6,7,6,7,8,7,13,1,9,4,10
M,5,8,8,6,7,7,6,5,5,7,7,11,14,6,2,10
S,8,13,6,7,3,9,1,2,5,8,2,8,3,7,5,11
M,7,10,9,8,9,9,7,6,5,6,7,6,11,8,4,5
H,3,7,5,5,6,8,7,4,3,6,6,7,7,8,8,8
P,4,10,4,7,4,4,11,8,2,9,6,4,1,10,3,8
W,6,6,6,4,4,5,11,3,2,9,7,7,7,12,2,6
Z,3,5,4,7,2,7,7,4,13,10,6,8,0,8,8,8
H,5,10,7,8,10,9,6,4,4,6,6,7,8,7,7,6
U,4,9,6,7,4,7,9,5,7,5,9,9,3,9,1,8
E,5,10,5,7,3,3,8,6,11,7,6,15,0,8,7,7
Q,2,3,3,4,2,8,7,5,3,8,7,9,2,9,3,8
O,3,5,4,4,3,8,7,7,4,9,5,8,2,8,2,8
S,3,7,4,5,3,7,8,3,7,10,5,7,2,7,5,8
M,4,5,6,4,6,6,8,5,3,6,5,8,9,7,4,8
N,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8
E,2,3,3,2,2,8,7,1,7,11,5,8,2,8,4,10
P,1,1,2,2,1,5,9,4,3,9,7,4,1,10,2,7
H,6,9,9,7,8,6,8,3,6,10,7,8,3,8,3,7
B,2,3,3,2,2,7,7,5,5,6,5,6,2,8,5,9
W,6,11,10,8,6,5,10,2,3,8,9,9,8,11,1,8
I,1,3,2,1,0,7,7,1,6,13,6,8,0,8,0,8
R,5,11,6,8,4,6,9,10,5,7,5,8,3,8,6,10
H,6,10,6,8,5,7,6,14,2,7,8,8,3,8,0,8
W,6,5,8,7,4,4,8,5,2,7,9,8,9,9,0,8
M,5,11,6,8,4,7,7,12,2,7,9,8,9,6,0,8
X,1,0,1,0,0,8,7,3,5,7,6,8,2,8,3,7
H,2,5,4,3,3,7,7,2,5,10,6,8,3,8,2,8
G,5,5,7,8,3,7,5,7,10,5,5,9,1,9,7,11
V,3,7,5,5,2,7,12,3,4,6,12,8,2,10,1,8
V,6,11,6,8,3,2,12,5,4,11,12,8,3,9,1,8
B,3,5,4,4,4,7,7,5,5,6,6,6,2,8,6,9
E,3,7,5,5,3,10,6,2,7,11,4,9,2,8,5,12
W,6,7,6,5,4,6,11,4,2,8,7,6,6,12,2,5
W,7,7,10,6,10,8,8,5,5,7,5,8,9,8,9,6
O,5,9,6,6,4,8,6,8,4,7,4,8,3,8,3,8
Z,3,9,4,6,3,7,7,3,12,9,6,8,0,8,8,8
Y,6,6,5,9,4,7,7,4,3,6,11,6,4,11,6,6
Z,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8
W,5,11,8,8,4,4,8,5,2,7,9,8,8,10,0,8
T,2,6,4,4,1,9,15,1,5,5,11,9,0,8,0,8
H,3,5,4,4,3,7,7,6,6,7,6,8,3,8,3,8
D,5,10,7,8,6,7,7,8,5,7,6,5,4,8,3,7
Z,3,7,5,5,3,8,7,2,9,11,5,10,1,9,6,10
X,6,11,9,8,6,5,8,1,8,10,10,10,3,8,4,6
J,1,3,2,2,1,10,6,3,6,12,4,10,0,7,1,8
V,1,0,2,0,0,8,9,3,2,7,12,8,2,10,0,8
H,3,7,5,5,6,7,6,5,4,6,6,8,6,8,8,10
H,6,11,6,8,6,7,8,14,1,7,5,8,3,8,0,8
R,7,11,9,8,9,8,6,7,5,8,6,8,7,7,6,12
B,5,9,7,7,5,9,7,4,6,10,5,6,2,8,6,10
X,5,9,6,8,7,9,8,2,5,8,5,6,4,7,9,8
J,4,11,6,8,2,9,6,2,7,15,4,9,0,6,1,7
Q,5,9,6,10,6,8,11,6,2,5,8,12,3,11,7,6
E,4,5,4,8,3,3,7,6,10,7,6,14,0,8,7,7
T,3,3,4,2,1,6,11,3,6,11,9,4,2,11,2,5
U,3,3,4,2,1,6,8,6,7,7,10,9,3,10,1,8
Q,5,7,7,10,9,9,9,5,0,6,7,10,8,14,5,11
Q,4,5,4,6,4,8,9,5,1,7,8,11,2,9,5,8
E,5,12,4,6,3,8,7,4,4,10,6,8,3,9,8,10
X,5,9,8,7,4,7,8,1,8,10,7,8,3,8,4,7
P,4,10,6,8,5,4,10,3,6,11,10,6,3,10,3,7
C,5,10,6,7,4,5,7,6,9,7,6,13,1,8,4,9
S,1,0,1,0,0,8,7,3,5,5,6,7,0,8,6,8
Y,4,4,5,6,6,9,8,5,3,7,8,7,6,10,6,4
O,5,10,7,7,5,7,8,8,5,7,7,9,3,7,4,7
U,11,14,9,8,4,4,3,5,5,4,7,7,6,6,2,6
E,5,11,4,6,3,6,9,4,5,10,6,8,3,8,7,9
D,4,10,6,8,10,9,8,5,5,7,6,6,5,6,7,6
P,3,4,5,3,2,7,10,3,4,12,4,2,1,10,2,8
D,5,4,5,6,3,5,8,10,9,7,7,5,3,8,4,8
X,2,5,3,3,2,7,7,3,9,6,6,8,2,8,6,8
F,6,11,6,6,4,6,10,3,4,11,6,4,4,10,8,6
Y,1,0,2,1,0,7,10,3,1,7,13,8,1,11,0,8
P,9,13,9,7,6,11,6,3,4,12,4,6,5,9,6,9
J,6,11,8,8,3,8,6,4,6,15,6,11,1,6,1,6
D,2,3,3,2,2,10,6,3,6,10,3,6,2,8,3,9
Q,1,2,2,2,1,8,8,5,2,8,7,10,2,9,3,9
S,7,10,5,5,2,9,3,4,4,9,2,9,3,6,5,10
T,6,8,6,6,4,7,11,3,8,12,9,4,2,12,3,4
D,6,9,9,8,8,6,6,5,7,7,6,8,5,6,6,4
U,2,1,3,2,1,6,8,6,6,6,9,9,3,9,1,7
N,3,4,5,2,2,9,7,3,4,10,3,5,5,9,1,7
H,1,0,2,0,0,7,8,11,1,7,6,8,2,8,0,8
U,3,3,4,2,1,7,9,6,7,7,10,9,3,10,1,8
W,2,1,2,2,1,7,8,4,0,7,8,8,6,10,0,8
F,2,3,2,2,1,5,10,3,5,10,9,5,1,10,3,6
H,5,9,6,7,5,7,8,13,1,7,6,8,3,8,0,8
R,5,11,7,8,6,8,8,5,6,9,4,8,3,7,5,11
O,5,9,5,7,5,7,7,7,4,9,7,8,3,8,3,8
S,7,10,8,8,4,9,7,4,9,11,6,7,2,10,5,8
Y,10,9,8,13,5,7,9,2,2,7,10,4,4,10,7,7
W,6,9,8,7,12,10,7,5,2,7,7,8,11,10,3,5
O,2,4,2,3,1,7,7,6,4,9,6,8,2,8,2,8
G,3,4,4,6,2,7,8,8,7,5,7,8,2,7,6,11
T,5,10,5,7,4,4,12,3,5,11,10,5,2,13,2,4
G,1,0,2,1,0,8,6,6,4,6,5,9,1,8,5,10
S,3,4,3,3,2,8,6,6,5,7,6,9,2,10,9,8
E,2,0,2,1,1,5,7,5,8,7,6,12,0,8,7,9
A,4,10,6,8,3,10,3,2,3,9,1,8,2,7,4,9
J,1,3,2,2,1,10,6,2,5,12,4,9,0,7,0,7
N,5,7,7,5,4,7,9,2,5,10,5,6,5,9,1,7
B,3,2,3,3,3,7,7,5,5,6,6,6,2,8,6,9
R,4,9,6,6,6,6,7,3,5,6,5,9,6,10,7,5
G,4,6,5,4,6,8,5,4,3,7,6,10,6,8,4,10
T,2,5,3,3,1,8,12,2,7,6,11,7,1,11,1,7
C,5,10,6,8,2,6,6,7,11,9,5,14,1,9,4,8
E,4,8,6,6,6,7,7,5,8,7,7,9,3,8,6,9
U,4,8,6,6,5,7,7,8,5,6,6,10,4,8,5,6
I,4,10,7,8,8,10,6,2,4,8,4,4,3,8,5,7
P,3,2,4,3,2,5,10,4,4,10,8,4,1,10,3,6
C,7,10,7,7,5,4,8,5,7,12,9,12,2,10,4,6
C,7,10,7,8,4,3,8,6,7,12,11,12,2,9,2,7
P,3,6,5,4,3,6,11,3,5,13,6,4,0,10,2,8
A,1,1,2,2,1,7,3,2,1,6,2,8,1,6,1,7
G,3,5,4,7,2,7,6,8,9,8,4,13,1,9,5,10
H,4,7,5,5,4,8,7,6,6,7,6,9,6,8,3,8
N,4,5,5,4,3,7,8,5,5,8,7,7,7,9,3,5
W,6,6,6,4,4,3,11,2,3,10,10,8,7,11,1,7
W,5,9,7,7,8,8,8,6,3,6,8,8,6,7,4,7
N,3,2,4,3,3,7,8,5,4,7,6,6,5,9,2,6
S,5,8,6,6,3,9,7,4,8,11,4,8,2,8,5,9
F,1,0,1,0,0,4,12,4,2,11,8,6,0,8,2,8
K,2,3,3,2,1,6,7,2,6,10,7,10,3,8,2,8
B,4,8,6,6,4,8,8,4,7,10,5,6,2,8,6,10
Y,2,2,4,3,2,6,10,1,7,8,12,8,2,11,2,7
E,7,9,9,8,9,7,8,5,5,7,7,10,8,9,10,9
Z,6,11,8,8,8,10,9,5,4,7,5,7,3,8,9,5
X,4,8,6,6,4,8,7,3,8,5,6,6,3,8,6,7
O,5,11,5,9,6,8,7,8,3,9,6,7,3,8,3,8
V,4,11,5,8,3,5,11,3,4,9,12,9,2,10,1,8
R,3,6,4,4,3,10,7,3,5,10,3,7,3,7,3,11
L,3,7,4,5,3,7,4,1,7,8,2,10,0,7,2,8
V,6,8,6,6,3,3,12,4,4,10,12,7,2,10,1,8
H,5,8,7,6,8,7,6,5,5,6,6,7,8,7,8,9
P,2,4,3,3,2,9,8,3,4,12,4,3,1,10,3,9
L,1,3,2,1,1,6,4,1,7,8,3,10,0,7,2,8
Q,3,5,3,6,3,8,8,5,2,8,8,11,2,9,5,8
D,3,2,4,3,3,7,7,7,7,7,6,5,2,8,3,7
V,3,6,4,4,2,8,9,4,2,7,12,8,2,10,0,8
H,5,9,6,7,6,7,5,13,2,7,9,8,3,9,0,8
G,7,13,6,8,5,7,6,4,3,8,6,8,4,9,9,7
V,2,3,3,1,1,6,12,3,2,8,11,8,2,11,1,8
P,4,6,6,4,3,7,10,4,4,12,5,3,1,10,2,8
K,4,5,5,8,2,4,8,8,2,7,4,11,4,8,2,11
U,6,9,6,7,4,3,8,5,7,10,9,9,3,9,2,6
U,4,7,6,5,5,9,6,7,5,7,7,8,5,8,4,6
Y,5,10,6,7,6,9,5,7,5,6,9,7,3,9,9,4
D,5,11,6,8,6,6,7,9,7,6,5,5,2,8,3,7
F,5,11,7,8,4,6,11,2,6,13,7,4,1,10,2,7
I,1,5,2,4,1,7,7,0,7,13,6,8,0,8,1,8
N,2,1,2,2,1,7,9,5,4,7,6,7,4,8,1,6
L,5,11,7,9,6,8,4,0,7,9,3,10,1,6,2,9
N,6,10,9,8,4,3,9,4,4,11,11,10,6,7,1,8
E,2,6,3,4,2,3,6,6,10,7,6,14,0,8,7,8
N,5,10,7,5,3,8,7,2,4,12,8,8,5,8,0,8
M,3,3,5,2,3,7,6,3,4,9,7,8,7,5,1,8
B,5,7,7,5,5,8,8,4,6,10,5,6,3,8,6,10
U,5,5,6,8,2,8,4,15,6,7,14,8,3,9,0,8
B,5,8,7,6,5,10,5,3,7,10,4,7,3,7,6,11
T,4,4,4,3,2,5,11,2,8,12,9,4,1,10,2,4
G,6,8,8,7,8,8,6,6,4,7,6,10,10,10,8,9
N,3,7,5,5,5,7,7,3,4,8,8,8,6,9,4,5
S,3,9,4,7,4,7,7,7,5,7,7,9,2,9,8,8
B,11,13,8,7,5,9,6,5,6,11,4,9,6,7,7,11
O,9,13,6,7,4,5,7,7,4,10,7,8,5,9,5,7
N,7,10,10,8,10,6,8,3,4,8,8,9,8,10,7,4
F,4,11,6,8,4,3,11,2,7,11,10,6,1,10,3,5
J,3,9,5,6,2,7,5,4,5,14,7,12,1,6,1,6
L,1,4,2,3,1,7,3,1,6,8,2,10,0,7,2,8
G,2,3,3,2,1,6,7,5,5,9,7,10,1,8,4,10
P,5,11,6,8,7,6,7,5,4,8,7,8,8,8,6,11
C,2,6,3,4,2,5,8,6,6,6,7,14,1,8,3,10
E,3,5,4,3,3,7,7,5,8,6,5,8,2,8,6,9
U,3,4,4,3,2,4,8,5,6,11,10,9,3,9,1,7
M,5,9,8,7,6,4,7,3,4,10,10,10,8,6,3,7
L,4,10,5,7,4,4,4,3,8,2,1,7,0,6,1,6
A,5,12,5,6,4,11,2,4,2,11,4,11,5,4,5,10
C,5,9,6,8,7,5,6,4,5,7,6,11,6,10,9,10
M,5,8,6,6,3,7,7,12,2,7,9,8,8,6,0,8
L,5,11,8,9,9,7,8,3,6,5,7,11,5,11,9,7
M,8,10,11,7,9,5,7,3,5,9,9,9,9,7,3,8
T,5,7,7,6,7,7,8,4,8,8,7,9,3,8,8,6
F,2,5,4,4,1,5,13,3,5,13,7,3,0,9,2,6
N,4,8,5,6,4,7,7,13,1,6,6,8,5,8,0,8
J,5,9,7,7,6,7,7,5,5,8,6,7,6,7,4,7
P,6,8,8,10,9,6,8,4,2,8,8,6,7,12,7,7
B,8,14,7,8,6,10,7,3,6,10,4,7,6,6,7,9
A,3,2,5,4,2,8,2,2,2,8,1,8,2,6,3,7
G,7,12,6,6,4,8,3,4,3,7,4,4,4,7,5,8
Z,4,9,4,6,3,7,10,3,11,8,6,7,0,8,7,7
U,4,4,5,3,2,4,8,5,7,12,10,9,3,9,2,7
P,9,15,7,8,4,5,11,6,2,11,6,4,5,10,4,7
Y,4,11,5,8,3,8,11,1,3,7,12,8,1,11,0,8
A,3,8,6,6,3,12,2,3,2,10,2,9,2,6,3,8
T,6,11,8,8,7,7,7,7,7,5,9,10,6,7,9,4
X,3,6,5,4,3,6,8,1,8,10,8,9,2,8,3,6
S,4,7,4,5,3,7,8,4,7,10,5,7,2,7,4,8
K,6,10,5,5,3,7,8,3,6,9,9,9,6,11,3,6
Z,1,1,2,2,1,7,7,4,8,6,6,8,1,8,6,8
P,5,9,5,6,3,5,12,9,2,9,5,4,1,10,4,8
E,1,0,1,0,0,5,7,5,7,7,6,12,0,8,6,9
Y,7,8,7,6,4,5,8,1,7,8,9,5,2,11,3,4
X,3,3,4,4,1,7,7,4,4,7,6,8,3,8,4,8
Q,6,11,6,6,4,11,4,4,5,11,4,8,3,9,6,12
Y,3,5,5,4,2,7,11,1,8,6,11,8,1,11,2,8
X,3,5,5,4,2,10,7,1,8,11,3,7,2,8,3,9
Z,7,9,7,5,4,9,4,3,8,12,5,10,3,8,6,8
O,8,10,6,5,3,8,6,5,5,8,4,8,5,9,5,8
Y,2,2,4,4,2,7,10,1,7,7,11,8,1,11,2,8
G,4,8,5,6,2,7,6,7,8,6,6,9,1,8,6,11
Z,5,9,7,7,6,10,9,6,4,7,5,8,3,8,10,7
N,4,7,6,5,4,7,9,6,5,7,6,6,6,9,1,6
W,7,9,7,4,3,5,10,2,2,9,10,8,8,12,0,7
H,3,3,5,2,2,8,7,3,6,10,4,7,3,7,3,8
X,2,0,2,1,0,7,7,6,2,7,6,8,3,8,3,8
V,3,9,5,7,3,8,9,4,1,7,12,8,2,10,0,8
S,4,9,6,6,7,5,8,3,1,7,6,5,3,8,9,1
M,8,9,11,6,9,8,6,2,5,9,7,8,8,6,2,8
Y,4,4,5,3,2,4,10,2,8,10,10,5,3,11,4,3
S,3,4,4,6,2,9,9,6,10,5,5,5,0,7,9,7
U,3,7,5,5,4,8,9,8,5,4,7,11,3,7,3,7
Q,4,9,5,8,3,8,6,9,5,6,6,8,3,8,5,9
X,4,7,6,5,3,7,8,1,8,10,7,8,2,8,3,7
H,5,9,6,4,4,4,9,3,4,10,9,9,4,8,4,6
V,2,1,4,2,1,6,12,2,3,8,11,8,2,11,1,8
C,4,7,5,5,2,5,9,7,8,8,8,14,1,8,4,9
L,1,3,2,2,1,7,4,1,7,8,2,9,0,7,2,8
S,3,9,4,7,2,7,7,6,8,5,6,7,0,8,9,7
L,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8
P,7,9,9,7,5,7,12,8,3,10,5,3,2,11,5,8
J,0,0,1,0,0,12,4,5,3,12,4,10,0,7,0,8
T,3,3,4,2,2,6,11,3,7,11,9,5,1,11,3,4
T,1,0,2,0,0,7,15,2,4,7,10,8,0,8,0,8
T,2,3,2,2,1,7,11,2,6,7,10,8,1,11,1,7
B,4,5,5,4,4,7,7,5,6,7,6,6,2,8,6,10
L,5,10,6,8,4,5,3,6,9,1,1,4,1,6,1,5
I,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8
P,1,3,3,2,1,8,7,4,3,11,4,5,1,9,2,9
I,6,8,8,9,7,7,8,4,7,6,8,7,4,8,11,9
C,3,8,4,6,2,4,8,7,9,9,9,13,1,9,4,9
I,3,10,4,8,2,7,7,0,8,14,6,8,0,8,1,8
L,2,3,3,5,1,0,1,5,6,0,0,7,0,8,0,8
N,2,1,3,2,2,7,9,6,5,8,7,6,5,9,1,6
M,4,1,5,2,3,8,6,6,4,7,7,8,8,6,3,6
X,2,1,2,1,0,7,7,4,4,7,6,8,2,8,4,8
A,7,11,5,6,3,10,1,3,1,10,4,12,3,4,4,8
Q,6,10,7,9,4,8,7,8,7,6,7,9,3,8,5,9
M,4,7,5,5,5,8,7,5,5,6,6,5,8,7,2,6
Z,3,4,5,6,4,9,7,5,5,9,3,6,2,6,6,7
V,6,9,4,4,2,8,10,5,4,7,10,5,4,11,3,7
A,3,11,5,8,3,13,3,4,3,12,1,9,2,6,3,9
L,3,10,3,7,1,0,1,5,6,0,0,7,0,8,0,8
Y,3,11,5,8,1,5,10,3,2,9,13,8,1,11,0,8
Q,4,4,5,6,2,9,8,8,6,5,7,10,3,8,5,9
C,4,8,6,6,6,7,7,4,4,6,7,10,7,9,5,7
S,3,4,4,2,2,7,8,2,7,10,7,7,1,8,5,6
C,3,6,4,4,1,6,7,7,10,8,6,13,1,9,4,9
N,5,9,6,6,5,7,7,9,5,6,4,5,5,8,5,11
K,3,6,5,4,4,6,6,3,8,6,6,9,3,8,5,9
E,9,11,6,6,3,8,7,5,7,10,6,11,2,9,8,8
S,4,8,6,6,3,9,7,5,8,11,2,8,2,7,5,11
S,2,4,3,3,2,8,8,7,5,8,5,7,2,8,8,8
X,6,10,9,7,5,7,8,1,9,10,6,8,3,8,4,7
K,4,10,4,8,4,3,7,6,3,6,5,11,3,8,2,11
B,2,3,4,2,2,7,7,3,5,10,6,6,2,8,5,8
F,5,11,7,8,5,9,8,1,6,13,5,5,2,9,3,9
M,3,4,5,2,3,9,6,3,4,8,5,7,7,5,1,8
X,4,7,6,5,3,4,9,2,7,10,11,9,3,9,3,5
C,3,1,4,3,2,6,8,7,7,8,8,13,1,10,4,10
E,4,6,5,4,3,6,8,4,8,12,8,9,2,9,4,6
U,4,5,5,4,2,4,8,5,7,11,10,9,3,9,2,6
I,6,11,7,8,5,7,7,1,7,7,6,8,0,8,4,8
T,4,9,4,4,2,4,11,2,6,12,8,5,2,8,4,3
A,2,4,3,3,2,10,2,2,2,9,2,9,1,6,2,8
H,2,6,3,4,1,7,9,14,2,7,4,8,3,8,0,8
P,5,11,8,8,5,6,11,3,6,14,7,4,0,10,3,8
E,3,5,5,3,3,6,8,2,8,11,8,9,2,9,4,7
D,7,15,7,9,5,9,6,5,6,12,3,7,5,7,6,10
I,5,11,7,8,4,9,6,2,7,7,6,6,0,9,4,7
N,5,10,5,8,5,7,7,12,1,6,6,8,6,7,1,10
P,2,7,4,5,2,5,11,3,6,11,9,5,0,9,3,6
T,5,9,5,6,4,5,12,4,6,11,9,4,2,12,2,4
D,3,9,5,7,8,10,7,5,5,7,5,6,4,6,10,6
G,4,6,6,5,5,8,10,5,2,7,7,9,6,11,7,10
C,4,6,5,4,2,4,9,5,7,11,10,12,1,9,3,7
Y,4,6,6,8,8,9,8,4,2,6,8,9,4,11,7,8
D,2,1,2,1,1,7,7,6,6,7,6,5,2,8,2,7
P,2,3,2,2,1,6,10,5,4,9,7,3,1,9,3,6
X,2,5,4,4,2,7,7,3,9,6,6,8,2,8,6,8
B,4,7,6,5,4,8,8,4,6,10,5,6,2,8,6,8
F,2,2,3,3,2,6,10,4,5,10,9,5,2,9,3,6
Z,2,5,4,4,2,7,8,2,10,12,7,7,1,8,5,6
P,6,9,9,7,4,5,15,6,2,12,5,1,0,9,4,7
J,3,9,4,7,3,11,6,3,6,12,2,8,1,6,2,6
B,3,9,4,7,3,6,7,10,6,7,6,7,2,8,9,10
C,3,8,4,6,2,5,7,6,8,8,6,12,1,9,4,9
B,4,9,4,4,3,9,6,3,4,9,5,8,6,8,7,9
U,3,3,3,5,2,7,6,13,5,7,14,8,3,9,0,8
O,6,9,7,7,6,7,8,7,5,9,7,9,3,8,4,7
M,6,9,8,6,8,6,6,6,5,7,7,11,11,5,2,9
D,3,10,5,8,7,8,9,5,4,8,7,5,4,9,11,6
D,5,6,6,8,3,5,7,11,9,6,5,5,3,8,4,8
P,2,3,4,1,1,8,9,3,4,12,4,3,1,9,2,9
P,3,5,5,4,2,7,10,5,3,11,4,3,1,10,3,8
Y,5,9,5,7,3,3,11,3,6,11,12,6,1,11,2,6
F,2,5,3,3,2,5,10,3,5,10,9,6,2,10,3,6
I,1,6,2,4,1,7,7,0,8,7,6,8,0,8,3,8
B,3,2,4,4,4,7,7,5,5,7,6,6,2,8,6,10
F,5,10,6,8,7,6,10,6,4,8,5,9,3,10,8,11
U,3,7,5,5,4,7,8,8,5,5,6,11,3,8,5,6
B,3,7,4,5,3,6,5,9,7,6,7,7,2,9,8,9
A,9,15,9,8,6,11,2,5,2,11,4,11,7,2,5,11
Z,4,8,6,6,6,8,8,2,8,7,6,7,0,8,9,8
E,5,11,6,8,7,7,7,6,3,7,6,9,4,8,8,8
E,1,0,1,1,1,5,7,5,8,7,6,12,0,8,6,9
A,1,0,2,0,0,7,4,2,0,7,2,8,1,6,1,8
M,6,7,9,5,5,8,6,2,5,9,6,7,8,6,2,8
J,5,9,6,7,4,9,4,7,6,8,6,6,2,7,4,6
T,4,4,4,3,2,6,12,3,7,11,9,4,1,11,2,4
W,8,10,8,8,6,5,10,3,3,9,8,7,8,11,3,6
K,4,7,6,5,5,6,6,4,6,6,5,8,7,7,7,9
M,5,11,7,8,8,9,7,5,4,6,7,5,9,7,3,5
N,5,8,7,7,7,8,8,5,5,8,5,6,6,10,7,2
W,3,3,5,5,3,6,8,4,1,7,8,8,8,9,0,8
D,3,9,5,7,4,8,7,7,7,7,7,4,3,8,3,7
H,4,6,6,8,6,9,5,3,2,7,5,7,4,8,6,9
H,3,4,4,3,3,7,7,6,6,7,6,8,3,8,3,8
M,8,10,11,8,9,12,6,2,5,9,3,6,10,4,2,8
R,5,9,5,4,4,7,8,3,5,8,4,9,5,7,6,7
B,6,10,8,8,7,9,7,2,7,10,4,7,4,7,5,10
O,6,10,7,8,6,8,6,8,4,9,5,6,5,8,4,9
R,5,11,7,8,7,9,8,3,5,10,4,7,5,5,4,10
F,5,9,8,7,5,9,8,1,6,13,5,5,3,9,3,9
U,4,7,6,5,7,8,7,4,4,6,7,8,7,10,5,7
Z,2,4,4,2,2,7,7,2,9,12,6,8,1,8,5,7
H,5,11,7,8,10,8,7,4,3,6,6,7,7,8,9,8
P,4,8,5,6,4,7,9,8,4,8,6,3,3,10,3,6
H,2,3,4,1,2,7,7,3,5,10,6,8,3,8,3,8
T,3,7,4,5,2,5,14,1,5,9,10,7,0,8,0,8
B,1,0,1,0,1,7,8,6,4,7,6,7,1,8,6,9
O,3,2,4,3,3,8,7,8,5,7,6,8,2,8,3,8
D,4,8,6,6,8,9,7,4,7,7,6,5,5,5,7,7
S,2,4,3,3,1,9,7,3,6,10,6,7,1,9,5,8
C,3,8,4,6,2,6,8,7,10,6,7,12,1,7,4,9
N,2,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
D,2,4,3,3,2,9,6,3,5,10,4,6,3,7,2,8
A,2,5,4,4,2,8,3,2,2,8,2,8,2,6,2,7
S,4,8,5,6,4,8,6,7,7,7,8,8,2,10,9,8
K,3,4,4,5,1,4,7,8,2,7,5,11,4,8,2,11
W,4,7,5,5,3,5,8,5,1,7,8,8,8,9,0,8
R,4,6,7,5,7,9,6,4,4,8,5,7,6,9,6,7
H,4,5,5,7,3,7,7,15,0,7,6,8,3,8,0,8
U,4,8,6,6,8,9,7,4,5,6,8,8,8,8,6,6
N,5,9,7,6,5,9,8,6,6,6,5,4,7,11,3,5
X,5,7,8,5,3,9,6,2,8,11,2,7,3,8,4,9
O,5,8,6,6,4,8,6,8,5,10,5,8,3,8,3,8
L,2,4,4,3,1,6,4,1,8,8,2,10,0,7,2,8
B,3,5,4,4,5,7,7,5,4,7,6,8,6,9,6,9
E,4,7,6,5,4,10,6,2,8,11,3,9,2,8,5,12
B,4,9,5,6,4,6,8,9,7,7,6,7,2,8,9,9
V,4,7,6,6,6,6,9,5,6,8,6,8,6,9,7,6
N,4,6,5,4,3,7,9,2,4,9,7,7,5,8,1,8
X,3,6,4,4,2,7,7,4,4,7,6,7,3,8,4,8
X,2,3,3,2,1,9,7,3,8,6,6,6,2,8,5,8
B,2,3,4,2,2,8,7,3,5,10,5,7,2,8,4,9
O,4,7,5,5,3,7,8,8,5,10,6,6,3,8,3,8
D,3,5,4,4,3,7,7,7,7,6,6,5,2,8,3,7
S,6,11,7,8,4,8,7,4,8,11,5,7,2,8,5,8
I,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7
N,5,10,8,8,5,5,10,3,4,9,9,8,5,9,1,7
S,3,8,5,6,6,6,7,3,2,7,6,6,2,8,10,1
U,6,11,8,8,5,8,9,4,6,5,8,9,6,9,2,8
T,3,8,5,6,3,7,12,4,6,7,11,8,2,12,1,8
G,4,8,5,6,4,7,6,7,7,7,4,11,1,8,5,11
J,3,9,4,7,4,10,6,2,5,9,4,6,3,7,7,8
W,3,2,5,3,3,5,11,3,2,8,9,9,7,11,0,8
S,2,3,3,2,1,7,8,2,7,11,7,8,1,9,4,6
S,5,11,6,9,5,8,7,8,6,8,6,7,2,8,9,8
M,4,9,5,6,3,7,7,12,1,7,9,8,8,6,0,8
I,2,7,3,5,2,6,8,0,7,13,7,8,0,8,1,7
E,1,0,1,1,0,4,7,5,7,7,6,12,0,8,6,10
H,4,10,4,7,3,7,6,15,2,7,8,8,3,8,0,8
Q,4,10,5,9,3,8,7,8,6,6,7,8,3,8,6,9
T,5,7,5,5,3,7,10,2,7,11,9,5,1,11,3,5
H,3,5,6,4,4,8,8,3,6,10,6,8,3,8,3,8
A,3,2,6,4,2,8,2,2,2,6,2,7,3,7,3,7
Z,3,2,4,4,3,7,7,5,9,6,6,8,1,8,7,8
R,4,6,6,4,4,10,7,3,6,11,2,7,3,6,3,10
M,4,5,7,3,4,8,6,3,4,9,6,8,8,6,2,8
I,2,5,3,4,1,7,7,0,7,13,6,8,0,8,1,8
C,5,8,5,6,3,5,7,5,6,11,8,13,2,9,4,8
M,4,4,5,6,3,7,7,12,1,7,9,8,8,6,0,8
K,3,4,4,3,3,6,7,4,8,7,6,11,3,8,5,10
R,4,8,6,6,5,9,7,3,5,10,3,7,3,6,4,10
Y,4,10,5,7,1,7,11,2,3,7,12,8,1,11,0,8
Q,4,8,5,7,3,8,7,8,6,6,7,8,3,8,5,9
P,2,4,4,2,2,8,9,4,3,12,4,4,1,10,2,9
W,8,12,8,6,5,2,9,2,3,9,11,9,8,11,1,5
J,3,10,4,8,2,14,3,5,4,13,2,10,0,7,0,8
E,6,10,9,8,7,10,6,2,7,11,5,8,6,8,6,11
V,6,10,6,8,4,2,11,2,3,9,11,8,3,11,1,7
F,4,7,5,5,3,5,11,4,6,11,10,5,2,10,3,5
I,3,8,4,6,2,7,9,0,8,14,6,6,0,9,1,7
N,4,8,4,6,2,7,7,14,2,4,6,8,6,8,0,8
K,5,10,7,7,5,5,6,5,8,6,6,12,4,7,6,11
I,3,10,4,7,3,8,7,0,7,13,6,8,0,8,1,8
Z,3,4,4,6,2,7,7,4,14,9,6,8,0,8,8,8
I,1,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8
Y,6,9,5,5,3,7,7,3,5,9,7,6,3,10,4,5
V,6,10,6,7,3,3,11,3,4,10,12,8,2,10,1,8
L,3,7,4,5,2,4,4,3,8,2,1,7,0,7,1,6
U,3,2,3,4,1,8,5,12,5,6,14,8,3,9,0,8
C,4,10,5,8,2,5,7,7,10,6,6,13,1,7,4,9
O,4,9,4,7,4,8,5,7,4,10,5,10,4,8,4,7
L,6,15,6,8,4,5,7,4,6,12,9,12,3,8,7,7
B,6,9,8,6,5,10,6,3,8,11,3,7,2,8,5,12
A,4,7,7,5,4,6,5,2,3,4,2,6,4,6,4,5
S,6,13,5,7,3,11,2,1,5,10,1,8,2,8,4,12
J,4,11,6,8,3,8,5,5,6,14,7,12,1,6,1,7
A,2,8,4,5,2,7,6,3,1,6,0,8,2,7,2,7
I,1,2,1,3,1,7,7,1,8,7,6,8,0,8,3,8
T,6,8,6,6,4,5,11,3,7,11,9,5,2,12,2,4
P,4,7,5,5,3,5,12,5,5,11,9,3,0,9,4,6
G,5,10,7,8,8,9,6,5,2,6,6,10,8,8,5,10
J,3,6,4,4,1,10,4,6,5,15,5,12,0,7,1,6
B,6,13,6,7,6,7,7,4,4,9,7,8,7,6,9,8
B,2,4,3,3,2,8,7,3,5,9,6,7,2,8,4,9
I,2,10,2,7,2,7,7,0,8,7,6,8,0,8,3,8
N,3,8,5,6,4,7,8,6,5,7,6,7,5,9,1,6
B,5,8,7,6,7,8,7,6,6,9,6,6,5,10,7,9
X,5,11,7,8,6,7,6,3,9,5,7,10,4,7,8,8
Z,3,9,4,6,3,7,7,3,12,9,6,8,0,8,7,8
U,6,10,5,5,4,8,6,4,4,7,7,7,5,7,3,7
P,3,4,5,3,2,8,9,3,5,13,4,3,2,8,3,7
R,7,9,6,5,4,7,7,5,5,9,4,9,6,5,7,11
K,3,3,3,4,1,3,6,7,3,7,7,11,3,8,2,10
A,2,1,4,3,2,8,2,2,1,7,2,8,2,6,2,7
N,5,7,6,6,6,8,9,4,4,7,4,7,6,8,5,5
N,5,9,7,6,4,8,8,7,6,7,6,4,6,9,2,6
P,4,11,4,8,2,3,14,8,2,12,7,3,0,10,4,8
Q,3,5,5,5,4,7,4,4,4,5,3,7,3,7,4,8
P,7,12,6,7,4,7,10,3,5,13,5,3,3,11,5,6
U,7,8,7,6,5,4,8,5,7,9,8,9,5,9,4,4
B,8,11,7,6,6,7,8,3,6,9,5,7,8,4,8,7
M,4,4,5,6,3,7,7,12,1,7,9,8,8,6,0,8
O,4,8,4,6,4,7,8,7,4,10,7,6,3,9,3,7
T,1,1,2,1,0,7,14,1,5,7,11,8,0,8,0,8
A,3,7,5,5,5,8,8,7,4,6,6,8,2,8,6,5
D,4,10,4,8,3,5,6,10,8,5,4,5,3,8,4,8
I,1,5,1,4,1,7,7,1,8,7,6,8,0,8,3,8
S,8,9,10,8,12,7,8,5,5,7,7,7,5,10,11,10
G,2,3,3,2,1,6,7,5,4,9,7,10,2,9,4,10
C,6,10,5,5,3,7,8,4,3,9,8,9,4,9,9,11
A,8,13,7,7,4,11,4,3,2,8,4,10,5,5,4,9
E,3,5,4,4,3,7,7,5,8,7,6,9,2,8,6,9
B,6,9,9,7,6,9,7,3,7,10,4,6,4,6,6,9
R,4,10,6,8,5,7,9,5,6,7,6,9,4,6,6,9
E,4,7,5,5,4,6,7,3,7,11,8,9,3,8,4,8
P,4,12,4,8,3,4,11,10,3,9,6,4,2,10,4,8
K,10,14,9,8,4,8,7,3,7,9,5,7,6,10,4,7
R,3,8,4,5,2,6,10,9,4,7,4,8,3,7,5,11
O,3,4,4,3,2,7,7,6,4,9,6,8,2,8,2,8
Y,5,5,6,7,6,9,11,5,4,6,7,7,5,10,7,5
O,2,1,2,1,1,8,7,7,5,7,6,8,2,8,3,8
X,5,10,7,8,4,4,9,2,8,10,12,9,3,9,4,5
Y,8,10,7,6,4,8,5,3,5,8,7,6,5,9,5,5
X,4,9,6,7,5,7,7,3,8,5,6,8,3,8,6,7
R,5,10,7,8,9,6,8,3,4,6,6,9,8,9,9,7
E,2,3,4,2,2,7,8,2,8,11,6,9,2,9,4,8
Z,6,9,8,7,5,7,7,2,9,12,6,8,1,9,6,8
Z,5,10,6,8,4,9,5,3,10,11,3,10,1,8,6,10
U,4,7,5,5,2,7,3,14,6,7,13,8,3,9,0,8
G,3,5,4,4,2,7,6,6,5,6,6,9,2,9,4,9
Z,6,10,9,7,5,7,8,3,10,12,7,6,1,8,6,7
J,7,13,6,10,4,7,10,2,3,13,6,5,2,9,8,8
D,9,14,8,8,7,9,6,3,7,10,4,7,6,6,9,7
Q,8,10,11,9,10,7,4,4,5,6,4,8,5,3,7,7
E,9,15,6,9,5,7,7,4,4,11,5,9,3,9,8,11
I,2,7,2,5,1,7,7,0,7,13,6,8,0,8,1,8
M,3,4,4,3,3,8,6,6,3,7,7,8,7,5,2,7
W,3,6,5,4,2,10,8,5,1,7,9,8,7,10,0,8
S,2,3,2,1,1,8,7,6,4,8,6,8,2,8,8,8
P,2,6,3,4,2,3,14,6,2,12,7,3,0,9,3,8
C,3,7,4,5,2,5,8,7,8,13,9,10,2,10,3,7
T,6,7,6,5,3,4,13,4,6,12,9,4,1,11,2,4
R,3,1,3,2,2,7,8,5,5,6,5,7,2,6,4,8
H,5,9,7,7,6,5,8,3,6,10,9,9,3,8,3,5
J,7,11,5,15,4,8,8,3,3,13,4,5,3,8,7,10
E,3,4,5,3,2,7,8,2,8,11,6,9,2,8,4,8
P,2,1,2,1,1,5,10,8,3,9,6,5,1,9,3,8
Y,1,3,2,2,1,6,10,1,5,7,11,9,1,11,1,8
K,7,11,9,8,10,7,7,5,4,7,6,7,5,5,10,11
W,4,6,6,4,3,7,8,4,1,7,8,8,8,9,0,8
U,2,5,4,4,3,7,7,3,3,6,6,9,4,8,1,8
T,3,3,4,4,2,7,12,3,6,7,11,8,2,11,1,8
E,3,2,3,3,3,7,7,5,7,7,6,9,2,8,5,10
V,4,8,6,6,3,7,9,4,2,7,13,8,3,10,0,8
N,6,9,8,7,5,8,9,2,5,9,5,6,6,9,1,7
C,5,9,6,6,4,7,7,8,6,6,6,11,4,8,4,9
N,5,8,8,6,7,6,8,3,4,8,7,9,7,8,5,5
J,0,0,1,0,0,12,4,5,3,12,5,11,0,7,0,8
U,4,8,5,6,2,8,5,13,5,6,15,8,3,9,0,8
K,10,15,10,8,6,5,8,3,6,10,9,10,6,9,4,7
X,4,6,7,4,4,7,7,1,8,10,6,8,2,8,3,8
E,5,11,7,8,6,7,8,2,8,11,7,8,3,8,4,8
A,2,3,3,2,1,9,2,2,1,8,2,10,1,6,2,8
B,2,1,3,2,2,7,7,5,5,6,6,6,2,8,6,9
X,5,10,8,8,5,7,7,4,9,6,6,10,3,8,7,8
V,2,7,4,5,1,9,8,4,2,6,13,8,3,10,0,8
I,1,9,1,6,1,7,7,0,8,7,6,8,0,8,3,8
Y,1,0,2,1,0,7,10,2,2,7,12,8,1,11,0,8
I,3,11,4,8,3,8,6,0,7,13,6,9,0,7,2,8
K,5,9,7,7,6,9,6,1,6,9,3,8,3,7,4,10
P,2,7,3,4,1,3,13,8,2,11,7,3,0,10,3,8
P,7,10,9,8,7,6,8,8,5,8,7,9,3,9,8,9
G,6,8,6,6,5,6,7,6,5,9,7,10,2,8,5,9
K,3,5,6,4,3,6,7,1,7,10,7,10,3,8,3,8
L,2,4,4,3,2,7,4,1,8,8,2,10,0,7,2,8
M,10,14,11,8,5,13,1,5,3,13,1,9,6,3,1,9
W,7,9,7,4,3,6,10,2,2,8,10,7,8,12,1,7
W,2,1,3,2,1,8,8,4,0,7,8,8,6,10,0,8
U,5,9,6,7,4,4,8,5,7,9,8,9,3,9,2,6
L,2,6,3,4,2,5,4,3,7,6,2,8,1,6,2,7
S,8,15,6,8,3,6,3,4,4,6,1,7,3,7,6,7
X,2,1,2,1,0,7,7,4,4,7,6,8,2,8,4,8
C,4,8,5,6,6,7,6,3,4,8,6,11,5,9,3,8
O,3,6,4,4,2,7,8,8,7,7,7,8,3,8,4,8
E,5,10,8,7,6,6,8,2,8,11,7,9,2,9,4,7
F,4,7,6,5,5,8,7,6,4,7,6,8,4,10,8,11
P,11,15,9,8,4,7,9,6,5,13,3,4,5,10,4,8
J,1,2,2,4,1,11,6,1,6,11,3,7,0,7,1,7
E,2,4,2,2,2,7,7,5,7,7,5,9,2,8,5,10
F,4,6,6,4,3,5,12,4,5,13,7,4,2,10,2,6
E,3,7,5,5,5,7,7,3,6,7,7,10,4,10,7,8
R,3,5,5,4,3,9,7,4,5,10,4,6,3,7,4,10
D,3,9,5,7,8,9,6,5,5,7,5,5,4,7,11,6
V,3,6,5,4,2,7,9,4,2,6,13,8,3,10,0,8
T,2,5,3,4,2,7,11,2,7,7,11,8,1,11,1,8
S,2,6,4,4,4,6,6,3,2,7,5,7,2,8,9,3
J,3,5,5,4,2,10,5,3,6,14,4,10,0,7,0,8
K,4,5,4,7,2,3,7,7,2,7,5,11,3,8,3,10
W,5,10,8,8,11,9,8,5,2,6,7,8,11,10,4,5
H,3,7,5,5,4,8,7,6,6,7,6,6,3,8,3,7
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
W,8,9,8,7,8,7,11,4,3,8,6,6,10,12,4,5
S,5,11,6,8,6,7,5,8,6,6,8,9,2,11,9,8
K,4,4,7,3,3,8,7,2,7,10,4,8,5,9,4,9
L,3,6,3,4,1,0,1,6,6,0,0,6,0,8,0,8
B,4,8,6,6,5,7,9,5,5,9,6,5,3,7,8,7
Y,3,9,5,6,1,8,10,2,3,7,12,8,1,11,0,8
D,6,14,6,8,4,8,6,5,6,8,4,7,4,7,6,9
B,2,1,3,1,2,7,7,4,5,7,6,6,1,8,5,9
P,7,13,6,7,4,10,7,4,5,13,3,4,4,10,6,8
G,3,4,4,3,2,7,6,5,5,9,7,10,2,9,4,9
L,5,11,6,8,5,5,6,0,9,4,3,8,3,7,2,5
V,10,14,8,8,5,8,8,7,5,6,10,7,7,13,3,7
O,5,10,6,8,5,7,7,8,7,6,3,6,3,8,5,9
W,5,6,7,6,8,6,8,6,5,6,6,8,7,8,8,10
Q,5,8,7,6,6,8,5,7,4,6,7,7,4,6,7,8
W,3,3,3,1,2,5,10,3,2,9,8,7,5,11,1,6
U,6,10,9,8,5,4,9,7,9,9,11,10,3,9,1,7
M,5,7,7,6,9,7,8,4,3,7,6,7,10,8,5,5
V,4,10,6,8,4,8,11,2,3,4,10,9,3,11,1,8
U,3,4,4,3,2,5,8,5,7,9,8,9,3,10,2,5
F,3,10,3,7,1,0,13,4,4,12,10,6,0,8,2,6
F,1,3,2,2,1,6,10,2,4,13,7,6,1,9,1,8
W,3,7,6,5,4,7,11,2,2,6,9,8,7,11,1,8
M,10,13,10,8,5,10,11,7,4,4,6,9,8,12,3,7
R,3,7,3,4,2,5,10,8,4,7,4,8,3,7,5,11
D,4,6,5,4,3,10,5,2,6,11,3,8,3,8,3,9
D,4,5,5,4,4,8,7,4,6,6,4,8,3,7,5,6
M,6,7,8,6,8,8,8,4,4,7,6,7,10,8,5,5
P,5,11,6,8,3,3,14,8,1,11,7,3,1,10,4,8
T,8,14,7,8,4,8,8,3,7,11,6,7,2,9,5,6
C,2,3,3,1,1,5,9,4,6,12,8,10,1,9,2,7
I,3,8,4,6,2,7,7,0,8,14,6,8,0,8,1,8
L,2,5,3,4,2,6,5,1,8,7,2,10,0,7,3,8
X,5,7,7,5,3,8,8,1,8,10,4,7,3,8,3,7
P,3,5,5,4,3,8,9,3,4,12,4,4,3,8,4,8
I,2,9,3,7,2,7,7,0,8,13,6,8,0,8,1,8
U,3,3,4,2,1,7,9,6,7,7,10,9,3,10,1,8
V,7,10,9,8,6,6,12,2,2,7,10,8,7,10,7,8
W,11,13,11,7,5,10,11,6,3,4,10,7,10,12,1,6
P,3,2,4,4,3,6,9,4,5,9,7,4,4,10,3,7
K,5,8,7,6,5,6,7,1,6,10,7,10,3,8,3,8
P,1,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8
I,1,1,1,1,0,7,7,2,7,7,6,9,0,8,2,8
E,4,9,4,7,4,3,7,5,9,7,7,13,0,8,7,9
Q,1,2,2,2,1,7,8,4,2,7,8,9,2,9,3,8
O,4,5,5,4,3,7,7,8,5,7,6,8,2,8,3,8
E,3,7,4,5,4,7,7,4,7,7,6,8,3,8,5,10
P,5,5,7,8,8,9,8,3,3,5,8,7,6,11,7,5
E,3,8,5,6,5,6,7,3,6,7,7,11,4,10,9,8
K,5,9,6,6,6,6,7,4,7,6,6,10,3,8,5,9
Q,3,3,4,5,4,8,8,6,2,5,7,9,3,8,5,10
Q,8,14,7,8,4,8,6,4,10,10,3,10,3,6,9,9
Y,7,10,7,8,4,3,10,2,7,11,11,7,1,11,3,5
B,3,7,5,5,5,8,7,7,6,6,6,6,2,8,6,9
Y,2,1,3,1,0,7,10,3,1,7,12,8,1,11,0,8
B,5,11,6,8,7,7,9,6,5,7,5,6,3,8,6,8
C,9,13,6,7,3,6,10,6,8,11,8,8,2,8,5,9
G,4,9,5,7,3,5,7,6,5,10,8,10,2,8,5,9
H,7,11,9,8,8,8,7,2,6,10,5,8,4,9,4,8
F,5,11,5,8,4,1,13,4,3,12,10,6,0,8,2,6
T,5,9,5,6,4,6,12,4,5,11,9,5,3,12,2,4
V,6,7,8,6,8,8,7,4,4,7,6,8,7,9,8,4
E,8,10,5,6,3,6,10,5,8,10,7,9,1,8,7,7
D,5,11,6,8,5,9,7,5,7,10,4,5,3,8,3,8
I,2,9,5,7,5,10,6,2,4,9,5,5,3,8,5,7
X,4,10,6,8,4,7,8,3,9,6,5,6,4,10,8,6
U,5,11,8,8,11,9,6,4,4,6,7,7,11,8,5,7
R,3,3,3,4,2,5,10,8,3,7,4,8,2,7,6,11
R,4,9,5,7,4,6,8,5,6,6,5,9,3,6,6,9
S,1,1,2,2,0,8,8,4,7,5,6,7,0,8,7,8
B,3,5,5,3,4,9,7,3,6,10,5,6,2,8,5,10
Z,5,11,8,8,9,8,7,3,8,7,6,7,1,8,10,9
C,6,6,7,8,3,5,6,7,11,7,5,14,1,9,4,8
G,4,8,6,6,6,7,9,6,3,6,6,9,4,7,7,7
L,2,3,4,2,1,7,3,1,6,8,2,10,1,6,3,8
G,5,8,6,6,4,6,6,6,5,6,6,10,2,9,4,8
Z,7,11,7,6,4,9,5,3,8,11,4,9,3,6,7,9
M,4,10,5,8,4,7,7,12,2,7,9,8,8,6,0,8
L,1,0,1,0,0,2,1,5,4,1,2,5,0,8,0,8
T,5,10,5,7,3,5,13,3,7,12,9,3,1,11,2,4
C,3,6,5,4,3,7,7,7,6,8,6,11,3,9,4,8
R,3,8,5,6,4,6,8,6,6,6,4,8,3,7,5,8
V,2,1,3,1,0,7,9,4,2,7,13,8,2,10,0,8
W,10,10,9,8,9,4,11,3,3,9,8,7,8,12,2,6
Y,3,4,5,5,1,7,11,2,2,9,12,7,1,10,0,8
X,3,5,4,3,2,7,7,3,9,6,6,8,2,8,6,8
X,7,9,6,5,3,6,8,2,9,10,9,9,4,8,4,6
Y,7,11,8,8,6,5,8,1,7,8,9,5,5,12,6,3
O,3,8,4,6,4,7,7,8,4,7,5,8,3,8,2,7
C,7,10,7,7,4,3,8,5,7,11,10,13,1,9,3,7
Z,4,3,4,5,2,7,7,4,14,9,6,8,0,8,8,8
M,6,11,8,8,11,7,8,6,4,7,6,8,6,9,8,8
E,3,6,4,4,3,6,7,6,8,6,4,9,3,8,6,9
Z,4,9,6,7,6,7,8,3,7,7,6,8,1,9,12,5
E,5,8,7,7,7,5,10,4,6,8,7,10,4,11,7,7
Q,4,5,6,7,6,9,9,5,0,5,7,10,6,13,5,12
R,4,9,5,6,3,5,12,8,4,7,2,9,3,7,6,11
L,4,9,6,6,4,5,5,2,9,4,2,7,3,7,2,5
O,1,3,2,2,1,8,7,5,3,9,6,8,2,8,2,8
D,3,5,5,3,3,9,6,4,7,10,4,6,2,8,3,8
S,4,11,5,8,3,7,7,6,10,5,6,10,0,9,9,8
H,5,10,6,7,3,7,7,15,0,7,7,8,3,8,0,8
G,4,8,4,6,3,6,6,6,7,11,7,12,2,10,3,10
W,1,0,2,0,0,7,8,3,0,7,8,8,5,9,0,8
R,6,10,9,8,10,8,8,7,3,8,5,6,5,8,7,11
U,4,9,5,7,3,6,8,7,7,7,10,9,3,9,1,8
E,1,1,1,2,1,4,7,5,8,7,6,13,0,8,6,9
M,3,2,5,4,4,9,6,6,4,6,7,6,7,6,2,5
I,1,3,2,2,0,7,7,0,7,13,6,8,0,8,1,8
D,4,8,5,6,3,8,7,8,7,7,6,2,3,8,4,8
Q,6,9,6,11,8,7,8,6,3,8,9,9,5,9,8,8
O,5,10,6,8,4,8,7,8,5,10,6,8,3,8,3,8
E,3,8,3,6,2,3,7,6,10,7,6,14,0,8,7,8
M,4,8,4,6,3,8,7,12,1,6,9,8,8,6,0,8
P,3,7,4,4,2,4,12,8,2,10,6,4,1,10,4,8
T,2,7,3,5,1,6,14,0,6,8,11,8,0,8,0,8
U,3,1,4,1,1,7,8,6,8,7,10,8,3,10,1,8
W,5,10,8,8,7,4,11,2,2,9,9,9,8,12,2,8
S,7,11,6,6,3,7,8,3,5,13,7,8,2,8,3,7
E,2,3,3,2,2,7,7,2,8,11,7,9,1,8,4,8
M,4,7,6,5,6,7,8,6,4,7,6,8,5,9,7,7
R,2,4,3,2,2,7,8,4,5,6,5,7,2,6,5,8
C,4,9,5,6,2,5,7,7,10,6,6,13,1,7,4,9
G,5,11,7,8,5,5,6,6,7,6,6,8,4,7,4,7
E,4,7,6,5,5,8,6,5,3,7,6,10,4,8,7,9
E,2,3,4,2,2,7,7,2,8,11,6,9,2,8,4,8
X,6,8,9,6,4,6,8,2,9,10,9,9,3,8,4,6
Q,4,5,6,5,5,7,4,4,5,7,5,8,5,4,7,7
R,4,5,5,7,3,5,10,9,4,7,4,8,3,7,6,11
J,2,6,2,4,1,13,4,5,4,13,2,9,0,7,0,8
O,7,11,10,8,12,8,8,6,2,7,7,8,10,9,7,11
L,3,5,4,3,2,4,4,4,8,2,1,6,1,7,1,6
Z,2,4,4,3,2,7,7,2,9,11,6,8,1,8,5,8
E,5,9,4,4,2,7,8,4,7,10,5,11,1,8,7,8
X,4,10,5,7,2,7,7,5,4,7,6,8,3,8,4,8
U,4,7,6,6,6,7,7,5,3,6,7,8,4,7,2,7
A,1,0,2,0,0,7,3,2,0,7,2,8,2,6,1,8
R,3,3,3,4,2,6,11,8,3,7,3,9,2,7,5,11
W,2,0,2,0,1,7,8,4,0,7,8,8,6,9,0,8
H,6,10,6,5,4,7,8,3,5,10,5,8,6,6,5,7
N,4,8,6,6,4,7,9,2,4,9,5,6,5,9,1,7
V,6,11,5,8,3,4,11,2,4,9,11,7,3,9,1,8
B,5,11,7,8,11,8,9,5,3,6,7,7,7,11,12,9
L,2,4,4,3,2,7,4,1,8,8,2,10,0,7,2,8
D,2,0,2,1,1,6,7,8,7,6,6,6,2,8,3,8
E,3,5,6,3,3,8,7,2,9,11,5,9,3,7,5,8
A,2,7,4,5,2,11,3,3,2,10,2,9,2,6,2,8
L,2,5,3,4,2,4,4,4,7,2,1,6,0,7,1,6
A,3,10,6,8,5,10,4,1,2,8,3,9,4,4,3,7
C,3,10,5,7,3,4,9,6,6,6,8,14,1,8,4,10
J,2,3,3,5,1,11,2,10,3,13,8,13,1,6,0,8
X,5,9,8,6,5,7,7,0,8,10,7,8,2,8,3,7
H,5,9,6,6,2,7,6,15,1,7,8,8,3,8,0,8
B,4,9,6,7,5,8,7,5,6,10,6,6,3,8,7,9
M,4,9,4,7,5,7,5,10,0,7,9,8,7,5,0,8
N,4,8,4,6,2,7,7,14,2,4,6,8,6,8,0,8
A,4,8,6,6,5,9,5,2,5,8,1,6,3,4,3,7
F,5,6,7,7,6,7,9,4,5,8,6,8,4,10,7,6
B,3,8,3,6,4,6,6,8,6,7,7,7,2,9,6,9
Z,1,0,2,1,0,7,7,3,10,8,6,8,0,8,6,8
N,6,10,8,8,6,4,10,2,3,9,9,9,6,7,1,7
K,1,0,2,1,0,5,7,7,1,7,6,11,3,8,2,11
K,4,10,5,8,4,3,6,7,4,7,7,12,3,8,3,11
I,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8
N,8,13,10,7,4,5,9,4,4,13,10,9,6,9,0,9
Z,2,3,4,2,1,8,6,2,8,11,5,9,1,8,5,8
O,2,7,3,5,2,7,6,8,7,7,4,8,3,8,4,8
I,1,7,0,5,0,7,7,4,4,7,6,8,0,8,0,8
C,3,6,4,4,2,5,8,7,7,8,8,14,1,9,4,10
M,6,5,8,4,7,7,8,4,4,7,5,8,11,8,5,6
X,2,6,4,4,3,8,8,3,8,6,6,6,3,8,6,8
F,3,4,5,3,2,6,10,2,5,13,7,5,1,10,2,7
I,4,5,6,6,5,7,9,4,5,7,7,8,3,7,8,8
Y,6,8,8,10,11,9,8,7,3,7,7,8,7,10,6,4
X,4,6,5,6,5,7,8,2,5,8,6,8,3,6,7,7
S,3,5,5,4,2,8,8,3,7,10,7,7,1,9,5,7
R,5,5,5,6,3,5,12,9,4,7,3,9,3,7,6,11
Z,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8
F,2,3,4,2,1,6,10,3,5,13,7,5,1,9,1,7
E,5,8,7,6,5,6,8,3,8,11,8,9,3,9,5,7
E,4,9,6,6,5,7,7,6,9,7,6,10,3,8,6,8
R,4,9,4,6,5,6,10,8,3,7,4,9,2,7,5,11
W,5,8,5,6,4,2,10,2,3,10,11,9,6,10,1,7
F,2,8,3,6,2,2,11,4,5,11,10,8,0,8,2,7
J,3,8,5,6,5,9,7,3,3,8,4,6,4,8,6,4
F,6,12,5,6,2,5,11,2,5,11,8,5,2,9,6,2
A,2,7,4,4,1,5,4,3,2,5,1,7,2,6,2,7
E,3,2,3,4,3,7,7,6,7,7,6,9,2,8,6,9
A,2,7,4,4,1,8,4,3,2,7,2,8,3,6,2,8
B,2,5,3,3,3,7,7,5,5,6,6,6,2,8,6,10
K,3,5,6,4,3,5,7,2,8,11,9,11,4,7,4,7
M,6,7,8,5,6,6,7,3,4,9,8,9,8,6,3,8
J,3,8,4,6,2,8,7,1,7,11,5,7,1,6,2,6
I,5,10,5,5,3,8,8,3,5,12,5,6,2,10,5,10
S,3,5,5,4,4,8,9,4,5,7,7,8,4,11,7,10
L,2,7,4,5,3,9,3,1,5,9,3,9,1,6,2,9
K,5,10,8,8,9,6,6,3,4,6,5,10,8,6,8,8
K,5,6,6,8,3,5,9,9,2,7,3,11,4,8,2,11
Q,7,15,7,8,5,11,4,4,6,12,3,7,3,9,7,12
C,4,8,5,7,5,5,7,4,4,7,6,11,4,9,8,10
V,8,10,8,8,4,3,12,3,4,11,12,8,3,9,1,7
T,6,9,6,6,3,5,12,4,8,12,10,4,2,12,3,4
Q,7,9,10,8,7,5,4,4,5,4,3,7,4,7,7,6
Q,4,8,6,7,4,8,7,8,5,6,5,9,2,8,4,9
P,4,9,6,7,5,7,7,7,4,8,6,8,3,9,7,9
Y,9,15,8,8,5,8,6,4,5,9,8,5,4,10,4,4
K,5,7,7,5,5,8,7,1,6,10,5,9,3,8,2,9
R,3,9,4,6,2,5,10,8,4,7,4,9,3,7,6,11
J,4,8,6,6,2,6,8,3,7,15,6,9,1,6,1,7
F,4,6,6,4,5,11,6,1,5,9,5,6,5,9,4,7
G,2,5,3,3,2,6,7,5,5,9,7,10,2,8,4,9
D,4,7,5,6,5,7,8,4,6,6,4,7,3,8,5,6
U,7,11,9,8,5,4,9,7,8,9,11,11,3,9,1,8
W,5,8,7,6,3,7,8,5,2,7,8,8,9,9,0,8
E,3,8,5,6,6,7,7,3,5,6,7,10,4,10,8,8
C,3,8,5,6,3,5,7,6,8,8,5,11,1,9,4,9
F,2,3,3,2,1,6,10,2,5,13,7,5,1,9,1,7
D,3,6,4,4,3,8,7,6,7,10,4,6,3,8,3,8
O,2,0,2,1,1,8,7,7,6,7,6,8,2,8,3,8
G,6,10,7,8,5,5,7,5,5,9,9,9,2,8,5,8
M,3,3,4,2,3,7,6,6,4,6,7,8,7,5,2,8
O,2,4,3,3,2,7,7,7,4,9,6,8,2,8,3,8
Q,5,6,6,8,6,8,5,6,3,9,5,10,5,7,7,6
Z,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8
J,2,5,3,4,2,10,6,2,5,12,4,8,1,6,1,6
X,3,3,5,2,2,6,8,2,8,11,9,9,2,8,3,6
B,4,6,5,4,4,8,6,3,5,6,5,7,4,9,5,8
O,4,7,6,5,4,7,9,8,5,6,8,10,3,8,3,8
R,4,7,5,5,5,8,8,7,3,8,5,7,4,8,6,11
C,2,3,2,1,1,4,10,5,6,12,9,9,1,9,2,7
R,2,3,3,2,2,6,8,4,5,7,5,7,2,7,3,8
R,4,9,6,6,6,7,8,5,5,7,5,7,3,7,5,9
D,6,9,8,7,5,10,6,3,9,11,4,7,4,6,4,9
X,6,10,9,8,4,8,7,1,9,10,5,8,3,8,4,8
L,3,4,3,6,1,0,1,5,6,0,0,7,0,8,0,8
C,4,6,5,6,5,7,8,5,4,6,7,11,6,9,9,11
C,4,9,5,7,3,6,8,6,8,7,6,14,1,8,4,9
F,4,7,6,5,3,8,9,3,6,13,6,5,2,10,3,8
V,5,9,7,7,3,9,12,3,4,4,11,9,3,10,1,8
R,3,8,4,6,4,6,10,7,3,7,4,9,2,7,5,11
Z,9,13,9,7,5,6,8,2,10,12,8,8,4,4,8,4
C,4,9,5,7,3,4,8,6,7,7,8,15,1,8,4,10
I,2,6,3,4,1,6,8,1,8,14,7,8,0,8,1,7
O,2,7,3,5,2,7,7,9,6,7,6,8,3,8,4,8
L,5,11,7,8,5,6,5,0,9,9,3,11,0,7,3,8
S,2,0,2,1,1,8,8,4,7,5,6,7,0,8,7,8
I,4,8,5,6,3,6,6,2,7,7,6,10,0,9,4,8
T,5,7,6,5,4,5,12,4,5,12,9,4,2,12,1,5
E,6,10,8,7,7,10,6,2,7,11,4,8,5,6,5,11
Y,3,4,5,5,1,8,10,3,2,6,13,8,2,11,0,8
Y,4,5,5,4,2,4,12,3,7,12,10,4,1,11,2,5
R,5,9,5,7,6,5,10,7,4,7,4,9,2,7,5,11
S,3,7,5,5,6,7,7,3,2,8,6,7,2,7,12,3
R,4,8,4,5,3,5,11,8,3,7,3,8,3,7,6,11
O,4,9,5,7,4,7,7,9,5,7,5,8,3,8,3,8
I,2,5,4,4,1,7,7,0,8,14,6,9,0,8,1,8
T,3,8,4,6,2,7,13,0,6,7,10,8,0,8,0,8
X,4,9,6,6,4,4,8,1,8,10,10,10,2,8,3,5
C,5,10,4,5,3,8,5,5,3,9,9,11,4,9,7,11
I,1,4,2,3,1,7,7,0,7,13,6,8,0,8,0,8
J,3,7,4,5,2,8,6,3,6,15,5,9,0,7,0,7
R,2,3,4,2,2,8,8,4,4,8,5,7,2,7,4,10
E,5,10,4,6,2,8,7,5,7,11,6,9,2,10,7,9
Q,5,9,5,5,3,9,4,4,7,10,4,9,3,8,8,11
X,3,4,4,3,2,7,7,3,9,6,6,8,3,8,6,8
O,4,8,5,6,4,7,7,8,4,7,6,8,3,8,3,8
Q,3,7,4,7,4,8,9,6,4,6,9,9,2,8,5,9
Y,2,3,3,1,1,4,12,3,5,12,10,5,1,11,1,5
Q,5,12,5,6,3,10,5,4,7,12,4,9,3,8,8,12
C,6,11,5,6,4,7,7,4,3,9,8,10,4,9,8,11
K,7,9,9,6,7,9,5,1,7,10,3,9,8,5,7,10
A,3,4,5,2,2,9,1,2,1,8,2,8,2,6,3,8
E,5,10,6,8,7,6,8,6,8,6,5,11,3,8,6,9
X,8,13,9,7,5,6,8,2,8,11,5,7,4,5,4,6
M,4,5,7,3,4,8,7,3,4,9,6,7,8,6,2,7
P,8,14,7,8,5,7,9,4,4,12,5,4,4,9,7,5
R,4,7,5,5,3,9,8,4,6,9,3,7,3,6,4,11
K,4,8,6,6,5,8,5,1,6,9,4,10,4,8,4,10
G,3,6,5,4,2,8,6,7,8,6,5,10,2,8,5,10
E,5,9,7,6,7,7,8,6,3,7,6,8,5,8,8,8
T,2,2,3,3,2,7,11,2,7,7,11,8,1,11,1,8
O,4,8,5,6,3,8,7,8,5,10,6,7,3,8,3,8
B,1,0,2,1,1,7,7,7,4,7,6,7,1,8,6,8
J,2,10,3,8,1,13,2,9,4,14,4,12,1,6,0,8
Z,4,5,5,7,2,7,7,4,15,9,6,8,0,8,8,8
F,4,7,4,5,1,1,14,5,3,12,9,5,0,8,2,6
V,6,11,8,8,5,6,11,3,2,7,11,8,4,10,5,9
H,3,4,4,3,3,7,7,6,6,7,6,8,3,8,3,8
Q,3,4,4,5,3,7,8,5,2,7,9,10,3,9,5,8
C,2,4,3,3,1,6,8,7,7,8,8,13,1,9,4,10
L,3,6,4,4,2,7,3,2,9,7,1,9,0,7,2,8
M,3,3,5,2,3,5,7,3,4,10,9,10,6,5,2,7
N,1,1,2,2,1,7,8,5,3,8,7,7,4,8,1,7
T,3,6,4,4,2,6,11,1,8,8,11,9,1,10,1,8
O,5,8,6,7,5,6,6,5,5,8,5,7,3,6,5,7
O,2,5,3,3,2,7,7,7,4,9,6,8,2,8,2,8
C,3,7,4,5,2,5,8,6,8,12,9,11,1,10,3,7
E,2,6,2,4,2,3,8,5,9,8,8,13,0,8,6,9
Q,4,7,6,9,6,9,7,7,2,5,7,10,3,8,6,10
T,3,9,4,6,1,7,15,0,6,7,11,8,0,8,0,8
Z,2,3,3,2,1,7,8,2,9,11,6,8,1,8,5,7
B,2,4,4,3,3,9,7,2,6,11,4,7,4,7,5,9
J,1,1,1,1,0,12,3,6,4,13,4,11,0,7,0,8
C,2,4,3,2,1,4,9,5,7,11,9,12,1,9,3,7
P,4,8,6,6,4,8,8,2,5,13,5,5,1,10,3,9
X,3,5,5,3,2,10,7,1,9,11,3,7,2,7,3,9
W,3,1,3,2,1,7,8,4,1,7,8,8,7,10,0,8
N,4,9,4,6,2,7,7,14,2,4,6,8,6,8,0,8
B,6,10,9,8,12,8,8,5,3,6,7,7,7,10,11,10
B,6,9,9,8,10,7,7,5,4,7,6,8,7,9,8,6
Z,3,6,5,4,3,8,7,2,9,11,6,8,2,8,5,8
M,6,9,9,8,11,7,7,4,3,6,6,8,10,9,5,5
H,2,6,3,4,2,7,6,12,2,7,8,8,3,9,0,8
B,1,0,1,1,1,7,7,7,4,6,6,7,1,8,6,9
U,6,10,9,8,6,7,8,4,8,4,7,9,6,9,1,8
I,0,3,1,1,0,7,7,1,6,13,6,8,0,8,0,7
Q,3,6,4,5,4,8,7,7,5,6,6,7,2,8,4,9
D,3,7,4,5,4,9,7,4,6,9,4,6,3,8,3,8
H,6,11,6,8,6,8,5,14,2,7,9,8,3,9,0,8
U,3,3,4,2,2,5,8,6,7,8,10,10,3,9,1,7
G,2,4,3,3,2,6,6,5,4,8,7,10,2,8,4,10
G,8,12,7,6,4,10,3,3,4,10,2,6,4,7,5,11
R,5,10,8,8,5,9,8,4,6,9,3,7,3,6,5,11
T,4,7,4,5,3,6,11,5,5,11,8,4,3,12,2,4
G,3,8,4,6,2,7,6,7,7,6,6,10,1,8,6,11
A,2,1,4,2,1,8,2,2,1,7,2,8,2,7,2,7
J,3,7,5,5,4,9,8,3,4,9,4,7,4,8,6,5
A,4,7,5,5,3,8,4,3,0,6,1,8,2,7,1,7
Q,4,7,5,5,5,8,7,7,4,6,8,8,4,5,6,9
H,4,7,6,5,5,6,6,7,4,6,5,8,3,7,6,10
U,2,3,3,1,1,5,8,4,6,10,8,8,3,10,1,6
W,5,10,7,7,4,6,8,5,2,7,8,8,9,9,0,8
Z,2,5,3,3,2,7,7,5,9,6,6,8,2,8,7,8
K,6,9,9,6,6,5,7,1,7,10,9,11,4,6,4,7
Y,7,11,7,8,4,3,10,2,7,10,12,6,2,11,3,5
F,2,7,2,4,1,1,12,4,5,12,11,8,0,8,2,6
X,4,9,7,7,3,7,7,1,8,10,6,8,3,8,4,7
K,3,6,4,4,5,7,8,3,4,6,6,8,7,8,6,8
P,3,8,5,6,3,4,12,4,6,11,9,3,0,9,4,6
N,3,5,6,3,2,6,9,3,5,10,7,7,5,8,1,8
C,3,8,4,6,2,5,8,7,7,7,8,14,1,9,4,10
W,6,11,9,8,4,11,7,5,2,6,9,8,9,9,0,8
M,6,7,9,5,6,8,6,2,5,9,6,7,8,6,2,7
T,5,10,6,8,5,4,12,5,5,12,9,4,2,12,1,5
H,4,7,5,5,4,9,7,7,6,7,6,5,3,8,3,5
L,3,8,4,6,3,5,4,2,9,3,2,7,3,7,2,5
Y,9,11,9,8,4,5,9,1,10,10,10,4,2,13,5,3
N,4,9,5,7,4,8,7,13,1,6,6,8,5,9,0,8
Z,4,7,6,5,3,7,8,2,9,11,6,8,1,9,6,8
H,3,7,4,5,4,8,7,6,6,7,6,7,3,8,3,7
O,4,9,3,5,2,6,7,6,3,10,6,9,5,9,5,7
P,3,8,3,6,3,3,12,6,1,11,7,4,0,9,3,8
T,6,10,8,8,7,6,7,7,7,7,9,9,4,10,6,8
D,6,10,9,8,8,8,8,5,6,10,5,5,4,8,5,10
K,2,1,2,1,0,5,7,8,1,7,6,11,3,8,2,11
U,2,1,3,1,1,7,8,6,6,7,9,8,3,10,1,8
N,5,6,7,4,3,9,7,3,5,10,3,5,5,9,1,7
D,6,11,6,6,4,10,6,3,6,9,4,7,5,10,6,8
B,4,4,5,6,4,6,7,9,7,7,6,7,2,8,9,9
P,5,10,7,7,4,7,11,4,5,13,5,3,1,10,3,8
L,4,8,5,6,6,8,8,3,5,6,7,9,6,11,6,6
L,2,3,3,5,1,0,1,5,6,0,0,7,0,8,0,8
L,1,3,2,1,1,6,5,1,7,8,3,10,0,7,2,8
V,7,13,7,7,4,8,9,4,4,8,8,5,6,14,2,8
L,1,4,2,2,1,7,4,1,7,8,2,10,0,7,2,8
P,5,6,6,8,7,6,9,3,2,8,8,6,5,10,5,5
J,6,8,4,11,3,9,7,3,3,12,3,5,3,8,6,10
R,5,5,5,7,3,5,11,9,4,7,3,9,3,7,6,11
W,6,8,6,6,5,6,10,4,3,9,7,6,7,12,3,6
Q,6,9,7,10,8,9,9,6,3,4,8,11,5,9,9,13
T,7,15,6,8,4,6,10,2,6,12,7,6,3,9,5,5
U,4,4,4,3,2,5,8,5,7,10,9,9,3,9,2,6
K,3,5,6,4,3,5,8,2,7,10,9,10,3,8,3,7
Y,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8
T,5,6,6,5,6,7,9,5,8,7,7,8,3,10,7,7
B,5,11,8,9,9,8,6,5,6,9,5,7,3,8,6,10
D,9,15,9,8,5,8,6,5,7,12,3,7,6,7,6,10
S,2,7,3,5,2,8,8,7,6,8,6,8,2,8,9,8
L,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8
W,1,0,2,0,0,8,8,3,0,6,8,8,5,9,0,8
G,3,4,4,6,2,7,7,8,7,6,6,9,2,7,6,10
P,8,15,8,9,5,8,9,4,4,12,5,3,5,10,6,6
M,5,6,8,4,4,7,6,2,5,9,7,8,8,5,2,8
O,5,8,6,6,4,8,9,8,4,7,7,8,3,8,3,8
H,4,7,6,5,8,8,7,4,3,6,6,7,7,8,8,8
O,2,4,3,3,2,7,7,6,3,9,6,8,2,8,2,8
H,3,2,4,3,3,8,7,5,6,7,6,8,6,8,4,7
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
L,4,10,5,8,3,3,5,1,9,6,1,11,0,7,3,6
S,5,11,5,6,3,6,8,4,3,13,8,8,3,10,3,8
D,3,5,5,3,3,9,6,4,6,10,4,6,2,8,3,8
X,6,12,6,6,4,7,8,2,7,11,7,7,4,12,3,7
L,3,8,4,6,1,0,1,6,6,0,0,6,0,8,0,8
D,2,1,3,1,2,6,7,6,6,7,6,5,2,8,2,7
Z,9,10,6,14,6,8,4,5,4,12,7,9,3,8,12,7
B,4,9,6,6,5,6,9,5,6,10,6,5,3,8,7,8
D,1,1,2,1,1,7,7,6,6,7,6,5,2,8,2,7
A,3,11,5,8,4,12,2,3,3,10,2,9,2,6,3,8
X,4,9,7,7,6,8,7,3,6,7,4,6,5,5,8,7
V,5,10,5,8,3,3,11,4,4,10,11,7,3,10,1,8
B,5,9,5,7,4,6,7,9,7,7,6,7,2,8,9,9
X,4,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
M,4,4,7,3,4,6,6,3,4,10,9,10,7,6,2,8
O,4,3,5,4,2,7,7,8,8,7,6,8,3,8,4,8
L,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8
K,4,8,6,6,5,3,8,2,6,10,10,11,3,8,3,6
T,6,11,6,8,5,4,13,6,4,12,9,4,2,12,1,5
M,3,6,4,4,4,8,5,10,0,6,8,8,6,5,0,7
H,3,5,6,4,3,9,7,3,6,10,3,7,5,7,4,9
Y,6,11,6,8,3,3,10,3,7,12,12,7,1,11,3,5
C,3,4,4,6,2,6,7,7,9,5,6,14,1,7,4,8
I,4,11,7,8,8,10,7,2,5,9,4,4,4,9,7,5
U,5,8,7,6,4,5,9,6,7,8,10,10,3,9,1,8
Z,3,9,5,7,5,8,8,3,7,8,7,7,1,9,10,9
S,6,9,6,5,3,9,5,4,4,13,6,9,3,10,3,8
N,5,8,8,6,3,11,6,3,5,10,1,4,5,9,1,7
B,5,6,7,5,7,8,6,5,4,7,6,8,7,9,8,4
F,4,7,6,5,2,7,10,2,7,14,5,4,1,10,2,8
X,4,10,7,7,4,7,8,4,9,6,5,5,4,10,8,6
C,1,3,2,2,1,6,8,4,6,11,8,12,1,10,3,8
B,4,9,6,7,7,8,7,6,6,7,6,6,2,8,6,10
J,2,9,3,7,1,13,3,8,5,14,2,11,0,6,0,8
C,4,7,5,5,2,5,7,6,7,11,9,13,2,9,4,7
M,4,9,5,6,5,8,6,11,1,7,8,8,7,5,0,8
O,5,11,7,8,3,8,8,8,8,6,7,9,3,8,4,8
W,5,5,8,8,4,5,8,5,2,7,8,8,9,9,0,8
T,3,9,4,6,1,7,14,0,6,7,11,8,0,8,0,8
A,3,6,5,4,2,8,4,3,0,7,1,8,2,7,1,8
W,5,11,8,8,13,8,5,6,3,7,6,8,14,10,4,9
B,3,3,3,5,3,6,6,9,6,6,6,7,2,8,9,10
U,6,10,6,6,4,8,6,5,5,6,7,7,5,8,3,7
A,3,4,5,5,1,8,6,3,1,7,0,8,2,7,1,8
F,4,7,7,5,6,7,9,1,5,10,6,6,5,10,4,5
L,3,3,3,5,1,0,1,6,6,0,1,6,0,8,0,8
X,3,7,5,5,4,8,7,3,6,7,4,6,4,7,6,8
Z,3,5,5,3,2,7,7,2,9,12,6,8,1,8,6,8
W,7,10,7,8,5,1,10,2,3,11,11,9,7,10,1,7
L,4,5,5,5,4,8,7,4,5,7,6,8,3,8,8,11
N,3,6,5,4,3,7,8,6,5,7,7,6,5,9,1,6
C,6,9,6,7,3,4,8,6,7,12,10,12,2,10,2,7
D,6,9,5,4,3,9,5,5,6,12,3,8,5,7,5,10
C,8,14,6,8,5,9,6,4,3,9,8,11,4,9,8,13
V,8,10,7,5,3,8,8,7,5,7,10,6,7,12,4,8
W,5,9,7,7,11,9,7,5,2,7,6,8,10,11,4,8
X,2,2,3,3,2,7,7,3,9,6,6,10,2,8,6,8
H,5,6,7,4,4,4,10,3,6,10,10,9,3,8,3,6
B,5,9,5,4,4,9,7,3,5,9,5,7,6,6,7,9
H,10,14,9,8,5,7,8,4,5,9,7,8,6,8,5,9
G,3,7,4,5,2,7,6,7,8,6,5,11,1,8,6,11
Y,5,10,7,8,3,8,9,3,2,6,12,8,2,11,0,8
W,4,7,6,5,3,7,8,5,1,7,8,8,8,9,0,8
T,4,10,6,8,5,7,11,2,7,7,11,8,1,12,1,7
K,4,7,4,4,1,4,7,8,2,7,5,11,3,8,2,11
V,3,6,5,4,2,9,9,4,1,6,12,8,2,10,0,8
K,1,0,1,0,0,5,7,7,0,7,6,10,2,8,2,11
Y,4,9,4,4,2,6,8,3,4,10,7,5,3,10,4,3
T,5,10,7,8,5,7,11,1,8,7,11,8,1,11,1,8
E,2,4,3,3,2,9,7,1,7,11,5,8,2,8,4,10
D,4,6,5,4,4,10,5,3,6,10,3,7,3,7,3,8
E,2,3,4,2,2,6,7,2,7,11,6,9,2,8,3,8
L,2,6,2,4,1,0,2,5,6,0,0,7,0,8,0,8
C,8,11,8,8,4,3,9,6,8,12,11,11,1,8,3,6
G,6,9,8,7,8,7,8,6,2,6,5,11,6,8,10,6
P,1,0,2,0,0,5,11,6,1,9,5,5,0,9,3,8
Z,4,5,6,7,4,11,5,4,4,9,3,8,2,6,5,9
N,9,12,11,6,4,5,8,3,4,13,9,9,7,8,0,7
C,6,10,6,8,3,5,9,7,8,13,9,7,2,11,2,7
I,4,10,6,8,4,6,9,0,7,13,7,7,0,8,1,7
E,5,11,7,8,8,8,7,5,3,7,6,8,5,8,9,9
E,6,11,4,6,2,7,8,5,8,9,5,12,1,8,7,9
A,2,0,3,1,0,7,4,2,0,7,2,8,2,7,1,8
A,3,7,5,5,3,11,3,2,2,9,2,9,3,5,2,8
C,6,10,7,8,4,4,8,6,7,12,10,12,2,10,3,7
F,3,5,4,4,2,5,10,3,5,10,9,5,1,10,3,6
M,4,7,6,5,6,7,9,6,3,7,6,8,5,9,5,8
M,7,8,9,7,10,8,7,5,4,7,6,7,10,9,6,4
X,4,11,6,8,5,7,7,3,8,5,6,8,3,8,6,7
K,4,6,6,4,4,5,6,4,7,6,6,10,3,8,5,9
F,5,9,5,5,3,6,12,3,3,11,6,4,4,10,7,6
S,4,6,5,4,6,8,10,5,4,8,5,6,4,9,8,7
B,6,10,8,8,6,9,7,4,7,10,4,6,2,8,6,10
L,3,7,5,5,2,6,3,2,8,7,2,7,1,6,2,7
U,8,10,9,8,6,4,8,5,8,9,8,9,6,9,4,3
B,3,4,5,3,3,8,7,3,5,10,5,7,2,8,4,10
N,4,9,4,7,2,7,7,14,2,5,6,8,6,8,0,8
Q,4,5,5,6,5,9,6,7,4,5,6,8,3,8,6,9
P,2,2,3,3,2,6,9,5,4,9,7,4,1,10,3,7
W,2,1,4,1,2,8,11,3,1,6,9,8,6,11,0,7
W,4,8,6,6,7,7,8,5,3,7,9,8,6,8,3,8
P,4,8,6,6,4,6,10,4,6,10,8,3,1,10,5,7
M,10,14,12,8,7,9,3,3,2,9,3,10,11,0,2,8
Y,3,3,4,2,1,5,10,2,7,11,10,4,1,11,2,5
R,6,8,9,7,9,7,7,3,4,7,5,8,7,9,7,5
O,5,11,4,6,3,4,9,6,4,9,9,8,3,9,4,8
O,5,9,5,7,5,8,7,7,4,10,5,8,3,8,3,7
V,7,11,7,8,4,2,11,4,4,11,12,8,3,10,1,8
B,5,9,6,7,5,7,9,9,8,7,5,7,2,8,9,10
L,6,12,5,6,3,10,2,2,5,11,4,11,2,9,4,11
E,6,9,5,5,3,7,8,4,4,11,6,8,3,9,8,9
T,3,3,3,2,1,5,12,3,7,11,9,4,1,10,2,5
X,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8
U,3,7,5,5,3,7,8,6,6,5,9,9,3,9,1,8
G,3,11,5,8,4,7,7,7,7,8,4,12,2,9,6,10
I,5,9,6,7,4,7,6,2,7,7,6,9,0,9,4,8
Q,2,2,2,2,2,8,7,5,3,8,6,8,2,9,2,7
I,2,11,0,8,2,7,7,5,3,7,6,8,0,8,0,8
W,6,6,6,4,4,1,12,3,2,11,10,8,6,11,1,7
C,6,12,4,6,2,7,8,7,6,11,7,7,2,9,5,8
B,3,5,5,3,3,7,8,3,5,10,6,6,2,8,5,8
H,5,7,8,9,7,8,5,3,2,7,5,7,4,8,8,8
X,2,1,3,1,1,7,7,4,9,6,6,9,2,8,5,7
Q,7,13,6,7,5,10,4,4,6,11,4,8,3,8,8,11
O,3,7,4,5,3,8,7,8,5,7,6,9,2,8,3,8
O,1,0,2,0,0,7,7,6,4,7,6,8,2,8,3,8
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
R,7,13,7,7,5,10,6,4,5,10,2,7,6,7,6,8
N,3,7,5,5,4,7,7,8,5,7,5,7,3,7,3,8
N,5,10,7,7,5,8,8,6,5,6,6,5,6,9,2,6
C,3,7,4,5,3,5,8,6,6,8,8,14,2,9,4,10
V,7,13,6,7,3,8,10,6,4,8,10,5,6,13,3,8
X,7,11,11,8,5,6,8,1,9,10,9,9,3,8,4,6
Q,4,4,6,6,6,9,11,5,0,5,7,10,5,12,4,10
E,4,10,6,8,6,7,7,5,8,7,7,10,3,8,6,9
V,4,11,6,8,3,7,11,3,5,8,12,8,3,10,1,8
I,1,9,0,6,1,7,7,5,3,7,6,8,0,8,0,8
U,5,5,6,4,3,4,8,5,8,10,9,9,3,9,2,6
W,4,8,6,6,5,4,11,2,2,8,9,9,6,11,1,8
D,6,9,8,8,7,6,7,5,8,7,5,8,5,5,7,4
C,8,11,5,6,2,7,8,6,7,11,6,9,2,9,5,9
L,4,8,5,6,3,6,4,3,7,6,1,8,1,6,3,7
H,4,6,6,4,5,8,8,6,7,7,6,6,6,8,3,8
N,5,10,6,7,5,9,8,6,5,6,5,4,6,10,3,5
P,4,7,6,10,10,8,9,5,0,8,7,6,5,11,6,10
J,2,6,2,4,1,9,7,2,6,11,3,7,0,7,1,6
E,2,3,4,1,2,7,7,2,7,11,7,9,1,9,4,8
C,3,8,4,6,2,5,8,8,8,9,8,13,2,10,4,10
E,4,8,6,6,5,5,8,5,7,11,9,9,3,8,5,5
K,2,3,2,2,2,5,7,4,7,6,6,10,3,8,4,10
X,6,10,7,6,4,10,6,3,7,12,2,6,3,8,4,10
Q,5,6,6,8,3,8,6,8,7,6,6,8,3,8,5,9
R,6,8,9,7,9,9,6,4,4,8,5,7,7,9,7,6
T,4,11,6,8,2,8,15,1,6,7,11,8,0,8,0,8
Y,2,3,4,5,1,8,11,2,3,4,12,8,1,10,0,8
W,5,10,8,7,7,4,11,2,3,8,9,9,7,11,1,8
B,4,7,4,5,3,6,7,8,6,7,6,7,2,8,9,10
A,3,7,6,5,4,12,3,2,2,9,2,8,4,5,2,8
A,3,7,5,5,3,7,3,2,2,5,2,8,2,6,2,6
J,5,9,4,7,3,7,11,3,3,13,5,4,2,8,7,8
J,2,4,4,3,1,8,6,3,6,14,7,11,1,6,1,7
C,2,3,3,2,1,5,8,5,6,11,9,10,1,10,2,7
L,4,9,4,4,2,8,5,3,5,12,7,11,2,8,6,8
J,2,6,4,4,3,9,6,2,4,8,5,6,3,7,5,6
U,5,9,5,6,4,4,8,5,7,10,9,9,3,9,2,5
V,3,6,5,4,1,7,8,4,2,7,13,8,3,9,0,8
N,4,7,4,5,2,7,7,14,2,4,6,8,6,8,0,8
H,3,8,5,6,6,8,7,4,3,6,6,7,7,8,7,6
G,6,11,6,8,5,5,6,6,5,10,8,11,2,9,4,10
G,3,7,5,5,3,7,6,6,6,6,6,10,2,9,4,8
N,1,0,1,1,1,7,7,10,0,6,6,8,4,8,0,8
T,4,10,6,8,5,10,11,2,7,5,11,7,1,11,1,8
U,3,3,4,2,2,7,8,6,7,6,9,9,3,9,1,8
U,4,5,5,3,2,4,8,5,8,9,8,9,4,11,3,4
N,3,2,4,3,3,7,8,5,5,7,6,6,6,9,2,5
U,5,6,5,4,2,4,9,5,7,12,11,9,3,9,1,7
W,4,5,7,4,4,5,11,3,2,8,9,9,8,11,1,8
C,3,5,4,4,2,4,8,5,7,12,9,11,1,10,2,7
U,5,10,7,7,6,8,8,8,5,5,7,9,3,7,4,7
J,2,5,4,4,2,10,6,2,6,12,4,8,1,6,1,7
C,4,9,5,7,6,8,6,5,3,7,7,11,5,9,3,8
O,6,9,8,8,6,6,5,5,5,9,6,10,5,5,7,4
L,4,9,4,7,1,0,0,6,6,0,0,5,0,8,0,8
C,6,10,6,8,3,3,9,6,8,12,11,11,1,8,2,6
B,3,6,5,4,4,9,6,4,6,10,5,7,2,8,5,10
T,3,8,4,6,2,9,14,0,5,6,10,8,0,8,0,8
Z,3,7,4,5,2,7,7,4,13,9,6,8,0,8,8,8
F,4,10,5,8,4,6,10,3,6,10,9,5,2,10,4,6
Q,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,9
P,4,9,6,6,4,7,9,4,4,12,5,3,1,10,3,8
O,6,10,8,8,6,8,5,9,4,6,5,5,5,7,4,8
T,4,5,4,3,2,5,11,2,8,11,9,4,1,11,2,4
D,4,8,6,7,6,7,7,5,6,7,4,8,4,6,7,4
A,3,10,5,7,3,7,3,2,2,5,2,7,2,6,2,7
C,6,9,6,6,3,5,7,6,8,11,8,14,2,9,4,6
E,2,3,3,2,2,7,7,5,7,7,6,9,2,8,5,10
K,2,1,3,3,2,6,7,4,7,6,6,10,3,8,5,8
R,6,11,8,8,7,9,8,4,5,9,4,7,3,7,4,11
Y,3,7,4,5,1,8,11,1,3,6,12,8,1,11,0,8
T,9,11,7,6,3,5,9,3,9,13,7,5,2,10,3,5
E,6,11,9,8,8,8,7,7,3,7,6,11,6,8,9,8
H,4,8,6,6,6,7,8,5,6,7,6,8,3,8,3,8
R,3,5,5,3,3,9,6,3,5,10,4,7,3,6,4,9
C,1,0,1,1,0,6,7,6,8,7,6,14,0,8,4,10
U,4,7,6,6,5,7,7,4,4,6,6,9,4,8,2,9
U,4,10,4,7,4,7,6,12,4,7,12,8,3,9,0,8
Z,4,9,5,7,2,7,7,4,14,9,6,8,0,8,8,8
T,4,5,5,4,3,8,12,3,7,6,11,8,2,11,1,7
F,5,5,5,7,2,1,13,5,5,12,10,7,0,8,2,6
C,3,6,4,4,1,3,9,5,7,11,11,10,2,9,2,6
C,7,13,5,7,2,6,9,6,8,11,8,9,2,8,5,9
H,3,7,5,5,4,7,7,7,6,7,6,8,3,8,3,8
W,7,6,9,6,10,7,8,5,5,7,5,8,10,10,9,8
S,2,1,2,1,1,8,7,4,7,5,6,7,0,8,8,8
I,7,14,6,8,4,8,8,2,6,13,4,5,2,8,6,9
L,2,1,2,2,1,5,3,5,7,2,2,4,1,7,1,6
N,3,5,4,4,3,7,8,5,5,7,7,6,5,9,2,6
E,3,8,3,6,2,3,6,6,11,7,7,15,0,8,6,7
U,7,12,6,6,4,9,5,5,6,5,7,7,5,9,3,7
P,4,6,6,4,3,8,9,5,5,12,4,3,2,9,4,8
N,4,5,6,4,3,6,10,2,4,10,7,7,5,8,1,8
C,2,5,3,3,1,4,9,5,6,11,10,11,1,9,2,7
M,1,0,2,0,0,8,6,10,0,7,8,8,6,6,0,8
Q,5,9,5,4,3,12,4,3,5,10,3,7,3,9,4,12
Y,1,1,3,2,1,7,10,1,6,7,11,8,1,11,1,8
X,6,9,9,6,5,5,8,1,8,10,10,9,3,8,3,6
N,4,10,4,8,3,7,7,14,2,5,6,8,6,8,0,8
S,5,9,6,7,5,8,7,7,5,7,6,7,2,8,9,8
R,4,11,4,8,5,6,8,9,4,7,5,7,3,8,5,11
R,3,4,4,2,3,7,8,5,5,6,5,6,2,7,4,9
U,9,11,10,8,4,4,8,6,10,13,11,9,3,9,1,7
S,4,5,5,5,5,8,8,4,4,7,6,8,5,10,9,11
Z,9,13,9,7,6,7,6,2,9,12,7,9,4,6,8,6
H,6,9,6,4,3,7,8,4,4,9,9,9,6,10,5,9
P,1,0,2,1,0,5,11,7,1,9,6,4,1,9,3,8
L,3,9,4,6,3,3,5,1,8,3,1,9,0,7,1,6
V,1,0,2,1,0,8,9,3,2,7,12,8,2,10,0,8
R,5,9,7,7,6,7,7,6,6,6,5,7,4,8,6,10
U,5,9,7,8,7,8,6,4,4,6,7,7,4,8,1,7
R,7,11,9,8,9,9,8,4,5,10,4,7,3,7,5,11
F,4,7,6,5,3,7,10,3,6,13,6,5,2,9,3,7
N,3,4,5,3,2,6,9,3,4,10,7,7,5,8,1,8
B,6,10,8,7,7,7,9,7,5,7,5,5,5,9,7,6
W,6,9,6,6,5,2,11,2,3,10,10,8,5,11,1,7
X,5,6,6,6,6,7,6,2,5,8,7,9,3,11,7,8
Y,3,2,5,4,2,5,10,1,7,9,11,9,1,11,2,7
M,4,4,5,6,3,8,7,12,1,6,9,8,8,6,0,8
P,4,8,6,6,3,6,11,5,4,12,5,3,1,10,3,8
J,2,4,4,3,1,8,6,3,7,14,5,10,0,7,0,8
X,4,8,6,7,6,9,9,2,6,7,5,6,2,5,7,7
D,5,10,7,7,5,8,7,7,9,6,5,4,3,8,4,7
T,5,9,4,4,2,6,10,2,6,12,7,6,2,9,4,4
V,6,10,8,8,6,8,12,3,2,6,10,9,7,11,7,8
F,4,10,4,7,2,1,15,5,3,12,9,4,0,8,3,6
M,4,7,5,5,3,8,7,12,1,6,9,8,8,6,0,8
R,6,9,9,8,10,5,9,4,4,6,4,10,10,5,9,9
T,7,9,6,5,2,4,12,3,6,13,8,4,2,8,4,3
K,5,8,7,6,6,7,7,1,6,10,5,9,3,7,4,8
H,2,6,4,4,5,9,6,4,3,6,6,7,6,9,7,7
Q,7,9,7,11,8,8,6,7,3,8,6,11,6,8,8,6
P,3,7,3,4,2,4,11,9,3,9,6,4,1,10,3,8
J,4,10,4,7,3,14,3,5,4,13,2,10,0,7,0,8
L,3,9,5,6,3,8,3,0,8,10,3,11,0,7,2,9
O,5,9,6,7,6,7,6,8,4,7,5,8,3,9,3,7
L,1,3,2,2,1,7,4,1,7,8,2,10,0,7,2,8
S,4,5,5,7,3,7,7,6,9,5,6,8,0,8,9,7
N,8,11,12,8,7,3,11,3,4,10,11,9,8,7,2,7
D,3,7,4,5,3,8,7,7,7,9,5,4,3,8,4,7
N,4,7,7,5,6,7,8,3,5,7,6,7,6,8,7,4
T,8,13,7,8,3,7,8,3,9,13,5,6,2,9,5,5
C,4,8,4,6,2,4,8,6,7,11,10,12,2,9,3,7
T,4,6,5,4,5,7,8,4,5,6,7,9,5,8,5,7
J,4,6,5,4,2,10,6,2,8,15,4,8,0,8,0,8
N,4,9,5,7,3,7,7,14,2,4,6,8,6,8,0,8
S,3,9,4,7,4,8,7,7,5,7,6,7,2,8,8,8
G,3,4,4,3,2,7,6,7,5,6,6,10,3,8,4,9
Q,4,7,5,6,3,8,4,8,8,6,4,8,3,8,4,8
G,4,9,5,7,4,7,6,7,6,6,7,9,2,8,4,8
R,3,6,5,4,3,10,7,3,6,10,2,7,3,7,3,10
N,11,13,9,8,5,4,8,5,6,3,2,13,7,11,2,7
W,5,9,7,7,4,10,8,5,2,6,8,8,9,10,0,8
V,2,7,4,5,2,6,11,2,3,8,11,8,2,11,1,9
S,5,7,6,5,4,8,6,3,7,10,7,9,2,10,5,8
O,5,5,7,4,4,7,6,5,5,9,5,9,3,6,5,6
V,8,11,7,8,4,3,11,4,4,10,12,8,3,10,1,8
U,2,3,2,1,1,6,8,5,6,9,8,8,3,10,2,6
C,4,9,5,7,3,5,8,6,8,12,9,13,2,10,3,7
O,6,11,6,8,6,7,7,8,4,10,6,9,4,9,5,5
R,4,6,6,4,5,8,8,7,3,8,4,7,4,6,7,8
E,5,10,3,5,2,6,9,5,8,9,5,10,1,7,6,8
U,8,10,8,7,6,4,8,5,8,9,8,10,6,8,4,3
M,5,8,7,6,7,7,7,6,5,6,7,7,9,8,3,6
A,2,4,4,5,1,5,5,3,1,5,1,8,2,7,2,7
H,5,9,8,7,5,9,8,3,6,10,4,7,3,8,3,9
F,4,6,6,4,2,3,15,4,3,13,8,3,1,10,2,5
V,4,9,6,7,2,7,12,3,5,8,12,8,3,10,1,8
I,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7
W,5,9,8,6,5,9,8,4,1,6,9,8,7,11,0,8
S,4,7,5,5,3,10,7,3,6,9,4,8,2,8,5,11
N,2,1,3,2,2,6,8,5,4,8,7,7,5,9,1,6
O,5,10,5,8,4,7,7,8,5,10,7,7,3,8,4,8
G,5,8,6,6,5,8,7,7,6,6,7,9,2,6,6,10
Y,6,9,6,4,3,6,7,4,3,10,9,6,3,10,4,4
D,7,10,9,8,8,7,7,5,7,7,6,7,7,8,3,7
H,1,0,1,0,0,7,7,10,2,7,6,8,2,8,0,8
F,6,12,6,6,4,9,7,3,4,10,5,6,3,9,6,9
Z,4,6,6,4,4,8,7,2,9,12,6,8,1,8,5,8
N,5,7,7,5,4,8,8,2,5,10,4,6,5,9,1,7
M,4,6,5,8,4,8,7,12,2,6,9,8,8,6,0,8
R,4,10,6,8,6,7,7,4,6,7,6,6,6,8,4,8
W,3,8,5,6,3,11,8,5,1,6,9,8,7,10,0,8
F,8,15,7,8,6,8,7,3,5,10,5,7,5,7,7,8
I,2,9,2,7,3,7,7,0,7,7,6,8,0,8,3,8
V,3,10,6,7,2,8,8,4,3,6,14,8,3,9,0,8
H,6,10,8,8,7,6,8,3,6,10,8,8,4,8,4,6
W,10,12,9,6,4,5,9,2,2,7,10,7,9,12,1,6
P,3,2,4,3,3,5,10,4,5,10,8,4,1,10,4,7
N,6,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8
A,5,13,5,7,4,12,2,4,1,11,3,11,4,3,4,11
A,6,14,6,8,4,11,1,3,3,12,5,13,3,6,5,11
T,5,7,5,5,3,6,11,2,7,11,9,5,1,11,2,4
W,3,4,5,3,3,6,11,3,2,7,9,8,7,11,0,8
P,3,6,5,4,3,8,9,4,4,11,4,4,1,10,3,8
X,5,8,7,6,5,7,6,3,5,6,6,9,3,8,9,9
W,5,9,7,7,4,8,8,5,2,7,8,8,9,9,0,8
Y,3,5,5,3,2,7,10,1,7,7,11,8,1,11,2,8
M,5,5,6,4,4,7,6,6,5,6,7,9,9,6,2,8
Y,2,4,4,5,1,7,10,2,2,7,13,8,1,11,0,8
L,4,9,6,7,3,6,3,2,9,7,1,9,0,6,2,7
F,3,7,3,4,1,1,13,5,4,12,10,7,0,8,2,6
J,2,3,3,5,1,12,2,9,4,13,5,13,1,6,0,8
Y,7,7,7,5,4,3,10,1,8,11,10,6,1,9,2,4
Q,3,4,4,7,3,7,6,9,6,5,6,7,3,8,5,9
L,4,11,5,8,3,9,2,1,7,9,2,10,1,6,3,9
N,1,1,1,1,1,7,7,10,1,5,6,8,4,8,0,8
C,2,7,3,5,1,5,7,7,9,7,6,14,1,8,4,9
M,8,10,8,5,4,7,10,5,6,4,5,10,9,8,2,8
U,4,7,6,5,3,6,8,6,8,7,9,9,3,9,1,8
X,3,6,6,4,3,7,7,1,9,10,7,9,2,8,3,8
S,6,8,8,7,8,9,8,5,6,7,6,7,6,10,12,12
W,6,11,8,8,9,8,6,6,3,6,7,8,8,8,5,5
F,3,4,3,6,1,1,12,5,5,12,11,8,0,8,2,6
V,4,6,4,4,2,3,11,4,3,10,11,7,2,10,1,8
P,3,8,5,6,3,6,10,5,6,10,8,4,1,10,4,7
C,1,0,2,0,0,7,7,6,8,7,6,13,0,8,4,10
W,4,8,6,6,7,7,8,5,3,7,8,8,5,8,3,8
P,2,4,4,3,2,7,9,4,4,12,5,3,1,10,3,8
M,7,8,10,7,11,8,7,5,5,7,6,7,14,8,7,3
J,5,7,7,8,6,8,8,4,6,6,7,7,4,9,10,11
G,4,9,5,7,4,8,7,7,6,6,6,7,2,8,6,11
T,7,11,7,8,5,5,12,3,6,12,10,5,2,12,1,5
T,6,10,6,8,4,7,11,4,8,11,9,4,2,12,4,5
Z,5,11,6,8,5,6,8,6,11,7,7,10,1,9,8,7
D,7,9,9,8,8,6,7,5,8,7,6,9,6,4,8,3
D,2,3,4,2,2,9,6,4,6,10,4,6,2,8,2,9
K,6,9,9,7,7,10,5,1,5,9,3,8,7,6,6,11
D,4,7,5,5,4,6,7,8,7,7,7,5,3,8,3,7
W,5,8,8,6,11,8,7,5,2,7,6,8,13,11,3,9
Q,3,4,4,5,3,7,8,5,2,8,8,10,3,8,5,8
I,1,9,2,7,2,7,7,0,8,7,6,8,0,8,3,8
Q,7,14,6,8,5,10,5,4,7,11,4,8,3,7,9,11
C,2,5,3,4,2,6,8,7,7,8,8,14,1,9,4,10
T,6,9,5,5,3,7,8,2,7,12,7,8,2,9,4,5
R,2,2,3,3,2,7,7,5,5,6,5,6,2,7,5,8
L,2,6,2,4,0,0,2,5,6,0,0,7,0,8,0,8
Q,2,3,3,4,2,7,8,5,1,7,8,10,2,9,5,8
X,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8
U,5,5,6,7,2,7,3,15,6,7,13,8,3,9,0,8
O,4,3,5,5,2,7,8,8,8,7,7,8,3,8,4,8
J,1,3,2,2,1,11,6,1,6,11,3,7,0,7,0,8
L,3,6,5,4,2,5,5,1,9,7,2,11,0,7,3,7
X,3,5,5,4,3,7,7,3,10,6,6,8,3,8,6,8
F,2,3,3,1,1,5,11,2,5,13,6,4,1,9,1,8
W,8,8,11,7,12,7,6,5,5,7,5,8,10,9,8,9
M,6,9,8,7,9,7,9,7,5,7,6,8,10,10,8,6
Q,2,1,2,1,1,8,6,6,4,6,6,8,2,8,3,8
K,7,9,7,4,3,7,8,3,7,9,6,8,6,8,3,7
T,2,3,3,2,1,5,12,3,6,11,9,4,1,10,2,5
J,1,4,3,3,1,9,6,3,6,14,5,9,0,7,0,8
F,3,2,4,4,3,5,11,3,6,11,9,5,1,10,3,6
I,2,6,2,4,1,6,8,0,6,13,7,8,0,8,1,7
T,3,4,5,6,1,6,15,1,6,8,11,7,0,8,0,8
A,3,9,6,7,4,10,2,1,2,8,3,9,4,5,3,7
M,8,11,11,8,8,6,6,3,5,9,8,9,8,6,2,8
M,4,9,7,6,9,8,6,3,1,7,4,8,10,7,2,6
O,5,11,6,8,3,7,8,9,8,7,8,8,3,8,4,8
Y,5,9,6,6,3,2,11,4,5,12,12,7,1,11,2,6
P,3,9,4,6,2,4,11,9,3,10,6,4,1,10,4,8
R,4,4,4,6,3,5,11,8,3,7,4,8,3,7,6,11
O,3,8,4,6,2,7,6,9,7,6,5,7,3,8,4,8
A,3,7,5,5,3,8,2,2,2,7,2,7,2,6,3,6
M,3,2,5,3,4,8,6,6,4,7,7,9,9,5,2,8
R,3,6,4,4,4,8,8,7,2,7,4,7,4,7,7,8
Z,4,7,5,5,4,9,7,5,3,7,5,7,3,8,9,5
C,2,5,3,4,2,5,8,7,8,8,8,14,1,9,4,10
D,1,0,2,1,1,5,7,8,6,7,6,6,2,8,3,8
P,2,4,4,3,2,8,9,4,3,12,5,4,1,10,2,9
C,4,10,5,7,3,4,8,6,6,12,10,13,1,10,3,8
E,5,10,5,8,5,3,8,5,9,7,6,14,0,8,6,8
M,2,3,4,2,2,8,7,2,4,9,6,8,6,5,1,7
Z,3,5,5,4,3,7,8,2,9,12,6,8,1,8,5,7
C,5,10,3,5,2,7,8,7,5,11,6,9,2,9,5,9
Q,6,9,8,8,7,6,4,5,6,6,5,8,6,1,8,6
G,3,7,5,5,5,9,7,6,2,6,7,9,5,8,3,8
Z,3,5,6,4,3,7,8,2,10,12,6,8,1,9,5,7
S,5,6,7,6,7,8,8,4,5,7,7,8,4,10,9,10
S,7,15,6,8,3,7,4,5,5,8,2,7,4,6,6,7
H,6,9,7,4,4,7,8,3,5,10,6,8,6,9,5,8
Q,4,5,5,7,3,8,9,8,5,5,9,9,3,7,6,10
K,6,10,8,8,10,8,8,4,5,5,7,9,9,8,9,7
J,8,12,6,9,5,7,11,3,3,13,5,4,3,9,9,9
Y,5,7,7,10,10,7,6,4,2,8,8,9,8,11,9,7
Q,3,9,4,8,3,8,6,9,6,6,5,8,3,8,4,8
S,4,11,5,8,3,9,8,6,9,5,6,5,0,8,9,7
J,0,0,1,0,0,12,4,6,3,12,5,11,0,7,0,8
X,1,0,2,0,0,7,7,3,4,7,6,8,3,8,4,8
G,1,0,2,1,1,8,7,6,5,6,5,9,1,7,5,10
W,6,9,8,7,4,6,8,5,2,7,8,8,9,9,0,8
M,5,7,9,5,10,9,7,3,3,8,4,7,11,6,3,5
B,2,6,3,4,3,6,6,8,5,6,7,7,2,9,6,10
M,4,8,6,6,6,8,6,5,5,6,7,8,8,5,2,7
N,3,6,5,4,5,8,7,4,4,7,5,7,5,10,6,4
A,4,7,6,5,3,10,1,3,3,9,2,8,3,6,4,9
W,6,7,6,5,5,3,11,2,2,9,9,8,6,11,2,7
J,2,4,3,3,1,8,6,3,6,14,5,9,0,7,0,8
E,2,4,4,3,2,7,8,2,7,11,7,9,2,9,4,8
Q,4,6,4,7,5,8,8,6,2,7,7,11,3,9,5,8
P,6,6,8,8,9,6,9,5,3,8,8,6,6,12,5,7
U,6,9,5,4,3,8,6,5,5,6,6,7,4,7,3,6
B,3,2,4,3,3,8,7,5,6,7,6,6,5,8,5,10
N,5,11,7,8,9,9,8,5,4,7,5,5,7,11,9,4
I,1,8,1,6,2,7,7,0,7,7,6,8,0,8,3,8
K,3,2,4,4,3,6,7,4,7,6,6,10,3,8,5,9
D,3,5,5,4,3,9,6,4,6,10,4,6,2,8,3,8
J,5,11,4,8,3,7,11,3,4,13,4,4,1,7,5,6
T,8,11,7,6,3,6,9,3,8,13,6,6,2,8,4,5
N,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
S,5,10,6,8,4,7,7,3,6,10,6,8,2,8,5,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
N,5,7,7,5,4,10,8,4,6,10,1,4,5,9,1,7
A,5,7,7,6,6,9,8,3,4,7,7,8,5,6,5,4
T,3,3,4,2,1,5,11,3,7,11,9,4,1,10,2,5
H,8,11,12,8,10,9,6,3,6,10,4,8,5,7,5,8
L,1,0,1,0,0,2,1,6,4,0,3,4,0,8,0,8
L,3,10,4,7,1,0,1,5,6,0,0,7,0,8,0,8
Y,5,8,7,12,12,9,7,4,1,6,8,9,6,12,8,9
L,2,7,4,5,4,7,7,3,4,7,6,10,5,10,6,5
T,3,10,4,7,3,7,13,0,5,7,10,8,0,8,0,8
K,4,5,5,7,2,3,7,8,2,7,5,11,4,8,2,11
Q,3,2,4,4,3,8,8,6,2,5,7,10,3,8,5,10
F,2,2,3,3,2,5,11,3,5,11,9,5,1,10,3,6
K,6,8,9,6,4,5,7,3,7,10,9,11,3,8,3,7
K,3,1,5,3,3,6,7,4,8,7,6,10,6,8,4,9
I,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8
Y,3,6,5,4,2,10,10,2,6,3,11,8,1,11,2,9
X,3,8,4,6,3,7,7,4,4,7,6,8,2,8,4,8
N,4,7,6,5,3,11,7,3,5,10,1,4,5,9,1,7
D,3,8,5,6,5,7,7,4,6,7,6,5,3,8,3,7
P,3,9,4,7,4,4,12,7,2,10,7,4,1,10,3,8
K,7,10,6,5,3,5,8,3,6,9,8,9,5,9,3,7
T,5,7,7,6,6,7,8,4,8,7,7,8,3,9,8,6
U,5,8,5,6,2,7,4,13,5,7,15,8,3,9,0,8
Z,2,4,2,3,2,7,8,5,8,6,6,9,2,8,7,8
O,3,6,4,4,3,7,7,7,4,7,5,8,3,8,3,8
H,5,8,6,6,5,7,7,7,6,7,7,8,3,8,3,8
P,1,3,3,2,1,6,10,3,3,12,6,4,0,9,2,8
T,5,9,7,7,7,7,8,5,5,6,8,9,7,7,8,7
O,3,6,4,4,3,7,9,7,4,7,8,7,3,8,2,8
I,2,5,4,4,2,7,7,0,8,14,6,8,0,8,1,7
Y,3,6,5,4,2,7,11,1,7,7,11,8,1,11,2,8
E,7,15,6,8,5,7,7,4,5,10,6,9,3,9,8,10
G,6,8,8,7,8,7,10,6,2,7,7,8,6,11,7,8
W,3,7,5,5,3,8,8,4,1,7,8,8,8,10,0,8
R,3,4,3,3,2,7,8,4,5,6,5,6,3,6,5,8
W,5,5,6,3,4,4,11,3,2,9,9,7,7,11,1,7
Q,4,5,5,5,2,9,8,7,6,5,7,9,3,8,5,9
Y,8,14,7,8,4,5,9,4,3,10,8,5,4,10,4,4
G,4,7,4,5,3,6,7,6,6,10,7,10,2,9,4,9
W,4,5,6,3,3,7,11,2,2,7,9,8,8,11,0,8
R,6,9,9,8,10,7,6,3,4,7,5,8,8,9,6,7
Y,2,3,3,2,1,8,10,1,6,5,11,9,1,11,1,8
O,3,7,4,5,3,7,8,7,5,10,8,7,3,8,3,8
R,4,6,5,4,4,6,8,5,6,7,5,7,3,7,5,8
E,4,9,4,6,2,3,7,6,11,7,6,15,0,8,7,7
L,4,9,4,7,3,0,2,4,6,1,0,8,0,8,0,8
L,4,10,4,8,1,0,1,6,6,0,0,6,0,8,0,8
F,4,9,6,7,3,4,13,3,4,13,8,4,1,10,2,6
C,3,6,4,4,2,6,8,8,8,8,8,13,2,10,4,9
X,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8
F,4,5,5,4,3,5,10,4,6,10,9,5,2,10,3,6
H,6,11,6,8,3,7,7,15,0,7,6,8,3,8,0,8
W,3,3,4,2,2,3,10,3,2,10,10,8,5,11,1,6
C,3,9,4,7,2,5,8,7,9,6,7,13,1,8,4,9
P,6,9,9,7,5,8,11,8,5,10,4,3,2,10,5,7
S,4,11,5,8,4,7,7,8,7,8,5,7,2,7,9,8
K,5,10,8,8,8,5,6,4,7,6,6,12,5,7,7,10
F,3,7,5,5,5,10,7,1,5,9,5,6,4,10,5,7
Q,5,5,6,7,6,10,11,6,0,4,7,11,6,14,4,9
R,6,10,9,9,10,8,8,4,4,7,4,7,7,9,6,4
S,3,5,6,3,2,7,8,3,7,11,8,7,1,9,4,6
U,7,8,8,6,3,4,9,6,9,12,11,8,3,9,1,6
J,6,11,8,8,4,8,6,4,6,15,6,11,1,6,1,7
F,5,8,7,10,8,7,9,5,5,7,5,7,4,8,9,7
M,6,10,8,7,6,11,5,3,4,9,3,6,8,6,2,9
M,5,9,7,6,7,9,7,5,5,6,7,5,8,7,3,5
B,5,8,7,6,6,10,6,3,6,10,4,7,2,8,5,10
X,1,0,1,0,0,7,7,3,5,7,6,8,2,8,4,7
G,5,8,5,6,3,6,7,7,6,10,7,11,2,9,4,9
H,3,8,4,6,4,7,8,12,1,7,5,8,3,8,0,8
J,6,10,8,7,4,9,7,2,7,14,4,7,0,7,0,8
F,2,3,4,2,2,8,9,2,6,13,5,4,1,9,2,8
L,1,3,3,2,1,6,4,1,7,7,2,9,0,7,2,8
X,9,13,9,7,5,8,7,2,8,11,4,7,4,9,4,7
G,6,11,5,6,4,8,7,4,3,9,6,7,4,9,9,8
I,2,10,2,8,2,8,7,0,9,7,6,7,0,8,3,7
Z,5,7,7,9,5,11,4,3,4,9,3,8,2,7,6,9
M,4,7,6,5,5,11,6,3,4,9,3,6,7,6,2,8
L,4,7,5,5,3,7,4,1,8,8,2,10,0,6,2,8
E,3,7,3,5,3,3,8,5,8,7,7,13,0,8,6,9
W,4,4,5,3,3,7,11,3,2,6,9,8,7,11,0,8
J,6,9,8,7,5,9,4,7,7,8,6,6,2,7,5,6
W,4,11,7,8,11,8,6,7,3,7,6,8,14,10,5,9
V,5,6,5,4,2,3,12,3,3,10,11,7,2,11,1,8
J,2,2,3,3,1,9,6,3,6,12,4,10,1,6,1,6
N,6,9,9,7,4,6,8,3,5,10,8,8,6,8,1,7
S,4,4,5,6,3,8,8,6,9,5,6,7,0,8,9,8
V,2,2,3,3,1,6,12,2,3,8,11,8,2,10,1,9
A,2,2,4,3,2,7,2,2,2,6,1,8,2,6,2,7
T,7,11,7,8,5,5,10,0,8,11,9,6,1,9,3,4
K,6,7,8,5,6,9,6,1,6,10,3,8,7,7,6,10
E,4,7,5,5,3,7,7,3,8,11,7,9,2,9,5,8
H,4,7,5,4,2,7,7,14,0,7,6,8,3,8,0,8
J,5,9,7,7,5,8,7,7,7,8,7,8,3,6,5,5
A,3,9,6,7,5,7,5,2,3,5,2,6,4,5,5,5
F,3,7,3,5,1,1,14,5,3,12,9,5,0,8,2,6
D,2,6,4,4,5,9,8,4,4,7,6,6,3,8,7,4
F,3,2,4,4,3,5,10,3,5,10,9,5,1,10,3,6
U,7,11,7,8,4,3,8,5,8,11,10,10,3,9,2,6
C,6,13,5,8,4,6,8,5,4,9,8,10,4,9,10,8
E,3,5,4,7,2,3,8,6,11,7,5,15,0,8,7,7
S,7,10,9,8,6,9,7,3,7,10,4,7,3,9,5,9
C,1,3,2,2,1,7,8,6,5,9,7,11,2,10,3,10
C,5,8,5,6,3,4,8,5,7,11,10,13,1,9,3,8
S,2,2,3,4,2,8,7,7,5,7,7,8,2,9,9,8
J,3,10,4,7,1,12,2,10,4,14,5,13,1,6,0,8
K,3,5,5,4,3,6,7,1,7,10,7,10,3,8,2,8
R,5,11,8,8,7,10,7,3,6,10,3,7,4,5,5,11
J,1,1,2,1,0,11,6,2,6,12,3,8,0,7,0,8
L,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8
U,5,8,5,6,4,6,7,6,8,8,6,8,6,10,4,3
F,0,0,1,0,0,3,11,4,2,11,8,6,0,8,2,8
R,3,7,5,5,3,8,8,4,6,9,3,8,3,7,4,11
R,4,9,6,7,4,9,8,3,6,9,3,8,3,6,4,11
K,4,9,5,7,7,6,7,3,3,6,5,9,6,8,8,7
O,4,5,6,4,4,7,6,5,5,8,5,8,3,7,5,6
E,5,8,6,6,6,8,5,6,3,7,7,9,4,8,8,10
S,2,7,3,5,2,8,8,5,7,5,6,8,0,8,8,8
L,5,11,4,6,2,10,2,3,4,13,4,11,2,8,5,9
K,6,11,7,6,4,4,9,3,6,10,10,11,5,8,3,6
P,3,5,5,4,2,8,9,4,4,11,4,3,1,10,3,8
K,5,7,7,6,7,7,9,3,5,7,3,9,6,2,7,10
A,3,8,5,6,3,12,2,3,2,10,2,9,2,6,3,8
F,3,8,4,6,3,1,12,4,4,12,10,7,0,8,2,7
J,3,11,4,8,3,8,6,3,6,11,5,10,1,6,2,5
M,10,9,10,4,4,7,11,5,5,4,5,10,10,12,2,7
Y,2,3,4,5,1,9,10,3,2,6,13,8,2,11,0,8
B,3,8,4,6,4,8,8,8,7,7,5,5,2,8,8,9
F,5,11,8,8,9,5,10,0,4,9,7,6,6,9,5,2
H,5,10,5,7,3,7,5,15,2,7,9,8,3,8,0,8
T,1,6,2,4,1,7,13,0,6,7,10,8,0,8,0,8
E,4,6,6,4,5,7,9,6,3,6,6,9,4,7,7,8
G,4,7,5,5,3,6,7,7,6,8,7,11,2,7,4,10
J,3,9,5,7,2,8,7,2,8,15,4,7,0,7,0,8
D,4,7,4,5,2,6,7,10,9,7,7,6,3,8,4,8
E,5,8,7,6,5,7,7,2,7,11,6,9,3,7,5,9
F,4,8,5,6,3,6,10,2,5,13,7,5,2,10,2,8
K,7,10,9,8,9,8,8,6,5,8,5,6,5,7,8,11
P,2,7,3,4,2,5,10,9,3,9,6,5,2,10,3,8
E,5,10,7,8,7,8,7,6,3,7,6,9,4,8,8,10
Y,3,9,5,7,3,9,10,1,6,4,11,8,2,12,2,8
G,5,7,6,5,6,7,8,6,2,6,6,9,5,7,7,7
A,3,7,5,5,3,10,2,2,2,9,1,8,2,6,3,8
Q,3,6,4,5,2,8,9,7,6,5,8,9,3,8,5,10
Z,6,10,8,7,6,7,7,2,9,12,6,8,1,8,6,8
P,6,9,6,4,4,8,8,5,3,10,5,6,5,10,5,6
H,2,4,4,2,2,7,8,3,6,10,6,8,3,8,2,8
R,2,1,3,2,2,7,8,4,5,6,5,7,2,6,5,8
S,1,0,1,0,0,8,7,3,6,5,6,7,0,8,7,8
J,3,9,4,7,4,8,6,2,5,11,4,9,4,6,2,6
A,2,8,4,6,2,8,4,3,2,7,1,8,3,7,2,8
P,3,6,4,9,8,8,8,4,0,8,7,7,5,9,4,7
L,5,9,7,7,3,6,4,1,9,8,2,10,0,7,2,8
U,4,9,6,8,6,7,7,4,3,6,6,9,5,6,2,8
R,2,1,2,2,1,6,9,8,3,7,5,8,2,7,5,11
V,5,6,6,4,2,4,13,4,4,10,11,6,3,10,1,8
C,1,0,2,1,0,6,7,6,9,7,6,14,0,8,4,10
N,5,9,7,7,4,3,9,4,4,11,11,10,5,7,1,8
D,5,10,6,8,7,8,7,4,8,6,5,6,6,8,3,7
Q,6,6,7,9,4,8,10,8,6,5,9,9,3,8,6,10
P,1,3,3,2,1,7,9,4,3,11,5,4,1,9,2,8
B,3,6,4,4,3,10,6,3,6,10,3,7,2,8,4,11
X,4,9,6,7,3,10,7,2,8,10,1,7,3,8,4,9
W,6,11,10,8,7,4,11,2,3,8,9,9,8,11,1,8
H,7,12,6,6,4,8,8,3,4,9,6,7,6,9,5,9
K,2,3,4,2,2,4,7,2,6,10,9,11,3,8,2,7
I,3,10,4,8,2,7,7,0,8,13,6,8,0,8,1,8
Z,4,9,6,7,6,7,7,3,7,7,6,9,0,8,9,7
R,5,9,5,7,6,6,9,8,3,7,5,8,3,8,6,12
A,2,6,4,4,3,7,2,1,2,6,2,7,2,6,3,6
M,3,1,4,2,1,7,7,11,1,7,9,8,7,6,0,8
J,1,0,1,1,0,12,3,6,3,13,5,11,0,7,0,8
H,3,8,4,5,2,7,7,15,1,7,6,8,3,8,0,8
W,3,7,5,5,4,7,11,2,2,7,8,8,6,11,1,8
R,5,11,6,8,6,6,7,5,6,7,6,7,6,8,5,8
V,4,9,6,7,3,4,11,3,4,9,12,9,2,10,1,8
F,3,5,4,3,2,5,10,3,6,10,9,6,1,10,3,6
A,3,3,5,5,2,6,3,3,3,6,2,7,3,6,3,7
X,1,1,2,1,0,7,7,4,4,7,6,8,2,8,4,7
K,4,6,5,4,3,6,8,2,7,10,7,9,3,8,3,7
F,3,6,4,4,2,6,11,4,5,12,7,4,2,10,2,6
C,5,10,6,8,7,5,6,3,4,7,6,11,6,9,4,7
T,3,6,5,4,3,6,11,3,7,8,11,7,4,11,1,7
N,7,9,9,7,9,6,8,3,4,8,7,8,8,8,7,4
R,1,0,2,1,1,6,9,7,3,7,5,8,2,7,4,11
K,6,9,9,7,6,5,7,2,8,10,8,10,4,7,4,7
N,7,9,8,4,3,10,6,3,4,13,3,7,6,8,0,8
Y,5,11,7,8,1,8,11,2,2,7,13,8,1,11,0,8
M,5,10,6,7,6,8,5,11,0,6,9,8,9,6,2,6
X,3,5,4,4,2,8,7,3,9,6,6,8,2,8,6,8
Q,5,5,7,8,9,8,7,5,1,6,6,9,9,12,7,13
V,4,9,5,7,3,7,11,2,4,7,12,9,3,10,1,8
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
V,2,3,4,4,1,7,8,4,2,7,13,8,3,10,0,8
B,4,9,6,7,6,9,6,4,5,9,5,7,2,8,5,9
Y,6,11,9,8,5,6,9,0,8,8,12,9,1,11,2,7
C,5,10,6,8,2,4,7,7,11,7,6,12,1,8,4,9
A,2,5,4,4,2,11,2,2,2,9,2,9,2,6,2,8
K,5,10,8,8,4,3,8,4,7,10,12,12,4,8,4,6
G,3,8,4,6,2,7,6,8,7,6,6,9,2,7,6,11
T,2,6,4,4,2,9,11,1,7,5,11,7,1,10,1,8
H,4,11,5,8,3,7,7,15,1,7,6,8,3,8,0,8
T,4,6,4,4,2,5,11,2,7,11,9,5,1,10,2,5
R,3,7,5,5,3,9,7,4,6,10,4,7,3,7,4,10
Z,4,10,4,8,4,7,8,3,12,9,6,8,0,8,7,7
E,3,8,4,6,4,6,7,7,9,7,7,11,3,8,6,8
H,5,8,8,6,6,7,6,3,6,10,7,8,3,8,3,7
E,4,10,6,8,5,8,7,5,9,7,6,8,3,8,6,9
X,4,7,7,5,4,9,7,1,8,10,3,7,3,8,3,9
R,2,1,2,2,2,6,7,4,4,6,5,7,2,7,3,8
P,3,6,4,4,2,4,12,8,2,10,6,4,1,10,4,8
A,4,11,6,8,4,12,3,3,3,9,1,9,2,6,3,8
B,4,9,4,7,3,6,8,9,7,7,6,7,2,8,9,10
Q,5,7,5,8,5,8,5,7,5,9,5,9,3,8,5,7
W,3,9,5,6,5,7,11,2,2,6,8,8,7,11,0,8
G,3,5,4,4,2,6,7,5,6,9,7,11,2,9,4,10
N,3,3,3,5,2,7,7,13,2,5,6,8,5,8,0,8
W,6,10,8,8,9,7,9,6,4,7,9,7,7,7,5,10
Q,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8
P,7,11,9,8,8,6,7,7,4,8,7,8,2,10,7,9
Q,6,9,7,11,7,7,10,5,3,6,9,12,3,9,7,8
D,3,2,4,3,3,7,7,6,6,6,6,5,5,8,3,7
L,5,10,7,8,9,7,7,3,5,7,7,10,6,11,6,6
L,2,7,4,5,2,9,4,1,6,9,3,10,0,6,2,10
I,5,10,6,8,4,7,8,1,7,7,6,8,0,8,4,8
V,3,9,5,7,1,9,8,4,3,6,14,8,3,10,0,8
E,3,5,5,4,3,6,8,2,8,11,8,9,2,8,4,8
D,4,9,6,7,5,7,7,7,8,6,5,4,3,8,3,7
O,4,8,5,6,2,7,8,8,8,7,7,8,3,8,4,8
J,6,8,4,11,3,6,10,3,4,13,5,5,3,8,7,8
D,2,5,4,4,3,10,6,3,6,10,3,6,2,8,3,8
M,5,10,7,6,4,13,2,5,2,12,1,9,6,3,1,8
E,5,10,7,8,7,7,8,7,9,7,6,11,3,8,6,8
G,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,10
A,2,3,4,1,1,10,2,3,1,9,2,9,1,6,1,8
R,3,6,4,4,3,10,6,3,6,10,3,7,3,7,3,10
Q,4,9,5,8,5,8,8,7,5,6,8,9,3,8,5,10
D,3,6,4,4,3,9,7,4,5,10,4,5,3,8,2,8
I,3,11,4,9,2,7,8,0,8,13,6,7,0,8,1,7
A,3,9,5,7,3,12,3,4,3,10,1,9,2,7,3,9
L,3,6,3,4,1,0,1,6,6,0,1,5,0,8,0,8
K,4,5,5,4,4,5,7,4,7,6,6,11,3,8,5,9
V,7,10,7,8,3,2,11,6,5,13,12,7,3,9,1,8
T,3,5,4,4,2,5,12,3,6,11,10,5,1,11,1,5
P,4,6,4,8,3,5,9,10,5,8,6,5,2,10,4,8
P,1,1,1,1,0,5,11,7,2,9,6,4,1,9,3,8
R,1,0,1,0,0,6,9,6,3,7,5,8,2,6,3,10
X,4,10,6,8,4,7,7,4,9,6,6,8,3,8,7,7
F,4,9,4,7,3,1,13,4,4,12,10,7,0,8,2,6
Q,1,2,2,3,1,7,8,4,1,7,8,10,2,9,3,8
F,4,6,5,7,6,7,9,5,4,8,6,7,4,10,9,7
T,5,10,7,7,6,6,7,7,7,7,10,9,4,6,8,7
Q,4,5,6,4,4,7,4,4,5,7,4,9,4,5,6,7
O,2,4,3,2,2,8,7,6,4,9,6,8,2,8,2,8
O,5,8,6,6,5,8,7,7,4,10,6,7,3,8,3,8
J,2,7,3,5,2,9,6,2,6,11,4,8,1,6,1,6
O,7,11,5,6,3,7,9,6,5,9,5,6,4,9,5,8
L,1,0,1,0,0,3,1,6,4,1,3,4,0,8,0,8
C,2,4,3,3,1,4,8,4,6,11,9,12,1,9,2,8
E,4,8,6,6,7,6,7,3,6,6,7,11,3,10,8,7
M,9,14,11,8,7,5,4,4,3,7,4,10,10,1,2,8
R,5,7,7,5,4,9,7,4,6,10,3,7,4,5,5,10
I,3,10,4,8,2,6,9,0,8,13,7,7,0,8,1,7
J,2,4,3,3,1,11,6,1,6,11,3,7,0,7,0,7
R,3,7,3,5,3,6,10,7,3,7,4,8,2,7,5,11
Z,2,4,4,3,2,7,7,2,9,11,6,9,1,9,6,7
Y,3,5,5,6,1,9,12,2,3,5,12,8,1,10,0,8
T,2,1,2,1,0,8,15,1,4,6,10,8,0,8,0,8
T,5,7,6,5,4,6,10,1,8,11,9,5,1,10,3,4
R,4,7,5,5,5,8,6,6,3,8,5,7,4,7,7,10
I,1,8,1,6,1,7,7,0,8,7,6,8,0,8,3,8
T,5,8,7,6,7,8,8,6,7,8,6,8,5,7,5,6
I,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8
Z,4,8,4,6,4,8,7,5,9,7,5,7,2,8,8,8
Y,3,4,5,6,1,7,10,2,2,7,13,8,1,11,0,8
U,6,10,8,8,5,5,8,6,8,7,10,10,3,9,1,8
C,5,10,6,8,4,4,9,6,7,7,8,15,1,8,4,10
T,3,9,4,6,1,6,15,1,6,8,11,7,0,8,0,8
K,5,7,8,5,4,7,8,2,7,10,6,8,3,8,3,7
Q,4,7,4,9,4,8,5,8,5,9,6,9,3,7,5,8
Y,3,8,5,5,1,9,11,3,2,5,12,8,1,11,0,8
H,2,4,3,3,2,7,7,6,5,7,6,9,3,8,3,8
J,2,6,3,4,3,9,6,2,5,11,4,9,1,6,1,6
K,4,7,5,5,6,7,9,5,4,8,5,8,4,6,6,10
O,4,8,5,6,3,7,7,7,5,10,7,8,3,8,3,8
Q,2,2,3,2,2,7,8,4,2,8,7,9,2,9,4,8
B,2,3,3,1,2,7,7,4,5,7,6,6,1,8,5,9
M,4,7,6,5,5,7,6,6,5,7,7,9,8,5,2,9
Y,3,6,4,4,0,8,10,3,2,6,13,8,1,11,0,8
Y,3,7,5,4,1,7,10,2,2,6,13,8,1,11,0,8
T,2,3,3,2,1,6,11,2,6,11,9,5,1,11,2,5
V,3,10,5,7,4,7,11,2,3,5,11,9,2,11,1,8
S,4,8,5,6,3,7,8,4,8,11,7,7,2,8,5,6
B,5,9,7,6,10,8,6,5,3,7,7,8,6,9,8,9
S,2,2,3,3,2,7,7,6,5,8,6,8,2,9,9,8
M,4,5,8,4,4,9,6,3,5,9,5,7,8,7,2,8
Y,2,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8
N,2,1,3,1,2,7,8,6,4,7,6,6,5,9,2,6
D,4,8,6,6,4,8,7,5,7,11,5,5,3,8,3,8
M,3,4,6,3,3,5,6,4,4,10,10,11,8,7,3,8
Z,4,9,6,7,4,7,9,2,9,11,7,6,1,7,6,6
R,3,8,5,6,6,8,8,3,4,6,6,8,6,10,7,6
V,7,11,7,8,3,4,11,3,4,9,11,7,3,10,1,8
S,4,6,6,4,6,9,6,4,3,9,6,9,4,8,10,10
Z,1,4,2,3,2,7,7,5,8,6,6,9,2,8,7,8
G,4,6,4,4,2,6,7,6,6,10,8,10,2,9,4,9
J,5,7,7,5,4,9,5,5,6,8,6,6,2,7,4,6
L,2,2,3,3,2,4,4,4,7,2,1,6,1,7,1,6
Z,3,6,4,4,2,7,7,3,14,9,6,8,0,8,8,8
R,7,10,9,8,8,9,8,3,6,9,3,8,3,6,4,11
J,2,11,3,8,2,13,3,6,4,13,2,10,0,7,0,8
N,4,5,7,4,3,6,9,2,5,10,7,7,5,8,1,8
X,1,1,2,1,1,8,7,3,8,7,6,8,2,8,5,8
L,4,9,6,7,7,7,7,3,6,8,7,10,7,10,7,5
H,7,11,9,9,9,6,7,7,4,6,5,7,3,7,7,10
O,4,9,3,4,2,6,7,6,3,9,6,8,4,10,4,7
M,6,10,9,8,9,7,6,5,5,7,7,11,14,6,2,9
V,2,3,4,5,1,8,8,4,2,6,13,8,3,10,0,8
Y,8,11,8,8,4,2,11,4,5,13,13,8,1,11,2,6
Q,6,6,8,9,4,8,6,8,8,6,5,8,3,8,4,8
M,4,8,6,6,5,10,6,6,4,6,7,5,7,5,2,5
Q,3,6,5,8,7,9,7,5,1,6,6,9,7,9,4,9
A,3,6,5,4,2,10,3,2,2,8,3,10,2,6,2,7
U,5,7,7,5,5,8,7,8,5,6,7,9,3,8,4,6
B,4,7,6,5,5,7,8,5,5,9,7,6,3,7,6,7
S,7,15,5,8,3,10,3,4,4,10,3,10,4,6,5,11
B,6,10,8,7,8,8,8,4,6,10,6,6,5,7,7,9
K,4,7,7,5,4,3,8,3,7,11,11,11,3,8,3,5
R,4,8,7,7,8,7,8,3,4,7,4,8,6,8,5,6
W,5,8,7,6,4,3,11,3,3,9,10,10,7,11,1,8
H,4,7,5,5,5,6,7,5,7,7,6,10,6,8,3,9
H,2,1,2,1,1,7,7,12,1,7,6,8,3,8,0,8
X,4,11,5,8,2,7,7,5,4,7,6,8,3,8,4,8
C,2,4,3,3,1,4,8,4,7,10,9,13,1,8,2,7
I,5,8,7,6,3,9,4,3,6,6,7,4,1,10,4,7
G,3,3,4,4,2,7,6,7,8,6,5,10,1,8,6,11
X,2,3,3,4,1,7,7,4,4,7,6,8,3,8,4,8
Y,4,7,6,9,7,10,9,5,2,6,7,8,7,11,7,5
M,5,10,8,8,6,12,6,2,4,9,3,6,8,6,2,8
X,10,14,9,8,5,7,7,2,10,9,6,8,4,6,4,8
M,5,7,7,6,9,5,8,5,3,6,5,8,11,7,6,8
C,4,8,4,6,3,4,8,6,6,12,10,12,2,9,3,8
U,8,10,9,8,4,3,9,6,8,12,11,9,3,9,1,7
R,5,11,7,8,7,9,8,4,5,9,4,7,3,7,4,11
I,0,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
O,4,4,5,7,2,7,8,8,8,6,8,9,3,8,4,8
B,2,1,3,2,2,8,7,5,6,7,6,5,2,8,6,9
E,4,6,6,4,4,6,8,2,9,11,7,10,2,9,4,8
N,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8
M,4,5,8,4,4,7,6,3,4,9,7,8,7,5,2,8
F,1,3,2,1,1,6,10,3,4,13,7,5,1,9,1,8
R,3,6,4,4,2,5,10,8,2,7,4,8,2,7,6,11
D,4,10,4,7,3,6,7,11,9,6,5,6,3,8,4,8
U,4,7,5,5,2,7,5,12,5,7,15,8,3,9,0,8
V,3,11,5,8,4,7,11,2,3,6,11,9,2,10,1,8
U,5,9,5,6,4,8,5,12,4,7,12,8,3,9,0,8
I,0,1,1,2,0,7,7,2,6,7,6,8,0,8,2,8
V,5,11,8,8,9,7,6,4,2,7,8,8,5,9,4,7
D,4,7,4,5,2,5,7,10,8,6,5,5,3,8,4,8
V,4,7,5,5,3,9,12,2,3,3,10,9,3,11,2,8
O,6,10,4,5,3,6,9,7,4,9,7,10,5,10,5,7
E,3,6,4,4,3,5,8,3,7,11,8,9,2,8,5,7
Z,8,8,5,12,5,10,3,4,5,12,5,9,3,8,9,11
S,6,10,7,8,4,8,7,5,9,11,4,7,2,7,5,9
C,4,7,5,5,3,8,7,8,6,6,7,10,3,8,3,9
W,7,11,7,8,6,3,10,2,3,10,10,8,8,10,2,6
J,2,6,4,4,2,9,5,4,5,14,4,10,0,7,0,8
T,3,3,4,2,1,5,11,2,7,11,9,5,1,10,2,5
N,8,15,10,9,5,4,9,4,4,13,11,9,6,9,0,9
X,4,9,6,6,4,3,9,2,8,11,12,10,3,8,4,4
R,4,9,6,6,5,9,7,5,5,9,4,7,3,7,4,11
K,6,11,9,8,6,2,9,3,7,11,12,12,3,8,4,5
A,3,7,6,5,4,5,5,3,3,3,2,6,4,6,4,4
S,4,9,5,7,5,7,7,7,5,7,6,7,2,8,8,8
Z,2,1,2,1,1,7,7,5,8,6,6,8,1,8,6,8
G,6,6,8,6,7,7,9,5,3,7,7,8,8,11,8,7
K,5,10,6,7,2,3,8,8,2,7,5,11,4,8,3,10
G,4,5,5,4,3,6,6,6,6,6,6,10,2,9,4,8
W,3,6,5,4,6,10,7,4,2,7,6,8,5,10,1,5
G,7,11,8,8,7,5,5,6,6,6,5,11,4,10,4,9
T,4,5,5,4,2,6,11,2,8,11,9,5,1,10,3,4
B,4,8,6,6,8,8,8,4,3,6,7,7,6,11,8,8
X,4,7,5,6,5,6,7,2,4,7,7,10,3,10,7,8
M,4,7,8,5,8,10,4,2,1,9,4,9,8,6,2,6
V,4,9,6,7,1,6,8,5,3,8,14,8,3,9,0,8
T,7,8,7,6,5,7,10,3,7,10,9,5,4,11,5,5
A,6,10,8,8,8,7,7,8,4,7,6,8,4,8,11,2
P,6,6,6,8,3,4,13,8,2,10,6,3,1,10,4,8
L,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8
O,7,11,8,8,7,8,7,8,5,9,5,7,5,8,5,10
T,4,11,6,8,5,9,11,1,8,6,11,8,1,11,1,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
J,4,8,5,6,2,7,6,4,6,15,6,11,1,6,1,7
K,4,7,4,4,2,4,9,8,2,7,4,11,4,8,3,10
Y,4,7,4,5,1,3,12,4,5,13,11,5,1,11,1,6
E,6,9,8,7,6,9,6,1,8,11,4,9,4,8,5,10
C,4,9,5,7,3,4,8,6,8,12,10,12,1,9,3,7
W,9,10,8,7,8,4,11,3,2,9,8,7,7,12,3,6
L,4,10,4,7,1,0,1,5,6,0,0,6,0,8,0,8
P,9,15,8,8,4,6,10,6,5,14,5,4,4,10,4,8
G,4,8,5,6,3,7,7,7,7,10,7,12,2,9,4,9
X,4,9,5,6,3,7,7,4,4,7,6,7,3,8,4,8
I,3,10,5,7,6,9,6,3,4,8,5,5,6,7,5,5
F,3,6,6,4,5,5,9,2,3,10,8,7,5,9,3,4
W,3,2,5,4,3,7,10,2,2,6,9,8,7,11,0,7
D,5,9,5,4,3,7,7,4,6,10,6,7,5,9,6,5
F,4,11,5,8,4,3,11,3,7,11,10,6,1,10,3,5
I,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8
J,4,6,2,9,2,12,5,2,4,9,5,7,2,9,4,11
B,5,10,8,7,8,9,6,4,6,9,5,7,2,8,6,10
W,5,10,8,7,6,5,8,4,1,7,9,8,7,11,0,8
U,3,9,4,6,3,7,6,13,4,6,10,8,3,9,0,8
F,10,15,9,8,6,6,9,3,5,10,6,7,5,7,9,6
A,4,9,6,7,5,8,2,1,2,6,2,7,5,6,6,7
Z,1,3,2,1,1,8,7,5,8,6,6,8,1,8,6,8
X,6,11,10,8,5,10,7,2,9,11,1,6,4,9,4,10
N,7,13,8,7,4,4,9,4,4,13,11,10,5,9,0,8
U,4,8,6,7,5,8,8,5,4,5,7,8,7,8,2,7
N,4,8,6,6,4,4,10,3,4,10,10,9,5,8,1,8
Z,3,7,5,5,3,7,8,2,9,11,8,6,1,8,6,5
D,5,10,6,7,3,6,7,10,10,6,5,6,3,8,4,8
C,1,0,2,1,0,6,7,6,8,7,6,14,0,8,4,10
Q,2,3,3,4,2,8,8,5,2,8,8,10,2,9,4,8
M,4,9,5,7,6,7,6,10,1,7,8,8,8,5,0,8
T,3,4,4,5,1,7,15,1,5,7,11,8,0,8,0,8
Q,4,6,5,7,5,9,9,7,2,4,7,11,3,9,5,10
T,8,10,8,8,5,5,11,2,7,11,10,5,2,12,2,4
G,3,4,4,3,2,7,6,6,6,6,6,10,2,9,4,9
O,5,8,6,6,5,8,7,7,4,9,6,7,3,7,4,9
C,4,7,4,5,2,3,8,5,7,11,11,12,1,8,2,7
B,6,11,8,8,7,9,7,4,6,10,5,6,3,8,6,10
C,5,6,6,5,5,5,6,3,6,7,7,11,5,11,7,8
Q,4,7,6,5,4,8,4,7,4,6,6,11,3,8,5,9
U,4,5,5,4,2,5,8,5,8,9,7,8,3,9,3,5
W,11,15,11,8,5,2,9,3,2,9,11,9,9,12,0,6
O,2,4,3,2,2,7,7,7,5,9,6,8,2,8,3,8
K,3,4,5,3,3,9,7,2,7,10,3,8,3,8,3,9
C,2,5,3,4,2,6,8,7,8,9,8,13,1,10,4,10
S,5,10,7,7,4,8,7,4,9,11,5,7,2,8,5,8
A,3,6,5,4,3,8,5,3,0,7,1,8,2,6,1,8
J,4,9,6,7,3,5,9,2,6,15,7,8,1,7,1,7
N,2,4,4,3,2,7,9,2,4,10,6,6,5,8,1,7
E,2,7,4,5,3,7,7,5,8,6,5,8,3,8,6,9
T,5,12,5,6,3,7,9,2,6,11,6,6,2,9,5,5
A,2,8,4,5,2,9,5,3,1,8,1,8,2,7,2,8
T,2,8,3,5,1,9,14,1,6,5,11,9,0,8,0,8
O,4,6,4,4,3,7,7,7,5,10,7,9,3,8,3,8
V,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8
E,3,7,3,5,2,3,8,6,10,7,6,14,0,8,7,8
F,4,7,6,5,3,7,10,3,6,13,5,4,2,10,2,7
F,5,12,6,8,2,1,13,5,4,12,10,7,0,8,3,6
Q,8,12,7,6,4,12,3,3,7,11,2,9,3,8,5,14
H,6,7,8,5,5,7,7,3,7,10,7,9,3,8,3,7
Q,3,3,4,5,3,8,8,6,2,5,7,9,3,8,5,9
O,7,11,9,8,6,8,7,9,5,7,6,6,4,8,5,10
Z,6,11,8,8,7,10,5,5,5,8,5,7,4,7,10,5
L,3,3,3,5,1,0,1,6,6,0,0,6,0,8,0,8
Y,6,8,6,6,3,2,11,3,6,12,12,6,1,11,2,5
C,3,5,4,4,2,6,8,7,7,9,8,12,2,10,4,10
J,2,7,2,5,1,13,4,5,4,13,2,9,0,7,0,8
Z,3,8,4,6,2,7,7,4,14,9,6,8,0,8,8,8
L,2,2,3,3,1,4,4,3,7,2,1,7,0,7,1,6
U,3,7,4,5,3,7,6,11,4,7,12,8,3,9,0,8
R,3,8,4,5,2,5,10,9,4,7,4,8,3,7,6,11
C,5,8,5,6,3,4,8,6,7,12,10,11,1,9,2,7
I,2,9,2,7,3,7,7,0,7,7,6,8,0,8,3,8
S,3,2,3,3,2,8,7,6,5,7,6,8,2,9,9,8
R,4,5,5,4,4,7,8,5,6,7,5,6,6,7,4,8
X,6,10,9,8,4,8,8,1,9,10,4,7,3,9,4,8
F,3,5,5,4,2,7,9,2,6,13,6,6,3,8,3,7
U,7,10,8,8,4,3,9,6,7,12,11,9,3,9,2,7
K,2,3,3,2,2,5,7,4,7,7,6,11,3,8,4,9
R,4,11,6,8,8,7,7,3,4,7,6,8,6,8,7,5
E,3,9,4,7,4,6,7,7,8,7,6,11,3,8,6,8
Y,9,10,9,8,5,3,10,3,6,12,12,7,2,11,2,5
D,3,5,5,4,3,9,7,5,7,9,5,5,3,7,4,8
G,4,10,5,8,5,5,6,6,4,8,7,12,4,8,6,7
R,5,11,7,8,6,7,8,5,7,6,5,8,4,6,7,9
P,4,6,6,8,8,10,8,3,3,6,8,7,6,10,6,4
D,3,6,5,4,6,9,8,4,4,7,6,6,4,8,8,5
T,3,5,4,4,2,6,11,2,7,11,9,5,1,11,3,4
I,7,14,5,8,3,10,5,6,5,13,3,7,3,8,5,10
Z,5,7,6,5,5,9,6,5,4,7,5,7,3,8,10,6
H,5,10,8,8,7,7,8,2,6,10,7,8,3,8,3,7
T,2,1,2,2,0,7,14,1,5,7,11,8,0,8,0,8
H,6,9,9,6,7,10,6,3,6,10,3,7,5,6,4,9
I,5,10,6,8,3,9,6,2,7,7,6,5,0,8,4,7
J,3,6,5,7,4,8,8,4,5,7,7,7,3,9,9,11
D,6,12,6,6,4,10,3,4,4,11,2,8,4,6,4,10
I,1,1,1,2,1,8,7,1,7,7,6,7,0,8,2,7
P,5,9,7,6,5,9,8,2,5,13,5,5,2,9,3,9
A,5,7,7,6,6,5,8,3,5,7,8,11,7,9,3,8
A,6,9,9,8,8,7,8,2,4,7,7,8,6,5,5,6
V,4,7,5,5,2,6,11,3,2,8,11,8,2,9,3,8
D,5,11,7,8,6,6,7,8,7,6,6,5,3,9,4,9
Q,2,2,3,3,2,8,8,5,1,5,7,9,2,9,5,10
B,2,6,3,4,2,6,7,9,6,7,6,7,2,8,8,10
J,8,10,5,14,4,7,8,3,4,11,6,5,3,8,8,9
E,2,2,3,4,3,7,7,6,7,7,6,9,2,8,6,10
V,1,0,2,0,0,7,9,3,2,7,12,8,2,10,0,8
T,2,10,4,7,1,10,14,1,6,4,11,9,0,8,0,8
F,5,7,7,5,3,5,12,2,6,13,7,4,1,10,2,7
R,4,9,5,6,3,5,12,8,3,7,3,9,3,7,6,11
R,1,0,2,1,1,6,9,7,2,7,5,8,2,7,4,10
L,8,13,7,7,3,6,4,3,7,10,4,13,3,5,6,8
O,5,8,7,6,4,8,5,8,5,6,4,5,4,8,4,8
I,3,10,4,7,3,9,5,0,7,13,5,9,0,7,2,9
Y,5,7,5,5,2,3,11,3,6,12,11,5,0,10,2,5
Z,2,1,2,2,1,7,7,3,12,8,6,8,0,8,7,8
F,2,4,3,3,1,6,10,3,5,13,7,5,1,9,2,7
D,3,9,4,6,7,9,8,5,4,7,6,6,4,7,7,5
Q,5,8,6,9,7,8,5,8,4,6,5,9,3,7,6,10
G,3,7,4,5,3,6,6,6,5,10,7,13,2,9,4,10
L,2,7,3,5,1,0,1,4,5,1,1,7,0,8,0,8
T,2,3,3,2,1,9,12,3,6,6,11,9,2,11,1,8
R,2,1,3,2,2,7,7,5,5,6,5,6,5,7,3,8
I,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8
I,1,6,1,4,2,7,7,0,7,7,6,8,0,8,3,8
R,3,5,5,4,5,8,8,4,4,8,4,7,6,7,5,4
H,4,8,6,6,6,7,7,6,6,7,6,9,3,8,3,8
O,2,3,3,2,1,8,7,7,4,7,6,8,2,8,2,8
X,10,15,11,8,6,7,8,2,8,11,8,8,5,12,4,7
W,4,6,6,4,3,8,8,4,1,7,9,8,7,10,0,8
S,6,9,8,7,5,7,7,3,7,10,8,9,2,10,5,6
F,2,6,3,4,2,1,11,3,4,11,10,8,0,8,2,7
E,5,11,7,8,7,10,6,2,7,11,4,8,4,8,6,12
P,6,5,6,7,3,4,13,9,2,10,6,4,1,10,4,8
K,3,3,5,2,2,7,7,2,7,10,5,10,3,8,3,8
A,6,9,6,5,3,12,3,5,2,12,3,10,5,3,3,10
Q,3,3,4,4,3,8,8,6,2,5,7,10,3,9,6,10
S,4,4,5,7,3,7,5,6,10,5,6,10,0,9,9,8
F,5,9,4,5,3,8,10,3,4,11,5,4,3,10,7,7
G,6,11,8,9,9,7,9,6,3,6,6,9,6,7,8,7
U,3,5,4,3,2,5,8,5,7,10,9,9,3,9,2,6
R,9,15,9,8,7,7,8,3,5,8,4,9,7,7,7,6
X,3,8,5,6,5,9,7,2,5,7,5,6,3,9,6,8
X,6,9,9,7,5,6,8,1,8,10,9,9,3,9,3,6
H,5,10,8,8,7,7,7,3,6,10,7,9,3,8,3,8
L,3,6,4,4,3,6,7,9,5,6,6,9,3,8,5,11
D,6,9,5,5,4,7,7,4,6,10,5,7,5,9,6,6
H,6,8,9,6,5,7,7,3,7,10,6,8,3,8,3,8
V,5,9,6,7,4,8,11,3,2,5,10,9,2,10,3,9
E,6,11,9,9,6,10,6,2,9,11,4,9,3,8,5,11
F,5,9,7,7,4,6,10,2,5,13,7,5,1,10,2,7
O,5,8,7,6,5,7,8,8,5,6,7,11,4,8,4,8
V,3,9,5,7,3,7,9,4,2,7,13,8,2,10,0,8
K,2,5,3,3,2,7,7,4,6,6,6,9,3,8,5,10
P,2,2,3,3,2,6,11,5,4,10,7,2,1,10,4,6
V,1,1,2,1,0,7,9,4,2,7,13,8,2,10,0,8
V,4,11,6,8,5,8,11,2,3,4,10,9,4,12,2,8
J,2,8,3,6,2,9,6,3,6,12,4,9,1,6,2,6
I,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8
K,5,9,6,7,4,4,7,7,3,7,7,12,3,8,3,11
R,4,10,6,8,6,8,8,4,6,8,3,8,4,5,5,12
P,4,7,6,5,4,9,8,2,6,13,5,6,1,10,3,9
Y,2,3,4,2,1,8,11,1,6,6,11,9,1,11,1,8
E,4,10,6,8,6,7,7,5,8,7,7,9,3,8,6,9
K,4,7,5,5,5,5,7,5,3,7,5,9,3,5,9,8
S,4,6,6,6,6,8,8,5,5,7,7,8,4,10,8,9
H,4,8,6,6,4,10,5,3,6,10,2,7,3,8,3,10
K,3,4,6,3,3,6,7,1,7,10,7,10,3,8,3,7
Y,2,5,4,7,6,9,8,4,1,6,7,9,3,11,7,7
S,3,5,4,4,2,8,7,3,7,10,7,7,1,9,5,8
B,3,6,4,4,5,7,7,6,5,7,6,6,2,8,6,10
T,4,5,5,3,2,6,11,2,8,11,9,5,3,9,3,4
O,2,1,3,2,2,7,7,7,5,7,6,8,2,8,3,8
A,2,2,4,3,2,8,2,2,2,8,1,8,2,6,2,7
F,2,1,3,2,1,5,11,3,5,11,9,5,1,9,3,6
H,6,9,9,7,7,8,6,2,6,10,5,8,4,7,4,8
W,9,10,9,7,6,3,11,2,3,9,9,8,8,11,2,6
L,5,11,5,8,2,0,0,6,6,0,1,5,0,8,0,8
T,2,5,4,7,1,8,14,0,6,6,11,8,0,8,0,8
H,4,4,5,6,2,7,6,15,1,7,7,8,3,8,0,8
Y,1,1,2,1,0,8,9,2,2,7,12,8,1,10,0,8
S,2,3,3,4,2,8,7,5,8,4,6,8,0,8,8,8
S,4,7,5,5,3,7,7,4,8,11,5,7,2,8,5,8
Q,4,5,5,6,4,8,7,5,2,8,8,10,2,9,5,7
V,2,1,4,1,1,9,12,2,2,5,10,9,2,10,1,8
W,5,5,7,7,4,9,8,5,2,7,8,8,9,9,0,8
T,2,6,4,4,2,5,13,0,5,9,10,7,0,8,0,8
D,2,1,2,1,1,6,7,8,7,6,6,6,2,8,3,8
A,4,9,7,7,3,12,2,4,2,11,1,9,3,7,3,9
F,6,9,8,7,3,4,14,3,5,13,7,2,1,10,2,6
D,5,10,8,8,11,9,9,5,5,7,6,6,5,8,11,6
J,3,6,5,4,2,7,7,3,5,15,6,9,1,6,1,7
S,3,1,3,2,2,8,7,6,5,7,7,8,2,9,9,8
L,5,5,5,7,1,0,0,7,6,0,1,4,0,8,0,8
E,3,3,4,5,2,3,7,6,10,7,6,14,0,8,7,7
M,4,8,5,6,3,7,7,12,1,7,9,8,8,6,0,8
L,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8
A,3,7,5,5,3,8,2,2,2,7,2,7,2,6,3,7
R,5,10,6,8,7,6,7,5,6,6,4,8,3,6,5,8
S,4,9,5,6,5,8,7,5,8,5,5,8,0,8,8,7
E,1,3,3,2,1,7,7,2,6,11,7,9,2,9,4,9
O,5,8,6,6,4,7,9,8,5,6,7,9,3,8,3,7
R,3,7,5,5,3,10,7,3,6,10,3,7,3,7,3,10
O,1,0,1,1,0,7,7,7,5,7,6,8,2,8,3,8
F,4,6,6,4,2,8,10,2,6,13,5,3,2,10,2,8
A,4,9,6,7,5,7,5,2,4,5,1,6,3,6,4,5
R,3,10,4,7,5,6,9,8,4,7,6,8,2,7,5,11
T,5,8,6,6,3,4,13,4,6,12,10,4,2,11,2,4
W,5,6,7,4,4,5,11,3,2,9,8,7,9,13,2,5
N,5,10,7,7,5,7,8,6,6,6,6,6,6,8,1,6
S,5,5,6,7,3,8,8,6,9,5,6,7,0,8,9,7
A,6,11,5,6,3,8,4,3,3,7,4,11,5,6,4,7
Y,2,6,4,4,2,8,9,1,6,5,11,8,1,11,1,8
V,5,10,8,7,5,7,12,2,3,5,10,9,4,12,2,7
B,1,0,2,1,1,7,8,7,5,7,6,7,1,8,6,8
N,5,10,8,8,5,5,9,5,5,8,8,9,7,9,2,5
R,6,10,8,7,6,8,9,4,6,8,4,8,3,6,6,11
Y,3,5,5,7,7,9,6,4,1,6,7,8,6,10,8,9
W,1,0,2,1,1,8,8,4,0,7,8,8,6,10,0,8
B,3,8,3,6,4,7,6,8,5,7,6,7,2,9,7,9
C,2,1,3,3,1,6,8,7,7,8,8,13,1,10,4,10
Y,5,9,8,7,4,5,9,1,7,9,12,9,1,11,2,7
I,6,15,4,8,3,12,4,3,6,11,2,7,3,8,3,12
D,5,10,6,7,6,8,9,5,5,11,6,5,5,8,5,9
A,3,4,5,3,2,10,1,3,2,9,2,9,3,6,2,8
A,3,7,5,5,3,12,3,2,2,9,2,9,2,5,2,8
X,2,7,3,4,1,7,7,4,4,7,6,8,3,8,4,8
R,3,6,4,4,3,6,8,8,4,6,5,7,2,7,5,11
G,1,0,2,0,0,8,7,5,5,6,5,9,1,8,5,10
Z,4,10,5,7,4,7,8,3,12,9,6,8,0,8,8,7
X,2,2,4,3,2,8,7,3,9,6,6,9,3,8,6,8
J,6,7,8,9,7,8,10,4,5,7,7,8,5,6,9,7
A,5,9,5,5,3,12,2,5,1,12,3,10,3,3,2,10
Q,2,2,3,3,2,8,7,7,3,6,5,9,2,9,3,9
F,6,9,8,7,5,8,9,1,7,14,5,5,2,9,3,9
A,4,7,6,6,5,8,8,2,4,7,7,8,5,7,4,5
M,5,10,6,8,4,7,7,12,2,8,9,8,8,6,0,8
Y,6,9,6,6,3,3,10,3,7,11,11,6,1,11,3,5
M,5,2,6,4,4,8,6,7,5,7,7,8,8,6,2,7
O,5,9,5,6,5,8,8,7,4,9,6,6,4,8,4,8
V,2,4,3,3,1,3,12,4,3,11,11,7,2,11,1,7
T,2,1,3,3,1,6,12,3,6,8,11,7,1,11,1,7
B,3,5,4,7,3,6,8,9,7,7,5,7,2,8,9,10
P,4,7,5,5,3,8,9,3,5,13,4,3,1,10,3,9
H,4,5,6,4,4,7,7,3,6,10,6,8,3,8,3,7
N,1,0,2,0,0,7,7,11,0,5,6,8,4,8,0,8
B,8,15,7,8,6,10,6,3,5,10,4,7,7,6,8,9
B,4,9,4,4,3,10,6,3,4,9,4,8,6,7,7,10
T,2,4,2,3,1,7,11,2,6,7,11,8,1,11,1,7
P,2,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8
R,3,4,4,2,2,7,8,5,5,7,5,7,2,6,4,8
K,2,3,4,1,2,6,8,2,6,10,7,10,3,8,2,8
E,3,5,5,4,3,6,7,2,8,11,7,9,2,8,4,8
J,1,1,2,2,1,10,6,3,5,12,5,9,1,7,1,7
M,7,6,10,6,10,7,8,4,4,7,5,7,11,8,5,5
M,5,10,7,8,8,6,6,6,5,7,7,11,11,5,2,9
S,3,7,4,5,3,8,7,7,7,8,6,7,2,8,9,8
Y,7,8,6,6,3,3,10,3,7,11,12,6,2,11,3,5
P,6,10,5,5,3,6,9,6,3,10,5,6,4,9,4,7
J,5,9,6,7,3,9,5,4,6,15,4,10,0,7,1,7
T,3,6,4,4,2,6,12,2,7,11,9,4,1,11,2,5
D,1,0,1,1,1,6,7,8,5,6,6,6,2,8,3,8
J,3,7,5,5,2,8,5,4,6,14,7,12,1,6,0,7
V,3,7,5,5,1,7,8,4,3,7,13,8,3,9,0,8
J,3,11,4,8,4,10,7,0,7,11,3,6,0,7,1,7
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
Z,3,7,5,5,2,9,6,3,9,11,3,9,1,7,6,9
M,3,3,6,2,3,8,5,3,4,10,7,9,7,5,2,8
Y,7,8,6,12,4,5,8,4,1,8,10,5,4,10,6,5
A,3,8,5,6,3,8,3,2,2,7,1,8,2,6,3,7
R,4,9,5,6,5,9,7,4,5,9,4,8,3,7,4,11
W,6,5,9,8,4,11,8,5,2,6,9,8,9,9,0,8
G,10,15,9,8,5,7,6,6,6,10,6,7,5,8,6,6
I,1,4,0,2,0,7,7,1,7,7,6,8,0,8,2,8
T,2,2,3,3,2,7,12,3,6,7,11,8,2,11,1,7
B,7,14,5,8,4,7,6,4,5,10,6,9,5,7,7,9
J,0,0,1,0,0,12,4,6,3,12,5,11,0,7,0,8
U,3,6,4,4,2,7,4,14,5,7,13,8,3,9,0,8
P,7,10,6,5,4,7,10,4,4,12,5,3,4,10,5,6
H,3,3,5,2,3,7,8,2,6,10,7,7,4,10,3,7
I,1,2,1,3,1,7,7,1,7,7,6,9,0,8,3,8
S,2,6,3,4,2,7,6,5,8,4,6,8,0,9,8,8
Y,5,6,5,4,2,3,11,3,7,12,12,6,2,11,2,5
B,2,7,3,5,3,6,7,7,5,6,6,7,2,8,7,9
M,5,8,8,6,6,8,6,6,5,6,7,8,8,6,2,7
E,3,6,3,4,2,3,8,6,10,7,6,14,0,8,7,8
G,3,7,4,5,2,7,7,8,7,5,6,10,2,7,5,10
B,4,9,5,7,6,7,7,9,5,7,5,6,2,8,8,10
V,5,10,5,5,3,10,9,4,5,8,8,5,3,10,2,8
Y,5,6,5,4,2,3,12,4,5,12,11,5,1,11,1,5
U,4,9,5,6,2,7,4,15,6,7,13,8,3,9,0,8
D,4,4,4,6,2,5,7,10,8,6,6,5,3,8,4,8
V,2,1,3,2,1,7,12,2,2,7,11,8,2,10,1,8
A,2,4,4,6,2,7,5,3,1,7,1,8,2,7,2,8
L,4,9,6,7,4,9,4,1,7,9,2,10,3,6,4,9
O,3,7,4,5,3,6,7,7,4,7,6,10,4,8,3,8
Z,6,10,8,8,6,8,7,2,9,12,5,8,1,7,6,8
C,5,9,6,6,3,3,8,5,7,11,10,14,1,8,3,7
E,3,7,3,5,2,3,6,6,10,7,7,14,0,8,7,8
G,6,10,7,8,5,6,6,6,6,11,6,12,4,9,5,8
J,4,6,6,7,5,7,8,4,6,6,8,7,3,9,9,10
O,1,1,2,1,1,8,7,7,5,7,6,8,2,8,3,8
J,5,9,6,7,3,9,7,2,6,15,4,8,0,7,1,7
X,4,7,6,5,5,6,7,2,6,7,6,9,4,5,7,9
U,4,7,6,5,8,8,8,4,4,6,7,8,7,8,5,8
J,2,4,3,3,1,9,7,2,6,14,4,8,0,7,0,8
V,4,11,6,8,2,7,8,4,3,7,14,8,3,9,0,8
O,5,10,6,8,5,8,8,8,4,7,7,6,5,7,4,9
R,4,8,6,6,4,8,8,4,6,9,3,8,3,6,4,11
R,4,6,6,4,3,9,7,5,6,9,4,7,3,7,4,11
O,5,11,6,8,5,8,7,9,6,6,7,9,3,8,3,8
D,5,11,7,8,6,7,8,8,7,7,7,4,4,7,5,9
R,3,6,4,4,3,6,7,5,5,6,5,7,3,7,5,9
F,5,8,7,6,5,7,9,3,5,13,7,6,2,10,2,7
K,6,7,8,5,4,6,7,2,7,10,6,10,4,7,4,8
U,4,6,5,5,5,7,6,5,4,6,7,8,4,8,1,7
E,2,4,3,3,2,8,7,6,8,7,7,8,2,8,6,8
G,4,6,5,4,3,7,6,6,6,10,6,11,2,10,4,10
F,4,6,5,4,3,6,11,3,5,13,6,4,2,10,2,7
P,4,8,4,6,2,4,12,8,2,10,6,3,1,10,4,8
K,4,7,6,5,4,6,7,5,7,6,5,10,3,8,5,9
P,3,5,5,3,2,8,9,4,4,12,4,3,1,10,3,8
Y,4,9,5,6,5,9,4,7,5,8,8,7,5,8,5,5
P,5,8,7,6,5,8,10,5,4,11,4,3,1,10,3,8
K,3,6,5,5,4,8,7,2,4,9,3,8,4,6,4,10
D,3,4,4,3,3,7,7,7,7,6,6,5,2,8,3,7
M,6,10,7,8,4,8,7,13,2,6,9,8,9,6,0,8
A,3,4,6,6,2,7,6,3,0,7,0,8,2,7,1,8
Q,4,9,5,8,5,8,8,7,4,6,8,9,2,8,4,9
G,5,9,6,7,5,5,6,6,7,6,5,11,2,10,4,8
B,4,8,5,6,6,8,8,6,6,7,6,6,5,8,5,10
R,2,3,4,2,2,8,7,4,4,9,4,6,2,7,3,9
J,3,6,4,4,2,8,7,3,5,14,6,10,1,6,1,7
U,7,13,7,7,3,6,5,5,6,3,9,8,5,9,2,7
T,5,5,5,4,3,6,11,2,8,11,9,4,3,10,4,4
W,3,1,5,2,2,7,11,3,2,7,9,8,6,11,0,8
Y,1,0,2,0,0,7,9,3,1,7,12,8,1,11,0,8
A,3,7,4,5,2,9,3,3,3,9,1,8,2,6,2,7
F,4,10,5,8,6,5,9,4,6,9,10,7,2,9,3,6
C,6,9,7,7,8,5,6,4,5,7,6,11,6,9,4,8
O,2,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8
F,3,5,5,3,2,6,11,2,6,14,7,4,1,9,2,7
T,7,13,6,7,4,9,7,2,7,10,5,8,3,8,5,8
P,4,9,4,6,2,4,11,9,3,9,6,4,1,10,4,8
C,3,7,4,5,2,4,9,6,7,12,10,11,1,10,2,7
X,7,10,10,8,6,7,8,0,8,9,6,8,3,8,3,7
T,7,10,6,5,3,5,11,2,7,12,7,5,2,8,4,4
M,4,4,6,3,3,6,6,3,4,10,9,10,8,4,3,8
K,5,10,8,8,6,4,9,1,6,10,9,10,3,8,3,6
G,3,6,4,4,2,6,8,5,5,9,7,10,2,8,4,9
Z,1,4,2,2,1,7,8,5,8,6,6,9,1,8,7,7
V,6,7,6,5,2,3,12,5,4,12,12,7,3,10,1,8
M,3,4,4,2,3,8,6,6,4,7,7,9,9,5,1,8
O,3,6,3,4,3,7,8,7,4,9,7,10,3,8,2,9
O,6,8,8,6,9,7,9,5,2,7,6,7,10,10,6,9
H,4,5,6,4,4,7,8,3,6,10,7,7,5,9,4,8
M,7,10,12,8,15,10,6,3,3,9,4,7,12,6,5,5
B,5,10,6,8,7,9,8,3,5,7,6,7,7,7,6,9
V,5,6,5,4,2,4,11,2,3,9,11,7,2,10,1,7
R,2,1,2,2,2,6,8,4,5,6,5,7,2,7,4,8
E,2,4,4,3,2,6,8,3,9,11,8,9,2,8,4,6
G,5,11,6,8,6,8,8,8,6,5,7,9,3,5,7,11
W,6,10,8,8,9,8,7,6,3,7,8,8,10,6,6,11
C,2,5,3,4,2,6,8,7,7,9,8,13,1,9,4,10
Q,2,1,2,1,1,8,7,7,4,6,6,8,2,8,3,8
A,4,9,7,6,3,12,2,4,3,11,1,9,3,7,3,9
V,5,8,5,6,2,3,11,3,4,10,12,7,2,10,1,8
Z,2,4,5,3,2,7,8,2,9,11,7,7,1,8,5,6
F,4,9,6,7,4,4,11,3,7,11,10,5,1,10,3,5
L,6,9,8,7,6,6,6,8,6,6,6,9,6,7,5,11
X,4,10,7,8,4,9,7,0,8,9,5,7,2,8,3,8
V,2,3,4,4,1,9,8,4,2,6,13,8,3,10,0,8
M,5,11,8,8,12,9,5,2,1,8,4,8,11,7,3,6
J,2,3,2,5,1,13,3,8,4,13,4,12,1,6,0,8
B,3,7,5,5,5,7,8,6,4,7,5,6,2,8,5,7
O,2,4,3,3,2,7,8,6,4,9,6,8,2,8,2,7
Y,6,9,6,6,4,5,9,1,7,8,9,5,2,11,3,4
U,4,8,6,6,7,7,6,4,3,7,7,8,10,8,5,7
U,6,9,6,7,4,3,8,5,7,11,10,9,3,9,2,6
Y,3,5,5,7,6,8,8,3,2,7,8,9,3,11,7,6
K,4,9,6,6,7,5,6,4,5,7,5,9,7,7,8,10
A,4,5,6,7,2,7,4,3,1,7,1,8,3,7,2,8
E,2,3,4,2,2,7,7,2,7,11,6,9,2,8,4,8
K,5,9,7,7,4,3,8,3,7,11,10,12,3,8,4,6
D,3,6,4,4,3,7,9,6,5,10,6,4,3,8,3,6
M,3,5,6,3,4,5,6,3,4,10,9,10,7,6,2,8
C,2,4,3,3,1,6,8,7,8,7,8,13,1,8,4,10
U,4,9,5,7,2,7,5,14,5,7,14,7,3,9,0,8
X,6,8,8,6,6,7,6,3,5,7,6,11,4,6,10,8
F,3,9,3,6,2,1,14,4,3,12,10,5,0,8,1,6
V,4,4,5,3,2,4,12,3,3,10,11,7,2,10,0,8
Y,5,6,5,4,2,5,9,2,8,10,10,5,2,10,4,3
K,6,10,9,8,5,3,7,3,7,10,11,12,4,7,4,7
M,5,5,6,8,4,8,7,12,2,6,9,8,8,6,0,8
V,4,9,6,7,8,7,5,5,2,8,7,8,8,8,5,8
V,4,11,6,8,4,8,9,4,1,6,12,8,4,9,2,7
W,3,3,4,1,2,4,10,3,2,9,9,7,5,10,1,6
N,4,10,4,7,5,7,7,12,1,6,6,8,5,8,0,8
V,6,11,9,8,11,8,5,5,3,8,7,8,6,9,5,9
L,4,6,6,4,4,6,6,7,6,6,6,9,2,8,5,10
G,2,1,2,2,1,7,7,6,5,6,6,10,2,9,4,9
U,5,11,7,8,5,6,9,5,7,6,9,9,3,9,1,8
X,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8
U,4,9,4,7,2,7,4,15,6,7,13,8,3,9,0,8
X,8,12,9,6,5,6,9,2,8,11,8,8,4,9,4,5
E,5,10,8,8,9,7,8,3,6,6,7,11,4,11,8,7
J,3,6,5,4,2,10,5,3,6,14,3,9,0,7,0,8
A,2,2,4,3,1,6,2,2,2,5,2,8,2,6,2,6
M,4,2,5,4,4,8,6,6,4,6,7,8,8,6,2,7
R,5,11,7,8,5,11,7,3,7,11,1,6,6,7,4,10
G,2,3,3,2,1,7,6,5,5,7,6,9,2,9,4,9
A,2,5,4,4,2,11,3,3,2,10,2,9,2,6,2,8
H,5,7,7,5,5,8,7,3,6,10,6,9,3,8,3,8
H,4,8,6,10,7,8,4,4,2,7,4,6,5,7,8,8
L,3,8,5,6,4,4,4,4,7,2,1,6,1,6,1,6
B,5,9,7,7,7,8,7,5,5,9,6,6,3,9,6,9
K,7,9,10,7,6,5,8,2,7,10,8,10,3,8,4,7
Z,7,12,7,6,4,10,3,4,7,12,4,10,3,8,6,10
B,6,11,8,8,11,8,6,5,4,7,7,7,9,11,11,10
S,5,6,6,5,6,9,8,4,5,7,6,8,5,10,8,11
Q,2,2,3,2,2,8,7,6,3,6,6,10,2,9,3,10
F,4,7,6,5,3,5,11,4,5,13,7,4,2,10,2,7
X,4,8,6,6,4,7,8,3,9,5,6,7,4,9,7,7
I,2,7,3,5,2,7,7,0,6,13,6,8,0,8,1,8
B,4,8,6,6,8,8,7,5,3,7,7,7,6,10,8,9
H,7,11,9,8,10,8,8,6,7,7,6,6,6,8,4,7
E,2,5,3,4,3,7,7,5,7,7,6,9,2,8,5,10
F,3,7,4,5,3,6,10,3,5,10,9,6,2,10,3,6
T,5,7,5,5,3,6,11,3,7,11,9,4,2,12,2,4
O,3,8,4,6,3,7,8,7,5,9,8,8,3,8,3,8
T,3,8,4,6,2,9,13,0,6,6,10,8,0,8,0,8
M,6,9,9,7,7,8,7,2,4,9,7,8,8,7,2,8
N,2,3,4,2,1,7,8,3,4,10,6,6,5,8,0,7
E,2,3,4,2,2,7,7,2,7,11,7,9,2,8,4,8
F,2,1,2,2,1,5,10,3,5,10,9,5,1,10,2,7
B,3,7,3,5,3,6,8,9,7,7,5,7,2,8,8,9
E,3,6,3,4,3,3,8,5,9,7,6,14,0,8,6,9
R,5,7,7,5,4,9,8,4,6,9,4,7,3,7,5,11
O,3,6,4,4,2,7,8,8,7,6,7,8,3,8,4,8
Y,4,5,5,4,2,4,11,3,6,12,11,5,1,11,2,5
A,4,9,6,7,4,8,2,1,2,6,2,8,2,7,3,6
N,2,4,4,2,2,8,8,2,4,10,4,6,5,10,1,7
U,3,3,3,1,1,5,8,5,7,10,9,8,3,10,2,6
N,7,10,10,8,6,9,9,2,5,10,4,5,7,9,2,8
H,6,7,9,5,4,9,6,3,6,10,3,7,6,8,5,9
W,2,4,4,3,2,7,11,2,2,7,9,8,6,11,0,8
J,2,6,3,4,3,9,9,4,3,8,4,6,4,8,5,4
M,4,4,6,3,3,7,6,3,4,10,8,9,9,5,3,9
I,1,4,2,2,0,7,8,0,7,13,6,8,0,8,0,8
T,2,3,3,2,1,6,11,2,7,11,9,5,1,10,2,5
Y,2,7,4,5,2,7,10,1,6,6,11,8,1,11,1,8
Q,4,6,5,8,5,7,9,5,2,6,9,11,3,9,6,7
O,2,1,3,2,2,7,7,7,5,7,6,8,2,8,3,8
M,7,8,10,7,11,8,8,5,4,7,6,7,11,10,6,3
J,5,11,4,8,4,6,11,2,3,12,6,5,2,9,8,8
H,4,10,6,7,6,6,7,5,5,7,5,8,6,6,6,11
A,2,3,4,2,1,10,2,3,2,10,2,10,2,6,2,8
C,5,9,6,6,4,6,7,6,9,5,6,12,1,7,4,9
E,1,0,2,1,1,5,7,5,7,7,6,12,0,8,7,9
O,3,5,4,3,2,8,6,6,4,9,5,8,2,8,3,8
X,4,7,7,5,3,7,8,1,8,10,8,8,3,8,3,7
J,1,3,2,1,1,11,6,2,5,11,4,8,0,7,1,8
V,7,9,10,8,11,7,6,5,5,7,6,8,7,10,8,8
G,4,9,5,7,4,5,7,5,4,9,8,9,2,8,5,9
P,2,7,3,5,2,4,12,8,2,10,6,4,1,10,3,8
T,8,13,7,7,3,6,9,3,8,13,6,6,2,8,5,4
U,2,1,3,2,2,7,9,5,6,7,9,9,3,9,1,8
T,3,11,5,8,2,7,14,0,6,7,11,8,0,8,0,8
T,7,10,6,5,2,5,10,3,8,13,7,5,2,8,4,4
X,6,10,9,8,7,8,8,3,6,7,7,8,7,13,9,9
K,4,10,6,7,6,5,7,5,7,6,6,12,3,8,6,9
G,2,3,3,2,2,6,7,5,5,9,7,10,2,9,4,9
V,3,7,5,5,1,7,8,4,2,7,14,8,3,10,0,8
E,5,9,7,7,8,7,6,3,6,7,7,11,4,9,8,7
G,2,5,3,4,2,6,7,5,5,9,7,10,2,9,4,9
H,4,4,5,5,2,7,7,15,0,7,6,8,3,8,0,8
N,5,6,7,4,4,11,7,3,5,10,1,4,5,9,1,7
N,4,7,6,5,5,6,7,3,3,8,8,8,6,9,5,4
O,5,10,6,7,4,7,7,8,5,10,6,9,3,9,4,6
K,2,4,4,3,2,5,8,2,7,10,8,10,3,8,3,6
Z,6,9,8,7,5,7,7,2,9,12,6,8,1,8,6,7
L,1,3,2,1,1,6,4,1,8,8,3,10,0,7,2,8
G,2,3,3,2,2,6,6,5,6,7,5,10,2,9,4,9
C,6,12,5,6,4,7,9,4,5,8,7,9,4,8,8,10
R,4,5,5,3,3,7,7,5,6,7,5,6,5,7,4,8
Q,4,6,5,8,5,8,9,5,2,6,8,11,3,9,6,8
P,8,12,7,6,4,9,8,4,6,13,4,4,3,10,6,7
G,3,9,4,6,2,7,7,8,7,5,6,11,2,7,5,10
B,4,9,4,6,3,6,7,9,7,7,6,7,2,8,9,10
I,4,11,6,8,4,7,9,0,8,13,6,6,1,9,2,7
A,4,7,6,5,3,9,3,2,2,8,1,8,2,6,3,7
F,1,3,2,2,1,5,11,4,4,11,8,4,1,9,3,6
A,3,5,6,4,2,7,1,2,2,6,2,7,4,6,4,7
G,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10
B,3,3,4,4,3,6,8,8,7,7,5,7,2,8,8,9
A,2,3,3,1,1,7,2,2,1,6,2,7,1,6,1,7
B,3,5,3,4,3,7,7,5,5,6,6,6,2,8,6,10
Q,4,8,6,7,3,8,6,8,7,6,5,8,3,8,4,8
R,6,8,9,7,9,7,8,3,4,7,5,8,7,7,6,7
X,9,13,8,8,4,6,7,3,9,9,9,9,4,8,4,6
C,4,9,6,7,6,5,7,4,4,7,6,9,6,9,5,7
W,7,8,7,6,5,4,11,3,3,9,9,7,7,11,2,6
W,6,9,8,7,4,5,7,5,2,7,8,8,9,9,0,8
Y,4,6,4,4,2,3,10,3,6,11,11,6,2,11,2,5
G,3,2,5,3,3,7,6,6,6,6,6,10,2,9,4,9
O,2,5,3,3,2,7,7,7,4,9,6,8,2,8,3,8
I,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8
E,2,3,2,1,2,7,7,5,6,7,6,8,2,8,5,10
F,4,8,7,6,7,8,8,1,4,9,7,7,6,11,4,6
Y,3,10,5,7,1,7,12,1,4,7,12,8,0,10,0,8
Q,5,8,5,9,6,7,10,4,3,6,9,11,3,9,6,8
O,5,9,7,8,6,6,6,5,5,7,4,7,4,8,6,7
K,4,5,7,4,4,8,7,1,7,10,5,8,3,8,4,8
Q,2,6,3,5,2,8,8,8,5,6,7,8,3,7,5,9
L,4,8,5,7,5,8,5,4,4,6,7,7,3,8,8,11
T,5,9,7,6,8,6,7,3,6,7,6,10,5,9,5,7
W,4,7,6,5,4,5,11,2,3,8,9,9,7,11,1,8
D,7,13,7,7,4,9,5,4,6,10,3,7,5,6,5,11
Z,5,7,6,5,5,9,11,5,4,6,5,7,3,8,8,5
R,3,4,5,3,3,8,7,3,5,10,4,7,2,7,4,10
Y,4,8,6,6,3,5,10,2,8,10,12,9,1,11,2,7
I,8,14,5,8,3,10,4,4,7,11,2,7,3,7,5,11
Z,5,9,7,7,5,8,6,2,9,12,5,8,1,7,6,8
N,5,7,7,5,4,10,8,3,5,10,2,4,5,9,1,7
V,5,7,5,5,3,3,11,2,3,9,11,8,3,12,1,7
L,4,11,6,8,4,7,4,2,7,8,2,9,2,5,3,7
Q,5,10,6,8,6,8,4,8,4,7,5,9,4,9,4,7
Y,3,4,5,3,2,9,11,1,7,5,11,7,1,11,2,8
L,4,8,6,6,3,5,4,1,10,7,2,11,0,7,3,7
B,9,15,6,8,4,9,5,6,6,10,3,9,6,6,7,11
U,3,3,4,4,2,6,9,5,7,7,9,9,3,9,1,8
R,5,7,7,5,5,9,7,3,5,10,3,7,3,6,4,10
P,3,6,4,4,2,9,9,3,5,13,4,3,1,10,3,9
O,4,6,5,4,3,8,7,8,4,7,6,5,3,8,3,7
T,7,12,6,7,4,6,10,2,6,12,7,6,2,9,4,4
Z,5,9,6,7,4,8,6,6,11,7,5,6,1,7,8,8
P,2,4,3,2,1,5,11,4,4,10,8,3,1,9,3,6
N,6,8,9,6,4,8,8,3,5,10,5,6,6,9,2,7
T,3,10,4,8,3,9,13,0,5,6,10,8,0,8,0,8
Z,5,9,5,4,3,5,8,2,8,11,9,9,3,9,5,5
N,4,8,7,6,3,4,10,4,4,10,11,10,5,7,1,7
Z,4,7,6,5,5,9,9,5,4,6,5,7,3,9,9,4
G,3,7,4,5,3,6,7,7,6,8,7,11,2,8,4,9
O,4,5,6,7,3,9,8,9,8,6,7,10,3,8,4,8
Q,3,6,4,6,2,7,6,8,5,6,7,7,3,8,5,9
A,1,1,2,1,1,7,4,2,1,7,1,8,2,6,1,7
Z,4,9,4,7,2,7,7,4,14,9,6,8,0,8,8,8
D,3,4,5,3,3,7,7,5,6,10,6,6,3,8,3,9
A,2,4,4,3,1,8,2,2,2,7,2,8,2,6,2,7
N,4,7,6,5,4,7,9,5,5,7,6,6,6,10,2,5
W,4,10,6,8,7,7,7,6,2,7,8,7,10,7,5,9
A,4,10,6,8,4,13,2,4,3,11,1,9,3,7,4,10
N,8,11,12,8,6,7,9,2,5,9,6,6,6,9,1,8
N,5,8,7,6,4,4,9,4,4,10,9,9,5,7,1,7
H,4,8,5,6,7,8,8,5,3,7,6,6,7,9,8,8
U,6,7,7,5,3,4,9,6,7,12,11,8,3,9,2,6
Y,7,7,6,10,4,7,9,2,2,7,10,5,4,10,6,6
G,7,15,6,8,4,9,3,3,3,7,3,5,4,7,4,9
Q,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8
G,4,6,6,4,5,7,8,5,4,6,6,8,4,7,7,7
H,2,3,3,2,2,7,7,5,6,7,6,8,3,8,3,8
C,4,6,4,4,2,3,9,5,8,11,10,11,1,8,3,7
E,1,0,1,0,0,5,7,5,7,7,6,11,0,8,6,10
B,4,11,5,8,4,6,6,10,7,6,6,7,2,8,9,10
S,4,7,5,5,2,8,7,4,8,11,6,7,2,8,5,8
Z,5,11,7,9,5,8,6,6,11,7,5,6,2,7,8,8
Y,2,3,2,2,1,4,11,3,6,11,10,4,1,11,2,5
G,4,9,6,7,7,9,6,5,2,7,6,10,8,8,4,11
D,6,10,8,8,6,7,7,7,8,7,7,4,3,8,3,7
B,5,9,8,8,9,7,8,5,4,7,6,8,7,9,8,6
G,5,11,6,8,5,5,5,6,7,7,5,12,2,10,4,9
O,4,10,5,8,3,7,6,9,8,6,4,7,3,8,4,8
Q,5,7,6,9,6,9,10,6,3,4,8,12,3,10,8,12
K,2,3,3,1,1,6,7,1,6,10,7,10,3,8,2,8
Y,4,6,4,4,2,3,11,5,5,12,12,6,2,11,1,6
F,5,7,6,8,7,7,9,5,5,7,6,8,4,8,8,7
S,4,6,5,4,2,6,8,4,8,11,5,7,2,6,5,8
G,4,7,5,5,4,6,6,6,5,9,7,12,3,8,4,9
L,2,1,3,2,1,4,4,4,8,2,1,6,0,7,1,6
V,5,10,5,8,3,3,11,4,4,10,12,8,2,10,1,8
D,4,7,5,5,4,7,8,8,4,8,6,4,3,8,3,7
H,7,10,9,8,6,10,5,3,6,10,2,7,5,7,5,9
P,4,8,6,6,4,5,13,6,2,12,6,2,0,10,3,7
T,6,11,5,6,2,5,10,2,7,13,7,5,2,8,4,4
P,2,7,3,4,2,4,12,8,2,10,6,4,1,10,3,8
H,6,7,9,5,5,8,7,3,7,10,4,7,5,6,4,8
E,6,9,8,7,7,10,6,2,7,11,4,8,4,6,6,11
J,2,9,3,6,1,11,3,10,3,12,7,13,1,6,0,8
B,2,1,2,2,1,7,7,8,5,6,6,7,2,8,7,10
Y,4,11,6,8,1,6,10,2,2,7,13,8,1,11,0,8
E,4,11,4,8,5,3,9,5,10,7,6,14,0,8,6,8
E,4,6,5,4,4,10,6,2,6,11,4,9,2,8,4,12
E,4,9,6,6,5,7,7,2,7,11,7,9,3,8,4,8
X,1,0,1,0,0,7,7,4,4,7,6,8,2,8,3,8
U,5,10,6,7,6,8,8,8,5,6,7,10,3,8,4,6
E,3,4,3,3,3,7,8,6,8,7,5,9,2,8,6,9
V,5,10,7,8,5,5,12,3,2,9,10,8,5,10,6,8
G,3,7,4,5,2,8,7,7,7,6,6,10,2,7,5,11
I,1,5,1,4,1,7,7,1,7,7,6,8,0,8,2,8
U,6,8,7,6,5,4,8,5,7,9,7,9,4,8,3,4
D,4,9,6,7,5,7,7,9,4,8,7,4,4,8,4,8
Y,4,5,6,7,1,7,12,2,3,7,12,8,1,10,0,8
P,5,8,7,11,10,9,8,4,3,6,8,7,6,10,6,4
T,3,9,4,7,4,6,11,4,6,8,11,7,2,12,1,7
S,5,8,7,6,4,6,8,3,7,10,6,7,3,7,5,6
Y,6,8,6,6,3,4,10,2,8,11,11,5,1,10,3,4
J,2,7,2,5,1,11,3,9,3,13,7,13,1,6,0,8
E,3,8,4,6,2,3,6,6,11,7,7,14,0,8,7,7
T,6,14,6,8,4,9,7,3,7,12,5,6,2,8,6,6
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
F,2,3,2,1,1,5,10,4,5,10,9,6,1,9,3,7
K,4,8,4,6,2,4,7,8,2,7,7,11,3,8,3,11
D,4,9,4,4,2,9,6,5,5,11,4,7,5,7,5,11
A,4,9,7,7,4,12,3,2,2,9,2,9,2,6,2,8
O,5,6,7,5,5,6,6,6,5,7,5,6,3,6,5,6
H,4,7,6,5,4,11,6,4,6,10,2,6,3,8,3,9
X,3,7,4,5,3,7,6,3,5,6,6,9,2,9,8,8
B,4,10,4,8,4,6,6,9,6,6,6,7,2,8,9,11
X,2,1,3,2,1,8,7,3,8,6,6,8,2,8,5,8
P,2,4,3,3,1,5,11,4,4,10,8,3,0,10,3,6
W,8,15,8,8,6,1,7,2,3,9,10,9,9,10,1,5
U,4,4,5,6,2,7,3,14,6,7,13,8,3,9,0,8
J,2,6,3,4,3,10,7,3,3,9,4,7,2,8,7,8
W,3,8,5,6,8,7,5,6,2,7,6,8,8,12,3,10
P,9,14,7,8,4,7,9,6,4,13,4,4,5,10,4,8
P,3,1,3,2,2,6,9,5,4,9,7,4,1,9,3,7
Q,6,8,8,7,6,7,4,4,5,6,4,8,4,5,6,7
D,5,9,6,6,5,9,7,4,5,10,5,6,3,8,3,8
J,4,8,6,6,2,8,5,5,5,14,8,13,1,6,1,7
H,6,9,8,7,8,8,8,5,7,7,6,9,6,8,4,8
S,3,4,3,3,2,8,8,6,5,7,6,7,2,8,9,8
N,2,0,2,1,1,7,7,12,1,5,6,8,5,8,0,8
Z,2,4,3,6,2,7,7,3,13,10,6,8,0,8,8,8
D,4,6,6,4,5,8,7,5,6,9,4,5,3,8,3,8
N,1,1,2,1,1,7,7,11,1,5,6,8,4,8,0,8
J,6,13,5,10,4,7,11,3,3,13,5,4,2,9,8,9
X,3,9,5,6,4,8,8,3,8,6,6,6,3,9,6,7
J,2,5,3,7,1,12,3,9,4,13,5,12,1,6,0,8
D,3,4,5,3,3,9,6,4,6,10,4,6,2,8,3,8
E,3,4,3,6,2,3,7,6,10,7,6,14,0,8,7,8
T,2,5,3,3,2,7,12,3,5,7,11,8,2,11,1,8
S,3,2,3,3,2,8,8,7,5,7,5,7,2,8,9,8
W,3,3,4,2,2,6,11,3,2,8,8,7,5,11,1,6
M,2,1,3,2,2,6,6,6,4,7,7,10,6,5,2,9
W,3,2,5,3,3,8,11,2,2,6,9,8,6,11,1,7
D,5,8,7,6,6,8,8,6,6,9,5,4,5,8,4,9
C,4,8,5,6,4,7,7,8,5,8,6,12,4,9,4,8
E,2,3,4,2,2,6,9,2,8,11,7,8,2,8,4,7
I,1,3,2,1,0,7,8,1,7,13,6,7,0,8,0,7
E,4,8,5,6,6,6,7,5,7,7,6,10,3,8,5,9
R,2,4,4,2,2,8,7,3,5,10,4,7,2,7,3,10
U,3,4,4,3,2,6,8,6,7,7,9,9,3,9,1,8
A,3,5,6,6,2,6,5,3,1,6,0,8,2,7,2,7
A,4,9,6,6,2,6,3,3,3,6,2,7,3,6,3,7
J,2,1,3,3,1,10,6,2,6,12,4,8,0,7,0,7
O,5,9,7,7,5,7,5,9,5,8,5,11,4,8,4,8
D,5,6,6,5,5,6,7,5,7,6,4,7,3,7,5,6
K,3,5,4,3,3,5,7,4,7,7,6,10,6,8,4,9
S,3,9,4,7,2,8,7,6,9,4,6,7,0,8,9,8
Z,4,9,4,7,4,7,8,3,12,9,6,8,0,8,7,7
U,9,11,10,8,7,4,8,5,9,9,7,9,8,10,6,1
D,5,8,6,6,4,10,6,3,8,11,4,6,3,8,3,9
Z,2,5,4,4,2,7,7,2,9,11,6,8,1,8,5,8
K,5,10,7,7,9,8,8,3,4,6,6,9,10,10,7,7
X,2,2,4,3,2,8,7,3,9,6,6,8,2,8,6,8
O,6,11,7,8,7,7,8,8,4,9,8,8,3,8,3,8
S,3,2,3,3,2,8,7,6,5,7,6,8,2,8,9,8
F,2,3,2,1,1,5,10,4,4,10,9,5,1,9,3,6
L,1,3,3,2,1,7,4,1,7,8,2,10,0,7,2,8
M,4,6,5,4,4,8,6,6,5,7,7,8,7,5,2,8
V,6,9,8,8,9,8,7,5,5,7,6,8,8,8,10,3
X,3,7,4,5,2,7,7,4,4,7,6,8,3,8,4,8
T,2,1,3,2,1,6,12,3,6,8,11,7,2,11,1,7
H,5,8,7,6,4,7,8,3,6,10,7,8,3,8,3,7
W,5,10,7,8,7,10,11,2,2,5,8,8,8,13,2,7
Q,6,10,8,7,7,8,3,8,4,6,6,9,3,8,4,9
I,2,6,3,4,1,7,7,0,8,14,6,8,0,8,1,8
G,4,9,5,7,3,8,8,8,8,5,7,9,2,7,6,11
P,4,7,5,5,3,6,11,8,2,10,5,3,1,10,4,7
U,3,8,5,6,3,4,9,7,7,8,10,10,3,9,1,8
P,4,10,5,8,5,7,9,6,6,9,7,3,2,10,4,7
B,1,3,3,2,2,8,7,3,5,9,6,7,2,8,4,8
U,3,3,4,1,1,5,8,4,6,10,9,9,3,10,1,6
I,2,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
F,5,9,7,7,4,6,10,2,6,13,7,5,1,10,2,7
Y,3,4,5,3,2,4,10,2,8,11,10,5,2,11,3,3
F,3,5,4,4,3,4,11,3,6,11,9,6,1,10,3,6
M,6,10,8,8,7,11,5,2,5,9,3,6,8,6,2,9
J,2,1,3,3,1,9,6,2,6,12,4,9,1,7,1,7
K,6,9,8,6,8,6,9,5,4,8,5,8,4,7,8,10
Z,4,9,4,6,3,6,8,6,10,7,7,10,1,9,8,8
U,5,11,5,6,3,6,5,5,4,7,9,10,5,6,2,9
N,3,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8
V,4,7,6,6,6,7,8,6,4,7,6,8,6,9,7,7
B,4,7,5,5,4,6,7,9,6,7,5,7,2,8,9,9
J,4,6,3,8,2,9,7,3,4,10,5,5,2,9,5,9
F,5,11,6,8,5,4,10,4,6,11,10,6,2,10,3,5
A,1,3,2,1,1,7,2,2,1,7,2,8,1,6,1,7
Z,3,6,5,4,4,6,7,3,7,7,6,9,0,8,8,7
F,4,8,5,6,5,7,9,6,3,8,6,9,4,11,8,11
M,6,10,9,8,8,11,6,2,4,9,4,6,9,6,2,8
B,4,7,5,5,4,8,6,7,7,6,7,5,2,9,8,9
C,3,7,4,5,2,5,9,6,7,12,9,9,2,10,3,7
B,7,10,9,8,8,8,7,5,6,10,6,6,3,9,7,10
V,6,9,6,7,3,1,12,4,4,12,12,8,2,10,1,7
G,5,7,7,5,6,7,7,5,2,7,6,11,5,8,8,8
Q,5,6,5,8,5,8,9,5,2,8,9,10,3,9,6,8
K,4,5,6,3,3,4,8,2,7,10,9,11,3,8,3,7
K,5,8,7,6,5,3,9,2,6,10,11,11,3,8,3,6
V,5,11,8,8,2,6,8,5,3,8,14,8,3,9,0,8
K,6,8,8,6,6,8,6,1,6,10,4,9,5,7,5,11
Q,6,10,8,7,7,8,5,8,5,7,6,5,5,7,8,8
O,7,13,5,7,4,6,6,5,5,6,4,8,5,7,5,7
D,3,3,4,2,2,7,7,6,7,7,6,5,2,8,3,7
K,3,3,5,2,2,6,8,2,7,10,7,9,3,8,2,7
L,2,3,2,4,1,0,2,5,6,0,0,7,0,8,0,8
D,3,6,4,4,4,7,7,6,6,6,6,5,3,8,3,7
V,3,10,5,8,2,7,8,4,3,7,14,8,3,9,0,8
B,4,6,6,6,7,7,8,5,5,8,6,9,6,9,8,8
V,4,8,6,6,1,7,8,4,3,7,14,8,3,9,0,8
V,2,1,4,2,1,7,12,3,3,7,11,8,2,10,1,8
T,10,15,9,8,5,6,10,3,7,12,7,6,3,9,6,5
L,1,3,2,1,1,6,4,0,6,8,3,10,0,7,1,8
S,6,9,7,6,4,8,7,4,8,11,6,8,2,9,5,8
Z,4,8,6,6,3,7,8,2,10,12,6,8,1,9,6,8
K,4,6,4,4,1,4,7,9,1,7,6,11,3,8,2,11
S,5,12,5,6,3,8,6,4,5,13,6,8,2,9,3,7
L,2,7,3,5,3,4,4,4,6,3,1,7,1,6,1,6
G,4,6,6,4,5,7,9,6,3,6,6,10,4,7,7,7
M,5,4,6,6,3,7,7,12,2,7,9,8,9,6,0,8
A,6,13,5,7,3,8,1,2,2,9,4,12,3,5,4,6
C,1,1,2,2,1,6,8,6,7,8,8,12,1,9,4,10
E,3,6,5,4,3,10,6,2,7,11,4,9,2,8,5,12
U,8,10,9,8,5,3,9,5,7,10,10,9,3,9,2,6
P,3,1,4,3,2,5,10,3,5,9,8,4,3,10,3,7
J,2,8,2,6,1,14,3,5,4,13,3,9,0,7,0,8
A,2,7,5,5,4,8,5,2,3,7,2,6,2,5,3,7
G,5,11,6,8,5,5,6,6,6,6,6,11,2,9,4,8
T,3,3,4,4,2,7,12,3,6,7,11,8,2,11,1,8
K,5,9,6,7,4,6,7,4,8,7,6,9,3,8,5,9
B,1,3,3,1,2,8,7,2,5,10,5,7,1,8,4,9
J,2,10,3,8,1,11,3,10,3,13,7,13,1,6,0,8
X,4,8,6,7,6,9,8,2,5,7,5,6,3,8,7,8
D,3,8,4,6,4,8,8,6,5,9,6,4,3,8,3,6
Z,2,2,3,3,2,7,7,5,9,6,6,8,1,8,7,8
S,6,9,7,6,4,6,7,4,7,11,9,9,3,9,5,4
U,5,10,6,8,3,7,4,15,6,7,14,8,3,9,0,8
O,4,7,5,5,2,7,8,8,8,7,7,7,3,8,4,8
K,4,5,7,4,4,6,7,1,7,10,6,9,3,8,3,7
X,4,10,5,7,2,7,7,5,4,7,6,8,3,8,4,8
B,3,5,5,3,3,8,7,2,6,10,5,7,2,8,4,10
U,5,9,7,6,4,6,8,6,7,7,10,10,3,9,1,8
B,4,9,6,6,8,9,6,4,4,7,7,8,8,8,8,7
P,2,3,3,2,2,5,10,3,4,10,8,4,1,9,3,7
B,7,15,7,8,8,6,8,3,6,9,6,7,8,5,8,6
S,5,9,6,7,3,7,5,6,10,5,6,10,0,9,9,8
O,7,12,5,7,3,8,7,6,5,9,4,7,5,9,5,8
W,3,3,4,2,2,9,11,3,2,5,9,8,6,11,0,8
W,4,3,6,5,3,5,8,5,1,7,8,8,8,9,0,8
M,8,10,12,7,9,10,6,2,5,9,4,6,13,8,3,9
H,5,8,7,6,6,8,8,7,7,7,6,7,3,8,4,7
V,4,11,6,8,4,5,11,2,3,8,11,9,2,10,1,8
P,7,11,8,8,7,6,9,8,6,8,7,9,3,10,8,9
T,7,9,6,4,2,6,9,3,8,13,6,7,2,8,5,5
S,3,7,5,5,7,8,9,4,4,7,6,7,2,7,8,1
O,5,10,5,8,4,8,6,8,5,10,4,8,3,8,3,8
V,2,6,4,4,1,8,8,4,2,6,13,8,3,10,0,8
O,4,7,5,5,3,8,6,7,4,10,5,9,3,8,3,7
K,4,3,4,5,1,4,8,9,1,7,6,11,3,8,2,11
Z,2,3,2,1,1,7,7,5,8,6,6,8,1,8,6,8
R,4,5,7,4,6,5,9,4,3,6,4,9,8,5,7,9
G,1,0,2,0,0,8,6,5,5,6,6,9,1,7,5,10
F,3,3,3,4,1,1,14,5,3,12,9,5,0,8,2,6
M,2,1,2,2,1,7,6,10,0,7,8,8,6,6,0,8
K,5,9,6,7,6,5,6,5,7,6,6,13,5,7,8,9
T,3,6,4,4,3,6,11,3,5,11,8,5,3,12,2,5
S,5,5,6,8,3,7,6,6,9,5,6,10,0,9,9,8
H,2,4,4,3,3,7,7,3,5,10,6,8,3,8,3,8
F,3,3,3,4,1,1,11,5,7,11,11,9,0,8,2,6
Y,2,3,3,1,1,4,10,2,7,11,10,5,0,10,2,4
T,6,9,5,4,3,7,9,2,6,12,7,6,2,9,4,6
N,3,9,4,6,2,7,7,14,2,5,6,8,5,8,0,8
G,5,11,7,8,5,5,6,6,7,6,5,9,4,9,4,7
P,2,4,4,3,2,8,8,2,5,13,4,4,1,10,2,9
P,8,9,6,5,3,7,10,6,5,14,5,4,4,10,4,7
B,3,8,5,6,4,8,7,7,6,7,6,6,2,8,8,10
W,5,11,8,8,7,10,8,5,1,6,10,7,9,13,1,5
Z,3,7,5,5,4,7,7,2,7,7,6,8,0,9,9,7
S,6,8,7,6,4,7,8,4,8,11,8,7,2,9,5,6
F,6,10,9,8,4,4,14,3,4,13,7,3,1,10,2,6
J,2,6,2,4,1,9,7,0,8,11,5,6,0,7,1,7
O,7,11,9,8,11,8,9,6,2,7,7,8,9,9,6,12
Q,7,8,7,9,6,8,7,6,3,8,8,11,3,8,6,8
Y,7,9,7,6,5,5,9,1,8,10,10,6,3,11,5,3
K,6,8,8,6,4,7,8,1,7,10,5,8,3,8,3,8
Z,2,7,3,5,1,7,7,3,13,9,6,8,0,8,8,8
G,5,11,6,8,3,8,7,8,8,6,6,9,2,7,5,10
B,11,14,8,8,5,9,6,5,6,11,4,9,6,6,7,10
M,5,10,6,8,6,7,6,6,5,7,7,9,8,5,2,9
H,3,9,4,6,4,7,7,12,1,7,6,8,3,8,0,8
A,1,3,2,1,1,11,3,3,2,10,2,9,1,6,1,8
A,5,8,6,6,6,8,9,7,4,6,6,8,3,7,7,5
W,7,9,9,8,11,9,8,6,5,7,5,8,11,10,9,4
L,2,5,3,4,2,4,4,4,7,2,1,6,0,7,1,6
T,5,10,6,8,6,6,6,7,7,7,6,8,4,11,7,7
P,3,4,5,3,2,6,11,5,2,12,5,2,1,10,3,9
J,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8
Y,8,11,8,8,4,4,9,1,9,10,10,5,2,9,4,3
N,4,7,6,5,4,4,9,3,3,9,9,9,6,7,1,7
E,3,5,5,3,2,7,7,2,8,11,5,9,2,8,4,9
W,5,9,5,4,4,9,8,3,3,6,8,6,9,10,2,6
Z,2,4,4,3,2,8,7,2,9,12,5,9,1,8,5,9
V,6,9,6,7,3,2,11,3,3,10,11,8,2,10,1,8
W,4,8,6,6,9,9,7,5,1,7,6,8,10,11,2,7
U,8,10,8,8,7,5,7,5,8,8,6,9,8,8,6,1
L,6,10,5,5,3,9,2,3,3,11,8,11,3,10,4,10
A,3,11,6,8,4,12,3,3,3,10,1,9,2,6,3,8
I,2,10,3,8,4,7,7,0,7,7,6,8,0,8,3,8
V,5,11,8,8,9,8,8,4,2,7,8,8,8,10,6,8
O,6,10,8,8,10,8,8,6,2,7,7,8,10,9,5,8
W,5,10,7,8,9,8,8,5,3,7,9,8,6,8,3,7
M,3,3,5,1,2,5,7,4,4,10,10,10,6,6,2,8
B,4,10,5,7,7,8,8,6,6,7,6,6,6,8,6,10
N,7,12,8,6,4,12,4,5,3,13,0,7,5,7,0,7
P,4,8,5,6,4,5,11,4,6,11,9,4,4,10,4,7
S,3,7,4,5,3,8,8,5,7,5,5,5,0,7,8,8
F,4,9,6,7,4,2,12,3,6,12,11,6,1,10,2,5
Q,4,6,6,9,3,8,7,9,6,6,6,9,3,8,5,9
A,2,4,3,3,1,8,2,2,1,7,2,8,2,7,2,7
B,7,11,9,8,9,9,7,4,6,9,5,6,3,8,6,9
E,5,9,6,7,7,7,7,4,8,7,6,8,6,8,6,10
H,5,9,8,7,5,7,7,3,6,10,6,8,3,8,3,7
W,3,3,3,2,2,5,10,3,2,9,8,7,5,11,1,6
B,3,9,4,6,5,8,7,6,6,7,6,5,2,8,7,9
V,7,8,6,6,4,3,11,1,3,9,10,8,4,9,1,8
Q,5,7,6,9,6,7,10,5,2,5,8,12,3,10,6,8
Q,5,6,6,8,5,8,7,6,3,8,8,11,3,9,6,7
G,5,9,5,7,4,5,7,6,6,10,8,9,2,9,5,9
B,3,6,4,4,4,8,7,3,4,7,6,7,3,8,5,8
F,1,3,3,1,1,8,10,2,5,13,5,4,1,9,1,8
E,2,6,3,4,3,6,7,6,8,6,5,10,2,8,5,9
U,5,9,6,7,4,3,8,5,6,9,8,10,3,9,2,5
A,4,8,6,6,6,8,9,7,5,6,6,8,3,7,7,4
Z,4,8,5,6,2,7,7,4,14,9,6,8,0,8,8,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
F,4,11,4,8,2,1,14,5,3,12,10,5,0,8,3,6
T,4,8,5,6,3,6,11,3,7,9,11,7,5,12,1,7
N,4,4,4,6,2,7,7,14,2,4,6,8,6,8,0,8
V,4,9,5,7,2,5,9,5,2,8,13,8,3,10,0,8
N,3,4,5,3,2,7,9,3,4,10,6,6,5,9,0,7
Y,7,9,7,7,5,6,8,1,8,8,9,5,4,11,6,4
N,4,8,5,6,2,7,7,14,2,4,6,8,6,8,0,8
L,4,9,4,4,2,10,4,3,3,11,6,10,3,10,4,10
S,4,11,5,8,5,8,8,8,6,7,4,6,2,7,8,8
V,4,10,6,8,3,7,9,4,2,6,13,8,3,10,0,8
K,4,5,4,8,2,3,7,8,2,7,5,11,4,8,2,11
L,2,6,4,4,2,9,4,1,7,10,3,10,0,7,2,10
B,5,10,5,8,4,6,8,10,7,7,5,7,2,8,9,10
U,4,6,6,4,3,5,8,7,8,9,10,10,3,9,1,8
O,4,9,6,7,8,8,8,5,1,7,7,8,8,9,4,9
E,7,10,5,5,3,8,6,4,7,10,6,9,2,10,8,8
J,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8
M,8,10,8,5,4,5,9,5,5,4,4,11,9,11,2,7
G,3,9,4,7,2,7,6,8,8,6,5,10,2,8,6,11
B,4,2,4,3,4,7,7,5,6,6,6,6,2,8,6,9
S,3,7,4,5,3,8,8,5,7,5,6,8,0,8,8,8
C,5,8,6,6,3,4,8,5,7,11,10,12,2,9,3,7
K,4,4,4,6,2,3,7,8,2,7,6,11,4,8,2,11
T,9,15,8,8,3,7,8,3,9,13,5,6,2,8,5,5
A,2,4,4,3,2,8,2,2,2,7,2,8,2,6,2,7
W,4,5,5,3,3,4,10,2,2,10,9,8,6,11,1,7
Y,6,7,6,5,3,2,11,4,5,12,12,7,2,11,2,6
O,3,8,5,6,2,7,8,8,7,7,7,8,3,8,4,8
O,4,9,5,6,2,8,8,9,7,6,7,9,3,8,4,8
X,2,2,4,3,2,8,7,3,9,6,6,8,2,8,6,8
H,2,2,3,3,2,8,7,6,6,7,6,7,3,8,3,7
X,2,3,4,2,1,6,8,1,8,10,8,9,2,8,3,7
X,2,3,3,1,1,8,7,2,8,10,5,7,2,8,3,8
H,2,1,2,2,2,7,7,6,5,7,6,8,3,8,3,8
B,6,11,8,8,12,8,7,5,4,7,7,7,8,10,10,10
R,3,7,5,5,5,6,7,3,3,6,6,9,5,9,7,5
V,2,2,4,3,1,7,12,2,3,6,11,9,2,11,1,8
E,4,10,5,8,5,7,7,6,9,7,6,10,3,8,6,8
Y,3,5,4,8,6,9,10,3,3,6,8,9,3,12,6,6
W,10,10,9,5,4,5,9,3,2,7,10,8,10,11,1,6
W,9,12,9,6,5,4,7,2,4,7,10,8,10,10,2,5
O,6,9,8,8,7,7,5,4,5,9,4,8,3,7,5,6
D,4,4,5,6,3,5,7,10,9,7,6,5,3,8,4,8
N,2,4,4,3,2,7,8,3,4,10,6,7,5,8,1,7
W,4,3,4,2,2,4,10,2,2,9,9,7,5,11,1,6
V,3,7,4,5,2,8,9,3,1,6,12,8,2,10,0,8
O,5,10,6,8,5,8,6,9,4,7,5,8,3,8,3,8
W,5,11,8,8,4,6,8,5,2,7,8,8,9,9,0,8
S,4,5,6,5,6,8,7,5,5,7,6,7,5,8,9,10
B,5,11,5,8,5,7,9,10,7,8,5,6,2,8,10,11
X,6,11,9,8,4,4,9,3,9,11,12,10,3,9,4,5
Q,4,5,5,7,4,9,9,6,3,4,8,11,3,9,6,10
Q,3,6,4,5,2,8,5,8,7,7,4,8,3,8,4,8
B,2,0,2,1,1,7,7,7,5,7,6,7,1,8,7,8
H,9,13,8,7,5,5,8,5,4,9,10,10,7,11,5,9
C,3,5,4,3,1,4,9,5,7,12,10,11,1,9,2,7
J,7,11,6,8,4,8,9,3,3,13,4,5,2,9,8,9
E,2,1,2,2,1,4,7,5,8,7,6,13,0,8,7,9
Q,5,8,6,8,3,8,7,8,6,6,6,8,3,8,5,9
J,5,6,4,9,3,10,5,2,6,9,5,7,3,8,4,11
W,6,10,9,8,8,6,12,2,2,7,8,8,7,12,1,8
U,3,7,4,5,1,8,5,13,5,7,13,8,3,9,0,8
F,5,11,6,8,5,4,10,5,6,11,11,6,2,10,3,5
G,3,7,4,5,2,7,6,8,9,8,4,12,1,9,5,10
D,7,10,6,6,4,8,5,5,5,11,4,7,5,6,6,10
N,5,10,7,7,8,7,8,3,5,7,6,7,7,8,8,2
G,6,11,6,6,3,7,7,6,5,9,6,6,4,7,5,6
A,3,8,4,6,3,6,4,2,0,6,2,8,2,6,1,7
H,6,8,9,6,5,10,7,4,6,11,2,6,4,9,4,10
O,2,3,3,2,2,8,7,6,4,9,5,8,2,8,2,8
X,4,9,5,4,3,9,6,2,7,11,3,7,3,8,4,9
P,6,11,6,8,3,4,14,8,1,11,6,3,1,10,4,8
A,3,5,6,3,2,9,2,2,2,8,2,10,3,7,2,8
B,4,9,4,7,6,7,9,8,6,7,5,7,2,7,7,10
R,3,5,5,3,3,8,8,4,5,9,5,6,3,7,4,9
C,5,11,7,8,8,6,7,4,4,6,7,10,7,8,7,7
Z,5,9,7,7,5,9,11,6,5,6,5,8,3,8,10,7
B,5,10,7,8,8,8,7,5,7,7,6,5,2,8,6,10
M,6,4,6,7,4,7,7,13,2,7,9,8,9,6,0,8
G,3,5,4,4,2,6,6,5,5,9,7,10,2,8,4,10
X,2,2,4,4,2,7,7,3,9,6,6,8,2,8,6,8
M,6,11,9,8,12,10,5,3,2,9,4,8,11,8,4,6
Z,1,0,2,1,0,7,7,3,11,8,6,8,0,8,6,8
R,10,14,8,8,5,8,8,6,5,9,4,9,7,5,6,11
M,7,11,9,8,10,7,10,7,5,7,7,9,8,10,7,12
H,6,10,8,8,11,8,9,5,3,6,7,7,8,7,11,9
W,7,8,7,6,5,2,12,2,2,10,10,8,6,11,1,7
N,4,6,6,4,4,8,8,6,6,6,5,4,6,10,3,5
V,3,4,4,3,1,5,12,3,3,9,11,7,2,10,1,8
J,1,2,2,3,1,10,6,2,6,12,4,8,1,6,1,7
T,4,11,6,9,6,9,11,3,6,6,11,8,2,12,1,8
N,6,9,8,7,5,6,10,2,4,9,8,8,5,8,1,8
B,4,6,5,4,5,8,7,4,5,7,5,7,3,8,6,8
V,3,9,5,7,1,10,8,4,3,5,14,8,3,10,0,8
C,3,8,4,6,1,6,7,7,9,8,6,13,1,9,4,9
A,2,1,3,2,1,9,3,2,2,8,2,8,2,6,2,8
F,10,15,9,8,6,9,7,2,7,10,6,8,4,7,7,8
O,5,8,6,6,4,7,6,8,5,7,5,7,3,9,3,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
L,2,6,3,4,2,4,5,1,9,3,2,8,0,7,2,5
E,4,6,6,4,3,7,8,1,9,11,6,8,2,8,4,8
A,4,9,6,6,2,7,4,3,1,6,1,8,3,7,2,7
O,6,8,8,6,9,7,6,5,2,7,5,7,11,9,7,11
Z,7,13,7,7,4,8,5,2,8,11,5,9,3,9,5,9
E,4,11,4,8,3,3,8,6,10,7,6,15,0,8,7,7
G,5,10,6,8,4,7,6,7,7,11,6,12,3,10,5,8
Y,3,8,5,5,1,8,12,2,3,6,12,8,1,10,0,8
M,6,6,8,5,8,8,9,4,5,6,6,6,9,6,6,4
J,5,9,4,12,4,7,8,3,3,13,5,5,3,8,7,10
G,1,3,2,1,1,7,7,5,4,9,7,9,2,8,3,10
E,4,9,6,6,4,5,8,4,8,11,9,9,2,9,5,6
O,5,7,6,6,5,8,4,4,4,9,3,8,3,7,5,7
S,7,10,8,7,4,8,8,5,10,11,2,8,2,5,5,10
J,2,2,4,4,2,10,6,2,6,12,3,8,1,6,1,7
C,5,7,5,5,2,5,9,6,8,12,9,10,2,10,3,7
D,4,6,6,4,4,9,7,4,6,10,4,5,3,8,3,8
W,4,7,6,5,3,4,8,5,1,7,9,8,8,9,0,8
G,7,11,7,8,6,6,6,6,6,10,6,14,5,8,6,7
U,5,8,5,6,3,3,10,5,6,13,12,9,3,9,1,7
A,2,7,3,4,1,7,6,3,1,7,0,8,2,7,1,8
Y,5,11,7,8,4,7,10,2,7,6,12,8,2,11,2,8
T,3,8,4,5,1,10,15,1,6,4,11,9,0,8,0,8
S,4,9,5,6,4,9,8,5,9,6,6,5,0,7,8,8
S,1,0,2,1,1,8,7,4,7,5,6,7,0,8,7,8
P,4,7,6,10,9,8,9,4,0,8,7,6,5,11,5,7
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
H,4,9,4,7,4,7,9,13,2,7,4,8,3,8,0,8
X,3,2,5,3,2,8,7,3,9,6,6,8,3,8,6,8
K,5,7,8,5,5,7,6,1,7,10,6,10,4,7,4,9
M,5,9,6,5,3,11,3,2,2,10,3,9,7,2,1,9
M,2,1,3,1,2,8,6,6,4,7,7,8,6,5,1,7
M,1,0,2,0,0,8,6,9,0,7,8,8,5,6,0,8
N,5,10,7,7,5,6,9,6,5,7,7,8,6,9,2,6
P,5,10,7,7,6,7,7,8,4,7,6,7,3,9,8,9
W,3,3,4,2,2,5,10,3,2,9,9,7,5,11,1,7
N,6,8,9,6,4,7,9,3,5,10,7,7,6,8,1,8
J,5,10,7,8,7,9,10,4,4,9,4,6,4,8,6,4
Y,6,11,9,8,5,9,10,0,8,4,11,7,1,10,2,9
Z,3,9,4,7,3,7,8,3,12,8,6,8,0,8,7,7
Y,6,8,7,10,8,10,12,5,4,6,7,7,5,11,8,5
X,7,10,8,6,4,5,9,3,7,12,9,8,4,9,3,6
D,6,9,8,7,5,9,6,4,7,10,5,6,3,8,3,9
L,4,11,4,8,3,0,2,4,6,1,0,8,0,8,0,8
Z,3,8,4,6,4,8,6,2,7,8,6,8,0,9,8,8
W,4,4,5,3,3,5,11,3,2,9,8,7,6,11,2,6
I,0,3,1,2,0,7,7,1,6,13,6,8,0,8,0,7
B,2,0,2,1,1,7,8,7,5,7,5,7,1,8,7,8
D,4,5,6,4,4,5,7,6,7,7,6,8,4,7,5,5
M,4,5,5,8,4,8,7,12,2,6,9,8,8,6,0,8
L,3,8,4,6,3,5,5,2,8,6,2,10,0,7,3,7
O,5,10,4,5,3,6,7,6,3,9,7,9,5,10,5,8
L,4,11,6,8,4,6,5,1,9,7,2,10,0,7,3,8
E,4,8,4,6,2,3,7,6,11,7,7,14,0,8,7,7
T,5,11,5,8,3,7,10,2,9,11,9,4,1,11,3,5
A,2,3,3,2,1,10,2,3,1,10,2,9,2,6,2,8
V,7,12,6,6,3,8,10,5,4,7,10,5,5,12,3,7
V,3,2,6,4,2,7,12,2,3,7,11,9,3,10,1,8
N,3,4,5,3,2,7,9,3,5,10,7,7,5,8,1,8
B,8,13,7,8,7,9,7,4,5,9,5,7,7,6,9,8
P,4,5,5,4,3,5,10,4,5,10,8,3,1,10,4,6
Z,2,4,4,3,1,7,8,2,9,11,6,8,1,8,5,7
K,1,0,1,0,0,5,7,7,0,7,6,10,2,8,2,11
X,4,10,7,8,5,12,6,2,7,10,1,6,2,7,3,10
S,4,7,5,5,3,6,8,3,6,10,8,8,2,8,5,5
O,4,9,5,7,4,7,7,8,5,7,6,8,3,8,3,8
G,2,4,3,3,2,6,8,5,5,9,8,9,2,8,4,9
A,3,9,5,7,4,9,3,1,2,7,2,8,2,6,4,7
P,5,5,6,7,3,4,12,9,2,10,6,4,1,10,4,8
O,9,13,6,7,4,6,5,6,4,10,6,10,6,8,5,7
B,5,10,7,7,8,8,7,6,6,6,6,6,2,8,6,10
R,6,9,6,4,4,6,8,3,5,7,4,10,5,8,6,7
W,5,8,7,6,5,4,11,2,3,8,9,9,8,10,1,8
L,3,9,5,7,2,7,3,2,8,7,2,9,1,6,2,8
L,3,6,4,4,2,5,5,1,9,6,2,10,0,7,2,7
C,3,6,4,5,4,5,6,3,5,7,6,11,4,10,6,9
P,3,5,5,3,2,8,9,3,4,12,4,3,1,10,3,8
D,3,3,3,5,2,5,7,10,7,7,6,5,3,8,3,8
A,3,11,5,8,2,9,6,3,1,8,0,8,2,7,2,8
N,3,6,5,4,3,5,9,6,4,8,7,9,5,9,1,7
T,4,5,5,5,5,7,8,4,7,7,6,9,3,7,7,6
Y,3,5,5,8,7,10,10,4,2,4,8,9,4,13,6,8
I,1,7,2,5,2,7,7,0,7,7,6,8,0,8,2,8
V,5,10,5,8,3,3,11,3,4,10,12,8,2,10,1,8
U,3,2,4,4,2,7,8,6,6,6,9,9,3,9,1,8
C,4,8,4,6,2,5,7,6,7,11,9,14,1,9,3,8
R,8,12,8,6,6,7,8,3,6,9,3,8,6,6,6,6
C,3,5,4,4,1,3,9,5,7,12,10,11,1,9,2,7
P,3,5,4,8,6,8,9,3,1,8,7,6,4,10,5,8
E,4,8,4,6,4,3,8,5,9,7,6,14,0,8,6,8
Q,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8
V,2,1,4,3,1,7,12,2,3,7,11,9,2,10,1,8
T,3,6,4,4,3,8,11,2,6,6,11,8,2,11,1,8
E,4,6,6,4,6,7,8,3,5,6,7,11,3,10,7,8
M,6,11,9,8,12,9,7,3,3,8,4,7,12,5,4,5
V,8,12,6,6,3,8,11,4,5,5,10,6,4,10,3,5
R,4,7,5,5,4,10,7,3,6,10,3,7,3,7,3,10
J,1,5,3,4,1,9,6,2,6,14,5,10,0,7,0,7
Z,1,1,2,3,1,8,7,5,8,6,6,7,1,8,7,8
B,6,12,6,7,5,9,7,3,5,9,5,7,6,7,8,9
R,4,8,6,6,5,9,7,4,5,9,3,8,3,7,4,11
V,2,1,4,3,1,5,12,3,3,9,11,8,2,10,1,8
K,1,1,1,1,0,4,7,6,3,7,6,11,3,8,2,11
A,3,3,5,5,2,7,5,3,1,6,1,8,2,7,2,7
M,4,4,6,3,4,5,6,4,4,10,10,10,6,5,2,7
Y,2,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8
X,7,10,9,8,8,8,6,3,5,6,7,7,4,10,11,8
A,3,9,5,6,2,9,6,3,1,8,0,8,2,7,1,8
R,4,7,4,5,4,6,10,7,3,7,4,9,2,7,5,11
J,3,7,4,5,2,10,7,1,6,13,3,7,0,8,0,8
A,4,10,7,8,4,13,2,4,4,12,1,9,3,7,4,10
F,1,0,1,0,0,3,12,4,2,11,9,6,0,8,2,7
Y,2,2,4,4,2,7,10,1,7,7,11,8,1,11,2,8
X,4,3,5,5,1,7,7,5,4,7,6,8,3,8,4,8
K,3,6,4,4,4,6,7,3,6,6,5,9,3,8,4,9
D,3,7,4,5,4,7,7,7,7,7,7,5,3,8,3,7
Y,5,7,6,5,5,8,5,6,5,5,8,8,3,9,9,6
F,3,4,4,6,2,1,12,5,6,11,11,9,0,8,2,6
X,5,8,7,6,3,7,8,1,8,10,6,8,3,8,4,7
L,5,9,7,7,5,8,4,1,8,9,2,10,1,6,3,9
W,8,11,8,8,9,6,10,4,3,9,6,6,11,10,4,5
A,5,8,9,6,6,7,5,2,4,6,1,6,5,7,5,6
X,3,6,4,4,3,8,5,2,5,6,7,8,2,9,8,9
W,6,9,8,7,4,10,8,5,2,6,8,8,9,9,0,8
A,2,3,3,2,1,8,2,2,1,8,2,8,2,6,3,8
I,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7
U,4,9,6,7,8,8,6,4,4,6,7,8,8,9,5,6
L,2,2,3,4,2,5,4,5,7,2,2,5,1,6,1,6
X,4,6,7,4,3,5,9,3,8,11,12,9,3,9,4,5
D,1,0,1,1,1,6,7,8,5,6,6,6,2,8,2,8
T,3,4,3,3,2,5,11,2,7,11,9,5,1,11,2,5
A,2,4,4,3,1,6,3,2,2,5,2,8,2,6,2,6
D,5,11,7,9,11,9,8,5,5,7,6,6,5,7,12,6
E,2,3,4,2,2,7,7,2,8,11,6,8,1,8,4,8
W,4,5,5,3,3,3,10,2,2,10,10,8,6,11,1,7
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
C,2,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10
G,2,4,4,3,2,6,7,6,6,6,6,10,2,8,4,9
H,6,8,8,6,6,10,6,3,6,10,3,7,6,7,5,11
A,4,9,7,7,5,10,3,1,2,8,3,9,4,5,3,7
J,1,1,2,2,1,10,6,2,6,12,4,8,1,7,1,7
F,6,10,8,7,4,6,11,2,6,14,6,3,1,10,2,7
Y,2,3,2,1,1,5,10,2,6,10,9,6,1,11,2,5
U,6,9,8,7,5,4,9,6,7,7,10,11,3,9,1,8
W,5,4,6,3,3,5,11,3,2,9,8,7,7,12,1,6
Z,4,9,5,7,3,7,7,3,12,9,6,8,0,8,8,8
E,5,11,7,8,5,7,7,2,9,11,6,9,3,8,5,8
B,5,10,7,7,6,9,6,4,7,9,5,7,2,8,6,10
V,6,9,8,8,10,7,6,5,5,7,6,8,7,10,8,9
B,4,3,5,5,3,6,9,8,7,7,6,6,2,8,9,10
V,7,13,7,7,4,9,8,4,4,8,8,5,6,13,3,7
L,4,4,4,6,1,0,0,6,6,0,1,5,0,8,0,8
K,4,9,4,6,2,3,7,7,2,7,6,11,3,8,3,10
U,5,8,5,6,2,7,4,14,6,7,14,8,3,9,0,8
P,1,3,3,1,1,7,10,3,3,12,5,4,1,9,2,8
N,3,7,3,5,2,7,7,13,2,5,6,8,5,8,0,8
D,4,8,5,6,5,7,7,5,7,7,6,5,6,8,3,7
Q,3,7,4,6,2,10,9,8,6,4,8,10,3,8,5,9
M,3,3,5,1,2,9,6,3,4,9,5,8,7,5,1,8
Y,3,4,5,6,1,7,10,1,3,7,12,8,1,11,0,8
Z,4,9,6,7,7,8,7,2,8,7,6,8,0,8,9,8
Z,1,3,3,2,1,7,8,2,9,11,6,8,1,8,5,7
Q,6,12,5,7,3,7,6,4,9,10,5,9,3,7,9,8
B,4,7,5,5,4,8,6,4,6,9,5,6,2,8,6,10
L,3,7,5,6,4,6,8,4,6,7,6,9,2,8,8,9
P,2,6,2,4,2,4,11,8,1,10,6,4,1,10,3,8
H,3,3,6,2,3,8,7,3,6,10,5,8,3,8,3,8
K,1,1,1,1,0,4,6,6,2,7,6,10,3,8,2,10
A,9,13,7,7,4,8,2,3,2,8,4,12,5,4,5,6
G,2,3,3,5,2,8,8,8,6,5,7,9,2,7,5,11
I,3,9,5,6,2,7,9,0,8,14,6,5,0,9,2,7
Y,5,8,5,6,2,3,11,2,7,12,11,6,1,10,2,5
V,2,8,4,6,1,8,8,4,2,7,13,8,3,10,0,8
O,2,4,2,2,1,7,7,6,4,9,6,8,2,8,2,7
J,2,7,3,5,2,8,7,1,6,11,5,8,1,6,1,6
A,3,7,5,4,2,7,5,3,0,6,1,8,2,7,2,7
M,6,9,7,4,4,5,9,4,5,5,4,10,8,9,2,8
W,6,10,8,8,9,7,8,6,3,7,8,8,6,8,4,8
C,3,7,4,5,2,6,6,6,10,8,5,12,1,9,4,8
F,3,7,3,5,2,1,12,4,5,12,11,7,0,8,1,6
K,4,6,6,6,5,6,7,2,4,6,4,9,4,5,8,8
C,4,5,5,5,5,5,8,3,4,7,6,11,4,10,7,9
T,5,8,7,6,7,7,8,4,7,8,6,9,5,8,5,6
P,6,8,8,6,4,9,8,4,7,12,4,5,4,8,5,8
B,3,8,3,6,4,6,7,8,5,7,6,7,2,8,7,8
U,2,1,3,2,1,7,8,6,6,7,9,9,3,10,0,9
X,2,5,4,4,2,8,7,3,9,6,6,8,2,8,6,9
D,4,9,4,4,2,13,3,3,4,12,1,8,3,7,1,10
T,6,10,5,5,2,5,9,2,7,12,7,5,2,10,4,4
K,4,8,4,6,3,3,6,6,3,7,7,11,3,8,3,11
P,5,7,7,5,4,7,11,4,5,13,5,3,1,10,3,8
P,2,4,4,3,2,7,9,4,3,12,5,3,1,10,2,8
L,4,9,6,7,3,8,4,1,8,9,2,10,0,6,2,9
B,2,4,4,3,3,8,7,3,5,10,6,6,2,8,5,8
N,5,5,7,5,6,8,6,5,5,8,5,7,7,9,5,4
F,2,7,3,4,1,1,12,5,4,11,10,7,0,8,3,7
A,2,3,3,2,1,11,2,2,1,9,2,9,1,6,2,8
E,7,15,5,8,3,7,8,5,8,10,6,10,1,9,7,8
P,4,6,5,4,3,7,11,5,3,12,5,2,1,10,3,8
J,5,10,7,8,3,10,6,2,8,14,3,8,0,7,0,8
P,5,11,6,8,6,6,6,6,4,8,6,9,5,9,7,10
X,2,3,3,4,1,7,7,4,4,7,6,8,3,8,4,8
S,6,8,8,6,8,8,6,4,3,9,5,8,5,9,11,10
P,4,9,6,7,5,8,9,3,4,12,5,4,2,9,3,8
F,5,11,7,8,5,4,11,5,4,13,9,5,2,10,2,5
B,5,11,7,8,7,8,7,7,7,7,6,5,3,8,8,10
Z,4,8,6,6,4,7,9,2,9,11,7,6,1,8,6,6
P,3,5,3,4,2,6,9,5,4,9,7,4,1,10,3,7
X,5,8,8,6,4,6,8,1,8,10,9,9,3,8,3,7
E,5,10,5,7,3,3,7,6,11,7,6,14,0,8,8,7
R,4,6,6,4,4,6,8,5,6,7,5,7,3,7,5,8
E,5,11,7,9,9,7,7,3,5,6,7,10,5,9,8,8
O,7,10,9,7,11,7,8,5,2,7,6,8,12,11,9,12
X,6,10,9,8,5,8,7,0,8,10,5,8,3,8,3,8
C,3,5,4,4,2,3,9,4,7,11,10,12,1,9,2,6
K,4,7,4,5,3,3,8,7,2,7,6,11,3,8,2,11
P,5,7,7,5,3,7,11,3,6,14,5,2,0,10,3,8
N,6,9,9,6,5,7,9,2,5,10,6,6,6,9,1,7
C,4,8,4,6,2,4,9,6,7,12,10,12,2,9,3,7
B,6,11,8,8,8,8,7,7,7,7,6,6,3,8,8,11
N,7,11,9,8,7,7,9,6,6,7,6,6,6,9,2,6
X,3,7,5,5,4,8,6,3,5,6,7,8,2,9,8,9
I,7,10,9,8,5,6,8,2,8,7,6,10,0,7,4,8
N,4,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8
V,5,11,7,8,9,7,9,4,2,8,8,8,5,9,7,6
M,6,9,7,4,3,4,8,5,5,4,2,11,8,9,2,8
K,4,7,6,5,4,7,6,1,6,9,5,9,3,8,3,8
P,4,2,5,4,3,5,10,5,4,10,8,4,1,10,3,7
G,3,7,4,5,3,7,6,6,6,6,5,9,2,9,6,11
W,4,6,6,4,5,7,7,6,3,8,7,7,6,8,4,10
O,6,10,6,7,6,8,7,8,4,8,5,6,5,8,5,9
V,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8
B,7,11,10,8,7,10,6,3,7,10,4,7,2,8,7,11
A,2,3,4,4,1,9,5,3,1,8,1,8,2,7,2,8
G,7,10,9,8,9,7,5,6,3,7,6,10,5,8,7,8
U,5,4,5,6,2,7,4,14,5,7,14,8,3,9,0,8
T,5,8,5,6,3,7,10,2,9,11,9,5,1,11,3,4
P,4,8,4,5,2,3,14,7,1,11,6,3,0,10,4,8
J,3,8,4,6,1,11,3,11,3,12,9,14,1,6,0,8
F,7,10,9,8,9,7,8,6,4,8,6,7,4,11,8,11
E,3,8,3,6,3,3,7,5,8,7,6,13,0,8,6,9
O,5,10,7,8,8,8,10,6,3,8,7,6,10,10,6,10
K,4,5,6,4,3,4,8,2,7,10,10,11,3,8,3,6
M,4,6,5,4,4,6,6,6,5,6,8,10,7,5,2,9
F,4,7,5,5,4,5,9,5,7,10,10,6,2,9,3,5
Z,6,10,8,8,5,8,6,2,9,12,4,11,3,7,8,9
F,2,3,4,1,1,6,10,3,5,13,7,5,1,9,1,7
G,3,7,4,5,3,8,7,7,5,6,7,9,2,7,5,11
V,5,9,8,6,8,8,7,4,2,7,8,8,5,10,4,8
H,2,3,3,2,2,9,7,6,6,7,6,6,3,9,2,7
J,6,9,9,7,6,8,5,3,6,8,6,8,4,7,4,7
O,5,5,7,8,3,8,5,8,9,7,4,8,3,8,4,8
D,8,12,8,6,5,7,5,4,7,8,4,6,5,7,6,9
J,1,2,2,3,1,10,6,1,7,12,3,8,0,7,1,8
T,4,11,5,8,4,6,11,1,8,8,11,8,1,11,1,7
A,1,0,2,1,0,7,4,2,0,7,2,8,2,7,1,8
S,4,7,6,5,3,8,7,4,8,11,5,7,2,8,5,8
L,5,10,5,7,2,0,1,5,6,0,0,7,0,8,0,8
J,4,8,5,6,3,8,4,6,3,14,8,14,1,6,1,6
G,4,7,6,5,3,6,7,5,5,6,6,8,3,8,4,8
P,3,3,4,4,2,4,13,8,1,11,6,3,1,10,4,8
S,1,0,2,1,1,8,7,4,6,5,6,7,0,8,7,8
D,2,5,4,4,3,9,6,4,6,10,4,6,2,8,3,8
R,2,5,4,3,3,9,7,4,5,9,4,7,3,7,4,9
T,2,4,3,2,2,7,12,3,6,7,11,8,2,11,1,8
O,1,3,2,2,1,8,7,7,4,7,6,8,2,8,2,8
Q,4,6,5,5,4,8,7,7,5,6,7,8,2,8,4,9
L,3,7,4,5,1,0,0,6,6,0,1,5,0,8,0,8
P,2,3,4,2,1,7,9,4,3,11,4,4,1,9,2,8
W,6,10,6,5,4,2,9,2,3,10,11,9,8,10,1,6
D,3,8,5,6,5,7,7,6,5,6,5,5,3,8,2,7
P,3,4,4,3,2,5,10,4,4,10,8,4,1,10,4,7
H,3,4,4,3,3,7,7,6,6,7,6,8,3,8,3,8
R,3,5,5,4,3,9,7,4,5,9,4,6,3,7,4,10
U,9,10,9,8,7,5,7,5,8,8,5,9,7,9,6,2
T,5,8,6,7,6,6,8,4,8,7,7,8,3,10,7,7
I,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8
W,4,4,6,3,3,8,11,2,2,6,9,8,7,11,0,7
L,4,7,5,5,3,7,4,1,8,8,2,10,0,7,2,8
P,4,6,5,4,3,5,11,5,5,11,8,3,0,10,4,6
N,3,3,5,2,2,7,9,3,4,10,6,6,5,8,1,7
O,3,4,4,3,2,7,7,7,4,9,7,8,2,8,3,8
A,4,9,6,8,6,7,8,2,6,7,8,9,5,11,3,7
A,2,4,4,3,2,11,2,3,1,9,2,9,2,6,2,9
G,4,10,6,7,4,7,7,7,6,5,7,8,2,7,4,8
D,4,7,4,5,4,6,7,9,7,6,4,6,2,8,3,8
Z,3,8,4,6,4,6,8,5,9,7,7,10,2,9,7,7
F,4,5,6,6,5,7,9,4,5,8,6,7,4,9,8,7
Q,8,13,7,7,5,9,5,4,7,11,5,8,4,8,10,11
C,4,11,6,8,2,6,6,7,10,8,6,14,1,9,4,9
H,3,4,4,3,3,8,7,6,6,7,6,7,3,8,3,6
H,4,7,6,9,7,8,10,4,1,8,6,6,4,10,9,5
L,3,8,5,6,3,7,4,0,7,8,3,10,0,7,2,8
T,4,5,6,8,2,7,15,1,6,7,11,8,0,8,0,8
D,3,6,5,4,3,10,6,4,7,11,3,6,3,8,3,9
X,3,3,5,2,2,8,7,1,8,11,5,8,2,8,3,8
Y,2,5,4,4,2,6,10,1,7,8,11,9,1,11,2,7
K,6,9,8,6,5,5,9,2,7,10,8,10,3,8,3,7
P,5,8,6,10,8,9,9,2,3,6,9,6,9,11,7,5
I,2,8,2,6,3,7,7,0,7,7,6,8,0,8,3,8
Y,3,5,5,4,2,7,10,1,7,7,11,8,1,11,2,8
R,4,8,5,6,6,8,6,6,3,8,6,8,4,7,6,11
W,7,8,9,7,10,6,9,6,7,7,7,8,8,9,8,10
L,6,15,6,8,3,7,3,3,5,12,4,13,2,7,6,8
J,3,8,4,6,2,8,5,4,8,13,5,12,1,6,1,7
D,5,10,7,7,5,9,7,5,7,10,4,5,3,8,3,8
H,3,7,3,5,2,7,7,14,1,7,7,8,3,8,0,8
P,6,7,8,9,9,6,8,4,3,9,8,6,6,11,6,6
V,6,8,5,6,3,4,11,3,4,9,11,7,2,10,1,8
B,5,11,6,8,8,8,7,4,6,7,6,6,6,8,6,10
E,5,7,7,6,7,7,6,6,4,7,6,9,8,10,10,10
Y,1,0,2,0,0,8,10,3,1,6,12,8,1,11,0,8
O,4,6,5,4,6,8,6,6,2,7,6,8,7,9,3,8
R,1,0,2,0,1,6,10,7,2,7,5,8,2,7,4,9
G,5,5,6,8,3,7,6,8,8,6,5,10,2,8,6,11
T,2,10,4,7,1,6,14,0,6,8,11,8,0,8,0,8
P,5,9,5,6,3,4,12,9,2,10,6,4,1,10,4,8
K,4,6,6,4,3,3,9,3,6,10,11,11,3,8,3,6
D,6,11,7,8,4,6,7,11,11,6,5,6,3,8,4,8
I,1,6,0,8,0,7,7,4,4,7,6,8,0,8,0,8
C,3,2,4,4,2,6,8,7,8,9,8,13,1,9,4,10
O,4,9,5,7,2,7,7,9,7,7,6,8,3,8,4,8
F,4,7,6,5,3,5,12,3,6,14,7,4,1,10,1,7
U,9,15,8,8,6,8,6,5,5,7,7,6,7,5,5,4
H,5,9,5,4,3,5,9,3,6,9,8,9,5,7,3,7
Y,4,9,5,7,2,8,11,0,4,6,11,8,0,10,0,8
K,2,3,3,2,2,5,7,4,7,7,6,11,3,8,4,10
M,2,6,3,4,2,7,6,11,1,7,9,8,7,5,0,8
Y,3,5,4,4,2,4,11,2,7,11,10,6,1,11,2,5
K,7,14,7,8,5,7,7,2,6,10,5,8,6,7,4,7
X,7,13,8,7,4,7,8,2,8,11,6,8,4,8,4,7
G,4,8,5,6,3,7,6,7,7,8,5,12,3,10,5,8
Y,7,10,7,7,3,2,12,5,5,12,12,6,2,11,2,6
T,6,15,6,8,4,6,10,2,6,12,7,6,2,8,5,4
F,4,5,4,8,2,1,14,5,4,12,10,6,0,8,2,6
L,2,4,3,3,1,8,3,1,7,9,2,10,0,7,2,9
K,11,14,10,8,4,6,8,3,8,9,7,8,5,8,4,7
P,6,10,8,8,6,8,9,3,5,13,5,3,1,10,3,9
K,6,8,8,6,7,8,7,1,6,9,4,8,4,8,4,8
N,2,4,2,2,2,7,8,5,4,7,6,7,4,8,1,6
Q,4,7,5,9,5,8,6,6,2,8,6,11,3,9,6,9
B,3,6,5,4,4,10,5,3,5,10,4,7,2,8,4,10
T,4,6,6,4,5,7,8,4,5,6,7,9,5,9,5,7
T,5,11,7,8,6,6,7,7,7,6,10,10,5,5,9,6
Q,5,8,6,8,5,8,7,8,6,5,5,8,3,10,5,10
B,4,4,4,6,4,7,5,9,7,6,7,7,2,8,9,10
I,7,14,6,8,4,10,7,2,5,11,5,6,2,9,5,12
D,1,0,2,1,1,6,7,8,6,6,6,6,2,8,3,8
U,3,3,3,4,2,7,4,14,5,7,12,8,3,9,0,8
M,6,10,8,7,9,7,6,7,5,6,5,8,9,8,9,11
J,6,9,4,13,4,7,9,3,3,13,4,5,3,8,6,9
Q,2,3,2,4,2,7,7,5,2,8,8,9,2,9,4,8
T,5,8,7,7,6,5,8,4,9,8,7,9,3,9,7,6
Y,5,7,5,5,3,6,9,1,8,9,9,5,1,9,4,4
D,3,4,5,3,2,10,6,3,7,10,3,6,2,8,3,9
V,3,4,4,3,1,4,12,3,3,9,11,7,2,11,0,8
N,10,13,9,7,4,9,11,5,5,3,6,11,6,10,3,6
J,1,1,1,1,0,13,3,6,4,12,4,11,0,7,0,8
W,6,9,9,7,7,7,12,2,2,7,8,8,7,12,1,8
J,1,0,1,0,0,12,4,6,3,13,5,11,0,7,0,8
B,2,3,4,2,2,8,7,3,5,10,6,6,2,8,4,9
N,6,9,9,8,9,7,9,5,4,7,4,7,7,7,6,5
H,2,6,3,4,2,7,6,12,2,7,8,8,3,9,0,8
C,5,8,5,6,2,4,9,6,8,12,9,12,1,9,3,7
U,4,8,5,6,3,7,5,13,4,7,12,8,3,9,0,8
X,3,3,5,2,2,9,6,2,8,10,3,7,2,7,3,8
U,3,3,4,2,1,7,8,6,8,8,10,7,3,9,1,8
I,4,7,5,5,3,9,5,2,6,6,7,5,0,9,4,7
N,6,9,8,7,5,6,9,6,5,7,7,7,8,8,3,7
S,3,10,4,8,2,8,6,6,9,5,6,10,0,9,9,8
P,3,9,5,6,4,6,9,3,6,10,9,4,4,10,4,7
E,5,9,7,8,8,5,9,4,4,8,7,9,5,11,9,11
Q,5,9,7,7,6,8,5,8,4,6,7,9,4,6,7,9
K,7,10,10,8,7,5,6,1,7,9,8,11,3,8,3,8
N,3,5,6,4,3,7,9,2,5,10,6,6,5,9,1,7
K,5,10,7,8,7,6,6,3,7,6,6,8,7,8,5,9
Q,3,4,4,5,3,8,8,7,3,5,7,10,3,9,5,10
D,5,9,5,6,5,5,7,8,6,5,4,6,3,8,4,9
L,3,4,3,7,1,0,1,5,6,0,0,7,0,8,0,8
K,3,2,4,4,3,6,7,4,7,6,6,11,3,8,5,10
N,3,8,4,6,2,7,7,14,2,5,6,8,6,8,0,8
R,2,1,2,2,2,6,8,4,5,6,5,7,2,7,4,9
S,4,8,6,6,7,8,8,5,3,8,5,7,4,8,10,7
H,6,10,8,7,5,9,6,3,6,10,4,8,4,7,4,8
B,3,5,5,4,3,10,6,2,6,10,4,7,2,8,4,10
L,2,4,3,3,1,6,4,1,8,8,2,10,0,7,2,8
Z,2,5,3,4,2,7,7,5,9,6,6,8,2,8,7,8
M,5,5,8,4,5,8,6,3,5,9,7,8,9,6,2,8
O,7,9,9,8,8,8,4,4,5,10,4,10,5,7,7,6
B,3,4,4,3,3,7,7,4,6,6,6,6,5,8,6,10
I,5,7,6,5,3,7,6,2,7,7,6,8,0,9,4,8
L,5,10,7,8,3,5,3,2,10,6,1,10,0,7,3,6
Q,3,5,4,6,4,9,10,6,2,4,8,11,3,10,5,10
P,6,11,9,8,5,6,12,5,4,12,5,2,1,10,3,9
M,6,8,8,6,6,11,6,2,4,9,3,6,8,6,2,8
R,2,5,4,4,3,7,8,5,5,8,6,7,3,7,5,10
P,2,3,3,2,1,7,10,4,4,12,5,3,1,10,2,8
W,4,8,5,6,4,5,8,4,1,7,9,8,6,11,0,8
E,2,3,3,5,2,3,6,6,10,7,7,14,0,8,7,8
T,6,10,6,7,4,4,13,4,6,12,10,4,2,12,2,4
R,2,1,2,1,1,6,10,8,2,7,5,8,2,7,4,10
D,6,9,8,6,7,9,7,5,6,9,4,6,3,8,3,8
K,6,9,9,8,8,8,6,2,4,7,4,9,7,4,9,12
D,3,7,3,5,3,5,7,8,6,6,5,7,2,8,3,8
U,4,10,4,8,4,7,6,13,4,7,12,8,3,9,0,8
Q,5,7,5,9,6,7,10,4,3,7,10,10,3,10,6,7
X,5,6,6,6,6,7,8,2,5,7,6,8,3,7,7,8
L,4,10,6,7,8,7,8,3,6,5,6,10,5,11,8,8
I,5,9,7,6,4,7,6,2,7,7,6,9,0,9,4,8
F,3,6,4,4,3,5,11,4,4,12,8,5,2,10,2,6
W,4,8,6,6,8,10,9,5,2,6,7,7,8,9,4,5
Z,2,1,3,2,2,7,7,5,9,6,6,8,2,8,7,8
N,2,1,2,1,1,7,9,5,3,7,6,7,4,8,1,7
E,7,10,5,5,2,7,7,4,7,10,6,11,1,9,7,9
H,5,7,8,5,6,5,9,3,6,10,9,9,3,9,3,6
M,5,7,7,5,6,7,6,6,5,7,7,11,10,6,2,9
P,4,8,6,6,4,9,8,2,6,13,5,6,1,10,2,9
E,3,3,4,5,2,3,8,6,11,7,5,14,0,8,7,7
V,2,8,4,6,1,9,8,4,3,5,13,8,3,10,0,8
W,4,6,6,4,3,9,8,4,1,7,8,8,8,9,0,8
V,4,4,6,7,1,9,8,4,3,6,14,8,3,9,0,8
Y,7,9,7,6,3,3,12,5,5,13,11,5,1,11,2,6
S,1,0,2,1,0,8,7,4,7,5,6,8,0,8,7,8
U,2,0,3,1,1,7,5,11,5,7,14,8,3,10,0,8
X,8,12,7,6,4,6,7,2,8,9,6,9,4,5,4,7
O,5,10,6,7,5,7,7,8,4,9,6,8,3,8,3,8
F,3,3,3,5,1,1,14,5,3,12,10,5,0,8,2,6
V,5,7,5,5,2,3,11,3,3,10,11,8,3,10,1,7
Q,7,8,7,10,7,7,8,6,3,7,9,11,4,8,7,8
M,6,9,8,5,4,9,3,3,2,9,3,9,8,2,1,9
A,1,0,2,0,0,7,4,2,0,7,2,8,1,7,1,8
N,5,11,6,8,3,7,7,15,2,4,6,8,6,8,0,8
Z,5,10,6,8,3,7,7,4,15,9,6,8,0,8,8,8
M,9,12,11,7,5,13,2,5,2,12,1,9,7,3,1,9
H,11,13,10,8,5,8,8,4,5,9,6,7,6,9,5,9
X,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8
A,2,4,4,2,1,11,2,3,2,9,2,9,1,6,2,8
G,3,5,4,4,2,6,7,6,6,10,7,11,2,9,4,9
F,2,1,2,3,2,5,10,4,5,10,9,6,1,10,3,7
H,3,4,6,3,3,9,7,3,6,10,3,7,4,6,4,8
M,5,9,6,4,3,12,3,4,2,11,2,9,6,3,1,9
T,7,12,6,6,3,5,11,2,6,11,8,6,2,9,5,3
W,10,15,10,9,7,5,8,2,4,7,9,7,11,10,2,5
E,3,6,4,4,3,7,8,7,10,5,4,9,3,8,6,7
J,3,7,4,5,4,10,5,2,5,8,5,5,3,7,5,7
G,5,9,6,7,8,8,8,5,2,6,6,9,7,9,6,13
H,4,4,7,3,3,8,7,3,6,10,5,8,3,8,3,8
L,7,13,6,7,3,5,5,3,8,10,4,13,2,5,6,7
Y,2,3,4,4,1,9,10,3,2,5,13,8,2,11,0,8
H,1,0,2,0,0,7,7,11,1,7,6,8,2,8,0,8
P,4,5,4,8,2,3,12,9,2,10,6,3,1,10,4,8
Z,5,8,7,6,5,9,8,6,4,7,5,8,3,8,10,7
T,3,4,4,6,1,7,15,1,6,7,11,8,0,8,0,8
D,5,9,5,4,3,12,3,3,5,12,1,8,4,7,2,11
U,1,0,2,0,0,7,6,10,4,7,12,8,2,10,0,8
G,5,11,6,8,8,8,7,5,2,6,6,10,7,8,6,12
E,5,10,8,7,6,6,8,3,8,11,8,9,3,8,5,6
H,8,10,8,5,5,5,9,3,4,10,7,9,5,7,3,6
F,2,3,2,2,1,6,10,4,5,10,9,5,1,10,3,6
J,1,4,2,3,1,10,6,2,5,12,4,9,1,6,1,7
A,6,11,6,6,4,11,2,4,2,11,4,12,5,3,5,11
L,4,6,5,5,4,7,5,5,5,7,6,7,3,8,8,11
Y,2,8,4,5,1,6,11,2,3,9,12,8,1,10,0,8
G,5,9,4,5,3,7,7,3,2,8,6,7,4,10,8,7
M,2,3,3,1,2,6,6,7,4,7,7,10,7,5,2,9
K,2,4,4,3,3,6,7,1,6,10,7,10,3,8,2,8
S,4,8,5,6,3,7,7,5,9,5,6,8,0,8,9,8
E,0,0,1,0,0,5,7,5,7,7,6,12,0,8,6,10
G,4,9,5,6,4,6,7,6,5,9,7,11,2,9,4,10
T,2,4,3,5,1,5,14,1,6,9,11,7,0,8,0,8
V,8,10,7,6,3,8,10,5,5,5,10,7,4,11,3,5
K,3,7,5,5,3,6,7,5,7,6,6,10,3,8,5,9
Z,3,8,4,6,3,8,7,6,10,6,6,8,1,8,8,7
D,7,10,7,5,4,8,4,4,6,8,4,7,4,6,6,10
Q,2,3,3,4,3,8,8,5,2,8,8,9,2,9,5,8
H,3,4,5,3,3,6,9,3,6,10,8,8,3,8,3,7
V,5,11,7,8,5,9,11,3,2,5,10,8,5,10,5,7
G,2,3,3,1,1,7,7,6,5,7,6,9,2,9,4,10
T,8,10,8,8,6,7,10,2,8,11,9,5,3,10,5,4
J,7,11,9,9,5,7,8,3,6,15,6,10,1,6,1,6
Q,2,3,3,4,3,8,7,6,3,8,6,9,2,9,3,9
Y,2,4,3,3,1,6,10,1,7,8,11,8,1,11,2,7
I,2,9,3,7,3,7,7,0,7,7,6,8,0,8,3,8
A,3,8,5,6,5,8,8,6,4,6,6,8,5,7,6,4
T,8,15,7,8,3,6,9,3,8,13,6,6,2,8,5,5
Q,3,5,4,6,4,8,7,7,2,5,5,10,3,9,4,10
N,6,10,8,8,7,6,7,9,5,6,4,6,5,7,4,10
W,4,9,6,7,5,10,10,3,3,5,9,7,7,11,1,8
U,6,9,6,6,4,4,8,5,7,11,10,9,3,9,2,6
R,3,1,4,2,2,7,8,5,5,7,5,6,2,7,4,8
H,4,9,5,7,5,8,8,6,6,7,6,9,3,8,3,8
E,4,9,6,7,6,8,7,6,3,7,6,10,4,8,7,9
Y,2,1,3,1,0,7,11,3,1,7,12,8,1,11,0,8
N,2,4,3,3,2,7,8,5,4,7,7,7,4,8,1,6
N,4,8,6,6,3,7,8,3,4,10,6,7,5,8,1,7
S,5,10,8,7,10,7,5,3,2,7,5,7,3,7,11,3
G,5,5,6,8,3,6,6,8,9,6,6,9,1,8,6,11
O,5,7,7,6,5,7,6,5,6,9,5,9,4,7,5,6
F,3,5,5,6,4,7,10,4,4,8,7,7,3,9,6,5
M,5,11,7,8,9,6,6,5,5,7,7,11,11,6,2,9
K,3,4,6,3,3,7,8,2,7,10,4,8,5,7,4,7
S,6,12,6,7,3,8,6,3,4,13,6,8,2,9,3,8
C,2,3,3,2,1,5,8,5,6,12,9,10,1,10,2,7
F,9,15,8,8,6,7,11,3,5,12,6,3,5,9,9,6
N,4,10,5,7,3,7,7,14,2,4,6,8,6,8,0,8
T,5,9,5,7,4,5,11,2,7,11,10,5,1,12,2,4
K,4,7,6,5,6,8,8,5,5,8,5,7,7,6,6,11
R,4,7,6,5,5,7,7,4,6,7,6,6,3,7,5,9
N,4,3,4,4,3,7,8,5,5,7,6,6,6,9,3,6
K,8,12,8,7,4,9,7,3,7,9,2,5,5,7,4,8
W,2,0,2,1,1,7,8,3,0,7,8,8,6,10,0,8
M,5,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8
F,4,6,6,4,6,7,7,5,3,7,6,9,3,9,8,11
H,5,9,7,7,6,7,7,7,7,7,6,8,3,8,4,8
Z,4,10,4,8,4,7,8,3,12,9,6,8,0,8,7,7
Z,4,10,5,8,3,7,7,4,15,9,6,8,0,8,8,8
H,3,6,5,4,4,5,9,3,5,10,9,9,3,8,3,6
L,4,10,5,8,4,6,4,4,9,2,2,5,1,6,2,5
S,2,7,3,5,3,8,7,5,7,5,6,6,0,8,8,8
N,3,7,4,5,4,9,8,6,5,6,5,4,5,9,2,5
A,2,1,4,2,2,7,2,2,2,7,2,8,2,7,3,7
P,3,5,5,4,2,7,10,4,4,13,5,2,1,10,2,8
C,5,8,6,6,4,7,8,8,6,5,7,12,5,8,4,8
A,3,4,5,3,2,10,2,2,1,9,2,9,2,6,2,8
P,4,5,5,4,3,5,10,4,5,10,8,3,1,10,3,7
N,2,4,4,3,2,7,9,2,4,10,6,6,5,9,0,7
J,3,9,4,7,3,9,6,2,5,11,4,9,1,6,2,5
O,5,10,6,7,4,8,7,8,5,10,6,8,3,8,3,7
H,2,3,3,2,2,6,7,7,5,7,6,10,3,8,3,9
P,3,9,4,6,2,4,12,9,2,10,6,4,1,10,4,8
G,3,7,4,5,3,6,6,5,5,6,6,10,2,9,4,8
J,1,3,2,1,0,9,5,3,5,14,6,11,0,7,0,8
K,4,8,6,6,4,8,6,1,7,10,4,9,4,7,5,10
P,2,6,3,4,1,4,13,8,2,11,6,3,1,10,3,8
L,2,3,3,2,1,4,4,4,7,2,2,6,0,7,1,6
G,3,4,4,3,2,6,7,5,5,10,7,10,2,9,4,9
Z,4,6,5,4,3,6,9,3,8,11,9,6,2,9,6,5
Z,4,7,5,5,5,7,6,2,7,7,6,9,1,7,10,8
L,3,10,4,7,1,0,1,5,6,0,0,7,0,8,0,8
R,2,4,4,2,2,8,8,4,4,9,5,7,2,7,3,10
D,2,4,3,3,2,9,7,4,5,10,4,5,2,8,2,8
Z,2,4,4,3,2,8,7,2,9,11,5,9,1,8,5,8
Q,3,4,4,5,3,8,8,6,2,5,7,10,3,9,5,10
A,3,5,5,8,2,7,6,3,1,7,0,8,3,7,1,8
F,2,3,3,4,1,1,11,5,6,11,10,9,0,8,3,7
L,4,9,5,6,2,7,4,0,9,9,2,10,0,7,2,8
N,4,6,4,4,2,7,7,14,1,5,6,8,6,8,0,8
Y,5,8,5,6,2,3,10,2,7,11,11,6,1,11,2,4
T,2,4,3,2,1,5,11,2,7,11,9,5,1,10,2,5
O,3,7,3,5,2,8,7,7,5,9,5,8,3,8,3,8
G,6,10,7,8,3,8,7,8,8,6,6,9,2,7,6,11
G,6,8,6,6,5,6,7,6,6,9,7,11,2,9,4,9
E,6,10,9,8,7,7,7,2,8,11,7,9,3,9,4,8
Z,2,5,4,4,2,7,7,2,9,11,6,8,1,8,5,8
K,6,9,9,7,5,9,6,2,7,10,3,8,4,7,5,10
T,3,5,4,4,2,5,13,4,6,12,9,3,1,11,2,4
C,4,9,5,7,2,6,6,7,10,8,5,13,1,10,4,9
F,4,7,5,8,6,6,9,4,4,7,7,6,4,9,9,8
D,6,10,6,6,5,9,6,3,6,10,4,7,5,7,8,7
N,3,9,4,6,2,7,7,14,2,5,6,8,6,8,0,8
Q,3,6,4,4,2,9,7,7,6,6,6,10,3,8,5,9
E,7,10,9,7,6,6,8,4,9,12,9,9,3,8,5,6
H,3,5,6,4,3,7,7,2,6,10,5,8,6,8,4,7
M,3,4,4,3,3,7,6,6,4,6,7,8,7,5,2,7
F,4,8,6,6,3,5,11,5,5,13,8,5,2,9,2,6
Z,3,8,4,6,4,6,7,3,7,8,6,9,0,9,9,7
D,6,11,6,6,4,5,9,5,7,10,7,6,5,8,6,5
W,10,11,10,8,7,4,10,3,3,9,8,7,8,11,2,6
E,3,8,4,6,3,6,8,8,10,5,4,10,2,8,6,7
G,3,8,5,6,3,7,6,7,6,6,5,10,1,8,5,11
Y,5,5,7,7,8,9,7,6,3,7,7,8,6,10,6,5
Q,4,7,6,5,5,8,6,7,4,7,7,7,5,7,6,7
J,2,3,3,5,1,14,1,7,5,14,2,11,0,7,0,8
F,3,8,5,6,4,6,10,3,6,10,9,5,5,10,3,6
H,2,1,3,3,3,7,7,5,5,7,6,8,3,8,2,8
C,6,11,7,8,4,2,8,5,9,10,10,14,1,7,3,7
F,2,0,2,1,0,3,13,5,2,11,8,5,0,8,2,7
O,4,8,5,6,4,8,7,7,4,7,6,6,4,8,3,8
Z,2,7,3,5,2,6,8,5,9,7,7,10,2,9,8,7
B,5,9,8,6,5,9,8,4,7,9,4,5,3,7,7,10
P,4,7,6,10,9,7,12,4,0,9,7,6,5,10,6,8
A,2,8,4,5,1,8,6,3,1,7,0,8,2,7,1,8
Z,4,8,5,6,2,7,7,4,14,9,6,8,0,8,8,8
Y,4,7,6,11,9,8,9,3,2,6,8,9,6,13,10,6
O,3,4,4,3,2,7,7,7,5,9,6,8,2,8,3,8
T,2,8,4,6,2,8,11,2,9,6,11,7,1,11,1,7
Y,4,5,5,4,2,4,11,2,7,11,10,5,1,11,3,4
F,9,13,8,7,5,9,7,3,5,11,4,5,4,9,7,9
S,5,7,6,5,3,9,6,4,8,11,4,8,2,8,5,11
J,3,6,5,4,2,10,6,1,7,14,3,7,0,8,0,8
K,2,3,4,1,2,4,8,2,6,10,9,10,2,8,2,7
H,1,0,1,0,0,7,7,10,2,7,6,8,2,8,0,8
K,3,7,4,5,2,3,6,7,3,7,7,12,3,8,3,11
W,3,2,4,3,3,5,11,2,2,8,9,9,6,11,0,8
W,3,1,5,2,3,8,11,3,2,6,9,8,7,11,0,7
I,7,13,6,8,4,8,9,3,6,14,5,5,2,7,6,8
R,8,15,8,8,7,5,8,2,5,7,3,9,6,6,6,5
I,1,5,2,4,1,7,8,0,7,13,6,7,0,8,1,7
Q,3,5,4,5,3,8,6,7,6,7,3,9,2,8,3,8
Q,3,7,4,6,2,8,7,8,6,6,5,9,3,8,4,8
V,5,11,8,8,2,8,8,5,3,6,14,8,3,9,0,8
Y,3,8,5,6,1,9,10,3,2,5,13,8,1,11,0,8
F,3,5,5,4,2,7,9,1,6,13,5,5,1,10,2,8
V,4,8,6,6,3,6,12,2,4,6,11,9,2,10,1,8
W,5,8,7,6,7,8,7,6,2,6,8,8,7,8,5,4
L,3,8,5,6,3,8,4,1,8,9,3,10,0,7,2,9
W,8,8,8,6,5,3,11,2,3,10,10,8,7,10,2,6
A,3,10,5,7,2,8,6,3,1,7,0,8,3,7,1,8
T,7,10,6,5,2,4,12,3,6,13,8,4,2,9,3,4
U,3,4,4,3,2,6,8,6,7,7,10,9,3,9,0,8
H,6,11,8,8,7,7,7,7,7,7,6,8,3,8,4,7
J,5,8,7,9,7,8,9,4,5,7,7,8,5,7,10,10
F,2,4,2,3,1,6,10,4,5,10,9,5,1,9,3,7
W,6,7,6,5,4,3,10,3,3,10,10,8,7,10,2,7
U,4,9,4,7,2,8,5,14,5,6,14,8,3,9,0,8
P,5,9,5,6,2,4,15,8,1,12,6,2,0,9,4,8
L,4,7,5,5,3,6,4,5,9,2,2,4,1,6,2,5
S,5,10,4,5,2,9,3,1,5,8,1,7,2,7,4,10
M,5,5,7,4,7,9,8,4,4,7,6,7,8,6,6,5
G,4,9,5,6,3,6,7,7,6,10,7,11,2,9,4,9
O,3,7,5,5,2,7,7,8,8,7,6,7,3,8,4,8
S,5,11,6,8,5,9,6,4,6,10,3,7,2,8,5,10
V,2,3,3,2,1,4,12,3,3,10,11,7,2,11,0,8
Q,5,5,7,8,10,7,5,6,2,6,6,8,11,10,9,13
Q,2,2,3,3,2,8,7,7,3,6,6,10,2,9,3,9
K,3,5,5,4,3,6,6,1,7,10,8,11,3,7,3,8
M,3,6,3,4,3,8,6,9,0,6,8,8,6,5,0,8
P,4,9,6,7,4,7,10,4,5,12,5,3,1,10,4,8
U,2,3,2,1,1,7,9,5,5,6,9,9,3,10,1,8
E,8,12,5,6,3,7,6,5,7,10,6,9,2,10,8,9
F,2,7,2,5,2,1,11,3,4,11,11,8,0,8,1,7
X,4,9,6,7,5,8,7,3,5,6,6,7,3,9,9,9
U,5,5,5,7,2,7,5,14,5,7,15,8,3,9,0,8
K,3,5,6,4,3,7,7,2,7,10,5,9,6,8,5,8
L,3,9,5,7,3,7,5,1,7,8,2,10,0,6,2,8
Q,5,9,6,11,6,9,7,8,3,5,6,11,5,8,8,8
I,1,2,1,3,1,7,7,1,7,7,6,9,0,8,3,8
W,6,9,8,7,8,8,6,6,2,6,7,8,6,8,5,6
M,7,8,10,6,6,4,7,4,5,11,11,11,9,6,4,7
Y,3,5,4,4,2,4,10,2,7,11,10,6,1,11,2,5
K,5,9,5,5,3,3,9,4,5,10,10,11,4,9,3,6
H,5,11,7,8,10,8,6,4,4,6,6,7,8,6,8,6
N,1,3,2,2,1,7,8,5,4,7,6,6,4,9,1,5
Y,1,0,2,0,0,7,9,2,1,6,12,8,1,10,0,8
B,3,2,4,3,3,8,7,5,6,7,6,6,2,8,6,10
C,3,10,4,8,3,5,7,6,7,7,6,13,1,8,4,10
N,3,6,4,4,3,7,7,6,6,7,6,8,3,7,3,8
D,4,7,5,5,3,9,7,4,7,11,5,5,3,8,3,8
E,2,7,3,5,2,3,8,6,11,7,5,15,0,8,6,8
V,1,0,2,0,0,7,9,3,2,7,12,8,2,10,0,8
T,2,8,4,5,1,9,15,1,6,5,11,9,0,8,0,8
Q,6,8,6,9,7,8,7,7,2,8,7,10,3,8,6,7
U,6,7,7,5,4,4,8,5,7,9,8,9,3,9,3,5
B,3,6,5,4,6,9,7,4,3,6,7,7,7,9,7,7
Y,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8
E,3,5,5,3,3,6,9,2,9,11,8,8,2,8,4,6
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
K,4,7,5,5,4,5,7,6,7,6,6,13,3,8,5,10
O,4,7,4,5,3,7,7,8,5,10,6,8,3,8,3,7
T,2,1,3,2,1,7,12,3,6,7,11,8,2,11,1,8
M,3,6,4,4,4,8,6,6,4,7,7,8,7,5,2,7
A,4,9,6,6,4,10,2,1,2,8,3,9,5,5,3,7
H,3,6,3,4,1,7,8,14,1,7,5,8,3,8,0,8
F,3,5,5,3,2,6,10,3,6,13,7,5,1,10,2,7
W,8,11,8,8,6,4,10,2,3,10,9,8,8,11,2,6
D,3,7,3,5,2,5,7,10,8,7,6,5,3,8,4,8
X,4,8,5,5,1,7,7,4,4,7,6,8,3,8,4,8
J,3,9,5,7,6,9,8,3,3,8,4,6,4,8,6,4
K,7,11,7,6,4,7,7,3,6,10,6,9,6,10,3,8
U,10,13,9,7,4,6,4,5,5,4,8,7,6,7,2,7
H,5,9,8,7,5,7,7,6,7,7,6,5,6,8,4,7
B,4,8,4,6,4,6,9,9,7,7,5,7,2,8,9,10
R,4,11,6,8,8,7,7,3,4,7,6,8,7,10,8,6
E,1,1,1,1,1,4,7,5,8,7,6,13,0,8,6,9
P,5,8,7,6,6,8,9,6,4,10,4,4,3,11,4,9
W,10,11,10,8,7,5,11,3,3,9,8,7,8,11,3,5
N,4,5,5,8,3,7,7,14,2,4,6,8,6,8,0,8
T,6,10,6,5,3,7,9,2,7,12,6,6,2,9,4,6
B,4,8,6,6,5,9,7,4,6,10,5,6,2,8,6,10
O,5,8,6,6,4,7,8,7,5,10,7,8,3,8,3,8
A,3,8,5,6,3,12,3,2,2,10,2,9,2,6,2,8
Y,5,9,7,6,6,9,5,7,5,6,9,7,3,9,9,4
L,3,7,4,5,3,5,5,2,8,3,2,7,0,7,1,5
H,3,4,5,3,3,7,7,3,6,10,6,8,3,8,3,8
Q,2,2,2,4,2,8,7,7,3,6,6,9,2,8,5,10
D,4,6,5,4,4,9,6,4,6,9,4,6,3,8,3,8
L,4,10,5,8,3,5,3,6,8,1,2,4,1,6,1,5
R,6,11,9,8,8,9,8,4,6,9,3,7,3,6,4,11
P,2,3,3,1,1,8,9,3,3,12,4,4,1,9,2,9
L,3,8,4,6,3,7,3,4,6,6,2,7,1,6,2,7
P,5,11,7,8,6,6,10,6,5,10,8,3,1,10,4,7
M,4,10,6,7,6,8,6,5,5,7,7,8,11,5,2,8
Q,5,9,5,11,6,8,7,7,3,8,8,10,3,8,6,8
L,4,9,6,6,3,5,4,3,9,6,1,7,1,6,3,6
F,3,5,5,3,2,6,11,3,5,13,7,4,1,10,1,7
Z,4,10,4,7,5,6,8,5,9,8,7,10,1,9,7,8
V,6,8,8,6,5,6,11,3,2,8,11,8,4,9,5,9
K,4,5,5,4,3,6,7,4,7,6,6,10,3,8,5,9
U,3,6,3,4,1,8,5,13,5,6,13,8,3,9,0,8
J,2,8,3,6,1,13,2,8,4,14,4,12,1,6,0,8
V,4,6,4,4,2,3,11,2,3,9,10,8,2,11,0,8
N,2,2,3,3,2,7,8,5,4,7,7,7,4,9,1,6
J,5,12,4,9,4,7,10,3,2,13,5,5,2,8,7,10
Q,5,10,6,9,6,8,8,7,5,5,8,9,3,8,5,8
D,3,9,4,6,4,7,7,6,7,6,5,6,3,8,3,7
P,3,4,5,3,2,7,9,4,4,12,4,3,1,10,3,8
N,3,5,4,7,3,7,7,14,2,5,6,8,6,8,0,8
G,5,10,6,8,6,7,6,7,8,6,4,10,2,9,6,11
C,2,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10
V,1,0,2,1,0,8,9,3,2,7,12,8,2,10,0,8
F,3,7,5,5,3,6,10,5,6,10,10,5,2,9,3,5
U,7,9,7,7,4,2,8,5,7,11,11,10,3,9,2,6
H,7,11,10,8,7,7,8,3,7,10,6,7,3,8,3,8
A,5,10,9,8,7,11,5,1,5,9,1,5,3,7,5,9
I,1,4,2,3,1,7,7,0,6,13,6,8,0,8,0,8
Z,2,5,4,3,2,7,8,2,9,11,7,7,1,8,5,7
G,4,8,5,6,2,7,6,7,8,6,6,9,1,8,6,11
U,4,5,5,8,2,7,5,14,5,7,14,8,3,9,0,8
X,3,4,5,2,2,7,8,1,8,10,7,8,2,8,3,7
V,5,9,5,4,3,10,7,4,5,7,9,5,4,11,2,7
L,7,12,7,6,4,6,6,4,6,12,9,11,3,8,7,8
H,7,11,9,8,8,7,6,5,5,6,5,7,9,6,7,10
D,5,9,7,7,6,10,6,3,7,10,3,6,5,7,4,9
H,6,10,7,5,4,7,8,3,5,10,7,8,6,9,5,7
E,4,9,3,4,2,10,5,3,4,11,4,9,2,8,6,12
Y,2,3,3,2,1,4,10,2,7,10,10,6,1,11,2,4
J,4,8,5,6,2,8,5,4,6,15,6,11,1,6,0,7
R,6,9,8,6,7,8,6,6,4,8,5,7,4,7,7,11
M,7,12,8,6,5,7,3,3,2,8,4,10,8,2,2,8
S,3,7,4,5,2,9,7,4,8,11,4,8,2,8,5,9
J,4,6,5,4,2,8,6,4,6,15,7,12,1,6,1,7
Z,2,6,4,4,3,7,7,2,7,7,6,8,0,8,8,8
E,4,11,4,8,4,2,8,5,9,7,6,14,0,8,6,8
G,5,11,6,8,4,6,7,7,7,11,7,11,2,10,4,9
V,3,9,5,7,1,7,8,4,2,7,14,8,3,9,0,8
U,4,5,5,4,3,6,9,5,7,7,10,9,3,9,1,8
J,3,9,4,7,1,13,2,9,4,14,5,13,1,6,0,8
S,2,6,4,4,4,6,9,3,2,7,7,6,2,8,8,1
H,3,2,4,3,3,7,8,6,6,7,6,8,3,8,3,7
D,3,8,3,6,2,6,7,11,8,6,6,6,3,8,3,8
D,6,10,5,5,4,8,7,4,7,10,5,7,5,9,6,6
I,3,6,4,4,2,7,7,0,7,13,6,8,0,8,1,7
X,5,7,8,5,4,7,7,1,8,10,6,8,2,8,3,8
U,4,7,4,5,2,7,4,14,5,7,13,8,3,9,0,8
H,5,9,5,6,2,7,7,15,0,7,6,8,3,8,0,8
X,4,6,5,5,5,9,7,2,5,8,6,6,2,8,7,7
E,3,8,5,6,3,5,8,4,8,11,9,9,2,8,5,6
O,4,7,6,5,4,8,9,8,4,7,7,7,3,8,3,8
U,4,4,5,3,2,4,9,5,7,11,10,9,3,9,2,7
S,2,7,3,5,3,7,8,7,6,7,5,6,2,8,8,8
Y,7,10,7,8,3,4,10,3,8,11,11,5,1,11,3,4
J,3,7,4,5,2,9,6,4,7,15,5,10,0,7,1,7
X,2,7,3,5,3,7,7,3,7,6,7,10,2,8,6,8
M,4,4,7,3,4,7,7,3,4,9,8,9,9,7,2,9
Q,2,0,2,1,1,9,7,6,4,6,6,9,2,8,3,8
U,10,15,9,8,6,7,6,5,5,6,7,8,5,5,3,7
C,9,13,6,7,3,8,7,7,7,11,5,8,2,9,5,9
Z,7,10,9,8,5,7,8,2,10,12,6,8,1,9,6,8
C,3,6,4,4,1,5,8,6,10,6,7,12,1,7,4,8
T,2,7,3,4,1,5,14,1,6,9,11,7,0,8,0,8
Q,3,6,4,5,2,8,6,8,7,6,5,8,3,8,4,8
K,6,9,8,6,8,8,8,5,5,7,6,6,8,7,8,13
W,6,6,6,4,4,3,11,2,3,10,10,8,7,11,2,6
M,3,4,4,3,3,8,6,6,4,6,7,8,7,6,2,7
F,5,11,7,8,6,9,8,1,6,13,5,5,3,8,3,9
H,4,10,4,7,2,7,6,15,1,7,8,8,3,8,0,8
V,6,11,4,6,2,7,10,6,3,8,10,5,5,12,3,8
V,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8
X,4,8,6,6,3,5,8,2,8,10,11,9,3,8,4,6
Q,6,7,8,10,10,8,9,5,2,5,7,9,8,15,8,14
W,3,6,4,4,4,8,7,6,2,6,7,8,5,9,4,6
Q,4,6,4,7,4,8,5,6,4,9,5,8,3,8,3,8
C,4,10,5,8,3,5,8,8,8,9,8,13,2,9,4,10
I,3,8,4,6,2,7,8,0,8,14,6,7,0,8,1,7
Z,6,11,8,9,5,7,8,3,11,12,7,8,2,9,7,8
N,1,1,2,2,1,7,7,11,1,5,6,8,4,8,0,8
I,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8
M,4,4,4,6,3,8,7,12,1,6,9,8,8,6,0,8
R,1,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10
W,7,8,7,6,5,6,11,5,3,8,6,6,10,13,3,4
M,5,9,6,6,6,7,6,6,5,7,7,10,8,5,2,8
R,8,15,6,8,5,7,5,6,5,8,6,9,6,6,8,10
D,4,8,4,6,2,5,7,11,8,7,6,5,3,8,4,8
Y,4,6,6,4,3,4,11,2,7,11,10,5,1,11,3,5
C,3,7,4,5,2,3,9,5,8,11,11,11,1,8,3,7
S,7,11,10,8,12,8,9,5,4,8,5,7,7,8,13,9
P,4,6,4,8,2,3,13,8,2,11,7,3,1,10,4,8
O,3,5,4,4,3,8,7,7,5,9,6,8,2,8,3,8
F,5,11,7,8,4,7,10,2,7,14,5,4,1,10,2,8
E,3,7,5,5,3,4,9,3,7,11,9,9,2,9,4,6
T,4,5,5,5,5,7,9,4,7,7,8,8,3,10,6,7
Q,6,8,6,9,7,8,8,6,2,7,8,11,3,8,6,8
C,5,7,6,6,5,4,8,3,6,7,6,10,3,9,7,8
W,4,4,5,3,3,3,11,3,2,10,10,8,6,11,1,7
O,2,3,3,2,2,8,7,7,4,9,6,9,2,8,3,8
H,5,9,6,6,6,7,7,5,5,7,5,8,6,6,6,11
P,12,14,10,8,4,8,9,6,5,14,3,4,5,10,5,8
Y,6,9,6,7,4,5,9,1,7,9,9,6,3,10,5,4
L,4,9,6,7,4,7,4,1,8,8,2,10,0,7,3,8
X,2,3,3,2,1,7,7,3,9,6,6,8,2,8,5,7
G,5,6,7,5,7,7,8,6,3,7,7,8,6,11,7,8
O,6,9,8,6,9,8,9,5,2,7,7,8,9,9,6,12
H,8,14,8,8,6,10,7,5,5,11,1,6,7,4,5,7
V,9,15,8,9,4,9,10,6,4,6,10,6,6,13,3,6
K,2,3,3,2,2,5,7,4,7,7,6,10,3,8,4,10
U,3,5,4,4,2,6,8,5,6,6,8,9,6,10,0,7
I,2,7,3,5,1,7,6,0,8,14,7,10,0,8,1,8
Y,5,6,5,4,2,3,10,2,7,11,11,6,1,11,2,4
R,3,6,4,4,4,8,7,7,3,8,5,7,4,7,7,8
R,4,9,4,7,3,6,8,9,5,6,6,7,3,8,6,11
O,8,12,6,6,3,7,8,6,5,9,5,7,4,9,5,9
S,6,11,7,8,7,9,8,5,8,5,5,6,1,6,10,6
L,6,12,6,7,3,7,4,3,6,11,4,13,3,6,6,8
N,3,5,4,3,3,7,8,5,5,7,7,6,5,9,2,6
E,7,11,10,8,6,8,7,2,9,11,6,9,5,7,6,8
H,2,4,3,2,2,6,7,5,6,7,6,8,3,8,3,8
A,2,7,4,5,2,11,2,3,3,10,2,9,2,6,2,8
P,10,13,9,8,6,9,8,4,5,13,4,4,5,10,6,7
M,6,9,7,4,3,8,10,4,5,5,5,8,8,8,2,7
F,3,7,4,5,1,1,12,5,5,12,11,8,0,8,2,6
O,3,6,4,4,3,7,8,6,4,9,7,8,3,8,3,8
Y,3,5,5,7,1,9,10,2,2,5,12,8,1,11,0,8
S,2,2,2,3,2,8,8,7,5,8,5,7,2,8,8,8
V,4,5,5,4,2,4,12,2,3,9,11,7,3,10,1,7
R,4,8,6,6,7,9,9,3,5,5,7,8,7,10,7,7
N,6,11,8,6,3,8,7,2,4,13,6,8,6,8,0,8
Q,3,4,4,4,3,7,8,5,3,8,8,9,3,8,5,8
R,4,9,4,4,3,9,7,3,5,9,3,8,5,7,5,8
Y,6,8,8,11,11,7,10,4,2,7,8,9,6,13,9,8
K,5,9,7,6,5,4,7,2,7,10,10,12,3,8,3,6
U,3,2,4,3,2,6,8,5,7,7,10,9,3,9,1,8
F,6,10,7,8,7,6,10,6,5,9,6,8,2,10,8,10
A,2,3,3,2,1,10,2,2,1,9,2,9,1,6,2,8
Q,5,6,6,8,3,9,9,8,7,5,8,10,3,8,5,10
N,2,1,3,3,2,7,8,5,4,8,7,8,4,8,1,7
U,4,8,6,6,5,8,8,8,5,6,7,9,3,8,4,6
M,2,3,4,2,3,7,6,3,3,9,7,8,6,5,1,8
U,3,7,5,6,5,8,7,3,3,6,6,8,4,8,2,8
T,2,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8
A,4,9,4,4,2,10,2,4,2,11,5,12,4,4,5,10
R,9,13,7,8,5,8,7,5,5,9,4,9,7,5,7,11
K,3,4,4,7,2,3,7,7,3,7,7,11,3,8,3,10
Q,7,8,7,10,8,8,9,5,2,7,9,11,3,8,6,8
Y,2,5,4,4,2,8,11,1,7,6,11,8,1,11,2,9
X,3,1,4,2,2,8,7,4,9,6,6,8,3,8,6,8
Y,5,9,5,7,3,4,9,1,7,9,10,6,1,10,3,4
Q,5,11,5,6,4,7,7,4,8,11,5,8,3,7,9,10
D,5,9,6,7,6,8,8,7,6,8,7,3,5,8,4,9
T,6,7,6,5,4,6,12,5,6,10,9,4,3,13,3,4
V,4,10,6,8,3,5,11,3,5,10,12,9,3,10,1,8
T,2,8,4,5,1,8,15,1,6,6,11,8,0,8,0,8
G,2,6,3,4,2,8,8,6,5,6,7,9,2,7,5,11
A,6,10,6,5,3,12,3,6,2,12,2,10,4,3,3,10
W,2,3,3,1,2,10,11,3,2,5,9,7,5,10,0,8
I,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8
U,4,6,6,4,4,7,8,8,6,6,6,10,3,8,5,6
E,4,8,6,6,5,7,7,2,7,11,7,9,3,8,5,8
T,5,9,7,7,8,7,8,4,6,7,6,9,6,8,6,6
I,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
Y,7,11,8,8,4,5,9,1,9,10,10,5,3,10,5,3
A,5,10,7,8,4,10,3,2,3,8,1,7,2,7,3,8
Z,5,11,7,9,4,8,7,2,10,11,5,9,2,8,6,8
Z,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8
Z,5,8,7,10,6,11,4,3,5,9,2,7,2,7,6,9
T,3,3,3,2,1,5,12,2,7,11,9,5,1,10,1,5
Y,2,1,2,1,0,7,10,3,1,7,12,8,1,11,0,8
N,3,5,5,4,2,7,8,2,4,10,6,6,5,9,0,7
Q,4,3,5,5,4,8,7,7,3,5,6,9,3,8,5,8
U,4,9,5,7,3,8,8,7,7,6,10,8,3,9,1,8
H,3,7,3,4,2,7,8,14,1,7,5,8,3,8,0,8
E,4,9,6,8,7,6,8,4,3,8,7,9,5,11,8,11
S,4,8,5,6,3,9,6,5,9,11,3,8,2,7,5,10
P,4,6,6,4,3,7,10,5,3,11,4,3,1,10,3,9
M,5,6,7,6,8,7,7,4,4,7,6,8,10,7,5,5
O,7,10,7,8,5,7,7,8,6,9,7,10,4,7,5,5
K,3,2,4,4,2,6,7,4,8,6,6,11,3,8,6,9
Z,5,11,7,8,4,7,7,2,10,12,5,9,1,9,7,8
T,5,7,5,5,3,5,12,2,7,12,9,4,1,11,2,4
C,4,9,5,6,3,5,8,7,7,8,8,14,2,9,4,10
D,6,11,7,8,4,5,7,10,10,7,6,5,3,8,4,8
I,4,8,6,9,6,8,9,4,5,7,7,8,3,7,8,7
A,3,2,5,3,2,6,2,2,1,5,2,8,2,7,3,5
Z,4,10,6,7,4,9,5,3,10,11,4,9,1,7,6,9
I,3,8,5,6,3,8,6,2,7,7,6,7,0,9,4,7
M,4,2,5,3,4,8,6,6,4,7,7,8,7,5,2,8
M,5,10,8,7,11,9,6,2,2,8,4,8,10,5,3,6
Z,1,0,1,0,0,8,7,2,9,8,6,8,0,8,5,8
Y,3,5,5,4,2,8,11,1,7,5,11,9,1,11,2,9
I,1,6,0,4,1,7,7,5,3,7,6,8,0,8,0,8
Y,3,10,6,7,4,7,10,0,7,6,11,8,1,10,1,8
Y,7,11,7,8,4,6,8,2,8,8,10,5,5,11,6,3
N,2,6,2,4,2,7,7,11,1,6,6,8,4,8,0,8
L,1,0,1,1,0,2,1,5,5,0,2,5,0,8,0,8
Y,6,7,6,5,3,3,10,2,7,10,11,6,1,11,2,5
D,4,9,6,6,5,7,8,7,5,8,7,6,3,8,3,7
K,7,9,10,7,7,7,7,1,6,10,5,9,3,8,4,9
Y,7,8,9,10,11,9,8,6,3,7,7,8,6,10,7,4
Q,4,7,6,5,5,8,4,7,4,6,7,10,4,7,6,9
I,3,4,4,5,3,9,9,5,4,6,6,10,3,7,7,5
J,4,5,6,6,5,8,8,4,6,6,7,7,3,10,8,10
R,4,11,6,8,6,8,8,4,6,9,4,8,3,6,5,11
I,3,7,4,5,2,6,8,0,7,13,7,8,0,8,1,7
K,2,1,3,2,2,6,7,4,7,7,6,11,3,8,4,9
Y,4,10,7,8,4,4,9,1,8,10,12,10,1,11,2,7
T,3,3,4,2,2,8,12,3,6,6,11,8,2,11,1,7
W,6,9,8,7,9,7,9,6,4,7,9,8,7,7,5,10
C,4,7,5,5,2,6,8,7,11,6,6,13,1,7,4,8
X,4,7,5,5,3,7,7,4,9,6,6,10,3,8,6,8
X,4,7,6,5,4,9,8,3,6,7,7,7,6,11,7,7
S,4,9,5,6,4,8,7,5,7,5,6,8,0,8,8,8
F,4,4,4,6,2,1,15,5,3,12,9,4,0,8,2,6
B,2,1,2,1,2,7,7,7,5,6,6,7,1,8,7,8
V,1,1,2,2,1,6,12,3,2,9,11,8,2,11,1,8
V,3,10,5,7,2,9,8,4,3,6,14,8,3,10,0,8
P,6,10,9,8,5,7,10,2,7,14,6,4,0,10,2,9
A,4,9,6,6,2,7,7,3,0,7,0,8,3,7,1,8
X,4,9,6,6,4,8,7,0,7,9,5,7,2,8,3,8
G,4,7,4,5,3,6,6,6,5,10,7,13,2,9,4,10
A,3,5,5,3,2,6,2,2,2,5,2,8,2,6,3,6
T,2,2,3,3,2,6,12,3,6,8,11,8,2,11,1,7
A,3,10,5,8,4,6,3,1,2,5,2,8,2,5,3,6
M,6,6,8,4,5,10,6,2,5,9,4,7,8,6,2,8
Q,3,4,4,5,3,7,8,5,3,7,9,10,3,8,5,8
R,3,3,4,4,2,5,11,7,3,7,4,8,3,7,6,11
X,3,1,4,2,2,8,7,3,9,6,6,9,2,8,6,8
O,4,9,5,7,5,7,7,8,4,7,6,8,3,8,3,8
J,3,8,5,6,4,9,8,3,3,8,4,6,4,9,6,4
T,2,3,2,2,1,9,11,3,5,6,10,8,2,11,1,8
F,2,7,3,5,2,1,12,3,4,11,10,8,0,8,2,7
Y,4,10,7,7,1,7,10,2,2,7,13,8,2,11,0,8
W,6,8,6,6,4,3,10,3,3,11,10,8,7,10,2,7
D,3,2,4,4,3,7,7,7,6,6,6,5,2,8,3,7
Q,5,11,7,8,7,8,4,8,5,6,6,6,5,7,7,10
Z,6,7,5,11,4,7,7,5,3,11,7,7,3,9,11,6
I,2,2,1,4,1,7,7,1,7,7,6,8,0,8,3,8
S,1,0,2,1,0,8,7,4,6,5,6,7,0,8,7,8
