[ { "path": ".gitignore", "content": "*.DS_Store\n*.pyc\n.ipynb_checkpoints/\n*.ipynb_checkpoints/\n*.bkbn\n.spyderworkspace\n.spyderproject\n\n# setup.py working directory\nbuild\n# sphinx build directory\ndoc/_build\n# setup.py dist directory\ndist\n# Egg metadata\n*.egg-info\n.eggs\npyBN.egg-info" }, { "path": "License.txt", "content": "The MIT License (MIT)\n\nCopyright (c) 2016, Nicholas Cullen \n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE." }, { "path": "Readme.md", "content": "\nNOTE: I wrote this code to go along with Daphne Koller's book and no longer\nmaintain the repository, although the code should be easily adaptable.\n\n

Bayesian Networks in Python

\n\n

Overview

\nThis 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.\n\nI 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.\n\nFor 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:\n\n\n

Current features

\n\n

Marginal Inference

\n- Exact Marginal Inference\n\t- Sum-Product Variable Elimination \n\t- Clique Tree Message Passing\n- Approximate Marginal Inference\n\t- Forward Sampling \n\t- Likelihood Weighted Sampling\n\t- Gibbs (MCMC) Sampling\n\t- Loopy Belief Propagation\n\n

MAP/MPE Inference

\n- Exact MAP Inference\n\t- Max-Product Variable Elimination\n\t- Integer Linear Programming\n- Approximate MAP Inference\n\t- LP Relaxation\n\n

Constraint-Based Structure Learning

\n- Algorithms\n\t- PC\n\t- Grow-Shrink\n\t- IAMB/Lambda-IAMB/Fast-IAMB\n- Independence Tests\n\t- Marginal Mutual Information\n\t- Conditional Mutual Information\n\t- Pearson Chi-Square\n\n

Score-Based Structure Learning

\n- Algorithms\n\t- Greedy Hill Climbing\n\t- Tabu Search\n\t- Random Restarts\n- Scoring Functions\n\t- BIC/AIC/MDL\n\t- BDe/BDeu/K2\n\n

Tree-Based Structure Learning

\n- Naive Bayes\n- Tree-Augmented Naive Bayes\n- Chow-Liu\n\n

Hybrid Structure Leanring

\n- MMPC\n- MMHC\n\n

Exact Structure Learning

\n- GOBNILP Solver\n\n

Parameter Learning

\n- Maximum Likelihood Estimation\n- Dirichlet-Multinomial Estimation\n\n

Classification

\n- Naive Bayes\n- Tree-Augmented Naive Bayes\n- General DAG\n\n

Multi-Dimensional Classification

\n- Empty/Tree/Polytree/Forest\n- General DAG\n\n

Comparing Two Bayesian Networks

\n- Structure-Based Distance Metrics\n\t- Missing Edges\n\t- Extra Edges\n\t- Incorrect Edge Orientation\n\t- Hamming Distance\n- Parameter-Based Distance Metrics\n\t- KL-Divergence and JS-Divergence\n\t- Manhattan and Euclidean\n\t- Hellinger\n\t- Minkowski\n\n

Utility Functionality

\n- Determine Class Equivalence\n- Discretize continuous data \n- Orient a PDAG\n- Generate random sample dataset from a BN\n- Markov Blanket operations\n\nI 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.\nThe wrappers can be found in the \"pyGOBN\" project at www.github.com/ncullen93/pyGOBN. For an overview of GOBNILP or to see its\ngreat benchmarks on even the most massive datasets, visit https://www.cs.york.ac.uk/aig/sw/gobnilp/.\n\n\n

Examples

\nThis 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.\n\nHere is a list of current examples:\n- 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.\n- Drawing : an introduction to the drawing/plotting capabilities of pyBN with both small and large Bayesian networks.\n- FactorOperations : an introduction to the Factor class, an exploration of the numerous attributes belonging to a Factor in\npyBN, an overview of every Factor operation function at the users' hands, and a short discussion of what makes Factor operations\nso fast and efficient in pyBN.\n\n

Usage

\nGetting up-and-running with this package is simple:\n\n1. Click \"Download ZIP\" button towards the upper right corner of the page.\n2. Unpack the ZIP file wherever you want on your local machine. You should now have a folder called \"pyBN-master\"\n3. 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!\n4. In your python terminal, simply type \"from pyBN import \". This will load all of the module's functions, classes, etc.\n5. You are now free to use the package! Perhaps you want to start by creating a BayesNet object using \"bn = BayesNet()\" and so on.\n\n