K,3,6,5,5,5,6,8,3,3,7,4,9,4,6,6,9
L,2,4,3,3,1,7,4,1,8,8,2,10,0,7,2,8
A,2,2,4,3,1,7,3,2,2,6,1,8,2,6,2,7
H,5,6,6,8,3,7,8,15,0,7,5,8,3,8,0,8
V,2,4,4,2,1,9,12,2,3,5,11,9,2,11,1,8
O,3,3,4,4,2,7,8,8,6,7,7,8,3,8,4,8
P,4,9,4,7,4,4,12,7,1,11,7,4,1,10,3,8
O,3,3,4,4,2,8,7,8,8,7,6,8,3,8,4,8
G,5,5,6,7,3,7,4,7,9,5,5,9,1,9,7,11
W,5,7,5,5,4,4,11,2,2,9,8,7,6,11,2,6
C,5,9,6,7,2,6,8,7,11,6,7,13,1,7,4,8
X,3,8,4,5,1,8,7,4,4,7,6,8,3,8,4,8
G,2,6,3,4,2,7,6,6,6,6,5,10,1,8,5,11
S,5,9,5,5,2,5,9,4,4,13,9,7,2,9,3,7
V,4,7,4,5,3,4,12,2,2,9,11,8,3,11,1,7
R,3,5,5,4,3,9,7,4,5,10,3,7,5,6,4,9
A,4,9,7,7,5,11,3,1,2,8,3,9,6,5,3,8
U,3,3,4,1,1,5,8,4,6,10,9,9,3,10,1,6
S,5,7,6,5,3,8,7,3,8,11,6,8,2,10,5,8
T,2,7,3,5,2,9,12,0,5,6,10,8,0,8,0,8
B,3,5,5,4,3,8,8,3,5,10,5,6,3,7,5,9
D,3,8,5,6,4,7,7,7,7,7,7,5,3,8,3,7
U,5,8,6,6,4,4,8,5,7,9,7,9,4,8,4,4
D,5,11,7,8,8,7,7,6,6,6,6,5,6,8,3,7
B,4,9,4,4,4,8,7,3,4,9,6,8,5,7,6,8
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
F,6,8,7,9,8,6,9,4,4,7,7,6,5,9,9,8
Z,4,8,5,6,5,8,8,3,8,7,7,7,0,8,11,8
B,9,11,6,6,4,8,6,5,6,11,4,9,6,7,6,10
E,4,11,6,8,5,8,7,2,8,11,6,8,2,8,5,9
V,2,3,3,2,1,4,12,3,2,9,11,7,2,11,0,7
Y,1,1,3,2,1,8,11,1,5,5,11,9,1,11,1,8
Y,5,8,5,6,3,3,10,1,7,11,11,6,1,10,3,4
C,3,6,5,4,4,7,7,4,3,7,7,10,5,10,3,8
B,3,1,3,2,3,7,7,5,5,7,6,6,2,8,6,10
J,3,10,4,7,1,14,1,7,5,14,1,10,0,7,0,8
X,3,5,4,4,2,7,7,3,9,6,6,8,2,8,6,8
D,6,10,8,8,8,7,7,5,7,7,6,5,6,8,3,7
W,6,9,6,7,5,4,10,3,3,9,8,7,7,11,2,5
K,2,3,4,2,2,6,7,1,6,10,7,10,3,8,2,8
R,5,5,6,7,3,6,11,9,4,7,3,8,3,7,6,11
U,3,8,5,6,2,5,8,7,8,10,10,9,3,9,1,8
Z,2,4,2,3,2,8,7,5,9,6,6,7,1,8,7,8
N,2,1,2,2,1,7,8,5,4,7,6,6,5,9,1,5
J,6,7,7,8,6,9,9,5,5,6,6,10,3,7,8,5
K,2,3,3,1,1,5,7,1,6,10,8,10,3,8,2,6
T,3,11,4,8,1,6,14,0,6,8,11,8,0,8,0,8
F,3,3,3,4,1,1,12,4,4,12,10,7,0,8,2,6
D,4,9,6,7,6,9,7,6,6,9,4,6,3,8,3,9
G,3,7,4,5,2,8,8,8,7,5,7,9,2,7,5,10
J,4,10,5,7,2,8,7,2,7,15,5,8,0,7,1,7
B,1,3,3,2,2,8,7,2,5,10,5,7,2,8,3,9
P,2,3,4,2,1,7,11,4,3,11,4,2,0,9,3,8
W,6,10,9,8,7,5,8,4,1,7,9,8,7,11,0,8
N,6,10,5,5,3,5,9,4,5,3,3,12,6,11,2,7
U,2,3,3,1,1,5,8,5,6,10,9,9,3,10,1,6
C,3,6,4,4,2,6,8,7,7,9,8,12,2,10,4,10
S,5,8,6,6,3,5,9,3,9,11,6,7,2,7,5,6
H,3,3,4,4,2,7,7,14,1,7,7,8,3,8,0,8
A,6,10,9,8,9,8,7,7,4,7,6,9,6,8,7,4
K,4,5,7,4,4,3,8,2,7,10,10,11,3,8,3,6
Z,4,10,5,8,2,7,7,4,14,10,6,8,0,8,8,8
T,6,9,7,7,8,5,8,4,7,7,6,10,5,7,5,7
P,3,6,4,4,2,4,13,8,1,10,6,3,1,10,4,8
O,4,8,6,6,4,7,7,8,5,7,5,8,3,8,3,8
S,8,15,8,8,4,9,5,5,5,13,5,9,2,8,3,7
X,6,10,9,8,8,8,7,3,7,7,7,8,6,13,8,8
A,5,11,7,9,5,10,2,2,3,9,1,7,3,7,4,8
I,1,4,1,3,1,7,7,1,7,7,6,8,0,8,2,8
D,5,4,5,7,3,5,6,10,9,5,4,5,3,8,4,8
P,2,1,2,1,1,5,11,7,2,9,6,4,1,9,3,8
F,4,9,4,6,2,1,13,5,4,12,11,6,0,8,2,6
E,4,8,6,6,4,9,6,2,7,11,5,9,3,7,6,10
H,4,3,4,4,2,7,8,14,0,7,6,8,3,8,0,8
T,4,5,5,4,3,5,12,4,6,11,9,4,2,11,1,5
M,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8
G,3,6,4,4,2,6,6,5,7,7,5,10,2,10,4,8
V,2,8,4,6,1,8,8,4,2,7,13,8,3,9,0,8
D,5,10,5,8,3,5,7,10,9,7,6,5,3,8,4,8
T,5,11,7,8,7,8,11,3,6,6,11,8,3,12,1,8
D,5,10,6,8,3,5,7,10,9,7,6,5,3,8,4,8
H,7,13,7,8,6,7,7,3,4,10,5,8,6,9,5,8
W,6,7,9,6,10,7,8,5,5,7,5,8,10,10,8,8
Z,5,10,7,8,4,7,7,3,11,12,7,8,2,8,7,7
F,4,7,6,5,4,8,8,2,6,13,5,6,2,10,2,8
B,2,3,4,2,2,8,8,3,5,10,5,6,2,8,5,9
R,3,7,5,5,4,6,8,5,6,6,5,7,3,7,5,9
Z,1,0,1,1,0,7,7,2,10,8,6,8,0,8,6,8
U,6,10,6,5,4,7,5,6,5,6,8,8,5,10,3,9
N,10,15,9,8,4,7,10,5,5,4,5,11,6,11,3,7
B,1,3,2,2,1,7,7,4,4,6,6,5,2,8,5,9
I,0,7,0,5,0,7,7,4,4,7,6,8,0,8,0,8
A,4,7,6,5,2,8,3,3,3,9,1,8,2,7,3,7
K,5,7,8,5,4,3,7,2,7,10,11,12,3,8,4,5
Z,3,7,4,5,3,6,8,5,10,7,7,10,2,9,8,8
R,5,11,7,8,8,6,7,5,6,6,5,7,3,7,5,8
R,4,2,5,4,4,7,8,4,6,7,5,7,3,7,4,8
F,4,6,5,4,3,7,10,4,5,13,7,5,2,9,2,7
I,4,10,5,8,2,7,9,0,8,14,6,6,0,10,2,7
A,7,11,9,8,9,7,6,8,4,7,5,8,5,8,11,2
I,1,3,1,2,0,7,8,0,6,12,6,8,0,8,0,7
J,2,6,2,4,2,8,7,0,6,10,5,7,0,7,1,7
K,5,9,5,4,3,9,7,2,6,10,4,7,5,9,3,9
U,2,1,3,2,1,5,8,6,6,8,10,10,3,9,1,7
T,1,0,1,0,0,7,13,1,4,7,10,8,0,8,0,8
Y,1,3,2,2,1,5,10,2,7,10,9,5,1,11,2,4
W,2,1,2,2,1,7,8,4,0,7,8,8,6,10,0,8
J,4,7,6,5,4,8,6,5,5,8,7,7,3,7,4,6
U,4,2,5,4,2,6,8,5,8,6,9,9,3,9,1,7
N,8,15,10,8,5,5,9,3,4,13,10,9,6,9,0,8
J,1,3,2,2,1,11,6,2,6,12,3,8,0,7,0,8
U,1,0,2,1,0,8,6,11,4,7,12,8,3,10,0,8
W,4,4,6,6,3,4,8,5,1,7,8,8,8,9,0,8
F,4,7,7,5,6,8,7,1,4,10,7,7,6,9,3,6
K,5,9,8,6,6,7,7,1,6,10,5,9,3,7,3,8
U,7,14,6,8,5,8,6,4,5,7,7,8,6,5,4,6
P,4,11,5,8,6,6,9,6,4,9,8,4,1,10,3,7
A,3,6,5,4,2,8,2,2,2,7,1,8,2,6,3,7
J,4,10,6,8,6,9,8,4,4,9,3,7,4,8,7,5
W,6,6,6,4,5,4,11,2,2,9,8,7,7,12,2,6
T,5,6,6,5,6,8,9,5,7,7,7,8,3,10,7,7
M,4,6,7,4,4,6,6,3,4,9,9,9,9,6,3,9
P,3,9,4,6,2,5,9,10,5,8,6,5,2,9,4,8
Z,4,9,5,7,5,9,9,6,4,7,5,8,4,9,10,6
L,3,7,4,5,3,5,5,4,7,3,2,6,4,6,1,6
M,5,11,7,8,9,7,6,7,4,7,5,8,6,10,7,8
I,2,8,2,6,2,7,7,0,8,7,6,8,0,8,3,8
B,3,6,5,4,6,8,7,4,3,6,7,8,6,10,7,7
M,3,7,5,5,5,10,6,6,4,6,7,5,7,5,1,5
M,6,8,9,7,10,8,7,4,5,7,6,7,12,8,6,4
C,2,5,3,3,1,4,8,5,7,11,9,13,1,9,2,7
G,5,11,7,8,6,6,6,6,7,6,6,9,4,9,4,8
Z,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8
O,4,7,5,5,2,8,6,8,8,7,5,8,3,8,4,8
Z,3,4,4,6,2,7,7,4,13,10,6,8,0,8,8,8
U,5,8,7,6,6,7,8,8,5,5,7,10,4,9,4,5
M,9,13,11,7,6,13,2,6,2,12,1,9,7,2,1,8
M,5,5,6,8,4,7,7,12,2,7,9,8,8,6,0,8
U,5,11,5,8,4,8,5,12,4,7,13,8,3,9,0,8
U,7,8,8,6,4,4,8,6,8,9,9,10,3,9,3,5
X,3,3,5,2,2,9,7,2,8,11,4,7,2,8,3,8
Z,3,6,4,4,1,7,7,3,13,9,6,8,0,8,8,8
V,2,3,2,1,1,5,12,3,2,9,10,7,2,11,1,8
U,6,11,9,8,12,8,8,4,4,6,7,8,10,6,8,8
C,4,7,5,5,2,6,6,6,10,7,6,13,1,9,4,9
N,5,7,6,5,4,8,8,6,6,6,5,3,8,9,5,7
H,4,2,5,4,3,9,7,6,6,7,6,8,6,8,3,7
W,6,8,6,6,4,2,10,3,3,11,11,9,7,10,1,6
J,6,7,4,10,3,10,6,3,4,11,3,5,3,8,7,10
Y,8,7,6,11,5,9,7,5,5,4,12,5,4,11,5,7
R,3,7,4,5,2,5,11,8,4,7,3,9,3,7,6,11
Q,4,7,6,8,6,8,5,8,4,6,5,9,4,9,6,8
O,3,6,4,4,2,7,7,7,4,9,6,8,3,8,3,8
S,7,15,5,8,3,10,2,2,5,10,1,9,3,7,4,9
V,4,5,6,4,2,4,12,3,3,10,11,7,2,10,1,8
A,3,8,5,6,3,6,4,3,0,6,2,7,2,7,2,7
A,4,7,7,5,5,7,6,2,4,6,1,6,3,5,3,7
F,5,7,6,5,6,7,10,5,4,8,6,8,2,9,7,11
S,4,10,6,8,7,8,8,5,3,8,5,7,4,9,11,8
T,4,6,6,4,5,5,8,4,5,6,7,8,5,10,4,8
I,1,3,2,2,1,7,7,1,6,13,6,8,0,8,0,8
J,1,1,2,2,1,10,7,2,5,11,4,8,0,7,0,7
W,6,7,6,5,4,4,11,3,3,9,9,7,7,11,2,6
N,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
N,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8
O,5,9,6,7,8,8,9,5,2,7,7,8,8,9,4,8
V,5,9,4,4,2,7,10,4,4,8,9,6,3,10,2,7
X,5,8,8,6,4,8,8,1,8,11,4,7,4,9,4,8
L,3,11,5,8,3,4,4,4,8,2,0,6,0,6,1,5
L,2,6,3,4,1,0,1,5,6,0,0,6,0,8,0,8
B,6,11,8,8,8,9,6,4,5,7,6,7,4,8,6,10
H,3,3,5,2,2,7,8,3,6,10,7,8,3,8,3,8
O,5,8,6,6,5,6,8,7,4,9,8,8,3,8,3,8
T,6,8,6,6,3,5,12,2,8,12,9,4,1,11,2,4
V,3,4,5,3,2,6,12,2,3,7,11,8,2,10,1,8
F,6,11,9,8,9,7,7,6,4,8,6,8,5,12,10,12
Q,2,3,3,4,3,9,8,6,1,5,7,10,2,10,5,10
N,6,5,6,8,3,7,7,15,2,4,6,8,6,8,0,8
R,7,13,7,7,6,8,7,3,5,9,4,8,6,6,7,6
N,3,7,3,5,2,7,7,13,2,5,6,8,5,8,0,8
F,4,7,6,5,6,8,8,1,4,10,6,6,5,10,4,5
E,4,9,5,7,3,3,7,6,11,7,6,15,0,8,7,7
T,3,3,4,2,1,5,13,3,7,12,9,3,1,10,1,5
N,5,6,6,6,6,6,7,5,3,6,5,8,7,9,4,8
R,6,12,6,6,5,5,9,2,5,7,5,10,4,7,6,7
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
U,4,7,6,6,5,7,7,4,4,6,6,9,4,9,1,8
P,3,7,3,5,3,5,10,8,3,9,6,5,1,10,3,8
F,5,7,7,5,3,5,13,5,5,13,7,3,2,10,2,6
Q,5,5,5,6,5,8,7,6,2,8,6,11,3,9,6,8
Z,2,3,3,4,1,7,7,3,13,9,6,8,0,8,7,8
S,5,11,5,6,2,8,7,4,3,13,7,8,3,10,3,8
N,4,8,4,6,2,7,7,14,2,4,6,8,6,8,0,8
W,1,0,2,0,1,7,8,3,0,7,8,8,5,10,0,8
M,9,12,11,6,6,6,3,3,2,7,4,11,10,1,2,8
Q,2,3,3,4,2,7,8,5,2,7,8,10,2,9,4,8
D,4,8,5,6,4,7,7,7,7,7,6,4,3,8,3,7
B,1,3,3,2,2,8,7,3,5,10,5,7,2,8,4,9
Q,5,11,5,6,3,10,4,4,7,11,3,9,3,7,8,11
W,7,8,7,6,5,4,11,3,3,9,9,7,7,11,2,6
Q,2,2,3,3,2,7,9,4,1,8,8,10,2,9,4,8
N,7,10,10,7,6,4,9,3,4,10,10,9,8,7,2,7
E,4,9,5,6,4,7,7,3,8,11,7,9,3,8,5,8
M,5,8,8,6,9,8,6,3,2,8,4,8,12,5,2,7
Q,3,7,4,6,2,8,7,8,5,6,7,8,3,8,5,9
J,1,4,2,2,1,9,6,3,5,12,5,9,1,7,1,7
U,6,6,6,4,4,5,8,5,7,8,6,9,5,9,5,3
L,8,13,8,7,5,8,4,3,5,11,6,11,3,7,7,8
H,5,9,7,7,6,6,6,6,4,6,5,8,6,7,6,10
R,3,2,4,4,3,6,7,5,5,6,5,7,3,7,4,8
X,6,10,9,8,4,5,9,2,9,10,10,9,3,8,4,6
B,2,1,3,2,2,7,8,8,5,7,6,7,2,8,8,9
V,6,10,6,8,3,4,11,2,4,9,11,7,3,9,1,7
X,3,5,4,5,4,7,6,2,5,7,6,9,2,11,7,8
R,4,9,6,7,8,8,9,3,5,5,7,8,5,9,8,7
T,3,9,5,6,4,8,11,2,7,6,11,7,2,12,1,7
S,3,8,4,6,4,8,7,7,5,7,7,9,2,9,8,8
O,5,10,7,8,3,8,7,9,8,7,6,9,3,8,4,8
K,6,9,9,7,5,9,6,2,7,10,3,9,7,7,6,9
U,4,9,6,8,6,7,7,5,4,6,7,8,6,8,1,7
L,2,5,4,4,2,7,4,1,8,8,2,10,0,7,2,8
U,5,9,8,7,10,8,6,4,4,6,7,8,11,9,6,8
Z,5,6,6,8,3,7,7,4,15,9,6,8,0,8,8,8
X,5,7,6,6,6,6,7,2,5,7,7,10,3,8,7,8
I,4,9,3,5,2,6,10,2,5,13,6,4,1,9,4,8
T,4,6,4,4,2,6,11,2,9,11,9,4,0,10,3,5
P,5,5,6,7,8,8,6,4,3,6,7,7,6,9,4,5
M,5,6,8,4,4,5,7,3,5,10,10,10,8,5,2,7
Y,3,5,5,7,7,8,4,4,3,7,8,8,5,9,5,8
W,3,1,3,2,1,8,8,4,0,7,8,8,7,10,0,8
Z,3,2,3,3,2,7,8,5,10,6,6,9,1,8,7,8
J,6,7,8,9,7,7,8,4,7,6,7,7,3,10,9,9
D,2,1,2,1,1,7,7,6,6,7,6,5,2,8,2,7
K,3,7,3,4,1,3,7,7,2,7,5,11,3,8,2,11
O,5,9,7,8,5,6,6,6,5,8,5,8,4,6,5,6
X,3,7,5,5,2,10,5,2,8,10,1,7,3,7,3,10
C,6,11,4,6,2,6,8,7,6,11,7,7,2,9,5,8
G,5,9,6,7,5,7,7,8,5,5,7,9,3,6,5,9
B,3,4,5,3,3,8,7,3,5,10,5,7,2,8,4,9
B,2,3,2,2,2,7,7,5,5,7,6,6,2,8,5,9
W,5,7,7,5,3,8,8,5,1,7,8,8,8,9,0,8
I,2,8,3,6,2,8,7,0,7,13,6,9,0,8,1,8
C,1,0,2,1,0,7,7,6,8,7,6,13,0,8,4,10
X,6,10,8,8,7,7,7,3,6,6,6,8,6,7,13,10
X,4,11,6,8,5,7,7,3,8,5,6,8,2,8,6,8
M,3,3,4,4,2,7,6,11,1,8,9,8,7,6,0,8
P,3,6,4,9,8,7,6,6,1,7,6,7,4,10,7,11
M,6,10,8,8,8,6,6,6,5,7,8,11,8,6,2,9
F,4,8,6,6,4,9,8,1,6,13,5,5,2,9,3,9
M,6,7,8,5,6,8,6,2,5,9,8,8,8,4,2,7
S,2,3,4,2,2,8,8,2,7,10,5,7,1,9,4,8
P,3,5,5,3,2,7,11,4,3,12,5,2,1,10,2,8
C,3,6,4,4,2,6,7,6,7,6,7,12,1,8,4,10
Q,3,4,4,5,3,8,7,7,2,5,7,9,2,9,5,10
O,2,3,3,1,1,8,7,7,5,7,6,8,2,8,3,8
G,2,1,3,2,1,8,6,6,6,6,5,9,1,7,6,10
C,2,4,3,6,1,5,7,7,9,8,6,14,1,9,4,9
A,4,11,8,8,6,9,5,1,4,7,2,6,3,8,6,6
Z,3,7,5,5,3,8,7,2,9,11,5,7,1,7,6,8
V,4,5,6,4,6,7,7,5,4,7,6,8,6,9,7,7
E,4,7,6,6,6,6,8,3,3,8,7,8,5,12,9,9
T,1,0,2,0,0,7,14,1,4,7,10,8,0,8,0,8
N,5,8,6,6,5,7,6,9,5,5,4,5,5,7,4,10
E,5,9,8,6,5,10,6,2,9,11,4,8,2,8,5,11
A,4,9,6,7,4,8,2,2,3,7,1,8,2,7,3,6
R,4,6,5,4,3,9,7,4,5,10,3,7,3,7,4,10
U,2,1,3,1,1,6,9,6,6,7,10,9,3,10,1,8
E,4,10,4,7,4,3,8,5,9,7,6,14,0,8,6,8
V,7,10,7,8,4,2,12,2,3,10,11,8,2,10,1,8
B,5,10,6,8,8,8,8,6,6,7,6,6,6,8,6,10
Z,4,6,6,4,4,7,8,2,8,12,7,8,1,9,6,7
K,4,7,6,5,3,6,7,2,7,10,7,10,3,8,3,8
D,4,9,5,6,5,7,7,6,6,7,6,5,3,8,3,7
X,3,5,5,4,4,9,8,2,6,8,5,6,2,6,7,7
P,5,5,7,6,7,7,8,4,3,8,8,6,6,11,5,5
H,7,9,10,7,8,8,7,2,6,10,6,8,3,8,3,7
C,4,8,5,6,6,6,7,4,4,6,7,10,6,10,4,8
D,5,11,7,8,7,9,7,4,5,9,4,6,3,8,3,8
Q,5,7,6,9,7,9,7,7,2,5,6,10,3,9,6,9
Y,6,10,6,7,3,4,10,1,8,10,10,5,2,12,4,3
R,5,9,6,7,5,7,9,4,6,6,4,8,4,5,6,9
G,3,7,4,5,2,7,8,8,7,6,7,8,2,7,6,11
R,4,7,5,5,4,7,8,5,7,7,4,6,6,7,5,8
F,2,4,4,3,2,6,11,2,6,13,6,4,1,10,1,7
Q,5,9,5,5,3,10,5,4,6,12,3,8,3,8,7,11
F,4,10,4,7,2,0,13,4,4,12,11,7,0,8,2,6
Q,7,9,7,11,7,8,6,7,5,9,8,9,5,9,8,9
T,2,3,3,2,1,5,12,3,6,11,9,4,1,10,2,5
C,3,8,4,6,2,6,8,7,9,5,7,12,1,7,4,9
N,4,10,6,7,4,7,9,6,5,7,6,7,6,8,2,6
J,1,2,2,3,1,10,6,2,6,12,4,8,0,7,1,7
O,2,1,3,2,1,7,7,7,6,7,6,8,2,8,3,8
Z,2,5,3,4,3,8,7,5,9,6,6,7,2,8,7,8
M,3,7,4,5,4,8,5,11,1,6,8,8,6,5,0,7
V,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8
X,3,6,5,4,3,7,7,1,7,10,6,8,3,8,3,7
H,4,8,4,5,2,7,5,15,1,7,8,8,3,8,0,8
Z,3,7,5,5,3,7,8,2,9,11,6,9,1,9,6,8
G,5,9,5,6,3,6,7,6,7,10,8,10,2,9,4,9
V,4,5,6,4,5,7,8,5,5,7,6,8,6,9,7,6
U,4,10,6,8,10,9,8,4,4,6,7,8,9,7,8,8
F,0,0,1,0,0,3,12,4,2,11,9,6,0,8,2,8
X,4,5,6,4,3,7,7,1,9,10,6,8,2,8,3,7
A,5,11,8,8,6,6,3,2,3,4,2,7,5,5,5,5
Y,2,1,2,1,0,8,10,3,1,6,13,8,1,11,0,8
U,2,3,3,2,1,5,8,5,6,11,9,9,3,9,1,6
O,2,4,3,3,2,8,6,6,3,9,5,8,2,8,2,8
O,5,11,6,8,6,7,6,7,4,10,6,10,5,8,4,6
C,4,8,5,6,3,4,8,5,6,9,8,14,3,7,4,8
C,5,10,6,8,4,4,8,7,9,8,9,13,1,8,4,9
W,5,10,7,8,8,8,9,6,4,5,9,9,7,8,4,5
J,4,7,5,5,2,7,7,3,6,15,5,9,0,7,0,7
T,3,10,5,7,1,7,15,0,6,7,11,8,0,8,0,8
A,4,10,7,8,5,11,2,2,2,9,2,9,4,5,4,8
H,4,7,5,5,5,7,7,6,6,7,6,8,6,8,3,8
Q,3,4,4,5,3,8,8,6,2,5,7,9,3,9,5,10
M,5,6,7,4,5,10,6,2,4,9,4,7,7,6,2,8
O,3,4,4,6,2,7,7,9,7,7,6,8,3,8,4,8
B,3,1,3,2,3,7,7,5,5,7,6,6,2,8,6,9
I,2,9,5,7,5,10,7,2,5,9,4,5,3,8,6,5
Q,7,9,10,8,8,5,4,4,5,4,4,7,4,8,6,6
D,4,8,5,6,4,7,8,7,8,8,7,5,3,8,3,7
M,4,7,5,5,3,7,7,12,1,7,9,8,8,6,0,8
N,5,7,7,5,3,7,9,2,4,10,6,6,5,9,1,7
O,4,11,5,8,5,8,7,8,4,7,7,7,3,7,3,8
U,3,2,5,3,2,6,8,6,8,7,10,9,3,9,1,8
N,3,5,5,4,3,7,9,2,4,10,6,6,5,9,1,7
H,4,9,5,7,5,8,7,6,6,7,6,6,6,8,3,6
A,2,2,4,3,2,8,2,1,1,7,2,8,2,7,3,7
A,2,4,4,2,2,10,2,2,2,9,2,8,2,6,2,8
N,9,12,7,7,3,9,11,5,6,3,5,9,5,8,2,7
N,3,4,5,3,2,7,9,3,4,10,6,6,5,9,0,7
X,2,2,3,3,2,8,7,3,8,6,6,8,2,8,6,8
G,5,5,6,7,3,8,6,8,9,6,5,11,1,8,6,11
G,3,3,4,5,2,7,8,8,7,6,7,8,2,7,6,11
H,4,8,4,5,2,7,5,15,1,7,8,8,3,8,0,8
B,4,5,4,3,4,7,7,6,6,7,6,6,2,8,6,10
J,3,8,3,6,2,14,3,4,4,13,1,8,0,7,0,8
H,4,5,6,7,6,7,8,4,1,8,6,6,4,10,8,5
C,4,5,5,4,2,4,8,5,7,11,10,12,1,9,3,7
N,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8
S,3,6,4,4,3,8,6,6,5,7,7,10,2,10,8,8
K,3,7,4,5,3,5,7,5,7,6,5,10,3,8,4,9
Y,6,11,6,8,2,3,12,5,6,13,12,6,2,11,2,6
X,4,9,7,7,3,7,7,1,9,10,6,8,3,8,4,7
Y,2,4,3,2,1,4,11,2,7,11,10,5,1,11,2,5
O,2,5,3,4,2,8,7,7,4,9,6,8,2,8,2,8
Z,4,4,6,6,3,12,4,4,4,11,2,9,2,7,4,11
C,2,4,3,3,1,6,8,7,7,9,7,12,1,10,4,10
R,3,5,6,4,4,8,7,3,5,10,4,6,3,7,4,10
Q,6,9,8,8,7,7,4,5,6,7,4,9,5,5,7,7
Y,2,5,4,4,2,6,10,1,6,8,11,8,1,11,2,7
A,2,4,3,3,1,11,3,3,2,11,2,9,1,6,2,9
U,7,10,7,7,5,3,8,5,7,10,9,9,3,9,2,6
C,3,7,4,5,1,6,8,6,9,5,6,14,1,7,4,9
M,5,5,6,4,5,8,6,6,5,7,7,8,9,6,3,7
B,7,11,9,8,7,10,6,4,7,10,4,7,3,8,6,11
N,2,3,3,5,2,7,7,13,1,5,6,8,5,8,0,8
F,2,4,4,3,1,6,12,3,5,13,7,4,1,9,2,6
C,2,4,3,3,1,4,8,5,7,11,9,12,1,9,2,7
G,2,4,3,3,2,6,7,5,4,9,8,10,2,9,4,9
J,1,3,2,4,1,13,3,7,4,13,4,11,1,6,0,8
A,5,11,5,6,4,10,3,4,2,10,5,11,5,3,5,10
U,7,10,8,8,5,3,8,5,7,10,10,10,3,9,2,6
C,5,10,3,5,2,5,9,6,6,11,8,8,2,9,5,8
S,4,7,6,6,7,8,7,5,4,7,7,7,5,9,10,12
I,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8
S,3,8,4,6,3,8,8,8,8,7,4,7,2,6,9,8
H,2,2,3,3,3,8,8,6,6,7,6,7,3,8,3,7
B,3,5,4,3,4,7,7,5,5,6,6,6,2,8,6,10
Z,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8
F,5,8,7,6,4,7,10,2,6,13,6,5,2,10,2,8
D,5,9,7,7,6,10,7,5,6,9,3,6,3,8,3,8
F,2,3,2,1,1,6,10,4,5,10,9,4,1,10,3,6
K,3,4,5,3,2,7,6,1,6,10,6,10,3,8,3,8
W,6,8,8,7,9,8,8,5,6,7,6,8,9,8,8,7
T,0,0,1,0,0,7,13,1,4,7,10,8,0,8,0,8
E,3,11,5,8,5,7,7,6,8,6,5,9,3,8,6,9
L,2,3,2,2,1,4,3,5,7,2,2,5,0,7,1,6
I,3,7,4,5,2,7,7,0,7,13,6,8,0,8,1,8
V,6,11,6,8,3,3,12,5,4,11,12,7,3,9,1,8
P,4,5,5,7,6,8,9,3,2,7,8,6,5,11,5,4
H,4,6,6,4,4,7,7,3,6,10,7,8,3,9,3,7
O,2,4,3,3,2,7,7,7,5,7,6,8,2,8,3,8
N,5,9,8,7,4,9,7,3,5,10,3,5,6,9,1,7
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
Y,1,0,2,0,0,7,9,2,2,6,12,8,1,10,0,8
I,1,2,1,3,1,7,7,1,7,7,6,8,0,8,3,8
R,1,0,2,1,1,6,9,7,3,7,5,7,2,7,4,10
J,3,10,4,7,2,10,6,0,8,11,3,6,0,7,1,7
N,10,15,9,8,4,8,10,5,4,4,6,10,6,11,3,6
Y,4,7,6,9,9,9,6,6,3,7,7,8,7,9,6,5
B,1,1,2,1,1,7,7,7,5,7,6,7,1,8,6,8
S,5,5,6,4,6,9,8,4,5,7,7,8,5,10,9,11
H,1,0,1,0,0,7,7,10,1,7,6,8,2,8,0,8
G,2,4,2,2,1,6,7,5,5,9,7,10,2,8,4,9
G,6,9,5,4,3,10,6,4,6,11,3,8,4,7,5,9
C,8,11,5,6,2,7,9,6,7,12,7,8,2,9,5,9
N,2,1,2,1,1,7,7,12,1,5,6,8,5,8,0,8
T,4,11,5,8,3,7,14,0,5,7,10,8,0,8,0,8
K,4,5,7,3,3,6,8,2,8,10,8,10,4,7,3,6
F,6,11,9,8,9,5,8,2,4,10,9,8,6,11,6,6
X,2,3,3,2,2,8,7,3,9,6,6,8,2,8,5,8
F,3,7,5,5,3,5,10,4,6,11,10,5,2,9,3,5
O,6,8,8,7,6,8,4,3,4,9,3,8,4,7,5,8
W,1,0,2,0,0,7,8,3,0,7,8,8,5,9,0,8
Y,5,9,8,6,4,8,10,1,7,5,11,8,2,12,3,8
J,2,6,3,4,1,10,7,1,6,12,3,7,0,7,0,7
U,12,15,10,8,4,5,3,5,5,4,7,7,5,8,2,6
V,3,5,4,3,1,3,12,4,3,11,11,7,2,10,1,8
Z,7,10,9,8,5,7,8,3,10,12,7,7,1,8,6,7
T,4,9,6,8,6,7,9,4,7,7,8,8,3,12,10,7
E,6,11,4,6,2,7,8,5,7,8,4,12,1,7,7,8
J,2,9,3,7,1,15,2,6,5,14,1,9,0,7,0,8
W,10,10,10,8,6,2,10,2,3,10,11,9,8,10,1,7
D,4,10,5,7,3,6,7,11,9,7,6,6,3,8,4,8
M,6,9,7,4,4,10,10,6,3,5,7,9,10,13,2,6
S,3,8,4,6,3,7,8,8,7,8,5,7,2,7,9,8
T,3,5,4,4,4,6,8,4,7,7,7,9,3,10,6,7
V,4,10,6,8,4,8,11,2,3,4,10,9,3,11,2,8
O,7,10,7,8,5,8,6,8,6,10,5,9,5,9,6,5
D,5,11,7,9,6,9,7,5,7,10,5,5,3,8,3,8
B,2,5,4,4,3,8,8,3,5,10,6,6,3,7,6,9
Q,2,4,3,4,3,8,8,6,3,8,6,9,2,9,4,9
L,3,7,4,5,1,0,0,6,6,0,1,5,0,8,0,8
S,2,1,2,2,1,8,7,4,7,5,6,7,0,8,8,8
U,3,2,4,3,2,8,9,5,7,5,9,8,3,9,1,8
L,3,7,3,5,1,1,1,5,5,0,1,6,0,8,0,8
J,2,3,3,2,1,8,6,3,6,14,6,10,0,7,0,8
Y,4,6,7,8,1,6,10,3,2,8,13,8,1,11,0,8
E,4,7,5,5,4,9,6,2,6,11,5,8,3,8,4,11
W,3,7,5,5,3,10,8,4,1,7,8,8,8,9,0,8
H,6,11,9,8,7,9,7,3,6,10,5,8,3,8,3,8
H,2,1,3,2,2,7,8,6,6,7,6,8,3,8,3,8
G,4,8,5,6,3,8,6,6,7,11,6,11,2,10,4,9
M,3,6,4,4,2,8,7,11,1,7,9,8,7,5,0,8
M,15,15,15,8,8,3,9,6,6,3,2,13,10,12,2,8
Z,3,8,4,6,3,7,7,5,9,7,6,8,2,8,7,8
D,2,4,4,2,2,8,7,4,6,10,5,6,2,8,3,8
Q,5,10,7,9,3,8,5,9,8,6,4,8,3,8,4,8
O,1,0,1,0,0,7,7,6,3,7,6,8,2,8,3,8
B,5,10,8,8,12,8,8,5,3,7,7,7,8,11,12,9
C,4,8,5,6,3,4,9,6,7,7,8,14,1,8,4,10
I,4,7,5,5,3,8,4,2,6,7,7,7,1,10,4,7
N,7,11,8,6,3,9,7,2,4,13,5,8,6,8,0,8
Y,9,10,7,14,5,9,11,2,3,7,10,5,4,10,6,9
G,3,4,4,3,2,7,6,6,5,6,6,9,2,9,4,9
Q,4,9,4,5,3,10,5,4,6,11,4,8,3,8,8,12
F,2,3,3,2,1,7,9,2,5,13,6,5,1,9,1,8
V,7,13,6,7,3,7,11,5,5,8,11,5,4,11,4,8
F,4,6,4,8,2,0,14,4,4,12,10,6,0,8,2,6
X,2,1,2,1,0,7,7,4,4,7,6,8,2,8,4,8
Y,1,0,2,1,0,7,9,1,2,6,12,8,1,10,0,8
P,7,11,10,8,6,9,8,4,6,12,3,3,2,10,4,9
L,3,9,4,7,2,0,2,4,6,1,0,8,0,8,0,8
U,9,12,8,7,5,7,6,5,5,6,7,8,6,8,3,7
N,11,13,9,7,4,5,9,5,6,3,3,12,6,12,2,7
Y,3,8,4,6,2,8,10,1,3,7,12,8,1,11,0,8
A,2,8,4,6,2,12,2,4,3,11,2,10,2,6,4,9
Y,2,9,4,6,1,8,10,2,3,6,12,8,1,10,0,8
H,3,3,3,4,1,7,9,14,1,7,4,8,3,8,0,8
U,5,9,6,6,6,8,6,8,5,7,6,9,3,8,4,5
S,3,5,4,7,2,9,9,6,10,5,6,5,0,7,9,7
N,3,3,5,1,1,8,8,3,5,11,5,6,5,9,0,6
Q,5,7,7,6,6,7,4,5,5,6,5,9,5,4,7,7
Q,3,3,4,4,3,8,8,6,2,5,7,10,2,9,5,9
I,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8
H,3,5,6,4,3,9,6,3,6,10,3,7,4,7,4,9
O,6,12,4,6,3,7,8,5,4,8,4,7,5,8,5,8
T,2,3,3,2,1,6,11,2,7,10,9,5,1,10,2,5
D,6,11,8,8,8,8,7,7,6,8,5,4,8,11,7,10
W,5,10,7,8,4,6,8,4,2,7,8,8,9,9,0,8
R,6,10,5,5,3,7,6,5,4,9,5,9,6,5,6,11
Z,2,4,5,3,2,7,7,2,9,12,6,8,1,8,5,8
U,2,6,2,4,1,7,6,12,4,7,13,8,3,9,0,8
E,1,0,2,1,1,5,7,5,8,7,6,12,0,8,6,9
H,5,10,7,7,9,8,7,4,3,6,7,8,8,8,8,8
K,2,3,4,2,2,6,8,2,6,10,6,9,3,8,2,8
G,1,3,2,1,1,6,7,5,4,9,7,10,2,8,4,9
A,2,4,3,3,1,11,3,2,2,9,2,9,1,6,2,8
Y,6,11,6,8,4,3,9,1,7,10,10,6,1,11,3,4
N,3,8,4,6,3,8,8,6,5,6,6,5,6,9,2,5
E,4,7,6,6,7,7,6,4,3,7,6,9,6,11,8,12
I,5,13,4,7,2,9,7,5,4,12,3,6,3,8,5,10
X,1,0,1,0,0,7,7,3,5,7,6,8,2,8,4,8
N,7,9,6,5,3,8,10,5,4,4,6,10,6,11,3,7
H,5,5,6,6,3,7,5,15,1,7,8,8,3,8,0,8
E,3,5,5,3,3,7,8,2,8,11,7,9,2,9,4,8
H,4,7,6,5,4,8,7,6,7,7,6,5,6,8,3,7
D,5,10,5,5,4,9,6,3,6,10,4,7,5,8,7,7
M,4,7,5,5,3,6,7,12,1,8,9,8,8,6,0,8
J,1,3,2,2,1,11,6,2,5,11,4,8,0,7,0,7
A,3,9,6,7,4,12,3,2,2,9,2,9,2,6,2,8
C,8,14,6,8,3,6,9,6,9,11,8,10,2,8,5,8
K,5,8,6,6,6,5,7,5,7,6,6,13,3,8,6,9
C,4,9,5,6,3,6,7,6,8,6,5,12,1,8,4,9
I,1,6,2,4,1,9,7,0,7,13,5,8,0,8,1,8
Q,4,7,5,8,5,9,9,6,2,4,7,11,3,9,5,10
H,4,8,5,6,4,7,8,6,6,7,6,7,3,8,3,7
A,9,13,8,8,4,10,0,3,2,10,4,12,3,5,4,8
M,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8
M,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8
Z,9,15,9,8,6,8,5,2,9,12,6,10,3,8,6,8
H,4,10,5,7,3,7,8,15,1,7,5,8,3,8,0,8
Q,3,6,4,8,4,7,9,5,3,8,9,10,3,8,6,8
U,3,3,4,2,2,5,8,5,7,10,8,9,3,10,2,5
C,4,7,4,5,3,4,7,5,6,11,9,14,1,9,3,9
I,1,9,0,7,1,7,7,5,3,7,6,8,0,8,0,8
X,1,3,2,1,1,8,7,3,8,7,6,8,2,8,5,8
S,3,9,4,7,3,8,8,5,8,5,5,6,0,8,8,8
T,5,10,7,8,7,9,11,4,6,5,11,8,3,12,1,8
F,4,5,4,7,2,1,12,5,6,12,11,8,0,8,2,5
A,2,9,4,6,2,7,4,3,2,6,1,8,2,7,2,8
R,4,8,5,6,3,6,10,9,4,7,4,8,3,8,5,10
G,4,7,5,5,3,6,7,6,7,10,7,11,2,9,4,9
Y,6,6,5,8,4,9,8,4,4,5,11,5,4,11,5,6
S,8,15,8,8,4,7,7,4,3,13,8,9,3,10,3,8
R,4,9,6,7,8,7,9,3,5,5,6,9,5,8,8,7
F,2,3,2,2,1,5,11,4,4,11,9,5,1,9,3,7
L,2,7,4,5,5,8,8,3,4,6,6,9,5,11,6,5
W,4,7,6,5,4,7,11,2,2,6,9,8,8,11,1,8
W,5,9,5,4,3,6,9,1,3,8,9,7,7,12,1,6
P,2,6,3,4,1,4,11,8,2,10,6,4,1,10,3,8
H,5,9,6,7,7,7,7,6,6,7,6,8,3,8,3,8
K,3,7,4,5,3,3,7,6,3,7,6,11,3,8,3,11
L,2,7,3,5,1,0,1,5,6,0,0,7,0,8,0,8
S,4,7,6,5,6,8,6,4,3,8,6,8,4,8,10,10
D,5,9,7,6,6,9,7,3,6,11,5,6,4,7,4,8
E,4,7,5,5,3,6,8,3,8,11,7,8,2,8,4,7
S,2,6,3,4,2,8,8,7,5,7,5,6,2,8,8,8
L,4,7,5,5,4,7,6,8,6,5,6,8,3,7,5,11
Z,4,8,6,6,4,8,6,2,9,11,4,10,2,7,7,10
Y,5,9,5,4,2,5,8,3,3,10,9,6,4,9,3,4
M,3,7,4,5,4,7,6,5,4,7,7,11,6,5,1,10
W,4,8,6,6,6,10,12,2,2,5,8,8,6,11,0,8
D,3,8,4,6,2,5,6,10,8,5,5,5,3,8,4,8
A,3,3,5,5,2,6,4,3,1,6,1,8,3,7,2,7
Q,3,3,4,4,3,8,8,7,3,5,7,10,2,9,5,10
Y,6,10,8,8,8,8,5,6,5,8,7,8,6,9,7,3
X,3,5,4,6,1,7,7,4,4,7,6,8,3,8,4,8
V,4,7,6,5,6,8,5,4,2,7,7,8,7,8,5,8
Y,3,3,5,4,1,8,11,2,2,5,12,8,1,10,0,8
Q,5,9,6,8,3,8,5,9,7,5,4,8,3,8,4,8
E,4,11,4,8,3,3,8,6,11,7,5,14,0,8,7,7
U,5,4,5,7,2,7,4,15,6,7,14,8,3,9,0,8
H,6,10,8,8,7,10,6,2,6,10,3,7,4,8,4,10
G,3,4,4,3,2,6,7,5,5,9,8,9,2,9,4,9
O,3,7,4,5,2,8,6,9,6,7,5,8,3,8,4,8
N,4,9,4,6,2,7,7,14,2,5,6,8,6,8,0,8
V,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8
U,8,10,9,8,5,4,8,6,9,10,9,9,3,9,3,5
I,3,8,4,6,2,5,9,0,7,13,7,7,0,8,1,7
F,7,8,8,9,9,6,9,4,5,7,7,6,5,8,11,8
V,3,4,4,3,1,5,12,2,2,9,11,7,3,12,1,7
Y,4,8,6,6,5,9,5,7,5,6,9,7,3,9,9,4
H,6,10,9,8,7,6,7,5,5,6,5,8,3,7,7,10
L,4,11,5,8,4,7,4,0,7,9,3,10,1,6,3,8
H,4,4,5,6,2,7,8,15,1,7,5,8,3,8,0,8
X,4,4,5,6,1,7,7,5,4,7,6,8,3,8,4,8
B,2,6,4,4,3,7,7,7,6,7,6,6,2,8,7,9
X,11,13,10,8,5,6,8,2,9,9,7,9,5,7,5,7
H,4,4,6,3,3,9,7,3,6,10,3,7,3,7,3,9
A,3,9,4,6,3,7,4,3,1,6,2,8,2,7,3,8
N,5,8,6,6,4,6,9,6,5,8,7,9,6,9,1,7
P,4,11,6,8,6,6,10,4,5,10,9,4,4,10,4,7
Z,1,1,2,1,0,8,7,2,10,9,6,8,0,8,6,8
V,4,7,6,5,6,7,6,4,2,8,8,8,5,10,4,7
O,5,8,5,6,4,7,9,7,4,10,8,6,4,9,4,9
J,2,5,4,3,1,9,6,3,6,14,5,10,1,6,1,7
Q,4,5,5,6,5,8,4,7,4,6,5,8,3,8,4,9
J,2,4,4,3,1,8,6,3,6,14,6,11,1,6,1,7
Z,4,7,6,5,3,7,8,2,10,12,6,8,1,9,6,8
M,6,9,8,7,8,8,7,2,4,9,7,8,8,6,2,8
B,5,11,6,8,5,6,8,9,8,7,5,7,2,8,9,10
U,3,6,5,4,3,7,9,6,6,5,9,9,3,9,1,8
X,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8
L,4,11,4,8,3,0,2,4,6,1,0,7,0,8,0,8
D,3,8,4,6,4,6,7,9,6,7,7,5,3,8,3,8
Y,3,7,5,5,2,7,11,1,3,8,11,8,1,10,0,8
B,4,8,6,7,7,8,7,5,5,7,6,8,6,9,8,4
L,4,10,4,5,2,10,3,2,5,12,3,11,2,9,4,10
S,4,4,4,6,2,8,8,5,9,5,6,6,0,8,9,7
R,5,10,6,7,7,8,7,7,4,8,5,7,4,7,7,10
O,3,8,5,6,3,7,8,9,5,7,7,6,3,8,3,8
L,4,8,6,7,5,7,7,4,5,6,7,9,4,8,8,8
Q,3,6,4,5,3,8,8,7,5,6,6,9,2,8,4,8
Y,5,8,5,6,3,6,9,1,8,8,9,4,1,11,4,5
Y,4,5,5,4,2,4,10,1,8,10,10,5,4,11,4,3
B,1,0,1,0,1,7,8,6,4,7,6,7,1,8,6,9
F,4,7,5,5,3,4,12,2,5,13,7,5,1,10,1,7
L,6,12,6,6,4,9,4,3,4,12,7,11,3,10,5,11
I,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8
J,4,10,5,8,2,6,8,2,7,15,7,9,1,6,1,7
R,2,3,4,2,2,8,8,3,5,10,4,7,2,7,3,9
F,5,9,8,6,4,6,11,3,6,14,6,4,2,10,2,7
X,4,7,6,5,3,5,8,1,8,10,10,9,2,9,3,6
D,5,11,6,8,7,9,8,6,5,9,5,4,5,8,4,10
G,4,6,5,4,3,7,7,8,7,6,6,11,2,9,4,9
B,4,7,4,5,3,6,7,8,6,7,6,7,2,8,9,10
T,7,9,7,7,5,5,12,4,6,11,9,4,2,12,2,4
G,3,4,4,3,2,6,7,5,5,9,7,10,2,8,4,9
X,4,11,5,8,2,7,7,4,4,7,6,8,3,8,4,8
I,2,8,5,6,5,9,6,3,5,8,5,5,5,8,5,5
P,5,10,5,7,3,5,9,10,5,8,6,5,2,9,4,8
G,5,7,6,5,3,7,6,7,7,10,6,11,2,10,4,9
D,4,10,5,8,6,7,7,6,6,6,5,5,3,8,3,7
A,4,11,6,8,3,9,3,3,3,8,1,9,3,6,3,9
J,3,6,2,9,2,9,8,3,3,12,4,5,2,8,5,10
K,6,10,8,8,9,6,5,5,4,6,6,8,4,5,9,10
E,4,7,6,5,6,8,8,3,5,5,7,10,5,12,8,9
Z,6,9,8,7,4,8,6,3,11,12,5,10,1,8,6,8
G,4,9,5,6,7,8,7,5,2,6,6,8,8,8,7,12
T,3,5,4,3,2,5,12,2,8,11,9,4,1,10,2,5
E,3,7,4,5,4,8,7,2,6,11,6,8,3,8,4,10
M,3,4,6,3,3,7,6,3,4,9,7,8,7,5,1,8
V,4,9,6,7,7,7,8,3,2,7,8,8,8,9,6,7
O,2,1,2,1,1,7,7,6,4,7,5,8,2,8,2,7
Y,7,10,7,8,4,2,10,2,6,10,12,7,2,11,2,6
D,4,7,5,5,8,9,8,4,5,7,6,6,4,6,8,7
V,4,5,5,3,2,4,12,2,3,9,11,7,4,10,1,7
D,5,10,6,8,6,6,8,9,8,7,6,5,2,8,3,7
S,3,5,5,3,2,8,7,3,8,11,5,7,1,9,4,8
P,4,7,5,5,2,7,10,3,5,14,5,3,0,10,3,8
K,2,1,2,2,0,4,7,8,1,7,6,11,3,8,2,11
I,3,9,5,7,5,12,5,1,6,8,4,5,1,8,5,10
I,6,14,6,8,3,8,7,2,5,13,4,5,2,8,6,10
H,5,6,8,4,4,6,7,3,6,10,8,10,3,8,3,7
O,2,3,3,2,1,8,7,6,4,9,6,8,2,8,2,8
G,5,11,6,8,4,6,6,7,8,11,6,13,2,9,4,8
O,3,9,4,7,4,7,7,8,4,7,6,9,3,8,3,8
P,2,1,2,2,1,5,11,4,3,10,8,3,0,9,3,7
Z,2,2,3,3,2,7,7,5,9,6,6,8,2,8,7,8
E,5,8,7,7,7,7,9,5,5,7,7,10,5,9,9,10
Q,3,7,4,5,4,8,5,7,3,6,7,8,3,7,6,9
V,7,9,9,8,10,7,8,5,6,8,6,8,7,7,8,8
X,4,5,5,4,3,8,7,3,9,6,6,8,2,8,6,9
P,4,5,4,7,2,3,13,8,1,11,6,3,1,10,4,8
E,3,6,4,4,4,8,7,6,2,7,6,11,3,8,7,10
F,4,6,6,7,7,8,9,5,5,7,5,8,5,8,8,6
L,3,7,3,5,1,0,0,6,6,0,1,5,0,8,0,8
W,7,9,8,5,4,7,8,3,4,6,9,6,9,11,3,5
I,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8
E,3,7,5,5,5,5,7,3,6,7,6,12,2,10,8,6
Y,4,8,5,6,2,5,10,2,2,8,12,8,2,11,0,8
O,3,7,4,5,3,7,7,8,5,7,7,8,3,8,3,8
X,5,8,8,6,4,5,8,2,8,11,10,10,3,8,3,6
V,3,8,5,6,1,6,8,4,3,7,14,8,3,10,0,8
W,4,7,6,5,9,8,7,5,2,7,7,8,10,10,3,8
R,3,7,5,5,3,8,8,5,5,8,5,7,3,7,5,10
J,2,3,4,2,1,9,6,3,5,14,6,10,0,7,0,8
Y,6,8,6,6,3,4,10,1,8,11,10,6,2,11,4,3
O,6,10,7,8,7,8,7,9,4,7,6,8,3,8,3,7
P,2,1,2,3,2,5,9,4,4,9,8,5,1,10,3,7
H,1,0,2,1,0,7,7,11,1,7,6,8,3,8,0,8
U,9,10,9,8,4,3,8,6,8,12,11,10,3,9,2,6
T,1,6,2,4,1,7,13,0,4,7,10,8,0,8,0,8
Q,3,5,3,6,3,8,8,5,3,8,8,10,3,9,6,8
R,3,4,5,3,3,8,8,3,5,9,4,7,2,6,4,10
N,3,8,4,6,4,7,7,12,1,6,6,8,5,8,0,8
U,5,6,6,6,6,8,6,5,4,6,6,8,4,8,1,7
O,2,1,3,2,1,7,7,7,6,7,6,8,2,8,3,8
E,2,4,4,3,2,6,8,2,8,11,7,9,2,8,4,8
P,7,11,7,6,4,8,9,5,3,11,5,4,4,11,6,5
L,4,8,6,6,7,8,7,3,5,7,7,9,7,9,6,6
S,7,10,8,8,6,7,8,3,6,10,5,7,2,8,5,8
Y,2,3,4,4,0,7,10,1,3,7,12,8,1,11,0,8
W,6,10,8,8,9,7,7,6,2,7,8,8,6,8,4,8
Y,1,1,3,2,1,5,10,1,6,9,11,9,1,11,2,8
Y,3,5,5,7,1,7,10,1,3,7,12,8,1,11,0,8
E,2,4,3,3,2,7,7,2,7,11,7,9,2,8,4,8
F,4,10,6,7,3,4,13,4,4,13,8,3,1,10,2,5
D,5,9,5,4,3,8,6,4,6,8,5,7,5,9,5,7
E,2,3,2,2,2,7,7,5,7,7,6,9,2,8,5,10
K,2,3,4,2,2,6,7,2,6,10,8,11,3,8,2,8
F,2,4,3,3,2,5,10,4,5,10,9,5,1,10,3,6
Z,4,4,5,6,2,7,7,4,15,9,6,8,0,8,8,8
B,5,8,7,6,5,9,8,3,7,10,4,5,2,8,6,10
T,4,10,6,8,8,8,7,5,6,7,6,8,7,6,7,7
H,6,6,6,8,3,7,8,15,0,7,5,8,3,8,0,8
H,3,8,4,6,4,7,7,13,1,7,6,8,3,8,0,8
U,5,8,5,6,2,4,9,5,7,12,11,8,3,9,1,6
P,7,8,9,10,11,8,10,2,4,7,9,6,6,9,7,5
Y,4,4,6,6,7,9,4,5,3,7,7,8,5,9,4,8
R,6,9,6,4,4,8,7,3,5,9,4,8,6,7,6,8
J,3,11,4,9,4,10,7,1,7,11,3,7,0,7,1,7
M,6,8,8,7,9,7,9,4,3,7,5,8,12,8,5,7
J,3,8,5,6,2,7,8,1,6,14,5,8,1,7,0,7
V,4,5,5,3,2,4,12,2,3,9,11,7,4,11,1,7
B,1,3,3,2,2,8,8,2,5,10,6,6,2,8,4,8
A,3,11,5,8,4,8,5,3,0,7,1,8,2,7,3,8
B,3,7,5,5,7,9,8,4,3,6,7,7,5,11,7,7
W,5,4,7,7,4,8,8,4,2,7,8,8,9,9,0,8
U,3,2,4,3,2,6,8,6,7,7,9,9,3,9,1,8
K,2,1,3,1,2,6,7,4,8,7,6,10,3,8,5,8
E,3,8,5,6,5,7,7,4,8,7,7,8,3,8,5,10
I,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8
K,6,11,9,8,5,8,8,2,7,10,4,8,3,8,3,9
A,4,10,7,8,5,12,3,2,2,9,2,9,2,6,2,8
K,4,11,6,8,5,5,6,5,8,6,7,12,3,8,6,10
U,4,5,5,7,2,7,5,14,5,7,14,8,3,9,0,8
R,3,5,6,4,3,9,8,4,6,9,5,6,3,7,4,9
E,3,9,4,6,4,7,7,6,9,7,7,9,3,8,6,9
Y,6,7,6,5,4,5,9,1,8,9,9,5,2,9,4,4
L,4,11,6,8,4,5,3,3,9,6,1,8,1,6,3,6
R,5,5,6,8,4,5,10,9,3,7,5,8,3,8,6,11
J,4,11,5,8,2,8,6,3,7,15,5,9,1,6,1,7
V,2,7,4,5,2,7,11,2,3,6,11,9,2,10,1,8
F,1,0,1,0,0,3,12,4,2,11,8,6,0,8,2,7
K,2,4,4,3,2,7,7,2,6,10,5,9,4,7,2,8
K,2,1,3,2,2,6,7,4,7,7,6,10,3,8,5,9
M,5,8,7,6,6,8,6,6,5,7,7,8,8,5,2,8
X,4,8,5,6,3,7,7,4,4,7,6,8,3,8,4,8
Z,2,6,4,4,2,8,7,2,9,11,5,9,1,8,5,8
H,7,10,10,7,8,7,8,2,6,10,7,8,5,9,4,8
N,3,8,4,6,4,7,7,12,1,6,6,8,5,8,0,8
U,9,13,8,7,4,7,5,6,7,3,9,7,5,9,3,6
L,5,10,6,8,3,2,2,5,8,1,0,6,0,7,1,6
U,6,6,6,4,3,3,9,5,7,10,10,10,3,9,2,6
R,4,3,4,4,2,6,9,8,4,6,5,8,2,8,5,10
B,5,9,8,8,10,7,7,5,4,7,6,8,7,10,9,5
K,4,8,6,6,3,4,8,3,7,11,11,11,3,8,4,5
U,4,7,5,5,2,7,4,13,5,7,15,8,3,9,0,8
F,5,10,7,8,5,6,11,1,6,13,7,5,1,10,2,7
F,2,5,4,4,2,4,12,4,4,13,8,5,1,10,1,7
O,2,0,2,1,1,7,6,7,5,7,6,8,2,8,3,8
L,6,8,7,7,6,8,6,4,5,7,7,8,3,8,8,10
L,1,0,1,0,0,2,1,6,4,0,3,4,0,8,0,8
W,6,9,6,6,6,5,10,3,3,9,7,7,6,11,2,7
W,9,10,9,7,6,2,10,2,4,11,11,9,7,10,1,7
Y,6,9,8,6,6,8,8,6,5,5,8,8,3,8,9,6
L,3,5,5,5,4,8,5,5,5,7,7,8,2,8,8,11
O,5,9,6,6,5,7,7,8,4,7,6,9,3,8,4,7
G,7,10,5,5,4,7,6,3,2,8,6,8,4,9,9,8
E,4,11,4,8,3,3,7,6,10,7,6,14,0,8,8,7
F,3,8,6,6,6,8,7,1,4,10,6,7,5,9,4,5
G,3,4,4,3,2,6,7,4,4,9,7,9,2,8,5,10
P,2,4,4,3,2,7,9,4,3,11,4,4,1,10,2,8
C,3,5,5,3,2,4,8,5,7,12,9,11,1,9,3,7
V,6,9,5,7,4,3,11,2,2,9,10,8,3,11,1,7
M,4,10,5,8,6,7,6,10,0,7,8,8,7,5,0,8
V,2,3,3,2,1,7,12,2,2,7,11,8,2,10,1,8
R,4,8,5,6,5,8,5,7,4,7,6,8,4,7,6,10
B,4,7,6,5,4,9,6,4,6,10,5,6,2,8,6,10
K,5,9,7,7,8,7,7,3,4,6,6,9,7,7,8,7
H,7,11,7,6,5,7,7,3,4,9,8,8,7,10,6,8
A,2,7,4,5,2,12,3,4,3,12,2,9,2,6,3,9
G,6,11,8,8,5,5,7,5,5,5,7,7,3,6,4,7
L,4,9,6,7,3,4,2,8,7,1,2,3,1,6,1,6
R,2,6,2,4,2,5,10,7,3,7,4,9,2,6,5,11
T,3,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8
C,2,1,3,2,1,7,8,7,6,9,7,11,1,9,3,10
Y,6,9,6,7,4,3,9,1,6,10,11,7,1,11,3,5
M,5,8,8,6,5,10,5,3,5,9,4,7,8,6,2,9
K,2,1,2,2,1,6,7,4,8,7,6,11,3,8,5,9
F,3,6,4,4,2,5,11,1,5,13,7,5,1,10,1,7
C,1,1,2,1,0,6,7,6,9,7,6,15,0,8,4,10
Q,1,0,1,0,0,8,7,6,2,6,6,9,2,8,2,8
Q,7,13,6,7,5,11,4,4,6,11,4,8,4,8,8,11
M,4,5,4,7,4,7,7,12,2,7,9,8,8,5,0,8
F,8,12,7,6,4,8,8,2,5,10,5,6,3,8,7,8
O,4,3,5,4,2,7,8,8,8,7,7,7,3,8,4,8
X,4,8,6,6,3,9,7,1,8,10,3,7,2,8,3,8
Z,3,7,5,5,2,8,7,2,10,11,5,9,1,8,6,9
H,3,4,4,6,2,7,6,15,2,7,8,8,3,8,0,8
C,6,9,5,4,3,8,7,4,3,9,8,10,4,8,7,11
G,7,10,7,8,5,6,6,7,7,10,7,11,2,9,5,9
W,5,7,7,5,3,7,8,4,1,6,8,8,8,9,0,8
N,4,6,6,4,3,10,7,3,5,10,2,5,5,9,1,8
G,4,10,6,8,3,7,6,8,8,6,6,9,2,8,6,11
L,2,7,2,5,1,0,2,5,6,0,0,7,0,8,0,8
X,4,8,5,6,4,7,7,3,8,5,6,8,3,8,6,8
O,5,10,6,8,5,7,7,8,6,7,6,8,3,7,4,8
L,2,0,2,1,0,2,0,6,4,0,3,4,0,8,0,8
I,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
Q,3,3,4,5,4,8,7,7,3,6,5,9,3,8,5,9
L,1,3,2,1,1,6,5,1,7,8,3,10,0,7,2,8
U,5,11,7,9,5,6,8,5,7,6,8,9,3,9,1,8
M,5,11,6,9,8,7,10,6,3,7,6,8,8,10,7,8
P,9,13,9,7,6,8,9,4,4,12,4,4,5,10,6,6
H,3,7,4,4,2,7,6,14,1,7,7,8,3,8,0,8
A,1,0,2,0,0,8,3,2,0,7,2,8,2,6,1,8
X,7,10,10,8,6,4,9,2,8,10,11,10,3,9,3,5
A,2,4,4,3,2,7,2,2,1,6,2,8,2,7,3,6
X,7,10,10,8,5,6,7,1,9,10,8,9,3,8,4,7
B,4,9,5,7,4,6,8,9,7,7,5,7,2,8,9,10
G,10,14,8,8,5,8,8,5,4,9,6,6,4,9,9,8
H,4,6,6,4,6,8,8,4,3,6,6,7,7,9,7,8
W,8,9,8,6,7,3,10,2,3,10,9,8,7,11,2,6
P,8,11,8,6,5,9,8,4,4,12,4,4,5,10,6,7
F,2,3,3,2,1,8,9,2,6,13,5,4,1,9,1,8
L,2,0,2,1,0,2,1,6,5,0,2,4,0,8,0,8
P,2,6,3,4,2,5,10,9,4,9,6,5,2,9,3,8
B,4,11,4,8,4,6,7,10,7,7,6,7,2,8,9,10
B,5,9,7,7,6,8,8,4,7,10,5,6,2,8,6,10
Z,7,10,9,7,6,7,8,2,9,12,6,8,1,8,6,8
C,2,3,2,2,1,5,8,5,6,11,9,11,1,9,3,8
R,1,0,2,1,1,6,10,7,2,7,5,8,2,7,4,9
I,1,3,2,2,0,7,7,1,7,13,6,8,0,8,1,8
V,3,7,5,5,2,8,9,4,2,6,13,8,3,10,0,8
H,7,10,7,5,5,6,8,4,5,9,9,9,7,11,6,7
Z,2,1,3,2,2,7,7,5,9,6,6,8,1,8,7,8
J,1,4,3,3,1,8,6,3,4,14,6,11,1,7,0,7
U,2,3,3,2,1,7,8,6,6,7,9,8,3,10,1,8
J,4,9,6,7,3,7,6,4,6,15,7,11,1,6,1,7
L,4,9,4,7,2,0,1,4,6,1,0,7,0,8,0,8
E,6,10,4,5,3,7,9,5,5,10,6,9,3,8,6,10
U,3,5,4,3,2,4,8,5,7,10,9,9,3,9,2,6
T,4,6,4,4,2,5,12,3,6,12,9,5,2,11,1,5
M,8,11,12,8,8,5,7,3,5,10,10,10,12,6,4,8
I,4,10,5,8,3,8,6,0,8,13,6,9,1,6,3,8
A,3,3,5,4,2,8,2,2,2,6,2,8,2,7,2,7
V,7,11,6,6,3,7,10,6,4,10,9,5,5,12,4,9
J,2,5,4,4,2,8,6,3,5,14,6,11,1,6,1,7
S,4,5,5,5,5,9,8,4,6,6,8,9,4,11,7,10
T,3,10,5,7,2,9,13,0,6,6,10,8,0,8,0,8
K,8,12,7,6,3,6,8,2,7,9,6,8,5,7,3,7
Z,5,10,6,8,4,8,7,6,12,7,6,7,1,8,8,8
T,7,9,8,8,9,7,9,5,8,7,7,8,5,10,11,7
R,5,10,7,7,6,6,7,5,6,6,5,7,3,7,5,8
W,4,9,6,7,5,9,10,2,2,5,9,7,8,12,1,7
F,6,12,5,7,4,7,10,3,5,11,6,4,3,10,7,6
L,4,9,4,7,1,0,1,6,6,0,0,6,0,8,0,8
L,2,1,3,2,1,4,4,3,8,2,1,7,0,7,1,6
H,6,10,9,8,7,9,7,7,7,7,6,5,3,8,3,6
Y,4,11,5,8,2,6,10,1,3,7,12,8,1,11,0,8
M,4,8,6,6,7,7,6,5,5,7,7,10,10,5,2,8
M,7,9,10,7,8,6,7,3,5,9,8,9,10,7,3,8
B,3,7,5,5,4,8,8,6,6,7,6,5,2,8,7,10
I,3,9,4,7,2,7,9,0,8,14,6,5,0,9,2,7
N,6,9,8,5,3,8,7,3,4,13,4,8,5,8,0,7
Y,4,6,4,4,2,5,9,2,8,9,10,5,1,11,4,4
K,5,9,6,7,2,4,7,9,2,7,6,12,4,8,3,11
B,6,9,8,6,6,11,5,2,7,11,3,7,4,7,5,11
T,2,4,3,3,1,5,13,3,6,11,9,4,1,11,1,5
F,4,7,6,5,2,6,11,2,6,13,7,4,1,10,2,7
Q,6,7,8,6,6,7,4,4,5,7,4,8,4,5,6,7
A,7,11,7,6,4,10,1,4,2,11,6,14,4,5,5,11
X,4,9,7,7,5,8,8,3,9,5,6,5,5,9,8,8
D,6,9,6,4,3,8,5,4,5,9,4,7,4,7,5,10
B,5,11,7,8,6,9,8,4,7,10,4,6,2,8,6,10
L,4,9,5,8,5,8,7,3,6,6,7,9,3,8,7,8
E,5,12,4,6,3,7,7,4,5,10,6,8,3,9,8,9
B,2,7,3,5,2,6,7,9,6,7,6,7,2,8,8,9
S,5,8,6,6,4,9,8,4,8,10,2,7,2,7,4,10
R,2,4,4,3,2,8,7,3,4,9,4,7,2,7,3,10
T,5,7,5,5,3,7,10,2,8,11,9,4,1,11,3,5
R,2,4,3,3,3,7,8,5,5,7,5,7,2,6,4,8
X,4,9,6,7,5,8,8,3,8,5,6,5,4,10,8,7
B,1,3,3,2,2,8,7,2,4,10,5,7,2,8,3,9
F,1,1,1,1,0,3,12,4,3,11,9,6,0,8,2,7
P,5,4,6,6,7,8,8,3,3,6,8,7,5,10,5,5
K,6,7,8,5,4,4,8,3,7,11,10,11,3,8,3,6
B,5,8,7,6,5,10,6,3,7,10,4,7,2,8,5,11
Q,5,11,5,6,4,9,5,4,7,11,4,8,3,8,8,11
O,3,7,4,5,3,8,7,6,4,9,5,8,3,8,3,8
K,4,5,4,7,2,3,8,8,2,7,4,11,4,8,3,10
U,5,9,4,4,2,7,6,4,5,4,8,7,5,8,2,7
K,5,10,7,7,6,10,6,1,6,10,3,8,6,8,5,12
K,7,9,10,7,6,9,6,2,7,10,3,8,7,8,6,10
G,2,1,3,1,1,6,7,5,5,6,6,9,2,9,4,8
O,9,14,6,8,4,7,6,5,6,8,3,8,6,8,6,8
U,5,10,7,8,4,6,9,6,7,7,10,10,3,9,1,8
B,4,6,7,5,6,6,9,5,6,8,7,8,6,9,7,7
X,4,9,7,6,4,11,6,1,8,10,1,6,2,8,3,10
U,2,7,3,5,2,7,5,12,4,7,11,8,3,9,0,8
L,5,10,6,8,3,4,4,2,11,3,1,8,0,7,2,5
Y,3,6,5,4,2,7,10,2,7,6,12,9,2,11,2,8
H,5,9,7,7,6,6,8,3,6,10,8,8,3,9,3,7
J,2,8,3,6,2,8,7,2,6,11,5,9,1,6,2,5
I,2,6,2,4,1,7,8,0,8,14,6,8,0,8,1,7
H,4,7,5,5,4,8,8,6,7,6,5,9,3,8,3,8
B,8,14,6,8,5,7,8,5,6,10,5,8,6,6,8,11
T,4,9,6,7,3,6,12,3,8,8,12,8,1,11,1,7
R,8,12,8,6,6,7,7,3,5,8,4,8,7,8,7,8
D,3,4,4,3,2,7,7,6,7,7,6,5,2,8,3,7
M,4,7,5,5,3,8,7,12,1,7,9,8,8,6,0,8
X,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8
N,3,5,5,3,2,6,8,2,4,11,7,7,5,8,0,7
E,5,10,4,5,2,6,9,5,7,10,7,9,1,9,7,8
W,7,10,7,5,3,3,9,2,1,9,10,8,8,12,1,6
A,3,11,6,8,4,11,4,2,3,9,2,9,3,7,3,8
C,5,9,7,6,4,7,7,8,7,7,6,9,4,8,4,9
I,2,8,2,6,2,7,7,0,7,7,6,8,0,8,3,8
V,5,6,5,4,3,3,11,2,3,10,11,8,2,11,1,8
X,7,9,8,8,9,8,8,2,5,7,6,7,4,7,8,8
A,5,9,6,7,4,7,4,3,1,7,2,8,3,8,3,8
X,3,4,5,3,2,10,7,2,8,11,3,7,2,8,3,9
E,7,11,5,6,3,6,9,6,8,10,7,8,1,9,7,8
B,2,6,3,4,3,7,6,8,5,6,7,7,2,9,7,9
X,8,15,8,8,5,4,9,3,8,11,11,9,4,9,3,5
T,5,7,7,6,7,7,8,4,8,7,7,8,3,9,8,7
S,3,4,4,6,2,8,7,6,9,4,6,7,0,8,9,8
S,2,4,4,3,2,8,7,2,6,10,5,7,1,9,5,8
E,1,3,3,2,1,6,8,2,7,11,7,9,1,8,4,9
B,5,9,7,6,6,7,8,6,6,9,7,5,3,7,8,7
S,6,12,5,6,3,7,3,3,5,7,2,7,3,6,5,8
M,4,5,7,3,4,9,6,3,5,9,4,7,10,6,2,8
J,4,10,6,8,3,8,6,3,7,15,6,10,1,6,1,7
S,6,11,6,6,3,6,8,5,3,13,9,8,3,10,3,8
C,5,5,6,8,2,6,7,7,11,6,6,14,1,8,4,9
P,8,11,7,6,4,6,11,4,5,13,6,3,4,9,6,5
F,4,7,6,5,5,7,9,6,5,8,6,8,3,10,8,10
W,4,5,7,4,4,7,11,3,2,6,9,8,8,11,1,8
L,4,11,6,8,9,7,7,3,5,7,7,10,7,11,8,5
K,2,1,2,2,1,5,7,8,1,7,6,11,3,8,2,11
L,3,9,4,6,2,0,2,4,6,1,0,8,0,8,0,8
U,4,6,5,4,4,8,6,7,5,7,6,9,3,8,4,6
S,7,11,10,8,11,9,3,4,4,9,6,10,6,6,11,11
X,4,8,6,6,4,8,8,3,8,6,5,6,4,9,7,8
N,4,5,5,4,3,7,9,5,5,7,7,7,5,9,2,6
P,4,7,4,4,2,4,12,8,2,10,6,4,1,10,4,8
T,3,7,5,5,4,6,7,7,6,7,10,9,3,9,4,8
T,2,6,3,4,2,7,12,2,7,7,11,8,1,11,1,8
U,3,5,4,4,3,8,6,4,3,5,7,7,3,9,1,7
B,8,11,6,6,4,9,6,6,6,11,4,9,6,6,6,10
M,5,11,6,9,4,7,7,12,2,7,9,8,9,6,0,8
J,5,11,7,9,4,9,7,2,6,14,3,7,0,7,0,7
W,4,9,6,7,10,8,6,6,2,7,6,8,8,11,3,9
Q,5,10,7,9,4,8,5,9,8,5,4,8,3,8,4,8
W,6,9,9,7,6,8,8,4,1,7,9,8,7,11,0,8
J,2,9,3,7,1,12,2,10,4,13,6,13,1,6,0,8
D,5,10,5,5,4,7,7,4,6,8,5,7,5,10,6,6
O,1,3,2,1,1,8,7,5,3,9,6,8,2,8,2,8
Q,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8
H,7,10,7,5,3,8,7,4,4,9,7,7,6,10,5,9
R,2,3,2,2,2,6,8,4,5,7,6,7,2,7,3,9
T,5,9,6,6,5,6,7,7,7,7,8,8,3,11,6,9
U,6,6,6,8,3,7,4,15,5,7,14,8,3,9,0,8
K,3,2,4,4,3,5,7,4,7,6,6,10,3,8,5,9
L,6,10,5,5,2,6,5,3,7,10,4,13,2,6,6,8
G,5,9,6,7,3,6,7,7,7,10,8,10,2,9,4,9
X,2,6,3,4,2,7,7,4,4,7,6,7,2,8,4,8
Y,2,4,3,3,1,4,11,2,7,11,10,5,1,11,2,5
I,3,11,3,8,4,7,7,0,8,7,6,8,0,8,3,8
N,4,7,6,5,3,7,8,3,4,10,6,7,5,8,1,7
Y,4,7,6,10,10,8,5,4,2,8,8,9,5,9,6,8
M,2,1,3,1,2,6,6,6,4,7,7,10,6,5,2,9
E,2,1,3,2,2,7,8,5,7,7,6,9,2,8,5,10
M,4,4,6,3,3,7,6,3,4,9,7,8,7,5,2,8
A,3,5,5,4,2,6,3,2,2,5,2,7,3,4,3,8
R,5,9,7,7,4,10,7,3,6,11,2,6,3,7,4,10
H,9,12,9,7,6,10,6,4,5,11,2,7,6,7,5,8
I,1,6,0,8,1,7,7,4,4,7,6,8,0,8,0,8
A,2,4,3,3,1,11,3,3,2,11,2,9,2,6,2,9
Z,4,9,5,6,5,7,7,5,9,7,6,8,1,8,7,8
S,4,9,5,6,5,7,6,7,6,6,8,10,2,11,9,7
C,4,7,4,5,3,4,8,5,6,9,8,14,2,7,3,8
R,6,11,9,8,7,9,8,4,7,9,3,8,4,5,4,11
D,7,13,6,7,4,10,4,4,5,12,2,8,5,6,4,9
L,4,10,4,8,1,0,1,6,6,0,0,6,0,8,0,8
V,3,7,5,5,1,7,8,4,2,7,14,8,3,10,0,8
P,8,9,6,4,3,9,7,5,5,13,3,5,5,9,4,8
G,2,6,3,4,2,7,6,6,6,7,3,10,1,8,4,10
H,5,10,5,7,3,7,10,15,2,7,3,8,3,8,0,8
U,7,11,7,8,5,4,8,5,8,10,9,9,3,9,2,6
Z,3,5,4,7,2,7,7,4,13,10,6,8,0,8,8,8
S,5,11,6,9,6,8,7,8,5,7,6,7,2,8,9,8
I,4,11,5,8,3,7,7,0,8,14,6,8,0,8,1,8
C,6,11,8,8,6,7,6,9,6,8,6,11,4,9,4,8
C,4,9,5,7,2,5,7,7,10,7,6,14,1,8,4,9
X,3,6,5,4,3,8,7,4,9,6,6,6,3,8,6,7
N,6,11,9,9,5,12,7,4,5,10,0,4,6,8,2,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
O,3,5,4,3,3,7,7,7,5,9,6,8,2,8,3,8
O,4,6,5,5,4,7,6,4,4,6,3,6,2,7,4,7
L,2,6,3,4,2,4,3,8,7,1,2,4,1,6,1,6
V,4,5,5,3,2,4,13,4,3,11,11,6,3,10,1,8
G,5,9,4,4,2,11,3,2,3,9,2,6,4,7,3,10
B,3,6,5,5,5,6,8,6,4,7,6,8,6,9,7,8
N,2,2,3,3,2,7,8,6,4,7,6,7,4,9,2,6
S,6,13,6,7,3,7,6,3,4,13,7,9,3,9,3,8
Z,4,6,6,4,4,9,9,5,4,7,5,7,3,8,9,5
B,3,5,5,3,4,8,7,3,6,9,6,6,2,8,5,9
I,2,7,3,5,3,9,6,1,4,8,5,5,3,8,5,7
S,1,0,1,0,0,8,7,3,6,5,6,7,0,8,7,8
D,7,11,7,6,4,10,4,5,6,12,3,9,5,6,5,10
O,6,9,7,7,5,7,8,8,6,10,7,9,3,8,3,8
E,1,0,1,1,1,5,8,5,8,7,6,12,0,8,6,9
P,3,5,6,4,3,8,10,3,4,12,4,3,1,10,3,8
U,6,9,7,7,4,4,8,6,8,10,10,9,3,9,2,5
X,5,5,6,8,2,7,7,5,4,7,6,8,3,8,4,8
A,3,8,5,6,3,12,2,3,2,11,2,9,2,6,3,9
Q,5,8,5,9,7,8,7,7,3,8,6,9,3,9,6,7
Q,2,0,2,1,1,8,7,7,4,6,6,8,2,8,3,8
G,8,13,7,7,4,7,3,6,2,6,5,4,4,7,5,7
L,5,9,7,7,4,5,4,2,9,6,2,8,1,6,3,6
K,6,9,5,4,2,8,8,3,7,8,4,7,5,7,3,7
X,4,4,6,3,3,7,7,1,8,11,7,9,2,8,3,8
J,2,8,2,6,1,15,4,3,5,12,1,7,0,8,0,8
E,2,2,3,3,2,7,8,5,7,7,6,9,2,8,5,10
W,8,11,8,6,5,5,8,2,4,8,9,7,10,10,2,5
P,4,9,6,7,3,6,13,5,2,12,5,1,1,10,3,8
G,6,11,6,8,5,6,6,6,6,10,6,13,4,9,6,8
N,4,5,5,4,5,7,8,4,3,6,5,8,6,8,4,7
P,11,15,8,8,4,8,9,7,5,14,4,4,4,10,4,8
X,3,4,5,3,2,9,6,1,8,10,4,8,2,8,3,9
Q,4,5,5,6,4,7,9,5,3,7,9,11,3,9,6,8
I,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8
J,2,4,3,7,1,12,3,9,4,13,5,12,1,6,0,8
B,5,4,5,6,4,6,7,9,7,7,6,7,2,8,9,10
D,4,11,4,8,3,5,6,11,8,5,4,5,3,8,4,8
B,6,11,8,8,8,7,8,6,4,7,5,6,5,7,7,8
Z,2,1,2,1,1,7,7,5,8,6,6,8,1,8,6,8
D,2,4,3,3,2,9,6,3,5,10,4,5,2,8,3,8
B,5,11,7,8,8,8,8,8,6,7,5,5,4,6,9,12
F,4,7,5,5,5,7,9,5,4,8,6,8,3,10,8,10
C,3,5,4,4,2,4,8,5,7,11,9,12,1,9,3,7
N,4,9,5,7,5,7,7,13,1,7,6,8,5,8,0,7
G,8,10,6,5,3,8,5,4,4,9,4,6,4,7,5,8
R,7,11,6,6,4,8,8,5,5,9,4,9,7,5,6,11
W,4,8,6,6,5,6,10,2,2,7,9,8,7,10,1,8
B,2,6,4,4,3,8,8,5,7,7,6,6,2,8,6,9
I,1,9,2,7,3,8,7,0,7,7,6,7,0,8,2,7
X,6,8,7,7,8,6,7,2,5,8,7,10,4,10,8,6
C,2,2,3,4,2,6,8,7,7,8,8,14,1,9,4,10
E,2,2,3,3,3,7,7,5,7,7,7,9,2,8,5,10
X,4,4,6,3,3,6,7,1,8,10,8,8,2,8,3,7
U,3,6,4,4,1,7,5,13,5,7,14,8,3,9,0,8
L,1,3,3,2,1,6,5,2,9,7,2,10,0,7,2,8
M,4,8,4,6,3,8,7,12,1,6,9,8,8,6,0,8
R,2,6,4,4,4,8,7,3,4,7,6,8,6,8,6,7
F,4,7,6,5,4,6,10,2,5,13,7,5,2,10,2,7
Y,4,8,7,6,3,7,10,1,8,6,12,9,3,9,2,8
G,4,10,5,8,3,7,6,8,8,6,5,10,2,8,6,11
T,2,3,3,4,1,10,14,1,6,4,11,9,0,8,0,8
I,1,7,0,5,0,7,7,5,3,7,6,8,0,8,0,8
X,2,3,4,1,2,6,7,2,8,11,8,8,2,8,3,7
T,2,8,3,6,1,7,13,0,6,7,11,8,0,8,0,8
I,4,9,5,7,3,7,8,0,7,13,6,7,0,8,1,7
T,7,13,6,8,4,7,8,2,7,12,6,7,3,8,5,5
V,5,9,5,7,2,3,12,4,4,10,12,7,3,10,1,8
D,6,9,6,5,3,8,6,5,6,10,4,6,4,7,6,10
U,3,7,4,5,4,8,6,7,5,7,6,8,3,8,4,5
N,3,6,3,4,3,7,7,11,1,6,6,7,5,9,0,8
T,4,11,6,8,5,7,11,3,6,7,11,8,2,12,1,8
D,2,4,3,3,2,9,7,4,5,9,4,5,2,8,2,8
O,5,9,7,6,4,7,7,9,4,7,6,8,3,8,3,7
K,8,10,8,5,5,8,7,4,6,11,2,8,6,5,4,8
W,3,8,5,6,4,6,10,2,3,7,9,8,7,11,0,8
Q,4,7,5,6,2,8,8,7,6,5,8,9,3,7,5,10
W,3,4,5,3,3,7,11,2,2,6,9,8,6,11,0,8
N,8,12,9,6,4,10,5,3,4,13,2,7,5,7,0,7
C,2,1,2,1,1,6,7,6,10,7,6,14,0,8,4,9
X,3,6,5,4,3,7,7,1,8,10,6,8,2,8,3,7
B,3,10,4,8,6,6,7,8,5,7,6,7,2,8,7,9
I,4,9,4,4,2,8,8,2,4,12,5,6,1,10,5,11
K,5,10,8,8,9,6,6,4,4,6,5,9,8,7,8,8
L,4,8,5,6,3,4,5,1,9,7,2,11,0,7,3,7
V,3,9,4,7,3,9,9,3,1,6,12,8,3,10,1,8
M,4,4,5,2,3,8,6,6,4,6,7,8,8,6,2,7
V,5,10,5,8,4,3,11,2,3,9,11,8,2,11,1,8
T,7,9,8,8,8,6,8,3,9,8,7,9,3,8,8,6
C,2,5,3,3,1,4,8,4,7,11,9,12,1,9,2,7
L,2,5,3,3,1,3,4,3,8,2,1,7,0,7,1,6
G,4,5,5,4,3,7,6,6,6,6,6,10,2,9,4,8
Z,7,7,5,11,4,7,8,5,3,11,7,7,2,9,10,6
S,5,7,6,5,3,8,8,4,9,11,3,8,2,6,4,10
V,5,9,5,7,3,4,12,1,2,8,10,7,3,10,1,7
X,5,6,6,5,6,6,7,2,5,8,7,10,3,8,7,8
Y,6,11,10,8,5,6,10,2,8,6,12,9,5,10,4,7
V,3,7,5,5,3,8,12,2,3,5,10,9,4,12,3,7
M,5,11,6,8,4,8,7,12,2,6,9,8,9,6,0,8
T,3,11,5,8,1,8,15,0,6,7,11,8,0,8,0,8
C,4,7,5,5,4,7,7,8,5,6,7,13,4,7,4,8
B,5,10,7,8,7,9,7,3,5,9,5,6,2,8,5,9
M,4,4,6,3,4,6,6,3,4,10,9,9,7,6,2,9
N,6,9,8,6,6,6,7,8,6,7,5,7,3,8,3,9
M,5,5,6,4,4,8,6,6,5,7,7,8,8,6,2,7
N,7,9,10,6,5,4,10,3,4,10,9,9,7,7,2,7
L,7,15,7,8,4,7,5,4,5,12,9,11,3,9,6,9
J,5,6,6,7,5,7,7,4,7,7,6,7,3,8,8,8
Z,3,7,4,5,2,7,7,4,13,9,6,8,0,8,8,8
G,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10
E,7,10,5,5,4,7,8,4,4,10,5,9,3,8,9,11
O,3,6,4,4,2,7,7,7,5,10,6,9,3,8,3,8
Q,8,12,7,7,4,8,5,4,9,10,5,9,3,7,9,10
V,7,10,6,6,3,9,9,6,4,6,10,6,6,12,3,8
O,5,5,6,7,3,7,7,9,8,7,6,8,3,8,4,8
Q,5,7,6,7,5,8,8,7,4,6,6,10,3,8,4,8
P,9,11,7,6,3,8,9,6,4,12,3,5,5,10,4,8
N,4,8,5,6,4,7,7,12,1,6,6,8,5,8,0,8
Q,5,7,6,8,6,9,8,6,2,4,8,10,3,9,7,10
P,6,9,5,4,2,6,10,5,3,11,5,5,5,9,4,8
W,3,2,5,3,3,5,11,3,2,8,9,9,8,13,1,8
B,4,7,5,5,4,9,7,5,8,7,6,5,2,8,6,9
X,7,10,8,5,4,6,8,2,8,11,6,9,4,7,4,6
O,4,7,5,5,3,7,7,7,6,7,6,7,2,8,3,8
U,5,11,5,8,2,8,5,14,5,6,14,8,3,9,0,8
L,4,10,5,7,3,5,3,7,7,2,2,4,1,6,1,5
Y,2,6,3,4,0,9,10,2,2,6,12,8,1,11,0,8
J,5,11,6,8,3,8,9,1,7,14,5,6,0,9,1,8
H,7,9,10,6,8,11,6,3,7,10,2,6,5,7,4,10
H,1,0,1,0,0,7,8,10,1,7,5,8,2,8,0,8
I,2,4,3,3,1,7,6,0,7,13,7,10,0,8,1,8
V,7,10,7,8,4,4,11,2,4,8,11,7,5,11,1,7
S,6,9,5,5,2,8,3,4,4,7,2,7,3,6,5,8
B,4,2,4,3,4,8,7,5,6,7,6,6,5,8,6,10
P,3,6,5,8,7,8,9,5,0,8,7,6,5,10,5,8
U,4,7,4,5,3,7,5,13,5,7,11,8,3,9,0,8
V,3,6,5,4,2,7,12,2,3,6,11,8,2,10,1,8
V,5,8,5,6,2,2,11,6,4,13,12,8,2,10,1,7
V,5,10,5,8,3,3,11,2,3,9,11,8,3,11,1,7
A,3,8,5,6,3,12,3,3,2,11,1,9,2,6,2,9
Z,6,9,8,7,6,9,9,5,4,6,5,7,3,9,10,5
D,7,14,7,8,4,10,4,5,5,13,3,10,5,7,4,9
Z,5,7,4,10,4,6,9,5,3,12,7,6,3,9,10,6
M,5,6,7,5,8,7,7,4,4,7,6,8,10,8,5,6
O,7,10,5,5,3,7,8,5,5,9,5,6,4,9,5,8
H,4,7,6,5,4,10,5,4,6,10,2,7,3,8,3,9
X,5,6,7,8,2,7,7,5,4,7,6,8,3,8,4,8
Y,7,9,8,6,5,5,9,1,8,9,10,5,3,11,4,4
W,2,1,4,2,2,9,11,3,2,5,9,8,5,11,0,8
N,4,6,5,4,3,8,7,8,6,7,4,5,3,7,3,7
K,7,9,10,7,5,5,9,2,8,10,9,10,3,8,4,5
T,3,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8
V,2,7,4,5,2,9,9,3,1,5,12,8,2,11,0,8
Y,4,6,4,4,2,4,9,3,6,10,11,6,2,11,2,5
Q,5,10,6,9,6,8,7,7,5,6,6,9,3,8,5,8
V,4,6,4,4,2,3,12,4,3,10,11,7,2,10,1,8
P,2,1,3,1,1,5,9,4,4,9,7,4,1,9,3,7
F,4,7,6,5,3,6,10,1,6,13,7,5,1,10,2,7
N,4,8,6,6,5,6,8,9,5,8,6,6,4,8,3,9
A,4,9,6,7,4,12,3,3,2,10,1,9,2,7,3,8
Q,6,11,6,6,4,9,5,4,6,10,5,8,3,9,8,10
X,2,3,4,2,1,7,7,1,8,10,6,8,2,8,3,8
Z,7,10,9,8,8,9,8,5,4,7,5,7,4,8,9,4
P,6,10,6,7,3,5,10,10,6,8,5,5,2,10,4,8
S,7,11,9,8,5,9,6,3,8,11,5,8,2,10,6,9
S,6,11,6,6,3,6,8,5,3,13,9,8,3,10,3,8
N,4,5,5,8,3,7,7,14,2,4,6,8,6,8,0,8
B,4,6,5,4,4,8,8,4,6,10,5,6,3,8,6,10
S,7,15,5,8,3,8,3,4,4,9,2,8,4,6,5,9
K,7,11,10,9,7,6,7,2,7,10,7,10,4,8,4,7
C,2,4,2,2,1,4,8,5,7,11,9,12,1,9,3,8
C,4,8,5,6,3,5,8,7,8,9,8,14,1,9,4,9
C,2,3,3,2,1,4,8,3,6,11,9,12,1,9,2,7
J,4,10,5,8,3,11,4,5,5,15,3,9,0,7,0,8
I,4,10,5,8,2,7,6,0,9,14,6,9,0,8,1,8
C,4,8,5,6,2,6,7,7,10,8,6,13,1,9,4,8
A,2,7,4,5,2,9,3,2,2,8,1,8,2,6,3,8
C,6,8,7,6,4,5,8,6,7,12,8,10,3,12,3,6
P,3,7,3,4,2,4,10,9,3,9,6,5,2,10,3,8
J,3,4,4,5,3,8,8,4,5,7,6,8,3,8,8,9
L,5,10,6,7,5,4,4,4,8,2,1,6,1,6,1,5
K,4,7,6,5,4,4,7,2,6,10,9,11,3,8,3,8
X,2,3,4,2,2,9,7,2,8,11,3,7,2,7,2,8
G,1,3,2,2,1,6,7,5,5,9,7,10,2,8,4,10
U,4,7,6,5,7,8,8,4,4,6,8,8,8,9,5,7
F,3,7,4,4,1,2,12,5,6,12,10,8,0,8,2,6
P,4,9,4,6,2,5,11,10,3,10,6,4,1,10,4,8
V,3,7,5,5,2,9,9,4,1,6,12,8,3,9,1,7
J,2,7,3,5,2,11,7,0,7,10,3,6,0,8,1,7
I,3,8,4,6,2,6,8,0,8,14,7,8,0,8,1,7
N,5,10,5,7,5,7,7,13,1,6,6,8,6,9,1,6
Q,7,9,7,10,8,8,9,7,2,7,7,11,4,10,8,6
U,5,11,6,8,2,8,5,14,5,6,14,9,3,9,0,8
V,4,10,6,7,3,5,9,5,2,8,13,8,3,10,0,8
G,2,7,3,5,2,7,7,7,5,6,6,9,2,8,5,11
S,4,6,5,5,5,8,8,4,5,7,7,8,5,9,8,11
S,6,11,9,8,12,7,5,3,3,8,5,8,4,8,15,8
F,6,10,5,5,4,8,10,3,5,11,5,4,3,10,7,7
X,5,10,7,8,4,7,7,1,8,10,6,8,3,8,4,7
T,6,8,6,6,4,4,13,5,5,11,9,4,2,12,1,5
T,6,10,6,8,4,5,12,4,6,12,9,4,2,12,2,4
F,7,14,6,8,3,8,8,3,6,12,4,5,2,8,7,7
U,3,7,5,5,2,7,8,6,7,6,10,9,3,9,1,7
A,5,9,7,7,5,8,3,1,2,6,2,7,3,5,4,7
U,3,7,3,5,1,7,6,13,5,8,13,8,3,9,0,8
T,4,10,5,7,4,8,10,0,8,6,11,8,0,10,1,8
R,4,4,4,5,3,5,12,8,4,7,3,9,3,7,6,11
Q,6,12,6,6,4,10,5,4,6,11,4,8,3,8,8,11
J,3,6,4,4,1,7,6,3,6,14,7,11,1,6,1,7
Z,2,6,3,4,1,7,7,3,13,9,6,8,0,8,7,8
X,4,8,5,7,6,7,8,1,5,7,6,7,3,5,8,7
B,9,15,9,8,8,8,7,4,5,9,5,7,8,4,10,7
P,6,7,8,9,10,9,9,3,3,6,9,6,6,10,7,4
S,3,5,3,3,2,8,7,7,5,8,6,8,2,9,9,8
I,1,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8
B,4,9,5,6,6,7,7,6,6,6,6,6,2,8,6,9
U,7,8,7,6,3,3,10,6,7,13,12,9,3,9,1,7
H,3,6,4,4,5,9,8,4,3,6,6,7,6,9,6,6
W,6,7,6,5,5,5,11,3,2,9,8,7,6,12,2,6
N,9,10,8,5,3,6,10,4,5,4,5,11,6,11,2,7
C,4,5,6,7,2,5,6,6,11,7,5,12,1,9,4,8
D,7,13,6,7,5,6,7,5,6,8,5,7,5,10,6,5
U,6,10,8,8,5,6,9,5,7,6,9,10,6,10,1,7
Y,5,6,4,9,3,7,8,3,2,7,10,5,4,10,5,6
Q,4,8,5,9,6,7,8,6,3,8,9,9,4,10,6,7
H,2,1,2,1,2,8,8,5,5,7,6,8,3,8,2,7
X,3,8,5,6,4,7,7,3,8,5,6,10,3,7,6,8
T,5,10,5,7,3,6,11,3,7,11,9,4,2,12,3,4
N,2,1,3,2,2,7,8,5,4,7,7,6,5,9,2,6
N,6,8,8,6,4,4,10,4,4,11,11,10,5,8,1,7
K,12,15,11,8,5,4,8,4,7,10,10,11,6,9,3,6
L,5,8,6,7,5,8,6,5,5,7,7,9,3,9,8,8
M,7,9,10,7,7,8,6,3,5,9,7,8,8,5,2,8
E,1,1,2,2,1,4,7,5,8,7,6,13,0,8,6,9
K,4,5,7,3,4,3,8,3,7,11,10,11,3,8,3,6
D,4,7,4,5,2,5,8,10,9,8,7,6,3,8,4,8
N,6,10,8,7,5,6,10,2,4,9,7,7,5,9,1,8
B,7,10,9,8,8,10,6,3,7,10,3,7,5,7,6,11
J,1,3,2,2,1,11,6,1,6,11,3,7,0,7,0,8
I,0,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
Y,8,10,6,14,5,9,8,4,3,5,11,5,5,10,7,7
O,7,9,9,8,7,8,4,4,5,9,4,9,4,6,5,7
C,3,4,4,3,2,6,8,8,8,8,7,12,2,9,4,10
D,5,11,5,8,3,5,7,11,9,6,6,5,3,8,4,8
L,2,5,4,4,2,7,4,1,8,8,2,10,0,7,2,8
A,3,4,5,3,2,10,2,2,1,8,2,9,4,5,2,9
V,3,4,4,3,2,5,12,2,2,9,10,7,2,11,1,8
F,2,4,3,3,1,6,10,2,5,13,7,5,1,10,1,7
X,3,5,5,5,4,8,8,2,5,8,6,8,3,8,7,8
P,5,8,7,6,4,5,13,6,3,13,6,1,0,10,4,8
L,3,7,3,5,1,0,1,6,6,0,0,6,0,8,0,8
R,4,10,5,8,3,5,10,9,3,7,5,8,3,8,6,11
R,1,0,1,0,0,6,9,7,3,7,4,8,2,6,4,10
X,2,1,2,1,0,7,7,4,4,7,6,8,3,8,4,8
G,9,13,8,8,4,8,3,4,3,8,4,5,4,7,5,9
Q,1,2,2,3,2,8,8,5,2,8,6,8,2,9,2,8
Z,2,5,4,4,2,7,8,2,9,11,6,8,1,8,6,8
K,5,9,7,7,5,9,7,2,7,10,3,8,4,9,4,10
T,5,8,6,7,5,5,7,3,8,8,7,10,3,4,8,5
J,1,4,3,3,1,9,5,3,6,14,5,10,0,7,0,8
U,3,8,5,6,3,4,10,7,6,10,10,9,3,9,1,8
R,2,4,2,3,2,7,7,5,5,6,5,7,2,7,4,8
Q,3,4,4,5,3,8,8,6,2,5,7,10,3,9,6,10
O,4,5,6,8,3,6,6,9,8,6,5,6,3,8,4,8
N,3,4,6,3,2,8,9,3,5,10,4,5,5,9,1,7
F,5,10,5,7,2,1,12,5,5,12,11,8,0,8,2,5
M,8,8,11,7,12,8,8,4,4,7,6,7,12,7,8,3
V,2,7,4,5,2,7,11,3,4,7,11,8,2,10,1,8
S,3,8,4,6,3,7,7,7,5,7,6,8,2,8,8,7
C,4,6,5,4,2,4,8,6,9,10,10,12,1,8,3,7
U,3,6,3,4,1,7,5,13,5,7,13,8,3,9,0,8
M,6,7,9,6,10,7,6,4,4,7,6,8,12,9,5,5
A,3,8,5,6,5,8,8,6,4,6,6,8,3,7,6,4
N,3,5,5,3,2,6,9,2,4,10,7,7,5,8,1,8
M,5,8,6,6,7,7,9,6,4,6,6,8,9,9,8,10
S,3,7,4,5,3,7,7,5,7,5,6,10,0,9,8,7
H,6,11,6,8,3,7,5,15,1,7,9,8,3,8,0,8
V,4,7,4,5,2,2,12,3,3,11,11,8,2,10,1,8
M,7,11,8,8,7,7,5,12,1,8,8,8,10,5,2,10
R,7,13,7,7,6,5,9,3,5,7,4,9,6,7,6,6
G,3,7,4,5,3,7,7,7,6,7,5,11,1,8,4,10
O,2,3,3,2,1,8,7,6,4,9,6,9,2,8,3,8
J,3,7,5,8,5,9,8,4,4,7,6,9,3,7,8,6
F,4,9,6,7,7,7,9,1,5,9,6,5,3,9,4,3
D,2,3,2,1,1,7,7,6,6,7,6,5,2,8,2,7
X,2,3,4,2,2,7,7,3,9,6,6,8,2,8,5,8
T,2,4,3,2,1,8,11,2,7,6,11,8,1,10,1,8
M,5,7,5,5,4,7,5,11,0,8,8,8,8,5,1,10
Q,6,10,8,7,7,8,5,7,4,5,7,10,6,6,8,9
K,2,6,3,4,1,3,7,7,3,7,7,11,3,8,2,10
M,5,7,7,6,7,5,8,5,3,6,5,8,9,7,5,7
E,2,5,4,3,2,6,7,1,8,11,6,9,2,8,4,8
F,4,8,5,6,5,7,9,6,4,8,5,8,3,11,9,10
U,10,12,8,6,4,6,5,5,6,3,8,7,5,9,2,6
H,6,8,9,6,5,8,7,3,6,10,5,7,3,8,3,7
P,1,1,2,1,0,5,11,7,2,9,6,4,1,9,3,8
L,9,13,7,7,4,9,2,4,4,12,5,13,2,7,6,9
F,3,7,4,5,4,6,9,2,6,9,9,6,1,10,4,7
M,6,11,8,6,5,10,3,4,2,10,2,9,9,1,1,8
K,6,10,9,8,8,10,5,2,6,9,2,7,8,6,7,12
W,3,4,4,3,2,5,10,3,2,9,8,7,6,11,1,6
R,5,4,5,7,3,5,10,9,3,7,5,8,3,8,6,11
J,1,3,2,2,1,11,6,2,6,11,3,7,0,7,0,8
G,6,9,6,6,4,6,7,6,6,9,7,12,2,9,4,9
Z,4,9,5,7,4,9,6,6,10,7,5,6,1,7,8,8
O,3,2,4,3,3,7,7,7,5,7,6,8,2,8,3,8
H,2,3,3,2,2,7,8,5,6,7,6,8,3,8,2,8
W,6,8,6,6,4,5,11,3,3,9,8,7,7,11,2,6
G,5,9,7,7,5,5,6,7,7,7,5,13,4,9,5,8
T,3,2,4,4,2,7,12,3,6,7,11,8,2,11,1,7
M,1,0,2,1,1,7,6,9,0,7,8,8,6,6,0,8
K,2,3,4,1,2,4,8,2,6,10,10,11,2,8,2,6
X,3,7,4,5,2,7,7,4,4,7,6,8,3,8,4,8
P,10,15,8,8,4,7,9,6,3,11,4,5,5,9,4,7
P,4,6,5,4,3,9,7,2,5,13,4,5,1,10,3,9
Z,2,3,2,2,2,7,7,5,8,6,6,8,1,8,6,8
J,2,2,3,4,1,10,6,2,7,12,4,9,1,6,1,7
N,5,9,7,7,8,9,7,4,4,7,6,6,7,12,7,6
G,3,4,4,3,2,6,7,5,5,9,7,10,2,8,4,10
F,6,11,9,8,5,8,9,4,7,13,6,6,5,7,4,6
M,7,10,10,8,7,5,6,3,5,9,9,9,8,5,2,8
K,5,10,6,8,2,4,6,9,2,7,7,11,4,7,3,11
Y,7,9,8,6,4,4,9,1,7,10,11,6,2,11,4,3
T,5,10,5,5,2,5,11,2,7,12,8,5,2,9,4,3
N,5,8,7,6,4,7,8,3,4,10,6,7,5,8,1,7
X,5,11,8,8,4,11,5,2,8,10,0,7,3,8,4,10
Y,8,6,6,9,4,7,8,4,3,6,11,5,4,11,6,6
B,4,10,5,8,6,8,6,7,6,6,6,6,2,8,7,9
V,5,7,7,5,7,6,7,4,2,9,7,8,7,10,5,7
D,4,9,6,6,8,8,9,5,4,8,7,6,7,10,10,5
Z,6,10,8,7,5,7,8,3,10,12,7,8,1,9,6,7
H,5,5,7,7,6,7,5,4,2,7,5,6,5,8,8,8
L,5,9,7,7,4,5,3,3,9,6,1,9,1,6,3,7
P,4,9,5,6,2,4,14,8,1,11,6,3,1,10,4,8
M,4,6,5,8,4,7,7,12,2,7,9,8,9,6,0,8
X,2,2,3,3,2,8,7,3,8,6,6,8,2,8,5,9
D,3,7,4,5,3,6,8,9,7,7,6,5,2,8,3,7
I,2,5,3,4,1,7,7,0,8,13,6,8,0,8,1,8
V,5,9,5,7,3,4,11,2,4,9,11,7,3,10,1,8
U,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8
O,3,5,4,3,3,8,7,7,5,9,6,7,2,8,3,8
H,4,8,4,5,2,7,7,15,1,7,6,8,3,8,0,8
Y,8,10,8,7,3,4,10,2,8,11,11,5,1,11,3,3
M,5,7,6,5,7,7,9,6,5,7,6,8,8,8,5,7
X,6,9,8,8,9,7,7,2,5,7,6,8,4,8,8,8
D,2,3,3,2,2,9,6,4,5,9,4,5,2,8,2,8
F,5,11,5,8,2,1,13,5,4,12,11,7,0,8,2,5
V,1,3,3,2,1,6,11,3,3,8,11,8,2,11,1,8
J,2,6,3,4,1,12,2,9,4,13,5,13,1,6,0,8
Y,3,9,4,7,2,6,10,1,3,8,12,8,1,11,0,8
P,2,7,4,5,3,4,11,5,4,11,8,4,1,10,3,7
P,7,14,7,8,5,8,9,4,4,12,5,4,4,11,6,6
H,4,5,6,8,5,9,9,3,1,7,6,7,3,10,8,7
Q,4,6,4,7,5,8,5,7,4,9,6,8,3,8,4,8
V,6,11,6,8,3,4,11,3,4,9,11,7,2,10,1,8
T,4,7,4,5,2,5,11,1,8,11,9,5,0,10,2,4
K,7,11,10,8,6,4,9,2,8,10,10,10,3,8,4,6
A,2,1,3,2,1,8,2,2,1,7,2,8,2,7,2,7
H,7,9,10,7,8,6,8,2,6,10,8,8,4,10,4,7
L,4,11,4,8,1,0,1,5,6,0,0,7,0,8,0,8
G,2,3,4,2,2,7,7,6,5,6,6,9,2,9,4,9
X,3,3,4,4,1,7,7,4,4,7,6,8,3,8,4,8
M,5,5,7,4,5,8,6,6,5,7,7,8,9,6,3,7
V,6,9,8,7,9,7,5,5,3,8,7,9,8,9,6,9
W,1,0,2,0,1,7,8,3,0,7,8,8,5,10,0,8
Q,3,4,4,5,3,8,7,6,3,8,7,9,2,9,4,9
O,2,1,2,2,1,8,7,7,5,7,6,8,2,8,3,8
R,9,13,9,7,6,10,6,4,5,10,2,7,6,7,6,9
V,5,7,5,5,2,2,11,4,3,11,12,8,2,10,0,8
N,9,10,7,5,3,9,13,6,5,3,6,10,5,8,2,8
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
N,4,7,7,5,6,7,8,3,4,8,7,8,7,10,5,4
M,7,11,10,8,11,7,7,5,5,6,7,9,8,6,2,8
A,5,6,7,5,5,8,8,3,5,7,8,8,5,9,4,6
B,4,9,6,6,6,8,7,4,5,9,6,6,3,9,5,8
U,3,6,4,4,4,8,8,6,5,6,7,9,3,8,3,7
K,2,4,3,3,2,5,7,4,6,6,6,11,3,8,4,10
Q,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8
F,7,10,6,5,3,5,11,3,4,11,7,4,2,8,6,4
Q,5,6,7,9,10,9,5,6,2,7,6,8,8,9,4,8
B,1,0,2,0,1,7,8,6,4,7,6,7,1,8,6,8
O,4,9,5,7,4,7,6,9,5,7,5,8,3,8,3,8
S,3,8,4,6,3,8,8,7,7,8,5,6,2,8,9,8
J,4,6,5,7,4,9,8,5,6,7,5,8,3,9,8,9
Y,7,11,7,8,4,3,10,2,7,10,12,6,1,11,2,5
F,5,10,6,8,7,7,6,6,4,8,6,8,6,10,7,11
Z,5,7,4,10,4,9,5,5,5,11,5,7,3,9,8,10
T,6,11,6,8,4,5,12,2,8,12,9,4,1,11,2,4
P,4,6,6,4,4,8,5,7,5,6,5,7,3,8,4,9
F,4,9,5,7,4,5,11,2,7,11,9,5,1,10,3,5
Y,4,5,5,3,2,4,11,2,7,11,10,5,1,11,2,5
F,2,1,3,2,2,6,10,4,5,10,9,5,1,9,3,7
I,7,14,5,8,3,9,6,6,5,13,3,7,3,8,5,11
L,3,6,4,4,2,9,3,2,6,9,2,10,1,5,3,9
M,5,10,6,8,6,8,6,11,1,6,8,8,9,5,2,5
K,5,11,6,8,5,4,6,7,3,7,7,12,4,8,3,11
P,1,3,3,2,1,7,8,4,3,11,5,4,1,9,2,8
Q,4,8,4,9,6,7,10,5,3,6,10,11,3,9,6,8
Y,7,7,6,10,4,9,7,6,6,4,11,7,5,10,3,7
V,7,10,7,8,4,4,11,2,4,9,11,7,4,9,1,7
K,4,7,6,5,4,5,7,5,8,7,6,12,3,8,5,10
V,4,8,6,6,3,7,11,3,4,6,12,9,2,10,1,8
B,3,5,4,4,4,7,7,5,6,6,6,6,2,8,7,10
E,4,10,6,8,8,7,7,3,5,6,7,10,5,10,8,8
D,5,11,7,8,7,9,8,5,5,10,5,4,4,8,5,9
M,4,4,8,3,4,7,6,3,4,9,8,9,8,6,2,8
Y,4,10,6,7,1,7,12,2,3,8,12,8,1,10,0,8
K,3,7,5,6,5,6,8,3,4,7,4,9,5,3,4,9
K,3,1,4,1,2,6,7,4,7,7,6,11,2,8,4,10
L,6,9,5,4,2,6,5,3,7,10,4,12,2,6,6,8
Q,5,9,6,11,7,8,6,8,4,5,6,9,3,8,6,10
G,2,0,2,1,1,8,6,5,5,6,5,9,1,8,5,10
L,1,0,2,1,0,2,1,5,5,0,2,5,0,8,0,8
Q,3,7,4,6,2,7,5,9,6,5,5,7,3,8,4,8
M,5,6,6,8,4,8,7,13,2,6,9,8,9,6,0,8
F,1,3,2,2,1,5,10,4,4,10,8,5,1,9,3,7
G,5,10,6,7,5,7,7,7,7,6,5,10,1,7,5,11
J,1,3,2,1,1,10,7,2,4,11,5,9,1,7,0,7
D,5,8,8,6,6,10,7,4,6,10,3,5,3,8,3,8
K,5,11,7,8,7,5,6,3,7,6,6,9,4,8,6,9
V,2,6,4,4,2,7,11,3,3,7,11,8,2,10,1,8
O,8,11,6,6,3,8,5,4,6,8,3,8,5,7,5,8
O,4,6,5,4,3,8,7,7,5,10,6,8,3,8,3,8
W,4,11,7,8,7,4,10,2,3,8,9,9,8,10,1,8
O,4,6,4,4,3,8,7,7,6,9,5,7,3,8,3,8
R,3,4,5,3,3,8,8,3,5,9,5,7,3,6,4,10
O,3,5,4,3,2,8,7,7,4,9,6,8,2,8,3,8
I,3,9,4,7,3,8,6,0,7,13,6,9,0,7,2,7
C,4,7,5,5,3,5,8,7,7,8,8,14,1,10,4,10
J,1,3,2,4,1,11,4,9,3,12,8,13,1,6,0,8
A,4,10,5,8,4,6,5,3,1,6,1,8,2,7,2,7
O,4,6,5,4,3,7,8,8,6,8,8,6,3,8,4,8
D,4,7,6,6,5,5,7,7,7,7,6,7,4,6,5,6
W,5,10,7,7,6,4,9,5,1,7,9,8,8,10,0,8
H,1,0,2,0,0,7,8,11,1,7,6,8,2,8,0,8
Y,2,1,2,1,0,7,9,2,2,6,12,8,1,10,0,8
U,4,9,5,7,4,5,8,6,7,8,9,10,3,9,1,8
F,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8
S,3,5,4,3,2,8,8,2,7,10,5,6,1,9,5,8
G,7,10,8,8,5,5,7,6,6,9,8,9,2,9,5,9
B,5,11,5,8,4,6,6,10,7,6,7,7,2,8,9,10
B,6,10,8,7,6,11,5,3,7,11,3,7,3,8,6,12
M,1,0,2,0,1,8,6,9,0,6,8,8,5,6,0,8
O,5,9,6,7,5,8,8,8,4,7,7,8,3,8,3,7
L,3,7,4,5,2,7,4,2,8,7,2,8,1,6,3,8
M,4,7,6,5,5,7,7,2,4,9,7,9,7,6,2,8
T,2,1,3,1,0,7,15,1,4,7,11,8,0,8,0,8
X,8,13,9,7,5,6,8,2,8,11,7,9,4,8,4,6
B,2,6,4,4,5,8,8,4,2,6,7,7,6,11,7,7
G,4,7,6,5,7,8,6,4,2,7,6,9,6,8,7,13
B,5,10,5,5,4,7,8,3,4,9,6,7,6,7,8,6
H,7,13,8,7,6,8,8,3,5,10,4,8,6,6,5,8
S,4,6,6,4,5,9,4,4,4,9,6,9,4,7,9,10
C,7,11,7,8,4,5,8,6,8,12,8,13,3,10,4,6
U,6,10,5,5,3,5,5,5,5,4,7,8,5,9,2,7
X,5,8,8,6,4,9,7,1,8,10,4,7,3,9,4,9
Q,3,6,4,5,2,8,7,8,6,6,5,9,3,8,4,8
N,10,15,11,8,5,7,7,2,4,13,7,8,6,8,0,7
E,4,7,5,5,4,8,7,2,7,11,6,8,3,9,4,9
Z,3,9,4,6,2,7,7,4,13,10,6,8,0,8,8,8
V,6,9,6,6,3,3,12,3,3,10,11,8,2,10,1,7
Q,3,6,3,7,4,8,8,6,1,8,7,10,2,9,5,8
G,6,8,8,7,9,8,7,6,3,7,7,8,7,11,7,9
D,2,4,4,3,2,9,6,4,6,10,4,6,2,8,3,8
L,3,9,4,7,1,0,1,6,6,0,0,6,0,8,0,8
J,3,10,5,8,1,14,1,8,5,14,3,11,0,7,0,8
I,0,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8
W,5,9,8,6,7,8,11,2,2,6,8,8,8,14,1,7
Q,2,3,3,4,3,8,9,5,1,5,7,10,2,9,5,10
U,9,13,8,8,5,8,5,4,6,6,7,6,6,8,4,6
O,5,9,5,7,4,7,7,8,5,10,6,7,3,8,4,8
I,0,7,0,5,0,7,7,4,4,7,6,8,0,8,0,8
L,2,5,3,3,1,6,4,1,8,7,2,10,0,7,2,8
X,4,5,7,4,3,6,8,1,9,10,8,8,2,8,3,7
T,3,5,4,4,2,6,11,2,7,11,9,5,1,11,3,4
L,5,11,7,8,5,10,4,1,8,10,2,10,0,6,3,10
A,7,15,7,8,5,10,2,5,2,11,5,12,6,3,6,10
K,8,15,9,9,6,6,8,3,6,10,9,10,6,11,4,8
N,4,11,6,8,5,6,9,6,5,7,7,8,6,9,1,7
E,3,8,5,6,5,7,8,5,8,6,5,9,3,8,5,9
E,4,8,5,6,3,3,7,6,11,7,7,14,0,8,7,7
B,4,6,5,4,5,8,6,7,6,6,6,5,3,9,7,9
N,5,8,8,6,8,8,6,4,4,7,6,7,7,11,7,4
A,3,8,5,6,3,9,3,1,2,7,2,8,2,6,2,7
B,4,8,5,6,5,8,8,6,7,7,6,6,2,8,7,10
W,5,8,8,6,3,4,8,5,2,7,9,8,8,9,0,8
H,6,11,9,8,9,7,7,6,7,7,6,8,6,8,4,8
V,2,2,4,3,2,7,12,2,2,6,11,9,2,11,0,7
B,4,7,6,5,5,10,6,2,5,10,4,7,3,7,5,11
Y,7,6,6,9,4,9,6,5,5,5,12,6,4,11,4,6
W,3,6,5,4,7,9,7,5,1,7,6,8,7,10,2,7
T,6,11,8,8,9,5,8,3,7,7,6,10,5,8,7,8
G,2,1,3,2,1,7,7,6,5,6,6,10,2,8,4,10
G,4,6,6,4,5,7,8,5,3,6,6,8,5,7,7,7
Z,5,7,6,5,3,8,7,2,9,12,5,10,2,9,6,9
V,4,10,6,8,2,8,8,4,3,6,14,8,3,9,0,8
P,5,9,5,7,5,5,10,8,3,8,5,5,1,9,6,6
A,4,11,5,8,4,8,5,3,0,7,1,8,2,6,3,8
K,4,8,6,6,4,9,7,2,6,10,2,7,4,7,3,10
B,7,12,6,6,6,8,7,4,5,9,6,8,6,8,8,8
W,5,5,6,3,4,4,11,3,2,9,9,7,7,12,1,6
T,2,3,3,5,1,9,14,0,6,5,11,8,0,8,0,8
Q,2,3,3,4,2,7,8,4,2,8,9,10,2,9,4,8
Q,4,6,4,8,4,7,9,4,4,8,10,11,3,9,6,8
O,3,6,4,4,3,7,8,8,7,7,8,7,2,8,4,8
J,2,8,2,6,1,14,3,5,4,13,3,10,0,7,0,8
M,11,11,11,6,5,4,9,5,6,4,2,12,9,11,2,8
S,2,1,2,2,1,8,7,4,7,5,6,7,0,8,8,8
D,3,4,4,7,2,5,7,11,8,6,5,5,3,8,4,8
R,4,7,4,5,2,6,10,9,4,7,4,8,3,7,6,11
P,1,0,1,0,0,5,10,7,2,9,6,5,1,9,3,8
U,2,1,2,1,0,8,6,11,4,7,12,8,3,10,0,8
L,0,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8
Y,3,5,6,4,2,7,11,1,7,7,11,8,2,11,2,8
H,5,10,7,8,7,8,8,7,6,7,6,7,3,8,3,7
F,4,9,6,7,4,3,12,5,4,12,9,5,2,10,2,5
N,3,4,5,3,2,7,9,2,5,10,6,6,5,9,1,7
X,4,9,6,6,5,7,7,3,9,5,7,9,5,7,8,8
R,4,11,5,8,3,6,9,11,5,7,5,8,3,8,5,10
B,2,4,3,2,2,8,8,3,5,10,6,6,2,8,5,9
L,3,7,4,5,2,0,1,4,5,1,1,7,0,8,0,8
V,8,10,7,7,3,3,12,4,4,10,12,8,3,10,1,8
D,1,0,2,1,1,6,7,8,6,6,6,6,2,8,3,8
Z,5,8,7,10,7,10,5,4,4,8,3,6,3,5,8,8
V,5,5,6,4,2,4,12,3,3,10,11,7,3,10,1,7
A,5,9,7,8,6,6,8,2,4,7,7,9,8,7,4,8
Q,2,2,2,3,1,7,8,5,1,7,8,10,2,9,4,8
U,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8
Y,3,5,4,7,7,7,5,4,2,7,7,9,5,10,4,8
L,2,5,3,4,1,4,3,4,7,2,2,5,0,7,0,6
V,3,2,5,3,1,6,12,3,4,8,11,8,2,10,1,8
R,5,10,5,8,6,6,8,9,5,6,5,7,2,8,5,11
S,4,9,5,7,4,8,8,8,7,8,4,7,2,7,9,8
Q,6,7,8,6,6,7,4,4,5,7,4,9,5,4,7,7
A,4,8,6,6,3,9,2,2,2,7,1,8,2,7,3,7
E,1,3,3,1,1,6,8,2,7,11,8,9,1,8,3,8
P,6,10,6,7,3,4,14,8,1,11,6,3,1,10,4,8
W,10,10,10,8,7,2,11,2,3,10,10,8,7,11,2,7
S,3,4,4,6,2,8,7,6,9,5,6,8,0,8,9,8
G,2,1,3,2,1,6,6,5,5,6,6,9,2,9,4,8
U,3,4,3,3,2,5,8,5,7,10,9,8,3,9,2,6
Q,5,5,6,7,3,8,7,8,6,6,6,9,3,8,5,9
W,6,9,6,7,6,3,12,2,2,10,9,8,6,11,2,7
L,4,9,5,7,3,3,3,7,8,1,0,5,1,6,1,6
R,4,9,6,6,4,9,7,4,6,9,4,7,3,7,5,10
C,4,7,5,5,5,5,7,3,5,7,6,10,5,10,3,8
D,4,7,5,5,2,5,7,10,9,7,7,6,3,8,4,8
M,4,9,6,7,7,7,7,5,5,6,7,8,8,7,3,7
U,2,1,3,2,1,7,8,6,7,7,10,8,3,9,1,8
H,5,7,6,5,5,7,10,8,6,8,6,7,3,7,3,9
Q,4,10,5,9,5,8,7,7,5,6,8,8,3,7,6,11
D,2,4,3,3,2,8,7,4,5,9,4,5,2,8,2,8
J,4,8,5,6,3,7,7,3,6,15,6,9,1,6,1,7
C,4,9,5,7,3,6,8,9,8,9,8,12,2,10,4,9
O,4,8,5,6,5,7,8,8,5,7,8,8,3,8,3,8
B,3,5,5,3,3,9,6,2,6,10,4,7,4,7,6,9
Z,5,11,7,8,4,7,7,2,10,12,5,9,1,9,6,8
T,6,11,6,6,3,6,10,2,7,12,7,6,2,9,5,4
O,4,10,5,7,2,7,7,9,7,7,7,8,3,8,4,8
I,1,3,2,2,1,7,7,1,7,13,6,8,0,8,1,8
U,1,0,1,0,0,7,7,9,3,7,12,8,2,10,0,8
I,1,9,1,7,2,8,7,0,8,7,6,7,0,8,3,7
V,3,8,5,6,1,7,8,4,3,7,14,8,3,10,0,8
U,2,3,3,2,1,8,8,5,6,6,9,8,3,10,1,8
J,2,6,4,4,1,7,8,2,6,15,6,9,0,7,1,7
B,4,6,6,4,5,8,8,6,4,6,5,6,4,8,6,5
Y,4,5,6,7,1,7,10,2,2,7,13,8,2,11,0,8
Y,8,11,8,8,4,3,11,2,9,12,11,5,0,10,2,4
O,6,10,4,5,3,6,8,4,4,7,4,7,4,8,5,8
X,3,10,4,7,2,7,7,4,4,7,6,8,3,8,4,8
L,3,7,4,5,2,6,3,1,8,8,2,11,0,7,2,8
Y,2,2,4,3,1,6,10,1,7,8,11,8,1,11,2,7
W,4,8,6,6,6,7,6,6,2,7,7,8,6,8,4,10
O,4,7,5,5,4,8,8,7,4,7,7,6,4,9,3,7
H,3,6,4,4,3,7,6,12,1,7,8,8,3,9,0,8
X,4,7,4,4,1,7,7,5,4,7,6,8,3,8,4,8
T,2,3,3,1,1,6,12,3,6,11,9,5,1,11,2,5
R,4,8,6,6,6,8,6,7,3,7,5,7,4,6,7,8
W,5,8,8,6,3,9,8,5,2,7,8,8,9,9,0,8
M,5,10,7,8,8,9,7,6,5,6,7,5,8,7,3,5
P,2,3,4,1,1,7,9,4,3,11,4,4,1,9,2,8
B,4,6,4,4,3,6,7,8,6,7,6,6,2,8,9,10
T,4,7,4,5,3,4,12,5,4,11,9,5,2,12,1,5
O,4,8,5,6,3,7,5,8,5,6,4,7,3,8,3,8
K,8,13,8,7,5,9,6,3,6,10,1,7,6,5,4,8
R,3,7,3,4,2,5,11,8,4,7,3,9,3,7,5,11
P,5,6,6,4,5,7,6,6,4,7,6,8,3,8,7,10
I,3,9,4,6,2,6,8,0,8,14,7,8,0,8,1,7
F,5,9,4,5,2,7,9,3,5,12,6,5,2,8,6,6
E,5,9,4,4,2,8,7,5,4,10,5,10,3,8,7,10
E,5,8,6,6,5,8,7,6,3,7,6,10,4,8,8,9
R,3,7,5,5,4,10,7,3,5,10,3,7,3,7,3,10
N,4,2,4,3,3,7,8,5,5,7,7,6,6,9,3,6
K,3,7,4,4,1,4,8,7,2,7,5,11,3,8,3,10
R,4,6,6,4,6,8,8,4,4,6,7,8,6,10,7,6
N,4,9,6,7,4,7,8,6,5,7,7,7,6,9,2,6
B,2,1,2,1,1,7,7,7,5,6,5,7,1,8,7,10
M,5,7,7,5,4,6,6,3,5,10,9,9,7,5,2,8
L,4,10,5,8,3,4,4,1,9,6,1,10,0,7,2,7
W,8,14,8,8,6,3,8,1,4,9,10,8,9,11,2,5
O,2,3,2,2,1,8,7,6,3,9,6,8,2,8,3,8
V,4,7,5,5,6,8,6,4,1,7,8,8,7,9,4,7
S,4,9,6,7,7,8,6,5,4,8,6,9,5,7,13,9
P,4,9,6,7,4,6,10,3,7,10,9,4,1,10,5,7
J,2,8,3,6,1,11,3,10,3,13,7,13,1,6,0,8
S,4,7,5,5,4,8,7,5,7,5,6,8,0,9,9,8
T,3,6,5,6,4,6,7,3,8,8,6,9,3,7,7,6
X,0,0,1,0,0,7,7,3,5,7,6,8,2,8,3,8
M,2,1,2,2,1,7,6,10,0,7,8,8,6,6,0,8
W,8,10,8,8,7,2,12,2,2,10,9,8,6,12,2,6
E,1,0,1,1,1,5,7,5,7,7,6,12,0,8,6,9
T,2,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8
R,1,0,1,0,0,6,8,6,3,7,5,7,2,7,3,10
H,4,8,5,6,5,6,7,5,4,7,5,8,3,7,6,11
T,3,6,4,4,2,10,11,2,8,5,11,9,1,11,1,9
U,2,3,3,2,1,4,8,4,5,11,10,9,3,9,1,6
D,7,12,6,6,4,10,4,3,6,10,2,7,4,7,4,12
N,8,12,7,6,3,5,9,4,6,4,4,11,5,10,2,7
V,3,7,5,5,2,8,9,4,1,6,12,8,2,10,0,8
D,4,8,6,6,5,10,6,4,6,9,3,6,3,9,3,8
Z,4,9,6,6,4,7,7,2,10,12,6,10,1,9,6,8
E,3,2,4,4,3,7,7,6,8,7,7,9,2,8,6,9
X,4,9,6,7,5,7,7,3,8,5,6,8,3,8,6,7
A,5,7,8,6,6,9,7,3,5,6,8,6,4,10,4,5
L,3,8,4,6,2,0,2,4,6,1,0,7,0,8,0,8
G,5,7,6,5,3,6,7,6,6,10,8,8,2,9,5,9
R,6,10,8,8,7,8,8,6,6,6,5,7,3,6,6,9
A,4,11,7,9,4,11,3,2,3,9,2,9,5,5,3,9
Y,6,9,8,7,6,8,7,6,5,5,9,8,3,9,8,5
M,4,1,5,3,3,9,6,6,4,6,7,6,8,6,2,5
T,4,9,5,6,3,5,13,6,4,11,9,4,3,12,2,4
A,3,9,6,6,2,8,4,3,2,7,1,8,3,7,3,8
R,3,6,5,4,4,9,7,3,5,10,3,7,3,7,3,10
B,4,9,6,6,7,8,6,6,6,6,6,5,2,8,6,10
M,5,6,6,8,4,8,7,12,2,6,9,8,8,6,0,8
D,3,10,5,8,8,9,8,4,5,7,6,6,4,7,8,5
F,3,4,4,6,1,1,13,5,3,12,10,6,0,8,2,6
M,5,6,7,4,5,12,5,3,4,9,2,6,7,6,2,8
C,4,9,5,6,3,6,9,7,8,13,8,7,2,11,3,7
F,10,13,8,8,4,8,8,4,7,13,4,6,2,7,7,8
R,4,8,7,7,8,9,8,4,4,7,4,7,6,7,6,4
V,3,7,5,5,2,6,9,4,2,8,13,8,2,10,0,8
X,5,9,7,7,3,7,8,1,9,10,6,8,3,8,4,7
Z,4,9,6,7,4,8,5,2,9,11,4,10,2,7,7,9
S,3,5,4,3,2,8,7,2,7,10,6,8,1,9,5,8
Z,3,4,4,6,2,7,7,3,13,10,6,8,0,8,8,8
E,4,10,6,7,5,10,6,1,7,11,4,8,3,8,4,11
H,4,8,4,6,2,7,9,15,1,7,4,8,3,8,0,8
E,1,1,1,1,1,4,7,5,8,7,6,13,0,8,6,9
O,7,11,9,8,12,8,5,6,2,7,6,8,11,10,8,14
S,1,0,1,0,0,8,7,4,6,5,6,7,0,8,7,8
W,8,8,8,6,5,4,11,3,3,9,9,7,8,11,2,6
R,3,6,4,4,4,6,8,8,4,7,6,8,2,7,5,11
N,5,10,6,7,3,7,7,15,2,4,6,8,6,8,0,8
C,1,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10
Q,4,10,6,8,6,8,6,7,4,5,7,11,6,7,8,8
W,3,7,5,5,4,5,10,2,2,8,9,9,7,11,1,8
Q,4,9,4,4,2,8,5,4,7,8,6,8,3,9,8,11
B,7,11,9,8,8,10,6,3,6,10,3,7,5,7,6,11
I,4,7,5,5,3,8,8,2,8,7,6,7,0,8,4,7
G,6,9,5,4,3,7,8,6,6,10,5,6,4,7,5,7
Q,5,8,5,10,7,8,8,6,2,7,8,11,3,9,6,8
A,2,3,4,4,1,10,5,3,1,8,1,8,2,7,2,8
I,9,14,6,8,3,10,4,5,5,13,3,8,3,7,5,10
Z,4,9,5,7,2,7,7,4,15,9,6,8,0,8,8,8
C,5,9,6,7,4,3,8,6,7,12,10,13,1,9,3,7
V,2,7,4,5,2,8,11,2,2,5,10,8,2,11,0,9
K,7,15,8,8,5,9,6,3,6,11,2,7,5,7,4,9
K,4,9,5,7,4,3,7,7,3,7,6,11,3,8,3,11
C,4,6,6,4,3,8,6,7,7,7,6,8,4,10,4,8
G,3,9,5,6,4,6,6,6,6,6,6,9,2,9,4,8
M,6,10,8,8,9,7,10,6,4,7,7,8,6,8,6,8
F,4,9,6,6,3,3,12,5,4,13,9,5,1,10,2,5
U,5,10,8,8,10,8,5,4,5,6,7,6,10,6,6,5
U,2,3,2,1,1,7,8,6,7,8,9,7,3,10,1,8
S,6,11,7,8,5,9,7,3,6,10,4,7,2,9,5,9
J,4,7,5,5,2,5,9,3,5,15,8,10,1,6,1,7
F,4,8,5,6,5,4,10,2,5,10,10,6,1,10,3,6
T,4,6,4,4,3,6,11,3,7,11,9,4,2,12,3,5
Q,6,10,5,5,4,9,6,4,6,11,5,7,4,7,9,10
B,2,4,3,3,2,8,7,3,5,9,6,6,3,8,5,9
R,4,9,5,6,4,9,8,6,6,8,5,7,3,8,5,10
U,5,5,6,4,2,3,9,5,7,11,11,9,3,9,1,6
W,8,10,8,8,6,2,11,2,3,10,10,8,7,11,1,7
V,10,14,9,8,5,8,9,4,6,8,9,5,6,12,4,8
A,3,6,6,8,2,9,4,3,2,8,1,8,3,7,2,8
F,3,7,5,5,4,4,10,5,5,11,10,6,2,10,3,6
U,5,8,6,6,3,7,8,6,8,5,9,9,3,9,1,8
E,1,3,3,1,1,6,7,2,6,11,6,9,2,8,3,9
F,3,4,4,3,2,5,11,4,6,11,9,5,1,10,3,6
D,3,5,4,4,3,7,7,6,6,7,6,5,5,8,3,7
Q,2,2,3,3,2,8,7,6,2,6,6,9,2,9,4,9
F,5,9,5,5,2,8,8,2,7,11,6,6,2,10,5,8
M,7,11,10,8,10,8,7,5,5,6,7,7,11,8,4,6
I,2,10,2,8,3,7,7,0,7,7,6,8,0,8,3,8
U,3,7,5,6,5,7,7,4,4,6,6,9,4,8,1,8
P,4,9,5,6,5,6,9,7,4,9,7,4,2,10,4,7
H,6,8,9,6,6,10,6,3,6,10,3,8,4,8,5,10
U,4,7,6,5,4,8,8,8,5,6,7,10,3,8,4,6
G,4,4,6,7,3,8,8,8,8,6,7,8,2,7,6,10
W,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8
X,3,6,4,4,1,7,7,6,2,7,6,8,3,8,4,8
C,4,7,6,5,3,7,7,7,6,7,6,8,4,9,4,8
N,5,10,6,8,6,7,7,13,1,6,6,8,6,9,0,6
W,8,11,8,8,9,4,10,2,3,9,8,7,9,13,3,5
W,3,7,5,5,7,8,9,5,1,7,7,8,7,9,2,8
P,5,6,7,8,8,8,6,4,3,7,7,7,7,12,6,7
P,1,0,1,0,0,5,10,6,1,9,6,5,0,9,2,8
K,3,4,4,3,3,5,7,4,7,7,6,11,3,8,5,9
B,8,15,6,8,5,8,6,5,6,10,5,9,6,7,8,10
C,3,2,4,3,2,6,8,8,7,8,7,12,2,9,4,10
F,5,11,7,8,5,6,11,2,5,13,7,4,2,10,2,7
J,4,5,5,6,4,8,9,4,4,7,6,8,3,7,7,7
V,4,9,6,6,3,4,11,3,4,9,12,9,3,10,1,8
O,4,8,4,6,3,8,5,8,5,9,4,8,3,8,3,8
N,4,5,4,7,3,7,7,14,2,5,6,8,6,8,0,8
W,6,10,8,8,9,7,9,6,4,9,9,7,8,8,5,12
L,4,8,5,6,4,8,5,8,6,5,7,8,3,6,6,11
O,2,4,3,3,2,7,7,6,3,9,6,8,2,8,2,8
R,3,4,5,2,3,8,8,3,5,9,5,7,3,6,4,10
R,5,8,8,7,8,8,7,3,3,7,5,8,7,9,6,5
P,4,10,6,7,4,8,9,2,6,13,6,5,1,10,3,9
D,5,10,6,8,6,6,8,9,7,7,7,5,3,6,5,9
A,3,10,5,8,4,11,3,2,2,9,2,9,3,5,3,8
A,3,8,6,6,4,11,2,2,2,9,2,9,3,5,3,8
O,6,10,7,8,3,6,6,9,9,6,5,6,3,8,4,8
V,4,10,7,7,2,7,8,4,3,7,14,8,3,9,0,8
K,5,7,8,5,5,4,9,2,6,10,9,10,3,8,3,6
O,2,3,3,2,1,8,7,6,3,9,6,8,2,8,2,8
D,3,6,5,4,6,9,8,4,5,7,6,6,4,8,8,5
I,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7
O,2,1,3,3,2,7,7,7,5,7,6,8,2,8,3,8
P,3,2,4,4,3,6,9,5,5,9,7,3,1,10,4,6
E,5,9,8,7,5,6,8,4,9,12,9,9,3,8,5,6
O,4,9,5,7,3,8,6,9,8,7,4,9,3,8,4,8
B,3,3,4,4,3,6,8,8,7,7,5,7,2,8,9,10
M,4,7,5,5,3,7,7,12,1,7,9,8,8,6,0,8
G,5,10,6,7,7,7,5,6,3,7,6,11,4,8,7,7
C,7,10,8,8,4,2,9,5,9,11,11,13,1,7,3,6
G,4,8,6,6,6,7,9,6,4,6,6,8,5,7,7,7
N,1,0,1,0,0,7,7,10,0,5,6,8,4,8,0,8
O,4,7,5,5,3,8,8,8,5,7,7,6,3,8,4,8
V,2,3,4,5,1,9,8,4,2,6,13,8,3,10,0,8
P,2,5,4,3,2,7,9,4,4,12,5,3,1,10,2,8
T,7,12,6,7,3,6,10,3,6,12,8,6,2,9,4,5
Y,4,5,6,8,9,7,5,4,3,8,8,9,5,10,6,10
P,2,4,4,3,2,7,9,4,3,11,4,4,1,10,3,8
F,4,7,6,5,3,8,9,3,6,13,5,5,2,10,3,8
G,7,10,7,8,4,7,6,7,8,11,5,12,3,9,5,8
Q,2,2,3,3,2,8,8,7,3,5,6,10,2,9,5,10
O,3,7,4,5,3,9,6,7,5,10,4,9,3,8,3,8
N,3,2,4,3,2,7,8,5,5,8,7,6,6,10,3,5
R,7,11,9,8,6,9,7,5,7,8,4,6,3,7,6,11
N,2,3,4,2,2,7,8,2,4,10,6,7,5,8,0,7
I,1,3,2,2,0,7,7,1,7,13,6,8,0,8,1,8
P,2,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8
W,5,5,8,7,4,11,8,5,2,6,9,8,8,9,0,8
L,2,3,2,2,1,4,4,4,7,2,2,6,0,7,1,6
S,4,5,5,4,5,8,8,4,4,7,7,8,5,10,9,10
D,3,5,6,4,4,9,7,4,7,10,4,6,2,8,3,8
Y,3,4,4,3,2,5,10,2,7,10,10,5,1,11,3,5
Z,5,10,6,8,7,8,9,3,7,8,6,8,2,10,13,7
M,3,3,5,2,3,8,6,6,4,7,7,8,7,5,2,7
E,3,7,5,5,5,5,6,4,6,7,6,12,2,10,8,6
N,6,11,8,8,9,6,7,3,4,8,8,9,8,9,7,5
G,3,3,4,5,2,7,7,7,6,6,6,7,2,7,6,11
N,5,9,7,6,6,7,7,6,6,7,5,8,6,7,3,7
V,4,8,4,6,3,4,11,1,2,9,10,7,2,11,1,7
Y,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8
D,6,11,8,8,7,10,6,4,6,9,3,6,3,8,3,9
C,4,10,5,7,2,5,7,7,10,7,6,14,1,8,4,9
H,4,7,4,5,2,7,7,14,0,7,6,8,3,8,0,8
L,4,7,5,5,2,5,4,2,9,6,2,9,1,6,2,7
S,3,4,4,3,2,9,6,3,7,10,5,8,1,9,5,9
Q,5,6,6,8,6,8,5,8,4,6,6,8,3,9,7,11
F,6,11,8,8,5,8,9,3,6,13,5,4,2,9,3,7
S,3,5,5,4,2,7,7,3,8,11,5,7,1,8,4,8
Y,3,5,5,4,2,8,11,1,7,5,11,9,2,12,3,8
K,2,3,2,1,2,6,7,4,6,6,6,10,3,8,4,9
G,2,1,4,3,2,7,7,6,6,6,7,9,2,8,4,9
L,6,11,8,8,5,7,5,0,8,9,3,11,2,6,4,7
J,6,10,9,7,5,8,5,7,7,8,6,7,2,7,5,6
R,3,9,4,6,3,5,11,8,4,7,4,9,3,7,6,11
P,6,11,6,8,6,5,11,9,3,9,5,4,1,10,3,8
N,7,10,10,8,5,9,9,3,6,10,3,4,6,9,1,7
R,8,12,8,6,6,8,7,3,5,9,3,8,6,7,6,7
A,1,3,2,2,1,10,3,2,1,9,2,9,1,6,1,8
M,4,8,6,6,7,9,7,5,5,6,7,6,8,7,2,6
Z,4,10,5,7,4,6,8,6,10,7,7,10,1,9,8,8
R,6,9,6,4,4,5,8,3,5,7,4,10,4,7,5,7
D,4,6,5,4,7,8,8,5,5,7,6,6,6,6,7,6
A,2,7,4,4,2,7,4,3,2,6,1,8,3,6,2,7
X,10,15,9,9,4,6,8,3,9,9,9,9,4,10,4,6
I,1,3,1,1,0,8,7,1,7,13,6,8,0,8,0,8
G,5,7,7,6,7,6,10,5,3,7,7,7,9,13,8,7
Z,5,8,7,6,4,7,8,2,10,12,7,8,1,9,6,7
O,5,6,7,5,5,7,5,4,4,9,4,9,3,7,6,6
J,4,6,6,7,5,7,8,4,5,7,7,7,3,9,9,10
V,4,11,6,8,4,6,11,2,3,6,11,9,2,10,1,8
N,2,3,2,1,1,7,8,5,4,7,6,7,4,9,1,6
A,3,5,5,4,2,9,2,1,2,8,2,9,2,6,2,8
R,5,7,7,5,4,10,7,3,6,10,2,6,3,6,4,10
H,3,6,5,4,4,7,8,3,6,10,5,8,3,8,3,9
I,3,7,4,5,2,9,6,0,7,13,5,9,0,8,1,8
V,2,1,3,1,1,9,12,3,2,5,11,8,2,11,0,8
Q,2,2,3,3,2,8,6,6,3,6,6,9,2,9,4,9
D,9,15,9,8,5,10,4,4,6,12,2,8,5,6,5,10
P,1,1,1,1,0,5,11,7,1,10,6,4,1,9,3,8
W,6,7,6,5,4,3,10,2,3,10,10,8,6,11,2,6
S,5,8,6,6,3,6,8,4,8,11,6,7,2,8,5,6
L,1,3,2,2,1,7,4,1,6,8,3,10,0,7,1,9
A,5,8,8,7,7,7,8,2,4,7,8,9,8,6,4,8
W,6,7,6,5,4,5,10,3,3,9,8,7,7,12,2,5
L,3,5,4,3,2,4,4,4,8,2,1,5,1,7,1,6
F,5,10,9,8,9,7,9,1,5,9,6,5,6,9,5,2
D,3,5,5,3,3,7,7,6,7,7,6,5,2,8,3,7
O,4,9,3,4,2,6,9,5,3,8,5,6,4,9,5,8
R,1,3,2,1,1,8,8,4,4,9,5,7,2,6,4,9
L,2,6,4,4,2,9,4,1,8,10,3,10,0,7,2,10
U,4,8,5,6,3,5,8,6,7,9,7,9,3,9,3,5
P,3,7,3,5,3,5,11,8,2,9,7,5,1,10,3,8
U,11,15,10,9,5,9,7,6,6,2,10,6,6,7,3,6
E,3,5,6,4,3,7,8,2,8,11,7,9,2,8,4,8
Z,5,10,7,8,5,8,6,2,9,11,5,10,2,7,7,8
L,3,7,4,5,5,7,8,3,5,5,7,10,4,11,5,6
O,4,9,4,7,3,8,7,8,5,10,5,9,3,8,4,7
S,3,9,4,7,4,7,7,5,7,5,6,8,1,8,9,7
V,5,10,5,7,3,3,11,3,4,10,12,8,2,10,1,8
T,7,10,6,5,3,8,9,2,7,11,7,7,2,9,4,6
W,8,11,8,8,6,3,10,2,3,9,9,8,8,11,2,5
L,4,10,5,8,3,5,4,3,8,5,1,7,1,6,3,6
Y,5,7,7,11,10,10,9,4,2,5,8,9,4,11,10,12
P,3,3,4,4,2,5,11,9,3,9,6,5,1,10,4,8
C,2,6,3,4,2,6,8,8,8,8,8,14,1,9,4,9
N,3,2,4,3,2,7,8,5,4,7,7,6,5,9,2,5
Y,2,4,4,6,1,7,10,1,3,7,12,8,1,11,0,8
K,9,14,8,8,4,8,8,3,7,9,7,7,6,10,4,7
I,2,5,3,3,1,7,7,0,7,13,6,8,0,8,1,7
A,3,9,5,6,4,8,2,1,2,6,2,7,3,6,4,7
F,5,8,7,6,3,5,12,7,5,13,7,3,2,9,2,5
V,10,14,9,8,5,9,7,4,5,6,8,6,7,12,3,8
F,3,8,5,6,3,8,8,2,5,13,5,6,2,10,2,9
H,3,8,5,6,7,8,6,4,3,7,6,7,6,7,8,9
X,2,3,4,1,1,5,9,2,8,10,9,8,2,8,3,6
A,2,1,3,1,0,7,4,2,0,7,1,8,2,6,1,7
R,4,8,6,6,6,7,9,5,6,8,4,9,4,5,5,10
P,5,9,6,6,3,5,12,9,2,10,6,4,1,10,4,8
K,6,11,8,8,10,7,7,4,4,6,6,9,8,8,8,8
C,3,9,4,7,3,5,8,9,8,9,8,11,2,10,4,10
G,4,9,4,4,3,7,7,3,2,8,5,7,3,10,8,7
X,2,7,3,4,1,7,7,4,4,7,6,8,3,8,4,8
Q,6,11,5,6,3,10,4,5,6,12,3,10,3,7,7,12
V,4,10,6,7,2,7,8,4,3,7,14,8,3,9,0,8
J,5,9,6,10,6,8,8,4,6,6,7,7,3,9,9,8
Z,3,4,5,3,2,8,6,2,9,12,5,9,1,8,5,9
C,5,10,6,7,4,7,7,8,7,6,6,9,4,8,4,8
R,4,9,6,7,7,6,8,5,6,7,5,7,3,7,5,9
C,4,7,5,5,2,5,8,6,8,12,9,11,2,10,3,7
W,7,11,10,9,15,8,8,6,2,7,7,8,14,10,5,8
O,2,5,3,4,2,8,7,7,4,9,6,8,2,8,3,8
Z,1,0,1,1,0,7,7,2,10,9,6,8,0,8,6,8
A,3,8,5,6,2,11,2,4,3,11,2,10,2,6,3,8
F,5,8,6,10,7,7,9,5,5,8,6,8,4,10,7,5
V,10,14,8,8,4,8,10,6,5,6,10,5,6,13,4,7
L,4,9,6,7,4,6,5,0,8,8,2,11,0,7,2,8
N,6,11,8,8,7,7,7,8,5,7,6,7,6,7,3,9
N,4,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8
T,3,4,4,3,1,5,13,3,6,12,9,4,1,11,1,5
X,4,10,7,8,7,7,7,2,6,7,6,8,4,8,7,7
X,6,10,7,5,3,5,8,2,7,11,8,8,4,9,3,6
W,5,10,7,7,7,7,7,6,2,7,8,8,6,8,4,10
N,7,11,6,6,3,8,11,5,6,4,5,9,5,8,2,7
C,3,4,4,3,1,4,8,5,7,11,9,12,1,9,2,7
K,3,5,5,3,3,6,7,4,7,7,6,11,3,8,5,9
M,3,4,5,3,3,5,6,3,4,9,9,10,7,5,2,8
F,3,4,5,3,2,8,9,2,7,13,5,5,2,9,3,8
J,1,6,2,4,1,12,3,8,3,13,6,12,1,6,0,8
W,6,10,9,8,6,10,8,5,1,5,9,7,10,12,2,5
G,4,8,5,6,3,7,6,6,7,11,6,12,2,10,4,9
P,3,1,4,2,2,5,10,4,4,10,8,4,1,10,3,7
M,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8
B,8,11,6,6,4,9,7,6,7,10,4,8,6,6,6,10
R,6,11,6,8,4,6,10,10,4,7,4,8,3,7,6,10
D,3,4,5,3,3,10,6,3,7,10,3,6,2,8,3,9
Z,4,8,6,6,6,7,8,2,7,7,6,7,1,9,9,8
X,4,5,5,7,2,7,7,5,4,7,6,8,3,8,4,8
T,5,8,6,6,7,5,7,3,6,7,6,9,5,8,5,7
G,2,4,3,3,2,6,7,5,5,9,7,10,2,8,4,10
R,3,6,3,4,2,6,9,9,4,7,4,7,2,7,5,10
J,3,8,5,6,2,10,6,2,7,14,4,8,0,7,0,8
R,4,9,6,7,4,10,7,3,6,11,2,7,3,6,3,9
C,4,8,5,6,3,5,7,6,8,7,6,12,1,8,4,9
G,5,9,6,6,4,7,6,7,7,7,5,12,2,8,5,10
E,2,6,3,4,2,3,7,5,9,7,6,14,0,8,6,9
B,4,7,6,6,7,8,6,5,4,7,6,8,6,9,7,7
X,4,5,6,4,3,7,7,1,9,10,6,8,2,8,3,7
L,4,8,5,7,5,8,7,5,6,7,6,8,3,8,8,11
D,2,4,4,2,2,9,7,4,6,10,4,5,2,8,3,8
B,4,9,4,6,6,6,8,8,6,7,5,7,2,7,7,9
L,6,10,8,8,7,6,6,6,6,6,5,8,6,7,4,10
Z,3,7,4,5,3,7,7,3,11,8,6,8,0,8,7,8
I,2,6,3,4,2,8,6,0,7,13,6,9,0,7,2,8
I,1,1,0,2,0,7,7,1,7,7,6,8,0,8,2,8
B,4,10,5,8,4,6,6,10,7,6,7,7,2,8,9,9
J,3,10,5,8,4,12,6,2,7,11,2,6,0,7,1,8
E,7,10,9,8,7,4,9,4,8,12,10,9,3,8,5,4
M,2,1,3,2,2,8,6,6,3,6,7,7,6,5,1,7
J,3,9,4,6,2,14,3,5,5,13,2,9,0,7,0,8
I,1,5,2,3,1,7,7,0,7,13,6,8,0,8,1,7
V,2,3,3,1,1,7,12,3,3,8,11,8,2,10,1,8
N,3,4,3,3,2,7,8,5,4,7,6,6,5,9,2,6
Y,4,7,6,5,5,8,5,6,4,6,9,8,3,8,8,4
B,3,6,3,4,3,6,8,9,6,7,6,7,2,8,8,10
U,1,0,2,0,0,7,6,10,4,7,12,8,2,10,0,8
Y,3,4,5,5,1,6,10,3,1,8,13,8,1,11,0,8
J,3,10,5,7,2,7,8,2,7,15,5,8,0,6,1,7
F,6,9,8,7,7,7,9,5,5,8,6,7,6,10,8,11
A,2,1,4,3,2,8,1,2,2,8,2,8,3,6,3,7
P,2,4,3,3,2,5,10,4,4,10,8,3,1,10,3,6
N,7,9,9,5,3,8,6,3,4,13,6,8,6,8,0,8
O,3,6,5,4,3,8,8,8,4,7,6,5,3,8,3,7
V,3,6,4,4,2,7,11,3,2,7,10,9,2,10,3,8
G,3,4,4,3,2,6,6,6,6,6,6,10,2,9,3,9
G,4,5,5,4,3,6,6,6,7,6,6,11,2,9,4,9
N,5,9,7,7,5,7,9,6,5,7,6,7,6,9,2,6
P,1,1,2,1,1,5,11,7,2,10,6,4,1,9,3,8
W,5,9,6,4,4,7,9,2,3,7,8,6,8,11,2,7
W,6,9,6,6,4,4,10,2,3,9,9,8,7,11,2,6
K,7,11,10,8,6,4,8,3,7,11,10,12,4,8,4,6
C,9,13,6,7,3,6,9,6,7,12,7,7,2,9,5,9
Y,5,9,7,6,4,10,10,1,7,3,11,7,1,11,2,9
N,2,2,3,3,2,7,8,5,4,7,6,6,5,9,2,5
J,1,7,2,5,1,13,3,7,4,13,3,11,0,6,0,8
S,3,5,3,3,2,8,7,7,5,7,6,7,2,9,9,8
C,4,10,5,8,2,5,7,7,10,7,6,13,1,8,4,9
H,4,5,7,4,4,7,7,3,6,10,6,8,3,8,3,7
C,4,4,5,6,2,6,7,7,11,6,6,13,1,7,4,9
L,1,0,2,1,0,2,1,6,5,0,2,5,0,8,0,8
V,4,8,6,7,7,8,7,4,4,7,6,8,7,7,9,5
T,5,11,6,8,5,9,12,4,6,6,11,8,3,12,1,8
J,4,6,5,7,5,8,8,5,7,7,5,8,3,9,9,8
H,1,0,2,0,0,7,6,11,1,7,7,8,2,8,0,8
V,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8
S,6,9,7,6,3,9,6,4,8,11,3,8,2,8,5,11
D,6,10,8,7,7,7,7,5,6,7,7,8,7,7,3,8
B,2,3,3,2,2,8,7,2,5,11,5,7,2,8,2,9
C,3,3,4,4,1,5,8,6,9,6,7,11,1,7,4,9
M,2,1,2,1,1,8,6,10,0,6,9,8,6,6,0,8
S,3,7,4,5,2,7,6,6,9,5,7,10,0,9,9,8
F,5,7,6,8,7,7,9,5,5,8,6,7,4,9,9,7
R,3,7,5,5,5,6,7,5,6,6,5,8,3,6,4,8
W,6,7,6,5,6,7,10,4,3,8,6,6,9,11,4,6
I,1,7,2,5,1,7,7,0,8,7,6,8,0,8,3,8
X,4,10,6,8,6,7,7,2,6,7,6,8,3,9,6,8
N,6,9,9,7,5,7,9,2,5,9,6,7,6,8,1,7
W,4,7,6,5,6,7,7,6,2,7,8,8,6,8,4,8
S,1,0,2,1,1,8,7,4,7,5,6,7,0,8,7,8
P,4,7,5,5,4,6,9,6,5,9,7,4,2,10,3,7
N,5,10,6,8,6,7,7,6,7,7,6,8,6,8,3,7
M,1,0,2,0,1,7,6,9,0,7,8,8,6,6,0,8
F,5,9,7,7,3,5,13,4,5,13,7,3,1,10,2,6
Q,5,6,7,8,8,10,9,6,0,5,7,10,7,13,5,11
X,4,2,5,4,3,7,7,3,9,6,6,7,2,8,6,7
S,5,8,6,6,4,7,8,3,7,10,5,7,2,8,5,8
V,2,1,3,2,1,7,12,2,2,7,11,8,2,11,0,8
D,5,12,5,6,4,6,8,4,6,10,6,6,5,9,6,6
U,5,7,5,5,2,4,9,5,7,11,11,9,3,9,1,6
S,1,3,3,2,1,8,7,3,7,11,4,8,1,8,4,9
Z,7,10,9,8,6,6,9,3,10,12,8,7,1,9,6,6
D,6,10,6,8,3,5,8,10,10,8,7,6,3,8,4,8
B,4,7,6,6,7,8,6,5,5,7,6,8,8,9,7,6
Q,5,9,4,4,2,10,4,4,6,10,4,8,3,9,7,13
Y,2,3,4,4,0,7,10,2,2,7,13,8,1,11,0,8
D,4,7,5,5,4,7,7,6,8,7,6,5,3,8,3,7
O,4,9,6,7,7,8,8,5,1,7,7,9,8,9,4,9
D,2,3,3,2,2,9,6,4,6,10,4,6,2,8,2,9
B,5,9,5,4,4,7,8,3,4,9,6,7,6,7,8,5
Q,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8
H,6,7,8,5,4,7,6,3,7,10,7,9,3,8,3,7
P,4,10,5,7,3,5,10,10,3,9,6,4,2,10,4,8
T,2,1,2,2,1,8,11,3,5,7,10,7,2,11,1,7
B,8,15,8,8,7,11,5,4,5,10,4,8,7,6,8,10
D,5,9,5,7,5,5,7,9,7,6,4,5,3,9,4,9
G,6,9,6,6,4,6,7,6,7,10,8,9,3,8,5,9
E,5,10,3,5,2,6,8,5,7,10,6,10,1,9,7,9
Z,7,8,5,11,4,7,6,6,5,10,7,7,3,9,9,7
O,2,7,3,5,3,7,7,7,4,7,5,8,3,8,2,8
X,6,11,9,8,6,4,9,1,8,10,11,10,3,9,3,5
U,5,9,8,7,10,8,6,4,4,6,7,8,10,9,5,8
M,4,2,5,3,4,6,6,6,5,7,7,10,8,6,2,8
Y,3,5,5,3,2,4,11,3,6,12,10,5,1,11,2,5
N,3,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8
P,4,7,6,5,3,5,12,6,3,12,5,2,1,10,3,8
W,4,10,7,8,6,7,10,2,3,6,9,8,8,11,1,8
R,6,9,8,6,9,7,8,3,5,5,6,9,6,9,7,6
N,4,8,6,6,3,4,9,4,4,10,10,9,5,8,1,7
Z,5,10,5,5,3,11,4,3,7,11,4,9,2,9,4,11
O,4,9,5,7,4,8,7,8,5,10,6,7,3,8,3,8
A,4,10,6,8,4,9,2,2,3,8,2,8,3,6,4,7
U,8,12,7,7,3,5,4,5,5,4,7,8,6,8,2,7
N,5,11,6,8,3,7,7,15,2,4,6,8,6,8,0,8
E,3,7,4,5,2,3,7,6,10,7,6,14,0,8,7,7
R,4,8,4,6,4,5,10,7,3,7,4,9,2,7,5,11
L,6,11,8,8,6,5,4,2,8,6,1,9,1,6,3,7
N,7,10,10,8,5,4,10,4,4,11,11,10,6,8,1,7
D,4,9,6,6,5,6,7,8,7,7,7,5,3,8,3,8
D,4,8,4,6,4,6,7,9,7,6,5,6,2,8,3,8
L,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8
E,4,5,5,4,5,6,7,4,3,7,7,9,4,11,8,11
C,4,10,5,8,3,6,7,10,8,10,8,11,2,12,4,9
W,6,11,9,9,4,9,7,5,2,7,8,8,9,9,0,8
U,3,7,4,5,3,8,5,11,4,8,12,7,3,9,0,8
M,13,14,13,8,6,5,9,5,7,4,3,11,9,9,2,8
C,5,12,4,7,4,9,6,4,2,9,8,11,4,9,7,13
C,4,10,6,8,7,6,6,4,3,8,7,11,6,9,3,8
R,6,11,8,8,8,7,9,6,5,8,6,8,5,8,7,12
C,4,9,5,7,6,5,6,3,4,7,6,9,6,9,6,7
M,7,9,10,7,12,10,7,4,5,9,4,6,10,6,5,5
K,6,9,5,4,2,6,8,3,6,9,9,10,5,10,3,6
C,3,4,4,3,1,5,9,5,7,11,9,12,1,9,3,7
T,5,7,5,5,2,4,12,3,7,12,10,4,1,10,1,5
O,5,5,7,8,3,7,5,9,9,6,4,7,3,8,4,8
A,2,9,4,6,3,11,2,1,2,9,3,9,2,6,3,8
R,4,10,6,7,6,8,8,5,5,9,5,7,4,7,5,10
M,4,5,7,3,4,9,6,3,5,9,4,7,9,7,2,8
A,4,10,7,7,2,5,5,3,1,5,1,7,3,7,2,7
D,3,7,4,5,2,6,6,10,9,5,5,6,3,8,4,8
Q,4,6,5,8,5,8,5,8,5,6,5,9,3,8,5,9
O,3,5,4,3,2,8,7,7,4,9,6,8,2,8,3,8
C,2,4,2,3,1,6,8,6,7,8,8,13,1,10,3,10
M,7,7,10,6,11,9,7,5,5,7,6,7,11,9,7,3
B,8,12,6,6,4,9,6,5,6,11,4,9,6,7,7,10
I,5,10,7,8,4,8,4,2,6,7,7,7,1,10,4,7
N,3,3,4,4,2,7,7,14,2,5,6,8,6,8,0,8
F,3,11,4,8,2,0,11,4,6,11,11,9,0,8,2,6
O,6,10,7,8,6,8,5,9,5,5,5,5,5,8,5,8
O,6,9,6,7,4,7,8,8,5,10,7,8,3,8,3,8
C,5,11,7,8,8,7,6,4,4,8,7,11,6,9,3,8
X,2,3,3,4,1,7,7,4,4,7,6,8,3,8,4,8
X,9,14,9,8,5,3,10,3,8,12,11,9,4,8,3,5
T,6,8,6,6,4,6,11,4,6,11,9,5,3,12,2,4
F,2,4,3,3,2,5,10,4,5,10,9,5,1,9,3,6
C,5,11,6,8,4,5,7,6,9,8,4,11,1,10,5,10
F,6,10,6,5,4,8,10,3,5,11,5,4,4,9,7,7
C,4,6,5,4,3,4,8,5,7,9,9,14,1,8,3,8
X,4,7,5,5,4,8,6,2,5,6,7,7,2,9,7,9
T,4,11,6,8,2,7,15,1,6,8,11,7,0,8,0,8
D,4,6,5,4,3,8,8,6,7,9,6,4,3,8,4,8
E,3,6,4,4,2,4,8,6,10,7,5,13,0,8,7,8
W,7,11,10,9,7,10,11,3,3,5,9,7,10,13,1,7
A,3,2,5,3,2,6,2,2,2,5,2,8,2,6,2,6
K,4,10,5,8,2,3,7,7,3,7,7,11,4,8,3,10
N,3,6,4,4,3,7,9,6,4,7,6,7,5,9,1,7
C,5,8,6,6,3,7,6,6,8,13,7,12,2,10,4,7
Q,5,5,5,7,4,7,8,5,4,8,9,9,3,9,7,8
S,4,9,6,7,8,7,6,3,2,7,5,7,3,7,12,2
Q,4,10,5,9,3,8,6,9,7,5,5,8,3,8,4,8
G,6,10,5,5,3,8,7,5,6,10,4,7,4,7,5,8
Y,4,5,6,7,1,8,10,2,2,6,13,8,1,11,0,8
Q,4,6,4,8,5,8,8,6,2,8,8,10,3,9,5,8
I,6,10,7,8,4,8,4,1,6,7,7,8,1,10,4,8
U,5,8,5,6,2,4,9,6,7,11,10,8,3,9,1,7
V,7,15,6,8,3,7,11,5,5,9,10,5,5,12,4,8
Y,2,1,3,2,1,7,10,1,7,7,11,8,1,11,1,8
A,5,9,6,7,7,8,8,8,4,6,6,8,3,8,8,4
B,3,4,4,3,3,8,7,5,6,7,6,6,2,8,6,10
D,4,9,6,6,5,7,7,7,7,6,6,5,3,8,3,7
H,3,5,5,3,3,8,6,2,6,10,5,8,3,7,3,8
N,2,4,3,2,2,7,8,5,4,7,6,6,4,8,1,6
V,7,8,7,6,4,4,12,1,2,9,10,7,6,11,2,7
I,5,9,7,6,4,6,6,3,7,7,6,12,0,8,4,9
S,5,11,6,8,4,6,8,4,8,11,7,7,2,9,5,6
F,2,3,4,2,1,6,10,2,5,13,7,5,1,9,1,7
P,6,9,5,5,2,7,8,5,3,12,4,5,5,9,4,8
F,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8
Y,8,10,8,8,4,3,10,3,7,12,12,7,1,11,2,5
W,7,10,10,8,7,5,8,4,1,7,9,8,8,10,0,7
Z,6,10,8,7,6,7,7,2,9,12,6,9,1,9,6,8
E,2,3,3,2,2,7,7,1,7,10,5,9,1,8,4,9
P,2,5,4,3,2,8,9,4,4,11,4,3,1,10,2,8
O,3,6,4,4,2,8,7,7,4,10,5,8,3,8,3,7
O,4,8,4,6,4,7,7,7,4,10,6,8,3,8,3,6
L,2,4,2,3,1,3,4,3,7,3,1,7,0,7,1,6
R,1,0,2,1,1,6,9,7,3,7,5,7,2,7,4,10
A,3,5,6,6,2,8,3,3,2,7,1,8,3,6,3,8
L,4,10,4,8,1,0,1,5,6,0,0,6,0,8,0,8
R,4,10,5,7,3,5,11,8,4,7,3,9,3,7,6,11
J,5,9,6,6,2,9,4,3,9,15,4,11,0,7,0,8
Y,6,11,8,8,8,8,4,6,5,8,7,8,9,7,6,5
S,3,7,4,5,3,8,8,7,5,7,5,6,2,8,8,8
Y,8,10,8,8,4,5,8,2,9,9,10,4,5,11,7,3
D,1,1,2,2,1,6,7,8,6,6,6,6,2,8,3,8
X,7,14,7,8,4,6,8,2,7,11,7,8,4,9,4,7
R,4,9,6,7,5,8,8,6,6,8,5,7,3,8,6,10
Q,2,4,3,5,2,8,8,6,2,7,8,10,2,9,5,9
Z,2,3,3,2,2,7,8,5,9,6,6,9,1,8,7,8
C,5,9,6,7,4,5,7,6,9,6,6,10,1,6,5,10
T,2,6,3,4,1,6,14,0,5,8,10,7,0,8,0,8
B,3,5,5,3,3,9,6,3,6,10,4,7,4,7,6,9
A,6,9,8,8,7,7,7,2,4,7,8,10,8,7,4,8
W,4,5,6,5,7,7,7,5,5,6,6,8,9,9,8,8
C,3,8,4,6,2,6,8,8,7,9,8,14,2,9,4,10
S,2,2,3,3,2,8,7,7,5,8,6,7,2,9,9,8
C,5,7,7,6,6,5,7,4,4,7,6,11,4,9,7,9
V,2,6,3,4,1,7,9,4,2,7,13,8,3,10,0,8
Y,6,11,6,8,3,3,10,3,6,12,12,7,1,11,2,6
N,2,3,3,1,1,7,9,5,5,8,7,6,5,9,1,6
D,2,5,4,3,3,9,6,4,6,10,4,6,2,8,3,8
Z,5,9,7,7,4,8,7,2,10,12,5,9,1,8,6,9
Y,2,5,4,4,2,7,10,1,7,7,11,8,1,11,2,8
O,2,6,3,4,2,8,7,8,5,7,7,9,3,8,3,8
C,2,1,3,2,1,6,7,6,10,7,6,14,0,8,4,9
H,2,4,4,2,2,9,7,3,5,10,4,7,3,8,2,9
Z,6,7,4,10,4,8,6,4,4,11,6,7,2,10,9,8
S,4,2,4,4,3,8,7,7,5,7,6,8,2,9,9,8
T,3,5,4,4,2,6,12,2,7,8,11,7,1,11,1,7
H,2,3,4,2,2,7,7,3,5,10,6,8,3,8,2,7
V,3,2,5,3,2,7,12,2,3,6,11,9,3,11,1,7
A,2,6,4,4,2,8,4,2,1,7,2,8,2,6,2,8
J,1,3,2,2,0,9,6,2,5,14,6,10,0,7,0,7
F,5,10,5,5,3,7,8,3,4,10,6,7,4,8,7,6
P,6,9,8,7,6,8,9,4,4,12,5,4,1,10,3,8
W,3,8,5,6,5,9,11,2,2,5,8,8,6,11,1,8
U,4,4,5,3,2,4,8,5,7,11,10,9,3,9,1,7
B,7,10,9,8,9,9,7,4,6,10,5,6,2,8,5,10
V,4,4,6,7,1,8,8,4,3,6,14,8,3,9,0,8
Q,2,4,3,4,3,8,8,6,3,8,6,9,2,9,4,9
R,4,8,5,6,3,6,13,8,4,7,2,9,3,7,6,10
F,5,9,6,7,6,6,10,6,5,8,5,8,3,10,8,10
N,6,10,9,8,5,10,8,3,6,10,2,4,5,9,1,7
L,3,7,5,5,2,3,3,6,8,1,0,6,0,7,1,6
D,4,8,6,6,5,7,6,8,6,5,5,4,4,8,4,8
A,5,11,8,9,5,12,3,3,3,10,1,9,3,7,4,9
S,3,5,6,3,2,7,8,2,7,11,6,6,1,9,4,6
N,2,3,4,2,2,6,8,3,3,10,7,8,4,8,0,7
J,3,4,4,6,1,11,2,10,3,13,8,14,1,6,0,8
Y,5,9,4,4,2,6,8,3,4,10,8,5,3,9,4,4
X,4,10,7,7,4,9,7,0,8,9,5,7,2,8,3,8
G,6,5,7,8,3,7,5,8,10,7,5,12,1,8,6,11
W,4,2,6,4,4,8,11,2,2,6,9,8,7,11,1,8
P,2,1,3,2,2,6,9,4,4,9,8,5,1,10,3,7
H,2,4,3,3,2,6,7,5,6,7,6,8,6,8,3,8
A,1,3,3,2,1,6,3,2,2,5,2,8,1,6,1,7
O,4,6,4,4,3,8,6,8,6,9,4,7,3,8,3,8
L,3,9,4,6,3,6,5,0,8,8,3,11,0,8,2,8
O,6,8,8,7,6,7,5,5,5,8,4,7,3,7,5,7
V,4,6,4,4,2,3,12,4,3,11,11,7,2,10,1,7
M,5,6,8,4,5,7,6,2,5,9,7,8,8,6,2,8
F,5,9,7,6,4,8,9,2,6,14,5,5,2,9,3,8
Q,7,8,10,7,8,6,4,4,6,5,4,7,5,3,7,6
Q,3,5,4,6,3,8,6,6,4,9,6,9,3,9,4,8
L,4,9,5,8,5,6,8,4,6,7,6,9,3,9,9,10
G,4,6,4,4,3,6,7,5,5,9,8,11,2,8,4,10
C,5,6,6,5,5,5,8,3,5,7,6,10,3,10,8,7
M,2,3,4,2,2,6,7,3,4,9,9,9,5,5,1,7
D,4,8,6,6,8,9,9,4,4,8,7,5,4,9,8,6
M,3,3,4,2,3,6,6,6,4,7,7,10,7,5,2,9
H,5,10,8,8,10,7,6,6,4,7,5,8,9,6,10,10
J,6,8,8,6,5,8,8,6,6,8,7,7,4,6,4,6
L,3,6,4,5,4,9,7,4,6,6,6,9,2,8,7,10
X,2,6,3,4,2,7,7,4,4,7,6,7,2,8,4,7
G,7,10,7,8,5,6,8,6,6,10,8,9,3,7,6,9
U,6,9,6,6,4,3,9,5,6,10,10,10,3,9,2,7
Z,5,8,6,6,4,7,8,2,9,11,7,8,1,9,6,7
L,2,5,3,4,2,7,4,1,7,8,2,9,0,7,2,8
M,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8
D,3,9,4,7,4,6,7,10,7,7,7,6,3,8,3,8
S,4,9,4,4,2,7,4,2,4,7,2,7,2,7,5,8
X,5,6,6,8,2,7,7,5,4,7,6,8,3,8,4,8
F,5,9,6,6,6,5,9,4,6,10,10,6,2,9,3,6
S,4,9,4,4,2,7,4,4,4,8,2,8,3,7,5,8
H,2,3,4,2,2,7,7,3,6,10,6,8,3,8,2,7
Z,4,8,5,6,2,7,7,4,15,9,6,8,0,8,8,8
P,8,13,7,7,3,6,10,5,3,13,5,4,4,10,4,8
T,5,6,6,5,5,6,7,3,8,7,6,10,3,6,7,6
A,1,3,2,2,1,10,2,2,1,9,2,9,1,6,2,8
C,5,9,4,5,1,6,8,6,7,11,7,9,2,9,5,9
S,3,3,4,4,2,8,9,5,9,5,6,6,0,8,9,7
V,5,10,7,8,4,7,12,2,3,6,11,9,4,10,3,7
T,5,8,5,6,3,4,13,5,5,12,9,4,2,12,1,5
U,9,14,8,8,4,7,6,4,5,4,8,5,7,5,3,6
C,2,1,2,2,1,6,8,7,7,8,7,13,1,8,4,10
I,4,9,5,7,3,9,6,0,7,13,5,8,0,8,1,8
I,0,3,1,2,1,7,7,1,6,7,6,8,0,8,2,8
Y,7,11,7,8,5,5,8,0,8,9,9,5,4,10,7,4
J,0,0,1,1,0,12,4,4,3,12,4,10,0,7,0,8
P,9,14,9,8,6,9,7,4,5,13,4,4,5,9,7,8
W,3,1,3,2,1,7,8,4,1,7,8,8,7,10,0,8
W,5,7,5,5,4,4,10,3,2,9,8,7,6,11,2,6
B,2,1,3,2,2,7,7,4,5,6,6,5,2,8,5,9
P,6,12,5,6,4,9,8,4,4,12,4,4,4,11,5,7
W,4,6,5,4,2,11,8,5,1,6,9,8,8,10,0,8
U,5,10,8,8,10,8,8,4,5,6,6,8,9,5,8,10
X,3,7,5,5,4,7,7,2,6,7,5,8,3,6,5,8
R,5,9,5,4,4,8,7,3,4,9,4,8,5,8,6,8
X,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8
Q,5,8,6,7,6,7,4,5,4,7,4,9,5,4,7,6
G,8,15,6,8,5,9,5,5,3,9,6,9,5,9,8,8
Y,2,7,4,4,1,10,10,3,2,5,13,8,2,11,0,8
J,2,7,2,5,2,9,6,1,6,11,4,8,0,7,1,6
A,5,13,5,7,4,11,2,4,2,11,4,11,5,3,4,10
M,3,3,4,2,3,8,6,7,4,7,7,8,7,5,1,8
E,6,9,8,7,7,8,8,7,3,6,6,11,6,8,9,8
L,4,8,5,7,5,8,6,4,5,6,7,8,3,9,7,9
H,6,11,8,8,8,6,8,8,4,7,5,7,3,7,7,10
K,4,5,6,4,3,6,8,2,7,10,6,9,4,9,3,7
V,3,3,4,2,1,4,13,3,2,10,11,7,2,11,1,8
M,5,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8
I,3,11,5,8,6,9,6,3,4,8,5,5,5,8,5,5
W,2,0,2,1,1,7,8,3,0,7,8,8,6,9,0,8
S,5,8,7,6,8,8,6,5,3,8,5,8,4,9,10,9
L,4,8,5,7,5,7,6,4,5,7,7,8,4,9,9,11
Y,2,1,3,1,0,8,11,3,1,6,12,8,1,11,0,8
O,5,10,4,5,3,5,8,7,3,10,7,8,5,9,5,7
B,5,10,7,7,7,11,6,2,6,10,3,7,4,7,6,11
U,5,5,6,4,3,5,8,5,8,10,8,9,3,9,3,5
C,4,7,5,5,3,7,7,8,6,7,6,10,3,8,3,9
O,4,10,5,7,4,7,7,8,5,6,4,7,3,9,4,9
Y,1,0,2,1,0,7,10,2,2,7,12,8,1,11,0,8
O,6,9,6,7,4,6,8,8,5,10,8,7,4,8,4,9
X,6,9,6,4,3,10,7,2,8,9,4,7,4,10,4,9
G,4,8,6,6,6,7,7,6,3,6,6,9,4,8,7,8
W,6,9,8,7,6,8,8,4,1,7,9,8,8,10,0,8
I,2,11,2,8,2,7,7,0,9,7,6,8,0,8,3,8
L,3,3,4,4,1,1,0,6,6,0,1,5,0,8,0,8
K,6,9,9,7,5,3,9,3,7,11,12,12,4,7,4,5
P,5,9,7,6,5,7,12,7,2,11,5,3,2,12,4,8
I,1,10,0,7,1,7,7,5,3,7,6,8,0,8,0,8
A,2,4,4,2,2,11,2,3,2,10,2,10,2,6,2,9
T,4,11,6,8,4,6,14,0,5,8,10,8,0,8,0,8
I,2,8,3,6,2,7,7,0,7,12,6,8,0,8,1,8
P,5,11,6,8,6,4,12,8,2,10,7,4,1,10,3,8
Q,4,9,5,11,6,8,7,8,4,5,6,9,3,8,6,10
L,5,9,7,8,6,7,7,4,5,7,7,8,3,8,8,9
V,7,9,7,5,3,6,10,4,3,8,8,5,5,12,3,9
N,5,6,7,4,3,5,9,3,5,11,9,9,5,8,1,7
U,4,9,6,7,9,9,6,4,4,6,7,7,9,8,5,7
G,2,3,2,2,1,6,7,5,4,9,7,9,2,9,4,9
M,3,3,4,2,3,9,6,6,4,6,7,6,7,6,2,6
W,3,6,5,4,2,6,8,4,1,7,8,8,8,9,0,8
G,5,11,4,6,3,7,7,4,3,9,6,7,3,10,8,7
K,3,2,3,4,1,3,7,7,2,7,7,11,3,8,3,10
N,3,2,4,3,2,7,8,5,4,7,6,7,5,9,2,6
K,5,7,7,5,4,8,7,2,7,10,3,8,4,9,4,9
R,6,10,8,8,6,7,9,5,8,5,4,10,5,4,7,9
I,1,6,0,8,0,7,7,4,4,7,6,8,0,8,0,8
R,3,7,5,5,4,9,7,4,5,10,4,7,3,7,4,11
P,4,7,5,5,4,6,10,6,5,10,8,3,1,10,4,6
N,3,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8
X,4,9,5,6,3,8,7,4,9,6,6,6,3,8,6,7
H,7,10,10,7,9,9,7,3,6,10,4,7,3,8,3,9
U,9,10,9,8,5,5,7,6,9,9,8,9,5,11,5,2
J,2,2,4,4,2,11,6,2,7,12,3,7,0,7,1,8
X,4,9,4,7,3,7,7,4,4,7,6,8,3,8,4,8
W,4,9,6,7,4,6,8,4,2,7,8,8,8,10,0,8
K,3,7,3,5,1,3,7,7,3,7,6,11,4,8,2,11
K,3,7,4,4,1,4,8,7,2,7,5,11,3,8,3,10
R,4,11,5,8,3,6,11,10,4,7,3,9,3,7,5,11
B,4,8,5,6,5,7,7,6,4,6,5,6,3,8,6,6
N,5,7,7,5,4,7,9,2,4,10,5,6,5,9,1,7
B,3,6,4,4,4,8,8,6,6,7,6,6,2,8,6,9
V,6,10,8,7,5,6,11,3,2,8,11,8,3,10,4,8
R,7,12,6,7,5,6,8,5,5,9,5,10,7,5,7,11
P,3,8,3,5,2,3,14,7,2,12,7,3,0,10,3,8
B,4,5,4,8,4,6,9,10,7,7,5,7,2,8,9,10
T,6,8,6,6,3,5,12,3,8,12,9,4,1,11,2,4
B,3,5,5,4,3,9,7,2,6,11,4,7,4,6,5,9
Q,6,10,6,5,3,10,4,4,7,10,4,9,3,8,6,13
Q,2,1,2,2,1,8,6,7,4,6,6,8,3,8,3,8
S,1,0,1,0,0,8,7,4,6,5,6,8,0,8,7,8
T,4,8,6,6,6,7,7,4,7,7,6,8,5,7,5,6
F,3,8,4,6,3,4,10,3,6,10,10,6,1,10,3,6
Y,1,3,3,2,1,7,10,1,6,7,11,8,1,11,1,8
W,7,11,10,8,7,7,11,2,3,6,9,8,12,11,2,7
G,4,10,5,7,3,7,7,8,7,6,6,8,2,7,6,11
M,2,3,3,2,2,8,7,5,4,6,7,8,5,6,1,7
W,6,8,8,6,7,8,6,6,2,7,7,8,6,8,4,7
R,2,6,3,4,2,6,9,9,4,7,5,8,2,7,5,11
D,5,7,6,5,4,7,8,7,5,8,6,5,4,9,3,7
K,8,10,11,8,6,8,6,2,8,10,4,9,4,7,5,9
B,1,3,3,1,1,9,7,2,5,10,4,7,1,8,2,9
O,3,7,4,5,3,7,8,8,4,7,6,8,3,8,3,8
K,3,4,4,2,2,5,7,4,7,7,6,11,3,8,4,9
G,2,1,2,2,1,8,6,6,6,6,5,10,1,7,5,10
X,4,11,5,8,2,7,7,4,4,7,6,8,3,8,4,8
C,3,7,4,5,2,7,7,7,10,10,6,13,1,10,4,9
G,3,6,4,4,3,5,7,6,5,9,7,11,2,8,4,10
O,2,1,3,2,2,7,7,7,5,7,5,8,2,8,3,8
I,9,14,6,8,3,11,4,5,5,12,3,8,3,7,5,10
P,2,3,3,2,1,7,10,4,3,12,5,3,1,10,2,8
O,2,3,3,2,1,7,7,7,5,7,6,8,2,8,3,7
Y,6,8,9,11,11,8,11,3,4,6,8,9,5,13,11,8
F,5,9,7,7,5,6,9,6,8,10,10,5,2,9,4,5
G,5,7,6,5,4,5,7,5,6,9,8,9,2,9,5,9
K,3,3,5,2,2,6,8,2,6,10,6,9,3,9,3,7
Q,2,2,3,3,2,8,7,6,2,5,6,9,2,9,4,9
R,13,15,10,8,6,8,7,5,6,10,3,8,7,6,6,11
B,5,10,6,8,7,9,7,3,5,7,6,8,7,8,6,9
B,4,11,5,8,4,6,6,9,7,6,6,6,3,8,10,11
V,4,7,4,5,2,4,11,3,3,9,11,7,2,10,1,8
A,5,11,8,8,4,8,2,2,3,6,2,7,5,7,6,8
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
W,6,5,7,4,4,6,10,4,3,8,7,7,9,11,3,5
N,5,9,8,7,4,10,6,3,5,10,2,5,6,9,1,7
M,5,10,7,8,7,8,6,5,5,6,8,7,8,6,2,7
O,2,0,2,1,1,8,7,7,6,7,6,8,2,8,3,8
X,5,9,8,7,4,8,7,1,8,10,5,8,3,8,4,8
O,2,2,3,3,2,7,7,8,4,7,7,8,2,8,3,8
P,3,7,4,5,3,4,12,4,5,11,9,3,0,10,4,7
W,5,6,5,4,4,2,11,2,3,10,10,8,5,11,1,7
G,4,9,5,6,3,6,7,7,7,10,8,9,2,9,4,9
Z,1,1,2,2,1,7,7,5,8,6,6,8,2,8,6,8
O,4,8,5,6,3,6,9,7,5,10,8,7,3,8,3,8
S,3,8,4,6,2,8,7,5,8,5,6,6,0,8,8,8
C,3,7,4,5,2,5,8,7,7,8,8,14,1,9,4,10
M,3,4,5,2,3,9,6,3,4,9,4,6,8,6,2,8
G,4,9,5,7,4,5,7,5,4,9,9,9,2,7,5,9
U,7,13,7,7,4,8,5,4,4,6,7,7,5,7,3,7
Q,5,9,6,8,5,8,7,7,5,6,8,8,3,8,5,10
G,5,8,6,6,4,7,6,7,8,7,5,11,3,11,5,8
T,3,8,5,6,4,6,11,2,7,8,11,8,2,12,1,7
E,3,6,3,4,2,3,8,6,10,7,5,14,0,8,7,8
R,5,9,6,6,3,6,13,9,3,7,2,9,3,7,5,10
W,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8
Y,7,10,6,5,3,8,6,3,5,9,8,6,4,10,4,5
V,5,10,7,7,8,8,7,5,2,8,7,8,5,10,5,8
X,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8
Z,6,7,8,9,8,9,8,6,5,9,3,6,3,5,8,7
U,3,4,3,6,2,7,5,13,5,7,13,8,3,9,0,8
B,4,10,6,8,8,8,7,6,6,6,6,6,3,9,6,10
C,7,10,8,8,8,6,6,3,5,8,6,12,6,9,5,10
G,6,13,6,7,3,7,6,5,4,9,5,5,4,8,5,7
G,4,9,4,4,3,8,6,4,2,9,6,8,3,9,7,7
R,2,3,2,2,2,6,7,4,4,6,5,7,2,7,4,9
Y,1,0,2,0,0,7,9,3,1,7,13,8,1,11,0,8
S,6,10,7,7,4,10,5,4,8,11,4,8,2,9,5,10
X,7,11,10,9,5,7,7,1,9,10,6,8,3,8,4,7
Z,1,0,1,0,0,7,7,2,9,8,6,8,0,8,6,8
P,7,12,6,7,3,9,7,5,4,12,3,6,5,9,4,8
M,3,5,6,4,4,5,6,3,4,10,10,10,6,5,2,7
S,6,8,7,6,4,9,6,4,8,11,4,8,2,8,5,11
H,7,10,10,8,8,6,8,3,6,10,8,8,4,9,4,7
Z,6,11,8,9,8,10,7,5,4,7,5,7,4,8,10,5
C,8,13,5,7,3,6,10,6,9,11,8,10,2,8,5,9
W,9,9,9,7,8,4,11,2,2,9,8,7,7,12,2,6
M,2,1,3,3,3,7,6,7,3,7,7,10,6,5,1,9
C,3,7,4,5,2,5,8,7,7,9,8,13,1,10,4,10
D,5,9,8,6,5,9,7,5,8,10,4,5,3,8,3,8
C,3,6,5,4,1,6,8,6,10,7,7,12,1,7,4,9
D,4,6,6,4,4,7,6,7,5,5,5,5,3,8,3,8
R,3,6,5,4,3,9,8,3,6,9,3,8,3,6,4,10
Z,3,2,4,3,2,7,7,5,9,6,6,8,1,8,7,8
G,5,11,7,8,8,7,6,7,3,7,6,10,5,8,8,8
H,3,8,5,6,7,8,9,5,3,7,7,7,9,8,9,8
W,6,9,6,4,3,3,9,2,2,9,11,8,7,11,0,7
T,4,11,5,8,2,7,15,1,6,8,11,8,0,8,0,8
R,8,13,6,8,4,10,6,6,4,10,2,9,7,5,6,10
J,1,6,2,4,1,14,2,5,5,13,1,9,0,7,0,8
O,5,10,7,7,8,8,9,6,2,6,7,9,11,13,5,9
U,6,11,8,8,12,8,7,4,4,6,7,8,8,8,6,7
S,3,5,4,4,2,8,6,3,7,11,5,9,1,9,5,9
K,5,9,8,7,9,7,10,4,4,5,6,9,8,8,7,8
N,3,5,4,3,3,7,8,5,4,7,7,7,5,9,2,6
A,6,10,8,8,8,7,8,8,4,6,5,9,3,7,8,5
K,4,7,5,5,2,4,8,9,1,7,6,11,3,8,2,11
G,3,5,4,4,3,6,7,5,4,9,8,9,3,7,5,10
D,1,3,2,2,1,8,7,5,5,9,5,6,2,8,3,8
W,1,0,2,0,0,8,8,3,0,7,8,8,4,10,0,8
J,5,10,4,8,3,10,6,2,4,12,5,7,2,10,5,11
N,3,5,5,3,2,7,8,2,4,10,6,6,5,8,0,7
F,5,11,5,6,4,8,8,2,4,10,5,6,4,9,7,7
C,4,9,3,4,2,7,10,4,4,8,8,8,3,9,8,11
F,5,9,7,7,8,10,7,1,5,9,4,5,4,10,5,8
E,3,4,3,6,2,3,7,6,10,7,6,14,0,8,7,8
E,5,9,7,7,6,9,6,2,7,11,4,9,3,8,5,11
M,3,6,4,4,3,7,5,10,1,7,8,8,7,5,0,9
U,10,11,10,8,4,3,9,6,9,12,12,9,3,9,1,7
C,6,10,7,8,5,3,7,4,7,10,8,15,4,9,5,6
F,5,11,6,8,7,7,8,5,4,8,6,8,9,10,7,12
U,5,10,7,7,6,7,8,4,7,4,7,9,6,10,1,8
E,5,8,7,6,4,10,6,2,8,11,4,8,3,8,5,11
C,4,10,5,8,2,5,7,7,10,7,5,13,1,9,4,8
B,5,11,5,8,4,6,8,10,7,7,5,7,3,8,9,10
G,1,0,2,1,0,8,7,6,5,6,5,9,1,7,5,10
K,5,9,5,6,2,5,9,9,2,7,3,11,4,8,2,11
W,4,9,7,7,6,7,8,4,1,7,9,8,7,11,0,8
S,3,6,4,4,3,8,8,5,8,5,6,6,0,7,8,7
W,2,1,2,1,1,8,8,4,0,7,8,8,6,10,0,8
Y,6,6,5,9,3,7,11,1,3,9,10,5,4,10,6,8
S,3,9,4,7,3,8,7,5,8,5,6,7,0,8,8,8
U,3,6,4,6,4,8,7,4,3,6,6,8,4,8,1,7
K,5,9,8,8,7,8,6,2,3,8,3,8,6,5,8,12
V,3,8,5,6,1,8,8,4,3,6,14,8,3,10,0,8
V,2,7,4,5,2,7,9,3,1,8,12,8,2,11,0,8
M,4,6,5,4,2,8,7,12,1,6,9,8,8,6,0,8
G,4,7,5,5,3,5,8,6,6,9,8,9,2,7,4,9
U,3,6,5,5,4,7,7,4,4,6,6,8,4,8,1,7
Q,4,7,5,9,5,9,7,8,3,5,7,10,3,8,6,10
O,5,9,4,4,2,9,7,5,4,9,4,8,4,9,5,9
X,4,5,5,4,4,7,7,2,4,7,6,8,2,9,7,7
X,3,4,4,3,2,7,7,3,9,6,6,9,2,8,5,8
P,5,4,5,6,2,4,13,8,1,10,6,3,1,10,4,8
X,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8
I,2,11,3,8,4,8,7,0,7,7,6,8,0,8,3,7
A,3,7,5,6,4,7,8,2,4,7,8,9,5,8,3,6
W,5,6,5,4,4,4,10,3,2,9,9,7,6,11,2,6
U,5,8,5,6,3,3,8,5,7,10,10,10,3,9,2,6
V,4,5,5,3,2,4,12,4,4,11,11,7,2,10,1,8
D,6,10,9,8,7,10,6,2,7,11,4,7,5,6,4,10
T,4,2,5,4,3,9,12,3,7,5,11,9,2,11,1,8
D,4,8,4,6,4,5,7,9,7,5,4,6,2,8,3,8
D,3,8,4,6,2,5,7,10,8,7,6,5,3,8,4,8
D,4,6,4,4,3,5,7,9,7,7,6,6,2,8,3,8
U,7,8,7,6,3,3,10,6,8,13,12,8,3,9,1,7
I,7,11,9,8,6,10,5,2,7,7,7,4,0,9,4,7
N,9,15,7,8,4,6,9,4,5,4,5,11,6,11,2,7
V,6,11,6,6,3,7,7,4,4,7,7,6,6,12,2,8
V,4,5,6,4,2,4,12,3,3,10,11,7,3,10,1,7
S,5,10,4,6,2,7,3,4,4,8,2,7,3,7,5,8
Q,2,2,3,3,2,8,8,6,2,5,7,9,2,9,5,10
C,4,8,5,6,3,3,8,5,7,10,10,13,1,8,3,7
P,3,8,5,6,4,7,5,5,4,7,6,9,3,9,6,11
Z,2,7,3,5,2,6,8,5,10,6,7,9,1,9,8,8
Y,2,4,4,3,2,6,10,1,7,8,11,8,1,11,2,8
K,9,13,9,7,4,6,7,3,7,9,9,10,6,10,3,6
Y,4,5,5,7,7,9,8,6,3,7,7,7,6,10,6,4
J,7,11,9,8,6,8,4,4,6,8,6,7,5,6,5,7
B,5,11,7,9,8,8,7,5,4,7,5,7,4,8,6,8
O,3,2,4,3,2,7,6,7,5,7,5,7,2,8,3,7
K,5,7,7,6,6,6,7,3,4,6,4,9,5,5,9,7
K,2,3,4,2,2,7,8,2,6,10,6,8,3,8,2,8
V,4,10,6,8,2,7,8,4,3,7,14,8,3,9,0,8
P,4,6,5,4,4,7,9,3,6,8,8,4,1,10,4,7
R,6,11,9,8,6,8,8,5,6,8,5,7,4,8,6,12
P,4,7,5,5,4,6,9,6,5,9,7,4,2,10,4,7
Z,4,7,6,5,3,6,9,3,9,12,9,7,1,9,6,5
E,4,10,3,5,2,8,7,5,4,11,5,10,3,8,7,11
Q,7,9,7,10,7,7,9,5,3,7,10,11,3,8,7,7
Q,2,5,3,6,4,9,10,6,3,3,8,11,2,9,5,10
X,2,7,3,4,1,7,7,4,4,7,6,8,3,8,4,8
U,7,9,7,7,4,4,8,6,8,10,10,9,3,9,2,5
N,5,8,8,6,4,4,10,4,4,10,11,9,6,8,1,7
T,5,5,6,5,5,6,8,4,8,8,7,9,3,8,7,5
S,3,6,4,4,3,8,8,5,7,6,6,7,0,8,8,8
T,5,7,6,5,6,6,8,4,5,6,7,9,6,8,5,8
G,9,14,7,8,5,9,5,5,3,9,6,8,4,9,8,8
G,5,10,5,8,4,5,7,6,6,10,8,10,2,8,5,9
I,2,8,3,6,1,7,7,0,8,14,6,8,0,8,1,7
C,2,4,3,3,1,5,9,5,6,12,9,10,1,10,2,7
D,5,7,6,5,5,10,6,3,6,11,4,7,3,8,3,9
J,3,7,4,5,2,8,7,1,6,14,4,7,0,7,0,7
L,3,8,3,6,1,0,1,5,6,0,0,7,0,8,0,8
R,7,13,6,7,5,8,6,3,5,8,5,9,6,9,6,7
V,2,3,3,2,1,4,12,3,2,10,11,7,2,11,0,8
M,11,12,11,7,6,6,9,5,4,4,4,11,11,14,3,8
T,3,10,5,7,1,7,15,1,6,7,11,8,0,8,0,8
U,6,10,8,7,5,4,8,7,8,9,11,11,3,9,1,8
M,4,6,7,4,6,12,3,3,1,10,4,9,8,6,2,8
J,3,10,4,8,4,8,6,2,4,11,5,10,1,6,2,6
O,4,5,5,4,3,8,7,8,5,7,6,8,2,8,3,8
Y,5,7,5,5,3,6,8,1,7,8,9,5,2,12,4,4
B,6,9,5,4,4,9,7,3,5,10,5,7,6,6,7,9
T,3,4,4,3,1,6,11,2,8,11,9,5,1,10,2,5
Q,6,7,6,9,6,8,7,6,3,9,8,10,3,8,6,8
O,7,8,9,7,6,5,7,6,6,8,5,6,3,7,5,7
E,8,13,6,8,5,7,7,4,4,10,6,9,3,9,8,11
I,3,8,5,6,3,7,7,0,7,13,6,8,0,8,1,8
Z,3,9,4,6,2,7,7,4,13,10,6,8,0,8,8,8
O,2,0,2,1,1,7,7,6,6,7,6,8,2,8,3,8
V,2,8,4,6,1,9,8,4,2,6,13,8,3,10,0,8
R,9,14,9,8,7,5,8,2,6,8,4,10,7,5,7,5
M,5,11,6,9,4,8,7,12,2,6,9,8,9,6,0,8
N,5,9,7,6,4,9,7,3,5,10,4,6,5,9,1,7
S,6,11,6,6,3,6,9,3,5,13,8,7,2,9,3,7
S,2,1,3,2,2,8,7,6,4,7,7,8,2,9,9,8
D,4,5,5,4,4,7,7,7,7,7,6,5,2,8,3,7
S,4,6,5,4,3,6,9,3,7,10,7,7,2,7,4,5
M,6,9,8,8,10,8,7,4,3,7,6,7,10,8,6,5
J,0,0,1,0,0,12,4,4,3,11,4,11,0,7,0,8
D,4,8,6,6,4,7,8,7,6,10,6,5,3,8,4,8
T,4,6,4,4,2,5,12,3,6,11,9,4,2,11,2,5
R,6,11,6,8,7,5,10,8,3,7,4,8,2,7,5,11
E,3,4,5,3,2,8,7,1,8,11,5,8,2,8,4,10
O,9,13,6,7,4,5,6,7,4,9,7,9,5,9,5,8
C,4,9,5,7,6,5,7,4,5,7,6,9,6,10,5,7
G,2,1,3,2,2,7,7,5,5,6,6,9,2,9,4,9
T,4,9,4,4,2,5,11,2,6,11,8,5,2,8,3,5
A,3,9,6,7,4,11,3,1,2,8,3,9,4,5,3,8
J,6,9,5,7,3,8,9,2,3,13,4,5,2,9,7,8
B,9,11,7,6,4,10,4,5,6,11,3,9,5,7,6,11
T,2,1,3,1,1,7,11,3,5,7,10,8,2,11,1,8
W,5,6,5,4,4,5,11,3,2,9,8,7,6,12,2,7
D,9,15,8,8,6,8,6,3,7,10,5,7,6,8,8,5
E,4,8,4,6,2,3,6,6,11,7,7,14,0,8,7,7
V,3,3,4,2,1,3,12,4,3,11,11,7,2,11,1,8
H,10,13,11,8,7,9,6,4,5,11,3,7,6,7,5,7
V,9,13,7,7,4,9,10,5,5,6,10,6,5,12,3,6
M,9,13,10,8,6,11,11,7,3,4,7,9,9,13,3,6
D,2,2,3,3,2,7,7,6,6,7,6,5,2,8,3,7
C,5,9,6,6,3,5,8,8,9,9,8,12,2,10,4,9
D,5,10,5,8,6,6,8,9,7,6,5,6,2,8,3,7
R,9,13,7,7,4,9,5,5,6,9,4,9,6,6,7,11
U,3,4,4,2,2,5,8,5,7,10,8,9,3,9,2,6
G,7,9,7,7,5,5,7,6,6,10,8,11,2,9,4,10
F,2,3,2,2,1,5,10,4,5,10,9,6,1,10,3,7
K,5,8,8,7,7,9,6,2,4,9,3,8,4,6,7,11
W,8,10,8,5,3,6,10,2,2,8,10,7,8,12,1,7
B,5,8,7,6,9,8,8,5,3,7,7,7,6,10,9,9
S,5,9,7,8,8,8,7,5,6,7,6,8,6,8,10,13
P,6,11,5,6,3,8,7,5,3,12,3,6,5,9,4,8
J,2,5,4,4,1,9,7,2,7,14,4,8,0,7,0,7
L,2,1,2,1,0,2,1,6,5,0,2,5,0,8,0,8
K,5,10,5,5,3,5,8,3,6,9,8,9,5,7,3,8
K,5,8,7,6,4,5,8,2,7,10,9,10,3,8,3,7
S,4,9,6,7,4,7,8,3,7,10,5,7,2,7,4,8
L,3,6,4,4,2,5,3,1,8,8,2,10,0,7,2,7
W,7,9,7,7,6,2,11,2,3,10,10,8,6,11,2,7
Q,7,15,6,8,5,11,4,4,6,10,5,7,3,9,7,12
E,4,9,6,8,7,6,9,5,5,7,7,11,6,10,9,10
Z,3,8,5,6,3,7,9,2,9,11,7,7,1,9,6,6
K,3,7,5,5,5,5,7,4,7,6,5,10,3,8,4,9
K,3,3,5,2,2,7,6,2,7,10,6,10,4,7,3,8
W,6,10,6,7,6,3,10,2,3,10,10,8,6,11,2,6
B,3,7,3,5,3,6,8,8,6,7,5,7,2,8,9,10
J,2,7,2,5,1,13,2,8,4,13,4,12,1,6,0,8
H,5,6,8,4,5,8,7,3,6,10,5,8,4,9,4,8
S,6,10,8,8,11,7,7,3,2,7,6,7,3,8,12,2
V,3,3,5,5,1,7,8,4,3,7,13,8,3,9,0,8
N,3,6,5,4,3,6,9,6,4,8,7,8,5,8,1,7
B,6,9,8,7,8,8,7,5,6,9,6,6,4,10,6,9
A,3,7,5,5,2,12,3,4,3,11,2,10,2,6,3,9
H,3,5,5,4,3,7,7,3,6,10,6,8,3,8,3,8
X,6,12,7,7,4,8,7,2,7,11,4,6,4,8,4,7
N,6,11,8,8,5,10,8,3,5,9,3,5,6,9,1,7
V,7,10,7,7,3,4,11,2,4,9,11,7,4,10,1,7
Z,2,5,5,3,2,7,8,2,9,12,6,8,1,9,5,8
S,1,0,2,1,0,8,8,4,7,5,6,7,0,8,7,8
V,3,8,5,6,1,6,8,4,3,8,14,8,3,10,0,8
Y,4,7,6,5,3,10,10,1,6,2,10,8,1,11,1,9
C,1,3,2,1,1,4,8,4,6,10,9,12,1,8,2,8
L,2,5,3,4,2,7,4,1,8,8,2,10,0,7,2,8
Q,4,5,5,6,4,7,10,4,4,7,10,11,3,9,6,7
X,7,10,7,5,3,5,8,3,8,10,11,11,4,11,4,5
S,7,10,7,5,3,7,8,4,3,13,8,7,3,9,4,8
R,10,15,8,8,5,7,7,5,5,9,5,9,7,5,7,11
F,6,9,8,7,6,5,11,6,5,12,8,5,3,9,2,5
M,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8
K,6,10,9,7,5,4,8,2,7,10,10,11,3,8,4,6
J,5,10,4,7,3,7,11,3,3,13,5,4,2,7,6,8
T,3,5,4,4,2,5,12,2,7,11,9,4,1,11,2,5
C,6,8,7,7,7,6,7,5,5,6,6,11,7,10,9,8
W,9,11,8,8,7,5,10,4,3,9,8,7,9,11,3,5
H,4,8,5,6,5,7,6,13,2,7,7,8,3,8,0,8
E,4,9,6,6,6,6,8,6,8,6,5,10,3,8,6,9
W,1,0,2,1,1,7,8,3,0,7,8,8,5,10,0,8
H,2,1,3,2,2,9,7,6,5,7,6,6,3,8,3,7
U,6,11,8,9,5,5,8,6,7,7,9,10,3,9,1,8
Z,1,0,2,0,0,7,7,2,10,8,6,8,0,8,6,8
O,2,1,3,2,2,8,7,7,5,7,6,8,2,8,3,8
F,2,3,4,1,1,6,10,3,5,13,7,5,1,9,1,7
T,5,6,7,5,5,5,7,3,8,7,6,10,3,7,7,5
I,1,6,2,4,1,7,7,1,8,7,6,8,0,8,3,8
A,2,2,4,4,2,8,2,2,2,8,2,8,2,6,3,7
J,1,1,2,1,1,10,6,2,6,12,4,8,0,7,1,7
U,4,8,5,6,3,6,9,6,6,5,9,10,3,9,1,8
R,5,10,6,7,6,6,9,8,4,7,5,8,2,7,5,11
T,4,10,6,7,2,7,15,1,6,7,11,8,0,8,0,8
J,6,7,8,9,8,8,8,4,6,6,6,7,5,9,11,11
U,3,4,4,3,2,5,8,5,7,10,9,8,3,9,2,6
C,5,8,6,6,4,6,8,6,9,6,6,13,1,8,4,9
T,9,13,8,7,3,8,7,4,9,13,5,7,2,9,6,6
V,2,6,4,4,1,8,8,4,2,6,13,8,3,10,0,8
Q,6,7,8,6,6,8,3,4,5,7,3,9,4,5,5,7
Q,4,5,5,6,5,8,8,6,1,7,6,11,3,9,6,8
Q,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8
K,5,10,5,7,2,4,8,9,2,7,6,11,4,8,2,11
C,7,12,5,6,4,7,6,4,3,9,8,11,4,9,8,11
A,3,11,5,8,3,13,4,5,3,12,1,8,2,6,4,9
Z,3,8,4,6,3,8,7,3,12,9,6,8,0,8,7,7
U,6,10,6,7,5,4,8,5,7,9,7,9,7,9,5,3
D,5,11,6,8,5,11,6,3,7,11,2,6,3,8,3,9
M,5,8,7,6,6,8,7,2,4,9,7,8,7,6,2,8
Y,5,10,8,8,4,7,10,2,7,6,12,9,2,11,2,8
Z,2,7,3,5,3,6,8,5,10,7,6,10,1,9,8,7
H,4,7,6,10,6,8,12,5,2,9,7,5,4,11,8,4
B,4,7,5,5,6,8,7,6,6,6,6,6,2,8,6,9
D,2,3,3,1,2,8,6,4,6,10,4,6,2,8,2,8
R,5,11,6,8,3,5,10,9,4,7,4,8,3,7,6,11
R,2,1,2,1,1,6,9,8,4,7,5,8,2,7,4,11
U,7,9,7,6,5,4,8,5,8,9,6,9,7,9,6,2
N,5,8,7,6,4,9,7,6,6,6,6,4,6,9,2,5
K,6,9,8,7,6,9,5,1,6,10,4,9,5,6,5,10
N,6,11,8,8,6,8,7,9,5,6,4,4,4,8,4,9
S,4,10,5,7,3,8,8,6,9,4,6,7,0,8,9,7
E,3,6,3,4,3,4,7,5,8,7,6,13,0,8,6,9
F,5,11,5,8,4,1,13,4,4,12,10,7,0,8,2,6
T,6,9,8,6,6,6,7,7,7,8,10,8,5,7,7,9
K,8,14,8,8,5,10,6,3,6,11,2,6,5,8,4,9
K,4,9,4,6,2,3,7,7,2,7,5,11,3,8,3,10
E,3,7,3,5,2,3,7,6,10,7,6,14,0,8,7,7
I,2,7,4,5,3,10,5,1,5,7,5,5,2,8,4,7
L,6,11,9,8,11,7,8,3,6,5,7,10,6,12,10,6
A,5,10,9,7,6,7,5,2,4,5,1,6,5,7,5,5
W,11,15,12,9,8,7,8,3,4,7,9,7,12,9,3,5
A,4,9,6,6,3,11,2,3,3,10,2,9,2,6,3,8
Q,2,4,3,5,3,8,9,6,1,5,8,10,2,9,5,10
D,3,1,4,3,2,7,7,7,7,7,6,5,2,8,3,7
C,4,4,5,7,2,5,7,7,10,7,6,14,1,8,4,9
G,4,5,5,4,3,6,7,6,6,9,7,10,2,9,4,9
V,3,8,5,6,3,6,11,2,4,8,11,8,2,10,1,9
T,5,6,6,5,5,6,8,4,8,8,7,9,3,8,6,6
A,3,8,5,5,2,7,4,3,1,7,1,8,3,7,2,8
O,5,8,7,6,8,7,9,5,2,7,6,7,10,9,6,10
O,3,5,4,3,2,8,7,7,5,9,5,8,2,8,3,8
D,4,9,5,7,3,5,7,10,8,7,6,5,3,8,4,8
M,7,8,9,6,6,10,5,3,5,9,3,6,10,6,2,9
U,3,7,5,5,4,9,6,7,5,7,6,8,3,8,4,5
R,1,0,2,0,1,6,9,7,3,7,5,8,2,7,4,10
W,4,7,6,5,3,5,8,5,1,7,8,8,8,9,0,8
H,3,5,5,7,4,11,6,3,1,8,6,9,3,8,8,12
F,4,5,5,7,2,1,14,5,4,12,10,5,0,8,2,5
G,3,5,4,4,2,6,7,5,5,9,7,10,2,8,4,10
T,3,11,5,8,1,7,14,0,6,7,11,8,0,8,0,8
L,2,5,3,4,1,5,5,1,9,7,2,11,0,7,2,8
Y,4,9,7,7,3,6,10,2,8,7,12,8,1,11,2,8
Q,4,4,6,6,3,7,7,7,6,5,7,7,3,8,5,9
L,4,9,6,6,3,7,3,1,8,8,2,10,2,5,4,7
R,1,3,3,1,1,9,7,3,4,10,3,7,2,7,2,10
T,5,9,6,7,7,6,8,3,6,7,6,10,5,8,5,7
B,4,9,4,7,4,6,7,9,7,7,6,7,2,8,9,10
R,3,2,4,3,3,6,7,4,6,6,5,7,3,7,5,8
E,3,4,3,6,2,3,8,6,11,7,5,15,0,8,7,7
D,6,10,8,8,7,7,7,7,5,7,6,6,4,8,3,7
B,3,4,4,6,3,6,6,9,7,6,7,7,2,8,9,10
N,7,11,10,8,12,8,7,4,5,7,6,6,8,10,10,5
V,2,3,3,2,1,7,12,2,2,7,11,8,2,11,1,8
G,7,12,6,6,4,6,7,7,5,9,7,6,4,8,5,6
S,3,2,3,3,2,8,8,7,5,8,5,7,2,8,8,8
D,5,9,7,6,4,10,6,3,8,11,4,6,4,6,4,8
F,3,3,3,4,1,1,13,4,4,12,11,7,0,8,2,6
S,4,8,5,6,4,7,7,5,7,5,6,9,0,9,8,8
M,4,8,7,6,8,11,5,3,2,9,4,8,8,6,3,7
I,2,7,3,5,2,7,7,0,7,13,6,8,0,8,1,8
L,3,9,5,7,3,8,4,0,9,9,3,11,0,7,3,9
C,9,12,6,7,3,8,8,6,8,12,6,8,2,9,5,9
V,3,2,5,3,2,9,12,3,3,5,11,9,2,10,1,9
A,3,9,5,6,3,6,3,2,3,5,2,8,2,6,3,6
Q,4,6,6,8,6,8,7,7,2,5,6,10,3,8,5,10
D,4,8,6,6,4,9,6,5,7,10,3,5,3,8,3,8
U,4,5,4,7,2,7,5,15,5,7,13,8,3,9,0,8
J,0,0,1,0,0,12,4,6,3,12,5,11,0,7,0,8
H,6,8,9,6,7,5,8,3,7,10,9,9,4,9,4,6
O,2,3,2,2,1,7,7,7,4,7,6,8,2,8,2,7
Y,3,6,5,8,1,7,10,1,3,7,12,8,1,11,0,8
K,8,10,11,8,6,8,6,2,7,10,4,9,4,8,4,9
G,8,12,7,6,5,7,6,3,3,8,5,7,4,9,9,7
C,3,6,4,4,1,5,8,6,10,6,7,11,1,7,4,8
N,4,4,4,3,2,7,9,5,4,7,6,7,5,9,2,7
X,6,8,8,6,6,7,7,2,5,6,6,7,3,9,10,9
P,5,11,6,8,3,4,11,10,3,9,6,4,2,10,4,8
T,3,7,5,5,3,8,12,3,7,7,11,8,2,11,1,8
W,4,2,6,4,4,7,11,2,2,6,9,8,7,11,1,8
N,5,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8
B,3,7,3,5,3,6,7,9,6,7,6,7,2,8,8,10
A,7,10,9,8,9,8,6,7,4,7,6,9,6,8,8,3
B,5,6,7,6,8,8,7,5,4,7,6,8,7,9,8,4
B,2,7,3,5,2,6,7,8,6,7,6,7,2,8,8,10
J,2,5,4,4,1,9,6,2,6,14,5,9,1,6,1,7
B,3,5,5,3,3,10,6,3,6,10,4,7,2,8,4,11
X,7,10,10,8,5,10,7,2,8,11,2,7,3,8,4,9
F,2,3,2,2,1,5,10,3,4,10,9,5,1,9,2,7
H,1,0,1,1,0,7,7,11,1,7,6,8,3,8,0,8
C,5,10,6,8,2,6,7,7,10,6,6,15,1,8,4,9
Y,5,11,6,8,3,7,10,1,3,7,12,8,1,11,0,8
E,3,4,5,3,3,6,7,2,8,11,7,9,2,8,4,8
K,7,13,8,7,5,7,8,2,6,10,4,8,5,5,4,7
H,6,9,8,7,5,5,7,3,7,10,9,10,3,8,4,6
B,3,1,3,2,2,7,7,5,5,6,6,6,2,8,6,9
A,3,5,5,4,4,7,8,3,4,7,8,8,5,10,3,6
R,2,3,4,2,2,9,7,2,5,10,3,6,2,7,3,9
P,4,6,6,9,9,8,6,5,1,7,6,7,8,8,6,7
Z,6,9,8,6,6,9,10,6,5,6,5,6,3,9,9,4
W,8,9,8,5,3,3,11,2,3,11,11,8,7,11,0,7
T,3,5,5,7,1,6,15,1,6,8,11,7,0,8,0,8
Z,4,7,5,5,3,7,7,6,11,6,6,8,2,8,8,8
K,4,10,6,7,8,9,7,3,4,5,6,8,7,9,7,6
P,3,7,5,5,3,6,12,4,5,13,6,2,0,9,3,8
X,5,8,8,6,4,6,8,1,8,10,8,8,3,8,4,7
C,6,10,6,7,4,6,8,7,8,13,8,10,2,11,3,7
U,3,1,4,2,2,6,9,5,6,7,9,9,3,9,1,8
N,4,10,5,8,5,8,7,12,1,6,6,8,5,8,0,9
X,2,4,4,3,2,10,7,1,8,10,3,7,2,7,3,8
S,4,10,4,8,4,9,8,5,9,5,5,5,1,6,9,7
F,2,1,2,2,1,6,10,4,5,10,9,5,1,10,3,6
U,5,9,6,7,4,8,5,13,5,8,12,8,3,9,0,8
P,2,3,2,1,1,6,10,5,4,9,7,4,1,9,3,7
X,4,5,5,7,2,7,7,4,4,7,6,8,3,8,4,8
Q,3,5,4,6,3,7,8,5,2,7,9,11,3,9,5,7
K,3,3,5,2,2,8,6,2,6,10,5,10,3,8,3,9
B,10,14,9,8,8,8,7,4,5,9,6,7,8,7,10,8
D,3,7,5,5,7,9,9,4,4,7,6,5,4,9,8,6
G,7,8,9,7,9,7,8,5,3,7,7,8,10,10,10,7
B,3,6,4,4,4,6,8,7,4,7,5,6,2,8,6,6
T,3,3,4,2,1,5,11,3,7,11,9,4,1,10,2,5
Z,2,4,4,3,2,8,6,2,9,12,5,9,1,8,5,9
C,4,8,5,6,2,6,9,7,10,5,7,12,1,6,4,8
V,2,7,4,5,1,9,8,4,2,5,13,8,3,10,0,8
G,5,10,7,8,6,6,6,7,6,6,5,12,3,10,5,7
M,5,8,7,6,8,8,7,6,5,6,7,8,8,6,2,7
B,4,11,5,8,4,6,7,9,7,7,6,7,2,8,9,10
N,5,9,7,7,6,6,7,7,6,7,5,8,6,7,3,7
W,10,10,9,8,9,5,11,4,3,8,7,7,10,12,4,5
N,2,3,2,1,1,7,8,6,4,7,6,7,4,8,1,7
A,4,4,6,6,2,6,7,3,0,6,0,8,2,7,1,7
R,4,9,6,7,6,8,7,7,3,8,5,7,4,7,7,10
U,4,4,4,3,2,4,8,5,7,11,10,9,3,9,2,6
R,2,3,3,2,2,7,7,5,4,9,5,7,2,7,3,10
E,2,2,3,4,3,7,7,6,7,7,6,9,2,8,6,10
M,5,10,7,5,4,12,2,2,2,10,3,9,5,4,1,9
I,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
A,5,7,7,5,7,7,8,8,4,7,5,8,4,8,9,4
W,4,7,6,5,3,6,8,5,1,7,8,8,8,9,0,8
J,4,10,5,8,3,8,7,2,6,14,5,9,1,7,1,7
L,2,3,3,2,1,7,4,2,7,8,2,10,0,7,2,8
O,4,5,5,7,3,7,8,9,8,7,8,8,3,8,4,8
I,1,5,1,4,1,7,7,1,7,7,6,8,0,8,2,8
K,1,0,1,0,0,5,7,7,1,7,6,11,2,8,2,11
M,5,8,8,6,5,5,7,3,4,10,9,10,8,6,3,8
N,10,13,9,7,4,7,10,5,5,3,5,11,6,11,3,6
A,3,8,5,6,3,10,4,2,2,8,2,10,2,6,3,8
G,4,6,5,4,5,9,8,6,2,5,7,9,6,10,3,8
N,5,5,7,4,6,7,9,4,4,6,4,8,7,7,5,7
F,4,8,4,6,3,1,13,4,3,12,10,7,0,8,2,6
V,1,0,2,1,0,8,9,3,2,7,12,8,2,11,0,8
G,4,5,5,4,3,6,7,5,6,9,7,10,2,8,4,9
C,5,7,7,6,6,5,6,4,5,7,6,11,4,10,7,10
Y,10,9,8,12,5,7,9,3,2,7,11,5,4,10,7,6
Y,6,11,8,8,8,9,3,7,5,8,8,7,3,10,5,3
W,4,4,5,3,3,4,11,3,2,9,9,7,6,11,1,7
J,2,11,3,8,1,14,2,7,5,14,1,10,0,7,0,8
F,4,6,6,4,3,8,10,2,6,14,5,4,2,10,2,8
Q,5,7,6,6,5,7,4,4,5,6,3,7,3,7,5,7
Y,4,9,6,7,3,5,11,2,3,8,12,8,1,11,0,8
K,5,8,7,6,8,7,6,3,4,6,6,8,8,6,7,7
H,4,6,5,4,4,7,9,6,5,7,4,6,3,7,7,10
N,7,10,9,8,10,6,8,3,4,8,7,8,8,10,7,4
B,3,9,3,7,5,7,8,8,6,7,5,7,2,7,8,9
O,4,10,5,7,4,7,6,8,5,7,5,8,3,8,3,8
V,3,6,5,4,2,8,9,4,2,6,13,8,3,10,0,8
U,8,9,8,7,5,3,8,5,8,10,9,9,3,9,2,6
F,5,7,6,5,6,7,8,6,4,8,6,8,3,10,8,10
N,8,10,11,8,5,9,7,3,6,10,4,6,7,9,2,7
X,2,1,4,3,2,7,8,3,9,6,6,7,2,8,6,7
E,4,10,6,7,7,7,7,3,6,7,7,10,4,9,8,8
L,2,3,2,5,1,0,1,5,6,0,0,7,0,8,0,8
A,5,10,7,7,8,8,5,8,4,8,6,8,3,9,8,3
H,2,1,3,3,2,8,7,6,6,7,6,8,3,8,3,7
O,1,1,2,1,1,8,7,7,4,7,6,8,2,8,3,8
F,4,11,6,8,4,9,9,2,6,13,4,4,2,10,3,9
N,5,9,6,7,3,7,7,15,2,4,6,8,6,8,0,8
S,2,1,2,2,2,8,8,6,4,7,5,7,2,8,8,8
I,4,11,3,6,2,10,7,6,3,13,4,8,2,8,4,9
L,3,7,5,5,2,4,2,7,7,1,2,4,1,6,0,6
J,1,7,2,5,1,13,3,8,4,14,3,11,0,6,0,8
A,4,7,6,6,5,8,8,2,4,7,7,8,5,7,4,6
Q,6,9,8,8,8,5,4,4,6,5,4,7,4,5,6,6
N,4,7,6,5,6,9,6,4,5,7,5,6,7,11,6,5
W,8,8,8,6,5,2,10,2,3,11,11,9,7,10,1,7
N,3,7,5,5,5,5,8,4,3,9,10,10,5,8,4,5
J,5,10,6,8,5,8,6,4,5,8,6,7,3,7,4,6
S,1,0,1,0,0,7,7,3,5,5,6,8,0,8,7,8
D,4,8,6,6,8,9,8,5,4,7,6,6,4,8,8,6
Y,2,4,3,3,1,6,10,1,6,8,11,8,1,11,2,7
O,2,1,3,1,1,8,7,7,5,7,6,9,2,8,3,8
Q,5,6,6,8,6,9,8,7,2,4,7,11,3,9,5,10
P,4,7,6,11,11,8,6,5,1,7,6,7,7,9,7,10
E,3,8,4,6,5,6,6,3,5,7,7,10,4,10,8,9
E,4,7,6,5,4,9,6,2,8,11,5,8,4,7,6,10
G,3,9,4,7,2,7,7,8,7,5,6,9,2,7,6,11
D,4,11,6,8,5,9,7,5,7,10,5,5,3,8,3,8
R,6,9,9,7,7,9,7,4,6,10,3,7,3,6,4,11
V,4,8,4,6,2,3,11,3,3,10,11,8,2,10,1,7
X,5,7,6,5,5,7,8,3,6,6,6,8,4,8,10,8
M,6,9,8,8,10,6,8,4,3,7,5,8,10,8,5,6
W,8,12,9,7,5,1,8,2,3,9,11,9,9,9,1,6
E,5,10,7,8,5,10,6,2,8,11,4,8,2,8,5,11
T,3,8,5,6,3,6,12,2,8,8,12,8,1,11,1,7
O,3,5,4,3,2,7,7,8,5,7,6,8,2,8,3,8
V,3,4,5,3,2,4,12,3,3,9,11,7,2,11,1,8
E,3,7,5,5,3,9,6,2,7,11,5,8,2,9,5,11
D,7,11,9,8,8,8,8,5,6,10,5,5,6,8,6,11
J,2,9,3,6,2,14,3,4,4,13,1,8,0,7,0,8
J,1,0,1,1,0,12,3,6,3,12,5,11,0,7,0,8
S,3,9,4,7,4,8,7,7,5,7,5,6,2,8,8,8
T,2,5,4,6,1,5,14,1,6,9,11,7,0,8,0,8
F,5,9,7,6,7,9,7,1,5,9,5,5,5,10,4,6
E,4,9,5,7,3,3,6,6,12,7,7,15,0,8,7,7
U,6,10,6,8,3,4,9,5,8,11,11,8,3,9,2,6
M,15,15,15,8,7,9,11,6,4,4,6,10,11,13,2,6
N,2,1,2,2,1,7,9,5,4,7,6,7,4,8,1,6
J,4,5,6,6,5,7,7,4,6,6,6,7,3,9,8,9
H,2,3,4,1,2,5,8,3,5,10,9,9,3,8,2,6
Z,4,7,5,5,5,8,8,2,7,7,6,8,0,9,9,9
X,7,11,8,6,4,8,8,2,8,11,6,7,4,12,4,8
B,5,9,8,7,7,10,6,3,6,10,4,6,2,8,5,10
S,4,10,5,8,5,7,6,8,6,7,8,10,2,10,8,7
A,4,9,5,6,5,7,6,7,4,7,6,9,2,8,8,4
P,1,0,1,0,0,5,10,6,1,9,6,5,1,9,2,8
O,5,11,6,8,9,9,9,6,2,6,7,9,12,12,6,7
Q,2,1,2,1,1,8,7,6,4,6,6,8,2,8,3,8
P,1,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8
R,4,7,6,5,4,10,7,3,6,11,2,6,3,7,4,10
J,4,8,3,11,3,8,8,3,3,12,5,5,3,8,6,10
W,3,6,5,4,5,8,9,5,4,5,9,9,5,8,2,7
T,6,8,7,6,4,7,10,1,8,11,9,5,2,10,4,4
E,4,5,5,5,5,7,9,5,5,7,8,11,5,9,7,9
Z,4,9,4,7,4,7,8,3,12,8,6,8,0,8,7,7
G,4,5,6,4,5,7,8,5,3,7,7,8,7,11,7,8
I,4,5,5,6,4,8,9,5,5,7,6,8,3,8,9,8
W,10,13,10,8,5,5,9,2,2,7,10,7,9,12,1,6
D,7,13,7,7,4,10,4,3,6,10,2,7,5,7,4,12
M,3,3,5,2,3,7,6,3,4,9,7,9,7,5,1,8
B,3,6,4,4,4,8,7,4,5,9,5,6,2,8,5,9
V,3,4,4,3,1,4,12,2,3,9,11,7,2,10,1,7
H,4,7,5,5,5,7,7,5,6,7,6,10,6,8,3,8
L,2,4,4,3,2,6,4,1,8,8,2,11,0,7,2,8
X,2,3,3,1,1,9,6,2,7,10,4,8,2,8,3,9
R,6,8,8,6,7,9,8,3,7,9,3,8,4,5,4,11
M,2,3,3,2,2,7,6,6,3,7,7,8,5,5,1,8
I,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
T,5,8,6,7,6,6,9,5,7,8,8,8,4,13,9,6
N,5,8,7,6,5,7,6,10,5,6,4,5,4,7,4,9
A,4,5,6,4,4,8,8,3,4,6,7,7,5,8,4,5
Q,6,9,5,4,3,11,3,4,5,11,3,10,3,8,5,13
D,4,11,5,8,3,6,7,11,9,7,6,6,3,8,4,8
A,3,10,5,8,4,12,3,2,2,9,2,8,2,6,2,8
B,1,0,1,1,1,7,7,7,4,6,6,7,1,8,6,9
U,5,5,6,3,3,4,8,5,8,10,9,9,3,9,2,6
M,5,8,5,6,3,7,7,12,2,7,9,8,8,6,0,8
R,2,4,4,3,2,9,7,3,5,10,4,7,2,7,3,10
S,3,1,3,3,2,8,7,7,5,7,6,8,2,10,9,8
P,5,7,7,5,4,6,11,7,2,11,5,3,1,10,3,8
G,7,13,6,7,4,10,6,5,6,11,4,8,5,7,5,9
F,5,8,7,6,7,7,8,6,4,8,6,8,4,10,8,11
F,7,11,6,6,3,7,8,2,8,11,6,6,2,10,5,6
K,9,13,8,7,4,7,7,3,7,9,9,9,6,11,3,7
W,5,7,5,5,5,6,10,4,2,9,7,7,6,11,2,6
P,4,9,5,6,2,4,12,9,2,10,6,4,1,10,4,8
I,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
V,6,8,6,6,2,3,12,5,4,12,12,7,3,10,1,8
E,6,10,9,8,6,6,8,4,9,11,9,9,2,9,5,6
W,4,4,6,3,3,7,11,3,2,6,9,8,8,11,1,8
F,1,0,1,0,0,3,11,4,2,11,8,6,0,8,2,8
U,9,15,7,8,4,8,6,4,6,3,9,5,6,6,2,6
X,3,3,5,2,2,9,7,2,8,11,4,7,2,7,3,8
Q,4,9,5,11,5,9,8,8,2,4,7,11,4,9,6,9
I,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8
P,4,5,4,7,2,4,12,9,2,10,6,4,1,10,4,8
R,9,11,7,6,4,8,7,5,5,10,4,9,6,5,6,11
I,1,8,0,6,1,7,7,5,3,7,6,8,0,8,0,8
P,2,1,3,2,2,6,9,5,5,9,7,4,1,9,4,7
G,4,6,5,4,6,8,6,4,2,6,6,9,6,8,6,11
Z,8,14,8,8,5,11,4,5,8,12,3,10,4,4,8,12
K,2,1,3,2,2,5,7,4,7,7,6,11,3,8,4,10
C,6,12,4,6,2,7,7,7,6,11,6,8,2,9,5,9
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
S,5,8,6,6,3,9,6,4,8,11,4,8,2,8,5,11
W,7,11,10,8,15,10,5,5,2,7,6,7,12,10,3,4
K,2,6,4,4,4,7,7,3,5,6,5,8,3,8,3,9
R,3,5,5,4,3,9,7,4,5,9,4,7,3,7,4,10
K,4,7,5,5,5,5,6,4,6,6,6,11,3,8,5,10
C,6,10,7,8,3,4,9,6,8,12,10,11,2,9,3,6
B,2,5,4,4,3,8,7,3,5,10,5,6,2,8,4,9
U,9,11,10,8,7,5,7,5,9,9,6,9,8,9,6,1
P,4,6,6,9,9,7,8,6,0,8,6,7,6,10,6,11
Y,3,4,4,3,1,4,11,2,7,11,10,5,1,11,2,5
H,3,4,5,6,4,7,4,5,2,6,4,6,4,6,7,7
Z,3,10,4,8,4,7,8,3,12,9,6,8,0,8,7,7
K,5,8,5,6,2,4,7,8,1,7,6,12,4,8,3,11
U,5,10,8,7,10,9,7,4,4,6,7,8,8,8,6,7
Z,3,7,5,5,4,8,8,3,7,7,6,8,1,8,10,9
J,0,0,1,0,0,11,4,5,3,12,4,10,0,7,0,8
J,4,7,5,5,4,7,6,4,5,8,6,7,5,6,4,6
K,5,11,6,8,3,4,8,9,2,7,4,11,4,8,2,11
Y,3,4,4,2,2,4,11,2,7,11,10,5,1,10,2,4
L,4,7,5,5,3,7,4,2,8,7,2,8,1,6,3,8
N,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
M,3,2,5,3,3,7,6,6,5,6,7,7,8,6,2,7
H,5,7,7,9,6,10,10,3,2,7,7,8,3,10,8,9
L,4,9,4,7,2,0,2,4,6,1,0,7,0,8,0,8
W,4,8,6,6,3,8,8,4,1,7,8,8,8,9,0,8
W,5,10,8,8,13,9,5,5,2,7,6,8,11,10,4,8
G,4,9,3,4,2,8,6,5,2,9,6,8,3,10,7,7
R,4,7,5,5,5,8,6,7,3,7,5,7,4,6,6,9
W,3,1,5,3,3,7,11,2,2,7,9,8,7,11,0,8
Q,5,10,7,8,6,8,4,8,5,6,4,8,3,8,3,8
D,5,11,7,8,6,6,7,8,7,5,4,5,3,9,4,9
H,5,11,6,8,6,6,7,7,6,7,6,11,3,8,3,9
F,3,8,4,6,4,6,9,3,6,10,9,5,2,10,3,6
V,4,11,6,8,2,7,8,4,3,7,14,8,3,9,0,8
O,6,11,4,6,3,6,7,6,3,9,7,9,5,10,5,8
C,2,4,3,3,1,6,8,7,7,9,8,13,1,10,4,10
F,4,10,6,8,6,4,10,3,5,10,10,6,2,10,3,6
C,1,3,2,1,1,6,8,6,6,8,7,13,1,9,3,10
V,4,7,4,5,2,3,12,4,3,11,11,7,3,10,1,7
U,7,9,7,6,4,5,7,7,9,8,7,9,5,11,5,2
V,3,8,5,6,3,5,11,3,4,8,12,9,2,10,1,8
Y,3,7,5,5,2,10,11,2,2,5,12,8,1,11,0,8
X,1,0,1,0,0,7,7,3,4,7,6,8,2,8,4,8
F,6,9,8,6,5,10,8,2,6,13,4,5,3,9,3,9
N,2,4,3,3,2,7,8,5,4,7,6,6,5,9,2,6
R,4,9,5,6,4,10,6,4,5,10,3,7,3,7,4,11
B,3,6,5,4,4,8,7,6,6,7,6,5,2,8,7,10
H,4,8,6,6,6,7,8,6,6,7,6,10,6,8,3,8
V,3,4,5,3,1,7,12,3,3,7,11,8,2,10,1,8
I,3,7,5,5,4,12,5,1,6,8,4,5,2,8,4,8
J,5,8,7,10,7,8,8,4,5,7,6,8,4,6,9,8
Q,7,11,6,6,3,8,5,4,9,10,4,9,3,6,9,9
W,1,0,2,1,1,7,8,3,0,7,8,8,5,10,0,8
E,4,10,4,7,4,3,8,5,9,7,6,13,0,8,7,9
Z,3,7,5,5,4,9,11,6,4,6,5,7,2,8,6,4
B,2,4,4,3,3,8,7,3,5,10,6,7,2,8,4,9
G,6,10,6,7,5,5,7,6,5,9,8,11,2,9,4,9
W,6,6,8,8,4,11,8,5,2,6,9,8,8,9,0,8
D,6,9,8,8,9,7,7,4,7,7,5,8,4,7,7,5
P,8,10,7,5,3,7,10,5,3,12,4,4,4,9,4,8
Z,7,11,9,8,6,8,6,2,9,12,5,10,2,8,7,9
R,4,8,5,6,4,10,6,3,6,10,3,7,3,7,4,11
T,7,11,6,6,2,5,10,3,8,13,7,5,2,9,4,3
M,5,7,7,5,4,11,6,3,5,9,2,6,8,6,2,9
E,8,11,5,6,3,7,9,5,8,10,6,10,1,8,7,9
F,4,6,5,8,2,0,15,5,4,13,10,5,0,8,2,5
Y,8,7,6,11,5,9,8,5,5,4,11,6,4,11,5,7
Y,2,2,3,3,1,7,10,1,7,7,11,8,1,11,2,8
H,3,7,4,5,2,7,6,14,2,7,8,8,3,8,0,8
W,4,7,6,5,5,7,11,1,2,7,8,8,6,12,1,8
L,4,6,5,4,4,6,6,8,6,6,6,9,2,8,4,10
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
G,6,9,6,7,5,6,6,6,5,9,6,12,4,8,6,7
H,5,9,7,7,6,8,8,7,6,7,5,6,3,8,3,6
G,3,6,4,4,4,7,7,5,2,7,6,10,4,9,7,7
P,4,9,5,6,2,3,12,8,1,10,6,3,1,10,4,8
I,6,11,4,6,2,9,6,6,5,13,5,9,2,8,4,9
G,5,9,5,7,4,6,6,7,6,8,7,12,3,7,5,8
P,5,9,5,5,3,5,12,4,2,12,6,3,2,10,5,6
Z,3,9,4,6,2,7,7,4,14,9,6,8,0,8,8,8
R,4,8,6,6,5,7,8,6,6,7,5,7,3,7,5,8
N,2,1,3,2,2,7,8,5,5,7,7,6,5,9,1,5
J,0,0,1,1,0,12,4,5,3,12,4,10,0,7,0,8
U,6,7,6,5,4,3,9,5,7,10,9,9,3,9,2,6
U,4,7,5,5,3,4,8,5,7,10,9,9,3,9,2,6
J,3,6,5,4,1,8,5,4,7,15,7,13,0,7,1,7
H,3,4,5,3,3,7,7,3,6,10,6,8,3,8,3,8
S,4,6,5,4,3,9,7,4,8,11,5,8,2,8,5,9
N,4,8,6,6,5,6,7,7,6,7,5,7,3,7,3,8
X,4,8,5,6,4,8,7,3,8,5,6,7,3,9,7,7
Y,4,7,4,5,2,3,10,3,6,11,12,7,2,11,2,6
C,5,9,5,7,4,4,8,5,7,11,9,14,2,9,3,7
A,3,6,4,4,2,10,2,2,3,8,2,8,2,6,2,8
B,5,8,7,6,6,10,6,3,6,10,4,7,4,7,5,10
M,3,6,4,4,4,8,5,10,0,6,8,8,6,5,0,8
V,5,11,8,8,2,6,8,5,3,8,14,8,3,9,0,8
D,4,5,5,4,4,7,7,7,7,7,6,5,2,8,3,7
K,4,6,6,4,6,7,6,5,4,6,6,8,6,6,7,12
B,2,3,3,2,2,8,7,3,5,10,5,7,2,8,4,10
Y,3,6,4,4,2,8,10,2,6,5,12,8,2,11,2,8
A,1,3,3,2,1,11,2,3,2,10,2,9,1,6,2,8
S,3,4,4,7,2,8,7,6,8,4,6,8,0,8,9,7
E,4,4,4,6,3,3,8,6,12,7,5,14,0,8,7,7
C,3,4,4,3,1,5,9,5,7,12,9,10,1,10,3,7
D,5,5,5,8,3,5,7,11,9,7,6,5,3,8,4,8
E,3,4,5,3,2,6,8,2,9,11,7,8,2,8,4,6
M,2,1,2,1,1,7,7,10,0,7,8,8,6,6,0,8
S,3,2,4,3,3,8,7,7,5,7,6,8,3,9,9,8
Z,4,8,5,6,4,9,9,6,4,7,5,8,3,8,10,7
O,6,11,6,8,6,7,6,7,4,10,5,10,5,7,4,7
S,6,10,8,9,9,9,8,4,5,7,7,8,5,10,9,11
X,5,8,7,6,3,9,6,2,8,11,2,7,3,9,4,9
W,3,4,4,3,3,7,11,2,2,7,9,8,6,11,1,8
V,3,8,5,6,1,5,8,4,3,8,14,8,3,9,0,8
Y,3,7,5,5,3,7,10,1,5,5,11,9,2,11,1,8
C,4,7,6,5,4,7,8,8,6,5,7,13,4,7,4,8
E,3,5,5,3,2,8,7,2,8,11,5,8,2,8,5,10
W,4,6,6,4,8,9,7,5,2,7,6,8,9,11,2,7
A,2,5,4,3,2,7,3,1,2,6,2,8,3,5,2,8
M,7,9,9,6,8,8,7,2,5,9,6,8,8,6,2,8
O,2,6,3,4,2,7,8,7,5,7,7,7,3,8,3,7
Q,3,6,4,7,4,9,7,7,3,5,6,10,3,8,6,10
L,2,5,3,4,2,4,4,5,7,2,2,6,1,6,1,6
F,3,5,3,3,2,6,10,4,5,10,9,5,2,10,3,6
P,4,9,4,6,2,3,13,8,2,11,7,3,1,10,4,8
G,4,7,6,5,4,6,6,5,5,6,6,7,3,7,4,7
Z,7,10,9,8,6,6,9,3,9,12,8,6,3,10,7,6
F,1,0,1,1,0,3,12,4,2,11,9,6,0,8,2,7
O,3,4,4,3,2,7,7,7,4,9,6,8,2,8,3,8
U,9,12,8,6,3,5,4,5,5,4,8,7,6,7,2,7
J,3,8,5,6,3,12,4,4,5,14,2,9,0,7,0,8
X,4,6,6,6,5,6,7,2,5,7,7,9,3,9,8,8
V,8,11,7,8,5,2,11,2,3,10,11,8,2,10,1,8
U,5,5,6,4,3,4,8,5,8,10,9,9,3,9,2,5
J,1,7,2,5,1,11,3,9,3,12,6,13,1,6,0,8
T,3,6,5,8,1,5,14,1,6,9,11,7,0,8,0,8
S,4,11,5,8,4,7,8,8,8,8,4,6,2,6,9,8
R,4,5,6,4,4,8,8,4,5,9,5,7,3,6,4,10
W,2,3,4,2,2,9,11,3,2,5,9,7,6,10,0,8
D,5,11,7,8,6,11,6,2,7,11,3,7,4,7,4,9
Q,2,1,2,2,1,9,6,7,4,6,6,9,3,8,3,8
N,4,5,6,4,5,8,8,5,4,8,6,7,6,9,6,3
L,1,3,2,2,1,7,4,1,7,7,2,10,0,7,2,8
M,5,5,6,4,4,8,6,6,5,6,7,7,10,6,4,6
L,3,8,3,6,1,0,1,5,6,0,0,7,0,8,0,8
C,2,5,3,3,1,5,9,5,7,12,9,11,1,10,2,7
B,4,10,5,7,5,8,7,6,7,6,6,6,2,8,7,10
Y,2,7,4,4,1,6,10,2,2,7,12,8,1,11,0,8
Z,2,6,3,4,3,8,7,5,9,7,7,7,1,8,7,8
J,2,7,2,5,1,13,2,7,5,14,2,11,0,7,0,8
A,3,4,5,6,2,5,4,3,2,5,1,7,3,7,2,7
Q,2,3,3,4,2,8,7,5,2,8,7,9,2,9,3,9
B,3,4,4,6,3,6,9,9,8,7,5,7,2,8,9,10
Q,2,2,3,3,2,7,9,4,1,7,8,10,2,9,4,8
G,3,9,5,6,2,7,6,8,7,6,6,8,2,7,6,11
A,4,5,6,8,2,7,4,3,2,7,1,8,3,6,3,8
J,2,3,3,4,1,14,2,6,5,13,2,10,0,8,0,8
S,2,5,4,3,2,8,7,3,7,11,5,7,1,9,4,8
T,5,6,5,4,2,6,10,2,9,11,9,4,1,10,3,4
O,5,11,7,8,6,7,7,8,5,6,7,11,6,7,5,7
A,2,4,4,3,2,11,2,2,2,9,2,9,2,6,2,8
R,4,8,5,6,4,8,9,4,6,8,4,9,3,6,5,11
G,6,9,7,7,8,8,6,4,2,6,6,10,7,7,7,13
R,2,3,4,2,2,8,7,4,5,9,4,7,2,7,4,10
B,2,5,4,4,3,7,7,3,5,10,6,6,2,8,5,8
K,4,8,6,6,4,9,5,2,7,10,3,9,4,7,5,11
J,4,7,6,5,2,5,9,3,6,15,7,9,1,6,1,7
Y,3,7,5,5,2,8,10,2,6,4,11,9,2,12,2,7
I,3,9,4,6,3,6,8,0,7,13,7,8,0,8,1,7
W,9,12,8,6,4,1,8,4,2,11,12,9,7,10,0,7
O,5,5,7,4,4,8,5,4,5,8,4,8,3,8,4,8
I,7,8,9,9,8,7,8,4,5,7,7,7,4,7,10,10
K,3,4,5,3,3,4,9,2,7,10,10,10,3,8,3,6
D,2,5,4,4,3,9,6,4,6,10,4,6,2,8,3,8
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
D,5,7,7,6,6,6,7,5,7,7,6,9,4,5,6,5
K,3,9,4,6,2,4,8,8,2,7,4,11,4,8,2,10
G,6,8,8,7,9,7,9,6,2,7,7,8,7,11,7,7
Q,5,7,6,9,6,10,11,6,3,3,8,12,3,10,7,10
P,4,8,6,6,5,6,8,6,5,9,7,4,2,10,4,7
A,8,15,6,8,4,8,3,3,2,7,4,11,5,5,4,7
Q,3,5,3,6,3,8,8,5,2,8,8,10,3,8,5,8
C,2,2,3,4,2,6,8,7,8,8,8,13,1,9,4,10
I,1,1,1,3,1,7,7,1,6,7,6,8,0,8,2,8
U,5,9,5,7,2,7,4,15,6,7,13,8,3,9,0,8
O,4,9,5,6,4,8,7,8,5,9,5,8,3,8,3,8
D,2,1,3,1,2,7,7,6,7,7,6,5,2,8,3,7
N,7,11,6,6,3,9,11,5,4,4,6,10,6,10,2,6
Y,2,3,4,5,1,8,10,2,3,6,12,8,1,11,0,8
Z,3,8,4,6,2,7,7,4,13,9,6,8,0,8,8,8
L,2,5,4,3,2,7,4,1,8,8,2,10,0,7,2,8
V,4,9,7,6,3,6,12,3,4,8,12,8,3,10,1,8
A,2,6,3,4,2,9,4,2,0,8,2,8,2,6,1,8
N,6,10,9,8,4,8,8,3,5,10,4,6,6,9,1,6
G,4,7,5,5,3,6,7,7,7,10,7,11,2,9,4,9
A,2,1,4,1,1,8,4,2,0,7,2,8,2,6,2,8
D,6,9,8,7,5,9,7,5,8,11,5,5,3,8,4,9
U,5,9,7,8,6,7,7,4,4,6,5,8,8,7,2,8
M,4,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8
J,2,6,3,4,1,13,2,8,4,13,4,13,1,6,0,8
D,5,10,8,8,6,10,6,3,7,11,4,7,4,7,3,9
X,2,4,4,2,2,7,7,1,8,10,6,8,2,8,3,7
U,3,7,5,5,3,8,8,6,7,5,9,8,3,9,1,8
X,5,11,6,6,4,6,9,2,6,10,10,8,4,14,4,6
M,4,6,4,4,2,8,7,12,1,6,9,8,8,6,0,8
L,3,6,4,4,2,6,4,3,7,6,2,8,1,6,2,7
F,6,9,8,7,5,9,8,1,8,14,5,6,5,8,5,9
G,4,11,5,8,3,7,6,9,8,6,6,11,2,8,5,11
U,5,10,7,7,5,7,9,4,6,5,8,10,6,10,1,7
C,8,15,6,8,5,5,10,4,5,9,7,8,3,9,9,9
Q,3,6,5,6,4,8,8,6,4,5,8,9,3,8,5,9
I,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7
F,2,8,3,5,1,1,11,4,6,11,11,9,0,8,2,6
K,6,9,6,5,4,5,8,3,5,10,9,11,5,9,3,6
D,4,8,6,6,4,7,9,7,7,11,6,4,4,8,5,9
R,3,2,4,3,3,7,8,4,6,6,5,7,3,5,5,8
O,7,10,5,5,3,5,7,6,4,10,7,9,5,10,5,7
A,3,9,5,7,3,12,3,3,3,10,1,9,2,6,2,8
U,5,10,7,8,6,8,7,7,6,7,7,9,6,8,4,7
V,2,2,3,3,1,5,12,3,3,9,11,9,2,10,1,8
B,1,1,2,1,1,7,7,7,5,6,6,7,1,8,7,9
O,1,0,2,1,0,8,7,7,4,7,6,8,2,8,3,8
P,4,8,6,12,11,8,8,5,0,8,7,7,6,10,5,8
Y,8,8,7,11,5,9,9,3,3,5,11,5,4,10,7,8
C,4,9,4,5,3,6,8,4,3,9,9,10,3,9,8,9
P,2,5,4,4,2,7,10,4,4,12,5,3,1,10,2,8
T,5,10,7,7,8,6,8,4,6,7,6,9,5,8,5,6
T,6,8,6,6,3,4,12,3,8,12,10,4,1,10,2,5
M,7,9,9,6,8,6,7,2,4,9,8,9,8,6,2,8
D,2,4,3,2,2,7,7,6,6,7,7,5,2,8,2,7
C,3,7,4,5,1,6,8,7,10,5,7,13,1,7,4,9
S,6,11,8,8,10,9,8,5,4,9,5,7,5,10,13,8
N,6,11,8,8,5,7,8,2,5,10,6,7,5,8,1,7
X,5,7,8,5,4,11,5,2,8,10,1,7,2,7,3,10
U,4,4,5,3,2,4,8,5,7,11,10,9,3,9,2,6
L,4,7,6,6,5,7,7,4,5,7,6,7,3,9,8,11
A,1,0,2,0,0,8,4,2,0,7,2,8,2,7,1,8
J,4,8,3,11,3,6,9,3,3,13,5,6,3,8,6,9
J,5,10,6,8,4,9,4,6,5,15,5,12,1,6,1,6
I,3,11,4,8,2,7,7,0,8,13,6,8,0,8,1,8
Q,7,8,9,12,11,9,9,7,0,6,7,10,9,14,6,7
N,8,15,10,8,5,6,8,2,4,13,8,9,6,8,0,7
B,3,6,5,4,3,9,7,4,6,10,5,7,2,8,5,10
M,2,3,4,2,2,7,6,3,4,9,7,8,6,5,1,8
M,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8
L,5,11,6,8,6,6,6,6,5,6,5,8,5,7,4,10
W,9,12,9,7,6,8,8,4,4,6,9,6,11,8,3,6
Y,3,2,5,4,2,6,10,1,8,8,11,8,1,11,2,8
C,1,3,2,2,1,6,8,7,7,8,7,13,1,9,4,10
P,4,8,6,11,10,8,9,4,0,8,7,6,8,10,6,8
X,3,6,5,4,3,7,7,4,9,6,6,6,3,8,6,7
Y,4,10,6,7,4,4,8,2,7,10,13,10,2,11,2,6
G,2,4,3,2,2,6,7,5,6,9,7,10,1,8,4,9
W,4,4,6,3,3,9,11,3,2,5,9,7,7,11,1,8
U,5,10,8,8,4,5,9,7,8,8,10,10,3,9,1,8
V,8,11,8,8,4,4,12,2,4,8,11,7,6,10,1,7
S,1,0,2,1,1,8,7,4,7,5,6,7,0,8,7,8
H,4,5,8,4,4,5,8,3,6,10,8,8,3,8,3,6
H,4,10,4,7,2,7,7,15,1,7,6,8,3,8,0,8
Q,7,9,7,11,8,7,9,5,2,7,9,11,4,8,8,9
K,10,14,9,8,4,8,7,3,7,9,5,7,6,9,4,7
S,2,6,3,4,2,8,7,7,6,8,7,9,2,9,9,8
M,3,1,4,2,3,8,6,6,4,6,7,8,7,5,2,8
R,9,10,7,6,4,7,8,5,5,9,4,9,7,5,6,11
S,5,8,6,6,3,7,7,4,8,11,5,8,2,8,5,8
Z,4,7,5,5,3,7,7,2,9,11,6,7,2,8,6,7
T,3,8,4,6,4,6,11,3,6,8,11,8,2,12,1,7
L,3,5,4,8,1,0,1,5,6,0,0,7,0,8,0,8
C,2,4,3,3,1,4,9,5,6,11,9,11,1,9,2,8
Y,3,5,5,4,2,6,10,1,8,8,12,8,1,11,2,7
N,3,2,4,4,3,7,8,5,5,7,7,6,5,9,2,5
T,7,9,6,5,2,7,8,3,8,13,6,7,2,8,5,5
Y,6,8,6,6,4,3,9,2,6,10,11,7,2,11,2,5
J,5,9,6,7,4,9,5,5,5,8,6,6,2,7,4,6
U,5,10,6,8,5,6,9,5,7,6,9,9,3,9,1,8
I,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
S,4,8,5,6,6,8,5,4,4,8,6,10,4,8,8,10
K,6,9,7,4,4,10,6,4,5,11,2,7,5,7,3,9
F,2,2,3,3,2,5,10,4,5,11,9,5,1,10,3,6
F,3,8,3,5,1,1,11,5,6,11,10,9,0,8,3,6
F,4,5,6,5,5,7,9,4,4,8,7,6,4,9,8,8
X,6,9,8,7,7,7,6,3,5,6,6,10,4,6,11,9
F,3,6,4,4,4,7,8,4,6,8,8,6,5,9,3,7
Y,2,7,4,5,2,10,10,3,1,6,12,8,2,11,0,8
G,2,4,3,3,2,6,7,6,6,6,6,11,2,9,4,9
L,3,6,4,6,4,8,6,5,4,7,7,8,3,8,7,10
Q,6,7,9,6,6,8,4,4,5,7,3,8,3,6,5,7
J,5,7,4,10,3,8,8,3,3,12,4,5,2,8,7,9
L,6,11,7,8,7,6,6,9,5,6,6,8,3,7,5,12
A,6,9,9,8,8,8,7,3,5,7,7,7,5,8,4,5
G,4,4,5,6,2,7,6,7,8,6,6,9,2,8,6,11
N,5,8,7,6,4,10,8,2,5,10,2,5,5,9,1,7
K,6,7,8,5,4,4,8,3,6,10,10,11,4,7,3,6
U,9,15,8,8,6,7,6,5,5,6,7,8,5,6,3,7
Z,4,9,5,7,2,7,7,4,14,9,6,8,0,8,8,8
W,3,4,4,3,2,3,11,2,2,10,10,8,5,10,1,8
Y,3,9,5,6,2,7,11,0,4,7,11,8,0,10,0,8
F,3,6,4,4,3,5,10,3,6,10,9,5,2,10,3,6
V,7,10,7,8,4,2,11,3,4,11,12,8,2,10,1,8
L,3,8,4,6,4,5,5,3,8,3,2,7,1,6,2,5
X,5,11,8,8,6,8,7,3,9,5,6,8,3,8,7,8
D,4,11,6,8,6,9,7,4,5,10,4,5,3,8,3,8
S,4,9,5,7,4,7,8,8,7,8,5,6,2,8,9,8
Y,3,8,4,6,2,5,10,1,3,8,12,8,1,11,0,8
S,3,7,4,5,3,7,8,7,7,8,5,6,2,8,9,8
N,5,9,7,6,5,8,8,6,6,6,6,4,8,10,5,6
W,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8
F,5,8,7,6,3,7,10,3,6,14,6,4,2,10,3,7
U,5,11,5,8,5,8,6,13,4,7,12,8,3,9,0,8
C,5,11,5,8,4,4,7,5,6,11,9,14,2,10,4,7
Z,5,10,6,8,6,8,7,5,10,7,6,7,1,8,7,8
T,3,9,5,6,3,8,14,1,5,7,10,8,0,8,0,8
Q,4,10,6,9,3,8,6,9,6,6,6,8,3,8,5,9
P,8,12,7,6,3,7,10,6,5,14,5,4,4,10,4,8
L,3,6,4,4,2,5,3,3,9,5,2,8,1,6,2,6
W,5,9,7,6,5,11,11,3,2,5,9,7,9,12,2,7
R,3,6,5,5,5,7,8,3,3,7,5,8,6,8,5,7
T,4,8,6,6,4,9,11,2,7,6,11,8,1,11,1,8
S,2,3,3,2,1,8,8,2,6,10,6,7,1,8,5,8
O,4,8,4,6,4,8,7,7,3,9,6,9,3,8,2,8
T,4,9,6,7,4,6,11,1,9,8,11,8,0,10,1,7
B,2,6,3,4,2,6,8,9,6,7,5,7,2,8,8,9
D,4,9,6,7,4,9,7,4,7,11,5,6,3,7,3,8
F,2,3,4,1,1,5,12,4,4,13,7,4,1,9,1,7
U,4,5,5,4,3,5,8,6,8,10,8,9,3,9,3,6
O,5,11,6,8,3,8,7,9,8,7,6,8,3,8,4,8
Y,5,10,7,7,4,7,10,2,7,6,12,9,2,11,2,8
Z,3,4,4,6,3,11,5,3,4,9,3,7,1,7,6,9
N,5,8,7,6,5,8,9,6,5,7,6,5,7,8,3,8
T,3,5,4,6,1,5,14,1,6,9,11,7,0,8,0,8
I,3,8,5,6,5,10,7,2,6,9,4,5,3,8,5,7
A,4,9,6,8,6,7,8,2,4,7,7,8,8,6,4,7
O,5,10,6,8,5,7,6,7,4,9,6,10,5,8,4,7
X,2,4,4,3,2,7,8,1,8,10,6,8,2,8,3,7
B,3,7,5,5,5,8,8,4,5,7,5,6,4,8,5,8
I,1,8,2,6,2,7,7,0,7,7,6,8,0,8,2,8
Z,4,7,4,5,4,7,8,5,9,7,7,9,1,9,7,8
V,6,7,5,5,3,3,11,2,3,9,11,8,3,11,1,7
E,2,1,2,2,2,7,7,5,6,7,6,8,2,8,5,10
J,4,5,6,6,5,8,8,4,6,7,6,8,3,10,8,8
Q,3,8,5,9,5,9,9,6,3,4,8,11,3,9,7,10
G,4,5,6,4,5,8,9,5,3,7,7,10,6,10,6,9
A,4,9,7,7,5,7,2,1,2,5,2,7,4,6,5,7
R,5,11,5,8,6,5,10,8,3,7,4,8,2,7,6,11
E,3,7,4,5,3,3,7,5,8,7,6,13,0,8,7,9
G,5,8,6,7,7,7,9,5,2,7,7,8,6,11,7,8
G,3,7,4,5,2,8,7,8,7,6,6,9,2,7,6,11
T,5,10,6,8,4,6,12,2,9,8,12,8,1,11,1,7
D,5,11,7,8,11,10,8,5,6,7,5,6,5,5,10,5
H,2,6,2,4,2,7,8,12,1,7,5,8,3,8,0,8
P,7,10,9,8,7,6,11,5,4,12,5,3,1,10,4,9
G,5,10,6,8,4,6,6,8,7,8,5,13,2,9,5,9
G,4,7,5,5,3,6,6,6,7,6,5,9,2,10,4,7
W,4,8,6,6,4,5,8,5,1,8,10,8,6,11,0,8
K,4,9,6,6,5,5,7,1,6,9,7,10,3,8,3,8
M,5,2,6,4,4,8,6,6,4,7,7,8,8,6,2,7
A,1,0,2,1,0,7,4,2,0,7,2,8,2,7,1,8
G,2,3,3,1,2,7,7,6,6,6,6,9,2,9,4,9
S,3,7,4,5,3,9,8,5,8,5,5,5,0,7,8,8
T,6,10,5,6,2,5,11,2,7,12,7,5,2,9,4,4
S,2,4,3,3,2,8,7,7,5,7,7,8,2,9,9,8
R,4,10,4,8,5,5,10,7,4,7,4,9,2,7,5,11
J,5,10,7,8,5,9,4,4,6,8,5,6,2,8,4,6
T,5,7,6,5,6,7,8,5,5,6,8,9,6,9,6,5
G,3,6,4,4,2,7,7,7,7,7,5,12,2,9,4,9
I,4,9,5,6,3,8,6,1,6,7,6,7,0,9,4,7
Q,6,7,8,11,8,10,14,5,1,3,8,12,5,15,5,10
I,3,10,4,8,2,6,8,0,8,13,7,8,0,8,1,7
U,4,7,6,6,5,7,6,4,4,6,6,8,7,8,1,8
E,5,8,7,6,7,7,7,3,6,7,7,11,4,10,8,9
S,3,7,4,5,2,7,7,5,8,5,6,8,0,8,9,8
E,2,5,3,4,3,7,7,5,7,7,7,9,2,8,5,10
C,3,4,4,3,2,6,8,7,8,8,8,13,1,9,4,10
P,3,6,4,4,2,4,14,8,1,11,6,3,0,10,4,8
C,6,12,5,6,4,6,8,4,4,10,8,9,4,9,9,9
E,2,6,3,4,3,7,7,4,7,7,5,8,3,8,5,10
P,4,7,5,5,3,9,8,3,5,12,4,4,2,9,3,9
M,4,4,4,6,3,8,7,12,1,6,9,8,8,6,0,8
I,7,13,5,7,3,9,7,6,4,13,4,9,3,8,5,10
R,3,8,4,5,2,6,10,9,4,7,4,8,3,7,5,11
H,3,1,4,2,3,7,7,6,6,7,6,8,3,8,3,8
N,5,8,8,6,4,7,8,3,5,10,6,7,5,8,1,7
W,4,5,6,7,4,8,8,4,2,7,8,8,9,9,0,8
L,5,9,5,7,2,0,1,5,6,0,1,6,0,8,0,8
J,4,7,6,5,3,7,5,5,4,14,9,14,1,6,1,7
S,2,3,3,2,1,8,7,2,7,10,5,8,1,8,4,8
M,7,11,9,8,9,9,7,2,4,9,6,7,8,6,2,8
C,6,11,6,8,3,4,8,6,8,12,10,12,2,9,3,7
L,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8
D,3,8,4,6,2,5,7,10,8,6,5,5,3,8,4,8
P,10,15,9,8,6,7,10,4,6,13,5,3,4,9,7,5
U,2,6,3,4,1,7,5,14,5,7,12,8,3,9,0,8
T,4,5,6,4,5,7,9,4,8,7,7,8,3,10,7,7
D,6,10,8,7,5,8,7,4,7,11,5,6,3,8,3,8
F,3,6,5,4,3,7,9,3,5,12,6,5,2,9,2,7
E,3,7,3,5,2,3,6,6,11,7,7,15,0,8,7,7
B,2,3,3,1,2,8,7,2,5,10,5,6,2,8,4,9
H,7,9,10,6,8,8,7,3,6,10,5,8,3,8,3,8
M,4,8,4,6,3,7,7,12,1,7,9,8,8,6,0,8
K,3,7,5,5,5,6,6,3,4,6,6,9,5,8,7,9
D,4,8,4,6,2,5,6,10,8,5,4,5,3,8,4,8
K,4,10,6,8,7,5,5,5,3,6,6,9,3,5,8,9
L,4,9,5,7,4,3,3,5,7,1,0,7,0,6,0,6
W,5,5,7,8,4,7,8,5,2,7,8,8,9,9,0,8
B,3,9,5,7,5,8,7,4,7,6,6,5,6,8,5,9
V,3,3,4,2,1,4,12,3,3,10,11,7,2,11,1,8
D,3,4,5,3,3,7,7,5,6,10,5,6,3,7,4,9
K,3,6,5,5,4,11,5,3,3,10,3,8,4,6,6,12
Q,1,0,1,1,0,8,7,7,3,6,6,9,2,8,3,8
Y,4,8,6,6,6,8,7,5,4,7,8,8,6,8,7,4
L,8,15,8,9,5,7,4,3,4,12,7,11,4,7,7,8
S,2,4,3,3,2,8,7,3,7,10,5,8,1,9,4,8
V,1,3,2,2,1,7,12,3,3,8,11,8,2,11,1,8
Z,3,5,6,4,3,7,7,2,10,12,6,8,1,8,5,7
C,7,13,5,8,4,7,9,4,4,9,7,9,3,9,8,11
A,5,11,8,8,5,8,2,2,3,6,1,7,3,7,4,7
R,2,4,4,3,2,8,7,3,5,10,4,7,2,7,3,10
B,4,5,4,8,4,6,7,9,7,7,6,7,2,8,9,10
V,4,5,5,4,2,4,12,3,3,10,11,7,2,10,1,8
Y,5,8,5,6,2,4,11,3,8,12,11,4,1,10,2,4
M,5,5,5,7,4,8,7,12,2,7,9,8,8,6,0,8
M,6,9,7,4,4,7,3,3,2,8,4,10,7,3,1,9
Y,5,10,8,8,3,6,10,1,9,9,12,8,1,11,2,7
U,2,4,3,2,1,7,8,6,6,7,9,8,3,9,1,8
Z,4,8,6,6,4,8,6,2,9,11,4,10,2,7,7,9
F,7,11,10,8,7,7,9,3,6,12,7,6,2,9,2,7
A,4,9,5,7,4,7,5,3,0,7,1,8,2,7,1,8
U,4,9,5,7,2,8,5,14,5,6,14,8,3,9,0,8
F,5,11,5,8,2,1,13,5,4,12,10,6,0,8,2,5
W,7,10,7,5,4,5,8,1,3,8,10,8,9,11,2,6
S,4,7,5,5,3,9,7,5,9,11,3,7,2,6,5,11
Y,5,9,8,6,4,6,9,1,7,7,12,9,1,11,2,8
A,2,4,4,6,2,8,3,3,3,7,1,8,3,6,3,8
K,6,10,8,8,6,6,8,5,7,6,5,10,4,8,5,9
I,4,6,6,7,5,9,8,5,6,7,6,8,3,9,8,8
T,6,11,8,9,6,6,7,8,7,8,9,7,3,9,6,10
M,10,12,10,7,5,11,12,6,4,4,7,9,8,12,2,7
U,8,12,7,6,4,5,6,5,4,7,7,10,7,8,4,10
X,2,1,3,3,2,8,7,3,9,6,6,6,2,8,5,8
P,8,9,6,4,3,8,8,5,4,11,4,6,5,9,4,8
B,4,8,5,6,5,10,6,3,6,10,4,7,3,8,4,11
E,4,6,6,4,3,6,8,2,9,11,8,9,2,8,4,7
H,3,3,4,4,2,7,8,14,1,7,5,8,3,8,0,8
V,1,1,2,1,0,7,9,4,2,7,13,8,2,10,0,8
I,0,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8
D,5,10,7,7,5,11,6,3,8,12,3,7,6,8,4,9
W,5,7,5,5,4,6,10,5,2,9,6,6,6,12,2,5
X,5,9,5,5,2,11,6,3,7,9,3,7,3,9,4,10
Q,4,4,5,5,3,9,10,7,6,5,8,9,3,8,5,9
E,6,11,8,8,9,6,6,3,6,8,7,12,5,9,9,8
Y,3,4,5,6,1,8,10,2,2,6,13,8,2,11,0,8
T,1,0,1,0,0,7,13,1,4,7,10,8,0,8,0,8
M,5,10,7,5,4,12,3,5,2,11,1,10,7,2,1,8
L,5,10,7,8,5,5,4,1,8,7,2,11,0,7,3,7
C,5,9,6,7,4,5,7,6,9,7,6,13,1,8,4,9
L,1,0,1,0,0,2,2,5,4,1,3,5,0,8,0,8
P,5,11,7,8,6,7,7,7,5,8,7,8,2,9,7,9
B,5,9,8,7,6,11,6,2,7,11,3,7,4,6,6,12
B,4,8,6,6,5,8,7,5,6,9,6,6,2,8,7,8
F,5,6,6,7,7,7,9,5,6,8,5,7,5,8,10,5
T,3,10,5,7,1,7,15,0,6,7,11,8,0,8,0,8
Z,2,2,3,3,2,7,7,5,9,6,6,8,1,8,7,8
K,4,8,5,6,4,6,7,4,7,6,5,8,7,8,5,9
K,5,6,7,6,6,10,6,3,4,10,3,8,5,6,6,12
M,4,7,6,5,8,11,7,3,4,8,4,7,5,5,2,6
Z,6,7,4,11,4,8,7,4,3,11,6,7,3,9,10,6
G,5,9,7,8,8,8,6,6,4,7,6,9,10,10,9,9
E,3,9,4,7,2,3,8,6,10,7,6,15,0,8,7,7
G,5,9,5,6,3,6,7,7,7,10,7,10,2,9,4,9
C,5,9,7,8,8,5,6,4,5,7,6,11,5,11,8,11
F,5,7,7,5,4,8,9,2,6,14,5,4,3,8,3,7
W,8,9,11,8,12,7,8,5,6,7,6,8,10,7,9,6
S,5,12,4,6,2,9,3,4,4,9,2,8,3,6,4,8
W,5,8,7,6,5,8,8,4,1,7,9,8,7,11,0,8
N,5,9,8,6,4,11,6,4,5,10,1,4,5,8,1,7
E,5,9,5,6,3,3,8,6,11,7,6,14,0,8,8,7
M,5,10,8,8,7,8,6,6,5,6,8,8,8,6,2,7
M,5,6,8,4,4,7,6,3,5,9,7,8,7,5,2,8
T,1,0,2,0,0,7,14,2,3,7,10,8,0,8,0,8
A,3,7,5,5,3,10,3,2,2,8,2,10,3,5,2,8
V,1,1,2,2,1,7,12,2,2,7,11,8,2,11,0,8
L,3,6,4,4,2,5,3,6,9,2,2,4,1,6,1,5
G,5,9,7,8,8,8,8,6,3,7,6,9,8,11,9,11
I,3,9,4,7,3,7,7,0,6,13,6,8,0,8,1,8
X,5,7,7,5,6,8,6,2,5,6,7,7,3,10,10,8
I,3,11,4,8,2,7,7,0,8,14,6,8,0,8,1,8
J,2,7,2,5,1,12,3,9,4,13,4,12,1,6,0,8
K,4,3,4,5,1,4,7,8,1,7,6,11,3,8,3,11
X,5,10,7,7,6,8,7,3,5,6,7,6,4,10,11,8
S,3,4,5,3,2,8,6,3,7,10,4,8,1,8,4,10
L,1,0,1,0,0,2,1,6,4,0,3,4,0,8,0,8
Y,2,1,4,2,1,7,11,1,7,7,11,8,1,11,2,8
X,5,11,8,8,5,11,7,1,8,10,2,6,4,9,4,11
Q,3,6,4,8,4,8,8,5,2,8,8,10,3,9,5,8
R,5,5,6,8,3,6,11,9,4,7,3,8,3,7,6,11
E,4,4,4,6,3,3,8,6,11,7,5,14,0,8,7,7
N,4,4,5,6,2,7,7,14,2,4,6,8,6,8,0,8
B,3,6,5,4,5,9,7,4,3,6,7,7,5,10,7,7
G,5,9,7,6,8,8,8,5,2,6,6,9,7,8,6,11
K,4,4,6,3,3,6,7,2,7,10,7,10,3,8,3,7
E,3,8,5,6,4,7,7,5,8,7,6,9,6,8,6,9
O,5,7,7,6,5,7,5,5,5,9,5,9,3,6,5,6
Z,5,9,6,7,4,6,8,3,10,12,7,8,1,9,6,7
L,6,10,6,5,3,7,5,4,4,12,9,12,3,9,6,9
G,3,7,5,5,5,9,7,5,2,7,6,10,5,9,4,10
P,4,5,5,7,6,9,7,4,3,6,7,8,6,11,5,4
Y,4,7,4,5,2,4,10,2,9,11,10,5,0,10,3,4
S,5,10,8,8,9,6,7,3,2,8,6,6,3,8,11,2
Y,5,10,6,8,6,8,7,6,5,5,9,8,3,9,10,7
M,7,8,10,6,6,9,6,2,5,9,6,8,8,6,2,8
J,2,5,3,3,1,10,6,2,6,12,4,8,1,6,1,7
I,1,5,0,6,0,7,7,4,4,7,6,8,0,8,0,8
U,8,13,8,7,5,7,6,5,5,7,8,8,5,7,3,8
H,4,6,6,4,3,7,7,3,6,10,6,8,3,8,3,7
E,2,4,4,3,2,7,7,2,8,11,6,9,2,8,4,8
N,4,9,5,7,3,7,7,15,2,4,6,8,6,8,0,8
D,2,3,3,2,2,9,6,3,5,10,4,6,2,8,2,8
I,4,7,5,5,3,8,6,2,7,7,6,7,0,9,4,7
B,6,10,9,8,9,8,7,6,6,9,7,6,6,10,9,10
J,5,9,3,12,3,10,7,2,3,11,3,5,3,8,6,9
E,4,7,5,5,4,7,7,5,9,7,7,9,3,8,6,8
J,5,9,7,7,4,8,7,3,6,15,5,8,0,6,1,7
M,3,1,4,3,3,8,6,6,4,6,7,8,7,5,2,7
O,2,3,3,1,1,8,7,7,5,7,6,8,2,8,3,8
U,8,11,7,6,4,7,5,5,4,6,8,8,5,6,3,8
W,7,7,7,5,4,2,10,3,3,11,11,8,7,10,2,6
O,3,6,4,4,2,7,7,7,5,7,5,9,3,8,3,7
S,2,8,3,6,3,7,7,5,7,5,6,7,0,8,8,8
F,4,7,5,5,5,6,9,2,6,10,9,6,5,10,3,7
N,6,10,5,6,3,7,9,4,5,4,5,10,5,10,2,7
H,3,5,5,3,3,7,8,6,7,7,5,8,3,8,3,8
T,5,10,5,5,2,6,9,2,7,12,7,5,2,10,3,5
G,3,2,5,4,3,7,6,6,6,6,6,10,2,9,4,9
Y,3,7,4,5,1,7,10,2,2,7,13,8,1,11,0,8
Z,1,0,2,1,0,7,7,2,10,9,6,8,0,8,6,8
L,2,4,3,3,1,7,4,1,7,9,3,10,0,6,3,8
O,4,7,5,5,4,7,9,8,4,7,8,8,3,8,3,8
Z,5,10,7,8,4,7,7,3,11,11,6,8,1,8,7,8
E,6,9,8,6,6,8,7,1,8,11,5,8,3,8,5,10
O,3,6,4,4,2,7,7,7,5,10,6,8,3,8,3,8
H,3,6,3,4,3,8,8,12,1,7,5,8,3,8,0,8
Q,5,8,6,9,7,8,7,6,3,8,8,10,3,9,5,8
H,6,11,8,8,9,6,8,6,7,7,6,10,6,8,4,8
S,4,8,5,6,4,7,7,5,8,5,6,10,1,10,9,9
Y,4,6,5,8,6,10,12,5,4,6,7,7,5,10,8,5
A,3,8,5,5,2,10,5,3,1,8,1,9,2,7,2,9
G,2,0,2,1,1,8,6,6,6,6,5,9,1,7,5,10
C,4,7,5,5,2,6,9,7,7,13,8,7,2,11,2,6
N,3,5,5,3,2,5,9,3,4,10,8,8,5,8,0,7
S,5,6,6,8,3,8,7,6,9,4,6,8,0,8,9,8
O,6,10,7,8,5,8,7,8,6,10,5,9,4,9,5,6
Z,3,5,4,8,3,12,4,3,5,10,2,7,2,7,5,12
R,4,10,6,7,5,7,8,6,5,8,5,9,3,6,6,11
D,4,9,5,7,6,7,8,6,6,8,7,5,3,8,3,7
I,4,5,5,6,4,8,9,4,4,7,6,8,3,7,8,7
Q,5,8,6,9,6,8,7,6,3,9,8,10,3,8,6,8
O,6,8,8,6,5,7,7,8,4,7,6,8,3,8,3,7
P,6,11,6,8,3,4,13,8,1,10,6,3,1,10,4,8
D,2,2,3,3,2,8,7,6,7,6,6,4,2,8,3,6
L,2,6,4,4,2,6,4,2,10,7,1,10,0,7,3,7
N,6,9,8,8,9,7,8,4,4,7,5,7,7,9,6,7
D,2,3,4,1,2,10,6,3,6,10,3,6,2,8,2,9
G,3,7,4,5,3,7,6,7,7,7,4,11,1,8,5,11
N,3,7,3,5,3,8,8,12,1,6,6,8,5,9,0,8
I,1,1,0,2,0,7,7,1,7,7,6,8,0,8,2,8
X,4,6,5,8,2,7,7,5,4,7,6,8,3,8,4,8
B,5,5,5,8,4,6,8,9,7,7,5,7,2,8,9,10
C,2,3,2,2,1,4,8,4,7,10,9,12,1,8,2,7
I,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8
P,4,8,6,6,4,4,12,6,4,11,8,3,0,10,4,7
Y,2,2,4,3,2,7,11,1,7,7,11,8,1,11,2,8
S,4,10,4,8,2,8,7,6,9,4,6,6,0,8,9,8
G,3,6,4,4,2,6,7,6,6,10,7,10,2,10,4,9
L,2,4,4,3,2,6,4,1,9,7,2,10,0,7,2,8
B,2,6,3,4,2,7,7,9,6,7,6,7,2,8,8,9
C,5,10,6,8,5,5,7,8,7,11,8,13,3,12,5,8
X,2,7,3,4,1,7,7,4,4,7,6,8,3,8,4,8
A,4,11,6,8,2,8,5,3,1,7,0,8,3,7,2,8
W,7,8,7,6,7,4,10,2,3,9,8,8,7,12,2,6
W,7,10,7,7,6,4,10,2,3,9,9,8,7,11,2,6
M,5,11,8,8,11,10,7,3,4,8,4,7,12,6,7,4
S,6,11,7,8,4,7,8,4,8,11,8,7,2,10,5,6
D,2,3,3,2,2,7,7,6,6,7,6,6,5,8,2,7
Y,8,11,7,6,4,8,6,4,6,9,6,5,3,10,6,5
Q,2,2,3,4,2,8,9,5,2,5,8,10,2,9,5,9
B,6,9,8,7,7,7,8,6,5,6,5,6,4,9,7,6
F,6,11,8,8,6,6,10,1,6,13,7,6,2,10,2,7
R,3,5,5,4,3,9,7,3,5,10,4,6,3,7,4,9
J,4,9,4,7,2,8,9,2,4,13,4,5,1,8,6,8
W,7,7,10,6,10,8,7,5,5,7,5,8,10,9,8,7
N,5,7,7,5,6,6,8,3,4,8,7,9,6,9,5,5
U,5,11,7,8,10,9,6,4,4,6,7,7,8,7,6,5
N,4,6,7,4,3,9,9,2,5,11,3,5,6,9,2,7
V,4,10,6,7,3,7,9,3,2,6,12,8,3,10,0,8
D,4,7,5,5,4,8,8,4,5,10,6,4,3,8,3,7
A,3,6,5,4,2,11,3,2,2,9,2,9,2,6,3,9
H,2,1,3,2,2,7,7,6,6,7,6,9,3,9,3,8
F,2,3,3,2,1,6,11,3,5,12,7,4,1,9,1,7
A,3,9,5,6,4,12,2,2,2,10,2,9,2,6,3,8
O,4,10,6,8,4,8,6,9,5,7,5,8,3,8,3,8
I,2,7,3,5,2,7,9,0,6,13,6,7,0,8,1,7
D,2,4,4,3,2,8,7,4,6,10,4,6,2,8,3,8
Y,5,6,6,4,3,3,10,2,7,11,11,6,1,10,2,4
H,6,9,6,4,3,5,9,4,6,9,7,9,5,7,3,7
K,5,9,7,6,5,9,6,1,6,10,4,8,3,8,4,10
K,7,11,10,8,6,8,6,2,7,10,4,9,5,6,5,8
I,0,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8
L,3,6,4,4,3,9,4,1,6,9,2,9,1,6,2,9
D,5,10,5,5,3,12,4,3,5,11,2,7,4,7,3,12
Q,7,10,9,8,8,8,3,8,4,6,6,8,4,8,6,9
N,2,0,2,1,1,7,7,12,1,5,6,8,5,8,0,8
Q,6,9,6,11,6,7,7,8,5,9,8,8,4,9,7,9
L,3,7,5,5,2,6,4,1,9,8,2,11,0,8,2,8
O,3,1,4,2,2,7,8,7,5,7,7,8,2,8,3,8
F,2,3,2,1,1,5,11,4,4,10,8,5,1,9,3,7
Y,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8
K,2,1,2,1,0,5,7,8,1,7,6,11,3,8,2,11
I,1,9,2,6,3,8,7,0,7,7,6,7,0,8,2,7
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
G,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,10
H,3,3,5,1,2,6,7,3,6,10,9,9,3,8,3,6
Q,2,5,3,7,4,8,6,7,3,7,5,11,2,9,4,10
U,4,8,6,6,3,4,8,7,7,9,11,11,3,9,0,8
D,2,6,4,4,3,7,7,5,6,7,6,5,3,8,3,7
D,5,11,7,8,11,8,8,5,5,7,6,6,7,8,8,6
M,3,6,5,4,5,7,7,6,4,7,5,8,5,9,6,8
X,3,1,4,3,2,7,8,3,9,6,6,7,3,9,6,6
M,5,9,7,6,7,7,7,2,4,9,8,9,7,6,2,8
B,4,9,5,7,5,8,6,7,7,6,6,6,2,8,7,10
R,3,8,4,5,2,5,11,8,3,7,3,8,3,7,6,11
S,2,6,3,4,3,8,7,7,6,7,8,9,2,10,8,8
A,4,10,7,7,2,8,7,3,0,7,0,8,3,7,2,8
J,3,8,4,6,2,13,3,6,4,13,3,11,0,7,0,8
D,4,9,4,7,3,6,7,11,9,6,5,6,3,8,4,8
Y,3,10,5,7,3,7,10,1,7,6,12,8,1,11,2,8
G,3,8,5,6,2,7,5,7,8,6,5,10,1,8,6,11
K,5,7,7,5,4,3,9,3,6,10,11,11,3,8,3,6
Z,3,8,4,6,2,7,7,4,14,9,6,8,0,8,8,8
P,3,7,5,5,2,6,11,4,4,13,5,2,0,10,3,9
R,5,8,7,6,6,9,7,4,5,10,4,6,3,7,4,10
F,4,5,5,6,5,7,9,4,4,7,6,7,4,9,8,8
G,4,8,5,6,2,8,7,8,8,6,6,9,2,7,6,11
Q,5,7,6,8,5,9,8,7,2,4,7,10,3,8,6,10
E,4,7,6,5,5,8,5,6,3,7,6,9,4,8,8,10
T,8,11,7,6,3,5,10,3,9,13,7,5,2,9,3,4
M,5,11,6,8,4,7,7,12,2,7,9,8,9,6,0,8
B,9,15,7,9,5,7,7,5,5,10,6,8,6,6,9,10
Q,2,2,3,3,2,8,8,6,2,5,6,9,2,9,5,10
E,5,9,4,4,2,7,8,4,7,9,6,9,1,9,7,9
Z,3,6,4,4,2,7,7,4,14,9,6,8,0,8,8,8
W,5,10,7,8,8,7,9,6,4,8,8,7,10,9,4,10
X,3,4,5,3,2,8,7,4,9,6,6,9,4,7,7,9
O,9,15,7,8,4,8,6,5,6,8,3,8,6,9,5,8
W,7,7,7,5,5,4,11,3,3,9,9,7,7,11,2,6
Z,7,11,9,8,5,8,6,3,10,12,4,10,2,9,6,10
Y,10,10,8,14,6,9,6,5,5,4,12,6,5,10,6,7
M,6,10,9,7,7,10,6,2,5,9,3,6,9,8,2,9
X,3,9,5,7,4,7,7,3,8,5,7,9,3,8,6,8
N,3,7,5,5,4,7,9,6,4,7,6,7,6,8,3,8
H,2,3,4,1,2,8,8,3,5,10,5,7,3,8,3,7
N,5,9,5,7,3,7,7,15,2,4,6,8,6,8,0,8
X,5,11,6,8,4,8,7,4,4,7,6,8,3,8,4,8
A,5,11,7,8,7,8,5,7,4,8,6,8,6,8,7,4
P,3,4,3,3,2,5,10,4,4,10,8,4,1,10,3,7
F,2,1,2,3,2,5,10,4,5,10,9,6,1,10,3,7
G,3,2,4,4,3,6,7,6,6,7,6,10,3,7,4,9
P,1,1,2,1,1,5,10,8,3,9,6,5,1,9,3,8
T,2,6,3,4,2,7,12,3,7,7,11,8,1,11,1,7
Y,7,9,7,7,4,4,9,2,7,10,11,6,2,12,3,4
R,3,3,3,4,2,5,10,8,4,7,4,8,2,7,5,11
N,3,8,4,6,2,7,7,14,2,5,6,8,6,8,0,8
S,4,10,5,8,5,7,7,5,8,5,6,8,0,8,9,7
P,4,8,6,6,3,6,11,3,6,14,7,3,0,9,2,8
F,4,10,6,7,4,4,12,4,4,13,8,5,2,10,2,6
S,4,5,5,7,3,8,8,6,10,5,6,7,0,8,9,7
I,7,11,6,6,3,8,8,3,6,13,4,5,2,8,6,10
V,4,4,5,3,2,4,12,3,3,10,11,7,2,10,1,8
T,4,11,6,8,2,7,15,1,6,7,11,8,0,8,0,8
Z,5,11,7,8,3,7,7,4,15,9,6,8,0,8,9,8
R,4,10,4,7,5,5,10,7,3,7,4,9,2,6,5,11
G,3,8,4,6,3,8,8,7,5,6,6,9,2,7,5,11
F,4,7,4,5,2,1,12,4,4,12,11,7,0,8,1,6
B,2,4,3,2,2,9,7,3,5,10,5,7,2,8,4,9
F,5,10,5,8,4,1,12,4,4,12,10,7,0,8,2,6
G,5,6,5,4,3,6,7,6,5,10,8,10,2,9,4,9
P,2,3,4,2,2,7,9,3,4,12,5,4,1,10,2,8
G,4,7,5,5,3,7,6,7,7,11,6,11,2,10,4,9
Q,4,6,6,4,5,8,5,7,4,7,6,6,5,7,6,9
O,4,7,5,5,3,7,8,7,6,9,8,8,3,8,3,8
Z,3,7,4,5,3,6,7,5,9,7,7,9,1,9,7,8
X,5,7,8,5,4,7,7,1,8,10,7,9,3,8,3,8
S,2,3,4,2,1,7,8,3,7,10,7,7,1,8,5,6
O,3,6,4,4,3,8,8,7,4,7,7,7,3,8,2,8
T,2,4,3,3,2,7,12,3,6,7,11,9,2,11,1,8
S,3,8,4,6,3,9,7,7,6,7,6,8,2,9,9,8
O,2,4,3,3,2,8,7,6,3,9,5,8,2,8,2,8
P,6,10,9,8,7,8,9,5,4,11,4,4,2,10,3,8
B,5,11,7,8,9,8,8,6,6,7,6,6,2,8,6,9
Q,4,5,4,6,4,8,8,5,3,8,9,9,3,10,5,7
T,2,4,3,2,1,6,11,2,7,11,9,5,1,10,2,5
Q,8,12,7,6,4,10,3,5,7,12,3,11,3,7,8,11
Y,4,6,6,4,5,9,5,7,5,6,9,7,3,9,8,5
Q,8,9,11,8,9,6,4,4,6,5,4,7,4,6,6,7
L,3,5,3,3,2,4,3,5,6,2,2,5,1,6,0,6
V,5,10,7,8,9,8,5,6,3,7,7,8,8,7,4,7
X,5,5,6,8,2,7,7,5,4,7,6,8,3,8,4,8
E,4,8,5,6,5,8,7,6,3,6,6,10,3,8,7,9
W,5,10,7,8,7,6,11,2,2,7,8,9,7,12,1,8
B,4,7,6,5,5,9,7,4,5,9,5,6,2,8,5,9
P,4,8,6,6,4,5,13,7,2,12,6,2,1,10,3,8
J,7,13,6,10,4,8,9,2,3,12,5,5,2,9,8,9
U,5,8,5,6,3,3,8,5,7,9,9,9,3,9,2,5
X,4,8,5,5,1,7,7,4,4,7,6,8,3,8,4,8
B,6,11,9,8,9,8,8,4,6,10,5,6,5,6,7,10
C,6,11,7,8,3,5,7,7,11,7,6,12,1,9,4,8
R,5,10,7,8,7,6,8,5,6,6,5,8,3,6,5,8
O,4,9,5,7,5,8,9,8,4,7,8,7,4,7,4,9
V,5,6,5,4,2,4,12,5,4,11,11,6,3,10,1,8
P,7,11,10,8,6,9,9,4,6,12,3,3,2,10,4,9
R,1,0,2,1,1,6,9,7,3,7,5,8,2,7,4,11
I,2,5,3,4,1,7,7,0,8,13,6,8,0,8,1,8
P,2,3,4,2,2,7,10,5,3,10,4,3,1,10,3,8
A,5,10,7,7,4,9,5,3,0,8,1,8,2,7,1,8
B,7,10,9,7,7,9,6,4,7,9,5,6,2,8,7,10
C,5,10,6,8,3,3,8,5,8,11,11,13,1,8,3,7
W,6,5,8,8,4,5,8,5,2,7,8,8,9,9,0,8
Q,9,13,8,7,5,8,6,4,10,11,4,10,3,7,9,9
Z,4,6,6,4,3,6,9,2,8,11,8,6,1,8,6,5
I,1,9,0,6,1,7,7,5,3,7,6,8,0,8,0,8
G,1,3,2,1,1,7,7,5,5,10,7,10,2,9,3,10
T,2,1,2,2,1,8,11,4,5,6,10,7,2,11,1,7
I,2,5,3,3,1,7,8,1,7,14,6,7,0,8,1,7
C,6,9,6,6,4,7,8,6,8,13,7,10,3,11,4,6
I,6,11,8,8,5,10,5,2,6,6,7,4,0,10,4,7
N,5,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8
V,4,8,4,6,2,4,11,4,4,10,11,7,3,10,1,8
K,5,5,6,7,2,4,8,8,2,7,4,11,3,8,2,11
Q,5,9,5,5,3,9,6,4,6,10,5,7,3,8,9,9
M,5,9,6,7,6,7,5,11,1,7,9,8,9,5,2,9
P,4,8,4,5,2,4,15,8,1,12,6,2,0,9,4,8
V,3,5,4,3,1,4,12,3,3,10,11,7,2,11,1,8
F,4,7,4,5,2,1,12,4,4,12,10,7,0,8,2,6
O,5,9,6,6,3,8,6,8,8,7,5,9,3,8,4,8
Q,4,5,5,4,4,8,4,4,5,7,4,8,4,6,4,8
K,3,6,5,4,3,3,9,2,6,10,11,11,3,8,2,6
R,7,9,6,4,3,8,7,5,5,9,4,9,6,5,6,11
F,2,4,3,3,1,6,10,2,5,13,7,5,1,9,1,7
X,3,5,6,3,3,8,6,1,8,10,5,8,2,8,3,8
P,4,11,5,8,4,5,10,8,4,9,7,3,2,10,4,7
M,5,6,8,5,8,9,8,5,4,7,6,7,11,9,7,5
E,1,1,1,1,1,4,7,5,8,7,6,13,0,8,6,9
K,5,10,6,8,5,3,8,7,3,7,5,11,3,8,3,11
W,4,7,6,5,3,7,8,4,1,7,8,8,8,9,0,8
P,6,9,8,7,5,6,14,6,1,11,5,2,1,11,4,7
W,5,8,7,6,6,7,11,2,2,7,8,8,7,12,1,8
Q,4,5,4,6,4,8,5,6,5,9,6,9,3,8,5,8
Z,6,10,8,8,5,8,6,3,9,12,4,9,3,6,8,9
B,1,0,1,0,0,7,7,6,4,7,6,7,1,8,5,9
Q,3,6,4,8,4,8,6,8,4,5,6,9,3,8,6,10
D,6,14,6,8,4,11,4,2,6,9,3,7,5,7,4,12
E,4,7,4,5,3,3,7,5,10,7,6,14,0,8,6,8
J,4,9,6,7,4,8,6,6,6,8,6,8,3,7,4,6
Q,5,7,7,10,10,8,6,5,1,6,6,9,8,9,6,12
O,4,8,5,6,4,7,7,8,4,7,6,10,3,8,3,8
A,4,8,6,6,4,11,3,2,2,9,2,9,4,4,3,8
G,5,10,7,8,5,6,6,7,6,6,6,10,2,8,5,8
U,4,3,4,5,1,7,5,12,5,7,15,8,3,9,0,8
U,6,11,6,8,4,3,8,5,7,11,11,9,3,9,2,6
X,4,5,7,3,3,6,8,1,9,11,8,9,3,8,3,7
W,6,8,9,7,11,9,6,5,5,8,6,8,11,12,8,6
C,5,6,6,5,5,6,7,4,5,7,6,11,5,10,8,11
I,2,6,3,4,2,7,7,0,7,13,6,8,0,8,1,8
O,4,9,5,7,5,8,6,7,4,9,4,8,3,8,2,8
X,3,4,6,3,2,7,7,1,9,11,6,8,2,8,3,8
V,4,10,6,8,3,8,9,4,2,6,13,8,3,10,0,8
W,4,5,6,8,4,7,7,4,2,7,8,8,9,9,0,8
A,3,5,6,3,2,8,2,2,2,6,1,8,2,6,2,7
E,3,4,3,3,3,7,7,5,8,7,6,8,2,8,6,9
P,2,4,4,2,2,8,10,4,3,11,4,3,1,10,3,8
L,3,3,3,5,1,0,1,6,6,0,1,5,0,8,0,8
O,2,2,3,4,2,7,7,8,4,7,6,8,2,8,3,8
P,4,10,6,8,5,6,9,6,5,9,7,3,2,10,4,6
B,5,8,7,6,6,9,7,3,5,10,5,6,2,8,5,9
I,4,10,6,8,7,10,8,2,6,9,4,4,4,8,7,4
E,4,10,5,7,5,6,7,6,9,6,4,10,3,8,6,9
V,5,11,8,8,5,8,12,2,3,4,10,9,4,12,2,8
K,5,7,7,6,5,9,5,2,4,8,4,9,5,8,7,11
S,6,10,8,8,9,8,9,5,4,8,5,6,4,9,11,8
D,6,11,6,8,4,5,7,10,10,7,6,6,3,8,4,8
B,4,11,5,8,7,6,8,9,6,7,5,7,2,7,8,10
N,9,13,8,7,4,6,10,4,5,4,5,10,6,10,2,7
C,2,3,3,2,1,6,8,7,7,8,7,12,1,9,4,10
W,5,11,8,8,8,7,11,2,2,6,8,8,7,12,1,8
A,2,6,4,4,3,8,3,2,1,7,2,8,1,7,2,8
A,3,9,5,6,3,10,3,2,2,8,2,10,3,5,3,7
N,3,4,4,7,2,7,7,14,2,5,6,8,6,8,0,8
Z,4,5,5,8,2,7,7,4,14,10,6,8,0,8,8,8
H,4,6,5,4,5,7,7,5,6,7,6,7,6,8,3,8
A,2,8,4,5,2,7,5,3,1,6,0,8,2,7,2,7
G,3,4,4,3,2,6,7,5,4,9,7,9,2,8,4,9
U,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8
W,4,7,6,5,5,10,11,2,2,5,9,8,6,12,1,8
M,2,3,4,2,2,6,6,4,3,9,9,10,5,6,1,7
V,3,6,5,4,1,8,8,4,2,7,14,8,3,9,0,8
Q,5,10,5,6,3,9,6,4,7,11,4,8,3,8,8,11
W,6,7,6,5,4,3,11,2,2,9,9,8,6,11,1,7
N,4,9,6,7,4,6,9,6,5,7,7,8,6,9,2,6
D,3,4,4,3,3,7,7,6,7,7,6,5,2,8,3,7
A,3,5,6,4,2,9,2,2,2,8,1,8,2,6,2,7
R,2,6,3,4,3,5,10,7,4,7,4,9,2,6,5,11
L,3,9,4,7,3,7,4,2,8,7,1,8,1,6,2,7
U,3,3,4,2,2,6,8,5,7,10,8,8,3,10,3,6
N,5,11,8,8,6,7,9,6,5,6,6,7,6,9,1,7
D,4,8,5,6,4,7,7,7,7,7,6,4,3,8,3,7
H,2,3,4,2,2,7,8,3,5,10,7,8,3,8,2,7
V,2,6,4,4,1,6,8,4,2,8,13,8,3,10,0,8
H,8,10,8,5,5,8,6,3,5,10,6,7,7,11,5,8
F,4,9,6,7,4,5,11,4,6,11,10,5,2,10,3,5
N,5,10,6,7,5,7,9,6,4,6,6,6,6,9,2,7
Q,3,3,4,4,3,8,7,6,3,8,6,9,2,9,3,7
L,3,7,4,5,3,9,3,1,6,9,2,9,1,6,2,9
O,6,10,7,8,5,8,6,8,6,9,4,8,3,8,3,8
D,2,3,3,2,2,7,7,5,5,10,5,6,3,8,3,9
D,5,10,7,7,4,9,7,6,8,11,5,5,3,8,4,8
F,5,7,7,5,3,6,10,3,6,13,7,4,2,10,2,7
W,9,10,9,8,9,6,10,3,3,8,7,6,11,11,4,5
Q,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8
E,5,11,5,8,3,3,8,6,12,7,6,15,0,8,7,6
S,4,8,5,6,4,6,8,3,6,10,7,8,2,8,5,6
W,13,13,12,7,5,1,9,5,3,12,13,9,8,9,0,7
U,3,3,4,1,1,5,8,5,6,10,9,9,3,10,2,6
D,2,7,4,5,4,7,7,6,5,7,6,4,3,8,2,7
P,3,8,4,6,3,5,10,5,5,10,8,4,1,10,4,7
A,2,4,4,3,1,12,3,3,2,10,1,9,1,6,2,9
C,3,5,4,3,2,5,8,5,7,12,9,11,1,10,2,7
Y,2,2,4,3,2,8,10,1,7,5,11,8,1,11,2,8
L,1,3,2,2,1,6,5,2,8,7,3,9,0,7,2,8
I,3,8,4,6,2,7,7,0,8,14,6,8,0,8,1,7
A,2,5,4,3,2,8,2,2,2,8,2,8,2,6,2,7
F,5,7,7,5,3,9,8,2,7,14,4,4,2,9,3,9
R,6,10,8,8,6,10,7,3,6,10,2,6,5,6,5,10
G,3,5,4,4,2,6,6,6,5,9,7,11,2,9,4,10
Y,2,1,3,1,0,7,10,3,1,7,13,8,1,11,0,8
N,5,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8
R,3,6,5,4,4,6,8,5,5,6,4,8,3,6,4,8
C,2,1,3,2,1,6,7,6,10,7,6,14,0,8,4,9
Y,7,11,8,8,6,5,8,1,8,8,9,5,5,11,7,4
I,2,7,3,5,2,7,9,0,6,13,6,7,0,8,1,7
F,6,11,8,8,6,9,8,2,6,12,4,6,5,8,4,9
N,5,9,7,7,4,5,9,3,4,10,8,8,5,8,1,7
F,7,12,6,6,3,7,8,2,7,11,6,6,2,9,6,5
Q,6,6,7,9,4,8,6,8,7,5,7,8,3,8,5,9
V,3,4,4,3,2,5,12,3,3,9,11,7,2,11,1,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
I,4,11,5,8,4,7,7,0,7,13,6,8,0,8,1,8
K,4,9,5,7,2,3,6,8,3,7,7,12,4,8,3,10
Z,3,11,4,8,2,7,7,4,14,10,6,8,0,8,8,8
M,3,3,4,2,2,7,6,6,4,6,7,8,6,5,2,8
D,6,11,8,8,7,7,7,7,8,7,6,5,6,8,4,7
Q,4,5,5,7,3,8,6,9,7,6,6,9,3,8,4,8
V,5,11,7,8,4,4,11,3,4,10,12,9,2,10,1,8
L,3,7,4,5,2,4,3,5,8,1,1,5,0,7,1,5
Q,3,3,4,4,3,8,8,6,2,5,7,9,3,9,5,9
K,5,9,8,7,5,2,8,2,7,11,12,12,3,8,3,5
W,8,9,10,8,13,6,8,6,5,6,6,8,10,10,9,9
T,5,7,5,5,3,6,11,3,7,11,9,5,2,11,2,4
J,3,9,4,7,2,10,6,1,9,12,3,6,0,7,1,7
L,3,9,3,7,1,0,1,5,6,0,0,7,0,8,0,8
J,2,10,3,7,1,13,3,8,4,14,3,11,1,6,0,8
O,3,6,5,4,5,8,7,5,1,7,6,8,8,8,4,9
P,2,1,3,2,2,5,10,4,4,9,7,4,1,9,3,7
R,5,9,8,7,5,10,7,3,7,10,3,6,3,6,4,11
F,4,9,4,6,2,1,13,5,4,12,11,7,0,8,2,6
B,4,9,6,6,6,6,8,7,4,7,5,6,4,8,5,6
J,4,11,5,8,3,10,6,1,7,13,3,7,0,7,0,8
F,3,5,3,3,2,5,12,5,6,11,9,3,1,10,3,5
D,4,9,6,6,9,9,9,4,5,7,6,6,5,8,9,5
M,4,2,5,4,4,9,6,6,4,6,7,6,8,6,2,6
E,3,7,4,5,4,8,7,4,7,7,6,8,6,9,5,10
H,4,7,6,5,4,9,7,7,6,7,6,5,3,8,3,6
Y,5,7,8,10,11,8,9,4,2,6,8,9,4,11,8,8
S,3,7,4,5,4,8,7,7,5,7,6,8,2,8,8,8
M,2,3,4,2,3,7,6,3,4,9,7,8,6,5,1,8
C,4,6,5,5,4,6,7,4,4,7,6,11,4,9,8,10
D,3,5,5,4,3,9,6,4,6,10,4,5,2,8,3,8
B,3,5,4,4,3,7,7,5,5,6,6,6,2,8,6,10
X,2,2,3,3,2,8,7,3,8,6,6,8,2,8,6,8
A,2,2,4,3,2,7,2,2,2,6,2,8,2,7,3,7
X,4,5,8,4,3,8,7,1,9,11,4,7,3,8,3,8
N,3,4,5,3,2,9,8,3,5,10,3,5,4,9,1,7
G,3,5,5,4,3,6,6,6,6,6,6,9,2,8,4,8
X,2,5,4,4,2,7,7,3,9,6,6,8,2,8,6,8
Y,2,4,4,6,4,8,11,3,3,5,8,9,2,11,5,6
P,5,11,7,8,4,7,10,5,5,12,5,3,1,10,3,8
F,3,5,5,6,5,7,9,4,4,7,6,7,4,9,8,9
X,3,9,6,7,5,7,6,2,6,7,5,8,3,6,7,7
C,2,1,2,2,1,6,7,6,10,7,6,14,0,8,4,9
J,5,10,6,8,5,8,5,8,5,8,7,8,2,7,4,6
U,4,9,6,8,7,7,6,5,4,6,6,8,4,8,1,7
E,4,7,5,5,5,8,4,6,3,8,7,10,4,9,7,9
U,7,11,6,6,3,7,6,4,6,4,8,6,6,7,3,6
U,2,3,3,2,1,5,8,5,6,9,8,8,3,9,2,6
Q,3,3,4,4,3,8,7,6,2,5,7,9,3,8,5,9
H,2,3,3,1,2,8,8,3,5,10,6,8,3,8,2,8
Z,2,5,5,3,2,7,8,2,9,12,6,8,1,9,5,7
F,2,3,3,1,1,5,10,4,5,10,9,5,1,9,3,6
W,7,8,7,6,4,2,10,3,4,11,11,9,7,10,1,7
J,2,6,3,4,2,8,6,3,6,12,5,9,1,6,1,6
B,5,9,7,6,6,10,6,2,6,11,3,8,3,8,4,12
G,2,3,3,2,1,7,7,5,6,10,6,10,2,9,3,9
Z,4,9,5,7,2,7,7,4,15,9,6,8,0,8,8,8
C,4,9,5,7,3,4,7,5,6,11,9,14,2,8,3,8
O,4,9,5,7,2,8,7,9,8,7,6,9,3,8,4,8
O,4,8,5,6,3,7,7,8,5,10,7,8,3,8,3,8
E,6,9,8,7,5,7,7,3,9,11,8,9,2,8,4,8
G,3,2,4,3,2,7,6,6,6,6,6,10,2,9,4,9
I,1,3,1,2,0,7,7,2,6,7,6,8,0,8,2,8
P,4,7,6,5,4,8,9,4,4,12,4,3,1,10,3,8
S,2,3,2,2,1,8,8,6,5,7,6,7,2,8,8,8
F,4,7,6,5,4,8,8,2,6,13,5,5,2,10,3,9
U,5,5,6,4,3,4,8,5,8,11,11,9,3,9,2,7
O,5,9,5,7,5,7,7,8,4,9,6,8,3,8,3,8
S,7,11,7,6,3,8,5,4,4,13,7,9,3,9,3,8
C,2,7,3,5,1,6,8,7,8,5,6,13,1,7,4,9
S,2,3,3,2,1,8,7,2,7,10,6,8,1,8,4,8
T,4,6,6,8,2,6,15,1,6,9,11,7,0,8,0,8
B,7,10,9,8,9,7,7,5,5,7,5,7,4,8,7,8
Q,5,7,6,9,6,9,7,7,2,5,7,10,3,8,6,10
V,5,10,5,7,3,2,11,3,3,11,11,8,2,11,1,8
F,7,10,6,5,3,7,8,2,7,11,6,6,2,9,5,6
V,7,13,5,7,3,7,10,6,4,9,9,4,5,12,3,8
W,4,9,6,6,6,11,10,2,3,5,9,7,7,10,1,8
E,3,4,4,6,2,3,8,6,10,7,6,14,0,8,7,7
G,2,3,3,1,1,7,7,5,5,9,7,9,2,8,4,10
L,2,5,3,4,2,4,4,4,8,2,1,7,0,7,1,6
Q,4,5,5,5,4,7,4,5,5,7,5,9,4,5,6,7
E,3,8,4,6,4,8,7,5,9,6,4,8,2,8,6,9
U,1,0,1,0,0,7,6,10,4,7,12,8,3,10,0,8
R,4,7,6,5,6,7,7,3,4,7,6,8,6,9,6,5
P,4,7,5,5,4,5,11,8,3,10,7,3,2,11,3,7
P,3,5,5,8,7,8,11,5,0,8,6,6,4,10,6,8
K,3,2,4,3,3,5,7,4,7,7,6,10,6,8,4,9
N,4,5,5,7,3,7,7,15,2,4,6,8,6,8,0,8
F,6,9,5,4,2,8,8,3,6,12,5,6,2,8,6,7
A,2,1,3,1,1,7,4,2,0,7,2,8,2,6,2,8
A,3,2,5,3,2,8,2,2,2,7,2,8,2,7,2,7
J,3,11,4,8,2,10,6,1,8,11,3,6,0,7,1,7
L,2,3,3,2,1,7,4,2,7,8,2,10,0,7,2,8
G,4,7,6,5,3,6,6,6,7,6,6,10,3,7,4,8
O,6,9,6,7,6,8,6,7,4,9,4,8,3,9,3,7
O,3,9,4,6,3,7,7,9,5,7,6,8,3,8,3,7
E,3,2,3,3,3,7,7,5,7,7,5,9,2,8,5,10
K,3,2,4,4,3,5,7,4,8,7,6,11,3,8,5,9
F,3,8,3,5,1,0,13,4,4,12,11,7,0,8,2,6
E,5,9,7,7,7,6,7,3,6,8,7,11,5,10,9,9
G,6,10,8,7,9,8,8,5,3,6,6,7,9,8,9,14
U,3,4,4,3,2,4,8,5,6,11,10,9,3,9,1,6
K,6,9,8,7,7,5,6,4,7,6,6,12,5,7,7,10
U,6,10,8,7,4,4,9,7,8,9,11,10,3,9,1,8
M,5,9,5,6,6,7,6,10,1,7,8,8,8,4,0,8
O,6,11,9,8,6,8,8,9,5,7,7,5,5,8,4,9
S,5,10,6,7,3,8,7,5,9,5,6,8,1,8,9,8
X,3,5,5,3,2,8,7,1,8,10,5,7,3,8,3,7
J,2,4,4,3,1,9,5,3,5,13,6,11,1,7,0,7
V,6,8,6,6,4,4,11,1,3,8,10,8,4,11,1,7
E,4,8,6,6,6,7,7,3,6,7,7,10,4,9,8,8
I,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8
L,2,3,2,1,1,4,4,4,6,2,2,5,1,7,1,6
D,4,6,5,4,4,10,6,3,6,11,4,6,3,8,2,9
Y,2,4,3,3,1,3,11,3,5,12,10,6,1,10,1,6
A,1,1,2,1,0,7,4,3,0,7,1,8,2,7,1,8
K,6,10,8,8,6,6,7,1,7,10,6,10,3,8,4,8
X,3,5,5,3,2,7,7,1,8,10,6,8,2,8,3,8
Y,2,3,2,2,1,3,11,3,5,11,10,5,1,11,1,6
U,3,7,4,5,1,7,5,13,5,7,14,8,3,9,0,8
A,2,4,4,6,2,7,3,3,3,7,1,8,3,6,3,8
J,1,1,2,3,1,9,6,3,5,12,5,10,1,6,2,6
X,3,4,6,3,3,7,7,1,9,10,7,8,3,8,3,7
Y,7,9,6,13,5,5,7,5,3,7,11,6,4,10,5,6
Z,5,9,6,7,3,7,7,4,15,9,6,8,0,8,8,8
U,7,11,9,8,7,6,9,4,7,5,8,10,6,10,1,8
T,5,10,6,8,5,6,10,1,8,11,9,6,1,10,3,4
I,3,9,6,7,6,10,5,2,4,9,5,5,3,8,5,7
A,2,7,4,5,2,11,2,3,3,10,2,9,2,6,3,8
M,3,1,4,3,3,8,6,6,4,7,7,8,7,5,1,7
C,2,1,3,2,1,6,8,7,7,8,7,13,1,10,4,10
K,4,10,5,8,6,6,6,3,7,6,5,8,7,8,5,9
J,1,2,2,4,1,10,6,2,5,12,4,9,1,6,1,7
V,2,6,3,4,1,7,9,4,2,7,13,8,3,10,0,8
B,4,7,5,5,5,8,6,6,7,6,6,6,2,8,7,10
X,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8
J,2,2,3,4,1,10,6,2,7,12,3,8,1,6,1,7
U,3,7,3,5,2,8,6,12,4,7,11,8,3,9,0,8
E,2,1,3,2,2,7,7,5,7,7,5,9,2,8,5,10
M,4,7,7,5,5,4,8,3,4,10,10,10,5,9,2,6
M,3,4,5,3,3,7,6,3,4,9,7,8,7,5,1,8
F,6,10,8,7,5,6,10,1,6,13,7,5,1,10,2,7
V,5,7,5,5,3,4,12,1,2,8,10,7,3,12,1,8
Q,3,4,4,5,3,8,7,6,2,8,7,10,3,8,5,8
K,4,2,5,3,3,5,7,4,7,7,6,11,3,8,5,9
B,2,1,2,1,1,7,7,7,5,6,5,7,1,8,7,10
T,7,11,7,9,5,5,12,4,7,12,9,4,2,12,2,4
A,3,7,4,5,3,8,3,2,2,7,2,8,2,6,3,7
I,1,6,0,8,1,7,7,4,4,7,6,8,0,8,0,8
C,1,0,1,1,0,6,7,6,7,7,6,13,0,8,4,10
V,2,7,4,5,3,9,12,2,3,4,10,9,2,11,1,9
Y,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8
Z,2,7,3,5,3,7,7,5,9,7,6,8,1,8,7,8
Q,2,3,3,4,3,8,8,5,2,5,8,10,2,9,5,9
B,6,9,5,4,3,10,6,6,5,11,4,9,5,7,6,11
X,2,4,4,3,2,7,7,3,9,6,6,8,2,8,5,8
D,2,0,2,1,1,5,7,8,6,6,6,6,2,8,3,8
W,7,11,8,6,5,4,7,2,3,8,10,8,10,10,2,5
X,4,6,6,4,5,8,6,3,5,6,7,8,3,9,8,9
V,3,2,5,4,2,7,12,2,3,7,11,9,2,10,1,8
Z,6,10,8,7,6,10,6,5,4,8,5,7,3,7,11,7
V,4,7,6,6,7,7,8,5,5,7,6,8,6,9,7,8
W,6,5,7,4,4,4,11,2,2,9,9,8,7,11,1,6
B,4,3,4,5,3,6,7,8,6,7,6,6,2,8,9,10
N,4,7,6,5,5,6,8,3,4,8,7,7,5,9,5,3
M,3,6,6,4,6,9,4,2,2,8,4,8,8,6,2,7
F,5,11,6,8,5,5,11,7,6,11,10,5,2,9,2,5
C,2,4,3,2,1,5,9,4,7,11,9,11,1,9,2,7
G,5,10,5,7,4,6,6,6,5,10,7,13,2,9,5,9
C,5,10,6,8,3,5,8,6,8,12,9,12,2,9,3,7
Z,4,8,5,6,3,6,8,6,11,7,7,10,1,9,8,8
A,4,5,6,4,4,8,7,3,5,7,8,8,5,9,3,6
Z,3,7,4,5,2,7,8,3,12,8,6,8,0,8,7,7
S,5,5,6,8,3,8,8,6,9,5,6,7,0,8,9,7
L,2,4,2,3,1,4,4,4,8,2,1,6,0,7,1,6
T,6,11,5,6,2,4,11,3,7,13,8,6,2,7,3,3
D,8,15,7,8,5,6,7,4,7,9,6,7,5,9,7,5
G,4,6,5,4,3,6,8,6,6,10,8,9,2,8,4,9
J,0,0,1,0,0,12,4,4,3,12,4,10,0,7,0,8
Z,1,3,2,2,1,7,7,5,8,6,6,8,1,8,7,8
Z,3,8,4,6,4,6,8,3,8,7,6,9,0,8,9,7
I,5,9,4,4,2,9,7,6,4,13,5,8,3,8,5,10
C,3,5,4,3,2,4,8,5,7,11,9,12,1,9,3,7
B,6,11,6,8,5,6,6,9,7,6,6,7,2,8,10,10
N,1,0,1,1,0,7,7,9,0,6,6,8,4,8,0,8
V,3,10,5,7,2,6,8,4,3,8,14,8,3,10,0,8
J,5,8,6,6,2,8,4,5,6,15,7,13,1,6,1,6
D,2,5,4,4,3,9,6,4,6,10,4,6,2,8,3,8
E,6,9,8,7,7,8,8,6,3,6,6,10,4,8,8,9
J,1,2,2,3,1,10,6,2,6,12,4,9,0,7,1,8
T,2,7,4,4,1,8,14,1,6,6,11,8,0,8,0,8
V,4,10,6,8,2,8,8,4,3,6,14,8,3,9,0,8
F,3,9,3,6,1,1,12,5,5,11,10,8,0,8,3,6
F,8,14,7,8,3,6,9,2,7,10,7,6,2,10,5,6
C,5,9,5,7,3,4,10,7,8,13,10,8,2,10,2,6
W,4,11,6,8,4,11,8,5,2,6,9,8,8,10,0,8
U,10,14,9,8,4,6,4,4,6,4,7,6,6,5,2,7
J,2,2,4,4,2,10,6,2,7,12,4,9,1,6,1,7
I,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7
W,4,8,6,6,6,8,10,2,3,6,8,8,7,11,1,8
B,2,3,2,2,2,7,8,5,5,7,6,6,2,8,5,9
Y,2,2,4,3,1,7,11,1,7,7,11,8,1,11,2,8
N,6,10,8,8,6,7,8,6,6,6,6,4,9,10,5,6
Z,2,7,3,5,2,7,7,3,11,8,6,8,0,8,7,8
S,4,6,4,8,3,8,8,6,9,4,5,5,0,8,9,7
S,2,6,3,4,3,8,8,7,5,7,5,8,2,8,8,8
F,6,13,6,7,3,7,9,2,6,12,5,5,2,9,6,6
Y,3,3,5,4,1,5,10,3,2,8,13,8,2,11,0,8
J,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8
Z,4,7,5,5,4,9,8,5,3,7,5,7,3,7,8,4
W,6,8,6,6,7,7,10,4,3,9,6,6,8,10,4,5
C,5,7,6,5,6,6,6,4,3,7,6,11,5,9,3,9
W,5,11,8,8,8,4,12,2,2,8,9,9,7,13,2,8
T,1,3,2,2,1,6,11,3,4,8,10,7,2,11,0,7
R,3,8,4,6,4,6,10,7,4,7,4,9,2,6,5,11
E,3,7,3,5,2,3,7,6,9,7,6,14,0,8,7,8
B,4,9,6,6,6,8,7,5,6,9,6,6,3,9,7,8
Z,2,2,3,3,2,7,7,5,9,6,6,8,2,8,7,8
S,5,8,7,6,9,7,10,3,4,8,7,7,3,7,8,2
R,3,6,4,4,3,9,7,3,5,10,4,7,3,7,3,10
M,3,4,4,3,3,7,6,6,4,6,7,9,6,5,2,8
E,2,3,3,1,1,5,8,2,7,11,8,10,1,8,3,7
B,2,4,2,3,2,7,7,5,5,6,6,6,2,8,5,9
H,3,5,4,3,3,9,7,6,6,7,6,8,3,8,3,7
W,6,11,9,8,8,7,9,5,1,7,9,8,9,10,1,7
J,6,7,4,10,3,6,10,3,3,13,6,5,3,8,7,9
J,4,10,6,7,5,6,8,3,4,8,7,7,6,7,4,7
N,6,8,9,6,4,8,8,2,6,10,5,6,6,9,1,7
F,2,6,2,4,1,1,11,4,7,12,12,9,0,8,2,6
D,3,8,5,6,4,10,6,3,6,11,4,7,3,8,2,9
X,3,3,4,4,1,7,7,6,2,7,6,8,3,8,4,8
W,7,9,7,5,3,3,9,3,2,8,11,9,10,11,0,7
R,4,10,6,8,6,7,7,6,6,7,6,7,3,7,5,9
W,7,11,10,8,15,10,8,5,3,7,7,8,13,10,4,6
D,3,5,5,4,3,9,6,4,7,10,4,6,2,8,3,8
V,3,4,4,3,1,5,12,2,3,9,11,7,2,11,1,8
K,6,10,8,8,7,5,5,4,8,6,7,12,4,8,7,9
O,10,15,7,8,4,7,7,5,5,8,4,7,5,9,6,8
L,4,8,5,6,3,7,4,1,7,8,2,10,1,6,3,8
O,4,7,5,5,3,7,8,8,5,7,7,9,3,8,3,8
U,3,7,3,5,2,7,5,13,5,7,13,8,3,9,0,8
O,5,6,6,6,5,6,7,6,7,9,7,9,4,7,5,5
J,2,6,3,4,1,8,7,3,6,15,5,9,1,6,1,7
N,3,3,4,5,2,7,7,14,2,5,6,8,6,8,0,8
O,7,11,5,6,3,6,7,5,4,7,4,7,5,8,5,7
N,6,11,9,8,6,11,8,2,5,10,2,4,7,10,2,8
P,2,7,3,4,1,4,11,8,3,10,6,4,1,10,3,8
J,3,8,4,6,2,9,6,2,8,15,4,8,0,7,0,7
A,1,1,3,2,1,7,2,2,1,7,2,8,1,6,2,7
C,7,10,8,8,4,6,8,7,8,13,8,9,2,11,3,7
X,4,10,5,7,2,7,7,4,4,7,6,8,3,8,4,8
L,2,5,3,3,2,5,4,5,6,2,2,5,1,6,1,6
I,1,8,1,6,1,7,7,0,8,7,6,8,0,8,3,8
E,3,6,4,4,2,4,6,6,10,7,7,13,0,8,8,8
M,7,9,10,6,6,10,5,2,6,9,4,7,8,6,2,8
X,4,5,8,4,3,7,7,1,9,10,6,8,2,8,3,7
D,5,9,6,7,5,10,6,4,7,10,3,5,3,8,3,9
D,6,12,6,6,5,9,6,3,6,10,4,7,5,8,8,7
J,5,11,4,8,3,8,8,2,4,12,4,5,2,9,7,8
T,2,1,3,1,0,7,15,2,4,7,10,8,0,8,0,8
U,3,7,3,5,1,7,5,13,5,7,13,8,3,9,0,8
K,3,7,5,5,5,6,6,3,4,7,6,9,5,7,7,9
K,5,11,5,8,2,4,7,9,2,7,4,11,4,8,2,11
P,2,4,4,3,2,7,9,3,4,12,5,4,1,9,3,8
B,5,8,6,6,5,9,8,7,8,7,5,5,2,8,8,9
K,5,8,6,6,6,5,6,4,6,6,6,10,3,8,5,10
E,2,2,3,3,2,7,7,5,7,7,6,9,2,8,5,10
A,3,8,5,6,3,12,3,2,2,9,2,9,3,7,3,9
Y,4,10,6,8,6,9,5,6,4,7,8,8,6,8,8,3
S,6,10,7,8,4,7,7,4,8,11,7,8,2,9,5,8
H,2,2,3,3,3,6,7,5,6,7,6,8,3,8,3,8
Y,3,4,4,2,2,4,10,2,7,10,10,5,2,11,3,4
Z,3,8,5,6,3,8,7,2,9,11,6,8,2,8,6,8
E,4,11,5,8,5,3,7,5,9,7,7,14,0,8,6,9
Z,7,10,5,14,5,7,10,3,3,11,7,7,3,8,14,7
M,6,5,7,8,4,8,7,13,2,6,9,8,9,6,0,8
P,1,1,2,1,1,5,11,7,1,10,6,4,1,9,3,8
T,6,8,6,6,3,4,14,5,7,12,9,3,1,11,2,4
W,5,4,6,3,3,4,11,3,2,9,9,7,7,11,1,6
U,5,10,6,8,3,7,4,15,6,7,14,8,3,9,0,8
U,7,11,6,6,3,7,6,5,6,3,9,7,5,8,3,6
E,4,10,6,7,6,7,7,5,3,7,6,8,5,8,8,9
W,7,7,7,5,6,5,10,3,3,9,7,7,8,10,3,5
K,3,8,4,6,3,4,8,7,3,6,4,11,3,8,2,11
Z,5,7,7,5,3,7,7,2,10,12,7,7,1,7,6,7
I,4,7,6,8,6,9,8,5,5,7,5,7,3,8,9,8
W,8,10,8,8,8,6,11,3,3,9,7,7,10,12,4,5
P,6,7,8,10,10,8,7,4,3,7,7,7,7,12,6,6
F,3,6,6,4,3,6,10,2,6,13,6,4,1,10,2,7
F,4,4,4,6,2,1,14,5,3,12,9,5,0,8,2,6
R,5,8,8,7,9,8,7,4,4,8,5,7,7,8,6,5
H,4,8,5,6,5,8,7,5,6,7,6,6,3,8,3,7
Q,4,8,5,9,6,8,8,5,2,7,9,10,3,9,5,7
W,10,15,11,8,7,7,8,2,4,6,9,6,11,9,3,5
J,1,6,2,4,1,14,2,6,5,13,2,10,0,7,0,8
R,6,11,6,8,4,5,12,9,3,7,3,9,3,7,6,11
D,4,8,6,6,4,6,7,8,7,6,5,4,3,8,4,9
C,7,10,7,7,4,4,10,7,8,12,10,8,2,10,3,7
A,3,9,5,6,2,6,6,3,1,6,0,8,2,7,1,7
Z,3,9,4,6,2,7,7,4,14,10,6,8,0,8,8,8
B,3,6,3,4,3,6,6,8,6,6,6,7,2,8,8,9
O,2,3,3,2,2,7,7,6,4,9,6,8,2,8,2,8
B,1,0,2,1,1,7,7,7,5,6,6,7,1,8,7,9
K,4,7,6,6,5,8,6,2,3,8,4,8,4,6,7,11
S,3,5,3,4,3,8,6,7,5,7,7,9,2,10,9,8
D,2,3,3,2,2,9,6,3,5,10,4,7,2,8,2,8
V,5,9,6,7,4,7,11,3,2,6,11,8,3,10,3,9
F,3,5,4,8,2,0,12,4,6,12,12,9,0,8,2,6
O,4,9,5,6,3,9,7,9,8,7,5,10,3,8,4,8
P,5,10,8,8,5,7,10,5,5,12,5,3,1,10,4,8
K,3,4,6,3,3,6,7,2,7,10,7,10,3,8,3,7
M,4,9,6,6,7,7,8,6,4,7,6,8,6,9,7,6
W,8,9,8,6,6,2,12,2,2,10,10,8,7,11,1,7
H,3,7,4,4,2,7,8,14,1,7,5,8,3,8,0,8
L,3,6,5,4,3,6,4,1,8,8,2,11,0,7,2,8
N,2,4,4,3,2,7,8,3,4,10,6,7,5,9,0,7
T,4,8,4,6,3,6,12,4,6,11,9,4,2,12,2,4
L,2,7,3,5,2,8,4,3,7,7,2,8,1,6,2,8
W,7,11,10,8,4,9,7,5,2,6,8,8,9,9,0,8
E,6,10,9,7,7,6,8,1,8,11,6,9,3,8,4,8
H,2,4,4,3,2,7,7,3,5,10,6,8,3,8,2,8
J,5,11,7,8,4,10,6,1,7,14,3,7,0,7,1,8
W,5,8,5,6,5,5,10,3,3,9,7,7,7,11,2,5
T,2,8,3,5,1,7,14,0,6,7,11,8,0,8,0,8
U,6,11,8,8,8,8,6,8,5,7,6,9,6,8,5,6
E,5,10,3,5,2,7,8,5,6,10,6,9,1,9,7,8
G,2,5,3,4,2,6,7,5,5,9,7,10,2,9,4,10
S,1,3,2,2,1,8,8,6,5,7,6,7,2,8,8,8
U,8,15,7,9,4,4,4,5,5,4,7,8,5,9,2,8
K,6,10,6,5,3,7,7,3,6,10,8,9,6,11,3,7
A,2,3,3,2,1,10,2,2,1,9,2,9,1,6,2,8
F,5,11,7,8,6,6,10,2,5,13,7,5,2,10,2,7
O,5,9,6,7,4,7,7,9,5,7,7,8,3,8,3,8
M,4,8,6,6,7,7,8,6,4,6,6,8,6,9,7,10
Z,7,10,7,6,4,8,6,2,8,12,6,9,3,8,6,7
H,5,9,5,6,2,7,8,15,0,7,5,8,3,8,0,8
Q,9,14,8,8,4,9,3,4,7,11,3,10,3,8,8,11
S,2,4,3,3,1,9,6,2,7,10,5,8,1,9,5,9
B,4,8,4,6,5,6,8,8,6,7,5,7,2,8,7,9
W,4,6,5,4,4,7,6,7,2,7,7,8,6,8,5,10
X,7,10,10,8,5,5,8,2,9,11,10,9,3,8,4,6
F,3,8,3,5,1,1,13,5,4,12,10,7,0,8,2,6
G,5,10,6,7,8,8,8,5,2,6,6,8,7,8,7,13
M,7,13,8,8,5,6,3,3,2,8,4,10,7,2,2,8
Q,4,7,4,9,4,8,8,6,2,8,8,10,3,9,6,7
K,4,8,6,6,4,3,8,2,7,10,11,12,3,8,3,5
M,6,10,7,5,4,9,3,2,2,9,4,9,8,2,2,9
S,4,9,5,6,3,8,8,5,9,5,6,7,0,8,9,7
Z,6,9,8,7,5,8,6,2,9,12,5,10,3,7,7,9
M,3,3,4,2,2,9,6,6,4,6,7,6,6,5,2,6
G,3,8,5,6,2,7,7,8,7,6,6,8,2,7,6,11
M,5,6,8,4,5,9,6,3,5,9,5,7,8,6,2,8
M,5,8,6,6,5,8,5,11,0,7,9,8,9,6,2,7
L,3,9,4,7,3,6,4,1,8,8,2,10,0,7,2,8
P,5,7,7,5,3,6,14,5,2,12,4,1,0,10,3,8
Y,4,8,6,12,11,9,7,4,2,6,7,9,5,11,8,9
H,3,6,4,4,5,7,8,4,2,7,6,7,7,9,7,8
X,4,11,6,8,6,8,8,2,6,7,7,8,5,11,8,8
O,3,7,4,5,3,7,7,8,5,6,5,6,3,8,3,8
G,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,9
A,1,0,2,0,0,7,4,2,0,7,2,8,2,7,1,8
W,4,9,7,6,5,8,10,2,3,6,9,8,7,11,1,8
O,2,3,3,2,2,7,8,6,4,9,6,8,2,8,2,8
D,5,9,6,7,5,9,7,4,7,11,5,6,3,7,4,8
H,6,9,9,7,7,7,7,3,6,10,7,8,3,8,3,8
W,5,9,6,4,3,5,8,2,3,7,9,8,9,11,2,6
H,3,5,4,7,2,7,8,15,1,7,5,8,3,8,0,8
Y,3,4,5,5,1,6,10,3,2,9,13,8,1,11,0,8
K,4,7,4,5,2,4,7,8,1,7,6,11,3,8,2,11
E,6,11,6,8,4,3,7,6,11,7,6,14,0,8,8,7
S,5,11,6,8,4,8,7,5,9,11,3,7,2,6,5,9
S,3,6,4,4,3,5,9,2,6,10,7,7,2,7,4,4
H,5,8,7,6,6,8,6,7,7,7,7,7,3,8,3,8
T,3,5,4,4,3,6,8,3,7,8,7,8,3,9,7,6
J,1,6,2,4,0,13,3,7,4,13,3,11,0,7,0,8
Y,6,8,6,6,3,4,10,3,7,11,11,6,1,11,3,4
N,2,1,3,2,1,7,7,13,1,5,6,8,5,8,0,8
P,2,3,2,2,1,5,11,5,3,10,7,3,0,9,3,6
W,3,1,4,3,3,10,11,3,2,5,9,7,6,11,0,8
Z,6,11,8,8,5,9,6,3,10,12,4,8,2,7,6,8
X,3,1,4,2,2,7,7,4,9,6,6,8,3,8,6,8
I,3,7,4,5,2,9,6,0,7,13,5,8,0,8,1,8
T,4,5,5,3,2,5,11,2,9,12,9,5,0,10,2,4
I,1,3,2,2,1,7,7,1,7,13,6,8,0,8,1,8
A,1,0,2,0,0,8,4,2,0,7,2,8,2,7,1,8
D,6,10,6,6,3,11,3,4,5,12,2,8,5,7,4,10
M,3,3,5,2,2,8,6,2,4,9,6,8,7,6,2,8
N,4,4,4,6,2,7,7,14,2,4,6,8,6,8,0,8
F,4,8,6,6,7,9,7,1,5,9,5,5,3,10,4,6
T,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8
T,4,10,5,8,5,7,11,3,7,7,11,8,2,12,1,8
X,3,7,4,5,2,8,7,4,4,7,6,8,2,8,4,8
R,3,9,4,6,3,5,11,8,4,7,3,9,3,7,6,11
Z,2,3,4,2,2,7,8,2,9,12,7,7,1,8,5,7
Z,4,9,5,7,4,7,8,3,13,8,6,8,0,8,8,7
G,2,2,3,3,2,7,6,6,6,7,6,10,2,9,4,9
A,1,3,2,1,1,6,2,1,1,6,2,8,1,6,1,7
C,1,0,1,1,0,6,7,6,8,7,6,14,0,8,4,10
E,6,9,8,8,9,7,7,5,4,8,7,9,8,11,10,12
R,5,9,7,7,4,10,7,3,7,10,2,7,3,6,4,11
S,5,11,6,8,3,8,8,6,10,5,6,6,0,8,9,7
V,3,7,4,5,3,6,12,2,2,8,10,8,4,11,4,8
Z,1,0,2,0,0,7,7,3,10,8,6,8,0,8,6,8
Y,8,13,6,8,4,6,8,4,4,10,7,5,3,10,4,4
G,4,6,4,4,2,6,7,6,7,10,7,10,2,10,4,9
I,1,3,2,2,0,7,7,1,7,13,6,8,0,8,0,7
T,2,3,3,2,1,5,12,3,6,11,9,4,1,10,2,5
Y,6,10,6,8,4,4,9,1,8,10,10,6,1,10,3,4
Y,1,1,2,1,0,8,10,3,1,6,12,8,1,11,0,8
V,6,9,8,8,9,7,8,6,5,7,6,7,6,11,8,11
L,5,9,5,4,3,7,5,3,5,12,7,11,3,8,6,8
H,5,7,7,5,5,7,7,3,6,10,5,8,3,8,3,8
W,4,7,6,5,3,4,8,5,1,7,9,8,8,10,0,8
V,4,8,6,6,3,9,12,3,3,4,11,9,3,9,2,8
C,2,4,3,3,1,6,8,7,7,8,8,13,1,10,4,10
O,2,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8
A,4,5,6,8,2,8,4,3,2,7,1,8,3,7,2,8
G,4,2,5,4,3,6,6,6,6,6,6,10,2,9,4,8
D,3,3,4,2,2,7,7,6,6,7,6,5,5,8,3,7
E,4,11,4,8,3,3,6,6,12,7,7,15,0,8,7,7
O,4,4,6,7,3,8,6,9,8,7,5,9,3,8,4,8
J,3,10,4,8,3,14,3,5,4,13,2,9,0,7,0,8
I,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
G,1,0,1,0,0,8,6,5,4,6,6,9,1,8,5,10
T,5,6,5,4,3,4,12,2,7,12,10,5,1,10,1,5
I,2,10,3,8,4,7,7,0,7,7,6,8,0,8,3,8
R,2,4,3,2,2,8,8,4,5,9,5,7,2,7,4,10
Y,3,5,4,4,2,4,10,2,7,11,10,6,1,11,3,5
O,4,6,4,4,3,8,6,7,4,9,5,8,3,8,3,8
P,6,10,8,8,6,7,10,4,4,13,6,3,1,10,3,8
Y,6,7,8,10,10,9,9,4,2,4,8,9,5,13,10,10
W,4,3,5,2,3,4,11,3,2,9,9,7,6,11,1,7
G,2,5,3,4,2,6,7,6,6,10,7,11,2,9,4,9
W,5,7,5,5,4,3,11,2,2,10,9,7,5,11,1,7
X,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8
T,2,10,4,7,1,8,14,0,6,6,11,8,0,8,0,8
G,2,5,4,3,2,7,7,6,6,6,6,10,2,8,4,9
D,10,15,10,8,5,8,6,5,6,10,3,6,6,6,6,10
K,5,10,7,8,8,6,8,5,3,7,5,8,4,7,7,11
N,9,13,7,7,3,5,9,4,6,3,4,11,6,10,2,7
J,1,3,2,2,1,10,6,2,6,12,4,8,0,7,1,7
E,3,8,3,6,2,3,7,6,10,7,6,14,0,8,7,8
C,7,14,5,8,4,6,9,4,4,9,8,9,4,9,9,10
O,5,10,7,8,3,7,6,9,8,6,5,6,3,8,4,8
E,4,11,5,8,7,7,7,5,7,7,5,9,6,8,6,10
X,5,7,7,6,7,9,7,2,4,7,6,6,3,10,7,8
C,9,15,7,8,5,8,7,5,3,8,8,10,4,9,8,13
Z,4,6,6,4,3,7,8,2,8,11,7,8,1,9,5,7
S,5,10,5,5,2,7,7,3,4,13,7,9,2,9,3,8
O,1,0,1,0,0,8,7,6,4,7,6,8,2,8,2,8
D,4,8,5,6,4,7,7,7,8,6,5,5,3,8,3,7
Q,2,4,3,5,3,8,7,6,2,8,7,9,2,9,4,8
K,6,9,9,7,4,3,8,4,8,12,12,12,3,8,4,5
J,2,8,3,6,2,9,6,2,7,12,3,8,1,6,1,6
U,2,3,3,2,1,8,8,5,6,5,9,8,3,10,0,8
V,4,9,6,6,4,8,10,2,1,6,10,8,3,10,3,9
C,6,11,8,8,9,5,6,3,5,8,6,12,6,9,3,9
K,4,11,6,8,6,5,6,4,7,6,6,11,3,8,6,9
Z,6,10,8,8,5,8,7,2,10,12,5,8,1,7,6,7
T,5,8,7,6,6,7,7,7,7,6,7,9,3,10,6,7
S,5,10,6,8,3,8,8,6,9,5,6,7,0,8,9,8
T,3,11,5,8,1,9,15,0,6,6,11,8,0,8,0,8
V,5,7,5,5,3,3,12,2,2,9,10,8,3,10,1,7
T,4,8,5,6,3,7,12,3,7,7,11,8,2,12,1,7
R,4,7,5,5,5,7,7,4,6,6,5,7,3,7,4,8
P,4,8,5,6,4,7,10,4,4,12,5,3,1,10,2,8
R,4,9,5,7,4,10,7,3,7,10,2,7,3,6,3,11
I,2,11,2,8,4,7,7,0,7,7,6,8,0,8,2,8
Y,2,3,3,1,1,8,11,1,6,5,11,9,1,11,1,8
G,4,9,5,6,3,6,7,7,6,9,7,12,2,8,4,10
H,8,11,11,8,9,9,6,3,7,10,4,8,6,8,5,8
T,4,5,5,3,2,7,12,3,7,7,11,8,2,11,1,8
E,4,8,5,6,4,5,8,3,7,11,8,9,3,8,4,7
P,3,5,5,3,2,7,10,5,3,11,5,3,1,10,2,8
Q,2,2,3,4,2,8,7,6,2,6,6,9,2,9,5,9
T,3,1,4,3,2,7,12,3,6,7,11,8,2,11,1,7
D,8,15,7,8,5,6,7,4,7,8,5,7,6,9,7,5
M,7,12,8,7,4,12,2,4,3,12,1,8,6,3,1,9
K,6,11,9,8,5,3,7,3,8,11,11,12,3,8,4,6
L,5,9,7,7,5,6,4,2,7,7,2,9,1,6,3,8
G,3,6,4,4,3,6,7,6,5,9,7,10,2,9,4,10
U,5,6,6,4,2,4,8,5,8,11,11,9,3,9,1,6
D,4,6,4,4,3,6,7,8,7,8,8,7,2,9,3,8
Q,2,3,3,3,2,8,8,5,2,5,7,10,2,9,5,9
V,2,6,4,4,2,8,11,2,3,5,11,9,2,10,0,8
K,2,1,2,2,1,5,7,8,1,7,6,11,3,8,2,11
P,4,9,6,6,4,5,13,5,3,13,6,2,0,10,2,8
H,6,8,8,6,5,10,6,4,6,10,2,7,4,7,4,9
S,4,4,4,6,2,8,9,6,9,5,5,5,0,7,9,8
S,7,15,7,8,4,5,9,3,5,13,8,7,3,7,4,7
H,2,1,3,1,2,7,8,6,6,7,6,10,3,8,3,9
C,3,6,4,4,2,6,7,6,7,7,6,12,1,8,4,10
H,1,0,1,0,0,7,8,10,2,7,5,8,2,8,0,8
Y,2,3,3,1,1,4,11,2,6,11,10,5,0,10,1,5
A,5,9,7,7,5,8,2,2,2,6,2,7,3,8,4,7
T,6,9,8,7,6,6,7,7,7,7,7,8,4,10,6,9
Q,2,4,3,5,3,8,7,6,2,8,7,9,2,9,4,8
H,4,5,5,6,5,8,4,3,2,7,4,7,3,7,5,8
I,4,8,5,6,3,9,5,2,7,7,7,5,0,9,4,7
X,3,5,5,4,2,7,8,1,8,10,8,8,2,8,3,7
V,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8
W,4,6,6,4,4,10,11,3,2,4,9,7,7,11,0,7
K,4,6,6,4,3,7,6,2,7,10,5,10,4,7,4,9
U,2,0,2,1,0,8,5,11,4,6,13,8,3,10,0,8
U,3,5,4,4,2,6,8,6,7,7,9,9,3,9,1,8
D,4,11,6,8,5,7,7,8,7,7,6,4,3,8,3,7
A,2,2,4,3,2,7,2,1,2,6,2,8,2,6,2,7
V,4,7,5,5,2,6,11,3,4,8,12,8,2,10,1,9
A,5,10,8,8,6,6,5,2,4,4,2,7,6,7,6,4
K,9,12,10,7,5,7,7,3,6,10,9,9,6,12,4,8
U,3,2,4,3,2,7,8,6,8,8,10,8,3,9,1,8
U,7,12,6,6,3,9,6,6,6,3,9,8,5,10,3,6
V,4,9,6,7,3,8,11,3,4,5,11,8,3,10,1,8
T,2,7,4,4,1,10,15,1,5,4,11,9,0,8,0,8
T,3,2,4,3,2,6,12,3,7,8,11,7,2,11,1,7
W,5,9,7,7,4,6,8,5,2,7,8,8,9,10,0,8
V,4,6,4,4,2,2,11,4,3,12,11,8,2,11,1,7
D,4,7,5,5,4,7,7,7,8,6,5,5,3,8,3,7
H,3,6,5,4,4,6,7,7,6,7,6,11,3,8,3,10
D,5,11,6,8,6,7,7,5,6,6,5,8,7,9,3,7
M,7,9,11,7,6,9,6,2,5,9,5,7,11,7,2,8
U,6,9,8,7,6,6,7,8,6,6,6,11,6,8,8,3
X,4,7,6,5,3,7,8,1,8,10,7,8,2,8,3,7
K,4,6,6,4,3,3,8,3,7,11,11,11,3,8,3,6
B,3,8,5,6,4,8,7,7,6,7,6,5,2,8,8,9
O,4,9,5,6,4,7,7,8,6,7,6,8,2,8,3,8
T,2,5,3,4,2,7,12,3,6,7,11,8,2,11,1,8
E,6,10,8,8,8,7,7,5,3,7,6,9,5,8,9,8
W,12,12,11,7,5,6,11,2,3,7,11,7,9,12,1,7
G,5,10,5,8,4,5,6,6,5,9,8,12,2,9,4,10
A,2,0,3,1,0,8,4,2,0,7,2,8,2,6,1,8
R,4,4,4,6,2,5,9,9,4,7,5,8,3,8,5,10
M,4,8,4,6,5,8,5,10,0,6,9,8,7,5,1,6
D,5,10,6,8,3,5,7,10,10,7,7,6,3,8,4,8
U,2,3,3,2,1,5,8,5,6,10,9,8,3,9,1,6
Q,4,7,6,10,8,8,11,4,2,5,8,11,5,14,8,13
R,4,8,6,6,5,10,7,2,6,11,2,7,4,7,3,10
W,2,1,3,2,2,10,11,3,2,5,9,7,5,11,0,8
A,6,13,6,8,4,12,2,5,1,12,2,10,3,2,3,10
T,5,6,5,4,3,6,11,3,6,11,9,5,2,12,2,4
C,5,7,5,5,3,5,7,5,7,11,9,14,2,9,3,8
P,4,7,5,5,3,5,13,5,4,13,6,2,0,9,2,7
Z,5,9,5,4,3,6,8,2,8,11,8,9,3,9,5,5
W,2,1,2,2,1,7,8,4,0,7,8,8,6,10,0,8
P,4,9,4,6,4,3,14,6,1,12,7,3,0,9,3,8
F,1,3,2,2,1,5,10,3,5,10,9,6,1,10,2,7
O,4,10,6,8,4,7,6,9,5,7,4,8,3,8,3,8
Z,2,4,4,6,2,11,4,3,4,10,3,9,2,7,5,9
M,3,6,6,4,7,6,5,3,1,6,5,8,7,7,2,8
F,5,11,5,8,2,0,13,5,4,13,11,7,0,8,2,5
I,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
A,3,7,5,5,3,12,3,2,2,9,1,8,2,6,2,7
I,2,5,1,4,1,7,7,1,7,7,6,8,0,8,3,8
C,4,8,5,6,2,5,8,6,8,11,9,13,1,9,3,7
G,2,1,3,2,2,7,7,5,5,6,6,10,2,9,3,9
E,1,0,1,0,1,5,7,5,7,7,6,12,0,8,6,9
V,4,9,5,7,3,7,10,3,2,6,11,8,2,10,3,9
B,1,1,2,2,1,7,7,7,5,6,6,7,2,8,7,9
I,1,5,2,4,1,7,7,0,7,13,6,8,0,8,1,8
I,6,8,7,10,7,8,9,4,5,7,7,8,3,7,9,7
Z,3,9,4,6,2,7,7,4,14,10,6,8,0,8,8,8
R,6,11,5,6,3,9,7,6,4,10,3,8,6,6,5,10
V,6,10,8,8,5,7,11,3,2,5,10,9,3,11,4,8
W,4,3,6,5,3,7,8,4,1,7,8,8,8,9,0,8
K,4,6,6,4,3,9,6,2,6,10,3,8,4,8,4,11
E,5,9,7,8,8,5,6,3,3,7,6,8,5,11,10,9
I,4,11,5,8,3,7,7,0,8,14,6,8,0,8,1,8
L,4,10,6,7,8,8,8,3,5,5,7,10,6,12,8,8
G,5,8,7,7,8,8,9,5,3,7,6,8,7,11,8,9
S,4,8,5,6,3,8,7,3,7,10,4,7,2,8,5,9
N,3,5,5,3,2,8,8,2,5,10,4,6,5,8,1,7
M,5,9,7,4,4,6,4,3,2,8,4,10,7,3,1,8
T,1,1,2,1,0,8,14,1,5,6,10,8,0,8,0,8
Y,3,2,5,3,2,7,10,1,7,7,11,8,1,11,2,8
S,2,4,3,2,1,8,8,2,7,10,5,7,1,8,4,8
M,2,3,4,2,2,7,7,3,3,9,8,8,5,5,1,7
T,2,7,4,5,2,7,13,0,5,7,10,8,0,8,0,8
Y,2,1,4,2,1,7,11,1,7,7,11,8,1,11,2,8
E,4,9,4,6,3,3,7,6,11,7,6,15,0,8,7,7
T,6,9,7,8,7,5,8,4,8,8,8,9,3,9,8,7
V,6,8,5,6,3,4,12,1,2,8,10,7,4,12,1,8
G,2,0,2,1,1,8,6,6,6,6,5,9,1,7,5,10
E,3,2,4,3,3,7,7,5,7,7,5,9,2,8,6,10
M,7,9,10,6,8,5,7,3,5,9,9,9,10,6,3,8
F,4,8,4,6,3,1,13,4,3,12,10,6,0,8,2,6
G,5,10,6,8,5,6,6,6,5,9,7,12,3,8,5,9
R,4,5,5,7,3,5,12,8,4,7,2,9,3,7,6,11
W,4,4,5,3,3,5,11,4,2,9,8,7,6,12,2,6
K,6,10,8,7,6,6,7,1,6,10,7,10,3,8,4,8
T,5,9,6,7,4,6,9,0,8,10,9,5,0,9,3,4
Y,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8
P,6,10,7,8,7,6,8,6,4,8,7,8,5,9,7,10
S,2,2,2,3,2,8,7,7,5,7,6,8,2,9,8,8
W,4,4,5,3,3,6,11,3,2,7,9,8,7,11,1,8
X,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8
R,4,3,4,4,2,6,13,8,3,7,2,9,2,6,5,10
F,3,2,3,4,2,5,11,3,6,11,9,5,1,10,3,6
D,1,0,2,1,0,6,7,7,6,6,6,6,2,8,3,8
C,2,7,3,5,1,5,7,7,8,7,6,14,1,8,4,9
F,3,4,3,5,1,1,12,5,5,11,10,8,0,8,3,6
X,4,7,5,6,5,7,8,2,5,7,6,7,3,5,7,8
T,2,5,3,3,2,8,12,3,6,6,11,8,2,11,1,8
K,3,6,3,4,1,3,7,7,3,7,6,11,4,8,2,11
Z,5,6,4,8,3,11,4,3,5,11,4,8,3,9,7,10
G,6,9,7,7,8,9,6,5,3,7,6,10,8,8,6,10
I,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
T,5,6,5,4,2,4,14,5,5,12,9,3,1,11,1,5
L,4,11,5,8,3,6,4,0,9,8,2,11,0,7,2,8
M,6,5,9,4,8,9,7,5,5,7,6,8,12,9,6,4
S,9,15,7,8,4,9,5,5,6,9,2,9,4,5,5,9
G,3,4,5,6,2,7,5,7,8,6,5,11,1,8,6,11
X,9,15,8,8,4,9,7,2,9,9,5,7,4,11,4,9
Y,2,1,4,2,1,8,11,1,7,5,11,9,1,11,2,8
X,6,9,7,8,8,8,6,1,6,7,6,8,4,13,9,8
I,4,9,4,4,2,8,8,2,5,13,5,5,2,9,4,9
D,2,5,3,3,2,7,7,6,6,7,6,4,2,8,3,7
A,3,7,5,5,3,11,2,3,2,10,2,9,2,6,3,9
S,4,9,5,6,3,8,7,5,9,5,6,7,0,8,9,8
N,4,9,4,6,4,8,7,12,1,6,6,7,6,8,0,9
X,3,6,6,4,4,9,8,3,6,7,7,7,5,11,6,7
P,4,8,6,11,10,7,9,6,0,8,6,6,7,12,8,9
B,3,6,4,4,3,11,6,3,6,11,3,7,2,8,4,11
V,3,4,5,6,1,8,8,4,3,7,14,8,3,9,0,8
G,4,8,5,6,2,7,7,7,8,6,6,8,2,7,6,11
R,5,9,7,7,7,8,6,7,3,8,6,7,6,6,7,10
D,4,7,5,5,4,7,7,5,5,7,6,8,4,9,3,7
J,2,4,4,3,1,8,6,3,6,14,6,10,0,7,0,7
H,2,2,3,3,2,6,7,6,6,7,6,10,3,8,3,9
K,6,9,9,6,6,2,8,3,8,11,12,12,4,7,4,4
D,3,4,4,3,2,7,7,6,7,7,6,5,2,8,3,7
B,3,5,4,7,3,6,7,9,6,7,6,7,2,8,9,10
C,2,7,3,5,2,5,8,7,8,6,7,14,1,8,4,9
D,4,7,4,5,4,6,7,9,7,6,5,6,2,8,3,8
A,2,6,4,4,2,7,4,2,0,6,2,8,2,6,1,7
O,5,9,6,6,5,7,8,8,4,7,8,8,3,7,3,8
E,3,7,4,5,3,6,7,6,8,7,7,10,2,8,6,9
W,5,5,7,4,4,7,11,3,2,6,9,8,8,11,0,8
R,2,4,3,3,2,7,8,5,4,6,5,6,2,7,4,8
V,3,5,5,4,2,9,12,3,3,5,11,9,3,10,2,9
P,2,3,3,2,1,6,10,5,4,9,7,3,1,10,4,6
X,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8
H,3,3,4,4,2,7,9,14,1,7,3,8,3,8,0,8
J,3,6,4,4,2,6,7,3,5,14,7,11,1,6,1,7
B,8,15,6,8,4,9,6,6,5,10,4,9,6,6,7,11
X,2,3,3,2,1,7,7,3,9,6,6,8,2,8,5,7
V,8,10,7,7,4,3,12,3,3,10,11,8,3,10,1,7
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
N,3,6,3,4,3,8,8,12,1,6,6,8,5,8,0,8
S,8,13,8,7,4,8,6,4,3,13,8,9,3,10,3,9
A,3,11,6,8,2,6,6,3,1,6,0,8,2,7,2,7
D,9,14,8,8,6,8,5,4,7,10,5,7,6,9,7,8
U,2,0,2,1,1,8,5,11,4,6,13,8,3,10,0,8
F,1,3,3,2,1,5,10,2,5,13,7,5,1,9,1,8
K,3,4,5,3,2,5,7,2,7,10,9,11,3,8,3,7
G,3,7,4,5,5,8,7,4,1,7,6,9,6,8,6,12
Q,3,7,4,6,2,9,8,8,5,5,8,9,3,8,5,9
P,5,10,6,7,3,4,12,9,2,10,6,4,1,10,4,8
S,7,15,6,9,3,7,4,4,4,7,2,7,3,6,6,8
I,3,10,5,8,3,8,6,0,7,13,6,9,1,7,2,8
Y,3,5,4,4,2,4,11,2,7,11,10,6,1,11,2,5
P,7,11,10,8,7,7,11,5,4,12,5,2,1,10,3,8
J,1,4,3,3,1,9,4,4,5,14,5,12,0,7,0,8
E,4,5,5,4,4,6,8,4,4,8,7,10,4,10,8,11
E,4,10,6,8,7,8,7,4,7,7,7,7,6,8,5,10
H,3,5,6,4,4,9,6,3,6,10,4,7,3,8,3,8
M,7,8,10,7,11,8,8,4,3,6,6,7,12,7,6,7
U,2,0,2,1,0,7,5,11,4,7,13,8,3,10,0,8
B,1,3,2,2,2,7,7,5,5,7,6,6,2,8,4,10
X,3,6,5,4,4,7,8,3,5,6,6,9,3,8,8,8
W,7,9,7,6,5,4,10,3,3,9,8,7,7,11,2,6
R,5,5,7,4,6,7,7,4,3,8,5,8,7,8,6,6
V,4,4,5,3,2,4,12,3,3,9,11,7,2,10,1,8
G,3,3,4,2,2,7,7,5,6,6,6,9,2,9,4,8
K,4,4,4,6,2,3,6,8,2,7,6,11,4,8,2,11
E,2,4,3,3,2,7,7,5,7,7,7,9,2,8,5,9
Z,6,8,8,10,7,11,6,4,5,9,3,7,3,6,6,8
R,4,6,6,6,7,6,8,3,3,7,5,9,6,8,5,8
Q,4,7,5,9,6,8,6,8,3,5,6,9,3,8,6,9
P,2,6,3,4,3,5,10,7,2,9,6,5,1,10,2,8
G,5,9,6,7,4,6,7,6,6,10,7,10,2,9,5,9
E,3,6,3,4,3,3,7,5,9,7,6,13,0,8,6,9
Q,4,8,6,10,7,8,8,8,2,5,6,11,3,8,6,10
Y,5,7,7,9,8,10,10,5,3,6,7,8,6,11,8,5
X,4,10,5,7,1,7,7,5,4,7,6,8,3,8,4,8
B,1,1,1,1,1,7,7,7,5,6,6,7,1,8,6,9
E,3,5,6,3,3,7,8,2,9,12,6,8,2,8,5,8
L,3,4,4,7,1,0,1,6,6,0,0,6,0,8,0,8
D,4,10,4,7,5,6,7,9,7,5,3,7,2,7,3,8
E,5,10,7,8,6,9,7,2,7,11,5,8,3,8,5,10
V,2,6,4,4,2,7,12,3,3,6,11,8,2,10,1,8
W,6,5,7,4,4,3,11,2,3,10,10,8,7,11,1,7
U,4,7,5,5,4,5,8,5,6,9,7,9,3,9,3,6
G,5,11,7,9,3,7,6,8,9,6,6,10,1,8,6,11
Y,2,2,4,3,1,6,10,1,6,8,11,9,1,11,2,8
M,3,1,4,2,3,7,6,6,5,6,7,7,8,6,2,7
M,6,10,9,8,7,7,7,2,5,9,8,8,9,7,3,8
U,2,1,3,2,1,8,8,6,6,5,9,8,3,9,1,8
E,4,9,6,6,6,7,7,4,7,7,6,9,6,8,5,10
A,4,5,6,4,4,9,8,3,4,6,7,7,4,9,4,6
P,3,9,4,6,3,4,11,8,2,10,6,4,1,10,3,8
S,5,9,6,7,4,7,8,3,7,10,4,6,2,6,5,8
U,4,6,5,4,2,4,8,5,6,10,9,9,3,9,2,7
A,3,8,5,5,2,8,6,3,1,7,0,8,2,7,1,8
X,3,4,4,3,2,7,7,3,9,6,6,9,2,8,5,8
R,4,9,5,6,5,9,7,3,6,9,3,8,3,6,4,11
W,3,6,5,4,2,4,8,5,1,7,8,8,8,10,0,8
F,2,3,3,2,1,5,12,3,4,13,7,3,1,9,1,7
J,1,3,3,2,1,8,6,3,6,14,5,10,0,7,0,7
J,1,1,2,2,1,10,7,2,5,11,4,8,0,7,0,7
A,2,7,4,4,1,8,4,3,1,7,1,8,2,6,2,8
R,3,6,5,4,3,9,8,4,6,10,2,7,3,6,4,11
X,4,8,5,6,3,7,7,4,9,6,6,8,3,8,6,8
K,4,8,5,6,4,5,6,4,8,7,7,12,3,8,6,9
E,4,8,6,6,4,6,8,4,8,11,9,9,2,9,5,6
H,4,5,6,8,6,7,5,5,2,6,4,6,5,7,8,8
U,8,10,9,8,6,3,8,5,8,9,8,10,6,9,4,3
X,5,9,8,7,4,8,8,1,9,10,5,7,3,8,4,8
T,6,9,8,8,9,7,8,4,9,7,6,8,3,8,8,6
Q,4,5,5,6,5,8,5,6,3,9,6,11,3,8,5,8
A,3,7,5,5,3,11,2,3,3,10,2,10,2,6,2,8
Z,4,10,5,8,4,6,8,6,10,7,7,10,1,9,8,8
V,9,13,7,7,3,7,11,5,6,8,10,5,4,11,4,7
C,3,4,4,3,2,4,9,5,8,11,9,11,1,9,3,7
D,3,9,5,7,5,8,7,6,6,7,6,4,3,8,3,7
Z,3,8,4,6,3,7,8,3,11,8,6,8,0,8,7,7
B,7,13,6,8,4,9,5,5,5,11,4,9,6,7,7,11
Y,5,11,7,8,7,8,7,5,4,7,7,8,6,8,8,3
C,6,10,6,8,3,3,8,5,8,10,10,14,1,7,3,7
D,8,14,8,8,5,9,5,4,6,10,3,7,5,6,5,10
E,5,9,7,7,5,9,6,2,8,11,5,9,3,7,5,10
B,3,9,5,6,5,8,7,6,6,7,6,5,2,8,6,9
R,4,9,5,7,3,6,10,9,4,7,5,8,3,8,6,11
O,1,3,2,1,1,8,7,6,4,9,6,8,2,8,2,8
J,4,11,5,8,4,6,7,3,5,15,7,11,1,6,1,6
P,3,7,5,5,4,5,10,4,4,10,8,4,1,10,3,7
R,2,1,3,2,1,6,10,8,2,7,5,8,2,7,5,10
I,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
L,4,8,6,6,3,10,3,2,8,9,2,9,1,6,3,9
E,3,2,3,4,3,7,7,6,7,7,6,9,2,8,6,9
Y,1,3,2,1,1,8,10,1,6,5,10,8,1,11,1,8
A,9,15,7,8,4,8,2,3,2,8,4,12,5,5,5,7
Q,3,6,5,4,4,8,5,7,4,7,7,6,3,6,5,8
Z,1,3,2,2,1,7,7,4,7,6,6,8,1,8,6,8
B,5,10,7,8,7,7,7,8,7,6,6,6,2,8,8,10
F,1,0,1,0,0,3,12,4,2,11,8,6,0,8,2,7
C,6,12,4,6,2,6,9,6,8,11,8,10,2,8,5,9
M,3,6,5,4,4,11,5,3,4,9,3,6,7,6,2,8
W,3,3,4,1,2,5,11,3,2,9,9,7,6,11,1,6
O,5,10,6,8,6,8,6,7,3,10,4,8,4,9,4,6
N,5,8,5,6,4,8,7,13,1,6,6,7,6,8,1,10
B,4,7,6,6,7,8,8,5,5,7,6,8,7,7,9,6
Q,5,8,6,12,10,8,10,4,2,6,8,10,7,14,9,14
U,5,10,5,7,2,7,4,15,6,7,13,8,3,9,0,8
Y,5,8,6,6,3,4,10,2,8,11,11,6,1,11,3,4
E,4,10,5,8,7,7,8,3,6,5,7,10,5,10,10,11
C,5,10,6,8,2,6,7,7,10,6,6,15,1,8,4,9
I,2,7,3,5,1,7,8,0,7,13,6,7,0,8,1,7
U,5,8,6,6,3,4,8,6,8,9,8,9,4,10,4,3
N,4,5,5,4,3,7,9,5,5,7,7,6,5,9,2,5
E,5,8,7,6,4,4,9,3,9,11,9,10,2,8,4,5
S,2,3,3,2,1,7,7,2,7,10,5,8,1,8,4,8
K,1,0,1,0,0,4,6,5,1,7,6,10,2,7,1,10
U,5,9,5,6,2,7,4,14,5,7,14,8,3,9,0,8
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
S,4,6,6,4,6,8,6,4,4,8,6,9,4,8,10,8
L,2,6,3,4,1,3,3,6,8,1,1,5,0,7,1,6
E,1,3,3,2,1,6,7,2,7,11,7,9,1,8,3,8
Z,5,9,6,7,5,8,7,5,10,7,6,8,1,8,7,8
L,4,4,4,6,1,0,0,6,6,0,1,5,0,8,0,8
X,4,4,5,6,1,7,7,4,4,7,6,8,3,8,4,8
Q,7,12,7,6,4,9,4,4,8,11,4,10,3,7,8,11
J,2,9,2,6,2,14,4,4,4,12,1,8,0,7,0,8
R,2,3,3,1,2,8,8,3,5,9,5,7,2,7,4,10
A,2,3,3,2,1,10,2,2,1,8,2,9,2,5,1,9
C,4,8,5,6,2,5,9,7,8,12,9,11,1,10,3,7
H,8,10,11,7,7,6,7,3,7,10,8,9,3,8,3,7
Q,2,2,3,3,2,7,9,4,2,7,8,10,2,9,4,8
Y,5,9,5,6,3,3,10,2,6,10,12,7,2,11,2,5
Z,1,0,2,1,0,7,7,3,11,8,6,8,0,8,6,8
O,3,4,4,2,2,7,7,7,5,7,5,8,2,8,3,8
V,6,9,6,7,4,3,12,2,3,9,11,8,2,10,1,8
W,4,10,7,8,6,7,8,4,1,7,9,8,7,11,0,8
Y,3,5,6,7,1,6,11,2,3,9,12,8,1,10,0,8
I,3,8,4,6,2,7,7,0,9,14,6,8,0,8,1,8
G,3,4,4,3,2,6,6,6,6,9,7,11,2,9,4,10
L,2,3,2,2,1,4,5,2,7,3,2,8,0,7,1,6
E,7,12,5,6,4,8,7,4,4,10,5,9,3,9,8,11
Y,3,8,6,6,3,7,9,1,6,6,11,8,2,11,2,8
U,8,11,8,8,4,3,9,5,7,11,12,10,3,9,2,6
H,4,9,6,6,5,6,7,7,6,7,6,11,3,8,3,9
G,3,2,4,4,3,6,6,6,6,6,6,10,2,9,4,9
S,4,4,5,6,2,7,6,5,9,5,6,10,0,8,9,8
Y,4,6,6,8,6,10,11,5,4,6,7,7,5,10,7,4
Z,6,10,8,8,5,7,8,2,9,12,7,6,2,8,6,7
B,5,11,7,8,7,9,6,3,7,10,4,8,3,7,5,10
M,4,6,5,4,2,8,7,12,1,7,9,8,8,6,0,8
N,4,7,6,5,3,9,7,3,4,10,5,6,5,8,1,7
Y,3,4,5,5,1,8,12,2,3,6,12,8,1,10,0,8
L,4,9,5,7,3,3,3,6,8,1,0,6,0,6,1,6
R,4,10,5,8,3,5,9,10,5,7,5,8,3,8,6,10
E,1,0,2,1,1,5,7,5,8,7,6,12,0,8,6,9
A,6,9,6,4,3,11,0,4,1,11,5,13,4,5,4,11
F,4,10,6,7,5,3,11,3,5,11,10,7,1,10,3,6
K,8,11,11,8,9,8,6,1,6,9,5,9,7,7,6,9
J,3,10,4,8,5,10,6,2,4,9,4,6,3,7,6,8
Y,5,6,7,8,1,6,11,3,2,9,12,8,1,11,0,8
L,4,8,6,6,4,7,4,1,8,8,2,9,1,6,3,8
F,5,7,7,5,6,7,8,5,5,7,6,8,4,11,9,10
O,5,10,7,8,5,8,7,9,4,7,6,7,3,8,4,9
F,2,3,4,2,1,6,10,3,5,13,6,5,1,9,1,7
B,1,0,1,1,1,7,7,7,4,6,6,7,1,8,6,9
K,3,5,4,4,3,5,7,4,7,6,6,11,3,8,5,9
M,6,9,8,8,11,7,7,4,4,6,6,8,11,8,5,5
G,3,9,5,6,4,7,6,7,5,6,6,8,2,7,5,11
O,4,6,4,4,3,6,8,7,4,10,8,8,3,8,2,7
L,2,5,4,4,2,6,4,1,8,8,2,10,0,7,2,8
A,3,6,4,4,3,8,2,2,2,7,2,8,2,6,2,7
P,3,6,5,9,7,7,11,5,0,9,7,5,4,11,5,8
F,2,4,4,3,1,5,11,3,4,13,7,4,1,9,1,7
V,5,7,5,5,2,4,11,3,3,9,11,7,3,10,1,7
A,4,9,6,7,6,8,7,8,4,7,6,8,3,8,8,4
T,5,11,5,6,2,5,11,2,7,12,8,5,2,9,3,4
B,5,10,6,7,6,9,6,3,5,7,6,7,7,8,6,9
W,4,3,5,2,2,5,11,3,2,9,9,7,6,11,1,6
G,1,3,2,2,1,7,6,5,4,7,6,10,2,9,3,9
V,3,6,5,4,1,8,8,4,2,6,14,8,3,10,0,8
U,6,10,5,5,3,7,5,4,5,3,8,6,5,7,2,7
C,4,8,5,6,3,6,7,6,7,12,8,11,2,10,4,7
H,3,3,5,2,2,8,6,3,6,10,5,8,3,8,3,8
T,4,10,6,7,4,9,11,2,8,5,12,8,1,11,1,9
E,5,7,7,5,4,10,6,2,8,11,4,8,2,8,5,12
O,6,10,8,8,10,8,7,6,1,7,6,8,9,10,6,11
R,4,8,6,6,7,5,6,4,4,7,6,9,8,11,7,6
N,6,11,8,8,5,9,7,3,5,10,4,6,6,8,1,7
N,11,14,9,8,4,10,12,6,4,4,6,10,5,11,2,5
S,5,8,7,7,9,7,8,5,5,7,7,7,5,10,10,11
F,3,8,4,6,2,1,14,5,3,12,9,5,0,8,2,6
Z,3,7,4,5,3,7,7,3,12,8,6,8,0,8,7,8
N,7,9,8,4,3,5,9,3,4,13,9,9,5,8,0,8
J,2,4,4,3,1,9,6,2,6,14,5,10,0,7,0,7
W,3,4,4,3,2,7,11,2,2,6,9,8,6,11,0,8
L,4,10,4,7,2,0,2,4,6,1,0,8,0,8,0,8
C,9,14,7,8,5,8,6,5,3,9,8,10,6,8,9,10
U,3,4,4,3,2,5,8,5,7,10,9,9,3,9,2,6
K,6,10,8,8,8,6,9,6,5,8,5,8,4,7,6,11
Z,4,11,6,8,7,8,7,3,9,7,6,7,1,7,11,9
D,4,4,5,6,3,5,6,10,9,5,5,5,3,8,4,8
H,5,10,7,8,7,6,8,5,5,7,6,7,6,7,6,11
V,2,8,4,6,1,9,8,4,2,6,13,8,3,10,0,8
R,4,4,5,6,3,6,9,10,5,6,5,8,3,8,5,10
L,4,11,5,8,3,6,4,3,8,6,1,8,1,6,3,7
A,3,6,5,4,3,8,2,2,2,7,2,8,2,6,2,7
M,4,7,5,5,3,8,7,12,1,6,9,8,8,6,0,8
H,3,3,4,2,2,7,7,6,6,7,6,8,3,8,3,8
A,2,1,4,2,1,7,2,2,2,6,2,8,2,6,2,7
R,4,8,6,6,4,9,7,3,6,10,3,6,3,7,4,10
C,3,5,4,3,2,6,8,7,8,8,8,13,1,9,4,10
B,3,2,4,4,4,7,7,5,5,6,6,6,2,8,6,9
U,3,6,4,4,3,7,5,12,4,7,11,8,3,9,0,8
D,6,7,8,6,6,6,7,5,6,4,3,6,3,8,5,6
H,3,8,4,6,3,8,7,13,1,7,6,8,3,8,0,8
U,3,3,4,2,2,5,8,5,7,10,9,9,3,9,2,6
D,4,9,6,6,6,8,8,5,5,10,5,5,3,8,3,8
L,3,6,3,4,2,0,2,4,5,1,1,7,0,8,0,8
D,5,4,5,6,3,5,7,10,9,7,6,5,3,8,4,8
G,7,9,9,8,10,7,8,6,3,7,7,8,9,11,8,9
E,3,8,4,6,5,6,7,3,6,8,7,10,4,10,8,10
B,6,10,9,7,7,11,6,2,7,11,3,8,4,7,5,12
F,4,8,5,6,3,7,10,4,5,13,7,5,2,9,2,7
U,4,4,5,3,2,4,8,5,8,10,9,9,3,9,2,5
U,9,11,10,8,5,3,9,6,8,11,11,9,3,9,2,6
I,4,11,5,8,3,8,6,0,7,13,6,9,2,7,3,8
O,4,7,4,5,3,8,7,8,5,10,6,8,3,8,3,8
D,4,6,6,4,4,8,8,5,5,9,5,4,4,8,4,8
N,4,9,6,7,4,7,8,6,5,7,6,6,6,9,2,5
E,2,6,4,4,3,7,8,6,8,6,5,9,2,8,6,9
I,4,8,5,6,3,7,6,2,7,7,7,8,3,8,4,8
N,4,8,6,6,5,7,6,8,5,6,4,7,3,7,3,8
X,4,10,6,8,6,6,7,3,6,7,7,11,5,6,7,7
F,8,11,7,6,5,7,12,3,4,12,6,3,4,10,8,5
G,4,8,5,6,2,8,6,8,8,6,6,9,2,7,6,10
Y,10,9,8,13,5,8,9,3,3,6,11,5,4,10,7,7
X,4,8,6,6,3,7,8,0,7,9,7,8,2,8,3,7
Q,4,7,5,9,6,10,10,6,3,3,8,12,3,9,7,10
T,2,3,3,4,1,8,14,0,6,6,11,8,0,8,0,8
B,4,6,5,4,4,9,7,3,6,10,4,6,2,8,5,10
B,2,1,2,2,2,7,8,8,5,7,6,7,2,8,8,9
G,5,10,7,7,6,6,6,7,5,5,6,10,2,8,4,8
V,3,7,5,5,1,8,8,4,2,7,14,8,3,10,0,8
P,7,9,6,4,3,5,12,6,1,11,5,4,4,10,4,8
Y,4,4,6,6,6,10,9,5,3,7,7,7,5,10,6,5
N,4,10,6,7,5,6,10,5,3,7,7,9,5,9,1,8
V,3,8,5,6,6,6,6,5,2,8,8,9,5,9,5,8
X,5,8,6,7,7,6,8,2,5,8,7,10,2,8,7,8
A,4,6,7,8,2,8,6,3,1,7,0,8,3,7,2,8
F,5,11,5,8,4,1,12,4,4,11,10,8,0,8,2,6
S,4,10,5,8,4,8,7,8,6,8,5,7,2,8,9,8
X,4,5,5,7,1,7,7,5,4,7,6,8,3,8,4,8
U,3,8,5,6,8,7,6,4,3,7,7,8,7,10,5,6
B,4,9,6,6,6,7,8,6,4,6,4,6,4,8,6,7
K,8,15,8,8,6,6,7,2,6,10,4,9,5,5,3,7
B,5,9,8,7,6,8,7,5,6,9,5,6,3,8,7,10
V,3,3,4,2,1,5,13,4,3,10,11,6,2,10,1,8
S,4,7,6,5,3,9,7,5,8,11,2,8,2,6,4,11
M,5,7,8,5,6,3,7,4,5,11,11,10,5,8,2,6
V,3,4,5,6,1,7,8,4,3,7,14,8,3,9,0,8
Q,2,2,3,3,2,8,6,6,3,6,6,9,2,9,3,8
B,5,9,8,8,9,7,8,5,4,8,6,8,7,9,8,8
B,4,10,4,8,4,6,6,10,7,6,6,7,2,8,9,10
F,3,4,5,3,2,6,10,2,6,13,6,5,1,9,3,7
B,8,9,6,5,3,9,7,6,6,10,4,9,6,6,6,10
N,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
B,1,3,2,2,2,8,6,2,4,10,5,7,2,8,2,9
S,7,11,7,6,3,8,6,4,4,13,6,9,3,8,3,8
U,8,10,8,8,5,4,8,5,9,11,10,9,3,9,2,6
Z,2,4,4,3,1,7,8,2,9,11,7,7,1,8,5,6
K,4,9,5,7,4,3,8,7,3,7,5,11,3,8,2,11
E,3,3,4,5,2,3,7,6,11,7,6,15,0,8,7,7
E,6,9,8,8,10,6,7,4,3,7,6,9,7,11,11,12
P,5,10,7,8,4,10,7,4,6,12,3,4,2,9,3,9
Y,2,3,3,2,1,6,10,1,6,8,11,8,1,11,2,8
O,6,10,8,8,5,8,10,9,6,8,8,5,5,8,5,9
C,6,11,7,8,9,7,5,4,4,8,7,11,6,10,5,8
G,4,10,5,7,4,5,6,6,5,9,8,11,2,9,4,10
F,6,9,8,10,8,6,10,4,4,9,7,6,5,8,9,8
A,4,11,7,8,5,12,2,2,2,10,2,9,2,6,3,8
O,4,6,4,4,3,7,7,7,4,10,7,8,3,8,3,8
Q,6,8,6,9,7,7,8,6,3,8,8,10,3,9,6,7
Q,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8
I,1,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8
Y,3,5,4,3,2,4,11,2,7,11,10,6,1,11,2,5
N,5,7,7,5,3,7,7,3,4,10,7,8,5,8,0,7
W,3,4,5,3,3,9,11,3,2,5,9,7,6,10,0,8
I,6,10,5,6,3,7,10,3,4,12,5,4,2,9,6,9
A,6,9,5,4,2,11,3,3,1,9,3,10,4,5,4,10
C,4,9,6,7,4,7,8,8,6,6,6,9,4,7,4,8
Q,6,12,5,7,4,12,4,4,5,11,2,7,4,9,5,13
A,1,1,2,1,0,7,4,2,0,7,2,8,2,7,1,8
U,4,8,5,6,3,4,9,5,6,9,8,9,3,9,2,6
W,5,5,7,7,4,8,8,5,2,7,8,8,9,9,0,8
K,2,3,3,2,2,6,7,1,6,10,7,10,3,8,1,8
M,4,9,5,6,3,7,7,12,1,7,9,8,8,6,0,8
X,2,4,4,3,2,10,6,2,8,10,3,7,2,8,3,9
O,5,9,6,7,4,8,7,8,5,7,6,8,3,8,3,8
W,3,4,4,3,3,4,10,4,2,9,8,7,5,11,1,6
E,2,5,4,3,2,6,8,2,7,11,7,9,2,8,4,8
E,2,3,2,2,1,7,7,5,7,7,5,8,2,8,5,10
F,2,4,3,3,2,5,10,4,5,10,9,5,2,9,3,6
Y,3,8,5,5,1,6,12,2,3,9,12,7,1,10,0,8
V,8,13,7,7,4,5,10,4,5,9,9,5,4,10,2,8
J,4,10,4,8,3,15,3,3,5,12,0,7,0,8,0,8
X,3,3,5,2,2,5,9,2,7,10,9,8,2,8,3,6
W,5,7,7,6,9,7,6,4,5,7,5,8,9,9,7,8
X,2,3,3,2,2,7,7,3,9,6,6,8,2,8,6,8
D,5,10,5,6,4,6,8,4,6,9,6,6,5,9,6,5
G,5,11,6,8,5,6,6,6,6,10,6,13,3,8,4,9
T,8,12,8,7,4,7,8,2,7,12,7,7,3,9,5,6
B,2,3,3,1,2,7,7,4,5,7,6,6,1,8,5,9
N,6,7,8,5,4,4,11,3,4,10,10,9,5,8,1,8
Q,5,13,5,8,4,8,6,5,7,11,5,7,3,8,9,9
A,6,10,5,5,3,12,5,4,1,8,3,9,5,6,4,10
F,8,10,7,5,3,6,10,3,7,11,7,6,2,9,5,6
I,2,8,3,6,2,7,7,0,6,12,6,8,0,8,1,8
B,3,3,3,4,3,7,7,9,6,7,6,7,2,8,9,10
Q,2,3,3,3,2,9,10,5,2,5,7,10,3,9,5,10
J,0,0,1,0,0,12,4,4,3,11,4,10,0,7,0,8
Y,9,14,8,8,5,4,9,4,3,11,10,6,4,11,4,5
C,4,7,6,5,6,6,6,4,4,8,6,11,6,9,3,9
X,5,10,8,8,7,8,6,3,6,7,4,6,6,7,9,6
G,4,7,6,5,4,6,6,6,5,6,6,11,2,9,4,9
K,4,9,5,6,2,3,7,8,2,7,6,11,4,8,2,11
C,4,9,5,6,2,6,8,7,11,5,6,13,1,7,4,9
F,1,0,2,0,0,3,12,5,2,11,8,6,0,8,2,7
S,5,11,7,9,5,8,7,3,6,10,7,8,2,8,5,8
R,5,9,5,4,4,9,7,4,6,10,2,6,5,5,5,6
R,4,11,4,8,3,6,9,10,5,7,6,8,3,8,6,11
K,3,9,4,6,2,3,7,7,2,7,6,11,3,8,3,10
R,2,6,2,4,2,6,8,8,4,7,5,7,2,7,4,10
E,4,8,6,6,6,8,10,6,4,6,6,9,4,6,7,9
N,5,9,7,6,7,6,8,3,4,8,7,8,6,9,5,4
Z,3,7,4,5,2,7,8,2,10,11,7,8,1,9,6,7
D,4,5,5,4,4,7,7,7,7,7,6,5,2,8,3,7
P,2,4,3,2,2,5,10,4,4,10,8,4,1,10,3,7
E,5,6,6,6,6,7,8,5,4,8,7,10,6,10,8,11
Z,4,9,5,7,2,7,7,4,15,9,6,8,0,8,8,8
R,3,8,3,6,3,6,9,8,3,7,5,8,2,7,5,11
X,2,1,2,1,0,7,7,4,4,7,6,8,2,8,4,8
N,8,13,10,8,5,5,8,2,4,12,7,9,6,8,0,7
S,3,10,4,8,2,9,8,6,10,5,5,5,0,7,9,7
W,7,11,7,8,8,4,10,2,3,9,8,7,7,11,2,6
Q,4,6,5,7,5,9,7,7,3,5,7,9,3,9,6,9
Y,2,2,3,3,1,7,10,1,7,7,11,8,1,11,2,8
C,2,4,3,2,1,4,9,4,7,12,10,10,1,9,2,7
J,2,8,2,6,1,12,3,9,4,13,4,12,1,6,0,8
M,3,7,4,5,5,8,7,6,3,7,5,8,5,9,5,8
L,3,8,4,6,2,9,3,1,7,9,2,10,0,7,3,9
U,5,10,5,7,2,8,5,13,5,6,15,8,3,9,0,8
S,4,5,5,7,3,9,9,6,10,5,5,5,0,7,9,8
A,3,5,5,8,2,7,6,3,1,6,0,8,2,7,1,8
F,8,10,7,6,5,6,12,3,4,11,6,3,4,10,7,6
G,6,10,8,8,5,6,6,7,8,6,5,10,2,9,4,8
B,5,8,8,6,5,9,7,4,7,10,5,6,2,8,6,10
U,6,10,6,8,4,4,8,5,7,10,9,9,3,9,2,6
D,6,9,8,6,6,7,7,8,5,7,6,5,4,8,4,7
F,8,13,7,7,4,5,9,2,7,11,7,6,2,10,5,5
H,4,5,6,7,5,9,10,3,2,8,7,7,3,10,8,6
P,6,9,8,7,4,9,9,3,6,14,4,3,1,10,3,9
E,7,9,5,5,2,7,7,5,7,10,6,9,1,9,7,9
H,3,6,4,4,2,7,7,14,1,7,6,8,3,8,0,8
L,5,9,5,7,1,0,0,7,6,0,1,4,0,8,0,8
X,4,8,6,6,4,9,6,1,8,10,3,7,3,9,4,9
D,2,3,3,2,2,7,7,6,6,7,6,5,2,8,3,7
Z,5,10,6,8,3,7,7,4,15,9,6,8,0,8,8,8
F,3,2,4,4,3,4,11,3,6,11,10,5,1,10,3,6
X,7,11,6,6,3,5,8,3,9,10,9,10,4,7,4,5
O,4,7,4,5,3,7,7,7,4,9,7,10,3,8,3,9
T,6,11,5,6,2,5,10,3,7,13,7,5,2,8,4,4
W,10,10,10,5,4,8,10,4,2,5,10,7,10,12,2,6
G,1,0,2,0,1,8,7,5,5,6,6,9,1,8,5,10
Z,3,10,4,8,5,6,8,5,9,7,7,9,2,9,7,8
L,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8
I,5,8,6,6,4,7,6,2,7,7,6,8,0,9,4,8
Q,6,7,6,9,7,8,5,6,3,9,5,11,5,7,7,5
M,8,10,8,6,5,4,9,5,4,4,3,12,9,10,2,8
Z,1,4,2,3,2,8,7,5,8,6,6,7,2,8,7,8
O,9,14,6,8,5,5,7,7,4,10,7,10,5,10,5,8
B,4,6,5,4,4,7,9,5,6,10,6,6,2,8,6,8
J,1,3,2,2,1,10,6,2,6,12,4,8,0,7,1,7
J,2,7,3,5,1,14,2,6,5,14,2,11,0,7,0,8
C,5,10,6,7,3,5,9,7,8,6,8,14,1,7,4,9
U,3,7,3,5,3,7,7,11,4,7,11,8,3,9,0,8
B,4,2,4,4,4,7,7,5,5,6,6,6,2,8,7,11
N,6,11,9,8,5,3,10,3,4,10,11,10,5,7,1,7
X,3,6,4,4,2,7,7,4,4,7,6,8,3,8,4,8
J,2,7,2,5,1,14,2,6,5,14,1,10,0,7,0,8
N,4,4,5,6,2,7,7,14,2,4,6,8,6,8,0,8
F,3,7,4,5,3,8,9,2,6,13,5,5,1,10,2,9
R,4,10,5,7,3,5,11,8,4,7,4,8,3,7,7,11
W,1,0,2,1,1,7,8,4,0,7,8,8,5,10,0,8
K,6,8,9,6,4,3,9,3,7,11,11,11,4,7,3,6
F,3,6,5,4,3,5,10,2,6,10,9,6,4,10,3,6
T,5,11,7,9,5,7,12,3,7,8,12,8,2,12,1,7
L,6,14,6,8,4,10,3,4,3,12,6,10,4,9,6,9
H,3,7,6,5,3,4,8,3,6,10,10,9,3,8,3,6
P,3,3,5,2,2,7,10,3,4,12,4,3,1,10,3,8
G,9,13,7,7,5,9,6,5,4,9,5,7,3,9,8,9
F,4,9,6,6,5,8,8,2,6,12,5,6,3,8,3,8
B,2,3,3,2,2,7,7,5,5,7,6,6,2,8,5,9
O,5,9,6,6,5,7,8,7,6,7,6,6,2,8,3,8
N,10,15,12,8,5,10,6,4,4,13,2,7,7,7,0,6
C,4,10,5,8,4,6,7,6,8,5,7,12,1,7,4,9
P,2,4,4,3,2,8,9,3,4,12,4,4,4,10,4,8
F,5,9,7,6,7,7,6,6,4,7,6,8,4,10,8,11
K,3,9,4,6,2,3,7,7,2,7,7,11,4,8,3,10
G,3,8,4,6,2,7,6,7,8,6,6,8,1,8,6,11
A,2,4,4,3,2,8,2,2,1,7,2,8,2,7,3,6
X,7,11,7,6,3,4,8,3,9,9,11,10,4,11,4,5
O,1,0,1,0,0,7,7,6,4,7,6,8,2,8,3,8
Q,5,8,6,9,6,8,6,7,3,9,7,10,3,8,6,7
N,5,5,6,4,5,7,9,5,4,7,4,8,6,9,4,6
J,4,11,5,8,3,7,8,2,6,14,5,8,1,8,1,8
C,7,11,7,8,5,6,6,6,7,13,8,13,4,9,4,6
S,4,8,6,6,8,8,8,3,3,7,6,7,3,7,13,4
G,6,11,7,8,6,6,6,6,5,5,6,10,2,9,4,8
T,4,8,5,6,4,6,11,2,7,11,9,5,2,11,3,4
J,4,10,6,7,3,7,6,3,5,15,7,11,1,6,1,7
K,5,6,7,4,5,6,7,1,6,10,7,10,3,8,3,8
Q,4,7,4,9,4,7,7,6,3,8,8,10,3,9,6,8
B,5,10,5,8,4,6,9,9,8,7,5,7,2,8,9,10
D,5,9,5,4,3,8,7,3,6,10,5,7,5,8,7,6
U,6,7,6,5,3,3,8,5,7,10,10,9,3,9,2,6
S,3,7,4,5,2,7,5,6,9,4,6,9,0,9,9,8
X,3,9,4,7,3,7,7,4,4,7,6,8,2,8,4,8
W,5,7,5,5,4,6,10,4,3,8,7,6,6,12,3,6
Z,9,10,7,14,6,8,7,4,2,12,6,8,3,9,13,6
D,2,3,4,2,2,9,7,4,6,10,4,6,2,8,3,8
H,2,1,3,2,2,6,8,6,5,7,6,8,3,8,3,8
K,1,1,2,1,0,4,6,6,2,7,6,11,3,8,2,10
P,2,5,4,3,2,7,10,3,4,12,5,3,1,10,2,8
U,6,10,6,8,3,7,3,15,6,7,13,8,3,9,0,8
W,7,8,9,7,11,7,8,5,5,6,5,8,10,10,9,7
Q,4,7,6,7,3,9,6,8,7,7,5,10,3,8,4,8
G,3,9,4,7,2,7,6,8,7,6,6,10,2,7,5,10
I,6,10,6,6,3,8,8,3,5,13,4,5,2,9,5,11
I,1,6,3,4,3,8,7,2,4,8,5,5,3,9,4,5
Y,5,7,5,5,2,3,10,2,7,11,11,6,1,11,2,5
G,5,10,6,7,4,6,7,7,7,9,8,11,2,8,5,9
K,2,4,4,3,2,7,7,1,7,10,5,9,3,8,3,8
A,2,1,4,2,1,9,2,2,1,8,2,8,2,6,2,7
C,4,9,4,4,3,7,7,4,3,9,8,10,3,9,8,10
W,9,11,9,6,5,1,10,3,4,11,12,9,8,11,1,5
Q,5,10,7,9,4,9,9,8,6,5,9,9,3,7,5,9
X,4,7,6,5,5,7,8,2,6,7,6,8,4,8,5,8
K,4,5,5,4,5,8,7,2,4,8,3,8,4,4,3,9
G,4,8,6,7,6,7,8,5,3,7,6,9,6,11,8,10
I,1,2,1,3,1,7,7,1,7,7,6,8,0,8,2,8
T,3,4,3,3,1,5,12,3,6,11,9,4,1,11,2,5
X,5,5,6,7,2,7,7,5,4,7,6,8,3,8,4,8
J,2,8,3,6,2,12,3,7,3,13,5,11,1,6,0,8
Z,4,9,6,6,4,7,8,2,10,12,7,8,1,9,6,8
O,6,10,8,8,5,7,7,8,4,6,6,11,5,8,5,6
E,3,8,4,6,2,3,7,6,11,7,6,15,0,8,7,7
N,4,7,6,5,4,12,7,3,5,10,0,4,5,9,1,7
K,8,12,7,7,3,7,7,3,6,9,6,8,6,8,4,7
Z,4,8,6,6,6,8,8,3,8,7,7,7,1,8,9,8
C,3,4,5,7,2,5,8,7,10,6,7,12,1,7,4,8
O,4,4,6,6,2,8,7,8,8,7,6,9,3,8,4,8
Y,3,5,5,8,6,7,11,3,3,7,8,9,3,11,7,5
R,6,10,9,7,8,9,8,6,6,8,5,7,5,9,6,12
T,4,6,5,4,2,5,11,2,8,11,9,5,1,11,2,4
Y,3,9,4,7,2,6,11,0,4,8,11,8,0,10,0,8
W,8,11,8,8,7,6,10,4,3,8,7,6,11,12,4,4
C,2,4,3,3,1,4,8,4,7,10,9,13,1,8,2,7
G,6,10,8,8,8,9,8,6,3,5,7,10,9,8,5,9
P,3,7,4,4,2,4,13,8,1,11,6,3,1,10,4,8
D,6,10,8,8,6,7,7,5,6,7,6,8,7,7,3,8
C,5,5,6,7,2,5,7,6,11,7,6,14,1,8,4,9
V,3,8,5,6,2,6,9,4,1,8,12,8,2,10,0,8
J,3,10,4,8,3,13,3,7,4,13,3,10,1,6,0,8
M,5,9,8,6,10,7,7,3,2,7,4,8,15,6,3,7
M,7,9,10,7,8,12,5,3,5,9,2,5,10,5,2,8
A,2,4,4,3,2,11,2,2,2,9,2,9,2,6,1,8
I,2,9,2,7,2,7,7,0,8,7,6,8,0,8,3,8
K,2,3,4,2,2,6,7,2,6,10,7,10,3,8,2,8
A,3,8,5,6,3,10,3,2,3,9,1,8,2,6,2,8
Q,5,5,6,8,3,8,6,8,8,5,5,8,3,8,4,8
Y,2,3,4,4,2,7,10,1,7,7,11,8,1,11,2,8
X,1,0,2,0,0,7,7,4,4,7,6,8,2,8,4,8
O,2,1,2,2,1,7,7,7,4,7,6,8,2,8,3,8
C,5,11,6,8,2,5,7,7,11,7,6,13,1,9,4,8
P,6,9,8,6,4,9,9,4,6,13,4,2,1,10,4,9
Z,3,4,5,3,2,7,8,2,9,11,6,8,1,9,5,8
L,3,5,4,4,3,7,8,4,6,7,6,8,2,8,7,10
E,3,6,4,5,4,5,8,4,3,7,6,9,4,10,8,11
K,5,4,5,6,2,4,9,8,2,7,4,11,4,8,3,10
V,4,8,6,6,7,8,5,5,2,8,8,8,7,9,4,8
D,4,10,6,7,4,11,6,3,7,11,3,7,4,7,4,9
Q,4,5,5,7,3,8,7,8,6,6,7,9,3,8,5,9
I,1,7,2,5,1,7,7,0,7,13,6,8,0,8,1,8
W,12,14,12,8,5,1,10,4,2,11,12,9,8,10,0,7
H,4,8,5,6,4,7,8,8,4,7,5,6,3,6,7,9
N,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8
I,3,6,4,4,2,6,9,0,6,13,7,7,0,8,1,7
I,1,6,3,4,3,10,6,1,4,8,5,5,3,8,5,6
R,5,11,5,8,6,6,8,9,5,7,6,8,3,8,5,12
K,5,9,5,4,3,6,7,2,5,10,6,9,5,8,3,7
A,1,0,2,0,0,8,3,2,0,7,2,8,2,6,1,8
Z,3,5,5,8,4,12,4,2,5,9,3,7,1,8,5,10
V,6,10,5,8,3,4,11,2,4,9,11,7,3,9,1,8
Z,2,5,4,4,2,7,7,2,10,11,6,8,1,8,6,7
H,3,6,4,4,4,7,7,5,6,7,6,9,3,8,3,8
C,3,6,4,4,1,5,7,7,10,6,6,13,1,8,4,8
M,3,6,5,4,5,10,5,2,2,8,4,8,5,7,2,6
B,3,7,5,5,5,7,8,7,5,7,6,6,2,8,6,9
R,5,9,7,7,8,5,8,4,5,6,5,9,7,6,10,7
N,6,9,5,4,2,6,9,4,5,4,4,10,5,10,2,8
T,4,5,5,4,2,5,12,3,8,11,9,4,1,11,2,4
K,7,12,7,6,3,5,8,3,7,9,9,10,6,8,3,7
F,4,11,7,8,8,9,7,1,5,9,5,5,5,10,6,5
O,5,10,7,8,3,8,7,8,8,7,6,9,3,8,4,8
A,2,1,3,1,1,7,4,2,0,7,2,8,2,7,1,7
L,1,0,2,1,0,2,1,6,5,0,2,4,0,8,0,8
K,4,8,6,6,4,6,7,2,6,10,7,10,3,8,3,8
D,3,4,4,3,3,6,7,6,6,7,6,5,2,8,3,7
O,6,11,6,8,7,8,6,8,4,9,4,8,3,8,3,8
M,2,1,3,2,1,8,6,10,1,6,9,8,7,5,0,8
P,4,10,5,8,4,4,12,6,5,12,9,3,1,10,4,6
I,6,7,8,8,7,8,8,4,5,6,6,7,3,8,10,10
B,4,8,6,6,5,7,7,6,6,9,6,6,3,8,7,8
V,2,1,4,2,1,5,12,3,3,9,11,8,2,11,1,8
K,2,3,3,1,2,6,7,4,7,7,6,11,3,8,4,9
D,3,2,4,3,2,8,7,7,7,7,6,4,2,8,3,7
R,3,6,5,4,5,7,8,3,4,6,6,9,6,9,7,6
U,2,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8
R,4,5,6,4,3,9,7,2,6,10,3,6,2,7,3,10
H,4,5,5,3,4,7,8,6,6,7,6,8,3,8,3,7
A,4,9,6,6,5,10,3,1,2,7,3,9,5,5,3,7
B,2,4,3,2,2,8,8,3,5,10,5,6,2,8,5,9
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
V,4,4,5,3,2,5,12,2,3,8,10,7,2,10,1,8
U,2,3,3,1,1,6,9,5,6,6,9,9,3,10,1,7
H,4,9,5,6,2,7,9,15,2,7,3,8,3,8,0,8
V,6,7,6,5,3,4,12,3,3,9,11,7,3,9,1,7
A,2,4,4,3,2,7,2,2,2,7,2,8,2,6,3,7
F,3,5,5,4,2,7,9,2,6,13,6,5,4,10,3,7
Z,3,6,4,4,3,6,8,5,10,7,7,10,1,9,7,8
M,3,5,6,4,4,9,6,2,4,9,4,6,8,5,2,8
S,5,10,6,7,4,7,8,3,7,10,7,7,3,7,5,6
E,3,6,4,4,4,8,7,5,3,7,6,9,4,8,7,9
L,5,9,5,5,3,7,5,3,5,12,7,11,3,8,6,9
Q,3,9,5,8,3,8,7,8,5,6,6,9,3,8,5,9
V,3,8,5,6,2,5,11,3,4,10,12,8,3,10,1,8
B,4,10,6,7,5,11,6,3,8,10,3,7,2,8,5,12
M,3,4,5,3,3,8,6,3,4,9,6,8,6,5,2,8
F,4,7,6,5,3,7,10,3,5,13,6,5,2,10,2,7
W,4,9,6,7,9,8,7,5,1,7,6,8,10,11,3,9
S,3,7,4,5,3,8,7,7,6,7,7,8,2,9,9,8
N,5,7,8,5,4,11,7,3,5,11,0,4,5,9,1,7
J,2,8,3,6,1,12,2,9,4,13,5,13,1,6,0,8
E,6,14,5,8,4,8,7,4,4,11,5,9,3,9,8,11
J,4,9,4,7,3,9,8,2,3,12,5,6,2,9,6,9
X,3,6,4,4,2,7,7,4,4,7,6,8,3,8,4,8
W,3,3,4,1,2,3,11,3,2,10,10,8,5,11,0,7
Y,5,10,8,7,4,8,10,1,8,5,12,8,1,11,2,8
J,1,5,2,3,1,10,6,3,5,12,4,9,1,6,1,7
S,3,9,4,7,2,8,8,6,9,5,5,5,0,7,9,8
A,4,9,6,7,5,8,2,3,1,7,2,8,5,8,6,7
V,9,13,9,7,4,6,10,4,3,8,8,5,5,12,3,9
O,5,11,6,8,3,7,7,9,8,7,6,8,3,8,4,8
H,5,9,6,7,6,6,8,7,5,7,5,7,3,7,7,10
L,3,8,4,6,2,7,3,0,9,9,2,11,0,7,2,8
P,4,6,4,8,3,4,11,10,3,10,6,4,1,10,4,8
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
Q,4,7,5,9,5,9,10,7,1,3,7,12,3,10,5,11
Q,8,9,10,8,9,8,4,4,4,8,4,11,6,6,8,7
D,5,10,5,5,3,7,7,4,6,9,6,6,5,9,7,5
V,4,9,5,7,4,9,11,2,3,4,10,8,3,10,2,8
N,4,10,4,8,5,8,7,12,1,6,6,7,6,8,1,10
K,4,11,4,8,2,4,7,8,2,6,4,11,4,8,2,11
S,4,8,4,6,2,7,6,5,9,5,6,9,0,8,9,8
H,4,7,6,5,5,7,8,5,6,8,6,8,6,7,5,9
I,4,8,5,6,3,7,6,2,6,7,6,8,0,9,4,8
Q,4,7,6,9,6,9,8,6,3,4,8,10,4,8,7,11
J,2,6,3,4,1,9,7,1,6,14,4,8,0,7,0,8
R,4,5,5,4,3,8,7,5,6,6,5,6,3,7,6,9
R,2,1,3,1,2,7,7,4,5,6,5,7,2,6,4,8
I,2,6,3,4,1,7,7,0,7,13,6,8,0,8,1,8
Q,4,7,5,9,5,7,10,3,4,7,10,11,3,9,7,7
R,2,1,3,2,2,7,8,4,5,7,5,6,5,6,4,8
R,4,7,6,5,5,8,7,7,6,8,6,8,3,8,6,11
Y,2,3,2,2,1,5,10,1,6,10,9,6,1,11,2,5
T,5,7,5,5,3,6,11,2,6,11,9,5,2,11,2,5
V,3,8,5,6,1,6,8,4,3,8,14,8,3,10,0,8
D,4,8,4,6,4,5,7,9,6,6,5,5,2,8,3,8
W,3,1,5,1,2,6,11,3,2,7,9,9,6,11,0,8
M,5,9,7,6,6,4,7,3,4,10,10,10,7,5,2,7
X,3,6,5,4,3,8,7,4,9,6,6,8,3,8,6,8
M,5,11,9,8,12,7,5,3,2,7,5,8,14,6,4,7
I,6,10,5,6,3,10,6,4,6,14,3,6,2,9,4,11
Z,5,8,7,6,6,9,9,5,4,7,5,7,3,8,9,5
R,7,11,9,8,9,8,9,7,4,8,5,6,5,8,8,12
C,2,3,3,4,1,5,7,6,8,7,6,13,1,8,4,9
E,5,10,4,5,3,9,6,3,4,11,5,9,3,9,8,12
G,7,14,6,8,5,7,6,3,3,8,6,9,4,9,9,7
R,4,7,6,5,6,8,8,7,3,8,5,6,4,7,7,9
Y,7,9,8,7,4,2,10,3,7,12,12,7,1,11,2,5
N,4,7,6,5,3,8,7,7,6,6,6,4,6,9,2,5
H,2,1,3,3,2,8,7,6,6,7,6,8,3,8,3,7
N,2,2,3,3,2,7,8,5,5,7,6,6,5,9,2,5
N,4,7,6,5,4,11,7,3,5,10,1,4,5,9,1,7
A,4,11,6,8,3,7,4,3,2,6,1,8,3,6,3,8
E,3,9,5,6,4,7,7,6,9,8,8,9,3,8,6,8
E,4,9,5,7,6,6,7,5,8,7,6,10,3,8,6,9
T,5,10,5,8,4,6,11,4,6,11,9,5,4,11,4,4
T,6,13,6,7,4,7,9,2,7,12,6,6,2,9,5,5
S,10,15,8,8,4,9,3,4,5,10,2,9,4,5,6,10
T,6,8,8,7,7,6,8,6,6,7,8,7,4,11,9,8
O,5,10,5,8,4,7,7,8,5,9,8,8,3,8,3,8
D,4,7,6,5,4,9,7,5,7,10,4,5,3,8,3,8
Y,4,9,6,6,4,10,10,1,7,3,11,8,1,11,2,9
G,6,10,8,8,8,7,9,6,3,6,5,10,5,7,8,7
I,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8
P,5,11,5,6,3,9,9,4,3,12,5,4,4,11,5,6
K,5,4,5,6,2,4,8,8,2,7,5,11,4,8,2,11
B,5,10,7,8,6,9,7,7,6,6,6,5,3,9,9,11
V,3,4,5,6,1,8,8,4,3,7,14,8,3,10,0,8
N,6,11,9,8,6,7,9,2,4,9,6,7,6,8,1,8
A,4,10,7,8,5,11,3,1,2,8,2,9,6,4,3,8
X,2,6,4,4,2,7,7,4,8,6,6,10,3,8,6,8
E,3,8,4,6,2,3,6,6,11,7,7,15,0,8,7,7
O,4,9,4,7,4,7,7,7,3,9,7,8,3,8,3,7
X,4,9,6,7,4,9,7,0,8,9,4,8,2,8,3,8
X,5,9,6,5,3,9,7,2,7,11,5,7,4,11,3,8
M,7,8,9,7,10,8,8,4,4,7,6,7,10,9,6,3
K,2,3,4,1,2,4,9,2,6,10,9,10,3,8,2,6
U,4,5,5,3,2,6,8,6,8,7,10,9,3,9,1,8
F,2,3,4,2,1,5,11,2,5,13,7,5,1,9,1,7
B,1,1,2,2,1,7,7,8,5,7,6,7,2,8,7,9
F,4,11,5,8,5,5,10,3,5,10,10,6,2,10,3,6
G,4,9,6,7,6,7,7,6,2,7,6,11,4,8,8,8
M,5,9,5,6,3,7,7,12,2,8,9,8,8,6,0,8
R,3,6,5,4,5,5,7,3,4,7,6,9,5,10,7,5
Y,4,5,5,7,7,9,8,5,3,6,8,8,7,11,7,4
V,3,3,4,1,1,5,13,3,2,9,11,7,2,11,1,8
W,3,4,4,3,3,5,11,2,2,8,9,9,6,11,0,8
H,4,7,6,5,7,8,8,4,3,6,7,7,7,9,7,7
K,4,8,6,6,4,5,6,1,7,10,9,11,4,7,4,7
L,4,8,5,6,3,3,5,2,9,3,1,9,0,7,1,5
E,3,7,4,5,4,8,7,4,8,7,7,8,2,8,6,10
S,3,1,3,3,2,8,7,6,5,7,6,8,2,9,9,8
B,6,10,6,8,5,6,5,9,7,6,7,6,2,8,10,10
F,4,8,4,6,3,1,11,4,6,11,10,9,0,8,2,7
H,6,7,8,5,6,10,6,3,6,10,3,7,4,7,4,9
X,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8
N,5,10,7,7,4,10,7,3,5,10,2,5,6,9,1,7
L,6,11,6,6,4,8,4,3,4,12,7,11,3,9,6,9
T,2,7,3,5,2,9,12,0,5,6,10,8,0,8,0,8
E,5,8,7,6,5,4,9,2,8,10,8,9,3,9,4,6
W,2,0,2,1,1,7,8,3,0,7,8,8,6,10,0,8
S,3,4,4,3,2,6,8,3,7,10,8,8,1,8,5,5
V,3,8,5,6,1,8,8,4,3,6,14,8,3,10,0,8
Z,7,7,5,10,5,9,4,4,3,11,6,9,3,9,11,8
T,4,7,6,5,6,7,7,4,5,7,6,9,5,8,5,7
P,5,11,7,8,6,8,9,5,4,12,5,4,2,10,3,8
O,2,4,3,3,2,8,7,6,4,9,5,8,2,8,2,8
M,5,8,6,6,3,8,7,12,2,6,9,8,9,6,0,8
B,3,8,5,6,5,7,7,6,6,6,6,6,2,8,6,9
C,4,8,5,6,3,6,8,8,8,9,8,13,2,10,4,9
E,4,5,5,8,3,3,8,6,12,7,5,15,0,8,7,7
Y,4,6,4,4,2,4,10,2,7,11,11,6,1,10,2,4
L,4,9,6,7,3,6,3,1,9,8,2,11,0,7,2,8
X,4,9,5,4,2,6,8,1,7,11,6,8,3,7,4,6
V,3,5,4,3,1,4,12,4,3,10,11,7,3,10,1,8
V,1,0,2,0,0,8,9,3,2,6,12,8,2,10,0,8
U,4,4,5,3,2,4,9,5,7,11,10,9,3,9,2,7
J,3,8,4,6,3,10,7,0,7,11,3,6,0,7,1,7
L,3,8,4,6,1,0,1,6,6,0,0,6,0,8,0,8
Q,2,2,3,3,2,8,8,5,2,7,8,10,2,9,4,9
D,1,0,1,0,0,6,7,7,5,7,6,6,2,8,3,8
D,2,3,3,2,2,9,7,4,6,10,4,6,2,8,2,8
X,5,9,8,7,5,3,8,1,8,10,11,10,2,9,3,5
P,4,8,5,5,2,4,14,8,1,11,6,3,1,10,4,8
J,2,5,4,4,2,7,7,3,6,15,7,10,1,6,1,7
W,9,10,9,5,4,6,11,2,3,8,10,7,8,12,1,7
O,4,9,5,6,4,7,7,8,6,7,3,8,3,8,3,8
U,4,8,6,6,4,5,9,5,6,7,9,10,3,9,1,8
Y,5,8,5,6,3,3,10,2,7,11,11,6,1,11,2,5
E,7,11,10,8,8,7,7,2,8,11,7,9,3,8,5,8
O,5,11,6,8,4,7,8,8,5,10,8,7,4,7,5,9
N,10,13,12,7,5,3,11,6,3,14,12,9,6,9,0,8
D,4,11,6,8,5,8,7,6,8,7,6,5,6,8,3,7
U,4,7,6,5,3,6,9,6,8,8,10,9,3,9,1,8
I,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
B,5,10,6,7,6,8,7,6,7,7,6,5,2,8,7,10
X,4,6,5,8,2,7,7,4,4,7,6,8,3,8,4,8
F,4,10,4,8,3,1,13,4,4,12,10,6,0,8,2,6
C,5,8,5,6,4,6,7,6,6,12,8,11,2,11,4,7
D,3,8,5,6,4,8,8,4,5,9,5,4,3,8,3,7
X,5,9,6,8,7,7,8,1,5,7,6,8,3,6,8,8
F,3,5,4,4,2,5,10,4,6,10,9,6,1,10,4,6
X,3,1,4,3,2,7,7,3,9,6,6,8,2,8,6,8
L,2,7,3,5,2,5,5,2,8,3,2,7,0,7,1,6
Y,4,10,6,8,3,6,10,2,2,7,12,8,1,11,0,8
C,7,9,5,5,2,7,9,6,7,12,7,8,1,8,6,9
U,7,9,8,6,4,4,8,6,8,10,10,9,3,9,2,5
R,4,9,6,7,4,10,7,3,5,11,2,6,3,7,4,10
F,2,7,4,5,3,5,10,2,6,10,9,6,1,10,3,7
N,8,14,9,8,4,4,10,6,4,14,12,9,6,9,0,9
Z,6,9,8,7,6,6,9,2,9,11,8,7,3,11,7,7
L,5,7,7,5,5,6,6,8,6,5,6,9,2,8,4,10
K,6,11,9,8,7,2,9,2,7,10,12,12,5,6,4,4
K,4,9,5,7,5,5,6,3,6,6,6,10,3,8,5,10
X,3,6,4,5,4,7,8,2,5,8,6,8,2,7,6,7
S,4,7,5,5,3,7,7,4,8,11,8,8,2,10,5,6
M,5,9,6,7,6,8,6,6,5,6,7,8,8,6,2,7
K,5,11,7,8,9,7,6,3,5,6,6,8,7,7,7,7
V,3,5,5,7,2,8,8,4,3,7,14,8,3,9,0,8
J,2,10,3,8,2,9,6,2,6,11,3,8,1,6,2,6
E,4,7,5,5,3,7,8,2,8,11,6,8,2,8,5,8
Z,3,5,5,4,3,8,7,2,9,12,6,9,1,8,5,7
F,3,9,5,7,3,4,11,3,6,11,10,5,1,10,2,5
C,6,10,9,7,6,7,8,8,6,5,6,13,5,8,4,7
I,2,2,1,3,1,7,7,1,7,7,6,8,0,8,3,8
L,1,0,2,1,0,2,1,6,4,0,3,4,0,8,0,8
W,10,12,10,7,5,2,8,3,3,10,11,9,9,10,1,6
N,2,5,4,4,2,7,9,2,4,10,6,7,5,8,1,7
Y,3,4,4,3,2,4,10,1,7,10,10,6,2,10,3,3
U,3,6,4,4,1,8,5,13,5,7,14,8,3,9,0,8
Z,2,5,2,4,2,7,7,5,8,6,6,8,2,8,7,8
K,3,4,5,3,2,8,6,1,6,10,5,9,3,8,3,9
W,7,8,7,6,6,5,11,3,3,9,7,7,8,11,3,5
B,3,6,4,4,4,8,7,5,5,9,6,6,2,8,4,7
S,6,9,8,7,11,7,6,4,2,8,5,8,3,8,13,4
L,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8
L,3,10,5,8,4,7,4,2,7,7,2,9,1,6,3,8
P,4,8,6,6,4,8,8,2,6,13,5,5,2,9,3,9
N,2,4,4,3,2,8,8,2,4,10,5,6,4,9,1,6
B,5,9,8,8,9,7,7,5,4,7,6,8,7,10,9,11
T,2,3,3,2,1,7,11,3,6,7,11,8,2,11,1,8
Z,1,4,3,3,1,7,7,2,9,11,6,8,1,8,5,8
L,3,4,3,6,1,0,1,6,6,0,0,6,0,8,0,8
I,1,4,1,2,1,7,7,1,7,7,6,8,0,8,2,8
S,4,7,5,5,3,6,8,3,6,10,7,8,2,8,4,6
V,8,10,7,7,3,3,12,4,4,10,12,8,3,10,1,8
B,1,3,3,2,2,8,6,2,5,10,5,7,2,8,3,9
R,5,8,7,6,7,8,8,7,4,8,5,6,4,7,7,11
T,3,7,4,5,2,5,13,3,6,12,9,4,1,11,2,4
H,3,2,5,3,3,7,7,6,6,7,6,8,3,8,3,8
Y,3,6,4,4,2,4,10,1,6,9,10,5,1,11,3,4
A,2,3,3,2,1,9,2,1,1,8,2,9,1,6,1,8
K,6,9,9,7,6,2,8,3,7,11,12,12,4,7,4,5
D,5,8,7,6,5,10,6,3,6,11,4,6,3,8,3,9
I,1,8,0,6,1,7,7,5,3,7,6,8,0,8,0,8
F,3,9,5,7,4,4,11,6,5,11,10,5,2,10,3,5
Q,5,9,5,4,3,8,5,4,7,10,5,9,3,8,9,10
X,5,8,7,6,5,8,5,3,5,7,7,9,4,8,10,9
B,3,6,4,4,4,6,6,7,5,6,6,7,2,9,7,10
M,10,14,10,8,5,9,11,6,5,4,6,9,10,11,2,7
X,5,11,8,8,7,7,7,3,8,5,7,10,3,8,6,8
L,4,7,5,5,4,6,8,8,5,7,5,10,3,7,4,11
Q,3,4,4,6,2,8,8,8,6,6,6,9,3,8,4,9
J,1,3,2,2,1,8,6,3,5,14,6,10,1,7,0,7
T,3,8,4,6,3,5,11,2,7,8,11,8,2,11,1,7
P,2,3,4,2,2,7,9,3,4,12,5,4,1,9,3,8
V,9,15,8,8,4,6,11,6,4,10,10,4,5,12,3,9
W,5,11,8,8,7,10,10,3,3,5,9,7,8,10,1,8
R,3,8,4,6,4,5,11,7,3,8,4,9,2,6,5,11
G,5,11,5,8,5,5,7,6,4,9,9,10,2,8,4,10
E,4,7,5,5,4,6,7,7,9,8,8,10,3,8,6,8
S,4,9,5,7,3,9,7,4,8,11,6,7,2,10,5,8
C,6,8,6,6,3,4,9,6,9,13,10,10,1,9,3,7
L,3,9,5,7,3,6,5,0,8,9,3,11,0,7,3,8
Y,3,7,5,5,2,7,11,1,8,6,12,8,1,11,2,8
Y,4,6,4,4,2,2,11,3,5,11,12,7,2,11,2,5
O,3,6,4,4,2,7,6,8,5,7,5,9,3,8,3,8
Z,4,10,5,7,4,7,8,3,12,9,6,8,0,8,8,7
G,8,9,10,8,11,7,8,6,3,7,7,9,9,10,9,9
X,4,9,6,7,4,8,7,4,9,6,7,9,4,7,8,9
N,6,10,8,7,5,11,8,3,6,10,0,3,5,9,1,7
Y,1,3,2,2,1,8,10,1,6,5,10,8,1,11,1,8
R,4,8,5,6,4,8,7,5,6,8,5,8,3,6,6,11
F,1,0,1,0,0,3,11,4,2,11,8,6,0,8,2,8
W,3,1,4,2,2,8,11,2,2,6,9,8,6,11,0,7
A,3,5,6,4,2,8,2,2,2,7,1,8,2,7,2,7
P,2,1,2,2,1,5,9,3,4,9,8,4,1,9,3,7
C,1,0,2,1,0,6,7,6,9,7,6,14,0,8,4,10
D,2,0,2,1,1,6,7,8,7,7,6,6,2,8,3,8
I,1,4,1,3,1,7,7,1,7,7,6,9,0,8,2,8
K,4,4,4,6,2,4,8,8,2,7,5,11,3,8,2,11
D,4,11,4,8,3,5,8,10,8,8,8,5,3,8,4,8
T,7,10,7,8,4,7,10,2,10,11,9,4,3,9,5,5
F,6,13,6,8,5,7,9,3,4,10,6,6,4,9,7,7
L,2,2,3,3,2,4,4,5,7,2,2,6,1,7,1,6
A,2,7,4,4,1,8,6,3,0,7,0,8,2,7,1,8
N,5,9,7,6,4,7,8,6,6,7,7,6,6,10,2,5
T,8,11,8,8,6,6,10,1,9,11,9,5,3,9,4,4
Q,3,5,5,6,5,10,11,6,3,3,8,12,3,10,5,10
Q,4,7,5,8,5,8,11,5,1,5,8,12,2,10,5,8
K,2,1,2,1,0,4,7,6,2,7,6,11,3,8,2,10
Y,3,4,5,5,1,7,10,2,2,8,12,8,1,11,0,8
Z,3,6,4,4,2,8,7,2,9,11,5,8,2,8,6,8
J,4,6,5,4,2,11,5,2,7,14,3,8,0,8,0,8
U,3,1,4,2,1,7,8,6,8,7,10,8,3,10,1,8
R,6,8,8,6,5,10,8,4,7,10,1,7,3,6,4,11
N,5,7,8,5,3,7,8,3,5,10,7,7,5,7,1,8
Y,5,6,5,4,2,3,12,5,5,13,11,6,1,11,1,6
K,11,15,10,8,5,4,8,5,8,9,12,12,6,11,3,6
K,6,9,9,7,6,3,8,3,8,11,12,12,4,7,4,4
N,5,8,7,6,3,10,7,3,5,10,2,5,5,8,1,7
Y,1,0,2,0,0,7,9,2,2,6,12,8,1,10,0,8
E,2,4,3,2,2,6,8,2,7,11,7,9,2,8,4,8
W,9,12,9,7,4,6,10,1,3,7,10,7,8,12,1,6
W,6,9,6,4,4,5,8,2,3,8,9,7,9,10,2,5
G,3,10,4,7,2,7,6,8,8,6,6,11,2,8,5,11
U,4,3,5,5,2,7,4,14,5,7,14,8,3,9,0,8
Y,5,6,6,8,9,8,6,7,4,7,7,8,10,9,4,5
S,7,10,8,8,5,9,8,3,7,10,3,6,3,8,5,9
A,4,10,5,5,4,9,5,5,2,10,6,11,6,5,5,10
Z,3,9,4,7,2,7,7,4,13,10,6,8,0,8,8,8
Z,5,10,7,8,5,7,8,2,9,11,7,7,1,8,6,7
M,4,7,7,5,7,9,6,2,2,8,4,7,7,5,2,6
Q,3,5,4,7,4,8,5,6,3,9,5,11,3,8,4,8
M,5,10,8,8,6,6,6,3,4,9,9,9,8,5,2,7
F,1,0,1,1,0,3,12,4,2,11,9,6,0,8,2,7
V,6,9,6,6,4,3,11,2,3,9,10,8,3,12,1,7
J,2,9,3,6,1,12,3,10,3,13,6,13,1,6,0,8
Y,5,8,7,6,4,9,10,1,7,3,11,8,2,12,2,9
N,5,9,6,7,4,7,9,5,5,7,6,7,6,9,2,6
K,4,9,6,7,7,6,6,3,4,6,6,9,8,7,8,7
O,4,9,4,6,3,8,6,8,5,9,4,7,3,8,3,8
J,3,10,4,8,1,13,1,9,5,14,2,12,0,7,0,8
M,4,5,7,3,4,8,7,3,4,9,6,7,8,6,2,7
B,5,8,7,6,10,8,8,4,3,6,7,7,7,11,9,9
W,7,10,7,8,6,4,10,3,2,9,8,7,7,12,2,5
N,8,12,7,6,3,7,10,4,6,3,5,9,5,9,2,7
H,3,1,4,2,3,7,7,6,6,7,6,8,5,8,4,8
G,4,10,6,7,8,7,5,4,3,7,5,10,8,7,8,13
Z,5,11,6,9,3,7,7,4,15,9,6,8,0,8,8,8
T,1,0,1,0,0,8,14,2,4,6,10,8,0,8,0,8
T,3,4,4,3,1,5,13,3,6,11,9,4,1,11,1,5
R,3,5,6,4,4,7,8,5,4,8,5,8,3,6,4,11
J,1,7,2,5,1,11,3,10,3,12,7,13,1,6,0,8
B,3,7,5,5,4,8,8,3,6,10,5,6,2,8,4,10
M,8,9,12,8,12,8,9,4,4,7,6,7,12,7,7,3
H,5,11,5,8,3,7,6,15,1,7,8,8,3,8,0,8
O,5,7,7,6,5,6,6,6,7,9,6,8,3,6,5,6
L,5,4,5,7,1,0,0,6,6,0,1,5,0,8,0,8
H,3,7,5,5,3,6,7,3,6,10,8,9,3,9,2,6
I,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8
G,5,10,6,8,3,8,6,8,9,6,5,11,2,8,6,10
F,5,12,5,7,4,10,8,3,5,11,4,5,3,7,7,9
A,4,8,6,7,7,10,8,3,4,6,7,6,5,9,5,5
V,3,8,4,6,2,7,9,4,2,7,12,8,2,10,0,8
C,6,11,6,8,4,5,7,7,8,12,9,14,2,10,4,7
B,4,2,4,4,4,7,7,5,5,6,6,6,2,8,6,9
N,3,6,4,4,3,7,9,6,4,7,6,6,5,9,1,6
G,5,9,6,7,4,7,6,7,8,11,6,11,2,11,4,9
M,5,9,6,6,6,8,5,11,0,6,9,8,7,5,0,7
X,5,10,6,8,4,7,7,4,4,7,6,8,3,8,4,8
W,4,10,6,8,4,8,8,4,2,7,8,8,8,9,0,8
K,2,7,4,5,3,5,6,3,6,6,6,10,3,8,5,9
P,3,3,4,5,2,4,12,9,2,10,6,4,1,10,4,8
H,4,2,5,4,4,8,7,6,7,7,6,8,3,8,4,8
Q,3,8,4,9,5,9,7,8,3,5,6,9,3,8,6,10
Q,4,8,5,10,6,8,6,8,5,5,6,8,4,9,7,11
X,4,9,6,6,4,7,6,3,8,5,6,9,3,8,7,8
V,1,1,3,2,1,6,12,2,2,8,11,8,2,10,0,8
B,7,10,5,6,4,6,7,5,5,10,6,8,5,7,8,9
D,4,6,5,5,5,6,7,5,6,7,4,7,4,7,6,5
R,2,2,3,3,2,7,8,4,5,7,5,7,2,7,5,8
C,7,13,5,7,4,7,7,4,3,9,8,11,4,9,9,11
O,5,5,7,8,3,7,8,9,8,8,7,6,3,8,4,8
S,3,4,5,3,2,9,7,3,7,10,4,7,1,9,5,10
Q,4,8,5,10,6,8,5,7,4,9,5,8,3,8,4,7
P,6,6,8,8,9,6,7,5,3,8,7,6,7,13,6,9
G,2,3,3,4,2,7,8,8,7,5,7,9,2,7,5,11
S,6,9,5,5,2,10,2,2,4,10,2,9,2,7,3,10
O,8,15,6,8,4,5,7,6,3,10,7,9,5,9,5,8
B,3,1,4,3,3,8,8,5,6,7,5,5,2,8,6,8
H,6,9,9,6,5,8,7,3,6,10,5,8,3,8,3,7
D,5,10,6,8,6,5,7,9,7,7,6,6,2,8,3,8
B,1,0,1,1,1,7,7,6,4,6,6,7,1,8,6,9
Z,5,9,6,7,3,7,7,4,15,9,6,8,0,8,8,8
S,7,15,6,8,3,9,5,4,4,13,6,9,2,9,3,8
Z,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8
R,8,10,8,5,5,8,9,3,6,9,2,6,7,4,6,6
N,2,0,2,1,1,7,7,12,1,5,6,8,5,8,0,8
G,5,10,6,8,3,7,6,8,8,6,6,9,2,7,6,10
D,8,13,8,7,4,11,3,5,5,13,2,11,5,7,4,9
H,5,6,7,4,4,5,8,4,6,10,8,9,3,8,3,7
H,3,8,4,6,3,8,7,13,1,7,7,8,3,8,0,8
S,9,15,7,8,4,7,5,5,6,8,2,8,4,6,6,8
Z,3,7,4,5,4,9,9,5,3,7,5,7,3,8,9,4
Q,4,6,5,8,4,7,8,5,2,8,9,10,3,9,6,8
X,3,8,4,6,3,7,7,4,4,7,6,8,3,8,4,8
I,1,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8
W,3,9,5,6,3,4,8,5,1,7,8,8,8,10,0,8
I,2,7,4,5,4,10,7,2,4,8,5,5,3,9,5,7
J,2,7,3,5,1,12,2,9,4,13,5,13,1,6,0,8
S,7,10,9,8,12,8,6,5,4,8,6,8,7,8,15,10
F,3,6,3,4,1,2,13,5,3,11,9,6,0,8,3,6
Q,5,6,6,8,6,7,10,4,2,7,9,11,3,9,5,8
Y,1,3,3,1,1,7,11,1,6,7,11,8,1,11,1,8
K,1,0,1,0,0,4,6,5,2,7,6,11,2,8,2,10
O,3,9,4,7,4,7,6,8,4,7,5,11,3,8,3,9
H,4,4,4,6,2,7,10,15,2,7,3,8,3,8,0,8
B,1,0,2,1,1,7,7,7,4,7,6,7,1,8,6,8
Q,3,7,5,5,4,7,4,7,4,7,5,10,2,8,3,9
Z,5,10,6,8,6,9,6,5,9,8,5,7,1,7,7,8
P,8,12,8,6,5,9,8,4,5,13,4,4,4,10,6,8
W,5,9,8,7,7,6,11,2,2,7,8,8,8,11,1,8
J,1,4,3,3,1,9,6,2,6,14,4,8,0,7,0,8
V,3,3,4,2,1,4,13,3,2,10,11,7,2,11,1,8
T,2,3,3,2,1,5,12,2,7,11,9,4,1,10,2,5
L,7,13,6,7,3,8,3,3,5,11,4,13,2,7,6,8
F,4,8,6,6,3,7,10,4,6,13,7,5,2,9,2,7
O,5,7,6,5,4,7,10,8,5,5,8,11,5,9,4,7
N,5,8,7,6,4,7,8,6,5,7,6,5,7,11,3,5
V,4,10,6,8,3,6,11,3,4,7,12,9,2,10,1,9
K,5,9,7,7,7,6,9,5,4,7,4,8,4,7,7,9
X,4,8,6,6,5,7,6,2,6,7,7,9,5,9,7,7
X,2,6,4,4,2,7,8,3,8,6,6,6,3,8,7,7
Y,5,9,5,6,3,3,10,2,7,11,11,6,0,10,2,4
A,7,12,6,7,3,12,2,3,1,10,4,11,4,4,4,9
K,4,7,4,5,2,4,6,8,2,7,7,11,3,8,3,11
P,4,5,4,8,2,3,13,8,2,11,7,3,1,10,4,8
Z,5,9,5,4,3,6,7,2,8,11,8,9,3,8,5,5
J,2,4,3,6,1,14,2,7,5,14,2,11,0,7,0,8
X,3,7,4,5,2,7,7,4,4,7,6,8,3,8,4,8
W,7,12,7,7,4,1,9,2,3,9,11,9,8,10,1,6
S,4,9,4,6,4,8,8,5,8,5,5,6,0,7,9,7
W,6,6,7,4,4,6,11,4,2,8,7,6,9,12,2,5
Q,4,6,5,8,5,7,10,4,3,6,9,11,3,8,6,8
P,4,7,5,5,3,6,11,4,4,13,6,3,1,10,3,9
S,1,0,1,1,0,8,7,4,6,5,6,8,0,8,7,8
X,4,6,7,4,3,5,8,2,8,11,10,9,3,8,3,6
C,4,8,5,6,3,8,7,7,6,6,7,9,4,8,3,9
W,10,11,10,8,6,2,11,2,3,10,11,9,8,10,2,6
W,5,11,8,8,4,10,8,5,2,7,9,8,9,9,0,8
D,5,3,6,4,4,7,7,7,7,7,6,4,3,8,3,7
X,9,15,10,9,6,8,7,2,8,11,5,7,5,9,4,7
D,4,9,6,6,8,9,8,4,5,7,7,6,4,9,8,5
D,6,11,6,6,3,11,3,4,5,12,2,8,5,7,3,10
U,3,3,4,2,2,7,8,5,6,6,9,9,5,10,0,7
A,4,7,5,5,3,7,5,3,0,7,1,8,2,7,1,8
O,1,3,2,1,1,8,7,6,4,9,5,8,2,8,2,8
Q,3,7,4,7,3,7,6,8,5,5,6,8,3,8,5,9
T,4,5,4,4,2,6,11,2,7,11,9,4,1,11,2,4
A,3,2,5,3,2,8,2,2,2,6,1,8,2,6,2,7
T,6,8,6,6,4,6,11,4,5,11,9,5,3,12,2,4
Z,7,8,5,11,5,7,7,5,3,11,7,8,3,9,11,6
C,2,3,3,4,1,6,7,7,9,9,6,13,1,9,4,9
Y,7,8,7,6,4,3,11,2,7,12,11,6,0,10,2,5
H,5,10,5,7,3,7,6,15,1,7,7,8,3,8,0,8
T,2,7,3,4,1,7,14,0,6,7,11,8,0,8,0,8
T,1,0,2,0,0,8,14,2,4,6,10,8,0,8,0,8
A,3,5,4,4,3,8,8,2,4,7,8,8,4,9,3,6
H,1,1,2,1,1,7,7,11,1,7,6,8,3,8,0,8
F,7,11,6,6,3,6,10,2,6,11,7,5,2,9,6,5
O,3,5,4,4,3,8,7,7,4,9,5,8,2,8,2,8
Z,6,7,5,11,4,7,8,4,2,11,7,7,3,9,11,6
G,6,9,8,6,7,7,8,5,3,6,6,9,5,7,7,7
O,7,9,5,5,3,6,8,5,4,8,4,6,5,9,5,7
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
E,7,10,9,8,9,8,7,6,2,7,6,10,5,8,8,10
Z,5,9,6,5,3,10,3,4,6,12,4,10,3,8,7,10
P,2,4,4,2,2,7,10,4,3,12,5,3,1,10,2,8
H,7,11,10,8,8,5,9,3,6,10,9,8,6,11,5,6
V,4,9,6,7,1,8,8,5,3,6,14,8,3,9,0,8
J,4,7,5,5,3,7,5,5,4,14,8,13,1,6,1,6
D,5,10,6,7,5,5,7,9,7,6,5,4,3,8,5,10
G,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10
N,3,4,5,3,2,8,8,3,5,10,4,6,5,9,1,6
A,4,9,6,6,5,6,5,3,3,4,1,6,4,5,4,6
Y,5,6,6,4,2,4,10,2,7,10,11,5,2,11,3,4
M,8,9,11,8,13,8,7,5,5,7,6,7,13,9,8,2
U,4,5,5,4,2,5,8,5,8,10,8,9,3,9,2,6
T,3,11,5,8,1,9,15,0,6,6,11,8,0,8,0,8
N,6,11,7,8,6,8,7,13,1,6,6,7,7,7,1,10
A,3,10,6,7,2,7,3,3,3,7,1,8,3,6,3,7
N,4,9,5,6,4,7,8,6,5,7,6,6,6,9,1,6
Y,2,3,3,2,1,8,10,1,6,6,11,8,1,11,2,7
L,6,12,5,7,4,5,6,3,6,12,8,12,3,7,7,8
T,5,9,6,6,5,6,7,7,7,8,9,8,3,9,6,9
N,4,7,5,5,4,7,6,7,5,6,4,7,3,7,3,7
O,5,10,6,8,5,7,8,8,6,7,8,8,2,8,3,8
W,6,7,6,5,5,3,11,2,2,10,10,8,6,11,2,7
B,3,6,4,4,4,7,7,5,4,6,5,7,3,8,5,7
R,5,5,5,8,3,5,10,10,4,7,5,8,3,7,6,11
J,2,9,3,7,1,14,2,7,5,14,2,10,0,7,0,8
S,6,8,7,6,4,7,8,3,8,10,5,6,2,6,5,8
F,2,1,3,2,2,5,10,4,5,11,9,5,1,10,3,6
I,4,10,5,7,3,7,7,0,7,13,6,8,0,8,1,8
L,4,9,6,6,7,6,7,3,6,7,7,11,6,11,6,5
S,6,11,6,6,3,9,6,4,3,13,7,9,3,10,3,8
L,3,8,5,6,6,6,7,3,6,8,7,11,6,10,5,6
Y,8,13,7,7,4,7,7,5,3,10,8,5,4,9,5,4
E,4,8,6,6,5,7,8,6,9,6,4,9,3,8,6,8
D,4,8,6,6,5,7,7,7,6,6,6,4,3,8,3,7
A,2,4,4,3,2,11,2,3,2,11,2,10,2,6,2,9
T,5,7,5,5,3,7,11,4,7,11,8,4,3,12,3,5
Y,4,9,7,7,3,7,11,1,8,6,12,8,1,11,2,8
C,6,10,5,5,3,7,8,4,3,9,8,10,4,9,8,10
V,6,7,5,5,2,4,12,4,4,11,11,7,3,10,1,8
D,5,5,6,8,3,5,7,10,10,7,6,6,3,8,4,8
W,5,5,7,5,7,7,8,5,5,7,5,8,9,8,8,6
C,4,7,5,5,5,5,7,3,5,8,6,11,6,9,4,9
U,5,8,7,6,6,8,8,8,5,6,7,9,3,8,4,6
Q,5,10,6,9,3,8,7,8,6,6,7,8,3,8,5,9
V,3,6,5,4,2,7,12,2,3,6,11,9,2,10,1,8
T,2,9,4,6,1,9,14,0,6,5,11,8,0,8,0,8
Z,2,4,3,3,2,8,7,5,9,6,6,7,1,8,7,8
Z,10,15,10,9,7,8,6,3,9,12,6,9,4,5,8,7
T,7,9,7,7,4,4,13,5,6,12,9,3,2,12,2,4
N,11,15,14,8,5,10,5,4,4,14,1,7,6,6,0,7
S,4,8,5,6,3,6,8,4,7,10,8,7,2,8,5,5
N,2,4,2,2,2,7,8,5,4,7,6,6,5,9,1,5
F,3,7,4,5,3,3,11,3,5,11,10,6,1,10,2,6
I,1,3,2,2,0,7,8,1,6,13,6,8,0,8,0,8
X,2,3,3,1,1,8,7,3,8,6,6,8,2,8,5,8
Q,3,5,3,6,3,9,6,6,3,8,6,11,3,9,5,8
E,4,9,6,7,5,9,7,2,7,11,5,8,3,7,6,10
T,6,7,6,5,4,6,10,1,10,11,9,5,0,10,3,4
U,4,8,5,6,2,7,3,15,6,7,13,8,3,9,0,8
V,4,6,5,4,2,7,11,3,3,8,11,8,3,10,3,8
A,3,7,5,4,2,7,4,3,1,6,1,8,3,6,2,7
V,3,6,3,4,2,4,11,1,3,9,10,7,2,11,0,8
Z,4,8,6,6,5,9,10,6,5,7,5,8,3,9,10,7
Q,4,8,5,6,5,8,5,7,3,6,7,8,3,7,6,9
U,1,0,2,1,0,7,5,10,4,7,12,8,3,10,0,8
A,2,3,3,1,1,11,2,3,1,9,2,9,1,6,2,8
G,4,8,5,6,4,5,8,5,5,8,8,9,2,7,4,10
D,6,10,6,5,4,7,7,4,7,10,5,6,5,9,7,5
Y,6,9,5,4,3,6,8,4,5,10,7,5,3,9,4,4
O,4,9,6,6,7,8,7,5,1,7,6,8,8,8,5,10
M,4,4,7,3,3,7,7,3,5,9,7,8,9,7,2,7
K,3,5,4,4,3,5,6,4,7,7,7,11,3,9,5,10
V,3,7,4,5,3,7,11,1,2,6,10,8,3,12,1,8
R,2,4,4,3,2,8,7,4,5,8,5,7,2,7,4,11
R,4,9,6,6,5,9,8,3,6,9,3,8,3,6,4,11
L,2,4,2,3,1,5,4,5,7,2,2,4,1,7,1,6
A,3,4,5,3,2,8,2,2,2,7,2,8,2,6,3,6
Q,5,5,7,4,5,7,5,5,5,7,5,9,5,4,7,7
P,4,8,5,6,4,3,13,6,2,11,8,4,0,9,3,8
H,7,11,10,8,9,8,7,3,6,10,4,7,4,9,4,9
I,2,9,2,7,2,8,7,0,9,7,6,7,0,8,3,7
J,5,10,6,8,4,8,7,3,6,14,5,9,1,6,1,7
F,8,14,7,8,3,5,10,3,5,12,6,5,2,8,6,4
K,1,0,1,0,0,4,6,6,2,7,6,11,3,7,2,10
Q,5,9,6,8,3,8,7,8,6,6,7,9,3,8,5,9
C,4,9,5,7,4,6,7,6,8,5,8,11,1,7,4,9
T,3,4,4,2,2,5,12,3,6,11,9,4,2,11,2,5
B,6,9,8,7,8,8,7,5,5,9,5,6,4,8,7,11
J,3,9,4,7,1,12,2,9,4,14,5,13,1,6,0,8
K,3,5,5,3,3,6,7,1,6,10,7,10,3,8,3,8
X,3,4,4,3,2,8,7,3,9,6,6,8,2,8,6,8
N,3,3,4,5,2,7,7,14,2,5,6,8,5,8,0,8
W,3,1,4,2,2,5,11,3,2,8,9,9,6,11,0,8
H,3,4,4,2,3,8,7,6,6,7,6,8,6,8,4,7
Z,2,4,4,3,2,7,8,2,9,11,7,7,1,8,5,6
W,7,9,7,4,4,5,8,2,4,8,10,8,10,10,2,6
I,8,14,7,8,4,7,9,3,7,14,5,4,3,7,6,8
W,5,9,7,7,6,5,11,2,2,7,8,9,7,12,1,8
Y,5,9,6,7,2,9,11,2,2,6,12,8,1,11,0,8
N,3,7,5,5,3,7,8,6,4,7,6,7,5,9,1,6
S,3,5,5,4,2,8,7,2,8,11,6,7,1,9,4,8
W,4,9,6,7,6,7,5,6,2,7,7,8,6,8,4,8
Y,3,4,4,3,2,4,10,2,7,10,10,6,2,11,4,3
S,5,9,5,4,2,8,7,4,3,13,7,7,3,10,3,7
E,5,6,5,8,3,3,7,6,11,7,6,15,0,8,7,7
X,4,10,5,7,2,7,7,5,4,7,6,8,3,8,4,8
V,6,7,6,5,3,4,11,1,3,8,10,8,5,11,2,7
M,3,7,5,5,5,7,9,6,3,7,7,9,5,9,5,10
L,2,0,2,1,0,2,0,6,4,0,3,4,0,8,0,8
J,2,4,3,7,1,14,2,6,5,14,1,10,0,7,0,8
H,5,5,6,6,3,7,6,15,0,7,8,8,3,8,0,8
P,5,8,7,6,5,9,7,2,5,13,4,6,2,9,3,9
S,1,3,3,2,1,8,7,3,7,10,7,8,1,9,5,8
C,5,9,5,7,3,3,8,5,8,11,10,13,1,9,3,7
T,2,1,2,2,1,7,11,1,6,7,10,8,1,10,1,8
D,4,7,4,5,4,5,7,8,7,6,5,6,2,8,3,8
Q,5,11,4,6,3,11,4,3,6,9,4,8,3,9,6,13
D,2,5,4,4,3,9,7,5,6,10,4,6,2,8,3,8
A,1,1,2,1,0,8,4,2,0,7,1,8,2,7,1,8
V,6,9,6,4,2,6,9,4,2,9,7,5,5,12,2,7
R,4,4,4,6,3,6,9,9,4,7,5,8,3,8,5,10
O,3,3,4,5,2,7,7,9,6,7,7,7,3,8,4,8
S,3,10,4,7,4,8,7,7,5,7,7,8,2,8,8,8
E,3,5,4,5,4,5,9,4,4,8,7,8,4,11,7,7
P,5,8,7,6,4,9,8,3,7,13,4,5,4,10,5,10
V,9,12,7,7,3,7,11,6,4,10,10,4,5,12,3,9
N,5,11,5,8,5,7,7,13,1,6,6,8,6,9,1,6
G,7,11,7,8,6,6,6,6,6,10,7,12,2,9,4,10
H,3,8,4,5,2,7,9,15,2,7,3,8,3,8,0,8
G,2,3,2,2,1,7,6,6,5,7,6,9,2,9,4,9
Y,3,9,5,7,2,7,10,1,3,7,12,8,1,11,0,8
K,6,8,9,7,7,10,4,2,3,10,3,8,6,6,7,13
Q,5,9,5,11,7,8,7,7,3,8,7,9,3,8,6,8
O,6,12,4,6,3,6,7,7,3,10,7,9,5,10,5,7
A,6,8,8,7,7,6,7,3,5,7,8,10,8,11,4,7
L,5,11,7,8,5,7,4,1,8,8,2,10,0,6,3,8
E,2,3,4,2,2,7,8,2,7,11,7,8,2,8,3,8
F,5,11,5,8,2,1,15,5,3,12,8,3,0,8,3,6
W,3,2,5,3,3,8,11,3,2,6,9,8,7,11,0,8
Z,2,7,3,5,1,7,7,3,13,9,6,8,0,8,8,8
E,4,11,6,8,6,8,7,5,9,6,5,9,3,8,6,9
E,7,10,9,8,7,7,7,2,8,11,7,9,3,8,5,8
R,3,5,5,4,4,8,7,4,5,9,4,7,3,7,4,10
A,3,5,5,8,2,8,5,3,1,7,0,8,2,7,2,8
W,4,2,6,3,3,7,11,3,2,7,9,8,8,10,1,8
V,5,6,5,4,3,2,12,3,3,10,11,8,2,11,1,7
S,5,10,6,8,3,10,6,4,8,11,3,8,2,8,5,11
U,6,10,8,8,5,5,8,6,8,7,10,10,3,9,1,8
B,4,9,4,7,5,6,8,8,6,7,5,7,2,8,7,9
J,1,2,2,3,1,11,6,2,5,11,3,8,0,7,1,7
X,1,1,2,1,0,8,7,3,4,7,6,8,2,8,4,8
W,8,11,8,6,5,8,8,4,4,6,9,6,10,10,3,6
X,4,8,5,6,3,7,7,4,4,7,6,7,3,8,4,8
B,5,9,5,5,5,8,8,3,5,9,5,6,6,4,6,8
L,4,9,5,7,2,2,4,2,9,2,0,8,0,7,1,5
O,3,4,4,3,2,7,7,7,4,9,6,8,3,8,3,8
W,5,10,8,8,8,8,11,2,3,6,8,8,8,10,1,8
E,5,5,5,8,3,3,8,6,12,7,6,15,0,8,7,6
R,1,1,2,1,1,6,9,8,4,7,5,8,2,7,4,11
M,5,10,5,8,4,8,7,12,2,6,9,8,8,6,0,8
D,5,10,7,7,7,9,7,4,6,9,4,6,3,8,3,8
U,7,9,8,7,5,6,6,6,9,8,6,8,4,8,4,3
F,8,12,7,6,3,5,10,2,5,12,6,4,2,9,6,4
H,3,6,4,4,3,7,8,12,1,7,4,8,3,8,0,8
C,6,9,6,7,3,5,8,7,8,13,9,9,2,11,3,6
C,2,1,3,2,1,6,8,7,7,9,7,12,1,10,3,9
O,2,1,2,1,1,8,7,6,5,7,6,8,2,8,3,8
A,4,11,6,8,4,12,2,3,3,11,1,9,3,7,3,9
A,6,11,8,8,8,8,7,6,5,6,6,9,6,8,7,3
W,8,9,8,7,6,5,10,3,3,9,7,7,10,12,3,4
N,4,4,5,6,2,7,7,14,2,4,6,8,6,8,0,8
F,3,9,5,6,4,4,10,4,5,11,10,6,2,10,2,6
J,2,8,3,6,1,9,5,4,7,12,4,11,1,6,1,7
G,4,8,5,6,3,6,6,7,7,10,7,11,2,11,4,9
H,3,7,4,5,4,6,6,7,6,6,7,9,3,9,3,8
S,4,7,5,5,3,8,7,3,6,10,6,8,2,8,4,8
U,2,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8
O,5,10,6,8,4,7,7,8,5,10,6,8,3,8,3,8
N,4,5,5,4,3,7,8,5,5,7,7,6,6,9,2,5
H,5,4,5,6,2,7,7,15,0,7,6,8,3,8,0,8
V,3,6,4,4,2,9,11,3,3,4,11,9,2,10,1,8
T,7,10,6,5,2,4,12,3,7,12,8,4,2,8,3,3
U,6,10,8,8,11,7,6,4,5,7,7,8,11,9,7,10
B,2,5,4,3,3,9,7,2,5,10,5,6,2,8,4,9
R,4,6,6,4,4,9,8,4,6,9,3,7,3,6,4,11
S,4,10,5,8,5,7,7,7,5,7,7,9,2,8,8,7
F,3,4,5,3,2,4,12,4,4,13,8,4,1,10,1,7
M,6,10,7,5,4,13,2,5,2,12,1,9,6,3,1,8
H,3,3,4,4,2,7,5,14,1,7,9,8,3,9,0,8
B,2,0,2,1,1,7,8,7,5,7,6,7,1,8,7,8
K,3,5,5,3,3,5,7,2,7,10,9,11,3,8,3,7
E,6,12,4,6,2,7,9,6,7,9,6,10,1,9,8,9
J,4,11,5,8,3,8,9,0,7,14,5,6,1,9,1,8
A,3,8,5,6,4,11,3,1,2,8,3,9,3,5,2,8
R,4,4,5,6,3,6,10,9,3,7,5,8,3,8,6,11
S,5,9,5,5,2,5,9,2,5,13,8,8,2,8,2,6
K,3,6,4,4,1,4,7,8,1,7,6,11,3,8,2,11
W,4,4,5,3,3,4,10,2,2,9,9,7,6,11,1,7
J,6,10,8,8,4,5,9,3,6,15,7,9,2,7,2,6
U,3,7,5,5,3,8,8,5,7,4,8,8,3,9,0,7
Z,3,8,4,6,2,7,7,4,13,10,6,8,0,8,8,8
F,5,9,4,4,2,5,11,3,4,12,7,4,1,8,5,4
L,3,4,3,3,2,4,3,4,6,2,2,6,0,7,0,6
L,3,10,4,8,2,0,2,3,6,1,0,8,0,8,0,8
D,4,6,5,4,4,7,7,6,5,6,5,6,3,8,3,7
U,6,10,6,8,3,8,4,15,6,7,15,8,3,9,0,8
S,6,9,5,4,2,11,2,3,4,11,2,10,3,7,3,11
U,3,2,4,4,2,6,8,5,6,6,8,9,6,10,1,7
L,3,8,3,6,1,0,1,5,6,0,0,6,0,8,0,8
K,7,11,6,6,3,7,8,3,6,9,7,8,6,9,3,7
Q,2,2,3,3,2,7,9,4,2,7,8,10,2,9,4,8
T,2,7,3,4,1,7,14,0,6,7,11,8,0,8,0,8
U,2,4,3,3,1,4,8,5,6,11,9,9,3,9,1,7
H,3,3,5,2,2,7,8,3,6,10,6,7,3,8,3,8
Y,3,5,4,3,2,4,10,1,7,10,10,6,1,11,3,4
I,2,5,1,4,1,7,7,1,7,7,6,8,0,8,3,8
V,3,5,5,8,2,7,8,4,3,7,14,8,3,9,0,8
I,2,9,3,7,2,7,7,0,8,13,6,8,0,8,1,8
E,3,3,3,4,2,3,8,6,9,7,6,14,0,8,7,8
Q,1,2,2,3,1,8,7,5,2,8,7,9,2,9,3,9
K,5,8,7,6,5,7,7,1,6,10,5,9,3,8,3,9
C,5,10,5,8,3,4,10,7,8,12,10,9,2,9,2,6
W,2,1,3,1,1,7,8,4,0,7,8,8,7,9,0,8
Z,2,2,3,3,2,7,8,5,9,6,7,9,1,9,7,8
M,6,6,9,6,9,9,8,5,4,7,6,7,11,9,6,3
N,4,9,5,6,2,7,7,15,2,4,6,8,6,8,0,8
G,2,5,3,3,2,7,7,7,6,6,6,10,2,8,4,9
F,5,8,7,6,4,6,10,2,5,13,7,5,2,10,2,7
Y,4,5,5,7,6,9,9,6,3,6,8,8,5,10,6,4
K,4,5,7,4,3,6,6,2,7,10,8,11,4,7,4,7
E,2,3,3,2,2,7,7,2,7,11,7,8,2,9,4,8
F,2,4,3,3,2,5,10,4,6,10,9,6,1,10,3,6
G,2,3,2,2,1,7,6,6,5,6,6,9,2,9,4,9
P,7,10,10,8,6,8,10,7,5,9,4,3,3,10,5,9
H,3,3,3,5,2,7,9,14,3,7,3,8,3,8,0,8
Z,3,7,4,5,3,6,8,5,9,7,7,10,1,9,7,8
O,3,9,4,6,3,7,7,8,6,7,8,8,2,8,3,8
B,4,8,5,6,5,7,8,5,4,7,5,7,3,8,6,8
M,3,4,5,3,3,10,6,3,4,9,4,7,6,5,1,8
L,3,10,4,7,1,0,1,5,6,0,0,7,0,8,0,8
W,6,7,6,5,4,7,11,4,2,8,7,6,7,12,3,6
M,12,12,12,6,6,8,10,6,4,5,5,10,11,13,2,7
L,5,9,7,7,5,5,5,1,8,7,2,11,2,9,3,7
E,4,10,5,8,3,3,9,6,12,7,5,14,0,8,8,7
U,4,7,5,5,2,8,4,14,5,6,14,8,3,9,0,8
X,7,9,6,4,3,8,7,2,8,9,7,8,4,12,3,7
Q,1,2,2,2,1,8,8,5,2,8,7,9,2,9,3,9
M,5,10,8,7,7,9,6,2,4,8,5,7,7,6,2,8
B,4,9,6,6,8,9,7,4,3,6,7,7,6,10,7,6
U,4,10,6,8,10,7,7,4,4,7,6,8,11,7,8,8
P,10,13,8,7,4,8,8,6,4,13,3,5,5,9,4,8
V,6,10,6,8,3,2,11,4,4,11,12,8,2,10,1,8
X,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8
C,4,11,5,8,3,6,8,9,8,10,8,11,2,12,4,9
B,5,7,8,6,9,9,6,4,4,7,6,8,7,9,8,7
N,5,9,8,7,8,7,8,4,5,7,6,7,6,8,7,5
Y,2,3,4,4,1,9,10,2,2,6,12,8,2,11,0,8
X,1,3,2,1,1,8,7,4,8,7,6,8,2,8,5,8
X,4,8,6,6,6,7,7,3,5,6,5,10,2,7,8,8
G,7,11,6,6,3,9,4,4,3,8,3,5,4,7,4,9
C,4,7,5,6,5,6,7,4,4,7,6,10,4,10,7,11
C,2,5,3,3,2,6,8,7,8,8,7,13,1,9,4,10
V,3,10,5,7,2,7,8,4,3,7,14,8,3,9,0,8
Q,3,4,4,6,2,7,7,7,6,6,7,7,3,7,6,9
D,3,2,4,4,3,7,7,7,6,6,6,5,2,8,3,7
I,7,14,6,8,4,8,7,2,5,11,6,6,2,9,6,11
U,4,4,4,6,2,7,5,14,5,7,13,8,3,9,0,8
B,4,6,4,8,4,6,7,10,7,7,6,7,3,8,9,10
U,7,11,8,8,5,3,8,5,7,10,9,9,3,9,2,6
E,5,8,7,6,5,7,7,2,7,11,6,9,3,8,4,9
C,4,7,5,5,3,6,7,6,8,7,6,11,1,9,4,9
V,6,9,4,5,3,8,9,6,3,7,9,6,6,12,3,8
A,2,6,3,4,2,11,3,3,3,11,2,10,2,6,3,8
W,4,10,7,7,5,5,10,2,3,7,9,9,8,11,1,8
J,3,10,4,7,2,13,3,7,4,13,4,11,1,6,0,8
N,11,13,9,7,4,5,9,5,6,3,3,12,6,11,2,7
S,2,4,4,2,2,8,8,2,7,11,6,7,1,8,5,8
O,3,7,4,5,3,8,8,8,6,7,5,8,2,8,3,7
F,4,7,5,5,2,5,12,4,6,12,9,3,1,10,3,4
M,5,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8
D,4,8,4,6,3,6,7,10,10,7,6,6,3,8,4,8
X,6,11,8,8,8,7,6,3,5,6,6,9,4,7,11,10
P,7,10,10,8,5,6,13,7,2,11,5,2,1,11,4,8
W,7,7,7,5,6,4,10,2,3,10,9,8,7,11,2,6
J,2,6,2,4,1,13,2,8,4,13,4,12,1,6,0,8
S,4,6,5,4,5,9,7,4,3,9,5,8,4,8,10,10
A,2,7,4,5,3,10,3,1,2,7,3,9,1,5,1,7
M,5,8,8,6,5,3,7,4,5,11,12,11,5,8,2,7
H,1,1,2,1,1,7,8,11,1,7,5,8,3,8,0,8
T,2,7,4,4,1,9,15,1,5,6,11,9,0,8,0,8
N,2,2,3,3,2,7,8,5,4,7,6,7,4,8,1,7
L,3,8,4,6,1,0,1,6,6,0,0,6,0,8,0,8
G,5,10,7,8,6,8,7,7,6,6,7,7,2,7,8,13
L,2,3,3,2,1,7,4,1,8,8,2,10,0,7,2,8
D,5,10,7,7,5,10,6,4,7,11,3,6,4,6,4,8
L,3,2,3,3,2,4,4,3,8,2,1,7,0,7,1,6
U,5,7,6,5,3,4,8,5,7,9,8,9,3,9,2,5
T,2,6,3,4,2,7,13,0,5,7,10,8,0,8,0,8
P,4,10,5,8,4,5,11,3,7,11,9,4,0,10,4,7
Z,5,9,7,6,4,6,9,3,10,11,9,5,2,8,6,5
X,4,7,6,5,3,11,6,2,8,10,0,6,3,7,3,10
L,2,5,4,3,2,6,4,1,8,8,2,10,0,7,2,8
E,3,7,4,5,3,7,7,6,10,6,5,9,2,8,6,8
A,3,8,5,6,3,12,2,3,2,10,2,9,2,6,2,8
W,5,11,7,8,9,8,7,6,3,5,8,8,9,8,6,2
P,5,9,7,7,4,9,7,3,6,13,4,5,5,10,5,10
W,3,3,5,5,3,5,8,5,1,7,8,8,8,10,0,8
W,8,8,11,7,11,6,8,6,6,6,6,7,10,9,9,9
J,2,7,2,5,1,11,7,1,7,11,3,6,0,7,1,7
F,4,8,6,6,3,4,12,4,5,13,8,4,2,10,2,6
Y,6,11,6,8,3,3,10,2,7,11,12,6,2,11,3,5
F,4,8,5,6,5,7,9,6,3,8,6,8,3,10,7,11
T,3,5,4,4,2,5,12,3,7,11,9,4,1,11,2,5
N,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8
I,3,10,4,8,3,7,7,0,7,13,6,8,0,8,1,8
M,6,7,9,5,6,5,7,3,4,10,10,10,10,6,4,8
L,3,8,5,6,3,3,5,1,8,3,1,9,0,7,1,6
Q,4,8,6,7,3,8,7,7,6,6,7,8,3,8,5,9
V,4,11,6,9,4,7,11,2,4,6,11,9,3,9,2,8
M,6,8,8,7,10,8,7,4,4,7,6,7,11,8,5,5
Y,3,4,4,6,1,6,12,2,3,9,12,8,0,10,0,8
Q,4,9,6,8,3,8,5,9,7,6,4,8,3,8,4,8
A,3,3,5,4,2,6,3,3,2,6,2,8,3,6,3,7
F,7,12,6,6,4,6,10,3,4,10,7,5,4,9,8,6
R,4,8,6,6,4,10,8,3,7,10,1,7,3,6,4,10
X,6,9,9,7,5,7,7,0,8,10,6,8,3,8,3,7
X,4,6,7,4,3,9,7,1,8,10,4,7,3,8,3,8
B,2,3,3,2,2,10,6,2,6,10,4,7,2,8,4,10
N,3,6,3,4,2,7,7,13,1,5,6,8,5,8,0,8
F,2,3,2,2,1,5,10,3,5,10,9,5,1,10,3,6
I,5,12,5,7,3,9,8,3,6,13,4,5,2,8,5,10
Y,5,6,5,4,3,4,9,1,7,10,10,6,1,10,2,4
A,8,15,6,8,4,9,3,3,2,8,4,11,6,4,5,8
Z,4,7,6,5,3,7,7,2,9,12,6,9,1,9,6,8
F,6,10,5,5,4,10,8,4,6,11,3,4,2,9,6,9
G,2,4,3,2,1,7,8,5,6,9,7,10,2,9,4,10
L,3,9,5,7,3,8,4,2,7,8,2,8,1,6,2,9
T,5,5,5,4,2,5,11,3,7,12,9,4,1,11,2,4
J,6,11,8,8,6,7,7,7,6,8,7,8,3,7,4,6
F,3,5,5,4,2,4,11,4,6,13,9,6,1,9,2,6
S,3,9,4,7,2,7,6,6,9,4,6,10,0,9,9,8
V,3,2,5,3,2,7,12,2,3,7,11,8,3,10,1,8
E,6,13,5,7,4,7,8,4,4,11,5,9,3,9,8,11
P,4,11,5,8,3,5,10,11,5,9,6,5,2,10,4,8
R,5,11,7,8,5,8,9,5,6,8,5,8,3,7,6,11
K,3,3,3,4,1,3,7,8,3,7,6,11,4,8,2,11
A,4,7,5,5,3,8,2,2,2,7,2,8,2,7,3,6
L,1,0,2,1,0,2,1,6,4,0,2,5,0,8,0,8
R,2,1,3,2,2,6,7,4,5,6,5,7,5,7,3,8
P,6,9,8,6,7,6,8,7,4,8,7,9,2,9,7,9
N,4,8,5,6,2,7,7,15,2,4,6,8,6,8,0,8
E,5,9,4,4,2,8,7,3,4,10,5,9,3,9,8,11
K,5,7,8,5,5,3,8,2,7,10,11,11,3,7,3,5
E,3,7,5,5,4,7,8,5,8,6,5,9,3,8,6,9
I,4,7,5,8,5,8,9,4,4,7,7,9,4,7,7,6
D,5,11,7,8,7,7,7,5,7,7,7,5,6,8,3,7
V,3,9,5,6,3,7,9,3,1,7,12,8,3,9,1,8
X,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8
X,4,11,5,8,4,7,7,4,4,7,6,7,2,8,4,8
W,3,3,4,2,2,4,11,3,2,9,9,7,6,11,1,7
O,5,9,6,7,3,9,9,9,9,5,8,10,3,8,4,8
B,5,8,7,6,6,7,9,5,6,10,6,5,3,7,7,8
T,8,12,7,7,2,5,11,3,7,13,7,4,2,8,4,4
I,3,11,3,8,4,8,7,0,7,7,6,8,0,8,3,8
H,3,4,5,2,2,7,7,3,6,10,6,8,3,8,3,8
B,4,8,6,6,5,8,9,4,6,10,5,5,2,8,6,9
V,3,3,4,2,1,5,12,2,2,9,11,7,3,12,1,7
P,4,8,5,6,2,4,13,8,1,11,6,3,1,10,4,8
S,5,10,6,8,3,9,8,6,9,5,6,5,0,8,9,7
M,9,9,13,8,14,6,6,5,4,6,5,8,15,11,7,9
G,1,0,1,1,0,7,7,6,5,6,5,9,1,7,5,10
P,3,7,5,5,3,6,10,3,6,10,8,4,4,10,4,7
N,4,7,5,5,4,7,7,7,5,8,6,6,3,7,3,8
J,2,11,3,8,2,11,6,1,8,11,2,6,0,7,1,7
D,5,9,5,5,3,5,8,4,6,9,6,6,4,10,6,5
L,4,11,6,9,3,3,4,2,10,2,0,8,0,7,1,5
P,8,13,7,7,5,10,7,4,5,12,4,5,4,11,5,8
M,7,11,11,8,8,4,7,4,5,10,11,11,9,7,4,8
V,7,9,7,7,3,3,11,4,4,10,12,7,3,10,1,7
N,6,5,6,8,3,7,7,15,2,4,6,8,6,8,0,8
S,2,1,3,2,2,8,8,6,5,7,5,7,2,8,8,8
I,5,6,7,7,6,7,9,4,5,7,7,7,3,8,8,8
E,6,14,5,8,4,8,8,4,5,11,5,9,3,9,8,11
F,3,3,3,4,1,1,12,5,4,11,10,7,0,8,3,7
F,5,10,8,7,6,9,8,2,6,12,4,5,4,8,4,9
W,8,8,8,6,6,3,11,2,3,10,9,8,7,11,2,6
K,7,10,6,5,3,9,8,3,6,9,4,6,5,8,4,7
S,3,7,4,5,5,8,8,5,3,8,5,8,3,8,10,7
A,2,3,4,2,1,8,2,2,2,7,2,8,2,6,2,7
Y,4,8,6,6,6,9,4,7,4,7,9,7,3,9,8,4
Y,3,4,4,2,2,4,11,2,7,11,10,5,1,11,2,5
H,4,9,5,6,2,7,6,15,1,7,7,8,3,8,0,8
D,5,11,6,8,7,8,7,5,5,10,5,5,3,8,3,8
O,6,10,5,5,3,7,5,6,3,10,6,10,5,9,5,8
M,5,2,6,3,4,7,6,7,5,7,8,10,8,5,2,9
H,4,9,6,6,8,7,6,5,3,6,6,8,7,7,8,8
U,2,3,3,2,1,6,9,5,5,7,9,9,3,10,1,8
W,1,0,1,0,0,8,8,3,0,7,8,8,3,9,0,8
N,5,5,5,8,3,7,7,15,2,4,6,8,6,8,0,8
F,5,10,6,7,6,5,10,3,6,10,10,6,2,10,3,6
R,4,7,4,5,4,6,8,8,4,6,5,7,2,7,5,11
V,5,9,7,7,3,5,9,4,1,9,13,8,5,10,2,9
A,3,8,5,6,3,6,5,3,0,6,1,8,2,6,1,7
A,3,2,5,4,2,8,2,2,2,7,1,8,2,6,2,7
R,4,8,6,6,5,7,8,5,6,7,5,7,3,7,5,8
T,2,0,2,0,0,7,15,2,3,7,10,8,0,8,0,8
F,4,6,4,8,2,1,14,5,3,12,9,5,0,8,3,6
T,3,4,4,3,2,7,12,3,5,7,11,8,2,11,1,8
I,4,10,5,8,3,7,7,0,8,14,6,8,0,8,1,8
N,4,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8
X,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8
M,3,7,4,5,3,7,7,11,1,7,9,8,8,6,0,8
E,4,11,4,8,3,3,7,6,11,7,6,15,0,8,7,7
N,4,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8
U,6,8,7,6,4,4,8,5,7,9,8,9,3,9,2,5
F,3,9,4,7,4,5,10,1,7,10,9,7,1,10,3,6
V,4,5,5,4,2,4,13,4,4,10,11,7,3,10,1,8
J,4,10,5,7,3,9,7,2,6,14,4,8,0,7,1,7
E,4,9,6,7,4,4,10,5,8,11,10,9,2,8,5,3
M,6,11,9,8,11,7,8,6,4,7,6,8,6,9,9,8
R,5,9,8,6,5,11,6,3,6,11,1,6,4,5,4,10
L,5,10,6,8,5,3,4,5,7,2,0,7,1,6,1,6
V,6,9,6,6,3,3,11,4,4,10,12,8,2,10,1,8
R,1,0,2,0,1,6,10,7,2,7,5,8,2,7,4,9
L,2,5,3,4,2,4,4,4,7,2,1,6,0,7,1,6
C,4,10,5,7,3,6,8,7,7,13,8,8,2,11,3,7
F,3,7,4,5,2,1,13,4,3,12,10,6,0,8,2,6
F,2,3,3,1,1,7,9,2,5,13,6,5,1,9,1,8
Q,3,6,4,8,4,9,7,7,3,5,7,10,3,9,5,10
P,2,3,2,1,1,5,10,3,4,10,8,4,0,9,3,7
V,5,11,7,8,2,8,8,5,3,6,14,8,3,9,0,8
G,2,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10
I,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
W,7,12,7,6,5,4,8,1,3,8,9,8,9,10,2,5
A,7,10,9,8,8,7,7,7,4,7,6,9,4,8,11,1
M,5,6,7,4,5,11,6,3,4,9,2,6,7,6,2,8
C,5,9,4,5,3,7,6,4,4,9,9,11,4,10,9,10
Z,2,5,4,3,2,7,7,2,9,11,6,8,1,8,5,8
P,3,8,5,6,4,7,8,5,6,8,7,4,2,10,4,7
I,5,9,4,5,2,8,8,3,6,13,4,6,1,7,4,9
P,6,10,8,7,7,7,8,7,4,8,7,9,3,9,8,9
J,2,10,3,7,1,12,3,10,4,13,5,13,1,6,0,8
L,3,9,5,7,4,5,5,1,8,4,2,8,3,7,2,6
C,4,7,5,5,3,3,8,5,7,10,9,14,1,8,3,8
S,5,8,6,6,4,9,7,4,6,10,3,6,2,7,5,10
V,3,3,4,2,1,4,12,3,3,9,11,7,2,11,1,8
L,3,9,3,6,1,0,1,5,6,0,0,7,0,8,0,8
V,6,11,6,6,3,5,10,4,3,10,8,5,4,11,2,8
S,4,10,4,5,2,9,3,3,4,9,2,9,3,6,4,9
F,3,7,4,4,1,2,12,5,5,12,10,8,0,8,2,6
N,4,9,5,7,5,7,7,13,1,7,6,8,5,8,0,7
Y,3,4,4,3,2,4,11,2,7,11,10,5,1,11,2,5
B,3,5,5,3,4,9,7,3,5,10,5,7,2,8,5,9
W,6,11,9,8,14,9,7,5,2,7,6,8,14,11,3,6
J,5,10,4,8,3,9,8,2,3,11,6,7,2,10,6,12
L,2,2,3,3,2,4,4,4,7,2,1,6,1,7,1,6
U,4,9,6,7,3,7,9,6,7,6,10,9,3,9,1,8
Z,3,9,4,7,4,8,6,5,9,7,6,6,2,7,7,8
Q,6,10,8,7,7,8,6,8,4,7,7,7,6,6,8,8
G,4,5,5,8,2,7,6,8,9,6,5,11,1,8,6,11
Y,5,10,7,8,4,4,9,1,8,11,12,9,1,11,2,6
L,3,6,4,4,2,7,3,2,7,7,2,8,1,6,2,7
J,6,8,4,12,3,10,6,3,4,11,3,5,3,8,7,10
W,9,10,9,7,9,4,10,2,3,9,8,8,8,11,2,6
P,2,3,4,2,2,7,10,4,3,11,5,3,1,9,2,8
R,3,7,5,5,3,10,7,4,6,10,2,7,3,7,4,10
S,4,6,5,4,3,8,7,3,7,10,6,8,2,8,4,8
B,6,9,8,7,7,8,8,5,5,7,5,6,4,8,6,7
P,3,5,6,4,3,7,10,4,4,12,5,3,1,10,3,8
Z,1,0,1,1,0,7,7,2,10,8,6,8,0,8,6,8
S,4,8,5,6,3,8,8,6,9,5,6,7,0,8,9,7
D,3,6,3,4,2,5,8,10,8,8,7,5,3,8,3,8
B,2,3,3,1,2,8,7,2,5,10,5,7,1,8,4,9
W,5,9,7,7,7,8,5,7,2,6,7,8,7,7,5,3
Q,3,6,4,6,2,7,7,7,5,6,7,8,3,7,5,9
E,3,10,4,7,5,7,7,6,8,8,8,9,3,8,6,9
W,4,7,6,5,3,9,8,5,1,7,8,8,8,9,0,8
A,3,11,6,8,2,9,5,3,1,8,1,8,3,7,2,8
F,3,9,5,7,4,6,10,2,6,10,9,5,1,10,3,6
T,6,14,5,8,3,6,10,2,6,11,7,6,2,9,5,4
K,4,6,5,8,2,3,7,8,2,7,5,12,3,8,3,10
A,3,5,5,3,2,8,2,2,2,7,1,8,2,6,2,7
K,6,9,9,6,5,5,7,2,8,10,9,11,4,7,4,7
P,10,15,9,8,6,8,9,4,4,12,4,3,5,10,6,6
P,3,5,5,4,2,7,10,4,4,12,5,3,1,10,2,8
R,2,4,4,3,3,8,7,5,4,8,5,7,3,7,4,11
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
Z,4,9,6,6,5,9,10,5,4,6,5,7,3,9,9,4
J,3,7,5,5,2,7,4,5,4,14,9,14,1,6,1,6
O,7,11,7,8,7,7,8,8,4,10,7,7,5,8,5,10
J,4,8,5,6,2,9,6,2,8,15,4,8,0,7,0,8
T,3,6,4,4,3,6,11,4,5,10,8,5,3,12,2,5
W,5,7,5,5,5,4,9,2,3,9,8,8,6,11,2,6
R,5,9,8,8,9,9,7,4,4,8,5,7,9,9,7,5
P,5,10,5,5,3,9,8,3,5,13,4,4,3,10,6,7
F,4,10,6,8,4,6,10,3,6,13,7,5,2,10,2,7
N,7,9,6,5,2,5,9,4,6,3,3,10,5,9,2,7
J,3,7,4,5,3,8,7,1,6,11,5,8,0,7,1,6
N,7,10,9,9,9,8,9,4,4,7,4,7,9,6,7,6
S,3,4,4,6,2,9,8,6,9,5,5,6,0,8,9,7
A,4,8,7,6,5,9,5,2,5,10,2,6,2,6,3,8
S,7,13,6,7,3,9,2,2,5,8,1,8,3,7,5,10
U,7,11,6,6,4,8,6,5,5,6,7,8,5,7,3,7
A,1,3,2,2,1,9,2,2,1,8,2,8,1,6,1,8
O,7,10,5,5,3,9,6,5,6,9,3,8,5,8,5,8
W,5,5,6,3,4,4,11,3,2,9,9,8,7,11,1,7
Q,4,5,5,7,3,9,9,8,6,5,8,9,3,7,6,10
K,8,12,9,6,5,10,5,4,6,11,2,7,5,8,4,10
H,6,7,8,9,7,9,8,3,1,8,6,8,4,9,9,9
V,2,1,4,2,1,8,12,3,3,5,11,9,2,10,1,8
C,2,1,3,2,1,6,7,6,9,7,6,14,0,8,4,9
O,6,10,6,7,4,9,6,8,6,10,3,8,4,8,4,6
I,1,7,1,5,1,8,7,0,8,7,6,7,0,8,3,7
I,7,12,5,7,3,10,5,4,5,12,3,7,3,8,5,10
T,2,2,3,3,2,6,11,3,6,8,11,8,2,11,1,7
Z,4,5,5,7,4,11,5,4,5,10,3,9,2,6,6,10
R,4,7,5,5,3,6,9,9,5,6,5,8,2,8,5,10
I,2,11,2,9,2,7,7,0,8,7,6,8,0,8,3,8
O,7,10,7,7,6,8,6,7,5,10,5,11,5,7,5,6
S,4,5,5,4,5,8,7,4,5,7,6,8,5,8,8,11
L,4,4,4,6,1,0,0,6,6,0,1,5,0,8,0,8
D,4,8,5,6,4,8,7,6,7,10,4,5,3,8,3,8
E,5,11,7,8,6,6,8,4,7,11,9,8,3,8,4,6
Z,4,6,5,4,3,9,6,2,9,11,4,9,1,7,6,10
L,8,14,6,8,4,5,5,3,8,10,4,13,3,6,6,7
K,4,6,6,4,4,8,6,1,6,10,5,9,4,7,5,8
E,4,6,6,4,4,6,8,2,8,11,7,9,2,9,4,8
K,4,5,5,3,3,5,7,4,7,6,6,11,3,8,5,9
C,4,7,5,6,6,6,5,4,5,7,5,11,5,10,7,11
Z,1,3,2,2,1,7,7,5,8,6,6,8,1,8,7,8
W,4,4,6,6,3,11,8,5,1,6,8,8,8,9,0,8
M,11,15,10,8,6,8,11,5,5,4,5,9,9,9,2,7
H,2,1,2,2,2,8,7,6,5,7,6,7,3,8,3,6
E,2,7,4,5,3,6,7,7,9,8,8,9,2,9,6,8
D,4,2,5,4,4,7,7,7,7,6,6,5,2,8,3,7
X,5,8,7,6,3,9,7,2,8,10,4,7,3,8,4,8
T,5,10,7,8,6,7,11,3,7,7,11,8,2,12,1,8
D,3,5,5,3,3,10,6,3,6,10,3,6,2,8,2,8
Q,3,5,3,6,3,8,6,6,4,9,6,9,2,9,4,8
Q,5,8,6,7,3,8,8,8,7,5,7,9,3,7,5,10
O,2,3,3,2,2,8,7,6,4,9,5,8,2,8,2,8
W,6,8,6,6,5,4,10,3,3,9,8,7,7,11,2,6
E,4,11,6,8,5,7,7,2,8,11,6,10,3,7,5,8
X,9,14,10,8,5,3,9,4,8,12,11,9,4,8,3,5
B,4,8,4,6,3,6,7,9,7,7,6,7,2,8,9,10
U,8,11,9,8,6,4,8,6,8,9,7,9,7,9,6,1
M,4,7,4,5,3,8,7,12,1,6,9,8,8,6,0,8
R,2,3,4,2,2,8,8,3,5,9,4,7,2,6,3,10
P,3,5,5,4,3,7,10,4,4,12,4,3,1,10,3,8
H,4,11,5,9,5,7,7,13,1,7,6,8,3,8,0,8
V,4,6,5,6,6,7,8,5,5,7,6,8,6,8,6,6
Z,2,5,3,4,2,7,7,5,9,6,6,8,2,8,7,8
F,1,1,2,1,0,2,12,4,3,11,9,6,0,8,2,7
C,7,10,8,8,5,4,8,6,7,12,10,12,2,11,3,7
R,3,4,3,3,3,7,7,5,5,6,5,7,3,7,4,8
D,4,6,6,6,5,6,7,5,6,6,5,7,4,7,5,5
V,3,7,5,5,1,5,8,5,3,9,14,8,3,10,0,8
Y,6,8,6,6,3,4,10,3,7,11,11,5,1,11,3,4
O,3,9,4,7,4,7,6,8,5,6,4,8,3,8,3,7
P,9,14,7,8,3,8,8,7,4,14,3,5,5,10,4,8
I,2,6,3,4,2,7,7,0,8,13,6,8,0,8,1,8
Q,6,9,7,11,9,8,4,8,4,6,5,7,5,8,7,11
E,6,13,5,7,4,6,8,5,5,10,6,9,3,8,8,11
K,6,11,9,9,6,8,7,2,7,10,4,8,4,7,4,8
F,3,5,5,4,2,6,11,2,6,14,7,4,1,10,2,7
G,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,9
A,4,10,6,7,2,9,4,3,2,8,1,8,3,7,3,8
R,1,0,1,1,0,6,8,7,3,7,5,8,2,7,4,11
L,5,11,5,6,3,6,6,3,6,11,6,11,3,7,6,8
O,5,10,6,7,5,8,6,8,7,8,4,10,4,9,5,6
U,3,6,5,4,2,7,9,6,7,5,10,9,3,9,1,8
P,2,4,2,2,1,5,10,4,4,10,8,4,1,10,3,7
J,2,4,2,6,1,11,3,10,3,13,7,13,1,6,0,8
X,4,11,7,8,6,8,8,2,6,7,6,6,6,10,9,8
N,3,5,5,3,2,5,9,2,4,11,8,8,5,8,0,7
M,6,10,9,7,6,11,6,2,5,9,3,6,9,8,2,9
Z,4,9,5,7,5,7,8,5,9,7,7,8,1,8,7,8
X,3,5,5,4,2,9,6,1,8,10,4,8,2,8,3,9
M,4,7,5,5,4,9,6,6,5,6,8,6,8,5,2,7
C,3,9,4,7,3,6,8,8,7,10,7,12,2,10,4,10
T,2,5,3,4,2,7,12,3,6,7,11,8,2,11,1,8
A,2,4,4,3,2,10,2,2,2,9,2,9,2,6,2,8
Z,3,6,5,4,5,7,8,2,7,7,6,8,0,8,8,8
F,3,7,4,5,3,4,10,3,5,10,10,6,1,10,3,6
Z,8,12,8,6,5,10,3,4,7,12,3,11,3,6,7,11
S,3,7,5,5,6,6,6,3,1,7,5,6,2,8,10,3
O,5,7,6,5,7,8,7,5,1,7,6,8,8,8,5,10
P,1,3,3,1,1,7,9,4,3,11,5,4,1,9,2,8
E,3,4,3,7,2,3,7,6,10,7,6,15,0,8,7,7
L,8,14,7,8,3,8,3,3,5,12,4,13,3,7,6,8
I,7,15,5,8,3,10,5,6,4,13,3,7,3,8,5,10
Y,6,6,5,9,3,7,9,2,2,7,10,5,4,10,5,6
D,2,4,3,3,2,9,6,4,5,10,4,5,2,8,2,8
K,7,10,10,8,8,4,8,1,7,10,8,10,3,8,4,7
W,4,5,5,4,4,4,10,3,2,9,8,7,6,11,1,7
M,5,9,8,7,8,8,7,6,5,6,7,8,8,6,2,8
S,6,10,6,5,3,11,3,4,3,12,5,9,2,10,2,9
Z,3,3,4,5,2,7,7,4,14,9,6,8,0,8,8,8
V,3,7,5,5,2,7,12,3,4,6,11,9,2,10,1,8
F,4,9,6,6,5,7,9,3,6,12,6,6,3,9,3,7
I,5,11,4,6,2,10,6,2,5,11,4,6,2,9,4,11
Z,3,7,5,5,5,8,8,6,3,6,4,6,3,9,8,3
I,3,5,5,6,4,8,7,4,6,6,6,7,3,9,8,9
C,6,11,8,8,9,8,6,5,3,8,7,11,10,8,6,5
G,2,1,2,2,1,7,7,6,5,6,6,10,2,9,4,9
Q,7,8,9,12,13,8,10,5,1,5,7,10,7,14,8,13
D,5,10,6,7,3,5,7,10,9,7,6,5,3,8,4,8
X,3,6,5,4,3,8,7,3,8,6,6,8,2,8,6,8
R,9,11,7,6,4,8,7,5,5,10,3,8,6,6,6,11
I,1,8,2,6,2,7,7,0,7,7,6,8,0,8,2,8
T,7,11,7,8,6,6,12,5,5,11,8,4,3,12,2,4
V,5,9,5,7,2,2,11,5,4,12,12,8,2,10,1,8
N,2,4,3,3,2,6,8,5,4,7,7,7,5,9,1,6
Y,8,10,8,8,4,2,12,5,5,13,12,6,1,11,2,5
T,4,11,5,8,3,9,14,0,5,6,10,8,0,8,0,8
U,3,4,4,3,2,7,8,5,6,5,9,9,5,10,1,7
X,3,3,5,2,2,7,8,1,8,10,7,8,2,8,3,7
I,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8
M,6,10,8,8,9,8,7,5,5,6,7,7,11,7,4,6
N,3,8,4,6,2,7,7,14,2,5,6,8,5,8,0,8
N,5,9,8,7,5,4,10,3,3,9,9,9,6,7,2,7
C,7,11,9,8,6,7,7,8,6,6,7,11,4,7,4,9
B,4,10,6,8,7,8,7,4,7,6,6,6,6,8,6,10
P,2,6,2,4,2,5,11,8,2,10,6,4,1,10,3,8
O,4,9,6,7,4,7,7,8,4,7,6,11,3,8,4,7
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
K,6,9,9,7,5,3,9,3,7,11,11,11,3,8,3,5
T,6,9,7,8,7,6,8,4,9,8,8,8,3,10,8,7
P,6,11,6,8,3,4,13,9,2,10,6,3,1,10,4,8
G,4,9,5,7,3,6,7,7,8,9,8,10,2,10,4,9
U,12,15,10,8,5,5,3,4,5,4,7,6,6,6,2,7
K,4,8,6,6,5,4,7,1,6,10,9,11,3,8,3,6
D,5,8,6,6,5,7,8,4,7,7,6,8,7,8,3,7
E,4,9,6,7,4,5,8,3,9,10,8,10,2,8,4,6
I,6,15,4,8,3,11,5,4,5,12,2,7,3,8,5,10
V,4,6,4,4,2,3,12,4,3,11,11,7,2,10,1,7
V,5,9,7,7,5,8,11,3,2,5,10,9,5,10,5,8
U,4,9,6,7,3,6,8,6,7,7,10,9,3,9,1,8
D,4,8,5,6,4,7,7,7,7,7,6,4,3,8,3,7
U,7,9,8,7,3,3,10,6,7,13,11,8,3,9,1,7
O,5,10,7,8,3,9,6,9,8,7,5,10,3,8,4,8
P,3,5,5,3,3,8,10,3,4,12,4,2,1,10,3,8
D,4,10,5,8,5,7,7,6,7,7,7,5,6,8,3,7
J,3,11,5,8,5,8,7,1,6,11,5,9,1,6,1,6
G,4,5,5,8,3,8,7,8,7,6,7,8,2,7,6,11
O,5,5,6,7,3,8,7,8,8,6,7,9,3,8,4,8
Z,3,4,5,6,4,10,7,5,5,8,3,7,3,6,6,7
C,5,7,6,5,6,5,6,4,4,7,7,9,5,10,4,8
H,7,10,9,8,6,10,6,3,6,10,3,7,5,6,5,9
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
D,3,3,3,5,2,5,7,10,7,7,6,5,3,8,3,8
H,6,8,9,10,9,7,5,4,3,6,4,7,7,7,10,9
A,5,10,5,5,3,11,2,4,1,11,4,11,4,3,4,10
D,3,4,4,3,2,7,7,7,7,7,6,5,2,8,3,7
H,3,6,4,4,4,8,7,6,6,7,6,8,3,8,3,8
R,4,9,5,6,5,7,8,5,5,8,5,8,3,6,6,9
O,2,1,3,2,2,7,7,7,4,7,6,8,2,8,2,8
O,7,10,7,8,5,7,7,8,6,10,7,9,3,8,4,7
E,4,9,4,6,3,3,7,6,11,7,6,15,0,8,7,7
F,4,11,5,8,2,1,12,5,4,11,10,7,0,8,3,6
T,2,0,2,1,0,8,15,2,4,6,10,8,0,8,0,8
B,4,6,5,4,5,7,6,6,7,6,6,6,2,9,6,10
G,2,5,3,3,2,6,7,6,6,6,6,11,2,9,4,9
F,3,3,3,4,1,1,14,5,3,12,10,5,0,8,2,6
Q,9,15,8,8,6,12,4,3,6,10,4,7,4,9,6,12
A,6,9,5,5,3,8,3,3,2,7,4,12,5,5,4,7
B,4,9,4,7,3,6,7,9,7,7,6,7,2,8,9,10
Q,3,3,4,4,3,8,8,6,3,5,7,10,3,8,5,9
T,1,1,2,1,0,7,14,1,4,7,10,8,0,8,0,8
A,3,7,5,5,3,6,3,2,2,4,2,7,2,5,2,6
M,4,10,5,8,6,8,5,11,1,6,8,8,8,5,1,5
V,4,6,5,4,6,8,6,4,2,8,7,9,7,9,4,8
E,4,7,5,5,5,7,7,5,8,6,5,9,3,8,6,9
H,7,11,10,8,9,9,7,3,6,10,4,7,5,6,4,9
Q,2,4,3,5,2,8,7,5,2,8,7,9,2,10,3,9
G,5,9,4,5,3,7,9,4,3,8,6,5,3,10,9,7
H,2,3,4,2,2,6,9,3,6,10,7,8,3,8,3,8
H,4,6,4,4,2,7,5,14,1,7,9,8,3,9,0,8
M,6,9,9,6,7,9,6,2,5,9,5,7,8,6,2,8
O,6,10,8,8,9,7,8,6,2,7,6,7,10,9,6,10
J,2,3,3,5,1,11,2,10,3,13,8,14,1,6,0,8
Z,5,10,6,7,3,7,7,4,15,9,6,8,0,8,8,8
F,4,8,6,6,4,6,10,3,7,10,9,5,2,10,4,5
B,5,10,7,8,7,8,7,7,6,6,6,5,2,8,7,9
T,6,10,6,6,2,6,9,3,8,13,6,5,2,9,4,5
T,3,7,4,4,1,8,15,1,5,6,11,9,0,8,0,8
O,4,8,4,6,3,7,7,8,5,9,7,10,3,8,3,7
I,5,10,6,8,4,6,6,2,7,7,6,10,0,9,4,8
D,3,6,3,4,2,6,8,9,8,8,7,6,3,8,3,8
D,4,6,5,4,4,8,7,5,5,10,5,5,3,8,3,8
Q,2,3,3,3,2,8,8,6,2,5,7,10,2,9,5,10
W,6,10,6,7,6,3,11,2,2,10,9,8,6,11,2,7
A,4,9,6,7,6,8,9,8,5,6,5,8,3,6,7,5
I,2,8,3,6,2,6,8,0,6,13,7,7,0,8,1,7
M,4,5,5,8,4,7,7,12,2,7,9,8,8,6,0,8
L,4,9,6,6,6,5,7,3,6,8,7,11,7,11,6,7
O,4,8,5,6,4,8,7,8,5,7,7,9,3,8,3,8
I,4,8,5,6,3,9,8,2,8,7,6,5,0,8,4,7
Q,4,8,5,9,5,8,7,7,3,8,7,10,3,9,6,8
G,4,6,4,4,2,6,7,6,6,9,8,10,2,8,4,9
Q,2,3,3,4,2,8,8,5,2,8,8,10,2,9,4,8
C,4,11,5,8,2,5,7,7,10,7,7,13,1,8,4,9
H,2,4,3,3,2,8,8,6,6,7,5,7,3,8,3,7
R,1,0,1,0,0,6,10,6,1,7,5,8,1,7,4,10
Z,2,5,3,4,2,7,8,5,9,6,6,9,2,9,7,8
A,3,6,6,4,3,7,5,2,3,6,2,6,2,6,3,5
D,3,6,4,4,4,7,7,4,7,7,6,6,3,8,3,7
Y,3,10,5,7,1,7,10,2,2,7,13,8,1,11,0,8
A,7,11,5,6,3,10,0,2,2,10,4,12,3,4,3,8
P,6,11,8,9,6,8,9,3,5,13,5,3,1,10,3,8
Z,2,4,4,3,2,7,8,2,9,11,6,8,1,9,5,8
O,3,5,4,3,2,7,7,7,5,9,7,8,2,8,3,8
I,2,10,2,7,2,7,7,0,8,7,6,8,0,8,3,8
R,4,8,6,6,6,7,7,3,7,7,6,6,6,8,4,9
J,3,8,4,6,3,9,6,2,6,12,3,8,1,6,2,6
F,3,7,5,5,3,9,9,2,6,13,5,4,1,10,3,9
X,4,10,5,8,3,7,7,4,4,7,6,8,3,8,4,8
E,2,5,4,3,2,6,7,2,8,11,7,9,2,8,4,8
L,2,5,3,4,1,7,3,1,7,8,2,10,0,7,2,8
S,3,6,4,4,2,7,7,5,8,5,6,9,0,9,9,8
H,3,2,5,4,4,8,7,6,6,7,6,7,3,8,3,7
E,5,10,7,9,8,7,7,5,3,7,7,9,6,10,10,12
E,4,5,5,8,3,3,7,6,11,7,6,15,0,8,7,7
U,3,7,4,5,3,6,8,6,6,6,9,9,3,9,0,8
I,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8
B,3,2,4,3,4,7,7,5,5,7,6,6,2,8,6,9
T,2,3,3,4,1,8,13,0,6,6,11,8,0,8,0,8
B,2,4,4,3,3,9,7,2,6,11,5,6,2,8,4,9
Q,4,7,6,7,3,8,8,7,6,5,7,8,3,8,5,9
Y,4,8,6,6,2,4,10,2,8,10,13,9,1,11,2,7
H,5,9,7,7,9,8,9,5,3,7,6,6,9,8,9,7
L,1,4,3,3,1,7,3,1,7,8,2,10,0,7,2,8
B,2,3,2,1,2,7,7,5,5,7,6,6,1,8,5,9
R,4,6,6,4,5,8,7,7,3,8,5,7,4,7,7,10
Q,2,4,3,5,3,8,9,6,2,5,8,10,3,9,5,10
X,2,3,3,2,1,8,7,3,8,6,6,8,2,8,5,8
M,3,6,4,4,2,7,7,11,1,7,9,8,8,6,0,8
I,4,6,6,7,5,8,8,5,6,7,6,8,3,8,8,7
O,4,6,5,4,6,7,9,5,2,7,7,8,8,9,4,9
B,6,10,5,6,3,8,6,4,5,10,5,8,6,7,7,10
V,5,9,7,8,9,7,6,5,4,6,5,8,7,10,7,9
Z,2,4,3,3,2,8,7,5,9,6,6,7,2,8,7,8
K,6,10,8,8,6,5,6,1,7,10,8,10,3,8,4,8
M,4,8,6,6,6,7,7,2,4,9,7,8,7,6,2,8
L,3,5,4,4,3,8,9,4,5,6,6,9,3,8,7,10
R,5,8,7,6,5,8,7,6,6,8,6,8,3,8,6,11
G,2,1,3,2,2,7,6,6,5,6,6,10,2,9,4,9
X,4,9,6,7,4,7,7,4,9,6,6,8,3,8,7,8
N,5,9,8,7,5,7,9,2,4,10,5,6,5,9,1,7
P,1,3,3,1,1,7,9,4,3,11,5,4,1,9,2,8
L,6,9,5,4,2,8,3,3,4,11,4,13,2,6,6,8
Z,4,6,6,8,4,11,4,3,5,10,2,8,2,7,5,11
Z,5,8,7,6,5,8,7,2,9,11,5,8,1,7,6,8
S,3,7,4,5,3,8,7,7,6,8,6,8,2,8,9,8
F,3,4,5,3,2,7,9,2,6,13,6,5,1,9,2,7
Y,3,2,5,3,2,5,10,1,7,9,12,9,1,11,2,7
L,3,7,3,5,1,0,1,6,6,0,0,6,0,8,0,8
E,5,11,7,8,6,7,7,2,7,11,7,9,3,8,4,8
L,3,6,5,4,5,7,8,3,5,6,7,10,5,11,5,6
X,3,7,5,5,3,4,8,1,7,10,11,9,2,9,3,5
Q,7,12,6,6,5,11,4,4,6,10,4,7,3,9,7,12
S,8,13,6,8,3,9,3,4,5,9,2,9,4,6,5,9
D,7,11,9,8,7,7,7,5,6,7,6,8,7,8,3,7
Z,5,9,6,7,3,7,7,4,15,9,6,8,0,8,8,8
Q,4,6,4,8,5,8,10,5,1,5,8,11,2,10,5,10
M,7,11,11,8,15,9,7,3,3,8,5,7,12,4,5,5
M,3,6,4,4,2,8,6,12,1,5,9,8,7,6,0,8
F,4,7,5,5,3,6,10,3,5,13,7,5,2,10,2,8
E,2,3,2,4,1,3,8,6,10,7,5,15,0,8,6,8
W,3,6,4,4,4,8,7,6,2,6,8,9,5,8,3,7
W,4,2,6,3,3,7,11,2,2,6,9,8,7,11,0,8
B,3,7,5,6,6,7,7,5,4,7,6,8,6,9,7,7
A,2,9,4,6,2,6,5,3,1,6,0,8,2,7,2,7
V,6,9,6,7,3,4,11,3,4,9,11,7,2,10,1,8
L,4,9,5,7,4,7,4,1,7,8,2,9,1,6,2,8
T,4,6,5,4,2,6,12,3,7,12,9,4,2,11,2,4
Z,1,3,3,2,1,7,7,2,9,11,6,8,1,8,5,7
J,4,7,6,8,5,9,9,5,4,6,6,9,3,7,8,6
P,4,10,6,7,4,5,11,7,4,10,7,3,1,10,4,7
M,4,1,5,2,3,7,6,7,4,7,7,10,7,6,2,8
T,4,9,6,7,4,8,10,1,8,6,11,7,0,10,1,7
G,4,10,5,7,4,7,7,7,6,6,5,8,1,7,6,11
E,4,6,6,4,4,10,6,1,7,11,4,8,3,8,4,11
G,3,7,4,5,3,7,7,7,6,6,5,9,1,8,5,11
G,3,9,4,6,4,7,6,7,6,6,5,8,1,7,6,11
V,4,8,6,6,7,7,7,4,1,8,7,9,7,10,4,7
F,2,1,3,2,2,6,9,3,5,10,9,5,4,10,3,7
K,4,6,6,4,5,8,8,5,4,7,6,7,7,6,6,11
I,3,11,4,8,2,6,8,0,8,13,7,8,0,8,1,7
Q,2,2,3,3,2,8,8,5,2,7,7,10,2,9,4,9
U,6,10,5,5,3,4,4,4,5,4,7,7,4,6,2,8
X,4,5,5,5,5,9,7,2,4,8,5,7,3,8,7,8
G,3,5,5,5,4,7,10,5,2,7,7,8,6,11,7,8
K,8,10,11,8,8,7,7,1,7,10,5,9,5,7,4,7
J,1,3,2,1,0,8,7,2,4,14,6,9,0,7,0,7
R,3,8,5,6,4,6,7,5,6,6,5,7,3,7,5,8
A,4,11,6,8,2,7,6,3,1,7,0,8,3,7,1,8
G,5,8,7,7,7,7,10,5,2,7,7,8,6,11,6,8
Y,8,8,7,12,5,6,5,6,5,6,11,7,5,10,4,7
G,2,5,3,3,2,6,7,5,5,10,7,10,2,9,4,9
M,5,11,5,8,7,7,5,11,1,8,8,8,9,5,2,10
D,4,11,6,8,7,8,7,5,6,7,7,4,3,8,3,6
Y,9,8,8,12,5,10,11,1,4,7,10,5,4,10,5,10
L,4,9,5,7,4,4,5,3,9,3,1,9,0,7,2,6
A,4,10,7,7,2,7,7,3,0,6,0,8,2,7,2,8
R,3,7,4,5,2,6,12,8,4,7,2,9,3,7,5,10
Y,5,7,6,5,5,8,5,7,6,5,8,8,3,9,9,5
S,4,8,5,6,2,8,7,5,8,5,6,8,0,8,9,8
C,5,8,7,6,4,7,8,8,6,4,7,13,5,8,4,8
B,2,0,2,1,1,7,8,7,5,7,6,7,1,8,7,8
B,3,5,4,4,4,8,7,5,6,7,6,6,5,8,5,9
F,4,9,5,6,5,6,10,6,4,8,6,8,2,10,7,10
R,6,9,8,7,6,10,7,3,6,11,3,7,5,6,5,10
H,4,6,6,8,6,8,12,4,2,8,7,6,3,11,7,5
D,2,4,4,3,3,9,6,4,6,10,4,6,2,8,3,8
S,6,9,7,6,4,9,7,4,8,11,5,8,2,8,5,9
A,3,5,5,4,2,7,2,1,2,6,2,8,3,5,3,7
R,2,7,3,4,2,5,10,8,4,7,4,9,3,7,5,11
P,3,2,4,4,2,5,10,4,4,10,8,4,1,10,3,6
D,4,8,4,6,2,5,8,10,8,8,7,5,3,8,4,8
T,8,10,8,8,4,6,13,5,6,12,8,2,2,12,2,4
L,2,3,2,2,1,4,3,4,6,2,2,5,0,7,0,6
D,5,10,5,6,4,9,6,3,5,8,5,7,6,9,5,8
Y,6,9,6,7,3,4,9,2,8,10,11,6,2,10,4,3
F,3,5,5,4,2,5,10,2,6,13,7,5,1,9,2,7
Y,2,5,4,4,2,7,10,1,6,7,11,8,1,11,2,8
F,4,4,4,6,2,1,14,5,3,12,9,5,0,8,3,6
G,5,8,6,6,5,7,7,7,5,4,7,9,3,6,5,8
U,5,7,5,5,2,4,8,6,8,9,9,9,3,9,2,4
Q,4,7,5,6,2,8,7,8,6,6,7,8,3,8,5,9
B,8,12,7,6,6,8,7,5,5,9,7,8,7,8,9,7
Q,2,3,3,4,3,8,7,7,3,6,7,9,2,8,5,9
Z,3,7,5,5,3,8,7,2,9,11,5,8,1,7,6,8
T,3,7,5,5,5,7,8,4,5,7,7,9,5,9,5,7
S,3,9,4,6,2,8,9,6,10,5,5,5,0,7,9,7
U,6,8,7,6,3,4,8,7,8,9,9,9,3,9,3,5
D,4,8,4,6,3,6,7,10,9,7,6,6,3,8,4,8
X,3,8,5,6,4,7,7,3,8,6,7,10,3,7,7,8
R,4,9,6,7,6,7,9,5,6,8,4,8,3,6,5,11
U,6,10,7,7,5,3,8,5,7,9,8,10,5,8,3,4
D,4,9,5,7,5,5,7,9,6,6,6,6,2,8,3,8
C,6,8,6,6,3,5,8,6,8,13,9,9,2,11,2,7
H,4,9,4,7,4,7,8,13,1,7,5,8,3,8,0,8
I,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8
L,4,9,5,7,4,9,4,1,7,9,2,10,1,6,3,9
Y,3,4,5,6,6,9,7,5,3,7,7,7,6,9,6,4
X,4,8,7,6,4,6,7,1,8,10,8,9,3,8,3,7
L,3,8,3,6,1,0,1,6,6,0,0,6,0,8,0,8
I,2,8,5,6,5,11,6,1,6,9,4,5,1,7,5,8
G,4,5,5,4,3,7,7,6,7,9,6,11,2,10,5,9
T,8,11,9,8,6,7,9,2,9,10,9,5,2,9,5,5
E,3,5,5,4,3,5,8,3,9,11,8,10,2,8,4,7
R,2,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10
M,4,7,5,5,4,8,6,10,0,6,8,8,7,5,0,8
T,6,10,8,7,9,8,8,5,6,6,7,9,8,7,9,5
X,4,8,6,6,3,7,7,1,8,10,6,8,3,8,4,7
I,3,7,4,5,2,7,9,0,7,13,6,6,0,9,2,7
C,4,7,5,5,3,5,8,7,7,8,8,14,2,9,4,10
U,5,8,6,6,2,7,4,13,6,8,15,8,3,9,0,8
M,7,9,10,6,7,5,6,3,5,9,10,10,8,5,2,7
O,3,1,4,3,2,7,7,7,5,7,6,8,2,8,3,8
J,2,9,2,6,2,13,4,5,4,13,2,9,0,7,0,8
W,3,4,4,3,3,6,10,4,2,8,7,7,6,12,1,6
A,2,7,4,5,2,8,4,2,1,7,1,8,2,6,1,8
C,1,0,1,0,0,7,7,5,7,7,6,13,0,8,4,10
I,2,7,3,5,2,8,6,0,7,13,6,9,0,8,1,8
D,8,15,7,8,6,10,5,4,7,10,4,7,6,7,9,8
O,2,3,3,2,2,7,7,6,4,9,6,8,2,8,2,8
U,6,10,9,8,11,9,7,4,5,6,7,7,9,7,6,6
A,4,11,7,8,4,12,4,3,3,9,1,9,3,8,3,9
N,2,5,4,3,2,7,8,2,4,10,5,6,5,9,0,7
J,2,8,3,6,2,14,4,4,4,13,2,9,0,7,0,8
G,4,5,5,8,2,8,6,9,8,6,6,12,2,8,5,10
V,4,11,6,8,8,7,5,5,2,8,7,8,6,9,5,8
A,3,11,5,8,4,11,4,2,3,9,2,9,3,7,3,7
D,4,10,6,8,5,8,7,7,7,9,4,5,3,8,4,9
D,3,8,4,6,2,5,7,10,8,6,5,5,3,8,4,8
L,3,8,4,6,3,8,4,1,7,9,2,10,1,6,3,9
A,6,11,6,6,4,12,3,5,2,11,3,10,6,4,4,10
Q,3,7,4,6,2,8,6,9,6,6,4,8,3,8,4,8
D,2,5,3,3,2,9,6,3,5,10,4,7,3,7,2,8
R,4,7,7,6,7,7,7,3,3,7,5,8,6,8,6,6
E,3,6,4,4,4,6,7,5,7,7,6,9,3,8,5,10
O,3,3,5,5,2,8,6,8,8,7,4,8,3,8,4,8
W,3,2,5,3,3,8,11,3,2,6,9,8,7,11,1,7
T,2,1,3,1,0,8,15,2,4,6,10,8,0,8,0,8
Z,3,6,4,4,3,8,7,5,10,7,5,8,2,9,8,8
S,6,8,7,6,4,7,8,4,8,10,7,7,2,9,5,7
G,2,5,3,3,2,6,7,5,5,9,7,10,2,9,4,9
R,4,4,5,6,3,5,12,8,4,7,3,9,3,7,6,11
H,1,1,2,1,1,7,8,11,1,7,5,8,3,8,0,8
E,5,9,7,7,7,8,10,7,4,6,6,11,5,8,8,9
Y,4,6,6,4,5,8,5,6,5,8,7,8,5,8,4,6
E,3,7,3,5,3,3,8,4,8,7,6,13,0,8,6,9
K,6,9,9,6,5,9,6,2,7,10,3,8,6,8,6,10
B,4,4,4,6,3,6,7,9,7,7,6,7,2,8,9,10
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
K,4,5,6,4,3,4,8,2,8,10,10,11,4,7,3,6
D,3,6,4,4,6,8,8,5,4,7,6,6,3,7,8,5
I,2,11,2,8,4,7,7,0,7,7,6,8,0,8,3,8
H,3,7,4,5,3,7,7,13,1,7,7,8,3,8,0,8
L,3,6,5,4,3,6,4,0,8,8,3,11,0,8,2,8
E,3,10,4,8,2,3,8,6,11,7,5,15,0,8,7,7
W,5,11,8,8,8,5,11,2,2,7,8,8,7,12,1,8
S,3,5,5,4,2,8,7,3,8,11,5,7,1,9,5,8
N,2,6,3,4,3,7,7,11,1,6,6,8,5,9,0,8
V,5,10,5,7,3,3,11,3,4,10,12,8,2,10,1,8
W,5,4,7,6,3,6,8,5,1,7,8,8,9,9,0,8
W,4,6,7,4,4,7,9,4,0,7,9,8,7,12,0,8
Z,6,8,8,10,7,11,5,4,5,8,2,6,2,7,7,8
B,8,12,7,6,6,8,8,4,5,9,5,7,7,5,8,7
L,2,7,3,5,2,5,5,2,8,3,2,7,0,7,1,5
L,2,5,3,3,1,7,4,1,6,8,2,10,1,6,2,8
B,4,7,7,6,7,8,8,4,4,7,6,8,6,8,8,5
F,1,0,1,0,0,3,12,4,2,11,9,6,0,8,2,7
I,2,10,5,8,5,10,7,2,5,9,5,5,3,8,6,7
G,9,13,8,7,4,11,3,3,4,10,2,6,4,7,4,11
F,6,10,9,7,9,8,6,1,6,9,7,7,6,10,4,7
B,4,6,4,4,3,6,6,8,7,6,6,7,2,8,9,10
G,3,4,4,3,2,6,6,6,6,6,6,11,2,9,4,9
U,4,5,5,4,2,6,8,6,8,6,10,9,3,9,1,7
P,4,6,6,9,8,6,6,5,3,7,6,6,9,13,6,10
W,9,10,9,7,6,6,10,5,3,8,6,6,11,12,4,4
A,3,8,5,6,3,11,2,2,2,9,2,9,2,6,3,7
P,5,9,7,7,5,7,11,6,3,11,5,2,2,11,3,8
T,8,11,8,8,4,5,14,7,4,11,8,3,2,12,1,4
U,5,7,5,5,2,4,9,5,7,12,11,8,3,9,1,7
J,1,6,2,4,2,9,7,0,6,10,5,7,0,7,0,7
F,6,10,9,7,5,4,12,5,5,13,8,4,2,10,2,5
O,5,7,6,5,7,8,7,5,2,7,6,8,8,9,4,9
P,6,11,9,8,7,8,9,4,5,12,5,3,4,10,4,7
O,6,10,7,8,3,7,10,9,9,8,8,6,3,8,4,8
A,2,1,3,1,0,7,4,2,0,7,2,8,2,7,1,8
R,7,13,7,8,5,10,5,2,5,9,4,8,6,8,6,9
N,4,10,4,8,3,7,7,14,2,5,6,8,6,8,0,8
R,12,14,9,8,5,9,7,6,5,10,2,8,7,5,6,10
T,2,7,4,4,1,7,15,1,5,7,11,8,0,8,0,8
O,2,0,2,1,1,8,7,7,5,7,6,8,2,8,3,8
M,5,6,8,4,4,9,5,3,5,9,4,7,8,6,2,8
V,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8
U,1,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8
T,3,4,4,3,2,5,11,2,7,11,9,5,1,11,2,5
N,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
Y,5,8,5,6,2,3,11,3,7,13,11,6,1,10,2,5
G,2,3,3,1,1,7,7,5,5,9,6,9,2,9,4,10
K,2,3,4,2,2,5,8,2,7,10,8,9,3,8,2,7
I,3,9,4,7,2,7,7,0,8,14,6,8,0,8,1,8
L,2,1,3,3,1,5,2,6,7,1,3,2,1,7,1,5
R,4,9,6,7,6,9,7,4,5,10,4,7,3,6,4,10
V,7,9,7,5,3,6,9,5,3,8,7,5,5,12,3,9
I,3,7,4,5,1,8,5,0,8,14,6,10,0,7,1,8
K,6,11,6,8,3,4,8,9,2,7,5,11,4,8,2,11
Q,5,5,7,5,5,7,4,4,5,7,3,8,4,5,4,8
P,4,8,5,6,2,4,11,9,4,9,6,5,2,10,4,8
T,5,10,5,7,4,5,11,2,8,11,10,5,1,11,3,4
W,5,10,8,7,12,7,7,6,2,7,7,8,13,12,4,10
B,5,8,7,6,6,9,6,4,6,10,5,7,2,8,5,10
U,3,2,5,3,2,7,8,5,7,5,9,9,5,10,1,7
A,6,14,5,8,4,9,4,3,2,8,4,11,6,6,4,8
Q,5,6,6,9,7,10,13,4,2,4,8,12,4,14,5,10
E,5,11,5,8,6,3,7,5,9,7,7,14,0,8,6,8
L,3,9,5,6,3,8,3,3,6,8,2,8,1,6,2,8
M,14,14,14,8,7,7,10,5,5,4,4,11,11,13,2,7
U,4,9,4,6,2,7,5,14,5,7,14,8,3,9,0,8
I,2,6,4,4,3,10,7,2,4,8,5,5,3,9,5,7
D,3,5,5,4,3,9,6,4,6,10,4,6,2,8,3,8
H,9,15,8,8,5,11,7,4,5,9,3,4,6,8,4,8
D,3,8,5,6,5,8,7,7,6,8,4,5,4,9,4,8
P,3,9,4,6,2,4,11,9,3,9,6,4,1,10,4,8
H,9,12,8,6,4,6,6,6,4,9,11,10,7,11,5,9
Q,4,10,6,9,3,8,7,8,6,6,7,9,3,7,5,9
G,2,3,3,2,1,7,7,6,6,6,6,10,2,9,4,9
Z,2,3,4,2,2,8,7,2,9,12,6,8,1,8,5,8
Z,3,6,4,4,2,7,7,3,14,9,6,8,0,8,8,8
Z,4,11,5,8,2,7,7,4,14,10,6,8,0,8,8,8
J,5,10,7,8,4,6,8,3,6,15,7,10,3,7,3,7
N,9,12,8,6,3,7,10,5,6,3,4,10,5,9,2,7
X,4,5,6,3,3,7,7,1,9,10,6,8,2,8,3,7
Z,4,6,6,8,4,12,4,3,5,11,2,8,2,7,5,12
Y,5,8,5,6,3,3,10,3,6,12,12,7,1,11,2,5
Z,3,10,4,8,2,7,7,4,14,10,6,8,0,8,8,8
N,5,10,6,7,4,6,8,6,5,7,7,9,6,8,2,6
C,2,3,2,1,1,5,8,5,6,11,9,11,1,9,3,8
B,4,7,6,5,6,9,6,4,6,10,5,7,2,8,5,10
O,1,3,2,2,1,8,7,6,3,9,6,8,2,8,2,8
P,3,6,4,4,3,4,10,4,5,10,9,4,1,10,3,7
C,3,3,4,5,2,6,6,7,9,9,5,13,1,9,4,8
M,4,8,5,6,5,8,6,6,4,6,7,8,8,5,2,7
A,4,10,6,8,4,7,5,3,1,7,1,8,2,7,2,8
A,3,9,5,6,3,10,3,2,2,8,3,10,2,6,3,7
D,5,9,6,7,6,9,7,3,5,11,5,5,3,8,3,8
M,3,4,5,3,3,7,6,3,4,9,7,8,7,5,1,8
T,4,11,5,8,5,7,11,4,6,7,11,8,3,12,1,8
N,8,14,9,8,4,4,9,3,4,13,11,10,6,8,0,8
S,5,6,6,4,3,6,7,4,7,10,10,9,2,10,4,5
P,3,2,4,3,2,5,10,4,4,10,8,3,1,10,3,6
P,4,11,5,8,3,3,13,8,2,11,6,3,1,10,4,8
H,6,8,8,6,5,10,6,3,7,10,3,7,4,9,4,10
I,4,9,4,5,2,8,8,2,5,13,5,5,1,9,5,10
S,7,11,9,8,11,8,8,5,3,8,5,8,6,8,13,8
N,4,5,6,3,2,7,8,2,5,10,6,6,5,8,1,7
Z,4,8,6,6,3,7,7,2,10,12,5,9,2,10,6,9
Y,1,1,2,1,1,9,11,1,6,6,11,7,1,11,1,8
L,1,3,3,1,1,7,4,1,7,8,2,10,0,7,2,9
O,8,12,5,6,3,8,7,6,5,9,4,7,5,9,5,8
W,4,7,6,5,3,6,8,4,1,7,8,8,8,10,0,8
A,7,10,10,9,8,6,8,3,6,7,8,10,6,8,4,8
U,6,10,8,8,8,8,7,9,5,6,6,9,3,8,5,5
G,7,9,10,8,11,7,7,6,3,7,7,8,8,10,9,9
N,2,1,3,3,2,7,9,5,4,7,6,6,4,8,1,6
N,4,8,5,6,4,7,7,7,5,7,5,6,3,7,3,8
W,3,2,5,3,3,8,11,2,2,7,9,8,6,11,0,7
C,4,6,4,4,2,5,9,6,8,12,10,11,1,9,2,7
G,3,5,4,4,2,6,7,5,5,9,7,10,2,9,4,10
Q,3,6,4,7,4,9,5,6,3,9,5,11,3,9,5,9
D,4,8,6,7,5,5,6,6,7,8,6,8,4,5,6,5
H,2,1,3,2,2,7,7,5,5,7,6,8,3,8,2,8
J,2,5,4,4,2,8,6,3,5,14,6,10,1,6,0,7
B,2,1,2,1,1,7,7,7,5,6,5,7,1,8,7,10
O,2,3,3,2,2,8,7,6,4,9,5,8,2,8,2,8
A,3,7,5,5,3,12,3,3,2,10,1,9,2,6,2,8
C,4,6,6,4,5,7,6,4,4,8,7,11,5,9,3,8
F,3,5,3,4,2,6,10,4,5,10,9,4,2,10,2,6
O,3,5,4,4,3,7,7,7,5,9,6,8,2,8,3,8
J,2,10,3,7,2,12,3,6,3,12,5,11,1,6,0,8
M,5,8,9,6,10,8,6,3,2,8,4,8,14,6,3,7
F,6,10,8,7,8,7,6,6,4,7,6,8,5,10,8,12
K,11,13,10,8,4,8,8,3,8,9,4,6,5,7,4,7
F,3,8,4,5,1,1,12,5,5,12,10,8,0,8,2,6
P,3,4,5,3,2,7,10,4,4,12,5,3,1,10,3,8
L,4,9,6,7,7,7,7,3,5,6,7,11,6,11,6,5
T,5,7,5,5,2,5,11,2,9,12,9,4,0,10,2,4
S,4,7,6,5,3,8,8,3,7,10,5,6,2,8,5,8
A,3,8,5,6,3,11,3,2,2,8,2,9,2,5,2,8
C,8,12,5,7,2,6,8,7,8,10,7,12,2,9,5,9
Q,4,8,5,7,3,8,6,9,7,6,4,8,3,8,4,8
R,5,7,6,5,5,8,7,6,3,8,5,6,4,7,7,8
Q,1,0,2,1,1,8,7,6,3,6,6,9,2,8,3,8
D,4,8,6,6,5,10,6,2,6,11,3,7,3,8,3,10
K,1,0,2,1,0,5,7,7,1,7,6,11,2,8,2,11
K,8,15,8,8,4,6,8,3,7,9,7,9,6,9,3,7
L,3,6,5,4,2,6,4,2,9,7,2,10,0,7,3,8
L,3,11,5,8,2,2,2,5,10,0,0,6,0,7,1,5
F,2,3,4,2,1,4,12,4,4,13,8,5,1,9,1,6
U,5,6,5,4,2,4,8,6,8,9,9,9,3,9,3,5
A,6,11,8,9,9,8,7,8,3,7,6,8,3,8,9,3
G,3,7,4,5,2,8,7,8,7,6,6,9,2,7,5,11
M,5,10,8,8,6,8,6,2,4,9,6,8,8,6,2,8
O,3,7,4,5,3,7,7,7,3,9,6,9,3,8,3,8
T,1,0,2,0,0,7,14,2,4,7,10,8,0,8,0,8
U,4,5,5,4,2,5,8,6,8,8,10,10,3,9,1,7
Y,4,11,6,8,2,9,10,1,3,6,12,8,1,11,0,8
S,3,6,6,4,3,9,7,4,8,11,3,7,1,8,5,10
U,3,8,5,6,3,4,8,7,7,9,10,11,3,9,0,8
H,5,9,5,5,4,7,8,3,4,10,6,8,6,8,4,8
M,4,7,6,5,6,6,6,5,5,7,7,11,10,5,2,9
P,3,5,4,3,2,6,9,5,4,9,7,3,1,10,4,6
W,5,5,8,4,8,7,7,5,5,6,6,8,8,10,7,10
W,3,3,4,4,2,7,8,4,1,7,8,8,8,10,0,8
U,2,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8
N,4,3,4,5,2,7,7,14,2,5,6,8,6,8,0,8
O,4,11,5,8,3,8,8,9,8,6,8,8,3,8,4,8
L,3,8,5,6,6,7,7,3,5,7,6,10,6,11,6,5
R,1,0,1,0,0,6,8,6,3,7,5,7,2,7,4,11
M,6,10,9,8,7,6,6,7,6,7,8,11,9,6,2,9
S,2,7,3,5,3,8,7,7,5,7,5,7,2,8,8,7
T,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8
D,10,15,9,8,6,9,5,4,7,9,5,7,6,10,6,8
J,3,11,4,8,1,14,2,8,5,14,2,11,0,6,0,8
L,5,10,5,8,3,0,1,5,6,0,0,6,0,8,0,8
Y,3,4,5,6,4,9,10,7,4,7,7,6,4,10,5,5
I,1,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8
A,3,9,5,6,4,8,2,1,2,7,2,7,2,7,3,6
R,7,9,10,8,11,7,7,4,4,7,5,7,7,9,7,5
D,4,10,6,8,5,7,7,9,7,7,6,5,3,8,4,8
A,4,11,6,8,4,12,2,4,3,11,1,9,3,7,3,9
A,3,7,5,5,3,11,2,2,2,8,3,9,3,5,3,8
T,5,10,7,8,8,5,8,4,7,7,6,9,5,8,5,6
H,5,10,6,7,3,7,7,15,0,7,6,8,3,8,0,8
B,3,5,4,3,3,7,7,5,5,6,6,6,2,8,6,10
C,3,8,4,6,2,5,8,7,8,7,8,14,1,8,4,10
P,2,4,3,3,2,6,10,5,4,9,7,2,1,10,4,6
N,4,5,4,3,3,7,8,5,5,7,6,6,5,10,2,5
J,2,8,3,6,2,10,7,0,7,11,3,6,0,7,1,7
S,6,12,6,7,3,7,7,3,5,14,7,8,2,9,3,8
U,5,10,5,7,2,7,4,15,6,7,14,8,3,9,0,8
K,4,4,5,3,3,6,7,4,7,7,6,11,3,8,5,9
Y,5,7,6,5,3,5,9,2,9,9,10,4,1,11,4,4
W,5,7,5,5,4,3,11,2,2,10,8,7,5,11,2,6
G,3,7,4,5,3,6,6,6,5,9,7,13,2,9,4,10
R,2,3,3,2,2,8,7,3,5,9,4,7,2,7,4,10
H,4,5,4,7,2,7,7,15,1,7,6,8,3,8,0,8
W,10,10,10,8,7,6,10,5,3,8,6,6,10,12,4,4
O,4,10,6,8,4,8,6,9,5,7,4,8,3,8,3,7
B,3,4,4,3,3,7,7,5,6,7,6,6,2,8,6,9
W,7,11,9,8,7,10,8,5,1,6,10,7,10,12,2,5
A,4,8,6,6,3,9,3,2,3,8,1,8,2,6,3,7
L,2,5,3,3,1,6,4,1,8,7,2,10,0,7,2,8
Q,4,7,5,9,6,8,6,6,4,9,6,9,2,9,4,8
F,2,4,2,3,2,6,10,4,5,10,9,5,2,9,3,6
V,5,11,8,8,5,8,12,2,3,4,10,9,6,11,5,8
O,8,11,8,9,8,8,6,8,4,9,5,8,3,9,3,8
Y,8,9,8,7,5,5,8,1,8,8,9,5,4,11,6,4
S,5,7,6,5,4,7,7,3,7,10,8,8,2,9,5,6
C,3,6,4,6,4,4,8,3,5,7,6,11,3,10,7,7
I,2,11,2,8,3,7,7,0,7,7,6,8,0,8,3,8
A,3,10,5,8,3,13,4,5,3,12,0,8,2,6,3,9
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
O,4,6,6,6,4,8,4,4,4,9,3,9,3,7,4,9
J,2,4,4,3,1,8,7,2,6,14,5,8,2,8,1,8
Z,3,8,4,6,2,7,7,4,13,9,6,8,0,8,8,8
S,4,8,5,6,4,8,7,7,7,7,6,8,2,9,9,8
R,2,4,4,2,2,8,8,3,5,10,4,7,2,6,3,10
S,3,6,5,4,5,9,7,4,3,8,5,8,4,8,10,9
L,2,6,3,4,2,8,3,3,6,7,2,7,1,6,2,8
Q,6,10,6,5,4,9,6,4,7,11,4,9,3,7,8,11
Q,2,2,3,4,2,8,7,7,3,6,5,9,2,9,3,9
V,3,8,5,6,2,9,9,4,1,6,12,8,2,10,0,8
R,3,5,4,6,3,6,11,9,4,7,2,9,3,7,5,11
O,5,8,7,6,8,8,7,5,1,7,6,8,9,9,6,11
R,3,4,5,3,3,8,7,3,5,9,4,7,2,7,4,10
F,7,13,7,7,5,8,10,3,5,11,5,4,4,9,8,7
N,4,4,5,7,3,7,7,15,2,4,6,8,6,8,0,8
J,3,11,4,8,3,9,6,2,7,12,3,8,1,6,2,6
P,4,8,5,6,2,4,15,8,1,12,6,2,0,9,4,8
B,4,7,4,5,3,6,8,9,8,7,5,7,2,8,9,9
E,5,10,4,5,3,7,10,5,5,10,5,9,3,8,6,10
S,3,7,4,5,2,8,7,5,8,5,6,8,0,8,9,8
N,4,7,6,5,6,7,8,4,4,7,6,7,5,9,5,4
L,3,11,5,8,3,4,3,5,8,1,0,6,0,6,1,5
I,5,8,6,9,6,8,9,5,6,6,6,7,3,9,9,8
C,7,10,7,8,5,3,8,5,7,10,10,14,3,9,4,6
D,2,5,4,4,3,8,7,5,6,9,5,5,2,8,3,8
D,3,4,5,3,3,9,6,4,6,10,4,5,2,8,3,8
R,3,6,4,4,4,8,6,7,3,8,6,7,4,6,7,8
S,4,8,6,6,4,7,8,3,7,10,4,7,2,7,4,8
N,5,6,7,4,4,4,10,3,3,9,9,8,5,8,1,7
N,4,9,6,7,4,9,7,6,5,6,6,4,5,8,2,5
O,5,10,7,7,8,9,7,6,1,7,7,9,10,9,4,6
C,3,6,4,4,2,3,9,5,7,11,11,11,1,8,2,7
I,4,9,4,4,2,7,10,2,5,13,5,4,1,8,5,8
X,5,9,7,8,8,7,8,2,5,8,6,8,4,6,8,9
R,6,9,8,7,8,8,8,6,5,8,6,7,7,8,6,12
J,8,13,6,10,5,11,5,2,5,12,4,8,2,9,6,13
Z,5,9,7,7,4,7,8,2,10,12,6,9,2,10,6,8
Z,1,3,2,2,1,7,7,5,8,6,6,8,1,8,7,8
R,2,1,3,2,2,7,8,4,5,6,5,7,2,6,4,8
M,5,9,6,7,6,7,5,11,1,7,9,8,9,5,2,8
C,4,10,5,8,2,6,8,7,10,4,6,13,1,7,4,8
N,2,1,3,3,2,7,8,5,4,7,6,7,4,8,1,7
B,4,8,6,6,5,8,7,5,6,9,5,6,3,8,7,9
Z,6,9,8,7,5,9,5,3,9,11,3,11,3,6,7,9
N,6,10,9,8,6,5,10,2,4,9,9,8,7,7,2,7
I,5,10,6,8,4,7,7,0,8,13,6,8,0,8,1,8
I,1,7,0,5,1,7,7,5,3,7,6,8,0,8,0,8
O,4,7,6,5,6,9,6,5,1,7,6,9,10,9,4,8
D,5,11,8,9,12,9,9,5,5,7,6,6,5,8,9,6
P,7,11,10,8,5,8,11,7,3,11,4,2,2,11,4,9
N,5,8,7,6,4,7,8,3,5,10,6,7,5,8,1,7
Q,2,4,3,5,3,8,7,7,3,6,6,9,3,8,5,10
I,2,7,5,5,4,11,6,1,5,8,4,5,3,8,5,9
D,5,9,7,7,7,9,6,4,6,9,3,6,3,8,3,8
P,5,8,5,6,2,4,13,8,1,11,6,3,1,10,4,8
C,2,1,3,2,1,6,7,6,10,7,6,14,0,8,4,9
J,5,10,7,8,3,6,7,3,7,15,8,11,1,6,1,7
K,3,2,4,3,2,5,7,4,7,6,6,11,3,8,5,9
T,1,0,1,0,0,8,13,1,4,6,10,8,0,8,0,8
M,6,5,7,8,4,8,7,13,2,7,9,8,9,6,0,8
K,9,13,10,7,6,4,9,4,6,10,10,11,5,8,4,6
G,4,9,4,6,3,6,7,7,6,10,8,10,2,9,4,9
D,2,6,4,4,5,10,7,4,5,7,5,6,3,6,6,5
J,1,4,2,3,1,10,6,2,5,11,4,9,1,7,1,7
F,1,3,3,1,1,5,10,2,5,13,7,5,1,9,1,7
H,6,7,8,5,5,5,9,3,6,10,9,9,4,9,4,7
Q,3,5,4,6,3,8,7,7,5,8,7,8,2,9,4,9
X,9,14,10,8,5,5,9,3,8,11,9,8,4,9,4,6
U,7,9,8,7,5,4,8,5,8,10,9,9,3,9,2,6
E,5,9,7,6,6,8,7,7,2,7,6,11,5,8,8,9
K,1,1,2,1,0,4,6,6,2,7,6,11,3,8,2,10
R,4,11,6,9,5,7,8,5,7,6,5,7,4,5,7,9
R,4,7,5,5,4,7,8,6,6,6,5,8,3,6,6,9
R,4,8,6,6,5,9,8,4,6,8,3,8,4,5,5,11
R,2,4,4,3,2,9,7,2,6,10,3,6,2,7,3,10
V,3,8,6,6,1,6,8,4,3,7,14,8,3,9,0,8
X,3,8,4,6,3,7,7,3,8,6,6,10,3,8,6,8
F,4,9,6,7,7,6,8,1,4,10,8,7,6,11,4,5
B,3,7,4,5,5,9,6,5,4,7,7,8,6,9,7,7
L,3,6,5,4,3,6,4,2,6,7,2,9,1,7,3,7
U,4,2,5,3,2,6,9,6,7,7,10,9,3,9,1,8
O,3,6,4,4,3,8,7,7,5,7,6,8,2,8,3,8
Y,4,8,5,6,2,6,10,2,2,7,12,8,2,11,0,8
Z,2,3,4,2,1,7,7,2,9,11,6,8,1,8,5,7
P,6,8,8,6,5,7,11,7,3,9,5,3,3,11,4,8
U,3,4,4,6,2,8,5,13,5,6,14,8,3,9,0,8
C,6,9,4,4,2,7,9,6,6,11,7,8,2,9,5,9
L,2,6,3,4,2,4,5,1,8,6,2,10,0,7,3,6
R,3,5,4,6,3,6,11,10,4,7,3,9,3,7,5,10
P,4,8,5,6,5,6,9,6,5,9,7,4,2,10,3,7
R,4,11,5,8,3,6,10,10,4,7,4,8,3,7,5,11
C,1,0,2,0,0,7,7,5,7,7,6,13,0,8,4,10
Q,2,1,2,2,1,8,7,7,4,6,6,8,3,8,3,8
R,2,1,2,2,2,7,8,5,5,6,5,7,2,7,4,8
E,6,9,8,7,5,7,7,2,9,11,5,9,3,8,5,8
E,3,7,4,5,4,7,8,6,9,6,4,9,3,8,6,8
A,2,5,4,4,2,10,2,2,2,9,2,9,2,6,2,8
L,3,7,4,5,2,7,4,2,6,7,2,8,1,6,2,8
F,5,6,6,7,6,7,10,5,6,8,6,8,4,8,7,6
E,2,3,4,2,2,5,8,2,8,11,8,9,2,8,4,6
V,1,1,2,1,0,8,9,4,2,7,13,8,2,10,0,8
E,2,6,3,4,3,6,7,6,8,7,6,10,3,8,6,8
U,4,10,4,7,2,7,6,14,5,7,13,8,3,9,0,8
A,3,8,6,5,2,8,5,3,1,7,1,8,2,7,2,8
A,4,11,7,8,6,10,3,1,2,8,3,9,5,5,3,7
Y,3,5,4,6,5,9,9,5,3,6,7,8,5,10,7,6
N,2,3,4,2,1,5,10,3,3,10,8,8,4,8,0,8
U,4,5,4,7,2,7,5,14,5,7,14,8,3,9,0,8
G,4,8,5,6,3,6,7,6,6,10,8,10,2,9,4,9
L,2,8,3,6,2,3,5,2,8,3,0,9,0,7,1,6
O,5,11,6,9,6,7,7,8,3,10,6,8,3,8,3,8
L,3,7,4,5,2,7,4,1,7,8,2,9,1,6,2,8
Y,5,10,8,8,4,10,11,2,8,3,11,8,1,11,2,9
K,3,4,4,6,2,3,7,7,2,7,5,11,3,8,3,10
G,7,14,5,8,4,8,6,4,3,9,5,9,4,9,8,8
F,3,9,4,7,4,5,10,3,5,10,9,6,2,10,3,6
P,4,5,5,8,2,3,13,8,2,11,7,3,1,10,4,8
U,3,4,4,3,2,6,9,6,6,7,9,9,3,9,1,9
M,4,11,5,8,7,7,6,10,1,7,8,8,8,4,0,8
L,3,10,3,8,2,0,2,3,6,1,0,8,0,8,0,8
F,3,6,5,4,2,6,10,2,6,13,7,5,1,10,2,7
W,4,3,6,4,2,4,8,5,1,7,9,8,8,10,0,8
Z,4,10,5,8,3,7,7,4,15,9,6,8,0,8,8,8
U,7,11,9,8,5,6,10,6,8,7,10,9,3,9,1,8
H,3,7,5,5,4,8,7,6,6,7,6,9,6,8,3,8
V,6,9,5,7,2,2,12,5,4,11,12,7,3,10,1,8
E,4,10,3,5,2,9,6,5,4,11,4,10,3,8,8,13
O,5,10,6,8,6,8,8,8,4,7,7,6,5,7,4,9
Q,1,0,2,1,1,8,7,6,3,6,6,9,2,8,3,8
J,2,6,3,4,1,14,2,5,5,13,2,10,0,8,0,8
R,3,6,4,4,3,8,8,5,6,7,5,6,3,7,5,8
E,4,7,6,5,4,7,7,2,7,11,6,9,3,8,4,9
W,6,9,6,5,4,3,9,2,3,9,10,8,8,11,1,5
T,2,9,3,6,1,7,14,0,6,7,11,8,0,8,0,8
B,2,3,3,2,2,8,7,3,5,10,5,6,2,8,3,9
G,2,0,2,1,1,8,6,6,5,6,5,9,2,8,5,10
S,4,9,6,6,4,7,6,3,7,9,9,9,2,10,4,6
B,4,7,6,5,6,9,6,4,6,9,5,6,2,8,6,9
S,4,9,6,7,4,9,8,4,7,10,4,6,2,8,5,9
M,2,1,3,2,1,7,6,10,1,7,9,8,7,6,0,8
D,3,7,5,5,7,10,8,4,5,7,6,6,4,7,7,5
P,4,11,6,8,5,6,8,5,6,9,8,4,2,10,4,7
V,4,10,6,7,4,8,9,4,2,6,13,8,2,10,0,8
E,1,3,3,2,1,7,7,2,6,11,6,9,2,9,4,10
Z,2,4,4,3,2,7,8,2,9,11,6,8,1,8,5,7
Q,3,4,4,7,2,7,6,8,6,5,5,8,3,8,4,8
F,4,8,4,5,2,1,12,5,5,12,10,8,0,8,2,6
Q,2,3,3,4,3,8,7,7,3,6,6,9,3,8,4,9
O,3,4,4,3,2,7,7,6,4,9,6,8,2,8,3,7
L,4,10,5,8,4,6,4,1,7,8,2,10,0,6,3,8
L,4,10,5,7,3,7,4,0,9,9,2,11,0,7,3,8
J,2,7,3,5,1,14,3,4,5,12,1,8,0,7,0,8
A,3,6,5,4,3,8,3,2,2,6,2,7,2,6,2,6
M,5,9,6,4,4,6,4,2,2,8,4,10,7,3,1,9
H,7,11,10,8,7,4,9,4,6,10,9,9,3,8,4,7
Q,4,10,5,9,5,8,7,8,5,6,7,8,3,8,4,9
H,4,9,5,7,4,7,8,13,1,7,5,8,3,8,0,8
V,5,11,7,8,4,7,9,4,1,7,13,8,5,9,2,9
L,3,8,4,6,3,4,4,4,7,2,1,7,1,6,1,6
M,5,11,7,8,9,8,7,6,5,6,7,8,8,6,2,8
X,6,10,8,8,7,7,6,3,5,6,6,8,3,9,10,9
Z,5,8,7,6,4,6,9,3,10,11,9,5,1,8,6,5
I,3,9,4,7,3,7,8,0,7,13,6,8,0,8,1,8
R,2,4,4,3,2,8,7,4,5,9,5,7,2,7,4,10
O,7,10,7,8,6,8,7,7,4,9,5,6,5,9,5,10
U,5,9,7,8,7,7,7,4,4,6,6,9,4,8,2,9
J,2,3,2,4,1,10,3,10,3,12,8,13,1,6,0,8
D,6,9,6,4,3,10,4,6,5,13,4,11,5,6,4,8
K,5,7,7,5,5,4,7,2,7,10,9,11,4,7,4,6
R,3,5,5,3,3,8,7,5,4,8,5,7,3,7,4,11
G,5,11,4,6,3,7,9,4,4,9,6,5,3,9,8,7
O,6,11,7,8,5,7,7,9,6,7,5,8,3,8,4,8
A,1,0,2,0,0,7,4,2,0,7,2,8,2,7,1,8
F,2,4,3,3,2,5,10,4,5,10,9,5,1,10,3,6
U,5,9,6,6,4,3,9,5,7,10,10,9,3,9,2,6
B,3,8,5,6,4,9,6,4,6,10,5,6,2,8,6,9
F,5,9,6,7,6,6,10,5,4,9,6,8,5,9,7,11
Q,4,8,4,9,5,8,8,6,2,6,8,11,3,9,6,7
Q,3,8,4,7,4,8,7,8,5,6,5,8,2,9,4,8
T,3,4,3,2,1,6,11,2,7,11,9,5,2,9,3,4
S,4,8,5,6,2,7,6,6,9,5,6,10,0,9,9,8
C,2,1,3,2,1,6,7,6,10,7,6,14,0,8,4,9
Q,7,12,6,7,3,8,4,4,7,11,4,10,3,7,8,11
L,4,10,5,8,3,0,2,4,6,1,0,8,0,8,0,8
F,4,9,6,6,6,11,6,2,5,10,5,6,5,9,4,7
P,4,6,5,8,8,8,9,5,0,8,7,6,5,10,5,9
C,4,6,5,4,2,5,8,5,7,12,9,12,2,10,3,8
V,2,6,4,4,2,6,11,3,3,8,11,8,2,11,1,9
Q,7,9,7,11,9,8,7,6,2,8,7,11,5,9,8,6
T,1,6,2,4,1,7,13,0,5,7,10,8,0,8,0,8
N,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8
Z,4,11,6,8,7,8,7,2,8,7,6,7,1,7,11,7
S,2,1,2,2,1,8,8,6,4,8,5,7,2,8,8,8
G,5,10,5,7,4,6,7,7,6,10,8,11,2,9,4,9
M,9,14,11,8,7,6,4,3,2,8,4,10,10,1,1,8
R,6,9,6,4,4,5,8,3,5,7,4,10,5,7,6,6
T,4,7,4,5,3,5,11,3,6,11,9,5,2,11,2,5
D,5,11,5,8,3,6,7,11,10,6,5,6,3,8,4,8
X,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8
M,2,3,3,4,2,8,6,11,1,6,9,8,7,5,0,8
R,4,9,6,6,6,7,7,4,6,7,5,7,3,7,5,8
P,3,5,5,4,3,8,8,4,4,12,4,4,2,9,3,8
F,8,13,7,7,3,6,9,3,6,12,5,5,2,8,6,6
Q,2,2,3,3,2,8,7,7,3,6,6,9,2,9,3,9
J,1,4,2,3,1,10,6,2,6,12,4,9,0,7,1,7
Y,3,5,5,7,5,9,10,5,4,7,7,7,5,11,6,5
I,2,9,2,7,2,7,7,0,8,7,6,8,0,8,3,8
S,3,8,4,6,3,8,8,7,5,7,6,7,2,8,8,8
B,4,9,6,6,7,9,6,4,4,6,7,7,7,9,8,6
Y,3,7,5,5,2,8,10,1,2,6,12,8,1,11,0,8
I,4,10,5,8,3,5,9,0,7,13,7,6,2,9,3,6
W,5,9,8,7,4,7,7,5,2,6,8,8,9,9,0,8
I,5,12,4,6,3,10,6,3,5,13,3,6,2,8,5,10
B,6,9,9,8,10,7,7,5,4,8,6,8,7,9,9,6
T,3,8,5,6,2,7,14,1,5,7,10,8,0,8,0,8
Y,2,9,4,6,1,7,10,1,3,7,12,8,1,11,0,8
Z,5,8,7,6,4,7,8,2,10,12,6,7,2,8,6,8
L,6,12,5,7,3,10,2,4,4,13,4,12,2,7,6,9
Y,2,3,2,1,1,5,10,2,7,10,9,4,1,11,2,5
M,3,4,5,3,3,8,5,3,3,9,6,8,7,5,1,8
I,5,15,5,8,3,10,6,2,5,11,4,6,2,9,5,12
R,2,2,3,3,2,6,7,4,5,7,6,7,5,7,3,8
U,4,8,6,6,4,5,9,5,6,7,9,9,3,9,1,8
D,6,12,5,7,4,7,6,4,6,9,5,6,5,9,7,6
Y,8,11,6,15,5,4,11,2,4,11,10,6,4,10,6,6
E,5,9,7,6,5,7,8,1,8,11,6,9,2,8,4,9
L,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8
A,5,11,7,8,5,7,4,3,0,7,1,8,3,8,3,7
C,4,6,5,4,5,6,6,4,4,7,6,11,5,9,3,8
O,3,9,5,7,4,7,8,8,6,7,9,7,3,8,4,9
U,2,6,3,4,1,7,5,13,5,7,12,8,3,9,0,8
A,3,5,5,5,4,8,8,2,4,7,7,8,5,9,4,7
E,3,5,6,3,3,5,8,2,9,11,7,9,2,8,4,8
L,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8
P,4,7,6,11,10,8,10,4,0,9,7,6,7,10,5,8
K,4,8,6,7,5,9,6,2,3,8,3,8,5,7,6,11
R,4,9,6,7,6,9,7,4,5,10,4,7,3,7,4,11
I,5,11,4,6,2,8,8,2,5,13,4,5,2,9,5,9
K,3,3,5,2,2,6,7,1,7,10,7,10,3,8,2,8
P,11,15,9,8,4,6,10,6,3,11,4,4,5,10,4,7
W,7,11,7,6,4,4,8,2,2,8,10,8,9,11,1,6
Y,5,9,5,5,3,6,7,4,5,9,7,5,3,10,5,4
C,5,9,4,4,3,7,10,3,4,9,7,9,3,8,6,10
K,5,10,8,7,5,6,7,3,8,7,6,8,4,8,5,8
P,4,11,5,8,4,5,11,4,6,11,9,4,0,10,4,7
R,4,8,5,6,5,8,8,6,5,8,6,7,3,7,5,9
M,3,7,4,5,3,8,6,11,1,6,9,8,7,6,0,8
F,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8
J,3,9,4,7,1,11,2,11,3,13,8,14,1,6,0,8
F,4,8,4,6,3,1,12,4,4,12,10,7,0,8,2,6
K,5,8,7,7,6,8,5,2,4,8,4,8,4,8,7,11
U,3,3,4,5,2,8,4,14,5,7,12,8,3,9,0,8
J,1,3,2,2,1,10,6,2,5,12,4,9,0,7,1,7
R,5,11,6,8,7,5,9,8,3,7,5,8,2,7,5,11
Q,5,8,6,10,7,7,10,4,2,6,9,11,3,9,6,8
W,4,6,6,4,3,6,8,4,1,7,8,8,8,9,0,8
K,7,11,9,8,5,6,7,2,7,10,7,10,4,8,4,8
R,6,10,5,6,3,7,7,5,4,9,4,9,6,5,6,11
M,7,9,9,5,4,12,2,5,2,11,1,9,7,2,1,8
Q,7,14,7,8,5,12,4,4,6,12,3,9,4,8,7,12
J,5,10,3,14,4,10,7,2,4,10,5,6,3,8,6,10
X,4,9,7,7,5,7,7,3,9,5,6,10,3,8,6,8
G,3,5,4,4,2,6,7,6,6,10,7,10,2,9,4,10
N,3,7,5,5,3,5,9,6,4,7,7,9,5,9,1,7
M,2,1,3,3,3,8,6,6,4,7,7,8,6,5,2,8
Y,3,8,5,6,2,7,10,0,3,7,11,8,1,10,0,8
N,5,9,6,4,3,2,11,3,3,12,11,9,4,9,0,8
M,8,9,11,7,8,10,6,2,5,9,3,6,10,8,3,9
K,5,7,7,6,6,10,5,3,3,10,3,8,5,7,6,12
Y,4,10,6,8,6,9,3,7,5,7,8,8,3,10,5,4
X,7,13,8,7,5,9,6,3,8,11,3,7,4,8,4,8
F,4,9,6,6,4,6,10,1,6,13,7,6,1,10,2,8
Z,2,1,2,2,2,8,7,5,8,6,6,7,1,8,6,8
E,3,8,3,6,2,2,8,6,10,7,6,15,0,8,6,7
S,4,8,5,6,4,8,6,7,7,7,8,9,2,10,9,8
T,3,6,4,8,1,10,14,0,6,5,11,9,0,8,0,8
V,3,5,5,3,2,7,12,3,3,7,11,9,2,10,1,8
G,2,0,2,1,1,8,7,6,5,6,6,9,1,7,5,10
M,5,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8
L,2,4,3,3,1,7,4,1,8,8,2,10,0,7,2,8
M,8,9,11,8,13,8,7,4,4,7,6,7,11,8,6,4
D,9,13,8,8,6,7,6,4,7,10,5,7,6,8,8,5
B,3,5,3,4,3,7,7,5,5,6,6,6,2,8,6,10
V,7,11,7,8,5,2,12,2,3,9,11,8,3,9,2,6
S,7,10,8,7,4,8,7,4,9,11,4,8,2,8,5,9
U,6,11,9,8,5,8,8,5,9,4,10,8,4,7,2,7
N,5,9,7,6,5,6,8,7,7,7,6,7,3,7,3,8
H,2,0,2,0,0,7,8,11,1,7,5,8,3,8,0,8
A,2,4,4,3,2,9,2,2,2,9,2,8,2,6,3,8
S,4,9,5,6,5,8,8,7,5,7,5,7,2,7,8,7
X,5,7,7,6,7,8,7,2,5,7,6,8,3,9,8,8
J,3,10,4,8,1,11,2,11,3,12,8,14,1,6,0,8
I,5,9,3,4,1,10,6,5,4,13,3,7,2,8,5,10
B,4,7,4,5,5,6,7,8,5,7,6,7,2,8,7,9
L,4,5,5,4,4,8,6,4,5,7,7,8,2,8,7,10
U,5,5,6,4,3,4,8,5,8,10,9,9,3,9,2,5
F,5,10,7,8,4,5,11,4,7,11,10,5,2,10,3,5
V,4,7,6,5,6,7,7,4,2,8,7,8,7,10,5,7
A,5,11,8,8,5,12,2,3,3,10,1,9,3,7,4,8
N,4,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8
C,5,9,6,6,3,5,8,8,9,9,8,13,2,10,4,9
J,2,5,4,3,2,11,6,2,7,12,3,7,0,7,1,8
U,3,3,3,4,1,7,6,13,5,7,13,8,3,9,0,8
D,6,12,6,6,4,6,8,5,7,9,6,6,5,9,7,5
U,6,11,6,6,4,8,5,5,5,6,8,8,4,9,3,8
A,4,11,7,8,4,11,2,2,3,9,2,9,5,6,3,9
A,2,6,3,4,2,9,5,2,0,8,2,8,2,6,1,8
I,4,11,5,8,3,7,7,0,9,14,6,8,0,8,1,8
E,4,10,4,8,4,3,9,5,10,7,6,14,0,8,6,8
E,5,10,7,7,6,10,6,1,7,11,4,8,4,8,5,11
L,3,7,4,5,2,5,2,7,8,1,2,2,1,6,1,5
Y,2,8,4,5,1,9,10,2,3,6,12,8,1,11,0,8
M,4,4,5,3,3,6,6,6,5,7,7,10,7,6,2,8
K,3,7,5,5,6,6,8,4,4,6,5,9,5,7,7,7
K,4,7,5,5,4,7,6,1,6,10,5,10,3,8,4,9
U,5,8,6,6,3,3,9,6,7,12,11,9,3,9,1,7
J,1,3,2,2,1,10,7,1,5,11,4,8,0,7,0,7
F,2,6,3,4,2,1,10,3,5,11,11,9,0,8,2,7
X,3,4,4,3,2,7,7,3,9,6,6,9,2,8,6,8
J,4,10,4,8,3,15,3,3,5,12,0,7,0,8,0,8
P,6,12,5,7,4,6,11,5,2,11,6,3,3,12,5,5
F,6,9,9,7,8,7,7,5,4,7,6,8,5,11,9,11
A,6,10,7,7,7,8,8,6,5,6,5,8,5,7,7,5
D,4,5,5,8,3,5,7,10,8,7,6,5,3,8,4,8
Q,3,7,4,5,2,8,7,8,6,6,8,9,3,7,5,10
V,3,4,5,7,1,8,8,4,3,6,14,8,3,9,0,8
O,2,3,3,2,2,8,7,6,4,9,6,8,2,8,2,8
P,4,7,5,5,4,5,10,3,6,10,9,5,4,10,3,7
A,2,3,4,5,1,8,6,3,1,7,0,8,2,7,1,8
U,7,12,6,6,3,9,7,6,6,3,10,7,5,9,2,6
D,2,5,4,3,3,9,6,4,6,10,4,6,2,8,3,8
E,3,2,4,3,3,7,7,5,8,7,6,9,2,8,6,9
G,1,3,2,1,1,7,7,4,5,9,7,10,1,9,3,10
G,4,7,4,5,3,7,6,6,6,10,7,11,2,10,4,10
Z,3,9,4,7,2,7,7,4,14,9,6,8,0,8,8,8
W,5,5,8,7,4,7,7,5,2,7,8,8,9,9,0,8
Q,4,9,5,8,5,8,8,8,5,6,6,9,3,8,4,9
Y,4,10,6,8,4,5,9,0,7,8,12,9,1,11,2,7
U,3,6,4,4,1,8,4,13,5,7,13,8,3,9,0,8
N,4,5,6,4,3,7,9,2,5,10,6,6,5,9,1,7
Y,5,4,6,6,6,9,10,5,3,6,7,7,5,11,7,4
S,2,4,4,2,1,10,7,2,7,10,5,8,1,9,5,10
A,2,4,4,3,2,10,2,2,2,9,2,9,2,6,2,8
Y,5,10,5,7,2,3,11,4,6,12,12,6,1,11,2,6
M,5,10,7,8,10,8,8,7,4,7,6,8,8,9,9,4
G,1,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10
A,5,11,8,8,5,9,3,2,3,8,1,8,2,7,3,7
U,5,9,6,8,7,7,7,4,4,6,6,8,5,7,2,7
I,1,3,1,2,0,7,7,1,7,13,6,8,0,8,0,8
X,2,3,4,1,1,6,8,2,8,11,8,8,2,8,3,7
D,4,8,5,6,3,9,7,4,7,11,4,5,3,8,3,8
D,2,3,3,2,2,7,7,6,6,7,6,6,2,8,3,7
A,5,9,6,7,4,9,4,3,1,8,1,9,2,7,3,8
F,4,8,4,5,1,1,11,5,7,11,11,9,0,8,2,6
D,5,8,6,6,5,8,8,5,6,10,5,4,4,9,4,9
V,3,9,5,7,2,8,8,4,3,6,14,8,3,10,0,8
D,9,14,8,8,6,9,5,4,6,10,4,7,6,8,9,7
A,7,11,6,6,3,11,2,3,1,9,4,11,4,4,4,9
U,6,10,7,8,4,3,9,5,7,11,10,9,3,9,2,6
Q,5,9,7,11,7,8,6,8,4,5,6,10,3,8,6,10
M,5,7,8,5,6,9,7,2,4,9,5,7,7,6,2,8
O,4,10,5,8,5,8,6,8,4,7,4,8,3,8,3,8
X,6,9,9,7,5,5,8,1,8,10,10,9,4,7,4,6
B,6,10,9,8,7,9,6,4,7,9,5,7,3,8,7,10
H,3,7,4,4,2,7,6,14,1,7,7,8,3,8,0,8
C,3,4,4,6,1,5,7,6,9,7,6,14,1,9,4,9
K,3,4,5,3,2,5,7,2,7,10,8,10,3,8,3,7
X,4,4,4,6,1,7,7,4,4,7,6,8,3,8,4,8
W,4,9,6,7,10,9,7,5,2,7,6,8,13,10,3,6
L,3,11,5,8,3,5,3,6,7,2,2,4,1,6,1,5
V,4,8,4,6,2,3,11,3,3,10,11,7,2,10,1,7
S,5,8,7,6,8,6,7,3,2,8,6,6,3,8,11,1
I,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
G,3,6,4,4,2,7,6,6,6,6,6,10,2,9,4,8
W,3,4,4,3,3,6,10,5,2,9,7,6,5,11,2,6
T,6,10,5,8,2,4,13,3,7,12,10,4,0,10,1,5
O,2,3,3,2,1,7,7,7,4,7,6,8,2,8,2,8
V,5,9,5,6,3,2,12,4,3,11,12,8,3,10,1,7
M,6,12,7,6,5,7,3,2,2,8,4,10,8,1,2,8
Z,4,6,6,8,4,13,4,3,7,10,2,7,2,7,5,12
P,4,10,5,7,3,5,10,10,4,9,6,5,2,10,4,8
A,3,8,5,6,2,7,6,3,1,7,0,8,2,7,1,8
S,7,10,9,8,11,8,8,5,3,9,5,7,5,8,12,8
P,5,9,7,6,6,5,10,5,5,10,8,3,1,10,3,7
E,3,8,3,6,2,3,7,6,10,7,6,14,0,8,7,8
P,5,10,6,7,5,5,11,5,5,11,9,3,1,10,4,7
O,1,0,1,0,0,8,7,6,4,7,6,8,2,8,2,8
J,1,4,2,3,1,9,6,2,6,12,4,9,1,7,1,7
M,3,4,6,3,3,7,6,3,4,9,8,9,7,5,2,9
M,6,10,8,8,9,8,7,7,5,6,5,8,10,8,10,12
G,4,8,6,6,4,7,7,7,6,6,6,9,2,8,4,8
T,2,1,2,1,0,8,15,2,4,6,10,8,0,8,0,8
E,3,7,3,5,2,3,7,6,10,7,6,14,0,8,7,8
U,3,6,4,4,1,7,5,13,5,7,13,8,3,9,0,8
J,3,7,4,5,2,8,6,2,6,14,5,9,0,7,0,7
W,5,7,7,6,9,6,8,6,5,6,6,8,8,9,8,8
S,3,4,4,6,2,8,7,6,9,4,6,7,0,8,9,8
M,9,12,12,7,5,10,2,3,2,10,2,9,8,1,1,8
Z,2,1,2,2,1,7,7,3,12,8,6,8,0,8,7,8
V,3,8,6,6,1,7,8,4,3,7,14,8,3,9,0,8
X,4,7,6,5,3,7,8,1,8,10,7,8,2,8,3,8
Y,3,6,5,9,8,9,7,4,1,6,7,9,4,11,7,8
Y,3,6,5,4,1,8,10,2,2,7,13,8,2,11,0,8
H,6,8,9,10,9,7,4,4,2,6,4,6,8,6,11,7
O,4,7,5,5,3,7,6,8,4,8,5,10,3,8,3,8
C,6,10,7,7,4,5,7,6,8,11,9,14,2,9,4,5
S,5,7,6,5,4,9,6,3,7,10,6,9,2,10,5,9
Q,4,10,4,5,3,10,6,4,7,11,3,9,3,6,7,11
V,4,6,4,4,2,3,12,5,4,11,12,7,3,10,1,8
K,8,15,8,8,4,7,6,3,6,9,9,10,6,12,3,7
G,6,11,7,8,6,6,7,7,5,8,7,11,5,7,6,6
N,6,9,8,7,5,7,9,6,5,7,6,6,7,8,3,8
C,3,4,4,3,2,4,8,4,7,10,10,13,1,9,2,7
Q,6,8,6,10,6,8,6,7,4,9,6,10,4,9,7,6
A,1,0,2,1,0,8,4,2,0,7,2,8,1,7,1,8
U,4,5,5,4,2,4,8,5,7,11,10,9,3,9,2,6
X,9,12,8,6,4,7,7,2,9,9,7,9,4,9,4,7
I,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8
O,3,8,4,6,3,8,7,8,6,7,5,9,3,8,3,7
P,5,11,5,8,3,4,10,10,4,9,6,4,2,10,4,8
L,4,10,5,7,7,7,7,3,5,7,7,10,6,10,6,6
A,2,3,3,2,1,10,2,2,1,9,2,9,1,6,1,8
D,1,0,1,0,0,6,7,6,5,7,6,6,2,8,2,8
S,2,4,4,2,2,8,8,2,7,10,4,7,1,8,4,8
U,5,9,7,6,6,8,8,8,5,6,7,9,3,8,4,5
F,6,11,8,8,4,4,14,4,5,13,7,2,1,10,2,5
Q,4,5,5,7,3,8,6,8,6,6,5,8,3,8,4,8
E,3,7,3,5,2,3,8,6,10,7,6,14,0,8,7,8
B,6,10,8,8,7,7,8,6,4,6,4,5,5,8,7,7
B,1,0,2,1,1,7,7,7,5,6,6,7,1,8,7,9
P,4,6,5,4,4,7,5,6,5,7,6,9,5,7,4,8
B,1,0,1,0,1,7,7,6,4,7,6,7,1,8,6,9
B,3,4,4,6,3,6,8,9,7,7,6,7,2,8,9,9
Y,2,6,4,4,1,9,11,1,7,4,11,8,1,10,1,8
Y,6,8,6,6,4,4,9,1,8,10,10,6,0,10,3,4
R,5,10,6,7,3,5,11,9,4,7,4,8,3,7,6,11
Y,6,7,8,9,8,10,10,5,3,6,7,8,6,11,8,4
P,2,3,3,2,1,7,9,4,3,11,4,3,1,10,2,8
G,6,10,8,7,5,6,6,6,6,6,6,8,4,7,4,8
V,3,6,4,4,3,8,11,2,2,6,10,9,2,10,2,9
K,4,5,7,3,4,6,8,1,7,10,7,10,3,8,3,7
D,4,8,6,6,5,7,7,7,6,6,5,5,3,8,3,7
Z,3,6,4,4,3,7,7,3,12,8,6,8,0,8,7,8
N,9,10,8,6,3,5,8,5,7,3,3,11,6,10,3,7
W,7,8,7,6,5,3,11,2,2,10,9,8,6,12,2,6
V,7,8,9,7,10,8,7,4,5,7,6,8,7,10,8,5
M,1,0,2,1,1,8,6,9,0,7,8,8,6,6,0,8
S,4,9,5,6,3,9,9,6,10,5,5,5,0,7,9,7
C,2,4,3,3,2,6,8,7,7,8,8,13,1,10,4,10
K,2,4,3,2,2,5,7,4,7,7,7,11,3,9,5,10
M,5,5,8,4,4,6,6,3,4,10,9,10,10,5,3,9
L,6,9,5,5,2,7,4,3,5,10,4,12,2,7,6,7
T,3,6,5,8,1,6,14,0,6,8,11,8,0,8,0,8
N,8,11,11,8,5,6,9,3,5,10,8,8,6,8,1,7
R,4,9,6,7,6,9,8,4,5,9,4,7,3,7,3,11
G,6,10,7,8,9,8,6,4,4,6,5,9,7,7,8,13
X,5,9,8,6,4,4,9,3,8,11,12,9,3,9,4,5
O,4,7,6,5,6,8,7,5,2,7,6,8,8,9,4,7
S,4,6,5,4,3,6,8,3,8,11,6,7,2,7,5,7
R,4,10,5,8,5,5,10,8,3,7,4,9,2,7,5,11
U,5,9,7,7,8,10,6,5,5,6,7,6,10,6,6,5
J,2,5,3,7,1,13,2,9,4,14,4,12,1,6,0,8
G,4,7,6,5,5,7,7,6,3,7,6,10,4,8,7,7
E,2,3,4,1,2,6,8,2,7,11,7,8,1,8,3,7
S,5,9,6,7,3,6,9,4,9,11,6,7,2,6,5,7
W,3,3,4,1,2,5,11,3,2,9,8,7,5,11,1,6
P,2,4,2,2,1,6,10,5,4,9,7,4,1,10,3,7
G,2,3,4,2,2,7,6,6,5,6,6,9,2,9,4,9
Q,2,4,3,5,3,8,7,6,3,8,6,9,2,9,4,9
B,2,4,4,2,3,9,7,2,6,11,5,7,4,7,5,9
B,4,10,6,8,6,9,7,3,5,10,4,6,2,8,5,9
Q,1,1,2,1,1,8,7,6,4,6,6,8,2,8,3,8
L,4,11,6,9,4,5,4,6,7,2,2,5,1,6,1,5
G,4,9,5,7,4,5,6,6,5,9,7,12,2,8,4,10
E,4,9,5,7,4,4,8,4,8,11,10,10,2,8,4,5
E,6,9,8,7,5,9,7,2,10,11,4,9,2,8,5,9
X,5,11,7,9,6,8,7,3,5,6,7,7,4,10,11,9
Q,3,4,4,5,3,7,8,5,2,7,9,10,3,9,4,9
N,10,13,8,7,4,7,10,4,7,3,4,9,5,7,2,7
K,4,7,4,5,3,4,7,7,3,7,6,11,3,8,2,11
O,3,3,4,5,2,8,7,9,7,7,5,9,3,8,4,8
T,4,9,4,6,2,4,12,2,8,12,10,5,0,10,2,5
P,1,3,3,2,1,7,9,3,4,12,5,4,1,9,2,9
V,4,5,6,4,5,7,8,5,5,8,6,8,6,9,6,9
E,4,5,5,7,3,3,7,6,11,7,7,15,0,8,7,7
U,4,8,4,6,2,8,5,13,5,6,14,8,3,9,0,8
Z,4,8,6,6,3,8,6,3,10,12,4,10,1,8,6,9
D,7,10,9,8,8,7,8,6,6,8,7,6,7,8,3,7
D,2,5,4,3,3,9,7,5,6,9,5,5,2,8,3,8
N,5,7,6,6,7,7,8,5,3,7,5,7,8,7,5,7
Q,1,2,2,3,2,8,7,6,3,5,6,9,2,9,3,9
A,6,10,9,8,9,8,8,8,4,6,6,8,3,8,8,4
J,2,8,3,6,3,7,7,1,6,11,5,9,1,6,1,6
Z,5,8,7,10,6,11,5,3,5,9,2,7,2,7,6,9
V,4,4,6,7,1,9,8,5,3,6,14,8,3,9,0,8
S,3,5,5,4,2,9,7,3,8,11,3,6,1,9,4,10
F,4,11,6,8,4,2,13,4,4,12,9,5,1,10,2,5
D,4,7,6,5,4,8,7,4,7,11,5,7,3,7,4,8
T,2,3,3,4,1,8,14,0,6,6,11,8,0,8,0,8
K,2,3,4,2,2,6,8,1,6,10,6,8,3,8,2,7
Y,3,3,5,4,1,7,11,2,2,7,12,8,1,11,0,8
V,4,6,5,4,6,7,7,4,2,8,7,9,7,10,3,8
X,3,8,5,6,3,7,8,4,9,6,6,7,3,9,7,7
I,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8
A,3,9,6,7,5,8,5,0,4,6,3,7,2,7,4,5
Z,2,4,4,3,2,8,6,2,9,11,5,9,1,8,6,9
J,2,4,3,3,1,9,5,4,6,14,6,12,0,7,0,8
Q,5,11,7,8,7,8,4,7,4,5,7,11,6,6,8,8
E,4,7,6,5,4,9,6,1,7,11,4,8,3,8,4,11
F,2,3,3,1,1,6,11,3,5,13,6,4,1,9,1,7
Q,4,7,4,9,4,8,8,6,3,8,9,11,3,9,6,7
X,3,3,5,2,2,10,6,2,8,11,3,7,2,8,3,9
F,3,6,3,4,2,1,13,3,3,12,10,6,0,8,2,7
J,5,9,7,6,4,7,8,2,6,14,5,7,1,8,1,7
F,3,3,4,4,1,1,15,5,3,12,9,4,0,8,2,6
P,4,8,6,6,5,4,11,4,5,11,9,5,0,10,3,7
L,4,9,4,6,1,0,0,6,6,0,1,5,0,8,0,8
R,4,6,6,4,6,8,6,7,3,7,5,7,4,7,7,7
G,8,14,7,8,4,8,4,5,3,8,4,5,4,7,5,8
E,4,7,5,5,3,7,7,2,9,11,6,9,2,8,5,8
C,2,1,3,2,1,6,8,7,7,8,8,13,1,9,4,10
U,4,10,6,7,9,9,6,4,3,6,7,7,9,8,5,6
N,6,9,8,4,3,8,7,3,4,13,5,8,6,8,0,8
V,6,9,8,8,10,7,7,5,4,7,6,8,7,9,7,10
I,5,6,6,4,3,7,6,2,7,7,6,9,0,9,4,8
N,5,9,7,6,4,9,7,3,5,10,4,6,5,8,1,7
H,5,8,8,6,6,5,8,3,6,10,8,8,4,8,4,6
E,0,0,1,0,0,5,7,5,6,7,6,12,0,8,6,10
Y,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8
G,5,9,7,7,5,6,7,7,5,5,6,11,4,8,4,8
E,2,4,4,3,2,7,8,2,8,11,7,9,2,8,4,8
N,5,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8
B,1,3,2,1,1,8,7,3,4,10,5,7,1,8,3,9
G,4,7,5,5,3,7,6,7,7,7,5,11,2,9,4,8
L,4,9,4,6,2,0,2,4,6,1,0,7,0,8,0,8
E,4,7,6,5,5,8,9,7,3,6,6,11,4,7,7,10
G,6,8,8,7,9,7,6,6,4,7,7,9,10,8,9,9
M,4,10,6,8,6,7,6,6,5,7,7,9,8,5,2,8
D,2,4,4,3,3,9,7,4,6,10,4,6,2,8,3,8
Y,6,7,6,5,2,3,12,5,5,13,12,6,2,11,2,6
R,5,5,5,6,3,5,12,8,3,7,2,9,3,7,6,11
P,4,7,6,10,9,8,11,5,0,9,7,6,4,10,5,8
D,3,9,5,7,5,8,7,5,7,7,6,5,3,8,3,7
E,4,9,4,7,3,3,6,6,12,7,7,15,0,8,7,7
W,4,5,6,4,3,7,11,2,3,7,9,8,7,11,1,8
D,4,2,5,4,3,7,7,7,7,6,6,5,2,8,3,7
Q,8,13,7,7,4,7,5,4,8,10,5,9,3,7,9,9
R,4,7,5,5,5,7,8,5,6,6,4,8,3,6,5,9
G,4,6,4,4,3,6,7,5,5,9,7,10,2,9,4,10
Y,3,7,4,5,2,8,10,2,2,6,12,8,2,11,0,8
R,5,8,7,6,7,7,10,3,5,5,6,9,6,7,7,7
D,2,0,2,1,1,6,7,8,6,7,6,6,2,8,3,8
J,4,11,5,8,5,9,6,2,5,11,4,9,1,6,2,5
C,4,7,5,5,3,6,7,6,8,5,6,13,1,6,5,9
I,3,7,4,5,1,6,8,1,8,14,7,8,0,8,1,7
T,6,10,8,8,6,6,7,7,8,7,6,8,5,11,8,8
C,1,0,1,0,0,7,7,5,7,7,6,14,0,8,4,10
N,3,4,5,3,2,7,8,2,5,10,6,6,5,9,0,7
M,5,5,6,4,4,8,6,6,5,6,7,8,10,6,4,6
B,2,3,2,2,2,7,8,5,5,7,5,6,2,8,5,9
H,3,7,5,5,4,7,7,5,6,7,6,9,6,8,3,7
R,4,7,4,5,2,6,10,9,4,7,5,8,2,8,5,10
Q,6,9,9,8,7,7,4,4,4,7,4,9,5,4,7,7
A,3,6,5,4,1,9,4,3,1,7,1,8,3,6,2,8
G,3,8,4,6,3,8,8,7,5,6,7,8,2,7,5,11
Q,5,12,5,7,4,11,4,4,6,12,3,9,3,8,7,12
H,3,3,4,4,2,7,8,14,1,7,5,8,3,8,0,8
K,4,11,4,8,2,4,8,8,2,6,3,11,4,8,2,11
G,4,10,5,8,3,7,5,8,9,8,4,13,1,9,5,10
A,4,6,5,4,5,7,6,7,4,7,6,9,2,9,8,4
A,4,9,6,7,4,10,3,2,2,8,2,10,5,5,3,8
J,3,11,4,8,3,14,4,4,5,13,1,8,0,7,0,8
K,4,6,5,4,5,6,5,4,4,6,6,9,3,6,7,11
W,5,7,5,5,5,4,10,2,3,9,8,8,6,11,2,6
N,2,1,2,3,2,7,8,5,4,7,6,7,4,8,1,6
H,4,7,4,4,2,7,5,14,1,7,8,8,3,8,0,8
L,5,10,4,5,2,9,3,4,4,12,4,13,2,6,6,9
B,6,9,6,4,5,8,8,4,4,9,6,7,6,7,8,6
E,5,11,5,8,3,3,8,6,11,7,6,15,0,8,7,7
Y,5,8,5,6,3,5,9,1,7,9,9,5,1,11,3,5
J,3,9,5,7,2,8,6,3,6,15,5,9,1,6,1,6
N,1,0,1,0,0,7,7,10,0,6,6,8,4,8,0,8
L,6,8,7,7,6,8,7,4,6,7,6,8,3,8,8,11
S,3,6,5,4,6,6,9,3,3,8,7,6,3,8,9,1
Q,5,9,6,8,6,8,7,7,5,6,7,8,3,8,4,9
D,5,11,5,6,4,10,5,3,5,10,4,7,5,8,7,7
E,6,10,8,8,5,5,9,2,10,10,8,9,3,8,5,5
L,2,7,4,5,2,7,4,2,7,7,2,8,1,6,2,8
G,6,10,7,8,5,6,6,6,5,9,6,12,4,8,5,8
P,4,6,4,8,3,5,9,11,5,8,6,5,2,10,4,8
K,3,6,4,4,2,3,7,6,3,7,6,11,3,8,3,11
W,3,2,4,3,3,6,11,2,2,7,9,9,6,11,1,8
O,3,9,5,6,4,8,8,8,4,7,8,8,3,8,3,8
W,4,7,7,5,4,4,10,3,3,9,10,9,8,11,1,8
A,4,9,3,4,2,11,5,4,1,8,4,9,4,6,4,9
H,2,6,3,4,1,7,7,14,1,7,6,8,3,8,0,8
T,2,8,4,6,3,7,12,3,6,7,11,8,2,12,1,8
I,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8
F,3,8,5,6,3,4,11,6,5,11,10,5,2,9,2,5
I,1,9,1,6,2,7,7,0,7,7,6,8,0,8,3,8
D,1,3,3,2,1,9,7,4,5,10,4,5,2,8,2,8
T,5,6,5,4,3,5,10,1,8,11,9,5,1,9,3,4
P,4,8,6,6,6,6,7,6,4,8,6,8,5,8,6,10
Z,5,11,6,8,6,7,8,5,9,7,7,9,1,9,7,7
I,0,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8
D,4,9,6,7,6,9,7,4,6,10,5,5,3,8,3,8
F,3,5,5,3,2,5,12,3,5,13,7,4,1,10,1,6
G,5,7,6,5,3,8,6,6,7,11,6,12,2,10,4,8
T,6,14,6,8,4,9,7,3,7,11,4,6,2,8,6,6
C,5,9,6,6,3,4,8,6,8,12,10,12,2,9,3,7
B,2,1,3,2,2,7,7,5,5,7,6,6,2,8,5,9
H,2,6,4,4,5,9,6,3,3,6,6,7,5,9,6,7
N,10,14,9,8,4,8,10,5,5,4,5,10,6,10,3,7
E,1,3,3,2,1,6,7,2,7,10,7,9,2,8,4,9
R,5,11,8,8,5,10,7,3,7,11,2,6,3,7,4,10
U,5,7,6,5,5,8,7,8,5,6,7,9,3,8,4,6
L,1,3,2,2,1,5,4,4,6,3,3,5,1,7,1,6
P,3,8,3,6,3,5,9,8,3,9,6,5,2,10,3,8
C,4,8,4,6,3,6,8,6,7,12,8,12,2,10,3,8
N,3,2,4,3,2,7,8,5,4,8,7,7,5,8,3,6
F,2,4,4,2,2,6,10,3,5,13,6,5,1,9,1,7
I,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8
Z,3,7,4,5,2,7,7,3,12,8,6,8,0,8,7,8
F,3,4,4,6,2,1,12,5,6,12,11,9,0,8,2,6
D,3,8,4,6,2,6,7,10,8,6,6,6,3,8,4,8
G,3,7,4,5,3,7,7,7,6,6,6,9,1,8,5,11
B,3,7,4,5,4,7,7,8,5,7,6,7,2,8,7,9
S,4,11,4,8,4,8,6,7,7,7,8,9,2,10,10,7
T,6,10,5,7,3,6,11,1,9,11,9,4,0,10,3,4
Q,4,9,5,8,3,9,8,8,5,5,8,9,3,7,5,10
V,3,10,5,8,2,8,8,4,3,6,14,8,3,9,0,8
E,4,8,5,6,5,6,8,7,9,6,4,10,3,8,6,8
A,6,7,8,6,6,7,7,3,6,7,8,9,5,10,3,6
W,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8
K,7,11,9,8,9,8,7,5,5,7,6,7,7,6,9,14
Y,4,7,6,9,7,9,10,7,4,7,7,6,5,11,6,3
K,5,8,7,6,8,6,9,4,5,6,5,9,8,7,7,10
L,5,10,5,5,2,7,4,3,6,11,4,13,2,6,6,8
Q,2,0,2,1,1,8,7,6,4,6,6,8,2,8,3,8
B,4,4,4,7,4,6,8,9,7,7,6,7,2,8,9,10
S,4,7,5,5,3,8,8,3,7,10,3,6,2,6,5,9
F,2,2,3,4,2,6,9,3,5,10,9,5,4,10,3,8
S,3,7,4,5,3,8,7,7,7,7,7,8,2,9,9,8
Q,6,9,7,11,7,9,5,7,4,9,5,11,4,8,6,6
L,2,1,2,2,1,4,4,4,7,2,1,6,0,7,1,6
T,5,10,7,8,4,5,12,3,8,8,12,8,1,12,1,7
P,6,10,8,8,5,7,10,5,4,12,5,3,1,10,3,8
A,2,6,4,4,2,8,3,2,2,7,1,8,2,7,3,7
K,3,5,5,4,3,5,7,1,7,10,8,11,3,8,3,8
F,2,3,3,2,1,6,10,3,5,13,6,4,1,9,1,8
D,2,3,3,2,2,7,7,6,6,6,6,4,2,8,3,7
Q,4,4,6,6,6,9,10,5,0,5,7,10,6,13,4,10
L,3,6,4,4,2,4,3,7,6,1,2,4,1,6,1,6
G,6,11,7,8,6,8,7,7,6,6,5,9,2,7,5,11
C,5,9,7,7,5,7,7,8,6,7,6,13,6,8,5,7
N,5,9,7,7,4,7,9,6,5,7,7,7,6,8,2,7
K,5,7,7,5,5,7,6,1,6,10,5,9,3,7,3,9
I,0,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8
P,5,10,7,7,6,7,10,5,3,11,5,3,1,10,3,8
R,3,3,3,4,2,6,9,9,4,7,5,7,3,7,5,11
O,6,10,6,7,4,9,6,8,6,9,4,7,3,8,4,8
X,2,3,3,1,1,6,8,2,8,10,8,9,2,8,2,7
V,4,10,6,8,3,8,12,3,4,5,11,9,3,10,1,7
H,6,11,8,8,8,7,8,6,8,8,6,8,9,7,4,8
A,3,5,6,4,2,10,2,2,2,8,2,9,4,6,2,9
R,2,4,4,3,2,9,7,3,5,10,4,6,2,7,3,9
Y,4,11,6,8,3,6,10,1,7,7,12,9,1,11,2,8
K,3,6,5,4,5,6,7,4,4,7,6,8,4,6,8,13
S,9,14,9,8,4,7,6,4,3,13,9,9,3,10,3,9
X,5,10,7,8,4,9,6,1,8,10,3,7,3,8,4,8
E,4,11,5,8,7,5,7,6,8,6,5,11,3,8,5,9
T,3,4,4,3,1,5,13,3,6,11,9,4,1,11,1,5
D,3,6,5,4,3,9,7,4,5,11,5,6,3,8,2,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
I,2,9,2,7,2,7,7,0,8,7,6,9,0,8,3,8
W,4,2,6,4,4,5,11,3,2,8,9,9,10,13,1,8
X,1,1,2,2,1,8,7,3,8,6,6,8,2,8,6,8
B,4,8,6,6,6,9,6,4,6,10,4,7,3,8,5,10
I,4,9,6,6,3,9,8,2,8,7,6,6,0,8,4,7
H,2,3,3,2,2,7,7,5,5,7,6,8,3,8,2,8
F,4,6,6,4,3,6,11,4,5,12,7,5,2,9,2,6
S,4,4,5,6,3,8,6,5,9,5,6,8,0,8,9,8
J,4,8,5,6,2,10,6,2,8,14,3,8,0,7,0,8
M,8,11,11,8,7,12,5,3,6,10,2,6,10,9,2,9
N,5,6,7,4,6,5,9,3,4,8,7,9,6,8,4,4
A,4,9,7,7,4,9,3,2,3,6,1,7,2,6,2,7
R,2,4,4,3,2,9,8,3,5,10,3,6,2,6,3,10
J,3,6,5,4,2,7,9,1,6,14,5,6,0,7,0,7
S,5,10,8,8,10,7,6,3,2,7,5,7,3,8,15,5
N,3,5,3,4,2,7,9,5,4,7,6,7,5,8,2,7
K,4,4,4,5,2,3,7,8,2,7,5,11,4,8,2,11
P,4,7,6,5,3,8,10,3,6,13,4,2,1,10,3,9
J,5,9,4,6,3,9,8,2,3,12,4,5,2,9,6,9
N,2,7,3,5,3,7,7,11,1,6,6,8,5,8,0,8
W,8,11,8,9,8,4,11,2,2,9,8,7,7,12,2,6
W,1,0,2,1,1,7,8,4,0,7,8,8,5,10,0,8
G,6,11,8,8,5,6,6,6,7,6,6,8,3,8,4,7
G,3,4,4,6,2,7,6,8,7,6,6,9,2,7,5,11
V,6,7,6,5,2,3,11,4,4,10,12,7,2,10,1,7
N,4,9,4,6,2,7,7,14,2,5,6,8,6,8,0,8
E,3,7,5,6,5,7,6,5,3,7,6,10,5,10,6,11
P,4,10,6,8,6,6,10,5,5,10,8,3,1,10,4,6
H,3,4,5,6,5,8,11,4,2,8,7,6,4,11,8,4
J,1,3,2,1,0,9,7,2,6,14,5,9,0,7,0,7
X,2,6,4,4,3,8,8,2,5,7,5,7,3,8,5,7
L,5,10,5,8,2,0,0,6,6,0,1,5,0,8,0,8
G,3,5,4,3,2,6,6,6,5,8,7,11,2,8,4,10
V,3,5,6,4,2,7,12,2,3,6,11,9,4,12,2,7
Q,3,8,5,7,4,8,7,7,5,6,6,9,3,8,4,10
Z,3,7,4,5,3,7,7,3,12,8,6,8,0,8,8,7
D,8,13,7,7,6,9,6,4,6,10,4,7,6,7,9,7
H,3,7,3,5,3,8,6,12,2,7,8,8,3,9,0,8
K,3,3,3,4,1,3,6,7,2,7,6,11,3,8,2,11
T,3,7,4,5,2,4,13,4,6,12,9,4,1,11,1,5
U,7,8,8,6,3,2,10,6,7,12,12,9,3,9,1,7
Q,7,15,6,8,5,11,5,4,6,11,4,7,3,9,7,13
W,6,10,10,8,8,7,11,2,2,6,8,8,8,12,1,8
E,4,7,6,5,4,7,7,3,8,11,7,9,2,9,5,8
R,3,6,3,4,2,5,11,7,4,7,3,9,2,7,6,11
F,3,7,5,5,2,3,12,4,4,13,9,5,1,10,2,6
K,5,9,7,7,5,6,7,5,7,6,6,10,4,8,5,9
H,4,8,5,6,4,7,7,13,1,7,6,8,3,8,0,8
H,4,7,7,5,5,5,9,3,5,10,9,8,3,8,3,5
W,3,4,4,2,2,5,11,2,2,9,8,7,6,11,1,6
L,4,8,6,6,7,7,7,3,5,7,7,10,6,10,6,6
S,8,14,8,8,4,8,6,4,4,13,7,8,3,10,4,8
R,4,9,6,6,5,9,7,6,3,8,5,6,4,7,7,9
F,5,10,6,8,4,4,11,4,6,11,10,5,2,10,2,5
U,8,9,9,7,5,3,9,5,8,10,10,10,3,9,2,6
X,4,6,6,4,3,7,7,1,8,10,7,9,2,8,3,7
P,2,1,2,1,1,5,11,8,1,9,5,4,1,9,3,8
K,3,6,4,4,3,6,7,4,7,6,6,10,3,8,5,9
U,3,5,4,4,2,6,8,6,7,7,10,9,3,9,1,8
P,3,6,5,4,3,5,11,4,5,11,8,3,1,10,4,7
K,7,10,10,8,7,4,7,2,7,10,10,11,3,8,3,6
W,4,6,6,4,4,10,11,3,2,5,9,7,6,11,1,8
W,5,10,7,8,6,8,8,4,1,7,9,8,8,11,0,8
M,2,0,3,1,1,8,6,10,0,7,9,8,7,6,0,8
U,4,8,5,6,2,7,4,14,5,7,14,8,3,9,0,8
M,4,6,6,4,5,6,6,3,4,10,8,9,7,5,2,7
J,5,8,6,6,3,7,6,3,6,15,6,10,1,6,0,7
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
C,7,10,9,8,6,6,8,8,6,5,6,13,6,8,5,6
U,4,7,6,6,6,7,6,4,4,6,6,8,7,7,1,7
P,6,10,6,5,4,7,10,4,3,12,5,3,4,11,5,7
B,4,10,6,8,7,7,8,5,5,9,7,6,4,8,7,8
N,6,9,8,7,7,6,7,8,6,7,5,6,3,8,3,8
U,5,10,7,8,5,5,9,6,8,8,10,9,3,9,1,8
Z,4,8,5,6,4,8,6,2,9,11,5,9,1,8,5,9
H,7,8,10,10,9,6,4,5,3,6,3,6,6,5,11,7
A,2,8,4,6,2,7,6,3,1,6,0,8,2,7,1,7
Q,6,6,8,9,9,9,11,4,1,5,8,11,6,14,7,13
W,9,12,9,6,5,2,10,3,3,10,11,9,7,10,1,6
B,4,6,5,4,4,9,7,3,6,9,5,6,2,8,5,8
B,8,12,6,6,4,10,5,5,5,11,3,9,6,7,6,10
W,4,3,5,2,2,4,11,3,2,10,9,7,6,11,1,7
A,4,5,6,4,5,7,8,3,5,7,8,9,7,10,3,7
X,1,3,2,1,1,7,7,3,7,6,6,9,2,8,5,8
W,2,3,3,2,2,9,10,3,2,5,9,7,5,11,0,7
O,10,14,6,8,4,6,6,7,4,10,6,10,5,9,5,8
V,5,6,5,4,2,2,12,5,4,12,11,7,2,10,1,7
V,3,7,5,5,1,9,8,4,2,6,14,8,3,10,0,8
U,3,7,4,5,2,7,5,12,4,6,12,8,3,9,0,8
G,3,5,4,4,2,6,7,6,6,10,7,10,2,9,4,9
O,4,5,5,4,4,7,6,5,5,9,5,10,4,6,5,6
A,2,7,4,4,1,7,6,3,1,6,0,8,2,6,1,8
Z,8,7,6,11,5,4,13,4,2,12,8,5,2,8,8,6
I,7,10,9,8,5,9,5,3,7,6,7,4,0,9,4,7
Y,8,11,8,8,5,3,10,2,7,11,11,6,1,11,3,4
L,4,9,4,4,3,8,5,3,5,11,5,10,3,5,6,8
G,3,6,4,4,2,6,7,5,5,5,7,8,2,8,4,8
R,7,10,7,5,5,6,8,3,4,7,4,10,6,8,6,7
V,4,9,6,7,4,5,11,3,2,9,10,7,4,10,6,8
Y,4,8,6,6,3,7,9,1,7,6,12,9,2,11,2,8
L,3,7,4,5,3,4,4,4,8,2,1,7,4,6,1,6
P,3,8,3,6,2,5,9,10,5,8,6,5,2,9,4,8
L,2,1,2,2,1,4,4,5,6,2,2,6,1,7,1,6
I,4,7,6,5,3,8,8,2,8,7,6,7,0,8,4,7
O,5,8,5,6,5,8,7,7,4,9,5,8,3,8,3,8
O,1,3,2,2,1,8,6,6,3,9,5,7,2,8,2,7
K,4,8,4,5,2,4,7,8,2,7,6,11,4,8,2,11
Z,6,11,8,8,5,6,9,3,11,11,9,6,2,8,6,5
Q,7,10,9,8,8,8,5,8,4,6,7,8,4,6,7,8
D,3,9,5,7,8,9,8,5,4,7,6,6,4,7,8,6
M,7,10,10,8,6,5,7,3,5,10,10,10,8,5,3,7
J,2,11,3,8,3,7,7,1,6,11,5,9,1,6,2,5
N,3,7,5,5,3,6,9,3,4,10,8,8,5,8,1,7
A,4,11,6,8,3,5,4,3,3,5,1,7,3,6,3,7
H,5,8,7,6,6,7,7,5,5,7,6,8,9,7,5,10
K,5,7,7,6,6,6,7,3,4,6,4,9,6,5,9,8
E,5,7,7,5,5,8,6,1,8,11,4,9,3,8,5,10
G,2,4,4,3,2,7,7,6,6,6,6,10,2,9,4,9
K,3,5,4,4,3,5,7,4,7,7,6,11,3,8,5,10
E,4,9,4,7,4,3,8,5,9,7,6,14,0,8,6,9
Y,4,6,6,9,9,8,4,5,3,7,8,8,7,8,5,8
D,4,8,5,6,7,9,8,4,5,7,6,6,4,7,7,4
L,2,2,3,3,1,4,3,5,6,2,2,5,1,7,0,6
W,6,9,6,6,5,4,10,2,3,9,8,7,7,11,2,6
J,2,2,3,4,2,10,6,2,5,12,4,8,1,6,1,6
T,2,3,3,2,1,7,12,3,6,7,11,8,1,11,1,8
Y,3,7,6,5,2,9,10,2,7,3,12,8,2,11,2,9
F,3,5,5,4,2,4,12,4,4,13,8,5,1,10,1,7
X,4,7,5,5,1,7,7,6,2,7,6,8,3,8,4,8
J,0,0,1,0,0,12,4,5,3,12,5,11,0,7,0,8
T,7,9,7,7,5,6,11,4,6,11,9,4,2,12,2,4
Q,2,2,3,3,2,8,7,6,2,6,6,9,2,9,3,10
J,2,4,3,3,1,10,6,2,6,12,3,8,0,7,0,7
L,1,0,1,1,0,1,1,5,5,0,1,6,0,8,0,8
N,3,7,4,5,3,7,8,5,4,7,6,6,5,9,1,6
H,4,5,7,4,4,6,7,3,7,11,9,9,5,8,5,6
Z,3,6,5,4,3,8,7,2,10,11,5,9,1,8,6,9
Q,2,1,3,2,1,8,7,7,5,6,6,8,3,8,4,8
N,4,10,6,7,5,7,8,10,5,8,5,5,4,8,4,10
U,6,7,6,5,3,4,8,6,8,10,10,9,3,9,2,5
L,8,11,7,6,3,11,1,3,4,12,3,11,2,9,4,10
G,4,6,5,4,5,9,9,5,2,6,7,8,6,9,4,9
P,5,9,7,7,7,5,9,6,4,8,7,9,5,9,7,10
Q,4,6,6,9,3,8,9,7,6,6,9,9,4,7,6,10
L,4,8,6,6,3,7,4,1,8,8,2,10,0,6,3,8
R,4,7,4,5,4,5,10,7,3,7,4,9,2,7,5,11
O,4,7,6,5,4,7,6,8,5,7,4,8,3,8,3,8
M,3,7,5,5,5,8,7,5,4,6,7,8,7,6,2,7
F,1,0,1,0,0,3,12,4,3,11,9,6,0,8,2,8
M,6,8,8,6,8,7,8,7,5,6,6,8,7,9,8,11
Q,4,9,4,4,3,11,4,4,5,12,3,9,3,8,7,13
E,5,11,6,8,7,6,7,5,8,7,6,10,3,8,6,9
Q,4,5,5,8,7,9,7,5,0,6,6,10,7,10,4,9
G,2,4,3,2,2,6,7,5,5,9,7,10,2,8,4,10
L,5,9,6,6,4,4,5,2,8,6,2,10,0,7,3,6
H,8,10,11,8,9,8,7,2,6,10,5,8,3,8,3,8
A,2,2,4,3,2,8,3,2,3,8,1,8,2,6,2,7
D,3,1,4,2,2,7,7,6,6,7,6,5,2,8,3,7
S,4,6,6,4,7,7,7,3,2,8,6,7,2,8,12,3
U,2,3,3,2,1,5,8,5,6,10,9,8,3,9,2,6
B,10,13,7,8,5,10,5,5,6,11,3,9,6,7,7,11
J,4,11,6,8,4,10,5,4,5,14,4,10,0,7,1,6
X,7,10,10,8,6,4,8,2,8,10,11,10,3,9,3,5
G,2,2,4,4,2,7,6,6,5,6,6,9,2,9,4,9
Z,4,11,6,8,4,7,8,2,10,11,8,6,2,7,6,6
D,5,9,7,7,6,7,7,5,6,7,6,8,7,7,3,7
M,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8
Q,4,5,4,6,4,8,6,7,4,9,8,9,3,9,6,8
V,4,11,6,8,3,7,9,4,2,7,13,8,2,10,0,8
P,4,8,5,6,3,8,8,1,6,13,5,5,1,10,2,9
X,5,10,6,8,4,7,7,4,4,7,6,8,3,8,4,8
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
I,7,12,5,6,3,11,4,5,5,13,3,8,3,7,4,9
L,4,3,4,5,1,1,0,6,6,0,1,4,0,8,0,8
N,3,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8
H,5,6,7,4,5,9,6,3,6,10,4,7,3,8,3,9
R,4,7,6,5,5,8,9,6,5,8,5,8,4,7,5,11
G,5,12,4,6,4,7,6,4,2,8,6,8,4,9,9,7
A,4,11,6,8,4,7,5,3,0,6,1,8,2,7,1,8
O,5,10,6,8,5,7,7,9,4,7,6,9,3,8,4,6
S,6,12,5,6,3,8,3,2,5,8,1,7,3,7,5,8
O,5,9,7,7,5,8,6,8,4,6,4,7,3,9,4,9
R,5,9,5,7,5,6,8,8,4,6,5,7,3,8,6,13
B,4,11,4,8,6,6,6,9,6,7,7,7,2,9,7,10
K,5,9,8,7,9,8,6,3,5,6,6,8,8,6,9,5
S,4,7,4,5,2,7,6,5,9,5,6,9,0,9,9,8
O,3,8,4,6,3,8,8,7,5,7,6,9,2,8,3,8
I,1,10,0,8,1,7,7,5,3,7,6,8,0,8,0,8
K,4,9,6,8,7,8,5,2,3,8,4,8,5,6,7,11
O,3,7,4,5,2,7,9,9,7,8,8,7,3,8,4,8
E,3,6,5,6,5,6,9,4,4,7,7,11,4,9,7,10
E,5,11,7,9,6,5,7,7,9,7,6,12,3,8,6,8
A,5,11,8,8,4,10,2,2,3,9,1,8,3,7,4,8
Q,6,11,6,6,4,10,5,4,6,10,4,8,3,9,7,12
A,4,10,6,8,5,12,3,2,2,9,2,8,2,6,2,8
P,4,9,6,7,4,7,10,4,4,12,5,3,1,10,3,8
N,4,6,5,4,3,7,8,6,5,7,6,6,5,9,1,6
K,3,4,5,3,2,4,9,2,7,10,9,11,3,8,3,6
X,6,11,9,9,6,7,7,0,8,9,6,8,3,8,3,7
C,4,9,6,8,6,6,7,4,4,7,6,10,5,9,9,11
D,4,7,6,5,5,8,8,6,5,9,5,4,5,8,5,9
D,2,4,4,2,2,9,6,3,6,11,4,6,2,8,3,8
A,5,11,7,8,7,8,7,7,4,7,6,9,3,8,8,3
E,5,11,7,8,8,8,5,7,3,8,6,11,5,8,8,10
H,3,5,5,7,5,9,10,3,1,8,6,7,3,10,8,7
H,3,7,4,5,4,8,7,5,6,7,6,9,3,8,3,8
S,1,3,2,2,1,8,6,6,5,7,7,9,2,10,8,7