Unit Tests

\nIf 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):\n- python -m unittest discover\n\n" }, { "path": "data/andes.bif", "content": "network unknown {\n}\nvariable GOAL_2 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_3 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_4 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_5 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_6 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_7 {\n type discrete [ 2 ] { false, true };\n}\nvariable DISPLACEM0 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp1 {\n type discrete [ 2 ] { false, true };\n}\nvariable GIVEN_1 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp2 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_8 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_9 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_10 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_11 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_12 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_13 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_14 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_15 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_16 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_17 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_18 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_19 {\n type discrete [ 2 ] { false, true };\n}\nvariable NEED1 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_20 {\n type discrete [ 2 ] { false, true };\n}\nvariable GRAV2 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_21 {\n type discrete [ 2 ] { false, true };\n}\nvariable VALUE3 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_24 {\n type discrete [ 2 ] { false, true };\n}\nvariable SLIDING4 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_25 {\n type discrete [ 2 ] { false, true };\n}\nvariable CONSTANT5 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_26 {\n type discrete [ 2 ] { false, true };\n}\nvariable KNOWN6 {\n type discrete [ 2 ] { false, true };\n}\nvariable VELOCITY7 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_47 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp3 {\n type discrete [ 2 ] { false, true };\n}\nvariable KNOWN8 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp4 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_27 {\n type discrete [ 2 ] { false, true };\n}\nvariable COMPO16 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_48 {\n type discrete [ 2 ] { false, true };\n}\nvariable TRY12 {\n type discrete [ 2 ] { false, true };\n}\nvariable TRY11 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_49 {\n type discrete [ 2 ] { false, true };\n}\nvariable CHOOSE19 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_50 {\n type discrete [ 2 ] { false, true };\n}\nvariable SYSTEM18 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_51 {\n type discrete [ 2 ] { false, true };\n}\nvariable KINEMATI17 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_52 {\n type discrete [ 2 ] { false, true };\n}\nvariable IDENTIFY10 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_53 {\n type discrete [ 2 ] { false, true };\n}\nvariable IDENTIFY9 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_28 {\n type discrete [ 2 ] { false, true };\n}\nvariable TRY13 {\n type discrete [ 2 ] { false, true };\n}\nvariable TRY14 {\n type discrete [ 2 ] { false, true };\n}\nvariable TRY15 {\n type discrete [ 2 ] { false, true };\n}\nvariable VAR20 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_29 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_31 {\n type discrete [ 2 ] { false, true };\n}\nvariable GIVEN21 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_33 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_34 {\n type discrete [ 2 ] { false, true };\n}\nvariable VECTOR27 {\n type discrete [ 2 ] { false, true };\n}\nvariable APPLY32 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_56 {\n type discrete [ 2 ] { false, true };\n}\nvariable CHOOSE35 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_57 {\n type discrete [ 2 ] { false, true };\n}\nvariable MAXIMIZE34 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_59 {\n type discrete [ 2 ] { false, true };\n}\nvariable AXIS33 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_60 {\n type discrete [ 2 ] { false, true };\n}\nvariable WRITE31 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_61 {\n type discrete [ 2 ] { false, true };\n}\nvariable WRITE30 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_62 {\n type discrete [ 2 ] { false, true };\n}\nvariable RESOLVE37 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_63 {\n type discrete [ 2 ] { false, true };\n}\nvariable NEED36 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_64 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_41 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_42 {\n type discrete [ 2 ] { false, true };\n}\nvariable IDENTIFY39 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_43 {\n type discrete [ 2 ] { false, true };\n}\nvariable RESOLVE38 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_66 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_67 {\n type discrete [ 2 ] { false, true };\n}\nvariable IDENTIFY41 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_54 {\n type discrete [ 2 ] { false, true };\n}\nvariable RESOLVE40 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_69 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_70 {\n type discrete [ 2 ] { false, true };\n}\nvariable IDENTIFY43 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_55 {\n type discrete [ 2 ] { false, true };\n}\nvariable RESOLVE42 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_72 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_73 {\n type discrete [ 2 ] { false, true };\n}\nvariable KINE29 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_74 {\n type discrete [ 2 ] { false, true };\n}\nvariable VECTOR44 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_75 {\n type discrete [ 2 ] { false, true };\n}\nvariable EQUATION28 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_79 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp5 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_80 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp6 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_81 {\n type discrete [ 2 ] { false, true };\n}\nvariable TRY25 {\n type discrete [ 2 ] { false, true };\n}\nvariable TRY24 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_83 {\n type discrete [ 2 ] { false, true };\n}\nvariable CHOOSE47 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_84 {\n type discrete [ 2 ] { false, true };\n}\nvariable SYSTEM46 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_86 {\n type discrete [ 2 ] { false, true };\n}\nvariable NEWTONS45 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_156 {\n type discrete [ 2 ] { false, true };\n}\nvariable DEFINE23 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_98 {\n type discrete [ 2 ] { false, true };\n}\nvariable IDENTIFY22 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_37 {\n type discrete [ 2 ] { false, true };\n}\nvariable TRY26 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_38 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_40 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_44 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_46 {\n type discrete [ 2 ] { false, true };\n}\nvariable NULL48 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_65 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_68 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_71 {\n type discrete [ 2 ] { false, true };\n}\nvariable FIND49 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_87 {\n type discrete [ 2 ] { false, true };\n}\nvariable NORMAL50 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_88 {\n type discrete [ 2 ] { false, true };\n}\nvariable STRAT_90 {\n type discrete [ 2 ] { SNode_92_1, SNode_91_2 };\n}\nvariable NORMAL52 {\n type discrete [ 2 ] { false, true };\n}\nvariable INCLINE51 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_91 {\n type discrete [ 2 ] { false, true };\n}\nvariable HORIZ53 {\n type discrete [ 2 ] { false, true };\n}\nvariable BUGGY54 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_92 {\n type discrete [ 2 ] { false, true };\n}\nvariable IDENTIFY55 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_93 {\n type discrete [ 2 ] { false, true };\n}\nvariable WEIGHT56 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_94 {\n type discrete [ 2 ] { false, true };\n}\nvariable WEIGHT57 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_95 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_97 {\n type discrete [ 2 ] { false, true };\n}\nvariable FIND58 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_99 {\n type discrete [ 2 ] { false, true };\n}\nvariable IDENTIFY59 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_100 {\n type discrete [ 2 ] { false, true };\n}\nvariable FORCE60 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_102 {\n type discrete [ 2 ] { false, true };\n}\nvariable APPLY61 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_103 {\n type discrete [ 2 ] { false, true };\n}\nvariable CHOOSE62 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_104 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_106 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_152 {\n type discrete [ 2 ] { false, true };\n}\nvariable WRITE63 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_107 {\n type discrete [ 2 ] { false, true };\n}\nvariable WRITE64 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_108 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_109 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL65 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_110 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL66 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_111 {\n type discrete [ 2 ] { false, true };\n}\nvariable NEED67 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp7 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp8 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_112 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL68 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_113 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_114 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_115 {\n type discrete [ 2 ] { false, true };\n}\nvariable VECTOR69 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_116 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_117 {\n type discrete [ 2 ] { false, true };\n}\nvariable VECTOR70 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_118 {\n type discrete [ 2 ] { false, true };\n}\nvariable EQUAL71 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_119 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_120 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL72 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_121 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_122 {\n type discrete [ 2 ] { false, true };\n}\nvariable VECTOR73 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_123 {\n type discrete [ 2 ] { false, true };\n}\nvariable NEWTONS74 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_124 {\n type discrete [ 2 ] { false, true };\n}\nvariable SUM75 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_125 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_126 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_127 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp9 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp10 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_128 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_129 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_130 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_131 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_132 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_133 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_134 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_135 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_154 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_136 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_137 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_142 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_143 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_146 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp11 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp12 {\n type discrete [ 2 ] { false, true };\n}\nvariable RApp13 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_147 {\n type discrete [ 2 ] { false, true };\n}\nvariable TRY76 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_149 {\n type discrete [ 2 ] { false, true };\n}\nvariable APPLY77 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_150 {\n type discrete [ 2 ] { false, true };\n}\nvariable GRAV78 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_151 {\n type discrete [ 2 ] { false, true };\n}\nvariable GOAL_153 {\n type discrete [ 2 ] { false, true };\n}\nvariable SNode_155 {\n type discrete [ 2 ] { false, true };\n}\nprobability ( GOAL_2 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_3 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_4 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_5 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_6 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_7 ) {\n table 0.02, 0.98;\n}\nprobability ( DISPLACEM0 ) {\n table 0.5, 0.5;\n}\nprobability ( RApp1 | DISPLACEM0, SNode_3 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( GIVEN_1 ) {\n table 0.02, 0.98;\n}\nprobability ( RApp2 | GIVEN_1 ) {\n (false) 1.0, 0.0;\n (true) 0.0001, 0.9999;\n}\nprobability ( SNode_8 | RApp1, RApp2 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.0, 1.0;\n (false, true) 0.0, 1.0;\n (true, true) 0.0, 1.0;\n}\nprobability ( SNode_9 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_10 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_11 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_12 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_13 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_14 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_15 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_16 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_17 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_18 ) {\n table 0.02, 0.98;\n}\nprobability ( SNode_19 ) {\n table 0.02, 0.98;\n}\nprobability ( NEED1 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_20 | SNode_16, NEED1 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( GRAV2 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_21 | SNode_20, GRAV2 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( VALUE3 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_24 | SNode_21, VALUE3 ) {\n (false, false) 0.9, 0.1;\n (true, false) 0.9, 0.1;\n (false, true) 0.9, 0.1;\n (true, true) 0.00009, 0.99991;\n}\nprobability ( SLIDING4 ) {\n table 0.1, 0.9;\n}\nprobability ( SNode_25 | SNode_15, SLIDING4 ) {\n (false, false) 0.9, 0.1;\n (true, false) 0.9, 0.1;\n (false, true) 0.9, 0.1;\n (true, true) 0.00009, 0.99991;\n}\nprobability ( CONSTANT5 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_26 | SNode_11, CONSTANT5 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( KNOWN6 ) {\n table 0.5, 0.5;\n}\nprobability ( VELOCITY7 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_47 | SNode_3, VELOCITY7 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( RApp3 | KNOWN6, SNode_26, SNode_47 ) {\n (false, false, false) 1.0, 0.0;\n (true, false, false) 1.0, 0.0;\n (false, true, false) 1.0, 0.0;\n (true, true, false) 1.0, 0.0;\n (false, false, true) 1.0, 0.0;\n (true, false, true) 1.0, 0.0;\n (false, true, true) 1.0, 0.0;\n (true, true, true) 0.0001, 0.9999;\n}\nprobability ( KNOWN8 ) {\n table 0.5, 0.5;\n}\nprobability ( RApp4 | KNOWN8, SNode_11 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( SNode_27 | RApp3, RApp4 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.0, 1.0;\n (false, true) 0.0, 1.0;\n (true, true) 0.0, 1.0;\n}\nprobability ( COMPO16 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_48 | GOAL_2, COMPO16 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( TRY12 ) {\n table 0.7, 0.3;\n}\nprobability ( TRY11 | TRY12 ) {\n (false) 0.8, 0.2;\n (true) 0.0, 1.0;\n}\nprobability ( GOAL_49 | SNode_5, SNode_6, GOAL_48, TRY11 ) {\n (false, false, false, false) 0.8, 0.2;\n (true, false, false, false) 0.8, 0.2;\n (false, true, false, false) 0.8, 0.2;\n (true, true, false, false) 0.8, 0.2;\n (false, false, true, false) 0.8, 0.2;\n (true, false, true, false) 0.8, 0.2;\n (false, true, true, false) 0.8, 0.2;\n (true, true, true, false) 0.8, 0.2;\n (false, false, false, true) 0.8, 0.2;\n (true, false, false, true) 0.8, 0.2;\n (false, true, false, true) 0.8, 0.2;\n (true, true, false, true) 0.8, 0.2;\n (false, false, true, true) 0.8, 0.2;\n (true, false, true, true) 0.8, 0.2;\n (false, true, true, true) 0.8, 0.2;\n (true, true, true, true) 0.00008, 0.99992;\n}\nprobability ( CHOOSE19 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_50 | GOAL_49, CHOOSE19 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SYSTEM18 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_51 | SNode_17, GOAL_50, SYSTEM18 ) {\n (false, false, false) 0.9, 0.1;\n (true, false, false) 0.9, 0.1;\n (false, true, false) 0.9, 0.1;\n (true, true, false) 0.9, 0.1;\n (false, false, true) 0.9, 0.1;\n (true, false, true) 0.9, 0.1;\n (false, true, true) 0.9, 0.1;\n (true, true, true) 0.00009, 0.99991;\n}\nprobability ( KINEMATI17 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_52 | SNode_51, KINEMATI17 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( IDENTIFY10 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_53 | GOAL_49, SNode_52, IDENTIFY10 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( IDENTIFY9 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_28 | SNode_27, GOAL_53, IDENTIFY9 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( TRY13 | TRY12 ) {\n (false) 0.8, 0.2;\n (true) 0.0, 1.0;\n}\nprobability ( TRY14 | TRY12 ) {\n (false) 0.9, 0.1;\n (true) 0.0, 1.0;\n}\nprobability ( TRY15 | TRY12 ) {\n (false) 0.9, 0.1;\n (true) 0.0, 1.0;\n}\nprobability ( VAR20 ) {\n table 0.3, 0.7;\n}\nprobability ( SNode_29 | SNode_28, VAR20 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_31 | SNode_29, VALUE3 ) {\n (false, false) 0.9, 0.1;\n (true, false) 0.9, 0.1;\n (false, true) 0.9, 0.1;\n (true, true) 0.00009, 0.99991;\n}\nprobability ( GIVEN21 ) {\n table 0.1, 0.9;\n}\nprobability ( SNode_33 | SNode_10, GIVEN21 ) {\n (false, false) 0.9, 0.1;\n (true, false) 0.9, 0.1;\n (false, true) 0.9, 0.1;\n (true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_34 | SNode_10, CONSTANT5 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( VECTOR27 ) {\n table 0.5, 0.5;\n}\nprobability ( APPLY32 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_56 | GOAL_49, SNode_52, APPLY32 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( CHOOSE35 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_57 | GOAL_56, CHOOSE35 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( MAXIMIZE34 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_59 | SNode_7, GOAL_57, MAXIMIZE34 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( AXIS33 ) {\n table 0.2, 0.8;\n}\nprobability ( SNode_60 | SNode_59, AXIS33 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( WRITE31 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_61 | GOAL_56, SNode_60, WRITE31 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( WRITE30 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_62 | GOAL_61, WRITE30 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( RESOLVE37 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_63 | SNode_28, GOAL_62, RESOLVE37 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( NEED36 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_64 | GOAL_63, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_41 | SNode_9, CONSTANT5 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_42 | SNode_8, SNode_41, KNOWN6 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( IDENTIFY39 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_43 | SNode_42, GOAL_53, IDENTIFY39 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( RESOLVE38 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_66 | SNode_43, GOAL_62, RESOLVE38 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_67 | GOAL_66, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( IDENTIFY41 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_54 | GOAL_53, IDENTIFY41 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( RESOLVE40 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_69 | SNode_54, GOAL_62, RESOLVE40 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_70 | GOAL_69, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( IDENTIFY43 ) {\n table 0.6, 0.4;\n}\nprobability ( SNode_55 | SNode_34, GOAL_53, IDENTIFY43 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( RESOLVE42 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_72 | SNode_55, GOAL_62, RESOLVE42 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_73 | GOAL_72, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( KINE29 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_74 | GOAL_62, SNode_64, SNode_67, SNode_70, SNode_73, KINE29 ) {\n (false, false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false, false) 0.9, 0.1;\n (true, true, false, false, false, false) 0.9, 0.1;\n (false, false, true, false, false, false) 0.9, 0.1;\n (true, false, true, false, false, false) 0.9, 0.1;\n (false, true, true, false, false, false) 0.9, 0.1;\n (true, true, true, false, false, false) 0.9, 0.1;\n (false, false, false, true, false, false) 0.9, 0.1;\n (true, false, false, true, false, false) 0.9, 0.1;\n (false, true, false, true, false, false) 0.9, 0.1;\n (true, true, false, true, false, false) 0.9, 0.1;\n (false, false, true, true, false, false) 0.9, 0.1;\n (true, false, true, true, false, false) 0.9, 0.1;\n (false, true, true, true, false, false) 0.9, 0.1;\n (true, true, true, true, false, false) 0.9, 0.1;\n (false, false, false, false, true, false) 0.9, 0.1;\n (true, false, false, false, true, false) 0.9, 0.1;\n (false, true, false, false, true, false) 0.9, 0.1;\n (true, true, false, false, true, false) 0.9, 0.1;\n (false, false, true, false, true, false) 0.9, 0.1;\n (true, false, true, false, true, false) 0.9, 0.1;\n (false, true, true, false, true, false) 0.9, 0.1;\n (true, true, true, false, true, false) 0.9, 0.1;\n (false, false, false, true, true, false) 0.9, 0.1;\n (true, false, false, true, true, false) 0.9, 0.1;\n (false, true, false, true, true, false) 0.9, 0.1;\n (true, true, false, true, true, false) 0.9, 0.1;\n (false, false, true, true, true, false) 0.9, 0.1;\n (true, false, true, true, true, false) 0.9, 0.1;\n (false, true, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, false, true) 0.9, 0.1;\n (true, true, false, false, false, true) 0.9, 0.1;\n (false, false, true, false, false, true) 0.9, 0.1;\n (true, false, true, false, false, true) 0.9, 0.1;\n (false, true, true, false, false, true) 0.9, 0.1;\n (true, true, true, false, false, true) 0.9, 0.1;\n (false, false, false, true, false, true) 0.9, 0.1;\n (true, false, false, true, false, true) 0.9, 0.1;\n (false, true, false, true, false, true) 0.9, 0.1;\n (true, true, false, true, false, true) 0.9, 0.1;\n (false, false, true, true, false, true) 0.9, 0.1;\n (true, false, true, true, false, true) 0.9, 0.1;\n (false, true, true, true, false, true) 0.9, 0.1;\n (true, true, true, true, false, true) 0.9, 0.1;\n (false, false, false, false, true, true) 0.9, 0.1;\n (true, false, false, false, true, true) 0.9, 0.1;\n (false, true, false, false, true, true) 0.9, 0.1;\n (true, true, false, false, true, true) 0.9, 0.1;\n (false, false, true, false, true, true) 0.9, 0.1;\n (true, false, true, false, true, true) 0.9, 0.1;\n (false, true, true, false, true, true) 0.9, 0.1;\n (true, true, true, false, true, true) 0.9, 0.1;\n (false, false, false, true, true, true) 0.9, 0.1;\n (true, false, false, true, true, true) 0.9, 0.1;\n (false, true, false, true, true, true) 0.9, 0.1;\n (true, true, false, true, true, true) 0.9, 0.1;\n (false, false, true, true, true, true) 0.9, 0.1;\n (true, false, true, true, true, true) 0.9, 0.1;\n (false, true, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( VECTOR44 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_75 | SNode_4, GOAL_72, SNode_73, VECTOR44 ) {\n (false, false, false, false) 0.9, 0.1;\n (true, false, false, false) 0.9, 0.1;\n (false, true, false, false) 0.9, 0.1;\n (true, true, false, false) 0.9, 0.1;\n (false, false, true, false) 0.9, 0.1;\n (true, false, true, false) 0.9, 0.1;\n (false, true, true, false) 0.9, 0.1;\n (true, true, true, false) 0.9, 0.1;\n (false, false, false, true) 0.9, 0.1;\n (true, false, false, true) 0.9, 0.1;\n (false, true, false, true) 0.9, 0.1;\n (true, true, false, true) 0.9, 0.1;\n (false, false, true, true) 0.9, 0.1;\n (true, false, true, true) 0.9, 0.1;\n (false, true, true, true) 0.9, 0.1;\n (true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( EQUATION28 ) {\n table 0.6, 0.4;\n}\nprobability ( GOAL_79 | SNode_74, SNode_75, EQUATION28 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( RApp5 | VECTOR27, GOAL_79 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( GOAL_80 | SNode_75, EQUATION28 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( RApp6 | COMPO16, GOAL_80 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( GOAL_81 | RApp5, RApp6 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.0, 1.0;\n (false, true) 0.0, 1.0;\n (true, true) 0.0, 1.0;\n}\nprobability ( TRY25 ) {\n table 0.1, 0.9;\n}\nprobability ( TRY24 | TRY25 ) {\n (false) 0.95, 0.05;\n (true) 0.0, 1.0;\n}\nprobability ( GOAL_83 | GOAL_81, TRY24 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( CHOOSE47 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_84 | GOAL_83, CHOOSE47 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SYSTEM46 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_86 | GOAL_84, SYSTEM46 ) {\n (false, false) 0.9, 0.1;\n (true, false) 0.9, 0.1;\n (false, true) 0.9, 0.1;\n (true, true) 0.00009, 0.99991;\n}\nprobability ( NEWTONS45 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_156 | SNode_86, NEWTONS45 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( DEFINE23 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_98 | GOAL_83, SNode_156, DEFINE23 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( IDENTIFY22 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_37 | GOAL_98, IDENTIFY22 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( TRY26 | TRY25 ) {\n (false) 0.9, 0.1;\n (true) 0.0, 1.0;\n}\nprobability ( SNode_38 | SNode_37, VAR20 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_40 | SNode_38, VALUE3 ) {\n (false, false) 0.9, 0.1;\n (true, false) 0.9, 0.1;\n (false, true) 0.9, 0.1;\n (true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_44 | SNode_43, VAR20 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_46 | SNode_44, VALUE3 ) {\n (false, false) 0.9, 0.1;\n (true, false) 0.9, 0.1;\n (false, true) 0.9, 0.1;\n (true, true) 0.00009, 0.99991;\n}\nprobability ( NULL48 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_65 | SNode_29, GOAL_63, SNode_64, NULL48 ) {\n (false, false, false, false) 0.9, 0.1;\n (true, false, false, false) 0.9, 0.1;\n (false, true, false, false) 0.9, 0.1;\n (true, true, false, false) 0.9, 0.1;\n (false, false, true, false) 0.9, 0.1;\n (true, false, true, false) 0.9, 0.1;\n (false, true, true, false) 0.9, 0.1;\n (true, true, true, false) 0.9, 0.1;\n (false, false, false, true) 0.9, 0.1;\n (true, false, false, true) 0.9, 0.1;\n (false, true, false, true) 0.9, 0.1;\n (true, true, false, true) 0.9, 0.1;\n (false, false, true, true) 0.9, 0.1;\n (true, false, true, true) 0.9, 0.1;\n (false, true, true, true) 0.9, 0.1;\n (true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_68 | GOAL_66, SNode_67, VECTOR44 ) {\n (false, false, false) 0.9, 0.1;\n (true, false, false) 0.9, 0.1;\n (false, true, false) 0.9, 0.1;\n (true, true, false) 0.9, 0.1;\n (false, false, true) 0.9, 0.1;\n (true, false, true) 0.9, 0.1;\n (false, true, true) 0.9, 0.1;\n (true, true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_71 | GOAL_69, SNode_70, VECTOR44 ) {\n (false, false, false) 0.9, 0.1;\n (true, false, false) 0.9, 0.1;\n (false, true, false) 0.9, 0.1;\n (true, true, false) 0.9, 0.1;\n (false, false, true) 0.9, 0.1;\n (true, false, true) 0.9, 0.1;\n (false, true, true) 0.9, 0.1;\n (true, true, true) 0.00009, 0.99991;\n}\nprobability ( FIND49 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_87 | GOAL_83, SNode_156, FIND49 ) {\n (false, false, false) 0.7, 0.3;\n (true, false, false) 0.7, 0.3;\n (false, true, false) 0.7, 0.3;\n (true, true, false) 0.7, 0.3;\n (false, false, true) 0.7, 0.3;\n (true, false, true) 0.7, 0.3;\n (false, true, true) 0.7, 0.3;\n (true, true, true) 0.00007, 0.99993;\n}\nprobability ( NORMAL50 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_88 | SNode_25, GOAL_87, NORMAL50 ) {\n (false, false, false) 0.9, 0.1;\n (true, false, false) 0.9, 0.1;\n (false, true, false) 0.9, 0.1;\n (true, true, false) 0.9, 0.1;\n (false, false, true) 0.9, 0.1;\n (true, false, true) 0.9, 0.1;\n (false, true, true) 0.9, 0.1;\n (true, true, true) 0.00009, 0.99991;\n}\nprobability ( STRAT_90 ) {\n table 0.5, 0.5;\n}\nprobability ( NORMAL52 ) {\n table 0.646, 0.354;\n}\nprobability ( INCLINE51 | NORMAL52 ) {\n (false) 1.0, 0.0;\n (true) 0.0, 1.0;\n}\nprobability ( SNode_91 | SNode_88, SNode_12, SNode_13, STRAT_90, INCLINE51 ) {\n (false, false, false, SNode_92_1, false) 0.8, 0.2;\n (true, false, false, SNode_92_1, false) 0.8, 0.2;\n (false, true, false, SNode_92_1, false) 0.8, 0.2;\n (true, true, false, SNode_92_1, false) 0.8, 0.2;\n (false, false, true, SNode_92_1, false) 0.8, 0.2;\n (true, false, true, SNode_92_1, false) 0.8, 0.2;\n (false, true, true, SNode_92_1, false) 0.8, 0.2;\n (true, true, true, SNode_92_1, false) 0.8, 0.2;\n (false, false, false, SNode_91_2, false) 0.8, 0.2;\n (true, false, false, SNode_91_2, false) 0.8, 0.2;\n (false, true, false, SNode_91_2, false) 0.8, 0.2;\n (true, true, false, SNode_91_2, false) 0.8, 0.2;\n (false, false, true, SNode_91_2, false) 0.8, 0.2;\n (true, false, true, SNode_91_2, false) 0.8, 0.2;\n (false, true, true, SNode_91_2, false) 0.8, 0.2;\n (true, true, true, SNode_91_2, false) 0.8, 0.2;\n (false, false, false, SNode_92_1, true) 0.8, 0.2;\n (true, false, false, SNode_92_1, true) 0.8, 0.2;\n (false, true, false, SNode_92_1, true) 0.8, 0.2;\n (true, true, false, SNode_92_1, true) 0.8, 0.2;\n (false, false, true, SNode_92_1, true) 0.8, 0.2;\n (true, false, true, SNode_92_1, true) 0.8, 0.2;\n (false, true, true, SNode_92_1, true) 0.8, 0.2;\n (true, true, true, SNode_92_1, true) 0.8, 0.2;\n (false, false, false, SNode_91_2, true) 0.8, 0.2;\n (true, false, false, SNode_91_2, true) 0.8, 0.2;\n (false, true, false, SNode_91_2, true) 0.8, 0.2;\n (true, true, false, SNode_91_2, true) 0.8, 0.2;\n (false, false, true, SNode_91_2, true) 0.8, 0.2;\n (true, false, true, SNode_91_2, true) 0.8, 0.2;\n (false, true, true, SNode_91_2, true) 0.8, 0.2;\n (true, true, true, SNode_91_2, true) 0.00008, 0.99992;\n}\nprobability ( HORIZ53 | NORMAL52 ) {\n (false) 0.8, 0.2;\n (true) 0.0, 1.0;\n}\nprobability ( BUGGY54 | NORMAL52 ) {\n (false) 0.2, 0.8;\n (true) 1.0, 0.0;\n}\nprobability ( SNode_92 | SNode_12, STRAT_90, BUGGY54 ) {\n (false, SNode_92_1, false) 0.8, 0.2;\n (true, SNode_92_1, false) 0.8, 0.2;\n (false, SNode_91_2, false) 0.8, 0.2;\n (true, SNode_91_2, false) 0.8, 0.2;\n (false, SNode_92_1, true) 0.8, 0.2;\n (true, SNode_92_1, true) 0.00008, 0.99992;\n (false, SNode_91_2, true) 0.8, 0.2;\n (true, SNode_91_2, true) 0.8, 0.2;\n}\nprobability ( IDENTIFY55 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_93 | GOAL_87, SNode_88, IDENTIFY55 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( WEIGHT56 ) {\n table 0.3, 0.7;\n}\nprobability ( SNode_94 | SNode_16, SNode_33, GOAL_87, WEIGHT56 ) {\n (false, false, false, false) 0.9, 0.1;\n (true, false, false, false) 0.9, 0.1;\n (false, true, false, false) 0.9, 0.1;\n (true, true, false, false) 0.9, 0.1;\n (false, false, true, false) 0.9, 0.1;\n (true, false, true, false) 0.9, 0.1;\n (false, true, true, false) 0.9, 0.1;\n (true, true, true, false) 0.9, 0.1;\n (false, false, false, true) 0.9, 0.1;\n (true, false, false, true) 0.9, 0.1;\n (false, true, false, true) 0.9, 0.1;\n (true, true, false, true) 0.9, 0.1;\n (false, false, true, true) 0.9, 0.1;\n (true, false, true, true) 0.9, 0.1;\n (false, true, true, true) 0.9, 0.1;\n (true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( WEIGHT57 ) {\n table 0.3, 0.7;\n}\nprobability ( SNode_95 | SNode_94, WEIGHT57 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_97 | GOAL_87, SNode_94, IDENTIFY55 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( FIND58 ) {\n table 0.7, 0.3;\n}\nprobability ( GOAL_99 | GOAL_98, FIND58 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( IDENTIFY59 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_100 | GOAL_98, IDENTIFY59 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( FORCE60 ) {\n table 0.2, 0.8;\n}\nprobability ( SNode_102 | GOAL_87, SNode_88, SNode_94, FORCE60 ) {\n (false, false, false, false) 0.9, 0.1;\n (true, false, false, false) 0.9, 0.1;\n (false, true, false, false) 0.9, 0.1;\n (true, true, false, false) 0.9, 0.1;\n (false, false, true, false) 0.9, 0.1;\n (true, false, true, false) 0.9, 0.1;\n (false, true, true, false) 0.9, 0.1;\n (true, true, true, false) 0.9, 0.1;\n (false, false, false, true) 0.9, 0.1;\n (true, false, false, true) 0.9, 0.1;\n (false, true, false, true) 0.9, 0.1;\n (true, true, false, true) 0.9, 0.1;\n (false, false, true, true) 0.9, 0.1;\n (true, false, true, true) 0.9, 0.1;\n (false, true, true, true) 0.9, 0.1;\n (true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( APPLY61 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_103 | GOAL_83, SNode_102, APPLY61 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( CHOOSE62 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_104 | GOAL_103, CHOOSE62 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_106 | GOAL_104, MAXIMIZE34 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_152 | SNode_106, AXIS33 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( WRITE63 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_107 | GOAL_103, SNode_152, WRITE63 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( WRITE64 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_108 | GOAL_107, WRITE64 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( GOAL_109 | GOAL_107, WRITE64 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( GOAL65 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_110 | GOAL_109, GOAL65 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( GOAL66 ) {\n table 0.6, 0.4;\n}\nprobability ( GOAL_111 | GOAL_109, GOAL66 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( NEED67 ) {\n table 0.5, 0.5;\n}\nprobability ( RApp7 | NEED67, GOAL_109 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( RApp8 | NEED36, GOAL_111 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( SNode_112 | RApp7, RApp8 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.0, 1.0;\n (false, true) 0.0, 1.0;\n (true, true) 0.0, 1.0;\n}\nprobability ( GOAL68 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_113 | GOAL_110, GOAL68 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( GOAL_114 | GOAL_110, GOAL68 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_115 | GOAL_114, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( VECTOR69 ) {\n table 0.7, 0.3;\n}\nprobability ( SNode_116 | SNode_95, SNode_97, GOAL_114, SNode_115, VECTOR69 ) {\n (false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false) 0.9, 0.1;\n (true, true, false, false, false) 0.9, 0.1;\n (false, false, true, false, false) 0.9, 0.1;\n (true, false, true, false, false) 0.9, 0.1;\n (false, true, true, false, false) 0.9, 0.1;\n (true, true, true, false, false) 0.9, 0.1;\n (false, false, false, true, false) 0.9, 0.1;\n (true, false, false, true, false) 0.9, 0.1;\n (false, true, false, true, false) 0.9, 0.1;\n (true, true, false, true, false) 0.9, 0.1;\n (false, false, true, true, false) 0.9, 0.1;\n (true, false, true, true, false) 0.9, 0.1;\n (false, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, true) 0.9, 0.1;\n (true, true, false, false, true) 0.9, 0.1;\n (false, false, true, false, true) 0.9, 0.1;\n (true, false, true, false, true) 0.9, 0.1;\n (false, true, true, false, true) 0.9, 0.1;\n (true, true, true, false, true) 0.9, 0.1;\n (false, false, false, true, true) 0.9, 0.1;\n (true, false, false, true, true) 0.9, 0.1;\n (false, true, false, true, true) 0.9, 0.1;\n (true, true, false, true, true) 0.9, 0.1;\n (false, false, true, true, true) 0.9, 0.1;\n (true, false, true, true, true) 0.9, 0.1;\n (false, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_117 | GOAL_113, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( VECTOR70 ) {\n table 0.1, 0.9;\n}\nprobability ( SNode_118 | SNode_91, GOAL_113, VECTOR70 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( EQUAL71 ) {\n table 0.4, 0.6;\n}\nprobability ( SNode_119 | SNode_93, SNode_117, SNode_118, EQUAL71 ) {\n (false, false, false, false) 0.9, 0.1;\n (true, false, false, false) 0.9, 0.1;\n (false, true, false, false) 0.9, 0.1;\n (true, true, false, false) 0.9, 0.1;\n (false, false, true, false) 0.9, 0.1;\n (true, false, true, false) 0.9, 0.1;\n (false, true, true, false) 0.9, 0.1;\n (true, true, true, false) 0.9, 0.1;\n (false, false, false, true) 0.9, 0.1;\n (true, false, false, true) 0.9, 0.1;\n (false, true, false, true) 0.9, 0.1;\n (true, true, false, true) 0.9, 0.1;\n (false, false, true, true) 0.9, 0.1;\n (true, false, true, true) 0.9, 0.1;\n (false, true, true, true) 0.9, 0.1;\n (true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_120 | SNode_92, SNode_93, GOAL_113, SNode_117, VECTOR69 ) {\n (false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false) 0.9, 0.1;\n (true, true, false, false, false) 0.9, 0.1;\n (false, false, true, false, false) 0.9, 0.1;\n (true, false, true, false, false) 0.9, 0.1;\n (false, true, true, false, false) 0.9, 0.1;\n (true, true, true, false, false) 0.9, 0.1;\n (false, false, false, true, false) 0.9, 0.1;\n (true, false, false, true, false) 0.9, 0.1;\n (false, true, false, true, false) 0.9, 0.1;\n (true, true, false, true, false) 0.9, 0.1;\n (false, false, true, true, false) 0.9, 0.1;\n (true, false, true, true, false) 0.9, 0.1;\n (false, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, true) 0.9, 0.1;\n (true, true, false, false, true) 0.9, 0.1;\n (false, false, true, false, true) 0.9, 0.1;\n (true, false, true, false, true) 0.9, 0.1;\n (false, true, true, false, true) 0.9, 0.1;\n (true, true, true, false, true) 0.9, 0.1;\n (false, false, false, true, true) 0.9, 0.1;\n (true, false, false, true, true) 0.9, 0.1;\n (false, true, false, true, true) 0.9, 0.1;\n (true, true, false, true, true) 0.9, 0.1;\n (false, false, true, true, true) 0.9, 0.1;\n (true, false, true, true, true) 0.9, 0.1;\n (false, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( GOAL72 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_121 | GOAL_109, GOAL72 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_122 | GOAL_121, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( VECTOR73 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_123 | SNode_4, SNode_100, GOAL_121, SNode_122, VECTOR73 ) {\n (false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false) 0.9, 0.1;\n (true, true, false, false, false) 0.9, 0.1;\n (false, false, true, false, false) 0.9, 0.1;\n (true, false, true, false, false) 0.9, 0.1;\n (false, true, true, false, false) 0.9, 0.1;\n (true, true, true, false, false) 0.9, 0.1;\n (false, false, false, true, false) 0.9, 0.1;\n (true, false, false, true, false) 0.9, 0.1;\n (false, true, false, true, false) 0.9, 0.1;\n (true, true, false, true, false) 0.9, 0.1;\n (false, false, true, true, false) 0.9, 0.1;\n (true, false, true, true, false) 0.9, 0.1;\n (false, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, true) 0.9, 0.1;\n (true, true, false, false, true) 0.9, 0.1;\n (false, false, true, false, true) 0.9, 0.1;\n (true, false, true, false, true) 0.9, 0.1;\n (false, true, true, false, true) 0.9, 0.1;\n (true, true, true, false, true) 0.9, 0.1;\n (false, false, false, true, true) 0.9, 0.1;\n (true, false, false, true, true) 0.9, 0.1;\n (false, true, false, true, true) 0.9, 0.1;\n (true, true, false, true, true) 0.9, 0.1;\n (false, false, true, true, true) 0.9, 0.1;\n (true, false, true, true, true) 0.9, 0.1;\n (false, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( NEWTONS74 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_124 | SNode_37, GOAL_109, SNode_112, SNode_122, NEWTONS74 ) {\n (false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false) 0.9, 0.1;\n (true, true, false, false, false) 0.9, 0.1;\n (false, false, true, false, false) 0.9, 0.1;\n (true, false, true, false, false) 0.9, 0.1;\n (false, true, true, false, false) 0.9, 0.1;\n (true, true, true, false, false) 0.9, 0.1;\n (false, false, false, true, false) 0.9, 0.1;\n (true, false, false, true, false) 0.9, 0.1;\n (false, true, false, true, false) 0.9, 0.1;\n (true, true, false, true, false) 0.9, 0.1;\n (false, false, true, true, false) 0.9, 0.1;\n (true, false, true, true, false) 0.9, 0.1;\n (false, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, true) 0.9, 0.1;\n (true, true, false, false, true) 0.9, 0.1;\n (false, false, true, false, true) 0.9, 0.1;\n (true, false, true, false, true) 0.9, 0.1;\n (false, true, true, false, true) 0.9, 0.1;\n (true, true, true, false, true) 0.9, 0.1;\n (false, false, false, true, true) 0.9, 0.1;\n (true, false, false, true, true) 0.9, 0.1;\n (false, true, false, true, true) 0.9, 0.1;\n (true, true, false, true, true) 0.9, 0.1;\n (false, false, true, true, true) 0.9, 0.1;\n (true, false, true, true, true) 0.9, 0.1;\n (false, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( SUM75 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_125 | GOAL_109, SNode_112, SUM75 ) {\n (false, false, false) 0.9, 0.1;\n (true, false, false) 0.9, 0.1;\n (false, true, false) 0.9, 0.1;\n (true, true, false) 0.9, 0.1;\n (false, false, true) 0.9, 0.1;\n (true, false, true) 0.9, 0.1;\n (false, true, true) 0.9, 0.1;\n (true, true, true) 0.00009, 0.99991;\n}\nprobability ( GOAL_126 | GOAL_108, GOAL65 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( GOAL_127 | GOAL_108, GOAL66 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( RApp9 | NEED67, GOAL_108 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( RApp10 | NEED36, GOAL_127 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( SNode_128 | RApp9, RApp10 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.0, 1.0;\n (false, true) 0.0, 1.0;\n (true, true) 0.0, 1.0;\n}\nprobability ( GOAL_129 | GOAL_126, GOAL68 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( GOAL_130 | GOAL_126, GOAL68 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_131 | GOAL_130, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_132 | SNode_95, SNode_97, GOAL_130, SNode_131, VECTOR69 ) {\n (false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false) 0.9, 0.1;\n (true, true, false, false, false) 0.9, 0.1;\n (false, false, true, false, false) 0.9, 0.1;\n (true, false, true, false, false) 0.9, 0.1;\n (false, true, true, false, false) 0.9, 0.1;\n (true, true, true, false, false) 0.9, 0.1;\n (false, false, false, true, false) 0.9, 0.1;\n (true, false, false, true, false) 0.9, 0.1;\n (false, true, false, true, false) 0.9, 0.1;\n (true, true, false, true, false) 0.9, 0.1;\n (false, false, true, true, false) 0.9, 0.1;\n (true, false, true, true, false) 0.9, 0.1;\n (false, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, true) 0.9, 0.1;\n (true, true, false, false, true) 0.9, 0.1;\n (false, false, true, false, true) 0.9, 0.1;\n (true, false, true, false, true) 0.9, 0.1;\n (false, true, true, false, true) 0.9, 0.1;\n (true, true, true, false, true) 0.9, 0.1;\n (false, false, false, true, true) 0.9, 0.1;\n (true, false, false, true, true) 0.9, 0.1;\n (false, true, false, true, true) 0.9, 0.1;\n (true, true, false, true, true) 0.9, 0.1;\n (false, false, true, true, true) 0.9, 0.1;\n (true, false, true, true, true) 0.9, 0.1;\n (false, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_133 | GOAL_129, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_134 | SNode_91, SNode_93, GOAL_129, SNode_133, VECTOR73 ) {\n (false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false) 0.9, 0.1;\n (true, true, false, false, false) 0.9, 0.1;\n (false, false, true, false, false) 0.9, 0.1;\n (true, false, true, false, false) 0.9, 0.1;\n (false, true, true, false, false) 0.9, 0.1;\n (true, true, true, false, false) 0.9, 0.1;\n (false, false, false, true, false) 0.9, 0.1;\n (true, false, false, true, false) 0.9, 0.1;\n (false, true, false, true, false) 0.9, 0.1;\n (true, true, false, true, false) 0.9, 0.1;\n (false, false, true, true, false) 0.9, 0.1;\n (true, false, true, true, false) 0.9, 0.1;\n (false, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, true) 0.9, 0.1;\n (true, true, false, false, true) 0.9, 0.1;\n (false, false, true, false, true) 0.9, 0.1;\n (true, false, true, false, true) 0.9, 0.1;\n (false, true, true, false, true) 0.9, 0.1;\n (true, true, true, false, true) 0.9, 0.1;\n (false, false, false, true, true) 0.9, 0.1;\n (true, false, false, true, true) 0.9, 0.1;\n (false, true, false, true, true) 0.9, 0.1;\n (true, true, false, true, true) 0.9, 0.1;\n (false, false, true, true, true) 0.9, 0.1;\n (true, false, true, true, true) 0.9, 0.1;\n (false, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_135 | SNode_92, SNode_93, GOAL_129, SNode_133, VECTOR69 ) {\n (false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false) 0.9, 0.1;\n (true, true, false, false, false) 0.9, 0.1;\n (false, false, true, false, false) 0.9, 0.1;\n (true, false, true, false, false) 0.9, 0.1;\n (false, true, true, false, false) 0.9, 0.1;\n (true, true, true, false, false) 0.9, 0.1;\n (false, false, false, true, false) 0.9, 0.1;\n (true, false, false, true, false) 0.9, 0.1;\n (false, true, false, true, false) 0.9, 0.1;\n (true, true, false, true, false) 0.9, 0.1;\n (false, false, true, true, false) 0.9, 0.1;\n (true, false, true, true, false) 0.9, 0.1;\n (false, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, true) 0.9, 0.1;\n (true, true, false, false, true) 0.9, 0.1;\n (false, false, true, false, true) 0.9, 0.1;\n (true, false, true, false, true) 0.9, 0.1;\n (false, true, true, false, true) 0.9, 0.1;\n (true, true, true, false, true) 0.9, 0.1;\n (false, false, false, true, true) 0.9, 0.1;\n (true, false, false, true, true) 0.9, 0.1;\n (false, true, false, true, true) 0.9, 0.1;\n (true, true, false, true, true) 0.9, 0.1;\n (false, false, true, true, true) 0.9, 0.1;\n (true, false, true, true, true) 0.9, 0.1;\n (false, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_154 | GOAL_121, NEED36 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_136 | SNode_37, GOAL_108, SNode_128, SNode_154, NEWTONS74 ) {\n (false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false) 0.9, 0.1;\n (true, true, false, false, false) 0.9, 0.1;\n (false, false, true, false, false) 0.9, 0.1;\n (true, false, true, false, false) 0.9, 0.1;\n (false, true, true, false, false) 0.9, 0.1;\n (true, true, true, false, false) 0.9, 0.1;\n (false, false, false, true, false) 0.9, 0.1;\n (true, false, false, true, false) 0.9, 0.1;\n (false, true, false, true, false) 0.9, 0.1;\n (true, true, false, true, false) 0.9, 0.1;\n (false, false, true, true, false) 0.9, 0.1;\n (true, false, true, true, false) 0.9, 0.1;\n (false, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, true) 0.9, 0.1;\n (true, true, false, false, true) 0.9, 0.1;\n (false, false, true, false, true) 0.9, 0.1;\n (true, false, true, false, true) 0.9, 0.1;\n (false, true, true, false, true) 0.9, 0.1;\n (true, true, true, false, true) 0.9, 0.1;\n (false, false, false, true, true) 0.9, 0.1;\n (true, false, false, true, true) 0.9, 0.1;\n (false, true, false, true, true) 0.9, 0.1;\n (true, true, false, true, true) 0.9, 0.1;\n (false, false, true, true, true) 0.9, 0.1;\n (true, false, true, true, true) 0.9, 0.1;\n (false, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true) 0.00009, 0.99991;\n}\nprobability ( SNode_137 | GOAL_108, SNode_128, SUM75 ) {\n (false, false, false) 0.9, 0.1;\n (true, false, false) 0.9, 0.1;\n (false, true, false) 0.9, 0.1;\n (true, true, false) 0.9, 0.1;\n (false, false, true) 0.9, 0.1;\n (true, false, true) 0.9, 0.1;\n (false, true, true) 0.9, 0.1;\n (true, true, true) 0.00009, 0.99991;\n}\nprobability ( GOAL_142 | SNode_116, SNode_125, EQUATION28 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( GOAL_143 | SNode_116, SNode_132, EQUATION28 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( GOAL_146 | SNode_132, SNode_137, EQUATION28 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.8, 0.2;\n (false, true, false) 0.8, 0.2;\n (true, true, false) 0.8, 0.2;\n (false, false, true) 0.8, 0.2;\n (true, false, true) 0.8, 0.2;\n (false, true, true) 0.8, 0.2;\n (true, true, true) 0.00008, 0.99992;\n}\nprobability ( RApp11 | VECTOR27, GOAL_142 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( RApp12 | COMPO16, GOAL_143 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( RApp13 | VECTOR27, GOAL_146 ) {\n (false, false) 1.0, 0.0;\n (true, false) 1.0, 0.0;\n (false, true) 1.0, 0.0;\n (true, true) 0.0001, 0.9999;\n}\nprobability ( GOAL_147 | RApp11, RApp12, RApp13 ) {\n (false, false, false) 0.8, 0.2;\n (true, false, false) 0.0, 1.0;\n (false, true, false) 0.0, 1.0;\n (true, true, false) 0.0, 1.0;\n (false, false, true) 0.0, 1.0;\n (true, false, true) 0.0, 1.0;\n (false, true, true) 0.0, 1.0;\n (true, true, true) 0.0, 1.0;\n}\nprobability ( TRY76 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_149 | GOAL_147, TRY76 ) {\n (false, false) 0.7, 0.3;\n (true, false) 0.7, 0.3;\n (false, true) 0.7, 0.3;\n (true, true) 0.00007, 0.99993;\n}\nprobability ( APPLY77 ) {\n table 0.5, 0.5;\n}\nprobability ( GOAL_150 | SNode_20, SNode_37, GOAL_149, APPLY77 ) {\n (false, false, false, false) 0.8, 0.2;\n (true, false, false, false) 0.8, 0.2;\n (false, true, false, false) 0.8, 0.2;\n (true, true, false, false) 0.8, 0.2;\n (false, false, true, false) 0.8, 0.2;\n (true, false, true, false) 0.8, 0.2;\n (false, true, true, false) 0.8, 0.2;\n (true, true, true, false) 0.8, 0.2;\n (false, false, false, true) 0.8, 0.2;\n (true, false, false, true) 0.8, 0.2;\n (false, true, false, true) 0.8, 0.2;\n (true, true, false, true) 0.8, 0.2;\n (false, false, true, true) 0.8, 0.2;\n (true, false, true, true) 0.8, 0.2;\n (false, true, true, true) 0.8, 0.2;\n (true, true, true, true) 0.00008, 0.99992;\n}\nprobability ( GRAV78 ) {\n table 0.5, 0.5;\n}\nprobability ( SNode_151 | GOAL_150, GRAV78 ) {\n (false, false) 0.9, 0.1;\n (true, false) 0.9, 0.1;\n (false, true) 0.9, 0.1;\n (true, true) 0.00009, 0.99991;\n}\nprobability ( GOAL_153 | GOAL_108, GOAL72 ) {\n (false, false) 0.8, 0.2;\n (true, false) 0.8, 0.2;\n (false, true) 0.8, 0.2;\n (true, true) 0.00008, 0.99992;\n}\nprobability ( SNode_155 | SNode_4, SNode_100, GOAL_153, SNode_154, VECTOR44 ) {\n (false, false, false, false, false) 0.9, 0.1;\n (true, false, false, false, false) 0.9, 0.1;\n (false, true, false, false, false) 0.9, 0.1;\n (true, true, false, false, false) 0.9, 0.1;\n (false, false, true, false, false) 0.9, 0.1;\n (true, false, true, false, false) 0.9, 0.1;\n (false, true, true, false, false) 0.9, 0.1;\n (true, true, true, false, false) 0.9, 0.1;\n (false, false, false, true, false) 0.9, 0.1;\n (true, false, false, true, false) 0.9, 0.1;\n (false, true, false, true, false) 0.9, 0.1;\n (true, true, false, true, false) 0.9, 0.1;\n (false, false, true, true, false) 0.9, 0.1;\n (true, false, true, true, false) 0.9, 0.1;\n (false, true, true, true, false) 0.9, 0.1;\n (true, true, true, true, false) 0.9, 0.1;\n (false, false, false, false, true) 0.9, 0.1;\n (true, false, false, false, true) 0.9, 0.1;\n (false, true, false, false, true) 0.9, 0.1;\n (true, true, false, false, true) 0.9, 0.1;\n (false, false, true, false, true) 0.9, 0.1;\n (true, false, true, false, true) 0.9, 0.1;\n (false, true, true, false, true) 0.9, 0.1;\n (true, true, true, false, true) 0.9, 0.1;\n (false, false, false, true, true) 0.9, 0.1;\n (true, false, false, true, true) 0.9, 0.1;\n (false, true, false, true, true) 0.9, 0.1;\n (true, true, false, true, true) 0.9, 0.1;\n (false, false, true, true, true) 0.9, 0.1;\n (true, false, true, true, true) 0.9, 0.1;\n (false, true, true, true, true) 0.9, 0.1;\n (true, true, true, true, true) 0.00009, 0.99991;\n}\n" }, { "path": "data/asia.bif", "content": "network unknown {\n}\nvariable asia {\n type discrete [ 2 ] { yes, no };\n}\nvariable tub {\n type discrete [ 2 ] { yes, no };\n}\nvariable smoke {\n type discrete [ 2 ] { yes, no };\n}\nvariable lung {\n type discrete [ 2 ] { yes, no };\n}\nvariable bronc {\n type discrete [ 2 ] { yes, no };\n}\nvariable either {\n type discrete [ 2 ] { yes, no };\n}\nvariable xray {\n type discrete [ 2 ] { yes, no };\n}\nvariable dysp {\n type discrete [ 2 ] { yes, no };\n}\nprobability ( asia ) {\n table 0.01, 0.99;\n}\nprobability ( tub | asia ) {\n (yes) 0.05, 0.95;\n (no) 0.01, 0.99;\n}\nprobability ( smoke ) {\n table 0.5, 0.5;\n}\nprobability ( lung | smoke ) {\n (yes) 0.1, 0.9;\n (no) 0.01, 0.99;\n}\nprobability ( bronc | smoke ) {\n (yes) 0.6, 0.4;\n (no) 0.3, 0.7;\n}\nprobability ( either | lung, tub ) {\n (yes, yes) 1.0, 0.0;\n (no, yes) 1.0, 0.0;\n (yes, no) 1.0, 0.0;\n (no, no) 0.0, 1.0;\n}\nprobability ( xray | either ) {\n (yes) 0.98, 0.02;\n (no) 0.05, 0.95;\n}\nprobability ( dysp | bronc, either ) {\n (yes, yes) 0.9, 0.1;\n (no, yes) 0.7, 0.3;\n (yes, no) 0.8, 0.2;\n (no, no) 0.1, 0.9;\n}\n" }, { "path": "data/bde_test.bn", "content": "{\n \"V\": [\"V1\", \"V2\", \"V3\", \"V4\", \"V5\"],\n \"E\": {\n \"V1\" : [],\n \"V2\" : [],\n \"V3\" : [\"V2\",\"V4\",\"V5\"],\n \"V4\" : [],\n \"V5\" : []\n },\n \"F\": {\n \"V1\": {\n \"values\": [\"No\", \"Yes\"],\n \"parents\": [],\n \"cpt\": [0.296, 0.704]\n },\n\n \"V2\": {\n \"values\": [\"No\", \"Yes\"],\n \"parents\": [\"V3\"],\n \"cpt\": [0.667, 0.333,0.897,0.103]\n },\n\n \"V3\": {\n \"values\": [\"No\",\"Yes\"],\n \"parents\": [],\n \"cpt\": [0.012, 0.988]\n },\n\n \"V4\": {\n \"values\": [\"No\",\"Yes\"],\n \"parents\": [\"V3\"],\n \"cpt\": [0.75,0.25,0.182,0.818]\n },\n\n \"V5\": {\n \"values\": [\"No\", \"Yes\"],\n \"parents\": [\"V3\"],\n \"cpt\": [0.75,0.25,0.31,0.689]\n }\n }\n}" }, { "path": "data/cancer.bif", "content": "network unknown {\n}\nvariable Pollution {\n type discrete [ 2 ] { low, high };\n}\nvariable Smoker {\n type discrete [ 2 ] { True, False };\n}\nvariable Cancer {\n type discrete [ 2 ] { True, False };\n}\nvariable Xray {\n type discrete [ 2 ] { positive, negative };\n}\nvariable Dyspnoea {\n type discrete [ 2 ] { True, False };\n}\nprobability ( Pollution ) {\n table 0.9, 0.1;\n}\nprobability ( Smoker ) {\n table 0.3, 0.7;\n}\nprobability ( Cancer | Pollution, Smoker ) {\n (low, True) 0.03, 0.97;\n (high, True) 0.05, 0.95;\n (low, False) 0.001, 0.999;\n (high, False) 0.02, 0.98;\n}\nprobability ( Xray | Cancer ) {\n (True) 0.9, 0.1;\n (False) 0.2, 0.8;\n}\nprobability ( Dyspnoea | Cancer ) {\n (True) 0.65, 0.35;\n (False) 0.3, 0.7;\n}\n" }, { "path": "data/cmu.bn", "content": "{\r\n \"V\": [\"Burglary\", \"Earthquake\", \"Alarm\", \"JohnCalls\", \"MaryCalls\"],\r\n \"E\": {\r\n \"Burglary\" : [\"Alarm\"],\r\n \"Earthquake\" : [\"Alarm\"],\r\n \"Alarm\" : [ \"JohnCalls\",\"MaryCalls\"],\r\n \"JohnCalls\" : [],\r\n \"MaryCalls\" : []\r\n },\r\n \"F\": {\r\n \"Burglary\": {\r\n \"values\": [\"No\", \"Yes\"],\r\n \"parents\": [],\r\n \"cpt\": [0.999,0.001]\r\n },\r\n\r\n \"Earthquake\": {\r\n \"values\": [\"No\", \"Yes\"],\r\n \"parents\": [],\r\n \"cpt\": [0.998,0.002]\r\n },\r\n\r\n \"Alarm\": {\r\n \"values\": [\"No\",\"Yes\"],\r\n \"parents\": [\"Earthquake\",\"Burglary\"],\r\n \"cpt\": [0.999,0.001,0.71,0.29,0.06,0.94,0.05,0.95]\r\n },\r\n\r\n \"JohnCalls\": {\r\n \"values\": [\"No\",\"Yes\"],\r\n \"parents\": [\"Alarm\"],\r\n \"cpt\": [0.95,0.05,0.1,0.9]\r\n },\r\n\r\n \"MaryCalls\": {\r\n \"values\": [\"No\", \"Yes\"],\r\n \"parents\": [\"Alarm\"],\r\n \"cpt\": [0.99,0.01,0.3,0.7]\r\n }\r\n }\r\n}" }, { "path": "data/earthquake.bn", "content": "{\n \"V\": [\"Burglary\", \"Earthquake\", \"Alarm\", \"JohnCalls\", \"MaryCalls\"],\n \"E\": {\n \"Burglary\" : [\"Alarm\"],\n \"Earthquake\" : [\"Alarm\"],\n \"Alarm\" : [ \"JohnCalls\",\"MaryCalls\"],\n \"JohnCalls\" : [],\n \"MaryCalls\" : []\n },\n \"F\": {\n \"Burglary\": {\n \"values\": [\"No\", \"Yes\"],\n \"parents\": [],\n \"cpt\": [0.99,0.01]\n },\n\n \"Earthquake\": {\n \"values\": [\"No\", \"Yes\"],\n \"parents\": [],\n \"cpt\": [0.98,0.02]\n },\n\n \"Alarm\": {\n \"values\": [\"No\",\"Yes\"],\n \"parents\": [\"Earthquake\",\"Burglary\"],\n \"cpt\": [0.999,0.001,0.71,0.29,0.06,0.94,0.05,0.95]\n },\n\n \"JohnCalls\": {\n \"values\": [\"No\",\"Yes\"],\n \"parents\": [\"Alarm\"],\n \"cpt\": [0.95,0.05,0.1,0.9]\n },\n\n \"MaryCalls\": {\n \"values\": [\"No\", \"Yes\"],\n \"parents\": [\"Alarm\"],\n \"cpt\": [0.99,0.01,0.3,0.7]\n }\n }\n}" }, { "path": "data/flags/README.txt", "content": "Flags Dataset\r\n=============\r\n\r\nThis dataset contains data about nations and their national flags. The multi-label \r\nclassification task is to predict the colors that appear on the flags.\r\n\r\nDataset characteristics:\r\n------------------------\r\n\r\ninstances = 194 \r\n\r\nattributes = 19 (9 nominal and 10 numeric)\r\n\r\nlabels = 7 (L = {red, green, blue, yellow, white, black, orange})\r\n\r\nlabel cardinality = 3.39 \r\n\r\ndomain = images\r\n\r\n\r\nCitation Request:\r\n-----------------\r\n\r\nPlease cite the following: \r\n\r\n@inproceedings{gpf13,\r\n author = {Eduardo Corr\\^{e}a Gon\\c{c}alves and Alexandre Plastino and Alex A. Freitas},\r\n title = {A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains},\r\n booktitle = {Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence},\r\n series = {ICTAI'13},\r\n address = {Washington, D.C., USA},\r\n month = {November},\r\n pages = {469--476},\r\n year = {2013}\r\n}\r\n\r\n\r\nThe original data can be found at the UCI repository \r\n(http://archive.ics.uci.edu/ml/machine-learning-databases/flags/). \r\n\r\n\r\n" }, { "path": "data/flags/flags-test.arff", "content": "@relation flags_ml\n\n@attribute landmass {1,2,3,4,5,6}\n@attribute zone {1,2,3,4}\n@attribute area numeric\n@attribute population numeric\n@attribute language {1,2,3,4,5,6,7,8,9,10}\n@attribute religion {0,1,2,3,4,5,6,7}\n@attribute bars numeric\n@attribute stripes numeric\n@attribute colours numeric\n@attribute circles numeric\n@attribute crosses numeric\n@attribute saltires numeric\n@attribute quarters numeric\n@attribute sunstars numeric\n@attribute crescent {0,1}\n@attribute triangle {0,1}\n@attribute icon {0,1}\n@attribute animate {0,1}\n@attribute text {0,1}\n@attribute red {0,1}\n@attribute green {0,1}\n@attribute blue {0,1}\n@attribute yellow {0,1}\n@attribute white {0,1}\n@attribute black {0,1}\n@attribute orange 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}, { "path": "data/flags/flags-train.arff", "content": "@relation flags_ml\n\n@attribute landmass {1,2,3,4,5,6}\n@attribute zone {1,2,3,4}\n@attribute area numeric\n@attribute population numeric\n@attribute language {1,2,3,4,5,6,7,8,9,10}\n@attribute religion {0,1,2,3,4,5,6,7}\n@attribute bars numeric\n@attribute stripes numeric\n@attribute colours numeric\n@attribute circles numeric\n@attribute crosses numeric\n@attribute saltires numeric\n@attribute quarters numeric\n@attribute sunstars numeric\n@attribute crescent {0,1}\n@attribute triangle {0,1}\n@attribute icon {0,1}\n@attribute animate {0,1}\n@attribute text {0,1}\n@attribute red {0,1}\n@attribute green {0,1}\n@attribute blue {0,1}\n@attribute yellow {0,1}\n@attribute white {0,1}\n@attribute black {0,1}\n@attribute orange 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2\n2 1 2 1 2\n2 1 2 2 1\n1 1 2 2 1\n" }, { "path": "data/letter.csv", "content": "T,2,8,3,5,1,8,13,0,6,6,10,8,0,8,0,8\nI,5,12,3,7,2,10,5,5,4,13,3,9,2,8,4,10\nD,4,11,6,8,6,10,6,2,6,10,3,7,3,7,3,9\nN,7,11,6,6,3,5,9,4,6,4,4,10,6,10,2,8\nG,2,1,3,1,1,8,6,6,6,6,5,9,1,7,5,10\nS,4,11,5,8,3,8,8,6,9,5,6,6,0,8,9,7\nB,4,2,5,4,4,8,7,6,6,7,6,6,2,8,7,10\nA,1,1,3,2,1,8,2,2,2,8,2,8,1,6,2,7\nJ,2,2,4,4,2,10,6,2,6,12,4,8,1,6,1,7\nM,11,15,13,9,7,13,2,6,2,12,1,9,8,1,1,8\nX,3,9,5,7,4,8,7,3,8,5,6,8,2,8,6,7\nO,6,13,4,7,4,6,7,6,3,10,7,9,5,9,5,8\nG,4,9,6,7,6,7,8,6,2,6,5,11,4,8,7,8\nM,6,9,8,6,9,7,8,6,5,7,5,8,8,9,8,6\nR,5,9,5,7,6,6,11,7,3,7,3,9,2,7,5,11\nF,6,9,5,4,3,10,6,3,5,10,5,7,3,9,6,9\nO,3,4,4,3,2,8,7,7,5,7,6,8,2,8,3,8\nC,7,10,5,5,2,6,8,6,8,11,7,11,2,8,5,9\nT,6,11,6,8,5,6,11,5,6,11,9,4,3,12,2,4\nJ,2,2,3,3,1,10,6,3,6,12,4,9,0,7,1,7\nJ,1,3,2,2,1,8,8,2,5,14,5,8,0,7,0,7\nH,4,5,5,4,4,7,7,6,6,7,6,8,3,8,3,8\nS,3,2,3,3,2,8,8,7,5,7,5,7,2,8,9,8\nO,6,11,7,8,5,7,6,9,6,7,5,9,4,8,5,5\nJ,3,6,4,4,2,6,6,4,4,14,8,12,1,6,1,6\nC,6,11,7,8,3,7,8,7,11,4,7,14,1,7,4,8\nM,7,11,11,8,9,3,8,4,5,10,11,10,10,9,5,7\nW,12,14,12,8,5,9,10,4,3,5,10,7,10,12,2,6\nH,6,9,8,7,6,8,6,6,7,7,7,9,6,8,4,8\nG,3,6,4,4,2,6,6,5,5,6,6,9,2,8,4,8\nL,2,3,3,4,1,0,1,5,6,0,0,6,0,8,0,8\nL,1,3,3,1,1,6,4,1,7,8,3,10,0,7,2,9\nX,8,12,8,6,4,3,10,4,7,12,11,9,3,7,3,4\nB,5,9,7,7,10,9,8,4,4,6,8,6,6,11,8,7\nM,6,9,9,7,6,5,6,3,5,10,9,9,8,5,2,7\nG,4,7,6,5,3,6,6,6,8,6,5,9,3,10,4,8\nO,4,7,5,5,3,7,7,8,6,7,6,8,3,8,3,8\nP,3,6,4,4,2,4,14,8,1,11,6,3,0,10,4,8\nG,4,9,5,6,6,8,5,4,3,7,5,11,6,8,5,11\nE,3,4,3,6,2,3,8,6,10,7,6,15,0,8,7,8\nX,5,11,8,8,4,8,8,1,8,10,5,7,3,8,4,8\nE,3,7,4,5,4,7,7,5,8,8,8,9,3,9,6,9\nX,4,6,7,4,3,9,7,2,8,11,3,7,3,8,4,9\nG,4,5,5,8,3,7,6,8,8,6,6,10,1,8,6,11\nV,7,9,6,5,2,8,10,4,5,8,10,5,4,12,3,8\nX,4,10,5,7,5,8,7,3,8,5,6,6,3,8,6,8\nW,5,9,6,7,8,7,9,5,3,7,9,8,6,8,3,8\nG,1,3,2,1,1,7,7,5,6,7,5,10,1,9,3,9\nR,8,10,8,6,6,7,7,3,5,8,4,8,6,6,7,7\nS,3,9,4,7,2,7,5,6,10,5,6,10,0,9,9,8\nY,7,11,9,8,8,9,5,6,4,7,8,8,3,9,8,3\nL,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8\nY,2,4,4,3,2,6,10,1,7,8,11,8,1,11,2,7\nJ,4,5,6,6,5,8,10,4,4,7,7,9,3,7,7,5\nS,4,9,5,6,4,7,8,7,7,8,4,7,2,7,9,8\nQ,6,9,7,11,7,8,5,7,5,9,6,9,3,8,6,8\nQ,3,5,5,4,4,9,3,4,3,8,4,9,3,6,4,8\nD,2,6,3,4,2,6,7,10,8,6,6,6,3,8,3,8\nC,6,10,4,5,2,6,9,6,6,11,7,7,2,9,5,9\nL,5,10,6,8,5,9,3,2,6,9,2,9,2,5,3,9\nN,3,3,3,5,2,7,7,13,2,5,6,8,5,8,0,8\nC,2,3,3,1,1,5,9,4,6,11,9,11,1,9,2,8\nE,6,9,4,4,2,7,7,4,7,10,6,10,1,9,7,9\nX,4,9,5,6,5,7,6,3,5,6,6,9,2,8,8,8\nH,3,3,4,1,2,8,7,5,6,7,6,8,5,8,3,7\nL,2,3,2,4,1,0,1,5,6,0,0,6,0,8,0,8\nH,3,5,5,4,3,7,8,3,6,10,6,8,3,8,3,8\nE,2,3,3,2,2,7,7,5,7,7,6,8,2,8,5,10\nY,5,10,6,7,6,9,6,6,4,7,8,7,6,9,8,3\nH,8,12,8,6,4,9,8,4,5,8,4,5,6,9,5,9\nQ,5,10,5,5,4,9,6,5,6,10,6,7,4,8,9,9\nQ,3,8,5,7,3,8,7,9,6,6,6,9,3,8,5,9\nQ,4,6,6,4,5,8,5,6,4,6,7,10,4,7,5,9\nJ,2,8,3,6,1,13,3,8,4,13,3,11,1,6,0,8\nJ,4,7,5,5,2,8,9,1,7,14,5,6,0,8,1,8\nD,4,5,5,4,3,7,7,7,7,7,6,5,2,8,3,7\nR,2,6,4,4,3,6,7,5,5,6,5,7,3,7,5,8\nM,4,5,7,4,4,6,6,3,4,9,9,10,7,5,2,8\nA,3,7,5,5,3,12,2,3,2,10,2,9,2,6,3,8\nN,3,5,4,4,2,7,8,5,4,7,7,7,6,9,2,5\nM,6,10,9,7,8,3,7,4,5,10,11,10,7,9,3,7\nP,8,14,7,8,4,5,10,6,3,12,5,4,4,10,4,8\nC,6,12,5,7,4,8,5,4,3,9,9,11,4,9,8,10\nP,6,10,8,8,7,8,5,7,5,7,6,6,3,9,8,9\nN,2,3,3,1,1,5,9,3,3,10,8,8,4,8,0,8\nW,3,4,4,3,2,9,10,3,2,5,9,7,6,11,0,8\nV,3,8,5,6,6,8,7,4,1,8,7,8,5,10,4,7\nV,6,9,9,8,10,8,7,5,5,7,6,8,8,9,9,3\nM,6,7,9,5,7,4,7,3,5,10,10,11,8,6,3,7\nE,4,8,5,6,4,7,7,4,8,11,8,9,2,9,5,7\nN,6,11,8,8,9,5,8,3,4,8,8,9,7,9,5,4\nY,8,10,8,7,4,3,10,3,7,11,12,6,1,11,3,5\nW,4,8,5,6,3,6,8,4,1,7,8,8,8,9,0,8\nO,6,7,8,6,6,6,6,5,6,8,5,8,3,6,5,6\nN,4,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nH,4,8,5,6,5,7,10,8,5,8,5,6,3,6,7,11\nO,4,7,5,5,3,8,7,8,5,10,6,8,3,8,3,8\nN,4,8,5,6,4,7,7,9,4,6,4,6,3,7,3,8\nH,4,9,5,6,2,7,6,15,1,7,7,8,3,8,0,8\nU,7,11,8,9,4,3,9,6,7,11,11,10,3,9,2,6\nY,2,7,3,5,1,6,10,0,3,7,11,8,1,11,0,8\nS,6,8,7,6,4,6,9,4,8,11,7,6,2,8,5,5\nS,5,9,6,7,5,7,7,7,5,7,6,7,2,8,9,8\nT,2,3,2,2,1,7,11,2,6,7,10,8,1,11,1,7\nC,2,3,3,2,1,4,7,4,6,10,9,13,1,9,2,7\nV,4,10,7,7,2,9,8,5,3,5,14,8,3,9,0,8\nT,5,8,5,6,3,6,12,4,7,12,9,4,2,12,2,4\nK,7,12,6,6,3,7,8,2,7,9,7,8,5,8,3,7\nD,3,8,4,6,4,7,8,7,6,7,7,5,3,8,3,7\nW,2,1,3,1,1,7,8,4,0,7,8,8,6,10,0,8\nI,2,9,3,7,2,8,7,0,7,13,6,9,0,8,1,8\nE,3,8,3,6,2,3,7,6,10,7,6,14,0,8,7,8\nF,6,9,8,7,4,6,12,6,6,13,7,3,2,9,2,5\nI,3,9,4,7,3,7,7,0,7,13,6,8,0,8,1,8\nH,5,10,7,8,8,7,7,6,6,7,6,9,3,8,4,8\nU,4,7,4,5,2,7,5,13,5,7,14,7,3,9,0,8\nC,2,3,3,2,1,6,8,7,7,9,7,12,1,10,3,9\nF,5,9,6,7,7,7,6,6,4,7,7,8,5,10,9,12\nA,3,8,5,6,3,9,2,2,3,8,2,8,2,6,3,7\nZ,3,7,5,5,3,7,8,2,9,11,7,7,1,8,6,7\nX,3,7,5,5,2,10,5,2,8,10,1,7,3,7,3,10\nN,3,3,5,2,2,5,10,3,4,10,8,7,5,8,1,8\nR,6,9,5,4,3,10,6,5,5,10,2,8,6,6,4,9\nB,3,3,3,4,3,7,7,5,5,7,6,6,5,8,5,10\nB,5,9,7,7,7,8,8,3,6,10,5,6,3,7,6,8\nU,5,6,5,4,2,5,8,6,8,9,8,8,3,9,2,5\nJ,4,4,6,5,4,8,9,4,5,7,6,9,3,7,8,8\nF,3,7,4,5,2,1,11,4,4,11,11,8,0,8,2,7\nM,6,8,9,6,6,11,6,2,5,9,3,6,9,7,2,9\nH,1,1,2,1,1,7,7,12,1,7,6,8,3,8,0,8\nA,2,1,4,2,1,8,1,2,2,7,2,8,2,5,2,7\nO,5,10,4,5,3,6,6,6,2,9,6,9,5,9,4,8\nE,1,0,1,1,1,4,7,5,8,7,6,13,0,8,6,10\nU,3,3,3,1,1,5,8,5,7,10,9,8,3,10,2,6\nA,3,7,5,5,3,10,4,1,2,8,3,9,2,4,2,7\nX,4,8,5,6,3,5,8,1,8,10,9,9,2,9,3,6\nS,5,8,7,6,4,8,8,4,9,11,3,8,2,6,4,9\nJ,1,3,2,2,1,10,7,1,5,11,4,8,0,7,0,7\nK,5,11,8,8,5,6,7,2,7,10,7,10,4,8,4,8\nI,2,6,2,4,1,9,5,0,6,13,5,9,0,8,1,8\nP,7,11,10,8,6,6,11,3,6,13,6,3,0,10,3,8\nT,2,1,2,1,0,8,15,1,4,6,10,8,0,8,0,8\nC,1,1,2,2,1,6,8,6,6,9,7,12,1,9,3,10\nH,4,9,4,7,4,7,8,13,1,7,5,8,3,8,0,8\nA,2,4,4,3,2,10,2,2,2,9,2,9,2,7,3,9\nL,6,11,9,8,11,7,7,3,6,6,7,11,6,11,9,7\nT,3,6,4,4,3,7,12,4,6,7,11,8,2,12,1,7\nA,2,8,4,6,2,12,2,4,3,11,2,10,3,6,3,9\nV,3,9,5,7,3,8,9,4,1,6,12,8,2,10,0,8\nU,5,7,6,5,5,8,8,8,5,6,7,9,4,8,4,5\nI,2,9,2,6,3,7,7,0,7,7,6,8,0,8,2,8\nZ,8,14,8,8,5,8,5,2,9,12,6,9,3,7,6,7\nV,4,7,6,5,6,8,8,4,2,8,7,8,7,11,5,8\nM,5,9,7,6,7,9,7,5,5,6,7,6,10,7,4,6\nL,4,8,6,6,4,6,5,2,8,7,2,8,1,6,3,7\nO,3,3,5,5,2,9,6,9,7,7,5,10,3,8,4,8\nB,4,2,5,4,4,7,7,5,6,7,6,6,2,8,7,10\nA,8,15,7,8,4,8,2,3,2,8,5,12,5,4,5,5\nK,6,9,9,6,7,9,6,1,7,10,3,8,3,7,3,9\nH,3,7,4,5,4,7,7,5,6,7,6,8,3,8,3,8\nT,7,9,7,7,5,6,10,1,8,11,9,5,2,9,3,4\nW,6,5,7,4,4,4,11,2,2,9,9,8,7,11,1,7\nF,3,8,3,5,1,1,12,4,5,12,11,8,0,8,2,6\nA,3,3,5,4,1,7,6,3,0,7,0,8,2,7,1,8\nE,3,8,3,6,3,3,6,5,9,5,4,13,0,8,6,9\nM,4,9,5,7,4,7,7,12,1,7,9,8,8,6,0,8\nB,4,9,5,7,6,7,8,9,6,7,6,6,2,8,8,10\nM,3,7,5,5,6,7,8,6,4,6,6,8,5,8,7,9\nY,6,9,6,7,4,5,8,0,8,8,9,5,2,11,4,4\nV,4,9,6,7,3,6,12,3,4,7,12,9,3,10,1,8\nS,2,8,3,6,3,8,8,7,6,7,4,6,2,7,8,8\nC,2,1,2,2,1,6,8,7,7,8,7,12,1,9,4,10\nH,4,9,6,6,8,7,7,6,3,7,6,7,8,7,9,9\nU,5,8,5,6,4,8,6,11,4,8,13,7,3,9,0,8\nP,6,10,6,6,4,7,10,5,2,11,5,4,4,11,5,7\nZ,3,8,4,6,2,7,7,4,13,10,6,8,0,8,8,8\nE,3,4,5,2,2,7,7,2,8,11,7,9,2,8,4,8\nN,5,9,7,6,4,4,10,3,4,9,10,9,5,8,1,8\nP,6,10,5,6,3,5,9,6,3,10,5,6,5,9,4,7\nH,4,5,5,4,4,7,7,6,6,7,6,7,3,8,3,6\nT,6,11,6,8,5,6,11,3,7,11,9,5,2,12,2,4\nB,5,5,5,8,4,7,9,10,8,7,5,7,2,8,9,10\nU,5,10,6,7,2,7,4,14,6,7,15,8,3,9,0,8\nS,3,4,4,6,2,8,8,6,9,5,5,5,0,8,9,7\nF,5,6,5,8,2,1,14,5,3,12,9,5,0,8,3,6\nJ,3,9,4,7,4,9,6,1,6,11,4,7,0,6,1,6\nR,3,6,4,4,4,6,7,5,5,7,6,7,3,7,4,9\nE,3,2,3,4,3,7,8,5,7,7,5,9,2,8,5,10\nP,6,9,6,4,4,8,9,5,3,10,5,5,5,10,5,6\nI,2,2,1,4,1,7,7,1,7,7,6,8,0,8,3,8\nK,7,10,9,8,9,7,7,5,5,7,6,7,7,6,9,13\nD,4,10,6,7,4,8,6,8,7,4,5,3,3,8,4,8\nB,4,7,6,5,7,9,6,4,4,6,6,7,7,8,6,7\nH,5,10,7,8,6,7,9,8,5,8,5,6,3,7,7,10\nM,7,10,10,7,7,9,6,2,5,9,5,7,8,6,2,8\nP,6,11,7,8,3,4,13,9,2,10,6,4,1,10,4,8\nO,4,8,6,6,4,8,5,8,4,7,5,8,3,8,3,8\nS,2,8,3,6,3,8,7,7,5,7,6,7,2,9,8,8\nK,4,7,6,5,4,3,8,3,7,11,11,12,3,8,3,5\nV,7,10,7,8,3,3,11,4,4,10,12,8,3,10,1,8\nF,7,11,10,8,5,6,11,2,7,14,7,4,1,10,2,7\nY,3,4,5,6,1,6,10,3,2,9,13,8,1,11,0,8\nD,4,6,6,4,3,11,6,3,8,11,3,6,3,8,3,9\nZ,1,0,1,1,0,7,7,2,10,8,6,8,0,8,6,8\nQ,3,5,3,6,3,7,8,6,3,8,8,9,3,8,5,7\nS,8,14,8,8,4,9,4,4,4,13,5,8,3,10,3,8\nB,5,7,7,5,6,8,6,5,6,9,6,7,3,9,6,10\nK,8,13,9,7,5,6,7,3,6,10,8,10,6,10,4,7\nV,3,10,5,7,2,5,8,5,3,9,14,8,3,10,0,8\nB,4,7,4,5,3,6,7,9,7,7,6,7,2,8,9,10\nY,2,2,4,4,2,6,10,1,7,7,11,9,1,11,1,8\nO,3,4,4,3,2,7,7,7,5,7,6,8,2,8,3,8\nZ,6,10,8,7,5,8,6,3,10,12,4,9,1,7,6,9\nL,2,3,3,2,1,6,4,1,8,7,2,10,0,7,2,8\nJ,1,3,2,2,1,10,6,2,6,12,4,9,0,7,1,7\nZ,1,3,3,2,1,7,8,1,9,11,7,7,1,8,5,7\nA,3,5,5,7,2,7,7,3,1,6,0,8,3,7,1,8\nL,3,5,4,5,3,8,7,4,5,7,6,8,3,8,7,10\nJ,2,5,3,3,1,10,7,1,7,12,3,7,0,7,1,7\nQ,5,6,6,8,5,8,7,6,3,7,7,12,3,8,6,8\nN,1,3,2,1,1,7,8,5,3,7,7,7,4,8,1,6\nZ,2,6,3,4,1,7,7,3,13,9,6,8,0,8,8,8\nE,4,9,5,7,3,3,7,6,11,7,6,15,0,8,7,7\nJ,4,7,3,10,3,9,7,3,3,11,4,5,3,8,6,9\nD,4,8,5,6,3,5,7,10,10,6,5,6,3,8,4,8\nO,5,10,6,8,5,7,8,9,6,8,8,6,4,8,5,10\nL,3,6,4,4,2,5,4,3,8,3,2,6,1,6,2,5\nV,4,5,5,4,2,4,12,4,4,11,11,6,2,10,1,8\nX,6,10,9,8,6,7,7,0,8,9,6,8,3,8,4,7\nJ,5,8,6,6,3,6,6,4,5,14,8,12,1,6,1,7\nN,7,10,10,8,5,7,8,3,5,10,6,7,7,8,1,8\nH,3,5,5,7,4,10,11,3,2,7,8,8,3,10,7,9\nA,2,7,4,5,3,13,3,3,2,10,1,8,2,6,2,8\nP,4,6,5,9,9,8,7,5,0,7,6,7,6,10,6,9\nA,4,9,6,6,2,10,5,3,1,8,1,9,2,7,2,8\nF,4,8,5,6,3,5,10,4,6,11,10,5,2,10,3,5\nX,3,4,6,3,2,7,7,1,9,10,6,8,2,8,3,7\nD,5,11,6,9,4,5,8,11,9,8,7,5,3,8,4,8\nV,3,7,5,5,3,9,11,2,2,4,10,8,2,12,1,8\nP,7,10,6,5,4,10,7,4,4,12,4,5,4,11,5,9\nF,5,9,6,7,6,6,9,4,6,9,9,6,5,9,3,6\nP,6,11,9,8,5,8,10,7,5,10,4,3,3,10,4,8\nY,4,10,6,8,2,9,11,0,4,6,11,8,0,10,0,8\nW,3,3,4,4,2,5,8,4,1,7,9,8,7,10,0,8\nP,6,9,8,7,5,6,11,7,3,11,5,3,2,10,4,8\nJ,5,6,7,7,6,7,8,4,5,7,7,8,4,6,8,6\nE,4,2,4,4,3,8,7,6,10,6,5,9,2,8,6,8\nS,4,8,5,6,3,7,5,6,9,5,6,10,0,9,9,8\nE,3,7,3,5,2,3,8,6,11,7,5,14,0,8,7,8\nB,5,9,7,7,6,7,9,5,6,10,6,6,3,8,7,8\nI,1,9,1,7,1,7,7,0,9,7,6,8,0,8,3,8\nS,6,8,7,6,4,9,6,4,6,10,3,8,2,8,5,10\nS,3,8,4,6,3,8,6,8,6,7,8,9,2,10,9,8\nC,3,7,4,5,2,5,8,6,7,6,8,13,1,8,4,10\nM,8,10,11,8,9,9,6,2,5,9,4,7,11,9,3,8\nP,9,10,7,5,3,6,11,6,2,11,5,5,4,10,4,8\nR,2,4,4,3,3,7,8,4,4,9,5,8,2,6,3,10\nU,3,8,5,6,6,8,7,4,4,6,7,8,7,9,5,6\nU,6,6,6,4,3,4,8,5,8,11,11,9,3,9,2,6\nD,3,8,4,6,2,5,7,10,8,7,7,5,3,8,4,8\nU,2,1,3,1,1,7,5,11,5,7,14,8,3,10,0,8\nJ,2,5,3,4,2,10,7,2,6,11,3,7,1,6,2,7\nZ,7,13,7,7,5,6,7,2,9,11,6,8,4,5,8,6\nF,5,11,7,8,6,7,9,3,6,12,7,6,3,9,2,7\nV,1,0,2,1,0,8,9,4,2,7,12,8,2,10,0,8\nM,6,5,8,4,7,6,9,5,3,6,4,8,13,8,5,7\nU,3,6,4,4,1,7,5,13,5,7,13,8,3,9,0,8\nB,3,7,4,5,5,7,7,4,6,6,6,6,2,8,5,10\nL,4,8,6,6,4,6,5,1,9,7,2,10,0,6,3,8\nT,4,10,5,8,3,8,13,0,5,7,10,8,0,8,0,8\nD,8,12,8,6,4,9,4,4,6,12,3,8,5,6,5,10\nT,6,11,6,8,4,6,10,1,10,11,9,5,2,8,4,4\nO,5,8,7,6,4,7,5,8,4,6,4,7,3,9,4,9\nT,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8\nU,4,3,5,2,2,5,8,5,7,10,10,9,3,9,2,6\nD,2,4,4,2,2,9,6,4,6,11,4,6,2,8,3,8\nR,4,8,5,5,3,5,11,8,3,7,4,8,3,7,6,11\nG,7,10,7,7,5,5,7,7,6,9,8,10,2,9,5,9\nD,2,1,2,2,1,6,7,8,7,6,6,6,2,8,3,8\nS,3,8,4,6,4,7,7,7,5,7,6,8,2,8,8,8\nS,7,10,9,8,10,9,5,5,4,9,6,9,6,9,12,11\nM,6,10,9,8,8,7,7,2,4,9,7,8,8,6,2,8\nM,4,8,6,6,8,9,6,2,2,8,4,8,7,7,2,5\nD,6,7,8,6,6,5,7,6,7,7,4,7,3,6,5,6\nZ,4,9,6,7,4,8,6,2,9,12,5,9,3,6,8,8\nW,4,9,7,7,5,9,8,4,1,6,9,8,7,11,0,8\nC,2,1,3,2,1,6,8,6,7,7,8,12,1,9,4,10\nK,3,6,4,4,4,6,7,3,7,6,6,9,3,8,6,10\nJ,6,9,9,7,5,8,5,7,7,8,6,7,2,6,4,6\nA,6,9,8,8,8,8,7,3,5,7,8,8,6,8,4,5\nD,5,12,5,6,4,8,7,3,6,10,5,7,5,8,7,6\nC,4,8,6,6,4,8,8,8,6,7,6,8,4,7,4,8\nV,3,4,4,3,1,4,12,3,3,10,11,7,2,11,1,8\nS,4,7,4,5,2,9,10,6,9,5,6,6,0,7,8,9\nF,4,9,6,6,4,9,8,1,6,13,5,5,1,10,2,9\nA,5,10,7,8,5,8,3,3,1,7,1,8,5,9,5,8\nB,3,2,4,3,3,8,7,5,6,7,6,6,2,8,6,9\nN,2,3,3,2,2,7,9,5,4,7,6,7,5,9,2,7\nU,6,10,5,6,3,6,6,4,4,6,7,8,5,5,2,8\nS,4,10,5,8,5,7,7,5,8,5,6,8,0,8,9,8\nQ,2,5,3,6,3,8,8,7,2,5,7,10,3,9,5,9\nF,4,8,4,5,1,1,13,5,5,12,11,7,0,8,2,6\nF,4,9,6,6,5,5,10,4,6,10,10,6,2,10,3,6\nX,5,10,8,8,4,8,7,4,10,6,6,8,4,6,8,9\nA,3,7,5,5,3,11,3,2,2,8,2,9,3,4,2,8\nB,4,9,4,6,3,6,7,9,7,7,6,7,2,8,9,10\nO,5,9,6,7,5,7,7,8,7,7,6,7,2,8,3,8\nJ,2,6,3,4,1,14,1,6,5,14,2,11,0,7,0,8\nH,4,8,5,5,2,7,5,15,1,7,9,8,3,8,0,8\nM,6,9,7,4,3,6,9,5,5,4,4,10,8,7,2,8\nI,3,7,4,5,2,7,7,0,7,13,6,8,0,8,1,7\nW,6,10,6,8,6,2,10,2,3,10,9,8,7,11,2,6\nN,3,6,4,4,3,6,7,8,5,7,5,7,3,7,3,8\nA,1,3,2,2,1,10,3,2,1,9,2,9,1,6,1,8\nS,2,3,4,1,1,7,8,2,6,10,5,7,1,8,4,8\nP,1,0,1,0,0,5,10,6,1,9,6,5,0,9,2,8\nD,6,9,8,8,8,7,6,5,7,7,5,9,6,5,9,4\nM,7,9,10,7,6,5,6,4,5,11,10,11,11,5,4,8\nT,4,8,5,6,5,8,11,3,6,6,11,8,3,12,1,7\nL,3,6,4,4,2,5,5,1,9,6,2,11,0,8,3,7\nX,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nG,2,4,3,6,2,7,7,8,6,5,7,9,2,7,5,11\nD,6,10,8,8,5,10,5,4,8,11,3,7,4,6,4,9\nE,3,4,4,6,2,3,8,6,10,7,6,14,0,8,7,7\nN,2,3,2,1,1,6,9,6,3,8,7,8,4,9,1,7\nG,2,4,3,3,2,6,6,6,5,6,6,9,2,8,4,8\nV,7,10,6,8,3,2,11,5,5,12,12,8,2,10,1,8\nA,2,7,4,4,2,7,3,3,2,7,2,8,3,7,2,8\nE,5,9,4,4,2,8,7,4,4,10,5,10,3,8,7,10\nK,4,5,5,8,2,3,6,7,3,7,8,12,3,8,3,10\nM,6,9,9,6,6,5,7,3,5,10,10,9,8,6,3,8\nL,5,11,5,6,3,7,5,3,5,12,7,11,3,8,6,9\nT,5,6,5,4,2,5,12,3,8,12,9,4,1,11,2,4\nG,2,4,4,3,2,7,6,6,6,6,6,10,2,9,4,9\nJ,2,4,4,3,1,8,8,2,7,15,5,8,0,7,1,8\nF,5,10,5,7,2,1,14,5,4,12,10,6,0,8,2,5\nW,7,8,7,6,6,6,11,5,2,9,6,6,8,13,4,4\nA,3,6,5,4,3,12,3,2,2,9,2,9,2,6,2,8\nK,6,11,8,8,5,9,5,1,7,10,3,9,5,9,5,11\nP,1,1,2,2,1,5,10,4,4,10,8,4,1,9,3,7\nM,7,6,9,5,9,7,7,5,4,6,5,8,10,8,5,5\nO,2,6,3,4,2,8,7,7,5,7,6,9,2,8,3,8\nS,4,11,5,8,3,6,6,6,9,4,6,9,0,9,9,8\nC,4,6,6,5,5,4,7,3,6,6,6,10,4,10,7,7\nO,1,0,2,1,0,8,7,6,4,7,6,8,2,8,3,8\nZ,2,1,3,2,1,7,7,5,9,6,6,9,1,8,7,8\nX,4,9,6,7,6,9,7,3,6,7,4,5,6,6,9,8\nW,4,4,5,3,3,6,11,5,2,8,7,6,6,11,2,6\nB,4,9,4,6,5,6,6,8,6,6,6,7,2,8,7,9\nC,6,11,6,8,4,4,8,5,6,12,10,11,2,10,3,7\nF,2,4,3,3,2,5,10,3,5,10,9,5,1,10,3,6\nI,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nB,1,3,2,2,1,8,7,3,5,10,5,7,2,8,3,9\nP,3,4,5,2,2,8,9,3,5,13,4,3,1,10,3,8\nK,11,14,10,8,4,6,8,3,7,9,8,10,6,8,4,6\nU,4,5,5,4,2,5,8,5,8,10,8,8,3,9,3,5\nN,3,8,4,6,2,7,7,14,2,5,6,8,5,8,0,8\nY,4,11,6,8,1,7,11,2,3,8,12,8,1,11,0,8\nI,3,5,5,6,4,8,8,4,4,7,6,8,3,7,8,8\nQ,8,15,7,8,4,8,5,4,9,11,4,10,3,6,9,9\nQ,4,5,4,7,4,9,7,6,2,8,6,11,3,9,6,8\nZ,1,0,1,0,0,8,7,2,9,8,6,8,0,8,5,8\nF,4,5,5,6,5,7,9,5,4,8,6,7,4,9,8,7\nM,8,10,11,8,8,9,6,2,5,9,6,7,8,6,2,8\nD,4,7,5,5,2,5,7,10,9,7,6,5,3,8,4,8\nN,2,3,3,1,1,8,9,3,4,10,4,6,4,9,1,7\nW,5,10,8,8,5,3,10,3,3,9,10,9,8,11,1,8\nU,4,9,4,6,4,7,5,13,5,7,10,8,3,9,0,8\nG,6,11,8,9,6,6,6,7,5,5,6,12,5,7,5,6\nG,7,11,9,8,11,8,5,4,3,6,5,10,8,7,9,13\nO,5,10,4,5,3,7,7,5,4,7,4,7,5,8,5,8\nU,2,6,3,4,1,8,6,13,5,7,13,8,3,9,0,8\nK,6,10,8,8,8,6,7,5,3,7,5,8,5,7,10,8\nW,4,8,6,6,3,4,8,5,1,7,9,8,8,10,0,8\nK,5,7,7,5,5,5,7,2,6,10,8,11,3,8,3,8\nE,5,11,4,6,2,9,6,4,6,10,5,9,1,10,7,9\nV,3,6,5,4,1,9,8,4,2,5,14,8,3,10,0,8\nR,8,12,8,7,6,9,6,3,5,9,4,8,6,8,6,8\nZ,4,11,5,8,5,9,6,6,10,7,5,6,2,7,8,8\nS,4,10,6,7,4,7,8,3,7,10,5,7,2,6,5,8\nN,3,6,5,4,5,7,8,3,3,8,7,8,5,9,5,4\nX,5,11,8,8,4,5,8,2,8,10,11,9,3,9,4,6\nI,3,6,4,4,2,5,7,3,7,7,6,12,0,8,4,9\nQ,1,2,2,3,1,8,7,4,1,7,8,10,2,9,3,9\nN,10,15,9,8,4,7,9,4,5,3,5,10,6,10,2,7\nC,5,7,5,5,3,6,7,6,8,12,8,12,2,10,4,7\nQ,6,9,5,4,3,10,4,4,7,11,3,10,3,7,7,11\nN,7,10,9,8,7,7,7,8,6,7,5,6,4,8,4,8\nK,6,9,8,8,8,7,7,2,4,7,3,9,7,4,9,10\nS,8,12,8,7,4,5,10,4,5,14,8,6,2,9,3,7\nE,2,3,3,2,1,6,8,2,8,11,8,8,1,8,4,7\nD,4,8,6,6,8,9,7,5,6,7,5,6,5,6,8,7\nX,4,4,5,5,1,7,7,4,4,7,6,8,3,8,4,8\nC,1,0,2,1,0,7,7,6,7,7,6,13,0,8,4,10\nJ,5,9,7,7,3,9,5,4,6,15,4,10,1,6,0,7\nB,5,7,7,5,5,7,6,6,7,8,7,6,3,9,8,7\nD,5,9,5,7,5,5,7,9,7,6,5,5,2,8,3,8\nR,7,10,7,5,5,7,6,3,5,8,4,9,6,8,6,7\nZ,2,1,2,1,1,7,7,3,11,8,6,8,0,8,6,8\nT,2,3,4,4,1,8,14,1,6,6,11,8,0,8,0,8\nK,5,9,5,7,2,4,7,9,2,7,6,12,3,8,3,11\nG,5,11,7,8,5,7,6,7,8,7,4,12,2,9,6,8\nB,11,15,10,8,9,9,8,3,6,10,4,6,8,3,9,7\nX,8,11,11,9,7,6,8,1,8,10,8,9,3,8,4,6\nD,6,9,8,7,6,10,6,3,7,11,4,7,4,7,4,9\nV,2,5,4,4,1,6,11,3,4,8,11,8,2,10,1,8\nI,2,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8\nZ,2,5,4,4,2,7,8,2,10,11,6,8,1,8,6,7\nG,2,3,3,2,1,7,6,5,4,6,6,9,2,9,4,9\nJ,3,5,4,8,1,13,2,9,5,14,3,12,0,6,0,8\nV,2,5,4,4,2,7,12,2,3,7,11,8,2,10,1,8\nV,8,15,8,8,5,5,8,5,4,8,7,6,6,12,3,8\nJ,6,12,5,9,4,10,7,2,4,12,4,6,2,9,7,9\nC,3,6,4,4,1,6,7,6,10,7,6,13,1,8,4,9\nU,2,3,3,1,1,4,8,5,6,11,10,8,3,10,1,6\nA,2,6,4,4,2,12,2,4,3,12,2,10,2,6,3,9\nY,3,10,5,7,1,10,10,3,2,5,13,8,2,11,0,8\nT,3,7,5,5,3,7,11,1,8,7,11,8,1,10,1,8\nM,5,8,7,6,6,8,7,2,4,9,5,7,7,6,2,8\nV,5,8,5,6,3,4,11,2,3,8,10,7,3,12,1,7\nS,4,8,5,6,3,6,8,4,6,10,9,8,2,9,5,4\nM,5,6,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nY,3,8,6,5,1,10,10,3,2,5,13,8,2,11,0,8\nZ,9,9,6,12,6,6,9,4,2,12,7,7,3,8,13,6\nM,3,8,4,6,3,8,6,12,1,5,9,8,7,6,0,8\nM,3,7,4,5,4,7,5,10,0,7,8,8,6,5,0,8\nJ,3,8,3,6,2,13,3,5,4,13,2,10,0,7,0,8\nK,7,13,7,7,4,8,7,2,6,10,7,9,6,11,4,8\nB,3,3,4,4,3,7,7,6,5,7,6,6,2,8,6,10\nK,4,10,6,8,7,7,7,5,4,7,5,7,4,6,9,14\nZ,5,8,7,6,4,5,10,3,10,11,9,6,1,9,6,5\nF,4,8,4,6,3,1,13,4,4,12,10,6,0,8,1,6\nN,1,1,2,1,1,7,8,5,3,7,7,7,4,8,1,6\nP,7,10,10,8,5,7,14,5,3,13,4,0,0,10,4,7\nC,4,3,5,5,2,6,7,6,10,7,6,13,1,8,4,9\nZ,4,8,6,6,3,9,5,3,10,11,4,9,1,7,6,9\nS,2,3,4,2,1,8,7,2,6,10,6,8,1,9,5,8\nW,4,6,7,4,4,9,11,2,3,5,9,8,7,11,1,8\nP,2,3,4,2,2,7,9,4,4,11,4,4,1,9,3,8\nL,4,8,4,6,1,0,0,6,6,0,1,4,0,8,0,8\nU,3,4,4,6,2,8,5,14,5,7,13,8,3,9,0,8\nJ,3,10,4,7,1,13,2,9,4,14,4,12,1,6,0,8\nJ,2,4,3,6,1,12,2,9,4,13,6,13,1,6,0,8\nZ,3,5,3,3,2,8,7,5,10,6,6,7,2,8,7,8\nM,4,2,5,4,4,8,6,6,4,7,7,8,8,6,3,6\nU,7,13,6,7,4,8,4,5,5,7,9,8,4,8,3,8\nJ,1,3,2,2,1,9,7,2,6,14,5,8,0,7,0,8\nB,5,9,5,7,7,6,7,8,5,7,6,7,2,8,7,9\nB,7,11,9,8,7,10,6,4,7,10,3,7,2,8,6,12\nQ,8,14,8,8,4,9,4,4,7,11,4,10,3,7,8,11\nN,3,4,5,3,2,7,9,2,5,10,6,6,5,9,1,7\nD,3,7,4,5,3,9,7,5,7,10,4,5,3,8,3,8\nB,1,0,1,0,0,7,7,6,4,7,6,7,1,8,5,9\nE,4,6,6,4,4,7,8,2,7,11,7,9,3,8,4,9\nE,3,3,5,2,2,6,8,3,8,11,8,9,2,9,4,7\nN,6,11,8,8,5,7,9,2,5,9,6,7,5,9,1,8\nT,2,4,4,6,1,7,14,0,6,8,11,8,0,8,0,8\nY,3,3,5,4,1,9,9,3,1,5,13,8,2,10,0,8\nQ,7,9,7,10,8,8,8,6,2,7,7,11,4,10,8,6\nU,9,15,8,8,5,5,5,4,4,7,8,10,7,5,3,9\nH,3,3,4,4,2,7,10,14,2,7,3,8,3,8,0,8\nJ,2,10,3,8,1,14,3,7,5,14,1,10,0,7,0,8\nD,1,4,3,3,2,10,6,3,5,10,3,6,2,8,2,9\nG,2,4,2,2,1,6,7,5,5,9,7,10,2,8,4,9\nV,5,9,7,8,9,7,7,5,4,7,6,8,7,9,7,8\nU,2,2,3,3,2,7,9,5,7,7,9,9,3,9,1,8\nU,7,11,9,8,7,5,9,5,7,6,9,10,3,9,1,8\nZ,3,3,4,4,2,7,7,3,14,9,6,8,0,8,8,8\nG,2,3,3,1,1,7,7,5,5,9,6,10,2,9,4,10\nL,4,11,5,8,4,7,4,1,7,8,2,10,1,6,3,8\nD,4,6,5,4,4,9,7,3,6,10,4,6,3,8,2,8\nJ,3,10,5,7,5,9,8,3,3,8,4,6,4,8,6,5\nK,3,6,5,4,5,7,6,3,4,6,6,8,7,7,6,9\nA,3,9,5,7,3,12,3,4,3,11,1,9,2,6,3,9\nL,3,4,4,7,1,0,1,6,6,0,0,6,0,8,0,8\nX,4,9,6,7,5,8,5,3,5,6,7,8,3,9,9,9\nQ,5,9,5,5,3,11,5,3,6,9,4,7,3,9,6,11\nF,3,8,3,6,2,2,12,4,3,12,10,6,0,8,2,7\nW,2,3,4,2,2,7,10,2,2,6,9,8,5,11,0,7\nK,6,9,9,6,6,5,7,1,7,10,8,10,3,8,4,7\nP,4,9,4,6,4,4,12,7,1,10,6,4,1,10,3,8\nV,6,9,5,5,2,8,11,4,5,7,10,5,4,10,2,6\nL,7,15,6,8,3,10,2,3,5,13,4,12,2,8,5,10\nA,3,8,5,5,1,6,6,3,1,6,0,8,2,6,1,7\nB,3,3,3,4,3,6,7,9,6,7,6,7,2,8,8,10\nS,6,9,5,5,2,8,4,4,5,9,2,8,3,6,5,8\nI,6,11,4,6,2,10,6,5,5,12,3,6,3,8,5,10\nB,1,0,1,0,1,7,8,6,4,7,6,7,1,8,6,9\nF,4,8,6,6,5,4,10,1,2,10,8,7,5,10,2,4\nI,3,4,4,5,3,7,8,4,5,7,8,7,2,9,8,9\nM,5,8,7,6,7,7,6,5,5,7,7,10,11,6,2,8\nK,2,1,3,2,2,5,7,4,7,7,6,11,3,8,4,9\nC,2,3,2,2,1,6,8,6,7,8,7,12,1,9,3,10\nH,4,7,6,5,4,9,8,3,7,10,4,7,3,8,3,9\nS,3,2,4,4,3,8,7,7,5,7,6,7,2,8,9,8\nB,4,5,5,7,4,7,7,10,7,7,6,8,2,8,9,9\nO,2,3,2,1,1,8,7,6,4,9,6,8,2,8,3,8\nM,9,11,12,8,8,4,6,4,5,10,11,11,11,6,4,8\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nW,7,9,