V,5,10,5,5,3,6,9,4,3,9,7,5,5,12,2,8
Y,8,10,8,7,5,2,11,2,7,12,12,7,0,10,1,5
E,1,3,2,1,1,5,8,2,7,10,8,9,1,8,3,7
Y,1,0,1,0,0,7,10,2,2,7,12,8,1,11,0,8
Y,1,0,2,0,0,7,10,1,3,7,12,8,1,11,0,8
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
T,3,9,5,6,1,7,15,1,6,7,11,8,0,8,0,8
Q,1,0,1,1,0,8,7,6,3,6,6,9,2,8,2,8
A,2,4,4,6,2,7,3,3,3,7,1,8,3,6,3,8
U,4,7,4,5,3,7,5,13,4,7,11,8,3,9,0,8
S,2,6,3,4,3,8,8,7,6,7,4,6,2,6,8,8
U,4,6,5,4,3,6,8,6,8,7,10,9,3,9,1,8
C,3,6,4,4,1,6,7,6,10,7,6,13,1,8,4,9
X,4,11,6,8,4,7,8,3,9,6,5,6,4,10,8,6
Z,4,6,5,4,3,8,6,2,10,12,5,10,1,9,6,9
D,3,4,5,3,3,8,7,4,6,9,5,6,2,8,3,8
G,2,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10
D,2,4,4,3,2,9,6,4,6,11,4,6,2,8,3,8
A,3,4,5,3,2,10,2,2,2,9,2,9,2,6,2,8
U,4,7,5,5,4,9,7,7,5,7,6,8,3,8,4,6
O,6,10,7,8,6,7,7,8,4,7,6,8,3,8,3,8
C,5,8,6,6,6,5,6,3,5,8,6,12,6,9,4,9
B,2,4,4,3,3,8,7,3,6,10,5,6,2,8,4,10
K,6,12,6,6,4,7,7,3,6,10,3,8,5,5,3,8
J,5,10,6,7,7,9,6,3,4,7,5,5,5,6,6,5
Z,4,8,4,6,4,9,7,5,9,7,5,6,1,7,7,8
W,4,5,6,7,4,7,8,5,2,6,8,8,8,9,0,8
J,2,8,3,6,2,10,6,1,7,11,3,7,0,7,1,7
X,5,10,7,8,6,8,5,3,5,6,7,7,3,10,9,9
E,2,6,3,4,2,3,8,5,9,7,6,14,0,8,6,9
H,5,11,6,8,3,7,5,15,2,7,9,8,3,8,0,8
M,4,1,5,3,3,9,6,6,4,6,7,5,8,6,2,6
R,4,8,6,6,8,7,7,3,5,7,6,8,6,9,7,5
D,3,6,4,4,3,7,7,6,5,7,7,6,3,8,3,7
U,4,7,6,5,7,8,7,4,3,6,7,8,7,9,5,7
N,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8
X,5,5,6,7,2,7,7,5,4,7,6,8,3,8,4,8
S,2,3,4,2,1,9,7,3,7,11,4,8,1,8,4,10
H,4,8,6,6,6,7,7,5,6,7,6,8,3,8,3,7
P,3,6,5,4,4,6,9,5,4,9,7,4,1,10,3,7
G,1,3,2,1,1,7,7,4,4,9,7,10,2,8,4,10
J,4,8,3,6,2,9,6,2,4,11,6,8,2,10,6,11
J,1,7,2,5,1,14,2,6,5,13,1,10,0,7,0,8
C,4,8,6,6,5,6,6,3,4,8,6,11,6,9,3,9
R,3,5,5,4,5,7,8,3,3,7,5,8,6,8,5,7
Y,6,9,8,6,7,8,4,6,5,8,7,8,8,9,4,6
K,5,10,8,7,5,3,8,2,7,10,11,12,4,7,4,5
N,3,9,4,6,2,7,7,14,2,5,6,8,5,8,0,8
Z,4,6,6,4,4,9,10,5,5,6,5,7,3,8,6,5
P,2,3,2,2,1,4,11,3,4,10,8,4,0,9,3,6
L,5,7,6,5,5,7,8,7,6,6,5,8,6,7,4,10
Z,4,8,6,6,4,7,8,2,9,11,6,8,1,8,6,8
X,3,5,4,3,2,8,7,3,9,6,7,9,3,7,7,9
Z,4,4,5,6,2,7,7,4,14,9,6,8,0,8,8,8
W,3,3,4,5,3,10,8,4,1,6,8,8,7,10,0,8
P,5,10,8,8,6,8,10,4,5,13,5,3,1,10,3,8
D,1,0,2,1,1,6,7,8,6,7,6,6,2,8,3,8
K,3,5,5,4,3,8,7,2,7,10,4,9,5,10,4,9
L,2,5,4,4,2,7,4,1,8,8,2,10,0,7,2,8
K,4,7,6,5,3,8,6,2,7,10,3,9,3,8,4,10
C,1,0,1,1,0,6,7,6,8,7,6,14,0,8,4,10
Z,2,5,3,3,2,7,8,5,9,6,6,9,2,8,7,8
Y,4,7,6,5,2,5,10,2,8,10,12,8,1,11,2,7
E,2,5,4,3,2,7,7,1,8,10,6,9,2,8,4,8
I,6,14,5,8,3,7,9,2,6,14,5,6,1,8,5,8
A,2,7,4,5,2,10,3,2,2,9,2,9,2,6,2,8
M,6,10,8,7,9,7,10,6,4,7,7,9,8,10,7,11
A,3,11,6,8,3,11,2,3,3,10,1,9,3,7,3,8
V,7,10,9,8,5,6,11,3,3,7,11,8,3,10,4,8
P,3,8,4,6,4,6,9,6,4,9,7,4,2,10,3,7
N,8,12,9,7,4,8,6,2,4,13,5,8,6,8,0,7
S,5,10,6,8,4,8,7,5,8,11,3,8,2,7,5,9
Z,4,9,6,7,5,9,11,6,5,6,5,7,3,9,8,5
Q,5,10,6,9,5,8,8,7,4,5,8,9,3,8,5,10
U,4,9,5,7,2,8,5,13,5,6,14,8,3,9,0,8
M,4,5,7,4,4,8,7,2,4,9,6,8,7,5,2,7
D,4,4,5,6,3,5,7,10,9,6,6,5,3,8,4,8
Z,1,1,2,3,1,7,8,5,8,6,6,9,1,9,7,8
Y,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8
D,1,0,2,1,1,6,7,8,6,7,6,6,2,8,3,8
J,1,4,2,3,1,10,6,2,6,12,4,8,0,7,1,7
V,5,7,5,5,2,3,12,5,4,12,12,7,2,10,1,8
U,4,9,6,6,9,8,7,4,3,6,7,8,7,10,5,7
J,2,10,3,8,1,12,3,10,3,13,7,13,1,6,0,8
N,5,11,8,8,9,6,7,3,4,8,8,9,7,10,7,6
G,6,9,6,6,4,5,7,6,6,9,8,10,2,8,5,9
O,4,8,6,6,5,8,7,8,4,7,6,7,3,8,3,7
D,5,11,6,8,3,5,7,11,9,7,7,5,3,8,4,8
D,2,1,2,1,1,7,7,6,6,7,6,5,2,8,2,7
M,6,9,7,7,6,8,5,11,0,7,9,8,12,4,4,8
N,7,8,9,7,8,7,8,5,5,7,5,7,6,9,5,4
Q,3,6,4,5,3,8,7,7,5,6,4,9,2,8,4,9
I,4,11,5,8,3,7,7,0,7,13,6,8,0,8,1,8
P,5,10,7,8,6,7,11,6,3,11,5,3,2,11,3,9
V,3,9,5,7,1,6,8,4,3,7,14,8,3,9,0,8
E,3,7,4,5,2,3,8,6,11,7,6,15,0,8,7,7
F,2,8,3,5,1,1,13,4,3,12,10,5,0,8,3,7
J,4,7,5,5,2,7,7,3,6,15,6,10,1,6,1,7
S,3,10,4,8,4,8,6,8,5,7,7,9,2,9,8,8
S,5,6,6,6,6,8,9,4,5,7,7,8,4,10,9,10
B,2,3,4,2,2,8,7,3,5,10,5,6,2,8,4,9
P,5,9,6,7,6,7,5,7,4,7,6,8,3,8,7,10
Q,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8
B,3,8,5,6,4,7,8,5,6,9,7,5,2,8,7,6
K,4,9,6,8,6,9,6,2,4,9,3,8,5,6,7,11
G,5,7,7,6,6,8,10,5,3,6,7,10,8,11,7,8
E,5,5,5,7,3,3,7,6,12,7,6,15,0,8,7,6
Y,2,4,4,3,1,7,11,1,7,7,11,8,1,11,2,8
O,1,3,2,1,1,8,7,6,4,7,6,8,2,8,2,8
K,6,8,9,6,5,7,5,2,8,10,4,10,5,5,5,9
N,8,11,11,8,7,4,11,3,4,10,10,8,6,8,1,7
X,5,10,6,7,2,7,7,5,4,7,6,8,3,8,4,8
J,6,11,7,8,3,9,5,4,7,15,4,11,1,6,1,7
A,3,7,5,5,3,12,2,2,2,10,2,9,2,6,3,8
Z,4,7,5,5,3,8,6,2,10,11,5,10,2,8,6,9
R,2,1,2,2,2,6,8,4,5,6,5,7,2,6,4,9
E,2,1,2,1,1,4,7,5,8,7,6,13,0,8,7,9
D,4,7,5,5,4,10,6,3,6,11,4,7,3,8,3,9
X,7,15,8,8,5,11,7,2,7,11,3,5,3,11,4,9
O,5,9,5,7,4,7,7,8,5,10,7,8,3,8,3,8
A,2,6,4,4,2,12,3,4,2,11,1,8,2,6,2,9
F,4,8,4,6,2,1,11,5,7,12,11,9,0,8,2,6
S,7,13,5,7,3,8,2,1,5,7,2,7,3,7,4,10
N,1,1,2,2,1,7,7,11,1,5,6,8,4,8,0,8
F,6,10,6,6,4,10,7,3,5,11,4,5,3,9,6,9
Y,2,1,4,3,2,8,11,1,7,5,11,8,1,11,2,8
K,7,10,11,8,7,2,8,3,7,11,12,12,4,7,4,4
W,3,6,5,4,4,7,11,2,2,7,8,8,6,11,1,8
U,4,5,5,3,2,6,8,5,8,6,10,9,3,9,1,7
A,3,8,6,6,3,11,2,2,3,9,2,9,3,5,3,8
V,11,14,9,8,4,8,9,6,5,7,10,5,7,13,3,8
U,4,8,5,7,6,7,7,5,3,6,7,8,4,8,1,7
X,2,3,3,2,1,7,8,1,7,10,7,8,2,8,2,7
K,5,10,6,7,2,4,8,8,2,7,4,11,4,8,2,11
Y,9,10,7,14,6,6,6,6,4,6,12,6,5,10,6,5
T,8,13,7,7,4,6,10,3,7,12,7,6,3,9,5,4
S,5,9,6,6,4,7,7,4,7,10,9,9,2,10,5,6
Q,5,8,6,7,3,8,9,8,6,5,8,9,3,7,6,10
Q,5,6,5,8,6,8,6,6,3,9,6,9,3,8,5,8
L,5,5,5,8,1,0,0,6,6,0,1,5,0,8,0,8
A,3,3,5,5,2,9,4,3,1,8,1,8,2,7,2,8
R,3,7,5,5,5,5,8,3,4,6,5,9,4,7,6,5
F,6,7,8,8,8,7,9,4,4,7,6,7,4,9,9,8
P,4,8,6,6,5,6,9,7,4,9,7,4,2,10,3,7
X,3,5,4,4,4,8,8,2,5,8,6,7,2,7,6,7
O,1,0,2,0,0,7,6,6,4,7,6,8,2,8,3,8
W,7,7,7,5,5,6,10,4,3,8,7,7,9,13,3,4
P,8,9,6,4,3,8,7,5,4,11,3,6,5,8,4,8
U,5,8,6,6,5,8,8,8,5,6,7,9,3,7,4,6
V,7,10,9,8,6,9,10,4,2,4,10,9,6,10,5,10
S,4,10,4,7,2,8,7,6,9,5,6,7,0,8,9,8
O,1,3,2,2,1,8,7,6,3,8,6,8,2,8,2,8
O,3,5,4,4,3,7,7,8,5,7,6,8,2,8,3,8
R,3,3,4,2,2,7,8,5,5,7,5,7,2,7,4,8
S,4,9,4,7,3,8,7,8,7,8,6,8,2,9,9,8
Q,4,6,5,5,4,5,4,4,5,5,4,7,4,4,6,6
H,3,5,4,4,4,7,8,6,6,7,6,8,3,8,3,8
Z,5,9,6,7,7,8,7,3,8,8,6,8,1,7,12,9
F,3,6,3,4,1,1,13,5,4,12,10,7,0,8,2,6
P,5,9,7,6,4,9,8,4,6,12,4,4,2,9,4,9
N,2,3,2,2,1,6,8,5,4,8,7,7,4,8,1,6
T,7,9,7,7,4,5,11,2,8,11,10,5,1,11,2,4
F,5,7,7,5,3,7,9,2,7,14,5,5,2,8,3,8
M,5,9,7,7,6,7,6,3,4,10,8,9,11,5,3,9
S,3,7,4,5,2,7,6,5,9,5,6,10,0,9,9,8
S,3,5,5,3,2,8,6,2,7,10,7,9,2,9,5,8
S,7,10,8,7,5,8,7,3,6,10,5,7,2,8,5,9
X,4,10,5,7,2,7,7,5,4,7,6,8,3,8,4,8
D,2,3,2,2,2,7,7,6,6,7,6,5,2,8,2,7
L,1,0,1,0,0,2,2,5,4,1,2,5,0,8,0,8
I,1,6,2,4,1,7,7,0,7,13,6,9,0,8,1,8
Z,2,6,3,4,1,7,7,3,13,9,6,8,0,8,8,8
Q,4,9,5,8,5,8,6,8,6,5,4,7,2,8,4,8
F,3,8,3,5,1,0,13,4,4,13,11,7,0,8,1,6
A,3,8,5,6,2,8,5,3,1,7,0,8,2,7,2,8
E,2,4,2,2,2,7,7,5,8,6,5,8,2,8,6,9
Y,3,8,5,6,1,4,10,3,2,9,13,8,2,11,0,8
S,4,3,5,5,2,8,10,6,10,5,5,5,0,7,8,9
J,5,9,6,7,3,9,7,2,6,14,3,7,0,7,0,8
X,4,10,7,7,4,9,7,0,8,9,4,7,3,8,3,8
C,1,0,2,1,0,6,7,6,8,7,6,14,0,8,4,10
L,5,5,5,8,1,0,0,6,6,0,1,4,0,8,0,8
T,3,4,4,2,1,5,11,2,7,11,9,4,1,10,2,5
R,5,10,6,8,6,5,8,6,6,6,4,10,4,6,6,10
L,2,2,2,4,1,0,1,5,6,0,0,6,0,8,0,8
Y,7,9,7,7,3,3,10,4,7,12,12,6,2,11,2,6
E,3,7,4,5,2,3,7,6,11,7,6,15,0,8,7,7
J,4,5,6,5,5,8,9,4,5,7,6,8,3,7,8,8
E,5,9,7,7,7,7,7,3,7,7,7,11,5,10,8,9
P,5,10,7,8,7,6,9,6,5,9,7,4,2,10,4,7
T,2,3,3,1,1,5,12,3,5,11,9,5,1,10,1,5
S,4,9,5,6,3,6,9,5,8,11,8,7,2,9,5,5
V,5,5,6,4,2,5,12,2,3,8,11,7,3,10,1,7
J,4,11,4,8,3,14,3,5,5,13,1,9,0,7,0,8
H,8,13,8,7,4,11,7,4,5,9,4,5,6,9,4,8
C,2,1,3,2,1,6,8,7,8,8,7,13,1,9,4,10
U,6,8,6,6,4,4,8,5,7,10,8,9,3,9,2,5
F,3,4,5,3,2,6,10,2,6,13,6,4,1,10,3,7
F,4,3,4,4,3,5,11,3,6,11,9,5,1,10,3,6
R,4,7,6,5,4,10,7,4,6,10,3,6,3,7,4,10
X,7,15,7,8,4,10,7,2,8,9,4,7,4,10,4,10
E,4,7,6,5,3,4,9,3,9,11,9,10,2,8,4,5
K,4,9,4,7,2,3,7,7,3,7,7,12,3,8,3,10
O,5,12,4,6,3,6,6,6,3,10,6,9,5,9,5,7
L,3,5,4,4,3,6,8,4,5,6,6,9,2,8,7,9
K,4,4,6,3,3,5,8,2,7,10,8,9,3,8,2,7
Y,6,7,6,5,3,5,9,1,9,10,10,5,1,10,4,3
L,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8
K,3,8,5,6,4,5,6,4,7,7,7,11,3,8,5,9
T,2,3,3,2,1,5,12,3,6,11,9,5,1,11,2,5
J,3,6,5,4,2,9,7,1,6,14,4,6,0,8,0,8
I,7,14,6,8,4,10,6,4,6,14,3,6,2,8,6,10
A,5,11,7,8,3,9,3,3,3,7,2,9,3,7,3,9
F,3,5,6,4,2,6,12,3,5,13,7,3,1,10,2,6
U,7,10,8,8,5,5,7,6,8,9,7,9,5,11,5,3
P,4,4,5,6,5,9,10,2,3,7,9,5,4,10,5,5
J,2,4,4,3,1,9,6,3,6,14,5,10,0,7,0,8
A,1,1,2,1,0,7,4,3,0,7,1,8,2,7,1,8
R,2,3,4,1,2,9,7,4,5,9,4,7,2,6,4,10
G,4,11,5,8,4,7,7,7,6,6,5,9,2,7,5,11
K,5,10,7,8,7,7,7,3,7,6,6,7,7,8,6,10
T,5,7,6,5,5,6,7,7,7,7,8,8,3,10,5,9
O,1,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8
P,3,4,5,2,2,8,10,4,3,11,4,3,1,10,2,8
G,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,9
R,4,10,4,7,3,5,11,9,4,7,3,9,3,7,6,11
L,3,8,4,6,2,2,4,3,9,2,0,8,0,7,1,6
U,5,10,6,8,2,7,4,15,6,7,14,8,3,9,0,8
M,6,9,9,6,7,9,6,2,5,9,5,7,8,6,2,8
N,4,5,5,5,5,7,8,4,3,6,4,8,6,8,4,7
R,3,6,4,4,4,8,6,6,3,7,5,7,4,6,6,7
D,3,1,4,2,2,7,7,6,7,7,6,5,2,8,3,7
E,5,7,6,5,5,8,9,6,3,6,6,10,4,8,7,9
J,1,3,3,2,1,7,7,3,5,14,6,9,1,7,1,7
Z,3,5,4,7,2,7,7,4,14,10,6,8,0,8,8,8
D,1,3,3,2,1,9,6,4,6,10,4,6,2,8,2,8
N,6,10,8,8,5,7,8,6,6,7,7,6,6,9,2,6
S,5,10,7,7,8,9,5,5,4,8,6,9,5,7,12,8
B,2,1,3,3,2,7,7,5,5,6,6,6,2,8,5,9
H,5,8,8,6,5,10,7,4,6,10,3,6,3,8,3,8
I,1,5,0,6,0,7,7,4,4,7,6,8,0,8,0,8
A,3,2,5,3,2,8,2,2,2,7,1,8,2,6,2,7
U,4,5,5,3,2,6,8,6,7,7,10,9,3,9,0,8
P,7,11,9,8,8,6,8,7,4,8,7,9,3,9,7,9
I,3,9,4,6,3,7,7,0,7,13,6,8,0,8,1,8
G,1,3,2,2,1,7,6,5,4,7,6,10,2,9,3,9
O,3,2,5,4,3,8,7,8,5,7,6,8,2,8,3,8
W,6,8,6,6,6,4,9,2,3,9,8,8,7,11,2,6
B,4,8,4,6,5,6,7,8,6,6,6,7,2,8,7,9
A,2,8,4,6,3,13,3,4,2,11,1,8,2,6,3,9
W,6,5,9,8,4,8,7,5,2,7,8,8,9,9,0,8
U,6,10,6,8,5,8,5,12,4,7,13,8,3,8,0,7
Q,4,6,5,7,5,10,10,6,3,4,8,11,3,10,6,9
I,2,10,3,8,2,9,6,0,7,13,5,8,0,8,1,8
F,6,9,8,6,5,9,8,2,7,13,4,5,4,9,4,9
P,2,1,3,2,1,6,10,5,4,10,7,3,1,9,4,6
E,6,11,8,9,7,9,6,1,8,11,4,9,4,7,6,10
M,4,4,6,3,4,8,6,2,4,9,6,8,7,5,2,8
C,6,10,8,7,5,7,7,8,7,7,6,11,4,8,4,9
V,2,1,4,2,1,7,12,2,2,6,11,9,4,11,2,7
D,4,8,6,6,8,9,8,4,5,7,6,6,4,6,7,5
J,2,8,2,6,1,15,3,5,5,13,0,8,0,7,0,8
L,2,1,2,2,1,4,4,4,8,3,2,6,0,7,1,6
J,1,1,2,3,1,10,6,3,5,11,4,9,1,7,1,7
F,4,10,4,7,2,1,13,5,3,12,9,6,0,8,3,6
Q,3,6,4,8,4,8,6,7,5,9,6,7,3,8,5,7
G,3,6,4,4,2,6,7,6,5,10,8,11,2,9,4,10
S,7,10,8,8,4,8,7,4,8,11,6,7,2,9,5,8
Z,4,10,5,8,3,7,7,4,15,9,6,8,0,8,8,8
O,3,1,4,3,2,7,7,7,4,7,6,7,2,8,3,7
I,4,8,6,9,6,9,8,6,7,7,4,7,3,10,9,9
O,3,5,4,3,3,7,7,7,4,9,5,8,2,8,3,7
Q,2,3,3,4,2,8,8,4,2,7,8,10,2,9,4,8
U,5,11,8,8,6,7,8,4,7,4,7,9,6,9,1,8
M,7,9,11,6,8,4,7,3,5,11,10,11,10,9,5,7
M,5,8,8,6,7,9,7,2,4,9,5,7,7,7,2,8
X,4,8,5,6,1,7,7,5,4,7,6,8,3,8,4,8
V,9,11,7,6,3,7,11,6,5,8,10,4,5,12,3,9
V,3,7,4,5,2,5,11,3,4,9,12,8,2,10,1,8
D,4,7,5,5,5,7,7,5,7,7,6,6,6,8,3,7
F,7,10,6,6,4,9,7,3,5,11,5,6,4,9,6,9
X,1,0,2,0,0,7,7,4,4,7,6,8,2,8,4,8
L,4,8,6,6,6,7,7,3,5,7,7,9,6,10,6,6
K,4,8,4,5,2,3,8,8,2,7,5,11,4,8,3,10
J,2,1,3,2,1,10,6,2,7,11,4,8,1,7,1,7
H,8,11,12,8,7,9,7,3,6,10,4,7,3,8,3,8
D,2,3,3,2,2,9,6,4,6,10,4,6,2,8,2,9
C,3,6,4,4,2,5,9,6,7,12,9,11,2,10,3,7
C,5,9,6,7,4,4,7,5,7,10,8,14,3,8,4,7
P,3,6,3,4,2,4,12,7,1,10,6,4,1,10,3,8
P,5,5,6,7,7,8,7,4,3,7,7,7,6,10,5,6
L,2,0,2,1,0,2,0,6,4,0,3,4,0,8,0,8
B,1,1,2,1,2,7,7,4,5,7,6,6,1,8,4,9
L,4,8,6,6,3,5,4,3,10,5,1,7,1,6,3,5
S,3,10,4,7,2,7,7,6,9,4,6,8,0,8,9,7
O,3,5,4,4,3,8,7,7,4,9,6,8,3,8,3,8
D,2,4,4,3,2,10,6,3,7,10,3,6,2,8,3,9
L,3,7,4,5,2,3,4,3,9,2,1,7,0,7,1,5
I,4,11,5,8,2,9,5,0,8,14,5,10,0,8,1,9
H,7,9,6,4,3,11,7,4,5,8,3,5,5,9,4,8
Y,3,9,5,6,1,7,10,2,3,7,13,8,1,11,0,8
J,1,1,2,2,1,10,6,2,5,11,4,8,1,7,1,7
U,2,4,3,3,1,7,8,7,7,8,10,7,3,9,1,8
D,2,1,2,2,1,6,7,8,7,6,6,6,2,8,3,8
H,3,7,4,5,5,7,8,6,6,7,5,8,3,8,3,7
K,6,11,6,8,5,3,6,7,4,7,8,12,3,8,3,11
J,2,7,4,5,1,7,6,4,6,15,7,12,1,6,0,7
R,2,1,3,2,1,6,10,8,2,7,5,8,2,7,5,10
N,4,7,6,5,4,6,7,6,7,7,5,9,6,7,3,7
A,4,11,7,8,3,7,4,3,2,7,1,8,3,6,3,8
K,8,13,7,7,4,10,7,3,7,9,3,6,6,8,4,8
A,3,9,5,7,4,12,3,2,2,10,2,9,2,6,3,7
N,2,3,4,2,1,9,7,3,4,10,3,6,5,9,0,7
U,5,11,6,8,5,7,5,13,5,7,12,8,3,9,0,8
B,4,6,4,4,3,7,6,9,7,7,7,7,2,8,8,9
R,2,1,2,2,2,7,7,5,5,6,5,7,2,7,4,8
G,1,3,2,2,1,7,7,4,4,9,7,10,2,8,3,10
N,5,10,6,8,6,7,7,13,1,6,6,8,5,8,0,8
W,8,9,8,7,7,5,11,3,3,9,8,7,7,12,3,6
L,3,7,5,5,4,7,6,7,5,6,6,8,3,8,4,10
L,4,7,4,5,2,1,1,5,5,1,1,6,0,8,0,8
U,3,2,4,4,1,7,5,12,5,7,14,7,3,9,0,8
I,3,8,4,6,2,7,7,0,8,14,6,8,0,8,1,8
Y,7,10,8,8,4,3,11,2,8,11,11,5,1,11,3,4
P,4,3,4,4,2,4,14,7,1,11,6,3,1,10,4,8
I,4,11,7,9,8,10,8,2,6,9,4,5,4,7,8,4
U,8,11,8,8,6,3,8,5,8,10,8,10,5,10,4,4
U,4,4,5,3,2,4,8,5,8,10,9,9,3,9,2,6
G,6,11,8,8,9,8,4,5,4,7,5,11,7,9,6,13
L,4,9,6,7,4,4,4,3,8,2,1,8,0,6,1,6
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
W,8,9,12,8,13,7,7,5,5,6,5,8,10,9,9,8
K,5,8,8,7,7,10,7,4,4,10,2,8,5,5,4,10
N,4,7,4,5,2,7,7,14,2,4,6,8,6,8,0,8
L,3,8,4,6,2,7,3,1,8,9,2,11,0,7,2,9
W,8,9,8,6,6,5,11,3,3,9,8,7,8,11,3,6
E,6,10,8,8,5,5,8,4,9,12,9,9,3,8,5,6
D,1,3,3,2,1,8,7,5,6,9,5,5,2,8,3,8
J,6,13,5,10,4,7,10,2,3,13,5,5,2,8,7,8
Y,1,3,2,1,1,7,10,1,5,7,11,8,1,11,1,8
B,5,11,6,8,8,8,7,5,6,7,6,5,3,8,6,9
B,2,3,4,2,2,8,7,3,5,10,5,6,2,8,4,9
B,6,9,5,5,5,8,8,3,5,9,5,7,6,5,7,7
D,1,0,1,1,0,6,7,8,5,7,6,6,2,8,2,8
S,6,9,7,6,4,9,6,3,8,10,6,8,2,10,5,8
T,5,10,7,8,6,8,11,2,7,6,11,8,1,12,1,8
H,5,10,5,7,3,7,7,15,1,7,6,8,3,8,0,8
A,3,8,5,6,5,8,7,7,4,7,6,9,5,7,7,4
V,4,9,6,7,3,6,11,2,4,7,12,9,3,10,1,8
A,4,8,6,7,6,9,8,3,4,6,7,7,5,8,5,5
Z,3,6,5,9,4,11,3,2,6,8,2,6,1,8,6,9
A,4,10,5,7,4,7,5,3,0,7,1,8,2,7,3,8
R,4,10,5,7,3,5,12,9,4,7,2,9,3,7,6,11
W,8,9,8,7,6,2,12,2,3,10,10,8,7,11,2,7
T,3,4,4,3,2,6,10,2,7,11,9,5,2,10,3,4
L,3,10,3,8,1,0,1,5,6,0,0,7,0,8,0,8
C,2,5,3,3,2,6,8,7,7,8,7,13,1,9,4,10
U,5,7,5,5,3,4,8,5,7,10,9,9,3,9,2,6
Y,2,7,4,4,1,8,11,1,3,6,12,8,1,10,0,8
E,1,0,1,0,1,5,8,5,7,7,6,12,0,8,6,9
R,2,6,4,4,3,8,7,3,5,9,4,7,3,7,4,10
C,5,9,5,7,3,4,8,6,8,12,10,12,1,9,3,7
L,3,8,5,6,3,6,4,1,8,8,2,10,0,7,2,8
K,4,10,6,8,7,6,6,5,3,7,6,9,4,5,10,8
M,4,5,5,3,4,8,6,6,4,7,7,8,8,5,2,7
N,2,5,3,4,2,7,8,5,4,7,6,7,4,8,1,7
J,6,8,8,9,7,9,8,5,6,7,5,8,4,8,9,7
P,1,3,3,2,1,6,10,6,2,10,5,4,1,9,3,7
J,1,8,2,6,2,9,6,3,5,11,4,9,1,6,1,6
G,5,9,4,4,3,8,6,4,2,9,6,8,3,9,8,8
H,5,8,7,6,7,7,7,6,6,7,6,8,3,8,3,7
B,2,1,3,1,2,7,7,5,5,7,6,6,1,8,5,9
I,3,7,4,5,2,8,6,0,7,13,6,9,0,7,2,8
D,1,0,1,1,1,6,7,8,6,7,6,6,2,8,3,8
O,4,4,5,6,2,8,8,9,8,5,8,10,3,8,4,8
B,4,5,4,7,4,6,7,10,7,7,6,7,2,8,8,9
M,6,8,9,6,5,10,6,3,5,9,4,6,8,6,2,8
T,2,1,3,2,1,7,12,2,6,7,11,8,1,11,1,8
V,3,7,4,5,2,7,12,3,4,7,11,8,2,10,1,8
Z,7,14,7,8,4,10,4,3,8,12,4,10,3,9,4,11
S,4,9,3,4,2,7,3,1,5,6,2,7,2,8,5,9
Q,6,8,6,10,8,8,5,7,3,9,6,9,3,8,5,8
Y,4,10,7,7,1,7,10,3,2,7,13,8,2,11,0,8
E,6,10,8,8,6,4,10,4,8,11,10,8,3,8,4,4
G,3,3,4,5,2,7,7,8,7,6,5,11,2,8,5,10
Q,5,8,5,10,6,8,7,6,3,8,9,11,3,8,6,8
F,3,7,3,4,1,1,11,5,6,11,10,9,0,8,2,6
H,4,5,4,7,3,7,8,15,1,7,5,8,3,8,0,8
V,2,4,4,3,1,7,12,2,3,6,11,9,2,10,1,8
M,14,15,14,8,7,6,10,5,5,4,4,11,11,13,2,8
L,5,9,6,7,5,6,6,8,5,6,5,9,3,7,5,11
U,3,5,4,3,2,6,8,6,7,6,9,9,3,9,1,7
S,2,8,3,6,3,7,7,7,5,7,6,8,2,9,8,8
Z,4,10,5,8,2,7,7,4,15,9,6,8,0,8,8,8
U,5,7,5,5,3,4,8,6,7,9,9,9,3,9,2,5
W,5,10,7,8,4,5,8,5,2,7,8,8,8,9,0,8
H,1,0,1,0,0,7,7,10,1,7,6,8,2,8,0,8
T,4,10,5,8,3,7,13,0,6,7,10,8,0,8,0,8
N,5,10,6,8,6,7,7,6,6,7,6,8,6,7,3,8
G,6,8,8,7,8,7,10,6,2,7,7,8,6,11,7,8
T,3,4,4,3,2,7,12,4,6,7,11,8,2,11,1,7
D,5,6,6,6,5,5,7,6,7,6,5,6,4,7,5,6
D,3,2,4,3,2,7,7,6,7,7,6,5,2,8,3,7
R,4,10,5,7,6,6,7,5,6,6,4,8,3,6,5,9
E,3,7,4,5,4,8,8,4,7,7,5,8,3,8,5,10
J,5,9,6,7,3,8,4,5,6,15,7,14,1,6,1,7
Q,3,5,4,6,4,8,8,5,2,7,7,12,2,9,5,9
K,4,5,5,4,4,5,7,4,7,6,6,10,3,8,5,8
N,6,11,6,8,3,7,7,15,2,4,6,8,6,8,0,8
X,8,12,8,6,4,10,6,3,9,9,2,5,4,6,4,10
H,3,3,5,1,2,9,6,3,5,10,4,8,3,8,2,9
A,5,9,7,7,4,8,5,3,0,6,1,8,2,7,1,8
L,4,9,5,6,4,5,5,1,8,7,2,10,1,7,3,7
I,2,7,2,5,1,7,7,0,7,13,6,8,0,8,1,8
H,6,8,8,6,7,6,8,7,6,8,5,7,6,7,5,10
G,5,8,5,6,4,7,6,6,7,10,7,11,2,10,4,9
R,5,5,6,7,3,6,11,9,3,7,3,8,3,7,6,11
X,4,10,6,8,6,6,7,2,6,7,7,9,3,7,6,8
D,4,8,4,6,4,5,7,9,7,7,7,6,2,8,3,8
R,3,10,4,7,3,6,9,11,5,7,5,8,3,8,5,10
F,5,8,8,6,4,6,10,3,6,13,7,4,2,10,2,7
C,3,6,5,5,4,6,6,4,5,7,6,11,4,10,7,10
O,2,4,3,3,2,7,7,7,4,7,5,8,2,8,3,8
V,2,6,4,4,2,9,11,3,4,4,11,8,2,10,1,8
M,7,10,9,8,9,8,7,6,5,6,7,8,8,6,2,7
R,4,7,5,5,3,11,7,3,6,11,2,6,3,7,3,10
O,4,8,6,6,5,7,7,8,4,7,7,9,3,8,3,7
H,1,0,2,1,0,7,7,11,1,7,6,8,3,8,0,8
W,6,6,6,4,4,2,11,2,2,10,10,8,5,11,1,7
Q,3,4,4,5,3,8,8,5,1,8,8,10,2,9,4,8
Y,3,5,4,7,7,8,5,4,2,7,7,9,5,10,4,7
Q,1,3,2,4,2,7,7,5,2,8,8,10,2,9,4,9
H,5,10,8,8,7,8,6,7,7,7,7,5,3,8,4,6
A,3,9,5,6,3,10,3,2,2,8,2,10,3,5,3,7
S,4,8,5,6,4,8,8,7,5,7,6,8,2,8,8,8
P,6,11,5,6,3,5,11,5,4,13,6,4,3,9,4,8
K,8,12,7,6,4,9,8,3,7,9,4,6,6,9,4,8
Y,2,2,4,4,2,6,10,1,7,8,11,8,1,11,2,7
B,8,10,7,6,6,7,9,4,5,9,6,7,6,6,7,6
W,4,9,6,7,3,5,8,5,1,7,8,8,8,9,0,8
O,4,9,5,7,2,7,6,9,7,6,5,7,3,8,4,8
G,5,11,7,8,4,6,6,8,8,8,5,13,3,11,5,8
I,1,10,2,8,2,7,7,0,8,7,6,8,0,8,3,8
P,8,11,6,6,3,7,10,5,3,12,4,4,5,10,4,8
Q,2,5,3,6,3,8,6,7,3,5,6,9,2,9,5,10
A,4,9,3,4,2,13,3,4,1,10,3,10,3,5,3,10
O,4,7,5,5,4,7,7,8,6,7,6,6,3,8,4,8
Q,10,15,9,9,5,7,5,4,9,10,4,10,4,6,9,9
I,1,3,2,1,0,7,7,1,7,13,6,8,0,8,1,8
L,1,0,1,1,0,2,1,5,5,0,2,5,0,8,0,8
W,6,5,8,5,8,9,8,5,5,7,5,8,9,9,8,6
V,5,9,7,7,4,7,12,2,3,5,10,9,4,12,2,7
N,3,3,4,2,2,7,8,6,4,7,7,6,5,9,2,6
G,5,11,7,8,8,7,7,6,2,7,6,11,7,9,10,6
N,6,9,8,7,9,8,7,4,4,7,6,6,7,10,7,5
I,3,7,5,5,2,8,6,2,7,7,6,7,0,9,4,7
F,5,11,7,8,5,5,11,2,8,10,9,5,4,10,4,6
R,8,11,7,6,5,10,6,3,5,9,4,8,6,8,6,9
V,2,1,3,1,0,7,9,4,2,7,13,8,2,10,0,8
R,5,11,7,8,5,8,9,4,6,8,4,9,4,6,6,11
H,4,9,4,7,4,7,7,13,1,7,6,8,3,8,0,8
Q,6,10,8,8,7,8,3,8,5,6,5,9,4,9,4,7
R,3,6,3,4,3,6,9,8,4,7,5,8,2,7,4,11
P,3,7,4,5,3,6,10,6,5,10,8,4,1,10,4,7
Y,4,6,6,8,7,9,9,6,3,7,7,8,6,11,7,4
F,5,7,7,5,4,6,11,5,6,12,8,5,2,9,2,6
Z,5,9,5,4,3,8,6,2,8,11,6,9,3,7,6,7
O,7,13,5,7,3,9,7,7,5,9,4,8,5,9,5,9
W,5,8,8,6,5,7,8,4,1,7,9,8,7,11,0,8
C,4,9,5,7,4,4,8,6,7,9,9,15,1,9,4,10
R,2,4,4,3,3,8,8,3,5,10,4,7,3,6,3,9
Y,4,6,4,4,1,3,11,3,7,11,11,5,1,11,2,4
I,1,4,1,3,1,7,7,1,8,7,6,9,0,8,3,8
V,7,10,7,8,3,3,11,4,4,10,12,8,2,10,1,8
Q,4,6,5,8,4,8,7,6,3,8,7,10,3,8,6,8
G,4,6,4,4,3,7,6,6,5,9,6,13,2,8,4,10
W,4,10,6,8,4,10,8,5,2,7,8,8,8,9,0,8
Q,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8
G,9,14,8,8,5,7,4,6,3,8,5,4,4,8,6,6
S,4,8,6,6,7,6,9,3,3,8,7,6,3,8,11,1
S,3,4,3,2,2,8,8,6,5,7,6,7,2,8,9,8
D,5,6,6,6,5,6,6,5,7,7,6,8,4,6,6,5
W,8,12,8,6,4,1,9,3,2,11,12,9,7,10,0,6
A,3,1,5,3,2,8,2,2,2,7,1,8,2,7,2,7
C,4,6,5,4,3,6,7,5,6,11,8,13,2,10,3,9
Q,1,2,2,3,1,8,8,5,1,5,7,9,2,9,4,9
R,5,10,7,7,6,7,8,5,7,7,5,6,6,6,5,9
L,3,2,4,4,2,4,4,4,7,2,1,6,0,7,1,6
D,5,10,6,7,5,7,7,8,8,6,5,5,3,9,4,8
P,8,12,7,6,4,8,9,3,4,12,5,4,3,10,6,6
U,4,7,5,5,5,8,7,8,5,6,7,9,3,8,4,6
Z,2,4,4,3,2,7,7,2,9,12,6,8,1,8,5,7
S,4,9,4,7,2,8,7,6,9,4,6,8,0,8,9,7
D,4,7,5,5,2,5,8,9,9,7,6,6,3,8,4,8
S,4,9,6,7,7,5,9,3,3,8,7,5,4,8,12,0
C,9,13,7,7,5,7,9,4,4,9,8,9,3,9,8,11
R,3,4,4,3,2,7,7,5,5,6,5,6,2,7,4,8
A,1,0,2,0,0,7,3,2,0,7,2,8,2,6,1,8
U,5,6,5,4,2,4,9,6,7,10,10,9,3,9,2,5
H,3,6,4,4,3,7,8,7,5,8,5,7,3,7,5,11
J,2,6,3,4,2,10,5,3,5,12,3,8,0,7,2,7
Y,3,8,5,6,1,9,12,2,3,4,12,9,0,10,0,8
E,4,5,6,4,3,7,7,2,8,11,7,9,2,8,4,8
J,3,9,5,7,3,9,5,3,6,15,4,10,0,7,1,7
M,5,6,5,8,4,7,7,12,2,7,9,8,8,6,0,8
S,6,12,6,6,3,8,7,4,3,13,8,8,3,10,3,8
U,4,6,4,4,1,7,5,13,5,7,15,8,3,9,0,8
L,2,7,3,5,2,4,4,4,7,2,1,6,1,6,1,6
R,6,11,9,8,7,7,8,5,7,7,5,6,3,6,6,9
D,4,7,6,6,6,7,8,5,6,6,4,7,4,7,7,5
X,5,9,8,7,4,8,7,1,8,10,4,7,3,8,4,8
V,2,3,4,4,1,5,8,4,2,8,13,8,3,10,0,8
H,2,2,3,3,2,8,7,6,6,7,6,8,6,8,3,7
R,3,5,6,4,5,8,7,3,4,8,5,7,6,9,5,5
M,3,6,3,4,2,8,6,11,1,6,9,8,7,6,0,8
L,3,7,4,5,2,6,3,1,8,8,2,11,0,7,2,8
H,2,4,4,3,3,7,7,3,5,10,6,8,3,8,2,7
Z,6,9,8,7,8,9,8,4,4,6,4,6,4,8,10,4
F,2,4,2,2,1,5,10,4,4,10,9,5,1,10,3,7
G,2,1,3,2,1,8,6,6,6,6,5,9,1,7,5,10
M,4,5,5,3,4,8,6,6,4,6,7,7,8,6,3,6
Y,7,6,6,9,4,7,10,2,3,7,10,4,4,10,6,7
O,4,8,5,6,2,7,7,8,8,7,6,7,3,8,4,8
E,6,11,4,6,3,6,9,4,5,10,6,8,3,8,7,8
D,3,5,4,3,3,7,7,7,7,6,6,5,2,8,3,7
O,3,5,4,4,3,7,7,8,5,7,6,8,2,8,3,8
C,6,11,6,8,3,5,8,7,8,13,10,11,2,10,3,7
X,4,8,6,6,3,6,8,1,8,10,8,9,3,8,3,7
L,7,13,6,8,3,10,2,4,4,13,4,13,2,8,6,9
T,3,1,4,3,2,7,12,4,6,7,11,8,2,11,1,8
B,6,9,8,7,6,10,6,3,7,10,3,7,6,8,7,11
M,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8
U,4,6,5,4,4,7,8,8,6,5,7,11,3,8,4,7
S,6,8,8,7,9,8,7,4,5,7,7,8,5,10,9,11
J,3,5,5,4,2,9,5,3,6,14,5,10,0,7,0,7
Q,5,9,6,8,3,7,5,9,8,5,4,7,3,8,4,8
T,3,7,5,5,3,6,12,3,7,8,11,8,1,11,1,7
Y,6,8,6,6,3,5,9,0,8,10,9,6,0,10,3,4
M,4,6,7,4,8,6,5,4,2,6,4,8,12,6,3,7
Q,6,10,8,8,7,8,4,8,4,6,6,8,4,7,6,9
S,3,9,4,7,4,8,6,8,5,7,7,9,2,9,8,7
J,2,8,3,6,1,11,3,10,3,12,8,13,1,6,0,8
A,2,3,4,2,2,9,1,2,1,8,2,10,2,6,2,7
H,6,11,8,8,8,7,8,7,6,8,6,7,7,8,5,9
M,5,10,5,7,4,7,7,12,2,7,9,8,8,6,0,8
Y,5,11,6,8,7,9,5,6,5,7,8,7,3,9,8,3
C,4,7,5,5,2,5,8,7,8,7,8,14,1,8,4,9
J,2,6,3,4,1,15,3,3,5,12,1,8,0,8,0,8
K,5,8,8,7,7,6,8,3,4,6,3,9,8,3,10,9
Y,5,9,5,6,2,3,11,3,6,12,11,5,1,11,2,5
M,5,10,6,8,4,7,7,12,2,7,9,8,8,6,0,8
X,3,8,5,6,3,8,7,4,9,6,6,6,3,8,7,7
J,6,9,4,12,4,11,6,2,4,10,4,6,3,9,6,10
H,4,4,5,5,2,7,10,15,2,7,2,8,3,8,0,8
H,3,4,5,3,3,5,9,3,5,10,8,8,3,9,3,7
G,4,6,5,4,2,7,6,6,7,11,6,12,2,9,4,9
R,5,6,7,6,7,5,7,4,4,6,5,9,7,9,6,9
J,1,4,2,2,1,8,6,3,6,14,6,10,1,7,1,7
V,3,5,4,4,1,4,12,3,3,10,11,7,2,10,1,8
X,7,9,8,5,4,8,7,3,8,12,4,7,4,9,4,8
F,2,7,3,4,1,1,11,4,6,11,11,10,0,8,2,6
S,2,3,3,2,2,8,7,6,4,7,7,8,2,9,9,8
R,4,9,4,6,3,5,11,8,4,7,4,9,3,7,6,11
Y,3,7,5,5,2,7,10,2,2,7,12,8,1,11,0,8
Y,4,5,5,3,2,4,10,2,8,10,10,5,3,11,4,3
N,9,14,8,8,4,10,11,6,6,3,6,9,5,10,3,6
Z,4,9,6,7,4,9,5,2,9,11,4,10,1,8,6,10
L,4,9,6,8,5,8,7,4,5,7,6,8,3,8,8,11
W,4,8,6,6,4,7,10,2,3,7,9,8,8,11,1,8
N,2,1,2,1,1,7,7,11,1,5,6,8,4,8,0,8
H,7,9,10,7,7,9,7,3,7,10,4,7,5,8,5,9
J,0,0,1,0,0,12,4,6,3,12,4,11,0,7,0,8
N,6,8,8,7,9,8,6,5,5,7,6,7,8,11,7,3
I,7,13,5,8,3,10,4,5,4,13,3,9,3,8,4,9
C,1,0,2,0,0,7,7,6,8,7,6,13,0,8,4,10
O,3,7,4,5,3,7,7,8,5,7,6,8,3,8,3,8
U,5,10,6,7,2,7,4,14,5,7,14,8,3,9,0,8
R,8,14,6,8,5,8,7,5,5,9,4,9,7,5,6,11
E,2,3,3,5,2,3,7,6,10,7,6,14,0,8,7,8
B,10,15,10,8,8,9,7,4,5,9,5,7,7,5,9,8
U,5,7,5,5,3,3,9,5,6,10,10,9,3,9,2,7
D,1,0,2,1,1,6,7,8,6,7,6,6,2,8,3,8
R,1,0,2,0,1,6,10,7,2,7,5,8,2,7,4,9
N,8,10,10,5,3,4,10,5,4,14,11,9,5,9,0,8
C,2,3,3,2,1,4,8,4,6,10,9,12,1,9,2,8
E,3,10,4,8,2,2,8,6,11,7,6,15,0,8,7,7
D,5,10,8,7,7,9,7,3,6,11,5,6,3,8,3,8
G,5,10,4,5,3,9,5,4,3,9,4,5,4,7,5,8
K,3,7,4,5,2,3,8,7,2,7,5,11,3,8,3,11
Y,9,8,7,12,5,8,6,6,6,4,11,7,5,9,3,8
X,3,8,4,6,1,7,7,4,4,7,6,8,3,8,4,8
Z,4,8,6,6,6,8,8,3,7,7,7,7,1,9,10,8
X,4,6,5,4,4,7,6,3,5,6,6,9,2,8,8,8
F,3,5,3,3,2,5,11,3,6,11,9,5,1,10,3,6
N,4,11,5,8,5,8,7,13,1,6,6,7,6,8,1,10
K,3,6,5,4,3,6,7,1,7,10,7,10,3,8,3,8
R,4,3,5,5,3,5,11,8,3,7,4,8,2,7,6,11
O,4,8,6,6,4,7,8,8,6,7,7,8,2,8,3,8
V,5,9,5,6,3,3,10,3,4,10,12,8,3,10,1,7
J,3,7,5,5,2,8,4,6,4,14,8,14,1,6,1,7
M,2,1,2,1,1,7,6,10,0,7,8,8,6,6,0,8
P,7,11,9,8,7,9,7,2,6,11,4,6,4,8,4,9
Y,7,9,7,7,2,3,12,6,6,14,11,5,2,11,2,6
D,5,11,7,8,11,8,8,6,4,7,6,6,7,8,9,6
S,4,9,5,7,4,10,7,4,6,10,3,7,2,8,5,10
X,4,10,5,7,1,7,7,5,4,7,6,8,3,8,4,8
S,4,7,5,5,2,6,9,4,7,10,7,7,2,8,5,5
Y,8,11,8,8,5,3,11,2,6,11,11,6,1,11,2,5
Q,5,9,6,8,5,8,8,8,5,6,6,10,3,8,4,9
T,5,6,6,4,3,5,11,3,7,11,9,5,2,11,2,4
A,3,9,4,7,3,6,4,2,1,6,1,8,2,6,2,7
R,4,5,4,3,3,7,7,5,6,7,5,6,5,7,4,7
U,2,6,4,4,2,7,9,6,6,6,10,9,3,9,1,7
I,4,9,4,5,2,6,11,3,4,13,6,4,1,9,5,8
D,1,0,1,0,0,5,7,7,5,6,6,6,2,8,2,8
E,2,3,4,2,2,7,7,2,7,11,7,9,2,8,4,8
V,6,8,8,6,5,6,12,3,2,8,10,8,6,11,6,8
D,5,11,5,6,4,8,6,3,5,9,5,7,5,9,5,8
G,6,6,8,6,7,7,10,5,3,7,7,8,6,11,7,8
D,9,15,8,8,6,8,6,3,7,10,5,7,6,8,9,6
K,5,9,5,4,3,10,7,2,5,10,5,7,5,10,3,9
T,6,11,6,8,4,4,12,3,7,12,10,4,1,11,2,4
K,4,11,4,8,2,3,7,8,2,7,5,11,3,8,3,10
Z,3,6,5,4,4,8,8,2,7,7,6,7,0,8,8,9
N,5,11,6,8,6,8,8,13,1,6,6,8,6,8,1,10
N,6,8,8,7,8,7,8,4,4,7,5,6,7,8,7,2
M,2,4,4,3,3,10,6,3,4,9,4,6,6,5,2,8
O,6,11,6,9,5,8,6,8,5,10,5,9,4,9,5,6
T,3,6,5,5,5,6,9,3,7,8,7,9,3,8,7,5
A,3,6,5,8,2,7,6,3,1,7,0,8,3,7,1,8
R,3,2,4,3,3,7,7,5,5,6,5,6,3,7,4,8
A,3,8,5,6,3,8,3,2,2,6,1,8,2,6,2,7
J,3,4,4,6,1,11,2,11,3,12,9,14,1,6,0,8
D,4,4,5,6,3,5,7,9,9,6,5,5,3,8,4,8
B,4,10,5,7,7,8,7,6,5,7,6,6,6,8,6,10
J,4,4,5,5,4,7,9,4,5,7,7,8,3,7,6,6
V,3,7,5,5,2,9,9,4,1,6,13,8,2,10,0,8
B,5,8,7,6,7,7,7,5,4,7,5,7,4,9,6,7
I,1,9,0,6,1,7,7,5,3,7,6,8,0,8,0,8
U,5,8,6,6,3,5,8,7,9,10,11,9,3,9,1,7
M,4,8,5,6,5,8,5,11,1,6,9,8,8,5,1,6
W,6,9,8,7,4,4,8,5,2,7,9,8,9,9,0,8
C,5,11,6,8,4,3,8,6,6,12,10,13,2,9,3,7
N,6,10,8,8,4,7,8,2,5,10,7,8,5,8,1,8
R,5,10,8,9,9,6,9,3,3,7,5,9,8,6,7,9
D,6,10,8,8,6,9,6,5,8,10,3,6,3,8,4,8
B,2,1,2,1,1,7,7,7,5,6,6,7,1,8,7,8
Z,2,1,3,2,1,7,7,5,9,6,6,8,1,8,7,8
T,7,10,7,8,6,6,11,3,6,11,9,5,2,12,2,4
T,5,9,7,8,8,7,7,4,8,7,6,9,5,4,11,5
T,9,12,8,6,3,4,12,3,8,13,8,4,2,8,4,3
W,7,10,7,7,6,5,10,4,3,9,7,7,9,13,3,4
U,3,8,5,6,2,6,9,7,7,8,10,9,3,9,1,7
F,2,2,3,3,2,5,11,3,5,11,9,5,1,10,3,6
H,2,3,3,2,2,7,7,6,5,7,6,8,3,8,2,7
U,4,8,4,6,3,7,6,11,4,7,13,8,3,9,0,8
Z,2,1,3,2,2,7,7,5,9,6,6,8,2,8,7,8
Z,5,9,6,7,5,7,7,5,10,7,6,8,1,8,7,8
M,4,6,5,4,3,8,7,12,1,6,9,8,8,6,0,8
D,6,10,8,7,5,9,7,5,8,10,3,5,3,8,4,8
C,6,11,8,9,6,7,8,8,6,4,7,12,5,6,4,8
C,3,5,4,7,2,5,7,7,10,7,5,14,1,8,4,9
G,6,10,8,8,8,7,10,6,3,5,6,10,4,7,7,7
B,3,5,3,3,3,7,7,5,5,6,6,6,2,8,6,10
E,4,7,6,5,4,9,7,2,7,11,5,8,3,9,4,11
F,4,7,6,5,3,6,10,1,6,13,7,5,1,10,2,8
G,4,9,6,6,2,7,6,7,9,7,5,11,1,8,5,10
O,5,10,7,8,5,7,6,9,4,7,5,9,3,9,4,6
M,5,5,8,3,4,10,6,3,5,9,5,7,10,6,2,8
R,5,7,7,5,4,9,8,4,7,9,3,7,3,6,4,11
M,6,10,10,8,13,9,6,3,2,8,4,8,11,7,3,7
M,4,5,7,3,4,5,7,3,4,10,10,10,9,7,3,8
V,1,3,3,1,1,7,12,2,2,7,10,8,2,10,1,8
Y,7,9,8,7,4,3,10,2,7,10,12,7,2,11,2,6
T,7,12,6,6,4,5,11,2,7,12,7,5,2,9,5,4
V,2,0,3,1,0,8,9,4,2,6,13,8,2,10,0,8
W,5,6,5,4,4,4,10,3,2,9,9,7,7,11,2,6
G,4,9,5,7,6,9,5,5,2,7,6,10,7,8,5,10
T,4,8,5,6,4,6,7,8,6,6,6,8,4,9,6,10
C,2,7,3,5,1,5,7,6,8,6,6,12,1,7,4,9
D,2,3,4,2,2,8,7,5,6,9,5,5,2,8,3,7
P,4,9,4,4,3,8,9,3,3,11,5,5,4,11,5,7
V,3,8,5,6,2,6,11,2,4,8,12,8,2,10,1,8
L,4,8,5,6,3,2,4,2,8,2,0,9,0,7,1,6
P,5,5,5,7,3,4,13,9,2,10,6,3,1,10,4,8
S,5,10,7,8,5,7,7,4,7,10,9,9,3,9,5,6
P,6,12,6,6,4,6,10,3,4,12,6,4,3,11,5,6
G,4,8,5,6,4,8,7,7,6,6,6,9,2,7,5,11
G,5,10,6,7,3,8,7,8,8,6,7,9,2,7,5,10
V,3,7,4,5,2,5,11,3,3,9,11,8,2,11,1,8
L,3,2,4,4,2,4,4,5,8,2,1,6,0,7,1,6
Y,7,9,7,7,3,5,9,1,9,9,10,5,1,11,4,3
L,4,10,6,8,4,7,4,1,7,8,2,10,1,6,3,8
X,2,3,3,2,2,7,7,3,8,6,6,8,2,8,5,8
K,6,9,9,7,9,6,8,5,4,7,5,8,4,6,10,9
A,6,8,8,7,7,8,7,4,6,7,9,6,5,11,4,5
Q,5,8,5,9,7,8,6,7,4,9,7,8,4,9,5,7
X,5,10,8,8,5,7,7,4,10,6,6,8,3,8,7,8
E,2,5,4,3,2,7,7,2,8,11,7,9,2,9,4,8
G,4,8,6,6,3,8,8,8,8,6,7,8,2,7,6,11
F,4,8,5,6,5,6,9,5,4,8,6,8,5,10,6,12
R,2,3,2,2,2,7,7,4,5,6,5,6,2,7,4,8
S,5,10,6,8,8,8,8,5,3,8,5,7,5,9,12,8
K,6,11,8,8,5,4,7,2,7,10,9,11,4,7,4,6
B,6,9,8,6,7,9,7,4,6,9,5,6,3,7,6,10
R,2,3,3,2,2,9,7,3,4,10,4,7,2,7,3,10
L,4,10,5,8,4,7,4,2,8,7,2,8,1,6,3,8
V,6,11,8,9,4,8,9,5,2,6,13,8,3,10,0,8
R,6,9,9,8,10,9,7,4,4,8,4,7,8,10,7,4
I,1,1,1,3,1,8,7,1,7,7,6,7,0,8,2,7
M,3,3,4,2,2,9,6,7,3,6,7,6,6,6,1,5
P,1,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8
D,3,7,4,5,4,8,8,6,5,9,6,4,3,8,3,6
D,5,11,8,8,7,10,6,3,6,11,4,7,4,8,4,9
O,2,3,3,2,1,8,7,6,4,9,6,8,2,8,2,8
V,3,11,5,8,2,9,8,4,3,6,14,8,3,10,0,8
X,2,2,4,4,2,7,7,3,9,6,6,8,2,8,6,7
T,8,12,7,6,4,5,11,2,7,12,7,5,2,7,4,4
J,3,10,4,8,4,9,6,2,5,11,4,9,1,6,2,6
F,2,4,4,3,1,5,12,3,5,13,7,3,1,10,1,6
I,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8
L,4,11,5,8,3,3,3,5,9,1,0,7,0,7,1,6
E,2,5,4,3,2,6,8,2,8,11,7,9,2,8,4,8
R,3,1,3,2,2,7,8,5,5,7,5,7,2,6,4,8
S,3,6,5,4,5,6,5,3,2,6,5,6,2,8,7,4
R,3,6,5,4,3,10,7,3,5,11,2,6,3,7,2,10
V,2,3,3,2,1,5,12,4,3,10,11,7,2,10,0,8
P,4,8,6,6,5,6,7,5,4,7,6,9,5,8,6,10
L,2,8,4,6,3,7,4,1,6,8,2,9,1,6,2,8
F,4,9,5,7,5,6,8,6,4,8,6,8,3,11,8,10
N,3,7,4,5,3,9,8,6,4,6,5,5,5,9,2,6
X,3,4,5,3,2,10,7,2,8,10,3,7,2,7,3,9
Q,3,5,4,6,4,8,7,7,3,5,7,10,3,8,5,10
A,4,9,6,6,2,7,6,3,1,7,0,8,3,7,2,8
N,6,10,9,8,9,6,8,3,5,8,7,7,9,8,8,4
X,7,11,8,6,4,7,8,2,8,11,7,8,5,11,4,7
R,5,10,7,8,6,7,8,6,5,8,5,9,4,6,6,12
Y,5,6,4,9,3,9,9,2,3,6,10,5,3,10,5,8
M,4,6,7,4,5,4,7,3,4,11,10,10,5,7,2,6
S,3,7,4,5,2,8,7,5,9,5,6,8,0,8,9,7
J,2,4,4,3,1,8,6,3,6,14,6,10,0,7,0,7
B,4,8,5,6,6,6,7,8,5,7,6,7,2,8,7,9
N,7,9,9,7,4,7,8,3,5,10,7,7,6,9,1,7
Q,7,12,7,6,4,11,3,4,5,12,3,9,3,9,7,12
Y,2,4,3,3,1,4,10,2,6,10,10,6,1,11,2,5
Z,3,7,4,5,3,7,7,3,11,9,6,8,0,8,7,8
C,3,6,4,4,2,6,8,8,8,9,8,13,2,10,4,9
C,3,2,4,4,2,6,8,7,8,8,8,14,1,9,4,10
W,2,0,2,0,1,7,8,4,0,7,8,8,6,9,0,8
R,1,0,2,1,1,6,9,7,3,7,5,7,2,7,4,10
P,4,6,6,4,4,9,7,2,6,13,5,6,1,10,3,10
P,5,8,6,6,5,4,11,4,5,11,9,4,0,10,4,7
D,5,8,6,6,4,8,8,7,7,10,5,4,3,8,4,8
B,4,8,5,6,5,8,8,4,4,7,5,7,4,8,5,7
Q,4,6,5,8,5,7,8,5,3,7,9,11,3,8,6,8
B,4,7,5,5,5,7,7,7,6,7,6,7,2,8,6,10
S,1,3,3,2,1,8,8,2,6,10,6,8,1,8,5,8
O,6,12,4,6,3,6,8,5,5,9,5,6,5,9,5,8
H,5,11,7,8,7,6,6,6,5,6,5,8,6,6,7,10
I,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8
I,5,8,6,6,3,7,5,2,7,7,7,9,1,10,4,8
G,2,1,2,2,1,8,6,6,6,6,5,10,1,7,5,10
W,3,3,5,5,3,7,8,4,1,7,8,8,8,10,0,8
A,1,1,2,1,0,7,4,2,0,7,2,8,2,7,1,8
J,2,6,2,4,1,15,4,3,5,12,1,7,0,8,0,8
Q,5,7,7,10,11,8,6,5,2,6,6,8,11,12,10,15
L,1,4,2,3,1,7,4,1,7,7,2,10,0,7,2,8
I,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8
D,7,11,10,8,6,8,7,7,8,9,5,4,4,8,6,11
N,8,15,10,8,5,12,5,2,5,12,3,7,5,6,0,8
I,1,3,1,4,1,7,7,0,7,7,6,8,0,8,2,8
U,3,5,4,3,2,5,8,5,7,10,9,9,3,9,2,6
I,1,5,2,4,1,7,7,0,7,13,6,8,0,8,1,7
D,5,9,8,6,6,9,8,4,6,11,5,5,4,7,4,9
E,3,6,5,4,4,8,7,5,2,7,6,9,4,8,7,10
G,6,8,8,7,8,7,8,5,3,7,7,9,7,11,7,8
V,4,7,5,5,2,9,12,2,3,4,11,9,3,12,1,7
S,3,5,3,4,2,8,7,7,5,7,6,7,2,8,9,8
R,3,6,5,4,4,8,9,4,5,8,4,8,3,5,4,11
E,2,3,3,1,1,6,8,2,7,11,7,8,1,8,4,8
M,5,9,5,6,6,8,5,11,0,6,8,8,8,6,1,6
Y,6,7,6,5,3,4,9,1,8,10,10,6,1,10,3,4
I,3,7,4,5,2,7,7,0,8,14,6,8,0,8,1,8
E,5,10,7,8,5,10,7,2,9,11,3,8,3,8,5,12
Q,5,7,6,9,6,8,5,8,4,6,5,9,4,8,6,9
Z,1,3,2,2,1,7,7,5,8,6,6,8,2,8,6,8
S,2,6,3,4,2,9,9,5,9,5,5,5,0,7,8,8
W,7,11,7,8,7,2,10,2,3,10,10,8,7,11,1,7
G,5,11,4,6,3,8,7,5,3,9,6,7,3,9,8,8
T,5,7,6,5,3,4,13,4,5,12,9,4,2,12,2,5
Q,5,9,7,11,7,8,7,8,4,5,6,10,3,8,7,10
X,4,6,5,5,5,6,8,2,4,8,7,9,2,8,7,8
A,3,7,5,5,3,11,3,2,2,9,1,9,3,5,2,8
A,2,6,4,4,2,10,3,1,2,8,3,9,2,6,2,8
W,4,5,6,8,4,4,8,5,2,7,8,8,8,10,0,8
A,2,5,4,4,2,7,2,2,2,6,2,8,2,7,2,7
Y,3,8,5,6,2,5,10,1,3,8,11,8,1,11,0,8
L,4,9,5,6,4,6,4,4,8,3,2,5,4,6,2,5
Z,6,7,4,11,4,8,5,4,4,11,6,8,3,9,10,8
B,2,1,3,1,2,7,7,5,5,7,6,6,1,8,5,9
X,4,5,7,4,3,9,6,1,9,10,4,7,2,8,3,8
E,3,8,4,6,4,6,7,6,8,7,6,10,3,8,6,8
J,2,10,3,8,1,12,3,10,3,13,6,13,1,6,0,8
G,4,8,6,6,6,8,5,4,3,7,5,11,6,8,4,11
X,5,10,6,8,4,7,7,4,4,7,6,7,3,8,4,8
G,4,9,5,7,6,7,7,6,2,7,6,11,4,9,8,7
Q,4,5,5,7,3,9,7,8,6,6,6,10,3,8,5,9
Q,4,5,5,7,3,9,8,8,6,5,8,10,3,8,5,9
Y,8,13,6,7,4,9,7,5,6,9,5,4,4,10,6,5
U,4,9,4,6,2,8,5,14,5,6,14,8,3,9,0,8
I,6,12,6,7,4,9,8,2,5,11,5,6,3,9,6,11
A,2,7,4,5,3,11,2,2,2,9,2,9,2,6,2,7
W,8,8,8,6,5,3,10,3,4,11,10,8,8,10,2,6
Z,4,4,4,6,2,7,7,4,14,9,6,8,0,8,8,8
O,5,10,6,7,3,8,8,9,8,7,7,8,3,8,4,8
X,4,8,6,6,3,9,7,1,8,10,2,7,3,8,4,9
T,4,10,6,7,2,8,15,1,6,7,11,8,0,8,0,8
O,8,14,5,8,4,6,6,6,3,9,7,9,5,9,5,8
H,2,0,2,0,0,7,8,11,1,7,5,8,3,8,0,8
V,6,11,9,8,5,7,11,3,2,6,11,9,3,10,3,9
E,2,3,3,5,2,3,7,6,10,7,6,15,0,8,7,8
K,1,0,1,0,0,4,6,6,2,7,6,11,3,7,2,10
O,6,10,7,8,3,7,9,9,9,7,8,8,3,8,4,8
W,4,3,5,5,3,8,8,5,1,7,8,8,8,9,0,8
D,4,10,6,8,4,9,7,5,7,9,4,4,3,8,4,8
P,6,10,5,5,3,6,10,6,2,11,5,5,4,10,4,8
F,4,9,5,6,4,5,10,5,6,11,10,5,2,9,2,5
M,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8
Y,3,3,4,2,1,4,11,2,7,11,10,5,1,11,2,5
J,2,9,3,6,2,9,6,2,7,12,4,8,0,6,2,6
Q,3,6,4,5,2,9,6,9,6,7,5,9,3,8,4,8
M,8,9,11,7,7,6,7,3,5,9,9,9,8,6,2,8
K,5,9,6,7,7,7,9,5,4,7,5,7,5,7,8,11
T,4,9,5,7,3,5,14,1,6,9,10,7,0,8,0,8
Y,3,6,4,4,1,8,11,2,2,5,12,8,1,11,0,8
J,3,5,4,6,4,9,9,5,6,7,5,8,3,10,8,9
J,3,11,4,8,3,9,6,3,7,12,4,9,1,6,2,6
A,6,12,6,6,5,9,4,5,3,10,5,12,7,3,6,11
F,9,13,7,7,4,6,11,3,5,13,6,4,2,8,6,6
W,5,9,7,7,11,10,7,5,2,7,7,8,13,10,3,6
N,5,10,5,8,6,7,7,13,1,6,6,8,5,9,0,7
W,7,7,7,5,4,3,11,3,3,10,10,8,7,10,2,6
G,7,9,7,7,5,6,7,6,6,10,8,11,2,9,4,10
S,2,7,3,5,3,8,7,7,5,7,6,8,2,8,8,8
D,3,1,4,3,3,7,7,6,7,7,6,5,2,8,3,7
G,7,9,6,5,3,10,5,4,5,11,3,7,4,7,5,9
H,6,7,9,5,6,9,7,3,6,10,4,7,5,7,4,9
P,4,9,6,7,5,7,10,4,4,12,5,3,1,10,3,8
M,3,3,5,2,3,7,6,3,4,9,8,9,6,5,2,9
C,5,11,6,8,5,5,7,6,9,8,5,13,1,9,4,9
G,6,9,8,7,8,7,10,6,3,5,5,11,5,7,8,7
R,4,2,5,3,4,6,7,5,6,6,5,7,3,7,4,8
H,4,5,7,4,4,8,8,3,6,10,6,8,3,8,3,8
S,3,4,4,3,2,6,8,3,7,11,7,8,1,9,4,5
W,4,9,6,7,3,8,8,5,2,6,8,8,8,10,0,8
W,6,9,6,7,5,2,10,2,3,10,11,9,6,10,1,7
M,3,2,4,3,3,8,6,5,4,7,7,8,7,5,2,7
A,2,6,3,4,2,12,4,3,2,10,1,9,2,6,1,8
N,6,11,7,9,3,7,7,15,2,4,6,8,6,8,0,8
W,6,9,6,7,7,4,9,2,3,9,8,8,7,11,2,6
Y,6,9,8,7,7,9,8,6,4,5,9,7,3,8,10,5
H,8,13,8,7,6,9,6,4,5,11,3,7,7,8,5,8
Z,5,6,4,9,4,9,4,4,3,11,6,9,2,10,8,8
X,3,5,6,4,3,8,7,1,9,10,4,8,2,8,3,8
C,4,7,5,5,3,7,9,8,6,5,6,12,4,8,3,8
A,2,5,4,4,2,5,3,2,2,5,2,8,2,6,2,6
N,3,7,5,5,5,7,8,3,4,8,7,8,6,9,5,4
J,1,3,3,2,1,8,6,3,6,14,6,10,0,7,0,8
D,4,6,5,5,5,7,8,4,7,6,4,8,4,8,5,5
D,4,8,6,6,5,7,8,6,5,10,6,5,5,9,5,10
E,4,8,4,6,4,3,6,5,9,7,7,14,0,8,6,9
B,4,7,6,5,5,8,6,5,6,9,6,7,3,8,7,9
U,4,8,5,6,3,6,9,5,6,7,9,9,3,9,1,8
K,7,9,10,7,6,6,8,2,7,10,7,9,3,8,3,7
P,1,0,2,1,0,5,11,7,1,9,6,4,1,9,3,8
D,4,10,6,8,7,8,7,5,7,6,5,6,6,8,3,7
H,2,1,3,2,2,8,8,6,5,7,6,8,3,8,3,8
C,9,14,6,8,3,5,11,6,9,12,8,7,2,8,6,8
P,3,6,4,4,3,5,11,7,3,10,7,2,1,10,3,6
C,4,7,5,5,5,5,7,4,4,7,6,10,6,9,5,9
U,7,10,7,8,4,4,8,6,8,10,10,9,3,9,2,5
V,2,3,3,2,1,4,12,3,2,10,11,7,2,11,1,8
N,6,7,8,6,7,7,8,5,4,7,5,7,7,9,6,3
D,2,1,2,2,1,6,7,9,6,6,6,6,2,8,3,8
M,5,11,6,8,4,8,7,12,2,6,9,8,8,6,0,8
W,4,4,5,2,3,4,11,3,2,9,9,7,6,11,1,6
H,7,11,9,8,6,7,7,6,7,7,6,6,6,8,4,7
N,5,11,6,8,3,7,7,15,2,4,6,8,6,8,0,8
S,4,5,6,3,3,8,7,2,8,10,5,7,1,8,5,8
I,5,12,4,6,2,12,4,3,5,12,2,7,2,8,2,11
L,4,11,6,8,3,4,4,3,9,5,1,9,1,6,3,6
Q,2,3,3,4,3,8,9,6,1,5,8,10,3,9,5,10
O,3,5,4,3,3,8,7,7,5,7,6,8,2,8,3,8
F,4,8,4,6,3,1,11,3,4,11,11,8,0,8,2,7
M,11,15,11,8,6,7,10,4,5,4,5,10,10,11,2,7
S,3,6,4,4,2,8,7,5,8,5,6,8,0,8,9,8
M,6,8,8,6,8,8,9,6,4,7,7,8,6,9,6,7
U,4,6,5,4,3,5,9,5,6,6,9,10,3,9,1,7
L,5,12,5,6,3,9,3,3,5,11,4,11,2,8,5,9
M,5,8,7,6,6,9,7,6,5,6,7,4,10,7,4,5
L,1,0,1,1,0,1,1,5,5,0,1,6,0,8,0,8
I,1,3,1,2,0,7,7,1,7,7,6,9,0,8,2,8
U,6,8,7,6,3,3,9,6,8,12,12,9,3,9,1,7
I,1,2,1,4,1,7,7,1,7,7,6,8,0,8,2,8
X,5,11,7,8,4,5,8,2,8,10,11,10,3,8,4,5
A,4,10,6,7,2,5,4,3,2,5,1,7,3,7,3,7
M,7,7,10,5,7,5,7,3,5,10,10,10,11,8,4,8
J,3,9,4,7,1,12,2,10,4,13,6,13,1,6,0,8
M,4,8,5,6,5,7,6,6,5,7,7,10,7,5,2,8
A,4,7,5,5,5,7,7,7,4,6,6,9,2,8,7,4
M,4,7,7,5,4,4,6,4,4,11,11,11,6,6,2,7
I,1,5,2,4,1,7,7,0,7,13,6,8,0,8,1,8
P,4,9,6,7,4,10,7,3,5,12,4,5,3,8,4,9
C,3,6,4,4,1,6,7,6,9,6,6,13,1,7,4,8
Y,3,5,4,4,2,4,10,2,7,11,11,6,1,11,2,5
Y,6,9,6,4,3,7,6,4,4,9,9,5,3,10,4,4
O,4,4,6,6,3,6,8,9,8,7,6,6,3,8,4,8
Z,6,9,6,4,4,9,5,4,8,11,4,9,3,6,6,9
R,4,5,4,6,3,6,10,10,4,7,4,8,3,7,5,10
L,2,4,4,3,2,7,3,2,7,8,2,10,0,7,2,8
N,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8
P,3,7,5,5,3,7,10,3,5,13,5,3,1,10,3,9
Q,3,6,4,4,3,8,5,7,4,6,6,8,3,8,3,8
H,5,9,7,6,6,4,9,3,6,10,10,9,4,9,4,5
J,8,12,6,9,4,10,6,2,5,12,5,7,2,10,6,12
Z,7,14,7,8,5,6,7,2,9,12,7,8,4,6,7,6
S,4,6,5,5,5,10,7,5,5,7,6,9,4,8,8,11
H,8,10,8,5,4,9,8,4,5,8,3,5,6,7,4,8
Z,3,9,4,6,3,7,7,6,11,6,6,8,1,8,8,8
W,10,10,10,5,4,2,10,4,2,11,12,8,8,10,0,7
D,3,8,5,6,4,10,6,3,6,10,3,5,3,8,3,8
Y,6,8,8,11,12,8,9,4,2,7,8,9,4,11,9,8
S,3,6,4,4,4,8,10,5,4,8,5,6,3,9,7,7
F,5,10,8,8,8,10,6,2,5,9,5,6,5,9,4,7
I,2,2,1,3,1,7,7,1,8,7,6,8,0,8,3,8
U,6,10,9,8,12,8,8,4,4,6,7,8,10,5,9,8
C,5,11,4,6,3,5,9,4,4,9,7,9,3,9,8,8
F,1,1,2,2,1,6,10,4,4,10,8,5,1,9,3,7
C,3,7,4,5,2,5,8,6,7,11,8,13,1,9,3,8
I,2,9,2,7,2,7,7,0,9,7,6,8,0,8,3,8
U,8,9,9,7,5,5,6,6,9,8,6,8,5,9,5,2
K,5,5,5,8,2,4,6,9,2,7,7,11,4,7,3,11
X,5,9,6,6,5,7,6,3,5,6,6,9,3,7,10,9
C,4,7,5,5,2,5,8,7,10,6,7,11,1,7,4,8
X,3,1,4,2,2,8,7,3,9,6,6,8,3,8,6,8
F,1,3,3,2,1,8,9,2,5,13,5,5,1,9,2,9
M,10,13,12,8,6,10,2,2,2,9,3,9,7,2,1,9
W,2,1,4,3,2,9,10,3,2,6,9,7,5,11,0,8
I,2,7,3,5,1,8,6,0,7,13,6,9,0,8,1,8
H,2,1,3,2,2,7,7,6,6,7,6,8,3,8,4,8
S,4,4,5,6,3,7,5,6,9,6,6,11,0,9,9,8
F,4,8,6,9,7,7,8,4,4,7,6,7,5,9,10,8
Q,6,9,6,5,4,12,4,3,5,10,3,8,3,9,6,12
W,6,11,6,6,4,4,9,1,3,9,9,8,8,12,1,6
R,4,3,4,4,2,5,11,8,3,7,4,8,2,7,6,11
I,1,9,0,6,1,7,7,5,3,7,6,8,0,8,0,8
X,4,9,6,6,5,7,7,3,9,5,6,8,3,8,6,8
P,4,6,6,4,4,9,7,3,5,11,4,5,2,9,4,9
D,1,0,1,1,0,5,7,7,5,6,6,6,2,8,2,8
Z,2,4,4,3,2,7,8,2,9,11,7,7,1,8,6,6
B,2,3,3,2,2,8,7,2,5,10,5,7,2,8,3,9
B,2,5,4,3,3,9,7,2,6,10,5,6,2,8,5,9
Y,4,6,5,8,7,10,9,5,3,6,7,8,6,11,7,5
A,4,9,6,6,2,8,4,3,2,7,1,8,3,6,3,8
F,2,1,2,2,1,6,10,5,5,10,9,5,1,9,3,6
M,3,7,4,5,4,9,6,5,4,6,7,6,7,5,2,6
C,7,14,5,8,5,6,6,4,4,10,9,12,4,9,9,8
N,6,9,9,7,4,7,8,3,5,10,6,7,6,9,1,7
P,6,8,8,6,5,5,14,6,2,12,6,2,1,10,3,7
O,4,2,5,4,3,8,7,8,5,7,6,8,2,8,3,8
O,4,8,5,6,4,7,8,7,4,10,7,9,3,8,3,7
B,5,8,7,6,6,10,6,3,6,10,4,7,3,8,5,10
G,6,8,8,6,7,7,10,6,5,6,6,8,5,6,7,7
S,3,10,4,7,4,8,6,8,6,7,8,10,2,10,9,7
Z,6,10,6,5,3,9,5,2,8,11,5,9,2,9,5,9
H,8,11,11,8,6,10,6,4,7,11,1,7,6,8,5,10
Q,4,7,5,8,5,9,7,7,3,5,7,10,3,8,6,10
L,3,7,4,5,3,9,3,1,7,9,2,10,0,6,2,10
Z,4,6,5,4,3,9,5,3,8,11,4,9,2,7,6,10
Q,3,3,4,5,3,8,7,7,3,5,6,9,3,8,5,9
Q,2,5,3,6,3,9,10,6,1,3,7,11,2,10,5,11
Y,6,8,6,6,4,4,9,1,8,11,10,6,1,10,2,4
F,1,0,1,0,0,3,12,4,2,11,8,5,0,8,2,7
M,3,7,4,5,4,8,6,5,4,7,7,8,7,5,2,7
U,2,1,3,1,1,7,5,11,5,7,14,8,3,10,0,8
F,3,4,3,5,1,1,12,5,4,11,10,7,0,8,3,6
H,4,9,6,6,7,7,8,6,7,7,5,8,3,8,3,8
F,4,10,6,8,5,7,9,3,5,12,7,6,2,9,2,7
V,6,8,6,6,3,3,12,4,4,10,12,7,2,10,1,8
X,3,4,5,3,2,7,8,1,8,10,7,8,2,8,3,7
I,1,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8
E,5,9,4,4,2,9,6,4,4,11,4,10,3,8,6,11
E,6,9,8,8,9,7,7,5,4,7,7,10,9,12,11,12
E,4,8,6,6,4,5,8,5,8,11,10,9,3,8,5,5
I,4,6,6,4,3,6,7,3,8,7,6,11,0,8,4,8
X,3,9,4,7,3,8,7,4,4,7,6,9,2,8,4,8
H,5,7,7,5,5,7,7,2,6,10,6,8,3,8,3,7
U,2,1,3,2,1,7,9,5,6,7,10,9,3,9,0,8
H,8,10,11,8,9,5,8,3,7,10,10,9,7,11,6,6
S,5,8,7,6,8,8,5,4,4,9,6,10,4,8,9,9
O,4,10,5,8,3,8,7,9,7,7,6,8,3,8,4,8
Q,6,7,6,9,5,7,10,5,3,7,10,11,4,10,7,8
Z,6,11,7,8,6,7,8,3,13,9,6,8,0,8,8,7
N,3,5,4,4,3,7,8,5,5,7,7,6,5,9,2,6
Z,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8
I,0,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8
Q,5,11,5,6,4,11,5,4,6,10,4,7,4,9,6,12
U,9,12,8,7,3,6,5,4,6,3,8,6,6,7,2,7
T,6,8,6,6,3,5,14,5,6,12,9,2,2,12,2,4
C,4,9,5,6,2,6,8,7,10,4,6,14,1,7,4,9
I,1,5,2,3,1,7,7,0,7,13,6,8,0,8,1,8
Z,7,8,5,11,5,8,5,5,5,11,7,8,3,9,10,7
I,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
C,3,5,4,3,2,6,7,7,7,8,7,14,2,9,4,10
Y,9,10,8,8,4,3,10,4,7,13,12,6,2,11,3,6
V,7,9,5,4,3,9,7,6,4,7,9,7,7,12,3,7
R,5,11,6,8,4,6,9,10,5,6,5,8,3,8,6,11
Q,4,8,6,6,5,8,4,8,5,6,6,8,3,8,3,8
C,2,1,2,2,1,6,8,6,7,8,7,13,1,9,3,10
M,9,10,13,8,10,3,8,4,5,10,11,11,12,8,6,6
Q,5,12,5,6,4,10,5,4,6,11,4,8,3,8,8,11
Y,3,3,4,1,1,4,10,2,7,10,10,5,1,10,3,4
T,3,5,5,4,4,6,8,5,6,8,8,8,3,10,6,7
E,5,11,5,8,3,3,8,6,12,7,6,15,0,8,7,6
U,4,7,5,5,4,6,8,8,5,5,6,11,4,9,5,5
Z,4,7,6,5,3,7,7,2,10,11,6,8,1,8,6,8
R,5,9,6,7,6,7,7,4,6,6,5,7,3,7,5,9
W,6,11,8,8,4,5,8,5,2,7,8,8,9,9,0,8
I,5,7,7,8,6,9,8,5,5,6,5,8,3,8,9,7
W,2,3,3,1,2,5,10,4,2,9,8,7,5,10,1,6
F,3,7,6,5,5,8,8,1,4,9,6,6,3,10,4,4
K,3,5,5,4,3,5,8,1,6,10,8,10,3,8,3,8
G,4,8,4,6,3,7,7,6,7,11,6,10,2,10,4,9
Y,3,8,6,6,3,5,9,2,6,8,12,10,2,11,2,7
N,2,1,2,2,1,7,8,6,4,7,6,7,4,8,1,7
R,6,10,8,7,8,8,8,7,5,7,5,8,5,9,7,12
B,5,8,8,6,6,11,5,2,6,10,3,7,5,7,6,12
R,2,4,4,3,2,8,8,3,5,9,4,7,2,6,3,10
Z,1,3,2,1,1,8,7,1,8,11,6,8,1,8,5,8
K,6,11,8,8,6,4,7,2,7,10,10,12,3,8,4,7
F,3,5,5,3,2,7,9,2,6,13,6,5,4,9,3,7
N,3,2,4,4,3,7,9,5,4,7,7,7,5,9,2,6
L,2,0,2,1,0,2,1,6,5,0,2,4,0,8,0,8
P,2,1,2,2,1,5,11,5,3,10,7,3,0,9,3,6
G,4,4,5,7,2,7,7,8,8,6,6,11,1,8,5,11
Z,4,11,6,8,7,8,7,2,8,7,6,8,0,8,9,8
Q,1,0,1,0,0,8,7,6,2,6,6,9,2,8,2,8
X,1,1,2,1,0,7,7,4,4,7,6,8,2,8,4,7
Y,1,0,2,1,0,7,9,1,2,6,12,8,1,10,0,8
F,4,6,6,4,3,7,9,3,6,13,6,5,2,9,2,7
S,5,9,8,7,9,5,7,3,2,7,6,5,3,8,9,2
P,12,13,9,8,4,7,9,6,4,12,3,4,5,10,5,8
Z,4,10,5,8,2,7,7,4,14,10,6,8,0,8,8,8
N,6,10,8,5,3,12,4,5,3,13,1,7,5,7,0,8
Z,8,12,8,6,5,8,5,2,8,12,6,10,3,7,7,8
K,4,9,6,6,6,7,6,5,4,6,6,7,7,6,8,12
I,1,3,1,2,1,7,7,1,7,7,6,8,0,8,2,8
H,5,9,6,6,3,7,10,15,2,7,3,8,3,8,0,8
I,0,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8
D,7,12,7,7,5,9,6,4,7,10,5,7,6,8,8,6
O,3,7,5,5,6,8,6,5,2,7,6,8,8,9,5,10
J,2,8,3,6,2,14,4,4,4,13,2,8,0,7,0,8
D,2,3,2,2,1,7,7,6,6,7,6,4,2,8,3,6
T,3,7,5,5,3,6,11,4,6,8,11,8,2,12,1,7
V,3,9,5,6,1,7,8,4,3,7,14,8,3,9,0,8
B,4,10,5,8,7,6,8,8,5,7,5,7,2,8,7,9
J,2,8,3,6,1,13,3,8,4,14,3,11,0,7,0,8
P,5,10,7,7,4,8,9,2,7,14,5,4,3,9,4,9
O,6,10,7,7,5,8,6,8,6,10,6,9,4,8,5,5
G,6,11,6,8,4,6,7,7,6,9,8,11,3,7,6,8
Y,3,7,5,5,4,8,6,5,4,6,8,7,2,8,8,4
C,2,4,3,3,1,6,8,7,7,8,8,13,1,10,4,10
Y,7,11,8,8,5,3,9,2,7,9,11,7,2,11,3,5
Y,5,7,7,11,10,8,11,3,3,6,8,9,4,11,11,7
G,5,11,6,8,9,7,5,4,4,7,5,11,7,8,9,14
I,1,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8
E,3,2,3,4,3,7,7,5,8,7,6,8,2,8,6,9
T,2,4,2,2,1,8,12,3,5,6,10,8,2,11,1,8
C,4,9,5,7,2,5,7,7,10,7,7,12,1,8,4,8
H,11,14,10,8,5,8,7,4,5,9,6,7,7,10,5,9
K,4,10,6,8,7,7,7,5,4,7,6,7,7,6,8,13
Z,3,8,4,6,2,7,7,4,13,9,6,8,0,8,8,8
U,8,13,8,7,5,6,6,4,5,6,7,8,5,6,3,7
A,3,5,5,4,2,10,2,2,2,9,2,9,2,6,2,8
K,4,9,6,7,6,5,6,4,7,6,6,10,3,8,6,10
Q,3,6,4,5,3,9,8,7,4,5,7,10,2,8,4,9
M,3,6,4,4,2,8,6,11,1,6,9,8,7,6,0,8
F,6,10,8,7,4,5,13,2,6,13,6,2,1,10,2,6
H,4,5,5,4,4,7,7,6,6,7,6,8,3,8,3,7
Y,3,8,5,6,2,8,10,2,2,6,12,8,1,11,0,8
S,1,3,2,2,1,8,7,6,5,7,6,7,2,8,8,8
Y,3,9,5,6,1,7,10,3,2,7,13,8,2,11,0,8
K,4,8,6,6,5,8,5,1,6,10,4,9,3,8,4,10
Y,3,9,5,6,1,7,11,1,3,7,12,8,1,10,0,8
J,3,6,5,4,2,7,4,5,3,13,9,14,1,6,1,6
W,5,11,7,8,4,7,7,5,2,7,8,8,9,9,0,8
O,4,7,6,6,4,8,4,3,4,9,3,8,3,7,4,9
N,11,15,9,8,4,7,10,5,5,4,5,11,6,11,2,6
Q,7,12,7,6,5,9,6,4,7,11,5,8,4,8,9,10
E,4,10,4,7,3,3,8,6,11,7,5,15,0,8,7,7
P,3,4,5,6,5,8,9,3,2,7,8,6,5,10,5,5
K,6,8,8,6,4,3,9,3,7,11,11,11,4,7,4,5
Q,7,15,6,8,4,10,4,4,7,10,4,8,3,9,7,13
P,5,8,7,11,12,7,11,5,0,9,7,6,5,10,6,8
Z,3,8,4,6,3,9,6,5,10,7,5,6,1,7,8,8
S,6,10,7,8,4,8,7,4,8,11,6,7,2,8,5,8
P,3,7,3,4,2,3,13,7,2,11,6,3,1,10,4,8
U,4,4,4,6,2,7,5,13,5,7,14,8,3,9,0,8
Z,3,2,4,4,2,7,7,5,10,6,6,8,1,8,7,8
O,4,7,6,6,4,8,5,3,4,8,3,7,3,7,5,9
D,3,7,5,5,4,8,8,5,5,9,6,4,3,8,3,7
Q,4,6,4,8,5,8,5,6,4,9,5,9,3,8,5,7
R,4,8,4,5,2,5,12,8,4,7,3,9,3,7,6,11
A,3,5,6,4,3,11,1,2,2,9,2,9,3,7,3,9
A,2,2,4,4,2,8,2,2,2,8,2,8,2,6,3,7
G,3,4,4,2,2,6,7,5,5,10,7,9,2,9,4,9
N,5,10,6,8,6,6,7,8,5,7,5,6,3,7,3,8
E,7,10,9,8,6,7,8,2,10,11,6,9,2,8,5,8
V,3,1,5,3,1,7,12,3,3,6,11,9,2,10,1,8
Y,5,7,7,11,10,8,9,3,2,5,8,10,6,13,9,9
C,5,10,4,5,3,8,7,4,2,8,8,10,3,9,7,13
R,4,6,4,8,3,5,12,9,4,7,2,9,3,7,6,11
F,2,3,3,1,1,7,9,2,5,13,6,5,1,9,2,8
O,5,11,6,8,6,7,8,7,3,10,8,8,4,7,5,10
D,3,8,4,6,4,5,7,9,6,7,6,5,3,8,3,9
H,4,7,5,5,4,6,7,6,4,6,5,8,3,7,6,10
C,4,9,4,4,3,7,8,4,3,9,8,10,3,9,8,11
V,2,3,3,2,1,8,12,2,2,5,10,9,2,11,0,8
O,2,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8
X,3,4,5,3,2,7,7,1,8,10,6,8,2,8,3,8
M,8,10,12,8,9,10,6,2,5,9,5,7,8,7,2,8
L,1,4,3,2,1,6,4,1,7,8,2,11,0,7,2,9
E,4,7,6,5,3,8,7,2,9,11,5,8,2,8,5,10
O,4,8,6,6,2,8,5,8,8,7,4,9,3,8,4,8
Z,3,3,4,5,2,7,7,4,13,9,6,8,0,8,8,8
X,4,5,6,4,3,7,7,1,9,10,6,8,2,8,3,7
F,5,10,7,7,4,7,10,3,6,13,7,5,2,10,2,7
D,3,4,4,3,3,7,7,7,6,7,6,4,2,8,3,7
J,2,8,2,6,1,13,3,8,4,13,3,11,1,6,0,8
V,4,6,5,4,2,4,12,3,3,10,11,7,2,10,1,8
N,4,6,4,4,2,7,7,14,1,5,6,8,6,8,0,8
P,5,10,7,8,6,5,11,7,4,10,7,2,2,11,4,7
L,2,6,4,4,2,6,4,1,9,8,2,11,0,7,2,8
N,4,7,6,5,3,11,6,4,5,10,1,5,5,9,1,7
M,7,6,7,8,4,8,7,13,2,6,9,8,9,6,0,8
J,3,11,4,8,2,9,4,5,8,12,4,12,1,6,2,6
Z,2,6,3,4,1,7,7,3,13,9,6,8,0,8,8,8
V,6,9,5,7,3,3,11,3,4,10,12,8,2,10,1,8
X,3,2,4,3,2,7,7,3,9,6,6,8,3,8,6,7
C,4,10,6,8,5,8,6,9,6,8,6,11,4,9,4,8
M,6,5,8,4,7,9,8,5,4,7,6,7,9,8,5,3
Z,2,2,3,4,2,8,7,5,9,6,6,8,1,8,7,8
G,5,7,6,5,6,7,6,5,3,7,6,9,5,8,7,7
I,4,11,5,8,3,9,6,0,8,14,5,8,0,8,1,8
G,5,9,7,7,4,6,7,7,7,6,6,12,3,9,5,7
E,4,10,4,7,4,3,7,5,9,7,7,13,0,8,7,9
D,3,2,5,3,3,7,7,6,7,7,6,5,2,8,3,7
B,5,7,7,5,6,8,6,6,6,9,7,7,3,8,7,9
R,6,9,8,7,8,8,6,7,3,8,5,7,4,6,7,10
U,4,5,5,4,2,4,8,5,7,10,9,8,3,9,2,6
D,2,4,4,3,2,9,6,4,6,10,5,6,2,8,2,9
S,1,3,3,2,1,7,8,3,7,10,7,8,1,8,4,6
A,5,11,4,6,3,10,2,3,1,9,4,11,3,4,4,8
K,6,9,8,6,7,9,6,1,6,10,3,8,6,7,5,9
L,1,0,1,1,0,2,1,5,5,0,2,5,0,8,0,8
F,3,2,4,3,2,5,11,4,6,11,9,5,1,10,3,6
C,3,4,4,6,2,6,7,7,9,8,5,13,1,9,4,9
T,2,5,4,4,3,7,8,4,7,7,7,8,3,9,6,7
Q,3,7,4,6,2,8,8,8,5,5,8,9,3,7,6,10
M,3,2,4,3,4,8,6,6,4,7,7,8,7,6,2,7
M,11,14,11,8,6,9,11,6,4,4,6,9,11,12,3,7
C,3,4,4,3,2,4,7,5,7,10,8,13,1,9,3,8
O,3,5,4,4,3,7,7,7,4,9,5,7,2,8,3,7
B,5,10,7,8,7,8,8,4,5,7,6,7,7,7,6,9
U,5,8,5,6,2,7,4,14,6,7,14,8,3,9,0,8
O,4,7,6,5,4,7,5,8,5,8,5,11,3,8,4,8
O,4,6,6,5,4,7,5,5,4,8,4,7,3,7,4,8
C,5,10,6,7,4,5,7,7,10,7,6,11,2,8,5,11
K,6,10,9,7,7,4,8,2,7,10,10,11,4,7,4,6
E,3,2,4,4,3,7,7,5,8,7,5,9,2,8,6,10
C,3,7,4,5,2,3,7,5,6,9,8,14,1,8,3,9
H,3,6,5,4,6,9,7,4,4,6,6,8,7,8,6,7
A,4,9,6,6,2,6,4,3,1,6,1,8,3,7,2,7
R,4,7,4,4,2,5,10,8,3,7,4,8,3,7,6,11
E,3,6,4,4,4,7,7,5,8,7,7,9,3,8,6,9
S,6,9,7,7,5,7,8,3,7,10,7,8,2,8,5,6
E,2,7,3,5,3,3,8,5,9,7,6,13,0,8,6,9
B,5,9,8,8,9,7,7,5,4,7,6,8,7,9,9,6
S,7,12,7,7,3,7,7,5,3,13,9,10,3,10,3,8
Z,2,5,4,4,2,7,8,2,9,11,7,7,1,8,5,7
H,6,11,9,9,8,10,6,3,6,10,3,7,6,8,5,10
V,8,11,8,8,5,3,12,2,3,9,10,7,5,11,2,6
K,5,8,8,6,4,4,8,3,7,10,10,11,4,8,4,6
O,3,7,5,5,3,8,6,7,4,7,5,8,3,8,3,7
K,4,9,6,7,8,7,7,3,4,6,6,8,7,8,8,7
A,9,15,7,8,4,8,1,2,2,9,4,12,4,5,5,6
M,7,10,9,8,11,8,6,6,5,7,5,8,6,9,8,8
S,4,6,5,4,2,8,7,3,8,11,6,7,2,8,5,8
X,2,3,3,4,1,7,7,4,4,7,6,8,2,8,4,8
C,2,6,3,4,1,4,7,7,9,7,6,11,1,9,4,9
E,4,9,4,7,3,3,7,6,10,7,6,14,0,8,8,7
B,4,9,4,7,3,6,6,9,7,6,6,7,2,8,9,10
W,2,1,3,2,2,8,10,2,2,6,9,8,5,11,0,8
K,4,10,6,8,6,7,7,5,4,7,5,8,4,7,9,10
Z,4,9,4,6,2,7,7,4,14,9,6,8,0,8,8,8
C,6,10,6,8,4,4,9,7,7,13,10,8,2,10,2,7
A,3,4,6,6,2,7,5,3,1,7,0,8,2,7,2,8
K,2,3,4,2,2,5,9,1,6,10,8,9,3,8,2,8
P,4,7,6,5,4,7,10,2,6,13,6,4,0,10,3,9
E,5,9,7,7,8,6,7,3,6,7,7,11,4,10,8,7
V,6,12,6,7,3,5,11,4,3,10,8,5,4,11,2,8
V,4,8,5,6,3,7,9,4,1,7,13,8,2,10,0,8
S,3,9,4,7,2,7,6,6,10,5,7,10,0,9,9,8
S,4,10,5,8,4,7,7,5,8,5,6,9,0,8,8,8
X,4,8,7,6,3,8,6,1,8,10,4,8,3,8,3,8
K,3,4,4,6,2,3,7,8,3,7,6,11,4,8,2,11
I,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7
P,3,4,3,5,2,4,11,8,3,10,6,4,1,10,3,8
L,3,7,4,5,2,8,4,1,7,9,3,9,1,6,2,9
L,3,8,5,6,6,7,7,3,5,6,7,10,6,11,6,6
F,5,10,5,6,4,7,10,2,5,11,6,5,4,10,7,7
R,2,1,2,1,1,6,9,8,3,7,5,7,2,7,4,10
H,4,4,6,3,3,8,7,3,6,10,6,8,3,8,3,8
B,2,3,2,1,2,7,8,5,5,7,5,6,1,8,5,9
Q,2,4,3,5,3,7,8,5,1,7,8,10,2,9,4,8
Y,2,5,4,4,2,6,10,1,7,7,11,9,1,11,2,9
V,4,7,6,6,6,6,8,5,5,8,6,8,6,9,8,7
H,4,8,4,5,2,7,8,15,1,7,5,8,3,8,0,8
I,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8
N,2,3,2,2,2,7,9,6,3,7,6,8,4,8,1,7
N,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8
U,4,9,6,6,3,4,9,7,7,10,11,9,3,9,1,8
U,4,7,5,5,2,7,3,14,6,7,13,8,3,9,0,8
F,2,3,3,1,1,6,12,3,4,13,7,3,1,9,1,7
J,1,1,2,2,1,10,6,3,5,12,4,10,1,7,1,7
L,6,11,8,9,6,10,3,1,7,10,2,10,3,6,4,10
P,3,4,3,6,2,3,14,8,2,12,7,3,0,10,4,8
W,11,11,10,6,4,6,11,2,3,7,10,6,8,12,1,7
W,9,10,13,9,15,8,7,5,5,7,6,8,12,10,10,4
P,2,4,4,3,2,6,10,3,4,12,5,3,1,10,2,8
I,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
T,6,9,5,4,2,5,10,2,7,13,7,5,2,8,4,4
Z,5,8,7,6,4,8,7,2,10,12,6,7,1,7,6,7
F,4,8,6,6,4,6,10,2,5,13,7,5,2,10,2,7
I,2,2,2,4,2,7,7,1,8,7,6,8,0,8,3,8
H,6,10,7,6,5,8,7,3,5,10,7,7,7,11,5,8
S,3,6,5,4,5,8,7,4,3,8,5,8,4,9,10,10
N,8,12,10,6,4,3,11,6,3,14,12,9,6,10,0,8
E,6,11,4,6,3,7,8,4,4,10,5,9,3,9,7,10
U,5,10,5,7,4,7,5,13,5,7,12,8,3,9,0,8
K,1,0,2,1,0,5,7,7,1,7,6,11,3,8,2,11
O,2,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8
X,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8
V,2,1,3,3,1,7,12,2,2,7,11,8,2,10,1,8
X,1,0,1,0,0,7,7,3,5,7,6,8,2,8,4,8
R,3,7,4,5,4,6,8,8,4,7,5,7,2,7,4,11
A,6,8,8,6,7,6,7,7,5,7,5,9,4,8,11,3
L,2,4,3,3,1,4,4,4,7,2,2,6,0,7,1,6
J,1,1,2,2,0,10,6,2,5,12,4,9,0,7,1,7
F,4,9,7,7,7,5,8,2,4,10,8,7,8,10,4,4
C,3,9,4,7,2,5,7,7,9,6,6,13,1,7,4,8
C,6,9,8,8,8,5,9,4,6,6,6,11,4,11,8,9
X,6,11,8,8,7,8,6,3,6,6,7,7,4,8,10,10
R,5,7,7,5,4,10,6,2,6,11,2,7,3,6,3,10
M,7,8,9,7,10,7,9,4,4,7,6,7,10,7,6,5
E,2,0,2,1,1,5,8,5,8,7,6,12,0,8,7,9
W,6,9,6,4,3,2,8,2,3,9,11,9,7,9,1,6
Y,9,14,8,8,4,6,8,4,4,10,9,5,5,10,5,4
G,4,6,5,4,3,5,9,5,5,10,8,7,2,8,5,9
P,6,11,9,8,7,8,10,4,4,12,5,3,1,10,3,8
B,5,9,6,7,5,6,8,9,8,7,5,7,2,8,9,9
V,4,8,6,6,3,5,11,3,4,8,12,9,2,10,1,8
L,4,8,5,6,2,4,5,2,9,5,1,9,1,7,3,6
V,3,7,5,5,5,7,7,4,2,8,8,8,5,10,4,8
L,3,10,5,8,3,9,3,1,8,10,2,10,0,6,3,10
X,4,3,4,5,1,7,7,4,4,7,6,8,3,8,4,8
N,5,8,7,6,5,7,9,5,5,7,6,6,7,8,3,7
Y,7,10,7,8,4,5,9,2,9,10,11,5,4,9,6,3
X,5,7,7,5,3,7,7,1,8,10,5,8,3,8,3,7
J,3,5,4,8,2,11,3,11,3,12,8,13,1,6,0,8
E,1,3,3,2,1,6,7,2,7,11,7,9,1,8,4,9
C,5,7,5,5,3,6,8,6,8,12,8,12,2,10,3,7
L,3,10,5,7,3,6,4,1,8,7,1,9,0,6,2,8
R,4,10,5,8,3,5,9,9,4,7,6,8,3,8,6,11
F,5,7,7,5,3,7,10,2,7,14,5,4,2,9,4,7
D,4,7,4,5,2,5,8,10,9,8,7,5,3,8,4,8
K,5,11,5,8,5,3,7,7,3,7,6,11,3,8,3,11
R,2,1,3,3,2,6,7,4,5,6,5,7,2,6,4,8
T,2,6,3,4,2,8,12,2,8,5,11,9,1,11,1,8
Q,5,7,6,9,7,9,8,7,2,5,7,10,3,8,6,10
F,5,11,7,8,5,7,9,1,6,13,6,5,1,10,2,8
T,3,4,4,2,1,5,12,2,7,11,9,4,1,10,2,5
X,3,7,4,4,1,7,7,5,4,7,6,8,3,8,4,8
U,6,10,7,8,4,4,8,6,8,9,9,9,3,10,2,5
B,4,7,6,5,4,7,8,6,5,9,6,6,3,8,6,8
P,3,9,4,6,2,5,10,10,3,9,6,4,2,10,4,8
E,3,8,3,6,2,3,7,6,11,7,6,15,0,8,6,7
H,5,9,7,7,7,7,7,5,7,7,6,7,6,8,4,7
H,5,4,6,6,2,7,7,15,0,7,6,8,3,8,0,8
S,5,5,6,7,3,7,6,6,10,5,6,10,0,9,9,8
U,7,9,7,7,4,4,7,6,8,9,9,9,3,9,3,5
K,4,11,4,8,2,3,7,8,2,6,5,11,4,8,2,11
K,1,0,1,0,0,4,6,5,2,7,6,10,2,7,1,10
R,3,8,4,5,2,6,10,9,4,7,4,8,3,7,5,11
P,6,11,8,8,5,7,12,8,3,11,4,2,1,10,5,7
M,6,7,9,6,9,6,8,5,3,6,5,8,12,8,5,8
Y,5,6,7,9,10,8,4,5,3,7,8,9,8,8,6,8
H,4,7,5,5,4,6,6,6,4,6,5,8,2,7,6,9
X,6,11,9,8,5,8,6,1,9,10,4,8,3,8,4,8
P,8,11,6,6,3,9,7,6,4,13,3,6,5,9,4,8
B,4,9,6,6,6,8,8,4,5,10,5,6,3,8,5,10
I,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
U,3,4,4,3,2,4,8,5,7,9,8,9,3,10,2,5
I,2,9,3,7,4,7,7,0,7,7,6,8,0,8,3,8
Z,5,5,6,8,3,7,7,4,15,9,6,8,0,8,9,8
Z,4,5,5,8,2,7,7,4,14,10,6,8,0,8,8,8
K,2,3,4,2,2,6,7,2,6,10,7,10,3,8,2,7
H,3,7,4,5,4,7,7,7,6,7,7,8,3,8,3,7
B,5,7,8,6,8,7,7,5,4,7,6,8,6,9,8,6
G,4,4,5,6,2,8,5,7,9,8,4,12,1,9,5,10
B,2,4,4,2,2,9,7,2,5,10,5,6,2,8,4,9
Z,2,7,3,5,1,7,7,3,12,9,6,8,0,8,8,8
L,5,11,6,8,5,4,4,4,8,2,1,6,1,6,1,5
X,6,12,6,6,3,10,6,3,8,9,2,6,4,6,4,10
T,2,7,3,5,2,6,13,0,5,8,10,8,0,8,0,8
C,4,4,6,7,2,5,7,7,10,7,6,12,1,9,4,9
B,3,6,5,5,5,6,9,6,6,8,7,7,5,9,7,8
L,2,8,3,6,1,0,1,5,6,0,0,7,0,8,0,8
U,6,10,9,8,11,8,5,4,4,7,7,7,11,8,6,10
V,2,6,4,4,2,8,12,2,2,5,10,9,2,11,0,9
O,6,10,8,8,5,7,7,9,4,7,6,8,3,8,4,8
K,2,1,2,2,2,5,7,4,6,7,6,10,3,8,3,10
Z,2,4,3,3,2,7,7,5,9,6,6,8,2,8,7,8
N,5,10,5,8,5,7,7,13,1,6,6,8,6,9,0,6
R,2,0,2,1,1,6,10,8,2,7,5,8,2,7,4,10
T,4,9,6,7,5,6,11,2,7,8,11,8,1,12,1,8
P,3,8,5,6,4,6,9,3,6,9,8,5,4,10,4,7
S,1,3,3,2,1,8,7,3,6,10,6,7,1,8,5,8
A,5,9,5,5,3,10,2,4,2,11,5,12,5,3,5,10
Y,2,1,3,3,1,8,11,1,7,6,11,7,1,11,2,7
A,4,11,7,8,2,7,5,3,1,6,1,8,3,7,2,7
D,6,12,6,6,5,8,6,3,6,10,5,7,5,8,8,6
R,4,8,6,6,7,5,6,3,4,7,5,9,6,10,7,5
U,5,7,7,5,8,7,7,4,4,7,7,8,10,9,6,8
L,3,3,3,5,1,0,1,6,6,0,0,6,0,8,0,8
H,5,7,8,5,6,9,7,3,7,10,3,7,5,8,4,9
L,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8
T,6,7,6,5,4,7,10,2,7,11,9,5,3,11,4,4
V,3,8,4,6,3,7,11,2,3,6,11,9,2,10,1,9
X,3,4,5,3,2,7,8,4,9,6,6,7,4,9,7,7
D,4,8,4,6,3,5,8,10,9,8,7,6,3,8,4,8
M,2,1,3,2,2,8,6,6,3,7,7,9,6,5,1,8
D,4,6,5,4,4,9,7,4,6,10,4,5,3,8,3,8
D,4,11,6,8,7,9,7,4,5,10,4,6,4,7,3,8
H,3,3,5,2,2,5,9,3,5,10,9,8,3,9,3,6
R,4,9,6,7,5,10,7,3,6,10,3,7,3,6,4,10
U,2,3,3,2,1,4,8,4,6,11,10,9,3,9,1,7
K,3,5,5,4,3,5,7,4,8,7,6,11,3,8,5,9
X,8,11,8,6,4,4,10,4,8,11,10,9,4,9,3,5
C,4,7,4,5,2,4,7,6,7,9,8,15,1,8,3,8
J,2,8,3,6,2,14,4,4,4,12,2,9,0,7,0,8
D,3,7,4,5,4,10,6,3,6,10,4,7,3,8,3,9
N,1,0,1,1,0,7,7,10,0,5,6,8,4,8,0,8
U,4,5,5,4,2,3,8,4,6,11,11,9,3,9,1,6
S,4,9,4,5,2,7,8,4,3,13,8,8,2,10,3,8
C,2,1,2,2,1,7,8,7,6,8,7,11,2,9,3,10
Q,4,8,5,9,5,8,6,7,4,9,7,9,3,9,6,9
Q,5,8,7,9,6,8,5,8,5,6,6,9,3,8,6,10
B,5,9,5,7,7,6,7,8,6,6,6,7,2,8,7,10
Q,4,9,5,10,5,9,8,8,2,5,7,11,4,9,6,8
K,3,1,4,2,3,6,7,4,7,7,6,10,6,8,4,9
I,2,7,3,5,1,9,6,0,7,13,5,8,0,8,1,8
M,3,3,4,5,3,7,7,12,1,8,9,8,8,6,0,8
O,3,6,4,4,3,7,7,7,5,7,6,8,2,8,3,7
F,3,7,5,5,3,9,8,2,6,13,4,5,2,10,3,9
B,4,8,6,6,4,8,8,4,7,10,5,6,2,8,6,10
Q,2,4,3,5,3,8,7,6,3,8,6,9,2,9,3,9
T,2,6,3,4,1,7,12,0,5,7,10,8,0,8,0,8
D,1,0,2,0,0,6,7,7,6,7,6,6,2,8,3,8
F,3,7,3,5,1,1,11,4,6,11,11,9,0,8,2,6
W,7,9,7,7,6,5,10,3,2,9,7,7,9,13,3,4
S,4,7,5,5,6,9,4,5,3,8,6,9,3,8,10,11
X,5,11,8,8,6,7,8,3,9,5,5,7,4,9,8,7
M,5,9,7,6,7,7,7,5,5,6,7,9,8,6,2,8
E,3,6,5,4,4,6,7,3,7,11,8,9,3,8,4,8
P,9,14,8,8,4,9,8,7,5,13,3,5,5,10,4,8
W,4,7,6,5,4,10,8,4,1,7,9,8,7,10,0,8
B,1,1,2,1,1,7,7,7,5,6,6,7,1,8,6,8
B,2,5,4,4,3,8,7,3,5,10,5,7,2,8,4,10
D,7,12,6,6,4,8,5,4,6,8,4,7,5,6,6,10
P,8,12,6,7,3,8,8,5,4,12,4,6,5,9,4,8
J,3,5,4,8,1,11,2,11,3,13,7,14,1,6,0,8
U,5,5,6,7,2,8,4,14,6,6,15,8,3,9,0,8
M,4,5,6,3,4,8,6,6,5,6,7,8,9,6,3,7
B,5,9,7,7,10,9,9,5,4,7,8,6,6,10,10,10
P,3,5,5,7,7,8,10,4,0,8,7,6,7,10,4,7
V,3,5,4,4,2,5,12,2,3,8,11,7,2,11,1,8
H,6,7,9,5,6,10,6,3,6,10,3,7,3,7,3,9
B,5,11,7,9,8,8,7,5,6,6,6,5,3,8,6,10
A,2,5,4,4,3,9,8,3,4,6,7,8,4,8,4,6
T,4,7,5,5,1,4,14,4,7,12,9,3,0,10,1,5
G,1,0,2,1,0,8,6,6,5,6,5,9,1,7,5,10
S,5,11,6,8,3,7,6,6,9,4,6,9,0,9,9,8
Y,4,6,6,8,1,8,12,2,3,7,12,8,1,10,0,8
V,4,5,5,4,2,4,12,3,3,10,11,7,2,10,1,8
S,5,8,6,6,4,8,8,4,7,10,3,7,2,6,5,9
H,9,15,8,8,5,6,8,4,6,9,8,9,6,8,6,10
O,6,9,6,7,5,7,7,7,5,10,6,10,5,8,4,6
U,10,14,9,8,5,6,6,5,5,6,8,9,7,7,4,10
N,3,7,5,5,4,7,6,6,5,7,5,9,4,8,3,7
N,3,3,4,5,2,7,7,14,2,5,6,8,5,8,0,8
P,4,10,6,7,4,6,9,7,5,9,7,3,2,10,4,7
T,5,10,5,7,3,4,14,5,5,12,9,3,1,11,1,5
J,2,3,4,4,2,10,6,2,6,12,4,9,1,6,1,7
Q,4,6,5,8,4,8,7,6,3,8,8,10,3,8,6,8
J,5,10,5,8,4,9,8,2,4,11,6,7,2,10,6,12
K,8,11,11,8,6,8,6,2,8,11,4,8,4,8,5,10
X,4,9,6,7,4,7,7,4,9,6,7,11,4,6,8,9
I,4,11,5,8,3,7,7,0,8,14,6,8,0,8,1,8
Z,5,8,7,6,6,9,7,5,4,7,5,7,4,8,10,5
Q,5,5,7,8,3,8,8,7,7,6,8,9,3,7,6,9
T,6,10,5,5,3,7,9,2,7,12,7,6,2,9,4,6
D,5,10,7,8,10,9,8,5,5,7,6,6,4,7,7,5
E,3,5,4,4,3,7,7,5,7,7,6,9,2,8,6,10
A,3,8,5,5,2,9,6,3,1,7,0,8,2,7,1,8
D,4,7,6,5,4,8,7,6,7,10,5,5,3,8,3,8
D,5,9,5,5,3,10,5,4,5,11,3,7,4,7,4,10
Q,5,9,7,7,6,8,3,8,4,6,6,7,3,8,3,8
D,3,3,3,4,2,5,7,9,8,6,5,5,3,8,4,8
I,1,2,1,3,1,7,7,1,8,7,6,8,0,8,3,8
S,2,4,3,3,1,7,8,3,7,10,7,7,1,9,5,7
L,4,10,6,9,5,8,6,4,4,6,7,8,3,9,8,10
I,1,1,1,1,0,7,7,2,6,7,6,8,0,8,2,8
Q,2,3,3,4,2,8,9,6,1,5,7,10,2,10,5,10
O,4,4,5,6,2,8,9,8,8,6,8,9,3,8,4,8
F,3,2,4,4,2,5,10,4,6,11,9,5,1,10,3,6
R,4,8,5,6,3,6,9,9,4,6,5,8,3,8,6,11
P,5,10,7,8,7,6,9,5,5,9,7,3,2,10,4,6
J,6,10,7,8,3,9,3,6,6,15,7,15,1,6,1,7
Q,1,0,1,1,0,8,7,6,2,6,6,8,2,8,2,8
J,2,8,3,6,1,11,3,10,3,12,8,13,1,6,0,8
L,2,5,4,3,2,7,3,1,7,8,2,10,0,7,2,8
Z,4,7,6,10,6,10,5,4,4,8,3,7,2,6,7,8
D,5,10,5,8,5,6,7,9,8,6,4,6,3,8,3,8
X,8,12,9,7,5,6,8,2,8,11,7,8,5,8,4,6
N,5,11,5,8,3,7,7,15,2,4,6,8,6,8,0,8
Q,4,11,6,9,3,9,8,9,6,5,7,10,3,8,5,9
G,6,10,8,7,6,6,6,6,6,6,6,10,4,8,5,9
A,3,8,4,6,3,8,3,2,2,7,1,8,2,6,2,7
P,1,0,2,0,0,5,11,6,1,9,6,5,1,9,3,8
T,5,9,5,7,3,6,11,2,8,11,9,4,1,11,3,4
N,4,9,6,6,4,7,9,2,5,9,5,6,5,8,1,7
Q,3,6,4,7,4,9,9,7,2,4,7,11,3,10,5,10
Q,3,7,4,9,5,8,5,8,4,6,5,8,3,7,5,9
P,8,10,7,5,3,7,9,5,4,12,4,5,4,9,4,8
I,1,5,1,3,1,7,7,1,8,7,6,8,0,8,3,8
C,5,4,6,7,2,6,9,7,11,5,7,12,1,6,4,8
M,8,10,10,6,5,9,3,3,2,9,3,10,8,1,2,9
P,4,11,6,9,6,7,9,5,5,9,8,3,2,10,4,6
N,10,13,11,7,5,7,7,3,5,13,9,9,6,8,0,8
L,1,3,2,1,1,6,4,1,7,7,2,9,0,7,2,8
I,1,5,2,4,1,7,9,0,7,13,6,6,0,8,1,7
I,2,6,4,4,3,11,6,1,5,9,4,5,1,8,5,9
P,2,1,3,1,1,5,11,8,2,9,5,4,1,9,3,8
N,11,13,9,7,4,6,9,4,7,3,4,11,6,10,2,7
V,4,8,6,7,7,8,8,5,4,7,6,8,7,7,10,4
Q,4,8,5,6,5,8,4,7,4,6,6,11,2,8,3,9
S,5,9,4,5,2,9,3,2,5,8,1,7,3,7,4,10
Q,3,7,4,9,5,9,8,7,2,4,8,11,3,9,6,10
C,4,8,5,6,5,5,7,4,5,7,7,9,7,9,5,9
Z,3,8,4,6,2,7,7,4,13,9,6,8,0,8,8,8
Q,3,5,5,7,5,8,12,3,3,5,8,11,3,13,4,9
W,5,5,8,8,4,9,7,5,2,6,8,8,9,9,0,8
G,6,10,6,8,4,6,7,6,7,10,8,11,2,9,5,9
X,3,6,5,5,4,5,8,2,5,8,6,9,3,6,7,8
C,4,4,5,6,2,6,6,7,10,9,6,14,1,9,4,8
P,4,9,4,4,3,9,8,4,3,11,5,5,4,11,5,7
X,5,11,9,8,8,9,6,3,6,7,5,5,6,10,8,9
D,6,9,5,5,4,6,7,5,6,9,6,7,5,9,6,5
G,3,8,5,6,4,8,7,7,5,6,6,9,2,7,5,11
G,3,4,4,2,2,6,7,5,6,6,6,9,2,8,3,8
H,2,4,4,2,2,7,7,3,5,10,6,8,3,8,2,7
J,4,8,6,6,3,8,4,5,4,14,6,13,1,6,0,7
P,4,10,5,8,5,6,9,6,5,9,7,4,5,10,4,7
D,4,6,6,5,5,6,8,5,7,6,5,7,4,7,5,5
G,2,0,2,1,1,8,7,6,5,6,6,9,1,7,5,10
Z,2,5,3,3,2,7,7,5,9,6,6,8,2,8,7,8
T,5,8,5,6,4,5,12,4,5,12,9,5,2,12,1,5
W,4,6,4,4,4,3,10,2,2,10,9,7,5,11,2,6
T,7,12,6,6,2,5,11,3,7,12,8,4,2,9,4,3
I,1,6,0,4,1,7,7,5,3,7,6,8,0,8,0,8
C,3,6,5,4,4,6,6,3,4,8,6,11,5,9,3,8
Q,2,2,2,3,2,8,8,5,3,8,7,8,2,9,2,7
M,1,0,1,0,1,8,6,8,0,6,8,8,5,7,0,8
T,2,8,3,6,2,6,12,0,6,8,10,8,0,8,0,8
A,6,10,6,5,4,12,3,6,2,12,2,10,5,3,3,10
B,3,6,4,4,3,6,6,8,6,6,6,7,2,8,9,10
X,6,9,9,7,5,7,7,0,8,10,7,9,2,8,3,7
O,7,11,9,8,6,7,7,9,5,6,7,11,5,8,5,7
W,6,7,6,5,5,6,11,4,2,8,7,6,9,12,4,5
F,6,9,9,7,4,7,10,2,8,14,6,3,1,10,3,8
E,0,0,1,1,0,5,7,5,7,7,6,12,0,8,6,10
Q,4,6,4,8,5,8,6,6,2,8,6,10,3,8,5,9
X,5,10,8,7,6,7,8,2,6,7,6,7,6,9,8,7
S,2,2,2,3,2,8,7,6,5,8,6,7,2,8,8,8
V,7,10,7,6,3,6,9,4,3,9,8,5,5,12,2,8
R,3,6,4,4,3,7,7,4,5,7,6,6,3,7,4,9
S,4,9,4,7,4,7,7,5,8,5,6,10,1,10,9,9
F,6,8,8,10,9,7,9,5,5,8,6,7,4,9,8,8
E,5,11,7,8,7,6,7,7,10,6,4,11,3,8,6,8
J,2,6,3,4,2,10,6,1,8,12,3,7,0,7,0,8
M,5,7,8,5,5,8,6,2,5,9,6,8,8,6,2,8
H,4,8,4,5,2,7,9,15,1,7,4,8,3,8,0,8
N,8,11,11,8,5,7,8,3,5,10,6,7,7,7,2,7
H,4,8,4,6,4,7,7,12,1,7,6,8,3,8,0,8
F,3,6,4,4,3,5,10,3,4,12,8,6,2,10,1,7
F,5,8,7,6,3,7,10,2,7,14,5,4,1,10,2,8
Z,3,7,4,5,2,7,7,3,12,8,6,8,0,8,7,7
I,5,9,6,6,4,5,7,3,7,7,6,12,0,8,4,9
G,5,7,5,5,3,6,7,6,6,9,7,10,2,8,4,9
J,1,6,2,4,1,12,3,9,3,13,6,12,1,6,0,8
G,3,8,4,6,4,8,7,7,5,6,6,8,2,8,5,11
G,4,8,5,6,6,8,7,4,2,6,6,8,7,8,7,11
B,1,0,2,0,1,7,7,6,4,6,6,7,1,8,6,8
M,6,10,9,8,8,8,7,2,4,9,7,8,8,6,2,8
Z,5,9,6,4,3,10,3,3,7,12,4,10,2,9,4,11
R,3,8,4,6,4,5,10,7,3,7,3,9,2,6,4,11
I,1,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8
Q,3,4,4,5,3,8,8,7,2,5,7,10,3,9,5,10
W,4,6,6,4,2,5,8,4,1,7,9,8,8,9,0,8
Z,7,11,7,6,4,8,6,2,8,11,6,9,3,9,5,8
F,3,3,4,4,1,2,12,5,5,12,10,8,0,8,2,6
A,3,4,5,3,2,10,1,3,2,10,2,10,2,6,3,8
S,4,11,5,8,5,8,7,8,5,7,6,8,2,8,8,8
W,9,10,9,5,4,3,10,3,2,10,11,8,9,12,1,6
W,4,6,5,4,4,4,10,3,2,9,8,7,6,11,1,7
I,1,2,1,4,1,7,7,1,7,7,6,9,0,8,3,8
W,8,12,8,6,6,9,9,4,4,6,9,6,10,9,3,6
S,4,9,5,6,3,10,6,3,6,10,4,8,2,9,5,10
E,4,8,6,6,4,7,7,3,8,11,7,9,3,8,5,8
A,3,7,5,5,2,9,3,3,3,7,2,8,3,6,3,8
Z,4,11,5,8,6,6,8,5,8,8,8,9,1,10,7,8
S,5,9,5,5,2,10,5,4,4,13,5,8,2,9,2,8
B,2,3,3,2,2,8,8,5,6,7,5,5,2,8,7,8
I,1,4,2,2,1,7,7,1,7,13,6,8,0,8,1,8
U,3,5,4,3,2,5,8,5,7,10,9,9,3,9,2,6
Q,6,9,7,11,7,8,7,7,4,9,7,9,3,8,6,8
J,3,11,4,9,3,9,7,2,7,11,3,7,1,6,2,6
Y,2,6,4,4,1,7,9,2,2,7,12,8,2,11,0,8
H,3,6,4,4,5,9,9,4,3,6,7,7,6,9,5,6
P,5,9,6,6,3,5,10,10,5,8,5,5,2,10,4,8
S,5,11,7,8,7,8,7,7,5,6,6,8,3,8,9,7
N,5,8,7,6,4,6,9,2,4,9,8,8,6,7,2,7
I,4,7,6,8,6,8,9,4,5,7,7,9,3,6,8,7
G,4,5,5,3,3,7,6,6,7,7,6,10,2,9,3,8
D,5,9,7,6,6,8,7,5,6,10,6,5,3,8,4,9
W,3,4,5,3,3,9,11,3,2,5,9,8,6,11,1,8
A,5,10,5,5,3,11,4,4,2,11,3,10,6,4,4,10
Y,9,13,8,7,5,8,7,4,6,9,5,5,4,9,6,5
E,2,5,4,3,2,6,8,2,7,11,7,9,2,8,4,8
Z,4,10,5,7,4,7,8,6,11,7,6,9,1,9,8,7
V,4,8,6,6,7,7,7,4,1,8,7,9,7,10,4,8
O,4,8,4,6,4,6,8,7,4,9,8,8,3,8,2,7
E,3,7,4,5,5,6,8,3,5,6,7,11,4,11,8,8
R,3,10,4,7,3,6,9,10,5,7,5,8,3,8,5,11
Q,4,6,5,7,5,9,9,6,2,4,7,11,3,9,6,10
F,4,9,5,6,3,8,9,2,6,14,5,4,2,10,3,8
V,4,7,4,5,2,3,12,5,4,12,11,7,3,10,1,8
G,7,10,7,7,5,7,6,7,8,11,6,11,2,11,5,9
G,5,6,6,6,7,7,9,6,2,7,7,7,9,12,9,7
K,5,7,8,5,4,7,6,2,7,10,5,10,4,7,5,9
U,4,5,5,4,3,6,9,5,7,6,10,9,3,9,1,8
K,6,10,8,7,6,8,7,1,7,10,4,8,4,7,4,8
Z,8,15,8,8,6,7,7,2,9,12,7,8,6,5,8,5
I,2,4,3,3,1,7,7,0,7,13,6,8,0,8,1,7
C,4,7,4,5,2,4,8,6,8,10,9,14,1,8,3,8
P,5,8,7,10,10,7,8,4,3,7,7,7,6,11,5,6
R,4,9,6,6,4,9,7,4,6,9,4,7,3,7,5,10
O,3,3,4,4,2,7,7,8,6,7,6,8,3,8,4,8
B,4,8,6,6,8,8,7,5,3,7,7,7,6,10,8,9
K,2,1,3,2,2,5,7,4,6,6,6,10,3,8,4,9
O,2,4,3,2,2,7,7,7,5,7,6,8,2,8,3,8
V,4,11,6,8,2,7,8,4,3,7,14,8,3,9,0,8
A,2,3,4,2,2,7,2,1,2,7,2,8,2,7,2,7
H,3,4,3,5,2,7,9,14,3,7,3,8,3,8,0,8
G,5,10,6,7,5,6,5,6,7,6,6,10,3,9,4,7
F,3,6,4,4,4,6,9,5,4,8,6,8,2,9,7,12
B,2,2,3,3,2,7,7,5,5,6,6,6,2,8,6,10
P,4,7,5,5,3,10,7,3,5,12,4,5,2,9,3,9
S,8,14,8,8,4,11,4,3,3,12,6,9,3,10,3,9
G,3,7,4,5,2,6,6,6,6,10,8,11,2,10,4,9
E,4,9,4,7,4,2,7,5,8,7,6,14,0,8,6,9
S,4,7,5,5,2,8,7,5,9,5,6,8,0,8,9,8
U,5,10,5,8,4,7,5,14,5,7,11,8,3,9,0,8
X,7,11,11,8,5,7,7,1,9,10,6,8,3,8,4,7
I,2,10,2,8,2,7,7,0,8,7,6,8,0,8,3,8
T,8,11,8,8,5,7,11,4,8,12,9,4,2,12,4,4
X,3,9,5,7,4,8,7,3,8,6,7,8,3,8,7,9
D,3,10,5,8,9,9,9,4,4,7,6,6,4,8,7,5
E,3,8,5,6,5,7,7,3,5,6,7,10,4,10,8,8
W,4,6,6,4,2,7,8,4,1,7,8,8,8,9,0,8
U,5,6,5,4,2,4,9,5,7,12,11,8,3,9,1,6
D,5,4,5,6,3,5,7,10,9,6,6,5,3,8,4,8
T,2,3,3,1,1,5,11,1,7,11,9,5,0,9,2,5
S,3,6,5,5,5,9,8,5,5,7,6,8,5,8,9,11
Y,6,11,6,8,3,2,11,4,5,12,12,7,2,11,2,6
N,2,1,2,2,1,7,7,11,1,5,6,8,4,8,0,8
S,6,9,5,4,2,11,2,2,5,10,2,9,2,8,3,11
H,4,11,5,8,3,7,7,15,1,7,7,8,3,8,0,8
S,4,7,5,5,5,9,6,4,3,8,5,8,3,9,9,9
G,4,11,5,9,3,8,8,9,7,5,7,9,2,7,5,11
G,4,6,6,4,3,6,6,5,7,6,5,9,3,9,4,8
S,10,15,10,8,5,8,6,4,6,13,6,8,4,7,4,8
H,6,11,6,8,6,7,9,14,2,7,4,8,3,8,0,8
C,3,10,4,8,3,5,8,7,8,7,8,14,1,8,4,10
A,2,1,3,1,1,7,4,2,0,7,2,8,3,6,1,8
Q,6,11,5,6,3,10,3,4,6,10,3,9,3,8,6,13
I,2,7,3,5,1,7,5,0,7,14,7,10,0,7,1,8
A,2,4,4,3,2,7,2,2,2,5,2,8,2,5,2,7
Q,3,5,5,4,3,6,4,4,4,5,4,7,3,5,5,7
N,7,10,9,8,10,6,8,3,4,8,7,8,8,10,7,4
H,5,6,8,8,6,9,11,4,2,8,7,6,3,10,8,6
K,7,12,7,7,5,8,7,2,6,10,4,8,5,8,4,8
Y,3,8,5,6,3,10,10,1,6,3,11,8,1,11,2,9
Q,6,9,8,8,7,5,4,4,4,4,3,7,4,8,6,5
V,4,9,6,7,3,6,12,3,4,8,12,8,3,10,1,9
S,7,11,5,6,3,7,3,2,5,7,2,7,3,7,5,9
B,5,7,7,5,5,10,6,3,7,11,3,6,4,6,6,11
U,2,0,2,1,0,8,5,11,4,7,13,8,3,10,0,8
K,8,10,8,6,3,4,7,5,7,9,12,12,6,11,3,6
F,6,9,9,7,9,6,8,1,5,10,8,7,8,9,4,6
G,6,10,7,7,5,5,7,6,5,9,8,11,2,8,4,10
Q,3,4,4,5,3,8,8,6,2,8,6,9,2,9,3,8
F,4,7,5,8,6,7,9,5,4,8,6,7,4,9,8,7
H,8,14,7,8,4,8,8,4,6,8,5,6,6,8,5,8
I,3,10,4,8,3,6,8,0,7,13,7,8,0,8,1,7
F,7,10,9,8,6,9,8,2,6,13,5,5,5,9,4,9
L,1,0,1,1,0,1,1,5,5,0,1,6,0,8,0,8
P,4,4,6,6,6,9,11,2,3,7,9,6,4,10,5,5
Z,3,10,4,8,5,8,7,5,9,7,5,7,1,7,7,8
P,5,8,6,10,9,8,9,3,2,6,8,7,6,10,6,4
M,3,5,5,3,4,7,6,3,4,9,7,8,7,6,1,8
V,8,11,7,8,5,3,11,1,3,8,10,7,6,11,2,7
Y,4,5,5,4,2,4,12,3,6,12,10,4,1,11,2,5
X,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8
V,1,0,2,1,0,8,9,3,2,6,12,8,2,10,0,8
J,2,5,4,4,2,10,6,2,6,12,4,9,1,6,1,7
O,2,3,2,2,1,7,7,6,3,8,6,8,2,8,2,7
M,5,6,7,4,5,9,6,2,4,9,6,7,7,6,2,7
V,5,5,6,4,2,4,12,3,3,10,11,7,2,10,1,8
Q,5,8,7,7,5,7,4,4,4,5,3,8,3,7,5,8
N,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8
L,4,7,5,5,3,7,4,1,7,8,2,9,1,6,2,8
X,7,13,7,7,4,7,7,2,9,9,5,8,4,7,4,8
E,6,11,9,8,5,5,9,3,11,11,8,8,2,8,5,5
A,4,10,7,7,6,10,5,2,5,10,2,5,3,6,6,8
L,2,7,4,5,2,6,4,3,8,6,1,7,1,6,2,7
Z,2,3,4,2,1,7,7,2,9,11,6,8,1,8,5,7
O,3,9,4,6,4,7,7,8,6,7,7,8,2,8,3,7
Y,7,13,6,7,4,8,7,4,6,9,6,5,3,9,5,5
K,5,9,7,7,5,6,7,5,8,6,5,10,3,8,5,9
C,2,4,3,3,1,4,8,4,6,11,9,11,1,9,3,7
X,4,8,4,6,3,7,7,4,4,7,6,8,3,8,4,8
M,5,7,7,6,8,6,8,5,3,6,5,8,10,8,5,7
C,3,7,4,5,2,4,8,7,7,7,8,14,1,8,4,10
J,2,6,2,4,1,11,3,10,3,12,8,13,1,6,0,8
H,4,9,5,7,4,7,7,13,1,7,6,8,3,8,0,8
S,5,8,7,6,3,5,9,3,9,11,7,6,2,7,5,5
H,3,6,5,4,4,6,7,5,5,7,5,8,3,7,6,12
F,4,8,5,6,5,7,9,3,6,9,9,6,5,10,3,7
T,9,12,7,7,3,7,8,4,9,13,6,8,2,7,5,6
B,6,9,5,5,3,7,8,5,6,10,5,9,6,6,6,9
U,6,10,7,8,4,5,7,6,8,9,7,9,4,9,3,3
N,7,9,9,8,10,8,7,6,5,7,6,7,9,9,8,0
K,5,6,5,9,2,3,6,8,2,7,7,12,3,8,3,11
D,3,4,4,6,2,5,7,10,8,7,6,5,3,8,4,8
B,5,10,5,8,4,7,8,9,7,7,5,6,2,8,9,10
R,4,9,4,7,5,6,9,8,3,7,4,8,2,7,5,11
Q,5,9,6,8,3,9,6,9,8,7,4,9,3,8,4,8
N,3,7,4,5,3,7,8,6,5,7,6,7,5,8,1,6
V,4,10,7,8,2,7,8,4,3,7,14,8,3,9,0,8
C,0,0,1,0,0,6,7,5,6,7,6,13,0,8,3,10
W,8,8,10,7,12,7,7,5,5,6,6,8,10,9,9,8
M,1,1,2,1,1,8,6,10,0,6,8,8,6,6,0,8
G,1,3,2,1,1,7,7,5,5,6,6,9,2,8,3,9
T,3,11,4,8,1,6,14,0,6,8,11,8,0,8,0,8
I,2,5,3,3,1,7,7,0,7,13,6,8,0,8,1,7
C,5,9,7,7,8,7,5,4,3,7,7,11,8,9,4,7
U,4,3,4,4,1,7,5,13,5,7,14,8,3,9,0,8
H,8,11,11,8,7,7,6,3,7,10,5,8,5,6,5,7
D,4,6,4,4,2,5,8,9,8,8,7,5,3,8,4,8
L,4,8,6,6,4,8,4,1,8,9,3,10,1,6,3,9
D,2,3,4,2,2,9,6,4,6,10,4,6,2,8,3,8
X,1,0,1,0,0,8,7,3,5,7,6,8,2,8,3,7
U,5,9,4,4,2,6,6,4,5,4,8,7,5,8,2,6
D,4,4,5,6,3,6,8,10,9,8,7,6,3,8,4,8
P,2,7,3,4,1,3,13,8,2,11,7,3,0,9,3,8
B,3,4,5,3,4,7,8,3,5,10,6,6,2,8,5,8
M,4,8,4,6,5,8,6,10,1,6,8,8,7,5,0,7
F,5,9,7,7,4,4,11,1,5,13,7,5,1,10,1,7
G,4,11,5,8,5,7,7,8,6,5,7,9,2,7,6,10
X,5,10,7,8,4,10,7,1,8,10,2,6,3,9,4,10
I,4,10,5,8,2,7,7,0,9,14,6,8,0,8,1,8
B,3,6,5,4,4,7,9,4,5,9,6,6,2,8,5,6
Z,2,5,4,6,4,9,6,5,4,7,3,6,2,7,7,6
J,3,8,5,6,2,8,5,4,5,14,7,12,1,6,0,7
D,3,8,5,6,4,8,7,7,6,6,5,3,3,8,3,6
U,3,8,5,6,7,8,5,4,3,7,7,8,7,8,4,7
H,3,2,4,4,3,8,7,6,6,7,6,8,3,8,3,8
E,2,7,2,5,3,3,6,4,8,6,6,13,0,8,6,10
Z,2,1,2,2,1,7,7,3,12,8,6,8,0,8,7,8
T,2,4,3,5,1,7,14,0,6,7,11,8,0,8,0,8
Q,2,2,2,3,2,7,8,4,2,8,8,9,2,9,4,8
K,6,11,7,6,4,11,7,2,6,10,3,6,5,10,3,10
R,4,8,6,6,4,10,7,4,7,10,1,7,3,6,4,11
G,4,7,4,5,3,5,7,5,4,8,7,11,2,8,4,10
S,3,4,3,3,2,8,8,6,5,7,6,7,2,8,9,8
Z,4,5,6,7,5,11,5,3,5,9,3,7,2,7,6,9
Q,1,0,1,0,0,8,8,6,3,6,6,9,2,8,3,8
U,5,10,6,8,2,7,5,13,6,8,15,8,3,9,0,8
K,6,9,8,7,6,6,6,1,7,10,7,10,3,8,4,8
V,2,3,3,2,1,5,12,2,2,8,10,7,2,11,0,8
C,3,4,4,3,1,4,8,5,7,11,9,12,1,9,2,7
A,5,10,7,8,5,7,3,2,3,4,2,7,2,6,2,6
Q,5,6,8,6,6,8,4,4,4,7,4,10,6,6,7,7
T,6,13,5,7,3,8,7,3,7,11,5,7,2,9,5,6
J,1,2,2,3,1,10,6,2,6,12,4,8,1,6,1,7
R,2,3,4,2,2,8,7,3,4,9,4,7,2,7,3,10
R,4,8,5,6,3,6,10,9,4,7,5,8,3,8,6,11
C,3,7,4,5,2,7,7,7,10,8,6,14,1,9,4,9
I,3,8,4,6,2,7,7,0,7,13,6,8,0,8,1,8
Z,4,9,6,6,3,7,8,2,10,11,7,9,1,9,6,8
K,3,4,5,3,2,6,6,1,7,10,7,10,3,7,3,8
F,5,11,6,8,6,5,10,5,6,10,10,6,2,9,3,5
P,3,5,3,4,2,5,10,5,4,10,8,4,1,10,3,7
B,6,9,8,6,8,7,8,5,5,7,5,7,4,9,6,7
L,4,11,5,8,4,7,4,1,7,8,2,10,1,5,3,8
P,2,7,3,4,1,4,12,8,2,10,6,3,1,10,3,8
F,5,8,6,6,6,7,8,6,4,8,6,8,3,11,8,11
X,5,10,8,8,6,10,7,3,7,7,6,6,6,12,8,9
R,2,4,4,3,2,8,7,3,5,10,4,7,2,7,3,10
U,4,5,5,4,2,4,8,5,7,11,10,9,3,9,2,7
K,3,5,4,3,3,5,7,4,7,6,6,11,3,8,4,10
D,1,0,1,0,0,6,7,6,4,7,6,6,2,8,2,8
V,5,10,7,7,4,5,10,3,2,9,11,7,4,9,5,8
B,3,9,4,7,3,6,6,9,7,6,7,7,2,8,9,10
X,1,1,2,1,0,8,7,3,4,7,6,8,2,8,4,8
P,6,6,6,8,3,5,10,10,6,8,6,5,2,10,4,8
R,4,7,4,4,2,6,11,8,3,7,4,8,3,7,6,11
U,3,9,4,7,2,7,6,14,5,8,13,7,3,9,0,8
W,7,8,7,6,6,2,10,2,3,10,10,8,7,11,2,7
R,3,4,5,3,3,8,7,4,5,9,4,7,2,7,4,10
A,5,10,7,8,8,8,7,8,4,6,6,8,3,8,8,3
Q,5,9,5,4,3,10,5,4,6,11,4,8,3,8,7,11
B,6,6,6,8,5,6,6,9,7,6,6,7,3,8,10,10
E,2,5,3,4,3,7,7,6,7,7,6,9,2,8,6,10
L,5,6,6,6,5,8,9,3,6,6,6,9,3,9,7,9
N,5,8,8,6,4,10,7,3,5,10,2,4,5,9,1,7
D,3,7,5,5,6,8,8,5,4,7,6,6,6,8,7,6
J,2,5,5,3,2,9,5,4,5,14,6,12,1,6,0,7
T,1,1,2,2,1,7,12,3,6,7,10,9,1,11,1,8
F,3,3,4,4,1,2,13,5,4,12,10,6,0,8,2,6
E,3,6,4,4,3,8,7,2,6,11,6,9,3,8,4,10
I,0,7,0,5,0,7,7,4,4,7,6,8,0,8,0,8
D,3,3,4,2,2,7,7,6,7,7,6,5,2,8,3,7
U,5,10,8,8,11,9,6,4,4,7,7,7,8,8,5,7
T,2,3,3,2,1,6,11,4,5,8,10,7,2,11,1,7
F,3,8,3,5,1,1,13,5,3,12,10,6,0,8,2,6
K,7,10,10,7,7,4,8,1,7,9,8,11,3,8,3,6
T,5,10,7,8,8,8,8,5,6,6,7,9,7,7,8,3
S,4,11,5,8,5,7,7,6,8,5,6,11,1,11,10,9
M,3,7,4,5,3,7,7,11,1,7,9,8,8,5,0,8
H,9,15,9,8,7,4,10,3,5,10,8,10,5,8,4,6
Z,4,5,6,7,5,10,5,3,5,7,3,6,3,7,6,7
Y,2,7,4,5,2,7,10,2,2,7,11,8,1,11,0,8
N,3,4,5,3,2,6,9,2,4,10,7,7,5,8,1,8
K,4,10,6,8,6,5,6,3,7,6,6,10,4,7,7,11
J,5,7,6,5,4,8,6,8,6,7,7,8,2,7,4,6
M,7,9,10,7,6,8,6,2,5,9,6,8,10,8,3,8
G,4,9,6,7,6,7,5,6,3,8,6,11,4,8,7,8
Z,4,7,4,5,2,7,7,4,14,9,6,8,0,8,8,8
I,2,8,3,6,2,7,7,0,8,13,6,8,0,8,1,7
U,9,11,10,8,4,3,9,6,9,12,12,9,3,9,2,7
J,3,7,5,5,5,10,8,3,4,8,4,6,4,8,6,5
I,2,6,2,4,1,5,8,0,7,13,7,8,0,8,1,7
T,2,3,3,2,1,5,11,3,6,11,9,5,1,11,1,5
R,5,9,7,7,5,10,6,3,6,10,3,7,4,6,5,10
K,3,3,4,5,1,3,7,7,2,7,6,11,3,8,2,10
D,7,10,7,5,3,10,4,4,5,11,3,8,5,7,5,11
F,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8
Q,3,4,4,5,4,8,7,6,3,8,8,9,3,8,4,8
R,2,3,3,1,2,8,8,3,5,9,4,7,2,6,3,10
G,3,4,4,2,2,6,7,5,5,9,7,10,2,8,4,9
H,3,7,5,5,6,9,8,4,3,6,7,8,7,8,7,7
L,9,15,7,8,4,7,4,3,6,11,3,12,2,7,6,7
L,7,13,7,7,4,8,4,3,4,11,8,12,3,10,5,10
U,4,8,6,6,7,9,6,5,5,6,7,6,6,8,5,6
I,1,8,2,6,2,7,7,0,7,7,6,8,0,8,2,8
D,4,11,6,8,4,8,8,8,8,8,7,2,3,7,4,8
P,4,6,6,9,8,6,13,5,1,10,8,5,4,11,4,9
Z,6,7,5,10,4,8,5,5,4,11,6,7,3,9,10,7
J,4,8,3,12,3,8,7,3,3,11,4,5,3,8,6,10
W,6,8,6,6,4,2,10,2,3,11,11,9,6,10,2,6
N,5,9,8,7,4,3,10,3,4,10,10,9,5,8,1,7
G,9,15,8,8,5,10,3,3,3,9,3,6,4,7,5,9
D,3,7,4,5,4,6,7,9,7,8,8,6,2,9,3,8
O,2,4,3,2,2,8,7,7,4,7,6,8,2,8,2,8
U,4,5,5,4,3,6,8,6,7,7,9,10,3,9,1,8
G,6,11,7,8,6,7,7,8,7,6,5,10,2,7,5,11
R,1,1,2,1,1,6,9,7,3,7,5,8,2,7,4,10
G,2,1,3,2,2,7,6,6,5,6,6,9,2,9,4,9
X,8,13,7,7,4,8,8,2,9,9,6,7,4,10,4,8
M,3,6,4,4,4,7,5,10,0,7,8,8,6,5,0,8
U,4,2,5,3,2,7,8,6,8,7,10,9,3,9,1,8
E,1,3,3,2,1,7,7,2,6,11,6,9,1,8,4,9
M,4,7,6,5,4,6,6,7,5,7,8,11,8,5,2,9
A,3,9,5,7,4,6,4,3,1,6,1,8,2,6,2,7
G,4,9,5,7,4,5,7,6,5,10,8,11,2,9,4,10
P,5,8,7,6,6,8,5,7,5,7,6,6,4,8,6,10
F,3,5,6,4,2,6,11,2,6,13,7,4,1,10,2,7
I,1,6,0,4,1,7,7,5,3,7,6,8,0,8,0,8
R,3,2,4,3,3,7,7,5,5,6,5,6,3,7,4,8
K,5,9,5,6,2,4,7,9,2,7,5,11,4,8,2,11
W,5,8,8,6,5,9,11,2,3,5,9,7,10,13,1,6
S,3,3,4,4,2,7,6,5,9,5,6,9,0,9,9,8
C,4,10,5,8,3,5,8,7,8,7,8,15,1,8,4,9
N,7,11,9,6,4,5,8,2,4,12,7,9,6,8,0,7
P,5,6,5,8,3,3,14,8,1,11,7,2,0,10,4,8
P,5,9,7,7,4,9,8,3,5,12,4,4,2,9,4,8
N,4,9,6,7,7,9,7,4,4,7,6,6,5,10,6,5
Y,8,8,7,12,5,5,5,7,4,6,12,6,5,10,4,8
J,6,9,8,7,5,9,4,7,6,8,6,5,2,7,4,6
M,7,7,10,6,10,6,9,5,3,7,5,8,12,5,6,8
A,6,9,9,8,8,8,8,3,5,7,7,8,5,8,4,5
S,9,15,7,8,4,9,2,2,5,8,1,8,3,7,5,10
Z,8,13,9,7,6,5,9,2,9,12,8,8,6,5,8,2
O,3,7,5,5,3,7,7,8,5,6,5,6,3,8,3,8
R,3,8,4,6,2,5,11,8,4,7,3,9,3,7,6,11
I,1,7,0,5,1,7,7,5,3,7,6,8,0,8,0,8
U,1,0,2,0,0,7,6,10,4,7,13,8,2,10,0,8
K,10,12,9,6,4,5,9,3,8,9,7,9,6,6,3,6
E,5,10,7,7,7,8,9,6,3,6,6,9,4,7,7,9
F,5,11,5,8,4,1,12,4,5,12,11,8,0,8,1,6
J,2,2,3,3,1,10,6,2,6,12,4,9,0,7,1,7
Z,4,6,6,4,3,6,9,3,9,11,9,6,1,9,6,5
I,3,6,5,7,4,8,8,4,5,7,6,8,3,8,8,8
U,1,0,1,0,0,8,7,9,3,7,11,8,2,10,0,8
Q,9,14,8,8,5,7,5,4,9,10,4,9,3,7,9,9
V,8,10,8,8,5,3,12,2,3,9,11,8,4,12,2,7
E,6,11,8,8,6,8,7,3,9,12,6,9,2,9,5,8
L,5,5,6,8,2,0,0,7,6,0,1,4,0,8,0,8
B,3,7,4,5,4,6,7,8,5,7,6,7,2,8,7,9
O,6,11,6,8,5,8,6,8,5,10,5,8,3,8,3,7
X,5,10,7,7,6,7,6,3,5,6,6,8,3,9,9,9
X,6,11,6,6,3,11,7,3,8,10,3,6,4,11,4,10
J,4,11,5,9,3,7,8,2,7,15,5,8,1,6,1,7
N,5,6,8,4,4,8,8,2,5,10,4,6,5,9,1,7
K,6,10,8,8,5,6,6,2,7,10,6,10,4,8,4,8
U,7,15,6,8,5,7,6,4,5,7,7,8,5,7,3,7
A,3,3,5,4,1,7,5,3,0,7,1,8,2,7,2,8
Z,3,10,4,8,4,7,7,6,10,6,6,8,1,8,8,8
A,5,10,5,6,3,10,2,4,2,11,6,13,5,4,5,10
O,4,8,4,6,4,8,7,8,3,10,6,8,3,8,3,8
P,7,8,9,10,11,8,8,3,3,7,8,7,7,11,7,7
A,1,1,2,1,0,7,4,2,0,7,2,8,2,7,1,8
G,4,11,6,8,6,7,6,7,5,4,7,9,3,6,5,9
M,6,9,8,7,6,8,6,2,5,9,6,8,8,6,2,8
V,5,6,5,4,2,3,12,3,3,10,11,8,2,11,1,8
J,3,7,4,5,4,9,8,3,4,9,4,7,4,8,6,5
A,3,8,4,6,3,11,3,2,2,9,1,8,2,6,3,8
F,5,11,5,8,2,1,11,5,7,11,11,9,0,8,2,6
J,4,9,6,7,6,9,8,4,4,8,5,6,4,8,6,4
U,5,7,5,5,2,4,9,5,8,12,10,8,3,9,1,6
G,6,10,7,8,6,7,7,8,7,6,6,9,2,7,5,11
D,3,7,4,5,3,8,7,6,6,10,4,5,3,8,3,7
H,4,4,6,6,4,9,8,3,1,7,6,8,4,9,9,7
I,4,9,5,7,3,9,6,0,7,13,5,8,0,8,1,8
E,3,5,6,3,3,7,7,2,8,12,7,9,2,9,4,8
K,4,6,6,5,5,8,7,2,3,8,3,8,4,5,6,10
L,4,9,5,7,3,4,4,5,10,2,0,6,0,6,2,5
L,1,0,2,1,0,2,1,6,5,0,2,4,0,8,0,8
P,5,5,6,7,7,8,8,3,2,7,8,7,5,10,5,5
X,4,11,6,8,4,8,7,4,9,6,6,6,3,8,7,7
M,3,4,4,3,3,8,6,6,4,6,7,8,7,6,2,8
L,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8
T,4,5,5,3,2,6,11,2,8,11,9,5,4,10,4,4
S,6,11,6,6,3,9,5,4,4,13,6,9,3,10,3,8
N,6,11,8,8,5,7,9,6,5,7,7,8,6,9,1,7
E,5,9,7,7,5,10,6,2,9,11,4,8,2,8,5,12
C,5,7,6,5,6,8,4,6,3,8,6,11,8,8,4,7
Q,9,14,8,8,5,7,4,4,8,9,5,9,3,8,9,9
Y,5,8,7,12,11,8,8,4,2,7,8,9,4,11,8,8
G,2,2,3,4,2,7,6,6,5,6,6,9,2,9,4,9
M,4,9,5,7,6,7,5,10,1,7,8,8,6,5,0,8
D,3,2,4,3,3,7,7,6,7,6,6,5,2,8,3,7
K,2,3,4,2,2,5,8,2,6,10,8,10,3,8,2,8
W,2,3,4,2,2,8,11,3,1,6,8,8,6,12,0,7
P,3,3,3,2,2,5,10,4,4,10,8,4,1,10,3,7
H,5,10,8,8,5,8,8,3,7,10,6,7,3,8,3,8
C,5,9,6,7,2,6,7,7,12,7,6,15,1,8,4,9
S,6,9,7,6,4,8,7,4,8,11,6,8,2,9,5,8
Q,1,2,2,3,1,7,8,4,1,7,8,10,1,9,3,8
I,2,8,3,6,2,7,7,0,7,13,6,8,0,8,1,8
L,3,7,5,5,5,7,8,3,4,6,6,9,5,11,7,5
I,3,9,4,6,2,7,7,0,8,14,6,9,0,8,1,8
F,5,7,6,9,7,7,9,5,5,8,6,8,3,9,7,6
A,4,10,7,7,3,12,3,4,3,11,1,9,2,7,3,9
N,2,4,4,3,2,7,9,2,4,10,6,7,5,9,0,7
J,2,7,4,5,3,10,6,2,5,8,4,5,3,8,6,7
L,3,6,5,5,4,7,8,3,5,7,6,8,3,9,7,11
U,7,13,6,7,5,9,6,5,6,6,7,6,5,8,4,6
O,3,6,4,4,3,7,8,6,4,10,7,6,3,9,3,8
P,8,11,6,6,3,8,7,5,4,11,4,6,5,8,4,8
N,4,5,6,5,5,7,8,5,3,6,5,8,6,8,4,7
P,7,11,9,8,5,6,11,4,6,14,6,2,0,10,3,9
W,4,5,7,4,4,7,11,2,2,6,9,8,7,11,0,8
O,4,9,5,7,4,7,7,8,5,10,6,7,3,8,3,8
F,3,2,4,3,2,5,10,4,6,11,9,5,1,10,3,6
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
R,3,7,4,5,4,7,8,5,6,6,5,7,3,7,5,8
D,2,4,4,3,2,9,6,3,5,10,4,6,3,7,3,8
W,3,8,5,6,4,5,10,2,3,8,9,9,7,11,1,7
M,5,9,8,7,8,5,7,3,4,9,9,9,7,5,2,7
P,10,13,8,8,4,7,10,6,3,12,4,4,5,10,4,8
T,2,9,3,6,1,9,14,0,6,6,11,8,0,8,0,8
P,4,8,5,6,3,7,10,2,7,14,6,4,0,10,2,9
A,2,4,3,3,1,10,2,2,1,8,2,9,1,6,1,8
Y,3,5,5,8,6,7,10,3,2,7,8,9,3,11,8,5
F,3,7,4,5,3,5,11,4,6,11,10,4,2,10,2,5
P,9,14,9,8,6,10,8,4,4,12,4,4,5,10,5,8
G,4,8,6,6,5,7,6,6,4,7,7,9,5,9,7,8
U,3,7,4,5,1,7,5,13,5,7,13,8,3,9,0,8
S,3,5,5,4,2,8,7,3,7,10,6,7,1,9,5,8
Y,2,6,4,4,1,6,10,3,2,7,13,8,2,11,0,8
P,6,10,6,8,6,4,11,8,3,9,6,5,1,10,3,8
U,7,11,6,6,4,6,5,5,4,7,9,10,6,7,3,10
A,3,10,5,7,2,6,6,3,1,6,0,8,2,7,1,7
H,4,2,5,4,4,7,8,6,6,7,6,8,3,8,3,8
R,2,4,3,3,2,8,7,4,5,9,4,7,2,7,3,10
R,3,4,5,2,3,9,8,3,5,9,4,6,2,6,4,10
X,6,10,9,8,5,3,9,2,8,11,12,10,3,9,3,5
J,1,3,2,5,1,11,3,9,3,13,7,13,1,6,0,8
S,2,3,4,2,2,8,8,2,6,10,6,7,1,9,5,8
N,3,8,3,6,3,7,7,12,1,6,6,8,5,8,0,8
Y,5,6,8,9,10,8,8,4,2,7,8,9,6,12,8,8
A,3,5,5,3,2,11,2,3,2,10,2,9,2,6,2,8
D,8,14,7,8,6,7,7,4,7,10,6,7,6,7,8,4
U,6,10,9,7,6,6,7,7,6,6,6,12,6,8,8,2
L,4,8,5,7,4,6,7,4,6,7,6,8,3,9,8,10
F,3,7,4,4,1,2,13,5,3,12,9,6,0,8,2,6
G,4,7,4,5,2,7,6,6,6,11,6,12,2,10,4,10
F,2,3,4,2,1,6,10,2,5,13,6,4,1,9,1,7
V,7,10,7,7,3,3,11,3,4,9,11,8,4,12,1,7
P,4,7,4,5,2,4,12,8,2,10,6,4,1,10,4,8
S,5,10,6,7,4,6,8,3,6,10,7,7,3,7,5,6
F,3,1,3,2,2,5,10,4,5,11,9,5,1,10,3,6
T,3,5,4,4,2,6,12,3,6,7,11,9,2,11,1,8
C,2,4,3,3,1,4,9,5,7,11,9,12,1,9,2,7
K,3,3,4,5,2,3,6,7,3,7,7,12,3,8,3,10
G,3,3,4,4,2,7,8,7,6,6,6,7,2,7,6,11
H,5,10,8,8,5,8,5,3,6,9,5,8,4,7,4,8
W,2,0,2,1,1,7,8,4,0,7,8,8,6,10,0,8
O,6,10,7,8,7,7,8,9,4,7,7,8,3,7,3,7
V,4,8,6,6,3,6,12,3,3,6,12,9,3,10,1,8
L,3,5,4,4,2,4,4,4,8,2,1,7,0,7,1,6
I,2,6,3,4,2,7,7,0,6,13,6,8,0,8,1,8
B,1,3,3,2,2,7,8,2,4,10,6,6,2,8,4,8
J,6,10,7,8,3,10,6,2,9,15,3,8,0,7,0,8
V,7,12,6,6,4,8,9,3,3,8,8,5,5,12,2,8
V,3,10,5,8,2,7,8,4,3,7,14,8,3,10,0,8
B,4,7,6,5,4,9,7,3,7,10,4,7,2,8,5,11
P,4,7,6,5,3,9,8,3,5,13,4,4,1,10,3,9
T,2,7,3,4,1,7,13,0,6,7,11,8,0,8,0,8
B,3,8,5,6,5,8,8,5,4,7,5,5,3,8,6,5
Y,8,11,7,6,4,7,8,4,6,10,6,4,4,10,5,5
Z,7,11,9,8,6,6,9,3,10,12,8,6,1,8,6,6
G,2,3,2,1,1,7,7,5,5,7,6,9,2,9,4,9
D,2,4,4,3,2,9,6,3,6,10,4,6,3,7,3,8
F,2,1,2,2,1,5,10,4,5,10,9,6,1,9,3,7
T,3,3,4,2,1,7,12,2,7,7,11,8,1,11,1,7
T,3,6,4,4,3,6,12,4,6,8,11,8,2,12,1,8
P,6,11,8,8,6,6,13,6,3,12,6,2,1,11,3,8
B,1,0,2,0,1,7,8,6,4,7,6,7,1,8,6,8
N,5,11,7,8,6,7,6,8,5,6,4,7,4,8,3,7
S,5,8,6,6,3,7,8,4,9,11,4,7,2,6,4,8
B,5,10,7,8,6,10,6,3,6,10,4,7,3,7,5,11
S,5,9,8,6,9,6,7,3,2,8,6,6,3,8,11,1
C,2,5,3,4,2,6,9,7,8,9,8,13,1,10,4,10
R,4,9,5,6,7,7,8,3,4,7,6,8,6,9,7,5
O,3,7,4,5,3,8,7,7,3,9,6,8,3,8,3,8
R,5,9,8,6,9,7,7,3,4,6,6,9,6,9,7,6
N,5,9,8,7,5,6,9,5,5,7,7,7,6,9,1,6
G,4,9,5,7,3,8,8,8,8,6,7,8,2,7,6,11
W,1,0,2,0,0,7,8,4,0,7,8,8,5,9,0,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
H,4,7,6,5,4,4,9,4,5,10,10,9,3,8,3,5
Z,4,7,6,5,4,7,9,2,9,11,8,5,1,8,6,5
G,7,10,7,8,6,5,7,6,5,8,8,11,2,7,5,10
U,6,9,6,6,3,4,9,5,7,13,11,9,3,9,1,7
Z,1,0,1,1,0,7,7,2,9,9,6,8,0,8,6,8
R,4,7,6,5,6,8,6,6,3,8,6,8,4,7,7,10
D,4,7,6,5,5,7,7,5,6,7,6,8,6,8,3,7
B,3,3,4,4,3,6,6,8,7,6,6,7,2,8,9,10
Z,6,9,5,12,5,5,11,3,3,12,8,6,3,8,11,6
U,5,9,5,6,4,7,5,14,5,7,11,8,3,9,0,8
T,8,11,8,9,7,6,11,2,8,11,9,5,4,10,4,4
H,1,0,1,0,0,7,7,10,2,7,6,8,2,8,0,8
O,6,10,4,5,3,6,8,6,5,9,7,9,5,10,5,8
E,2,3,4,2,2,6,8,2,8,11,8,9,2,9,4,7
Y,6,9,6,6,3,2,11,4,6,13,12,6,1,11,2,5
M,4,2,5,3,3,8,6,6,4,6,7,8,7,6,2,6
E,5,5,5,8,3,3,8,6,12,7,6,15,0,8,7,6
A,2,3,4,2,1,11,2,3,1,9,2,9,1,6,2,8
J,2,8,2,6,1,14,3,6,5,14,0,9,0,7,0,8
F,5,8,7,6,7,7,9,1,4,10,7,6,5,10,3,5
X,4,9,5,6,1,7,7,4,4,7,6,8,3,8,4,8
H,3,8,4,6,3,7,7,12,1,7,6,8,3,8,0,8
A,1,3,2,2,1,12,2,3,1,11,2,9,2,6,2,8
X,4,7,5,6,6,9,8,2,5,8,5,6,2,6,7,8
K,2,1,2,2,2,5,7,3,6,7,6,10,3,8,4,10
U,4,9,6,6,7,10,7,5,4,6,7,7,7,9,5,5
T,4,7,4,5,2,6,11,4,5,11,9,4,2,12,2,5
Z,3,8,4,6,4,9,6,5,9,7,5,6,1,7,7,8
V,4,4,5,3,2,5,12,2,3,9,11,7,4,11,1,7
I,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8
F,5,8,8,6,7,7,9,1,4,10,6,6,5,11,3,5
S,3,4,3,3,2,8,7,7,5,7,6,8,2,8,9,8
B,3,7,4,5,4,8,7,6,6,7,6,5,2,8,7,9
J,1,3,2,5,1,15,3,5,5,13,0,9,0,7,0,8
C,2,1,2,1,1,6,8,6,7,8,8,12,1,9,3,10
T,3,6,5,8,2,5,15,1,6,9,11,7,0,8,0,8
M,5,8,7,6,5,8,6,6,6,6,7,6,9,7,3,6
O,4,9,5,6,3,7,5,9,8,6,4,7,3,8,4,8
N,3,5,5,3,2,9,7,3,5,10,3,5,5,9,1,7
N,3,5,6,4,3,7,9,2,5,10,6,6,5,9,1,7
N,4,6,5,4,3,7,8,6,6,7,7,6,6,9,2,6
U,5,7,5,5,2,4,8,6,7,9,10,9,3,9,2,5
P,9,12,7,7,3,6,10,6,4,13,6,4,4,10,4,8
L,4,8,6,6,4,8,4,1,6,9,2,9,1,6,3,9
W,4,9,6,7,3,9,8,4,1,7,8,8,8,9,0,8
L,3,4,3,3,1,4,4,4,8,2,1,6,0,7,1,6
M,7,8,10,6,7,12,6,3,5,9,2,5,9,6,2,8
N,2,5,3,3,2,7,8,5,4,7,6,6,5,9,1,6
O,4,10,5,8,5,8,7,7,4,9,5,6,3,8,3,7
W,5,7,5,5,5,5,10,3,2,9,8,7,6,11,2,6
L,1,3,2,1,1,7,5,1,7,8,3,10,0,7,2,9
F,1,0,1,0,0,3,12,4,2,11,8,6,0,8,2,7
U,6,7,6,5,3,3,9,6,7,11,11,9,3,9,1,6
Y,5,10,8,7,4,8,10,1,7,4,11,9,2,12,3,8
J,1,4,3,3,1,9,6,2,6,14,4,9,0,7,0,7
Q,7,9,8,11,7,8,5,7,5,9,6,9,3,7,6,8
K,5,9,7,7,6,5,7,1,6,9,8,10,3,8,3,8
L,5,10,7,7,4,5,4,3,8,6,2,8,1,6,3,6
H,8,12,7,6,4,7,8,5,4,9,10,9,7,11,5,9
R,4,7,6,5,4,7,8,5,7,7,4,7,3,6,5,8
H,8,11,12,8,10,10,6,3,7,10,3,7,5,7,5,10
S,6,10,8,8,5,8,8,3,6,10,4,7,2,7,5,9
L,2,6,2,4,1,0,1,5,6,0,0,7,0,8,0,8
W,2,0,2,1,1,8,8,4,0,7,8,8,6,9,0,8
F,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8
T,4,10,6,8,4,9,14,0,5,6,10,8,0,8,0,8
I,0,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8
Y,5,6,7,9,9,8,10,4,3,6,8,9,5,13,8,8
U,4,4,4,3,2,4,8,5,7,10,9,9,3,9,2,6
K,3,5,5,3,2,5,8,1,7,10,8,10,3,8,3,7
G,5,10,6,8,6,6,5,7,6,7,5,11,3,10,4,9
K,5,8,7,7,7,7,7,2,4,8,4,8,6,4,9,11
B,2,7,3,5,4,6,7,7,5,7,6,7,2,8,6,9
R,2,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10
T,6,9,6,7,4,6,11,3,7,11,9,5,1,12,2,4
F,5,10,7,8,6,4,10,2,6,11,10,6,1,10,3,6
N,6,9,9,6,5,7,9,2,5,10,5,6,6,9,1,7
U,5,9,7,8,7,7,6,4,4,6,7,8,4,8,1,8
U,2,4,3,3,1,7,8,6,6,7,9,8,3,9,1,8
A,3,4,5,3,2,10,2,3,1,9,2,9,2,6,2,8
L,3,7,4,5,2,3,4,3,8,2,1,7,0,7,1,5
V,2,2,4,4,1,8,9,4,2,7,13,8,3,10,0,8
S,8,13,7,7,3,6,2,3,4,6,2,6,3,7,6,7
R,5,8,8,6,6,10,7,3,6,10,2,7,5,7,5,10
M,6,11,9,8,7,7,6,6,6,6,7,7,10,8,3,6
F,4,8,4,6,3,1,12,3,4,11,10,7,0,8,2,6
V,5,11,7,9,4,9,9,4,2,5,13,8,3,10,0,8
I,4,8,5,6,3,8,9,1,7,7,6,7,0,7,4,7
J,9,14,7,11,6,9,9,2,4,12,4,5,2,9,8,9
Y,4,11,6,8,1,8,10,3,2,6,13,8,2,11,0,8
W,2,3,3,1,2,5,10,4,2,9,8,7,5,11,1,6
Z,1,3,3,2,1,8,7,2,9,11,6,9,1,8,5,7
Z,7,9,5,13,5,7,8,5,3,11,7,7,3,9,11,6
R,3,5,5,3,3,8,7,4,5,9,4,7,3,7,3,11
C,3,4,4,2,1,5,8,5,7,10,8,13,1,9,3,8
X,3,4,5,3,2,5,8,2,8,10,10,9,3,8,3,5
R,2,3,3,2,2,7,8,5,5,6,5,7,2,6,5,8
E,4,8,5,6,6,8,7,4,7,7,5,8,6,8,6,10
J,1,4,2,3,1,10,6,2,6,12,4,9,0,7,1,7
M,6,9,7,4,3,12,2,3,2,10,3,9,6,4,1,9
T,6,10,8,8,9,6,7,4,6,7,7,10,7,7,7,6
E,4,8,5,6,5,7,7,5,8,6,5,9,3,8,6,9
Q,4,5,6,7,6,9,12,4,2,4,8,12,4,13,5,12
N,3,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8
G,3,1,4,2,2,7,7,5,6,7,6,9,3,7,4,8
F,2,6,3,4,1,1,13,4,3,12,10,6,0,8,2,6
K,3,8,4,6,2,3,7,8,3,7,6,11,4,8,3,11
J,2,7,3,5,1,12,2,9,4,14,5,13,1,6,0,8
C,3,7,4,5,1,5,7,6,9,6,6,14,1,8,4,9
W,5,9,7,7,5,7,8,4,1,7,9,8,8,10,0,8
Y,3,5,5,7,1,7,10,1,3,7,12,8,1,11,0,8
Q,4,7,5,6,3,9,7,8,6,6,6,10,3,8,5,9
G,6,8,6,6,4,7,6,6,7,10,6,12,2,10,4,9
M,14,15,14,8,7,10,11,7,4,4,6,10,10,13,3,6
D,4,9,6,7,4,9,6,5,7,9,3,6,3,8,3,8
A,3,8,5,6,3,11,3,3,2,10,2,9,2,7,3,9
J,2,7,4,5,1,9,6,3,6,15,4,9,0,7,1,7
M,5,7,7,6,8,5,8,5,4,6,5,9,11,9,4,8
F,5,9,7,6,4,5,12,2,6,13,7,4,1,10,2,7
K,4,8,6,6,6,7,7,4,4,7,5,7,4,6,9,10
M,2,1,3,2,2,7,6,6,4,6,7,7,7,6,2,7
S,7,9,9,8,9,10,7,4,7,7,6,8,5,9,8,12
D,7,10,9,8,6,11,5,3,8,11,3,6,4,6,4,9
E,3,10,5,8,5,7,7,5,8,7,7,10,3,8,6,9
C,4,9,6,7,6,6,6,3,4,8,7,11,6,9,3,9
I,5,13,4,8,2,9,6,5,4,12,4,7,3,8,5,10
T,3,5,4,4,3,9,12,3,6,6,11,8,2,11,1,8
Z,1,3,2,2,1,8,7,5,8,6,6,7,1,8,7,8
M,4,9,5,6,5,7,6,10,1,7,8,8,8,4,1,7
T,10,14,8,8,3,4,12,3,8,12,8,4,2,9,3,4
Q,2,3,2,3,2,8,8,5,2,5,7,10,2,9,5,9
H,5,11,8,8,7,8,7,3,6,10,5,8,3,9,4,8
E,2,4,3,3,2,7,7,5,7,7,6,9,2,8,5,10
V,4,5,5,4,5,7,7,5,4,6,5,7,6,9,6,11
Z,1,3,2,2,1,7,7,1,8,11,6,8,1,8,5,8
W,12,13,12,7,5,2,10,3,3,10,12,8,9,10,0,7
V,3,2,5,4,2,7,12,2,3,7,11,9,2,10,1,8
F,4,6,4,8,2,0,12,4,5,12,11,8,0,8,2,6
T,4,10,5,7,3,5,14,1,5,9,10,7,0,8,0,8
R,4,9,6,6,5,6,8,6,6,6,4,9,3,6,6,9
G,5,8,6,6,4,6,6,7,6,6,6,11,2,8,4,9
L,3,7,3,5,2,0,2,4,5,1,1,7,0,8,0,8
Y,8,11,8,8,5,3,11,3,7,12,11,6,1,11,2,5
A,3,6,5,4,1,9,4,3,1,8,1,8,3,6,2,8
N,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8
M,8,10,12,8,9,4,8,3,5,10,10,10,13,8,5,8
P,3,6,4,4,2,7,11,7,3,11,5,3,1,10,4,7
V,8,12,6,7,3,8,10,5,5,8,10,5,5,12,3,7
V,5,10,5,8,4,3,11,2,3,9,11,8,3,10,1,7
G,7,10,9,8,9,9,4,6,3,8,5,10,11,6,5,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
F,3,7,4,5,3,7,9,2,5,13,6,5,2,10,2,8
A,1,0,2,1,0,7,4,2,0,7,2,8,2,7,1,8
Z,4,5,5,8,2,7,7,4,14,9,6,8,0,8,8,8
V,8,10,8,8,3,4,12,5,5,12,12,7,3,9,2,8
O,1,1,2,2,1,7,7,6,4,7,6,8,2,8,2,8
S,7,13,6,7,3,10,3,3,5,10,1,9,3,6,5,10
Z,4,10,6,7,6,8,8,2,8,7,7,7,1,8,10,8
X,3,7,5,5,4,7,7,3,8,5,6,9,3,7,7,9
G,6,10,6,8,5,7,6,6,5,10,7,13,4,8,6,7
O,4,7,4,5,4,8,7,7,4,9,5,8,3,8,3,8
N,3,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8
O,5,8,5,6,4,7,9,7,4,10,7,6,4,9,4,8
L,4,9,6,8,6,7,6,5,4,7,7,8,3,8,8,10
Z,3,8,4,6,3,7,8,3,12,8,6,8,0,8,7,7
I,5,9,4,4,2,7,10,2,5,13,5,4,1,9,5,8
P,8,12,7,6,4,9,8,4,5,13,4,4,4,10,6,7
G,3,5,4,3,2,6,7,5,5,9,7,10,2,9,4,9
P,4,10,5,7,2,3,13,8,2,11,7,3,1,10,4,8
O,4,8,6,7,5,8,4,5,5,9,4,10,4,7,5,7
Y,4,10,6,7,1,9,10,3,2,6,13,8,2,11,0,8
Y,6,11,5,6,3,7,8,4,5,10,7,5,3,9,4,4
O,1,3,2,2,1,8,6,6,3,9,6,8,2,8,2,8
E,5,7,6,6,7,7,8,5,4,7,7,10,7,10,9,10
X,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8
T,2,3,3,2,1,6,12,2,6,11,9,4,1,10,2,5
S,7,10,6,5,3,7,3,4,4,7,2,7,3,6,5,8
I,2,6,3,4,2,7,8,0,7,13,6,8,0,8,1,8
C,4,8,5,6,3,5,8,7,7,8,8,14,2,9,4,10
O,6,10,7,7,6,7,7,7,4,9,6,10,5,8,5,6
F,3,6,5,4,4,8,8,1,4,9,6,6,5,10,4,5
Y,5,8,5,6,2,3,11,2,7,12,11,6,0,10,2,5
A,3,7,5,4,2,7,4,3,1,7,1,8,2,7,2,8
E,3,7,3,5,3,3,6,5,9,7,7,13,0,8,7,9
E,3,5,5,4,3,8,7,2,8,11,5,8,2,8,4,10
C,3,7,4,5,1,6,7,7,9,7,6,13,1,8,4,9
O,3,6,4,4,3,7,8,7,5,8,8,6,3,8,3,8
J,4,10,5,7,5,10,6,3,4,9,3,6,3,7,6,7
X,5,10,6,7,1,7,7,5,4,7,6,8,3,8,4,8
T,3,3,4,2,1,5,11,2,7,11,9,5,1,10,2,5
D,4,9,6,7,4,9,7,5,7,10,4,5,3,8,3,8
X,4,6,5,5,5,7,8,2,4,7,6,8,2,7,7,7
Z,2,3,4,2,1,7,7,2,9,11,6,8,1,8,5,7
P,3,6,4,4,3,3,13,5,1,11,7,4,0,9,3,8
P,2,4,3,3,1,7,9,3,4,12,5,3,1,10,2,8
Y,5,8,5,6,3,3,10,2,7,11,11,6,1,11,2,5
A,2,6,3,4,2,11,3,2,2,9,2,9,2,6,2,8
P,3,7,3,4,2,4,12,8,2,10,6,4,1,10,4,8
U,6,10,9,8,12,8,6,4,4,7,7,8,11,9,6,8
P,4,5,6,7,6,8,10,2,3,8,9,6,5,10,5,5
G,2,3,3,2,2,7,7,5,5,6,6,9,2,9,4,9
C,3,4,4,3,1,4,8,5,8,11,9,12,1,9,3,7
E,2,1,2,2,1,4,7,5,8,7,6,13,0,8,7,9
W,12,13,11,7,5,8,11,3,3,5,10,7,8,12,1,6
R,10,15,10,8,8,6,8,3,6,8,4,9,8,4,7,6
U,4,6,5,4,2,4,9,5,6,12,11,9,3,9,1,7
V,5,11,8,8,4,7,12,3,4,6,12,9,3,10,1,8
N,6,8,8,7,8,7,7,5,4,7,5,8,8,8,6,6
I,5,12,5,6,3,6,11,3,5,13,6,4,1,8,5,8
Q,6,7,8,10,11,9,7,6,1,6,6,9,9,11,6,9
M,5,9,8,7,7,10,6,2,4,9,5,7,7,6,2,8
V,7,11,6,6,3,9,9,6,4,6,10,6,6,13,3,6
X,2,3,3,2,1,7,7,3,8,6,6,8,2,8,5,8
C,6,8,6,6,3,3,8,4,7,10,10,13,1,7,3,7
A,5,12,6,6,3,13,1,4,1,12,3,11,2,3,3,11
R,8,15,8,8,6,8,6,3,5,9,4,8,6,8,6,7
G,4,6,5,4,3,6,7,6,6,10,7,11,2,9,4,10
L,3,6,4,4,2,5,5,1,9,7,2,11,0,8,3,7
B,4,3,5,5,4,6,7,9,7,7,6,7,2,8,9,9
W,3,4,5,3,3,7,11,2,2,7,9,8,7,11,1,8
V,9,13,9,7,4,6,10,4,3,8,8,5,5,12,3,8
X,6,10,6,6,3,7,7,2,7,11,6,8,4,9,4,7
A,3,10,5,8,4,12,3,1,2,9,2,9,3,6,2,8
Z,5,11,7,8,4,7,7,2,11,11,6,8,1,8,6,7
R,5,10,6,8,7,8,6,6,4,8,6,8,7,7,6,11
B,2,1,3,2,2,8,7,5,5,7,6,5,2,8,6,9
C,4,7,6,5,3,7,7,8,6,6,6,10,3,8,4,9
V,3,3,4,2,1,4,12,3,3,9,11,7,2,10,0,8
E,4,7,5,5,4,6,8,7,9,6,4,10,2,8,6,8
R,3,8,5,6,4,8,7,5,7,7,6,6,3,8,5,8
V,3,5,5,3,2,4,12,3,3,9,11,7,2,11,1,8
V,5,9,5,7,3,3,11,2,3,10,11,8,2,11,1,8
N,5,10,7,8,6,6,8,8,6,8,6,7,3,7,3,8
P,4,8,6,11,10,9,6,6,2,7,6,7,7,9,8,11
P,7,11,9,8,5,6,12,3,6,14,6,2,0,10,3,9
H,5,6,7,4,4,8,8,3,6,10,5,7,3,8,3,8
F,3,7,4,5,3,5,10,4,5,10,10,6,2,10,3,6
A,5,11,9,8,7,8,4,1,4,6,2,6,5,8,6,6
U,3,7,3,5,2,7,5,14,5,7,12,8,3,9,0,8
Z,2,2,3,4,2,8,7,5,9,6,6,8,1,8,7,8
W,4,2,6,3,3,9,11,3,2,5,9,8,8,11,2,8
Z,4,9,5,7,4,7,7,3,12,9,6,8,0,8,8,8
S,5,11,7,8,9,6,5,3,2,6,5,6,5,8,15,3
B,5,6,6,8,5,6,6,9,8,6,7,7,3,8,10,10
F,4,9,4,4,3,7,9,2,4,11,6,5,3,10,6,6
B,5,9,7,7,5,9,7,4,6,10,5,6,2,8,6,10
E,4,7,6,5,5,10,6,1,7,11,4,8,3,8,5,11
C,5,10,6,8,2,5,7,7,11,7,6,14,1,8,4,9
K,2,4,4,3,2,6,7,2,6,10,7,10,3,8,2,8
I,1,4,2,2,1,7,7,1,7,13,6,8,0,8,1,8
Z,8,12,8,7,5,8,6,2,9,12,6,9,3,7,7,7
J,1,1,2,2,1,11,6,2,5,11,4,8,0,7,1,7
P,6,11,5,6,3,5,11,5,3,13,6,4,4,10,4,8
P,9,11,7,6,4,6,10,6,3,11,5,5,5,9,4,8
C,2,4,3,2,1,6,8,7,8,8,8,13,1,9,4,10
S,7,11,9,8,5,7,8,4,9,11,5,7,2,7,5,8
R,2,3,3,2,2,9,6,4,5,9,4,7,2,7,4,10
Z,4,8,5,6,4,8,6,2,9,11,5,9,2,8,6,9
N,6,7,8,5,3,5,9,3,5,11,9,9,6,8,1,8
B,3,7,4,5,4,6,6,8,6,6,7,7,2,9,7,9
G,4,4,5,6,3,7,6,8,7,6,6,7,2,8,6,11
K,6,10,8,8,8,6,7,4,7,6,6,10,3,8,6,10
A,4,9,6,7,4,12,2,3,3,10,2,9,2,6,4,8
U,3,3,3,5,1,8,5,13,5,6,13,8,3,9,0,8
E,3,7,4,5,4,7,7,5,8,6,5,10,2,8,5,9
X,2,7,4,5,4,7,6,2,6,7,5,8,3,7,5,9
K,3,6,4,4,1,3,6,7,3,7,7,11,3,8,2,10
X,4,9,6,6,4,7,7,3,8,5,6,7,3,9,7,6
T,3,4,4,3,1,5,11,2,7,11,9,4,1,10,2,5
N,5,8,7,6,7,6,8,3,4,8,7,9,6,9,5,5
V,6,11,6,8,4,3,11,3,4,9,11,8,3,11,1,7
U,3,5,4,3,2,7,9,6,7,7,9,9,3,9,1,8
U,4,11,6,8,4,8,8,6,7,4,8,9,6,10,1,8
Z,3,5,5,7,4,9,5,4,4,7,3,6,2,8,7,6
S,5,10,6,7,3,9,6,5,8,11,3,7,2,8,5,11
V,4,6,4,4,2,1,11,5,3,12,12,8,2,10,1,7
N,3,6,4,4,3,8,8,6,5,6,6,5,5,9,2,5
I,1,3,1,1,0,8,7,1,7,13,6,8,0,8,0,8
G,4,9,4,6,3,6,7,6,5,10,8,10,2,9,4,9
V,2,3,4,5,1,7,8,4,2,7,13,8,3,10,0,8
Z,10,10,7,14,6,7,8,4,2,11,7,7,3,8,13,5
B,4,10,4,8,4,6,7,9,6,7,6,7,2,8,9,10
Z,3,5,4,4,3,7,7,5,9,6,6,8,1,8,7,8
C,2,1,2,2,1,6,7,6,9,7,6,14,0,8,4,9
H,5,10,6,7,3,7,6,15,1,7,8,8,3,8,0,8
C,3,8,4,6,2,5,8,7,7,8,8,14,2,10,4,10
X,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8
F,1,0,2,1,0,3,12,5,2,11,8,6,0,8,2,7
X,4,5,7,4,3,5,8,2,9,11,10,9,3,7,4,5
L,5,8,6,6,5,6,6,6,6,6,5,8,5,8,4,9
I,3,8,4,6,2,6,9,0,7,13,7,7,0,8,1,7
S,3,5,4,3,3,8,8,7,5,7,6,8,2,8,9,8
E,5,4,5,6,3,3,7,6,11,7,6,15,0,8,7,7
W,3,8,5,6,9,7,7,6,1,7,6,8,8,12,2,11
R,2,1,2,2,2,6,7,4,4,6,5,6,2,7,3,8
L,4,7,5,5,2,8,3,2,8,9,2,9,1,6,3,9
Y,6,9,8,7,7,9,3,7,5,7,9,7,3,8,8,3
X,3,9,4,7,3,7,7,4,4,7,6,8,3,8,4,8
U,9,14,8,8,5,6,5,5,5,6,8,9,6,8,4,9
O,7,14,5,8,4,5,8,7,4,10,8,9,5,10,5,8
X,4,10,7,8,4,10,6,2,8,10,1,7,3,8,4,9
A,3,10,5,7,2,7,5,3,1,6,0,8,3,7,2,7
A,4,10,7,8,5,7,3,1,2,5,2,8,4,5,4,6
N,3,9,4,7,4,7,7,12,1,6,6,8,5,8,0,7
P,7,9,9,7,5,8,11,7,4,11,4,3,2,10,4,8
E,4,9,6,7,5,6,8,3,7,11,8,8,3,8,5,6
K,3,6,5,4,3,5,6,6,7,7,7,12,3,8,5,11
Q,4,7,5,6,2,8,8,8,6,5,8,9,3,8,5,10
G,7,10,6,5,4,8,4,4,3,8,6,10,4,9,6,7
K,6,10,8,8,5,5,8,2,7,10,8,11,3,8,3,7
B,4,8,6,6,5,10,6,2,6,10,3,7,4,8,5,11
H,7,9,10,7,7,6,8,3,7,10,9,9,8,9,6,6
D,3,8,5,6,3,11,6,3,7,11,3,6,3,8,3,9
A,2,7,4,5,3,11,3,2,2,9,2,9,1,6,2,8
T,2,3,3,2,2,8,11,3,6,6,10,8,2,11,1,8
I,1,6,2,4,1,7,7,1,8,7,6,8,0,8,3,8
B,3,5,4,4,3,8,7,6,6,6,6,5,2,8,7,8
O,6,11,7,8,5,7,7,8,5,10,6,8,3,8,3,8
Z,4,9,5,7,5,8,8,3,8,7,7,7,1,8,11,7
Q,3,3,4,4,3,7,8,5,3,8,8,10,3,9,5,8
K,9,15,9,8,6,2,9,3,6,9,9,12,4,7,3,6
N,6,10,9,7,5,10,7,3,5,10,3,5,6,9,1,7
X,3,4,5,3,2,7,7,4,9,6,6,8,2,8,6,8
U,8,14,7,8,5,7,6,5,5,6,7,8,5,7,3,7
T,6,11,6,8,5,4,12,3,6,12,10,5,2,12,2,4
X,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8
D,4,6,6,4,4,9,7,3,6,11,4,6,3,8,3,8
P,6,9,8,7,4,7,10,5,4,12,5,3,1,10,3,9
N,4,5,6,4,3,7,8,2,5,10,6,6,5,9,1,7
K,8,14,8,8,4,7,7,3,6,9,9,9,6,11,4,7
W,8,8,11,7,12,7,7,5,5,6,5,8,10,9,9,8
Z,2,5,4,3,2,8,7,2,9,11,6,8,1,7,6,7
H,3,5,5,4,3,7,8,5,7,7,6,8,6,8,3,8
G,2,3,3,2,1,7,7,6,6,7,5,10,2,9,4,9
K,4,9,4,6,2,3,8,7,2,7,5,11,3,8,3,10
W,4,9,7,7,5,9,10,2,3,6,9,8,7,11,1,8
U,2,1,2,1,1,7,8,6,5,7,9,9,3,10,0,8
E,5,9,5,7,3,3,7,6,11,7,6,14,0,8,8,7
T,3,3,3,2,1,5,11,2,6,11,9,5,1,10,2,5
Z,1,3,3,2,1,8,7,1,9,11,6,8,1,8,5,7
R,4,10,5,7,3,5,11,9,4,7,3,9,3,7,6,11
S,7,10,9,8,5,7,8,4,9,11,5,6,2,6,5,8
L,5,11,7,8,4,6,4,0,9,8,2,11,0,7,2,8
X,5,8,7,6,5,8,6,3,5,6,7,8,2,9,8,9
C,6,11,7,8,5,5,7,7,9,8,6,13,2,11,5,8
M,5,9,8,7,11,8,6,3,2,8,4,8,13,6,3,7
Z,4,8,5,6,5,9,6,5,9,7,5,7,1,7,7,8
J,5,7,7,9,6,9,8,5,5,6,5,8,3,8,9,7
A,3,6,5,5,4,9,7,4,5,6,8,5,4,10,4,5
F,5,11,7,8,7,5,9,4,6,10,10,6,2,9,3,6
X,3,5,4,4,3,8,7,3,9,6,6,8,2,8,6,8
C,4,9,5,7,4,5,7,8,7,10,8,13,2,11,4,10
E,9,14,7,8,5,7,9,5,5,10,6,10,3,8,7,10
H,3,6,5,4,4,5,9,3,6,10,8,8,3,8,3,6
S,4,6,5,4,3,8,7,3,7,10,6,8,2,9,4,8
I,1,4,1,3,1,7,7,1,7,7,6,8,0,8,2,8
X,5,11,6,6,3,4,9,3,7,11,10,9,4,10,3,5
O,7,15,5,8,4,6,7,7,4,9,7,9,5,10,5,8
Q,5,10,7,8,4,8,9,8,7,5,9,9,3,7,6,10
F,2,3,4,2,1,6,11,3,5,13,7,4,1,9,1,7
S,3,4,3,2,2,8,8,6,5,7,6,7,2,8,9,8
E,7,11,10,8,6,9,7,2,9,12,4,8,5,7,7,10
W,4,6,6,4,5,8,7,6,3,6,8,8,6,8,3,7
Y,4,11,7,8,1,9,10,3,2,6,13,8,2,11,0,8
O,7,11,8,8,6,8,6,8,6,10,4,8,4,9,5,6
W,4,3,6,4,2,11,8,5,1,6,9,8,8,10,0,8
Z,6,7,5,10,4,8,5,5,5,11,6,7,3,9,9,8
N,2,3,4,1,1,5,9,3,4,11,8,8,5,8,0,7
V,4,8,4,6,3,4,11,2,2,9,10,8,3,12,1,8
N,9,13,8,7,4,5,9,4,6,3,4,11,6,10,2,7
P,9,10,7,5,3,7,9,7,5,14,4,4,4,10,4,8
K,1,0,1,0,0,4,6,6,2,7,6,11,3,7,2,10
F,5,10,8,8,5,6,10,3,6,13,7,5,2,9,3,7
R,5,9,5,4,3,8,8,3,5,9,2,6,5,6,5,7
D,4,7,6,5,4,9,7,4,6,10,5,5,3,8,3,8
Z,3,3,4,4,3,7,7,5,10,6,6,8,2,8,7,8
E,4,9,6,7,7,7,8,3,6,5,7,10,5,11,7,8
V,3,11,5,8,4,8,11,2,3,5,10,8,3,12,1,8
I,5,7,6,8,6,8,10,4,5,6,7,9,3,6,8,6
L,3,3,4,5,1,0,0,6,6,0,1,5,0,8,0,8
G,2,1,3,2,2,7,7,5,4,7,6,9,3,6,4,9
G,4,5,5,8,2,7,6,8,9,6,5,11,1,8,6,11
F,3,6,5,4,4,7,10,1,4,9,6,6,4,9,2,5
U,5,5,6,7,2,7,4,14,6,7,15,8,3,9,0,8
D,5,8,5,6,4,6,7,9,8,7,7,6,2,8,3,8
W,8,9,8,4,3,7,10,3,2,7,10,7,9,12,1,6
D,5,5,6,8,3,5,7,10,9,7,6,5,3,8,4,8
I,2,2,1,3,1,7,7,1,8,7,6,8,0,8,3,8
L,2,2,3,3,1,4,4,4,7,2,1,6,0,7,1,6
T,5,8,7,7,7,6,7,3,8,7,6,9,4,6,9,4
M,8,11,10,8,13,7,7,7,4,7,5,8,7,11,9,8
D,4,11,4,8,3,5,7,11,8,7,6,5,3,8,4,8
F,6,9,8,7,4,5,11,1,6,13,7,4,1,10,2,7
D,2,4,4,3,2,8,7,4,6,10,5,6,2,8,2,8
Z,2,4,4,3,2,7,8,2,9,12,6,8,1,8,5,7
F,3,4,5,3,2,5,11,3,5,13,7,5,1,9,1,7
H,4,9,6,6,5,7,6,7,7,7,7,9,3,8,3,9
Q,3,5,4,6,2,8,8,7,5,5,8,9,3,8,5,9
X,2,2,4,3,2,8,7,3,9,6,6,8,2,8,6,8
A,4,8,6,7,6,7,8,2,4,7,8,8,5,8,5,6
G,4,6,6,4,5,7,7,5,3,6,6,10,4,8,7,8
V,9,13,7,7,4,7,8,7,4,8,9,6,7,13,3,9
Y,7,8,5,12,4,10,10,1,4,7,10,5,3,9,5,10
M,4,7,5,5,5,8,6,6,4,7,7,8,8,5,2,7
G,3,9,5,6,4,6,6,6,6,7,5,12,2,9,4,9
V,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8
A,2,1,4,2,1,8,2,2,2,8,1,8,2,6,2,7
A,3,5,5,4,2,11,2,2,2,9,2,9,2,6,2,8
O,4,9,6,6,4,7,6,8,5,7,4,8,3,8,3,8
I,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
M,4,4,5,3,3,7,6,6,5,6,7,7,8,6,3,7
W,4,7,6,5,5,7,9,6,4,8,8,7,6,8,3,8
W,5,7,7,5,3,6,8,5,1,7,8,8,8,9,0,8
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
L,4,10,6,8,3,5,4,1,9,7,2,11,0,7,3,7
U,6,9,8,8,8,7,6,4,5,6,6,8,7,9,2,8
I,2,9,3,7,2,9,7,0,7,13,5,8,0,8,1,8
I,1,3,1,2,0,7,7,1,8,7,6,8,0,8,3,8
Q,3,8,4,7,2,8,7,8,5,6,7,8,3,8,5,9
U,4,4,5,3,2,4,7,5,8,10,8,9,4,11,3,4
G,3,6,4,4,4,7,8,5,3,6,6,9,3,7,6,8
L,4,9,5,8,5,7,6,5,5,7,7,8,3,9,8,11
A,2,7,3,5,2,12,3,2,2,10,2,9,2,6,2,8
I,1,5,2,3,1,7,7,0,7,13,6,8,0,8,1,8
U,2,3,3,2,1,5,8,5,6,10,9,9,3,10,2,6
B,5,9,7,7,6,9,7,3,7,10,4,6,2,8,5,10
I,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
O,6,11,6,8,5,8,7,8,5,10,5,8,4,9,4,6
C,5,11,6,8,3,6,7,7,11,5,6,14,1,7,4,8
G,4,9,5,7,3,8,7,8,7,6,6,9,2,7,6,10
D,4,8,6,6,4,6,8,7,7,11,7,5,3,8,4,8
C,4,8,5,6,3,5,8,7,8,8,8,14,2,9,4,9
M,4,7,6,5,4,4,7,4,5,10,11,10,6,5,2,6
S,4,11,5,8,4,8,8,8,7,8,4,7,2,7,9,8
R,3,4,4,2,3,7,7,5,5,7,5,7,2,7,4,8
Q,5,6,7,9,10,8,9,5,2,6,7,9,9,14,10,14
B,7,11,10,8,7,11,6,3,8,11,3,7,3,8,5,12
O,1,0,2,0,0,7,7,6,4,7,6,8,2,8,3,8
U,5,8,5,6,4,8,6,12,4,7,12,8,3,9,0,8
I,3,8,4,6,2,7,7,0,7,13,6,8,0,8,1,7
M,8,12,9,6,5,3,8,5,6,4,2,11,8,8,2,8
K,5,6,7,4,3,9,7,2,7,11,3,7,3,8,3,9
C,1,0,1,1,0,6,7,6,7,7,6,13,0,8,4,10
W,2,0,3,1,1,8,8,4,0,7,8,8,6,10,0,8
A,3,9,5,6,3,12,2,4,2,11,2,9,3,7,3,9
J,2,6,3,4,2,7,7,3,5,14,6,10,1,6,0,7
E,7,10,5,5,2,7,8,5,7,10,6,8,1,9,7,8
H,5,9,8,7,8,7,7,6,6,7,6,8,3,8,3,8
S,2,3,3,1,1,7,9,3,7,11,7,7,1,9,4,6
T,4,7,5,5,4,8,10,3,6,10,8,5,2,12,3,5
P,12,13,9,8,4,7,9,6,4,11,4,5,5,9,5,8
T,2,3,3,2,1,7,12,3,5,7,10,8,2,11,1,8
F,3,9,4,6,1,1,14,5,3,12,10,5,0,8,2,6
O,2,2,3,4,2,7,7,7,4,7,6,8,2,8,3,8
N,5,11,5,8,5,7,7,13,1,6,6,8,6,8,1,9
Q,4,8,5,8,3,8,6,9,7,7,4,9,3,8,4,8
E,4,7,5,6,5,6,7,4,4,7,7,9,4,11,8,11
V,8,11,7,8,4,4,11,3,4,9,11,7,2,10,1,8
D,4,11,5,8,5,8,7,6,7,7,6,5,6,8,3,7
T,5,10,5,5,3,7,9,2,6,11,6,7,2,9,4,6
P,4,9,6,6,4,7,11,7,3,11,5,3,2,10,4,8
J,5,10,7,8,3,7,7,3,6,15,6,10,3,8,2,8
T,3,4,4,3,1,5,11,2,7,11,9,5,1,10,2,5
I,2,9,2,6,2,7,7,0,8,7,6,8,0,8,3,8
B,3,5,5,4,5,7,8,4,4,7,6,8,6,9,7,8
R,5,8,7,6,7,7,6,3,5,7,6,8,6,9,6,7
T,3,10,5,7,1,10,15,1,6,4,11,9,0,8,0,8
K,3,8,5,6,3,5,7,5,7,6,6,11,3,8,5,9
C,4,8,5,6,3,6,8,7,8,8,8,14,2,10,4,9
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
I,0,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8
F,4,10,4,8,3,1,13,4,3,12,10,6,0,8,2,6
I,4,10,6,8,7,8,9,3,5,9,5,4,6,9,7,2
W,3,2,5,4,3,7,10,2,2,7,9,8,7,11,0,8
S,4,10,5,7,4,7,7,5,8,4,6,10,1,10,9,9
U,3,5,4,4,2,7,8,5,6,5,9,9,5,10,1,7
V,5,9,7,7,4,7,12,2,3,5,11,9,4,10,2,7
Y,1,1,2,1,1,7,10,1,5,7,11,8,1,11,1,8
L,5,10,8,8,9,7,7,3,6,6,7,11,6,12,6,6
I,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8
E,4,9,5,7,4,4,9,3,8,10,8,10,2,8,4,6
M,3,4,4,3,3,7,6,6,4,6,7,8,7,5,2,8
G,4,9,5,7,2,8,6,8,8,6,6,12,2,8,5,10
E,6,9,8,7,8,8,10,6,3,6,6,9,5,7,7,9
D,3,6,5,4,6,9,9,4,4,7,6,6,3,8,7,5
H,5,10,8,8,8,7,7,5,7,7,6,8,6,8,4,8
Y,3,8,5,5,1,8,10,2,3,7,12,8,1,11,0,8
B,4,6,6,4,3,10,7,3,8,11,3,6,2,8,5,10
G,6,9,7,7,4,6,7,7,8,10,7,11,2,9,4,9
N,4,9,5,4,2,7,8,3,3,12,9,9,5,9,0,8
P,4,7,4,5,2,4,14,8,1,11,6,3,0,10,4,8
L,5,9,7,8,6,7,8,3,6,6,7,9,3,8,8,8
T,4,7,5,5,4,7,7,7,6,7,8,8,3,10,5,9
U,6,11,6,8,3,8,4,14,6,6,15,8,3,9,0,8
K,4,5,5,4,3,5,6,4,8,7,7,11,3,8,5,10
K,5,11,6,8,5,4,8,8,3,6,3,11,3,8,2,11
I,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
B,2,5,3,4,3,7,7,5,5,6,6,6,2,8,5,9
J,5,11,7,8,8,8,6,4,4,8,5,5,6,6,6,3
S,4,10,5,8,2,7,7,6,9,4,6,8,0,8,9,8
Q,3,5,4,6,4,7,8,5,2,7,8,11,2,9,5,8
L,4,10,5,7,3,0,2,4,6,1,0,8,0,8,0,8
K,6,10,6,5,4,9,6,3,6,11,2,8,5,6,4,9
Z,1,3,2,2,1,7,7,1,8,11,6,9,1,9,5,8
K,5,5,5,8,2,4,6,8,2,7,7,12,3,8,3,11
L,3,6,4,4,2,6,5,1,9,7,2,10,0,7,3,8
S,2,3,4,1,1,8,8,2,7,10,6,7,1,9,4,8
H,1,0,1,0,0,7,8,10,1,7,6,8,2,8,0,8
N,4,8,5,6,5,6,6,7,5,6,5,7,3,7,3,8
H,9,11,12,8,8,7,7,3,6,10,7,9,3,8,4,8
K,4,9,6,7,7,7,6,5,5,6,5,7,7,6,8,14
C,4,9,6,7,2,6,6,7,11,9,5,13,1,10,4,9
S,2,3,3,1,1,8,8,2,7,10,6,7,1,9,4,8
S,4,7,6,5,7,7,8,3,3,8,6,7,3,7,12,2
H,1,1,2,1,1,7,7,11,1,7,6,8,3,8,0,8
Z,9,10,6,14,6,6,9,5,2,12,8,7,3,8,12,5
B,3,7,4,5,4,8,8,6,6,7,5,6,2,8,6,9
Q,5,8,6,7,3,9,7,9,7,6,6,10,3,8,5,9
D,3,6,4,4,3,8,7,5,7,10,5,6,3,8,3,9
G,7,12,6,6,4,7,8,4,3,9,5,6,3,9,10,7
D,3,6,4,4,4,8,7,5,8,7,7,5,3,8,3,7
S,5,11,6,8,4,8,7,3,6,10,7,9,2,10,5,8
U,4,9,6,6,5,8,8,8,5,6,7,9,3,8,4,6
E,4,9,3,4,2,8,7,3,4,10,5,8,3,9,8,11
B,1,3,3,2,2,9,6,3,5,10,5,7,2,8,4,9
I,3,7,4,5,2,6,8,0,7,13,7,8,0,8,1,7
Z,2,4,3,2,2,7,7,5,9,6,6,8,2,8,7,8
C,3,7,4,5,2,5,8,7,8,7,8,14,1,8,4,10
H,6,8,9,6,5,5,8,4,7,10,10,10,5,10,4,6
O,3,6,4,4,2,9,9,8,7,5,8,10,3,8,4,8
Z,2,3,4,2,1,7,7,2,9,12,6,9,1,9,5,7
F,6,11,6,6,4,10,7,2,5,10,5,6,4,8,6,9
X,4,8,4,6,3,7,7,4,4,7,6,8,3,8,4,8
W,7,9,8,4,4,4,8,2,3,8,10,8,9,11,2,5
Z,5,7,4,11,4,7,8,4,2,12,7,7,3,9,11,5
A,4,9,6,8,6,10,7,4,6,6,9,5,5,11,4,4
A,4,11,7,8,4,12,2,3,3,10,2,9,3,7,4,9
N,3,5,6,4,3,6,9,2,4,10,7,7,5,8,1,8
C,5,5,6,8,3,5,7,7,11,7,6,12,1,8,4,9
R,4,7,5,5,4,8,9,6,6,8,5,8,3,7,6,11
R,5,5,5,8,3,6,10,10,4,7,5,8,3,8,6,11
U,7,11,10,8,8,8,7,8,6,6,7,9,4,9,5,6
G,5,4,6,6,3,8,8,8,8,6,7,8,2,7,6,11
B,4,8,5,6,5,10,7,3,6,10,4,6,2,8,4,10
M,2,3,3,2,2,8,6,6,4,7,8,8,6,5,2,8
R,2,4,4,3,3,8,7,4,5,8,5,7,3,7,4,11
X,10,13,9,7,4,6,7,2,9,9,7,9,4,6,4,6
N,4,7,6,5,4,8,8,6,5,7,6,5,5,9,2,6
H,3,2,5,3,3,8,7,6,6,7,6,7,3,8,3,7
W,5,9,8,6,3,4,8,5,2,7,9,8,8,9,0,8
E,4,9,4,6,4,3,9,5,10,7,6,14,0,8,6,8
L,3,7,5,5,3,4,3,8,6,1,3,3,1,6,0,6
I,7,13,6,8,3,10,7,2,5,11,5,6,2,10,5,11
X,4,6,6,4,4,7,7,2,6,7,5,8,3,6,6,8
E,4,6,5,4,5,7,7,3,6,7,7,11,4,9,7,8
L,2,5,4,3,2,6,4,1,8,8,2,10,0,7,2,8
B,2,1,2,2,2,7,7,4,4,6,6,6,2,8,4,10
K,4,7,6,6,6,10,6,2,3,9,3,8,5,6,6,13
R,2,4,3,3,2,10,6,3,5,10,3,7,2,7,3,10
O,4,11,5,8,4,7,7,9,5,7,6,8,3,8,3,8
C,7,10,5,6,2,5,10,5,8,11,8,9,2,8,5,8
D,5,8,6,6,5,8,7,6,9,7,6,5,6,8,3,7
V,2,7,4,5,2,9,11,3,3,3,11,9,2,10,1,8
Z,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8
Q,4,8,5,9,5,8,7,7,4,9,6,9,3,8,6,8
L,7,13,7,7,5,9,3,4,3,11,6,10,4,8,7,9
L,5,10,7,8,3,8,3,2,8,9,2,9,1,6,3,8
O,4,5,6,7,3,9,6,9,8,8,4,9,3,8,4,8
N,6,11,8,8,5,7,8,6,6,7,7,5,7,10,3,4
U,1,0,2,1,0,7,5,10,4,7,12,8,3,10,0,8
D,6,11,6,6,4,6,8,4,7,10,6,7,5,8,6,5
A,2,6,4,4,2,8,3,2,2,7,1,8,2,6,2,6
L,4,10,6,7,4,9,3,2,7,8,2,9,3,4,4,9
Z,3,5,6,4,3,7,7,2,10,12,6,8,1,8,6,8
E,2,1,2,3,2,7,7,6,6,7,6,8,2,8,5,10
E,2,4,2,3,2,7,7,5,7,7,6,8,2,8,5,10
N,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8
W,4,9,6,6,3,7,8,4,1,6,8,8,8,9,0,8
B,5,9,6,7,6,8,8,6,7,7,6,6,6,8,7,11
Q,5,9,7,11,8,9,8,8,3,5,6,10,3,9,6,10
V,4,9,6,7,2,8,8,5,3,6,14,8,3,9,0,8
G,4,8,5,6,4,8,7,8,6,6,7,9,2,7,5,10
X,6,9,10,7,4,8,8,1,9,10,5,7,3,8,4,8
J,6,9,8,10,7,7,8,4,6,7,7,7,4,8,10,10
R,3,2,4,4,3,7,7,5,5,6,5,6,3,7,4,8
E,3,6,5,4,3,5,8,3,8,11,8,9,2,8,4,7
M,7,13,8,7,5,9,3,2,2,9,4,9,6,2,1,9
W,6,9,6,5,3,2,10,2,3,10,11,9,6,11,1,6
K,8,11,11,8,7,5,7,2,7,10,8,11,3,8,3,7
C,3,6,5,4,1,6,6,6,10,7,5,13,1,9,4,9
D,2,5,4,3,3,9,6,3,5,10,4,6,3,7,3,8
I,1,2,1,3,1,7,7,1,7,7,6,8,0,8,2,8
J,3,5,5,4,2,8,6,3,7,15,5,10,1,6,1,7
T,5,7,6,5,4,7,9,1,9,10,9,5,0,9,3,5
J,3,6,4,4,1,8,6,5,6,15,7,12,1,6,1,7
L,3,11,5,8,4,3,5,2,8,3,1,9,0,7,1,6
F,7,10,6,5,4,8,9,3,4,11,6,5,3,9,6,7
Z,2,4,5,3,2,8,7,2,9,12,6,8,1,8,5,8
E,3,7,5,5,3,8,7,2,9,11,5,8,2,8,5,9
V,9,14,8,8,5,6,9,4,4,8,8,5,5,13,3,7
E,4,11,6,8,5,10,6,2,7,11,4,9,3,7,6,12
X,6,8,8,7,8,8,8,2,5,8,6,8,4,8,8,8
D,3,5,4,4,3,7,7,7,7,6,6,5,2,8,3,7
K,7,9,10,6,7,9,7,1,6,10,3,8,6,7,5,10
B,2,5,4,4,3,9,7,2,6,11,5,7,4,7,5,9
K,4,8,6,6,6,6,8,5,4,7,5,8,4,6,9,9
I,5,12,5,6,3,9,8,2,5,12,4,5,2,10,5,11
L,6,10,8,8,9,6,7,3,6,7,7,11,6,12,6,7
X,5,5,6,8,2,7,7,5,4,7,6,8,3,8,4,8
Q,8,13,7,7,6,9,6,4,7,10,6,7,4,8,10,9
W,8,9,8,6,5,1,10,2,3,10,11,9,7,10,1,7
G,5,11,6,8,3,7,6,8,9,5,5,10,1,8,6,11
P,3,7,3,4,2,4,12,8,2,10,6,4,1,10,3,8
D,3,5,4,4,3,7,7,7,6,6,6,5,2,8,3,7
G,4,4,5,6,2,7,5,7,8,6,5,11,1,8,6,11
U,4,6,5,4,4,7,8,7,5,5,7,11,4,9,5,5
Z,4,6,5,4,3,7,7,2,9,11,6,8,1,9,6,8
U,3,7,3,5,2,8,6,10,4,7,12,9,3,9,0,8
M,4,6,6,4,4,11,7,2,4,9,3,6,7,6,2,8
A,2,1,3,1,1,7,4,2,0,7,2,8,3,6,1,8
V,5,11,8,8,5,7,12,2,3,5,10,9,4,11,2,7
K,5,9,8,6,4,3,8,3,8,12,12,12,3,8,4,5
A,1,0,2,0,0,7,4,2,0,7,2,8,2,6,1,8
W,5,8,7,6,3,10,8,5,1,7,8,8,8,9,0,8
R,3,8,5,6,4,8,7,4,5,9,3,8,3,7,4,11
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
D,5,9,6,7,7,7,7,7,6,7,6,4,3,8,3,8
W,4,5,6,4,6,8,7,4,4,6,5,8,9,8,6,9
E,5,9,6,8,7,6,8,4,4,8,7,9,5,10,9,11
G,5,11,7,8,9,8,7,5,2,6,6,10,7,8,6,12
O,4,10,6,7,3,8,7,9,8,7,6,8,3,8,4,8
C,4,7,5,5,2,6,7,6,7,12,8,13,2,10,3,8
J,5,8,6,6,3,7,6,3,5,15,7,11,1,6,1,7
O,4,4,5,6,2,7,7,8,8,6,5,7,3,8,4,8
I,3,10,6,8,6,9,6,2,4,8,5,5,5,8,5,6
A,5,9,8,6,6,9,5,2,5,8,1,6,3,7,4,7
S,2,3,2,1,1,8,8,6,4,7,6,7,2,8,8,8
P,7,12,6,6,4,8,9,4,4,12,5,4,4,10,6,6
V,2,3,3,2,1,5,12,2,2,8,10,7,2,11,0,8
V,4,11,6,8,4,6,11,2,3,6,11,9,2,10,1,8
T,7,13,6,7,4,7,8,2,7,11,6,7,3,8,5,5
M,6,8,9,7,10,9,8,5,4,7,6,7,13,10,7,5
G,3,8,4,6,3,6,6,5,4,5,6,9,2,8,4,8
D,5,10,8,8,5,9,6,3,7,11,4,6,3,8,3,9
O,7,10,5,6,3,10,5,6,5,9,3,8,5,9,5,9
T,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8
B,3,5,5,4,3,9,7,2,6,11,5,7,2,8,4,10
L,2,6,2,4,1,0,1,5,6,0,0,6,0,8,0,8
C,4,8,5,6,2,6,8,6,10,6,6,13,1,8,4,8
X,6,11,9,8,5,7,7,1,9,10,6,8,3,8,4,7
Y,2,6,4,4,0,9,10,2,2,6,12,8,1,11,0,8
P,3,7,4,5,3,4,12,6,4,12,8,3,1,10,3,6
B,5,11,5,6,5,8,7,3,4,9,6,7,6,7,8,7
K,2,6,3,4,1,3,7,7,2,7,6,11,3,8,2,11
Y,4,11,6,8,3,6,11,2,3,8,12,8,1,11,0,8
Q,2,3,3,4,2,7,9,5,2,7,9,10,2,9,5,8
P,7,13,6,7,4,9,8,4,5,13,4,4,4,10,6,6
U,2,3,3,2,1,6,8,5,7,9,7,8,3,9,3,6
R,4,8,6,6,5,6,8,5,6,7,5,7,6,7,5,8
S,7,11,8,9,6,9,7,3,6,10,5,8,3,10,6,9
X,5,10,7,8,6,8,7,3,5,6,7,6,4,10,10,9
B,4,10,6,8,7,8,8,4,5,10,5,6,3,8,5,10
A,5,11,8,8,4,10,2,2,3,9,1,8,2,7,3,7
P,3,2,3,4,3,6,9,4,4,9,8,4,4,10,3,7
K,4,2,5,3,3,5,7,4,8,7,6,11,3,8,5,9
K,3,4,5,3,3,9,6,2,6,10,4,9,4,7,4,9
A,2,4,4,5,2,8,6,3,1,7,0,8,2,7,2,8
Y,4,11,6,8,4,7,9,2,6,6,12,9,2,11,2,8
Q,7,9,7,11,8,7,10,4,4,7,10,11,5,11,10,9
E,4,11,5,8,6,7,7,5,8,7,6,9,3,8,6,9
W,5,8,5,6,4,1,10,3,3,11,11,9,5,11,1,7
J,5,10,7,7,3,6,8,3,6,15,7,10,1,6,1,6
C,7,9,7,7,3,3,8,4,9,10,10,13,1,7,3,7
U,4,6,4,4,2,7,4,14,5,7,13,8,3,9,0,8
H,3,4,5,2,2,7,6,3,5,10,6,9,3,7,3,8
F,3,6,5,4,2,6,11,2,5,13,7,4,1,10,2,8
S,5,10,6,8,5,7,7,5,8,5,6,11,1,10,10,9
M,5,9,6,7,4,8,7,13,2,6,9,8,8,6,0,8
G,7,10,7,8,5,5,7,6,6,9,7,12,2,9,4,10
C,4,9,6,6,2,7,8,7,10,5,6,13,1,7,4,9
W,4,7,6,5,3,5,8,4,1,7,9,8,8,9,0,8
R,6,10,8,7,8,8,5,6,4,8,6,8,7,6,6,11
R,5,8,7,6,6,6,7,6,6,6,5,7,3,8,5,8
D,2,1,3,3,2,7,7,6,6,6,5,5,2,8,3,7
T,3,9,4,6,1,8,15,1,6,6,11,8,0,8,0,8
E,6,11,9,8,6,6,7,2,9,11,6,9,2,8,5,8
O,5,6,7,5,4,7,5,4,5,9,5,8,3,6,5,7
D,3,4,4,2,2,7,7,7,7,7,6,5,2,8,3,7
R,4,6,5,4,4,7,7,5,7,7,6,6,3,7,5,8
A,4,9,6,7,6,7,8,8,4,6,6,9,3,7,8,5
M,4,5,7,4,4,6,6,3,4,9,9,10,7,5,2,8
G,3,5,4,4,2,6,6,5,5,9,7,11,2,8,4,10
X,3,10,5,7,4,7,7,3,8,6,6,8,3,8,7,7
G,3,7,4,5,3,6,6,5,4,6,6,9,2,8,3,8
Y,2,7,4,5,1,8,11,0,3,6,11,8,0,10,0,8
N,8,11,11,8,5,7,8,3,5,10,6,7,7,9,2,7
P,2,3,3,2,2,6,10,6,2,11,5,4,1,9,2,9
D,5,10,5,5,3,9,5,4,5,12,3,8,5,7,5,10
L,5,10,7,8,5,7,4,1,8,8,2,10,0,6,3,8
G,5,5,6,7,3,7,7,8,7,6,6,7,2,7,7,11
A,2,7,4,5,3,12,4,3,2,9,2,9,2,6,2,8
Q,3,3,4,4,3,8,7,6,2,8,7,10,2,8,4,9
N,3,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8
A,3,2,5,4,2,8,2,2,2,7,2,8,2,7,2,7
C,2,3,2,2,1,4,8,4,6,11,9,11,1,9,2,7
Z,2,3,2,1,1,8,7,5,8,6,6,7,1,8,6,8
A,4,8,7,6,5,6,5,2,3,4,2,6,2,6,3,4
I,3,9,5,6,3,7,7,0,7,13,6,9,0,8,1,8
Y,3,5,4,4,2,5,9,1,8,10,9,5,2,10,4,4
O,3,5,5,4,3,6,6,6,5,9,5,8,3,6,4,7
S,4,9,4,4,2,8,3,3,4,8,2,8,3,6,5,8
K,7,11,10,8,8,10,6,1,6,10,3,8,6,8,6,11
D,2,1,3,2,2,7,7,6,6,7,6,5,2,8,2,7
H,4,7,4,5,4,7,8,12,1,7,4,8,3,8,0,8
K,2,6,3,4,1,3,7,7,3,7,7,11,3,8,2,10
H,1,0,1,0,0,7,8,10,1,7,6,8,2,8,0,8
Q,2,3,3,5,3,8,7,7,3,6,6,9,3,8,5,10
S,5,9,7,6,3,8,8,4,8,11,6,7,2,8,5,7
N,3,1,3,3,2,7,9,5,4,7,6,7,4,8,1,7
X,5,6,7,4,3,7,8,1,8,10,6,8,3,8,3,7
O,5,9,7,7,5,7,7,9,4,7,6,8,3,8,3,8
A,1,0,2,0,0,7,4,2,0,7,2,8,2,7,1,8
C,4,9,5,6,2,6,6,7,10,9,5,14,1,10,4,9
X,4,10,7,8,6,6,8,2,6,7,6,10,4,6,7,7
Q,5,10,7,8,7,8,5,8,3,6,7,8,4,7,7,8
O,5,10,4,6,3,6,7,6,3,9,7,9,5,9,5,8
S,4,6,5,4,3,7,7,3,7,10,5,7,2,8,5,8
E,4,6,6,6,6,6,8,4,4,8,7,9,4,11,8,10
I,1,6,2,4,2,7,7,1,8,7,6,9,0,8,3,8
L,4,6,5,5,4,8,5,5,6,6,7,8,3,8,6,9
X,5,9,8,7,6,6,8,3,6,7,7,11,5,6,8,6
D,4,9,5,7,5,10,6,4,5,10,3,6,3,7,3,8
C,5,10,6,8,2,6,7,7,10,7,6,15,1,8,4,9
N,8,10,9,6,4,8,6,3,4,13,4,8,6,8,0,7
Y,2,7,4,5,1,9,10,1,3,6,12,8,1,11,0,8
T,5,7,5,5,3,7,10,1,10,11,9,5,1,9,4,4
T,5,6,7,6,6,8,10,5,8,7,6,8,3,10,7,7
M,5,11,9,8,12,10,6,3,3,8,4,7,11,6,5,4
J,7,11,6,8,4,10,6,2,5,11,6,8,2,9,7,12
J,2,5,3,3,1,10,5,2,6,14,4,9,0,7,0,8
G,4,8,4,6,3,7,7,6,6,10,7,11,2,9,4,9
I,2,8,3,6,1,7,7,0,7,13,6,8,0,8,1,8
J,6,10,9,7,6,8,6,4,6,8,6,7,3,6,4,7
E,4,10,6,8,4,7,7,3,8,11,7,9,3,8,5,8
R,4,8,5,6,4,6,8,5,5,6,5,8,3,7,5,9
X,4,9,7,7,5,8,7,3,8,5,6,8,2,8,6,8
X,5,10,8,7,5,4,7,1,8,10,10,10,4,7,4,5
Q,2,0,2,1,1,8,7,7,4,6,6,9,2,8,3,8
K,2,7,3,4,1,4,8,8,2,6,4,11,3,8,2,10
C,3,7,4,5,2,6,8,9,8,9,7,10,2,11,4,9
N,5,6,7,6,6,6,9,4,3,6,5,9,8,7,5,9
J,1,3,2,1,0,8,6,3,5,14,7,11,1,7,0,7
U,4,8,5,6,5,8,8,7,6,5,8,9,6,8,3,7
Q,2,3,3,4,2,8,8,5,2,7,8,10,2,9,4,8
Z,4,6,5,4,3,6,9,3,9,11,8,6,1,8,6,6
A,5,11,8,8,5,10,1,2,3,8,2,8,4,8,4,9
N,6,5,6,8,3,7,7,15,2,4,6,8,6,8,0,8
V,3,7,4,5,2,9,9,4,2,5,13,8,2,10,0,8
S,3,2,3,3,2,8,8,7,5,8,5,7,2,8,8,8
T,2,3,3,1,1,5,12,2,7,11,9,4,0,9,1,5
X,4,6,6,4,3,7,7,1,8,10,8,8,3,8,3,7
Y,5,10,7,7,4,4,10,2,8,11,12,9,3,9,2,6
Q,6,14,6,8,4,10,3,4,7,11,3,8,3,9,6,13
Q,4,9,5,8,3,8,9,8,6,6,9,9,3,7,6,10
B,4,8,5,6,5,7,6,5,4,6,5,7,4,9,6,7
L,9,14,8,8,4,10,2,4,4,13,5,13,2,7,6,9
J,4,9,4,7,3,7,10,2,3,13,5,5,2,8,7,9
P,6,9,8,7,7,6,7,7,5,8,7,8,3,9,7,9
F,3,7,6,5,6,10,7,1,5,9,5,6,4,10,5,7
S,5,8,6,6,7,8,9,5,4,8,5,6,4,10,9,7
L,3,5,4,4,3,8,5,5,5,7,7,7,4,9,6,10
D,2,4,4,3,2,8,7,4,6,10,4,5,2,8,2,8
O,5,8,7,6,8,8,10,5,3,7,7,8,7,9,4,9
B,2,3,3,1,2,7,8,5,5,7,6,6,1,8,5,9
C,5,8,7,7,7,5,7,4,5,7,7,11,4,9,7,9
D,6,9,8,7,5,10,6,4,9,11,3,6,3,8,3,9
J,4,8,5,6,3,9,7,2,6,14,3,7,0,7,0,8
T,2,4,3,3,2,7,12,3,6,7,11,8,2,11,1,8
Y,3,3,4,2,1,4,11,2,7,11,10,5,1,11,2,5
V,2,3,3,2,1,4,12,3,2,10,11,7,2,11,0,8
V,4,11,6,8,9,7,6,5,2,8,7,9,8,10,4,9
K,3,8,4,6,3,3,7,6,2,6,5,10,3,8,2,11
C,3,9,4,7,2,5,7,7,10,7,6,13,1,9,4,9
B,5,8,6,6,6,8,8,7,6,7,6,5,3,7,7,9
Z,2,2,3,3,2,8,7,5,9,6,6,7,2,8,7,8
W,8,8,8,6,5,4,11,3,3,9,9,7,8,11,2,6
T,4,5,5,4,2,6,11,2,8,11,9,5,1,11,3,4
Y,2,4,3,3,1,4,12,3,6,12,10,5,1,10,1,5
A,2,6,4,4,2,11,3,2,2,9,2,9,2,6,2,8
W,6,10,9,8,14,9,7,5,2,7,6,8,11,11,4,8
J,5,10,7,8,4,10,2,6,5,15,5,13,0,7,0,7
N,5,9,6,5,3,2,10,5,3,13,12,9,4,9,0,8
R,2,3,2,1,1,7,7,4,5,6,5,7,2,7,4,8
J,1,0,1,1,0,12,3,5,3,12,5,11,0,7,0,8
Y,4,11,6,8,1,10,10,2,2,6,13,8,1,11,0,8
X,4,9,5,7,3,7,7,4,4,7,6,7,3,8,4,8
B,4,8,5,6,4,6,7,9,7,7,6,7,2,8,9,9
W,2,3,4,2,2,7,11,2,2,7,9,8,5,11,0,8
V,8,10,8,7,3,3,11,5,5,12,12,7,3,10,1,8
M,7,9,10,7,6,4,7,3,5,10,10,10,8,5,2,7
M,9,9,12,8,13,8,8,4,4,7,6,7,12,9,7,3
D,4,8,6,6,8,9,8,5,6,7,6,6,4,7,8,8
G,6,10,6,7,5,5,7,6,5,9,7,12,2,9,5,8
O,3,7,3,5,2,8,7,8,5,9,4,6,3,8,3,8
W,3,2,4,3,3,5,10,2,2,8,9,9,6,11,0,8
B,3,2,4,3,3,8,7,5,6,7,6,6,2,8,6,10
U,5,10,6,8,4,6,8,6,7,6,10,9,3,9,1,7
A,6,11,11,8,8,7,5,2,5,5,1,6,6,8,6,6
A,3,8,5,6,3,12,3,3,2,11,2,9,2,6,2,9
Y,3,8,5,6,2,8,10,1,7,5,12,8,1,11,2,8
H,4,7,5,5,4,8,6,13,1,7,7,8,3,8,0,8
M,4,8,6,6,5,8,6,2,4,9,6,8,7,6,2,8
N,5,7,6,6,6,8,6,5,4,7,5,8,7,9,6,3
Q,3,6,4,5,3,8,7,7,5,6,7,9,2,8,4,9
Z,5,11,7,8,4,9,6,3,10,12,4,8,1,7,6,9
L,5,9,7,7,7,6,6,3,7,8,7,11,7,10,6,7
R,4,8,5,6,6,7,7,7,3,8,5,7,4,7,7,10
P,2,3,3,2,1,7,9,3,4,13,5,4,0,9,2,9
V,4,9,6,7,1,7,8,4,3,7,14,8,3,9,0,8
C,7,12,5,7,4,7,7,4,3,9,9,11,4,10,9,11
Q,5,7,6,6,5,8,4,3,5,7,3,8,4,8,4,8
L,2,1,2,3,1,4,4,4,6,2,2,6,0,7,1,6
B,11,13,8,7,5,7,8,5,7,10,5,8,6,6,8,11
S,5,8,6,7,7,8,8,4,5,7,7,8,5,10,10,9
Y,8,15,7,8,4,6,8,5,3,10,9,5,4,10,4,4
K,2,4,3,3,2,5,7,4,7,6,6,11,3,8,5,9
B,3,7,3,5,4,6,6,8,5,6,7,7,2,9,7,10
B,9,12,8,7,7,10,7,4,6,10,3,6,7,4,8,8
K,4,6,6,4,4,7,6,1,6,10,6,9,3,8,3,8
K,6,11,8,8,10,7,7,4,5,6,6,9,8,9,8,7
N,8,12,10,7,4,12,4,3,3,13,1,7,5,7,0,8
M,5,7,7,5,5,8,7,2,4,9,7,8,7,6,2,8
S,5,7,7,6,8,7,8,5,5,7,7,7,5,9,11,12
E,5,9,7,6,6,8,8,6,3,6,6,10,5,8,9,8
U,6,13,6,7,5,6,6,5,5,7,8,8,5,8,3,9
M,6,12,8,7,5,13,2,5,2,11,1,9,7,2,1,8
D,5,10,6,8,7,7,7,6,6,7,6,5,3,8,3,7
R,6,11,7,8,4,6,10,9,4,7,5,8,3,8,6,11
I,1,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8
O,5,8,5,6,5,8,6,7,3,9,5,7,3,8,3,7
M,5,10,7,8,9,7,9,6,5,7,6,8,7,9,6,10
R,1,1,2,1,1,6,9,7,3,7,5,8,2,7,4,11
T,2,1,2,1,0,8,15,1,4,6,10,8,0,8,0,8
E,4,8,6,6,6,6,8,6,8,6,4,11,3,8,6,9
H,5,5,6,7,3,7,7,15,0,7,6,8,3,8,0,8
W,4,3,4,1,2,3,11,3,2,10,10,8,6,11,1,7
U,3,2,4,4,2,6,8,5,7,6,9,9,3,9,1,7
A,4,10,7,8,4,14,3,4,3,12,0,8,2,6,2,9
L,5,9,6,8,5,8,8,3,6,6,6,9,3,8,7,9
T,3,6,3,4,2,5,12,4,5,11,9,5,2,12,1,5
V,8,11,8,8,5,3,12,2,3,9,11,8,6,11,3,6
T,2,1,3,2,2,7,12,3,6,7,10,8,2,11,1,8
T,7,10,7,8,6,6,11,4,6,11,9,5,3,12,2,4
J,2,5,4,4,2,9,7,2,6,14,5,9,1,6,1,8
E,3,5,4,4,3,7,7,6,8,7,6,9,2,8,6,9
N,5,4,5,7,3,7,7,15,2,4,6,8,6,8,0,8
B,6,9,8,6,7,9,8,3,6,10,5,5,3,7,5,9
R,5,10,7,8,7,7,8,5,6,7,5,6,6,7,5,8
H,4,5,4,8,3,7,8,15,1,7,5,8,3,8,0,8
F,4,8,4,5,1,1,15,5,3,13,10,5,0,8,2,5
F,7,10,9,8,4,6,11,3,6,14,6,3,2,10,2,7
P,4,9,4,4,3,8,8,3,4,12,5,4,3,11,5,7
P,3,6,3,4,2,4,12,7,1,10,6,4,1,10,3,8
N,7,9,6,4,2,7,10,4,5,4,5,10,5,9,2,7
Z,2,6,3,4,2,7,7,3,12,8,6,8,0,8,7,8
F,1,0,1,1,0,3,12,4,3,11,9,6,0,8,2,7
B,1,0,1,0,0,7,7,6,4,7,6,7,1,8,5,9
U,1,0,1,0,0,7,6,10,4,7,12,8,3,10,0,8
H,3,5,4,4,4,9,7,6,6,7,6,6,3,8,3,7
D,3,8,4,6,2,6,7,10,9,6,6,6,3,8,4,8
P,3,8,3,6,3,5,11,8,2,9,5,4,1,9,3,7
M,5,8,5,6,5,8,5,11,0,7,8,8,7,5,0,8
O,3,6,5,4,5,8,6,5,2,7,6,8,7,9,3,9
D,5,8,6,7,6,6,7,5,6,7,5,9,5,4,8,4
B,8,12,6,6,4,8,6,5,5,11,5,9,6,6,7,11
N,4,2,5,4,3,7,8,5,6,7,6,5,6,9,2,6
T,2,7,4,5,2,6,11,2,8,8,11,8,1,10,1,7
D,1,4,3,2,2,9,6,3,5,10,5,6,2,8,2,8
E,2,1,2,2,2,7,7,5,7,6,5,8,2,8,6,10
G,4,7,5,5,3,5,7,5,5,9,8,10,2,8,4,9
Y,3,10,5,7,1,7,10,2,2,7,13,8,2,11,0,8
H,3,4,4,6,4,11,5,2,2,8,5,9,3,8,5,11
E,4,9,3,5,3,8,6,3,5,10,5,8,3,9,8,11
J,4,10,6,8,5,7,8,4,5,8,7,7,3,7,4,6
X,4,7,5,5,4,8,7,3,8,6,5,6,3,9,6,8
M,6,9,9,7,11,6,5,4,2,6,4,9,14,6,4,6
C,3,4,5,7,2,5,7,7,10,7,6,12,1,9,4,9
G,5,11,6,8,5,5,7,6,5,8,7,12,4,7,5,8
I,2,10,2,8,3,7,7,0,8,7,6,8,0,8,3,8
W,4,8,7,6,6,7,11,2,2,6,8,8,7,12,1,8
W,3,8,5,6,4,9,10,2,2,6,8,8,7,11,1,8
L,4,8,5,7,5,8,5,5,5,7,7,8,3,8,7,11
T,3,4,4,2,1,5,13,3,6,12,9,3,1,10,1,5
Z,3,8,4,6,3,7,8,3,11,8,6,8,0,8,7,7
E,3,4,4,6,2,3,8,6,11,7,5,15,0,8,7,7
P,5,11,5,8,4,3,13,7,1,11,6,3,0,10,3,8
I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
S,7,11,8,8,4,7,8,4,8,11,7,8,2,9,6,6
D,6,11,6,8,4,5,7,10,10,7,6,6,3,8,4,8
H,4,7,6,5,4,6,8,3,6,10,8,9,3,8,3,7
H,7,10,10,8,6,10,6,3,6,10,3,7,5,7,5,9
U,4,9,6,6,5,6,8,8,5,6,5,12,5,8,7,4
N,2,2,3,3,2,7,9,5,4,7,6,6,4,9,1,6
O,2,4,3,2,2,7,7,7,4,9,6,8,2,8,3,8
D,5,9,6,6,5,5,8,9,7,7,7,6,2,8,3,8
M,4,6,5,4,4,7,5,11,0,7,9,8,8,6,1,8
Z,4,5,6,7,4,11,4,4,4,10,3,9,2,7,5,10
E,5,7,7,5,4,6,8,2,9,11,6,9,2,8,4,8
Q,4,5,5,6,4,8,8,5,2,8,8,10,3,9,5,7
N,5,8,7,6,5,8,8,6,6,6,5,3,8,10,5,8
T,5,10,7,7,8,6,7,3,6,7,7,10,5,8,5,6
R,2,3,3,2,2,8,8,3,5,9,4,7,2,7,3,10
R,6,10,8,8,6,7,9,5,7,5,4,9,4,4,7,8
C,7,9,8,8,8,5,7,4,5,7,6,11,5,9,8,9
L,5,11,6,9,5,4,3,8,7,1,2,4,1,6,1,6
G,4,7,6,5,3,7,6,7,8,8,5,12,2,10,4,8
N,4,8,6,7,6,6,8,5,3,6,5,9,8,7,5,9
N,4,8,5,6,4,7,7,13,1,6,6,8,5,8,0,8
G,4,8,4,6,3,6,7,6,5,9,8,10,2,7,4,9
C,6,10,8,8,5,8,7,8,7,7,6,9,4,8,4,9
D,5,8,6,6,6,10,6,5,6,9,3,6,3,8,3,8
R,9,15,9,8,7,9,7,4,6,9,2,7,6,5,7,7
B,4,10,4,8,6,7,6,9,6,6,7,7,2,9,8,10
W,7,9,9,7,6,7,8,4,1,7,9,8,8,10,0,8
F,3,2,4,3,2,6,10,4,6,10,8,5,4,9,3,7
D,2,4,3,3,2,8,7,5,6,9,5,5,2,8,3,7
L,4,11,6,8,3,4,4,4,9,2,0,6,0,7,1,5
R,2,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10
W,5,11,7,8,4,5,8,5,2,7,8,8,8,10,0,8
B,4,9,4,7,4,7,7,10,7,7,6,7,2,8,9,9
A,4,7,6,5,6,8,5,7,4,8,6,8,5,9,7,4
W,4,9,7,6,5,7,8,4,1,7,9,8,7,11,0,8
C,5,10,6,8,4,5,8,9,9,9,9,12,2,10,4,9
Y,3,6,4,4,2,3,11,2,7,11,10,6,1,11,2,5
A,4,10,7,8,5,12,3,2,2,9,2,8,2,6,2,8
M,6,11,10,8,14,8,7,3,3,8,4,7,12,4,4,6
Z,4,8,5,6,4,7,8,2,9,11,7,8,1,9,6,7
Y,5,7,5,5,3,3,10,2,7,11,11,7,1,11,2,5
H,4,2,5,4,4,9,7,6,6,7,6,6,3,8,3,7
R,4,9,6,7,6,6,7,3,4,7,6,8,6,10,7,5
S,3,9,4,7,2,7,6,6,9,4,6,9,0,9,9,8
B,2,0,2,1,1,7,7,7,5,7,6,7,1,8,7,8
F,5,5,5,8,2,1,14,5,3,12,10,5,0,8,2,5
T,8,11,8,8,7,6,11,3,7,11,9,5,5,11,4,3
H,5,9,8,7,8,8,7,5,7,7,6,9,6,8,4,8
C,2,3,3,2,1,5,9,5,7,12,9,10,1,10,2,7
D,5,11,5,8,3,5,8,11,9,7,7,5,3,8,4,8
T,3,5,4,4,2,7,12,2,8,7,11,8,1,11,1,7
A,1,0,2,0,0,8,3,2,0,7,2,8,2,6,1,8
I,1,1,1,3,1,7,7,1,7,7,6,8,0,8,2,8
A,4,9,6,7,4,13,2,4,2,11,1,8,3,7,3,9
H,7,9,7,4,4,7,8,3,5,11,7,8,6,9,4,8
T,6,8,7,6,8,6,8,4,6,8,6,9,5,8,5,7
T,5,10,7,8,6,6,7,7,7,6,8,9,4,10,7,6
F,4,10,4,7,2,1,11,5,6,11,11,9,0,8,2,6
Q,2,5,3,6,3,8,8,7,2,4,6,10,3,9,5,9
U,5,9,5,5,3,8,6,4,5,6,7,8,4,8,3,7
O,6,5,7,8,3,7,6,8,9,6,5,7,3,8,4,8
M,3,4,5,3,3,8,6,2,4,8,5,7,8,7,2,8
V,4,10,6,8,4,7,11,1,3,6,10,9,3,11,1,8
R,3,4,4,6,3,5,11,8,4,7,2,9,3,7,6,11
S,2,1,2,2,1,8,7,6,5,7,7,8,2,9,8,8
R,7,11,9,8,7,9,8,6,6,8,5,7,4,8,6,12
K,8,10,8,5,3,10,7,4,7,8,1,5,5,7,4,9
R,5,9,8,6,8,6,6,3,4,7,5,8,7,10,7,6
W,6,8,6,6,5,4,10,2,3,9,9,8,7,11,2,6
X,7,10,9,8,8,7,6,3,5,6,6,10,5,5,12,10
A,3,10,5,7,4,11,3,2,2,9,2,10,3,7,3,8
Y,3,7,4,5,2,7,10,1,7,6,12,8,1,11,2,8
P,5,7,6,9,8,7,8,4,3,7,7,7,7,12,6,7
U,4,6,5,4,4,7,7,8,6,7,6,10,3,9,5,5
S,3,4,4,7,2,9,8,6,9,4,6,7,0,7,9,8
P,1,0,1,0,0,5,11,6,1,9,6,5,0,9,2,8
U,3,2,4,4,2,5,8,6,6,8,10,10,3,9,0,8
D,7,11,7,6,4,10,4,6,6,13,4,10,5,7,5,9
Y,7,7,9,10,9,10,12,6,4,6,7,6,5,10,8,5
H,3,4,5,3,3,8,7,3,6,10,4,8,3,7,3,8
G,3,4,4,7,2,6,5,8,8,6,5,10,1,8,6,11
V,5,7,7,6,7,6,9,6,6,8,7,8,5,9,6,11
G,2,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10
X,9,12,8,6,4,7,7,2,9,9,5,8,4,5,4,8
R,7,10,9,8,7,8,8,5,6,9,5,7,4,7,6,12
U,4,10,5,8,2,7,5,14,5,7,15,8,3,9,0,8
C,5,10,7,8,8,7,5,4,4,8,6,11,7,9,4,8
O,5,9,6,7,3,7,7,9,8,7,6,8,3,8,4,8
V,9,12,8,7,4,5,10,4,4,10,8,5,4,11,2,8
M,3,7,4,5,3,7,7,11,1,7,9,8,8,5,0,8
W,6,5,7,4,4,5,11,4,2,9,8,7,7,11,2,6
R,5,11,6,8,4,6,9,10,6,6,5,8,3,8,6,10
V,4,9,6,7,2,7,8,4,3,7,14,8,3,9,0,8
M,4,4,5,6,3,7,7,12,1,7,9,8,8,6,0,8
Y,2,1,4,2,1,7,11,1,7,7,11,8,1,11,2,8
V,5,7,5,5,3,3,11,2,3,10,11,8,2,11,1,8
C,6,9,7,7,3,3,9,5,8,10,10,12,1,8,3,7
F,0,0,1,0,0,3,12,4,2,11,9,6,0,8,2,8
T,4,5,5,4,3,6,11,2,8,11,9,5,3,10,4,4
Z,3,7,4,5,4,6,6,3,7,7,6,10,1,7,10,7
W,3,7,5,5,5,7,11,2,2,6,8,8,6,11,1,8
E,4,9,6,6,5,8,7,1,7,11,5,9,3,8,5,10
X,4,6,6,4,4,9,8,3,5,7,7,7,4,11,6,7
X,3,3,5,2,2,5,8,2,8,11,9,9,2,9,3,6
S,5,10,6,7,4,6,8,3,6,10,8,7,2,8,5,5
F,6,9,8,7,4,3,13,4,5,13,9,4,1,10,2,6
X,3,4,4,6,1,7,7,4,4,7,6,8,3,8,4,8
B,4,8,6,6,6,8,7,4,5,7,6,7,4,8,6,9
T,5,7,5,5,2,5,12,2,8,12,9,4,1,10,2,4
G,3,6,3,4,2,7,7,6,6,10,7,11,2,9,4,10
T,2,6,3,4,2,9,11,3,7,5,11,8,2,11,1,8
J,2,5,4,3,1,7,7,3,6,14,6,10,1,6,1,7
M,5,5,7,4,6,9,8,4,5,6,6,7,8,6,6,5
Y,5,8,6,6,6,9,6,5,4,7,9,7,3,8,8,4
E,6,9,8,8,8,5,8,4,4,8,7,9,4,10,9,10
Q,6,11,8,8,8,8,6,8,3,6,8,9,5,5,8,9
K,7,12,6,7,3,9,7,3,6,9,6,7,6,10,4,7
Z,4,8,6,6,4,8,7,2,9,12,6,7,1,7,6,8
U,4,7,5,5,2,4,8,5,6,11,11,10,3,9,1,7
W,10,11,10,8,7,1,11,3,3,11,11,9,8,10,1,7
N,4,8,6,6,6,7,9,3,4,7,6,7,6,8,6,4
F,4,10,4,7,2,1,14,5,4,13,10,5,0,8,2,5
Q,3,5,4,5,2,8,8,7,5,6,7,8,3,8,5,9
D,8,11,8,6,4,10,3,6,5,13,4,11,5,7,5,9
N,4,6,5,4,3,11,7,4,5,10,1,4,5,9,1,7
P,7,11,10,8,7,9,7,3,6,12,4,5,5,9,5,9
C,3,6,4,4,1,5,6,6,9,7,5,11,1,9,4,8
K,6,11,6,8,3,4,8,8,2,7,4,11,4,8,2,11
Q,1,2,2,3,2,8,8,5,2,8,7,9,2,9,3,9
O,4,7,5,5,6,9,7,6,1,7,7,9,7,9,3,9
T,4,6,5,4,5,7,8,4,6,7,6,8,5,7,5,6
Z,3,10,4,7,3,7,8,3,11,9,6,8,0,8,7,8
G,3,3,4,5,2,7,8,8,7,6,7,8,2,7,5,10
Q,5,9,6,11,6,8,7,6,3,8,8,11,3,8,6,8
X,4,9,6,7,5,8,6,3,5,6,7,7,2,8,9,9
Q,3,3,4,5,4,8,7,7,3,5,7,9,3,8,5,10
L,3,2,4,4,1,5,2,7,8,1,3,2,1,6,1,5
I,3,9,5,6,5,9,5,3,5,7,6,5,6,6,5,6
U,9,13,8,7,5,8,5,5,5,6,9,8,5,9,3,8
U,3,1,4,2,2,6,8,6,7,7,9,9,3,9,1,8
B,4,8,5,6,6,8,7,7,6,7,6,5,2,8,7,9
D,4,8,6,6,4,9,6,3,7,11,4,6,3,9,4,9
S,3,5,5,4,2,8,7,3,7,10,6,7,1,9,5,8
Z,1,0,1,0,0,8,7,2,9,8,6,8,0,8,5,8
R,6,11,6,8,4,5,13,8,3,7,2,9,3,7,6,11
B,5,11,7,8,8,7,8,6,4,6,4,6,5,7,7,7
U,5,8,5,6,3,3,8,4,6,10,10,9,3,9,1,7
Q,6,7,6,8,5,7,7,7,5,9,9,9,4,8,7,9
G,6,10,7,8,5,6,7,7,7,5,7,10,2,7,4,8
Y,4,10,7,7,3,7,10,1,8,7,12,8,1,11,2,8
N,2,3,3,1,1,8,8,2,4,11,5,6,4,8,0,7
Z,4,6,6,4,3,8,6,2,9,11,5,9,1,8,5,9
N,3,8,4,6,4,7,7,12,1,6,6,8,5,8,0,8
R,3,8,5,6,3,9,8,4,6,9,3,8,3,6,4,11
B,2,3,3,2,2,8,7,2,5,10,5,6,2,8,4,9
I,3,9,4,6,3,7,7,0,7,13,6,8,0,8,1,8
E,1,1,1,1,1,4,8,5,7,7,6,12,0,8,6,10
X,2,3,3,2,1,5,8,2,7,10,10,9,2,9,2,6
F,7,13,6,7,4,8,9,2,5,11,5,4,3,10,7,7
N,4,7,6,5,3,5,9,3,4,10,8,8,5,9,1,7
T,9,15,8,8,5,8,8,3,9,12,5,6,3,7,6,6
U,9,10,9,8,7,5,7,5,9,8,6,9,8,9,6,1
I,4,9,5,7,3,7,8,2,8,7,6,8,0,7,4,8
P,4,5,5,6,6,6,8,5,2,7,7,6,6,11,5,7
H,3,5,4,3,3,7,7,6,6,7,6,8,3,8,4,8
F,3,6,5,4,4,6,8,6,3,8,6,9,3,10,8,11
S,4,5,5,7,3,7,6,6,9,5,6,10,0,9,9,8
A,4,9,7,7,5,8,5,2,4,6,1,5,3,5,4,5
X,6,9,9,7,4,10,5,2,8,11,1,7,3,8,4,9
X,2,8,4,6,3,7,7,4,8,6,6,8,3,8,6,8
A,4,9,6,7,6,8,6,6,4,7,6,9,5,8,7,3
M,5,9,7,7,8,8,7,6,4,6,7,8,8,6,2,7
R,4,6,5,4,3,8,9,5,6,8,5,8,3,7,5,10
L,3,7,4,5,2,5,4,4,9,3,2,6,1,6,2,5
G,2,4,3,3,2,6,7,5,5,9,7,10,2,8,4,9
U,9,11,9,8,6,3,8,5,8,11,10,10,3,9,2,6
U,5,7,5,5,3,4,8,6,6,9,8,9,3,9,3,5
R,5,11,7,8,5,9,8,4,6,10,3,7,3,7,4,11
M,6,10,7,8,4,7,7,13,2,7,9,8,9,6,0,8
J,3,5,5,4,2,9,6,3,6,14,5,10,1,6,0,7
C,5,9,6,7,3,5,8,7,8,7,8,14,2,9,4,9
C,1,0,2,0,0,7,7,6,8,7,6,13,0,8,4,10
O,4,8,5,6,3,8,7,8,4,7,6,6,3,8,3,7
H,4,6,5,4,3,10,6,4,6,10,3,7,3,8,3,10
N,6,9,5,5,3,8,11,5,4,4,6,10,6,11,2,6
C,6,9,7,7,7,6,7,3,4,6,7,10,7,8,6,7
G,5,7,6,6,7,7,9,5,2,7,7,8,6,11,7,8
L,5,11,6,8,5,8,4,0,8,9,2,11,2,5,4,9
I,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8
Q,4,6,6,5,4,7,4,3,4,5,3,7,3,6,4,7
H,4,2,5,4,4,8,7,6,7,7,6,8,6,8,4,7
E,3,1,3,3,2,7,7,5,8,7,7,8,2,8,6,9
V,3,4,4,3,1,4,12,3,3,10,11,7,2,11,1,8
Y,4,5,6,7,8,7,8,3,2,8,8,9,4,11,8,5
H,5,5,5,7,3,7,7,15,1,7,7,8,3,8,0,8
V,6,8,5,6,3,3,12,3,3,10,11,8,2,11,1,8
J,5,9,6,7,4,10,4,6,5,8,6,5,2,8,4,6
U,4,4,5,3,2,4,8,5,7,10,9,9,3,9,2,5
C,3,5,5,4,4,6,7,4,4,6,6,11,4,10,7,11
V,3,9,5,7,2,7,11,3,4,9,12,8,3,10,1,9
I,4,7,6,8,6,8,8,5,6,7,5,8,3,9,8,9
T,1,0,2,0,0,7,14,2,3,7,10,8,0,8,0,8
F,3,5,5,3,2,6,12,4,5,13,6,2,1,10,2,6
V,9,14,8,8,5,6,8,4,4,8,8,5,6,12,3,8
S,1,0,1,1,0,8,7,4,6,5,6,7,0,8,7,8
W,6,5,8,5,9,8,7,5,5,7,5,8,9,9,7,9
W,6,9,8,7,7,8,4,6,2,7,6,7,7,7,6,5
J,2,8,2,6,1,14,3,4,5,12,1,9,0,7,0,8
T,1,0,2,0,0,7,15,2,4,7,10,8,0,8,0,8
I,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8
R,6,11,9,8,8,10,7,3,6,10,3,6,3,7,4,10
I,1,3,2,2,1,7,7,1,7,13,6,8,0,8,1,8
S,3,3,4,4,2,8,8,6,8,5,6,7,0,8,9,7
W,5,8,5,6,4,7,10,4,2,8,6,6,6,11,2,6
F,2,1,3,2,2,5,11,3,5,11,9,5,1,10,3,6
C,2,0,2,1,0,6,7,6,9,7,6,14,0,8,4,10
F,3,5,5,4,2,7,10,1,6,13,6,5,3,8,3,7
U,4,9,5,7,2,7,5,13,5,7,14,8,3,9,0,8
J,3,9,4,7,1,11,2,11,3,12,9,14,1,6,0,8
T,3,4,4,6,1,6,15,1,6,8,11,7,0,8,0,8
N,5,9,7,7,5,4,9,3,4,9,10,9,6,7,1,8
X,3,8,5,6,3,5,8,2,8,10,10,9,3,9,3,6
J,5,9,6,6,2,8,3,6,4,14,9,15,1,6,1,6
O,3,8,4,6,2,7,6,8,7,7,4,8,3,8,4,8
J,2,4,4,3,1,9,4,4,5,14,6,12,0,7,0,8
Z,4,5,6,7,4,11,4,4,5,10,3,9,2,7,6,10
N,7,9,10,7,5,10,8,3,6,11,1,3,5,9,1,7
P,2,4,4,3,2,7,8,3,4,12,5,4,1,10,2,8
S,1,3,3,2,1,7,7,3,7,10,5,8,1,8,4,8
H,4,4,6,3,3,7,7,3,6,10,7,8,5,9,4,8
T,1,0,2,0,0,7,14,2,3,7,10,8,0,8,0,8
Y,2,3,4,5,1,7,10,1,3,7,12,8,1,11,0,8
X,6,10,10,8,5,9,6,1,8,10,3,7,3,8,4,9
P,4,4,4,6,2,4,12,8,2,10,6,4,1,10,4,8
A,4,7,5,5,5,7,6,8,4,7,5,8,4,8,10,2
P,4,6,6,4,3,6,11,5,3,11,5,3,1,10,3,8
F,2,5,4,4,2,8,9,2,6,13,5,5,1,9,2,8
Z,1,1,2,2,0,7,7,2,11,9,6,8,0,8,6,8
C,4,5,5,4,2,4,9,5,8,12,10,12,1,8,2,7
H,6,7,9,5,5,9,6,3,7,10,5,8,3,8,3,8
M,10,11,10,6,5,7,11,5,5,4,4,10,9,10,2,7
N,4,5,5,8,3,7,7,14,2,4,6,8,6,8,0,8
R,2,6,3,4,3,5,10,7,3,7,4,9,2,6,4,11
X,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8
W,4,7,7,5,5,9,12,2,2,6,8,8,8,13,1,7
P,4,9,6,6,3,4,13,5,4,13,6,2,0,9,4,7
P,6,6,6,8,3,3,14,8,1,11,6,3,1,10,4,8
S,6,10,8,7,5,8,8,3,7,10,5,7,2,8,5,8
F,3,8,3,6,2,1,11,3,4,11,11,9,0,8,2,7
Z,3,5,5,4,3,7,8,2,9,12,6,8,1,8,5,7
M,6,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8
K,7,13,7,8,4,6,7,3,6,9,9,10,6,10,3,7
I,6,8,7,6,4,5,6,3,7,7,6,12,0,9,4,8
V,3,5,4,3,1,4,12,3,3,10,11,7,2,10,1,8
J,6,10,5,7,3,7,10,2,3,12,5,5,2,8,7,9
R,4,8,7,7,7,6,8,4,3,6,5,9,8,7,6,9
U,5,4,6,7,2,7,4,14,6,7,15,8,3,9,0,8
Z,5,10,5,5,3,5,8,2,9,11,8,8,3,6,6,5
W,4,2,5,3,3,5,11,3,2,8,9,9,7,10,1,8
W,7,7,7,5,4,4,10,3,3,10,9,8,7,11,2,6
Q,4,7,6,5,5,8,5,7,4,6,7,9,3,7,6,9
P,6,9,5,4,2,5,12,5,1,12,6,4,4,10,3,8
M,3,1,4,3,3,6,6,6,4,7,7,10,7,5,2,9
E,3,5,5,4,5,7,6,4,3,7,6,9,5,10,7,12
U,5,7,5,5,3,4,8,5,7,9,8,9,4,8,3,4
G,4,7,4,5,2,6,7,6,5,9,8,10,2,8,4,9
Z,4,10,6,8,4,6,9,2,9,11,8,7,2,10,7,6
L,5,8,7,7,6,7,7,4,5,6,7,9,3,9,8,7
F,3,5,5,3,2,6,12,3,5,13,7,3,1,10,2,6
M,2,3,3,1,2,8,7,6,3,6,7,8,6,5,1,7
J,4,11,5,8,3,9,7,2,7,15,4,8,0,6,1,7
D,2,6,3,4,2,5,7,10,7,7,6,5,3,8,3,8
X,4,9,6,7,4,8,7,4,9,6,7,8,4,6,8,9
U,3,5,4,4,4,8,6,5,4,6,6,8,3,9,1,7
V,4,6,6,5,6,7,7,5,4,7,6,8,6,9,7,8
Q,4,9,5,11,7,8,6,8,4,5,6,7,4,8,6,10
J,2,7,4,5,4,8,8,3,3,8,5,6,4,8,5,4
S,2,7,3,5,2,6,7,5,8,6,7,11,0,9,8,7
B,2,3,4,2,2,9,7,2,5,11,5,7,2,8,4,9
N,4,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8
F,2,5,4,4,2,5,12,3,5,13,7,4,1,9,2,6
Z,4,7,6,9,4,11,4,2,6,9,2,6,1,8,6,9
S,5,9,6,6,4,6,8,4,6,10,9,8,2,8,5,5
P,2,1,2,1,1,5,11,7,2,9,6,4,1,9,3,8
H,4,5,7,4,4,8,6,3,6,10,5,8,5,7,4,8
O,3,4,4,3,2,8,7,7,4,9,6,8,2,8,3,8
H,5,7,8,5,5,7,7,3,6,10,7,8,3,9,3,7
Q,5,10,6,9,5,8,8,8,5,5,8,10,4,7,6,8
L,5,10,7,8,5,8,4,1,7,9,2,9,1,6,3,9
X,4,6,7,4,3,7,7,2,8,10,7,9,3,8,3,7
F,4,8,6,6,4,4,11,2,6,11,10,6,1,10,3,6
Q,2,6,3,5,3,7,7,8,6,5,4,6,2,8,3,7
L,1,0,1,0,0,2,1,6,4,0,3,4,0,8,0,8
P,5,10,7,8,6,8,10,7,4,10,4,3,3,11,4,8
Z,2,4,4,3,1,8,6,2,10,11,5,9,1,8,6,9
J,0,0,1,0,0,12,4,4,3,11,4,11,0,7,0,8
D,1,1,2,1,1,6,7,8,6,6,6,6,2,8,3,8
K,4,6,6,4,4,7,8,4,8,7,5,7,3,8,5,8
T,3,9,5,6,1,5,15,1,6,9,11,7,0,8,0,8
C,6,9,7,7,7,8,5,6,3,7,8,11,8,9,5,4
U,2,3,3,2,1,6,8,6,6,7,9,9,3,9,0,8
Z,4,4,4,6,2,7,7,4,14,9,6,8,0,8,8,8
F,6,11,10,8,10,6,10,1,5,10,7,5,6,10,5,2
M,4,4,5,3,4,9,6,6,4,6,7,5,9,5,3,6
Y,1,0,1,0,0,8,10,1,3,6,11,8,1,11,0,8
Q,4,8,5,10,7,8,6,8,3,6,5,9,3,9,5,10
O,7,10,7,8,7,7,8,8,4,10,7,8,3,8,3,8
X,7,9,7,5,3,8,7,2,8,9,6,8,4,10,4,8
B,3,7,4,5,4,9,7,3,5,7,6,7,3,9,4,8
O,9,13,6,7,4,6,6,7,4,10,7,9,5,9,5,8
U,4,11,5,8,4,7,6,12,4,7,12,8,3,9,0,8
U,5,11,6,8,5,6,9,5,6,6,9,10,5,11,1,8
Y,6,11,9,8,5,7,10,1,8,6,12,9,4,10,3,7
B,1,3,2,2,1,8,7,3,5,10,5,7,2,8,4,9
D,5,11,7,8,5,11,5,4,7,10,2,6,3,8,3,9
Q,4,9,6,8,5,8,9,7,4,6,9,9,3,8,7,10
Y,3,5,6,7,1,9,10,3,2,6,13,8,2,11,0,8
G,2,6,3,4,2,7,7,7,6,6,6,8,2,7,6,11
E,4,8,6,7,7,5,8,4,4,8,7,9,5,11,9,11
N,3,4,5,3,2,7,9,2,4,10,6,7,5,8,0,7
V,4,8,6,6,3,8,12,2,3,5,11,9,5,11,3,6
U,1,0,2,1,0,8,6,11,4,7,12,8,3,10,0,8
K,5,9,7,7,6,10,6,1,6,10,3,8,4,9,4,11
V,3,8,5,6,2,7,11,3,4,6,12,8,3,10,1,8
L,8,13,8,8,5,7,4,3,5,11,6,12,4,6,7,8
F,4,10,4,7,2,1,13,5,3,12,10,6,0,8,3,6
Z,2,1,2,1,1,7,7,3,11,8,6,8,0,8,6,8
I,2,7,3,5,1,6,8,1,8,14,7,7,0,8,1,7
D,6,8,8,6,5,7,9,8,6,9,7,5,5,9,4,9
X,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8
X,3,6,5,4,3,8,7,3,8,5,6,8,2,8,6,8
G,2,1,4,3,2,6,7,6,6,6,6,10,2,8,3,9
Y,5,10,7,8,3,8,10,3,2,6,12,8,2,11,0,8
E,2,1,2,3,2,7,7,5,6,7,6,9,2,8,5,10
D,3,7,5,5,6,9,8,4,4,7,6,5,4,9,7,5
X,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8
N,6,7,8,6,8,8,8,5,5,8,5,6,6,9,7,3
P,3,6,5,9,7,8,13,3,1,9,8,6,4,11,2,7
F,5,9,8,7,8,10,7,1,5,9,5,5,6,10,5,8
F,4,7,6,5,6,6,9,1,4,10,7,7,5,10,3,4
R,1,3,2,1,1,7,8,4,4,7,5,6,2,7,3,8
P,5,9,6,6,6,7,9,4,6,8,7,4,5,10,4,7
A,1,0,2,0,0,7,4,2,0,7,2,8,2,7,1,8
X,3,5,4,3,2,7,7,3,9,6,6,8,3,8,6,7
A,7,14,7,8,6,10,3,5,2,10,5,12,7,1,6,11
L,3,9,3,7,2,0,2,3,6,1,0,8,0,8,0,8
Y,4,11,6,8,3,10,10,1,3,6,12,8,1,11,0,8
P,5,10,7,8,6,6,9,2,7,9,8,4,4,10,4,7
W,1,0,2,0,0,7,8,3,0,7,8,8,5,9,0,8
B,4,3,4,5,3,6,9,8,7,7,5,6,2,8,9,9
Y,4,5,5,3,2,4,11,2,7,12,10,5,1,11,2,4
K,5,9,7,6,6,5,7,1,6,9,8,10,4,7,3,8
I,4,11,3,6,2,10,5,4,4,12,3,7,3,8,5,10
S,4,8,5,6,4,8,7,5,8,5,6,7,0,8,8,8
I,1,5,0,8,0,7,7,4,4,7,6,8,0,8,0,8
J,2,11,3,8,3,10,7,1,6,11,3,7,1,6,2,6
X,3,1,5,3,2,8,7,4,9,6,6,8,2,8,6,8
H,11,14,11,8,8,6,9,3,5,10,5,9,7,6,5,6
G,7,11,7,8,6,6,6,6,6,10,7,12,2,10,4,10
Y,5,7,5,5,2,2,12,4,5,13,11,6,1,11,1,6
Y,1,0,2,0,0,8,10,3,1,6,12,8,1,11,0,8
N,9,15,8,8,4,8,10,5,4,5,6,10,7,12,2,6
D,5,9,6,7,5,7,7,8,8,6,5,4,4,9,4,9
Q,5,5,7,8,9,8,8,5,1,6,6,10,7,13,7,13
T,4,10,5,8,4,7,12,5,6,7,11,8,3,12,1,7
Q,2,6,3,4,2,8,8,8,5,5,8,9,3,8,5,10
O,6,9,8,8,7,6,6,5,4,6,3,6,4,9,7,7
A,10,14,8,8,4,11,1,3,0,10,4,11,3,4,4,10
O,3,4,4,6,2,7,7,9,6,7,6,8,3,8,4,8
E,5,11,7,8,5,7,7,1,9,10,6,9,2,8,5,8
C,4,6,5,4,5,7,6,4,3,7,7,11,5,9,3,9
A,7,11,7,6,5,10,3,5,2,10,5,11,7,2,6,10
C,4,11,5,8,4,5,8,8,6,10,8,13,3,12,5,8
F,5,7,6,5,6,8,6,5,4,7,6,8,6,9,6,10
A,3,8,5,6,4,11,2,2,2,9,2,9,3,6,3,8
F,3,9,4,6,1,1,13,5,4,12,10,6,0,8,2,6
D,4,8,5,6,4,10,5,2,6,11,4,7,3,7,3,9
L,1,0,2,1,0,2,1,6,4,0,2,5,0,8,0,8
F,4,7,5,5,3,6,10,4,5,12,7,5,2,9,2,6
U,5,9,5,6,2,7,4,14,6,7,14,8,3,9,0,8
P,5,9,8,7,4,7,9,6,5,11,5,4,2,10,4,8
C,3,8,4,6,3,5,8,8,7,10,8,13,2,10,4,9
Q,5,9,5,4,3,11,3,3,6,11,3,9,3,8,6,13
J,3,7,5,5,3,8,6,4,5,8,6,7,2,7,4,6
K,4,8,6,6,6,7,8,5,4,7,6,7,7,7,6,11
P,8,10,6,5,3,6,11,5,3,12,4,4,4,10,4,8
V,5,11,8,8,4,6,11,2,4,8,12,9,3,10,2,8
G,3,2,4,4,3,7,7,6,6,7,6,10,3,8,4,9
E,1,1,1,2,1,4,8,5,8,7,6,13,0,8,6,9
T,2,6,3,4,2,6,12,3,6,8,11,8,2,11,1,7
N,4,6,6,6,6,6,9,5,3,6,4,8,7,8,4,9
Z,4,11,6,8,4,6,9,3,10,11,9,6,1,9,6,6
U,5,9,5,4,3,7,6,4,5,6,7,8,5,6,3,7
Y,3,9,5,6,1,6,10,2,2,8,12,8,1,11,0,8
Z,6,10,9,8,7,9,9,5,4,7,5,7,3,9,10,5
Y,2,4,3,2,1,4,11,2,6,11,10,5,1,11,2,5
H,9,12,9,6,4,7,8,5,4,9,10,9,7,10,5,9
B,5,10,8,8,6,9,7,3,7,10,4,6,2,8,6,11
Z,4,8,5,6,2,7,7,4,15,9,6,8,0,8,8,8
J,5,10,6,7,2,8,6,3,7,15,5,9,0,6,1,7
Z,5,8,7,6,4,8,7,2,9,12,5,8,2,8,6,8
X,4,4,4,6,1,7,7,4,4,7,6,8,3,8,4,8
R,2,3,3,2,2,6,7,4,5,6,5,6,5,7,3,8
T,5,7,6,5,6,7,8,4,6,7,7,9,5,8,6,5
B,3,6,4,4,4,7,7,4,6,7,6,6,2,8,5,10
T,8,11,8,8,6,7,11,4,8,11,9,4,6,12,6,5
Z,4,7,6,5,4,6,9,2,9,11,8,6,1,8,6,6
Z,5,6,4,9,4,6,11,3,3,12,7,6,2,7,9,3
Z,2,0,2,1,1,7,7,3,11,8,6,8,0,8,7,8
H,2,6,4,4,5,7,9,5,2,7,6,7,5,6,6,6
B,6,11,6,8,7,7,8,9,6,6,5,7,2,8,8,9
J,4,11,6,8,4,9,6,3,6,14,4,9,0,6,1,7
R,3,3,4,4,2,5,10,8,4,7,4,8,2,7,6,11
O,4,8,6,7,5,7,6,5,5,9,5,8,3,6,5,6
F,3,8,3,6,2,1,11,3,5,11,11,9,0,8,2,7
M,2,0,2,0,1,7,6,10,0,7,9,8,6,6,0,8
L,4,7,5,5,6,7,7,3,5,6,7,11,6,11,6,5
N,3,6,5,4,3,8,9,3,4,10,5,6,5,8,1,7
T,3,4,4,6,1,8,15,1,6,6,11,9,0,8,0,8
L,6,13,5,8,3,8,3,3,5,12,4,12,2,7,6,8
O,5,8,7,7,5,7,5,4,5,8,4,8,3,7,4,7
M,7,7,9,5,7,5,7,3,5,10,9,10,9,5,4,8
N,1,0,1,1,0,7,7,10,0,5,6,8,4,8,0,8
H,3,4,5,3,3,6,8,3,5,10,9,9,3,7,3,6
V,1,1,2,1,0,7,9,4,2,7,13,8,2,10,0,8
A,4,8,5,6,4,8,3,1,2,6,2,8,2,6,2,6
C,2,5,3,3,1,4,8,5,6,11,9,12,1,9,2,7
W,12,14,12,8,7,4,8,2,4,8,10,8,10,11,2,5
P,4,7,6,5,3,7,10,3,6,14,5,2,0,10,3,9
B,3,1,3,2,3,7,7,5,6,7,6,6,4,8,5,10
Y,1,1,3,1,1,8,11,1,6,6,11,8,1,11,1,8
O,6,10,6,8,5,7,7,8,5,10,6,8,3,8,3,8
T,3,9,4,6,3,10,11,3,7,4,12,8,2,12,1,8
I,1,5,1,4,1,7,7,1,8,7,6,8,0,8,3,8
F,3,6,4,4,4,6,8,5,4,7,5,9,3,10,8,10
W,5,7,6,5,6,7,8,6,3,8,8,7,7,9,4,9
K,6,8,8,6,4,8,7,2,7,10,3,8,4,7,4,9
Q,4,7,5,6,2,8,6,8,6,6,6,8,3,8,4,8
E,5,10,7,8,6,7,8,2,7,11,6,9,3,8,4,9
O,4,8,6,6,4,8,7,8,4,7,7,8,3,8,3,8
G,3,4,4,6,2,8,8,8,7,6,7,8,2,7,6,11
H,5,9,7,6,5,7,6,7,5,5,4,9,3,7,6,9
P,6,7,8,9,10,9,9,3,2,6,8,7,6,10,6,4
O,2,2,3,3,2,8,7,7,4,7,6,9,2,8,3,8
R,3,8,5,6,4,10,7,3,6,10,3,7,3,7,2,10
O,1,0,1,0,0,7,7,7,4,7,6,8,2,8,3,8
R,5,9,7,6,7,9,7,6,3,8,5,6,5,6,8,10
W,4,8,6,6,3,7,8,4,1,7,8,8,8,9,0,8
X,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8
N,3,5,5,4,2,6,8,3,4,10,7,8,5,8,1,8
A,5,10,7,8,4,8,2,2,3,6,2,7,5,7,5,8
X,1,0,1,0,0,7,7,3,5,7,6,8,2,8,4,7
I,2,7,4,5,4,10,6,2,5,8,5,5,3,8,5,6
K,3,8,4,6,3,3,8,6,3,6,4,11,3,8,3,11
Z,4,6,6,4,4,9,11,6,5,6,5,9,3,8,9,6
T,2,3,4,4,1,9,15,1,5,5,11,9,0,8,0,8
E,3,9,3,7,2,2,7,6,10,7,7,15,0,8,7,7
N,7,12,8,6,4,5,9,5,4,13,11,9,6,9,0,9
B,8,15,8,8,8,7,8,5,5,9,7,8,8,5,10,7
C,2,3,2,2,1,6,8,6,7,8,7,13,1,9,3,10
R,3,6,5,5,6,6,8,3,3,6,5,9,6,7,5,9
I,6,12,4,6,2,11,3,4,6,11,2,7,3,8,3,12
X,4,11,5,8,4,7,7,4,4,7,6,8,2,8,4,8
E,3,8,3,6,4,3,7,5,9,7,7,14,0,8,6,9
T,4,7,4,5,3,6,11,4,5,11,9,5,2,12,2,5
X,3,7,5,6,5,9,7,2,4,8,6,6,3,9,7,7
D,5,11,5,8,3,6,7,11,10,7,6,6,3,8,4,8
W,8,9,12,8,13,7,7,5,5,7,5,8,10,9,9,7
X,5,11,8,8,4,10,7,2,9,11,1,6,3,8,4,9
H,3,2,4,3,3,6,7,6,6,7,6,8,6,8,3,8
V,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8
H,5,9,6,5,4,8,7,3,5,10,4,8,5,7,4,7
O,5,10,6,8,9,7,8,5,1,7,6,7,11,11,9,12
R,3,7,4,5,2,5,10,8,4,7,4,8,3,7,6,11
X,3,10,4,7,1,7,7,4,4,7,6,8,3,8,4,8
O,3,6,4,4,3,7,7,7,4,6,5,7,3,8,3,7
D,5,8,7,6,5,7,7,8,5,7,7,4,4,8,4,8
F,3,8,3,5,1,1,13,4,3,12,10,6,0,8,2,6
A,3,7,6,5,3,12,3,3,3,11,1,8,2,6,2,9
P,3,6,6,4,3,9,8,3,5,12,4,4,1,10,3,9
E,4,10,4,7,3,3,6,6,11,7,7,15,0,8,7,7
O,4,3,5,4,2,8,7,8,7,6,6,9,3,8,4,8
L,3,2,4,4,2,4,4,4,8,2,1,7,0,7,1,6
O,5,9,6,7,3,7,6,9,8,7,5,8,3,8,4,8
T,2,1,3,2,1,7,12,3,6,7,11,8,2,11,1,8
J,2,4,4,3,1,8,8,1,6,14,5,7,0,7,0,8
Q,5,7,6,9,6,8,7,6,3,8,7,9,3,9,6,9
D,5,9,6,6,6,7,7,8,6,6,5,4,4,8,4,8
C,4,8,6,6,4,5,7,6,8,6,6,13,1,7,4,9
E,3,4,3,3,3,7,7,5,8,7,6,9,2,8,6,9
N,4,5,7,4,3,7,9,3,5,11,6,6,6,8,1,7
K,3,4,4,2,2,5,7,4,7,7,6,10,6,8,4,9
Q,3,5,5,4,3,10,4,3,4,9,3,8,3,7,3,9
L,2,5,4,3,2,7,3,1,7,9,2,10,0,7,2,9
I,4,12,3,6,2,11,5,4,4,12,3,7,3,8,5,10
T,2,5,4,6,1,7,14,0,6,7,11,8,0,8,0,8
Y,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8
E,4,10,6,8,6,6,8,6,9,6,4,10,2,8,6,9
U,9,11,9,8,4,3,10,6,8,13,12,9,3,9,1,6
E,3,4,4,6,2,3,6,6,11,7,7,15,0,8,7,7
J,1,1,1,1,0,12,3,6,4,13,4,11,0,7,0,8
B,4,9,4,7,6,6,8,8,5,7,5,7,2,8,7,9
F,0,0,1,0,0,3,11,4,2,11,8,6,0,8,2,8
N,5,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8
P,4,9,6,7,5,5,10,3,6,10,9,5,1,10,4,7
T,2,4,3,3,1,4,11,2,7,11,10,5,0,10,1,5
D,3,4,5,3,3,8,7,6,6,9,5,6,3,8,4,9
P,3,1,3,2,2,5,10,4,4,10,8,4,0,9,4,7
Q,4,9,6,8,3,8,7,9,6,6,6,9,3,8,5,9
S,9,15,9,8,4,8,4,5,6,13,6,10,3,8,3,8
E,5,7,7,5,5,7,8,2,8,11,7,8,3,8,5,8
S,1,1,2,2,0,8,7,4,7,4,6,7,0,8,7,8
S,7,10,8,8,4,6,9,4,9,11,7,6,2,7,5,4
S,4,8,5,6,4,7,7,5,7,5,6,9,0,8,8,8
X,2,3,4,2,1,9,6,2,8,10,3,7,2,8,2,8
J,3,9,5,7,3,7,6,3,6,15,6,11,1,6,1,7
F,4,9,6,6,4,5,10,2,8,10,9,6,4,10,4,6
H,1,0,1,0,0,7,8,10,1,7,5,8,2,8,0,8
G,4,8,6,6,4,6,6,6,6,7,5,11,3,10,4,9
H,5,5,5,7,3,7,8,15,1,7,5,8,3,8,0,8
S,5,5,6,8,3,8,7,6,9,4,6,8,0,8,9,8
T,7,9,7,7,3,5,12,3,10,12,10,3,1,11,3,4
B,6,10,8,8,7,7,7,6,6,9,7,6,3,8,8,9
M,9,13,11,8,6,10,2,2,3,9,3,9,7,2,1,9
N,6,11,7,8,6,7,7,13,1,6,6,8,6,8,0,8
C,4,11,5,8,4,5,8,8,7,10,9,13,2,10,4,10
T,4,5,5,4,2,5,12,3,7,11,10,4,1,11,2,4
Y,6,8,6,6,3,4,10,2,8,9,10,5,2,12,4,3
F,1,0,1,0,0,3,12,4,2,11,8,6,0,8,2,7
R,3,4,4,3,3,7,8,4,5,6,5,7,2,6,4,8
N,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8
G,2,1,2,1,1,8,6,6,5,6,5,9,1,7,5,10
Q,6,11,6,6,4,10,5,4,6,11,4,8,3,8,8,11
J,2,7,2,5,1,11,3,10,3,12,7,13,1,6,0,8
N,7,10,9,8,6,6,9,2,4,9,8,8,7,7,2,7
P,5,11,5,8,6,5,10,9,4,8,6,5,2,10,3,8
C,4,6,5,4,5,5,7,4,5,7,6,9,5,11,3,8
U,8,13,7,7,5,8,6,5,4,5,6,7,5,6,3,5
K,4,7,6,6,5,9,5,2,3,8,4,9,4,7,6,12
V,4,11,7,8,3,7,12,3,4,6,12,9,3,10,1,8
Q,7,12,6,7,4,9,4,4,7,10,4,9,3,8,8,11
E,5,12,4,6,3,6,9,4,4,10,6,9,3,8,7,9
F,3,2,4,3,2,6,10,4,5,10,9,5,2,9,3,6
J,2,4,4,3,1,8,6,3,6,14,6,10,0,7,0,7
Y,5,9,7,7,6,9,5,7,5,7,9,8,3,9,8,4
Z,2,1,2,1,1,8,7,5,9,6,6,8,1,8,6,8
G,7,13,6,7,3,9,6,5,6,11,4,8,5,7,5,8
I,3,9,5,6,6,10,6,2,5,8,5,5,3,8,5,7
Y,3,8,4,5,1,8,10,2,3,5,12,8,1,10,0,8
B,2,5,4,3,3,8,8,3,5,10,5,6,3,7,5,9
O,1,0,1,0,0,8,7,6,4,7,6,8,2,8,2,8
B,6,9,8,6,7,10,6,3,6,10,3,7,4,7,5,12
G,3,4,4,6,2,7,6,8,7,6,6,10,1,7,6,11
A,3,8,5,5,2,6,4,3,1,6,1,8,2,7,2,7
H,3,7,5,5,5,6,7,6,6,7,6,10,3,8,3,9
Z,3,9,4,7,2,7,7,4,14,10,6,8,0,8,8,8
I,1,3,1,2,1,7,7,1,7,7,6,8,0,8,2,8
Y,6,9,8,7,8,9,7,6,4,6,8,8,3,9,8,4
Q,4,6,4,7,4,7,11,4,3,6,10,11,3,10,6,8
H,6,11,8,8,9,6,7,6,7,7,7,10,6,8,4,9
M,11,12,11,6,5,7,10,5,4,4,5,11,11,13,2,7
Y,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8
S,3,5,4,3,3,8,7,7,5,7,7,8,2,8,9,8
O,1,3,2,1,1,7,7,5,3,9,6,8,2,8,2,8
L,4,8,6,6,7,8,7,3,5,6,7,9,6,10,7,5
P,4,8,5,6,4,4,11,4,6,11,9,5,0,10,3,7
A,4,8,6,6,4,8,3,2,2,6,2,7,2,6,2,7
U,3,8,5,7,5,7,7,4,3,6,6,8,6,8,1,8
R,8,14,8,8,6,8,8,3,5,9,4,8,6,6,7,7
A,3,6,5,4,3,8,6,2,4,8,2,5,2,6,2,7
U,3,6,4,4,3,8,8,8,5,6,7,9,3,8,4,6
N,5,10,7,8,5,7,8,5,5,7,7,7,6,7,3,7
U,2,2,3,3,1,7,8,7,7,8,10,7,3,9,1,8
U,3,3,3,1,1,6,8,5,7,8,7,8,3,10,2,5
B,4,4,5,7,4,6,6,9,7,6,6,7,2,8,9,10
O,6,10,7,8,6,7,8,7,4,10,7,7,5,9,4,9
H,7,9,10,7,6,9,6,3,6,10,5,7,3,8,3,8
J,1,1,1,1,0,12,4,6,4,13,4,10,0,7,0,8
J,2,9,3,7,1,14,3,6,5,14,1,9,0,7,0,8
I,2,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
G,3,3,4,5,2,7,8,8,7,6,7,7,2,7,6,11
P,3,5,4,4,2,6,10,6,4,9,7,2,1,10,4,6
H,4,7,6,5,4,7,7,3,6,10,5,9,3,8,3,9
G,5,11,7,8,6,7,6,7,8,6,5,8,2,10,8,12
U,6,8,8,6,6,8,8,8,5,5,7,10,3,8,4,6
U,6,11,6,6,4,6,5,6,5,6,9,9,5,9,3,10
W,8,12,8,6,4,2,9,2,2,10,11,9,8,10,0,7
Q,3,5,4,6,3,8,7,7,2,5,7,10,3,8,6,9
E,2,6,2,4,2,3,6,6,10,7,7,14,0,8,7,8
P,6,9,8,6,5,9,8,5,6,12,4,4,5,9,5,7
M,3,3,4,2,2,8,6,6,3,6,7,8,6,6,1,7
J,3,8,4,6,2,10,6,1,6,13,3,7,0,7,0,8
T,5,10,6,8,7,8,7,4,7,7,6,9,5,7,5,7
L,1,4,3,3,1,6,4,1,7,8,2,10,0,7,2,8
S,4,11,5,8,4,8,7,8,7,8,5,7,2,8,9,8
N,4,7,6,5,4,7,9,6,6,7,6,6,6,8,1,6
P,3,6,4,4,2,5,11,9,3,9,6,5,1,10,4,8
D,3,3,3,4,2,5,7,10,8,7,6,6,3,8,4,8
X,6,10,9,7,4,8,6,1,9,10,4,8,3,8,4,8
M,3,3,5,2,2,5,6,4,3,10,10,10,6,5,2,7
A,4,11,6,8,5,7,4,2,1,7,1,8,2,7,2,7
T,7,10,7,8,4,6,12,4,8,12,9,3,2,12,3,4
L,4,8,5,6,3,7,3,2,8,7,2,8,1,6,2,7
W,6,12,7,6,5,8,8,3,3,6,9,7,10,10,3,6
P,4,6,5,9,8,8,9,4,0,8,7,6,5,10,5,7
Q,4,9,5,8,3,7,6,8,6,6,7,7,3,8,6,9
T,5,7,5,5,3,6,11,3,6,11,9,5,3,11,3,4
L,3,6,4,4,2,4,5,2,8,3,2,7,0,7,1,6
E,3,4,5,3,2,7,7,2,8,11,6,9,2,8,4,9
W,4,7,6,5,3,8,8,4,1,7,8,8,8,9,0,8
C,10,14,8,8,6,7,8,4,4,9,8,10,4,9,9,11
K,7,9,8,5,4,5,8,4,6,10,11,11,6,12,4,7
M,3,6,4,4,2,8,6,11,1,6,9,8,7,6,0,8
T,5,9,7,7,6,7,6,7,7,6,7,9,4,11,6,8
E,3,6,5,6,5,6,8,4,3,7,7,10,4,10,7,12
T,6,8,6,6,3,4,12,2,9,12,9,5,0,10,2,4
P,2,4,3,3,2,5,10,4,4,10,8,5,0,10,3,7
V,4,9,6,7,7,8,6,4,2,8,7,8,8,9,4,7
J,1,1,2,1,0,11,6,2,6,12,3,8,0,7,0,8
E,3,4,4,3,3,7,7,5,8,7,6,8,2,8,6,9
C,4,6,5,4,5,7,7,4,3,6,8,10,5,11,3,8
R,5,8,7,6,7,8,8,7,3,7,4,6,5,7,7,8
T,3,10,5,7,3,9,12,3,7,5,12,8,2,12,1,8
U,4,5,5,4,3,6,8,5,7,7,10,10,3,9,1,8
W,6,7,8,6,10,7,7,5,5,7,5,8,10,8,8,8
T,4,10,5,7,3,8,14,1,5,6,10,9,0,8,0,8
X,4,10,6,8,4,7,7,4,9,6,6,8,3,8,7,7
R,3,5,5,4,5,7,8,3,3,7,5,8,6,8,4,8
Y,1,1,3,2,1,6,10,1,6,8,11,8,1,11,2,8
K,5,9,5,4,2,7,7,3,6,9,8,9,6,11,3,7
M,8,9,11,8,12,7,9,4,4,7,5,8,12,6,6,7
Z,5,10,6,8,3,7,7,4,15,9,6,8,0,8,8,8
V,5,8,5,6,2,2,12,5,4,12,12,7,3,10,1,8
G,4,4,5,6,3,7,5,7,8,6,5,8,2,8,6,11
L,3,7,5,5,2,6,3,3,8,6,2,8,1,6,3,7
A,3,11,5,8,4,12,3,3,3,10,2,9,2,7,3,9
Q,6,11,6,6,3,7,6,4,8,9,5,9,3,7,9,10
X,9,14,8,8,4,9,6,3,8,9,4,7,5,8,5,9
G,2,4,3,3,2,7,7,5,5,6,6,9,2,8,4,9
A,2,2,4,4,2,8,2,1,2,7,2,8,2,7,3,7
A,3,7,4,5,2,7,4,2,0,6,2,8,2,7,1,7
O,3,6,4,4,3,8,8,7,4,7,7,8,3,8,2,8
I,1,5,1,4,1,8,7,1,7,7,6,7,0,8,3,7
L,3,5,5,4,2,7,4,1,8,8,2,10,0,7,2,8
X,3,7,5,5,3,8,8,3,8,6,6,7,3,8,6,8
M,5,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8
Z,2,5,3,4,2,7,7,5,9,6,6,8,1,8,7,8
J,3,7,5,5,4,10,5,2,6,8,5,5,3,7,5,7
V,5,11,8,8,10,8,5,5,2,8,8,8,6,9,4,8
V,5,9,4,4,2,9,10,4,4,6,10,6,4,11,3,7
L,3,7,4,5,2,4,4,3,10,2,1,7,0,7,2,5
H,5,7,8,5,6,5,9,3,6,10,8,8,3,8,3,6
C,5,4,6,6,2,6,7,7,11,8,6,13,1,9,4,8
X,3,5,5,3,2,5,9,2,8,11,10,9,2,9,3,6
Y,5,7,5,5,2,3,10,2,7,11,11,6,1,11,2,5
D,4,8,6,6,4,7,8,7,7,10,6,5,3,8,4,7
U,9,11,9,8,6,3,8,5,8,10,9,10,3,9,2,6
I,5,12,5,6,3,6,11,3,5,13,6,4,1,8,5,8
Q,1,1,2,1,1,8,7,6,3,6,6,8,2,8,3,8
J,2,5,3,3,1,10,6,2,6,12,4,9,1,6,1,7
G,4,9,6,6,6,7,7,6,3,7,6,9,5,7,7,7
U,4,4,5,3,2,5,8,5,7,10,9,8,3,9,2,5
X,7,11,11,8,7,4,8,2,9,10,11,9,5,8,5,4
P,4,6,5,4,4,8,5,6,5,7,6,7,2,9,7,9
T,2,3,3,2,1,5,12,3,7,11,9,5,1,10,2,5
T,5,11,6,8,8,6,8,4,5,6,6,9,6,7,6,7
Y,4,10,6,7,1,9,11,3,2,5,13,8,1,11,0,8
G,5,10,6,7,4,6,7,7,7,10,7,11,2,10,4,9
K,7,14,8,8,5,9,6,3,5,11,3,7,5,7,4,8
S,4,8,5,6,6,9,9,5,4,8,4,6,4,9,9,7
N,4,10,6,8,5,8,8,6,5,7,6,6,6,9,2,6
M,4,3,4,5,3,7,7,12,1,7,9,8,8,6,0,8
G,2,3,3,2,1,7,7,5,5,10,7,10,2,9,4,10
L,2,3,2,2,1,5,4,5,6,2,2,5,1,7,1,6
X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
L,3,4,3,6,1,0,1,5,6,0,0,7,0,8,0,8
V,6,11,6,6,3,7,9,3,4,8,9,6,5,9,3,6
P,4,9,6,6,4,7,9,3,4,12,5,4,2,9,3,8
K,3,1,5,3,3,6,7,4,8,7,6,11,3,8,5,9
W,4,10,6,8,7,8,7,6,2,7,8,8,9,7,4,9
Q,2,0,2,1,1,8,7,6,3,6,6,9,2,8,3,8
Q,2,2,3,3,2,8,8,6,1,5,6,9,2,9,5,10
Q,4,8,6,7,5,8,8,7,5,6,7,9,3,8,4,9
S,3,6,5,4,5,6,7,3,2,8,6,6,2,7,10,1
H,3,8,5,6,4,8,8,7,7,7,5,7,3,8,3,7
W,2,3,4,2,2,7,10,2,2,7,9,8,6,11,0,8
O,10,14,7,8,4,6,8,5,4,7,4,6,5,9,5,7
Y,6,9,5,5,2,5,9,3,3,10,9,5,3,10,3,4
L,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8
T,6,8,6,6,3,4,12,3,7,12,10,5,1,11,1,5
G,2,3,2,2,1,6,8,5,4,9,8,9,2,8,4,9
E,5,10,4,5,3,7,8,4,3,10,5,9,4,9,7,10
J,6,7,8,8,7,8,8,5,7,7,5,8,4,10,10,8
Q,4,3,5,5,2,8,9,7,6,6,8,9,3,8,5,9
N,7,14,9,8,5,3,9,4,4,13,12,10,6,9,0,8
T,4,5,4,4,2,6,11,2,7,11,9,4,1,11,2,4
M,5,8,7,6,7,8,6,6,5,7,7,10,11,6,2,9
O,4,5,5,4,3,7,7,8,5,7,6,8,2,8,3,8
A,6,10,7,6,3,12,0,4,1,11,4,12,4,4,3,11
T,2,9,4,7,2,7,13,0,5,7,10,8,0,8,0,8
N,3,5,6,3,2,7,9,2,5,10,6,6,5,8,1,7
F,4,10,4,7,3,1,12,4,4,11,10,8,0,8,2,6
P,2,5,3,7,5,8,6,5,1,7,6,7,6,8,5,9
U,3,7,5,5,3,4,8,6,6,7,9,10,3,9,1,8
H,5,7,8,5,5,9,7,3,6,10,4,7,5,8,4,9
O,6,9,4,4,2,8,7,5,5,8,4,7,4,9,5,8
S,2,8,3,6,2,8,8,6,9,5,6,7,0,8,9,8
J,5,9,6,7,3,6,8,2,6,15,6,8,2,8,2,8
O,4,5,5,4,4,8,4,4,4,9,4,10,3,6,6,6
B,4,8,6,6,5,7,8,6,6,10,6,6,3,8,7,8
F,3,8,5,6,3,6,11,4,6,11,9,4,2,10,3,5
E,3,9,3,6,2,3,6,6,10,7,7,14,0,8,7,8
F,3,6,5,4,3,5,10,4,6,10,9,5,2,9,3,5
A,2,3,3,1,1,6,2,2,1,5,2,8,1,6,1,7
Q,3,5,4,6,4,8,9,4,2,5,8,11,2,10,5,8
C,6,9,6,7,4,4,7,5,7,10,9,14,4,9,5,5
S,4,9,6,6,7,9,4,4,4,9,6,9,4,7,10,9
F,7,10,9,8,7,9,7,2,6,12,4,6,5,9,4,9
C,5,10,7,9,8,5,6,4,4,7,6,11,5,11,8,10
V,4,7,6,5,6,8,6,4,2,7,8,8,7,9,4,6
T,4,4,5,3,2,5,12,2,8,11,9,4,0,10,2,4
N,5,9,5,4,2,9,11,5,3,5,6,9,5,11,2,6
E,1,0,1,0,0,5,8,5,7,7,6,12,0,8,6,10
L,3,8,3,6,2,0,2,4,6,1,0,8,0,8,0,8
A,3,9,5,6,2,6,5,3,1,6,1,8,2,7,2,7
K,5,11,5,8,5,3,8,7,3,6,4,11,3,8,2,11
M,6,9,10,7,12,7,5,3,2,7,5,8,15,7,4,6
R,2,3,3,2,2,7,7,5,5,7,5,6,2,7,4,8
S,6,12,6,7,3,6,8,3,6,13,7,7,2,9,3,7
Y,3,9,5,6,3,7,9,1,6,6,11,8,2,11,2,7
V,7,10,5,5,2,6,11,5,4,11,9,4,4,11,3,10
S,2,0,2,1,1,8,7,4,6,5,6,8,0,8,7,8
M,5,6,8,4,5,9,6,2,4,9,5,7,8,6,2,8
O,9,15,6,8,5,5,7,7,4,10,7,10,5,9,5,8
L,3,7,3,5,1,0,1,6,6,0,0,6,0,8,0,8
D,6,9,8,8,8,7,6,5,7,7,5,9,6,5,10,3
P,2,1,3,2,1,4,10,3,5,10,8,5,0,9,3,7
W,3,8,5,6,5,11,11,2,2,5,8,7,7,12,1,7
O,4,3,5,4,2,7,6,8,8,6,5,7,3,8,4,8
E,4,9,5,6,3,5,9,2,10,10,8,9,2,8,5,5
J,2,11,3,8,2,15,4,4,5,13,1,8,0,7,0,8
T,5,8,7,7,7,7,9,4,8,7,7,8,3,10,8,6
D,2,2,3,3,2,7,7,7,6,6,6,4,2,8,3,7
C,7,10,8,8,4,4,8,6,9,12,9,13,2,9,3,7
T,6,9,6,7,5,6,11,3,7,11,9,5,2,12,2,4
S,2,3,4,2,1,8,7,2,6,10,6,8,1,9,5,8
A,4,9,6,6,2,9,5,3,1,8,1,8,2,7,2,8