10,8,11,6,6,6,5,5,5,8,11,11,9,12\nU,4,6,4,4,1,8,5,12,5,7,15,8,3,9,0,8\nY,4,5,5,4,2,4,11,2,7,11,10,6,1,11,2,5\nA,3,9,6,7,3,11,2,3,3,10,2,9,2,6,3,8\nP,2,3,4,2,2,7,10,3,4,12,4,3,1,10,2,8\nB,4,7,6,5,5,7,8,5,5,9,6,6,3,7,6,7\nO,4,7,5,5,3,8,6,8,5,10,6,9,3,8,3,8\nY,5,7,5,5,2,3,10,3,7,11,12,7,2,11,3,5\nI,4,5,6,5,4,8,9,4,5,7,6,9,3,8,8,7\nR,3,9,4,6,3,5,10,9,3,7,4,8,3,7,6,11\nN,6,8,9,6,5,10,8,3,6,10,2,4,5,9,1,7\nB,3,7,3,5,4,6,7,7,5,7,6,7,2,8,7,10\nQ,9,15,8,8,4,9,3,4,7,11,4,10,3,8,8,11\nH,6,11,8,8,6,9,7,6,7,7,6,8,6,8,4,7\nJ,2,4,3,3,1,11,6,2,7,11,3,7,0,7,1,8\nW,4,4,5,6,3,7,8,4,1,7,8,8,8,10,0,8\nY,5,11,6,8,2,3,11,3,8,13,11,5,0,10,2,4\nF,4,9,5,7,3,4,11,3,7,11,10,6,1,10,3,5\nA,4,9,6,6,2,7,4,3,2,7,1,8,3,7,3,8\nX,1,0,2,0,0,7,7,4,4,7,6,8,2,8,3,8\nZ,5,10,7,8,5,7,8,2,9,12,7,8,1,9,6,7\nH,3,7,4,4,2,7,9,14,2,7,3,8,3,8,0,8\nK,6,11,9,8,8,7,6,1,6,10,5,9,7,7,6,9\nJ,4,7,5,8,5,8,8,4,5,7,6,8,3,8,9,8\nN,2,4,4,3,2,6,9,2,4,10,7,7,5,8,1,8\nW,5,9,7,7,7,7,5,6,2,7,7,8,7,7,6,12\nU,4,6,5,4,2,6,8,7,9,9,10,8,3,9,1,8\nY,2,6,4,4,1,9,10,3,2,5,13,8,2,11,0,8\nQ,4,7,4,9,4,7,7,6,3,8,9,10,3,8,6,8\nV,3,3,4,2,1,4,12,4,3,11,11,6,2,11,1,8\nD,6,13,6,7,5,11,5,4,6,10,4,7,5,9,6,10\nV,3,8,4,6,2,7,9,4,2,8,12,8,2,10,0,8\nR,4,8,5,6,3,5,12,8,4,8,3,9,3,7,6,11\nL,1,0,1,1,0,2,2,5,5,1,1,6,0,8,0,8\nV,4,10,7,8,3,8,12,3,4,5,11,8,3,10,1,8\nF,5,10,7,8,5,6,10,2,6,13,7,5,1,10,2,7\nB,5,8,7,7,8,7,8,5,5,8,6,8,7,8,9,6\nR,3,4,4,5,2,6,10,9,4,7,4,8,3,7,5,10\nJ,3,6,4,4,3,9,6,6,5,8,6,7,2,8,4,6\nV,3,7,5,5,1,7,8,4,2,7,14,8,3,10,0,8\nY,9,9,8,13,5,9,10,2,4,5,11,5,4,10,7,8\nM,8,9,11,7,8,10,6,2,5,9,4,7,8,7,2,8\nX,3,2,4,3,2,7,7,3,9,6,6,8,2,8,6,8\nS,3,9,4,7,2,7,7,6,8,5,6,7,0,8,9,7\nK,2,3,4,2,2,8,7,1,6,10,5,8,3,8,2,8\nT,3,7,5,5,3,6,11,1,7,8,11,9,1,11,1,8\nH,3,4,6,3,3,7,8,3,6,10,6,8,3,8,3,7\nL,4,9,4,7,2,0,2,4,6,1,0,8,0,8,0,8\nJ,3,8,4,6,2,10,6,2,8,12,3,7,0,6,2,6\nD,2,1,2,1,1,5,7,8,7,6,6,6,2,8,3,8\nB,2,1,3,2,2,8,7,5,5,7,6,6,2,8,5,9\nN,6,9,9,7,5,10,8,3,6,10,2,4,5,9,1,7\nR,6,10,9,8,11,6,7,4,4,6,6,9,8,9,9,7\nM,13,15,13,8,7,3,8,6,6,4,2,13,9,11,2,8\nZ,3,6,4,4,2,7,7,4,14,9,6,8,0,8,8,8\nR,6,9,8,7,9,6,7,4,5,7,6,8,6,9,7,5\nK,5,11,6,8,2,3,7,8,2,7,6,11,4,8,2,11\nW,5,9,8,7,7,7,12,2,2,6,8,8,7,12,1,8\nP,3,6,4,4,2,7,10,4,4,12,5,3,1,10,3,8\nE,3,8,4,6,4,6,7,5,8,7,5,10,3,8,5,9\nE,7,12,5,6,4,8,7,4,4,11,5,9,3,9,8,11\nY,3,3,4,4,1,10,13,3,3,3,11,9,0,10,0,8\nJ,3,10,4,7,3,11,5,2,6,11,2,7,0,7,1,7\nL,3,7,4,5,2,4,5,2,10,3,1,8,0,7,2,5\nI,2,5,1,3,1,7,7,1,7,7,6,8,0,8,3,8\nI,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8\nM,6,10,7,6,4,13,2,4,3,12,1,9,5,5,1,9\nV,2,5,4,3,2,7,12,2,2,7,11,9,2,10,1,8\nP,7,10,9,8,6,7,10,4,4,12,5,3,1,10,3,8\nH,5,10,7,8,7,7,7,6,7,7,6,8,6,8,4,8\nR,9,13,7,8,5,8,6,5,5,9,5,9,6,6,7,11\nX,4,9,6,7,5,8,7,3,5,6,7,6,4,11,10,8\nP,2,5,3,7,6,8,7,4,1,7,6,7,5,8,4,8\nV,4,10,5,7,3,7,9,4,2,7,13,8,2,10,0,8\nN,3,6,3,4,3,8,7,11,1,6,6,8,5,9,0,6\nO,3,1,4,3,2,8,7,7,5,7,6,8,2,8,3,8\nG,6,11,7,8,5,5,7,6,6,9,8,10,2,8,5,9\nT,4,5,5,4,2,5,12,3,7,11,10,4,1,11,1,5\nR,8,12,7,6,6,6,7,3,5,7,5,9,6,9,6,6\nW,4,3,5,5,3,8,8,4,1,6,8,8,8,9,0,8\nU,6,9,6,5,3,7,5,5,5,7,8,8,5,7,2,8\nL,6,12,6,6,4,8,3,3,4,12,8,12,3,10,5,11\nS,7,12,7,6,3,10,4,5,4,13,5,8,2,9,2,7\nQ,4,9,5,11,7,9,8,8,2,5,7,10,3,8,6,10\nM,3,1,4,3,3,7,6,6,4,6,7,7,8,6,2,7\nF,2,2,3,3,2,5,10,4,5,10,9,5,1,10,3,6\nV,5,8,5,6,2,2,11,5,4,12,12,8,2,10,1,8\nV,5,7,5,5,2,3,11,4,3,10,12,8,2,10,1,8\nF,4,8,4,6,3,1,13,4,3,12,10,7,0,8,1,6\nM,4,11,6,8,6,6,6,6,5,7,7,10,8,5,2,9\nI,3,8,4,6,2,7,7,0,6,13,6,8,0,8,1,8\nP,3,7,3,4,2,4,12,8,2,10,6,4,1,10,4,8\nU,4,2,5,3,2,6,8,5,8,7,10,9,3,9,1,8\nB,3,4,3,3,3,7,7,5,5,7,6,6,2,8,6,9\nQ,4,10,6,9,6,8,7,8,5,6,4,8,3,8,4,7\nH,5,8,5,6,4,7,8,13,1,7,5,8,3,8,0,8\nX,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nM,4,7,6,5,6,9,7,5,5,6,7,5,8,6,2,6\nJ,3,7,4,5,1,7,7,3,7,15,6,10,0,7,1,7\nV,6,12,5,7,3,7,10,6,4,9,9,5,5,12,3,8\nB,2,3,2,2,2,7,7,5,5,6,6,5,2,8,6,8\nT,5,8,5,6,3,4,13,4,6,12,10,4,1,11,1,5\nU,8,10,9,8,5,4,7,6,8,10,10,9,3,9,2,5\nD,4,7,6,5,4,7,8,5,6,10,6,4,3,8,4,8\nY,4,9,6,7,2,7,12,1,3,7,11,8,0,10,0,8\nO,2,1,2,2,1,8,7,7,4,7,6,8,2,8,3,8\nO,2,0,2,1,1,7,6,7,5,7,6,8,2,8,3,8\nK,4,4,4,6,2,4,7,8,1,7,6,11,3,8,2,11\nW,8,11,8,6,5,9,8,4,5,6,9,6,11,8,3,6\nA,5,11,7,8,4,8,3,2,3,7,1,8,2,7,3,7\nO,8,12,6,6,4,5,9,6,4,9,8,9,5,10,5,8\nS,5,9,6,8,7,9,7,5,6,7,6,7,6,8,10,12\nT,5,7,5,5,3,6,11,3,7,11,9,5,2,12,3,4\nS,2,7,3,5,3,8,8,7,5,7,5,6,2,8,8,8\nX,3,4,5,3,2,7,7,1,8,10,6,8,2,8,3,7\nT,2,9,4,6,1,5,14,1,6,9,11,7,0,8,0,8\nU,5,9,6,7,5,6,6,8,5,7,6,10,5,8,7,3\nY,5,6,5,4,3,5,9,1,7,9,10,6,2,11,3,5\nV,3,3,4,2,1,4,12,3,3,10,11,7,2,11,1,7\nD,2,4,4,3,2,9,7,4,6,10,4,6,2,8,3,8\nH,5,10,8,8,7,7,8,7,7,7,5,9,3,8,3,8\nB,4,8,6,6,8,9,7,4,4,6,7,7,7,10,7,6\nS,5,11,6,8,3,7,8,4,8,11,7,7,2,8,5,6\nO,2,2,3,3,2,8,7,7,4,7,6,8,2,8,3,8\nO,6,11,7,9,3,7,8,9,8,7,7,6,3,8,4,8\nJ,2,6,2,4,1,12,2,9,3,13,5,13,1,6,0,8\nP,3,4,4,6,6,8,9,4,0,8,7,7,5,9,5,7\nO,3,7,5,5,5,8,6,5,1,7,6,8,7,8,3,8\nI,3,7,4,5,2,7,7,0,7,13,6,8,0,8,1,7\nZ,4,9,6,6,6,7,7,3,8,8,6,9,1,8,11,7\nW,3,6,5,4,7,9,7,4,2,7,6,8,8,11,1,6\nF,2,4,3,5,1,1,13,4,4,12,11,7,0,8,2,6\nH,5,7,8,5,6,8,7,3,6,10,6,8,3,8,3,8\nD,2,4,3,3,2,9,6,3,5,10,4,6,2,8,2,8\nB,2,1,2,2,1,7,7,8,5,6,6,7,2,8,7,10\nK,0,0,1,0,0,4,7,5,3,7,6,10,3,8,2,11\nT,4,10,6,8,3,5,11,1,9,9,12,8,0,10,1,7\nN,3,8,3,6,2,7,7,13,2,5,6,8,5,8,0,8\nF,2,6,2,4,1,1,11,3,6,11,11,9,0,8,1,6\nA,3,8,5,5,2,7,4,3,1,7,1,8,3,7,2,8\nV,8,11,7,8,5,4,11,1,3,8,10,8,5,12,1,7\nF,9,13,7,7,3,4,13,3,5,13,7,3,2,7,5,4\nY,2,1,3,1,0,7,10,3,1,7,13,8,1,11,0,8\nR,4,7,6,5,4,9,8,3,6,10,3,6,3,6,4,10\nQ,5,9,5,11,7,8,7,6,2,8,7,10,3,8,5,8\nM,6,9,9,7,6,10,5,3,5,9,3,6,8,6,2,9\nP,3,5,3,3,2,4,10,3,5,10,8,4,0,10,3,7\nK,3,7,4,5,3,6,7,4,7,6,6,9,3,8,5,9\nY,6,6,8,9,8,10,12,6,4,6,7,6,5,10,7,4\nD,4,6,6,6,5,6,7,4,7,6,4,7,3,7,5,6\nR,4,3,5,5,3,6,9,10,5,7,5,8,2,8,5,10\nB,4,7,6,5,5,10,7,3,6,10,4,6,2,8,4,10\nT,4,8,6,6,4,6,7,7,7,8,9,8,3,10,5,9\nW,6,10,8,8,4,7,7,4,2,7,8,8,9,9,0,8\nK,5,9,7,7,8,7,9,5,5,8,5,7,7,6,8,12\nG,10,15,9,8,5,8,4,5,4,9,4,5,4,7,5,8\nP,3,7,3,5,2,4,12,8,2,10,6,4,1,10,3,8\nC,1,0,1,1,0,6,7,6,8,7,6,14,0,8,4,10\nS,5,11,7,8,4,10,6,4,8,11,3,8,2,8,5,11\nJ,5,9,6,7,4,7,4,5,4,14,8,14,1,6,1,6\nP,5,11,8,8,6,7,11,7,3,10,5,3,3,11,4,8\nX,3,2,4,4,2,7,7,3,9,6,6,8,2,8,6,8\nL,8,13,8,8,5,9,3,3,3,11,6,11,4,8,7,9\nB,4,5,4,8,4,6,8,10,7,7,5,7,2,8,9,10\nE,3,5,6,4,3,8,7,2,8,11,5,8,2,8,5,10\nH,3,4,4,6,2,7,9,15,2,7,4,8,3,8,0,8\nT,3,4,4,3,2,6,11,3,7,11,9,4,2,11,3,4\nP,2,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8\nS,2,3,4,2,2,6,8,2,6,10,8,8,1,8,5,6\nN,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8\nP,5,4,5,6,3,4,12,9,3,10,6,4,1,10,4,8\nD,3,5,5,4,3,9,7,5,7,9,4,5,3,8,3,8\nT,2,7,3,4,1,6,14,0,6,8,11,8,0,8,0,8\nB,2,4,4,2,2,9,7,3,5,11,5,7,2,7,4,9\nY,3,7,5,5,2,7,10,2,2,7,12,8,1,11,0,8\nJ,3,11,4,8,2,15,3,4,5,13,0,8,0,7,0,8\nA,3,9,6,7,4,11,3,2,2,9,2,9,3,4,3,8\nJ,3,7,5,5,2,11,6,2,7,14,3,8,0,8,0,8\nS,2,3,3,4,1,9,10,5,9,5,6,5,0,7,8,7\nE,4,8,4,6,2,3,7,6,11,7,6,15,0,8,7,7\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nH,5,9,7,6,7,8,8,6,7,7,6,9,6,8,4,7\nG,4,5,5,8,2,7,6,8,8,6,5,10,2,8,6,11\nZ,2,2,3,3,2,7,8,5,9,6,6,9,1,9,7,8\nY,4,6,6,4,3,9,10,1,7,3,11,8,1,11,1,9\nH,3,7,4,5,5,7,5,4,4,6,5,8,4,8,5,7\nB,4,8,6,6,7,8,8,7,6,7,5,6,2,7,7,10\nA,4,9,6,7,4,10,3,2,2,8,2,10,4,4,3,8\nB,6,10,6,6,5,10,6,3,5,9,5,8,6,8,7,9\nM,6,10,8,8,7,9,6,6,5,6,7,6,8,6,2,6\nP,4,9,4,6,2,5,10,9,4,9,6,5,2,10,4,8\nW,5,11,8,8,6,4,11,2,3,8,9,9,9,11,2,7\nS,2,4,4,2,1,6,8,2,7,11,7,8,1,8,4,6\nJ,3,10,4,8,1,13,2,9,4,14,5,13,1,6,0,8\nN,8,14,9,8,5,7,7,2,4,12,4,8,6,8,0,7\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nP,5,8,7,11,11,8,9,4,1,8,7,6,8,10,6,8\nG,2,3,3,2,1,6,7,5,5,9,7,10,2,8,4,10\nE,6,8,8,6,5,4,9,4,9,11,10,9,2,9,4,5\nQ,6,8,7,10,6,8,7,7,4,8,7,10,3,8,6,8\nR,3,8,4,6,2,5,11,8,4,7,4,9,3,7,6,11\nK,2,3,2,2,2,5,7,4,7,6,6,10,3,8,4,10\nY,4,5,6,6,6,9,9,6,4,7,7,7,5,11,6,4\nP,4,5,6,6,6,6,9,4,2,8,7,6,6,10,4,6\nG,6,9,6,4,3,7,3,5,2,6,5,4,4,8,5,7\nG,4,7,6,5,6,7,7,6,2,7,6,11,4,9,7,9\nP,7,9,8,7,7,6,7,5,5,7,6,9,5,9,7,10\nL,7,15,7,8,5,9,3,3,4,12,6,11,3,8,6,9\nJ,1,2,2,3,1,10,6,2,6,12,4,9,0,7,1,7\nY,3,10,5,8,2,8,10,1,3,6,12,8,1,11,0,8\nD,6,11,8,8,8,8,8,6,6,9,5,4,6,9,5,10\nM,6,10,9,8,8,8,7,2,4,9,7,8,8,6,2,8\nG,2,1,2,2,1,8,6,6,6,6,5,10,1,7,5,10\nU,4,9,5,6,4,6,9,8,5,3,7,12,4,8,5,6\nI,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nI,4,11,5,8,3,8,6,0,7,13,6,9,1,7,3,8\nB,6,11,6,8,5,6,6,9,7,6,6,7,2,8,10,10\nQ,1,2,2,3,2,7,7,5,2,8,8,9,2,9,4,9\nJ,2,7,3,5,1,13,2,9,4,14,4,12,1,6,0,8\nB,4,11,5,8,4,6,7,9,7,7,6,7,2,8,9,10\nA,3,5,5,5,4,9,8,3,4,6,8,7,4,10,4,5\nS,3,4,5,2,2,8,8,2,7,10,6,7,1,8,5,7\nU,4,4,4,6,2,8,5,14,5,6,13,8,3,9,0,8\nE,2,3,4,2,2,7,7,2,7,11,7,9,2,8,4,8\nR,3,8,3,6,3,6,10,7,4,6,3,10,2,6,5,11\nL,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8\nN,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8\nB,6,9,9,8,10,7,8,5,5,7,7,8,7,9,9,6\nK,6,9,8,6,9,7,6,3,5,6,6,9,8,6,7,7\nX,5,8,6,7,7,6,8,2,5,7,6,9,4,7,8,7\nP,6,9,5,4,2,5,11,5,3,13,5,4,4,9,3,8\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nG,8,14,7,8,5,8,6,4,3,9,6,8,4,9,8,8\nA,3,10,6,8,4,12,2,3,3,10,2,9,2,6,4,8\nK,4,4,6,3,3,8,6,1,7,10,4,9,4,7,4,9\nB,5,10,7,8,7,8,6,7,7,6,7,6,2,8,8,11\nY,3,8,5,6,3,5,9,1,6,8,12,10,1,11,2,8\nN,2,4,4,3,2,5,10,3,3,10,8,8,5,8,1,7\nY,2,4,4,6,1,5,10,3,2,8,13,8,2,11,0,8\nO,9,12,6,7,3,8,7,6,5,9,4,7,5,9,5,9\nE,6,11,9,9,7,6,8,2,8,10,7,9,3,8,5,7\nU,6,10,6,8,4,3,9,5,6,11,11,9,3,9,1,7\nU,7,8,8,6,3,4,8,5,8,10,10,9,3,9,2,6\nX,3,5,6,4,3,9,7,1,9,10,4,7,2,8,3,8\nD,1,0,1,0,0,6,7,7,5,7,6,6,2,8,3,8\nD,2,1,3,2,2,8,7,7,7,7,7,4,2,8,3,6\nS,4,5,6,4,3,7,7,2,8,11,5,7,1,8,5,8\nK,4,5,7,4,4,5,7,2,8,10,9,10,3,8,4,6\nI,2,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nF,4,5,5,5,5,7,8,4,5,7,5,7,4,9,8,7\nA,1,1,3,2,1,6,2,2,1,6,2,7,1,6,2,6\nR,3,6,5,4,5,6,7,3,3,7,6,9,5,9,7,5\nP,3,2,4,3,3,6,9,4,4,9,7,4,1,10,4,7\nY,2,7,4,5,2,8,10,1,6,5,11,8,1,11,1,8\nV,4,9,6,7,3,9,11,2,4,4,11,8,2,10,1,8\nM,5,11,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nK,4,9,6,6,5,6,6,3,7,6,6,9,3,8,5,10\nF,4,8,7,6,7,10,6,1,5,9,6,6,5,10,4,6\nX,7,10,7,6,4,9,6,2,7,11,4,7,4,9,4,9\nH,4,9,6,6,7,7,7,6,6,7,6,9,3,8,3,8\nL,4,9,5,7,1,0,0,6,6,0,1,5,0,8,0,8\nH,6,9,8,6,7,11,6,3,6,10,3,7,3,7,3,10\nJ,5,11,7,8,8,10,7,4,4,8,4,5,4,7,6,5\nR,2,3,3,2,2,6,7,5,5,7,6,7,2,7,4,9\nG,1,1,2,1,1,7,7,5,5,6,6,10,2,9,3,9\nF,5,9,8,6,7,4,10,1,4,10,7,6,7,10,3,4\nD,6,9,6,4,3,12,2,4,5,12,2,9,4,7,2,10\nK,9,13,9,7,4,4,9,3,7,10,9,11,5,7,3,6\nL,3,9,5,6,3,5,4,2,8,6,1,10,0,6,3,7\nP,5,4,5,6,3,5,9,10,5,8,5,5,2,10,4,8\nA,2,3,3,2,1,8,2,2,1,6,2,8,2,7,2,7\nI,6,10,8,7,5,7,6,2,8,7,6,8,0,9,4,8\nX,3,7,5,5,2,9,7,1,8,10,4,7,3,8,3,8\nM,3,6,5,4,5,12,5,3,2,9,4,8,5,6,2,6\nR,4,2,4,3,3,7,8,5,5,6,5,6,3,7,4,8\nY,1,0,1,0,0,7,10,3,1,7,12,8,1,11,0,8\nI,1,3,2,1,0,7,7,1,7,13,6,8,0,8,0,7\nT,5,11,6,8,8,7,8,4,5,6,7,9,7,7,8,7\nF,3,9,4,6,2,1,12,5,4,11,10,7,0,8,3,6\nR,5,10,8,8,5,11,7,3,6,11,1,6,4,5,4,10\nZ,9,11,6,15,6,5,11,3,3,12,8,6,3,9,11,5\nK,4,8,6,6,3,7,7,1,7,10,6,9,3,8,4,8\nR,6,9,6,5,4,9,8,4,6,9,2,6,5,5,5,7\nE,4,8,5,6,3,3,9,6,12,7,5,14,0,8,7,7\nY,10,12,8,7,5,6,8,4,5,10,7,4,3,9,5,4\nM,6,10,7,8,4,7,7,13,2,8,9,8,9,6,0,8\nG,8,15,7,8,6,8,5,4,3,8,6,8,4,9,8,8\nX,4,6,6,4,2,7,7,2,8,11,6,8,3,8,3,7\nZ,3,10,4,7,3,7,8,3,11,9,6,8,0,8,7,8\nT,3,8,4,5,1,8,15,1,6,6,11,9,0,8,0,8\nA,3,3,5,4,1,9,3,3,2,8,2,9,3,6,2,8\nA,2,4,4,5,1,8,6,3,1,7,0,8,2,6,1,8\nP,3,5,5,4,3,8,9,3,4,12,4,4,2,8,3,8\nH,4,8,4,5,2,7,8,15,1,7,5,8,3,8,0,8\nT,5,10,7,8,7,7,6,7,7,6,8,9,4,9,8,6\nM,7,10,9,8,8,9,6,2,5,9,5,7,9,8,2,8\nK,4,9,6,7,7,6,6,3,4,6,5,9,8,6,8,8\nT,1,1,2,1,0,7,14,1,4,7,11,8,0,8,0,8\nJ,2,7,3,5,2,8,7,2,5,11,5,8,3,8,2,6\nI,2,8,3,6,2,9,6,0,7,13,5,9,0,8,1,8\nJ,1,5,2,3,1,10,6,2,6,11,3,8,0,7,1,7\nC,4,8,5,6,2,5,9,8,8,13,9,6,2,10,2,6\nY,3,3,4,2,1,4,11,2,7,11,10,5,1,11,2,5\nL,7,15,7,8,5,9,3,4,4,12,7,11,4,8,7,9\nB,2,4,3,2,2,8,7,3,5,9,6,6,2,8,4,8\nP,6,10,8,8,6,8,10,5,4,11,4,3,1,10,3,8\nQ,4,10,5,9,6,8,7,8,5,6,3,8,3,9,4,8\nD,3,6,4,4,5,9,7,5,4,7,5,7,4,7,6,5\nE,5,11,4,6,3,9,6,3,5,11,4,8,3,9,7,11\nD,5,7,6,6,5,6,7,6,7,7,5,7,4,7,5,5\nP,6,11,8,8,8,7,5,6,5,7,6,9,6,9,8,9\nO,4,7,6,5,4,7,7,8,4,7,6,8,3,8,3,8\nH,4,7,6,5,5,8,6,6,7,7,7,6,3,9,3,7\nN,7,11,10,8,6,12,7,3,5,10,0,3,7,11,2,9\nA,3,6,5,4,2,9,2,2,2,7,1,8,2,7,3,7\nT,4,8,6,6,5,7,7,7,7,5,9,10,4,8,7,6\nC,1,1,2,1,1,6,8,6,6,8,7,12,1,9,3,10\nL,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8\nP,2,7,4,5,2,6,11,5,4,10,8,3,1,10,4,6\nP,4,9,5,7,4,5,10,4,5,10,9,4,4,10,4,7\nJ,3,5,4,7,1,12,2,9,4,14,5,13,1,6,0,8\nD,3,1,4,2,2,7,7,6,7,6,5,5,2,8,3,7\nI,0,3,0,1,0,7,7,1,7,7,6,8,0,8,2,8\nH,2,3,4,2,2,8,7,3,5,10,6,8,3,8,2,8\nT,9,11,9,8,7,7,10,2,9,11,9,5,4,11,5,5\nY,7,9,7,7,4,3,10,2,7,11,11,7,1,11,2,5\nC,3,6,4,4,2,6,7,5,6,11,8,12,1,9,3,9\nS,4,5,6,4,6,8,8,4,5,7,7,8,4,9,9,10\nL,5,11,5,8,2,0,0,6,6,0,1,5,0,8,0,8\nN,3,2,4,4,3,7,9,5,5,7,6,6,5,9,2,6\nG,5,7,6,5,4,5,7,6,5,9,7,10,2,9,4,10\nQ,7,9,8,11,9,8,7,6,2,7,7,12,6,9,9,6\nI,1,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nV,4,8,6,6,3,7,11,2,3,5,11,9,2,10,1,8\nR,4,9,5,7,4,9,8,4,6,9,3,7,3,7,4,11\nN,1,3,3,1,1,9,8,3,4,10,3,5,4,8,1,8\nC,6,14,5,8,4,7,8,4,3,9,8,10,4,9,9,11\nT,4,7,5,5,4,7,8,7,7,7,8,9,3,10,5,8\nR,4,9,5,7,5,9,7,3,5,9,3,8,3,6,3,11\nZ,3,8,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nB,2,2,3,3,2,7,7,5,5,6,6,6,2,8,5,9\nT,2,2,3,3,2,7,11,2,7,7,11,8,1,11,1,8\nR,1,0,1,0,0,6,8,6,3,7,5,7,2,7,4,11\nJ,2,3,4,2,1,9,6,3,5,14,6,10,0,7,0,8\nM,5,10,5,8,4,7,7,12,2,7,9,8,8,6,0,8\nX,9,13,9,7,5,4,9,3,8,12,10,9,4,9,4,5\nS,5,10,6,8,4,9,6,5,8,11,2,8,2,7,5,11\nS,3,6,4,4,2,8,7,5,8,5,6,8,0,8,9,8\nG,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10\nB,9,14,7,8,4,9,6,6,6,11,4,9,6,7,7,10\nM,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8\nS,3,6,4,4,2,6,5,5,9,5,6,10,0,9,9,8\nM,3,7,4,5,3,7,6,11,1,8,9,8,7,6,0,8\nL,2,6,4,4,1,5,4,1,9,7,1,9,0,7,2,7\nG,3,6,4,4,3,7,6,6,6,6,6,10,2,9,4,8\nC,7,10,5,5,2,6,10,6,7,12,8,8,2,9,5,8\nS,5,8,7,6,4,5,8,4,6,10,9,8,2,8,5,4\nC,4,10,5,8,2,5,7,7,10,7,5,14,1,9,4,9\nR,2,4,3,3,2,7,7,5,5,6,5,6,2,7,4,8\nG,6,11,8,8,9,8,7,6,2,7,6,11,5,8,9,7\nC,2,4,3,3,2,6,8,7,8,8,7,13,1,9,4,10\nI,3,8,4,6,2,7,8,0,8,14,6,6,0,8,1,7\nY,8,11,8,8,4,2,11,3,6,12,12,7,1,11,2,5\nC,3,6,4,4,4,8,6,5,2,7,7,10,6,9,4,7\nY,4,11,6,8,3,10,10,1,3,6,12,8,1,11,0,8\nK,4,2,5,3,3,5,7,4,8,7,6,11,3,8,5,9\nI,7,8,8,9,8,7,8,4,6,6,8,7,4,9,10,9\nE,5,9,7,7,6,8,7,6,3,7,6,10,5,9,9,8\nO,2,5,3,4,2,7,7,8,5,7,5,8,2,8,3,8\nM,5,7,7,5,6,4,7,3,4,10,10,10,6,6,2,6\nQ,4,8,5,9,5,8,7,7,2,8,7,12,3,9,6,8\nZ,3,9,4,6,3,7,7,6,11,6,6,8,1,8,8,8\nH,3,8,5,6,6,8,6,4,5,6,6,8,6,7,6,7\nZ,8,8,6,12,5,8,5,5,4,11,6,8,3,9,11,7\nH,4,9,6,7,7,7,7,5,6,7,6,8,3,8,3,8\nO,5,9,6,7,4,7,8,8,6,7,8,8,3,7,4,9\nU,5,9,5,6,4,7,5,14,5,7,11,8,3,9,0,8\nW,6,9,6,7,7,4,9,2,3,9,8,8,7,11,2,7\nL,3,8,3,6,1,0,1,5,6,0,0,6,0,8,0,8\nJ,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8\nL,5,10,7,8,5,5,5,1,9,6,2,10,3,7,4,5\nO,5,8,7,7,6,7,5,5,5,9,5,10,5,5,7,5\nK,6,11,8,8,7,6,6,4,7,6,6,11,5,7,8,10\nJ,4,6,6,7,5,8,9,5,5,7,7,9,3,7,8,6\nK,3,6,4,4,4,7,9,4,4,7,5,8,4,7,6,9\nT,3,7,4,5,2,7,11,3,7,10,9,4,2,11,3,5\nG,4,10,6,8,7,7,9,6,3,5,5,11,5,7,8,7\nM,5,5,6,7,4,7,7,12,2,7,9,8,9,6,0,8\nJ,2,10,3,7,1,15,2,6,6,14,0,9,0,7,0,8\nV,3,4,5,2,1,3,13,4,2,11,11,7,2,10,1,7\nT,6,8,6,6,3,6,11,2,9,12,9,4,1,11,3,4\nY,2,1,4,2,1,6,10,2,7,8,11,8,1,11,2,7\nC,3,3,4,5,1,5,7,6,9,6,6,12,1,8,4,8\nA,3,4,5,6,2,8,5,3,1,7,1,8,2,7,2,8\nR,8,12,6,6,4,10,6,6,5,11,2,8,7,6,5,10\nM,5,9,8,7,7,12,6,2,4,9,2,6,8,6,2,8\nU,9,10,9,8,3,4,9,6,9,13,12,9,3,9,1,7\nQ,6,8,6,9,8,8,7,6,3,8,8,10,3,8,6,8\nX,3,6,5,4,3,8,7,4,9,6,6,8,3,8,6,8\nC,4,7,5,5,5,6,7,4,4,7,7,10,6,9,3,8\nY,4,10,6,7,1,6,10,3,2,8,13,8,1,11,0,8\nL,3,6,4,4,2,4,4,4,9,2,1,7,0,7,1,6\nM,4,8,7,6,9,9,5,2,2,8,4,8,10,6,2,6\nS,1,0,1,0,0,8,7,3,5,5,6,7,0,8,6,8\nI,2,7,2,5,2,7,7,0,7,7,6,8,0,8,3,8\nJ,6,7,4,11,3,6,9,3,4,13,5,5,3,8,6,9\nE,10,15,7,8,5,7,7,4,5,10,6,8,3,9,8,9\nM,5,9,8,7,10,7,6,4,2,7,4,8,13,6,3,7\nL,3,4,3,6,1,0,1,5,6,0,0,7,0,8,0,8\nT,3,8,5,6,3,7,12,4,6,8,11,7,2,12,1,7\nX,5,11,6,8,2,7,7,5,4,7,6,8,3,8,4,8\nX,5,11,6,8,2,7,7,5,4,7,6,8,3,8,4,8\nV,8,11,8,8,3,2,11,6,5,13,12,8,3,10,1,8\nD,2,4,4,2,2,9,6,4,6,10,4,6,2,8,2,9\nX,8,15,8,9,4,8,8,2,8,9,7,8,4,12,4,7\nS,3,2,4,4,3,8,7,7,5,7,7,8,2,9,9,8\nB,1,3,2,1,1,8,7,2,5,10,5,7,1,8,3,9\nE,1,3,3,2,2,6,8,2,7,11,7,9,2,8,4,9\nS,5,11,6,8,3,9,10,6,10,5,6,5,0,7,9,8\nK,5,8,8,6,5,3,8,2,6,10,11,11,3,7,3,5\nP,4,8,6,12,10,8,9,5,0,8,6,7,5,11,6,6\nZ,2,2,3,4,2,7,7,5,9,6,6,8,2,8,7,8\nR,3,5,6,3,4,8,8,3,5,9,4,7,3,6,3,11\nS,2,3,3,2,1,9,7,2,6,10,5,7,1,9,5,9\nF,2,1,3,2,1,5,11,4,5,10,9,5,1,9,3,6\nL,4,8,5,6,3,4,3,5,9,1,1,5,1,6,1,5\nN,5,9,6,7,4,8,6,9,6,4,4,4,5,7,3,7\nN,6,7,8,5,4,10,7,3,6,10,1,4,6,10,2,8\nN,1,0,1,1,0,7,7,10,0,6,6,8,4,8,0,8\nD,4,8,6,6,4,8,7,6,7,10,5,5,3,8,3,8\nM,6,10,7,8,4,8,7,13,2,6,9,8,9,6,0,8\nY,5,10,7,8,6,8,6,7,5,5,8,8,3,8,10,6\nR,9,13,7,8,5,7,7,5,5,9,5,9,7,5,7,11\nD,6,9,8,8,8,6,8,5,7,5,3,6,5,9,8,5\nE,3,6,3,4,2,3,8,6,10,7,6,15,0,8,7,7\nR,2,0,2,1,1,7,11,8,2,7,5,8,2,7,4,10\nM,4,7,6,5,5,6,6,2,4,9,8,9,7,5,2,7\nI,6,10,8,8,5,9,5,2,6,6,7,6,0,10,4,7\nF,4,9,5,6,3,2,11,5,6,11,10,9,0,8,2,6\nP,5,10,7,8,7,6,9,3,7,9,8,5,4,10,4,7\nS,6,10,7,8,4,6,8,4,8,11,8,7,2,8,5,5\nX,3,5,4,4,4,5,8,2,4,8,7,9,2,7,7,8\nT,2,7,3,4,1,7,14,0,6,7,11,8,0,8,0,8\nR,5,10,5,8,7,6,8,8,4,7,5,7,3,8,6,12\nO,2,3,2,1,1,8,7,6,4,9,6,8,2,8,3,8\nA,4,9,6,7,3,11,2,4,3,10,2,9,3,7,3,8\nP,5,8,8,6,3,7,11,3,7,14,5,2,0,9,3,8\nX,5,8,6,7,6,7,7,2,5,7,6,8,3,10,8,7\nM,3,1,4,2,1,8,7,11,1,7,9,8,7,6,0,8\nU,3,5,5,4,4,8,6,4,4,6,6,8,4,10,1,7\nB,2,6,3,4,3,6,7,7,5,7,6,7,2,8,6,9\nZ,3,8,4,6,3,7,7,6,10,6,6,8,1,8,8,8\nG,5,8,6,6,4,6,7,5,4,9,8,9,2,7,5,9\nP,4,9,5,7,5,5,8,6,4,8,7,9,3,8,7,10\nS,6,10,9,8,11,6,7,3,2,7,5,6,3,8,11,4\nM,7,8,9,7,10,5,8,5,4,6,5,8,13,7,6,9\nE,5,5,5,8,3,3,8,6,12,7,5,15,0,8,7,6\nK,5,11,7,8,7,9,6,1,6,10,3,8,7,7,6,11\nT,3,4,5,6,1,7,15,1,5,7,11,8,0,8,0,8\nA,7,15,5,8,4,10,3,3,2,8,4,11,5,5,4,8\nS,2,6,3,4,3,7,6,7,5,7,7,8,2,9,8,7\nR,4,8,4,6,4,6,8,9,4,7,5,8,2,7,5,11\nD,3,8,4,6,6,10,7,4,5,7,6,6,4,6,9,6\nH,6,10,9,8,8,7,7,3,6,10,6,8,3,8,3,7\nG,5,9,4,4,3,9,3,5,2,9,7,10,4,9,6,8\nE,6,10,8,8,7,9,6,2,7,11,4,9,5,7,6,11\nO,5,10,5,8,5,8,6,8,4,9,5,8,3,8,3,8\nJ,6,10,8,8,4,5,9,3,6,15,7,9,2,7,2,7\nC,5,8,7,7,6,5,7,3,4,7,6,11,4,10,7,10\nI,1,3,2,2,0,7,7,1,7,13,6,9,0,8,1,8\nD,4,6,5,5,4,6,6,6,7,7,6,8,4,6,5,5\nF,1,0,1,1,0,3,11,4,3,11,9,7,0,8,2,8\nU,2,3,3,2,1,4,8,4,6,11,10,9,3,9,1,7\nQ,2,3,2,3,2,8,8,5,2,8,7,10,2,9,3,8\nQ,3,7,5,5,4,8,4,7,4,6,5,7,3,8,3,9\nW,6,10,9,7,7,4,11,2,2,8,9,9,7,12,1,8\nR,4,6,5,4,4,6,7,4,6,7,6,6,6,7,4,8\nJ,1,3,3,2,1,8,6,3,5,14,6,11,1,7,0,7\nZ,6,10,8,8,5,7,8,3,10,12,8,7,2,8,7,6\nC,4,9,5,8,6,5,7,3,4,7,6,11,4,10,7,10\nQ,2,3,3,3,2,8,7,6,3,6,6,9,2,8,5,9\nQ,2,2,2,2,1,7,8,4,2,8,7,9,2,9,3,9\nT,2,7,4,4,1,8,15,1,5,6,11,8,0,8,0,8\nE,3,5,3,4,3,7,7,5,7,7,6,9,2,8,5,10\nE,3,5,3,3,3,7,7,5,7,7,6,9,2,8,5,10\nP,4,7,5,5,2,6,14,5,3,12,5,1,0,10,3,7\nF,2,3,4,2,1,6,11,2,5,13,6,4,1,10,1,8\nC,4,4,5,6,2,6,6,7,11,8,5,12,1,9,4,9\nS,1,3,3,2,1,7,9,3,7,10,7,6,1,8,4,6\nS,3,6,4,4,3,7,6,7,5,7,7,9,2,9,8,7\nN,3,2,4,4,3,7,8,5,5,7,7,6,5,10,2,5\nT,6,8,6,6,3,7,11,3,8,11,9,4,2,12,3,4\nA,1,3,2,2,1,9,3,2,1,8,2,9,1,6,1,7\nX,3,2,4,3,2,8,7,3,9,6,6,8,2,8,6,8\nW,5,7,7,5,3,4,8,5,1,7,9,8,8,10,0,8\nV,3,6,5,4,5,8,5,5,2,7,7,7,4,9,4,6\nF,3,6,4,4,2,6,11,2,6,14,6,4,1,10,2,8\nD,2,2,3,3,3,7,7,6,6,7,6,5,2,8,3,7\nQ,4,9,5,8,3,9,8,8,6,5,8,9,3,8,5,9\nH,1,1,2,1,1,7,7,12,1,7,6,8,3,8,0,8\nO,4,6,6,5,4,6,6,5,5,8,4,8,3,7,5,6\nV,4,6,4,4,2,5,11,3,4,9,11,7,2,10,1,8\nT,3,8,5,6,1,7,15,1,5,7,11,8,0,8,0,8\nU,3,6,4,4,1,7,4,14,5,7,13,8,3,9,0,8\nY,2,7,3,5,2,5,10,2,2,8,12,8,1,11,0,8\nG,1,0,2,0,1,8,7,5,5,6,6,9,1,7,5,10\nH,4,7,6,10,7,8,9,5,1,8,6,6,5,11,10,6\nU,2,1,2,1,1,8,5,11,4,6,13,8,3,10,0,8\nI,3,10,4,8,2,7,7,0,8,13,6,8,0,8,1,8\nL,4,10,5,8,3,0,2,4,6,1,0,8,0,8,0,8\nP,4,7,5,5,4,8,9,4,4,11,5,5,3,10,3,7\nL,4,10,6,7,8,7,7,3,5,6,7,10,6,10,7,4\nN,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8\nW,7,10,7,8,8,5,10,3,3,9,7,7,8,11,3,6\nF,5,10,5,7,3,0,13,3,3,11,9,6,0,8,2,6\nA,6,14,6,8,4,12,2,3,2,12,4,11,4,4,4,11\nG,5,10,6,8,5,7,7,7,6,6,5,9,2,8,6,11\nY,4,7,6,5,2,9,11,1,7,3,11,8,1,11,2,9\nL,5,10,7,7,3,6,4,1,10,8,2,11,0,7,3,7\nM,4,1,5,3,3,8,6,6,5,6,7,8,8,5,2,7\nO,2,4,3,2,1,8,7,7,5,7,6,8,2,8,3,8\nG,4,7,5,5,4,6,7,6,4,5,7,9,2,7,4,9\nY,3,5,4,4,2,4,10,1,8,10,10,6,1,10,3,4\nM,3,3,5,2,3,9,6,3,4,9,5,7,6,5,1,8\nA,3,6,5,4,3,11,2,2,2,9,2,9,2,6,3,8\nP,4,8,4,5,2,3,14,8,1,11,7,3,1,10,4,8\nU,4,8,4,6,3,7,6,12,4,7,12,8,3,9,0,8\nF,2,3,4,2,1,6,10,3,5,13,6,5,1,9,2,7\nA,3,6,5,4,1,7,5,3,1,6,1,8,2,7,2,7\nP,1,3,2,1,1,5,10,3,4,10,8,5,0,9,3,7\nH,3,4,4,5,2,7,9,15,2,7,3,8,3,8,0,8\nA,4,10,7,7,5,7,5,2,3,5,2,6,3,7,4,4\nF,4,9,5,6,2,1,15,5,3,12,9,4,0,8,2,6\nG,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10\nX,11,14,10,8,4,8,7,2,10,9,6,8,4,8,4,8\nS,9,15,8,9,4,11,2,2,5,10,2,9,3,7,4,12\nK,5,8,7,6,5,5,7,1,7,10,8,10,3,8,4,7\nD,4,4,5,6,3,5,6,10,9,5,5,5,3,8,4,8\nJ,4,5,5,6,4,9,8,5,4,6,5,8,3,8,9,8\nT,7,9,7,7,5,6,12,5,6,11,9,4,3,13,3,4\nP,3,3,5,2,2,7,10,3,4,12,4,3,1,9,3,8\nK,4,8,6,6,5,5,8,1,6,9,7,9,3,8,3,8\nA,2,7,4,5,2,12,2,4,3,11,2,9,2,6,3,9\nA,3,4,6,6,2,9,3,3,3,8,2,9,3,6,3,9\nC,4,7,5,5,3,6,7,6,9,8,4,10,1,10,4,10\nM,2,3,4,2,2,6,6,3,3,9,8,9,5,5,1,8\nV,3,4,4,3,1,4,12,4,3,10,11,7,2,10,1,8\nM,5,8,7,6,8,7,7,7,5,7,5,8,7,9,8,6\nJ,2,6,2,4,1,15,3,3,5,12,1,8,0,8,0,8\nO,3,7,4,5,2,7,8,8,7,7,7,8,3,8,4,8\nS,5,10,7,8,5,10,6,4,6,10,3,7,2,8,5,10\nN,3,5,4,7,3,7,7,14,2,5,6,8,6,8,0,8\nL,5,11,6,9,4,4,4,1,9,6,1,10,0,6,3,6\nV,2,5,4,4,2,6,12,2,3,8,11,8,2,10,1,8\nH,3,3,3,4,2,7,8,14,1,7,5,8,3,8,0,8\nZ,3,5,4,7,2,7,7,4,14,10,6,8,0,8,8,8\nJ,2,7,3,5,1,13,2,8,4,13,4,12,1,6,0,8\nK,11,15,10,8,4,7,8,3,8,9,7,8,6,8,4,7\nX,6,8,9,6,4,6,8,2,9,10,9,8,3,8,4,7\nC,7,10,7,7,4,6,7,6,8,13,8,12,3,11,5,5\nD,3,6,3,4,3,5,7,8,6,7,7,6,2,8,3,8\nE,4,10,4,8,5,2,8,5,9,7,7,14,0,8,6,9\nM,5,9,8,7,6,5,7,3,4,10,9,9,8,6,3,8\nA,7,13,7,7,4,12,3,6,2,12,2,10,5,3,4,10\nM,6,9,8,6,8,7,7,6,5,6,7,9,8,6,2,8\nC,3,7,4,5,2,5,8,6,7,7,6,12,1,9,4,10\nJ,3,10,4,8,1,12,2,10,4,14,4,13,1,6,0,8\nJ,2,5,4,3,2,9,5,4,5,14,6,11,1,6,0,7\nL,4,10,4,8,1,0,1,6,6,0,0,6,0,8,0,8\nQ,3,4,4,5,4,8,8,7,2,5,7,9,3,8,5,9\nM,4,3,4,4,3,7,7,12,1,7,9,8,8,6,0,8\nZ,2,3,4,2,2,7,8,2,9,12,6,8,1,9,5,8\nQ,6,7,8,6,6,7,4,4,5,7,4,9,5,5,6,7\nN,5,6,6,6,6,7,7,4,3,7,5,7,6,9,5,5\nV,2,3,3,1,1,4,12,3,2,10,11,7,2,11,1,7\nB,3,6,4,4,3,8,6,6,7,6,6,6,2,8,7,10\nK,5,7,8,5,5,9,5,1,6,9,3,8,4,7,4,10\nQ,6,12,6,6,4,11,4,4,6,12,3,9,3,9,7,12\nA,3,7,5,5,3,11,2,2,2,9,2,9,3,6,3,9\nR,4,8,6,6,4,7,8,6,6,6,5,8,4,6,7,9\nI,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nM,6,8,9,6,9,10,6,3,3,9,4,7,9,8,3,6\nH,6,11,6,8,3,7,6,15,0,7,7,8,3,8,0,8\nD,4,6,6,4,4,7,7,6,5,7,6,6,4,8,3,7\nD,2,4,4,3,2,8,7,5,6,9,5,5,2,8,3,7\nH,3,4,4,3,3,7,8,5,7,7,6,8,6,8,4,8\nR,3,3,4,5,2,5,11,8,4,7,3,9,3,7,6,11\nZ,4,7,5,5,3,7,8,2,9,11,7,8,1,8,6,7\nQ,4,7,5,6,3,7,6,8,6,6,7,7,3,8,5,9\nB,4,9,5,6,4,6,7,8,7,7,6,7,2,8,9,10\nG,2,4,3,3,2,6,7,5,5,9,7,10,2,8,4,9\nB,1,0,2,1,1,7,8,7,5,7,6,7,1,8,6,8\nC,4,5,5,7,2,5,7,7,10,7,6,13,1,8,4,8\nK,8,15,8,8,5,3,8,4,6,10,11,11,5,11,4,7\nC,8,13,5,8,2,8,6,7,7,12,5,9,2,10,5,9\nW,4,7,5,5,5,7,7,6,2,7,8,8,5,8,3,9\nQ,3,7,4,6,2,8,6,8,6,6,4,8,3,8,4,8\nU,6,10,6,5,3,7,6,5,4,6,7,7,5,6,3,6\nX,5,8,8,6,5,10,6,1,8,10,2,7,4,9,4,10\nD,4,2,5,3,3,7,7,6,7,6,6,5,5,8,3,7\nF,6,7,8,8,8,7,9,4,5,7,7,6,5,9,9,9\nZ,5,9,7,6,5,7,8,2,9,12,6,8,1,9,6,8\nK,7,9,10,7,7,2,9,2,7,10,11,11,5,7,4,4\nS,2,3,3,2,2,8,7,6,4,7,6,8,2,8,9,8\nO,6,11,7,9,5,8,7,9,6,7,5,10,4,8,5,5\nX,10,12,9,7,4,8,7,2,9,9,6,8,4,11,4,8\nN,3,7,4,5,3,7,7,12,1,6,6,8,5,8,0,8\nQ,3,4,4,5,3,7,8,5,2,8,9,10,2,9,5,9\nJ,6,11,7,8,3,9,5,3,7,15,4,10,0,6,1,7\nJ,3,9,4,6,2,9,5,3,6,14,4,9,0,7,1,7\nH,6,9,8,7,7,7,7,7,5,6,5,7,3,7,7,10\nO,4,6,5,4,2,7,7,8,8,7,6,8,3,8,4,8\nB,4,6,6,4,5,9,8,3,6,7,6,8,6,8,6,9\nP,1,0,2,0,0,5,10,7,2,9,6,5,1,9,3,8\nN,6,10,9,8,6,11,8,3,5,10,1,4,7,11,2,8\nE,2,4,2,3,2,7,7,5,7,7,6,8,2,8,5,10\nI,7,13,6,7,3,12,5,2,6,12,3,6,2,10,4,12\nL,1,3,3,2,1,6,4,1,7,7,2,10,0,7,2,8\nH,4,8,4,5,2,7,7,15,1,7,6,8,3,8,0,8\nH,3,5,5,3,3,8,6,3,5,10,6,9,3,7,3,8\nM,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8\nH,6,11,6,6,3,8,8,3,4,9,6,7,6,9,5,9\nX,1,0,1,0,0,8,7,3,5,7,6,8,2,8,3,7\nU,9,10,7,5,3,5,3,5,5,3,7,7,5,8,2,7\nI,5,11,6,8,4,9,6,0,8,13,5,9,0,8,1,9\nX,6,10,9,7,5,7,7,0,8,10,6,8,3,8,3,8\nC,2,6,3,4,2,7,8,8,7,10,6,11,2,11,4,9\nB,2,3,2,2,2,7,7,5,5,7,6,6,2,8,5,9\nD,2,3,3,2,2,7,7,6,7,7,6,4,2,8,3,7\nV,3,2,6,4,2,7,12,2,3,6,11,9,4,12,2,7\nK,4,9,5,6,5,6,5,4,4,6,6,9,3,6,8,10\nK,4,8,6,6,7,7,7,3,4,6,6,8,7,7,7,7\nB,4,11,6,8,8,8,8,6,6,7,6,5,2,8,6,9\nQ,4,9,4,4,2,11,4,4,5,12,3,8,2,7,6,12\nT,6,9,6,7,3,5,11,2,10,12,9,4,0,10,3,4\nH,3,4,6,3,3,8,7,3,6,10,5,8,3,8,3,7\nL,5,11,7,8,5,5,3,6,8,2,2,4,1,6,1,5\nW,4,11,7,8,8,10,11,2,2,5,8,7,9,12,2,8\nR,5,9,7,7,6,7,8,5,7,7,5,6,3,7,5,8\nY,2,7,4,5,2,5,9,1,6,9,12,9,1,11,2,7\nA,3,3,5,4,1,8,6,3,1,7,1,8,2,7,1,8\nU,8,10,9,8,6,3,9,5,8,10,10,10,3,9,2,6\nZ,3,8,4,6,4,7,8,5,9,7,6,9,1,9,7,7\nD,5,8,7,6,6,8,8,6,6,9,6,4,6,10,5,7\nJ,6,11,8,9,3,8,7,3,7,15,5,10,1,6,1,7\nX,4,9,6,7,6,7,8,2,6,7,7,9,4,10,6,7\nS,4,5,6,4,5,8,7,5,5,7,6,8,5,8,9,11\nO,8,15,5,8,3,7,9,6,6,9,6,6,4,10,5,9\nV,3,5,6,4,2,6,13,3,3,8,11,8,3,10,1,8\nC,5,8,7,6,6,6,5,4,4,9,6,12,6,8,4,9\nH,3,5,5,7,4,8,12,3,2,8,8,7,3,10,7,6\nR,2,3,4,2,2,8,7,3,4,9,4,6,2,7,3,9\nM,6,8,9,7,10,6,8,5,3,6,5,8,13,9,5,8\nT,3,10,5,8,5,7,11,4,5,7,11,8,3,12,1,8\nY,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nY,2,4,3,3,1,3,12,3,6,12,10,5,1,11,2,5\nJ,2,6,3,4,1,13,2,8,4,14,4,12,0,7,0,8\nF,2,7,3,5,1,1,12,4,4,12,10,7,0,8,2,6\nJ,5,9,6,7,3,9,5,4,6,15,5,11,1,6,0,7\nC,5,11,6,9,3,4,9,7,8,13,11,12,2,9,3,7\nG,1,0,2,0,1,8,6,5,5,6,5,9,1,8,5,10\nQ,6,7,8,6,6,7,4,5,6,7,4,8,5,4,6,7\nP,3,8,4,6,2,3,14,8,1,11,7,3,1,10,4,8\nQ,2,2,3,2,2,8,7,6,4,6,5,9,2,9,3,9\nD,4,9,6,7,9,8,8,5,4,7,6,5,6,8,8,5\nV,7,12,6,6,3,9,10,5,4,6,10,6,5,12,3,7\nG,5,9,4,4,3,7,6,3,3,8,6,8,4,9,8,6\nY,2,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nL,3,6,3,4,1,0,1,6,6,0,1,5,0,8,0,8\nN,5,11,5,9,6,7,7,13,1,6,6,8,5,9,0,7\nV,4,6,5,4,3,9,11,3,1,4,10,9,3,11,4,9\nC,2,1,2,2,0,6,7,6,9,7,6,15,0,8,4,10\nI,1,4,2,3,1,7,7,1,8,13,6,8,0,8,1,8\nY,2,6,3,4,0,6,10,2,2,8,12,8,1,10,0,8\nO,4,7,5,5,3,7,7,8,5,8,7,10,3,8,3,7\nM,6,7,9,6,9,5,9,5,3,6,4,8,13,6,5,8\nE,4,10,5,8,3,3,7,6,11,7,6,15,0,8,7,6\nI,1,5,2,3,1,7,7,0,7,13,6,8,0,8,1,8\nC,5,7,5,5,3,3,8,5,7,11,10,13,2,9,3,7\nE,4,7,6,5,4,5,8,5,8,12,10,9,3,8,5,5\nI,7,12,5,6,2,10,5,6,4,13,3,8,3,8,5,10\nE,2,4,4,3,2,7,7,2,7,11,6,8,2,8,4,9\nX,1,0,1,0,0,8,7,3,4,7,6,8,2,8,4,8\nX,4,2,6,4,3,7,7,3,10,6,6,8,2,8,6,8\nG,3,5,4,4,2,6,7,5,5,9,7,10,2,8,4,9\nE,3,6,4,4,3,7,7,6,9,7,7,9,3,8,6,8\nJ,3,7,4,5,2,9,4,5,4,14,6,12,0,6,1,7\nD,3,5,4,4,2,8,7,7,7,6,6,3,2,8,3,7\nS,4,10,5,7,3,6,8,5,8,11,8,7,2,9,5,5\nZ,4,9,6,6,4,8,7,2,9,11,5,9,1,8,6,8\nW,7,10,10,8,14,9,7,5,2,7,7,8,13,10,4,6\nM,4,3,4,5,3,7,7,12,1,7,9,8,8,6,0,8\nL,4,8,5,6,3,3,4,2,8,2,1,8,0,7,1,6\nR,3,9,3,6,4,5,9,8,3,7,5,8,2,7,5,11\nH,4,7,6,5,4,7,8,3,6,10,7,8,3,8,3,8\nD,3,7,3,5,3,6,7,9,7,6,5,6,2,8,3,7\nS,2,4,4,3,2,7,7,2,7,10,6,8,1,9,5,8\nY,3,10,5,7,1,8,11,1,3,6,12,8,1,11,0,8\nN,3,3,4,4,2,7,7,14,2,5,6,8,6,8,0,8\nY,4,4,5,6,7,8,8,3,2,7,8,9,4,11,7,7\nR,3,9,4,6,4,6,8,8,4,7,5,8,2,7,5,11\nD,2,4,4,3,2,8,7,4,6,10,5,6,2,8,3,8\nO,3,4,4,6,2,7,6,9,6,6,5,6,3,8,4,8\nG,3,7,4,5,2,7,6,8,7,6,6,8,2,8,6,11\nH,9,15,10,8,7,8,7,3,5,10,4,7,7,6,6,7\nY,6,10,8,8,7,9,7,7,4,6,9,7,3,9,9,4\nM,7,10,9,8,8,8,6,6,5,6,8,8,9,6,2,7\nP,9,10,7,5,3,6,12,6,4,13,5,3,4,10,4,8\nH,3,4,5,6,4,9,12,3,2,8,8,7,3,10,6,7\nV,6,9,5,4,2,7,10,7,3,9,9,5,5,12,3,9\nB,5,10,7,7,6,8,8,6,8,7,6,6,6,8,6,10\nX,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nO,3,3,4,2,2,7,8,7,5,7,7,8,2,8,3,8\nK,4,11,5,8,2,3,7,8,2,7,5,11,3,8,2,11\nG,1,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10\nT,3,7,5,5,3,7,11,1,8,7,11,8,1,10,1,8\nK,2,4,3,2,2,5,7,4,6,7,6,11,3,8,4,9\nH,3,7,4,4,2,7,8,14,1,7,6,8,3,8,0,8\nG,3,7,5,5,5,7,9,6,3,6,6,10,3,7,7,8\nB,4,4,5,6,4,6,6,9,7,6,6,7,2,8,9,9\nV,4,9,6,7,2,7,8,4,3,7,14,8,3,9,0,8\nT,4,9,6,8,6,6,8,4,8,8,7,8,3,9,8,7\nN,5,5,6,7,3,7,7,15,2,4,6,8,6,8,0,8\nU,3,3,3,2,1,5,8,5,7,9,8,8,3,9,2,5\nZ,4,6,6,4,3,6,9,3,9,11,8,6,1,9,6,6\nK,1,0,1,0,0,5,8,7,0,7,6,10,3,8,2,11\nH,8,13,8,7,4,10,8,4,6,9,2,5,6,7,4,9\nI,1,3,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nA,2,1,3,2,1,8,2,2,1,7,2,8,2,7,2,7\nH,4,8,6,6,8,7,6,5,3,6,6,8,7,7,9,9\nG,4,8,5,6,2,8,6,8,8,7,5,12,2,8,5,10\nO,8,13,6,8,3,7,7,5,5,8,4,7,5,9,5,8\nE,1,1,2,2,1,4,7,5,8,7,6,13,0,8,6,9\nX,4,8,7,6,4,4,8,2,8,10,12,10,3,8,4,5\nJ,4,10,5,7,3,8,6,3,6,14,4,9,0,6,1,7\nO,2,4,3,3,2,7,7,6,4,9,7,7,2,8,3,8\nX,1,1,2,2,1,7,7,3,8,6,6,8,2,8,5,8\nV,4,4,6,7,1,8,8,4,3,7,14,8,3,9,0,8\nC,5,10,5,8,3,6,8,7,7,13,7,9,2,11,3,7\nM,5,5,6,4,4,8,6,6,5,6,7,7,10,6,4,6\nQ,4,6,5,8,5,7,9,5,3,8,9,9,4,10,6,7\nK,3,1,4,2,2,6,7,4,7,7,6,10,3,8,4,9\nW,2,0,2,1,1,7,8,3,0,7,8,8,6,9,0,8\nR,2,3,3,2,2,9,6,3,4,10,4,7,2,7,3,10\nG,3,9,5,6,3,6,7,7,6,5,7,9,2,7,4,9\nS,7,10,8,8,5,7,7,4,6,9,8,9,2,11,5,6\nO,4,6,6,4,3,7,6,8,5,8,5,11,4,8,4,7\nY,3,10,5,7,3,7,10,2,6,6,12,9,2,11,2,8\nP,5,4,5,6,3,4,13,8,2,10,6,3,1,10,4,8\nT,3,4,3,3,1,5,12,3,6,11,9,5,1,11,1,5\nR,4,6,6,4,4,9,7,4,5,9,3,7,3,7,4,11\nH,6,10,6,6,4,5,8,3,4,10,9,9,6,10,5,8\nE,6,9,8,7,7,8,6,6,2,7,6,9,4,8,8,10\nB,3,7,5,5,3,9,7,4,6,10,5,6,2,8,6,9\nF,3,4,3,3,2,5,11,3,6,11,9,5,1,10,3,6\nN,5,10,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nH,3,4,3,5,2,7,7,14,1,7,6,8,3,8,0,8\nH,5,9,8,7,5,6,8,3,6,10,8,9,3,8,3,7\nL,3,7,4,5,2,6,4,2,8,7,2,10,0,7,2,8\nQ,5,7,7,11,10,9,10,5,0,5,7,10,6,13,6,12\nI,1,3,2,2,0,7,8,1,6,13,6,7,0,8,0,7\nF,3,9,4,6,2,1,13,5,4,12,10,7,0,8,2,6\nD,3,6,5,4,3,9,7,4,5,10,4,5,3,8,2,8\nB,5,9,4,5,3,7,7,5,5,10,6,9,5,7,7,9\nD,2,4,4,3,2,8,7,4,6,9,5,5,2,8,3,7\nA,1,3,2,2,1,10,2,2,1,9,2,9,1,6,2,8\nW,4,8,7,6,5,7,10,2,3,7,9,8,8,11,1,8\nD,7,11,9,8,8,7,8,7,6,8,7,6,7,7,3,7\nD,3,5,4,4,3,7,7,7,6,7,6,5,2,8,3,7\nT,3,6,4,5,4,6,7,3,8,8,7,10,3,7,7,6\nW,5,6,5,4,4,4,10,2,3,9,9,7,7,11,2,6\nE,3,2,3,3,3,7,7,5,7,7,5,9,2,8,5,10\nZ,3,9,5,7,5,8,6,3,7,7,6,8,1,7,11,9\nY,3,8,5,6,2,5,10,3,2,8,12,8,1,11,0,8\nU,6,9,8,7,10,9,8,4,5,5,8,8,10,7,8,6\nJ,6,8,8,9,7,8,9,5,5,7,7,9,4,6,9,6\nE,8,15,6,8,5,8,6,5,5,11,5,10,3,8,7,11\nW,6,5,7,4,3,3,10,3,3,10,10,8,7,10,1,7\nY,6,8,8,10,10,10,10,6,3,6,7,7,6,11,8,3\nI,1,7,0,5,1,7,7,5,3,7,6,8,0,8,0,8\nJ,3,9,4,7,3,10,6,0,8,11,3,6,0,7,1,7\nI,1,4,2,3,0,7,8,0,7,13,6,8,0,8,0,8\nP,8,15,7,8,4,6,9,6,4,13,5,5,4,10,4,8\nN,4,9,6,6,4,8,9,9,6,8,5,5,3,7,3,7\nH,3,9,4,6,2,7,8,15,1,7,4,8,3,8,0,8\nP,11,13,8,7,4,6,12,6,5,14,6,2,4,10,4,8\nY,6,9,6,7,4,5,9,0,8,8,9,5,1,11,4,5\nE,3,7,3,5,2,3,7,6,11,7,7,15,0,8,6,7\nS,4,6,5,6,6,8,8,5,4,7,7,8,5,11,9,10\nT,4,9,5,7,5,7,11,4,6,7,11,8,2,12,1,7\nM,6,11,7,8,7,8,6,11,1,7,8,8,9,4,1,8\nL,2,6,2,4,0,0,2,5,6,0,0,7,0,8,0,8\nV,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8\nB,4,11,6,8,8,7,7,6,5,7,6,6,3,8,6,10\nV,2,3,2,1,1,5,11,2,2,9,10,7,1,11,1,7\nS,4,9,5,7,3,8,6,6,9,5,6,8,0,9,9,8\nQ,6,13,5,7,3,8,4,4,7,10,4,9,3,8,8,10\nS,3,4,5,3,2,8,7,2,7,10,5,8,1,9,5,8\nO,5,9,6,7,5,7,8,8,4,6,7,9,3,7,4,7\nE,1,0,1,1,1,5,7,5,8,7,6,12,0,8,6,9\nA,2,6,4,4,1,8,5,3,1,7,1,8,2,7,2,8\nT,3,3,4,2,1,5,11,3,7,11,9,4,1,10,2,5\nG,2,1,3,2,2,6,7,6,6,6,6,10,2,9,3,9\nR,4,11,6,8,6,6,7,6,6,6,5,8,3,6,6,9\nH,4,7,4,4,2,7,10,14,2,7,3,8,3,8,0,8\nM,8,9,11,7,9,6,8,3,4,9,9,9,9,8,3,8\nD,4,7,6,5,3,9,6,4,8,11,4,6,3,8,3,9\nX,3,7,5,5,4,7,7,3,8,5,6,8,2,8,6,8\nC,6,11,6,8,3,4,8,6,8,12,10,13,1,9,3,7\nS,3,7,4,5,3,8,8,5,7,5,6,8,0,8,8,8\nP,5,10,7,8,6,7,10,5,4,12,5,3,1,10,3,8\nP,4,8,6,6,4,6,9,7,6,9,7,4,2,10,4,6\nJ,0,0,1,0,0,11,4,5,3,12,4,10,0,7,0,8\nR,5,11,7,8,6,6,8,5,6,6,5,7,3,6,6,9\nE,7,11,4,6,2,8,7,5,8,10,6,9,2,10,7,9\nJ,2,10,3,7,3,8,6,2,4,11,5,10,1,6,2,6\nQ,2,3,3,5,3,8,8,6,1,5,7,10,3,9,5,10\nV,7,12,5,6,3,8,10,5,5,7,10,5,5,12,3,7\nO,2,6,3,4,2,7,7,6,3,7,6,8,3,8,2,8\nG,2,4,3,3,2,6,7,5,4,9,7,10,2,9,4,10\nP,5,7,6,5,5,6,9,6,4,8,7,9,2,9,7,10\nI,0,3,0,2,0,9,7,2,6,7,6,7,0,8,1,7\nG,3,2,5,3,3,7,7,6,6,6,7,9,2,8,4,8\nD,3,4,4,3,2,7,7,7,7,7,6,5,2,8,3,7\nU,6,9,8,6,6,7,8,8,6,5,7,11,4,8,5,6\nD,5,8,7,7,6,6,7,5,7,7,5,7,4,7,6,5\nA,1,0,2,0,0,8,4,2,0,7,2,8,1,6,1,8\nU,1,0,1,0,0,7,7,10,4,7,12,8,3,10,0,8\nV,3,4,5,3,2,8,12,2,3,6,11,9,2,10,1,8\nD,5,9,6,7,3,6,8,11,10,8,7,6,3,8,4,8\nA,3,6,5,4,2,11,2,4,2,10,2,10,2,7,3,8\nU,7,11,6,6,4,6,6,5,5,7,8,9,5,8,3,9\nM,4,7,4,5,3,7,7,12,1,7,9,8,8,6,0,8\nX,7,10,7,5,4,6,9,3,7,11,10,8,4,14,4,6\nP,5,11,7,8,5,8,8,1,6,13,6,5,1,9,3,9\nZ,4,5,6,8,5,11,4,2,5,8,3,6,1,8,6,9\nJ,6,8,7,9,7,8,7,4,6,6,6,7,4,9,10,9\nX,7,15,8,8,5,10,5,3,8,11,2,8,4,5,4,9\nG,4,8,5,6,3,7,7,7,7,11,6,11,2,9,4,9\nC,2,2,3,4,2,6,8,7,7,8,8,13,1,9,4,10\nR,4,5,7,4,6,7,7,3,4,7,5,8,6,9,5,7\nB,3,7,5,5,5,7,8,7,6,7,5,7,2,7,6,10\nT,3,5,4,4,3,7,11,3,7,7,11,8,2,11,1,8\nO,3,7,4,5,2,7,6,8,8,7,4,8,3,8,4,8\nG,5,8,7,7,7,7,8,6,3,7,7,9,7,10,8,8\nN,6,12,7,6,3,7,7,2,3,12,5,8,5,8,0,7\nK,3,4,5,3,3,6,8,1,7,10,7,10,3,8,2,7\nN,5,9,7,6,4,7,9,6,5,7,6,7,7,9,3,7\nS,3,4,3,3,2,8,7,7,5,7,6,8,2,9,9,8\nK,4,2,5,3,3,5,7,4,8,7,6,11,3,8,5,9\nA,4,12,7,8,3,7,4,3,2,6,1,8,3,7,3,7\nI,3,10,5,7,2,7,7,0,9,14,6,8,0,8,1,8\nI,1,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nJ,5,6,6,7,5,8,9,4,5,7,6,8,3,8,8,8\nO,3,4,4,3,2,8,6,7,4,9,5,8,2,8,3,8\nC,3,4,4,6,1,5,6,7,9,6,5,13,1,8,4,9\nL,2,5,4,4,2,7,4,1,8,8,2,10,0,7,2,8\nT,3,7,4,5,2,6,12,2,7,8,11,8,1,11,1,7\nK,5,10,7,7,6,6,7,4,7,6,6,9,7,8,5,9\nP,6,9,8,7,6,5,14,6,2,12,6,2,1,10,3,7\nU,6,7,7,5,4,4,8,5,7,9,8,9,3,9,3,5\nP,4,10,6,7,6,7,6,7,4,7,6,8,3,8,7,10\nT,6,13,5,7,2,6,9,3,7,13,6,6,2,8,4,4\nC,3,2,4,4,2,5,8,7,7,9,8,13,1,9,4,10\nN,2,3,3,2,2,7,9,6,4,8,6,7,4,8,1,7\nH,4,2,5,3,4,7,7,6,6,7,6,8,3,8,3,9\nK,5,11,6,8,2,4,5,9,2,7,7,12,4,7,3,11\nF,4,8,6,9,8,7,9,4,4,8,6,7,4,9,8,8\nC,4,7,5,5,2,7,7,6,7,12,7,11,2,10,3,8\nF,5,11,6,8,6,6,9,4,7,10,9,6,2,9,4,6\nL,4,9,5,4,3,9,4,3,4,12,7,11,3,10,4,10\nY,4,7,4,5,2,4,9,2,6,10,11,6,1,11,2,5\nS,7,11,9,8,11,8,6,5,3,8,6,8,5,7,11,8\nU,3,8,5,6,3,6,8,8,9,9,9,8,3,9,1,8\nN,1,1,2,2,1,7,7,11,1,5,6,8,4,8,0,8\nZ,4,8,5,6,3,8,6,2,10,11,4,9,2,8,6,9\nO,2,1,3,2,2,8,7,7,4,7,6,8,2,8,3,8\nP,6,9,5,5,2,7,9,5,3,12,4,5,4,9,4,8\nJ,3,10,4,8,2,14,3,5,5,13,1,9,0,7,0,8\nO,4,9,6,7,3,8,7,9,8,7,5,8,3,8,4,8\nZ,2,4,4,2,2,7,7,2,9,12,6,8,1,8,5,8\nX,4,4,6,3,3,7,6,1,9,11,7,9,3,7,3,7\nH,5,10,7,8,8,7,7,6,7,7,6,9,3,8,4,8\nX,5,9,6,6,1,7,7,5,4,7,6,8,3,8,4,8\nH,5,6,7,4,5,5,9,3,6,10,9,9,3,9,3,6\nK,3,7,3,5,2,5,7,7,2,6,5,11,3,8,2,11\nD,5,11,6,8,7,10,6,3,6,10,3,6,3,8,3,9\nT,4,9,5,7,4,9,11,3,7,5,11,8,2,12,1,8\nA,4,11,6,8,4,8,3,2,3,6,1,8,2,7,3,7\nH,4,5,5,3,3,7,7,6,6,7,6,8,3,8,3,8\nQ,2,3,3,3,2,8,9,5,2,5,8,10,2,9,5,9\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nP,8,11,7,6,4,8,9,4,4,12,4,4,4,10,5,7\nF,6,10,8,8,9,7,6,6,4,8,7,8,4,10,8,11\nK,1,0,1,0,0,4,6,5,1,7,6,10,2,7,1,10\nH,1,0,2,0,0,7,8,11,1,7,6,8,2,8,0,8\nE,3,6,3,4,2,3,8,6,10,7,5,14,0,8,7,7\nA,2,4,4,3,2,9,2,2,1,8,2,9,2,7,2,8\nC,7,11,8,8,5,4,8,6,7,12,9,11,2,11,3,7\nH,7,12,8,6,5,11,6,3,5,10,4,6,5,9,4,8\nI,1,10,0,8,1,7,7,5,3,7,6,8,0,8,0,8\nJ,1,7,2,5,1,9,6,2,7,11,4,8,0,7,1,6\nY,9,9,7,13,5,7,11,2,3,9,10,5,4,10,6,8\nG,3,5,4,3,2,6,7,5,5,9,7,10,2,8,4,9\nX,5,10,6,7,2,7,7,5,4,7,6,8,3,8,4,8\nT,2,6,4,4,1,8,15,1,5,6,11,8,0,8,0,8\nD,3,7,5,5,4,9,7,3,5,10,4,6,3,8,2,8\nQ,5,8,5,9,5,8,6,7,2,8,7,12,3,8,6,7\nY,3,8,5,6,1,6,9,3,1,8,13,8,2,11,0,8\nO,4,7,4,5,4,7,8,7,4,9,7,8,3,8,3,8\nB,5,9,7,7,5,8,8,4,7,10,5,5,2,8,6,10\nE,3,5,5,3,2,6,7,1,8,11,6,9,2,8,4,8\nZ,3,3,4,5,2,7,7,4,14,9,6,8,0,8,8,8\nX,7,15,8,8,5,11,7,2,8,11,4,6,3,11,4,9\nG,2,2,4,3,2,6,7,6,6,7,6,10,2,8,4,9\nM,6,10,8,8,10,7,6,7,4,6,5,8,8,8,8,9\nU,4,7,5,5,2,7,4,14,5,7,14,8,3,9,0,8\nX,4,9,7,6,4,7,7,0,8,9,6,8,2,8,3,7\nW,3,2,4,3,3,8,11,2,2,6,8,8,6,11,0,8\nM,2,3,4,2,2,8,6,3,4,9,6,8,6,5,1,7\nT,3,4,4,3,2,5,12,3,7,11,9,4,1,11,2,5\nB,2,3,4,1,2,8,7,2,5,10,5,6,1,8,4,9\nJ,1,6,2,4,1,14,2,6,5,13,1,9,0,7,0,8\nI,1,3,2,2,1,7,8,1,7,13,6,7,0,8,1,7\nL,2,6,3,4,2,3,4,3,8,2,1,8,0,7,1,6\nF,3,9,5,6,3,4,11,2,6,11,10,6,1,10,3,5\nL,3,7,4,5,3,5,5,2,8,7,2,9,1,7,3,7\nN,4,9,4,6,4,8,7,12,1,6,6,8,5,9,0,8\nJ,3,7,4,5,2,9,6,1,6,11,4,7,0,7,1,7\nE,4,6,6,4,4,6,8,3,7,11,8,9,3,9,4,7\nB,3,5,5,3,3,9,7,3,6,10,5,6,2,8,5,10\nG,1,0,2,0,0,8,6,5,4,6,5,9,1,8,5,10\nY,3,9,6,6,1,5,11,3,2,10,12,7,1,11,0,8\nH,2,3,4,2,2,6,8,2,5,10,7,8,3,8,2,7\nB,6,11,8,8,7,9,7,4,7,10,4,6,3,8,6,10\nY,7,9,5,13,4,5,11,2,4,11,10,6,4,11,6,7\nA,3,9,5,6,3,9,3,2,3,7,1,8,2,6,2,7\nR,1,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10\nR,3,4,3,5,2,5,11,7,4,7,3,9,3,7,6,11\nH,7,9,7,4,3,8,8,4,5,8,5,7,6,7,4,8\nA,5,8,8,7,7,8,7,2,5,7,8,8,5,10,3,6\nD,2,6,3,4,3,8,7,5,6,6,5,4,3,8,3,7\nP,4,7,6,5,5,6,7,7,4,7,6,8,3,10,7,9\nA,2,4,3,2,1,8,2,2,2,8,2,8,2,6,2,7\nE,5,9,7,6,5,7,8,1,8,11,6,9,3,8,4,9\nY,2,3,4,4,0,7,10,2,2,8,12,8,1,11,0,8\nV,6,8,6,6,2,4,12,4,4,10,12,7,3,9,1,8\nQ,6,7,8,11,8,10,12,6,0,4,7,11,6,15,5,8\nF,2,1,2,1,1,5,10,4,4,10,9,5,1,10,2,7\nG,5,8,6,6,6,7,7,6,2,6,6,10,4,8,7,7\nN,3,5,5,3,2,6,9,2,4,10,7,7,5,8,1,8\nQ,3,4,4,5,3,8,8,5,2,8,7,10,2,9,4,8\nX,2,5,4,3,2,7,7,3,9,6,6,8,2,8,6,8\nK,4,5,7,4,4,6,7,2,7,10,7,10,3,8,3,8\nI,1,3,2,1,0,7,7,1,6,13,6,8,0,8,0,8\nV,4,7,5,5,3,9,12,2,3,4,10,9,3,12,2,8\nK,5,9,7,8,8,8,8,2,5,8,3,8,5,4,4,10\nV,7,12,7,7,4,7,8,4,4,7,7,6,6,12,2,9\nL,3,6,5,4,5,8,8,3,5,5,7,9,6,11,6,5\nW,7,10,7,8,7,4,11,2,2,9,8,7,7,12,2,6\nD,5,8,8,6,6,8,8,5,6,10,6,5,5,9,4,10\nU,2,6,4,4,3,6,9,4,5,6,9,9,3,9,0,8\nS,6,11,8,8,5,8,7,3,6,10,5,7,2,8,5,8\nD,1,0,1,0,0,6,7,7,5,7,6,6,2,8,3,8\nV,9,14,7,8,3,7,11,5,6,10,10,5,4,11,3,8\nZ,3,8,5,6,3,6,9,2,9,11,8,7,2,9,6,7\nE,4,7,6,5,3,7,8,3,9,11,7,8,2,8,5,7\nY,4,10,6,8,2,5,11,1,4,9,11,8,0,10,0,8\nT,2,8,3,6,2,7,13,0,5,7,10,8,0,8,0,8\nM,4,6,6,4,4,10,6,7,5,6,7,4,8,5,2,5\nL,2,1,2,3,1,4,4,5,7,2,2,5,1,7,1,6\nS,2,1,2,2,1,8,7,6,4,8,5,8,2,8,8,8\nJ,1,3,3,2,1,8,6,3,6,14,5,10,0,7,0,7\nB,2,2,3,3,2,8,7,5,5,6,6,5,2,8,7,9\nZ,7,10,5,14,5,5,11,3,3,12,8,6,3,8,12,5\nS,4,8,5,6,3,10,7,4,8,11,5,7,2,9,5,9\nA,7,12,6,6,3,9,0,3,2,9,4,12,3,5,4,6\nR,3,6,3,4,3,6,9,7,3,7,5,8,2,7,5,11\nY,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nU,5,6,5,4,3,4,8,5,7,9,8,9,3,9,3,5\nY,5,8,5,6,2,3,13,4,6,13,11,4,1,11,2,5\nZ,5,11,7,8,8,6,7,3,9,7,6,10,1,8,11,5\nX,1,0,1,0,0,7,7,3,4,7,6,8,2,8,3,8\nR,7,12,7,6,5,11,5,3,5,10,3,8,6,8,6,10\nS,4,8,5,6,3,7,7,4,8,11,4,8,2,7,5,8\nQ,4,7,5,6,3,8,5,8,7,7,4,9,3,8,4,8\nD,2,5,4,4,3,8,7,5,6,9,5,5,3,8,4,8\nC,1,3,2,1,1,5,9,4,6,11,8,10,1,9,2,8\nP,6,9,5,4,2,7,9,6,3,13,5,4,4,9,4,7\nG,7,15,6,8,5,8,7,4,4,8,6,7,4,9,9,8\nZ,3,4,5,6,4,9,7,5,6,8,3,7,3,6,6,8\nN,4,8,6,6,4,5,9,2,3,9,8,8,5,8,1,7\nM,5,9,6,7,4,8,7,13,2,6,9,8,8,6,0,8\nA,3,6,4,4,2,8,2,2,2,7,2,8,2,6,3,7\nN,4,7,6,5,4,8,8,5,6,7,6,4,7,9,4,6\nZ,2,4,4,6,3,11,5,3,4,9,3,7,2,7,6,8\nT,4,8,6,6,5,6,8,7,7,8,7,8,3,10,6,9\nI,3,6,4,4,2,7,7,0,7,13,6,8,0,8,1,7\nN,3,3,5,2,2,7,9,3,4,10,6,6,5,9,1,7\nD,4,6,5,5,5,7,7,5,6,7,5,9,4,6,6,5\nX,4,11,6,8,6,6,7,2,7,7,7,10,6,5,8,7\nZ,1,1,2,2,1,7,8,5,8,6,6,9,1,8,7,7\nN,4,8,6,6,5,8,8,5,5,6,6,5,6,10,2,5\nQ,3,4,4,5,3,8,7,6,3,8,8,10,3,8,5,8\nQ,5,7,5,9,5,7,9,4,3,7,9,11,3,9,6,8\nA,1,0,2,1,0,8,4,2,0,7,2,8,2,7,1,8\nG,3,5,4,4,2,5,7,5,5,9,7,10,2,8,4,9\nW,2,4,4,2,2,8,10,2,2,6,9,8,6,11,0,8\nB,5,8,7,6,6,8,6,6,6,9,7,7,3,8,7,8\nQ,3,4,4,5,3,8,8,6,2,5,7,10,3,9,5,10\nO,2,2,3,3,2,7,7,8,4,7,6,8,2,8,3,8\nN,3,7,4,5,3,8,8,6,4,7,6,6,5,9,1,6\nI,2,8,2,6,2,7,7,0,7,7,6,8,0,8,3,8\nN,4,5,5,4,3,7,8,6,5,7,6,6,6,9,2,5\nU,4,5,5,3,2,3,8,4,6,11,11,9,3,9,1,6\nZ,4,7,6,5,5,8,8,5,3,6,4,6,4,8,8,3\nK,4,10,5,8,4,3,6,7,4,7,7,12,3,8,3,11\nC,4,6,4,4,2,3,9,5,8,10,10,12,1,8,3,7\nN,4,5,7,4,3,6,10,3,5,10,8,7,5,8,1,7\nK,3,5,5,4,3,6,7,1,7,10,7,10,3,8,3,8\nC,4,7,5,5,2,4,9,6,8,12,10,12,1,9,3,7\nR,4,6,5,4,3,10,7,3,6,10,3,6,3,7,4,10\nO,6,9,6,7,4,8,6,8,5,10,6,10,3,8,4,6\nQ,1,0,2,1,1,8,7,6,4,6,6,8,2,8,3,8\nI,1,4,2,3,1,7,8,0,6,13,6,8,0,8,0,8\nW,4,10,6,7,4,11,8,5,2,7,9,8,8,10,0,8\nX,2,1,3,2,2,8,7,3,8,6,6,8,2,8,5,8\nT,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8\nR,4,6,4,4,2,6,12,8,3,7,3,9,2,7,5,11\nJ,3,6,5,4,4,8,8,4,3,8,4,7,4,9,5,4\nK,5,9,8,7,5,8,6,1,7,10,3,9,5,6,6,10\nJ,3,9,4,7,1,12,2,9,4,13,7,13,1,6,0,8\nE,3,5,5,4,3,7,7,2,7,11,6,8,2,9,5,8\nA,1,0,2,0,0,7,4,2,0,7,2,8,1,6,1,8\nC,5,7,5,5,3,5,8,6,8,12,9,13,1,9,3,7\nX,3,6,4,4,2,7,7,4,4,7,6,8,2,8,4,8\nL,2,7,3,5,2,5,5,3,8,3,2,7,0,6,2,6\nS,3,5,5,3,2,7,7,3,7,11,5,8,1,8,5,8\nN,2,1,2,2,1,7,7,12,1,5,6,8,5,8,0,8\nF,3,6,4,4,3,5,10,2,6,10,9,6,1,10,3,6\nS,6,9,8,7,5,6,7,4,7,10,10,9,2,9,5,5\nK,3,6,4,4,3,6,7,5,7,7,6,10,3,8,5,9\nO,5,9,6,6,8,7,8,5,1,7,7,8,8,9,6,10\nJ,5,10,7,8,5,9,4,5,6,8,5,6,2,8,4,6\nT,3,4,4,2,1,5,11,2,7,11,9,5,1,10,2,5\nH,5,8,7,6,6,4,9,3,5,10,9,9,3,8,3,5\nZ,2,0,2,1,1,7,7,3,11,8,6,8,0,8,7,8\nX,7,15,8,8,5,6,8,2,8,11,7,8,4,9,4,6\nF,3,5,4,7,2,0,14,4,4,12,11,6,0,8,2,6\nD,6,11,8,8,8,10,7,4,6,9,3,6,3,8,3,8\nC,3,10,5,8,4,5,8,7,6,9,8,14,2,9,4,10\nB,3,7,3,5,3,6,8,9,6,6,5,7,2,8,8,9\nC,4,9,6,6,7,7,7,4,3,6,7,10,8,10,6,6\nH,5,10,5,8,5,8,7,13,1,7,7,8,3,8,0,8\nT,3,3,3,2,1,5,12,3,5,11,9,4,2,11,1,5\nJ,2,7,3,5,2,9,6,1,6,11,4,8,0,7,1,7\nS,2,3,2,1,1,8,8,6,5,7,5,7,2,8,8,8\nM,3,2,4,3,3,8,6,5,3,7,7,9,9,5,1,8\nH,2,3,4,1,2,8,7,3,6,10,5,7,3,8,2,7\nN,4,7,4,5,3,8,8,12,1,6,6,7,5,8,0,9\nK,3,6,5,4,5,6,7,3,6,6,5,8,3,8,4,9\nA,2,5,4,3,2,10,2,3,2,10,2,9,2,6,2,8\nD,1,1,2,2,1,6,7,8,6,6,6,6,2,8,3,8\nP,4,6,6,4,3,8,10,3,5,13,4,3,1,10,3,9\nI,1,4,3,3,1,7,7,1,7,14,6,8,0,8,1,8\nJ,6,11,5,8,4,9,8,2,3,12,5,6,2,9,7,9\nJ,2,10,3,8,2,12,3,7,3,12,5,11,1,6,0,8\nF,3,8,3,6,1,1,13,5,3,12,10,6,0,8,2,6\nK,6,7,8,5,5,7,8,2,7,10,5,9,4,7,3,7\nW,4,9,6,6,3,4,8,5,1,7,9,8,8,10,0,8\nV,4,5,7,8,2,7,8,4,3,7,14,8,3,9,0,8\nI,1,1,1,1,0,7,7,2,8,7,6,8,0,8,3,8\nO,2,3,3,2,2,7,7,7,5,7,6,8,2,8,3,8\nP,4,7,6,5,3,7,11,5,4,12,4,1,1,10,3,8\nW,4,4,6,6,3,8,8,4,1,7,8,8,8,9,0,8\nU,4,8,5,6,5,7,6,9,5,7,6,9,4,9,6,4\nO,3,8,4,6,3,7,7,8,4,10,6,8,3,8,3,7\nU,5,9,7,6,4,4,8,7,9,9,10,11,3,9,1,7\nI,3,9,4,6,2,7,8,0,8,14,6,6,0,8,1,7\nX,2,3,3,2,1,7,7,1,8,10,6,8,2,8,2,7\nJ,2,8,3,6,2,9,6,3,6,11,4,9,1,6,2,6\nB,4,2,5,4,4,8,7,5,6,6,5,5,2,8,7,10\nQ,2,3,3,4,2,7,9,4,1,7,8,10,2,9,4,8\nG,4,7,4,5,3,7,6,6,6,10,6,12,2,10,4,9\nQ,7,8,7,9,7,8,7,6,2,7,7,11,5,9,7,7\nJ,7,11,9,8,6,9,4,5,6,8,5,6,2,7,5,6\nD,8,12,8,6,4,11,3,3,6,10,2,8,4,7,3,12\nU,4,10,6,7,9,10,8,4,4,5,8,8,9,8,8,6\nP,4,10,5,8,4,6,10,6,5,9,7,3,2,10,4,7\nX,4,8,5,6,1,7,7,4,4,7,6,8,3,8,4,8\nB,3,6,4,4,4,8,7,5,6,7,6,6,2,8,6,9\nG,4,8,5,6,3,7,7,8,6,7,6,12,3,8,4,9\nW,3,4,5,6,3,6,8,4,1,7,8,8,8,10,0,8\nG,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,9\nA,4,9,5,6,4,7,5,3,0,6,1,8,2,7,1,7\nR,4,8,6,6,6,6,7,5,6,7,6,7,3,7,5,9\nB,2,4,3,2,2,10,7,2,6,10,4,6,2,8,4,10\nX,8,11,7,6,3,7,7,2,9,9,7,9,4,11,4,7\nN,3,4,5,3,2,6,9,3,4,11,8,8,5,7,1,8\nK,7,10,10,7,8,8,6,1,7,9,5,9,5,7,5,8\nB,4,8,6,6,6,7,8,6,4,7,5,6,4,7,6,6\nE,3,7,4,5,3,4,9,2,8,10,8,10,2,8,4,5\nE,4,8,4,6,2,3,6,6,11,7,7,15,0,8,7,7\nW,4,6,6,4,5,7,11,2,2,7,8,8,6,11,1,8\nA,3,9,5,7,3,13,3,3,2,11,1,8,2,6,3,9\nU,3,5,4,4,2,6,8,6,7,7,9,9,3,9,1,8\nM,3,4,5,2,3,8,6,6,4,7,7,9,9,5,2,8\nJ,5,9,7,7,3,9,6,3,7,15,5,10,1,6,1,7\nC,5,4,6,6,2,6,7,7,11,7,6,14,1,8,4,9\nM,3,6,4,4,4,7,6,6,4,6,7,8,7,5,2,7\nP,4,7,6,5,3,8,9,3,5,13,5,4,1,10,2,8\nI,1,5,1,3,1,7,7,1,7,7,6,8,0,8,3,8\nD,5,10,7,7,6,11,6,2,6,11,3,7,3,8,3,9\nE,5,11,7,8,5,5,9,4,9,11,9,8,3,8,5,5\nS,4,7,5,5,3,10,6,4,8,11,3,8,2,8,5,10\nI,3,8,6,6,5,8,8,4,5,9,6,4,5,10,4,6\nW,8,13,8,7,5,4,8,1,3,8,9,8,9,11,2,5\nC,3,7,4,5,2,3,8,5,8,9,9,13,1,8,3,7\nF,4,9,4,6,2,1,13,5,3,12,10,6,0,8,2,6\nC,3,7,4,5,2,3,9,6,7,11,11,11,1,8,2,6\nZ,4,8,5,6,3,7,7,3,12,9,6,8,0,8,8,8\nF,5,10,7,8,5,8,9,2,6,13,6,5,2,10,2,8\nR,7,11,10,8,11,5,8,3,5,6,5,10,8,9,9,6\nA,5,10,8,8,5,11,3,1,2,8,2,9,4,6,3,8\nV,5,9,5,7,4,3,11,2,3,9,11,8,2,11,1,8\nZ,4,5,5,8,2,7,7,4,14,9,6,8,0,8,8,8\nI,1,3,2,2,1,7,8,0,6,13,6,7,0,8,0,7\nO,10,13,7,8,4,7,7,5,5,8,4,7,5,9,6,8\nD,1,0,1,0,0,6,7,6,4,7,6,6,2,8,2,8\nM,3,4,4,2,3,7,6,6,4,6,7,8,6,6,2,8\nS,6,12,6,6,3,7,8,3,6,13,6,7,2,8,3,7\nE,6,10,9,8,6,4,9,4,9,12,10,9,3,8,5,5\nD,6,11,9,8,8,7,8,6,6,10,7,5,7,9,6,11\nI,2,5,4,4,1,7,7,0,7,13,6,8,0,8,1,8\nQ,5,5,7,8,4,8,9,7,6,6,8,9,3,7,6,10\nM,5,5,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nP,4,7,5,5,3,6,10,7,3,10,5,4,2,10,3,8\nN,6,10,9,8,5,8,8,3,5,10,5,6,6,8,2,7\nF,5,5,5,7,2,1,14,5,4,12,10,6,0,8,2,5\nP,6,10,8,8,5,6,12,5,3,12,5,2,1,11,3,8\nT,5,10,6,8,4,7,9,0,8,11,9,6,1,10,3,4\nU,2,6,3,4,1,8,5,13,5,6,13,8,3,9,0,8\nO,4,10,6,8,3,8,8,9,8,6,7,10,3,8,4,8\nO,5,9,6,6,5,7,7,7,4,9,6,10,4,8,4,7\nS,5,10,7,7,4,9,6,3,7,10,6,9,2,10,5,9\nX,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nB,4,10,5,8,6,8,7,7,6,7,6,6,2,8,7,10\nN,3,2,3,3,2,7,8,5,4,7,6,7,4,8,1,7\nX,3,7,5,5,3,8,7,4,9,6,6,8,3,8,7,9\nQ,3,6,4,6,2,8,6,8,6,6,6,8,3,8,4,8\nP,2,1,2,2,1,5,10,4,4,9,7,4,1,10,3,7\nL,5,10,7,8,4,7,3,2,9,7,1,9,1,6,3,7\nW,3,5,5,4,4,9,11,3,2,5,9,7,7,11,1,8\nN,3,7,5,5,3,6,9,6,5,8,7,8,5,8,1,7\nO,4,9,5,6,4,8,7,8,5,7,7,7,3,8,3,8\nV,7,11,9,8,6,5,12,3,2,9,10,7,5,10,6,8\nG,4,8,6,6,3,6,6,6,7,7,5,9,3,10,4,8\nF,3,4,3,5,1,1,14,5,3,12,9,5,0,8,2,6\nL,2,4,4,3,2,6,4,1,8,8,2,11,0,7,2,8\nB,4,5,5,8,4,6,7,10,7,7,6,7,2,8,9,9\nI,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nX,4,4,5,6,1,7,7,5,4,7,6,8,3,8,4,8\nD,4,8,5,6,8,9,7,5,5,7,6,6,4,7,7,6\nI,1,8,2,6,2,7,7,0,8,7,6,8,0,8,3,8\nD,2,7,4,5,4,7,7,4,6,7,6,6,3,8,2,7\nC,6,11,7,8,4,5,8,8,9,9,9,13,2,9,4,9\nG,4,6,5,4,3,6,7,6,6,9,8,9,2,8,4,9\nV,1,1,2,1,0,7,9,4,2,7,13,8,2,10,0,8\nT,5,5,5,4,2,5,12,3,7,12,9,4,1,11,1,5\nK,5,7,7,5,4,4,8,2,7,10,10,11,3,8,4,6\nC,7,11,7,8,4,5,7,6,8,12,9,14,2,10,4,7\nZ,4,5,5,7,2,7,7,4,15,9,6,8,0,8,8,8\nW,6,10,6,8,7,4,10,3,2,9,7,8,8,12,3,5\nE,4,9,4,7,3,3,8,6,12,7,5,15,0,8,7,7\nB,7,12,6,6,4,8,6,4,5,10,5,8,6,7,7,10\nB,4,7,6,5,4,10,6,3,7,10,4,7,3,8,5,11\nK,2,1,3,3,2,6,7,4,7,7,6,10,3,8,5,9\nM,2,3,4,1,2,8,6,3,4,9,6,7,6,5,1,7\nH,4,4,6,6,5,9,9,3,1,7,7,8,4,9,8,8\nC,6,8,8,7,7,5,7,4,5,7,6,11,4,9,8,9\nV,7,12,7,6,4,9,8,4,5,8,8,5,6,10,3,6\nF,4,10,6,8,3,4,13,6,5,12,10,3,2,10,2,4\nT,3,8,4,6,2,9,13,0,5,6,10,8,0,8,0,8\nY,5,10,7,8,3,7,10,1,3,7,12,8,1,11,0,8\nF,3,7,3,4,1,2,14,5,2,12,9,4,0,8,3,6\nX,2,3,3,2,2,8,7,3,8,6,6,6,2,8,6,8\nD,5,8,7,6,6,8,7,5,6,9,4,5,3,8,4,9\nR,2,0,2,1,1,6,10,7,2,7,5,7,2,7,4,9\nY,1,3,2,2,1,6,11,1,7,8,11,9,1,11,2,8\nX,4,8,6,6,4,7,7,3,9,5,6,8,3,8,7,8\nO,4,6,4,4,4,6,9,7,4,9,8,8,3,8,3,7\nC,8,12,5,7,3,5,10,6,8,11,8,8,1,8,6,8\nH,3,3,4,4,2,7,7,14,1,7,6,8,3,8,0,8\nA,3,9,5,7,3,9,3,2,2,8,1,8,2,6,3,7\nX,6,10,9,8,4,5,8,2,9,11,12,10,4,7,4,5\nR,5,8,8,7,8,8,8,3,3,7,5,8,7,8,6,4\nT,5,7,6,6,6,5,8,4,8,9,7,10,3,7,7,5\nC,5,4,6,7,2,7,6,7,11,9,5,13,1,9,4,8\nD,8,14,8,8,4,11,4,3,6,10,2,7,5,7,4,12\nN,1,3,3,2,1,5,9,3,3,10,8,8,4,8,0,7\nA,4,6,6,5,5,8,8,3,5,7,8,7,5,10,4,6\nA,3,8,5,6,3,7,5,3,0,7,1,8,2,7,1,8\nA,3,11,6,8,4,12,2,2,2,9,2,9,2,6,3,8\nB,4,9,6,7,5,8,7,6,6,9,6,6,3,8,7,8\nV,1,0,2,0,0,8,9,3,2,6,12,8,2,10,0,8\nD,5,5,6,7,3,5,7,10,10,6,6,6,3,8,4,8\nX,2,3,3,2,2,7,7,3,9,6,6,8,2,8,5,8\nD,6,11,6,6,3,10,4,3,6,9,3,7,4,7,4,11\nJ,2,5,4,4,1,9,6,2,7,14,5,10,1,6,1,7\nH,5,11,5,8,3,7,9,15,2,7,3,8,3,8,0,8\nE,2,3,4,2,2,7,7,2,7,11,7,9,2,8,4,8\nI,3,9,4,6,3,8,7,0,7,13,6,8,0,8,1,8\nU,7,9,6,5,3,4,4,5,5,4,7,8,5,7,2,7\nC,7,10,9,8,6,7,7,8,7,6,7,8,5,6,5,9\nI,7,14,5,8,2,9,7,6,4,13,3,7,3,7,6,11\nT,4,6,5,6,5,5,8,4,7,8,8,9,3,8,7,6\nA,2,4,3,3,1,10,2,3,2,10,2,9,2,6,2,8\nK,8,13,8,8,4,8,7,3,6,9,7,8,6,11,4,7\nW,5,8,8,6,3,7,7,5,2,7,8,8,9,9,0,8\nQ,4,7,5,9,6,8,6,8,2,5,6,9,3,8,5,10\nT,5,10,6,8,4,5,11,1,9,8,11,9,1,10,1,7\nZ,1,0,2,1,0,7,7,2,11,8,6,8,0,8,6,8\nB,5,4,5,6,4,7,9,9,8,7,5,7,2,8,9,9\nP,4,6,6,4,3,7,10,3,4,13,6,3,1,10,3,9\nV,6,11,6,6,3,5,9,4,3,10,8,5,5,12,2,7\nP,2,1,2,1,1,5,11,7,2,9,6,4,1,9,3,8\nH,3,6,4,4,2,7,8,14,1,7,5,8,3,8,0,8\nX,7,10,10,8,6,9,6,1,8,10,3,7,3,8,3,9\nP,4,9,6,6,4,5,10,6,5,10,8,3,1,10,4,6\nC,0,0,1,0,0,6,7,5,7,7,6,14,0,8,3,10\nG,3,4,4,3,2,7,6,6,6,6,6,10,2,9,4,9\nG,3,5,4,4,3,6,7,5,5,9,7,10,2,8,4,10\nX,9,11,8,6,3,7,7,2,9,9,6,8,4,9,4,8\nZ,5,8,6,6,4,7,8,2,10,12,7,6,1,7,6,6\nW,6,11,9,8,7,5,9,5,1,7,9,8,8,11,0,7\nI,2,8,3,6,1,9,7,0,8,14,5,8,0,8,1,8\nZ,2,4,4,3,2,8,7,2,9,12,6,7,1,8,5,8\nC,2,6,3,4,2,6,8,8,8,9,8,13,1,9,4,10\nW,3,7,6,5,4,5,10,2,3,8,9,9,7,11,1,8\nG,4,7,5,5,3,6,7,7,7,10,7,9,2,10,4,9\nX,3,3,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nR,3,7,3,5,2,6,9,9,5,7,5,8,3,8,5,10\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nN,5,9,8,7,5,10,7,3,5,10,2,5,6,9,1,7\nW,8,10,8,5,4,1,9,3,3,11,12,9,8,10,1,6\nK,7,10,10,8,6,4,9,2,7,10,9,10,5,6,4,5\nU,4,7,4,5,3,7,6,12,4,7,12,8,3,9,0,8\nB,8,12,6,6,4,7,7,5,6,10,5,8,6,6,7,10\nH,4,5,6,4,4,7,7,3,6,10,6,8,3,8,3,8\nE,4,7,5,5,4,10,6,2,7,11,4,8,4,9,5,12\nC,3,7,5,5,5,5,7,3,4,7,6,10,5,10,3,8\nQ,3,6,4,8,4,8,11,4,3,5,9,11,2,10,5,8\nP,6,11,8,8,5,5,11,7,4,11,7,2,1,10,4,6\nL,3,7,3,5,1,0,1,5,6,0,0,6,0,8,0,8\nE,0,0,1,0,0,5,7,5,6,7,6,12,0,8,6,10\nK,1,1,2,1,1,6,7,4,7,7,6,11,3,8,4,9\nR,4,4,4,6,3,5,11,8,4,7,2,9,3,7,6,11\nO,1,0,1,0,0,7,6,6,3,7,6,7,2,8,3,8\nK,4,9,6,7,4,5,7,5,7,6,6,10,4,8,5,9\nV,5,6,5,4,2,3,12,4,4,10,12,7,2,10,1,7\nF,5,8,7,6,4,7,9,3,6,13,6,5,2,9,3,7\nT,2,4,3,3,1,7,11,2,7,6,11,8,1,10,1,8\nS,2,6,3,4,2,7,6,5,8,4,6,8,0,9,8,8\nQ,3,7,4,6,3,8,7,7,4,6,6,9,2,8,4,9\nV,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8\nU,3,6,4,4,2,7,4,14,5,7,12,8,3,9,0,8\nP,1,0,2,0,0,5,11,6,1,9,6,5,1,9,3,8\nJ,1,1,2,3,1,11,6,2,6,12,3,7,0,7,1,8\nS,6,9,7,6,4,8,8,4,10,11,4,7,2,6,5,9\nM,10,15,12,9,6,10,2,3,2,10,3,9,8,1,1,8\nT,3,9,4,7,4,7,11,3,6,7,11,9,2,12,1,8\nU,5,6,6,4,3,3,9,5,7,11,11,9,3,9,2,6\nF,3,7,3,4,1,1,13,5,4,12,10,7,0,8,2,6\nX,3,5,6,3,3,7,7,1,8,10,6,8,2,8,3,8\nL,7,11,6,6,3,10,2,4,4,12,5,13,2,7,5,9\nX,4,6,5,5,5,8,8,2,4,7,6,8,3,8,7,7\nF,7,12,6,6,3,8,8,3,7,12,4,5,2,8,7,7\nJ,7,8,4,12,4,7,9,3,4,13,4,5,3,8,7,10\nT,5,9,5,6,3,5,12,3,8,12,9,4,1,11,2,4\nY,3,9,5,6,2,9,11,1,3,5,11,8,1,10,0,8\nA,3,8,5,5,2,7,5,3,1,6,1,8,2,7,2,7\nF,4,7,6,5,3,5,11,3,5,13,8,5,1,10,2,7\nI,2,9,2,7,3,7,7,0,7,7,6,8,0,8,3,8\nU,3,2,4,4,2,6,8,6,7,6,9,9,3,9,1,7\nI,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nE,4,5,6,4,5,6,8,4,4,8,7,9,4,11,8,10\nH,8,10,11,8,6,7,8,3,7,10,8,8,6,8,5,7\nM,4,2,6,3,4,8,6,6,5,6,7,8,8,7,3,6\nN,3,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8\nS,3,8,4,6,2,7,6,6,10,5,7,10,0,9,9,8\nL,10,15,8,8,4,9,2,4,5,12,4,13,2,7,6,8\nF,3,5,5,6,4,6,9,3,4,8,7,7,4,10,8,9\nH,5,9,8,7,7,8,8,5,7,7,6,7,6,8,4,7\nW,5,6,7,5,8,6,7,6,5,6,5,8,8,10,8,10\nN,4,7,6,5,3,11,6,4,5,10,1,5,5,9,1,7\nH,7,9,10,7,6,5,8,4,7,10,9,10,3,8,3,6\nI,2,8,4,6,4,10,6,1,4,8,5,5,3,8,5,7\nD,3,5,5,4,3,9,6,4,7,10,4,6,2,8,3,9\nD,4,11,5,8,5,5,7,10,7,6,5,6,3,8,3,8\nQ,4,7,5,7,4,8,7,7,5,6,4,9,2,8,3,8\nJ,6,7,8,9,7,8,8,4,6,7,6,7,4,8,8,8\nV,3,8,5,6,3,6,11,2,3,7,11,9,2,10,1,8\nJ,2,7,3,5,1,9,6,3,7,12,4,9,0,7,2,6\nC,2,4,3,3,2,6,8,7,7,8,7,13,1,9,4,10\nO,7,11,7,8,5,8,7,8,5,10,6,8,3,8,3,8\nJ,7,10,5,8,4,8,10,3,3,13,4,5,2,9,7,9\nF,5,6,6,7,6,7,9,6,6,7,6,9,5,8,8,6\nT,6,10,6,7,5,5,11,3,7,11,10,5,2,12,2,4\nX,3,6,4,4,3,8,7,3,8,6,6,8,2,8,6,8\nG,6,10,7,8,4,6,7,7,8,10,7,10,2,10,5,9\nS,3,9,4,6,3,8,7,8,7,7,7,9,2,10,9,8\nO,2,4,2,3,2,8,7,6,3,9,6,8,2,8,2,8\nE,5,8,7,6,4,7,7,3,8,12,7,9,3,8,5,8\nR,4,4,4,5,2,5,10,8,4,7,4,8,3,7,6,11\nY,4,7,6,5,3,9,10,1,7,3,11,8,1,11,2,9\nB,3,6,5,4,4,7,8,5,5,9,6,6,3,8,7,8\nT,3,11,4,8,1,5,14,1,6,9,11,7,0,8,0,8\nF,4,8,5,6,4,4,11,6,5,11,10,5,2,9,2,5\nQ,6,9,7,11,8,7,9,5,3,8,10,10,5,8,8,10\nR,5,7,8,6,8,9,7,4,4,8,5,7,8,10,7,5\nE,3,9,4,6,2,3,7,6,11,7,6,15,0,8,7,7\nV,3,5,5,4,2,4,12,3,3,9,11,8,2,11,0,8\nO,4,8,5,6,2,8,6,8,7,7,5,8,3,8,4,8\nW,12,14,12,8,5,2,9,3,2,10,12,8,9,10,1,6\nK,4,2,5,3,3,6,7,4,8,7,6,10,6,8,5,9\nA,3,9,6,6,3,11,3,2,2,9,2,9,3,5,3,8\nU,5,7,6,5,3,4,8,6,8,10,10,9,3,9,2,5\nX,4,10,6,7,6,7,7,2,6,7,6,7,4,7,6,7\nW,6,9,9,7,7,4,10,2,3,8,9,9,10,9,1,8\nM,7,10,10,8,9,11,6,2,5,9,4,6,10,7,2,8\nL,3,8,4,6,1,0,0,6,6,0,1,5,0,8,0,8\nQ,4,9,6,8,3,9,8,8,6,5,8,9,3,8,5,9\nH,6,9,6,4,3,9,6,4,4,9,7,8,6,12,4,9\nN,1,0,1,0,0,7,7,9,0,6,6,8,3,8,0,8\nN,4,8,6,6,3,5,9,3,4,10,9,9,5,8,1,7\nQ,2,2,3,3,2,8,8,6,1,5,6,9,2,9,4,10\nE,5,8,7,6,6,6,8,3,7,11,9,9,3,8,5,6\nK,2,6,3,4,2,3,7,6,2,7,6,10,3,8,2,11\nG,3,6,4,4,4,7,8,5,3,6,6,10,3,8,6,8\nI,2,8,3,6,1,7,7,0,8,14,6,9,0,8,1,8\nC,4,9,6,7,4,5,8,6,6,8,8,15,3,10,4,9\nE,2,4,2,3,2,7,7,5,6,7,6,8,2,8,5,11\nG,2,1,3,2,1,6,7,5,5,6,6,9,2,9,4,8\nB,3,5,5,4,4,9,6,3,6,10,5,7,2,8,5,10\nT,4,10,6,8,4,6,12,3,7,7,12,8,2,12,1,7\nP,2,3,4,2,2,8,8,3,3,12,4,5,1,9,2,8\nJ,4,9,6,7,3,6,7,3,5,15,6,10,1,6,1,7\nM,2,3,3,1,2,6,6,6,4,7,7,10,6,5,1,9\nU,4,3,4,2,2,5,8,5,7,10,9,9,3,9,2,6\nL,7,10,9,8,7,6,7,7,7,6,6,8,6,8,5,10\nN,5,7,7,5,3,5,9,3,5,10,9,9,6,7,1,7\nW,5,10,8,8,13,9,7,5,2,7,7,8,10,10,3,5\nI,7,12,5,6,2,10,5,6,4,13,3,9,3,8,4,9\nV,10,15,10,8,5,6,8,4,4,8,8,5,6,12,4,6\nZ,6,9,8,7,4,9,6,3,10,11,4,9,1,7,6,8\nM,5,5,6,8,4,8,7,12,2,6,9,8,9,6,0,8\nE,5,5,5,7,3,3,8,6,12,7,5,15,0,8,7,7\nA,2,2,4,3,2,8,2,2,2,8,2,8,2,6,3,7\nM,4,7,8,5,8,10,6,3,3,9,4,7,10,5,3,6\nD,1,0,2,0,0,6,7,7,6,7,6,6,2,8,3,8\nZ,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nN,8,11,11,8,6,12,7,3,6,10,0,3,7,11,2,8\nJ,2,5,5,3,1,8,6,3,7,15,6,10,1,6,1,7\nY,6,11,9,8,8,8,4,6,5,8,7,8,6,8,8,3\nX,4,9,6,7,4,8,7,4,9,6,6,6,3,8,7,7\nB,2,3,4,2,2,8,7,3,5,10,5,6,2,8,4,9\nA,3,11,5,8,4,12,3,3,3,10,1,9,2,6,3,8\nT,5,7,6,5,6,6,8,4,6,7,6,10,5,8,5,7\nH,3,2,4,4,3,7,7,5,6,7,6,8,6,8,4,8\nH,4,9,5,6,2,7,6,15,1,7,8,8,3,8,0,8\nH,3,7,3,4,2,7,6,14,1,7,7,8,3,8,0,8\nY,4,5,5,7,5,10,11,5,4,5,8,8,5,10,9,5\nV,4,6,4,4,2,3,11,4,3,10,12,7,2,10,1,8\nK,5,11,6,8,2,4,6,9,2,7,7,12,4,8,3,11\nX,3,7,5,5,2,9,7,1,8,10,4,7,3,8,3,8\nQ,5,8,6,7,3,9,8,8,6,5,8,9,3,7,5,9\nS,5,8,6,6,4,8,8,4,8,10,4,6,2,7,5,8\nC,4,9,5,6,3,4,8,5,6,12,10,12,2,9,2,7\nB,1,0,2,1,1,7,7,7,5,6,6,7,1,8,7,9\nB,5,11,7,8,7,8,7,7,7,6,6,5,2,8,8,10\nH,5,9,7,6,4,7,6,3,7,10,8,9,3,8,3,7\nX,3,5,5,4,3,7,7,3,9,6,6,8,3,8,6,8\nH,3,7,4,4,2,7,7,14,1,7,6,8,3,8,0,8\nZ,3,8,5,6,3,8,7,2,9,11,5,9,1,8,6,8\nS,7,11,7,6,3,7,6,3,4,13,8,8,3,8,4,7\nM,5,2,6,4,4,9,6,7,4,6,7,5,10,5,3,5\nV,1,0,1,0,0,8,10,3,1,7,12,8,2,11,0,8\nW,4,7,6,5,3,9,8,5,1,7,8,8,8,9,0,8\nF,4,8,6,6,4,7,9,2,6,13,6,5,2,10,2,8\nO,5,9,5,7,4,7,7,8,5,9,7,10,3,8,4,6\nT,4,9,5,7,3,6,11,2,8,8,12,8,1,11,1,7\nO,4,10,5,8,3,9,7,9,7,7,6,10,3,8,4,8\nB,5,7,7,5,5,8,6,6,7,9,7,6,3,8,8,7\nT,4,8,5,6,4,6,7,7,6,8,10,8,3,9,5,9\nX,3,7,4,5,2,7,7,4,4,7,6,7,2,8,4,8\nC,9,12,7,7,5,7,9,4,5,9,8,9,4,9,8,11\nN,6,5,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nX,6,10,9,8,5,7,8,1,8,10,6,8,3,8,4,8\nQ,5,7,6,5,5,8,6,7,4,4,8,10,6,6,7,10\nV,2,1,4,1,1,7,12,3,2,6,11,9,2,11,0,7\nD,2,3,4,2,2,9,6,4,6,10,4,6,2,8,3,8\nK,5,9,8,7,4,4,9,2,7,10,10,11,3,8,3,6\nA,3,11,6,8,2,8,6,3,1,7,0,8,3,7,2,8\nV,3,6,4,4,2,2,11,4,3,11,12,8,2,10,1,8\nH,4,8,5,6,5,8,7,5,6,7,6,8,6,8,3,7\nD,1,0,1,0,0,6,7,7,5,7,6,6,2,8,2,8\nG,3,8,4,6,2,7,6,8,7,6,5,11,1,8,5,11\nJ,2,5,3,8,1,12,3,10,4,13,5,13,1,6,0,8\nC,2,5,3,4,2,6,8,7,7,8,8,13,1,9,4,10\nG,5,10,6,8,3,7,6,8,8,6,6,8,2,8,6,11\nK,5,5,5,8,2,3,6,8,3,7,7,11,4,8,3,11\nV,3,9,5,6,1,7,8,4,2,7,14,8,3,9,0,8\nC,3,4,4,3,2,4,8,4,6,11,10,12,1,9,2,7\nM,5,10,6,8,4,8,7,12,2,6,9,8,8,6,0,8\nF,5,8,7,6,7,7,8,1,4,10,6,6,5,10,3,5\nY,2,3,3,4,0,8,11,1,3,5,11,8,0,10,0,8\nC,3,5,4,3,2,4,8,5,8,11,9,13,1,9,3,7\nQ,6,8,9,12,14,8,5,6,1,6,6,9,10,10,8,14\nH,9,15,8,8,5,6,7,5,4,8,10,9,7,11,5,9\nU,9,11,8,6,4,9,6,6,7,3,9,7,5,9,3,5\nD,5,10,6,8,6,7,7,7,7,7,6,5,3,8,3,7\nH,5,10,7,8,10,8,9,5,3,6,7,7,7,9,9,8\nO,6,10,8,8,10,8,9,6,1,7,7,8,9,8,5,9\nW,3,7,5,5,7,9,5,5,2,7,6,8,5,10,2,5\nS,4,7,6,5,6,8,9,4,4,9,5,7,4,9,10,9\nU,2,3,3,2,1,5,8,4,7,10,8,8,3,9,2,6\nY,6,11,6,8,3,4,10,2,7,10,11,6,1,11,3,4\nN,3,8,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nN,7,12,6,6,3,5,9,4,5,4,4,11,5,9,2,7\nM,4,2,5,4,4,8,6,6,4,7,7,8,7,5,2,7\nE,2,7,4,5,4,7,8,3,5,5,7,10,3,11,7,8\nI,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7\nZ,2,3,2,2,2,7,7,5,9,6,6,8,1,8,6,8\nB,2,4,2,3,2,7,7,5,5,6,6,6,2,8,5,9\nB,7,11,9,8,7,9,6,4,7,10,5,6,3,8,6,10\nG,6,10,8,8,8,6,6,6,3,6,6,10,5,8,8,7\nP,4,9,5,6,2,3,14,7,1,11,6,3,0,10,4,8\nP,3,4,5,6,7,8,5,5,2,7,6,7,6,9,6,9\nU,3,5,5,3,2,7,9,6,7,7,10,9,3,9,1,8\nK,8,10,11,8,5,4,8,4,9,12,12,11,3,8,4,5\nV,6,10,5,5,2,8,10,6,3,7,9,5,6,13,3,7\nC,9,14,7,8,5,7,6,4,4,9,8,11,4,9,9,11\nW,3,8,5,6,5,6,11,2,2,7,8,8,6,11,1,8\nV,4,7,6,5,4,8,11,2,2,6,10,8,4,10,4,9\nC,5,5,6,8,2,6,7,7,10,8,6,15,1,9,4,9\nE,2,3,3,2,1,7,7,2,7,11,7,9,1,8,3,8\nO,4,9,5,7,2,7,7,9,8,7,5,8,3,8,4,8\nO,1,1,2,1,1,7,7,6,4,7,6,8,2,8,2,8\nE,4,7,6,5,4,6,8,3,7,11,8,8,3,8,4,6\nC,2,1,2,1,1,6,7,6,9,7,6,13,0,8,4,9\nO,3,5,4,4,2,8,6,7,4,9,5,8,2,8,3,8\nA,3,8,6,6,4,8,5,2,4,6,2,6,2,6,2,6\nN,4,10,4,7,3,7,7,14,2,4,6,8,6,8,0,8\nZ,3,9,4,7,3,7,8,3,11,8,6,8,0,8,7,7\nA,6,9,6,5,3,13,0,5,1,12,3,11,2,3,2,10\nS,4,5,5,5,6,7,7,5,4,7,7,7,5,8,10,10\nI,3,10,6,8,7,10,6,2,5,8,4,5,3,8,5,7\nF,6,10,8,7,6,6,10,2,6,13,7,6,2,10,2,7\nL,3,7,4,5,2,4,5,1,9,7,2,11,0,8,2,7\nV,7,8,9,7,10,8,7,4,5,7,6,8,8,10,9,4\nG,5,11,6,8,8,8,4,4,3,7,5,11,6,8,6,13\nG,5,9,4,5,2,7,4,4,2,7,4,5,4,7,5,8\nL,5,10,7,8,4,7,3,2,9,7,1,9,1,6,3,7\nP,3,6,4,4,2,5,12,7,3,12,6,3,1,10,4,7\nA,3,8,5,6,3,11,2,3,3,10,2,9,2,6,3,8\nP,3,7,5,10,8,7,12,4,1,9,8,5,4,11,4,8\nC,5,8,6,6,2,6,7,7,10,6,6,15,1,8,4,9\nM,4,2,5,4,4,8,6,6,4,7,7,8,7,5,2,8\nU,2,1,2,2,1,7,9,5,5,6,9,9,3,9,1,8\nY,5,6,5,4,3,5,9,1,7,9,10,6,1,11,3,5\nD,7,10,9,8,7,7,7,5,6,7,6,7,7,8,3,8\nY,9,9,7,13,5,7,8,4,3,6,11,5,5,10,6,6\nY,4,5,5,4,2,4,11,2,7,11,10,6,1,11,2,5\nT,4,9,4,4,2,5,10,2,6,11,8,6,2,9,4,3\nW,4,9,5,6,6,7,6,6,2,7,7,8,6,8,4,8\nB,5,11,8,8,11,9,7,4,4,6,7,7,8,8,8,7\nE,3,5,6,4,3,6,8,2,9,11,8,8,2,8,4,6\nX,3,9,5,7,4,8,7,3,8,5,6,8,3,8,6,8\nS,2,7,3,5,3,8,7,7,5,7,7,8,2,9,8,8\nU,5,5,6,4,3,4,8,5,8,10,8,9,3,9,2,6\nT,4,6,5,4,4,7,7,7,7,7,6,8,3,10,6,8\nU,3,4,4,3,2,5,8,5,7,9,8,8,3,9,2,5\nV,3,8,5,6,2,5,11,3,4,9,12,9,3,10,1,9\nJ,4,6,5,4,2,9,7,0,7,13,5,7,0,8,0,8\nB,5,10,5,5,5,7,8,3,5,9,6,7,6,5,7,8\nH,1,0,1,0,0,7,8,10,1,7,6,8,2,8,0,8\nD,6,11,8,8,7,7,7,6,6,7,7,7,7,7,3,7\nS,3,3,4,4,2,7,7,5,8,5,6,8,0,8,8,8\nL,1,4,2,3,1,7,4,1,7,7,2,10,0,7,2,8\nQ,6,8,7,10,6,8,6,7,4,8,7,11,4,9,7,7\nH,3,3,5,2,2,9,6,3,6,10,4,8,3,8,3,9\nM,4,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8\nC,1,0,1,1,0,7,7,5,7,7,6,14,0,8,4,10\nR,3,2,4,4,3,7,8,5,5,6,5,6,3,7,4,8\nG,1,0,2,1,0,8,6,6,4,6,5,9,1,8,5,10\nA,2,6,5,4,3,6,5,1,3,5,2,7,2,6,3,4\nO,4,7,4,5,3,9,6,7,5,10,4,10,3,8,3,8\nI,1,6,3,4,3,9,6,3,4,7,6,5,3,8,5,5\nV,4,8,4,6,2,3,11,4,4,11,12,8,2,10,1,8\nV,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8\nA,5,12,5,6,3,14,0,4,2,12,3,11,2,4,2,10\nK,3,2,4,4,3,5,7,3,7,6,6,10,6,8,4,9\nV,3,5,4,3,1,4,12,4,4,11,11,7,2,11,1,8\nC,7,10,7,7,4,4,10,7,8,12,10,9,2,10,3,6\nL,4,6,5,4,3,6,6,7,6,6,6,8,2,8,4,10\nH,2,1,2,1,2,8,8,6,6,7,6,8,3,8,3,8\nU,5,6,6,4,3,4,9,5,7,11,11,9,3,9,2,7\nN,3,5,4,3,3,7,9,5,5,7,6,6,5,9,2,6\nD,4,10,6,8,4,8,7,7,7,10,5,4,3,7,5,9\nQ,3,3,4,4,3,8,7,6,2,8,7,9,2,9,4,8\nX,3,5,5,3,2,8,7,3,9,6,6,9,3,7,7,9\nN,4,11,5,8,5,7,7,13,1,6,6,8,6,9,1,6\nN,1,0,1,0,0,7,7,10,0,5,6,8,4,8,0,8\nL,3,9,5,7,7,7,8,3,5,6,6,10,6,11,7,5\nF,4,11,5,8,4,4,11,4,6,11,10,6,2,10,3,5\nN,5,9,5,5,2,5,8,4,6,4,4,9,4,7,2,8\nI,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nY,5,8,5,6,3,5,9,1,8,9,9,5,1,11,3,4\nZ,2,4,4,3,2,7,8,2,9,12,7,7,1,8,5,6\nN,5,11,7,8,6,7,7,8,5,7,5,6,3,7,3,8\nN,4,7,5,5,4,6,7,8,5,7,5,7,3,7,3,8\nP,4,10,4,7,2,4,10,10,3,9,6,4,2,10,4,8\nK,12,15,11,9,5,6,8,3,7,9,9,10,6,10,4,6\nA,1,0,2,1,0,7,4,2,0,7,2,8,2,7,1,8\nK,5,10,6,7,2,4,7,9,2,7,6,12,3,8,3,11\nG,7,11,8,8,5,6,6,7,8,10,7,11,2,10,5,9\nK,4,5,7,4,4,6,7,1,7,10,7,10,3,8,3,8\nA,3,10,6,7,4,11,3,2,2,9,2,9,3,7,3,9\nT,2,1,3,2,1,6,12,2,7,8,11,8,1,10,1,7\nQ,4,9,5,8,3,8,6,9,7,6,5,9,3,8,4,8\nY,3,4,5,6,1,7,12,2,3,8,12,8,1,10,0,8\nN,3,7,5,5,3,7,9,6,5,7,6,7,5,9,2,6\nL,2,6,2,4,1,0,1,5,6,0,0,7,0,8,0,8\nE,7,13,5,7,3,6,8,6,8,10,6,10,1,8,8,8\nJ,1,3,2,5,1,12,3,9,3,13,5,13,1,6,0,8\nZ,2,1,2,2,1,7,7,3,11,9,6,8,0,8,7,8\nG,1,0,1,0,0,8,7,5,4,6,5,9,1,8,4,10\nU,5,9,6,7,6,8,8,8,5,5,7,9,3,8,4,6\nC,3,2,4,4,2,6,8,7,8,9,8,13,1,9,4,10\nG,8,12,6,6,5,8,8,4,3,9,5,6,4,9,9,8\nW,6,10,6,8,6,2,11,2,2,10,9,7,6,11,2,6\nJ,4,9,6,6,4,7,6,4,5,8,6,7,5,6,4,6\nL,4,11,6,8,8,7,8,3,5,6,6,10,6,11,7,5\nR,5,10,7,8,6,6,8,5,6,6,5,8,3,6,6,9\nF,2,4,4,3,2,7,9,1,7,13,5,5,1,9,2,8\nY,6,12,5,6,3,4,9,4,2,10,9,6,3,9,3,4\nY,3,5,5,4,2,7,10,1,7,7,11,8,1,11,2,8\nH,2,1,3,3,2,6,7,5,6,7,6,8,3,8,3,8\nX,3,6,4,4,3,7,8,3,8,6,6,8,2,8,6,8\nR,5,11,7,8,10,6,7,3,4,6,6,9,7,10,8,6\nP,3,6,5,4,3,5,12,6,3,10,7,2,1,10,3,6\nU,3,3,3,5,1,7,5,13,5,7,14,8,3,9,0,8\nC,2,0,2,1,1,6,7,6,10,7,6,14,0,8,4,9\nR,1,0,2,1,1,6,9,7,3,7,5,8,2,7,4,11\nT,8,15,7,9,3,6,10,3,8,13,6,6,2,8,5,4\nY,6,8,8,11,12,8,8,4,2,7,8,9,4,12,9,8\nO,4,7,4,5,3,7,7,8,6,9,7,10,3,8,3,7\nJ,0,0,1,0,0,12,4,6,3,12,4,11,0,7,0,8\nR,3,4,5,3,3,9,7,3,5,11,3,6,2,7,3,9\nZ,1,3,3,2,1,8,7,2,9,11,6,8,1,8,5,7\nX,3,11,4,8,4,7,7,4,4,7,6,8,3,8,4,8\nU,2,3,3,2,1,5,8,5,6,10,9,9,3,9,2,6\nC,5,11,6,8,5,5,8,8,7,10,8,13,2,11,4,10\nV,5,11,7,8,8,8,5,5,3,8,7,9,9,9,6,11\nC,4,7,5,5,2,6,7,6,10,6,6,12,1,7,4,8\nM,8,10,11,7,7,5,6,4,5,11,10,11,11,5,4,7\nI,1,1,1,1,1,7,7,2,7,7,6,8,0,8,2,8\nU,1,0,2,1,0,8,6,11,4,7,12,8,3,10,0,8\nL,5,11,7,8,9,8,7,3,5,6,7,9,7,10,7,6\nJ,1,3,1,2,0,10,6,2,5,11,4,9,0,7,1,7\nF,5,8,7,6,3,4,12,3,6,14,8,4,1,10,1,7\nG,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,9\nG,4,10,6,8,7,8,6,5,2,6,6,10,7,8,5,10\nA,2,1,3,1,1,7,4,2,0,7,2,8,2,6,2,8\nQ,2,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8\nB,4,8,6,6,6,8,7,6,5,9,7,6,3,8,7,8\nP,1,0,1,0,0,5,10,7,1,9,6,5,1,9,2,8\nC,3,8,4,6,3,5,8,6,6,9,8,14,1,9,3,10\nE,2,4,4,3,2,6,8,2,8,11,7,9,2,8,4,8\nM,10,13,12,7,6,10,2,3,2,10,2,9,8,1,2,8\nL,2,6,2,4,1,0,2,5,6,0,0,7,0,8,0,8\nD,4,11,6,8,7,8,7,5,7,7,6,6,6,8,3,7\nB,2,6,3,4,3,6,7,7,5,7,6,7,2,8,6,9\nE,4,9,6,7,5,5,9,3,7,11,9,9,3,9,4,6\nQ,6,15,6,8,4,7,5,5,8,9,5,8,3,7,9,9\nM,7,6,9,5,9,7,8,5,4,6,5,8,13,7,5,8\nG,4,6,6,6,6,7,7,5,4,7,7,8,7,10,6,8\nU,3,6,5,4,4,8,8,8,5,6,7,9,3,8,4,6\nX,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nN,8,10,10,9,11,7,7,5,4,7,6,7,8,9,6,5\nW,5,6,5,4,4,3,10,2,3,10,9,8,6,11,2,6\nI,0,1,1,2,1,7,7,1,6,7,6,8,0,8,2,8\nF,5,5,5,7,2,1,15,5,3,12,9,4,0,8,3,6\nE,5,10,5,8,5,2,7,5,9,7,6,14,0,8,6,8\nY,4,10,6,7,1,8,10,2,2,6,13,8,1,11,0,8\nB,4,8,6,6,5,10,6,3,6,10,4,7,3,8,5,11\nD,4,8,5,6,4,6,7,8,7,6,5,4,3,8,4,8\nO,2,1,2,2,1,8,7,7,5,7,6,8,2,8,3,8\nH,3,3,5,2,2,7,8,3,5,10,7,8,3,8,3,7\nQ,4,5,5,5,2,8,5,8,8,6,4,8,3,8,4,8\nY,4,8,4,6,2,3,12,4,5,12,12,6,1,11,2,6\nB,3,8,4,6,5,8,7,8,5,7,6,6,3,8,7,10\nO,4,9,5,7,3,6,9,7,5,10,8,7,3,8,3,8\nP,3,4,5,3,3,7,10,6,3,11,4,3,1,10,3,8\nE,1,0,1,0,0,5,8,5,7,7,6,12,0,8,6,10\nO,3,7,4,5,2,8,8,8,6,6,7,9,3,8,4,8\nV,3,8,5,6,1,9,8,4,3,6,14,8,3,9,0,8\nQ,6,9,6,10,8,8,5,7,4,9,5,8,4,8,5,7\nK,4,8,5,6,7,8,7,3,4,6,6,8,6,8,7,5\nT,6,10,6,7,4,6,12,4,7,11,9,4,2,13,4,4\nC,5,9,6,7,3,4,8,6,9,12,9,12,1,9,3,7\nU,4,5,5,3,2,6,8,6,7,7,10,10,3,9,1,8\nN,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nC,3,2,4,3,2,6,8,7,7,9,8,12,2,10,3,9\nK,7,10,9,8,5,4,8,3,8,11,11,11,4,6,5,5\nH,6,11,8,8,8,6,6,8,4,6,4,8,4,6,8,11\nP,6,10,5,5,3,4,12,6,1,12,6,4,3,10,4,8\nP,1,0,2,0,0,5,11,6,1,9,6,5,1,9,3,8\nU,3,3,4,2,1,5,8,5,7,11,10,8,3,9,2,6\nC,5,9,5,7,4,4,8,5,6,11,9,14,2,9,3,7\nU,5,4,6,3,2,4,8,5,8,10,9,9,3,9,2,6\nE,2,3,2,1,1,7,7,5,7,7,6,8,2,8,5,10\nV,10,15,9,8,5,6,9,4,4,9,8,5,5,12,3,8\nJ,2,6,4,4,3,10,5,2,4,7,5,5,3,8,4,6\nY,5,9,5,6,3,3,10,2,7,11,11,6,1,11,2,5\nB,4,6,5,4,4,8,9,3,5,10,5,5,3,8,5,8\nF,4,9,5,6,2,1,12,5,5,12,10,8,0,8,2,6\nL,8,12,8,6,4,8,4,4,5,12,8,11,3,9,6,9\nS,4,9,6,7,3,9,7,4,8,12,4,7,2,8,5,9\nO,2,6,3,4,3,7,8,7,5,7,9,8,2,9,3,7\nU,6,7,7,5,3,4,10,6,7,12,11,8,3,9,2,7\nR,3,7,4,5,2,5,10,8,3,7,5,8,2,8,6,11\nT,3,6,4,4,3,7,8,7,6,7,7,8,3,10,5,9\nZ,1,3,2,1,1,7,7,5,8,6,6,8,1,8,6,8\nO,5,6,6,9,3,8,7,9,8,7,6,9,3,8,4,8\nG,7,11,9,9,12,8,7,4,3,6,5,9,8,7,9,14\nK,6,9,9,8,9,6,7,2,4,7,4,9,7,5,10,10\nY,8,10,6,15,5,4,9,3,1,9,10,5,4,10,7,5\nG,6,9,5,4,3,8,3,4,3,7,3,5,4,7,4,9\nH,4,5,7,4,4,7,8,3,6,10,6,8,3,8,3,8\nJ,3,9,4,7,1,12,2,10,4,14,5,13,1,6,0,8\nT,3,5,4,4,2,6,10,1,8,11,9,5,1,9,3,4\nW,5,9,7,7,3,8,8,4,2,7,8,8,8,9,0,8\nB,2,4,4,3,3,8,7,2,5,10,5,6,2,8,4,9\nU,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8\nF,1,3,3,2,1,5,11,3,4,13,7,5,1,9,1,7\nR,4,10,6,8,6,7,7,4,6,6,5,7,3,7,5,9\nU,3,9,4,7,2,7,5,15,5,7,12,8,3,9,0,8\nM,8,9,12,8,12,7,7,4,4,6,5,8,14,9,6,9\nP,3,4,5,2,2,7,11,4,3,12,4,2,1,9,2,9\nI,2,11,2,8,4,7,7,0,6,7,6,8,0,9,3,8\nJ,3,5,5,6,4,8,9,4,5,7,6,8,3,8,8,9\nP,6,9,8,7,7,6,6,8,4,7,6,8,4,9,8,11\nU,4,8,6,6,6,10,6,4,5,6,7,6,5,8,4,6\nI,1,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8\nP,4,7,6,5,3,7,12,4,5,14,5,1,0,10,3,8\nH,3,3,5,2,3,7,7,2,6,10,6,8,3,8,3,7\nV,4,2,6,4,2,7,13,3,4,8,12,8,3,10,2,8\nN,6,9,9,7,5,7,9,2,5,10,6,6,6,9,1,8\nG,6,11,7,8,4,7,6,7,7,11,6,13,3,9,5,8\nW,4,2,6,4,4,9,11,2,2,5,9,7,9,11,2,7\nS,5,11,6,8,6,8,8,5,8,5,5,8,0,8,9,8\nJ,1,3,3,2,1,9,4,4,4,13,7,12,1,7,0,7\nB,3,7,4,5,3,6,6,9,7,6,6,7,2,8,9,9\nV,3,10,5,8,2,8,8,4,3,6,14,8,3,9,0,8\nE,4,9,4,6,4,3,6,5,9,6,7,13,0,8,7,9\nQ,5,6,5,8,5,8,4,6,5,10,5,10,4,7,6,7\nG,6,10,8,7,5,7,7,7,6,5,7,9,3,8,4,8\nX,3,6,5,5,5,9,6,2,6,8,6,7,3,10,8,6\nD,3,4,4,3,3,7,7,6,7,7,6,5,2,8,3,7\nG,5,9,6,6,7,9,6,5,2,6,6,10,6,7,5,11\nX,3,5,6,3,2,5,8,2,9,11,10,9,2,9,3,6\nF,6,10,9,7,5,5,12,4,5,13,8,4,2,10,2,6\nW,3,7,5,5,3,11,8,5,1,6,9,8,7,10,0,8\nQ,4,9,5,8,5,8,8,7,4,5,9,9,2,9,4,9\nZ,4,8,5,6,5,7,7,5,10,7,6,8,1,8,7,7\nW,6,9,8,6,7,7,7,6,3,8,8,7,7,9,5,10\nF,2,5,3,4,2,6,9,4,5,10,9,6,2,10,3,6\nY,6,9,6,4,3,7,7,4,4,9,8,6,4,10,4,4\nI,1,9,0,7,1,7,7,5,3,7,6,8,0,8,0,8\nU,2,3,3,2,1,5,8,4,5,10,8,9,3,10,1,7\nA,4,11,7,8,6,7,5,1,4,5,2,6,5,6,6,6\nU,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8\nD,1,3,2,2,1,9,6,4,4,10,3,5,2,8,2,8\nS,6,12,6,6,3,10,4,4,3,12,6,9,3,10,2,8\nU,1,3,2,1,1,9,8,6,5,5,9,7,3,10,0,8\nD,3,9,5,6,4,6,7,9,6,6,6,5,3,8,3,8\nI,2,6,3,4,1,7,7,1,8,14,6,8,0,8,1,8\nO,5,9,4,4,3,6,8,6,4,9,8,9,4,9,4,8\nV,3,7,5,5,1,8,8,4,2,6,14,8,3,10,0,8\nP,5,9,7,7,6,9,8,4,5,11,4,4,2,9,4,8\nT,2,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8\nS,3,7,4,5,3,8,8,7,6,7,6,8,2,8,9,8\nG,3,6,4,4,2,8,6,7,7,6,6,9,1,7,6,11\nO,4,11,5,8,3,7,7,9,7,7,6,8,3,8,4,8\nX,5,9,8,6,4,10,6,2,8,10,2,7,3,8,3,9\nG,5,9,7,7,7,7,9,6,3,5,5,10,4,8,7,8\nU,6,9,6,7,4,5,7,7,8,8,6,9,3,9,3,4\nF,4,9,5,6,2,1,11,5,7,12,11,9,0,8,2,6\nQ,5,8,6,10,6,9,5,7,4,9,5,11,4,8,6,7\nM,1,0,2,0,1,7,6,9,0,7,8,8,6,6,0,8\nB,7,13,7,7,6,10,6,4,5,9,5,8,6,8,8,9\nC,5,8,6,6,2,3,10,5,8,12,11,10,1,8,2,6\nK,7,9,10,8,10,9,7,3,4,9,2,8,8,4,8,12\nR,1,1,2,1,1,6,9,8,4,7,5,8,2,7,4,11\nA,6,11,8,9,8,7,7,8,4,8,5,8,4,8,10,1\nQ,6,7,6,9,7,8,5,7,4,8,6,7,6,8,6,9\nK,4,10,5,7,2,3,8,8,2,7,5,11,3,8,3,10\nI,1,6,1,4,1,7,7,0,7,7,6,9,0,8,3,8\nV,5,6,5,4,3,4,12,1,2,8,10,7,3,11,1,7\nF,2,1,3,2,1,5,11,3,5,11,9,5,1,10,3,6\nO,4,8,5,6,4,7,8,8,5,10,7,8,3,8,3,8\nA,2,7,4,5,3,12,3,2,2,9,2,9,3,7,3,9\nN,6,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nC,2,1,2,2,1,6,8,7,7,9,7,12,1,10,4,10\nW,1,0,1,0,0,7,8,3,0,7,8,8,3,9,0,8\nM,5,7,6,5,7,8,8,6,5,7,6,8,7,9,7,6\nQ,5,9,6,11,7,7,9,4,3,8,10,10,4,10,8,8\nC,3,8,4,6,3,5,7,6,7,7,5,11,1,8,4,10\nA,7,9,10,8,8,6,8,2,4,7,7,9,8,6,4,8\nY,3,5,5,8,1,6,10,2,2,7,13,8,2,11,0,8\nI,1,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8\nB,5,9,7,8,9,7,7,5,4,7,6,8,6,9,8,7\nG,4,7,4,5,2,6,7,6,6,10,8,9,2,9,4,9\nL,4,9,4,5,2,9,3,4,5,12,4,13,2,6,5,9\nI,2,8,3,6,2,8,7,0,7,13,6,8,0,8,1,8\nY,8,10,7,5,4,8,6,4,6,9,6,5,4,10,4,6\nK,3,6,5,4,5,8,6,4,4,7,6,8,6,7,6,8\nX,5,11,9,9,7,6,8,2,6,7,6,9,6,5,8,7\nV,8,10,7,8,5,3,12,2,3,9,11,8,7,11,2,6\nS,8,11,9,8,6,7,8,3,6,10,5,7,4,6,6,8\nK,3,7,5,5,5,5,6,3,4,6,5,9,5,7,8,7\nA,3,8,5,5,2,7,5,3,1,7,1,8,2,7,2,7\nS,9,13,8,7,4,9,4,4,3,13,8,10,4,9,3,9\nD,2,4,4,3,2,8,7,5,6,9,5,5,2,8,3,7\nQ,6,9,8,7,6,8,4,9,5,7,4,8,3,8,4,8\nN,10,13,9,7,4,4,9,4,7,3,3,11,5,8,2,7\nZ,5,7,7,9,6,10,5,4,4,8,2,6,2,7,7,8\nA,4,9,7,7,4,8,2,2,3,6,1,7,2,7,3,7\nB,4,6,5,4,4,8,7,5,6,9,6,6,2,8,6,9\nU,5,8,5,6,2,7,4,14,5,7,14,8,3,9,0,8\nS,3,5,6,3,2,6,8,2,7,10,7,8,1,9,5,6\nP,3,1,4,2,2,5,10,4,4,10,8,4,1,9,4,7\nC,3,2,4,4,2,6,8,7,7,8,7,13,1,9,4,10\nQ,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8\nQ,5,6,7,9,8,8,11,4,3,5,8,11,5,13,7,11\nE,3,8,5,6,4,4,8,5,8,11,10,9,2,9,4,5\nD,2,4,4,3,2,9,6,4,6,10,4,5,2,8,3,8\nG,4,2,5,4,3,7,6,6,6,6,6,9,2,8,4,8\nV,2,1,4,2,1,6,13,3,2,8,11,8,2,10,1,8\nA,4,10,7,7,2,7,5,3,1,7,1,8,3,7,2,8\nA,2,8,4,6,2,12,3,3,2,10,1,9,2,6,2,8\nR,3,2,4,4,3,7,7,5,5,6,5,7,3,6,6,9\nS,3,5,4,4,2,8,7,3,7,10,6,8,1,9,5,8\nQ,5,9,5,4,3,8,5,4,8,10,4,9,3,7,8,10\nZ,6,9,9,7,6,8,6,2,9,11,5,9,3,5,7,8\nC,7,10,8,8,5,3,7,5,7,11,10,14,2,9,3,7\nF,6,12,5,6,3,4,12,3,5,12,7,4,2,7,5,5\nB,4,10,5,8,7,7,8,9,6,7,6,7,2,7,8,10\nR,3,7,3,5,3,5,9,7,3,7,5,8,2,7,4,11\nW,8,10,9,8,6,1,10,3,3,11,11,9,7,10,1,7\nK,6,10,8,7,6,6,6,1,7,10,6,10,4,8,4,8\nP,2,4,4,3,2,8,8,3,5,12,4,4,1,10,2,8\nO,3,6,4,4,3,7,8,7,4,9,7,8,3,8,2,8\nJ,5,8,4,6,3,8,7,2,4,11,6,8,2,10,6,11\nA,6,9,5,4,2,11,3,3,1,9,4,11,4,5,4,9\nP,3,7,5,5,3,9,8,2,5,12,4,5,1,10,2,9\nN,10,13,12,8,5,4,9,3,4,13,10,9,6,8,0,7\nM,3,2,5,3,3,9,6,6,4,6,7,6,7,5,2,6\nP,3,7,5,11,8,8,9,5,0,8,6,6,5,11,6,9\nZ,3,7,5,5,3,6,8,2,9,11,7,7,1,9,6,7\nS,4,6,5,4,3,9,8,3,7,10,4,7,2,7,4,9\nX,1,0,1,0,0,8,7,3,4,7,6,8,2,8,4,8\nP,4,8,4,5,2,5,10,9,5,9,6,5,2,10,4,8\nC,6,11,6,8,4,5,8,7,8,12,9,12,2,10,4,6\nP,5,8,7,6,5,9,8,2,6,13,5,5,2,9,3,9\nD,3,7,3,5,2,5,6,10,7,5,4,5,3,8,3,8\nZ,2,4,5,3,2,7,8,2,9,12,7,7,1,8,5,6\nY,7,6,6,9,4,6,8,4,2,7,10,5,4,10,5,6\nL,1,3,3,1,1,6,4,1,7,7,2,9,0,7,2,8\nK,4,5,5,4,3,5,7,4,8,7,6,10,6,8,5,9\nT,6,9,6,6,4,7,11,3,9,12,9,4,2,12,3,4\nR,2,1,2,1,1,6,10,7,2,7,5,8,2,7,4,10\nT,4,11,5,8,2,5,15,1,6,9,11,7,0,8,0,8\nA,8,14,8,8,6,9,4,5,4,10,6,12,9,2,7,11\nH,5,10,5,7,3,7,5,15,1,7,8,8,3,8,0,8\nZ,5,7,6,5,4,8,5,2,9,11,4,10,2,8,6,10\nG,4,4,6,6,2,8,5,8,9,6,5,10,2,8,5,10\nU,3,1,4,1,1,7,9,5,6,7,10,9,3,10,1,8\nB,4,8,6,6,4,11,6,3,7,11,3,7,2,8,5,11\nL,7,13,6,7,3,7,4,3,6,11,4,13,2,7,6,8\nV,7,9,9,8,10,7,6,5,4,7,6,7,7,10,8,11\nB,2,5,4,3,3,8,7,3,6,9,5,7,2,8,5,10\nQ,1,1,2,2,2,8,7,5,2,5,6,9,2,9,4,10\nG,6,11,7,8,5,6,7,7,7,10,8,10,2,8,5,9\nD,4,8,6,6,5,7,8,8,6,9,7,4,4,9,3,7\nK,7,10,7,5,3,7,6,3,6,9,8,9,6,11,3,7\nY,4,5,5,7,7,9,6,6,3,7,7,8,7,8,6,4\nW,4,8,6,6,3,6,8,4,1,7,8,8,8,9,0,8\nY,4,4,5,6,5,9,9,5,3,7,7,7,5,10,6,4\nH,4,4,4,5,2,7,6,15,1,7,8,8,3,8,0,8\nS,7,10,5,5,2,9,2,3,4,9,2,9,3,6,5,10\nQ,8,15,8,8,5,11,4,5,7,12,3,10,3,7,8,12\nD,6,5,6,8,3,5,7,10,10,7,6,5,3,8,4,8\nG,3,8,5,6,4,6,6,6,6,7,5,11,2,10,4,9\nF,4,11,4,8,3,1,11,4,5,11,11,9,0,8,2,6\nU,2,3,3,2,1,5,8,5,6,10,9,8,3,9,2,6\nS,2,5,2,4,2,8,8,7,5,8,5,7,2,8,8,8\nA,3,9,5,7,3,11,3,2,2,9,2,9,3,5,3,8\nC,2,2,3,3,2,6,8,7,8,8,7,13,1,9,4,10\nI,3,9,5,7,3,7,7,0,7,13,6,8,0,8,1,8\nI,1,1,1,2,1,7,7,1,6,7,6,8,0,8,2,8\nH,3,3,5,2,2,7,8,3,6,10,7,7,3,8,3,8\nY,3,5,5,8,6,7,10,3,3,7,8,9,4,10,9,5\nW,4,8,7,6,9,9,8,5,2,7,7,7,12,10,4,6\nP,3,7,5,11,9,9,8,4,1,8,6,7,7,9,5,6\nF,6,11,6,8,2,1,12,5,6,12,11,8,0,8,2,5\nJ,0,0,1,1,0,12,4,6,3,12,5,11,0,7,0,8\nU,2,4,3,3,2,5,8,5,6,10,9,9,3,9,2,6\nH,8,11,12,9,10,6,7,3,7,10,8,9,5,8,5,7\nR,3,5,4,4,3,7,7,4,5,6,5,6,3,7,4,8\nC,3,8,4,6,3,5,7,6,8,5,6,12,1,7,4,10\nR,4,9,6,7,6,6,7,5,6,7,6,6,3,7,5,9\nB,4,8,6,6,8,8,8,4,3,6,7,7,6,11,8,9\nM,6,9,10,7,11,10,5,3,2,9,4,8,11,6,4,7\nO,4,8,5,6,4,7,7,7,6,7,5,8,2,8,3,8\nC,6,11,7,8,5,5,8,7,6,8,8,15,4,9,6,6\nE,3,4,5,3,2,6,8,2,8,11,8,9,2,8,4,7\nZ,1,4,2,3,1,8,7,5,8,6,6,8,1,8,7,7\nM,6,11,8,8,8,8,6,5,5,6,7,8,8,6,2,7\nE,3,7,3,5,2,3,7,6,11,7,7,15,0,8,7,7\nU,4,10,5,8,4,8,6,12,4,7,13,7,3,8,0,9\nR,3,8,3,6,4,5,9,7,3,7,5,8,2,7,5,11\nD,4,11,4,8,3,5,7,11,8,7,6,5,3,8,4,8\nF,5,7,6,5,6,7,8,6,4,8,6,8,3,10,8,11\nC,7,11,8,8,5,5,7,5,7,12,9,13,4,11,5,5\nL,5,10,6,8,4,7,4,2,8,7,1,8,1,6,3,7\nF,5,11,6,8,7,6,9,6,4,8,6,8,3,10,8,11\nS,2,4,4,3,2,9,6,3,7,10,4,8,1,9,5,9\nC,6,9,8,8,8,5,7,3,5,7,6,11,4,11,8,8\nU,4,6,4,4,3,7,5,12,4,7,12,8,3,9,0,8\nP,6,13,6,7,4,8,9,4,3,12,4,4,4,11,5,6\nH,3,6,4,4,2,7,7,14,0,7,6,8,3,8,0,8\nH,3,5,6,3,3,7,7,3,6,10,6,8,3,8,3,7\nH,4,5,5,4,4,8,7,6,6,7,6,8,3,8,3,7\nV,4,7,4,5,2,2,11,3,3,11,11,8,2,11,0,8\nJ,4,11,5,8,4,8,5,3,7,12,4,10,1,6,2,6\nZ,2,1,3,3,2,7,7,5,9,6,6,8,1,8,7,8\nM,4,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8\nI,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nS,7,11,7,6,3,9,5,4,4,13,6,8,2,9,3,8\nJ,1,4,2,3,1,10,7,1,7,11,4,7,0,7,1,7\nW,10,15,10,8,5,4,9,2,2,8,10,8,9,12,1,6\nN,6,9,9,7,5,9,8,2,5,10,2,4,7,10,2,7\nI,1,1,1,3,1,7,7,1,6,7,6,8,0,8,2,8\nM,5,6,7,4,6,7,6,5,5,8,6,11,10,5,2,9\nE,3,8,4,6,3,7,8,6,10,6,4,9,3,8,6,8\nG,4,7,5,5,3,7,7,6,7,10,7,11,2,9,4,9\nP,2,3,3,1,1,5,9,4,4,9,8,5,1,9,3,7\nR,5,11,7,8,6,10,7,3,5,10,3,7,3,7,4,10\nZ,2,3,3,2,2,7,7,5,9,6,6,8,1,8,7,8\nO,2,4,3,3,2,7,7,6,4,9,6,8,2,8,2,8\nW,4,8,6,6,6,5,11,2,2,7,8,8,7,11,1,8\nA,2,4,3,3,1,11,3,3,2,11,2,9,2,6,2,9\nJ,0,0,1,0,0,12,4,5,3,12,5,11,0,7,0,8\nT,3,5,4,4,3,9,12,4,5,5,11,9,2,12,1,8\nX,2,1,3,3,2,7,7,3,8,6,6,8,2,8,6,8\nJ,2,7,3,5,1,14,2,6,5,14,2,11,0,7,0,8\nD,2,2,3,3,2,7,7,6,6,7,6,5,2,8,3,7\nJ,2,9,3,6,2,10,6,1,7,11,3,6,0,7,1,7\nX,3,8,5,6,3,8,7,4,8,6,6,8,3,8,6,8\nJ,1,7,2,5,2,10,6,1,5,11,4,8,0,6,1,7\nW,4,4,6,3,3,8,11,3,2,6,9,8,8,12,1,7\nA,3,9,5,7,3,11,4,2,2,8,2,9,3,5,3,8\nR,3,5,5,4,3,9,7,3,5,10,4,6,3,7,4,10\nV,6,11,6,8,4,3,11,1,2,9,10,8,4,11,1,7\nT,8,11,7,6,3,7,9,4,8,13,6,8,2,7,4,6\nV,5,8,5,6,2,3,11,3,4,10,11,7,2,10,1,8\nB,1,0,1,0,1,7,7,6,4,6,6,7,1,8,5,9\nG,2,1,3,2,2,7,7,5,5,7,6,10,3,7,4,9\nB,4,9,6,6,5,9,6,4,6,10,5,7,3,7,6,9\nN,5,9,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nK,2,3,3,2,2,6,7,4,7,6,6,10,3,8,4,9\nU,3,6,4,4,1,7,4,13,5,7,13,8,3,9,0,8\nL,3,7,4,5,3,5,4,5,7,2,2,5,1,6,1,5\nO,2,4,3,3,2,8,7,6,3,9,6,8,2,8,2,8\nW,5,5,7,8,4,9,8,4,2,7,8,8,9,9,0,8\nH,4,11,6,8,7,7,7,5,6,7,6,9,6,8,3,8\nB,8,12,6,6,4,9,5,4,5,10,5,9,5,7,7,10\nV,5,8,5,6,3,3,12,3,3,10,11,8,2,10,1,8\nD,4,6,6,4,5,9,6,4,5,10,3,6,3,7,3,8\nQ,3,5,4,8,7,8,7,5,0,6,6,9,7,10,6,12\nG,7,12,6,7,3,10,4,4,4,10,3,6,4,7,5,9\nD,4,7,5,5,3,6,7,10,10,6,5,6,3,8,4,8\nG,6,10,8,8,6,6,6,6,6,5,6,11,2,9,4,8\nL,1,4,2,3,1,6,4,2,7,7,2,9,0,7,2,8\nD,6,9,6,5,3,8,6,5,5,10,4,6,4,7,6,10\nH,1,0,2,0,0,7,7,11,1,7,6,8,3,8,0,8\nV,3,3,5,4,1,8,8,4,2,6,14,8,3,10,0,8\nT,3,9,5,7,3,7,12,3,7,7,12,8,1,12,1,8\nW,4,8,7,6,4,7,10,2,3,7,9,8,8,11,1,8\nI,1,5,3,4,1,7,7,0,7,13,6,8,0,8,1,8\nM,3,6,4,4,4,9,6,6,4,6,7,5,7,5,2,6\nE,3,8,4,6,5,6,7,3,4,6,7,10,4,11,8,7\nU,5,5,6,7,2,7,4,14,6,7,15,8,3,9,0,8\nA,3,5,5,3,2,10,2,2,2,10,2,9,2,6,2,7\nM,11,15,11,8,6,9,11,6,4,4,6,9,10,12,2,7\nM,3,4,5,3,3,8,6,6,4,7,7,8,7,5,2,7\nA,3,9,5,7,3,10,2,2,2,8,1,8,2,7,3,8\nL,4,8,6,7,6,7,6,5,5,7,7,7,3,9,8,11\nR,4,8,5,6,4,7,9,6,5,7,6,8,3,7,6,9\nU,6,11,7,8,6,8,5,13,5,6,12,8,3,9,0,8\nE,0,0,1,0,0,5,7,5,6,7,6,12,0,8,5,11\nE,4,7,6,6,5,5,9,3,5,8,7,9,4,10,7,7\nP,7,15,7,8,5,8,9,4,4,12,5,4,4,11,6,6\nU,3,7,4,5,3,8,5,12,4,7,11,8,3,9,0,8\nN,5,10,7,8,4,3,9,4,4,10,11,10,5,7,1,8\nK,3,7,5,5,5,8,7,4,4,7,6,7,4,6,8,13\nX,5,5,6,8,2,7,7,5,4,7,6,8,3,8,4,8\nO,1,0,1,0,0,7,7,6,3,7,6,8,2,8,3,8\nD,6,5,6,8,3,5,7,10,9,7,6,5,3,8,4,8\nJ,3,7,4,5,1,8,6,3,7,15,5,10,0,7,1,7\nE,5,10,7,8,4,5,9,3,9,11,9,9,2,9,5,5\nD,3,6,3,4,2,6,8,10,8,7,7,6,3,8,4,8\nQ,3,3,4,5,3,8,8,7,2,5,7,9,2,9,5,9\nD,9,15,8,8,6,8,6,3,7,10,5,6,6,7,9,6\nO,3,4,4,3,2,7,7,7,4,9,7,9,2,8,3,8\nZ,8,8,6,11,5,7,7,4,3,11,6,8,3,9,11,6\nR,5,6,6,8,4,5,12,8,3,7,3,9,3,7,7,11\nR,4,7,5,5,4,11,7,2,5,11,3,7,3,7,3,11\nE,5,10,5,8,3,3,8,6,12,7,5,15,0,8,7,7\nY,7,10,7,7,3,2,11,5,6,13,13,7,2,11,2,6\nI,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nW,4,7,6,5,3,4,8,5,1,7,9,8,8,10,0,8\nK,4,10,6,7,6,6,7,3,7,6,5,8,3,8,5,9\nF,2,6,3,4,3,6,10,3,5,10,9,5,2,10,3,6\nB,5,5,5,7,4,6,7,9,6,7,6,6,2,8,9,10\nD,3,7,4,5,3,6,7,8,6,7,6,4,3,8,3,7\nQ,5,8,6,9,6,8,7,7,4,9,8,9,4,9,7,9\nU,8,10,9,8,4,3,8,6,8,10,10,9,3,9,2,5\nN,7,10,10,7,4,6,8,3,5,10,8,8,6,7,1,7\nC,2,3,2,2,1,4,9,5,6,12,9,10,1,9,2,7\nA,5,9,9,7,6,5,5,2,4,3,2,7,6,7,7,2\nB,2,2,3,3,2,8,7,5,5,6,5,5,2,8,6,9\nC,9,14,6,8,3,7,8,7,7,11,6,7,2,9,6,8\nT,3,5,5,6,1,8,15,1,5,7,11,8,0,8,0,8\nY,2,4,4,5,1,8,10,2,2,6,12,8,1,11,0,8\nQ,4,6,6,5,6,8,4,4,4,7,4,10,5,6,7,7\nG,6,11,5,6,3,11,4,3,4,10,2,7,4,7,4,9\nG,5,11,7,8,5,6,6,7,7,6,5,10,2,9,4,8\nA,3,8,6,5,2,6,5,3,0,6,1,8,2,7,2,7\nL,4,10,6,8,3,7,4,0,9,9,2,10,0,7,3,8\nL,3,6,4,5,4,8,6,5,5,7,7,9,3,8,7,10\nY,9,12,8,7,5,6,6,5,4,9,9,6,5,11,4,6\nD,1,1,2,1,1,6,7,8,6,6,6,6,2,8,3,8\nD,5,10,8,8,5,9,8,5,8,10,5,4,4,7,5,9\nA,3,9,5,7,4,9,3,2,2,7,2,8,2,6,3,6\nP,7,15,7,8,5,6,11,3,5,13,6,3,3,9,5,5\nD,4,10,5,7,3,5,7,10,9,6,6,5,3,8,4,8\nO,4,8,5,6,4,8,6,8,4,7,4,8,3,8,3,8\nU,5,6,5,4,4,5,8,5,6,9,7,10,3,9,3,6\nS,4,7,5,5,3,5,9,3,6,10,7,6,3,7,4,5\nE,7,10,9,7,7,5,9,2,8,11,8,9,3,8,5,6\nU,5,8,6,6,5,8,6,8,5,7,6,9,3,8,4,6\nC,5,9,6,7,4,4,8,7,8,7,8,14,2,8,4,10\nP,5,11,6,8,3,5,9,11,6,8,6,5,2,10,4,8\nU,2,0,2,1,0,7,5,11,4,7,13,8,3,10,0,8\nB,3,6,4,4,4,9,6,3,5,10,4,7,2,8,4,10\nD,6,9,8,6,5,10,6,4,7,10,4,6,5,9,5,8\nY,4,6,6,8,1,7,11,2,3,7,12,8,1,11,0,8\nA,3,7,5,5,3,8,2,2,2,7,1,8,2,7,3,7\nG,7,12,6,6,4,7,6,4,3,9,6,8,4,9,9,8\nO,6,11,7,8,6,7,8,8,7,7,6,6,4,7,5,10\nH,4,3,5,4,4,6,7,6,6,7,6,10,3,8,3,8\nY,3,6,5,8,1,10,11,2,4,4,12,8,0,10,0,8\nK,3,2,4,4,3,5,7,4,7,7,6,11,3,8,5,9\nB,5,10,5,7,7,6,7,8,5,7,6,7,2,8,7,10\nM,2,3,4,1,2,5,7,4,3,10,10,10,4,7,1,6\nC,3,7,5,6,5,5,8,3,5,7,7,11,3,9,6,10\nQ,4,7,6,6,5,6,4,4,5,6,4,7,3,5,6,6\nS,6,9,8,8,9,8,8,4,5,7,7,7,5,10,10,10\nI,4,10,6,8,4,5,6,2,6,7,7,12,0,9,4,9\nR,7,11,9,8,6,9,8,4,7,9,3,7,3,6,5,11\nD,8,11,8,6,4,9,6,5,6,12,4,8,6,6,6,10\nX,5,9,8,6,4,5,8,2,8,11,11,9,3,9,4,6\nQ,3,5,4,5,3,8,7,7,4,6,6,9,2,8,4,9\nL,3,5,4,4,2,4,4,4,8,2,1,6,0,7,1,6\nR,3,7,5,5,5,8,7,7,5,8,6,7,3,8,5,9\nH,9,13,9,7,5,10,6,4,5,9,5,5,7,10,5,9\nK,4,5,5,7,2,4,7,9,2,7,5,11,4,8,2,11\nI,1,9,2,6,2,7,7,0,6,7,6,8,0,8,2,8\nA,2,6,5,4,3,5,5,2,2,4,2,7,2,6,3,3\nD,4,6,6,4,4,7,7,4,6,7,6,8,6,8,3,7\nG,3,7,4,5,2,8,7,8,7,6,6,9,2,7,5,11\nY,3,9,5,6,1,8,10,3,2,6,13,8,2,11,0,8\nA,3,9,6,7,4,13,3,3,3,11,1,9,2,6,2,9\nU,7,10,6,5,3,6,6,4,5,4,8,7,5,8,2,6\nI,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nQ,2,1,2,1,1,8,7,6,4,6,6,8,2,8,3,8\nJ,1,3,2,2,0,9,6,3,6,14,6,10,0,7,0,8\nN,6,9,9,8,8,7,9,4,4,7,4,7,9,7,6,6\nA,2,3,3,2,1,10,2,2,1,8,2,9,1,6,1,8\nO,4,5,5,8,2,8,7,9,7,7,5,8,3,8,4,8\nK,4,7,6,5,3,6,6,2,7,10,7,10,3,8,4,8\nM,4,8,5,6,5,7,5,10,0,7,8,8,8,5,2,10\nL,5,11,5,8,3,0,1,5,6,0,0,7,0,8,0,8\nE,5,7,6,6,6,5,7,4,3,7,6,8,5,11,9,8\nG,4,7,5,5,5,9,5,5,2,7,6,10,5,10,3,9\nH,3,5,5,4,3,9,6,3,6,10,4,8,3,8,3,9\nS,4,9,5,6,6,9,7,5,3,8,6,8,4,8,10,9\nT,6,7,6,5,3,7,11,3,7,11,8,4,4,10,4,4\nB,3,6,4,4,3,6,6,8,6,6,6,7,2,8,9,10\nA,2,7,4,5,3,11,3,2,2,9,2,9,2,6,3,8\nW,5,8,7,6,3,5,8,5,1,7,8,8,8,9,0,8\nM,6,9,9,6,10,10,3,3,2,9,4,8,11,5,3,7\nT,5,10,6,7,5,8,11,3,7,6,11,8,2,12,1,8\nO,4,8,6,6,2,6,7,8,8,7,6,7,3,8,4,8\nN,6,9,5,4,2,9,11,5,4,4,6,10,5,11,3,7\nM,4,5,7,3,4,7,6,3,4,9,7,8,7,5,2,8\nU,7,9,7,7,5,3,8,5,7,10,9,9,3,9,2,6\nH,3,5,4,3,3,8,7,6,6,7,6,6,3,8,3,6\nN,6,10,9,7,5,6,10,5,4,8,7,9,7,7,2,8\nJ,7,12,5,9,5,8,9,2,4,11,6,7,2,9,8,9\nI,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8\nO,3,8,4,6,2,7,8,9,7,7,7,8,3,8,4,8\nB,3,8,5,6,5,9,7,4,5,10,5,7,2,8,5,9\nQ,4,6,6,9,9,9,7,6,1,7,6,9,10,9,5,7\nK,5,11,6,8,2,3,7,8,2,7,6,11,4,8,2,11\nZ,7,9,7,5,4,7,7,2,9,12,6,8,3,7,6,6\nR,2,3,3,2,2,8,8,3,4,9,5,7,2,7,3,9\nB,3,4,5,3,3,7,8,3,5,10,6,6,2,8,5,9\nV,7,9,7,7,4,3,12,2,3,9,11,8,4,12,1,7\nR,6,12,6,7,5,10,6,3,5,10,4,8,6,8,6,10\nZ,2,1,2,2,1,7,7,3,11,9,6,8,0,8,6,8\nF,4,9,6,6,3,6,11,3,6,13,7,4,1,10,2,7\nS,4,8,5,6,2,7,7,6,9,5,6,8,0,8,9,7\nT,2,7,3,4,1,10,14,1,6,4,11,9,0,8,0,8\nB,3,6,5,4,6,8,6,5,4,7,7,8,7,9,7,8\nP,5,6,7,4,3,9,8,4,6,12,4,5,2,9,4,9\nY,7,10,7,7,3,4,10,2,8,11,11,6,1,10,3,3\nA,5,6,7,5,5,8,8,3,4,7,8,8,5,9,3,6\nV,5,6,5,4,2,3,12,5,4,11,11,7,3,10,1,8\nP,3,9,5,7,5,6,9,4,5,9,8,4,1,10,4,7\nC,7,11,5,6,2,7,7,7,7,12,6,9,2,9,5,9\nH,4,8,6,6,7,8,7,4,3,6,6,7,7,8,7,6\nA,3,4,5,3,2,10,2,2,2,8,2,9,4,5,2,9\nA,3,6,6,4,4,8,5,2,4,7,2,6,2,6,4,6\nL,5,10,5,5,3,8,5,3,5,12,7,11,2,9,6,9\nJ,3,8,4,6,2,8,7,1,8,12,5,8,0,6,2,6\nR,8,12,6,6,4,11,5,5,5,11,2,8,6,6,6,10\nT,2,6,3,4,1,7,13,0,5,7,10,8,0,8,0,8\nM,5,10,6,8,4,7,7,12,2,8,9,8,8,6,0,8\nW,7,7,7,5,6,4,11,2,2,10,9,7,7,11,2,6\nY,5,7,7,10,10,8,8,4,2,7,8,9,4,11,9,8\nO,3,10,5,8,4,8,8,8,6,6,6,11,3,8,4,7\nH,8,10,12,8,7,7,8,3,7,10,5,7,3,8,3,8\nR,4,9,5,7,5,7,7,5,6,6,5,7,3,7,5,8\nB,6,11,7,8,8,8,7,5,5,7,5,7,4,8,6,8\nN,6,8,9,6,5,4,10,3,4,10,10,8,5,8,1,8\nS,2,4,2,2,1,8,8,6,5,7,5,7,2,8,8,8\nJ,3,10,5,8,5,8,7,2,5,11,5,9,5,6,3,4\nL,2,7,3,5,1,0,2,3,5,1,0,8,0,8,0,8\nB,9,14,7,8,5,6,8,5,7,10,6,8,6,6,7,9\nA,3,9,5,7,3,11,3,3,4,11,2,9,2,6,3,8\nD,3,7,5,5,4,7,7,7,4,6,5,6,3,8,3,7\nJ,7,10,5,8,4,7,10,3,2,12,5,5,2,9,8,9\nX,3,5,6,4,3,6,7,1,9,11,9,9,3,8,3,7\nU,2,0,3,1,1,7,5,11,5,7,14,8,3,10,0,8\nQ,1,0,2,1,0,8,7,7,3,6,6,9,2,8,3,8\nN,2,1,3,2,2,7,8,5,4,7,6,6,4,8,1,6\nY,4,7,5,5,5,9,6,5,4,7,8,7,3,9,8,4\nY,7,6,6,9,3,6,12,2,3,10,10,6,4,11,5,7\nM,5,11,8,8,8,9,7,5,5,6,7,5,10,8,3,5\nD,4,8,4,6,2,5,7,10,9,6,6,5,3,8,4,8\nH,4,7,6,9,5,10,11,3,2,7,8,8,3,11,7,10\nC,7,10,8,8,5,5,8,6,7,12,9,12,3,11,4,6\nQ,1,2,2,2,1,8,8,4,2,8,7,9,2,9,3,9\nA,2,3,4,4,2,8,3,1,2,7,2,8,2,6,2,7\nT,2,7,4,5,2,8,12,4,6,6,11,8,2,12,1,8\nJ,6,8,8,9,7,8,8,4,7,6,7,7,4,11,11,9\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nF,9,13,8,7,6,7,9,3,5,11,6,6,5,8,8,7\nP,6,9,8,7,7,7,7,5,5,7,6,9,6,8,7,10\nU,4,6,5,4,3,6,7,5,6,8,6,8,3,9,3,6\nX,6,11,8,9,7,8,6,3,5,6,7,8,3,9,9,9\nP,4,5,4,3,3,5,10,5,4,10,8,4,1,10,4,7\nG,3,8,4,6,3,6,6,6,6,7,5,11,3,10,4,8\nW,3,5,5,3,3,5,11,2,2,8,9,9,6,10,0,8\nH,2,3,4,2,2,7,7,2,5,10,5,8,3,7,2,8\nZ,3,8,4,6,2,7,7,3,13,10,6,8,0,8,8,8\nS,2,1,2,2,1,8,8,6,5,8,6,7,2,8,8,8\nS,7,12,7,7,4,10,3,4,3,13,6,10,3,9,3,9\nM,5,10,5,8,4,7,7,12,2,7,9,8,8,6,0,8\nG,4,8,5,6,3,5,6,5,6,6,6,9,2,8,4,7\nR,6,11,6,8,4,5,11,9,3,7,4,8,3,8,6,11\nR,9,11,7,6,4,10,6,6,5,11,2,8,6,6,5,10\nI,1,6,0,4,1,7,7,5,3,7,6,8,0,8,0,8\nU,6,9,8,8,7,7,7,5,4,5,7,8,7,7,2,6\nO,5,5,6,7,3,7,7,9,8,7,5,7,3,8,4,8\nB,9,12,8,7,7,7,8,4,5,9,6,7,8,5,9,7\nN,3,7,5,5,3,4,10,3,3,10,10,9,5,8,1,7\nJ,4,9,6,7,3,6,9,2,6,14,6,7,1,7,1,7\nA,3,10,6,7,2,7,3,3,3,7,1,8,3,6,3,7\nP,3,2,4,3,2,5,10,4,5,10,8,3,1,10,4,7\nY,2,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nY,5,5,7,8,8,8,8,7,3,7,7,8,6,10,6,4\nN,6,7,8,5,4,9,7,3,5,10,2,5,5,9,1,7\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nF,5,10,5,6,2,6,10,3,5,13,6,5,2,8,5,6\nB,4,6,7,6,6,7,10,6,6,8,7,8,5,9,7,9\nH,6,11,8,8,7,10,7,3,6,10,3,7,6,8,5,9\nQ,5,9,5,4,3,12,3,3,4,10,3,8,3,9,5,12\nL,4,10,6,8,3,3,4,3,10,2,0,7,0,7,1,5\nE,4,8,5,6,5,7,7,5,8,7,7,9,3,8,6,9\nN,4,11,4,8,5,8,7,12,1,6,6,7,5,8,0,9\nR,4,7,6,5,4,9,7,4,6,9,4,7,3,7,5,11\nG,4,9,5,7,4,5,7,6,5,9,8,10,2,8,4,9\nL,5,12,5,7,3,10,2,3,4,12,4,11,2,8,4,10\nS,8,11,9,9,6,5,8,3,6,10,8,8,3,8,5,5\nY,7,9,6,5,3,4,7,5,3,9,11,6,4,10,3,4\nE,2,0,2,1,1,5,7,5,8,7,6,12,0,8,7,9\nT,5,7,6,5,5,6,7,7,6,7,6,8,3,9,6,10\nJ,2,5,5,4,2,10,5,4,5,14,5,11,1,6,1,7\nF,3,8,3,6,1,1,14,5,3,12,10,5,0,8,2,6\nZ,4,8,5,6,2,7,7,4,14,9,6,8,0,8,8,8\nV,6,9,6,4,2,5,12,5,2,11,7,4,3,11,2,7\nA,2,3,3,1,1,8,2,2,1,7,2,8,2,6,2,7\nI,2,2,1