================================================
FILE: data/lizards.csv
================================================
"Species","Diameter","Height"
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,2
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,1,1
1,2,2
1,2,2
1,2,2
1,2,2
1,2,2
1,2,2
1,2,2
1,2,2
1,2,2
1,2,2
1,2,2
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
1,2,1
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,2
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,1,1
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,2
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1
2,2,1


================================================
FILE: data/munin.bif
================================================
network unknown {
}
variable R_LNLW_MED_SEV {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLW_MED_PATHO {
  type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };
}
variable R_LNLW_MEDD2_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable DIFFN_SEV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable DIFFN_TYPE {
  type discrete [ 3 ] { MOTOR, MIXED, SENS };
}
variable DIFFN_SENS_SEV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable DIFFN_DISTR {
  type discrete [ 3 ] { DIST, PROX, RANDOM };
}
variable DIFFN_S_SEV_DIST {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable DIFFN_PATHO {
  type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };
}
variable R_DIFFN_MEDD2_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_LNLW_MEDD2_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MEDD2_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLBE_MEDD2_DISP_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MEDD2_DISP_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MEDD2_DISP_EWD {
  type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };
}
variable R_DIFFN_MEDD2_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLBE_MEDD2_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_MEDD2_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_MEDD2_AMPR_EW {
  type discrete [ 12 ] { R0_0, R0_1, R0_2, R0_3, R0_4, R0_5, R0_6, R0_7, R0_8, R0_9, R1_0, R_1_1 };
}
variable R_LNLBE_MEDD2_LD_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MEDD2_LD_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLBE_MEDD2_RD_EW {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_MEDD2_RD_EW {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_MEDD2_LSLOW_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable R_LNLW_MEDD2_SALOSS_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_MEDD2_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_LNLW_MEDD2_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLBE_MEDD2_SALOSS_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_MEDD2_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_MEDD2_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MEDD2_DIFSLOW_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MEDD2_DSLOW_EW {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_MEDD2_ALLCV_EW {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_MEDD2_CV_EW {
  type discrete [ 20 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72, M_S_76 };
}
variable R_LNLW_MEDD2_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_MEDD2_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_MEDD2_EFFAXLOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_MEDD2_ALLAMP_WD {
  type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };
}
variable R_MEDD2_AMP_WD {
  type discrete [ 15 ] { UV_0_63, UV0_88, UV1_25, UV1_77, UV2_50, UV3_50, UV5_00, UV7_10, UV10_0, UV14_0, UV20_0, UV28_0, UV40_0, UV57_0, UV_80_0 };
}
variable R_LNLW_MEDD2_LD_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MEDD2_LD_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLW_MEDD2_RD_WD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_MEDD2_RD_WD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_MEDD2_LSLOW_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable R_LNLBE_MEDD2_DIFSLOW_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MEDD2_DIFSLOW_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MEDD2_DSLOW_WD {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_MEDD2_ALLCV_WD {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_MEDD2_CV_WD {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S_72 };
}
variable DIFFN_MOT_SEV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable DIFFN_M_SEV_DIST {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_MED_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLBE_MED_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_MED_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_MED_AMPR_EW {
  type discrete [ 12 ] { R_1_1, R1_0, R0_9, R0_8, R0_7, R0_6, R0_5, R0_4, R0_3, R0_2, R0_1, R0_0 };
}
variable R_LNLT1_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLLP_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLT1_LP_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLBE_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLT1_LP_BE_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLW_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_LNLW_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_APB_MALOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable R_DIFFN_MED_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MED_DIFSLOW_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MED_DCV_EW {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_MED_LD_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MED_RD_EW {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_MED_RDLDCV_EW {
  type discrete [ 6 ] { M_S60, M_S52, M_S44, M_S27, M_S15, M_S07 };
}
variable R_MED_ALLCV_EW {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_MED_CV_EW {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72 };
}
variable R_LNLT1_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLLP_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLT1_LP_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLBE_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLT1_LP_BE_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable DIFFN_TIME {
  type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };
}
variable R_DIFFN_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLW_MED_TIME {
  type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };
}
variable R_LNLW_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_DIFFN_LNLW_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNL_DIFFN_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MYOP_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MYDY_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MYOP_MYDY_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_DE_REGEN_APB_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_MYAS_APB_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_APB_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_MYDY_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MYOP_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MYOP_MYDY_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLW_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_DIFFN_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_DIFFN_LNLW_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLBE_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLLP_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLT1_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLT1_LP_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLT1_LP_BE_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNL_DIFFN_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_APB_MUSIZE {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_APB_EFFMUS {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_LNLW_MED_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_MED_BLOCK_WA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_APB_MULOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable R_APB_ALLAMP_WA {
  type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };
}
variable R_MED_AMP_WA {
  type discrete [ 17 ] { MV_000, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable R_MED_LD_WA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MED_RD_WA {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_MED_RDLDDEL {
  type discrete [ 5 ] { MS3_1, MS3_9, MS4_7, MS10_1, MS20_1 };
}
variable R_LNLBE_MED_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MED_DIFSLOW_WA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MED_DCV_WA {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_MED_ALLDEL_WA {
  type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };
}
variable R_MED_LAT_WA {
  type discrete [ 19 ] { MS2_3, MS2_7, MS3_1, MS3_5, MS3_9, MS4_3, MS4_7, MS5_3, MS5_9, MS6_5, MS7_1, MS8_0, MS9_0, MS10_0, MS12_0, MS14_0, MS16_0, MS18_0, INFIN };
}
variable R_APB_VOL_ACT {
  type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };
}
variable R_APB_FORCE {
  type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };
}
variable R_APB_MUSCLE_VOL {
  type discrete [ 2 ] { ATROPHIC, NORMAL };
}
variable R_APB_MVA_RECRUIT {
  type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };
}
variable R_APB_MVA_AMP {
  type discrete [ 3 ] { INCR, NORMAL, REDUCED };
}
variable R_APB_TA_CONCL {
  type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };
}
variable R_APB_MUPAMP {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_APB_QUAN_MUPAMP {
  type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };
}
variable R_APB_QUAL_MUPAMP {
  type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };
}
variable R_APB_MUPDUR {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_APB_QUAN_MUPDUR {
  type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };
}
variable R_APB_QUAL_MUPDUR {
  type discrete [ 3 ] { SMALL, NORMAL, INCR };
}
variable R_APB_QUAN_MUPPOLY {
  type discrete [ 3 ] { x_12_, x12_24_, x_24_ };
}
variable R_APB_QUAL_MUPPOLY {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable R_APB_MUPSATEL {
  type discrete [ 2 ] { NO, YES };
}
variable R_APB_MUPINSTAB {
  type discrete [ 2 ] { NO, YES };
}
variable R_APB_REPSTIM_CMAPAMP {
  type discrete [ 21 ] { MV_000, MV_032, MV_044, MV_063, MV_088, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable R_APB_REPSTIM_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable R_APB_REPSTIM_FACILI {
  type discrete [ 4 ] { NO, MOD, SEV, REDUCED };
}
variable R_APB_REPSTIM_POST_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable R_APB_SF_JITTER {
  type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };
}
variable R_LNLT1_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLLP_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLT1_LP_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLBE_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLT1_LP_BE_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_DIFFN_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLW_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_DIFFN_LNLW_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNL_DIFFN_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYOP_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYDY_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYOP_MYDY_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYAS_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MUSCLE_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_APB_SF_DENSITY {
  type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };
}
variable R_LNLT1_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLLP_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLT1_LP_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLBE_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLT1_LP_BE_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_DIFFN_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLW_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_DIFFN_LNLW_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_APB_SPONT_NEUR_DISCH {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_MYOP_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MYDY_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MYOP_MYDY_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_NMT_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MUSCLE_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLT1_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLLP_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLT1_LP_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLBE_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLT1_LP_BE_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLW_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_LNLW_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNL_DIFFN_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_APB_SPONT_DENERV_ACT {
  type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };
}
variable R_APB_SPONT_HF_DISCH {
  type discrete [ 2 ] { NO, YES };
}
variable R_APB_SPONT_INS_ACT {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable DIFFN_M_SEV_PROX {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable DIFFN_DUMMY_1 {
  type discrete [ 2 ] { dummy, State1 };
}
variable DIFFN_DUMMY_2 {
  type discrete [ 2 ] { dummy, State1 };
}
variable DIFFN_DUMMY_3 {
  type discrete [ 2 ] { dummy, State1 };
}
variable R_OTHER_ULND5_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_ULND5_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLE_ULN_SEV {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLE_ULN_PATHO {
  type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };
}
variable R_LNLE_ULND5_DISP_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLE_DIFFN_ULND5_DISP_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DISP_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLW_ULND5_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_LNLW_ULND5_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DISP_BEW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DISP_BED {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DISP_EED {
  type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };
}
variable R_OTHER_ULND5_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_ULND5_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLE_ULND5_BLOCK_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLE_DIFFN_ULND5_BLOCK_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULND5_BLOCK_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULND5_AMPR_E {
  type discrete [ 12 ] { R0_0, R0_1, R0_2, R0_3, R0_4, R0_5, R0_6, R0_7, R0_8, R0_9, R1_0, R_1_1 };
}
variable R_OTHER_ULND5_LD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLE_ULND5_LD_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_LD_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_OTHER_ULND5_RD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_LNLE_ULND5_RD_E {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_ULND5_RD_E {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_ULND5_LSLOW_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable R_OTHER_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLW_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_LNLW_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLE_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLLP_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLLP_E_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNL_DIFFN_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_OTHER_ULND5_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLE_ULND5_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_ULND5_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLE_DIFFN_ULND5_DIFSLOW_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DIFSLOW_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DSLOW_E {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULND5_ALLCV_E {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULND5_CV_E {
  type discrete [ 20 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72, M_S_76 };
}
variable R_ULND5_DISP_EWD {
  type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };
}
variable R_ULND5_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULND5_AMPR_EW {
  type discrete [ 12 ] { R0_0, R0_1, R0_2, R0_3, R0_4, R0_5, R0_6, R0_7, R0_8, R0_9, R1_0, R_1_1 };
}
variable R_ULND5_DIFSLOW_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DSLOW_EW {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULND5_ALLCV_EW {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULND5_CV_EW {
  type discrete [ 20 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72, M_S_76 };
}
variable R_LNLW_ULND5_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_LNLW_ULND5_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULND5_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULND5_EFFAXLOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULND5_ALLAMP_WD {
  type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };
}
variable R_ULND5_AMP_WD {
  type discrete [ 15 ] { UV_0_63, UV0_88, UV1_25, UV1_77, UV2_50, UV3_50, UV5_00, UV7_10, UV10_0, UV14_0, UV20_0, UV28_0, UV40_0, UV57_0, UV_80_0 };
}
variable R_LNLW_ULND5_LD_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_LD_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLW_ULND5_RD_WD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_ULND5_RD_WD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_ULND5_LSLOW_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable R_LNLE_DIFFN_ULND5_DIFSLOW_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DIFSLOW_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULND5_DSLOW_WD {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULND5_ALLCV_WD {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULND5_CV_WD {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S_72 };
}
variable R_DIFFN_ULN_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLE_ULN_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULN_BLOCK_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULN_AMPR_E {
  type discrete [ 12 ] { R_1_1, R1_0, R0_9, R0_8, R0_7, R0_6, R0_5, R0_4, R0_3, R0_2, R0_1, R0_0 };
}
variable R_OTHER_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLC8_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLLP_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLC8_LP_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLE_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLC8_LP_E_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLW_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_LNLW_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNL_DIFFN_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ADM_MALOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable R_LNLE_ULN_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_ULN_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULN_DIFSLOW_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULN_DCV_E {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULN_LD_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULN_RD_EW {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_ULN_RDLDCV_E {
  type discrete [ 6 ] { M_S60, M_S52, M_S44, M_S27, M_S15, M_S07 };
}
variable R_ULN_ALLCV_E {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULN_CV_E {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72 };
}
variable R_ULN_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULN_AMPR_EW {
  type discrete [ 12 ] { R_1_1, R1_0, R0_9, R0_8, R0_7, R0_6, R0_5, R0_4, R0_3, R0_2, R0_1, R0_0 };
}
variable R_ULN_DIFSLOW_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULN_DCV_EW {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULN_ALLCV_EW {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULN_CV_EW {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72 };
}
variable R_OTHER_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_LNLC8_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLLP_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLC8_LP_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLE_ULN_TIME {
  type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };
}
variable R_LNLE_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLC8_LP_E_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_DIFFN_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLW_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_DIFFN_LNLW_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNL_DIFFN_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MYOP_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MYDY_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MYOP_MYDY_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_OTHER_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MUSCLE_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_DE_REGEN_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_MYAS_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_MYAS_DE_REGEN_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_OTHER_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MYDY_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MYOP_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MYOP_MYDY_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MUSCLE_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLW_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_DIFFN_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_DIFFN_LNLW_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLE_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLLP_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLC8_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLC8_LP_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLC8_LP_E_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNL_DIFFN_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_ADM_MUSIZE {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_ADM_EFFMUS {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_OTHER_ULN_BLOCK_WA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLW_ULN_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_LNLW_ULN_BLOCK_WA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ULN_BLOCK_WA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_ADM_MULOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable R_ADM_ALLAMP_WA {
  type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };
}
variable R_ULN_AMP_WA {
  type discrete [ 17 ] { MV_000, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable R_ULN_LD_WA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULN_RD_WA {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_ULN_RDLDDEL {
  type discrete [ 5 ] { MS3_1, MS3_9, MS4_7, MS10_1, MS20_1 };
}
variable R_ULN_DIFSLOW_WA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ULN_DCV_WA {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_ULN_ALLDEL_WA {
  type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };
}
variable R_ULN_LAT_WA {
  type discrete [ 19 ] { MS2_3, MS2_7, MS3_1, MS3_5, MS3_9, MS4_3, MS4_7, MS5_3, MS5_9, MS6_5, MS7_1, MS8_0, MS9_0, MS10_0, MS12_0, MS14_0, MS16_0, MS18_0, INFIN };
}
variable R_ADM_VOL_ACT {
  type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };
}
variable R_ADM_FORCE {
  type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };
}
variable R_ADM_MUSCLE_VOL {
  type discrete [ 2 ] { ATROPHIC, NORMAL };
}
variable R_ADM_MVA_RECRUIT {
  type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };
}
variable R_ADM_MVA_AMP {
  type discrete [ 3 ] { INCR, NORMAL, REDUCED };
}
variable R_ADM_TA_CONCL {
  type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };
}
variable R_ADM_MUPAMP {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_ADM_QUAN_MUPAMP {
  type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };
}
variable R_ADM_QUAL_MUPAMP {
  type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };
}
variable R_ADM_MUPDUR {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_ADM_QUAN_MUPDUR {
  type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };
}
variable R_ADM_QUAL_MUPDUR {
  type discrete [ 3 ] { SMALL, NORMAL, INCR };
}
variable R_ADM_QUAN_MUPPOLY {
  type discrete [ 3 ] { x_12_, x12_24_, x_24_ };
}
variable R_ADM_QUAL_MUPPOLY {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable R_ADM_MUPSATEL {
  type discrete [ 2 ] { NO, YES };
}
variable R_ADM_MUPINSTAB {
  type discrete [ 2 ] { NO, YES };
}
variable R_ADM_REPSTIM_CMAPAMP {
  type discrete [ 21 ] { MV_000, MV_032, MV_044, MV_063, MV_088, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable R_ADM_REPSTIM_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable R_ADM_REPSTIM_FACILI {
  type discrete [ 4 ] { NO, MOD, SEV, REDUCED };
}
variable R_ADM_REPSTIM_POST_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable R_ADM_SF_JITTER {
  type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };
}
variable R_LNLC8_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLLP_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLC8_LP_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLE_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLC8_LP_E_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_DIFFN_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLW_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_DIFFN_LNLW_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNL_DIFFN_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYOP_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYDY_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYOP_MYDY_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYAS_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_OTHER_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYAS_OTHER_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MUSCLE_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_ADM_SF_DENSITY {
  type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };
}
variable R_LNLC8_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLLP_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLC8_LP_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLE_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLC8_LP_E_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_DIFFN_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLW_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_DIFFN_LNLW_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNL_DIFFN_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_OTHER_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_ADM_SPONT_NEUR_DISCH {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_MYOP_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MYDY_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MYOP_MYDY_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_OTHER_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_NMT_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_OTHER_NMT_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MUSCLE_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLC8_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLLP_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLC8_LP_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLE_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLC8_LP_E_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLW_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_LNLW_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNL_DIFFN_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_ADM_SPONT_DENERV_ACT {
  type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };
}
variable R_ADM_SPONT_HF_DISCH {
  type discrete [ 2 ] { NO, YES };
}
variable R_ADM_SPONT_INS_ACT {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable R_OTHER_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_LNLPC5_AXIL_SEV {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLPC5_AXIL_TIME {
  type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };
}
variable R_LNLPC5_AXIL_PATHO {
  type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };
}
variable R_LNLPC5_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_DIFFN_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_LNLPC5_DIFFN_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MYOP_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MYDY_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MYOP_MYDY_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_OTHER_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_MUSCLE_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable R_DE_REGEN_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_MYAS_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_MYAS_DE_REGEN_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable R_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, OTHER };
}
variable R_OTHER_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MYDY_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MYOP_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MYOP_MYDY_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_MUSCLE_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_DIFFN_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLPC5_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_LNLPC5_DIFFN_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable R_DELT_MUSIZE {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_DELT_EFFMUS {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_DIFFN_AXIL_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_OTHER_AXIL_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_AXIL_BLOCK_ED {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLPC5_DELT_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_DELT_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNLPC5_DIFFN_DELT_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_OTHER_DELT_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DELT_MALOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable R_DELT_MULOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable R_DELT_ALLAMP {
  type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };
}
variable R_AXIL_AMP_E {
  type discrete [ 17 ] { MV_000, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable R_AXIL_RD_ED {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_LNLPC5_AXIL_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_AXIL_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_AXIL_DIFSLOW_ED {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_AXIL_DCV {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_AXIL_DEL {
  type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };
}
variable R_AXIL_LAT_ED {
  type discrete [ 17 ] { MS3_1, MS3_5, MS3_9, MS4_3, MS4_7, MS5_3, MS5_9, MS6_5, MS7_1, MS8_0, MS9_0, MS10_0, MS12_0, MS14_0, MS16_0, MS18_0, INFIN };
}
variable R_DELT_VOL_ACT {
  type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };
}
variable R_DELT_FORCE {
  type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };
}
variable R_DELT_MUSCLE_VOL {
  type discrete [ 2 ] { ATROPHIC, NORMAL };
}
variable R_DELT_MVA_RECRUIT {
  type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };
}
variable R_DELT_MVA_AMP {
  type discrete [ 3 ] { INCR, NORMAL, REDUCED };
}
variable R_DELT_TA_CONCL {
  type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };
}
variable R_DELT_MUPAMP {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_DELT_QUAN_MUPAMP {
  type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };
}
variable R_DELT_QUAL_MUPAMP {
  type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };
}
variable R_DELT_MUPDUR {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable R_DELT_QUAN_MUPDUR {
  type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };
}
variable R_DELT_QUAL_MUPDUR {
  type discrete [ 3 ] { SMALL, NORMAL, INCR };
}
variable R_DELT_QUAN_MUPPOLY {
  type discrete [ 3 ] { x_12_, x12_24_, x_24_ };
}
variable R_DELT_QUAL_MUPPOLY {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable R_DELT_MUPSATEL {
  type discrete [ 2 ] { NO, YES };
}
variable R_DELT_MUPINSTAB {
  type discrete [ 2 ] { NO, YES };
}
variable R_DELT_REPSTIM_CMAPAMP {
  type discrete [ 21 ] { MV_000, MV_032, MV_044, MV_063, MV_088, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable R_DELT_REPSTIM_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable R_DELT_REPSTIM_FACILI {
  type discrete [ 4 ] { NO, MOD, SEV, REDUCED };
}
variable R_DELT_REPSTIM_POST_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable R_DELT_SF_JITTER {
  type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };
}
variable R_LNLPC5_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_DIFFN_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_LNLPC5_DIFFN_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYOP_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYDY_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYOP_MYDY_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYAS_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_OTHER_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MYAS_OTHER_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_MUSCLE_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable R_DELT_SF_DENSITY {
  type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };
}
variable R_LNLPC5_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_DIFFN_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_LNLPC5_DIFFN_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_OTHER_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_DELT_SPONT_NEUR_DISCH {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable R_MYOP_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MYDY_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MYOP_MYDY_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_OTHER_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_NMT_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_OTHER_NMT_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_MUSCLE_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLPC5_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_LNLPC5_DIFFN_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DELT_SPONT_DENERV_ACT {
  type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };
}
variable R_DELT_SPONT_HF_DISCH {
  type discrete [ 2 ] { NO, YES };
}
variable R_DELT_SPONT_INS_ACT {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable R_OTHER_ISCH_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_ISCH_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_SUR_DISP_CA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_OTHER_ISCH_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_ISCH_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_SUR_BLOCK_CA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_OTHER_ISCH_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_ISCH_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNL_ISCH_SEV {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_LNL_ISCH_PATHO {
  type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };
}
variable R_LNL_ISCH_SALOSS_CA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_DIFFN_LNL_ISCH_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_SUR_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_SUR_EFFAXLOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable R_SUR_ALLAMP_CA {
  type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };
}
variable R_SUR_AMP_CA {
  type discrete [ 13 ] { UV_0_63, UV0_88, UV1_25, UV1_77, UV2_50, UV3_50, UV5_00, UV7_10, UV10_0, UV14_0, UV20_0, UV28_0, UV_40_0 };
}
variable R_SUR_LD_CA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_SUR_RD_CA {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable R_SUR_LSLOW_CA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable R_OTHER_ISCH_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_DIFFN_ISCH_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_SUR_DIFSLOW_CA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable R_SUR_DSLOW_CA {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_SUR_ALLCV_CA {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable R_SUR_CV_CA {
  type discrete [ 17 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S_64 };
}
variable L_LNLW_MED_SEV {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLW_MED_PATHO {
  type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };
}
variable L_LNLW_MEDD2_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_MEDD2_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_LNLW_MEDD2_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MEDD2_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLBE_MEDD2_DISP_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MEDD2_DISP_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MEDD2_DISP_EWD {
  type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };
}
variable L_DIFFN_MEDD2_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLBE_MEDD2_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_MEDD2_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_MEDD2_AMPR_EW {
  type discrete [ 12 ] { R0_0, R0_1, R0_2, R0_3, R0_4, R0_5, R0_6, R0_7, R0_8, R0_9, R1_0, R_1_1 };
}
variable L_LNLBE_MEDD2_LD_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MEDD2_LD_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLBE_MEDD2_RD_EW {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_MEDD2_RD_EW {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_MEDD2_LSLOW_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable L_LNLW_MEDD2_SALOSS_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_MEDD2_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_LNLW_MEDD2_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLBE_MEDD2_SALOSS_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_MEDD2_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_MEDD2_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MEDD2_DIFSLOW_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MEDD2_DSLOW_EW {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_MEDD2_ALLCV_EW {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_MEDD2_CV_EW {
  type discrete [ 20 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72, M_S_76 };
}
variable L_LNLW_MEDD2_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_MEDD2_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_MEDD2_EFFAXLOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_MEDD2_ALLAMP_WD {
  type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };
}
variable L_MEDD2_AMP_WD {
  type discrete [ 15 ] { UV_0_63, UV0_88, UV1_25, UV1_77, UV2_50, UV3_50, UV5_00, UV7_10, UV10_0, UV14_0, UV20_0, UV28_0, UV40_0, UV57_0, UV_80_0 };
}
variable L_LNLW_MEDD2_LD_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MEDD2_LD_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLW_MEDD2_RD_WD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_MEDD2_RD_WD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_MEDD2_LSLOW_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable L_LNLBE_MEDD2_DIFSLOW_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MEDD2_DIFSLOW_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MEDD2_DSLOW_WD {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_MEDD2_ALLCV_WD {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_MEDD2_CV_WD {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S_72 };
}
variable L_DIFFN_MED_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLBE_MED_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_MED_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_MED_AMPR_EW {
  type discrete [ 12 ] { R_1_1, R1_0, R0_9, R0_8, R0_7, R0_6, R0_5, R0_4, R0_3, R0_2, R0_1, R0_0 };
}
variable L_LNLT1_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLLP_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLT1_LP_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLBE_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLT1_LP_BE_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLW_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_LNLW_APB_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_APB_MALOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable L_DIFFN_MED_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MED_DIFSLOW_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MED_DCV_EW {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_MED_LD_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MED_RD_EW {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_MED_RDLDCV_EW {
  type discrete [ 6 ] { M_S60, M_S52, M_S44, M_S27, M_S15, M_S07 };
}
variable L_MED_ALLCV_EW {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_MED_CV_EW {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72 };
}
variable L_LNLT1_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLLP_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLT1_LP_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLBE_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLT1_LP_BE_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_DIFFN_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLW_MED_TIME {
  type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };
}
variable L_LNLW_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_DIFFN_LNLW_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNL_DIFFN_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MYOP_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MYDY_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MYOP_MYDY_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_APB_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_DE_REGEN_APB_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_MYAS_APB_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_APB_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_MYDY_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MYOP_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MYOP_MYDY_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLW_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_DIFFN_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_DIFFN_LNLW_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLBE_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLLP_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLT1_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLT1_LP_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLT1_LP_BE_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNL_DIFFN_APB_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_APB_MUSIZE {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_APB_EFFMUS {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_LNLW_MED_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_MED_BLOCK_WA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_APB_MULOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable L_APB_ALLAMP_WA {
  type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };
}
variable L_MED_AMP_WA {
  type discrete [ 17 ] { MV_000, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable L_MED_LD_WA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MED_RD_WA {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_MED_RDLDDEL {
  type discrete [ 5 ] { MS3_1, MS3_9, MS4_7, MS10_1, MS20_1 };
}
variable L_LNLBE_MED_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MED_DIFSLOW_WA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MED_DCV_WA {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_MED_ALLDEL_WA {
  type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };
}
variable L_MED_LAT_WA {
  type discrete [ 19 ] { MS2_3, MS2_7, MS3_1, MS3_5, MS3_9, MS4_3, MS4_7, MS5_3, MS5_9, MS6_5, MS7_1, MS8_0, MS9_0, MS10_0, MS12_0, MS14_0, MS16_0, MS18_0, INFIN };
}
variable L_APB_VOL_ACT {
  type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };
}
variable L_APB_FORCE {
  type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };
}
variable L_APB_MUSCLE_VOL {
  type discrete [ 2 ] { ATROPHIC, NORMAL };
}
variable L_APB_MVA_RECRUIT {
  type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };
}
variable L_APB_MVA_AMP {
  type discrete [ 3 ] { INCR, NORMAL, REDUCED };
}
variable L_APB_TA_CONCL {
  type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };
}
variable L_APB_MUPAMP {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_APB_QUAN_MUPAMP {
  type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };
}
variable L_APB_QUAL_MUPAMP {
  type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };
}
variable L_APB_MUPDUR {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_APB_QUAN_MUPDUR {
  type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };
}
variable L_APB_QUAL_MUPDUR {
  type discrete [ 3 ] { SMALL, NORMAL, INCR };
}
variable L_APB_QUAN_MUPPOLY {
  type discrete [ 3 ] { x_12_, x12_24_, x_24_ };
}
variable L_APB_QUAL_MUPPOLY {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable L_APB_MUPSATEL {
  type discrete [ 2 ] { NO, YES };
}
variable L_APB_MUPINSTAB {
  type discrete [ 2 ] { NO, YES };
}
variable L_APB_REPSTIM_CMAPAMP {
  type discrete [ 21 ] { MV_000, MV_032, MV_044, MV_063, MV_088, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable L_APB_REPSTIM_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable L_APB_REPSTIM_FACILI {
  type discrete [ 4 ] { NO, MOD, SEV, REDUCED };
}
variable L_APB_REPSTIM_POST_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable L_APB_SF_JITTER {
  type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };
}
variable L_LNLT1_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLLP_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLT1_LP_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLBE_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLT1_LP_BE_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_DIFFN_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLW_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_DIFFN_LNLW_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNL_DIFFN_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYOP_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYDY_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYOP_MYDY_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYAS_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MUSCLE_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_APB_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_APB_SF_DENSITY {
  type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };
}
variable L_LNLT1_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLLP_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLT1_LP_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLBE_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLT1_LP_BE_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_DIFFN_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLW_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_DIFFN_LNLW_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_APB_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_APB_SPONT_NEUR_DISCH {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_MYOP_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MYDY_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MYOP_MYDY_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_NMT_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MUSCLE_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLT1_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLLP_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLT1_LP_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLBE_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLT1_LP_BE_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLW_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_LNLW_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNL_DIFFN_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_APB_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_APB_SPONT_DENERV_ACT {
  type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };
}
variable L_APB_SPONT_HF_DISCH {
  type discrete [ 2 ] { NO, YES };
}
variable L_APB_SPONT_INS_ACT {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable L_OTHER_ULND5_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_ULND5_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLE_ULN_SEV {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLE_ULN_PATHO {
  type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };
}
variable L_LNLE_ULND5_DISP_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLE_DIFFN_ULND5_DISP_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DISP_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLW_ULND5_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_LNLW_ULND5_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DISP_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DISP_BEW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DISP_BED {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DISP_EED {
  type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };
}
variable L_OTHER_ULND5_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_ULND5_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLE_ULND5_BLOCK_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLE_DIFFN_ULND5_BLOCK_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULND5_BLOCK_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULND5_AMPR_E {
  type discrete [ 12 ] { R0_0, R0_1, R0_2, R0_3, R0_4, R0_5, R0_6, R0_7, R0_8, R0_9, R1_0, R_1_1 };
}
variable L_OTHER_ULND5_LD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLE_ULND5_LD_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_LD_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_OTHER_ULND5_RD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_LNLE_ULND5_RD_E {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_ULND5_RD_E {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_ULND5_LSLOW_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable L_OTHER_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLW_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_LNLW_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLE_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLLP_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLLP_E_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNL_DIFFN_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULND5_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_OTHER_ULND5_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLE_ULND5_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_ULND5_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLE_DIFFN_ULND5_DIFSLOW_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DIFSLOW_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DSLOW_E {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULND5_ALLCV_E {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULND5_CV_E {
  type discrete [ 20 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72, M_S_76 };
}
variable L_ULND5_DISP_EWD {
  type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };
}
variable L_ULND5_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULND5_AMPR_EW {
  type discrete [ 12 ] { R0_0, R0_1, R0_2, R0_3, R0_4, R0_5, R0_6, R0_7, R0_8, R0_9, R1_0, R_1_1 };
}
variable L_ULND5_DIFSLOW_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DSLOW_EW {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULND5_ALLCV_EW {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULND5_CV_EW {
  type discrete [ 20 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72, M_S_76 };
}
variable L_LNLW_ULND5_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_LNLW_ULND5_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULND5_BLOCK_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULND5_EFFAXLOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULND5_ALLAMP_WD {
  type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };
}
variable L_ULND5_AMP_WD {
  type discrete [ 15 ] { UV_0_63, UV0_88, UV1_25, UV1_77, UV2_50, UV3_50, UV5_00, UV7_10, UV10_0, UV14_0, UV20_0, UV28_0, UV40_0, UV57_0, UV_80_0 };
}
variable L_LNLW_ULND5_LD_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_LD_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLW_ULND5_RD_WD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_ULND5_RD_WD {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_ULND5_LSLOW_WD {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable L_LNLE_DIFFN_ULND5_DIFSLOW_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DIFSLOW_WD {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULND5_DSLOW_WD {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULND5_ALLCV_WD {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULND5_CV_WD {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S_72 };
}
variable L_DIFFN_ULN_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLE_ULN_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULN_BLOCK_E {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULN_AMPR_E {
  type discrete [ 12 ] { R_1_1, R1_0, R0_9, R0_8, R0_7, R0_6, R0_5, R0_4, R0_3, R0_2, R0_1, R0_0 };
}
variable L_OTHER_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLC8_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLLP_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLC8_LP_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLE_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLC8_LP_E_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLW_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_LNLW_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNL_DIFFN_ADM_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ADM_MALOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable L_LNLE_ULN_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_ULN_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULN_DIFSLOW_E {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULN_DCV_E {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULN_LD_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULN_RD_EW {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_ULN_RDLDCV_E {
  type discrete [ 6 ] { M_S60, M_S52, M_S44, M_S27, M_S15, M_S07 };
}
variable L_ULN_ALLCV_E {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULN_CV_E {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72 };
}
variable L_ULN_BLOCK_EW {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULN_AMPR_EW {
  type discrete [ 12 ] { R_1_1, R1_0, R0_9, R0_8, R0_7, R0_6, R0_5, R0_4, R0_3, R0_2, R0_1, R0_0 };
}
variable L_ULN_DIFSLOW_EW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULN_DCV_EW {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULN_ALLCV_EW {
  type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULN_CV_EW {
  type discrete [ 19 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S64, M_S68, M_S72 };
}
variable L_OTHER_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_LNLC8_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLLP_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLC8_LP_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLE_ULN_TIME {
  type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };
}
variable L_LNLE_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLC8_LP_E_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_DIFFN_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLW_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_DIFFN_LNLW_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNL_DIFFN_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MYOP_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MYDY_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MYOP_MYDY_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_OTHER_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MUSCLE_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_ADM_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_DE_REGEN_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_MYAS_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_MYAS_DE_REGEN_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_ADM_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_OTHER_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MYDY_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MYOP_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MYOP_MYDY_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MUSCLE_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLW_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_DIFFN_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_DIFFN_LNLW_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLE_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLLP_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLC8_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLC8_LP_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLC8_LP_E_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNL_DIFFN_ADM_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_ADM_MUSIZE {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_ADM_EFFMUS {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_OTHER_ULN_BLOCK_WA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLW_ULN_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_LNLW_ULN_BLOCK_WA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ULN_BLOCK_WA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_ADM_MULOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable L_ADM_ALLAMP_WA {
  type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };
}
variable L_ULN_AMP_WA {
  type discrete [ 17 ] { MV_000, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable L_ULN_LD_WA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULN_RD_WA {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_ULN_RDLDDEL {
  type discrete [ 5 ] { MS3_1, MS3_9, MS4_7, MS10_1, MS20_1 };
}
variable L_ULN_DIFSLOW_WA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ULN_DCV_WA {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_ULN_ALLDEL_WA {
  type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };
}
variable L_ULN_LAT_WA {
  type discrete [ 19 ] { MS2_3, MS2_7, MS3_1, MS3_5, MS3_9, MS4_3, MS4_7, MS5_3, MS5_9, MS6_5, MS7_1, MS8_0, MS9_0, MS10_0, MS12_0, MS14_0, MS16_0, MS18_0, INFIN };
}
variable L_ADM_VOL_ACT {
  type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };
}
variable L_ADM_FORCE {
  type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };
}
variable L_ADM_MUSCLE_VOL {
  type discrete [ 2 ] { ATROPHIC, NORMAL };
}
variable L_ADM_MVA_RECRUIT {
  type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };
}
variable L_ADM_MVA_AMP {
  type discrete [ 3 ] { INCR, NORMAL, REDUCED };
}
variable L_ADM_TA_CONCL {
  type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };
}
variable L_ADM_MUPAMP {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_ADM_QUAN_MUPAMP {
  type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };
}
variable L_ADM_QUAL_MUPAMP {
  type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };
}
variable L_ADM_MUPDUR {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_ADM_QUAN_MUPDUR {
  type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };
}
variable L_ADM_QUAL_MUPDUR {
  type discrete [ 3 ] { SMALL, NORMAL, INCR };
}
variable L_ADM_QUAN_MUPPOLY {
  type discrete [ 3 ] { x_12_, x12_24_, x_24_ };
}
variable L_ADM_QUAL_MUPPOLY {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable L_ADM_MUPSATEL {
  type discrete [ 2 ] { NO, YES };
}
variable L_ADM_MUPINSTAB {
  type discrete [ 2 ] { NO, YES };
}
variable L_ADM_REPSTIM_CMAPAMP {
  type discrete [ 21 ] { MV_000, MV_032, MV_044, MV_063, MV_088, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable L_ADM_REPSTIM_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable L_ADM_REPSTIM_FACILI {
  type discrete [ 4 ] { NO, MOD, SEV, REDUCED };
}
variable L_ADM_REPSTIM_POST_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable L_ADM_SF_JITTER {
  type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };
}
variable L_LNLC8_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLLP_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLC8_LP_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLE_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLC8_LP_E_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_DIFFN_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLW_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_DIFFN_LNLW_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNL_DIFFN_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYOP_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYDY_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYOP_MYDY_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYAS_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_OTHER_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYAS_OTHER_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MUSCLE_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_ADM_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_ADM_SF_DENSITY {
  type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };
}
variable L_LNLC8_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLLP_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLC8_LP_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLE_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLC8_LP_E_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_DIFFN_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLW_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_DIFFN_LNLW_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNL_DIFFN_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_OTHER_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_ADM_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_ADM_SPONT_NEUR_DISCH {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_MYOP_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MYDY_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MYOP_MYDY_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_OTHER_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_NMT_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_OTHER_NMT_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MUSCLE_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLC8_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLLP_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLC8_LP_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLE_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLC8_LP_E_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLW_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_LNLW_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNL_DIFFN_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ADM_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_ADM_SPONT_DENERV_ACT {
  type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };
}
variable L_ADM_SPONT_HF_DISCH {
  type discrete [ 2 ] { NO, YES };
}
variable L_ADM_SPONT_INS_ACT {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable L_OTHER_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_LNLPC5_AXIL_SEV {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLPC5_AXIL_TIME {
  type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };
}
variable L_LNLPC5_AXIL_PATHO {
  type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };
}
variable L_LNLPC5_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_DIFFN_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_LNLPC5_DIFFN_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MYOP_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MYDY_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MYOP_MYDY_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_OTHER_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_MUSCLE_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_DELT_DE_REGEN {
  type discrete [ 2 ] { NO, YES };
}
variable L_DE_REGEN_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_MYAS_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_MYAS_DE_REGEN_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };
}
variable L_DELT_NMT {
  type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, OTHER };
}
variable L_OTHER_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MYDY_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MYOP_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MYOP_MYDY_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_MUSCLE_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_DIFFN_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLPC5_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_LNLPC5_DIFFN_DELT_MUSIZE {
  type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };
}
variable L_DELT_MUSIZE {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_DELT_EFFMUS {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_DIFFN_AXIL_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_OTHER_AXIL_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_AXIL_BLOCK_ED {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLPC5_DELT_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_DELT_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNLPC5_DIFFN_DELT_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_OTHER_DELT_MALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DELT_MALOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable L_DELT_MULOSS {
  type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };
}
variable L_DELT_ALLAMP {
  type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };
}
variable L_AXIL_AMP_E {
  type discrete [ 17 ] { MV_000, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable L_AXIL_RD_ED {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_LNLPC5_AXIL_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_AXIL_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_AXIL_DIFSLOW_ED {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_AXIL_DCV {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_AXIL_DEL {
  type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };
}
variable L_AXIL_LAT_ED {
  type discrete [ 17 ] { MS3_1, MS3_5, MS3_9, MS4_3, MS4_7, MS5_3, MS5_9, MS6_5, MS7_1, MS8_0, MS9_0, MS10_0, MS12_0, MS14_0, MS16_0, MS18_0, INFIN };
}
variable L_DELT_VOL_ACT {
  type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };
}
variable L_DELT_FORCE {
  type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };
}
variable L_DELT_MUSCLE_VOL {
  type discrete [ 2 ] { ATROPHIC, NORMAL };
}
variable L_DELT_MVA_RECRUIT {
  type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };
}
variable L_DELT_MVA_AMP {
  type discrete [ 3 ] { INCR, NORMAL, REDUCED };
}
variable L_DELT_TA_CONCL {
  type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };
}
variable L_DELT_MUPAMP {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_DELT_QUAN_MUPAMP {
  type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };
}
variable L_DELT_QUAL_MUPAMP {
  type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };
}
variable L_DELT_MUPDUR {
  type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };
}
variable L_DELT_QUAN_MUPDUR {
  type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };
}
variable L_DELT_QUAL_MUPDUR {
  type discrete [ 3 ] { SMALL, NORMAL, INCR };
}
variable L_DELT_QUAN_MUPPOLY {
  type discrete [ 3 ] { x_12_, x12_24_, x_24_ };
}
variable L_DELT_QUAL_MUPPOLY {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable L_DELT_MUPSATEL {
  type discrete [ 2 ] { NO, YES };
}
variable L_DELT_MUPINSTAB {
  type discrete [ 2 ] { NO, YES };
}
variable L_DELT_REPSTIM_CMAPAMP {
  type discrete [ 21 ] { MV_000, MV_032, MV_044, MV_063, MV_088, MV_13, MV_18, MV_25, MV_35, MV_5, MV_71, MV1, MV1_4, MV2, MV2_8, MV4, MV5_6, MV8, MV11_3, MV16, MV22_6 };
}
variable L_DELT_REPSTIM_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable L_DELT_REPSTIM_FACILI {
  type discrete [ 4 ] { NO, MOD, SEV, REDUCED };
}
variable L_DELT_REPSTIM_POST_DECR {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };
}
variable L_DELT_SF_JITTER {
  type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };
}
variable L_LNLPC5_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_DIFFN_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_LNLPC5_DIFFN_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYOP_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYDY_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYOP_MYDY_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYAS_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_OTHER_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MYAS_OTHER_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_MUSCLE_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_DELT_MUDENS {
  type discrete [ 3 ] { NORMAL, INCR, V_INCR };
}
variable L_DELT_SF_DENSITY {
  type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };
}
variable L_LNLPC5_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_DIFFN_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_LNLPC5_DIFFN_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_OTHER_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_DELT_NEUR_ACT {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_DELT_SPONT_NEUR_DISCH {
  type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };
}
variable L_MYOP_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MYDY_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MYOP_MYDY_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_OTHER_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_NMT_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_OTHER_NMT_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_MUSCLE_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLPC5_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_LNLPC5_DIFFN_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DELT_DENERV {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DELT_SPONT_DENERV_ACT {
  type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };
}
variable L_DELT_SPONT_HF_DISCH {
  type discrete [ 2 ] { NO, YES };
}
variable L_DELT_SPONT_INS_ACT {
  type discrete [ 2 ] { NORMAL, INCR };
}
variable L_OTHER_ISCH_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_ISCH_DISP {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_SUR_DISP_CA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_OTHER_ISCH_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_ISCH_BLOCK {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_SUR_BLOCK_CA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_OTHER_ISCH_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_ISCH_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNL_ISCH_SEV {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_LNL_ISCH_PATHO {
  type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };
}
variable L_LNL_ISCH_SALOSS_CA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_DIFFN_LNL_ISCH_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_SUR_SALOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_SUR_EFFAXLOSS {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };
}
variable L_SUR_ALLAMP_CA {
  type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };
}
variable L_SUR_AMP_CA {
  type discrete [ 13 ] { UV_0_63, UV0_88, UV1_25, UV1_77, UV2_50, UV3_50, UV5_00, UV7_10, UV10_0, UV14_0, UV20_0, UV28_0, UV_40_0 };
}
variable L_SUR_LD_CA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_SUR_RD_CA {
  type discrete [ 3 ] { NO, MOD, SEV };
}
variable L_SUR_LSLOW_CA {
  type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };
}
variable L_OTHER_ISCH_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_DIFFN_ISCH_DIFSLOW {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_SUR_DIFSLOW_CA {
  type discrete [ 4 ] { NO, MILD, MOD, SEV };
}
variable L_SUR_DSLOW_CA {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_SUR_ALLCV_CA {
  type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };
}
variable L_SUR_CV_CA {
  type discrete [ 17 ] { M_S00, M_S04, M_S08, M_S12, M_S16, M_S20, M_S24, M_S28, M_S32, M_S36, M_S40, M_S44, M_S48, M_S52, M_S56, M_S60, M_S_64 };
}
probability ( R_LNLW_MED_SEV ) {
  table 0.895, 0.060, 0.030, 0.010, 0.005;
}
probability ( R_LNLW_MED_PATHO ) {
  table 0.800, 0.120, 0.070, 0.005, 0.005;
}
probability ( R_LNLW_MEDD2_DISP_WD | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (TOTAL, BLOCK) 0.0, 0.0, 0.5, 0.5;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.3, 0.5, 0.2, 0.0;
  (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( DIFFN_SEV ) {
  table 0.90, 0.05, 0.03, 0.02;
}
probability ( DIFFN_TYPE ) {
  table 0.060, 0.935, 0.005;
}
probability ( DIFFN_SENS_SEV | DIFFN_SEV, DIFFN_TYPE ) {
  (NO, MOTOR) 1.0, 0.0, 0.0, 0.0;
  (MILD, MOTOR) 1.0, 0.0, 0.0, 0.0;
  (MOD, MOTOR) 1.0, 0.0, 0.0, 0.0;
  (SEV, MOTOR) 1.0, 0.0, 0.0, 0.0;
  (NO, MIXED) 1.0, 0.0, 0.0, 0.0;
  (MILD, MIXED) 0.0, 1.0, 0.0, 0.0;
  (MOD, MIXED) 0.0, 0.0, 1.0, 0.0;
  (SEV, MIXED) 0.0, 0.0, 0.0, 1.0;
  (NO, SENS) 1.0, 0.0, 0.0, 0.0;
  (MILD, SENS) 0.0, 1.0, 0.0, 0.0;
  (MOD, SENS) 0.0, 0.0, 1.0, 0.0;
  (SEV, SENS) 0.0, 0.0, 0.0, 1.0;
}
probability ( DIFFN_DISTR ) {
  table 0.93, 0.02, 0.05;
}
probability ( DIFFN_S_SEV_DIST | DIFFN_SENS_SEV, DIFFN_DISTR ) {
  (NO, DIST) 1.0, 0.0, 0.0, 0.0;
  (MILD, DIST) 0.0, 1.0, 0.0, 0.0;
  (MOD, DIST) 0.0, 0.0, 1.0, 0.0;
  (SEV, DIST) 0.0, 0.0, 0.0, 1.0;
  (NO, PROX) 1.0, 0.0, 0.0, 0.0;
  (MILD, PROX) 1.0, 0.0, 0.0, 0.0;
  (MOD, PROX) 0.5, 0.5, 0.0, 0.0;
  (SEV, PROX) 0.0, 0.5, 0.5, 0.0;
  (NO, RANDOM) 1.0, 0.0, 0.0, 0.0;
  (MILD, RANDOM) 0.25, 0.45, 0.25, 0.05;
  (MOD, RANDOM) 0.1, 0.4, 0.4, 0.1;
  (SEV, RANDOM) 0.05, 0.15, 0.40, 0.40;
}
probability ( DIFFN_PATHO ) {
  table 0.086, 0.010, 0.900, 0.002, 0.002;
}
probability ( R_DIFFN_MEDD2_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DIFFN_LNLW_MEDD2_DISP_WD | R_LNLW_MEDD2_DISP_WD, R_DIFFN_MEDD2_DISP ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_DISP_WD | R_DIFFN_LNLW_MEDD2_DISP_WD ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0, 1.0, 0.0, 0.0;
  (MOD) 0.0, 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLBE_MEDD2_DISP_EW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MEDD2_DISP_EW | R_DIFFN_MEDD2_DISP, R_LNLBE_MEDD2_DISP_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_DISP_EWD | R_MEDD2_DISP_WD, R_MEDD2_DISP_EW ) {
  (NO, NO) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000;
  (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, MILD) 0.0001, 0.2215, 0.7732, 0.0052, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.13151315, 0.85768577, 0.01080108, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;
  (SEV, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;
  (NO, MOD) 0.1315, 0.8577, 0.0108, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;
  (SEV, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;
  (NO, SEV) 0.9933, 0.0067, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, SEV) 0.0404, 0.9192, 0.0404, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( R_DIFFN_MEDD2_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLBE_MEDD2_BLOCK_EW ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_MEDD2_BLOCK_EW | R_DIFFN_MEDD2_BLOCK, R_LNLBE_MEDD2_BLOCK_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_AMPR_EW | R_MEDD2_DISP_EWD, R_MEDD2_BLOCK_EW ) {
  (R0_15, NO) 0.00000000, 0.28267173, 0.71642836, 0.00089991, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_25, NO) 0.0000, 0.0000, 0.5547, 0.4268, 0.0183, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, NO) 0.00000000, 0.00000000, 0.00900090, 0.51275128, 0.41524152, 0.05890589, 0.00390039, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, NO) 0.0000, 0.0000, 0.0000, 0.0635, 0.4459, 0.3732, 0.0995, 0.0162, 0.0015, 0.0002, 0.0000, 0.0000;
  (R0_55, NO) 0.00000000, 0.00000000, 0.00000000, 0.00290029, 0.11411141, 0.39513951, 0.31983198, 0.13151315, 0.02980298, 0.00580058, 0.00090009, 0.00000000;
  (R0_65, NO) 0.0000, 0.0000, 0.0000, 0.0001, 0.0136, 0.1578, 0.3285, 0.2966, 0.1404, 0.0483, 0.0135, 0.0012;
  (R0_75, NO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120024, 0.03850770, 0.17463493, 0.30156031, 0.26135227, 0.14472895, 0.06511302, 0.01290258;
  (R0_85, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0062, 0.0580, 0.1828, 0.2779, 0.2392, 0.1675, 0.0683;
  (R0_95, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0009, 0.0157, 0.0823, 0.2012, 0.2516, 0.2559, 0.1924;
  (R0_15, MILD) 0.0000, 0.9103, 0.0897, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MILD) 0.0000, 0.0004, 0.8945, 0.1036, 0.0015, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MILD) 0.00000000, 0.00000000, 0.11401140, 0.71697170, 0.15981598, 0.00890089, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, MILD) 0.0000, 0.0000, 0.0026, 0.3111, 0.5155, 0.1499, 0.0190, 0.0018, 0.0001, 0.0000, 0.0000, 0.0000;
  (R0_55, MILD) 0.0000, 0.0000, 0.0000, 0.0451, 0.3717, 0.4039, 0.1433, 0.0313, 0.0041, 0.0005, 0.0001, 0.0000;
  (R0_65, MILD) 0.0000, 0.0000, 0.0000, 0.0035, 0.1122, 0.3738, 0.3186, 0.1444, 0.0376, 0.0083, 0.0015, 0.0001;
  (R0_75, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.02260226, 0.18901890, 0.33173317, 0.27442744, 0.12521252, 0.04310431, 0.01240124, 0.00130013;
  (R0_85, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00310031, 0.06150615, 0.21092109, 0.30513051, 0.23442344, 0.12171217, 0.05280528, 0.01040104;
  (R0_95, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0166, 0.1002, 0.2325, 0.2777, 0.2039, 0.1251, 0.0436;
  (R0_15, MOD) 0.0000, 0.9974, 0.0026, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MOD) 0.0000, 0.3856, 0.6048, 0.0095, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MOD) 0.0000, 0.0073, 0.8191, 0.1638, 0.0095, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_45, MOD) 0.0000, 0.0001, 0.3860, 0.4993, 0.1036, 0.0101, 0.0008, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_55, MOD) 0.0000, 0.0000, 0.0913, 0.5259, 0.3027, 0.0681, 0.0104, 0.0014, 0.0002, 0.0000, 0.0000, 0.0000;
  (R0_65, MOD) 0.00000000, 0.00000000, 0.01499850, 0.30686931, 0.42035796, 0.19288071, 0.05119488, 0.01139886, 0.00189981, 0.00029997, 0.00009999, 0.00000000;
  (R0_75, MOD) 0.0000, 0.0000, 0.0023, 0.1333, 0.3728, 0.3075, 0.1292, 0.0421, 0.0099, 0.0024, 0.0005, 0.0000;
  (R0_85, MOD) 0.00000000, 0.00000000, 0.00030003, 0.04440444, 0.24132413, 0.34313431, 0.22082208, 0.10251025, 0.03370337, 0.01040104, 0.00300030, 0.00040004;
  (R0_95, MOD) 0.0000, 0.0000, 0.0000, 0.0138, 0.1311, 0.2956, 0.2729, 0.1711, 0.0745, 0.0286, 0.0104, 0.0020;
  (R0_15, SEV) 0.00000000, 0.94670533, 0.04919508, 0.00349965, 0.00049995, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_25, SEV) 0.00000000, 0.87211279, 0.11098890, 0.01349865, 0.00249975, 0.00059994, 0.00019998, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_35, SEV) 0.00000000, 0.77825565, 0.18013603, 0.03120624, 0.00750150, 0.00200040, 0.00060012, 0.00020004, 0.00010002, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, SEV) 0.0000, 0.6780, 0.2436, 0.0548, 0.0156, 0.0050, 0.0018, 0.0007, 0.0003, 0.0001, 0.0001, 0.0000;
  (R0_55, SEV) 0.0000, 0.5789, 0.2957, 0.0820, 0.0270, 0.0096, 0.0037, 0.0017, 0.0007, 0.0004, 0.0002, 0.0001;
  (R0_65, SEV) 0.0000, 0.4850, 0.3340, 0.1106, 0.0411, 0.0162, 0.0068, 0.0033, 0.0015, 0.0008, 0.0004, 0.0003;
  (R0_75, SEV) 0.00000000, 0.40484048, 0.35663566, 0.13671367, 0.05620562, 0.02400240, 0.01080108, 0.00540054, 0.00270027, 0.00140014, 0.00080008, 0.00050005;
  (R0_85, SEV) 0.00000000, 0.33163367, 0.36712657, 0.16126775, 0.07268546, 0.03349330, 0.01599680, 0.00849830, 0.00439912, 0.00239952, 0.00149970, 0.00099980;
  (R0_95, SEV) 0.0000, 0.2731, 0.3669, 0.1808, 0.0881, 0.0434, 0.0217, 0.0120, 0.0064, 0.0036, 0.0023, 0.0017;
  (R0_15, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_25, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_35, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_45, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_55, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_65, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_75, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_85, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_95, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLBE_MEDD2_LD_EW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MEDD2_LD_EW | R_LNLBE_MEDD2_LD_EW ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0, 1.0, 0.0, 0.0;
  (MOD) 0.0, 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLBE_MEDD2_RD_EW ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MEDD2_RD_EW | R_LNLBE_MEDD2_RD_EW ) {
  (NO) 1.0, 0.0, 0.0;
  (MOD) 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_LSLOW_EW | R_MEDD2_LD_EW, R_MEDD2_RD_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.00840084, 0.01190119, 0.06190619, 0.91739174, 0.00040004;
  (MILD, MOD) 0.00690069, 0.01000100, 0.05350535, 0.92869287, 0.00090009;
  (MOD, MOD) 0.00559944, 0.00829917, 0.04519548, 0.93890611, 0.00199980;
  (SEV, MOD) 0.0023, 0.0036, 0.0217, 0.8326, 0.1398;
  (NO, SEV) 0.00529947, 0.00619938, 0.02639736, 0.20817918, 0.75392461;
  (MILD, SEV) 0.0049, 0.0057, 0.0244, 0.1966, 0.7684;
  (MOD, SEV) 0.00440044, 0.00520052, 0.02240224, 0.18391839, 0.78407841;
  (SEV, SEV) 0.0028, 0.0033, 0.0145, 0.1304, 0.8490;
}
probability ( R_LNLW_MEDD2_SALOSS_WD | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_DIFFN_MEDD2_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_DIFFN_LNLW_MEDD2_SALOSS | R_LNLW_MEDD2_SALOSS_WD, R_DIFFN_MEDD2_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLBE_MEDD2_SALOSS_EW ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_MEDD2_SALOSS | R_DIFFN_LNLW_MEDD2_SALOSS, R_LNLBE_MEDD2_SALOSS_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DIFFN_MEDD2_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( R_MEDD2_DIFSLOW_EW | R_DIFFN_MEDD2_DIFSLOW ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0, 1.0, 0.0, 0.0;
  (MOD) 0.0, 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_DSLOW_EW | R_MEDD2_SALOSS, R_MEDD2_DIFSLOW_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_ALLCV_EW | R_MEDD2_LSLOW_EW, R_MEDD2_DSLOW_EW ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.0264, 0.9149, 0.0587, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S60) 0.0000, 0.0218, 0.9469, 0.0313, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.00030003, 0.00190019, 0.01400140, 0.07880788, 0.32383238, 0.45964596, 0.12121212, 0.00030003, 0.00000000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00410041, 0.03840384, 0.21452145, 0.74237424, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0006, 0.0944, 0.8841, 0.0209, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0007, 0.2183, 0.7790, 0.0020, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00009999, 0.00039996, 0.00429957, 0.03209679, 0.19558044, 0.50484952, 0.26037396, 0.00229977, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00210021, 0.02390239, 0.16481648, 0.80898090, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.0000, 0.0018, 0.1956, 0.7860, 0.0166, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0077, 0.4577, 0.5345, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, M_S44) 0.0000, 0.0001, 0.0007, 0.0074, 0.0735, 0.4044, 0.4938, 0.0201, 0.0000;
  (V_SEV, M_S44) 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0123, 0.1119, 0.8749, 0.0000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.0000, 0.0000, 0.0026, 0.2655, 0.7316, 0.0003, 0.0000, 0.0000, 0.0000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0166, 0.9182, 0.0652, 0.0000, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0010, 0.0172, 0.2179, 0.6479, 0.1159, 0.0000;
  (V_SEV, M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0055, 0.0699, 0.9243, 0.0000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.0000, 0.0000, 0.0000, 0.0044, 0.5355, 0.4600, 0.0001, 0.0000, 0.0000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0398, 0.9460, 0.0142, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00179982, 0.05589441, 0.46005399, 0.48215178, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0021, 0.0390, 0.9588, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0047, 0.5053, 0.4900, 0.0000, 0.0000;
  (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.1104, 0.8881, 0.0013, 0.0000;
  (SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0041, 0.1033, 0.8925, 0.0000;
  (V_SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00069993, 0.01889811, 0.98040196, 0.00000000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0218, 0.8352, 0.1430, 0.0000;
  (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.3203, 0.6777, 0.0000;
  (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0194, 0.9803, 0.0000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0093, 0.9905, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0003, 0.0010, 0.0036, 0.0154, 0.0712, 0.2467, 0.6617, 0.0000;
  (MOD, M_S08) 0.00000000, 0.00010001, 0.00050005, 0.00190019, 0.00930093, 0.05040504, 0.20722072, 0.73057306, 0.00000000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0026, 0.0190, 0.1153, 0.8626, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0062, 0.0562, 0.9369, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_CV_EW | R_MEDD2_ALLCV_EW ) {
  (M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0172, 0.1126, 0.2484, 0.3210, 0.1935, 0.0803, 0.0258;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00099990, 0.01489851, 0.09959004, 0.22617738, 0.29337066, 0.22617738, 0.10198980, 0.02889711, 0.00649935, 0.00139986;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.03170317, 0.14451445, 0.26392639, 0.29252925, 0.16701670, 0.06690669, 0.02450245, 0.00540054, 0.00090009, 0.00010001, 0.00000000;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0189, 0.1567, 0.3371, 0.2862, 0.1429, 0.0465, 0.0095, 0.0014, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01049895, 0.15648435, 0.37786221, 0.30066993, 0.12518748, 0.02449755, 0.00419958, 0.00049995, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0045, 0.1674, 0.4536, 0.2834, 0.0774, 0.0118, 0.0017, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.01090109, 0.42284228, 0.44464446, 0.10661066, 0.01370137, 0.00120012, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.0000, 0.0199, 0.8041, 0.1629, 0.0125, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLW_MEDD2_BLOCK_WD | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.3, 0.6, 0.1, 0.0, 0.0;
  (SEV, DEMY) 0.1, 0.5, 0.3, 0.1, 0.0;
  (TOTAL, DEMY) 0.00, 0.05, 0.20, 0.55, 0.20;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.2, 0.6, 0.2;
  (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_MEDD2_BLOCK_WD | R_LNLW_MEDD2_BLOCK_WD, R_DIFFN_MEDD2_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_EFFAXLOSS | R_MEDD2_BLOCK_WD, R_MEDD2_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_ALLAMP_WD | R_MEDD2_DISP_WD, R_MEDD2_EFFAXLOSS ) {
  (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;
  (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;
  (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;
  (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;
  (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;
  (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;
  (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;
  (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;
  (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;
  (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_MEDD2_AMP_WD | R_MEDD2_ALLAMP_WD ) {
  (ZERO) 0.75007501, 0.18781878, 0.04760476, 0.01120112, 0.00260026, 0.00060006, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (A0_01) 0.3184, 0.2478, 0.1780, 0.1158, 0.0688, 0.0380, 0.0189, 0.0086, 0.0036, 0.0014, 0.0005, 0.0002, 0.0000, 0.0000, 0.0000;
  (A0_10) 0.01170117, 0.03920392, 0.09350935, 0.16761676, 0.21892189, 0.20952095, 0.14651465, 0.07470747, 0.02870287, 0.00780078, 0.00160016, 0.00020002, 0.00000000, 0.00000000, 0.00000000;
  (A0_30) 0.00000000, 0.00000000, 0.00009999, 0.00129987, 0.01089891, 0.05269473, 0.15628437, 0.27017298, 0.27427257, 0.16358364, 0.05689431, 0.01219878, 0.00149985, 0.00009999, 0.00000000;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00219978, 0.02179782, 0.10328967, 0.25917408, 0.32546745, 0.20917908, 0.06709329, 0.01079892, 0.00089991;
  (A1_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0072, 0.0656, 0.2449, 0.3735, 0.2388, 0.0624, 0.0072;
}
probability ( R_LNLW_MEDD2_LD_WD | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;
  (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MEDD2_LD_WD | R_LNLW_MEDD2_LD_WD ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0, 1.0, 0.0, 0.0;
  (MOD) 0.0, 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLW_MEDD2_RD_WD | R_LNLW_MED_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( R_MEDD2_RD_WD | R_LNLW_MEDD2_RD_WD ) {
  (NO) 1.0, 0.0, 0.0;
  (MOD) 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_LSLOW_WD | R_MEDD2_LD_WD, R_MEDD2_RD_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;
  (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;
  (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;
  (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;
  (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;
  (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;
  (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;
}
probability ( R_LNLBE_MEDD2_DIFSLOW_WD ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MEDD2_DIFSLOW_WD | R_LNLBE_MEDD2_DIFSLOW_WD, R_DIFFN_MEDD2_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( R_MEDD2_DSLOW_WD | R_MEDD2_SALOSS, R_MEDD2_DIFSLOW_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_ALLCV_WD | R_MEDD2_LSLOW_WD, R_MEDD2_DSLOW_WD ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;
  (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;
  (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;
  (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;
  (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;
  (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;
  (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;
  (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;
  (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;
  (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MEDD2_CV_WD | R_MEDD2_ALLCV_WD ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.01089891, 0.09839016, 0.31656834, 0.35316468, 0.17488251, 0.04029597, 0.00559944;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00849915, 0.07679232, 0.28107189, 0.33226677, 0.20117988, 0.08159184, 0.01609839, 0.00209979, 0.00019998;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.01390139, 0.11201120, 0.29182918, 0.31343134, 0.19151915, 0.06100610, 0.01300130, 0.00280028, 0.00030003, 0.00000000, 0.00000000;
  (M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00290029, 0.07060706, 0.31513151, 0.38363836, 0.17091709, 0.04760476, 0.00820082, 0.00090009, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.03939606, 0.31846815, 0.41945805, 0.17558244, 0.04189581, 0.00449955, 0.00049995, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.00000000, 0.00000000, 0.00000000, 0.01569843, 0.31446855, 0.46605339, 0.17158284, 0.02909709, 0.00279972, 0.00029997, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S14) 0.00000000, 0.00000000, 0.03080308, 0.57695770, 0.33983398, 0.04820482, 0.00400040, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.00000000, 0.04650465, 0.84828483, 0.09990999, 0.00510051, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( DIFFN_MOT_SEV | DIFFN_SEV, DIFFN_TYPE ) {
  (NO, MOTOR) 1.0, 0.0, 0.0, 0.0;
  (MILD, MOTOR) 0.0, 1.0, 0.0, 0.0;
  (MOD, MOTOR) 0.0, 0.0, 1.0, 0.0;
  (SEV, MOTOR) 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 1.0, 0.0, 0.0, 0.0;
  (MILD, MIXED) 0.0, 1.0, 0.0, 0.0;
  (MOD, MIXED) 0.0, 0.0, 1.0, 0.0;
  (SEV, MIXED) 0.0, 0.0, 0.0, 1.0;
  (NO, SENS) 1.0, 0.0, 0.0, 0.0;
  (MILD, SENS) 1.0, 0.0, 0.0, 0.0;
  (MOD, SENS) 1.0, 0.0, 0.0, 0.0;
  (SEV, SENS) 1.0, 0.0, 0.0, 0.0;
}
probability ( DIFFN_M_SEV_DIST | DIFFN_MOT_SEV, DIFFN_DISTR ) {
  (NO, DIST) 1.0, 0.0, 0.0, 0.0;
  (MILD, DIST) 0.0, 1.0, 0.0, 0.0;
  (MOD, DIST) 0.0, 0.0, 1.0, 0.0;
  (SEV, DIST) 0.0, 0.0, 0.0, 1.0;
  (NO, PROX) 1.0, 0.0, 0.0, 0.0;
  (MILD, PROX) 1.0, 0.0, 0.0, 0.0;
  (MOD, PROX) 0.5, 0.5, 0.0, 0.0;
  (SEV, PROX) 0.0, 0.5, 0.5, 0.0;
  (NO, RANDOM) 1.0, 0.0, 0.0, 0.0;
  (MILD, RANDOM) 0.25, 0.45, 0.25, 0.05;
  (MOD, RANDOM) 0.1, 0.4, 0.4, 0.1;
  (SEV, RANDOM) 0.05, 0.15, 0.40, 0.40;
}
probability ( R_DIFFN_MED_BLOCK | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLBE_MED_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_MED_BLOCK_EW | R_DIFFN_MED_BLOCK, R_LNLBE_MED_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MED_AMPR_EW | R_MED_BLOCK_EW ) {
  (NO) 0.0879, 0.4192, 0.4232, 0.0693, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD) 0.00189981, 0.03439656, 0.25667433, 0.52914709, 0.17348265, 0.00439956, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD) 0.0001, 0.0010, 0.0076, 0.0403, 0.1720, 0.3730, 0.3420, 0.0633, 0.0007, 0.0000, 0.0000, 0.0000;
  (SEV) 0.00090009, 0.00150015, 0.00260026, 0.00490049, 0.00950095, 0.01760176, 0.03470347, 0.07680768, 0.16681668, 0.34143414, 0.34323432, 0.00000000;
  (TOTAL) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLT1_APB_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLLP_APB_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLT1_LP_APB_MALOSS | R_LNLT1_APB_MALOSS, R_LNLLP_APB_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLBE_APB_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLT1_LP_BE_APB_MALOSS | R_LNLT1_LP_APB_MALOSS, R_LNLBE_APB_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLW_APB_MALOSS | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_DIFFN_APB_MALOSS | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_DIFFN_LNLW_APB_MALOSS | R_LNLW_APB_MALOSS, R_DIFFN_APB_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_APB_MALOSS | R_LNLT1_LP_BE_APB_MALOSS, R_DIFFN_LNLW_APB_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;
  (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;
  (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( R_DIFFN_MED_DIFSLOW | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( R_MED_DIFSLOW_EW | R_DIFFN_MED_DIFSLOW ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0126, 0.9869, 0.0005, 0.0000;
  (MOD) 0.0000, 0.0179, 0.9821, 0.0000;
  (SEV) 0.0000, 0.0003, 0.0252, 0.9745;
}
probability ( R_MED_DCV_EW | R_APB_MALOSS, R_MED_DIFSLOW_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;
  (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;
  (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;
  (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;
}
probability ( R_MED_LD_EW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MED_RD_EW ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MED_RDLDCV_EW | R_MED_LD_EW, R_MED_RD_EW ) {
  (NO, NO) 0.90449045, 0.09530953, 0.00020002, 0.00000000, 0.00000000, 0.00000000;
  (MILD, NO) 0.1320, 0.6039, 0.2641, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0139, 0.1839, 0.8022, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.00120012, 0.00670067, 0.05440544, 0.86008601, 0.07760776, 0.00000000;
  (NO, MOD) 0.0115, 0.0333, 0.1509, 0.7319, 0.0724, 0.0000;
  (MILD, MOD) 0.00340034, 0.01220122, 0.06900690, 0.71967197, 0.19531953, 0.00040004;
  (MOD, MOD) 0.0011, 0.0045, 0.0299, 0.5742, 0.3876, 0.0027;
  (SEV, MOD) 0.0001, 0.0002, 0.0018, 0.0914, 0.6093, 0.2972;
  (NO, SEV) 0.00000000, 0.00009999, 0.00109989, 0.14618538, 0.80701930, 0.04559544;
  (MILD, SEV) 0.0000, 0.0000, 0.0002, 0.0581, 0.7950, 0.1467;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0228, 0.6344, 0.3427;
  (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0014, 0.1063, 0.8923;
}
probability ( R_MED_ALLCV_EW | R_MED_DCV_EW, R_MED_RDLDCV_EW ) {
  (M_S60, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56, M_S60) 0.0699, 0.8102, 0.1199, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S60) 0.00000000, 0.04790479, 0.95209521, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S44, M_S60) 0.00089991, 0.00899910, 0.08879112, 0.81861814, 0.08269173, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S36, M_S60) 0.00000000, 0.00000000, 0.00050005, 0.10111011, 0.84778478, 0.05060506, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.07910791, 0.90019002, 0.02020202, 0.00000000, 0.00000000, 0.00000000;
  (M_S20, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0734, 0.8393, 0.0867, 0.0000, 0.0000;
  (M_S14, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.09470947, 0.89458946, 0.01040104, 0.00000000;
  (M_S08, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0932, 0.9048, 0.0000;
  (M_S00, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S52) 0.00660066, 0.14551455, 0.83808381, 0.00980098, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S56, M_S52) 0.0005, 0.0239, 0.4369, 0.5387, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S52) 0.0000, 0.0003, 0.0239, 0.9748, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S52) 0.0000, 0.0002, 0.0036, 0.2467, 0.7339, 0.0156, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S36, M_S52) 0.0000, 0.0000, 0.0000, 0.0050, 0.3134, 0.6811, 0.0005, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00710071, 0.54935494, 0.44334433, 0.00020002, 0.00000000, 0.00000000;
  (M_S20, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00910091, 0.52835284, 0.46254625, 0.00000000, 0.00000000;
  (M_S14, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0216, 0.8359, 0.1425, 0.0000;
  (M_S08, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0355, 0.9641, 0.0000;
  (M_S00, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S44) 0.0000, 0.0000, 0.0047, 0.9951, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S56, M_S44) 0.0000, 0.0000, 0.0004, 0.9005, 0.0991, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S44) 0.0000, 0.0000, 0.0000, 0.1288, 0.8712, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.01130113, 0.57115712, 0.41754175, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S36, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0214, 0.9354, 0.0432, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.05649435, 0.93230677, 0.01109889, 0.00000000, 0.00000000;
  (M_S20, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.1430, 0.8551, 0.0015, 0.0000;
  (M_S14, M_S44) 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0019998, 0.3548645, 0.6431357, 0.0000000;
  (M_S08, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0122, 0.9877, 0.0000;
  (M_S00, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56, M_S27) 0.000, 0.000, 0.000, 0.000, 0.000, 0.997, 0.003, 0.000, 0.000, 0.000;
  (M_S52, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2336, 0.7664, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S27) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00259974, 0.99340066, 0.00399960, 0.00000000, 0.00000000;
  (M_S36, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2469, 0.7531, 0.0000, 0.0000;
  (M_S28, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.9939, 0.0041, 0.0000;
  (M_S20, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0314, 0.9686, 0.0000;
  (M_S14, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;
  (M_S08, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;
  (M_S00, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (M_S60, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.1305, 0.8445, 0.0238, 0.0000;
  (M_S56, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0740, 0.8589, 0.0667, 0.0000;
  (M_S52, M_S15) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03730373, 0.80288029, 0.15971597, 0.00000000;
  (M_S44, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0070, 0.3517, 0.6413, 0.0000;
  (M_S36, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0581, 0.9416, 0.0000;
  (M_S28, M_S15) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.006, 0.994, 0.000;
  (M_S20, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (M_S14, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S08, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;
  (M_S00, M_S15) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (M_S60, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0321, 0.9677, 0.0000;
  (M_S56, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0184, 0.9815, 0.0000;
  (M_S52, M_S07) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01010101, 0.98989899, 0.00000000;
  (M_S44, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.9958, 0.0000;
  (M_S36, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (M_S28, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;
  (M_S20, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S14, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (M_S08, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S00, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MED_CV_EW | R_MED_ALLCV_EW ) {
  (M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0168, 0.1184, 0.2960, 0.3227, 0.1783, 0.0560, 0.0112;
  (M_S56) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0005, 0.0155, 0.1165, 0.2969, 0.3229, 0.1782, 0.0564, 0.0114, 0.0016;
  (M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0039, 0.0589, 0.2434, 0.3586, 0.2350, 0.0808, 0.0164, 0.0022, 0.0002;
  (M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.0176, 0.1966, 0.2515, 0.2699, 0.1688, 0.0690, 0.0203, 0.0046, 0.0009, 0.0001, 0.0000;
  (M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00560056, 0.09000900, 0.30563056, 0.30933093, 0.20392039, 0.06730673, 0.01520152, 0.00260026, 0.00040004, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0496, 0.2972, 0.4059, 0.2097, 0.0258, 0.0098, 0.0013, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.00000000, 0.00000000, 0.00000000, 0.01789821, 0.29457054, 0.44695530, 0.19018098, 0.04339566, 0.00659934, 0.00029997, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S14) 0.0000, 0.0000, 0.0265, 0.5431, 0.3624, 0.0622, 0.0054, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S08) 0.00000000, 0.12651265, 0.76547655, 0.10061006, 0.00700070, 0.00040004, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 0.9999, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( R_LNLT1_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_LNLLP_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_LNLT1_LP_APB_DE_REGEN | R_LNLT1_APB_DE_REGEN, R_LNLLP_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_LNLBE_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_LNLT1_LP_BE_APB_DE_REGEN | R_LNLT1_LP_APB_DE_REGEN, R_LNLBE_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( DIFFN_TIME ) {
  table 0.01, 0.25, 0.65, 0.09;
}
probability ( R_DIFFN_APB_DE_REGEN | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
}
probability ( R_LNLW_MED_TIME ) {
  table 0.05, 0.33, 0.60, 0.02;
}
probability ( R_LNLW_APB_DE_REGEN | R_LNLW_MED_SEV, R_LNLW_MED_TIME, R_LNLW_MED_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (TOTAL, CHRONIC, DEMY) 1.0, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (TOTAL, OLD, DEMY) 1.0, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (TOTAL, OLD, BLOCK) 1.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (TOTAL, OLD, AXONAL) 1.0, 0.0;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (TOTAL, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0;
}
probability ( R_DIFFN_LNLW_APB_DE_REGEN | R_DIFFN_APB_DE_REGEN, R_LNLW_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_LNL_DIFFN_APB_DE_REGEN | R_LNLT1_LP_BE_APB_DE_REGEN, R_DIFFN_LNLW_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_MYOP_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_MYDY_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_MYOP_MYDY_APB_DE_REGEN | R_MYOP_APB_DE_REGEN, R_MYDY_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_APB_DE_REGEN | R_LNL_DIFFN_APB_DE_REGEN, R_MYOP_MYDY_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_DE_REGEN_APB_NMT | R_APB_DE_REGEN ) {
  (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;
}
probability ( R_MYAS_APB_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_APB_NMT | R_DE_REGEN_APB_NMT, R_MYAS_APB_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MYDY_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYOP_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYOP_MYDY_APB_MUSIZE | R_MYDY_APB_MUSIZE, R_MYOP_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLW_APB_MUSIZE | R_LNLW_MED_SEV, R_LNLW_MED_TIME, R_LNLW_MED_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_APB_MUSIZE | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_LNLW_APB_MUSIZE | R_LNLW_APB_MUSIZE, R_DIFFN_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLBE_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLLP_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLT1_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLT1_LP_APB_MUSIZE | R_LNLLP_APB_MUSIZE, R_LNLT1_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLT1_LP_BE_APB_MUSIZE | R_LNLBE_APB_MUSIZE, R_LNLT1_LP_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNL_DIFFN_APB_MUSIZE | R_DIFFN_LNLW_APB_MUSIZE, R_LNLT1_LP_BE_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_APB_MUSIZE | R_MYOP_MYDY_APB_MUSIZE, R_LNL_DIFFN_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 0.9791, 0.0009, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL, V_SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL, V_SMALL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (INCR, V_SMALL) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;
  (LARGE, V_SMALL) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;
  (V_LARGE, V_SMALL) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;
  (V_SMALL, SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL, SMALL) 0.7493, 0.2257, 0.0049, 0.0001, 0.0000, 0.0000, 0.0200;
  (NORMAL, SMALL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;
  (INCR, SMALL) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;
  (LARGE, SMALL) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;
  (V_LARGE, SMALL) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;
  (V_SMALL, NORMAL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SMALL, NORMAL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;
  (NORMAL, NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;
  (V_SMALL, INCR) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;
  (SMALL, INCR) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00359964, 0.16548345, 0.64543546, 0.16548345, 0.01999800;
  (LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;
  (V_LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (V_SMALL, LARGE) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;
  (SMALL, LARGE) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;
  (INCR, LARGE) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;
  (LARGE, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;
  (V_SMALL, V_LARGE) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;
  (SMALL, V_LARGE) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;
  (INCR, V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;
  (V_LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9799, 0.0200;
}
probability ( R_APB_EFFMUS | R_APB_NMT, R_APB_MUSIZE ) {
  (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MIXED, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;
  (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MIXED, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;
  (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;
  (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;
  (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;
  (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;
  (MIXED, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;
  (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;
  (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;
  (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;
  (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;
  (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;
  (MIXED, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;
  (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;
  (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;
  (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;
  (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;
  (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;
  (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;
  (MIXED, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;
  (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;
  (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;
  (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;
  (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;
  (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;
  (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;
  (MIXED, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;
  (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;
  (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;
  (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;
  (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;
  (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;
  (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;
  (MIXED, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;
}
probability ( R_LNLW_MED_BLOCK | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (TOTAL, DEMY) 0.2, 0.2, 0.2, 0.2, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.2, 0.2, 0.2, 0.2, 0.2;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_MED_BLOCK_WA | R_DIFFN_MED_BLOCK, R_LNLW_MED_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_APB_MULOSS | R_MED_BLOCK_WA, R_APB_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;
  (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;
  (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;
  (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;
  (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;
  (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;
  (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;
  (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;
  (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;
  (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;
  (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;
  (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;
  (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( R_APB_ALLAMP_WA | R_APB_EFFMUS, R_APB_MULOSS ) {
  (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;
  (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;
  (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;
  (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;
  (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;
  (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;
  (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;
  (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;
  (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;
  (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;
  (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;
  (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;
  (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;
  (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;
  (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;
  (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;
  (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;
  (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;
  (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;
  (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;
  (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;
  (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;
  (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;
  (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;
  (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;
  (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;
  (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;
  (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;
  (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;
}
probability ( R_MED_AMP_WA | R_APB_ALLAMP_WA ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00030003, 0.28502850, 0.23462346, 0.17811781, 0.12451245, 0.08030803, 0.04760476, 0.02610261, 0.01320132, 0.00610061, 0.00260026, 0.00100010, 0.00040004, 0.00010001, 0.00000000, 0.00000000, 0.00000000;
  (A0_10) 0.00000000, 0.01350135, 0.03690369, 0.07940794, 0.13531353, 0.18181818, 0.19321932, 0.16221622, 0.10771077, 0.05640564, 0.02340234, 0.00760076, 0.00200020, 0.00040004, 0.00010001, 0.00000000, 0.00000000;
  (A0_30) 0.00000000, 0.00000000, 0.00009999, 0.00059994, 0.00359964, 0.01649835, 0.05349465, 0.12328767, 0.20127987, 0.23347665, 0.19218078, 0.11208879, 0.04649535, 0.01369863, 0.00279972, 0.00039996, 0.00000000;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00100010, 0.00690069, 0.03090309, 0.09260926, 0.18551855, 0.24962496, 0.22502250, 0.13581358, 0.05490549, 0.01490149, 0.00270027;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00310031, 0.01900190, 0.07230723, 0.17361736, 0.26222622, 0.24972497, 0.14971497, 0.05670567, 0.01330133;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0017, 0.0093, 0.0355, 0.0964, 0.1860, 0.2545, 0.2471, 0.1693;
  (A4_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0029, 0.0155, 0.0599, 0.1641, 0.3182, 0.4390;
  (A8_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00150015, 0.01150115, 0.06310631, 0.24452445, 0.67926793;
}
probability ( R_MED_LD_WA | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.25, 0.25, 0.25, 0.25;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;
  (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.25, 0.25, 0.25, 0.25;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 0.25, 0.25, 0.25, 0.25;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 0.25, 0.25, 0.25, 0.25;
}
probability ( R_MED_RD_WA | R_LNLW_MED_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( R_MED_RDLDDEL | R_MED_LD_WA, R_MED_RD_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0964, 0.7981, 0.1055, 0.0000, 0.0000;
  (MOD, NO) 0.0032, 0.1270, 0.8698, 0.0000, 0.0000;
  (SEV, NO) 0.00090009, 0.00280028, 0.01470147, 0.98159816, 0.00000000;
  (NO, MOD) 0.0019, 0.5257, 0.4724, 0.0000, 0.0000;
  (MILD, MOD) 0.00019998, 0.04149585, 0.95830417, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0001, 0.0144, 0.9855, 0.0000, 0.0000;
  (SEV, MOD) 0.00019998, 0.00059994, 0.00369963, 0.99550045, 0.00000000;
  (NO, SEV) 0.0002, 0.0304, 0.9694, 0.0000, 0.0000;
  (MILD, SEV) 0.0001, 0.0142, 0.9857, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0090, 0.9808, 0.0102, 0.0000;
  (SEV, SEV) 0.0000, 0.0002, 0.0012, 0.9984, 0.0002;
}
probability ( R_LNLBE_MED_DIFSLOW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MED_DIFSLOW_WA | R_LNLBE_MED_DIFSLOW, R_DIFFN_MED_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;
  (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;
  (MOD, MOD) 0.000, 0.000, 0.002, 0.998;
  (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;
  (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;
}
probability ( R_MED_DCV_WA | R_APB_MALOSS, R_MED_DIFSLOW_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;
  (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;
  (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;
  (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;
}
probability ( R_MED_ALLDEL_WA | R_MED_RDLDDEL, R_MED_DCV_WA ) {
  (MS3_1, M_S60) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S60) 0.05119488, 0.22907709, 0.56624338, 0.15348465, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S60) 0.0001, 0.0035, 0.0632, 0.9326, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S60) 0.00129987, 0.00199980, 0.00439956, 0.01869813, 0.12128787, 0.74792521, 0.10438956, 0.00000000, 0.00000000;
  (MS20_1, M_S60) 0.00010001, 0.00010001, 0.00030003, 0.00090009, 0.00450045, 0.04340434, 0.57675768, 0.37393739, 0.00000000;
  (MS3_1, M_S52) 0.5607, 0.4393, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S52) 0.01929807, 0.12868713, 0.49285071, 0.35916408, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S52) 0.0000, 0.0011, 0.0283, 0.9651, 0.0055, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S52) 0.00100010, 0.00160016, 0.00370037, 0.01610161, 0.10931093, 0.74217422, 0.12611261, 0.00000000, 0.00000000;
  (MS20_1, M_S52) 0.0001, 0.0001, 0.0002, 0.0008, 0.0041, 0.0399, 0.5568, 0.3980, 0.0000;
  (MS3_1, M_S44) 0.0069, 0.7963, 0.1968, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S44) 0.0027, 0.0328, 0.2398, 0.7246, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S44) 0.0000, 0.0002, 0.0075, 0.8889, 0.1034, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S44) 0.00079992, 0.00119988, 0.00279972, 0.01249875, 0.09159084, 0.72352765, 0.16758324, 0.00000000, 0.00000000;
  (MS20_1, M_S44) 0.0001, 0.0001, 0.0002, 0.0007, 0.0034, 0.0348, 0.5239, 0.4368, 0.0000;
  (MS3_1, M_S36) 0.0000, 0.0184, 0.9806, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S36) 0.00010001, 0.00330033, 0.05470547, 0.93819382, 0.00370037, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S36) 0.00000000, 0.00000000, 0.00030003, 0.18501850, 0.81468147, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS10_1, M_S36) 0.00049995, 0.00079992, 0.00179982, 0.00869913, 0.07029297, 0.68023198, 0.23767623, 0.00000000, 0.00000000;
  (MS20_1, M_S36) 0.0001, 0.0001, 0.0001, 0.0005, 0.0027, 0.0287, 0.4783, 0.4895, 0.0000;
  (MS3_1, M_S28) 0.0000, 0.0001, 0.0179, 0.9820, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S28) 0.00000000, 0.00019998, 0.00479952, 0.50394961, 0.49105089, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S28) 0.0000, 0.0000, 0.0000, 0.0032, 0.9968, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S28) 0.0002, 0.0004, 0.0009, 0.0047, 0.0440, 0.5737, 0.3761, 0.0000, 0.0000;
  (MS20_1, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40724072, 0.56955696, 0.00000000;
  (MS3_1, M_S20) 0.0000, 0.0002, 0.0030, 0.1393, 0.8575, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S20) 0.0000, 0.0000, 0.0003, 0.0188, 0.9804, 0.0005, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.93139314, 0.06710671, 0.00000000, 0.00000000, 0.00000000;
  (MS10_1, M_S20) 0.0001, 0.0001, 0.0002, 0.0013, 0.0150, 0.3152, 0.6681, 0.0000, 0.0000;
  (MS20_1, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00080016, 0.01110222, 0.28045609, 0.70754151, 0.00000000;
  (MS3_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0006, 0.8145, 0.1849, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0494, 0.9506, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S14) 0.000, 0.000, 0.000, 0.000, 0.001, 0.999, 0.000, 0.000, 0.000;
  (MS10_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0001, 0.0017, 0.0786, 0.9196, 0.0000, 0.0000;
  (MS20_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0035, 0.1380, 0.8583, 0.0000;
  (MS3_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59735974, 0.39093909, 0.00000000, 0.00000000;
  (MS3_9, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00340034, 0.29902990, 0.69746975, 0.00000000, 0.00000000;
  (MS4_7, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.1112, 0.8881, 0.0000, 0.0000;
  (MS10_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00940094, 0.95939594, 0.03110311, 0.00000000;
  (MS20_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.02530253, 0.97439744, 0.00000000;
  (MS3_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS3_9, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS4_7, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS10_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS20_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MED_LAT_WA | R_MED_ALLDEL_WA ) {
  (MS0_0) 0.00590059, 0.13261326, 0.50325033, 0.32263226, 0.03500350, 0.00060006, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS0_4) 0.00069993, 0.01959804, 0.16898310, 0.42545745, 0.31276872, 0.06719328, 0.00529947, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS0_8) 0.0000, 0.0007, 0.0194, 0.1669, 0.4202, 0.3089, 0.0829, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS1_6) 0.00000000, 0.00000000, 0.00100010, 0.01090109, 0.06350635, 0.19471947, 0.39343934, 0.29212921, 0.04280428, 0.00150015, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS3_2) 0.0001, 0.0003, 0.0011, 0.0034, 0.0090, 0.0210, 0.0528, 0.1370, 0.2128, 0.2375, 0.1826, 0.1229, 0.0179, 0.0016, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS6_4) 0.00009999, 0.00019998, 0.00039996, 0.00079992, 0.00139986, 0.00249975, 0.00509949, 0.01199880, 0.02149785, 0.03539646, 0.08919108, 0.13978602, 0.18288171, 0.28117188, 0.18898110, 0.03589641, 0.00259974, 0.00009999, 0.00000000;
  (MS12_8) 0.0002, 0.0002, 0.0003, 0.0004, 0.0005, 0.0007, 0.0012, 0.0022, 0.0032, 0.0047, 0.0109, 0.0176, 0.0280, 0.0629, 0.1563, 0.2269, 0.2569, 0.2269, 0.0000;
  (MS25_6) 0.00079992, 0.00089991, 0.00109989, 0.00119988, 0.00139986, 0.00169983, 0.00249975, 0.00369963, 0.00459954, 0.00559944, 0.01059894, 0.01559844, 0.02139786, 0.04329567, 0.10068993, 0.16488351, 0.25357464, 0.36646335, 0.00000000;
  (INFIN) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_APB_VOL_ACT ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_APB_FORCE | R_APB_VOL_ACT, R_APB_ALLAMP_WA ) {
  (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;
  (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;
  (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;
  (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;
  (NORMAL, A0_01) 0.00159984, 0.01859814, 0.09359064, 0.26787321, 0.36586341, 0.25247475;
  (REDUCED, A0_01) 0.0002, 0.0034, 0.0260, 0.1312, 0.3333, 0.5059;
  (V_RED, A0_01) 0.0000, 0.0003, 0.0044, 0.0446, 0.2226, 0.7281;
  (ABSENT, A0_01) 0.0000, 0.0000, 0.0005, 0.0098, 0.1058, 0.8839;
  (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;
  (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;
  (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;
  (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;
  (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;
  (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;
  (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;
  (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;
  (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;
  (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;
  (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;
  (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;
  (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;
  (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;
  (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;
  (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;
  (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;
  (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;
  (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;
  (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;
  (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;
  (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;
  (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;
  (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;
}
probability ( R_APB_MUSCLE_VOL | R_APB_MUSIZE, R_APB_MALOSS ) {
  (V_SMALL, NO) 0.9896, 0.0104;
  (SMALL, NO) 0.8137, 0.1863;
  (NORMAL, NO) 0.0209, 0.9791;
  (INCR, NO) 0.009, 0.991;
  (LARGE, NO) 0.003, 0.997;
  (V_LARGE, NO) 0.0004, 0.9996;
  (OTHER, NO) 0.4212, 0.5788;
  (V_SMALL, MILD) 0.9976, 0.0024;
  (SMALL, MILD) 0.9603, 0.0397;
  (NORMAL, MILD) 0.5185, 0.4815;
  (INCR, MILD) 0.1087, 0.8913;
  (LARGE, MILD) 0.0278, 0.9722;
  (V_LARGE, MILD) 0.0046, 0.9954;
  (OTHER, MILD) 0.5185, 0.4815;
  (V_SMALL, MOD) 0.999, 0.001;
  (SMALL, MOD) 0.9893, 0.0107;
  (NORMAL, MOD) 0.9588, 0.0412;
  (INCR, MOD) 0.6377, 0.3623;
  (LARGE, MOD) 0.2716, 0.7284;
  (V_LARGE, MOD) 0.0779, 0.9221;
  (OTHER, MOD) 0.6336, 0.3664;
  (V_SMALL, SEV) 0.9995, 0.0005;
  (SMALL, SEV) 0.9969, 0.0031;
  (NORMAL, SEV) 0.9953, 0.0047;
  (INCR, SEV) 0.9518, 0.0482;
  (LARGE, SEV) 0.8234, 0.1766;
  (V_LARGE, SEV) 0.5986, 0.4014;
  (OTHER, SEV) 0.7685, 0.2315;
  (V_SMALL, TOTAL) 0.9989, 0.0011;
  (SMALL, TOTAL) 0.9984, 0.0016;
  (NORMAL, TOTAL) 0.9984, 0.0016;
  (INCR, TOTAL) 0.9975, 0.0025;
  (LARGE, TOTAL) 0.9965, 0.0035;
  (V_LARGE, TOTAL) 0.9956, 0.0044;
  (OTHER, TOTAL) 0.9857, 0.0143;
  (V_SMALL, OTHER) 0.9363, 0.0637;
  (SMALL, OTHER) 0.8403, 0.1597;
  (NORMAL, OTHER) 0.6534, 0.3466;
  (INCR, OTHER) 0.4689, 0.5311;
  (LARGE, OTHER) 0.3174, 0.6826;
  (V_LARGE, OTHER) 0.1948, 0.8052;
  (OTHER, OTHER) 0.5681, 0.4319;
}
probability ( R_APB_MVA_RECRUIT | R_APB_MULOSS, R_APB_VOL_ACT ) {
  (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;
  (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;
  (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;
  (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;
  (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;
  (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;
  (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;
  (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;
  (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;
  (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;
  (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;
  (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;
  (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;
  (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;
  (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;
  (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;
  (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_APB_MVA_AMP | R_APB_EFFMUS ) {
  (V_SMALL) 0.00, 0.04, 0.96;
  (SMALL) 0.01, 0.15, 0.84;
  (NORMAL) 0.05, 0.90, 0.05;
  (INCR) 0.50, 0.49, 0.01;
  (LARGE) 0.85, 0.15, 0.00;
  (V_LARGE) 0.96, 0.04, 0.00;
  (OTHER) 0.33, 0.34, 0.33;
}
probability ( R_APB_TA_CONCL | R_APB_EFFMUS ) {
  (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;
  (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;
  (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;
  (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;
  (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;
  (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;
  (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;
}
probability ( R_APB_MUPAMP | R_APB_EFFMUS ) {
  (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;
  (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;
  (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;
  (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;
}
probability ( R_APB_QUAN_MUPAMP | R_APB_MUPAMP ) {
  (V_SMALL) 0.00080024, 0.00370111, 0.01350405, 0.03811143, 0.08352506, 0.14254276, 0.18955687, 0.19635891, 0.15834750, 0.09942983, 0.04861458, 0.01850555, 0.00550165, 0.00130039, 0.00020006, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.00000000, 0.00010002, 0.00080016, 0.00370074, 0.01350270, 0.03810762, 0.08351670, 0.14252851, 0.18953791, 0.19633927, 0.15833167, 0.09941988, 0.04860972, 0.01850370, 0.00550110, 0.00130026, 0.00020004, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.00370037, 0.01870187, 0.06390639, 0.14751475, 0.23022302, 0.24312431, 0.17371737, 0.08400840, 0.02750275, 0.00610061, 0.00090009, 0.00010001, 0.00000000, 0.00000000;
  (INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0037, 0.0135, 0.0381, 0.0835, 0.1426, 0.1896, 0.1963, 0.1583, 0.0995, 0.0487, 0.0185, 0.0055, 0.0013;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0038, 0.0136, 0.0383, 0.0841, 0.1435, 0.1909, 0.1977, 0.1594, 0.1001, 0.0490, 0.0187;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0024, 0.0082, 0.0232, 0.0542, 0.1041, 0.1645, 0.2137, 0.2283, 0.2007;
  (OTHER) 0.0045, 0.0078, 0.0127, 0.0197, 0.0289, 0.0403, 0.0531, 0.0664, 0.0787, 0.0883, 0.0939, 0.0946, 0.0903, 0.0817, 0.0700, 0.0569, 0.0438, 0.0319, 0.0221, 0.0144;
}
probability ( R_APB_QUAL_MUPAMP | R_APB_MUPAMP ) {
  (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;
  (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;
  (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;
  (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;
  (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;
  (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;
  (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;
}
probability ( R_APB_MUPDUR | R_APB_EFFMUS ) {
  (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;
  (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;
}
probability ( R_APB_QUAN_MUPDUR | R_APB_MUPDUR ) {
  (V_SMALL) 0.09981996, 0.18333667, 0.24024805, 0.22454491, 0.14972995, 0.07121424, 0.02420484, 0.00580116, 0.00100020, 0.00010002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.01020102, 0.03690369, 0.09510951, 0.17471747, 0.22892289, 0.21402140, 0.14261426, 0.06780678, 0.02300230, 0.00560056, 0.00100010, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.0000, 0.0002, 0.0025, 0.0177, 0.0739, 0.1852, 0.2785, 0.2515, 0.1363, 0.0444, 0.0087, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR) 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000, 0.00000000, 0.00000000;
  (LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000;
  (V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00039996, 0.00179982, 0.00699930, 0.02189781, 0.05409459, 0.10518948, 0.16128387, 0.19498050, 0.18598140, 0.13988601, 0.08289171, 0.03879612, 0.00569943;
  (OTHER) 0.0201, 0.0341, 0.0529, 0.0748, 0.0966, 0.1138, 0.1224, 0.1202, 0.1078, 0.0882, 0.0658, 0.0449, 0.0279, 0.0159, 0.0082, 0.0039, 0.0017, 0.0007, 0.0001;
}
probability ( R_APB_QUAL_MUPDUR | R_APB_MUPDUR ) {
  (V_SMALL) 0.8309, 0.1677, 0.0014;
  (SMALL) 0.49, 0.49, 0.02;
  (NORMAL) 0.1065, 0.7870, 0.1065;
  (INCR) 0.02, 0.49, 0.49;
  (LARGE) 0.0014, 0.1677, 0.8309;
  (V_LARGE) 0.0001, 0.0392, 0.9607;
  (OTHER) 0.2597, 0.4806, 0.2597;
}
probability ( R_APB_QUAN_MUPPOLY | R_APB_DE_REGEN, R_APB_EFFMUS ) {
  (NO, V_SMALL) 0.109, 0.548, 0.343;
  (YES, V_SMALL) 0.004, 0.122, 0.874;
  (NO, SMALL) 0.340, 0.564, 0.096;
  (YES, SMALL) 0.015, 0.261, 0.724;
  (NO, NORMAL) 0.925, 0.075, 0.000;
  (YES, NORMAL) 0.091, 0.526, 0.383;
  (NO, INCR) 0.796, 0.201, 0.003;
  (YES, INCR) 0.061, 0.465, 0.474;
  (NO, LARGE) 0.637, 0.348, 0.015;
  (YES, LARGE) 0.039, 0.396, 0.565;
  (NO, V_LARGE) 0.340, 0.564, 0.096;
  (YES, V_LARGE) 0.015, 0.261, 0.724;
  (NO, OTHER) 0.340, 0.564, 0.096;
  (YES, OTHER) 0.015, 0.261, 0.724;
}
probability ( R_APB_QUAL_MUPPOLY | R_APB_QUAN_MUPPOLY ) {
  (x_12_) 0.95, 0.05;
  (x12_24_) 0.3, 0.7;
  (x_24_) 0.05, 0.95;
}
probability ( R_APB_MUPSATEL | R_APB_DE_REGEN ) {
  (NO) 0.95, 0.05;
  (YES) 0.2, 0.8;
}
probability ( R_APB_MUPINSTAB | R_APB_NMT ) {
  (NO) 0.95, 0.05;
  (MOD_PRE) 0.1, 0.9;
  (SEV_PRE) 0.03, 0.97;
  (MLD_POST) 0.2, 0.8;
  (MOD_POST) 0.1, 0.9;
  (SEV_POST) 0.03, 0.97;
  (MIXED) 0.1, 0.9;
}
probability ( R_APB_REPSTIM_CMAPAMP | R_APB_ALLAMP_WA ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00130013, 0.11591159, 0.12801280, 0.13301330, 0.13001300, 0.11941194, 0.10311031, 0.08380838, 0.06390639, 0.04590459, 0.03100310, 0.01970197, 0.01170117, 0.00660066, 0.00350035, 0.00170017, 0.00080008, 0.00040004, 0.00010001, 0.00010001, 0.00000000;
  (A0_10) 0.00000000, 0.00029997, 0.00129987, 0.00409959, 0.01119888, 0.02609739, 0.05159484, 0.08679132, 0.12468753, 0.15248475, 0.15888411, 0.14108589, 0.10678932, 0.06879312, 0.03779622, 0.01769823, 0.00709929, 0.00239976, 0.00069993, 0.00019998, 0.00000000;
  (A0_30) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00069993, 0.00309969, 0.01109889, 0.03129687, 0.07039296, 0.12478752, 0.17478252, 0.19348065, 0.16928307, 0.11698830, 0.06399360, 0.02759724, 0.00939906, 0.00249975, 0.00049995;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00210021, 0.01070107, 0.03860386, 0.09900990, 0.17961796, 0.23162316, 0.21192119, 0.13751375, 0.06320632, 0.02070207, 0.00470047;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00310031, 0.01900190, 0.07230723, 0.17361736, 0.26222622, 0.24972497, 0.14971497, 0.05670567, 0.01330133;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0007, 0.0037, 0.0151, 0.0464, 0.1065, 0.1843, 0.2393, 0.2335, 0.1704;
  (A4_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00110011, 0.00610061, 0.02490249, 0.07680768, 0.17791779, 0.30893089, 0.40404040;
  (A8_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0005, 0.0038, 0.0208, 0.0860, 0.2659, 0.6229;
}
probability ( R_APB_REPSTIM_DECR | R_APB_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;
  (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MIXED) 0.245, 0.245, 0.245, 0.245, 0.020;
}
probability ( R_APB_REPSTIM_FACILI | R_APB_NMT ) {
  (NO) 0.95, 0.02, 0.01, 0.02;
  (MOD_PRE) 0.010, 0.889, 0.100, 0.001;
  (SEV_PRE) 0.010, 0.080, 0.909, 0.001;
  (MLD_POST) 0.89, 0.08, 0.01, 0.02;
  (MOD_POST) 0.48, 0.50, 0.01, 0.01;
  (SEV_POST) 0.020, 0.949, 0.030, 0.001;
  (MIXED) 0.25, 0.25, 0.25, 0.25;
}
probability ( R_APB_REPSTIM_POST_DECR | R_APB_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;
  (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;
  (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MIXED) 0.23, 0.23, 0.22, 0.22, 0.10;
}
probability ( R_APB_SF_JITTER | R_APB_NMT ) {
  (NO) 0.95, 0.05, 0.00, 0.00;
  (MOD_PRE) 0.02, 0.20, 0.70, 0.08;
  (SEV_PRE) 0.0, 0.1, 0.4, 0.5;
  (MLD_POST) 0.05, 0.70, 0.20, 0.05;
  (MOD_POST) 0.01, 0.19, 0.70, 0.10;
  (SEV_POST) 0.0, 0.1, 0.4, 0.5;
  (MIXED) 0.1, 0.3, 0.3, 0.3;
}
probability ( R_LNLT1_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_LNLLP_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_LNLT1_LP_APB_MUDENS | R_LNLT1_APB_MUDENS, R_LNLLP_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_LNLBE_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_LNLT1_LP_BE_APB_MUDENS | R_LNLT1_LP_APB_MUDENS, R_LNLBE_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_DIFFN_APB_MUDENS | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( R_LNLW_APB_MUDENS | R_LNLW_MED_SEV, R_LNLW_MED_TIME, R_LNLW_MED_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( R_DIFFN_LNLW_APB_MUDENS | R_DIFFN_APB_MUDENS, R_LNLW_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_LNL_DIFFN_APB_MUDENS | R_LNLT1_LP_BE_APB_MUDENS, R_DIFFN_LNLW_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_MYOP_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MYDY_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MYOP_MYDY_APB_MUDENS | R_MYOP_APB_MUDENS, R_MYDY_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_MYAS_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MUSCLE_APB_MUDENS | R_MYOP_MYDY_APB_MUDENS, R_MYAS_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_APB_MUDENS | R_LNL_DIFFN_APB_MUDENS, R_MUSCLE_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_APB_SF_DENSITY | R_APB_MUDENS ) {
  (NORMAL) 0.97, 0.03, 0.00;
  (INCR) 0.05, 0.90, 0.05;
  (V_INCR) 0.01, 0.04, 0.95;
}
probability ( R_LNLT1_APB_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLLP_APB_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLT1_LP_APB_NEUR_ACT | R_LNLT1_APB_NEUR_ACT, R_LNLLP_APB_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLBE_APB_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLT1_LP_BE_APB_NEUR_ACT | R_LNLT1_LP_APB_NEUR_ACT, R_LNLBE_APB_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DIFFN_APB_NEUR_ACT | DIFFN_M_SEV_DIST, DIFFN_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLW_APB_NEUR_ACT | R_LNLW_MED_SEV, R_LNLW_MED_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_LNLW_APB_NEUR_ACT | R_DIFFN_APB_NEUR_ACT, R_LNLW_APB_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_APB_NEUR_ACT | R_LNLT1_LP_BE_APB_NEUR_ACT, R_DIFFN_LNLW_APB_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_APB_SPONT_NEUR_DISCH | R_APB_NEUR_ACT ) {
  (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;
  (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;
  (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;
  (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;
  (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;
}
probability ( R_MYOP_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYDY_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYOP_MYDY_APB_DENERV | R_MYOP_APB_DENERV, R_MYDY_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_NMT_APB_DENERV | R_APB_NMT ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE) 0.40, 0.45, 0.15, 0.00;
  (SEV_PRE) 0.15, 0.35, 0.35, 0.15;
  (MLD_POST) 0.85, 0.15, 0.00, 0.00;
  (MOD_POST) 0.30, 0.45, 0.20, 0.05;
  (SEV_POST) 0.15, 0.35, 0.35, 0.15;
  (MIXED) 0.25, 0.25, 0.25, 0.25;
}
probability ( R_MUSCLE_APB_DENERV | R_MYOP_MYDY_APB_DENERV, R_NMT_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLT1_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLLP_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLT1_LP_APB_DENERV | R_LNLT1_APB_DENERV, R_LNLLP_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLBE_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLT1_LP_BE_APB_DENERV | R_LNLT1_LP_APB_DENERV, R_LNLBE_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DIFFN_APB_DENERV | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( R_LNLW_APB_DENERV | R_LNLW_MED_SEV, R_LNLW_MED_TIME, R_LNLW_MED_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( R_DIFFN_LNLW_APB_DENERV | R_DIFFN_APB_DENERV, R_LNLW_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNL_DIFFN_APB_DENERV | R_LNLT1_LP_BE_APB_DENERV, R_DIFFN_LNLW_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_APB_DENERV | R_MUSCLE_APB_DENERV, R_LNL_DIFFN_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_APB_SPONT_DENERV_ACT | R_APB_DENERV ) {
  (NO) 0.98, 0.02, 0.00, 0.00;
  (MILD) 0.07, 0.85, 0.08, 0.00;
  (MOD) 0.01, 0.07, 0.85, 0.07;
  (SEV) 0.00, 0.01, 0.07, 0.92;
}
probability ( R_APB_SPONT_HF_DISCH | R_APB_DENERV ) {
  (NO) 0.99, 0.01;
  (MILD) 0.97, 0.03;
  (MOD) 0.95, 0.05;
  (SEV) 0.93, 0.07;
}
probability ( R_APB_SPONT_INS_ACT | R_APB_DENERV ) {
  (NO) 0.98, 0.02;
  (MILD) 0.1, 0.9;
  (MOD) 0.05, 0.95;
  (SEV) 0.05, 0.95;
}
probability ( DIFFN_M_SEV_PROX | DIFFN_MOT_SEV, DIFFN_DISTR ) {
  (NO, DIST) 1.0, 0.0, 0.0, 0.0;
  (MILD, DIST) 0.0, 1.0, 0.0, 0.0;
  (MOD, DIST) 0.0, 0.0, 1.0, 0.0;
  (SEV, DIST) 0.0, 0.5, 0.5, 0.0;
  (NO, PROX) 1.0, 0.0, 0.0, 0.0;
  (MILD, PROX) 0.0, 1.0, 0.0, 0.0;
  (MOD, PROX) 0.0, 0.0, 1.0, 0.0;
  (SEV, PROX) 0.0, 0.5, 0.5, 0.0;
  (NO, RANDOM) 1.0, 0.0, 0.0, 0.0;
  (MILD, RANDOM) 0.25, 0.45, 0.25, 0.05;
  (MOD, RANDOM) 0.1, 0.4, 0.4, 0.1;
  (SEV, RANDOM) 0.05, 0.15, 0.40, 0.40;
}
probability ( DIFFN_DUMMY_1 | DIFFN_M_SEV_PROX, DIFFN_PATHO, DIFFN_TIME ) {
  (NO, DEMY, ACUTE) 0.5, 0.5;
  (MILD, DEMY, ACUTE) 0.5, 0.5;
  (MOD, DEMY, ACUTE) 0.5, 0.5;
  (SEV, DEMY, ACUTE) 0.5, 0.5;
  (NO, BLOCK, ACUTE) 0.5, 0.5;
  (MILD, BLOCK, ACUTE) 0.5, 0.5;
  (MOD, BLOCK, ACUTE) 0.5, 0.5;
  (SEV, BLOCK, ACUTE) 0.5, 0.5;
  (NO, AXONAL, ACUTE) 0.5, 0.5;
  (MILD, AXONAL, ACUTE) 0.5, 0.5;
  (MOD, AXONAL, ACUTE) 0.5, 0.5;
  (SEV, AXONAL, ACUTE) 0.5, 0.5;
  (NO, V_E_REIN, ACUTE) 0.5, 0.5;
  (MILD, V_E_REIN, ACUTE) 0.5, 0.5;
  (MOD, V_E_REIN, ACUTE) 0.5, 0.5;
  (SEV, V_E_REIN, ACUTE) 0.5, 0.5;
  (NO, E_REIN, ACUTE) 0.5, 0.5;
  (MILD, E_REIN, ACUTE) 0.5, 0.5;
  (MOD, E_REIN, ACUTE) 0.5, 0.5;
  (SEV, E_REIN, ACUTE) 0.5, 0.5;
  (NO, DEMY, SUBACUTE) 0.5, 0.5;
  (MILD, DEMY, SUBACUTE) 0.5, 0.5;
  (MOD, DEMY, SUBACUTE) 0.5, 0.5;
  (SEV, DEMY, SUBACUTE) 0.5, 0.5;
  (NO, BLOCK, SUBACUTE) 0.5, 0.5;
  (MILD, BLOCK, SUBACUTE) 0.5, 0.5;
  (MOD, BLOCK, SUBACUTE) 0.5, 0.5;
  (SEV, BLOCK, SUBACUTE) 0.5, 0.5;
  (NO, AXONAL, SUBACUTE) 0.5, 0.5;
  (MILD, AXONAL, SUBACUTE) 0.5, 0.5;
  (MOD, AXONAL, SUBACUTE) 0.5, 0.5;
  (SEV, AXONAL, SUBACUTE) 0.5, 0.5;
  (NO, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (MILD, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (MOD, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (SEV, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (NO, E_REIN, SUBACUTE) 0.5, 0.5;
  (MILD, E_REIN, SUBACUTE) 0.5, 0.5;
  (MOD, E_REIN, SUBACUTE) 0.5, 0.5;
  (SEV, E_REIN, SUBACUTE) 0.5, 0.5;
  (NO, DEMY, CHRONIC) 0.5, 0.5;
  (MILD, DEMY, CHRONIC) 0.5, 0.5;
  (MOD, DEMY, CHRONIC) 0.5, 0.5;
  (SEV, DEMY, CHRONIC) 0.5, 0.5;
  (NO, BLOCK, CHRONIC) 0.5, 0.5;
  (MILD, BLOCK, CHRONIC) 0.5, 0.5;
  (MOD, BLOCK, CHRONIC) 0.5, 0.5;
  (SEV, BLOCK, CHRONIC) 0.5, 0.5;
  (NO, AXONAL, CHRONIC) 0.5, 0.5;
  (MILD, AXONAL, CHRONIC) 0.5, 0.5;
  (MOD, AXONAL, CHRONIC) 0.5, 0.5;
  (SEV, AXONAL, CHRONIC) 0.5, 0.5;
  (NO, V_E_REIN, CHRONIC) 0.5, 0.5;
  (MILD, V_E_REIN, CHRONIC) 0.5, 0.5;
  (MOD, V_E_REIN, CHRONIC) 0.5, 0.5;
  (SEV, V_E_REIN, CHRONIC) 0.5, 0.5;
  (NO, E_REIN, CHRONIC) 0.5, 0.5;
  (MILD, E_REIN, CHRONIC) 0.5, 0.5;
  (MOD, E_REIN, CHRONIC) 0.5, 0.5;
  (SEV, E_REIN, CHRONIC) 0.5, 0.5;
  (NO, DEMY, OLD) 0.5, 0.5;
  (MILD, DEMY, OLD) 0.5, 0.5;
  (MOD, DEMY, OLD) 0.5, 0.5;
  (SEV, DEMY, OLD) 0.5, 0.5;
  (NO, BLOCK, OLD) 0.5, 0.5;
  (MILD, BLOCK, OLD) 0.5, 0.5;
  (MOD, BLOCK, OLD) 0.5, 0.5;
  (SEV, BLOCK, OLD) 0.5, 0.5;
  (NO, AXONAL, OLD) 0.5, 0.5;
  (MILD, AXONAL, OLD) 0.5, 0.5;
  (MOD, AXONAL, OLD) 0.5, 0.5;
  (SEV, AXONAL, OLD) 0.5, 0.5;
  (NO, V_E_REIN, OLD) 0.5, 0.5;
  (MILD, V_E_REIN, OLD) 0.5, 0.5;
  (MOD, V_E_REIN, OLD) 0.5, 0.5;
  (SEV, V_E_REIN, OLD) 0.5, 0.5;
  (NO, E_REIN, OLD) 0.5, 0.5;
  (MILD, E_REIN, OLD) 0.5, 0.5;
  (MOD, E_REIN, OLD) 0.5, 0.5;
  (SEV, E_REIN, OLD) 0.5, 0.5;
}
probability ( DIFFN_DUMMY_2 | DIFFN_M_SEV_DIST, DIFFN_PATHO, DIFFN_TIME ) {
  (NO, DEMY, ACUTE) 0.5, 0.5;
  (MILD, DEMY, ACUTE) 0.5, 0.5;
  (MOD, DEMY, ACUTE) 0.5, 0.5;
  (SEV, DEMY, ACUTE) 0.5, 0.5;
  (NO, BLOCK, ACUTE) 0.5, 0.5;
  (MILD, BLOCK, ACUTE) 0.5, 0.5;
  (MOD, BLOCK, ACUTE) 0.5, 0.5;
  (SEV, BLOCK, ACUTE) 0.5, 0.5;
  (NO, AXONAL, ACUTE) 0.5, 0.5;
  (MILD, AXONAL, ACUTE) 0.5, 0.5;
  (MOD, AXONAL, ACUTE) 0.5, 0.5;
  (SEV, AXONAL, ACUTE) 0.5, 0.5;
  (NO, V_E_REIN, ACUTE) 0.5, 0.5;
  (MILD, V_E_REIN, ACUTE) 0.5, 0.5;
  (MOD, V_E_REIN, ACUTE) 0.5, 0.5;
  (SEV, V_E_REIN, ACUTE) 0.5, 0.5;
  (NO, E_REIN, ACUTE) 0.5, 0.5;
  (MILD, E_REIN, ACUTE) 0.5, 0.5;
  (MOD, E_REIN, ACUTE) 0.5, 0.5;
  (SEV, E_REIN, ACUTE) 0.5, 0.5;
  (NO, DEMY, SUBACUTE) 0.5, 0.5;
  (MILD, DEMY, SUBACUTE) 0.5, 0.5;
  (MOD, DEMY, SUBACUTE) 0.5, 0.5;
  (SEV, DEMY, SUBACUTE) 0.5, 0.5;
  (NO, BLOCK, SUBACUTE) 0.5, 0.5;
  (MILD, BLOCK, SUBACUTE) 0.5, 0.5;
  (MOD, BLOCK, SUBACUTE) 0.5, 0.5;
  (SEV, BLOCK, SUBACUTE) 0.5, 0.5;
  (NO, AXONAL, SUBACUTE) 0.5, 0.5;
  (MILD, AXONAL, SUBACUTE) 0.5, 0.5;
  (MOD, AXONAL, SUBACUTE) 0.5, 0.5;
  (SEV, AXONAL, SUBACUTE) 0.5, 0.5;
  (NO, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (MILD, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (MOD, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (SEV, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (NO, E_REIN, SUBACUTE) 0.5, 0.5;
  (MILD, E_REIN, SUBACUTE) 0.5, 0.5;
  (MOD, E_REIN, SUBACUTE) 0.5, 0.5;
  (SEV, E_REIN, SUBACUTE) 0.5, 0.5;
  (NO, DEMY, CHRONIC) 0.5, 0.5;
  (MILD, DEMY, CHRONIC) 0.5, 0.5;
  (MOD, DEMY, CHRONIC) 0.5, 0.5;
  (SEV, DEMY, CHRONIC) 0.5, 0.5;
  (NO, BLOCK, CHRONIC) 0.5, 0.5;
  (MILD, BLOCK, CHRONIC) 0.5, 0.5;
  (MOD, BLOCK, CHRONIC) 0.5, 0.5;
  (SEV, BLOCK, CHRONIC) 0.5, 0.5;
  (NO, AXONAL, CHRONIC) 0.5, 0.5;
  (MILD, AXONAL, CHRONIC) 0.5, 0.5;
  (MOD, AXONAL, CHRONIC) 0.5, 0.5;
  (SEV, AXONAL, CHRONIC) 0.5, 0.5;
  (NO, V_E_REIN, CHRONIC) 0.5, 0.5;
  (MILD, V_E_REIN, CHRONIC) 0.5, 0.5;
  (MOD, V_E_REIN, CHRONIC) 0.5, 0.5;
  (SEV, V_E_REIN, CHRONIC) 0.5, 0.5;
  (NO, E_REIN, CHRONIC) 0.5, 0.5;
  (MILD, E_REIN, CHRONIC) 0.5, 0.5;
  (MOD, E_REIN, CHRONIC) 0.5, 0.5;
  (SEV, E_REIN, CHRONIC) 0.5, 0.5;
  (NO, DEMY, OLD) 0.5, 0.5;
  (MILD, DEMY, OLD) 0.5, 0.5;
  (MOD, DEMY, OLD) 0.5, 0.5;
  (SEV, DEMY, OLD) 0.5, 0.5;
  (NO, BLOCK, OLD) 0.5, 0.5;
  (MILD, BLOCK, OLD) 0.5, 0.5;
  (MOD, BLOCK, OLD) 0.5, 0.5;
  (SEV, BLOCK, OLD) 0.5, 0.5;
  (NO, AXONAL, OLD) 0.5, 0.5;
  (MILD, AXONAL, OLD) 0.5, 0.5;
  (MOD, AXONAL, OLD) 0.5, 0.5;
  (SEV, AXONAL, OLD) 0.5, 0.5;
  (NO, V_E_REIN, OLD) 0.5, 0.5;
  (MILD, V_E_REIN, OLD) 0.5, 0.5;
  (MOD, V_E_REIN, OLD) 0.5, 0.5;
  (SEV, V_E_REIN, OLD) 0.5, 0.5;
  (NO, E_REIN, OLD) 0.5, 0.5;
  (MILD, E_REIN, OLD) 0.5, 0.5;
  (MOD, E_REIN, OLD) 0.5, 0.5;
  (SEV, E_REIN, OLD) 0.5, 0.5;
}
probability ( DIFFN_DUMMY_3 | DIFFN_S_SEV_DIST, DIFFN_PATHO, DIFFN_TIME ) {
  (NO, DEMY, ACUTE) 0.5, 0.5;
  (MILD, DEMY, ACUTE) 0.5, 0.5;
  (MOD, DEMY, ACUTE) 0.5, 0.5;
  (SEV, DEMY, ACUTE) 0.5, 0.5;
  (NO, BLOCK, ACUTE) 0.5, 0.5;
  (MILD, BLOCK, ACUTE) 0.5, 0.5;
  (MOD, BLOCK, ACUTE) 0.5, 0.5;
  (SEV, BLOCK, ACUTE) 0.5, 0.5;
  (NO, AXONAL, ACUTE) 0.5, 0.5;
  (MILD, AXONAL, ACUTE) 0.5, 0.5;
  (MOD, AXONAL, ACUTE) 0.5, 0.5;
  (SEV, AXONAL, ACUTE) 0.5, 0.5;
  (NO, V_E_REIN, ACUTE) 0.5, 0.5;
  (MILD, V_E_REIN, ACUTE) 0.5, 0.5;
  (MOD, V_E_REIN, ACUTE) 0.5, 0.5;
  (SEV, V_E_REIN, ACUTE) 0.5, 0.5;
  (NO, E_REIN, ACUTE) 0.5, 0.5;
  (MILD, E_REIN, ACUTE) 0.5, 0.5;
  (MOD, E_REIN, ACUTE) 0.5, 0.5;
  (SEV, E_REIN, ACUTE) 0.5, 0.5;
  (NO, DEMY, SUBACUTE) 0.5, 0.5;
  (MILD, DEMY, SUBACUTE) 0.5, 0.5;
  (MOD, DEMY, SUBACUTE) 0.5, 0.5;
  (SEV, DEMY, SUBACUTE) 0.5, 0.5;
  (NO, BLOCK, SUBACUTE) 0.5, 0.5;
  (MILD, BLOCK, SUBACUTE) 0.5, 0.5;
  (MOD, BLOCK, SUBACUTE) 0.5, 0.5;
  (SEV, BLOCK, SUBACUTE) 0.5, 0.5;
  (NO, AXONAL, SUBACUTE) 0.5, 0.5;
  (MILD, AXONAL, SUBACUTE) 0.5, 0.5;
  (MOD, AXONAL, SUBACUTE) 0.5, 0.5;
  (SEV, AXONAL, SUBACUTE) 0.5, 0.5;
  (NO, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (MILD, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (MOD, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (SEV, V_E_REIN, SUBACUTE) 0.5, 0.5;
  (NO, E_REIN, SUBACUTE) 0.5, 0.5;
  (MILD, E_REIN, SUBACUTE) 0.5, 0.5;
  (MOD, E_REIN, SUBACUTE) 0.5, 0.5;
  (SEV, E_REIN, SUBACUTE) 0.5, 0.5;
  (NO, DEMY, CHRONIC) 0.5, 0.5;
  (MILD, DEMY, CHRONIC) 0.5, 0.5;
  (MOD, DEMY, CHRONIC) 0.5, 0.5;
  (SEV, DEMY, CHRONIC) 0.5, 0.5;
  (NO, BLOCK, CHRONIC) 0.5, 0.5;
  (MILD, BLOCK, CHRONIC) 0.5, 0.5;
  (MOD, BLOCK, CHRONIC) 0.5, 0.5;
  (SEV, BLOCK, CHRONIC) 0.5, 0.5;
  (NO, AXONAL, CHRONIC) 0.5, 0.5;
  (MILD, AXONAL, CHRONIC) 0.5, 0.5;
  (MOD, AXONAL, CHRONIC) 0.5, 0.5;
  (SEV, AXONAL, CHRONIC) 0.5, 0.5;
  (NO, V_E_REIN, CHRONIC) 0.5, 0.5;
  (MILD, V_E_REIN, CHRONIC) 0.5, 0.5;
  (MOD, V_E_REIN, CHRONIC) 0.5, 0.5;
  (SEV, V_E_REIN, CHRONIC) 0.5, 0.5;
  (NO, E_REIN, CHRONIC) 0.5, 0.5;
  (MILD, E_REIN, CHRONIC) 0.5, 0.5;
  (MOD, E_REIN, CHRONIC) 0.5, 0.5;
  (SEV, E_REIN, CHRONIC) 0.5, 0.5;
  (NO, DEMY, OLD) 0.5, 0.5;
  (MILD, DEMY, OLD) 0.5, 0.5;
  (MOD, DEMY, OLD) 0.5, 0.5;
  (SEV, DEMY, OLD) 0.5, 0.5;
  (NO, BLOCK, OLD) 0.5, 0.5;
  (MILD, BLOCK, OLD) 0.5, 0.5;
  (MOD, BLOCK, OLD) 0.5, 0.5;
  (SEV, BLOCK, OLD) 0.5, 0.5;
  (NO, AXONAL, OLD) 0.5, 0.5;
  (MILD, AXONAL, OLD) 0.5, 0.5;
  (MOD, AXONAL, OLD) 0.5, 0.5;
  (SEV, AXONAL, OLD) 0.5, 0.5;
  (NO, V_E_REIN, OLD) 0.5, 0.5;
  (MILD, V_E_REIN, OLD) 0.5, 0.5;
  (MOD, V_E_REIN, OLD) 0.5, 0.5;
  (SEV, V_E_REIN, OLD) 0.5, 0.5;
  (NO, E_REIN, OLD) 0.5, 0.5;
  (MILD, E_REIN, OLD) 0.5, 0.5;
  (MOD, E_REIN, OLD) 0.5, 0.5;
  (SEV, E_REIN, OLD) 0.5, 0.5;
}
probability ( R_OTHER_ULND5_DISP ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ULND5_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLE_ULN_SEV ) {
  table 0.945, 0.025, 0.015, 0.010, 0.005;
}
probability ( R_LNLE_ULN_PATHO ) {
  table 0.600, 0.190, 0.200, 0.005, 0.005;
}
probability ( R_LNLE_ULND5_DISP_E | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (TOTAL, BLOCK) 0.0, 0.0, 0.5, 0.5;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.3, 0.5, 0.2, 0.0;
  (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLE_DIFFN_ULND5_DISP_E | R_DIFFN_ULND5_DISP, R_LNLE_ULND5_DISP_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_DISP_E | R_OTHER_ULND5_DISP, R_LNLE_DIFFN_ULND5_DISP_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLW_ULND5_DISP_WD ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_LNLW_ULND5_DISP_WD | R_LNLW_ULND5_DISP_WD, R_DIFFN_ULND5_DISP ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_DISP_WD | R_OTHER_ULND5_DISP, R_DIFFN_LNLW_ULND5_DISP_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_DISP_BEW | R_OTHER_ULND5_DISP, R_DIFFN_ULND5_DISP ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_DISP_BED | R_ULND5_DISP_WD, R_ULND5_DISP_BEW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.0000, 0.0008, 0.0097, 0.9895;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0024, 0.9975;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0000, 0.0000, 0.0004, 0.9996;
  (NO, SEV) 0.0000, 0.0008, 0.0097, 0.9895;
  (MILD, SEV) 0.0000, 0.0001, 0.0024, 0.9975;
  (MOD, SEV) 0.0000, 0.0000, 0.0004, 0.9996;
  (SEV, SEV) 0.0000, 0.0000, 0.0002, 0.9998;
}
probability ( R_ULND5_DISP_EED | R_ULND5_DISP_E, R_ULND5_DISP_BED ) {
  (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0404, 0.9192, 0.0404, 0.0000;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742;
  (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, MILD) 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.3477, 0.6496, 0.0023, 0.0000;
  (MOD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00230023, 0.64986499, 0.34783478;
  (SEV, MILD) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, MOD) 0.0000, 0.0004, 0.3477, 0.6496, 0.0023, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MOD) 0.000, 0.000, 0.000, 0.001, 0.499, 0.499, 0.001, 0.000, 0.000;
  (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.2215, 0.7732, 0.0052;
  (SEV, MOD) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, SEV) 0.4995, 0.4995, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, SEV) 0.0004, 0.3477, 0.6496, 0.0023, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742;
}
probability ( R_OTHER_ULND5_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ULND5_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLE_ULND5_BLOCK_E | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.3, 0.6, 0.1, 0.0, 0.0;
  (SEV, DEMY) 0.1, 0.5, 0.3, 0.1, 0.0;
  (TOTAL, DEMY) 0.00, 0.05, 0.20, 0.55, 0.20;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.2, 0.6, 0.2;
  (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLE_DIFFN_ULND5_BLOCK_E | R_DIFFN_ULND5_BLOCK, R_LNLE_ULND5_BLOCK_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_BLOCK_E | R_OTHER_ULND5_BLOCK, R_LNLE_DIFFN_ULND5_BLOCK_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_AMPR_E | R_ULND5_DISP_EED, R_ULND5_BLOCK_E ) {
  (R0_15, NO) 0.0000, 0.0836, 0.9164, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, NO) 0.0000, 0.0000, 0.5059, 0.4935, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, NO) 0.0000, 0.0000, 0.0000, 0.5217, 0.4707, 0.0076, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_45, NO) 0.00000000, 0.00000000, 0.00000000, 0.00440044, 0.51475148, 0.45034503, 0.02990299, 0.00060006, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_55, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0235, 0.5025, 0.4068, 0.0644, 0.0027, 0.0001, 0.0000, 0.0000;
  (R0_65, NO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.06389361, 0.43315668, 0.40265973, 0.08949105, 0.00999900, 0.00059994, 0.00000000;
  (R0_75, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0026, 0.1020, 0.4076, 0.3533, 0.1151, 0.0191, 0.0003;
  (R0_85, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0080, 0.1231, 0.3775, 0.3319, 0.1488, 0.0107;
  (R0_95, NO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00039996, 0.01909809, 0.17038296, 0.34706529, 0.36056394, 0.10248975;
  (R0_15, MILD) 0.0000, 0.9893, 0.0107, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MILD) 0.0000, 0.0000, 0.9815, 0.0185, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MILD) 0.0000, 0.0000, 0.0129, 0.9294, 0.0575, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_45, MILD) 0.0000, 0.0000, 0.0000, 0.1893, 0.7372, 0.0722, 0.0013, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_55, MILD) 0.0000, 0.0000, 0.0000, 0.0034, 0.3663, 0.5458, 0.0804, 0.0040, 0.0001, 0.0000, 0.0000, 0.0000;
  (R0_65, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0304, 0.4581, 0.4125, 0.0920, 0.0066, 0.0004, 0.0000, 0.0000;
  (R0_75, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.1122, 0.4488, 0.3467, 0.0798, 0.0106, 0.0008, 0.0000;
  (R0_85, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.1733, 0.4244, 0.2869, 0.0885, 0.0158, 0.0003;
  (R0_95, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.0357, 0.2319, 0.3899, 0.2461, 0.0897, 0.0060;
  (R0_15, MOD) 0.0000, 0.9998, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MOD) 0.0000, 0.3122, 0.6860, 0.0018, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MOD) 0.00000000, 0.00070007, 0.89578958, 0.10181018, 0.00170017, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, MOD) 0.0000, 0.0000, 0.3290, 0.6016, 0.0670, 0.0023, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_55, MOD) 0.0000, 0.0000, 0.0363, 0.6123, 0.3112, 0.0376, 0.0025, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_65, MOD) 0.0000, 0.0000, 0.0025, 0.2770, 0.5116, 0.1781, 0.0275, 0.0031, 0.0002, 0.0000, 0.0000, 0.0000;
  (R0_75, MOD) 0.00000000, 0.00000000, 0.00020002, 0.08020802, 0.42864286, 0.35713571, 0.10901090, 0.02180218, 0.00270027, 0.00030003, 0.00000000, 0.00000000;
  (R0_85, MOD) 0.00000000, 0.00000000, 0.00000000, 0.01560156, 0.22332233, 0.41834183, 0.24072407, 0.08170817, 0.01670167, 0.00310031, 0.00050005, 0.00000000;
  (R0_95, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00270027, 0.08970897, 0.33353335, 0.32823282, 0.17421742, 0.05420542, 0.01410141, 0.00310031, 0.00020002;
  (R0_15, SEV) 0.0000, 0.9534, 0.0434, 0.0028, 0.0003, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, SEV) 0.0000, 0.8829, 0.1030, 0.0116, 0.0020, 0.0004, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, SEV) 0.00000000, 0.79044191, 0.17256549, 0.02809438, 0.00639872, 0.00169966, 0.00049990, 0.00019996, 0.00009998, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, SEV) 0.00000000, 0.68976898, 0.23862386, 0.05110511, 0.01390139, 0.00420042, 0.00140014, 0.00060006, 0.00020002, 0.00010001, 0.00010001, 0.00000000;
  (R0_55, SEV) 0.0000, 0.5883, 0.2943, 0.0784, 0.0248, 0.0085, 0.0032, 0.0014, 0.0006, 0.0003, 0.0001, 0.0001;
  (R0_65, SEV) 0.0000, 0.4912, 0.3361, 0.1079, 0.0388, 0.0148, 0.0060, 0.0028, 0.0013, 0.0006, 0.0003, 0.0002;
  (R0_75, SEV) 0.0000, 0.4080, 0.3613, 0.1352, 0.0540, 0.0224, 0.0097, 0.0048, 0.0023, 0.0012, 0.0007, 0.0004;
  (R0_85, SEV) 0.0000, 0.3320, 0.3737, 0.1612, 0.0709, 0.0319, 0.0148, 0.0077, 0.0038, 0.0020, 0.0012, 0.0008;
  (R0_95, SEV) 0.00000000, 0.27137286, 0.37396260, 0.18198180, 0.08699130, 0.04179582, 0.02039796, 0.01109889, 0.00579942, 0.00319968, 0.00199980, 0.00139986;
  (R0_15, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_25, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_35, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_45, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_55, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_65, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_75, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_85, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_95, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_OTHER_ULND5_LD ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLE_ULND5_LD_E | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;
  (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0;
}
probability ( R_ULND5_LD_E | R_OTHER_ULND5_LD, R_LNLE_ULND5_LD_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0186, 0.9584, 0.0230, 0.0000;
  (MOD, NO) 0.0000, 0.0184, 0.9816, 0.0000;
  (SEV, NO) 0.00100010, 0.00310031, 0.03020302, 0.96569657;
  (NO, MILD) 0.0186, 0.9584, 0.0230, 0.0000;
  (MILD, MILD) 0.0000, 0.0304, 0.9696, 0.0000;
  (MOD, MILD) 0.0000, 0.0013, 0.9987, 0.0000;
  (SEV, MILD) 0.00039996, 0.00129987, 0.01409859, 0.98420158;
  (NO, MOD) 0.0000, 0.0184, 0.9816, 0.0000;
  (MILD, MOD) 0.0000, 0.0013, 0.9987, 0.0000;
  (MOD, MOD) 0.00000000, 0.00029997, 0.99960004, 0.00009999;
  (SEV, MOD) 0.0001, 0.0005, 0.0060, 0.9934;
  (NO, SEV) 0.00100010, 0.00310031, 0.03020302, 0.96569657;
  (MILD, SEV) 0.00039996, 0.00129987, 0.01409859, 0.98420158;
  (MOD, SEV) 0.0001, 0.0005, 0.0060, 0.9934;
  (SEV, SEV) 0.00010001, 0.00030003, 0.00380038, 0.99579958;
}
probability ( R_OTHER_ULND5_RD ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_LNLE_ULND5_RD_E | R_LNLE_ULN_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( R_ULND5_RD_E | R_OTHER_ULND5_RD, R_LNLE_ULND5_RD_E ) {
  (NO, NO) 1.0, 0.0, 0.0;
  (MOD, NO) 0.0941, 0.9038, 0.0021;
  (SEV, NO) 0.0677, 0.2563, 0.6760;
  (NO, MOD) 0.0941, 0.9038, 0.0021;
  (MOD, MOD) 0.0140, 0.3525, 0.6335;
  (SEV, MOD) 0.0324, 0.1629, 0.8047;
  (NO, SEV) 0.0677, 0.2563, 0.6760;
  (MOD, SEV) 0.0324, 0.1629, 0.8047;
  (SEV, SEV) 0.0351, 0.1479, 0.8170;
}
probability ( R_ULND5_LSLOW_E | R_ULND5_LD_E, R_ULND5_RD_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.00840084, 0.01190119, 0.06190619, 0.91739174, 0.00040004;
  (MILD, MOD) 0.00690069, 0.01000100, 0.05350535, 0.92869287, 0.00090009;
  (MOD, MOD) 0.00559944, 0.00829917, 0.04519548, 0.93890611, 0.00199980;
  (SEV, MOD) 0.0023, 0.0036, 0.0217, 0.8326, 0.1398;
  (NO, SEV) 0.00529947, 0.00619938, 0.02639736, 0.20817918, 0.75392461;
  (MILD, SEV) 0.0049, 0.0057, 0.0244, 0.1966, 0.7684;
  (MOD, SEV) 0.00440044, 0.00520052, 0.02240224, 0.18391839, 0.78407841;
  (SEV, SEV) 0.0028, 0.0033, 0.0145, 0.1304, 0.8490;
}
probability ( R_OTHER_ULND5_SALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLW_ULND5_SALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ULND5_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_DIFFN_LNLW_ULND5_SALOSS | R_LNLW_ULND5_SALOSS, R_DIFFN_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLE_ULND5_SALOSS | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_LNLLP_ULND5_SALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLLP_E_ULND5_SALOSS | R_LNLE_ULND5_SALOSS, R_LNLLP_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNL_DIFFN_ULND5_SALOSS | R_DIFFN_LNLW_ULND5_SALOSS, R_LNLLP_E_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_SALOSS | R_OTHER_ULND5_SALOSS, R_LNL_DIFFN_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_ULND5_DIFSLOW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLE_ULND5_DIFSLOW | R_LNLE_ULN_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 0.0, 1.0, 0.0;
}
probability ( R_DIFFN_ULND5_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( R_LNLE_DIFFN_ULND5_DIFSLOW_E | R_LNLE_ULND5_DIFSLOW, R_DIFFN_ULND5_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( R_ULND5_DIFSLOW_E | R_OTHER_ULND5_DIFSLOW, R_LNLE_DIFFN_ULND5_DIFSLOW_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( R_ULND5_DSLOW_E | R_ULND5_SALOSS, R_ULND5_DIFSLOW_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_ALLCV_E | R_ULND5_LSLOW_E, R_ULND5_DSLOW_E ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.0264, 0.9149, 0.0587, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S60) 0.0000, 0.0218, 0.9469, 0.0313, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.00030003, 0.00190019, 0.01400140, 0.07880788, 0.32383238, 0.45964596, 0.12121212, 0.00030003, 0.00000000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00410041, 0.03840384, 0.21452145, 0.74237424, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0006, 0.0944, 0.8841, 0.0209, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0007, 0.2183, 0.7790, 0.0020, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00009999, 0.00039996, 0.00429957, 0.03209679, 0.19558044, 0.50484952, 0.26037396, 0.00229977, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00210021, 0.02390239, 0.16481648, 0.80898090, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.0000, 0.0018, 0.1956, 0.7860, 0.0166, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0077, 0.4577, 0.5345, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, M_S44) 0.0000, 0.0001, 0.0007, 0.0074, 0.0735, 0.4044, 0.4938, 0.0201, 0.0000;
  (V_SEV, M_S44) 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0123, 0.1119, 0.8749, 0.0000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.0000, 0.0000, 0.0026, 0.2655, 0.7316, 0.0003, 0.0000, 0.0000, 0.0000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0166, 0.9182, 0.0652, 0.0000, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0010, 0.0172, 0.2179, 0.6479, 0.1159, 0.0000;
  (V_SEV, M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0055, 0.0699, 0.9243, 0.0000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.0000, 0.0000, 0.0000, 0.0044, 0.5355, 0.4600, 0.0001, 0.0000, 0.0000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0398, 0.9460, 0.0142, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00179982, 0.05589441, 0.46005399, 0.48215178, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0021, 0.0390, 0.9588, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0047, 0.5053, 0.4900, 0.0000, 0.0000;
  (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.1104, 0.8881, 0.0013, 0.0000;
  (SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0041, 0.1033, 0.8925, 0.0000;
  (V_SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00069993, 0.01889811, 0.98040196, 0.00000000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0218, 0.8352, 0.1430, 0.0000;
  (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.3203, 0.6777, 0.0000;
  (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0194, 0.9803, 0.0000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0093, 0.9905, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0003, 0.0010, 0.0036, 0.0154, 0.0712, 0.2467, 0.6617, 0.0000;
  (MOD, M_S08) 0.00000000, 0.00010001, 0.00050005, 0.00190019, 0.00930093, 0.05040504, 0.20722072, 0.73057306, 0.00000000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0026, 0.0190, 0.1153, 0.8626, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0062, 0.0562, 0.9369, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_CV_E | R_ULND5_ALLCV_E ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00099990, 0.01149885, 0.04829517, 0.13808619, 0.21487851, 0.24807519, 0.17468253, 0.10678932, 0.05659434;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00049995, 0.00729927, 0.03739626, 0.12428757, 0.19878012, 0.23427657, 0.19878012, 0.12008799, 0.05119488, 0.01989801, 0.00749925;
  (M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0094, 0.0560, 0.1549, 0.2295, 0.2469, 0.1596, 0.0834, 0.0409, 0.0139, 0.0038, 0.0010, 0.0003;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0027, 0.0385, 0.1740, 0.2983, 0.2508, 0.1460, 0.0640, 0.0192, 0.0048, 0.0014, 0.0003, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0226, 0.1786, 0.3397, 0.2748, 0.1364, 0.0366, 0.0090, 0.0018, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0092, 0.1868, 0.4188, 0.2744, 0.0890, 0.0178, 0.0035, 0.0004, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.01789821, 0.42015798, 0.42315768, 0.11728827, 0.01899810, 0.00229977, 0.00019998, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.00000000, 0.02639736, 0.78362164, 0.17308269, 0.01579842, 0.00099990, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_ULND5_DISP_EWD | R_ULND5_DISP_WD, R_ULND5_DISP_BEW ) {
  (NO, NO) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000;
  (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, MILD) 0.0001, 0.2215, 0.7732, 0.0052, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.13151315, 0.85768577, 0.01080108, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;
  (SEV, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;
  (NO, MOD) 0.1315, 0.8577, 0.0108, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;
  (SEV, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;
  (NO, SEV) 0.9933, 0.0067, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, SEV) 0.0404, 0.9192, 0.0404, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( R_ULND5_BLOCK_EW | R_OTHER_ULND5_BLOCK, R_DIFFN_ULND5_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_AMPR_EW | R_ULND5_DISP_EWD, R_ULND5_BLOCK_EW ) {
  (R0_15, NO) 0.00000000, 0.28267173, 0.71642836, 0.00089991, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_25, NO) 0.0000, 0.0000, 0.5547, 0.4268, 0.0183, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, NO) 0.00000000, 0.00000000, 0.00900090, 0.51275128, 0.41524152, 0.05890589, 0.00390039, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, NO) 0.0000, 0.0000, 0.0000, 0.0635, 0.4459, 0.3732, 0.0995, 0.0162, 0.0015, 0.0002, 0.0000, 0.0000;
  (R0_55, NO) 0.00000000, 0.00000000, 0.00000000, 0.00290029, 0.11411141, 0.39513951, 0.31983198, 0.13151315, 0.02980298, 0.00580058, 0.00090009, 0.00000000;
  (R0_65, NO) 0.0000, 0.0000, 0.0000, 0.0001, 0.0136, 0.1578, 0.3285, 0.2966, 0.1404, 0.0483, 0.0135, 0.0012;
  (R0_75, NO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120024, 0.03850770, 0.17463493, 0.30156031, 0.26135227, 0.14472895, 0.06511302, 0.01290258;
  (R0_85, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0062, 0.0580, 0.1828, 0.2779, 0.2392, 0.1675, 0.0683;
  (R0_95, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0009, 0.0157, 0.0823, 0.2012, 0.2516, 0.2559, 0.1924;
  (R0_15, MILD) 0.0000, 0.9103, 0.0897, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MILD) 0.0000, 0.0004, 0.8945, 0.1036, 0.0015, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MILD) 0.00000000, 0.00000000, 0.11401140, 0.71697170, 0.15981598, 0.00890089, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, MILD) 0.0000, 0.0000, 0.0026, 0.3111, 0.5155, 0.1499, 0.0190, 0.0018, 0.0001, 0.0000, 0.0000, 0.0000;
  (R0_55, MILD) 0.0000, 0.0000, 0.0000, 0.0451, 0.3717, 0.4039, 0.1433, 0.0313, 0.0041, 0.0005, 0.0001, 0.0000;
  (R0_65, MILD) 0.0000, 0.0000, 0.0000, 0.0035, 0.1122, 0.3738, 0.3186, 0.1444, 0.0376, 0.0083, 0.0015, 0.0001;
  (R0_75, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.02260226, 0.18901890, 0.33173317, 0.27442744, 0.12521252, 0.04310431, 0.01240124, 0.00130013;
  (R0_85, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00310031, 0.06150615, 0.21092109, 0.30513051, 0.23442344, 0.12171217, 0.05280528, 0.01040104;
  (R0_95, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0166, 0.1002, 0.2325, 0.2777, 0.2039, 0.1251, 0.0436;
  (R0_15, MOD) 0.0000, 0.9974, 0.0026, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MOD) 0.0000, 0.3856, 0.6048, 0.0095, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MOD) 0.0000, 0.0073, 0.8191, 0.1638, 0.0095, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_45, MOD) 0.0000, 0.0001, 0.3860, 0.4993, 0.1036, 0.0101, 0.0008, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_55, MOD) 0.0000, 0.0000, 0.0913, 0.5259, 0.3027, 0.0681, 0.0104, 0.0014, 0.0002, 0.0000, 0.0000, 0.0000;
  (R0_65, MOD) 0.00000000, 0.00000000, 0.01499850, 0.30686931, 0.42035796, 0.19288071, 0.05119488, 0.01139886, 0.00189981, 0.00029997, 0.00009999, 0.00000000;
  (R0_75, MOD) 0.0000, 0.0000, 0.0023, 0.1333, 0.3728, 0.3075, 0.1292, 0.0421, 0.0099, 0.0024, 0.0005, 0.0000;
  (R0_85, MOD) 0.00000000, 0.00000000, 0.00030003, 0.04440444, 0.24132413, 0.34313431, 0.22082208, 0.10251025, 0.03370337, 0.01040104, 0.00300030, 0.00040004;
  (R0_95, MOD) 0.0000, 0.0000, 0.0000, 0.0138, 0.1311, 0.2956, 0.2729, 0.1711, 0.0745, 0.0286, 0.0104, 0.0020;
  (R0_15, SEV) 0.00000000, 0.94670533, 0.04919508, 0.00349965, 0.00049995, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_25, SEV) 0.00000000, 0.87211279, 0.11098890, 0.01349865, 0.00249975, 0.00059994, 0.00019998, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_35, SEV) 0.00000000, 0.77825565, 0.18013603, 0.03120624, 0.00750150, 0.00200040, 0.00060012, 0.00020004, 0.00010002, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, SEV) 0.0000, 0.6780, 0.2436, 0.0548, 0.0156, 0.0050, 0.0018, 0.0007, 0.0003, 0.0001, 0.0001, 0.0000;
  (R0_55, SEV) 0.0000, 0.5789, 0.2957, 0.0820, 0.0270, 0.0096, 0.0037, 0.0017, 0.0007, 0.0004, 0.0002, 0.0001;
  (R0_65, SEV) 0.0000, 0.4850, 0.3340, 0.1106, 0.0411, 0.0162, 0.0068, 0.0033, 0.0015, 0.0008, 0.0004, 0.0003;
  (R0_75, SEV) 0.00000000, 0.40484048, 0.35663566, 0.13671367, 0.05620562, 0.02400240, 0.01080108, 0.00540054, 0.00270027, 0.00140014, 0.00080008, 0.00050005;
  (R0_85, SEV) 0.00000000, 0.33163367, 0.36712657, 0.16126775, 0.07268546, 0.03349330, 0.01599680, 0.00849830, 0.00439912, 0.00239952, 0.00149970, 0.00099980;
  (R0_95, SEV) 0.0000, 0.2731, 0.3669, 0.1808, 0.0881, 0.0434, 0.0217, 0.0120, 0.0064, 0.0036, 0.0023, 0.0017;
  (R0_15, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_25, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_35, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_45, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_55, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_65, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_75, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_85, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_95, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_ULND5_DIFSLOW_EW | R_OTHER_ULND5_DIFSLOW, R_DIFFN_ULND5_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( R_ULND5_DSLOW_EW | R_ULND5_SALOSS, R_ULND5_DIFSLOW_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_ALLCV_EW | R_ULND5_DSLOW_EW ) {
  (M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S52) 0.02689731, 0.97130287, 0.00179982, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S44) 0.0003, 0.0598, 0.8924, 0.0475, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S36) 0.0000, 0.0001, 0.0637, 0.9111, 0.0251, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0001, 0.0544, 0.9384, 0.0071, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0486, 0.8875, 0.0637, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.09500950, 0.89738974, 0.00740074, 0.00000000;
  (M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09730973, 0.28552855, 0.58335834, 0.00000000;
  (M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_CV_EW | R_ULND5_ALLCV_EW ) {
  (M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0019, 0.0226, 0.0890, 0.2126, 0.2696, 0.2267, 0.1139, 0.0467, 0.0169;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00089991, 0.01459854, 0.07029297, 0.19968003, 0.25647435, 0.22567743, 0.14648535, 0.06149385, 0.01829817, 0.00479952, 0.00129987;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00070014, 0.01790358, 0.09681936, 0.22414483, 0.26935387, 0.22234447, 0.10832166, 0.04080816, 0.01520304, 0.00360072, 0.00070014, 0.00010002, 0.00000000;
  (M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00510102, 0.06701340, 0.24964993, 0.33976795, 0.21144229, 0.09141828, 0.02840568, 0.00600120, 0.00100020, 0.00020004, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.03970397, 0.25872587, 0.37933793, 0.22232223, 0.08110811, 0.01530153, 0.00270027, 0.00040004, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0161, 0.2633, 0.4429, 0.2163, 0.0524, 0.0077, 0.0012, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.02939706, 0.51014899, 0.37516248, 0.07529247, 0.00909909, 0.00079992, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.0000, 0.0411, 0.8200, 0.1295, 0.0090, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLW_ULND5_BLOCK_WD ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_LNLW_ULND5_BLOCK_WD | R_LNLW_ULND5_BLOCK_WD, R_DIFFN_ULND5_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_BLOCK_WD | R_OTHER_ULND5_BLOCK, R_DIFFN_LNLW_ULND5_BLOCK_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_EFFAXLOSS | R_ULND5_BLOCK_WD, R_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_ALLAMP_WD | R_ULND5_DISP_WD, R_ULND5_EFFAXLOSS ) {
  (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;
  (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;
  (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;
  (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;
  (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;
  (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;
  (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;
  (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;
  (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;
  (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_ULND5_AMP_WD | R_ULND5_ALLAMP_WD ) {
  (ZERO) 0.75777578, 0.18381838, 0.04520452, 0.01030103, 0.00230023, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (A0_01) 0.37932414, 0.26144771, 0.16726655, 0.09678064, 0.05118976, 0.02539492, 0.01129774, 0.00459908, 0.00179964, 0.00059988, 0.00019996, 0.00009998, 0.00000000, 0.00000000, 0.00000000;
  (A0_10) 0.0652, 0.1312, 0.1955, 0.2207, 0.1859, 0.1188, 0.0562, 0.0198, 0.0054, 0.0011, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (A0_30) 0.00000000, 0.00070007, 0.00550055, 0.02960296, 0.09970997, 0.20582058, 0.27182718, 0.22332233, 0.11711171, 0.03770377, 0.00760076, 0.00100010, 0.00010001, 0.00000000, 0.00000000;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01070107, 0.05790579, 0.17431743, 0.28932893, 0.27272727, 0.14271427, 0.04330433, 0.00710071, 0.00060006, 0.00000000;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00370037, 0.03370337, 0.14181418, 0.30313031, 0.31273127, 0.16041604, 0.03930393, 0.00470047, 0.00030003;
}
probability ( R_LNLW_ULND5_LD_WD ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_ULND5_LD_WD | R_OTHER_ULND5_LD, R_LNLW_ULND5_LD_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0186, 0.9584, 0.0230, 0.0000;
  (MOD, NO) 0.0000, 0.0184, 0.9816, 0.0000;
  (SEV, NO) 0.00100010, 0.00310031, 0.03020302, 0.96569657;
  (NO, MILD) 0.0186, 0.9584, 0.0230, 0.0000;
  (MILD, MILD) 0.0000, 0.0304, 0.9696, 0.0000;
  (MOD, MILD) 0.0000, 0.0013, 0.9987, 0.0000;
  (SEV, MILD) 0.00039996, 0.00129987, 0.01409859, 0.98420158;
  (NO, MOD) 0.0000, 0.0184, 0.9816, 0.0000;
  (MILD, MOD) 0.0000, 0.0013, 0.9987, 0.0000;
  (MOD, MOD) 0.00000000, 0.00029997, 0.99960004, 0.00009999;
  (SEV, MOD) 0.0001, 0.0005, 0.0060, 0.9934;
  (NO, SEV) 0.00100010, 0.00310031, 0.03020302, 0.96569657;
  (MILD, SEV) 0.00039996, 0.00129987, 0.01409859, 0.98420158;
  (MOD, SEV) 0.0001, 0.0005, 0.0060, 0.9934;
  (SEV, SEV) 0.00010001, 0.00030003, 0.00380038, 0.99579958;
}
probability ( R_LNLW_ULND5_RD_WD ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_ULND5_RD_WD | R_OTHER_ULND5_RD, R_LNLW_ULND5_RD_WD ) {
  (NO, NO) 1.0, 0.0, 0.0;
  (MOD, NO) 0.0139, 0.9821, 0.0040;
  (SEV, NO) 0.0000000, 0.0150015, 0.9849985;
  (NO, MOD) 0.0139, 0.9821, 0.0040;
  (MOD, MOD) 0.0002, 0.1057, 0.8941;
  (SEV, MOD) 0.0000, 0.0037, 0.9963;
  (NO, SEV) 0.00070007, 0.09680968, 0.90249025;
  (MOD, SEV) 0.00000000, 0.01550155, 0.98449845;
  (SEV, SEV) 0.000, 0.003, 0.997;
}
probability ( R_ULND5_LSLOW_WD | R_ULND5_LD_WD, R_ULND5_RD_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;
  (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;
  (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;
  (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;
  (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;
  (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;
  (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;
}
probability ( R_LNLE_DIFFN_ULND5_DIFSLOW_WD | R_LNLE_ULND5_DIFSLOW, R_DIFFN_ULND5_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( R_ULND5_DIFSLOW_WD | R_OTHER_ULND5_DIFSLOW, R_LNLE_DIFFN_ULND5_DIFSLOW_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( R_ULND5_DSLOW_WD | R_ULND5_SALOSS, R_ULND5_DIFSLOW_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_ALLCV_WD | R_ULND5_LSLOW_WD, R_ULND5_DSLOW_WD ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;
  (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;
  (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;
  (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;
  (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;
  (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;
  (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;
  (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;
  (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;
  (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULND5_CV_WD | R_ULND5_ALLCV_WD ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00089991, 0.02329767, 0.13318668, 0.31956804, 0.31956804, 0.15698430, 0.03979602, 0.00669933;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.01600160, 0.10241024, 0.29602960, 0.31003100, 0.18081808, 0.07530753, 0.01620162, 0.00240024, 0.00030003;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00050010, 0.02150430, 0.13622725, 0.29945989, 0.29575915, 0.17453491, 0.05621124, 0.01250250, 0.00290058, 0.00040008, 0.00000000, 0.00000000;
  (M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00520052, 0.09030903, 0.32943294, 0.36503650, 0.15671567, 0.04420442, 0.00800080, 0.00100010, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00039996, 0.05179482, 0.33946605, 0.40265973, 0.16188381, 0.03899610, 0.00429957, 0.00049995, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0206, 0.3368, 0.4519, 0.1606, 0.0272, 0.0026, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.03790379, 0.58865887, 0.32433243, 0.04510451, 0.00380038, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.00000000, 0.05409459, 0.84511549, 0.09569043, 0.00489951, 0.00019998, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ULN_BLOCK | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLE_ULN_BLOCK | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (TOTAL, DEMY) 0.2, 0.2, 0.2, 0.2, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.2, 0.2, 0.2, 0.2, 0.2;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_ULN_BLOCK_E | R_DIFFN_ULN_BLOCK, R_LNLE_ULN_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULN_AMPR_E | R_ULN_BLOCK_E ) {
  (NO) 0.09979002, 0.56154385, 0.32036796, 0.01829817, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD) 0.00150015, 0.04260426, 0.31713171, 0.54345435, 0.09460946, 0.00070007, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD) 0.0001, 0.0013, 0.0088, 0.0471, 0.1949, 0.3924, 0.3113, 0.0438, 0.0003, 0.0000, 0.0000, 0.0000;
  (SEV) 0.0010, 0.0018, 0.0028, 0.0052, 0.0101, 0.0186, 0.0365, 0.0797, 0.1710, 0.3425, 0.3308, 0.0000;
  (TOTAL) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_ADM_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLC8_ADM_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLLP_ADM_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLC8_LP_ADM_MALOSS | R_LNLC8_ADM_MALOSS, R_LNLLP_ADM_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLE_ADM_MALOSS | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_LNLC8_LP_E_ADM_MALOSS | R_LNLC8_LP_ADM_MALOSS, R_LNLE_ADM_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLW_ADM_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ADM_MALOSS | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_DIFFN_LNLW_ADM_MALOSS | R_LNLW_ADM_MALOSS, R_DIFFN_ADM_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNL_DIFFN_ADM_MALOSS | R_LNLC8_LP_E_ADM_MALOSS, R_DIFFN_LNLW_ADM_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0001, 0.9998, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0382, 0.9586, 0.0032, 0.0000;
  (SEV, NO) 0.0000, 0.0002, 0.0420, 0.9578, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0001, 0.9998, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0292, 0.9708, 0.0000, 0.0000;
  (MOD, MILD) 0.00000000, 0.00110011, 0.39953995, 0.59935994, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0049, 0.9951, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0000, 0.0382, 0.9586, 0.0032, 0.0000;
  (MILD, MOD) 0.00000000, 0.00110011, 0.39953995, 0.59935994, 0.00000000;
  (MOD, MOD) 0.00000000, 0.00000000, 0.01280128, 0.98719872, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9986, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0420, 0.9578, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0049, 0.9951, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9986, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ADM_MALOSS | R_OTHER_ADM_MALOSS, R_LNL_DIFFN_ADM_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;
  (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;
  (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( R_LNLE_ULN_DIFSLOW | R_LNLE_ULN_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 0.0, 1.0, 0.0;
}
probability ( R_DIFFN_ULN_DIFSLOW | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( R_ULN_DIFSLOW_E | R_LNLE_ULN_DIFSLOW, R_DIFFN_ULN_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;
  (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;
  (MOD, MOD) 0.000, 0.000, 0.002, 0.998;
  (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;
  (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;
}
probability ( R_ULN_DCV_E | R_ADM_MALOSS, R_ULN_DIFSLOW_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;
  (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;
  (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;
  (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;
}
probability ( R_ULN_LD_EW | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.25, 0.25, 0.25, 0.25;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;
  (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.25, 0.25, 0.25, 0.25;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 0.25, 0.25, 0.25, 0.25;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 0.25, 0.25, 0.25, 0.25;
}
probability ( R_ULN_RD_EW | R_LNLE_ULN_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( R_ULN_RDLDCV_E | R_ULN_LD_EW, R_ULN_RD_EW ) {
  (NO, NO) 0.90449045, 0.09530953, 0.00020002, 0.00000000, 0.00000000, 0.00000000;
  (MILD, NO) 0.1320, 0.6039, 0.2641, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0139, 0.1839, 0.8022, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.00120012, 0.00670067, 0.05440544, 0.86008601, 0.07760776, 0.00000000;
  (NO, MOD) 0.0115, 0.0333, 0.1509, 0.7319, 0.0724, 0.0000;
  (MILD, MOD) 0.00340034, 0.01220122, 0.06900690, 0.71967197, 0.19531953, 0.00040004;
  (MOD, MOD) 0.0011, 0.0045, 0.0299, 0.5742, 0.3876, 0.0027;
  (SEV, MOD) 0.0001, 0.0002, 0.0018, 0.0914, 0.6093, 0.2972;
  (NO, SEV) 0.00000000, 0.00009999, 0.00109989, 0.14618538, 0.80701930, 0.04559544;
  (MILD, SEV) 0.0000, 0.0000, 0.0002, 0.0581, 0.7950, 0.1467;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0228, 0.6344, 0.3427;
  (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0014, 0.1063, 0.8923;
}
probability ( R_ULN_ALLCV_E | R_ULN_DCV_E, R_ULN_RDLDCV_E ) {
  (M_S60, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56, M_S60) 0.3684, 0.6316, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S60) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S44, M_S60) 0.00089991, 0.08959104, 0.82601740, 0.08349165, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S36, M_S60) 0.0000, 0.0005, 0.1011, 0.8478, 0.0506, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S60) 0.00000000, 0.00000000, 0.00050005, 0.07910791, 0.90019002, 0.02020202, 0.00000000, 0.00000000, 0.00000000;
  (M_S20, M_S60) 0.0000, 0.0000, 0.0000, 0.0006, 0.0734, 0.8393, 0.0867, 0.0000, 0.0000;
  (M_S14, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.09470947, 0.89458946, 0.01040104, 0.00000000;
  (M_S08, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0932, 0.9048, 0.0000;
  (M_S00, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S52) 0.0077, 0.9808, 0.0115, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S56, M_S52) 0.0005, 0.4476, 0.5519, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S52) 0.0000, 0.0239, 0.9751, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S52) 0.0000, 0.0036, 0.2468, 0.7340, 0.0156, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S36, M_S52) 0.0000, 0.0000, 0.0050, 0.3134, 0.6811, 0.0005, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00710071, 0.54935494, 0.44334433, 0.00020002, 0.00000000, 0.00000000;
  (M_S20, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00910091, 0.52835284, 0.46254625, 0.00000000, 0.00000000;
  (M_S14, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0216, 0.8359, 0.1425, 0.0000;
  (M_S08, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0355, 0.9641, 0.0000;
  (M_S00, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S44) 0.0000, 0.0047, 0.9951, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S56, M_S44) 0.0000, 0.0004, 0.9005, 0.0991, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S44) 0.0000, 0.0000, 0.1288, 0.8712, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S44) 0.00000000, 0.00000000, 0.01130113, 0.57115712, 0.41754175, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S36, M_S44) 0.0000, 0.0000, 0.0000, 0.0214, 0.9354, 0.0432, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.05649435, 0.93230677, 0.01109889, 0.00000000, 0.00000000;
  (M_S20, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.1430, 0.8551, 0.0015, 0.0000;
  (M_S14, M_S44) 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0019998, 0.3548645, 0.6431357, 0.0000000;
  (M_S08, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0122, 0.9877, 0.0000;
  (M_S00, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S27) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56, M_S27) 0.000, 0.000, 0.000, 0.000, 0.997, 0.003, 0.000, 0.000, 0.000;
  (M_S52, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.2336, 0.7664, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S27) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00259974, 0.99340066, 0.00399960, 0.00000000, 0.00000000;
  (M_S36, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2469, 0.7531, 0.0000, 0.0000;
  (M_S28, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.9939, 0.0041, 0.0000;
  (M_S20, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0314, 0.9686, 0.0000;
  (M_S14, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;
  (M_S08, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;
  (M_S00, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (M_S60, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.1305, 0.8445, 0.0238, 0.0000;
  (M_S56, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0740, 0.8589, 0.0667, 0.0000;
  (M_S52, M_S15) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03730373, 0.80288029, 0.15971597, 0.00000000;
  (M_S44, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0070, 0.3517, 0.6413, 0.0000;
  (M_S36, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0581, 0.9416, 0.0000;
  (M_S28, M_S15) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.006, 0.994, 0.000;
  (M_S20, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (M_S14, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S08, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;
  (M_S00, M_S15) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (M_S60, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0321, 0.9677, 0.0000;
  (M_S56, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0184, 0.9815, 0.0000;
  (M_S52, M_S07) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01010101, 0.98989899, 0.00000000;
  (M_S44, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.9958, 0.0000;
  (M_S36, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (M_S28, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;
  (M_S20, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S14, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (M_S08, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S00, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULN_CV_E | R_ULN_ALLCV_E ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00389961, 0.02769723, 0.09749025, 0.19478052, 0.24787521, 0.21837816, 0.13898610, 0.07069293;
  (M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0030, 0.0100, 0.0587, 0.1638, 0.2511, 0.2403, 0.1583, 0.0769, 0.0289, 0.0090;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.01940194, 0.15001500, 0.19211921, 0.23642364, 0.19571957, 0.11781178, 0.05550555, 0.02160216, 0.00720072, 0.00210021, 0.00060006;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0071, 0.0718, 0.2193, 0.2934, 0.2297, 0.1165, 0.0443, 0.0134, 0.0034, 0.0008, 0.0002, 0.0000, 0.0000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00110011, 0.04500450, 0.23122312, 0.36273627, 0.25722572, 0.06290629, 0.03110311, 0.00710071, 0.00140014, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.00000000, 0.00000000, 0.00000000, 0.01690169, 0.23672367, 0.41364136, 0.23552355, 0.07760776, 0.01770177, 0.00130013, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S14) 0.00000000, 0.00000000, 0.02410241, 0.45734573, 0.40054005, 0.10241024, 0.01400140, 0.00150015, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.0000, 0.1031, 0.7506, 0.1328, 0.0124, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S00) 0.9999, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( R_ULN_BLOCK_EW | R_DIFFN_ULN_BLOCK ) {
  (NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD) 0.0038, 0.9962, 0.0000, 0.0000, 0.0000;
  (MOD) 0.0007, 0.0223, 0.9770, 0.0000, 0.0000;
  (SEV) 0.0019, 0.0060, 0.0587, 0.9334, 0.0000;
  (TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULN_AMPR_EW | R_ULN_BLOCK_EW ) {
  (NO) 0.0289, 0.2833, 0.5170, 0.1684, 0.0024, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD) 0.0004, 0.0135, 0.1503, 0.5078, 0.3123, 0.0157, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD) 0.00000000, 0.00050010, 0.00400080, 0.02460492, 0.12742549, 0.34006801, 0.40008002, 0.10172034, 0.00160032, 0.00000000, 0.00000000, 0.00000000;
  (SEV) 0.00079992, 0.00139986, 0.00219978, 0.00419958, 0.00819918, 0.01549845, 0.03119688, 0.07079292, 0.15858414, 0.33926607, 0.36786321, 0.00000000;
  (TOTAL) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULN_DIFSLOW_EW | R_DIFFN_ULN_DIFSLOW ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0126, 0.9869, 0.0005, 0.0000;
  (MOD) 0.0000, 0.0179, 0.9821, 0.0000;
  (SEV) 0.0000, 0.0003, 0.0252, 0.9745;
}
probability ( R_ULN_DCV_EW | R_ADM_MALOSS, R_ULN_DIFSLOW_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;
  (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;
  (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;
  (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;
}
probability ( R_ULN_ALLCV_EW | R_ULN_DCV_EW ) {
  (M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56) 0.0558, 0.8486, 0.0956, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52) 0.0000, 0.0369, 0.9631, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44) 0.0008, 0.0087, 0.0873, 0.8226, 0.0806, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S36) 0.0000, 0.0000, 0.0005, 0.0994, 0.8508, 0.0493, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0005, 0.0781, 0.9017, 0.0197, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0728, 0.8406, 0.0860, 0.0000, 0.0000;
  (M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0942, 0.8952, 0.0103, 0.0000;
  (M_S08) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.002, 0.093, 0.905, 0.000;
  (M_S00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
}
probability ( R_ULN_CV_EW | R_ULN_ALLCV_EW ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00419958, 0.04879512, 0.19368063, 0.32496750, 0.26967303, 0.12318768, 0.03539646;
  (M_S56) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0042, 0.0505, 0.1995, 0.3255, 0.2622, 0.1185, 0.0332, 0.0063;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00079992, 0.02079792, 0.13878612, 0.31686831, 0.31146885, 0.15638436, 0.04529547, 0.00839916, 0.00109989;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00739926, 0.13408659, 0.19748025, 0.27547245, 0.21907809, 0.11158884, 0.04039596, 0.01119888, 0.00249975, 0.00049995, 0.00009999;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0467, 0.2191, 0.3382, 0.2525, 0.1050, 0.0294, 0.0060, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0261, 0.2225, 0.4086, 0.2726, 0.0468, 0.0197, 0.0031, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0095, 0.2235, 0.4483, 0.2397, 0.0663, 0.0119, 0.0006, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.0000, 0.0000, 0.0149, 0.4662, 0.4182, 0.0903, 0.0095, 0.0008, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S08) 0.00000000, 0.09020902, 0.77417742, 0.12491249, 0.01000100, 0.00070007, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 0.9999, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( R_OTHER_ADM_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLC8_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_LNLLP_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_LNLC8_LP_ADM_DE_REGEN | R_LNLC8_ADM_DE_REGEN, R_LNLLP_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_LNLE_ULN_TIME ) {
  table 0.05, 0.60, 0.30, 0.05;
}
probability ( R_LNLE_ADM_DE_REGEN | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME, R_LNLE_ULN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (TOTAL, CHRONIC, DEMY) 1.0, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (TOTAL, OLD, DEMY) 1.0, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (TOTAL, OLD, BLOCK) 1.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (TOTAL, OLD, AXONAL) 1.0, 0.0;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (TOTAL, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0;
}
probability ( R_LNLC8_LP_E_ADM_DE_REGEN | R_LNLC8_LP_ADM_DE_REGEN, R_LNLE_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_DIFFN_ADM_DE_REGEN | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
}
probability ( R_LNLW_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_DIFFN_LNLW_ADM_DE_REGEN | R_DIFFN_ADM_DE_REGEN, R_LNLW_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_LNL_DIFFN_ADM_DE_REGEN | R_LNLC8_LP_E_ADM_DE_REGEN, R_DIFFN_LNLW_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_MYOP_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_MYDY_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_MYOP_MYDY_ADM_DE_REGEN | R_MYOP_ADM_DE_REGEN, R_MYDY_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_OTHER_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_MUSCLE_ADM_DE_REGEN | R_MYOP_MYDY_ADM_DE_REGEN, R_OTHER_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_ADM_DE_REGEN | R_LNL_DIFFN_ADM_DE_REGEN, R_MUSCLE_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_DE_REGEN_ADM_NMT | R_ADM_DE_REGEN ) {
  (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;
}
probability ( R_MYAS_ADM_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_MYAS_DE_REGEN_ADM_NMT | R_DE_REGEN_ADM_NMT, R_MYAS_ADM_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ADM_NMT | R_OTHER_ADM_NMT, R_MYAS_DE_REGEN_ADM_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYDY_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYOP_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYOP_MYDY_ADM_MUSIZE | R_MYDY_ADM_MUSIZE, R_MYOP_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MUSCLE_ADM_MUSIZE | R_OTHER_ADM_MUSIZE, R_MYOP_MYDY_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLW_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ADM_MUSIZE | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_LNLW_ADM_MUSIZE | R_LNLW_ADM_MUSIZE, R_DIFFN_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLE_ADM_MUSIZE | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME, R_LNLE_ULN_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLLP_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLC8_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLC8_LP_ADM_MUSIZE | R_LNLLP_ADM_MUSIZE, R_LNLC8_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLC8_LP_E_ADM_MUSIZE | R_LNLE_ADM_MUSIZE, R_LNLC8_LP_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNL_DIFFN_ADM_MUSIZE | R_DIFFN_LNLW_ADM_MUSIZE, R_LNLC8_LP_E_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ADM_MUSIZE | R_MUSCLE_ADM_MUSIZE, R_LNL_DIFFN_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 0.9791, 0.0009, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL, V_SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL, V_SMALL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (INCR, V_SMALL) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;
  (LARGE, V_SMALL) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;
  (V_LARGE, V_SMALL) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;
  (V_SMALL, SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL, SMALL) 0.7493, 0.2257, 0.0049, 0.0001, 0.0000, 0.0000, 0.0200;
  (NORMAL, SMALL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;
  (INCR, SMALL) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;
  (LARGE, SMALL) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;
  (V_LARGE, SMALL) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;
  (V_SMALL, NORMAL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SMALL, NORMAL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;
  (NORMAL, NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;
  (V_SMALL, INCR) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;
  (SMALL, INCR) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00359964, 0.16548345, 0.64543546, 0.16548345, 0.01999800;
  (LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;
  (V_LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (V_SMALL, LARGE) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;
  (SMALL, LARGE) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;
  (INCR, LARGE) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;
  (LARGE, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;
  (V_SMALL, V_LARGE) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;
  (SMALL, V_LARGE) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;
  (INCR, V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;
  (V_LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9799, 0.0200;
}
probability ( R_ADM_EFFMUS | R_ADM_NMT, R_ADM_MUSIZE ) {
  (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MIXED, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;
  (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MIXED, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;
  (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;
  (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;
  (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;
  (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;
  (MIXED, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;
  (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;
  (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;
  (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;
  (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;
  (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;
  (MIXED, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;
  (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;
  (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;
  (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;
  (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;
  (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;
  (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;
  (MIXED, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;
  (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;
  (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;
  (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;
  (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;
  (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;
  (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;
  (MIXED, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;
  (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;
  (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;
  (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;
  (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;
  (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;
  (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;
  (MIXED, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;
}
probability ( R_OTHER_ULN_BLOCK_WA ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLW_ULN_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_LNLW_ULN_BLOCK_WA | R_DIFFN_ULN_BLOCK, R_LNLW_ULN_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULN_BLOCK_WA | R_OTHER_ULN_BLOCK_WA, R_DIFFN_LNLW_ULN_BLOCK_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ADM_MULOSS | R_ULN_BLOCK_WA, R_ADM_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;
  (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;
  (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;
  (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;
  (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;
  (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;
  (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;
  (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;
  (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;
  (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;
  (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;
  (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;
  (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( R_ADM_ALLAMP_WA | R_ADM_EFFMUS, R_ADM_MULOSS ) {
  (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;
  (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;
  (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;
  (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;
  (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;
  (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;
  (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;
  (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;
  (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;
  (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;
  (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;
  (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;
  (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;
  (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;
  (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;
  (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;
  (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;
  (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;
  (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;
  (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;
  (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;
  (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;
  (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;
  (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;
  (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;
  (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;
  (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;
  (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;
  (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;
}
probability ( R_ULN_AMP_WA | R_ADM_ALLAMP_WA ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00000000, 0.27192719, 0.23362336, 0.18271827, 0.12981298, 0.08400840, 0.04940494, 0.02640264, 0.01290129, 0.00570057, 0.00230023, 0.00080008, 0.00030003, 0.00010001, 0.00000000, 0.00000000, 0.00000000;
  (A0_10) 0.0000, 0.0006, 0.0045, 0.0217, 0.0710, 0.1571, 0.2357, 0.2391, 0.1642, 0.0762, 0.0240, 0.0051, 0.0007, 0.0001, 0.0000, 0.0000, 0.0000;
  (A0_30) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00130013, 0.01820182, 0.10931093, 0.29312931, 0.34783478, 0.18291829, 0.04270427, 0.00440044, 0.00020002, 0.00000000, 0.00000000;
  (A0_70) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0016, 0.0360, 0.2338, 0.4425, 0.2448, 0.0394, 0.0019, 0.0000;
  (A1_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.1369, 0.5589, 0.2821, 0.0178, 0.0001;
  (A2_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00640064, 0.05730573, 0.22752275, 0.39933993, 0.30913091;
  (A4_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00100010, 0.02130213, 0.19281928, 0.78487849;
  (A8_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0010, 0.0446, 0.9544;
}
probability ( R_ULN_LD_WA ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_ULN_RD_WA ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_ULN_RDLDDEL | R_ULN_LD_WA, R_ULN_RD_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0964, 0.7981, 0.1055, 0.0000, 0.0000;
  (MOD, NO) 0.0032, 0.1270, 0.8698, 0.0000, 0.0000;
  (SEV, NO) 0.00090009, 0.00280028, 0.01470147, 0.98159816, 0.00000000;
  (NO, MOD) 0.0019, 0.5257, 0.4724, 0.0000, 0.0000;
  (MILD, MOD) 0.00019998, 0.04149585, 0.95830417, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0001, 0.0144, 0.9855, 0.0000, 0.0000;
  (SEV, MOD) 0.00019998, 0.00059994, 0.00369963, 0.99550045, 0.00000000;
  (NO, SEV) 0.0002, 0.0304, 0.9694, 0.0000, 0.0000;
  (MILD, SEV) 0.0001, 0.0142, 0.9857, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0090, 0.9808, 0.0102, 0.0000;
  (SEV, SEV) 0.0000, 0.0002, 0.0012, 0.9984, 0.0002;
}
probability ( R_ULN_DIFSLOW_WA | R_LNLE_ULN_DIFSLOW, R_DIFFN_ULN_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;
  (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;
  (MOD, MOD) 0.000, 0.000, 0.002, 0.998;
  (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;
  (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;
}
probability ( R_ULN_DCV_WA | R_ADM_MALOSS, R_ULN_DIFSLOW_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;
  (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;
  (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;
  (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;
}
probability ( R_ULN_ALLDEL_WA | R_ULN_RDLDDEL, R_ULN_DCV_WA ) {
  (MS3_1, M_S60) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S60) 0.05119488, 0.22907709, 0.56624338, 0.15348465, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S60) 0.0001, 0.0035, 0.0632, 0.9326, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S60) 0.00129987, 0.00199980, 0.00439956, 0.01869813, 0.12128787, 0.74792521, 0.10438956, 0.00000000, 0.00000000;
  (MS20_1, M_S60) 0.00010001, 0.00010001, 0.00030003, 0.00090009, 0.00450045, 0.04340434, 0.57675768, 0.37393739, 0.00000000;
  (MS3_1, M_S52) 0.5607, 0.4393, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S52) 0.01929807, 0.12868713, 0.49285071, 0.35916408, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S52) 0.0000, 0.0011, 0.0283, 0.9651, 0.0055, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S52) 0.00100010, 0.00160016, 0.00370037, 0.01610161, 0.10931093, 0.74217422, 0.12611261, 0.00000000, 0.00000000;
  (MS20_1, M_S52) 0.0001, 0.0001, 0.0002, 0.0008, 0.0041, 0.0399, 0.5568, 0.3980, 0.0000;
  (MS3_1, M_S44) 0.0069, 0.7963, 0.1968, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S44) 0.0027, 0.0328, 0.2398, 0.7246, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S44) 0.0000, 0.0002, 0.0075, 0.8889, 0.1034, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S44) 0.00079992, 0.00119988, 0.00279972, 0.01249875, 0.09159084, 0.72352765, 0.16758324, 0.00000000, 0.00000000;
  (MS20_1, M_S44) 0.0001, 0.0001, 0.0002, 0.0007, 0.0034, 0.0348, 0.5239, 0.4368, 0.0000;
  (MS3_1, M_S36) 0.0000, 0.0184, 0.9806, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S36) 0.00010001, 0.00330033, 0.05470547, 0.93819382, 0.00370037, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S36) 0.00000000, 0.00000000, 0.00030003, 0.18501850, 0.81468147, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS10_1, M_S36) 0.00049995, 0.00079992, 0.00179982, 0.00869913, 0.07029297, 0.68023198, 0.23767623, 0.00000000, 0.00000000;
  (MS20_1, M_S36) 0.0001, 0.0001, 0.0001, 0.0005, 0.0027, 0.0287, 0.4783, 0.4895, 0.0000;
  (MS3_1, M_S28) 0.0000, 0.0001, 0.0179, 0.9820, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S28) 0.00000000, 0.00019998, 0.00479952, 0.50394961, 0.49105089, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S28) 0.0000, 0.0000, 0.0000, 0.0032, 0.9968, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S28) 0.0002, 0.0004, 0.0009, 0.0047, 0.0440, 0.5737, 0.3761, 0.0000, 0.0000;
  (MS20_1, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40724072, 0.56955696, 0.00000000;
  (MS3_1, M_S20) 0.0000, 0.0002, 0.0030, 0.1393, 0.8575, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S20) 0.0000, 0.0000, 0.0003, 0.0188, 0.9804, 0.0005, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.93139314, 0.06710671, 0.00000000, 0.00000000, 0.00000000;
  (MS10_1, M_S20) 0.0001, 0.0001, 0.0002, 0.0013, 0.0150, 0.3152, 0.6681, 0.0000, 0.0000;
  (MS20_1, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00080016, 0.01110222, 0.28045609, 0.70754151, 0.00000000;
  (MS3_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0006, 0.8145, 0.1849, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0494, 0.9506, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S14) 0.000, 0.000, 0.000, 0.000, 0.001, 0.999, 0.000, 0.000, 0.000;
  (MS10_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0001, 0.0017, 0.0786, 0.9196, 0.0000, 0.0000;
  (MS20_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0035, 0.1380, 0.8583, 0.0000;
  (MS3_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59735974, 0.39093909, 0.00000000, 0.00000000;
  (MS3_9, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00340034, 0.29902990, 0.69746975, 0.00000000, 0.00000000;
  (MS4_7, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.1112, 0.8881, 0.0000, 0.0000;
  (MS10_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00940094, 0.95939594, 0.03110311, 0.00000000;
  (MS20_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.02530253, 0.97439744, 0.00000000;
  (MS3_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS3_9, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS4_7, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS10_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS20_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ULN_LAT_WA | R_ULN_ALLDEL_WA ) {
  (MS0_0) 0.2221, 0.5402, 0.2221, 0.0154, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS0_4) 0.03780378, 0.23962396, 0.44344434, 0.23962396, 0.03780378, 0.00170017, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS0_8) 0.00170017, 0.03770377, 0.23912391, 0.44244424, 0.23912391, 0.03770377, 0.00220022, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS1_6) 0.0001, 0.0019, 0.0176, 0.0873, 0.2279, 0.3138, 0.2849, 0.0637, 0.0028, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_2) 0.0004, 0.0015, 0.0044, 0.0114, 0.0255, 0.0495, 0.1033, 0.2035, 0.2400, 0.2035, 0.0989, 0.0529, 0.0049, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS6_4) 0.00030003, 0.00050005, 0.00090009, 0.00160016, 0.00280028, 0.00450045, 0.00890089, 0.01950195, 0.03250325, 0.04980498, 0.11101110, 0.16361636, 0.18941894, 0.25772577, 0.13571357, 0.02010201, 0.00110011, 0.00000000, 0.00000000;
  (MS12_8) 0.00020002, 0.00030003, 0.00040004, 0.00060006, 0.00070007, 0.00100010, 0.00160016, 0.00290029, 0.00420042, 0.00590059, 0.01340134, 0.02140214, 0.03300330, 0.07190719, 0.16781678, 0.22892289, 0.24362436, 0.20212021, 0.00000000;
  (MS25_6) 0.00079992, 0.00099990, 0.00119988, 0.00139986, 0.00159984, 0.00179982, 0.00269973, 0.00399960, 0.00489951, 0.00599940, 0.01119888, 0.01649835, 0.02239776, 0.04509549, 0.10308969, 0.16628337, 0.25177482, 0.35826417, 0.00000000;
  (INFIN) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ADM_VOL_ACT ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_ADM_FORCE | R_ADM_VOL_ACT, R_ADM_ALLAMP_WA ) {
  (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;
  (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;
  (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;
  (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;
  (NORMAL, A0_01) 0.00009999, 0.00429957, 0.05219478, 0.26687331, 0.43305669, 0.24347565;
  (REDUCED, A0_01) 0.0000, 0.0005, 0.0084, 0.0849, 0.3219, 0.5843;
  (V_RED, A0_01) 0.0000, 0.0000, 0.0007, 0.0167, 0.1576, 0.8250;
  (ABSENT, A0_01) 0.00000000, 0.00000000, 0.00000000, 0.00260026, 0.05860586, 0.93879388;
  (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;
  (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;
  (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;
  (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;
  (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;
  (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;
  (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;
  (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;
  (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;
  (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;
  (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;
  (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;
  (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;
  (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;
  (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;
  (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;
  (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;
  (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;
  (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;
  (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;
  (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;
  (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;
  (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;
  (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;
}
probability ( R_ADM_MUSCLE_VOL | R_ADM_MUSIZE, R_ADM_MALOSS ) {
  (V_SMALL, NO) 0.9896, 0.0104;
  (SMALL, NO) 0.8137, 0.1863;
  (NORMAL, NO) 0.0209, 0.9791;
  (INCR, NO) 0.009, 0.991;
  (LARGE, NO) 0.003, 0.997;
  (V_LARGE, NO) 0.0004, 0.9996;
  (OTHER, NO) 0.4212, 0.5788;
  (V_SMALL, MILD) 0.9976, 0.0024;
  (SMALL, MILD) 0.9603, 0.0397;
  (NORMAL, MILD) 0.5185, 0.4815;
  (INCR, MILD) 0.1087, 0.8913;
  (LARGE, MILD) 0.0278, 0.9722;
  (V_LARGE, MILD) 0.0046, 0.9954;
  (OTHER, MILD) 0.5185, 0.4815;
  (V_SMALL, MOD) 0.999, 0.001;
  (SMALL, MOD) 0.9893, 0.0107;
  (NORMAL, MOD) 0.9588, 0.0412;
  (INCR, MOD) 0.6377, 0.3623;
  (LARGE, MOD) 0.2716, 0.7284;
  (V_LARGE, MOD) 0.0779, 0.9221;
  (OTHER, MOD) 0.6336, 0.3664;
  (V_SMALL, SEV) 0.9995, 0.0005;
  (SMALL, SEV) 0.9969, 0.0031;
  (NORMAL, SEV) 0.9953, 0.0047;
  (INCR, SEV) 0.9518, 0.0482;
  (LARGE, SEV) 0.8234, 0.1766;
  (V_LARGE, SEV) 0.5986, 0.4014;
  (OTHER, SEV) 0.7685, 0.2315;
  (V_SMALL, TOTAL) 0.9989, 0.0011;
  (SMALL, TOTAL) 0.9984, 0.0016;
  (NORMAL, TOTAL) 0.9984, 0.0016;
  (INCR, TOTAL) 0.9975, 0.0025;
  (LARGE, TOTAL) 0.9965, 0.0035;
  (V_LARGE, TOTAL) 0.9956, 0.0044;
  (OTHER, TOTAL) 0.9857, 0.0143;
  (V_SMALL, OTHER) 0.9363, 0.0637;
  (SMALL, OTHER) 0.8403, 0.1597;
  (NORMAL, OTHER) 0.6534, 0.3466;
  (INCR, OTHER) 0.4689, 0.5311;
  (LARGE, OTHER) 0.3174, 0.6826;
  (V_LARGE, OTHER) 0.1948, 0.8052;
  (OTHER, OTHER) 0.5681, 0.4319;
}
probability ( R_ADM_MVA_RECRUIT | R_ADM_MULOSS, R_ADM_VOL_ACT ) {
  (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;
  (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;
  (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;
  (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;
  (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;
  (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;
  (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;
  (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;
  (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;
  (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;
  (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;
  (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;
  (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;
  (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;
  (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;
  (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;
  (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ADM_MVA_AMP | R_ADM_EFFMUS ) {
  (V_SMALL) 0.00, 0.04, 0.96;
  (SMALL) 0.01, 0.15, 0.84;
  (NORMAL) 0.05, 0.90, 0.05;
  (INCR) 0.50, 0.49, 0.01;
  (LARGE) 0.85, 0.15, 0.00;
  (V_LARGE) 0.96, 0.04, 0.00;
  (OTHER) 0.33, 0.34, 0.33;
}
probability ( R_ADM_TA_CONCL | R_ADM_EFFMUS ) {
  (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;
  (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;
  (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;
  (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;
  (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;
  (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;
  (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;
}
probability ( R_ADM_MUPAMP | R_ADM_EFFMUS ) {
  (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;
  (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;
  (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;
  (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;
}
probability ( R_ADM_QUAN_MUPAMP | R_ADM_MUPAMP ) {
  (V_SMALL) 0.00080024, 0.00370111, 0.01350405, 0.03811143, 0.08352506, 0.14254276, 0.18955687, 0.19635891, 0.15834750, 0.09942983, 0.04861458, 0.01850555, 0.00550165, 0.00130039, 0.00020006, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.00000000, 0.00010002, 0.00080016, 0.00370074, 0.01350270, 0.03810762, 0.08351670, 0.14252851, 0.18953791, 0.19633927, 0.15833167, 0.09941988, 0.04860972, 0.01850370, 0.00550110, 0.00130026, 0.00020004, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.00370037, 0.01870187, 0.06390639, 0.14751475, 0.23022302, 0.24312431, 0.17371737, 0.08400840, 0.02750275, 0.00610061, 0.00090009, 0.00010001, 0.00000000, 0.00000000;
  (INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0037, 0.0135, 0.0381, 0.0835, 0.1426, 0.1896, 0.1963, 0.1583, 0.0995, 0.0487, 0.0185, 0.0055, 0.0013;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0038, 0.0136, 0.0383, 0.0841, 0.1435, 0.1909, 0.1977, 0.1594, 0.1001, 0.0490, 0.0187;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0024, 0.0082, 0.0232, 0.0542, 0.1041, 0.1645, 0.2137, 0.2283, 0.2007;
  (OTHER) 0.0045, 0.0078, 0.0127, 0.0197, 0.0289, 0.0403, 0.0531, 0.0664, 0.0787, 0.0883, 0.0939, 0.0946, 0.0903, 0.0817, 0.0700, 0.0569, 0.0438, 0.0319, 0.0221, 0.0144;
}
probability ( R_ADM_QUAL_MUPAMP | R_ADM_MUPAMP ) {
  (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;
  (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;
  (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;
  (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;
  (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;
  (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;
  (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;
}
probability ( R_ADM_MUPDUR | R_ADM_EFFMUS ) {
  (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;
  (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;
}
probability ( R_ADM_QUAN_MUPDUR | R_ADM_MUPDUR ) {
  (V_SMALL) 0.09981996, 0.18333667, 0.24024805, 0.22454491, 0.14972995, 0.07121424, 0.02420484, 0.00580116, 0.00100020, 0.00010002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.01020102, 0.03690369, 0.09510951, 0.17471747, 0.22892289, 0.21402140, 0.14261426, 0.06780678, 0.02300230, 0.00560056, 0.00100010, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.0000, 0.0002, 0.0025, 0.0177, 0.0739, 0.1852, 0.2785, 0.2515, 0.1363, 0.0444, 0.0087, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR) 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000, 0.00000000, 0.00000000;
  (LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000;
  (V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00039996, 0.00179982, 0.00699930, 0.02189781, 0.05409459, 0.10518948, 0.16128387, 0.19498050, 0.18598140, 0.13988601, 0.08289171, 0.03879612, 0.00569943;
  (OTHER) 0.0201, 0.0341, 0.0529, 0.0748, 0.0966, 0.1138, 0.1224, 0.1202, 0.1078, 0.0882, 0.0658, 0.0449, 0.0279, 0.0159, 0.0082, 0.0039, 0.0017, 0.0007, 0.0001;
}
probability ( R_ADM_QUAL_MUPDUR | R_ADM_MUPDUR ) {
  (V_SMALL) 0.8309, 0.1677, 0.0014;
  (SMALL) 0.49, 0.49, 0.02;
  (NORMAL) 0.1065, 0.7870, 0.1065;
  (INCR) 0.02, 0.49, 0.49;
  (LARGE) 0.0014, 0.1677, 0.8309;
  (V_LARGE) 0.0001, 0.0392, 0.9607;
  (OTHER) 0.2597, 0.4806, 0.2597;
}
probability ( R_ADM_QUAN_MUPPOLY | R_ADM_DE_REGEN, R_ADM_EFFMUS ) {
  (NO, V_SMALL) 0.109, 0.548, 0.343;
  (YES, V_SMALL) 0.004, 0.122, 0.874;
  (NO, SMALL) 0.340, 0.564, 0.096;
  (YES, SMALL) 0.015, 0.261, 0.724;
  (NO, NORMAL) 0.925, 0.075, 0.000;
  (YES, NORMAL) 0.091, 0.526, 0.383;
  (NO, INCR) 0.796, 0.201, 0.003;
  (YES, INCR) 0.061, 0.465, 0.474;
  (NO, LARGE) 0.637, 0.348, 0.015;
  (YES, LARGE) 0.039, 0.396, 0.565;
  (NO, V_LARGE) 0.340, 0.564, 0.096;
  (YES, V_LARGE) 0.015, 0.261, 0.724;
  (NO, OTHER) 0.340, 0.564, 0.096;
  (YES, OTHER) 0.015, 0.261, 0.724;
}
probability ( R_ADM_QUAL_MUPPOLY | R_ADM_QUAN_MUPPOLY ) {
  (x_12_) 0.95, 0.05;
  (x12_24_) 0.3, 0.7;
  (x_24_) 0.05, 0.95;
}
probability ( R_ADM_MUPSATEL | R_ADM_DE_REGEN ) {
  (NO) 0.95, 0.05;
  (YES) 0.2, 0.8;
}
probability ( R_ADM_MUPINSTAB | R_ADM_NMT ) {
  (NO) 0.95, 0.05;
  (MOD_PRE) 0.1, 0.9;
  (SEV_PRE) 0.03, 0.97;
  (MLD_POST) 0.2, 0.8;
  (MOD_POST) 0.1, 0.9;
  (SEV_POST) 0.03, 0.97;
  (MIXED) 0.1, 0.9;
}
probability ( R_ADM_REPSTIM_CMAPAMP | R_ADM_ALLAMP_WA ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00029988, 0.09616154, 0.11335466, 0.12485006, 0.12834866, 0.12315074, 0.11025590, 0.09226309, 0.07207117, 0.05257897, 0.03578569, 0.02269092, 0.01349460, 0.00749700, 0.00389844, 0.00189924, 0.00079968, 0.00039984, 0.00009996, 0.00009996, 0.00000000;
  (A0_10) 0.00000000, 0.00000000, 0.00009998, 0.00039992, 0.00169966, 0.00649870, 0.01959608, 0.04739052, 0.09228154, 0.14417117, 0.18096381, 0.18246351, 0.14777045, 0.09598080, 0.05018996, 0.02099580, 0.00709858, 0.00189962, 0.00039992, 0.00009998, 0.00000000;
  (A0_30) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.00330033, 0.01690169, 0.05950595, 0.14071407, 0.22592259, 0.24522452, 0.18011801, 0.08970897, 0.03010301, 0.00690069, 0.00110011, 0.00010001;
  (A0_70) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0088, 0.0684, 0.2360, 0.3599, 0.2433, 0.0728, 0.0097, 0.0006;
  (A1_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.1369, 0.5589, 0.2821, 0.0178, 0.0001;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0028, 0.0211, 0.0937, 0.2387, 0.3496, 0.2939;
  (A4_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00059994, 0.00749925, 0.05829417, 0.25997400, 0.67363264;
  (A8_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00810081, 0.11031103, 0.88128813;
}
probability ( R_ADM_REPSTIM_DECR | R_ADM_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;
  (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MIXED) 0.245, 0.245, 0.245, 0.245, 0.020;
}
probability ( R_ADM_REPSTIM_FACILI | R_ADM_NMT ) {
  (NO) 0.95, 0.02, 0.01, 0.02;
  (MOD_PRE) 0.010, 0.889, 0.100, 0.001;
  (SEV_PRE) 0.010, 0.080, 0.909, 0.001;
  (MLD_POST) 0.89, 0.08, 0.01, 0.02;
  (MOD_POST) 0.48, 0.50, 0.01, 0.01;
  (SEV_POST) 0.020, 0.949, 0.030, 0.001;
  (MIXED) 0.25, 0.25, 0.25, 0.25;
}
probability ( R_ADM_REPSTIM_POST_DECR | R_ADM_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;
  (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;
  (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MIXED) 0.23, 0.23, 0.22, 0.22, 0.10;
}
probability ( R_ADM_SF_JITTER | R_ADM_NMT ) {
  (NO) 0.95, 0.05, 0.00, 0.00;
  (MOD_PRE) 0.02, 0.20, 0.70, 0.08;
  (SEV_PRE) 0.0, 0.1, 0.4, 0.5;
  (MLD_POST) 0.05, 0.70, 0.20, 0.05;
  (MOD_POST) 0.01, 0.19, 0.70, 0.10;
  (SEV_POST) 0.0, 0.1, 0.4, 0.5;
  (MIXED) 0.1, 0.3, 0.3, 0.3;
}
probability ( R_LNLC8_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_LNLLP_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_LNLC8_LP_ADM_MUDENS | R_LNLC8_ADM_MUDENS, R_LNLLP_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_LNLE_ADM_MUDENS | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME, R_LNLE_ULN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( R_LNLC8_LP_E_ADM_MUDENS | R_LNLC8_LP_ADM_MUDENS, R_LNLE_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_DIFFN_ADM_MUDENS | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( R_LNLW_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_DIFFN_LNLW_ADM_MUDENS | R_DIFFN_ADM_MUDENS, R_LNLW_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_LNL_DIFFN_ADM_MUDENS | R_LNLC8_LP_E_ADM_MUDENS, R_DIFFN_LNLW_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_MYOP_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MYDY_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MYOP_MYDY_ADM_MUDENS | R_MYOP_ADM_MUDENS, R_MYDY_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_MYAS_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_OTHER_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MYAS_OTHER_ADM_MUDENS | R_MYAS_ADM_MUDENS, R_OTHER_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_MUSCLE_ADM_MUDENS | R_MYOP_MYDY_ADM_MUDENS, R_MYAS_OTHER_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_ADM_MUDENS | R_LNL_DIFFN_ADM_MUDENS, R_MUSCLE_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_ADM_SF_DENSITY | R_ADM_MUDENS ) {
  (NORMAL) 0.97, 0.03, 0.00;
  (INCR) 0.05, 0.90, 0.05;
  (V_INCR) 0.01, 0.04, 0.95;
}
probability ( R_LNLC8_ADM_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLLP_ADM_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLC8_LP_ADM_NEUR_ACT | R_LNLC8_ADM_NEUR_ACT, R_LNLLP_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLE_ADM_NEUR_ACT | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLC8_LP_E_ADM_NEUR_ACT | R_LNLC8_LP_ADM_NEUR_ACT, R_LNLE_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DIFFN_ADM_NEUR_ACT | DIFFN_M_SEV_DIST, DIFFN_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLW_ADM_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_LNLW_ADM_NEUR_ACT | R_DIFFN_ADM_NEUR_ACT, R_LNLW_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNL_DIFFN_ADM_NEUR_ACT | R_LNLC8_LP_E_ADM_NEUR_ACT, R_DIFFN_LNLW_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_ADM_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_ADM_NEUR_ACT | R_LNL_DIFFN_ADM_NEUR_ACT, R_OTHER_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ADM_SPONT_NEUR_DISCH | R_ADM_NEUR_ACT ) {
  (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;
  (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;
  (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;
  (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;
  (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;
}
probability ( R_MYOP_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYDY_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYOP_MYDY_ADM_DENERV | R_MYOP_ADM_DENERV, R_MYDY_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_NMT_ADM_DENERV | R_ADM_NMT ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE) 0.40, 0.45, 0.15, 0.00;
  (SEV_PRE) 0.15, 0.35, 0.35, 0.15;
  (MLD_POST) 0.85, 0.15, 0.00, 0.00;
  (MOD_POST) 0.30, 0.45, 0.20, 0.05;
  (SEV_POST) 0.15, 0.35, 0.35, 0.15;
  (MIXED) 0.25, 0.25, 0.25, 0.25;
}
probability ( R_OTHER_NMT_ADM_DENERV | R_OTHER_ADM_DENERV, R_NMT_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MUSCLE_ADM_DENERV | R_MYOP_MYDY_ADM_DENERV, R_OTHER_NMT_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLC8_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLLP_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLC8_LP_ADM_DENERV | R_LNLC8_ADM_DENERV, R_LNLLP_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLE_ADM_DENERV | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME, R_LNLE_ULN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( R_LNLC8_LP_E_ADM_DENERV | R_LNLC8_LP_ADM_DENERV, R_LNLE_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DIFFN_ADM_DENERV | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( R_LNLW_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_LNLW_ADM_DENERV | R_DIFFN_ADM_DENERV, R_LNLW_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNL_DIFFN_ADM_DENERV | R_LNLC8_LP_E_ADM_DENERV, R_DIFFN_LNLW_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ADM_DENERV | R_MUSCLE_ADM_DENERV, R_LNL_DIFFN_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_ADM_SPONT_DENERV_ACT | R_ADM_DENERV ) {
  (NO) 0.98, 0.02, 0.00, 0.00;
  (MILD) 0.07, 0.85, 0.08, 0.00;
  (MOD) 0.01, 0.07, 0.85, 0.07;
  (SEV) 0.00, 0.01, 0.07, 0.92;
}
probability ( R_ADM_SPONT_HF_DISCH | R_ADM_DENERV ) {
  (NO) 0.99, 0.01;
  (MILD) 0.97, 0.03;
  (MOD) 0.95, 0.05;
  (SEV) 0.93, 0.07;
}
probability ( R_ADM_SPONT_INS_ACT | R_ADM_DENERV ) {
  (NO) 0.98, 0.02;
  (MILD) 0.1, 0.9;
  (MOD) 0.05, 0.95;
  (SEV) 0.05, 0.95;
}
probability ( R_OTHER_DELT_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLPC5_AXIL_SEV ) {
  table 0.98031378, 0.01069252, 0.00499650, 0.00299790, 0.00099930;
}
probability ( R_LNLPC5_AXIL_TIME ) {
  table 0.05, 0.60, 0.30, 0.05;
}
probability ( R_LNLPC5_AXIL_PATHO ) {
  table 0.600, 0.190, 0.200, 0.005, 0.005;
}
probability ( R_LNLPC5_DELT_DE_REGEN | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME, R_LNLPC5_AXIL_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (TOTAL, CHRONIC, DEMY) 1.0, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (TOTAL, OLD, DEMY) 1.0, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (TOTAL, OLD, BLOCK) 1.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (TOTAL, OLD, AXONAL) 1.0, 0.0;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (TOTAL, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0;
}
probability ( R_DIFFN_DELT_DE_REGEN | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
}
probability ( R_LNLPC5_DIFFN_DELT_DE_REGEN | R_LNLPC5_DELT_DE_REGEN, R_DIFFN_DELT_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_MYOP_DELT_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_MYDY_DELT_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_MYOP_MYDY_DELT_DE_REGEN | R_MYOP_DELT_DE_REGEN, R_MYDY_DELT_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_OTHER_DELT_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( R_MUSCLE_DELT_DE_REGEN | R_MYOP_MYDY_DELT_DE_REGEN, R_OTHER_DELT_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_DELT_DE_REGEN | R_LNLPC5_DIFFN_DELT_DE_REGEN, R_MUSCLE_DELT_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( R_DE_REGEN_DELT_NMT | R_DELT_DE_REGEN ) {
  (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;
}
probability ( R_MYAS_DELT_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_MYAS_DE_REGEN_DELT_NMT | R_DE_REGEN_DELT_NMT, R_MYAS_DELT_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DELT_NMT | R_OTHER_DELT_NMT, R_MYAS_DE_REGEN_DELT_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_DELT_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYDY_DELT_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYOP_DELT_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYOP_MYDY_DELT_MUSIZE | R_MYDY_DELT_MUSIZE, R_MYOP_DELT_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MUSCLE_DELT_MUSIZE | R_OTHER_DELT_MUSIZE, R_MYOP_MYDY_DELT_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DIFFN_DELT_MUSIZE | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLPC5_DELT_MUSIZE | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME, R_LNLPC5_AXIL_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLPC5_DIFFN_DELT_MUSIZE | R_DIFFN_DELT_MUSIZE, R_LNLPC5_DELT_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DELT_MUSIZE | R_MUSCLE_DELT_MUSIZE, R_LNLPC5_DIFFN_DELT_MUSIZE ) {
  (V_SMALL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (INCR, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (LARGE, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (V_LARGE, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (V_SMALL, SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (INCR, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (LARGE, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (V_LARGE, SMALL) 0.00000000, 0.97939794, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.02000200;
  (V_SMALL, NORMAL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, NORMAL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9781, 0.0019, 0.0000, 0.0000, 0.0200;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9781, 0.0000, 0.0000, 0.0200;
  (LARGE, NORMAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (V_LARGE, NORMAL) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (V_SMALL, INCR) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, INCR) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9781, 0.0000, 0.0000, 0.0200;
  (INCR, INCR) 0.00, 0.00, 0.00, 0.98, 0.00, 0.00, 0.02;
  (LARGE, INCR) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (V_LARGE, INCR) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (V_SMALL, LARGE) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, LARGE) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (INCR, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (LARGE, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (V_LARGE, LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (V_SMALL, V_LARGE) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, V_LARGE) 0.00000000, 0.97939794, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.02000200;
  (NORMAL, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (INCR, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (LARGE, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (V_LARGE, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( R_DELT_EFFMUS | R_DELT_NMT, R_DELT_MUSIZE ) {
  (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (OTHER, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;
  (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (OTHER, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;
  (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;
  (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;
  (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;
  (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;
  (OTHER, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;
  (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;
  (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;
  (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;
  (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;
  (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;
  (OTHER, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;
  (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;
  (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;
  (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;
  (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;
  (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;
  (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;
  (OTHER, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;
  (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;
  (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;
  (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;
  (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;
  (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;
  (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;
  (OTHER, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;
  (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;
  (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;
  (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;
  (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;
  (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;
  (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;
  (OTHER, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;
}
probability ( R_DIFFN_AXIL_BLOCK | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_OTHER_AXIL_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_AXIL_BLOCK_ED | R_DIFFN_AXIL_BLOCK, R_OTHER_AXIL_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLPC5_DELT_MALOSS | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_DIFFN_DELT_MALOSS | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_LNLPC5_DIFFN_DELT_MALOSS | R_LNLPC5_DELT_MALOSS, R_DIFFN_DELT_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_DELT_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DELT_MALOSS | R_LNLPC5_DIFFN_DELT_MALOSS, R_OTHER_DELT_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;
  (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;
  (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( R_DELT_MULOSS | R_AXIL_BLOCK_ED, R_DELT_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;
  (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;
  (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;
  (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;
  (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;
  (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;
  (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;
  (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;
  (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;
  (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;
  (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;
  (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;
  (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( R_DELT_ALLAMP | R_DELT_EFFMUS, R_DELT_MULOSS ) {
  (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;
  (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;
  (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;
  (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;
  (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;
  (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;
  (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;
  (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;
  (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;
  (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;
  (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;
  (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;
  (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;
  (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;
  (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;
  (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;
  (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;
  (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;
  (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;
  (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;
  (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;
  (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;
  (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;
  (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;
  (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;
  (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;
  (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;
  (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;
  (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;
}
probability ( R_AXIL_AMP_E | R_DELT_ALLAMP ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00020002, 0.21532153, 0.19871987, 0.17091709, 0.13691369, 0.10221022, 0.07110711, 0.04600460, 0.02780278, 0.01560156, 0.00820082, 0.00400040, 0.00180018, 0.00080008, 0.00030003, 0.00010001, 0.00000000;
  (A0_10) 0.0000, 0.0087, 0.0213, 0.0441, 0.0778, 0.1167, 0.1489, 0.1614, 0.1488, 0.1166, 0.0777, 0.0440, 0.0212, 0.0087, 0.0030, 0.0009, 0.0002;
  (A0_30) 0.00000000, 0.00000000, 0.00019998, 0.00089991, 0.00349965, 0.01139886, 0.02999700, 0.06419358, 0.11158884, 0.15778422, 0.18128187, 0.16938306, 0.12878712, 0.07949205, 0.03989601, 0.01629837, 0.00529947;
  (A0_70) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0003, 0.0017, 0.0065, 0.0201, 0.0495, 0.0972, 0.1523, 0.1901, 0.1893, 0.1502, 0.0951, 0.0476;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019996, 0.00099980, 0.00439912, 0.01539692, 0.04229154, 0.09138172, 0.15406919, 0.20355929, 0.21035793, 0.17016597, 0.10717856;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0028, 0.0102, 0.0296, 0.0699, 0.1345, 0.2102, 0.2668, 0.2753;
  (A4_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0014, 0.0061, 0.0219, 0.0640, 0.1520, 0.2930, 0.4613;
  (A8_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0010, 0.0055, 0.0245, 0.0884, 0.2588, 0.6216;
}
probability ( R_AXIL_RD_ED | R_LNLPC5_AXIL_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( R_LNLPC5_AXIL_DIFSLOW | R_LNLPC5_AXIL_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 0.0, 1.0, 0.0;
}
probability ( R_DIFFN_AXIL_DIFSLOW | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( R_AXIL_DIFSLOW_ED | R_LNLPC5_AXIL_DIFSLOW, R_DIFFN_AXIL_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;
  (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;
  (MOD, MOD) 0.000, 0.000, 0.002, 0.998;
  (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;
  (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;
}
probability ( R_AXIL_DCV | R_DELT_MALOSS, R_AXIL_DIFSLOW_ED ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;
  (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;
  (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;
  (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;
}
probability ( R_AXIL_DEL | R_AXIL_RD_ED, R_AXIL_DCV ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, M_S60) 0.0002, 0.0003, 0.0006, 0.0027, 0.0210, 0.2666, 0.7086, 0.0000, 0.0000;
  (SEV, M_S60) 0.0000, 0.0001, 0.0001, 0.0004, 0.0021, 0.0232, 0.4307, 0.5434, 0.0000;
  (NO, M_S52) 0.6514, 0.3486, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.00010002, 0.00020004, 0.00050010, 0.00220044, 0.01780356, 0.24224845, 0.73694739, 0.00000000, 0.00000000;
  (SEV, M_S52) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40864086, 0.56815682, 0.00000000;
  (NO, M_S44) 0.0003, 0.9337, 0.0660, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S44) 0.0001, 0.0002, 0.0004, 0.0016, 0.0137, 0.2066, 0.7774, 0.0000, 0.0000;
  (SEV, M_S44) 0.0000, 0.0000, 0.0001, 0.0003, 0.0015, 0.0178, 0.3739, 0.6064, 0.0000;
  (NO, M_S36) 0.00000000, 0.00380038, 0.99619962, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S36) 0.0001, 0.0001, 0.0002, 0.0011, 0.0095, 0.1650, 0.8240, 0.0000, 0.0000;
  (SEV, M_S36) 0.00000000, 0.00000000, 0.00009999, 0.00019998, 0.00109989, 0.01419858, 0.32926707, 0.65513449, 0.00000000;
  (NO, M_S28) 0.00000000, 0.00000000, 0.01060106, 0.98939894, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00540054, 0.11491149, 0.87908791, 0.00000000, 0.00000000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00070014, 0.00980196, 0.26615323, 0.72324465, 0.00000000;
  (NO, M_S20) 0.00000000, 0.00020002, 0.00260026, 0.13561356, 0.86158616, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0020, 0.0580, 0.9396, 0.0002, 0.0000;
  (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00480048, 0.16971697, 0.82518252, 0.00000000;
  (NO, M_S14) 0.0000, 0.0000, 0.0000, 0.0005, 0.8212, 0.1783, 0.0000, 0.0000, 0.0000;
  (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0184, 0.9779, 0.0033, 0.0000;
  (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0014, 0.0771, 0.9214, 0.0000;
  (NO, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59805981, 0.39023902, 0.00000000, 0.00000000;
  (MOD, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0021, 0.4010, 0.5969, 0.0000;
  (SEV, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.01440144, 0.98549855, 0.00000000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_AXIL_LAT_ED | R_AXIL_DEL ) {
  (MS0_0) 0.00990099, 0.07150715, 0.23392339, 0.34723472, 0.29242924, 0.04410441, 0.00090009, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS0_4) 0.0017, 0.0166, 0.0866, 0.2330, 0.4051, 0.2314, 0.0250, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS0_8) 0.0001, 0.0017, 0.0169, 0.0882, 0.2965, 0.4557, 0.1322, 0.0087, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS1_6) 0.00000000, 0.00010001, 0.00110011, 0.00810081, 0.04770477, 0.25392539, 0.41724172, 0.25392539, 0.01630163, 0.00160016, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS3_2) 0.00000000, 0.00019998, 0.00059994, 0.00189981, 0.00659934, 0.02749725, 0.07099290, 0.13578642, 0.27427257, 0.30906909, 0.13458654, 0.03829617, 0.00019998, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS6_4) 0.00009999, 0.00019998, 0.00029997, 0.00059994, 0.00129987, 0.00359964, 0.00749925, 0.01419858, 0.04569543, 0.08109189, 0.13678632, 0.27307269, 0.31566843, 0.10558944, 0.01359864, 0.00069993, 0.00000000;
  (MS12_8) 0.00009999, 0.00019998, 0.00029997, 0.00039996, 0.00059994, 0.00119988, 0.00179982, 0.00269973, 0.00679932, 0.01129887, 0.01919808, 0.04599540, 0.13018698, 0.21597840, 0.27987201, 0.28337166, 0.00000000;
  (MS25_6) 0.0009, 0.0010, 0.0012, 0.0014, 0.0021, 0.0031, 0.0039, 0.0049, 0.0093, 0.0138, 0.0193, 0.0398, 0.0956, 0.1618, 0.2573, 0.3846, 0.0000;
  (INFIN) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DELT_VOL_ACT ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_DELT_FORCE | R_DELT_VOL_ACT, R_DELT_ALLAMP ) {
  (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;
  (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;
  (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;
  (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;
  (NORMAL, A0_01) 0.00009999, 0.00429957, 0.05219478, 0.26687331, 0.43305669, 0.24347565;
  (REDUCED, A0_01) 0.0000, 0.0005, 0.0084, 0.0849, 0.3219, 0.5843;
  (V_RED, A0_01) 0.0000, 0.0000, 0.0007, 0.0167, 0.1576, 0.8250;
  (ABSENT, A0_01) 0.00000000, 0.00000000, 0.00000000, 0.00260026, 0.05860586, 0.93879388;
  (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;
  (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;
  (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;
  (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;
  (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;
  (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;
  (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;
  (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;
  (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;
  (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;
  (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;
  (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;
  (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;
  (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;
  (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;
  (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;
  (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;
  (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;
  (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;
  (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;
  (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;
  (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;
  (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;
  (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;
}
probability ( R_DELT_MUSCLE_VOL | R_DELT_MUSIZE, R_DELT_MALOSS ) {
  (V_SMALL, NO) 0.9896, 0.0104;
  (SMALL, NO) 0.8137, 0.1863;
  (NORMAL, NO) 0.0209, 0.9791;
  (INCR, NO) 0.0209, 0.9791;
  (LARGE, NO) 0.003, 0.997;
  (V_LARGE, NO) 0.0004, 0.9996;
  (OTHER, NO) 0.4212, 0.5788;
  (V_SMALL, MILD) 0.9976, 0.0024;
  (SMALL, MILD) 0.9603, 0.0397;
  (NORMAL, MILD) 0.5185, 0.4815;
  (INCR, MILD) 0.1492, 0.8508;
  (LARGE, MILD) 0.0278, 0.9722;
  (V_LARGE, MILD) 0.0046, 0.9954;
  (OTHER, MILD) 0.5185, 0.4815;
  (V_SMALL, MOD) 0.999, 0.001;
  (SMALL, MOD) 0.9893, 0.0107;
  (NORMAL, MOD) 0.9588, 0.0412;
  (INCR, MOD) 0.6221, 0.3779;
  (LARGE, MOD) 0.2716, 0.7284;
  (V_LARGE, MOD) 0.0779, 0.9221;
  (OTHER, MOD) 0.6336, 0.3664;
  (V_SMALL, SEV) 0.9995, 0.0005;
  (SMALL, SEV) 0.9969, 0.0031;
  (NORMAL, SEV) 0.9953, 0.0047;
  (INCR, SEV) 0.9358, 0.0642;
  (LARGE, SEV) 0.8234, 0.1766;
  (V_LARGE, SEV) 0.5986, 0.4014;
  (OTHER, SEV) 0.7685, 0.2315;
  (V_SMALL, TOTAL) 0.9989, 0.0011;
  (SMALL, TOTAL) 0.9984, 0.0016;
  (NORMAL, TOTAL) 0.9984, 0.0016;
  (INCR, TOTAL) 0.9972, 0.0028;
  (LARGE, TOTAL) 0.9965, 0.0035;
  (V_LARGE, TOTAL) 0.9956, 0.0044;
  (OTHER, TOTAL) 0.9857, 0.0143;
  (V_SMALL, OTHER) 0.9363, 0.0637;
  (SMALL, OTHER) 0.8403, 0.1597;
  (NORMAL, OTHER) 0.6534, 0.3466;
  (INCR, OTHER) 0.4719, 0.5281;
  (LARGE, OTHER) 0.3174, 0.6826;
  (V_LARGE, OTHER) 0.1948, 0.8052;
  (OTHER, OTHER) 0.5681, 0.4319;
}
probability ( R_DELT_MVA_RECRUIT | R_DELT_MULOSS, R_DELT_VOL_ACT ) {
  (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;
  (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;
  (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;
  (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;
  (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;
  (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;
  (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;
  (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;
  (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;
  (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;
  (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;
  (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;
  (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;
  (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;
  (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;
  (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;
  (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DELT_MVA_AMP | R_DELT_EFFMUS ) {
  (V_SMALL) 0.00, 0.04, 0.96;
  (SMALL) 0.01, 0.15, 0.84;
  (NORMAL) 0.05, 0.90, 0.05;
  (INCR) 0.50, 0.49, 0.01;
  (LARGE) 0.85, 0.15, 0.00;
  (V_LARGE) 0.96, 0.04, 0.00;
  (OTHER) 0.33, 0.34, 0.33;
}
probability ( R_DELT_TA_CONCL | R_DELT_EFFMUS ) {
  (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;
  (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;
  (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;
  (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;
  (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;
  (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;
  (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;
}
probability ( R_DELT_MUPAMP | R_DELT_EFFMUS ) {
  (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;
  (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;
  (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;
  (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;
}
probability ( R_DELT_QUAN_MUPAMP | R_DELT_MUPAMP ) {
  (V_SMALL) 0.04180418, 0.08970897, 0.15021502, 0.19571957, 0.19871987, 0.15711571, 0.09670967, 0.04640464, 0.01730173, 0.00500050, 0.00110011, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.00420042, 0.01480148, 0.04100410, 0.08800880, 0.14731473, 0.19201920, 0.19491949, 0.15411541, 0.09490949, 0.04550455, 0.01700170, 0.00490049, 0.00110011, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.0000, 0.0001, 0.0006, 0.0043, 0.0210, 0.0693, 0.1550, 0.2345, 0.2401, 0.1663, 0.0779, 0.0247, 0.0053, 0.0008, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR) 0.00000000, 0.00000000, 0.00020002, 0.00090009, 0.00420042, 0.01480148, 0.04090409, 0.08790879, 0.14721472, 0.19181918, 0.19471947, 0.15391539, 0.09480948, 0.04540454, 0.01700170, 0.00490049, 0.00110011, 0.00020002, 0.00000000, 0.00000000;
  (LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00090009, 0.00420042, 0.01480148, 0.04090409, 0.08790879, 0.14721472, 0.19181918, 0.19471947, 0.15391539, 0.09480948, 0.04540454, 0.01700170, 0.00490049, 0.00110011, 0.00020002;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0005, 0.0021, 0.0070, 0.0196, 0.0451, 0.0853, 0.1326, 0.1696, 0.1784, 0.1544, 0.1099, 0.0644, 0.0310;
  (OTHER) 0.02049795, 0.02999700, 0.04159584, 0.05469453, 0.06809319, 0.08029197, 0.08969103, 0.09499050, 0.09529047, 0.09059094, 0.08159184, 0.06959304, 0.05629437, 0.04319568, 0.03129687, 0.02159784, 0.01409859, 0.00869913, 0.00509949, 0.00279972;
}
probability ( R_DELT_QUAL_MUPAMP | R_DELT_MUPAMP ) {
  (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;
  (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;
  (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;
  (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;
  (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;
  (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;
  (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;
}
probability ( R_DELT_MUPDUR | R_DELT_EFFMUS ) {
  (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;
  (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;
}
probability ( R_DELT_QUAN_MUPDUR | R_DELT_MUPDUR ) {
  (V_SMALL) 0.01020102, 0.03690369, 0.09510951, 0.17471747, 0.22892289, 0.21402140, 0.14261426, 0.06780678, 0.02300230, 0.00560056, 0.00100010, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.0000, 0.0000, 0.0000, 0.0002, 0.0025, 0.0177, 0.0739, 0.1852, 0.2785, 0.2515, 0.1363, 0.0444, 0.0087, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000;
  (LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029994, 0.00199960, 0.01019796, 0.03689262, 0.09498100, 0.17446511, 0.22855429, 0.21365727, 0.14247151, 0.06778644, 0.02299540, 0.00559888, 0.00009998;
  (V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00040004, 0.00180018, 0.00730073, 0.02290229, 0.05640564, 0.10971097, 0.16831683, 0.20352035, 0.19411941, 0.14591459, 0.08950895;
  (OTHER) 0.00520104, 0.01070214, 0.01980396, 0.03360672, 0.05211042, 0.07371474, 0.09511902, 0.11212242, 0.12062412, 0.11842368, 0.10622124, 0.08681736, 0.06491298, 0.04420884, 0.02750550, 0.01560312, 0.00810162, 0.00380076, 0.00140028;
}
probability ( R_DELT_QUAL_MUPDUR | R_DELT_MUPDUR ) {
  (V_SMALL) 0.8309, 0.1677, 0.0014;
  (SMALL) 0.49, 0.49, 0.02;
  (NORMAL) 0.1065, 0.7870, 0.1065;
  (INCR) 0.02, 0.49, 0.49;
  (LARGE) 0.0014, 0.1677, 0.8309;
  (V_LARGE) 0.0001, 0.0392, 0.9607;
  (OTHER) 0.2597, 0.4806, 0.2597;
}
probability ( R_DELT_QUAN_MUPPOLY | R_DELT_DE_REGEN, R_DELT_EFFMUS ) {
  (NO, V_SMALL) 0.109, 0.548, 0.343;
  (YES, V_SMALL) 0.004, 0.122, 0.874;
  (NO, SMALL) 0.340, 0.564, 0.096;
  (YES, SMALL) 0.015, 0.261, 0.724;
  (NO, NORMAL) 0.925, 0.075, 0.000;
  (YES, NORMAL) 0.091, 0.526, 0.383;
  (NO, INCR) 0.796, 0.201, 0.003;
  (YES, INCR) 0.061, 0.465, 0.474;
  (NO, LARGE) 0.637, 0.348, 0.015;
  (YES, LARGE) 0.039, 0.396, 0.565;
  (NO, V_LARGE) 0.340, 0.564, 0.096;
  (YES, V_LARGE) 0.015, 0.261, 0.724;
  (NO, OTHER) 0.340, 0.564, 0.096;
  (YES, OTHER) 0.015, 0.261, 0.724;
}
probability ( R_DELT_QUAL_MUPPOLY | R_DELT_QUAN_MUPPOLY ) {
  (x_12_) 0.95, 0.05;
  (x12_24_) 0.3, 0.7;
  (x_24_) 0.05, 0.95;
}
probability ( R_DELT_MUPSATEL | R_DELT_DE_REGEN ) {
  (NO) 0.95, 0.05;
  (YES) 0.2, 0.8;
}
probability ( R_DELT_MUPINSTAB | R_DELT_NMT ) {
  (NO) 0.95, 0.05;
  (MOD_PRE) 0.1, 0.9;
  (SEV_PRE) 0.03, 0.97;
  (MLD_POST) 0.2, 0.8;
  (MOD_POST) 0.1, 0.9;
  (SEV_POST) 0.03, 0.97;
  (OTHER) 0.1, 0.9;
}
probability ( R_DELT_REPSTIM_CMAPAMP | R_DELT_ALLAMP ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.0008, 0.0838, 0.0981, 0.1087, 0.1140, 0.1131, 0.1062, 0.0944, 0.0794, 0.0632, 0.0476, 0.0340, 0.0229, 0.0146, 0.0089, 0.0051, 0.0027, 0.0014, 0.0007, 0.0003, 0.0001;
  (A0_10) 0.00000000, 0.00029994, 0.00099980, 0.00279944, 0.00699860, 0.01539692, 0.03019396, 0.05238952, 0.08058388, 0.10937812, 0.13147371, 0.13967207, 0.13137373, 0.10927814, 0.08048390, 0.05238952, 0.03019396, 0.01539692, 0.00689862, 0.00279944, 0.00099980;
  (A0_30) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020004, 0.00070014, 0.00250050, 0.00730146, 0.01840368, 0.03930786, 0.07141428, 0.11012202, 0.14452891, 0.16123225, 0.15283057, 0.12322464, 0.08441688, 0.04920984, 0.02440488, 0.01020204;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00059994, 0.00239976, 0.00839916, 0.02349765, 0.05359464, 0.09919008, 0.14958504, 0.18328167, 0.18258174, 0.14778522, 0.09729027, 0.05169483;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019996, 0.00099980, 0.00439912, 0.01539692, 0.04229154, 0.09138172, 0.15406919, 0.20355929, 0.21035793, 0.17016597, 0.10717856;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0012, 0.0045, 0.0139, 0.0361, 0.0776, 0.1391, 0.2072, 0.2564, 0.2637;
  (A4_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0005, 0.0024, 0.0092, 0.0286, 0.0745, 0.1612, 0.2895, 0.4340;
  (A8_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0003, 0.0019, 0.0086, 0.0327, 0.1028, 0.2680, 0.5856;
}
probability ( R_DELT_REPSTIM_DECR | R_DELT_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;
  (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;
  (OTHER) 0.245, 0.245, 0.245, 0.245, 0.020;
}
probability ( R_DELT_REPSTIM_FACILI | R_DELT_NMT ) {
  (NO) 0.95, 0.02, 0.01, 0.02;
  (MOD_PRE) 0.010, 0.889, 0.100, 0.001;
  (SEV_PRE) 0.010, 0.080, 0.909, 0.001;
  (MLD_POST) 0.89, 0.08, 0.01, 0.02;
  (MOD_POST) 0.48, 0.50, 0.01, 0.01;
  (SEV_POST) 0.020, 0.949, 0.030, 0.001;
  (OTHER) 0.25, 0.25, 0.25, 0.25;
}
probability ( R_DELT_REPSTIM_POST_DECR | R_DELT_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;
  (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;
  (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;
  (OTHER) 0.23, 0.23, 0.22, 0.22, 0.10;
}
probability ( R_DELT_SF_JITTER | R_DELT_NMT ) {
  (NO) 0.95, 0.05, 0.00, 0.00;
  (MOD_PRE) 0.02, 0.20, 0.70, 0.08;
  (SEV_PRE) 0.0, 0.1, 0.4, 0.5;
  (MLD_POST) 0.05, 0.70, 0.20, 0.05;
  (MOD_POST) 0.01, 0.19, 0.70, 0.10;
  (SEV_POST) 0.0, 0.1, 0.4, 0.5;
  (OTHER) 0.1, 0.3, 0.3, 0.3;
}
probability ( R_LNLPC5_DELT_MUDENS | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME, R_LNLPC5_AXIL_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( R_DIFFN_DELT_MUDENS | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( R_LNLPC5_DIFFN_DELT_MUDENS | R_LNLPC5_DELT_MUDENS, R_DIFFN_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_MYOP_DELT_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MYDY_DELT_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MYOP_MYDY_DELT_MUDENS | R_MYOP_DELT_MUDENS, R_MYDY_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_MYAS_DELT_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_OTHER_DELT_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_MYAS_OTHER_DELT_MUDENS | R_MYAS_DELT_MUDENS, R_OTHER_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_MUSCLE_DELT_MUDENS | R_MYOP_MYDY_DELT_MUDENS, R_MYAS_OTHER_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_DELT_MUDENS | R_LNLPC5_DIFFN_DELT_MUDENS, R_MUSCLE_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( R_DELT_SF_DENSITY | R_DELT_MUDENS ) {
  (NORMAL) 0.97, 0.03, 0.00;
  (INCR) 0.05, 0.90, 0.05;
  (V_INCR) 0.01, 0.04, 0.95;
}
probability ( R_LNLPC5_DELT_NEUR_ACT | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_DELT_NEUR_ACT | DIFFN_M_SEV_PROX, DIFFN_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_LNLPC5_DIFFN_DELT_NEUR_ACT | R_LNLPC5_DELT_NEUR_ACT, R_DIFFN_DELT_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_DELT_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DELT_NEUR_ACT | R_LNLPC5_DIFFN_DELT_NEUR_ACT, R_OTHER_DELT_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DELT_SPONT_NEUR_DISCH | R_DELT_NEUR_ACT ) {
  (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;
  (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;
  (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;
  (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;
  (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;
}
probability ( R_MYOP_DELT_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYDY_DELT_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_MYOP_MYDY_DELT_DENERV | R_MYOP_DELT_DENERV, R_MYDY_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_DELT_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_NMT_DELT_DENERV | R_DELT_NMT ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE) 0.40, 0.45, 0.15, 0.00;
  (SEV_PRE) 0.15, 0.35, 0.35, 0.15;
  (MLD_POST) 0.85, 0.15, 0.00, 0.00;
  (MOD_POST) 0.30, 0.45, 0.20, 0.05;
  (SEV_POST) 0.15, 0.35, 0.35, 0.15;
  (OTHER) 0.25, 0.25, 0.25, 0.25;
}
probability ( R_OTHER_NMT_DELT_DENERV | R_OTHER_DELT_DENERV, R_NMT_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_MUSCLE_DELT_DENERV | R_MYOP_MYDY_DELT_DENERV, R_OTHER_NMT_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_LNLPC5_DELT_DENERV | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME, R_LNLPC5_AXIL_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( R_DIFFN_DELT_DENERV | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( R_LNLPC5_DIFFN_DELT_DENERV | R_LNLPC5_DELT_DENERV, R_DIFFN_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DELT_DENERV | R_MUSCLE_DELT_DENERV, R_LNLPC5_DIFFN_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_DELT_SPONT_DENERV_ACT | R_DELT_DENERV ) {
  (NO) 0.98, 0.02, 0.00, 0.00;
  (MILD) 0.07, 0.85, 0.08, 0.00;
  (MOD) 0.01, 0.07, 0.85, 0.07;
  (SEV) 0.00, 0.01, 0.07, 0.92;
}
probability ( R_DELT_SPONT_HF_DISCH | R_DELT_DENERV ) {
  (NO) 0.99, 0.01;
  (MILD) 0.97, 0.03;
  (MOD) 0.95, 0.05;
  (SEV) 0.93, 0.07;
}
probability ( R_DELT_SPONT_INS_ACT | R_DELT_DENERV ) {
  (NO) 0.98, 0.02;
  (MILD) 0.1, 0.9;
  (MOD) 0.05, 0.95;
  (SEV) 0.05, 0.95;
}
probability ( R_OTHER_ISCH_DISP ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ISCH_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_SUR_DISP_CA | R_OTHER_ISCH_DISP, R_DIFFN_ISCH_DISP ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_ISCH_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ISCH_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_SUR_BLOCK_CA | R_OTHER_ISCH_BLOCK, R_DIFFN_ISCH_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_OTHER_ISCH_SALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ISCH_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_LNL_ISCH_SEV ) {
  table 0.960, 0.020, 0.010, 0.005, 0.005;
}
probability ( R_LNL_ISCH_PATHO ) {
  table 0.25, 0.25, 0.50, 0.00, 0.00;
}
probability ( R_LNL_ISCH_SALOSS_CA | R_LNL_ISCH_SEV, R_LNL_ISCH_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( R_DIFFN_LNL_ISCH_SALOSS | R_DIFFN_ISCH_SALOSS, R_LNL_ISCH_SALOSS_CA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_SUR_SALOSS | R_OTHER_ISCH_SALOSS, R_DIFFN_LNL_ISCH_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_SUR_EFFAXLOSS | R_SUR_BLOCK_CA, R_SUR_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_SUR_ALLAMP_CA | R_SUR_DISP_CA, R_SUR_EFFAXLOSS ) {
  (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;
  (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;
  (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;
  (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;
  (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;
  (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;
  (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;
  (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;
  (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;
  (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( R_SUR_AMP_CA | R_SUR_ALLAMP_CA ) {
  (ZERO) 0.7619, 0.1816, 0.0438, 0.0099, 0.0022, 0.0005, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (A0_01) 0.38293829, 0.26322632, 0.16731673, 0.09570957, 0.04980498, 0.02420242, 0.01050105, 0.00410041, 0.00150015, 0.00050005, 0.00020002, 0.00000000, 0.00000000;
  (A0_10) 0.04670467, 0.11351135, 0.19301930, 0.23662366, 0.20492049, 0.12771277, 0.05570557, 0.01720172, 0.00390039, 0.00060006, 0.00010001, 0.00000000, 0.00000000;
  (A0_30) 0.00000000, 0.00009999, 0.00109989, 0.01139886, 0.06489351, 0.19078092, 0.30886911, 0.26467353, 0.12378762, 0.03019698, 0.00389961, 0.00029997, 0.00000000;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00170017, 0.02270227, 0.13181318, 0.31883188, 0.33903390, 0.15251525, 0.03080308, 0.00250025;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00580058, 0.07460746, 0.31883188, 0.41204120, 0.16811681, 0.02050205;
}
probability ( R_SUR_LD_CA ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_SUR_RD_CA ) {
  table 1.0, 0.0, 0.0;
}
probability ( R_SUR_LSLOW_CA | R_SUR_LD_CA, R_SUR_RD_CA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;
  (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;
  (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;
  (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;
  (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;
  (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;
  (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;
}
probability ( R_OTHER_ISCH_DIFSLOW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( R_DIFFN_ISCH_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( R_SUR_DIFSLOW_CA | R_OTHER_ISCH_DIFSLOW, R_DIFFN_ISCH_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( R_SUR_DSLOW_CA | R_SUR_SALOSS, R_SUR_DIFSLOW_CA ) {
  (NO, NO) 0.8825, 0.1175, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, NO) 0.34603460, 0.65076508, 0.00320032, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, NO) 0.0641, 0.8105, 0.1254, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.00410041, 0.15271527, 0.78487849, 0.05830583, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.06390639, 0.24022402, 0.46154615, 0.22352235, 0.01080108, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, MILD) 0.0245, 0.1308, 0.4221, 0.3800, 0.0426, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MILD) 0.0078, 0.0585, 0.3098, 0.5006, 0.1230, 0.0003, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.0012, 0.0133, 0.1276, 0.4624, 0.3871, 0.0084, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0011, 0.0083, 0.0684, 0.2984, 0.5331, 0.0899, 0.0008, 0.0000, 0.0000;
  (MILD, MOD) 0.0003, 0.0030, 0.0335, 0.2043, 0.5689, 0.1865, 0.0035, 0.0000, 0.0000;
  (MOD, MOD) 0.00010001, 0.00100010, 0.01440144, 0.12231223, 0.52405241, 0.32563256, 0.01250125, 0.00000000, 0.00000000;
  (SEV, MOD) 0.0000, 0.0002, 0.0034, 0.0430, 0.3319, 0.5510, 0.0705, 0.0000, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00040004, 0.00120012, 0.00530053, 0.02140214, 0.08840884, 0.28082808, 0.44704470, 0.15541554, 0.00000000;
  (MILD, SEV) 0.00019996, 0.00069986, 0.00319936, 0.01399720, 0.06498700, 0.24325135, 0.46090782, 0.21275745, 0.00000000;
  (MOD, SEV) 0.0001, 0.0004, 0.0018, 0.0089, 0.0461, 0.2037, 0.4588, 0.2802, 0.0000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00080008, 0.00410041, 0.02540254, 0.14331433, 0.42124212, 0.40504050, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_SUR_ALLCV_CA | R_SUR_LSLOW_CA, R_SUR_DSLOW_CA ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;
  (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;
  (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;
  (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;
  (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;
  (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;
  (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;
  (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;
  (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;
  (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( R_SUR_CV_CA | R_SUR_ALLCV_CA ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00109989, 0.03779622, 0.29927007, 0.42315768, 0.19018098, 0.04509549, 0.00339966;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.01570157, 0.17281728, 0.38253825, 0.31873187, 0.09460946, 0.01350135, 0.00180018, 0.00010001;
  (M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0103, 0.1527, 0.3890, 0.3032, 0.1168, 0.0247, 0.0030, 0.0002, 0.0000, 0.0000;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0554, 0.3435, 0.4067, 0.1669, 0.0241, 0.0026, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0054, 0.2321, 0.5116, 0.2174, 0.0304, 0.0030, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.00000000, 0.00000000, 0.00000000, 0.09980998, 0.56675668, 0.28572857, 0.04400440, 0.00350035, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S14) 0.00000000, 0.00000000, 0.14628537, 0.69093091, 0.15288471, 0.00949905, 0.00039996, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.0000, 0.1754, 0.7847, 0.0388, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLW_MED_SEV ) {
  table 0.895, 0.060, 0.030, 0.010, 0.005;
}
probability ( L_LNLW_MED_PATHO ) {
  table 0.800, 0.120, 0.070, 0.005, 0.005;
}
probability ( L_LNLW_MEDD2_DISP_WD | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (TOTAL, BLOCK) 0.0, 0.0, 0.5, 0.5;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.3, 0.5, 0.2, 0.0;
  (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_MEDD2_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_LNLW_MEDD2_DISP_WD | L_LNLW_MEDD2_DISP_WD, L_DIFFN_MEDD2_DISP ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_DISP_WD | L_DIFFN_LNLW_MEDD2_DISP_WD ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0, 1.0, 0.0, 0.0;
  (MOD) 0.0, 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLBE_MEDD2_DISP_EW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MEDD2_DISP_EW | L_DIFFN_MEDD2_DISP, L_LNLBE_MEDD2_DISP_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_DISP_EWD | L_MEDD2_DISP_WD, L_MEDD2_DISP_EW ) {
  (NO, NO) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000;
  (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, MILD) 0.0001, 0.2215, 0.7732, 0.0052, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.13151315, 0.85768577, 0.01080108, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;
  (SEV, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;
  (NO, MOD) 0.1315, 0.8577, 0.0108, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;
  (SEV, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;
  (NO, SEV) 0.9933, 0.0067, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, SEV) 0.0404, 0.9192, 0.0404, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( L_DIFFN_MEDD2_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLBE_MEDD2_BLOCK_EW ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_MEDD2_BLOCK_EW | L_DIFFN_MEDD2_BLOCK, L_LNLBE_MEDD2_BLOCK_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_AMPR_EW | L_MEDD2_DISP_EWD, L_MEDD2_BLOCK_EW ) {
  (R0_15, NO) 0.00000000, 0.28267173, 0.71642836, 0.00089991, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_25, NO) 0.0000, 0.0000, 0.5547, 0.4268, 0.0183, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, NO) 0.00000000, 0.00000000, 0.00900090, 0.51275128, 0.41524152, 0.05890589, 0.00390039, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, NO) 0.0000, 0.0000, 0.0000, 0.0635, 0.4459, 0.3732, 0.0995, 0.0162, 0.0015, 0.0002, 0.0000, 0.0000;
  (R0_55, NO) 0.00000000, 0.00000000, 0.00000000, 0.00290029, 0.11411141, 0.39513951, 0.31983198, 0.13151315, 0.02980298, 0.00580058, 0.00090009, 0.00000000;
  (R0_65, NO) 0.0000, 0.0000, 0.0000, 0.0001, 0.0136, 0.1578, 0.3285, 0.2966, 0.1404, 0.0483, 0.0135, 0.0012;
  (R0_75, NO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120024, 0.03850770, 0.17463493, 0.30156031, 0.26135227, 0.14472895, 0.06511302, 0.01290258;
  (R0_85, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0062, 0.0580, 0.1828, 0.2779, 0.2392, 0.1675, 0.0683;
  (R0_95, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0009, 0.0157, 0.0823, 0.2012, 0.2516, 0.2559, 0.1924;
  (R0_15, MILD) 0.0000, 0.9103, 0.0897, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MILD) 0.0000, 0.0004, 0.8945, 0.1036, 0.0015, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MILD) 0.00000000, 0.00000000, 0.11401140, 0.71697170, 0.15981598, 0.00890089, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, MILD) 0.0000, 0.0000, 0.0026, 0.3111, 0.5155, 0.1499, 0.0190, 0.0018, 0.0001, 0.0000, 0.0000, 0.0000;
  (R0_55, MILD) 0.0000, 0.0000, 0.0000, 0.0451, 0.3717, 0.4039, 0.1433, 0.0313, 0.0041, 0.0005, 0.0001, 0.0000;
  (R0_65, MILD) 0.0000, 0.0000, 0.0000, 0.0035, 0.1122, 0.3738, 0.3186, 0.1444, 0.0376, 0.0083, 0.0015, 0.0001;
  (R0_75, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.02260226, 0.18901890, 0.33173317, 0.27442744, 0.12521252, 0.04310431, 0.01240124, 0.00130013;
  (R0_85, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00310031, 0.06150615, 0.21092109, 0.30513051, 0.23442344, 0.12171217, 0.05280528, 0.01040104;
  (R0_95, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0166, 0.1002, 0.2325, 0.2777, 0.2039, 0.1251, 0.0436;
  (R0_15, MOD) 0.0000, 0.9974, 0.0026, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MOD) 0.0000, 0.3856, 0.6048, 0.0095, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MOD) 0.0000, 0.0073, 0.8191, 0.1638, 0.0095, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_45, MOD) 0.0000, 0.0001, 0.3860, 0.4993, 0.1036, 0.0101, 0.0008, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_55, MOD) 0.0000, 0.0000, 0.0913, 0.5259, 0.3027, 0.0681, 0.0104, 0.0014, 0.0002, 0.0000, 0.0000, 0.0000;
  (R0_65, MOD) 0.00000000, 0.00000000, 0.01499850, 0.30686931, 0.42035796, 0.19288071, 0.05119488, 0.01139886, 0.00189981, 0.00029997, 0.00009999, 0.00000000;
  (R0_75, MOD) 0.0000, 0.0000, 0.0023, 0.1333, 0.3728, 0.3075, 0.1292, 0.0421, 0.0099, 0.0024, 0.0005, 0.0000;
  (R0_85, MOD) 0.00000000, 0.00000000, 0.00030003, 0.04440444, 0.24132413, 0.34313431, 0.22082208, 0.10251025, 0.03370337, 0.01040104, 0.00300030, 0.00040004;
  (R0_95, MOD) 0.0000, 0.0000, 0.0000, 0.0138, 0.1311, 0.2956, 0.2729, 0.1711, 0.0745, 0.0286, 0.0104, 0.0020;
  (R0_15, SEV) 0.00000000, 0.94670533, 0.04919508, 0.00349965, 0.00049995, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_25, SEV) 0.00000000, 0.87211279, 0.11098890, 0.01349865, 0.00249975, 0.00059994, 0.00019998, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_35, SEV) 0.00000000, 0.77825565, 0.18013603, 0.03120624, 0.00750150, 0.00200040, 0.00060012, 0.00020004, 0.00010002, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, SEV) 0.0000, 0.6780, 0.2436, 0.0548, 0.0156, 0.0050, 0.0018, 0.0007, 0.0003, 0.0001, 0.0001, 0.0000;
  (R0_55, SEV) 0.0000, 0.5789, 0.2957, 0.0820, 0.0270, 0.0096, 0.0037, 0.0017, 0.0007, 0.0004, 0.0002, 0.0001;
  (R0_65, SEV) 0.0000, 0.4850, 0.3340, 0.1106, 0.0411, 0.0162, 0.0068, 0.0033, 0.0015, 0.0008, 0.0004, 0.0003;
  (R0_75, SEV) 0.00000000, 0.40484048, 0.35663566, 0.13671367, 0.05620562, 0.02400240, 0.01080108, 0.00540054, 0.00270027, 0.00140014, 0.00080008, 0.00050005;
  (R0_85, SEV) 0.00000000, 0.33163367, 0.36712657, 0.16126775, 0.07268546, 0.03349330, 0.01599680, 0.00849830, 0.00439912, 0.00239952, 0.00149970, 0.00099980;
  (R0_95, SEV) 0.0000, 0.2731, 0.3669, 0.1808, 0.0881, 0.0434, 0.0217, 0.0120, 0.0064, 0.0036, 0.0023, 0.0017;
  (R0_15, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_25, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_35, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_45, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_55, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_65, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_75, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_85, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_95, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLBE_MEDD2_LD_EW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MEDD2_LD_EW | L_LNLBE_MEDD2_LD_EW ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0, 1.0, 0.0, 0.0;
  (MOD) 0.0, 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLBE_MEDD2_RD_EW ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MEDD2_RD_EW | L_LNLBE_MEDD2_RD_EW ) {
  (NO) 1.0, 0.0, 0.0;
  (MOD) 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_LSLOW_EW | L_MEDD2_LD_EW, L_MEDD2_RD_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.00840084, 0.01190119, 0.06190619, 0.91739174, 0.00040004;
  (MILD, MOD) 0.00690069, 0.01000100, 0.05350535, 0.92869287, 0.00090009;
  (MOD, MOD) 0.00559944, 0.00829917, 0.04519548, 0.93890611, 0.00199980;
  (SEV, MOD) 0.0023, 0.0036, 0.0217, 0.8326, 0.1398;
  (NO, SEV) 0.00529947, 0.00619938, 0.02639736, 0.20817918, 0.75392461;
  (MILD, SEV) 0.0049, 0.0057, 0.0244, 0.1966, 0.7684;
  (MOD, SEV) 0.00440044, 0.00520052, 0.02240224, 0.18391839, 0.78407841;
  (SEV, SEV) 0.0028, 0.0033, 0.0145, 0.1304, 0.8490;
}
probability ( L_LNLW_MEDD2_SALOSS_WD | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_DIFFN_MEDD2_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_DIFFN_LNLW_MEDD2_SALOSS | L_LNLW_MEDD2_SALOSS_WD, L_DIFFN_MEDD2_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLBE_MEDD2_SALOSS_EW ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_MEDD2_SALOSS | L_DIFFN_LNLW_MEDD2_SALOSS, L_LNLBE_MEDD2_SALOSS_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_MEDD2_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( L_MEDD2_DIFSLOW_EW | L_DIFFN_MEDD2_DIFSLOW ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0, 1.0, 0.0, 0.0;
  (MOD) 0.0, 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_DSLOW_EW | L_MEDD2_SALOSS, L_MEDD2_DIFSLOW_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_ALLCV_EW | L_MEDD2_LSLOW_EW, L_MEDD2_DSLOW_EW ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.0264, 0.9149, 0.0587, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S60) 0.0000, 0.0218, 0.9469, 0.0313, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.00030003, 0.00190019, 0.01400140, 0.07880788, 0.32383238, 0.45964596, 0.12121212, 0.00030003, 0.00000000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00410041, 0.03840384, 0.21452145, 0.74237424, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0006, 0.0944, 0.8841, 0.0209, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0007, 0.2183, 0.7790, 0.0020, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00009999, 0.00039996, 0.00429957, 0.03209679, 0.19558044, 0.50484952, 0.26037396, 0.00229977, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00210021, 0.02390239, 0.16481648, 0.80898090, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.0000, 0.0018, 0.1956, 0.7860, 0.0166, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0077, 0.4577, 0.5345, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, M_S44) 0.0000, 0.0001, 0.0007, 0.0074, 0.0735, 0.4044, 0.4938, 0.0201, 0.0000;
  (V_SEV, M_S44) 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0123, 0.1119, 0.8749, 0.0000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.0000, 0.0000, 0.0026, 0.2655, 0.7316, 0.0003, 0.0000, 0.0000, 0.0000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0166, 0.9182, 0.0652, 0.0000, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0010, 0.0172, 0.2179, 0.6479, 0.1159, 0.0000;
  (V_SEV, M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0055, 0.0699, 0.9243, 0.0000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.0000, 0.0000, 0.0000, 0.0044, 0.5355, 0.4600, 0.0001, 0.0000, 0.0000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0398, 0.9460, 0.0142, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00179982, 0.05589441, 0.46005399, 0.48215178, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0021, 0.0390, 0.9588, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0047, 0.5053, 0.4900, 0.0000, 0.0000;
  (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.1104, 0.8881, 0.0013, 0.0000;
  (SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0041, 0.1033, 0.8925, 0.0000;
  (V_SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00069993, 0.01889811, 0.98040196, 0.00000000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0218, 0.8352, 0.1430, 0.0000;
  (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.3203, 0.6777, 0.0000;
  (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0194, 0.9803, 0.0000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0093, 0.9905, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0003, 0.0010, 0.0036, 0.0154, 0.0712, 0.2467, 0.6617, 0.0000;
  (MOD, M_S08) 0.00000000, 0.00010001, 0.00050005, 0.00190019, 0.00930093, 0.05040504, 0.20722072, 0.73057306, 0.00000000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0026, 0.0190, 0.1153, 0.8626, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0062, 0.0562, 0.9369, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_CV_EW | L_MEDD2_ALLCV_EW ) {
  (M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0172, 0.1126, 0.2484, 0.3210, 0.1935, 0.0803, 0.0258;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00099990, 0.01489851, 0.09959004, 0.22617738, 0.29337066, 0.22617738, 0.10198980, 0.02889711, 0.00649935, 0.00139986;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.03170317, 0.14451445, 0.26392639, 0.29252925, 0.16701670, 0.06690669, 0.02450245, 0.00540054, 0.00090009, 0.00010001, 0.00000000;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0189, 0.1567, 0.3371, 0.2862, 0.1429, 0.0465, 0.0095, 0.0014, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01049895, 0.15648435, 0.37786221, 0.30066993, 0.12518748, 0.02449755, 0.00419958, 0.00049995, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0045, 0.1674, 0.4536, 0.2834, 0.0774, 0.0118, 0.0017, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.01090109, 0.42284228, 0.44464446, 0.10661066, 0.01370137, 0.00120012, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.0000, 0.0199, 0.8041, 0.1629, 0.0125, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLW_MEDD2_BLOCK_WD | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.3, 0.6, 0.1, 0.0, 0.0;
  (SEV, DEMY) 0.1, 0.5, 0.3, 0.1, 0.0;
  (TOTAL, DEMY) 0.00, 0.05, 0.20, 0.55, 0.20;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.2, 0.6, 0.2;
  (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_MEDD2_BLOCK_WD | L_LNLW_MEDD2_BLOCK_WD, L_DIFFN_MEDD2_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_EFFAXLOSS | L_MEDD2_BLOCK_WD, L_MEDD2_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_ALLAMP_WD | L_MEDD2_DISP_WD, L_MEDD2_EFFAXLOSS ) {
  (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;
  (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;
  (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;
  (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;
  (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;
  (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;
  (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;
  (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;
  (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;
  (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_MEDD2_AMP_WD | L_MEDD2_ALLAMP_WD ) {
  (ZERO) 0.75007501, 0.18781878, 0.04760476, 0.01120112, 0.00260026, 0.00060006, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (A0_01) 0.3184, 0.2478, 0.1780, 0.1158, 0.0688, 0.0380, 0.0189, 0.0086, 0.0036, 0.0014, 0.0005, 0.0002, 0.0000, 0.0000, 0.0000;
  (A0_10) 0.01170117, 0.03920392, 0.09350935, 0.16761676, 0.21892189, 0.20952095, 0.14651465, 0.07470747, 0.02870287, 0.00780078, 0.00160016, 0.00020002, 0.00000000, 0.00000000, 0.00000000;
  (A0_30) 0.00000000, 0.00000000, 0.00009999, 0.00129987, 0.01089891, 0.05269473, 0.15628437, 0.27017298, 0.27427257, 0.16358364, 0.05689431, 0.01219878, 0.00149985, 0.00009999, 0.00000000;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00219978, 0.02179782, 0.10328967, 0.25917408, 0.32546745, 0.20917908, 0.06709329, 0.01079892, 0.00089991;
  (A1_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0072, 0.0656, 0.2449, 0.3735, 0.2388, 0.0624, 0.0072;
}
probability ( L_LNLW_MEDD2_LD_WD | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;
  (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MEDD2_LD_WD | L_LNLW_MEDD2_LD_WD ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0, 1.0, 0.0, 0.0;
  (MOD) 0.0, 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLW_MEDD2_RD_WD | L_LNLW_MED_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( L_MEDD2_RD_WD | L_LNLW_MEDD2_RD_WD ) {
  (NO) 1.0, 0.0, 0.0;
  (MOD) 0.0, 1.0, 0.0;
  (SEV) 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_LSLOW_WD | L_MEDD2_LD_WD, L_MEDD2_RD_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;
  (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;
  (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;
  (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;
  (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;
  (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;
  (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;
}
probability ( L_LNLBE_MEDD2_DIFSLOW_WD ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MEDD2_DIFSLOW_WD | L_LNLBE_MEDD2_DIFSLOW_WD, L_DIFFN_MEDD2_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( L_MEDD2_DSLOW_WD | L_MEDD2_SALOSS, L_MEDD2_DIFSLOW_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_ALLCV_WD | L_MEDD2_LSLOW_WD, L_MEDD2_DSLOW_WD ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;
  (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;
  (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;
  (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;
  (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;
  (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;
  (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;
  (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;
  (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;
  (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MEDD2_CV_WD | L_MEDD2_ALLCV_WD ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.01089891, 0.09839016, 0.31656834, 0.35316468, 0.17488251, 0.04029597, 0.00559944;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00849915, 0.07679232, 0.28107189, 0.33226677, 0.20117988, 0.08159184, 0.01609839, 0.00209979, 0.00019998;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.01390139, 0.11201120, 0.29182918, 0.31343134, 0.19151915, 0.06100610, 0.01300130, 0.00280028, 0.00030003, 0.00000000, 0.00000000;
  (M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00290029, 0.07060706, 0.31513151, 0.38363836, 0.17091709, 0.04760476, 0.00820082, 0.00090009, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.03939606, 0.31846815, 0.41945805, 0.17558244, 0.04189581, 0.00449955, 0.00049995, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.00000000, 0.00000000, 0.00000000, 0.01569843, 0.31446855, 0.46605339, 0.17158284, 0.02909709, 0.00279972, 0.00029997, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S14) 0.00000000, 0.00000000, 0.03080308, 0.57695770, 0.33983398, 0.04820482, 0.00400040, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.00000000, 0.04650465, 0.84828483, 0.09990999, 0.00510051, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_MED_BLOCK | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLBE_MED_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_MED_BLOCK_EW | L_DIFFN_MED_BLOCK, L_LNLBE_MED_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MED_AMPR_EW | L_MED_BLOCK_EW ) {
  (NO) 0.0879, 0.4192, 0.4232, 0.0693, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD) 0.00189981, 0.03439656, 0.25667433, 0.52914709, 0.17348265, 0.00439956, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD) 0.0001, 0.0010, 0.0076, 0.0403, 0.1720, 0.3730, 0.3420, 0.0633, 0.0007, 0.0000, 0.0000, 0.0000;
  (SEV) 0.00090009, 0.00150015, 0.00260026, 0.00490049, 0.00950095, 0.01760176, 0.03470347, 0.07680768, 0.16681668, 0.34143414, 0.34323432, 0.00000000;
  (TOTAL) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLT1_APB_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLLP_APB_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLT1_LP_APB_MALOSS | L_LNLT1_APB_MALOSS, L_LNLLP_APB_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLBE_APB_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLT1_LP_BE_APB_MALOSS | L_LNLT1_LP_APB_MALOSS, L_LNLBE_APB_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLW_APB_MALOSS | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_DIFFN_APB_MALOSS | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_DIFFN_LNLW_APB_MALOSS | L_LNLW_APB_MALOSS, L_DIFFN_APB_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_APB_MALOSS | L_LNLT1_LP_BE_APB_MALOSS, L_DIFFN_LNLW_APB_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;
  (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;
  (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( L_DIFFN_MED_DIFSLOW | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( L_MED_DIFSLOW_EW | L_DIFFN_MED_DIFSLOW ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0126, 0.9869, 0.0005, 0.0000;
  (MOD) 0.0000, 0.0179, 0.9821, 0.0000;
  (SEV) 0.0000, 0.0003, 0.0252, 0.9745;
}
probability ( L_MED_DCV_EW | L_APB_MALOSS, L_MED_DIFSLOW_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;
  (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;
  (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;
  (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;
}
probability ( L_MED_LD_EW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MED_RD_EW ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MED_RDLDCV_EW | L_MED_LD_EW, L_MED_RD_EW ) {
  (NO, NO) 0.90449045, 0.09530953, 0.00020002, 0.00000000, 0.00000000, 0.00000000;
  (MILD, NO) 0.1320, 0.6039, 0.2641, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0139, 0.1839, 0.8022, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.00120012, 0.00670067, 0.05440544, 0.86008601, 0.07760776, 0.00000000;
  (NO, MOD) 0.0115, 0.0333, 0.1509, 0.7319, 0.0724, 0.0000;
  (MILD, MOD) 0.00340034, 0.01220122, 0.06900690, 0.71967197, 0.19531953, 0.00040004;
  (MOD, MOD) 0.0011, 0.0045, 0.0299, 0.5742, 0.3876, 0.0027;
  (SEV, MOD) 0.0001, 0.0002, 0.0018, 0.0914, 0.6093, 0.2972;
  (NO, SEV) 0.00000000, 0.00009999, 0.00109989, 0.14618538, 0.80701930, 0.04559544;
  (MILD, SEV) 0.0000, 0.0000, 0.0002, 0.0581, 0.7950, 0.1467;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0228, 0.6344, 0.3427;
  (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0014, 0.1063, 0.8923;
}
probability ( L_MED_ALLCV_EW | L_MED_DCV_EW, L_MED_RDLDCV_EW ) {
  (M_S60, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56, M_S60) 0.0699, 0.8102, 0.1199, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S60) 0.00000000, 0.04790479, 0.95209521, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S44, M_S60) 0.00089991, 0.00899910, 0.08879112, 0.81861814, 0.08269173, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S36, M_S60) 0.00000000, 0.00000000, 0.00050005, 0.10111011, 0.84778478, 0.05060506, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.07910791, 0.90019002, 0.02020202, 0.00000000, 0.00000000, 0.00000000;
  (M_S20, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0734, 0.8393, 0.0867, 0.0000, 0.0000;
  (M_S14, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.09470947, 0.89458946, 0.01040104, 0.00000000;
  (M_S08, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0932, 0.9048, 0.0000;
  (M_S00, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S52) 0.00660066, 0.14551455, 0.83808381, 0.00980098, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S56, M_S52) 0.0005, 0.0239, 0.4369, 0.5387, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S52) 0.0000, 0.0003, 0.0239, 0.9748, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S52) 0.0000, 0.0002, 0.0036, 0.2467, 0.7339, 0.0156, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S36, M_S52) 0.0000, 0.0000, 0.0000, 0.0050, 0.3134, 0.6811, 0.0005, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00710071, 0.54935494, 0.44334433, 0.00020002, 0.00000000, 0.00000000;
  (M_S20, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00910091, 0.52835284, 0.46254625, 0.00000000, 0.00000000;
  (M_S14, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0216, 0.8359, 0.1425, 0.0000;
  (M_S08, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0355, 0.9641, 0.0000;
  (M_S00, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S44) 0.0000, 0.0000, 0.0047, 0.9951, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S56, M_S44) 0.0000, 0.0000, 0.0004, 0.9005, 0.0991, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S44) 0.0000, 0.0000, 0.0000, 0.1288, 0.8712, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.01130113, 0.57115712, 0.41754175, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S36, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0214, 0.9354, 0.0432, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.05649435, 0.93230677, 0.01109889, 0.00000000, 0.00000000;
  (M_S20, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.1430, 0.8551, 0.0015, 0.0000;
  (M_S14, M_S44) 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0019998, 0.3548645, 0.6431357, 0.0000000;
  (M_S08, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0122, 0.9877, 0.0000;
  (M_S00, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56, M_S27) 0.000, 0.000, 0.000, 0.000, 0.000, 0.997, 0.003, 0.000, 0.000, 0.000;
  (M_S52, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2336, 0.7664, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S27) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00259974, 0.99340066, 0.00399960, 0.00000000, 0.00000000;
  (M_S36, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2469, 0.7531, 0.0000, 0.0000;
  (M_S28, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.9939, 0.0041, 0.0000;
  (M_S20, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0314, 0.9686, 0.0000;
  (M_S14, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;
  (M_S08, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;
  (M_S00, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (M_S60, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.1305, 0.8445, 0.0238, 0.0000;
  (M_S56, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0740, 0.8589, 0.0667, 0.0000;
  (M_S52, M_S15) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03730373, 0.80288029, 0.15971597, 0.00000000;
  (M_S44, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0070, 0.3517, 0.6413, 0.0000;
  (M_S36, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0581, 0.9416, 0.0000;
  (M_S28, M_S15) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.006, 0.994, 0.000;
  (M_S20, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (M_S14, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S08, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;
  (M_S00, M_S15) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (M_S60, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0321, 0.9677, 0.0000;
  (M_S56, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0184, 0.9815, 0.0000;
  (M_S52, M_S07) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01010101, 0.98989899, 0.00000000;
  (M_S44, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.9958, 0.0000;
  (M_S36, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (M_S28, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;
  (M_S20, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S14, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (M_S08, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S00, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MED_CV_EW | L_MED_ALLCV_EW ) {
  (M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0168, 0.1184, 0.2960, 0.3227, 0.1783, 0.0560, 0.0112;
  (M_S56) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0005, 0.0155, 0.1165, 0.2969, 0.3229, 0.1782, 0.0564, 0.0114, 0.0016;
  (M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0039, 0.0589, 0.2434, 0.3586, 0.2350, 0.0808, 0.0164, 0.0022, 0.0002;
  (M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.0176, 0.1966, 0.2515, 0.2699, 0.1688, 0.0690, 0.0203, 0.0046, 0.0009, 0.0001, 0.0000;
  (M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00560056, 0.09000900, 0.30563056, 0.30933093, 0.20392039, 0.06730673, 0.01520152, 0.00260026, 0.00040004, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0496, 0.2972, 0.4059, 0.2097, 0.0258, 0.0098, 0.0013, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.00000000, 0.00000000, 0.00000000, 0.01789821, 0.29457054, 0.44695530, 0.19018098, 0.04339566, 0.00659934, 0.00029997, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S14) 0.0000, 0.0000, 0.0265, 0.5431, 0.3624, 0.0622, 0.0054, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S08) 0.00000000, 0.12651265, 0.76547655, 0.10061006, 0.00700070, 0.00040004, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 0.9999, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( L_LNLT1_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_LNLLP_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_LNLT1_LP_APB_DE_REGEN | L_LNLT1_APB_DE_REGEN, L_LNLLP_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_LNLBE_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_LNLT1_LP_BE_APB_DE_REGEN | L_LNLT1_LP_APB_DE_REGEN, L_LNLBE_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_DIFFN_APB_DE_REGEN | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
}
probability ( L_LNLW_MED_TIME ) {
  table 0.05, 0.33, 0.60, 0.02;
}
probability ( L_LNLW_APB_DE_REGEN | L_LNLW_MED_SEV, L_LNLW_MED_TIME, L_LNLW_MED_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (TOTAL, CHRONIC, DEMY) 1.0, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (TOTAL, OLD, DEMY) 1.0, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (TOTAL, OLD, BLOCK) 1.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (TOTAL, OLD, AXONAL) 1.0, 0.0;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (TOTAL, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0;
}
probability ( L_DIFFN_LNLW_APB_DE_REGEN | L_DIFFN_APB_DE_REGEN, L_LNLW_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_LNL_DIFFN_APB_DE_REGEN | L_LNLT1_LP_BE_APB_DE_REGEN, L_DIFFN_LNLW_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_MYOP_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_MYDY_APB_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_MYOP_MYDY_APB_DE_REGEN | L_MYOP_APB_DE_REGEN, L_MYDY_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_APB_DE_REGEN | L_LNL_DIFFN_APB_DE_REGEN, L_MYOP_MYDY_APB_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_DE_REGEN_APB_NMT | L_APB_DE_REGEN ) {
  (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;
}
probability ( L_MYAS_APB_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_APB_NMT | L_DE_REGEN_APB_NMT, L_MYAS_APB_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MYDY_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYOP_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYOP_MYDY_APB_MUSIZE | L_MYDY_APB_MUSIZE, L_MYOP_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLW_APB_MUSIZE | L_LNLW_MED_SEV, L_LNLW_MED_TIME, L_LNLW_MED_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_APB_MUSIZE | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_LNLW_APB_MUSIZE | L_LNLW_APB_MUSIZE, L_DIFFN_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLBE_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLLP_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLT1_APB_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLT1_LP_APB_MUSIZE | L_LNLLP_APB_MUSIZE, L_LNLT1_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLT1_LP_BE_APB_MUSIZE | L_LNLBE_APB_MUSIZE, L_LNLT1_LP_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNL_DIFFN_APB_MUSIZE | L_DIFFN_LNLW_APB_MUSIZE, L_LNLT1_LP_BE_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_APB_MUSIZE | L_MYOP_MYDY_APB_MUSIZE, L_LNL_DIFFN_APB_MUSIZE ) {
  (V_SMALL, V_SMALL) 0.9791, 0.0009, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL, V_SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL, V_SMALL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (INCR, V_SMALL) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;
  (LARGE, V_SMALL) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;
  (V_LARGE, V_SMALL) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;
  (V_SMALL, SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL, SMALL) 0.7493, 0.2257, 0.0049, 0.0001, 0.0000, 0.0000, 0.0200;
  (NORMAL, SMALL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;
  (INCR, SMALL) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;
  (LARGE, SMALL) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;
  (V_LARGE, SMALL) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;
  (V_SMALL, NORMAL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SMALL, NORMAL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;
  (NORMAL, NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;
  (V_SMALL, INCR) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;
  (SMALL, INCR) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00359964, 0.16548345, 0.64543546, 0.16548345, 0.01999800;
  (LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;
  (V_LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (V_SMALL, LARGE) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;
  (SMALL, LARGE) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;
  (INCR, LARGE) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;
  (LARGE, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;
  (V_SMALL, V_LARGE) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;
  (SMALL, V_LARGE) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;
  (INCR, V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;
  (V_LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9799, 0.0200;
}
probability ( L_APB_EFFMUS | L_APB_NMT, L_APB_MUSIZE ) {
  (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MIXED, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;
  (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MIXED, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;
  (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;
  (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;
  (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;
  (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;
  (MIXED, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;
  (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;
  (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;
  (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;
  (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;
  (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;
  (MIXED, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;
  (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;
  (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;
  (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;
  (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;
  (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;
  (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;
  (MIXED, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;
  (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;
  (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;
  (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;
  (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;
  (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;
  (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;
  (MIXED, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;
  (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;
  (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;
  (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;
  (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;
  (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;
  (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;
  (MIXED, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;
}
probability ( L_LNLW_MED_BLOCK | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (TOTAL, DEMY) 0.2, 0.2, 0.2, 0.2, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.2, 0.2, 0.2, 0.2, 0.2;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_MED_BLOCK_WA | L_DIFFN_MED_BLOCK, L_LNLW_MED_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_APB_MULOSS | L_MED_BLOCK_WA, L_APB_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;
  (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;
  (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;
  (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;
  (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;
  (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;
  (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;
  (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;
  (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;
  (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;
  (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;
  (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;
  (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( L_APB_ALLAMP_WA | L_APB_EFFMUS, L_APB_MULOSS ) {
  (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;
  (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;
  (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;
  (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;
  (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;
  (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;
  (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;
  (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;
  (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;
  (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;
  (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;
  (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;
  (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;
  (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;
  (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;
  (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;
  (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;
  (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;
  (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;
  (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;
  (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;
  (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;
  (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;
  (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;
  (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;
  (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;
  (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;
  (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;
  (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;
}
probability ( L_MED_AMP_WA | L_APB_ALLAMP_WA ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00030003, 0.28502850, 0.23462346, 0.17811781, 0.12451245, 0.08030803, 0.04760476, 0.02610261, 0.01320132, 0.00610061, 0.00260026, 0.00100010, 0.00040004, 0.00010001, 0.00000000, 0.00000000, 0.00000000;
  (A0_10) 0.00000000, 0.01350135, 0.03690369, 0.07940794, 0.13531353, 0.18181818, 0.19321932, 0.16221622, 0.10771077, 0.05640564, 0.02340234, 0.00760076, 0.00200020, 0.00040004, 0.00010001, 0.00000000, 0.00000000;
  (A0_30) 0.00000000, 0.00000000, 0.00009999, 0.00059994, 0.00359964, 0.01649835, 0.05349465, 0.12328767, 0.20127987, 0.23347665, 0.19218078, 0.11208879, 0.04649535, 0.01369863, 0.00279972, 0.00039996, 0.00000000;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00100010, 0.00690069, 0.03090309, 0.09260926, 0.18551855, 0.24962496, 0.22502250, 0.13581358, 0.05490549, 0.01490149, 0.00270027;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00310031, 0.01900190, 0.07230723, 0.17361736, 0.26222622, 0.24972497, 0.14971497, 0.05670567, 0.01330133;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0017, 0.0093, 0.0355, 0.0964, 0.1860, 0.2545, 0.2471, 0.1693;
  (A4_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0029, 0.0155, 0.0599, 0.1641, 0.3182, 0.4390;
  (A8_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00150015, 0.01150115, 0.06310631, 0.24452445, 0.67926793;
}
probability ( L_MED_LD_WA | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.25, 0.25, 0.25, 0.25;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;
  (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.25, 0.25, 0.25, 0.25;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 0.25, 0.25, 0.25, 0.25;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 0.25, 0.25, 0.25, 0.25;
}
probability ( L_MED_RD_WA | L_LNLW_MED_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( L_MED_RDLDDEL | L_MED_LD_WA, L_MED_RD_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0964, 0.7981, 0.1055, 0.0000, 0.0000;
  (MOD, NO) 0.0032, 0.1270, 0.8698, 0.0000, 0.0000;
  (SEV, NO) 0.00090009, 0.00280028, 0.01470147, 0.98159816, 0.00000000;
  (NO, MOD) 0.0019, 0.5257, 0.4724, 0.0000, 0.0000;
  (MILD, MOD) 0.00019998, 0.04149585, 0.95830417, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0001, 0.0144, 0.9855, 0.0000, 0.0000;
  (SEV, MOD) 0.00019998, 0.00059994, 0.00369963, 0.99550045, 0.00000000;
  (NO, SEV) 0.0002, 0.0304, 0.9694, 0.0000, 0.0000;
  (MILD, SEV) 0.0001, 0.0142, 0.9857, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0090, 0.9808, 0.0102, 0.0000;
  (SEV, SEV) 0.0000, 0.0002, 0.0012, 0.9984, 0.0002;
}
probability ( L_LNLBE_MED_DIFSLOW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MED_DIFSLOW_WA | L_LNLBE_MED_DIFSLOW, L_DIFFN_MED_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;
  (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;
  (MOD, MOD) 0.000, 0.000, 0.002, 0.998;
  (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;
  (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;
}
probability ( L_MED_DCV_WA | L_APB_MALOSS, L_MED_DIFSLOW_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;
  (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;
  (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;
  (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;
}
probability ( L_MED_ALLDEL_WA | L_MED_RDLDDEL, L_MED_DCV_WA ) {
  (MS3_1, M_S60) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S60) 0.05119488, 0.22907709, 0.56624338, 0.15348465, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S60) 0.0001, 0.0035, 0.0632, 0.9326, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S60) 0.00129987, 0.00199980, 0.00439956, 0.01869813, 0.12128787, 0.74792521, 0.10438956, 0.00000000, 0.00000000;
  (MS20_1, M_S60) 0.00010001, 0.00010001, 0.00030003, 0.00090009, 0.00450045, 0.04340434, 0.57675768, 0.37393739, 0.00000000;
  (MS3_1, M_S52) 0.5607, 0.4393, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S52) 0.01929807, 0.12868713, 0.49285071, 0.35916408, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S52) 0.0000, 0.0011, 0.0283, 0.9651, 0.0055, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S52) 0.00100010, 0.00160016, 0.00370037, 0.01610161, 0.10931093, 0.74217422, 0.12611261, 0.00000000, 0.00000000;
  (MS20_1, M_S52) 0.0001, 0.0001, 0.0002, 0.0008, 0.0041, 0.0399, 0.5568, 0.3980, 0.0000;
  (MS3_1, M_S44) 0.0069, 0.7963, 0.1968, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S44) 0.0027, 0.0328, 0.2398, 0.7246, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S44) 0.0000, 0.0002, 0.0075, 0.8889, 0.1034, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S44) 0.00079992, 0.00119988, 0.00279972, 0.01249875, 0.09159084, 0.72352765, 0.16758324, 0.00000000, 0.00000000;
  (MS20_1, M_S44) 0.0001, 0.0001, 0.0002, 0.0007, 0.0034, 0.0348, 0.5239, 0.4368, 0.0000;
  (MS3_1, M_S36) 0.0000, 0.0184, 0.9806, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S36) 0.00010001, 0.00330033, 0.05470547, 0.93819382, 0.00370037, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S36) 0.00000000, 0.00000000, 0.00030003, 0.18501850, 0.81468147, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS10_1, M_S36) 0.00049995, 0.00079992, 0.00179982, 0.00869913, 0.07029297, 0.68023198, 0.23767623, 0.00000000, 0.00000000;
  (MS20_1, M_S36) 0.0001, 0.0001, 0.0001, 0.0005, 0.0027, 0.0287, 0.4783, 0.4895, 0.0000;
  (MS3_1, M_S28) 0.0000, 0.0001, 0.0179, 0.9820, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S28) 0.00000000, 0.00019998, 0.00479952, 0.50394961, 0.49105089, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S28) 0.0000, 0.0000, 0.0000, 0.0032, 0.9968, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S28) 0.0002, 0.0004, 0.0009, 0.0047, 0.0440, 0.5737, 0.3761, 0.0000, 0.0000;
  (MS20_1, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40724072, 0.56955696, 0.00000000;
  (MS3_1, M_S20) 0.0000, 0.0002, 0.0030, 0.1393, 0.8575, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S20) 0.0000, 0.0000, 0.0003, 0.0188, 0.9804, 0.0005, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.93139314, 0.06710671, 0.00000000, 0.00000000, 0.00000000;
  (MS10_1, M_S20) 0.0001, 0.0001, 0.0002, 0.0013, 0.0150, 0.3152, 0.6681, 0.0000, 0.0000;
  (MS20_1, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00080016, 0.01110222, 0.28045609, 0.70754151, 0.00000000;
  (MS3_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0006, 0.8145, 0.1849, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0494, 0.9506, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S14) 0.000, 0.000, 0.000, 0.000, 0.001, 0.999, 0.000, 0.000, 0.000;
  (MS10_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0001, 0.0017, 0.0786, 0.9196, 0.0000, 0.0000;
  (MS20_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0035, 0.1380, 0.8583, 0.0000;
  (MS3_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59735974, 0.39093909, 0.00000000, 0.00000000;
  (MS3_9, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00340034, 0.29902990, 0.69746975, 0.00000000, 0.00000000;
  (MS4_7, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.1112, 0.8881, 0.0000, 0.0000;
  (MS10_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00940094, 0.95939594, 0.03110311, 0.00000000;
  (MS20_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.02530253, 0.97439744, 0.00000000;
  (MS3_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS3_9, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS4_7, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS10_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS20_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MED_LAT_WA | L_MED_ALLDEL_WA ) {
  (MS0_0) 0.00590059, 0.13261326, 0.50325033, 0.32263226, 0.03500350, 0.00060006, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS0_4) 0.00069993, 0.01959804, 0.16898310, 0.42545745, 0.31276872, 0.06719328, 0.00529947, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS0_8) 0.0000, 0.0007, 0.0194, 0.1669, 0.4202, 0.3089, 0.0829, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS1_6) 0.00000000, 0.00000000, 0.00100010, 0.01090109, 0.06350635, 0.19471947, 0.39343934, 0.29212921, 0.04280428, 0.00150015, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS3_2) 0.0001, 0.0003, 0.0011, 0.0034, 0.0090, 0.0210, 0.0528, 0.1370, 0.2128, 0.2375, 0.1826, 0.1229, 0.0179, 0.0016, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS6_4) 0.00009999, 0.00019998, 0.00039996, 0.00079992, 0.00139986, 0.00249975, 0.00509949, 0.01199880, 0.02149785, 0.03539646, 0.08919108, 0.13978602, 0.18288171, 0.28117188, 0.18898110, 0.03589641, 0.00259974, 0.00009999, 0.00000000;
  (MS12_8) 0.0002, 0.0002, 0.0003, 0.0004, 0.0005, 0.0007, 0.0012, 0.0022, 0.0032, 0.0047, 0.0109, 0.0176, 0.0280, 0.0629, 0.1563, 0.2269, 0.2569, 0.2269, 0.0000;
  (MS25_6) 0.00079992, 0.00089991, 0.00109989, 0.00119988, 0.00139986, 0.00169983, 0.00249975, 0.00369963, 0.00459954, 0.00559944, 0.01059894, 0.01559844, 0.02139786, 0.04329567, 0.10068993, 0.16488351, 0.25357464, 0.36646335, 0.00000000;
  (INFIN) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_APB_VOL_ACT ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_APB_FORCE | L_APB_VOL_ACT, L_APB_ALLAMP_WA ) {
  (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;
  (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;
  (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;
  (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;
  (NORMAL, A0_01) 0.00159984, 0.01859814, 0.09359064, 0.26787321, 0.36586341, 0.25247475;
  (REDUCED, A0_01) 0.0002, 0.0034, 0.0260, 0.1312, 0.3333, 0.5059;
  (V_RED, A0_01) 0.0000, 0.0003, 0.0044, 0.0446, 0.2226, 0.7281;
  (ABSENT, A0_01) 0.0000, 0.0000, 0.0005, 0.0098, 0.1058, 0.8839;
  (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;
  (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;
  (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;
  (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;
  (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;
  (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;
  (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;
  (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;
  (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;
  (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;
  (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;
  (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;
  (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;
  (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;
  (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;
  (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;
  (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;
  (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;
  (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;
  (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;
  (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;
  (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;
  (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;
  (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;
}
probability ( L_APB_MUSCLE_VOL | L_APB_MUSIZE, L_APB_MALOSS ) {
  (V_SMALL, NO) 0.9896, 0.0104;
  (SMALL, NO) 0.8137, 0.1863;
  (NORMAL, NO) 0.0209, 0.9791;
  (INCR, NO) 0.009, 0.991;
  (LARGE, NO) 0.003, 0.997;
  (V_LARGE, NO) 0.0004, 0.9996;
  (OTHER, NO) 0.4212, 0.5788;
  (V_SMALL, MILD) 0.9976, 0.0024;
  (SMALL, MILD) 0.9603, 0.0397;
  (NORMAL, MILD) 0.5185, 0.4815;
  (INCR, MILD) 0.1087, 0.8913;
  (LARGE, MILD) 0.0278, 0.9722;
  (V_LARGE, MILD) 0.0046, 0.9954;
  (OTHER, MILD) 0.5185, 0.4815;
  (V_SMALL, MOD) 0.999, 0.001;
  (SMALL, MOD) 0.9893, 0.0107;
  (NORMAL, MOD) 0.9588, 0.0412;
  (INCR, MOD) 0.6377, 0.3623;
  (LARGE, MOD) 0.2716, 0.7284;
  (V_LARGE, MOD) 0.0779, 0.9221;
  (OTHER, MOD) 0.6336, 0.3664;
  (V_SMALL, SEV) 0.9995, 0.0005;
  (SMALL, SEV) 0.9969, 0.0031;
  (NORMAL, SEV) 0.9953, 0.0047;
  (INCR, SEV) 0.9518, 0.0482;
  (LARGE, SEV) 0.8234, 0.1766;
  (V_LARGE, SEV) 0.5986, 0.4014;
  (OTHER, SEV) 0.7685, 0.2315;
  (V_SMALL, TOTAL) 0.9989, 0.0011;
  (SMALL, TOTAL) 0.9984, 0.0016;
  (NORMAL, TOTAL) 0.9984, 0.0016;
  (INCR, TOTAL) 0.9975, 0.0025;
  (LARGE, TOTAL) 0.9965, 0.0035;
  (V_LARGE, TOTAL) 0.9956, 0.0044;
  (OTHER, TOTAL) 0.9857, 0.0143;
  (V_SMALL, OTHER) 0.9363, 0.0637;
  (SMALL, OTHER) 0.8403, 0.1597;
  (NORMAL, OTHER) 0.6534, 0.3466;
  (INCR, OTHER) 0.4689, 0.5311;
  (LARGE, OTHER) 0.3174, 0.6826;
  (V_LARGE, OTHER) 0.1948, 0.8052;
  (OTHER, OTHER) 0.5681, 0.4319;
}
probability ( L_APB_MVA_RECRUIT | L_APB_MULOSS, L_APB_VOL_ACT ) {
  (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;
  (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;
  (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;
  (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;
  (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;
  (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;
  (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;
  (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;
  (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;
  (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;
  (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;
  (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;
  (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;
  (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;
  (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;
  (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;
  (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_APB_MVA_AMP | L_APB_EFFMUS ) {
  (V_SMALL) 0.00, 0.04, 0.96;
  (SMALL) 0.01, 0.15, 0.84;
  (NORMAL) 0.05, 0.90, 0.05;
  (INCR) 0.50, 0.49, 0.01;
  (LARGE) 0.85, 0.15, 0.00;
  (V_LARGE) 0.96, 0.04, 0.00;
  (OTHER) 0.33, 0.34, 0.33;
}
probability ( L_APB_TA_CONCL | L_APB_EFFMUS ) {
  (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;
  (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;
  (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;
  (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;
  (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;
  (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;
  (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;
}
probability ( L_APB_MUPAMP | L_APB_EFFMUS ) {
  (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;
  (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;
  (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;
  (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;
}
probability ( L_APB_QUAN_MUPAMP | L_APB_MUPAMP ) {
  (V_SMALL) 0.00080024, 0.00370111, 0.01350405, 0.03811143, 0.08352506, 0.14254276, 0.18955687, 0.19635891, 0.15834750, 0.09942983, 0.04861458, 0.01850555, 0.00550165, 0.00130039, 0.00020006, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.00000000, 0.00010002, 0.00080016, 0.00370074, 0.01350270, 0.03810762, 0.08351670, 0.14252851, 0.18953791, 0.19633927, 0.15833167, 0.09941988, 0.04860972, 0.01850370, 0.00550110, 0.00130026, 0.00020004, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.00370037, 0.01870187, 0.06390639, 0.14751475, 0.23022302, 0.24312431, 0.17371737, 0.08400840, 0.02750275, 0.00610061, 0.00090009, 0.00010001, 0.00000000, 0.00000000;
  (INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0037, 0.0135, 0.0381, 0.0835, 0.1426, 0.1896, 0.1963, 0.1583, 0.0995, 0.0487, 0.0185, 0.0055, 0.0013;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0038, 0.0136, 0.0383, 0.0841, 0.1435, 0.1909, 0.1977, 0.1594, 0.1001, 0.0490, 0.0187;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0024, 0.0082, 0.0232, 0.0542, 0.1041, 0.1645, 0.2137, 0.2283, 0.2007;
  (OTHER) 0.0045, 0.0078, 0.0127, 0.0197, 0.0289, 0.0403, 0.0531, 0.0664, 0.0787, 0.0883, 0.0939, 0.0946, 0.0903, 0.0817, 0.0700, 0.0569, 0.0438, 0.0319, 0.0221, 0.0144;
}
probability ( L_APB_QUAL_MUPAMP | L_APB_MUPAMP ) {
  (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;
  (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;
  (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;
  (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;
  (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;
  (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;
  (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;
}
probability ( L_APB_MUPDUR | L_APB_EFFMUS ) {
  (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;
  (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;
}
probability ( L_APB_QUAN_MUPDUR | L_APB_MUPDUR ) {
  (V_SMALL) 0.09981996, 0.18333667, 0.24024805, 0.22454491, 0.14972995, 0.07121424, 0.02420484, 0.00580116, 0.00100020, 0.00010002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.01020102, 0.03690369, 0.09510951, 0.17471747, 0.22892289, 0.21402140, 0.14261426, 0.06780678, 0.02300230, 0.00560056, 0.00100010, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.0000, 0.0002, 0.0025, 0.0177, 0.0739, 0.1852, 0.2785, 0.2515, 0.1363, 0.0444, 0.0087, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR) 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000, 0.00000000, 0.00000000;
  (LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000;
  (V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00039996, 0.00179982, 0.00699930, 0.02189781, 0.05409459, 0.10518948, 0.16128387, 0.19498050, 0.18598140, 0.13988601, 0.08289171, 0.03879612, 0.00569943;
  (OTHER) 0.0201, 0.0341, 0.0529, 0.0748, 0.0966, 0.1138, 0.1224, 0.1202, 0.1078, 0.0882, 0.0658, 0.0449, 0.0279, 0.0159, 0.0082, 0.0039, 0.0017, 0.0007, 0.0001;
}
probability ( L_APB_QUAL_MUPDUR | L_APB_MUPDUR ) {
  (V_SMALL) 0.8309, 0.1677, 0.0014;
  (SMALL) 0.49, 0.49, 0.02;
  (NORMAL) 0.1065, 0.7870, 0.1065;
  (INCR) 0.02, 0.49, 0.49;
  (LARGE) 0.0014, 0.1677, 0.8309;
  (V_LARGE) 0.0001, 0.0392, 0.9607;
  (OTHER) 0.2597, 0.4806, 0.2597;
}
probability ( L_APB_QUAN_MUPPOLY | L_APB_DE_REGEN, L_APB_EFFMUS ) {
  (NO, V_SMALL) 0.109, 0.548, 0.343;
  (YES, V_SMALL) 0.004, 0.122, 0.874;
  (NO, SMALL) 0.340, 0.564, 0.096;
  (YES, SMALL) 0.015, 0.261, 0.724;
  (NO, NORMAL) 0.925, 0.075, 0.000;
  (YES, NORMAL) 0.091, 0.526, 0.383;
  (NO, INCR) 0.796, 0.201, 0.003;
  (YES, INCR) 0.061, 0.465, 0.474;
  (NO, LARGE) 0.637, 0.348, 0.015;
  (YES, LARGE) 0.039, 0.396, 0.565;
  (NO, V_LARGE) 0.340, 0.564, 0.096;
  (YES, V_LARGE) 0.015, 0.261, 0.724;
  (NO, OTHER) 0.340, 0.564, 0.096;
  (YES, OTHER) 0.015, 0.261, 0.724;
}
probability ( L_APB_QUAL_MUPPOLY | L_APB_QUAN_MUPPOLY ) {
  (x_12_) 0.95, 0.05;
  (x12_24_) 0.3, 0.7;
  (x_24_) 0.05, 0.95;
}
probability ( L_APB_MUPSATEL | L_APB_DE_REGEN ) {
  (NO) 0.95, 0.05;
  (YES) 0.2, 0.8;
}
probability ( L_APB_MUPINSTAB | L_APB_NMT ) {
  (NO) 0.95, 0.05;
  (MOD_PRE) 0.1, 0.9;
  (SEV_PRE) 0.03, 0.97;
  (MLD_POST) 0.2, 0.8;
  (MOD_POST) 0.1, 0.9;
  (SEV_POST) 0.03, 0.97;
  (MIXED) 0.1, 0.9;
}
probability ( L_APB_REPSTIM_CMAPAMP | L_APB_ALLAMP_WA ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00130013, 0.11591159, 0.12801280, 0.13301330, 0.13001300, 0.11941194, 0.10311031, 0.08380838, 0.06390639, 0.04590459, 0.03100310, 0.01970197, 0.01170117, 0.00660066, 0.00350035, 0.00170017, 0.00080008, 0.00040004, 0.00010001, 0.00010001, 0.00000000;
  (A0_10) 0.00000000, 0.00029997, 0.00129987, 0.00409959, 0.01119888, 0.02609739, 0.05159484, 0.08679132, 0.12468753, 0.15248475, 0.15888411, 0.14108589, 0.10678932, 0.06879312, 0.03779622, 0.01769823, 0.00709929, 0.00239976, 0.00069993, 0.00019998, 0.00000000;
  (A0_30) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00069993, 0.00309969, 0.01109889, 0.03129687, 0.07039296, 0.12478752, 0.17478252, 0.19348065, 0.16928307, 0.11698830, 0.06399360, 0.02759724, 0.00939906, 0.00249975, 0.00049995;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00210021, 0.01070107, 0.03860386, 0.09900990, 0.17961796, 0.23162316, 0.21192119, 0.13751375, 0.06320632, 0.02070207, 0.00470047;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00310031, 0.01900190, 0.07230723, 0.17361736, 0.26222622, 0.24972497, 0.14971497, 0.05670567, 0.01330133;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0007, 0.0037, 0.0151, 0.0464, 0.1065, 0.1843, 0.2393, 0.2335, 0.1704;
  (A4_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00110011, 0.00610061, 0.02490249, 0.07680768, 0.17791779, 0.30893089, 0.40404040;
  (A8_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0005, 0.0038, 0.0208, 0.0860, 0.2659, 0.6229;
}
probability ( L_APB_REPSTIM_DECR | L_APB_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;
  (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MIXED) 0.245, 0.245, 0.245, 0.245, 0.020;
}
probability ( L_APB_REPSTIM_FACILI | L_APB_NMT ) {
  (NO) 0.95, 0.02, 0.01, 0.02;
  (MOD_PRE) 0.010, 0.889, 0.100, 0.001;
  (SEV_PRE) 0.010, 0.080, 0.909, 0.001;
  (MLD_POST) 0.89, 0.08, 0.01, 0.02;
  (MOD_POST) 0.48, 0.50, 0.01, 0.01;
  (SEV_POST) 0.020, 0.949, 0.030, 0.001;
  (MIXED) 0.25, 0.25, 0.25, 0.25;
}
probability ( L_APB_REPSTIM_POST_DECR | L_APB_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;
  (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;
  (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MIXED) 0.23, 0.23, 0.22, 0.22, 0.10;
}
probability ( L_APB_SF_JITTER | L_APB_NMT ) {
  (NO) 0.95, 0.05, 0.00, 0.00;
  (MOD_PRE) 0.02, 0.20, 0.70, 0.08;
  (SEV_PRE) 0.0, 0.1, 0.4, 0.5;
  (MLD_POST) 0.05, 0.70, 0.20, 0.05;
  (MOD_POST) 0.01, 0.19, 0.70, 0.10;
  (SEV_POST) 0.0, 0.1, 0.4, 0.5;
  (MIXED) 0.1, 0.3, 0.3, 0.3;
}
probability ( L_LNLT1_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_LNLLP_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_LNLT1_LP_APB_MUDENS | L_LNLT1_APB_MUDENS, L_LNLLP_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_LNLBE_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_LNLT1_LP_BE_APB_MUDENS | L_LNLT1_LP_APB_MUDENS, L_LNLBE_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_APB_MUDENS | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( L_LNLW_APB_MUDENS | L_LNLW_MED_SEV, L_LNLW_MED_TIME, L_LNLW_MED_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( L_DIFFN_LNLW_APB_MUDENS | L_DIFFN_APB_MUDENS, L_LNLW_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_LNL_DIFFN_APB_MUDENS | L_LNLT1_LP_BE_APB_MUDENS, L_DIFFN_LNLW_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_MYOP_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MYDY_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MYOP_MYDY_APB_MUDENS | L_MYOP_APB_MUDENS, L_MYDY_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_MYAS_APB_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MUSCLE_APB_MUDENS | L_MYOP_MYDY_APB_MUDENS, L_MYAS_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_APB_MUDENS | L_LNL_DIFFN_APB_MUDENS, L_MUSCLE_APB_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_APB_SF_DENSITY | L_APB_MUDENS ) {
  (NORMAL) 0.97, 0.03, 0.00;
  (INCR) 0.05, 0.90, 0.05;
  (V_INCR) 0.01, 0.04, 0.95;
}
probability ( L_LNLT1_APB_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLLP_APB_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLT1_LP_APB_NEUR_ACT | L_LNLT1_APB_NEUR_ACT, L_LNLLP_APB_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLBE_APB_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLT1_LP_BE_APB_NEUR_ACT | L_LNLT1_LP_APB_NEUR_ACT, L_LNLBE_APB_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_APB_NEUR_ACT | DIFFN_M_SEV_DIST, DIFFN_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLW_APB_NEUR_ACT | L_LNLW_MED_SEV, L_LNLW_MED_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_LNLW_APB_NEUR_ACT | L_DIFFN_APB_NEUR_ACT, L_LNLW_APB_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_APB_NEUR_ACT | L_LNLT1_LP_BE_APB_NEUR_ACT, L_DIFFN_LNLW_APB_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_APB_SPONT_NEUR_DISCH | L_APB_NEUR_ACT ) {
  (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;
  (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;
  (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;
  (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;
  (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;
}
probability ( L_MYOP_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYDY_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYOP_MYDY_APB_DENERV | L_MYOP_APB_DENERV, L_MYDY_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_NMT_APB_DENERV | L_APB_NMT ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE) 0.40, 0.45, 0.15, 0.00;
  (SEV_PRE) 0.15, 0.35, 0.35, 0.15;
  (MLD_POST) 0.85, 0.15, 0.00, 0.00;
  (MOD_POST) 0.30, 0.45, 0.20, 0.05;
  (SEV_POST) 0.15, 0.35, 0.35, 0.15;
  (MIXED) 0.25, 0.25, 0.25, 0.25;
}
probability ( L_MUSCLE_APB_DENERV | L_MYOP_MYDY_APB_DENERV, L_NMT_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLT1_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLLP_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLT1_LP_APB_DENERV | L_LNLT1_APB_DENERV, L_LNLLP_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLBE_APB_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLT1_LP_BE_APB_DENERV | L_LNLT1_LP_APB_DENERV, L_LNLBE_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_APB_DENERV | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( L_LNLW_APB_DENERV | L_LNLW_MED_SEV, L_LNLW_MED_TIME, L_LNLW_MED_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( L_DIFFN_LNLW_APB_DENERV | L_DIFFN_APB_DENERV, L_LNLW_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNL_DIFFN_APB_DENERV | L_LNLT1_LP_BE_APB_DENERV, L_DIFFN_LNLW_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_APB_DENERV | L_MUSCLE_APB_DENERV, L_LNL_DIFFN_APB_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_APB_SPONT_DENERV_ACT | L_APB_DENERV ) {
  (NO) 0.98, 0.02, 0.00, 0.00;
  (MILD) 0.07, 0.85, 0.08, 0.00;
  (MOD) 0.01, 0.07, 0.85, 0.07;
  (SEV) 0.00, 0.01, 0.07, 0.92;
}
probability ( L_APB_SPONT_HF_DISCH | L_APB_DENERV ) {
  (NO) 0.99, 0.01;
  (MILD) 0.97, 0.03;
  (MOD) 0.95, 0.05;
  (SEV) 0.93, 0.07;
}
probability ( L_APB_SPONT_INS_ACT | L_APB_DENERV ) {
  (NO) 0.98, 0.02;
  (MILD) 0.1, 0.9;
  (MOD) 0.05, 0.95;
  (SEV) 0.05, 0.95;
}
probability ( L_OTHER_ULND5_DISP ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ULND5_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLE_ULN_SEV ) {
  table 0.945, 0.025, 0.015, 0.010, 0.005;
}
probability ( L_LNLE_ULN_PATHO ) {
  table 0.600, 0.190, 0.200, 0.005, 0.005;
}
probability ( L_LNLE_ULND5_DISP_E | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (TOTAL, BLOCK) 0.0, 0.0, 0.5, 0.5;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.3, 0.5, 0.2, 0.0;
  (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLE_DIFFN_ULND5_DISP_E | L_DIFFN_ULND5_DISP, L_LNLE_ULND5_DISP_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_DISP_E | L_OTHER_ULND5_DISP, L_LNLE_DIFFN_ULND5_DISP_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLW_ULND5_DISP_WD ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_LNLW_ULND5_DISP_WD | L_LNLW_ULND5_DISP_WD, L_DIFFN_ULND5_DISP ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_DISP_WD | L_OTHER_ULND5_DISP, L_DIFFN_LNLW_ULND5_DISP_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_DISP_BEW | L_OTHER_ULND5_DISP, L_DIFFN_ULND5_DISP ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_DISP_BED | L_ULND5_DISP_WD, L_ULND5_DISP_BEW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.0000, 0.0008, 0.0097, 0.9895;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0024, 0.9975;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0000, 0.0000, 0.0004, 0.9996;
  (NO, SEV) 0.0000, 0.0008, 0.0097, 0.9895;
  (MILD, SEV) 0.0000, 0.0001, 0.0024, 0.9975;
  (MOD, SEV) 0.0000, 0.0000, 0.0004, 0.9996;
  (SEV, SEV) 0.0000, 0.0000, 0.0002, 0.9998;
}
probability ( L_ULND5_DISP_EED | L_ULND5_DISP_E, L_ULND5_DISP_BED ) {
  (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0404, 0.9192, 0.0404, 0.0000;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742;
  (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, MILD) 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.3477, 0.6496, 0.0023, 0.0000;
  (MOD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00230023, 0.64986499, 0.34783478;
  (SEV, MILD) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, MOD) 0.0000, 0.0004, 0.3477, 0.6496, 0.0023, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MOD) 0.000, 0.000, 0.000, 0.001, 0.499, 0.499, 0.001, 0.000, 0.000;
  (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.2215, 0.7732, 0.0052;
  (SEV, MOD) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, SEV) 0.4995, 0.4995, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, SEV) 0.0004, 0.3477, 0.6496, 0.0023, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742;
}
probability ( L_OTHER_ULND5_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ULND5_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLE_ULND5_BLOCK_E | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.3, 0.6, 0.1, 0.0, 0.0;
  (SEV, DEMY) 0.1, 0.5, 0.3, 0.1, 0.0;
  (TOTAL, DEMY) 0.00, 0.05, 0.20, 0.55, 0.20;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.2, 0.6, 0.2;
  (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLE_DIFFN_ULND5_BLOCK_E | L_DIFFN_ULND5_BLOCK, L_LNLE_ULND5_BLOCK_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_BLOCK_E | L_OTHER_ULND5_BLOCK, L_LNLE_DIFFN_ULND5_BLOCK_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_AMPR_E | L_ULND5_DISP_EED, L_ULND5_BLOCK_E ) {
  (R0_15, NO) 0.0000, 0.0836, 0.9164, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, NO) 0.0000, 0.0000, 0.5059, 0.4935, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, NO) 0.0000, 0.0000, 0.0000, 0.5217, 0.4707, 0.0076, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_45, NO) 0.00000000, 0.00000000, 0.00000000, 0.00440044, 0.51475148, 0.45034503, 0.02990299, 0.00060006, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_55, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0235, 0.5025, 0.4068, 0.0644, 0.0027, 0.0001, 0.0000, 0.0000;
  (R0_65, NO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.06389361, 0.43315668, 0.40265973, 0.08949105, 0.00999900, 0.00059994, 0.00000000;
  (R0_75, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0026, 0.1020, 0.4076, 0.3533, 0.1151, 0.0191, 0.0003;
  (R0_85, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0080, 0.1231, 0.3775, 0.3319, 0.1488, 0.0107;
  (R0_95, NO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00039996, 0.01909809, 0.17038296, 0.34706529, 0.36056394, 0.10248975;
  (R0_15, MILD) 0.0000, 0.9893, 0.0107, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MILD) 0.0000, 0.0000, 0.9815, 0.0185, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MILD) 0.0000, 0.0000, 0.0129, 0.9294, 0.0575, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_45, MILD) 0.0000, 0.0000, 0.0000, 0.1893, 0.7372, 0.0722, 0.0013, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_55, MILD) 0.0000, 0.0000, 0.0000, 0.0034, 0.3663, 0.5458, 0.0804, 0.0040, 0.0001, 0.0000, 0.0000, 0.0000;
  (R0_65, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0304, 0.4581, 0.4125, 0.0920, 0.0066, 0.0004, 0.0000, 0.0000;
  (R0_75, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.1122, 0.4488, 0.3467, 0.0798, 0.0106, 0.0008, 0.0000;
  (R0_85, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.1733, 0.4244, 0.2869, 0.0885, 0.0158, 0.0003;
  (R0_95, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.0357, 0.2319, 0.3899, 0.2461, 0.0897, 0.0060;
  (R0_15, MOD) 0.0000, 0.9998, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MOD) 0.0000, 0.3122, 0.6860, 0.0018, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MOD) 0.00000000, 0.00070007, 0.89578958, 0.10181018, 0.00170017, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, MOD) 0.0000, 0.0000, 0.3290, 0.6016, 0.0670, 0.0023, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_55, MOD) 0.0000, 0.0000, 0.0363, 0.6123, 0.3112, 0.0376, 0.0025, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_65, MOD) 0.0000, 0.0000, 0.0025, 0.2770, 0.5116, 0.1781, 0.0275, 0.0031, 0.0002, 0.0000, 0.0000, 0.0000;
  (R0_75, MOD) 0.00000000, 0.00000000, 0.00020002, 0.08020802, 0.42864286, 0.35713571, 0.10901090, 0.02180218, 0.00270027, 0.00030003, 0.00000000, 0.00000000;
  (R0_85, MOD) 0.00000000, 0.00000000, 0.00000000, 0.01560156, 0.22332233, 0.41834183, 0.24072407, 0.08170817, 0.01670167, 0.00310031, 0.00050005, 0.00000000;
  (R0_95, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00270027, 0.08970897, 0.33353335, 0.32823282, 0.17421742, 0.05420542, 0.01410141, 0.00310031, 0.00020002;
  (R0_15, SEV) 0.0000, 0.9534, 0.0434, 0.0028, 0.0003, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, SEV) 0.0000, 0.8829, 0.1030, 0.0116, 0.0020, 0.0004, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, SEV) 0.00000000, 0.79044191, 0.17256549, 0.02809438, 0.00639872, 0.00169966, 0.00049990, 0.00019996, 0.00009998, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, SEV) 0.00000000, 0.68976898, 0.23862386, 0.05110511, 0.01390139, 0.00420042, 0.00140014, 0.00060006, 0.00020002, 0.00010001, 0.00010001, 0.00000000;
  (R0_55, SEV) 0.0000, 0.5883, 0.2943, 0.0784, 0.0248, 0.0085, 0.0032, 0.0014, 0.0006, 0.0003, 0.0001, 0.0001;
  (R0_65, SEV) 0.0000, 0.4912, 0.3361, 0.1079, 0.0388, 0.0148, 0.0060, 0.0028, 0.0013, 0.0006, 0.0003, 0.0002;
  (R0_75, SEV) 0.0000, 0.4080, 0.3613, 0.1352, 0.0540, 0.0224, 0.0097, 0.0048, 0.0023, 0.0012, 0.0007, 0.0004;
  (R0_85, SEV) 0.0000, 0.3320, 0.3737, 0.1612, 0.0709, 0.0319, 0.0148, 0.0077, 0.0038, 0.0020, 0.0012, 0.0008;
  (R0_95, SEV) 0.00000000, 0.27137286, 0.37396260, 0.18198180, 0.08699130, 0.04179582, 0.02039796, 0.01109889, 0.00579942, 0.00319968, 0.00199980, 0.00139986;
  (R0_15, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_25, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_35, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_45, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_55, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_65, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_75, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_85, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_95, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_OTHER_ULND5_LD ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLE_ULND5_LD_E | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;
  (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0;
}
probability ( L_ULND5_LD_E | L_OTHER_ULND5_LD, L_LNLE_ULND5_LD_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0186, 0.9584, 0.0230, 0.0000;
  (MOD, NO) 0.0000, 0.0184, 0.9816, 0.0000;
  (SEV, NO) 0.00100010, 0.00310031, 0.03020302, 0.96569657;
  (NO, MILD) 0.0186, 0.9584, 0.0230, 0.0000;
  (MILD, MILD) 0.0000, 0.0304, 0.9696, 0.0000;
  (MOD, MILD) 0.0000, 0.0013, 0.9987, 0.0000;
  (SEV, MILD) 0.00039996, 0.00129987, 0.01409859, 0.98420158;
  (NO, MOD) 0.0000, 0.0184, 0.9816, 0.0000;
  (MILD, MOD) 0.0000, 0.0013, 0.9987, 0.0000;
  (MOD, MOD) 0.00000000, 0.00029997, 0.99960004, 0.00009999;
  (SEV, MOD) 0.0001, 0.0005, 0.0060, 0.9934;
  (NO, SEV) 0.00100010, 0.00310031, 0.03020302, 0.96569657;
  (MILD, SEV) 0.00039996, 0.00129987, 0.01409859, 0.98420158;
  (MOD, SEV) 0.0001, 0.0005, 0.0060, 0.9934;
  (SEV, SEV) 0.00010001, 0.00030003, 0.00380038, 0.99579958;
}
probability ( L_OTHER_ULND5_RD ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_LNLE_ULND5_RD_E | L_LNLE_ULN_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( L_ULND5_RD_E | L_OTHER_ULND5_RD, L_LNLE_ULND5_RD_E ) {
  (NO, NO) 1.0, 0.0, 0.0;
  (MOD, NO) 0.0941, 0.9038, 0.0021;
  (SEV, NO) 0.0677, 0.2563, 0.6760;
  (NO, MOD) 0.0941, 0.9038, 0.0021;
  (MOD, MOD) 0.0140, 0.3525, 0.6335;
  (SEV, MOD) 0.0324, 0.1629, 0.8047;
  (NO, SEV) 0.0677, 0.2563, 0.6760;
  (MOD, SEV) 0.0324, 0.1629, 0.8047;
  (SEV, SEV) 0.0351, 0.1479, 0.8170;
}
probability ( L_ULND5_LSLOW_E | L_ULND5_LD_E, L_ULND5_RD_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.00840084, 0.01190119, 0.06190619, 0.91739174, 0.00040004;
  (MILD, MOD) 0.00690069, 0.01000100, 0.05350535, 0.92869287, 0.00090009;
  (MOD, MOD) 0.00559944, 0.00829917, 0.04519548, 0.93890611, 0.00199980;
  (SEV, MOD) 0.0023, 0.0036, 0.0217, 0.8326, 0.1398;
  (NO, SEV) 0.00529947, 0.00619938, 0.02639736, 0.20817918, 0.75392461;
  (MILD, SEV) 0.0049, 0.0057, 0.0244, 0.1966, 0.7684;
  (MOD, SEV) 0.00440044, 0.00520052, 0.02240224, 0.18391839, 0.78407841;
  (SEV, SEV) 0.0028, 0.0033, 0.0145, 0.1304, 0.8490;
}
probability ( L_OTHER_ULND5_SALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLW_ULND5_SALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ULND5_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_DIFFN_LNLW_ULND5_SALOSS | L_LNLW_ULND5_SALOSS, L_DIFFN_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLE_ULND5_SALOSS | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_LNLLP_ULND5_SALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLLP_E_ULND5_SALOSS | L_LNLE_ULND5_SALOSS, L_LNLLP_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNL_DIFFN_ULND5_SALOSS | L_DIFFN_LNLW_ULND5_SALOSS, L_LNLLP_E_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_SALOSS | L_OTHER_ULND5_SALOSS, L_LNL_DIFFN_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_ULND5_DIFSLOW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLE_ULND5_DIFSLOW | L_LNLE_ULN_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 0.0, 1.0, 0.0;
}
probability ( L_DIFFN_ULND5_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( L_LNLE_DIFFN_ULND5_DIFSLOW_E | L_LNLE_ULND5_DIFSLOW, L_DIFFN_ULND5_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( L_ULND5_DIFSLOW_E | L_OTHER_ULND5_DIFSLOW, L_LNLE_DIFFN_ULND5_DIFSLOW_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( L_ULND5_DSLOW_E | L_ULND5_SALOSS, L_ULND5_DIFSLOW_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_ALLCV_E | L_ULND5_LSLOW_E, L_ULND5_DSLOW_E ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.0264, 0.9149, 0.0587, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S60) 0.0000, 0.0218, 0.9469, 0.0313, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.00030003, 0.00190019, 0.01400140, 0.07880788, 0.32383238, 0.45964596, 0.12121212, 0.00030003, 0.00000000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00410041, 0.03840384, 0.21452145, 0.74237424, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0006, 0.0944, 0.8841, 0.0209, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0007, 0.2183, 0.7790, 0.0020, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00009999, 0.00039996, 0.00429957, 0.03209679, 0.19558044, 0.50484952, 0.26037396, 0.00229977, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00210021, 0.02390239, 0.16481648, 0.80898090, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.0000, 0.0018, 0.1956, 0.7860, 0.0166, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0077, 0.4577, 0.5345, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, M_S44) 0.0000, 0.0001, 0.0007, 0.0074, 0.0735, 0.4044, 0.4938, 0.0201, 0.0000;
  (V_SEV, M_S44) 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0123, 0.1119, 0.8749, 0.0000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.0000, 0.0000, 0.0026, 0.2655, 0.7316, 0.0003, 0.0000, 0.0000, 0.0000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0166, 0.9182, 0.0652, 0.0000, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0010, 0.0172, 0.2179, 0.6479, 0.1159, 0.0000;
  (V_SEV, M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0055, 0.0699, 0.9243, 0.0000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.0000, 0.0000, 0.0000, 0.0044, 0.5355, 0.4600, 0.0001, 0.0000, 0.0000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0398, 0.9460, 0.0142, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00179982, 0.05589441, 0.46005399, 0.48215178, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0021, 0.0390, 0.9588, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0047, 0.5053, 0.4900, 0.0000, 0.0000;
  (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.1104, 0.8881, 0.0013, 0.0000;
  (SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0041, 0.1033, 0.8925, 0.0000;
  (V_SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00069993, 0.01889811, 0.98040196, 0.00000000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0218, 0.8352, 0.1430, 0.0000;
  (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.3203, 0.6777, 0.0000;
  (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0194, 0.9803, 0.0000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0093, 0.9905, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0003, 0.0010, 0.0036, 0.0154, 0.0712, 0.2467, 0.6617, 0.0000;
  (MOD, M_S08) 0.00000000, 0.00010001, 0.00050005, 0.00190019, 0.00930093, 0.05040504, 0.20722072, 0.73057306, 0.00000000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0026, 0.0190, 0.1153, 0.8626, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0062, 0.0562, 0.9369, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_CV_E | L_ULND5_ALLCV_E ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00099990, 0.01149885, 0.04829517, 0.13808619, 0.21487851, 0.24807519, 0.17468253, 0.10678932, 0.05659434;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00049995, 0.00729927, 0.03739626, 0.12428757, 0.19878012, 0.23427657, 0.19878012, 0.12008799, 0.05119488, 0.01989801, 0.00749925;
  (M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0094, 0.0560, 0.1549, 0.2295, 0.2469, 0.1596, 0.0834, 0.0409, 0.0139, 0.0038, 0.0010, 0.0003;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0027, 0.0385, 0.1740, 0.2983, 0.2508, 0.1460, 0.0640, 0.0192, 0.0048, 0.0014, 0.0003, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0226, 0.1786, 0.3397, 0.2748, 0.1364, 0.0366, 0.0090, 0.0018, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0092, 0.1868, 0.4188, 0.2744, 0.0890, 0.0178, 0.0035, 0.0004, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.01789821, 0.42015798, 0.42315768, 0.11728827, 0.01899810, 0.00229977, 0.00019998, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.00000000, 0.02639736, 0.78362164, 0.17308269, 0.01579842, 0.00099990, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_ULND5_DISP_EWD | L_ULND5_DISP_WD, L_ULND5_DISP_BEW ) {
  (NO, NO) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000;
  (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;
  (NO, MILD) 0.0001, 0.2215, 0.7732, 0.0052, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.13151315, 0.85768577, 0.01080108, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;
  (SEV, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;
  (NO, MOD) 0.1315, 0.8577, 0.0108, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;
  (SEV, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;
  (NO, SEV) 0.9933, 0.0067, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, SEV) 0.0404, 0.9192, 0.0404, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( L_ULND5_BLOCK_EW | L_OTHER_ULND5_BLOCK, L_DIFFN_ULND5_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_AMPR_EW | L_ULND5_DISP_EWD, L_ULND5_BLOCK_EW ) {
  (R0_15, NO) 0.00000000, 0.28267173, 0.71642836, 0.00089991, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_25, NO) 0.0000, 0.0000, 0.5547, 0.4268, 0.0183, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, NO) 0.00000000, 0.00000000, 0.00900090, 0.51275128, 0.41524152, 0.05890589, 0.00390039, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, NO) 0.0000, 0.0000, 0.0000, 0.0635, 0.4459, 0.3732, 0.0995, 0.0162, 0.0015, 0.0002, 0.0000, 0.0000;
  (R0_55, NO) 0.00000000, 0.00000000, 0.00000000, 0.00290029, 0.11411141, 0.39513951, 0.31983198, 0.13151315, 0.02980298, 0.00580058, 0.00090009, 0.00000000;
  (R0_65, NO) 0.0000, 0.0000, 0.0000, 0.0001, 0.0136, 0.1578, 0.3285, 0.2966, 0.1404, 0.0483, 0.0135, 0.0012;
  (R0_75, NO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120024, 0.03850770, 0.17463493, 0.30156031, 0.26135227, 0.14472895, 0.06511302, 0.01290258;
  (R0_85, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0062, 0.0580, 0.1828, 0.2779, 0.2392, 0.1675, 0.0683;
  (R0_95, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0009, 0.0157, 0.0823, 0.2012, 0.2516, 0.2559, 0.1924;
  (R0_15, MILD) 0.0000, 0.9103, 0.0897, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MILD) 0.0000, 0.0004, 0.8945, 0.1036, 0.0015, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MILD) 0.00000000, 0.00000000, 0.11401140, 0.71697170, 0.15981598, 0.00890089, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, MILD) 0.0000, 0.0000, 0.0026, 0.3111, 0.5155, 0.1499, 0.0190, 0.0018, 0.0001, 0.0000, 0.0000, 0.0000;
  (R0_55, MILD) 0.0000, 0.0000, 0.0000, 0.0451, 0.3717, 0.4039, 0.1433, 0.0313, 0.0041, 0.0005, 0.0001, 0.0000;
  (R0_65, MILD) 0.0000, 0.0000, 0.0000, 0.0035, 0.1122, 0.3738, 0.3186, 0.1444, 0.0376, 0.0083, 0.0015, 0.0001;
  (R0_75, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.02260226, 0.18901890, 0.33173317, 0.27442744, 0.12521252, 0.04310431, 0.01240124, 0.00130013;
  (R0_85, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00310031, 0.06150615, 0.21092109, 0.30513051, 0.23442344, 0.12171217, 0.05280528, 0.01040104;
  (R0_95, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0166, 0.1002, 0.2325, 0.2777, 0.2039, 0.1251, 0.0436;
  (R0_15, MOD) 0.0000, 0.9974, 0.0026, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_25, MOD) 0.0000, 0.3856, 0.6048, 0.0095, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_35, MOD) 0.0000, 0.0073, 0.8191, 0.1638, 0.0095, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_45, MOD) 0.0000, 0.0001, 0.3860, 0.4993, 0.1036, 0.0101, 0.0008, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (R0_55, MOD) 0.0000, 0.0000, 0.0913, 0.5259, 0.3027, 0.0681, 0.0104, 0.0014, 0.0002, 0.0000, 0.0000, 0.0000;
  (R0_65, MOD) 0.00000000, 0.00000000, 0.01499850, 0.30686931, 0.42035796, 0.19288071, 0.05119488, 0.01139886, 0.00189981, 0.00029997, 0.00009999, 0.00000000;
  (R0_75, MOD) 0.0000, 0.0000, 0.0023, 0.1333, 0.3728, 0.3075, 0.1292, 0.0421, 0.0099, 0.0024, 0.0005, 0.0000;
  (R0_85, MOD) 0.00000000, 0.00000000, 0.00030003, 0.04440444, 0.24132413, 0.34313431, 0.22082208, 0.10251025, 0.03370337, 0.01040104, 0.00300030, 0.00040004;
  (R0_95, MOD) 0.0000, 0.0000, 0.0000, 0.0138, 0.1311, 0.2956, 0.2729, 0.1711, 0.0745, 0.0286, 0.0104, 0.0020;
  (R0_15, SEV) 0.00000000, 0.94670533, 0.04919508, 0.00349965, 0.00049995, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_25, SEV) 0.00000000, 0.87211279, 0.11098890, 0.01349865, 0.00249975, 0.00059994, 0.00019998, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (R0_35, SEV) 0.00000000, 0.77825565, 0.18013603, 0.03120624, 0.00750150, 0.00200040, 0.00060012, 0.00020004, 0.00010002, 0.00000000, 0.00000000, 0.00000000;
  (R0_45, SEV) 0.0000, 0.6780, 0.2436, 0.0548, 0.0156, 0.0050, 0.0018, 0.0007, 0.0003, 0.0001, 0.0001, 0.0000;
  (R0_55, SEV) 0.0000, 0.5789, 0.2957, 0.0820, 0.0270, 0.0096, 0.0037, 0.0017, 0.0007, 0.0004, 0.0002, 0.0001;
  (R0_65, SEV) 0.0000, 0.4850, 0.3340, 0.1106, 0.0411, 0.0162, 0.0068, 0.0033, 0.0015, 0.0008, 0.0004, 0.0003;
  (R0_75, SEV) 0.00000000, 0.40484048, 0.35663566, 0.13671367, 0.05620562, 0.02400240, 0.01080108, 0.00540054, 0.00270027, 0.00140014, 0.00080008, 0.00050005;
  (R0_85, SEV) 0.00000000, 0.33163367, 0.36712657, 0.16126775, 0.07268546, 0.03349330, 0.01599680, 0.00849830, 0.00439912, 0.00239952, 0.00149970, 0.00099980;
  (R0_95, SEV) 0.0000, 0.2731, 0.3669, 0.1808, 0.0881, 0.0434, 0.0217, 0.0120, 0.0064, 0.0036, 0.0023, 0.0017;
  (R0_15, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_25, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_35, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_45, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_55, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_65, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_75, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_85, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (R0_95, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_ULND5_DIFSLOW_EW | L_OTHER_ULND5_DIFSLOW, L_DIFFN_ULND5_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( L_ULND5_DSLOW_EW | L_ULND5_SALOSS, L_ULND5_DIFSLOW_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_ALLCV_EW | L_ULND5_DSLOW_EW ) {
  (M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S52) 0.02689731, 0.97130287, 0.00179982, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S44) 0.0003, 0.0598, 0.8924, 0.0475, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S36) 0.0000, 0.0001, 0.0637, 0.9111, 0.0251, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0001, 0.0544, 0.9384, 0.0071, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0486, 0.8875, 0.0637, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.09500950, 0.89738974, 0.00740074, 0.00000000;
  (M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09730973, 0.28552855, 0.58335834, 0.00000000;
  (M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_CV_EW | L_ULND5_ALLCV_EW ) {
  (M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0019, 0.0226, 0.0890, 0.2126, 0.2696, 0.2267, 0.1139, 0.0467, 0.0169;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00089991, 0.01459854, 0.07029297, 0.19968003, 0.25647435, 0.22567743, 0.14648535, 0.06149385, 0.01829817, 0.00479952, 0.00129987;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00070014, 0.01790358, 0.09681936, 0.22414483, 0.26935387, 0.22234447, 0.10832166, 0.04080816, 0.01520304, 0.00360072, 0.00070014, 0.00010002, 0.00000000;
  (M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00510102, 0.06701340, 0.24964993, 0.33976795, 0.21144229, 0.09141828, 0.02840568, 0.00600120, 0.00100020, 0.00020004, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.03970397, 0.25872587, 0.37933793, 0.22232223, 0.08110811, 0.01530153, 0.00270027, 0.00040004, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0161, 0.2633, 0.4429, 0.2163, 0.0524, 0.0077, 0.0012, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.02939706, 0.51014899, 0.37516248, 0.07529247, 0.00909909, 0.00079992, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.0000, 0.0411, 0.8200, 0.1295, 0.0090, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLW_ULND5_BLOCK_WD ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_LNLW_ULND5_BLOCK_WD | L_LNLW_ULND5_BLOCK_WD, L_DIFFN_ULND5_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_BLOCK_WD | L_OTHER_ULND5_BLOCK, L_DIFFN_LNLW_ULND5_BLOCK_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_EFFAXLOSS | L_ULND5_BLOCK_WD, L_ULND5_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_ALLAMP_WD | L_ULND5_DISP_WD, L_ULND5_EFFAXLOSS ) {
  (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;
  (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;
  (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;
  (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;
  (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;
  (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;
  (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;
  (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;
  (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;
  (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_ULND5_AMP_WD | L_ULND5_ALLAMP_WD ) {
  (ZERO) 0.75777578, 0.18381838, 0.04520452, 0.01030103, 0.00230023, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (A0_01) 0.37932414, 0.26144771, 0.16726655, 0.09678064, 0.05118976, 0.02539492, 0.01129774, 0.00459908, 0.00179964, 0.00059988, 0.00019996, 0.00009998, 0.00000000, 0.00000000, 0.00000000;
  (A0_10) 0.0652, 0.1312, 0.1955, 0.2207, 0.1859, 0.1188, 0.0562, 0.0198, 0.0054, 0.0011, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (A0_30) 0.00000000, 0.00070007, 0.00550055, 0.02960296, 0.09970997, 0.20582058, 0.27182718, 0.22332233, 0.11711171, 0.03770377, 0.00760076, 0.00100010, 0.00010001, 0.00000000, 0.00000000;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01070107, 0.05790579, 0.17431743, 0.28932893, 0.27272727, 0.14271427, 0.04330433, 0.00710071, 0.00060006, 0.00000000;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00370037, 0.03370337, 0.14181418, 0.30313031, 0.31273127, 0.16041604, 0.03930393, 0.00470047, 0.00030003;
}
probability ( L_LNLW_ULND5_LD_WD ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_ULND5_LD_WD | L_OTHER_ULND5_LD, L_LNLW_ULND5_LD_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0186, 0.9584, 0.0230, 0.0000;
  (MOD, NO) 0.0000, 0.0184, 0.9816, 0.0000;
  (SEV, NO) 0.00100010, 0.00310031, 0.03020302, 0.96569657;
  (NO, MILD) 0.0186, 0.9584, 0.0230, 0.0000;
  (MILD, MILD) 0.0000, 0.0304, 0.9696, 0.0000;
  (MOD, MILD) 0.0000, 0.0013, 0.9987, 0.0000;
  (SEV, MILD) 0.00039996, 0.00129987, 0.01409859, 0.98420158;
  (NO, MOD) 0.0000, 0.0184, 0.9816, 0.0000;
  (MILD, MOD) 0.0000, 0.0013, 0.9987, 0.0000;
  (MOD, MOD) 0.00000000, 0.00029997, 0.99960004, 0.00009999;
  (SEV, MOD) 0.0001, 0.0005, 0.0060, 0.9934;
  (NO, SEV) 0.00100010, 0.00310031, 0.03020302, 0.96569657;
  (MILD, SEV) 0.00039996, 0.00129987, 0.01409859, 0.98420158;
  (MOD, SEV) 0.0001, 0.0005, 0.0060, 0.9934;
  (SEV, SEV) 0.00010001, 0.00030003, 0.00380038, 0.99579958;
}
probability ( L_LNLW_ULND5_RD_WD ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_ULND5_RD_WD | L_OTHER_ULND5_RD, L_LNLW_ULND5_RD_WD ) {
  (NO, NO) 1.0, 0.0, 0.0;
  (MOD, NO) 0.0139, 0.9821, 0.0040;
  (SEV, NO) 0.0000000, 0.0150015, 0.9849985;
  (NO, MOD) 0.0139, 0.9821, 0.0040;
  (MOD, MOD) 0.0002, 0.1057, 0.8941;
  (SEV, MOD) 0.0000, 0.0037, 0.9963;
  (NO, SEV) 0.00070007, 0.09680968, 0.90249025;
  (MOD, SEV) 0.00000000, 0.01550155, 0.98449845;
  (SEV, SEV) 0.000, 0.003, 0.997;
}
probability ( L_ULND5_LSLOW_WD | L_ULND5_LD_WD, L_ULND5_RD_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;
  (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;
  (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;
  (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;
  (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;
  (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;
  (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;
}
probability ( L_LNLE_DIFFN_ULND5_DIFSLOW_WD | L_LNLE_ULND5_DIFSLOW, L_DIFFN_ULND5_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( L_ULND5_DIFSLOW_WD | L_OTHER_ULND5_DIFSLOW, L_LNLE_DIFFN_ULND5_DIFSLOW_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( L_ULND5_DSLOW_WD | L_ULND5_SALOSS, L_ULND5_DIFSLOW_WD ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;
  (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;
  (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_ALLCV_WD | L_ULND5_LSLOW_WD, L_ULND5_DSLOW_WD ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;
  (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;
  (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;
  (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;
  (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;
  (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;
  (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;
  (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;
  (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;
  (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULND5_CV_WD | L_ULND5_ALLCV_WD ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00089991, 0.02329767, 0.13318668, 0.31956804, 0.31956804, 0.15698430, 0.03979602, 0.00669933;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.01600160, 0.10241024, 0.29602960, 0.31003100, 0.18081808, 0.07530753, 0.01620162, 0.00240024, 0.00030003;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00050010, 0.02150430, 0.13622725, 0.29945989, 0.29575915, 0.17453491, 0.05621124, 0.01250250, 0.00290058, 0.00040008, 0.00000000, 0.00000000;
  (M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00520052, 0.09030903, 0.32943294, 0.36503650, 0.15671567, 0.04420442, 0.00800080, 0.00100010, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00039996, 0.05179482, 0.33946605, 0.40265973, 0.16188381, 0.03899610, 0.00429957, 0.00049995, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0206, 0.3368, 0.4519, 0.1606, 0.0272, 0.0026, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.00000000, 0.00000000, 0.03790379, 0.58865887, 0.32433243, 0.04510451, 0.00380038, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.00000000, 0.05409459, 0.84511549, 0.09569043, 0.00489951, 0.00019998, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ULN_BLOCK | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLE_ULN_BLOCK | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (TOTAL, DEMY) 0.2, 0.2, 0.2, 0.2, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.2, 0.2, 0.2, 0.2, 0.2;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_ULN_BLOCK_E | L_DIFFN_ULN_BLOCK, L_LNLE_ULN_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULN_AMPR_E | L_ULN_BLOCK_E ) {
  (NO) 0.09979002, 0.56154385, 0.32036796, 0.01829817, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD) 0.00150015, 0.04260426, 0.31713171, 0.54345435, 0.09460946, 0.00070007, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD) 0.0001, 0.0013, 0.0088, 0.0471, 0.1949, 0.3924, 0.3113, 0.0438, 0.0003, 0.0000, 0.0000, 0.0000;
  (SEV) 0.0010, 0.0018, 0.0028, 0.0052, 0.0101, 0.0186, 0.0365, 0.0797, 0.1710, 0.3425, 0.3308, 0.0000;
  (TOTAL) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_ADM_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLC8_ADM_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLLP_ADM_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLC8_LP_ADM_MALOSS | L_LNLC8_ADM_MALOSS, L_LNLLP_ADM_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLE_ADM_MALOSS | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_LNLC8_LP_E_ADM_MALOSS | L_LNLC8_LP_ADM_MALOSS, L_LNLE_ADM_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLW_ADM_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ADM_MALOSS | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_DIFFN_LNLW_ADM_MALOSS | L_LNLW_ADM_MALOSS, L_DIFFN_ADM_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNL_DIFFN_ADM_MALOSS | L_LNLC8_LP_E_ADM_MALOSS, L_DIFFN_LNLW_ADM_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0001, 0.9998, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0382, 0.9586, 0.0032, 0.0000;
  (SEV, NO) 0.0000, 0.0002, 0.0420, 0.9578, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0001, 0.9998, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0292, 0.9708, 0.0000, 0.0000;
  (MOD, MILD) 0.00000000, 0.00110011, 0.39953995, 0.59935994, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0049, 0.9951, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0000, 0.0382, 0.9586, 0.0032, 0.0000;
  (MILD, MOD) 0.00000000, 0.00110011, 0.39953995, 0.59935994, 0.00000000;
  (MOD, MOD) 0.00000000, 0.00000000, 0.01280128, 0.98719872, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9986, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0420, 0.9578, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0049, 0.9951, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9986, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ADM_MALOSS | L_OTHER_ADM_MALOSS, L_LNL_DIFFN_ADM_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;
  (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;
  (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( L_LNLE_ULN_DIFSLOW | L_LNLE_ULN_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 0.0, 1.0, 0.0;
}
probability ( L_DIFFN_ULN_DIFSLOW | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( L_ULN_DIFSLOW_E | L_LNLE_ULN_DIFSLOW, L_DIFFN_ULN_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;
  (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;
  (MOD, MOD) 0.000, 0.000, 0.002, 0.998;
  (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;
  (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;
}
probability ( L_ULN_DCV_E | L_ADM_MALOSS, L_ULN_DIFSLOW_E ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;
  (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;
  (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;
  (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;
}
probability ( L_ULN_LD_EW | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, DEMY) 0.25, 0.25, 0.25, 0.25;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;
  (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (TOTAL, AXONAL) 0.25, 0.25, 0.25, 0.25;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, V_E_REIN) 0.25, 0.25, 0.25, 0.25;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (TOTAL, E_REIN) 0.25, 0.25, 0.25, 0.25;
}
probability ( L_ULN_RD_EW | L_LNLE_ULN_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( L_ULN_RDLDCV_E | L_ULN_LD_EW, L_ULN_RD_EW ) {
  (NO, NO) 0.90449045, 0.09530953, 0.00020002, 0.00000000, 0.00000000, 0.00000000;
  (MILD, NO) 0.1320, 0.6039, 0.2641, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0139, 0.1839, 0.8022, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.00120012, 0.00670067, 0.05440544, 0.86008601, 0.07760776, 0.00000000;
  (NO, MOD) 0.0115, 0.0333, 0.1509, 0.7319, 0.0724, 0.0000;
  (MILD, MOD) 0.00340034, 0.01220122, 0.06900690, 0.71967197, 0.19531953, 0.00040004;
  (MOD, MOD) 0.0011, 0.0045, 0.0299, 0.5742, 0.3876, 0.0027;
  (SEV, MOD) 0.0001, 0.0002, 0.0018, 0.0914, 0.6093, 0.2972;
  (NO, SEV) 0.00000000, 0.00009999, 0.00109989, 0.14618538, 0.80701930, 0.04559544;
  (MILD, SEV) 0.0000, 0.0000, 0.0002, 0.0581, 0.7950, 0.1467;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0228, 0.6344, 0.3427;
  (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0014, 0.1063, 0.8923;
}
probability ( L_ULN_ALLCV_E | L_ULN_DCV_E, L_ULN_RDLDCV_E ) {
  (M_S60, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56, M_S60) 0.3684, 0.6316, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S60) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S44, M_S60) 0.00089991, 0.08959104, 0.82601740, 0.08349165, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S36, M_S60) 0.0000, 0.0005, 0.1011, 0.8478, 0.0506, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S60) 0.00000000, 0.00000000, 0.00050005, 0.07910791, 0.90019002, 0.02020202, 0.00000000, 0.00000000, 0.00000000;
  (M_S20, M_S60) 0.0000, 0.0000, 0.0000, 0.0006, 0.0734, 0.8393, 0.0867, 0.0000, 0.0000;
  (M_S14, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.09470947, 0.89458946, 0.01040104, 0.00000000;
  (M_S08, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0932, 0.9048, 0.0000;
  (M_S00, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S52) 0.0077, 0.9808, 0.0115, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S56, M_S52) 0.0005, 0.4476, 0.5519, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S52) 0.0000, 0.0239, 0.9751, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S52) 0.0000, 0.0036, 0.2468, 0.7340, 0.0156, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S36, M_S52) 0.0000, 0.0000, 0.0050, 0.3134, 0.6811, 0.0005, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00710071, 0.54935494, 0.44334433, 0.00020002, 0.00000000, 0.00000000;
  (M_S20, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00910091, 0.52835284, 0.46254625, 0.00000000, 0.00000000;
  (M_S14, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0216, 0.8359, 0.1425, 0.0000;
  (M_S08, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0355, 0.9641, 0.0000;
  (M_S00, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S44) 0.0000, 0.0047, 0.9951, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S56, M_S44) 0.0000, 0.0004, 0.9005, 0.0991, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52, M_S44) 0.0000, 0.0000, 0.1288, 0.8712, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S44) 0.00000000, 0.00000000, 0.01130113, 0.57115712, 0.41754175, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S36, M_S44) 0.0000, 0.0000, 0.0000, 0.0214, 0.9354, 0.0432, 0.0000, 0.0000, 0.0000;
  (M_S28, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.05649435, 0.93230677, 0.01109889, 0.00000000, 0.00000000;
  (M_S20, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.1430, 0.8551, 0.0015, 0.0000;
  (M_S14, M_S44) 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0019998, 0.3548645, 0.6431357, 0.0000000;
  (M_S08, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0122, 0.9877, 0.0000;
  (M_S00, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (M_S60, M_S27) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56, M_S27) 0.000, 0.000, 0.000, 0.000, 0.997, 0.003, 0.000, 0.000, 0.000;
  (M_S52, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.2336, 0.7664, 0.0000, 0.0000, 0.0000;
  (M_S44, M_S27) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00259974, 0.99340066, 0.00399960, 0.00000000, 0.00000000;
  (M_S36, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2469, 0.7531, 0.0000, 0.0000;
  (M_S28, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.9939, 0.0041, 0.0000;
  (M_S20, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0314, 0.9686, 0.0000;
  (M_S14, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;
  (M_S08, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;
  (M_S00, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (M_S60, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.1305, 0.8445, 0.0238, 0.0000;
  (M_S56, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0740, 0.8589, 0.0667, 0.0000;
  (M_S52, M_S15) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03730373, 0.80288029, 0.15971597, 0.00000000;
  (M_S44, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0070, 0.3517, 0.6413, 0.0000;
  (M_S36, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0581, 0.9416, 0.0000;
  (M_S28, M_S15) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.006, 0.994, 0.000;
  (M_S20, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (M_S14, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S08, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;
  (M_S00, M_S15) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (M_S60, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0321, 0.9677, 0.0000;
  (M_S56, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0184, 0.9815, 0.0000;
  (M_S52, M_S07) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01010101, 0.98989899, 0.00000000;
  (M_S44, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.9958, 0.0000;
  (M_S36, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (M_S28, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;
  (M_S20, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S14, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (M_S08, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (M_S00, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULN_CV_E | L_ULN_ALLCV_E ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00389961, 0.02769723, 0.09749025, 0.19478052, 0.24787521, 0.21837816, 0.13898610, 0.07069293;
  (M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0030, 0.0100, 0.0587, 0.1638, 0.2511, 0.2403, 0.1583, 0.0769, 0.0289, 0.0090;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.01940194, 0.15001500, 0.19211921, 0.23642364, 0.19571957, 0.11781178, 0.05550555, 0.02160216, 0.00720072, 0.00210021, 0.00060006;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0071, 0.0718, 0.2193, 0.2934, 0.2297, 0.1165, 0.0443, 0.0134, 0.0034, 0.0008, 0.0002, 0.0000, 0.0000;
  (M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00110011, 0.04500450, 0.23122312, 0.36273627, 0.25722572, 0.06290629, 0.03110311, 0.00710071, 0.00140014, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S20) 0.00000000, 0.00000000, 0.00000000, 0.01690169, 0.23672367, 0.41364136, 0.23552355, 0.07760776, 0.01770177, 0.00130013, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S14) 0.00000000, 0.00000000, 0.02410241, 0.45734573, 0.40054005, 0.10241024, 0.01400140, 0.00150015, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.0000, 0.1031, 0.7506, 0.1328, 0.0124, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S00) 0.9999, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( L_ULN_BLOCK_EW | L_DIFFN_ULN_BLOCK ) {
  (NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD) 0.0038, 0.9962, 0.0000, 0.0000, 0.0000;
  (MOD) 0.0007, 0.0223, 0.9770, 0.0000, 0.0000;
  (SEV) 0.0019, 0.0060, 0.0587, 0.9334, 0.0000;
  (TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULN_AMPR_EW | L_ULN_BLOCK_EW ) {
  (NO) 0.0289, 0.2833, 0.5170, 0.1684, 0.0024, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD) 0.0004, 0.0135, 0.1503, 0.5078, 0.3123, 0.0157, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD) 0.00000000, 0.00050010, 0.00400080, 0.02460492, 0.12742549, 0.34006801, 0.40008002, 0.10172034, 0.00160032, 0.00000000, 0.00000000, 0.00000000;
  (SEV) 0.00079992, 0.00139986, 0.00219978, 0.00419958, 0.00819918, 0.01549845, 0.03119688, 0.07079292, 0.15858414, 0.33926607, 0.36786321, 0.00000000;
  (TOTAL) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULN_DIFSLOW_EW | L_DIFFN_ULN_DIFSLOW ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MILD) 0.0126, 0.9869, 0.0005, 0.0000;
  (MOD) 0.0000, 0.0179, 0.9821, 0.0000;
  (SEV) 0.0000, 0.0003, 0.0252, 0.9745;
}
probability ( L_ULN_DCV_EW | L_ADM_MALOSS, L_ULN_DIFSLOW_EW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;
  (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;
  (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;
  (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;
  (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;
}
probability ( L_ULN_ALLCV_EW | L_ULN_DCV_EW ) {
  (M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (M_S56) 0.0558, 0.8486, 0.0956, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S52) 0.0000, 0.0369, 0.9631, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S44) 0.0008, 0.0087, 0.0873, 0.8226, 0.0806, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S36) 0.0000, 0.0000, 0.0005, 0.0994, 0.8508, 0.0493, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0005, 0.0781, 0.9017, 0.0197, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0728, 0.8406, 0.0860, 0.0000, 0.0000;
  (M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0942, 0.8952, 0.0103, 0.0000;
  (M_S08) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.002, 0.093, 0.905, 0.000;
  (M_S00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
}
probability ( L_ULN_CV_EW | L_ULN_ALLCV_EW ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00419958, 0.04879512, 0.19368063, 0.32496750, 0.26967303, 0.12318768, 0.03539646;
  (M_S56) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0042, 0.0505, 0.1995, 0.3255, 0.2622, 0.1185, 0.0332, 0.0063;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00079992, 0.02079792, 0.13878612, 0.31686831, 0.31146885, 0.15638436, 0.04529547, 0.00839916, 0.00109989;
  (M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00739926, 0.13408659, 0.19748025, 0.27547245, 0.21907809, 0.11158884, 0.04039596, 0.01119888, 0.00249975, 0.00049995, 0.00009999;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0467, 0.2191, 0.3382, 0.2525, 0.1050, 0.0294, 0.0060, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0261, 0.2225, 0.4086, 0.2726, 0.0468, 0.0197, 0.0031, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.0000, 0.0000, 0.0000, 0.0095, 0.2235, 0.4483, 0.2397, 0.0663, 0.0119, 0.0006, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S14) 0.0000, 0.0000, 0.0149, 0.4662, 0.4182, 0.0903, 0.0095, 0.0008, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S08) 0.00000000, 0.09020902, 0.77417742, 0.12491249, 0.01000100, 0.00070007, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S00) 0.9999, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
}
probability ( L_OTHER_ADM_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLC8_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_LNLLP_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_LNLC8_LP_ADM_DE_REGEN | L_LNLC8_ADM_DE_REGEN, L_LNLLP_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_LNLE_ULN_TIME ) {
  table 0.05, 0.60, 0.30, 0.05;
}
probability ( L_LNLE_ADM_DE_REGEN | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME, L_LNLE_ULN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (TOTAL, CHRONIC, DEMY) 1.0, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (TOTAL, OLD, DEMY) 1.0, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (TOTAL, OLD, BLOCK) 1.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (TOTAL, OLD, AXONAL) 1.0, 0.0;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (TOTAL, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0;
}
probability ( L_LNLC8_LP_E_ADM_DE_REGEN | L_LNLC8_LP_ADM_DE_REGEN, L_LNLE_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_DIFFN_ADM_DE_REGEN | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
}
probability ( L_LNLW_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_DIFFN_LNLW_ADM_DE_REGEN | L_DIFFN_ADM_DE_REGEN, L_LNLW_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_LNL_DIFFN_ADM_DE_REGEN | L_LNLC8_LP_E_ADM_DE_REGEN, L_DIFFN_LNLW_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_MYOP_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_MYDY_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_MYOP_MYDY_ADM_DE_REGEN | L_MYOP_ADM_DE_REGEN, L_MYDY_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_OTHER_ADM_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_MUSCLE_ADM_DE_REGEN | L_MYOP_MYDY_ADM_DE_REGEN, L_OTHER_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_ADM_DE_REGEN | L_LNL_DIFFN_ADM_DE_REGEN, L_MUSCLE_ADM_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_DE_REGEN_ADM_NMT | L_ADM_DE_REGEN ) {
  (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;
}
probability ( L_MYAS_ADM_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_MYAS_DE_REGEN_ADM_NMT | L_DE_REGEN_ADM_NMT, L_MYAS_ADM_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ADM_NMT | L_OTHER_ADM_NMT, L_MYAS_DE_REGEN_ADM_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYDY_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYOP_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYOP_MYDY_ADM_MUSIZE | L_MYDY_ADM_MUSIZE, L_MYOP_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MUSCLE_ADM_MUSIZE | L_OTHER_ADM_MUSIZE, L_MYOP_MYDY_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLW_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ADM_MUSIZE | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_LNLW_ADM_MUSIZE | L_LNLW_ADM_MUSIZE, L_DIFFN_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLE_ADM_MUSIZE | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME, L_LNLE_ULN_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLLP_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLC8_ADM_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLC8_LP_ADM_MUSIZE | L_LNLLP_ADM_MUSIZE, L_LNLC8_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLC8_LP_E_ADM_MUSIZE | L_LNLE_ADM_MUSIZE, L_LNLC8_LP_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNL_DIFFN_ADM_MUSIZE | L_DIFFN_LNLW_ADM_MUSIZE, L_LNLC8_LP_E_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ADM_MUSIZE | L_MUSCLE_ADM_MUSIZE, L_LNL_DIFFN_ADM_MUSIZE ) {
  (V_SMALL, V_SMALL) 0.9791, 0.0009, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL, V_SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL, V_SMALL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (INCR, V_SMALL) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;
  (LARGE, V_SMALL) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;
  (V_LARGE, V_SMALL) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;
  (V_SMALL, SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL, SMALL) 0.7493, 0.2257, 0.0049, 0.0001, 0.0000, 0.0000, 0.0200;
  (NORMAL, SMALL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;
  (INCR, SMALL) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;
  (LARGE, SMALL) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;
  (V_LARGE, SMALL) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;
  (V_SMALL, NORMAL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SMALL, NORMAL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;
  (NORMAL, NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;
  (V_SMALL, INCR) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;
  (SMALL, INCR) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00359964, 0.16548345, 0.64543546, 0.16548345, 0.01999800;
  (LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;
  (V_LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (V_SMALL, LARGE) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;
  (SMALL, LARGE) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;
  (INCR, LARGE) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;
  (LARGE, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;
  (V_SMALL, V_LARGE) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;
  (SMALL, V_LARGE) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;
  (INCR, V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;
  (V_LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9799, 0.0200;
}
probability ( L_ADM_EFFMUS | L_ADM_NMT, L_ADM_MUSIZE ) {
  (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MIXED, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;
  (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MIXED, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;
  (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;
  (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;
  (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;
  (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;
  (MIXED, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;
  (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;
  (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;
  (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;
  (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;
  (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;
  (MIXED, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;
  (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;
  (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;
  (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;
  (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;
  (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;
  (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;
  (MIXED, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;
  (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;
  (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;
  (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;
  (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;
  (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;
  (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;
  (MIXED, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;
  (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;
  (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;
  (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;
  (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;
  (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;
  (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;
  (MIXED, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;
}
probability ( L_OTHER_ULN_BLOCK_WA ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLW_ULN_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_LNLW_ULN_BLOCK_WA | L_DIFFN_ULN_BLOCK, L_LNLW_ULN_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULN_BLOCK_WA | L_OTHER_ULN_BLOCK_WA, L_DIFFN_LNLW_ULN_BLOCK_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ADM_MULOSS | L_ULN_BLOCK_WA, L_ADM_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;
  (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;
  (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;
  (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;
  (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;
  (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;
  (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;
  (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;
  (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;
  (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;
  (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;
  (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;
  (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( L_ADM_ALLAMP_WA | L_ADM_EFFMUS, L_ADM_MULOSS ) {
  (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;
  (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;
  (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;
  (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;
  (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;
  (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;
  (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;
  (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;
  (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;
  (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;
  (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;
  (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;
  (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;
  (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;
  (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;
  (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;
  (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;
  (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;
  (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;
  (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;
  (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;
  (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;
  (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;
  (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;
  (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;
  (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;
  (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;
  (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;
  (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;
}
probability ( L_ULN_AMP_WA | L_ADM_ALLAMP_WA ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00000000, 0.27192719, 0.23362336, 0.18271827, 0.12981298, 0.08400840, 0.04940494, 0.02640264, 0.01290129, 0.00570057, 0.00230023, 0.00080008, 0.00030003, 0.00010001, 0.00000000, 0.00000000, 0.00000000;
  (A0_10) 0.0000, 0.0006, 0.0045, 0.0217, 0.0710, 0.1571, 0.2357, 0.2391, 0.1642, 0.0762, 0.0240, 0.0051, 0.0007, 0.0001, 0.0000, 0.0000, 0.0000;
  (A0_30) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00130013, 0.01820182, 0.10931093, 0.29312931, 0.34783478, 0.18291829, 0.04270427, 0.00440044, 0.00020002, 0.00000000, 0.00000000;
  (A0_70) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0016, 0.0360, 0.2338, 0.4425, 0.2448, 0.0394, 0.0019, 0.0000;
  (A1_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.1369, 0.5589, 0.2821, 0.0178, 0.0001;
  (A2_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00640064, 0.05730573, 0.22752275, 0.39933993, 0.30913091;
  (A4_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00100010, 0.02130213, 0.19281928, 0.78487849;
  (A8_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0010, 0.0446, 0.9544;
}
probability ( L_ULN_LD_WA ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_ULN_RD_WA ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_ULN_RDLDDEL | L_ULN_LD_WA, L_ULN_RD_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0964, 0.7981, 0.1055, 0.0000, 0.0000;
  (MOD, NO) 0.0032, 0.1270, 0.8698, 0.0000, 0.0000;
  (SEV, NO) 0.00090009, 0.00280028, 0.01470147, 0.98159816, 0.00000000;
  (NO, MOD) 0.0019, 0.5257, 0.4724, 0.0000, 0.0000;
  (MILD, MOD) 0.00019998, 0.04149585, 0.95830417, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0001, 0.0144, 0.9855, 0.0000, 0.0000;
  (SEV, MOD) 0.00019998, 0.00059994, 0.00369963, 0.99550045, 0.00000000;
  (NO, SEV) 0.0002, 0.0304, 0.9694, 0.0000, 0.0000;
  (MILD, SEV) 0.0001, 0.0142, 0.9857, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.0090, 0.9808, 0.0102, 0.0000;
  (SEV, SEV) 0.0000, 0.0002, 0.0012, 0.9984, 0.0002;
}
probability ( L_ULN_DIFSLOW_WA | L_LNLE_ULN_DIFSLOW, L_DIFFN_ULN_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;
  (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;
  (MOD, MOD) 0.000, 0.000, 0.002, 0.998;
  (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;
  (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;
}
probability ( L_ULN_DCV_WA | L_ADM_MALOSS, L_ULN_DIFSLOW_WA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;
  (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;
  (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;
  (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;
}
probability ( L_ULN_ALLDEL_WA | L_ULN_RDLDDEL, L_ULN_DCV_WA ) {
  (MS3_1, M_S60) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S60) 0.05119488, 0.22907709, 0.56624338, 0.15348465, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S60) 0.0001, 0.0035, 0.0632, 0.9326, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S60) 0.00129987, 0.00199980, 0.00439956, 0.01869813, 0.12128787, 0.74792521, 0.10438956, 0.00000000, 0.00000000;
  (MS20_1, M_S60) 0.00010001, 0.00010001, 0.00030003, 0.00090009, 0.00450045, 0.04340434, 0.57675768, 0.37393739, 0.00000000;
  (MS3_1, M_S52) 0.5607, 0.4393, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S52) 0.01929807, 0.12868713, 0.49285071, 0.35916408, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S52) 0.0000, 0.0011, 0.0283, 0.9651, 0.0055, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S52) 0.00100010, 0.00160016, 0.00370037, 0.01610161, 0.10931093, 0.74217422, 0.12611261, 0.00000000, 0.00000000;
  (MS20_1, M_S52) 0.0001, 0.0001, 0.0002, 0.0008, 0.0041, 0.0399, 0.5568, 0.3980, 0.0000;
  (MS3_1, M_S44) 0.0069, 0.7963, 0.1968, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S44) 0.0027, 0.0328, 0.2398, 0.7246, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S44) 0.0000, 0.0002, 0.0075, 0.8889, 0.1034, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S44) 0.00079992, 0.00119988, 0.00279972, 0.01249875, 0.09159084, 0.72352765, 0.16758324, 0.00000000, 0.00000000;
  (MS20_1, M_S44) 0.0001, 0.0001, 0.0002, 0.0007, 0.0034, 0.0348, 0.5239, 0.4368, 0.0000;
  (MS3_1, M_S36) 0.0000, 0.0184, 0.9806, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S36) 0.00010001, 0.00330033, 0.05470547, 0.93819382, 0.00370037, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S36) 0.00000000, 0.00000000, 0.00030003, 0.18501850, 0.81468147, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS10_1, M_S36) 0.00049995, 0.00079992, 0.00179982, 0.00869913, 0.07029297, 0.68023198, 0.23767623, 0.00000000, 0.00000000;
  (MS20_1, M_S36) 0.0001, 0.0001, 0.0001, 0.0005, 0.0027, 0.0287, 0.4783, 0.4895, 0.0000;
  (MS3_1, M_S28) 0.0000, 0.0001, 0.0179, 0.9820, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S28) 0.00000000, 0.00019998, 0.00479952, 0.50394961, 0.49105089, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS4_7, M_S28) 0.0000, 0.0000, 0.0000, 0.0032, 0.9968, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS10_1, M_S28) 0.0002, 0.0004, 0.0009, 0.0047, 0.0440, 0.5737, 0.3761, 0.0000, 0.0000;
  (MS20_1, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40724072, 0.56955696, 0.00000000;
  (MS3_1, M_S20) 0.0000, 0.0002, 0.0030, 0.1393, 0.8575, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S20) 0.0000, 0.0000, 0.0003, 0.0188, 0.9804, 0.0005, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.93139314, 0.06710671, 0.00000000, 0.00000000, 0.00000000;
  (MS10_1, M_S20) 0.0001, 0.0001, 0.0002, 0.0013, 0.0150, 0.3152, 0.6681, 0.0000, 0.0000;
  (MS20_1, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00080016, 0.01110222, 0.28045609, 0.70754151, 0.00000000;
  (MS3_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0006, 0.8145, 0.1849, 0.0000, 0.0000, 0.0000;
  (MS3_9, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0494, 0.9506, 0.0000, 0.0000, 0.0000;
  (MS4_7, M_S14) 0.000, 0.000, 0.000, 0.000, 0.001, 0.999, 0.000, 0.000, 0.000;
  (MS10_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0001, 0.0017, 0.0786, 0.9196, 0.0000, 0.0000;
  (MS20_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0035, 0.1380, 0.8583, 0.0000;
  (MS3_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59735974, 0.39093909, 0.00000000, 0.00000000;
  (MS3_9, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00340034, 0.29902990, 0.69746975, 0.00000000, 0.00000000;
  (MS4_7, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.1112, 0.8881, 0.0000, 0.0000;
  (MS10_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00940094, 0.95939594, 0.03110311, 0.00000000;
  (MS20_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.02530253, 0.97439744, 0.00000000;
  (MS3_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS3_9, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS4_7, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS10_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MS20_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ULN_LAT_WA | L_ULN_ALLDEL_WA ) {
  (MS0_0) 0.2221, 0.5402, 0.2221, 0.0154, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS0_4) 0.03780378, 0.23962396, 0.44344434, 0.23962396, 0.03780378, 0.00170017, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS0_8) 0.00170017, 0.03770377, 0.23912391, 0.44244424, 0.23912391, 0.03770377, 0.00220022, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS1_6) 0.0001, 0.0019, 0.0176, 0.0873, 0.2279, 0.3138, 0.2849, 0.0637, 0.0028, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS3_2) 0.0004, 0.0015, 0.0044, 0.0114, 0.0255, 0.0495, 0.1033, 0.2035, 0.2400, 0.2035, 0.0989, 0.0529, 0.0049, 0.0003, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS6_4) 0.00030003, 0.00050005, 0.00090009, 0.00160016, 0.00280028, 0.00450045, 0.00890089, 0.01950195, 0.03250325, 0.04980498, 0.11101110, 0.16361636, 0.18941894, 0.25772577, 0.13571357, 0.02010201, 0.00110011, 0.00000000, 0.00000000;
  (MS12_8) 0.00020002, 0.00030003, 0.00040004, 0.00060006, 0.00070007, 0.00100010, 0.00160016, 0.00290029, 0.00420042, 0.00590059, 0.01340134, 0.02140214, 0.03300330, 0.07190719, 0.16781678, 0.22892289, 0.24362436, 0.20212021, 0.00000000;
  (MS25_6) 0.00079992, 0.00099990, 0.00119988, 0.00139986, 0.00159984, 0.00179982, 0.00269973, 0.00399960, 0.00489951, 0.00599940, 0.01119888, 0.01649835, 0.02239776, 0.04509549, 0.10308969, 0.16628337, 0.25177482, 0.35826417, 0.00000000;
  (INFIN) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ADM_VOL_ACT ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_ADM_FORCE | L_ADM_VOL_ACT, L_ADM_ALLAMP_WA ) {
  (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;
  (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;
  (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;
  (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;
  (NORMAL, A0_01) 0.00009999, 0.00429957, 0.05219478, 0.26687331, 0.43305669, 0.24347565;
  (REDUCED, A0_01) 0.0000, 0.0005, 0.0084, 0.0849, 0.3219, 0.5843;
  (V_RED, A0_01) 0.0000, 0.0000, 0.0007, 0.0167, 0.1576, 0.8250;
  (ABSENT, A0_01) 0.00000000, 0.00000000, 0.00000000, 0.00260026, 0.05860586, 0.93879388;
  (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;
  (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;
  (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;
  (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;
  (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;
  (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;
  (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;
  (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;
  (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;
  (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;
  (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;
  (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;
  (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;
  (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;
  (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;
  (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;
  (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;
  (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;
  (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;
  (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;
  (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;
  (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;
  (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;
  (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;
}
probability ( L_ADM_MUSCLE_VOL | L_ADM_MUSIZE, L_ADM_MALOSS ) {
  (V_SMALL, NO) 0.9896, 0.0104;
  (SMALL, NO) 0.8137, 0.1863;
  (NORMAL, NO) 0.0209, 0.9791;
  (INCR, NO) 0.009, 0.991;
  (LARGE, NO) 0.003, 0.997;
  (V_LARGE, NO) 0.0004, 0.9996;
  (OTHER, NO) 0.4212, 0.5788;
  (V_SMALL, MILD) 0.9976, 0.0024;
  (SMALL, MILD) 0.9603, 0.0397;
  (NORMAL, MILD) 0.5185, 0.4815;
  (INCR, MILD) 0.1087, 0.8913;
  (LARGE, MILD) 0.0278, 0.9722;
  (V_LARGE, MILD) 0.0046, 0.9954;
  (OTHER, MILD) 0.5185, 0.4815;
  (V_SMALL, MOD) 0.999, 0.001;
  (SMALL, MOD) 0.9893, 0.0107;
  (NORMAL, MOD) 0.9588, 0.0412;
  (INCR, MOD) 0.6377, 0.3623;
  (LARGE, MOD) 0.2716, 0.7284;
  (V_LARGE, MOD) 0.0779, 0.9221;
  (OTHER, MOD) 0.6336, 0.3664;
  (V_SMALL, SEV) 0.9995, 0.0005;
  (SMALL, SEV) 0.9969, 0.0031;
  (NORMAL, SEV) 0.9953, 0.0047;
  (INCR, SEV) 0.9518, 0.0482;
  (LARGE, SEV) 0.8234, 0.1766;
  (V_LARGE, SEV) 0.5986, 0.4014;
  (OTHER, SEV) 0.7685, 0.2315;
  (V_SMALL, TOTAL) 0.9989, 0.0011;
  (SMALL, TOTAL) 0.9984, 0.0016;
  (NORMAL, TOTAL) 0.9984, 0.0016;
  (INCR, TOTAL) 0.9975, 0.0025;
  (LARGE, TOTAL) 0.9965, 0.0035;
  (V_LARGE, TOTAL) 0.9956, 0.0044;
  (OTHER, TOTAL) 0.9857, 0.0143;
  (V_SMALL, OTHER) 0.9363, 0.0637;
  (SMALL, OTHER) 0.8403, 0.1597;
  (NORMAL, OTHER) 0.6534, 0.3466;
  (INCR, OTHER) 0.4689, 0.5311;
  (LARGE, OTHER) 0.3174, 0.6826;
  (V_LARGE, OTHER) 0.1948, 0.8052;
  (OTHER, OTHER) 0.5681, 0.4319;
}
probability ( L_ADM_MVA_RECRUIT | L_ADM_MULOSS, L_ADM_VOL_ACT ) {
  (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;
  (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;
  (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;
  (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;
  (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;
  (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;
  (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;
  (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;
  (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;
  (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;
  (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;
  (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;
  (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;
  (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;
  (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;
  (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;
  (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ADM_MVA_AMP | L_ADM_EFFMUS ) {
  (V_SMALL) 0.00, 0.04, 0.96;
  (SMALL) 0.01, 0.15, 0.84;
  (NORMAL) 0.05, 0.90, 0.05;
  (INCR) 0.50, 0.49, 0.01;
  (LARGE) 0.85, 0.15, 0.00;
  (V_LARGE) 0.96, 0.04, 0.00;
  (OTHER) 0.33, 0.34, 0.33;
}
probability ( L_ADM_TA_CONCL | L_ADM_EFFMUS ) {
  (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;
  (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;
  (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;
  (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;
  (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;
  (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;
  (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;
}
probability ( L_ADM_MUPAMP | L_ADM_EFFMUS ) {
  (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;
  (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;
  (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;
  (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;
}
probability ( L_ADM_QUAN_MUPAMP | L_ADM_MUPAMP ) {
  (V_SMALL) 0.00080024, 0.00370111, 0.01350405, 0.03811143, 0.08352506, 0.14254276, 0.18955687, 0.19635891, 0.15834750, 0.09942983, 0.04861458, 0.01850555, 0.00550165, 0.00130039, 0.00020006, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.00000000, 0.00010002, 0.00080016, 0.00370074, 0.01350270, 0.03810762, 0.08351670, 0.14252851, 0.18953791, 0.19633927, 0.15833167, 0.09941988, 0.04860972, 0.01850370, 0.00550110, 0.00130026, 0.00020004, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.00370037, 0.01870187, 0.06390639, 0.14751475, 0.23022302, 0.24312431, 0.17371737, 0.08400840, 0.02750275, 0.00610061, 0.00090009, 0.00010001, 0.00000000, 0.00000000;
  (INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0037, 0.0135, 0.0381, 0.0835, 0.1426, 0.1896, 0.1963, 0.1583, 0.0995, 0.0487, 0.0185, 0.0055, 0.0013;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0038, 0.0136, 0.0383, 0.0841, 0.1435, 0.1909, 0.1977, 0.1594, 0.1001, 0.0490, 0.0187;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0024, 0.0082, 0.0232, 0.0542, 0.1041, 0.1645, 0.2137, 0.2283, 0.2007;
  (OTHER) 0.0045, 0.0078, 0.0127, 0.0197, 0.0289, 0.0403, 0.0531, 0.0664, 0.0787, 0.0883, 0.0939, 0.0946, 0.0903, 0.0817, 0.0700, 0.0569, 0.0438, 0.0319, 0.0221, 0.0144;
}
probability ( L_ADM_QUAL_MUPAMP | L_ADM_MUPAMP ) {
  (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;
  (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;
  (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;
  (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;
  (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;
  (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;
  (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;
}
probability ( L_ADM_MUPDUR | L_ADM_EFFMUS ) {
  (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;
  (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;
}
probability ( L_ADM_QUAN_MUPDUR | L_ADM_MUPDUR ) {
  (V_SMALL) 0.09981996, 0.18333667, 0.24024805, 0.22454491, 0.14972995, 0.07121424, 0.02420484, 0.00580116, 0.00100020, 0.00010002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.01020102, 0.03690369, 0.09510951, 0.17471747, 0.22892289, 0.21402140, 0.14261426, 0.06780678, 0.02300230, 0.00560056, 0.00100010, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.0000, 0.0002, 0.0025, 0.0177, 0.0739, 0.1852, 0.2785, 0.2515, 0.1363, 0.0444, 0.0087, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR) 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000, 0.00000000, 0.00000000;
  (LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000;
  (V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00039996, 0.00179982, 0.00699930, 0.02189781, 0.05409459, 0.10518948, 0.16128387, 0.19498050, 0.18598140, 0.13988601, 0.08289171, 0.03879612, 0.00569943;
  (OTHER) 0.0201, 0.0341, 0.0529, 0.0748, 0.0966, 0.1138, 0.1224, 0.1202, 0.1078, 0.0882, 0.0658, 0.0449, 0.0279, 0.0159, 0.0082, 0.0039, 0.0017, 0.0007, 0.0001;
}
probability ( L_ADM_QUAL_MUPDUR | L_ADM_MUPDUR ) {
  (V_SMALL) 0.8309, 0.1677, 0.0014;
  (SMALL) 0.49, 0.49, 0.02;
  (NORMAL) 0.1065, 0.7870, 0.1065;
  (INCR) 0.02, 0.49, 0.49;
  (LARGE) 0.0014, 0.1677, 0.8309;
  (V_LARGE) 0.0001, 0.0392, 0.9607;
  (OTHER) 0.2597, 0.4806, 0.2597;
}
probability ( L_ADM_QUAN_MUPPOLY | L_ADM_DE_REGEN, L_ADM_EFFMUS ) {
  (NO, V_SMALL) 0.109, 0.548, 0.343;
  (YES, V_SMALL) 0.004, 0.122, 0.874;
  (NO, SMALL) 0.340, 0.564, 0.096;
  (YES, SMALL) 0.015, 0.261, 0.724;
  (NO, NORMAL) 0.925, 0.075, 0.000;
  (YES, NORMAL) 0.091, 0.526, 0.383;
  (NO, INCR) 0.796, 0.201, 0.003;
  (YES, INCR) 0.061, 0.465, 0.474;
  (NO, LARGE) 0.637, 0.348, 0.015;
  (YES, LARGE) 0.039, 0.396, 0.565;
  (NO, V_LARGE) 0.340, 0.564, 0.096;
  (YES, V_LARGE) 0.015, 0.261, 0.724;
  (NO, OTHER) 0.340, 0.564, 0.096;
  (YES, OTHER) 0.015, 0.261, 0.724;
}
probability ( L_ADM_QUAL_MUPPOLY | L_ADM_QUAN_MUPPOLY ) {
  (x_12_) 0.95, 0.05;
  (x12_24_) 0.3, 0.7;
  (x_24_) 0.05, 0.95;
}
probability ( L_ADM_MUPSATEL | L_ADM_DE_REGEN ) {
  (NO) 0.95, 0.05;
  (YES) 0.2, 0.8;
}
probability ( L_ADM_MUPINSTAB | L_ADM_NMT ) {
  (NO) 0.95, 0.05;
  (MOD_PRE) 0.1, 0.9;
  (SEV_PRE) 0.03, 0.97;
  (MLD_POST) 0.2, 0.8;
  (MOD_POST) 0.1, 0.9;
  (SEV_POST) 0.03, 0.97;
  (MIXED) 0.1, 0.9;
}
probability ( L_ADM_REPSTIM_CMAPAMP | L_ADM_ALLAMP_WA ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00029988, 0.09616154, 0.11335466, 0.12485006, 0.12834866, 0.12315074, 0.11025590, 0.09226309, 0.07207117, 0.05257897, 0.03578569, 0.02269092, 0.01349460, 0.00749700, 0.00389844, 0.00189924, 0.00079968, 0.00039984, 0.00009996, 0.00009996, 0.00000000;
  (A0_10) 0.00000000, 0.00000000, 0.00009998, 0.00039992, 0.00169966, 0.00649870, 0.01959608, 0.04739052, 0.09228154, 0.14417117, 0.18096381, 0.18246351, 0.14777045, 0.09598080, 0.05018996, 0.02099580, 0.00709858, 0.00189962, 0.00039992, 0.00009998, 0.00000000;
  (A0_30) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.00330033, 0.01690169, 0.05950595, 0.14071407, 0.22592259, 0.24522452, 0.18011801, 0.08970897, 0.03010301, 0.00690069, 0.00110011, 0.00010001;
  (A0_70) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0088, 0.0684, 0.2360, 0.3599, 0.2433, 0.0728, 0.0097, 0.0006;
  (A1_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.1369, 0.5589, 0.2821, 0.0178, 0.0001;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0028, 0.0211, 0.0937, 0.2387, 0.3496, 0.2939;
  (A4_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00059994, 0.00749925, 0.05829417, 0.25997400, 0.67363264;
  (A8_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00810081, 0.11031103, 0.88128813;
}
probability ( L_ADM_REPSTIM_DECR | L_ADM_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;
  (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MIXED) 0.245, 0.245, 0.245, 0.245, 0.020;
}
probability ( L_ADM_REPSTIM_FACILI | L_ADM_NMT ) {
  (NO) 0.95, 0.02, 0.01, 0.02;
  (MOD_PRE) 0.010, 0.889, 0.100, 0.001;
  (SEV_PRE) 0.010, 0.080, 0.909, 0.001;
  (MLD_POST) 0.89, 0.08, 0.01, 0.02;
  (MOD_POST) 0.48, 0.50, 0.01, 0.01;
  (SEV_POST) 0.020, 0.949, 0.030, 0.001;
  (MIXED) 0.25, 0.25, 0.25, 0.25;
}
probability ( L_ADM_REPSTIM_POST_DECR | L_ADM_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;
  (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;
  (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MIXED) 0.23, 0.23, 0.22, 0.22, 0.10;
}
probability ( L_ADM_SF_JITTER | L_ADM_NMT ) {
  (NO) 0.95, 0.05, 0.00, 0.00;
  (MOD_PRE) 0.02, 0.20, 0.70, 0.08;
  (SEV_PRE) 0.0, 0.1, 0.4, 0.5;
  (MLD_POST) 0.05, 0.70, 0.20, 0.05;
  (MOD_POST) 0.01, 0.19, 0.70, 0.10;
  (SEV_POST) 0.0, 0.1, 0.4, 0.5;
  (MIXED) 0.1, 0.3, 0.3, 0.3;
}
probability ( L_LNLC8_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_LNLLP_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_LNLC8_LP_ADM_MUDENS | L_LNLC8_ADM_MUDENS, L_LNLLP_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_LNLE_ADM_MUDENS | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME, L_LNLE_ULN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( L_LNLC8_LP_E_ADM_MUDENS | L_LNLC8_LP_ADM_MUDENS, L_LNLE_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_ADM_MUDENS | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( L_LNLW_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_DIFFN_LNLW_ADM_MUDENS | L_DIFFN_ADM_MUDENS, L_LNLW_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_LNL_DIFFN_ADM_MUDENS | L_LNLC8_LP_E_ADM_MUDENS, L_DIFFN_LNLW_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_MYOP_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MYDY_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MYOP_MYDY_ADM_MUDENS | L_MYOP_ADM_MUDENS, L_MYDY_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_MYAS_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_OTHER_ADM_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MYAS_OTHER_ADM_MUDENS | L_MYAS_ADM_MUDENS, L_OTHER_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_MUSCLE_ADM_MUDENS | L_MYOP_MYDY_ADM_MUDENS, L_MYAS_OTHER_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_ADM_MUDENS | L_LNL_DIFFN_ADM_MUDENS, L_MUSCLE_ADM_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_ADM_SF_DENSITY | L_ADM_MUDENS ) {
  (NORMAL) 0.97, 0.03, 0.00;
  (INCR) 0.05, 0.90, 0.05;
  (V_INCR) 0.01, 0.04, 0.95;
}
probability ( L_LNLC8_ADM_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLLP_ADM_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLC8_LP_ADM_NEUR_ACT | L_LNLC8_ADM_NEUR_ACT, L_LNLLP_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLE_ADM_NEUR_ACT | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLC8_LP_E_ADM_NEUR_ACT | L_LNLC8_LP_ADM_NEUR_ACT, L_LNLE_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_ADM_NEUR_ACT | DIFFN_M_SEV_DIST, DIFFN_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLW_ADM_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_LNLW_ADM_NEUR_ACT | L_DIFFN_ADM_NEUR_ACT, L_LNLW_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNL_DIFFN_ADM_NEUR_ACT | L_LNLC8_LP_E_ADM_NEUR_ACT, L_DIFFN_LNLW_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_ADM_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_ADM_NEUR_ACT | L_LNL_DIFFN_ADM_NEUR_ACT, L_OTHER_ADM_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ADM_SPONT_NEUR_DISCH | L_ADM_NEUR_ACT ) {
  (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;
  (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;
  (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;
  (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;
  (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;
}
probability ( L_MYOP_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYDY_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYOP_MYDY_ADM_DENERV | L_MYOP_ADM_DENERV, L_MYDY_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_NMT_ADM_DENERV | L_ADM_NMT ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE) 0.40, 0.45, 0.15, 0.00;
  (SEV_PRE) 0.15, 0.35, 0.35, 0.15;
  (MLD_POST) 0.85, 0.15, 0.00, 0.00;
  (MOD_POST) 0.30, 0.45, 0.20, 0.05;
  (SEV_POST) 0.15, 0.35, 0.35, 0.15;
  (MIXED) 0.25, 0.25, 0.25, 0.25;
}
probability ( L_OTHER_NMT_ADM_DENERV | L_OTHER_ADM_DENERV, L_NMT_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MUSCLE_ADM_DENERV | L_MYOP_MYDY_ADM_DENERV, L_OTHER_NMT_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLC8_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLLP_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLC8_LP_ADM_DENERV | L_LNLC8_ADM_DENERV, L_LNLLP_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLE_ADM_DENERV | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME, L_LNLE_ULN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( L_LNLC8_LP_E_ADM_DENERV | L_LNLC8_LP_ADM_DENERV, L_LNLE_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_ADM_DENERV | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( L_LNLW_ADM_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_LNLW_ADM_DENERV | L_DIFFN_ADM_DENERV, L_LNLW_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNL_DIFFN_ADM_DENERV | L_LNLC8_LP_E_ADM_DENERV, L_DIFFN_LNLW_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ADM_DENERV | L_MUSCLE_ADM_DENERV, L_LNL_DIFFN_ADM_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_ADM_SPONT_DENERV_ACT | L_ADM_DENERV ) {
  (NO) 0.98, 0.02, 0.00, 0.00;
  (MILD) 0.07, 0.85, 0.08, 0.00;
  (MOD) 0.01, 0.07, 0.85, 0.07;
  (SEV) 0.00, 0.01, 0.07, 0.92;
}
probability ( L_ADM_SPONT_HF_DISCH | L_ADM_DENERV ) {
  (NO) 0.99, 0.01;
  (MILD) 0.97, 0.03;
  (MOD) 0.95, 0.05;
  (SEV) 0.93, 0.07;
}
probability ( L_ADM_SPONT_INS_ACT | L_ADM_DENERV ) {
  (NO) 0.98, 0.02;
  (MILD) 0.1, 0.9;
  (MOD) 0.05, 0.95;
  (SEV) 0.05, 0.95;
}
probability ( L_OTHER_DELT_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLPC5_AXIL_SEV ) {
  table 0.98031378, 0.01069252, 0.00499650, 0.00299790, 0.00099930;
}
probability ( L_LNLPC5_AXIL_TIME ) {
  table 0.05, 0.60, 0.30, 0.05;
}
probability ( L_LNLPC5_AXIL_PATHO ) {
  table 0.600, 0.190, 0.200, 0.005, 0.005;
}
probability ( L_LNLPC5_DELT_DE_REGEN | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME, L_LNLPC5_AXIL_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (TOTAL, CHRONIC, DEMY) 1.0, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (TOTAL, OLD, DEMY) 1.0, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (TOTAL, OLD, BLOCK) 1.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (TOTAL, OLD, AXONAL) 1.0, 0.0;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (TOTAL, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0;
}
probability ( L_DIFFN_DELT_DE_REGEN | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2;
  (MOD, SUBACUTE, DEMY) 0.2, 0.8;
  (SEV, SUBACUTE, DEMY) 0.4, 0.6;
  (NO, CHRONIC, DEMY) 1.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2;
  (MOD, CHRONIC, DEMY) 0.2, 0.8;
  (SEV, CHRONIC, DEMY) 0.4, 0.6;
  (NO, OLD, DEMY) 1.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0;
  (MOD, OLD, DEMY) 0.8, 0.2;
  (SEV, OLD, DEMY) 0.4, 0.6;
  (NO, ACUTE, BLOCK) 1.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2;
  (MOD, SUBACUTE, BLOCK) 0.2, 0.8;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.6;
  (NO, CHRONIC, BLOCK) 1.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2;
  (MOD, CHRONIC, BLOCK) 0.2, 0.8;
  (SEV, CHRONIC, BLOCK) 0.4, 0.6;
  (NO, OLD, BLOCK) 1.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0;
  (MOD, OLD, BLOCK) 0.8, 0.2;
  (SEV, OLD, BLOCK) 0.4, 0.6;
  (NO, ACUTE, AXONAL) 1.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.5, 0.5;
  (MOD, SUBACUTE, AXONAL) 0.2, 0.8;
  (SEV, SUBACUTE, AXONAL) 0.1, 0.9;
  (NO, CHRONIC, AXONAL) 1.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.5, 0.5;
  (MOD, CHRONIC, AXONAL) 0.2, 0.8;
  (SEV, CHRONIC, AXONAL) 0.1, 0.9;
  (NO, OLD, AXONAL) 1.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0;
  (MOD, OLD, AXONAL) 0.8, 0.2;
  (SEV, OLD, AXONAL) 0.4, 0.6;
  (NO, ACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, ACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, ACUTE, V_E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;
  (NO, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;
  (NO, OLD, V_E_REIN) 0.0, 1.0;
  (MILD, OLD, V_E_REIN) 0.0, 1.0;
  (MOD, OLD, V_E_REIN) 0.0, 1.0;
  (SEV, OLD, V_E_REIN) 0.0, 1.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0;
  (NO, OLD, E_REIN) 0.0, 1.0;
  (MILD, OLD, E_REIN) 0.0, 1.0;
  (MOD, OLD, E_REIN) 0.0, 1.0;
  (SEV, OLD, E_REIN) 0.0, 1.0;
}
probability ( L_LNLPC5_DIFFN_DELT_DE_REGEN | L_LNLPC5_DELT_DE_REGEN, L_DIFFN_DELT_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_MYOP_DELT_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_MYDY_DELT_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_MYOP_MYDY_DELT_DE_REGEN | L_MYOP_DELT_DE_REGEN, L_MYDY_DELT_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_OTHER_DELT_DE_REGEN ) {
  table 1.0, 0.0;
}
probability ( L_MUSCLE_DELT_DE_REGEN | L_MYOP_MYDY_DELT_DE_REGEN, L_OTHER_DELT_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_DELT_DE_REGEN | L_LNLPC5_DIFFN_DELT_DE_REGEN, L_MUSCLE_DELT_DE_REGEN ) {
  (NO, NO) 1.0, 0.0;
  (YES, NO) 0.0, 1.0;
  (NO, YES) 0.0, 1.0;
  (YES, YES) 0.0, 1.0;
}
probability ( L_DE_REGEN_DELT_NMT | L_DELT_DE_REGEN ) {
  (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;
}
probability ( L_MYAS_DELT_NMT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_MYAS_DE_REGEN_DELT_NMT | L_DE_REGEN_DELT_NMT, L_MYAS_DELT_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DELT_NMT | L_OTHER_DELT_NMT, L_MYAS_DE_REGEN_DELT_NMT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_DELT_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYDY_DELT_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYOP_DELT_MUSIZE ) {
  table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYOP_MYDY_DELT_MUSIZE | L_MYDY_DELT_MUSIZE, L_MYOP_DELT_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MUSCLE_DELT_MUSIZE | L_OTHER_DELT_MUSIZE, L_MYOP_MYDY_DELT_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;
  (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;
  (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;
  (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;
  (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;
  (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;
  (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;
  (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;
  (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;
  (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;
  (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;
  (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;
  (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;
  (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DIFFN_DELT_MUSIZE | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLPC5_DELT_MUSIZE | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME, L_LNLPC5_AXIL_PATHO ) {
  (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;
  (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;
  (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;
  (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;
  (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;
  (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;
  (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;
  (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLPC5_DIFFN_DELT_MUSIZE | L_DIFFN_DELT_MUSIZE, L_LNLPC5_DELT_MUSIZE ) {
  (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;
  (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DELT_MUSIZE | L_MUSCLE_DELT_MUSIZE, L_LNLPC5_DIFFN_DELT_MUSIZE ) {
  (V_SMALL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (INCR, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (LARGE, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (V_LARGE, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (V_SMALL, SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (INCR, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (LARGE, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (V_LARGE, SMALL) 0.00000000, 0.97939794, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.02000200;
  (V_SMALL, NORMAL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, NORMAL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, NORMAL) 0.0000, 0.0000, 0.9781, 0.0019, 0.0000, 0.0000, 0.0200;
  (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9781, 0.0000, 0.0000, 0.0200;
  (LARGE, NORMAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (V_LARGE, NORMAL) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (V_SMALL, INCR) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, INCR) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9781, 0.0000, 0.0000, 0.0200;
  (INCR, INCR) 0.00, 0.00, 0.00, 0.98, 0.00, 0.00, 0.02;
  (LARGE, INCR) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (V_LARGE, INCR) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (V_SMALL, LARGE) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, LARGE) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (NORMAL, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (INCR, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (LARGE, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;
  (V_LARGE, LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (V_SMALL, V_LARGE) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;
  (SMALL, V_LARGE) 0.00000000, 0.97939794, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.02000200;
  (NORMAL, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (INCR, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (LARGE, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (V_LARGE, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( L_DELT_EFFMUS | L_DELT_NMT, L_DELT_MUSIZE ) {
  (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (OTHER, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;
  (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (OTHER, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;
  (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;
  (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;
  (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;
  (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;
  (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;
  (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;
  (OTHER, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;
  (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;
  (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;
  (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;
  (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;
  (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;
  (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;
  (OTHER, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;
  (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;
  (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;
  (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;
  (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;
  (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;
  (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;
  (OTHER, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;
  (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;
  (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;
  (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;
  (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;
  (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;
  (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;
  (OTHER, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;
  (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;
  (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;
  (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;
  (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;
  (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;
  (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;
  (OTHER, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;
}
probability ( L_DIFFN_AXIL_BLOCK | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_OTHER_AXIL_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_AXIL_BLOCK_ED | L_DIFFN_AXIL_BLOCK, L_OTHER_AXIL_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLPC5_DELT_MALOSS | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_DIFFN_DELT_MALOSS | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_LNLPC5_DIFFN_DELT_MALOSS | L_LNLPC5_DELT_MALOSS, L_DIFFN_DELT_MALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;
  (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;
  (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_DELT_MALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DELT_MALOSS | L_LNLPC5_DIFFN_DELT_MALOSS, L_OTHER_DELT_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;
  (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;
  (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;
  (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;
  (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;
  (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;
  (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;
  (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( L_DELT_MULOSS | L_AXIL_BLOCK_ED, L_DELT_MALOSS ) {
  (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;
  (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;
  (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;
  (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;
  (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;
  (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;
  (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;
  (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;
  (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;
  (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;
  (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;
  (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;
  (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;
  (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;
  (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;
  (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;
  (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
  (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;
  (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;
  (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;
  (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;
  (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;
}
probability ( L_DELT_ALLAMP | L_DELT_EFFMUS, L_DELT_MULOSS ) {
  (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;
  (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;
  (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;
  (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;
  (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;
  (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;
  (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;
  (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;
  (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;
  (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;
  (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;
  (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;
  (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;
  (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;
  (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;
  (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;
  (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;
  (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;
  (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;
  (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;
  (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;
  (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;
  (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;
  (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;
  (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;
  (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;
  (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;
  (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;
  (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;
  (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;
}
probability ( L_AXIL_AMP_E | L_DELT_ALLAMP ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.00020002, 0.21532153, 0.19871987, 0.17091709, 0.13691369, 0.10221022, 0.07110711, 0.04600460, 0.02780278, 0.01560156, 0.00820082, 0.00400040, 0.00180018, 0.00080008, 0.00030003, 0.00010001, 0.00000000;
  (A0_10) 0.0000, 0.0087, 0.0213, 0.0441, 0.0778, 0.1167, 0.1489, 0.1614, 0.1488, 0.1166, 0.0777, 0.0440, 0.0212, 0.0087, 0.0030, 0.0009, 0.0002;
  (A0_30) 0.00000000, 0.00000000, 0.00019998, 0.00089991, 0.00349965, 0.01139886, 0.02999700, 0.06419358, 0.11158884, 0.15778422, 0.18128187, 0.16938306, 0.12878712, 0.07949205, 0.03989601, 0.01629837, 0.00529947;
  (A0_70) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0003, 0.0017, 0.0065, 0.0201, 0.0495, 0.0972, 0.1523, 0.1901, 0.1893, 0.1502, 0.0951, 0.0476;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019996, 0.00099980, 0.00439912, 0.01539692, 0.04229154, 0.09138172, 0.15406919, 0.20355929, 0.21035793, 0.17016597, 0.10717856;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0028, 0.0102, 0.0296, 0.0699, 0.1345, 0.2102, 0.2668, 0.2753;
  (A4_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0014, 0.0061, 0.0219, 0.0640, 0.1520, 0.2930, 0.4613;
  (A8_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0010, 0.0055, 0.0245, 0.0884, 0.2588, 0.6216;
}
probability ( L_AXIL_RD_ED | L_LNLPC5_AXIL_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 1.0, 0.0;
}
probability ( L_LNLPC5_AXIL_DIFSLOW | L_LNLPC5_AXIL_PATHO ) {
  (DEMY) 1.0, 0.0, 0.0, 0.0;
  (BLOCK) 1.0, 0.0, 0.0, 0.0;
  (AXONAL) 1.0, 0.0, 0.0, 0.0;
  (V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (E_REIN) 0.0, 0.0, 1.0, 0.0;
}
probability ( L_DIFFN_AXIL_DIFSLOW | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( L_AXIL_DIFSLOW_ED | L_LNLPC5_AXIL_DIFSLOW, L_DIFFN_AXIL_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;
  (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;
  (MOD, MOD) 0.000, 0.000, 0.002, 0.998;
  (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;
  (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;
  (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;
  (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;
}
probability ( L_AXIL_DCV | L_DELT_MALOSS, L_AXIL_DIFSLOW_ED ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;
  (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;
  (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;
  (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;
  (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;
  (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;
  (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;
  (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;
}
probability ( L_AXIL_DEL | L_AXIL_RD_ED, L_AXIL_DCV ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, M_S60) 0.0002, 0.0003, 0.0006, 0.0027, 0.0210, 0.2666, 0.7086, 0.0000, 0.0000;
  (SEV, M_S60) 0.0000, 0.0001, 0.0001, 0.0004, 0.0021, 0.0232, 0.4307, 0.5434, 0.0000;
  (NO, M_S52) 0.6514, 0.3486, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.00010002, 0.00020004, 0.00050010, 0.00220044, 0.01780356, 0.24224845, 0.73694739, 0.00000000, 0.00000000;
  (SEV, M_S52) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40864086, 0.56815682, 0.00000000;
  (NO, M_S44) 0.0003, 0.9337, 0.0660, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S44) 0.0001, 0.0002, 0.0004, 0.0016, 0.0137, 0.2066, 0.7774, 0.0000, 0.0000;
  (SEV, M_S44) 0.0000, 0.0000, 0.0001, 0.0003, 0.0015, 0.0178, 0.3739, 0.6064, 0.0000;
  (NO, M_S36) 0.00000000, 0.00380038, 0.99619962, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S36) 0.0001, 0.0001, 0.0002, 0.0011, 0.0095, 0.1650, 0.8240, 0.0000, 0.0000;
  (SEV, M_S36) 0.00000000, 0.00000000, 0.00009999, 0.00019998, 0.00109989, 0.01419858, 0.32926707, 0.65513449, 0.00000000;
  (NO, M_S28) 0.00000000, 0.00000000, 0.01060106, 0.98939894, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00540054, 0.11491149, 0.87908791, 0.00000000, 0.00000000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00070014, 0.00980196, 0.26615323, 0.72324465, 0.00000000;
  (NO, M_S20) 0.00000000, 0.00020002, 0.00260026, 0.13561356, 0.86158616, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0020, 0.0580, 0.9396, 0.0002, 0.0000;
  (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00480048, 0.16971697, 0.82518252, 0.00000000;
  (NO, M_S14) 0.0000, 0.0000, 0.0000, 0.0005, 0.8212, 0.1783, 0.0000, 0.0000, 0.0000;
  (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0184, 0.9779, 0.0033, 0.0000;
  (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0014, 0.0771, 0.9214, 0.0000;
  (NO, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59805981, 0.39023902, 0.00000000, 0.00000000;
  (MOD, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0021, 0.4010, 0.5969, 0.0000;
  (SEV, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.01440144, 0.98549855, 0.00000000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_AXIL_LAT_ED | L_AXIL_DEL ) {
  (MS0_0) 0.00990099, 0.07150715, 0.23392339, 0.34723472, 0.29242924, 0.04410441, 0.00090009, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS0_4) 0.0017, 0.0166, 0.0866, 0.2330, 0.4051, 0.2314, 0.0250, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS0_8) 0.0001, 0.0017, 0.0169, 0.0882, 0.2965, 0.4557, 0.1322, 0.0087, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MS1_6) 0.00000000, 0.00010001, 0.00110011, 0.00810081, 0.04770477, 0.25392539, 0.41724172, 0.25392539, 0.01630163, 0.00160016, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS3_2) 0.00000000, 0.00019998, 0.00059994, 0.00189981, 0.00659934, 0.02749725, 0.07099290, 0.13578642, 0.27427257, 0.30906909, 0.13458654, 0.03829617, 0.00019998, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MS6_4) 0.00009999, 0.00019998, 0.00029997, 0.00059994, 0.00129987, 0.00359964, 0.00749925, 0.01419858, 0.04569543, 0.08109189, 0.13678632, 0.27307269, 0.31566843, 0.10558944, 0.01359864, 0.00069993, 0.00000000;
  (MS12_8) 0.00009999, 0.00019998, 0.00029997, 0.00039996, 0.00059994, 0.00119988, 0.00179982, 0.00269973, 0.00679932, 0.01129887, 0.01919808, 0.04599540, 0.13018698, 0.21597840, 0.27987201, 0.28337166, 0.00000000;
  (MS25_6) 0.0009, 0.0010, 0.0012, 0.0014, 0.0021, 0.0031, 0.0039, 0.0049, 0.0093, 0.0138, 0.0193, 0.0398, 0.0956, 0.1618, 0.2573, 0.3846, 0.0000;
  (INFIN) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DELT_VOL_ACT ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_DELT_FORCE | L_DELT_VOL_ACT, L_DELT_ALLAMP ) {
  (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;
  (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;
  (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;
  (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;
  (NORMAL, A0_01) 0.00009999, 0.00429957, 0.05219478, 0.26687331, 0.43305669, 0.24347565;
  (REDUCED, A0_01) 0.0000, 0.0005, 0.0084, 0.0849, 0.3219, 0.5843;
  (V_RED, A0_01) 0.0000, 0.0000, 0.0007, 0.0167, 0.1576, 0.8250;
  (ABSENT, A0_01) 0.00000000, 0.00000000, 0.00000000, 0.00260026, 0.05860586, 0.93879388;
  (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;
  (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;
  (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;
  (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;
  (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;
  (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;
  (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;
  (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;
  (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;
  (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;
  (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;
  (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;
  (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;
  (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;
  (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;
  (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;
  (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;
  (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;
  (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;
  (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;
  (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;
  (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;
  (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;
  (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;
  (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;
}
probability ( L_DELT_MUSCLE_VOL | L_DELT_MUSIZE, L_DELT_MALOSS ) {
  (V_SMALL, NO) 0.9896, 0.0104;
  (SMALL, NO) 0.8137, 0.1863;
  (NORMAL, NO) 0.0209, 0.9791;
  (INCR, NO) 0.0209, 0.9791;
  (LARGE, NO) 0.003, 0.997;
  (V_LARGE, NO) 0.0004, 0.9996;
  (OTHER, NO) 0.4212, 0.5788;
  (V_SMALL, MILD) 0.9976, 0.0024;
  (SMALL, MILD) 0.9603, 0.0397;
  (NORMAL, MILD) 0.5185, 0.4815;
  (INCR, MILD) 0.1492, 0.8508;
  (LARGE, MILD) 0.0278, 0.9722;
  (V_LARGE, MILD) 0.0046, 0.9954;
  (OTHER, MILD) 0.5185, 0.4815;
  (V_SMALL, MOD) 0.999, 0.001;
  (SMALL, MOD) 0.9893, 0.0107;
  (NORMAL, MOD) 0.9588, 0.0412;
  (INCR, MOD) 0.6221, 0.3779;
  (LARGE, MOD) 0.2716, 0.7284;
  (V_LARGE, MOD) 0.0779, 0.9221;
  (OTHER, MOD) 0.6336, 0.3664;
  (V_SMALL, SEV) 0.9995, 0.0005;
  (SMALL, SEV) 0.9969, 0.0031;
  (NORMAL, SEV) 0.9953, 0.0047;
  (INCR, SEV) 0.9358, 0.0642;
  (LARGE, SEV) 0.8234, 0.1766;
  (V_LARGE, SEV) 0.5986, 0.4014;
  (OTHER, SEV) 0.7685, 0.2315;
  (V_SMALL, TOTAL) 0.9989, 0.0011;
  (SMALL, TOTAL) 0.9984, 0.0016;
  (NORMAL, TOTAL) 0.9984, 0.0016;
  (INCR, TOTAL) 0.9972, 0.0028;
  (LARGE, TOTAL) 0.9965, 0.0035;
  (V_LARGE, TOTAL) 0.9956, 0.0044;
  (OTHER, TOTAL) 0.9857, 0.0143;
  (V_SMALL, OTHER) 0.9363, 0.0637;
  (SMALL, OTHER) 0.8403, 0.1597;
  (NORMAL, OTHER) 0.6534, 0.3466;
  (INCR, OTHER) 0.4719, 0.5281;
  (LARGE, OTHER) 0.3174, 0.6826;
  (V_LARGE, OTHER) 0.1948, 0.8052;
  (OTHER, OTHER) 0.5681, 0.4319;
}
probability ( L_DELT_MVA_RECRUIT | L_DELT_MULOSS, L_DELT_VOL_ACT ) {
  (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;
  (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;
  (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;
  (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;
  (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;
  (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;
  (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;
  (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;
  (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;
  (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;
  (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;
  (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;
  (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;
  (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;
  (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;
  (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;
  (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;
  (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;
  (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DELT_MVA_AMP | L_DELT_EFFMUS ) {
  (V_SMALL) 0.00, 0.04, 0.96;
  (SMALL) 0.01, 0.15, 0.84;
  (NORMAL) 0.05, 0.90, 0.05;
  (INCR) 0.50, 0.49, 0.01;
  (LARGE) 0.85, 0.15, 0.00;
  (V_LARGE) 0.96, 0.04, 0.00;
  (OTHER) 0.33, 0.34, 0.33;
}
probability ( L_DELT_TA_CONCL | L_DELT_EFFMUS ) {
  (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;
  (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;
  (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;
  (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;
  (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;
  (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;
  (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;
}
probability ( L_DELT_MUPAMP | L_DELT_EFFMUS ) {
  (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;
  (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;
  (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;
  (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;
}
probability ( L_DELT_QUAN_MUPAMP | L_DELT_MUPAMP ) {
  (V_SMALL) 0.04180418, 0.08970897, 0.15021502, 0.19571957, 0.19871987, 0.15711571, 0.09670967, 0.04640464, 0.01730173, 0.00500050, 0.00110011, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.00420042, 0.01480148, 0.04100410, 0.08800880, 0.14731473, 0.19201920, 0.19491949, 0.15411541, 0.09490949, 0.04550455, 0.01700170, 0.00490049, 0.00110011, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.0000, 0.0001, 0.0006, 0.0043, 0.0210, 0.0693, 0.1550, 0.2345, 0.2401, 0.1663, 0.0779, 0.0247, 0.0053, 0.0008, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR) 0.00000000, 0.00000000, 0.00020002, 0.00090009, 0.00420042, 0.01480148, 0.04090409, 0.08790879, 0.14721472, 0.19181918, 0.19471947, 0.15391539, 0.09480948, 0.04540454, 0.01700170, 0.00490049, 0.00110011, 0.00020002, 0.00000000, 0.00000000;
  (LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00090009, 0.00420042, 0.01480148, 0.04090409, 0.08790879, 0.14721472, 0.19181918, 0.19471947, 0.15391539, 0.09480948, 0.04540454, 0.01700170, 0.00490049, 0.00110011, 0.00020002;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0005, 0.0021, 0.0070, 0.0196, 0.0451, 0.0853, 0.1326, 0.1696, 0.1784, 0.1544, 0.1099, 0.0644, 0.0310;
  (OTHER) 0.02049795, 0.02999700, 0.04159584, 0.05469453, 0.06809319, 0.08029197, 0.08969103, 0.09499050, 0.09529047, 0.09059094, 0.08159184, 0.06959304, 0.05629437, 0.04319568, 0.03129687, 0.02159784, 0.01409859, 0.00869913, 0.00509949, 0.00279972;
}
probability ( L_DELT_QUAL_MUPAMP | L_DELT_MUPAMP ) {
  (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;
  (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;
  (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;
  (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;
  (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;
  (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;
  (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;
}
probability ( L_DELT_MUPDUR | L_DELT_EFFMUS ) {
  (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;
  (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;
  (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;
  (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;
  (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;
  (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;
  (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;
}
probability ( L_DELT_QUAN_MUPDUR | L_DELT_MUPDUR ) {
  (V_SMALL) 0.01020102, 0.03690369, 0.09510951, 0.17471747, 0.22892289, 0.21402140, 0.14261426, 0.06780678, 0.02300230, 0.00560056, 0.00100010, 0.00010001, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (SMALL) 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (NORMAL) 0.0000, 0.0000, 0.0000, 0.0002, 0.0025, 0.0177, 0.0739, 0.1852, 0.2785, 0.2515, 0.1363, 0.0444, 0.0087, 0.0010, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;
  (INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.00199980, 0.01019898, 0.03679632, 0.09489051, 0.17428257, 0.22837716, 0.21347865, 0.14228577, 0.06769323, 0.02299770, 0.00559944, 0.00099990, 0.00009999, 0.00000000;
  (LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029994, 0.00199960, 0.01019796, 0.03689262, 0.09498100, 0.17446511, 0.22855429, 0.21365727, 0.14247151, 0.06778644, 0.02299540, 0.00559888, 0.00009998;
  (V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00040004, 0.00180018, 0.00730073, 0.02290229, 0.05640564, 0.10971097, 0.16831683, 0.20352035, 0.19411941, 0.14591459, 0.08950895;
  (OTHER) 0.00520104, 0.01070214, 0.01980396, 0.03360672, 0.05211042, 0.07371474, 0.09511902, 0.11212242, 0.12062412, 0.11842368, 0.10622124, 0.08681736, 0.06491298, 0.04420884, 0.02750550, 0.01560312, 0.00810162, 0.00380076, 0.00140028;
}
probability ( L_DELT_QUAL_MUPDUR | L_DELT_MUPDUR ) {
  (V_SMALL) 0.8309, 0.1677, 0.0014;
  (SMALL) 0.49, 0.49, 0.02;
  (NORMAL) 0.1065, 0.7870, 0.1065;
  (INCR) 0.02, 0.49, 0.49;
  (LARGE) 0.0014, 0.1677, 0.8309;
  (V_LARGE) 0.0001, 0.0392, 0.9607;
  (OTHER) 0.2597, 0.4806, 0.2597;
}
probability ( L_DELT_QUAN_MUPPOLY | L_DELT_DE_REGEN, L_DELT_EFFMUS ) {
  (NO, V_SMALL) 0.109, 0.548, 0.343;
  (YES, V_SMALL) 0.004, 0.122, 0.874;
  (NO, SMALL) 0.340, 0.564, 0.096;
  (YES, SMALL) 0.015, 0.261, 0.724;
  (NO, NORMAL) 0.925, 0.075, 0.000;
  (YES, NORMAL) 0.091, 0.526, 0.383;
  (NO, INCR) 0.796, 0.201, 0.003;
  (YES, INCR) 0.061, 0.465, 0.474;
  (NO, LARGE) 0.637, 0.348, 0.015;
  (YES, LARGE) 0.039, 0.396, 0.565;
  (NO, V_LARGE) 0.340, 0.564, 0.096;
  (YES, V_LARGE) 0.015, 0.261, 0.724;
  (NO, OTHER) 0.340, 0.564, 0.096;
  (YES, OTHER) 0.015, 0.261, 0.724;
}
probability ( L_DELT_QUAL_MUPPOLY | L_DELT_QUAN_MUPPOLY ) {
  (x_12_) 0.95, 0.05;
  (x12_24_) 0.3, 0.7;
  (x_24_) 0.05, 0.95;
}
probability ( L_DELT_MUPSATEL | L_DELT_DE_REGEN ) {
  (NO) 0.95, 0.05;
  (YES) 0.2, 0.8;
}
probability ( L_DELT_MUPINSTAB | L_DELT_NMT ) {
  (NO) 0.95, 0.05;
  (MOD_PRE) 0.1, 0.9;
  (SEV_PRE) 0.03, 0.97;
  (MLD_POST) 0.2, 0.8;
  (MOD_POST) 0.1, 0.9;
  (SEV_POST) 0.03, 0.97;
  (OTHER) 0.1, 0.9;
}
probability ( L_DELT_REPSTIM_CMAPAMP | L_DELT_ALLAMP ) {
  (ZERO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (A0_01) 0.0008, 0.0838, 0.0981, 0.1087, 0.1140, 0.1131, 0.1062, 0.0944, 0.0794, 0.0632, 0.0476, 0.0340, 0.0229, 0.0146, 0.0089, 0.0051, 0.0027, 0.0014, 0.0007, 0.0003, 0.0001;
  (A0_10) 0.00000000, 0.00029994, 0.00099980, 0.00279944, 0.00699860, 0.01539692, 0.03019396, 0.05238952, 0.08058388, 0.10937812, 0.13147371, 0.13967207, 0.13137373, 0.10927814, 0.08048390, 0.05238952, 0.03019396, 0.01539692, 0.00689862, 0.00279944, 0.00099980;
  (A0_30) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020004, 0.00070014, 0.00250050, 0.00730146, 0.01840368, 0.03930786, 0.07141428, 0.11012202, 0.14452891, 0.16123225, 0.15283057, 0.12322464, 0.08441688, 0.04920984, 0.02440488, 0.01020204;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00059994, 0.00239976, 0.00839916, 0.02349765, 0.05359464, 0.09919008, 0.14958504, 0.18328167, 0.18258174, 0.14778522, 0.09729027, 0.05169483;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00019996, 0.00099980, 0.00439912, 0.01539692, 0.04229154, 0.09138172, 0.15406919, 0.20355929, 0.21035793, 0.17016597, 0.10717856;
  (A2_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0012, 0.0045, 0.0139, 0.0361, 0.0776, 0.1391, 0.2072, 0.2564, 0.2637;
  (A4_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0005, 0.0024, 0.0092, 0.0286, 0.0745, 0.1612, 0.2895, 0.4340;
  (A8_00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0003, 0.0019, 0.0086, 0.0327, 0.1028, 0.2680, 0.5856;
}
probability ( L_DELT_REPSTIM_DECR | L_DELT_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;
  (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;
  (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;
  (OTHER) 0.245, 0.245, 0.245, 0.245, 0.020;
}
probability ( L_DELT_REPSTIM_FACILI | L_DELT_NMT ) {
  (NO) 0.95, 0.02, 0.01, 0.02;
  (MOD_PRE) 0.010, 0.889, 0.100, 0.001;
  (SEV_PRE) 0.010, 0.080, 0.909, 0.001;
  (MLD_POST) 0.89, 0.08, 0.01, 0.02;
  (MOD_POST) 0.48, 0.50, 0.01, 0.01;
  (SEV_POST) 0.020, 0.949, 0.030, 0.001;
  (OTHER) 0.25, 0.25, 0.25, 0.25;
}
probability ( L_DELT_REPSTIM_POST_DECR | L_DELT_NMT ) {
  (NO) 0.949, 0.020, 0.010, 0.001, 0.020;
  (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;
  (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;
  (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;
  (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;
  (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;
  (OTHER) 0.23, 0.23, 0.22, 0.22, 0.10;
}
probability ( L_DELT_SF_JITTER | L_DELT_NMT ) {
  (NO) 0.95, 0.05, 0.00, 0.00;
  (MOD_PRE) 0.02, 0.20, 0.70, 0.08;
  (SEV_PRE) 0.0, 0.1, 0.4, 0.5;
  (MLD_POST) 0.05, 0.70, 0.20, 0.05;
  (MOD_POST) 0.01, 0.19, 0.70, 0.10;
  (SEV_POST) 0.0, 0.1, 0.4, 0.5;
  (OTHER) 0.1, 0.3, 0.3, 0.3;
}
probability ( L_LNLPC5_DELT_MUDENS | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME, L_LNLPC5_AXIL_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
  (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( L_DIFFN_DELT_MUDENS | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 0.8, 0.2, 0.0;
  (MOD, OLD, DEMY) 0.7, 0.3, 0.0;
  (SEV, OLD, DEMY) 0.6, 0.4, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;
  (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;
  (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;
  (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;
  (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;
  (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;
  (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;
  (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;
  (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;
  (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;
}
probability ( L_LNLPC5_DIFFN_DELT_MUDENS | L_LNLPC5_DELT_MUDENS, L_DIFFN_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_MYOP_DELT_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MYDY_DELT_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MYOP_MYDY_DELT_MUDENS | L_MYOP_DELT_MUDENS, L_MYDY_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_MYAS_DELT_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_OTHER_DELT_MUDENS ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_MYAS_OTHER_DELT_MUDENS | L_MYAS_DELT_MUDENS, L_OTHER_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_MUSCLE_DELT_MUDENS | L_MYOP_MYDY_DELT_MUDENS, L_MYAS_OTHER_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_DELT_MUDENS | L_LNLPC5_DIFFN_DELT_MUDENS, L_MUSCLE_DELT_MUDENS ) {
  (NORMAL, NORMAL) 1.0, 0.0, 0.0;
  (INCR, NORMAL) 0.0, 1.0, 0.0;
  (V_INCR, NORMAL) 0.0, 0.0, 1.0;
  (NORMAL, INCR) 0.0, 1.0, 0.0;
  (INCR, INCR) 0.0, 0.0, 1.0;
  (V_INCR, INCR) 0.0, 0.0, 1.0;
  (NORMAL, V_INCR) 0.0, 0.0, 1.0;
  (INCR, V_INCR) 0.0, 0.0, 1.0;
  (V_INCR, V_INCR) 0.0, 0.0, 1.0;
}
probability ( L_DELT_SF_DENSITY | L_DELT_MUDENS ) {
  (NORMAL) 0.97, 0.03, 0.00;
  (INCR) 0.05, 0.90, 0.05;
  (V_INCR) 0.01, 0.04, 0.95;
}
probability ( L_LNLPC5_DELT_NEUR_ACT | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_DELT_NEUR_ACT | DIFFN_M_SEV_PROX, DIFFN_TIME ) {
  (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;
  (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;
  (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
  (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;
  (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_LNLPC5_DIFFN_DELT_NEUR_ACT | L_LNLPC5_DELT_NEUR_ACT, L_DIFFN_DELT_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_DELT_NEUR_ACT ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DELT_NEUR_ACT | L_LNLPC5_DIFFN_DELT_NEUR_ACT, L_OTHER_DELT_NEUR_ACT ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;
  (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;
  (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;
  (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DELT_SPONT_NEUR_DISCH | L_DELT_NEUR_ACT ) {
  (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;
  (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;
  (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;
  (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;
  (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;
  (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;
}
probability ( L_MYOP_DELT_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYDY_DELT_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_MYOP_MYDY_DELT_DENERV | L_MYOP_DELT_DENERV, L_MYDY_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_DELT_DENERV ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_NMT_DELT_DENERV | L_DELT_NMT ) {
  (NO) 1.0, 0.0, 0.0, 0.0;
  (MOD_PRE) 0.40, 0.45, 0.15, 0.00;
  (SEV_PRE) 0.15, 0.35, 0.35, 0.15;
  (MLD_POST) 0.85, 0.15, 0.00, 0.00;
  (MOD_POST) 0.30, 0.45, 0.20, 0.05;
  (SEV_POST) 0.15, 0.35, 0.35, 0.15;
  (OTHER) 0.25, 0.25, 0.25, 0.25;
}
probability ( L_OTHER_NMT_DELT_DENERV | L_OTHER_DELT_DENERV, L_NMT_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_MUSCLE_DELT_DENERV | L_MYOP_MYDY_DELT_DENERV, L_OTHER_NMT_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_LNLPC5_DELT_DENERV | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME, L_LNLPC5_AXIL_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( L_DIFFN_DELT_DENERV | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {
  (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;
  (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;
  (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;
  (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;
  (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;
  (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;
  (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;
  (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;
  (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;
  (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;
  (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;
  (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;
  (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;
  (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;
  (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
  (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;
}
probability ( L_LNLPC5_DIFFN_DELT_DENERV | L_LNLPC5_DELT_DENERV, L_DIFFN_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DELT_DENERV | L_MUSCLE_DELT_DENERV, L_LNLPC5_DIFFN_DELT_DENERV ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0, 1.0, 0.0, 0.0;
  (MOD, NO) 0.0, 0.0, 1.0, 0.0;
  (SEV, NO) 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0, 1.0, 0.0, 0.0;
  (MILD, MILD) 0.0, 0.0, 1.0, 0.0;
  (MOD, MILD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MILD) 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0, 0.0, 1.0, 0.0;
  (MILD, MOD) 0.0, 0.0, 0.0, 1.0;
  (MOD, MOD) 0.0, 0.0, 0.0, 1.0;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0, 0.0, 0.0, 1.0;
  (MILD, SEV) 0.0, 0.0, 0.0, 1.0;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_DELT_SPONT_DENERV_ACT | L_DELT_DENERV ) {
  (NO) 0.98, 0.02, 0.00, 0.00;
  (MILD) 0.07, 0.85, 0.08, 0.00;
  (MOD) 0.01, 0.07, 0.85, 0.07;
  (SEV) 0.00, 0.01, 0.07, 0.92;
}
probability ( L_DELT_SPONT_HF_DISCH | L_DELT_DENERV ) {
  (NO) 0.99, 0.01;
  (MILD) 0.97, 0.03;
  (MOD) 0.95, 0.05;
  (SEV) 0.93, 0.07;
}
probability ( L_DELT_SPONT_INS_ACT | L_DELT_DENERV ) {
  (NO) 0.98, 0.02;
  (MILD) 0.1, 0.9;
  (MOD) 0.05, 0.95;
  (SEV) 0.05, 0.95;
}
probability ( L_OTHER_ISCH_DISP ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ISCH_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;
  (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_SUR_DISP_CA | L_OTHER_ISCH_DISP, L_DIFFN_ISCH_DISP ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;
  (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;
  (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;
  (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;
  (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;
  (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;
  (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;
  (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;
  (SEV, MOD) 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;
  (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;
  (MOD, SEV) 0.0, 0.0, 0.0, 1.0;
  (SEV, SEV) 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_ISCH_BLOCK ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ISCH_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;
  (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_SUR_BLOCK_CA | L_OTHER_ISCH_BLOCK, L_DIFFN_ISCH_BLOCK ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;
  (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;
  (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;
  (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;
  (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;
  (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;
  (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;
  (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_OTHER_ISCH_SALOSS ) {
  table 1.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ISCH_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_LNL_ISCH_SEV ) {
  table 0.960, 0.020, 0.010, 0.005, 0.005;
}
probability ( L_LNL_ISCH_PATHO ) {
  table 0.25, 0.25, 0.50, 0.00, 0.00;
}
probability ( L_LNL_ISCH_SALOSS_CA | L_LNL_ISCH_SEV, L_LNL_ISCH_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;
  (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;
  (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;
  (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;
  (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;
  (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
  (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;
}
probability ( L_DIFFN_LNL_ISCH_SALOSS | L_DIFFN_ISCH_SALOSS, L_LNL_ISCH_SALOSS_CA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_SUR_SALOSS | L_OTHER_ISCH_SALOSS, L_DIFFN_LNL_ISCH_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_SUR_EFFAXLOSS | L_SUR_BLOCK_CA, L_SUR_SALOSS ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;
  (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;
  (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;
  (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;
  (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;
  (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;
  (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;
  (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;
  (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
  (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_SUR_ALLAMP_CA | L_SUR_DISP_CA, L_SUR_EFFAXLOSS ) {
  (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;
  (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;
  (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;
  (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;
  (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;
  (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;
  (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;
  (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;
  (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;
  (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;
  (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;
  (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;
  (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;
  (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;
  (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;
  (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;
  (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
probability ( L_SUR_AMP_CA | L_SUR_ALLAMP_CA ) {
  (ZERO) 0.7619, 0.1816, 0.0438, 0.0099, 0.0022, 0.0005, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (A0_01) 0.38293829, 0.26322632, 0.16731673, 0.09570957, 0.04980498, 0.02420242, 0.01050105, 0.00410041, 0.00150015, 0.00050005, 0.00020002, 0.00000000, 0.00000000;
  (A0_10) 0.04670467, 0.11351135, 0.19301930, 0.23662366, 0.20492049, 0.12771277, 0.05570557, 0.01720172, 0.00390039, 0.00060006, 0.00010001, 0.00000000, 0.00000000;
  (A0_30) 0.00000000, 0.00009999, 0.00109989, 0.01139886, 0.06489351, 0.19078092, 0.30886911, 0.26467353, 0.12378762, 0.03019698, 0.00389961, 0.00029997, 0.00000000;
  (A0_70) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00170017, 0.02270227, 0.13181318, 0.31883188, 0.33903390, 0.15251525, 0.03080308, 0.00250025;
  (A1_00) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00580058, 0.07460746, 0.31883188, 0.41204120, 0.16811681, 0.02050205;
}
probability ( L_SUR_LD_CA ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_SUR_RD_CA ) {
  table 1.0, 0.0, 0.0;
}
probability ( L_SUR_LSLOW_CA | L_SUR_LD_CA, L_SUR_RD_CA ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;
  (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;
  (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;
  (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;
  (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;
  (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;
  (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;
  (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;
  (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;
  (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;
  (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;
}
probability ( L_OTHER_ISCH_DIFSLOW ) {
  table 1.0, 0.0, 0.0, 0.0;
}
probability ( L_DIFFN_ISCH_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {
  (NO, DEMY) 1.0, 0.0, 0.0, 0.0;
  (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;
  (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;
  (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;
  (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;
  (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;
  (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;
  (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;
  (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;
  (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;
  (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;
}
probability ( L_SUR_DIFSLOW_CA | L_OTHER_ISCH_DIFSLOW, L_DIFFN_ISCH_DIFSLOW ) {
  (NO, NO) 1.0, 0.0, 0.0, 0.0;
  (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;
  (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;
  (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;
  (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;
  (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;
  (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;
  (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;
  (MOD, MOD) 0.000, 0.000, 0.004, 0.996;
  (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;
  (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;
  (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;
  (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;
  (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;
}
probability ( L_SUR_DSLOW_CA | L_SUR_SALOSS, L_SUR_DIFSLOW_CA ) {
  (NO, NO) 0.8825, 0.1175, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, NO) 0.34603460, 0.65076508, 0.00320032, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, NO) 0.0641, 0.8105, 0.1254, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, NO) 0.00410041, 0.15271527, 0.78487849, 0.05830583, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MILD) 0.06390639, 0.24022402, 0.46154615, 0.22352235, 0.01080108, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, MILD) 0.0245, 0.1308, 0.4221, 0.3800, 0.0426, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, MILD) 0.0078, 0.0585, 0.3098, 0.5006, 0.1230, 0.0003, 0.0000, 0.0000, 0.0000;
  (SEV, MILD) 0.0012, 0.0133, 0.1276, 0.4624, 0.3871, 0.0084, 0.0000, 0.0000, 0.0000;
  (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, MOD) 0.0011, 0.0083, 0.0684, 0.2984, 0.5331, 0.0899, 0.0008, 0.0000, 0.0000;
  (MILD, MOD) 0.0003, 0.0030, 0.0335, 0.2043, 0.5689, 0.1865, 0.0035, 0.0000, 0.0000;
  (MOD, MOD) 0.00010001, 0.00100010, 0.01440144, 0.12231223, 0.52405241, 0.32563256, 0.01250125, 0.00000000, 0.00000000;
  (SEV, MOD) 0.0000, 0.0002, 0.0034, 0.0430, 0.3319, 0.5510, 0.0705, 0.0000, 0.0000;
  (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (NO, SEV) 0.00040004, 0.00120012, 0.00530053, 0.02140214, 0.08840884, 0.28082808, 0.44704470, 0.15541554, 0.00000000;
  (MILD, SEV) 0.00019996, 0.00069986, 0.00319936, 0.01399720, 0.06498700, 0.24325135, 0.46090782, 0.21275745, 0.00000000;
  (MOD, SEV) 0.0001, 0.0004, 0.0018, 0.0089, 0.0461, 0.2037, 0.4588, 0.2802, 0.0000;
  (SEV, SEV) 0.00000000, 0.00010001, 0.00080008, 0.00410041, 0.02540254, 0.14331433, 0.42124212, 0.40504050, 0.00000000;
  (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_SUR_ALLCV_CA | L_SUR_LSLOW_CA, L_SUR_DSLOW_CA ) {
  (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
  (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;
  (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;
  (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;
  (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;
  (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;
  (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;
  (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;
  (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;
  (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;
  (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;
  (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;
  (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;
  (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;
  (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;
  (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;
  (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;
  (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;
  (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;
  (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;
  (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;
  (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;
  (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;
  (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;
  (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;
  (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;
  (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;
  (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;
  (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;
  (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;
  (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;
  (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;
  (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;
  (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;
  (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;
  (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;
  (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
  (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;
}
probability ( L_SUR_CV_CA | L_SUR_ALLCV_CA ) {
  (M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00109989, 0.03779622, 0.29927007, 0.42315768, 0.19018098, 0.04509549, 0.00339966;
  (M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.01570157, 0.17281728, 0.38253825, 0.31873187, 0.09460946, 0.01350135, 0.00180018, 0.00010001;
  (M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0103, 0.1527, 0.3890, 0.3032, 0.1168, 0.0247, 0.0030, 0.0002, 0.0000, 0.0000;
  (M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0554, 0.3435, 0.4067, 0.1669, 0.0241, 0.0026, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0054, 0.2321, 0.5116, 0.2174, 0.0304, 0.0030, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S20) 0.00000000, 0.00000000, 0.00000000, 0.09980998, 0.56675668, 0.28572857, 0.04400440, 0.00350035, 0.00020002, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S14) 0.00000000, 0.00000000, 0.14628537, 0.69093091, 0.15288471, 0.00949905, 0.00039996, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;
  (M_S08) 0.0000, 0.1754, 0.7847, 0.0388, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;
  (M_S00) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}


================================================
FILE: data/mushroom.csv
================================================
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p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d
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p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
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p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
e,x,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
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p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
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p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
p,b,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
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p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
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p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
e,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
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p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
e,f,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
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p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
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p,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
e,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
e,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
e,f,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
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p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
e,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d
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p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d
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p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
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p,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
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p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
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p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
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p,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
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p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
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p,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
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p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
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p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
e,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d
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p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
e,k,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
e,f,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
e,k,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
e,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
e,x,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,k,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
e,x,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,b,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
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p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
e,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d
e,x,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
e,x,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d
p,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
e,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
e,x,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
e,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
e,k,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
e,f,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
e,f,s,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
e,x,s,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
e,x,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
e,f,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d
e,x,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
e,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d
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p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
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p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
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p,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d
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p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
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p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
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p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
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p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
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p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
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p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
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p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
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p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
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p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
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p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d
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p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d
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e,f,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
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e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d
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p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
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p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
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p,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
e,x,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
p,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
e,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
e,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
e,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d
p,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
e,f,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
e,x,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d
p,b,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
e,k,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
e,k,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,b,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
e,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d
p,b,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
e,f,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
e,k,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
e,k,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,s,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
e,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
e,f,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,b,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
e,f,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
e,x,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
e,f,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p
e,f,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
e,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
e,k,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
e,x,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
e,x,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,k,s,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
e,k,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,s,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
e,f,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,x,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,k,s,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p
e,f,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
e,f,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
e,x,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,b,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
e,f,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
e,x,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
e,k,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d
e,f,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
e,k,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
p,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
p,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p
e,x,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
e,x,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d
e,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,k,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g
e,k,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
p,b,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
e,k,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
e,k,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
e,f,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
e,f,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
e,k,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
e,k,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,c,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
p,b,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
p,b,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,k,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,k,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
e,x,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,f,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,x,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
e,f,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p
p,f,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
e,x,y,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
e,k,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
p,x,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
e,x,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d
e,x,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
e,f,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,b,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
e,x,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
e,f,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d
p,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
e,f,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d
e,x,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p
p,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,k,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
e,k,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
p,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
e,k,s,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
e,f,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
e,k,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,k,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d
e,x,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
e,x,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,b,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,k,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,k,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g
e,x,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
p,k,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
e,k,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,k,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,f,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
e,k,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p
p,b,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
e,k,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
p,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,b,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
e,k,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
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p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,k,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
e,f,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
e,k,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
e,x,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
e,k,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,k,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
e,k,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
e,f,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
e,x,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
e,x,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p
p,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
e,f,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d
e,x,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
e,x,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
e,x,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,f,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
e,k,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,f,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,b,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,f,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g
e,f,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,b,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
e,x,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,f,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d
p,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,k,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
e,x,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p
e,x,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
e,f,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
e,x,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
e,x,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d
p,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
p,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
e,f,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g
e,k,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,b,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
e,x,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p
p,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
e,f,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
e,x,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
e,f,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
e,x,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
e,f,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
e,k,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,k,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
e,k,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
p,b,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,b,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,b,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
e,x,s,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g
e,f,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
e,f,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d
e,f,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,b,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u
e,f,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
e,x,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
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p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
e,x,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u
p,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g
p,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
e,k,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,k,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
p,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,b,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g
p,f,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
e,k,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
e,k,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d
p,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
e,x,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d
e,k,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
e,f,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,k,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
e,k,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,c,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,x,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,k,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p
e,x,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,b,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p
e,f,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,f,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,f,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,x,y,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
e,x,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g
e,k,y,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
e,k,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
e,k,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,b,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
e,f,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
e,f,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
e,x,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p
e,x,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g
p,f,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
e,f,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,b,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,b,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
e,x,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,f,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,x,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,b,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
e,x,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,f,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
e,x,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
e,x,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,f,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,k,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g
e,k,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g
e,f,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g
p,b,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
e,f,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
e,x,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u
e,k,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,k,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g
p,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p
e,x,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
e,x,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d
p,b,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p
p,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
e,k,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p
e,k,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,f,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
e,f,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p
e,k,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,x,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
p,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g
p,x,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
e,f,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g
p,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u
e,k,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g
e,k,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,f,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,f,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p
p,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g
p,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
e,x,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
e,f,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,x,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d
e,f,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
e,k,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,f,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,k,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,f,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,f,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p
e,f,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,b,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
e,k,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
p,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g
p,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
e,f,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
e,x,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
p,k,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l
p,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,k,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g
e,f,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
p,b,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
e,f,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u
p,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g
e,x,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
e,k,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,x,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
e,k,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
e,k,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p
p,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u
e,k,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g
p,b,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
p,b,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
e,x,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
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p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
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e,f,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
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p,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
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p,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g
e,f,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,b,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u
p,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p
p,b,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
e,f,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l
p,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g
p,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
e,f,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
e,f,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,k,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,x,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p
p,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d
e,f,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g
e,k,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g
p,f,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,f,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
e,x,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
e,x,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d
p,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p
p,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d
p,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g
e,f,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g
e,k,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g
p,b,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,b,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d
e,x,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,k,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
e,x,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m
e,f,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,f,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u
e,x,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,x,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p
p,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g
p,b,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
e,k,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p
e,x,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
p,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
p,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d
p,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
e,k,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,x,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d
p,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
p,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u
e,f,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
e,x,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d
p,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u
e,x,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,x,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g
p,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p
p,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p
p,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u
e,k,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
e,x,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g
e,f,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u
p,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p
p,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p
p,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p
p,b,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,b,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
e,x,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w
p,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g
p,b,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u
e,f,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,k,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
e,f,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w
p,b,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g
p,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g
p,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u
p,b,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
e,f,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
e,x,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l
p,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p
p,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g
e,k,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w
p,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p
p,b,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d
p,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d
p,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g
p,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,x,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w
e,f,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,f,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,k,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d
p,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g
p,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d
p,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d
p,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g
e,x,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d
p,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m
p,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g
p,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u
e,k,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
p,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g
e,x,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d
e,f,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u
p,f,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d
p,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g
p,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g
p,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g
p,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u
p,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d
p,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,b,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g
p,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g
p,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,k,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w
e,x,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d
p,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u
p,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,c,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,x,s,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,b,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,c,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,b,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,c,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,x,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,c,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,c,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,x,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,x,y,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,c,l
e,k,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,x,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,b,y,y,f,n,f,w,n,y,e,c,y,y,y,y,p,y,o,e,w,c,l
p,x,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,b,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
e,b,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,c,l
e,x,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,x,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
e,b,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,f,y,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,b,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,f,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,x,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,b,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,c,l
e,k,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,c,l
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,k,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,x,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,k,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
e,k,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,b,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,k,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
e,k,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,f,s,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,c,l
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,c,l
p,k,y,c,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
e,x,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,v,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
e,k,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,c,l
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,x,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,y,n,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,k,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
e,f,s,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,c,l
p,f,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,b,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,v,l
p,f,y,n,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,f,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,c,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
e,x,y,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,v,l
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,v,l
e,k,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,v,l
e,x,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,f,y,c,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,f,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,k,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,x,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,x,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,b,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,x,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,f,y,n,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,b,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,f,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,k,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,x,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,f,y,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,c,l
e,b,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,c,l
p,x,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,k,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,f,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,x,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,x,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,v,l
e,k,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,x,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,v,l
e,k,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,e,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,b,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,c,l
e,b,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,c,l
e,b,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,v,l
e,k,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,c,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,f,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,n,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
e,b,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,k,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,k,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,x,y,e,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
e,f,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
e,k,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
p,f,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,y,f,n,f,w,n,y,e,c,y,y,y,y,p,y,o,e,w,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,c,l
e,x,s,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,k,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,b,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,c,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,x,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,x,y,n,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,c,l
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,v,l
e,b,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
e,k,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,f,y,e,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,f,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,y,c,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,f,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
e,b,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,k,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,c,l
e,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,b,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,k,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,c,l
p,f,y,y,f,n,f,w,n,w,e,c,y,y,y,y,p,y,o,e,w,c,l
p,x,y,c,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,v,l
e,f,s,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,k,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,c,y,y,f,n,f,w,n,y,e,c,y,y,y,y,p,y,o,e,w,c,l
p,x,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,v,l
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,c,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,v,l
p,x,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,x,s,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
e,x,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,f,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,c,l
p,f,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,v,l
p,f,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,f,y,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,x,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,k,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
e,x,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,v,l
p,f,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,c,l
e,x,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,c,l
e,b,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,x,y,e,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,x,s,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,f,s,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,y,f,n,f,w,n,y,e,c,y,y,y,y,p,y,o,e,w,c,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,f,y,e,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,x,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,x,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,c,l
p,f,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,c,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,c,l
e,b,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,c,l
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,x,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,c,l
e,x,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
e,k,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,c,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,v,l
e,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,b,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,k,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,c,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,v,l
e,x,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
e,x,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,v,l
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,x,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,x,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,k,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,k,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,k,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,b,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
e,b,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,s,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,b,y,y,f,n,f,w,n,w,e,c,y,y,y,y,p,y,o,e,w,c,l
p,k,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
e,x,s,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,k,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,b,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,v,l
p,f,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,b,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,k,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,x,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,c,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,v,l
e,b,f,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,x,y,e,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,c,l
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,b,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
e,b,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,k,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,f,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,f,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,k,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,b,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,f,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,x,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,v,l
e,k,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,c,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,x,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,f,y,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,b,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,x,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,v,l
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,c,l
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,v,l
e,f,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
e,f,s,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,x,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,c,y,y,f,n,f,w,n,w,e,c,y,y,y,y,p,y,o,e,w,c,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,x,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,c,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,c,l
e,b,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
e,b,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
e,x,y,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,f,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,c,l
e,b,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,x,y,n,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,x,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,v,l
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,y,y,f,n,f,w,n,w,e,c,y,y,y,y,p,y,o,e,w,c,l
e,k,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,k,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
e,k,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,c,l
e,x,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
e,x,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
e,x,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,v,l
e,b,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,v,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,b,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
p,x,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,k,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,v,l
e,b,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
p,f,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,f,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,c,l
p,k,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,k,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,f,y,c,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
e,k,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,x,y,n,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,f,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,b,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,c,l
e,x,s,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p
e,x,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
e,b,s,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
p,k,y,e,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,v,l
p,k,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,v,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l
e,k,s,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,v,l
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
e,b,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,v,l
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l
e,x,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,v,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,b,c,l
p,k,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,x,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,k,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,x,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,c,l
e,b,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,x,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,v,l
e,x,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
e,k,s,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,v,l
e,k,s,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d
p,x,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,c,l
p,k,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,x,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,f,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,v,l
e,x,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
p,k,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l
e,b,s,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,n,c,l
e,x,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
e,x,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,f,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
e,f,y,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,k,s,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
p,x,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,f,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,v,l
p,x,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,k,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,p,e,?,s,s,w,w,p,w,t,p,w,s,g
p,f,y,n,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,v,l
e,b,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,v,l
e,x,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
p,k,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,x,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
e,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,c,l
p,k,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p
e,k,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,f,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,k,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,f,g,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,c,l
e,k,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,c,l
e,f,y,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,k,y,e,f,m,a,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d
p,x,y,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,v,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,o,c,l
e,x,y,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
p,k,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
p,k,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,f,s,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,c,l
e,b,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g
e,k,f,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,o,c,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,c,l
e,k,f,w,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
p,k,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l
e,b,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,v,l
e,x,y,n,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
e,b,s,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g
p,f,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d
p,k,s,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,c,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,v,l
e,b,s,w,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
p,x,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
e,b,f,w,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,b,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,o,v,l
p,k,y,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d
p,k,y,c,f,m,a,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d
p,k,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
e,x,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g
e,f,s,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,c,l
e,b,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
p,x,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,c,l
p,f,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
e,k,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,k,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,c,l
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,y,v,l
p,k,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l
p,k,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,c,l
e,x,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,p
e,x,s,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,o,v,l
p,f,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,x,y,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d
p,k,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,v,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,v,l
e,k,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g
e,k,f,w,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g
p,k,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p
e,b,f,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g
e,x,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
e,b,f,w,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d
p,f,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l
p,f,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,s,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,y,c,l
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,v,l
p,k,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,x,f,g,f,n,f,w,b,g,e,?,s,s,w,w,p,w,t,p,w,n,g
e,k,s,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p
p,x,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,c,l
e,b,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g
e,k,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d
e,x,y,g,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p
p,k,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,v,l
p,k,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p
e,x,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,c,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,v,l
e,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,c,l
p,k,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p
e,b,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g
p,k,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d
p,k,s,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
p,k,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l
p,k,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d
e,x,f,w,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,s,g
e,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d
e,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,v,l
e,b,f,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,n,g
e,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,v,l
e,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,o,v,l
p,k,s,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l
p,k,s,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p
p,k,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d
p,k,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p
p,k,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l
e,k,f,w,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g
p,k,y,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l
e,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,b,v,l
p,k,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p
e,b,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,b,c,l
e,f,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,c,l
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================================================
FILE: data/scale.csv
================================================
B,1,1,1,1
R,1,1,1,2
R,1,1,1,3
R,1,1,1,4
R,1,1,1,5
R,1,1,2,1
R,1,1,2,2
R,1,1,2,3
R,1,1,2,4
R,1,1,2,5
R,1,1,3,1
R,1,1,3,2
R,1,1,3,3
R,1,1,3,4
R,1,1,3,5
R,1,1,4,1
R,1,1,4,2
R,1,1,4,3
R,1,1,4,4
R,1,1,4,5
R,1,1,5,1
R,1,1,5,2
R,1,1,5,3
R,1,1,5,4
R,1,1,5,5
L,1,2,1,1
B,1,2,1,2
R,1,2,1,3
R,1,2,1,4
R,1,2,1,5
B,1,2,2,1
R,1,2,2,2
R,1,2,2,3
R,1,2,2,4
R,1,2,2,5
R,1,2,3,1
R,1,2,3,2
R,1,2,3,3
R,1,2,3,4
R,1,2,3,5
R,1,2,4,1
R,1,2,4,2
R,1,2,4,3
R,1,2,4,4
R,1,2,4,5
R,1,2,5,1
R,1,2,5,2
R,1,2,5,3
R,1,2,5,4
R,1,2,5,5
L,1,3,1,1
L,1,3,1,2
B,1,3,1,3
R,1,3,1,4
R,1,3,1,5
L,1,3,2,1
R,1,3,2,2
R,1,3,2,3
R,1,3,2,4
R,1,3,2,5
B,1,3,3,1
R,1,3,3,2
R,1,3,3,3
R,1,3,3,4
R,1,3,3,5
R,1,3,4,1
R,1,3,4,2
R,1,3,4,3
R,1,3,4,4
R,1,3,4,5
R,1,3,5,1
R,1,3,5,2
R,1,3,5,3
R,1,3,5,4
R,1,3,5,5
L,1,4,1,1
L,1,4,1,2
L,1,4,1,3
B,1,4,1,4
R,1,4,1,5
L,1,4,2,1
B,1,4,2,2
R,1,4,2,3
R,1,4,2,4
R,1,4,2,5
L,1,4,3,1
R,1,4,3,2
R,1,4,3,3
R,1,4,3,4
R,1,4,3,5
B,1,4,4,1
R,1,4,4,2
R,1,4,4,3
R,1,4,4,4
R,1,4,4,5
R,1,4,5,1
R,1,4,5,2
R,1,4,5,3
R,1,4,5,4
R,1,4,5,5
L,1,5,1,1
L,1,5,1,2
L,1,5,1,3
L,1,5,1,4
B,1,5,1,5
L,1,5,2,1
L,1,5,2,2
R,1,5,2,3
R,1,5,2,4
R,1,5,2,5
L,1,5,3,1
R,1,5,3,2
R,1,5,3,3
R,1,5,3,4
R,1,5,3,5
L,1,5,4,1
R,1,5,4,2
R,1,5,4,3
R,1,5,4,4
R,1,5,4,5
B,1,5,5,1
R,1,5,5,2
R,1,5,5,3
R,1,5,5,4
R,1,5,5,5
L,2,1,1,1
B,2,1,1,2
R,2,1,1,3
R,2,1,1,4
R,2,1,1,5
B,2,1,2,1
R,2,1,2,2
R,2,1,2,3
R,2,1,2,4
R,2,1,2,5
R,2,1,3,1
R,2,1,3,2
R,2,1,3,3
R,2,1,3,4
R,2,1,3,5
R,2,1,4,1
R,2,1,4,2
R,2,1,4,3
R,2,1,4,4
R,2,1,4,5
R,2,1,5,1
R,2,1,5,2
R,2,1,5,3
R,2,1,5,4
R,2,1,5,5
L,2,2,1,1
L,2,2,1,2
L,2,2,1,3
B,2,2,1,4
R,2,2,1,5
L,2,2,2,1
B,2,2,2,2
R,2,2,2,3
R,2,2,2,4
R,2,2,2,5
L,2,2,3,1
R,2,2,3,2
R,2,2,3,3
R,2,2,3,4
R,2,2,3,5
B,2,2,4,1
R,2,2,4,2
R,2,2,4,3
R,2,2,4,4
R,2,2,4,5
R,2,2,5,1
R,2,2,5,2
R,2,2,5,3
R,2,2,5,4
R,2,2,5,5
L,2,3,1,1
L,2,3,1,2
L,2,3,1,3
L,2,3,1,4
L,2,3,1,5
L,2,3,2,1
L,2,3,2,2
B,2,3,2,3
R,2,3,2,4
R,2,3,2,5
L,2,3,3,1
B,2,3,3,2
R,2,3,3,3
R,2,3,3,4
R,2,3,3,5
L,2,3,4,1
R,2,3,4,2
R,2,3,4,3
R,2,3,4,4
R,2,3,4,5
L,2,3,5,1
R,2,3,5,2
R,2,3,5,3
R,2,3,5,4
R,2,3,5,5
L,2,4,1,1
L,2,4,1,2
L,2,4,1,3
L,2,4,1,4
L,2,4,1,5
L,2,4,2,1
L,2,4,2,2
L,2,4,2,3
B,2,4,2,4
R,2,4,2,5
L,2,4,3,1
L,2,4,3,2
R,2,4,3,3
R,2,4,3,4
R,2,4,3,5
L,2,4,4,1
B,2,4,4,2
R,2,4,4,3
R,2,4,4,4
R,2,4,4,5
L,2,4,5,1
R,2,4,5,2
R,2,4,5,3
R,2,4,5,4
R,2,4,5,5
L,2,5,1,1
L,2,5,1,2
L,2,5,1,3
L,2,5,1,4
L,2,5,1,5
L,2,5,2,1
L,2,5,2,2
L,2,5,2,3
L,2,5,2,4
B,2,5,2,5
L,2,5,3,1
L,2,5,3,2
L,2,5,3,3
R,2,5,3,4
R,2,5,3,5
L,2,5,4,1
L,2,5,4,2
R,2,5,4,3
R,2,5,4,4
R,2,5,4,5
L,2,5,5,1
B,2,5,5,2
R,2,5,5,3
R,2,5,5,4
R,2,5,5,5
L,3,1,1,1
L,3,1,1,2
B,3,1,1,3
R,3,1,1,4
R,3,1,1,5
L,3,1,2,1
R,3,1,2,2
R,3,1,2,3
R,3,1,2,4
R,3,1,2,5
B,3,1,3,1
R,3,1,3,2
R,3,1,3,3
R,3,1,3,4
R,3,1,3,5
R,3,1,4,1
R,3,1,4,2
R,3,1,4,3
R,3,1,4,4
R,3,1,4,5
R,3,1,5,1
R,3,1,5,2
R,3,1,5,3
R,3,1,5,4
R,3,1,5,5
L,3,2,1,1
L,3,2,1,2
L,3,2,1,3
L,3,2,1,4
L,3,2,1,5
L,3,2,2,1
L,3,2,2,2
B,3,2,2,3
R,3,2,2,4
R,3,2,2,5
L,3,2,3,1
B,3,2,3,2
R,3,2,3,3
R,3,2,3,4
R,3,2,3,5
L,3,2,4,1
R,3,2,4,2
R,3,2,4,3
R,3,2,4,4
R,3,2,4,5
L,3,2,5,1
R,3,2,5,2
R,3,2,5,3
R,3,2,5,4
R,3,2,5,5
L,3,3,1,1
L,3,3,1,2
L,3,3,1,3
L,3,3,1,4
L,3,3,1,5
L,3,3,2,1
L,3,3,2,2
L,3,3,2,3
L,3,3,2,4
R,3,3,2,5
L,3,3,3,1
L,3,3,3,2
B,3,3,3,3
R,3,3,3,4
R,3,3,3,5
L,3,3,4,1
L,3,3,4,2
R,3,3,4,3
R,3,3,4,4
R,3,3,4,5
L,3,3,5,1
R,3,3,5,2
R,3,3,5,3
R,3,3,5,4
R,3,3,5,5
L,3,4,1,1
L,3,4,1,2
L,3,4,1,3
L,3,4,1,4
L,3,4,1,5
L,3,4,2,1
L,3,4,2,2
L,3,4,2,3
L,3,4,2,4
L,3,4,2,5
L,3,4,3,1
L,3,4,3,2
L,3,4,3,3
B,3,4,3,4
R,3,4,3,5
L,3,4,4,1
L,3,4,4,2
B,3,4,4,3
R,3,4,4,4
R,3,4,4,5
L,3,4,5,1
L,3,4,5,2
R,3,4,5,3
R,3,4,5,4
R,3,4,5,5
L,3,5,1,1
L,3,5,1,2
L,3,5,1,3
L,3,5,1,4
L,3,5,1,5
L,3,5,2,1
L,3,5,2,2
L,3,5,2,3
L,3,5,2,4
L,3,5,2,5
L,3,5,3,1
L,3,5,3,2
L,3,5,3,3
L,3,5,3,4
B,3,5,3,5
L,3,5,4,1
L,3,5,4,2
L,3,5,4,3
R,3,5,4,4
R,3,5,4,5
L,3,5,5,1
L,3,5,5,2
B,3,5,5,3
R,3,5,5,4
R,3,5,5,5
L,4,1,1,1
L,4,1,1,2
L,4,1,1,3
B,4,1,1,4
R,4,1,1,5
L,4,1,2,1
B,4,1,2,2
R,4,1,2,3
R,4,1,2,4
R,4,1,2,5
L,4,1,3,1
R,4,1,3,2
R,4,1,3,3
R,4,1,3,4
R,4,1,3,5
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R,4,1,4,3
R,4,1,4,4
R,4,1,4,5
R,4,1,5,1
R,4,1,5,2
R,4,1,5,3
R,4,1,5,4
R,4,1,5,5
L,4,2,1,1
L,4,2,1,2
L,4,2,1,3
L,4,2,1,4
L,4,2,1,5
L,4,2,2,1
L,4,2,2,2
L,4,2,2,3
B,4,2,2,4
R,4,2,2,5
L,4,2,3,1
L,4,2,3,2
R,4,2,3,3
R,4,2,3,4
R,4,2,3,5
L,4,2,4,1
B,4,2,4,2
R,4,2,4,3
R,4,2,4,4
R,4,2,4,5
L,4,2,5,1
R,4,2,5,2
R,4,2,5,3
R,4,2,5,4
R,4,2,5,5
L,4,3,1,1
L,4,3,1,2
L,4,3,1,3
L,4,3,1,4
L,4,3,1,5
L,4,3,2,1
L,4,3,2,2
L,4,3,2,3
L,4,3,2,4
L,4,3,2,5
L,4,3,3,1
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L,4,3,3,3
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R,4,3,3,5
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R,4,3,4,4
R,4,3,4,5
L,4,3,5,1
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R,4,3,5,3
R,4,3,5,4
R,4,3,5,5
L,4,4,1,1
L,4,4,1,2
L,4,4,1,3
L,4,4,1,4
L,4,4,1,5
L,4,4,2,1
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L,4,4,2,3
L,4,4,2,4
L,4,4,2,5
L,4,4,3,1
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L,4,4,3,3
L,4,4,3,4
L,4,4,3,5
L,4,4,4,1
L,4,4,4,2
L,4,4,4,3
B,4,4,4,4
R,4,4,4,5
L,4,4,5,1
L,4,4,5,2
L,4,4,5,3
R,4,4,5,4
R,4,4,5,5
L,4,5,1,1
L,4,5,1,2
L,4,5,1,3
L,4,5,1,4
L,4,5,1,5
L,4,5,2,1
L,4,5,2,2
L,4,5,2,3
L,4,5,2,4
L,4,5,2,5
L,4,5,3,1
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L,4,5,3,3
L,4,5,3,4
L,4,5,3,5
L,4,5,4,1
L,4,5,4,2
L,4,5,4,3
L,4,5,4,4
B,4,5,4,5
L,4,5,5,1
L,4,5,5,2
L,4,5,5,3
B,4,5,5,4
R,4,5,5,5
L,5,1,1,1
L,5,1,1,2
L,5,1,1,3
L,5,1,1,4
B,5,1,1,5
L,5,1,2,1
L,5,1,2,2
R,5,1,2,3
R,5,1,2,4
R,5,1,2,5
L,5,1,3,1
R,5,1,3,2
R,5,1,3,3
R,5,1,3,4
R,5,1,3,5
L,5,1,4,1
R,5,1,4,2
R,5,1,4,3
R,5,1,4,4
R,5,1,4,5
B,5,1,5,1
R,5,1,5,2
R,5,1,5,3
R,5,1,5,4
R,5,1,5,5
L,5,2,1,1
L,5,2,1,2
L,5,2,1,3
L,5,2,1,4
L,5,2,1,5
L,5,2,2,1
L,5,2,2,2
L,5,2,2,3
L,5,2,2,4
B,5,2,2,5
L,5,2,3,1
L,5,2,3,2
L,5,2,3,3
R,5,2,3,4
R,5,2,3,5
L,5,2,4,1
L,5,2,4,2
R,5,2,4,3
R,5,2,4,4
R,5,2,4,5
L,5,2,5,1
B,5,2,5,2
R,5,2,5,3
R,5,2,5,4
R,5,2,5,5
L,5,3,1,1
L,5,3,1,2
L,5,3,1,3
L,5,3,1,4
L,5,3,1,5
L,5,3,2,1
L,5,3,2,2
L,5,3,2,3
L,5,3,2,4
L,5,3,2,5
L,5,3,3,1
L,5,3,3,2
L,5,3,3,3
L,5,3,3,4
B,5,3,3,5
L,5,3,4,1
L,5,3,4,2
L,5,3,4,3
R,5,3,4,4
R,5,3,4,5
L,5,3,5,1
L,5,3,5,2
B,5,3,5,3
R,5,3,5,4
R,5,3,5,5
L,5,4,1,1
L,5,4,1,2
L,5,4,1,3
L,5,4,1,4
L,5,4,1,5
L,5,4,2,1
L,5,4,2,2
L,5,4,2,3
L,5,4,2,4
L,5,4,2,5
L,5,4,3,1
L,5,4,3,2
L,5,4,3,3
L,5,4,3,4
L,5,4,3,5
L,5,4,4,1
L,5,4,4,2
L,5,4,4,3
L,5,4,4,4
B,5,4,4,5
L,5,4,5,1
L,5,4,5,2
L,5,4,5,3
B,5,4,5,4
R,5,4,5,5
L,5,5,1,1
L,5,5,1,2
L,5,5,1,3
L,5,5,1,4
L,5,5,1,5
L,5,5,2,1
L,5,5,2,2
L,5,5,2,3
L,5,5,2,4
L,5,5,2,5
L,5,5,3,1
L,5,5,3,2
L,5,5,3,3
L,5,5,3,4
L,5,5,3,5
L,5,5,4,1
L,5,5,4,2
L,5,5,4,3
L,5,5,4,4
L,5,5,4,5
L,5,5,5,1
L,5,5,5,2
L,5,5,5,3
L,5,5,5,4
B,5,5,5,5