,4,1,7,7,1,8,7,6,8,0,8,3,8\nV,2,7,4,5,2,5,11,3,3,9,11,9,2,11,1,8\nV,2,7,4,5,2,8,11,2,3,5,10,8,2,11,1,8\nB,2,4,3,3,3,7,8,5,5,7,6,6,2,8,6,9\nC,7,9,7,7,4,4,7,5,7,11,9,14,3,10,4,7\nU,7,15,7,8,5,8,5,5,5,6,6,7,5,7,3,6\nZ,2,7,3,5,2,7,7,3,12,8,6,8,0,8,7,8\nR,10,13,8,7,5,7,7,5,5,9,4,9,7,6,7,11\nH,8,11,11,9,7,8,6,3,6,10,5,8,3,8,3,8\nQ,4,5,4,6,4,8,5,6,3,9,6,9,3,8,4,8\nQ,3,5,4,6,3,8,10,5,2,6,9,12,2,10,6,8\nY,5,8,5,6,3,5,8,0,8,9,9,6,1,10,3,4\nA,5,10,7,8,4,9,2,3,3,8,1,8,2,7,4,8\nB,4,6,5,4,3,8,8,4,6,9,6,5,2,8,7,7\nJ,2,4,4,3,1,9,5,3,7,15,6,12,0,7,0,8\nE,2,3,4,1,2,7,7,2,7,11,7,9,1,9,4,8\nK,4,5,5,4,3,4,7,5,8,7,6,13,3,8,5,9\nQ,3,7,4,9,5,8,7,8,3,5,6,9,3,8,6,10\nR,3,8,4,6,4,5,8,7,5,6,4,9,3,6,5,8\nW,8,14,8,8,6,4,8,1,3,8,9,8,10,11,2,6\nG,5,8,7,6,4,6,7,6,6,6,6,9,4,7,4,8\nT,3,5,4,3,2,5,12,3,7,11,9,5,1,11,2,5\nL,2,5,2,3,1,4,3,3,7,2,2,6,0,7,0,6\nG,3,4,4,6,2,7,5,7,7,5,5,10,1,8,6,11\nK,3,6,4,4,1,4,8,8,1,7,6,11,3,8,3,11\nZ,4,9,6,7,6,7,9,3,8,7,6,8,2,10,13,6\nE,4,9,6,7,6,8,9,7,2,6,6,11,4,7,8,9\nS,4,10,6,7,4,9,6,3,7,10,6,8,2,9,4,9\nX,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nM,6,11,9,8,10,7,10,6,4,7,7,8,6,8,6,8\nX,3,4,4,3,2,7,7,3,9,6,6,10,2,8,6,7\nC,3,9,4,6,3,5,8,8,7,10,8,12,2,11,4,10\nT,4,10,6,8,8,7,7,4,6,7,7,9,5,9,5,7\nA,2,6,4,4,1,7,5,3,1,6,0,8,2,7,1,7\nB,5,9,7,6,9,8,6,5,4,7,7,8,7,9,7,9\nK,4,7,6,5,4,3,8,2,6,10,9,11,3,8,2,6\nK,3,4,6,2,3,7,7,2,7,10,6,9,4,8,4,7\nT,7,12,6,7,4,9,8,3,7,11,5,6,2,8,6,7\nW,11,14,12,8,7,8,9,3,4,6,9,6,11,8,2,6\nR,3,7,5,5,3,9,8,6,6,8,5,8,3,7,6,10\nM,6,8,9,6,10,6,8,4,3,7,4,8,14,3,4,7\nU,4,9,5,7,4,8,6,13,4,7,11,8,3,9,0,8\nM,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8\nF,5,11,5,8,4,1,13,5,4,12,10,6,0,8,2,6\nL,3,7,5,5,3,7,4,1,7,8,2,9,1,6,2,8\nI,1,6,1,4,1,7,7,1,6,7,6,8,0,8,2,8\nD,7,11,9,8,8,6,7,7,5,7,7,6,4,7,4,8\nY,6,8,6,6,2,2,12,4,6,13,12,6,1,11,1,6\nU,3,4,4,6,2,8,5,14,5,6,13,8,3,9,0,8\nZ,6,10,9,8,7,10,9,6,5,7,6,8,4,9,10,7\nU,7,11,8,8,4,4,8,7,8,10,9,9,3,9,3,5\nD,3,7,4,5,3,10,6,3,7,11,3,6,3,8,3,9\nL,9,14,9,8,5,7,4,4,5,12,8,12,4,9,6,10\nI,1,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8\nO,4,9,5,7,4,7,6,8,4,7,5,9,3,8,3,8\nD,5,11,7,8,7,9,7,4,5,10,4,5,3,8,3,8\nD,2,1,3,3,2,7,7,6,6,7,6,5,2,8,2,7\nN,10,12,8,6,4,9,11,6,5,3,5,10,6,9,2,7\nB,6,9,9,8,11,7,7,5,4,8,6,8,7,9,8,7\nX,1,0,1,0,0,7,7,3,4,7,6,8,2,8,3,8\nQ,2,2,3,2,2,7,9,4,2,7,8,10,2,9,4,9\nP,5,10,6,8,6,5,10,3,7,10,9,6,4,10,4,7\nE,6,10,8,8,7,5,9,4,8,12,10,8,3,8,5,4\nY,4,7,6,10,9,9,7,6,3,7,7,8,7,10,6,4\nM,4,9,6,7,6,8,6,6,4,7,7,8,8,5,2,7\nM,4,10,5,8,4,7,7,12,2,7,9,8,8,6,0,8\nB,1,0,1,0,0,7,7,6,4,7,6,7,1,8,5,9\nL,4,6,5,6,4,7,7,4,5,7,6,8,2,9,7,10\nN,6,8,8,6,4,10,8,3,6,10,2,4,5,9,1,7\nW,2,1,3,2,2,5,10,3,2,8,9,9,5,11,0,8\nL,3,9,4,6,2,7,4,0,9,9,2,11,0,7,2,8\nV,9,11,7,6,4,9,9,6,5,6,10,5,6,13,3,7\nM,5,10,6,5,3,10,2,1,3,9,3,9,5,4,1,9\nQ,7,12,6,6,4,10,5,4,7,11,4,8,3,8,8,11\nV,4,6,6,6,6,7,6,5,4,6,5,8,7,9,7,9\nC,7,10,7,8,5,5,7,5,7,12,8,13,4,9,5,6\nX,6,13,7,7,4,9,7,2,7,11,4,6,4,10,4,8\nD,2,1,3,2,2,7,7,6,6,7,6,5,2,8,3,7\nP,9,13,8,7,4,8,7,5,5,11,3,6,5,9,4,8\nE,2,5,4,3,2,7,7,2,8,11,7,9,2,9,4,8\nA,7,9,9,8,8,5,8,3,5,7,8,10,8,8,4,8\nL,3,7,5,5,2,6,3,2,8,7,2,10,0,7,2,8\nP,2,5,4,4,2,7,11,4,3,12,4,2,1,10,3,8\nS,3,8,4,6,3,8,7,8,6,8,6,7,2,8,9,8\nI,1,4,1,3,0,8,7,0,7,13,6,8,0,8,0,8\nU,5,9,6,8,7,7,6,4,4,6,7,8,7,8,2,7\nT,7,10,9,7,7,6,7,7,7,8,10,8,3,9,6,8\nS,4,7,4,5,2,7,6,6,9,6,7,11,0,9,9,8\nB,3,7,5,5,6,8,6,5,3,7,6,8,6,10,8,7\nM,6,5,6,8,4,7,7,13,2,7,9,8,9,6,0,8\nF,6,9,9,7,6,7,9,3,6,12,7,6,3,9,3,7\nC,4,8,5,6,3,5,8,6,6,8,8,15,3,10,4,10\nD,2,4,4,3,2,9,6,4,6,10,4,6,2,8,3,8\nX,3,9,5,6,4,7,7,3,8,5,6,8,2,8,6,8\nJ,3,8,4,6,3,8,7,1,7,11,5,8,0,6,1,6\nR,2,3,4,2,2,8,7,4,5,9,4,7,2,7,4,10\nE,1,3,2,1,2,7,7,5,6,7,6,8,2,8,5,10\nQ,4,8,5,10,5,10,11,6,2,3,8,12,3,10,6,10\nL,2,5,3,3,1,4,4,4,7,2,2,6,1,7,1,6\nE,4,5,4,4,4,7,7,5,8,7,7,9,2,8,6,9\nY,7,6,5,9,4,7,11,2,3,8,10,5,3,9,5,7\nL,2,3,2,5,1,0,1,5,6,0,0,7,0,8,0,8\nF,2,4,2,2,1,6,10,4,5,10,9,4,1,10,3,6\nX,4,8,5,6,3,8,7,4,4,7,6,8,3,8,4,8\nT,4,6,4,4,2,5,12,4,5,11,9,4,2,11,1,5\nK,4,10,4,7,2,4,8,9,2,6,3,11,4,8,2,11\nB,5,10,5,8,7,7,8,8,6,8,6,7,2,8,8,9\nE,4,7,6,5,4,9,6,2,8,11,5,9,3,8,5,10\nR,3,5,6,4,4,9,7,3,5,9,4,6,3,7,4,10\nP,3,6,4,9,8,8,9,5,0,8,7,6,5,10,3,7\nX,6,11,7,6,4,7,8,2,7,11,7,7,4,10,4,6\nK,6,10,9,8,6,5,8,2,7,10,8,10,3,8,3,8\nE,4,9,6,6,6,7,7,6,3,7,6,11,4,8,8,9\nY,3,9,5,6,1,6,12,2,3,9,12,8,1,10,0,8\nE,4,5,4,8,3,3,8,6,11,7,5,14,0,8,7,7\nA,2,4,4,5,2,7,4,3,1,7,1,8,2,7,2,8\nX,4,7,7,5,3,10,6,1,8,10,3,7,2,8,3,9\nA,4,7,5,5,3,8,2,2,2,6,2,8,2,6,3,6\nZ,6,12,6,6,4,6,7,2,9,11,7,8,3,7,6,6\nH,4,3,4,4,2,7,7,14,0,7,6,8,3,8,0,8\nA,4,6,6,5,5,8,8,2,4,7,8,8,5,8,4,6\nQ,6,10,6,5,4,9,6,4,6,10,5,8,3,8,9,9\nO,4,6,4,4,3,9,6,7,5,10,4,10,3,8,4,7\nM,2,1,3,2,2,7,6,6,4,6,7,8,6,5,1,8\nF,4,5,4,8,2,1,12,5,4,11,10,7,0,8,3,6\nA,4,6,5,6,5,7,9,2,4,7,7,9,7,7,3,8\nD,5,11,5,6,4,6,8,5,6,9,6,7,5,10,6,5\nH,5,11,6,8,3,7,9,15,1,7,3,8,3,8,0,8\nO,2,5,3,4,2,7,7,8,5,7,6,8,2,8,3,8\nA,5,7,6,5,6,8,7,7,4,7,6,8,2,8,8,3\nT,7,15,6,8,4,9,7,3,7,12,5,7,3,9,5,6\nL,2,7,4,5,5,7,8,2,4,6,7,10,5,11,6,5\nF,3,8,4,6,2,1,14,5,3,12,10,5,0,8,2,6\nV,4,4,6,6,1,10,8,5,3,5,14,8,3,9,0,8\nP,2,3,4,2,2,7,9,3,4,12,5,4,1,10,2,8\nT,4,7,5,5,2,6,11,2,8,11,9,5,1,11,3,4\nR,5,10,5,5,4,7,7,3,4,7,5,9,5,8,6,7\nJ,1,7,2,5,1,13,3,8,4,13,4,12,1,6,0,8\nL,2,3,2,5,1,0,1,5,6,0,0,7,0,8,0,8\nE,5,10,7,8,6,4,9,4,7,12,10,8,3,9,4,5\nZ,4,10,5,7,4,7,7,3,12,9,6,8,0,8,8,8\nV,3,5,6,7,2,6,8,4,3,7,14,8,3,9,0,8\nI,1,10,2,8,2,7,7,0,8,7,6,8,0,8,3,8\nE,3,10,4,7,2,3,7,6,11,7,6,15,0,8,7,7\nK,4,9,5,6,5,6,7,3,6,6,6,9,3,8,5,10\nO,4,9,5,7,4,7,7,8,6,7,6,6,2,8,3,8\nP,2,3,3,2,1,7,10,3,4,12,5,3,0,9,2,8\nN,3,5,3,3,2,7,8,5,5,7,6,6,5,10,2,5\nF,4,7,6,5,4,9,8,2,5,13,5,6,2,9,2,9\nX,4,5,5,7,2,7,7,5,4,7,6,8,3,8,4,8\nE,4,9,4,6,2,3,7,6,11,7,6,15,0,8,7,7\nD,2,5,4,4,3,9,6,4,6,10,4,6,2,8,3,8\nZ,2,8,3,6,3,7,8,3,11,8,6,8,0,8,7,8\nS,4,6,5,4,2,8,8,4,8,11,8,7,2,10,5,6\nV,2,4,4,3,1,7,12,2,2,7,11,9,2,10,1,8\nG,3,5,4,4,2,6,6,5,5,9,7,11,2,9,4,10\nO,3,5,4,3,2,7,7,7,4,9,6,7,2,8,3,7\nP,1,1,2,1,1,5,11,7,2,10,6,4,1,9,3,8\nG,4,6,5,4,3,7,6,5,6,11,6,12,2,10,3,10\nV,6,8,6,6,3,3,12,3,3,9,11,8,3,11,1,7\nE,2,5,4,3,2,7,8,2,8,11,7,9,2,9,4,8\nC,2,1,3,2,1,6,8,7,7,8,7,13,1,9,4,10\nO,2,4,3,3,2,7,7,8,5,7,6,8,2,8,3,8\nG,4,8,5,6,3,6,6,7,6,10,7,12,2,9,4,9\nU,2,0,2,1,0,7,5,10,4,7,12,8,3,10,0,8\nJ,4,8,6,6,2,7,6,4,6,15,7,11,1,6,1,7\nC,3,6,5,5,4,5,6,3,4,7,6,11,4,10,7,8\nE,3,5,5,4,3,5,8,3,8,11,9,10,2,8,4,6\nQ,3,6,4,5,3,8,8,6,4,6,9,8,3,8,5,9\nS,4,8,5,6,3,9,7,4,8,11,7,7,2,10,5,8\nI,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7\nT,3,10,5,7,1,5,15,1,6,9,11,7,0,8,0,8\nC,5,10,6,8,2,6,7,7,11,6,6,13,1,8,4,8\nX,5,11,8,8,7,7,7,3,8,5,7,10,3,8,6,8\nA,3,9,4,6,3,9,5,2,0,8,1,8,2,6,1,8\nA,4,7,5,5,5,7,7,6,3,7,6,8,2,7,8,3\nW,3,2,5,4,3,7,11,2,2,7,9,8,7,11,0,8\nG,3,3,5,5,2,8,6,7,8,6,5,10,1,8,6,11\nY,6,11,9,8,5,6,9,1,7,6,12,9,2,11,2,8\nP,3,7,4,5,3,4,12,7,1,10,6,4,1,10,3,8\nG,2,4,3,3,2,6,7,5,5,10,7,10,2,9,4,9\nG,3,2,4,4,2,6,6,5,6,6,6,9,2,9,3,8\nH,4,7,4,5,3,7,7,12,0,7,7,7,3,8,0,8\nO,1,0,1,1,0,7,6,6,3,7,6,8,2,8,3,8\nT,2,5,3,3,2,8,12,3,6,6,11,7,2,11,1,7\nL,2,6,4,4,2,6,4,3,8,6,2,7,1,6,2,7\nX,5,8,8,6,4,6,8,1,8,10,9,9,2,9,3,7\nO,2,3,3,1,1,8,7,7,5,7,6,8,2,8,3,8\nA,4,9,4,4,3,10,3,4,2,10,5,11,5,4,4,10\nG,4,7,4,5,3,6,7,5,5,9,7,12,2,9,4,10\nJ,3,9,5,7,5,9,9,3,3,8,4,6,4,8,5,5\nJ,3,11,4,8,5,7,7,2,5,11,5,9,4,7,2,6\nT,3,3,4,2,1,5,11,3,7,11,9,5,1,11,2,5\nJ,4,8,5,6,3,7,5,5,4,14,8,14,1,6,1,7\nM,6,11,9,8,9,9,7,5,5,6,7,5,11,8,4,6\nM,4,2,5,3,4,9,6,6,4,6,7,6,9,6,3,6\nH,3,6,4,4,2,7,6,14,1,7,7,8,3,8,0,8\nN,2,1,2,1,1,7,7,12,1,5,6,8,5,8,0,8\nD,1,0,1,1,0,5,7,7,5,6,6,6,2,8,3,8\nX,7,13,6,7,3,8,7,2,8,9,6,8,4,8,4,8\nZ,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8\nT,3,5,4,7,1,9,14,0,6,5,11,8,0,8,0,8\nB,5,8,8,7,9,8,6,5,5,7,6,7,8,11,9,6\nP,8,15,8,8,5,9,8,4,4,11,4,5,5,11,6,7\nI,1,8,0,6,1,7,7,5,3,7,6,8,0,8,0,8\nZ,3,5,4,4,3,7,7,5,10,6,6,8,2,8,7,8\nG,2,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10\nN,5,11,6,8,6,8,8,13,1,6,6,8,6,8,1,10\nR,3,6,4,4,4,6,10,7,3,7,4,8,2,6,5,11\nZ,3,6,6,4,3,7,7,2,10,12,6,8,1,8,6,8\nJ,3,8,5,6,5,9,7,3,3,8,4,5,4,8,6,5\nS,2,4,4,3,2,7,7,2,7,10,5,7,1,8,5,8\nB,2,7,3,5,2,6,6,9,7,6,7,7,2,8,8,10\nZ,5,8,7,6,5,8,7,2,9,12,6,7,1,7,6,7\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nV,5,5,6,4,2,4,12,3,3,10,11,7,2,10,1,8\nI,0,1,1,2,0,7,7,2,6,7,6,8,0,8,2,8\nO,4,8,6,6,3,8,6,8,8,7,5,8,3,8,4,8\nQ,2,5,3,6,4,8,7,8,3,6,6,10,2,9,4,9\nP,7,11,10,9,8,7,11,6,4,11,5,3,2,11,4,8\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nN,4,11,5,8,3,7,7,15,2,4,6,8,6,8,0,8\nF,2,7,3,5,2,1,11,3,5,11,11,9,0,8,2,7\nZ,8,10,6,14,5,6,11,3,3,12,7,6,3,9,13,5\nW,4,7,6,5,4,6,11,2,3,7,9,9,8,11,1,8\nI,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nC,8,15,5,8,3,7,7,8,8,12,7,10,2,9,5,9\nC,3,3,4,4,1,6,7,7,9,7,6,13,1,9,4,9\nE,5,9,7,7,8,8,7,3,5,6,7,9,5,10,8,8\nO,4,5,6,4,4,6,6,5,5,8,4,7,3,7,4,7\nD,5,7,6,6,6,6,7,5,6,6,4,7,4,7,6,5\nV,6,8,6,6,3,3,12,3,3,10,11,8,2,10,1,8\nE,2,1,3,2,2,7,7,5,7,7,6,8,2,8,5,10\nR,3,9,4,7,4,6,9,8,3,7,5,8,2,7,5,11\nZ,2,3,3,2,2,7,7,5,9,6,6,9,1,8,7,8\nJ,4,5,6,6,5,8,9,4,5,7,6,8,3,8,8,7\nX,4,9,6,7,6,9,7,2,6,7,5,6,3,8,6,8\nE,3,9,5,7,4,7,7,6,9,6,4,9,2,8,6,9\nE,4,7,5,5,3,7,7,6,10,7,7,9,3,8,6,8\nF,9,14,8,8,6,9,8,3,5,12,4,5,5,7,8,9\nA,1,1,2,1,1,7,3,2,1,6,2,8,1,6,1,7\nT,10,14,8,8,4,7,8,3,10,13,6,6,2,9,5,5\nV,2,3,3,2,1,4,12,3,3,10,11,7,2,11,0,8\nH,4,7,6,5,5,7,7,2,6,10,6,8,3,8,3,8\nQ,6,10,6,5,3,12,3,3,7,11,2,8,2,9,5,13\nY,6,9,5,4,2,5,9,4,4,11,9,6,4,10,4,4\nF,3,5,5,3,2,6,10,2,6,13,7,5,1,9,2,7\nI,1,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8\nN,2,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nU,4,9,4,4,3,6,6,5,4,7,8,8,4,7,3,8\nV,4,6,4,4,2,2,12,3,3,11,11,8,2,11,1,8\nN,6,7,8,5,4,7,9,2,5,10,5,6,5,9,1,7\nH,3,7,4,5,3,8,7,7,6,7,6,7,3,8,4,7\nC,6,12,5,6,4,8,6,4,3,9,8,11,4,9,7,11\nM,6,8,10,6,7,4,7,3,5,11,10,11,9,8,4,7\nR,1,0,1,1,1,6,9,7,3,7,5,8,2,7,4,10\nF,3,3,5,2,2,6,11,2,5,13,7,4,1,9,1,7\nK,8,11,11,8,7,9,7,2,7,10,3,8,3,8,3,9\nZ,5,10,6,8,3,7,7,4,15,9,6,8,0,8,9,8\nY,1,1,2,2,1,7,11,1,6,7,11,8,1,11,1,8\nY,5,7,6,9,9,8,8,7,3,7,7,8,9,11,6,4\nW,3,7,5,5,3,6,8,4,1,7,8,8,8,9,0,8\nQ,2,0,2,1,1,8,7,7,5,6,6,9,2,8,3,8\nR,4,6,6,6,6,6,8,4,3,6,5,9,7,6,7,8\nP,6,11,9,8,6,6,12,4,4,13,6,3,1,10,3,8\nS,7,11,8,8,6,5,9,3,7,10,6,6,3,7,5,5\nK,4,8,6,6,4,6,8,4,7,6,5,10,3,8,4,9\nT,10,14,8,8,3,4,12,3,8,12,8,4,2,9,3,3\nD,6,11,6,8,6,5,7,10,7,5,4,6,4,10,5,9\nO,5,10,5,8,4,8,7,8,5,10,6,8,3,8,3,8\nZ,1,0,2,1,0,7,7,3,11,8,6,8,0,8,6,8\nR,6,9,9,8,9,7,7,4,4,7,5,8,8,7,7,9\nA,6,11,8,8,8,8,9,8,5,6,6,8,3,7,8,4\nO,6,7,8,6,6,7,5,4,5,8,5,11,5,6,7,6\nN,3,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8\nS,2,1,3,2,2,8,8,6,5,7,6,7,2,8,9,8\nZ,9,10,6,14,6,6,10,4,2,12,7,6,3,9,14,6\nZ,2,7,3,5,2,7,8,3,11,8,6,8,0,8,7,7\nX,3,8,5,6,4,7,7,3,8,6,6,8,3,8,6,7\nH,5,5,5,7,3,7,8,15,1,7,4,8,3,8,0,8\nG,4,9,6,7,7,7,9,6,3,6,6,10,4,8,7,7\nY,3,5,4,7,6,7,9,3,2,7,7,8,3,11,7,6\nI,1,4,3,2,1,7,7,1,8,14,6,8,0,8,1,8\nN,8,10,7,6,3,7,10,5,5,4,5,10,6,11,3,7\nP,4,9,4,6,4,5,10,8,2,9,6,5,1,10,3,8\nP,4,7,5,5,5,5,9,6,4,8,7,9,2,9,7,10\nY,6,9,5,4,3,6,8,3,4,10,8,5,3,10,4,4\nN,3,7,4,5,3,5,9,6,4,8,7,9,5,8,1,7\nL,5,11,6,8,4,10,3,2,7,9,2,9,4,5,5,9\nE,6,8,8,7,8,5,10,4,5,8,7,9,4,10,9,7\nS,5,9,6,7,5,8,8,7,5,7,5,7,2,7,9,8\nB,3,5,4,4,3,8,7,6,6,6,5,6,2,8,6,10\nD,2,3,4,1,2,9,6,4,6,10,4,6,2,8,3,8\nV,4,4,5,3,1,4,12,4,4,11,11,6,2,10,1,8\nA,4,5,7,7,2,7,6,3,0,7,0,8,3,7,1,8\nI,3,9,4,6,2,5,8,0,8,14,7,8,0,8,1,7\nQ,5,6,5,8,5,8,8,5,2,8,8,10,3,9,6,8\nC,2,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10\nR,3,5,5,4,3,8,7,3,5,10,4,6,3,7,3,9\nK,9,12,9,6,5,8,7,3,6,10,7,9,6,12,4,9\nG,3,4,4,3,2,6,6,5,5,7,6,9,3,7,4,8\nH,8,12,7,6,4,9,9,4,7,8,2,5,6,6,4,8\nY,4,5,5,8,7,10,8,5,3,7,7,7,6,10,8,4\nL,4,9,5,7,3,6,4,4,8,5,2,6,1,6,2,6\nV,3,6,5,4,3,8,11,3,2,5,10,9,3,10,3,10\nJ,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8\nL,2,5,4,3,2,7,4,1,8,8,2,10,0,7,3,8\nL,3,8,4,6,2,4,3,6,7,1,1,5,1,6,1,6\nE,6,11,8,8,6,4,9,2,8,10,8,9,3,8,5,5\nY,5,6,6,9,6,10,12,5,4,6,7,7,6,11,8,6\nJ,1,2,2,3,1,10,6,3,5,12,4,9,1,6,1,7\nA,2,8,4,5,2,6,5,3,1,6,1,8,2,7,2,7\nK,2,1,2,1,0,5,7,8,1,7,6,11,3,8,2,11\nR,4,7,7,6,6,7,7,3,3,7,5,8,6,8,5,7\nY,7,9,7,7,3,4,11,2,8,12,11,5,1,10,2,4\nU,4,4,5,3,2,5,8,5,8,10,8,8,3,9,2,5\nC,5,4,6,7,2,5,7,7,11,7,5,12,1,9,4,8\nO,5,8,5,6,4,7,7,8,5,10,7,8,3,8,4,8\nF,5,7,7,8,7,7,10,5,5,8,7,9,5,9,7,5\nP,2,1,3,2,2,5,9,5,4,9,7,4,1,10,3,7\nD,3,6,4,4,3,7,8,8,7,8,7,3,3,8,3,7\nB,2,2,2,3,2,7,7,6,5,7,6,6,2,8,5,10\nX,5,9,8,6,4,6,8,1,8,10,9,9,2,9,3,7\nH,3,5,5,3,3,6,7,6,6,7,6,9,3,8,3,8\nI,2,5,4,4,2,7,7,0,8,13,6,8,0,8,1,8\nU,3,6,3,4,1,7,5,13,5,7,13,8,3,9,0,8\nU,5,5,6,8,2,8,5,14,5,6,15,8,3,9,0,8\nM,4,3,5,4,4,8,6,6,4,6,7,8,9,5,3,6\nR,4,2,4,3,3,7,8,5,5,6,5,6,3,7,4,8\nC,1,3,2,1,1,5,9,4,6,11,9,11,1,9,2,8\nC,4,9,5,6,4,5,8,7,6,9,8,14,2,10,4,10\nB,3,7,5,5,4,7,8,7,6,7,5,6,2,8,7,10\nO,7,9,9,8,6,5,7,6,8,9,7,8,4,5,6,5\nL,2,4,3,3,1,5,4,4,7,2,2,5,1,7,1,6\nG,2,1,4,2,2,7,7,5,5,7,6,9,3,7,4,9\nB,3,5,5,4,5,7,7,4,4,7,6,8,6,9,8,6\nP,6,7,8,9,8,7,9,2,3,7,9,6,6,10,6,4\nF,2,4,3,2,2,5,10,4,5,10,9,5,1,10,3,6\nS,4,4,5,6,3,7,6,5,9,5,6,8,0,9,9,8\nX,3,6,5,4,3,7,7,3,8,6,6,9,3,8,6,8\nF,4,9,6,7,4,5,11,4,6,11,10,5,2,10,3,5\nT,2,3,3,1,1,5,12,3,5,11,9,5,1,10,1,5\nO,6,10,4,5,2,8,7,6,5,9,4,7,5,9,5,9\nV,4,7,6,5,7,7,8,4,1,8,7,8,5,10,5,8\nX,2,2,3,3,2,8,7,3,8,6,6,7,2,8,5,7\nU,5,7,6,5,4,5,7,5,7,9,7,9,3,9,3,6\nN,4,3,4,5,2,7,7,14,2,5,6,8,6,8,0,8\nM,4,9,5,6,5,6,6,6,4,7,7,11,7,5,2,10\nN,4,6,6,4,3,7,8,3,4,10,6,7,5,8,0,7\nR,3,9,5,6,4,6,7,6,6,6,6,6,3,8,6,8\nB,4,6,6,5,6,8,8,5,5,7,6,8,6,8,8,3\nW,4,7,7,5,5,5,11,2,2,8,9,8,7,12,1,8\nZ,1,0,1,1,0,7,7,2,9,9,6,8,0,8,6,8\nP,7,14,7,8,5,8,9,4,4,12,5,4,4,11,6,6\nB,6,8,9,7,9,7,8,5,5,8,6,8,7,7,9,6\nO,3,2,4,3,2,7,7,7,5,7,7,7,2,8,3,7\nY,4,7,6,5,3,8,10,1,6,4,11,9,2,11,2,8\nD,2,3,3,1,2,9,6,3,6,10,4,6,2,8,2,9\nK,2,6,3,4,1,4,8,8,2,7,4,11,3,8,2,10\nA,5,11,8,8,5,11,2,3,3,9,2,9,5,7,4,9\nC,1,0,2,0,0,7,7,6,8,7,6,13,0,8,4,10\nZ,4,10,5,8,5,8,7,6,10,7,5,7,1,7,8,8\nA,3,6,5,5,4,9,7,3,5,7,8,6,4,10,3,5\nI,5,11,6,9,4,9,5,2,5,6,7,5,1,10,4,7\nH,6,9,6,5,4,8,7,3,4,10,5,7,6,9,5,7\nD,3,2,4,4,3,7,7,7,7,7,6,5,2,8,3,7\nB,3,6,4,4,4,6,7,6,3,6,5,7,2,8,6,6\nB,4,6,6,4,6,9,9,4,4,5,7,7,7,12,6,8\nM,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8\nA,1,3,2,1,1,9,3,2,1,8,2,9,1,6,0,8\nL,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8\nM,4,7,7,5,7,9,5,2,1,8,4,8,10,5,2,6\nB,6,10,6,8,5,6,6,9,8,6,6,7,2,8,10,10\nF,2,3,3,4,1,1,14,4,3,12,9,5,0,8,2,6\nL,4,3,4,5,1,1,0,6,6,0,1,4,0,8,0,8\nA,3,7,5,5,3,11,2,3,2,10,2,9,2,6,3,8\nQ,2,2,2,3,2,8,8,5,2,7,8,10,2,9,4,8\nY,3,7,5,5,2,7,10,1,2,7,12,8,1,11,0,8\nX,3,7,5,5,2,6,9,1,8,10,8,8,2,8,3,7\nT,1,1,2,1,0,7,14,1,4,7,10,8,0,8,0,8\nQ,2,4,3,4,3,8,8,6,3,8,7,9,2,9,4,9\nN,6,11,9,9,7,7,7,8,5,7,5,6,3,7,3,8\nD,3,6,5,5,5,7,7,4,6,7,4,8,4,7,6,5\nM,5,6,8,4,4,9,6,3,5,9,5,7,8,6,2,8\nQ,4,6,5,7,4,8,6,6,4,9,6,10,3,8,5,8\nF,3,9,4,7,3,0,11,3,4,11,11,8,0,8,2,7\nV,3,5,6,4,2,6,12,2,3,8,11,8,2,10,1,9\nV,4,9,7,7,2,9,8,5,3,6,14,8,3,9,0,8\nO,4,6,5,4,5,8,5,5,2,7,5,8,8,8,3,9\nE,6,8,8,6,5,7,8,2,10,11,6,9,2,8,4,8\nH,3,7,4,5,3,7,7,12,1,7,6,8,3,8,0,8\nS,2,7,3,5,3,8,7,7,6,7,8,9,2,10,8,8\nI,3,9,5,7,6,10,8,2,5,9,5,4,3,9,6,5\nX,2,7,4,5,2,7,7,4,8,6,6,8,3,8,6,8\nU,2,0,2,1,1,8,5,11,5,7,13,8,3,10,0,8\nX,3,5,4,3,2,7,7,3,9,6,6,7,3,8,6,7\nT,3,5,4,3,2,9,12,3,7,5,11,9,1,11,1,8\nN,2,4,4,3,2,6,8,2,4,10,7,8,4,8,0,8\nQ,4,7,6,6,5,6,4,4,4,5,3,7,3,6,5,6\nA,2,4,4,3,2,9,2,2,2,8,2,9,1,6,1,7\nO,7,12,5,6,3,5,8,6,4,10,7,9,5,10,5,8\nV,7,9,7,7,3,3,11,2,4,9,11,7,5,11,1,6\nR,4,7,5,5,3,6,10,9,4,6,4,8,2,8,5,10\nR,3,7,5,5,4,7,8,5,6,8,5,9,3,6,5,11\nV,8,14,7,8,3,9,10,4,6,6,11,6,4,11,3,6\nF,2,1,2,2,1,5,10,4,5,10,9,5,1,10,3,7\nY,3,6,5,9,8,8,6,4,1,7,8,9,7,10,8,8\nX,4,6,6,4,3,8,7,2,8,10,5,8,3,8,4,8\nM,5,6,7,5,7,7,9,4,4,7,5,7,9,8,5,5\nY,2,1,3,1,0,7,10,3,1,7,13,8,1,11,0,8\nX,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nU,4,6,6,5,5,7,8,4,3,6,6,8,4,7,1,7\nG,6,11,5,6,4,8,5,4,3,9,6,9,4,9,8,8\nS,3,7,4,5,2,7,8,4,8,11,8,8,2,10,5,7\nF,6,10,10,8,9,8,8,1,6,10,6,5,5,10,4,6\nL,2,4,3,2,1,6,4,1,8,7,2,10,0,7,2,8\nS,5,9,8,7,9,7,4,3,3,7,5,7,4,8,11,6\nM,5,8,7,6,5,11,5,2,4,9,3,6,7,6,2,8\nU,4,9,4,7,2,7,5,14,5,7,14,8,3,9,0,8\nA,2,1,3,3,1,7,2,1,2,6,2,7,2,5,2,8\nS,3,7,5,5,6,7,5,3,3,6,5,7,3,8,9,3\nJ,4,9,5,7,3,7,9,2,6,14,5,7,0,6,1,7\nX,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8\nJ,3,7,5,5,4,8,9,4,3,8,5,6,6,9,6,4\nT,4,7,5,5,5,6,8,3,5,7,6,9,5,9,4,8\nE,3,6,4,4,4,8,7,6,3,7,6,10,3,8,7,9\nT,4,7,6,5,6,5,8,4,6,7,6,10,5,8,5,7\nL,6,11,6,6,3,8,4,3,5,12,7,12,3,8,6,9\nF,3,5,5,3,2,6,10,2,6,13,7,5,1,10,2,7\nR,5,9,7,8,8,7,8,3,4,7,5,8,7,8,6,8\nD,3,6,4,4,4,7,7,4,6,7,6,6,3,8,2,7\nM,5,9,6,4,3,7,3,3,2,7,4,10,6,3,2,8\nA,1,3,2,2,1,10,2,3,1,9,2,9,2,6,1,7\nQ,4,9,5,10,7,8,6,8,3,6,5,9,3,8,6,10\nM,4,4,5,6,3,7,7,12,1,7,9,8,8,6,0,8\nW,2,1,4,2,2,7,11,2,2,7,9,8,5,11,0,8\nM,4,4,7,3,4,7,6,3,4,9,8,9,7,5,2,8\nQ,4,6,4,7,4,7,9,5,3,7,9,11,3,9,7,8\nB,3,8,4,6,4,7,7,7,6,7,6,6,2,8,7,9\nU,2,1,2,1,1,8,5,11,5,7,13,8,3,10,0,8\nI,1,5,1,4,1,7,7,1,7,7,6,9,0,8,3,8\nN,6,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nI,1,2,1,3,1,7,7,1,7,7,6,8,0,8,3,8\nU,6,11,9,8,5,6,8,6,8,7,9,10,3,9,1,8\nC,6,11,6,8,4,2,9,5,7,11,11,13,1,8,2,7\nW,8,9,8,6,6,2,10,2,3,10,10,9,7,10,2,7\nZ,3,5,5,6,3,10,3,2,6,7,3,6,1,8,7,7\nU,6,8,6,6,3,4,8,6,8,10,10,9,3,9,2,5\nW,3,7,5,5,4,7,10,2,3,6,9,8,7,11,1,8\nU,5,8,6,6,3,4,9,5,7,12,11,9,3,9,2,7\nM,2,1,3,1,2,7,6,6,4,6,7,7,7,6,2,7\nB,5,11,8,8,10,9,5,5,5,7,7,7,7,9,8,9\nW,4,8,6,6,3,9,8,4,1,7,8,8,8,9,0,8\nZ,2,2,3,3,2,7,7,5,9,6,6,8,2,8,7,8\nT,3,1,4,3,2,7,12,4,6,7,11,8,2,11,1,8\nU,2,5,3,4,2,7,8,7,8,8,9,7,3,9,1,8\nT,1,0,2,0,0,7,14,1,4,7,10,8,0,8,0,8\nZ,4,9,5,7,5,8,7,5,9,7,5,7,1,7,7,8\nE,6,10,5,5,3,7,8,4,4,10,6,8,3,9,9,9\nS,2,6,3,4,2,8,8,7,6,7,5,8,2,8,9,8\nX,4,9,5,6,1,7,7,4,4,7,6,8,3,8,4,8\nS,4,8,5,6,3,7,6,6,10,5,6,10,0,9,9,8\nV,5,10,5,7,3,4,11,2,3,9,11,7,2,10,1,8\nA,7,12,7,7,4,12,1,4,1,11,4,12,5,4,4,11\nB,6,10,8,8,9,8,7,5,6,7,6,6,2,8,6,10\nJ,4,8,3,12,3,8,7,3,3,11,4,5,3,8,7,10\nZ,2,1,3,2,2,7,7,5,9,6,6,8,1,8,7,8\nL,5,9,6,6,5,6,7,7,5,6,6,9,6,8,5,11\nA,3,11,5,8,4,12,3,2,2,9,2,9,2,6,2,8\nH,5,10,5,8,5,7,8,13,1,7,5,8,3,8,0,8\nS,4,7,5,5,3,8,7,3,6,9,6,7,2,8,5,8\nY,2,5,4,3,2,7,10,1,7,7,11,8,2,11,2,8\nC,2,2,3,4,2,6,8,8,7,9,8,12,1,10,4,10\nK,4,8,4,6,2,3,6,8,3,7,7,11,4,8,3,10\nC,5,9,6,8,7,6,7,5,5,6,7,12,6,9,8,10\nY,1,1,3,2,1,8,11,1,6,6,10,8,1,11,1,8\nY,3,6,5,4,1,6,10,3,2,8,13,8,2,11,0,8\nP,4,8,4,5,2,3,14,7,1,11,7,3,0,10,4,8\nD,3,4,5,3,3,9,6,4,6,10,4,6,2,8,3,8\nQ,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8\nT,4,4,4,3,2,5,12,3,7,12,9,4,1,11,1,5\nS,3,9,4,7,2,9,9,6,10,5,5,5,0,7,9,7\nO,3,5,4,4,3,8,7,7,4,9,6,8,2,8,3,7\nV,6,8,6,6,3,3,12,4,4,10,12,7,2,10,1,8\nO,5,11,5,8,6,8,6,8,4,9,4,8,3,8,3,8\nK,3,4,4,3,3,6,7,4,7,7,6,11,3,8,5,10\nF,5,8,7,6,5,7,9,2,5,13,6,5,2,10,2,8\nT,3,7,4,5,3,7,11,1,8,7,11,8,0,10,1,8\nT,3,4,4,3,1,5,12,2,7,11,9,5,0,10,1,5\nP,3,4,5,3,2,7,10,3,4,12,4,2,1,10,2,8\nL,4,10,6,8,4,5,5,1,9,6,2,10,1,7,3,7\nH,5,8,7,6,5,5,8,3,6,10,8,9,3,8,3,6\nC,2,5,3,3,2,6,8,7,7,9,7,12,2,9,4,10\nT,4,10,6,7,5,9,11,3,7,5,11,8,2,12,1,8\nW,4,7,6,5,8,9,6,5,2,7,6,8,10,10,2,7\nD,5,11,7,8,5,8,8,6,7,10,6,4,4,7,5,10\nS,5,10,6,8,4,10,7,4,6,10,3,7,3,9,5,11\nC,1,0,2,1,0,6,7,6,8,7,6,14,0,8,4,10\nF,4,9,5,6,2,1,14,5,3,12,9,4,0,8,3,6\nX,3,6,5,4,4,7,7,3,5,6,6,9,2,8,8,8\nN,4,7,6,5,3,4,9,4,4,10,10,9,5,8,1,7\nC,6,9,8,7,5,7,7,8,6,8,5,11,4,9,4,8\nW,6,9,8,7,4,11,8,5,2,6,9,8,8,9,0,8\nR,6,11,8,8,7,9,7,3,6,10,3,7,3,6,3,11\nQ,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8\nH,2,1,3,2,2,7,8,6,6,7,6,9,3,8,3,8\nY,2,7,3,4,0,7,11,1,3,8,12,8,1,11,0,8\nJ,2,3,2,5,1,12,3,9,3,13,5,12,1,6,0,8\nP,6,6,8,8,8,6,11,2,3,8,9,6,5,10,5,6\nM,3,6,4,4,4,7,5,10,0,6,8,8,6,5,0,7\nJ,2,3,3,5,1,13,3,8,4,13,4,11,1,6,0,8\nY,5,10,8,8,5,8,10,1,7,4,11,8,1,11,2,8\nA,1,3,3,2,1,8,3,2,2,7,2,8,2,6,2,7\nS,5,10,6,7,4,6,8,5,8,11,10,7,2,10,5,5\nN,5,4,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nI,2,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nI,1,1,1,3,0,7,7,1,8,7,6,8,0,8,3,8\nA,6,11,6,6,4,10,2,4,2,11,5,12,5,4,6,10\nV,5,8,7,6,5,7,12,2,1,7,10,8,5,11,6,8\nC,4,7,5,5,2,4,9,6,8,13,11,10,1,9,2,6\nC,5,8,5,6,3,6,7,5,6,12,8,13,3,9,4,7\nG,3,7,5,5,5,8,9,6,3,6,6,9,4,8,6,7\nW,6,10,6,7,7,4,10,2,3,9,8,8,7,11,2,7\nW,6,8,9,7,11,9,7,5,5,7,5,8,12,10,9,4\nC,3,7,4,5,2,6,8,8,8,9,7,12,1,10,4,9\nY,4,10,6,8,3,7,10,1,7,7,12,8,1,11,2,8\nL,4,7,5,5,6,6,7,3,6,7,7,11,6,11,6,5\nK,2,1,2,1,1,6,7,4,7,6,6,10,3,8,4,9\nF,1,3,3,2,1,7,9,2,4,13,6,6,1,9,1,8\nS,2,3,2,2,2,8,7,6,5,7,7,8,2,9,8,8\nI,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nR,5,9,7,7,6,6,8,6,6,6,5,8,3,6,6,9\nY,3,5,5,8,7,8,7,4,2,6,8,9,4,11,7,8\nW,4,7,6,5,4,7,8,4,0,6,9,8,7,12,0,8\nN,6,7,8,5,3,8,8,3,5,10,5,6,6,9,1,7\nI,1,6,1,4,1,7,7,0,7,7,6,8,0,8,2,8\nX,3,7,4,5,3,8,7,2,5,6,6,7,2,8,8,9\nZ,6,10,8,7,5,6,9,3,10,12,9,7,1,9,6,6\nU,5,10,7,7,5,6,8,8,5,5,6,12,5,9,7,3\nH,4,11,6,8,10,8,9,5,3,6,7,7,8,8,10,8\nU,5,8,5,6,2,7,4,14,5,7,14,8,3,9,0,8\nF,2,7,4,5,2,5,11,2,7,10,9,5,1,10,3,5\nK,4,9,5,7,2,3,7,8,2,7,6,12,3,8,3,11\nV,2,6,4,4,2,9,12,2,3,4,10,9,2,11,1,9\nH,2,3,3,1,2,7,8,6,6,7,6,9,3,8,3,8\nG,2,1,3,1,1,8,6,6,6,6,5,9,1,7,6,10\nZ,2,7,4,5,4,8,8,2,7,7,7,7,0,8,8,9\nL,6,11,8,8,10,7,7,3,6,6,7,11,6,11,7,5\nS,3,4,5,3,2,8,8,2,8,11,5,6,1,9,4,8\nF,4,9,4,7,3,1,12,4,4,11,10,7,0,8,2,7\nZ,2,4,3,2,1,8,7,2,9,11,5,9,1,9,5,8\nP,7,10,7,5,4,7,10,3,6,13,5,4,3,10,5,6\nG,5,11,5,8,5,5,7,5,4,9,9,9,2,8,5,9\nE,6,9,8,7,7,8,5,7,3,8,6,11,5,8,8,10\nU,9,11,9,8,7,4,8,5,8,9,7,9,6,8,4,3\nD,4,5,4,8,3,5,7,11,8,7,7,5,3,8,4,8\nE,5,9,7,6,4,7,7,2,9,11,6,9,2,8,4,8\nI,1,8,2,6,2,7,7,0,7,7,6,8,0,8,2,8\nW,9,12,9,6,5,4,8,2,4,8,10,8,10,10,2,5\nK,1,0,2,1,0,5,8,8,1,7,6,11,3,8,2,11\nR,5,6,7,5,7,6,7,4,3,6,5,9,7,9,6,10\nH,11,13,10,7,5,8,9,4,6,9,5,7,6,8,5,9\nI,2,8,3,6,1,7,7,0,7,13,6,9,0,8,1,8\nC,5,5,6,7,2,5,7,7,11,7,6,12,1,8,4,9\nJ,1,1,2,2,1,11,7,1,5,11,4,7,0,7,0,8\nN,3,6,4,4,3,6,9,6,4,7,6,8,5,9,1,7\nP,7,10,6,5,4,6,12,5,3,12,6,3,3,11,5,6\nD,2,5,4,3,3,9,6,3,6,10,4,7,3,7,3,8\nT,3,7,5,5,3,7,12,3,7,8,11,7,2,11,1,7\nF,2,2,3,3,2,5,11,4,5,11,9,5,1,10,3,6\nK,1,0,1,0,0,5,7,7,0,7,6,10,2,8,2,11\nW,8,11,7,6,4,1,9,3,2,11,12,9,7,10,0,6\nH,2,1,3,2,2,6,7,6,5,7,6,8,3,8,3,8\nI,5,8,6,9,6,7,8,5,6,7,7,7,3,9,9,9\nY,3,9,5,6,1,7,12,1,3,7,12,8,1,10,0,8\nH,3,3,5,2,3,7,7,3,6,10,6,8,3,8,3,7\nF,7,13,6,7,3,7,9,3,6,12,5,5,2,8,6,6\nV,7,11,7,8,4,2,12,2,3,9,11,8,2,10,1,7\nO,4,9,4,7,4,7,8,7,4,9,7,8,3,8,3,7\nC,4,9,5,6,3,5,7,6,8,5,7,12,1,7,4,9\nE,3,9,5,6,5,6,8,3,6,6,7,11,4,11,8,8\nH,8,10,8,5,5,8,8,3,5,10,3,7,6,5,4,7\nE,3,7,4,5,3,3,7,5,9,7,6,14,0,8,6,9\nB,3,7,4,5,3,7,6,9,7,6,7,7,2,9,8,9\nN,8,11,11,8,5,9,7,3,6,10,3,5,6,8,2,7\nP,2,4,4,3,2,7,11,5,2,11,5,3,1,10,2,9\nN,2,3,4,2,1,7,9,3,4,10,7,7,5,8,1,8\nU,4,4,5,3,2,4,8,5,8,10,10,9,3,9,2,6\nO,6,11,7,8,3,8,8,8,9,6,7,9,3,8,4,8\nC,3,4,4,3,2,6,8,7,7,9,8,12,2,10,3,9\nF,2,2,3,3,2,5,10,4,5,10,9,5,1,10,3,6\nQ,2,1,2,1,1,8,7,7,4,6,6,8,2,8,3,8\nF,2,6,3,4,2,4,11,5,5,11,10,6,2,10,2,6\nT,3,10,5,7,1,8,14,0,6,6,11,8,0,8,0,8\nI,0,1,1,2,1,7,7,1,6,7,6,8,0,8,2,8\nR,3,6,5,4,5,7,8,3,4,6,6,9,6,8,7,6\nT,3,8,5,6,3,6,12,4,7,9,12,7,2,12,1,7\nX,3,7,4,5,4,6,6,3,5,6,6,10,2,8,8,7\nA,2,4,4,2,2,11,2,2,1,9,2,9,2,7,2,9\nL,3,9,5,6,6,7,8,3,6,5,7,10,5,12,6,5\nC,5,8,6,6,2,6,6,7,11,8,5,12,1,9,4,8\nR,4,7,6,5,4,10,7,3,7,11,2,6,4,5,3,9\nP,6,6,8,9,8,7,9,3,2,8,8,6,8,11,6,5\nG,4,7,5,5,2,7,6,7,9,6,5,10,1,8,6,11\nX,5,11,7,8,5,4,8,1,8,9,10,10,2,9,3,5\nM,4,10,5,8,4,8,7,12,2,6,9,8,8,6,0,8\nS,5,9,6,7,3,8,7,4,8,11,6,7,2,9,5,8\nL,6,10,7,8,6,6,6,7,6,6,5,8,5,7,4,10\nL,2,5,4,4,2,7,4,1,7,9,2,10,0,7,2,8\nY,3,10,5,7,1,7,10,1,3,7,12,8,1,11,0,8\nU,3,3,4,2,1,4,8,4,6,11,11,9,3,9,0,7\nF,2,5,4,4,2,6,10,2,6,13,7,6,1,9,2,7\nG,2,3,3,2,2,7,7,5,5,9,7,10,2,8,4,9\nQ,4,7,6,7,3,8,9,8,5,6,7,9,3,8,5,9\nV,8,9,10,8,12,7,6,5,5,7,6,8,7,10,6,9\nH,3,4,6,2,3,9,6,3,5,10,5,7,3,8,3,7\nK,4,9,6,7,8,7,9,3,5,5,6,8,8,9,8,7\nT,2,7,4,5,1,9,14,1,6,5,11,9,0,8,0,8\nL,5,11,4,6,2,5,5,3,7,9,4,12,2,6,5,7\nM,3,5,6,4,4,7,7,2,4,9,7,8,7,5,1,8\nO,4,9,5,7,4,8,7,8,4,7,6,4,4,8,4,8\nH,4,7,4,5,4,7,9,12,1,7,4,8,3,7,0,8\nU,2,0,2,1,0,8,5,11,4,7,13,8,3,10,0,8\nR,4,2,4,3,3,7,8,5,6,6,5,7,3,7,4,8\nB,7,10,9,8,7,9,7,4,7,10,5,6,2,8,6,10\nD,3,6,4,4,3,8,7,4,5,10,5,5,3,8,3,8\nL,3,10,3,8,1,0,1,5,6,0,0,7,0,8,0,8\nV,3,4,4,3,1,4,12,3,3,10,11,7,2,11,1,8\nE,1,0,1,1,1,5,7,5,8,7,6,12,0,8,6,9\nV,4,11,6,8,4,6,11,2,3,7,11,9,2,10,1,9\nU,4,6,5,4,3,6,8,6,7,6,9,9,3,9,1,8\nO,3,6,5,4,3,7,6,8,6,6,4,7,3,8,3,8\nV,10,13,8,7,4,8,10,5,6,6,10,5,5,12,4,7\nD,2,3,3,1,2,7,7,6,6,7,6,5,2,8,3,7\nH,8,10,11,7,8,7,7,2,6,10,6,9,5,7,4,7\nR,5,10,7,8,7,8,8,4,6,8,4,8,4,5,5,11\nJ,5,6,3,9,3,8,8,3,3,12,5,5,3,8,6,10\nH,3,3,4,4,2,7,9,14,1,7,4,8,3,8,0,8\nJ,6,11,8,8,4,8,9,1,7,14,4,6,2,8,2,7\nH,4,3,4,4,2,7,6,14,1,7,8,8,3,8,0,8\nQ,2,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8\nC,3,7,4,5,3,6,8,7,7,10,8,13,2,10,4,10\nH,3,4,4,6,4,8,7,4,0,7,6,7,3,9,8,5\nS,5,7,6,5,3,8,8,4,9,11,4,7,2,6,5,9\nM,3,7,3,5,3,8,7,11,1,6,9,8,7,6,0,8\nQ,2,2,3,3,2,8,8,5,2,5,7,10,2,9,5,10\nM,7,9,10,7,6,4,6,4,5,11,11,11,9,3,4,6\nC,4,4,5,7,2,7,6,7,10,9,5,13,1,10,4,9\nF,3,8,3,6,1,1,12,4,5,12,11,8,0,8,2,6\nY,5,9,5,6,3,3,11,3,7,12,11,6,1,10,2,5\nV,6,8,6,6,4,3,12,2,3,9,11,8,4,12,1,7\nX,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nL,2,5,4,4,2,6,4,1,8,8,2,10,0,7,2,8\nC,2,3,2,2,1,4,8,4,6,11,10,11,1,9,2,7\nT,2,8,4,6,2,6,11,3,7,8,11,7,2,11,1,7\nC,9,13,6,8,3,6,9,7,7,11,7,6,2,9,6,8\nG,5,9,6,7,3,8,6,8,9,6,6,10,1,8,6,10\nK,1,0,1,1,0,5,7,7,1,7,6,11,2,8,2,11\nS,9,14,7,8,4,8,3,4,5,8,1,7,3,6,6,9\nY,7,9,7,7,4,5,8,2,8,8,10,5,5,11,6,3\nQ,3,3,4,4,3,8,7,6,3,8,6,9,2,9,3,9\nF,2,3,4,2,1,6,10,3,5,13,7,5,1,9,1,7\nW,4,5,4,3,3,3,10,2,2,10,10,8,5,11,1,7\nT,4,11,6,8,6,9,11,3,7,5,12,8,2,12,1,9\nU,2,6,3,4,1,8,6,12,5,7,13,8,3,9,0,8\nI,1,5,1,4,1,7,7,1,7,7,6,8,0,8,2,8\nG,5,10,6,7,3,7,5,8,10,7,5,12,1,9,5,10\nG,3,5,4,7,3,7,7,8,7,7,7,7,2,7,6,11\nL,7,15,6,8,3,8,4,3,6,11,4,12,2,7,6,9\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nR,3,4,5,3,3,8,7,3,5,9,4,7,3,7,4,10\nX,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8\nK,2,1,3,2,2,5,7,4,7,7,6,11,3,8,4,9\nT,5,5,5,4,2,5,12,2,7,11,9,4,1,11,2,4\nP,6,9,5,5,3,6,11,6,2,11,5,4,4,10,4,8\nN,11,14,9,8,4,8,11,5,5,3,6,10,6,10,3,7\nK,4,5,7,3,4,7,6,2,7,10,6,10,4,8,4,8\nQ,4,6,6,9,3,7,6,8,6,5,7,8,3,8,5,9\nM,5,7,7,5,6,7,7,3,4,9,8,9,7,5,2,8\nG,6,11,7,9,5,6,7,8,7,6,6,13,3,8,5,7\nJ,7,10,4,14,4,7,9,3,4,13,5,5,3,8,7,10\nI,4,7,6,8,5,8,8,5,6,7,7,7,4,8,9,9\nB,3,6,5,4,5,8,8,6,6,7,6,6,2,8,6,9\nS,6,10,8,8,4,9,7,4,8,11,7,8,2,10,5,7\nL,2,3,3,2,1,6,4,2,8,8,2,10,0,7,2,8\nO,3,7,4,5,3,9,5,7,5,10,4,9,3,8,3,8\nI,1,3,2,2,0,7,7,1,7,13,6,8,0,8,0,7\nK,3,2,4,3,2,5,7,4,8,7,6,11,3,8,5,9\nZ,1,1,2,1,0,8,7,2,10,9,6,8,0,8,6,8\nO,6,9,6,7,5,8,6,8,4,9,5,8,4,7,4,9\nG,5,5,6,8,3,7,6,8,8,6,6,10,2,8,6,11\nL,3,7,4,5,3,6,5,7,6,6,6,9,2,8,4,10\nC,3,5,4,4,2,6,7,7,8,8,8,13,1,9,4,10\nX,6,10,7,5,3,5,9,2,8,11,8,8,4,9,3,6\nF,1,1,1,1,0,2,12,4,3,11,9,6,0,8,2,7\nI,8,13,7,7,4,9,7,3,6,12,4,5,3,8,6,11\nZ,2,7,3,5,2,8,6,5,10,7,6,7,1,7,8,8\nZ,7,11,9,8,6,8,7,3,10,12,6,8,2,7,6,8\nI,7,15,6,8,4,9,7,3,6,13,3,5,3,8,6,10\nF,2,7,3,4,1,1,13,5,3,12,10,6,0,8,3,7\nX,4,5,5,7,2,7,7,5,4,7,6,8,3,8,4,8\nG,5,10,6,8,5,5,7,6,6,10,8,11,2,9,4,10\nI,1,3,1,2,1,8,7,1,7,7,6,7,0,8,2,7\nI,3,5,4,6,4,7,8,5,5,6,8,7,3,10,8,8\nK,3,5,5,4,4,6,7,3,3,6,4,9,4,6,7,8\nJ,3,5,4,7,1,13,2,8,5,14,3,12,0,6,0,8\nI,1,5,2,3,1,7,7,0,7,13,6,8,0,8,0,8\nP,3,5,5,4,2,7,12,4,3,12,4,1,1,10,3,8\nM,5,9,6,7,4,7,7,12,2,7,9,8,9,6,0,8\nZ,3,9,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nJ,5,11,4,8,3,9,7,2,4,12,5,7,2,9,7,9\nO,2,5,3,3,2,8,7,7,4,9,5,8,2,8,3,8\nG,4,8,5,6,2,7,6,7,9,6,6,9,2,8,6,11\nE,3,8,3,6,2,3,7,6,9,7,6,14,0,8,7,8\nL,6,11,6,6,3,6,6,5,5,12,9,11,3,10,6,9\nP,3,6,3,4,2,3,12,6,1,11,7,4,0,9,3,8\nV,4,5,6,3,2,4,13,3,3,10,11,7,3,10,1,7\nV,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8\nS,3,5,3,4,2,8,7,7,5,8,5,7,2,9,9,8\nF,3,8,5,6,2,3,13,6,3,13,9,4,2,10,2,5\nL,3,6,4,6,4,8,6,5,5,6,7,8,3,8,8,11\nP,5,8,7,6,6,7,6,6,4,7,6,9,5,8,7,10\nC,4,8,5,6,3,5,8,8,8,8,8,14,1,9,4,9\nV,8,10,8,8,5,3,12,3,3,10,11,8,3,9,2,6\nR,4,7,6,5,6,6,8,3,4,6,6,9,6,10,7,5\nU,5,9,4,5,3,7,7,4,5,7,7,8,5,6,3,7\nT,6,11,6,8,4,3,13,4,6,12,10,4,1,11,1,4\nP,5,6,5,8,3,4,12,9,2,10,6,4,1,10,4,8\nE,4,8,6,6,5,8,6,6,3,7,6,10,4,8,8,9\nV,6,10,6,8,4,2,11,2,3,10,11,8,2,10,1,8\nI,3,10,4,7,2,7,8,0,8,13,6,7,0,8,1,7\nS,3,5,3,4,2,8,7,7,5,8,6,7,2,9,9,8\nO,5,10,5,8,4,8,7,8,5,10,5,8,3,8,3,8\nQ,7,9,8,11,7,8,7,6,3,8,7,11,5,8,8,6\nA,6,12,6,6,3,13,1,5,2,12,2,10,2,3,2,10\nP,3,9,4,7,3,5,11,9,2,10,6,4,1,10,3,7\nH,6,11,6,8,3,7,7,15,0,7,6,8,3,8,0,8\nN,4,6,6,5,5,7,7,4,4,6,5,8,6,8,4,6\nB,6,10,6,8,5,6,6,9,8,6,6,7,2,8,10,10\nW,5,8,8,6,11,7,7,7,2,7,6,8,15,12,4,11\nH,6,8,9,6,7,6,9,3,6,10,8,8,3,8,4,6\nC,1,3,2,2,1,6,8,7,7,8,7,13,1,9,4,10\nO,2,1,2,1,1,8,7,7,6,7,6,8,2,8,3,8\nW,6,9,9,8,11,10,7,5,5,7,5,8,10,11,9,4\nH,3,3,4,2,2,7,8,6,6,7,6,8,3,8,3,7\nT,4,5,5,8,2,9,15,1,6,5,11,9,0,8,0,8\nH,4,6,6,4,5,7,8,5,6,7,5,8,3,8,3,8\nG,4,5,5,8,2,7,6,8,8,6,5,11,2,8,6,11\nY,7,15,6,8,4,5,8,4,3,10,9,6,4,10,4,4\nZ,5,9,7,7,5,6,9,3,9,12,8,6,3,10,8,6\nD,4,7,5,5,5,7,8,6,6,8,7,5,3,8,3,7\nZ,4,8,5,6,5,8,7,3,8,8,6,7,1,8,10,9\nR,2,4,4,3,2,8,7,3,4,9,4,6,3,7,3,9\nM,6,8,9,6,7,8,7,2,5,9,6,8,9,6,2,8\nJ,4,9,4,7,3,8,9,2,3,13,5,5,2,9,7,9\nL,4,8,5,6,4,3,4,4,7,2,0,8,0,6,1,6\nI,1,5,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nR,2,1,2,2,2,6,7,5,5,6,5,6,2,7,4,8\nL,5,9,6,7,5,5,6,5,6,6,5,9,8,7,4,10\nV,3,7,5,5,3,7,12,2,3,6,11,9,2,11,1,8\nJ,4,7,5,5,2,8,6,3,7,15,5,10,0,7,1,7\nR,6,9,6,5,5,7,7,3,4,7,5,9,5,8,6,7\nD,4,8,5,6,4,7,7,8,7,7,6,5,3,8,3,7\nX,3,5,4,5,4,6,7,2,5,8,7,10,2,10,7,8\nE,5,9,6,7,6,6,7,6,9,8,7,10,3,8,6,8\nD,6,10,6,5,3,8,6,5,6,12,4,7,5,7,5,9\nL,2,6,3,4,1,0,1,5,6,0,0,6,0,8,0,8\nU,6,7,7,5,3,4,9,5,8,11,11,9,3,9,2,7\nS,5,9,6,7,3,8,7,4,8,11,7,8,2,10,5,8\nQ,6,8,6,9,7,8,9,6,2,6,8,11,3,8,6,8\nK,5,9,8,6,6,6,7,1,6,9,6,9,3,8,3,8\nF,4,5,4,8,2,1,12,5,5,11,11,8,0,8,2,6\nH,3,2,5,3,3,7,7,6,6,7,6,8,3,8,3,8\nG,3,7,4,5,2,8,7,8,8,6,6,11,1,8,5,10\nN,6,9,8,8,8,7,7,5,4,7,5,8,7,9,5,7\nX,4,8,6,6,4,8,8,3,9,5,5,6,3,9,7,8\nV,1,0,2,0,0,7,9,3,1,7,11,8,2,11,0,8\nG,5,9,4,4,3,7,8,4,3,8,6,7,3,10,9,7\nZ,7,15,7,9,5,11,3,4,6,12,3,10,3,7,7,11\nH,2,3,4,1,2,7,8,3,5,10,7,7,3,9,2,7\nZ,7,11,9,8,7,7,7,2,9,12,6,9,2,9,6,8\nF,7,13,6,8,3,6,9,3,6,12,5,5,2,9,6,6\nT,5,9,5,4,2,7,8,2,7,11,7,8,2,9,4,5\nI,4,10,7,8,8,10,8,1,6,9,5,5,3,8,7,6\nX,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8\nE,3,4,5,3,2,7,7,2,8,11,7,9,2,8,4,8\nC,6,11,7,8,4,6,7,10,9,10,7,11,2,12,4,9\nT,5,8,6,6,4,4,12,3,6,12,10,5,1,11,1,5\nC,2,2,3,4,2,6,8,7,7,8,8,13,1,9,4,10\nM,4,4,6,3,3,10,6,3,4,9,4,7,7,6,2,8\nG,4,3,5,5,2,8,5,7,9,7,4,11,1,9,5,10\nK,7,9,6,5,3,6,8,3,7,9,8,9,5,8,3,7\nK,4,10,5,7,2,3,6,8,3,7,7,11,4,8,2,11\nE,2,1,3,2,2,7,7,5,7,7,6,8,2,8,5,10\nN,5,9,8,7,5,7,9,2,4,9,5,6,5,9,1,7\nZ,2,4,5,3,2,7,7,2,9,12,6,8,1,8,5,8\nM,4,3,5,5,3,8,7,12,1,6,9,8,8,6,0,8\nI,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nI,1,7,1,5,2,8,7,0,7,7,6,7,0,8,2,7\nY,6,9,6,4,3,6,6,4,4,9,9,6,4,10,3,5\nH,4,10,5,7,3,7,7,15,1,7,6,8,3,8,0,8\nF,5,9,4,5,2,7,8,2,7,10,6,6,2,10,5,7\nW,3,3,4,1,2,5,11,3,2,9,8,7,6,11,1,6\nO,4,6,6,6,4,7,5,4,4,8,3,7,3,7,4,8\nB,3,4,4,3,3,7,7,5,5,7,6,6,2,8,6,9\nX,7,13,8,7,5,8,7,2,8,11,4,7,4,9,4,7\nR,4,5,4,8,3,5,11,8,4,7,4,9,3,7,7,11\nO,5,9,6,7,5,8,6,8,4,6,5,5,4,8,4,8\nI,3,7,4,5,2,8,6,2,7,7,6,7,0,9,4,7\nY,3,7,5,5,4,8,6,6,5,5,8,8,2,8,9,5\nV,2,6,4,4,4,8,7,4,2,8,7,8,5,10,4,7\nL,3,2,3,3,2,4,4,5,7,2,1,6,1,7,1,6\nF,5,9,4,4,2,6,10,3,5,12,6,5,2,8,5,6\nO,3,5,4,7,2,7,7,9,6,7,6,8,3,8,4,8\nV,2,3,4,4,1,7,8,4,2,7,13,8,3,10,0,8\nU,1,0,2,1,0,7,6,10,4,7,12,8,3,10,0,8\nH,5,10,6,8,8,8,8,6,6,7,6,6,6,8,3,8\nT,5,9,5,7,3,5,12,4,6,12,9,4,2,12,2,4\nL,3,3,3,4,1,1,0,6,6,0,1,5,0,8,0,8\nD,8,14,7,8,5,7,6,3,7,10,5,7,6,7,8,5\nZ,4,6,5,4,4,9,9,5,4,7,5,7,3,8,9,5\nB,3,7,5,5,4,9,8,3,5,9,5,6,2,8,5,9\nE,5,7,7,5,5,7,8,1,8,11,6,9,3,8,4,9\nO,3,3,4,2,2,8,7,7,5,7,6,7,2,8,3,7\nF,4,9,4,4,2,6,10,2,5,11,7,5,2,9,5,5\nC,2,3,3,1,1,5,8,4,6,11,9,11,1,9,2,8\nJ,5,8,7,6,5,8,6,9,6,7,7,8,4,5,6,3\nC,5,9,6,6,3,5,9,7,8,5,8,14,2,7,5,8\nG,3,5,5,4,5,7,8,5,2,7,7,8,6,11,7,8\nV,3,4,4,3,1,4,12,3,3,10,11,7,2,11,1,8\nJ,1,1,1,1,0,12,3,6,4,13,4,11,0,7,0,8\nC,6,9,8,7,8,6,6,4,5,7,6,10,6,9,4,8\nS,4,8,6,6,3,9,6,4,8,11,4,8,2,8,5,10\nT,3,6,4,4,3,6,7,7,6,7,6,7,3,9,5,9\nC,6,8,6,6,3,5,7,6,8,11,8,14,2,10,4,7\nW,2,3,3,1,2,8,11,2,1,6,9,8,5,11,0,8\nB,2,6,3,4,4,6,6,7,5,6,6,7,2,9,7,10\nJ,5,9,6,7,5,8,7,6,5,8,7,7,3,6,4,6\nO,5,9,6,7,4,8,6,8,5,10,5,9,3,8,3,8\nO,6,11,5,6,3,5,8,6,4,10,8,9,5,10,4,8\nD,2,3,3,1,1,8,7,4,5,10,4,5,2,8,2,8\nA,1,3,2,1,1,9,3,1,1,8,3,9,1,6,1,8\nD,5,6,7,5,5,6,7,5,7,7,4,7,3,7,5,6\nJ,7,9,5,13,4,6,9,3,3,12,6,5,3,8,8,8\nY,6,9,8,7,7,9,7,6,5,5,9,7,3,8,9,5\nD,4,7,4,5,2,5,6,10,9,5,5,6,3,8,4,8\nN,5,6,7,4,3,6,8,3,5,11,8,8,5,8,1,8\nY,2,1,3,2,1,9,10,1,6,5,11,7,1,11,2,8\nW,6,9,6,4,3,1,10,3,3,12,11,9,6,11,0,6\nC,4,8,5,6,3,5,7,6,9,6,7,13,1,7,4,9\nL,2,3,3,2,1,7,4,1,8,8,2,10,0,7,2,8\nC,3,5,4,5,4,5,7,3,4,7,6,11,4,9,7,9\nA,3,5,5,3,2,8,2,2,2,8,2,8,2,6,3,7\nF,3,3,4,4,1,1,15,5,3,12,9,4,0,8,2,6\nC,4,6,5,5,5,5,6,3,5,7,6,11,4,10,6,10\nP,3,7,3,5,2,3,14,7,2,12,7,3,0,10,4,8\nQ,9,14,8,8,5,9,3,4,7,11,4,10,3,7,8,11\nE,2,4,4,3,2,7,8,2,7,11,7,8,2,8,4,8\nM,4,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8\nM,4,8,6,6,7,7,8,6,4,7,5,8,6,9,7,7\nG,4,7,5,5,5,8,5,4,3,7,6,10,6,8,4,10\nH,4,7,6,5,5,7,8,5,5,7,6,8,6,7,5,9\nH,5,8,8,6,5,7,7,3,6,10,6,8,3,8,3,7\nI,2,7,3,5,2,7,7,0,7,13,6,8,0,8,1,8\nO,4,5,6,7,3,8,7,9,8,7,6,8,3,8,4,8\nS,7,12,6,7,3,9,3,4,4,9,2,8,3,6,5,9\nQ,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8\nB,3,7,3,5,4,7,7,8,5,7,6,7,2,8,7,9\nW,6,9,8,7,8,8,4,7,3,7,6,7,8,7,6,3\nZ,2,7,3,5,3,7,8,5,8,7,6,10,1,9,7,7\nO,4,7,6,5,6,8,10,5,2,7,7,8,7,9,4,9\nS,3,5,5,4,2,7,7,3,7,11,6,8,1,9,5,7\nQ,1,2,2,2,1,8,7,6,2,6,6,9,2,9,3,10\nU,5,10,7,7,7,8,9,7,6,6,8,9,6,7,4,8\nH,2,1,2,2,2,7,8,5,5,7,6,8,3,8,2,8\nT,5,6,5,4,3,4,11,1,7,11,9,6,1,10,2,5\nL,5,11,6,8,4,3,4,3,9,2,0,8,0,7,1,5\nC,4,8,6,6,5,5,6,4,5,8,6,11,6,9,4,8\nC,5,9,5,7,3,5,8,6,8,12,9,12,2,10,3,7\nH,1,0,2,0,0,7,8,11,1,7,5,8,3,8,0,8\nX,3,5,6,3,2,9,6,1,8,11,4,8,2,8,3,9\nH,6,9,9,7,7,8,6,2,6,10,6,9,3,8,4,7\nE,3,7,3,5,2,3,7,6,10,7,6,15,0,8,7,7\nA,2,7,4,5,2,12,4,4,3,11,1,8,2,6,2,8\nF,4,5,5,8,2,1,14,5,4,12,10,5,0,8,2,5\nX,6,9,6,4,3,8,7,2,8,9,6,8,4,9,4,8\nB,8,13,6,7,4,9,6,5,5,11,4,9,6,6,7,10\nV,5,9,8,6,8,7,7,4,2,8,8,8,5,10,4,8\nP,5,9,5,4,3,10,8,4,4,12,4,5,4,10,5,9\nD,5,5,6,7,3,5,8,10,9,7,7,5,3,8,4,8\nS,3,4,5,3,2,9,6,2,7,10,4,7,1,9,5,10\nH,6,7,9,9,8,9,12,3,2,8,7,6,4,11,8,6\nH,4,8,5,5,2,7,5,15,1,7,8,8,3,8,0,8\nH,7,9,10,7,8,10,6,3,6,10,3,8,5,7,5,9\nU,7,10,6,5,4,8,6,4,5,7,7,7,6,7,4,6\nR,5,7,6,5,5,9,9,6,3,7,4,6,5,7,8,8\nN,2,2,3,3,2,7,8,5,4,7,6,6,5,8,1,6\nP,3,4,5,6,6,8,9,5,0,8,6,6,5,9,5,8\nC,3,8,4,6,2,6,7,7,10,6,6,14,1,8,4,9\nB,3,5,5,4,4,9,6,3,6,11,4,7,4,7,5,9\nZ,4,10,6,7,4,8,7,2,10,11,6,8,1,7,6,8\nE,5,9,7,8,7,5,8,4,3,8,6,9,5,11,10,9\nE,6,9,8,7,5,9,7,2,9,11,4,9,2,8,5,10\nW,7,9,7,4,3,6,10,1,3,8,10,7,7,12,1,7\nH,4,9,4,7,4,7,7,13,1,7,6,8,3,8,0,8\nL,5,10,5,5,3,10,3,3,3,11,6,10,3,10,4,10\nO,7,14,5,8,4,5,6,7,4,10,6,9,6,9,5,7\nG,5,9,7,8,8,7,6,6,4,8,7,9,10,8,10,7\nZ,3,5,5,8,3,11,5,3,5,10,2,7,2,7,5,11\nO,4,11,5,8,5,8,7,8,6,7,6,8,3,8,3,8\nS,4,9,5,7,3,9,8,6,9,5,6,8,0,8,9,8\nJ,4,9,6,7,4,8,5,3,5,8,6,8,4,6,4,6\nM,5,11,6,8,4,9,7,13,2,6,9,8,8,6,0,8\nH,5,11,5,8,5,7,8,14,1,7,5,8,3,8,0,8\nT,2,3,3,2,1,7,12,2,7,7,11,8,1,11,1,8\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nP,3,4,4,6,2,3,14,7,2,11,7,3,0,10,4,8\nX,2,4,3,3,2,8,7,3,8,6,6,6,2,8,5,8\nY,3,5,5,4,2,4,10,1,8,10,10,5,3,11,5,3\nO,5,10,5,8,5,7,7,8,4,9,6,8,3,8,3,8\nZ,3,5,5,4,2,7,7,2,10,12,6,8,1,8,6,8\nU,5,8,7,7,6,7,7,4,5,6,7,8,7,8,2,7\nX,3,4,4,3,2,8,7,3,9,6,6,6,2,8,6,8\nR,3,6,5,4,5,7,7,3,4,7,6,8,6,9,6,6\nD,3,8,5,6,7,9,8,4,4,7,6,6,4,7,7,6\nL,2,7,3,5,2,4,4,6,7,2,2,5,1,6,1,6\nP,5,5,7,8,8,7,6,4,3,7,7,7,7,11,5,6\nE,3,5,4,4,3,7,8,5,8,7,5,9,2,8,6,9\nG,5,10,6,8,8,9,6,5,2,7,6,10,8,8,5,10\nX,7,10,10,7,5,5,9,2,9,11,12,9,3,9,4,5\nA,2,6,4,4,2,11,3,3,2,10,1,9,2,6,2,8\nU,4,9,5,7,4,7,6,11,4,6,13,8,3,9,0,8\nX,6,11,9,8,5,10,7,1,8,10,2,7,3,8,4,9\nG,3,7,5,5,5,8,5,4,3,7,6,10,6,8,4,10\nV,2,2,4,3,2,7,12,2,3,7,11,9,2,10,1,8\nT,3,7,4,5,3,7,12,3,7,7,11,8,2,11,1,8\nK,5,9,5,5,2,4,8,4,6,9,11,11,5,9,3,6\nF,6,10,8,7,6,7,9,3,6,12,7,6,3,9,2,7\nI,4,7,5,5,3,7,8,2,7,7,6,9,0,8,4,8\nN,8,10,11,7,5,11,7,3,6,11,0,3,6,9,2,8\nU,3,6,4,5,4,6,7,5,4,6,5,9,4,8,1,8\nO,4,11,5,8,5,8,7,8,4,7,6,5,4,8,3,8\nP,5,10,7,8,6,5,10,4,5,11,9,4,1,10,4,7\nU,4,7,4,5,1,8,5,13,5,7,14,8,3,9,0,8\nT,7,10,7,8,5,7,11,4,7,11,8,4,3,12,3,4\nB,8,10,7,5,5,9,7,4,5,9,4,7,6,7,8,9\nF,3,8,5,6,4,4,11,2,6,11,9,6,1,10,3,6\nL,2,6,2,4,1,0,1,5,6,0,0,6,0,8,0,8\nF,2,1,3,3,2,5,11,4,5,11,9,5,1,10,3,6\nD,5,11,7,8,5,8,7,9,8,7,6,2,4,8,5,9\nT,8,10,8,7,6,6,11,4,6,11,9,5,3,12,2,4\nC,3,2,4,3,2,6,8,7,7,9,8,13,1,9,4,10\nS,7,9,9,8,9,10,8,4,7,7,7,8,5,9,9,11\nU,6,10,6,5,4,8,5,5,5,6,8,7,4,9,3,7\nV,8,14,7,8,4,9,10,6,4,7,10,5,6,13,4,7\nU,6,9,8,7,10,9,7,4,5,5,8,7,11,9,6,6\nM,3,1,4,2,1,7,7,11,1,7,9,8,7,6,0,8\nJ,5,6,3,9,3,9,7,3,3,11,4,5,3,8,6,9\nJ,1,1,2,2,0,10,6,3,5,12,4,9,1,7,1,7\nX,6,9,9,6,4,5,8,2,8,11,11,9,3,9,4,5\nI,1,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8\nD,6,11,8,8,7,7,6,7,5,5,5,6,7,9,3,7\nB,4,6,6,4,7,8,7,5,2,6,7,8,5,10,9,8\nD,3,6,5,6,4,7,7,5,6,7,5,7,4,7,6,5\nE,2,1,2,2,1,4,7,5,8,7,6,13,0,8,7,9\nP,4,10,6,8,6,7,7,8,4,8,6,7,3,9,7,9\nZ,3,5,4,7,2,7,7,4,14,10,6,8,0,8,8,8\nG,3,8,5,6,4,6,6,6,5,5,7,10,2,8,4,9\nL,5,9,5,5,3,7,5,3,6,11,6,11,2,7,6,8\nJ,4,11,5,9,3,14,3,5,4,13,2,10,0,7,0,8\nW,5,11,8,8,12,10,8,4,2,6,7,8,11,10,4,5\nI,6,11,7,8,5,7,8,1,8,7,6,8,0,7,4,8\nE,3,2,4,3,3,7,8,5,8,7,6,9,2,8,6,9\nI,1,3,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nY,4,5,5,4,2,4,10,2,8,11,10,5,3,10,4,3\nC,5,6,5,4,2,5,9,6,8,12,9,10,2,9,3,7\nV,3,8,5,6,1,6,8,4,3,8,13,8,3,10,0,8\nP,4,8,6,6,5,7,5,6,5,7,6,8,3,8,4,8\nR,12,15,9,8,5,9,6,6,5,10,2,8,7,6,6,11\nU,2,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8\nA,1,0,2,0,0,7,4,2,0,7,2,8,1,7,1,8\nM,5,8,7,6,6,4,7,3,4,10,10,10,5,6,2,6\nK,6,8,9,6,4,6,8,2,7,11,6,8,3,8,4,7\nT,3,9,4,6,3,10,10,1,8,5,11,8,1,10,1,8\nQ,4,5,4,6,4,7,8,6,3,9,9,9,3,9,6,7\nX,4,5,5,7,2,7,7,5,4,7,6,8,3,8,4,8\nJ,4,9,6,7,2,10,5,4,7,15,3,9,0,7,0,8\nX,2,1,3,1,1,8,7,4,8,6,6,7,2,8,5,8\nF,2,6,4,4,3,5,10,3,5,10,9,6,2,10,3,6\nS,4,10,5,8,3,8,7,6,9,4,6,8,0,8,9,8\nV,8,10,8,8,3,2,11,6,5,13,12,8,3,10,1,8\nX,3,4,5,3,2,9,7,2,8,10,3,7,2,8,3,9\nM,11,13,11,7,6,11,11,7,3,4,7,9,10,13,3,6\nT,7,10,9,8,7,6,7,7,8,7,8,9,4,10,7,7\nS,1,0,1,1,0,8,7,4,6,5,6,7,0,8,7,8\nC,4,8,5,6,2,5,7,7,10,7,5,12,1,9,4,9\nI,4,5,6,5,5,8,8,4,5,7,6,7,4,8,8,9\nP,5,9,5,7,5,4,11,9,2,10,6,4,1,10,3,8\nD,4,8,6,6,5,7,5,8,5,5,5,5,3,9,3,8\nC,5,9,6,6,5,7,8,8,6,6,6,11,4,8,4,8\nT,2,3,2,1,1,5,12,2,5,11,9,5,1,10,1,5\nD,5,7,6,5,5,7,7,4,6,7,7,9,6,7,3,7\nP,5,11,7,8,5,5,12,5,5,11,8,2,1,10,4,6\nG,3,7,5,5,6,7,6,4,3,6,5,10,6,8,6,10\nJ,1,3,3,1,1,8,8,2,5,14,5,8,1,7,0,8\nW,4,4,5,3,3,4,11,3,2,9,9,8,6,11,1,7\nY,4,8,6,6,3,7,10,1,7,5,11,8,1,11,2,8\nU,1,0,2,0,0,7,6,10,4,7,13,8,2,10,0,8\nY,3,4,5,5,1,6,11,2,2,9,12,8,1,10,0,8\nW,4,2,5,4,3,6,10,2,3,7,9,8,7,11,0,8\nF,6,12,6,7,4,8,8,3,4,10,5,5,3,9,7,8\nU,3,6,3,4,2,8,6,12,4,8,11,8,3,9,0,8\nC,3,7,5,6,5,5,8,3,5,7,6,11,3,9,7,9\nS,5,7,6,5,3,6,9,3,8,11,5,7,2,6,5,8\nW,5,4,6,3,3,5,11,3,2,9,8,7,7,12,2,6\nT,2,7,3,5,1,9,14,0,6,5,11,8,0,8,0,8\nS,5,10,8,7,9,5,10,3,4,8,7,6,3,8,10,1\nW,3,1,3,2,1,8,8,4,0,7,8,8,7,10,0,8\nI,3,7,4,5,1,7,9,0,7,14,6,6,0,9,2,7\nC,3,10,4,8,3,5,7,6,8,7,5,12,1,8,4,10\nZ,2,1,3,3,2,7,8,5,9,6,6,9,1,9,7,8\nA,7,13,6,8,4,8,2,3,2,8,4,12,5,5,4,7\nS,4,9,6,7,4,10,6,5,6,10,2,7,2,7,4,11\nF,6,8,8,6,7,8,7,5,4,7,6,8,5,11,9,11\nI,2,9,3,7,2,7,7,0,7,13,6,8,0,8,1,8\nG,5,10,7,7,8,7,4,5,3,8,6,11,7,7,10,6\nQ,6,7,8,10,8,10,13,5,2,3,8,12,5,15,4,10\nO,5,10,7,8,5,7,8,9,6,7,7,8,3,8,4,7\nV,2,6,3,4,1,7,9,3,1,7,12,8,2,11,0,8\nO,5,11,7,8,5,8,5,9,5,6,6,4,5,7,5,9\nS,5,11,6,8,6,8,7,8,5,7,6,7,2,8,9,8\nD,4,10,5,8,7,7,7,6,6,7,6,5,6,8,3,7\nU,2,3,3,2,1,7,8,6,6,7,9,8,3,10,1,8\nT,5,8,7,6,7,7,8,4,6,7,6,9,5,8,5,7\nU,4,7,5,5,3,7,9,6,7,5,10,9,3,9,1,8\nA,4,9,7,7,5,6,5,2,3,4,1,6,5,7,5,4\nS,4,10,5,8,5,8,8,5,9,5,5,6,1,6,9,6\nJ,5,10,7,8,3,8,8,1,8,14,4,6,1,9,1,8\nW,4,8,5,6,3,4,8,5,1,7,8,8,8,10,0,8\nM,4,7,7,5,8,7,7,3,2,7,5,8,8,5,2,7\nE,6,11,4,6,2,6,8,4,6,10,6,9,2,9,8,9\nY,4,5,5,3,2,4,10,2,8,11,10,5,1,11,3,4\nX,4,6,6,4,3,10,7,1,8,10,2,6,3,8,3,9\nX,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nK,3,9,4,6,2,3,7,7,2,7,5,11,3,8,2,10\nU,7,10,6,5,3,6,4,5,5,4,8,8,5,10,2,7\nD,1,3,3,2,2,8,7,4,5,9,4,6,2,8,2,8\nM,3,7,4,5,3,7,7,11,1,7,9,8,8,5,0,8\nT,3,3,3,2,1,5,13,4,5,11,8,4,2,11,1,5\nK,5,6,7,4,4,4,7,2,7,10,9,11,3,8,3,6\nO,4,6,6,4,3,7,5,7,5,8,4,10,3,7,3,8\nP,2,3,3,2,1,5,11,3,4,10,8,3,0,9,3,6\nQ,7,8,7,10,6,7,8,6,4,8,9,10,4,8,8,8\nY,3,8,6,6,3,9,10,1,6,4,11,8,1,11,2,9\nS,4,5,5,7,3,8,8,6,9,5,6,7,0,8,9,8\nX,4,7,6,5,3,5,8,1,8,10,9,9,2,9,3,6\nM,4,4,6,3,4,5,7,3,4,10,10,10,7,7,2,8\nC,3,2,4,4,2,6,8,8,8,9,8,12,2,10,4,9\nJ,1,4,2,2,1,8,7,2,5,14,6,9,1,7,0,7\nL,5,11,6,8,2,0,0,7,6,0,1,4,0,8,0,8\nF,4,8,6,6,5,7,8,6,4,8,6,8,3,10,8,10\nX,3,5,5,3,3,8,7,3,10,6,6,8,4,7,7,9\nE,2,6,3,4,2,3,8,5,8,7,6,13,0,8,6,10\nD,3,8,3,6,2,5,7,10,8,7,6,5,3,8,4,8\nW,4,6,6,4,5,7,7,6,2,8,8,8,6,7,4,8\nL,2,4,3,6,1,0,1,5,6,0,0,7,0,8,0,8\nU,4,4,5,3,2,5,8,5,8,10,9,9,3,9,2,6\nH,4,7,6,5,4,8,8,3,6,10,5,7,3,8,3,7\nK,2,3,3,1,1,4,8,2,6,10,10,10,2,8,2,6\nW,9,10,9,5,3,3,10,2,2,10,11,8,8,12,0,7\nW,3,1,5,2,2,8,11,3,2,6,9,8,6,11,1,7\nR,4,7,4,4,2,5,11,8,3,7,4,8,3,7,6,11\nQ,4,6,5,8,3,8,7,8,6,6,8,8,3,7,6,10\nS,4,9,5,7,3,8,8,6,9,5,6,6,0,8,9,7\nU,9,10,9,8,5,4,7,6,9,9,8,9,6,10,4,2\nO,5,11,6,8,6,8,7,9,4,7,6,7,3,8,3,8\nA,3,7,5,5,4,8,5,1,3,7,2,6,2,6,4,5\nN,6,11,6,8,6,7,7,13,1,6,6,8,5,8,0,8\nD,5,9,7,7,5,7,6,8,5,6,5,4,5,8,5,9\nF,5,11,6,8,2,1,15,5,3,12,9,4,0,8,2,5\nT,4,6,4,4,2,6,12,3,6,11,9,4,2,11,2,5\nH,6,10,6,8,6,7,6,13,2,7,8,8,3,9,0,8\nX,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nO,2,5,3,4,2,8,7,7,4,9,6,8,2,8,2,8\nY,6,8,9,12,12,7,9,3,3,7,8,9,7,13,12,5\nC,10,15,6,8,3,7,8,6,7,12,6,7,2,9,5,8\nN,3,3,5,2,2,6,10,3,4,10,8,7,5,8,0,8\nZ,6,7,5,11,4,9,6,4,3,11,6,8,3,9,11,7\nP,4,6,6,4,3,6,13,7,2,11,5,2,1,10,4,7\nV,9,13,8,7,4,6,10,4,4,8,8,5,5,11,3,6\nM,4,10,5,8,6,7,6,10,1,7,8,8,7,5,0,8\nR,4,6,6,4,5,7,7,3,4,6,6,8,6,9,6,6\nB,6,9,8,6,8,7,9,6,5,7,5,6,4,7,6,8\nZ,5,8,7,6,4,7,8,2,10,12,7,9,1,9,6,8\nY,4,9,4,4,2,7,7,4,4,9,9,5,3,11,3,4\nX,4,6,6,4,3,10,7,1,8,10,2,6,3,8,3,10\nL,2,6,4,4,2,4,5,1,8,6,2,10,0,7,2,7\nL,3,8,4,6,2,0,2,4,6,1,0,7,0,8,0,8\nX,3,10,6,7,6,8,7,2,6,7,6,8,3,8,6,8\nS,4,10,6,8,7,7,6,3,2,7,5,6,3,7,14,4\nS,4,5,6,5,6,8,7,5,5,7,6,7,5,8,9,11\nO,4,8,5,6,4,7,8,8,4,7,7,8,3,8,3,8\nE,4,7,6,5,5,6,8,3,7,11,8,9,3,8,4,7\nY,1,1,2,1,0,8,9,2,2,7,12,8,1,10,0,8\nO,9,14,6,8,4,4,9,6,5,10,8,9,5,10,5,7\nG,7,10,6,6,4,8,7,4,3,9,5,6,4,9,8,8\nA,3,4,5,3,2,8,2,2,2,8,2,8,2,7,3,7\nD,4,9,5,6,4,8,7,6,6,10,5,5,3,8,3,8\nY,4,10,6,7,3,7,10,1,8,6,12,8,1,11,2,8\nT,2,6,4,4,2,6,12,3,7,8,11,8,1,11,1,7\nV,3,5,4,3,2,4,12,2,3,9,11,7,3,10,1,7\nG,4,7,5,5,2,7,6,7,9,6,5,10,1,8,6,11\nN,5,9,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nG,6,9,8,7,10,8,7,4,3,6,5,9,8,7,9,13\nE,6,11,8,8,7,10,6,1,8,11,4,8,4,7,5,11\nD,4,5,5,4,4,6,7,5,7,6,4,7,3,8,5,6\nQ,2,1,3,2,1,8,7,7,5,6,6,8,3,8,4,8\nC,5,11,6,8,5,7,8,8,6,5,6,12,4,7,4,7\nW,4,10,6,8,8,7,7,6,3,8,8,7,6,8,4,9\nO,4,6,6,5,4,7,6,4,4,7,3,7,3,7,4,8\nI,3,11,5,8,6,10,6,1,4,9,4,4,3,8,5,8\nW,3,7,5,5,5,7,7,6,2,7,8,8,5,8,3,8\nP,4,7,6,5,4,6,12,5,3,12,5,1,1,10,3,8\nE,2,7,3,5,2,3,7,6,10,7,6,14,0,8,7,8\nV,4,7,4,5,3,3,11,2,2,9,11,8,2,11,1,8\nD,8,13,8,7,4,9,5,4,6,11,3,7,5,6,6,11\nN,2,2,3,3,2,7,8,5,4,7,7,6,5,9,2,5\nH,5,7,7,5,5,6,7,6,7,7,6,8,6,8,3,8\nW,4,9,6,7,3,8,8,4,1,6,8,8,8,10,0,8\nA,5,12,5,6,3,12,2,4,2,11,3,11,4,4,4,11\nN,7,13,7,7,3,6,9,4,5,4,5,11,6,11,2,7\nA,2,1,4,2,1,8,2,2,1,7,2,8,2,6,3,6\nZ,4,8,5,6,5,8,11,6,5,6,5,7,3,9,6,4\nN,5,7,8,5,3,7,8,3,5,10,6,7,6,8,1,7\nZ,2,4,4,3,2,8,6,2,9,11,5,9,1,8,5,9\nV,3,9,5,7,1,7,8,4,2,7,14,8,3,9,0,8\nB,8,13,8,7,7,8,8,4,5,9,5,7,8,4,9,7\nM,3,4,4,3,3,7,6,6,4,7,7,10,6,5,1,9\nR,6,11,9,8,6,9,9,4,7,8,3,8,3,6,5,11\nB,6,11,4,6,3,10,5,6,5,11,3,9,6,7,6,10\nE,1,3,3,1,1,6,7,2,6,10,7,10,1,8,4,9\nK,2,1,3,2,2,7,7,4,6,6,6,9,3,8,5,10\nE,2,3,3,5,2,3,8,6,10,7,6,14,0,8,7,8\nX,4,9,7,6,4,7,7,4,10,6,6,8,3,8,7,8\nZ,3,7,5,5,2,9,5,3,9,11,4,9,2,7,6,9\nA,3,7,5,4,2,8,3,3,3,7,2,8,3,6,3,8\nF,5,7,6,8,7,6,11,4,4,8,7,6,4,9,7,7\nO,4,8,5,6,4,8,7,8,6,7,7,10,3,7,4,7\nM,5,9,6,5,3,14,2,4,3,12,1,9,5,4,0,9\nO,3,5,4,3,3,8,7,7,5,7,6,8,2,8,3,8\nK,4,8,6,6,5,5,6,4,7,6,6,10,3,8,5,10\nQ,3,5,4,6,4,9,8,7,2,4,7,11,3,9,6,10\nI,5,7,6,8,6,7,9,4,5,8,7,7,4,8,9,10\nU,4,5,4,4,2,4,8,5,7,10,9,9,3,9,2,6\nG,5,10,6,7,3,7,5,8,9,6,5,10,1,8,6,11\nX,1,0,2,1,0,7,7,4,5,7,6,8,2,8,4,8\nA,2,5,3,3,2,11,3,2,2,9,2,9,2,6,2,8\nT,1,6,2,4,1,7,13,0,4,7,10,8,0,8,0,8\nJ,2,6,4,4,3,9,7,2,3,8,4,6,4,8,6,5\nA,3,5,5,4,2,10,2,2,2,8,2,9,3,5,2,8\nP,1,0,1,0,0,5,11,6,1,9,6,5,0,9,3,8\nK,4,8,4,6,2,3,6,9,2,7,7,11,3,8,2,11\nY,2,8,3,6,1,9,10,1,3,6,11,8,1,11,0,8\nW,5,11,8,8,4,8,8,5,2,7,8,8,9,9,0,8\nK,4,7,6,5,5,5,7,4,7,6,6,11,3,8,5,9\nW,4,2,6,4,4,8,11,3,2,6,9,8,8,12,1,7\nM,11,11,11,6,5,6,10,5,5,4,4,11,10,12,2,7\nW,8,10,11,9,14,7,7,5,5,6,5,8,11,9,10,9\nF,8,11,6,6,3,9,6,3,8,11,4,6,2,10,5,9\nK,4,7,5,5,5,6,8,5,3,7,5,8,4,6,7,10\nT,4,8,5,6,4,6,11,3,7,8,11,8,2,12,1,7\nU,3,7,5,5,4,8,7,7,5,6,7,9,5,8,3,7\nU,3,8,4,6,3,8,6,12,4,7,11,8,3,9,0,8\nI,1,6,2,4,1,7,7,0,7,14,6,8,0,8,1,8\nD,5,8,5,6,5,6,7,9,8,6,5,6,2,8,3,7\nL,1,0,1,1,0,2,2,5,5,1,1,6,0,8,0,8\nA,4,9,6,7,4,12,2,3,2,10,2,9,2,7,3,8\nI,3,10,4,7,2,7,7,0,8,14,6,8,0,8,1,8\nV,4,6,4,4,2,4,12,1,2,9,10,7,2,11,0,8\nZ,4,8,5,6,5,8,10,5,4,6,5,6,3,8,8,3\nH,4,9,4,6,2,7,8,15,1,7,5,8,3,8,0,8\nC,2,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10\nY,1,0,2,0,0,7,9,3,1,7,12,8,1,11,0,8\nH,6,10,6,6,3,7,8,3,5,9,5,7,6,7,5,8\nF,1,3,3,2,1,5,11,3,4,12,7,5,1,9,1,7\nA,3,2,5,4,2,10,2,2,2,9,1,8,2,6,3,8\nB,5,7,6,5,6,8,7,4,4,7,5,7,6,8,5,8\nT,7,9,7,7,4,5,10,1,8,11,9,6,0,9,2,4\nG,10,14,8,8,4,11,4,4,5,11,3,8,5,7,5,10\nE,6,9,8,7,6,5,8,3,8,11,9,9,3,9,4,6\nY,1,0,2,0,0,7,10,1,3,7,12,8,1,11,0,8\nX,9,15,10,9,6,8,8,2,7,11,6,6,5,12,4,8\nY,3,4,5,6,6,7,8,4,1,6,8,9,4,11,7,7\nV,3,4,5,6,1,9,8,4,3,6,14,8,3,10,0,8\nM,3,8,4,6,3,7,6,11,1,7,9,8,8,6,0,8\nX,3,8,5,6,3,7,7,4,9,6,6,8,3,8,6,8\nH,4,10,6,7,10,8,8,5,2,6,6,7,8,9,10,8\nS,5,5,6,8,4,8,9,6,10,5,6,6,0,7,9,7\nY,3,6,4,4,2,10,11,2,2,5,12,8,1,11,0,8\nR,4,8,6,6,7,6,7,3,4,6,6,9,7,10,7,5\nG,4,7,5,5,3,6,7,6,7,10,7,11,2,9,4,9\nB,3,2,4,4,4,7,7,5,6,7,6,6,2,8,6,10\nL,3,10,3,8,1,0,1,5,6,0,0,7,0,8,0,8\nT,4,11,5,8,3,7,14,0,5,7,10,8,0,8,0,8\nS,6,12,5,6,2,10,2,1,5,9,1,8,2,8,4,11\nI,1,3,1,2,1,8,7,1,7,7,6,7,0,8,2,7\nR,4,6,6,4,3,10,8,3,7,11,1,6,3,7,3,10\nE,3,9,4,7,4,7,7,7,9,8,8,10,3,8,6,7\nO,7,13,5,7,4,6,6,6,3,10,7,9,5,9,5,8\nY,7,11,7,8,5,4,9,1,7,9,10,6,2,12,4,3\nX,4,4,6,3,3,5,8,2,9,11,10,9,3,7,3,6\nO,4,8,6,6,5,7,7,8,4,7,5,8,3,8,3,8\nV,3,7,5,5,5,8,5,4,1,7,7,8,5,9,3,7\nH,3,7,4,5,2,7,8,14,1,7,6,8,3,8,0,8\nC,8,12,5,6,2,7,8,7,7,11,7,9,2,9,5,9\nP,7,10,6,5,3,5,11,5,2,12,5,4,4,9,4,8\nR,7,11,10,8,7,11,6,3,6,11,2,6,6,7,5,10\nA,2,1,3,1,0,7,4,2,0,7,2,8,2,7,1,8\nP,2,6,2,4,2,3,13,5,1,11,7,4,0,9,2,7\nR,3,8,3,6,2,6,10,9,4,7,4,8,3,7,5,10\nY,3,3,4,2,1,4,10,2,7,10,10,5,2,12,3,4\nT,3,8,4,6,1,8,15,1,6,6,11,9,0,8,0,8\nQ,5,10,7,9,3,8,7,8,7,6,7,9,3,8,5,9\nV,3,2,5,3,2,7,12,2,3,7,11,9,2,10,1,8\nJ,6,10,9,8,6,9,5,6,5,8,6,7,3,7,4,6\nN,5,9,7,7,4,8,7,3,5,10,5,6,6,8,1,7\nM,4,5,6,4,4,8,6,2,4,9,6,8,7,5,2,8\nV,7,9,7,7,3,3,11,5,5,12,12,7,3,10,1,8\nW,4,4,6,6,3,4,8,5,1,7,8,8,8,9,0,8\nE,1,0,1,1,0,5,7,5,7,7,6,12,0,8,6,10\nM,4,2,5,3,4,8,6,6,4,7,7,9,9,5,2,8\nS,3,5,3,4,2,8,7,7,5,8,5,7,2,8,9,8\nH,5,8,8,6,5,5,9,4,6,10,10,9,4,9,4,6\nJ,4,9,6,7,3,7,7,3,6,15,6,11,1,6,1,7\nT,6,9,6,7,2,5,13,3,9,12,9,3,0,10,2,4\nJ,3,6,4,4,2,9,4,5,4,14,7,13,1,6,0,7\nB,6,9,8,7,6,9,7,3,7,11,4,6,2,8,6,10\nJ,2,8,3,6,1,11,5,2,9,12,2,8,0,7,1,8\nS,2,4,4,3,2,9,6,2,7,11,5,8,1,9,4,9\nK,3,9,4,6,3,4,7,7,3,7,6,12,3,8,3,11\nL,4,9,6,6,3,7,3,2,8,7,2,8,1,6,2,7\nN,4,4,4,6,2,7,7,14,2,4,6,8,6,8,0,8\nF,5,7,7,8,7,7,8,4,6,7,6,6,4,9,9,9\nN,6,10,8,8,6,7,9,5,5,7,6,6,7,7,3,7\nV,1,0,2,1,0,8,9,3,2,7,12,8,2,10,0,8\nU,3,2,4,4,2,8,8,6,7,5,9,8,3,9,1,8\nP,5,7,6,10,11,8,6,6,1,7,6,7,9,8,6,8\nO,3,7,4,5,3,6,8,7,5,9,8,8,3,8,3,8\nI,4,10,5,7,3,7,7,0,7,13,6,8,0,8,1,8\nJ,3,7,5,5,2,9,5,5,5,15,6,11,0,6,1,7\nM,3,7,4,5,3,7,7,11,1,7,9,8,8,5,0,8\nP,8,9,7,4,3,6,11,6,2,10,4,5,4,9,4,8\nQ,5,10,6,9,6,8,8,7,4,5,10,9,3,8,5,9\nX,6,9,9,7,5,4,8,2,8,10,12,11,3,8,3,5\nZ,3,8,4,6,3,9,6,5,10,7,5,6,1,7,8,8\nM,1,0,2,0,1,7,6,9,0,7,8,8,6,6,0,8\nG,5,11,6,8,7,7,8,6,2,6,6,10,4,8,7,7\nT,4,5,5,7,2,5,15,1,6,9,11,7,0,8,0,8\nI,6,12,4,7,3,11,3,4,5,12,2,8,3,8,4,10\nI,4,10,5,8,4,6,8,0,7,13,7,8,0,8,1,7\nL,5,10,5,5,3,5,7,3,7,12,8,11,2,8,6,7\nJ,2,6,4,4,3,9,7,3,3,8,5,6,4,8,6,5\nJ,4,9,6,7,6,9,9,4,3,8,4,6,4,8,5,4\nP,3,3,4,5,2,4,11,9,2,10,6,4,1,10,4,8\nM,5,5,7,4,4,7,6,6,6,6,7,7,9,7,3,6\nI,3,11,4,8,3,9,7,0,7,13,5,8,0,8,1,8\nU,3,1,4,3,2,6,9,5,7,7,10,9,3,9,1,8\nB,2,3,4,2,2,8,8,3,5,10,5,6,3,7,5,8\nU,5,10,6,8,6,9,6,6,6,7,6,8,9,8,4,7\nF,3,8,3,5,1,1,13,4,4,12,11,6,0,8,2,6\nF,5,9,5,5,4,5,12,3,3,11,7,4,4,10,7,5\nL,5,10,7,8,4,7,3,1,9,9,2,11,0,7,3,9\nQ,6,10,8,9,4,8,6,8,8,6,5,8,3,8,5,8\nX,2,0,2,1,0,7,7,6,2,7,6,8,3,8,3,8\nP,2,3,3,2,2,5,9,4,4,9,7,4,1,10,3,7\nZ,2,1,2,2,1,7,7,5,9,6,6,8,1,8,6,8\nB,7,10,9,8,6,11,5,3,7,11,3,7,5,7,7,12\nE,6,9,8,8,8,6,9,4,4,8,7,10,5,11,8,10\nE,3,2,3,3,3,7,7,5,7,7,5,9,2,8,5,10\nF,6,9,8,6,5,6,11,3,5,13,7,4,2,10,2,7\nV,3,6,5,4,1,7,8,4,2,7,13,8,3,10,0,8\nM,4,11,5,8,4,7,7,12,2,7,9,8,8,6,0,8\nF,4,10,7,8,8,9,6,1,5,9,6,6,6,11,5,6\nK,4,10,6,8,6,7,6,5,4,6,6,8,4,6,10,11\nX,1,1,2,1,1,7,7,3,7,6,6,8,2,8,5,8\nY,5,8,5,6,2,4,10,1,7,10,10,6,1,11,3,3\nL,4,8,5,6,2,5,3,1,9,6,1,10,0,7,2,7\nT,5,8,5,6,3,5,11,2,8,12,10,5,1,10,2,4\nY,9,13,8,7,4,6,7,5,4,10,9,5,4,10,4,5\nS,8,15,6,9,3,11,3,4,5,11,2,9,4,6,5,11\nG,3,8,4,6,2,8,5,8,9,8,4,12,1,9,5,10\nB,6,10,8,7,6,10,7,3,7,11,3,6,2,8,5,11\nH,4,5,5,4,4,8,7,6,6,7,6,8,3,8,4,7\nJ,3,6,5,4,2,7,6,3,6,15,6,11,1,6,0,8\nV,4,5,7,8,2,7,8,4,3,7,14,8,3,9,0,8\nV,3,6,5,4,1,8,8,4,2,6,14,8,3,9,0,8\nY,4,7,6,5,3,5,9,1,7,8,12,9,1,11,2,7\nZ,3,9,5,7,5,8,6,2,7,8,6,8,1,7,11,10\nQ,2,4,3,5,3,8,7,6,2,8,7,9,2,8,4,9\nF,7,11,6,6,3,7,8,3,6,12,5,6,2,8,6,6\nY,3,7,5,4,1,7,10,3,2,7,13,8,2,11,0,8\nT,5,6,7,6,6,7,9,5,8,7,7,7,3,11,7,7\nE,4,9,5,7,6,6,7,5,8,7,6,10,3,8,6,9\nQ,3,5,4,6,4,8,7,7,3,8,6,9,2,9,3,8\nZ,3,5,6,4,3,8,7,2,10,12,6,9,1,8,6,8\nU,12,14,10,8,4,6,5,4,7,3,9,6,6,7,2,7\nX,2,3,4,1,2,7,7,4,9,7,6,8,2,8,6,7\nD,8,14,7,8,5,6,7,5,6,9,6,6,5,8,7,4\nI,5,10,4,6,2,9,7,3,6,13,4,5,2,9,4,10\nC,4,9,5,6,3,4,8,6,6,11,10,12,2,10,3,7\nN,6,9,5,5,2,4,8,4,6,4,4,11,5,9,2,7\nR,5,9,8,6,9,8,7,4,4,6,7,8,7,9,8,6\nU,4,5,5,4,2,4,8,5,8,10,9,9,3,9,2,6\nX,3,6,5,4,4,8,6,2,5,6,6,7,2,9,8,9\nL,7,11,9,9,7,9,7,8,6,6,6,7,6,7,9,15\nB,7,11,9,8,9,8,8,4,6,7,6,7,7,7,7,9\nF,3,10,4,8,3,1,12,3,4,12,11,8,0,8,2,6\nG,5,11,6,8,4,6,7,7,7,10,7,11,2,9,4,9\nK,4,5,6,5,5,9,5,2,4,8,4,8,4,6,6,11\nJ,3,9,5,7,3,5,7,3,5,15,8,11,1,6,1,7\nX,3,8,5,6,4,8,7,3,8,5,6,8,2,8,6,8\nR,1,0,1,0,0,6,8,7,3,7,5,7,2,7,4,11\nR,2,3,3,2,2,6,8,4,5,7,5,7,2,7,4,9\nR,3,8,5,6,5,8,8,7,4,8,5,7,4,7,6,11\nR,5,9,7,6,6,6,8,5,6,7,5,8,3,7,5,9\nJ,1,3,2,2,1,8,6,3,4,14,6,10,1,7,0,7\nZ,4,7,4,5,3,7,7,6,11,6,6,8,1,8,8,8\nA,4,9,7,6,4,8,3,1,2,5,2,8,3,5,3,7\nV,2,1,3,2,1,9,12,2,2,5,10,9,2,11,0,8\nG,6,9,6,7,5,5,6,6,5,9,8,11,2,9,4,10\nK,7,12,7,7,4,11,6,4,6,11,2,6,5,8,4,10\nI,3,6,4,4,2,7,7,0,7,13,6,8,0,8,1,8\nN,6,9,8,7,9,7,7,3,5,8,6,7,6,9,7,5\nX,5,8,7,6,3,8,7,2,8,10,4,7,3,8,4,8\nN,4,4,4,6,2,7,7,14,2,4,6,8,6,8,0,8\nE,5,10,7,8,6,7,7,6,9,7,6,9,6,8,6,9\nJ,3,7,5,5,2,8,5,5,5,15,7,12,1,6,1,6\nW,5,11,7,8,4,7,7,5,2,7,8,8,9,9,0,8\nZ,4,8,6,6,3,7,7,2,10,12,6,7,1,7,6,7\nD,7,12,7,6,4,12,2,4,5,12,1,9,4,7,2,11\nJ,4,5,6,5,5,8,8,4,6,7,6,8,3,9,8,8\nD,5,8,7,6,5,10,5,2,7,11,3,7,4,7,3,9\nJ,4,11,6,9,5,7,7,1,6,11,5,9,1,6,2,5\nZ,1,0,1,1,0,7,7,2,10,9,6,8,0,8,6,8\nW,6,10,8,8,4,4,8,5,2,7,9,8,9,9,0,8\nB,5,11,5,8,7,6,7,8,5,6,6,7,2,8,8,9\nS,4,5,4,4,3,8,8,7,5,7,6,7,2,9,9,8\nL,4,9,4,7,1,0,0,6,6,0,1,5,0,8,0,8\nC,5,11,6,8,3,4,7,7,11,7,7,12,1,7,4,8\nE,4,6,5,5,5,6,8,4,3,8,7,9,4,10,8,11\nJ,5,8,3,12,3,9,6,2,4,9,5,6,3,9,5,10\nE,5,9,8,7,5,10,6,2,8,11,4,9,3,8,5,11\nM,7,11,9,8,11,7,8,6,4,7,6,8,8,10,9,11\nW,7,10,7,8,7,4,11,2,2,9,8,7,7,12,2,6\nW,6,8,8,7,10,7,7,5,5,6,5,8,10,8,8,10\nX,5,10,7,8,5,8,7,3,9,5,6,9,5,7,9,8\nW,4,4,5,3,3,4,11,3,2,9,9,7,6,12,1,6\nJ,2,7,2,5,1,15,3,3,5,12,1,8,0,8,0,8\nD,2,3,4,2,2,9,6,4,6,10,4,6,2,8,3,8\nR,3,7,4,5,4,8,8,7,2,7,4,6,4,6,8,8\nH,4,7,6,5,5,7,9,7,5,8,5,7,3,7,5,10\nE,2,1,3,3,2,7,7,5,7,7,6,9,2,8,5,10\nC,4,7,5,5,2,4,9,6,8,12,10,11,1,9,3,7\nT,2,3,3,1,1,7,11,2,6,7,11,8,1,10,1,7\nO,4,7,5,5,3,7,6,8,5,7,5,8,3,8,3,8\nM,2,1,3,2,1,7,6,10,1,7,9,8,7,6,0,8\nF,3,11,4,8,2,1,13,5,4,12,10,7,0,8,3,6\nL,3,9,5,7,3,5,5,2,8,6,1,10,0,6,3,7\nS,4,6,5,4,5,8,9,4,4,8,5,6,3,9,9,7\nY,1,0,2,0,0,8,10,3,1,6,12,8,1,11,0,8\nR,4,5,5,7,3,5,10,9,4,7,4,8,3,7,6,11\nF,2,4,3,2,1,4,11,4,4,13,8,5,1,9,1,7\nZ,5,11,6,8,5,6,8,6,11,7,7,10,1,9,8,8\nY,3,2,5,4,2,6,10,1,8,8,12,8,1,11,3,7\nJ,5,9,7,7,4,6,7,3,6,15,7,11,1,6,2,6\nD,6,11,9,8,8,8,8,5,6,10,6,5,4,8,4,9\nT,2,1,3,2,1,8,12,3,6,7,11,8,2,11,1,8\nM,3,5,4,4,4,8,6,6,4,7,7,8,7,5,2,7\nQ,3,5,4,8,2,8,7,9,5,6,7,9,3,8,5,9\nO,1,0,2,0,0,7,7,6,4,7,6,8,2,8,3,8\nB,4,11,5,8,4,6,8,10,7,7,5,7,2,8,9,9\nD,2,4,4,3,2,9,6,4,6,10,4,6,2,8,3,8\nU,4,9,5,6,4,7,6,12,4,6,12,8,3,9,0,8\nZ,3,9,4,7,5,9,9,3,7,7,7,5,0,9,12,9\nC,4,7,4,5,3,5,8,5,6,11,9,13,2,9,3,7\nN,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nF,6,11,8,8,8,6,10,6,5,9,6,9,3,10,8,10\nJ,5,10,6,8,3,6,9,3,6,15,7,8,2,8,2,8\nC,5,11,6,8,3,5,8,8,8,8,8,14,2,9,4,9\nP,2,3,2,2,1,5,9,3,4,9,8,5,1,9,2,7\nL,7,10,9,7,7,6,6,9,5,6,6,9,3,7,6,13\nI,4,5,6,5,4,8,9,4,6,7,7,8,3,8,8,8\nM,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8\nS,4,6,5,4,3,7,8,3,7,10,4,7,2,6,4,8\nW,6,7,6,5,6,7,10,4,2,8,6,6,8,12,4,5\nZ,5,6,4,8,3,8,6,5,5,10,6,7,3,9,9,9\nC,1,3,2,2,1,6,8,6,7,7,8,12,1,9,4,10\nJ,5,7,7,8,6,8,9,4,5,7,6,7,3,8,9,8\nF,2,4,3,3,2,7,9,2,5,13,6,6,2,11,2,7\nS,4,7,6,5,3,9,7,4,8,11,3,8,2,8,5,11\nY,3,5,4,7,6,8,8,3,1,7,7,9,4,11,6,7\nT,3,4,4,2,1,5,13,3,6,12,9,3,1,10,1,5\nT,4,9,4,4,1,6,10,2,7,12,7,5,1,10,3,5\nN,4,2,5,4,3,7,9,5,5,7,6,6,7,8,3,7\nY,5,6,6,8,9,9,8,6,3,7,7,8,6,10,7,4\nC,3,3,4,2,1,4,8,4,7,10,9,12,1,9,2,7\nG,6,9,5,5,4,8,4,5,2,9,6,9,4,8,7,8\nF,8,15,7,8,5,8,10,3,5,12,5,4,4,9,7,7\nA,3,7,4,5,3,9,4,3,1,8,1,8,2,6,1,8\nQ,3,4,4,5,3,8,7,5,3,8,8,9,3,8,4,9\nU,4,7,6,5,7,7,7,4,4,6,7,8,10,9,5,8\nG,3,4,4,3,2,6,7,5,5,9,7,9,2,9,4,9\nQ,3,4,4,6,2,8,7,8,6,6,7,8,3,8,5,9\nK,5,9,6,5,3,3,9,3,6,9,8,11,4,7,3,6\nC,1,3,2,2,1,6,7,7,6,8,7,13,1,9,3,10\nO,4,5,5,8,3,8,8,9,7,6,7,9,3,8,4,8\nC,5,11,6,9,4,6,8,9,7,10,7,11,2,11,4,9\nW,4,6,6,4,3,9,8,4,1,7,8,8,8,9,0,8\nZ,4,10,5,7,5,7,8,5,9,7,6,9,1,9,7,7\nU,3,1,4,3,2,6,8,6,7,7,9,9,3,9,1,8\nR,3,7,4,5,4,6,9,8,3,7,5,8,2,7,5,11\nC,3,2,4,4,2,6,8,8,8,9,8,12,2,10,4,10\nC,2,1,3,2,1,6,8,7,8,8,8,13,1,9,4,10\nP,6,11,6,8,3,4,13,9,2,10,6,4,1,10,4,8\nN,4,6,7,4,6,7,8,3,4,8,7,7,6,9,5,4\nW,3,3,4,2,2,4,11,3,2,9,9,7,6,11,1,7\nU,6,8,7,6,5,4,8,5,7,9,8,10,5,8,3,4\nK,6,9,9,7,10,7,7,4,4,6,6,9,8,8,8,8\nD,4,8,5,6,4,7,7,5,5,7,6,7,4,8,3,7\nK,1,0,1,0,0,4,6,6,1,7,6,10,3,7,1,10\nM,5,6,8,4,5,7,6,2,5,9,7,8,8,6,2,8\nI,3,11,4,9,2,6,8,0,7,13,7,8,0,8,1,7\nW,4,8,6,6,5,8,8,4,1,6,9,8,7,11,0,8\nZ,3,5,3,3,2,8,7,5,10,6,6,7,2,8,7,8\nB,3,7,3,5,4,6,7,8,5,7,6,7,2,8,7,10\nG,9,14,7,8,5,8,7,5,4,9,5,6,4,9,8,8\nZ,5,5,7,8,3,7,7,4,15,9,6,8,0,8,9,8\nF,4,11,5,8,2,1,11,5,7,12,12,10,0,8,2,6\nT,2,5,3,4,2,7,12,3,6,7,11,8,2,11,1,8\nG,4,3,5,4,3,6,6,5,6,6,6,8,2,8,4,8\nJ,3,7,4,5,2,9,6,1,6,13,4,8,0,7,0,8\nA,8,12,8,6,4,11,0,4,3,12,6,14,4,5,5,11\nS,3,10,4,8,5,8,8,8,5,7,5,8,2,8,8,8\nN,5,6,7,4,3,7,8,3,5,10,6,7,5,8,1,7\nJ,2,9,3,6,3,11,5,2,6,11,2,7,0,7,1,7\nK,6,11,9,8,8,3,8,1,6,9,9,11,3,8,3,6\nU,4,6,6,6,5,7,6,5,4,6,6,8,4,8,1,7\nS,4,7,6,5,7,6,6,3,2,8,6,7,3,7,11,2\nY,4,7,6,10,10,7,5,4,3,8,8,9,9,10,8,8\nE,2,1,2,1,2,7,7,5,6,7,6,8,2,8,5,10\nO,5,11,5,8,5,8,7,8,4,9,6,6,4,9,4,8\nV,3,9,5,7,3,7,11,2,3,6,11,9,2,10,1,8\nW,7,10,9,7,6,4,8,5,1,7,9,8,8,10,0,8\nA,3,7,5,5,3,10,2,2,2,9,2,8,2,6,4,8\nI,2,6,4,4,2,7,7,0,8,13,6,8,0,8,1,7\nZ,2,2,2,3,2,7,8,5,9,6,6,9,1,9,7,8\nO,4,7,4,5,3,8,6,6,3,10,5,10,3,8,2,8\nP,4,9,6,7,4,6,10,5,6,10,8,3,1,10,4,7\nX,2,1,4,2,2,7,7,3,9,6,6,8,2,8,5,8\nM,5,10,8,7,10,12,4,3,1,9,4,9,10,6,3,8\nR,1,1,2,1,1,6,9,7,3,7,5,8,2,7,4,11\nM,4,7,7,5,9,8,7,3,3,8,4,7,10,5,2,6\nZ,2,5,3,3,3,8,7,5,9,6,6,7,2,8,7,8\nN,5,5,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nM,5,10,6,8,4,8,7,13,2,6,9,8,8,6,0,8\nL,3,6,4,4,2,5,3,5,8,2,2,4,1,6,1,5\nM,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8\nN,4,8,6,6,4,8,6,9,5,4,4,5,4,6,3,8\nW,9,10,8,7,6,5,10,3,3,9,8,7,10,11,4,5\nY,4,9,5,6,1,7,10,3,2,7,13,8,1,11,0,8\nN,3,6,5,4,3,4,9,3,3,10,10,9,5,8,0,8\nJ,3,8,4,6,2,9,5,4,5,15,6,12,1,6,1,7\nL,4,11,6,8,3,4,4,1,10,6,1,11,0,7,3,6\nC,7,11,8,8,4,2,10,5,8,11,11,11,1,8,2,6\nA,4,9,6,7,4,7,5,3,0,6,1,8,2,7,1,7\nF,2,3,2,1,1,5,11,3,5,11,9,5,1,9,3,6\nT,2,7,4,5,1,7,14,0,5,7,10,8,0,8,0,8\nN,2,3,4,2,2,8,9,3,4,10,4,5,5,9,1,7\nI,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nH,3,2,4,4,3,8,8,6,6,7,6,8,3,8,3,9\nA,3,7,5,6,4,6,8,2,4,7,7,9,5,9,3,7\nR,2,3,3,5,2,6,9,9,4,7,5,7,2,8,5,10\nH,5,8,7,6,7,6,7,6,6,7,6,10,3,8,3,9\nE,4,8,6,6,4,6,8,4,9,11,9,9,2,9,5,5\nY,3,5,4,3,2,4,10,1,7,10,10,5,2,11,3,4\nL,2,6,3,4,1,0,1,5,6,0,0,6,0,8,0,8\nJ,4,7,5,5,2,9,5,3,7,15,4,9,0,7,0,8\nA,1,1,2,1,0,7,4,2,0,7,2,8,2,6,1,8\nU,4,5,5,4,3,4,8,5,7,10,9,9,3,9,2,6\nY,2,7,4,4,1,9,10,2,3,6,12,8,1,11,0,8\nI,7,12,5,6,2,9,6,5,5,13,3,8,3,7,5,10\nO,6,9,8,8,7,7,5,4,5,9,3,7,4,8,6,9\nT,4,5,5,3,2,6,11,2,8,11,9,4,1,11,3,4\nK,4,8,6,6,4,4,7,6,7,6,6,13,4,7,6,10\nQ,3,6,5,5,3,8,7,7,5,6,6,8,2,8,4,9\nP,2,1,2,1,1,5,11,7,1,9,6,4,1,9,3,8\nP,5,8,8,6,4,6,12,7,2,11,5,2,1,10,4,9\nU,4,4,4,6,2,8,5,14,5,6,13,8,3,9,0,8\nV,5,9,5,6,3,2,11,2,3,10,11,8,2,11,1,8\nO,2,3,2,1,1,8,7,6,4,9,6,8,2,8,3,8\nL,3,2,4,3,2,4,4,5,7,2,2,5,1,6,1,6\nW,6,6,6,4,4,7,11,5,2,8,6,6,7,12,2,6\nI,6,15,4,8,3,11,5,5,4,13,3,8,3,8,5,9\nN,4,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8\nV,6,9,5,4,2,6,11,6,3,11,9,4,4,12,3,9\nK,4,8,6,6,4,5,7,4,8,7,6,10,4,8,5,9\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nI,0,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nH,2,6,3,4,1,7,8,14,1,7,5,8,3,8,0,8\nB,7,9,6,4,5,9,7,3,5,9,4,7,7,5,7,8\nD,8,12,7,6,5,8,6,3,7,10,5,7,6,7,8,6\nA,6,14,6,8,5,11,3,4,2,11,3,11,5,3,4,11\nR,7,10,9,8,11,7,7,3,5,6,6,9,7,9,7,6\nP,3,2,3,3,2,5,11,5,4,10,7,2,1,10,4,6\nL,5,11,7,8,5,9,3,1,8,9,2,10,3,6,5,10\nL,1,0,1,1,0,2,2,5,5,1,1,6,0,8,0,8\nE,2,4,4,3,2,6,7,2,7,11,7,9,2,8,4,8\nM,3,4,4,3,3,8,6,6,4,6,7,8,7,5,2,7\nJ,2,6,4,4,1,10,6,2,7,15,4,7,0,7,0,8\nI,4,9,5,7,3,9,6,0,7,13,5,9,0,8,1,8\nH,7,9,10,6,6,6,7,3,7,10,8,9,3,8,3,7\nV,3,8,5,6,1,8,8,4,3,6,14,8,3,10,0,8\nD,3,5,4,3,2,8,7,7,8,6,6,3,2,8,3,7\nD,10,15,9,8,5,7,6,5,6,9,4,5,5,8,6,9\nK,5,6,7,4,5,6,7,1,6,10,6,10,4,8,4,8\nD,5,8,8,6,5,8,8,7,8,10,5,4,3,8,5,8\nL,4,10,5,8,7,8,8,3,5,5,7,9,5,11,9,10\nY,9,15,8,8,4,7,8,4,4,10,7,5,4,9,5,3\nD,2,1,2,2,1,6,7,9,6,6,6,6,2,8,3,8\nV,6,11,9,8,10,7,5,5,3,8,8,9,9,9,6,8\nX,3,4,6,3,3,6,9,1,8,10,9,8,2,8,3,6\nN,2,3,4,2,1,5,9,3,3,10,8,8,5,8,0,7\nJ,1,5,3,4,1,9,6,3,6,14,5,10,0,7,0,7\nP,4,5,6,7,7,6,8,5,3,7,7,6,7,12,6,9\nW,5,9,7,7,7,10,11,2,2,6,8,7,9,13,2,8\nV,4,9,7,7,3,5,12,3,4,8,12,9,2,10,1,8\nF,4,7,6,5,6,7,8,5,4,8,6,9,4,11,9,11\nK,8,10,7,5,3,8,7,3,7,9,7,7,6,10,4,8\nE,3,7,4,5,3,7,7,6,8,7,6,9,3,8,6,8\nT,3,11,5,8,1,6,14,0,6,8,11,8,0,8,0,8\nO,5,9,5,6,4,7,7,8,5,10,6,8,3,8,3,8\nK,7,9,10,7,6,2,8,3,8,11,12,12,5,6,5,3\nR,5,8,7,6,6,8,8,5,5,9,5,8,3,8,5,11\nT,4,8,6,6,5,6,7,6,6,7,9,9,3,10,5,8\nI,2,8,2,6,2,7,7,0,8,7,6,8,0,8,3,8\nD,1,3,2,2,1,9,6,3,4,10,4,6,2,8,2,8\nK,4,4,5,6,2,3,7,8,2,7,6,11,3,8,2,11\nJ,1,6,2,4,1,11,3,9,3,13,7,13,1,6,0,8\nU,4,4,5,2,2,4,8,5,7,10,9,9,3,9,2,6\nH,2,4,4,2,2,7,8,3,5,10,6,7,3,8,2,8\nP,7,11,9,8,6,8,9,2,6,14,6,5,3,10,4,9\nK,3,4,6,3,3,6,7,2,7,10,7,10,3,8,3,7\nF,3,7,3,5,2,1,12,4,4,11,10,8,0,8,2,6\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nK,4,8,5,6,2,4,8,9,1,7,6,11,3,8,3,11\nQ,1,0,1,1,0,8,6,7,5,6,6,9,2,7,3,9\nQ,3,4,4,5,3,8,8,6,2,8,7,9,2,9,3,9\nL,4,11,5,8,3,9,2,2,7,9,2,10,1,5,3,9\nI,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nT,7,13,6,7,4,8,8,3,7,11,5,6,2,8,6,7\nA,4,8,6,6,3,13,3,4,3,11,1,8,2,6,2,9\nG,5,10,6,7,4,6,7,7,7,10,7,11,2,9,5,9\nO,6,9,8,7,5,7,6,8,4,7,5,8,3,8,3,8\nP,2,2,3,3,2,5,10,4,4,9,8,4,1,10,3,7\nN,4,10,6,8,4,8,8,6,5,6,6,6,6,9,2,5\nT,3,1,4,2,1,7,11,2,7,7,11,8,1,10,1,8\nV,5,9,6,7,4,8,11,3,2,5,10,9,5,11,5,8\nH,5,6,7,4,4,10,7,4,6,10,2,7,3,8,3,9\nG,1,0,1,0,0,7,6,6,5,6,5,9,1,7,4,10\nP,4,9,4,7,4,5,10,9,3,9,6,5,1,10,3,8\nD,3,6,5,4,3,9,7,5,7,10,5,4,3,8,3,8\nK,5,6,7,5,6,7,7,2,4,7,3,9,6,4,8,10\nZ,1,0,1,0,0,7,7,2,9,8,6,8,0,8,5,8\nX,4,6,5,4,3,7,8,1,8,10,7,8,3,8,3,7\nK,7,9,9,8,9,10,6,3,5,10,2,8,8,6,7,12\nG,4,7,5,5,5,8,7,6,2,7,6,11,4,8,7,9\nJ,4,9,5,7,3,9,6,2,6,14,4,8,0,7,0,8\nQ,9,15,8,8,5,9,4,5,9,11,3,11,3,6,8,11\nM,5,5,8,4,4,10,5,3,5,9,3,7,10,7,3,9\nQ,1,2,2,3,2,8,7,6,2,5,6,10,2,9,4,9\nC,6,9,6,7,3,3,8,5,7,12,10,12,2,9,2,7\nY,7,11,6,6,3,5,9,4,3,11,10,6,4,10,3,3\nF,3,10,4,7,2,0,13,4,4,12,11,7,0,8,2,6\nU,4,10,4,7,2,7,5,15,5,7,13,8,3,9,0,8\nT,6,9,5,4,2,5,11,3,6,13,7,5,2,8,3,4\nI,6,7,8,8,7,9,8,5,5,7,5,9,3,8,9,7\nF,4,8,4,6,3,2,12,5,6,11,10,8,0,8,2,7\nL,2,3,2,2,1,4,4,4,7,2,2,6,0,7,1,6\nL,3,7,5,6,4,8,5,5,4,6,7,8,3,8,8,11\nG,4,6,6,6,6,8,9,6,3,6,6,9,7,11,6,9\nH,2,4,3,3,3,7,7,5,5,7,6,8,3,8,2,8\nK,8,13,8,7,4,9,7,3,7,9,3,6,5,8,4,8\nC,2,5,3,3,2,6,8,7,8,8,7,13,1,9,4,10\nV,2,7,4,5,1,9,8,4,2,6,13,8,3,10,0,8\nQ,2,2,2,3,2,8,9,5,2,8,8,10,2,9,4,9\nY,1,0,2,0,0,8,10,3,1,6,12,8,1,11,0,8\nO,2,1,3,2,2,7,8,7,5,7,7,8,2,8,3,8\nY,8,14,6,8,4,7,8,4,4,9,8,5,4,10,4,4\nX,5,11,7,8,7,8,7,3,5,6,7,6,4,10,11,9\nA,1,0,2,0,0,8,4,2,0,7,2,8,1,6,1,8\nP,6,9,9,7,4,9,9,3,7,14,4,3,2,9,4,9\nL,2,2,3,4,1,3,4,3,8,2,1,7,0,7,1,6\nV,9,12,7,7,3,7,9,6,4,8,9,5,6,13,4,9\nF,2,5,3,4,2,5,11,3,5,11,9,5,1,10,3,6\nE,3,8,5,6,5,7,7,3,6,7,7,10,4,10,8,8\nZ,5,12,6,6,4,9,5,3,8,12,4,9,3,9,5,11\nU,3,3,4,1,1,4,9,5,6,11,10,9,3,10,2,7\nJ,2,5,3,4,1,11,6,2,6,11,3,8,1,6,1,7\nF,2,4,4,3,2,6,10,2,6,13,7,5,1,10,1,7\nF,4,6,6,4,5,10,7,1,5,9,5,6,4,10,4,7\nZ,1,3,2,2,1,7,7,5,8,6,6,8,2,8,7,8\nW,3,4,4,3,3,6,10,4,2,8,7,7,6,11,1,6\nJ,1,0,1,1,0,12,3,5,3,12,5,11,0,7,0,8\nP,4,9,5,6,3,5,10,10,5,9,6,5,2,10,4,8\nL,0,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8\nC,5,11,6,8,2,5,7,7,10,7,6,14,1,8,4,9\nA,5,8,7,6,6,8,9,7,5,6,6,8,3,7,7,4\nX,6,9,8,8,8,8,7,2,5,7,6,7,4,9,8,8\nQ,6,9,7,11,8,9,10,6,4,3,9,12,4,9,9,14\nA,3,2,6,4,2,10,2,2,2,9,1,8,2,6,2,8\nW,4,4,5,2,3,7,11,3,2,6,9,8,7,11,0,8\nK,4,9,5,6,2,3,8,8,2,7,5,11,4,8,3,10\nF,5,9,7,7,8,10,6,2,4,9,5,6,6,9,5,7\nE,4,8,4,6,3,3,9,6,12,7,5,14,0,8,7,7\nH,7,10,10,8,9,7,8,3,6,10,6,7,3,8,3,8\nG,8,11,7,6,4,7,6,6,5,9,5,6,4,7,5,7\nY,5,6,6,8,8,9,7,6,3,7,8,8,7,10,6,5\nT,2,3,3,4,1,7,14,0,6,7,11,8,0,8,0,8\nJ,2,8,3,6,1,12,2,9,4,14,6,13,1,6,0,8\nT,8,11,8,8,4,4,12,2,8,13,10,5,0,10,2,4\nV,4,10,5,8,3,5,11,3,3,9,12,9,2,10,1,8\nA,5,6,7,5,6,6,6,3,5,7,8,10,8,11,3,8\nV,5,9,4,4,2,9,9,5,4,5,10,6,4,11,3,5\nM,3,7,5,5,5,9,6,6,4,6,7,6,7,5,2,6\nU,5,10,6,8,5,6,8,8,5,5,6,12,5,9,7,3\nD,5,10,7,8,6,6,7,8,7,7,7,5,4,7,4,9\nQ,6,9,6,10,6,8,6,7,4,9,6,10,4,10,7,6\nX,4,6,6,4,3,7,7,1,8,10,7,9,3,8,3,7\nH,3,9,4,7,4,8,9,13,2,7,4,8,3,8,0,8\nV,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8\nG,4,6,4,4,3,6,7,5,6,9,8,10,2,9,4,9\nF,3,4,3,6,1,0,11,4,7,12,12,9,0,8,2,6\nH,6,7,9,10,8,7,11,5,1,8,7,5,4,12,10,4\nU,6,10,6,6,4,7,6,5,5,7,8,9,5,8,3,8\nW,4,4,5,2,3,4,11,3,2,10,9,8,6,11,1,6\nH,4,10,4,7,2,7,7,15,1,7,7,8,3,8,0,8\nM,6,10,8,8,7,6,7,3,5,9,9,9,8,6,2,7\nU,10,14,9,8,5,6,6,5,5,6,8,9,7,7,4,9\nE,3,3,3,5,2,3,9,6,11,7,5,14,0,8,7,8\nZ,6,6,5,9,4,7,8,5,3,11,7,7,2,9,9,7\nU,2,5,4,4,3,7,7,4,3,6,7,8,3,8,1,7\nU,3,6,4,4,1,7,5,13,5,7,14,8,3,9,0,8\nY,3,6,5,4,2,10,11,2,7,3,11,8,1,11,1,9\nT,1,0,1,0,0,7,14,1,4,7,10,8,0,8,0,8\nD,6,11,6,8,4,6,7,11,11,6,5,6,3,8,4,8\nI,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nM,2,0,3,1,1,7,6,10,0,7,9,8,7,6,0,8\nI,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nI,3,10,4,8,2,7,7,0,8,13,6,8,0,8,1,7\nE,2,3,3,2,1,6,8,1,7,11,6,9,2,8,3,8\nQ,8,13,8,7,5,12,4,3,6,10,3,7,3,9,6,13\nW,2,0,2,0,1,7,8,4,0,7,8,8,6,9,0,8\nH,7,9,10,7,8,6,8,2,6,10,7,8,5,9,4,8\nI,2,5,3,3,1,7,8,1,8,14,6,7,0,8,1,7\nQ,4,6,5,9,6,8,13,4,3,4,8,12,4,14,7,13\nD,3,6,5,4,3,7,7,8,7,7,6,5,3,8,3,7\nQ,6,10,8,8,7,7,4,8,4,6,7,10,5,7,7,9\nC,3,8,4,6,2,6,8,8,7,10,8,13,2,10,4,10\nV,5,7,5,5,2,3,11,4,3,10,12,7,2,10,1,8\nD,3,6,4,4,3,5,8,8,7,8,7,6,2,8,3,8\nJ,4,10,6,7,6,10,8,3,4,8,3,6,4,8,7,7\nN,3,6,4,4,3,7,7,12,1,7,6,8,5,9,0,8\nV,3,3,4,2,1,4,12,3,2,10,11,7,2,11,1,8\nU,5,7,5,5,3,3,9,5,6,11,11,9,3,9,1,6\nS,2,6,3,4,2,8,7,7,7,7,7,9,2,10,9,8\nA,3,7,5,5,3,11,2,3,2,10,2,9,3,7,3,9\nW,7,7,9,6,10,7,7,5,5,7,6,8,9,9,9,8\nF,4,11,6,8,4,5,11,4,6,11,10,5,2,10,3,5\nN,2,1,2,1,1,7,7,11,1,5,6,8,4,8,0,8\nN,2,4,3,3,2,7,8,5,4,7,6,6,4,8,1,6\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nP,4,6,5,4,4,6,7,6,4,7,6,8,2,9,7,10\nT,2,7,3,4,1,7,14,0,6,7,11,8,0,8,0,8\nD,6,9,9,8,8,6,7,5,7,7,4,7,4,6,6,5\nR,5,8,7,6,7,8,6,7,3,8,6,8,6,6,6,11\nE,5,9,5,7,3,3,8,6,11,7,6,15,0,8,7,7\nZ,7,10,9,8,6,7,8,2,9,12,7,7,1,8,6,8\nF,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8\nP,4,7,5,5,3,7,10,6,4,11,5,4,1,10,3,8\nT,7,15,6,8,3,6,9,3,8,13,6,6,2,8,5,4\nP,3,10,4,8,4,3,12,7,2,11,8,4,0,10,3,8\nK,3,7,4,5,1,3,7,7,2,7,7,11,3,8,3,10\nE,3,6,4,4,2,5,9,4,8,12,9,8,2,9,5,5\nW,10,11,10,8,8,2,11,2,3,10,11,9,7,10,2,6\nZ,4,9,4,7,2,7,7,4,14,9,6,8,0,8,8,8\nB,4,5,5,8,4,6,9,9,7,8,5,6,2,8,9,10\nR,3,7,4,5,4,7,9,6,5,8,5,9,3,6,5,10\nY,9,8,7,12,5,8,5,5,5,5,12,6,5,10,4,7\nZ,5,10,6,8,5,7,8,3,12,9,6,8,0,8,8,7\nZ,2,5,4,4,2,7,8,2,10,11,6,7,1,8,6,7\nI,2,10,2,8,3,7,7,0,7,7,6,8,0,8,3,8\nZ,4,8,5,6,5,8,8,3,7,7,6,7,1,9,10,8\nL,2,3,3,2,1,5,4,5,8,3,2,6,1,7,1,6\nE,3,5,3,4,3,7,8,5,8,7,5,9,2,8,6,9\nK,3,9,4,7,2,3,7,7,3,7,7,11,3,8,3,10\nG,3,7,4,5,3,6,6,5,4,6,6,9,2,9,4,8\nP,4,9,6,6,3,9,9,2,6,14,5,4,1,10,3,10\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nP,7,10,7,5,4,8,9,4,3,12,5,4,4,11,5,6\nK,6,11,8,8,5,6,7,2,7,10,7,10,3,8,3,8\nV,4,8,6,6,1,6,8,5,3,8,14,8,3,9,0,8\nT,3,4,3,3,1,5,13,3,5,11,9,3,1,11,1,5\nQ,2,2,3,4,3,8,8,6,2,5,7,9,3,9,5,9\nR,5,8,7,6,5,10,6,3,5,10,3,7,3,7,4,10\nP,6,7,8,10,9,7,9,4,2,7,8,7,6,11,6,5\nN,7,12,7,6,3,8,10,5,4,4,6,9,6,10,3,6\nB,4,9,5,6,5,7,8,7,4,7,5,6,4,7,6,6\nN,3,5,5,4,2,7,8,3,5,10,6,7,5,8,1,7\nR,5,11,6,8,7,6,8,9,5,6,5,8,2,8,8,9\nC,5,5,6,8,2,6,7,7,10,7,6,15,1,8,4,9\nM,4,7,4,5,3,7,7,12,1,7,9,8,8,6,0,8\nH,5,8,8,6,5,6,8,4,7,10,9,9,4,9,4,6\nQ,4,8,5,8,3,8,7,8,6,6,7,8,3,8,5,9\nQ,3,5,5,8,3,7,5,9,6,6,4,8,3,8,4,8\nS,4,7,5,5,4,7,7,5,8,5,6,9,0,9,9,8\nJ,4,9,5,7,2,10,6,2,8,14,3,8,0,7,0,8\nR,5,10,5,8,6,6,9,9,4,6,5,7,3,8,5,11\nF,5,8,6,10,8,7,9,5,5,7,6,7,4,9,9,7\nP,1,0,1,0,0,5,11,6,1,9,6,5,0,9,3,8\nI,1,11,0,8,1,7,7,5,3,7,6,8,0,8,0,8\nI,3,11,6,8,7,11,6,1,6,8,4,5,3,8,5,9\nS,2,7,3,5,3,8,8,8,6,8,4,6,2,6,8,8\nQ,5,6,6,8,5,8,6,8,4,5,6,9,3,8,6,10\nV,7,11,6,6,3,9,8,6,4,7,10,7,7,12,3,8\nA,3,8,6,6,3,11,2,2,2,9,2,9,3,7,3,9\nQ,4,5,5,8,3,8,7,8,6,5,6,8,3,8,5,9\nJ,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8\nU,2,0,2,1,0,8,5,11,4,7,13,8,3,10,0,8\nG,4,7,6,5,5,7,8,5,3,6,6,11,4,8,7,8\nH,4,2,5,4,4,8,7,6,6,7,6,8,3,8,3,9\nK,5,11,7,8,8,5,7,5,4,7,5,8,4,6,10,8\nB,3,4,5,3,3,8,7,2,6,11,5,7,2,8,4,9\nO,6,10,9,8,10,8,7,5,1,7,6,8,9,9,7,11\nB,3,5,5,3,3,8,7,2,6,10,5,7,2,8,4,10\nH,3,6,4,4,3,7,6,12,1,7,7,8,3,8,0,8\nH,5,11,8,8,9,8,8,6,7,7,5,8,3,8,4,7\nJ,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8\nK,3,6,4,4,3,5,7,4,8,6,6,11,3,8,6,9\nE,3,8,5,6,6,5,7,3,7,8,7,12,4,9,8,8\nR,10,11,8,6,4,10,5,5,5,10,3,8,6,6,6,11\nF,4,9,5,7,4,4,11,4,6,11,10,6,2,10,3,5\nI,4,6,5,7,5,8,9,4,5,8,7,8,4,7,8,7\nB,4,10,6,7,7,8,6,4,4,6,5,7,4,9,5,8\nQ,4,5,5,7,5,9,11,6,2,3,7,12,3,10,5,10\nG,4,9,5,7,2,7,6,8,9,7,5,11,1,8,5,10\nA,5,10,7,8,5,8,4,3,0,7,1,8,2,7,4,8\nB,2,3,3,2,2,8,7,3,5,10,6,7,2,8,4,9\nY,2,1,3,1,0,7,10,3,1,7,12,8,1,11,0,8\nA,3,5,5,3,2,11,2,2,2,9,2,9,2,6,2,8\nV,8,11,8,6,4,7,10,4,5,8,9,6,5,11,3,7\nZ,6,10,9,7,5,5,10,4,10,11,10,6,1,9,6,5\nW,11,12,10,6,4,4,10,2,3,9,11,8,9,12,1,6\nH,5,6,7,4,4,7,7,3,7,10,7,9,3,8,3,7\nH,4,5,5,4,4,7,7,6,6,7,6,8,3,8,3,7\nG,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,9\nZ,4,6,5,4,4,9,9,5,4,6,5,7,3,8,8,4\nN,5,11,6,9,3,7,7,15,2,4,6,8,6,8,0,8\nS,5,9,6,6,4,6,7,4,7,10,10,9,2,9,5,5\nB,7,15,7,8,6,10,6,4,5,10,4,7,7,6,8,9\nQ,5,7,6,6,5,9,3,4,4,8,3,10,3,6,6,7\nZ,5,11,7,8,5,7,7,3,13,9,6,8,0,8,8,8\nQ,2,3,2,3,2,8,8,5,1,8,7,9,2,9,4,8\nM,6,10,7,8,7,8,5,11,0,7,9,8,10,5,3,7\nG,7,11,7,8,5,7,6,7,7,10,7,13,3,8,5,7\nY,4,6,6,8,8,9,6,6,3,7,8,8,6,10,6,4\nA,2,7,4,5,2,8,3,2,2,7,1,8,2,6,2,7\nJ,2,6,2,4,1,13,2,7,4,13,4,12,0,7,0,8\nJ,2,8,2,6,1,15,3,4,5,12,1,8,0,7,0,8\nE,8,12,5,6,3,8,6,5,7,11,6,9,2,10,8,9\nD,5,11,7,8,8,7,7,4,8,6,6,6,3,8,3,7\nZ,4,7,4,5,3,7,7,5,11,7,6,9,1,8,8,8\nN,2,2,3,4,2,7,8,5,4,7,7,7,5,9,2,6\nO,4,8,5,6,4,7,7,7,4,10,6,8,3,8,3,8\nE,2,1,2,1,1,4,8,5,8,7,6,13,0,8,7,9\nG,2,2,3,3,2,7,6,6,5,6,6,10,2,9,4,9\nO,8,9,5,5,3,7,9,6,5,9,5,6,4,9,5,8\nG,4,7,4,5,3,6,7,6,7,9,8,9,2,10,4,9\nT,7,9,6,5,2,8,8,4,8,13,5,7,2,8,5,6\nY,5,8,5,6,4,4,9,1,6,9,10,7,3,11,4,4\nG,4,7,6,5,3,5,5,5,7,6,5,9,2,10,4,7\nT,6,8,6,6,4,6,12,4,6,11,9,4,3,12,3,4\nA,1,1,2,1,0,7,4,2,0,6,2,8,2,7,1,8\nO,5,11,6,8,9,8,10,5,2,7,7,8,8,9,5,10\nA,3,9,5,7,4,12,2,3,3,10,2,9,3,8,4,9\nM,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8\nO,4,7,4,5,3,7,7,7,5,10,6,8,3,8,3,8\nT,3,4,4,3,2,5,12,3,6,11,9,4,1,11,1,5\nX,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nK,6,9,7,4,3,5,8,3,5,9,9,10,5,10,3,6\nD,4,8,6,6,5,8,8,5,6,10,5,4,3,8,4,8\nX,5,9,8,7,7,6,7,3,6,8,8,11,6,6,8,6\nB,6,9,8,7,6,9,6,4,7,9,5,7,3,8,6,10\nF,7,12,6,6,3,6,8,2,7,10,7,6,2,10,5,6\nA,5,11,7,9,5,8,3,3,1,7,2,8,5,8,5,10\nD,4,9,4,4,3,7,8,4,7,10,5,6,4,8,5,6\nC,6,10,6,7,3,4,8,6,8,12,10,12,1,9,3,7\nE,4,7,6,5,5,8,7,6,3,7,6,10,4,8,8,9\nE,3,9,4,7,2,3,8,6,11,7,5,15,0,8,7,7\nB,4,4,5,6,4,6,8,9,7,7,5,7,2,8,9,9\nY,5,10,5,7,3,3,10,2,8,11,11,6,0,10,2,4\nD,2,5,4,3,3,9,6,4,6,10,4,6,2,8,3,8\nE,4,6,5,4,5,7,9,5,4,6,6,9,4,7,7,7\nN,10,14,12,8,5,12,5,3,4,13,1,6,6,7,0,8\nV,8,10,8,8,4,2,13,5,4,11,12,7,3,9,2,7\nQ,4,8,5,9,5,8,9,6,2,6,8,12,3,10,6,8\nY,3,7,5,5,2,4,9,1,7,10,12,9,1,10,2,7\nW,12,15,11,8,5,6,10,2,3,7,10,7,10,12,1,6\nU,8,9,9,7,6,4,7,5,8,9,7,9,6,8,4,3\nM,3,1,3,1,1,8,6,11,0,7,9,8,7,6,0,8\nZ,3,7,4,5,2,7,7,4,13,9,6,8,0,8,8,8\nE,3,6,5,4,3,5,9,5,8,11,10,8,3,8,4,4\nW,3,1,4,2,1,7,8,4,0,7,8,8,7,9,0,8\nO,5,9,4,4,2,9,7,5,4,9,4,8,4,9,5,8\nT,5,10,7,7,6,6,7,7,7,7,6,8,4,11,7,8\nX,3,7,5,5,3,5,8,1,7,10,10,9,2,9,3,6\nF,5,9,7,6,4,10,7,2,6,13,4,6,6,9,5,9\nL,1,4,3,3,1,6,5,2,9,7,2,10,0,7,3,7\nM,5,2,7,4,5,8,6,6,5,6,7,7,10,6,3,6\nP,8,9,6,4,3,8,8,5,4,12,3,6,5,9,4,8\nW,6,10,7,5,4,7,8,3,4,6,9,6,9,9,2,6\nE,3,9,4,7,4,6,7,7,9,8,8,10,3,8,6,8\nU,6,8,7,6,3,3,9,5,8,11,11,9,3,9,2,6\nI,3,7,5,5,5,9,7,2,4,8,5,5,3,9,5,6\nH,5,7,8,5,4,8,7,3,6,10,4,7,3,8,3,8\nE,4,11,5,8,5,2,8,5,10,7,5,14,0,8,6,9\nD,4,9,5,7,3,5,7,10,9,6,5,6,3,8,4,8\nQ,1,2,2,3,2,8,7,6,2,6,6,9,2,9,3,10\nR,6,11,6,8,4,5,13,8,4,7,3,9,3,7,7,11\nM,2,1,2,1,1,8,6,10,0,6,9,8,6,6,0,8\nB,5,10,8,8,11,8,7,5,3,6,7,7,7,10,9,9\nM,2,4,3,3,3,8,6,6,4,6,7,6,6,6,2,6\nY,7,8,7,6,3,2,12,4,6,13,11,5,1,11,1,5\nG,5,9,4,4,3,8,6,5,2,9,6,8,3,10,7,8\nY,2,3,3,2,1,4,10,2,7,10,10,5,1,11,2,4\nU,4,4,5,3,2,4,8,5,7,11,10,9,3,9,1,7\nO,1,0,2,0,0,7,7,6,4,7,6,8,2,8,3,8\nK,4,7,6,5,4,6,7,1,6,10,7,10,3,8,3,8\nX,2,2,4,3,2,8,7,3,9,6,6,8,3,8,6,8\nX,2,1,3,3,2,7,8,3,8,6,6,7,2,8,6,7\nC,2,1,2,2,1,6,8,7,6,9,7,11,2,10,4,10\nU,6,10,7,7,4,3,8,5,7,10,9,9,3,9,2,6\nX,4,8,7,6,6,8,7,2,6,7,6,7,5,7,7,8\nN,5,5,6,7,3,7,7,15,2,4,6,8,6,8,0,8\nM,4,7,5,5,6,7,7,7,4,6,5,8,6,9,7,8\nD,3,6,5,4,4,7,7,6,6,6,5,5,3,8,3,7\nE,4,7,6,5,4,5,9,2,8,11,7,9,3,9,5,7\nR,4,8,5,6,4,8,8,5,7,6,4,7,3,6,5,8\nA,4,8,6,6,4,9,5,3,0,8,1,8,2,7,1,8\nU,5,10,6,8,4,5,8,7,7,8,10,10,3,9,1,8\nZ,4,4,5,7,2,7,7,4,15,9,6,8,0,8,8,8\nI,2,10,3,7,2,9,7,0,7,13,5,8,0,8,1,8\nH,2,1,3,1,2,6,7,5,6,7,6,8,5,8,3,8\nF,5,7,6,8,7,7,9,4,4,7,6,7,4,9,9,8\nK,5,10,5,8,4,4,7,7,3,7,6,12,3,8,3,11\nR,4,7,4,5,4,6,9,8,3,7,5,8,2,7,5,11\nP,2,3,4,2,2,7,10,3,4,12,4,3,1,10,2,8\nH,8,11,12,8,10,9,6,3,7,10,5,8,6,8,5,8\nQ,6,6,8,6,6,8,3,4,5,7,3,9,5,5,6,8\nG,7,10,6,6,3,7,6,6,5,10,5,7,4,7,5,7\nD,6,7,8,6,8,7,6,5,6,7,5,9,5,6,8,3\nB,6,10,8,8,9,8,6,5,6,9,5,7,4,9,7,10\nF,4,9,4,6,2,1,14,5,3,12,9,4,0,8,3,6\nU,3,7,4,5,3,7,5,12,4,7,11,8,3,9,0,8\nK,3,4,6,3,3,7,7,1,6,10,5,9,4,7,4,8\nR,3,7,4,4,2,5,11,8,3,7,3,9,3,7,5,11\nX,5,9,7,8,8,8,7,2,4,8,6,8,4,10,8,6\nW,3,6,5,4,3,7,10,2,2,7,9,8,6,11,0,8\nN,4,7,6,5,4,7,9,6,5,7,6,6,5,9,1,6\nW,1,0,2,0,1,8,8,4,0,7,8,8,5,10,0,8\nK,5,8,7,6,5,4,7,2,6,10,10,11,3,8,3,6\nE,2,3,4,2,2,7,7,2,7,11,6,9,2,8,4,8\nE,2,3,3,2,2,7,7,5,7,7,6,8,2,8,5,10\nA,3,8,6,6,3,12,2,2,2,10,2,9,2,6,2,8\nR,2,1,3,2,2,7,8,5,5,7,5,6,2,7,4,8\nS,2,3,4,2,1,9,6,3,7,10,4,8,1,8,5,10\nP,6,10,5,5,2,6,10,6,5,14,5,5,3,9,4,8\nF,1,1,2,1,0,3,12,4,3,11,9,6,0,8,2,7\nG,5,7,5,5,4,6,8,5,5,9,8,8,2,8,4,9\nO,4,7,5,5,4,7,7,7,4,8,5,10,4,8,3,7\nX,5,8,7,7,7,9,8,3,5,8,5,6,3,6,8,8\nB,5,11,7,8,8,7,6,8,6,6,6,6,3,8,8,11\nZ,1,0,1,1,0,7,7,2,10,8,6,8,0,8,6,8\nM,3,3,3,4,2,8,6,11,1,6,9,8,7,6,0,8\nN,1,3,3,2,1,6,8,3,3,10,7,8,4,8,0,7\nB,3,2,4,4,3,7,7,5,5,6,6,5,2,8,6,10\nV,4,7,6,6,7,6,7,5,4,7,6,8,6,9,7,10\nJ,4,11,5,8,3,11,5,4,7,12,2,9,1,6,2,6\nD,5,10,7,8,7,9,7,6,6,8,5,5,5,9,4,10\nV,5,8,5,6,2,4,12,4,4,10,12,7,3,10,1,8\nG,6,11,6,8,5,5,6,5,5,9,8,11,2,9,4,9\nG,7,14,6,8,5,7,7,5,4,9,5,6,4,9,9,8\nQ,3,6,4,7,4,9,8,7,2,5,7,10,3,9,5,9\nC,4,5,5,5,4,6,8,3,5,6,6,11,4,9,7,9\nF,9,14,8,8,6,7,11,3,4,12,6,4,6,8,9,5\nD,6,11,9,8,8,8,8,5,6,10,6,5,6,9,6,11\nH,6,9,8,7,7,6,7,5,5,7,6,8,6,7,7,11\nM,5,8,8,6,5,3,7,4,5,11,12,11,6,9,3,7\nL,3,2,4,3,2,5,4,5,7,2,2,5,1,7,1,6\nS,5,9,7,8,8,9,8,4,5,7,6,8,5,10,9,11\nP,5,11,5,8,3,5,10,10,4,9,6,5,2,10,4,8\nY,7,10,6,5,4,7,6,4,4,9,8,5,3,10,4,4\nS,5,7,6,5,4,9,7,4,7,10,3,7,2,7,5,10\nQ,3,5,4,6,4,9,10,6,2,4,7,11,2,9,5,10\nT,2,4,3,5,1,5,14,1,6,9,11,7,0,8,0,8\nY,4,11,7,8,4,7,10,1,7,6,12,9,1,11,2,8\nM,3,7,4,5,3,8,7,11,1,6,9,8,8,6,0,8\nO,4,8,5,6,2,7,7,8,7,7,6,8,3,8,4,8\nX,4,9,6,7,3,6,8,1,8,10,9,8,3,8,4,7\nN,6,11,8,8,8,5,9,3,4,9,8,9,9,8,7,3\nB,4,8,6,6,6,8,7,4,5,9,5,6,3,8,6,9\nE,2,3,4,2,2,8,6,1,7,11,5,9,2,8,4,10\nH,3,7,4,5,2,7,9,14,2,7,3,8,3,8,0,8\nX,5,9,8,7,4,5,8,2,8,11,11,9,3,9,4,6\nK,4,8,4,6,2,3,7,8,2,7,6,11,4,8,2,11\nO,4,9,5,7,5,8,6,7,3,10,5,9,3,8,3,7\nY,4,6,6,9,7,9,10,6,3,7,7,7,6,11,6,5\nC,6,10,7,8,4,6,8,7,8,13,8,10,2,11,3,7\nW,6,9,8,6,7,7,7,6,3,5,8,9,11,8,6,4\nU,5,5,6,8,2,8,4,14,6,6,14,8,3,9,0,8\nF,6,10,8,7,8,8,8,6,4,8,6,7,5,11,9,11\nC,1,0,1,1,0,7,7,5,7,7,6,13,0,8,4,10\nO,5,9,6,7,5,8,7,8,7,7,6,8,3,8,4,8\nX,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8\nE,4,6,6,4,5,7,9,5,3,6,6,8,4,8,6,7\nU,5,8,8,6,4,7,8,6,9,5,9,8,3,9,1,8\nQ,4,7,5,7,3,8,7,8,6,6,7,9,3,8,5,9\nR,5,11,7,8,6,7,7,4,8,7,6,6,3,8,5,8\nP,6,12,6,7,4,6,10,3,5,13,6,3,3,10,5,6\nS,3,9,4,7,4,8,8,5,7,5,5,6,0,8,8,8\nZ,2,4,5,3,2,7,8,2,9,11,7,6,1,8,5,7\nK,7,12,7,7,4,5,8,3,5,10,9,11,5,9,3,7\nR,6,9,8,7,8,9,5,7,4,7,5,7,6,7,8,9\nP,5,10,8,8,5,8,9,5,5,12,4,3,2,10,4,8\nD,3,5,4,7,2,5,6,11,7,5,5,5,3,8,4,8\nD,7,9,9,8,9,6,7,5,7,6,3,7,5,10,9,5\nQ,2,2,2,2,1,7,8,5,2,7,8,10,2,9,4,8\nL,4,11,6,9,5,8,4,0,8,9,3,10,2,5,3,9\nN,6,10,8,7,5,8,9,2,5,9,4,6,5,9,1,7\nV,3,6,5,4,2,7,12,3,4,8,12,8,3,10,1,8\nA,1,1,2,1,0,7,4,2,0,7,2,8,2,7,1,8\nS,6,10,5,5,2,8,4,5,4,9,3,8,4,6,5,9\nR,6,10,6,8,4,6,10,10,5,7,5,8,3,8,6,10\nD,5,10,7,8,7,7,8,7,5,8,7,7,7,8,3,7\nT,4,10,6,8,5,6,7,8,7,8,8,7,3,10,6,10\nV,4,6,5,5,6,8,7,5,4,7,6,9,6,8,8,4\nE,6,11,8,8,5,4,10,4,9,11,10,9,2,8,5,4\nC,4,10,6,8,4,4,9,5,6,5,7,14,4,8,5,7\nW,6,10,9,8,8,5,12,2,2,8,8,9,9,14,2,8\nV,1,3,2,1,1,6,12,2,3,8,11,8,2,11,0,8\nH,2,7,3,4,2,7,6,14,2,7,8,8,3,8,0,8\nJ,2,8,3,6,1,13,3,9,4,13,4,12,1,6,0,8\nB,4,7,4,5,3,6,7,9,7,7,6,7,2,8,8,9\nN,5,8,8,6,3,3,9,4,4,11,11,10,5,8,1,7\nI,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nL,5,11,5,6,3,7,4,3,5,11,7,12,3,7,6,8\nZ,5,8,7,10,7,11,5,5,4,9,3,8,3,6,6,9\nK,10,12,10,7,4,9,8,4,8,9,1,5,5,6,3,9\nZ,3,6,4,4,3,7,8,5,10,7,7,9,1,9,7,8\nN,6,7,8,5,4,11,7,3,6,10,1,4,5,9,1,7\nI,2,6,3,4,1,7,7,0,7,13,6,8,0,8,1,8\nM,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8\nZ,3,7,4,5,2,7,7,4,14,9,6,8,0,8,8,8\nX,10,12,9,6,4,10,6,3,8,10,4,7,4,12,4,9\nO,4,9,3,5,3,6,8,6,4,9,7,9,5,9,4,8\nT,2,4,4,6,1,7,14,0,6,7,11,8,0,8,0,8\nZ,4,7,6,5,3,7,7,2,10,12,6,8,1,9,6,8\nP,5,10,7,8,6,6,6,6,4,7,6,9,5,8,7,10\nM,7,11,8,8,4,7,7,13,2,7,10,8,9,6,0,8\nI,1,10,0,8,1,7,7,5,3,7,6,8,0,8,0,8\nY,2,3,2,2,1,4,11,3,5,11,10,5,1,11,2,6\nG,6,6,7,8,3,8,5,8,9,6,5,10,2,8,6,11\nN,5,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nN,3,5,6,4,3,9,8,3,5,10,3,5,5,9,1,8\nD,5,11,6,8,4,5,7,10,10,6,5,6,3,8,4,8\nD,4,7,4,5,2,5,7,10,8,7,6,5,3,8,4,8\nT,2,9,4,6,1,7,14,0,6,7,11,8,0,8,0,8\nL,2,5,4,3,2,7,3,2,8,8,2,9,1,7,3,8\nG,3,6,4,4,3,6,7,5,5,10,8,10,2,9,4,9\nO,5,8,7,6,8,8,6,5,2,7,5,8,9,9,5,11\nR,5,10,5,7,3,5,10,9,4,7,4,8,3,7,6,11\nS,6,13,5,7,3,10,4,5,5,10,3,10,4,5,4,10\nX,3,1,4,2,2,7,7,3,9,6,6,8,3,8,6,8\nL,1,3,2,1,1,7,3,1,6,9,3,10,0,7,2,9\nW,6,6,6,4,4,2,11,2,2,10,10,8,6,11,1,7\nJ,0,0,1,1,0,12,4,5,3,12,4,10,0,7,0,8\nF,3,7,5,5,2,5,12,4,6,12,9,4,1,10,3,4\nF,2,5,3,4,2,5,10,4,5,10,9,5,2,10,3,6\nC,2,4,3,3,1,6,8,7,8,8,7,14,1,9,4,10\nC,2,7,3,5,2,5,8,7,7,8,8,13,2,9,4,10\nU,5,9,5,7,2,7,4,14,5,7,14,8,3,9,0,8\nV,8,11,7,8,4,3,11,3,4,10,12,8,3,10,1,7\nL,2,5,4,4,2,6,4,1,8,8,2,10,0,7,2,8\nX,2,3,3,2,2,7,8,3,8,6,6,7,2,8,5,7\nV,4,6,6,5,6,7,7,4,4,7,6,8,6,10,7,7\nG,7,8,9,7,10,7,6,5,4,7,7,9,8,10,9,7\nD,6,10,6,5,4,9,6,3,7,10,4,7,5,7,8,7\nL,4,9,4,7,2,0,2,4,6,1,0,8,0,8,0,8\nP,1,0,1,1,0,5,10,6,1,9,6,4,1,9,2,8\nX,3,4,5,3,2,9,6,1,8,10,4,7,2,8,3,8\nC,8,12,6,6,2,5,11,5,8,11,8,9,1,7,5,8\nM,6,10,7,8,4,7,7,13,2,7,9,8,9,6,0,8\nY,3,6,4,4,1,5,11,2,2,8,12,8,1,11,0,8\nR,6,11,9,8,9,6,8,5,6,6,5,8,3,7,5,9\nA,3,8,5,5,2,9,6,3,1,8,0,8,2,7,1,8\nQ,4,3,5,5,4,8,8,6,2,5,7,10,3,9,6,9\nM,3,3,4,1,2,8,6,6,4,6,7,8,6,5,2,7\nU,5,5,6,3,3,4,8,5,8,10,9,9,3,9,2,6\nT,6,9,6,6,5,6,11,4,6,11,9,5,3,12,2,4\nJ,2,8,3,6,1,13,2,8,4,13,4,12,1,6,0,8\nM,4,7,6,5,5,9,7,2,4,9,6,7,7,5,2,7\nO,4,11,5,8,3,8,8,9,7,7,8,8,3,8,4,8\nU,5,9,6,7,6,8,6,8,5,7,6,9,3,8,4,6\nE,7,10,9,8,8,7,4,6,4,7,6,9,5,9,9,8\nN,4,5,6,5,5,7,8,5,4,7,5,7,6,9,5,3\nL,3,7,4,5,2,6,4,1,9,8,2,11,0,7,2,8\nY,4,9,6,7,3,8,10,1,8,5,12,8,1,11,2,8\nZ,5,9,7,7,5,9,6,2,9,11,4,9,3,7,7,9\nW,9,11,9,6,5,5,8,2,4,7,9,7,10,10,2,5\nK,2,3,4,1,1,4,9,2,6,10,9,10,3,8,2,7\nQ,1,0,1,0,0,8,7,6,3,6,6,8,2,8,3,8\nC,2,3,3,4,1,5,8,6,8,7,7,12,1,7,4,9\nR,2,3,2,2,2,6,8,4,4,7,5,7,2,7,3,8\nT,3,4,3,3,1,5,11,3,7,11,9,5,1,11,2,5\nY,3,5,4,4,2,4,11,2,7,11,10,6,1,11,2,5\nZ,3,5,5,4,2,8,7,2,10,12,5,9,1,8,6,9\nZ,3,4,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nM,4,3,4,4,2,7,7,12,1,7,9,8,8,6,0,8\nI,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7\nO,4,10,5,8,5,8,8,8,5,6,8,9,3,8,3,7\nE,2,1,3,2,2,7,7,5,7,7,6,8,2,8,5,10\nY,5,9,5,4,3,6,6,5,4,9,10,6,3,9,2,5\nE,2,3,4,2,2,7,7,1,8,11,6,9,1,8,4,8\nQ,7,10,6,5,3,10,4,4,7,12,3,11,3,7,7,11\nR,2,2,3,4,3,7,7,5,6,7,6,7,3,7,5,8\nT,7,10,7,8,5,5,11,3,6,11,10,5,2,12,2,4\nE,4,9,6,7,6,7,7,5,8,7,7,9,3,8,5,9\nV,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8\nU,3,4,4,3,2,4,8,5,6,11,10,9,3,9,1,7\nT,4,7,5,5,4,7,7,7,6,6,7,9,3,10,6,7\nG,3,6,5,5,5,7,8,5,2,7,7,8,6,11,7,7\nD,3,7,4,5,2,5,7,10,8,7,6,5,3,8,4,8\nW,4,4,5,3,2,4,10,3,2,9,9,7,6,11,1,7\nN,5,5,5,8,3,7,7,15,2,4,6,8,6,8,0,8\nM,4,4,7,3,4,7,5,3,4,10,8,9,7,5,2,8\nR,5,7,7,5,4,8,9,4,6,8,4,8,3,6,5,11\nY,4,10,7,8,3,4,10,2,8,10,12,9,1,10,2,7\nT,5,7,5,5,3,7,11,3,8,11,9,4,2,12,3,5\nM,8,12,10,7,5,9,4,3,2,10,2,9,10,1,1,8\nR,4,9,4,6,4,6,10,8,3,7,4,8,2,7,5,11\nY,3,7,4,5,4,9,8,6,4,5,9,7,2,8,7,5\nJ,3,6,5,4,1,7,8,2,8,15,6,8,0,7,0,7\nT,2,3,3,2,1,5,12,3,6,11,9,5,1,10,1,5\nP,2,2,3,4,2,5,10,4,4,10,8,4,1,10,3,7\nD,5,5,6,5,5,7,7,4,6,7,4,8,4,7,5,6\nK,4,10,6,8,6,5,7,4,7,6,6,11,3,8,6,9\nV,5,10,7,8,4,9,9,4,2,5,13,8,4,9,1,7\nV,5,10,5,8,3,3,11,5,4,12,12,8,2,10,1,8\nV,5,9,5,6,3,2,11,4,4,11,12,8,2,10,1,7\nP,5,11,6,8,3,4,12,9,2,10,6,4,1,10,4,8\nX,4,7,6,5,3,6,8,1,8,10,8,9,3,8,3,7\nB,7,12,6,6,6,8,8,4,5,9,6,7,6,7,8,7\nC,3,5,5,3,2,4,8,5,7,11,10,12,1,9,2,7\nP,5,8,7,6,5,8,9,5,4,11,4,4,2,10,3,8\nM,7,9,10,7,7,3,7,4,5,10,11,11,10,6,4,7\nS,5,9,7,6,8,7,8,5,3,8,5,8,4,8,10,7\nH,5,11,6,8,3,7,7,15,1,7,7,8,3,8,0,8\nJ,2,7,3,5,1,12,2,9,4,13,5,13,1,6,0,8\nN,4,7,5,5,4,8,8,13,1,6,6,7,6,8,1,10\nS,4,8,6,6,4,9,7,3,6,10,5,8,2,9,5,9\nB,4,9,4,7,6,6,8,8,5,7,5,7,2,8,7,10\nR,5,7,6,5,6,8,9,7,3,8,4,6,5,7,7,9\nT,3,5,4,7,1,7,14,0,6,7,11,8,0,8,0,8\nJ,2,7,3,5,3,9,7,1,5,10,4,7,0,7,1,6\nI,2,11,2,8,2,8,7,0,8,7,6,7,0,8,3,7\nA,2,4,4,3,2,8,2,2,2,7,2,8,2,6,2,7\nE,4,8,5,6,4,6,8,2,8,11,7,9,2,8,5,7\nS,9,15,8,9,4,11,2,4,5,11,2,10,3,6,5,12\nY,3,7,4,5,4,9,5,6,5,7,8,8,3,9,7,4\nT,3,9,4,6,2,9,13,0,6,6,10,8,0,8,0,8\nS,1,0,2,0,0,8,8,4,6,5,6,7,0,8,7,8\nV,2,3,4,4,1,8,8,4,2,6,13,8,3,10,0,8\nE,3,5,6,3,3,7,6,2,8,11,6,10,2,8,4,9\nH,3,5,5,6,4,11,5,3,2,8,4,9,4,8,5,11\nO,2,1,3,2,1,7,7,7,6,7,6,8,2,8,3,8\nV,5,6,7,6,7,7,8,6,5,7,6,7,6,9,7,10\nT,3,4,4,3,2,6,10,2,8,11,9,5,1,10,3,4\nZ,6,6,5,9,4,7,8,4,2,11,7,7,3,9,10,6\nC,3,4,4,3,1,5,9,5,7,12,9,11,1,10,3,7\nO,5,10,6,8,5,7,8,8,5,10,8,8,3,8,3,8\nV,2,6,4,4,1,8,8,4,2,6,13,8,3,10,0,8\nP,6,11,8,8,5,7,11,6,4,12,5,2,1,11,4,9\nU,6,11,6,6,3,6,5,4,5,6,8,8,5,7,2,8\nY,2,3,2,2,1,4,11,2,6,11,10,5,1,10,1,5\nR,4,10,5,7,3,5,10,9,4,7,4,8,3,7,6,11\nV,5,7,7,6,7,8,6,5,5,7,6,8,7,10,6,7\nS,2,7,3,5,2,8,7,7,7,7,7,8,2,9,9,8\nN,7,9,8,4,3,9,6,4,4,13,2,7,6,8,0,7\nB,2,5,4,4,3,8,8,3,5,10,6,6,3,7,5,9\nF,2,3,4,2,2,7,9,2,6,13,6,5,2,9,2,7\nT,3,2,4,3,2,7,12,2,7,7,11,8,1,11,1,7\nB,2,4,3,3,2,8,7,3,5,9,6,6,2,8,5,9\nK,5,8,8,7,7,7,7,2,4,7,3,8,6,4,8,11\nF,6,11,5,6,3,7,9,2,6,11,6,5,2,10,5,6\nT,3,9,5,6,3,7,12,2,8,7,12,8,1,11,1,8\nP,3,5,6,4,3,7,10,4,4,12,4,3,1,10,3,8\nT,5,11,7,8,6,7,11,2,7,7,11,8,2,12,1,8\nO,2,1,2,2,1,8,7,7,4,7,6,8,2,8,3,8\nF,3,5,3,4,2,5,10,4,5,10,9,5,1,10,3,7\nR,3,7,3,5,3,6,10,8,4,7,3,9,2,6,4,10\nJ,2,9,4,6,4,10,7,3,3,9,3,6,2,7,7,7\nW,9,13,9,7,5,5,9,2,2,7,10,8,10,12,1,6\nX,4,8,7,6,3,7,7,1,8,10,7,9,3,8,3,7\nT,3,7,4,5,2,6,14,1,5,8,10,7,0,8,0,8\nF,3,11,4,8,3,1,13,4,4,12,10,7,0,8,2,6\nN,8,15,7,8,4,7,9,4,7,4,4,9,5,8,2,8\nC,3,6,4,4,2,4,9,6,7,12,9,10,2,10,3,7\nZ,3,8,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nB,4,8,5,6,4,6,6,8,7,6,6,7,2,8,9,10\nP,6,9,9,7,6,6,13,7,2,12,5,2,1,11,4,8\nR,4,8,6,6,4,8,7,6,6,9,5,7,3,8,5,11\nF,2,4,4,3,2,7,9,2,6,13,6,6,1,9,2,8\nK,6,11,6,8,3,4,9,9,2,7,3,11,4,8,2,11\nO,4,8,5,6,3,7,6,8,5,7,5,8,3,8,3,8\nV,6,10,6,7,4,4,12,1,2,8,10,7,6,12,2,7\nE,4,7,6,5,4,7,8,2,7,11,6,9,3,8,4,9\nD,5,11,6,8,5,9,7,5,7,10,4,5,3,8,3,8\nL,9,15,8,8,4,9,2,4,5,12,4,13,2,7,6,8\nR,4,8,6,6,5,6,8,5,6,6,5,7,3,7,5,8\nW,5,7,5,5,5,5,9,3,3,9,7,7,6,11,3,6\nB,2,3,4,2,2,9,7,2,5,10,5,6,2,8,4,9\nC,6,12,4,6,2,5,10,5,8,11,8,9,2,8,5,8\nL,4,10,4,8,1,0,0,6,6,0,0,5,0,8,0,8\nP,3,5,4,4,3,5,10,4,4,10,8,4,1,10,3,7\nV,5,10,7,9,8,7,7,6,4,7,6,8,7,10,8,11\nY,4,4,6,6,1,7,10,3,2,7,13,8,2,11,0,8\nS,1,1,2,1,0,8,7,4,7,5,6,7,0,8,7,8\nL,6,14,6,8,4,8,4,3,4,12,8,11,4,9,6,10\nB,6,10,9,7,7,8,7,5,6,9,5,6,3,7,7,10\nR,6,11,6,6,5,7,8,3,6,8,3,8,6,6,6,7\nZ,2,2,3,3,2,7,7,5,9,6,6,8,1,8,7,8\nC,4,9,5,7,4,6,8,6,8,7,6,13,1,8,4,9\nZ,8,14,8,8,5,7,7,2,9,11,7,9,4,7,7,5\nM,7,11,10,8,13,11,5,3,3,9,4,8,11,8,4,8\nH,5,7,7,5,4,5,8,4,6,10,9,9,3,8,3,7\nT,5,10,7,8,6,6,7,7,7,8,9,8,4,9,7,7\nT,8,14,7,8,4,5,11,2,7,12,8,6,3,8,5,3\nG,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,9\nO,1,0,2,1,0,8,7,7,4,7,6,8,2,8,3,8\nH,3,4,6,3,3,7,7,3,6,10,6,8,3,8,3,7\nP,3,5,4,8,7,8,7,5,1,7,6,7,6,9,5,9\nK,7,9,9,7,7,6,7,1,7,9,6,10,4,8,4,8\nQ,6,8,6,9,6,8,7,7,4,8,7,10,3,8,6,8\nX,4,6,5,5,5,7,9,2,6,8,6,8,3,6,6,7\nZ,4,6,6,9,4,12,3,3,6,10,2,8,1,7,5,11\nL,4,8,5,6,2,3,1,7,9,0,1,3,0,7,1,5\nX,9,13,10,7,5,7,7,2,9,11,5,8,4,7,4,7\nQ,6,11,6,6,4,11,3,4,5,12,3,9,3,9,7,12\nW,4,5,6,3,4,6,11,2,2,7,9,8,8,11,1,8\nJ,3,6,4,4,2,6,8,2,5,14,7,9,1,6,0,7\nA,2,3,3,1,1,8,2,2,1,7,2,8,2,7,2,7\nP,1,0,2,0,0,5,11,7,1,9,6,4,1,9,3,8\nM,2,3,4,1,2,5,6,3,4,10,10,10,4,7,1,7\nT,4,11,6,8,2,8,15,1,6,7,11,8,0,8,0,8\nF,3,2,4,3,2,6,10,4,5,10,9,4,2,10,3,6\nY,9,15,8,8,5,5,7,4,3,9,9,6,4,9,4,4\nJ,2,6,2,4,1,13,2,8,4,13,4,12,0,7,0,8\nP,4,8,6,6,4,9,7,2,5,13,4,5,1,10,3,10\nD,5,5,5,7,3,6,7,10,10,7,7,6,3,8,4,8\nM,5,7,7,6,8,5,7,5,4,6,5,8,11,7,5,9\nF,3,4,5,3,2,6,10,3,6,13,7,5,1,10,2,7\nW,3,1,5,3,3,7,11,3,2,6,9,8,7,11,0,8\nU,3,3,4,2,2,6,8,5,7,6,9,9,3,10,1,7\nJ,4,8,6,6,2,9,6,2,8,15,4,8,0,7,0,8\nP,5,5,7,7,8,8,7,4,2,7,8,7,7,12,5,7\nZ,5,10,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nC,5,9,6,7,7,5,6,3,4,7,6,11,6,9,3,8\nE,3,2,4,3,3,7,7,5,8,7,5,8,2,8,6,9\nK,4,7,5,5,4,6,6,1,6,10,7,10,3,8,3,8\nU,5,11,7,8,5,4,9,6,7,9,11,10,3,9,1,8\nB,7,11,7,6,5,9,7,3,5,10,5,7,6,7,7,8\nX,3,4,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nZ,4,11,4,9,4,6,8,6,10,7,7,9,2,9,8,8\nO,2,3,3,2,1,7,7,7,4,7,6,8,2,8,2,8\nX,5,5,6,7,2,7,7,5,4,7,6,8,3,8,4,8\nR,5,10,6,8,7,8,5,7,3,8,6,8,6,5,6,10\nY,2,3,4,4,1,8,10,2,2,6,13,8,2,11,0,8\nI,3,10,4,8,3,7,9,0,7,13,6,7,0,9,2,7\nV,5,11,7,8,9,8,5,5,2,8,7,8,6,8,5,8\nJ,5,11,7,8,8,9,7,4,4,8,4,6,4,7,6,4\nC,5,10,5,7,3,3,8,5,7,11,10,13,1,9,3,8\nN,3,5,4,7,3,7,7,14,2,5,6,8,6,8,0,8\nP,3,8,4,5,2,5,10,10,4,8,6,5,2,10,4,8\nO,2,5,3,3,2,7,7,8,4,7,6,8,2,8,3,8\nO,3,7,4,5,3,7,8,7,4,8,7,10,3,8,3,9\nA,2,6,4,4,2,11,3,3,2,10,1,9,2,6,2,8\nR,3,6,4,4,4,7,8,4,6,6,5,7,3,7,5,8\nW,4,2,6,4,4,8,11,2,2,6,9,8,9,13,1,7\nD,4,6,5,4,3,8,8,7,8,9,5,3,3,8,4,6\nT,3,8,4,6,2,8,14,0,5,6,10,8,0,8,0,8\nN,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8\nH,4,8,6,10,7,9,10,3,1,8,7,6,3,10,8,7\nD,3,7,5,5,4,8,7,3,5,10,5,6,3,8,3,8\nR,5,9,7,7,6,10,6,3,5,10,3,7,4,6,4,10\nB,7,10,6,6,4,8,5,4,5,10,6,9,5,7,7,10\nA,5,8,8,7,7,7,7,2,5,7,9,10,5,11,3,7\nA,2,1,3,1,1,7,4,2,0,7,1,8,3,6,1,8\nT,4,10,5,8,4,5,12,4,7,9,12,7,2,12,1,7\nO,9,13,6,7,4,5,8,6,4,10,8,9,5,10,5,8\nJ,5,8,6,6,3,8,8,1,6,14,5,7,1,6,1,8\nY,4,4,6,6,6,9,10,5,4,6,7,7,5,10,7,5\nL,6,15,6,8,4,7,5,3,5,11,7,11,3,8,6,9\nQ,4,6,5,7,5,8,5,7,3,6,5,9,3,8,5,9\nB,5,7,7,5,6,7,9,4,6,10,6,6,3,7,6,8\nR,5,8,7,7,7,7,8,3,3,7,5,7,6,8,6,6\nE,4,8,4,6,2,3,6,6,11,7,7,15,0,8,7,7\nM,7,10,10,8,6,10,5,2,5,9,4,7,8,6,2,9\nQ,4,9,5,10,5,7,7,6,3,8,9,10,3,8,7,9\nU,4,5,4,8,2,7,4,15,6,7,12,8,3,9,0,8\nE,6,9,8,8,8,7,9,5,6,6,8,11,8,9,9,8\nN,4,6,6,4,5,4,10,2,4,8,7,9,6,7,4,5\nG,5,8,6,6,3,7,7,7,8,10,6,11,2,10,4,9\nD,2,4,4,3,2,9,7,4,6,10,4,6,2,8,3,8\nU,8,9,9,7,4,3,8,6,8,10,11,10,3,9,2,5\nA,5,11,9,8,7,10,5,1,5,9,1,5,3,7,4,8\nP,4,10,5,8,5,4,10,4,5,11,9,5,1,10,3,7\nL,5,11,6,8,5,3,4,3,8,2,0,8,0,7,1,5\nP,4,11,5,8,3,4,12,9,2,10,6,4,1,10,4,8\nL,2,2,3,4,2,4,4,5,7,2,2,5,1,6,1,6\nB,3,6,5,4,4,7,8,4,5,9,6,5,2,8,6,6\nE,3,4,4,6,2,3,8,6,10,7,6,14,0,8,7,7\nC,3,8,4,6,3,6,8,6,5,9,8,14,1,9,3,11\nH,4,5,6,3,3,8,8,3,6,10,6,8,3,8,3,8\nL,3,7,3,5,1,0,1,6,6,0,1,6,0,8,0,8\nT,5,7,7,6,6,5,8,3,8,7,7,9,3,7,7,5\nO,2,2,3,3,2,7,7,7,4,7,6,8,2,8,3,8\nD,7,11,6,6,5,8,8,4,7,10,5,6,6,6,7,6\nK,4,6,6,4,4,3,8,2,6,10,10,11,3,8,3,6\nB,3,2,4,3,3,7,7,5,6,6,6,6,2,8,7,10\nF,2,1,2,2,1,5,11,3,5,11,9,5,1,10,3,6\nY,7,8,6,11,5,7,6,5,5,6,11,6,4,11,3,7\nR,8,10,6,6,4,6,8,5,5,9,6,9,7,5,7,11\nY,7,9,7,7,4,3,10,2,7,11,11,6,1,11,2,5\nK,5,9,7,7,7,6,6,3,7,6,6,9,7,8,5,9\nM,3,3,5,2,3,8,6,6,4,7,7,8,7,5,2,7\nY,3,8,5,5,1,7,10,3,2,7,13,8,2,11,0,8\nG,3,5,4,7,2,7,7,8,7,5,6,10,2,7,5,11\nK,5,4,5,6,2,4,8,9,1,7,6,11,3,8,3,11\nJ,1,3,2,2,1,8,5,4,4,13,7,12,1,7,0,8\nX,4,11,6,8,4,7,7,4,9,6,6,8,3,8,7,7\nW,6,10,6,8,6,1,10,2,3,10,10,9,6,10,1,7\nA,4,9,6,6,2,8,4,3,1,7,1,8,3,7,2,8\nV,9,15,8,8,4,5,10,4,4,9,9,6,4,11,2,8\nH,3,1,4,2,3,7,7,6,6,7,6,8,3,8,4,8\nI,7,14,6,8,4,8,9,3,7,14,4,5,2,8,5,9\nB,3,5,5,4,4,8,8,3,6,10,5,5,3,7,6,9\nD,4,8,6,6,4,8,8,6,7,9,5,4,3,7,4,9\nM,6,11,9,9,10,9,7,5,5,6,7,6,10,9,3,6\nG,3,5,4,4,2,6,7,6,7,7,5,10,2,9,4,9\nH,2,1,3,2,2,8,7,6,5,7,6,8,3,8,3,8\nD,2,3,3,2,2,9,6,4,6,10,5,6,2,8,2,8\nG,3,4,5,7,2,8,7,9,7,5,6,10,2,7,5,10\nM,3,8,4,6,3,7,7,11,1,7,9,8,8,5,0,8\nG,6,11,8,9,9,9,7,5,2,6,6,10,8,8,5,10\nA,2,8,4,6,3,11,3,3,3,10,2,9,2,6,3,8\nO,5,9,6,6,3,7,7,8,8,7,6,8,3,8,4,8\nY,3,5,4,6,5,8,10,6,4,7,7,6,4,10,6,3\nD,2,5,3,3,3,7,7,6,6,7,6,5,2,8,2,7\nJ,6,11,5,8,4,5,13,3,3,13,6,4,2,8,6,6\nC,2,5,3,4,2,6,8,7,7,9,8,13,1,9,4,10\nM,4,10,5,8,6,7,5,11,1,7,9,8,9,5,2,8\nT,3,4,5,6,1,7,15,1,6,7,11,8,0,8,0,8\nL,5,11,7,8,4,7,3,2,8,7,2,8,1,6,3,7\nJ,1,6,2,4,1,10,6,1,7,11,3,7,0,7,1,7\nD,4,9,5,6,8,8,10,5,4,8,6,5,6,10,9,6\nZ,5,7,7,5,6,9,5,5,4,7,5,7,3,7,10,5\nR,4,6,5,4,5,8,7,6,3,8,5,7,4,7,7,10\nV,5,10,5,7,4,4,11,1,2,8,10,7,3,10,1,8\nH,5,8,7,6,7,8,7,6,7,7,6,7,3,8,4,7\nK,9,14,9,8,6,3,8,4,6,10,11,12,5,8,4,6\nO,6,11,9,8,11,8,6,6,1,7,6,8,11,8,6,8\nM,7,9,9,7,7,6,6,3,5,10,9,9,10,7,3,8\nR,4,4,5,6,3,5,13,8,4,8,3,9,3,7,7,11\nX,5,10,6,8,4,7,7,4,4,7,6,7,3,8,4,8\nO,9,15,6,9,5,4,8,6,4,10,8,9,5,10,5,8\nL,5,7,6,5,4,6,6,7,7,5,6,10,2,8,4,10\nZ,4,7,6,5,4,9,6,2,8,11,4,10,2,7,6,10\nF,4,7,6,5,4,5,11,3,5,13,7,5,1,10,2,7\nQ,5,5,7,4,5,8,5,5,4,7,4,10,5,6,6,8\nC,8,12,6,6,5,7,7,4,4,9,8,10,4,9,8,10\nG,3,7,4,5,3,6,5,5,6,6,6,9,2,9,3,7\nV,4,5,6,3,2,4,12,3,3,10,11,7,2,10,1,8\nI,6,9,8,7,4,6,7,2,7,7,6,11,0,8,4,8\nC,4,5,6,8,2,6,8,7,10,5,7,13,1,7,4,9\nD,4,8,5,6,4,7,6,7,8,6,5,5,3,8,3,7\nX,5,11,6,8,2,7,7,5,4,7,6,8,3,8,4,8\nE,5,11,5,8,3,3,7,6,11,7,6,14,0,8,8,7\nE,1,1,2,2,1,4,7,5,8,7,6,13,0,8,7,9\nS,4,10,5,7,5,8,9,8,6,7,4,6,2,6,9,8\nV,5,9,7,7,3,9,12,3,4,4,11,9,3,10,1,8\nD,2,6,4,4,3,9,6,4,6,10,4,6,3,8,3,8\nL,4,10,4,8,1,0,1,5,6,0,0,6,0,8,0,8\nD,3,8,5,6,7,9,8,4,5,7,6,6,5,6,8,6\nU,3,5,5,5,4,8,7,4,4,6,7,7,4,8,1,6\nI,2,6,3,4,1,7,7,0,7,13,6,8,0,8,1,8\nV,6,7,8,6,9,8,6,5,5,7,6,8,8,10,7,4\nF,8,14,7,8,5,10,6,3,5,10,4,6,4,8,7,10\nM,5,7,8,5,7,8,7,2,4,9,6,7,7,5,2,7\nI,4,10,6,8,4,7,7,0,8,13,6,8,0,8,1,8\nW,4,5,5,4,3,4,10,3,2,9,9,7,6,11,1,6\nI,4,5,5,6,5,7,10,4,5,8,7,8,4,7,6,6\nH,8,10,8,5,5,8,7,3,5,10,5,7,6,9,4,8\nA,4,10,6,7,2,7,5,3,1,7,0,8,3,7,2,8\nN,6,8,9,7,9,6,7,4,4,6,5,8,8,9,5,8\nP,6,9,9,7,4,7,10,2,7,14,5,3,3,8,4,8\nV,4,5,6,8,2,7,8,4,3,7,14,8,3,9,0,8\nR,7,9,10,8,11,7,7,4,4,7,5,8,7,8,6,6\nR,4,8,5,6,5,8,8,7,5,8,5,7,7,8,6,11\nI,4,11,6,8,3,7,7,0,8,14,6,8,0,8,1,8\nE,3,5,5,4,3,8,7,2,7,11,5,8,2,9,5,10\nF,5,9,5,6,2,1,12,5,6,12,11,9,0,8,2,6\nG,4,5,5,4,3,6,7,5,5,9,8,9,2,8,5,9\nO,5,9,6,7,5,8,6,8,6,7,5,8,3,8,4,7\nA,4,8,6,6,6,8,8,7,4,6,6,8,3,7,7,4\nS,4,8,4,6,4,8,8,5,7,5,5,7,0,8,8,8\nJ,2,5,3,8,1,12,3,10,3,13,6,13,1,6,0,8\nE,3,2,3,3,3,7,7,5,7,7,6,9,2,8,5,10\nQ,6,14,5,8,5,9,6,4,6,11,5,7,4,8,9,10\nF,3,8,4,6,3,3,12,4,5,12,10,5,1,10,3,5\nX,4,8,6,6,4,8,7,1,8,10,4,7,3,8,3,8\nX,3,3,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nG,5,9,4,4,3,7,8,3,3,8,6,6,3,9,8,7\nU,3,5,4,3,2,6,8,6,7,7,10,9,3,9,0,8\nZ,2,7,4,5,3,8,7,2,6,7,6,7,0,9,8,8\nU,6,8,7,6,3,3,9,6,7,11,11,9,3,9,1,6\nQ,4,7,5,8,5,8,6,7,5,9,6,9,3,8,5,7\nV,3,5,5,4,2,4,12,3,3,9,11,7,2,10,1,8\nK,3,4,4,3,2,5,7,4,7,7,6,11,3,8,5,9\nU,5,9,7,6,9,7,6,4,4,7,7,8,10,8,6,6\nC,6,10,6,8,4,5,8,6,8,12,9,13,2,9,4,7\nN,4,7,4,5,3,8,7,12,1,6,6,8,5,8,0,9\nH,4,4,6,3,3,7,6,3,6,10,5,9,4,6,4,7\nY,6,10,6,5,3,6,8,4,3,10,8,5,3,10,4,4\nG,2,1,4,2,2,7,7,6,6,6,6,10,2,9,4,9\nT,4,6,6,6,5,5,8,4,8,8,8,9,3,9,7,6\nB,5,9,8,7,7,9,7,3,7,11,4,7,3,7,5,9\nB,3,5,6,4,4,9,6,3,6,10,5,7,2,8,5,9\nV,5,9,5,7,4,3,11,2,3,9,11,8,3,12,1,7\nZ,4,8,5,6,2,7,7,4,14,9,6,8,0,8,8,8\nX,3,9,5,6,4,7,7,3,8,5,6,8,2,8,6,7\nS,7,11,8,8,6,6,7,3,6,10,7,8,3,7,5,6\nV,7,9,6,7,3,4,11,3,4,9,11,7,3,10,1,7\nR,5,9,6,6,7,8,8,7,3,8,4,6,5,7,8,9\nU,2,1,3,2,1,6,8,6,6,6,9,9,3,9,0,8\nJ,4,8,3,12,3,9,7,3,3,11,5,5,3,8,6,10\nQ,5,9,6,11,6,8,6,7,4,9,7,10,3,8,6,8\nV,11,14,8,8,4,9,10,6,5,6,10,6,6,13,3,6\nS,4,6,5,4,3,8,7,3,6,10,6,8,2,9,5,8\nS,3,8,4,6,2,7,7,6,9,5,7,10,0,8,9,8\nC,6,11,5,6,3,7,8,4,3,8,8,10,4,8,7,11\nQ,4,6,6,8,8,9,9,5,0,6,7,10,6,12,5,10\nH,1,0,1,0,0,7,7,10,2,7,6,8,2,8,0,8\nM,5,11,7,8,9,8,9,7,4,7,7,8,8,8,9,5\nV,3,7,4,5,2,8,9,3,1,7,12,8,2,10,0,8\nS,5,11,6,8,5,7,8,3,7,10,4,6,2,6,5,8\nE,4,10,4,8,4,3,8,5,9,7,6,14,0,8,6,8\nG,5,11,5,8,4,5,6,7,6,7,7,11,3,7,5,8\nT,2,1,3,2,0,7,15,1,4,7,11,8,0,8,0,8\nF,4,9,4,6,2,1,14,5,3,12,9,5,0,8,2,6\nJ,5,11,7,8,4,8,6,3,6,15,4,9,0,7,0,7\nP,4,9,5,6,4,5,11,8,2,9,5,4,1,10,3,7\nD,3,7,5,5,3,10,6,4,7,10,3,6,3,8,3,9\nL,3,3,4,5,1,1,0,6,6,0,1,5,0,8,0,8\nG,5,10,6,7,5,6,6,6,6,10,7,13,3,9,5,9\nQ,2,2,3,3,2,8,7,6,3,6,6,9,2,9,3,10\nL,5,10,5,8,3,0,1,5,6,0,0,7,0,8,0,8\nT,6,9,6,7,3,4,14,5,6,12,9,3,1,11,2,4\nF,1,3,3,1,1,6,10,3,4,13,7,5,1,9,1,7\nN,5,7,7,5,4,4,10,3,4,10,10,9,5,8,1,8\nX,2,3,4,2,2,8,7,1,7,10,5,8,2,8,2,8\nI,0,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8\nC,4,9,3,4,2,6,8,4,3,9,8,10,3,9,8,9\nL,4,10,6,7,4,8,4,0,8,10,3,10,0,7,3,9\nA,3,7,6,5,3,11,2,3,2,10,2,9,2,6,3,9\nT,2,8,3,5,1,8,13,0,6,6,11,8,0,8,0,8\nN,4,7,6,5,4,5,9,3,4,10,9,8,5,8,1,8\nV,3,1,5,3,1,7,12,2,3,7,11,9,2,10,1,8\nL,2,3,2,2,1,4,3,4,8,2,2,5,0,7,1,6\nH,5,9,8,7,7,5,9,3,6,10,9,9,3,9,4,6\nX,8,12,9,6,5,8,7,2,7,11,4,7,5,10,4,8\nS,4,8,5,6,5,8,6,7,5,6,8,9,3,9,9,8\nZ,3,7,4,5,3,6,8,5,9,7,7,9,1,9,7,7\nY,5,8,7,11,11,8,8,4,2,7,8,9,4,11,8,8\nY,2,2,3,3,1,7,10,1,6,7,11,9,1,11,1,8\nK,6,11,9,8,7,7,7,4,7,6,5,8,7,8,5,9\nE,3,3,3,4,2,3,7,6,10,7,6,14,0,8,7,7\nF,3,5,5,3,2,6,12,3,5,13,7,4,1,10,2,6\nK,5,9,7,7,9,8,7,3,4,6,7,8,10,8,8,7\nH,3,7,3,4,2,7,9,14,3,7,3,8,3,8,0,8\nO,3,6,5,4,3,8,8,8,4,7,6,7,3,8,3,8\nR,5,10,8,8,9,6,7,3,4,7,6,9,8,10,8,5\nC,2,5,3,3,2,6,8,7,7,8,8,13,1,9,4,10\nH,7,10,10,8,6,8,7,3,7,10,5,8,3,8,3,8\nX,6,9,8,8,9,6,8,2,5,7,7,10,4,5,8,7\nE,4,10,5,8,7,8,7,3,5,6,7,10,4,9,8,8\nA,3,6,5,8,2,7,5,3,1,7,0,8,3,7,2,8\nZ,3,5,5,4,3,7,8,2,9,12,6,8,1,9,5,7\nY,5,7,6,5,3,4,9,1,8,10,10,6,1,10,3,4\nA,2,1,4,2,1,7,2,2,1,6,2,8,2,7,2,7\nA,3,9,6,6,4,11,3,1,2,8,2,9,2,6,2,7\nJ,2,7,4,5,2,7,8,2,5,14,5,7,0,6,1,7\nC,4,5,5,3,2,4,8,4,8,11,9,12,1,9,3,7\nQ,6,8,6,9,6,7,7,7,4,9,9,9,4,8,7,9\nF,5,8,7,6,4,8,9,3,6,13,5,5,2,9,2,7\nU,5,10,6,8,4,3,9,5,7,11,10,9,3,9,2,7\nS,5,11,6,8,6,8,8,7,5,7,5,7,2,8,9,8\nE,3,9,4,6,4,3,8,5,9,7,7,14,0,8,6,9\nI,1,3,1,1,1,7,7,2,7,7,6,8,0,8,2,8\nE,4,9,6,7,5,7,7,2,7,11,7,9,3,8,4,8\nF,3,5,4,4,3,5,10,3,5,10,9,5,1,10,3,6\nU,3,2,4,4,2,6,8,6,6,7,9,9,3,9,1,8\nO,2,2,3,3,2,7,7,7,4,7,6,8,2,8,3,8\nH,10,12,9,7,5,7,7,5,5,8,9,8,7,11,5,9\nX,2,1,3,2,2,7,7,3,9,6,6,8,2,8,5,8\nQ,3,5,4,6,4,8,7,5,2,8,7,10,3,9,5,8\nL,4,9,5,7,3,3,4,3,9,2,0,7,0,7,1,5\nN,5,9,7,6,4,6,9,6,5,8,7,8,5,8,1,7\nS,4,10,5,8,5,8,9,8,6,7,4,5,2,6,8,8\nT,4,8,5,6,3,7,11,3,8,11,9,4,2,11,3,5\nE,5,9,8,7,8,6,7,3,7,5,6,11,4,10,11,8\nT,2,7,4,5,2,6,12,3,7,8,11,7,1,11,1,7\nJ,2,7,2,5,1,12,2,9,4,13,5,12,1,6,0,8\nL,1,3,2,1,0,6,4,1,7,8,2,10,0,7,2,9\nX,5,10,6,7,2,7,7,5,4,7,6,8,3,8,4,8\nO,4,6,5,5,4,8,5,4,4,9,4,10,3,7,5,7\nP,3,5,5,7,6,8,11,3,1,8,8,6,6,10,4,6\nU,4,9,5,7,2,7,5,15,5,7,13,8,3,9,0,8\nU,7,11,9,8,8,7,6,9,6,7,6,9,6,8,7,2\nN,4,6,6,4,3,7,8,3,5,10,6,7,5,8,1,7\nY,9,12,8,6,5,7,5,4,5,9,9,5,4,10,4,5\nM,4,2,5,4,4,8,6,6,4,6,7,8,8,6,2,7\nP,3,10,4,8,4,4,12,7,2,10,7,4,1,10,3,8\nA,2,3,4,2,1,6,2,2,2,5,2,8,2,6,2,6\nK,3,7,5,5,6,8,6,3,4,6,6,8,6,8,6,8\nS,3,4,5,3,2,9,6,3,8,11,4,7,1,9,4,9\nG,4,7,5,5,2,8,6,7,7,6,6,9,2,8,6,11\nB,4,10,5,7,4,6,8,9,7,7,5,7,2,8,9,9\nU,4,4,5,6,2,7,4,14,5,7,14,8,3,9,0,8\nQ,4,9,6,7,6,8,5,7,4,6,6,8,3,7,5,9\nZ,5,6,4,9,3,6,9,5,3,12,7,7,2,9,10,6\nN,6,9,8,7,4,6,8,3,4,10,8,8,5,8,1,7\nR,5,5,6,8,4,6,9,10,5,6,5,8,3,8,6,11\nV,2,3,3,2,1,7,12,2,2,6,10,9,2,11,0,8\nM,5,9,5,6,6,7,5,11,0,7,8,8,7,5,1,9\nL,3,4,4,6,1,0,0,6,6,0,1,5,0,8,0,8\nN,4,5,4,3,3,7,8,5,5,7,7,6,6,9,3,6\nE,4,9,6,7,5,9,6,2,7,11,5,9,3,7,6,10\nJ,4,6,5,4,2,8,6,4,7,15,6,10,1,6,1,7\nV,4,11,5,8,4,6,9,4,1,8,12,8,3,9,1,8\nA,2,7,4,4,1,8,5,3,1,7,0,8,2,7,2,8\nP,1,1,2,1,1,5,11,7,2,10,6,4,1,9,3,8\nY,9,11,9,8,6,5,8,0,9,8,9,5,4,13,7,3\nL,4,5,5,4,2,4,4,5,8,2,2,5,1,6,1,5\nQ,7,8,7,10,7,8,5,7,5,9,6,9,3,8,6,7\nU,5,11,5,8,4,7,5,12,4,7,13,8,3,9,0,7\nD,4,9,5,7,3,5,7,10,9,6,5,5,3,8,4,8\nB,4,11,5,8,4,6,8,9,8,7,5,7,3,8,9,10\nN,3,6,3,4,3,8,7,11,1,6,6,8,5,9,0,6\nL,4,9,5,8,6,7,6,5,4,7,7,8,2,9,7,10\nU,5,9,5,6,2,8,4,14,6,7,14,8,3,9,0,8\nV,4,10,5,8,3,6,9,4,1,8,12,8,4,9,1,8\nP,6,11,9,8,7,9,8,2,5,13,5,5,3,9,4,9\nE,6,8,8,6,6,7,7,2,8,11,5,9,3,8,4,9\nA,4,8,5,6,3,7,5,2,0,7,1,8,2,7,1,8\nY,7,9,7,6,3,4,10,2,8,10,11,5,2,13,4,3\nW,5,9,8,6,6,7,10,2,3,7,9,8,8,11,1,8\nK,8,13,8,7,4,9,7,3,7,9,3,6,5,8,4,8\nD,5,9,7,8,7,6,7,6,7,7,6,9,5,5,7,4\nP,0,0,1,0,0,5,10,6,1,9,6,5,1,9,2,8\nC,2,2,3,3,2,6,8,7,7,8,7,13,1,9,4,10\nY,3,7,5,4,1,8,11,2,2,5,12,8,1,11,0,8\nJ,2,5,3,3,1,9,6,3,6,12,4,9,1,6,1,7\nC,6,11,8,9,6,7,7,8,6,6,6,13,6,8,5,7\nQ,5,9,5,10,6,8,6,7,5,9,6,9,3,8,6,8\nD,3,5,5,4,3,9,6,4,7,9,4,6,2,8,3,8\nI,5,12,4,6,2,11,5,3,6,12,3,7,2,9,4,11\nG,4,7,5,5,3,5,8,5,5,9,9,8,2,8,4,9\nM,6,9,8,7,8,7,7,5,5,6,7,8,9,7,3,7\nA,4,9,6,6,2,8,3,3,3,7,2,8,3,6,3,8\nI,2,8,2,6,2,7,7,0,7,7,6,8,0,8,3,8\nG,6,7,8,6,7,7,8,5,3,7,7,8,9,10,9,7\nQ,4,7,5,9,5,9,7,7,3,5,7,10,3,8,7,10\nD,4,8,4,6,4,6,7,9,7,7,7,6,2,8,3,8\nD,3,8,5,6,7,10,8,4,5,7,6,6,4,7,7,4\nD,4,9,6,7,6,7,7,8,6,6,5,5,3,9,4,8\nT,3,3,3,2,1,6,11,2,7,11,9,5,1,10,2,5\nZ,2,5,5,3,2,7,8,2,9,12,6,8,1,9,5,8\nD,3,9,4,6,3,6,7,11,9,7,6,6,3,8,4,8\nM,6,10,9,8,7,10,6,2,5,9,4,7,8,6,2,8\nZ,2,1,3,2,2,7,7,5,8,6,6,9,2,8,7,8\nG,5,8,6,6,3,5,7,6,6,10,8,10,2,8,5,9\nD,3,2,4,3,2,7,7,6,7,6,6,5,2,8,3,7\nK,5,10,7,8,6,10,6,1,6,10,3,8,4,9,4,11\nZ,6,10,8,8,6,8,7,2,9,12,5,8,1,8,6,8\nC,6,10,6,8,4,4,7,6,6,13,10,12,2,11,3,7\nK,6,11,9,8,7,6,6,1,6,9,6,10,5,7,5,8\nO,4,9,5,6,4,8,6,7,3,10,4,8,3,8,3,7\nM,5,5,7,4,5,8,6,6,5,7,7,8,8,6,2,8\nR,4,5,4,6,3,5,11,8,4,7,3,9,3,7,6,11\nU,4,10,6,7,4,5,8,7,7,8,10,10,3,9,1,8\nQ,7,13,6,7,4,10,3,4,7,10,4,9,3,9,6,13\nO,4,9,6,7,4,8,6,9,3,6,5,7,3,8,4,9\nU,3,5,4,3,2,5,8,4,7,10,8,9,3,9,2,6\nH,3,7,5,5,4,6,7,6,4,6,5,8,3,7,6,11\nA,2,3,4,4,1,7,5,3,1,6,1,8,2,7,2,7\nW,5,10,7,8,4,11,8,5,2,6,9,8,8,10,0,8\nF,2,3,4,2,1,5,11,3,5,13,7,4,1,9,1,7\nU,5,5,6,3,2,4,8,5,8,10,10,9,3,9,2,5\nU,1,0,1,0,0,7,6,10,4,7,11,8,3,10,0,8\nJ,5,9,6,7,3,7,7,3,6,15,6,10,1,6,1,7\nS,3,3,4,5,2,8,6,5,9,5,6,8,0,9,9,8\nW,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8\nQ,2,2,3,2,2,8,8,5,2,8,7,9,2,9,3,8\nA,3,9,5,7,3,13,2,4,4,12,1,9,3,6,3,10\nU,6,7,7,5,4,3,8,5,7,10,9,10,3,9,2,5\nG,3,3,4,5,2,8,7,8,7,6,6,10,2,7,5,11\nQ,4,7,5,6,3,8,7,8,6,6,7,9,3,8,5,9\nB,6,11,8,8,8,9,7,3,6,9,4,6,3,8,5,9\nE,5,8,7,6,4,5,9,4,10,12,10,8,2,8,5,3\nE,5,7,7,5,4,6,7,4,9,12,8,9,2,8,5,7\nM,6,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nE,4,9,4,7,2,3,7,6,11,7,7,15,0,8,7,7\nI,4,11,6,8,3,7,7,0,8,14,6,8,0,8,1,8\nD,2,4,4,3,2,9,7,4,6,10,4,6,2,8,3,8\nW,4,5,5,4,3,7,11,3,2,6,9,8,7,11,0,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nF,8,12,7,6,4,9,7,4,7,12,4,6,2,8,8,8\nT,4,5,6,4,5,8,9,5,7,7,7,8,3,10,7,7\nG,7,11,8,8,6,6,6,7,7,9,8,11,2,11,5,9\nH,7,11,8,6,5,6,9,3,5,10,6,8,6,7,5,7\nH,4,10,6,8,7,8,7,5,6,7,6,6,3,8,4,7\nF,5,6,6,7,6,6,12,3,5,9,7,6,4,9,5,6\nN,4,10,5,8,3,7,7,14,2,4,6,8,6,8,0,8\nC,4,7,4,5,2,5,9,6,7,12,9,9,2,10,2,7\nX,4,5,6,3,3,7,7,1,9,10,6,8,2,8,3,7\nP,3,7,4,5,3,5,11,5,4,11,8,4,1,10,4,7\nN,3,8,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nD,4,8,5,6,5,8,8,6,5,9,5,5,3,8,3,7\nU,6,11,8,8,8,9,7,8,5,6,7,9,6,8,4,6\nP,6,6,8,8,9,7,9,3,3,8,8,6,6,11,6,5\nB,0,0,1,0,0,6,7,6,4,7,6,8,1,8,5,10\nX,4,7,7,5,3,5,9,2,8,10,10,8,3,8,4,6\nT,3,5,4,7,1,10,14,1,6,4,11,9,0,8,0,8\nO,3,10,5,8,5,8,6,8,4,6,5,6,3,8,3,7\nZ,4,7,5,5,4,9,9,6,4,7,5,8,3,9,10,7\nT,1,1,2,1,0,7,14,1,5,7,11,8,0,8,0,8\nW,3,4,4,2,2,6,11,3,2,8,7,7,6,11,1,6\nT,7,9,6,4,2,4,12,3,7,13,8,4,2,8,3,4\nT,2,6,3,4,1,7,12,0,5,7,10,8,0,8,0,8\nA,5,11,5,6,4,9,4,5,3,10,6,12,7,4,6,10\nR,5,10,6,8,6,5,8,6,6,6,5,9,4,8,6,10\nR,1,0,1,0,0,6,9,7,3,7,5,8,2,7,3,10\nT,3,2,4,4,3,7,11,3,6,7,11,8,2,11,1,8\nL,1,0,2,1,0,2,1,6,5,0,2,5,0,8,0,8\nT,4,8,5,6,3,7,10,1,8,11,9,5,1,10,3,5\nY,2,3,3,5,0,7,10,1,3,7,12,8,1,11,0,8\nT,5,11,5,8,3,5,11,2,8,12,10,4,1,11,2,4\nS,2,6,3,4,1,6,5,5,8,5,6,11,0,9,8,8\nN,6,8,8,6,4,9,9,3,5,10,3,5,5,9,1,7\nF,4,7,6,5,5,5,9,2,4,10,8,7,5,10,3,4\nM,3,1,4,3,3,8,6,6,4,7,7,8,7,6,2,7\nH,9,11,12,8,8,5,7,4,7,10,9,10,3,8,4,6\nD,6,9,6,5,3,9,5,4,5,12,3,8,5,7,5,10\nJ,4,9,6,6,5,9,8,3,4,8,4,6,4,8,5,4\nO,4,7,5,5,2,8,7,8,7,7,6,9,3,8,4,8\nP,2,4,4,3,2,8,9,3,4,12,4,3,1,10,3,8\nC,3,9,5,7,3,4,8,7,7,9,9,14,2,9,4,9\nB,3,5,5,3,3,9,7,3,6,10,5,6,2,8,5,9\nT,2,1,3,3,1,7,12,3,6,7,11,8,2,11,1,8\nF,4,4,4,6,2,1,13,5,4,12,10,7,0,8,2,6\nJ,1,3,2,4,0,14,3,6,4,13,2,10,0,7,0,8\nL,3,7,5,5,3,7,3,2,7,8,2,9,2,5,3,8\nE,3,5,6,3,3,7,7,2,8,11,6,9,2,8,4,8\nS,4,7,5,5,6,8,8,4,3,8,5,7,3,8,10,8\nP,7,12,6,6,3,8,9,7,5,14,4,4,4,10,4,7\nA,2,6,4,4,2,8,2,2,2,7,2,8,2,6,3,6\nF,5,10,7,8,4,5,13,5,4,13,8,3,2,10,2,5\nH,6,9,6,4,3,8,8,4,4,8,6,6,6,10,4,8\nS,5,11,7,8,4,8,9,6,10,5,6,6,0,8,9,7\nB,3,7,4,5,3,6,7,8,6,7,6,6,2,8,9,10\nG,7,11,8,9,5,7,6,7,7,12,6,12,3,11,5,8\nZ,2,6,3,4,1,7,7,3,13,9,6,8,0,8,8,8\nS,2,1,2,2,1,8,7,6,5,7,6,7,2,8,8,8\nW,3,3,5,4,2,9,8,4,1,7,8,8,8,9,0,8\nZ,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nA,4,11,6,8,5,7,3,1,2,5,2,7,4,6,4,7\nR,7,9,10,8,10,8,8,4,4,8,4,7,8,6,7,6\nU,3,8,4,6,2,8,6,13,5,6,13,8,3,9,0,8\nO,5,9,6,7,3,8,8,9,8,6,8,9,3,8,4,8\nI,5,10,6,8,4,6,8,0,7,13,7,8,0,8,1,7\nZ,3,5,5,7,4,9,4,3,5,7,3,6,3,8,7,6\nV,6,11,5,6,3,9,10,5,4,7,10,5,4,11,3,6\nR,6,10,8,8,6,8,8,5,7,8,3,9,4,5,5,12\nN,7,9,9,8,9,8,7,5,4,7,5,7,7,9,6,5\nC,4,7,5,5,2,6,8,6,10,6,7,12,1,7,4,8\nF,3,8,6,6,6,11,6,1,5,9,5,6,4,10,4,7\nA,3,8,6,6,4,10,5,1,4,8,2,6,2,7,4,8\nX,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nA,3,5,5,5,4,9,8,2,4,7,7,7,4,8,4,5\nP,3,4,4,5,2,4,13,8,2,11,6,3,1,10,4,8\nW,6,9,6,6,6,3,11,2,3,10,10,8,6,11,2,7\nV,4,10,6,8,4,6,11,2,3,7,11,9,2,10,1,9\nQ,3,5,3,6,4,8,9,5,1,6,7,11,2,9,5,9\nB,5,10,5,8,4,6,8,9,7,7,6,7,2,8,9,10\nC,5,10,6,8,2,6,7,7,11,7,6,14,1,8,4,9\nL,4,8,4,6,2,0,1,5,5,0,1,6,0,8,0,8\nS,5,10,6,7,3,7,7,6,9,5,6,8,0,8,9,7\nV,2,2,4,3,1,7,12,2,3,7,11,9,2,10,1,8\nF,2,4,4,2,1,6,10,2,5,13,7,5,1,9,1,7\nE,4,9,4,6,2,3,8,6,10,7,6,14,0,8,7,7\nT,6,7,6,5,3,7,10,2,10,11,9,4,1,11,4,5\nQ,6,7,8,6,7,8,4,5,5,7,4,10,4,5,7,7\nZ,1,3,3,2,1,7,8,2,9,11,6,8,1,8,5,7\nK,1,0,1,0,0,5,7,7,1,7,6,11,2,8,2,11\nM,3,4,6,3,3,7,6,3,4,9,7,8,7,5,1,8\nE,1,3,2,2,2,7,7,5,6,7,6,8,2,8,5,10\nU,6,9,8,8,7,7,6,5,4,6,7,8,7,8,2,7\nD,3,8,3,6,2,5,7,10,8,6,6,5,3,8,4,8\nE,4,6,4,8,3,3,7,6,11,7,6,15,0,8,7,7\nW,9,11,9,9,7,7,11,4,3,8,6,6,11,13,4,4\nW,5,11,7,8,9,8,7,6,2,7,8,8,6,8,4,8\nH,4,4,5,3,4,6,7,6,6,7,6,10,3,8,3,9\nQ,3,4,4,5,4,8,7,7,3,6,6,9,2,8,5,9\nS,6,10,8,8,4,8,7,4,8,11,5,7,2,8,5,8\nG,3,2,5,4,3,6,6,6,6,6,6,9,2,9,4,9\nZ,4,10,5,7,4,6,8,6,10,7,7,10,1,9,8,8\nA,8,12,6,6,3,10,1,2,2,10,4,12,3,5,4,8\nR,4,9,5,6,3,5,12,8,4,7,3,9,3,7,6,11\nF,6,11,8,8,8,7,6,5,5,7,6,8,6,10,8,12\nY,0,0,1,0,0,7,10,1,3,7,11,8,1,11,0,8\nL,4,10,6,7,7,6,7,3,6,7,7,11,5,11,6,5\nZ,5,8,6,6,5,9,11,6,6,6,5,9,3,8,8,5\nB,3,7,5,5,5,8,7,5,6,6,6,6,2,8,5,9\nV,3,3,4,2,1,3,12,4,3,11,11,7,2,11,1,8\nE,2,4,4,3,2,7,7,2,7,11,6,8,2,8,5,9\nE,7,13,5,7,4,9,5,4,5,11,4,9,3,9,7,11\nY,3,4,4,3,1,4,11,2,7,11,10,5,1,11,2,5\nR,8,10,6,5,3,10,5,5,5,10,3,9,6,6,6,10\nP,1,1,2,1,1,5,11,7,1,9,6,4,1,9,3,8\nO,5,8,6,6,8,8,6,5,2,7,6,8,8,9,4,10\nY,4,5,5,4,2,3,11,3,6,12,11,6,1,11,2,5\nZ,6,10,8,8,4,6,9,3,10,11,9,5,2,8,7,5\nH,3,8,5,6,6,8,7,4,2,6,6,7,6,8,8,7\nY,7,10,7,8,4,4,10,2,8,10,11,6,2,10,4,3\nT,6,9,6,6,3,7,10,2,9,11,9,4,3,9,4,4\nP,2,4,3,2,2,5,10,4,4,9,8,5,3,10,3,7\nG,2,2,4,3,2,6,6,6,6,7,6,11,2,9,4,9\nW,7,8,7,6,6,4,12,3,2,9,8,7,6,12,2,6\nX,3,2,5,4,3,8,7,3,9,6,6,8,3,8,6,8\nD,5,9,8,6,5,10,6,5,8,10,3,5,3,8,4,9\nK,4,5,5,7,2,4,8,8,2,7,4,11,3,8,2,11\nJ,3,9,5,6,3,7,7,3,6,15,5,9,1,7,1,7\nH,5,5,6,7,3,7,6,15,0,7,7,8,3,8,0,8\nT,2,5,4,7,1,5,14,1,6,9,11,7,0,8,0,8\nK,7,9,7,5,4,6,8,2,6,10,4,9,5,6,3,8\nX,4,8,6,6,3,5,8,2,8,10,10,9,3,8,3,6\nP,2,1,2,1,1,5,10,4,4,10,8,4,0,9,3,7\nS,3,6,4,4,3,8,7,7,5,7,6,8,2,8,9,8\nU,6,10,7,8,4,4,8,6,8,10,9,9,3,9,2,5\nS,3,8,4,6,2,8,7,5,9,5,6,7,0,8,9,8\nM,7,8,10,6,7,11,6,2,5,9,3,6,9,8,2,9\nQ,4,7,4,8,5,8,7,6,4,8,7,9,3,8,6,8\nD,6,14,6,8,5,8,6,4,6,9,5,7,5,9,7,6\nC,1,3,2,2,1,5,8,4,5,12,8,10,1,10,2,8\nN,4,2,5,4,3,7,8,5,5,7,7,7,5,9,2,6\nX,5,9,7,7,4,6,8,1,8,10,8,9,3,8,3,6\nP,7,10,9,8,5,9,8,3,7,13,3,3,2,10,4,9\nK,2,3,4,1,2,6,8,1,6,10,6,9,3,8,2,8\nT,3,6,4,4,2,9,11,2,9,5,11,8,1,10,1,8\nD,6,7,8,6,6,5,7,6,8,7,6,7,4,7,5,5\nU,8,10,8,8,5,3,8,5,7,10,10,9,3,9,2,6\nT,3,5,5,3,2,8,12,3,7,6,11,7,2,11,1,7\nI,1,10,2,8,3,7,7,0,7,7,6,8,0,8,3,8\nD,3,9,5,6,5,7,8,7,5,8,7,5,3,8,3,7\nT,2,4,3,5,1,7,14,0,6,7,11,8,0,8,0,8\nM,3,4,5,3,3,9,6,3,4,9,5,7,6,5,1,8\nH,3,8,4,5,2,7,8,15,1,7,5,8,3,8,0,8\nZ,5,11,7,8,5,8,7,2,9,12,6,7,1,7,6,7\nH,8,10,11,8,7,9,7,3,6,10,4,7,3,8,4,8\nI,1,5,3,4,1,7,7,0,8,13,6,8,0,8,1,7\nA,4,10,5,7,4,9,4,3,1,8,2,8,2,7,2,8\nG,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,9\nL,3,7,4,5,2,5,4,1,9,7,2,10,0,7,2,7\nO,3,5,4,3,2,8,7,8,5,7,7,8,2,8,3,8\nG,6,10,8,8,9,7,7,6,2,7,6,11,7,9,10,7\nW,3,6,4,4,3,9,10,2,2,6,9,8,6,11,1,8\nJ,2,9,3,7,2,10,7,0,7,10,4,6,0,7,1,7\nN,10,12,8,6,4,4,8,4,6,3,2,12,6,11,2,7\nF,3,7,3,5,2,1,11,3,4,11,11,8,0,8,1,7\nS,3,2,3,3,2,8,8,6,5,7,6,7,2,8,9,8\nG,4,10,6,7,3,7,5,7,10,5,4,10,1,9,6,11\nH,5,9,7,7,5,9,7,3,6,10,5,7,3,8,3,9\nB,5,9,7,6,7,8,7,4,4,7,6,7,4,8,6,8\nW,5,7,7,5,4,5,8,5,1,8,10,9,8,11,0,8\nU,4,5,5,4,2,4,8,5,7,10,9,9,3,9,2,5\nS,8,15,6,9,3,8,4,4,5,8,2,8,4,6,6,9\nL,5,9,5,5,3,10,4,3,3,12,7,11,3,10,4,10\nW,4,9,6,6,3,6,8,4,2,7,8,8,8,10,0,8\nN,7,9,6,4,3,4,9,4,7,3,3,11,5,9,2,8\nO,2,1,2,2,1,7,7,7,4,7,6,8,2,8,2,7\nO,7,11,9,8,10,7,10,5,3,8,7,7,11,11,7,10\nL,7,14,7,8,4,6,5,4,5,12,9,11,3,9,7,8\nS,3,7,4,5,5,8,8,5,3,8,5,8,4,9,10,6\nB,6,9,9,7,6,10,6,3,7,11,3,7,6,8,7,11\nH,2,6,2,4,1,7,8,14,1,7,5,8,3,8,0,8\nG,3,1,4,2,2,7,7,6,6,6,6,9,2,9,4,9\nL,2,1,3,2,1,4,3,5,6,2,3,4,1,7,0,6\nQ,1,2,2,3,1,8,6,6,3,8,6,9,2,9,3,8\nM,4,5,5,7,4,8,7,12,2,6,9,8,8,6,0,8\nZ,2,4,3,3,1,8,6,2,9,11,5,8,1,8,5,8\nU,5,11,6,8,5,6,9,5,6,6,9,9,3,8,1,8\nV,3,5,4,4,5,8,8,5,4,7,6,8,6,8,7,4\nT,1,0,1,1,0,8,13,1,5,6,10,8,0,8,0,8\nT,4,10,6,7,7,7,8,5,5,6,7,9,6,8,8,6\nK,1,1,2,1,1,6,7,4,7,6,6,10,3,8,4,9\nO,2,4,3,3,2,7,7,7,4,9,6,8,2,8,3,8\nK,6,11,9,8,6,7,6,2,7,10,5,9,4,7,5,9\nU,3,6,5,4,3,6,9,5,7,7,9,9,3,9,1,8\nL,3,7,5,5,2,5,4,2,10,6,1,9,0,7,3,6\nR,10,15,8,8,5,9,7,7,5,10,2,9,7,5,6,10\nM,6,7,9,5,6,9,6,2,5,9,5,7,8,4,2,8\nA,2,2,4,3,2,6,2,2,2,5,2,8,2,6,2,6\nH,12,15,11,8,6,8,7,4,5,9,7,7,7,10,5,9\nJ,5,9,6,7,3,10,6,1,7,14,3,7,0,7,0,8\nH,8,13,8,8,5,6,9,4,5,9,8,9,6,8,6,8\nM,7,11,11,8,8,7,6,3,5,9,8,9,8,6,2,8\nF,4,10,4,7,2,1,12,5,5,12,11,8,0,8,2,6\nS,4,8,5,6,4,7,9,7,7,8,4,6,2,7,9,9\nY,8,7,6,10,4,9,7,6,6,4,10,7,5,10,3,7\nM,5,6,8,4,5,8,6,2,5,9,6,8,7,5,2,8\nO,2,5,3,4,2,8,7,7,4,9,4,8,2,8,2,8\nG,2,1,2,2,1,7,7,6,5,6,6,9,2,9,4,9\nL,1,4,3,3,1,6,5,1,6,8,3,10,1,7,2,9\nG,4,5,5,4,3,6,6,6,6,6,6,9,2,9,4,8\nX,2,3,3,4,1,7,7,4,4,7,6,8,3,8,4,8\nA,2,7,3,5,2,7,4,2,0,7,2,8,1,6,1,8\nS,4,6,5,8,2,8,7,6,9,4,6,7,0,8,9,8\nT,4,8,5,6,4,6,11,3,6,8,11,8,2,12,1,7\nT,9,13,8,7,4,8,6,4,10,13,5,7,2,9,6,6\nX,7,11,10,8,5,10,7,2,9,11,1,6,4,9,4,10\nC,2,4,3,3,1,4,9,4,7,11,9,12,1,9,2,7\nZ,4,6,5,4,3,7,8,2,9,11,8,6,1,8,6,6\nZ,4,11,5,9,6,7,7,5,10,7,5,9,3,10,9,8\nT,2,7,4,4,1,9,14,1,6,5,11,9,0,8,0,8\nV,3,2,5,4,2,6,12,3,4,8,12,8,3,10,1,8\nX,3,4,6,3,2,9,6,2,8,10,3,7,2,7,3,9\nL,3,3,3,5,1,0,1,6,6,0,1,5,0,8,0,8\nR,4,9,4,6,3,6,11,9,3,7,2,9,3,7,5,10\nH,7,11,10,8,8,9,7,3,6,10,4,7,6,8,5,8\nQ,5,8,7,9,7,9,7,7,2,5,8,9,5,9,7,11\nP,8,12,7,6,3,7,9,6,4,13,4,5,4,10,4,8\nJ,2,7,4,5,2,7,7,3,5,15,6,9,1,6,1,7\nV,6,11,5,6,3,8,10,5,5,7,10,5,5,12,3,7\nQ,3,3,4,5,4,8,7,6,2,5,7,9,3,9,5,10\nS,4,5,6,4,5,10,8,5,5,6,8,9,4,11,6,10\nY,3,5,5,7,1,9,10,3,2,6,13,8,2,11,0,8\nL,3,10,4,7,4,3,4,4,7,2,0,8,0,6,1,6\nL,5,11,6,8,4,3,4,4,9,2,0,7,0,7,1,5\nA,5,8,7,7,6,9,7,3,4,7,7,6,5,10,4,6\nX,8,13,8,7,5,7,6,2,8,11,4,8,4,5,4,7\nF,4,11,4,8,2,0,13,5,4,12,11,6,0,8,2,5\nM,4,10,5,8,6,7,5,11,1,7,9,8,8,6,1,9\nI,2,9,3,7,3,8,7,0,7,7,6,8,0,8,3,7\nH,3,4,6,3,3,8,8,3,6,10,6,7,3,8,3,8\nL,6,9,8,8,7,7,7,4,6,7,7,8,3,8,9,10\nE,2,1,3,3,2,7,7,5,7,7,6,9,2,8,5,10\nV,4,10,6,7,2,9,8,4,3,6,14,8,3,10,0,8\nG,4,9,5,6,2,7,6,8,8,6,5,10,2,8,5,11\nP,4,7,6,9,7,6,8,5,2,8,7,6,9,12,7,8\nG,5,9,7,8,8,7,9,5,2,7,7,8,6,11,7,9\nA,3,8,5,6,3,5,4,3,0,5,2,7,2,7,2,7\nS,5,10,6,8,3,8,8,6,10,5,6,6,0,8,9,7\nX,11,15,10,8,5,11,6,3,9,10,4,7,4,11,4,11\nI,2,9,3,6,2,6,8,0,7,14,7,8,0,8,1,7\nD,3,6,5,4,3,7,7,7,8,6,5,4,3,8,3,7\nL,2,2,3,4,2,4,4,4,7,2,1,6,0,7,1,6\nW,4,3,4,2,2,4,11,3,2,9,9,7,6,11,1,6\nA,3,2,6,4,3,8,1,2,2,7,2,8,2,7,3,7\nP,5,9,6,7,5,7,9,5,6,9,8,4,5,10,5,7\nA,4,6,6,6,5,7,8,2,4,7,7,9,5,7,3,7\nK,9,13,9,7,5,10,6,2,6,11,4,7,6,11,3,9\nN,3,7,5,5,3,5,10,6,4,7,7,9,5,9,1,8\nX,4,4,5,6,1,7,7,4,4,7,6,8,3,8,4,8\nT,5,10,7,8,6,7,7,7,6,5,10,10,5,6,9,7\nR,5,10,6,8,4,5,11,8,3,7,4,8,3,8,6,11\nP,6,10,9,8,5,7,12,7,3,11,4,2,2,11,4,8\nL,2,5,4,4,2,7,3,1,8,9,2,10,0,7,2,8\nX,5,9,8,7,5,4,8,1,8,10,11,10,3,9,3,5\nW,7,11,9,8,10,8,7,6,3,6,8,8,6,8,5,7\nP,5,11,8,8,6,8,9,3,5,13,5,4,4,10,4,8\nB,3,7,4,5,3,6,7,8,6,7,6,7,2,8,9,10\nC,2,1,3,2,1,6,7,6,7,8,7,13,1,9,4,10\nM,5,8,8,6,7,7,6,5,5,7,7,11,14,6,2,10\nS,8,13,6,7,3,9,1,2,5,8,2,8,3,7,5,11\nM,7,10,9,8,9,9,7,6,5,6,7,6,11,8,4,5\nH,3,7,5,5,6,8,7,4,3,6,6,7,7,8,8,8\nP,4,10,4,7,4,4,11,8,2,9,6,4,1,10,3,8\nW,6,6,6,4,4,5,11,3,2,9,7,7,7,12,2,6\nZ,3,5,4,7,2,7,7,4,13,10,6,8,0,8,8,8\nH,5,10,7,8,10,9,6,4,4,6,6,7,8,7,7,6\nU,4,9,6,7,4,7,9,5,7,5,9,9,3,9,1,8\nE,5,10,5,7,3,3,8,6,11,7,6,15,0,8,7,7\nQ,2,3,3,4,2,8,7,5,3,8,7,9,2,9,3,8\nO,3,5,4,4,3,8,7,7,4,9,5,8,2,8,2,8\nS,3,7,4,5,3,7,8,3,7,10,5,7,2,7,5,8\nM,4,5,6,4,6,6,8,5,3,6,5,8,9,7,4,8\nN,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8\nE,2,3,3,2,2,8,7,1,7,11,5,8,2,8,4,10\nP,1,1,2,2,1,5,9,4,3,9,7,4,1,10,2,7\nH,6,9,9,7,8,6,8,3,6,10,7,8,3,8,3,7\nB,2,3,3,2,2,7,7,5,5,6,5,6,2,8,5,9\nW,6,11,10,8,6,5,10,2,3,8,9,9,8,11,1,8\nI,1,3,2,1,0,7,7,1,6,13,6,8,0,8,0,8\nR,5,11,6,8,4,6,9,10,5,7,5,8,3,8,6,10\nH,6,10,6,8,5,7,6,14,2,7,8,8,3,8,0,8\nW,6,5,8,7,4,4,8,5,2,7,9,8,9,9,0,8\nM,5,11,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nX,1,0,1,0,0,8,7,3,5,7,6,8,2,8,3,7\nH,2,5,4,3,3,7,7,2,5,10,6,8,3,8,2,8\nG,5,5,7,8,3,7,5,7,10,5,5,9,1,9,7,11\nV,3,7,5,5,2,7,12,3,4,6,12,8,2,10,1,8\nV,6,11,6,8,3,2,12,5,4,11,12,8,3,9,1,8\nB,3,5,4,4,4,7,7,5,5,6,6,6,2,8,6,9\nE,3,7,5,5,3,10,6,2,7,11,4,9,2,8,5,12\nW,6,7,6,5,4,6,11,4,2,8,7,6,6,12,2,5\nW,7,7,10,6,10,8,8,5,5,7,5,8,9,8,9,6\nO,5,9,6,6,4,8,6,8,4,7,4,8,3,8,3,8\nZ,3,9,4,6,3,7,7,3,12,9,6,8,0,8,8,8\nY,6,6,5,9,4,7,7,4,3,6,11,6,4,11,6,6\nZ,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8\nW,5,11,8,8,4,4,8,5,2,7,9,8,8,10,0,8\nT,2,6,4,4,1,9,15,1,5,5,11,9,0,8,0,8\nH,3,5,4,4,3,7,7,6,6,7,6,8,3,8,3,8\nD,5,10,7,8,6,7,7,8,5,7,6,5,4,8,3,7\nZ,3,7,5,5,3,8,7,2,9,11,5,10,1,9,6,10\nX,6,11,9,8,6,5,8,1,8,10,10,10,3,8,4,6\nJ,1,3,2,2,1,10,6,3,6,12,4,10,0,7,1,8\nV,1,0,2,0,0,8,9,3,2,7,12,8,2,10,0,8\nH,3,7,5,5,6,7,6,5,4,6,6,8,6,8,8,10\nH,6,11,6,8,6,7,8,14,1,7,5,8,3,8,0,8\nR,7,11,9,8,9,8,6,7,5,8,6,8,7,7,6,12\nB,5,9,7,7,5,9,7,4,6,10,5,6,2,8,6,10\nX,5,9,6,8,7,9,8,2,5,8,5,6,4,7,9,8\nJ,4,11,6,8,2,9,6,2,7,15,4,9,0,6,1,7\nQ,5,9,6,10,6,8,11,6,2,5,8,12,3,11,7,6\nE,4,5,4,8,3,3,7,6,10,7,6,14,0,8,7,7\nT,3,3,4,2,1,6,11,3,6,11,9,4,2,11,2,5\nU,3,3,4,2,1,6,8,6,7,7,10,9,3,10,1,8\nQ,5,7,7,10,9,9,9,5,0,6,7,10,8,14,5,11\nQ,4,5,4,6,4,8,9,5,1,7,8,11,2,9,5,8\nE,5,12,4,6,3,8,7,4,4,10,6,8,3,9,8,10\nX,5,9,8,7,4,7,8,1,8,10,7,8,3,8,4,7\nP,4,10,6,8,5,4,10,3,6,11,10,6,3,10,3,7\nC,5,10,6,7,4,5,7,6,9,7,6,13,1,8,4,9\nS,1,0,1,0,0,8,7,3,5,5,6,7,0,8,6,8\nY,4,4,5,6,6,9,8,5,3,7,8,7,6,10,6,4\nO,5,10,7,7,5,7,8,8,5,7,7,9,3,7,4,7\nU,11,14,9,8,4,4,3,5,5,4,7,7,6,6,2,6\nE,5,11,4,6,3,6,9,4,5,10,6,8,3,8,7,9\nD,4,10,6,8,10,9,8,5,5,7,6,6,5,6,7,6\nP,3,4,5,3,2,7,10,3,4,12,4,2,1,10,2,8\nD,5,4,5,6,3,5,8,10,9,7,7,5,3,8,4,8\nX,2,5,3,3,2,7,7,3,9,6,6,8,2,8,6,8\nF,6,11,6,6,4,6,10,3,4,11,6,4,4,10,8,6\nY,1,0,2,1,0,7,10,3,1,7,13,8,1,11,0,8\nP,9,13,9,7,6,11,6,3,4,12,4,6,5,9,6,9\nJ,6,11,8,8,3,8,6,4,6,15,6,11,1,6,1,6\nD,2,3,3,2,2,10,6,3,6,10,3,6,2,8,3,9\nQ,1,2,2,2,1,8,8,5,2,8,7,10,2,9,3,9\nS,7,10,5,5,2,9,3,4,4,9,2,9,3,6,5,10\nT,6,8,6,6,4,7,11,3,8,12,9,4,2,12,3,4\nD,6,9,9,8,8,6,6,5,7,7,6,8,5,6,6,4\nU,2,1,3,2,1,6,8,6,6,6,9,9,3,9,1,7\nN,3,4,5,2,2,9,7,3,4,10,3,5,5,9,1,7\nH,1,0,2,0,0,7,8,11,1,7,6,8,2,8,0,8\nU,3,3,4,2,1,7,9,6,7,7,10,9,3,10,1,8\nW,2,1,2,2,1,7,8,4,0,7,8,8,6,10,0,8\nF,2,3,2,2,1,5,10,3,5,10,9,5,1,10,3,6\nH,5,9,6,7,5,7,8,13,1,7,6,8,3,8,0,8\nR,5,11,7,8,6,8,8,5,6,9,4,8,3,7,5,11\nO,5,9,5,7,5,7,7,7,4,9,7,8,3,8,3,8\nS,7,10,8,8,4,9,7,4,9,11,6,7,2,10,5,8\nY,10,9,8,13,5,7,9,2,2,7,10,4,4,10,7,7\nW,6,9,8,7,12,10,7,5,2,7,7,8,11,10,3,5\nO,2,4,2,3,1,7,7,6,4,9,6,8,2,8,2,8\nG,3,4,4,6,2,7,8,8,7,5,7,8,2,7,6,11\nT,5,10,5,7,4,4,12,3,5,11,10,5,2,13,2,4\nG,1,0,2,1,0,8,6,6,4,6,5,9,1,8,5,10\nS,3,4,3,3,2,8,6,6,5,7,6,9,2,10,9,8\nE,2,0,2,1,1,5,7,5,8,7,6,12,0,8,7,9\nA,4,10,6,8,3,10,3,2,3,9,1,8,2,7,4,9\nJ,1,3,2,2,1,10,6,2,5,12,4,9,0,7,0,7\nN,5,7,7,5,4,7,9,2,5,10,5,6,5,9,1,7\nB,3,2,3,3,3,7,7,5,5,6,6,6,2,8,6,9\nR,4,9,6,6,6,6,7,3,5,6,5,9,6,10,7,5\nG,4,6,5,4,6,8,5,4,3,7,6,10,6,8,4,10\nT,2,5,3,3,1,8,12,2,7,6,11,7,1,11,1,7\nC,5,10,6,8,2,6,6,7,11,9,5,14,1,9,4,8\nE,4,8,6,6,6,7,7,5,8,7,7,9,3,8,6,9\nU,4,8,6,6,5,7,7,8,5,6,6,10,4,8,5,6\nI,4,10,7,8,8,10,6,2,4,8,4,4,3,8,5,7\nP,3,2,4,3,2,5,10,4,4,10,8,4,1,10,3,6\nC,7,10,7,7,5,4,8,5,7,12,9,12,2,10,4,6\nC,7,10,7,8,4,3,8,6,7,12,11,12,2,9,2,7\nP,3,6,5,4,3,6,11,3,5,13,6,4,0,10,2,8\nA,1,1,2,2,1,7,3,2,1,6,2,8,1,6,1,7\nG,3,5,4,7,2,7,6,8,9,8,4,13,1,9,5,10\nH,4,7,5,5,4,8,7,6,6,7,6,9,6,8,3,8\nN,4,5,5,4,3,7,8,5,5,8,7,7,7,9,3,5\nW,6,6,6,4,4,3,11,2,3,10,10,8,7,11,1,7\nW,5,9,7,7,8,8,8,6,3,6,8,8,6,7,4,7\nN,3,2,4,3,3,7,8,5,4,7,6,6,5,9,2,6\nS,5,8,6,6,3,9,7,4,8,11,4,8,2,8,5,9\nF,1,0,1,0,0,4,12,4,2,11,8,6,0,8,2,8\nK,2,3,3,2,1,6,7,2,6,10,7,10,3,8,2,8\nB,4,8,6,6,4,8,8,4,7,10,5,6,2,8,6,10\nY,2,2,4,3,2,6,10,1,7,8,12,8,2,11,2,7\nE,7,9,9,8,9,7,8,5,5,7,7,10,8,9,10,9\nZ,6,11,8,8,8,10,9,5,4,7,5,7,3,8,9,5\nX,4,8,6,6,4,8,7,3,8,5,6,6,3,8,6,7\nO,5,11,5,9,6,8,7,8,3,9,6,7,3,8,3,8\nV,4,11,5,8,3,5,11,3,4,9,12,9,2,10,1,8\nR,3,6,4,4,3,10,7,3,5,10,3,7,3,7,3,11\nL,3,7,4,5,3,7,4,1,7,8,2,10,0,7,2,8\nV,6,8,6,6,3,3,12,4,4,10,12,7,2,10,1,8\nH,5,8,7,6,8,7,6,5,5,6,6,7,8,7,8,9\nP,2,4,3,3,2,9,8,3,4,12,4,3,1,10,3,9\nL,1,3,2,1,1,6,4,1,7,8,3,10,0,7,2,8\nQ,3,5,3,6,3,8,8,5,2,8,8,11,2,9,5,8\nD,3,2,4,3,3,7,7,7,7,7,6,5,2,8,3,7\nV,3,6,4,4,2,8,9,4,2,7,12,8,2,10,0,8\nH,5,9,6,7,6,7,5,13,2,7,9,8,3,9,0,8\nG,7,13,6,8,5,7,6,4,3,8,6,8,4,9,9,7\nV,2,3,3,1,1,6,12,3,2,8,11,8,2,11,1,8\nP,4,6,6,4,3,7,10,4,4,12,5,3,1,10,2,8\nK,4,5,5,8,2,4,8,8,2,7,4,11,4,8,2,11\nU,6,9,6,7,4,3,8,5,7,10,9,9,3,9,2,6\nU,4,7,6,5,5,9,6,7,5,7,7,8,5,8,4,6\nY,5,10,6,7,6,9,5,7,5,6,9,7,3,9,9,4\nD,5,11,6,8,6,6,7,9,7,6,5,5,2,8,3,7\nF,5,11,7,8,4,6,11,2,6,13,7,4,1,10,2,7\nI,1,5,2,4,1,7,7,0,7,13,6,8,0,8,1,8\nN,2,1,2,2,1,7,9,5,4,7,6,7,4,8,1,6\nL,5,11,7,9,6,8,4,0,7,9,3,10,1,6,2,9\nN,6,10,9,8,4,3,9,4,4,11,11,10,6,7,1,8\nE,2,6,3,4,2,3,6,6,10,7,6,14,0,8,7,8\nN,5,10,7,5,3,8,7,2,4,12,8,8,5,8,0,8\nM,3,3,5,2,3,7,6,3,4,9,7,8,7,5,1,8\nB,5,7,7,5,5,8,8,4,6,10,5,6,3,8,6,10\nU,5,5,6,8,2,8,4,15,6,7,14,8,3,9,0,8\nB,5,8,7,6,5,10,5,3,7,10,4,7,3,7,6,11\nT,4,4,4,3,2,5,11,2,8,12,9,4,1,10,2,4\nG,6,8,8,7,8,8,6,6,4,7,6,10,10,10,8,9\nN,3,7,5,5,5,7,7,3,4,8,8,8,6,9,4,5\nS,3,9,4,7,4,7,7,7,5,7,7,9,2,9,8,8\nB,11,13,8,7,5,9,6,5,6,11,4,9,6,7,7,11\nO,9,13,6,7,4,5,7,7,4,10,7,8,5,9,5,7\nN,7,10,10,8,10,6,8,3,4,8,8,9,8,10,7,4\nF,4,11,6,8,4,3,11,2,7,11,10,6,1,10,3,5\nJ,3,9,5,6,2,7,5,4,5,14,7,12,1,6,1,6\nL,1,4,2,3,1,7,3,1,6,8,2,10,0,7,2,8\nG,2,3,3,2,1,6,7,5,5,9,7,10,1,8,4,10\nP,5,11,6,8,7,6,7,5,4,8,7,8,8,8,6,11\nC,2,6,3,4,2,5,8,6,6,6,7,14,1,8,3,10\nE,3,5,4,3,3,7,7,5,8,6,5,8,2,8,6,9\nU,3,4,4,3,2,4,8,5,6,11,10,9,3,9,1,7\nM,5,9,8,7,6,4,7,3,4,10,10,10,8,6,3,7\nL,4,10,5,7,4,4,4,3,8,2,1,7,0,6,1,6\nA,5,12,5,6,4,11,2,4,2,11,4,11,5,4,5,10\nC,5,9,6,8,7,5,6,4,5,7,6,11,6,10,9,10\nM,5,8,6,6,3,7,7,12,2,7,9,8,8,6,0,8\nL,5,11,8,9,9,7,8,3,6,5,7,11,5,11,9,7\nM,8,10,11,7,9,5,7,3,5,9,9,9,9,7,3,8\nT,5,7,7,6,7,7,8,4,8,8,7,9,3,8,8,6\nF,2,5,4,4,1,5,13,3,5,13,7,3,0,9,2,6\nN,4,8,5,6,4,7,7,13,1,6,6,8,5,8,0,8\nJ,5,9,7,7,6,7,7,5,5,8,6,7,6,7,4,7\nP,6,8,8,10,9,6,8,4,2,8,8,6,7,12,7,7\nB,8,14,7,8,6,10,7,3,6,10,4,7,6,6,7,9\nA,3,2,5,4,2,8,2,2,2,8,1,8,2,6,3,7\nG,7,12,6,6,4,8,3,4,3,7,4,4,4,7,5,8\nZ,4,9,4,6,3,7,10,3,11,8,6,7,0,8,7,7\nU,4,4,5,3,2,4,8,5,7,12,10,9,3,9,2,7\nP,9,15,7,8,4,5,11,6,2,11,6,4,5,10,4,7\nY,4,11,5,8,3,8,11,1,3,7,12,8,1,11,0,8\nA,3,8,6,6,3,12,2,3,2,10,2,9,2,6,3,8\nT,6,11,8,8,7,7,7,7,7,5,9,10,6,7,9,4\nX,3,6,5,4,3,6,8,1,8,10,8,9,2,8,3,6\nS,4,7,4,5,3,7,8,4,7,10,5,7,2,7,4,8\nK,6,10,5,5,3,7,8,3,6,9,9,9,6,11,3,6\nZ,1,1,2,2,1,7,7,4,8,6,6,8,1,8,6,8\nP,5,9,5,6,3,5,12,9,2,9,5,4,1,10,4,8\nE,1,0,1,0,0,5,7,5,7,7,6,12,0,8,6,9\nY,7,8,7,6,4,5,8,1,7,8,9,5,2,11,3,4\nX,3,3,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nQ,6,11,6,6,4,11,4,4,5,11,4,8,3,9,6,12\nY,3,5,5,4,2,7,11,1,8,6,11,8,1,11,2,8\nX,3,5,5,4,2,10,7,1,8,11,3,7,2,8,3,9\nZ,7,9,7,5,4,9,4,3,8,12,5,10,3,8,6,8\nO,8,10,6,5,3,8,6,5,5,8,4,8,5,9,5,8\nY,2,2,4,4,2,7,10,1,7,7,11,8,1,11,2,8\nG,4,8,5,6,2,7,6,7,8,6,6,9,1,8,6,11\nZ,5,9,7,7,6,10,9,6,4,7,5,8,3,8,10,7\nN,4,7,6,5,4,7,9,6,5,7,6,6,6,9,1,6\nW,7,9,7,4,3,5,10,2,2,9,10,8,8,12,0,7\nH,3,3,5,2,2,8,7,3,6,10,4,7,3,7,3,8\nX,2,0,2,1,0,7,7,6,2,7,6,8,3,8,3,8\nV,3,9,5,7,3,8,9,4,1,7,12,8,2,10,0,8\nS,4,9,6,6,7,5,8,3,1,7,6,5,3,8,9,1\nM,8,9,11,6,9,8,6,2,5,9,7,8,8,6,2,8\nY,4,4,5,3,2,4,10,2,8,10,10,5,3,11,4,3\nS,3,4,4,6,2,9,9,6,10,5,5,5,0,7,9,7\nU,3,7,5,5,4,8,9,8,5,4,7,11,3,7,3,7\nQ,4,9,5,8,3,8,6,9,5,6,6,8,3,8,5,9\nX,4,7,6,5,3,7,8,1,8,10,7,8,2,8,3,7\nH,5,9,6,4,4,4,9,3,4,10,9,9,4,8,4,6\nV,2,1,4,2,1,6,12,2,3,8,11,8,2,11,1,8\nC,4,7,5,5,2,5,9,7,8,8,8,14,1,8,4,9\nL,1,3,2,2,1,7,4,1,7,8,2,9,0,7,2,8\nS,3,9,4,7,2,7,7,6,8,5,6,7,0,8,9,7\nL,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8\nP,7,9,9,7,5,7,12,8,3,10,5,3,2,11,5,8\nJ,0,0,1,0,0,12,4,5,3,12,4,10,0,7,0,8\nT,3,3,4,2,2,6,11,3,7,11,9,5,1,11,3,4\nT,1,0,2,0,0,7,15,2,4,7,10,8,0,8,0,8\nT,2,3,2,2,1,7,11,2,6,7,10,8,1,11,1,7\nB,4,5,5,4,4,7,7,5,6,7,6,6,2,8,6,10\nL,5,10,6,8,4,5,3,6,9,1,1,4,1,6,1,5\nI,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8\nP,1,3,3,2,1,8,7,4,3,11,4,5,1,9,2,9\nI,6,8,8,9,7,7,8,4,7,6,8,7,4,8,11,9\nC,3,8,4,6,2,4,8,7,9,9,9,13,1,9,4,9\nI,3,10,4,8,2,7,7,0,8,14,6,8,0,8,1,8\nL,2,3,3,5,1,0,1,5,6,0,0,7,0,8,0,8\nN,2,1,3,2,2,7,9,6,5,8,7,6,5,9,1,6\nM,4,1,5,2,3,8,6,6,4,7,7,8,8,6,3,6\nX,2,1,2,1,0,7,7,4,4,7,6,8,2,8,4,8\nA,7,11,5,6,3,10,1,3,1,10,4,12,3,4,4,8\nQ,6,10,7,9,4,8,7,8,7,6,7,9,3,8,5,9\nM,4,7,5,5,5,8,7,5,5,6,6,5,8,7,2,6\nZ,3,4,5,6,4,9,7,5,5,9,3,6,2,6,6,7\nV,6,9,4,4,2,8,10,5,4,7,10,5,4,11,3,7\nA,3,11,5,8,3,13,3,4,3,12,1,9,2,6,3,9\nL,3,10,3,7,1,0,1,5,6,0,0,7,0,8,0,8\nY,3,11,5,8,1,5,10,3,2,9,13,8,1,11,0,8\nQ,4,4,5,6,2,9,8,8,6,5,7,10,3,8,5,9\nC,4,8,6,6,6,7,7,4,4,6,7,10,7,9,5,7\nS,3,4,4,2,2,7,8,2,7,10,7,7,1,8,5,6\nC,3,6,4,4,1,6,7,7,10,8,6,13,1,9,4,9\nN,5,9,6,6,5,7,7,9,5,6,4,5,5,8,5,11\nK,3,6,5,4,4,6,6,3,8,6,6,9,3,8,5,9\nE,9,11,6,6,3,8,7,5,7,10,6,11,2,9,8,8\nS,4,8,6,6,3,9,7,5,8,11,2,8,2,7,5,11\nS,2,4,3,3,2,8,8,7,5,8,5,7,2,8,8,8\nX,6,10,9,7,5,7,8,1,9,10,6,8,3,8,4,7\nK,4,10,4,8,4,3,7,6,3,6,5,11,3,8,2,11\nB,2,3,4,2,2,7,7,3,5,10,6,6,2,8,5,8\nF,5,11,7,8,5,9,8,1,6,13,5,5,2,9,3,9\nM,3,4,5,2,3,9,6,3,4,8,5,7,7,5,1,8\nX,4,7,6,5,3,4,9,2,7,10,11,9,3,9,3,5\nC,3,1,4,3,2,6,8,7,7,8,8,13,1,10,4,10\nE,4,6,5,4,3,6,8,4,8,12,8,9,2,9,4,6\nU,4,5,5,4,2,4,8,5,7,11,10,9,3,9,2,6\nI,6,11,7,8,5,7,7,1,7,7,6,8,0,8,4,8\nT,4,9,4,4,2,4,11,2,6,12,8,5,2,8,4,3\nA,2,4,3,3,2,10,2,2,2,9,2,9,1,6,2,8\nH,2,6,3,4,1,7,9,14,2,7,4,8,3,8,0,8\nP,5,11,8,8,5,6,11,3,6,14,7,4,0,10,3,8\nE,3,5,5,3,3,6,8,2,8,11,8,9,2,9,4,7\nD,7,15,7,9,5,9,6,5,6,12,3,7,5,7,6,10\nI,5,11,7,8,4,9,6,2,7,7,6,6,0,9,4,7\nN,5,10,5,8,5,7,7,12,1,6,6,8,6,7,1,10\nP,2,7,4,5,2,5,11,3,6,11,9,5,0,9,3,6\nT,5,9,5,6,4,5,12,4,6,11,9,4,2,12,2,4\nD,3,9,5,7,8,10,7,5,5,7,5,6,4,6,10,6\nG,4,6,6,5,5,8,10,5,2,7,7,9,6,11,7,10\nC,4,6,5,4,2,4,9,5,7,11,10,12,1,9,3,7\nY,4,6,6,8,8,9,8,4,2,6,8,9,4,11,7,8\nD,2,1,2,1,1,7,7,6,6,7,6,5,2,8,2,7\nP,2,3,2,2,1,6,10,5,4,9,7,3,1,9,3,6\nX,2,5,4,4,2,7,7,3,9,6,6,8,2,8,6,8\nB,4,7,6,5,4,8,8,4,6,10,5,6,2,8,6,8\nF,2,2,3,3,2,6,10,4,5,10,9,5,2,9,3,6\nZ,2,5,4,4,2,7,8,2,10,12,7,7,1,8,5,6\nP,6,9,9,7,4,5,15,6,2,12,5,1,0,9,4,7\nJ,3,9,4,7,3,11,6,3,6,12,2,8,1,6,2,6\nB,3,9,4,7,3,6,7,10,6,7,6,7,2,8,9,10\nC,3,8,4,6,2,5,7,6,8,8,6,12,1,9,4,9\nB,4,9,4,4,3,9,6,3,4,9,5,8,6,8,7,9\nU,3,3,3,5,2,7,6,13,5,7,14,8,3,9,0,8\nO,6,9,7,7,6,7,8,7,5,9,7,9,3,8,4,7\nM,6,9,8,6,8,6,6,6,5,7,7,11,11,5,2,9\nD,3,10,5,8,7,8,9,5,4,8,7,5,4,9,11,6\nD,5,6,6,8,3,5,7,11,9,6,5,5,3,8,4,8\nP,2,3,4,1,1,8,9,3,4,12,4,3,1,9,2,9\nP,3,5,5,4,2,7,10,5,3,11,4,3,1,10,3,8\nY,5,9,5,7,3,3,11,3,6,11,12,6,1,11,2,6\nF,2,5,3,3,2,5,10,3,5,10,9,6,2,10,3,6\nI,1,6,2,4,1,7,7,0,8,7,6,8,0,8,3,8\nB,3,2,4,4,4,7,7,5,5,7,6,6,2,8,6,10\nF,5,10,6,8,7,6,10,6,4,8,5,9,3,10,8,11\nU,3,7,5,5,4,7,8,8,5,5,6,11,3,8,5,6\nB,3,7,4,5,3,6,5,9,7,6,7,7,2,9,8,9\nA,9,15,9,8,6,11,2,5,2,11,4,11,7,2,5,11\nZ,4,8,6,6,6,8,8,2,8,7,6,7,0,8,9,8\nE,5,11,6,8,7,7,7,6,3,7,6,9,4,8,8,8\nE,1,0,1,1,1,5,7,5,8,7,6,12,0,8,6,9\nA,1,0,2,0,0,7,4,2,0,7,2,8,1,6,1,8\nM,6,7,9,5,5,8,6,2,5,9,6,7,8,6,2,8\nJ,5,9,6,7,4,9,4,7,6,8,6,6,2,7,4,6\nT,4,4,4,3,2,6,12,3,7,11,9,4,1,11,2,4\nW,8,10,8,8,6,5,10,3,3,9,8,7,8,11,3,6\nK,4,7,6,5,5,6,6,4,6,6,5,8,7,7,7,9\nM,5,11,7,8,8,9,7,5,4,6,7,5,9,7,3,5\nN,5,8,7,7,7,8,8,5,5,8,5,6,6,10,7,2\nW,3,3,5,5,3,6,8,4,1,7,8,8,8,9,0,8\nD,3,9,5,7,4,8,7,7,7,7,7,4,3,8,3,7\nH,4,6,6,8,6,9,5,3,2,7,5,7,4,8,6,9\nH,3,4,4,3,3,7,7,6,6,7,6,8,3,8,3,8\nM,8,10,11,8,9,12,6,2,5,9,3,6,10,4,2,8\nR,5,9,5,4,4,7,8,3,5,8,4,9,5,7,6,7\nB,6,10,8,8,7,9,7,2,7,10,4,7,4,7,5,10\nO,6,10,7,8,6,8,6,8,4,9,5,6,5,8,4,9\nR,5,11,7,8,7,9,8,3,5,10,4,7,5,5,4,10\nF,5,9,8,7,5,9,8,1,6,13,5,5,3,9,3,9\nU,4,7,6,5,7,8,7,4,4,6,7,8,7,10,5,7\nZ,2,4,4,2,2,7,7,2,9,12,6,8,1,8,5,7\nH,5,11,7,8,10,8,7,4,3,6,6,7,7,8,9,8\nP,4,8,5,6,4,7,9,8,4,8,6,3,3,10,3,6\nH,2,3,4,1,2,7,7,3,5,10,6,8,3,8,3,8\nT,3,7,4,5,2,5,14,1,5,9,10,7,0,8,0,8\nB,1,0,1,0,1,7,8,6,4,7,6,7,1,8,6,9\nO,3,2,4,3,3,8,7,8,5,7,6,8,2,8,3,8\nD,4,8,6,6,8,9,7,4,7,7,6,5,5,5,7,7\nS,2,4,3,3,1,9,7,3,6,10,6,7,1,9,5,8\nC,3,8,4,6,2,6,8,7,10,6,7,12,1,7,4,9\nN,2,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nD,2,4,3,3,2,9,6,3,5,10,4,6,3,7,2,8\nA,2,5,4,4,2,8,3,2,2,8,2,8,2,6,2,7\nS,4,8,5,6,4,8,6,7,7,7,8,8,2,10,9,8\nK,3,4,4,5,1,4,7,8,2,7,5,11,4,8,2,11\nW,4,7,5,5,3,5,8,5,1,7,8,8,8,9,0,8\nR,4,6,7,5,7,9,6,4,4,8,5,7,6,9,6,7\nH,4,5,5,7,3,7,7,15,0,7,6,8,3,8,0,8\nU,4,8,6,6,8,9,7,4,5,6,8,8,8,8,6,6\nN,5,9,7,6,5,9,8,6,6,6,5,4,7,11,3,5\nX,5,7,8,5,3,9,6,2,8,11,2,7,3,8,4,9\nO,5,8,6,6,4,8,6,8,5,10,5,8,3,8,3,8\nL,2,4,4,3,1,6,4,1,8,8,2,10,0,7,2,8\nB,3,5,4,4,5,7,7,5,4,7,6,8,6,9,6,9\nE,4,7,6,5,4,10,6,2,8,11,3,9,2,8,5,12\nB,4,9,5,6,4,6,8,9,7,7,6,7,2,8,9,9\nV,4,7,6,6,6,6,9,5,6,8,6,8,6,9,7,6\nN,4,6,5,4,3,7,9,2,4,9,7,7,5,8,1,8\nX,3,6,4,4,2,7,7,4,4,7,6,7,3,8,4,8\nX,2,3,3,2,1,9,7,3,8,6,6,6,2,8,5,8\nB,2,3,4,2,2,8,7,3,5,10,5,7,2,8,4,9\nO,4,7,5,5,3,7,8,8,5,10,6,6,3,8,3,8\nD,3,5,4,4,3,7,7,7,7,6,6,5,2,8,3,7\nS,6,11,7,8,4,8,7,4,8,11,5,7,2,8,5,8\nI,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7\nN,5,10,8,8,5,5,10,3,4,9,9,8,5,9,1,7\nS,3,8,5,6,6,6,7,3,2,7,6,6,2,8,10,1\nU,6,11,8,8,5,8,9,4,6,5,8,9,6,9,2,8\nT,3,8,5,6,3,7,12,4,6,7,11,8,2,12,1,8\nG,4,8,5,6,4,7,6,7,7,7,4,11,1,8,5,11\nJ,3,9,4,7,4,10,6,2,5,9,4,6,3,7,7,8\nW,3,2,5,3,3,5,11,3,2,8,9,9,7,11,0,8\nS,2,3,3,2,1,7,8,2,7,11,7,8,1,9,4,6\nS,5,11,6,9,5,8,7,8,6,8,6,7,2,8,9,8\nM,4,9,5,6,3,7,7,12,1,7,9,8,8,6,0,8\nI,2,7,3,5,2,6,8,0,7,13,7,8,0,8,1,7\nE,1,0,1,1,0,4,7,5,7,7,6,12,0,8,6,10\nH,4,10,4,7,3,7,6,15,2,7,8,8,3,8,0,8\nQ,4,10,5,9,3,8,7,8,6,6,7,8,3,8,6,9\nT,5,7,5,5,3,7,10,2,7,11,9,5,1,11,3,5\nH,3,5,6,4,4,8,8,3,6,10,6,8,3,8,3,8\nA,3,2,6,4,2,8,2,2,2,6,2,7,3,7,3,7\nZ,3,2,4,4,3,7,7,5,9,6,6,8,1,8,7,8\nR,4,6,6,4,4,10,7,3,6,11,2,7,3,6,3,10\nM,4,5,7,3,4,8,6,3,4,9,6,8,8,6,2,8\nI,2,5,3,4,1,7,7,0,7,13,6,8,0,8,1,8\nC,5,8,5,6,3,5,7,5,6,11,8,13,2,9,4,8\nM,4,4,5,6,3,7,7,12,1,7,9,8,8,6,0,8\nK,3,4,4,3,3,6,7,4,8,7,6,11,3,8,5,10\nR,4,8,6,6,5,9,7,3,5,10,3,7,3,6,4,10\nY,4,10,5,7,1,7,11,2,3,7,12,8,1,11,0,8\nQ,4,8,5,7,3,8,7,8,6,6,7,8,3,8,5,9\nP,2,4,4,2,2,8,9,4,3,12,4,4,1,10,2,9\nW,8,12,8,6,5,2,9,2,3,9,11,9,8,11,1,5\nJ,3,10,4,8,2,14,3,5,4,13,2,10,0,7,0,8\nE,6,10,9,8,7,10,6,2,7,11,5,8,6,8,6,11\nV,6,10,6,8,4,2,11,2,3,9,11,8,3,11,1,7\nF,4,7,5,5,3,5,11,4,6,11,10,5,2,10,3,5\nI,3,8,4,6,2,7,9,0,8,14,6,6,0,9,1,7\nN,4,8,4,6,2,7,7,14,2,4,6,8,6,8,0,8\nK,5,10,7,7,5,5,6,5,8,6,6,12,4,7,6,11\nI,3,10,4,7,3,8,7,0,7,13,6,8,0,8,1,8\nZ,3,4,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nI,1,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nY,6,9,5,5,3,7,7,3,5,9,7,6,3,10,4,5\nV,6,10,6,7,3,3,11,3,4,10,12,8,2,10,1,8\nL,3,7,4,5,2,4,4,3,8,2,1,7,0,7,1,6\nU,3,2,3,4,1,8,5,12,5,6,14,8,3,9,0,8\nC,4,10,5,8,2,5,7,7,10,6,6,13,1,7,4,9\nO,4,9,4,7,4,8,5,7,4,10,5,10,4,8,4,7\nL,6,15,6,8,4,5,7,4,6,12,9,12,3,8,7,7\nB,6,9,8,6,5,10,6,3,8,11,3,7,2,8,5,12\nA,4,7,7,5,4,6,5,2,3,4,2,6,4,6,4,5\nS,6,13,5,7,3,11,2,1,5,10,1,8,2,8,4,12\nJ,4,11,6,8,3,8,5,5,6,14,7,12,1,6,1,7\nA,2,8,4,5,2,7,6,3,1,6,0,8,2,7,2,7\nI,1,2,1,3,1,7,7,1,8,7,6,8,0,8,3,8\nT,6,8,6,6,4,5,11,3,7,11,9,5,2,12,2,4\nP,4,7,5,5,3,5,12,5,5,11,9,3,0,9,4,6\nG,5,10,7,8,8,9,6,5,2,6,6,10,8,8,5,10\nJ,3,6,4,4,1,10,4,6,5,15,5,12,0,7,1,6\nB,6,13,6,7,6,7,7,4,4,9,7,8,7,6,9,8\nB,2,4,3,3,2,8,7,3,5,9,6,7,2,8,4,9\nI,2,10,2,7,2,7,7,0,8,7,6,8,0,8,3,8\nN,3,8,5,6,4,7,8,6,5,7,6,7,5,9,1,6\nB,5,8,7,6,7,8,7,6,6,9,6,6,5,10,7,9\nX,5,11,7,8,6,7,6,3,9,5,7,10,4,7,8,8\nZ,3,9,4,6,3,7,7,3,12,9,6,8,0,8,7,8\nU,6,10,5,5,4,8,6,4,4,7,7,7,5,7,3,7\nP,3,4,5,3,2,8,9,3,5,13,4,3,2,8,3,7\nR,7,9,6,5,4,7,7,5,5,9,4,9,6,5,7,11\nK,3,3,3,4,1,3,6,7,3,7,7,11,3,8,2,10\nA,2,1,4,3,2,8,2,2,1,7,2,8,2,6,2,7\nN,5,7,6,6,6,8,9,4,4,7,4,7,6,8,5,5\nN,5,9,7,6,4,8,8,7,6,7,6,4,6,9,2,6\nP,4,11,4,8,2,3,14,8,2,12,7,3,0,10,4,8\nQ,3,5,5,5,4,7,4,4,4,5,3,7,3,7,4,8\nP,7,12,6,7,4,7,10,3,5,13,5,3,3,11,5,6\nU,7,8,7,6,5,4,8,5,7,9,8,9,5,9,4,4\nB,8,11,7,6,6,7,8,3,6,9,5,7,8,4,8,7\nM,4,4,5,6,3,7,7,12,1,7,9,8,8,6,0,8\nO,4,8,4,6,4,7,8,7,4,10,7,6,3,9,3,7\nT,1,1,2,1,0,7,14,1,5,7,11,8,0,8,0,8\nA,3,7,5,5,5,8,8,7,4,6,6,8,2,8,6,5\nD,4,10,4,8,3,5,6,10,8,5,4,5,3,8,4,8\nI,1,5,1,4,1,7,7,1,8,7,6,8,0,8,3,8\nS,8,9,10,8,12,7,8,5,5,7,7,7,5,10,11,10\nG,2,3,3,2,1,6,7,5,4,9,7,10,2,9,4,10\nC,6,10,5,5,3,7,8,4,3,9,8,9,4,9,9,11\nA,8,13,7,7,4,11,4,3,2,8,4,10,5,5,4,9\nE,3,5,4,4,3,7,7,5,8,7,6,9,2,8,6,9\nB,6,9,9,7,6,9,7,3,7,10,4,6,4,6,6,9\nR,4,10,6,8,5,7,9,5,6,7,6,9,4,6,6,9\nE,4,7,5,5,4,6,7,3,7,11,8,9,3,8,4,8\nP,4,12,4,8,3,4,11,10,3,9,6,4,2,10,4,8\nK,10,14,9,8,4,8,7,3,7,9,5,7,6,10,4,7\nR,3,8,4,5,2,6,10,9,4,7,4,8,3,7,5,11\nO,3,4,4,3,2,7,7,6,4,9,6,8,2,8,2,8\nY,5,5,6,7,6,9,11,5,4,6,7,7,5,10,7,5\nO,2,1,2,1,1,8,7,7,5,7,6,8,2,8,3,8\nX,5,10,7,8,4,4,9,2,8,10,12,9,3,9,4,5\nY,8,10,7,6,4,8,5,3,5,8,7,6,5,9,5,5\nX,4,9,6,7,5,7,7,3,8,5,6,8,3,8,6,7\nR,5,10,7,8,9,6,8,3,4,6,6,9,8,9,9,7\nE,2,3,4,2,2,7,8,2,8,11,6,9,2,9,4,8\nZ,6,9,8,7,5,7,7,2,9,12,6,8,1,9,6,8\nZ,5,10,6,8,4,9,5,3,10,11,3,10,1,8,6,10\nU,4,7,5,5,2,7,3,14,6,7,13,8,3,9,0,8\nG,3,5,4,4,2,7,6,6,5,6,6,9,2,9,4,9\nZ,6,10,9,7,5,7,8,3,10,12,7,6,1,8,6,7\nJ,7,13,6,10,4,7,10,2,3,13,6,5,2,9,8,8\nD,9,14,8,8,7,9,6,3,7,10,4,7,6,6,9,7\nQ,8,10,11,9,10,7,4,4,5,6,4,8,5,3,7,7\nE,9,15,6,9,5,7,7,4,4,11,5,9,3,9,8,11\nI,2,7,2,5,1,7,7,0,7,13,6,8,0,8,1,8\nM,3,4,4,3,3,8,6,6,3,7,7,8,7,5,2,7\nW,3,6,5,4,2,10,8,5,1,7,9,8,7,10,0,8\nS,2,3,2,1,1,8,7,6,4,8,6,8,2,8,8,8\nP,2,6,3,4,2,3,14,6,2,12,7,3,0,9,3,8\nC,3,7,4,5,2,5,8,7,8,13,9,10,2,10,3,7\nT,6,7,6,5,3,4,13,4,6,12,9,4,1,11,2,4\nR,3,1,3,2,2,7,8,5,5,6,5,7,2,6,4,8\nH,5,9,7,7,6,5,8,3,6,10,9,9,3,8,3,5\nJ,7,11,5,15,4,8,8,3,3,13,4,5,3,8,7,10\nE,3,4,5,3,2,7,8,2,8,11,6,9,2,8,4,8\nP,2,1,2,1,1,5,10,8,3,9,6,5,1,9,3,8\nY,1,3,2,2,1,6,10,1,5,7,11,9,1,11,1,8\nK,7,11,9,8,10,7,7,5,4,7,6,7,5,5,10,11\nW,4,6,6,4,3,7,8,4,1,7,8,8,8,9,0,8\nU,2,5,4,4,3,7,7,3,3,6,6,9,4,8,1,8\nT,3,3,4,4,2,7,12,3,6,7,11,8,2,11,1,8\nE,3,2,3,3,3,7,7,5,7,7,6,9,2,8,5,10\nV,4,8,6,6,3,7,9,4,2,7,13,8,3,10,0,8\nN,6,9,8,7,5,8,9,2,5,9,5,6,6,9,1,7\nC,5,9,6,6,4,7,7,8,6,6,6,11,4,8,4,9\nN,5,8,8,6,7,6,8,3,4,8,7,9,7,8,5,5\nJ,0,0,1,0,0,12,4,5,3,12,5,11,0,7,0,8\nU,4,8,5,6,2,8,5,13,5,6,15,8,3,9,0,8\nK,10,15,10,8,6,5,8,3,6,10,9,10,6,9,4,7\nX,4,6,7,4,4,7,7,1,8,10,6,8,2,8,3,8\nE,5,11,7,8,6,7,8,2,8,11,7,8,3,8,4,8\nA,2,3,3,2,1,9,2,2,1,8,2,10,1,6,2,8\nB,2,1,3,2,2,7,7,5,5,6,6,6,2,8,6,9\nX,5,10,8,8,5,7,7,4,9,6,6,10,3,8,7,8\nV,2,7,4,5,1,9,8,4,2,6,13,8,3,10,0,8\nI,1,9,1,6,1,7,7,0,8,7,6,8,0,8,3,8\nY,1,0,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nI,3,11,4,8,3,8,6,0,7,13,6,9,0,7,2,8\nK,5,9,7,7,6,9,6,1,6,9,3,8,3,7,4,10\nP,2,7,3,4,1,3,13,8,2,11,7,3,0,10,3,8\nP,7,10,9,8,7,6,8,8,5,8,7,9,3,9,8,9\nG,6,8,6,6,5,6,7,6,5,9,7,10,2,8,5,9\nK,3,5,6,4,3,6,7,1,7,10,7,10,3,8,3,8\nL,2,4,4,3,2,7,4,1,8,8,2,10,0,7,2,8\nM,10,14,11,8,5,13,1,5,3,13,1,9,6,3,1,9\nW,7,9,7,4,3,6,10,2,2,8,10,7,8,12,1,7\nW,2,1,3,2,1,8,8,4,0,7,8,8,6,10,0,8\nU,5,9,6,7,4,4,8,5,7,9,8,9,3,9,2,6\nL,2,6,3,4,2,5,4,3,7,6,2,8,1,6,2,7\nS,8,15,6,8,3,6,3,4,4,6,1,7,3,7,6,7\nX,2,1,2,1,0,7,7,4,4,7,6,8,2,8,4,8\nC,4,8,5,6,6,7,6,3,4,8,6,11,5,9,3,8\nO,3,6,4,4,2,7,8,8,7,7,7,8,3,8,4,8\nE,5,10,8,7,6,6,8,2,8,11,7,9,2,9,4,7\nF,4,7,6,5,5,8,7,6,4,7,6,8,4,10,8,11\nP,11,15,9,8,4,7,9,6,5,13,3,4,5,10,4,8\nJ,1,2,2,4,1,11,6,1,6,11,3,7,0,7,1,7\nE,2,4,2,2,2,7,7,5,7,7,5,9,2,8,5,10\nF,4,6,6,4,3,5,12,4,5,13,7,4,2,10,2,6\nE,3,7,5,5,5,7,7,3,6,7,7,10,4,10,7,8\nR,3,5,5,4,3,9,7,4,5,10,4,6,3,7,4,10\nD,3,9,5,7,8,9,6,5,5,7,5,5,4,7,11,6\nV,3,6,5,4,2,7,9,4,2,6,13,8,3,10,0,8\nT,2,5,3,4,2,7,11,2,7,7,11,8,1,11,1,8\nS,2,6,4,4,4,6,6,3,2,7,5,7,2,8,9,3\nJ,3,5,5,4,2,10,5,3,6,14,4,10,0,7,0,8\nK,4,5,4,7,2,3,7,7,2,7,5,11,3,8,3,10\nW,5,10,8,8,11,9,8,5,2,6,7,8,11,10,4,5\nH,3,7,5,5,4,8,7,6,6,7,6,6,3,8,3,7\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nW,8,9,8,7,8,7,11,4,3,8,6,6,10,12,4,5\nS,5,11,6,8,6,7,5,8,6,6,8,9,2,11,9,8\nK,4,4,7,3,3,8,7,2,7,10,4,8,5,9,4,9\nL,3,6,3,4,1,0,1,6,6,0,0,6,0,8,0,8\nB,4,8,6,6,5,7,9,5,5,9,6,5,3,7,8,7\nY,3,9,5,6,1,8,10,2,3,7,12,8,1,11,0,8\nD,6,14,6,8,4,8,6,5,6,8,4,7,4,7,6,9\nB,2,1,3,1,2,7,7,4,5,7,6,6,1,8,5,9\nP,7,13,6,7,4,10,7,4,5,13,3,4,4,10,6,8\nG,3,4,4,3,2,7,6,5,5,9,7,10,2,9,4,9\nL,5,11,6,8,5,5,6,0,9,4,3,8,3,7,2,5\nV,10,14,8,8,5,8,8,7,5,6,10,7,7,13,3,7\nO,5,10,6,8,5,7,7,8,7,6,3,6,3,8,5,9\nW,5,6,7,6,8,6,8,6,5,6,6,8,7,8,8,10\nQ,5,8,7,6,6,8,5,7,4,6,7,7,4,6,7,8\nW,3,3,3,1,2,5,10,3,2,9,8,7,5,11,1,6\nU,6,10,9,8,5,4,9,7,9,9,11,10,3,9,1,7\nM,5,7,7,6,9,7,8,4,3,7,6,7,10,8,5,5\nV,4,10,6,8,4,8,11,2,3,4,10,9,3,11,1,8\nU,3,4,4,3,2,5,8,5,7,9,8,9,3,10,2,5\nF,3,10,3,7,1,0,13,4,4,12,10,6,0,8,2,6\nF,1,3,2,2,1,6,10,2,4,13,7,6,1,9,1,8\nW,3,7,6,5,4,7,11,2,2,6,9,8,7,11,1,8\nM,10,13,10,8,5,10,11,7,4,4,6,9,8,12,3,7\nR,3,7,3,4,2,5,10,8,4,7,4,8,3,7,5,11\nD,4,6,5,4,3,10,5,2,6,11,3,8,3,8,3,9\nD,4,5,5,4,4,8,7,4,6,6,4,8,3,7,5,6\nM,6,7,8,6,8,8,8,4,4,7,6,7,10,8,5,5\nP,5,11,6,8,3,3,14,8,1,11,7,3,1,10,4,8\nT,8,14,7,8,4,8,8,3,7,11,6,7,2,9,5,6\nC,2,3,3,1,1,5,9,4,6,12,8,10,1,9,2,7\nI,3,8,4,6,2,7,7,0,8,14,6,8,0,8,1,8\nL,2,5,3,4,2,6,5,1,8,7,2,10,0,7,3,8\nX,5,7,7,5,3,8,8,1,8,10,4,7,3,8,3,7\nP,3,5,5,4,3,8,9,3,4,12,4,4,3,8,4,8\nI,2,9,3,7,2,7,7,0,8,13,6,8,0,8,1,8\nU,3,3,4,2,1,7,9,6,7,7,10,9,3,10,1,8\nV,7,10,9,8,6,6,12,2,2,7,10,8,7,10,7,8\nW,11,13,11,7,5,10,11,6,3,4,10,7,10,12,1,6\nP,3,2,4,4,3,6,9,4,5,9,7,4,4,10,3,7\nK,5,8,7,6,5,6,7,1,6,10,7,10,3,8,3,8\nP,1,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8\nI,1,1,1,1,0,7,7,2,7,7,6,9,0,8,2,8\nE,4,9,4,7,4,3,7,5,9,7,7,13,0,8,7,9\nQ,1,2,2,2,1,7,8,4,2,7,8,9,2,9,3,8\nO,4,5,5,4,3,7,7,8,5,7,6,8,2,8,3,8\nE,3,7,4,5,4,7,7,4,7,7,6,8,3,8,5,10\nP,5,5,7,8,8,9,8,3,3,5,8,7,6,11,7,5\nE,3,8,5,6,5,6,7,3,6,7,7,11,4,10,9,8\nK,5,9,6,6,6,6,7,4,7,6,6,10,3,8,5,9\nQ,3,3,4,5,4,8,8,6,2,5,7,9,3,8,5,10\nQ,8,14,7,8,4,8,6,4,10,10,3,10,3,6,9,9\nY,7,10,7,8,4,3,10,2,7,11,11,7,1,11,3,5\nB,3,7,5,5,5,8,7,7,6,6,6,6,2,8,6,9\nY,2,1,3,1,0,7,10,3,1,7,12,8,1,11,0,8\nB,5,11,6,8,7,7,9,6,5,7,5,6,3,8,6,8\nC,9,13,6,7,3,6,10,6,8,11,8,8,2,8,5,9\nG,4,9,5,7,3,5,7,6,5,10,8,10,2,8,5,9\nH,7,11,9,8,8,8,7,2,6,10,5,8,4,9,4,8\nF,5,11,5,8,4,1,13,4,3,12,10,6,0,8,2,6\nT,5,9,5,6,4,6,12,4,5,11,9,5,3,12,2,4\nV,6,7,8,6,8,8,7,4,4,7,6,8,7,9,8,4\nE,8,10,5,6,3,6,10,5,8,10,7,9,1,8,7,7\nD,5,11,6,8,5,9,7,5,7,10,4,5,3,8,3,8\nI,2,9,5,7,5,10,6,2,4,9,5,5,3,8,5,7\nX,4,10,6,8,4,7,8,3,9,6,5,6,4,10,8,6\nU,5,11,8,8,11,9,6,4,4,6,7,7,11,8,5,7\nR,3,3,3,4,2,5,10,8,3,7,4,8,2,7,6,11\nR,4,9,5,7,4,6,8,5,6,6,5,9,3,6,6,9\nS,1,1,2,2,0,8,8,4,7,5,6,7,0,8,7,8\nB,3,5,5,3,4,9,7,3,6,10,5,6,2,8,5,10\nZ,5,11,8,8,9,8,7,3,8,7,6,7,1,8,10,9\nC,6,6,7,8,3,5,6,7,11,7,5,14,1,9,4,8\nG,4,8,6,6,6,7,9,6,3,6,6,9,4,7,7,7\nL,2,3,4,2,1,7,3,1,6,8,2,10,1,6,3,8\nG,5,8,6,6,4,6,6,6,5,6,6,10,2,9,4,8\nZ,7,11,7,6,4,9,5,3,8,11,4,9,3,6,7,9\nM,4,10,5,8,4,7,7,12,2,7,9,8,8,6,0,8\nL,1,0,1,0,0,2,1,5,4,1,2,5,0,8,0,8\nT,5,10,5,7,3,5,13,3,7,12,9,3,1,11,2,4\nC,3,6,5,4,3,7,7,7,6,8,6,11,3,9,4,8\nR,3,8,5,6,4,6,8,6,6,6,4,8,3,7,5,8\nV,2,1,3,1,0,7,9,4,2,7,13,8,2,10,0,8\nW,10,10,9,8,9,4,11,3,3,9,8,7,8,12,2,6\nY,3,4,5,5,1,7,11,2,2,9,12,7,1,10,0,8\nX,3,5,4,3,2,7,7,3,9,6,6,8,2,8,6,8\nX,7,9,6,5,3,6,8,2,9,10,9,9,4,8,4,6\nY,7,11,8,8,6,5,8,1,7,8,9,5,5,12,6,3\nO,3,8,4,6,4,7,7,8,4,7,5,8,3,8,2,7\nC,7,10,7,7,4,3,8,5,7,11,10,13,1,9,3,7\nZ,4,3,4,5,2,7,7,4,14,9,6,8,0,8,8,8\nM,6,11,8,8,11,7,8,6,4,7,6,8,6,9,8,8\nE,3,6,4,4,3,6,7,6,8,6,4,9,3,8,6,9\nZ,4,9,6,7,6,7,8,3,7,7,6,8,1,9,12,5\nE,5,8,7,7,7,5,10,4,6,8,7,10,4,11,7,7\nQ,4,5,6,7,6,9,9,5,0,5,7,10,6,13,5,12\nR,4,9,5,6,3,5,12,8,4,7,2,9,3,7,6,11\nL,4,9,6,6,4,5,5,2,9,4,2,7,3,7,2,5\nO,1,3,2,2,1,8,7,5,3,9,6,8,2,8,2,8\nD,3,5,5,3,3,9,6,4,7,10,4,6,2,8,3,8\nS,4,11,5,8,3,7,7,6,10,5,6,10,0,9,9,8\nH,5,10,6,7,3,7,7,15,0,7,7,8,3,8,0,8\nG,4,8,4,6,3,6,6,6,7,11,7,12,2,10,3,10\nW,1,0,2,0,0,7,8,3,0,7,8,8,5,9,0,8\nR,6,10,9,8,10,8,8,7,3,8,5,6,5,8,7,11\nU,4,9,5,7,3,6,8,7,7,7,10,9,3,9,1,8\nE,1,1,1,2,1,4,7,5,8,7,6,13,0,8,6,9\nM,3,2,5,4,4,9,6,6,4,6,7,6,7,6,2,5\nI,1,3,2,2,0,7,7,0,7,13,6,8,0,8,1,8\nD,4,8,5,6,3,8,7,8,7,7,6,2,3,8,4,8\nQ,6,9,6,11,8,7,8,6,3,8,9,9,5,9,8,8\nO,5,10,6,8,4,8,7,8,5,10,6,8,3,8,3,8\nE,3,8,3,6,2,3,7,6,10,7,6,14,0,8,7,8\nM,4,8,4,6,3,8,7,12,1,6,9,8,8,6,0,8\nP,3,7,4,4,2,4,12,8,2,10,6,4,1,10,4,8\nT,2,7,3,5,1,6,14,0,6,8,11,8,0,8,0,8\nU,3,1,4,1,1,7,8,6,8,7,10,8,3,10,1,8\nW,5,10,8,8,7,4,11,2,2,9,9,9,8,12,2,8\nS,7,11,6,6,3,7,8,3,5,13,7,8,2,8,3,7\nE,2,3,3,2,2,7,7,2,8,11,7,9,1,8,4,8\nM,4,7,6,5,6,7,8,6,4,7,6,8,5,9,7,7\nR,2,4,3,2,2,7,8,4,5,6,5,7,2,6,5,8\nC,4,9,5,6,2,5,7,7,10,6,6,13,1,7,4,9\nG,5,11,7,8,5,5,6,6,7,6,6,8,4,7,4,7\nE,4,7,6,5,5,8,6,5,3,7,6,10,4,8,7,9\nE,2,3,4,2,2,7,7,2,8,11,6,9,2,8,4,8\nX,6,8,9,6,4,6,8,2,9,10,9,9,3,8,4,6\nQ,4,5,6,5,5,7,4,4,5,7,5,8,5,4,7,7\nR,4,5,5,7,3,5,10,9,4,7,4,8,3,7,6,11\nJ,2,6,2,4,1,13,4,5,4,13,2,9,0,7,0,8\nO,7,11,10,8,12,8,8,6,2,7,7,8,10,9,7,11\nL,3,5,4,3,2,4,4,4,8,2,1,6,1,7,1,6\nZ,2,4,4,3,2,7,7,2,9,11,6,8,1,8,5,8\nE,5,9,4,4,2,7,8,4,7,10,5,11,1,8,7,8\nX,4,10,5,7,2,7,7,5,4,7,6,8,3,8,4,8\nU,4,7,6,6,6,7,7,5,3,6,7,8,4,7,2,7\nA,1,0,2,0,0,7,3,2,0,7,2,8,2,6,1,8\nR,3,3,3,4,2,6,11,8,3,7,3,9,2,7,5,11\nW,2,0,2,0,1,7,8,4,0,7,8,8,6,9,0,8\nH,6,10,6,5,4,7,8,3,5,10,5,8,6,6,5,7\nN,4,8,6,6,4,7,9,2,4,9,5,6,5,9,1,7\nV,6,11,5,8,3,4,11,2,4,9,11,7,3,9,1,8\nB,5,11,7,8,11,8,9,5,3,6,7,7,7,11,12,9\nL,2,4,4,3,2,7,4,1,8,8,2,10,0,7,2,8\nD,2,0,2,1,1,6,7,8,7,6,6,6,2,8,3,8\nE,3,5,6,3,3,8,7,2,9,11,5,9,3,7,5,8\nA,2,7,4,5,2,11,3,3,2,10,2,9,2,6,2,8\nL,2,5,3,4,2,4,4,4,7,2,1,6,0,7,1,6\nA,3,10,6,8,5,10,4,1,2,8,3,9,4,4,3,7\nC,3,10,5,7,3,4,9,6,6,6,8,14,1,8,4,10\nJ,2,3,3,5,1,11,2,10,3,13,8,13,1,6,0,8\nX,5,9,8,6,5,7,7,0,8,10,7,8,2,8,3,7\nH,5,9,6,6,2,7,6,15,1,7,8,8,3,8,0,8\nB,4,9,6,7,5,8,7,5,6,10,6,6,3,8,7,9\nM,4,9,4,7,5,7,5,10,0,7,9,8,7,5,0,8\nN,4,8,4,6,2,7,7,14,2,4,6,8,6,8,0,8\nA,4,8,6,6,5,9,5,2,5,8,1,6,3,4,3,7\nF,5,6,7,7,6,7,9,4,5,8,6,8,4,10,7,6\nB,3,8,3,6,4,6,6,8,6,7,7,7,2,9,6,9\nZ,1,0,2,1,0,7,7,3,10,8,6,8,0,8,6,8\nN,6,10,8,8,6,4,10,2,3,9,9,9,6,7,1,7\nK,1,0,2,1,0,5,7,7,1,7,6,11,3,8,2,11\nK,4,10,5,8,4,3,6,7,4,7,7,12,3,8,3,11\nI,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nN,8,13,10,7,4,5,9,4,4,13,10,9,6,9,0,9\nZ,2,3,4,2,1,8,6,2,8,11,5,9,1,8,5,8\nO,2,7,3,5,2,7,6,8,7,7,4,8,3,8,4,8\nI,1,7,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nC,3,6,4,4,2,5,8,7,7,8,8,14,1,9,4,10\nM,6,5,8,4,7,7,8,4,4,7,5,8,11,8,5,6\nX,2,6,4,4,3,8,8,3,8,6,6,6,3,8,6,8\nF,3,4,5,3,2,6,10,2,5,13,7,5,1,10,2,7\nI,4,5,6,6,5,7,9,4,5,7,7,8,3,7,8,8\nY,6,8,8,10,11,9,8,7,3,7,7,8,7,10,6,4\nX,4,6,5,6,5,7,8,2,5,8,6,8,3,6,7,7\nS,3,5,5,4,2,8,8,3,7,10,7,7,1,9,5,7\nR,5,5,5,6,3,5,12,9,4,7,3,9,3,7,6,11\nZ,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nF,2,3,4,2,1,6,10,3,5,13,7,5,1,9,1,7\nE,5,8,7,6,5,6,8,3,8,11,8,9,3,9,5,7\nE,4,9,6,6,5,7,7,6,9,7,6,10,3,8,6,8\nR,4,9,4,6,5,6,10,8,3,7,4,9,2,7,5,11\nW,5,8,5,6,4,2,10,2,3,10,11,9,6,10,1,7\nF,2,8,3,6,2,2,11,4,5,11,10,8,0,8,2,7\nJ,3,8,5,6,5,9,7,3,3,8,4,6,4,8,6,4\nF,6,12,5,6,2,5,11,2,5,11,8,5,2,9,6,2\nA,2,7,4,4,1,5,4,3,2,5,1,7,2,6,2,7\nE,3,2,3,4,3,7,7,6,7,7,6,9,2,8,6,9\nA,2,7,4,4,1,8,4,3,2,7,2,8,3,6,2,8\nB,2,5,3,3,3,7,7,5,5,6,6,6,2,8,6,10\nK,3,5,6,4,3,5,7,2,8,11,9,11,4,7,4,7\nM,6,7,8,5,6,6,7,3,4,9,8,9,8,6,3,8\nJ,3,8,4,6,2,8,7,1,7,11,5,7,1,6,2,6\nI,5,10,5,5,3,8,8,3,5,12,5,6,2,10,5,10\nS,3,5,5,4,4,8,9,4,5,7,7,8,4,11,7,10\nL,2,7,4,5,3,9,3,1,5,9,3,9,1,6,2,9\nK,5,10,8,8,9,6,6,3,4,6,5,10,8,6,8,8\nK,5,6,6,8,3,5,9,9,2,7,3,11,4,8,2,11\nQ,7,15,7,8,5,11,4,4,6,12,3,7,3,9,7,12\nC,4,8,5,7,5,5,7,4,4,7,6,11,4,9,8,10\nV,8,10,8,8,4,3,12,3,4,11,12,8,3,9,1,7\nT,6,9,6,6,3,5,12,4,8,12,10,4,2,12,3,4\nQ,7,9,10,8,7,5,4,4,5,4,3,7,4,7,7,6\nQ,4,8,6,7,4,8,7,8,5,6,5,9,2,8,4,9\nP,4,9,6,7,5,7,7,7,4,8,6,8,3,9,7,9\nY,9,15,8,8,5,8,6,4,5,9,8,5,4,10,4,4\nK,5,7,7,5,5,8,7,1,6,10,5,9,3,8,2,9\nR,3,9,4,6,2,5,10,8,4,7,4,9,3,7,6,11\nJ,4,8,6,6,2,6,8,3,7,15,6,9,1,6,1,7\nF,4,6,6,4,5,11,6,1,5,9,5,6,5,9,4,7\nG,2,5,3,3,2,6,7,5,5,9,7,10,2,8,4,9\nD,4,7,5,6,5,7,8,4,6,6,4,7,3,8,5,6\nU,7,11,9,8,5,4,9,7,8,9,11,11,3,9,1,8\nW,5,8,7,6,3,7,8,5,2,7,8,8,9,9,0,8\nE,3,8,5,6,6,7,7,3,5,6,7,10,4,10,8,8\nC,3,8,5,6,3,5,7,6,8,8,5,11,1,9,4,9\nF,2,3,3,2,1,6,10,2,5,13,7,5,1,9,1,7\nD,3,6,4,4,3,8,7,6,7,10,4,6,3,8,3,8\nO,2,0,2,1,1,8,7,7,6,7,6,8,2,8,3,8\nG,6,10,7,8,5,5,7,5,5,9,9,9,2,8,5,8\nM,3,3,4,2,3,7,6,6,4,6,7,8,7,5,2,8\nO,2,4,3,3,2,7,7,7,4,9,6,8,2,8,3,8\nQ,5,6,6,8,6,8,5,6,3,9,5,10,5,7,7,6\nZ,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nJ,2,5,3,4,2,10,6,2,5,12,4,8,1,6,1,6\nX,3,3,5,2,2,6,8,2,8,11,9,9,2,8,3,6\nB,4,6,5,4,4,8,6,3,5,6,5,7,4,9,5,8\nO,4,7,6,5,4,7,9,8,5,6,8,10,3,8,3,8\nR,4,7,5,5,5,8,8,7,3,8,5,7,4,8,6,11\nC,2,3,2,1,1,4,10,5,6,12,9,9,1,9,2,7\nR,2,3,3,2,2,6,8,4,5,7,5,7,2,7,3,8\nR,4,9,6,6,6,7,8,5,5,7,5,7,3,7,5,9\nD,6,9,8,7,5,10,6,3,9,11,4,7,4,6,4,9\nX,6,10,9,8,4,8,7,1,9,10,5,8,3,8,4,8\nL,3,4,3,6,1,0,1,5,6,0,0,7,0,8,0,8\nC,4,6,5,6,5,7,8,5,4,6,7,11,6,9,9,11\nC,4,9,5,7,3,6,8,6,8,7,6,14,1,8,4,9\nF,4,7,6,5,3,8,9,3,6,13,6,5,2,10,3,8\nV,5,9,7,7,3,9,12,3,4,4,11,9,3,10,1,8\nR,3,8,4,6,4,6,10,7,3,7,4,9,2,7,5,11\nZ,9,13,9,7,5,6,8,2,10,12,8,8,4,4,8,4\nC,4,9,5,7,3,4,8,6,7,7,8,15,1,8,4,10\nI,2,6,3,4,1,6,8,1,8,14,7,8,0,8,1,7\nO,2,7,3,5,2,7,7,9,6,7,6,8,3,8,4,8\nL,5,11,7,8,5,6,5,0,9,9,3,11,0,7,3,8\nS,2,0,2,1,1,8,8,4,7,5,6,7,0,8,7,8\nI,4,8,5,6,3,6,6,2,7,7,6,10,0,9,4,8\nT,5,7,6,5,4,5,12,4,5,12,9,4,2,12,1,5\nE,6,10,8,7,7,10,6,2,7,11,4,8,5,6,5,11\nY,3,4,5,5,1,8,10,3,2,6,13,8,2,11,0,8\nY,4,5,5,4,2,4,12,3,7,12,10,4,1,11,2,5\nR,5,9,5,7,6,5,10,7,4,7,4,9,2,7,5,11\nS,3,7,5,5,6,7,7,3,2,8,6,7,2,7,12,3\nR,4,8,4,5,3,5,11,8,3,7,3,8,3,7,6,11\nO,4,9,5,7,4,7,7,9,5,7,5,8,3,8,3,8\nI,2,5,4,4,1,7,7,0,8,14,6,9,0,8,1,8\nT,3,8,4,6,2,7,13,0,6,7,10,8,0,8,0,8\nX,4,9,6,6,4,4,8,1,8,10,10,10,2,8,3,5\nC,5,10,4,5,3,8,5,5,3,9,9,11,4,9,7,11\nI,1,4,2,3,1,7,7,0,7,13,6,8,0,8,0,8\nJ,3,7,4,5,2,8,6,3,6,15,5,9,0,7,0,7\nR,2,3,4,2,2,8,8,4,4,8,5,7,2,7,4,10\nE,5,10,4,6,2,8,7,5,7,11,6,9,2,10,7,9\nQ,5,9,5,5,3,9,4,4,7,10,4,9,3,8,8,11\nX,3,4,4,3,2,7,7,3,9,6,6,8,3,8,6,8\nO,4,8,5,6,4,7,7,8,4,7,6,8,3,8,3,8\nQ,3,7,4,7,4,8,9,6,4,6,9,9,2,8,5,9\nY,2,3,3,1,1,4,12,3,5,12,10,5,1,11,1,5\nQ,5,12,5,6,3,10,5,4,7,12,4,9,3,8,8,12\nC,6,11,5,6,4,7,7,4,3,9,8,10,4,9,8,11\nK,7,9,9,6,7,9,5,1,7,10,3,9,8,5,7,10\nA,3,4,5,2,2,9,1,2,1,8,2,8,2,6,3,8\nE,5,10,6,8,7,6,8,6,8,6,5,11,3,8,6,9\nX,8,13,9,7,5,6,8,2,8,11,5,7,4,5,4,6\nM,4,5,7,3,4,8,7,3,4,9,6,7,8,6,2,7\nP,8,14,7,8,5,7,9,4,4,12,5,4,4,9,7,5\nR,4,7,5,5,3,9,8,4,6,9,3,7,3,6,4,11\nK,4,8,6,6,5,8,5,1,6,9,4,10,4,8,4,10\nG,3,6,5,4,2,8,6,7,8,6,5,10,2,8,5,10\nE,5,9,7,6,7,7,8,6,3,7,6,8,5,8,8,8\nT,2,2,3,3,2,7,11,2,7,7,11,8,1,11,1,8\nO,4,8,5,6,3,8,7,8,5,10,6,7,3,8,3,8\nB,1,0,2,1,1,7,7,7,4,7,6,7,1,8,6,8\nJ,2,10,3,8,1,13,2,9,4,14,4,12,1,6,0,8\nZ,4,5,5,7,2,7,7,4,15,9,6,8,0,8,8,8\nF,4,7,4,5,1,1,14,5,3,12,9,5,0,8,2,6\nV,6,11,8,8,5,6,11,3,2,7,11,8,4,10,5,9\nH,3,4,4,3,3,7,7,6,6,7,6,8,3,8,3,8\nQ,3,4,4,5,3,7,8,5,2,7,9,10,3,9,5,8\nC,2,4,3,3,1,6,8,7,7,8,8,13,1,9,4,10\nL,3,6,4,4,2,7,3,2,9,7,1,9,0,7,2,8\nM,3,3,5,2,3,5,7,3,4,10,9,10,6,5,2,7\nN,1,1,2,2,1,7,8,5,3,8,7,7,4,8,1,7\nT,3,6,4,4,2,6,11,1,8,8,11,9,1,10,1,8\nO,5,8,6,7,5,6,6,5,5,8,5,7,3,6,5,7\nO,2,5,3,3,2,7,7,7,4,9,6,8,2,8,2,8\nC,3,7,4,5,2,5,8,6,8,12,9,11,1,10,3,7\nE,2,6,2,4,2,3,8,5,9,8,8,13,0,8,6,9\nQ,4,7,6,9,6,9,7,7,2,5,7,10,3,8,6,10\nT,3,9,4,6,1,7,15,0,6,7,11,8,0,8,0,8\nZ,2,3,3,2,1,7,8,2,9,11,6,8,1,8,5,7\nB,2,4,4,3,3,9,7,2,6,11,4,7,4,7,5,9\nJ,1,1,1,1,0,12,3,6,4,13,4,11,0,7,0,8\nC,2,4,3,2,1,4,9,5,7,11,9,12,1,9,3,7\nP,4,8,6,6,4,8,8,2,5,13,5,5,1,10,3,9\nX,3,5,5,3,2,10,7,1,9,11,3,7,2,7,3,9\nW,3,1,3,2,1,7,8,4,1,7,8,8,7,10,0,8\nN,4,9,4,6,2,7,7,14,2,4,6,8,6,8,0,8\nB,6,10,9,8,12,8,8,5,3,6,7,7,7,10,11,10\nB,6,9,9,8,10,7,7,5,4,7,6,8,7,9,8,6\nZ,3,6,5,4,3,8,7,2,9,11,6,8,2,8,5,8\nM,6,9,9,8,11,7,7,4,3,6,6,8,10,9,5,5\nH,2,6,3,4,2,7,6,12,2,7,8,8,3,9,0,8\nB,1,0,1,1,1,7,7,7,4,6,6,7,1,8,6,9\nU,6,10,9,8,6,7,8,4,8,4,7,9,6,9,1,8\nI,0,3,1,1,0,7,7,1,6,13,6,8,0,8,0,7\nQ,3,6,4,5,4,8,7,7,5,6,6,7,2,8,4,9\nD,3,7,4,5,4,9,7,4,6,9,4,6,3,8,3,8\nH,6,11,6,8,6,8,5,14,2,7,9,8,3,9,0,8\nU,3,3,4,2,2,5,8,6,7,8,10,10,3,9,1,7\nG,2,4,3,3,2,6,6,5,4,8,7,10,2,8,4,10\nG,8,12,7,6,4,10,3,3,4,10,2,6,4,7,5,11\nR,5,10,8,8,5,9,8,4,6,9,3,7,3,6,5,11\nT,4,7,4,5,3,6,11,5,5,11,8,4,3,12,2,4\nG,3,8,4,6,2,7,6,7,7,6,6,10,1,8,6,11\nA,2,1,4,2,1,8,2,2,1,7,2,8,2,7,2,7\nJ,3,7,5,5,4,9,8,3,4,9,4,7,4,8,6,5\nA,4,7,5,5,3,8,4,3,0,6,1,8,2,7,1,7\nQ,4,7,5,5,5,8,7,7,4,6,8,8,4,5,6,9\nH,4,7,6,5,5,6,6,7,4,6,5,8,3,7,6,10\nU,2,3,3,1,1,5,8,4,6,10,8,8,3,10,1,6\nW,5,10,7,7,4,6,8,5,2,7,8,8,9,9,0,8\nZ,2,5,3,3,2,7,7,5,9,6,6,8,2,8,7,8\nK,6,9,9,6,6,5,7,1,7,10,9,11,4,6,4,7\nY,7,11,7,8,4,3,10,2,7,10,12,6,2,11,3,5\nF,2,7,2,4,1,1,12,4,5,12,11,8,0,8,2,6\nX,4,9,7,7,3,7,7,1,8,10,6,8,3,8,4,7\nK,3,6,4,4,5,7,8,3,4,6,6,8,7,8,6,8\nP,3,8,5,6,3,4,12,4,6,11,9,3,0,9,4,6\nN,3,5,6,3,2,6,9,3,5,10,7,7,5,8,1,8\nC,3,8,4,6,2,5,8,7,7,7,8,14,1,9,4,10\nW,6,11,9,8,4,11,7,5,2,6,9,8,9,9,0,8\nM,6,7,9,5,6,8,6,2,5,9,6,7,8,6,2,7\nT,5,10,6,8,5,4,12,5,5,12,9,4,2,12,1,5\nH,4,7,5,5,4,9,7,7,6,7,6,5,3,8,3,5\nL,3,8,4,6,3,5,4,2,9,3,2,7,3,7,2,5\nY,9,11,9,8,4,5,9,1,10,10,10,4,2,13,5,3\nN,4,9,5,7,4,8,7,13,1,6,6,8,5,9,0,8\nZ,4,7,6,5,3,7,8,2,9,11,6,8,1,9,6,8\nH,3,7,4,5,4,8,7,6,6,7,6,7,3,8,3,7\nO,4,9,3,5,2,6,7,6,3,10,6,9,5,9,5,7\nP,3,8,3,6,3,3,12,6,1,11,7,4,0,9,3,8\nT,6,10,8,8,7,6,7,7,7,7,9,9,4,10,6,8\nD,6,10,9,8,8,8,8,5,6,10,5,5,4,8,5,10\nK,2,1,2,1,0,5,7,8,1,7,6,11,3,8,2,11\nU,2,1,3,1,1,7,8,6,6,7,9,8,3,10,1,8\nN,5,6,7,4,3,9,7,3,5,10,3,5,5,9,1,7\nD,6,11,6,6,4,10,6,3,6,9,4,7,5,10,6,8\nB,4,4,5,6,4,6,7,9,7,7,6,7,2,8,9,9\nP,5,10,7,7,4,7,11,4,5,13,5,3,1,10,3,8\nL,4,8,5,6,6,8,8,3,5,6,7,9,6,11,6,6\nL,2,3,3,5,1,0,1,5,6,0,0,7,0,8,0,8\nL,1,3,2,1,1,6,5,1,7,8,3,10,0,7,2,8\nV,7,13,7,7,4,8,9,4,4,8,8,5,6,14,2,8\nL,1,4,2,2,1,7,4,1,7,8,2,10,0,7,2,8\nP,5,6,6,8,7,6,9,3,2,8,8,6,5,10,5,5\nJ,6,8,4,11,3,9,7,3,3,12,3,5,3,8,6,10\nR,5,5,5,7,3,5,11,9,4,7,3,9,3,7,6,11\nW,6,8,6,6,5,6,10,4,3,9,7,6,7,12,3,6\nQ,6,9,7,10,8,9,9,6,3,4,8,11,5,9,9,13\nT,7,15,6,8,4,6,10,2,6,12,7,6,3,9,5,5\nU,4,4,4,3,2,5,8,5,7,10,9,9,3,9,2,6\nK,3,5,6,4,3,5,8,2,7,10,9,10,3,8,3,7\nY,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8\nT,5,6,6,5,6,7,9,5,8,7,7,8,3,10,7,7\nB,5,11,8,9,9,8,6,5,6,9,5,7,3,8,6,10\nD,9,15,9,8,5,8,6,5,7,12,3,7,6,7,6,10\nS,2,7,3,5,2,8,8,7,6,8,6,8,2,8,9,8\nL,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8\nW,1,0,2,0,0,8,8,3,0,6,8,8,5,9,0,8\nG,3,4,4,6,2,7,7,8,7,6,6,9,2,7,6,10\nP,8,15,8,9,5,8,9,4,4,12,5,3,5,10,6,6\nM,5,6,8,4,4,7,6,2,5,9,7,8,8,5,2,8\nO,5,8,6,6,4,8,9,8,4,7,7,8,3,8,3,8\nH,4,7,6,5,8,8,7,4,3,6,6,7,7,8,8,8\nO,2,4,3,3,2,7,7,6,3,9,6,8,2,8,2,8\nH,3,2,4,3,3,8,7,5,6,7,6,8,6,8,4,7\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nL,4,10,5,8,3,3,5,1,9,6,1,11,0,7,3,6\nS,5,11,5,6,3,6,8,4,3,13,8,8,3,10,3,8\nD,3,5,5,3,3,9,6,4,6,10,4,6,2,8,3,8\nX,6,12,6,6,4,7,8,2,7,11,7,7,4,12,3,7\nL,3,8,4,6,1,0,1,6,6,0,0,6,0,8,0,8\nD,2,1,3,1,2,6,7,6,6,7,6,5,2,8,2,7\nZ,9,10,6,14,6,8,4,5,4,12,7,9,3,8,12,7\nB,4,9,6,6,5,6,9,5,6,10,6,5,3,8,7,8\nD,1,1,2,1,1,7,7,6,6,7,6,5,2,8,2,7\nA,3,11,5,8,4,12,2,3,3,10,2,9,2,6,3,8\nX,4,9,7,7,6,8,7,3,6,7,4,6,5,5,8,7\nV,5,10,5,8,3,3,11,4,4,10,11,7,3,10,1,8\nB,5,9,5,7,4,6,7,9,7,7,6,7,2,8,9,9\nX,4,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nM,4,4,7,3,4,6,6,3,4,10,9,10,7,6,2,8\nO,4,3,5,4,2,7,7,8,8,7,6,8,3,8,4,8\nL,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8\nK,4,8,6,6,5,3,8,2,6,10,10,11,3,8,3,6\nT,6,11,6,8,5,4,13,6,4,12,9,4,2,12,1,5\nM,3,6,4,4,4,8,5,10,0,6,8,8,6,5,0,7\nH,3,5,6,4,3,9,7,3,6,10,3,7,5,7,4,9\nY,6,11,6,8,3,3,10,3,7,12,12,7,1,11,3,5\nC,3,4,4,6,2,6,7,7,9,5,6,14,1,7,4,8\nI,4,11,7,8,8,10,7,2,5,9,4,4,4,9,7,5\nU,5,8,7,6,4,5,9,6,7,8,10,10,3,9,1,8\nZ,3,9,5,7,5,8,8,3,7,8,7,7,1,9,10,9\nS,6,9,6,5,3,9,5,4,4,13,6,9,3,10,3,8\nN,5,8,8,6,3,11,6,3,5,10,1,4,5,9,1,7\nB,5,6,7,5,7,8,6,5,4,7,6,8,7,9,8,4\nF,4,7,6,5,2,7,10,2,7,14,5,4,1,10,2,8\nX,4,10,7,7,4,7,8,4,9,6,5,5,4,10,8,6\nC,1,3,2,2,1,6,8,4,6,11,8,12,1,10,3,8\nB,4,9,6,7,7,8,7,6,6,7,6,6,2,8,6,10\nJ,2,9,3,7,1,13,3,8,5,14,2,11,0,6,0,8\nC,4,7,5,5,2,5,7,6,7,11,9,13,2,9,4,7\nM,4,9,5,6,5,8,6,11,1,7,8,8,7,5,0,8\nO,5,11,7,8,3,8,8,8,8,6,7,9,3,8,4,8\nW,5,5,8,8,4,5,8,5,2,7,8,8,9,9,0,8\nT,3,9,4,6,1,7,14,0,6,7,11,8,0,8,0,8\nA,3,6,5,4,2,8,4,3,0,7,1,8,2,7,1,8\nW,5,11,8,8,13,8,5,6,3,7,6,8,14,10,4,9\nB,3,3,3,5,3,6,6,9,6,6,6,7,2,8,9,10\nU,6,10,6,6,4,8,6,5,5,6,7,7,5,8,3,7\nA,3,4,5,5,1,8,6,3,1,7,0,8,2,7,1,8\nF,4,7,7,5,6,7,9,1,5,10,6,6,5,10,4,5\nL,3,3,3,5,1,0,1,6,6,0,1,6,0,8,0,8\nX,3,7,5,5,4,8,7,3,6,7,4,6,4,7,6,8\nZ,3,5,5,3,2,7,7,2,9,12,6,8,1,8,6,8\nW,7,10,7,8,5,1,10,2,3,11,11,9,7,10,1,7\nL,4,5,5,5,4,8,7,4,5,7,6,8,3,8,8,11\nN,3,6,5,4,3,7,8,6,5,7,7,6,5,9,1,6\nC,6,9,6,7,3,4,8,6,7,12,10,12,2,10,2,7\nD,6,9,5,4,3,9,5,5,6,12,3,8,5,7,5,10\nC,8,14,6,8,5,9,6,4,3,9,8,11,4,9,8,13\nV,8,10,7,5,3,8,8,7,5,7,10,6,7,12,4,8\nW,5,9,7,7,11,9,7,5,2,7,6,8,10,11,4,8\nX,2,2,3,3,2,7,7,3,9,6,6,10,2,8,6,8\nH,5,6,7,4,4,4,10,3,6,10,10,9,3,8,3,6\nB,5,9,5,4,4,9,7,3,5,9,5,7,6,6,7,9\nH,10,14,9,8,5,7,8,4,5,9,7,8,6,8,5,9\nG,3,7,4,5,2,7,6,7,8,6,5,11,1,8,6,11\nY,5,10,7,8,3,8,9,3,2,6,12,8,2,11,0,8\nW,4,7,6,5,3,7,8,5,1,7,8,8,8,9,0,8\nT,4,10,6,8,5,7,11,2,7,7,11,8,1,12,1,7\nK,4,7,4,4,1,4,7,8,2,7,5,11,3,8,2,11\nV,3,6,5,4,2,9,9,4,1,6,12,8,2,10,0,8\nK,1,0,1,0,0,5,7,7,0,7,6,10,2,8,2,11\nY,4,9,4,4,2,6,8,3,4,10,7,5,3,10,4,3\nT,5,10,7,8,5,7,11,1,8,7,11,8,1,11,1,8\nE,2,4,3,3,2,9,7,1,7,11,5,8,2,8,4,10\nD,4,6,5,4,4,10,5,3,6,10,3,7,3,7,3,8\nE,2,3,4,2,2,6,7,2,7,11,6,9,2,8,3,8\nL,2,6,2,4,1,0,2,5,6,0,0,7,0,8,0,8\nC,8,11,8,8,4,3,9,6,8,12,11,11,1,8,3,6\nG,6,9,8,7,8,7,8,6,2,6,5,11,6,8,10,6\nP,1,0,2,0,0,5,11,6,1,9,5,5,0,9,3,8\nZ,4,5,6,7,4,11,5,4,4,9,3,8,2,6,5,9\nN,9,12,11,6,4,5,8,3,4,13,9,9,7,8,0,7\nC,6,10,6,8,3,5,9,7,8,13,9,7,2,11,2,7\nI,4,10,6,8,4,6,9,0,7,13,7,7,0,8,1,7\nE,5,11,7,8,8,8,7,5,3,7,6,8,5,8,9,9\nE,6,11,4,6,2,7,8,5,8,9,5,12,1,8,7,9\nA,2,0,3,1,0,7,4,2,0,7,2,8,2,7,1,8\nA,3,7,5,5,3,11,3,2,2,9,2,9,3,5,2,8\nC,6,10,7,8,4,4,8,6,7,12,10,12,2,10,3,7\nF,3,5,4,4,2,5,10,3,5,10,9,5,1,10,3,6\nM,4,7,6,5,6,7,9,6,3,7,6,8,5,9,5,8\nM,7,8,9,7,10,8,7,5,4,7,6,7,10,9,6,4\nX,4,11,6,8,5,7,7,3,8,5,6,8,3,8,6,7\nK,4,6,6,4,4,5,6,4,7,6,6,10,3,8,5,9\nF,5,9,5,5,3,6,12,3,3,11,6,4,4,10,7,6\nS,4,6,5,4,6,8,10,5,4,8,5,6,4,9,8,7\nB,6,10,8,8,6,9,7,4,7,10,4,6,2,8,6,10\nL,3,7,5,5,2,6,3,2,8,7,2,7,1,6,2,7\nU,8,10,9,8,6,4,8,5,8,9,8,9,6,9,4,3\nB,3,4,5,3,3,8,7,3,5,10,5,7,2,8,4,10\nN,4,9,4,7,2,7,7,14,2,5,6,8,6,8,0,8\nQ,4,5,5,6,5,9,6,7,4,5,6,8,3,8,6,9\nP,2,2,3,3,2,6,9,5,4,9,7,4,1,10,3,7\nW,2,1,4,1,2,8,11,3,1,6,9,8,6,11,0,7\nW,4,8,6,6,7,7,8,5,3,7,9,8,6,8,3,8\nP,4,8,6,6,4,6,10,4,6,10,8,3,1,10,5,7\nM,10,14,12,8,7,9,3,3,2,9,3,10,11,0,2,8\nY,3,3,4,2,1,5,10,2,7,11,10,4,1,11,2,5\nR,6,8,9,7,9,7,7,3,4,7,5,8,7,9,7,5\nO,5,11,4,6,3,4,9,6,4,9,9,8,3,9,4,8\nO,5,9,5,7,5,8,7,7,4,10,5,8,3,8,3,7\nV,7,11,7,8,4,2,11,4,4,11,12,8,3,10,1,8\nB,5,9,6,7,5,7,9,9,8,7,5,7,2,8,9,10\nL,6,12,5,6,3,10,2,2,5,11,4,11,2,9,4,11\nE,6,9,5,5,3,7,8,4,4,11,6,8,3,9,8,9\nT,3,3,3,2,1,5,12,3,7,11,9,4,1,10,2,5\nX,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8\nU,3,7,5,5,3,7,8,6,6,5,9,9,3,9,1,8\nG,3,11,5,8,4,7,7,7,7,8,4,12,2,9,6,10\nI,5,9,6,7,4,7,6,2,7,7,6,9,0,9,4,8\nQ,2,2,2,2,2,8,7,5,3,8,6,8,2,9,2,7\nI,2,11,0,8,2,7,7,5,3,7,6,8,0,8,0,8\nW,6,6,6,4,4,1,12,3,2,11,10,8,6,11,1,7\nC,6,12,4,6,2,7,8,7,6,11,7,7,2,9,5,8\nB,3,5,5,3,3,7,8,3,5,10,6,6,2,8,5,8\nH,5,7,8,9,7,8,5,3,2,7,5,7,4,8,8,8\nX,2,1,3,1,1,7,7,4,9,6,6,9,2,8,5,7\nQ,7,13,6,7,5,10,4,4,6,11,4,8,3,8,8,11\nO,3,7,4,5,3,8,7,8,5,7,6,9,2,8,3,8\nO,1,0,2,0,0,7,7,6,4,7,6,8,2,8,3,8\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nR,7,13,7,7,5,10,6,4,5,10,2,7,6,7,6,8\nN,3,7,5,5,4,7,7,8,5,7,5,7,3,7,3,8\nN,5,10,7,7,5,8,8,6,5,6,6,5,6,9,2,6\nC,3,7,4,5,3,5,8,6,6,8,8,14,2,9,4,10\nV,7,13,6,7,3,8,10,6,4,8,10,5,6,13,3,8\nX,7,11,11,8,5,6,8,1,9,10,9,9,3,8,4,6\nQ,4,4,6,6,6,9,11,5,0,5,7,10,5,12,4,10\nE,4,10,6,8,6,7,7,5,8,7,7,10,3,8,6,9\nV,4,11,6,8,3,7,11,3,5,8,12,8,3,10,1,8\nI,1,9,0,6,1,7,7,5,3,7,6,8,0,8,0,8\nU,5,5,6,4,3,4,8,5,8,10,9,9,3,9,2,6\nW,4,8,6,6,5,4,11,2,2,8,9,9,6,11,1,8\nD,6,9,8,8,7,6,7,5,8,7,5,8,5,5,7,4\nC,8,11,5,6,2,7,8,6,7,11,6,9,2,9,5,9\nL,4,8,5,6,3,6,4,3,7,6,1,8,1,6,3,7\nH,4,6,6,4,5,8,8,6,7,7,6,6,6,8,3,8\nN,5,10,6,7,5,9,8,6,5,6,5,4,6,10,3,5\nP,4,7,6,10,10,8,9,5,0,8,7,6,5,11,6,10\nJ,2,6,2,4,1,9,7,2,6,11,3,7,0,7,1,6\nE,2,3,4,1,2,7,7,2,7,11,7,9,1,9,4,8\nC,3,8,4,6,2,5,8,8,8,9,8,13,2,10,4,10\nE,4,8,6,6,5,5,8,5,7,11,9,9,3,8,5,5\nK,2,3,2,2,2,5,7,4,7,6,6,10,3,8,4,10\nX,6,10,7,6,4,10,6,3,7,12,2,6,3,8,4,10\nQ,5,6,6,8,3,8,6,8,7,6,6,8,3,8,5,9\nR,6,8,9,7,9,9,6,4,4,8,5,7,7,9,7,6\nT,4,11,6,8,2,8,15,1,6,7,11,8,0,8,0,8\nY,2,3,4,5,1,8,11,2,3,4,12,8,1,10,0,8\nW,5,10,8,7,7,4,11,2,3,8,9,9,7,11,1,8\nB,4,7,4,5,3,6,7,8,6,7,6,7,2,8,9,10\nA,3,7,6,5,4,12,3,2,2,9,2,8,4,5,2,8\nA,3,7,5,5,3,7,3,2,2,5,2,8,2,6,2,6\nJ,5,9,4,7,3,7,11,3,3,13,5,4,2,8,7,8\nJ,2,4,4,3,1,8,6,3,6,14,7,11,1,6,1,7\nC,2,3,3,2,1,5,8,5,6,11,9,10,1,10,2,7\nL,4,9,4,4,2,8,5,3,5,12,7,11,2,8,6,8\nJ,2,6,4,4,3,9,6,2,4,8,5,6,3,7,5,6\nU,5,9,5,6,4,4,8,5,7,10,9,9,3,9,2,5\nV,3,6,5,4,1,7,8,4,2,7,13,8,3,9,0,8\nN,4,7,4,5,2,7,7,14,2,4,6,8,6,8,0,8\nH,3,8,5,6,6,8,7,4,3,6,6,7,7,8,7,6\nG,6,11,6,8,5,5,6,6,5,10,8,11,2,9,4,10\nG,3,7,5,5,3,7,6,6,6,6,6,10,2,9,4,8\nN,1,0,1,1,1,7,7,10,0,6,6,8,4,8,0,8\nT,4,10,6,8,5,10,11,2,7,5,11,7,1,11,1,8\nU,3,3,4,2,2,7,8,6,7,6,9,9,3,9,1,8\nU,4,5,5,3,2,4,8,5,8,9,8,9,4,11,3,4\nN,3,2,4,3,3,7,8,5,5,7,6,6,6,9,2,5\nU,5,6,5,4,2,4,9,5,7,12,11,9,3,9,1,7\nW,4,5,7,4,4,5,11,3,2,8,9,9,8,11,1,8\nC,3,5,4,4,2,4,8,5,7,12,9,11,1,10,2,7\nU,5,10,7,7,6,8,8,8,5,5,7,9,3,7,4,7\nJ,2,5,4,4,2,10,6,2,6,12,4,8,1,6,1,7\nC,4,9,5,7,6,8,6,5,3,7,7,11,5,9,3,8\nO,6,9,8,8,6,6,5,5,5,9,6,10,5,5,7,4\nL,4,9,4,7,1,0,0,6,6,0,0,5,0,8,0,8\nC,6,10,6,8,3,3,9,6,8,12,11,11,1,8,2,6\nB,3,6,5,4,4,9,6,4,6,10,5,7,2,8,5,10\nT,3,8,4,6,2,9,14,0,5,6,10,8,0,8,0,8\nZ,3,7,4,5,2,7,7,4,13,9,6,8,0,8,8,8\nF,4,10,5,8,4,6,10,3,6,10,9,5,2,10,4,6\nQ,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,9\nP,4,9,6,6,4,7,9,4,4,12,5,3,1,10,3,8\nO,6,10,8,8,6,8,5,9,4,6,5,5,5,7,4,8\nT,4,5,4,3,2,5,11,2,8,11,9,4,1,11,2,4\nD,4,8,6,7,6,7,7,5,6,7,4,8,4,6,7,4\nA,3,10,5,7,3,7,3,2,2,5,2,7,2,6,2,7\nC,6,9,6,6,3,5,7,6,8,11,8,14,2,9,4,6\nE,2,3,3,2,2,7,7,5,7,7,6,9,2,8,5,10\nK,2,1,3,3,2,6,7,4,7,6,6,10,3,8,5,8\nR,6,11,8,8,7,9,8,4,5,9,4,7,3,7,4,11\nY,3,7,4,5,1,8,11,1,3,6,12,8,1,11,0,8\nT,9,11,7,6,3,5,9,3,9,13,7,5,2,10,3,5\nE,6,11,9,8,8,8,7,7,3,7,6,11,6,8,9,8\nH,4,8,6,6,6,7,8,5,6,7,6,8,3,8,3,8\nR,3,5,5,3,3,9,6,3,5,10,4,7,3,6,4,9\nC,1,0,1,1,0,6,7,6,8,7,6,14,0,8,4,10\nU,4,7,6,6,5,7,7,4,4,6,6,9,4,8,2,9\nU,4,10,4,7,4,7,6,12,4,7,12,8,3,9,0,8\nZ,4,9,5,7,2,7,7,4,14,9,6,8,0,8,8,8\nT,4,5,5,4,3,8,12,3,7,6,11,8,2,11,1,7\nF,5,5,5,7,2,1,13,5,5,12,10,7,0,8,2,6\nC,3,6,4,4,1,3,9,5,7,11,11,10,2,9,2,6\nC,7,13,5,7,2,6,9,6,8,11,8,9,2,8,5,9\nH,3,7,5,5,4,7,7,7,6,7,6,8,3,8,3,8\nW,7,6,9,6,10,7,8,5,5,7,5,8,10,10,9,8\nS,2,1,2,1,1,8,7,4,7,5,6,7,0,8,8,8\nI,7,14,6,8,4,8,8,2,6,13,4,5,2,8,6,9\nL,2,1,2,2,1,5,3,5,7,2,2,4,1,7,1,6\nN,3,5,4,4,3,7,8,5,5,7,7,6,5,9,2,6\nE,3,8,3,6,2,3,6,6,11,7,7,15,0,8,6,7\nU,7,12,6,6,4,9,5,5,6,5,7,7,5,9,3,7\nP,4,6,6,4,3,8,9,5,5,12,4,3,2,9,4,8\nN,4,5,6,4,3,6,10,2,4,10,7,7,5,8,1,8\nC,2,5,3,3,1,4,9,5,6,11,10,11,1,9,2,7\nM,1,0,2,0,0,8,6,10,0,7,8,8,6,6,0,8\nQ,5,9,5,4,3,12,4,3,5,10,3,7,3,9,4,12\nY,1,1,3,2,1,7,10,1,6,7,11,8,1,11,1,8\nX,6,9,9,6,5,5,8,1,8,10,10,9,3,8,3,6\nN,4,10,4,8,3,7,7,14,2,5,6,8,6,8,0,8\nS,5,9,6,7,5,8,7,7,5,7,6,7,2,8,9,8\nR,4,11,4,8,5,6,8,9,4,7,5,7,3,8,5,11\nR,3,4,4,2,3,7,8,5,5,6,5,6,2,7,4,9\nU,9,11,10,8,4,4,8,6,10,13,11,9,3,9,1,7\nS,4,5,5,5,5,8,8,4,4,7,6,8,5,10,9,11\nZ,9,13,9,7,6,7,6,2,9,12,7,9,4,6,8,6\nH,6,9,6,4,3,7,8,4,4,9,9,9,6,10,5,9\nP,1,0,2,1,0,5,11,7,1,9,6,4,1,9,3,8\nL,3,9,4,6,3,3,5,1,8,3,1,9,0,7,1,6\nV,1,0,2,1,0,8,9,3,2,7,12,8,2,10,0,8\nR,5,9,7,7,6,7,7,6,6,6,5,7,4,8,6,10\nU,5,9,7,8,7,8,6,4,4,6,7,7,4,8,1,7\nR,7,11,9,8,9,9,8,4,5,10,4,7,3,7,5,11\nF,4,7,6,5,3,7,10,3,6,13,6,5,2,9,3,7\nN,3,4,5,3,2,6,9,3,4,10,7,7,5,8,1,8\nB,6,10,8,7,7,7,9,7,5,7,5,5,5,9,7,6\nW,6,9,6,6,5,2,11,2,3,10,10,8,5,11,1,7\nX,5,6,6,6,6,7,6,2,5,8,7,9,3,11,7,8\nY,3,2,5,4,2,5,10,1,7,9,11,9,1,11,2,7\nM,4,4,5,6,3,8,7,12,1,6,9,8,8,6,0,8\nP,4,8,6,6,3,6,11,5,4,12,5,3,1,10,3,8\nJ,2,4,4,3,1,8,6,3,7,14,5,10,0,7,0,8\nX,4,8,6,7,6,9,9,2,6,7,5,6,2,5,7,7\nD,5,10,7,7,5,8,7,7,9,6,5,4,3,8,4,7\nT,5,9,4,4,2,6,10,2,6,12,7,6,2,9,4,4\nV,6,10,8,8,6,8,12,3,2,6,10,9,7,11,7,8\nF,4,10,4,7,2,1,15,5,3,12,9,4,0,8,3,6\nM,4,7,5,5,3,8,7,12,1,6,9,8,8,6,0,8\nR,6,9,9,8,10,5,9,4,4,6,4,10,10,5,9,9\nT,7,9,6,5,2,4,12,3,6,13,8,4,2,8,4,3\nK,5,8,7,6,6,7,7,1,6,10,5,9,3,7,4,8\nH,2,6,4,4,5,9,6,4,3,6,6,7,6,9,7,7\nQ,7,9,7,11,8,8,6,7,3,8,6,11,6,8,8,6\nP,3,7,3,4,2,4,11,9,3,9,6,4,1,10,3,8\nJ,4,10,4,7,3,14,3,5,4,13,2,10,0,7,0,8\nL,3,9,5,6,3,8,3,0,8,10,3,11,0,7,2,9\nO,5,9,6,7,6,7,6,8,4,7,5,8,3,9,3,7\nL,1,3,2,2,1,7,4,1,7,8,2,10,0,7,2,8\nS,4,5,5,7,3,7,7,6,9,5,6,8,0,8,9,7\nN,8,11,12,8,7,3,11,3,4,10,11,9,8,7,2,7\nD,3,7,4,5,3,8,7,7,7,9,5,4,3,8,4,7\nN,4,7,7,5,6,7,8,3,5,7,6,7,6,8,7,4\nT,8,13,7,8,3,7,8,3,9,13,5,6,2,9,5,5\nC,4,8,4,6,2,4,8,6,7,11,10,12,2,9,3,7\nT,4,6,5,4,5,7,8,4,5,6,7,9,5,8,5,7\nJ,4,6,5,4,2,10,6,2,8,15,4,8,0,8,0,8\nN,4,9,5,7,3,7,7,14,2,4,6,8,6,8,0,8\nS,3,9,4,7,4,8,7,7,5,7,6,7,2,8,8,8\nG,3,4,4,3,2,7,6,7,5,6,6,10,3,8,4,9\nQ,4,7,5,6,3,8,4,8,8,6,4,8,3,8,4,8\nG,4,9,5,7,4,7,6,7,6,6,7,9,2,8,4,8\nR,3,6,5,4,3,10,7,3,6,10,2,7,3,7,3,10\nN,11,13,9,8,5,4,8,5,6,3,2,13,7,11,2,7\nW,5,9,7,7,4,10,8,5,2,6,8,8,9,10,0,8\nV,2,7,4,5,2,6,11,2,3,8,11,8,2,11,1,9\nS,5,7,6,5,4,8,6,3,7,10,7,9,2,10,5,8\nO,5,5,7,4,4,7,6,5,5,9,5,9,3,6,5,6\nV,8,11,7,8,4,3,11,4,4,10,12,8,3,10,1,8\nU,2,3,2,1,1,6,8,5,6,9,8,8,3,10,2,6\nC,4,9,5,7,3,5,8,6,8,12,9,13,2,10,3,7\nO,6,11,6,8,6,7,7,8,4,10,6,9,4,9,5,5\nR,4,6,6,4,5,8,8,7,3,8,4,7,4,6,7,8\nE,5,10,3,5,2,6,9,5,8,9,5,10,1,7,6,8\nU,8,10,8,7,6,4,8,5,8,9,8,10,6,8,4,3\nM,5,8,7,6,7,7,7,6,5,6,7,7,9,8,3,6\nA,2,4,4,5,1,5,5,3,1,5,1,8,2,7,2,7\nH,5,9,8,7,5,9,8,3,6,10,4,7,3,8,3,9\nF,4,6,6,4,2,3,15,4,3,13,8,3,1,10,2,5\nV,4,9,6,7,2,7,12,3,5,8,12,8,3,10,1,8\nI,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7\nW,5,9,8,6,5,9,8,4,1,6,9,8,7,11,0,8\nS,4,7,5,5,3,10,7,3,6,9,4,8,2,8,5,11\nN,2,1,3,2,2,6,8,5,4,8,7,7,5,9,1,6\nO,5,10,5,8,4,7,7,8,5,10,7,7,3,8,4,8\nG,5,8,6,6,5,8,7,7,6,6,7,9,2,6,6,10\nY,6,9,6,4,3,6,7,4,3,10,9,6,3,10,4,4\nD,7,10,9,8,8,7,7,5,7,7,6,7,7,8,3,7\nH,1,0,1,0,0,7,7,10,2,7,6,8,2,8,0,8\nF,6,12,6,6,4,9,7,3,4,10,5,6,3,9,6,9\nZ,4,6,6,4,4,8,7,2,9,12,6,8,1,8,5,8\nN,5,7,7,5,4,8,8,2,5,10,4,6,5,9,1,7\nM,4,6,5,8,4,8,7,12,2,6,9,8,8,6,0,8\nR,4,10,6,8,6,7,7,4,6,7,6,6,6,8,4,8\nW,3,8,5,6,3,11,8,5,1,6,9,8,7,10,0,8\nF,8,15,7,8,6,8,7,3,5,10,5,7,5,7,7,8\nI,2,9,2,7,3,7,7,0,7,7,6,8,0,8,3,8\nV,3,10,6,7,2,8,8,4,3,6,14,8,3,9,0,8\nH,6,10,8,8,7,6,8,3,6,10,8,8,4,8,4,6\nW,10,12,9,6,4,5,9,2,2,7,10,7,9,12,1,6\nP,3,2,4,3,3,5,10,4,5,10,8,4,1,10,4,7\nN,6,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nA,5,13,5,7,4,12,2,4,1,11,3,11,4,3,4,11\nA,6,14,6,8,4,11,1,3,3,12,5,13,3,6,5,11\nT,5,7,5,5,3,6,11,2,7,11,9,5,1,11,2,4\nW,3,4,5,3,3,6,11,3,2,7,9,8,7,11,0,8\nP,3,6,5,4,3,8,9,4,4,11,4,4,1,10,3,8\nX,5,8,7,6,5,7,6,3,5,6,6,9,3,8,9,9\nW,5,9,7,7,4,8,8,5,2,7,8,8,9,9,0,8\nY,3,5,5,3,2,7,10,1,7,7,11,8,1,11,2,8\nM,5,5,6,4,4,7,6,6,5,6,7,9,9,6,2,8\nY,2,4,4,5,1,7,10,2,2,7,13,8,1,11,0,8\nL,4,9,6,7,3,6,3,2,9,7,1,9,0,6,2,7\nF,3,7,3,4,1,1,13,5,4,12,10,7,0,8,2,6\nJ,2,3,3,5,1,12,2,9,4,13,5,13,1,6,0,8\nY,7,7,7,5,4,3,10,1,8,11,10,6,1,9,2,4\nQ,3,4,4,7,3,7,6,9,6,5,6,7,3,8,5,9\nL,4,11,5,8,3,9,2,1,7,9,2,10,1,6,3,9\nN,1,1,1,1,1,7,7,10,1,5,6,8,4,8,0,8\nC,2,7,3,5,1,5,7,7,9,7,6,14,1,8,4,9\nM,8,10,8,5,4,7,10,5,6,4,5,10,9,8,2,8\nU,4,7,6,5,3,6,8,6,8,7,9,9,3,9,1,8\nX,3,6,6,4,3,7,7,1,9,10,7,9,2,8,3,8\nS,6,8,8,7,8,9,8,5,6,7,6,7,6,10,12,12\nW,6,11,8,8,9,8,6,6,3,6,7,8,8,8,5,5\nF,3,4,3,6,1,1,12,5,5,12,11,8,0,8,2,6\nV,4,6,4,4,2,3,11,4,3,10,11,7,2,10,1,8\nP,3,8,5,6,3,6,10,5,6,10,8,4,1,10,4,7\nC,1,0,2,0,0,7,7,6,8,7,6,13,0,8,4,10\nW,4,8,6,6,7,7,8,5,3,7,8,8,5,8,3,8\nP,2,4,4,3,2,7,9,4,4,12,5,3,1,10,3,8\nM,7,8,10,7,11,8,7,5,5,7,6,7,14,8,7,3\nJ,5,7,7,8,6,8,8,4,6,6,7,7,4,9,10,11\nG,4,9,5,7,4,8,7,7,6,6,6,7,2,8,6,11\nT,7,11,7,8,5,5,12,3,6,12,10,5,2,12,1,5\nT,6,10,6,8,4,7,11,4,8,11,9,4,2,12,4,5\nZ,5,11,6,8,5,6,8,6,11,7,7,10,1,9,8,7\nD,7,9,9,8,8,6,7,5,8,7,6,9,6,4,8,3\nD,2,3,4,2,2,9,6,4,6,10,4,6,2,8,2,9\nK,6,9,9,7,7,10,5,1,5,9,3,8,7,6,6,11\nD,4,7,5,5,4,6,7,8,7,7,7,5,3,8,3,7\nW,5,8,8,6,11,8,7,5,2,7,6,8,13,11,3,9\nQ,3,4,4,5,3,7,8,5,2,8,8,10,3,8,5,8\nI,1,9,2,7,2,7,7,0,8,7,6,8,0,8,3,8\nQ,7,14,6,8,5,10,5,4,7,11,4,8,3,7,9,11\nC,2,5,3,4,2,6,8,7,7,8,8,14,1,9,4,10\nT,6,9,5,5,3,7,8,2,7,12,7,8,2,9,4,5\nR,2,2,3,3,2,7,7,5,5,6,5,6,2,7,5,8\nL,2,6,2,4,0,0,2,5,6,0,0,7,0,8,0,8\nQ,2,3,3,4,2,7,8,5,1,7,8,10,2,9,5,8\nX,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nU,5,5,6,7,2,7,3,15,6,7,13,8,3,9,0,8\nO,4,3,5,5,2,7,8,8,8,7,7,8,3,8,4,8\nJ,1,3,2,2,1,11,6,1,6,11,3,7,0,7,0,8\nL,3,6,5,4,2,5,5,1,9,7,2,11,0,7,3,7\nX,3,5,5,4,3,7,7,3,10,6,6,8,3,8,6,8\nF,2,3,3,1,1,5,11,2,5,13,6,4,1,9,1,8\nW,8,8,11,7,12,7,6,5,5,7,5,8,10,9,8,9\nM,6,9,8,7,9,7,9,7,5,7,6,8,10,10,8,6\nQ,2,1,2,1,1,8,6,6,4,6,6,8,2,8,3,8\nK,7,9,7,4,3,7,8,3,7,9,6,8,6,8,3,7\nT,2,3,3,2,1,5,12,3,6,11,9,4,1,10,2,5\nJ,1,4,3,3,1,9,6,3,6,14,5,9,0,7,0,8\nF,3,2,4,4,3,5,11,3,6,11,9,5,1,10,3,6\nI,2,6,2,4,1,6,8,0,6,13,7,8,0,8,1,7\nT,3,4,5,6,1,6,15,1,6,8,11,7,0,8,0,8\nA,3,9,6,7,4,10,2,1,2,8,3,9,4,5,3,7\nM,8,11,11,8,8,6,6,3,5,9,8,9,8,6,2,8\nM,4,9,7,6,9,8,6,3,1,7,4,8,10,7,2,6\nO,5,11,6,8,3,7,8,9,8,7,8,8,3,8,4,8\nY,5,9,6,6,3,2,11,4,5,12,12,7,1,11,2,6\nP,3,9,4,6,2,4,11,9,3,10,6,4,1,10,4,8\nR,4,4,4,6,3,5,11,8,3,7,4,8,3,7,6,11\nO,3,8,4,6,2,7,6,9,7,6,5,7,3,8,4,8\nA,3,7,5,5,3,8,2,2,2,7,2,7,2,6,3,6\nM,3,2,5,3,4,8,6,6,4,7,7,9,9,5,2,8\nR,3,6,4,4,4,8,8,7,2,7,4,7,4,7,7,8\nZ,4,7,5,5,4,9,7,5,3,7,5,7,3,8,9,5\nC,2,5,3,4,2,5,8,7,8,8,8,14,1,9,4,10\nD,1,0,2,1,1,5,7,8,6,7,6,6,2,8,3,8\nP,2,4,4,3,2,8,9,4,3,12,5,4,1,10,2,9\nC,4,10,5,7,3,4,8,6,6,12,10,13,1,10,3,8\nE,5,10,5,8,5,3,8,5,9,7,6,14,0,8,6,8\nM,2,3,4,2,2,8,7,2,4,9,6,8,6,5,1,7\nZ,3,5,5,4,3,7,8,2,9,12,6,8,1,8,5,7\nC,5,10,3,5,2,7,8,7,5,11,6,9,2,9,5,9\nQ,6,9,8,8,7,6,4,5,6,6,5,8,6,1,8,6\nG,3,7,5,5,5,9,7,6,2,6,7,9,5,8,3,8\nZ,3,5,6,4,3,7,8,2,10,12,6,8,1,9,5,7\nS,5,6,7,6,7,8,8,4,5,7,7,8,4,10,9,10\nS,7,15,6,8,3,7,4,5,5,8,2,7,4,6,6,7\nH,6,9,7,4,4,7,8,3,5,10,6,8,6,9,5,8\nQ,4,5,5,7,3,8,9,8,5,5,9,9,3,7,6,10\nK,6,10,8,8,10,8,8,4,5,5,7,9,9,8,9,7\nJ,8,12,6,9,5,7,11,3,3,13,5,4,3,9,9,9\nY,5,7,7,10,10,7,6,4,2,8,8,9,8,11,9,7\nQ,3,9,4,8,3,8,6,9,6,6,5,8,3,8,4,8\nS,4,11,5,8,3,9,8,6,9,5,6,5,0,8,9,7\nJ,0,0,1,0,0,12,4,6,3,12,5,11,0,7,0,8\nX,1,0,2,0,0,7,7,3,4,7,6,8,3,8,4,8\nG,1,0,2,1,1,8,7,6,5,6,5,9,1,7,5,10\nW,6,9,8,7,4,6,8,5,2,7,8,8,9,9,0,8\nM,5,7,9,5,10,9,7,3,3,8,4,7,11,6,3,5\nB,2,6,3,4,3,6,6,8,5,6,7,7,2,9,6,10\nM,4,8,6,6,6,8,6,5,5,6,7,8,8,5,2,7\nN,3,6,5,4,5,8,7,4,4,7,5,7,5,10,6,4\nA,4,7,6,5,3,10,1,3,3,9,2,8,3,6,4,9\nW,6,7,6,5,5,3,11,2,2,9,9,8,6,11,2,7\nJ,2,4,3,3,1,8,6,3,6,14,5,9,0,7,0,8\nE,2,4,4,3,2,7,8,2,7,11,7,9,2,9,4,8\nQ,4,6,4,7,5,8,8,6,2,7,7,11,3,9,5,8\nP,6,6,8,8,9,6,9,5,3,8,8,6,6,12,5,7\nU,6,9,5,4,3,8,6,5,5,6,6,7,4,7,3,6\nB,3,2,4,3,3,8,7,5,6,7,6,6,5,8,5,10\nN,5,11,7,8,9,9,8,5,4,7,5,5,7,11,9,4\nI,1,8,1,6,2,7,7,0,7,7,6,8,0,8,3,8\nK,3,2,4,4,3,6,7,4,7,6,6,10,3,8,5,9\nD,3,5,5,4,3,9,6,4,6,10,4,6,2,8,3,8\nJ,5,11,4,8,3,7,11,3,4,13,4,4,1,7,5,6\nT,8,11,7,6,3,6,9,3,8,13,6,6,2,8,4,5\nN,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nS,5,10,6,8,4,7,7,3,6,10,6,8,2,8,5,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nN,5,7,7,5,4,10,8,4,6,10,1,4,5,9,1,7\nA,5,7,7,6,6,9,8,3,4,7,7,8,5,6,5,4\nT,3,3,4,2,1,5,11,3,7,11,9,4,1,10,2,5\nH,8,11,12,8,10,9,6,3,6,10,4,8,5,7,5,8\nL,1,0,1,0,0,2,1,6,4,0,3,4,0,8,0,8\nL,3,10,4,7,1,0,1,5,6,0,0,7,0,8,0,8\nY,5,8,7,12,12,9,7,4,1,6,8,9,6,12,8,9\nL,2,7,4,5,4,7,7,3,4,7,6,10,5,10,6,5\nT,3,10,4,7,3,7,13,0,5,7,10,8,0,8,0,8\nK,4,5,5,7,2,3,7,8,2,7,5,11,4,8,2,11\nQ,3,2,4,4,3,8,8,6,2,5,7,10,3,8,5,10\nF,2,2,3,3,2,5,11,3,5,11,9,5,1,10,3,6\nK,6,8,9,6,4,5,7,3,7,10,9,11,3,8,3,7\nK,3,1,5,3,3,6,7,4,8,7,6,10,6,8,4,9\nI,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nY,3,6,5,4,2,10,10,2,6,3,11,8,1,11,2,9\nX,3,8,4,6,3,7,7,4,4,7,6,8,2,8,4,8\nN,4,7,6,5,3,11,7,3,5,10,1,4,5,9,1,7\nD,3,8,5,6,5,7,7,4,6,7,6,5,3,8,3,7\nP,3,9,4,7,4,4,12,7,2,10,7,4,1,10,3,8\nK,7,10,6,5,3,5,8,3,6,9,8,9,5,9,3,7\nT,5,7,7,6,6,7,8,4,8,7,7,8,3,9,8,6\nU,5,8,5,6,2,7,4,13,5,7,15,8,3,9,0,8\nZ,2,4,2,3,2,7,8,5,8,6,6,9,2,8,7,8\nO,3,6,4,4,3,7,7,7,4,7,5,8,3,8,3,8\nH,5,8,6,6,5,7,7,7,6,7,7,8,3,8,3,8\nP,1,3,3,2,1,6,10,3,3,12,6,4,0,9,2,8\nT,5,9,7,7,7,7,8,5,5,6,8,9,7,7,8,7\nO,3,6,4,4,3,7,9,7,4,7,8,7,3,8,2,8\nI,2,5,4,4,2,7,7,0,8,14,6,8,0,8,1,7\nY,3,6,5,4,2,7,11,1,7,7,11,8,1,11,2,8\nE,7,15,6,8,5,7,7,4,5,10,6,9,3,9,8,10\nG,6,8,8,7,8,7,10,6,2,7,7,8,6,11,7,8\nW,3,7,5,5,3,8,8,4,1,7,8,8,8,10,0,8\nR,3,4,3,3,2,7,8,4,5,6,5,6,3,6,5,8\nW,5,5,6,3,4,4,11,3,2,9,9,7,7,11,1,7\nQ,4,5,5,5,2,9,8,7,6,5,7,9,3,8,5,9\nY,8,14,7,8,4,5,9,4,3,10,8,5,4,10,4,4\nG,4,7,4,5,3,6,7,6,6,10,7,10,2,9,4,9\nW,4,5,6,3,3,7,11,2,2,7,9,8,8,11,0,8\nR,6,9,9,8,10,7,6,3,4,7,5,8,8,9,6,7\nY,2,3,3,2,1,8,10,1,6,5,11,9,1,11,1,8\nO,3,7,4,5,3,7,8,7,5,10,8,7,3,8,3,8\nR,4,6,5,4,4,6,8,5,6,7,5,7,3,7,5,8\nE,4,9,4,6,2,3,7,6,11,7,6,15,0,8,7,7\nL,4,9,4,7,3,0,2,4,6,1,0,8,0,8,0,8\nL,4,10,4,8,1,0,1,6,6,0,0,6,0,8,0,8\nF,4,9,6,7,3,4,13,3,4,13,8,4,1,10,2,6\nC,3,6,4,4,2,6,8,8,8,8,8,13,2,10,4,9\nX,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nF,4,5,5,4,3,5,10,4,6,10,9,5,2,10,3,6\nH,6,11,6,8,3,7,7,15,0,7,6,8,3,8,0,8\nW,3,3,4,2,2,3,10,3,2,10,10,8,5,11,1,6\nC,3,9,4,7,2,5,8,7,9,6,7,13,1,8,4,9\nP,6,9,9,7,5,8,11,8,5,10,4,3,2,10,5,7\nS,4,11,5,8,4,7,7,8,7,8,5,7,2,7,9,8\nK,5,10,8,8,8,5,6,4,7,6,6,12,5,7,7,10\nF,3,7,5,5,5,10,7,1,5,9,5,6,4,10,5,7\nQ,5,5,6,7,6,10,11,6,0,4,7,11,6,14,4,9\nR,6,10,9,9,10,8,8,4,4,7,4,7,7,9,6,4\nS,3,5,6,3,2,7,8,3,7,11,8,7,1,9,4,6\nU,7,8,8,6,3,4,9,6,9,12,11,8,3,9,1,6\nJ,6,11,8,8,4,8,6,4,6,15,6,11,1,6,1,7\nF,5,8,7,10,8,7,9,5,5,7,5,7,4,8,9,7\nM,6,10,8,7,6,11,5,3,4,9,3,6,8,6,2,9\nM,5,9,7,6,7,9,7,5,5,6,7,5,8,7,3,5\nB,5,8,7,6,6,10,6,3,6,10,4,7,2,8,5,10\nX,1,0,1,0,0,7,7,3,5,7,6,8,2,8,4,7\nG,5,8,5,6,3,6,7,7,6,10,7,11,2,9,4,9\nH,3,8,4,6,4,7,8,12,1,7,5,8,3,8,0,8\nJ,6,10,8,7,4,9,7,2,7,14,4,7,0,7,0,8\nF,2,3,4,2,2,8,9,2,6,13,5,4,1,9,2,8\nL,1,3,3,2,1,6,4,1,7,7,2,9,0,7,2,8\nX,9,13,9,7,5,8,7,2,8,11,4,7,4,9,4,7\nG,6,11,5,6,4,8,7,4,3,9,6,7,4,9,9,8\nI,2,10,2,8,2,8,7,0,9,7,6,7,0,8,3,7\nZ,5,7,7,9,5,11,4,3,4,9,3,8,2,7,6,9\nM,4,7,6,5,5,11,6,3,4,9,3,6,7,6,2,8\nL,4,7,5,5,3,7,4,1,8,8,2,10,0,6,2,8\nE,3,7,3,5,3,3,8,5,8,7,7,13,0,8,6,9\nW,4,4,5,3,3,7,11,3,2,6,9,8,7,11,0,8\nJ,6,9,8,7,5,9,4,7,7,8,6,6,2,7,5,6\nW,4,11,7,8,11,8,6,7,3,7,6,8,14,10,5,9\nV,5,6,5,4,2,3,12,3,3,10,11,7,2,11,1,8\nJ,2,2,3,3,1,9,6,3,6,12,4,10,1,6,1,6\nN,6,9,9,7,4,6,8,3,5,10,8,8,6,8,1,7\nS,4,4,5,6,3,8,8,6,9,5,6,7,0,8,9,8\nV,2,2,3,3,1,6,12,2,3,8,11,8,2,10,1,9\nA,2,2,4,3,2,7,2,2,2,6,1,8,2,6,2,7\nT,7,11,7,8,5,5,10,0,8,11,9,6,1,9,3,4\nK,6,7,8,5,6,9,6,1,6,10,3,8,7,7,6,10\nE,4,7,5,5,3,7,7,3,8,11,7,9,2,9,5,8\nH,4,7,5,4,2,7,7,14,0,7,6,8,3,8,0,8\nJ,5,9,7,7,5,8,7,7,7,8,7,8,3,6,5,5\nA,3,9,6,7,5,7,5,2,3,5,2,6,4,5,5,5\nF,3,7,3,5,1,1,14,5,3,12,9,5,0,8,2,6\nD,2,6,4,4,5,9,8,4,4,7,6,6,3,8,7,4\nF,3,2,4,4,3,5,10,3,5,10,9,5,1,10,3,6\nU,7,11,7,8,4,3,8,5,8,11,10,10,3,9,2,6\nC,6,13,5,8,4,6,8,5,4,9,8,10,4,9,10,8\nE,3,5,4,7,2,3,8,6,11,7,5,15,0,8,7,7\nS,7,10,9,8,6,9,7,3,7,10,4,7,3,9,5,9\nC,1,3,2,2,1,7,8,6,5,9,7,11,2,10,3,10\nC,5,8,5,6,3,4,8,5,7,11,10,13,1,9,3,8\nS,2,2,3,4,2,8,7,7,5,7,7,8,2,9,9,8\nJ,3,10,4,7,1,12,2,10,4,14,5,13,1,6,0,8\nK,3,5,5,4,3,6,7,1,7,10,7,10,3,8,2,8\nR,5,11,8,8,7,10,7,3,6,10,3,7,4,5,5,11\nJ,1,1,2,1,0,11,6,2,6,12,3,8,0,7,0,8\nL,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8\nU,5,8,5,6,4,6,7,6,8,8,6,8,6,10,4,3\nF,0,0,1,0,0,3,11,4,2,11,8,6,0,8,2,8\nR,3,7,5,5,3,8,8,4,6,9,3,8,3,7,4,11\nR,4,9,6,7,4,9,8,3,6,9,3,8,3,6,4,11\nK,4,9,5,7,7,6,7,3,3,6,5,9,6,8,8,7\nO,4,5,6,4,4,7,6,5,5,8,5,8,3,7,5,6\nE,5,8,6,6,6,8,5,6,3,7,7,9,4,8,8,10\nS,2,7,3,5,2,8,8,5,7,5,6,8,0,8,8,8\nL,5,11,4,6,2,10,2,3,4,13,4,11,2,8,5,9\nK,6,11,7,6,4,4,9,3,6,10,10,11,5,8,3,6\nP,3,5,5,4,2,8,9,4,4,11,4,3,1,10,3,8\nK,5,7,7,6,7,7,9,3,5,7,3,9,6,2,7,10\nA,3,8,5,6,3,12,2,3,2,10,2,9,2,6,3,8\nF,3,8,4,6,3,1,12,4,4,12,10,7,0,8,2,7\nJ,3,11,4,8,3,8,6,3,6,11,5,10,1,6,2,5\nM,10,9,10,4,4,7,11,5,5,4,5,10,10,12,2,7\nY,2,3,4,5,1,9,10,3,2,6,13,8,2,11,0,8\nB,3,8,4,6,4,8,8,8,7,7,5,5,2,8,8,9\nF,5,11,8,8,9,5,10,0,4,9,7,6,6,9,5,2\nH,5,10,5,7,3,7,5,15,2,7,9,8,3,8,0,8\nT,1,6,2,4,1,7,13,0,6,7,10,8,0,8,0,8\nE,4,6,6,4,5,7,9,6,3,6,6,9,4,7,7,8\nG,4,7,5,5,3,6,7,7,6,8,7,11,2,7,4,10\nJ,3,9,5,7,2,8,7,2,8,15,4,7,0,7,0,8\nD,4,7,4,5,2,6,7,10,9,7,7,6,3,8,4,8\nE,5,8,7,6,5,7,7,2,7,11,6,9,3,7,5,9\nF,4,8,5,6,3,6,10,2,5,13,7,5,2,10,2,8\nK,7,10,9,8,9,8,8,6,5,8,5,6,5,7,8,11\nP,2,7,3,4,2,5,10,9,3,9,6,5,2,10,3,8\nE,5,10,7,8,7,8,7,6,3,7,6,9,4,8,8,10\nY,3,9,5,7,3,9,10,1,6,4,11,8,2,12,2,8\nG,5,7,6,5,6,7,8,6,2,6,6,9,5,7,7,7\nA,3,7,5,5,3,10,2,2,2,9,1,8,2,6,3,8\nQ,3,6,4,5,2,8,9,7,6,5,8,9,3,8,5,10\nZ,6,10,8,7,6,7,7,2,9,12,6,8,1,8,6,8\nP,6,9,6,4,4,8,8,5,3,10,5,6,5,10,5,6\nH,2,4,4,2,2,7,8,3,6,10,6,8,3,8,2,8\nR,2,1,3,2,2,7,8,4,5,6,5,7,2,6,5,8\nS,1,0,1,0,0,8,7,3,6,5,6,7,0,8,7,8\nJ,3,9,4,7,4,8,6,2,5,11,4,9,4,6,2,6\nA,2,8,4,6,2,8,4,3,2,7,1,8,3,7,2,8\nP,3,6,4,9,8,8,8,4,0,8,7,7,5,9,4,7\nL,5,9,7,7,3,6,4,1,9,8,2,10,0,7,2,8\nU,4,9,6,8,6,7,7,4,3,6,6,9,5,6,2,8\nR,2,1,2,2,1,6,9,8,3,7,5,8,2,7,5,11\nV,5,6,6,4,2,4,13,4,4,10,11,6,3,10,1,8\nC,1,0,2,1,0,6,7,6,9,7,6,14,0,8,4,10\nN,5,9,7,7,4,3,9,4,4,11,11,10,5,7,1,8\nD,5,10,6,8,7,8,7,4,8,6,5,6,6,8,3,7\nQ,6,6,7,9,4,8,10,8,6,5,9,9,3,8,6,10\nP,1,3,3,2,1,7,9,4,3,11,5,4,1,9,2,8\nB,3,6,4,4,3,10,6,3,6,10,3,7,2,8,4,11\nX,4,9,6,7,3,10,7,2,8,10,1,7,3,8,4,9\nW,6,11,10,8,7,4,11,2,3,8,9,9,8,11,1,8\nH,7,12,6,6,4,8,8,3,4,9,6,7,6,9,5,9\nK,2,3,4,2,2,4,7,2,6,10,9,11,3,8,2,7\nI,3,10,4,8,2,7,7,0,8,13,6,8,0,8,1,8\nZ,4,9,6,7,6,7,7,3,7,7,6,9,0,8,9,7\nR,5,9,5,7,6,6,9,8,3,7,5,8,3,8,6,12\nA,2,6,4,4,3,7,2,1,2,6,2,7,2,6,3,6\nM,3,1,4,2,1,7,7,11,1,7,9,8,7,6,0,8\nJ,1,0,1,1,0,12,3,6,3,13,5,11,0,7,0,8\nH,3,8,4,5,2,7,7,15,1,7,6,8,3,8,0,8\nW,3,7,5,5,4,7,11,2,2,7,8,8,6,11,1,8\nR,5,11,6,8,6,6,7,5,6,7,6,7,6,8,5,8\nV,4,9,6,7,3,4,11,3,4,9,12,9,2,10,1,8\nF,3,5,4,3,2,5,10,3,6,10,9,6,1,10,3,6\nA,3,3,5,5,2,6,3,3,3,6,2,7,3,6,3,7\nX,1,1,2,1,0,7,7,4,4,7,6,8,2,8,4,7\nK,4,6,5,4,3,6,8,2,7,10,7,9,3,8,3,7\nF,3,6,4,4,2,6,11,4,5,12,7,4,2,10,2,6\nC,5,10,6,8,7,5,6,3,4,7,6,11,6,9,4,7\nT,3,6,5,4,3,6,11,3,7,8,11,7,4,11,1,7\nN,7,9,9,7,9,6,8,3,4,8,7,8,8,8,7,4\nR,1,0,2,1,1,6,9,7,3,7,5,8,2,7,4,11\nK,6,9,9,7,6,5,7,2,8,10,8,10,4,7,4,7\nN,7,9,8,4,3,10,6,3,4,13,3,7,6,8,0,8\nY,5,11,7,8,1,8,11,2,2,7,13,8,1,11,0,8\nM,5,10,6,7,6,8,5,11,0,6,9,8,9,6,2,6\nX,3,5,4,4,2,8,7,3,9,6,6,8,2,8,6,8\nQ,5,5,7,8,9,8,7,5,1,6,6,9,9,12,7,13\nV,4,9,5,7,3,7,11,2,4,7,12,9,3,10,1,8\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nV,2,3,4,4,1,7,8,4,2,7,13,8,3,10,0,8\nB,4,9,6,7,6,9,6,4,5,9,5,7,2,8,5,9\nY,6,11,9,8,5,6,9,0,8,8,12,9,1,11,2,7\nC,5,10,6,8,2,4,7,7,11,7,6,12,1,8,4,9\nA,2,5,4,4,2,11,2,2,2,9,2,9,2,6,2,8\nK,5,10,8,8,4,3,8,4,7,10,12,12,4,8,4,6\nG,3,8,4,6,2,7,6,8,7,6,6,9,2,7,6,11\nT,2,6,4,4,2,9,11,1,7,5,11,7,1,10,1,8\nH,4,11,5,8,3,7,7,15,1,7,6,8,3,8,0,8\nT,4,6,4,4,2,5,11,2,7,11,9,5,1,10,2,5\nR,3,7,5,5,3,9,7,4,6,10,4,7,3,7,4,10\nZ,4,10,4,8,4,7,8,3,12,9,6,8,0,8,7,7\nE,3,8,4,6,4,6,7,7,9,7,7,11,3,8,6,8\nH,5,8,8,6,6,7,6,3,6,10,7,8,3,8,3,7\nE,4,10,6,8,5,8,7,5,9,7,6,8,3,8,6,9\nX,4,7,7,5,4,9,7,1,8,10,3,7,3,8,3,9\nR,2,1,2,2,2,6,7,4,4,6,5,7,2,7,3,8\nP,3,6,4,4,2,4,12,8,2,10,6,4,1,10,4,8\nA,4,11,6,8,4,12,3,3,3,9,1,9,2,6,3,8\nB,4,9,4,7,3,6,8,9,7,7,6,7,2,8,9,10\nQ,5,7,5,8,5,8,5,7,5,9,5,9,3,8,5,7\nW,3,9,5,6,5,7,11,2,2,6,8,8,7,11,0,8\nG,3,5,4,4,2,6,7,5,6,9,7,11,2,9,4,10\nN,3,3,3,5,2,7,7,13,2,5,6,8,5,8,0,8\nW,6,10,8,8,9,7,9,6,4,7,9,7,7,7,5,10\nQ,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8\nP,7,11,9,8,8,6,7,7,4,8,7,8,2,10,7,9\nQ,6,9,7,11,7,7,10,5,3,6,9,12,3,9,7,8\nD,3,2,4,3,3,7,7,6,6,6,6,5,5,8,3,7\nL,5,10,7,8,9,7,7,3,5,7,7,10,6,11,6,6\nL,2,7,4,5,2,9,4,1,6,9,3,10,0,6,2,10\nI,5,10,6,8,4,7,8,1,7,7,6,8,0,8,4,8\nV,3,9,5,7,1,9,8,4,3,6,14,8,3,10,0,8\nE,3,5,5,4,3,6,8,2,8,11,8,9,2,8,4,8\nD,4,9,6,7,5,7,7,7,8,6,5,4,3,8,3,7\nO,4,8,5,6,2,7,8,8,8,7,7,8,3,8,4,8\nJ,6,8,4,11,3,6,10,3,4,13,5,5,3,8,7,8\nD,2,5,4,4,3,10,6,3,6,10,3,6,2,8,3,8\nM,5,10,7,6,4,13,2,5,2,12,1,9,6,3,1,8\nE,5,10,7,8,7,7,8,7,9,7,6,11,3,8,6,8\nG,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,10\nA,2,3,4,1,1,10,2,3,1,9,2,9,1,6,1,8\nR,3,6,4,4,3,10,6,3,6,10,3,7,3,7,3,10\nQ,4,9,5,8,5,8,8,7,5,6,8,9,3,8,5,10\nD,3,6,4,4,3,9,7,4,5,10,4,5,3,8,2,8\nI,3,11,4,9,2,7,8,0,8,13,6,7,0,8,1,7\nA,3,9,5,7,3,12,3,4,3,10,1,9,2,7,3,9\nL,3,6,3,4,1,0,1,6,6,0,1,5,0,8,0,8\nK,4,5,5,4,4,5,7,4,7,6,6,11,3,8,5,9\nV,7,10,7,8,3,2,11,6,5,13,12,7,3,9,1,8\nT,3,5,4,4,2,5,12,3,6,11,10,5,1,11,1,5\nP,4,6,4,8,3,5,9,10,5,8,6,5,2,10,4,8\nP,1,1,1,1,0,5,11,7,2,9,6,4,1,9,3,8\nR,1,0,1,0,0,6,9,6,3,7,5,8,2,6,3,10\nX,4,10,6,8,4,7,7,4,9,6,6,8,3,8,7,7\nF,4,9,4,7,3,1,13,4,4,12,10,7,0,8,2,6\nQ,1,2,2,3,1,7,8,4,1,7,8,10,2,9,3,8\nF,4,6,5,7,6,7,9,5,4,8,6,7,4,10,9,7\nT,5,10,7,7,6,6,7,7,7,7,10,9,4,6,8,7\nQ,4,5,6,4,4,7,4,4,5,7,4,9,4,5,6,7\nO,2,4,3,2,2,8,7,6,4,9,6,8,2,8,2,8\nO,5,8,6,6,5,8,7,7,4,10,6,7,3,8,3,8\nJ,2,7,3,5,2,9,6,2,6,11,4,8,1,6,1,6\nO,7,11,5,6,3,7,9,6,5,9,5,6,4,9,5,8\nL,1,0,1,0,0,3,1,6,4,1,3,4,0,8,0,8\nC,2,4,3,3,1,4,8,4,6,11,9,12,1,9,2,8\nE,4,8,6,6,7,6,7,3,6,6,7,11,3,10,8,7\nM,9,14,11,8,7,5,4,4,3,7,4,10,10,1,2,8\nR,5,7,7,5,4,9,7,4,6,10,3,7,4,5,5,10\nI,3,10,4,8,2,6,9,0,8,13,7,7,0,8,1,7\nJ,2,4,3,3,1,11,6,1,6,11,3,7,0,7,0,7\nR,3,7,3,5,3,6,10,7,3,7,4,8,2,7,5,11\nZ,2,4,4,3,2,7,7,2,9,11,6,9,1,9,6,7\nY,3,5,5,6,1,9,12,2,3,5,12,8,1,10,0,8\nT,2,1,2,1,0,8,15,1,4,6,10,8,0,8,0,8\nT,5,7,6,5,4,6,10,1,8,11,9,5,1,10,3,4\nR,4,7,5,5,5,8,6,6,3,8,5,7,4,7,7,10\nI,1,8,1,6,1,7,7,0,8,7,6,8,0,8,3,8\nT,5,8,7,6,7,8,8,6,7,8,6,8,5,7,5,6\nI,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nZ,4,8,4,6,4,8,7,5,9,7,5,7,2,8,8,8\nY,3,4,5,6,1,7,10,2,2,7,13,8,1,11,0,8\nU,6,10,8,8,5,5,8,6,8,7,10,10,3,9,1,8\nC,5,10,6,8,4,4,9,6,7,7,8,15,1,8,4,10\nT,3,9,4,6,1,6,15,1,6,8,11,7,0,8,0,8\nK,5,7,8,5,4,7,8,2,7,10,6,8,3,8,3,7\nQ,4,7,4,9,4,8,5,8,5,9,6,9,3,7,5,8\nY,3,8,5,5,1,9,11,3,2,5,12,8,1,11,0,8\nH,2,4,3,3,2,7,7,6,5,7,6,9,3,8,3,8\nJ,2,6,3,4,3,9,6,2,5,11,4,9,1,6,1,6\nK,4,7,5,5,6,7,9,5,4,8,5,8,4,6,6,10\nO,4,8,5,6,3,7,7,7,5,10,7,8,3,8,3,8\nQ,2,2,3,2,2,7,8,4,2,8,7,9,2,9,4,8\nB,2,3,3,1,2,7,7,4,5,7,6,6,1,8,5,9\nM,4,7,6,5,5,7,6,6,5,7,7,9,8,5,2,9\nY,3,6,4,4,0,8,10,3,2,6,13,8,1,11,0,8\nY,3,7,5,4,1,7,10,2,2,6,13,8,1,11,0,8\nT,2,3,3,2,1,6,11,2,6,11,9,5,1,11,2,5\nV,3,10,5,7,4,7,11,2,3,5,11,9,2,11,1,8\nS,4,8,5,6,3,7,8,4,8,11,7,7,2,8,5,6\nB,5,9,7,6,10,8,6,5,3,7,7,8,6,9,8,9\nS,2,2,3,3,2,7,7,6,5,8,6,8,2,9,9,8\nM,4,5,8,4,4,9,6,3,5,9,5,7,8,7,2,8\nY,2,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8\nN,2,1,3,1,2,7,8,6,4,7,6,6,5,9,2,6\nD,4,8,6,6,4,8,7,5,7,11,5,5,3,8,3,8\nM,3,4,6,3,3,5,6,4,4,10,10,11,8,7,3,8\nZ,4,9,6,7,4,7,9,2,9,11,7,6,1,7,6,6\nR,3,8,5,6,6,8,8,3,4,6,6,8,6,10,7,6\nV,7,11,7,8,3,4,11,3,4,9,11,7,3,10,1,8\nS,4,6,6,4,6,9,6,4,3,9,6,9,4,8,10,10\nZ,1,4,2,3,2,7,7,5,8,6,6,9,2,8,7,8\nG,4,6,4,4,2,6,7,6,6,10,8,10,2,9,4,9\nJ,5,7,7,5,4,9,5,5,6,8,6,6,2,7,4,6\nL,2,2,3,3,2,4,4,4,7,2,1,6,1,7,1,6\nZ,3,6,4,4,2,7,7,3,14,9,6,8,0,8,8,8\nR,7,10,9,8,8,9,8,3,6,9,3,8,3,6,4,11\nJ,2,11,3,8,2,13,3,6,4,13,2,10,0,7,0,8\nN,4,5,7,4,3,6,9,2,5,10,7,7,5,8,1,8\nX,1,1,2,1,1,8,7,3,8,7,6,8,2,8,5,8\nL,4,9,6,7,7,7,7,3,6,8,7,10,7,10,7,5\nH,7,11,9,9,9,6,7,7,4,6,5,7,3,7,7,10\nO,4,9,3,4,2,6,7,6,3,9,6,8,4,10,4,7\nM,6,10,9,8,9,7,6,5,5,7,7,11,14,6,2,9\nV,2,3,4,5,1,8,8,4,2,6,13,8,3,10,0,8\nY,8,11,8,8,4,2,11,4,5,13,13,8,1,11,2,6\nQ,6,6,8,9,4,8,6,8,8,6,5,8,3,8,4,8\nM,4,8,6,6,5,10,6,6,4,6,7,5,7,5,2,5\nQ,3,6,5,8,7,9,7,5,1,6,6,9,7,9,4,9\nA,3,6,5,4,2,10,3,2,2,8,3,10,2,6,2,7\nU,5,7,7,5,5,8,7,8,5,6,7,9,3,8,4,6\nB,4,7,6,5,5,7,8,5,5,9,7,6,3,7,6,7\nS,7,15,5,8,3,10,3,4,4,10,3,10,4,6,5,11\nB,6,10,8,7,8,8,8,4,6,10,6,6,5,7,7,9\nK,4,7,7,5,4,3,8,3,7,11,11,11,3,8,3,5\nR,4,8,7,7,8,7,8,3,4,7,4,8,6,8,5,6\nW,5,8,7,6,4,3,11,3,3,9,10,10,7,11,1,8\nH,4,7,5,5,5,6,7,5,7,7,6,10,6,8,3,9\nH,2,1,2,1,1,7,7,12,1,7,6,8,3,8,0,8\nX,4,11,5,8,2,7,7,5,4,7,6,8,3,8,4,8\nC,2,4,3,3,1,4,8,4,7,10,9,13,1,8,2,7\nI,5,8,7,6,3,9,4,3,6,6,7,4,1,10,4,7\nG,3,3,4,4,2,7,6,7,8,6,5,10,1,8,6,11\nX,2,3,3,4,1,7,7,4,4,7,6,8,3,8,4,8\nY,4,7,6,9,7,10,9,5,2,6,7,8,7,11,7,5\nM,5,10,8,8,6,12,6,2,4,9,3,6,8,6,2,8\nX,10,14,9,8,5,7,7,2,10,9,6,8,4,6,4,8\nM,5,7,7,6,9,5,8,5,3,6,5,8,11,7,6,8\nC,4,8,4,6,3,4,8,6,6,12,10,12,2,9,3,8\nU,8,10,9,8,4,3,9,6,8,12,11,9,3,9,1,7\nR,5,11,7,8,7,9,8,4,5,9,4,7,3,7,4,11\nI,0,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nO,4,4,5,7,2,7,8,8,8,6,8,9,3,8,4,8\nB,2,1,3,2,2,8,7,5,6,7,6,5,2,8,6,9\nE,4,6,6,4,4,6,8,2,9,11,7,10,2,9,4,8\nN,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8\nM,4,5,8,4,4,7,6,3,4,9,7,8,7,5,2,8\nF,1,3,2,1,1,6,10,3,4,13,7,5,1,9,1,8\nR,3,6,4,4,2,5,10,8,2,7,4,8,2,7,6,11\nD,4,10,4,7,3,6,7,11,9,6,5,6,3,8,4,8\nU,4,7,5,5,2,7,5,12,5,7,15,8,3,9,0,8\nV,3,11,5,8,4,7,11,2,3,6,11,9,2,10,1,8\nU,5,9,5,6,4,8,5,12,4,7,12,8,3,9,0,8\nI,0,1,1,2,0,7,7,2,6,7,6,8,0,8,2,8\nV,5,11,8,8,9,7,6,4,2,7,8,8,5,9,4,7\nD,4,7,4,5,2,5,7,10,8,6,5,5,3,8,4,8\nV,4,7,5,5,3,9,12,2,3,3,10,9,3,11,2,8\nO,6,10,4,5,3,6,9,7,4,9,7,10,5,10,5,7\nE,3,6,4,4,3,5,8,3,7,11,8,9,2,8,5,7\nZ,8,8,5,12,5,10,3,4,5,12,5,9,3,8,9,11\nS,6,10,7,8,4,8,7,5,9,11,4,7,2,7,5,9\nC,4,7,5,5,3,8,7,8,6,6,7,10,3,8,3,9\nW,7,11,7,8,6,3,10,2,3,10,10,8,8,10,2,6\nJ,2,6,4,4,2,9,5,4,5,14,4,10,0,7,0,8\nT,3,3,4,2,1,5,11,2,7,11,9,5,1,10,2,5\nN,8,15,10,9,5,4,9,4,4,13,11,9,6,9,0,9\nX,4,9,6,6,4,3,9,2,8,11,12,10,3,8,4,4\nR,4,9,6,6,5,9,7,5,5,9,4,7,3,7,4,11\nK,6,11,9,8,6,2,9,3,7,11,12,12,3,8,4,5\nA,3,7,6,5,4,5,5,3,3,3,2,6,4,6,4,4\nS,4,9,5,7,5,7,7,7,5,7,6,7,2,8,8,8\nZ,2,1,2,1,1,7,7,5,8,6,6,8,1,8,6,8\nG,6,6,8,6,7,7,9,5,3,7,7,8,8,11,8,7\nK,5,10,6,7,2,3,8,8,2,7,5,11,4,8,3,10\nG,4,5,5,4,3,6,6,6,6,6,6,10,2,9,4,8\nW,3,6,5,4,6,10,7,4,2,7,6,8,5,10,1,5\nG,7,11,8,8,7,5,5,6,6,6,5,11,4,10,4,9\nT,4,5,5,4,2,6,11,2,8,11,9,5,1,10,3,4\nB,4,8,6,6,8,8,8,4,3,6,7,7,6,11,8,8\nX,4,7,5,6,5,6,7,2,4,7,7,10,3,10,7,8\nM,4,7,8,5,8,10,4,2,1,9,4,9,8,6,2,6\nV,4,9,6,7,1,6,8,5,3,8,14,8,3,9,0,8\nT,7,8,7,6,5,7,10,3,7,10,9,5,4,11,5,5\nA,6,10,8,8,8,7,7,8,4,7,6,8,4,8,11,2\nP,6,6,6,8,3,4,13,8,2,10,6,3,1,10,4,8\nL,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8\nO,7,11,8,8,7,8,7,8,5,9,5,7,5,8,5,10\nT,4,11,6,8,5,9,11,1,8,6,11,8,1,11,1,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nJ,4,8,5,6,2,7,6,4,6,15,6,11,1,6,1,7\nK,4,7,4,4,2,4,9,8,2,7,4,11,4,8,3,10\nY,4,7,4,5,1,3,12,4,5,13,11,5,1,11,1,6\nE,6,9,8,7,6,9,6,1,8,11,4,9,4,8,5,10\nC,4,9,5,7,3,4,8,6,8,12,10,12,1,9,3,7\nW,9,10,8,7,8,4,11,3,2,9,8,7,7,12,3,6\nL,4,10,4,7,1,0,1,5,6,0,0,6,0,8,0,8\nP,9,15,8,8,4,6,10,6,5,14,5,4,4,10,4,8\nG,4,8,5,6,3,7,7,7,7,10,7,12,2,9,4,9\nX,4,9,5,6,3,7,7,4,4,7,6,7,3,8,4,8\nI,3,10,5,7,6,9,6,3,4,8,5,5,6,7,5,5\nF,3,6,6,4,5,5,9,2,3,10,8,7,5,9,3,4\nW,3,2,5,4,3,7,10,2,2,6,9,8,7,11,0,7\nD,5,9,5,4,3,7,7,4,6,10,6,7,5,9,6,5\nF,4,11,5,8,4,3,11,3,7,11,10,6,1,10,3,5\nI,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nJ,4,6,2,9,2,12,5,2,4,9,5,7,2,9,4,11\nB,5,10,8,7,8,9,6,4,6,9,5,7,2,8,6,10\nW,5,10,8,7,6,5,8,4,1,7,9,8,7,11,0,8\nU,3,9,4,6,3,7,6,13,4,6,10,8,3,9,0,8\nF,10,15,9,8,6,6,9,3,5,10,6,7,5,7,9,6\nA,4,9,6,7,5,8,2,1,2,6,2,7,5,6,6,7\nZ,1,3,2,1,1,8,7,5,8,6,6,8,1,8,6,8\nX,6,11,10,8,5,10,7,2,9,11,1,6,4,9,4,10\nN,7,13,8,7,4,4,9,4,4,13,11,10,5,9,0,8\nU,4,8,6,7,5,8,8,5,4,5,7,8,7,8,2,7\nN,4,8,6,6,4,4,10,3,4,10,10,9,5,8,1,8\nZ,3,7,5,5,3,7,8,2,9,11,8,6,1,8,6,5\nD,5,10,6,7,3,6,7,10,10,6,5,6,3,8,4,8\nC,1,0,2,1,0,6,7,6,8,7,6,14,0,8,4,10\nQ,2,3,3,4,2,8,8,5,2,8,8,10,2,9,4,8\nM,4,9,5,7,6,7,6,10,1,7,8,8,8,5,0,8\nT,3,4,4,5,1,7,15,1,5,7,11,8,0,8,0,8\nQ,4,6,5,7,5,9,9,7,2,4,7,11,3,9,5,10\nT,8,10,8,8,5,5,11,2,7,11,10,5,2,12,2,4\nG,3,4,4,3,2,7,6,6,6,6,6,10,2,9,4,9\nO,5,8,6,6,5,8,7,7,4,9,6,7,3,7,4,9\nC,4,7,4,5,2,3,8,5,7,11,11,12,1,8,2,7\nB,6,11,8,8,7,9,7,4,6,10,5,6,3,8,6,10\nC,5,6,6,5,5,5,6,3,6,7,7,11,5,11,7,8\nQ,4,7,6,5,4,8,4,7,4,6,6,11,3,8,5,9\nU,4,5,5,4,2,5,8,5,8,9,7,8,3,9,3,5\nW,11,15,11,8,5,2,9,3,2,9,11,9,9,12,0,6\nO,2,4,3,2,2,7,7,7,5,9,6,8,2,8,3,8\nK,3,4,5,3,3,9,7,2,7,10,3,8,3,8,3,9\nC,2,5,3,4,2,6,8,7,8,9,8,13,1,10,4,10\nS,5,10,7,7,4,8,7,4,9,11,5,7,2,8,5,8\nA,3,6,5,4,3,8,5,3,0,7,1,8,2,6,1,8\nJ,4,9,6,7,3,5,9,2,6,15,7,8,1,7,1,7\nN,2,4,4,3,2,7,9,2,4,10,6,6,5,8,1,7\nE,2,7,4,5,3,7,7,5,8,6,5,8,3,8,6,9\nT,5,12,5,6,3,7,9,2,6,11,6,6,2,9,5,5\nA,2,8,4,5,2,9,5,3,1,8,1,8,2,7,2,8\nT,2,8,3,5,1,9,14,1,6,5,11,9,0,8,0,8\nO,4,6,4,4,3,7,7,7,5,10,7,9,3,8,3,8\nV,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8\nE,3,7,3,5,2,3,8,6,10,7,6,14,0,8,7,8\nF,4,7,6,5,3,7,10,3,6,13,5,4,2,10,2,7\nF,5,12,6,8,2,1,13,5,4,12,10,7,0,8,3,6\nQ,8,12,7,6,4,12,3,3,7,11,2,9,3,8,5,14\nH,6,7,8,5,5,7,7,3,7,10,7,9,3,8,3,7\nQ,3,3,4,5,3,8,8,6,2,5,7,9,3,8,5,9\nO,7,11,9,8,6,8,7,9,5,7,6,6,4,8,5,10\nZ,6,11,8,8,7,10,5,5,5,8,5,7,4,7,10,5\nL,3,3,3,5,1,0,1,6,6,0,0,6,0,8,0,8\nY,6,8,6,6,3,2,11,3,6,12,12,6,1,11,2,5\nC,3,5,4,4,2,6,8,7,7,9,8,12,2,10,4,10\nJ,2,7,2,5,1,13,4,5,4,13,2,9,0,7,0,8\nZ,3,8,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nL,2,2,3,3,1,4,4,3,7,2,1,7,0,7,1,6\nU,3,7,4,5,3,7,6,11,4,7,12,8,3,9,0,8\nR,3,8,4,5,2,5,10,9,4,7,4,8,3,7,6,11\nC,5,8,5,6,3,4,8,6,7,12,10,11,1,9,2,7\nI,2,9,2,7,3,7,7,0,7,7,6,8,0,8,3,8\nS,3,2,3,3,2,8,7,6,5,7,6,8,2,9,9,8\nR,4,5,5,4,4,7,8,5,6,7,5,6,6,7,4,8\nX,6,10,9,8,4,8,8,1,9,10,4,7,3,9,4,8\nF,3,5,5,4,2,7,9,2,6,13,6,6,3,8,3,7\nU,7,10,8,8,4,3,9,6,7,12,11,9,3,9,2,7\nK,2,3,3,2,2,5,7,4,7,7,6,11,3,8,4,9\nR,4,11,6,8,8,7,7,3,4,7,6,8,6,8,7,5\nE,3,9,4,7,4,6,7,7,8,7,6,11,3,8,6,8\nY,9,10,9,8,5,3,10,3,6,12,12,7,2,11,2,5\nD,3,5,5,4,3,9,7,5,7,9,5,5,3,7,4,8\nG,4,10,5,8,5,5,6,6,4,8,7,12,4,8,6,7\nR,5,11,7,8,6,7,8,5,7,6,5,8,4,6,7,9\nP,4,6,6,8,8,10,8,3,3,6,8,7,6,10,6,4\nD,3,6,5,4,6,9,8,4,4,7,6,6,4,8,8,5\nT,3,5,4,4,2,6,11,2,7,11,9,5,1,11,3,4\nI,7,14,5,8,3,10,5,6,5,13,3,7,3,8,5,10\nZ,5,7,6,5,5,9,6,5,4,7,5,7,3,8,10,6\nH,5,10,8,8,7,7,8,2,6,10,7,8,3,8,3,7\nT,2,1,2,2,0,7,14,1,5,7,11,8,0,8,0,8\nH,6,9,9,6,7,10,6,3,6,10,3,7,5,6,4,9\nI,5,10,6,8,3,9,6,2,7,7,6,5,0,8,4,7\nJ,3,6,5,7,4,8,8,4,5,7,7,7,3,9,9,11\nD,6,12,6,6,4,10,3,4,4,11,2,8,4,6,4,10\nI,1,1,1,2,1,8,7,1,7,7,6,7,0,8,2,7\nP,5,9,7,6,5,9,8,2,5,13,5,5,2,9,3,9\nA,5,7,7,6,6,5,8,3,5,7,8,11,7,9,3,8\nA,6,9,9,8,8,7,8,2,4,7,7,8,6,5,5,6\nV,4,7,5,5,2,6,11,3,2,8,11,8,2,9,3,8\nD,5,11,7,8,6,6,7,8,7,6,6,5,3,9,4,9\nQ,2,2,3,3,2,8,8,5,1,5,7,9,2,9,5,10\nB,2,6,3,4,2,6,7,9,6,7,6,7,2,8,8,10\nJ,8,10,5,14,4,7,8,3,4,11,6,5,3,8,8,9\nE,2,2,3,4,3,7,7,6,7,7,6,9,2,8,6,10\nV,1,0,2,0,0,7,9,3,2,7,12,8,2,10,0,8\nT,2,10,4,7,1,10,14,1,6,4,11,9,0,8,0,8\nF,5,7,7,5,3,5,12,2,6,13,7,4,1,10,2,7\nR,4,9,5,6,3,5,12,8,3,7,3,9,3,7,6,11\nR,1,0,2,1,1,6,9,7,2,7,5,8,2,7,4,10\nL,8,13,7,7,3,6,4,3,7,10,4,13,3,5,6,8\nO,5,8,7,6,4,8,5,8,5,6,4,5,4,8,4,8\nI,3,10,4,7,3,9,5,0,7,13,5,9,0,7,2,9\nY,5,7,5,5,2,3,11,3,6,12,11,5,0,10,2,5\nZ,2,1,2,2,1,7,7,3,12,8,6,8,0,8,7,8\nF,2,4,3,3,1,6,10,3,5,13,7,5,1,9,2,7\nD,3,9,4,6,7,9,8,5,4,7,6,6,4,7,7,5\nQ,5,8,6,9,7,8,5,8,4,6,5,9,3,7,6,10\nG,3,7,4,5,3,6,6,6,5,10,7,13,2,9,4,10\nL,2,7,3,5,1,0,1,4,5,1,1,7,0,8,0,8\nT,2,3,3,2,1,9,12,3,6,6,11,9,2,11,1,8\nR,2,1,3,2,2,7,7,5,5,6,5,6,5,7,3,8\nI,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nI,1,6,1,4,2,7,7,0,7,7,6,8,0,8,3,8\nR,3,5,5,4,5,8,8,4,4,8,4,7,6,7,5,4\nH,4,8,6,6,6,7,7,6,6,7,6,9,3,8,3,8\nO,2,3,3,2,1,8,7,7,4,7,6,8,2,8,2,8\nX,10,15,11,8,6,7,8,2,8,11,8,8,5,12,4,7\nW,4,6,6,4,3,8,8,4,1,7,9,8,7,10,0,8\nS,6,9,8,7,5,7,7,3,7,10,8,9,2,10,5,6\nF,2,6,3,4,2,1,11,3,4,11,10,8,0,8,2,7\nE,5,11,7,8,7,10,6,2,7,11,4,8,4,8,6,12\nP,6,5,6,7,3,4,13,9,2,10,6,4,1,10,4,8\nK,3,3,5,2,2,7,7,2,7,10,5,10,3,8,3,8\nA,6,9,6,5,3,12,3,5,2,12,3,10,5,3,3,10\nQ,3,3,4,4,3,8,8,6,2,5,7,10,3,9,6,10\nS,4,4,5,7,3,7,5,6,10,5,6,10,0,9,9,8\nF,5,9,4,5,3,8,10,3,4,11,5,4,3,10,7,7\nG,6,11,8,9,9,7,9,6,3,6,6,9,6,7,8,7\nU,3,5,4,3,2,5,8,5,7,10,9,9,3,9,2,6\nR,9,15,9,8,7,7,8,3,5,8,4,9,7,7,7,6\nX,3,8,5,6,5,9,7,2,5,7,5,6,3,9,6,8\nX,6,9,9,7,5,6,8,1,8,10,9,9,3,9,3,6\nH,5,10,8,8,7,7,7,3,6,10,7,9,3,8,3,8\nL,3,6,4,4,3,6,7,9,5,6,6,9,3,8,5,11\nD,6,9,5,5,4,7,7,4,6,10,5,7,5,9,6,6\nH,6,8,9,6,5,7,7,3,7,10,6,8,3,8,3,8\nV,5,9,6,7,4,8,11,3,2,5,10,9,2,10,3,9\nE,6,11,9,9,6,10,6,2,9,11,4,9,3,8,5,11\nF,5,9,7,7,4,6,10,2,5,13,7,5,1,10,2,7\nO,5,8,7,6,5,7,8,8,5,6,7,11,4,8,4,8\nV,3,9,5,7,3,7,9,4,2,7,13,8,2,10,0,8\nK,2,5,3,3,2,7,7,4,6,6,6,9,3,8,5,10\nP,2,2,3,3,2,6,11,5,4,10,7,2,1,10,4,6\nV,1,1,2,1,0,7,9,4,2,7,13,8,2,10,0,8\nV,4,11,6,8,5,8,11,2,3,4,10,9,4,12,2,8\nJ,2,8,3,6,2,9,6,3,6,12,4,9,1,6,2,6\nI,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nK,5,9,6,7,4,4,7,7,3,7,7,12,3,8,3,11\nR,4,10,6,8,6,8,8,4,6,8,3,8,4,5,5,12\nP,4,7,6,5,4,9,8,2,6,13,5,6,1,10,3,9\nY,2,3,4,2,1,8,11,1,6,6,11,9,1,11,1,8\nE,4,10,6,8,6,7,7,5,8,7,7,9,3,8,6,9\nK,4,7,5,5,5,5,7,5,3,7,5,9,3,5,9,8\nS,4,6,6,6,6,8,8,5,5,7,7,8,4,10,8,9\nH,4,8,6,6,4,10,5,3,6,10,2,7,3,8,3,10\nK,3,4,6,3,3,6,7,1,7,10,7,10,3,8,3,7\nY,2,5,4,7,6,9,8,4,1,6,7,9,3,11,7,7\nS,3,5,4,4,2,8,7,3,7,10,7,7,1,9,5,8\nB,3,6,4,4,5,7,7,6,5,7,6,6,2,8,6,10\nT,4,5,5,3,2,6,11,2,8,11,9,5,3,9,3,4\nO,2,1,3,2,2,7,7,7,5,7,6,8,2,8,3,8\nA,2,2,4,3,2,8,2,2,2,8,1,8,2,6,2,7\nF,2,1,3,2,1,5,11,3,5,11,9,5,1,9,3,6\nH,6,9,9,7,7,8,6,2,6,10,5,8,4,7,4,8\nW,9,10,9,7,6,3,11,2,3,9,9,8,8,11,2,6\nL,5,11,5,8,2,0,0,6,6,0,1,5,0,8,0,8\nT,2,5,4,7,1,8,14,0,6,6,11,8,0,8,0,8\nH,4,4,5,6,2,7,6,15,1,7,7,8,3,8,0,8\nY,1,1,2,1,0,8,9,2,2,7,12,8,1,10,0,8\nS,2,3,3,4,2,8,7,5,8,4,6,8,0,8,8,8\nS,4,7,5,5,3,7,7,4,8,11,5,7,2,8,5,8\nQ,4,5,5,6,4,8,7,5,2,8,8,10,2,9,5,7\nV,2,1,4,1,1,9,12,2,2,5,10,9,2,10,1,8\nW,5,5,7,7,4,9,8,5,2,7,8,8,9,9,0,8\nT,2,6,4,4,2,5,13,0,5,9,10,7,0,8,0,8\nD,2,1,2,1,1,6,7,8,7,6,6,6,2,8,3,8\nA,4,9,7,7,3,12,2,4,2,11,1,9,3,7,3,9\nF,6,9,8,7,3,4,14,3,5,13,7,2,1,10,2,6\nD,5,10,8,8,11,9,9,5,5,7,6,6,5,8,11,6\nJ,3,6,5,4,2,7,7,3,5,15,6,9,1,6,1,7\nS,3,1,3,2,2,8,7,6,5,7,7,8,2,9,9,8\nL,5,5,5,7,1,0,0,7,6,0,1,4,0,8,0,8\nE,3,3,4,5,2,3,7,6,10,7,6,14,0,8,7,7\nM,4,8,5,6,3,7,7,12,1,7,9,8,8,6,0,8\nL,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8\nA,3,7,5,5,3,8,2,2,2,7,2,7,2,6,3,7\nR,5,10,6,8,7,6,7,5,6,6,4,8,3,6,5,8\nS,4,9,5,6,5,8,7,5,8,5,5,8,0,8,8,7\nE,1,3,3,2,1,7,7,2,6,11,7,9,2,9,4,9\nO,5,8,6,6,4,7,9,8,5,6,7,9,3,8,3,7\nR,3,7,5,5,3,10,7,3,6,10,3,7,3,7,3,10\nO,1,0,1,1,0,7,7,7,5,7,6,8,2,8,3,8\nF,4,6,6,4,2,8,10,2,6,13,5,3,2,10,2,8\nA,4,9,6,7,5,7,5,2,4,5,1,6,3,6,4,5\nR,3,10,4,7,5,6,9,8,4,7,6,8,2,7,5,11\nT,5,8,6,6,3,4,13,4,6,12,10,4,2,11,2,4\nW,5,6,7,4,4,5,11,3,2,9,8,7,9,13,2,5\nN,5,10,7,7,5,7,8,6,6,6,6,6,6,8,1,6\nS,5,5,6,7,3,8,8,6,9,5,6,7,0,8,9,7\nA,6,11,5,6,3,8,4,3,3,7,4,11,5,6,4,7\nY,2,6,4,4,2,8,9,1,6,5,11,8,1,11,1,8\nV,5,10,8,7,5,7,12,2,3,5,10,9,4,12,2,7\nB,1,0,2,1,1,7,8,7,5,7,6,7,1,8,6,8\nN,5,10,8,8,5,5,9,5,5,8,8,9,7,9,2,5\nR,6,10,8,7,6,8,9,4,6,8,4,8,3,6,6,11\nY,3,5,5,7,7,9,6,4,1,6,7,8,6,10,8,9\nW,1,0,2,1,1,8,8,4,0,7,8,8,6,10,0,8\nB,3,8,3,6,4,7,6,8,5,7,6,7,2,9,7,9\nC,2,1,3,3,1,6,8,7,7,8,8,13,1,10,4,10\nY,5,9,8,7,4,5,9,1,7,9,12,9,1,11,2,7\nI,6,15,4,8,3,12,4,3,6,11,2,7,3,8,3,12\nD,5,10,6,7,6,8,9,5,5,11,6,5,5,8,5,9\nA,3,4,5,3,2,10,1,3,2,9,2,9,3,6,2,8\nA,3,7,5,5,3,12,3,2,2,9,2,9,2,5,2,8\nX,2,7,3,4,1,7,7,4,4,7,6,8,3,8,4,8\nR,3,6,4,4,3,6,8,8,4,6,5,7,2,7,5,11\nG,1,0,2,0,0,8,7,5,5,6,5,9,1,8,5,10\nZ,4,10,5,7,4,7,8,3,12,9,6,8,0,8,8,7\nX,2,2,4,3,2,8,7,3,9,6,6,9,3,8,6,8\nJ,6,7,8,9,7,8,10,4,5,7,7,8,5,6,9,7\nA,5,9,5,5,3,12,2,5,1,12,3,10,3,3,2,10\nQ,2,2,3,3,2,8,7,7,3,6,5,9,2,9,3,9\nF,6,9,8,7,5,8,9,1,7,14,5,5,2,9,3,9\nA,4,7,6,6,5,8,8,2,4,7,7,8,5,7,4,5\nM,5,10,6,8,4,7,7,12,2,8,9,8,8,6,0,8\nY,6,9,6,6,3,3,10,3,7,11,11,6,1,11,3,5\nM,5,2,6,4,4,8,6,7,5,7,7,8,8,6,2,7\nO,5,9,5,6,5,8,8,7,4,9,6,6,4,8,4,8\nV,2,4,3,3,1,3,12,4,3,11,11,7,2,11,1,7\nT,2,1,3,3,1,6,12,3,6,8,11,7,1,11,1,7\nB,3,5,4,7,3,6,8,9,7,7,5,7,2,8,9,10\nP,4,7,5,5,3,8,9,3,5,13,4,3,1,10,3,9\nH,4,5,6,4,4,7,7,3,6,10,6,8,3,8,3,7\nN,1,0,2,0,0,7,7,11,0,5,6,8,4,8,0,8\nB,8,15,7,8,6,10,6,3,5,10,4,7,7,6,8,9\nB,4,9,4,4,3,10,6,3,4,9,4,8,6,7,7,10\nT,2,4,2,3,1,7,11,2,6,7,11,8,1,11,1,7\nP,2,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8\nR,3,4,4,2,2,7,8,5,5,7,5,7,2,6,4,8\nK,2,3,4,1,2,6,8,2,6,10,7,10,3,8,2,8\nE,3,5,5,4,3,6,7,2,8,11,7,9,2,8,4,8\nJ,1,1,2,2,1,10,6,3,5,12,5,9,1,7,1,7\nM,7,6,10,6,10,7,8,4,4,7,5,7,11,8,5,5\nM,5,10,7,8,8,6,6,6,5,7,7,11,11,5,2,9\nS,3,7,4,5,3,8,7,7,7,8,6,7,2,8,9,8\nY,7,8,6,6,3,3,10,3,7,11,12,6,2,11,3,5\nP,6,10,5,5,3,6,9,6,3,10,5,6,4,9,4,7\nJ,5,9,6,7,3,9,5,4,6,15,4,10,0,7,1,7\nT,3,6,4,4,2,6,12,2,7,11,9,4,1,11,2,5\nD,1,0,1,1,1,6,7,8,5,6,6,6,2,8,3,8\nJ,3,7,5,5,2,8,5,4,6,14,7,12,1,6,0,7\nV,3,7,5,5,1,7,8,4,3,7,13,8,3,9,0,8\nJ,3,11,4,8,4,10,7,0,7,11,3,6,0,7,1,7\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nZ,3,7,5,5,2,9,6,3,9,11,3,9,1,7,6,9\nM,3,3,6,2,3,8,5,3,4,10,7,9,7,5,2,8\nY,7,8,6,12,4,5,8,4,1,8,10,5,4,10,6,5\nA,3,8,5,6,3,8,3,2,2,7,1,8,2,6,3,7\nR,4,9,5,6,5,9,7,4,5,9,4,8,3,7,4,11\nW,6,5,9,8,4,11,8,5,2,6,9,8,9,9,0,8\nG,10,15,9,8,5,7,6,6,6,10,6,7,5,8,6,6\nI,1,4,0,2,0,7,7,1,7,7,6,8,0,8,2,8\nT,2,2,3,3,2,7,12,3,6,7,11,8,2,11,1,7\nB,7,14,5,8,4,7,6,4,5,10,6,9,5,7,7,9\nJ,0,0,1,0,0,12,4,6,3,12,5,11,0,7,0,8\nU,3,6,4,4,2,7,4,14,5,7,13,8,3,9,0,8\nP,7,10,6,5,4,7,10,4,4,12,5,3,4,10,5,6\nH,3,3,5,2,3,7,8,2,6,10,7,7,4,10,3,7\nI,1,2,1,3,1,7,7,1,7,7,6,9,0,8,3,8\nS,2,6,3,4,2,7,6,5,8,4,6,8,0,9,8,8\nY,5,6,5,4,2,3,11,3,7,12,12,6,2,11,2,5\nB,2,7,3,5,3,6,7,7,5,6,6,7,2,8,7,9\nM,5,8,8,6,6,8,6,6,5,6,7,8,8,6,2,7\nE,3,6,3,4,2,3,8,6,10,7,6,14,0,8,7,8\nG,3,7,4,5,2,7,7,8,7,5,6,10,2,7,5,10\nB,4,9,5,7,6,7,7,9,5,7,5,6,2,8,8,10\nV,5,10,5,5,3,10,9,4,5,8,8,5,3,10,2,8\nY,5,6,5,4,2,3,12,4,5,12,11,5,1,11,1,5\nU,4,9,5,6,2,7,4,15,6,7,13,8,3,9,0,8\nD,4,4,4,6,2,5,7,10,8,6,6,5,3,8,4,8\nV,2,1,3,2,1,7,12,2,2,7,11,8,2,10,1,8\nA,2,4,4,6,2,7,5,3,1,7,1,8,2,7,2,8\nL,4,9,6,7,4,9,4,1,7,9,2,10,3,6,4,9\nO,3,7,4,5,3,6,7,7,4,7,6,10,4,8,3,8\nZ,6,10,8,8,6,8,7,2,9,12,5,8,1,7,6,8\nC,5,9,6,6,3,3,8,5,7,11,10,14,1,8,3,7\nE,3,7,3,5,2,3,6,6,10,7,7,14,0,8,7,8\nG,6,10,7,8,5,6,6,6,6,11,6,12,4,9,5,8\nJ,4,6,6,7,5,7,8,4,6,6,8,7,3,9,9,10\nO,1,1,2,1,1,8,7,7,5,7,6,8,2,8,3,8\nJ,5,9,6,7,3,9,7,2,6,15,4,8,0,7,1,7\nX,4,7,6,5,5,6,7,2,6,7,6,9,4,5,7,9\nU,4,7,6,5,8,8,8,4,4,6,7,8,7,8,5,8\nJ,2,4,3,3,1,9,7,2,6,14,4,8,0,7,0,8\nV,4,11,6,8,2,7,8,4,3,7,14,8,3,9,0,8\nO,5,10,6,8,5,8,8,8,4,7,7,6,5,7,4,9\nR,4,8,6,6,4,8,8,4,6,9,3,8,3,6,4,11\nR,4,6,6,4,3,9,7,5,6,9,4,7,3,7,4,11\nO,5,11,6,8,5,8,7,9,6,6,7,9,3,8,3,8\nD,5,11,7,8,6,7,8,8,7,7,7,4,4,7,5,9\nR,3,6,4,4,3,6,7,5,5,6,5,7,3,7,5,9\nF,5,8,7,6,5,7,9,3,5,13,7,6,2,10,2,7\nK,6,7,8,5,4,6,7,2,7,10,6,10,4,7,4,8\nU,4,6,5,5,5,7,6,5,4,6,7,8,4,8,1,7\nE,2,4,3,3,2,8,7,6,8,7,7,8,2,8,6,8\nG,4,6,5,4,3,7,6,6,6,10,6,11,2,10,4,10\nF,4,6,5,4,3,6,11,3,5,13,6,4,2,10,2,7\nP,4,8,4,6,2,4,12,8,2,10,6,3,1,10,4,8\nK,4,7,6,5,4,6,7,5,7,6,5,10,3,8,5,9\nP,3,5,5,3,2,8,9,4,4,12,4,3,1,10,3,8\nY,4,9,5,6,5,9,4,7,5,8,8,7,5,8,5,5\nP,5,8,7,6,5,8,10,5,4,11,4,3,1,10,3,8\nK,3,6,5,5,4,8,7,2,4,9,3,8,4,6,4,10\nD,3,4,4,3,3,7,7,7,7,6,6,5,2,8,3,7\nM,6,10,7,8,4,8,7,13,2,6,9,8,9,6,0,8\nA,3,4,6,6,2,7,6,3,0,7,0,8,2,7,1,8\nQ,4,9,5,8,5,8,8,7,4,6,8,9,2,8,4,9\nG,5,9,6,7,5,5,6,6,7,6,5,11,2,10,4,8\nB,4,8,5,6,6,8,8,6,6,7,6,6,5,8,5,10\nR,2,3,4,2,2,8,7,4,4,9,4,6,2,7,3,9\nJ,3,6,4,4,2,8,7,3,5,14,6,10,1,6,1,7\nU,7,13,7,7,3,6,5,5,6,3,9,8,5,9,2,7\nT,5,5,5,4,3,6,11,2,8,11,9,4,3,10,4,4\nW,3,1,5,2,2,7,11,3,2,7,9,8,6,11,0,8\nY,1,0,2,0,0,7,9,3,1,7,12,8,1,11,0,8\nA,3,7,4,5,2,9,3,3,3,9,1,8,2,6,2,7\nF,4,10,5,8,6,5,9,4,6,9,10,7,2,9,3,6\nC,6,9,7,7,8,5,6,4,5,7,6,11,6,9,4,8\nO,2,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8\nF,3,5,5,3,2,6,11,2,6,14,7,4,1,9,2,7\nT,7,13,6,7,4,9,7,2,7,10,5,8,3,8,5,8\nP,4,9,4,6,2,4,11,9,3,9,6,4,1,10,4,8\nC,3,7,4,5,2,4,9,6,7,12,10,11,1,10,2,7\nX,7,10,10,8,6,7,8,0,8,9,6,8,3,8,3,7\nT,7,10,6,5,3,5,11,2,7,12,7,5,2,8,4,4\nM,4,4,6,3,3,6,6,3,4,10,9,10,8,4,3,8\nK,5,10,8,8,6,4,9,1,6,10,9,10,3,8,3,6\nG,3,6,4,4,2,6,8,5,5,9,7,10,2,8,4,9\nZ,1,4,2,2,1,7,8,5,8,6,6,9,1,8,7,7\nV,6,7,6,5,2,3,12,5,4,12,12,7,3,10,1,8\nM,3,4,4,2,3,8,6,6,4,7,7,9,9,5,1,8\nO,3,6,3,4,3,7,8,7,4,9,7,10,3,8,2,9\nO,6,8,8,6,9,7,9,5,2,7,6,7,10,10,6,9\nH,4,5,6,4,4,7,8,3,6,10,7,7,5,9,4,8\nM,7,10,12,8,15,10,6,3,3,9,4,7,12,6,5,5\nB,5,10,6,8,7,9,8,3,5,7,6,7,7,7,6,9\nV,5,6,5,4,2,4,11,2,3,9,11,7,2,10,1,7\nR,2,1,2,2,2,6,8,4,5,6,5,7,2,7,4,8\nE,2,4,4,3,2,6,8,3,9,11,8,9,2,8,4,6\nG,5,11,6,8,6,8,8,8,6,5,7,9,3,5,7,11\nW,6,10,8,8,9,8,7,6,3,7,8,8,10,6,6,11\nC,2,5,3,4,2,6,8,7,7,9,8,13,1,9,4,10\nQ,2,1,2,1,1,8,7,7,4,6,6,8,2,8,3,8\nA,4,9,7,6,3,12,2,4,3,11,1,9,3,7,3,9\nV,5,8,5,6,2,3,11,3,4,10,12,7,2,10,1,8\nZ,2,4,5,3,2,7,8,2,9,11,7,7,1,8,5,6\nF,4,9,6,7,4,4,11,3,7,11,10,5,1,10,3,5\nL,6,9,8,7,6,6,6,8,6,6,6,9,6,7,5,11\nX,4,10,7,8,4,9,7,0,8,9,5,7,2,8,3,8\nV,2,3,4,4,1,9,8,4,2,6,13,8,3,10,0,8\nM,5,11,8,8,12,9,5,2,1,8,4,8,11,7,3,6\nJ,2,3,2,5,1,13,3,8,4,13,4,12,1,6,0,8\nB,3,7,5,5,5,7,8,6,4,7,5,6,2,8,5,7\nO,2,4,3,3,2,7,8,6,4,9,6,8,2,8,2,7\nY,6,9,6,6,4,5,9,1,7,8,9,5,2,11,3,4\nU,4,8,6,6,7,7,6,4,3,7,7,8,10,8,5,7\nU,6,9,6,7,4,3,8,5,7,11,10,9,3,9,2,6\nY,3,5,5,7,6,8,8,3,2,7,8,9,3,11,7,6\nK,4,9,6,6,7,5,6,4,5,7,5,9,7,7,8,10\nA,4,5,6,7,2,7,4,3,1,7,1,8,3,7,2,8\nE,2,3,4,2,2,7,7,2,7,11,6,9,2,8,4,8\nK,5,9,7,7,4,3,8,3,7,11,10,12,3,8,4,6\nD,3,6,4,4,3,7,9,6,5,10,6,4,3,8,3,6\nM,3,5,6,3,4,5,6,3,4,10,9,10,7,6,2,8\nC,2,4,3,3,1,6,8,7,8,7,8,13,1,8,4,10\nU,4,9,5,7,2,7,5,14,5,7,14,7,3,9,0,8\nX,6,8,8,6,6,7,6,3,5,7,6,11,4,6,10,8\nF,3,9,3,6,2,1,14,4,3,12,10,5,0,8,1,6\nV,4,4,5,3,2,4,12,3,3,10,11,7,2,10,0,8\nY,5,6,5,4,2,5,9,2,8,10,10,5,2,10,4,3\nK,6,10,9,8,5,3,7,3,7,10,11,12,4,7,4,7\nM,5,5,6,8,4,8,7,12,2,6,9,8,8,6,0,8\nV,4,9,6,7,8,7,5,5,2,8,7,8,8,8,5,8\nV,4,11,6,8,4,8,9,4,1,6,12,8,4,9,2,7\nW,3,3,4,1,2,4,10,3,2,9,9,7,5,10,1,6\nN,4,10,4,7,5,7,7,12,1,6,6,8,5,8,0,8\nV,6,11,9,8,11,8,5,5,3,8,7,8,6,9,5,9\nL,4,6,6,4,4,6,6,7,6,6,6,9,2,8,5,10\nG,2,1,2,2,1,7,7,6,5,6,6,10,2,9,4,9\nU,5,11,7,8,5,6,9,5,7,6,9,9,3,9,1,8\nX,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8\nU,4,9,4,7,2,7,4,15,6,7,13,8,3,9,0,8\nX,8,12,9,6,5,6,9,2,8,11,8,8,4,9,4,5\nE,5,10,8,8,9,7,8,3,6,6,7,11,4,11,8,7\nJ,3,6,5,4,2,10,5,3,6,14,3,9,0,7,0,8\nA,2,2,4,3,1,6,2,2,2,5,2,8,2,6,2,6\nM,4,2,5,4,4,8,6,6,4,6,7,8,8,6,2,7\nR,5,11,7,8,5,11,7,3,7,11,1,6,6,7,4,10\nG,2,3,3,2,1,7,6,5,5,7,6,9,2,9,4,9\nA,2,5,4,4,2,11,3,3,2,10,2,9,2,6,2,8\nH,5,7,7,5,5,8,7,3,6,10,6,9,3,8,3,8\nH,4,8,6,10,7,8,4,4,2,7,4,6,5,7,8,8\nL,3,8,5,6,4,4,4,4,7,2,1,6,1,6,1,6\nB,5,9,7,7,7,8,7,5,5,9,6,6,3,9,6,9\nK,7,9,10,7,6,5,8,2,7,10,8,10,3,8,4,7\nZ,7,12,7,6,4,10,3,4,7,12,4,10,3,8,6,10\nB,6,11,8,8,11,8,6,5,4,7,7,7,9,11,11,10\nS,5,6,6,5,6,9,8,4,5,7,6,8,5,10,8,11\nQ,2,2,3,2,2,8,7,6,3,6,6,10,2,9,3,10\nF,4,7,6,5,3,5,11,4,5,13,7,4,2,10,2,7\nX,4,8,6,6,4,7,8,3,9,5,6,7,4,9,7,7\nI,2,7,3,5,2,7,7,0,6,13,6,8,0,8,1,8\nB,4,8,6,6,8,8,7,5,3,7,7,7,6,10,8,9\nH,7,11,9,8,10,8,8,6,7,7,6,6,6,8,4,7\nE,2,5,3,4,3,7,7,5,7,7,6,9,2,8,5,10\nF,3,7,4,5,3,6,10,3,5,10,9,6,2,10,3,6\nT,5,7,5,5,3,6,11,3,7,11,9,4,2,12,2,4\nO,3,8,4,6,3,7,8,7,5,9,8,8,3,8,3,8\nT,3,8,4,6,2,9,13,0,6,6,10,8,0,8,0,8\nM,6,9,9,7,7,8,7,2,4,9,7,8,8,7,2,8\nN,2,3,4,2,1,7,8,3,4,10,6,6,5,8,0,7\nE,2,3,4,2,2,7,7,2,7,11,7,9,2,8,4,8\nF,2,1,2,2,1,5,10,3,5,10,9,5,1,10,2,7\nB,3,7,3,5,3,6,8,9,7,7,5,7,2,8,8,9\nE,3,6,3,4,3,3,8,5,9,7,6,14,0,8,6,9\nR,5,7,7,5,4,9,8,4,6,9,4,7,3,7,5,11\nO,3,6,4,4,2,7,8,8,7,6,7,8,3,8,4,8\nY,4,5,5,4,2,4,11,3,6,12,11,5,1,11,2,5\nA,4,9,6,7,4,8,2,1,2,6,2,8,2,7,3,6\nN,2,4,4,2,2,8,8,2,4,10,4,6,5,10,1,7\nU,3,3,3,1,1,5,8,5,7,10,9,8,3,10,2,6\nN,7,10,10,8,6,9,9,2,5,10,4,5,7,9,2,8\nH,6,7,9,5,4,9,6,3,6,10,3,7,6,8,5,9\nW,2,4,4,3,2,7,11,2,2,7,9,8,6,11,0,8\nJ,2,6,3,4,3,9,9,4,3,8,4,6,4,8,5,4\nM,4,4,6,3,3,7,6,3,4,10,8,9,9,5,3,9\nI,1,4,2,2,0,7,8,0,7,13,6,8,0,8,0,8\nT,2,3,3,2,1,6,11,2,7,11,9,5,1,10,2,5\nY,2,7,4,5,2,7,10,1,6,6,11,8,1,11,1,8\nQ,4,6,5,8,5,7,9,5,2,6,9,11,3,9,6,7\nO,2,1,3,2,2,7,7,7,5,7,6,8,2,8,3,8\nM,7,8,10,7,11,8,8,5,4,7,6,7,11,10,6,3\nJ,5,11,4,8,4,6,11,2,3,12,6,5,2,9,8,8\nH,4,10,6,7,6,6,7,5,5,7,5,8,6,6,6,11\nA,2,3,4,2,1,10,2,3,2,10,2,10,2,6,2,8\nC,5,9,6,6,4,6,7,6,9,5,6,12,1,7,4,9\nE,1,0,2,1,1,5,7,5,7,7,6,12,0,8,7,9\nO,3,5,4,3,2,8,6,6,4,9,5,8,2,8,3,8\nX,4,7,7,5,3,7,8,1,8,10,8,8,3,8,3,7\nJ,1,3,2,1,1,11,6,2,5,11,4,8,0,7,1,8\nV,7,9,10,8,11,7,6,5,5,7,6,8,7,10,8,8\nG,4,9,5,7,4,5,7,5,4,9,8,9,2,8,5,9\nP,2,7,3,5,2,4,12,8,2,10,6,4,1,10,3,8\nT,8,13,7,7,3,6,9,3,8,13,6,6,2,8,5,4\nU,2,1,3,2,2,7,9,5,6,7,9,9,3,9,1,8\nT,3,11,5,8,2,7,14,0,6,7,11,8,0,8,0,8\nT,7,10,6,5,2,5,10,3,8,13,7,5,2,8,4,4\nX,6,10,9,8,7,8,8,3,6,7,7,8,7,13,9,9\nK,4,10,6,7,6,5,7,5,7,6,6,12,3,8,6,9\nG,2,3,3,2,2,6,7,5,5,9,7,10,2,9,4,9\nV,3,7,5,5,1,7,8,4,2,7,14,8,3,10,0,8\nE,5,9,7,7,8,7,6,3,6,7,7,11,4,9,8,7\nG,2,5,3,4,2,6,7,5,5,9,7,10,2,9,4,9\nH,4,4,5,5,2,7,7,15,0,7,6,8,3,8,0,8\nN,5,6,7,4,4,11,7,3,5,10,1,4,5,9,1,7\nN,4,7,6,5,5,6,7,3,3,8,8,8,6,9,5,4\nO,5,10,6,7,4,7,7,8,5,10,6,9,3,9,4,6\nK,2,4,4,3,2,5,8,2,7,10,8,10,3,8,3,6\nZ,6,9,8,7,5,7,7,2,9,12,6,8,1,8,6,7\nL,1,3,2,1,1,6,4,1,8,8,3,10,0,7,2,8\nG,2,3,3,2,2,6,6,5,6,7,5,10,2,9,4,9\nC,6,12,5,6,4,7,9,4,5,8,7,9,4,8,8,10\nR,4,5,5,3,3,7,7,5,6,7,5,6,5,7,4,8\nQ,4,6,5,8,5,8,9,5,2,6,8,11,3,9,6,8\nP,8,12,7,6,4,9,8,4,6,13,4,4,3,10,6,7\nG,3,9,4,6,2,7,7,8,7,5,6,11,2,7,5,10\nB,4,9,4,6,3,6,7,9,7,7,6,7,2,8,9,10\nI,4,11,6,8,4,7,9,0,8,13,6,6,1,9,2,7\nA,4,7,6,5,3,9,3,2,2,8,1,8,2,6,3,7\nF,1,3,2,2,1,5,11,4,4,11,8,4,1,9,3,6\nA,3,5,6,4,2,7,1,2,2,6,2,7,4,6,4,7\nG,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10\nB,3,3,4,4,3,6,8,8,7,7,5,7,2,8,8,9\nA,2,3,3,1,1,7,2,2,1,6,2,7,1,6,1,7\nB,3,5,3,4,3,7,7,5,5,6,6,6,2,8,6,10\nQ,4,8,6,7,3,8,6,8,7,6,5,8,3,8,4,8\nR,6,8,9,7,9,7,8,3,4,7,5,8,7,7,6,7\nX,9,13,8,8,4,6,7,3,9,9,9,9,4,8,4,6\nC,4,9,6,7,6,5,7,4,4,7,6,9,6,9,5,7\nW,7,8,7,6,5,4,11,3,3,9,9,7,7,11,2,6\nW,6,9,8,7,4,5,7,5,2,7,8,8,9,9,0,8\nY,4,6,4,4,2,3,10,3,6,11,11,6,2,11,2,5\nG,3,2,5,3,3,7,6,6,6,6,6,10,2,9,4,9\nO,2,5,3,3,2,7,7,7,4,9,6,8,2,8,3,8\nI,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nE,2,3,2,1,2,7,7,5,6,7,6,8,2,8,5,10\nF,4,8,7,6,7,8,8,1,4,9,7,7,6,11,4,6\nY,3,10,5,7,1,7,12,1,4,7,12,8,0,10,0,8\nQ,5,8,5,9,6,7,10,4,3,6,9,11,3,9,6,8\nO,5,9,7,8,6,6,6,5,5,7,4,7,4,8,6,7\nK,4,5,7,4,4,8,7,1,7,10,5,8,3,8,4,8\nQ,2,6,3,5,2,8,8,8,5,6,7,8,3,7,5,9\nL,4,8,5,7,5,8,5,4,4,6,7,7,3,8,8,11\nT,5,9,7,6,8,6,7,3,6,7,6,10,5,9,5,7\nW,4,7,6,5,4,5,11,2,3,8,9,9,7,11,1,8\nD,7,13,7,7,4,9,5,4,6,10,3,7,5,6,5,11\nZ,5,7,6,5,5,9,11,5,4,6,5,7,3,8,8,5\nR,3,4,5,3,3,8,7,3,5,10,4,7,2,7,4,10\nY,4,8,6,6,3,5,10,2,8,10,12,9,1,11,2,7\nI,8,14,5,8,3,10,4,4,7,11,2,7,3,7,5,11\nZ,5,9,7,7,5,8,6,2,9,12,5,8,1,7,6,8\nN,5,7,7,5,4,10,8,3,5,10,2,4,5,9,1,7\nV,5,7,5,5,3,3,11,2,3,9,11,8,3,12,1,7\nL,4,11,6,8,4,7,4,2,7,8,2,9,2,5,3,7\nQ,5,10,6,8,6,8,4,8,4,7,5,9,4,9,4,7\nY,3,4,5,3,2,9,11,1,7,5,11,7,1,11,2,8\nL,4,8,6,6,3,5,4,1,10,7,2,11,0,7,3,7\nB,9,15,6,8,4,9,5,6,6,10,3,9,6,6,7,11\nU,3,3,4,4,2,6,9,5,7,7,9,9,3,9,1,8\nR,5,7,7,5,5,9,7,3,5,10,3,7,3,6,4,10\nP,3,6,4,4,2,9,9,3,5,13,4,3,1,10,3,9\nO,4,6,5,4,3,8,7,8,4,7,6,5,3,8,3,7\nT,7,12,6,7,4,6,10,2,6,12,7,6,2,9,4,4\nZ,5,9,6,7,4,8,6,6,11,7,5,6,1,7,8,8\nP,2,4,3,2,1,5,11,4,4,10,8,3,1,9,3,6\nN,6,8,9,6,4,8,8,3,5,10,5,6,6,9,2,7\nT,3,10,4,8,3,9,13,0,5,6,10,8,0,8,0,8\nZ,5,9,5,4,3,5,8,2,8,11,9,9,3,9,5,5\nN,4,8,7,6,3,4,10,4,4,10,11,10,5,7,1,7\nZ,4,7,6,5,5,9,9,5,4,6,5,7,3,9,9,4\nG,3,7,4,5,3,6,7,7,6,8,7,11,2,8,4,9\nO,4,5,6,7,3,9,8,9,8,6,7,10,3,8,4,8\nQ,3,6,4,6,2,7,6,8,5,6,7,7,3,8,5,9\nA,1,1,2,1,1,7,4,2,1,7,1,8,2,6,1,7\nZ,4,9,4,7,2,7,7,4,14,9,6,8,0,8,8,8\nD,3,4,5,3,3,7,7,5,6,10,6,6,3,8,3,9\nA,2,4,4,3,1,8,2,2,2,7,2,8,2,6,2,7\nN,4,7,6,5,4,7,9,5,5,7,6,6,6,10,2,5\nW,4,10,6,8,7,7,7,6,2,7,8,7,10,7,5,9\nA,4,10,6,8,4,13,2,4,3,11,1,9,3,7,4,10\nN,8,11,12,8,6,7,9,2,5,9,6,6,6,9,1,8\nN,5,8,7,6,4,4,9,4,4,10,9,9,5,7,1,7\nH,4,8,5,6,7,8,8,5,3,7,6,6,7,9,8,8\nU,6,7,7,5,3,4,9,6,7,12,11,8,3,9,2,6\nY,7,7,6,10,4,7,9,2,2,7,10,5,4,10,6,6\nG,7,15,6,8,4,9,3,3,3,7,3,5,4,7,4,9\nQ,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8\nG,4,6,6,4,5,7,8,5,4,6,6,8,4,7,7,7\nH,2,3,3,2,2,7,7,5,6,7,6,8,3,8,3,8\nC,4,6,4,4,2,3,9,5,8,11,10,11,1,8,3,7\nE,1,0,1,0,0,5,7,5,7,7,6,11,0,8,6,10\nB,4,11,5,8,4,6,6,10,7,6,6,7,2,8,9,10\nS,4,7,5,5,2,8,7,4,8,11,6,7,2,8,5,8\nZ,5,11,7,9,5,8,6,6,11,7,5,6,2,7,8,8\nY,2,3,2,2,1,4,11,3,6,11,10,4,1,11,2,5\nG,4,9,6,7,7,9,6,5,2,7,6,10,8,8,4,11\nD,6,10,8,8,6,7,7,7,8,7,7,4,3,8,3,7\nB,5,9,8,8,9,7,8,5,4,7,6,8,7,9,8,6\nG,5,11,6,8,5,5,5,6,7,7,5,12,2,10,4,9\nO,4,10,5,8,3,7,6,9,8,6,4,7,3,8,4,8\nQ,5,7,6,9,6,9,10,6,3,4,8,12,3,10,8,12\nK,2,3,3,1,1,6,7,1,6,10,7,10,3,8,2,8\nY,4,6,4,4,2,3,11,5,5,12,12,6,2,11,1,6\nF,5,7,6,8,7,7,9,5,5,7,6,8,4,8,8,7\nS,4,6,5,4,2,6,8,4,8,11,5,7,2,6,5,8\nG,4,7,5,5,4,6,6,6,5,9,7,12,3,8,4,9\nL,2,1,3,2,1,4,4,4,8,2,1,6,0,7,1,6\nV,5,10,5,8,3,3,11,4,4,10,12,8,2,10,1,8\nD,4,7,5,5,4,7,8,8,4,8,6,4,3,8,3,7\nH,7,10,9,8,6,10,5,3,6,10,2,7,5,7,5,9\nP,4,8,6,6,4,5,13,6,2,12,6,2,0,10,3,7\nT,6,11,5,6,2,5,10,2,7,13,7,5,2,8,4,4\nP,2,7,3,4,2,4,12,8,2,10,6,4,1,10,3,8\nH,6,7,9,5,5,8,7,3,7,10,4,7,5,6,4,8\nE,6,9,8,7,7,10,6,2,7,11,4,8,4,6,6,11\nJ,2,9,3,6,1,11,3,10,3,12,7,13,1,6,0,8\nB,2,1,2,2,1,7,7,8,5,6,6,7,2,8,7,10\nY,4,11,6,8,1,6,10,2,2,7,13,8,1,11,0,8\nE,4,11,4,8,5,3,9,5,10,7,6,14,0,8,6,8\nE,4,6,5,4,4,10,6,2,6,11,4,9,2,8,4,12\nE,4,9,6,6,5,7,7,2,7,11,7,9,3,8,4,8\nX,1,0,1,0,0,7,7,4,4,7,6,8,2,8,3,8\nU,5,10,6,7,6,8,8,8,5,6,7,10,3,8,4,6\nE,3,4,3,3,3,7,8,6,8,7,5,9,2,8,6,9\nV,5,10,7,8,5,5,12,3,2,9,10,8,5,10,6,8\nG,3,7,4,5,2,8,7,7,7,6,6,10,2,7,5,11\nI,1,5,1,4,1,7,7,1,7,7,6,8,0,8,2,8\nU,6,8,7,6,5,4,8,5,7,9,7,9,4,8,3,4\nD,4,9,6,7,5,7,7,9,4,8,7,4,4,8,4,8\nY,4,5,6,7,1,7,12,2,3,7,12,8,1,10,0,8\nP,5,8,7,11,10,9,8,4,3,6,8,7,6,10,6,4\nT,3,9,4,7,4,6,11,4,6,8,11,7,2,12,1,7\nS,5,8,7,6,4,6,8,3,7,10,6,7,3,7,5,6\nY,6,8,6,6,3,4,10,2,8,11,11,5,1,10,3,4\nJ,2,7,2,5,1,11,3,9,3,13,7,13,1,6,0,8\nE,3,8,4,6,2,3,6,6,11,7,7,14,0,8,7,7\nT,6,14,6,8,4,9,7,3,7,12,5,6,2,8,6,6\nX,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nF,2,3,2,1,1,5,10,4,5,10,9,6,1,9,3,7\nK,4,8,4,6,2,4,7,8,2,7,7,11,3,8,3,11\nD,4,9,4,4,2,9,6,5,5,11,4,7,5,7,5,11\nA,4,9,7,7,4,12,3,2,2,9,2,9,2,6,2,8\nO,5,6,7,5,5,6,6,6,5,7,5,6,3,6,5,6\nH,4,7,6,5,4,11,6,4,6,10,2,6,3,8,3,9\nX,3,7,4,5,3,7,6,3,5,6,6,9,2,9,8,8\nB,4,10,4,8,4,6,6,9,6,6,6,7,2,8,9,11\nX,2,1,3,2,1,8,7,3,8,6,6,8,2,8,5,8\nP,2,4,3,3,1,5,11,4,4,10,8,3,0,10,3,6\nW,8,15,8,8,6,1,7,2,3,9,10,9,9,10,1,5\nU,4,4,5,6,2,7,3,14,6,7,13,8,3,9,0,8\nJ,2,6,3,4,3,10,7,3,3,9,4,7,2,8,7,8\nW,3,8,5,6,8,7,5,6,2,7,6,8,8,12,3,10\nP,9,14,7,8,4,7,9,6,4,13,4,4,5,10,4,8\nP,3,1,3,2,2,6,9,5,4,9,7,4,1,9,3,7\nQ,6,8,8,7,6,7,4,4,5,6,4,8,4,5,6,7\nD,5,9,6,6,5,9,7,4,5,10,5,6,3,8,3,8\nJ,4,8,6,6,2,8,5,5,5,14,8,13,1,6,1,7\nH,6,9,8,7,8,8,8,5,7,7,6,9,6,8,4,8\nS,3,4,3,3,2,8,8,6,5,7,6,7,2,8,9,8\nN,2,0,2,1,1,7,7,12,1,5,6,8,5,8,0,8\nZ,2,4,3,6,2,7,7,3,13,10,6,8,0,8,8,8\nD,4,6,6,4,5,8,7,5,6,9,4,5,3,8,3,8\nN,1,1,2,1,1,7,7,11,1,5,6,8,4,8,0,8\nJ,6,13,5,10,4,7,11,3,3,13,5,4,2,9,8,9\nX,3,9,5,6,4,8,8,3,8,6,6,6,3,9,6,7\nJ,2,5,3,7,1,12,3,9,4,13,5,12,1,6,0,8\nD,3,4,5,3,3,9,6,4,6,10,4,6,2,8,3,8\nE,3,4,3,6,2,3,7,6,10,7,6,14,0,8,7,8\nT,2,5,3,3,2,7,12,3,5,7,11,8,2,11,1,8\nS,3,2,3,3,2,8,8,7,5,7,5,7,2,8,9,8\nW,3,3,4,2,2,6,11,3,2,8,8,7,5,11,1,6\nM,2,1,3,2,2,6,6,6,4,7,7,10,6,5,2,9\nW,3,2,5,3,3,8,11,2,2,6,9,8,6,11,1,7\nD,5,8,7,6,6,8,8,6,6,9,5,4,5,8,4,9\nC,4,8,5,6,4,7,7,8,5,8,6,12,4,9,4,8\nE,2,3,4,2,2,6,9,2,8,11,7,8,2,8,4,7\nI,1,3,2,1,0,7,8,1,7,13,6,7,0,8,0,7\nE,4,8,5,6,6,6,7,5,7,7,6,10,3,8,5,9\nR,2,4,4,2,2,8,7,3,5,10,4,7,2,7,3,10\nU,3,4,4,3,2,6,8,6,7,7,9,9,3,9,1,8\nA,3,5,6,6,2,6,5,3,1,6,0,8,2,7,2,7\nA,4,9,6,6,2,6,3,3,3,6,2,7,3,6,3,7\nJ,2,1,3,3,1,10,6,2,6,12,4,8,0,7,0,7\nO,5,9,7,7,5,7,5,9,5,8,5,11,4,8,4,8\nD,5,6,6,5,5,6,7,5,7,6,4,7,3,7,5,6\nK,3,5,4,3,3,5,7,4,7,7,6,10,6,8,4,9\nS,3,9,4,7,2,8,7,6,9,4,6,7,0,8,9,8\nZ,4,9,4,7,4,7,8,3,12,9,6,8,0,8,7,7\nU,9,11,10,8,7,4,8,5,9,9,7,9,8,10,6,1\nD,5,8,6,6,4,10,6,3,8,11,4,6,3,8,3,9\nZ,2,5,4,4,2,7,7,2,9,11,6,8,1,8,5,8\nK,5,10,7,7,9,8,8,3,4,6,6,9,10,10,7,7\nX,2,2,4,3,2,8,7,3,9,6,6,8,2,8,6,8\nO,6,11,7,8,7,7,8,8,4,9,8,8,3,8,3,8\nS,3,2,3,3,2,8,7,6,5,7,6,8,2,8,9,8\nF,2,3,2,1,1,5,10,4,4,10,9,5,1,9,3,6\nL,1,3,3,2,1,7,4,1,7,8,2,10,0,7,2,8\nM,4,6,5,4,4,8,6,6,5,7,7,8,7,5,2,8\nV,6,9,8,8,9,8,7,5,5,7,6,8,8,8,10,3\nX,3,7,4,5,2,7,7,4,4,7,6,8,3,8,4,8\nT,2,1,3,2,1,6,12,3,6,8,11,7,2,11,1,7\nH,5,8,7,6,4,7,8,3,6,10,7,8,3,8,3,7\nW,5,10,7,8,7,10,11,2,2,5,8,8,8,13,2,7\nQ,6,10,8,7,7,8,3,8,4,6,6,9,3,8,4,9\nI,2,6,3,4,1,7,7,0,8,14,6,8,0,8,1,8\nG,4,9,5,7,3,8,8,8,8,5,7,9,2,7,6,11\nP,4,7,5,5,3,6,11,8,2,10,5,3,1,10,4,7\nU,3,8,5,6,3,4,9,7,7,8,10,10,3,9,1,8\nP,4,10,5,8,5,7,9,6,6,9,7,3,2,10,4,7\nB,1,3,3,2,2,8,7,3,5,9,6,7,2,8,4,8\nU,3,3,4,1,1,5,8,4,6,10,9,9,3,10,1,6\nI,2,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nF,5,9,7,7,4,6,10,2,6,13,7,5,1,10,2,7\nY,3,4,5,3,2,4,10,2,8,11,10,5,2,11,3,3\nF,3,5,4,4,3,4,11,3,6,11,9,6,1,10,3,6\nM,6,10,8,8,7,11,5,2,5,9,3,6,8,6,2,9\nJ,2,1,3,3,1,9,6,2,6,12,4,9,1,7,1,7\nK,6,9,8,6,8,6,9,5,4,8,5,8,4,7,8,10\nZ,4,9,4,6,3,6,8,6,10,7,7,10,1,9,8,8\nU,5,11,5,6,3,6,5,5,4,7,9,10,5,6,2,9\nN,3,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8\nV,4,7,6,6,6,7,8,6,4,7,6,8,6,9,7,7\nB,4,7,5,5,4,6,7,9,6,7,5,7,2,8,9,9\nJ,4,6,3,8,2,9,7,3,4,10,5,5,2,9,5,9\nF,5,11,6,8,5,4,10,4,6,11,10,6,2,10,3,5\nA,1,3,2,1,1,7,2,2,1,7,2,8,1,6,1,7\nZ,3,6,5,4,4,6,7,3,7,7,6,9,0,8,8,7\nF,4,8,5,6,5,7,9,6,3,8,6,9,4,11,8,11\nM,6,10,9,8,8,11,6,2,4,9,4,6,9,6,2,8\nB,4,7,5,5,4,8,6,7,7,6,7,5,2,9,8,9\nC,3,7,4,5,2,5,9,6,7,12,9,9,2,10,3,7\nB,7,10,9,8,8,8,7,5,6,10,6,6,3,9,7,10\nV,6,9,6,7,3,1,12,4,4,12,12,8,2,10,1,7\nG,5,7,7,5,6,7,7,5,2,7,6,11,5,8,8,8\nQ,5,6,5,8,5,8,9,5,2,8,9,10,3,9,6,8\nK,4,5,6,3,3,4,8,2,7,10,9,11,3,8,3,7\nK,5,8,7,6,5,3,9,2,6,10,11,11,3,8,3,6\nV,5,11,8,8,2,6,8,5,3,8,14,8,3,9,0,8\nK,6,8,8,6,6,8,6,1,6,10,4,9,5,7,5,11\nQ,6,10,8,7,7,8,5,8,5,7,6,5,5,7,8,8\nO,7,13,5,7,4,6,6,5,5,6,4,8,5,7,5,7\nD,3,3,4,2,2,7,7,6,7,7,6,5,2,8,3,7\nK,3,3,5,2,2,6,8,2,7,10,7,9,3,8,2,7\nL,2,3,2,4,1,0,2,5,6,0,0,7,0,8,0,8\nD,3,6,4,4,4,7,7,6,6,6,6,5,3,8,3,7\nV,3,10,5,8,2,7,8,4,3,7,14,8,3,9,0,8\nB,4,6,6,6,7,7,8,5,5,8,6,9,6,9,8,8\nV,4,8,6,6,1,7,8,4,3,7,14,8,3,9,0,8\nV,2,1,4,2,1,7,12,3,3,7,11,8,2,10,1,8\nT,10,15,9,8,5,6,10,3,7,12,7,6,3,9,6,5\nL,1,3,2,1,1,6,4,0,6,8,3,10,0,7,1,8\nS,6,9,7,6,4,8,7,4,8,11,6,8,2,9,5,8\nZ,4,8,6,6,3,7,8,2,10,12,6,8,1,9,6,8\nK,4,6,4,4,1,4,7,9,1,7,6,11,3,8,2,11\nS,5,12,5,6,3,8,6,4,5,13,6,8,2,9,3,7\nL,2,7,3,5,3,4,4,4,6,3,1,7,1,6,1,6\nG,4,6,6,4,5,7,9,6,3,6,6,10,4,7,7,7\nM,5,4,6,6,3,7,7,12,2,7,9,8,9,6,0,8\nA,6,13,5,7,3,8,1,2,2,9,4,12,3,5,4,6\nC,1,1,2,2,1,6,8,6,7,8,8,12,1,9,4,10\nE,3,6,5,4,3,10,6,2,7,11,4,9,2,8,5,12\nU,8,10,9,8,5,3,9,5,7,10,10,9,3,9,2,6\nP,3,1,4,3,2,5,10,3,5,9,8,4,3,10,3,7\nJ,2,8,2,6,1,14,3,5,4,13,3,9,0,7,0,8\nA,2,7,5,5,4,8,5,2,3,7,2,6,2,5,3,7\nG,5,11,6,8,5,5,6,6,6,6,6,11,2,9,4,8\nT,3,3,4,4,2,7,12,3,6,7,11,8,2,11,1,8\nK,5,9,6,7,4,6,7,4,8,7,6,9,3,8,5,9\nB,1,3,3,1,2,8,7,2,5,10,5,7,1,8,4,9\nJ,2,10,3,8,1,11,3,10,3,13,7,13,1,6,0,8\nX,4,8,6,7,6,9,8,2,5,7,5,6,3,8,7,8\nD,3,8,4,6,4,8,8,6,5,9,6,4,3,8,3,6\nZ,2,2,3,3,2,7,7,5,9,6,6,8,1,8,7,8\nS,6,9,7,6,4,6,7,4,7,11,9,9,3,9,5,4\nU,5,10,6,8,3,7,4,15,6,7,14,8,3,9,0,8\nO,4,7,5,5,2,7,8,8,8,7,7,7,3,8,4,8\nK,4,5,7,4,4,6,7,1,7,10,6,9,3,8,3,7\nX,4,10,5,7,2,7,7,5,4,7,6,8,3,8,4,8\nB,3,5,5,3,3,8,7,2,6,10,5,7,2,8,4,10\nU,5,9,7,6,4,6,8,6,7,7,10,10,3,9,1,8\nB,4,9,6,6,8,9,6,4,4,7,7,8,8,8,8,7\nP,2,3,3,2,2,5,10,3,4,10,8,4,1,9,3,7\nB,7,15,7,8,8,6,8,3,6,9,6,7,8,5,8,6\nS,5,9,6,7,3,7,5,6,10,5,6,10,0,9,9,8\nO,7,12,5,7,3,8,7,6,5,9,4,7,5,9,5,8\nW,3,3,4,2,2,9,11,3,2,5,9,8,6,11,0,8\nW,4,3,6,5,3,5,8,5,1,7,8,8,8,9,0,8\nM,8,10,12,7,9,10,6,2,5,9,4,6,13,8,3,9\nH,5,8,7,6,6,8,8,7,7,7,6,7,3,8,4,7\nV,4,11,6,8,4,5,11,2,3,8,11,9,2,10,1,8\nP,7,11,8,8,7,6,9,8,6,8,7,9,3,10,8,9\nT,7,9,6,4,2,6,9,3,8,13,6,7,2,8,5,5\nS,3,7,5,5,7,8,9,4,4,7,6,7,2,7,8,1\nO,5,10,5,8,4,8,6,8,5,10,4,8,3,8,3,8\nV,2,6,4,4,1,8,8,4,2,6,13,8,3,10,0,8\nO,4,7,5,5,3,8,6,7,4,10,5,9,3,8,3,7\nK,4,3,4,5,1,4,8,9,1,7,6,11,3,8,2,11\nZ,2,3,2,1,1,7,7,5,8,6,6,8,1,8,6,8\nR,4,5,7,4,6,5,9,4,3,6,4,9,8,5,7,9\nG,1,0,2,0,0,8,6,5,5,6,6,9,1,7,5,10\nF,3,3,3,4,1,1,14,5,3,12,9,5,0,8,2,6\nM,2,1,2,2,1,7,6,10,0,7,8,8,6,6,0,8\nK,5,9,6,7,6,5,6,5,7,6,6,13,5,7,8,9\nT,3,6,4,4,3,6,11,3,5,11,8,5,3,12,2,5\nS,5,5,6,8,3,7,6,6,9,5,6,10,0,9,9,8\nH,2,4,4,3,3,7,7,3,5,10,6,8,3,8,3,8\nF,3,3,3,4,1,1,11,5,7,11,11,9,0,8,2,6\nY,2,3,3,1,1,4,10,2,7,11,10,5,0,10,2,4\nT,6,9,5,4,3,7,9,2,6,12,7,6,2,9,4,6\nN,3,9,4,6,2,7,7,14,2,5,6,8,5,8,0,8\nG,5,11,7,8,5,5,6,6,7,6,5,9,4,9,4,7\nP,2,4,4,3,2,8,8,2,5,13,4,4,1,10,2,9\nP,8,9,6,5,3,7,10,6,5,14,5,4,4,10,4,7\nB,3,8,5,6,4,8,7,7,6,7,6,6,2,8,8,10\nW,5,11,8,8,7,10,8,5,1,6,10,7,9,13,1,5\nZ,3,7,5,5,4,7,7,2,7,7,6,8,0,9,9,7\nS,6,8,7,6,4,7,8,4,8,11,8,7,2,9,5,6\nF,6,10,9,8,4,4,14,3,4,13,7,3,1,10,2,6\nJ,2,6,2,4,1,9,7,0,8,11,5,6,0,7,1,7\nO,7,11,9,8,11,8,9,6,2,7,7,8,9,9,6,12\nQ,7,8,7,9,6,8,7,6,3,8,8,11,3,8,6,8\nY,7,9,7,6,5,5,9,1,8,10,10,6,3,11,5,3\nK,6,8,8,6,4,7,8,1,7,10,5,8,3,8,3,8\nZ,2,7,3,5,1,7,7,3,13,9,6,8,0,8,8,8\nG,5,11,6,8,3,8,7,8,8,6,6,9,2,7,5,10\nB,11,14,8,8,5,9,6,5,6,11,4,9,6,6,7,10\nM,5,10,6,8,6,7,6,6,5,7,7,9,8,5,2,9\nH,3,9,4,6,4,7,7,12,1,7,6,8,3,8,0,8\nA,1,3,2,1,1,11,3,3,2,10,2,9,1,6,1,8\nA,5,8,6,6,6,8,9,7,4,6,6,8,3,7,7,5\nW,7,9,9,8,11,9,8,6,5,7,5,8,11,10,9,4\nL,2,5,3,4,2,4,4,4,7,2,1,6,0,7,1,6\nT,5,10,6,8,6,6,6,7,7,7,6,8,4,11,7,7\nP,3,4,5,3,2,6,11,5,2,12,5,2,1,10,3,9\nJ,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8\nY,8,11,8,8,4,4,9,1,9,10,10,5,2,9,4,3\nN,4,7,6,5,4,4,9,3,3,9,9,9,6,7,1,7\nE,3,5,5,3,2,7,7,2,8,11,5,9,2,8,4,9\nW,5,9,5,4,4,9,8,3,3,6,8,6,9,10,2,6\nZ,2,4,4,3,2,8,7,2,9,12,5,9,1,8,5,9\nV,6,9,6,7,3,2,11,3,3,10,11,8,2,10,1,8\nW,4,8,6,6,9,9,7,5,1,7,6,8,10,11,2,7\nU,8,10,8,8,7,5,7,5,8,8,6,9,8,8,6,1\nL,6,10,5,5,3,9,2,3,3,11,8,11,3,10,4,10\nA,3,11,6,8,4,12,3,3,3,10,1,9,2,6,3,8\nI,2,10,3,8,4,7,7,0,7,7,6,8,0,8,3,8\nV,5,11,8,8,9,8,8,4,2,7,8,8,8,10,6,8\nO,6,10,8,8,10,8,8,6,2,7,7,8,10,9,5,8\nW,5,10,7,8,9,8,8,5,3,7,9,8,6,8,3,7\nM,3,3,5,1,2,5,7,4,4,10,10,10,6,6,2,8\nB,4,10,5,7,7,8,8,6,6,7,6,6,6,8,6,10\nN,7,12,8,6,4,12,4,5,3,13,0,7,5,7,0,7\nP,4,8,5,6,4,5,11,4,6,11,9,4,4,10,4,7\nS,3,7,4,5,3,8,8,5,7,5,5,5,0,7,8,8\nF,4,9,6,7,4,2,12,3,6,12,11,6,1,10,2,5\nQ,4,6,6,9,3,8,7,9,6,6,6,9,3,8,5,9\nA,2,4,3,3,1,8,2,2,1,7,2,8,2,7,2,7\nB,7,11,9,8,9,9,7,4,6,9,5,6,3,8,6,9\nE,5,9,6,7,7,7,7,4,8,7,6,8,6,8,6,10\nH,5,9,8,7,5,7,7,3,6,10,6,8,3,8,3,7\nW,3,3,3,2,2,5,10,3,2,9,8,7,5,11,1,6\nB,3,9,4,6,5,8,7,6,6,7,6,5,2,8,7,9\nV,7,8,6,6,4,3,11,1,3,9,10,8,4,9,1,8\nQ,5,7,6,9,6,7,10,5,2,5,8,12,3,10,6,8\nQ,5,6,6,8,5,8,7,6,3,8,8,11,3,9,6,7\nG,5,9,5,7,4,5,7,6,6,10,8,9,2,9,5,9\nB,3,6,4,4,4,8,7,3,4,7,6,7,3,8,5,8\nF,1,3,3,1,1,8,10,2,5,13,5,4,1,9,1,8\nE,2,6,3,4,3,6,7,6,8,6,5,10,2,8,5,9\nU,5,9,6,7,4,3,8,5,6,9,8,10,3,9,2,5\nA,4,8,6,6,6,8,9,7,5,6,6,8,3,7,7,4\nZ,4,8,5,6,2,7,7,4,14,9,6,8,0,8,8,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nF,4,11,4,8,2,1,14,5,3,12,10,5,0,8,3,6\nT,4,8,5,6,3,6,11,3,7,9,11,7,5,12,1,7\nN,4,4,4,6,2,7,7,14,2,4,6,8,6,8,0,8\nV,4,9,5,7,2,5,9,5,2,8,13,8,3,10,0,8\nN,3,4,5,3,2,7,9,3,4,10,6,6,5,9,0,7\nY,7,9,7,7,5,6,8,1,8,8,9,5,4,11,6,4\nN,4,8,5,6,2,7,7,14,2,4,6,8,6,8,0,8\nL,4,9,4,4,2,10,4,3,3,11,6,10,3,10,4,10\nS,4,11,5,8,5,8,8,8,6,7,4,6,2,7,8,8\nV,4,10,6,8,3,7,9,4,2,6,13,8,3,10,0,8\nK,4,5,4,8,2,3,7,8,2,7,5,11,4,8,2,11\nL,2,6,4,4,2,9,4,1,7,10,3,10,0,7,2,10\nB,5,10,5,8,4,6,8,10,7,7,5,7,2,8,9,10\nU,4,6,6,4,3,5,8,7,8,9,10,10,3,9,1,8\nO,4,9,6,7,8,8,8,5,1,7,7,8,8,9,4,9\nE,7,10,5,5,3,8,6,4,7,10,6,9,2,10,8,8\nJ,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8\nM,8,10,8,5,4,5,9,5,5,4,4,11,9,11,2,7\nG,3,9,4,7,2,7,6,8,8,6,5,10,2,8,6,11\nB,4,2,4,3,4,7,7,5,6,6,6,6,2,8,6,9\nS,3,7,4,5,3,8,8,5,7,5,6,8,0,8,8,8\nC,5,8,6,6,3,4,8,5,7,11,10,12,2,9,3,7\nK,4,4,4,6,2,3,7,8,2,7,6,11,4,8,2,11\nT,9,15,8,8,3,7,8,3,9,13,5,6,2,8,5,5\nA,2,4,4,3,2,8,2,2,2,7,2,8,2,6,2,7\nW,4,5,5,3,3,4,10,2,2,10,9,8,6,11,1,7\nY,6,7,6,5,3,2,11,4,5,12,12,7,2,11,2,6\nO,3,8,5,6,2,7,8,8,7,7,7,8,3,8,4,8\nO,4,9,5,6,2,8,8,9,7,6,7,9,3,8,4,8\nX,2,2,4,3,2,8,7,3,9,6,6,8,2,8,6,8\nH,2,2,3,3,2,8,7,6,6,7,6,7,3,8,3,7\nX,2,3,4,2,1,6,8,1,8,10,8,9,2,8,3,7\nX,2,3,3,1,1,8,7,2,8,10,5,7,2,8,3,8\nH,2,1,2,2,2,7,7,6,5,7,6,8,3,8,3,8\nB,6,11,8,8,12,8,7,5,4,7,7,7,8,10,10,10\nR,3,7,5,5,5,6,7,3,3,6,6,9,5,9,7,5\nV,2,2,4,3,1,7,12,2,3,6,11,9,2,11,1,8\nE,4,10,5,8,5,7,7,6,9,7,6,10,3,8,6,8\nY,3,5,4,8,6,9,10,3,3,6,8,9,3,12,6,6\nW,10,10,9,5,4,5,9,3,2,7,10,8,10,11,1,6\nW,9,12,9,6,5,4,7,2,4,7,10,8,10,10,2,5\nO,6,9,8,8,7,7,5,4,5,9,4,8,3,7,5,6\nD,4,4,5,6,3,5,7,10,9,7,6,5,3,8,4,8\nN,2,4,4,3,2,7,8,3,4,10,6,7,5,8,1,7\nW,4,3,4,2,2,4,10,2,2,9,9,7,5,11,1,6\nV,3,7,4,5,2,8,9,3,1,6,12,8,2,10,0,8\nO,5,10,6,8,5,8,6,9,4,7,5,8,3,8,3,8\nW,5,11,8,8,4,6,8,5,2,7,8,8,9,9,0,8\nS,4,5,6,5,6,8,7,5,5,7,6,7,5,8,9,10\nB,5,11,5,8,5,7,9,10,7,8,5,6,2,8,10,11\nX,6,11,9,8,4,4,9,3,9,11,12,10,3,9,4,5\nQ,4,5,5,7,4,9,9,6,3,4,8,11,3,9,6,10\nQ,3,6,4,5,2,8,5,8,7,7,4,8,3,8,4,8\nB,2,0,2,1,1,7,7,7,5,7,6,7,1,8,7,8\nH,9,13,8,7,5,5,8,5,4,9,10,10,7,11,5,9\nC,3,5,4,3,1,4,9,5,7,12,10,11,1,9,2,7\nJ,7,11,6,8,4,8,9,3,3,13,4,5,2,9,8,9\nE,2,1,2,2,1,4,7,5,8,7,6,13,0,8,7,9\nQ,5,8,6,8,3,8,7,8,6,6,6,8,3,8,5,9\nJ,5,6,4,9,3,10,5,2,6,9,5,7,3,8,4,11\nW,6,10,9,8,8,6,12,2,2,7,8,8,7,12,1,8\nU,3,7,4,5,1,8,5,13,5,7,13,8,3,9,0,8\nF,5,11,6,8,5,4,10,5,6,11,11,6,2,10,3,5\nG,3,7,4,5,2,7,6,8,9,8,4,12,1,9,5,10\nD,7,10,6,6,4,8,5,5,5,11,4,7,5,6,6,10\nN,5,10,7,7,8,7,8,3,5,7,6,7,7,8,8,2\nG,6,11,6,6,3,7,7,6,5,9,6,6,4,7,5,6\nA,3,8,4,6,3,6,4,2,0,6,2,8,2,6,1,7\nH,6,8,9,6,5,10,7,4,6,11,2,6,4,9,4,10\nO,2,3,3,2,2,8,7,6,4,9,5,8,2,8,2,8\nX,4,9,5,4,3,9,6,2,7,11,3,7,3,8,4,9\nP,6,11,6,8,3,4,14,8,1,11,6,3,1,10,4,8\nA,3,5,6,3,2,9,2,2,2,8,2,10,3,7,2,8\nB,4,9,4,7,6,7,9,8,6,7,5,7,2,7,7,10\nR,3,5,5,3,3,8,8,4,5,9,5,6,3,7,4,9\nC,5,11,7,8,8,6,7,4,4,6,7,10,7,8,7,7\nZ,5,9,7,7,5,9,11,6,5,6,5,8,3,8,10,7\nB,5,10,7,8,8,8,7,5,7,7,6,5,2,8,6,10\nM,6,4,6,7,4,7,7,13,2,7,9,8,9,6,0,8\nG,3,5,4,4,2,6,6,5,5,9,7,10,2,8,4,10\nX,2,2,4,4,2,7,7,3,9,6,6,8,2,8,6,8\nM,6,11,9,8,12,10,5,3,2,9,4,8,11,8,4,6\nZ,1,0,2,1,0,7,7,3,11,8,6,8,0,8,6,8\nR,10,14,8,8,5,8,8,6,5,9,4,9,7,5,6,11\nM,7,11,9,8,10,7,10,7,5,7,7,9,8,10,7,12\nH,6,10,8,8,11,8,9,5,3,6,7,7,8,7,11,9\nW,7,8,7,6,5,2,12,2,2,10,10,8,6,11,1,7\nN,4,6,6,4,4,8,8,6,6,6,5,4,6,10,3,5\nV,3,4,4,3,1,5,12,3,3,9,11,7,2,10,1,8\nJ,1,2,2,3,1,10,6,2,6,12,4,8,1,6,1,7\nT,4,11,6,9,6,9,11,3,6,6,11,8,2,12,1,8\nN,6,9,8,7,5,6,10,2,4,9,8,8,5,8,1,8\nB,4,6,5,4,5,8,7,4,5,7,5,7,3,8,6,8\nV,3,9,5,7,1,10,8,4,3,5,14,8,3,10,0,8\nC,3,8,4,6,1,6,7,7,9,8,6,13,1,9,4,9\nA,2,1,3,2,1,9,3,2,2,8,2,8,2,6,2,8\nF,10,15,9,8,6,9,7,2,7,10,6,8,4,7,7,8\nO,5,8,6,6,4,7,6,8,5,7,5,7,3,9,3,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nL,2,6,3,4,2,4,5,1,9,3,2,8,0,7,2,5\nE,4,6,6,4,3,7,8,1,9,11,6,8,2,8,4,8\nA,4,9,6,6,2,7,4,3,1,6,1,8,3,7,2,7\nO,6,8,8,6,9,7,6,5,2,7,5,7,11,9,7,11\nZ,7,13,7,7,4,8,5,2,8,11,5,9,3,9,5,9\nE,4,11,4,8,3,3,8,6,10,7,6,15,0,8,7,7\nG,5,10,6,8,4,7,6,7,7,11,6,12,3,10,5,8\nY,3,8,5,5,1,8,12,2,3,6,12,8,1,10,0,8\nM,6,6,8,5,8,8,9,4,5,6,6,6,9,6,6,4\nJ,5,9,4,12,4,7,8,3,3,13,5,5,3,8,7,10\nG,1,3,2,1,1,7,7,5,4,9,7,9,2,8,3,10\nE,4,9,6,6,4,5,8,4,8,11,9,9,2,9,5,6\nO,5,7,6,6,5,8,4,4,4,9,3,8,3,7,5,7\nS,7,10,8,7,4,8,8,5,10,11,2,8,2,5,5,10\nJ,2,2,4,4,2,10,6,2,6,12,3,8,1,6,1,7\nC,5,7,5,5,2,5,9,6,8,12,9,10,2,10,3,7\nD,4,6,6,4,4,9,7,4,6,10,4,5,3,8,3,8\nW,4,7,6,5,3,4,8,5,1,7,9,8,8,9,0,8\nG,7,11,7,8,6,6,6,6,6,10,6,14,5,8,6,7\nU,5,8,5,6,3,3,10,5,6,13,12,9,3,9,1,7\nA,2,7,3,4,1,7,6,3,1,7,0,8,2,7,1,8\nY,5,11,7,8,4,7,10,2,7,6,12,8,2,11,2,8\nT,3,8,4,5,1,10,15,1,6,4,11,9,0,8,0,8\nS,4,9,5,6,4,9,8,5,9,6,6,5,0,7,8,8\nS,1,0,2,1,1,8,7,4,7,5,6,7,0,8,7,8\nP,4,7,6,10,9,8,9,4,0,8,7,6,5,11,5,7\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nH,4,9,4,7,4,7,9,13,2,7,4,8,3,8,0,8\nX,3,2,5,3,2,8,7,3,9,6,6,8,3,8,6,8\nK,5,7,8,5,5,7,6,1,7,10,6,10,4,7,4,9\nM,5,9,6,5,3,11,3,2,2,10,3,9,7,2,1,9\nM,2,1,3,1,2,8,6,6,4,7,7,8,6,5,1,7\nM,1,0,2,0,0,8,6,9,0,7,8,8,5,6,0,8\nN,5,10,7,7,5,6,9,6,5,7,7,8,6,9,2,6\nP,5,10,7,7,6,7,7,8,4,7,6,7,3,9,8,9\nW,3,3,4,2,2,5,10,3,2,9,9,7,5,11,1,7\nN,6,8,9,6,4,7,9,3,5,10,7,7,6,8,1,8\nJ,5,10,7,8,7,9,10,4,4,9,4,6,4,8,6,4\nY,6,11,9,8,5,9,10,0,8,4,11,7,1,10,2,9\nZ,3,9,4,7,3,7,8,3,12,8,6,8,0,8,7,7\nY,6,8,7,10,8,10,12,5,4,6,7,7,5,11,8,5\nX,7,10,8,6,4,5,9,3,7,12,9,8,4,9,3,6\nD,6,9,8,7,5,9,6,4,7,10,5,6,3,8,3,9\nL,4,11,4,8,3,0,2,4,6,1,0,8,0,8,0,8\nZ,3,8,4,6,4,8,6,2,7,8,6,8,0,9,8,8\nW,4,4,5,3,3,5,11,3,2,9,8,7,6,11,2,6\nI,0,3,1,2,0,7,7,1,6,13,6,8,0,8,0,7\nB,2,0,2,1,1,7,8,7,5,7,5,7,1,8,7,8\nD,4,5,6,4,4,5,7,6,7,7,6,8,4,7,5,5\nM,4,5,5,8,4,8,7,12,2,6,9,8,8,6,0,8\nL,3,8,4,6,3,5,5,2,8,6,2,10,0,7,3,7\nO,5,10,4,5,3,6,7,6,3,9,7,9,5,10,5,8\nL,4,11,6,8,4,6,5,1,9,7,2,10,0,7,3,8\nE,4,8,4,6,2,3,7,6,11,7,7,14,0,8,7,7\nT,5,11,5,8,3,7,10,2,9,11,9,4,1,11,3,5\nA,2,3,3,2,1,10,2,3,1,10,2,9,2,6,2,8\nV,7,12,6,6,3,8,10,5,4,7,10,5,5,12,3,7\nV,3,2,6,4,2,7,12,2,3,7,11,9,3,10,1,8\nN,3,4,5,3,2,7,9,3,5,10,7,7,5,8,1,8\nB,8,13,7,8,7,9,7,4,5,9,5,7,7,6,9,8\nP,4,5,5,4,3,5,10,4,5,10,8,3,1,10,4,6\nZ,2,4,4,3,1,7,8,2,9,11,6,8,1,8,5,7\nK,1,0,1,0,0,5,7,7,0,7,6,10,2,8,2,11\nX,4,10,7,8,5,12,6,2,7,10,1,6,2,7,3,10\nS,4,7,5,5,3,6,8,3,6,10,8,8,2,8,5,5\nO,4,9,5,7,4,7,7,8,5,7,6,8,3,8,3,8\nG,2,4,3,3,2,6,8,5,5,9,8,9,2,8,4,9\nA,3,9,5,7,4,9,3,1,2,7,2,8,2,6,4,7\nP,5,5,6,7,3,4,12,9,2,10,6,4,1,10,4,8\nO,9,13,6,7,4,6,5,6,4,10,6,10,6,8,5,7\nB,5,10,7,7,8,8,7,6,6,6,6,6,2,8,6,10\nR,6,9,6,4,4,6,8,3,5,7,4,10,5,8,6,7\nW,5,8,7,6,5,4,11,2,3,8,9,9,8,10,1,8\nL,3,9,5,7,2,7,3,2,8,7,2,9,1,6,2,8\nL,3,6,4,4,2,5,5,1,9,6,2,10,0,7,2,7\nC,3,6,4,5,4,5,6,3,5,7,6,11,4,10,6,9\nP,3,5,5,3,2,8,9,3,4,12,4,3,1,10,3,8\nD,3,3,3,5,2,5,7,10,7,7,6,5,3,8,3,8\nA,3,11,5,8,2,9,6,3,1,8,0,8,2,7,2,8\nN,3,6,5,4,3,5,9,6,4,8,7,9,5,9,1,7\nT,4,5,5,5,5,7,8,4,7,7,6,9,3,7,7,6\nY,3,5,5,8,7,10,10,4,2,4,8,9,4,13,6,8\nI,1,7,2,5,2,7,7,0,7,7,6,8,0,8,2,8\nV,5,10,5,8,3,3,11,3,4,10,12,8,2,10,1,8\nU,3,2,4,4,2,7,8,6,6,6,9,9,3,9,1,8\nC,4,8,4,6,2,5,7,6,7,11,9,14,1,9,3,8\nR,8,12,8,6,6,7,8,3,6,9,3,8,6,6,6,6\nC,3,5,4,4,1,3,9,5,7,12,10,11,1,9,2,7\nP,3,5,4,8,6,8,9,3,1,8,7,6,4,10,5,8\nE,4,8,4,6,4,3,8,5,9,7,6,14,0,8,6,8\nQ,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8\nV,2,1,4,3,1,7,12,2,3,7,11,9,2,10,1,8\nT,3,6,4,4,3,8,11,2,6,6,11,8,2,11,1,8\nE,4,6,6,4,6,7,8,3,5,6,7,11,3,10,7,8\nM,6,11,9,8,12,9,7,3,3,8,4,7,12,5,4,5\nV,8,12,6,6,3,8,11,4,5,5,10,6,4,10,3,5\nR,4,7,5,5,4,10,7,3,6,10,3,7,3,7,3,10\nJ,1,5,3,4,1,9,6,2,6,14,5,10,0,7,0,7\nZ,1,1,2,3,1,8,7,5,8,6,6,7,1,8,7,8\nB,6,12,6,7,5,9,7,3,5,9,5,7,6,7,8,9\nR,4,8,6,6,5,9,7,4,5,9,3,8,3,7,4,11\nV,2,1,4,3,1,5,12,3,3,9,11,8,2,10,1,8\nK,1,1,1,1,0,4,7,6,3,7,6,11,3,8,2,11\nA,3,3,5,5,2,7,5,3,1,6,1,8,2,7,2,7\nM,4,4,6,3,4,5,6,4,4,10,10,10,6,5,2,7\nY,2,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8\nX,7,10,9,8,8,8,6,3,5,6,7,7,4,10,11,8\nA,3,9,5,6,2,9,6,3,1,8,0,8,2,7,1,8\nR,4,7,4,5,4,6,10,7,3,7,4,9,2,7,5,11\nJ,3,7,4,5,2,10,7,1,6,13,3,7,0,8,0,8\nA,4,10,7,8,4,13,2,4,4,12,1,9,3,7,4,10\nF,1,0,1,0,0,3,12,4,2,11,9,6,0,8,2,7\nY,2,2,4,4,2,7,10,1,7,7,11,8,1,11,2,8\nX,4,3,5,5,1,7,7,5,4,7,6,8,3,8,4,8\nK,3,6,4,4,4,6,7,3,6,6,5,9,3,8,4,9\nD,3,7,4,5,4,7,7,7,7,7,7,5,3,8,3,7\nY,5,7,6,5,5,8,5,6,5,5,8,8,3,9,9,6\nF,3,4,4,6,2,1,12,5,6,11,11,9,0,8,2,6\nX,5,8,7,6,3,7,8,1,8,10,6,8,3,8,4,7\nL,5,9,7,7,5,8,4,1,8,9,2,10,1,6,3,9\nW,8,11,8,8,9,6,10,4,3,9,6,6,11,10,4,5\nA,5,8,9,6,6,7,5,2,4,6,1,6,5,7,5,6\nX,3,6,4,4,3,8,5,2,5,6,7,8,2,9,8,9\nW,6,9,8,7,4,10,8,5,2,6,8,8,9,9,0,8\nA,2,3,3,2,1,8,2,2,1,8,2,8,2,6,3,8\nI,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7\nU,4,9,6,7,8,8,6,4,4,6,7,8,8,9,5,6\nL,2,2,3,4,2,5,4,5,7,2,2,5,1,6,1,6\nX,4,6,7,4,3,5,9,3,8,11,12,9,3,9,4,5\nD,1,0,1,1,1,6,7,8,5,6,6,6,2,8,2,8\nT,3,4,3,3,2,5,11,2,7,11,9,5,1,11,2,5\nA,2,4,4,3,1,6,3,2,2,5,2,8,2,6,2,6\nD,5,11,7,9,11,9,8,5,5,7,6,6,5,7,12,6\nE,2,3,4,2,2,7,7,2,8,11,6,8,1,8,4,8\nW,4,5,5,3,3,3,10,2,2,10,10,8,6,11,1,7\nX,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nC,2,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10\nG,2,4,4,3,2,6,7,6,6,6,6,10,2,8,4,9\nH,6,8,8,6,6,10,6,3,6,10,3,7,6,7,5,11\nA,4,9,7,7,5,10,3,1,2,8,3,9,4,5,3,7\nJ,1,1,2,2,1,10,6,2,6,12,4,8,1,7,1,7\nF,6,10,8,7,4,6,11,2,6,14,6,3,1,10,2,7\nY,2,3,2,1,1,5,10,2,6,10,9,6,1,11,2,5\nU,6,9,8,7,5,4,9,6,7,7,10,11,3,9,1,8\nW,5,4,6,3,3,5,11,3,2,9,8,7,7,12,1,6\nZ,4,9,5,7,3,7,7,3,12,9,6,8,0,8,8,8\nE,5,11,7,8,5,7,7,2,9,11,6,9,3,8,5,8\nB,5,10,7,7,6,9,6,4,7,9,5,7,2,8,6,10\nV,6,9,8,8,10,7,6,5,5,7,6,8,7,10,8,9\nB,4,3,5,5,3,6,9,8,7,7,6,6,2,8,9,10\nV,7,13,7,7,4,9,8,4,4,8,8,5,6,13,3,7\nL,4,4,4,6,1,0,0,6,6,0,1,5,0,8,0,8\nK,4,9,4,6,2,3,7,7,2,7,6,11,3,8,3,10\nU,5,8,5,6,2,7,4,14,6,7,14,8,3,9,0,8\nP,1,3,3,1,1,7,10,3,3,12,5,4,1,9,2,8\nN,3,7,3,5,2,7,7,13,2,5,6,8,5,8,0,8\nD,4,8,5,6,5,7,7,5,7,7,6,5,6,8,3,7\nQ,3,7,4,6,2,10,9,8,6,4,8,10,3,8,5,9\nM,3,3,5,1,2,9,6,3,4,9,5,8,7,5,1,8\nY,3,4,5,6,1,7,10,1,3,7,12,8,1,11,0,8\nZ,4,9,6,7,7,8,7,2,8,7,6,8,0,8,9,8\nZ,1,3,3,2,1,7,8,2,9,11,6,8,1,8,5,7\nQ,6,12,5,7,3,7,6,4,9,10,5,9,3,7,9,8\nB,4,7,5,5,4,8,6,4,6,9,5,6,2,8,6,10\nL,3,7,5,6,4,6,8,4,6,7,6,9,2,8,8,9\nP,2,6,2,4,2,4,11,8,1,10,6,4,1,10,3,8\nH,3,3,6,2,3,8,7,3,6,10,5,8,3,8,3,8\nK,1,1,1,1,0,4,6,6,2,7,6,10,3,8,2,10\nA,9,13,7,7,4,8,2,3,2,8,4,12,5,4,5,6\nG,2,3,3,5,2,8,8,8,6,5,7,9,2,7,5,11\nI,3,9,5,6,2,7,9,0,8,14,6,5,0,9,2,7\nY,5,8,5,6,2,3,11,2,7,12,11,6,1,10,2,5\nV,2,8,4,6,1,8,8,4,2,7,13,8,3,10,0,8\nO,2,4,2,2,1,7,7,6,4,9,6,8,2,8,2,7\nJ,2,7,3,5,2,8,7,1,6,11,5,8,1,6,1,6\nA,3,7,5,4,2,7,5,3,0,6,1,8,2,7,2,7\nM,6,9,7,4,4,5,9,4,5,5,4,10,8,9,2,8\nW,6,10,8,8,9,7,8,6,3,7,8,8,6,8,4,8\nC,3,7,4,5,2,6,6,6,10,8,5,12,1,9,4,8\nF,3,7,3,5,2,1,12,4,5,12,11,7,0,8,1,6\nK,4,6,6,6,5,6,7,2,4,6,4,9,4,5,8,8\nC,4,5,5,5,5,5,8,3,4,7,6,11,4,10,7,9\nT,5,8,7,6,7,7,8,4,7,8,6,9,5,8,5,6\nP,6,8,8,6,4,9,8,4,7,12,4,5,4,8,5,8\nB,3,8,3,6,4,6,7,8,5,7,6,7,2,8,7,8\nU,2,1,3,2,1,7,8,6,6,7,9,9,3,10,0,9\nX,2,5,4,4,2,8,7,3,9,6,6,8,2,8,6,9\nD,4,9,4,4,2,13,3,3,4,12,1,8,3,7,1,10\nT,6,10,5,5,2,5,9,2,7,12,7,5,2,10,4,4\nK,4,8,4,6,3,3,6,6,3,7,7,11,3,8,3,11\nP,5,7,7,5,4,7,11,4,5,13,5,3,1,10,3,8\nP,2,4,4,3,2,7,9,4,3,12,5,3,1,10,2,8\nL,4,9,6,7,3,8,4,1,8,9,2,10,0,6,2,9\nB,2,4,4,3,3,8,7,3,5,10,6,6,2,8,5,8\nN,5,5,7,5,6,8,6,5,5,8,5,7,7,9,5,4\nF,2,7,3,4,1,1,12,5,4,11,10,7,0,8,3,7\nA,2,3,3,2,1,11,2,2,1,9,2,9,1,6,2,8\nE,7,15,5,8,3,7,8,5,8,10,6,10,1,9,7,8\nP,4,6,5,4,3,7,11,5,3,12,5,2,1,10,3,8\nJ,5,10,7,8,3,10,6,2,8,14,3,8,0,7,0,8\nP,5,11,6,8,6,6,6,6,4,8,6,9,5,9,7,10\nX,2,3,3,4,1,7,7,4,4,7,6,8,3,8,4,8\nS,6,8,8,6,8,8,6,4,3,9,5,8,5,9,11,10\nP,4,9,6,7,5,8,9,3,4,12,5,4,2,9,3,8\nF,5,11,7,8,5,4,11,5,4,13,9,5,2,10,2,5\nB,5,11,7,8,7,8,7,7,7,7,6,5,3,8,8,10\nZ,4,8,6,6,4,7,9,2,9,11,7,6,1,8,6,6\nP,3,5,3,4,2,6,9,5,4,9,7,4,1,10,3,7\nX,5,8,8,6,4,6,8,1,8,10,9,9,3,8,3,7\nE,5,10,5,7,3,3,7,6,11,7,6,14,0,8,8,7\nR,4,6,6,4,4,6,8,5,6,7,5,7,3,7,5,8\nE,5,11,7,9,9,7,7,3,5,6,7,10,5,9,8,8\nO,7,10,9,7,11,7,8,5,2,7,6,8,12,11,9,12\nX,6,10,9,8,5,8,7,0,8,10,5,8,3,8,3,8\nC,3,5,4,4,2,3,9,4,7,11,10,12,1,9,2,6\nK,4,7,4,5,3,3,8,7,2,7,6,11,3,8,2,11\nP,5,7,7,5,3,7,11,3,6,14,5,2,0,10,3,8\nN,6,9,9,6,5,7,9,2,5,10,6,6,6,9,1,7\nC,4,8,4,6,2,4,9,6,7,12,10,12,2,9,3,7\nB,6,11,8,8,8,8,7,7,7,7,6,6,3,8,8,11\nN,7,11,9,8,7,7,9,6,6,7,6,6,6,9,2,6\nX,3,7,5,5,4,8,6,3,5,6,7,8,2,9,8,9\nI,7,10,9,8,5,6,8,2,8,7,6,10,0,7,4,8\nN,4,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8\nV,5,11,7,8,9,7,9,4,2,8,8,8,5,9,7,6\nM,6,9,7,4,3,4,8,5,5,4,2,11,8,9,2,8\nK,4,7,6,5,4,7,6,1,6,9,5,9,3,8,3,8\nP,4,2,5,4,3,5,10,5,4,10,8,4,1,10,3,7\nG,3,7,4,5,3,7,6,6,6,6,5,9,2,9,6,11\nW,4,6,6,4,5,7,7,6,3,8,7,7,6,8,4,10\nO,6,10,6,7,6,8,7,8,4,8,5,6,5,8,5,9\nV,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8\nB,7,11,10,8,7,10,6,3,7,10,4,7,2,8,7,11\nA,2,3,4,4,1,9,5,3,1,8,1,8,2,7,2,8\nG,7,10,9,8,9,7,5,6,3,7,6,10,5,8,7,8\nU,5,4,5,6,2,7,4,14,5,7,14,8,3,9,0,8\nT,5,8,5,6,3,7,10,2,9,11,9,5,1,11,3,4\nP,4,8,4,5,2,3,14,7,1,11,6,3,0,10,4,8\nJ,3,8,4,6,1,11,3,11,3,12,9,14,1,6,0,8\nF,7,10,9,8,9,7,8,6,4,8,6,7,4,11,8,11\nE,3,8,3,6,3,3,7,5,8,7,6,13,0,8,6,9\nO,5,10,7,8,8,8,10,6,3,8,7,6,10,10,6,10\nK,4,5,6,4,3,4,8,2,7,10,10,11,3,8,3,6\nM,4,6,5,4,4,6,6,6,5,6,8,10,7,5,2,9\nF,4,7,5,5,4,5,9,5,7,10,10,6,2,9,3,5\nZ,6,10,8,8,5,8,6,2,9,12,4,11,3,7,8,9\nF,2,3,4,1,1,6,10,3,5,13,7,5,1,9,1,7\nG,3,7,4,5,3,8,7,7,5,6,7,9,2,7,5,11\nV,5,9,8,6,8,8,7,4,2,7,8,8,5,10,4,8\nH,2,3,3,2,2,9,7,6,6,7,6,6,3,9,2,7\nJ,6,9,9,7,6,8,5,3,6,8,6,8,4,7,4,7\nO,5,5,7,8,3,8,5,8,9,7,4,8,3,8,4,8\nD,8,12,8,6,5,7,5,4,7,8,4,6,5,7,6,9\nJ,1,2,2,3,1,10,6,1,7,12,3,8,0,7,1,8\nT,4,11,5,8,4,6,11,1,8,8,11,8,1,11,1,7\nA,1,0,2,1,0,7,4,2,0,7,2,8,2,7,1,8\nS,4,7,6,5,3,8,7,4,8,11,5,7,2,8,5,8\nL,5,10,5,7,2,0,1,5,6,0,0,7,0,8,0,8\nJ,4,8,5,6,3,8,4,6,3,14,8,14,1,6,1,6\nG,4,7,6,5,3,6,7,5,5,6,6,8,3,8,4,8\nP,3,3,4,4,2,4,13,8,1,11,6,3,1,10,4,8\nS,1,0,2,1,1,8,7,4,6,5,6,7,0,8,7,8\nD,2,5,4,4,3,9,6,4,6,10,4,6,2,8,3,8\nR,2,5,4,3,3,9,7,4,5,9,4,7,3,7,4,9\nT,2,4,3,2,2,7,12,3,6,7,11,8,2,11,1,8\nO,1,3,2,2,1,8,7,7,4,7,6,8,2,8,2,8\nQ,4,6,5,5,4,8,7,7,5,6,7,8,2,8,4,9\nL,3,7,4,5,1,0,0,6,6,0,1,5,0,8,0,8\nP,2,3,4,2,1,7,9,4,3,11,4,4,1,9,2,8\nW,6,10,6,5,4,2,9,2,3,10,11,9,8,10,1,6\nD,3,8,5,6,5,7,7,6,5,6,5,5,3,8,2,7\nP,3,4,4,3,2,5,10,4,4,10,8,4,1,10,4,7\nH,3,4,4,3,3,7,7,6,6,7,6,8,3,8,3,8\nR,3,5,5,4,3,9,7,4,5,9,4,6,3,7,4,10\nU,9,10,9,8,7,5,7,5,8,8,5,9,7,9,6,2\nT,5,8,6,7,6,6,8,4,8,7,7,8,3,10,7,7\nI,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nW,4,4,6,3,3,8,11,2,2,6,9,8,7,11,0,7\nL,4,7,5,5,3,7,4,1,8,8,2,10,0,7,2,8\nP,4,6,5,4,3,5,11,5,5,11,8,3,0,10,4,6\nN,3,3,5,2,2,7,9,3,4,10,6,6,5,8,1,7\nO,3,4,4,3,2,7,7,7,4,9,7,8,2,8,3,8\nA,4,9,6,8,6,7,8,2,6,7,8,9,5,11,3,7\nA,2,4,4,3,2,11,2,3,1,9,2,9,2,6,2,9\nG,4,10,6,7,4,7,7,7,6,5,7,8,2,7,4,8\nD,4,7,4,5,4,6,7,9,7,6,4,6,2,8,3,8\nZ,3,8,4,6,4,6,8,5,9,7,7,10,2,9,7,7\nF,4,5,6,6,5,7,9,4,5,8,6,7,4,9,8,7\nQ,8,13,7,7,5,9,5,4,7,11,5,8,4,8,10,11\nC,4,11,6,8,2,6,6,7,10,8,6,14,1,9,4,9\nH,3,4,4,3,3,8,7,6,6,7,6,7,3,8,3,6\nH,4,7,6,9,7,8,10,4,1,8,6,6,4,10,9,5\nL,3,8,5,6,3,7,4,0,7,8,3,10,0,7,2,8\nT,4,5,6,8,2,7,15,1,6,7,11,8,0,8,0,8\nD,3,6,5,4,3,10,6,4,7,11,3,6,3,8,3,9\nX,3,3,5,2,2,8,7,1,8,11,5,8,2,8,3,8\nY,2,5,4,4,2,6,10,1,7,8,11,9,1,11,2,7\nK,6,9,8,6,5,5,9,2,7,10,8,10,3,8,3,7\nP,5,8,6,10,8,9,9,2,3,6,9,6,9,11,7,5\nI,2,8,2,6,3,7,7,0,7,7,6,8,0,8,3,8\nY,3,5,5,4,2,7,10,1,7,7,11,8,1,11,2,8\nR,4,8,5,6,6,8,6,6,3,8,6,8,4,7,6,11\nW,7,8,9,7,10,6,9,6,7,7,7,8,8,9,8,10\nL,6,15,6,8,3,7,3,3,5,12,4,13,2,7,6,8\nJ,3,8,4,6,2,8,5,4,8,13,5,12,1,6,1,7\nD,5,10,7,7,5,9,7,5,7,10,4,5,3,8,3,8\nH,3,7,3,5,2,7,7,14,1,7,7,8,3,8,0,8\nP,6,7,8,9,9,6,8,4,3,9,8,6,6,11,6,6\nV,6,8,5,6,3,4,11,3,4,9,11,7,2,10,1,8\nB,5,11,6,8,8,8,7,4,6,7,6,6,6,8,6,10\nE,5,7,7,6,7,7,6,6,4,7,6,9,8,10,10,10\nY,1,0,2,0,0,8,10,3,1,6,12,8,1,11,0,8\nO,4,6,5,4,6,8,6,6,2,7,6,8,7,9,3,8\nR,1,0,2,0,1,6,10,7,2,7,5,8,2,7,4,9\nG,5,5,6,8,3,7,6,8,8,6,5,10,2,8,6,11\nT,2,10,4,7,1,6,14,0,6,8,11,8,0,8,0,8\nP,5,9,5,6,3,4,12,9,2,10,6,4,1,10,4,8\nK,4,6,6,4,3,3,9,3,6,10,11,11,3,8,3,6\nD,6,11,7,8,4,6,7,11,11,6,5,6,3,8,4,8\nI,1,6,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nC,3,2,4,4,2,6,8,7,8,9,8,13,1,9,4,10\nO,4,9,5,7,2,7,7,9,7,7,6,8,3,8,4,8\nF,4,7,6,5,3,5,12,3,6,14,7,4,1,10,1,7\nU,9,15,8,8,6,8,6,5,5,7,7,6,7,5,5,4\nH,5,9,5,4,3,5,9,3,6,9,8,9,5,7,3,7\nY,4,9,5,7,2,8,11,0,4,6,11,8,0,10,0,8\nK,2,3,3,2,2,5,7,4,7,7,6,11,3,8,4,10\nM,2,6,3,4,2,7,6,11,1,7,9,8,7,5,0,8\nY,3,5,4,4,2,4,11,2,7,11,10,6,1,11,2,5\nK,7,14,7,8,5,7,7,2,6,10,5,8,6,7,4,7\nX,7,13,8,7,4,7,8,2,8,11,6,8,4,8,4,7\nG,4,8,5,6,3,7,6,7,7,8,5,12,3,10,5,8\nY,7,10,7,7,3,2,12,5,5,12,12,6,2,11,2,6\nT,6,15,6,8,4,6,10,2,6,12,7,6,2,8,5,4\nF,4,5,4,8,2,1,14,5,4,12,10,6,0,8,2,6\nL,2,4,3,3,1,8,3,1,7,9,2,10,0,7,2,9\nK,11,14,10,8,4,6,8,3,8,9,7,8,5,8,4,7\nP,6,10,8,8,6,8,9,3,5,13,5,3,1,10,3,9\nK,6,8,8,6,7,8,7,1,6,9,4,8,4,8,4,8\nN,2,4,2,2,2,7,8,5,4,7,6,7,4,8,1,6\nQ,4,7,5,9,5,8,6,6,2,8,6,11,3,9,6,9\nB,3,6,5,4,4,10,5,3,5,10,4,7,2,8,4,10\nT,4,6,6,4,5,7,8,4,5,6,7,9,5,9,5,7\nT,5,11,7,8,6,6,7,7,7,6,10,10,5,5,9,6\nQ,5,8,6,8,5,8,7,8,6,5,5,8,3,10,5,10\nB,4,4,4,6,4,7,5,9,7,6,7,7,2,8,9,10\nI,7,14,6,8,4,10,7,2,5,11,5,6,2,9,5,12\nD,1,0,2,1,1,6,7,8,6,6,6,6,2,8,3,8\nU,3,3,3,4,2,7,4,14,5,7,12,8,3,9,0,8\nM,6,10,8,7,9,7,6,7,5,6,5,8,9,8,9,11\nJ,6,9,4,13,4,7,9,3,3,13,4,5,3,8,6,9\nQ,2,3,2,4,2,7,7,5,2,8,8,9,2,9,4,8\nT,5,8,7,7,6,5,8,4,9,8,7,9,3,9,7,6\nY,5,7,5,5,3,6,9,1,8,9,9,5,1,9,4,4\nD,3,4,5,3,2,10,6,3,7,10,3,6,2,8,3,9\nV,3,4,4,3,1,4,12,3,3,9,11,7,2,11,0,8\nN,10,13,9,7,4,9,11,5,5,3,6,11,6,10,3,6\nJ,1,1,1,1,0,13,3,6,4,12,4,11,0,7,0,8\nW,6,9,9,7,7,7,12,2,2,7,8,8,7,12,1,8\nJ,1,0,1,0,0,12,4,6,3,13,5,11,0,7,0,8\nB,2,3,4,2,2,8,7,3,5,10,6,6,2,8,4,9\nN,6,9,9,8,9,7,9,5,4,7,4,7,7,7,6,5\nH,2,6,3,4,2,7,6,12,2,7,8,8,3,9,0,8\nC,5,8,5,6,2,4,9,6,8,12,9,12,1,9,3,7\nU,4,8,5,6,3,7,5,13,4,7,12,8,3,9,0,8\nX,3,3,5,2,2,9,6,2,8,10,3,7,2,7,3,8\nU,3,3,4,2,1,7,8,6,8,8,10,7,3,9,1,8\nI,4,7,5,5,3,9,5,2,6,6,7,5,0,9,4,7\nN,6,9,8,7,5,6,9,6,5,7,7,7,8,8,3,7\nS,3,10,4,8,2,8,6,6,9,5,6,10,0,9,9,8\nP,3,9,5,6,4,6,9,3,6,10,9,4,4,10,4,7\nE,5,9,7,8,8,5,9,4,4,8,7,9,5,11,9,11\nQ,5,9,7,7,6,8,5,8,4,6,7,9,4,6,7,9\nK,7,10,10,8,7,5,6,1,7,9,8,11,3,8,3,8\nN,3,5,6,4,3,7,9,2,5,10,6,6,5,9,1,7\nK,5,10,7,8,7,6,6,3,7,6,6,8,7,8,5,9\nQ,3,4,4,5,3,8,8,7,3,5,7,10,3,9,5,10\nD,5,9,5,6,5,5,7,8,6,5,4,6,3,8,4,9\nL,3,4,3,7,1,0,1,5,6,0,0,7,0,8,0,8\nK,3,2,4,4,3,6,7,4,7,6,6,11,3,8,5,10\nN,3,8,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nR,2,1,2,2,2,6,8,4,5,6,5,7,2,7,4,9\nS,4,8,6,6,7,8,8,5,3,8,5,7,4,8,10,7\nH,6,10,8,7,5,9,6,3,6,10,4,8,4,7,4,8\nB,3,5,5,4,3,10,6,2,6,10,4,7,2,8,4,10\nL,2,4,3,3,1,6,4,1,8,8,2,10,0,7,2,8\nZ,2,5,3,4,2,7,7,5,9,6,6,8,2,8,7,8\nM,5,5,8,4,5,8,6,3,5,9,7,8,9,6,2,8\nO,7,9,9,8,8,8,4,4,5,10,4,10,5,7,7,6\nB,3,4,4,3,3,7,7,4,6,6,6,6,5,8,6,10\nI,5,7,6,5,3,7,6,2,7,7,6,8,0,9,4,8\nL,5,10,7,8,3,5,3,2,10,6,1,10,0,7,3,6\nQ,3,5,4,6,4,9,10,6,2,4,8,11,3,10,5,10\nP,6,11,9,8,5,6,12,5,4,12,5,2,1,10,3,9\nM,6,8,8,6,6,11,6,2,4,9,3,6,8,6,2,8\nR,2,5,4,4,3,7,8,5,5,8,6,7,3,7,5,10\nP,2,3,3,2,1,7,10,4,4,12,5,3,1,10,2,8\nW,4,8,5,6,4,5,8,4,1,7,9,8,6,11,0,8\nE,2,3,3,5,2,3,6,6,10,7,7,14,0,8,7,8\nT,6,10,6,7,4,4,13,4,6,12,10,4,2,12,2,4\nR,2,1,2,1,1,6,10,8,2,7,5,8,2,7,4,10\nD,6,9,8,6,7,9,7,5,6,9,4,6,3,8,3,8\nK,6,9,9,8,8,8,6,2,4,7,4,9,7,4,9,12\nD,3,7,3,5,3,5,7,8,6,6,5,7,2,8,3,8\nU,4,10,4,8,4,7,6,13,4,7,12,8,3,9,0,8\nQ,5,7,5,9,6,7,10,4,3,7,10,10,3,10,6,7\nX,5,6,6,6,6,7,8,2,5,7,6,8,3,7,7,8\nL,4,10,6,7,8,7,8,3,6,5,6,10,5,11,8,8\nI,5,9,7,6,4,7,6,2,7,7,6,9,0,9,4,8\nF,3,6,4,4,3,5,11,4,4,12,8,5,2,10,2,6\nW,4,8,6,6,8,10,9,5,2,6,7,7,8,9,4,5\nZ,2,1,3,2,2,7,7,5,9,6,6,8,2,8,7,8\nN,2,1,2,1,1,7,9,5,3,7,6,7,4,8,1,7\nE,7,10,5,5,2,7,7,4,7,10,6,11,1,9,7,9\nH,5,7,8,5,6,5,9,3,6,10,9,9,3,9,3,6\nM,5,7,7,5,6,7,6,6,5,7,7,11,10,6,2,9\nP,4,8,6,6,4,9,8,2,6,13,5,6,1,10,2,9\nE,3,3,4,5,2,3,8,6,11,7,5,14,0,8,7,7\nV,2,8,4,6,1,9,8,4,3,5,13,8,3,10,0,8\nW,4,6,6,4,3,9,8,4,1,7,8,8,8,9,0,8\nV,4,4,6,7,1,9,8,4,3,6,14,8,3,9,0,8\nY,7,9,7,6,3,3,12,5,5,13,11,5,1,11,2,6\nS,1,0,2,1,0,8,7,4,7,5,6,8,0,8,7,8\nU,2,0,3,1,1,7,5,11,5,7,14,8,3,10,0,8\nX,8,12,7,6,4,6,7,2,8,9,6,9,4,5,4,7\nO,5,10,6,7,5,7,7,8,4,9,6,8,3,8,3,8\nF,3,3,3,5,1,1,14,5,3,12,10,5,0,8,2,6\nV,5,7,5,5,2,3,11,3,3,10,11,8,3,10,1,7\nQ,7,8,7,10,7,7,8,6,3,7,9,11,4,8,7,8\nM,6,9,8,5,4,9,3,3,2,9,3,9,8,2,1,9\nA,1,0,2,0,0,7,4,2,0,7,2,8,1,7,1,8\nN,5,11,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nZ,5,10,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nM,9,12,11,7,5,13,2,5,2,12,1,9,7,3,1,9\nH,11,13,10,8,5,8,8,4,5,9,6,7,6,9,5,9\nX,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8\nA,2,4,4,2,1,11,2,3,2,9,2,9,1,6,2,8\nG,3,5,4,4,2,6,7,6,6,10,7,11,2,9,4,9\nF,2,1,2,3,2,5,10,4,5,10,9,6,1,10,3,7\nH,3,4,6,3,3,9,7,3,6,10,3,7,4,6,4,8\nM,5,9,6,4,3,12,3,4,2,11,2,9,6,3,1,9\nT,7,12,6,6,3,5,11,2,6,11,8,6,2,9,5,3\nW,10,15,10,9,7,5,8,2,4,7,9,7,11,10,2,5\nE,3,6,4,4,3,7,8,7,10,5,4,9,3,8,6,7\nJ,3,7,4,5,4,10,5,2,5,8,5,5,3,7,5,7\nG,5,9,6,7,8,8,8,5,2,6,6,9,7,9,6,13\nH,4,4,7,3,3,8,7,3,6,10,5,8,3,8,3,8\nL,7,13,6,7,3,5,5,3,8,10,4,13,2,5,6,7\nY,2,3,4,4,1,9,10,3,2,5,13,8,2,11,0,8\nH,1,0,2,0,0,7,7,11,1,7,6,8,2,8,0,8\nP,4,5,4,8,2,3,12,9,2,10,6,3,1,10,4,8\nZ,5,8,7,6,5,9,8,6,4,7,5,8,3,8,10,7\nT,3,4,4,6,1,7,15,1,6,7,11,8,0,8,0,8\nD,5,9,5,4,3,12,3,3,5,12,1,8,4,7,2,11\nU,1,0,2,0,0,7,6,10,4,7,12,8,2,10,0,8\nG,5,11,6,8,8,8,7,5,2,6,6,10,7,8,6,12\nE,5,10,8,7,6,6,8,3,8,11,8,9,3,8,5,6\nH,8,10,8,5,5,5,9,3,4,10,7,9,5,7,3,6\nF,2,3,2,2,1,6,10,4,5,10,9,5,1,10,3,6\nJ,1,4,2,3,1,10,6,2,5,12,4,9,1,6,1,7\nA,6,11,6,6,4,11,2,4,2,11,4,12,5,3,5,11\nL,4,6,5,5,4,7,5,5,5,7,6,7,3,8,8,11\nY,2,8,4,5,1,6,11,2,3,9,12,8,1,10,0,8\nG,5,9,4,5,3,7,7,3,2,8,6,7,4,10,8,7\nM,2,3,3,1,2,6,6,7,4,7,7,10,7,5,2,9\nK,2,4,4,3,3,6,7,1,6,10,7,10,3,8,2,8\nS,4,8,5,6,3,7,7,5,9,5,6,8,0,8,9,8\nE,0,0,1,0,0,5,7,5,7,7,6,12,0,8,6,10\nG,4,9,5,6,4,6,7,6,5,9,7,11,2,9,4,10\nT,2,4,3,5,1,5,14,1,6,9,11,7,0,8,0,8\nV,8,10,7,6,3,8,10,5,5,5,10,7,4,11,3,5\nK,3,7,5,5,3,6,7,5,7,6,6,10,3,8,5,9\nZ,3,8,4,6,3,8,7,6,10,6,6,8,1,8,8,7\nD,7,10,7,5,4,8,4,4,6,8,4,7,4,6,6,10\nQ,2,3,3,4,3,8,8,5,2,8,8,9,2,9,5,8\nH,3,4,5,3,3,6,9,3,6,10,8,8,3,8,3,7\nV,5,11,7,8,5,9,11,3,2,5,10,8,5,10,5,7\nG,2,3,3,1,1,7,7,6,5,7,6,9,2,9,4,10\nT,8,10,8,8,6,7,10,2,8,11,9,5,3,10,5,4\nJ,7,11,9,9,5,7,8,3,6,15,6,10,1,6,1,6\nQ,2,3,3,4,3,8,7,6,3,8,6,9,2,9,3,9\nY,2,4,3,3,1,6,10,1,7,8,11,8,1,11,2,7\nI,2,9,3,7,3,7,7,0,7,7,6,8,0,8,3,8\nA,3,8,5,6,5,8,8,6,4,6,6,8,5,7,6,4\nT,8,15,7,8,3,6,9,3,8,13,6,6,2,8,5,5\nQ,3,5,4,6,4,8,7,7,2,5,5,10,3,9,4,10\nN,6,10,8,8,7,6,7,9,5,6,4,6,5,7,4,10\nW,4,9,6,7,5,10,10,3,3,5,9,7,7,11,1,8\nU,6,9,6,6,4,4,8,5,7,11,10,9,3,9,2,6\nR,3,1,4,2,2,7,8,5,5,7,5,6,2,7,4,8\nH,4,9,5,7,5,8,8,6,6,7,6,9,3,8,3,8\nE,4,9,6,7,6,8,7,6,3,7,6,10,4,8,7,9\nY,2,1,3,1,0,7,11,3,1,7,12,8,1,11,0,8\nN,2,4,3,3,2,7,8,5,4,7,7,7,4,8,1,6\nN,4,8,6,6,3,7,8,3,4,10,6,7,5,8,1,7\nS,5,10,8,7,10,7,5,3,2,7,5,7,3,7,11,3\nG,5,5,6,8,3,6,6,8,9,6,6,9,1,8,6,11\nO,5,7,7,6,5,7,6,5,6,9,5,9,4,7,5,6\nF,3,5,5,6,4,7,10,4,4,8,7,7,3,9,6,5\nM,5,11,7,8,9,6,6,5,5,7,7,11,11,6,2,9\nK,3,4,6,3,3,7,8,2,7,10,4,8,5,7,4,7\nS,6,12,6,7,3,8,6,3,4,13,6,8,2,9,3,8\nC,2,3,3,2,1,5,8,5,6,12,9,10,1,10,2,7\nF,9,15,8,8,6,7,11,3,5,12,6,3,5,9,9,6\nN,4,10,5,7,3,7,7,14,2,4,6,8,6,8,0,8\nT,5,9,5,7,4,5,11,2,7,11,10,5,1,12,2,4\nK,4,7,6,5,6,8,8,5,5,8,5,7,7,6,6,11\nR,4,7,6,5,5,7,7,4,6,7,6,6,3,7,5,9\nN,4,3,4,4,3,7,8,5,5,7,6,6,6,9,3,6\nK,8,12,8,7,4,9,7,3,7,9,2,5,5,7,4,8\nW,2,0,2,1,1,7,8,3,0,7,8,8,6,10,0,8\nM,5,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nF,4,6,6,4,6,7,7,5,3,7,6,9,3,9,8,11\nH,5,9,7,7,6,7,7,7,7,7,6,8,3,8,4,8\nZ,4,10,4,8,4,7,8,3,12,9,6,8,0,8,7,7\nZ,4,10,5,8,3,7,7,4,15,9,6,8,0,8,8,8\nH,3,6,5,4,4,5,9,3,5,10,9,9,3,8,3,6\nL,4,10,5,8,4,6,4,4,9,2,2,5,1,6,2,5\nS,2,7,3,5,3,8,7,5,7,5,6,6,0,8,8,8\nN,3,7,4,5,4,9,8,6,5,6,5,4,5,9,2,5\nA,2,1,4,2,2,7,2,2,2,7,2,8,2,7,3,7\nP,3,5,5,4,2,7,10,4,4,13,5,2,1,10,2,8\nC,5,8,6,6,4,7,8,8,6,5,7,12,5,8,4,8\nA,3,4,5,3,2,10,2,2,1,9,2,9,2,6,2,8\nP,4,5,5,4,3,5,10,4,5,10,8,3,1,10,3,7\nN,2,4,4,3,2,7,9,2,4,10,6,6,5,9,0,7\nJ,3,9,4,7,3,9,6,2,5,11,4,9,1,6,2,5\nO,5,10,6,7,4,8,7,8,5,10,6,8,3,8,3,7\nH,2,3,3,2,2,6,7,7,5,7,6,10,3,8,3,9\nP,3,9,4,6,2,4,12,9,2,10,6,4,1,10,4,8\nG,3,7,4,5,3,6,6,5,5,6,6,10,2,9,4,8\nJ,1,3,2,1,0,9,5,3,5,14,6,11,0,7,0,8\nK,4,8,6,6,4,8,6,1,7,10,4,9,4,7,5,10\nP,2,6,3,4,1,4,13,8,2,11,6,3,1,10,3,8\nL,2,3,3,2,1,4,4,4,7,2,2,6,0,7,1,6\nG,3,4,4,3,2,6,7,5,5,10,7,10,2,9,4,9\nZ,4,6,5,4,3,6,9,3,8,11,9,6,2,9,6,5\nZ,4,7,5,5,5,7,6,2,7,7,6,9,1,7,10,8\nL,3,10,4,7,1,0,1,5,6,0,0,7,0,8,0,8\nR,2,4,4,2,2,8,8,4,4,9,5,7,2,7,3,10\nD,2,4,3,3,2,9,7,4,5,10,4,5,2,8,2,8\nZ,2,4,4,3,2,8,7,2,9,11,5,9,1,8,5,8\nQ,3,4,4,5,3,8,8,6,2,5,7,10,3,9,5,10\nA,3,5,5,8,2,7,6,3,1,7,0,8,3,7,1,8\nF,2,3,3,4,1,1,11,5,6,11,10,9,0,8,3,7\nL,4,9,5,6,2,7,4,0,9,9,2,10,0,7,2,8\nN,4,6,4,4,2,7,7,14,1,5,6,8,6,8,0,8\nY,5,8,5,6,2,3,10,2,7,11,11,6,1,11,2,4\nT,2,4,3,2,1,5,11,2,7,11,9,5,1,10,2,5\nO,3,7,3,5,2,8,7,7,5,9,5,8,3,8,3,8\nG,6,10,7,8,3,8,7,8,8,6,6,9,2,7,6,11\nG,6,8,6,6,5,6,7,6,6,9,7,11,2,9,4,9\nE,6,10,9,8,7,7,7,2,8,11,7,9,3,9,4,8\nZ,2,5,4,4,2,7,7,2,9,11,6,8,1,8,5,8\nK,6,9,9,7,5,9,6,2,7,10,3,8,4,7,5,10\nT,3,5,4,4,2,5,13,4,6,12,9,3,1,11,2,4\nC,4,9,5,7,2,6,6,7,10,8,5,13,1,10,4,9\nF,4,7,5,8,6,6,9,4,4,7,7,6,4,9,9,8\nD,6,10,6,6,5,9,6,3,6,10,4,7,5,7,8,7\nN,3,9,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nQ,3,6,4,4,2,9,7,7,6,6,6,10,3,8,5,9\nE,7,10,9,7,6,6,8,4,9,12,9,9,3,8,5,6\nH,3,5,6,4,3,7,7,2,6,10,5,8,6,8,4,7\nM,3,4,4,3,3,7,6,6,4,6,7,8,7,5,2,7\nF,4,8,6,6,3,5,11,5,5,13,8,5,2,9,2,6\nZ,3,8,4,6,4,6,7,3,7,8,6,9,0,9,9,7\nD,6,11,6,6,4,5,9,5,7,10,7,6,5,8,6,5\nW,10,11,10,8,7,4,10,3,3,9,8,7,8,11,2,6\nE,3,8,4,6,3,6,8,8,10,5,4,10,2,8,6,7\nG,3,8,5,6,3,7,6,7,6,6,5,10,1,8,5,11\nY,5,5,7,7,8,9,7,6,3,7,7,8,6,10,6,5\nQ,4,7,6,5,5,8,6,7,4,7,7,7,5,7,6,7\nJ,2,3,3,5,1,14,1,7,5,14,2,11,0,7,0,8\nF,3,8,5,6,4,6,10,3,6,10,9,5,5,10,3,6\nH,2,1,3,3,3,7,7,5,5,7,6,8,3,8,2,8\nC,6,11,7,8,4,2,8,5,9,10,10,14,1,7,3,7\nF,2,0,2,1,0,3,13,5,2,11,8,5,0,8,2,7\nO,4,8,5,6,4,8,7,7,4,7,6,6,4,8,3,8\nZ,2,7,3,5,2,6,8,5,9,7,7,10,2,9,8,7\nB,5,9,8,6,5,9,8,4,7,9,4,5,3,7,7,10\nP,4,7,6,10,9,7,12,4,0,9,7,6,5,10,6,8\nA,2,8,4,5,1,8,6,3,1,7,0,8,2,7,1,8\nZ,4,8,5,6,2,7,7,4,14,9,6,8,0,8,8,8\nY,4,7,6,11,9,8,9,3,2,6,8,9,6,13,10,6\nO,3,4,4,3,2,7,7,7,5,9,6,8,2,8,3,8\nT,2,8,4,6,2,8,11,2,9,6,11,7,1,11,1,7\nY,4,5,5,4,2,4,11,2,7,11,10,5,1,11,3,4\nF,9,13,8,7,5,9,7,3,5,11,4,5,4,9,7,9\nS,5,7,6,5,3,9,6,4,8,11,4,8,2,8,5,11\nJ,3,6,5,4,2,10,6,1,7,14,3,7,0,8,0,8\nK,2,3,4,1,2,4,8,2,6,10,9,10,2,8,2,7\nH,1,0,1,0,0,7,7,10,2,7,6,8,2,8,0,8\nK,3,7,4,5,2,3,6,7,3,7,7,12,3,8,3,11\nW,3,2,4,3,3,5,11,2,2,8,9,9,6,11,0,8\nW,3,1,5,2,3,8,11,3,2,6,9,8,7,11,0,7\nI,7,13,6,8,4,8,9,3,6,14,5,5,2,7,6,8\nR,8,15,8,8,7,5,8,2,5,7,3,9,6,6,6,5\nI,1,5,2,4,1,7,8,0,7,13,6,7,0,8,1,7\nQ,3,5,4,5,3,8,6,7,6,7,3,9,2,8,3,8\nQ,3,7,4,6,2,8,7,8,6,6,5,9,3,8,4,8\nV,5,11,8,8,2,8,8,5,3,6,14,8,3,9,0,8\nY,3,8,5,6,1,9,10,3,2,5,13,8,1,11,0,8\nF,3,5,5,4,2,7,9,1,6,13,5,5,1,10,2,8\nV,4,8,6,6,3,6,12,2,4,6,11,9,2,10,1,8\nW,5,8,7,6,7,8,7,6,2,6,8,8,7,8,5,4\nL,3,8,5,6,3,8,4,1,8,9,3,10,0,7,2,9\nW,8,8,8,6,5,3,11,2,3,10,10,8,7,10,2,6\nA,3,10,5,7,2,8,6,3,1,7,0,8,3,7,1,8\nT,7,10,6,5,2,4,12,3,6,13,8,4,2,9,3,4\nU,3,4,4,3,2,6,8,6,7,7,10,9,3,9,0,8\nH,6,11,8,8,7,7,7,7,7,7,6,8,3,8,4,7\nJ,5,8,7,9,7,8,9,4,5,7,7,8,5,7,10,10\nF,2,4,2,3,1,6,10,4,5,10,9,5,1,9,3,7\nW,6,7,6,5,4,3,10,3,3,10,10,8,7,10,2,7\nU,4,9,4,7,2,8,5,14,5,6,14,8,3,9,0,8\nP,5,9,5,6,2,4,15,8,1,12,6,2,0,9,4,8\nL,4,7,5,5,3,6,4,5,9,2,2,4,1,6,2,5\nS,5,10,4,5,2,9,3,1,5,8,1,7,2,7,4,10\nM,5,5,7,4,7,9,8,4,4,7,6,7,8,6,6,5\nG,4,9,5,6,3,6,7,7,6,10,7,11,2,9,4,9\nO,3,7,5,5,2,7,7,8,8,7,6,7,3,8,4,8\nS,5,11,6,8,5,9,6,4,6,10,3,7,2,8,5,10\nV,2,3,3,2,1,4,12,3,3,10,11,7,2,11,0,8\nQ,5,5,7,8,10,7,5,6,2,6,6,8,11,10,9,13\nQ,2,2,3,3,2,8,7,7,3,6,6,10,2,9,3,9\nK,3,5,5,4,3,6,6,1,7,10,8,11,3,7,3,8\nM,3,6,3,4,3,8,6,9,0,6,8,8,6,5,0,8\nP,4,9,6,7,4,7,10,4,5,12,5,3,1,10,4,8\nU,2,3,2,1,1,7,9,5,5,6,9,9,3,10,1,8\nE,8,12,5,6,3,7,6,5,7,10,6,9,2,10,8,9\nF,2,7,2,5,2,1,11,3,4,11,11,8,0,8,1,7\nX,4,9,6,7,5,8,7,3,5,6,6,7,3,9,9,9\nU,5,5,5,7,2,7,5,14,5,7,15,8,3,9,0,8\nK,3,5,6,4,3,7,7,2,7,10,5,9,6,8,5,8\nL,3,9,5,7,3,7,5,1,7,8,2,10,0,6,2,8\nQ,5,9,6,11,6,9,7,8,3,5,6,11,5,8,8,8\nI,1,2,1,3,1,7,7,1,7,7,6,9,0,8,3,8\nW,6,9,8,7,8,8,6,6,2,6,7,8,6,8,5,6\nM,7,8,10,6,6,4,7,4,5,11,11,11,9,6,4,7\nY,3,5,4,4,2,4,10,2,7,11,10,6,1,11,2,5\nK,5,9,5,5,3,3,9,4,5,10,10,11,4,9,3,6\nH,5,11,7,8,10,8,6,4,4,6,6,7,8,6,8,6\nN,1,3,2,2,1,7,8,5,4,7,6,6,4,9,1,5\nY,1,0,2,0,0,7,9,2,1,6,12,8,1,10,0,8\nB,3,2,4,3,3,8,7,5,6,7,6,6,2,8,6,10\nC,3,10,4,8,3,5,7,6,7,7,6,13,1,8,4,10\nN,3,6,4,4,3,7,7,6,6,7,6,8,3,7,3,8\nD,4,7,5,5,3,9,7,4,7,11,5,5,3,8,3,8\nE,2,7,3,5,2,3,8,6,11,7,5,15,0,8,6,8\nV,1,0,2,0,0,7,9,3,2,7,12,8,2,10,0,8\nT,2,8,4,5,1,9,15,1,6,5,11,9,0,8,0,8\nQ,6,8,6,9,7,8,7,7,2,8,7,10,3,8,6,7\nU,6,7,7,5,4,4,8,5,7,9,8,9,3,9,3,5\nB,3,6,5,4,6,9,7,4,3,6,7,7,7,9,7,7\nY,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nE,3,5,5,3,3,6,9,2,9,11,8,8,2,8,4,6\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nK,4,7,5,5,4,5,7,6,7,6,6,13,3,8,5,10\nO,4,7,4,5,3,7,7,8,5,10,6,8,3,8,3,7\nT,2,1,3,2,1,7,12,3,6,7,11,8,2,11,1,8\nM,3,6,4,4,4,8,6,6,4,7,7,8,7,5,2,7\nA,4,9,6,6,4,10,2,1,2,8,3,9,5,5,3,7\nH,3,6,3,4,1,7,8,14,1,7,5,8,3,8,0,8\nF,3,5,5,3,2,6,10,3,6,13,7,5,1,10,2,7\nW,8,11,8,8,6,4,10,2,3,10,9,8,8,11,2,6\nD,3,7,3,5,2,5,7,10,8,7,6,5,3,8,4,8\nX,4,8,5,5,1,7,7,4,4,7,6,8,3,8,4,8\nJ,3,9,5,7,6,9,8,3,3,8,4,6,4,8,6,4\nK,7,11,7,6,4,7,7,3,6,10,6,9,6,10,3,8\nU,10,13,9,7,4,6,4,5,5,4,8,7,6,7,2,7\nH,5,9,8,7,5,7,7,6,7,7,6,5,6,8,4,7\nB,4,8,4,6,4,6,9,9,7,7,5,7,2,8,9,10\nR,4,11,6,8,8,7,7,3,4,7,6,8,7,10,8,6\nE,1,1,1,1,1,4,7,5,8,7,6,13,0,8,6,9\nP,5,8,7,6,6,8,9,6,4,10,4,4,3,11,4,9\nW,10,11,10,8,7,5,11,3,3,9,8,7,8,11,3,5\nN,4,5,5,8,3,7,7,14,2,4,6,8,6,8,0,8\nT,6,10,6,5,3,7,9,2,7,12,6,6,2,9,4,6\nB,4,8,6,6,5,9,7,4,6,10,5,6,2,8,6,10\nO,5,8,6,6,4,7,8,7,5,10,7,8,3,8,3,8\nA,3,8,5,6,3,12,3,2,2,10,2,9,2,6,2,8\nY,5,9,7,6,6,9,5,7,5,6,9,7,3,9,9,4\nL,3,7,4,5,3,5,5,2,8,3,2,7,0,7,1,5\nH,3,4,5,3,3,7,7,3,6,10,6,8,3,8,3,8\nQ,2,2,2,4,2,8,7,7,3,6,6,9,2,8,5,10\nD,4,6,5,4,4,9,6,4,6,9,4,6,3,8,3,8\nL,4,10,5,8,3,5,3,6,8,1,2,4,1,6,1,5\nR,6,11,9,8,8,9,8,4,6,9,3,7,3,6,4,11\nP,2,3,3,1,1,8,9,3,3,12,4,4,1,9,2,9\nL,3,8,4,6,3,7,3,4,6,6,2,7,1,6,2,7\nP,5,11,7,8,6,6,10,6,5,10,8,3,1,10,4,7\nM,4,10,6,7,6,8,6,5,5,7,7,8,11,5,2,8\nQ,5,9,5,11,6,8,7,7,3,8,8,10,3,8,6,8\nL,4,9,6,6,3,5,4,3,9,6,1,7,1,6,3,6\nF,3,5,5,3,2,6,11,3,5,13,7,4,1,10,1,7\nZ,4,10,4,7,5,6,8,5,9,8,7,10,1,9,7,8\nV,6,8,8,6,5,6,11,3,2,8,11,8,4,9,5,9\nK,4,5,5,4,3,6,7,4,7,6,6,10,3,8,5,9\nU,3,6,3,4,1,8,5,13,5,6,13,8,3,9,0,8\nJ,2,8,3,6,1,13,2,8,4,14,4,12,1,6,0,8\nV,4,6,4,4,2,3,11,2,3,9,10,8,2,11,0,8\nN,2,2,3,3,2,7,8,5,4,7,7,7,4,9,1,6\nJ,5,12,4,9,4,7,10,3,2,13,5,5,2,8,7,10\nQ,5,10,6,9,6,8,8,7,5,5,8,9,3,8,5,8\nD,3,9,4,6,4,7,7,6,7,6,5,6,3,8,3,7\nP,3,4,5,3,2,7,9,4,4,12,4,3,1,10,3,8\nN,3,5,4,7,3,7,7,14,2,5,6,8,6,8,0,8\nG,5,10,6,8,6,7,6,7,8,6,4,10,2,9,6,11\nC,2,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10\nV,1,0,2,1,0,8,9,3,2,7,12,8,2,10,0,8\nF,3,7,5,5,3,6,10,5,6,10,10,5,2,9,3,5\nU,7,9,7,7,4,2,8,5,7,11,11,10,3,9,2,6\nH,7,11,10,8,7,7,8,3,7,10,6,7,3,8,3,8\nA,5,10,9,8,7,11,5,1,5,9,1,5,3,7,5,9\nI,1,4,2,3,1,7,7,0,6,13,6,8,0,8,0,8\nZ,2,5,4,3,2,7,8,2,9,11,7,7,1,8,5,7\nG,4,8,5,6,2,7,6,7,8,6,6,9,1,8,6,11\nU,4,5,5,8,2,7,5,14,5,7,14,8,3,9,0,8\nX,3,4,5,2,2,7,8,1,8,10,7,8,2,8,3,7\nV,5,9,5,4,3,10,7,4,5,7,9,5,4,11,2,7\nL,7,12,7,6,4,6,6,4,6,12,9,11,3,8,7,8\nH,7,11,9,8,8,7,6,5,5,6,5,7,9,6,7,10\nD,5,9,7,7,6,10,6,3,7,10,3,6,5,7,4,9\nH,6,10,7,5,4,7,8,3,5,10,7,8,6,9,5,7\nE,4,9,3,4,2,10,5,3,4,11,4,9,2,8,6,12\nY,2,3,3,2,1,4,10,2,7,10,10,6,1,11,2,4\nJ,4,8,5,6,2,8,5,4,6,15,6,11,1,6,0,7\nR,6,9,8,6,7,8,6,6,4,8,5,7,4,7,7,11\nM,7,12,8,6,5,7,3,3,2,8,4,10,8,2,2,8\nS,3,7,4,5,2,9,7,4,8,11,4,8,2,8,5,9\nJ,4,6,5,4,2,8,6,4,6,15,7,12,1,6,1,7\nZ,2,6,4,4,3,7,7,2,7,7,6,8,0,8,8,8\nE,4,11,4,8,4,2,8,5,9,7,6,14,0,8,6,8\nG,5,11,6,8,4,6,7,7,7,11,7,11,2,10,4,9\nV,3,9,5,7,1,7,8,4,2,7,14,8,3,9,0,8\nU,4,5,5,4,3,6,9,5,7,7,10,9,3,9,1,8\nJ,3,9,4,7,1,13,2,9,4,14,5,13,1,6,0,8\nS,2,6,4,4,4,6,9,3,2,7,7,6,2,8,8,1\nH,3,2,4,3,3,7,8,6,6,7,6,8,3,8,3,7\nD,3,8,3,6,2,6,7,11,8,6,6,6,3,8,3,8\nD,6,10,5,5,4,8,7,4,7,10,5,7,5,9,6,6\nI,3,6,4,4,2,7,7,0,7,13,6,8,0,8,1,7\nX,5,7,8,5,4,7,7,1,8,10,6,8,2,8,3,8\nU,4,7,4,5,2,7,4,14,5,7,13,8,3,9,0,8\nH,5,9,5,6,2,7,7,15,0,7,6,8,3,8,0,8\nX,4,6,5,5,5,9,7,2,5,8,6,6,2,8,7,7\nE,3,8,5,6,3,5,8,4,8,11,9,9,2,8,5,6\nO,4,7,6,5,4,8,9,8,4,7,7,7,3,8,3,8\nU,4,4,5,3,2,4,9,5,7,11,10,9,3,9,2,7\nS,2,7,3,5,3,7,8,7,6,7,5,6,2,8,8,8\nY,7,10,7,8,3,4,10,3,8,11,11,5,1,11,3,4\nJ,3,7,4,5,2,9,6,4,7,15,5,10,0,7,1,7\nX,2,7,3,5,3,7,7,3,7,6,7,10,2,8,6,8\nM,4,4,7,3,4,7,7,3,4,9,8,9,9,7,2,9\nQ,2,0,2,1,1,9,7,6,4,6,6,9,2,8,3,8\nU,10,15,9,8,6,7,6,5,5,6,7,8,5,5,3,7\nC,9,13,6,7,3,8,7,7,7,11,5,8,2,9,5,9\nZ,7,10,9,8,5,7,8,2,10,12,6,8,1,9,6,8\nC,3,6,4,4,1,5,8,6,10,6,7,12,1,7,4,8\nT,2,7,3,4,1,5,14,1,6,9,11,7,0,8,0,8\nQ,3,6,4,5,2,8,6,8,7,6,5,8,3,8,4,8\nK,6,9,8,6,8,8,8,5,5,7,6,6,8,7,8,13\nW,6,6,6,4,4,3,11,2,3,10,10,8,7,11,2,6\nM,3,4,4,3,3,8,6,6,4,6,7,8,7,6,2,7\nF,5,11,7,8,6,9,8,1,6,13,5,5,3,8,3,9\nH,4,10,4,7,2,7,6,15,1,7,8,8,3,8,0,8\nV,6,11,4,6,2,7,10,6,3,8,10,5,5,12,3,8\nV,2,0,3,1,0,7,9,4,2,7,13,8,2,10,0,8\nX,4,8,6,6,3,5,8,2,8,10,11,9,3,8,4,6\nQ,6,7,8,10,10,8,9,5,2,5,7,9,8,15,8,14\nW,3,6,4,4,4,8,7,6,2,6,7,8,5,9,4,6\nQ,4,6,4,7,4,8,5,6,4,9,5,8,3,8,3,8\nC,4,10,5,8,3,5,8,8,8,9,8,13,2,9,4,10\nI,3,8,4,6,2,7,8,0,8,14,6,7,0,8,1,7\nZ,6,11,8,9,5,7,8,3,11,12,7,8,2,9,7,8\nN,1,1,2,2,1,7,7,11,1,5,6,8,4,8,0,8\nI,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nM,4,4,4,6,3,8,7,12,1,6,9,8,8,6,0,8\nR,1,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10\nW,7,8,7,6,5,6,11,5,3,8,6,6,10,13,3,4\nM,5,9,6,6,6,7,6,6,5,7,7,10,8,5,2,8\nR,8,15,6,8,5,7,5,6,5,8,6,9,6,6,8,10\nD,4,8,4,6,2,5,7,11,8,7,6,5,3,8,4,8\nY,4,6,6,4,3,4,11,2,7,11,10,5,1,11,3,5\nC,3,7,4,5,2,3,9,5,8,11,11,11,1,8,3,7\nS,7,11,10,8,12,8,9,5,4,8,5,7,7,8,13,9\nP,4,6,4,8,2,3,13,8,2,11,7,3,1,10,4,8\nO,3,5,4,4,3,8,7,7,5,9,6,8,2,8,3,8\nF,5,11,7,8,4,7,10,2,7,14,5,4,1,10,2,8\nE,3,7,5,5,3,4,9,3,7,11,9,9,2,9,4,6\nT,4,5,5,5,5,7,9,4,7,7,8,8,3,10,6,7\nQ,6,8,6,9,7,8,8,6,2,7,8,11,3,8,6,8\nC,5,7,6,6,5,4,8,3,6,7,6,10,3,9,7,8\nW,4,4,5,3,3,3,11,3,2,10,10,8,6,11,1,7\nO,2,3,3,2,2,8,7,7,4,9,6,9,2,8,3,8\nH,5,9,6,6,6,7,7,5,5,7,5,8,6,6,6,11\nP,12,14,10,8,4,8,9,6,5,14,3,4,5,10,5,8\nY,6,9,6,7,4,5,9,1,7,9,9,6,3,10,5,4\nL,4,9,6,7,4,7,4,1,8,8,2,10,0,7,3,8\nX,2,3,3,2,1,7,7,3,9,6,6,8,2,8,5,7\nG,5,6,7,5,7,7,8,6,3,7,7,8,6,11,7,8\nO,6,9,8,6,9,8,9,5,2,7,7,8,9,9,6,12\nH,8,14,8,8,6,10,7,5,5,11,1,6,7,4,5,7\nV,9,15,8,9,4,9,10,6,4,6,10,6,6,13,3,6\nK,2,3,3,2,2,5,7,4,7,7,6,10,3,8,4,10\nU,3,5,4,4,2,6,8,5,6,6,8,9,6,10,0,7\nI,2,7,3,5,1,7,6,0,8,14,7,10,0,8,1,8\nY,5,6,5,4,2,3,10,2,7,11,11,6,1,11,2,4\nR,3,6,4,4,4,8,7,7,3,8,5,7,4,7,7,8\nR,4,9,4,7,3,6,8,9,5,6,6,7,3,8,6,11\nO,8,12,6,6,3,7,8,6,5,9,5,7,4,9,5,9\nS,6,11,7,8,7,9,8,5,8,5,5,6,1,6,10,6\nL,6,12,6,7,3,7,4,3,6,11,4,13,3,6,6,8\nN,3,5,4,3,3,7,8,5,5,7,7,6,5,9,2,6\nE,7,11,10,8,6,8,7,2,9,11,6,9,5,7,6,8\nH,2,4,3,2,2,6,7,5,6,7,6,8,3,8,3,8\nA,2,7,4,5,2,11,2,3,3,10,2,9,2,6,2,8\nP,10,13,9,8,6,9,8,4,5,13,4,4,5,10,6,7\nM,6,9,7,4,3,8,10,4,5,5,5,8,8,8,2,7\nF,3,7,4,5,1,1,12,5,5,12,11,8,0,8,2,6\nO,3,6,4,4,3,7,8,6,4,9,7,8,3,8,3,8\nY,3,5,5,7,1,9,10,2,2,5,12,8,1,11,0,8\nS,2,2,2,3,2,8,8,7,5,8,5,7,2,8,8,8\nV,4,5,5,4,2,4,12,2,3,9,11,7,3,10,1,7\nR,4,8,6,6,7,9,9,3,5,5,7,8,7,10,7,7\nN,6,11,8,6,3,8,7,2,4,13,6,8,6,8,0,8\nQ,3,4,4,4,3,7,8,5,3,8,8,9,3,8,5,8\nR,4,9,4,4,3,9,7,3,5,9,3,8,5,7,5,8\nY,6,8,8,11,11,7,10,4,2,7,8,9,6,13,9,8\nK,5,9,7,6,5,4,7,2,7,10,10,12,3,8,3,6\nU,3,2,4,3,2,6,8,5,7,7,10,9,3,9,1,8\nF,6,10,7,8,7,6,10,6,5,9,6,8,2,10,8,10\nA,2,3,3,2,1,10,2,2,1,9,2,9,1,6,2,8\nQ,5,6,6,8,3,9,9,8,7,5,8,10,3,8,5,10\nN,2,1,3,3,2,7,8,5,4,8,7,8,4,8,1,7\nU,4,8,6,6,5,8,8,8,5,6,7,9,3,8,4,6\nM,2,3,4,2,3,7,6,3,3,9,7,8,6,5,1,8\nU,3,7,5,6,5,8,7,3,3,6,6,8,4,8,2,8\nT,2,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8\nA,4,9,4,4,2,10,2,4,2,11,5,12,4,4,5,10\nR,9,13,7,8,5,8,7,5,5,9,4,9,7,5,7,11\nK,3,4,4,7,2,3,7,7,3,7,7,11,3,8,3,10\nQ,7,8,7,10,8,8,9,5,2,7,9,11,3,8,6,8\nY,2,5,4,4,2,8,11,1,7,6,11,8,1,11,2,9\nX,3,1,4,2,2,8,7,4,9,6,6,8,3,8,6,8\nY,5,9,5,7,3,4,9,1,7,9,10,6,1,10,3,4\nQ,5,11,5,6,4,7,7,4,8,11,5,8,3,7,9,10\nD,5,9,6,7,6,8,8,7,6,8,7,3,5,8,4,9\nT,6,7,6,5,4,6,12,5,6,10,9,4,3,13,3,4\nV,4,10,6,8,3,5,11,3,5,10,12,9,3,10,1,8\nT,2,8,4,5,1,8,15,1,6,6,11,8,0,8,0,8\nG,2,6,3,4,2,8,8,6,5,6,7,9,2,7,5,11\nA,6,10,6,5,3,12,3,6,2,12,2,10,4,3,3,10\nW,2,3,3,1,2,10,11,3,2,5,9,7,5,10,0,8\nI,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nU,4,6,6,4,4,7,8,8,6,6,6,10,3,8,5,6\nE,4,8,6,6,5,7,7,2,7,11,7,9,3,8,5,8\nT,5,9,7,7,8,7,8,4,6,7,6,9,6,8,6,6\nI,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nY,7,11,8,8,4,5,9,1,9,10,10,5,3,10,5,3\nA,5,10,7,8,4,10,3,2,3,8,1,7,2,7,3,8\nZ,5,11,7,9,4,8,7,2,10,11,5,9,2,8,6,8\nZ,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nZ,5,8,7,10,6,11,4,3,5,9,2,7,2,7,6,9\nT,3,3,3,2,1,5,12,2,7,11,9,5,1,10,1,5\nY,2,1,2,1,0,7,10,3,1,7,12,8,1,11,0,8\nN,3,5,5,4,2,7,8,2,4,10,6,6,5,9,0,7\nQ,4,3,5,5,4,8,7,7,3,5,6,9,3,8,5,8\nU,4,9,5,7,3,8,8,7,7,6,10,8,3,9,1,8\nH,3,7,3,4,2,7,8,14,1,7,5,8,3,8,0,8\nE,4,9,6,8,7,6,8,4,3,8,7,9,5,11,8,11\nS,4,8,5,6,3,9,6,5,9,11,3,8,2,7,5,10\nP,4,6,6,4,3,7,10,5,3,11,4,3,1,10,3,9\nM,5,6,7,6,8,7,7,4,4,7,6,8,10,7,5,5\nO,7,10,7,8,5,7,7,8,6,9,7,10,4,7,5,5\nK,3,2,4,4,2,6,7,4,8,6,6,11,3,8,6,9\nZ,5,11,7,8,4,7,7,2,10,12,5,9,1,9,7,8\nT,5,7,5,5,3,5,12,2,7,12,9,4,1,11,2,4\nC,4,9,5,6,3,5,8,7,7,8,8,14,2,9,4,10\nD,6,11,7,8,4,5,7,10,10,7,6,5,3,8,4,8\nI,4,8,6,9,6,8,9,4,5,7,7,8,3,7,8,7