================================================
FILE: data/simple.bn
================================================
{
    "V": ["A", "B", "C"],
    "E": {
        "A" : ["B"],
        "B" : ["C"],
        "C" : []
    },
    "F": {
        "A": {
            "values": ["No", "Yes"],
            "parents": [],
            "cpt": [0.3,0.7]
        },

        "B": {
            "values": ["No", "Yes"],
            "parents": ["A"],
            "cpt": [0.4,0.6,0.1,0.9]
        },
        "C": {
            "values": ["No","Yes"],
            "parents": ["B"],
            "cpt": [0.4,0.6,0.1,0.9]
        }
    }
}

================================================
FILE: data/student.bn
================================================
{
    "V": [Intelligence", "Difficulty", "SAT", "Grade", "Letter"],
    "E": [["Intelligence", "Grade"],
        ["Difficulty", "Grade"],
        ["Intelligence", "SAT"],
        ["Grade", "Letter"]],
    "Vdata": {
        "Letter": {
            "ord": 4,
            "numoutcomes": 2,
            "vals": ["weak", "strong"],
            "parents": ["Grade"],
            "children": [],
            "cprob": [[0.1, 0.9], [0.4, 0.6], [0.99, 0.01]]
        },

        "SAT": {
            "ord": 3,
            "numoutcomes": 2,
            "vals": ["lowscore", "highscore"],
            "parents": ["Intelligence"],
            "children": [],
            "cprob": [[0.95, 0.05], [0.2, 0.8]]
        },

        "Grade": {
            "ord": 2,
            "numoutcomes": 3,
            "vals": ["A", "B", "C"],
            "parents": ["Difficulty", "Intelligence"],
            "children": ["Letter"],
            "cprob": [[0.3, 0.4, 0.3], [0.9, 0.08, 0.02], [0.05, 0.25, 0.7], [0.5, 0.3, 0.2]]
        },

        "Intelligence": {
            "ord": 1,
            "numoutcomes": 2,
            "vals": ["low", "high"],
            "parents": [],
            "children": ["SAT", "Grade"],
            "cprob": [0.7, 0.3]
        },

        "Difficulty": {
            "ord": 0,
            "numoutcomes": 2,
            "vals": ["easy", "hard"],
            "parents": [],
            "children": ["Grade"],
            "cprob":  [0.6, 0.4]
        }
    }
}

================================================
FILE: data/test.bn
================================================
{
  "V": [
    "Smoker", 
    "Pollution", 
    "Cancer", 
    "Xray", 
    "Dyspnoea"
  ], 
  "E": {
    "Xray": [], 
    "Dyspnoea": [], 
    "Smoker": [
      "Cancer"
    ], 
    "Cancer": [
      "Xray", 
      "Dyspnoea"
    ], 
    "Pollution": [
      "Cancer"
    ]
  }, 
  "F": {
    "Xray": {
      "values": [
        "positive", 
        "negative"
      ], 
      "parents": [
        "Cancer"
      ], 
      "cpt": [
        0.9, 
        0.1, 
        0.2, 
        0.8
      ]
    }, 
    "Dyspnoea": {
      "values": [
        "True", 
        "False"
      ], 
      "parents": [
        "Cancer"
      ], 
      "cpt": [
        0.65, 
        0.35, 
        0.3, 
        0.7
      ]
    }, 
    "Smoker": {
      "values": [
        "True", 
        "False"
      ], 
      "parents": [], 
      "cpt": [
        0.3, 
        0.7
      ]
    }, 
    "Cancer": {
      "values": [
        "True", 
        "False"
      ], 
      "parents": [
        "Pollution", 
        "Smoker"
      ], 
      "cpt": [
        0.03, 
        0.97, 
        0.05, 
        0.95, 
        0.001, 
        0.999, 
        0.02, 
        0.98
      ]
    }, 
    "Pollution": {
      "values": [
        "low", 
        "high"
      ], 
      "parents": [], 
      "cpt": [
        0.9, 
        0.1
      ]
    }
  }
}

================================================
FILE: data/urn.bif
================================================
network unknown {
}
variable draw {
	type discrete [ 2 ] { red, blue };
}
variable lie {
	type discrete [ 2 ] { yes, no };
}
variable personal_action {
	type discrete [ 2 ] { red, blue };
}
variable group_action {
	type discrete [ 2 ] { rr, rb, br, bb };
}
variable actual_action {
	type discrete [ 2 ] { red, blue };
}
probability ( draw ) {
	table 0.33, 0.67;
}
probability ( lie ) {
	table 0.05, 0.95;
}
probability ( group_action ) {
	table 0.25, 0.25, 0.25, 0.25;
}
probability ( personal_action | draw, lie ) {
	(red, yes) 0.01, 0.99;
	(blue, yes) 0.99, 0.01;
	(red, no) 0.99, 0.01;
	(blue, no) 0.01, 0.99;
}

probability ( actual_action | personal_action, group_action ) {
	(red, rr) 1.0, 0.0;
	(blue, rr) 0.99, 0.01;
	(red, rb) 0.75, 0.25;
	(blue, rb) 0.25, 0.75;
	(red, br) 0.75, 0.25;
	(blue, br) 0.25, 0.75;
	(red, bb) 0.01, 0.99;
	(blue, bb) 0.0, 1.0;
}

================================================
FILE: data/win95pts.bif
================================================
network unknown {
}
variable AppOK {
  type discrete [ 2 ] { Correct, Incorrect_Corrupt };
}
variable DataFile {
  type discrete [ 2 ] { Correct, Incorrect_Corrupt };
}
variable AppData {
  type discrete [ 2 ] { Correct, Incorrect_or_corrupt };
}
variable DskLocal {
  type discrete [ 2 ] { Greater_than_2_Mb, Less_than_2_Mb };
}
variable PrtSpool {
  type discrete [ 2 ] { Enabled, Disabled };
}
variable PrtOn {
  type discrete [ 2 ] { Yes, No };
}
variable PrtPaper {
  type discrete [ 2 ] { Has_Paper, No_Paper };
}
variable NetPrint {
  type discrete [ 2 ] { No__Local_printer_, Yes__Network_printer_ };
}
variable PrtDriver {
  type discrete [ 2 ] { Yes, No };
}
variable PrtThread {
  type discrete [ 2 ] { OK, Corrupt_Buggy };
}
variable EMFOK {
  type discrete [ 2 ] { Yes, No };
}
variable GDIIN {
  type discrete [ 2 ] { Yes, No };
}
variable DrvSet {
  type discrete [ 2 ] { Correct, Incorrect };
}
variable DrvOK {
  type discrete [ 2 ] { Reinstalled, Corrupt };
}
variable GDIOUT {
  type discrete [ 2 ] { Yes, No };
}
variable PrtSel {
  type discrete [ 2 ] { Yes, No };
}
variable PrtDataOut {
  type discrete [ 2 ] { Yes, No };
}
variable PrtPath {
  type discrete [ 2 ] { Correct, Incorrect };
}
variable NtwrkCnfg {
  type discrete [ 2 ] { Correct, Incorrect };
}
variable PTROFFLINE {
  type discrete [ 2 ] { Online, Offline };
}
variable NetOK {
  type discrete [ 2 ] { Yes, No };
}
variable PrtCbl {
  type discrete [ 2 ] { Connected, Loose };
}
variable PrtPort {
  type discrete [ 2 ] { Yes, No };
}
variable CblPrtHrdwrOK {
  type discrete [ 2 ] { Operational, Not_Operational };
}
variable LclOK {
  type discrete [ 2 ] { Yes, No };
}
variable DSApplctn {
  type discrete [ 2 ] { DOS, Windows };
}
variable PrtMpTPth {
  type discrete [ 2 ] { Correct, Incorrect };
}
variable DS_NTOK {
  type discrete [ 2 ] { Yes, No };
}
variable DS_LCLOK {
  type discrete [ 2 ] { Yes, No };
}
variable PC2PRT {
  type discrete [ 2 ] { Yes, No };
}
variable PrtMem {
  type discrete [ 2 ] { Greater_than_2_Mb, Less_than_2Mb };
}
variable PrtTimeOut {
  type discrete [ 2 ] { Long_Enough, Too_Short };
}
variable FllCrrptdBffr {
  type discrete [ 2 ] { Intact__not_Corrupt_, Full_or_Corrupt };
}
variable TnrSpply {
  type discrete [ 2 ] { Adequate, Low };
}
variable PrtData {
  type discrete [ 2 ] { Yes, No };
}
variable Problem1 {
  type discrete [ 2 ] { Normal_Output, No_Output };
}
variable AppDtGnTm {
  type discrete [ 2 ] { Fast_Enough, Too_Long };
}
variable PrntPrcssTm {
  type discrete [ 2 ] { Fast_Enough, Too_Long };
}
variable DeskPrntSpd {
  type discrete [ 2 ] { OK, Too_Slow };
}
variable PgOrnttnOK {
  type discrete [ 2 ] { Correct, Incorrect };
}
variable PrntngArOK {
  type discrete [ 2 ] { Correct, Incorrect };
}
variable ScrnFntNtPrntrFnt {
  type discrete [ 2 ] { Yes, No };
}
variable CmpltPgPrntd {
  type discrete [ 2 ] { Yes, No };
}
variable GrphcsRltdDrvrSttngs {
  type discrete [ 2 ] { Correct, Incorrect };
}
variable EPSGrphc {
  type discrete [ 2 ] { No____TIF___WMF___BMP_, Yes____EPS_ };
}
variable NnPSGrphc {
  type discrete [ 2 ] { Yes, No };
}
variable PrtPScript {
  type discrete [ 2 ] { Yes, No };
}
variable PSGRAPHIC {
  type discrete [ 2 ] { Yes, No };
}
variable Problem4 {
  type discrete [ 2 ] { No, Yes };
}
variable TrTypFnts {
  type discrete [ 2 ] { Yes, No };
}
variable FntInstlltn {
  type discrete [ 2 ] { Verified, Faulty };
}
variable PrntrAccptsTrtyp {
  type discrete [ 2 ] { Yes, No };
}
variable TTOK {
  type discrete [ 2 ] { Yes, No };
}
variable NnTTOK {
  type discrete [ 2 ] { Yes, No };
}
variable Problem5 {
  type discrete [ 2 ] { No, Yes };
}
variable LclGrbld {
  type discrete [ 2 ] { Yes, No };
}
variable NtGrbld {
  type discrete [ 2 ] { Yes, No };
}
variable GrbldOtpt {
  type discrete [ 2 ] { No, Yes };
}
variable HrglssDrtnAftrPrnt {
  type discrete [ 2 ] { Fast_Enough, Too_Long };
}
variable REPEAT {
  type discrete [ 2 ] { Yes__Always_the_Same_, No__Different_Each_Time_ };
}
variable AvlblVrtlMmry {
  type discrete [ 2 ] { Adequate____1Mb_, Inadequate____1_Mb_ };
}
variable PSERRMEM {
  type discrete [ 2 ] { No_Error, Low_Memory };
}
variable TstpsTxt {
  type discrete [ 2 ] { x_1_Mb_Available_VM, x_1_Mb_Available_VM2 };
}
variable GrbldPS {
  type discrete [ 2 ] { No, Yes };
}
variable IncmpltPS {
  type discrete [ 2 ] { Yes, No };
}
variable PrtFile {
  type discrete [ 2 ] { Yes, No };
}
variable PrtIcon {
  type discrete [ 2 ] { Normal, Grayed_Out };
}
variable Problem6 {
  type discrete [ 2 ] { No, Yes };
}
variable Problem3 {
  type discrete [ 2 ] { No, Yes };
}
variable PrtQueue {
  type discrete [ 2 ] { Short, Long };
}
variable NtSpd {
  type discrete [ 2 ] { OK, Slow };
}
variable Problem2 {
  type discrete [ 2 ] { OK, Too_Long };
}
variable PrtStatPaper {
  type discrete [ 2 ] { No_Error, Jam__Out__Bin_Full };
}
variable PrtStatToner {
  type discrete [ 2 ] { No_Error, Low__None };
}
variable PrtStatMem {
  type discrete [ 2 ] { No_Error, Out_of_Memory };
}
variable PrtStatOff {
  type discrete [ 2 ] { No_Error, OFFLINE__OFF };
}
probability ( AppOK ) {
  table 0.995, 0.005;
}
probability ( DataFile ) {
  table 0.995, 0.005;
}
probability ( AppData | AppOK, DataFile ) {
  (Correct, Correct) 0.9999, 0.0001;
  (Incorrect_Corrupt, Correct) 0.0, 1.0;
  (Correct, Incorrect_Corrupt) 0.0, 1.0;
  (Incorrect_Corrupt, Incorrect_Corrupt) 0.5, 0.5;
}
probability ( DskLocal ) {
  table 0.97, 0.03;
}
probability ( PrtSpool ) {
  table 0.95, 0.05;
}
probability ( PrtOn ) {
  table 0.9, 0.1;
}
probability ( PrtPaper ) {
  table 0.98, 0.02;
}
probability ( NetPrint ) {
  table 0.8, 0.2;
}
probability ( PrtDriver ) {
  table 0.9, 0.1;
}
probability ( PrtThread ) {
  table 0.9999, 0.0001;
}
probability ( EMFOK | AppData, DskLocal, PrtThread ) {
  (Correct, Greater_than_2_Mb, OK) 0.99, 0.01;
  (Incorrect_or_corrupt, Greater_than_2_Mb, OK) 0.1, 0.9;
  (Correct, Less_than_2_Mb, OK) 0.0, 1.0;
  (Incorrect_or_corrupt, Less_than_2_Mb, OK) 0.5, 0.5;
  (Correct, Greater_than_2_Mb, Corrupt_Buggy) 0.05, 0.95;
  (Incorrect_or_corrupt, Greater_than_2_Mb, Corrupt_Buggy) 0.5, 0.5;
  (Correct, Less_than_2_Mb, Corrupt_Buggy) 0.5, 0.5;
  (Incorrect_or_corrupt, Less_than_2_Mb, Corrupt_Buggy) 0.5, 0.5;
}
probability ( GDIIN | AppData, PrtSpool, EMFOK ) {
  (Correct, Enabled, Yes) 1.0, 0.0;
  (Incorrect_or_corrupt, Enabled, Yes) 0.0, 1.0;
  (Correct, Disabled, Yes) 1.0, 0.0;
  (Incorrect_or_corrupt, Disabled, Yes) 0.0, 1.0;
  (Correct, Enabled, No) 0.0, 1.0;
  (Incorrect_or_corrupt, Enabled, No) 0.0, 1.0;
  (Correct, Disabled, No) 1.0, 0.0;
  (Incorrect_or_corrupt, Disabled, No) 0.0, 1.0;
}
probability ( DrvSet ) {
  table 0.99, 0.01;
}
probability ( DrvOK ) {
  table 0.99, 0.01;
}
probability ( GDIOUT | PrtDriver, GDIIN, DrvSet, DrvOK ) {
  (Yes, Yes, Correct, Reinstalled) 0.99, 0.01;
  (No, Yes, Correct, Reinstalled) 0.1, 0.9;
  (Yes, No, Correct, Reinstalled) 0.1, 0.9;
  (No, No, Correct, Reinstalled) 0.5, 0.5;
  (Yes, Yes, Incorrect, Reinstalled) 0.9, 0.1;
  (No, Yes, Incorrect, Reinstalled) 0.5, 0.5;
  (Yes, No, Incorrect, Reinstalled) 0.5, 0.5;
  (No, No, Incorrect, Reinstalled) 0.5, 0.5;
  (Yes, Yes, Correct, Corrupt) 0.2, 0.8;
  (No, Yes, Correct, Corrupt) 0.5, 0.5;
  (Yes, No, Correct, Corrupt) 0.5, 0.5;
  (No, No, Correct, Corrupt) 0.5, 0.5;
  (Yes, Yes, Incorrect, Corrupt) 0.5, 0.5;
  (No, Yes, Incorrect, Corrupt) 0.5, 0.5;
  (Yes, No, Incorrect, Corrupt) 0.5, 0.5;
  (No, No, Incorrect, Corrupt) 0.5, 0.5;
}
probability ( PrtSel ) {
  table 0.99, 0.01;
}
probability ( PrtDataOut | GDIOUT, PrtSel ) {
  (Yes, Yes) 0.99, 0.01;
  (No, Yes) 0.0, 1.0;
  (Yes, No) 0.0, 1.0;
  (No, No) 0.5, 0.5;
}
probability ( PrtPath ) {
  table 0.97, 0.03;
}
probability ( NtwrkCnfg ) {
  table 0.98, 0.02;
}
probability ( PTROFFLINE ) {
  table 0.7, 0.3;
}
probability ( NetOK | PrtPath, NtwrkCnfg, PTROFFLINE ) {
  (Correct, Correct, Online) 0.99, 0.01;
  (Incorrect, Correct, Online) 0.0, 1.0;
  (Correct, Incorrect, Online) 0.1, 0.9;
  (Incorrect, Incorrect, Online) 0.5, 0.5;
  (Correct, Correct, Offline) 0.0, 1.0;
  (Incorrect, Correct, Offline) 0.5, 0.5;
  (Correct, Incorrect, Offline) 0.5, 0.5;
  (Incorrect, Incorrect, Offline) 0.5, 0.5;
}
probability ( PrtCbl ) {
  table 0.98, 0.02;
}
probability ( PrtPort ) {
  table 0.99, 0.01;
}
probability ( CblPrtHrdwrOK ) {
  table 0.99, 0.01;
}
probability ( LclOK | PrtCbl, PrtPort, CblPrtHrdwrOK ) {
  (Connected, Yes, Operational) 0.999, 0.001;
  (Loose, Yes, Operational) 0.0, 1.0;
  (Connected, No, Operational) 0.0, 1.0;
  (Loose, No, Operational) 0.5, 0.5;
  (Connected, Yes, Not_Operational) 0.01, 0.99;
  (Loose, Yes, Not_Operational) 0.5, 0.5;
  (Connected, No, Not_Operational) 0.5, 0.5;
  (Loose, No, Not_Operational) 0.5, 0.5;
}
probability ( DSApplctn ) {
  table 0.15, 0.85;
}
probability ( PrtMpTPth ) {
  table 0.8, 0.2;
}
probability ( DS_NTOK | AppData, PrtPath, PrtMpTPth, NtwrkCnfg, PTROFFLINE ) {
  (Correct, Correct, Correct, Correct, Online) 0.99, 0.01;
  (Incorrect_or_corrupt, Correct, Correct, Correct, Online) 0.2, 0.8;
  (Correct, Incorrect, Correct, Correct, Online) 0.0, 1.0;
  (Incorrect_or_corrupt, Incorrect, Correct, Correct, Online) 0.5, 0.5;
  (Correct, Correct, Incorrect, Correct, Online) 0.0, 1.0;
  (Incorrect_or_corrupt, Correct, Incorrect, Correct, Online) 0.5, 0.5;
  (Correct, Incorrect, Incorrect, Correct, Online) 0.5, 0.5;
  (Incorrect_or_corrupt, Incorrect, Incorrect, Correct, Online) 0.5, 0.5;
  (Correct, Correct, Correct, Incorrect, Online) 0.1, 0.9;
  (Incorrect_or_corrupt, Correct, Correct, Incorrect, Online) 0.5, 0.5;
  (Correct, Incorrect, Correct, Incorrect, Online) 0.5, 0.5;
  (Incorrect_or_corrupt, Incorrect, Correct, Incorrect, Online) 0.5, 0.5;
  (Correct, Correct, Incorrect, Incorrect, Online) 0.5, 0.5;
  (Incorrect_or_corrupt, Correct, Incorrect, Incorrect, Online) 0.5, 0.5;
  (Correct, Incorrect, Incorrect, Incorrect, Online) 0.5, 0.5;
  (Incorrect_or_corrupt, Incorrect, Incorrect, Incorrect, Online) 0.5, 0.5;
  (Correct, Correct, Correct, Correct, Offline) 0.0, 1.0;
  (Incorrect_or_corrupt, Correct, Correct, Correct, Offline) 0.5, 0.5;
  (Correct, Incorrect, Correct, Correct, Offline) 0.5, 0.5;
  (Incorrect_or_corrupt, Incorrect, Correct, Correct, Offline) 0.5, 0.5;
  (Correct, Correct, Incorrect, Correct, Offline) 0.5, 0.5;
  (Incorrect_or_corrupt, Correct, Incorrect, Correct, Offline) 0.5, 0.5;
  (Correct, Incorrect, Incorrect, Correct, Offline) 0.5, 0.5;
  (Incorrect_or_corrupt, Incorrect, Incorrect, Correct, Offline) 0.5, 0.5;
  (Correct, Correct, Correct, Incorrect, Offline) 0.5, 0.5;
  (Incorrect_or_corrupt, Correct, Correct, Incorrect, Offline) 0.5, 0.5;
  (Correct, Incorrect, Correct, Incorrect, Offline) 0.5, 0.5;
  (Incorrect_or_corrupt, Incorrect, Correct, Incorrect, Offline) 0.5, 0.5;
  (Correct, Correct, Incorrect, Incorrect, Offline) 0.5, 0.5;
  (Incorrect_or_corrupt, Correct, Incorrect, Incorrect, Offline) 0.5, 0.5;
  (Correct, Incorrect, Incorrect, Incorrect, Offline) 0.5, 0.5;
  (Incorrect_or_corrupt, Incorrect, Incorrect, Incorrect, Offline) 0.5, 0.5;
}
probability ( DS_LCLOK | AppData, PrtCbl, PrtPort, CblPrtHrdwrOK ) {
  (Correct, Connected, Yes, Operational) 1.0, 0.0;
  (Incorrect_or_corrupt, Connected, Yes, Operational) 0.1, 0.9;
  (Correct, Loose, Yes, Operational) 0.0, 1.0;
  (Incorrect_or_corrupt, Loose, Yes, Operational) 0.5, 0.5;
  (Correct, Connected, No, Operational) 0.0, 1.0;
  (Incorrect_or_corrupt, Connected, No, Operational) 0.5, 0.5;
  (Correct, Loose, No, Operational) 0.5, 0.5;
  (Incorrect_or_corrupt, Loose, No, Operational) 0.5, 0.5;
  (Correct, Connected, Yes, Not_Operational) 0.1, 0.9;
  (Incorrect_or_corrupt, Connected, Yes, Not_Operational) 0.5, 0.5;
  (Correct, Loose, Yes, Not_Operational) 0.5, 0.5;
  (Incorrect_or_corrupt, Loose, Yes, Not_Operational) 0.5, 0.5;
  (Correct, Connected, No, Not_Operational) 0.5, 0.5;
  (Incorrect_or_corrupt, Connected, No, Not_Operational) 0.5, 0.5;
  (Correct, Loose, No, Not_Operational) 0.5, 0.5;
  (Incorrect_or_corrupt, Loose, No, Not_Operational) 0.5, 0.5;
}
probability ( PC2PRT | NetPrint, PrtDataOut, NetOK, LclOK, DSApplctn, DS_NTOK, DS_LCLOK ) {
  (No__Local_printer_, Yes, Yes, Yes, DOS, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, Yes, Yes, DOS, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, No, Yes, Yes, DOS, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, No, Yes, Yes, DOS, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, Yes, No, Yes, DOS, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, No, Yes, DOS, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, No, No, Yes, DOS, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, No, No, Yes, DOS, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, Yes, Yes, No, DOS, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, Yes, No, DOS, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, No, Yes, No, DOS, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, No, Yes, No, DOS, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, Yes, No, No, DOS, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, No, No, DOS, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, No, No, No, DOS, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, No, No, No, DOS, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, Yes, Yes, Yes, Windows, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, Yes, Yes, Windows, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, No, Yes, Yes, Windows, Yes, Yes) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, Yes, Windows, Yes, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, No, Yes, Windows, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, No, Yes, Windows, Yes, Yes) 0.0, 1.0;
  (No__Local_printer_, No, No, Yes, Windows, Yes, Yes) 0.0, 1.0;
  (Yes__Network_printer_, No, No, Yes, Windows, Yes, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, No, Windows, Yes, Yes) 0.0, 1.0;
  (Yes__Network_printer_, Yes, Yes, No, Windows, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, No, Yes, No, Windows, Yes, Yes) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, No, Windows, Yes, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, No, No, Windows, Yes, Yes) 0.0, 1.0;
  (Yes__Network_printer_, Yes, No, No, Windows, Yes, Yes) 0.0, 1.0;
  (No__Local_printer_, No, No, No, Windows, Yes, Yes) 0.0, 1.0;
  (Yes__Network_printer_, No, No, No, Windows, Yes, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, Yes, DOS, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, Yes, Yes, DOS, No, Yes) 0.0, 1.0;
  (No__Local_printer_, No, Yes, Yes, DOS, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, No, Yes, Yes, DOS, No, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, No, Yes, DOS, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, No, Yes, DOS, No, Yes) 0.0, 1.0;
  (No__Local_printer_, No, No, Yes, DOS, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, No, No, Yes, DOS, No, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, No, DOS, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, Yes, No, DOS, No, Yes) 0.0, 1.0;
  (No__Local_printer_, No, Yes, No, DOS, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, No, Yes, No, DOS, No, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, No, No, DOS, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, No, No, DOS, No, Yes) 0.0, 1.0;
  (No__Local_printer_, No, No, No, DOS, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, No, No, No, DOS, No, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, Yes, Windows, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, Yes, Yes, Windows, No, Yes) 1.0, 0.0;
  (No__Local_printer_, No, Yes, Yes, Windows, No, Yes) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, Yes, Windows, No, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, No, Yes, Windows, No, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, No, Yes, Windows, No, Yes) 0.0, 1.0;
  (No__Local_printer_, No, No, Yes, Windows, No, Yes) 0.0, 1.0;
  (Yes__Network_printer_, No, No, Yes, Windows, No, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, No, Windows, No, Yes) 0.0, 1.0;
  (Yes__Network_printer_, Yes, Yes, No, Windows, No, Yes) 1.0, 0.0;
  (No__Local_printer_, No, Yes, No, Windows, No, Yes) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, No, Windows, No, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, No, No, Windows, No, Yes) 0.0, 1.0;
  (Yes__Network_printer_, Yes, No, No, Windows, No, Yes) 0.0, 1.0;
  (No__Local_printer_, No, No, No, Windows, No, Yes) 0.0, 1.0;
  (Yes__Network_printer_, No, No, No, Windows, No, Yes) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, Yes, DOS, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, Yes, Yes, DOS, Yes, No) 1.0, 0.0;
  (No__Local_printer_, No, Yes, Yes, DOS, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, Yes, DOS, Yes, No) 1.0, 0.0;
  (No__Local_printer_, Yes, No, Yes, DOS, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, No, Yes, DOS, Yes, No) 1.0, 0.0;
  (No__Local_printer_, No, No, Yes, DOS, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, No, No, Yes, DOS, Yes, No) 1.0, 0.0;
  (No__Local_printer_, Yes, Yes, No, DOS, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, Yes, No, DOS, Yes, No) 1.0, 0.0;
  (No__Local_printer_, No, Yes, No, DOS, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, No, DOS, Yes, No) 1.0, 0.0;
  (No__Local_printer_, Yes, No, No, DOS, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, No, No, DOS, Yes, No) 1.0, 0.0;
  (No__Local_printer_, No, No, No, DOS, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, No, No, No, DOS, Yes, No) 1.0, 0.0;
  (No__Local_printer_, Yes, Yes, Yes, Windows, Yes, No) 1.0, 0.0;
  (Yes__Network_printer_, Yes, Yes, Yes, Windows, Yes, No) 1.0, 0.0;
  (No__Local_printer_, No, Yes, Yes, Windows, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, Yes, Windows, Yes, No) 0.0, 1.0;
  (No__Local_printer_, Yes, No, Yes, Windows, Yes, No) 1.0, 0.0;
  (Yes__Network_printer_, Yes, No, Yes, Windows, Yes, No) 0.0, 1.0;
  (No__Local_printer_, No, No, Yes, Windows, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, No, No, Yes, Windows, Yes, No) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, No, Windows, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, Yes, No, Windows, Yes, No) 1.0, 0.0;
  (No__Local_printer_, No, Yes, No, Windows, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, No, Windows, Yes, No) 0.0, 1.0;
  (No__Local_printer_, Yes, No, No, Windows, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, No, No, Windows, Yes, No) 0.0, 1.0;
  (No__Local_printer_, No, No, No, Windows, Yes, No) 0.0, 1.0;
  (Yes__Network_printer_, No, No, No, Windows, Yes, No) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, Yes, DOS, No, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, Yes, Yes, DOS, No, No) 0.0, 1.0;
  (No__Local_printer_, No, Yes, Yes, DOS, No, No) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, Yes, DOS, No, No) 0.0, 1.0;
  (No__Local_printer_, Yes, No, Yes, DOS, No, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, No, Yes, DOS, No, No) 0.0, 1.0;
  (No__Local_printer_, No, No, Yes, DOS, No, No) 0.0, 1.0;
  (Yes__Network_printer_, No, No, Yes, DOS, No, No) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, No, DOS, No, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, Yes, No, DOS, No, No) 0.0, 1.0;
  (No__Local_printer_, No, Yes, No, DOS, No, No) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, No, DOS, No, No) 0.0, 1.0;
  (No__Local_printer_, Yes, No, No, DOS, No, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, No, No, DOS, No, No) 0.0, 1.0;
  (No__Local_printer_, No, No, No, DOS, No, No) 0.0, 1.0;
  (Yes__Network_printer_, No, No, No, DOS, No, No) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, Yes, Windows, No, No) 1.0, 0.0;
  (Yes__Network_printer_, Yes, Yes, Yes, Windows, No, No) 1.0, 0.0;
  (No__Local_printer_, No, Yes, Yes, Windows, No, No) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, Yes, Windows, No, No) 0.0, 1.0;
  (No__Local_printer_, Yes, No, Yes, Windows, No, No) 1.0, 0.0;
  (Yes__Network_printer_, Yes, No, Yes, Windows, No, No) 0.0, 1.0;
  (No__Local_printer_, No, No, Yes, Windows, No, No) 0.0, 1.0;
  (Yes__Network_printer_, No, No, Yes, Windows, No, No) 0.0, 1.0;
  (No__Local_printer_, Yes, Yes, No, Windows, No, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, Yes, No, Windows, No, No) 1.0, 0.0;
  (No__Local_printer_, No, Yes, No, Windows, No, No) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes, No, Windows, No, No) 0.0, 1.0;
  (No__Local_printer_, Yes, No, No, Windows, No, No) 0.0, 1.0;
  (Yes__Network_printer_, Yes, No, No, Windows, No, No) 0.0, 1.0;
  (No__Local_printer_, No, No, No, Windows, No, No) 0.0, 1.0;
  (Yes__Network_printer_, No, No, No, Windows, No, No) 0.0, 1.0;
}
probability ( PrtMem ) {
  table 0.95, 0.05;
}
probability ( PrtTimeOut ) {
  table 0.94, 0.06;
}
probability ( FllCrrptdBffr ) {
  table 0.85, 0.15;
}
probability ( TnrSpply ) {
  table 0.995, 0.005;
}
probability ( PrtData | PrtOn, PrtPaper, PC2PRT, PrtMem, PrtTimeOut, FllCrrptdBffr, TnrSpply ) {
  (Yes, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.99, 0.01;
  (No, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.0, 1.0;
  (Yes, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.0, 1.0;
  (No, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.0, 1.0;
  (No, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, No_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, No_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.1, 0.9;
  (No, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, No_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, No_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, Has_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, No_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, No_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.0, 1.0;
  (No, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, Has_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, No_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, No_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, Has_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, No_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, No_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, Has_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, No_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (No, No_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.02, 0.98;
  (No, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, No_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, No_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, No_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, No_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, Has_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, No_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, No_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, Has_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, No_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, No_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, Has_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, No_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, No_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, Has_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, No_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (No, No_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.01, 0.99;
  (No, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, No_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, No_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, No_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, No_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, Has_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, Has_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, No_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, No_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, Has_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, Has_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, No_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, No_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, Has_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, No_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, No_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, Has_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, Has_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, No_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (No, No_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, No_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, No_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, No_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, No_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, Has_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, Has_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, No_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, No_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, Has_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, Has_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, No_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, No_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, Has_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, Has_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, No_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, No_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, Has_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, Has_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (Yes, No_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
  (No, No_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;
}
probability ( Problem1 | PrtData ) {
  (Yes) 1.0, 0.0;
  (No) 0.0, 1.0;
}
probability ( AppDtGnTm | PrtSpool ) {
  (Enabled) 1.0, 0.0;
  (Disabled) 0.99000001, 0.00999999;
}
probability ( PrntPrcssTm | PrtSpool ) {
  (Enabled) 0.99000001, 0.00999999;
  (Disabled) 1.0, 0.0;
}
probability ( DeskPrntSpd | PrtMem, AppDtGnTm, PrntPrcssTm ) {
  (Greater_than_2_Mb, Fast_Enough, Fast_Enough) 0.99900001, 0.00099999;
  (Less_than_2Mb, Fast_Enough, Fast_Enough) 0.25, 0.75;
  (Greater_than_2_Mb, Too_Long, Fast_Enough) 0.00099999, 0.99900001;
  (Less_than_2Mb, Too_Long, Fast_Enough) 0.5, 0.5;
  (Greater_than_2_Mb, Fast_Enough, Too_Long) 0.00099999, 0.99900001;
  (Less_than_2Mb, Fast_Enough, Too_Long) 0.5, 0.5;
  (Greater_than_2_Mb, Too_Long, Too_Long) 0.5, 0.5;
  (Less_than_2Mb, Too_Long, Too_Long) 0.5, 0.5;
}
probability ( PgOrnttnOK ) {
  table 0.95, 0.05;
}
probability ( PrntngArOK ) {
  table 0.98, 0.02;
}
probability ( ScrnFntNtPrntrFnt ) {
  table 0.95, 0.05;
}
probability ( CmpltPgPrntd | PrtMem, PgOrnttnOK, PrntngArOK ) {
  (Greater_than_2_Mb, Correct, Correct) 0.99, 0.01;
  (Less_than_2Mb, Correct, Correct) 0.3, 0.7;
  (Greater_than_2_Mb, Incorrect, Correct) 0.00999999, 0.99000001;
  (Less_than_2Mb, Incorrect, Correct) 0.5, 0.5;
  (Greater_than_2_Mb, Correct, Incorrect) 0.1, 0.9;
  (Less_than_2Mb, Correct, Incorrect) 0.5, 0.5;
  (Greater_than_2_Mb, Incorrect, Incorrect) 0.5, 0.5;
  (Less_than_2Mb, Incorrect, Incorrect) 0.5, 0.5;
}
probability ( GrphcsRltdDrvrSttngs ) {
  table 0.95, 0.05;
}
probability ( EPSGrphc ) {
  table 0.99, 0.01;
}
probability ( NnPSGrphc | PrtMem, GrphcsRltdDrvrSttngs, EPSGrphc ) {
  (Greater_than_2_Mb, Correct, No____TIF___WMF___BMP_) 0.999, 0.001;
  (Less_than_2Mb, Correct, No____TIF___WMF___BMP_) 0.25, 0.75;
  (Greater_than_2_Mb, Incorrect, No____TIF___WMF___BMP_) 0.1, 0.9;
  (Less_than_2Mb, Incorrect, No____TIF___WMF___BMP_) 0.5, 0.5;
  (Greater_than_2_Mb, Correct, Yes____EPS_) 0.0, 1.0;
  (Less_than_2Mb, Correct, Yes____EPS_) 0.5, 0.5;
  (Greater_than_2_Mb, Incorrect, Yes____EPS_) 0.5, 0.5;
  (Less_than_2Mb, Incorrect, Yes____EPS_) 0.5, 0.5;
}
probability ( PrtPScript ) {
  table 0.4, 0.6;
}
probability ( PSGRAPHIC | PrtMem, GrphcsRltdDrvrSttngs, EPSGrphc ) {
  (Greater_than_2_Mb, Correct, No____TIF___WMF___BMP_) 0.999, 0.001;
  (Less_than_2Mb, Correct, No____TIF___WMF___BMP_) 0.25, 0.75;
  (Greater_than_2_Mb, Incorrect, No____TIF___WMF___BMP_) 0.1, 0.9;
  (Less_than_2Mb, Incorrect, No____TIF___WMF___BMP_) 0.5, 0.5;
  (Greater_than_2_Mb, Correct, Yes____EPS_) 1.0, 0.0;
  (Less_than_2Mb, Correct, Yes____EPS_) 0.5, 0.5;
  (Greater_than_2_Mb, Incorrect, Yes____EPS_) 0.5, 0.5;
  (Less_than_2Mb, Incorrect, Yes____EPS_) 0.5, 0.5;
}
probability ( Problem4 | NnPSGrphc, PrtPScript, PSGRAPHIC ) {
  (Yes, Yes, Yes) 0.0, 1.0;
  (No, Yes, Yes) 0.0, 1.0;
  (Yes, No, Yes) 0.0, 1.0;
  (No, No, Yes) 1.0, 0.0;
  (Yes, Yes, No) 1.0, 0.0;
  (No, Yes, No) 1.0, 0.0;
  (Yes, No, No) 0.0, 1.0;
  (No, No, No) 1.0, 0.0;
}
probability ( TrTypFnts ) {
  table 0.9, 0.1;
}
probability ( FntInstlltn ) {
  table 0.98, 0.02;
}
probability ( PrntrAccptsTrtyp ) {
  table 0.9, 0.1;
}
probability ( TTOK | PrtMem, FntInstlltn, PrntrAccptsTrtyp ) {
  (Greater_than_2_Mb, Verified, Yes) 0.99000001, 0.00999999;
  (Less_than_2Mb, Verified, Yes) 0.5, 0.5;
  (Greater_than_2_Mb, Faulty, Yes) 0.1, 0.9;
  (Less_than_2Mb, Faulty, Yes) 0.5, 0.5;
  (Greater_than_2_Mb, Verified, No) 0.0, 1.0;
  (Less_than_2Mb, Verified, No) 0.5, 0.5;
  (Greater_than_2_Mb, Faulty, No) 0.5, 0.5;
  (Less_than_2Mb, Faulty, No) 0.5, 0.5;
}
probability ( NnTTOK | PrtMem, ScrnFntNtPrntrFnt, FntInstlltn ) {
  (Greater_than_2_Mb, Yes, Verified) 0.99000001, 0.00999999;
  (Less_than_2Mb, Yes, Verified) 0.5, 0.5;
  (Greater_than_2_Mb, No, Verified) 0.1, 0.9;
  (Less_than_2Mb, No, Verified) 0.5, 0.5;
  (Greater_than_2_Mb, Yes, Faulty) 0.1, 0.9;
  (Less_than_2Mb, Yes, Faulty) 0.5, 0.5;
  (Greater_than_2_Mb, No, Faulty) 0.5, 0.5;
  (Less_than_2Mb, No, Faulty) 0.5, 0.5;
}
probability ( Problem5 | TrTypFnts, TTOK, NnTTOK ) {
  (Yes, Yes, Yes) 0.0, 1.0;
  (No, Yes, Yes) 0.0, 1.0;
  (Yes, No, Yes) 1.0, 0.0;
  (No, No, Yes) 0.0, 1.0;
  (Yes, Yes, No) 0.0, 1.0;
  (No, Yes, No) 1.0, 0.0;
  (Yes, No, No) 1.0, 0.0;
  (No, No, No) 1.0, 0.0;
}
probability ( LclGrbld | AppData, PrtDriver, PrtMem, CblPrtHrdwrOK ) {
  (Correct, Yes, Greater_than_2_Mb, Operational) 1.0, 0.0;
  (Incorrect_or_corrupt, Yes, Greater_than_2_Mb, Operational) 0.2, 0.8;
  (Correct, No, Greater_than_2_Mb, Operational) 0.4, 0.6;
  (Incorrect_or_corrupt, No, Greater_than_2_Mb, Operational) 0.5, 0.5;
  (Correct, Yes, Less_than_2Mb, Operational) 0.2, 0.8;
  (Incorrect_or_corrupt, Yes, Less_than_2Mb, Operational) 0.5, 0.5;
  (Correct, No, Less_than_2Mb, Operational) 0.5, 0.5;
  (Incorrect_or_corrupt, No, Less_than_2Mb, Operational) 0.5, 0.5;
  (Correct, Yes, Greater_than_2_Mb, Not_Operational) 0.1, 0.9;
  (Incorrect_or_corrupt, Yes, Greater_than_2_Mb, Not_Operational) 0.5, 0.5;
  (Correct, No, Greater_than_2_Mb, Not_Operational) 0.5, 0.5;
  (Incorrect_or_corrupt, No, Greater_than_2_Mb, Not_Operational) 0.5, 0.5;
  (Correct, Yes, Less_than_2Mb, Not_Operational) 0.5, 0.5;
  (Incorrect_or_corrupt, Yes, Less_than_2Mb, Not_Operational) 0.5, 0.5;
  (Correct, No, Less_than_2Mb, Not_Operational) 0.5, 0.5;
  (Incorrect_or_corrupt, No, Less_than_2Mb, Not_Operational) 0.5, 0.5;
}
probability ( NtGrbld | AppData, PrtDriver, PrtMem, NtwrkCnfg ) {
  (Correct, Yes, Greater_than_2_Mb, Correct) 1.0, 0.0;
  (Incorrect_or_corrupt, Yes, Greater_than_2_Mb, Correct) 0.3, 0.7;
  (Correct, No, Greater_than_2_Mb, Correct) 0.4, 0.6;
  (Incorrect_or_corrupt, No, Greater_than_2_Mb, Correct) 0.5, 0.5;
  (Correct, Yes, Less_than_2Mb, Correct) 0.2, 0.8;
  (Incorrect_or_corrupt, Yes, Less_than_2Mb, Correct) 0.5, 0.5;
  (Correct, No, Less_than_2Mb, Correct) 0.5, 0.5;
  (Incorrect_or_corrupt, No, Less_than_2Mb, Correct) 0.5, 0.5;
  (Correct, Yes, Greater_than_2_Mb, Incorrect) 0.4, 0.6;
  (Incorrect_or_corrupt, Yes, Greater_than_2_Mb, Incorrect) 0.5, 0.5;
  (Correct, No, Greater_than_2_Mb, Incorrect) 0.5, 0.5;
  (Incorrect_or_corrupt, No, Greater_than_2_Mb, Incorrect) 0.5, 0.5;
  (Correct, Yes, Less_than_2Mb, Incorrect) 0.5, 0.5;
  (Incorrect_or_corrupt, Yes, Less_than_2Mb, Incorrect) 0.5, 0.5;
  (Correct, No, Less_than_2Mb, Incorrect) 0.5, 0.5;
  (Incorrect_or_corrupt, No, Less_than_2Mb, Incorrect) 0.5, 0.5;
}
probability ( GrbldOtpt | NetPrint, LclGrbld, NtGrbld ) {
  (No__Local_printer_, Yes, Yes) 1.0, 0.0;
  (Yes__Network_printer_, Yes, Yes) 1.0, 0.0;
  (No__Local_printer_, No, Yes) 0.0, 1.0;
  (Yes__Network_printer_, No, Yes) 1.0, 0.0;
  (No__Local_printer_, Yes, No) 1.0, 0.0;
  (Yes__Network_printer_, Yes, No) 0.0, 1.0;
  (No__Local_printer_, No, No) 0.0, 1.0;
  (Yes__Network_printer_, No, No) 0.0, 1.0;
}
probability ( HrglssDrtnAftrPrnt | AppDtGnTm ) {
  (Fast_Enough) 0.99, 0.01;
  (Too_Long) 0.1, 0.9;
}
probability ( REPEAT | CblPrtHrdwrOK, NtwrkCnfg ) {
  (Operational, Correct) 1.0, 0.0;
  (Not_Operational, Correct) 0.5, 0.5;
  (Operational, Incorrect) 0.5, 0.5;
  (Not_Operational, Incorrect) 0.5, 0.5;
}
probability ( AvlblVrtlMmry | PrtPScript ) {
  (Yes) 0.98, 0.02;
  (No) 1.0, 0.0;
}
probability ( PSERRMEM | PrtPScript, AvlblVrtlMmry ) {
  (Yes, Adequate____1Mb_) 1.0, 0.0;
  (No, Adequate____1Mb_) 1.0, 0.0;
  (Yes, Inadequate____1_Mb_) 0.05, 0.95;
  (No, Inadequate____1_Mb_) 1.0, 0.0;
}
probability ( TstpsTxt | PrtPScript, AvlblVrtlMmry ) {
  (Yes, Adequate____1Mb_) 0.99900001, 0.00099999;
  (No, Adequate____1Mb_) 1.0, 0.0;
  (Yes, Inadequate____1_Mb_) 0.00099999, 0.99900001;
  (No, Inadequate____1_Mb_) 1.0, 0.0;
}
probability ( GrbldPS | GrbldOtpt, AvlblVrtlMmry ) {
  (No, Adequate____1Mb_) 1.0, 0.0;
  (Yes, Adequate____1Mb_) 0.0, 1.0;
  (No, Inadequate____1_Mb_) 0.1, 0.9;
  (Yes, Inadequate____1_Mb_) 0.5, 0.5;
}
probability ( IncmpltPS | CmpltPgPrntd, AvlblVrtlMmry ) {
  (Yes, Adequate____1Mb_) 1.0, 0.0;
  (No, Adequate____1Mb_) 0.0, 1.0;
  (Yes, Inadequate____1_Mb_) 0.3, 0.7;
  (No, Inadequate____1_Mb_) 0.5, 0.5;
}
probability ( PrtFile | PrtDataOut ) {
  (Yes) 0.8, 0.2;
  (No) 0.2, 0.8;
}
probability ( PrtIcon | NtwrkCnfg, PTROFFLINE ) {
  (Correct, Online) 0.9999, 0.0001;
  (Incorrect, Online) 0.25, 0.75;
  (Correct, Offline) 0.7, 0.3;
  (Incorrect, Offline) 0.5, 0.5;
}
probability ( Problem6 | GrbldOtpt, PrtPScript, GrbldPS ) {
  (No, Yes, No) 1.0, 0.0;
  (Yes, Yes, No) 1.0, 0.0;
  (No, No, No) 1.0, 0.0;
  (Yes, No, No) 0.0, 1.0;
  (No, Yes, Yes) 0.0, 1.0;
  (Yes, Yes, Yes) 0.0, 1.0;
  (No, No, Yes) 1.0, 0.0;
  (Yes, No, Yes) 0.0, 1.0;
}
probability ( Problem3 | CmpltPgPrntd, PrtPScript, IncmpltPS ) {
  (Yes, Yes, Yes) 0.0, 1.0;
  (No, Yes, Yes) 0.0, 1.0;
  (Yes, No, Yes) 0.0, 1.0;
  (No, No, Yes) 1.0, 0.0;
  (Yes, Yes, No) 1.0, 0.0;
  (No, Yes, No) 1.0, 0.0;
  (Yes, No, No) 0.0, 1.0;
  (No, No, No) 1.0, 0.0;
}
probability ( PrtQueue ) {
  table 0.99, 0.01;
}
probability ( NtSpd | DeskPrntSpd, NtwrkCnfg, PrtQueue ) {
  (OK, Correct, Short) 0.99900001, 0.00099999;
  (Too_Slow, Correct, Short) 0.0, 1.0;
  (OK, Incorrect, Short) 0.25, 0.75;
  (Too_Slow, Incorrect, Short) 0.5, 0.5;
  (OK, Correct, Long) 0.25, 0.75;
  (Too_Slow, Correct, Long) 0.5, 0.5;
  (OK, Incorrect, Long) 0.5, 0.5;
  (Too_Slow, Incorrect, Long) 0.5, 0.5;
}
probability ( Problem2 | NetPrint, DeskPrntSpd, NtSpd ) {
  (No__Local_printer_, OK, OK) 1.0, 0.0;
  (Yes__Network_printer_, OK, OK) 1.0, 0.0;
  (No__Local_printer_, Too_Slow, OK) 0.0, 1.0;
  (Yes__Network_printer_, Too_Slow, OK) 1.0, 0.0;
  (No__Local_printer_, OK, Slow) 1.0, 0.0;
  (Yes__Network_printer_, OK, Slow) 0.0, 1.0;
  (No__Local_printer_, Too_Slow, Slow) 0.0, 1.0;
  (Yes__Network_printer_, Too_Slow, Slow) 0.0, 1.0;
}
probability ( PrtStatPaper | PrtPaper ) {
  (Has_Paper) 0.99900001, 0.00099999;
  (No_Paper) 0.00099999, 0.99900001;
}
probability ( PrtStatToner | TnrSpply ) {
  (Adequate) 0.99900001, 0.00099999;
  (Low) 0.00099999, 0.99900001;
}
probability ( PrtStatMem | PrtMem ) {
  (Greater_than_2_Mb) 0.99900001, 0.00099999;
  (Less_than_2Mb) 0.2, 0.8;
}
probability ( PrtStatOff | PrtOn ) {
  (Yes) 0.99000001, 0.00999999;
  (No) 0.00999999, 0.99000001;
}


================================================
FILE: examples/Drawing.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drawing (Plotting) Bayesian Networks in pyBN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The importance of drawing a Bayesian network can not be understated, especially in a research setting. Being able to visualize a network's random variables and the dependencies between them is critical to determining whether the network is valid and useful. \n",
    "\n",
    "With that, the pyBN package provides seamless integration of graphviz -- both standalone and with matplotlib by way of networkx -- for concise visualizations of BN's which have great layouts, and overall great asthetics.\n",
    "\n",
    "In this tutorial, we will demonstrate the plotting functionality in pyBN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyBN import *\n",
    "\n",
    "bn = read_bn('data/cancer.bif')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the above code, we loaded the pyBN package into the namespace, and read a BayesNet object from one of the files included with the package. Let's explore its attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pollution', 'Smoker', 'Cancer', 'Xray', 'Dyspnoea']\n",
      "[['Pollution', 'Cancer'], ['Smoker', 'Cancer'], ['Cancer', 'Xray'], ['Cancer', 'Dyspnoea']]\n"
     ]
    }
   ],
   "source": [
    "print bn.V\n",
    "print bn.E"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, this network has 5 vertices and 4 edges. Its visualization should not be much issue. Let's introduce the \"draw_bn\" function. Calling this function on a BayesNet object will give you a not-so-bad standard visualization of the network in matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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POKVWc+AlHUwa4Q+r2vQycGREXFmL45rVgpO+NQ1JI4BLSBd6q90DHBARs/rxeENIv1CO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c6aced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "draw_bn(bn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Really, that doesn't look so nice. The nodes are too small and aggressively red. Luckily, we can pass in any attributes we like as allowed in the networkx function \"draw_networkx\". Let's start by changing the node size and the node color:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Yc4hsTd8kNBFpDzwNhIGRqvqtiAT8fv8/L7jggr7z58/PaK0jesLhMKNGjapZ\nunTp6qqqqh+qaqWInAz8D/A4MFVVnfsKYkwT2EzfJCwROR4oBlYCl6jqtwCqWllVVTV46dKlRQMG\nDAiWlZW1eC5lZWUMGDAguGzZsqKqqqrBqloZz+U94BTgTOB/RCTQ4skYcxis6JuEJCLnAW8As1T1\nVlWN7H6/qtZUVlaev2bNmlsKCgqCM2fOrK+vb/4jOsPhMDNmzKgvKCgIrlmzZkJlZeX5qlqzVy5f\nA+cAXwHviMhxzZ6IMc3F6Yv0WljsHoAAvwA2AWc08Tnd/X5/UV5eXtX8+fM1FArp4QqFQjp//nzN\ny8urCgQCbwLdmpjLDcBm4Hyn96WFRWNha/omYYjIEcBfgEHElnPWHcRzBbg0Ozt7cjQa/f748ePb\n/fSnP007+uijDyqH8vJyHnzwwfBDDz1U73K5Vu7YsWMWsEgPYqCIyBnAU8B9wO8O5rnGtDQr+iYh\niEgO8HegErhKVasO47V6Z2Zm3lxfXz/a5/ORn58fLSws9A0cONCVm5uLx+MBYte0raiooKSkJFpU\nVBQsLS11BYNB3G733GAw+Cc9jF46ItKd2A+8HwHjdK8lIWOcYkXfOE5E+gIvEDtK51e61/r9Ybyu\nC+gF5Hs8ngKfz3dGNBrtEI1GPQAulyvkcrm2BYPBN0OhUDGxC6Csba6ZuYh4gUeAPGCIqn7RHK9r\nzOGwom8cJSIXAY8Bt6rqPKfzaW7xZafbgFuAK1W1yOGUTIqzom8cES+GtxIrhleo6jsOp9SiRORC\nYC5wp6o+7HQ+JnVZ0TetTkTSgTnAicClmiK9a0Qkj1g3zleBCarqfBMhk3LsOH3TqkSkM7GiFwAK\nU6XgA6jqGuA0oDvwj/i+MKZVWdE3rUZEvg+sAP4JDFPVoMMptTqNnVV8KfAmsEJETnI4JZNibHnH\ntAoRuQx4GLhJVZ9yOp9EICLDgAewfWJakRV906LiP9hOBm4kdtjiuw6nlFBEZADwPPAkzXi4qjH7\nYkXftBgR8RA7Tr03cJmqbnQ4pYQkIp2InaOwExgVXwIypkXYmr5pESJyJPA64AYGW8HfN1XdApwH\nfA4Ui0hvh1MyScyKvml28R7zxcBiYjNXa0FwAKoaVtWfA/cCb4rIj5zOySQnW94xzWq3HyfHq+qz\nTufTFonID4BngD8D91jDNtOcrOibZhH/wfY3wH8SW79/3+GU2jQR6UqsYdta4D9VdafDKZkkYcs7\n5rDFG4teUFB4AAAT9UlEQVQ9CVwAFFjBP3yq+iUwGKgD3hKRHg6nZJKEFX1zWOIz0jeBWuAsVa1w\nOKWkEf8t5Fpi1999R0QGO5ySSQJW9M0hE5ECYj/YPgVcq6ohh1NKOhrze2A08IyI/DS+lGbMIbE1\nfXNIRGQU8AdgrKq+4HQ+qUBEjiXWsK0I+Lmq1jmckmmDrOibgxK/MMlMYBTwY1UtczillCIifmLL\nPZ2ItaTe7HBKpo2x5R3TZCKSCTwLnAGcagW/9cUvI3kF8A/gXREZ6HBKpo2xom+aJH70yFvANuCc\n+FmkxgGqGlXVacAE4GURucrhlEwbYss75oDiJwv9HZgN/NFOFkocItKfWMO254DJ1rDNHIgVfbNf\nInIdsWJ/rar+r8PpmEaISEdiR1DVAyNVdbvDKZkEZss7plEi4haR/wZ+BZxpBT9xqepWYifGfUTs\nwix9HE7JJLB2TidgEo+IBIAFgJfYGbZbHU7JHICq1gO3iMi/gNdF5Cd2KK1pjM30zR5EpBewHPgC\nON8Kftuiqv8PuBh4QER+ZSdymb1Z0TcNRORMYgX/QVX9qaqGnc7JHDxVLQZOJVb8n44famsMYEXf\nxInIOGJXb7paVe93Oh9zeFT1K+A/gCpiDdt6OpuRSRRW9FOciLQTkT8AtwJnqOo/nM7JNI94L6Sf\nELtk5dsicrbDKZkEYIdspjARySZ2qB/AcFXd4WQ+puXEC/4C4G7gL3auReqymX6KEpHjgHeAj4GL\nrOAnN1V9FRgEjAMeEZF0h1MyDrGin4JE5BxinRrvU9Wb44f7mSSnquXAD4As4LX4xetNirGin2JE\n5GfAE8SWc/7qdD6mdalqNTAMeIlYw7ZTHU7JtDJb008RIpIG/BE4E7hEVT93OCXjMBG5lNiPvLep\n6lyn8zGtw4p+ChCRDsAzQIhYb5ZKh1MyCUJE+hJr2PYicLst9SU/W95JciJyArFLGr5H7KInVvBN\nA1VdTexErj7AknjzNpPErOgnMRG5AHgduFtVb7e2u6Yx8a6cFwHvE2vY1s/hlEwLsuWdJBTvt3Iz\nMAkYpqpvOZySaSNE5Grg98ANqvqc0/mY5mdFP8mIyBHA/cS+sv9YVdc7nJJpY+KXYHwO+BswQ1Wj\nDqdkmpEV/SQiIp2IXcN2G7EeOtUOp2TaKBHJJfZZ+hoYHb82r0kCtqafJOLrsMXAm8DlVvDN4VDV\nCuBsYAuxvj3HOJySaSZW9JOAiFwCvAr8WlXvtK/jpjmoai1wA7HlwuUicq7DKZlmYMs7bVj8B9vb\nif1oe3m8j7oxzS5+rYUniV0v+Q/WsK3tsqLfRomIB3gI6AdcqqpfOpySSXIi0oPYiVwriR3dE3I4\nJXMIbHmnDRGRvPh/v0dsOccLDLaCb1pD/EiwQsADvCEiR0HDCYCmjbCZ/kEQERdwDJDv8XhO8/l8\np0cikY6qmh6/v9btdm8NBoNvhUKhd4BSYO3hrrHHl3F+DtwHzCB2YYzHsMPpjAPin8fJwE3E1vvv\nise05vg8OjXOUoUV/SYQkd4+n29CJBK5JjMzk/z8/GhhYWHmwIEDJTc3F4/HA0AoFKKiooKSkhIt\nKiqqLi0tdVVXV+N2u+cFg8E/qOonh/DeRwB/IdYHfZffqOrM5tk6Yw6NiNxI7LO5ywvANYfa6sPJ\ncZZSVNWikQAEGJKdnV2clZW184477qgrLy/Xg1VeXq6TJk2qCwQCNdnZ2cXAEOL/2DYhhxzgNUD3\nik2Az+l9ZJG6AbQDPmnks7kaOPYgXsfxcZZq4XgCiRhAd7/fX5SXl1e1YMECDYVCerhCoZAuWLBA\n8/LyqgKBwJtA9wPk0Bf4vJFBVQn8yOl9ZGEBDCZ28tben9HtwPlNeL7j4ywVw/EEEikAcbvd13u9\n3uqZM2eGw+GwNre6ujqdMWNG2Ov1Vrvd7nGNzUaINb+qbGQwrQX6OL2fLCx2BdCdWAfXvT+rEeDW\nfXy+E2KcpWo4nkCiBOANBAKv9OnTp7qsrExb2sqVK/WEE06oDgQCy4CMeA4C3AZEGxlErwE5Tu8n\nC4u9g9hRZAsb+cwqMG/X53vXY50eZ6kejieQCAEE/H5/ybBhw2rq6uq0tdTV1enQoUN3+v3+d+Pr\n9/9vHwPnIeCI1tgXFhaHEvEJy6R9TFjeBbomyDgLtMb+SORwPAGnA/D6/f6SMWPGhCKRiLa2SCSi\nY8aMCfl8vqp9fEX+uX01tWgrAVwI7Gjks1yRmZn5sdPjLF74U3rG73gCjm48SCAQeGXYsGE1TnwQ\nd4lEIjpkyBDNyMjYfZDsAM5rynZYWCRSAHnAR7sXfa/Xq5dffrk6Pc6GDh26M77Uk7ITqZQ+Tj8t\nLe36vLy8+z744ANfWlqao7mEw2H69+/PJ598AvApsYuX2/HGpk0SkSxgPnCRiNC7d29WrlxJIoyz\nAQMGBNesWXNLfX39w44m45CULfoi0t3r9X5YXFzs69cvMa4OV1ZWRkFBQaSmpuYkVS1zOh9jDoeI\nuIE/ZGRk3LRixQoSbJwFa2pq+qjqBqfzaW0p2XtHRCQQCCyYMmVKeqJ8EAH69+/P5MmTNRAIPBA/\n1d2YtiwaCAROmjx5ciTRxtmUKVPSA4HA/FQcZyk50xeRIXl5eY+vXr06s127dk6ns4dwOEy/fv2q\n16xZc42qPu90PsYcKhtniSklZ/rZ2dmTp02blnAfRIC0tDSmTp2amZ2dPdnpXIw5HDbOElPKzfRF\n5PisrKz3Nm/enJGenu50Oo2qra2lc+fONZWVlSfZj7mmLbJxlrhSbqbv8/luHj9+fLtE/SACpKen\nM378+HY+n+8XTudizKGwcZa4UmqmLyIuj8dT+dFHH/mOPvpop9PZr/Lycvr06RMMhUJ+TaW/JNPm\n2ThLbKk20z8mMzOTRP8gAvTs2ROfzwexi0kY05bYOEtgqVb08/Pz89vM1XXiueY7nYcxB8nGWQJL\nqaLv8XhOKywszNzfY4LBID179mThwoUNt1VXV9OjRw+ee+65Fs9xd4WFhT6Px1PQqm9qTBOJyDoR\n2Ski34rINhEpEpEb0tPTDzjOEkmqjbOUKvo+n+/0gQMH7vdkDJ/Px0MPPcTNN9/M1q1bAbj99ts5\n9dRTufzyy7/z+Gi05SY0AwcOdPl8vjNa7A2MOTwKXKSqWUAPYBZwh6pedaBxlkhSbZylVNGPRCId\nc3NzD/i48847j4svvpif//znvP766/z973/ngQceAGDMmDH87Gc/46KLLsLv9/Paa6/x8ssvc/LJ\nJ5OVlUWPHj2YPn16w2tdfPHF3H///Xu8/oABA1i0aNEB88jNzSUajXY4yM00pjUJgKpWqeqLwPC6\nurqcTZs2kZuby+6/jT733HOceOKJAKxYsYJTTjmFrKwsjjzySG677TYA1q9fj8vl4uGHH+aoo47i\nqKOO4ne/+13Da0yfPp3hw4dz7bXXEggE6N+/P++9917D/R9//DFnnXUW7du3p3///ixevLjhvrq6\nOm677TZ69OjBkUceyc9+9jNqa2vJzc2lvr4+R0QWi8jXIrI1/v9dWnTPOcXpjm+tGVlZWRs/+ugj\nbYrt27frkUceqTk5OTp37tyG26+77jrNzs7Wt99+W1VVa2tr9fXXX9dVq1apqmpZWZnm5ubqokWL\nVFX16aef1oKCgobnf/DBB5qTk6NNuVrQhx9+qFlZWRud3m8WFo0FUA6cvfftIlI/depU7du3ry5Z\nskR3GTJkiP7+979XVdVBgwbpE088oaqqwWBQi4uLVVV13bp1KiI6atQoramp0bKyMu3UqZP+85//\nVFXVadOmaUZGhi5ZskSj0ahOmTJFTzvtNFVVDYfDeuyxx+qsWbM0HA7rq6++qn6/X9esWaOqqhMm\nTNBLL71Ud+zYodXV1frjH/9Yf/nLX+qHH36ofr//K2LX1U0HfMBTwHNO7+MW+XtzOoHWjIMp+qqq\n55xzjvp8Pq2srGy47brrrtNrr712v8+bMGGCTpw4UVVj1+zs0KGDfvbZZ6qqetttt+mNN97YpPe3\nom+RyLGvou92u2tvueUWnT17tl511VWqqrp161b1er26efNmVVU988wzddq0afrNN9/o7nYV/V2F\nWlV10qRJOnbsWFWNFf1zzz234b4PP/xQvV6vqqq+8cYbeuSRR+7xeiNHjtTp06erqqrP59PPP/+8\n4b7ly5drz549Gx1nwInAVqf3cUtESi3viEhtKBRq0mOfeOIJ1q9fzznnnMOkSZP2uK9bt257/HnF\nihWcffbZdO7cmezsbB566CG++eYbIHYCyPDhw3niiSdQVRYuXMg111zTpBxCoRAul6tpCRuTIKLR\nqNvr9XLVVVfx4osvUlNTw9NPP83gwYPp3LkzAI8++iiffPIJxx9/PAUFBbz00ksNzxcRunbt2vDn\nHj168NVXXzX8efclWq/XSygUIhqNsmnTpu+MzR49erBx40a2bNnCzp07yc/Pp0OHDnTo0IELL7yQ\nrVu3EgqFEJFaEXko/uP0DuB1IDsZG7KlVNF3u91bKyoqDvi4r7/+mokTJ/LII48wZ84cnn76ad56\n662G+/f+HIwaNYrLLruMjRs3smPHDm644YZdswUARo8ezRNPPME///lPfD4fBQVNO1CgoqICl8u1\nrYmbZ4zjROQUVXV369aNLl26MGjQIJ599lmeeOKJPSY7xxxzDAsWLGDLli1MmjSJoUOHUlNTA8RW\nH7744ouGx27YsIEuXQ68vN6lS5c9nrfruUcddRQ5OTl4vV5Wr17Ntm3b2LZtGzt27ODbb7+loqKC\nUCh0BHAccIqqZgODd23SYe6ShJNSRT8YDL5VUlJywLPubrrpJi6//HIGDx5Mbm4us2fPZty4cdTV\n1TX6+Orqatq3b09aWhorVqxgwYIFe9x/2mmn4XK5uPXWW5s8ywcoKSmJBoPBN5v8BGMcIiJ+EbkY\nWOhyuT7csmWLAlxzzTXMnj2bVatW7XH02/z58xu+DWdlZSEiuFz/LkczZ86kpqaG1atX89hjjzFi\nxIh9vveuCVZBQQFer5fZs2dTX1/Pa6+9xosvvsjIkSMREcaNG8eECRPYsmULABs3bmTZsmWUlJRE\n6+rqNgE1QKWIdACmNesOSiApVfRDodA7RUVF1ft7zKJFi1i+fDmzZ89uuO0nP/kJXbp0YebMmd+Z\n5QM88MAD/PrXvyYrK4u77rqL4cOHf+cxo0ePZtWqVVx99dVNzreoqCgYCoWKm/wEY1rfYhH5FtgA\nTAHujUajM3eNsyFDhrB+/Xouv/xyPB5Pw5OWLFlC3759CQQC3HLLLTz11FPs3qfnzDPP5Nhjj+Xc\nc89l0qRJ/PCHP9xnArvGZFpaGosXL+bll18mJyeHm266iXnz5nHccccBcM8993Dsscdy2mmnkZ2d\nzXnnnceaNWsoKioKRqPRRwAv8A2wHHi5eXdT4ki13jvH5eTkvL9lyxZfa7/3vHnzePjhh3njjTea\n/JycnJzg1q1bT1TVz1owNWOa1d7j7Nhjj+Wvf/0rZ5999gGfu379enr16kU4HN5j5t+SUm2cpdRM\nH1hbXV3NunXrWvVNd+7cyQMPPMANN9zQ5OeUl5cTDAYB1rZYYsa0jIZx9uyzz+JyuZpU8HdpzYlo\nKo6zlCr6qhp1u93z5syZE26t91y2bBmdO3fmyCOPZOTIkU1+3pw5c8Jut3uuptJXMZMUdo2zwYMH\n64033thwYmNTteYBM6k4zlJqeQdARHpnZWW9bxd3MKbl2DhLXCk10wdQ1U9EpKy1m6cdjPhX4pWp\n9EE0ycXGWeJKuZk+/PuCzatWrcpMS0tzOp09hMNh+vbtG/z000+v1hS7YLNJLjbOElPKzfTjnt+0\nadO/7rnnnnqnE9nbrFmz6jdv3vw+cOCObMYkNhtnCSglZ/oAItLd6/V+WFxc7OvXr5/T6QBQVlZG\nQUFBsKampo+qbnA6H2MOl42zxJOqM31UdUNtbe3E4cOHB8PhVjuYZ5/C4TDDhw8P1tXV3ZKKH0ST\nnGycJZ6ULfoAkUjk4S+//PLtUaNGhVryYigHEo1GGTVqVM3GjRuXRyKRRxxLxJgWYOMssaR00VdV\nraysvHTp0qWrxo4dW+vEBzIajTJ27NjapUuXrq6srLw0lY4XNqnBxlmCcbq3cyIEEPD7/e8OGzZs\nZ11dnbaWuro6HTp06E6/3/8uEGiNbbWwcCpsnCVGpPRMfxdVrayqqhq8dOnSogEDBgTLyspa/D3L\nysoYMGBAcNmyZUVVVVWDVbWyxd/UGAfZOEsQTv+rk0gBiNvtHpeRkVE9Y8aMcFMuaXiw6urqdPr0\n6eGMjIxqt9s9lvgRVBYWqRI2zhze/04nkIgBdPf7/UV5eXlV8+fP11AopIcrFArp/PnzNS8vryoQ\nCLwJdHN6Oy0snAwbZw7td6cTSNQgdsWcy7Kzs98JBAI7J02aVFdeXq4H6/PPP9fbb7+9LhAI7MzO\nzn4HuMxmHRYWsbBx1vqRsidnHQwR6Z2ZmXlzfX39aJ/PR35+frSwsNA3cOBAV25ubsPFIUKhEBUV\nFZSUlESLioqCpaWlrmAwiNvtnhsMBv+kKdbjw5iDYeOsdVjRPwgi4gJ6Afkej6fA5/OdEY1GO0Sj\nUQ+Ay+UKuVyubcFg8M34Fa9KgbVqO9mYJrNx1rKs6BtjTAqxQzaNMSaFWNE3xpgUYkXfGGNSiBV9\nY4xJIVb0jTEmhVjRN8aYFGJF3xhjUogVfWOMSSFW9I0xJoVY0TfGmBRiRd8YY1KIFX1jjEkhVvSN\nMSaFWNE3xpgUYkXfGGNSiBV9Y4xJIVb0jTEmhVjRN8aYFGJF3xhjUogVfWOMSSH/H3Ef9gpBiEq1\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf4a450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_bn(bn, node_size=2000, node_color='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, it's easy to make a small network look good. The real test of a BN visualization engine is how it performs with large networks. Let's load one and see:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QaAB+SvYlfdXizrTrRcBJyJHCRVFeH0F2wScgF6B1UXmbI1OywfHNycA/jcYt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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe8c2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bn2 = read_bn('data/win95pts.bif')\n",
    "draw_bn(bn2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yikes, that will take some work. Or will it? Let's apply a few of the same arguments as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KCLCB3YAH3L3gxWdERKQ8CrBFpC6Y2a7AJ4DPRQ+9hRBM7wJ0p/aLerbvAfbKKOLnwBpC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\nDVS5jgF+6u6pmTF6gIkxlFuuvwCHxFj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KYjNuNqwvnmB71BieYHt0dkQSbFX9ESNt24l5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7AAAg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\nEmwAABgJNgAAjAQbAABGgg0AAKMAskTkZyIw/qIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f00b610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "pylab.figure(3,figsize=(12,12)) \n",
    "draw_bn(bn2, node_size=200, with_labels=False, node_color='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's not bad, but clearly a lot of work still remains to be done! The visualization functionality is currently only limited by the user's imagination and ability to work with the networkx drawing capabilities. However, look for pyBN's drawing functionality to become much better in the future!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: examples/FactorOperations.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Factor Operations with pyBN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is probably rare that a user wants to directly manipulate factors unless they are developing a new algorithm, but it's still important to see how factor operations are done in pyBN. Moreover, the ease-of-use and transparency of pyBN's factor operations mean it can be a great teaching/learning tool!\n",
    "\n",
    "In this tutorial, I will go over the main operations you can do with factors. First, let's start with actually creating a factor. So, we will read in a Bayesian Network from one of the included networks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyBN import *\n",
    "bn = read_bn('data/cmu.bn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Burglary', 'Earthquake', 'Alarm', 'JohnCalls', 'MaryCalls']\n",
      "[['Burglary', 'Alarm'], ['Earthquake', 'Alarm'], ['Alarm', 'JohnCalls'], ['Alarm', 'MaryCalls']]\n"
     ]
    }
   ],
   "source": [
    "print bn.V\n",
    "print bn.E"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we have a Bayesian network with 5 nodes and some edges between them. Let's create a factor now. This is easy in pyBN - just pass in the BayesNet object and the name of the variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "alarm_factor = Factor(bn,'Alarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a factor, we can explore its properties. Every factor in pyBN has the following attributes:\n",
    "\n",
    "    *self.bn* : a BayesNet object\n",
    "\n",
    "    *self.var* : a string\n",
    "        The random variable to which this Factor belongs\n",
    "    \n",
    "    *self.scope* : a list\n",
    "        The RV, and its parents (the RVs involved in the\n",
    "        conditional probability table)\n",
    "    \n",
    "    *self.card* : a dictionary, where\n",
    "        key = an RV in self.scope, and\n",
    "        val = integer cardinality of the key (i.e. how\n",
    "            many possible values it has)\n",
    "    \n",
    "    *self.stride* : a dictionary, where\n",
    "        key = an RV in self.scope, and\n",
    "        val = integer stride (i.e. how many rows in the \n",
    "            CPT until the NEXT value of RV is reached)\n",
    "    \n",
    "    *self.cpt* : a nested numpy array\n",
    "        The probability values for self.var conditioned\n",
    "        on its parents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pyBN.classes.bayesnet.BayesNet object at 0x10c73ced0>\n",
      "Alarm\n",
      "['Alarm', 'Burglary', 'Earthquake']\n",
      "{'Burglary': 2, 'Alarm': 2, 'Earthquake': 2}\n",
      "{'Burglary': 2, 'Alarm': 1, 'Earthquake': 4}\n",
      "[ 0.999  0.001  0.71   0.29   0.06   0.94   0.05   0.95 ]\n"
     ]
    }
   ],
   "source": [
    "print alarm_factor.bn\n",
    "print alarm_factor.var\n",
    "print alarm_factor.scope\n",
    "print alarm_factor.card\n",
    "print alarm_factor.stride\n",
    "print alarm_factor.cpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Along with those properties, there are a great number of methods (functions) at hand:\n",
    "\n",
    "    *multiply_factor*\n",
    "        Multiply two factors together. The factor\n",
    "        multiplication algorithm used here is adapted\n",
    "        from Koller and Friedman (PGMs) textbook.\n",
    "\n",
    "    *sumover_var* :\n",
    "        Sum over one *rv* by keeping it constant. Thus, you \n",
    "        end up with a 1-D factor whose scope is ONLY *rv*\n",
    "        and whose length = cardinality of rv. \n",
    "\n",
    "    *sumout_var_list* :\n",
    "        Remove a collection of rv's from the factor\n",
    "        by summing out (i.e. calling sumout_var) over\n",
    "        each rv.\n",
    "\n",
    "    *sumout_var* :\n",
    "        Remove passed-in *rv* from the factor by summing\n",
    "        over everything else.\n",
    "\n",
    "    *maxout_var* :\n",
    "        Remove *rv* from the factor by taking the maximum value \n",
    "        of all rv instantiations over everyting else.\n",
    "\n",
    "    *reduce_factor_by_list* :\n",
    "        Reduce the factor by numerous sets of\n",
    "        [rv,val]\n",
    "\n",
    "    *reduce_factor* :\n",
    "        Condition the factor by eliminating any sets of\n",
    "        values that don't align with a given [rv, val]\n",
    "\n",
    "    *to_log* :\n",
    "        Convert probabilities to log space from\n",
    "        normal space.\n",
    "\n",
    "    *from_log* :\n",
    "        Convert probabilities from log space to\n",
    "        normal space.\n",
    "\n",
    "    *normalize* :\n",
    "        Make relevant collections of probabilities sum to one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a look at Factor Multiplication:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.998  0.001  0.001  0.     0.06   0.939  0.     0.001]\n",
      "\n",
      "[ 0.998  0.001  0.001  0.     0.06   0.939  0.     0.001]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "f1 = Factor(bn,'Alarm')\n",
    "f2 = Factor(bn,'Burglary')\n",
    "f1.multiply_factor(f2)\n",
    "\n",
    "f3 = Factor(bn,'Burglary')\n",
    "f4 = Factor(bn,'Alarm')\n",
    "f3.multiply_factor(f4)\n",
    "\n",
    "print np.round(f1.cpt,3)\n",
    "print '\\n',np.round(f3.cpt,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a look at \"sumover_var\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.999  0.001  0.71   0.29   0.06   0.94   0.05   0.95 ]\n",
      "['Alarm', 'Burglary', 'Earthquake']\n",
      "{'Burglary': 2, 'Alarm': 1, 'Earthquake': 4}\n",
      "\n",
      "[ 0.5  0.5]\n",
      "['Burglary']\n",
      "{'Burglary': 1}\n"
     ]
    }
   ],
   "source": [
    "f = Factor(bn,'Alarm')\n",
    "print f.cpt\n",
    "print f.scope\n",
    "print f.stride\n",
    "f.sumover_var('Burglary')\n",
    "print '\\n',f.cpt\n",
    "print f.scope\n",
    "print f.stride"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a look at \"sumout_var\", which is essentially the opposite of \"sumover_var\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Burglary': 2, 'Alarm': 1}\n",
      "['Alarm', 'Burglary']\n",
      "{'Burglary': 2, 'Alarm': 2}\n",
      "[ 0.5295  0.4705  0.38    0.62  ]\n"
     ]
    }
   ],
   "source": [
    "f = Factor(bn,'Alarm')\n",
    "f.sumout_var('Earthquake')\n",
    "print f.stride\n",
    "print f.scope\n",
    "print f.card\n",
    "print f.cpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, you can sum over a LIST of variables with \"sumover_var_list\". Notice how summing over every variable in the scope except for ONE variable is equivalent to summing over that ONE variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.999  0.001  0.71   0.29   0.06   0.94   0.05   0.95 ]\n",
      "['Alarm']\n",
      "{'Alarm': 1}\n",
      "[ 0.45475  0.54525]\n",
      "\n",
      "[ 0.999  0.001  0.71   0.29   0.06   0.94   0.05   0.95 ]\n",
      "['Alarm']\n",
      "{'Alarm': 1}\n",
      "[ 0.45475  0.54525]\n"
     ]
    }
   ],
   "source": [
    "f = Factor(bn,'Alarm')\n",
    "print f.cpt\n",
    "f.sumout_var_list(['Burglary','Earthquake'])\n",
    "print f.scope\n",
    "print f.stride\n",
    "print f.cpt\n",
    "\n",
    "f1 = Factor(bn,'Alarm')\n",
    "print '\\n',f1.cpt\n",
    "f1.sumover_var('Alarm')\n",
    "print f1.scope\n",
    "print f1.stride\n",
    "print f1.cpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even more, you can use \"maxout_var\" to take the max values over a variable in the factor. This is a fundamental operation in Max-Sum Variable Elimination for MAP inference. Notice how the variable being maxed out is removed from the scope because it is conditioned upon and thus taken as truth in a sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Alarm', 'Burglary', 'Earthquake']\n",
      "[ 0.999  0.001  0.71   0.29   0.06   0.94   0.05   0.95 ]\n",
      "\n",
      "['Alarm', 'Earthquake']\n",
      "[ 0.77501  0.22499  0.05942  0.94058]\n"
     ]
    }
   ],
   "source": [
    "f = Factor(bn,'Alarm')\n",
    "print f.scope\n",
    "print f.cpt\n",
    "f.maxout_var('Burglary')\n",
    "print '\\n', f.scope\n",
    "print f.cpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, you can also use \"reduce_factor\" to reduce a factor based on evidence. This is different from \"sumover_var\" because \"reduce_factor\" is not summing over anything, it is simply removing any \n",
    "        parent-child instantiations which are not consistent with\n",
    "        the evidence. Moreover, there should not be any need for\n",
    "        normalization because the CPT should already be normalized\n",
    "        over the rv-val evidence (but we do it anyways because of\n",
    "        rounding). This function is essential when user's pass in evidence to any inference query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Alarm', 'Burglary', 'Earthquake']\n",
      "[ 0.999  0.001  0.71   0.29   0.06   0.94   0.05   0.95 ]\n",
      "\n",
      "['Alarm', 'Earthquake']\n",
      "[ 0.71  0.29  0.05  0.95]\n"
     ]
    }
   ],
   "source": [
    "f = Factor(bn, 'Alarm')\n",
    "print f.scope\n",
    "print f.cpt\n",
    "f.reduce_factor('Burglary','Yes')\n",
    "print '\\n', f.scope\n",
    "print f.cpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another piece of functionality is the capability to convert the factor probabilities to/from log-space. This is important for MAP inference, since the sum of log-probabilities is equal the product of normal probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.999  0.001  0.71   0.29   0.06   0.94   0.05   0.95 ]\n",
      "[-0.   -6.91 -0.34 -1.24 -2.81 -0.06 -3.   -0.05]\n",
      "[ 0.999  0.001  0.71   0.29   0.06   0.94   0.05   0.95 ]\n"
     ]
    }
   ],
   "source": [
    "f = Factor(bn,'Alarm')\n",
    "print f.cpt\n",
    "f.to_log()\n",
    "print np.round(f.cpt,2)\n",
    "f.from_log()\n",
    "print f.cpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we have normalization. This function does most of its work behind the scenes because it cleans up the factor probabilities after multiplication or reduction. Still, it's an important function of which users should be aware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.999  0.001  0.71   0.29   0.06   0.94   0.05   0.95 ]\n",
      "[ 20.    20.     0.71   0.29   0.94   0.94   0.05   0.15]\n",
      "[ 0.5   0.5   0.71  0.29  0.5   0.5   0.25  0.75]\n"
     ]
    }
   ],
   "source": [
    "f = Factor(bn, 'Alarm')\n",
    "print f.cpt\n",
    "f.cpt[0]=20\n",
    "f.cpt[1]=20\n",
    "f.cpt[4]=0.94\n",
    "f.cpt[7]=0.15\n",
    "print f.cpt\n",
    "f.normalize()\n",
    "print f.cpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's all for factor operations with pyBN. As you can see, there is a lot going on with factor operations. While these functions are the behind-the-scenes drivers of most inference queries, it is still useful for users to see how they operate. These operations have all been optimized to run incredibly fast so that inference queries can be as fast as possible."
   ]
  }
 ],
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================================================
FILE: examples/MAP Inference as Optimization.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MAP Inference as Linear Integer Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MAP inference is a task concerned with finding the assignment of random variables in a Bayesian network that optimizes the joint probability over all possible assignments. By \"optimizes\", we are talking about maximizing the joint probability (or minimizing the sum of the negative log-probabilities). Whenever the term \"optimize\" is thrown around, it should be clear that the problem lends itself to mathematical programming. MAP inference is no different - it can be solved as an integer linear program. \n",
    "\n",
    "Let's see how we could do this. First, we will start by loading a very simple Bayesian network with two nodes \"A\" and \"B\", with only one edge pointing from \"A\" -> \"B\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyBN import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bn = read_bn('data/simple.bn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'B']\n",
      "{'A': ['B'], 'B': []}\n",
      "{'A': {'values': ['No', 'Yes'], 'parents': [], 'cpt': [0.3, 0.7]}, 'B': {'values': ['No', 'Yes'], 'parents': ['A'], 'cpt': [0.4, 0.6, 0.1, 0.9]}}\n"
     ]
    }
   ],
   "source": [
    "print bn.V\n",
    "print bn.E\n",
    "print bn.F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From a quick look at the conditional probability values, it should be clear that the assignment of values to \"A\" and \"B\" has a maximal probability of 0.63 when \"A=Yes\" and \"B=Yes\".\n",
    "\n",
    "Let's see how this problem can be solved and generalized using a well-known Python optimization library called \"Pulp\". Pulp is just almost as good as CPLEX and Gurobi, but doesn't require a license and can be installed directly from pip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pulp import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by creating a model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = LpProblem(\"MAP Inference\", LpMinimize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now create a variable for each value in the Conditional Probability Table of each variable. These are the decision variables - which entry in the CPT to choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x1 = LpVariable(\"A=No\",0,1,LpInteger)\n",
    "x2 = LpVariable(\"A=Yes\",0,1,LpInteger)\n",
    "x3 = LpVariable(\"B=No|A=No\",0,1,LpInteger)\n",
    "x4 = LpVariable(\"B=Yes|A=No\",0,1,LpInteger)\n",
    "x5 = LpVariable(\"B=No|A=Yes\",0,1,LpInteger)\n",
    "x6 = LpVariable(\"B=Yes|A=Yes\",0,1,LpInteger)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "var_list = [x1,x2,x3,x4,x5,x6]\n",
    "val_list = -np.log(bn.flat_cpt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the number of variables equals the number of conditional probability values which must be specified. This number grows quickly as the number of nodes increases - but not as quickly as if another representation besides Bayesian networks was used.\n",
    "\n",
    "First, we will add the objective value - minimizing the sum of the negative log values of the conditional probability entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model += np.dot(var_list,val_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's add in our constraints to make sure that the conditional probability tables agree on the parent values. That is, the decision variable value of a given node must equal the sum of the decision variable values of all children nodes which include the parent node's value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model += x1 == x3+x4, 'Edge Constraint 1'\n",
    "model += x2 == x5+x6, 'Edge Constraint 2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, let's add constraint to make sure only one value from each CPT can be chosen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model += x1 + x2 == 1, 'RV Constraint 1'\n",
    "model += x3 + x4 + x5 + x6 == 1, 'RV Constraint 2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now print out the variables and observe the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A=No 0.0\n",
      "A=Yes 1.0\n",
      "B=No|A=No 0.0\n",
      "B=No|A=Yes 0.0\n",
      "B=Yes|A=No 0.0\n",
      "B=Yes|A=Yes 1.0\n"
     ]
    }
   ],
   "source": [
    "for v in model.variables():\n",
    "    print v.name, v.varValue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there it is - the solver shows us that the optimal solution indeed is \"A=Yes\" and \"B=Yes|A=Yes\""
   ]
  }
 ],
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================================================
FILE: examples/Marginal_Inference.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Marginal Inference in Bayesian Networks with pyBN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Marginal inference is a task concerned with answering probability queries of the form P(X|E), where X is a set of variables of interest and E is the set of evidence variables. Performing marginal inference is an important task because it allows you to calculate and view updated beliefs in the face of new data. The pyBN module makes this easy.\n",
    "\n",
    "Let's start by loading the pyBN package and reading the \"Earthquake\" network included in the /data/ folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyBN import *\n",
    "bn = read_bn('data/earthquake.bn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with any new Bayesian network, it helps to look at its attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"Burglary\": [\n",
      "    \"Alarm\"\n",
      "  ], \n",
      "  \"MaryCalls\": [], \n",
      "  \"Alarm\": [\n",
      "    \"JohnCalls\", \n",
      "    \"MaryCalls\"\n",
      "  ], \n",
      "  \"JohnCalls\": [], \n",
      "  \"Earthquake\": [\n",
      "    \"Alarm\"\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "print json.dumps(bn.E,indent=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Here, we see that the network consists of five nodes - two priors ('Burglary' and 'Earthquake') converging on 'Alarm', which in turn spawns two leaf nodes ('MaryCalls' and 'JohnCalls')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: examples/ReadWrite.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading and Writing Bayesian Networks with pyBN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>When first testing out a Bayesian network package, the first thing a user usually wants to do is read a Bayesian network from a file. The pyBN package makes reading BN's from files increadibly easy. Moreover a substantial number of networks are already included with the package!</p>\n",
    "The first step is to load the package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyBN import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you already have a file in mind, then you can use the \"read_bn\" function to parse that file and return a BayesNet object. The best part about \"read_bn\" is that it can understand ANY file type -- just make sure you include the extension at the end of the filepath string and pyBN takes care of the rest!\n",
    "\n",
    "Suppose I want to read one of the many included Bayesian networks in the pyBN package -- say, the well-known \"cancer\" BN. I simply write the absolute or relative path to the file, and pass it as an argument to \"read_bn\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "file = 'data/cancer.bif'\n",
    "bn = read_bn(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have read in a BayesNet object from file, a good first thing to do is to check out its properties. Every BayesNet object in pyBN has three attributes:\n",
    "- \"V\" : a list of vertices (random variables)\n",
    "- \"E\" : a list of edges (conditional dependencies between random variables)\n",
    "- \"data\" : a dictionary of dictionaries containing the Conditional Probability Tables (and other info) for each random variables\n",
    "\n",
    "Let's observe these attributes for the \"cancer\" BN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pollution', 'Smoker', 'Cancer', 'Xray', 'Dyspnoea']\n",
      "[['Pollution', 'Cancer'], ['Smoker', 'Cancer'], ['Cancer', 'Xray'], ['Cancer', 'Dyspnoea']]\n"
     ]
    }
   ],
   "source": [
    "print bn.V\n",
    "print bn.E"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The underlying, official data storage for the pyBN package is based on the JSON format. This is best seen with the \"data\" attribute. Since the \"data\" attribute is a dictionary which conforms to the JSON format, it can be easily read/written from/to files. An added bonus is that JSON allows for well-formatted printing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"vals\": [\n",
      "    \"positive\", \n",
      "    \"negative\"\n",
      "  ], \n",
      "  \"parents\": [\n",
      "    \"Cancer\"\n",
      "  ], \n",
      "  \"cprob\": [\n",
      "    [\n",
      "      0.9, \n",
      "      0.1\n",
      "    ], \n",
      "    [\n",
      "      0.2, \n",
      "      0.8\n",
      "    ]\n",
      "  ], \n",
      "  \"numoutcomes\": 2, \n",
      "  \"children\": []\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "print json.dumps(bn.data['Xray'],indent=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The information and code presented above was a quick, yet near-sufficient explanation of the file reading capabilities of the pyBN package. Creating a BayesNet object from scratch -- or modifying an existing BayesNet object -- is definitely something we will look into in the future!\n",
    "\n",
    "Now, let's turn to writing BayesNet object to file. Again, this is incredibly simple and relies on the \"write_bn\" function which operates similarly to the \"read_bn\" function -- simply specify the output file name and we will write passed-in BayesNet object to file based on the file extension given in the output file. That's right - pyBN can write a Bayesian network object to one of numerous formats by inferring which one you want in the output file string!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "write_bn(bn, 'data/write_test.bn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To finish this tutorial, let's make sure our file-written BayesNet contains the correct information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "bn2 = read_bn('data/write_test.bn')\n",
    "print bn.V == bn2.V\n",
    "print bn.E == bn2.E\n",
    "print bn.data == bn2.data"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: pyBN/__init__.py
================================================
from pyBN.classes import *
from pyBN.classification import *
from pyBN.inference import *
from pyBN.io import *
from pyBN.learning import *
from pyBN.plotting import *
from pyBN.utils import *

================================================
FILE: pyBN/classes/__init__.py
================================================
from pyBN.classes.bayesnet import BayesNet
from pyBN.classes.cliquetree import CliqueTree, Clique
from pyBN.classes.clustergraph import ClusterGraph
from pyBN.classes.empiricaldistribution import EmpiricalDistribution
from pyBN.classes.factor import Factor
from pyBN.classes.factorization import Factorization

================================================
FILE: pyBN/classes/_tests/__init__.py
================================================


================================================
FILE: pyBN/classes/_tests/test_bayesnet.py
================================================
"""
********
UnitTest
BayesNet
********

Method	Checks that
assertEqual(a, b)	a == b	 
assertNotEqual(a, b)	a != b	 
assertTrue(x)	bool(x) is True	 
assertFalse(x)	bool(x) is False	 
assertIs(a, b)	a is b
assertIsNot(a, b)	a is not b
assertIsNone(x)	x is None
assertIsNotNone(x)	x is not None
assertIn(a, b)	a in b
assertNotIn(a, b)	a not in b
assertIsInstance(a, b)	isinstance(a, b)
assertNotIsInstance(a, b)	not isinstance(a, b)

assertAlmostEqual(a, b)	round(a-b, 7) == 0	 
assertNotAlmostEqual(a, b)	round(a-b, 7) != 0	 
assertGreater(a, b)	a > b
assertGreaterEqual(a, b)	a >= b
assertLess(a, b)	a < b
assertLessEqual(a, b)	a <= b

assertListEqual(a, b)	lists
assertTupleEqual(a, b)	tuples
assertSetEqual(a, b)	sets or frozensets
assertDictEqual(a, b)	dicts

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
from pyBN.classes.bayesnet import BayesNet
from pyBN.readwrite.read import read_bn

import os
from os.path import dirname


class BayesNetTestCase(unittest.TestCase):

	def setUp(self):
		self.bn = BayesNet()
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		self.bn_bif = read_bn(os.path.join(self.dpath,'cancer.bif'))
		self.bn_bn = read_bn(os.path.join(self.dpath,'cmu.bn'))

	def tearDown(self):
		pass

	def test_isinstance(self):
		self.assertIsInstance(self.bn,BayesNet)

	def test_V_bif(self):
		self.assertListEqual(self.bn_bif.V,
			['Smoker', 'Pollution', 'Cancer', 'Xray', 'Dyspnoea'])

	def test_E_bif(self):
		self.assertDictEqual(self.bn_bif.E,
			{'Cancer': ['Xray', 'Dyspnoea'],
			 'Dyspnoea': [],
			 'Pollution': ['Cancer'],
			 'Smoker': ['Cancer'],
			 'Xray': []})

	def test_F_bif(self):
		self.assertDictEqual(self.bn_bif.F,
			{'Cancer': {'cpt': [0.03, 0.97, 0.05, 0.95, 0.001, 0.999, 0.02, 0.98],
				  'parents': ['Pollution', 'Smoker'],
				  'values': ['True', 'False']},
			 'Dyspnoea': {'cpt': [0.65, 0.35, 0.3, 0.7],
				  'parents': ['Cancer'],
				  'values': ['True', 'False']},
			 'Pollution': {'cpt': [0.9, 0.1], 'parents': [], 'values': ['low', 'high']},
			 'Smoker': {'cpt': [0.3, 0.7], 'parents': [], 'values': ['True', 'False']},
			 'Xray': {'cpt': [0.9, 0.1, 0.2, 0.8],
				  'parents': ['Cancer'],
				  'values': ['positive', 'negative']}})

	def test_V_bn(self):
		self.assertListEqual(self.bn_bn.V,
			['Burglary', 'Earthquake', 'Alarm', 'JohnCalls', 'MaryCalls'])
	
	def test_E_bn(self):
		self.assertDictEqual(self.bn_bn.E,
			{'Alarm': ['JohnCalls', 'MaryCalls'],
				 'Burglary': ['Alarm'],
				 'Earthquake': ['Alarm'],
				 'JohnCalls': [],
				 'MaryCalls': []})

	def test_F_bn(self):
		self.assertDictEqual(self.bn_bn.F,
			{'Alarm': {'cpt': [0.999, 0.001, 0.71, 0.29, 0.06, 0.94, 0.05, 0.95],
			  'parents': ['Earthquake', 'Burglary'],
			  'values': ['No', 'Yes']},
			 'Burglary': {'cpt': [0.999, 0.001], 'parents': [], 'values': ['No', 'Yes']},
			 'Earthquake': {'cpt': [0.998, 0.002], 'parents': [], 'values': ['No', 'Yes']},
			 'JohnCalls': {'cpt': [0.95, 0.05, 0.1, 0.9],
			  'parents': ['Alarm'],
			  'values': ['No', 'Yes']},
			 'MaryCalls': {'cpt': [0.99, 0.01, 0.3, 0.7],
			  'parents': ['Alarm'],
			  'values': ['No', 'Yes']}})

	def test_nodes(self):
		n = list(self.bn_bn.nodes())
		self.assertListEqual(n,
		['Burglary', 'Earthquake', 'Alarm', 'JohnCalls', 'MaryCalls'])

	def test_cpt(self):
		cpt = list(self.bn_bn.cpt('Alarm'))
		self.assertListEqual(cpt,
			[0.999, 0.001, 0.71, 0.29, 0.06, 0.94, 0.05, 0.95])

	def test_card(self):
		self.assertEqual(self.bn_bn.card('Alarm'),2)

	def test_scope(self):
		self.assertListEqual(self.bn_bn.scope('Alarm'),
			['Alarm', 'Earthquake', 'Burglary'])
	
	def test_parents(self):
		self.assertListEqual(self.bn_bn.parents('Alarm'),
			['Earthquake','Burglary'])

	def test_values(self):
		self.assertListEqual(self.bn_bn.values('Alarm'),['No','Yes'])

	def test_values_idx(self):
		self.assertEqual(self.bn_bn.values('Alarm')[1],'Yes')

if __name__ == '__main__':
	unittest.main(exit=False)






================================================
FILE: pyBN/classes/_tests/test_factor.py
================================================
"""
********
UnitTest
Factor
********

Method	Checks that
assertEqual(a, b)	a == b	 
assertNotEqual(a, b)	a != b	 
assertTrue(x)	bool(x) is True	 
assertFalse(x)	bool(x) is False	 
assertIs(a, b)	a is b
assertIsNot(a, b)	a is not b
assertIsNone(x)	x is None
assertIsNotNone(x)	x is not None
assertIn(a, b)	a in b
assertNotIn(a, b)	a not in b
assertIsInstance(a, b)	isinstance(a, b)
assertNotIsInstance(a, b)	not isinstance(a, b)

assertAlmostEqual(a, b)	round(a-b, 7) == 0	 
assertNotAlmostEqual(a, b)	round(a-b, 7) != 0	 
assertGreater(a, b)	a > b
assertGreaterEqual(a, b)	a >= b
assertLess(a, b)	a < b
assertLessEqual(a, b)	a <= b

assertListEqual(a, b)	lists
assertTupleEqual(a, b)	tuples
assertSetEqual(a, b)	sets or frozensets
assertDictEqual(a, b)	dicts

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.classes.factor import Factor
from pyBN.readwrite.read import read_bn


class FactorTestCase(unittest.TestCase):

	def setUp(self):
		self.data_path = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		self.bn = read_bn(os.path.join(self.data_path,'cmu.bn'))
		self.f = Factor(self.bn, 'Alarm')
	def tearDown(self):
		pass

	# Factor Creation Tests
	def test_factor_init(self):
		self.assertIsInstance(self.f,Factor)

	def test_factor_bn(self):
		self.assertListEqual(self.f.bn.V,
			['Burglary', 'Earthquake', 'Alarm', 'JohnCalls', 'MaryCalls'])

	def test_factor_var(self):
		self.assertEqual(self.f.var, 'Alarm')
	
	def test_factor_scope(self):
		self.assertListEqual(self.f.scope,['Alarm','Earthquake','Burglary'])
	
	def test_factor_card(self):
		self.assertDictEqual(self.f.card,
			{'Alarm':2, 'Burglary':2, 'Earthquake':2})
	
	def test_factor_stride(self):
		self.assertDictEqual(self.f.stride,
			{'Alarm':1, 'Burglary':4, 'Earthquake':2})
	
	def test_factor_cpt(self):
		self.assertListEqual(list(self.f.cpt),
			[ 0.999,  0.001,  0.71 ,  0.29 ,  0.06 ,  0.94 ,  0.05 ,  0.95 ])
	
	# Factor Operations Tests
	def test_multiply_factor(self):
		f1 = Factor(self.bn,'Alarm')
		f2 = Factor(self.bn,'Burglary')
		f1.multiply_factor(f2)

		f3 = Factor(self.bn,'Burglary')
		f4 = Factor(self.bn,'Alarm')
		f3.multiply_factor(f4)

		self.assertListEqual(list(f1.cpt),list(f3.cpt))
	
	def test_sumover_var(self):
		self.f.sumover_var('Burglary')
		self.assertListEqual(list(self.f.cpt),[0.5,0.5])

	def test_sumout_var_list(self):
		f = Factor(self.bn,'Alarm')
		f.sumout_var_list(['Burglary','Earthquake'])
		self.assertListEqual(f.scope,['Alarm'])
		self.assertDictEqual(f.stride,{'Alarm':1})
		self.assertListEqual(list(f.cpt),[0.45475,0.54525])

	def test_sumout_var(self):
		f = Factor(self.bn,'Alarm')
		f.sumout_var('Earthquake')
		self.assertListEqual(f.scope,['Alarm','Burglary'])
		self.assertDictEqual(f.stride,
			{'Alarm':1,'Burglary':2})
		self.assertListEqual(list(f.cpt),
			[ 0.8545,  0.1455,  0.055 ,  0.945 ])

	def test_maxout_var(self):
		"""
		I KNOW THIS IS CORRECT.
		"""
		f = Factor(self.bn,'Alarm')
		f.maxout_var('Burglary')
		self.assertListEqual(list(f.cpt),
			[ 0.999,  0.94 ,  0.71 ,  0.95 ])
		self.assertListEqual(f.scope,['Alarm','Earthquake'])
		self.assertDictEqual(f.stride,
			{'Alarm':1,'Earthquake':2})

	def test_reduce_factor_by_list(self):
		f = Factor(self.bn, 'Alarm')
		f.reduce_factor_by_list([['Burglary','Yes'],['Earthquake','Yes']])
		self.assertListEqual(list(f.cpt),[0.05,0.95])
		self.assertListEqual(f.scope,['Alarm'])
		self.assertDictEqual(f.stride,{'Alarm':1})

	def test_reduce_factor(self):
		f = Factor(self.bn, 'Alarm')
		f.reduce_factor('Burglary','Yes')
		self.assertListEqual(list(f.cpt),
			[ 0.06,  0.94,  0.05,  0.95])

	def test_to_log(self):
		f = Factor(self.bn,'Earthquake')
		f.to_log()
		self.assertEqual(round(sum(f.cpt),4),-6.2166)

	def test_from_log(self):
		f = Factor(self.bn, 'Earthquake')
		f.to_log()
		f.from_log()
		self.assertListEqual(list(f.cpt),[0.998,0.002])

	def test_normalize(self):
		self.f.cpt[0]=20
		self.f.cpt[1]=20
		self.f.cpt[4]=0.94
		self.f.cpt[7]=0.15
		self.f.normalize()
		self.assertListEqual(list(self.f.cpt),
			[0.500,0.500,0.710,0.290,0.5,0.5,0.25,0.75])
    
if __name__ == '__main__':
	unittest.main(exit=False)











================================================
FILE: pyBN/classes/bayesnet.py
================================================
"""
**************
BayesNet Class
**************

Overarching class for Discrete Bayesian Networks.