\nA,3,2,5,3,2,6,2,2,1,5,2,8,2,7,3,5\nZ,4,10,6,7,4,9,5,3,10,11,4,9,1,7,6,9\nI,3,8,5,6,3,8,6,2,7,7,6,7,0,9,4,7\nM,4,2,5,3,4,8,6,6,4,7,7,8,7,5,2,8\nM,5,10,8,7,11,9,6,2,2,8,4,8,10,5,3,6\nZ,1,0,1,0,0,8,7,2,9,8,6,8,0,8,5,8\nY,3,5,5,4,2,8,11,1,7,5,11,9,1,11,2,9\nI,1,6,0,4,1,7,7,5,3,7,6,8,0,8,0,8\nY,3,10,6,7,4,7,10,0,7,6,11,8,1,10,1,8\nY,7,11,7,8,4,6,8,2,8,8,10,5,5,11,6,3\nN,2,6,2,4,2,7,7,11,1,6,6,8,4,8,0,8\nL,1,0,1,1,0,2,1,5,5,0,2,5,0,8,0,8\nY,6,7,6,5,3,3,10,2,7,10,11,6,1,11,2,5\nD,4,9,6,6,5,7,8,7,5,8,7,6,3,8,3,7\nK,7,9,10,7,7,7,7,1,6,10,5,9,3,8,4,9\nY,7,8,9,10,11,9,8,6,3,7,7,8,6,10,7,4\nQ,4,7,6,5,5,8,4,7,4,6,7,10,4,7,6,9\nI,3,4,4,5,3,9,9,5,4,6,6,10,3,7,7,5\nJ,4,5,6,6,5,8,8,4,6,6,7,7,3,10,8,10\nR,4,11,6,8,6,8,8,4,6,9,4,8,3,6,5,11\nI,3,7,4,5,2,6,8,0,7,13,7,8,0,8,1,7\nK,2,1,3,2,2,6,7,4,7,7,6,11,3,8,4,9\nY,4,10,7,8,4,4,9,1,8,10,12,10,1,11,2,7\nT,3,3,4,2,2,8,12,3,6,6,11,8,2,11,1,7\nW,6,9,8,7,9,7,9,6,4,7,9,8,7,7,5,10\nC,4,7,5,5,2,6,8,7,11,6,6,13,1,7,4,8\nX,4,7,5,5,3,7,7,4,9,6,6,10,3,8,6,8\nX,4,7,6,5,4,9,8,3,6,7,7,7,6,11,7,7\nS,4,9,5,6,4,8,7,5,7,5,6,8,0,8,8,8\nF,4,4,4,6,2,1,15,5,3,12,9,4,0,8,2,6\nB,2,1,2,1,2,7,7,7,5,6,6,7,1,8,7,8\nV,1,1,2,2,1,6,12,3,2,9,11,8,2,11,1,8\nV,3,10,5,7,2,9,8,4,3,6,14,8,3,10,0,8\nP,6,10,9,8,5,7,10,2,7,14,6,4,0,10,2,9\nA,4,9,6,6,2,7,7,3,0,7,0,8,3,7,1,8\nX,4,9,6,6,4,8,7,0,7,9,5,7,2,8,3,8\nG,4,7,4,5,3,6,6,6,5,10,7,13,2,9,4,10\nA,3,5,5,3,2,6,2,2,2,5,2,8,2,6,3,6\nT,2,2,3,3,2,6,12,3,6,8,11,8,2,11,1,7\nA,3,10,5,8,4,6,3,1,2,5,2,8,2,5,3,6\nM,6,6,8,4,5,10,6,2,5,9,4,7,8,6,2,8\nQ,3,4,4,5,3,7,8,5,3,7,9,10,3,8,5,8\nR,3,3,4,4,2,5,11,7,3,7,4,8,3,7,6,11\nX,3,1,4,2,2,8,7,3,9,6,6,9,2,8,6,8\nO,4,9,5,7,5,7,7,8,4,7,6,8,3,8,3,8\nJ,3,8,5,6,4,9,8,3,3,8,4,6,4,9,6,4\nT,2,3,2,2,1,9,11,3,5,6,10,8,2,11,1,8\nF,2,7,3,5,2,1,12,3,4,11,10,8,0,8,2,7\nY,4,10,7,7,1,7,10,2,2,7,13,8,2,11,0,8\nW,6,8,6,6,4,3,10,3,3,11,10,8,7,10,2,7\nD,3,2,4,4,3,7,7,7,6,6,6,5,2,8,3,7\nQ,5,11,7,8,7,8,4,8,5,6,6,6,5,7,7,10\nZ,6,7,5,11,4,7,7,5,3,11,7,7,3,9,11,6\nI,2,2,1,4,1,7,7,1,7,7,6,8,0,8,3,8\nS,1,0,2,1,0,8,7,4,6,5,6,7,0,8,7,8\nK,3,6,5,5,5,6,8,3,3,7,4,9,4,6,6,9\nL,2,4,3,3,1,7,4,1,8,8,2,10,0,7,2,8\nA,2,2,4,3,1,7,3,2,2,6,1,8,2,6,2,7\nH,5,6,6,8,3,7,8,15,0,7,5,8,3,8,0,8\nV,2,4,4,2,1,9,12,2,3,5,11,9,2,11,1,8\nO,3,3,4,4,2,7,8,8,6,7,7,8,3,8,4,8\nP,4,9,4,7,4,4,12,7,1,11,7,4,1,10,3,8\nO,3,3,4,4,2,8,7,8,8,7,6,8,3,8,4,8\nG,5,5,6,7,3,7,4,7,9,5,5,9,1,9,7,11\nW,5,7,5,5,4,4,11,2,2,9,8,7,6,11,2,6\nC,5,9,6,7,2,6,8,7,11,6,7,13,1,7,4,8\nX,3,8,4,5,1,8,7,4,4,7,6,8,3,8,4,8\nG,2,6,3,4,2,7,6,6,6,6,5,10,1,8,5,11\nS,5,9,5,5,2,5,9,4,4,13,9,7,2,9,3,7\nV,4,7,4,5,3,4,12,2,2,9,11,8,3,11,1,7\nR,3,5,5,4,3,9,7,4,5,10,3,7,5,6,4,9\nA,4,9,7,7,5,11,3,1,2,8,3,9,6,5,3,8\nU,3,3,4,1,1,5,8,4,6,10,9,9,3,10,1,6\nS,5,7,6,5,3,8,7,3,8,11,6,8,2,10,5,8\nT,2,7,3,5,2,9,12,0,5,6,10,8,0,8,0,8\nB,3,5,5,4,3,8,8,3,5,10,5,6,3,7,5,9\nD,3,8,5,6,4,7,7,7,7,7,7,5,3,8,3,7\nU,5,8,6,6,4,4,8,5,7,9,7,9,4,8,4,4\nD,5,11,7,8,8,7,7,6,6,6,6,5,6,8,3,7\nB,4,9,4,4,4,8,7,3,4,9,6,8,5,7,6,8\nX,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nF,6,8,7,9,8,6,9,4,4,7,7,6,5,9,9,8\nZ,4,8,5,6,5,8,8,3,8,7,7,7,0,8,11,8\nB,9,11,6,6,4,8,6,5,6,11,4,9,6,7,6,10\nE,4,11,6,8,5,8,7,2,8,11,6,8,2,8,5,9\nV,2,3,3,2,1,4,12,3,2,9,11,7,2,11,0,7\nY,1,1,3,2,1,8,11,1,5,5,11,9,1,11,1,8\nY,5,8,5,6,3,3,10,1,7,11,11,6,1,10,3,4\nC,3,6,5,4,4,7,7,4,3,7,7,10,5,10,3,8\nB,3,1,3,2,3,7,7,5,5,7,6,6,2,8,6,10\nJ,3,10,4,7,1,14,1,7,5,14,1,10,0,7,0,8\nX,3,5,4,4,2,7,7,3,9,6,6,8,2,8,6,8\nD,6,10,8,8,8,7,7,5,7,7,6,5,6,8,3,7\nW,6,9,6,7,5,4,10,3,3,9,8,7,7,11,2,5\nK,2,3,4,2,2,6,7,1,6,10,7,10,3,8,2,8\nR,5,5,6,7,3,6,11,9,4,7,3,8,3,7,6,11\nU,3,8,5,6,2,5,8,7,8,10,10,9,3,9,1,8\nZ,2,4,2,3,2,8,7,5,9,6,6,7,1,8,7,8\nN,2,1,2,2,1,7,8,5,4,7,6,6,5,9,1,5\nJ,6,7,7,8,6,9,9,5,5,6,6,10,3,7,8,5\nK,2,3,3,1,1,5,7,1,6,10,8,10,3,8,2,6\nT,3,11,4,8,1,6,14,0,6,8,11,8,0,8,0,8\nF,3,3,3,4,1,1,12,4,4,12,10,7,0,8,2,6\nD,4,9,6,7,6,9,7,6,6,9,4,6,3,8,3,9\nG,3,7,4,5,2,8,8,8,7,5,7,9,2,7,5,10\nJ,4,10,5,7,2,8,7,2,7,15,5,8,0,7,1,7\nB,1,3,3,2,2,8,7,2,5,10,5,7,2,8,3,9\nP,2,3,4,2,1,7,11,4,3,11,4,2,0,9,3,8\nW,6,10,9,8,7,5,8,4,1,7,9,8,7,11,0,8\nN,6,10,5,5,3,5,9,4,5,3,3,12,6,11,2,7\nU,2,3,3,1,1,5,8,5,6,10,9,9,3,10,1,6\nC,3,6,4,4,2,6,8,7,7,9,8,12,2,10,4,10\nS,5,8,6,6,3,5,9,3,9,11,6,7,2,7,5,6\nH,3,3,4,4,2,7,7,14,1,7,7,8,3,8,0,8\nA,6,10,9,8,9,8,7,7,4,7,6,9,6,8,7,4\nK,4,5,7,4,4,3,8,2,7,10,10,11,3,8,3,6\nZ,4,10,5,8,2,7,7,4,14,10,6,8,0,8,8,8\nT,6,9,7,7,8,5,8,4,7,7,6,10,5,7,5,7\nP,3,6,4,4,2,4,13,8,1,10,6,3,1,10,4,8\nO,4,8,6,6,4,7,7,8,5,7,5,8,3,8,3,8\nS,8,15,8,8,4,9,5,5,5,13,5,9,2,8,3,7\nX,6,10,9,8,8,8,7,3,7,7,7,8,6,13,8,8\nA,5,11,7,9,5,10,2,2,3,9,1,7,3,7,4,8\nI,1,4,1,3,1,7,7,1,7,7,6,8,0,8,2,8\nD,5,4,5,7,3,5,6,10,9,5,4,5,3,8,4,8\nP,2,1,2,1,1,5,11,7,2,9,6,4,1,9,3,8\nF,4,9,4,6,2,1,13,5,4,12,11,6,0,8,2,6\nE,4,8,6,6,4,9,6,2,7,11,5,9,3,7,6,10\nH,4,3,4,4,2,7,8,14,0,7,6,8,3,8,0,8\nT,4,5,5,4,3,5,12,4,6,11,9,4,2,11,1,5\nM,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8\nG,3,6,4,4,2,6,6,5,7,7,5,10,2,10,4,8\nV,2,8,4,6,1,8,8,4,2,7,13,8,3,9,0,8\nD,5,10,5,8,3,5,7,10,9,7,6,5,3,8,4,8\nT,5,11,7,8,7,8,11,3,6,6,11,8,3,12,1,8\nD,5,10,6,8,3,5,7,10,9,7,6,5,3,8,4,8\nH,7,13,7,8,6,7,7,3,4,10,5,8,6,9,5,8\nW,6,7,9,6,10,7,8,5,5,7,5,8,10,10,8,8\nZ,5,10,7,8,4,7,7,3,11,12,7,8,2,8,7,7\nF,4,7,6,5,4,8,8,2,6,13,5,6,2,10,2,8\nB,2,3,4,2,2,8,8,3,5,10,5,6,2,8,5,9\nR,3,7,5,5,4,6,8,5,6,6,5,7,3,7,5,9\nZ,1,0,1,1,0,7,7,2,10,8,6,8,0,8,6,8\nU,6,10,6,5,4,7,5,6,5,6,8,8,5,10,3,9\nN,10,15,9,8,4,7,10,5,5,4,5,11,6,11,3,7\nB,1,3,2,2,1,7,7,4,4,6,6,5,2,8,5,9\nI,0,7,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nA,4,7,6,5,2,8,3,3,3,9,1,8,2,7,3,7\nK,5,7,8,5,4,3,7,2,7,10,11,12,3,8,4,5\nZ,3,7,4,5,3,6,8,5,10,7,7,10,2,9,8,8\nR,5,11,7,8,8,6,7,5,6,6,5,7,3,7,5,8\nR,4,2,5,4,4,7,8,4,6,7,5,7,3,7,4,8\nF,4,6,5,4,3,7,10,4,5,13,7,5,2,9,2,7\nI,4,10,5,8,2,7,9,0,8,14,6,6,0,10,2,7\nA,7,11,9,8,9,7,6,8,4,7,5,8,5,8,11,2\nI,1,3,1,2,0,7,8,0,6,12,6,8,0,8,0,7\nJ,2,6,2,4,2,8,7,0,6,10,5,7,0,7,1,7\nK,5,9,5,4,3,9,7,2,6,10,4,7,5,9,3,9\nU,2,1,3,2,1,5,8,6,6,8,10,10,3,9,1,7\nT,1,0,1,0,0,7,13,1,4,7,10,8,0,8,0,8\nY,1,3,2,2,1,5,10,2,7,10,9,5,1,11,2,4\nW,2,1,2,2,1,7,8,4,0,7,8,8,6,10,0,8\nJ,4,7,6,5,4,8,6,5,5,8,7,7,3,7,4,6\nU,4,2,5,4,2,6,8,5,8,6,9,9,3,9,1,7\nN,8,15,10,8,5,5,9,3,4,13,10,9,6,9,0,8\nJ,1,3,2,2,1,11,6,2,6,12,3,8,0,7,0,8\nU,1,0,2,1,0,8,6,11,4,7,12,8,3,10,0,8\nW,4,4,6,6,3,4,8,5,1,7,8,8,8,9,0,8\nF,4,7,7,5,6,8,7,1,4,10,7,7,6,9,3,6\nK,5,9,8,6,6,7,7,1,6,10,5,9,3,7,3,8\nU,7,14,6,8,5,8,6,4,5,7,7,8,6,5,4,6\nP,4,11,5,8,6,6,9,6,4,9,8,4,1,10,3,7\nA,3,6,5,4,2,8,2,2,2,7,1,8,2,6,3,7\nJ,4,10,6,8,6,9,8,4,4,9,3,7,4,8,7,5\nW,6,6,6,4,5,4,11,2,2,9,8,7,7,12,2,6\nT,5,6,6,5,6,8,9,5,7,7,7,8,3,10,7,7\nM,4,6,7,4,4,6,6,3,4,9,9,9,9,6,3,9\nP,3,9,4,6,2,5,9,10,5,8,6,5,2,9,4,8\nZ,4,9,5,7,5,9,9,6,4,7,5,8,4,9,10,6\nL,3,7,4,5,3,5,5,4,7,3,2,6,4,6,1,6\nM,5,11,7,8,9,7,6,7,4,7,5,8,6,10,7,8\nI,2,8,2,6,2,7,7,0,8,7,6,8,0,8,3,8\nB,3,6,5,4,6,8,7,4,3,6,7,8,6,10,7,7\nM,3,7,5,5,5,10,6,6,4,6,7,5,7,5,1,5\nM,6,8,9,7,10,8,7,4,5,7,6,7,12,8,6,4\nC,2,5,3,3,1,4,8,5,7,11,9,13,1,9,2,7\nG,5,11,7,8,6,6,6,6,7,6,6,9,4,9,4,8\nZ,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nO,4,7,5,5,2,8,6,8,8,7,5,8,3,8,4,8\nZ,3,4,4,6,2,7,7,4,13,10,6,8,0,8,8,8\nU,5,8,7,6,6,7,8,8,5,5,7,10,4,9,4,5\nM,9,13,11,7,6,13,2,6,2,12,1,9,7,2,1,8\nM,5,5,6,8,4,7,7,12,2,7,9,8,8,6,0,8\nU,5,11,5,8,4,8,5,12,4,7,13,8,3,9,0,8\nU,7,8,8,6,4,4,8,6,8,9,9,10,3,9,3,5\nX,3,3,5,2,2,9,7,2,8,11,4,7,2,8,3,8\nZ,3,6,4,4,1,7,7,3,13,9,6,8,0,8,8,8\nV,2,3,2,1,1,5,12,3,2,9,10,7,2,11,1,8\nU,6,11,9,8,12,8,8,4,4,6,7,8,10,6,8,8\nC,4,7,5,5,2,6,6,6,10,7,6,13,1,9,4,9\nN,5,7,6,5,4,8,8,6,6,6,5,3,8,9,5,7\nH,4,2,5,4,3,9,7,6,6,7,6,8,6,8,3,7\nW,6,8,6,6,4,2,10,3,3,11,11,9,7,10,1,6\nJ,6,7,4,10,3,10,6,3,4,11,3,5,3,8,7,10\nY,8,7,6,11,5,9,7,5,5,4,12,5,4,11,5,7\nR,3,7,4,5,2,5,11,8,4,7,3,9,3,7,6,11\nQ,4,7,6,8,6,8,5,8,4,6,5,9,4,9,6,8\nO,3,6,4,4,2,7,7,7,4,9,6,8,3,8,3,8\nS,7,15,5,8,3,10,2,2,5,10,1,9,3,7,4,9\nV,4,5,6,4,2,4,12,3,3,10,11,7,2,10,1,8\nA,3,8,5,6,3,6,4,3,0,6,2,7,2,7,2,7\nA,4,7,7,5,5,7,6,2,4,6,1,6,3,5,3,7\nF,5,7,6,5,6,7,10,5,4,8,6,8,2,9,7,11\nS,4,10,6,8,7,8,8,5,3,8,5,7,4,9,11,8\nT,4,6,6,4,5,5,8,4,5,6,7,8,5,10,4,8\nI,1,3,2,2,1,7,7,1,6,13,6,8,0,8,0,8\nJ,1,1,2,2,1,10,7,2,5,11,4,8,0,7,0,7\nW,6,7,6,5,4,4,11,3,3,9,9,7,7,11,2,6\nN,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nN,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8\nO,5,9,6,7,8,8,9,5,2,7,7,8,8,9,4,8\nV,5,9,4,4,2,7,10,4,4,8,9,6,3,10,2,7\nX,5,8,8,6,4,8,8,1,8,11,4,7,4,9,4,8\nL,3,11,5,8,3,4,4,4,8,2,0,6,0,6,1,5\nL,2,6,3,4,1,0,1,5,6,0,0,6,0,8,0,8\nB,6,11,8,8,8,9,6,4,5,7,6,7,4,8,6,10\nH,3,3,5,2,2,7,8,3,6,10,7,8,3,8,3,8\nO,5,8,6,6,5,6,8,7,4,9,8,8,3,8,3,8\nT,6,8,6,6,3,5,12,2,8,12,9,4,1,11,2,4\nV,3,4,5,3,2,6,12,2,3,7,11,8,2,10,1,8\nF,6,11,9,8,9,7,7,6,4,8,6,8,5,12,10,12\nQ,2,3,3,4,3,9,8,6,1,5,7,10,2,10,5,10\nN,6,5,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nR,7,13,7,7,6,8,7,3,5,9,4,8,6,6,7,6\nN,3,7,3,5,2,7,7,13,2,5,6,8,5,8,0,8\nF,4,7,6,5,6,8,8,1,4,10,6,6,5,10,4,5\nE,4,9,5,7,3,3,7,6,11,7,6,15,0,8,7,7\nT,3,3,4,2,1,5,13,3,7,12,9,3,1,10,1,5\nN,5,6,6,6,6,6,7,5,3,6,5,8,7,9,4,8\nR,6,12,6,6,5,5,9,2,5,7,5,10,4,7,6,7\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nU,4,7,6,6,5,7,7,4,4,6,6,9,4,9,1,8\nP,3,7,3,5,3,5,10,8,3,9,6,5,1,10,3,8\nF,5,7,7,5,3,5,13,5,5,13,7,3,2,10,2,6\nQ,5,5,5,6,5,8,7,6,2,8,6,11,3,9,6,8\nZ,2,3,3,4,1,7,7,3,13,9,6,8,0,8,7,8\nS,5,11,5,6,2,8,7,4,3,13,7,8,3,10,3,8\nN,4,8,4,6,2,7,7,14,2,4,6,8,6,8,0,8\nW,1,0,2,0,1,7,8,3,0,7,8,8,5,10,0,8\nM,9,12,11,6,6,6,3,3,2,7,4,11,10,1,2,8\nQ,2,3,3,4,2,7,8,5,2,7,8,10,2,9,4,8\nD,4,8,5,6,4,7,7,7,7,7,6,4,3,8,3,7\nB,1,3,3,2,2,8,7,3,5,10,5,7,2,8,4,9\nQ,5,11,5,6,3,10,4,4,7,11,3,9,3,7,8,11\nW,7,8,7,6,5,4,11,3,3,9,9,7,7,11,2,6\nQ,2,2,3,3,2,7,9,4,1,8,8,10,2,9,4,8\nN,7,10,10,7,6,4,9,3,4,10,10,9,8,7,2,7\nE,4,9,5,6,4,7,7,3,8,11,7,9,3,8,5,8\nM,5,8,8,6,9,8,6,3,2,8,4,8,12,5,2,7\nQ,3,7,4,6,2,8,7,8,5,6,7,8,3,8,5,9\nJ,1,4,2,2,1,9,6,3,5,12,5,9,1,7,1,7\nU,6,6,6,4,4,5,8,5,7,8,6,9,5,9,5,3\nL,8,13,8,7,5,8,4,3,5,11,6,11,3,7,7,8\nH,5,9,7,7,6,6,6,6,4,6,5,8,6,7,6,10\nR,3,2,4,4,3,6,7,5,5,6,5,7,3,7,4,8\nX,6,10,9,8,4,5,9,2,9,10,10,9,3,8,4,6\nB,2,1,3,2,2,7,8,8,5,7,6,7,2,8,8,9\nV,6,10,6,8,3,4,11,2,4,9,11,7,3,9,1,7\nX,3,5,4,5,4,7,6,2,5,7,6,9,2,11,7,8\nR,4,9,6,7,8,8,9,3,5,5,7,8,5,9,8,7\nT,3,9,5,6,4,8,11,2,7,6,11,7,2,12,1,7\nS,3,8,4,6,4,8,7,7,5,7,7,9,2,9,8,8\nO,5,10,7,8,3,8,7,9,8,7,6,9,3,8,4,8\nK,6,9,9,7,5,9,6,2,7,10,3,9,7,7,6,9\nU,4,9,6,8,6,7,7,5,4,6,7,8,6,8,1,7\nL,2,5,4,4,2,7,4,1,8,8,2,10,0,7,2,8\nU,5,9,8,7,10,8,6,4,4,6,7,8,11,9,6,8\nZ,5,6,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nX,5,7,6,6,6,6,7,2,5,7,7,10,3,8,7,8\nI,4,9,3,5,2,6,10,2,5,13,6,4,1,9,4,8\nT,4,6,4,4,2,6,11,2,9,11,9,4,0,10,3,5\nP,5,5,6,7,8,8,6,4,3,6,7,7,6,9,4,5\nM,5,6,8,4,4,5,7,3,5,10,10,10,8,5,2,7\nY,3,5,5,7,7,8,4,4,3,7,8,8,5,9,5,8\nW,3,1,3,2,1,8,8,4,0,7,8,8,7,10,0,8\nZ,3,2,3,3,2,7,8,5,10,6,6,9,1,8,7,8\nJ,6,7,8,9,7,7,8,4,7,6,7,7,3,10,9,9\nD,2,1,2,1,1,7,7,6,6,7,6,5,2,8,2,7\nK,3,7,3,4,1,3,7,7,2,7,5,11,3,8,2,11\nO,5,9,7,8,5,6,6,6,5,8,5,8,4,6,5,6\nX,3,7,5,5,2,10,5,2,8,10,1,7,3,7,3,10\nC,6,11,4,6,2,6,8,7,6,11,7,7,2,9,5,8\nG,5,9,6,7,5,7,7,8,5,5,7,9,3,6,5,9\nB,3,4,5,3,3,8,7,3,5,10,5,7,2,8,4,9\nB,2,3,2,2,2,7,7,5,5,7,6,6,2,8,5,9\nW,5,7,7,5,3,8,8,5,1,7,8,8,8,9,0,8\nI,2,8,3,6,2,8,7,0,7,13,6,9,0,8,1,8\nC,1,0,2,1,0,7,7,6,8,7,6,13,0,8,4,10\nX,6,10,8,8,7,7,7,3,6,6,6,8,6,7,13,10\nX,4,11,6,8,5,7,7,3,8,5,6,8,2,8,6,8\nM,3,3,4,4,2,7,6,11,1,8,9,8,7,6,0,8\nP,3,6,4,9,8,7,6,6,1,7,6,7,4,10,7,11\nM,6,10,8,8,8,6,6,6,5,7,8,11,8,6,2,9\nF,4,8,6,6,4,9,8,1,6,13,5,5,2,9,3,9\nM,6,7,8,5,6,8,6,2,5,9,8,8,8,4,2,7\nS,2,3,4,2,2,8,8,2,7,10,5,7,1,9,4,8\nP,3,5,5,3,2,7,11,4,3,12,5,2,1,10,2,8\nC,3,6,4,4,2,6,7,6,7,6,7,12,1,8,4,10\nQ,3,4,4,5,3,8,7,7,2,5,7,9,2,9,5,10\nO,2,3,3,1,1,8,7,7,5,7,6,8,2,8,3,8\nG,2,1,3,2,1,8,6,6,6,6,5,9,1,7,6,10\nC,2,4,3,6,1,5,7,7,9,8,6,14,1,9,4,9\nA,4,11,8,8,6,9,5,1,4,7,2,6,3,8,6,6\nZ,3,7,5,5,3,8,7,2,9,11,5,7,1,7,6,8\nV,4,5,6,4,6,7,7,5,4,7,6,8,6,9,7,7\nE,4,7,6,6,6,6,8,3,3,8,7,8,5,12,9,9\nT,1,0,2,0,0,7,14,1,4,7,10,8,0,8,0,8\nN,5,8,6,6,5,7,6,9,5,5,4,5,5,7,4,10\nE,5,9,8,6,5,10,6,2,9,11,4,8,2,8,5,11\nA,4,9,6,7,4,8,2,2,3,7,1,8,2,7,3,6\nR,4,6,5,4,3,9,7,4,5,10,3,7,3,7,4,10\nU,2,1,3,1,1,6,9,6,6,7,10,9,3,10,1,8\nE,4,10,4,7,4,3,8,5,9,7,6,14,0,8,6,8\nV,7,10,7,8,4,2,12,2,3,10,11,8,2,10,1,8\nB,5,10,6,8,8,8,8,6,6,7,6,6,6,8,6,10\nZ,4,6,6,4,4,7,8,2,8,12,7,8,1,9,6,7\nK,4,7,6,5,3,6,7,2,7,10,7,10,3,8,3,8\nD,4,9,5,6,5,7,7,6,6,7,6,5,3,8,3,7\nX,3,5,5,4,4,9,8,2,6,8,5,6,2,6,7,7\nP,5,5,7,6,7,7,8,4,3,8,8,6,6,11,5,5\nH,7,9,10,7,8,8,7,2,6,10,6,8,3,8,3,7\nC,4,8,5,6,6,6,7,4,4,6,7,10,6,10,4,8\nD,5,11,7,8,7,9,7,4,5,9,4,6,3,8,3,8\nQ,5,7,6,9,7,9,7,7,2,5,6,10,3,9,6,9\nY,6,10,6,7,3,4,10,1,8,10,10,5,2,12,4,3\nR,5,9,6,7,5,7,9,4,6,6,4,8,4,5,6,9\nG,3,7,4,5,2,7,8,8,7,6,7,8,2,7,6,11\nR,4,7,5,5,4,7,8,5,7,7,4,6,6,7,5,8\nF,2,4,4,3,2,6,11,2,6,13,6,4,1,10,1,7\nQ,5,9,5,5,3,10,5,4,6,12,3,8,3,8,7,11\nF,4,10,4,7,2,0,13,4,4,12,11,7,0,8,2,6\nQ,7,9,7,11,7,8,6,7,5,9,8,9,5,9,8,9\nT,2,3,3,2,1,5,12,3,6,11,9,4,1,10,2,5\nC,3,8,4,6,2,6,8,7,9,5,7,12,1,7,4,9\nN,4,10,6,7,4,7,9,6,5,7,6,7,6,8,2,6\nJ,1,2,2,3,1,10,6,2,6,12,4,8,0,7,1,7\nO,2,1,3,2,1,7,7,7,6,7,6,8,2,8,3,8\nZ,2,5,3,4,3,8,7,5,9,6,6,7,2,8,7,8\nM,3,7,4,5,4,8,5,11,1,6,8,8,6,5,0,7\nV,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8\nX,3,6,5,4,3,7,7,1,7,10,6,8,3,8,3,7\nH,4,8,4,5,2,7,5,15,1,7,8,8,3,8,0,8\nZ,3,7,5,5,3,7,8,2,9,11,6,9,1,9,6,8\nG,5,9,5,6,3,6,7,6,7,10,8,10,2,9,4,9\nV,4,5,6,4,5,7,8,5,5,7,6,8,6,9,7,6\nU,4,10,6,8,10,9,8,4,4,6,7,8,9,7,8,8\nF,0,0,1,0,0,3,12,4,2,11,9,6,0,8,2,8\nX,4,5,6,4,3,7,7,1,9,10,6,8,2,8,3,7\nA,5,11,8,8,6,6,3,2,3,4,2,7,5,5,5,5\nY,2,1,2,1,0,8,10,3,1,6,13,8,1,11,0,8\nU,2,3,3,2,1,5,8,5,6,11,9,9,3,9,1,6\nO,2,4,3,3,2,8,6,6,3,9,5,8,2,8,2,8\nO,5,11,6,8,6,7,6,7,4,10,6,10,5,8,4,6\nC,4,8,5,6,3,4,8,5,6,9,8,14,3,7,4,8\nC,5,10,6,8,4,4,8,7,9,8,9,13,1,8,4,9\nW,5,10,7,8,8,8,9,6,4,5,9,9,7,8,4,5\nJ,4,7,5,5,2,7,7,3,6,15,5,9,0,7,0,7\nT,3,10,5,7,1,7,15,0,6,7,11,8,0,8,0,8\nA,4,10,7,8,5,11,2,2,2,9,2,9,4,5,4,8\nH,4,7,5,5,5,7,7,6,6,7,6,8,6,8,3,8\nQ,3,4,4,5,3,8,8,6,2,5,7,9,3,9,5,10\nM,5,6,7,4,5,10,6,2,4,9,4,7,7,6,2,8\nO,3,4,4,6,2,7,7,9,7,7,6,8,3,8,4,8\nB,3,1,3,2,3,7,7,5,5,7,6,6,2,8,6,9\nI,2,9,5,7,5,10,7,2,5,9,4,5,3,8,6,5\nQ,7,9,10,8,8,5,4,4,5,4,4,7,4,8,6,6\nD,4,8,5,6,4,7,8,7,8,8,7,5,3,8,3,7\nM,4,7,5,5,3,7,7,12,1,7,9,8,8,6,0,8\nN,5,7,7,5,3,7,9,2,4,10,6,6,5,9,1,7\nO,4,11,5,8,5,8,7,8,4,7,7,7,3,7,3,8\nU,3,2,5,3,2,6,8,6,8,7,10,9,3,9,1,8\nN,3,5,5,4,3,7,9,2,4,10,6,6,5,9,1,7\nH,4,9,5,7,5,8,7,6,6,7,6,6,6,8,3,6\nA,2,2,4,3,2,8,2,1,1,7,2,8,2,7,3,7\nA,2,4,4,2,2,10,2,2,2,9,2,8,2,6,2,8\nN,9,12,7,7,3,9,11,5,6,3,5,9,5,8,2,7\nN,3,4,5,3,2,7,9,3,4,10,6,6,5,9,0,7\nX,2,2,3,3,2,8,7,3,8,6,6,8,2,8,6,8\nG,5,5,6,7,3,8,6,8,9,6,5,11,1,8,6,11\nG,3,3,4,5,2,7,8,8,7,6,7,8,2,7,6,11\nH,4,8,4,5,2,7,5,15,1,7,8,8,3,8,0,8\nB,4,5,4,3,4,7,7,6,6,7,6,6,2,8,6,10\nJ,3,8,3,6,2,14,3,4,4,13,1,8,0,7,0,8\nH,4,5,6,7,6,7,8,4,1,8,6,6,4,10,8,5\nC,4,5,5,4,2,4,8,5,7,11,10,12,1,9,3,7\nN,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8\nS,3,6,4,4,3,8,6,6,5,7,7,10,2,10,8,8\nK,3,7,4,5,3,5,7,5,7,6,5,10,3,8,4,9\nY,6,11,6,8,2,3,12,5,6,13,12,6,2,11,2,6\nX,4,9,7,7,3,7,7,1,9,10,6,8,3,8,4,7\nY,2,4,3,2,1,4,11,2,7,11,10,5,1,11,2,5\nO,2,5,3,4,2,8,7,7,4,9,6,8,2,8,2,8\nZ,4,4,6,6,3,12,4,4,4,11,2,9,2,7,4,11\nC,2,4,3,3,1,6,8,7,7,9,7,12,1,10,4,10\nR,3,5,6,4,4,8,7,3,5,10,4,6,3,7,4,10\nQ,6,9,8,8,7,7,4,5,6,7,4,9,5,5,7,7\nY,2,5,4,4,2,6,10,1,6,8,11,8,1,11,2,7\nA,2,4,3,3,1,11,3,3,2,11,2,9,1,6,2,9\nU,7,10,7,7,5,3,8,5,7,10,9,9,3,9,2,6\nC,3,7,4,5,1,6,8,6,9,5,6,14,1,7,4,9\nM,5,5,6,4,5,8,6,6,5,7,7,8,9,6,3,7\nB,7,11,9,8,7,10,6,4,7,10,4,7,3,8,6,11\nN,2,3,3,5,2,7,7,13,1,5,6,8,5,8,0,8\nF,2,4,4,3,1,6,12,3,5,13,7,4,1,9,2,6\nC,2,4,3,3,1,4,8,5,7,11,9,12,1,9,2,7\nG,2,4,3,3,2,6,7,5,4,9,8,10,2,9,4,9\nJ,1,3,2,4,1,13,3,7,4,13,4,11,1,6,0,8\nA,5,11,5,6,4,10,3,4,2,10,5,11,5,3,5,10\nU,7,10,8,8,5,3,8,5,7,10,10,10,3,9,2,6\nC,5,10,3,5,2,5,9,6,6,11,8,8,2,9,5,8\nS,4,7,6,6,7,8,7,5,4,7,7,7,5,9,10,12\nI,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nS,3,8,4,6,3,8,8,8,8,7,4,7,2,6,9,8\nH,2,2,3,3,3,8,8,6,6,7,6,7,3,8,3,7\nB,3,5,4,3,4,7,7,5,5,6,6,6,2,8,6,10\nZ,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8\nF,5,8,7,6,4,7,10,2,6,13,6,5,2,10,2,8\nD,5,9,7,7,6,10,7,5,6,9,3,6,3,8,3,8\nF,2,3,2,1,1,6,10,4,5,10,9,4,1,10,3,6\nK,3,4,5,3,2,7,6,1,6,10,6,10,3,8,3,8\nW,6,8,8,7,9,8,8,5,6,7,6,8,9,8,8,7\nT,0,0,1,0,0,7,13,1,4,7,10,8,0,8,0,8\nE,3,11,5,8,5,7,7,6,8,6,5,9,3,8,6,9\nL,2,3,2,2,1,4,3,5,7,2,2,5,0,7,1,6\nI,3,7,4,5,2,7,7,0,7,13,6,8,0,8,1,8\nV,6,11,6,8,3,3,12,5,4,11,12,7,3,9,1,8\nP,4,5,5,7,6,8,9,3,2,7,8,6,5,11,5,4\nH,4,6,6,4,4,7,7,3,6,10,7,8,3,9,3,7\nO,2,4,3,3,2,7,7,7,5,7,6,8,2,8,3,8\nN,5,9,8,7,4,9,7,3,5,10,3,5,6,9,1,7\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nY,1,0,2,0,0,7,9,2,2,6,12,8,1,10,0,8\nI,1,2,1,3,1,7,7,1,7,7,6,8,0,8,3,8\nR,1,0,2,1,1,6,9,7,3,7,5,7,2,7,4,10\nJ,3,10,4,7,2,10,6,0,8,11,3,6,0,7,1,7\nN,10,15,9,8,4,8,10,5,4,4,6,10,6,11,3,6\nY,4,7,6,9,9,9,6,6,3,7,7,8,7,9,6,5\nB,1,1,2,1,1,7,7,7,5,7,6,7,1,8,6,8\nS,5,5,6,4,6,9,8,4,5,7,7,8,5,10,9,11\nH,1,0,1,0,0,7,7,10,1,7,6,8,2,8,0,8\nG,2,4,2,2,1,6,7,5,5,9,7,10,2,8,4,9\nG,6,9,5,4,3,10,6,4,6,11,3,8,4,7,5,9\nC,8,11,5,6,2,7,9,6,7,12,7,8,2,9,5,9\nN,2,1,2,1,1,7,7,12,1,5,6,8,5,8,0,8\nT,4,11,5,8,3,7,14,0,5,7,10,8,0,8,0,8\nK,4,5,7,3,3,6,8,2,8,10,8,10,4,7,3,6\nF,6,11,9,8,9,5,8,2,4,10,9,8,6,11,6,6\nX,2,3,3,2,2,8,7,3,9,6,6,8,2,8,5,8\nF,3,7,5,5,3,5,10,4,6,11,10,5,2,9,3,5\nO,6,8,8,7,6,8,4,3,4,9,3,8,4,7,5,8\nW,1,0,2,0,0,7,8,3,0,7,8,8,5,9,0,8\nY,5,9,8,6,4,8,10,1,7,5,11,8,2,12,3,8\nJ,2,6,3,4,1,10,7,1,6,12,3,7,0,7,0,7\nU,12,15,10,8,4,5,3,5,5,4,7,7,5,8,2,6\nV,3,5,4,3,1,3,12,4,3,11,11,7,2,10,1,8\nZ,7,10,9,8,5,7,8,3,10,12,7,7,1,8,6,7\nT,4,9,6,8,6,7,9,4,7,7,8,8,3,12,10,7\nE,6,11,4,6,2,7,8,5,7,8,4,12,1,7,7,8\nJ,2,9,3,7,1,15,2,6,5,14,1,9,0,7,0,8\nW,10,10,10,8,6,2,10,2,3,10,11,9,8,10,1,7\nD,4,10,5,7,3,6,7,11,9,7,6,6,3,8,4,8\nM,6,9,7,4,4,10,10,6,3,5,7,9,10,13,2,6\nS,3,8,4,6,3,7,8,8,7,8,5,7,2,7,9,8\nT,3,5,4,4,4,6,8,4,7,7,7,9,3,10,6,7\nV,4,10,6,8,4,8,11,2,3,4,10,9,3,11,2,8\nO,7,10,7,8,5,8,6,8,6,10,5,9,5,9,6,5\nD,5,11,7,9,6,9,7,5,7,10,5,5,3,8,3,8\nB,2,5,4,4,3,8,8,3,5,10,6,6,3,7,6,9\nQ,2,4,3,4,3,8,8,6,3,8,6,9,2,9,4,9\nL,3,7,4,5,1,0,0,6,6,0,1,5,0,8,0,8\nS,2,1,2,2,1,8,7,4,7,5,6,7,0,8,8,8\nU,3,2,4,3,2,8,9,5,7,5,9,8,3,9,1,8\nL,3,7,3,5,1,1,1,5,5,0,1,6,0,8,0,8\nJ,2,3,3,2,1,8,6,3,6,14,6,10,0,7,0,8\nY,4,6,7,8,1,6,10,3,2,8,13,8,1,11,0,8\nE,4,7,5,5,4,9,6,2,6,11,5,8,3,8,4,11\nW,3,7,5,5,3,10,8,4,1,7,8,8,8,9,0,8\nH,6,11,9,8,7,9,7,3,6,10,5,8,3,8,3,8\nH,2,1,3,2,2,7,8,6,6,7,6,8,3,8,3,8\nG,4,8,5,6,3,8,6,6,7,11,6,11,2,10,4,9\nM,3,6,4,4,2,8,7,11,1,7,9,8,7,5,0,8\nM,15,15,15,8,8,3,9,6,6,3,2,13,10,12,2,8\nZ,3,8,4,6,3,7,7,5,9,7,6,8,2,8,7,8\nD,2,4,4,2,2,8,7,4,6,10,5,6,2,8,3,8\nQ,5,10,7,9,3,8,5,9,8,6,4,8,3,8,4,8\nO,1,0,1,0,0,7,7,6,3,7,6,8,2,8,3,8\nB,5,10,8,8,12,8,8,5,3,7,7,7,8,11,12,9\nC,4,8,5,6,3,4,9,6,7,7,8,14,1,8,4,10\nI,4,7,5,5,3,8,4,2,6,7,7,7,1,10,4,7\nN,7,11,8,6,3,9,7,2,4,13,5,8,6,8,0,8\nY,9,10,7,14,5,9,11,2,3,7,10,5,4,10,6,9\nG,3,4,4,3,2,7,6,6,5,6,6,9,2,9,4,9\nQ,4,9,4,5,3,10,5,4,6,11,4,8,3,8,8,12\nF,2,3,3,2,1,7,9,2,5,13,6,5,1,9,1,8\nV,7,13,6,7,3,7,11,5,5,8,11,5,4,11,4,8\nF,4,6,4,8,2,0,14,4,4,12,10,6,0,8,2,6\nX,2,1,2,1,0,7,7,4,4,7,6,8,2,8,4,8\nY,1,0,2,1,0,7,9,1,2,6,12,8,1,10,0,8\nP,7,11,10,8,6,9,8,4,6,12,3,3,2,10,4,9\nL,3,9,4,7,2,0,2,4,6,1,0,8,0,8,0,8\nU,9,12,8,7,5,7,6,5,5,6,7,8,6,8,3,7\nN,11,13,9,7,4,5,9,5,6,3,3,12,6,12,2,7\nY,3,8,4,6,2,8,10,1,3,7,12,8,1,11,0,8\nA,2,8,4,6,2,12,2,4,3,11,2,10,2,6,4,9\nY,2,9,4,6,1,8,10,2,3,6,12,8,1,10,0,8\nH,3,3,3,4,1,7,9,14,1,7,4,8,3,8,0,8\nU,5,9,6,6,6,8,6,8,5,7,6,9,3,8,4,5\nS,3,5,4,7,2,9,9,6,10,5,6,5,0,7,9,7\nN,3,3,5,1,1,8,8,3,5,11,5,6,5,9,0,6\nQ,5,7,7,6,6,7,4,5,5,6,5,9,5,4,7,7\nQ,3,3,4,4,3,8,8,6,2,5,7,10,2,9,5,9\nI,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nH,3,5,6,4,3,9,6,3,6,10,3,7,4,7,4,9\nO,6,12,4,6,3,7,8,5,4,8,4,7,5,8,5,8\nT,2,3,3,2,1,6,11,2,7,10,9,5,1,10,2,5\nD,6,11,8,8,8,8,7,7,6,8,5,4,8,11,7,10\nW,5,10,7,8,4,6,8,4,2,7,8,8,9,9,0,8\nR,6,10,5,5,3,7,6,5,4,9,5,9,6,5,6,11\nZ,2,4,5,3,2,7,7,2,9,12,6,8,1,8,5,8\nU,2,6,2,4,1,7,6,12,4,7,13,8,3,9,0,8\nE,1,0,2,1,1,5,7,5,8,7,6,12,0,8,6,9\nH,5,10,7,7,9,8,7,4,3,6,7,8,8,8,8,8\nK,2,3,4,2,2,6,8,2,6,10,6,9,3,8,2,8\nG,1,3,2,1,1,6,7,5,4,9,7,10,2,8,4,9\nA,2,4,3,3,1,11,3,2,2,9,2,9,1,6,2,8\nY,6,11,6,8,4,3,9,1,7,10,10,6,1,11,3,4\nN,3,8,4,6,3,8,8,6,5,6,6,5,6,9,2,5\nE,4,7,6,6,7,7,6,4,3,7,6,9,6,11,8,12\nI,5,13,4,7,2,9,7,5,4,12,3,6,3,8,5,10\nX,1,0,1,0,0,7,7,3,5,7,6,8,2,8,4,8\nN,7,9,6,5,3,8,10,5,4,4,6,10,6,11,3,7\nH,5,5,6,6,3,7,5,15,1,7,8,8,3,8,0,8\nE,3,5,5,3,3,7,8,2,8,11,7,9,2,9,4,8\nH,4,7,6,5,4,8,7,6,7,7,6,5,6,8,3,7\nD,5,10,5,5,4,9,6,3,6,10,4,7,5,8,7,7\nM,4,7,5,5,3,6,7,12,1,8,9,8,8,6,0,8\nJ,1,3,2,2,1,11,6,2,5,11,4,8,0,7,0,7\nA,3,9,6,7,4,12,3,2,2,9,2,9,2,6,2,8\nC,8,14,6,8,3,6,9,6,9,11,8,10,2,8,5,8\nK,5,8,6,6,6,5,7,5,7,6,6,13,3,8,6,9\nC,4,9,5,6,3,6,7,6,8,6,5,12,1,8,4,9\nI,1,6,2,4,1,9,7,0,7,13,5,8,0,8,1,8\nQ,4,7,5,8,5,9,9,6,2,4,7,11,3,9,5,10\nH,4,8,5,6,4,7,8,6,6,7,6,7,3,8,3,7\nA,9,13,8,8,4,10,0,3,2,10,4,12,3,5,4,8\nM,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8\nM,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8\nZ,9,15,9,8,6,8,5,2,9,12,6,10,3,8,6,8\nH,4,10,5,7,3,7,8,15,1,7,5,8,3,8,0,8\nQ,3,6,4,8,4,7,9,5,3,8,9,10,3,8,6,8\nU,3,3,4,2,2,5,8,5,7,10,8,9,3,10,2,5\nC,4,7,4,5,3,4,7,5,6,11,9,14,1,9,3,9\nI,1,9,0,7,1,7,7,5,3,7,6,8,0,8,0,8\nX,1,3,2,1,1,8,7,3,8,7,6,8,2,8,5,8\nS,3,9,4,7,3,8,8,5,8,5,5,6,0,8,8,8\nT,5,10,7,8,7,9,11,4,6,5,11,8,3,12,1,8\nF,4,5,4,7,2,1,12,5,6,12,11,8,0,8,2,5\nA,2,9,4,6,2,7,4,3,2,6,1,8,2,7,2,8\nR,4,8,5,6,3,6,10,9,4,7,4,8,3,8,5,10\nG,4,7,5,5,3,6,7,6,7,10,7,11,2,9,4,9\nY,6,6,5,8,4,9,8,4,4,5,11,5,4,11,5,6\nS,8,15,8,8,4,7,7,4,3,13,8,9,3,10,3,8\nR,4,9,6,7,8,7,9,3,5,5,6,9,5,8,8,7\nF,2,3,2,2,1,5,11,4,4,11,9,5,1,9,3,7\nL,2,7,4,5,5,8,8,3,4,6,6,9,5,11,6,5\nW,4,7,6,5,4,7,11,2,2,6,9,8,8,11,1,8\nW,5,9,5,4,3,6,9,1,3,8,9,7,7,12,1,6\nP,2,6,3,4,1,4,11,8,2,10,6,4,1,10,3,8\nH,5,9,6,7,7,7,7,6,6,7,6,8,3,8,3,8\nK,3,7,4,5,3,3,7,6,3,7,6,11,3,8,3,11\nL,2,7,3,5,1,0,1,5,6,0,0,7,0,8,0,8\nS,4,7,6,5,6,8,6,4,3,8,6,8,4,8,10,10\nD,5,9,7,6,6,9,7,3,6,11,5,6,4,7,4,8\nE,4,7,5,5,3,6,8,3,8,11,7,8,2,8,4,7\nS,2,6,3,4,2,8,8,7,5,7,5,6,2,8,8,8\nL,4,7,5,5,4,7,6,8,6,5,6,8,3,7,5,11\nZ,4,8,6,6,4,8,6,2,9,11,4,10,2,7,7,10\nY,5,9,5,4,2,5,8,3,3,10,9,6,4,9,3,4\nM,3,7,4,5,4,7,6,5,4,7,7,11,6,5,1,10\nW,4,8,6,6,6,10,12,2,2,5,8,8,6,11,0,8\nD,3,8,4,6,2,5,6,10,8,5,5,5,3,8,4,8\nA,3,3,5,5,2,6,4,3,1,6,1,8,3,7,2,7\nQ,3,3,4,4,3,8,8,7,3,5,7,10,2,9,5,10\nY,6,10,8,8,8,8,5,6,5,8,7,8,6,9,7,3\nX,3,5,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nV,4,7,6,5,6,8,5,4,2,7,7,8,7,8,5,8\nY,3,3,5,4,1,8,11,2,2,5,12,8,1,10,0,8\nQ,5,9,6,8,3,8,5,9,7,5,4,8,3,8,4,8\nE,4,11,4,8,3,3,8,6,11,7,5,14,0,8,7,7\nU,5,4,5,7,2,7,4,15,6,7,14,8,3,9,0,8\nH,6,10,8,8,7,10,6,2,6,10,3,7,4,8,4,10\nG,3,4,4,3,2,6,7,5,5,9,8,9,2,9,4,9\nO,3,7,4,5,2,8,6,9,6,7,5,8,3,8,4,8\nN,4,9,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nV,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8\nU,8,10,9,8,5,4,8,6,9,10,9,9,3,9,3,5\nI,3,8,4,6,2,5,9,0,7,13,7,7,0,8,1,7\nF,7,8,8,9,9,6,9,4,5,7,7,6,5,8,11,8\nV,3,4,4,3,1,5,12,2,2,9,11,7,3,12,1,7\nY,4,8,6,6,5,9,5,7,5,6,9,7,3,9,9,4\nH,6,10,9,8,7,6,7,5,5,6,5,8,3,7,7,10\nL,4,11,5,8,4,7,4,0,7,9,3,10,1,6,3,8\nH,4,4,5,6,2,7,8,15,1,7,5,8,3,8,0,8\nX,4,4,5,6,1,7,7,5,4,7,6,8,3,8,4,8\nB,2,6,4,4,3,7,7,7,6,7,6,6,2,8,7,9\nX,11,13,10,8,5,6,8,2,9,9,7,9,5,7,5,7\nH,4,4,6,3,3,9,7,3,6,10,3,7,3,7,3,9\nA,3,9,4,6,3,7,4,3,1,6,2,8,2,7,3,8\nN,5,8,6,6,4,6,9,6,5,8,7,9,6,9,1,7\nP,4,11,6,8,6,6,10,4,5,10,9,4,4,10,4,7\nZ,1,1,2,1,0,8,7,2,10,9,6,8,0,8,6,8\nV,4,7,6,5,6,7,6,4,2,8,8,8,5,10,4,7\nO,5,8,5,6,4,7,9,7,4,10,8,6,4,9,4,9\nJ,2,5,4,3,1,9,6,3,6,14,5,10,1,6,1,7\nQ,4,5,5,6,5,8,4,7,4,6,5,8,3,8,4,9\nJ,2,4,4,3,1,8,6,3,6,14,6,11,1,6,1,7\nZ,4,7,6,5,3,7,8,2,10,12,6,8,1,9,6,8\nM,6,9,8,7,8,8,7,2,4,9,7,8,8,6,2,8\nB,5,11,6,8,5,6,8,9,8,7,5,7,2,8,9,10\nU,3,6,5,4,3,7,9,6,6,5,9,9,3,9,1,8\nX,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8\nL,4,11,4,8,3,0,2,4,6,1,0,7,0,8,0,8\nD,3,8,4,6,4,6,7,9,6,7,7,5,3,8,3,8\nY,3,7,5,5,2,7,11,1,3,8,11,8,1,10,0,8\nB,4,8,6,7,7,8,7,5,5,7,6,8,6,9,8,4\nL,4,10,4,5,2,10,3,2,5,12,3,11,2,9,4,10\nS,4,4,4,6,2,8,8,5,9,5,6,6,0,8,9,7\nR,5,10,6,7,7,8,7,7,4,8,5,7,4,7,7,10\nO,3,8,5,6,3,7,8,9,5,7,7,6,3,8,3,8\nL,4,8,6,7,5,7,7,4,5,6,7,9,4,8,8,8\nQ,3,6,4,5,3,8,8,7,5,6,6,9,2,8,4,8\nY,5,8,5,6,3,6,9,1,8,8,9,4,1,11,4,5\nY,4,5,5,4,2,4,10,1,8,10,10,5,4,11,4,3\nB,1,0,1,0,1,7,8,6,4,7,6,7,1,8,6,9\nF,4,7,5,5,3,4,12,2,5,13,7,5,1,10,1,7\nL,6,12,6,6,4,9,4,3,4,12,7,11,3,10,5,11\nI,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nJ,4,10,5,8,2,6,8,2,7,15,7,9,1,6,1,7\nR,2,3,4,2,2,8,8,3,5,10,4,7,2,7,3,9\nF,5,9,8,6,4,6,11,3,6,14,6,4,2,10,2,7\nX,4,7,6,5,3,5,8,1,8,10,10,9,2,9,3,6\nD,5,11,6,8,7,9,8,6,5,9,5,4,5,8,4,10\nG,4,6,5,4,3,7,7,8,7,6,6,11,2,9,4,9\nB,4,7,4,5,3,6,7,8,6,7,6,7,2,8,9,10\nT,7,9,7,7,5,5,12,4,6,11,9,4,2,12,2,4\nG,3,4,4,3,2,6,7,5,5,9,7,10,2,8,4,9\nX,4,11,5,8,2,7,7,4,4,7,6,8,3,8,4,8\nI,2,8,5,6,5,9,6,3,5,8,5,5,5,8,5,5\nP,5,10,5,7,3,5,9,10,5,8,6,5,2,9,4,8\nG,5,7,6,5,3,7,6,7,7,10,6,11,2,10,4,9\nD,4,10,5,8,6,7,7,6,6,6,5,5,3,8,3,7\nA,4,11,6,8,3,9,3,3,3,8,1,9,3,6,3,9\nJ,3,6,2,9,2,9,8,3,3,12,4,5,2,8,5,10\nK,6,10,8,8,9,6,5,5,4,6,6,8,4,5,9,10\nE,4,7,6,5,6,8,8,3,5,5,7,10,5,12,8,9\nZ,6,9,8,7,4,8,6,3,11,12,5,10,1,8,6,8\nG,4,9,5,6,7,8,7,5,2,6,6,8,8,8,7,12\nT,3,5,4,3,2,5,12,2,8,11,9,4,1,10,2,5\nE,3,7,4,5,4,8,7,2,6,11,6,8,3,8,4,10\nM,3,4,6,3,3,7,6,3,4,9,7,8,7,5,1,8\nV,4,9,6,7,7,7,8,3,2,7,8,8,8,9,6,7\nO,2,1,2,1,1,7,7,6,4,7,5,8,2,8,2,7\nY,7,10,7,8,4,2,10,2,6,10,12,7,2,11,2,6\nD,4,7,5,5,8,9,8,4,5,7,6,6,4,6,8,7\nV,4,5,5,3,2,4,12,2,3,9,11,7,4,10,1,7\nD,5,10,6,8,6,6,8,9,8,7,6,5,2,8,3,7\nS,3,5,5,3,2,8,7,3,8,11,5,7,1,9,4,8\nP,4,7,5,5,2,7,10,3,5,14,5,3,0,10,3,8\nK,2,1,2,2,0,4,7,8,1,7,6,11,3,8,2,11\nI,3,9,5,7,5,12,5,1,6,8,4,5,1,8,5,10\nI,6,14,6,8,3,8,7,2,5,13,4,5,2,8,6,10\nH,5,6,8,4,4,6,7,3,6,10,8,10,3,8,3,7\nO,2,3,3,2,1,8,7,6,4,9,6,8,2,8,2,8\nG,5,11,6,8,4,6,6,7,8,11,6,13,2,9,4,8\nO,3,9,4,7,4,7,7,8,4,7,6,9,3,8,3,8\nP,2,1,2,2,1,5,11,4,3,10,8,3,0,9,3,7\nZ,2,2,3,3,2,7,7,5,9,6,6,8,2,8,7,8\nE,5,8,7,7,7,7,9,5,5,7,7,10,5,9,9,10\nQ,3,7,4,5,4,8,5,7,3,6,7,8,3,7,6,9\nV,7,9,9,8,10,7,8,5,6,8,6,8,7,7,8,8\nX,4,5,5,4,3,8,7,3,9,6,6,8,2,8,6,9\nP,4,5,4,7,2,3,13,8,1,11,6,3,1,10,4,8\nE,3,6,4,4,4,8,7,6,2,7,6,11,3,8,7,10\nF,4,6,6,7,7,8,9,5,5,7,5,8,5,8,8,6\nL,3,7,3,5,1,0,0,6,6,0,1,5,0,8,0,8\nW,7,9,8,5,4,7,8,3,4,6,9,6,9,11,3,5\nI,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nE,3,7,5,5,5,5,7,3,6,7,6,12,2,10,8,6\nY,4,8,5,6,2,5,10,2,2,8,12,8,2,11,0,8\nO,3,7,4,5,3,7,7,8,5,7,7,8,3,8,3,8\nX,5,8,8,6,4,5,8,2,8,11,10,10,3,8,3,6\nV,3,8,5,6,1,6,8,4,3,7,14,8,3,10,0,8\nW,4,7,6,5,9,8,7,5,2,7,7,8,10,10,3,8\nR,3,7,5,5,3,8,8,5,5,8,5,7,3,7,5,10\nJ,2,3,4,2,1,9,6,3,5,14,6,10,0,7,0,8\nY,6,8,6,6,3,4,10,1,8,11,10,6,2,11,4,3\nO,6,10,7,8,7,8,7,9,4,7,6,8,3,8,3,7\nP,2,1,2,3,2,5,9,4,4,9,8,5,1,10,3,7\nH,1,0,2,1,0,7,7,11,1,7,6,8,3,8,0,8\nU,9,10,9,8,4,3,8,6,8,12,11,10,3,9,2,6\nT,1,6,2,4,1,7,13,0,4,7,10,8,0,8,0,8\nQ,3,5,3,6,3,8,8,5,3,8,8,10,3,9,6,8\nR,3,4,5,3,3,8,8,3,5,9,4,7,2,6,4,10\nN,3,8,4,6,4,7,7,12,1,6,6,8,5,8,0,8\nU,5,6,6,6,6,8,6,5,4,6,6,8,4,8,1,7\nO,2,1,3,2,1,7,7,7,6,7,6,8,2,8,3,8\nE,2,4,4,3,2,6,8,2,8,11,7,9,2,8,4,8\nP,7,11,7,6,4,8,9,5,3,11,5,4,4,11,6,5\nL,4,8,6,6,7,8,7,3,5,7,7,9,7,9,6,6\nS,7,10,8,8,6,7,8,3,6,10,5,7,2,8,5,8\nY,2,3,4,4,0,7,10,1,3,7,12,8,1,11,0,8\nW,6,10,8,8,9,7,7,6,2,7,8,8,6,8,4,8\nY,1,1,3,2,1,5,10,1,6,9,11,9,1,11,2,8\nY,3,5,5,7,1,7,10,1,3,7,12,8,1,11,0,8\nE,2,4,3,3,2,7,7,2,7,11,7,9,2,8,4,8\nF,4,10,6,7,3,4,13,4,4,13,8,3,1,10,2,5\nD,5,9,5,4,3,8,6,4,6,8,5,7,5,9,5,7\nE,2,3,2,2,2,7,7,5,7,7,6,9,2,8,5,10\nK,2,3,4,2,2,6,7,2,6,10,8,11,3,8,2,8\nF,2,4,3,3,2,5,10,4,5,10,9,5,1,10,3,6\nZ,4,4,5,6,2,7,7,4,15,9,6,8,0,8,8,8\nB,5,8,7,6,5,9,8,3,7,10,4,5,2,8,6,10\nT,4,10,6,8,8,8,7,5,6,7,6,8,7,6,7,7\nH,6,6,6,8,3,7,8,15,0,7,5,8,3,8,0,8\nH,3,8,4,6,4,7,7,13,1,7,6,8,3,8,0,8\nU,5,8,5,6,2,4,9,5,7,12,11,8,3,9,1,6\nP,7,8,9,10,11,8,10,2,4,7,9,6,6,9,7,5\nY,4,4,6,6,7,9,4,5,3,7,7,8,5,9,4,8\nR,6,9,6,4,4,8,7,3,5,9,4,8,6,7,6,8\nJ,3,11,4,9,4,10,7,1,7,11,3,7,0,7,1,7\nM,6,8,8,7,9,7,9,4,3,7,5,8,12,8,5,7\nJ,3,8,5,6,2,7,8,1,6,14,5,8,1,7,0,7\nV,4,5,5,3,2,4,12,2,3,9,11,7,4,11,1,7\nB,1,3,3,2,2,8,8,2,5,10,6,6,2,8,4,8\nA,3,11,5,8,4,8,5,3,0,7,1,8,2,7,3,8\nB,3,7,5,5,7,9,8,4,3,6,7,7,5,11,7,7\nW,5,4,7,7,4,8,8,4,2,7,8,8,9,9,0,8\nU,3,2,4,3,2,6,8,6,7,7,9,9,3,9,1,8\nK,2,1,3,1,2,6,7,4,8,7,6,10,3,8,5,8\nE,3,8,5,6,5,7,7,4,8,7,7,8,3,8,5,10\nI,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8\nK,6,11,9,8,5,8,8,2,7,10,4,8,3,8,3,9\nA,4,10,7,8,5,12,3,2,2,9,2,9,2,6,2,8\nK,4,11,6,8,5,5,6,5,8,6,7,12,3,8,6,10\nU,4,5,5,7,2,7,5,14,5,7,14,8,3,9,0,8\nR,3,5,6,4,3,9,8,4,6,9,5,6,3,7,4,9\nE,3,9,4,6,4,7,7,6,9,7,7,9,3,8,6,9\nY,6,7,6,5,4,5,9,1,8,9,9,5,2,9,4,4\nL,4,11,6,8,4,5,3,3,9,6,1,8,1,6,3,6\nR,5,5,6,8,4,5,10,9,3,7,5,8,3,8,6,11\nJ,4,11,5,8,2,8,6,3,7,15,5,9,1,6,1,7\nV,2,7,4,5,2,7,11,2,3,6,11,9,2,10,1,8\nF,1,0,1,0,0,3,12,4,2,11,8,6,0,8,2,7\nK,2,4,4,3,2,7,7,2,6,10,5,9,4,7,2,8\nK,2,1,3,2,2,6,7,4,7,7,6,10,3,8,5,9\nM,5,8,7,6,6,8,6,6,5,7,7,8,8,5,2,8\nX,4,8,5,6,3,7,7,4,4,7,6,8,3,8,4,8\nZ,2,6,4,4,2,8,7,2,9,11,5,9,1,8,5,8\nH,7,10,10,7,8,7,8,2,6,10,7,8,5,9,4,8\nN,3,8,4,6,4,7,7,12,1,6,6,8,5,8,0,8\nU,9,13,8,7,4,7,5,6,7,3,9,7,5,9,3,6\nL,5,10,6,8,3,2,2,5,8,1,0,6,0,7,1,6\nU,6,6,6,4,3,3,9,5,7,10,10,10,3,9,2,6\nR,4,3,4,4,2,6,9,8,4,6,5,8,2,8,5,10\nB,5,9,8,8,10,7,7,5,4,7,6,8,7,10,9,5\nK,4,8,6,6,3,4,8,3,7,11,11,11,3,8,4,5\nU,4,7,5,5,2,7,4,13,5,7,15,8,3,9,0,8\nF,5,10,7,8,5,6,11,1,6,13,7,5,1,10,2,7\nF,2,5,4,4,2,4,12,4,4,13,8,5,1,10,1,7\nO,2,0,2,1,1,7,6,7,5,7,6,8,2,8,3,8\nL,6,8,7,7,6,8,6,4,5,7,7,8,3,8,8,10\nL,1,0,1,0,0,2,1,6,4,0,3,4,0,8,0,8\nW,6,9,6,6,6,5,10,3,3,9,7,7,6,11,2,7\nW,9,10,9,7,6,2,10,2,4,11,11,9,7,10,1,7\nY,6,9,8,6,6,8,8,6,5,5,8,8,3,8,9,6\nL,3,5,5,5,4,8,5,5,5,7,7,8,2,8,8,11\nO,5,9,6,6,5,7,7,8,4,7,6,9,3,8,4,7\nG,7,10,5,5,4,7,6,3,2,8,6,8,4,9,9,8\nE,4,11,4,8,3,3,7,6,10,7,6,14,0,8,8,7\nF,3,8,6,6,6,8,7,1,4,10,6,7,5,9,4,5\nG,3,4,4,3,2,6,7,4,4,9,7,9,2,8,5,10\nP,2,4,4,3,2,7,9,4,3,11,4,4,1,10,2,8\nC,3,5,5,3,2,4,8,5,7,12,9,11,1,9,3,7\nV,6,9,5,7,4,3,11,2,2,9,10,8,3,11,1,7\nM,4,10,5,8,6,7,6,10,0,7,8,8,7,5,0,8\nV,2,3,3,2,1,7,12,2,2,7,11,8,2,10,1,8\nR,4,8,5,6,5,8,5,7,4,7,6,8,4,7,6,10\nB,4,7,6,5,4,9,6,4,6,10,5,6,2,8,6,10\nK,5,9,7,7,8,7,7,3,4,6,6,9,7,7,8,7\nH,7,11,7,6,5,7,7,3,4,9,8,8,7,10,6,8\nA,2,7,4,5,2,12,3,4,3,12,2,9,2,6,3,9\nG,6,11,8,8,5,5,7,5,5,5,7,7,3,6,4,7\nL,4,9,6,7,3,4,2,8,7,1,2,3,1,6,1,6\nR,2,6,2,4,2,5,10,7,3,7,4,9,2,6,5,11\nT,3,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8\nC,2,1,3,2,1,7,8,7,6,9,7,11,1,9,3,10\nY,6,9,6,7,4,3,9,1,6,10,11,7,1,11,3,5\nM,5,8,8,6,5,10,5,3,5,9,4,7,8,6,2,9\nK,2,1,2,2,1,6,7,4,8,7,6,11,3,8,5,9\nF,3,6,4,4,2,5,11,1,5,13,7,5,1,10,1,7\nC,1,1,2,1,0,6,7,6,9,7,6,15,0,8,4,10\nQ,1,0,1,0,0,8,7,6,2,6,6,9,2,8,2,8\nQ,7,13,6,7,5,11,4,4,6,11,4,8,4,8,8,11\nM,4,5,4,7,4,7,7,12,2,7,9,8,8,5,0,8\nF,8,12,7,6,4,8,8,2,5,10,5,6,3,8,7,8\nO,4,3,5,4,2,7,8,8,8,7,7,7,3,8,4,8\nX,4,8,6,6,3,9,7,1,8,10,3,7,2,8,3,8\nZ,3,7,5,5,2,8,7,2,10,11,5,9,1,8,6,9\nH,3,4,4,6,2,7,6,15,2,7,8,8,3,8,0,8\nC,6,9,5,4,3,8,7,4,3,9,8,10,4,8,7,11\nG,7,10,7,8,5,6,6,7,7,10,7,11,2,9,5,9\nW,5,7,7,5,3,7,8,4,1,6,8,8,8,9,0,8\nN,4,6,6,4,3,10,7,3,5,10,2,5,5,9,1,8\nG,4,10,6,8,3,7,6,8,8,6,6,9,2,8,6,11\nL,2,7,2,5,1,0,2,5,6,0,0,7,0,8,0,8\nX,4,8,5,6,4,7,7,3,8,5,6,8,3,8,6,8\nO,5,10,6,8,5,7,7,8,6,7,6,8,3,7,4,8\nL,2,0,2,1,0,2,0,6,4,0,3,4,0,8,0,8\nI,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nQ,3,3,4,5,4,8,7,7,3,6,5,9,3,8,5,9\nL,1,3,2,1,1,6,5,1,7,8,3,10,0,7,2,8\nU,5,11,7,9,5,6,8,5,7,6,8,9,3,9,1,8\nM,5,11,6,9,8,7,10,6,3,7,6,8,8,10,7,8\nP,9,13,9,7,6,8,9,4,4,12,4,4,5,10,6,6\nH,3,7,4,4,2,7,6,14,1,7,7,8,3,8,0,8\nA,1,0,2,0,0,8,3,2,0,7,2,8,2,6,1,8\nX,7,10,10,8,6,4,9,2,8,10,11,10,3,9,3,5\nA,2,4,4,3,2,7,2,2,1,6,2,8,2,7,3,6\nX,7,10,10,8,5,6,7,1,9,10,8,9,3,8,4,7\nB,4,9,5,7,4,6,8,9,7,7,5,7,2,8,9,10\nG,10,14,8,8,5,8,8,5,4,9,6,6,4,9,9,8\nH,4,6,6,4,6,8,8,4,3,6,6,7,7,9,7,8\nW,8,9,8,6,7,3,10,2,3,10,9,8,7,11,2,6\nP,8,11,8,6,5,9,8,4,4,12,4,4,5,10,6,7\nF,2,3,3,2,1,8,9,2,6,13,5,4,1,9,1,8\nL,2,0,2,1,0,2,1,6,5,0,2,4,0,8,0,8\nP,2,6,3,4,2,5,10,9,4,9,6,5,2,9,3,8\nB,4,11,4,8,4,6,7,10,7,7,6,7,2,8,9,10\nB,5,9,7,7,6,8,8,4,7,10,5,6,2,8,6,10\nZ,7,10,9,7,6,7,8,2,9,12,6,8,1,8,6,8\nC,2,3,2,2,1,5,8,5,6,11,9,11,1,9,3,8\nR,1,0,2,1,1,6,10,7,2,7,5,8,2,7,4,9\nI,1,3,2,2,0,7,7,1,7,13,6,8,0,8,1,8\nV,3,7,5,5,2,8,9,4,2,6,13,8,3,10,0,8\nH,7,10,7,5,5,6,8,4,5,9,9,9,7,11,6,7\nZ,2,1,3,2,2,7,7,5,9,6,6,8,1,8,7,8\nJ,1,4,3,3,1,8,6,3,4,14,6,11,1,7,0,7\nU,2,3,3,2,1,7,8,6,6,7,9,8,3,10,1,8\nJ,4,9,6,7,3,7,6,4,6,15,7,11,1,6,1,7\nL,4,9,4,7,2,0,1,4,6,1,0,7,0,8,0,8\nE,6,10,4,5,3,7,9,5,5,10,6,9,3,8,6,10\nU,3,5,4,3,2,4,8,5,7,10,9,9,3,9,2,6\nT,4,6,4,4,2,5,12,3,6,12,9,5,2,11,1,5\nM,8,11,12,8,8,5,7,3,5,10,10,10,12,6,4,8\nI,4,10,5,8,3,8,6,0,8,13,6,9,1,6,3,8\nA,3,3,5,4,2,8,2,2,2,6,2,8,2,7,2,7\nV,7,11,6,6,3,7,10,6,4,10,9,5,5,12,4,9\nJ,2,5,4,4,2,8,6,3,5,14,6,11,1,6,1,7\nS,4,5,5,5,5,9,8,4,6,6,8,9,4,11,7,10\nT,3,10,5,7,2,9,13,0,6,6,10,8,0,8,0,8\nK,8,12,7,6,3,6,8,2,7,9,6,8,5,7,3,7\nZ,5,10,6,8,4,8,7,6,12,7,6,7,1,8,8,8\nT,7,9,8,8,9,7,9,5,8,7,7,8,5,10,11,7\nR,5,10,7,7,6,6,7,5,6,6,5,7,3,7,5,8\nW,4,9,6,7,5,9,10,2,2,5,9,7,8,12,1,7\nF,6,12,5,7,4,7,10,3,5,11,6,4,3,10,7,6\nL,4,9,4,7,1,0,1,6,6,0,0,6,0,8,0,8\nL,2,1,3,2,1,4,4,3,8,2,1,7,0,7,1,6\nH,6,10,9,8,7,9,7,7,7,7,6,5,3,8,3,6\nY,4,11,5,8,2,6,10,1,3,7,12,8,1,11,0,8\nM,4,8,6,6,7,7,6,5,5,7,7,10,10,5,2,8\nM,7,9,10,7,8,6,7,3,5,9,8,9,10,7,3,8\nB,3,7,5,5,4,8,8,6,6,7,6,5,2,8,7,10\nI,3,9,4,7,2,7,9,0,8,14,6,5,0,9,2,7\nN,6,9,8,5,3,8,7,3,4,13,4,8,5,8,0,7\nY,4,6,4,4,2,5,9,2,8,9,10,5,1,11,4,4\nK,5,9,6,7,2,4,7,9,2,7,6,12,4,8,3,11\nB,6,9,8,6,6,11,5,2,7,11,3,7,4,7,5,11\nT,2,4,3,3,1,5,13,3,6,11,9,4,1,11,1,5\nF,4,7,6,5,2,6,11,2,6,13,7,4,1,10,2,7\nQ,6,7,8,6,6,7,4,4,5,7,4,8,4,5,6,7\nA,7,11,7,6,4,10,1,4,2,11,6,14,4,5,5,11\nX,4,9,7,7,5,8,8,3,9,5,6,5,5,9,8,8\nD,6,9,6,4,3,8,5,4,5,9,4,7,4,7,5,10\nB,5,11,7,8,6,9,8,4,7,10,4,6,2,8,6,10\nL,4,9,5,8,5,8,7,3,6,6,7,9,3,8,7,8\nE,5,12,4,6,3,7,7,4,5,10,6,8,3,9,8,9\nB,2,7,3,5,2,6,7,9,6,7,6,7,2,8,8,9\nS,5,8,6,6,4,9,8,4,8,10,2,7,2,7,4,10\nR,2,4,4,3,2,8,7,3,4,9,4,7,2,7,3,10\nT,5,7,5,5,3,7,10,2,8,11,9,4,1,11,3,5\nR,2,4,3,3,3,7,8,5,5,7,5,7,2,6,4,8\nX,4,9,6,7,5,8,8,3,8,5,6,5,4,10,8,7\nB,1,3,3,2,2,8,7,2,4,10,5,7,2,8,3,9\nF,1,1,1,1,0,3,12,4,3,11,9,6,0,8,2,7\nP,5,4,6,6,7,8,8,3,3,6,8,7,5,10,5,5\nK,6,7,8,5,4,4,8,3,7,11,10,11,3,8,3,6\nB,5,8,7,6,5,10,6,3,7,10,4,7,2,8,5,11\nQ,5,11,5,6,4,9,5,4,7,11,4,8,3,8,8,11\nO,3,7,4,5,3,8,7,6,4,9,5,8,3,8,3,8\nK,4,5,4,7,2,3,8,8,2,7,4,11,4,8,3,10\nU,5,9,4,4,2,7,6,4,5,4,8,7,5,8,2,7\nK,5,10,7,7,6,10,6,1,6,10,3,8,6,8,5,12\nK,7,9,10,7,6,9,6,2,7,10,3,8,7,8,6,10\nG,2,1,3,1,1,6,7,5,5,6,6,9,2,9,4,8\nO,9,14,6,8,4,7,6,5,6,8,3,8,6,8,6,8\nU,5,10,7,8,4,6,9,6,7,7,10,10,3,9,1,8\nB,4,6,7,5,6,6,9,5,6,8,7,8,6,9,7,7\nX,4,9,7,6,4,11,6,1,8,10,1,6,2,8,3,10\nU,2,7,3,5,2,7,5,12,4,7,11,8,3,9,0,8\nL,5,10,6,8,3,4,4,2,11,3,1,8,0,7,2,5\nY,3,6,5,4,2,7,10,2,7,6,12,9,2,11,2,8\nH,5,9,7,7,6,6,8,3,6,10,8,8,3,9,3,7\nJ,2,8,3,6,2,8,7,2,6,11,5,9,1,6,2,5\nI,2,6,2,4,1,7,8,0,8,14,6,8,0,8,1,7\nH,4,7,5,5,4,8,8,6,7,6,5,9,3,8,3,8\nB,8,14,6,8,5,7,8,5,6,10,5,8,6,6,8,11\nT,4,9,6,7,3,6,12,3,8,8,12,8,1,11,1,7\nR,8,12,8,6,6,7,7,3,5,8,4,8,7,8,7,8\nD,3,4,4,3,2,7,7,6,7,7,6,5,2,8,3,7\nM,4,7,5,5,3,8,7,12,1,7,9,8,8,6,0,8\nX,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8\nN,3,5,5,3,2,6,8,2,4,11,7,7,5,8,0,7\nE,5,10,4,5,2,6,9,5,7,10,7,9,1,9,7,8\nW,7,10,7,5,3,3,9,2,1,9,10,8,8,12,1,6\nA,3,11,6,8,4,11,4,2,3,9,2,9,3,7,3,8\nC,5,9,7,6,4,7,7,8,7,7,6,9,4,8,4,9\nI,2,8,2,6,2,7,7,0,7,7,6,8,0,8,3,8\nV,5,6,5,4,3,3,11,2,3,10,11,8,2,11,1,8\nX,7,9,8,8,9,8,8,2,5,7,6,7,4,7,8,8\nA,5,9,6,7,4,7,4,3,1,7,2,8,3,8,3,8\nX,3,4,5,3,2,10,7,2,8,11,3,7,2,8,3,9\nE,7,11,5,6,3,6,9,6,8,10,7,8,1,9,7,8\nB,2,6,3,4,3,7,6,8,5,6,7,7,2,9,7,9\nX,8,15,8,8,5,4,9,3,8,11,11,9,4,9,3,5\nT,5,7,7,6,7,7,8,4,8,7,7,8,3,9,8,7\nS,3,4,4,6,2,8,7,6,9,4,6,7,0,8,9,8\nS,2,4,4,3,2,8,7,2,6,10,5,7,1,9,5,8\nE,1,3,3,2,1,6,8,2,7,11,7,9,1,8,4,9\nB,5,9,7,6,6,7,8,6,6,9,7,5,3,7,8,7\nS,6,12,5,6,3,7,3,3,5,7,2,7,3,6,5,8\nM,4,5,7,3,4,9,6,3,5,9,4,7,10,6,2,8\nJ,4,10,6,8,3,8,6,3,7,15,6,10,1,6,1,7\nS,6,11,6,6,3,6,8,5,3,13,9,8,3,10,3,8\nC,5,5,6,8,2,6,7,7,11,6,6,14,1,8,4,9\nP,8,11,7,6,4,6,11,4,5,13,6,3,4,9,6,5\nF,4,7,6,5,5,7,9,6,5,8,6,8,3,10,8,10\nW,4,5,7,4,4,7,11,3,2,6,9,8,8,11,1,8\nL,4,11,6,8,9,7,7,3,5,7,7,10,7,11,8,5\nK,2,1,2,2,1,5,7,8,1,7,6,11,3,8,2,11\nL,3,9,4,6,2,0,2,4,6,1,0,8,0,8,0,8\nU,4,6,5,4,4,8,6,7,5,7,6,9,3,8,4,6\nS,7,11,10,8,11,9,3,4,4,9,6,10,6,6,11,11\nX,4,8,6,6,4,8,8,3,8,6,5,6,4,9,7,8\nN,4,5,5,4,3,7,9,5,5,7,7,7,5,9,2,6\nP,4,7,4,4,2,4,12,8,2,10,6,4,1,10,4,8\nT,3,7,5,5,4,6,7,7,6,7,10,9,3,9,4,8\nT,2,6,3,4,2,7,12,2,7,7,11,8,1,11,1,8\nU,3,5,4,4,3,8,6,4,3,5,7,7,3,9,1,7\nB,8,11,6,6,4,9,6,6,6,11,4,9,6,6,6,10\nM,5,11,6,9,4,7,7,12,2,7,9,8,9,6,0,8\nJ,5,11,7,9,4,9,7,2,6,14,3,7,0,7,0,7\nW,4,9,6,7,10,8,6,6,2,7,6,8,8,11,3,9\nQ,5,10,7,9,4,8,5,9,8,5,4,8,3,8,4,8\nW,6,9,9,7,6,8,8,4,1,7,9,8,7,11,0,8\nJ,2,9,3,7,1,12,2,10,4,13,6,13,1,6,0,8\nD,5,10,5,5,4,7,7,4,6,8,5,7,5,10,6,6\nO,1,3,2,1,1,8,7,5,3,9,6,8,2,8,2,8\nQ,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8\nH,7,10,7,5,3,8,7,4,4,9,7,7,6,10,5,9\nR,2,3,2,2,2,6,8,4,5,7,6,7,2,7,3,9\nT,5,9,6,6,5,6,7,7,7,7,8,8,3,11,6,9\nU,6,6,6,8,3,7,4,15,5,7,14,8,3,9,0,8\nK,3,2,4,4,3,5,7,4,7,6,6,10,3,8,5,9\nL,6,10,5,5,2,6,5,3,7,10,4,13,2,6,6,8\nG,5,9,6,7,3,6,7,7,7,10,8,10,2,9,4,9\nX,2,6,3,4,2,7,7,4,4,7,6,7,2,8,4,8\nY,2,4,3,3,1,4,11,2,7,11,10,5,1,11,2,5\nI,3,11,3,8,4,7,7,0,8,7,6,8,0,8,3,8\nN,4,7,6,5,3,7,8,3,4,10,6,7,5,8,1,7\nY,4,7,6,10,10,8,5,4,2,8,8,9,5,9,6,8\nM,2,1,3,1,2,6,6,6,4,7,7,10,6,5,2,9\nE,2,1,3,2,2,7,8,5,7,7,6,9,2,8,5,10\nM,4,4,6,3,3,7,6,3,4,9,7,8,7,5,2,8\nA,3,5,5,4,2,6,3,2,2,5,2,7,3,4,3,8\nR,5,9,7,7,4,10,7,3,6,11,2,6,3,7,4,10\nH,9,12,9,7,6,10,6,4,5,11,2,7,6,7,5,8\nI,1,6,0,8,1,7,7,4,4,7,6,8,0,8,0,8\nA,2,4,3,3,1,11,3,3,2,11,2,9,2,6,2,9\nZ,4,9,5,6,5,7,7,5,9,7,6,8,1,8,7,8\nS,4,9,5,6,5,7,6,7,6,6,8,10,2,11,9,7\nC,4,7,4,5,3,4,8,5,6,9,8,14,2,7,3,8\nR,6,11,9,8,7,9,8,4,7,9,3,8,4,5,4,11\nD,7,13,6,7,4,10,4,4,5,12,2,8,5,6,4,9\nL,4,10,4,8,1,0,1,6,6,0,0,6,0,8,0,8\nV,3,7,5,5,1,7,8,4,2,7,14,8,3,10,0,8\nP,8,9,6,4,3,9,7,5,5,13,3,5,5,9,4,8\nG,2,6,3,4,2,7,6,6,6,7,3,10,1,8,4,10\nH,5,10,5,7,3,7,10,15,2,7,3,8,3,8,0,8\nU,7,11,7,8,5,4,8,5,8,10,9,9,3,9,2,6\nZ,3,5,4,7,2,7,7,4,13,10,6,8,0,8,8,8\nS,5,11,6,9,6,8,7,8,5,7,6,7,2,8,9,8\nI,4,11,5,8,3,7,7,0,8,14,6,8,0,8,1,8\nC,6,11,8,8,6,7,6,9,6,8,6,11,4,9,4,8\nC,4,9,5,7,2,5,7,7,10,7,6,14,1,8,4,9\nX,3,6,5,4,3,8,7,4,9,6,6,6,3,8,6,7\nN,6,11,9,9,5,12,7,4,5,10,0,4,6,8,2,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nO,3,5,4,3,3,7,7,7,5,9,6,8,2,8,3,8\nO,4,6,5,5,4,7,6,4,4,6,3,6,2,7,4,7\nL,2,6,3,4,2,4,3,8,7,1,2,4,1,6,1,6\nV,4,5,5,3,2,4,13,4,3,11,11,6,3,10,1,8\nG,5,9,4,4,2,11,3,2,3,9,2,6,4,7,3,10\nB,3,6,5,5,5,6,8,6,4,7,6,8,6,9,7,8\nN,2,2,3,3,2,7,8,6,4,7,6,7,4,9,2,6\nS,6,13,6,7,3,7,6,3,4,13,7,9,3,9,3,8\nZ,4,6,6,4,4,9,9,5,4,7,5,7,3,8,9,5\nB,3,5,5,3,4,8,7,3,6,9,6,6,2,8,5,9\nI,2,7,3,5,3,9,6,1,4,8,5,5,3,8,5,7\nS,1,0,1,0,0,8,7,3,6,5,6,7,0,8,7,8\nD,7,11,7,6,4,10,4,5,6,12,3,9,5,6,5,10\nO,6,9,7,7,5,7,8,8,6,10,7,9,3,8,3,8\nE,1,0,1,1,1,5,8,5,8,7,6,12,0,8,6,9\nP,3,5,6,4,3,8,10,3,4,12,4,3,1,10,3,8\nU,6,9,7,7,4,4,8,6,8,10,10,9,3,9,2,5\nX,5,5,6,8,2,7,7,5,4,7,6,8,3,8,4,8\nA,3,8,5,6,3,12,2,3,2,11,2,9,2,6,3,9\nQ,5,8,5,9,7,8,7,7,3,8,6,9,3,9,6,7\nQ,2,0,2,1,1,8,7,7,4,6,6,8,2,8,3,8\nG,8,13,7,7,4,7,3,6,2,6,5,4,4,7,5,7\nL,5,9,7,7,4,5,4,2,9,6,2,8,1,6,3,6\nK,6,9,5,4,2,8,8,3,7,8,4,7,5,7,3,7\nX,4,4,6,3,3,7,7,1,8,11,7,9,2,8,3,8\nJ,2,8,2,6,1,15,4,3,5,12,1,7,0,8,0,8\nE,2,2,3,3,2,7,8,5,7,7,6,9,2,8,5,10\nW,8,11,8,6,5,5,8,2,4,8,9,7,10,10,2,5\nP,4,9,6,7,3,6,13,5,2,12,5,1,1,10,3,8\nG,6,11,6,8,5,6,6,6,6,10,6,13,4,9,6,8\nN,4,5,5,4,5,7,8,4,3,6,5,8,6,8,4,7\nP,11,15,8,8,4,8,9,7,5,14,4,4,4,10,4,8\nX,3,4,5,3,2,9,6,1,8,10,4,8,2,8,3,9\nQ,4,5,5,6,4,7,9,5,3,7,9,11,3,9,6,8\nI,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nJ,2,4,3,7,1,12,3,9,4,13,5,12,1,6,0,8\nB,5,4,5,6,4,6,7,9,7,7,6,7,2,8,9,10\nD,4,11,4,8,3,5,6,11,8,5,4,5,3,8,4,8\nB,6,11,8,8,8,7,8,6,4,7,5,6,5,7,7,8\nZ,2,1,2,1,1,7,7,5,8,6,6,8,1,8,6,8\nD,2,4,3,3,2,9,6,3,5,10,4,5,2,8,3,8\nB,5,11,7,8,8,8,8,8,6,7,5,5,4,6,9,12\nF,4,7,5,5,5,7,9,5,4,8,6,8,3,10,8,10\nC,3,5,4,4,2,4,8,5,7,11,9,12,1,9,3,7\nN,4,9,5,7,5,7,7,13,1,7,6,8,5,8,0,7\nG,8,10,6,5,3,8,5,4,4,9,4,6,4,7,5,8\nR,7,11,6,6,4,8,8,5,5,9,4,9,7,5,6,11\nW,4,8,6,6,5,6,10,2,2,7,9,8,7,10,1,8\nB,2,6,4,4,3,8,8,5,7,7,6,6,2,8,6,9\nI,1,9,2,7,3,8,7,0,7,7,6,7,0,8,2,7\nX,6,8,7,7,8,6,7,2,5,8,7,10,4,10,8,6\nC,2,2,3,4,2,6,8,7,7,8,8,14,1,9,4,10\nE,2,2,3,3,3,7,7,5,7,7,7,9,2,8,5,10\nX,4,4,6,3,3,6,7,1,8,10,8,8,2,8,3,7\nU,3,6,4,4,1,7,5,13,5,7,14,8,3,9,0,8\nL,1,3,3,2,1,6,5,2,9,7,2,10,0,7,2,8\nM,4,8,4,6,3,8,7,12,1,6,9,8,8,6,0,8\nR,2,6,4,4,4,8,7,3,4,7,6,8,6,8,6,7\nF,4,7,6,5,4,6,10,2,5,13,7,5,2,10,2,7\nY,4,8,7,6,3,7,10,1,8,6,12,9,3,9,2,8\nG,4,10,5,8,3,7,6,8,8,6,5,10,2,8,6,11\nT,2,3,3,4,1,10,14,1,6,4,11,9,0,8,0,8\nI,1,7,0,5,0,7,7,5,3,7,6,8,0,8,0,8\nX,2,3,4,1,2,6,7,2,8,11,8,8,2,8,3,7\nT,2,8,3,6,1,7,13,0,6,7,11,8,0,8,0,8\nI,4,9,5,7,3,7,8,0,7,13,6,7,0,8,1,7\nT,7,13,6,8,4,7,8,2,7,12,6,7,3,8,5,5\nV,5,9,5,7,2,3,12,4,4,10,12,7,3,10,1,8\nD,6,9,6,5,3,8,6,5,6,10,4,6,4,7,6,10\nU,3,7,4,5,4,8,6,7,5,7,6,8,3,8,4,5\nN,3,6,3,4,3,7,7,11,1,6,6,7,5,9,0,8\nT,4,11,6,8,5,7,11,3,6,7,11,8,2,12,1,8\nD,2,4,3,3,2,9,7,4,5,9,4,5,2,8,2,8\nO,5,9,7,6,4,7,7,9,4,7,6,8,3,8,3,7\nK,8,10,8,5,5,8,7,4,6,11,2,8,6,5,4,8\nW,3,8,5,6,4,6,10,2,3,7,9,8,7,11,0,8\nQ,4,7,5,6,2,8,8,7,6,5,8,9,3,7,5,10\nW,3,4,5,3,3,7,11,2,2,6,9,8,6,11,0,8\nN,8,12,9,6,4,10,5,3,4,13,2,7,5,7,0,7\nC,2,1,2,1,1,6,7,6,10,7,6,14,0,8,4,9\nX,3,6,5,4,3,7,7,1,8,10,6,8,2,8,3,7\nB,3,10,4,8,6,6,7,8,5,7,6,7,2,8,7,9\nI,4,9,4,4,2,8,8,2,4,12,5,6,1,10,5,11\nK,5,10,8,8,9,6,6,4,4,6,5,9,8,7,8,8\nL,4,8,5,6,3,4,5,1,9,7,2,11,0,7,3,7\nV,3,9,4,7,3,9,9,3,1,6,12,8,3,10,1,8\nM,4,4,5,2,3,8,6,6,4,6,7,8,8,6,2,7\nV,5,10,5,8,4,3,11,2,3,9,11,8,2,11,1,8\nT,7,9,8,8,8,6,8,3,9,8,7,9,3,8,8,6\nC,2,5,3,3,1,4,8,4,7,11,9,12,1,9,2,7\nL,2,5,3,3,1,3,4,3,8,2,1,7,0,7,1,6\nG,4,5,5,4,3,7,6,6,6,6,6,10,2,9,4,8\nZ,7,7,5,11,4,7,8,5,3,11,7,7,2,9,10,6\nS,5,7,6,5,3,8,8,4,9,11,3,8,2,6,4,10\nV,5,9,5,7,3,4,12,1,2,8,10,7,3,10,1,7\nX,5,6,6,5,6,6,7,2,5,8,7,10,3,8,7,8\nY,6,11,10,8,5,6,10,2,8,6,12,9,5,10,4,7\nV,3,7,5,5,3,8,12,2,3,5,10,9,4,12,3,7\nM,5,11,6,8,4,8,7,12,2,6,9,8,9,6,0,8\nT,3,11,5,8,1,8,15,0,6,7,11,8,0,8,0,8\nC,4,7,5,5,4,7,7,8,5,6,7,13,4,7,4,8\nB,5,10,7,8,7,9,7,3,5,9,5,6,2,8,5,9\nM,4,4,6,3,4,6,6,3,4,10,9,9,7,6,2,9\nN,6,9,8,6,6,6,7,8,6,7,5,7,3,8,3,9\nM,5,5,6,4,4,8,6,6,5,7,7,8,8,6,2,7\nN,7,9,10,6,5,4,10,3,4,10,9,9,7,7,2,7\nL,7,15,7,8,4,7,5,4,5,12,9,11,3,9,6,9\nJ,5,6,6,7,5,7,7,4,7,7,6,7,3,8,8,8\nZ,3,7,4,5,2,7,7,4,13,9,6,8,0,8,8,8\nG,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10\nE,7,10,5,5,4,7,8,4,4,10,5,9,3,8,9,11\nO,3,6,4,4,2,7,7,7,5,10,6,9,3,8,3,8\nQ,8,12,7,7,4,8,5,4,9,10,5,9,3,7,9,10\nV,7,10,6,6,3,9,9,6,4,6,10,6,6,12,3,8\nO,5,5,6,7,3,7,7,9,8,7,6,8,3,8,4,8\nQ,5,7,6,7,5,8,8,7,4,6,6,10,3,8,4,8\nP,9,11,7,6,3,8,9,6,4,12,3,5,5,10,4,8\nN,4,8,5,6,4,7,7,12,1,6,6,8,5,8,0,8\nQ,5,7,6,8,6,9,8,6,2,4,8,10,3,9,7,10\nP,6,9,5,4,2,6,10,5,3,11,5,5,5,9,4,8\nW,3,2,5,3,3,5,11,3,2,8,9,9,8,13,1,8\nB,4,7,5,5,4,9,7,5,8,7,6,5,2,8,6,9\nX,7,10,8,5,4,6,8,2,8,11,6,9,4,7,4,6\nO,4,7,5,5,3,7,7,7,6,7,6,7,2,8,3,8\nU,5,11,5,8,2,8,5,14,5,6,14,8,3,9,0,8\nL,4,10,5,7,3,5,3,7,7,2,2,4,1,6,1,5\nY,2,6,3,4,0,9,10,2,2,6,12,8,1,11,0,8\nJ,5,11,6,8,3,8,9,1,7,14,5,6,0,9,1,8\nH,7,9,10,6,8,11,6,3,7,10,2,6,5,7,4,10\nH,1,0,1,0,0,7,8,10,1,7,5,8,2,8,0,8\nI,2,4,3,3,1,7,6,0,7,13,7,10,0,8,1,8\nV,7,10,7,8,4,4,11,2,4,8,11,7,5,11,1,7\nS,6,9,5,5,2,8,3,4,4,7,2,7,3,6,5,8\nB,4,2,4,3,4,8,7,5,6,7,6,6,5,8,6,10\nP,3,6,5,8,7,8,9,5,0,8,7,6,5,10,5,8\nU,4,7,4,5,3,7,5,13,5,7,11,8,3,9,0,8\nV,3,6,5,4,2,7,12,2,3,6,11,8,2,10,1,8\nV,5,8,5,6,2,2,11,6,4,13,12,8,2,10,1,7\nV,5,10,5,8,3,3,11,2,3,9,11,8,3,11,1,7\nA,3,8,5,6,3,12,3,3,2,11,1,9,2,6,2,9\nZ,6,9,8,7,6,9,9,5,4,6,5,7,3,9,10,5\nD,7,14,7,8,4,10,4,5,5,13,3,10,5,7,4,9\nZ,5,7,4,10,4,6,9,5,3,12,7,6,3,9,10,6\nM,5,6,7,5,8,7,7,4,4,7,6,8,10,8,5,6\nO,7,10,5,5,3,7,8,5,5,9,5,6,4,9,5,8\nH,4,7,6,5,4,10,5,4,6,10,2,7,3,8,3,9\nX,5,6,7,8,2,7,7,5,4,7,6,8,3,8,4,8\nY,7,9,8,6,5,5,9,1,8,9,10,5,3,11,4,4\nW,2,1,4,2,2,9,11,3,2,5,9,8,5,11,0,8\nN,4,6,5,4,3,8,7,8,6,7,4,5,3,7,3,7\nK,7,9,10,7,5,5,9,2,8,10,9,10,3,8,4,5\nT,3,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8\nV,2,7,4,5,2,9,9,3,1,5,12,8,2,11,0,8\nY,4,6,4,4,2,4,9,3,6,10,11,6,2,11,2,5\nQ,5,10,6,9,6,8,7,7,5,6,6,9,3,8,5,8\nV,4,6,4,4,2,3,12,4,3,10,11,7,2,10,1,8\nP,2,1,3,1,1,5,9,4,4,9,7,4,1,9,3,7\nF,4,7,6,5,3,6,10,1,6,13,7,5,1,10,2,7\nN,4,8,6,6,5,6,8,9,5,8,6,6,4,8,3,9\nA,4,9,6,7,4,12,3,3,2,10,1,9,2,7,3,8\nQ,6,11,6,6,4,9,5,4,6,10,5,8,3,9,8,10\nX,2,3,4,2,1,7,7,1,8,10,6,8,2,8,3,8\nZ,7,10,9,8,8,9,8,5,4,7,5,7,4,8,9,4\nP,6,10,6,7,3,5,10,10,6,8,5,5,2,10,4,8\nS,7,11,9,8,5,9,6,3,8,11,5,8,2,10,6,9\nS,6,11,6,6,3,6,8,5,3,13,9,8,3,10,3,8\nN,4,5,5,8,3,7,7,14,2,4,6,8,6,8,0,8\nB,4,6,5,4,4,8,8,4,6,10,5,6,3,8,6,10\nS,7,15,5,8,3,8,3,4,4,9,2,8,4,6,5,9\nK,7,11,10,9,7,6,7,2,7,10,7,10,4,8,4,7\nC,2,4,2,2,1,4,8,5,7,11,9,12,1,9,3,8\nC,4,8,5,6,3,5,8,7,8,9,8,14,1,9,4,9\nC,2,3,3,2,1,4,8,3,6,11,9,12,1,9,2,7\nJ,4,10,5,8,3,11,4,5,5,15,3,9,0,7,0,8\nI,4,10,5,8,2,7,6,0,9,14,6,9,0,8,1,8\nC,4,8,5,6,2,6,7,7,10,8,6,13,1,9,4,8\nA,2,7,4,5,2,9,3,2,2,8,1,8,2,6,3,8\nC,6,8,7,6,4,5,8,6,7,12,8,10,3,12,3,6\nP,3,7,3,4,2,4,10,9,3,9,6,5,2,10,3,8\nJ,3,4,4,5,3,8,8,4,5,7,6,8,3,8,8,9\nL,5,10,6,7,5,4,4,4,8,2,1,6,1,6,1,5\nK,4,7,6,5,4,4,7,2,6,10,9,11,3,8,3,8\nX,2,3,4,2,2,9,7,2,8,11,3,7,2,7,2,8\nG,1,3,2,2,1,6,7,5,5,9,7,10,2,8,4,10\nU,4,7,6,5,7,8,8,4,4,6,8,8,8,9,5,7\nF,3,7,4,4,1,2,12,5,6,12,10,8,0,8,2,6\nP,4,9,4,6,2,5,11,10,3,10,6,4,1,10,4,8\nV,3,7,5,5,2,9,9,4,1,6,12,8,3,9,1,7\nJ,2,7,3,5,2,11,7,0,7,10,3,6,0,8,1,7\nI,3,8,4,6,2,6,8,0,8,14,7,8,0,8,1,7\nN,5,10,5,7,5,7,7,13,1,6,6,8,6,9,1,6\nQ,7,9,7,10,8,8,9,7,2,7,7,11,4,10,8,6\nU,5,11,6,8,2,8,5,14,5,6,14,9,3,9,0,8\nV,4,10,6,7,3,5,9,5,2,8,13,8,3,10,0,8\nG,2,7,3,5,2,7,7,7,5,6,6,9,2,8,5,11\nS,4,6,5,5,5,8,8,4,5,7,7,8,5,9,8,11\nS,6,11,9,8,12,7,5,3,3,8,5,8,4,8,15,8\nF,6,10,5,5,4,8,10,3,5,11,5,4,3,10,7,7\nX,5,10,7,8,4,7,7,1,8,10,6,8,3,8,4,7\nT,6,8,6,6,4,4,13,5,5,11,9,4,2,12,1,5\nT,6,10,6,8,4,5,12,4,6,12,9,4,2,12,2,4\nF,7,14,6,8,3,8,8,3,6,12,4,5,2,8,7,7\nU,3,7,5,5,2,7,8,6,7,6,10,9,3,9,1,7\nA,5,9,7,7,5,8,3,1,2,6,2,7,3,5,4,7\nU,3,7,3,5,1,7,6,13,5,8,13,8,3,9,0,8\nT,4,10,5,7,4,8,10,0,8,6,11,8,0,10,1,8\nR,4,4,4,5,3,5,12,8,4,7,3,9,3,7,6,11\nQ,6,12,6,6,4,10,5,4,6,11,4,8,3,8,8,11\nJ,3,6,4,4,1,7,6,3,6,14,7,11,1,6,1,7\nZ,2,6,3,4,1,7,7,3,13,9,6,8,0,8,7,8\nX,4,8,5,7,6,7,8,1,5,7,6,7,3,5,8,7\nB,9,15,9,8,8,8,7,4,5,9,5,7,8,4,10,7\nP,6,7,8,9,10,9,9,3,3,6,9,6,6,10,7,4\nS,3,5,3,3,2,8,7,7,5,8,6,8,2,9,9,8\nI,1,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8\nB,4,9,5,6,6,7,7,6,6,6,6,6,2,8,6,9\nU,7,8,7,6,3,3,10,6,7,13,12,9,3,9,1,7\nH,3,6,4,4,5,9,8,4,3,6,6,7,6,9,6,6\nW,6,7,6,5,5,5,11,3,2,9,8,7,6,12,2,6\nN,9,10,8,5,3,6,10,4,5,4,5,11,6,11,2,7\nC,4,5,6,7,2,5,6,6,11,7,5,12,1,9,4,8\nD,7,13,6,7,5,6,7,5,6,8,5,7,5,10,6,5\nU,6,10,8,8,5,6,9,5,7,6,9,10,6,10,1,7\nY,5,6,4,9,3,7,8,3,2,7,10,5,4,10,5,6\nQ,4,8,5,9,6,7,8,6,3,8,9,9,4,10,6,7\nH,2,1,2,1,2,8,8,5,5,7,6,8,3,8,2,7\nX,3,8,5,6,4,7,7,3,8,5,6,10,3,7,6,8\nT,5,10,5,7,3,6,11,3,7,11,9,4,2,12,3,4\nN,2,1,3,2,2,7,8,5,4,7,7,6,5,9,2,6\nN,6,8,8,6,4,4,10,4,4,11,11,10,5,8,1,7\nK,12,15,11,8,5,4,8,4,7,10,10,11,6,9,3,6\nL,5,8,6,7,5,8,6,5,5,7,7,9,3,9,8,8\nM,7,9,10,7,7,8,6,3,5,9,7,8,8,5,2,8\nE,1,1,2,2,1,4,7,5,8,7,6,13,0,8,6,9\nK,4,5,7,3,4,3,8,3,7,11,10,11,3,8,3,6\nD,4,7,4,5,2,5,8,10,9,8,7,6,3,8,4,8\nN,6,10,8,7,5,6,10,2,4,9,7,7,5,9,1,8\nB,7,10,9,8,8,10,6,3,7,10,3,7,5,7,6,11\nJ,1,3,2,2,1,11,6,1,6,11,3,7,0,7,0,8\nI,0,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nY,8,10,6,14,5,9,8,4,3,5,11,5,5,10,7,7\nO,7,9,9,8,7,8,4,4,5,9,4,9,4,6,5,7\nC,3,4,4,3,2,6,8,8,8,8,7,12,2,9,4,10\nD,5,11,5,8,3,5,7,11,9,6,6,5,3,8,4,8\nL,2,5,4,4,2,7,4,1,8,8,2,10,0,7,2,8\nA,3,4,5,3,2,10,2,2,1,8,2,9,4,5,2,9\nV,3,4,4,3,2,5,12,2,2,9,10,7,2,11,1,8\nF,2,4,3,3,1,6,10,2,5,13,7,5,1,10,1,7\nX,3,5,5,5,4,8,8,2,5,8,6,8,3,8,7,8\nP,5,8,7,6,4,5,13,6,3,13,6,1,0,10,4,8\nL,3,7,3,5,1,0,1,6,6,0,0,6,0,8,0,8\nR,4,10,5,8,3,5,10,9,3,7,5,8,3,8,6,11\nR,1,0,1,0,0,6,9,7,3,7,4,8,2,6,4,10\nX,2,1,2,1,0,7,7,4,4,7,6,8,3,8,4,8\nG,9,13,8,8,4,8,3,4,3,8,4,5,4,7,5,9\nQ,1,2,2,3,2,8,8,5,2,8,6,8,2,9,2,8\nZ,2,5,4,4,2,7,8,2,9,11,6,8,1,8,6,8\nK,5,9,7,7,5,9,7,2,7,10,3,8,4,9,4,10\nT,5,8,6,7,5,5,7,3,8,8,7,10,3,4,8,5\nJ,1,4,3,3,1,9,5,3,6,14,5,10,0,7,0,8\nU,3,8,5,6,3,4,10,7,6,10,10,9,3,9,1,8\nR,2,4,2,3,2,7,7,5,5,6,5,7,2,7,4,8\nQ,3,4,4,5,3,8,8,6,2,5,7,10,3,9,6,10\nO,4,5,6,8,3,6,6,9,8,6,5,6,3,8,4,8\nN,3,4,6,3,2,8,9,3,5,10,4,5,5,9,1,7\nF,5,10,5,7,2,1,12,5,5,12,11,8,0,8,2,5\nM,8,8,11,7,12,8,8,4,4,7,6,7,12,7,8,3\nV,2,7,4,5,2,7,11,3,4,7,11,8,2,10,1,8\nS,3,8,4,6,3,7,7,7,5,7,6,8,2,8,8,7\nC,4,6,5,4,2,4,8,6,9,10,10,12,1,8,3,7\nU,3,6,3,4,1,7,5,13,5,7,13,8,3,9,0,8\nM,6,7,9,6,10,7,6,4,4,7,6,8,12,9,5,5\nA,3,8,5,6,5,8,8,6,4,6,6,8,3,7,6,4\nN,3,5,5,3,2,6,9,2,4,10,7,7,5,8,1,8\nM,5,8,6,6,7,7,9,6,4,6,6,8,9,9,8,10\nS,3,7,4,5,3,7,7,5,7,5,6,10,0,9,8,7\nH,6,11,6,8,3,7,5,15,1,7,9,8,3,8,0,8\nV,4,7,4,5,2,2,12,3,3,11,11,8,2,10,1,8\nM,7,11,8,8,7,7,5,12,1,8,8,8,10,5,2,10\nR,7,13,7,7,6,5,9,3,5,7,4,9,6,7,6,6\nG,3,7,4,5,3,7,7,7,6,7,5,11,1,8,4,10\nO,2,3,3,2,1,8,7,6,4,9,6,9,2,8,3,8\nJ,3,7,5,8,5,9,8,4,4,7,6,9,3,7,8,6\nF,4,9,6,7,7,7,9,1,5,9,6,5,3,9,4,3\nD,2,3,2,1,1,7,7,6,6,7,6,5,2,8,2,7\nX,2,3,4,2,2,7,7,3,9,6,6,8,2,8,5,8\nT,2,4,3,2,1,8,11,2,7,6,11,8,1,10,1,8\nM,5,7,5,5,4,7,5,11,0,8,8,8,8,5,1,10\nQ,6,10,8,7,7,8,5,7,4,5,7,10,6,6,8,9\nK,2,6,3,4,1,3,7,7,3,7,7,11,3,8,2,10\nM,5,7,7,6,7,5,8,5,3,6,5,8,9,7,5,7\nE,2,5,4,3,2,6,7,1,8,11,6,9,2,8,4,8\nF,4,8,5,6,5,7,9,6,4,8,5,8,3,11,9,10\nU,10,12,8,6,4,6,5,5,6,3,8,7,5,9,2,6\nH,6,8,9,6,5,8,7,3,6,10,5,7,3,8,3,7\nP,1,1,2,1,0,5,11,7,2,9,6,4,1,9,3,8\nL,9,13,7,7,4,9,2,4,4,12,5,13,2,7,6,9\nF,3,7,4,5,4,6,9,2,6,9,9,6,1,10,4,7\nM,6,11,8,6,5,10,3,4,2,10,2,9,9,1,1,8\nK,6,10,9,8,8,10,5,2,6,9,2,7,8,6,7,12\nW,3,4,4,3,2,5,10,3,2,9,8,7,6,11,1,6\nR,5,4,5,7,3,5,10,9,3,7,5,8,3,8,6,11\nJ,1,3,2,2,1,11,6,2,6,11,3,7,0,7,0,8\nG,6,9,6,6,4,6,7,6,6,9,7,12,2,9,4,9\nZ,4,9,5,7,4,9,6,6,10,7,5,6,1,7,8,8\nO,3,2,4,3,3,7,7,7,5,7,6,8,2,8,3,8\nH,2,3,3,2,2,7,8,5,6,7,6,8,3,8,2,8\nW,6,8,6,6,4,5,11,3,3,9,8,7,7,11,2,6\nG,5,9,7,7,5,5,6,7,7,7,5,13,4,9,5,8\nT,3,2,4,4,2,7,12,3,6,7,11,8,2,11,1,7\nM,1,0,2,1,1,7,6,9,0,7,8,8,6,6,0,8\nK,2,3,4,1,2,4,8,2,6,10,10,11,2,8,2,6\nX,3,7,4,5,2,7,7,4,4,7,6,8,3,8,4,8\nP,10,15,8,8,4,7,9,6,3,11,4,5,5,9,4,7\nP,4,6,5,4,3,9,7,2,5,13,4,5,1,10,3,9\nZ,2,3,2,2,2,7,7,5,8,6,6,8,1,8,6,8\nJ,2,2,3,4,1,10,6,2,7,12,4,9,1,6,1,7\nN,5,9,7,7,8,9,7,4,4,7,6,6,7,12,7,6\nG,3,4,4,3,2,6,7,5,5,9,7,10,2,8,4,10\nF,6,11,9,8,5,8,9,4,7,13,6,6,5,7,4,6\nM,7,10,10,8,7,5,6,3,5,9,9,9,8,5,2,8\nK,5,10,6,8,2,4,6,9,2,7,7,11,4,7,3,11\nY,7,9,8,6,4,4,9,1,7,10,11,6,2,11,4,3\nT,5,10,5,5,2,5,11,2,7,12,8,5,2,9,4,3\nN,5,8,7,6,4,7,8,3,4,10,6,7,5,8,1,7\nX,5,11,8,8,4,11,5,2,8,10,0,7,3,8,4,10\nY,8,6,6,9,4,7,8,4,3,6,11,5,4,11,6,6\nB,4,10,5,8,6,8,6,7,6,6,6,6,2,8,7,9\nV,5,7,7,5,7,6,7,4,2,9,7,8,7,10,5,7\nD,4,9,6,6,8,8,9,5,4,8,7,6,7,10,10,5\nZ,6,10,8,7,5,7,8,3,10,12,7,8,1,9,6,7\nH,5,5,7,7,6,7,5,4,2,7,5,6,5,8,8,8\nL,5,9,7,7,4,5,3,3,9,6,1,9,1,6,3,7\nP,4,9,5,6,2,4,14,8,1,11,6,3,1,10,4,8\nM,4,6,5,8,4,7,7,12,2,7,9,8,9,6,0,8\nX,2,2,3,3,2,8,7,3,8,6,6,8,2,8,5,9\nD,3,7,4,5,3,6,8,9,7,7,6,5,2,8,3,7\nI,2,5,3,4,1,7,7,0,8,13,6,8,0,8,1,8\nV,5,9,5,7,3,4,11,2,4,9,11,7,3,10,1,8\nU,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8\nO,3,5,4,3,3,8,7,7,5,9,6,7,2,8,3,8\nH,4,8,4,5,2,7,7,15,1,7,6,8,3,8,0,8\nY,8,10,8,7,3,4,10,2,8,11,11,5,1,11,3,3\nM,5,7,6,5,7,7,9,6,5,7,6,8,8,8,5,7\nX,6,9,8,8,9,7,7,2,5,7,6,8,4,8,8,8\nD,2,3,3,2,2,9,6,4,5,9,4,5,2,8,2,8\nF,5,11,5,8,2,1,13,5,4,12,11,7,0,8,2,5\nV,1,3,3,2,1,6,11,3,3,8,11,8,2,11,1,8\nJ,2,6,3,4,1,12,2,9,4,13,5,13,1,6,0,8\nY,3,9,4,7,2,6,10,1,3,8,12,8,1,11,0,8\nP,2,7,4,5,3,4,11,5,4,11,8,4,1,10,3,7\nP,7,14,7,8,5,8,9,4,4,12,5,4,4,11,6,6\nH,4,5,6,8,5,9,9,3,1,7,6,7,3,10,8,7\nQ,4,6,4,7,5,8,5,7,4,9,6,8,3,8,4,8\nV,6,11,6,8,3,4,11,3,4,9,11,7,2,10,1,8\nT,4,7,4,5,2,5,11,1,8,11,9,5,0,10,2,4\nK,7,11,10,8,6,4,9,2,8,10,10,10,3,8,4,6\nA,2,1,3,2,1,8,2,2,1,7,2,8,2,7,2,7\nH,7,9,10,7,8,6,8,2,6,10,8,8,4,10,4,7\nL,4,11,4,8,1,0,1,5,6,0,0,7,0,8,0,8\nG,2,3,4,2,2,7,7,6,5,6,6,9,2,9,4,9\nX,3,3,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nM,5,5,7,4,5,8,6,6,5,7,7,8,9,6,3,7\nV,6,9,8,7,9,7,5,5,3,8,7,9,8,9,6,9\nW,1,0,2,0,1,7,8,3,0,7,8,8,5,10,0,8\nQ,3,4,4,5,3,8,7,6,3,8,7,9,2,9,4,9\nO,2,1,2,2,1,8,7,7,5,7,6,8,2,8,3,8\nR,9,13,9,7,6,10,6,4,5,10,2,7,6,7,6,9\nV,5,7,5,5,2,2,11,4,3,11,12,8,2,10,0,8\nN,9,10,7,5,3,9,13,6,5,3,6,10,5,8,2,8\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nN,4,7,7,5,6,7,8,3,4,8,7,8,7,10,5,4\nM,7,11,10,8,11,7,7,5,5,6,7,9,8,6,2,8\nA,5,6,7,5,5,8,8,3,5,7,8,8,5,9,4,6\nB,4,9,6,6,6,8,7,4,5,9,6,6,3,9,5,8\nU,3,6,4,4,4,8,8,6,5,6,7,9,3,8,3,7\nK,2,4,3,3,2,5,7,4,6,6,6,11,3,8,4,10\nQ,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8\nF,7,10,6,5,3,5,11,3,4,11,7,4,2,8,6,4\nQ,5,6,7,9,10,9,5,6,2,7,6,8,8,9,4,8\nB,1,0,2,0,1,7,8,6,4,7,6,7,1,8,6,8\nO,4,9,5,7,4,7,6,9,5,7,5,8,3,8,3,8\nS,3,8,4,6,3,8,8,7,7,8,5,6,2,8,9,8\nJ,4,6,5,7,4,9,8,5,6,7,5,8,3,9,8,9\nY,7,11,7,8,4,3,10,2,7,10,12,6,1,11,2,5\nF,5,10,6,8,7,7,6,6,4,8,6,8,6,10,7,11\nZ,5,7,4,10,4,9,5,5,5,11,5,7,3,9,8,10\nT,6,11,6,8,4,5,12,2,8,12,9,4,1,11,2,4\nP,4,6,6,4,4,8,5,7,5,6,5,7,3,8,4,9\nF,4,9,5,7,4,5,11,2,7,11,9,5,1,10,3,5\nY,4,5,5,3,2,4,11,2,7,11,10,5,1,11,2,5\nF,2,1,3,2,2,6,10,4,5,10,9,5,1,9,3,7\nI,7,14,5,8,3,9,6,6,5,13,3,7,3,8,5,11\nL,3,6,4,4,2,9,3,2,6,9,2,10,1,5,3,9\nM,5,10,6,8,6,8,6,11,1,6,8,8,9,5,2,5\nK,5,11,6,8,5,4,6,7,3,7,7,12,4,8,3,11\nP,1,3,3,2,1,7,8,4,3,11,5,4,1,9,2,8\nQ,4,8,4,9,6,7,10,5,3,6,10,11,3,9,6,8\nY,7,7,6,10,4,9,7,6,6,4,11,7,5,10,3,7\nV,7,10,7,8,4,4,11,2,4,9,11,7,4,9,1,7\nK,4,7,6,5,4,5,7,5,8,7,6,12,3,8,5,10\nV,4,8,6,6,3,7,11,3,4,6,12,9,2,10,1,8\nB,3,5,4,4,4,7,7,5,6,6,6,6,2,8,7,10\nE,4,10,6,8,8,7,7,3,5,6,7,10,5,10,8,8\nD,5,11,7,8,7,9,8,5,5,10,5,4,4,8,5,9\nM,4,4,8,3,4,7,6,3,4,9,8,9,8,6,2,8\nY,4,10,6,7,1,7,12,2,3,8,12,8,1,10,0,8\nK,3,7,5,6,5,6,8,3,4,7,4,9,5,3,4,9\nK,3,1,4,1,2,6,7,4,7,7,6,11,2,8,4,10\nL,6,9,5,4,2,6,5,3,7,10,4,12,2,6,6,8\nQ,5,9,6,11,7,8,6,8,4,5,6,9,3,8,6,10\nG,2,0,2,1,1,8,6,5,5,6,5,9,1,8,5,10\nL,1,0,2,1,0,2,1,5,5,0,2,5,0,8,0,8\nQ,3,7,4,6,2,7,5,9,6,5,5,7,3,8,4,8\nM,5,6,6,8,4,8,7,13,2,6,9,8,9,6,0,8\nF,1,3,2,2,1,5,10,4,4,10,8,5,1,9,3,7\nG,5,10,6,7,5,7,7,7,7,6,5,10,1,7,5,11\nJ,1,3,2,1,1,10,7,2,4,11,5,9,1,7,0,7\nD,5,8,8,6,6,10,7,4,6,10,3,5,3,8,3,8\nK,5,11,7,8,7,5,6,3,7,6,6,9,4,8,6,9\nV,2,6,4,4,2,7,11,3,3,7,11,8,2,10,1,8\nO,8,11,6,6,3,8,5,4,6,8,3,8,5,7,5,8\nO,4,6,5,4,3,8,7,7,5,10,6,8,3,8,3,8\nW,4,11,7,8,7,4,10,2,3,8,9,9,8,10,1,8\nO,4,6,4,4,3,8,7,7,6,9,5,7,3,8,3,8\nR,3,4,5,3,3,8,8,3,5,9,5,7,3,6,4,10\nO,3,5,4,3,2,8,7,7,4,9,6,8,2,8,3,8\nI,3,9,4,7,3,8,6,0,7,13,6,9,0,7,2,7\nC,4,7,5,5,3,5,8,7,7,8,8,14,1,10,4,10\nJ,1,3,2,4,1,11,4,9,3,12,8,13,1,6,0,8\nA,4,10,5,8,4,6,5,3,1,6,1,8,2,7,2,7\nO,4,6,5,4,3,7,8,8,6,8,8,6,3,8,4,8\nD,4,7,6,6,5,5,7,7,7,7,6,7,4,6,5,6\nW,5,10,7,7,6,4,9,5,1,7,9,8,8,10,0,8\nH,1,0,2,0,0,7,8,11,1,7,6,8,2,8,0,8\nY,2,1,2,1,0,7,9,2,2,6,12,8,1,10,0,8\nU,4,9,5,7,4,5,8,6,7,8,9,10,3,9,1,8\nF,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8\nS,3,5,4,3,2,8,8,2,7,10,5,6,1,9,5,8\nG,7,10,8,8,5,5,7,6,6,9,8,9,2,9,5,9\nB,5,11,5,8,4,6,6,10,7,6,7,7,2,8,9,10\nB,6,10,8,7,6,11,5,3,7,11,3,7,3,8,6,12\nM,1,0,2,0,1,8,6,9,0,6,8,8,5,6,0,8\nO,5,9,6,7,5,8,8,8,4,7,7,8,3,8,3,7\nL,3,7,4,5,2,7,4,2,8,7,2,8,1,6,3,8\nM,4,7,6,5,5,7,7,2,4,9,7,9,7,6,2,8\nT,2,1,3,1,0,7,15,1,4,7,11,8,0,8,0,8\nX,8,13,9,7,5,6,8,2,8,11,7,9,4,8,4,6\nB,2,6,4,4,5,8,8,4,2,6,7,7,6,11,7,7\nG,4,7,6,5,7,8,6,4,2,7,6,9,6,8,7,13\nB,5,10,5,5,4,7,8,3,4,9,6,7,6,7,8,6\nH,7,13,8,7,6,8,8,3,5,10,4,8,6,6,5,8\nS,4,6,6,4,5,9,4,4,4,9,6,9,4,7,9,10\nC,7,11,7,8,4,5,8,6,8,12,8,13,3,10,4,6\nU,6,10,5,5,3,5,5,5,5,4,7,8,5,9,2,7\nX,5,8,8,6,4,9,7,1,8,10,4,7,3,9,4,9\nQ,3,6,4,5,2,8,7,8,6,6,5,9,3,8,4,8\nN,10,15,11,8,5,7,7,2,4,13,7,8,6,8,0,7\nE,4,7,5,5,4,8,7,2,7,11,6,8,3,9,4,9\nZ,3,9,4,6,2,7,7,4,13,10,6,8,0,8,8,8\nV,6,9,6,6,3,3,12,3,3,10,11,8,2,10,1,7\nQ,3,6,3,7,4,8,8,6,1,8,7,10,2,9,5,8\nG,6,8,8,7,9,8,7,6,3,7,7,8,7,11,7,9\nD,2,4,4,3,2,9,6,4,6,10,4,6,2,8,3,8\nL,3,9,4,7,1,0,1,6,6,0,0,6,0,8,0,8\nJ,3,10,5,8,1,14,1,8,5,14,3,11,0,7,0,8\nI,0,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nW,5,9,8,6,7,8,11,2,2,6,8,8,8,14,1,7\nQ,2,3,3,4,3,8,9,5,1,5,7,10,2,9,5,10\nU,9,13,8,8,5,8,5,4,6,6,7,6,6,8,4,6\nO,5,9,5,7,4,7,7,8,5,10,6,7,3,8,4,8\nI,0,7,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nL,2,5,3,3,1,6,4,1,8,7,2,10,0,7,2,8\nX,4,5,7,4,3,6,8,1,9,10,8,8,2,8,3,7\nT,3,5,4,4,2,6,11,2,7,11,9,5,1,11,3,4\nL,5,11,7,8,5,10,4,1,8,10,2,10,0,6,3,10\nA,7,15,7,8,5,10,2,5,2,11,5,12,6,3,6,10\nK,8,15,9,9,6,6,8,3,6,10,9,10,6,11,4,8\nN,4,11,6,8,5,6,9,6,5,7,7,8,6,9,1,7\nE,3,8,5,6,5,7,8,5,8,6,5,9,3,8,5,9\nE,4,8,5,6,3,3,7,6,11,7,7,14,0,8,7,7\nB,4,6,5,4,5,8,6,7,6,6,6,5,3,9,7,9\nN,5,8,8,6,8,8,6,4,4,7,6,7,7,11,7,4\nA,3,8,5,6,3,9,3,1,2,7,2,8,2,6,2,7\nB,4,8,5,6,5,8,8,6,7,7,6,6,2,8,7,10\nW,5,8,8,6,3,4,8,5,2,7,9,8,8,9,0,8\nH,6,11,9,8,9,7,7,6,7,7,6,8,6,8,4,8\nV,2,2,4,3,2,7,12,2,2,6,11,9,2,11,0,7\nB,4,7,6,5,5,10,6,2,5,10,4,7,3,7,5,11\nY,7,6,6,9,4,9,6,5,5,5,12,6,4,11,4,6\nW,3,6,5,4,7,9,7,5,1,7,6,8,7,10,2,7\nT,6,11,8,8,9,5,8,3,7,7,6,10,5,8,7,8\nG,2,1,3,2,1,7,7,6,5,6,6,10,2,8,4,10\nG,4,6,6,4,5,7,8,5,3,6,6,8,5,7,7,7\nZ,5,7,6,5,3,8,7,2,9,12,5,10,2,9,6,9\nV,4,10,6,8,2,8,8,4,3,6,14,8,3,9,0,8\nP,5,9,5,7,5,5,10,8,3,8,5,5,1,9,6,6\nA,4,11,5,8,4,8,5,3,0,7,1,8,2,6,3,8\nK,4,8,6,6,4,9,7,2,6,10,2,7,4,7,3,10\nB,7,12,6,6,6,8,7,4,5,9,6,8,6,8,8,8\nW,5,5,6,3,4,4,11,3,2,9,9,7,7,12,1,6\nT,2,3,3,5,1,9,14,0,6,5,11,8,0,8,0,8\nQ,2,3,3,4,2,7,8,4,2,8,9,10,2,9,4,8\nQ,4,6,4,8,4,7,9,4,4,8,10,11,3,9,6,8\nO,3,6,4,4,3,7,8,8,7,7,8,7,2,8,4,8\nJ,2,8,2,6,1,14,3,5,4,13,3,10,0,7,0,8\nM,11,11,11,6,5,4,9,5,6,4,2,12,9,11,2,8\nS,2,1,2,2,1,8,7,4,7,5,6,7,0,8,8,8\nD,3,4,4,7,2,5,7,11,8,6,5,5,3,8,4,8\nR,4,7,4,5,2,6,10,9,4,7,4,8,3,7,6,11\nP,1,0,1,0,0,5,10,7,2,9,6,5,1,9,3,8\nU,2,1,2,1,0,8,6,11,4,7,12,8,3,10,0,8\nL,0,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8\nY,3,5,6,4,2,7,11,1,7,7,11,8,2,11,2,8\nH,5,10,7,8,7,8,8,7,6,7,6,7,3,8,3,7\nF,4,9,6,7,4,3,12,5,4,12,9,5,2,10,2,5\nN,3,4,5,3,2,7,9,2,5,10,6,6,5,9,1,7\nX,4,9,6,6,5,7,7,3,9,5,7,9,5,7,8,8\nR,4,11,5,8,3,6,9,11,5,7,5,8,3,8,5,10\nB,2,4,3,2,2,8,8,3,5,10,6,6,2,8,5,9\nL,3,7,4,5,2,0,1,4,5,1,1,7,0,8,0,8\nV,8,10,7,7,3,3,12,4,4,10,12,8,3,10,1,8\nD,1,0,2,1,1,6,7,8,6,6,6,6,2,8,3,8\nZ,5,8,7,10,7,10,5,4,4,8,3,6,3,5,8,8\nV,5,5,6,4,2,4,12,3,3,10,11,7,3,10,1,7\nA,5,9,7,8,6,6,8,2,4,7,7,9,8,7,4,8\nQ,2,2,2,3,1,7,8,5,1,7,8,10,2,9,4,8\nU,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8\nY,3,5,4,7,7,7,5,4,2,7,7,9,5,10,4,8\nL,2,5,3,4,1,4,3,4,7,2,2,5,0,7,0,6\nV,3,2,5,3,1,6,12,3,4,8,11,8,2,10,1,8\nR,5,10,5,8,6,6,8,9,5,6,5,7,2,8,5,11\nS,4,9,5,7,4,8,8,8,7,8,4,7,2,7,9,8\nQ,6,7,8,6,6,7,4,4,5,7,4,9,5,4,7,7\nA,4,8,6,6,3,9,2,2,2,7,1,8,2,7,3,7\nE,1,3,3,1,1,6,8,2,7,11,8,9,1,8,3,8\nP,6,10,6,7,3,4,14,8,1,11,6,3,1,10,4,8\nW,10,10,10,8,7,2,11,2,3,10,10,8,7,11,2,7\nS,3,4,4,6,2,8,7,6,9,5,6,8,0,8,9,8\nG,2,1,3,2,1,6,6,5,5,6,6,9,2,9,4,8\nU,3,4,3,3,2,5,8,5,7,10,9,8,3,9,2,6\nQ,5,5,6,7,3,8,7,8,6,6,6,9,3,8,5,9\nW,6,9,6,7,6,3,12,2,2,10,9,8,6,11,2,7\nL,4,9,5,7,3,3,3,7,8,1,0,5,1,6,1,6\nR,4,9,6,6,4,9,7,4,6,9,4,7,3,7,5,10\nC,4,7,5,5,5,5,7,3,5,7,6,10,5,10,3,8\nD,4,7,5,5,2,5,7,10,9,7,7,6,3,8,4,8\nM,4,9,6,7,7,7,7,5,5,6,7,8,8,7,3,7\nU,2,1,3,2,1,7,8,6,7,7,10,8,3,9,1,8\nH,5,7,6,5,5,7,10,8,6,8,6,7,3,7,3,9\nQ,4,10,5,9,5,8,7,7,5,6,8,8,3,7,6,11\nD,2,4,3,3,2,8,7,4,5,9,4,5,2,8,2,8\nJ,4,8,5,6,3,7,7,3,6,15,6,9,1,6,1,7\nC,4,9,5,7,3,6,8,9,8,9,8,12,2,10,4,9\nO,4,8,5,6,5,7,8,8,5,7,8,8,3,8,3,8\nB,3,5,5,3,3,9,6,2,6,10,4,7,4,7,6,9\nZ,5,11,7,8,4,7,7,2,10,12,5,9,1,9,6,8\nT,6,11,6,6,3,6,10,2,7,12,7,6,2,9,5,4\nO,4,10,5,7,2,7,7,9,7,7,7,8,3,8,4,8\nI,1,3,2,2,1,7,7,1,7,13,6,8,0,8,1,8\nU,1,0,1,0,0,7,7,9,3,7,12,8,2,10,0,8\nI,1,9,1,7,2,8,7,0,8,7,6,7,0,8,3,7\nV,3,8,5,6,1,7,8,4,3,7,14,8,3,10,0,8\nU,2,3,3,2,1,8,8,5,6,6,9,8,3,10,1,8\nJ,2,6,4,4,1,7,8,2,6,15,6,9,0,7,1,7\nB,4,6,6,4,5,8,8,6,4,6,5,6,4,8,6,5\nY,4,5,6,7,1,7,10,2,2,7,13,8,2,11,0,8\nY,8,11,8,8,4,3,11,2,9,12,11,5,0,10,2,4\nO,6,10,4,5,3,6,8,4,4,7,4,7,4,8,5,8\nX,3,10,4,7,2,7,7,4,4,7,6,8,3,8,4,8\nL,3,7,4,5,2,6,3,1,8,8,2,11,0,7,2,8\nY,2,2,4,3,1,6,10,1,7,8,11,8,1,11,2,7\nW,4,8,6,6,6,7,6,6,2,7,7,8,6,8,4,10\nO,4,7,5,5,4,8,8,7,4,7,7,6,4,9,3,7\nH,3,6,4,4,3,7,6,12,1,7,8,8,3,9,0,8\nX,4,7,4,4,1,7,7,5,4,7,6,8,3,8,4,8\nT,2,3,3,1,1,6,12,3,6,11,9,5,1,11,2,5\nR,4,8,6,6,6,8,6,7,3,7,5,7,4,6,7,8\nW,5,8,8,6,3,9,8,5,2,7,8,8,9,9,0,8\nM,5,10,7,8,8,9,7,6,5,6,7,5,8,7,3,5\nP,2,3,4,1,1,7,9,4,3,11,4,4,1,9,2,8\nB,4,6,4,4,3,6,7,8,6,7,6,6,2,8,9,10\nT,4,7,4,5,3,4,12,5,4,11,9,5,2,12,1,5\nO,4,8,5,6,3,7,5,8,5,6,4,7,3,8,3,8\nK,8,13,8,7,5,9,6,3,6,10,1,7,6,5,4,8\nR,3,7,3,4,2,5,11,8,4,7,3,9,3,7,5,11\nP,5,6,6,4,5,7,6,6,4,7,6,8,3,8,7,10\nI,3,9,4,6,2,6,8,0,8,14,7,8,0,8,1,7\nF,5,9,4,5,2,7,9,3,5,12,6,5,2,8,6,6\nE,5,9,4,4,2,8,7,5,4,10,5,10,3,8,7,10\nE,5,8,6,6,5,8,7,6,3,7,6,10,4,8,8,9\nR,3,7,5,5,4,10,7,3,5,10,3,7,3,7,3,10\nN,4,2,4,3,3,7,8,5,5,7,7,6,6,9,3,6\nK,3,7,4,4,1,4,8,7,2,7,5,11,3,8,3,10\nR,4,6,6,4,6,8,8,4,4,6,7,8,6,10,7,6\nN,4,9,6,7,4,7,8,6,5,7,7,7,6,9,2,6\nB,2,1,2,1,1,7,7,7,5,6,5,7,1,8,7,10\nM,5,7,7,5,4,6,6,3,5,10,9,9,7,5,2,8\nL,4,10,5,8,3,4,4,1,9,6,1,10,0,7,2,7\nW,8,14,8,8,6,3,8,1,4,9,10,8,9,11,2,5\nO,2,3,2,2,1,8,7,6,3,9,6,8,2,8,3,8\nV,4,7,5,5,6,8,6,4,1,7,8,8,7,9,4,7\nS,4,9,6,7,7,8,6,5,4,8,6,9,5,7,13,9\nP,4,9,6,7,4,6,10,3,7,10,9,4,1,10,5,7\nJ,2,8,3,6,1,11,3,10,3,13,7,13,1,6,0,8\nS,4,7,5,5,4,8,7,5,7,5,6,8,0,9,9,8\nT,3,6,5,6,4,6,7,3,8,8,6,9,3,7,7,6\nX,0,0,1,0,0,7,7,3,5,7,6,8,2,8,3,8\nM,2,1,2,2,1,7,6,10,0,7,8,8,6,6,0,8\nW,8,10,8,8,7,2,12,2,2,10,9,8,6,12,2,6\nE,1,0,1,1,1,5,7,5,7,7,6,12,0,8,6,9\nT,2,10,4,7,1,7,14,0,6,7,11,8,0,8,0,8\nR,1,0,1,0,0,6,8,6,3,7,5,7,2,7,3,10\nH,4,8,5,6,5,6,7,5,4,7,5,8,3,7,6,11\nT,3,6,4,4,2,10,11,2,8,5,11,9,1,11,1,9\nU,2,3,3,2,1,4,8,4,5,11,10,9,3,9,1,6\nD,7,12,6,6,4,10,4,3,6,10,2,7,4,7,4,12\nN,8,12,7,6,3,5,9,4,6,4,4,11,5,10,2,7\nV,3,7,5,5,2,8,9,4,1,6,12,8,2,10,0,8\nD,4,8,6,6,5,10,6,4,6,9,3,6,3,9,3,8\nZ,4,9,6,6,4,7,7,2,10,12,6,10,1,9,6,8\nE,3,2,4,4,3,7,7,6,8,7,7,9,2,8,6,9\nX,4,9,6,7,5,7,7,3,8,5,6,8,3,8,6,7\nA,5,7,8,6,6,9,7,3,5,6,8,6,4,10,4,5\nL,3,8,4,6,2,0,2,4,6,1,0,7,0,8,0,8\nG,5,7,6,5,3,6,7,6,6,10,8,8,2,9,5,9\nR,6,10,8,8,7,8,8,6,6,6,5,7,3,6,6,9\nA,4,11,7,9,4,11,3,2,3,9,2,9,5,5,3,9\nY,6,9,8,7,6,8,7,6,5,5,9,8,3,9,8,5\nM,4,1,5,3,3,9,6,6,4,6,7,6,8,6,2,5\nT,4,9,5,6,3,5,13,6,4,11,9,4,3,12,2,4\nA,3,9,6,6,2,8,4,3,2,7,1,8,3,7,3,8\nR,3,6,5,4,4,9,7,3,5,10,3,7,3,7,3,10\nB,4,9,6,6,7,8,6,6,6,6,6,5,2,8,6,10\nM,5,6,6,8,4,8,7,12,2,6,9,8,8,6,0,8\nD,3,10,5,8,8,9,8,4,5,7,6,6,4,7,8,5\nF,3,4,4,6,1,1,13,5,3,12,10,6,0,8,2,6\nM,5,6,7,4,5,12,5,3,4,9,2,6,7,6,2,8\nC,4,9,5,6,3,6,9,7,8,13,8,7,2,11,3,7\nF,10,13,8,8,4,8,8,4,7,13,4,6,2,7,7,8\nR,4,8,7,7,8,9,8,4,4,7,4,7,6,7,6,4\nV,3,7,5,5,2,6,9,4,2,8,13,8,2,10,0,8\nX,5,9,7,7,3,7,8,1,9,10,6,8,3,8,4,7\nZ,4,9,6,7,4,8,5,2,9,11,4,10,2,7,7,9\nS,3,5,4,3,2,8,7,2,7,10,6,8,1,9,5,8\nZ,3,4,4,6,2,7,7,3,13,10,6,8,0,8,8,8\nE,4,10,6,7,5,10,6,1,7,11,4,8,3,8,4,11\nH,4,8,4,6,2,7,9,15,1,7,4,8,3,8,0,8\nE,1,1,1,1,1,4,7,5,8,7,6,13,0,8,6,9\nO,7,11,9,8,12,8,5,6,2,7,6,8,11,10,8,14\nS,1,0,1,0,0,8,7,4,6,5,6,7,0,8,7,8\nW,8,8,8,6,5,4,11,3,3,9,9,7,8,11,2,6\nR,3,6,4,4,4,6,8,8,4,7,6,8,2,7,5,11\nN,5,10,6,7,3,7,7,15,2,4,6,8,6,8,0,8\nC,1,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10\nQ,4,10,6,8,6,8,6,7,4,5,7,11,6,7,8,8\nW,3,7,5,5,4,5,10,2,2,8,9,9,7,11,1,8\nQ,4,9,4,4,2,8,5,4,7,8,6,8,3,9,8,11\nB,7,11,9,8,8,10,6,3,6,10,3,7,5,7,6,11\nI,4,7,5,5,3,8,8,2,8,7,6,7,0,8,4,7\nG,6,9,5,4,3,7,8,6,6,10,5,6,4,7,5,7\nQ,5,8,5,10,7,8,8,6,2,7,8,11,3,9,6,8\nA,2,3,4,4,1,10,5,3,1,8,1,8,2,7,2,8\nI,9,14,6,8,3,10,4,5,5,13,3,8,3,7,5,10\nZ,4,9,5,7,2,7,7,4,15,9,6,8,0,8,8,8\nC,5,9,6,7,4,3,8,6,7,12,10,13,1,9,3,7\nV,2,7,4,5,2,8,11,2,2,5,10,8,2,11,0,9\nK,7,15,8,8,5,9,6,3,6,11,2,7,5,7,4,9\nK,4,9,5,7,4,3,7,7,3,7,6,11,3,8,3,11\nC,4,6,6,4,3,8,6,7,7,7,6,8,4,10,4,8\nG,3,9,5,6,4,6,6,6,6,6,6,9,2,9,4,8\nM,6,10,8,8,9,7,10,6,4,7,7,8,6,8,6,8\nF,4,9,6,6,3,3,12,5,4,13,9,5,1,10,2,5\nU,5,10,8,8,10,8,5,4,5,6,7,6,10,6,6,5\nU,2,3,2,1,1,7,8,6,7,8,9,7,3,10,1,8\nS,6,11,7,8,5,9,7,3,6,10,4,7,2,9,5,9\nJ,4,7,5,5,2,5,9,3,5,15,8,10,1,6,1,7\nF,4,8,5,6,5,4,10,2,5,10,10,6,1,10,3,6\nT,4,6,4,4,3,6,11,3,7,11,9,4,2,12,3,5\nQ,6,10,5,5,4,9,6,4,6,11,5,7,4,7,9,10\nB,2,4,3,3,2,8,7,3,5,9,6,6,3,8,5,9\nR,4,9,5,6,4,9,8,6,6,8,5,7,3,8,5,10\nU,5,5,6,4,2,3,9,5,7,11,11,9,3,9,1,6\nW,8,10,8,8,6,2,11,2,3,10,10,8,7,11,1,7\nV,10,14,9,8,5,8,9,4,6,8,9,5,6,12,4,8\nA,3,6,6,8,2,9,4,3,2,8,1,8,3,7,2,8\nF,3,7,5,5,4,4,10,5,5,11,10,6,2,10,3,6\nU,5,8,6,6,3,7,8,6,8,5,9,9,3,9,1,8\nE,1,3,3,1,1,6,7,2,6,11,6,9,2,8,3,9\nF,3,4,4,3,2,5,11,4,6,11,9,5,1,10,3,6\nD,3,5,4,4,3,7,7,6,6,7,6,5,5,8,3,7\nQ,2,2,3,3,2,8,7,6,2,6,6,9,2,9,4,9\nF,5,9,5,5,2,8,8,2,7,11,6,6,2,10,5,8\nM,7,11,10,8,10,8,7,5,5,6,7,7,11,8,4,6\nI,2,10,2,8,3,7,7,0,7,7,6,8,0,8,3,8\nU,3,7,5,6,5,7,7,4,4,6,6,9,4,8,1,8\nP,4,9,5,6,5,6,9,7,4,9,7,4,2,10,4,7\nH,6,8,9,6,6,10,6,3,6,10,3,8,4,8,5,10\nU,4,7,6,5,4,8,8,8,5,6,7,10,3,8,4,6\nG,4,4,6,7,3,8,8,8,8,6,7,8,2,7,6,10\nW,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8\nX,3,6,4,4,1,7,7,6,2,7,6,8,3,8,4,8\nC,4,7,6,5,3,7,7,7,6,7,6,8,4,9,4,8\nN,5,10,6,8,6,7,7,13,1,6,6,8,6,9,0,6\nW,8,11,8,8,9,4,10,2,3,9,8,7,9,13,3,5\nW,3,7,5,5,7,8,9,5,1,7,7,8,7,9,2,8\nP,5,6,7,8,8,8,6,4,3,7,7,7,7,12,6,7\nP,1,0,1,0,0,5,10,6,1,9,6,5,0,9,2,8\nK,3,4,4,3,3,5,7,4,7,7,6,11,3,8,5,9\nB,8,15,6,8,5,8,6,5,6,10,5,9,6,7,8,10\nC,3,2,4,3,2,6,8,8,7,8,7,12,2,9,4,10\nF,5,11,7,8,5,6,11,2,5,13,7,4,2,10,2,7\nJ,4,5,5,6,4,8,9,4,4,7,6,8,3,7,7,7\nV,4,9,6,6,3,4,11,3,4,9,12,9,3,10,1,8\nO,4,8,4,6,3,8,5,8,5,9,4,8,3,8,3,8\nN,4,5,4,7,3,7,7,14,2,5,6,8,6,8,0,8\nW,6,10,8,8,9,7,9,6,4,9,9,7,8,8,5,12\nL,4,8,5,6,4,8,5,8,6,5,7,8,3,6,6,11\nO,2,4,3,3,2,7,7,6,3,9,6,8,2,8,2,8\nR,3,4,5,2,3,8,8,3,5,9,5,7,3,6,4,10\nR,5,8,8,7,8,8,7,3,3,7,5,8,7,9,6,5\nP,4,10,6,7,4,8,9,2,6,13,6,5,1,10,3,9\nD,5,10,6,8,6,6,8,9,7,7,7,5,3,6,5,9\nA,3,10,5,8,4,11,3,2,2,9,2,9,3,5,3,8\nA,3,8,6,6,4,11,2,2,2,9,2,9,3,5,3,8\nO,6,10,7,8,3,6,6,9,9,6,5,6,3,8,4,8\nV,4,10,7,7,2,7,8,4,3,7,14,8,3,9,0,8\nK,5,7,8,5,5,4,9,2,6,10,9,10,3,8,3,6\nO,2,3,3,2,1,8,7,6,3,9,6,8,2,8,2,8\nD,3,6,5,4,6,9,8,4,5,7,6,6,4,8,8,5\nI,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7\nO,2,1,3,3,2,7,7,7,5,7,6,8,2,8,3,8\nP,3,2,4,4,3,6,9,5,5,9,7,3,1,10,4,6\nE,5,9,8,7,5,6,8,4,9,12,9,9,3,8,5,6\nO,4,9,5,7,3,8,6,9,8,7,4,9,3,8,4,8\nB,3,3,4,4,3,6,8,8,7,7,5,7,2,8,9,10\nM,4,7,5,5,3,7,7,12,1,7,9,8,8,6,0,8\nG,5,10,6,7,7,7,5,6,3,7,6,11,4,8,7,7\nC,7,10,8,8,4,2,9,5,9,11,11,13,1,7,3,6\nG,4,8,6,6,6,7,9,6,4,6,6,8,5,7,7,7\nN,1,0,1,0,0,7,7,10,0,5,6,8,4,8,0,8\nO,4,7,5,5,3,8,8,8,5,7,7,6,3,8,4,8\nV,2,3,4,5,1,9,8,4,2,6,13,8,3,10,0,8\nP,2,5,4,3,2,7,9,4,4,12,5,3,1,10,2,8\nT,7,12,6,7,3,6,10,3,6,12,8,6,2,9,4,5\nY,4,5,6,8,9,7,5,4,3,8,8,9,5,10,6,10\nP,2,4,4,3,2,7,9,4,3,11,4,4,1,10,3,8\nF,4,7,6,5,3,8,9,3,6,13,5,5,2,10,3,8\nG,7,10,7,8,4,7,6,7,8,11,5,12,3,9,5,8\nQ,2,2,3,3,2,8,8,7,3,5,6,10,2,9,5,10\nO,3,7,4,5,3,9,6,7,5,10,4,9,3,8,3,8\nN,3,2,4,3,2,7,8,5,5,8,7,6,6,10,3,5\nR,7,11,9,8,6,9,7,5,7,8,4,6,3,7,6,11\nN,2,3,4,2,2,7,8,2,4,10,6,7,5,8,0,7\nI,1,3,2,2,0,7,7,1,7,13,6,8,0,8,1,8\nP,2,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8\nW,5,5,8,7,4,11,8,5,2,6,9,8,8,9,0,8\nL,2,3,2,2,1,4,4,4,7,2,2,6,0,7,1,6\nS,4,5,5,4,5,8,8,4,4,7,7,8,5,10,9,10\nD,3,5,6,4,4,9,7,4,7,10,4,6,2,8,3,8\nY,3,4,4,3,2,5,10,2,7,10,10,5,1,11,3,5\nZ,5,10,6,8,7,8,9,3,7,8,6,8,2,10,13,7\nM,3,3,5,2,3,8,6,6,4,7,7,8,7,5,2,7\nE,3,7,5,5,5,5,6,4,6,7,6,12,2,10,8,6\nN,6,11,8,8,9,6,7,3,4,8,8,9,8,9,7,5\nG,3,3,4,5,2,7,7,7,6,6,6,7,2,7,6,11\nN,5,9,7,6,6,7,7,6,6,7,5,8,6,7,3,7\nV,4,8,4,6,3,4,11,1,2,9,10,7,2,11,1,7\nY,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nD,6,11,8,8,7,10,6,4,6,9,3,6,3,8,3,9\nC,4,10,5,7,2,5,7,7,10,7,6,14,1,8,4,9\nH,4,7,4,5,2,7,7,14,0,7,6,8,3,8,0,8\nL,4,7,5,5,2,5,4,2,9,6,2,9,1,6,2,7\nS,3,4,4,3,2,9,6,3,7,10,5,8,1,9,5,9\nQ,5,6,6,8,6,8,5,8,4,6,6,8,3,9,7,11\nF,6,11,8,8,5,8,9,3,6,13,5,4,2,9,3,7\nS,3,5,5,4,2,7,7,3,8,11,5,7,1,8,4,8\nY,3,5,5,4,2,8,11,1,7,5,11,9,2,12,3,8\nK,2,3,2,1,2,6,7,4,6,6,6,10,3,8,4,9\nG,2,1,4,3,2,7,7,6,6,6,7,9,2,8,4,9\nL,6,11,8,8,5,7,5,0,8,9,3,11,2,6,4,7\nJ,6,10,9,7,5,8,5,7,7,8,6,7,2,7,5,6\nR,3,9,4,6,3,5,11,8,4,7,4,9,3,7,6,11\nP,6,11,6,8,6,5,11,9,3,9,5,4,1,10,3,8\nN,7,10,10,8,5,9,9,3,6,10,3,4,6,9,1,7\nR,8,12,8,6,6,8,7,3,5,9,3,8,6,7,6,7\nA,1,3,2,2,1,10,3,2,1,9,2,9,1,6,1,8\nM,4,8,6,6,7,9,7,5,5,6,7,6,8,7,2,6\nZ,4,10,5,7,4,6,8,6,10,7,7,10,1,9,8,8\nR,6,9,6,4,4,5,8,3,5,7,4,10,4,7,5,7\nD,4,6,5,4,7,8,8,5,5,7,6,6,6,6,7,6\nA,2,7,4,4,2,7,4,3,2,6,1,8,3,6,2,7\nX,10,15,9,9,4,6,8,3,9,9,9,9,4,10,4,6\nI,1,3,1,1,0,8,7,1,7,13,6,8,0,8,0,8\nG,5,7,7,6,7,6,10,5,3,7,7,7,9,13,8,7\nZ,5,8,7,6,4,7,8,2,10,12,7,8,1,9,6,7\nO,5,6,7,5,5,7,5,4,4,9,4,9,3,7,6,6\nJ,4,6,6,7,5,7,8,4,5,7,7,7,3,9,9,10\nV,4,11,6,8,4,6,11,2,3,6,11,9,2,10,1,8\nN,2,3,2,1,1,7,8,5,4,7,6,7,4,9,1,6\nA,3,5,5,4,2,9,2,1,2,8,2,9,2,6,2,8\nR,5,7,7,5,4,10,7,3,6,10,2,6,3,6,4,10\nH,3,6,5,4,4,7,8,3,6,10,5,8,3,8,3,9\nI,3,7,4,5,2,9,6,0,7,13,5,9,0,8,1,8\nV,2,1,3,1,1,9,12,3,2,5,11,8,2,11,0,8\nQ,2,2,3,3,2,8,6,6,3,6,6,9,2,9,4,9\nD,9,15,9,8,5,10,4,4,6,12,2,8,5,6,5,10\nP,1,1,1,1,0,5,11,7,1,10,6,4,1,9,3,8\nW,6,7,6,5,4,3,10,2,3,10,10,8,6,11,2,6\nS,5,8,6,6,3,6,8,4,8,11,6,7,2,8,5,6\nL,1,3,2,2,1,7,4,1,6,8,3,10,0,7,1,9\nA,5,8,8,7,7,7,8,2,4,7,8,9,8,6,4,8\nW,6,7,6,5,4,5,10,3,3,9,8,7,7,12,2,5\nL,3,5,4,3,2,4,4,4,8,2,1,5,1,7,1,6\nF,5,10,9,8,9,7,9,1,5,9,6,5,6,9,5,2\nD,3,5,5,3,3,7,7,6,7,7,6,5,2,8,3,7\nO,4,9,3,4,2,6,9,5,3,8,5,6,4,9,5,8\nR,1,3,2,1,1,8,8,4,4,9,5,7,2,6,4,9\nL,2,6,4,4,2,9,4,1,8,10,3,10,0,7,2,10\nU,4,8,5,6,3,5,8,6,7,9,7,9,3,9,3,5\nP,3,7,3,5,3,5,11,8,2,9,7,5,1,10,3,8\nU,11,15,10,9,5,9,7,6,6,2,10,6,6,7,3,6\nE,3,5,6,4,3,7,8,2,8,11,7,9,2,8,4,8\nZ,5,10,7,8,5,8,6,2,9,11,5,10,2,7,7,8\nL,3,7,4,5,5,7,8,3,5,5,7,10,4,11,5,6\nO,4,9,4,7,3,8,7,8,5,10,5,9,3,8,4,7\nS,3,9,4,7,4,7,7,5,7,5,6,8,1,8,9,7\nV,5,10,5,7,3,3,11,3,4,10,12,8,2,10,1,8\nT,7,10,6,5,3,8,9,2,7,11,7,7,2,9,4,6\nW,8,11,8,8,6,3,10,2,3,9,9,8,8,11,2,5\nL,4,10,5,8,3,5,4,3,8,5,1,7,1,6,3,6\nY,5,7,7,11,10,10,9,4,2,5,8,9,4,11,10,12\nP,3,3,4,4,2,5,11,9,3,9,6,5,1,10,4,8\nC,2,6,3,4,2,6,8,8,8,8,8,14,1,9,4,9\nN,3,2,4,3,2,7,8,5,4,7,7,6,5,9,2,5\nY,2,4,4,6,1,7,10,1,3,7,12,8,1,11,0,8\nK,9,14,8,8,4,8,8,3,7,9,7,7,6,10,4,7\nI,2,5,3,3,1,7,7,0,7,13,6,8,0,8,1,7\nA,3,9,5,6,4,8,2,1,2,6,2,7,3,6,4,7\nF,5,8,7,6,3,5,12,7,5,13,7,3,2,9,2,5\nV,10,14,9,8,5,9,7,4,5,6,8,6,7,12,3,8\nF,3,8,5,6,3,8,8,2,5,13,5,6,2,10,2,9\nH,3,8,5,6,7,8,6,4,3,7,6,7,6,7,8,9\nX,2,3,4,1,1,5,9,2,8,10,9,8,2,8,3,6\nA,2,1,3,1,0,7,4,2,0,7,1,8,2,6,1,7\nR,4,8,6,6,6,7,9,5,6,8,4,9,4,5,5,10\nP,5,9,6,6,3,5,12,9,2,10,6,4,1,10,4,8\nK,6,11,8,8,10,7,7,4,4,6,6,9,8,8,8,8\nC,3,9,4,7,3,5,8,9,8,9,8,11,2,10,4,10\nG,4,9,4,4,3,7,7,3,2,8,5,7,3,10,8,7\nX,2,7,3,4,1,7,7,4,4,7,6,8,3,8,4,8\nQ,6,11,5,6,3,10,4,5,6,12,3,10,3,7,7,12\nV,4,10,6,7,2,7,8,4,3,7,14,8,3,9,0,8\nJ,5,9,6,10,6,8,8,4,6,6,7,7,3,9,9,8\nZ,3,4,5,3,2,8,6,2,9,12,5,9,1,8,5,9\nC,5,10,6,7,4,7,7,8,7,6,6,9,4,8,4,8\nR,4,9,6,7,7,6,8,5,6,7,5,7,3,7,5,9\nC,4,7,5,5,2,5,8,6,8,12,9,11,2,10,3,7\nW,7,11,10,9,15,8,8,6,2,7,7,8,14,10,5,8\nO,2,5,3,4,2,8,7,7,4,9,6,8,2,8,3,8\nZ,1,0,1,1,0,7,7,2,10,9,6,8,0,8,6,8\nA,3,8,5,6,2,11,2,4,3,11,2,10,2,6,3,8\nF,5,8,6,10,7,7,9,5,5,8,6,8,4,10,7,5\nV,10,14,8,8,4,8,10,6,5,6,10,5,6,13,4,7\nL,4,9,6,7,4,6,5,0,8,8,2,11,0,7,2,8\nN,6,11,8,8,7,7,7,8,5,7,6,7,6,7,3,9\nN,4,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8\nT,3,4,4,3,1,5,13,3,6,12,9,4,1,11,1,5\nX,4,10,7,8,7,7,7,2,6,7,6,8,4,8,7,7\nX,6,10,7,5,3,5,8,2,7,11,8,8,4,9,3,6\nW,5,10,7,7,7,7,7,6,2,7,8,8,6,8,4,10\nN,7,11,6,6,3,8,11,5,6,4,5,9,5,8,2,7\nC,3,4,4,3,1,4,8,5,7,11,9,12,1,9,2,7\nK,3,5,5,3,3,6,7,4,7,7,6,11,3,8,5,9\nM,3,4,5,3,3,5,6,3,4,9,9,10,7,5,2,8\nF,3,4,5,3,2,8,9,2,7,13,5,5,2,9,3,8\nJ,1,6,2,4,1,12,3,8,3,13,6,12,1,6,0,8\nW,6,10,9,8,6,10,8,5,1,5,9,7,10,12,2,5\nG,4,8,5,6,3,7,6,6,7,11,6,12,2,10,4,9\nP,3,1,4,2,2,5,10,4,4,10,8,4,1,10,3,7\nM,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8\nB,8,11,6,6,4,9,7,6,7,10,4,8,6,6,6,10\nR,6,11,6,8,4,6,10,10,4,7,4,8,3,7,6,10\nD,3,4,5,3,3,10,6,3,7,10,3,6,2,8,3,9\nZ,4,8,6,6,6,7,8,2,7,7,6,7,1,9,9,8\nX,4,5,5,7,2,7,7,5,4,7,6,8,3,8,4,8\nT,5,8,6,6,7,5,7,3,6,7,6,9,5,8,5,7\nG,2,4,3,3,2,6,7,5,5,9,7,10,2,8,4,10\nR,3,6,3,4,2,6,9,9,4,7,4,7,2,7,5,10\nJ,3,8,5,6,2,10,6,2,7,14,4,8,0,7,0,8\nR,4,9,6,7,4,10,7,3,6,11,2,7,3,6,3,9\nC,4,8,5,6,3,5,7,6,8,7,6,12,1,8,4,9\nG,5,9,6,6,4,7,6,7,7,7,5,12,2,8,5,10\nE,2,6,3,4,2,3,7,5,9,7,6,14,0,8,6,9\nB,4,7,6,6,7,8,6,5,4,7,6,8,6,9,7,7\nX,4,5,6,4,3,7,7,1,9,10,6,8,2,8,3,7\nL,4,8,5,7,5,8,7,5,6,7,6,8,3,8,8,11\nD,2,4,4,2,2,9,7,4,6,10,4,5,2,8,3,8\nB,4,9,4,6,6,6,8,8,6,7,5,7,2,7,7,9\nL,6,10,8,8,7,6,6,6,6,6,5,8,6,7,4,10\nZ,3,7,4,5,3,7,7,3,11,8,6,8,0,8,7,8\nI,2,6,3,4,2,8,6,0,7,13,6,9,0,7,2,8\nI,1,1,0,2,0,7,7,1,7,7,6,8,0,8,2,8\nB,4,10,5,8,4,6,6,10,7,6,7,7,2,8,9,9\nJ,3,10,5,8,4,12,6,2,7,11,2,6,0,7,1,8\nE,7,10,9,8,7,4,9,4,8,12,10,9,3,8,5,4\nM,2,1,3,2,2,8,6,6,3,6,7,7,6,5,1,7\nJ,3,9,4,6,2,14,3,5,5,13,2,9,0,7,0,8\nI,1,5,2,3,1,7,7,0,7,13,6,8,0,8,1,7\nV,2,3,3,1,1,7,12,3,3,8,11,8,2,10,1,8\nN,3,4,3,3,2,7,8,5,4,7,6,6,5,9,2,6\nY,4,7,6,5,5,8,5,6,4,6,9,8,3,8,8,4\nB,3,6,3,4,3,6,8,9,6,7,6,7,2,8,8,10\nU,1,0,2,0,0,7,6,10,4,7,12,8,2,10,0,8\nY,3,4,5,5,1,6,10,3,1,8,13,8,1,11,0,8\nJ,3,10,5,7,2,7,8,2,7,15,5,8,0,6,1,7\nF,6,9,8,7,7,7,9,5,5,8,6,7,6,10,8,11\nA,2,1,4,3,2,8,1,2,2,8,2,8,3,6,3,7\nP,2,4,3,3,2,5,10,4,4,10,8,3,1,10,3,6\nN,7,9,9,5,3,8,6,3,4,13,6,8,6,8,0,8\nO,3,6,5,4,3,8,8,8,4,7,6,5,3,8,3,7\nV,3,6,4,4,2,7,11,3,2,7,10,9,2,10,3,8\nG,3,4,4,3,2,6,6,6,6,6,6,10,2,9,3,9\nG,4,5,5,4,3,6,6,6,7,6,6,11,2,9,4,9\nN,5,9,7,7,5,7,9,6,5,7,6,7,6,9,2,6\nP,1,1,2,1,1,5,11,7,2,10,6,4,1,9,3,8\nW,5,9,6,4,4,7,9,2,3,7,8,6,8,11,2,7\nW,6,9,6,6,4,4,10,2,3,9,9,8,7,11,2,6\nK,7,11,10,8,6,4,8,3,7,11,10,12,4,8,4,6\nC,9,13,6,7,3,6,9,6,7,12,7,7,2,9,5,9\nY,5,9,7,6,4,10,10,1,7,3,11,7,1,11,2,9\nN,2,2,3,3,2,7,8,5,4,7,6,6,5,9,2,5\nJ,1,7,2,5,1,13,3,7,4,13,3,11,0,6,0,8\nS,3,5,3,3,2,8,7,7,5,7,6,7,2,9,9,8\nC,4,10,5,8,2,5,7,7,10,7,6,13,1,8,4,9\nH,4,5,7,4,4,7,7,3,6,10,6,8,3,8,3,7\nC,4,4,5,6,2,6,7,7,11,6,6,13,1,7,4,9\nL,1,0,2,1,0,2,1,6,5,0,2,5,0,8,0,8\nV,4,8,6,7,7,8,7,4,4,7,6,8,7,7,9,5\nT,5,11,6,8,5,9,12,4,6,6,11,8,3,12,1,8\nJ,4,6,5,7,5,8,8,5,7,7,5,8,3,9,9,8\nH,1,0,2,0,0,7,6,11,1,7,7,8,2,8,0,8\nV,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8\nS,6,9,7,6,3,9,6,4,8,11,3,8,2,8,5,11\nD,6,10,8,7,7,7,7,5,6,7,7,8,7,7,3,8\nB,2,3,3,2,2,8,7,2,5,11,5,7,2,8,2,9\nC,3,3,4,4,1,5,8,6,9,6,7,11,1,7,4,9\nM,2,1,2,1,1,8,6,10,0,6,9,8,6,6,0,8\nS,3,7,4,5,2,7,6,6,9,5,7,10,0,9,9,8\nF,5,7,6,8,7,7,9,5,5,8,6,7,4,9,9,7\nR,3,7,5,5,5,6,7,5,6,6,5,8,3,6,4,8\nW,6,7,6,5,6,7,10,4,3,8,6,6,9,11,4,6\nI,1,7,2,5,1,7,7,0,8,7,6,8,0,8,3,8\nX,4,10,6,8,6,7,7,2,6,7,6,8,3,9,6,8\nN,6,9,9,7,5,7,9,2,5,9,6,7,6,8,1,7\nW,4,7,6,5,6,7,7,6,2,7,8,8,6,8,4,8\nS,1,0,2,1,1,8,7,4,7,5,6,7,0,8,7,8\nP,4,7,5,5,4,6,9,6,5,9,7,4,2,10,3,7\nN,5,10,6,8,6,7,7,6,7,7,6,8,6,8,3,7\nM,1,0,2,0,1,7,6,9,0,7,8,8,6,6,0,8\nF,5,9,7,7,3,5,13,4,5,13,7,3,1,10,2,6\nQ,5,6,7,8,8,10,9,6,0,5,7,10,7,13,5,11\nX,4,2,5,4,3,7,7,3,9,6,6,7,2,8,6,7\nS,5,8,6,6,4,7,8,3,7,10,5,7,2,8,5,8\nV,2,1,3,2,1,7,12,2,2,7,11,8,2,11,0,8\nD,5,12,5,6,4,6,8,4,6,10,6,6,5,9,6,6\nU,5,7,5,5,2,4,9,5,7,11,11,9,3,9,1,6\nS,1,3,3,2,1,8,7,3,7,11,4,8,1,8,4,9\nZ,7,10,9,8,6,6,9,3,10,12,8,7,1,9,6,6\nD,6,10,6,8,3,5,8,10,10,8,7,6,3,8,4,8\nB,4,7,6,6,7,8,6,5,5,7,6,8,8,9,7,6\nQ,5,9,4,4,2,10,4,4,6,10,4,8,3,9,7,13\nY,2,3,4,4,0,7,10,2,2,7,13,8,1,11,0,8\nD,4,7,5,5,4,7,7,6,8,7,6,5,3,8,3,7\nO,4,9,6,7,7,8,8,5,1,7,7,9,8,9,4,9\nD,2,3,3,2,2,9,6,4,6,10,4,6,2,8,2,9\nB,5,9,5,4,4,7,8,3,4,9,6,7,6,7,8,5\nQ,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8\nH,6,7,8,5,4,7,6,3,7,10,7,9,3,8,3,7\nP,4,10,5,7,3,5,10,10,3,9,6,4,2,10,4,8\nT,2,1,2,2,1,8,11,3,5,7,10,7,2,11,1,7\nB,8,15,8,8,7,11,5,4,5,10,4,8,7,6,8,10\nD,5,9,5,7,5,5,7,9,7,6,4,5,3,9,4,9\nG,6,9,6,6,4,6,7,6,7,10,8,9,3,8,5,9\nE,5,10,3,5,2,6,8,5,7,10,6,10,1,9,7,9\nZ,7,8,5,11,4,7,6,6,5,10,7,7,3,9,9,7\nO,2,7,3,5,3,7,7,7,4,7,5,8,3,8,2,8\nX,6,11,9,8,6,4,9,1,8,10,11,10,3,9,3,5\nU,5,9,8,7,10,8,6,4,4,6,7,8,10,9,5,8\nM,4,2,5,3,4,6,6,6,5,7,7,10,8,6,2,8\nY,3,5,5,3,2,4,11,3,6,12,10,5,1,11,2,5\nN,3,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nP,4,7,6,5,3,5,12,6,3,12,5,2,1,10,3,8\nW,4,10,7,8,6,7,10,2,3,6,9,8,8,11,1,8\nR,6,9,8,6,9,7,8,3,5,5,6,9,6,9,7,6\nN,4,8,6,6,3,4,9,4,4,10,10,9,5,8,1,7\nZ,5,10,5,5,3,11,4,3,7,11,4,9,2,9,4,11\nO,4,9,5,7,4,8,7,8,5,10,6,7,3,8,3,8\nA,4,10,6,8,4,9,2,2,3,8,2,8,3,6,4,7\nU,8,12,7,7,3,5,4,5,5,4,7,8,6,8,2,7\nN,5,11,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nE,3,7,4,5,2,3,7,6,10,7,6,14,0,8,7,7\nR,4,8,4,6,4,5,10,7,3,7,4,9,2,7,5,11\nL,6,11,8,8,6,5,4,2,8,6,1,9,1,6,3,7\nN,7,10,10,8,5,4,10,4,4,11,11,10,6,8,1,7\nD,4,9,6,6,5,6,7,8,7,7,7,5,3,8,3,8\nD,4,8,4,6,4,6,7,9,7,6,5,6,2,8,3,8\nL,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8\nE,4,5,5,4,5,6,7,4,3,7,7,9,4,11,8,11\nC,4,10,5,8,3,6,7,10,8,10,8,11,2,12,4,9\nW,6,11,9,9,4,9,7,5,2,7,8,8,9,9,0,8\nU,3,7,4,5,3,8,5,11,4,8,12,7,3,9,0,8\nM,13,14,13,8,6,5,9,5,7,4,3,11,9,9,2,8\nC,5,12,4,7,4,9,6,4,2,9,8,11,4,9,7,13\nC,4,10,6,8,7,6,6,4,3,8,7,11,6,9,3,8\nR,6,11,8,8,8,7,9,6,5,8,6,8,5,8,7,12\nC,4,9,5,7,6,5,6,3,4,7,6,9,6,9,6,7\nM,7,9,10,7,12,10,7,4,5,9,4,6,10,6,5,5\nK,6,9,5,4,2,6,8,3,6,9,9,10,5,10,3,6\nC,3,4,4,3,1,5,9,5,7,11,9,12,1,9,3,7\nT,5,7,5,5,2,4,12,3,7,12,10,4,1,10,1,5\nO,5,5,7,8,3,7,5,9,9,6,4,7,3,8,4,8\nA,2,9,4,6,3,11,2,1,2,9,3,9,2,6,3,8\nR,4,10,6,7,6,8,8,5,5,9,5,7,4,7,5,10\nM,4,5,7,3,4,9,6,3,5,9,4,7,9,7,2,8\nA,4,10,7,7,2,5,5,3,1,5,1,7,3,7,2,7\nD,3,7,4,5,2,6,6,10,9,5,5,6,3,8,4,8\nQ,4,6,5,8,5,8,5,8,5,6,5,9,3,8,5,9\nO,3,5,4,3,2,8,7,7,4,9,6,8,2,8,3,8\nC,2,4,2,3,1,6,8,6,7,8,8,13,1,10,3,10\nM,7,7,10,6,11,9,7,5,5,7,6,7,11,9,7,3\nB,8,12,6,6,4,9,6,5,6,11,4,9,6,7,7,10\nI,5,10,7,8,4,8,4,2,6,7,7,7,1,10,4,7\nN,3,3,4,4,2,7,7,14,2,5,6,8,6,8,0,8\nF,3,11,4,8,2,0,11,4,6,11,11,9,0,8,2,6\nO,6,10,7,8,6,8,5,9,5,5,5,5,5,8,5,8\nO,6,9,6,7,4,7,8,8,5,10,7,8,3,8,3,8\nC,5,11,7,8,8,7,6,4,4,8,7,11,6,9,3,8\nX,2,3,3,4,1,7,7,4,4,7,6,8,3,8,4,8\nX,9,14,9,8,5,3,10,3,8,12,11,9,4,8,3,5\nT,6,8,6,6,4,6,11,4,6,11,9,5,3,12,2,4\nF,2,4,3,3,2,5,10,4,5,10,9,5,1,9,3,6\nC,5,11,6,8,4,5,7,6,9,8,4,11,1,10,5,10\nF,6,10,6,5,4,8,10,3,5,11,5,4,4,9,7,7\nC,4,6,5,4,3,4,8,5,7,9,9,14,1,8,3,8\nX,4,7,5,5,4,8,6,2,5,6,7,7,2,9,7,9\nT,4,11,6,8,2,7,15,1,6,8,11,7,0,8,0,8\nD,4,6,5,4,3,8,8,6,7,9,6,4,3,8,4,8\nE,3,6,4,4,2,4,8,6,10,7,5,13,0,8,7,8\nW,7,11,10,9,7,10,11,3,3,5,9,7,10,13,1,7\nA,3,2,5,3,2,6,2,2,2,5,2,8,2,6,2,6\nK,4,10,5,8,2,3,7,7,3,7,7,11,4,8,3,10\nN,3,6,4,4,3,7,9,6,4,7,6,7,5,9,1,7\nC,5,8,6,6,3,7,6,6,8,13,7,12,2,10,4,7\nQ,5,5,5,7,4,7,8,5,4,8,9,9,3,9,7,8\nS,4,9,6,7,8,7,6,3,2,7,5,7,3,7,12,2\nQ,4,10,5,9,3,8,6,9,7,5,5,8,3,8,4,8\nG,6,10,5,5,3,8,7,5,6,10,4,7,4,7,5,8\nY,4,5,6,7,1,8,10,2,2,6,13,8,1,11,0,8\nQ,4,6,4,8,5,8,8,6,2,8,8,10,3,9,5,8\nI,6,10,7,8,4,8,4,1,6,7,7,8,1,10,4,8\nU,5,8,5,6,2,4,9,6,7,11,10,8,3,9,1,7\nV,7,15,6,8,3,7,11,5,5,9,10,5,5,12,4,8\nY,2,1,3,2,1,7,10,1,7,7,11,8,1,11,1,8\nA,5,9,6,7,7,8,8,8,4,6,6,8,3,8,8,4\nB,3,4,4,3,3,8,7,5,6,7,6,6,2,8,6,10\nD,4,9,6,6,5,7,7,7,7,6,6,5,3,8,3,7\nH,3,5,5,3,3,8,6,2,6,10,5,8,3,7,3,8\nN,2,4,3,2,2,7,8,5,4,7,6,6,4,8,1,6\nV,7,8,7,6,4,4,12,1,2,9,10,7,6,11,2,7\nI,5,9,7,6,4,6,6,3,7,7,6,12,0,8,4,9\nS,5,11,6,8,4,6,8,4,8,11,7,7,2,9,5,6\nF,2,3,4,2,1,6,10,2,5,13,7,5,1,9,1,7\nP,6,9,5,5,2,7,8,5,3,12,4,5,5,9,4,8\nF,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8\nY,8,10,8,8,4,3,10,3,7,12,12,7,1,11,2,5\nW,7,10,10,8,7,5,8,4,1,7,9,8,8,10,0,7\nZ,6,10,8,7,6,7,7,2,9,12,6,9,1,9,6,8\nE,2,3,3,2,2,7,7,1,7,10,5,9,1,8,4,9\nP,2,5,4,3,2,8,9,4,4,11,4,3,1,10,2,8\nO,3,6,4,4,2,8,7,7,4,10,5,8,3,8,3,7\nO,4,8,4,6,4,7,7,7,4,10,6,8,3,8,3,6\nL,2,4,2,3,1,3,4,3,7,3,1,7,0,7,1,6\nR,1,0,2,1,1,6,9,7,3,7,5,7,2,7,4,10\nA,3,5,6,6,2,8,3,3,2,7,1,8,3,6,3,8\nL,4,10,4,8,1,0,1,5,6,0,0,6,0,8,0,8\nR,4,10,5,7,3,5,11,8,4,7,3,9,3,7,6,11\nJ,5,9,6,6,2,9,4,3,9,15,4,11,0,7,0,8\nY,6,11,8,8,8,8,4,6,5,8,7,8,9,7,6,5\nS,3,7,4,5,3,8,8,7,5,7,5,6,2,8,8,8\nY,8,10,8,8,4,5,8,2,9,9,10,4,5,11,7,3\nD,1,1,2,2,1,6,7,8,6,6,6,6,2,8,3,8\nX,7,14,7,8,4,6,8,2,7,11,7,8,4,9,4,7\nR,4,9,6,7,5,8,8,6,6,8,5,7,3,8,6,10\nQ,2,4,3,5,2,8,8,6,2,7,8,10,2,9,5,9\nZ,2,3,3,2,2,7,8,5,9,6,6,9,1,8,7,8\nC,5,9,6,7,4,5,7,6,9,6,6,10,1,6,5,10\nT,2,6,3,4,1,6,14,0,5,8,10,7,0,8,0,8\nB,3,5,5,3,3,9,6,3,6,10,4,7,4,7,6,9\nA,6,9,8,8,7,7,7,2,4,7,8,10,8,7,4,8\nW,4,5,6,5,7,7,7,5,5,6,6,8,9,9,8,8\nC,3,8,4,6,2,6,8,8,7,9,8,14,2,9,4,10\nS,2,2,3,3,2,8,7,7,5,8,6,7,2,9,9,8\nC,5,7,7,6,6,5,7,4,4,7,6,11,4,9,7,9\nV,2,6,3,4,1,7,9,4,2,7,13,8,3,10,0,8\nY,6,11,6,8,3,3,10,3,6,12,12,7,1,11,2,6\nN,2,3,3,1,1,7,9,5,5,8,7,6,5,9,1,6\nD,2,5,4,3,3,9,6,4,6,10,4,6,2,8,3,8\nZ,5,9,7,7,4,8,7,2,10,12,5,9,1,8,6,9\nY,2,5,4,4,2,7,10,1,7,7,11,8,1,11,2,8\nO,2,6,3,4,2,8,7,8,5,7,7,9,3,8,3,8\nC,2,1,3,2,1,6,7,6,10,7,6,14,0,8,4,9\nH,2,4,4,2,2,9,7,3,5,10,4,7,3,8,2,9\nZ,6,7,4,10,4,8,6,4,4,11,6,7,2,10,9,8\nS,4,2,4,4,3,8,7,7,5,7,6,8,2,9,9,8\nT,3,5,4,4,2,6,12,2,7,8,11,7,1,11,1,7\nH,2,3,4,2,2,7,7,3,5,10,6,8,3,8,2,7\nV,3,2,5,3,2,7,12,2,3,6,11,9,3,11,1,7\nA,2,6,4,4,2,8,4,2,1,7