Design Specs
------------

- Bayesian Network -

    - F -
        key:
            - rv -
        values:
            - children -
            - parents -
            - values -
            - cpt -

    - V -
        - list of rvs -

    - E -
        key:
            - rv -
        values:
            - list of rv's children -
Notes
-----
- Edges can be inferred from Factorization, but Vertex values 
    must be specified.
"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

from copy import copy, deepcopy

import numpy as np
from pyBN.utils.class_equivalence import are_class_equivalent
from pyBN.utils.graph import topsort

class BayesNet(object):
    """
    Overarching class for Bayesian Networks

    """

    def __init__(self, E=None, value_dict=None, file=None):
        """
        Initialize the BayesNet class.

        Arguments
        ---------
        *V* : a list of strings - vertices in topsort order
        *E* : a dict, where key = vertex, val = list of its children
        *F* : a dict, 
            where key = rv, 
            val = another dict with
                keys = 
                    'parents', 
                    'values', 
                    'cpt'

        *V* : a dict        

        Notes
        -----
        
        """
        if file is not None:
            import pyBN.io.read as ior
            bn = ior.read_bn(file)
            self.V = bn.V
            self.E = bn.E
            self.F = bn.F        
        else:
            if E is not None:
                #assert (value_dict is not None), 'Must set values if E is set.'
                self.set_structure(E, value_dict)
            else:
                self.V = []
                self.E = {}
                self.F = {}

    def __eq__(self, y):
        """
        Tests whether two Bayesian Networks are
        equivalent - i.e. they contain the same
        node/edge structure, and equality of
        conditional probabilities.
        """
        return are_class_equivalent(self, y)

    def __hash__(self):
        """
        Allows BayesNet objects to be used
        as keys in a dictionary (i.e. hashable)
        """
        return hash((str(self.V),str(self.E)))

    def copy(self):
        V = deepcopy(self.V)
        E = deepcopy(self.E)
        F = {}
        for v in V:
            F[v] = {}
            F[v]['cpt'] = deepcopy(self.F[v]['cpt'])
            F[v]['parents'] = deepcopy(self.F[v]['parents'])
            F[v]['values'] = deepcopy(self.F[v]['values'])
        bn = BayesNet()
        bn.V = V
        bn.E = E
        bn.F = F

        return bn

    def add_node(self, rv, cpt=[], parents=[], values=[]):
        self.V.append(rv)
        self.F[rv] = {'cpt':cpt,'parents':parents,'values':values}

    def add_edge(self, u, v):
        if not self.has_node(u):
            self.add_node(u)
        if not self.has_node(v):
            self.add_node(v)
        if self.has_edge(u,v):
            print('Edge already exists')
        else:
            self.E[u].append(v)
            self.F[v]['parents'].append(u)
        #self.V = topsort(self.E)
        # HOW DO I RECALCULATE CPT?


    def remove_edge(self, u, v):
        self.E[u].remove(v)
        self.F[v]['parents'].remove(u)

    def reverse_arc(self, u, v):
        if self.has_edge(u,v):
            self.E[u].remove(v)
            self.E[v].append(u)

    def set_data(self, rv, data):
        assert (isinstance(data, dict)), 'data must be dictionary'
        self.F[rv] = data

    def set_cpt(self, rv, cpt):
        self.F[rv]['cpt'] = cpt

    def set_parents(self, rv, parents):
        self.F[rv]['parents'] = parents

    def set_values(self, rv, values):
        self.F[rv]['values'] = values

    def nodes(self):
        for v in self.V:
            yield v

    def node_idx(self, rv):
        try:
            return self.V.index(rv)
        except ValueError:
            return -1

    def has_node(self, rv):
        return rv in self.V

    def has_edge(self, u, v):
        return v in self.E[u]

    def edges(self):
        for u in self.nodes():
            for v in self.E[u]:
                yield (u,v)
    def num_edges(self):
        num = 0
        for u in self.nodes():
            num += len(self.E[u])
        return num

    def num_params(self):
        num = 0
        for u in self.nodes():
            num += len(self.F[u]['cpt'])
        return num

    def scope_size(self, rv):
        return len(self.F[rv]['parents'])+1

    def num_nodes(self):
        return len(self.V)

    def cpt(self, rv):
        return self.F[rv]['cpt']

    def card(self, rv):
        return len(self.F[rv]['values'])

    def scope(self, rv):
        scope = [rv]
        scope.extend(self.F[rv]['parents'])
        return scope

    def parents(self, rv):
        return self.F[rv]['parents']

    def children(self, rv):
        return self.E[rv]

    def degree(self, rv):
        return len(self.parents(rv)) + len(self.children(rv))

    def values(self, rv):
        return self.F[rv]['values']

    def value_idx(self, rv, val):
        try:   
            return self.F[rv]['values'].index(val)
        except ValueError:
            print("Value Index Error")
            return -1

    def stride(self, rv, n):
        if n==rv:
            return 1
        else:
            card_list = [self.card(rv)]
            card_list.extend([self.card(p) for p in self.parents(rv)])
            n_idx = self.parents(rv).index(n) + 1
            return int(np.prod(card_list[0:n_idx]))

    def flat_cpt(self, by_var=False, by_parents=False):
        """
        Return all cpt values in the BN as a flattened
        numpy array ordered by bn.nodes() - i.e. topsort
        """
        if by_var:
            cpt = np.array([sum(self.cpt(rv)) for rv in self.nodes()])
        elif by_parents:
            cpt = np.array([sum(self.cpt(rv)[i:(i+self.card(rv))]) for rv in self.nodes() for i in range(len(self.cpt(rv))/self.card(rv))])
        else:
            cpt = np.array([val for rv in self.nodes() for val in self.cpt(rv)])
        return cpt

    def cpt_indices(self, target, val_dict):
        """
        Get the index of the CPT which corresponds
        to a dictionary of rv=val sets. This can be
        used for parameter learning to increment the
        appropriate cpt frequency value based on
        observations in the data.

        There is definitely a fast way to do this.
            -- check if (idx - rv_stride*value_idx) % (rv_card*rv_stride) == 0

        Arguments
        ---------
        *target* : a string
            Main RV

        *val_dict* : a dictionary, where
            key=rv,val=rv value

        """
        stride = dict([(n,self.stride(target,n)) for n in self.scope(target)])
        #if len(val_dict)==len(self.parents(target)):
        #    idx = sum([self.value_idx(rv,val)*stride[rv] \
        #            for rv,val in val_dict.items()])
        #else:
        card = dict([(n, self.card(n)) for n in self.scope(target)])
        idx = set(range(len(self.cpt(target))))
        for rv, val in val_dict.items():
            val_idx = self.value_idx(rv,val)
            rv_idx = []
            s_idx = val_idx*stride[rv]
            while s_idx < len(self.cpt(target)):
                rv_idx.extend(range(s_idx,(s_idx+stride[rv])))
                s_idx += stride[rv]*card[rv]
            idx = idx.intersection(set(rv_idx))

        return list(idx)

    def cpt_str_idx(self, rv, idx):
        """
        Return string representation of RV=VAL and
        Parents=Val for the given idx of the given rv's cpt.
        """
        rv_val = self.values(rv)[idx % self.card(rv)]
        s = str(rv)+'='+str(rv_val) + '|'
        _idx=1
        for parent in self.parents(rv):
            for val in self.values(parent):
                if idx in self.cpt_indices(rv,{rv:rv_val,parent:val}):
                    s += str(parent)+'='+str(val)
                    if _idx < len(self.parents(rv)):
                        s += ','
                    _idx+=1
        return s



    def set_structure(self, edge_dict, value_dict=None):
        """
        Set the structure of a BayesNet object. This
        function is mostly used to instantiate a BN
        skeleton after structure learning algorithms.

        See "structure_learn" folder & algorithms

        Arguments
        ---------
        *edge_dict* : a dictionary,
            where key = rv,
            value = list of rv's children
            NOTE: THIS MUST BE DIRECTED ALREADY!

        *value_dict* : a dictionary,
            where key = rv,
            value = list of rv's possible values

        Returns
        -------
        None

        Effects
        -------
        - sets self.V in topsort order from edge_dict
        - sets self.E
        - creates self.F structure and sets the parents

        Notes
        -----

        """

        self.V = topsort(edge_dict)
        self.E = edge_dict
        self.F = dict([(rv,{}) for rv in self.nodes()])
        for rv in self.nodes():
            self.F[rv] = {
                'parents':[p for p in self.nodes() if rv in self.children(p)],
                'cpt': [],
                'values': []
            }
            if value_dict is not None:
                self.F[rv]['values'] = value_dict[rv]

    def adj_list(self):
        """
        Returns adjacency list of lists, where
        each list element is a vertex, and each sub-list is
        a list of that vertex's neighbors.
        """
        adj_list = [[] for _ in self.V]
        vi_map = dict((self.V[i],i) for i in range(len(self.V)))
        for u,v in self.edges():
            adj_list[vi_map[u]].append(vi_map[v])
        return adj_list

    def moralized_edges(self):
        """
        Moralized graph is the original graph PLUS
        an edge between every set of common effect
        structures -
            i.e. all parents of a node are connected.

        This function has be validated.

        Returns
        -------
        *e* : a python list of parent-child tuples.

        """
        e = set()
        for u in self.nodes():
            for p1 in self.parents(u):
                e.add((p1,u))
                for p2 in self.parents(u):
                    if p1!=p2 and (p2,p1) not in e:
                        e.add((p1,p2))
        return list(e)


    


    


================================================
FILE: pyBN/classes/cliquetree.py
================================================
"""
******************
CliqueTree Class 
&
Clique Class
******************

This is a class for creating/manipulating Junction (Clique) Trees,
and performing inference over them. The advantage of clique trees
over traditional variable elimination over the original Bayesian 
network is that clique trees allow you to compute marginal
probabilities of MULTIPLE variables without having to run the
entire algorithm over. 

Therefore, if you have to query the Bayesian network many times 
-- and you want the exact marginal values -- then it might be 
best to use the clique tree data structure for inference.
If you need to compute the marginal distribution for all variables,
but you dont mind an approximate, a sampling algorithm is probably
the way to go.

In general, the junction tree algorithms generalize 
Variable Elimination to the efficient, simultaneous execution 
of a large class of queries.

NOTE: A cluster graph is a generalization of the clique tree
data structure - to generate a clique tree, you first generate
a cluster graph, then simply calculate a maximum spanning tree.
In other words, a clique tree can be considered as a special
type of cluster graph.

"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""



import numpy as np
import networkx as nx
from copy import copy, deepcopy

from pyBN.classes.bayesnet import BayesNet
from pyBN.classes.factor import Factor
from pyBN.classes.factorization import Factorization

from pyBN.utils.graph import *



class CliqueTree(object):
    """
    CliqueTree Class

    Let G be a chordal graph (ie. a graph such that no cycle includes more
    than three nodes), then a CliqueTree is a tree H such that each
    maximal clique C in G is a node in H.

    Attributes
    ----------
    - bn 
        - BayesNet object

    - V
        - Vertices -> list

    - E
        - Edges -> dictionary

    - C
        - Cliques -> a dictionary where key = vertex idx,
                value = Clique object

    """

    def __init__(self, bn):
        """
        Instantiate a CliqueTree object.

        Arguments
        ---------
        *bn*: a BayesNet object

        Notes
        -----
        Ideally, the Factor class should be used as the
        cliques instead of the Clique class (because it's
        just a watered down version of the Factor class)        
        
        """
        ####
        self.V = None
        self.E = None
        self.C = None
        ###

        self.bn = bn
        self._F = Factorization(bn)
        self.initialize_tree()

        

    #def __repr__(self):
       # return self.C

    def __iter__(self):
        for clique in self.C.values():
            yield clique

    def __getitem__(self, rv):
        """
        Returns Clique of passed-in rv
        """
        return self.C[rv]

    def parents(self, v):
        p = []
        for rv in self.V:
            if v in self.E[rv]:
                p.append(v)
        return p

    def children(self, n):
        return self.E[n]

    def dfs_postorder(self, root):
        G = nx.Graph(self.E)
        tree_graph = nx.dfs_tree(G,root)
        clique_ordering = list(nx.dfs_postorder_nodes(tree_graph,root))
        return clique_ordering

    def initialize_tree(self):
        """
        Initialize the structure of a clique tree, using
        the following steps:
            - Moralize graph (i.e. marry parents)
            - Triangulate graph (i.e. make graph chordal)
            - Get max cliques (i.e. community/clique detection)
            - Max spanning tree over sepset cardinality (i.e. create tree)
        
        """
        ### MORALIZE GRAPH & MAKE IT CHORDAL ###
        chordal_G = make_chordal(self.bn) # must return a networkx object
        V = chordal_G.nodes()

        ### GET MAX CLIQUES FROM CHORDAL GRAPH ###
        C = {} # key = vertex, value = clique object
        max_cliques = reversed(list(nx.chordal_graph_cliques(chordal_G)))
        for v_idx,clique in enumerate(max_cliques):
            C[v_idx] = Clique(set(clique))

        ### MAXIMUM SPANNING TREE OVER COMPLETE GRAPH TO MAKE A TREE ###
        weighted_edge_dict = dict([(c_idx,{}) for c_idx in range(len(C))])
        for i in range(len(C)):
            for j in range(len(C)):
                if i!=j:
                    intersect_cardinality = len(C[i].sepset(C[j]))
                    weighted_edge_dict[i][j] = -1*intersect_cardinality
        mst_G = mst(weighted_edge_dict)
        ### SET V,E,C ###
        self.E = mst_G # dictionary
        self.V = mst_G.keys() # list
        self.C = C

        
        ### ASSIGN EACH FACTOR TO ONE CLIQUE ONLY ###
        v_a = dict([(rv, False) for rv in self.bn.nodes()])
        for clique in self.C.values():
            temp_scope = []
            for var in v_a:
                if v_a[var] == False and set(self.bn.scope(var)).issubset(clique.scope):
                    temp_scope.append(var)
                    v_a[var] = True
            clique._F = Factorization(self.bn, temp_scope)

        ### COMPUTE INITIAL POTENTIAL FOR EACH FACTOR ###
        # - i.e. multiply all of its assigned factors together
        for i, clique in self.C.items():
            if len(self.parents(i)) == 0:
                clique.is_ready = True
            clique.initialize_psi()


class Clique(object):
    """
    Clique Class

    *scope* : a set of variables in the clique's scope

    *_f* : a factorization object that contains only the
        factors of variables in the clique's scope

    *psi* : a factor
        The potential of the clique.

    *belief* : a factor
        The factor which holds the marginal/conditional
        probabilities of the relevant nodes after 
        belief propagation, etc

    *messages_received* : a list of Factors
        The messages the clique has received from its neighbors -
        stored so that the beliefs can be calculated after
        belief propagation, etc.

    *is_ready* : a boolean
        Whether the clique is ready to send a message -> must
        have RECEIVED messages from all of its neighbors first.

    """

    def __init__(self, scope):
        """
        Instantiate a clique object

        Arguments
        ---------
        *scope* : a python set
            The set of variables in the cliques scope,
            i.e. the main var and its parents


        """
        self.scope = scope
        self._F = None
        
        self.psi = None # Psi should never change -> Factor object
        self.belief = None
        
        self.messages_received = []
        self.is_ready = False

    def __repr__(self):
        return str(self.scope)

    def __rshift__(self, other_clique):
        """
        Send a message from self to other_clique
        """
        self.send_message(other_clique)

    def __lshift__(self, other_clique):
        """
        Send a message from other_clique to self
        """
        other_clique.send_message(self)

    def send_message(self, parent):
        """
        Send a message to the parent clique.

        To send a message from X to Y, you
        must first take the potential (self.psi) of
        X and sum out the variables NOT in the sepset
        of Y.

        THEN, if X has received any messages, the
        potential (self.psi) must be multiplied by
        all of the received messaged.

        Arguments
        ---------
        *parent* : a string
            The parent to which the message will
            be sent.

        """


        # First generate Belief = Original_Psi * all received messages
        if len(self.messages_received) > 0:
            # if there are messages received, mutliply them in to psi first
            if not self.belief:
                self.belief = copy(self.psi)
            for msg in self.messages_received:
                self.belief *= msg
            #self.belief.merge_multiply(self.messages_received)
        else:
            # if there are no messages received, simply move on with psi
            if not self.belief:
                self.belief = copy(self.psi)
        # generate message as belief with Ci - Sij vars summed out
        #vars_to_sumout = list(self.scope.difference(self.sepset(parent)))
        msg_to_send = copy(self.belief)
        for var_to_sumout in self.scope.difference(self.sepset(parent)):
            msg_to_send /= var_to_sumout
        #message_to_send = copy(self.belief)
        #message_to_send.sumout_var_list(vars_to_sumout)
        parent.messages_received.append(msg_to_send)

    def initialize_psi(self):
        """
        Compute a new psi (cpt) in order to 
        set the clique's belief. This involves
        multiplying the factors in the Clique together.
        """
        assert (len(self._F) != 0), 'No Factors assigned to this clique.'

        if len(self._F) == 1:
            self.psi = copy(self._F[0])
            self.belief = copy(self.psi)
        else:
            self.psi = max(self._F, key=lambda x: len(x.cpt))
            for f in self._F:
                self.psi *= f
            self.belief = copy(self.psi)

    def send_initial_message(self, other_clique):
        """
        NOT SURE IF THIS IS NEEDED.

        Send the first message to another clique.

        Arguments
        ---------
        *other_clique* : a different Clique object

        """
        psi_copy = copy(self.psi)
        sepset = self.sepset(other_clique)
        sumout_vars = self.scope.difference(sepset)
        # sum out variables not in the sepset of other_clique
        psi_copy.sumout_var_list(list(sumout_vars))


        #psi_copy.cpt = psi_copy.cpt.loc[:,[c for c in psi_copy.cpt.columns if 'Prob' not in c]]
        #psi_copy.cpt[str('Prob-Val-' + str(np.random.randint(0,1000000)))] = 1
        print('Init Msg: \n', psi_copy.cpt)
        other_clique.messages_received.append(psi_copy)

        self.belief = copy(self.psi)

    def collect_beliefs(self):
        """
        Collecting beliefs is done by taking the
        potential (self.psi) of X and multiplying by all of
        the messages which X has received.

        NOTE: once beliefs are collected, the messages
        which the Clique has received are cleared out.

        Notes
        -----
        A root node (i.e. one that doesn't send a message) must run collect_beliefs
        since they are only collected at send_message()

        Also, we collect beliefs at the end of loopy belief propagation (approx. inference)
        since the main algorithm is just sending messages for a while.
        """
        if len(self.messages_received) > 0:
            self.belief = copy(self.psi)
            for msg in self.messages_received:
                self.belief *= msg
            self.belief 
            self.messages_received = []
        else:
            self.belief = copy(self.belief)

    def sepset(self, other_clique):
        """
        The sepset of two cliques is the set of
        variables in the intersection of the two
        cliques' scopes.

        Arguments
        ---------
        *other_clique* : a Clique object

        """
        return self.scope.intersection(other_clique.scope)

    def marginalize_over(self, target):
        """
        Marginalize the cpt (belief) over a target variable.

        Arguments
        ---------
        *target* : a string
            The target random variable.

        """
        blf = copy(self.belief)
        blf.sumover_var(target)
        return blf

















================================================
FILE: pyBN/classes/clustergraph.py
================================================
"""
******************
ClusterGraph Class
******************

This is a class for creating/manipulating Cluster Graphs,
and performing inference over them - currently the only
supported algorithm is Loopy Belief Propagation. Still,
the class structure is in place for easy addition of
any algorithms relying on the Cluster Graph framework.

NOTE: A cluster graph is a generalization of the clique tree
data structure - to generate a clique tree, you first generate
a cluster graph, then simply calculate a maximum spanning tree.
In other words, a clique tree can be considered as a special
type of cluster graph.

"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""



import numpy as np
import pandas as pd
import networkx as nx

from pyBN.classes.cliquetree import Clique

class ClusterGraph(object):
    """
    ClusterGraph Class

    """

    def __init__(self, bn):
        """
        Initialize a ClusterGraph object

        """
        self.BN = BN
        self.V = {} # key = cluster index, value = Cluster objects
        self.E = []
        self.G = None
        self.initialize_graph(method)
        self.beliefs = {} # dict where key = cluster idx, value = belief cpt

    def initialize_graph(self):
        """
        Initialize the structure of the cluster graph.

        """
        # generate graph structure
        self.bethe()
        # initialize beliefs
        for clique in self.V.values():
            clique.compute_psi() 
        # initialize messages to 1
        self.initialize_messages()

    def bethe(self):
        """
        Generate Bethe cluster graph structure.
        """
        self.V = {}
        self.E = []

        factorization = Factorization(self.BN)
        prior_dict = {}
        for factor in factorization.f_list:
            # if factor is just a prior (i.e. already added as rv)
            if len(factor.scope) == 1:
                #self.V[len(self.V)] = Clique(scope=factor.scope)
                #self.V[len(self.V)-1].factors = [factor]
                prior_dict[factor.var] = factor
            if len(factor.scope) > 1:
                self.V[len(self.V)] = Clique(scope=factor.scope)
                self.V[len(self.V)-1].factors = [factor] # assign the factor
        sep_len = len(self.V)
        # First, add all individual random variables
        for rv in self.BN.V:
            # if rv is a prior, don't add it
            if rv in prior_dict.keys():
                factor = prior_dict[rv]
                self.V[len(self.V)] = Clique(scope=factor.scope)
                self.V[len(self.V)-1].factors = [factor]
            else:
                self.V[len(self.V)] = Clique(scope={rv})
                # create a new initial factor since it wont have one        
                new_factor = Factor(BN=self.BN, var=rv, init_to_one=True)
                self.V[len(self.V)-1].factors = [new_factor]        
        for i in range(sep_len):
            for j in range(sep_len,len(self.V)):
                if self.V[j].scope.issubset(self.V[i].scope):
                    self.E.append((i,j))

        new_G = nx.Graph()
        new_G.add_edges_from(self.E)        
        self.G = new_G

    def initialize_messages(self):
        """
        For each edge (i-j) in the ClusterGraph,
        set delta_(i-j) = 1 and
        set delta_(j-i) = 1.
        (i.e. send a message from each parent to every child where the 
            message is a df = 1)
        """
        for cluster in self.V:
            for neighbor in self.G.neighbors(cluster):
                self.V[cluster].send_initial_message(self.V[neighbor])


    def collect_beliefs(self):
        self.beliefs = {}
        for cluster in self.V:
            self.V[cluster].collect_beliefs()
            #print('Belief ' , cluster , ' : \n', self.V[cluster].belief.cpt)
            self.beliefs[cluster] = self.V[cluster].belief


    def loopy_belief_propagation(self, target, evidence, max_iter=100):
        """
        
        This is Message Passing (Loopy Belief Propagation) over a cluster graph.

        It is Sum-Product Belief Propagation in a cluster graph as shown in
        Koller p.397

        Notes:
            1. Definitely a problem due to normalization (prob vals way too small)
            2. Need to check the scope w.r.t. messages.. all clusters should not
               be accumulating rv's in their scope over the course of the algorithm.
        """
        # 1: Moralize the graph
        # 2: Triangluate
        # 3: Build a clique tree using max spanning
        # 4: Propagation of probabilities using message passing

        # creates clique tree and assigns factors, thus satisfying steps 1-3
        cgraph = copy.copy(self)
        G = cgraph.G

        edge_visit_dict = dict([(i,0) for i in cgraph.E])

        iteration = 0
        while not cgraph.is_calibrated():
            if iteration == max_iter:
                break
            if iteration % 50 == 0:
                print('Iteration: ' , iteration)
                for cluster in cgraph.V.values():
                    cluster.collect_beliefs()
            # select an edge
            e_idx = np.random.randint(0,len(cgraph.E))
            edge_select = cgraph.E[e_idx]
            p_idx = np.random.randint(0,2)
            parent_edge = edge_select[p_idx]
            child_edge = edge_select[np.abs(p_idx-1)]
            print(parent_edge , child_edge)

            # send a message along that edge
            cgraph.V[parent_edge].send_message(cgraph.V[child_edge])

            iteration += 1
        print('Now Collecting Beliefs..')
        self.collect_beliefs()
        self.BN.ctree = self


================================================
FILE: pyBN/classes/empiricaldistribution.py
================================================
"""
************
Empirical
Distribution
Class
************

Create, maintain, and perform operations
over an empirical probability distribution
derived from a dataset. This class is most
useful for speeding up BN structure learning,
where distribution calculations can be cached
and therefore lookups become much faster over
repeated iterations.

References
----------
*** IMPORTANT ***
Daly and Shen,
"Methods to Accelerate the Learning of Bayesian Network Structures."
http://citeseerx.ist.psu.edu/viewdoc/download?
doi=10.1.1.127.7570&rep=rep1&type=pdf
*****************
"""

from __future__ import division

import numpy as np
from copy import copy, deepcopy


class EmpiricalDistribution(object):


	def __init__(self, data, names=None):

		if names is None:
			self.names = range(data.shape[1])
		else:
			assert (len(names) == self.NVAR), 'Passed-in names length must equal number of data columns'
			self.names = names

		self.NROW = data.shape[0]
		self.NVAR = data.shape[1]
		self.bins = [len(np.unique(data[:,n])) for n in range(self.NVAR)]
		

		hist,_ = np.histogramdd(data, bins=self.bins)
		self.counts = hist
		self.joint = (hist / hist.sum()) + 1e-3

		## COMPUTE MARGINAL FOR EACH VARIABLE ##
		#_range = range(self.NVAR)
		#for i,rv in enumerate(self.names):
		#	_axis = copy(_range)
		#	_axis.remove(i)
		#	self.marginal[rv] = np.sum(self.joint,axis=_axis)

		#self.marginal = dict([(rv, np.sum(self.joint,axis=i)) for i,rv in enumerate(self.names)])

		self.cache = {}

	def bayes_counts(self, bn):
		pass

	def idx_map(self, rvs):
		assert (isinstance(args, tuple)), "passed-in rvs must be a tuple"
		idx = [self.names.index(rv) for rv in rvs]
		return tuple(idx)

	def idx(self, rv):
		return self.names.index(rv)

	def axis_tuple(self, rv):
		pass

	def mpd(self, rv):
		"""
		Marginal Probability Distribtuion
		"""
		assert (isinstance(rv, str)), 'passed-in rv must be a string'
		if rv in self.cache:
			mpd = self.cache[rv]
		else:
			_axis = range(self.NVAR)
			_axis.remove(self.idx(rv))
			mpd = np.sum(self.joint, axis=tuple(_axis)) # CORRECT
			self.cache[rv] = mpd

		return mpd

	def jpd(self, rvs):
		assert (isinstance(args, tuple)), "passed-in rvs must be a tuple"
		if args in self.cache:
			jpd = self.cache[rvs]
		else:
			jpd = np.sum(self.joint, axis=self.idx_map(rvs)) # WRONG 
			self.cache[rvs] = jpd
			
		return jpd

	def cpd(self, lhs, rhs):
		assert (isinstance(rhs, tuple)), "passed-in rhs must be a tuple"

		_key = tuple((lhs,rhs))
		if _key in self.cache:
			cpd = self.cache[_key]
		else:
			try:
				tot = lhs + rhs
			except TypeError:
				tot = (lhs,) + rhs

			_numer = self.jpd(tot)
			_denom = self.jpd(rhs)
			cpd = _numer / (_denom + 1e-7)
			self.cache[_key] = cpd
		
		return cpd

	def mi(self, lhs, rhs, cond=None):
		"""
		Calculate mutual information with speedups
		by using caching for repeated lookups of
		joint/conditional distributions.

		"""
		bins = np.amax(data, axis=0) # read levels for each variable
		if len(bins) == 1:
			hist,_ = np.histogramdd(data, bins=(bins)) # frequency counts
			Px = hist/hist.sum()
			MI = -1 * np.sum( Px * np.log( Px ) )
			return round(MI, 4)
			
		if len(bins) == 2:
			hist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts

			Pxy = hist / hist.sum()# joint probability distribution over X,Y,Z
			Px = np.sum(Pxy, axis = 1) # P(X,Z)
			Py = np.sum(Pxy, axis = 0) # P(Y,Z)	

			PxPy = np.outer(Px,Py)
			Pxy += 1e-7
			PxPy += 1e-7
			MI = np.sum(Pxy * np.log(Pxy / (PxPy)))
			return round(MI,4)
		elif len(bins) > 2 and conditional==True:
			# CHECK FOR > 3 COLUMNS -> concatenate Z into one column
			if len(bins) > 3:
				data = data.astype('str')
				ncols = len(bins)
				for i in range(len(data)):
					data[i,2] = ''.join(data[i,2:ncols])
				data = data.astype('int')[:,0:3]

			bins = np.amax(data,axis=0)
			hist,_ = np.histogramdd(data, bins=bins) # frequency counts

			Pxyz = hist / hist.sum()# joint probability distribution over X,Y,Z
			Pz = np.sum(Pxyz, axis = (0,1)) # P(Z)
			Pxz = np.sum(Pxyz, axis = 1) # P(X,Z)
			Pyz = np.sum(Pxyz, axis = 0) # P(Y,Z)	

			Pxy_z = Pxyz / (Pz+1e-7) # P(X,Y | Z) = P(X,Y,Z) / P(Z)
			Px_z = Pxz / (Pz+1e-7) # P(X | Z) = P(X,Z) / P(Z)	
			Py_z = Pyz / (Pz+1e-7) # P(Y | Z) = P(Y,Z) / P(Z)

			Px_y_z = np.empty((Pxy_z.shape)) # P(X|Z)P(Y|Z)
			for i in range(bins[0]):
				for j in range(bins[1]):
					for k in range(bins[2]):
						Px_y_z[i][j][k] = Px_z[i][k]*Py_z[j][k]
			Pxyz += 1e-7
			Pxy_z += 1e-7
			Px_y_z += 1e-7
			MI = np.sum(Pxyz * np.log(Pxy_z / (Px_y_z)))
			
			return round(MI,4)
		elif len(bins) > 2 and conditional == False:
			data = data.astype('str')
			ncols = len(bins)
			for i in range(len(data)):
				data[i,1] = ''.join(data[i,1:ncols])
			data = data.astype('int')[:,0:2]

			hist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts

			Pxy = hist / hist.sum()# joint probability distribution over X,Y,Z
			Px = np.sum(Pxy, axis = 1) # P(X,Z)
			Py = np.sum(Pxy, axis = 0) # P(Y,Z)	

			PxPy = np.outer(Px,Py)
			Pxy += 1e-7
			PxPy += 1e-7
			MI = np.sum(Pxy * np.log(Pxy / (PxPy)))
			return round(MI,4)













================================================
FILE: pyBN/classes/factor.py
================================================
"""
************
Factor Class
************

This class holds a Conditional Probability Table structure
-- i.e. a factor. The benefit of this class structure is that
all factor manipulation happens in a centralized location,
thereby making it easier to write fast and readable code.

The Joint Probability Distribution of a Bayesian Network is
simply a product of its factors. Much of the functionality
is derived from algorithms presented in [1].

The tests for this class are found in "test_factor.py".

For accessing the flattened array based on RV values/indices
and respective strides, use this formula:
sum( value_index[i]*stride[i] for i = all variables in the scope )


References
----------
[1] Koller, Friedman (2009). "Probabilistic Graphical Models."

"""
from __future__ import division

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np

class Factor(object):
    """
    A Factor uses a flattened numpy array for the cpt.
    By storing the cpt in this manner and taking advantage 
    of efficient algorithms, significant speedups occur.

    Attributes
    ----------

    *self.var* : a string
        The random variable to which this Factor belongs
    
    *self.scope* : a list
        The RV, and its parents (the RVs involved in the
        conditional probability table)
    
    *self.stride* : a dictionary, where
        key = an RV in self.scope, and
        val = integer stride (i.e. how many rows in the 
            CPT until the NEXT value of RV is reached)
    
    *self.cpt* : a 1D numpy array
        The probability values for self.var conditioned
        on its parents
    

    Methods
    -------
    *multiply_factor*
        Multiply two factors together. The factor
        multiplication algorithm used here is adapted
        from Koller and Friedman (PGMs) textbook.

    *sumover_var* :
        Sum over one *rv* by keeping it constant. Thus, you 
        end up with a 1-D factor whose scope is ONLY *rv*
        and whose length = cardinality of rv. 

    *sumout_var_list* :
        Remove a collection of rv's from the factor
        by summing out (i.e. calling sumout_var) over
        each rv.

    *sumout_var* :
        Remove passed-in *rv* from the factor by summing
        over everything else.

    *maxout_var* :
        Remove *rv* from the factor by taking the maximum value 
        of all rv instantiations over everyting else.

    *reduce_factor_by_list* :
        Reduce the factor by numerous sets of
        [rv,val]

    *reduce_factor* :
        Condition the factor by eliminating any sets of
        values that don't align with a given [rv, val]

    *to_log* :
        Convert probabilities to log space from
        normal space.

    *from_log* :
        Convert probabilities from log space to
        normal space.

    *normalize* :
        Make relevant collections of probabilities sum to one.


    Notes
    -----
    """            


    def __init__(self, bn, var):
        """
        Initialize a Factor from a BayesNet object
        for a given random variable.

        Note, it's assumed that the FIRST variable
        of *scope* is the main variable (i.e. NOT a
        parent).

        Arguments
        ---------

        *var* : a string
            The RV for which the Factor will be extracted.

        Effects
        -------
        - sets *self.var*
        - sets *self.cpt*
        - sets *self.card*
        - sets *self.scope*
        - sets *self.stride*

        Notes
        -----
        - self.card is no longer an attribute, but is now a function
        - self.bn is no longer an attribute

        """
        self.bn = bn
        self.var = var
        self.cpt = np.array(bn.cpt(var))
        self.scope = bn.scope(var)
        self.card = dict([(rv, bn.card(rv)) for rv in self.scope])

        self.stride = {self.var:1}
        s=self.card[self.var]
        for v in bn.parents(var):
            self.stride[v]=s
            s*=self.card[v]


    def __repr__(self):
        """
        Internal representation of the factor,
        to be used when the object is called
        in the console without print.
        """
        s = self.var + ' | '
        s += ', '.join(self.parents())
        return s

    def __str__(self):
        """
        String representation of the factor,
        to be used when print is called.
        """
        s = self.var + ' | '
        s += ', '.join(self.parents())
        return s

    def __mul__(self, other_factor):
        """
        Overloads multiplication operator to
        be used as multiplying two factors together.
        """
        self.multiply_factor(other_factor)
        return self

    def __sub__(self, rv_val):
        """
        Overloads subtraction operator to
        be used as reducing a factor by evidence.
        """
        self.reduce_factor(rv_val[0],rv_val[1])
        return self

    def __div__(self, rv):
        """
        Overloads division operator to
        be used as summing out a variable.
        """
        self.sumout_var(rv)
        return self

    def __floordiv__(self, rv):
        """
        Overloads floor division operator to
        be used as maxing out a variable
        """
        self.maxout_var(rv)
        return self


    def parents(self):
        """
        Return parents of self.var ...
        Should make this an iterator
        """
        for rv in self.scope:
            if rv != self.var:
                yield rv
                
    def values(self, rv):
        return self.bn.values(rv)

    def value_indices(self, val_dict):
        """
        Return the indices in the cpt
        where RV=Value in val_dict
        For accessing the flattened array based 
        on RV values/indices
        and respective strides, use this formula:
        sum( value_index[i]*stride[i] for i = all 
            variables in the scope )
        """
        idx = sum([self.bn.value_idx(rv,val)*self.stride[rv] \
            for rv,val in val_dict.items()])
        return idx

    def sepset(self, other_factor):
        """
        The sepset of two cliques is the set of
        variables in the intersection of the two
        cliques' scopes.

        Arguments
        ---------
        *other_clique* : a Clique object
        """
        return set(self.scope).intersection(set(other_factor.scope))



    ##### FACTOR OPERATIONS #####

    def multiply_factor(self, other_factor):
        """
        Multiply two factors together. The factor
        multiplication algorithm used here is adapted
        from Koller and Friedman (PGMs) textbook.

        In essence, the scope of the merged factor is the
        union of the two scopes.

        Arguments
        ---------
        *other_factor* : a different Factor object

        Returns
        -------
        None

        Effects
        -------
        - alters self.cpt
        - alters self.stride
        - alters self.card
        - alters self.scope

        Notes
        -----
        - What is done about normalization here? I guess
        assume it's already normalized

        """
        if len(self.scope)>=len(other_factor.scope):
            phi1=self
            phi2=other_factor
        else:
            phi1=other_factor
            phi2=self
        # go in order of strides to keep them in order after the fact
        rv_order = sorted(phi1.stride, key=phi1.stride.__getitem__)        
        scope_set=set(phi1.scope).union(set(phi2.scope))

        ########### HAD TO ADD THESE FOR V/E TO WORK... #################
        ## I think this is necessary when phi2 has vars not in phi1 ##
        rv_order.extend(list(set(phi2.scope).difference(set(phi1.scope))))
        phi1.card.update(phi2.card)
        #################################################################

        j,k=0,0
        assignment = dict([(rv, 0) for rv in scope_set])
        psi = np.zeros(np.product(phi1.card.values()))

        for i in range(len(psi)):
            psi[i] = phi1.cpt[j]*phi2.cpt[k]
            for rv in rv_order:
                assignment[rv] += 1
                if assignment[rv] == phi1.card[rv]:
                    assignment[rv] = 0
                    if rv in phi1.scope:
                        j=j-(phi1.card[rv]-1)*phi1.stride[rv]
                    if rv in phi2.scope:
                        k=k-(phi2.card[rv]-1)*phi2.stride[rv]
                else:
                    if rv in phi1.scope:
                        j=j+phi1.stride[rv]
                    if rv in phi2.scope:
                        k=k+phi2.stride[rv]
                    break
        self.cpt = psi
        self.scope = list(scope_set)
        self.card=phi1.card
        self.stride={}
        s=1
        for v in rv_order:
            self.stride[v]=s
            s*=phi1.card[v]

        #self.normalize()


    def sumover_var(self, rv):
        """
        Sum over one *rv* by keeping it constant. Thus, you 
        end up with a factor whose scope is ONLY *rv*
        and whose length = cardinality of rv. 

        This is equivalent to calling self.sumout_var() over
        EVERY other variable in the scope and is thus faster
        when you want to do just that.

        Arguments
        ---------
        *rv* : a string
            The random variable to sum over.

        Returns
        -------
        None

        Effects
        -------
        - alters self.cpt
        - alters self.stride
        - alters self.card
        - alters self.scope

        Notes
        -----

        """
        exp_len = self.card[rv]
        new_cpt= np.zeros(exp_len)

        rv_card = self.card[rv]
        rv_stride = self.stride[rv]

        for i in range(exp_len):
            idx=i*rv_stride
            while idx < len(self.cpt):
                new_cpt[i] += np.sum(self.cpt[idx:(idx+rv_stride)])
                idx+=rv_card*rv_stride

        self.cpt=new_cpt
        self.card = {rv:rv_card}
        self.stride = {rv:1}
        self.scope = [rv]
        self.var = rv

        #self.normalize()

    def sumout_var_list(self, var_list):
        """
        Remove a collection of rv's from the factor
        by summing out (i.e. calling sumout_var) over
        each rv.

        Arguments
        ---------
        *var_list* : a list
            The list of rv's to sum out.

        Returns
        -------
        None

        Effects
        -------
        - see "self.sumout_var"

        Notes
        -----

        """
        for var in var_list:
            self.sumout_var(var)

    def sumout_var(self, rv):
        """
        Remove passed-in *rv* from the factor by summing
        over everything else.

        Arguments
        ---------
        *rv* : a string
            The random variable to sum out

        Returns
        -------
        None

        Effects
        -------
        - alters self.cpt
        - alters self.stride
        - alters self.card
        - alters self.scope

        Notes
        -----     
        
        """
        exp_len = int(len(self.cpt)/self.card[rv])
        new_cpt = np.zeros(exp_len)
        
        rv_card = self.card[rv]
        rv_stride = self.stride[rv]

        k=0
        p = np.prod([self.card[r] for r in self.scope if self.stride[r] < self.stride[rv]])
        for i in range(exp_len):
            for c in range(rv_card):
                new_cpt[i] += self.cpt[k + (rv_stride*c)]
            k+=1
            if (k % p== 0):
                k += (p * (rv_card - 1))
        self.cpt=new_cpt

        del self.card[rv]
        self.stride.update((k,v/rv_card) for k,v in self.stride.items() if v > rv_stride)
        del self.stride[rv]
        self.scope.remove(rv)

        if rv == self.var:
            l = [k for k,v in self.stride.items() if v==1]
            if len(l)>0:
                self.var = l[0]

        #if len([k for k,v in self.stride.items() if v==1]) > 0:
        #self.normalize()

    def maxout_var(self, rv):
        """
        Remove *rv* from the factor by taking the maximum value 
        of all instantiations of the passed-in rv

        Used in MAP inference (i.e. Algorithm 13.1 in Koller p.557)

        Arguments
        ---------
        *rv* : a string
            The random variable

        Returns
        -------
        None

        Effects
        -------
        - alters self.cpt
        - alters self.stride
        - alters self.card
        - alters self.scope

        Notes
        -----        
        
        """
        #self.cpt += 0.00002
        exp_len = int(len(self.cpt)/self.card[rv])
        new_cpt = np.zeros(exp_len)

        rv_card = self.card[rv]
        rv_stride = self.stride[rv]

        k=0
        p = np.prod([self.card[r] for r in self.scope if self.stride[r] < self.stride[rv]])
        for i in range(exp_len):
            max_val = 0
            for c in range(rv_card):
                if self.cpt[k + (rv_stride*c)] > max_val:
                    max_val = self.cpt[k + (rv_stride*c)]
                    new_cpt[i] = max_val
            k+=1
            if (k % p == 0):
                k += (p* (rv_card - 1))
        self.cpt=new_cpt

        del self.card[rv]
        self.stride.update((k,v/rv_card) for k,v in self.stride.items() if v > rv_stride)
        del self.stride[rv]
        self.scope.remove(rv)

        #if rv == self.var:
            #self.var = [k for k,v in self.stride.items() if v==1][0]

        #if len(self.scope) > 0:
            #self.normalize()

    def reduce_factor_by_list(self, evidence):
        """
        Reduce the factor by numerous sets of
        [rv,val] -- this is done by running
        self.reduce_factor over the list of
        lists (*evidence*)

        Arguments
        ---------
        *evidence* : a list of lists/tuples
            The collection of rv-val pairs to
            remove from (condition upon) the factor


        Returns
        -------
        None

        Effects
        -------
        - see "self.reduce_factor"

        Notes
        -----
        - Again, might be good to check that each
            rv-val pair is actually in the factor
        """
        if isinstance(evidence, list):
            for rv,val in evidence:
                self.reduce_factor(rv,val)
        elif isinstance(evidence, dict):
            for rv,val in evidence.items():
                self.reduce_factor(rv,val)

    def reduce_factor(self, rv, val):
        """
        Condition the factor over evidence by eliminating any
        sets of values that don't align with [rv, val].

        This is different from "sumover_var" because "reduce_factor"
        is not summing over anything, it is simply removing any 
        parent-child instantiations which are not consistent with
        the evidence. Moreover, there should not be any need for
        normalization because the CPT should already be normalized
        over the rv-val evidence (but we do it anyways because of
        rounding)

        Note, this will completely eliminate "rv" from the factor,
        including from the scope and cpt.

        Arguments
        ---------
        *rv* : a string
            The random variable to eliminate/condition upon.

        *val* : a string
            The value of RV

        Returns
        -------
        None

        Effects
        -------
        - alters self.cpt
        - alters self.scope
        - alters self.card
        - alters self.stride

        Notes
        -----
        - There are no fail-safes here to make sure the
            rv-val pair is actually in the factor..

        """
        exp_len = len(self.cpt)/float(self.card[rv])
        new_cpt = np.zeros((exp_len,))

        val_idx = self.bn.F[rv]['values'].index(val)
        rv_card = self.card[rv]
        rv_stride = self.stride[rv]

        add_idx=0
        idx = val_idx*rv_stride
        while add_idx < exp_len:
            for j in self.cpt[idx:(idx+rv_stride)]:
                new_cpt[add_idx] = j
                add_idx+=1
            idx+=rv_card*rv_stride

        self.cpt=new_cpt
        del self.card[rv]
        self.stride.update((k,v/rv_card) for k,v in self.stride.items() if v > rv_stride)
        del self.stride[rv]
        self.scope.remove(rv)

        if rv == self.var:
            l = [k for k,v in self.stride.items() if v==1]
            if len(l)>0:
                self.var = l[0]

    def to_log(self):
        """
        Convert probabilities to log space from
        normal space.

        """
        self.cpt = np.round(np.log(self.cpt),5)

    def from_log(self):
        """
        Convert probabilities from log space to
        normal space.

        """
        self.cpt = np.round(np.exp(self.cpt),5)

    def perturb(self):
        """
        Add some noise to avoid "nan" when dividing by zero.
        This will probably make cpt values have many 
        decimal points (bad).
        """
        self.cpt += 1e-7

    def normalize(self):
        """
        Make relevant collections of probabilities sum to one.

        This function is ALWAYS going to normalize the variable
        for which the stride = 1, because it's assumed that's the
        main/child variable.

        Effects
        -------
        - alters self.cpt

        Notes
        -----

        """
        self.perturb() # stops nan's from happening
        var = [k for k,v in self.stride.items() if v==1]
        if len(var) > 0:
            var = var[0]
            for i in range(0,len(self.cpt),self.card[var]):
                temp_sum = float(np.sum(self.cpt[i:(i+self.card[var])]))
                for j in range(self.card[var]):
                    self.cpt[i+j] /= temp_sum
        else:
            for i in range(len(self.cpt)):
                self.cpt[i] /= (np.sum(self.cpt))






================================================
FILE: pyBN/classes/factorization.py
================================================
"""
*************
Factorization 
Class
*************
"""
from pyBN.classes.factor import Factor

from collections import OrderedDict
from copy import copy,deepcopy
import numpy as np

class Factorization(object):

	def __init__(self, bn, nodes=None):
		"""
		Initialize a Factorization object.

		The *nodes* argument allows you to make a Factorization object
		that includes only a subset of the random variables in the
		passed-in BayesNet object. This is mostly useful for CliqueTree
		algorithms.
		
		"""
		self.bn = bn

		if nodes is not None:
			self._phi = [Factor(bn,rv) for rv in nodes]
		else:
			self._phi = [Factor(bn,rv) for rv in bn.nodes()]

		## MAP-BASED ATTRIBUTES ##
		self.map_factors = OrderedDict()
		self.map_assignment = dict()
		self.map_prob = -1

	def refresh(self):
		"""
		Refresh Factorization attributes.
		"""
		self._phi = [Factor(self.bn,rv) for rv in self.bn.nodes()]
		self.map_factors = OrderedDict()
		self.map_assignment = dict()
		self.map_prob = -1

	def __getitem__(self, idx):
		return self._phi[idx]

	def __iter__(self):
		for phi in self._phi:
			yield phi

	def __len__(self):
		return len(self._phi)

	def __div__(self, rv):
		"""
		Overloads division operator for
		Sum-Product Variable Elimination pass
		over passed-in var.
		"""
		self.sum_product_eliminate_var(rv)
		return self

	def __floordiv__(self, rv):
		"""
		Overloads floor division operator for
		Max-Product Variable Elimination pass
		over passed-in var.
		"""
		self.max_product_eliminate_var(rv)
		return self

	def __sub__(self, rv_val):
		"""
		Overloads subtract operator for
		reducing EVERY relevant factor
		by passed-in rv_val
		"""
		self.map_assignment[rv_val[0]] = rv_val[1] # append
		for phi in self._phi:
			if rv_val[0] in phi.scope:
				phi -= (rv_val[0],rv_val[1]) # reduce by evidence
		return self

	def sum_product_eliminate_var(self, rv):
		relevant_factors = self.relevant_factors(rv)
		irrelevant_factors = self.irrelevant_factors(rv)

		# mutliply all relevant factors
		psi = relevant_factors[0]
		for i in range(1,len(relevant_factors)):
			psi *= relevant_factors[i]

		# Take Sum over psi for rv
		psi /= rv # sumout
		irrelevant_factors.append(psi) # add sum-prod factor back in

		self._phi = irrelevant_factors

	def max_product_eliminate_var(self, rv):
		relevant_factors = self.relevant_factors(rv)
		irrelevant_factors = self.irrelevant_factors(rv)

		# mutliply all relevant factors
		psi = relevant_factors[0]
		for i in range(1,len(relevant_factors)):
			psi *= relevant_factors[i]

		self.map_factors[rv] = deepcopy(psi)
		# Take Max over psi for rv
		psi //= rv # maxout
		irrelevant_factors.append(psi) # add sum-prod factor back in

		self._phi = irrelevant_factors

	def traceback_map(self):
		nodes = self.map_factors.keys()
		idx = len(self.map_factors)
		for rv in reversed(self.map_factors.keys()):
			f = self.map_factors[rv]
			# reduce factor by variables upstream
			for i in range(idx,len(self.map_factors)):
				if nodes[i] in f.scope:
					f -= (nodes[i],self.map_assignment[nodes[i]])
			# choose maximizing assignment conditioned on upstream vars
			self.map_assignment[rv] = self.bn.values(rv)[np.argmax(self.map_factors[rv].cpt)]
			idx-=1
		return self.map_assignment

	def consolidate(self):
		final_phi = self._phi[0]
		for i in range(1,len(self._phi)):
			final_phi *= _phi[i]
		#final_phi.normalize()
		return final_phi

	def relevant_factors(self, rv):
		return [f for f in self._phi if rv in f.scope]

	def irrelevant_factors(self, rv):
		return [f for f in self._phi if rv not in f.scope]












================================================
FILE: pyBN/classification/__init__.py
================================================
from pyBN.classification.classification import *
from pyBN.classification.feature_selection import *

================================================
FILE: pyBN/classification/classification.py
================================================
"""
****************
Bayesian Network
Classifiers
****************

Implementation of various Bayesian network
classifiers.

"""
from __future__ import division

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np
from pyBN.inference.marginal_approx import *

def mbc_predict(data, targets, classifier=None, c_struct='DAG',f_struct='DAG', wrapper=False):
	pass

def predict(data, target, classifier=None, method='nb'):
	"""
	Wrapper for a unified interface to the 
	various classification algorithms. The pyBN
	user can call any algorithm from this function for
	convienence.

	The prediction algorithm works as follows:
		- For each row of data, set the observed attribute
			variables as evidence
		- Pick the most likely value of the target variable
			by either a) running MAP inference and simply
			returning the chosen value, or b) running Marginal
			inference and selecting the value of the target variable
			with the highest probability.
		- Compare the chosen value to the actual value.
		- Return the actual values, the chosen values, and the accuracy score.

	Arguments
	---------
	*data* : a nested numpy array

	*target* : an integer
		The data column corresponding to the
		predictor/target variable.

	*classifer* : a BayesNet object (optional)
		The BayesNet model to use as the classifier model. 
		NOTE: The user can supply a BayesNet class which has
		been learned as a general bn, naive bayes, tan, or ban model 
		from the appropriate structure learning algorithm or file...
		OR the user can leave classifier as "None" and supply a string
		to *method*, in which class the corresponding model will be
		structure/parameter learned IN THIS FUNCTION and used as the
		classifier.

	*method* : a string
		Which BN classifier to use if *classifier* is None.
		Options:
			- 'bn' : general bayesian network classifier
			- 'nb' : naive bayes classifier
			- 'tan' : tree-augmented naive bayes
			- 'ban' : bayesian augmented naive bayes ?

	

	Returns
	-------
	*c_dict* : a dictionary, where
		keys = 'y', 'yp', and 'acc', where
		'y' is the true target values (np array),
		'yp' is the predicted target values (np array),
		'acc' is the prediction accuracy percentage (float).

	Effects
	-------
	None

	Notes
	-----
	"""
	if classifier is None:
		print('Learning structure..')
		classifier = learn_structure(data=data, target=target, method=method)
		learn_parameters(classifier)

	non_target_cols = list(set(range(data.shape[1]))-{target})
	y = np.empty(data.shape[0],dtype=np.int32)
	yp = np.empty(data.shape[0],dtype=np.int32)
	### CLASSIFIER PREDICTION FROM INFERENCE ###
	for row in range(data.shape[0]):
		if row % 100 == 0:
			print('Iteration: ' , row)
		vals = [data[row,i] for i in non_target_cols]
		evidence = dict(zip(non_target_cols,vals))
		y[row] = data[row,target] # true value
		predicted_val = max(marginal_lws_a(bn=classifier,
									n=100,
									evidence=evidence,
									target=target))
		yp[row] = predicted_val
		
	acc = np.sum(y==yp)/data.shape[0]
	return y, yp, acc

		
			

























================================================
FILE: pyBN/classification/feature_selection.py
================================================
"""
******************
Feature Selection
for Classification
******************

Many constraint-based Bayesian network structure 
learning algorithms are actually quite useful for 
general feature selection as a pre-processing task
for absolute any classification algorithm.

The way Bayesian network-based feature selection works
is by essentially learning the Markov Blanket of the
target variable from the data. As has been pointed out
in numerous papers - starting from Daphne Koller's well-known
paper "Toward Optimal Feature Selection" -- identifying the 
Markov Blanket of a target variable gives you the theoretically
optimal selection of feature variables. 

The theoretical guaruntee of Markov Blanket feature selection
stems from the definition of "Markov Blanket" - the set of
nodes which, when conditioned upon, render the target variable 
independent from EVERY other variable in the network. In other
words, if you observe the values of the Markov Blanket for the
target variable, then it is useless to observe the values of 
any variables OUTSIDE the markov blanket - because the target
variable is independent of those variables.

"""

from pyBN.learning.structure.constraint import *


def fs_gambit(data, target):
	""" 
	The Feature Selection Gambit is just a wrapper method
	for calling every possible feature selection method. This
	is a useful method if you want to just run every algorithm
	and choose the best or compare.

	It returns a single dictionary where key = the algorithm 
	method and value = the result from the algorithm 
	(i.e. the set of variables in the Markov Blanket 
	of the target variable).
	
	There are currently five algorithms:
		- iamb
		- fast_iamb
		- lambda_iamb
		- gs (grow-shrink)
		
	Arguments
	---------
	*data* : a nested numpy array

	*target* : an integer
		The column of data that corresponds
		to the target variable.

	Returns
	-------
	*fs_gambit* : a dictionary, where
		key = feature selection algorithm name, and
		value = the result from the given algorithm: the
		set of relevant features for the target (i.e. the
		target variable's Markov Blanket)
	
	Effects
	-------
	None

	Notes
	-----
	None

	"""
	algorithms = ['iamb', 'fast_iamb', 'lambda_iamb', 'gs']
	fs_gambit = {}

	for algo in algorithms:
		fs_gambit[algo] = feature_selection(data=data, target=target, method=algo)

	return fs_gambit

def feature_selection(data, target, method='iamb'):
	"""
	Wrapper for Markov Blanket-based Feature Selection. This
	function provides a unified interface for using the
	various constraint-based structure learning algorithms
	for feature selection instead of full Bayesian Network
	model learning. It simply calls "learn_structure" which
	is already a structure learning wrapper, but also passes
	in "target" denoting that feature selection should take
	place instead of full structure learning.

	Arguments
	---------
	*data* : a nested numpy array

	*method* : a string
		Which feature selection algorithm to run. The
		default is "iamb", and if an unintelligable
		method is given, "iamb" is called.

	"""
	features = learn_structure(data, method=method, feature_selection=target)
	return features























================================================
FILE: pyBN/inference/__init__.py
================================================
from pyBN.inference.map_exact import *
from pyBN.inference.marginal_approx import *
from pyBN.inference.marginal_exact import *

================================================
FILE: pyBN/inference/_tests/__init__.py
================================================


================================================
FILE: pyBN/inference/_tests/test_map_exact.py
================================================
"""
********
UnitTest
Map Exact
********

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.readwrite.read import read_bn
from pyBN.inference.map_exact import map_ve_e

class MapExactTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		self.bn = read_bn(os.path.join(self.dpath,'cmu.bn'))

	def tearDown(self):
		pass

	#def test_map_noevidence(self):
		#p = list(map_ve_e(self.bn,target='Alarm'))
		#self.assertListEqual(p,['No',0.9367])

	#def test_map_prior(self):
		#p = list(map_ve_e(self.bn,target='Burglary'))
		#self.assertListEqual(p,['No',0.9367])

	#def test_map_leaf(self):
		#p = list(map_ve_e(self.bn,target='JohnCalls'))
		#self.assertlistEqual(p,['No',0.9367])
		

================================================
FILE: pyBN/inference/_tests/test_marginal_approx.py
================================================
"""
***************
UnitTest
Marginal Approx
***************

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.inference.marginal_approx import marginal_fs_a, marginal_lws_a, marginal_gs_a
from pyBN.readwrite.read import read_bn

class MarginalApproxTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		self.bn = read_bn(os.path.join(self.dpath,'cmu.bn'))

	def tearDown(self):
		pass

	def test_forward_sample(self):
		np.random.seed(3636)
		self.assertDictEqual(marginal_fs_a(self.bn,n=1000),
			{'Alarm': {'No': 0.996, 'Yes': 0.004},
			 'Burglary': {'No': 0.998, 'Yes': 0.002},
			 'Earthquake': {'No': 1.0, 'Yes': 0.0},
			 'JohnCalls': {'No': 0.946, 'Yes': 0.054},
			 'MaryCalls': {'No': 0.99, 'Yes': 0.01}}
			 )

	def test_likelihood_weighted_sample(self):
		"""
		THIS HAS BEEN VALIDATED AGAINST BNLEARN
		"""
		np.random.seed(3636)
		self.assertDictEqual(marginal_lws_a(self.bn,evidence={'Burglary':'Yes'}),
			{'Alarm': {'No': 0.058, 'Yes': 0.942},
			 'Burglary': {'No': 0.0, 'Yes': 1.0},
			 'Earthquake': {'No': 0.997, 'Yes': 0.003},
			 'JohnCalls': {'No': 0.136, 'Yes': 0.864},
			 'MaryCalls': {'No': 0.343, 'Yes': 0.657}})

	def test_lws_allevidence(self):
		self.assertDictEqual(marginal_lws_a(self.bn,evidence={'Burglary':'Yes','Alarm':'Yes',
			'Earthquake':'Yes','JohnCalls':'Yes','MaryCalls':'Yes'}),
			{'Alarm': {'No': 0.0, 'Yes': 1.0},
			 'Burglary': {'No': 0.0, 'Yes': 1.0},
			 'Earthquake': {'No': 0.0, 'Yes': 1.0},
			 'JohnCalls': {'No': 0.0, 'Yes': 1.0},
			 'MaryCalls': {'No': 0.0, 'Yes': 1.0}})

	def test_gibbs(self):
		np.random.seed(3636)
		self.assertDictEqual(marginal_gs_a(self.bn,n=1000,burn=200),
			{'Alarm': {'No': 0.9988, 'Yes': 0.0},
				 'Burglary': {'No': 0.9988, 'Yes': 0.0},
				 'Earthquake': {'No': 0.9975, 'Yes': 0.0013},
				 'JohnCalls': {'No': 0.9313, 'Yes': 0.0675},
				 'MaryCalls': {'No': 0.9838, 'Yes': 0.015}})












================================================
FILE: pyBN/inference/_tests/test_marginal_exact.py
================================================
"""
**************
UnitTest
Marginal Exact
**************

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.readwrite.read import read_bn
from pyBN.inference.marginal_exact import marginal_ve_e



class MarginalExactTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		self.bn = read_bn(os.path.join(self.dpath,'cmu.bn'))
		
	def tearDown(self):
		pass

	def test_marginal_ve_e_prior1(self):
		self.assertListEqual(list(marginal_ve_e(self.bn,'Burglary')),
			self.bn.cpt('Burglary'))

	def test_marginal_ve_e_prior2(self):
		self.assertListEqual(list(marginal_ve_e(self.bn,'Earthquake')),
			self.bn.cpt('Earthquake'))

	#def test_marginal_ve_e_middle(self):
	#	self.assertListEqual(list(marginal_ve_e(self.bn,'Alarm')),
	#		[0.9975,0.0025])

	#def test_marginal_ve_e_leaf1(self):
	#	self.assertListEqual(list(marginal_ve_e(self.bn,'JohnCalls')),
	#		[ 0.88567,  0.11433])

	#def test_marginal_ve_e_prior1_prior_ev(self):
	#	self.assertListEqual(list(marginal_ve_e(self.bn,'Earthquake')),
	#		marginal_ve_e(self.bn,'Earthquake',evidence={'Burglary':'Yes'}))

	#def test_marginal_ve_e_prior2_prior_ev(self):
#		self.assertListEqual(list(marginal_ve_e(self.bn,'Burglary')),
#			marginal_ve_e(self.bn,'Burglary',evidence={'Earthquake':'Yes'}))

	def test_marginal_ve_e_prior_middle_ev(self):
		self.assertListEqual(list(marginal_ve_e(self.bn,'Burglary',
			evidence={'Alarm':'Yes'})),[ 0.62963,  0.37037])

	def test_marginal_ve_e_prior_leaf_ev(self):
		self.assertListEqual(list(marginal_ve_e(self.bn,'Burglary',
			evidence={'JohnCalls':'Yes'})),[ 0.999,  0.001])

	def test_marginal_ve_e_middle_prior_ev(self):
		self.assertListEqual(list(marginal_ve_e(self.bn,'Alarm',
			evidence={'Burglary':'Yes'})),[0.06,0.94])

#	def test_marginal_ve_e_middle_leaf_ev(self):
#		self.assertListEqual(list(marginal_ve_e(self.bn,'Alarm',
#			evidence={'JohnCalls':'Yes'})),[ 0.95769,  0.04231])

	#def test_marginal_ve_e_leaf_prior_ev(self):
	#	self.assertListEqual(list(marginal_ve_e(self.bn,'JohnCalls',
#			evidence={'Burglary':'Yes'})),[ 0.17431,  0.82569])








================================================
FILE: pyBN/inference/map_exact/__init__.py
================================================
from pyBN.inference.map_exact.ilp_map import *
from pyBN.inference.map_exact.ve_map import *

================================================
FILE: pyBN/inference/map_exact/ilp_map.py
================================================

__author__ = """N. Cullen <ncullen.th@dartmouth.edu>"""

from copy import copy
import numpy as np

from pyBN.classes.factor import Factor
from pyBN.classes.factorization import Factorization



def ilp_map(bn, evidence={}):
    """
    Solve MAP Inference as an integer linear optimization
    problem, as formulated in Sontag's notes:
    http://cs.nyu.edu/~dsontag/courses/pgm12/slides/lecture6.pdf

    Arguments
    ---------
    *bn* : a BayesNet object

    *evidence* : a dictionary, where
        key = rv and value = rv's value

    """
    try:
        import pulp as pl
    except ImportError:
        print("You must 'pip install pulp' to use Map Optimization methods.")
        return

    model = pl.LpProblem("MAP Inference",pl.LpMinimize)

    # CREATE VARIABLE FOR EVERY CPT ENTRY
    var_dict = {} # key = rv, value = list of variables for each cpt entry
    weight_list = []
    var_list = []
    ii = 0
    for rv in bn.nodes():
        var_dict[rv] = {}
        for idx in range(len(bn.cpt(rv))):
            str_rep = bn.cpt_str_idx(rv,idx)
            #str_rep = str(rv)+'-'+str(idx)
            new_var = pl.LpVariable(str_rep,0,1,pl.LpInteger)
            var_dict[rv][idx] = new_var
            var_list.append(new_var)
            weight_list.append(round(-np.log(bn.cpt(rv)[idx]),3))
            ii+=1

    # OBJECTIVE FUNCTION
    model += np.dot(var_list,weight_list)
    
    # RV - VALUE CONSTRAINTS
    for rv in bn.nodes():
        model += sum([var_dict[rv][idx] \
            for idx in range(len(bn.cpt(rv)))]) == 1, \
            'RV Constraint - ' + str(rv)
    
    # OUTGOING EDGE CONSTRAINTS
    k=0
    for rv in bn.nodes():
        for val_idx in range(len(bn.cpt(rv))):
            val = bn.values(rv)[val_idx % bn.card(rv)] # actual rv-val
            for child in bn.children(rv):
                # get indices of the child cpt which correspond to rv-val
                child_cpt_indices = bn.cpt_indices(child, {rv:val})
                # rv-val variable = sum(child[child_cpt_indices] variables)
                model += var_dict[rv][val_idx] <= \
                    sum([var_dict[child][i] for i in child_cpt_indices]), \
                    'OUT-CPT Constraint - ' + str(k)
                k+=1
    
    # INGOING EDGE CONSTRAINTS
    k=0
    for rv in bn.nodes():
        if len(bn.parents(rv)) > 0:
            for val_idx in range(len(bn.cpt(rv))):
                # find which parent-vals that index belongs to
                p_cpt = {}
                for parent in bn.parents(rv):
                    p_cpt[parent] = []
                    for p_idx in range(len(bn.cpt(parent))):
                        p_val = bn.values(parent)[p_idx % bn.card(parent)]
                        if val_idx in bn.cpt_indices(rv, {parent:p_val}):
                            p_cpt[parent].append(p_idx)
                model += var_dict[rv][val_idx] <= \
                    sum([var_dict[p][pi] for p in bn.parents(rv) for pi in p_cpt[p]]), \
                    'IN-CPT Constraint - ' + str(k)
                k+=1
    
    #model.writeLP("MapInference.lp")
    model.solve()

    for v in model.variables():
        if v.varValue == 1.0:
            print(v.name)

================================================
FILE: pyBN/inference/map_exact/ve_map.py
================================================

__author__ = """N. Cullen <ncullen.th@dartmouth.edu>"""

from copy import copy
import numpy as np

from pyBN.classes.factor import Factor
from pyBN.classes.factorization import Factorization



def ve_map(bn,
            evidence={},
            target=None,
            prob=False):
    """
    Perform Max-Sum Variable Elimination over a BayesNet object
    for exact maximum a posteriori inference.

    This has been validated w/ and w/out evidence
    
    """
    _phi = Factorization(bn)

    order = copy(list(bn.nodes()))
    #### EVIDENCE PROCESSING ####
    for E, e in evidence.items():
        _phi -= (E,e)
        order.remove(E)

    #### MAX-PRODUCT ELIMINATE VAR ####
    for var in order:
        _phi //= var 
    
    #### TRACEBACK MAP ASSIGNMENT ####
    max_assignment = _phi.traceback_map()

    #### RETURN ####
    if prob:
        # multiply phi's together if there is evidence
        final_phi = _phi.consolidate()
        max_prob = round(final_phi.cpt[0],5)

        if target is not None:
            return max_prob, max_assignment[target]
        else:
            return max_prob, max_assignment
    else:
        if target is not None:
            return max_assignment[target]
        else:
            return max_assignment

================================================
FILE: pyBN/inference/marginal_approx/__init__.py
================================================
from pyBN.inference.marginal_approx.forward_sample import *
from pyBN.inference.marginal_approx.gibbs_sample import *
from pyBN.inference.marginal_approx.loopy_bp import *
from pyBN.inference.marginal_approx.lw_sample import *

================================================
FILE: pyBN/inference/marginal_approx/forward_sample.py
================================================
from pyBN.classes.bayesnet import BayesNet
from pyBN.classes.factor import Factor 
from pyBN.utils.graph import topsort

import numpy as np


def forward_sample(bn, n=1000):
	"""
	Approximate marginal probabilities from
	forward sampling algorithm on a BayesNet object.

	This algorithm works by
	repeatedly sampling from the BN and taking
	the ratio of observations as their marginal probabilities.

	One sample is done by first sampling from any prior random
	variables, then moving down the network in topological sort
	order - sampling from each successive random variable by
	conditioning on its parents (which have already been sampled
	higher up the network).

	Note that there is no evidence to include in this algorithm - 
	the comparative algorithm which includes evidence is the 
	likelihood weighted algorithm (see "lw_sample" function).

	Arguments
	---------
	*bn* : a BayesNet object

	*n* : an integer
		The number of samples to take

	Returns
	-------
	*sample_dict* : a dictionary, where key = rv, value = another dict
					where key = instance, value = its probability value

	Notes
	-----
	- Evidence is not currently implemented.
	"""

	sample_dict = {}
	for var in bn.nodes():
	    sample_dict[var] = {}
	    for val in bn.values(var):
	        sample_dict[var][val] = 0

	for i in range(n):
	    #if i % (n/float(10)) == 0:
	     #   print 'Sample: ' , i
	    new_sample = {}
	    for rv in bn.nodes():
	        f = Factor(bn,rv)
	        for p in bn.parents(rv):
	            f.reduce_factor(p,new_sample[p])
	        choice_vals = bn.values(rv)
	        choice_probs = f.cpt
	        chosen_val = np.random.choice(choice_vals, p=choice_probs)

	        sample_dict[rv][chosen_val] += 1
	        new_sample[rv] = chosen_val

	for rv in sample_dict:
	    for val in sample_dict[rv]:
	        sample_dict[rv][val] = int(sample_dict[rv][val]) / float(n)
	
	return sample_dict

================================================
FILE: pyBN/inference/marginal_approx/gibbs_sample.py
================================================

__author__ = """N. Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.classes.bayesnet import BayesNet
from pyBN.classes.factor import Factor 
from pyBN.utils.graph import topsort

import numpy as np



def gibbs_sample(bn, n=1000, burn=200):
	"""
	Approximate Marginal probabilities from Gibbs Sampling
	over a BayesNet object.

	Arguments
	---------
	*bn* : a BayesNet object

	*n* : an integer
		The number of samples to take

	*burn* : an integer
		The number of beginning samples to
		throw away for the MCMC mixing.

	Returns
	-------
	*sample_dict* : a dictionary where key = rv
		and value = another dictionary where
		key = rv instantiation and value = marginal
		probability

	Notes
	-----

	"""

	sample_dict ={}
	for rv in bn.nodes():
	    sample_dict[rv]={}
	    for val in bn.values(rv):
	        sample_dict[rv][val] = 0

	state = {}
	for rv in bn.nodes():
	    state[rv] = np.random.choice(bn.values(rv)) # uniform sample

	for i in range(n):
	    #if i % (n/float(10)) == 0:
	      #  print 'Sample: ' , i
	    for rv in bn.nodes():
	        # get possible values conditioned on everything else
	        parents = bn.parents(rv)
	        # no parents - prior
	        if len(parents) == 0:
	            choice_vals = bn.values(rv)
	            choice_probs = bn.cpt(rv)
	        # has parent - filter cpt
	        else:
	            f = Factor(bn,rv)
	            for p in parents:
	                f.reduce_factor(p,state[p])
	            choice_vals = bn.values(rv)
	            choice_probs = f.cpt
	        # sample over remaining possibilities
	        chosen_val = np.random.choice(choice_vals,p=choice_probs)
	        state[rv]=chosen_val
	    # update sample_dict dictionary
	    if i > burn:
	        for rv,val in state.items():
	            sample_dict[rv][val] +=1

	for rv in sample_dict:
	    for val in sample_dict[rv]:
	        sample_dict[rv][val] = round(int(sample_dict[rv][val]) / float(n-burn),4)
	
	return sample_dict

================================================
FILE: pyBN/inference/marginal_approx/loopy_bp.py
================================================

__author__ = """N. Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.classes.bayesnet import BayesNet
from pyBN.classes.factor import Factor 
from pyBN.utils.graph import topsort

import numpy as np


def loopy_bp(target=None, evidence=None, max_iter=100):
	"""
	Perform Message Passing (Loopy Belief Propagation) 
	over a cluster graph.

	This is a good candidate for Numba JIT.

	See Koller pg. 397.


	Parameters
	----------


	Returns
	-------


	Notes
	-----
	- Copied from clustergraph class... not tested.
	- Definitely a problem due to normalization (prob vals way too small)
	- Need to check the scope w.r.t. messages.. all clusters should not
	be accumulating rv's in their scope over the course of the algorithm.
	"""
	def collect_beliefs(cgraph):
		cgraph.beliefs = {}
		for cluster in self.V:
			cgraph.V[cluster].collect_beliefs()
			#print('Belief ' , cluster , ' : \n', self.V[cluster].belief.cpt)
			cgraph.beliefs[cluster] = cgraph.V[cluster].belief

	# 1: Moralize the graph
	# 2: Triangluate
	# 3: Build a clique tree using max spanning
	# 4: Propagation of probabilities using message passing

	# creates clique tree and assigns factors, thus satisfying steps 1-3
	cgraph = ClusterGraph(bn)

	edge_visit_dict = dict([(i,0) for i in cgraph.E])

	iteration = 0
	while not cgraph.is_calibrated():
		if iteration == max_iter:
			break
		if iteration % 50 == 0:
			print('Iteration: ' , iteration)
			for cluster in cgraph.V.values():
				cluster.collect_beliefs()
		# select an edge
		e_idx = np.random.randint(0,len(cgraph.E))
		edge_select = cgraph.E[e_idx]
		p_idx = np.random.randint(0,2)
		parent_edge = edge_select[p_idx]
		child_edge = edge_select[np.abs(p_idx-1)]
		print(parent_edge , child_edge)

		# send a message along that edge
		cgraph.V[parent_edge].send_message(cgraph.V[child_edge])

		iteration += 1
	print('Now Collecting Beliefs..')
	collect_beliefs(cgraph)
	#bn.ctree = self

================================================
FILE: pyBN/inference/marginal_approx/lw_sample.py
================================================
__author__ = """N. Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.classes.bayesnet import BayesNet
from pyBN.classes.factor import Factor 
from pyBN.utils.graph import topsort

import numpy as np


def lw_sample(bn, evidence={}, target=None, n=1000):
	"""
	Approximate Marginal probabilities from
	likelihood weighted sample algorithm on
	a BayesNet object.

	Arguments
	---------
	*bn* : a BayesNet object

	*n* : an integer
		The number of samples to take

	*evidence* : a dictionary, where
		key = rv, value = instantiation

	Returns
	-------
	*sample_dict* : a dictionary where key = rv
		and value = another dictionary where
		key = rv instantiation and value = marginal
		probability

	Effects
	-------
	None

	Notes
	-----

	"""
	sample_dict = {}
	weight_list = np.ones(n)

	#factor_dict = dict([(var,Factor(bn, var)) for var in bn.V])
	#parent_dict = dict([(var, bn.data[var]['parents']) for var in bn.V])

	for var in bn.nodes():
	    sample_dict[var] = {}
	    for val in bn.values(var):
	        sample_dict[var][val] = 0

	for i in range(n):
	    #if i % (n/float(10)) == 0:
	     #   print 'Sample: ' , i
	    new_sample = {}
	    for rv in bn.nodes():
	        f = Factor(bn,rv)
	        # reduce_factor by parent samples
	        for p in bn.parents(rv):
	            f.reduce_factor(p,new_sample[p])
	        # if rv in evidence, choose that value and weight
	        if rv in evidence:
	            chosen_val = evidence[rv]
	            weight_list[i] *= f.cpt[bn.values(rv).index(evidence[rv])]
	        # if rv not in evidence, sample as usual
	        else:
	            choice_vals = bn.values(rv)
	            choice_probs = f.cpt
	            chosen_val = np.random.choice(choice_vals, p=choice_probs)
	            
	        new_sample[rv] = chosen_val
	    # weight the choice by the evidence likelihood    
	    for rv in new_sample:
	        sample_dict[rv][new_sample[rv]] += 1*weight_list[i]

	weight_sum = sum(weight_list)

	for rv in sample_dict:
	    for val in sample_dict[rv]:
	        sample_dict[rv][val] /= weight_sum
	        sample_dict[rv][val] = round(sample_dict[rv][val],4)
	
	if target is not None:
		return sample_dict[target]
	else:
		return sample_dict

================================================
FILE: pyBN/inference/marginal_exact/__init__.py
================================================
from pyBN.inference.marginal_exact.exact_bp import *
from pyBN.inference.marginal_exact.ve_marginal import *

================================================
FILE: pyBN/inference/marginal_exact/exact_bp.py
================================================

__author__ = """N. Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.classes.factor import Factor
from pyBN.classes.factorization import Factorization
from pyBN.utils.graph import *

from copy import deepcopy, copy
import numpy as np
import json


def exact_bp(bn, target=None, evidence=None, downward_pass=False):
	"""
	Perform Belief Propagation (Message Passing) over a Clique Tree. This
	is sometimes referred to as the "Junction Tree Algorithm" or
	the "Hugin Algorithm".

	It involves an Upward Pass (see [1] pg. 353) along with
	Downward Pass (Calibration) ([1] pg. 357) if the target involves
	multiple random variables - i.e. is a list

	Steps Involved:
		1. Build a Clique Tree from a Bayesian Network
			a. Moralize the BN
			b. Triangulate the graph
			c. Find maximal cliques and collapse into nodes
			d. Create complete graph and make edge weights = sepset cardinality
			e. Using Max Spanning Tree to create a tree of cliques
		2. Assign each factor to only one clique
		3. Compute the initial potentials of each clique
			- multiply all of the clique's factors together
		4. Perform belief propagation based on message passing protocol.


	Arguments
	---------
	*bn* : a BayesNet object

	Returns
	-------


	Notes
	-----

	"""
	# 1: Moralize the graph
	# 2: Triangluate
	# 3: Build a clique tree using max spanning
	# 4: Propagation of probabilities using message passing

	# creates clique tree and assigns factors, thus satisfying steps 1-3
	ctree = CliqueTree(bn) # might not be initialized?
	#G = ctree.G
	#cliques = copy.copy(ctree.V)

	# select a clique as root where target is in scope of root
	root = ctree.V[0]
	if target is not None:
		for v in ctree.V:
			if target in ctree[v].scope:
				root = v
				break

	
	clique_ordering = ctree.dfs_postorder(root=root)

	# UPWARD PASS
	# send messages up the tree from the leaves to the single root
	for i in clique_ordering:
		#clique = ctree[i]
		for j in ctree.parents(i):
			ctree[i] >> ctree[j] 
			#clique.send_message(ctree[j])
		# if root node, collect its beliefs
		#if len(ctree.parents(i)) == 0:
			#ctree[root].collect_beliefs()
	ctree[root].collect_beliefs()
	marginal_target = ctree[root].marginalize_over(target)

	# DOWNWARD PASS
	if downward_pass == True:
		# send messages down the tree from the root to the leaves
		# (not needed unless *target* involves more than one variable)
		new_ordering = list(reversed(clique_ordering))
		for j in new_ordering:
			for i in ctree.children(j):
				ctree[j] >> ctree[i]
			# if leaf node, collect its beliefs
			if len(ctree.children(j)) == 0:                    
				ctree[j].collect_beliefs()

	return marginal_target

	# beliefs hold the answers

================================================
FILE: pyBN/inference/marginal_exact/ve_marginal.py
================================================

__author__ = """N. Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.classes.factor import Factor
from pyBN.classes.factorization import Factorization
from pyBN.utils.graph import *

from copy import deepcopy, copy
import numpy as np
import json

def marginal_ve_e(bn, target, evidence={}):
	"""
	Perform Sum-Product Variable Elimination on
	a Discrete Bayesian Network.

	Arguments
	---------
	*bn* : a BayesNet object

	*target* : a list of target RVs

	*evidence* : a dictionary, where
		key = rv and value = rv value

	Returns
	-------
	*marginal_dict* : a dictionary, where
		key = an rv in target and value =
		a numpy array containing the key's
		marginal conditional probability distribution.

	Notes
	-----
	- Mutliple pieces of evidence often returns "nan"...numbers too small?
		- dividing by zero -> perturb values in Factor class
	"""
	_phi = Factorization(bn)

	order = copy(list(bn.nodes()))
	order.remove(target)

	#### EVIDENCE PROCESSING ####
	for E, e in evidence.items():
		_phi -= (E,e)
		order.remove(E)

	#### SUM-PRODUCT ELIMINATE VAR ####
	for var in order:
		_phi /= var

	# multiply phi's together if there is evidence
	final_phi = _phi.consolidate()

	return np.round(final_phi.cpt,4)

================================================
FILE: pyBN/io/__init__.py
================================================
from pyBN.io.read import *
from pyBN.io.write import *

================================================
FILE: pyBN/io/_tests/__init__.py
================================================


================================================
FILE: pyBN/io/_tests/test_reading.py
================================================
"""
********
UnitTest
Reading
********

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
from pyBN.readwrite.read import read_bn
import os
from os.path import dirname


class ReadingTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		self.bn_bif = read_bn(os.path.join(self.dpath,'cancer.bif'))
		self.bn_bn = read_bn(os.path.join(self.dpath,'cmu.bn'))

	def tearDown(self):
		pass

	def test_read_bn_vertices(self):
		self.assertListEqual(self.bn_bn.V, ['Burglary','Earthquake','Alarm','JohnCalls','MaryCalls'])

	def test_read_bif_vertices(self):
		self.assertListEqual(self.bn_bif.V, ['Smoker', 'Pollution', 'Cancer', 'Xray', 'Dyspnoea'])

================================================
FILE: pyBN/io/_tests/test_writing.py
================================================
"""
********
UnitTest
Writing
********

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest


def suite():
	suite = unittest.TestLoader().loadTestsFromTestCase(WritingTestCase)
	return suite


class WritingTestCase(unittest.TestCase):

	def setUp(self):
		pass
	def tearDown(self):
		pass

================================================
FILE: pyBN/io/read.py
================================================
"""
*************
Read BayesNet
from File
*************

Read a BayesNet object from a file. These
functions all currently work, but support
for more formats should be added in the future.

"""

__author__ = """Nicholas Cullen <ncullen.th@dartouth.edu>"""

import json
import numpy as np
import copy

from pyBN.classes.bayesnet import BayesNet
from pyBN.classes.factor import Factor
from pyBN.utils.graph import topsort


def read_bn(path):
    """
    Wrapper function for reading BayesNet objects
    from various file types.

    Arguments
    ---------
    *path* : a string
        The path (relative or absolute) - MUST
        include extension -> that's how we know
        which file reader to call.

    Returns
    -------
    *bn* : a BayesNet object

    Effects
    -------
    None

    Notes
    -----

    """
    if '.bif' in path:
        return read_bif(path)
    elif '.bn' in path:
        return read_json(path)
    elif '.mat' in path:
        return read_mat(path)
    else:
        print("Path Extension not recognized")

def read_bif(path):
    """
    This function reads a .bif file into a
    BayesNet object. It's probably not the 
    fastest or prettiest but it gets the job
    done.

    Arguments
    ---------
    *path* : a string
        The path

    Returns
    -------
    *bn* : a BayesNet object

    Effects
    -------
    None

    Notes
    -----
    *V* : a list of strings
    *E* : a dict, where key = vertex, val = list of its children
    *F* : a dict, where key = rv, val = another dict with
                keys = 'parents', 'values', cpt'

    """
    _parents = {} # key = vertex, value = list of vertices in the scope (includind itself)
    _cpt = {} # key = vertex, value = list (then numpy array)
    _vals = {} # key=vertex, val=list of its possible values

    with open(path, 'r') as f:
        while True:
            line = f.readline()
            if 'variable' in line:
                new_vertex = line.split()[1]

                _parents[new_vertex] = []
                _cpt[new_vertex] = []
                #_vals[new_vertex] = []

                new_line = f.readline()
                new_vals = new_line.replace(',', ' ').split()[6:-1] # list of vals
                _vals[new_vertex] = new_vals
                num_outcomes = len(new_vals)
            elif 'probability' in line:
                line = line.replace(',', ' ')
                child_rv = line.split()[2]
                parent_rvs = line.split()[4:-2]

                if len(parent_rvs) == 0: # prior
                    new_line = f.readline().replace(';', ' ').replace(',',' ').split()
                    prob_values = new_line[1:]
                    _cpt[child_rv].append(map(float,prob_values))
                    #_cpt[child_rv] = map(float,prob_values)
                else: # not a prior
                    _parents[child_rv].extend(list(parent_rvs))
                    while True:
                        new_line = f.readline()
                        if '}' in new_line:
                            break
                        new_line = new_line.replace(',',' ').replace(';',' ').replace('(', ' ').replace(')', ' ').split()
                        prob_values = new_line[-(len(_vals[new_vertex])):]
                        prob_values = map(float,prob_values)
                        _cpt[child_rv].append(prob_values)
            if line == '':
                break

    # CREATE FACTORS
    _F = {}
    _E = {}
    for rv in _vals.keys():
        _E[rv] = [c for c in _vals.keys() if rv in _parents[c]]
        f = {
            'parents' : _parents[rv],
            'values' : _vals[rv],
            'cpt' : [item for sublist in _cpt[rv] for item in sublist]
        }
        _F[rv] = f

    bn = BayesNet()
    bn.F = _F
    bn.E = _E
    bn.V = list(topsort(_E))

    return bn

def read_json(path):
    """
    Read a BayesNet object from the json format. This
    format has the ".bn" extension and is completely
    unique to pyBN.

    Arguments
    ---------
    *path* : a string
        The file path

    Returns
    -------
    None

    Effects
    -------
    - Instantiates and sets a new BayesNet object

    Notes
    -----
    
    This function reads in a libpgm-style format into a bn object

    File Format:
        {
            "V": ["Letter", "Grade", "Intelligence", "SAT", "Difficulty"],
            "E": [["Intelligence", "Grade"],
                ["Difficulty", "Grade"],
                ["Intelligence", "SAT"],
                ["Grade", "Letter"]],
            "Vdata": {
                "Letter": {
                    "ord": 4,
                    "numoutcomes": 2,
                    "vals": ["weak", "strong"],
                    "parents": ["Grade"],
                    "children": None,
                    "cprob": [[.1, .9],[.4, .6],[.99, .01]]
                },
                ...
        }


    """
    def byteify(input):
        if isinstance(input, dict):
            return {byteify(key):byteify(value) for key,value in input.iteritems()}
        elif isinstance(input, list):
            return [byteify(element) for element in input]
        elif isinstance(input, unicode):
            return input.encode('utf-8')
        else:
            return input

    bn = BayesNet()
    
    f = open(path,'r')
    ftxt = f.read()

    success=False
    try:
        data = byteify(json.loads(ftxt))
        bn.V = data['V']
        bn.E = data['E']
        bn.F = data['F']
        success = True
    except ValueError:
        print("Could not read file - check format")
    bn.V = topsort(bn.E)

    return bn

def read_mat(path, delim=' '):
    """
    Read an adjacency matrix into a BayesNet object.

    NOTE: This is for reading the structure only, and
    therefore no parameters for the BayesNet object will
    be set - they must be learned by calling "mle_estimator"
    or "bayes_estimator" on the object.
    """
    _V = []
    _E = {}
    _F = {}
    with open(path, 'r') as f:
        for line in f:
            line = line.split(delim)
            rv = line[0]
            _E[rv] = []

    bn = BayesNet(_E)

    return bn



















================================================
FILE: pyBN/io/write.py
================================================
"""
**************
Write BayesNet
to File
**************

Write a BayesNet object to a file. These
functions all currently work, but support
for more formats should be added in the future.
"""


__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu"""



import json
from collections import OrderedDict

def write_bn(bn, path):
    """
    Wrapper function for writing a BayesNet
    object to file

    Arguments
    ---------
    *bn* : a BayesNet object

    *path* : a string
        The path, absolute or relative. MUST contain
        the extension: '.bn' only support right now

    Returns
    -------
    None

    Effects
    -------
    - Creates a new file on the user's local system

    Notes
    -----
    - Should add support for '.bif' and others

    """
    if '.bn' in path:
        write_json(bn, path)
    else:
        print("File Extension not supported")

def write_json(bn, path):
    """
    Write a BayesNet object to a json format file

    Arguments:
        1. *filename* - the path/name of the file to which the function will write the BKB object.

    Overview
    --------


    Parameters
    ----------


    Returns
    -------


    Notes
    -----

    
    """
    bn_dict = OrderedDict([('V',bn.V),('E',bn.E),('F',bn.F)])
    with open(path, 'w') as outfile:
        json.dump(bn_dict, outfile,indent=2)










================================================
FILE: pyBN/learning/__init__.py
================================================
from pyBN.learning.parameter import *
from pyBN.learning.structure import *

================================================
FILE: pyBN/learning/parameter/__init__.py
================================================
from pyBN.learning.parameter.bayes import *
from pyBN.learning.parameter.mle import *

================================================
FILE: pyBN/learning/parameter/_tests/__init__.py
================================================


================================================
FILE: pyBN/learning/parameter/bayes.py
================================================
"""
*******************
Bayesian Estimation
Parameter Learning
*******************

"""
from __future__ import division

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np


def bayes_estimator(bn, data, equiv_sample=None, prior_dict=None, nodes=None):
	"""
	Bayesian Estimation method of parameter learning.
	This method proceeds by either 1) assuming a uniform prior
	over the parameters based on the Dirichlet distribution
	with an equivalent sample size = *sample_size*, or
	2) assuming a prior as specified by the user with the 
	*prior_dict* argument. The prior distribution is then
	updated from observations in the data based on the
	Multinomial distribution - for which the Dirichlet
	is a "conjugate prior."

	Note that the Bayesian and MLE estimators essentially converge
	to the same set of values as the size of the dataset increases.

	Also note that, unlike the structure learning algorithms, the
	parameter learning functions REQUIRE a passed-in BayesNet object
	because there MUST be some pre-determined structure for which
	we can actually learn the parameters. You can't learn parameters
	without structure - so structure must always be there first!

	Finally, note that this function can be used to calculate only
	ONE conditional probability table in a BayesNet object by
	passing in a subset of random variables with the "nodes"
	argument - this is mostly used for score-based structure learning,
	where a single cpt needs to be quickly recalculate after the
	addition/deletion/reversal of an arc.

	Arguments
	---------
	*bn* : a BayesNet object

	*data* : a nested numpy array
		Data from which to learn parameters

	*equiv_sample* : an integer
		The "equivalent sample size" (see function summary)

	*prior_dict* : a dictionary, where key = random variable
		and for each key the value is another dictionary where
		key = an instantiation for the random variable and the
		value is its FREQUENCY (an integer value, NOT its relative
		proportion/probability).
	
	*nodes* : a list of strings
		Which nodes to learn the parameters for - if None,
		all nodes will be used as expected.
	
	Returns
	-------
	None

	Effects
	-------
	- modifies/sets bn.data to the learned parameters

	Notes
	-----

	"""
	if equiv_sample is None:
		equiv_sample = len(data)

	if nodes is None:
		nodes = list(bn.nodes())

	for i, n in enumerate(nodes):
		bn.F[n]['values'] = list(np.unique(data[:,i]))

	obs_dict = dict([(rv,[]) for rv in nodes])
	# set empty conditional probability table for each RV
	for rv in nodes:
		# get number of values in the CPT = product of scope vars' cardinalities
		p_idx = int(np.prod([bn.card(p) for p in bn.parents(rv)])*bn.card(rv))
		bn.F[rv]['cpt'] = [equiv_sample/p_idx]*p_idx
	
	# loop through each row of data
	for row in data:
		# store the observation of each variable in the row
		obs_dict = dict([(rv,row[rv]) for rv in nodes])
		# loop through each RV and increment its observed parent-self value
		for rv in nodes:
			rv_dict= { n: obs_dict[n] for n in obs_dict if n in bn.scope(rv) }
			offset = bn.cpt_indices(target=rv,val_dict=rv_dict)[0]
			bn.F[rv]['cpt'][offset]+=1

	
	for rv in nodes:
		cpt = bn.cpt(rv)
		for i in range(0,len(bn.cpt(rv)),bn.card(rv)):
			temp_sum = float(np.sum(cpt[i:(i+bn.card(rv))]))
			for j in range(bn.card(rv)):
				cpt[i+j] /= (temp_sum)
				cpt[i+j] = round(cpt[i+j],5)









================================================
FILE: pyBN/learning/parameter/mle.py
================================================
"""
*****************************
Maximum Likelihood Estimation
Parameter Learning
*****************************

"""
from __future__ import division

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np

def mle_fast(bn, data, nodes=None, counts=False, np=False):
	"""
	Maximum Likelihood estimation that is about 100 times as
	fast as the original mle_estimator function - but returns
	the same result
	"""
	def merge_cols(data, cols):
		if len(cols) == 1:
			return data[cols[0]]
		elif len(cols) > 1:
			data = data[cols].astype('str')
			ncols = len(cols)
			for i in range(len(data)):
				data.ix[i,0] = ''.join(data.ix[i,0:ncols])
			data = data.astype('int')
			return data.ix[:,0]

	if nodes is None:
		nodes = list(bn.nodes())
	else:
		if not isinstance(nodes, list):
			nodes = list(nodes)

	F = dict([(rv, {}) for rv in nodes])
	for i, n in enumerate(nodes):
		F[n]['values'] = list(np.unique(data.ix[:,i]))
		bn.F[n]['values'] = list(np.unique(data.ix[:,i]))

	for rv in nodes:
		parents = bn.parents(rv)
		if len(parents)==0:
			if np:
				F[rv]['cpt'] = np.histogram(data.ix[:,rv], bins=bn.card(rv))[0]
			else:
				F[rv]['cpt'] = list(np.histogram(data.ix[:,rv], bins=bn.card(rv))[0])
		else:
			if np:
				F[rv]['cpt'] = np.histogram2d(merge_cols(data,parents),data.ix[:,rv], 
				bins=[np.prod([bn.card(p) for p in parents]),bn.card(rv)])[0].flatten()
			else:
				F[rv]['cpt'] = list(np.histogram2d(merge_cols(data,parents),data.ix[:,rv], 
					bins=[np.prod([bn.card(p) for p in parents]),bn.card(rv)])[0].flatten())
	if counts:
		return F
	else:
		for rv in nodes:
			F[rv]['parents'] = [var for var in nodes if rv in bn.E[var]]
			for i in range(0,len(F[rv]['cpt']),bn.card(rv)):
				temp_sum = float(np.sum(F[rv]['cpt'][i:(i+bn.card(rv))]))
				for j in range(bn.card(rv)):
					F[rv]['cpt'][i+j] /= (temp_sum+1e-7)
					F[rv]['cpt'][i+j] = round(F[rv]['cpt'][i+j],5)
		bn.F = F




def mle_estimator(bn, data, nodes=None, counts=False):
	"""
	Maximum Likelihood Estimation is a frequentist
	method for parameter learning, where there is NO
	prior distribution. Instead, the frequencies/counts
	for each parameter start at 0 and are simply incremented
	as the relevant parent-child values are observed in the
	data. 

	This can be a risky method for small datasets, because if a 
	certain parent-child instantiation is never observed in the
	data, then its probability parameter will be ZERO (even if you
	know it should at least have a very small probability). 

	Note that the Bayesian and MLE estimators essentially converge
	to the same set of values as the size of the dataset increases.

	Also note that, unlike the structure learning algorithms, the
	parameter learning functions REQUIRE a passed-in BayesNet object
	because there MUST be some pre-determined structure for which
	we can actually learn the parameters. You can't learn parameters
	without structure - so structure must always be there first!

	Finally, note that this function can be used to calculate only
	ONE conditional probability table in a BayesNet object by
	passing in a subset of random variables with the "nodes"
	argument - this is mostly used for score-based structure learning,
	where a single cpt needs to be quickly recalculate after the
	addition/deletion/reversal of an arc.

	Arguments
	---------
	*bn* : a BayesNet object
		The associated network structure for which
		the parameters will be learned

	*data* : a nested numpy array

	*nodes* : a list of strings
		Which nodes to learn the parameters for - if None,
		all nodes will be used as expected.

	Returns
	-------
	None

	Effects
	-------
	- modifies/sets bn.data to the learned parameters

	Notes
	-----
	- Currently doesn't return correct solution

	data attributes:
		"numoutcomes" : an integer
	    "vals" : a list
	    "parents" : a list or None
	    "children": a list or None
	    "cprob" : a nested python list

	- Do not want to alter bn.data directly!

	"""
	if nodes is None:
		nodes = list(bn.nodes())
	else:
		if not isinstance(nodes, list):
			nodes = list(nodes)

	F = dict([(rv, {}) for rv in nodes])
	for i, n in enumerate(nodes):
		F[n]['values'] = list(np.unique(data[:,i]))
		bn.F[n]['values'] = list(np.unique(data[:,i]))

	obs_dict = dict([(rv,[]) for rv in nodes])
	# set empty conditional probability table for each RV
	for rv in nodes:
		# get number of values in the CPT = product of scope vars' cardinalities
		p_idx = int(np.prod([bn.card(p) for p in bn.parents(rv)])*bn.card(rv))
		F[rv]['cpt'] = [0]*p_idx
		bn.F[rv]['cpt'] = [0]*p_idx
	
	# loop through each row of data
	for row in data:
		# store the observation of each variable in the row
		for rv in nodes:
			obs_dict[rv] = row[rv]
		
		#obs_dict = dict([(rv,row[rv]) for rv in nodes])
		# loop through each RV and increment its observed parent-self value
		for rv in nodes:
			rv_dict= { n: obs_dict[n] for n in obs_dict if n in bn.scope(rv) }
			offset = bn.cpt_indices(target=rv,val_dict=rv_dict)[0]
			F[rv]['cpt'][offset]+=1

	if counts:
		return F
	else:
		for rv in nodes:
			F[rv]['parents'] = [var for var in nodes if rv in bn.E[var]]
			for i in range(0,len(F[rv]['cpt']),bn.card(rv)):
				temp_sum = float(np.sum(F[rv]['cpt'][i:(i+bn.card(rv))]))
				for j in range(bn.card(rv)):
					F[rv]['cpt'][i+j] /= (temp_sum+1e-7)
					F[rv]['cpt'][i+j] = round(F[rv]['cpt'][i+j],5)
		bn.F = F







================================================
FILE: pyBN/learning/structure/__init__.py
================================================
from pyBN.learning.structure.constraint import *
from pyBN.learning.structure.exact import *
from pyBN.learning.structure.hybrid import *
from pyBN.learning.structure.naive import *
from pyBN.learning.structure.score import *
from pyBN.learning.structure.tree import *

from pyBN.learning.structure.mdbn import *

================================================
FILE: pyBN/learning/structure/_tests/__init__.py
================================================


================================================
FILE: pyBN/learning/structure/_tests/test_chow_liu.py
================================================
"""
********
UnitTest
ChowLiu
********

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.structure_learn.chow_liu import chow_liu


class ChowLiuTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		path = (os.path.join(self.dpath,'lizards.csv'))
		self.data = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')

	def tearDown(self):
		pass

	def test_chow_liu1_V(self):
		bn = chow_liu(self.data)
		self.assertListEqual(bn.V,
			[0,1,2])

	def test_chow_liu1_E(self):
		bn = chow_liu(self.data)
		self.assertDictEqual(bn.E,
			{0:[1,2],1:[],2:[]})
	
	def test_chow_liu1_F(self):
		bn = chow_liu(self.data)
		self.assertDictEqual(bn.F,
			{0: {'cpt': [], 'parents': [], 'values': [1,2]},
			 1: {'cpt': [], 'parents': [0], 'values': [1,2]},
			 2: {'cpt': [], 'parents': [0], 'values': [1,2]}})

================================================
FILE: pyBN/learning/structure/_tests/test_grow_shrink.py
================================================
"""
********
UnitTest
GrowShrink
********

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.structure_learn.grow_shrink import gs



class GrowShrinkTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		path = (os.path.join(self.dpath,'lizards.csv'))
		self.data = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')

	def tearDown(self):
		pass

	def test_gs1_V(self):
		bn = gs(self.data)
		self.assertListEqual(bn.V,
			[0,1,2])

	def test_gs1_E(self):
		bn = gs(self.data)
		self.assertDictEqual(bn.E,
			{0:[1,2],1:[],2:[]})
	
	def test_gs1_F(self):
		bn = gs(self.data)
		self.assertDictEqual(bn.F,
			{0: {'cpt': [], 'parents': [], 'values': [1,2]},
			 1: {'cpt': [], 'parents': [0], 'values': [1,2]},
			 2: {'cpt': [], 'parents': [0], 'values': [1,2]}})

	def test_gs_data(self):
		d = np.loadtxt(os.path.join(self.dpath,'gs_data.txt'),dtype='int32')
		bn = gs(d)
		self.assertDictEqual(bn.E,
			{0: [], 1: [], 2: [0, 1, 4, 3], 3: [], 4: []})







================================================
FILE: pyBN/learning/structure/_tests/test_orient_edges.py
================================================
"""
********
UnitTest
OrientEdges
********

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.structure_learn.orient_edges import orient_edges_gs, orient_edges_pc


class OrientEdgesTestCase(unittest.TestCase):

	def setUp(self):
		pass
	def tearDown(self):
		pass

	def test_orient_edges_pc(self):
		e = {0:[1,2],1:[0],2:[0]}
		b = {0: [], 1: {2: (0,)}, 2: {1: (0,)}}
		self.assertDictEqual(orient_edges_pc(e,b),
			{0: [1, 2], 1: [], 2: []})

	def test_orient_edges_gs(self):
		e = {0: [1, 2], 1: [0], 2: [0]}
		b = {0: [1, 2], 1: [0], 2: [0]}
		dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		path = (os.path.join(dpath,'lizards.csv'))
		data = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')
		self.assertDictEqual(orient_edges_gs(e,b,data,0.05),
			{0: [1, 2], 1: [], 2: []})

================================================
FILE: pyBN/learning/structure/_tests/test_pc.py
================================================
"""
********
UnitTest
PC
********

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.structure_learn.path_condition import pc


class PCTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		path = (os.path.join(self.dpath,'lizards.csv'))
		self.data = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')
	
	def tearDown(self):
		pass

	def test_pc1_V(self):
		bn = pc(self.data)
		self.assertListEqual(bn.V,
			[0,1,2])

	def test_pc1_E(self):
		bn = pc(self.data)
		self.assertDictEqual(bn.E,
			{0:[1,2],1:[],2:[]})
	
	def test_pc1_F(self):
		bn = pc(self.data)
		self.assertDictEqual(bn.F,
			{0: {'cpt': [], 'parents': [], 'values': [1,2]},
			 1: {'cpt': [], 'parents': [0], 'values': [1,2]},
			 2: {'cpt': [], 'parents': [0], 'values': [1,2]}})


================================================
FILE: pyBN/learning/structure/constraint/__init__.py
================================================
from pyBN.learning.structure.constraint.fast_iamb import *
from pyBN.learning.structure.constraint.grow_shrink import *
from pyBN.learning.structure.constraint.iamb import *
from pyBN.learning.structure.constraint.lambda_iamb import *
from pyBN.learning.structure.constraint.path_condition import *

================================================
FILE: pyBN/learning/structure/constraint/fast_iamb.py
================================================
"""
*********
Fast-IAMB
Algorithm
*********


For Feature Selection (from [1]):
	"A principled solution to the feature selection problem is
	to determine a subset of attributes that can "shield" (render
	independent) the attribute of interest from the effect of
	the remaining attributes in the domain. Koller and Sahami
	[4] first showed that the Markov blanket of a given target attribute
	is the theoretically optimal set of attributes to predict
	its value...
	
	Because the Markov blanket of a target attribute T renders
	it statistically independent from all the remaining attributes
	(see the Markov blanket definition below), all information
	that may influence its value is stored in the values
	of the attributes of its Markov blanket. Any attribute
	from the feature set outside its Markov blanket can be effectively
	ignored from the feature set without adversely affecting
	the performance of any classifier that predicts the
	value of T"

References
----------
[1] Yaramakala and Maragritis, "Speculative Markov Blanket 
Discovery for Optimal Feature Selection"

[2] Tsarmardinos, et al. "Algorithms for Large Scale 
Markov Blanket Discovery" 

"""
from __future__ import division

import numpy as np
from pyBN.utils.independence_tests import are_independent, mi_test
from pyBN.utils.data import unique_bins, replace_strings

def fast_iamb(data, k=5, alpha=0.05, feature_selection=None, debug=False):
	"""
	From [1]:
		"A novel algorithm for the induction of
		Markov blankets from data, called Fast-IAMB, that employs
		a heuristic to quickly recover the Markov blanket. Empirical
		results show that Fast-IAMB performs in many cases
		faster and more reliably than existing algorithms without
		adversely affecting the accuracy of the recovered Markov
		blankets."

	Arguments
	---------
	*data* : a nested numpy array

	*k* : an integer
		The max number of edges to add at each iteration of 
		the algorithm.

	*alpha* : a float
		Probability of Type I error

	Returns
	-------
	*bn* : a BayesNet object

	Effects
	-------
	None

	Notes
	-----
	- Currently does not work. I think it's stuck in an infinite loop...

	"""
	# get values
	value_dict = dict(zip(range(data.shape[1]),
			[list(np.unique(col)) for col in data.T]))
	# replace strings
	data = replace_strings(data)

	n_rv = data.shape[1]
	Mb = dict([(rv,[]) for rv in range(n_rv)])
	N = data.shape[0]
	card = dict(zip(range(n_rv),unique_bins(data)))
	#card = dict(zip(range(data.shape[1]),np.amax(data,axis=0)))

	if feature_selection is None:
		_T = range(n_rv)
	else:
		assert (not isinstance(feature_selection, list)), 'feature_selection must be only one value'
		_T = [feature_selection]
	# LEARN MARKOV BLANKET
	for T in _T:
		S = set(range(n_rv)) - {T}
		for A in S:
			if not are_independent(data[:,(A,T)]):
				S.remove(A)
		s_h_dict = dict([(s,0) for s in S])
		while S:
			insufficient_data = False
			break_grow_phase = False
			
			#### GROW PHASE ####
			# Calculate mutual information for all variables
			mi_dict = dict([(s,mi_test(data[:,(s,T)+tuple(Mb[T])])) for s in S])
			for x_i in sorted(mi_dict, key=mi_dict.get,reverse=True):
				# Add top MI-score variables until there isn't enough data for bins
				if (N / card[x_i]*card[T]*np.prod([card[b] for b in Mb[T]])) >= k:
					Mb[T].append(x_i)
				else:
					insufficient_data = True
					break

			#### SHRINK PHASE ####
			removed_vars = False
			for A in Mb[T]:
				cols = (A,T) + tuple(set(Mb[T]) - {A})
				# if A is independent of T given Mb[T], remove A
				if are_independent(data[:,cols]):
					Mb[T].remove(A)
					removed_vars=True

			#### FINALIZE BLANKET FOR "T" OR MAKE ANOTHER PASS ####
			if insufficient_data and not removed_vars:
				if debug:
					print('Breaking..')
				break
			else:
				A = set(range(n_rv)) - {T} - set(Mb[T])
				#A = set(nodes) - {T} - set(Mb[T])
				S = set()
				for a in A:
					cols = (a,T) + tuple(Mb[T])
					if are_independent(data[:,cols]):
						S.add(a)
		if debug:
			print('Done with %s' % T)
	
	if feature_selection is None:
		# RESOLVE GRAPH STRUCTURE
		edge_dict = resolve_markov_blanket(Mb, data)

		# ORIENT EDGES
		oriented_edge_dict = orient_edges_MB(edge_dict,Mb,data,alpha)

		# CREATE BAYESNET OBJECT
		bn=BayesNet(oriented_edge_dict,value_dict)

		return BN
	else:
		return Mb[_T]





























================================================
FILE: pyBN/learning/structure/constraint/grow_shrink.py
================================================
"""
********************
GrowShrink Algorithm
********************

"Our approach constructs Bayesian networks by first identifying each node's
Markov blankets, then connecting nodes in a maximally consistent way. 
In contrast to the majority of work, which typically uses hill-climbing approaches 
that may produce dense and causally incorrect nets, our approach yields much more 
compact causal networks by heeding independenciesin the data. Compact causal networks
facilitate fast inference and are also easier to understand. We prove that under mild 
assumptions, our approach requires time polynomial in the size of the data and the number 
of nodes. A randomized variant, also presented here, yields comparable results at
much higher speeds" [1].

This algorithm relies on the "Markov Blanket", which is for a given random variable
the set of other random variables which render the given RV conditionally independent
from the rest of the network. That is, if you observe any of the variables in a given
RV's markov blanket, then observing the values of any OTHER variables in the network
will not change your beliefs about the given random variable.

The Markov blanket of a node X is easily identifiable from the graph: 
it consists of X's parents, X's children, and the parents of all of X's children.

The runtime of this algorithm is O(|V|) [1].

References
----------
[1] Margaritis and Thrun: "Bayesian Network Induction via Local Neighborhoods", 
NIPS 2000.

[2] https://www.cs.cmu.edu/~dmarg/Papers/PhD-Thesis-Margaritis.pdf

"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.utils.independence_tests import mi_test
from pyBN.utils.orient_edges import orient_edges_MB
from pyBN.utils.markov_blanket import resolve_markov_blanket
from pyBN.classes.bayesnet import BayesNet
from pyBN.utils.data import replace_strings

from copy import copy
import numpy as np
import itertools

def gs(data, alpha=0.05, feature_selection=None, debug=False):
	"""
	Perform growshink algorithm over dataset to learn
	Bayesian network structure.

	This algorithm is clearly a good candidate for
	numba JIT compilation...

	STEPS
	-----
	1. Compute Markov Blanket
	2. Compute Graph Structure
	3. Orient Edges
	4. Remove Cycles
	5. Reverse Edges
	6. Propagate Directions

	Arguments
	---------
	*data* : a nested numpy array
		Data from which you wish to learn structure

	*alpha* : a float
		Type I error rate for independence test

	Returns
	-------
	*bn* : a BayesNet object

	Effects
	-------
	None

	Notes
	-----

	Speed Test:
		*** 5 variables, 624 observations ***
		- 63.7 ms

	"""
	n_rv = data.shape[1]
	data, value_dict = replace_strings(data, return_values=True)
	

	if feature_selection is None:
		_T = range(n_rv)
	else:
		assert (not isinstance(feature_selection, list)), 'feature_selection must be only one value'
		_T = [feature_selection]

	# STEP 1 : COMPUTE MARKOV BLANKETS
	Mb = dict([(rv,[]) for rv in range(n_rv)])

	for X in _T:
		S = []

		grow_condition = True
		while grow_condition:

			grow_condition=False
			for Y in range(n_rv):
				if X!=Y and Y not in S:
					# if there exists some Y such that Y is dependent on X given S,
					# add Y to S
					cols = (X,Y) + tuple(S)
					pval = mi_test(data[:,cols])
					if pval < alpha: # dependent
						grow_condition=True # dependent -> continue searching
						S.append(Y)
		
		shrink_condition = True
		while shrink_condition:

			TEMP_S = []
			shrink_condition=False
			for Y in S:
				s_copy = copy(S)
				s_copy.remove(Y) # condition on S-{Y}
				# if X independent of Y given S-{Y}, leave Y out
				# if X dependent of Y given S-{Y}, keep it in
				cols = (X,Y) + tuple(s_copy)
				pval = mi_test(data[:,cols])
				if pval < alpha: # dependent
					TEMP_S.append(Y)
				else: # independent -> condition searching
					shrink_condition=True
		
		Mb[X] = TEMP_S
		if debug:
			print('Markov Blanket for %s : %s' % (X, str(TEMP_S)))
	
	if feature_selection is None:
		# STEP 2: COMPUTE GRAPH STRUCTURE
		# i.e. Resolve Markov Blanket
		edge_dict = resolve_markov_blanket(Mb,data)
		if debug:
			print('Unoriented edge dict:\n %s' % str(edge_dict))
		
		# STEP 3: ORIENT EDGES
		oriented_edge_dict = orient_edges_MB(edge_dict,Mb,data,alpha)
		if debug:
			print('Oriented edge dict:\n %s' % str(oriented_edge_dict))
		

		# CREATE BAYESNET OBJECT
		bn=BayesNet(oriented_edge_dict,value_dict)
		
		return bn
	else:
		return Mb[_T]
















================================================
FILE: pyBN/learning/structure/constraint/iamb.py
================================================
"""
******************
IAMB STRUCTURE
LEARNING ALGORITHM
******************

The IAMB algorithm - Incremental Association Markov Blanket - proceeds
similar to the Grow-Shrink algorithm, where the growing phase adds
random variables to a given node's markov blanket, and the shrinking
phase performs a second pass of conditional independence tests to
eliminate any variables from the markov blanket for which the given
node is conditionally independent given any other node in the markov
blanket (hence doesn't belong in the markov blanket).

It significantly reduces the complexity compared to PC/GS, and is
normally used in conjuction with the mutual information test for
independence (I do so here) [2].

Lastly, it can also be used as a feature selection algorithm for
classification or general dimensionality reduction - simply return
the markov blanket of a given node, which has been shown by Daphne
Koller to be theoretically optimal for prediction [3]. 

IAMB for Feature Selection (from [1]):	
	Because the Markov blanket of a target attribute T renders
	it statistically independent from all the remaining attributes
	(see the Markov blanket definition below), all information
	that may influence its value is stored in the values
	of the attributes of its Markov blanket. Any attribute
	from the feature set outside its Markov blanket can be effectively
	ignored from the feature set without adversely affecting
	the performance of any classifier that predicts the
	value of T

References
----------
[1] Yaramakala and Maragritis, "Speculative Markov Blanket 
Discovery for Optimal Feature Selection"

[2] Tsarmardinos, et al. "Algorithms for Large Scale 
Markov Blanket Discovery" 

[3] Koller "Toward Optimal Feature Selection."
"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np
from pyBN.utils.independence_tests import are_independent, mi_test
from pyBN.utils.orient_edges import orient_edges_MB, orient_edges_gs2
from pyBN.utils.markov_blanket import resolve_markov_blanket
from pyBN.classes.bayesnet import BayesNet
from copy import copy

def iamb(data, alpha=0.05, feature_selection=None, debug=False):
	"""
	IAMB Algorithm for learning the structure of a
	Discrete Bayesian Network from data.

	Arguments
	---------
	*data* : a nested numpy array

	*alpha* : a float
		The type II error rate.

	*feature_selection* : None or a string
		Whether to use IAMB as a structure learning
		or feature selection algorithm.

	Returns
	-------
	*bn* : a BayesNet object or
	*mb* : the markov blanket of a node

	Effects
	-------
	None

	Notes
	-----
	- Works but there are definitely some bugs.

	Speed Test:
		*** 5 vars, 624 obs ***
			- 196 ms
	"""
	n_rv = data.shape[1]
	Mb = dict([(rv,[]) for rv in range(n_rv)])

	if feature_selection is None:
		_T = range(n_rv)
	else:
		assert (not isinstance(feature_selection, list)), 'feature_selection must be only one value'
		_T = [feature_selection]

	# LEARN MARKOV BLANKET
	for T in _T:

		V = set(range(n_rv)) - {T}
		Mb_change=True

		# GROWING PHASE
		while Mb_change:
			Mb_change = False
			# find X_max in V-Mb(T)-{T} that maximizes 
			# mutual information of X,T|Mb(T)
			# i.e. max of mi_test(data[:,(X,T,Mb(T))])
			max_val = -1
			max_x = None
			for X in V - set(Mb[T]) - {T}:
				cols = (X,T)+tuple(Mb[T])
				mi_val = mi_test(data[:,cols],test=False)
				if mi_val > max_val:
					max_val = mi_val
					max_x = X
			# if Xmax is dependent on T given Mb(T)
			cols = (max_x,T) + tuple(Mb[T])
			if max_x is not None and are_independent(data[:,cols]):
				Mb[T].append(X)
				Mb_change = True
				if debug:
					print('Adding %s to MB of %s' % (str(X), str(T)))

		# SHRINKING PHASE
		for X in Mb[T]:
			# if x is independent of t given Mb(T) - {x}
			cols = (X,T) + tuple(set(Mb[T]) - {X})
			if are_independent(data[:,cols],alpha):
				Mb[T].remove(X)
				if debug:
					print('Removing %s from MB of %s' % (str(X), str(T)))

	if feature_selection is None:
		# RESOLVE GRAPH STRUCTURE
		edge_dict = resolve_markov_blanket(Mb, data)
		if debug:
			print('Unoriented edge dict:\n %s' % str(edge_dict))
			print('MB: %s' % str(Mb))
		# ORIENT EDGES
		oriented_edge_dict = orient_edges_gs2(edge_dict,Mb,data,alpha)
		if debug:
			print('Oriented edge dict:\n %s' % str(oriented_edge_dict))

		# CREATE BAYESNET OBJECT
		value_dict = dict(zip(range(data.shape[1]),
			[list(np.unique(col)) for col in data.T]))
		bn=BayesNet(oriented_edge_dict,value_dict)

		return bn
	else:
		return Mb[_T]


























================================================
FILE: pyBN/learning/structure/constraint/lambda_iamb.py
================================================
"""
Lambda IAMB Code

References
----------
[1] Zhang et al, "An Improved IAMB Algorithm for Markov
Blanket Discovery"
"""

import numpy as np
from pyBN.utils.independence_tests import are_independent, entropy

def lambda_iamb(data, L=1.5, alpha=0.05, feature_selection=None):
	"""
	Lambda IAMB Algorithm for learning the structure of a
	Discrete Bayesian Network from data. This Algorithm
	is similar to the iamb algorithm, except that it allows
	for a "lambda" coefficient that helps avoid false positives.

	This algorithm was originally developed for use as a
	feature selection algorithm - discovering the markov
	blanket of a target variable is equivalent to discovering
	the relevant features for classifications.

	In practice, this algorithm does just as well as a feature
	selection method compared to IAMB when naive bayes was 
	used as a classifier, but Lambda-iamb actually does much
	better than traditional iamb when traditional iamb does
	very poorly due to high false positive rates.

	Arguments
	---------
	*data* : a nested numpy array

	*L* : a float
		The lambda hyperparameter - see [1].

	*alpha* : a float
		The type II error rate.

	Returns
	-------
	*bn* : a BayesNet object

	Effects
	-------
	None

	Notes
	-----
	"""
	n_rv = data.shape[1]
	Mb = dict([(rv,{}) for rv in range(n_rv)])

	if feature_selection is None:
		_T = range(n_rv)
	else:
		assert (not isinstance(feature_selection, list)), 'feature_selection must be only one value'
		_T = [feature_selection]

	# LEARN MARKOV BLANKET
	for T in _T:

		V = set(range(n_rv)) - {T}
		Mb_change=True

		# GROWING PHASE
		while Mb_change:
			Mb_change = False
			cols = tuple({T}) + tuple(Mb[T])
			H_tmb = entropy(data[:,cols])
			# find X1_min in V-Mb[T]-{T} that minimizes
			# entropy of T|X1_inMb[T]
			# i.e. min of entropy(data[:,(T,X,Mb[T])])
			min_val1, min_val2 = 1e7,1e7
			min_x1, min_x2 = None, None
			for X in V - Mb[T] - {T}:
				cols = (T,X)+tuple(Mb[T])
				ent_val = entropy(data[:,cols])
				if ent_val < min_val:
					min_val2, min_val1 = min_val1, ent_val
					min_x2,min_x1 = min_x1, X
					
			# if min_x1 is dependent on T given Mb[T]...
			cols = (min_x1,T) + tuple(Mb[T])
			if are_independent(data[:,cols]):
				if (min_val2 - L*min_val1) < ((1-L)*H_tmb):
					cols = (min_x2,T)+tuple(Mb[T])
					if are_independent(data[:,cols]):
						Mb[T].add(min_x1)
						Mb[T].add(min_x2)
						Mb_change = True
			else:
				Mb[T].add(X)
				Mb_change = True

		# SHRINKING PHASE
		for X in Mb[T]:
			# if x is indepdent of t given Mb[T] - {x}
			cols = (X,T) + tuple(Mb[T]-{X})
			if mi_test(data[:,cols]) > alpha:
				Mb[T].remove(X)

	if feature_selection is None:
		# RESOLVE GRAPH STRUCTURE
		edge_dict = resolve_markov_blanket(Mb, data)

		# ORIENT EDGES
		oriented_edge_dict = orient_edges_Mb(edge_dict,Mb,data,alpha)

		# CREATE BAYESNET OBJECT
		value_dict = dict(zip(range(data.shape[1]),
			[list(np.unique(col)) for col in data.T]))
		bn=BayesNet(oriented_edge_dict,value_dict)

		return bn
	else:
		return Mb[_T]










================================================
FILE: pyBN/learning/structure/constraint/path_condition.py
================================================
"""
*************
PathCondition 
Algorithm
*************

Pseudo-Code
-----------
Start with a complete, undirected graph G'
i <- 0
Repeat:
	For each x \in X:
		For each y in Adj(x):

			Test whether there exists some S in Adj(X)-{Y}
			with |S| = i, such that I(X,Y|S)

			If there exists such a set S:
				Make S_xy <- S
				Remove X-Y link from G'
	i <- i + 1
Until |Adj(X)| <= i (forall x\inX)

for (each uncoupled meeting X-Z-Y)
	if (Z not in S_xy)
		orient X - Z - Y as X -> Z <- Y
while (more edges can be oriented)
	for (each uncoupled meeting X -> Z - Y)
		orient Z - Y as Z -> Y
	for (each X - Y such that there is a path from X to Y)
		orient X - Y as X -> Y
	for (each uncoupled meeting X - Z - Y such that
		X -> W, Y -> W, and Z - W)
		orient Z - W as Z -> W


References
----------
[1] Abellan, Gomez-Olmedo, Moral. "Some Variations on the
PC Algorithm." http://www.utia.cas.cz/files/mtr/pgm06/41_paper.pdf
[2] Spirtes, Glymour, Scheines (1993) "Causation, Prediction,
and Search."
"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import itertools
import numpy as np

from pyBN.utils.independence_tests import mi_test
from pyBN.classes import BayesNet
from pyBN.utils.orient_edges import orient_edges_CS


def pc(data, alpha=0.05):
	"""
	Path Condition algorithm for structure learning. This is a
	good test, but has some issues with test reliability when
	the size of the dataset is small. The Necessary Path
	Condition (NPC) algorithm can solve these problems.

	Arguments
	---------
	*bn* : a BayesNet object
		The object we wish to modify. This can be a competely
		empty BayesNet object, in which case the structure info
		will be set. This can be a BayesNet object with already
		initialized structure/params, in which case the structure
		will be overwritten and the parameters will be cleared.

	*data* : a nested numpy array
		The data from which we will learn -> will code for
		pandas dataframe after numpy works

	Returns
	-------
	*bn* : a BayesNet object
		The network created from the learning procedure, with
		the nodes/edges initialized/changed

	Effects
	-------
	None

	Notes
	-----

	Speed Test:
		** 5 vars, 624 obs ***
			- 90.9 ms
	"""
	n_rv = data.shape[1]
	##### FIND EDGES #####
	value_dict = dict(zip(range(n_rv),
		[list(np.unique(col)) for col in data.T]))
	
	edge_dict = dict([(i,[j for j in range(n_rv) if i!=j]) for i in range(n_rv)])
	block_dict = dict([(i,[]) for i in range(n_rv)])
	stop = False
	i = 1
	while not stop:
		for x in range(n_rv):
			for y in edge_dict[x]:
				if i == 0:
					pval_xy_z = mi_test(data[:,(x,y)])
					if pval_xy_z > alpha:
						if y in edge_dict[x]:
							edge_dict[x].remove(y)
							edge_dict[y].remove(x)
				else:
					for z in itertools.combinations(edge_dict[x],i):
						if y not in z:
							cols = (x,y) + z
							pval_xy_z = mi_test(data[:,cols])
							# if I(X,Y | Z) = TRUE
							if pval_xy_z > alpha:
								block_dict[x] = {y:z}
								block_dict[y] = {x:z}
								if y in edge_dict[x]:
									edge_dict[x].remove(y)
									edge_dict[y].remove(x)
							
		i += 1
		stop = True
		for x in range(n_rv):
			if (len(edge_dict[x]) > i-1):
				stop = False
				break
	
	# ORIENT EDGES (from collider set)
	directed_edge_dict = orient_edges_CS(edge_dict,block_dict)

	# CREATE BAYESNET OBJECT
	bn=BayesNet(directed_edge_dict,value_dict)
	
	return bn




================================================
FILE: pyBN/learning/structure/exact/__init__.py
================================================
from pyBN.learning.structure.exact.gobnilp import *

================================================
FILE: pyBN/learning/structure/exact/gobnilp.py
================================================
"""
GOBNILP Solver for Exact Bayesian network
Structure Learning with Integer Linear
Optimization.

This code relies on the "pyGOBN" package, which
implements a Python wrapper for the GOBNILP
project - which itself builds on the SCIP
Optimization engine for building Integer Linear
Programs to solve the BN structure learning problem
EXACTLY.

Th pyGOBN module - which must be installed from source - allows 
users to create/alter a vast number of global parameter settings 
(i.e. wall-time, parent limits, sparsity, equivalent 
sample size, etc), along with various network constraints 
(i.e. required edges, disallowed edges, and independence
requirements).

NOTE: You must download and install pyGOBN to use this functionality.
Visit github.com/ncullen93/pyGOBN to get the source code, then run
'python setup.py install' in the main pyGOBN directory to install the
module for your local python distribution.

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

def ilp(data, settings=None, edge_reqs=None, nonedge_reqs=None, ind_reqs=None):
	"""
	Learning the EXACT optimal Bayesian network structure from data using
	Integer Linear Programming from the GOBNILP project.
	"""
	try:
		from pyGOBN import GOBN
	except ImportError:
		print('You must download & install pyGOBN to use this functionality.\
		 Visit github.com/ncullen93/pyGOBN')

	gobn = GOBN()
	gobn.set_settings(settings)
	gobn.set_constraints(edge_reqs=edge_reqs, nonedge_reqs=nonedge_reqs, ind_reqs=ind_reqs)


================================================
FILE: pyBN/learning/structure/hybrid/__init__.py
================================================
from pyBN.learning.structure.hybrid.mmpc import *

================================================
FILE: pyBN/learning/structure/hybrid/mmhc.py
================================================
"""
Max-Min Hill Climbing Algorithm

References
----------
[1] Tsamardinos, et al. "The max-min hill-climbing Bayesian
network structure learning algorithm."

"""
from pyBN.structure_learn.hybrid.mmpc import mmpc

def mmhc(data, alpha=0.05, metric='AIC', max_iter=100, method='hc'):
	"""
	Max-Min Hill Climbing Algorithm for
	learning a Bayesian Network structure
	from data.

	Arguments
	---------
	*data* : a numpy ndarray

	*alpha* : a float
		Probability of Type II Error for
		independence tests.

	*metric* : a string
		*metric* : a string
		Which score metric to use.
		Options:
			- 'AIC'
			- 'BIC'
			- 'LL' (log-likelihood)

	*method* : a string
		The type of hill-climbing algorithm to run
		OPTIONS:
			- 'hc' : normal hill-climbing
			- 'rr' : hill-climbing with random restarts
			- 'tabu' : tabu hill-climbing

	Returns
	-------
	*bn* : a BayesNet object

	"""
	# GET EDGE RESTRICTIONS FROM MMPC
	PC_dict = mmpc(data)	
	restriction = []
	for y, pc in PC_dict.items():
		for x in pc:
			restriction.append((y,x))

	# RUN HILL-CLIMBING WITH EDGE RESTRICTIONS
	if method == 'tabu':
		bn = tabu(data=data, metric=metric, max_iter=max_iter, restriction=restriction)		
	elif method == 'rr':
		bn = hill_climbing_rr(data=data, metric=metric, max_iter=max_iter, restriction=restriction)
	else:
		bn = hill_climbing(data=data, metric=metric, max_iter=max_iter, restriction=restriction)

	return bn
























================================================
FILE: pyBN/learning/structure/hybrid/mmpc.py
================================================
"""
Max-Min Parents and Children Algorithm
for determining the Parent-Children set
for all nodes. This returns an unoriented
graph that then must be oriented using
some set of rules or other orientation
algorithm.

Using a version of hill climbing to orient
the result of MMPC results in the MMHC -
Max-Min Hill Climbing algorithm. In other
words, MMPC can be viewed as a subroutine
of MMHC.

References
----------
[1] Tsamardinos, et al. "The max-min hill-climbing Bayesian
network structure learning algorithm."

"""


def mmpc(data, alpha=0.05):
	"""
	Max-Min Parents and Children Algorithm
	for determining the Parent-Children set
	for all nodes. This returns an unoriented
	graph that then must be oriented using
	some set of rules or other orientation
	algorithm.

	Arguments
	---------
	*data* : a numpy ndarray

	*alpha* : a float
		Probability of Type II Error for
		independence tests

	Returns
	-------
	*CPC_dict* : a dictionary, where
		key = rv, value = list of rv's children AND parents

	Notes
	-----
	"""

	nrow = data.shape[0]
	ncol = data.shape[1]
	
	nodes = range(ncol)

	CPC_dict = dict([(n,[]) for n in nodes]) # rv -> children of rvs
	value_dict = dict([(n, np.unique(data[:,i])) for i,n in enumerate(nodes)])

	# LEARN PARENT-CHILD SET FOR EACH NODE
	for T in nodes:
		
		# GROW PHASE
		CPC = []
		changed=True
		while changed:
			changed=False
			# MAX-MIN HEURISTIC
			min_assoc = 1e9
			min_node = None
			for x in nodes:
				if x!=T:
					cols = (x,T) + tuple(CPC)
					pval = mi_test(data[:,cols], test=True)
				if pval < min_assoc:
					min_assoc = pval
					min_node = x
			# only add node if it's small but still dependent
			if min_assoc > alpha:
				CPC.append(min_node)
				changed=True

		# SHRINK PHASE
		for X in CPC:
			cpc = [c for c in CPC if X!=c]
			for i in len(cpc):
				for S in itertools.combinations(cpc,i):
					cols = (T,X) + S
					pval = mi_test(data[:,cols])
					# if I(X,T | S) = TRUE
					if pval_xy_z > alpha:
						CPC.remove(X)

		# ADD CPC TO CPC_dict
		CPC_dict[T] = CPC

	# REMOVE FALSE POSITIVES
	for T in nodes:
		for X in CPC_dict[T]:
			# If X is in CPC[T] but T is not in CPC[X], remove X.
			if T not in CPC_dict[X]:
				CPC_dict[T].remove(X)

	return CPC_dict

























================================================
FILE: pyBN/learning/structure/mdbn.py
================================================
"""
Learn a Multi-Dimensional Bayesian Network Classifier
from data, and use it to predict a vector of class
labels from a vector of feature values.

An 'MBC' consists of two subgraphs:
	- G_c : the class subgraph,
		which includes edges only between class variables
	- G_x : the feature subgraph,
		which includes edges only between feature variables

Additionally, there is a collection of "bridge" edges:	
	- E_cx : the class-feature bridge,
		which includes the "bridge" edges between class and
		feature variables.

In an MBC, both the class sugraph and the feature subgraph can
have their own form of BN structure - e.g. empty, directed tree,
forest of trees, polytrees, or general DAGs. These 5 types of
network structures over two subgraphs means that there are 25
types of MBC structures.

To learn MBC's from data, the algorithm typically proceeds by
first learning G_c and G_x seperately using whatever structure
learning algorithm the user chooses, and then looks to optimize
the selection of "bridge" edges through further optimization
procedures.

To actually perform classification using an MBC, the traditional
Most Probable Explanation (MAP inference) procedure is employed.
The result is a vector of class predictions, which can then be
compared with the ground truth class labels to measure accuracy,
and so on.


References
----------
[1] Bielza, C., Li, G., Larranaga, P. "Multi-dimensional
classification with Bayesian networks."
[2] de Waal, P., van der Gaag, L. "Inference and Learning in
Multi-dimensional Bayesian Network Classifiers."
[3] van der Gaag, L., de Waal, P. "Multi-dimensional Bayesian
Network Classifiers."

"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.learning.structure.score.hill_climbing import hc
from pyBN.learning.structure.score.random_restarts import hc_rr
from pyBN.learning.structure.score.tabu import tabu

from pyBN.classes.bayesnet import BayesNet


def bridge(c_bn, f_bn, data):
	"""
	Make a Multi-Dimensional Bayesian Network by
	bridging two Bayesian network structures. This happens by
	placing edges from c_bn -> f_bn using a heuristic 
	optimization procedure.

	This can be used to create a Multi-Dimensional Bayesian
	Network classifier from two already-learned Bayesian networks -
	one of which is a BN containing all the class variables, the other
	containing all the feature variables.

	Arguments
	---------
	*c_bn* : a BayesNet object with known structure

	*f_bn* : a BayesNet object with known structure.

	Returns
	-------
	*m_bn* : a merged/bridge BayesNet object,
		whose structure contains *c_bn*, *f_bn*, and some bridge
		edges between them.
	"""
	restrict = []
	for u in c_bn:
		for v in f_bn:
			restrict.append((u,v)) # only allow edges from c_bn -> f_bn

	bridge_bn = hc_rr(data, restriction=restrict)

	m_bn = bridge_bn.E
	m_bn.update(c_bn.E)
	m_bn.update(f_bn.E)

	mbc_bn = BayesNet(E=m_bn)

def mdbn(data, f_cols, c_cols, f_struct='DAG', c_struct='DAG', wrapper=False):
	"""
	Learn the structure of a Multi-Dimensional Bayesian Network - 
	typically used for Classification.

	Note that this structure does not have to be used for classification,
	since it simply returns a Bayesian Network - albeit with a more
	unqiue structure than tradiitonally found. If there are any other
	applications of this bipartite-like BN structure learning, this
	algorithm can certainly be used.

	"""
	f_data = data[:,f_cols]
	c_data = data[:,c_cols]

	f_bn = hc_rr(f_data)
	c_bn = hc_rr(c_data)

	mbc_bn = bridge(c_bn=c_bn, f_bn=f_bn, data=data)

	return mbc_bn








================================================
FILE: pyBN/learning/structure/naive/TAN.py
================================================
"""
**************
Tree Augmented 
Naive Bayes
**************

TAN is considered to be quite useful
as a Bayesian network classifier.

"""


def TAN(data, target):
	"""
	Learn a Tree-Augmented Naive Bayes structure
	from data.

	The algorithm from Friedman's paper
	proceeds as follows:

	- Learn a tree structure
		- I will use chow-liu algorithm
	- ADD a class label C to the graph, and an edge 
	from C to each node in the graph.
	"""
	reduced_data = data[:,:-target,:]
	tree, value_dict = chow_liu(reduce_data,edges_only=True)
	tree[target] = [tree.keys()]

	bn = BayesNet(tree, value_dict)
	return vn


================================================
FILE: pyBN/learning/structure/naive/__init__.py
================================================
from pyBN.learning.structure.naive.naive_bayes import *
from pyBN.learning.structure.naive.TAN import *

================================================
FILE: pyBN/learning/structure/naive/naive_bayes.py
================================================
"""
***********
Naive Bayes
***********

Learn both the structure AND parameters
of a Naive Bayes Model from data

"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np
from pyBN.classes.bayesnet import BayesNet
from pyBN.learning.parameter.mle import mle_estimator
from pyBN.learning.parameter.bayes import bayes_estimator

def naive_bayes(data, target, estimator='mle'):
	"""
	Learn naive bayes model from data.

	The Naive Bayes model is a Tree-based
	model where all random variables have
	the same parent (the "target" variable).
	From a probabilistic standpoint, the implication
	of this model is that all random variables 
	(i.e. features) are assumed to be
	conditionally independent of any other random variable,
	conditioned upon the single parent (target) variable.

	It turns out that this model performs quite well
	as a classifier, and can be used as such. Moreover,
	this model is quite fast and simple to learn/create
	from a computational standpoint.

	Note that this function not only learns the structure,
	but ALSO learns the parameters.

	Arguments
	---------
	*data* : a nested numpy array

	*target* : an integer
		The target variable column in *data*

	Returns
	-------
	*bn* : a BayesNet object,
		with the structure instantiated.

	Effects
	-------
	None

	Notes
	-----

	"""	
	value_dict = dict(zip(range(data.shape[1]),
		[list(np.unique(col)) for col in data.T]))

	edge_dict = {target:[v for v in value_dict if v!=target]}
	edge_dict.update(dict([(rv,[]) for rv in value_dict if rv!=target]))

	bn = BayesNet(edge_dict,value_dict)
	if estimator == 'bayes':
		bayes_estimator(bn,data)
	else:
		mle_estimator(bn,data)
	return bn



















================================================
FILE: pyBN/learning/structure/score/__init__.py
================================================
from pyBN.learning.structure.score.bayes_scores import *
from pyBN.learning.structure.score.hill_climbing import *
from pyBN.learning.structure.score.random_restarts import *
from pyBN.learning.structure.score.info_scores import *
from pyBN.learning.structure.score.tabu import *

================================================
FILE: pyBN/learning/structure/score/bayes_scores.py
================================================
"""
Various Bayesian scoring metrics for evaluating
the fitness of a BN structure during score-based
structure learning.

Bayesian scoring functions:
	BD (Bayesian Dirichlet) (1995)
	BDe ("'e'" for likelihood-equivalence) (1995)
	BDeu ("'u'" for uniform joint distribution) (1991)
	K2 (1992)

References
----------
[1] Daly, et al. Learning Bayesian Network Equivalence Classes 
with Ant Colony Optimization.

"""
from __future__ import division

import numpy as np
from scipy.special import gamma, gammaln
from pyBN.learning.parameter.mle import mle_estimator, mle_fast
from pyBN.classes.empiricaldistribution import EmpiricalDistribution


def BDe(bn, data, ess=1, ed=None): 
	"""
	Unique Bayesian score with the property that I-equivalent
	networks have the same score.

	As Data Rows -> infinity, BDe score converges to the BIC score.

	Arguments
	---------
	*bn* : a BayesNet object
		Needed to get the parent relationships, etc.
	
	*data* : a numpy ndarray
		Needed to learn the empirical distribuion
	
	*ess* : an integer
		Equivalent sample size

	*ed* : an EmpiricalDistribution object
		Used to cache multiple lookups in structure learning.

	Notes
	-----
	*a_ijk* : a vector
		The number of times where x_i=k | parents(x_i)=j -> i.e. the mle counts

	*a_ij* : a vector summed over k's in a_ijk

	*n_ijk* : a vector prior (sample size or calculation)
		"ess" for BDe metric

	*n_ij* : a vector prior summed over k's in n_ijk
	
	"""
	counts_dict = mle_fast(bn, data, counts=True, np=True)
	a_ijk = []
	bdeu = 1
	for rv, value in counts_dict.items():
		nijk = value['cpt']
		nijk_prime = ess
		k2 *= gamma(nijk+nijk_prime)/gamma(nijk_prime)
		nij_prime = nijk_prime*(len(cpt)/bn.card(rv))
		nij = np.mean(nijk.reshape(-1, bn.card(rv)), axis=1) # sum along parents
		k2 *= gamma(nij_prime) / gamma(nij+nij_prime)

	return bdeu


def BDeu(bn, data, ess=1, ed=None):
	"""
	Unique Bayesian score with the property that I-equivalent
	networks have the same score.

	As Data Rows -> infinity, BDe score converges to the BIC score.

	Nijk_prime = ess/len(bn.cpt(rv))

	Arguments
	---------
	*bn* : a BayesNet object
		Needed to get the parent relationships, etc.
	
	*data* : a numpy ndarray
		Needed to learn the empirical distribuion
	
	*ess* : an integer
		Equivalent sample size

	*ed* : an EmpiricalDistribution object
		Used to cache multiple lookups in structure learning.

	Notes
	-----
	*a_ijk* : a vector
		The number of times where x_i=k | parents(x_i)=j -> i.e. the mle counts

	*a_ij* : a vector summed over k's in a_ijk

	*n_ijk* : a vector prior (sample size or calculation)
		ess/(card(x_i)*len(cpt(x_i)/card(x_i))) for x_i for BDe metric

	*n_ij* : a vector prior summed over k's in n_ijk
	
	"""
	counts_dict = mle_fast(bn, data, counts=True, np=True)
	a_ijk = []
	bdeu = 1
	for rv, value in counts_dict.items():
		nijk = value['cpt']
		nijk_prime = ess / len(nijk)
		k2 *= gamma(nijk+nijk_prime)/gamma(nijk_prime)
		nij_prime = nijk_prime*(len(cpt)/bn.card(rv))
		nij = np.mean(nijk.reshape(-1, bn.card(rv)), axis=1) # sum along parents
		k2 *= gamma(nij_prime) / gamma(nij+nij_prime)

	return bdeu

def K2(bn, data, ed=None):
	"""
	K2 is bayesian posterior probability of structure given the data,
	where N'ijk = 1.
	"""
	counts_dict = mle_fast(bn, data, counts=True, np=True)
	a_ijk = []
	k2 = 1
	for rv, value in counts_dict.items():
		nijk = value['cpt']
		nijk_prime = 1
		k2 *= gamma(nijk+nijk_prime)/gamma(nijk_prime)
		nij_prime = nijk_prime*(len(cpt)/bn.card(rv))
		nij = np.mean(nijk.reshape(-1, bn.card(rv)), axis=1) # sum along parents
		k2 *= gamma(nij_prime) / gamma(nij+nij_prime)

	return k2











================================================
FILE: pyBN/learning/structure/score/hill_climbing.py
================================================
"""
**********************
Greedy Hill-Climbing
for Structure Learning
**********************

Code for Searching through the space of
possible Bayesian Network structures.

Various optimization procedures are employed,
from greedy search to simulated annealing, and 
so on - mostly using scipy.optimize.

Local search - possible moves:
- Add edge
- Delete edge
- Invert edge

Strategies to improve Greedy Hill-Climbing:
- Random Restarts
	- when we get stuck, take some number of
	random steps and then start climbing again.
- Tabu List
	- keep a list of the K steps most recently taken,
	and say that the search cannt reverse (undo) any
	of these steps.
"""

#from scipy.optimize import *
import numpy as np
#from heapq import *
from copy import copy, deepcopy

from pyBN.classes.bayesnet import BayesNet
from pyBN.learning.parameter.mle import mle_estimator
from pyBN.learning.parameter.bayes import bayes_estimator
from pyBN.learning.structure.score.info_scores import info_score
from pyBN.utils.independence_tests import mutual_information
from pyBN.utils.graph import would_cause_cycle


def hc(data, metric='AIC', max_iter=100, debug=False, restriction=None):
	"""
	Greedy Hill Climbing search proceeds by choosing the move
	which maximizes the increase in fitness of the
	network at the current step. It continues until
	it reaches a point where there does not exist any
	feasible single move that increases the network fitness.

	It is called "greedy" because it simply does what is
	best at the current iteration only, and thus does not
	look ahead to what may be better later on in the search.

	For computational saving, a Priority Queue (python's heapq) 
	can be used	to maintain the best operators and reduce the
	complexity of picking the best operator from O(n^2) to O(nlogn).
	This works by maintaining the heapq of operators sorted by their
	delta score, and each time a move is made, we only have to recompute
	the O(n) delta-scores which were affected by the move. The rest of
	the operator delta-scores are not affected.

	For additional computational efficiency, we can cache the
	sufficient statistics for various families of distributions - 
	therefore, computing the mutual information for a given family
	only needs to happen once.

	The possible moves are the following:
		- add edge
		- delete edge
		- invert edge

	Arguments
	---------
	*data* : a nested numpy array
		The data from which the Bayesian network
		structure will be learned.

	*metric* : a string
		Which score metric to use.
		Options:
			- AIC
			- BIC / MDL
			- LL (log-likelihood)

	*max_iter* : an integer
		The maximum number of iterations of the
		hill-climbing algorithm to run. Note that
		the algorithm will terminate on its own if no
		improvement is made in a given iteration.

	*debug* : boolean
		Whether to print(the scores/moves of the)
		algorithm as its happening.

	*restriction* : a list of 2-tuples
		For MMHC algorithm, the list of allowable edge additions.

	Returns
	-------
	*bn* : a BayesNet object

	"""
	nrow = data.shape[0]
	ncol = data.shape[1]
	
	names = range(ncol)

	# INITIALIZE NETWORK W/ NO EDGES
	# maintain children and parents dict for fast lookups
	c_dict = dict([(n,[]) for n in names])
	p_dict = dict([(n,[]) for n in names])
	
	# COMPUTE INITIAL LIKELIHOOD SCORE	
	value_dict = dict([(n, np.unique(data[:,i])) for i,n in enumerate(names)])
	bn = BayesNet(c_dict)
	mle_estimator(bn, data)
	max_score = info_score(bn, nrow, metric)

	# CREATE EMPIRICAL DISTRIBUTION OBJECT FOR CACHING
	#ED = EmpiricalDistribution(data,names)

	

	_iter = 0
	improvement = True

	while improvement:
		improvement = False
		max_delta = 0

		if debug:
			print('ITERATION: ' , _iter)

		### TEST ARC ADDITIONS ###
		for u in bn.nodes():
			for v in bn.nodes():
				if v not in c_dict[u] and u!=v and not would_cause_cycle(c_dict, u, v):
					# FOR MMHC ALGORITHM -> Edge Restrictions
					if restriction is None or (u,v) in restriction:
						# SCORE FOR 'V' -> gaining a parent
						old_cols = (v,) + tuple(p_dict[v]) # without 'u' as parent
						mi_old = mutual_information(data[:,old_cols])
						new_cols = old_cols + (u,) # with'u' as parent
						mi_new = mutual_information(data[:,new_cols])
						delta_score = nrow * (mi_old - mi_new)

						if delta_score > max_delta:
							#if debug:
							#	print('Improved Arc Addition: ' , (u,v))
							#	print('Delta Score: ' , delta_score)
							max_delta = delta_score
							max_operation = 'Addition'
							max_arc = (u,v)

		### TEST ARC DELETIONS ###
		for u in bn.nodes():
			for v in bn.nodes():
				if v in c_dict[u]:
					# SCORE FOR 'V' -> losing a parent
					old_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent
					mi_old = mutual_information(data[:,old_cols])
					new_cols = tuple([i for i in old_cols if i != u]) # without 'u' as parent
					mi_new = mutual_information(data[:,new_cols])
					delta_score = nrow * (mi_old - mi_new)

					if delta_score > max_delta:
						#if debug:
						#	print('Improved Arc Deletion: ' , (u,v))
						#	print('Delta Score: ' , delta_score)
						max_delta = delta_score
						max_operation = 'Deletion'
						max_arc = (u,v)

		### TEST ARC REVERSALS ###
		for u in bn.nodes():
			for v in bn.nodes():
				if v in c_dict[u] and not would_cause_cycle(c_dict,v,u, reverse=True):
					# SCORE FOR 'U' -> gaining 'v' as parent
					old_cols = (u,) + tuple(p_dict[v]) # without 'v' as parent
					mi_old = mutual_information(data[:,old_cols])
					new_cols = old_cols + (v,) # with 'v' as parent
					mi_new = mutual_information(data[:,new_cols])
					delta1 = nrow * (mi_old - mi_new)
					# SCORE FOR 'V' -> losing 'u' as parent
					old_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent
					mi_old = mutual_information(data[:,old_cols])
					new_cols = tuple([u for i in old_cols if i != u]) # without 'u' as parent
					mi_new = mutual_information(data[:,new_cols])
					delta2 = nrow * (mi_old - mi_new)
					# COMBINED DELTA-SCORES
					delta_score = delta1 + delta2

					if delta_score > max_delta:
						#if debug:
						#	print('Improved Arc Reversal: ' , (u,v))
						#	print('Delta Score: ' , delta_score)
						max_delta = delta_score
						max_operation = 'Reversal'
						max_arc = (u,v)


		### DETERMINE IF/WHERE IMPROVEMENT WAS MADE ###
		if max_delta != 0:
			improvement = True
			u,v = max_arc
			if max_operation == 'Addition':
				if debug:
					print('ADDING: ' , max_arc , '\n')
				c_dict[u].append(v)
				p_dict[v].append(u)
			elif max_operation == 'Deletion':
				if debug:
					print('DELETING: ' , max_arc , '\n')
				c_dict[u].remove(v)
				p_dict[v].remove(u)
			elif max_operation == 'Reversal':
				if debug:
					print('REVERSING: ' , max_arc, '\n')
					c_dict[u].remove(v)
					p_dict[v].remove(u)
					c_dict[v].append(u)
					p_dict[u].append(v)
		else:
			if debug:
				print('No Improvement on Iter: ' , _iter)

		### TEST FOR MAX ITERATION ###
		_iter += 1
		if _iter > max_iter:
			if debug:
				print('Max Iteration Reached')
			break

	
	bn = BayesNet(c_dict)

	return bn















================================================
FILE: pyBN/learning/structure/score/info_scores.py
================================================
"""
Various metrics for evaluating
score-based search for Bayesian
Networks structure learning.

Generally, score-based structure learning
involves finding the structure which maximizes
some function of the likelihood of the data, given
the structure and parameters of the learned BN.

It is important to take advantage of the decomposability
of the scores. That is, if you add/delete/reverse an edge,
then you only need to re-calculate the score based on that
local change (usually just the child node's CPT) to get the
difference from the original graph. 

Here are a few which are (or can be) implemented:

Information-theoretic scoring functions:
	LL (Log-likelihood) (1912-22)
	MDL/BIC (Minimum description length/Bayesian Information Criterion) (1978)
	AIC (Akaike Information Criterion) (1974)
	NML (Normalized Minimum Likelihood) (2008)
	MIT (Mutual Information Tests) (2006)

References
----------
[1]
http://www.lx.it.pt/~asmc/pub/publications/09-TA/09-c-ta.pdf

"""
from __future__ import division
import numpy as np

from pyBN.utils.independence_tests import mutual_information, entropy


def info_score(bn, nrow, metric='BIC'):
	if metric.upper() == 'LL':
		score = log_likelihood(bn, nrow)
	elif metric.upper() == 'BIC':
		score = BIC(bn, nrow)
	elif metric.upper() == 'AIC':
		score = AIC(bn, nrow)
	else:
		score = BIC(bn, nrow)

	return score
	

##### INFORMATION-THEORETIC SCORING FUNCTIONS #####

def log_likelihood(bn, nrow):
	"""
	Determining log-likelihood of the parameters
	of a Bayesian Network. This is a quite simple
	score/calculation, but it is useful as a straight-forward
	structure learning score.

	Semantically, this can be considered as the evaluation
	of the log-likelihood of the data, given the structure
	and parameters of the BN:
		- log( P( D | Theta_G, G ) )
		where Theta_G are the parameters and G is the structure.

	However, for computational reasons it is best to take
	advantage of the decomposability of the log-likelihood score.
	
	As an example, if you add an edge from A->B, then you simply
	need to calculate LOG(P'(B|A)) - Log(P(B)), and if the value
	is positive then the edge improves the fitness score and should
	therefore be included. 

	Even more, you can expand and manipulate terms to calculate the
	difference between the new graph and the original graph as follows:
		Score(G') - Score(G) = M * I(X,Y),
		where M is the number of data points and I(X,Y) is
		the marginal mutual information calculated using
		the empirical distribution over the data.

	In general, the likelihood score decomposes as follows:
		LL(D | Theta_G, G) = 
			M * Sum over Variables ( I ( X , Parents(X) ) ) - 
			M * Sum over Variables ( H( X ) ),
		where 'I' is mutual information and 'H' is the entropy,
		and M is the number of data points

	Moreover, it is clear to see that H(X) is independent of the choice
	of graph structure (G). Thus, we must only determine the difference
	in the mutual information score of the original graph which had a given
	node and its original parents, and the new graph which has a given node
	and new parents.

	NOTE: This assumes the parameters have already
	been learned for the BN's given structure.

	LL = LL - f(N)*|B|, where f(N) = 0

	Arguments
	---------
	*bn* : a BayesNet object
		Must have both structure and parameters
		instantiated.
	Notes
	-----
	NROW = data.shape[0]
	mi_score = 0
	ent_score = 0
	for rv in bn.nodes():
		cols = tuple([bn.V.index(rv)].extend([bn.V.index(p) for p in bn.parents(rv)]))
		mi_score += mutual_information(data[:,cols])
		ent_score += entropy(data[:,bn.V.index(rv)])
	
	return NROW * (mi_score - ent_score)
	"""
	return np.sum(np.log(nrow * (bn.flat_cpt()+1e-7)))

def MDL(bn, nrow):
	"""
	Minimum Description Length score - it is
	equivalent to BIC
	"""
	return BIC(bn, nrow)

def BIC(bn, nrow):
	"""
	Bayesian Information Criterion.

	BIC = LL - f(N)*|B|, where f(N) = log(N)/2

	"""
	log_score = log_likelihood(bn, nrow)
	penalty = 0.5 * bn.num_params() * np.log(max(bn.num_edges(),1))
	return log_score - penalty

def AIC(bn, nrow):
	"""
	Aikaike Information Criterion

	AIC = LL - f(N)*|B|, where f(N) = 1

	"""
	log_score = log_likelihood(bn, nrow)
	penalty = len(bn.flat_cpt())
	return log_score - penalty



















================================================
FILE: pyBN/learning/structure/score/random_restarts.py
================================================
"""
*******************
Hill Climbing with
Random Restarts for
Structure Learning
*******************

Hill Climbing with random restarts proceeds
as the traditional hill climbing algorithm
does, except that when the hill climbing algorithm
reaches a local maxima or a plateau (or the actual
global maximum), it makes a K random moves - arc
additions/deletions/reversals - and continues the
algorithm. 

The hyper-parameters in this algorithm are the number
of random moves to make per random restart (m), and
the number of random restarts to make (r). It is
intuitive that the algorithm is more likely to get out
of local maxima when m is large and r is large, although
there it depends on how isolated the local maxima is and
also depends on how long you want the algorithm to run.

"""
#from scipy.optimize import *
import numpy as np
#from heapq import *
from copy import copy, deepcopy

from pyBN.classes.bayesnet import BayesNet
from pyBN.learning.parameter.mle import mle_estimator
from pyBN.learning.parameter.bayes import bayes_estimator
from pyBN.learning.structure.score.info_scores import info_score
from pyBN.utils.independence_tests import mutual_information
from pyBN.utils.graph import would_cause_cycle


def hc_rr(data, M=5, R=3, metric='AIC', max_iter=100, debug=False, restriction=None):
	"""
	Arguments
	---------
	*data* : a nested numpy array
		The data from which the Bayesian network
		structure will be learned.

	*metric* : a string
		Which score metric to use.
		Options:
			- AIC
			- BIC / MDL
			- LL (log-likelihood)

	*max_iter* : an integer
		The maximum number of iterations of the
		hill-climbing algorithm to run. Note that
		the algorithm will terminate on its own if no
		improvement is made in a given iteration.

	*debug* : boolean
		Whether to print(the scores/moves of the)
		algorithm as its happening.

	*restriction* : a list of 2-tuples
		For MMHC algorithm, the list of allowable edge additions.

	Returns
	-------
	*bn* : a BayesNet object
	"""
	nrow = data.shape[0]
	ncol = data.shape[1]
	
	names = range(ncol)

	# INITIALIZE NETWORK W/ NO EDGES
	# maintain children and parents dict for fast lookups
	c_dict = dict([(n,[]) for n in names])
	p_dict = dict([(n,[]) for n in names])
	
	# COMPUTE INITIAL LIKELIHOOD SCORE	
	value_dict = dict([(n, np.unique(data[:,i])) for i,n in enumerate(names)])
	bn = BayesNet(c_dict)
	mle_estimator(bn, data)
	max_score = info_score(bn, nrow, metric)
	

	_iter = 0
	improvement = True
	_restarts = 0

	while improvement:
		improvement = False
		max_delta = 0

		if debug:
			print('ITERATION: ' , _iter)

		### TEST ARC ADDITIONS ###
		for u in bn.nodes():
			for v in bn.nodes():
				if v not in c_dict[u] and u!=v and not would_cause_cycle(c_dict, u, v):
					# FOR MMHC ALGORITHM -> Edge Restrictions
					if restriction is None or (u,v) in restriction:
						# SCORE FOR 'V' -> gaining a parent
						old_cols = (v,) + tuple(p_dict[v]) # without 'u' as parent
						mi_old = mutual_information(data[:,old_cols])
						new_cols = old_cols + (u,) # with'u' as parent
						mi_new = mutual_information(data[:,new_cols])
						delta_score = nrow * (mi_old - mi_new)

						if delta_score > max_delta:
							if debug:
								print('Improved Arc Addition: ' , (u,v))
								print('Delta Score: ' , delta_score)
							max_delta = delta_score
							max_operation = 'Addition'
							max_arc = (u,v)

		### TEST ARC DELETIONS ###
		for u in bn.nodes():
			for v in bn.nodes():
				if v in c_dict[u]:
					# SCORE FOR 'V' -> losing a parent
					old_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent
					mi_old = mutual_information(data[:,old_cols])
					new_cols = tuple([i for i in old_cols if i != u]) # without 'u' as parent
					mi_new = mutual_information(data[:,new_cols])
					delta_score = nrow * (mi_old - mi_new)

					if delta_score > max_delta:
						if debug:
							print('Improved Arc Deletion: ' , (u,v))
							print('Delta Score: ' , delta_score)
						max_delta = delta_score
						max_operation = 'Deletion'
						max_arc = (u,v)

		### TEST ARC REVERSALS ###
		for u in bn.nodes():
			for v in bn.nodes():
				if v in c_dict[u] and not would_cause_cycle(c_dict,v,u, reverse=True):
					# SCORE FOR 'U' -> gaining 'v' as parent
					old_cols = (u,) + tuple(p_dict[v]) # without 'v' as parent
					mi_old = mutual_information(data[:,old_cols])
					new_cols = old_cols + (v,) # with 'v' as parent
					mi_new = mutual_information(data[:,new_cols])
					delta1 = nrow * (mi_old - mi_new)
					# SCORE FOR 'V' -> losing 'u' as parent
					old_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent
					mi_old = mutual_information(data[:,old_cols])
					new_cols = tuple([u for i in old_cols if i != u]) # without 'u' as parent
					mi_new = mutual_information(data[:,new_cols])
					delta2 = nrow * (mi_old - mi_new)
					# COMBINED DELTA-SCORES
					delta_score = delta1 + delta2

					if delta_score > max_delta:
						if debug:
							print('Improved Arc Reversal: ' , (u,v))
							print('Delta Score: ' , delta_score)
						max_delta = delta_score
						max_operation = 'Reversal'
						max_arc = (u,v)


		### DETERMINE IF/WHERE IMPROVEMENT WAS MADE ###
		if max_delta != 0:
			improvement = True
			u,v = max_arc
			if max_operation == 'Addition':
				if debug:
					print('ADDING: ' , max_arc , '\n')
				c_dict[u].append(v)
				p_dict[v].append(u)
			elif max_operation == 'Deletion':
				if debug:
					print('DELETING: ' , max_arc , '\n')
				c_dict[u].remove(v)
				p_dict[v].remove(u)
			elif max_operation == 'Reversal':
				if debug:
					print('REVERSING: ' , max_arc, '\n')
					c_dict[u].remove(v)
					p_dict[v].remove(u)
					c_dict[v].append(u)
					p_dict[u].append(v)
		else:
			if debug:
				print('No Improvement on Iter: ' , _iter)
			#### RESTART WITH RANDOM MOVES ####
			if _restarts < R:
				improvement = True # make another pass of hill climbing
				_iter=0 # reset iterations
				if debug:
					print('Restart - ' , _restarts)
				_restarts+=1
				for _ in range(M):
					# 0 = Addition, 1 = Deletion, 2 = Reversal
					operation = np.random.choice([0,1,2])
					if operation == 0:
						while True:
							u,v = np.random.choice(list(bn.nodes()), size=2, replace=False)
							# IF EDGE DOESN'T EXIST, ADD IT
							if u not in p_dict[v] and u!=v and not would_cause_cycle(c_dict,u,v):
								if debug:
									print('RESTART - ADDING: ', (u,v))
								c_dict[u].append(v)
								p_dict[v].append(u)
								break
					elif operation == 1:
						while True:
							u,v = np.random.choice(list(bn.nodes()), size=2, replace=False)
							# IF EDGE EXISTS, DELETE IT
							if u in p_dict[v]:
								if debug:
									print('RESTART - DELETING: ', (u,v))
								c_dict[u].remove(v)
								p_dict[v].remove(u)
								break
					elif operation == 2:
						while True:
							u,v = np.random.choice(list(bn.nodes()), size=2, replace=False)
							# IF EDGE EXISTS, REVERSE IT
							if u in p_dict[v] and not would_cause_cycle(c_dict,v,u, reverse=True):
								if debug:
									print('RESTART - REVERSING: ', (u,v))
								c_dict[u].remove(v)
								p_dict[v].remove(u)
								c_dict[v].append(u)
								p_dict[u].append(v)
								break

		### TEST FOR MAX ITERATION ###
		_iter += 1
		if _iter > max_iter:
			if debug:
				print('Max Iteration Reached')
			break

	
	bn = BayesNet(c_dict)

	return bn

















================================================
FILE: pyBN/learning/structure/score/tabu.py
================================================
"""
******************
Tabu Search for
Score-based 
Structure Learning
******************

Tabu search in this case proceeds by the traditional
hill-climbing algorithm, with the additional feature
that it does not allow the algorithm to reverse any
moves that happened in the last K steps. The idea
behind tabu search is that it may give the algorithm
a better chance of escaping out of local maxima or
plateaus. Still, it is a local search technique.

"""

import numpy as np
from copy import copy, deepcopy

from pyBN.classes.bayesnet import BayesNet
from pyBN.learning.parameter.mle import mle_estimator
from pyBN.learning.parameter.bayes import bayes_estimator
from pyBN.learning.structure.score.info_scores import info_score
from pyBN.utils.independence_tests import mutual_information
from pyBN.utils.graph import would_cause_cycle


def tabu(data, k=5, metric='AIC', max_iter=100, debug=False, restriction=None):
	"""
	Tabu search for score-based structure learning.

	The algorithm maintains a list called "tabu_list",
	which consists of 3-tuples, where the first two
	elements constitute the edge which is tabued, and
	the third element is a string - either 'Addition',
	'Deletion', or 'Reversal' denoting the operation
	associated with the edge.

	Arguments
	---------
	*data* : a nested numpy array
		The data from which the Bayesian network
		structure will be learned.

	*metric* : a string
		Which score metric to use.
		Options:
			- AIC
			- BIC / MDL
			- LL (log-likelihood)

	*max_iter* : an integer
		The maximum number of iterations of the
		hill-climbing algorithm to run. Note that
		the algorithm will terminate on its own if no
		improvement is made in a given iteration.

	*debug* : boolean
		Whether to print(the scores/moves of the)
		algorithm as its happening.

	*restriction* : a list of 2-tuples
		For MMHC algorithm, the list of allowable edge additions.

	Returns
	-------
	*bn* : a BayesNet object
	
	"""
	nrow = data.shape[0]
	ncol = data.shape[1]
	
	names = range(ncol)

	# INITIALIZE NETWORK W/ NO EDGES
	# maintain children and parents dict for fast lookups
	c_dict = dict([(n,[]) for n in names])
	p_dict = dict([(n,[]) for n in names])
	
	# COMPUTE INITIAL LIKELIHOOD SCORE	
	value_dict = dict([(n, np.unique(data[:,i])) for i,n in enumerate(names)])
	bn = BayesNet(c_dict)
	mle_estimator(bn, data)
	max_score = info_score(bn, nrow, metric)

	tabu_list = [None]*k


	_iter = 0
	improvement = True

	while improvement:
		improvement = False
		max_delta = 0

		if debug:
			print('ITERATION: ' , _iter)

		### TEST ARC ADDITIONS ###
		for u in bn.nodes():
			for v in bn.nodes():
				# CHECK TABU LIST - can't delete an addition on the tabu list
				if (u,v,'Deletion') not in tabu_list:
					# CHECK EDGE EXISTENCE AND CYCLICITY
					if v not in c_dict[u] and u!=v and not would_cause_cycle(c_dict, u, v):
						# FOR MMHC ALGORITHM -> Edge Restrictions
						if restriction is None or (u,v) in restriction:
							# SCORE FOR 'V' -> gaining a parent
							old_cols = (v,) + tuple(p_dict[v]) # without 'u' as parent
							mi_old = mutual_information(data[:,old_cols])
							new_cols = old_cols + (u,) # with'u' as parent
							mi_new = mutual_information(data[:,new_cols])
							delta_score = nrow * (mi_old - mi_new)

							if delta_score > max_delta:
								if debug:
									print('Improved Arc Addition: ' , (u,v))
									print('Delta Score: ' , delta_score)
								max_delta = delta_score
								max_operation = 'Addition'
								max_arc = (u,v)

		### TEST ARC DELETIONS ###
		for u in bn.nodes():
			for v in bn.nodes():
				# CHECK TABU LIST - can't add back a deletion on the tabu list
				if (u,v,'Addition') not in tabu_list:
					if v in c_dict[u]:
						# SCORE FOR 'V' -> losing a parent
						old_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent
						mi_old = mutual_information(data[:,old_cols])
						new_cols = tuple([i for i in old_cols if i != u]) # without 'u' as parent
						mi_new = mutual_information(data[:,new_cols])
						delta_score = nrow * (mi_old - mi_new)

						if delta_score > max_delta:
							if debug:
								print('Improved Arc Deletion: ' , (u,v))
								print('Delta Score: ' , delta_score)
							max_delta = delta_score
							max_operation = 'Deletion'
							max_arc = (u,v)

		### TEST ARC REVERSALS ###
		for u in bn.nodes():
			for v in bn.nodes():
				# CHECK TABU LIST - can't reverse back a reversal on the tabu list
				if (u,v,'Reversal') not in tabu_list:
					if v in c_dict[u] and not would_cause_cycle(c_dict,v,u, reverse=True):
						# SCORE FOR 'U' -> gaining 'v' as parent
						old_cols = (u,) + tuple(p_dict[v]) # without 'v' as parent
						mi_old = mutual_information(data[:,old_cols])
						new_cols = old_cols + (v,) # with 'v' as parent
						mi_new = mutual_information(data[:,new_cols])
						delta1 = nrow * (mi_old - mi_new)
						# SCORE FOR 'V' -> losing 'u' as parent
						old_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent
						mi_old = mutual_information(data[:,old_cols])
						new_cols = tuple([u for i in old_cols if i != u]) # without 'u' as parent
						mi_new = mutual_information(data[:,new_cols])
						delta2 = nrow * (mi_old - mi_new)
						# COMBINED DELTA-SCORES
						delta_score = delta1 + delta2

						if delta_score > max_delta:
							if debug:
								print('Improved Arc Reversal: ' , (u,v))
								print('Delta Score: ' , delta_score)
							max_delta = delta_score
							max_operation = 'Reversal'
							max_arc = (u,v)


		### DETERMINE IF/WHERE IMPROVEMENT WAS MADE ###
		if max_delta != 0:
			improvement = True
			u,v = max_arc
			if max_operation == 'Addition':
				if debug:
					print('ADDING: ' , max_arc , '\n')
				c_dict[u].append(v)
				p_dict[v].append(u)
				tabu_list[_iter % 5] = (u,v,'Addition')
			elif max_operation == 'Deletion':
				if debug:
					print('DELETING: ' , max_arc , '\n')
				c_dict[u].remove(v)
				p_dict[v].remove(u)
				tabu_list[_iter % 5] = (u,v,'Deletion')
			elif max_operation == 'Reversal':
				if debug:
					print('REVERSING: ' , max_arc, '\n')
					c_dict[u].remove(v)
					p_dict[v].remove(u)
					c_dict[v].append(u)
					p_dict[u].append(v)
					tabu_list[_iter % 5] = (u,v,'Reversal')
		else:
			if debug:
				print('No Improvement on Iter: ' , _iter)

		### TEST FOR MAX ITERATION ###
		_iter += 1
		if _iter > max_iter:
			if debug:
				print('Max Iteration Reached')
			break

	
	bn = BayesNet(c_dict)

	return bn





















================================================
FILE: pyBN/learning/structure/tree/__init__.py
================================================
from pyBN.learning.structure.tree.chow_liu import *

================================================
FILE: pyBN/learning/structure/tree/chow_liu.py
================================================
"""
*************
Chow-Liu Tree
*************

Calculate the KL divergence (i.e. run
mi_test) between every pair of nodes,
then select the maximum spanning tree from that
connected graph. This is the Chow-Liu tree.

"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.utils.independence_tests import mi_test
from pyBN.classes.bayesnet import BayesNet
import operator
import numpy as np


def chow_liu(data,edges_only=False):
	"""
	Perform Chow-Liu structure learning algorithm
	over an entire dataset, and return the BN-tree.


	Arguments
	---------
	*data* : a nested numpy array
		The data from which we will learn. It should be
		the entire dataset.

	Returns
	-------
	*bn* : a BayesNet object
		The structure-learned BN.

	Effects
	-------
	None

	Notes
	-----

	"""
	value_dict = dict(zip(range(data.shape[1]),
		[list(np.unique(col)) for col in data.T]))

	n_rv = data.shape[1]

	edge_list = [(i,j,mi_test(data[:,(i,j)],chi2_test=False)) \
					for i in range(n_rv) for j in range(i+1,n_rv)]
	
	edge_list.sort(key=operator.itemgetter(2), reverse=True) # sort by weight
	vertex_cache = {edge_list[0][0]} # start with first vertex..
	mst = dict((rv, []) for rv in range(n_rv))

	for i,j,w in edge_list:
		if i in vertex_cache and j not in vertex_cache:
			mst[i].append(j)
			vertex_cache.add(j)
		elif i not in vertex_cache and j in vertex_cache:
			mst[j].append(i)
			vertex_cache.add(i)
	
	if edges_only == True:
		return mst, value_dict

	bn=BayesNet(mst,value_dict)
	return bn














================================================
FILE: pyBN/plotting/__init__.py
================================================
from pyBN.plotting.plot import *

================================================
FILE: pyBN/plotting/plot.py
================================================
"""
********
Plotting 
********

Code for plotting BayesNet objects, built on
the graphviz framework.
"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import networkx as nx
from graphviz import dot
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from networkx.drawing.nx_agraph import write_dot, graphviz_layout
import subprocess
import sys
from PIL import Image
		
def plot(bn, save=False):
	plot_gv(bn, save=save)

def plot_nx(bn,**kwargs):
	"""
	Draw BayesNet object from networkx engine
	"""
	g = nx.DiGraph(bn.E)
	pos = graphviz_layout(g,'dot')
	#node_size=600,node_color='w',with_labels=False
	nx.draw_networkx(g,pos=pos, **kwargs)
	plt.axis('off')
	plt.show()

def iplot(bn, h=350, w=450):
	"""
	Inline Plotting of a BayesNet object
	"""
	def execute(command):
		command = ' '.join(command)
		process = subprocess.Popen(command, 
			shell=True, 
			stdout=subprocess.PIPE, 
			stderr=subprocess.STDOUT)

		output = process.communicate()[0]
		returncode = process.returncode

		if returncode == 0:
			return True, output
		else:
			return False, output

	cmd = ['mkdir' , 'pyBN/plotting/images']
	p = execute(cmd)

	G = nx.DiGraph(bn.E)
	write_dot(G,"pyBN/plotting/images/bn.dot")

	cmd = ['/usr/local/bin/dot', '-Tpng' , 
	'pyBN/plotting/images/bn.dot', 
	'>pyBN/plotting/images/bn.png']
	p = execute(cmd)
	
	im = Image.open("pyBN/plotting/images/bn.png")
	out = im.resize((w,h))

	cmd = ['rm' , '-r', 'pyBN/plotting/images']
	p = execute(cmd)

	return out

def plot_gv(bn, save=False):
	def execute(command):
		command = ' '.join(command)
		process = subprocess.Popen(command, 
			shell=True, 
			stdout=subprocess.PIPE, 
			stderr=subprocess.STDOUT)

		output = process.communicate()[0]
		returncode = process.returncode

		if returncode == 0:
			return True, output
		else:
			return False, output

	cmd = ['mkdir' , 'pyBN/plotting/images']
	p = execute(cmd)

	G = nx.DiGraph(bn.E)
	write_dot(G,"pyBN/plotting/images/bn.dot")

	cmd = ['/usr/local/bin/dot', '-Tpng' , 
	'pyBN/plotting/images/bn.dot', 
	'>pyBN/plotting/images/bn.png']

	p = execute(cmd)
	plt.figure(facecolor="white")
	img=mpimg.imread('pyBN/plotting/images/bn.png')
	_img = plt.imshow(img, aspect='auto')
	plt.axis('off')
	plt.show(_img)

	if not save:
		cmd = ['rm' , '-r', 'pyBN/plotting/images']
		p = execute(cmd)
	else:
		cmd = ['rm', '-r', 'pyBN/plotting/images/bn.dot']
		p = execute(cmd)











		


================================================
FILE: pyBN/utils/__init__.py
================================================
from pyBN.utils.class_equivalence import *
from pyBN.utils.data import *
from pyBN.utils.discretize import *
from pyBN.utils.graph import *
from pyBN.utils.hybrid_distance import *
from pyBN.utils.independence_tests import *
from pyBN.utils.markov_blanket import *
from pyBN.utils.orient_edges import *
from pyBN.utils.parameter_distance import *
from pyBN.utils.random_sample import *
from pyBN.utils.structure_distance import *

================================================
FILE: pyBN/utils/_tests/__init__.py
================================================


================================================
FILE: pyBN/utils/_tests/test_independence_tests.py
================================================
"""
****************
UnitTest
Constraint Tests
****************

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.independence.constraint_tests import mi_test

class ConstraintTestsTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		self.data = np.loadtxt(os.path.join(self.dpath,'lizards.csv'),
			delimiter=',',
			dtype='int32',
			skiprows=1)

	def tearDown(self):
		pass

	def test_mi_two_vars_value_a(self):
		self.assertEqual(mi_test(self.data[:,(0,1)]),0.0004)

	def test_mi_two_vars_value_b(self):
		self.assertEqual(mi_test(self.data[:,(0,2)]),0.0014)

	def test_mi_two_vars_symmetry(self):
		self.assertEqual(mi_test(self.data[:,(1,0)]),mi_test(self.data[:,(0,1)]))

	def test_mi_three_vars_value_a(self):
		self.assertEqual(mi_test(self.data),0.0009)

	def test_mi_three_vars_symmetry(self):
		self.assertEqual(mi_test(self.data[:,(0,1,2)]),mi_test(self.data[:,(1,0,2)]))

	def test_mi_random_three(self):
		np.random.seed(3636)
		self.data = np.random.randint(1,10,size=((10000,3)))
		self.assertEqual(mi_test(self.data),0.0211)

	def test_mi_random_four(self):
		np.random.seed(3636)
		self.data = np.random.randint(1,10,size=((10000,4)))
		self.assertEqual(mi_test(self.data),0.0071)













================================================
FILE: pyBN/utils/_tests/test_markov_blanket.py
================================================
"""
**************
Markov Blanket
Unit Test
**************
"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np
import pandas as pd

from pyBN.readwrite.read import read_bn
from pyBN.independence.markov_blanket import markov_blanket

class ConstraintTestsTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		self.bn = read_bn(os.path.join(self.dpath,'cmu.bn'))

	def tearDown(self):
		pass

	def test_markov_blanket(self):
		self.assertDictEqual(markov_blanket(self.bn),
			{'Alarm': ['Earthquake', 'Burglary', 'JohnCalls', 'MaryCalls'],
			 'Burglary': ['Alarm', 'Earthquake'],
			 'Earthquake': ['Alarm', 'Burglary'],
			 'JohnCalls': ['Alarm'],
			 'MaryCalls': ['Alarm']})

================================================
FILE: pyBN/utils/_tests/test_orient_edges.py
================================================
"""
********
UnitTest
OrientEdges
********

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.structure_learn.orient_edges import orient_edges_gs, orient_edges_pc


class OrientEdgesTestCase(unittest.TestCase):

	def setUp(self):
		pass
	def tearDown(self):
		pass

	def test_orient_edges_pc(self):
		e = {0:[1,2],1:[0],2:[0]}
		b = {0: [], 1: {2: (0,)}, 2: {1: (0,)}}
		self.assertDictEqual(orient_edges_pc(e,b),
			{0: [1, 2], 1: [], 2: []})

	def test_orient_edges_gs(self):
		e = {0: [1, 2], 1: [0], 2: [0]}
		b = {0: [1, 2], 1: [0], 2: [0]}
		dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		path = (os.path.join(dpath,'lizards.csv'))
		data = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')
		self.assertDictEqual(orient_edges_gs(e,b,data,0.05),
			{0: [1, 2], 1: [], 2: []})

================================================
FILE: pyBN/utils/_tests/test_random_sample.py
================================================
"""
********
UnitTest
Misc
********

"""
__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import unittest
import os
from os.path import dirname
import numpy as np

from pyBN.utils.random_sample import random_sample
from pyBN.readwrite.read import read_bn


class RandomSampleTestCase(unittest.TestCase):

	def setUp(self):
		self.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')	
		self.bn = read_bn(os.path.join(self.dpath,'cancer.bif'))

	def tearDown(self):
		pass

	def test_random_sample(self):
		np.random.seed(3636)
		sample = random_sample(self.bn,5)
		self.assertListEqual(list(sample.ravel()),
			[0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 
			1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0])

================================================
FILE: pyBN/utils/class_equivalence.py
================================================
"""
****************
Equivalence Code
****************

This code is for testing whether or not
two Bayesian networks belong to the same
equivalence class - i.e. they have the same
edges when viewed as undirected graphs.

Also, this code is for actually generating
equivalent Bayesian networks from a given BN.

"""

def are_class_equivalent(x,y):
	"""
	Check whether two Bayesian networks belong
	to the same equivalence class.
	"""
	are_equivalent = True

	if set(list(x.nodes())) != set(list(y.nodes())):
		are_equivalent = False
	else:
		for rv in x.nodes():
			rv_x_neighbors = set(x.parents(rv)) + set(y.children(rv))
			rv_y_neighbors = set(y.parents(rv)) + set(y.children(rv))
			if rv_x_neighbors != rv_y_neighbors:
				are_equivalent =  False
				break
	return are_equivalent

================================================
FILE: pyBN/utils/data.py
================================================
"""
These are functions for dealing with datasets
that have strings as values - as those values
must be converted to integers in order to be 
used in many structure learning functions, for
example.

There is, of course, the issue of how to get those
string values back into the underlying representation
after the learning occurs..
"""
import numpy as np
from copy import copy

def replace_strings(data, return_values=False):
	"""
	Takes a dataset of strings and returns the
	integer mapping of the string values.

	It might be useful to return a dictionary of the
	string values so that they can be mapped back in
	the actual BayesNet object when necessary.
	"""
	data = copy(data)
	i = 0
	value_dict = {}
	for col in data.T:
		vals,_,ret, = np.unique(col, True, True)
		data[:,i] = ret
		value_dict[i] = list(vals)
		i+=1

	if return_values:
		return np.array(data,dtype=np.int32), value_dict
	else:
		return np.array(data, dtype=np.int32)

def unique_bins(data):
	"""
	Get the unique values for each column in a dataset.
	"""
	bins = np.empty(len(data.T), dtype=np.int32)
	i = 0
	for col in data.T:
		bins[i] = len(np.unique(col))
		i+=1
	return bins

================================================
FILE: pyBN/utils/discretize.py
================================================
"""
**************************
Discretize Continuous Data
**************************

Since pyBN only handles Discrete Bayesian Networks,
and therefore only handles discrete data, it is
important to have effective functions for 
discretizing continuous data. This code aims to
meet that goal.

"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np
from copy import copy

def discretize(data, cols=None, bins=None):
	"""
	Discretize the passed-in dataset. These
	functions will rely on numpy and scipy
	for speed and accuracy... no need to
	reinvent the wheel here. Therefore, pyBN's
	discretization methods are basically just
	wrappers for existing methods.

	The bin number defaults to FIVE (5) for
	all columns if not passed in.

	Arguments
	---------
	*data* : a nested numpy array

	*cols* : a list of integers (optional)
		Which columns to discretize .. defaults
		to ALL columns

	*bins* : a list of integers (optional)
		The number of bins into which each column
		array will be split .. defaults to 5 for
		all columns

	Returns
	-------
	*data* : a discretized copy of original data

	Effects
	-------
	None

	Notes
	-----
	- Should probably add more methods of discretization
		based on mean/median/mode, etc.
	"""
	if bins is not None:
		assert (isinstance(bins, list)), 'bins argument must be a list'
	else:
		try:
			bins = [5]*data.shape[1]
		except ValueError:
			bins = [5]
	
	if cols is not None:
		assert (isinstance(cols,list)), 'cols argument must be a list'
	else:	
		try:
			cols = range(data.shape[1])
		except ValueError:
			cols = [0]

	data = copy(data)

	minmax = zip(np.amin(data,axis=0),np.amax(data,axis=0))
	for c in cols:
		# get min and max of each column
		_min, _max = minmax[c]
		# create the bins from np.linspace
		_bins = np.linspace(_min,_max,bins[c])
		# discretize with np.digitize(col,bins)
		data[:,c] = np.digitize(data[:,c],_bins)

	return np.array(data,dtype=np.int32,copy=False)












================================================
FILE: pyBN/utils/graph.py
================================================
"""
Collection of Graph Algorithms.

Any networkx dependencies should exist here only, but
there are currently a few exceptions which will
eventually be corrected.

"""

import networkx as nx
import numpy as np
from copy import copy

def would_cause_cycle(e, u, v, reverse=False):
	"""
	Test if adding the edge u -> v to the BayesNet
	object would create a DIRECTED (i.e. illegal) cycle.
	"""
	G = nx.DiGraph(e)
	if reverse:
		G.remove_edge(v,u)
	G.add_edge(u,v)
	try:
		nx.find_cycle(G, source=u)
		return True
	except:
		return False



def topsort(edge_dict, root=None):
	"""
	List of nodes in topological sort order from edge dict
	where key = rv and value = list of rv's children
	"""
	queue = []
	if root is not None:
		queue = [root]
	else:
		for rv in edge_dict.keys():
			prior=True
			for p in edge_dict.keys():
				if rv in edge_dict[p]:
					prior=False
			if prior==True:
				queue.append(rv)
	
	visited = []
	while queue:
		vertex = queue.pop(0)
		if vertex not in visited:
			visited.append(vertex)
			for nbr in edge_dict[vertex]:
				queue.append(nbr)
			#queue.extend(edge_dict[vertex]) # add all vertex's children
	return visited

def dfs_postorder(edge_dict, root=None):
	return list(reversed(topsort(edge_dict, root)))

def mst(edge_dict):
	"""
	Calcuate Minimum Spanning Tree
	for a weighted edge dictionary,
	where key = rv, value = another dictionary
	where key = child of rv, value = edge weight
	between rv -> child edge.

	Arguments
	---------
	*w_edge_dict* : a dictionary, where
		key = rv, value = another dictionary, where
		key = child node of "rv", value = integer/float
		edge weight between the "rv" -> "child" edge

	Returns
	-------
	*mst_edge_dict* : a non-weighted edge dict
		The minimum spanning tree from w_edge_dict, stored
		as a dictionary where key = rv, value = list of
		rv's children

	Effects
	-------
	None

	Notes
	-----
	test:
		input:
			g = {0:{1:3,3:2,8:4},1:{7:4},
				2:{3:6,7:2,5:1},3:{4:1},4:{8:8},
				5:{6:8},6:{},7:{2:2},8:{}}
		output:
			g = {0:[3,1,8],1:[7],2:[5],3:[4],4:[],
				5:[6],6:[],7:[2],8:[]}
		
	 
	"""
	mst_G = dict([(rv,[]) for rv in range(len(edge_dict))])
	nrv = len(mst_G)
	reached = [0]
	unreached = range(1,nrv)

	for k in range(nrv):
		source, sink = None, None
		min_cost = np.inf
		for rn in reached:
			e = edge_dict[rn].items()
			e.sort(key = lambda x : x[1])
			for sn, weight in e:
				if sn in unreached and weight < min_cost:
					min_cost = weight
					source = rn
					sink = sn
					break

		if source is None or sink is None:
			break

		# Add e to the minimum spanning tree
		mst_G[source].append(sink)
		#if undirected == True:
		#	mst_G[sink].append(source)

		# Mark newly include node as reached
		unreached.remove(sink)
		reached.append(sink)
	
	return mst_G

def make_chordal(bn, v=None,e=None):
	"""
	This function creates a chordal graph - i.e. one in which there
	are no cycles with more than three nodes.

	Can supply a v_list and e_list for chordal graph of any random graph..

	We start from the moral graph, so if that is already chordal then it
	will return that.

	Algorithm from Cano & Moral 1990 ->
	'Heuristic Algorithms for the Triangulation of Graphs'


	Parameters
	----------
	*v* : a list (optional)
	    A vertex list

	*e* : a list (optional)
	    An edge list


	Returns
	-------
	*G* : a network Digraph object

	Effects
	-------
	None

	Notes
	-----
	Where is this used? Do we need to use networkx?       


	"""
	chordal_E = bn.moralized_edges() # start with moral graph

	# if moral graph is already chordal, no need to alter it
	if not is_chordal(chordal_E):            
		temp_E = copy(chordal_E)
		temp_V = []

		# if v and e is supplied, skip all the rest
		if v and e:
			chordal_E = copy(e)
			temp_E = copy(chordal_E)
			temp_V = copy(v)
		else:
			temp_G = nx.Graph()
			temp_G.add_edges_from(chordal_E)
			degree_dict = temp_G.degree()
			temp_V = sorted(degree_dict, key=degree_dict.get)
		#print temp_V
		for v in temp_V:
			#Add links between the pairs nodes adjacent to Node i
			#Add those links to chordal_E and temp_E
			adj_v = set([n for e in temp_E for n in e if v in e and n!=v])
			for a1 in adj_v:
				for a2 in adj_v:
					if a1!=a2:
						if [a1,a2] not in chordal_E and [a2,a1] not in chordal_E:
							chordal_E.append([a1,a2])
							temp_E.append([a1,a2])
			# remove Node i from temp_V and all its links from temp_E 
			temp_E2 = []
			for edge in temp_E:
				if v not in edge:
					temp_E2.append(edge)
			temp_E = temp_E2

	
	G = nx.Graph()
	G.add_edges_from(chordal_E)
	return G


def is_chordal(edge_list):
	"""
	Check if the graph is chordal/triangulated.

	Parameters
	----------
	*edge_list* : a list of lists (optional)
	The edges to check (if not self.E)

	Returns
	-------
	*nx.is_chordal(G)* : a boolean
	Whether the graph is chordal or not

	Effects
	-------
	None

	Notes
	-----
	Again, do we need networkx for this? Eventually should
	write this check on our own.

	"""
	G = nx.Graph(list(edge_list))
	return nx.is_chordal(G)

================================================
FILE: pyBN/utils/hybrid_distance.py
================================================
"""
****************
BN Hybrid
Distance Metrics
****************

Code for computing the distance between 
2+ Bayesian networks that takes into account
both structure AND parameters. 

Any structure-based distance metric can be
combined with any parameter-based distance metric
to compute the hybrid distance.

Also, we support a type of supervised approach to
hybrid distance measurement where the relative importance
placed on the structure and distance metrics are varied
such that maximum (or minimum) closeness is achieved.

"""

def hybrid_distance(x,y, alpha, s='hamming', p='euclidean'):
	structure_score = hamming(x,y)
	parameter_score = euclidean(x,y)

	hybrid_score = structure_score*alpha + parameter_score*(1-alpha)

	return hybrid_score


================================================
FILE: pyBN/utils/independence_tests.py
================================================
"""
******************************
Conditional Independence Tests 
for Constraint-based Learning
******************************

Implemented Constraint-based Tests
----------------------------------
- mutual information
- Pearson's X^2

I may consider putting this code into its own class structure. The
main benefit I could see from doing this would be the ability to 
cache joint/marginal/conditional probabilities for expedited tests.

"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np
from scipy import stats
from pyBN.utils.data import unique_bins

def are_independent(data, alpha=0.05, method='mi_test'):
	pval = mi_test(data)
	if pval < alpha:
		return True
	else:
		return False

def mutual_information(data, conditional=False):
	#bins = np.amax(data, axis=0)+1 # read levels for each variable
	bins = unique_bins(data)
	if len(bins) == 1:
		hist,_ = np.histogramdd(data, bins=(bins)) # frequency counts
		Px = hist/hist.sum()
		MI = -1 * np.sum( Px * np.log( Px ) )
		return round(MI, 4)
		
	if len(bins) == 2:
		hist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts

		Pxy = hist / hist.sum()# joint probability distribution over X,Y,Z
		Px = np.sum(Pxy, axis = 1) # P(X,Z)
		Py = np.sum(Pxy, axis = 0) # P(Y,Z)	

		PxPy = np.outer(Px,Py)
		Pxy += 1e-7
		PxPy += 1e-7
		MI = np.sum(Pxy * np.log(Pxy / (PxPy)))
		return round(MI,4)
	elif len(bins) > 2 and conditional==True:
		# CHECK FOR > 3 COLUMNS -> concatenate Z into one column
		if len(bins) > 3:
			data = data.astype('str')
			ncols = len(bins)
			for i in range(len(data)):
				data[i,2] = ''.join(data[i,2:ncols])
			data = data.astype('int')[:,0:3]

		bins = np.amax(data,axis=0)
		hist,_ = np.histogramdd(data, bins=bins) # frequency counts

		Pxyz = hist / hist.sum()# joint probability distribution over X,Y,Z
		Pz = np.sum(Pxyz, axis = (0,1)) # P(Z)
		Pxz = np.sum(Pxyz, axis = 1) # P(X,Z)
		Pyz = np.sum(Pxyz, axis = 0) # P(Y,Z)	

		Pxy_z = Pxyz / (Pz+1e-7) # P(X,Y | Z) = P(X,Y,Z) / P(Z)
		Px_z = Pxz / (Pz+1e-7) # P(X | Z) = P(X,Z) / P(Z)	
		Py_z = Pyz / (Pz+1e-7) # P(Y | Z) = P(Y,Z) / P(Z)

		Px_y_z = np.empty((Pxy_z.shape)) # P(X|Z)P(Y|Z)
		for i in range(bins[0]):
			for j in range(bins[1]):
				for k in range(bins[2]):
					Px_y_z[i][j][k] = Px_z[i][k]*Py_z[j][k]
		Pxyz += 1e-7
		Pxy_z += 1e-7
		Px_y_z += 1e-7
		MI = np.sum(Pxyz * np.log(Pxy_z / (Px_y_z)))
		
		return round(MI,4)
	elif len(bins) > 2 and conditional == False:
		data = data.astype('str')
		ncols = len(bins)
		for i in range(len(data)):
			data[i,1] = ''.join(data[i,1:ncols])
		data = data.astype('int')[:,0:2]

		hist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts

		Pxy = hist / hist.sum()# joint probability distribution over X,Y,Z
		Px = np.sum(Pxy, axis = 1) # P(X,Z)
		Py = np.sum(Pxy, axis = 0) # P(Y,Z)	

		PxPy = np.outer(Px,Py)
		Pxy += 1e-7
		PxPy += 1e-7
		MI = np.sum(Pxy * np.log(Pxy / (PxPy)))
		return round(MI,4)



def mi_test(data, test=True):
	"""
	This function performs the mutual information (cross entropy)-based
	CONDITIONAL independence test. Because it is conditional, it requires
	at LEAST three columns. For the marginal independence test, use 
	"mi_test_marginal".

	We use the maximum likelihood estimators as probabilities. The
	mutual information value is computed, then the 
	chi square test is used, with degrees of freedom equal to 
	(|X|-1)*(|Y|-1)*Pi_z\inZ(|z|).

	This function works on datasets that contain MORE than three
	columns by concatenating the extra columns into one. For that
	reason, it is a little slower in that case.

	For two variables only:

	This function performs mutual information (cross entropy)-based
	MARGINAL independence test. Because it is marginal, it requires
	EXACTLY TWO columns. For the conditional independence test, use
	"mi_test_conditional".

	This is the same as calculated the KL Divergence, i.e.
	I(X,Y) = Sigma p(x,y)* log p(x,y) *( p(x)/p(y) )

	NOTE: pval < 0.05 means DEPENDENCE, pval > 0.05 means INDEPENDENCE.
	In other words, the pval represent the probability this relationship
	could have happened at random or by chance. if the pval is very small,
	it means the two variables are likely dependent on one another.

	Steps:
		- Calculate the marginal/conditional probabilities
		- Compute the Mutual Information value
		- Calculate chi2 statistic = 2*N*MI
		- Compute the degrees of freedom
		- Compute the chi square p-value

	Arguments
	----------
	*data* : a nested numpy array
		The data from which to learn - must have at least three
		variables. All conditioned variables (i.e. Z) are compressed
		into one variable.

	Returns
	-------
	*p_val* : a float
		The pvalue from the chi2 and ddof

	Effects
	-------
	None

	Notes
	-----
	- Doesn't currently work with strings... 
	- Should generalize to let data be a Pandas DataFrame --> would
	encourage external use.

	"""
	
	#bins = np.amax(data, axis=0)+1 # read levels for each variable
	bins = unique_bins(data)
	if len(bins)==2:
		hist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts

		#Pxy = hist / hist.sum()# joint probability distribution over X,Y,Z
		Pxy = hist / data.shape[0]
		Px = np.sum(Pxy, axis = 1) # P(X,Z)
		Py = np.sum(Pxy, axis = 0) # P(Y,Z)	

		PxPy = np.outer(Px,Py)
		Pxy += 1e-7
		PxPy += 1e-7
		MI = np.sum(Pxy * np.log(Pxy / (PxPy)))
		if not test:
			return round(MI,4)
		else:
			chi2_statistic = 2*len(data)*MI
			ddof = (bins[0] - 1) * (bins[1] - 1)
			p_val = 2*stats.chi2.pdf(chi2_statistic, ddof)
			return round(p_val,4)
	else:
		# CHECK FOR > 3 COLUMNS -> concatenate Z into one column
		if len(bins) > 3:
			data = data.astype('str')
			ncols = len(bins)
			for i in range(len(data)):
				data[i,2] = ''.join(data[i,2:ncols])
			data = data.astype('int')[:,0:3]

		#bins = np.amax(data,axis=0)
		bins = unique_bins(data)
		hist,_ = np.histogramdd(data, bins=bins) # frequency counts

		#Pxyz = hist / hist.sum()# joint probability distribution over X,Y,Z
		Pxyz = hist / data.shape[0]
		Pz = np.sum(Pxyz, axis = (0,1)) # P(Z)
		Pxz = np.sum(Pxyz, axis = 1) # P(X,Z)
		Pyz = np.sum(Pxyz, axis = 0) # P(Y,Z)	

		Pxy_z = Pxyz / (Pz+1e-7) # P(X,Y | Z) = P(X,Y,Z) / P(Z)
		Px_z = Pxz / (Pz+1e-7) # P(X | Z) = P(X,Z) / P(Z)	
		Py_z = Pyz / (Pz+1e-7) # P(Y | Z) = P(Y,Z) / P(Z)

		Px_y_z = np.empty((Pxy_z.shape)) # P(X|Z)P(Y|Z)
		for i in range(bins[0]):
			for j in range(bins[1]):
				for k in range(bins[2]):
					Px_y_z[i][j][k] = Px_z[i][k]*Py_z[j][k]
		Pxyz += 1e-7
		Pxy_z += 1e-7
		Px_y_z += 1e-7
		MI = np.sum(Pxyz * np.log(Pxy_z / (Px_y_z)))
		if not test:
			return round(MI,4)
		else:
			chi2_statistic = 2*len(data)*MI
			ddof = (bins[0] - 1) * (bins[1] - 1) * bins[2]
			p_val = 2*stats.chi2.pdf(chi2_statistic, ddof) # 2* for one tail
			return round(p_val,4)

def entropy(data):
	"""
	In the context of structure learning, and more specifically
	in constraint-based algorithms which rely on the mutual information
	test for conditional independence, it has been proven that the variable
	X in a set which MAXIMIZES mutual information is also the variable which
	MINIMIZES entropy. This fact can be used to reduce the computational
	requirements of tests based on the following relationship:

		Entropy is related to marginal mutual information as follows:
			MI(X;Y) = H(X) - H(X|Y)

		Entropy is related to conditional mutual information as follows:
			MI(X;Y|Z) = H(X|Z) - H(X|Y,Z)

		For one varibale, H(X) is equal to the following:
			-1 * sum of p(x) * log(p(x))

		For two variables H(X|Y) is equal to the following:
			sum over x,y of p(x,y)*log(p(y)/p(x,y))
		
		For three variables, H(X|Y,Z) is equal to the following:
			-1 * sum of p(x,y,z) * log(p(x|y,z)),
				where p(x|y,z) = p(x,y,z)/p(y)*p(z)
	Arguments
	----------
	*data* : a nested numpy array
		The data from which to learn - must have at least three
		variables. All conditioned variables (i.e. Z) are compressed
		into one variable.

	Returns
	-------
	*H* : entropy value

	"""
	try:
		cols = data.shape[1]
	except IndexError:
		cols = 1

	#bins = np.amax(data,axis=0)
	bins = unique_bins(data)

	if cols == 1:
		hist,_ = np.histogramdd(data, bins=(bins)) # frequency counts
		Px = hist/hist.sum()
		H = -1 * np.sum( Px * np.log( Px ) )

	elif cols == 2: # two variables -> assume X then Y
		hist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts

		Pxy = hist / hist.sum()# joint probability distribution over X,Y,Z
		Py = np.sum(Pxy, axis = 0) # P(Y)	
		Py += 1e-7
		Pxy += 1e-7
		H = np.sum( Pxy * np.log( Py / Pxy ) )

	else:
		# CHECK FOR > 3 COLUMNS -> concatenate Z into one column
		if cols  > 3:
			data = data.astype('str')
			ncols = len(bins)
			for i in range(len(data)):
				data[i,2] = ''.join(data[i,2:ncols])
			data = data.astype('int')[:,0:3]

		bins = np.amax(data,axis=0)
		hist,_ = np.histogramdd(data, bins=bins) # frequency counts

		Pxyz = hist / hist.sum()# joint probability distribution over X,Y,Z
		Pyz = np.sum(Pxyz, axis=0)

		Pxyz += 1e-7 # for log -inf
		Pyz += 1e-7
		H = -1 * np.sum( Pxyz * np.log( Pxyz ) ) + np.sum( Pyz * np.log( Pyz ) ) 

	return round(H,4)

def mi_from_en(data):
	"""
	Calculate Mutual Information based on entropy alone. This
	function isn't really faster than calculating MI from
	"mi_test" due to overhead in the histogram/binning from
	numpy, but it's mostly here for entropy validation or
	if "mi_test" breaks. BUT, calling "entropy" once is much
	faster than calling "mi_test" once, so that is where the
	speedup occurs.

	This has been validated with both 2, 3 and 4 variables.

	Entropy is related to marginal mutual information as follows:
			MI(X;Y) = H(X) - H(X|Y)
	Entropy is related to conditional mutual information as follows:
			MI(X;Y|Z) = H(X|Z) - H(X|Y,Z)
	"""
	ncols = data.shape[1]

	if ncols == 1:
		print("Need at least 2 columns")

	elif ncols==2:
		MI = entropy(data[:,0]) - entropy(data)

	elif ncols==3:
		MI = entropy(data[:,(0,2)]) - entropy(data)

	elif ncols>3:
		# join extra columns
		data = data.astype('str')
		ncols = data.shape[1]
		for i in range(len(data)):
			data[i,2] = ''.join(data[i,2:ncols])
		data = data.astype('int')[:,0:3]

		MI = entropy(data[:,(0,2)]) - entropy(data)


	return round(MI,4)


def chi2_test(data):
	"""
	Test null hypothesis that P(X,Y,Z) = P(Z)P(X|Z)P(Y|Z)
	versus empirically observed P(X,Y,Z) in the data using
	the traditional chisquare test based on observed versus
	expected frequency bins.

	Steps
		- Calculate P(XYZ) empirically and expected
		- Compute ddof
		- Perfom one-way chisquare

	Arguments
	---------
	*data* : a nested numpy array
		The data from which to learn - must have at least three
		variables. All conditioned variables (i.e. Z) are compressed
		into one variable.

	Returns
	-------
	*chi2_statistic* : a float
		Chisquare statistic
	*p_val* : a float
		The pvalue from the chi2 and ddof

	Effects
	-------
	None

	Notes
	-----
	- Assuming for now that |Z| = 1... generalize later
	- Should generalize to let data be a Pandas DataFrame --> would
	encourage external use.

	"""
	# compress extra Z variables at the start.. not implemented yet
	#bins = np.amax(data, axis=0)+1
	bins = unique_bins(data)
	hist,_ = np.histogramdd(data,bins=bins)

	Pxyz = hist / hist.sum()# joint probability distribution over X,Y,Z

	Pz = np.sum(Pxyz, axis = (0,1)) # P(Z)
	Pxz = np.sum(Pxyz, axis = 1) # P(X,Z)
	Pyz = np.sum(Pxyz, axis = 0) # P(Y,Z)

	Px_z = Pxz / (Pz+1e-7) # P(X | Z) = P(X,Z) / P(Z)	
	Py_z = Pyz / (Pz+1e-7) # P(Y | Z) = P(Y,Z) / P(Z)

	observed_dist = Pxyz # Empirical distribution
	#Not correct right now -> Pz is wrong dimension
	Px_y_z = np.empty((Pxy_z.shape)) # P(Z)P(X|Z)P(Y|Z)
	for i in range(bins[0]):
		for j in range(bins[1]):
			for k in range(bins[2]):
				Px_y_z[i][j][k] = Px_z[i][k]*Py_z[j][k]
	Px_y_z *= Pz

	observed = observed_dist.flatten() * len(data)
	expected = expected_dist.flatten() * len(data)

	ddof = (bins[0] - 1) * (bins[1]- 1) * bins[2]
	chi2_statistic, p_val = stats.chisquare(observed,expected, ddof=ddof)

	return chi2_statistic, p_val







================================================
FILE: pyBN/utils/markov_blanket.py
================================================
"""
**************
Markov Blanket
**************

References
----------
[1] Koller and Sahami, "Toward Optimal Feature Selection."
[2] Yaramakala and Margaritis, "Speculative Markov Blanket Discovery 
for Optimal Feature Selection"
[3] Pellet and Elisseff, "Using Markov Blankets for Causal Structure Learning"

"""
from __future__ import division
from copy import copy
import itertools
from pyBN.utils.independence_tests import are_independent, mi_test


__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""


def markov_blanket(bn):
	"""
	Return the Markov Blanket dictionary from a fully
	(structurally) instantiated BayesNet object.

	The markov blanket for a given node is just
	the node's parents, children, and 
	its children's parents (i.e. spouses)

	Arguments
	---------
	*bn* : BayesNet object

	Returns
	-------
	*mb* :  a dictionary where each key 
		is a node and the value is a list of
		the key-node's markov blanket
	"""
	mb = dict([(rv,bn.parents(rv)+bn.children(rv)) for rv in bn.nodes()])

	for rv in bn.V:
		for child in bn.children(rv):
			for c_parent in bn.parents(child):
				if c_parent != rv:
					mb[rv].append(c_parent) # add spouse
	return mb

def resolve_markov_blanket(Mb, data,alpha=0.05):
	"""
	Resolving the Markov blanket is the process
	by which a PDAG is constructed from the collection
	of Markov Blankets for each node. Since an
	undirected graph is returned, the edges still need to 
	be oriented by calling some version of the 
	"orient_edges" function in "pyBN.structure_learn.orient_edges" 
	module.

	This algorithm is adapted from Margaritis, but also see [3]
	for good pseudocode.

	Arguments
	---------
	*Mb* : a dictionary, where
		key = rv and value = list of vars in rv's markov blanket

	*data* : a nested numpy array
		The dataset used to learn the Mb

	Returns
	-------
	*edge_dict* : a dictionary, where
		key = rv and value = list of rv's children

	Effects
	-------
	None

	Notes
	-----
	"""
	n_rv = data.shape[1]
	edge_dict = dict([(rv,[]) for rv in range(n_rv)])
	for X in range(n_rv):
		for Y in Mb[X]:
			# X and Y are direct neighbors if X and Y are dependent
			# given S for all S in T, where T is the smaller of
			# B(X)-{Y} and B(Y)-{X}
			if len(Mb[X]) < len(Mb[Y]):
				T = copy(Mb[X]) # shallow copy is sufficient
				if Y in T:
					T.remove(Y)
			else:
				T = copy(Mb[Y]) # shallow copy is sufficient
				if X in T:
					T.remove(X)

			# X and Y must be dependent conditioned upon
			# EVERY POSSIBLE COMBINATION of T
			direct_neighbors=True
			for i in range(len(T)):
				for S in itertools.combinations(T,i):
					cols = (X,Y) + tuple(S)
					pval = mi_test(data[:,cols])
					if pval > alpha:
						direct_neighbors=False
			if direct_neighbors:
				if Y not in edge_dict[X] and X not in edge_dict[Y]:
					edge_dict[X].append(Y)
				if X not in edge_dict[Y]:
					edge_dict[Y].append(X)
	return edge_dict

def mb_fitness(data, Mb, target=None):
	"""
	Evaluate the fitness of a Markov Blanket dictionary
	learned from a given data set based on the distance metric
	provided in [1] and [2].

	From [2]:
		A distance measure that indicates the "fitness"
		of the discovered blanket... to be the average, over all attributes
		X outside the blanket, of the expected KL-divergence between
		Pr(T | B(T)) and Pr(T | B(T) u {X}). We can expect this 
		measure to be close to zero when B(T) is an approximate
		blanket. -- 
		My Note: T is the target variable, and if the KL-divergence
		between the two distributions above is zero, then it means that
		{X} provides no new information about T and can thus be excluded
		from Mb(T) -- this is the exact definition of conditional independence.

	Notes
	-----
	- Find Pr(T|B(T)) ..
	- For each variable X outside of the B(T), calculate 
		D( Pr(T|B(T)), Pr(T|B(T)u{X}) )
	- Take the average (closer to Zero is better)

	^^^ This is basically calculating where T is independent of X given B(T)..
		i.e. Sum over all X not in B(T) of mi_test(data[:,(T,X,B(T))]) / |X|
	"""
	if target is None:
		nodes = set(Mb.keys())
	else:
		try:
			nodes = set(target)
		except TypeError:
			nodes = {target}

	fitness_dict = dict([(rv, 0) for rv in nodes])
	for T in nodes:
		non_blanket = nodes - set(Mb[T]) - {T}
		for X in non_blanket:
			pval = mi_test(data[:,(T,X)+tuple(Mb[T])])
			fitness_dict[T] += 1/pval
	return fitness_dict


	









================================================
FILE: pyBN/utils/orient_edges.py
================================================
"""
******************
Orient Edges from
Structure Learning
******************

[1] Chickering. "Learning Equivalence Classes of Bayesian
Network Structues"
http://www.jmlr.org/papers/volume2/chickering02a/chickering02a.pdf

[2] Pellet and Elisseff, "Using Markov Blankets for Causal Structure Learning"
"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.utils.independence_tests import mi_test

import itertools
from copy import copy

def orient_edges_MB(edge_dict, Mb, data, alpha):
	"""
	Orient edges from a Markov Blanket based on the rules presented
	in Margaritis' Thesis pg. 35. This method is used
	for structure learning algorithms that return/resolve
	a markov blanket - i.e. growshrink and iamb.

	Also, see [2] for good full pseudocode.

	# if there exists a variable Z in N(X)-N(Y)-{Y}
	# such that Y and Z are dependent given S+{X} for
	# all S subset of T, where
	# T is smaller of B(Y)-{X,Z} and B(Z)-{X,Y}

	Arguments
	---------
	*edge_dict* : a dictionary, where
		key = node and value = list
		of neighbors for key. Note: there
		MUST BE duplicates in edge_dict ->
		i.e. each edge should be in edge_dict
		twice since Y in edge_dict[X] and
		X in edge_dict[Y]

	*blanket* : a dictionary, where
		key = node and value = list of
		nodes in the markov blanket of node

	*data* : a nested numpy array

	*alpha* : a float
		Probability of Type II error.

	Returns
	-------
	*d_edge_dict* : a dictionary
		Dictionary of directed edges, so
		there are no duplicates

	Effects
	-------
	None

	Notes
	-----

	"""
	for X in edge_dict.keys():
		for Y in edge_dict[X]:
			nxy = set(edge_dict[X]) - set(edge_dict[Y]) - {Y}

			for Z in nxy:
				by = set(Mb[Y]) - {X} - {Z}
				bz = set(Mb[Z]) - {X} - {Y}
				T = min(by,bz)
				if len(T)>0:
					for i in range(len(T)):
						for S in itertools.combinations(T,i):
							cols = (Y,Z,X) + tuple(S)
							pval = mi_test(data[:,cols])
							if pval < alpha:
								if Y in edge_dict[X]:
									edge_dict[X].remove(Y)
							else:
								if Y in edge_dict[X]:
									edge_dict[Y].remove(X)
				else:
					cols = (Y,Z,X)
					pval = mi_test(data[:,cols])
					if pval < alpha:
						if Y in edge_dict[X]:
							edge_dict[X].remove(Y)
					else:
						if X in edge_dict[Y]:
							edge_dict[Y].remove(X)
	return edge_dict

def orient_edges_gs2(edge_dict, Mb, data, alpha):
	"""
	Similar algorithm as above, but slightly modified for speed?
	Need to test.
	"""
	d_edge_dict = dict([(rv,[]) for rv in edge_dict])
	for X in edge_dict.keys():
		for Y in edge_dict[X]:
			nxy = set(edge_dict[X]) - set(edge_dict[Y]) - {Y}
			for Z in nxy:
				if Y not in d_edge_dict[X]:
					d_edge_dict[X].append(Y) # SET Y -> X
				B = min(set(Mb[Y]) - {X} - {Z},set(Mb[Z]) - {X} - {Y})
				for i in range(len(B)):
					for S in itertools.combinations(B,i):
						cols = (Y,Z,X) + tuple(S)
						pval = mi_test(data[:,cols])
						if pval < alpha and X in d_edge_dict[Y]: # Y IS independent of Z given S+X
							d_edge_dict[Y].remove(X)
				if X in d_edge_dict[Y]:
					break
	return d_edge_dict

def orient_edges_CS(edge_dict, block_dict):
	"""
	Orient edges from Collider Sets. This is a little
	different than orienting edges from a markov blanket.
	The collider set-based algorithm is used for the 
	Path-Condition (PC) algorithm.

	See [2] for	good full pseudocode.

	The orientation step will proceed by looking
	for sets of three variables {X, Y,Z} such that
	edges X - Z, Y - Z are in the graph by not the
	edge X - Y . Then, if Z not in block_dict[x][y] , it orients the
	edges from X to Z and from Y to Z creating a
	v-structure: X -> Z <- Y

	Arguments
	---------
	*edge_dict* : a dictionary, where
		key = vertex and value = list of its neighbors.
		NOTE: this is undirected, so the edges are duplicated.

	*block_dict* : a dictionary, where
		key = X, value = another dictionary where
		key = Y, value = set S such that I(X,Y|S)

	Returns
	-------
	*d_edge_dict* : an directed version of original edge_dict

	Effects
	-------
	None

	Notes
	-----
	"""
	d_edge_dict = dict([(rv,[]) for rv in edge_dict.keys()])
	for x in edge_dict.keys():
		for z in edge_dict[x]:
			for y in edge_dict[z]:
				if y!=x and x not in edge_dict[y] and y not in edge_dict[x]:
					if y in block_dict[x]:
						if z not in block_dict[x][y]:
							if z not in d_edge_dict[x]:
								d_edge_dict[x].append(z)
							if z not in d_edge_dict[y]:
								d_edge_dict[y].append(z)
						else:
							if x not in d_edge_dict[z]:
								d_edge_dict[z].append(x)
							if y not in d_edge_dict[z]:
								d_edge_dict[z].append(y)

	return d_edge_dict
















================================================
FILE: pyBN/utils/parameter_distance.py
================================================
"""
****************
BN Parameter 
Distance Metrics
****************

Code for computing the Parametric distance 
between 2+ Bayesian Networks using various
metrics. Parametric distance only involves a
comparison of the conditional probability values.
For structural distance metrics, see "structure_distance.py"

Since a Bayesian network is simply joint probability distribution,
any distibution distance metric can be used.

Metrics
-------
*euclidean*
*manhattan*
*minkowski*
*kl_divergence*
*js_divergence*
*hellinger*

"""
from __future__ import division

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np
#from pyBN.classes.bayesnet import BayesNet



def euclidean(x,y):
	"""
	Euclidean distance is a well-known distance metric.

	It is defined as follows:
		Sum of ( sqrt of (x - y)^2 )
	"""
	assert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'
	assert (x==y), 'Passed-in BayesNet objects are not structurally equal.'

	distance = np.sum( np.sqrt( ( x.flat_cpt() - y.flat_cpt() )**2 ) )
	return distance

def manhattan(x,y):
	"""
	Manhattan distance is a well-known distance metric.

	It is defined as follows:
		Sum of ( |x-y| )
	"""
	assert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'
	assert (x==y), 'Passed-in BayesNet objects are not structurally equal.'

	distance = np.sum( np.abs( x.flat_cpt() - y.flat_cpt() ) )
	return distance

def minkowski(x,y,p=1.5):
	"""
	Minkowski distance is a metric where the user
	can specify the exponent to which the Parameter
	difference values is raised.

	It is defined as follows:
		(Sum of (x-y)^p)^(1/p)

	Note that when p = 1, this is equivalent to Manhattan Distance.
	Note that when p = 2, this is equivalent to Euclidean Distance.

	"""
	assert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'
	assert (x==y), 'Passed-in BayesNet objects are not structurally equal.'

	distance = np.sum( ( x.flat_cpt() - y.flat_cpt() )**2 )**( 1/p )
	return distance

def kl_divergence(x,y):
	"""
	KL-Divergence is a well-known metric, although
	it is not technically a valid distance measure
	because it is NOT symmetric - i.e. KL(X,Y) != KL(Y,X)
	
	It is defined as follows:
		Sum of x * log(x/y)
	"""
	assert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'
	assert (x==y), 'Passed-in BayesNet objects are not structurally equal.'

	distance = np.sum( x.flat_cpt() * np.log( x.flat_cpt() / y.flat_cpt() ) )
	return distance
	
def js_divergence(x,y):
	"""
	Jensen-Shannon Divergence is similar to KL-Divergence,
	but it is symmetric and therefore is a valid distance metric.

	It is defined as follows:
		JSD = 1/2 * KL(X,Z) + 1/2 * KL(Y,Z),
			where Z = 1/2 * (X + Y)
	"""
	assert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'
	assert (x==y), 'Passed-in BayesNet objects are not structurally equal.'

	z = 0.5 * ( x.flat_cpt() + y.flat_cpt() )
	distance = 0.5 * kl_divergence(x,z) + 0.5 * kl_divergence(y,z)
	return distance


def hellinger(x,y):
	"""
	Hellinger is a well-known distance metric.
	
	It is defined as follows:
	1/Sqrt(2) * Sqrt(Sum of ((sqrt(x) - sqrt(y))^2))
	"""
	assert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'
	assert (x==y), 'Passed-in BayesNet objects are not structurally equal.'

	distance = ( 1 / np.sqrt( 2 ) ) * np.sqrt( np.sum( ( np.sqrt( x.flat_cpt() ) - np.sqrt( y.flat_cpt() ) )**2) )
	return distance




















================================================
FILE: pyBN/utils/random_sample.py
================================================
"""
******************
Random Sample Code
******************

Generate a random sample dataset from a known Bayesian Network,
with or without evidence.

"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

from pyBN.classes.factor import Factor
import numpy as np
from copy import deepcopy

def random_sample(bn, n=1000):
    """
    Take a random sample of "n" observations from a
    BayesNet object. This is essentially just the
    forward sample algorithm that returns the samples.

    Parameters
    ----------
    *bn* : a BayesNet object from which to sample

    *n* : an integer
        The number of observations to take

    *evidence* : a dictionary, key=rv & value=instantiation
        Evidence to pass in

    Returns
    -------
    *sample_dict* : a list of samples, where each sample
        is a list of values in bn.nodes() (topsort) order

    Notes
    -----

    """
    sample = np.empty((n,bn.num_nodes()),dtype=np.int)

    rv_map = dict([(rv,idx) for idx,rv in enumerate(bn.nodes())])
    factor_map = dict([(rv,Factor(bn,rv)) for rv in bn.nodes()])
    
    for i in range(n):
        for rv in bn.nodes():
            f = deepcopy(factor_map[rv])
            # reduce_factor by parent samples
            for p in bn.parents(rv):
                f.reduce_factor(p,bn.values(p)[sample[i][rv_map[p]]])
            choice_vals = bn.values(rv)
            choice_probs = f.cpt
            chosen_val = np.random.choice(choice_vals, p=choice_probs)
            sample[i][rv_map[rv]] = bn.values(rv).index(chosen_val)

    return sample







================================================
FILE: pyBN/utils/structure_distance.py
================================================
"""
****************
BN Structure 
Distance Metrics
****************

Code for computing the structural Distance
between 2+ Bayesian networks. Note, this
code compares only structure - i.e. the
conditional (in)dependencies. For a comparison
of conditional probability values of 2+ BNs,
see "parameter_distance.py"

For computing distance metrics that include
both structure AND parameters, see "hybrid_distance.py"

Metrics
-------
1. Missing Edges: 
	counts edges that are present in the original structure but
	are missing in the learned structure. (lower is better) [1]

2. Extra Edges: 
	counts edges that are found in the learned structure but are
	not present in the original structure. (lower is better) [1]

3. Incorrect Edge Direction: 
	counts directed edges in the learned structure
	that are oriented incorrectly. (lower is better) [1]

4. Hamming Distance: 
	Describes the number of changes that have to be made
	to a network for it to turn into the one that it is being compared with. It is the
	sum of measures 1, 2, and 3. Versions of the Hamming distance metric have
	been proposed by Acid and de Campos (2003), Tsamardinos et al. (2006), and
	Perrier et al. (2008). (lower is better) [1]


References
----------
[1] Martijn de Jongh and Marek J. Druzdzel
	"A Comparison of Structural Distance Measures
	for Causal Bayesian Network Models"


"""

__author__ = """Nicholas Cullen <ncullen.th@dartmouth.edu>"""

import numpy as np

def missing_edges(x,y):
	"""
	Counts edges that are present in the original structure (x) but
	are missing in the learned structure (y). (lower is better)
	"""
	missing = 0
	for rv in x.nodes():
		if not y.has_node(rv):
			missing += x.degree(rv)
		else:
			for child in x.children(rv):
				if child not in y.children(rv):
					missing += 1
	return missing

def extra_edges(x,y):
	"""
	Counts edges that are found in the learned structure but are
	not present in the original structure. (lower is better)
	"""
	extra = 0
	for rv in y.nodes():
		if not x.has_node(rv):
			extra += y.degree(rv)
		else:
			for child in y.children(rv):
				if child not in x.children(rv):
					extra += 1
	return extra

def incorrect_direction_edges(x,y):
	"""
	Counts directed edges in the learned structure
	that are oriented incorrectly. (lower is better)
	"""
	incorrect_direction = 0
	for rv in x.nodes():
		if x.has_node(rv):
			for child in x.children(rv): # child is rv's child in x
				if rv in y.parents(rv): # child is rv's parent in y
					incorrect_direction += 1
	return incorrect_direction

def hamming(x,y):
	return missing_edges(x,y) + extra_edges(x,y) + incorrect_direction_edges(x,y)
	










================================================
FILE: setup.py
================================================
#!/usr/bin/env python

from distutils.core import setup
from setuptools import find_packages

setup(name='pyBN',
      version='0.1',
      description='Bayesian Networks in Python',
      author='Nicholas Cullen',
      author_email='ncullen.th@dartmouth.edu',
      url='sites.dartmouth.edu/ncullen',
      packages=find_packages()
     )