,2,8,2,6,2,8\nJ,1,3,2,2,0,9,6,2,5,14,6,10,0,7,0,7\nF,5,10,5,5,3,7,8,3,4,10,6,7,4,8,7,6\nP,6,9,8,7,6,8,9,4,4,12,5,4,1,10,3,8\nW,3,8,5,6,5,9,11,2,2,5,8,8,6,11,1,8\nU,4,4,5,3,2,4,8,5,7,11,10,9,3,9,1,7\nB,7,10,9,8,9,9,7,4,6,10,5,6,2,8,5,10\nV,4,4,6,7,1,8,8,4,3,6,14,8,3,9,0,8\nQ,2,4,3,4,3,8,8,6,3,8,6,9,2,9,4,9\nR,4,8,5,6,3,6,13,8,4,7,2,9,3,7,6,10\nF,5,9,6,7,6,6,10,6,5,8,5,8,3,10,8,10\nN,6,10,9,8,5,10,8,3,6,10,2,4,5,9,1,7\nL,3,7,5,5,2,3,3,6,8,1,0,6,0,7,1,6\nD,4,8,6,6,5,7,6,8,6,5,5,4,4,8,4,8\nA,5,11,8,9,5,12,3,3,3,10,1,9,3,7,4,9\nS,3,5,6,3,2,7,8,2,7,11,6,6,1,9,4,6\nN,2,3,4,2,2,6,8,3,3,10,7,8,4,8,0,7\nJ,3,4,4,6,1,11,2,10,3,13,8,14,1,6,0,8\nY,5,9,4,4,2,6,8,3,4,10,8,5,3,9,4,4\nX,4,10,7,7,4,9,7,0,8,9,5,7,2,8,3,8\nG,6,5,7,8,3,7,5,8,10,7,5,12,1,8,6,11\nW,4,2,6,4,4,8,11,2,2,6,9,8,7,11,1,8\nP,2,1,3,2,2,6,9,4,4,9,8,5,1,10,3,7\nH,2,4,3,3,2,6,7,5,6,7,6,8,6,8,3,8\nA,1,3,3,2,1,6,3,2,2,5,2,8,1,6,1,7\nO,4,6,4,4,3,8,6,8,6,9,4,7,3,8,3,8\nL,3,9,4,6,3,6,5,0,8,8,3,11,0,8,2,8\nO,6,8,8,7,6,7,5,5,5,8,4,7,3,7,5,7\nV,4,6,4,4,2,3,12,4,3,11,11,7,2,10,1,7\nM,5,6,8,4,5,7,6,2,5,9,7,8,8,6,2,8\nF,5,9,7,6,4,8,9,2,6,14,5,5,2,9,3,8\nQ,7,8,10,7,8,6,4,4,6,5,4,7,5,3,7,6\nQ,3,5,4,6,3,8,6,6,4,9,6,9,3,9,4,8\nL,4,9,5,8,5,6,8,4,6,7,6,9,3,9,9,10\nG,4,6,4,4,3,6,7,5,5,9,8,11,2,8,4,10\nC,5,6,6,5,5,5,8,3,5,7,6,10,3,10,8,7\nM,2,3,4,2,2,6,7,3,4,9,9,9,5,5,1,7\nD,4,8,6,6,8,9,9,4,4,8,7,5,4,9,8,6\nM,3,3,4,2,3,6,6,6,4,7,7,10,7,5,2,9\nH,5,10,8,8,10,7,6,6,4,7,5,8,9,6,10,10\nJ,6,8,8,6,5,8,8,6,6,8,7,7,4,6,4,6\nL,3,6,4,5,4,9,7,4,6,6,6,9,2,8,7,10\nX,2,6,3,4,2,7,7,4,4,7,6,7,2,8,4,7\nG,7,10,7,8,5,6,8,6,6,10,8,9,3,7,6,9\nU,6,9,6,6,4,3,9,5,6,10,10,10,3,9,2,7\nZ,5,8,6,6,4,7,8,2,9,11,7,8,1,9,6,7\nL,2,5,3,4,2,7,4,1,7,8,2,9,0,7,2,8\nM,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8\nD,3,9,4,7,4,6,7,10,7,7,7,6,3,8,3,8\nS,4,9,4,4,2,7,4,2,4,7,2,7,2,7,5,8\nX,5,6,6,8,2,7,7,5,4,7,6,8,3,8,4,8\nF,5,9,6,6,6,5,9,4,6,10,10,6,2,9,3,6\nS,4,9,4,4,2,7,4,4,4,8,2,8,3,7,5,8\nH,2,3,4,2,2,7,7,3,6,10,6,8,3,8,2,7\nZ,4,8,5,6,2,7,7,4,15,9,6,8,0,8,8,8\nP,8,13,7,7,3,6,10,5,3,13,5,4,4,10,4,8\nT,5,6,6,5,5,6,7,3,8,7,6,10,3,6,7,6\nA,1,3,2,2,1,10,2,2,1,9,2,9,1,6,2,8\nC,5,9,4,5,1,6,8,6,7,11,7,9,2,9,5,9\nS,3,3,4,4,2,8,9,5,9,5,6,6,0,8,9,7\nV,5,10,7,8,4,7,12,2,3,6,11,9,4,10,3,7\nT,5,8,5,6,3,4,13,5,5,12,9,4,2,12,1,5\nU,9,14,8,8,4,7,6,4,5,4,8,5,7,5,3,6\nC,2,1,2,2,1,6,8,7,7,8,7,13,1,8,4,10\nI,4,9,5,7,3,9,6,0,7,13,5,8,0,8,1,8\nI,0,3,1,2,1,7,7,1,6,7,6,8,0,8,2,8\nY,7,11,7,8,5,5,8,0,8,9,9,5,4,10,7,4\nJ,0,0,1,1,0,12,4,4,3,12,4,10,0,7,0,8\nP,9,14,9,8,6,9,7,4,5,13,4,4,5,9,7,8\nW,3,1,3,2,1,7,8,4,1,7,8,8,7,10,0,8\nW,5,7,5,5,4,4,10,3,2,9,8,7,6,11,2,6\nB,2,1,3,2,2,7,7,4,5,6,6,5,2,8,5,9\nP,6,12,5,6,4,9,8,4,4,12,4,4,4,11,5,7\nW,4,6,5,4,2,11,8,5,1,6,9,8,8,10,0,8\nU,5,10,8,8,10,8,8,4,5,6,6,8,9,5,8,10\nX,3,7,5,5,4,7,7,2,6,7,5,8,3,6,5,8\nR,5,9,5,4,4,8,7,3,4,9,4,8,5,8,6,8\nX,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nQ,5,8,6,7,6,7,4,5,4,7,4,9,5,4,7,6\nG,8,15,6,8,5,9,5,5,3,9,6,9,5,9,8,8\nY,2,7,4,4,1,10,10,3,2,5,13,8,2,11,0,8\nJ,2,7,2,5,2,9,6,1,6,11,4,8,0,7,1,6\nA,5,13,5,7,4,11,2,4,2,11,4,11,5,3,4,10\nM,3,3,4,2,3,8,6,7,4,7,7,8,7,5,1,8\nE,6,9,8,7,7,8,8,7,3,6,6,11,6,8,9,8\nL,4,8,5,7,5,8,6,4,5,6,7,8,3,9,7,9\nH,6,11,8,8,8,6,8,8,4,7,5,7,3,7,7,10\nK,4,5,6,4,3,6,8,2,7,10,6,9,4,9,3,7\nV,3,3,4,2,1,4,13,3,2,10,11,7,2,11,1,8\nM,5,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nI,3,11,5,8,6,9,6,3,4,8,5,5,5,8,5,5\nW,2,0,2,1,1,7,8,3,0,7,8,8,6,9,0,8\nS,5,8,7,6,8,8,6,5,3,8,5,8,4,9,10,9\nL,4,8,5,7,5,7,6,4,5,7,7,8,4,9,9,11\nY,2,1,3,1,0,8,11,3,1,6,12,8,1,11,0,8\nO,5,10,4,5,3,5,8,7,3,10,7,8,5,9,5,7\nB,5,10,7,7,7,11,6,2,6,10,3,7,4,7,6,11\nU,5,5,6,4,3,5,8,5,8,10,8,9,3,9,3,5\nC,4,7,5,5,3,7,7,8,6,7,6,10,3,8,3,9\nO,4,10,5,7,4,7,7,8,5,6,4,7,3,9,4,9\nY,1,0,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nO,6,9,6,7,4,6,8,8,5,10,8,7,4,8,4,9\nX,6,9,6,4,3,10,7,2,8,9,4,7,4,10,4,9\nG,4,8,6,6,6,7,7,6,3,6,6,9,4,8,7,8\nW,6,9,8,7,6,8,8,4,1,7,9,8,8,10,0,8\nI,2,11,2,8,2,7,7,0,9,7,6,8,0,8,3,8\nL,3,3,4,4,1,1,0,6,6,0,1,5,0,8,0,8\nK,6,9,9,7,5,3,9,3,7,11,12,12,4,7,4,5\nP,5,9,7,6,5,7,12,7,2,11,5,3,2,12,4,8\nI,1,10,0,7,1,7,7,5,3,7,6,8,0,8,0,8\nA,2,4,4,2,2,11,2,3,2,10,2,10,2,6,2,9\nT,4,11,6,8,4,6,14,0,5,8,10,8,0,8,0,8\nI,2,8,3,6,2,7,7,0,7,12,6,8,0,8,1,8\nP,5,11,6,8,6,4,12,8,2,10,7,4,1,10,3,8\nQ,4,9,5,11,6,8,7,8,4,5,6,9,3,8,6,10\nL,5,9,7,8,6,7,7,4,5,7,7,8,3,8,8,9\nV,7,9,7,5,3,6,10,4,3,8,8,5,5,12,3,9\nN,5,6,7,4,3,5,9,3,5,11,9,9,5,8,1,7\nU,4,9,6,7,9,9,6,4,4,6,7,7,9,8,5,7\nG,2,3,2,2,1,6,7,5,4,9,7,9,2,9,4,9\nM,3,3,4,2,3,9,6,6,4,6,7,6,7,6,2,6\nW,3,6,5,4,2,6,8,4,1,7,8,8,8,9,0,8\nG,5,11,4,6,3,7,7,4,3,9,6,7,3,10,8,7\nK,3,2,3,4,1,3,7,7,2,7,7,11,3,8,3,10\nN,3,2,4,3,2,7,8,5,4,7,6,7,5,9,2,6\nK,5,7,7,5,4,8,7,2,7,10,3,8,4,9,4,9\nR,6,10,8,8,6,7,9,5,8,5,4,10,5,4,7,9\nI,1,6,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nR,3,7,5,5,4,9,7,4,5,10,4,7,3,7,4,11\nP,4,7,5,5,4,6,10,6,5,10,8,3,1,10,4,6\nN,3,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8\nX,4,9,5,6,3,8,7,4,9,6,6,6,3,8,6,7\nH,7,10,10,7,9,9,7,3,6,10,4,7,3,8,3,9\nU,9,10,9,8,5,5,7,6,9,9,8,9,5,11,5,2\nJ,2,2,4,4,2,11,6,2,7,12,3,7,0,7,1,8\nX,4,9,4,7,3,7,7,4,4,7,6,8,3,8,4,8\nW,4,9,6,7,4,6,8,4,2,7,8,8,8,10,0,8\nK,3,7,3,5,1,3,7,7,3,7,6,11,4,8,2,11\nK,3,7,4,4,1,4,8,7,2,7,5,11,3,8,3,10\nR,4,11,5,8,3,6,11,10,4,7,3,9,3,7,5,11\nB,4,8,5,6,5,7,7,6,4,6,5,6,3,8,6,6\nN,5,7,7,5,4,7,9,2,4,10,5,6,5,9,1,7\nB,3,6,4,4,4,8,8,6,6,7,6,6,2,8,6,9\nV,6,10,8,7,5,6,11,3,2,8,11,8,3,10,4,8\nR,7,12,6,7,5,6,8,5,5,9,5,10,7,5,7,11\nP,3,8,3,5,2,3,14,7,2,12,7,3,0,10,3,8\nB,4,5,4,8,4,6,9,10,7,7,5,7,2,8,9,10\nT,6,8,6,6,3,5,12,3,8,12,9,4,1,11,2,4\nB,3,5,5,4,3,9,7,2,6,11,4,7,4,6,5,9\nQ,6,10,6,5,3,10,4,4,7,10,4,9,3,8,6,13\nQ,2,1,2,2,1,8,6,7,4,6,6,8,3,8,3,8\nS,1,0,1,0,0,8,7,4,6,5,6,8,0,8,7,8\nT,4,8,6,6,6,7,7,4,7,7,6,8,5,7,5,6\nF,3,8,4,6,3,4,10,3,6,10,10,6,1,10,3,6\nY,1,3,3,2,1,7,10,1,6,7,11,8,1,11,1,8\nW,7,11,10,8,7,7,11,2,3,6,9,8,12,11,2,7\nG,4,10,5,7,3,7,7,8,7,6,6,8,2,7,6,11\nM,2,3,3,2,2,8,7,5,4,6,7,8,5,6,1,7\nW,6,8,8,6,7,8,6,6,2,7,7,8,6,8,4,7\nR,2,6,3,4,2,6,9,9,4,7,5,8,2,7,5,11\nD,5,7,6,5,4,7,8,7,5,8,6,5,4,9,3,7\nK,8,10,11,8,6,8,6,2,8,10,4,9,4,7,5,9\nB,1,3,3,1,1,9,7,2,5,10,4,7,1,8,2,9\nO,3,7,4,5,3,7,8,8,4,7,6,8,3,8,3,8\nK,3,4,4,2,2,5,7,4,7,7,6,11,3,8,4,9\nG,2,1,2,2,1,8,6,6,6,6,5,10,1,7,5,10\nX,4,11,5,8,2,7,7,4,4,7,6,8,3,8,4,8\nC,3,7,4,5,2,7,7,7,10,10,6,13,1,10,4,9\nG,3,6,4,4,3,5,7,6,5,9,7,11,2,8,4,10\nO,2,1,3,2,2,7,7,7,5,7,5,8,2,8,3,8\nI,9,14,6,8,3,11,4,5,5,12,3,8,3,7,5,10\nP,2,3,3,2,1,7,10,4,3,12,5,3,1,10,2,8\nO,2,3,3,2,1,7,7,7,5,7,6,8,2,8,3,7\nY,6,8,9,11,11,8,11,3,4,6,8,9,5,13,11,8\nF,5,9,7,7,5,6,9,6,8,10,10,5,2,9,4,5\nG,5,7,6,5,4,5,7,5,6,9,8,9,2,9,5,9\nK,3,3,5,2,2,6,8,2,6,10,6,9,3,9,3,7\nQ,2,2,3,3,2,8,7,6,2,5,6,9,2,9,4,9\nR,13,15,10,8,6,8,7,5,6,10,3,8,7,6,6,11\nB,5,10,6,8,7,9,7,3,5,7,6,8,7,8,6,9\nB,4,11,5,8,4,6,6,9,7,6,6,6,3,8,10,11\nV,4,7,4,5,2,4,11,3,3,9,11,7,2,10,1,8\nA,5,11,8,8,4,8,2,2,3,6,2,7,5,7,6,8\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nW,6,5,7,4,4,6,10,4,3,8,7,7,9,11,3,5\nN,5,9,8,7,4,10,6,3,5,10,2,5,6,9,1,7\nM,5,10,7,8,7,8,6,5,5,6,8,7,8,6,2,7\nO,2,0,2,1,1,8,7,7,6,7,6,8,2,8,3,8\nX,5,9,8,7,4,8,7,1,8,10,5,8,3,8,4,8\nO,2,2,3,3,2,7,7,8,4,7,7,8,2,8,3,8\nP,3,7,4,5,3,4,12,4,5,11,9,3,0,10,4,7\nW,5,6,5,4,4,2,11,2,3,10,10,8,5,11,1,7\nG,4,9,5,6,3,6,7,7,7,10,8,9,2,9,4,9\nZ,1,1,2,2,1,7,7,5,8,6,6,8,2,8,6,8\nO,4,8,5,6,3,6,9,7,5,10,8,7,3,8,3,8\nS,3,8,4,6,2,8,7,5,8,5,6,6,0,8,8,8\nC,3,7,4,5,2,5,8,7,7,8,8,14,1,9,4,10\nM,3,4,5,2,3,9,6,3,4,9,4,6,8,6,2,8\nG,4,9,5,7,4,5,7,5,4,9,9,9,2,7,5,9\nU,7,13,7,7,4,8,5,4,4,6,7,7,5,7,3,7\nQ,5,9,6,8,5,8,7,7,5,6,8,8,3,8,5,10\nG,5,8,6,6,4,7,6,7,8,7,5,11,3,11,5,8\nT,3,8,5,6,4,6,11,2,7,8,11,8,2,12,1,7\nE,3,6,3,4,2,3,8,6,10,7,5,14,0,8,7,8\nR,5,9,6,6,3,6,13,9,3,7,2,9,3,7,5,10\nW,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8\nY,7,10,6,5,3,8,6,3,5,9,8,6,4,10,4,5\nV,5,10,7,7,8,8,7,5,2,8,7,8,5,10,5,8\nX,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8\nZ,6,7,8,9,8,9,8,6,5,9,3,6,3,5,8,7\nU,3,4,3,6,2,7,5,13,5,7,13,8,3,9,0,8\nB,4,10,6,8,8,8,7,6,6,6,6,6,3,9,6,10\nC,7,10,8,8,8,6,6,3,5,8,6,12,6,9,5,10\nG,6,13,6,7,3,7,6,5,4,9,5,5,4,8,5,7\nG,4,9,4,4,3,8,6,4,2,9,6,8,3,9,7,7\nR,2,3,2,2,2,6,7,4,4,6,5,7,2,7,4,9\nY,1,0,2,0,0,7,9,3,1,7,13,8,1,11,0,8\nS,6,10,7,7,4,10,5,4,8,11,4,8,2,9,5,10\nX,7,11,10,9,5,7,7,1,9,10,6,8,3,8,4,7\nZ,1,0,1,0,0,7,7,2,9,8,6,8,0,8,6,8\nP,7,12,6,7,3,9,7,5,4,12,3,6,5,9,4,8\nM,3,5,6,4,4,5,6,3,4,10,10,10,6,5,2,7\nS,6,8,7,6,4,9,6,4,8,11,4,8,2,8,5,11\nH,7,10,10,8,8,6,8,3,6,10,8,8,4,9,4,7\nZ,6,11,8,9,8,10,7,5,4,7,5,7,4,8,10,5\nC,8,13,5,7,3,6,10,6,9,11,8,10,2,8,5,9\nW,9,9,9,7,8,4,11,2,2,9,8,7,7,12,2,6\nM,2,1,3,3,3,7,6,7,3,7,7,10,6,5,1,9\nC,3,7,4,5,2,5,8,7,7,9,8,13,1,10,4,10\nD,5,9,8,6,5,9,7,5,8,10,4,5,3,8,3,8\nC,3,6,5,4,1,6,8,6,10,7,7,12,1,7,4,9\nD,4,6,6,4,4,7,6,7,5,5,5,5,3,8,3,8\nR,3,6,5,4,3,9,8,3,6,9,3,8,3,6,4,10\nZ,3,2,4,3,2,7,7,5,9,6,6,8,1,8,7,8\nG,5,11,7,8,8,7,6,7,3,7,6,10,5,8,8,8\nH,3,8,5,6,7,8,9,5,3,7,7,7,9,8,9,8\nW,6,9,6,4,3,3,9,2,2,9,11,8,7,11,0,7\nT,4,11,5,8,2,7,15,1,6,8,11,8,0,8,0,8\nR,8,13,6,8,4,10,6,6,4,10,2,9,7,5,6,10\nJ,1,6,2,4,1,14,2,5,5,13,1,9,0,7,0,8\nO,5,10,7,7,8,8,9,6,2,6,7,9,11,13,5,9\nU,6,11,8,8,12,8,7,4,4,6,7,8,8,8,6,7\nS,3,5,4,4,2,8,6,3,7,11,5,9,1,9,5,9\nK,5,9,8,7,9,7,10,4,4,5,6,9,8,8,7,8\nN,3,5,4,3,3,7,8,5,4,7,7,7,5,9,2,6\nA,6,10,8,8,8,7,8,8,4,6,5,9,3,7,8,5\nK,4,7,5,5,2,4,8,9,1,7,6,11,3,8,2,11\nG,3,5,4,4,3,6,7,5,4,9,8,9,3,7,5,10\nD,1,3,2,2,1,8,7,5,5,9,5,6,2,8,3,8\nW,1,0,2,0,0,8,8,3,0,7,8,8,4,10,0,8\nJ,5,10,4,8,3,10,6,2,4,12,5,7,2,10,5,11\nN,3,5,5,3,2,7,8,2,4,10,6,6,5,8,0,7\nF,5,11,5,6,4,8,8,2,4,10,5,6,4,9,7,7\nC,4,9,3,4,2,7,10,4,4,8,8,8,3,9,8,11\nF,5,9,7,7,8,10,7,1,5,9,4,5,4,10,5,8\nE,3,4,3,6,2,3,7,6,10,7,6,14,0,8,7,8\nE,5,9,7,7,6,9,6,2,7,11,4,9,3,8,5,11\nM,3,6,4,4,3,7,5,10,1,7,8,8,7,5,0,9\nU,10,11,10,8,4,3,9,6,9,12,12,9,3,9,1,7\nC,6,10,7,8,5,3,7,4,7,10,8,15,4,9,5,6\nF,5,11,6,8,7,7,8,5,4,8,6,8,9,10,7,12\nU,5,10,7,7,6,7,8,4,7,4,7,9,6,10,1,8\nE,5,8,7,6,4,10,6,2,8,11,4,8,3,8,5,11\nC,4,10,5,8,2,5,7,7,10,7,5,13,1,9,4,8\nB,5,11,5,8,4,6,8,10,7,7,5,7,3,8,9,10\nG,1,0,2,1,0,8,7,6,5,6,5,9,1,7,5,10\nK,5,9,5,6,2,5,9,9,2,7,3,11,4,8,2,11\nW,4,9,7,7,6,7,8,4,1,7,9,8,7,11,0,8\nS,3,6,4,4,3,8,8,5,8,5,6,6,0,7,8,7\nW,2,1,2,1,1,8,8,4,0,7,8,8,6,10,0,8\nY,6,6,5,9,3,7,11,1,3,9,10,5,4,10,6,8\nS,3,9,4,7,3,8,7,5,8,5,6,7,0,8,8,8\nU,3,6,4,6,4,8,7,4,3,6,6,8,4,8,1,7\nK,5,9,8,8,7,8,6,2,3,8,3,8,6,5,8,12\nV,3,8,5,6,1,8,8,4,3,6,14,8,3,10,0,8\nV,2,7,4,5,2,7,9,3,1,8,12,8,2,11,0,8\nM,4,6,5,4,2,8,7,12,1,6,9,8,8,6,0,8\nG,4,7,5,5,3,5,8,6,6,9,8,9,2,7,4,9\nU,3,6,5,5,4,7,7,4,4,6,6,8,4,8,1,7\nQ,4,7,5,9,5,9,7,8,3,5,7,10,3,8,6,10\nO,5,9,4,4,2,9,7,5,4,9,4,8,4,9,5,9\nX,4,5,5,4,4,7,7,2,4,7,6,8,2,9,7,7\nX,3,4,4,3,2,7,7,3,9,6,6,9,2,8,5,8\nP,5,4,5,6,2,4,13,8,1,10,6,3,1,10,4,8\nX,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nI,2,11,3,8,4,8,7,0,7,7,6,8,0,8,3,7\nA,3,7,5,6,4,7,8,2,4,7,8,9,5,8,3,6\nW,5,6,5,4,4,4,10,3,2,9,9,7,6,11,2,6\nU,5,8,5,6,3,3,8,5,7,10,10,10,3,9,2,6\nV,4,5,5,3,2,4,12,4,4,11,11,7,2,10,1,8\nD,6,10,9,8,7,10,6,2,7,11,4,7,5,6,4,10\nT,4,2,5,4,3,9,12,3,7,5,11,9,2,11,1,8\nD,4,8,4,6,4,5,7,9,7,5,4,6,2,8,3,8\nD,3,8,4,6,2,5,7,10,8,7,6,5,3,8,4,8\nD,4,6,4,4,3,5,7,9,7,7,6,6,2,8,3,8\nU,7,8,7,6,3,3,10,6,8,13,12,8,3,9,1,7\nI,7,11,9,8,6,10,5,2,7,7,7,4,0,9,4,7\nN,9,15,7,8,4,6,9,4,5,4,5,11,6,11,2,7\nV,6,11,6,6,3,7,7,4,4,7,7,6,6,12,2,8\nV,4,5,6,4,2,4,12,3,3,10,11,7,3,10,1,7\nS,5,10,4,6,2,7,3,4,4,8,2,7,3,7,5,8\nQ,2,2,3,3,2,8,8,6,2,5,7,9,2,9,5,10\nC,4,8,5,6,3,3,8,5,7,10,10,13,1,8,3,7\nP,3,8,5,6,4,7,5,5,4,7,6,9,3,9,6,11\nZ,2,7,3,5,2,6,8,5,10,6,7,9,1,9,8,8\nY,2,4,4,3,2,6,10,1,7,8,11,8,1,11,2,8\nK,9,13,9,7,4,6,7,3,7,9,9,10,6,10,3,6\nY,4,5,5,7,7,9,8,6,3,7,7,7,6,10,6,4\nJ,7,11,9,8,6,8,4,4,6,8,6,7,5,6,5,7\nB,5,11,7,9,8,8,7,5,4,7,5,7,4,8,6,8\nO,3,2,4,3,2,7,6,7,5,7,5,7,2,8,3,7\nK,5,7,7,6,6,6,7,3,4,6,4,9,5,5,9,7\nK,2,3,4,2,2,7,8,2,6,10,6,8,3,8,2,8\nV,4,10,6,8,2,7,8,4,3,7,14,8,3,9,0,8\nP,4,6,5,4,4,7,9,3,6,8,8,4,1,10,4,7\nR,6,11,9,8,6,8,8,5,6,8,5,7,4,8,6,12\nP,4,7,5,5,4,6,9,6,5,9,7,4,2,10,4,7\nZ,4,7,6,5,3,6,9,3,9,12,9,7,1,9,6,5\nE,4,10,3,5,2,8,7,5,4,11,5,10,3,8,7,11\nQ,7,9,7,10,7,7,9,5,3,7,10,11,3,8,7,7\nQ,2,5,3,6,4,9,10,6,3,3,8,11,2,9,5,10\nX,2,7,3,4,1,7,7,4,4,7,6,8,3,8,4,8\nU,7,9,7,7,4,4,8,6,8,10,10,9,3,9,2,5\nN,5,8,8,6,4,4,10,4,4,10,11,9,6,8,1,7\nT,5,5,6,5,5,6,8,4,8,8,7,9,3,8,7,5\nS,3,6,4,4,3,8,8,5,7,6,6,7,0,8,8,8\nT,5,7,6,5,6,6,8,4,5,6,7,9,6,8,5,8\nG,9,14,7,8,5,9,5,5,3,9,6,8,4,9,8,8\nG,5,10,5,8,4,5,7,6,6,10,8,10,2,8,5,9\nI,2,8,3,6,1,7,7,0,8,14,6,8,0,8,1,7\nC,2,4,3,3,1,5,9,5,6,12,9,10,1,10,2,7\nD,5,7,6,5,5,10,6,3,6,11,4,7,3,8,3,9\nJ,3,7,4,5,2,8,7,1,6,14,4,7,0,7,0,7\nL,3,8,3,6,1,0,1,5,6,0,0,7,0,8,0,8\nR,7,13,6,7,5,8,6,3,5,8,5,9,6,9,6,7\nV,2,3,3,2,1,4,12,3,2,10,11,7,2,11,0,8\nM,11,12,11,7,6,6,9,5,4,4,4,11,11,14,3,8\nT,3,10,5,7,1,7,15,1,6,7,11,8,0,8,0,8\nU,6,10,8,7,5,4,8,7,8,9,11,11,3,9,1,8\nM,4,6,7,4,6,12,3,3,1,10,4,9,8,6,2,8\nJ,3,10,4,8,4,8,6,2,4,11,5,10,1,6,2,6\nO,4,5,5,4,3,8,7,8,5,7,6,8,2,8,3,8\nY,5,7,5,5,3,6,8,1,7,8,9,5,2,12,4,4\nB,6,9,5,4,4,9,7,3,5,10,5,7,6,6,7,9\nT,3,4,4,3,1,6,11,2,8,11,9,5,1,10,2,5\nQ,6,7,6,9,6,8,7,6,3,9,8,10,3,8,6,8\nO,7,8,9,7,6,5,7,6,6,8,5,6,3,7,5,7\nE,8,13,6,8,5,7,7,4,4,10,6,9,3,9,8,11\nI,3,8,5,6,3,7,7,0,7,13,6,8,0,8,1,8\nZ,3,9,4,6,2,7,7,4,13,10,6,8,0,8,8,8\nO,2,0,2,1,1,7,7,6,6,7,6,8,2,8,3,8\nV,2,8,4,6,1,9,8,4,2,6,13,8,3,10,0,8\nR,9,14,9,8,7,5,8,2,6,8,4,10,7,5,7,5\nM,5,11,6,9,4,8,7,12,2,6,9,8,9,6,0,8\nN,5,9,7,6,4,9,7,3,5,10,4,6,5,9,1,7\nS,6,11,6,6,3,6,9,3,5,13,8,7,2,9,3,7\nS,2,1,3,2,2,8,7,6,4,7,7,8,2,9,9,8\nD,4,5,5,4,4,7,7,7,7,7,6,5,2,8,3,7\nS,4,6,5,4,3,6,9,3,7,10,7,7,2,7,4,5\nM,6,9,8,8,10,8,7,4,3,7,6,7,10,8,6,5\nJ,0,0,1,0,0,12,4,4,3,11,4,11,0,7,0,8\nD,4,8,6,6,4,7,8,7,6,10,6,5,3,8,4,8\nT,4,6,4,4,2,5,12,3,6,11,9,4,2,11,2,5\nR,6,11,6,8,7,5,10,8,3,7,4,8,2,7,5,11\nE,3,4,5,3,2,8,7,1,8,11,5,8,2,8,4,10\nO,9,13,6,7,4,5,6,7,4,9,7,9,5,9,5,8\nC,4,9,5,7,6,5,7,4,5,7,6,9,6,10,5,7\nG,2,1,3,2,2,7,7,5,5,6,6,9,2,9,4,9\nT,4,9,4,4,2,5,11,2,6,11,8,5,2,8,3,5\nA,3,9,6,7,4,11,3,1,2,8,3,9,4,5,3,8\nJ,6,9,5,7,3,8,9,2,3,13,4,5,2,9,7,8\nB,9,11,7,6,4,10,4,5,6,11,3,9,5,7,6,11\nT,2,1,3,1,1,7,11,3,5,7,10,8,2,11,1,8\nW,5,6,5,4,4,5,11,3,2,9,8,7,6,12,2,7\nD,9,15,8,8,6,8,6,3,7,10,5,7,6,8,8,5\nE,4,8,4,6,2,3,6,6,11,7,7,14,0,8,7,7\nV,3,3,4,2,1,3,12,4,3,11,11,7,2,11,1,8\nH,10,13,11,8,7,9,6,4,5,11,3,7,6,7,5,7\nV,9,13,7,7,4,9,10,5,5,6,10,6,5,12,3,6\nM,9,13,10,8,6,11,11,7,3,4,7,9,9,13,3,6\nD,2,2,3,3,2,7,7,6,6,7,6,5,2,8,3,7\nC,5,9,6,6,3,5,8,8,9,9,8,12,2,10,4,9\nD,5,10,5,8,6,6,8,9,7,6,5,6,2,8,3,7\nR,9,13,7,7,4,9,5,5,6,9,4,9,6,6,7,11\nU,3,4,4,2,2,5,8,5,7,10,8,9,3,9,2,6\nG,7,9,7,7,5,5,7,6,6,10,8,11,2,9,4,10\nF,2,3,2,2,1,5,10,4,5,10,9,6,1,10,3,7\nK,5,8,8,7,7,9,6,2,4,9,3,8,4,6,7,11\nW,8,10,8,5,3,6,10,2,2,8,10,7,8,12,1,7\nB,5,8,7,6,9,8,8,5,3,7,7,7,6,10,9,9\nS,5,9,7,8,8,8,7,5,6,7,6,8,6,8,10,13\nP,6,11,5,6,3,8,7,5,3,12,3,6,5,9,4,8\nJ,2,5,4,4,1,9,7,2,7,14,4,8,0,7,0,7\nL,2,1,2,1,0,2,1,6,5,0,2,5,0,8,0,8\nK,5,10,5,5,3,5,8,3,6,9,8,9,5,7,3,8\nK,5,8,7,6,4,5,8,2,7,10,9,10,3,8,3,7\nS,4,9,6,7,4,7,8,3,7,10,5,7,2,7,4,8\nL,3,6,4,4,2,5,3,1,8,8,2,10,0,7,2,7\nW,7,9,7,7,6,2,11,2,3,10,10,8,6,11,2,7\nQ,7,15,6,8,5,11,4,4,6,10,5,7,3,9,7,12\nE,4,9,6,8,7,6,9,5,5,7,7,11,6,10,9,10\nZ,3,8,5,6,3,7,9,2,9,11,7,7,1,9,6,6\nK,3,7,5,5,5,5,7,4,7,6,5,10,3,8,4,9\nK,3,3,5,2,2,7,6,2,7,10,6,10,4,7,3,8\nW,6,10,6,7,6,3,10,2,3,10,10,8,6,11,2,6\nB,3,7,3,5,3,6,8,8,6,7,5,7,2,8,9,10\nJ,2,7,2,5,1,13,2,8,4,13,4,12,1,6,0,8\nH,5,6,8,4,5,8,7,3,6,10,5,8,4,9,4,8\nS,6,10,8,8,11,7,7,3,2,7,6,7,3,8,12,2\nV,3,3,5,5,1,7,8,4,3,7,13,8,3,9,0,8\nN,3,6,5,4,3,6,9,6,4,8,7,8,5,8,1,7\nB,6,9,8,7,8,8,7,5,6,9,6,6,4,10,6,9\nA,3,7,5,5,2,12,3,4,3,11,2,10,2,6,3,9\nH,3,5,5,4,3,7,7,3,6,10,6,8,3,8,3,8\nX,6,12,7,7,4,8,7,2,7,11,4,6,4,8,4,7\nN,6,11,8,8,5,10,8,3,5,9,3,5,6,9,1,7\nV,7,10,7,7,3,4,11,2,4,9,11,7,4,10,1,7\nZ,2,5,5,3,2,7,8,2,9,12,6,8,1,9,5,8\nS,1,0,2,1,0,8,8,4,7,5,6,7,0,8,7,8\nV,3,8,5,6,1,6,8,4,3,8,14,8,3,10,0,8\nY,4,7,6,5,3,10,10,1,6,2,10,8,1,11,1,9\nC,1,3,2,1,1,4,8,4,6,10,9,12,1,8,2,8\nL,2,5,3,4,2,7,4,1,8,8,2,10,0,7,2,8\nQ,4,5,5,6,4,7,10,4,4,7,10,11,3,9,6,7\nX,7,10,7,5,3,5,8,3,8,10,11,11,4,11,4,5\nS,7,10,7,5,3,7,8,4,3,13,8,7,3,9,4,8\nR,10,15,8,8,5,7,7,5,5,9,5,9,7,5,7,11\nF,6,9,8,7,6,5,11,6,5,12,8,5,3,9,2,5\nM,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8\nK,6,10,9,7,5,4,8,2,7,10,10,11,3,8,4,6\nJ,5,10,4,7,3,7,11,3,3,13,5,4,2,7,6,8\nT,3,5,4,4,2,5,12,2,7,11,9,4,1,11,2,5\nC,6,8,7,7,7,6,7,5,5,6,6,11,7,10,9,8\nW,9,11,8,8,7,5,10,4,3,9,8,7,9,11,3,5\nH,4,8,5,6,5,7,6,13,2,7,7,8,3,8,0,8\nE,4,9,6,6,6,6,8,6,8,6,5,10,3,8,6,9\nW,1,0,2,1,1,7,8,3,0,7,8,8,5,10,0,8\nH,2,1,3,2,2,9,7,6,5,7,6,6,3,8,3,7\nU,6,11,8,9,5,5,8,6,7,7,9,10,3,9,1,8\nZ,1,0,2,0,0,7,7,2,10,8,6,8,0,8,6,8\nO,2,1,3,2,2,8,7,7,5,7,6,8,2,8,3,8\nF,2,3,4,1,1,6,10,3,5,13,7,5,1,9,1,7\nT,5,6,7,5,5,5,7,3,8,7,6,10,3,7,7,5\nI,1,6,2,4,1,7,7,1,8,7,6,8,0,8,3,8\nA,2,2,4,4,2,8,2,2,2,8,2,8,2,6,3,7\nJ,1,1,2,1,1,10,6,2,6,12,4,8,0,7,1,7\nU,4,8,5,6,3,6,9,6,6,5,9,10,3,9,1,8\nR,5,10,6,7,6,6,9,8,4,7,5,8,2,7,5,11\nT,4,10,6,7,2,7,15,1,6,7,11,8,0,8,0,8\nJ,6,7,8,9,8,8,8,4,6,6,6,7,5,9,11,11\nU,3,4,4,3,2,5,8,5,7,10,9,8,3,9,2,6\nC,5,8,6,6,4,6,8,6,9,6,6,13,1,8,4,9\nT,9,13,8,7,3,8,7,4,9,13,5,7,2,9,6,6\nV,2,6,4,4,1,8,8,4,2,6,13,8,3,10,0,8\nQ,6,7,8,6,6,8,3,4,5,7,3,9,4,5,5,7\nQ,4,5,5,6,5,8,8,6,1,7,6,11,3,9,6,8\nQ,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8\nK,5,10,5,7,2,4,8,9,2,7,6,11,4,8,2,11\nC,7,12,5,6,4,7,6,4,3,9,8,11,4,9,8,11\nA,3,11,5,8,3,13,4,5,3,12,1,8,2,6,4,9\nZ,3,8,4,6,3,8,7,3,12,9,6,8,0,8,7,7\nU,6,10,6,7,5,4,8,5,7,9,7,9,7,9,5,3\nD,5,11,6,8,5,11,6,3,7,11,2,6,3,8,3,9\nM,5,8,7,6,6,8,7,2,4,9,7,8,7,6,2,8\nY,5,10,8,8,4,7,10,2,7,6,12,9,2,11,2,8\nZ,2,7,3,5,3,6,8,5,10,7,6,10,1,9,8,7\nH,4,7,6,10,6,8,12,5,2,9,7,5,4,11,8,4\nB,4,7,5,5,6,8,7,6,6,6,6,6,2,8,6,9\nD,2,3,3,1,2,8,6,4,6,10,4,6,2,8,2,8\nR,5,11,6,8,3,5,10,9,4,7,4,8,3,7,6,11\nR,2,1,2,1,1,6,9,8,4,7,5,8,2,7,4,11\nU,7,9,7,6,5,4,8,5,8,9,6,9,7,9,6,2\nN,5,8,7,6,4,9,7,6,6,6,6,4,6,9,2,5\nK,6,9,8,7,6,9,5,1,6,10,4,9,5,6,5,10\nN,6,11,8,8,6,8,7,9,5,6,4,4,4,8,4,9\nS,4,10,5,7,3,8,8,6,9,4,6,7,0,8,9,7\nE,3,6,3,4,3,4,7,5,8,7,6,13,0,8,6,9\nF,5,11,5,8,4,1,13,4,4,12,10,7,0,8,2,6\nT,6,9,8,6,6,6,7,7,7,8,10,8,5,7,7,9\nK,8,14,8,8,5,10,6,3,6,11,2,6,5,8,4,9\nK,4,9,4,6,2,3,7,7,2,7,5,11,3,8,3,10\nE,3,7,3,5,2,3,7,6,10,7,6,14,0,8,7,7\nI,2,7,4,5,3,10,5,1,5,7,5,5,2,8,4,7\nL,6,11,9,8,11,7,8,3,6,5,7,10,6,12,10,6\nA,5,10,9,7,6,7,5,2,4,5,1,6,5,7,5,5\nW,11,15,12,9,8,7,8,3,4,7,9,7,12,9,3,5\nA,4,9,6,6,3,11,2,3,3,10,2,9,2,6,3,8\nQ,2,4,3,5,3,8,9,6,1,5,8,10,2,9,5,10\nD,3,1,4,3,2,7,7,7,7,7,6,5,2,8,3,7\nC,4,4,5,7,2,5,7,7,10,7,6,14,1,8,4,9\nG,4,5,5,4,3,6,7,6,6,9,7,10,2,9,4,9\nV,3,8,5,6,3,6,11,2,4,8,11,8,2,10,1,9\nT,5,6,6,5,5,6,8,4,8,8,7,9,3,8,6,6\nA,3,8,5,5,2,7,4,3,1,7,1,8,3,7,2,8\nO,5,8,7,6,8,7,9,5,2,7,6,7,10,9,6,10\nO,3,5,4,3,2,8,7,7,5,9,5,8,2,8,3,8\nD,4,9,5,7,3,5,7,10,8,7,6,5,3,8,4,8\nM,7,8,9,6,6,10,5,3,5,9,3,6,10,6,2,9\nU,3,7,5,5,4,9,6,7,5,7,6,8,3,8,4,5\nR,1,0,2,0,1,6,9,7,3,7,5,8,2,7,4,10\nW,4,7,6,5,3,5,8,5,1,7,8,8,8,9,0,8\nH,3,5,5,7,4,11,6,3,1,8,6,9,3,8,8,12\nF,4,5,5,7,2,1,14,5,4,12,10,5,0,8,2,5\nG,3,5,4,4,2,6,7,5,5,9,7,10,2,8,4,10\nT,3,11,5,8,1,7,14,0,6,7,11,8,0,8,0,8\nL,2,5,3,4,1,5,5,1,9,7,2,11,0,7,2,8\nY,4,9,7,7,3,6,10,2,8,7,12,8,1,11,2,8\nQ,4,4,6,6,3,7,7,7,6,5,7,7,3,8,5,9\nL,4,9,6,6,3,7,3,1,8,8,2,10,2,5,4,7\nR,1,3,3,1,1,9,7,3,4,10,3,7,2,7,2,10\nT,5,9,6,7,7,6,8,3,6,7,6,10,5,8,5,7\nB,4,9,4,7,4,6,7,9,7,7,6,7,2,8,9,10\nR,3,2,4,3,3,6,7,4,6,6,5,7,3,7,5,8\nE,3,4,3,6,2,3,8,6,11,7,5,15,0,8,7,7\nD,6,10,8,8,7,7,7,7,5,7,6,6,4,8,3,7\nB,3,4,4,6,3,6,6,9,7,6,7,7,2,8,9,10\nN,7,11,10,8,12,8,7,4,5,7,6,6,8,10,10,5\nV,2,3,3,2,1,7,12,2,2,7,11,8,2,11,1,8\nG,7,12,6,6,4,6,7,7,5,9,7,6,4,8,5,6\nS,3,2,3,3,2,8,8,7,5,8,5,7,2,8,8,8\nD,5,9,7,6,4,10,6,3,8,11,4,6,4,6,4,8\nF,3,3,3,4,1,1,13,4,4,12,11,7,0,8,2,6\nS,4,8,5,6,4,7,7,5,7,5,6,9,0,9,8,8\nM,4,8,7,6,8,11,5,3,2,9,4,8,8,6,3,7\nI,2,7,3,5,2,7,7,0,7,13,6,8,0,8,1,8\nL,3,9,5,7,3,8,4,0,9,9,3,11,0,7,3,9\nC,9,12,6,7,3,8,8,6,8,12,6,8,2,9,5,9\nV,3,2,5,3,2,9,12,3,3,5,11,9,2,10,1,9\nA,3,9,5,6,3,6,3,2,3,5,2,8,2,6,3,6\nQ,4,6,6,8,6,8,7,7,2,5,6,10,3,8,5,10\nD,4,8,6,6,4,9,6,5,7,10,3,5,3,8,3,8\nU,4,5,4,7,2,7,5,15,5,7,13,8,3,9,0,8\nJ,0,0,1,0,0,12,4,6,3,12,5,11,0,7,0,8\nH,6,8,9,6,7,5,8,3,7,10,9,9,4,9,4,6\nO,2,3,2,2,1,7,7,7,4,7,6,8,2,8,2,7\nY,3,6,5,8,1,7,10,1,3,7,12,8,1,11,0,8\nK,8,10,11,8,6,8,6,2,7,10,4,9,4,8,4,9\nG,8,12,7,6,5,7,6,3,3,8,5,7,4,9,9,7\nC,3,6,4,4,1,5,8,6,10,6,7,11,1,7,4,8\nN,4,4,4,3,2,7,9,5,4,7,6,7,5,9,2,7\nX,6,8,8,6,6,7,7,2,5,6,6,7,3,9,10,9\nP,5,11,6,8,3,4,11,10,3,9,6,4,2,10,4,8\nT,3,7,5,5,3,8,12,3,7,7,11,8,2,11,1,8\nW,4,2,6,4,4,7,11,2,2,6,9,8,7,11,1,8\nN,5,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8\nB,3,7,3,5,3,6,7,9,6,7,6,7,2,8,8,10\nA,7,10,9,8,9,8,6,7,4,7,6,9,6,8,8,3\nB,5,6,7,6,8,8,7,5,4,7,6,8,7,9,8,4\nB,2,7,3,5,2,6,7,8,6,7,6,7,2,8,8,10\nJ,2,5,4,4,1,9,6,2,6,14,5,9,1,6,1,7\nB,3,5,5,3,3,10,6,3,6,10,4,7,2,8,4,11\nX,7,10,10,8,5,10,7,2,8,11,2,7,3,8,4,9\nF,2,3,2,2,1,5,10,3,4,10,9,5,1,9,2,7\nH,1,0,1,1,0,7,7,11,1,7,6,8,3,8,0,8\nC,5,10,6,8,2,6,7,7,10,6,6,15,1,8,4,9\nY,5,11,6,8,3,7,10,1,3,7,12,8,1,11,0,8\nE,3,4,5,3,3,6,7,2,8,11,7,9,2,8,4,8\nK,7,13,8,7,5,7,8,2,6,10,4,8,5,5,4,7\nH,6,9,8,7,5,5,7,3,7,10,9,10,3,8,4,6\nB,3,1,3,2,2,7,7,5,5,6,6,6,2,8,6,9\nA,3,5,5,4,4,7,8,3,4,7,8,8,5,10,3,6\nR,2,3,4,2,2,9,7,2,5,10,3,6,2,7,3,9\nP,4,6,6,9,9,8,6,5,1,7,6,7,8,8,6,7\nZ,6,9,8,6,6,9,10,6,5,6,5,6,3,9,9,4\nW,8,9,8,5,3,3,11,2,3,11,11,8,7,11,0,7\nT,3,5,5,7,1,6,15,1,6,8,11,7,0,8,0,8\nZ,4,7,5,5,3,7,7,6,11,6,6,8,2,8,8,8\nK,4,10,6,7,8,9,7,3,4,5,6,8,7,9,7,6\nP,3,7,5,5,3,6,12,4,5,13,6,2,0,9,3,8\nX,5,8,8,6,4,6,8,1,8,10,8,8,3,8,4,7\nC,6,10,6,7,4,6,8,7,8,13,8,10,2,11,3,7\nU,3,1,4,2,2,6,9,5,6,7,9,9,3,9,1,8\nN,4,10,5,8,5,8,7,12,1,6,6,8,5,8,0,9\nX,2,4,4,3,2,10,7,1,8,10,3,7,2,7,3,8\nS,4,10,4,8,4,9,8,5,9,5,5,5,1,6,9,7\nF,2,1,2,2,1,6,10,4,5,10,9,5,1,10,3,6\nU,5,9,6,7,4,8,5,13,5,8,12,8,3,9,0,8\nP,2,3,2,1,1,6,10,5,4,9,7,4,1,9,3,7\nX,4,5,5,7,2,7,7,4,4,7,6,8,3,8,4,8\nQ,3,5,4,6,3,7,8,5,2,7,9,11,3,9,5,7\nK,3,3,5,2,2,8,6,2,6,10,5,10,3,8,3,9\nB,10,14,9,8,8,8,7,4,5,9,6,7,8,7,10,8\nD,3,7,5,5,7,9,9,4,4,7,6,5,4,9,8,6\nG,7,8,9,7,9,7,8,5,3,7,7,8,10,10,10,7\nB,3,6,4,4,4,6,8,7,4,7,5,6,2,8,6,6\nT,3,3,4,2,1,5,11,3,7,11,9,4,1,10,2,5\nZ,2,4,4,3,2,8,6,2,9,12,5,9,1,8,5,9\nC,4,8,5,6,2,6,9,7,10,5,7,12,1,6,4,8\nV,2,7,4,5,1,9,8,4,2,5,13,8,3,10,0,8\nG,5,10,7,8,6,6,6,7,6,6,5,12,3,10,5,7\nM,5,8,7,6,8,8,7,6,5,6,7,8,8,6,2,7\nB,4,11,5,8,4,6,7,9,7,7,6,7,2,8,9,10\nN,5,9,7,7,6,6,7,7,6,7,5,8,6,7,3,7\nW,10,10,9,8,9,5,11,4,3,8,7,7,10,12,4,5\nN,2,3,2,1,1,7,8,6,4,7,6,7,4,8,1,7\nA,4,4,6,6,2,6,7,3,0,6,0,8,2,7,1,7\nR,4,9,6,7,6,8,7,7,3,8,5,7,4,7,7,10\nU,4,4,4,3,2,4,8,5,7,11,10,9,3,9,2,6\nR,2,3,3,2,2,7,7,5,4,9,5,7,2,7,3,10\nE,2,2,3,4,3,7,7,6,7,7,6,9,2,8,6,10\nM,5,10,7,5,4,12,2,2,2,10,3,9,5,4,1,9\nI,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nA,5,7,7,5,7,7,8,8,4,7,5,8,4,8,9,4\nW,4,7,6,5,3,6,8,5,1,7,8,8,8,9,0,8\nJ,4,10,5,8,3,8,7,2,6,14,5,9,1,7,1,7\nL,2,3,3,2,1,7,4,2,7,8,2,10,0,7,2,8\nO,4,5,5,7,3,7,8,9,8,7,8,8,3,8,4,8\nI,1,5,1,4,1,7,7,1,7,7,6,8,0,8,2,8\nK,1,0,1,0,0,5,7,7,1,7,6,11,2,8,2,11\nM,5,8,8,6,5,5,7,3,4,10,9,10,8,6,3,8\nN,10,13,9,7,4,7,10,5,5,3,5,11,6,11,3,6\nA,3,8,5,6,3,10,4,2,2,8,2,10,2,6,3,8\nG,4,6,5,4,5,9,8,6,2,5,7,9,6,10,3,8\nN,5,5,7,4,6,7,9,4,4,6,4,8,7,7,5,7\nF,4,8,4,6,3,1,13,4,3,12,10,7,0,8,2,6\nV,1,0,2,1,0,8,9,3,2,7,12,8,2,11,0,8\nG,4,5,5,4,3,6,7,5,6,9,7,10,2,8,4,9\nC,5,7,7,6,6,5,6,4,5,7,6,11,4,10,7,10\nY,10,9,8,12,5,7,9,3,2,7,11,5,4,10,7,6\nY,6,11,8,8,8,9,3,7,5,8,8,7,3,10,5,3\nW,4,4,5,3,3,4,11,3,2,9,9,7,6,11,1,7\nJ,2,11,3,8,1,14,2,7,5,14,1,10,0,7,0,8\nF,4,6,6,4,3,8,10,2,6,14,5,4,2,10,2,8\nQ,5,7,6,6,5,7,4,4,5,6,3,7,3,7,5,7\nY,4,9,6,7,3,5,11,2,3,8,12,8,1,11,0,8\nK,5,8,7,6,8,7,6,3,4,6,6,8,8,6,7,7\nH,4,6,5,4,4,7,9,6,5,7,4,6,3,7,7,10\nN,7,10,9,8,10,6,8,3,4,8,7,8,8,10,7,4\nB,3,9,3,7,5,7,8,8,6,7,5,7,2,7,8,9\nO,4,10,5,7,4,7,6,8,5,7,5,8,3,8,3,8\nV,3,6,5,4,2,8,9,4,2,6,13,8,3,10,0,8\nU,8,9,8,7,5,3,8,5,8,10,9,9,3,9,2,6\nF,5,7,6,5,6,7,8,6,4,8,6,8,3,10,8,10\nN,8,10,11,8,5,9,7,3,6,10,4,6,7,9,2,7\nX,2,1,4,3,2,7,8,3,9,6,6,7,2,8,6,7\nE,4,10,6,7,7,7,7,3,6,7,7,10,4,9,8,8\nL,2,3,2,5,1,0,1,5,6,0,0,7,0,8,0,8\nA,5,10,7,7,8,8,5,8,4,8,6,8,3,9,8,3\nH,2,1,3,3,2,8,7,6,6,7,6,8,3,8,3,7\nO,1,1,2,1,1,8,7,7,4,7,6,8,2,8,3,8\nF,4,11,6,8,4,9,9,2,6,13,4,4,2,10,3,9\nN,5,9,6,7,3,7,7,15,2,4,6,8,6,8,0,8\nS,2,1,2,2,2,8,8,6,4,7,5,7,2,8,8,8\nI,4,11,3,6,2,10,7,6,3,13,4,8,2,8,4,9\nL,3,7,5,5,2,4,2,7,7,1,2,4,1,6,0,6\nJ,1,7,2,5,1,13,3,8,4,14,3,11,0,6,0,8\nA,4,7,6,6,5,8,8,2,4,7,7,8,5,7,4,6\nQ,6,9,8,8,8,5,4,4,6,5,4,7,4,5,6,6\nN,4,7,6,5,6,9,6,4,5,7,5,6,7,11,6,5\nW,8,8,8,6,5,2,10,2,3,11,11,9,7,10,1,7\nN,3,7,5,5,5,5,8,4,3,9,10,10,5,8,4,5\nJ,5,10,6,8,5,8,6,4,5,8,6,7,3,7,4,6\nS,1,0,1,0,0,7,7,3,5,5,6,8,0,8,7,8\nD,4,8,6,6,8,9,8,5,4,7,6,6,4,8,8,6\nY,2,4,3,3,1,6,10,1,6,8,11,8,1,11,2,7\nO,2,1,3,1,1,8,7,7,5,7,6,9,2,8,3,8\nQ,5,6,6,8,6,9,8,7,2,4,7,11,3,9,5,10\nP,4,7,6,11,11,8,6,5,1,7,6,7,7,9,7,10\nE,3,8,4,6,5,6,6,3,5,7,7,10,4,10,8,9\nE,4,7,6,5,4,9,6,2,8,11,5,8,4,7,6,10\nG,3,9,4,7,2,7,7,8,7,5,6,9,2,7,6,11\nD,4,11,6,8,5,9,7,5,7,10,5,5,3,8,3,8\nR,6,9,9,7,7,9,7,4,6,10,3,7,3,6,4,11\nV,4,8,4,6,2,3,11,3,3,10,11,8,2,10,1,7\nX,5,7,6,5,5,7,8,3,6,6,6,8,4,8,10,8\nM,6,9,8,8,10,6,8,4,3,7,5,8,10,8,5,6\nW,8,12,9,7,5,1,8,2,3,9,11,9,9,9,1,6\nE,5,10,7,8,5,10,6,2,8,11,4,8,2,8,5,11\nT,3,8,5,6,3,6,12,2,8,8,12,8,1,11,1,7\nO,3,5,4,3,2,7,7,8,5,7,6,8,2,8,3,8\nV,3,4,5,3,2,4,12,3,3,9,11,7,2,11,1,8\nE,3,7,5,5,3,9,6,2,7,11,5,8,2,9,5,11\nD,7,11,9,8,8,8,8,5,6,10,5,5,6,8,6,11\nJ,2,9,3,6,2,14,3,4,4,13,1,8,0,7,0,8\nJ,1,0,1,1,0,12,3,6,3,12,5,11,0,7,0,8\nS,3,9,4,7,4,8,7,7,5,7,5,6,2,8,8,8\nT,2,5,4,6,1,5,14,1,6,9,11,7,0,8,0,8\nF,5,9,7,6,7,9,7,1,5,9,5,5,5,10,4,6\nE,4,9,5,7,3,3,6,6,12,7,7,15,0,8,7,7\nU,6,10,6,8,3,4,9,5,8,11,11,8,3,9,2,6\nM,15,15,15,8,7,9,11,6,4,4,6,10,11,13,2,6\nN,2,1,2,2,1,7,9,5,4,7,6,7,4,8,1,6\nJ,4,5,6,6,5,7,7,4,6,6,6,7,3,9,8,9\nH,2,3,4,1,2,5,8,3,5,10,9,9,3,8,2,6\nZ,4,7,5,5,5,8,8,2,7,7,6,8,0,9,9,9\nX,7,11,8,6,4,8,8,2,8,11,6,7,4,12,4,8\nB,5,9,8,7,7,10,6,3,6,10,4,6,2,8,5,10\nS,4,10,5,8,5,7,6,8,6,7,8,10,2,10,8,7\nA,4,9,5,6,5,7,6,7,4,7,6,9,2,8,8,4\nP,1,0,1,0,0,5,10,6,1,9,6,5,1,9,2,8\nO,5,11,6,8,9,9,9,6,2,6,7,9,12,12,6,7\nQ,2,1,2,1,1,8,7,6,4,6,6,8,2,8,3,8\nP,1,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8\nR,4,7,6,5,4,10,7,3,6,11,2,6,3,7,4,10\nJ,4,8,3,11,3,8,8,3,3,12,5,5,3,8,6,10\nW,3,6,5,4,5,8,9,5,4,5,9,9,5,8,2,7\nT,6,8,7,6,4,7,10,1,8,11,9,5,2,10,4,4\nE,4,5,5,5,5,7,9,5,5,7,8,11,5,9,7,9\nZ,4,9,4,7,4,7,8,3,12,8,6,8,0,8,7,7\nG,4,5,6,4,5,7,8,5,3,7,7,8,7,11,7,8\nI,4,5,5,6,4,8,9,5,5,7,6,8,3,8,9,8\nW,10,13,10,8,5,5,9,2,2,7,10,7,9,12,1,6\nD,7,13,7,7,4,10,4,3,6,10,2,7,5,7,4,12\nM,3,3,5,2,3,7,6,3,4,9,7,9,7,5,1,8\nB,3,6,4,4,4,8,7,4,5,9,5,6,2,8,5,9\nV,3,4,4,3,1,4,12,2,3,9,11,7,2,10,1,7\nH,4,7,5,5,5,7,7,5,6,7,6,10,6,8,3,8\nL,2,4,4,3,2,6,4,1,8,8,2,11,0,7,2,8\nX,2,3,3,1,1,9,6,2,7,10,4,8,2,8,3,9\nR,6,8,8,6,7,9,8,3,7,9,3,8,4,5,4,11\nM,2,3,3,2,2,7,6,6,3,7,7,8,5,5,1,8\nI,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nT,5,8,6,7,6,6,9,5,7,8,8,8,4,13,9,6\nN,5,8,7,6,5,7,6,10,5,6,4,5,4,7,4,9\nA,4,5,6,4,4,8,8,3,4,6,7,7,5,8,4,5\nQ,6,9,5,4,3,11,3,4,5,11,3,10,3,8,5,13\nD,4,11,5,8,3,6,7,11,9,7,6,6,3,8,4,8\nA,3,10,5,8,4,12,3,2,2,9,2,8,2,6,2,8\nB,1,0,1,1,1,7,7,7,4,6,6,7,1,8,6,9\nU,5,5,6,3,3,4,8,5,8,10,9,9,3,9,2,6\nM,5,8,5,6,3,7,7,12,2,7,9,8,8,6,0,8\nR,2,4,4,3,2,9,7,3,5,10,4,7,2,7,3,10\nS,3,1,3,3,2,8,7,7,5,7,6,8,2,10,9,8\nP,5,7,7,5,4,6,11,7,2,11,5,3,1,10,3,8\nG,7,13,6,7,4,10,6,5,6,11,4,8,5,7,5,9\nF,5,8,7,6,7,7,8,6,4,8,6,8,4,10,8,11\nF,7,11,6,6,3,7,8,2,8,11,6,6,2,10,5,6\nK,9,13,8,7,4,7,7,3,7,9,9,9,6,11,3,7\nW,5,7,5,5,5,6,10,4,2,9,7,7,6,11,2,6\nP,4,9,5,6,2,4,12,9,2,10,6,4,1,10,4,8\nI,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nV,6,8,6,6,2,3,12,5,4,12,12,7,3,10,1,8\nE,6,10,9,8,6,6,8,4,9,11,9,9,2,9,5,6\nW,4,4,6,3,3,7,11,3,2,6,9,8,8,11,1,8\nF,1,0,1,0,0,3,11,4,2,11,8,6,0,8,2,8\nU,9,15,7,8,4,8,6,4,6,3,9,5,6,6,2,6\nX,3,3,5,2,2,9,7,2,8,11,4,7,2,7,3,8\nQ,4,9,5,11,5,9,8,8,2,4,7,11,4,9,6,9\nI,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nP,4,5,4,7,2,4,12,9,2,10,6,4,1,10,4,8\nR,9,11,7,6,4,8,7,5,5,10,4,9,6,5,6,11\nI,1,8,0,6,1,7,7,5,3,7,6,8,0,8,0,8\nP,2,1,3,2,2,6,9,5,5,9,7,4,1,9,4,7\nG,4,6,5,4,6,8,6,4,2,6,6,9,6,8,6,11\nZ,8,14,8,8,5,11,4,5,8,12,3,10,4,4,8,12\nK,2,1,3,2,2,5,7,4,7,7,6,11,3,8,4,10\nC,6,12,4,6,2,7,7,7,6,11,6,8,2,9,5,9\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nS,5,8,6,6,3,9,6,4,8,11,4,8,2,8,5,11\nW,7,11,10,8,15,10,5,5,2,7,6,7,12,10,3,4\nK,2,6,4,4,4,7,7,3,5,6,5,8,3,8,3,9\nR,3,5,5,4,3,9,7,4,5,9,4,7,3,7,4,10\nK,4,7,5,5,5,5,6,4,6,6,6,11,3,8,5,10\nC,6,10,7,8,3,4,9,6,8,12,10,11,2,9,3,6\nB,2,5,4,4,3,8,7,3,5,10,5,6,2,8,4,9\nU,9,11,10,8,7,5,7,5,9,9,6,9,8,9,6,1\nP,4,6,6,9,9,7,8,6,0,8,6,7,6,10,6,11\nY,3,4,4,3,1,4,11,2,7,11,10,5,1,11,2,5\nH,3,4,5,6,4,7,4,5,2,6,4,6,4,6,7,7\nZ,3,10,4,8,4,7,8,3,12,9,6,8,0,8,7,7\nK,5,8,5,6,2,4,7,8,1,7,6,12,4,8,3,11\nU,5,10,8,7,10,9,7,4,4,6,7,8,8,8,6,7\nZ,3,7,5,5,4,8,8,3,7,7,6,8,1,8,10,9\nJ,0,0,1,0,0,11,4,5,3,12,4,10,0,7,0,8\nJ,4,7,5,5,4,7,6,4,5,8,6,7,5,6,4,6\nK,5,11,6,8,3,4,8,9,2,7,4,11,4,8,2,11\nY,3,4,4,2,2,4,11,2,7,11,10,5,1,10,2,4\nL,4,7,5,5,3,7,4,2,8,7,2,8,1,6,3,8\nN,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nM,3,2,5,3,3,7,6,6,5,6,7,7,8,6,2,7\nH,5,7,7,9,6,10,10,3,2,7,7,8,3,10,8,9\nL,4,9,4,7,2,0,2,4,6,1,0,7,0,8,0,8\nW,4,8,6,6,3,8,8,4,1,7,8,8,8,9,0,8\nW,5,10,8,8,13,9,5,5,2,7,6,8,11,10,4,8\nG,4,9,3,4,2,8,6,5,2,9,6,8,3,10,7,7\nR,4,7,5,5,5,8,6,7,3,7,5,7,4,6,6,9\nW,3,1,5,3,3,7,11,2,2,7,9,8,7,11,0,8\nQ,5,10,7,8,6,8,4,8,5,6,4,8,3,8,3,8\nD,5,11,7,8,6,6,7,8,7,5,4,5,3,9,4,9\nH,5,11,6,8,6,6,7,7,6,7,6,11,3,8,3,9\nF,3,8,4,6,4,6,9,3,6,10,9,5,2,10,3,6\nV,4,11,6,8,2,7,8,4,3,7,14,8,3,9,0,8\nO,6,11,4,6,3,6,7,6,3,9,7,9,5,10,5,8\nC,2,4,3,3,1,6,8,7,7,9,8,13,1,10,4,10\nF,4,10,6,8,6,4,10,3,5,10,10,6,2,10,3,6\nC,1,3,2,1,1,6,8,6,6,8,7,13,1,9,3,10\nV,4,7,4,5,2,3,12,4,3,11,11,7,3,10,1,7\nU,7,9,7,6,4,5,7,7,9,8,7,9,5,11,5,2\nV,3,8,5,6,3,5,11,3,4,8,12,9,2,10,1,8\nY,3,7,5,5,2,10,11,2,2,5,12,8,1,11,0,8\nX,1,0,1,0,0,7,7,3,4,7,6,8,2,8,4,8\nF,6,9,8,6,5,10,8,2,6,13,4,5,3,9,3,9\nN,2,4,3,3,2,7,8,5,4,7,6,6,5,9,2,6\nR,4,9,5,6,4,10,6,4,5,10,3,7,3,7,4,11\nB,3,6,5,4,4,8,7,6,6,7,6,5,2,8,7,10\nH,4,8,6,6,6,7,8,6,6,7,6,10,6,8,3,8\nV,3,4,5,3,1,7,12,3,3,7,11,8,2,10,1,8\nI,3,7,5,5,4,12,5,1,6,8,4,5,2,8,4,8\nJ,5,8,7,10,7,8,8,4,5,7,6,8,4,6,9,8\nQ,7,11,6,6,3,8,5,4,9,10,4,9,3,6,9,9\nW,1,0,2,1,1,7,8,3,0,7,8,8,5,10,0,8\nE,4,10,4,7,4,3,8,5,9,7,6,13,0,8,7,9\nZ,3,7,5,5,4,9,11,6,4,6,5,7,2,8,6,4\nB,2,4,4,3,3,8,7,3,5,10,6,7,2,8,4,9\nG,6,10,6,7,5,5,7,6,5,9,8,11,2,9,4,9\nW,6,6,8,8,4,11,8,5,2,6,9,8,8,9,0,8\nD,6,9,8,8,9,7,7,4,7,7,5,8,4,7,7,5\nP,8,10,7,5,3,7,10,5,3,12,4,4,4,9,4,8\nZ,7,11,9,8,6,8,6,2,9,12,5,10,2,8,7,9\nR,4,8,5,6,4,10,6,3,6,10,3,7,3,7,4,11\nT,7,11,6,6,2,5,10,3,8,13,7,5,2,9,4,3\nM,5,7,7,5,4,11,6,3,5,9,2,6,8,6,2,9\nE,8,11,5,6,3,7,9,5,8,10,6,10,1,8,7,9\nF,4,6,5,8,2,0,15,5,4,13,10,5,0,8,2,5\nY,8,7,6,11,5,9,8,5,5,4,11,6,4,11,5,7\nY,2,2,3,3,1,7,10,1,7,7,11,8,1,11,2,8\nH,3,7,4,5,2,7,6,14,2,7,8,8,3,8,0,8\nW,4,7,6,5,5,7,11,1,2,7,8,8,6,12,1,8\nL,4,6,5,4,4,6,6,8,6,6,6,9,2,8,4,10\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nG,6,9,6,7,5,6,6,6,5,9,6,12,4,8,6,7\nH,5,9,7,7,6,8,8,7,6,7,5,6,3,8,3,6\nG,3,6,4,4,4,7,7,5,2,7,6,10,4,9,7,7\nP,4,9,5,6,2,3,12,8,1,10,6,3,1,10,4,8\nI,6,11,4,6,2,9,6,6,5,13,5,9,2,8,4,9\nG,5,9,5,7,4,6,6,7,6,8,7,12,3,7,5,8\nP,5,9,5,5,3,5,12,4,2,12,6,3,2,10,5,6\nZ,3,9,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nR,4,8,6,6,5,7,8,6,6,7,5,7,3,7,5,8\nN,2,1,3,2,2,7,8,5,5,7,7,6,5,9,1,5\nJ,0,0,1,1,0,12,4,5,3,12,4,10,0,7,0,8\nU,6,7,6,5,4,3,9,5,7,10,9,9,3,9,2,6\nU,4,7,5,5,3,4,8,5,7,10,9,9,3,9,2,6\nJ,3,6,5,4,1,8,5,4,7,15,7,13,0,7,1,7\nH,3,4,5,3,3,7,7,3,6,10,6,8,3,8,3,8\nS,4,6,5,4,3,9,7,4,8,11,5,8,2,8,5,9\nN,4,8,6,6,5,6,7,7,6,7,5,7,3,7,3,8\nX,4,8,5,6,4,8,7,3,8,5,6,7,3,9,7,7\nY,4,7,4,5,2,3,10,3,6,11,12,7,2,11,2,6\nC,5,9,5,7,4,4,8,5,7,11,9,14,2,9,3,7\nA,3,6,4,4,2,10,2,2,3,8,2,8,2,6,2,8\nB,5,8,7,6,6,10,6,3,6,10,4,7,4,7,5,10\nM,3,6,4,4,4,8,5,10,0,6,8,8,6,5,0,8\nV,5,11,8,8,2,6,8,5,3,8,14,8,3,9,0,8\nD,4,5,5,4,4,7,7,7,7,7,6,5,2,8,3,7\nK,4,6,6,4,6,7,6,5,4,6,6,8,6,6,7,12\nB,2,3,3,2,2,8,7,3,5,10,5,7,2,8,4,10\nY,3,6,4,4,2,8,10,2,6,5,12,8,2,11,2,8\nA,1,3,3,2,1,11,2,3,2,10,2,9,1,6,2,8\nS,3,4,4,7,2,8,7,6,8,4,6,8,0,8,9,7\nE,4,4,4,6,3,3,8,6,12,7,5,14,0,8,7,7\nC,3,4,4,3,1,5,9,5,7,12,9,10,1,10,3,7\nD,5,5,5,8,3,5,7,11,9,7,6,5,3,8,4,8\nE,3,4,5,3,2,6,8,2,9,11,7,8,2,8,4,6\nM,2,1,2,1,1,7,7,10,0,7,8,8,6,6,0,8\nS,3,2,4,3,3,8,7,7,5,7,6,8,3,9,9,8\nZ,4,8,5,6,4,9,9,6,4,7,5,8,3,8,10,7\nO,6,11,6,8,6,7,6,7,4,10,5,10,5,7,4,7\nS,6,10,8,9,9,9,8,4,5,7,7,8,5,10,9,11\nX,5,8,7,6,3,9,6,2,8,11,2,7,3,9,4,9\nW,3,4,4,3,3,7,11,2,2,7,9,8,6,11,1,8\nV,3,8,5,6,1,5,8,4,3,8,14,8,3,9,0,8\nY,3,7,5,5,3,7,10,1,5,5,11,9,2,11,1,8\nC,4,7,6,5,4,7,8,8,6,5,7,13,4,7,4,8\nE,3,5,5,3,2,8,7,2,8,11,5,8,2,8,5,10\nW,4,6,6,4,8,9,7,5,2,7,6,8,9,11,2,7\nA,2,5,4,3,2,7,3,1,2,6,2,8,3,5,2,8\nM,7,9,9,6,8,8,7,2,5,9,6,8,8,6,2,8\nO,2,6,3,4,2,7,8,7,5,7,7,7,3,8,3,7\nQ,3,6,4,7,4,9,7,7,3,5,6,10,3,8,6,10\nL,2,5,3,4,2,4,4,5,7,2,2,6,1,6,1,6\nF,3,5,3,3,2,6,10,4,5,10,9,5,2,10,3,6\nP,4,9,4,6,2,3,13,8,2,11,7,3,1,10,4,8\nG,4,7,6,5,4,6,6,5,5,6,6,7,3,7,4,7\nZ,7,10,9,8,6,6,9,3,9,12,8,6,3,10,7,6\nF,1,0,1,1,0,3,12,4,2,11,9,6,0,8,2,7\nO,3,4,4,3,2,7,7,7,4,9,6,8,2,8,3,8\nU,9,12,8,6,3,5,4,5,5,4,8,7,6,7,2,7\nJ,3,8,5,6,3,12,4,4,5,14,2,9,0,7,0,8\nX,4,6,6,6,5,6,7,2,5,7,7,9,3,9,8,8\nV,8,11,7,8,5,2,11,2,3,10,11,8,2,10,1,8\nU,5,5,6,4,3,4,8,5,8,10,9,9,3,9,2,5\nJ,1,7,2,5,1,11,3,9,3,12,6,13,1,6,0,8\nT,3,6,5,8,1,5,14,1,6,9,11,7,0,8,0,8\nS,4,11,5,8,4,7,8,8,8,8,4,6,2,6,9,8\nR,4,5,6,4,4,8,8,4,5,9,5,7,3,6,4,10\nW,2,3,4,2,2,9,11,3,2,5,9,7,6,10,0,8\nD,5,11,7,8,6,11,6,2,7,11,3,7,4,7,4,9\nQ,2,1,2,2,1,9,6,7,4,6,6,9,3,8,3,8\nN,4,5,6,4,5,8,8,5,4,8,6,7,6,9,6,3\nL,1,3,2,2,1,7,4,1,7,7,2,10,0,7,2,8\nM,5,5,6,4,4,8,6,6,5,6,7,7,10,6,4,6\nL,3,8,3,6,1,0,1,5,6,0,0,7,0,8,0,8\nC,2,5,3,3,1,5,9,5,7,12,9,11,1,10,2,7\nB,4,10,5,7,5,8,7,6,7,6,6,6,2,8,7,10\nY,2,7,4,4,1,6,10,2,2,7,12,8,1,11,0,8\nZ,2,6,3,4,3,8,7,5,9,7,7,7,1,8,7,8\nJ,2,7,2,5,1,13,2,7,5,14,2,11,0,7,0,8\nA,3,4,5,6,2,5,4,3,2,5,1,7,3,7,2,7\nQ,2,3,3,4,2,8,7,5,2,8,7,9,2,9,3,9\nB,3,4,4,6,3,6,9,9,8,7,5,7,2,8,9,10\nQ,2,2,3,3,2,7,9,4,1,7,8,10,2,9,4,8\nG,3,9,5,6,2,7,6,8,7,6,6,8,2,7,6,11\nA,4,5,6,8,2,7,4,3,2,7,1,8,3,6,3,8\nJ,2,3,3,4,1,14,2,6,5,13,2,10,0,8,0,8\nS,2,5,4,3,2,8,7,3,7,11,5,7,1,9,4,8\nT,5,6,5,4,2,6,10,2,9,11,9,4,1,10,3,4\nO,5,11,7,8,6,7,7,8,5,6,7,11,6,7,5,7\nA,2,4,4,3,2,11,2,2,2,9,2,9,2,6,2,8\nR,4,8,5,6,4,8,9,4,6,8,4,9,3,6,5,11\nG,6,9,7,7,8,8,6,4,2,6,6,10,7,7,7,13\nR,2,3,4,2,2,8,7,4,5,9,4,7,2,7,4,10\nB,2,5,4,4,3,7,7,3,5,10,6,6,2,8,5,8\nK,4,8,6,6,4,9,5,2,7,10,3,9,4,7,5,11\nJ,4,7,6,5,2,5,9,3,6,15,7,9,1,6,1,7\nY,3,7,5,5,2,8,10,2,6,4,11,9,2,12,2,7\nI,3,9,4,6,3,6,8,0,7,13,7,8,0,8,1,7\nW,9,12,8,6,4,1,8,4,2,11,12,9,7,10,0,7\nO,5,5,7,4,4,8,5,4,5,8,4,8,3,8,4,8\nI,7,8,9,9,8,7,8,4,5,7,7,7,4,7,10,10\nK,3,4,5,3,3,4,9,2,7,10,10,10,3,8,3,6\nD,2,5,4,4,3,9,6,4,6,10,4,6,2,8,3,8\nX,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nD,5,7,7,6,6,6,7,5,7,7,6,9,4,5,6,5\nK,3,9,4,6,2,4,8,8,2,7,4,11,4,8,2,10\nG,6,8,8,7,9,7,9,6,2,7,7,8,7,11,7,7\nQ,5,7,6,9,6,10,11,6,3,3,8,12,3,10,7,10\nP,4,8,6,6,5,6,8,6,5,9,7,4,2,10,4,7\nA,8,15,6,8,4,8,3,3,2,7,4,11,5,5,4,7\nQ,3,5,3,6,3,8,8,5,2,8,8,10,3,8,5,8\nC,2,2,3,4,2,6,8,7,8,8,8,13,1,9,4,10\nI,1,1,1,3,1,7,7,1,6,7,6,8,0,8,2,8\nU,5,9,5,7,2,7,4,15,6,7,13,8,3,9,0,8\nO,4,9,5,6,4,8,7,8,5,9,5,8,3,8,3,8\nD,2,1,3,1,2,7,7,6,7,7,6,5,2,8,3,7\nN,7,11,6,6,3,9,11,5,4,4,6,10,6,10,2,6\nY,2,3,4,5,1,8,10,2,3,6,12,8,1,11,0,8\nZ,3,8,4,6,2,7,7,4,13,9,6,8,0,8,8,8\nL,2,5,4,3,2,7,4,1,8,8,2,10,0,7,2,8\nV,4,9,7,6,3,6,12,3,4,8,12,8,3,10,1,8\nA,2,6,3,4,2,9,4,2,0,8,2,8,2,6,1,8\nN,6,10,9,8,4,8,8,3,5,10,4,6,6,9,1,6\nG,4,7,5,5,3,6,7,7,7,10,7,11,2,9,4,9\nA,2,1,4,1,1,8,4,2,0,7,2,8,2,6,2,8\nD,6,9,8,7,5,9,7,5,8,11,5,5,3,8,4,9\nU,5,9,7,8,6,7,7,4,4,6,5,8,8,7,2,8\nM,4,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8\nJ,2,6,3,4,1,13,2,8,4,13,4,13,1,6,0,8\nD,5,10,8,8,6,10,6,3,7,11,4,7,4,7,3,9\nX,2,4,4,2,2,7,7,1,8,10,6,8,2,8,3,7\nU,3,7,5,5,3,8,8,6,7,5,9,8,3,9,1,8\nX,5,11,6,6,4,6,9,2,6,10,10,8,4,14,4,6\nM,4,6,4,4,2,8,7,12,1,6,9,8,8,6,0,8\nL,3,6,4,4,2,6,4,3,7,6,2,8,1,6,2,7\nF,6,9,8,7,5,9,8,1,8,14,5,6,5,8,5,9\nG,4,11,5,8,3,7,6,9,8,6,6,11,2,8,5,11\nU,5,10,7,7,5,7,9,4,6,5,8,10,6,10,1,7\nC,8,15,6,8,5,5,10,4,5,9,7,8,3,9,9,9\nQ,3,6,5,6,4,8,8,6,4,5,8,9,3,8,5,9\nI,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7\nF,2,8,3,5,1,1,11,4,6,11,11,9,0,8,2,6\nK,6,9,6,5,4,5,8,3,5,10,9,11,5,9,3,6\nD,4,8,6,6,4,7,9,7,7,11,6,4,4,8,5,9\nR,3,2,4,3,3,7,8,4,6,6,5,7,3,5,5,8\nO,7,10,5,5,3,5,7,6,4,10,7,9,5,10,5,7\nA,3,9,5,7,3,12,3,3,3,10,1,9,2,6,2,8\nU,5,10,7,8,6,8,7,7,6,7,7,9,6,8,4,7\nV,2,2,3,3,1,5,12,3,3,9,11,9,2,10,1,8\nB,1,1,2,1,1,7,7,7,5,6,6,7,1,8,7,9\nO,1,0,2,1,0,8,7,7,4,7,6,8,2,8,3,8\nP,4,8,6,12,11,8,8,5,0,8,7,7,6,10,5,8\nY,8,8,7,11,5,9,9,3,3,5,11,5,4,10,7,8\nC,4,9,4,5,3,6,8,4,3,9,9,10,3,9,8,9\nP,2,5,4,4,2,7,10,4,4,12,5,3,1,10,2,8\nT,5,10,7,7,8,6,8,4,6,7,6,9,5,8,5,6\nT,6,8,6,6,3,4,12,3,8,12,10,4,1,10,2,5\nM,7,9,9,6,8,6,7,2,4,9,8,9,8,6,2,8\nD,2,4,3,2,2,7,7,6,6,7,7,5,2,8,2,7\nC,3,7,4,5,1,6,8,7,10,5,7,13,1,7,4,9\nS,6,11,8,8,10,9,8,5,4,9,5,7,5,10,13,8\nN,6,11,8,8,5,7,8,2,5,10,6,7,5,8,1,7\nX,5,7,8,5,4,11,5,2,8,10,1,7,2,7,3,10\nU,4,4,5,3,2,4,8,5,7,11,10,9,3,9,2,6\nL,4,7,6,6,5,7,7,4,5,7,6,7,3,9,8,11\nA,1,0,2,0,0,8,4,2,0,7,2,8,2,7,1,8\nJ,4,8,3,11,3,6,9,3,3,13,5,6,3,8,6,9\nJ,5,10,6,8,4,9,4,6,5,15,5,12,1,6,1,6\nI,3,11,4,8,2,7,7,0,8,13,6,8,0,8,1,8\nQ,7,8,9,12,11,9,9,7,0,6,7,10,9,14,6,7\nN,8,15,10,8,5,6,8,2,4,13,8,9,6,8,0,7\nB,3,6,5,4,3,9,7,4,6,10,5,7,2,8,5,10\nM,2,3,4,2,2,7,6,3,4,9,7,8,6,5,1,8\nM,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8\nL,5,11,6,8,6,6,6,6,5,6,5,8,5,7,4,10\nW,9,12,9,7,6,8,8,4,4,6,9,6,11,8,3,6\nY,3,2,5,4,2,6,10,1,8,8,11,8,1,11,2,8\nC,1,3,2,2,1,6,8,7,7,8,7,13,1,9,4,10\nP,4,8,6,11,10,8,9,4,0,8,7,6,8,10,6,8\nX,3,6,5,4,3,7,7,4,9,6,6,6,3,8,6,7\nY,4,10,6,7,4,4,8,2,7,10,13,10,2,11,2,6\nG,2,4,3,2,2,6,7,5,6,9,7,10,1,8,4,9\nW,4,4,6,3,3,9,11,3,2,5,9,7,7,11,1,8\nU,5,10,8,8,4,5,9,7,8,8,10,10,3,9,1,8\nV,8,11,8,8,4,4,12,2,4,8,11,7,6,10,1,7\nS,1,0,2,1,1,8,7,4,7,5,6,7,0,8,7,8\nH,4,5,8,4,4,5,8,3,6,10,8,8,3,8,3,6\nH,4,10,4,7,2,7,7,15,1,7,6,8,3,8,0,8\nQ,7,9,7,11,8,7,9,5,2,7,9,11,4,8,8,9\nK,10,14,9,8,4,8,7,3,7,9,5,7,6,9,4,7\nS,2,6,3,4,2,8,7,7,6,8,7,9,2,9,9,8\nM,3,1,4,2,3,8,6,6,4,6,7,8,7,5,2,8\nR,9,10,7,6,4,7,8,5,5,9,4,9,7,5,6,11\nS,5,8,6,6,3,7,7,4,8,11,5,8,2,8,5,8\nZ,4,7,5,5,3,7,7,2,9,11,6,7,2,8,6,7\nT,3,8,4,6,4,6,11,3,6,8,11,8,2,12,1,7\nL,3,5,4,8,1,0,1,5,6,0,0,7,0,8,0,8\nC,2,4,3,3,1,4,9,5,6,11,9,11,1,9,2,8\nY,3,5,5,4,2,6,10,1,8,8,12,8,1,11,2,7\nN,3,2,4,4,3,7,8,5,5,7,7,6,5,9,2,5\nT,7,9,6,5,2,7,8,3,8,13,6,7,2,8,5,5\nY,6,8,6,6,4,3,9,2,6,10,11,7,2,11,2,5\nJ,5,9,6,7,4,9,5,5,5,8,6,6,2,7,4,6\nU,5,10,6,8,5,6,9,5,7,6,9,9,3,9,1,8\nI,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nS,4,8,5,6,6,8,5,4,4,8,6,10,4,8,8,10\nK,6,9,7,4,4,10,6,4,5,11,2,7,5,7,3,9\nF,2,2,3,3,2,5,10,4,5,11,9,5,1,10,3,6\nF,3,8,3,5,1,1,11,5,6,11,10,9,0,8,3,6\nF,4,5,6,5,5,7,9,4,4,8,7,6,4,9,8,8\nX,6,9,8,7,7,7,6,3,5,6,6,10,4,6,11,9\nF,3,6,4,4,4,7,8,4,6,8,8,6,5,9,3,7\nY,2,7,4,5,2,10,10,3,1,6,12,8,2,11,0,8\nG,2,4,3,3,2,6,7,6,6,6,6,11,2,9,4,9\nL,3,6,4,6,4,8,6,5,4,7,7,8,3,8,7,10\nQ,6,7,9,6,6,8,4,4,5,7,3,8,3,6,5,7\nJ,5,7,4,10,3,8,8,3,3,12,4,5,2,8,7,9\nL,6,11,7,8,7,6,6,9,5,6,6,8,3,7,5,12\nA,6,9,9,8,8,8,7,3,5,7,7,7,5,8,4,5\nG,4,4,5,6,2,7,6,7,8,6,6,9,2,8,6,11\nN,5,8,7,6,4,10,8,2,5,10,2,5,5,9,1,7\nK,6,7,8,5,4,4,8,3,6,10,10,11,4,7,3,6\nU,9,15,8,8,6,7,6,5,5,6,7,8,5,6,3,7\nZ,4,9,5,7,2,7,7,4,14,9,6,8,0,8,8,8\nW,3,4,4,3,2,3,11,2,2,10,10,8,5,10,1,8\nY,3,9,5,6,2,7,11,0,4,7,11,8,0,10,0,8\nF,3,6,4,4,3,5,10,3,6,10,9,5,2,10,3,6\nV,7,10,7,8,4,2,11,3,4,11,12,8,2,10,1,8\nL,3,8,4,6,4,5,5,3,8,3,2,7,1,6,2,5\nX,5,11,8,8,6,8,7,3,9,5,6,8,3,8,7,8\nD,4,11,6,8,6,9,7,4,5,10,4,5,3,8,3,8\nS,4,9,5,7,4,7,8,8,7,8,5,6,2,8,9,8\nY,3,8,4,6,2,5,10,1,3,8,12,8,1,11,0,8\nS,3,7,4,5,3,7,8,7,7,8,5,6,2,8,9,8\nN,5,9,7,6,5,8,8,6,6,6,6,4,8,10,5,6\nW,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8\nF,5,8,7,6,3,7,10,3,6,14,6,4,2,10,3,7\nU,5,11,5,8,5,8,6,13,4,7,12,8,3,9,0,8\nC,5,11,5,8,4,4,7,5,6,11,9,14,2,10,4,7\nZ,5,10,6,8,6,8,7,5,10,7,6,7,1,8,7,8\nT,3,9,5,6,3,8,14,1,5,7,10,8,0,8,0,8\nQ,4,10,6,9,3,8,6,9,6,6,6,8,3,8,5,9\nP,8,12,7,6,3,7,10,6,5,14,5,4,4,10,4,8\nL,3,6,4,4,2,5,3,3,9,5,2,8,1,6,2,6\nW,5,9,7,6,5,11,11,3,2,5,9,7,9,12,2,7\nR,3,6,5,5,5,7,8,3,3,7,5,8,6,8,5,7\nT,4,8,6,6,4,9,11,2,7,6,11,8,1,11,1,8\nS,2,3,3,2,1,8,8,2,6,10,6,7,1,8,5,8\nO,4,8,4,6,4,8,7,7,3,9,6,9,3,8,2,8\nT,4,9,6,7,4,6,11,1,9,8,11,8,0,10,1,7\nB,2,6,3,4,2,6,8,9,6,7,5,7,2,8,8,9\nD,4,9,6,7,4,9,7,4,7,11,5,6,3,7,3,8\nF,2,3,4,1,1,5,12,4,4,13,7,4,1,9,1,7\nU,4,5,5,4,3,5,8,6,8,10,8,9,3,9,3,6\nO,5,11,6,8,3,8,7,9,8,7,6,8,3,8,4,8\nY,5,10,7,7,4,7,10,2,7,6,12,9,2,11,2,8\nZ,3,4,4,6,3,11,5,3,4,9,3,7,1,7,6,9\nN,5,8,7,6,5,8,9,6,5,7,6,5,7,8,3,8\nT,3,5,4,6,1,5,14,1,6,9,11,7,0,8,0,8\nI,3,8,5,6,5,10,7,2,6,9,4,5,3,8,5,7\nA,4,9,6,8,6,7,8,2,4,7,7,8,8,6,4,7\nO,5,10,6,8,5,7,6,7,4,9,6,10,5,8,4,7\nX,2,4,4,3,2,7,8,1,8,10,6,8,2,8,3,7\nB,3,7,5,5,5,8,8,4,5,7,5,6,4,8,5,8\nI,1,8,2,6,2,7,7,0,7,7,6,8,0,8,2,8\nZ,4,7,4,5,4,7,8,5,9,7,7,9,1,9,7,8\nV,6,7,5,5,3,3,11,2,3,9,11,8,3,11,1,7\nE,2,1,2,2,2,7,7,5,6,7,6,8,2,8,5,10\nJ,4,5,6,6,5,8,8,4,6,7,6,8,3,10,8,8\nQ,3,8,5,9,5,9,9,6,3,4,8,11,3,9,7,10\nG,4,5,6,4,5,8,9,5,3,7,7,10,6,10,6,9\nA,4,9,7,7,5,7,2,1,2,5,2,7,4,6,5,7\nR,5,11,5,8,6,5,10,8,3,7,4,8,2,7,6,11\nE,3,7,4,5,3,3,7,5,8,7,6,13,0,8,7,9\nG,5,8,6,7,7,7,9,5,2,7,7,8,6,11,7,8\nG,3,7,4,5,2,8,7,8,7,6,6,9,2,7,6,11\nT,5,10,6,8,4,6,12,2,9,8,12,8,1,11,1,7\nD,5,11,7,8,11,10,8,5,6,7,5,6,5,5,10,5\nH,2,6,2,4,2,7,8,12,1,7,5,8,3,8,0,8\nP,7,10,9,8,7,6,11,5,4,12,5,3,1,10,4,9\nG,5,10,6,8,4,6,6,8,7,8,5,13,2,9,5,9\nG,4,7,5,5,3,6,6,6,7,6,5,9,2,10,4,7\nW,4,8,6,6,4,5,8,5,1,8,10,8,6,11,0,8\nK,4,9,6,6,5,5,7,1,6,9,7,10,3,8,3,8\nM,5,2,6,4,4,8,6,6,4,7,7,8,8,6,2,7\nA,1,0,2,1,0,7,4,2,0,7,2,8,2,7,1,8\nG,2,3,3,1,2,7,7,6,6,6,6,9,2,9,4,9\nS,3,7,4,5,3,9,8,5,8,5,5,5,0,7,8,8\nT,6,10,5,6,2,5,11,2,7,12,7,5,2,9,4,4\nS,2,4,3,3,2,8,7,7,5,7,7,8,2,9,9,8\nR,4,10,4,8,5,5,10,7,4,7,4,9,2,7,5,11\nJ,5,10,7,8,5,9,4,4,6,8,5,6,2,8,4,6\nT,5,7,6,5,6,7,8,5,5,6,8,9,6,9,6,5\nG,3,6,4,4,2,7,7,7,7,7,5,12,2,9,4,9\nI,4,9,5,6,3,8,6,1,6,7,6,7,0,9,4,7\nQ,6,7,8,11,8,10,14,5,1,3,8,12,5,15,5,10\nI,3,10,4,8,2,6,8,0,8,13,7,8,0,8,1,7\nU,4,7,6,6,5,7,6,4,4,6,6,8,7,8,1,8\nE,5,8,7,6,7,7,7,3,6,7,7,11,4,10,8,9\nS,3,7,4,5,2,7,7,5,8,5,6,8,0,8,9,8\nE,2,5,3,4,3,7,7,5,7,7,7,9,2,8,5,10\nC,3,4,4,3,2,6,8,7,8,8,8,13,1,9,4,10\nP,3,6,4,4,2,4,14,8,1,11,6,3,0,10,4,8\nC,6,12,5,6,4,6,8,4,4,10,8,9,4,9,9,9\nE,2,6,3,4,3,7,7,4,7,7,5,8,3,8,5,10\nP,4,7,5,5,3,9,8,3,5,12,4,4,2,9,3,9\nM,4,4,4,6,3,8,7,12,1,6,9,8,8,6,0,8\nI,7,13,5,7,3,9,7,6,4,13,4,9,3,8,5,10\nR,3,8,4,5,2,6,10,9,4,7,4,8,3,7,5,11\nH,3,1,4,2,3,7,7,6,6,7,6,8,3,8,3,8\nN,5,8,8,6,4,7,8,3,5,10,6,7,5,8,1,7\nW,4,5,6,7,4,8,8,4,2,7,8,8,9,9,0,8\nL,5,9,5,7,2,0,1,5,6,0,1,6,0,8,0,8\nJ,4,7,6,5,3,7,5,5,4,14,9,14,1,6,1,7\nS,2,3,3,2,1,8,7,2,7,10,5,8,1,8,4,8\nM,7,11,9,8,9,9,7,2,4,9,6,7,8,6,2,8\nC,6,11,6,8,3,4,8,6,8,12,10,12,2,9,3,7\nL,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8\nD,3,8,4,6,2,5,7,10,8,6,5,5,3,8,4,8\nP,10,15,9,8,6,7,10,4,6,13,5,3,4,9,7,5\nU,2,6,3,4,1,7,5,14,5,7,12,8,3,9,0,8\nT,4,5,6,4,5,7,9,4,8,7,7,8,3,10,7,7\nD,6,10,8,7,5,8,7,4,7,11,5,6,3,8,3,8\nF,3,6,5,4,3,7,9,3,5,12,6,5,2,9,2,7\nE,3,7,3,5,2,3,6,6,11,7,7,15,0,8,7,7\nB,2,3,3,1,2,8,7,2,5,10,5,6,2,8,4,9\nH,7,9,10,6,8,8,7,3,6,10,5,8,3,8,3,8\nM,4,8,4,6,3,7,7,12,1,7,9,8,8,6,0,8\nK,3,7,5,5,5,6,6,3,4,6,6,9,5,8,7,9\nD,4,8,4,6,2,5,6,10,8,5,4,5,3,8,4,8\nK,4,10,6,8,7,5,5,5,3,6,6,9,3,5,8,9\nL,4,9,5,7,4,3,3,5,7,1,0,7,0,6,0,6\nW,5,5,7,8,4,7,8,5,2,7,8,8,9,9,0,8\nB,3,9,5,7,5,8,7,4,7,6,6,5,6,8,5,9\nV,3,3,4,2,1,4,12,3,3,10,11,7,2,11,1,8\nD,3,4,5,3,3,7,7,5,6,10,5,6,3,7,4,9\nK,3,6,5,5,4,11,5,3,3,10,3,8,4,6,6,12\nQ,1,0,1,1,0,8,7,7,3,6,6,9,2,8,3,8\nY,4,8,6,6,6,8,7,5,4,7,8,8,6,8,7,4\nL,8,15,8,9,5,7,4,3,4,12,7,11,4,7,7,8\nS,2,4,3,3,2,8,7,3,7,10,5,8,1,9,4,8\nV,1,3,2,2,1,7,12,3,3,8,11,8,2,11,1,8\nZ,3,5,6,4,3,7,7,2,10,12,6,8,1,8,5,7\nC,7,13,5,8,4,7,9,4,4,9,7,9,3,9,8,11\nA,5,11,8,8,5,8,2,2,3,6,1,7,3,7,4,7\nR,2,4,4,3,2,8,7,3,5,10,4,7,2,7,3,10\nB,4,5,4,8,4,6,7,9,7,7,6,7,2,8,9,10\nV,4,5,5,4,2,4,12,3,3,10,11,7,2,10,1,8\nY,5,8,5,6,2,4,11,3,8,12,11,4,1,10,2,4\nM,5,5,5,7,4,8,7,12,2,7,9,8,8,6,0,8\nM,6,9,7,4,4,7,3,3,2,8,4,10,7,3,1,9\nY,5,10,8,8,3,6,10,1,9,9,12,8,1,11,2,7\nU,2,4,3,2,1,7,8,6,6,7,9,8,3,9,1,8\nZ,4,8,6,6,4,8,6,2,9,11,4,10,2,7,7,9\nF,7,11,10,8,7,7,9,3,6,12,7,6,2,9,2,7\nA,4,9,5,7,4,7,5,3,0,7,1,8,2,7,1,8\nU,4,9,5,7,2,8,5,14,5,6,14,8,3,9,0,8\nF,5,11,5,8,2,1,13,5,4,12,10,6,0,8,2,5\nW,7,10,7,5,4,5,8,1,3,8,10,8,9,11,2,6\nS,4,7,5,5,3,9,7,5,9,11,3,7,2,6,5,11\nY,5,9,8,6,4,6,9,1,7,7,12,9,1,11,2,8\nA,2,4,4,6,2,8,3,3,3,7,1,8,3,6,3,8\nK,6,10,8,8,6,6,8,5,7,6,5,10,4,8,5,9\nI,4,6,6,7,5,9,8,5,6,7,6,8,3,9,8,8\nT,6,11,8,9,6,6,7,8,7,8,9,7,3,9,6,10\nM,10,12,10,7,5,11,12,6,4,4,7,9,8,12,2,7\nU,8,12,7,6,4,5,6,5,4,7,7,10,7,8,4,10\nX,2,1,3,3,2,8,7,3,9,6,6,6,2,8,5,8\nP,8,9,6,4,3,8,8,5,4,11,4,6,5,9,4,8\nB,4,8,5,6,5,10,6,3,6,10,4,7,3,8,4,11\nE,4,6,6,4,3,6,8,2,9,11,8,9,2,8,4,7\nH,3,3,4,4,2,7,8,14,1,7,5,8,3,8,0,8\nV,1,1,2,1,0,7,9,4,2,7,13,8,2,10,0,8\nI,0,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8\nD,5,10,7,7,5,11,6,3,8,12,3,7,6,8,4,9\nW,5,7,5,5,4,6,10,5,2,9,6,6,6,12,2,5\nX,5,9,5,5,2,11,6,3,7,9,3,7,3,9,4,10\nQ,4,4,5,5,3,9,10,7,6,5,8,9,3,8,5,9\nE,6,11,8,8,9,6,6,3,6,8,7,12,5,9,9,8\nY,3,4,5,6,1,8,10,2,2,6,13,8,2,11,0,8\nT,1,0,1,0,0,7,13,1,4,7,10,8,0,8,0,8\nM,5,10,7,5,4,12,3,5,2,11,1,10,7,2,1,8\nL,5,10,7,8,5,5,4,1,8,7,2,11,0,7,3,7\nC,5,9,6,7,4,5,7,6,9,7,6,13,1,8,4,9\nL,1,0,1,0,0,2,2,5,4,1,3,5,0,8,0,8\nP,5,11,7,8,6,7,7,7,5,8,7,8,2,9,7,9\nB,5,9,8,7,6,11,6,2,7,11,3,7,4,6,6,12\nB,4,8,6,6,5,8,7,5,6,9,6,6,2,8,7,8\nF,5,6,6,7,7,7,9,5,6,8,5,7,5,8,10,5\nT,3,10,5,7,1,7,15,0,6,7,11,8,0,8,0,8\nZ,2,2,3,3,2,7,7,5,9,6,6,8,1,8,7,8\nK,4,8,5,6,4,6,7,4,7,6,5,8,7,8,5,9\nK,5,6,7,6,6,10,6,3,4,10,3,8,5,6,6,12\nM,4,7,6,5,8,11,7,3,4,8,4,7,5,5,2,6\nZ,6,7,4,11,4,8,7,4,3,11,6,7,3,9,10,6\nG,5,9,7,8,8,8,6,6,4,7,6,9,10,10,9,9\nE,3,9,4,7,2,3,8,6,10,7,6,15,0,8,7,7\nG,5,9,5,6,3,6,7,7,7,10,7,10,2,9,4,9\nC,5,9,7,8,8,5,6,4,5,7,6,11,5,11,8,11\nF,5,7,7,5,4,8,9,2,6,14,5,4,3,8,3,7\nW,8,9,11,8,12,7,8,5,6,7,6,8,10,7,9,6\nS,5,12,4,6,2,9,3,4,4,9,2,8,3,6,4,8\nW,5,8,7,6,5,8,8,4,1,7,9,8,7,11,0,8\nN,5,9,8,6,4,11,6,4,5,10,1,4,5,8,1,7\nE,5,9,5,6,3,3,8,6,11,7,6,14,0,8,8,7\nM,5,10,8,8,7,8,6,6,5,6,8,8,8,6,2,7\nM,5,6,8,4,4,7,6,3,5,9,7,8,7,5,2,8\nT,1,0,2,0,0,7,14,2,3,7,10,8,0,8,0,8\nA,3,7,5,5,3,10,3,2,2,8,2,10,3,5,2,8\nV,1,1,2,2,1,7,12,2,2,7,11,8,2,11,0,8\nL,3,6,4,4,2,5,3,6,9,2,2,4,1,6,1,5\nG,5,9,7,8,8,8,8,6,3,7,6,9,8,11,9,11\nI,3,9,4,7,3,7,7,0,6,13,6,8,0,8,1,8\nX,5,7,7,5,6,8,6,2,5,6,7,7,3,10,10,8\nI,3,11,4,8,2,7,7,0,8,14,6,8,0,8,1,8\nJ,2,7,2,5,1,12,3,9,4,13,4,12,1,6,0,8\nK,4,3,4,5,1,4,7,8,1,7,6,11,3,8,3,11\nX,5,10,7,7,6,8,7,3,5,6,7,6,4,10,11,8\nS,3,4,5,3,2,8,6,3,7,10,4,8,1,8,4,10\nL,1,0,1,0,0,2,1,6,4,0,3,4,0,8,0,8\nY,2,1,4,2,1,7,11,1,7,7,11,8,1,11,2,8\nX,5,11,8,8,5,11,7,1,8,10,2,6,4,9,4,11\nQ,3,6,4,8,4,8,8,5,2,8,8,10,3,9,5,8\nR,5,5,6,8,3,6,11,9,4,7,3,8,3,7,6,11\nE,4,4,4,6,3,3,8,6,11,7,5,14,0,8,7,7\nN,4,4,5,6,2,7,7,14,2,4,6,8,6,8,0,8\nB,3,6,5,4,5,9,7,4,3,6,7,7,5,10,7,7\nG,5,9,7,6,8,8,8,5,2,6,6,9,7,8,6,11\nK,4,4,6,3,3,6,7,2,7,10,7,10,3,8,3,7\nE,3,8,5,6,4,7,7,5,8,7,6,9,6,8,6,9\nO,5,7,7,6,5,7,5,5,5,9,5,9,3,6,5,6\nZ,5,9,6,7,4,6,8,3,10,12,7,8,1,9,6,7\nL,6,10,6,5,3,7,5,4,4,12,9,12,3,9,6,9\nG,3,7,5,5,5,9,7,5,2,7,6,10,5,9,4,10\nP,4,5,5,7,6,9,7,4,3,6,7,8,6,11,5,4\nY,4,7,4,5,2,4,10,2,9,11,10,5,0,10,3,4\nS,5,10,8,8,9,6,7,3,2,8,6,6,3,8,11,2\nY,5,10,6,8,6,8,7,6,5,5,9,8,3,9,10,7\nM,7,8,10,6,6,9,6,2,5,9,6,8,8,6,2,8\nJ,2,5,3,3,1,10,6,2,6,12,4,8,1,6,1,7\nI,1,5,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nU,8,13,8,7,5,7,6,5,5,7,8,8,5,7,3,8\nH,4,6,6,4,3,7,7,3,6,10,6,8,3,8,3,7\nE,2,4,4,3,2,7,7,2,8,11,6,9,2,8,4,8\nN,4,9,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nD,2,3,3,2,2,9,6,3,5,10,4,6,2,8,2,8\nI,4,7,5,5,3,8,6,2,7,7,6,7,0,9,4,7\nB,6,10,9,8,9,8,7,6,6,9,7,6,6,10,9,10\nJ,5,9,3,12,3,10,7,2,3,11,3,5,3,8,6,9\nE,4,7,5,5,4,7,7,5,9,7,7,9,3,8,6,8\nJ,5,9,7,7,4,8,7,3,6,15,5,8,0,6,1,7\nM,3,1,4,3,3,8,6,6,4,6,7,8,7,5,2,7\nO,2,3,3,1,1,8,7,7,5,7,6,8,2,8,3,8\nU,8,11,7,6,4,7,5,5,4,6,8,8,5,6,3,8\nW,7,7,7,5,4,2,10,3,3,11,11,8,7,10,2,6\nO,3,6,4,4,2,7,7,7,5,7,5,9,3,8,3,7\nS,2,8,3,6,3,7,7,5,7,5,6,7,0,8,8,8\nF,4,7,5,5,5,6,9,2,6,10,9,6,5,10,3,7\nN,6,10,5,6,3,7,9,4,5,4,5,10,5,10,2,7\nH,3,5,5,3,3,7,8,6,7,7,5,8,3,8,3,8\nT,5,10,5,5,2,6,9,2,7,12,7,5,2,10,3,5\nG,3,2,5,4,3,7,6,6,6,6,6,10,2,9,4,9\nY,3,7,4,5,1,7,10,2,2,7,13,8,1,11,0,8\nZ,1,0,2,1,0,7,7,2,10,9,6,8,0,8,6,8\nL,2,4,3,3,1,7,4,1,7,9,3,10,0,6,3,8\nO,4,7,5,5,4,7,9,8,4,7,8,8,3,8,3,8\nZ,5,10,7,8,4,7,7,3,11,11,6,8,1,8,7,8\nE,6,9,8,6,6,8,7,1,8,11,5,8,3,8,5,10\nO,3,6,4,4,2,7,7,7,5,10,6,8,3,8,3,8\nH,3,6,3,4,3,8,8,12,1,7,5,8,3,8,0,8\nQ,5,8,6,9,7,8,7,6,3,8,8,10,3,9,5,8\nH,6,11,8,8,9,6,8,6,7,7,6,10,6,8,4,8\nS,4,8,5,6,4,7,7,5,8,5,6,10,1,10,9,9\nY,4,6,5,8,6,10,12,5,4,6,7,7,5,10,8,5\nA,3,8,5,5,2,10,5,3,1,8,1,9,2,7,2,9\nG,2,0,2,1,1,8,6,6,6,6,5,9,1,7,5,10\nC,4,7,5,5,2,6,9,7,7,13,8,7,2,11,2,6\nN,3,5,5,3,2,5,9,3,4,10,8,8,5,8,0,7\nS,5,6,6,8,3,8,7,6,9,4,6,8,0,8,9,8\nO,6,10,7,8,5,8,7,8,6,10,5,9,4,9,5,6\nZ,3,5,4,8,3,12,4,3,5,10,2,7,2,7,5,12\nR,4,10,6,7,5,7,8,6,5,8,5,9,3,6,6,11\nD,4,9,5,7,6,7,8,6,6,8,7,5,3,8,3,7\nI,4,5,5,6,4,8,9,4,4,7,6,8,3,7,8,7\nQ,5,8,6,9,6,8,7,6,3,9,8,10,3,8,6,8\nO,6,8,8,6,5,7,7,8,4,7,6,8,3,8,3,7\nP,6,11,6,8,3,4,13,8,1,10,6,3,1,10,4,8\nD,2,2,3,3,2,8,7,6,7,6,6,4,2,8,3,6\nL,2,6,4,4,2,6,4,2,10,7,1,10,0,7,3,7\nN,6,9,8,8,9,7,8,4,4,7,5,7,7,9,6,7\nD,2,3,4,1,2,10,6,3,6,10,3,6,2,8,2,9\nG,3,7,4,5,3,7,6,7,7,7,4,11,1,8,5,11\nN,3,7,3,5,3,8,8,12,1,6,6,8,5,9,0,8\nI,1,1,0,2,0,7,7,1,7,7,6,8,0,8,2,8\nX,4,6,5,8,2,7,7,5,4,7,6,8,3,8,4,8\nB,5,5,5,8,4,6,8,9,7,7,5,7,2,8,9,10\nC,2,3,2,2,1,4,8,4,7,10,9,12,1,8,2,7\nI,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nP,4,8,6,6,4,4,12,6,4,11,8,3,0,10,4,7\nY,2,2,4,3,2,7,11,1,7,7,11,8,1,11,2,8\nS,4,10,4,8,2,8,7,6,9,4,6,6,0,8,9,8\nG,3,6,4,4,2,6,7,6,6,10,7,10,2,10,4,9\nL,2,4,4,3,2,6,4,1,9,7,2,10,0,7,2,8\nB,2,6,3,4,2,7,7,9,6,7,6,7,2,8,8,9\nC,5,10,6,8,5,5,7,8,7,11,8,13,3,12,5,8\nX,2,7,3,4,1,7,7,4,4,7,6,8,3,8,4,8\nA,4,11,6,8,2,8,5,3,1,7,0,8,3,7,2,8\nW,7,8,7,6,7,4,10,2,3,9,8,8,7,12,2,6\nW,7,10,7,7,6,4,10,2,3,9,9,8,7,11,2,6\nM,5,11,8,8,11,10,7,3,4,8,4,7,12,6,7,4\nS,6,11,7,8,4,7,8,4,8,11,8,7,2,10,5,6\nD,2,3,3,2,2,7,7,6,6,7,6,6,5,8,2,7\nY,8,11,7,6,4,8,6,4,6,9,6,5,3,10,6,5\nQ,2,2,3,4,2,8,9,5,2,5,8,10,2,9,5,9\nB,6,9,8,7,7,7,8,6,5,6,5,6,4,9,7,6\nF,6,11,8,8,6,6,10,1,6,13,7,6,2,10,2,7\nR,3,5,5,4,3,9,7,3,5,10,4,6,3,7,4,9\nJ,4,9,4,7,2,8,9,2,4,13,4,5,1,8,6,8\nW,7,7,10,6,10,8,7,5,5,7,5,8,10,9,8,7\nN,5,7,7,5,6,6,8,3,4,8,7,9,6,9,5,5\nU,5,11,7,8,10,9,6,4,4,6,7,7,8,7,6,5\nN,4,6,7,4,3,9,9,2,5,11,3,5,6,9,2,7\nV,4,10,6,7,3,7,9,3,2,6,12,8,3,10,0,8\nD,4,7,5,5,4,8,8,4,5,10,6,4,3,8,3,7\nA,3,6,5,4,2,11,3,2,2,9,2,9,2,6,3,9\nH,2,1,3,2,2,7,7,6,6,7,6,9,3,9,3,8\nF,2,3,3,2,1,6,11,3,5,12,7,4,1,9,1,7\nA,3,9,5,6,4,12,2,2,2,10,2,9,2,6,3,8\nO,4,10,6,8,4,8,6,9,5,7,5,8,3,8,3,8\nI,2,7,3,5,2,7,9,0,6,13,6,7,0,8,1,7\nD,2,4,4,3,2,8,7,4,6,10,4,6,2,8,3,8\nY,5,6,6,4,3,3,10,2,7,11,11,6,1,10,2,4\nH,6,9,6,4,3,5,9,4,6,9,7,9,5,7,3,7\nK,5,9,7,6,5,9,6,1,6,10,4,8,3,8,4,10\nK,7,11,10,8,6,8,6,2,7,10,4,9,5,6,5,8\nI,0,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8\nL,3,6,4,4,3,9,4,1,6,9,2,9,1,6,2,9\nD,5,10,5,5,3,12,4,3,5,11,2,7,4,7,3,12\nQ,7,10,9,8,8,8,3,8,4,6,6,8,4,8,6,9\nN,2,0,2,1,1,7,7,12,1,5,6,8,5,8,0,8\nQ,6,9,6,11,6,7,7,8,5,9,8,8,4,9,7,9\nL,3,7,5,5,2,6,4,1,9,8,2,11,0,8,2,8\nO,3,1,4,2,2,7,8,7,5,7,7,8,2,8,3,8\nF,2,3,2,1,1,5,11,4,4,10,8,5,1,9,3,7\nY,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nK,2,1,2,1,0,5,7,8,1,7,6,11,3,8,2,11\nI,1,9,2,6,3,8,7,0,7,7,6,7,0,8,2,7\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nG,2,4,3,3,2,6,7,5,5,9,7,10,2,9,4,10\nH,3,3,5,1,2,6,7,3,6,10,9,9,3,8,3,6\nQ,2,5,3,7,4,8,6,7,3,7,5,11,2,9,4,10\nU,4,8,6,6,3,4,8,7,7,9,11,11,3,9,0,8\nD,2,6,4,4,3,7,7,5,6,7,6,5,3,8,3,7\nD,5,11,7,8,11,8,8,5,5,7,6,6,7,8,8,6\nM,3,6,5,4,5,7,7,6,4,7,5,8,5,9,6,8\nX,3,1,4,3,2,7,8,3,9,6,6,7,3,9,6,6\nM,5,9,7,6,7,7,7,2,4,9,8,9,7,6,2,8\nB,4,9,5,7,5,8,6,7,7,6,6,6,2,8,7,10\nR,3,8,4,5,2,5,11,8,3,7,3,8,3,7,6,11\nS,2,6,3,4,3,8,7,7,6,7,8,9,2,10,8,8\nA,4,10,7,7,2,8,7,3,0,7,0,8,3,7,2,8\nJ,3,8,4,6,2,13,3,6,4,13,3,11,0,7,0,8\nD,4,9,4,7,3,6,7,11,9,6,5,6,3,8,4,8\nY,3,10,5,7,3,7,10,1,7,6,12,8,1,11,2,8\nG,3,8,5,6,2,7,5,7,8,6,5,10,1,8,6,11\nK,5,7,7,5,4,3,9,3,6,10,11,11,3,8,3,6\nZ,3,8,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nP,3,7,5,5,2,6,11,4,4,13,5,2,0,10,3,9\nR,5,8,7,6,6,9,7,4,5,10,4,6,3,7,4,10\nF,4,5,5,6,5,7,9,4,4,7,6,7,4,9,8,8\nG,4,8,5,6,2,8,7,8,8,6,6,9,2,7,6,11\nQ,5,7,6,8,5,9,8,7,2,4,7,10,3,8,6,10\nE,4,7,6,5,5,8,5,6,3,7,6,9,4,8,8,10\nT,8,11,7,6,3,5,10,3,9,13,7,5,2,9,3,4\nM,5,11,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nB,9,15,7,9,5,7,7,5,5,10,6,8,6,6,9,10\nQ,2,2,3,3,2,8,8,6,2,5,6,9,2,9,5,10\nE,5,9,4,4,2,7,8,4,7,9,6,9,1,9,7,9\nZ,3,6,4,4,2,7,7,4,14,9,6,8,0,8,8,8\nW,5,10,7,8,8,7,9,6,4,8,8,7,10,9,4,10\nX,3,4,5,3,2,8,7,4,9,6,6,9,4,7,7,9\nO,9,15,7,8,4,8,6,5,6,8,3,8,6,9,5,8\nW,7,7,7,5,5,4,11,3,3,9,9,7,7,11,2,6\nZ,7,11,9,8,5,8,6,3,10,12,4,10,2,9,6,10\nY,10,10,8,14,6,9,6,5,5,4,12,6,5,10,6,7\nM,6,10,9,7,7,10,6,2,5,9,3,6,9,8,2,9\nX,3,9,5,7,4,7,7,3,8,5,7,9,3,8,6,8\nN,3,7,5,5,4,7,9,6,4,7,6,7,6,8,3,8\nH,2,3,4,1,2,8,8,3,5,10,5,7,3,8,3,7\nN,5,9,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nX,5,11,6,8,4,8,7,4,4,7,6,8,3,8,4,8\nA,5,11,7,8,7,8,5,7,4,8,6,8,6,8,7,4\nP,3,4,3,3,2,5,10,4,4,10,8,4,1,10,3,7\nF,2,1,2,3,2,5,10,4,5,10,9,6,1,10,3,7\nG,3,2,4,4,3,6,7,6,6,7,6,10,3,7,4,9\nP,1,1,2,1,1,5,10,8,3,9,6,5,1,9,3,8\nT,2,6,3,4,2,7,12,3,7,7,11,8,1,11,1,7\nY,7,9,7,7,4,4,9,2,7,10,11,6,2,12,3,4\nR,3,3,3,4,2,5,10,8,4,7,4,8,2,7,5,11\nN,3,8,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nS,4,10,5,8,5,7,7,5,8,5,6,8,0,8,9,7\nP,4,8,6,6,3,6,11,3,6,14,7,3,0,9,2,8\nF,4,10,6,7,4,4,12,4,4,13,8,5,2,10,2,6\nS,4,5,5,7,3,8,8,6,10,5,6,7,0,8,9,7\nI,7,11,6,6,3,8,8,3,6,13,4,5,2,8,6,10\nV,4,4,5,3,2,4,12,3,3,10,11,7,2,10,1,8\nT,4,11,6,8,2,7,15,1,6,7,11,8,0,8,0,8\nZ,5,11,7,8,3,7,7,4,15,9,6,8,0,8,9,8\nR,4,10,4,7,5,5,10,7,3,7,4,9,2,6,5,11\nG,3,8,4,6,3,8,8,7,5,6,6,9,2,7,5,11\nF,4,7,4,5,2,1,12,4,4,12,11,7,0,8,1,6\nB,2,4,3,2,2,9,7,3,5,10,5,7,2,8,4,9\nF,5,10,5,8,4,1,12,4,4,12,10,7,0,8,2,6\nG,5,6,5,4,3,6,7,6,5,10,8,10,2,9,4,9\nP,2,3,4,2,2,7,9,3,4,12,5,4,1,10,2,8\nG,4,7,5,5,3,7,6,7,7,11,6,11,2,10,4,9\nQ,4,6,6,4,5,8,5,7,4,7,6,6,5,7,6,9\nO,4,7,5,5,3,7,8,7,6,9,8,8,3,8,3,8\nZ,3,7,4,5,3,6,7,5,9,7,7,9,1,9,7,8\nX,5,7,8,5,4,7,7,1,8,10,7,9,3,8,3,8\nS,2,3,4,2,1,7,8,3,7,10,7,7,1,8,5,6\nO,3,6,4,4,3,8,8,7,4,7,7,7,3,8,2,8\nT,2,4,3,3,2,7,12,3,6,7,11,9,2,11,1,8\nS,3,8,4,6,3,9,7,7,6,7,6,8,2,9,9,8\nO,2,4,3,3,2,8,7,6,3,9,5,8,2,8,2,8\nP,6,10,9,8,7,8,9,5,4,11,4,4,2,10,3,8\nB,5,11,7,8,9,8,8,6,6,7,6,6,2,8,6,9\nQ,4,5,4,6,4,8,8,5,3,8,9,9,3,10,5,7\nT,2,4,3,2,1,6,11,2,7,11,9,5,1,10,2,5\nQ,8,12,7,6,4,10,3,5,7,12,3,11,3,7,8,11\nY,4,6,6,4,5,9,5,7,5,6,9,7,3,9,8,5\nQ,8,9,11,8,9,6,4,4,6,5,4,7,4,6,6,7\nL,3,5,3,3,2,4,3,5,6,2,2,5,1,6,0,6\nV,5,10,7,8,9,8,5,6,3,7,7,8,8,7,4,7\nX,5,5,6,8,2,7,7,5,4,7,6,8,3,8,4,8\nE,4,8,5,6,5,8,7,6,3,6,6,10,3,8,7,9\nW,5,10,7,8,7,6,11,2,2,7,8,9,7,12,1,8\nB,4,7,6,5,5,9,7,4,5,9,5,6,2,8,5,9\nP,4,8,6,6,4,5,13,7,2,12,6,2,1,10,3,8\nJ,7,13,6,10,4,8,9,2,3,12,5,5,2,9,8,9\nU,5,8,5,6,3,3,8,5,7,9,9,9,3,9,2,5\nX,4,8,5,5,1,7,7,4,4,7,6,8,3,8,4,8\nB,6,11,9,8,9,8,8,4,6,10,5,6,5,6,7,10\nC,6,11,7,8,3,5,7,7,11,7,6,12,1,9,4,8\nR,5,10,7,8,7,6,8,5,6,6,5,8,3,6,5,8\nO,4,9,5,7,5,8,9,8,4,7,8,7,4,7,4,9\nV,5,6,5,4,2,4,12,5,4,11,11,6,3,10,1,8\nP,7,11,10,8,6,9,9,4,6,12,3,3,2,10,4,9\nR,1,0,2,1,1,6,9,7,3,7,5,8,2,7,4,11\nI,2,5,3,4,1,7,7,0,8,13,6,8,0,8,1,8\nP,2,3,4,2,2,7,10,5,3,10,4,3,1,10,3,8\nA,5,10,7,7,4,9,5,3,0,8,1,8,2,7,1,8\nB,7,10,9,7,7,9,6,4,7,9,5,6,2,8,7,10\nC,5,10,6,8,3,3,8,5,8,11,11,13,1,8,3,7\nW,6,5,8,8,4,5,8,5,2,7,8,8,9,9,0,8\nQ,9,13,8,7,5,8,6,4,10,11,4,10,3,7,9,9\nZ,4,6,6,4,3,6,9,2,8,11,8,6,1,8,6,5\nI,1,9,0,6,1,7,7,5,3,7,6,8,0,8,0,8\nG,1,3,2,1,1,7,7,5,5,10,7,10,2,9,3,10\nT,2,1,2,2,1,8,11,4,5,6,10,7,2,11,1,7\nI,2,5,3,3,1,7,8,1,7,14,6,7,0,8,1,7\nC,6,9,6,6,4,7,8,6,8,13,7,10,3,11,4,6\nI,6,11,8,8,5,10,5,2,6,6,7,4,0,10,4,7\nN,5,10,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nV,4,8,4,6,2,4,11,4,4,10,11,7,3,10,1,8\nK,5,5,6,7,2,4,8,8,2,7,4,11,3,8,2,11\nQ,5,9,5,5,3,9,6,4,6,10,5,7,3,8,9,9\nM,5,9,6,7,6,7,5,11,1,7,9,8,9,5,2,9\nP,4,8,4,5,2,4,15,8,1,12,6,2,0,9,4,8\nV,3,5,4,3,1,4,12,3,3,10,11,7,2,11,1,8\nF,4,7,4,5,2,1,12,4,4,12,10,7,0,8,2,6\nO,5,9,6,6,3,8,6,8,8,7,5,9,3,8,4,8\nQ,4,5,5,4,4,8,4,4,5,7,4,8,4,6,4,8\nK,3,6,5,4,3,3,9,2,6,10,11,11,3,8,2,6\nR,7,9,6,4,3,8,7,5,5,9,4,9,6,5,6,11\nF,2,4,3,3,1,6,10,2,5,13,7,5,1,9,1,7\nX,3,5,6,3,3,8,6,1,8,10,5,8,2,8,3,8\nP,4,11,5,8,4,5,10,8,4,9,7,3,2,10,4,7\nM,5,6,8,5,8,9,8,5,4,7,6,7,11,9,7,5\nE,1,1,1,1,1,4,7,5,8,7,6,13,0,8,6,9\nK,5,10,6,8,5,3,8,7,3,7,5,11,3,8,3,11\nW,4,7,6,5,3,7,8,4,1,7,8,8,8,9,0,8\nP,6,9,8,7,5,6,14,6,1,11,5,2,1,11,4,7\nW,5,8,7,6,6,7,11,2,2,7,8,8,7,12,1,8\nQ,4,5,4,6,4,8,5,6,5,9,6,9,3,8,5,8\nZ,6,10,8,8,5,8,6,3,9,12,4,9,3,6,8,9\nB,1,0,1,0,0,7,7,6,4,7,6,7,1,8,5,9\nQ,3,6,4,8,4,8,6,8,4,5,6,9,3,8,6,10\nD,6,14,6,8,4,11,4,2,6,9,3,7,5,7,4,12\nE,4,7,4,5,3,3,7,5,10,7,6,14,0,8,6,8\nJ,4,9,6,7,4,8,6,6,6,8,6,8,3,7,4,6\nQ,5,7,7,10,10,8,6,5,1,6,6,9,8,9,6,12\nO,4,8,5,6,4,7,7,8,4,7,6,10,3,8,3,8\nA,4,8,6,6,4,11,3,2,2,9,2,9,4,4,3,8\nG,5,10,7,8,5,6,6,7,6,6,6,10,2,8,5,8\nU,4,3,4,5,1,7,5,12,5,7,15,8,3,9,0,8\nU,6,11,6,8,4,3,8,5,7,11,11,9,3,9,2,6\nX,4,5,7,3,3,6,8,1,9,11,8,9,3,8,3,7\nW,6,8,9,7,11,9,6,5,5,8,6,8,11,12,8,6\nC,5,6,6,5,5,6,7,4,5,7,6,11,5,10,8,11\nI,2,6,3,4,2,7,7,0,7,13,6,8,0,8,1,8\nO,4,9,5,7,5,8,6,7,4,9,4,8,3,8,2,8\nX,3,4,6,3,2,7,7,1,9,11,6,8,2,8,3,8\nV,4,10,6,8,3,8,9,4,2,6,13,8,3,10,0,8\nW,4,5,6,8,4,7,7,4,2,7,8,8,9,9,0,8\nA,3,5,6,3,2,8,2,2,2,6,1,8,2,6,2,7\nE,3,4,3,3,3,7,7,5,8,7,6,8,2,8,6,9\nP,2,4,4,2,2,8,10,4,3,11,4,3,1,10,3,8\nL,3,3,3,5,1,0,1,6,6,0,1,5,0,8,0,8\nO,2,2,3,4,2,7,7,8,4,7,6,8,2,8,3,8\nP,4,10,6,8,5,6,9,6,5,9,7,3,2,10,4,6\nB,5,8,7,6,6,9,7,3,5,10,5,6,2,8,5,9\nI,4,10,6,8,7,10,8,2,6,9,4,4,4,8,7,4\nE,4,10,5,7,5,6,7,6,9,6,4,10,3,8,6,9\nV,5,11,8,8,5,8,12,2,3,4,10,9,4,12,2,8\nK,5,7,7,6,5,9,5,2,4,8,4,9,5,8,7,11\nS,6,10,8,8,9,8,9,5,4,8,5,6,4,9,11,8\nD,6,11,6,8,4,5,7,10,10,7,6,6,3,8,4,8\nB,4,11,5,8,7,6,8,9,6,7,5,7,2,7,8,10\nN,9,13,8,7,4,6,10,4,5,4,5,10,6,10,2,7\nC,2,3,3,2,1,6,8,7,7,8,7,12,1,9,4,10\nW,5,11,8,8,8,7,11,2,2,6,8,8,7,12,1,8\nA,2,6,4,4,3,8,3,2,1,7,2,8,1,7,2,8\nA,3,9,5,6,3,10,3,2,2,8,2,10,3,5,3,7\nN,3,4,4,7,2,7,7,14,2,5,6,8,6,8,0,8\nZ,4,5,5,8,2,7,7,4,14,10,6,8,0,8,8,8\nH,4,6,5,4,5,7,7,5,6,7,6,7,6,8,3,8\nA,2,8,4,5,2,7,5,3,1,6,0,8,2,7,2,7\nG,3,4,4,3,2,6,7,5,4,9,7,9,2,8,4,9\nU,3,8,4,6,2,7,5,14,5,7,13,8,3,9,0,8\nW,4,7,6,5,5,10,11,2,2,5,9,8,6,12,1,8\nM,2,3,4,2,2,6,6,4,3,9,9,10,5,6,1,7\nV,3,6,5,4,1,8,8,4,2,7,14,8,3,9,0,8\nQ,5,10,5,6,3,9,6,4,7,11,4,8,3,8,8,11\nW,6,7,6,5,4,3,11,2,2,9,9,8,6,11,1,7\nN,4,9,6,7,4,6,9,6,5,7,7,8,6,9,2,6\nD,3,4,4,3,3,7,7,6,7,7,6,5,2,8,3,7\nA,3,5,6,4,2,9,2,2,2,8,1,8,2,6,2,7\nR,2,6,3,4,3,5,10,7,4,7,4,9,2,6,5,11\nL,3,9,4,7,3,7,4,2,8,7,1,8,1,6,2,7\nU,3,3,4,2,2,6,8,5,7,10,8,8,3,10,3,6\nN,5,11,8,8,6,7,9,6,5,6,6,7,6,9,1,7\nD,4,8,5,6,4,7,7,7,7,7,6,4,3,8,3,7\nH,2,3,4,2,2,7,8,3,5,10,7,8,3,8,2,7\nV,2,6,4,4,1,6,8,4,2,8,13,8,3,10,0,8\nH,8,10,8,5,5,8,6,3,5,10,6,7,7,11,5,8\nF,4,9,6,7,4,5,11,4,6,11,10,5,2,10,3,5\nN,5,10,6,7,5,7,9,6,4,6,6,6,6,9,2,7\nQ,3,3,4,4,3,8,7,6,3,8,6,9,2,9,3,7\nL,3,7,4,5,3,9,3,1,6,9,2,9,1,6,2,9\nO,6,10,7,8,5,8,6,8,6,9,4,8,3,8,3,8\nD,2,3,3,2,2,7,7,5,5,10,5,6,3,8,3,9\nD,5,10,7,7,4,9,7,6,8,11,5,5,3,8,4,8\nF,5,7,7,5,3,6,10,3,6,13,7,4,2,10,2,7\nW,9,10,9,8,9,6,10,3,3,8,7,6,11,11,4,5\nQ,1,0,2,1,1,8,7,6,4,6,6,9,2,8,3,8\nE,5,11,5,8,3,3,8,6,12,7,6,15,0,8,7,6\nS,4,8,5,6,4,6,8,3,6,10,7,8,2,8,5,6\nW,13,13,12,7,5,1,9,5,3,12,13,9,8,9,0,7\nU,3,3,4,1,1,5,8,5,6,10,9,9,3,10,2,6\nD,2,7,4,5,4,7,7,6,5,7,6,4,3,8,2,7\nP,3,8,4,6,3,5,10,5,5,10,8,4,1,10,4,7\nA,2,4,4,3,1,12,3,3,2,10,1,9,1,6,2,9\nC,3,5,4,3,2,5,8,5,7,12,9,11,1,10,2,7\nY,2,2,4,3,2,8,10,1,7,5,11,8,1,11,2,8\nL,1,3,2,2,1,6,5,2,8,7,3,9,0,7,2,8\nI,3,8,4,6,2,7,7,0,8,14,6,8,0,8,1,7\nA,2,5,4,3,2,8,2,2,2,8,2,8,2,6,2,7\nF,5,7,7,5,3,9,8,2,7,14,4,4,2,9,3,9\nR,6,10,8,8,6,10,7,3,6,10,2,6,5,6,5,10\nG,3,5,4,4,2,6,6,6,5,9,7,11,2,9,4,10\nY,2,1,3,1,0,7,10,3,1,7,13,8,1,11,0,8\nN,5,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8\nR,3,6,5,4,4,6,8,5,5,6,4,8,3,6,4,8\nC,2,1,3,2,1,6,7,6,10,7,6,14,0,8,4,9\nY,7,11,8,8,6,5,8,1,8,8,9,5,5,11,7,4\nI,2,7,3,5,2,7,9,0,6,13,6,7,0,8,1,7\nF,6,11,8,8,6,9,8,2,6,12,4,6,5,8,4,9\nN,5,9,7,7,4,5,9,3,4,10,8,8,5,8,1,7\nF,7,12,6,6,3,7,8,2,7,11,6,6,2,9,6,5\nQ,6,6,7,9,4,8,6,8,7,5,7,8,3,8,5,9\nV,3,4,4,3,2,5,12,3,3,9,11,7,2,11,1,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nI,4,11,5,8,4,7,7,0,7,13,6,8,0,8,1,8\nK,4,9,5,7,2,3,6,8,3,7,7,12,4,8,3,10\nZ,3,11,4,8,2,7,7,4,14,10,6,8,0,8,8,8\nM,3,3,4,2,2,7,6,6,4,6,7,8,6,5,2,8\nD,6,11,8,8,7,7,7,7,8,7,6,5,6,8,4,7\nQ,4,5,5,7,3,8,6,9,7,6,6,9,3,8,4,8\nV,5,11,7,8,4,4,11,3,4,10,12,9,2,10,1,8\nL,3,7,4,5,2,4,3,5,8,1,1,5,0,7,1,5\nQ,3,3,4,4,3,8,8,6,2,5,7,9,3,9,5,9\nK,5,9,8,7,5,2,8,2,7,11,12,12,3,8,3,5\nW,8,9,10,8,13,6,8,6,5,6,6,8,10,10,9,9\nT,5,7,5,5,3,6,11,3,7,11,9,5,2,11,2,4\nJ,3,9,4,7,2,10,6,1,9,12,3,6,0,7,1,7\nL,3,9,3,7,1,0,1,5,6,0,0,7,0,8,0,8\nJ,2,10,3,7,1,13,3,8,4,14,3,11,1,6,0,8\nO,3,6,5,4,5,8,7,5,1,7,6,8,8,8,4,9\nP,2,1,3,2,2,5,10,4,4,9,7,4,1,9,3,7\nR,5,9,8,7,5,10,7,3,7,10,3,6,3,6,4,11\nF,4,9,4,6,2,1,13,5,4,12,11,7,0,8,2,6\nB,4,9,6,6,6,6,8,7,4,7,5,6,4,8,5,6\nJ,4,11,5,8,3,10,6,1,7,13,3,7,0,7,0,8\nF,3,5,3,3,2,5,12,5,6,11,9,3,1,10,3,5\nD,4,9,6,6,9,9,9,4,5,7,6,6,5,8,9,5\nM,4,2,5,4,4,9,6,6,4,6,7,6,8,6,2,6\nE,3,7,4,5,4,8,7,4,7,7,6,8,6,9,5,10\nH,4,7,6,5,4,9,7,7,6,7,6,5,3,8,3,6\nY,5,7,8,10,11,8,9,4,2,6,8,9,4,11,8,8\nS,3,7,4,5,4,8,7,7,5,7,6,8,2,8,8,8\nM,2,3,4,2,3,7,6,3,4,9,7,8,6,5,1,8\nC,4,6,5,5,4,6,7,4,4,7,6,11,4,9,8,10\nD,3,5,5,4,3,9,6,4,6,10,4,5,2,8,3,8\nB,3,5,4,4,3,7,7,5,5,6,6,6,2,8,6,10\nX,2,2,3,3,2,8,7,3,8,6,6,8,2,8,6,8\nA,2,2,4,3,2,7,2,2,2,6,2,8,2,7,3,7\nX,4,5,8,4,3,8,7,1,9,11,4,7,3,8,3,8\nN,3,4,5,3,2,9,8,3,5,10,3,5,4,9,1,7\nG,3,5,5,4,3,6,6,6,6,6,6,9,2,8,4,8\nX,2,5,4,4,2,7,7,3,9,6,6,8,2,8,6,8\nY,2,4,4,6,4,8,11,3,3,5,8,9,2,11,5,6\nP,5,11,7,8,4,7,10,5,5,12,5,3,1,10,3,8\nF,3,5,5,6,5,7,9,4,4,7,6,7,4,9,8,9\nX,3,9,6,7,5,7,6,2,6,7,5,8,3,6,7,7\nC,2,1,2,2,1,6,7,6,10,7,6,14,0,8,4,9\nJ,5,10,6,8,5,8,5,8,5,8,7,8,2,7,4,6\nU,4,9,6,8,7,7,6,5,4,6,6,8,4,8,1,7\nE,4,7,5,5,5,8,4,6,3,8,7,10,4,9,7,9\nU,7,11,6,6,3,7,6,4,6,4,8,6,6,7,3,6\nU,2,3,3,2,1,5,8,5,6,9,8,8,3,9,2,6\nQ,3,3,4,4,3,8,7,6,2,5,7,9,3,8,5,9\nH,2,3,3,1,2,8,8,3,5,10,6,8,3,8,2,8\nZ,2,5,5,3,2,7,8,2,9,12,6,8,1,9,5,7\nF,2,3,3,1,1,5,10,4,5,10,9,5,1,9,3,6\nW,7,8,7,6,4,2,10,3,4,11,11,9,7,10,1,7\nJ,2,6,3,4,2,8,6,3,6,12,5,9,1,6,1,6\nB,5,9,7,6,6,10,6,2,6,11,3,8,3,8,4,12\nG,2,3,3,2,1,7,7,5,6,10,6,10,2,9,3,9\nZ,4,9,5,7,2,7,7,4,15,9,6,8,0,8,8,8\nC,4,9,5,7,3,4,7,5,6,11,9,14,2,8,3,8\nO,4,9,5,7,2,8,7,9,8,7,6,9,3,8,4,8\nO,4,8,5,6,3,7,7,8,5,10,7,8,3,8,3,8\nE,6,9,8,7,5,7,7,3,9,11,8,9,2,8,4,8\nG,3,2,4,3,2,7,6,6,6,6,6,10,2,9,4,9\nI,1,3,1,2,0,7,7,2,6,7,6,8,0,8,2,8\nP,4,7,6,5,4,8,9,4,4,12,4,3,1,10,3,8\nS,2,3,2,2,1,8,8,6,5,7,6,7,2,8,8,8\nF,4,7,6,5,4,8,8,2,6,13,5,5,2,10,3,9\nU,5,5,6,4,3,4,8,5,8,11,11,9,3,9,2,7\nO,5,9,5,7,5,7,7,8,4,9,6,8,3,8,3,8\nS,7,11,7,6,3,8,5,4,4,13,7,9,3,9,3,8\nC,2,7,3,5,1,6,8,7,8,5,6,13,1,7,4,9\nS,2,3,3,2,1,8,7,2,7,10,6,8,1,8,4,8\nT,4,6,6,8,2,6,15,1,6,9,11,7,0,8,0,8\nB,7,10,9,8,9,7,7,5,5,7,5,7,4,8,7,8\nQ,5,7,6,9,6,9,7,7,2,5,7,10,3,8,6,10\nV,5,10,5,7,3,2,11,3,3,11,11,8,2,11,1,8\nF,7,10,6,5,3,7,8,2,7,11,6,6,2,9,5,6\nV,7,13,5,7,3,7,10,6,4,9,9,4,5,12,3,8\nW,4,9,6,6,6,11,10,2,3,5,9,7,7,10,1,8\nE,3,4,4,6,2,3,8,6,10,7,6,14,0,8,7,7\nG,2,3,3,1,1,7,7,5,5,9,7,9,2,8,4,10\nL,2,5,3,4,2,4,4,4,8,2,1,7,0,7,1,6\nQ,4,5,5,5,4,7,4,5,5,7,5,9,4,5,6,7\nE,3,8,4,6,4,8,7,5,9,6,4,8,2,8,6,9\nU,1,0,1,0,0,7,6,10,4,7,12,8,3,10,0,8\nR,4,7,6,5,6,7,7,3,4,7,6,8,6,9,6,5\nP,4,7,5,5,4,5,11,8,3,10,7,3,2,11,3,7\nP,3,5,5,8,7,8,11,5,0,8,6,6,4,10,6,8\nK,3,2,4,3,3,5,7,4,7,7,6,10,6,8,4,9\nN,4,5,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nF,6,9,5,4,2,8,8,3,6,12,5,6,2,8,6,7\nA,2,1,3,1,1,7,4,2,0,7,2,8,2,6,2,8\nA,3,2,5,3,2,8,2,2,2,7,2,8,2,7,2,7\nJ,3,11,4,8,2,10,6,1,8,11,3,6,0,7,1,7\nL,2,3,3,2,1,7,4,2,7,8,2,10,0,7,2,8\nG,4,7,6,5,3,6,6,6,7,6,6,10,3,7,4,8\nO,6,9,6,7,6,8,6,7,4,9,4,8,3,9,3,7\nO,3,9,4,6,3,7,7,9,5,7,6,8,3,8,3,7\nE,3,2,3,3,3,7,7,5,7,7,5,9,2,8,5,10\nK,3,2,4,4,3,5,7,4,8,7,6,11,3,8,5,9\nF,3,8,3,5,1,0,13,4,4,12,11,7,0,8,2,6\nE,5,9,7,7,7,6,7,3,6,8,7,11,5,10,9,9\nG,6,10,8,7,9,8,8,5,3,6,6,7,9,8,9,14\nU,3,4,4,3,2,4,8,5,6,11,10,9,3,9,1,6\nK,6,9,8,7,7,5,6,4,7,6,6,12,5,7,7,10\nU,6,10,8,7,4,4,9,7,8,9,11,10,3,9,1,8\nM,5,9,5,6,6,7,6,10,1,7,8,8,8,4,0,8\nO,6,11,9,8,6,8,8,9,5,7,7,5,5,8,4,9\nS,5,10,6,7,3,8,7,5,9,5,6,8,1,8,9,8\nX,3,5,5,3,2,8,7,1,8,10,5,7,3,8,3,7\nJ,2,4,4,3,1,9,5,3,5,13,6,11,1,7,0,7\nV,6,8,6,6,4,4,11,1,3,8,10,8,4,11,1,7\nE,4,8,6,6,6,7,7,3,6,7,7,10,4,9,8,8\nI,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nL,2,3,2,1,1,4,4,4,6,2,2,5,1,7,1,6\nD,4,6,5,4,4,10,6,3,6,11,4,6,3,8,2,9\nY,2,4,3,3,1,3,11,3,5,12,10,6,1,10,1,6\nA,1,1,2,1,0,7,4,3,0,7,1,8,2,7,1,8\nK,6,10,8,8,6,6,7,1,7,10,6,10,3,8,4,8\nX,3,5,5,3,2,7,7,1,8,10,6,8,2,8,3,8\nY,2,3,2,2,1,3,11,3,5,11,10,5,1,11,1,6\nU,3,7,4,5,1,7,5,13,5,7,14,8,3,9,0,8\nA,2,4,4,6,2,7,3,3,3,7,1,8,3,6,3,8\nJ,1,1,2,3,1,9,6,3,5,12,5,10,1,6,2,6\nX,3,4,6,3,3,7,7,1,9,10,7,8,3,8,3,7\nY,7,9,6,13,5,5,7,5,3,7,11,6,4,10,5,6\nZ,5,9,6,7,3,7,7,4,15,9,6,8,0,8,8,8\nU,7,11,9,8,7,6,9,4,7,5,8,10,6,10,1,8\nT,5,10,6,8,5,6,10,1,8,11,9,6,1,10,3,4\nI,3,9,6,7,6,10,5,2,4,9,5,5,3,8,5,7\nA,2,7,4,5,2,11,2,3,3,10,2,9,2,6,3,8\nM,3,1,4,3,3,8,6,6,4,7,7,8,7,5,1,7\nC,2,1,3,2,1,6,8,7,7,8,7,13,1,10,4,10\nK,4,10,5,8,6,6,6,3,7,6,5,8,7,8,5,9\nJ,1,2,2,4,1,10,6,2,5,12,4,9,1,6,1,7\nV,2,6,3,4,1,7,9,4,2,7,13,8,3,10,0,8\nB,4,7,5,5,5,8,6,6,7,6,6,6,2,8,7,10\nX,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8\nJ,2,2,3,4,1,10,6,2,7,12,3,8,1,6,1,7\nU,3,7,3,5,2,8,6,12,4,7,11,8,3,9,0,8\nE,2,1,3,2,2,7,7,5,7,7,5,9,2,8,5,10\nM,4,7,7,5,5,4,8,3,4,10,10,10,5,9,2,6\nM,3,4,5,3,3,7,6,3,4,9,7,8,7,5,1,8\nF,6,10,8,7,5,6,10,1,6,13,7,5,1,10,2,7\nV,5,7,5,5,3,4,12,1,2,8,10,7,3,12,1,8\nQ,3,4,4,5,3,8,7,6,2,8,7,10,3,8,5,8\nK,4,2,5,3,3,5,7,4,7,7,6,11,3,8,5,9\nB,2,1,2,1,1,7,7,7,5,6,5,7,1,8,7,10\nT,7,11,7,9,5,5,12,4,7,12,9,4,2,12,2,4\nA,3,7,4,5,3,8,3,2,2,7,2,8,2,6,3,7\nI,1,6,0,8,1,7,7,4,4,7,6,8,0,8,0,8\nC,1,0,1,1,0,6,7,6,7,7,6,13,0,8,4,10\nV,2,7,4,5,3,9,12,2,3,4,10,9,2,11,1,9\nY,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nZ,2,7,3,5,3,7,7,5,9,7,6,8,1,8,7,8\nQ,2,3,3,4,3,8,8,5,2,5,8,10,2,9,5,9\nB,6,9,5,4,3,10,6,6,5,11,4,9,5,7,6,11\nX,2,4,4,3,2,7,7,3,9,6,6,8,2,8,5,8\nD,2,0,2,1,1,5,7,8,6,6,6,6,2,8,3,8\nW,7,11,8,6,5,4,7,2,3,8,10,8,10,10,2,5\nX,4,6,6,4,5,8,6,3,5,6,7,8,3,9,8,9\nV,3,2,5,4,2,7,12,2,3,7,11,9,2,10,1,8\nZ,6,10,8,7,6,10,6,5,4,8,5,7,3,7,11,7\nV,4,7,6,6,7,7,8,5,5,7,6,8,6,9,7,8\nW,6,5,7,4,4,4,11,2,2,9,9,8,7,11,1,6\nB,4,3,4,5,3,6,7,8,6,7,6,6,2,8,9,10\nN,4,7,6,5,5,6,8,3,4,8,7,7,5,9,5,3\nM,3,6,6,4,6,9,4,2,2,8,4,8,8,6,2,7\nF,5,11,6,8,5,5,11,7,6,11,10,5,2,9,2,5\nC,2,4,3,2,1,5,9,4,7,11,9,11,1,9,2,7\nG,5,10,5,7,4,6,6,6,5,10,7,13,2,9,5,9\nC,5,10,6,8,3,5,8,6,8,12,9,12,2,9,3,7\nZ,4,8,5,6,3,6,8,6,11,7,7,10,1,9,8,8\nA,4,5,6,4,4,8,7,3,5,7,8,8,5,9,3,6\nZ,3,7,4,5,2,7,8,3,12,8,6,8,0,8,7,7\nS,5,5,6,8,3,8,8,6,9,5,6,7,0,8,9,7\nL,2,4,2,3,1,4,4,4,8,2,1,6,0,7,1,6\nT,6,11,5,6,2,4,11,3,7,13,8,6,2,7,3,3\nD,8,15,7,8,5,6,7,4,7,9,6,7,5,9,7,5\nG,4,6,5,4,3,6,8,6,6,10,8,9,2,8,4,9\nJ,0,0,1,0,0,12,4,4,3,12,4,10,0,7,0,8\nZ,1,3,2,2,1,7,7,5,8,6,6,8,1,8,7,8\nZ,3,8,4,6,4,6,8,3,8,7,6,9,0,8,9,7\nI,5,9,4,4,2,9,7,6,4,13,5,8,3,8,5,10\nC,3,5,4,3,2,4,8,5,7,11,9,12,1,9,3,7\nB,6,11,6,8,5,6,6,9,7,6,6,7,2,8,10,10\nN,1,0,1,1,0,7,7,9,0,6,6,8,4,8,0,8\nV,3,10,5,7,2,6,8,4,3,8,14,8,3,10,0,8\nJ,5,8,6,6,2,8,4,5,6,15,7,13,1,6,1,6\nD,2,5,4,4,3,9,6,4,6,10,4,6,2,8,3,8\nE,6,9,8,7,7,8,8,6,3,6,6,10,4,8,8,9\nJ,1,2,2,3,1,10,6,2,6,12,4,9,0,7,1,8\nT,2,7,4,4,1,8,14,1,6,6,11,8,0,8,0,8\nV,4,10,6,8,2,8,8,4,3,6,14,8,3,9,0,8\nF,3,9,3,6,1,1,12,5,5,11,10,8,0,8,3,6\nF,8,14,7,8,3,6,9,2,7,10,7,6,2,10,5,6\nC,5,9,5,7,3,4,10,7,8,13,10,8,2,10,2,6\nW,4,11,6,8,4,11,8,5,2,6,9,8,8,10,0,8\nU,10,14,9,8,4,6,4,4,6,4,7,6,6,5,2,7\nJ,2,2,4,4,2,10,6,2,7,12,4,9,1,6,1,7\nI,1,3,2,2,1,7,8,1,7,13,6,8,0,8,1,7\nW,4,8,6,6,6,8,10,2,3,6,8,8,7,11,1,8\nB,2,3,2,2,2,7,8,5,5,7,6,6,2,8,5,9\nY,2,2,4,3,1,7,11,1,7,7,11,8,1,11,2,8\nN,6,10,8,8,6,7,8,6,6,6,6,4,9,10,5,6\nZ,2,7,3,5,2,7,7,3,11,8,6,8,0,8,7,8\nS,4,6,4,8,3,8,8,6,9,4,5,5,0,8,9,7\nS,2,6,3,4,3,8,8,7,5,7,5,8,2,8,8,8\nF,6,13,6,7,3,7,9,2,6,12,5,5,2,9,6,6\nY,3,3,5,4,1,5,10,3,2,8,13,8,2,11,0,8\nJ,0,0,1,1,0,12,4,5,3,12,4,11,0,7,0,8\nZ,4,7,5,5,4,9,8,5,3,7,5,7,3,7,8,4\nW,6,8,6,6,7,7,10,4,3,9,6,6,8,10,4,5\nC,5,7,6,5,6,6,6,4,3,7,6,11,5,9,3,9\nW,5,11,8,8,8,4,12,2,2,8,9,9,7,13,2,8\nT,1,3,2,2,1,6,11,3,4,8,10,7,2,11,0,7\nR,3,8,4,6,4,6,10,7,4,7,4,9,2,6,5,11\nE,3,7,3,5,2,3,7,6,9,7,6,14,0,8,7,8\nB,4,9,6,6,6,8,7,5,6,9,6,6,3,9,7,8\nZ,2,2,3,3,2,7,7,5,9,6,6,8,2,8,7,8\nS,5,8,7,6,9,7,10,3,4,8,7,7,3,7,8,2\nR,3,6,4,4,3,9,7,3,5,10,4,7,3,7,3,10\nM,3,4,4,3,3,7,6,6,4,6,7,9,6,5,2,8\nE,2,3,3,1,1,5,8,2,7,11,8,10,1,8,3,7\nB,2,4,2,3,2,7,7,5,5,6,6,6,2,8,5,9\nH,3,5,4,3,3,9,7,6,6,7,6,8,3,8,3,7\nW,6,11,9,8,8,7,9,5,1,7,9,8,9,10,1,7\nJ,6,7,4,10,3,6,10,3,3,13,6,5,3,8,7,9\nJ,4,10,6,7,5,6,8,3,4,8,7,7,6,7,4,7\nN,6,8,9,6,4,8,8,2,6,10,5,6,6,9,1,7\nF,2,6,2,4,1,1,11,4,7,12,12,9,0,8,2,6\nD,3,8,5,6,4,10,6,3,6,11,4,7,3,8,2,9\nX,3,3,4,4,1,7,7,6,2,7,6,8,3,8,4,8\nW,7,9,7,5,3,3,9,3,2,8,11,9,10,11,0,7\nR,4,10,6,8,6,7,7,6,6,7,6,7,3,7,5,9\nW,7,11,10,8,15,10,8,5,3,7,7,8,13,10,4,6\nD,3,5,5,4,3,9,6,4,7,10,4,6,2,8,3,8\nV,3,4,4,3,1,5,12,2,3,9,11,7,2,11,1,8\nK,6,10,8,8,7,5,5,4,8,6,7,12,4,8,7,9\nO,10,15,7,8,4,7,7,5,5,8,4,7,5,9,6,8\nL,4,8,5,6,3,7,4,1,7,8,2,10,1,6,3,8\nO,4,7,5,5,3,7,8,8,5,7,7,9,3,8,3,8\nU,3,7,3,5,2,7,5,13,5,7,13,8,3,9,0,8\nO,5,6,6,6,5,6,7,6,7,9,7,9,4,7,5,5\nJ,2,6,3,4,1,8,7,3,6,15,5,9,1,6,1,7\nN,3,3,4,5,2,7,7,14,2,5,6,8,6,8,0,8\nO,7,11,5,6,3,6,7,5,4,7,4,7,5,8,5,7\nN,6,11,9,8,6,11,8,2,5,10,2,4,7,10,2,8\nP,2,7,3,4,1,4,11,8,3,10,6,4,1,10,3,8\nJ,3,8,4,6,2,9,6,2,8,15,4,8,0,7,0,7\nA,1,1,3,2,1,7,2,2,1,7,2,8,1,6,2,7\nC,7,10,8,8,4,6,8,7,8,13,8,9,2,11,3,7\nX,4,10,5,7,2,7,7,4,4,7,6,8,3,8,4,8\nL,2,5,3,3,2,5,4,5,6,2,2,5,1,6,1,6\nI,1,8,1,6,1,7,7,0,8,7,6,8,0,8,3,8\nE,3,6,4,4,2,4,6,6,10,7,7,13,0,8,8,8\nM,7,9,10,6,6,10,5,2,6,9,4,7,8,6,2,8\nX,4,5,8,4,3,7,7,1,9,10,6,8,2,8,3,7\nD,5,9,6,7,5,10,6,4,7,10,3,5,3,8,3,9\nD,6,12,6,6,5,9,6,3,6,10,4,7,5,8,8,7\nJ,5,11,4,8,3,8,8,2,4,12,4,5,2,9,7,8\nT,2,1,3,1,0,7,15,2,4,7,10,8,0,8,0,8\nU,3,7,3,5,1,7,5,13,5,7,13,8,3,9,0,8\nK,3,7,5,5,5,6,6,3,4,7,6,9,5,7,7,9\nK,5,11,5,8,2,4,7,9,2,7,4,11,4,8,2,11\nP,2,4,4,3,2,7,9,3,4,12,5,4,1,9,3,8\nB,5,8,6,6,5,9,8,7,8,7,5,5,2,8,8,9\nK,5,8,6,6,6,5,6,4,6,6,6,10,3,8,5,10\nE,2,2,3,3,2,7,7,5,7,7,6,9,2,8,5,10\nA,3,8,5,6,3,12,3,2,2,9,2,9,3,7,3,9\nY,4,10,6,8,6,9,5,6,4,7,8,8,6,8,8,3\nS,6,10,7,8,4,7,7,4,8,11,7,8,2,9,5,8\nH,2,2,3,3,3,6,7,5,6,7,6,8,3,8,3,8\nY,3,4,4,2,2,4,10,2,7,10,10,5,2,11,3,4\nZ,3,8,5,6,3,8,7,2,9,11,6,8,2,8,6,8\nE,4,11,5,8,5,3,7,5,9,7,7,14,0,8,6,9\nZ,7,10,5,14,5,7,10,3,3,11,7,7,3,8,14,7\nM,6,5,7,8,4,8,7,13,2,6,9,8,9,6,0,8\nP,1,1,2,1,1,5,11,7,1,10,6,4,1,9,3,8\nT,6,8,6,6,3,4,14,5,7,12,9,3,1,11,2,4\nW,5,4,6,3,3,4,11,3,2,9,9,7,7,11,1,6\nU,5,10,6,8,3,7,4,15,6,7,14,8,3,9,0,8\nU,7,11,6,6,3,7,6,5,6,3,9,7,5,8,3,6\nE,4,10,6,7,6,7,7,5,3,7,6,8,5,8,8,9\nW,7,7,7,5,6,5,10,3,3,9,7,7,8,10,3,5\nK,3,8,4,6,3,4,8,7,3,6,4,11,3,8,2,11\nZ,5,7,7,5,3,7,7,2,10,12,7,7,1,7,6,7\nI,4,7,6,8,6,9,8,5,5,7,5,7,3,8,9,8\nW,8,10,8,8,8,6,11,3,3,9,7,7,10,12,4,5\nP,6,7,8,10,10,8,7,4,3,7,7,7,7,12,6,6\nF,3,6,6,4,3,6,10,2,6,13,6,4,1,10,2,7\nF,4,4,4,6,2,1,14,5,3,12,9,5,0,8,2,6\nR,5,8,8,7,9,8,7,4,4,8,5,7,7,8,6,5\nH,4,8,5,6,5,8,7,5,6,7,6,6,3,8,3,7\nQ,4,8,5,9,6,8,8,5,2,7,9,10,3,9,5,7\nW,10,15,11,8,7,7,8,2,4,6,9,6,11,9,3,5\nJ,1,6,2,4,1,14,2,6,5,13,2,10,0,7,0,8\nR,6,11,6,8,4,5,12,9,3,7,3,9,3,7,6,11\nD,4,8,6,6,4,6,7,8,7,6,5,4,3,8,4,9\nC,7,10,7,7,4,4,10,7,8,12,10,8,2,10,3,7\nA,3,9,5,6,2,6,6,3,1,6,0,8,2,7,1,7\nZ,3,9,4,6,2,7,7,4,14,10,6,8,0,8,8,8\nB,3,6,3,4,3,6,6,8,6,6,6,7,2,8,8,9\nO,2,3,3,2,2,7,7,6,4,9,6,8,2,8,2,8\nB,1,0,2,1,1,7,7,7,5,6,6,7,1,8,7,9\nK,4,7,6,6,5,8,6,2,3,8,4,8,4,6,7,11\nS,3,5,3,4,3,8,6,7,5,7,7,9,2,10,9,8\nD,2,3,3,2,2,9,6,3,5,10,4,7,2,8,2,8\nV,5,9,6,7,4,7,11,3,2,6,11,8,3,10,3,9\nF,3,5,4,8,2,0,12,4,6,12,12,9,0,8,2,6\nO,4,9,5,6,3,9,7,9,8,7,5,10,3,8,4,8\nP,5,10,8,8,5,7,10,5,5,12,5,3,1,10,4,8\nK,3,4,6,3,3,6,7,2,7,10,7,10,3,8,3,7\nM,4,9,6,6,7,7,8,6,4,7,6,8,6,9,7,6\nW,8,9,8,6,6,2,12,2,2,10,10,8,7,11,1,7\nH,3,7,4,4,2,7,8,14,1,7,5,8,3,8,0,8\nL,3,6,5,4,3,6,4,1,8,8,2,11,0,7,2,8\nN,2,4,4,3,2,7,8,3,4,10,6,7,5,9,0,7\nT,4,8,4,6,3,6,12,4,6,11,9,4,2,12,2,4\nL,2,7,3,5,2,8,4,3,7,7,2,8,1,6,2,8\nW,7,11,10,8,4,9,7,5,2,6,8,8,9,9,0,8\nE,6,10,9,7,7,6,8,1,8,11,6,9,3,8,4,8\nH,2,4,4,3,2,7,7,3,5,10,6,8,3,8,2,8\nJ,5,11,7,8,4,10,6,1,7,14,3,7,0,7,1,8\nW,5,8,5,6,5,5,10,3,3,9,7,7,7,11,2,5\nT,2,8,3,5,1,7,14,0,6,7,11,8,0,8,0,8\nU,6,11,8,8,8,8,6,8,5,7,6,9,6,8,5,6\nE,5,10,3,5,2,7,8,5,6,10,6,9,1,9,7,8\nG,2,5,3,4,2,6,7,5,5,9,7,10,2,9,4,10\nS,1,3,2,2,1,8,8,6,5,7,6,7,2,8,8,8\nU,8,15,7,9,4,4,4,5,5,4,7,8,5,9,2,8\nK,6,10,6,5,3,7,7,3,6,10,8,9,6,11,3,7\nA,2,3,3,2,1,10,2,2,1,9,2,9,1,6,2,8\nF,5,11,7,8,6,6,10,2,5,13,7,5,2,10,2,7\nO,5,9,6,7,4,7,7,9,5,7,7,8,3,8,3,8\nM,4,8,6,6,7,7,8,6,4,6,6,8,6,9,7,10\nZ,7,10,7,6,4,8,6,2,8,12,6,9,3,8,6,7\nH,5,9,5,6,2,7,8,15,0,7,5,8,3,8,0,8\nQ,9,14,8,8,4,9,3,4,7,11,3,10,3,8,8,11\nS,2,4,3,3,1,9,6,2,7,10,5,8,1,9,5,9\nB,4,8,4,6,5,6,8,8,6,7,5,7,2,8,7,9\nW,4,6,5,4,4,7,6,7,2,7,7,8,6,8,5,10\nX,7,10,10,8,5,5,8,2,9,11,10,9,3,8,4,6\nF,3,8,3,5,1,1,13,5,4,12,10,7,0,8,2,6\nG,5,10,6,7,8,8,8,5,2,6,6,8,7,8,7,13\nM,7,13,8,8,5,6,3,3,2,8,4,10,7,2,2,8\nQ,4,7,4,9,4,8,8,6,2,8,8,10,3,9,6,7\nK,4,8,6,6,4,3,8,2,7,10,11,12,3,8,3,5\nM,6,10,7,5,4,9,3,2,2,9,4,9,8,2,2,9\nS,4,9,5,6,3,8,8,5,9,5,6,7,0,8,9,7\nZ,6,9,8,7,5,8,6,2,9,12,5,10,3,7,7,9\nM,3,3,4,2,2,9,6,6,4,6,7,6,6,5,2,6\nG,3,8,5,6,2,7,7,8,7,6,6,8,2,7,6,11\nM,5,6,8,4,5,9,6,3,5,9,5,7,8,6,2,8\nM,5,8,6,6,5,8,5,11,0,7,9,8,9,6,2,7\nL,3,9,4,7,3,6,4,1,8,8,2,10,0,7,2,8\nP,5,7,7,5,3,6,14,5,2,12,4,1,0,10,3,8\nY,4,8,6,12,11,9,7,4,2,6,7,9,5,11,8,9\nH,3,6,4,4,5,7,8,4,2,7,6,7,7,9,7,8\nX,4,11,6,8,6,8,8,2,6,7,7,8,5,11,8,8\nO,3,7,4,5,3,7,7,8,5,6,5,6,3,8,3,8\nG,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,9\nA,1,0,2,0,0,7,4,2,0,7,2,8,2,7,1,8\nW,4,9,7,6,5,8,10,2,3,6,9,8,7,11,1,8\nO,2,3,3,2,2,7,8,6,4,9,6,8,2,8,2,8\nD,5,9,6,7,5,9,7,4,7,11,5,6,3,7,4,8\nH,6,9,9,7,7,7,7,3,6,10,7,8,3,8,3,8\nW,5,9,6,4,3,5,8,2,3,7,9,8,9,11,2,6\nH,3,5,4,7,2,7,8,15,1,7,5,8,3,8,0,8\nY,3,4,5,5,1,6,10,3,2,9,13,8,1,11,0,8\nK,4,7,4,5,2,4,7,8,1,7,6,11,3,8,2,11\nE,6,11,6,8,4,3,7,6,11,7,6,14,0,8,8,7\nS,5,11,6,8,4,8,7,5,9,11,3,7,2,6,5,9\nS,3,6,4,4,3,5,9,2,6,10,7,7,2,7,4,4\nH,5,8,7,6,6,8,6,7,7,7,7,7,3,8,3,8\nT,3,5,4,4,3,6,8,3,7,8,7,8,3,9,7,6\nJ,1,6,2,4,0,13,3,7,4,13,3,11,0,7,0,8\nY,6,8,6,6,3,4,10,3,7,11,11,6,1,11,3,4\nN,2,1,3,2,1,7,7,13,1,5,6,8,5,8,0,8\nP,2,3,2,2,1,5,11,5,3,10,7,3,0,9,3,6\nW,3,1,4,3,3,10,11,3,2,5,9,7,6,11,0,8\nZ,6,11,8,8,5,9,6,3,10,12,4,8,2,7,6,8\nX,3,1,4,2,2,7,7,4,9,6,6,8,3,8,6,8\nI,3,7,4,5,2,9,6,0,7,13,5,8,0,8,1,8\nT,4,5,5,3,2,5,11,2,9,12,9,5,0,10,2,4\nI,1,3,2,2,1,7,7,1,7,13,6,8,0,8,1,8\nA,1,0,2,0,0,8,4,2,0,7,2,8,2,7,1,8\nD,6,10,6,6,3,11,3,4,5,12,2,8,5,7,4,10\nM,3,3,5,2,2,8,6,2,4,9,6,8,7,6,2,8\nN,4,4,4,6,2,7,7,14,2,4,6,8,6,8,0,8\nF,4,8,6,6,7,9,7,1,5,9,5,5,3,10,4,6\nT,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8\nT,4,10,5,8,5,7,11,3,7,7,11,8,2,12,1,8\nX,3,7,4,5,2,8,7,4,4,7,6,8,2,8,4,8\nR,3,9,4,6,3,5,11,8,4,7,3,9,3,7,6,11\nZ,2,3,4,2,2,7,8,2,9,12,7,7,1,8,5,7\nZ,4,9,5,7,4,7,8,3,13,8,6,8,0,8,8,7\nG,2,2,3,3,2,7,6,6,6,7,6,10,2,9,4,9\nA,1,3,2,1,1,6,2,1,1,6,2,8,1,6,1,7\nC,1,0,1,1,0,6,7,6,8,7,6,14,0,8,4,10\nE,6,9,8,8,9,7,7,5,4,8,7,9,8,11,10,12\nR,5,9,7,7,4,10,7,3,7,10,2,7,3,6,4,11\nS,5,11,6,8,3,8,8,6,10,5,6,6,0,8,9,7\nV,3,7,4,5,3,6,12,2,2,8,10,8,4,11,4,8\nZ,1,0,2,0,0,7,7,3,10,8,6,8,0,8,6,8\nY,8,13,6,8,4,6,8,4,4,10,7,5,3,10,4,4\nG,4,6,4,4,2,6,7,6,7,10,7,10,2,10,4,9\nI,1,3,2,2,0,7,7,1,7,13,6,8,0,8,0,7\nT,2,3,3,2,1,5,12,3,6,11,9,4,1,10,2,5\nY,6,10,6,8,4,4,9,1,8,10,10,6,1,10,3,4\nY,1,1,2,1,0,8,10,3,1,6,12,8,1,11,0,8\nV,6,9,8,8,9,7,8,6,5,7,6,7,6,11,8,11\nL,5,9,5,4,3,7,5,3,5,12,7,11,3,8,6,8\nH,5,7,7,5,5,7,7,3,6,10,5,8,3,8,3,8\nW,4,7,6,5,3,4,8,5,1,7,9,8,8,10,0,8\nV,4,8,6,6,3,9,12,3,3,4,11,9,3,9,2,8\nC,2,4,3,3,1,6,8,7,7,8,8,13,1,10,4,10\nO,2,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8\nA,4,5,6,8,2,8,4,3,2,7,1,8,3,7,2,8\nG,4,2,5,4,3,6,6,6,6,6,6,10,2,9,4,8\nD,3,3,4,2,2,7,7,6,6,7,6,5,5,8,3,7\nE,4,11,4,8,3,3,6,6,12,7,7,15,0,8,7,7\nO,4,4,6,7,3,8,6,9,8,7,5,9,3,8,4,8\nJ,3,10,4,8,3,14,3,5,4,13,2,9,0,7,0,8\nI,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nG,1,0,1,0,0,8,6,5,4,6,6,9,1,8,5,10\nT,5,6,5,4,3,4,12,2,7,12,10,5,1,10,1,5\nI,2,10,3,8,4,7,7,0,7,7,6,8,0,8,3,8\nR,2,4,3,2,2,8,8,4,5,9,5,7,2,7,4,10\nY,3,5,4,4,2,4,10,2,7,11,10,6,1,11,3,5\nO,4,6,4,4,3,8,6,7,4,9,5,8,3,8,3,8\nP,6,10,8,8,6,7,10,4,4,13,6,3,1,10,3,8\nY,6,7,8,10,10,9,9,4,2,4,8,9,5,13,10,10\nW,4,3,5,2,3,4,11,3,2,9,9,7,6,11,1,7\nG,2,5,3,4,2,6,7,6,6,10,7,11,2,9,4,9\nW,5,7,5,5,4,3,11,2,2,10,9,7,5,11,1,7\nX,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nT,2,10,4,7,1,8,14,0,6,6,11,8,0,8,0,8\nG,2,5,4,3,2,7,7,6,6,6,6,10,2,8,4,9\nD,10,15,10,8,5,8,6,5,6,10,3,6,6,6,6,10\nK,5,10,7,8,8,6,8,5,3,7,5,8,4,7,7,11\nN,9,13,7,7,3,5,9,4,6,3,4,11,6,10,2,7\nJ,1,3,2,2,1,10,6,2,6,12,4,8,0,7,1,7\nE,3,8,3,6,2,3,7,6,10,7,6,14,0,8,7,8\nC,7,14,5,8,4,6,9,4,4,9,8,9,4,9,9,10\nO,5,10,7,8,3,7,6,9,8,6,5,6,3,8,4,8\nE,4,11,5,8,7,7,7,5,7,7,5,9,6,8,6,10\nX,5,7,7,6,7,9,7,2,4,7,6,6,3,10,7,8\nC,9,15,7,8,5,8,7,5,3,8,8,10,4,9,8,13\nZ,4,6,6,4,3,7,8,2,8,11,7,8,1,9,5,7\nS,5,10,5,5,2,7,7,3,4,13,7,9,2,9,3,8\nO,1,0,1,0,0,8,7,6,4,7,6,8,2,8,2,8\nD,4,8,5,6,4,7,7,7,8,6,5,5,3,8,3,7\nQ,2,4,3,5,3,8,7,6,2,8,7,9,2,9,4,8\nK,6,9,9,7,4,3,8,4,8,12,12,12,3,8,4,5\nJ,2,8,3,6,2,9,6,2,7,12,3,8,1,6,1,6\nU,2,3,3,2,1,8,8,5,6,5,9,8,3,10,0,8\nV,4,9,6,6,4,8,10,2,1,6,10,8,3,10,3,9\nC,6,11,8,8,9,5,6,3,5,8,6,12,6,9,3,9\nK,4,11,6,8,6,5,6,4,7,6,6,11,3,8,6,9\nZ,6,10,8,8,5,8,7,2,10,12,5,8,1,7,6,7\nT,5,8,7,6,6,7,7,7,7,6,7,9,3,10,6,7\nS,5,10,6,8,3,8,8,6,9,5,6,7,0,8,9,8\nT,3,11,5,8,1,9,15,0,6,6,11,8,0,8,0,8\nV,5,7,5,5,3,3,12,2,2,9,10,8,3,10,1,7\nT,4,8,5,6,3,7,12,3,7,7,11,8,2,12,1,7\nR,4,7,5,5,5,7,7,4,6,6,5,7,3,7,4,8\nP,4,8,5,6,4,7,10,4,4,12,5,3,1,10,2,8\nR,4,9,5,7,4,10,7,3,7,10,2,7,3,6,3,11\nI,2,11,2,8,4,7,7,0,7,7,6,8,0,8,2,8\nY,2,3,3,1,1,8,11,1,6,5,11,9,1,11,1,8\nG,4,9,5,6,3,6,7,7,6,9,7,12,2,8,4,10\nH,8,11,11,8,9,9,6,3,7,10,4,8,6,8,5,8\nT,4,5,5,3,2,7,12,3,7,7,11,8,2,11,1,8\nE,4,8,5,6,4,5,8,3,7,11,8,9,3,8,4,7\nP,3,5,5,3,2,7,10,5,3,11,5,3,1,10,2,8\nQ,2,2,3,4,2,8,7,6,2,6,6,9,2,9,5,9\nT,3,1,4,3,2,7,12,3,6,7,11,8,2,11,1,7\nD,8,15,7,8,5,6,7,4,7,8,5,7,6,9,7,5\nM,7,12,8,7,4,12,2,4,3,12,1,8,6,3,1,9\nK,6,11,9,8,5,3,7,3,8,11,11,12,3,8,4,6\nL,5,9,7,7,5,6,4,2,7,7,2,9,1,6,3,8\nG,3,6,4,4,3,6,7,6,5,9,7,10,2,9,4,10\nU,5,6,6,4,2,4,8,5,8,11,11,9,3,9,1,6\nD,4,6,4,4,3,6,7,8,7,8,8,7,2,9,3,8\nQ,2,3,3,3,2,8,8,5,2,5,7,10,2,9,5,9\nV,2,6,4,4,2,8,11,2,3,5,11,9,2,10,0,8\nK,2,1,2,2,1,5,7,8,1,7,6,11,3,8,2,11\nP,4,9,6,6,4,5,13,5,3,13,6,2,0,10,2,8\nH,6,8,8,6,5,10,6,4,6,10,2,7,4,7,4,9\nS,4,4,4,6,2,8,9,6,9,5,5,5,0,7,9,8\nS,7,15,7,8,4,5,9,3,5,13,8,7,3,7,4,7\nH,2,1,3,1,2,7,8,6,6,7,6,10,3,8,3,9\nC,3,6,4,4,2,6,7,6,7,7,6,12,1,8,4,10\nH,1,0,1,0,0,7,8,10,2,7,5,8,2,8,0,8\nY,2,3,3,1,1,4,11,2,6,11,10,5,0,10,1,5\nA,5,9,7,7,5,8,2,2,2,6,2,7,3,8,4,7\nT,6,9,8,7,6,6,7,7,7,7,7,8,4,10,6,9\nQ,2,4,3,5,3,8,7,6,2,8,7,9,2,9,4,8\nH,4,5,5,6,5,8,4,3,2,7,4,7,3,7,5,8\nI,4,8,5,6,3,9,5,2,7,7,7,5,0,9,4,7\nX,3,5,5,4,2,7,8,1,8,10,8,8,2,8,3,7\nV,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8\nW,4,6,6,4,4,10,11,3,2,4,9,7,7,11,0,7\nK,4,6,6,4,3,7,6,2,7,10,5,10,4,7,4,9\nU,2,0,2,1,0,8,5,11,4,6,13,8,3,10,0,8\nU,3,5,4,4,2,6,8,6,7,7,9,9,3,9,1,8\nD,4,11,6,8,5,7,7,8,7,7,6,4,3,8,3,7\nA,2,2,4,3,2,7,2,1,2,6,2,8,2,6,2,7\nV,4,7,5,5,2,6,11,3,4,8,12,8,2,10,1,9\nA,5,10,8,8,6,6,5,2,4,4,2,7,6,7,6,4\nK,9,12,10,7,5,7,7,3,6,10,9,9,6,12,4,8\nU,3,2,4,3,2,7,8,6,8,8,10,8,3,9,1,8\nU,7,12,6,6,3,9,6,6,6,3,9,8,5,10,3,6\nV,4,9,6,7,3,8,11,3,4,5,11,8,3,10,1,8\nT,2,7,4,4,1,10,15,1,5,4,11,9,0,8,0,8\nT,3,2,4,3,2,6,12,3,7,8,11,7,2,11,1,7\nW,5,9,7,7,4,6,8,5,2,7,8,8,9,10,0,8\nV,4,6,4,4,2,2,11,4,3,12,11,8,2,11,1,7\nD,4,7,5,5,4,7,7,7,8,6,5,5,3,8,3,7\nH,3,6,5,4,4,6,7,7,6,7,6,11,3,8,3,10\nD,5,11,6,8,6,7,7,5,6,6,5,8,7,9,3,7\nM,7,9,11,7,6,9,6,2,5,9,5,7,11,7,2,8\nU,6,9,8,7,6,6,7,8,6,6,6,11,6,8,8,3\nX,4,7,6,5,3,7,8,1,8,10,7,8,2,8,3,7\nK,4,6,6,4,3,3,8,3,7,11,11,11,3,8,3,6\nB,3,8,5,6,4,8,7,7,6,7,6,5,2,8,8,9\nO,4,9,5,6,4,7,7,8,6,7,6,8,2,8,3,8\nT,2,5,3,4,2,7,12,3,6,7,11,8,2,11,1,8\nE,6,10,8,8,8,7,7,5,3,7,6,9,5,8,9,8\nW,12,12,11,7,5,6,11,2,3,7,11,7,9,12,1,7\nG,5,10,5,8,4,5,6,6,5,9,8,12,2,9,4,10\nA,2,0,3,1,0,8,4,2,0,7,2,8,2,6,1,8\nR,4,4,4,6,2,5,9,9,4,7,5,8,3,8,5,10\nM,4,8,4,6,5,8,5,10,0,6,9,8,7,5,1,6\nD,5,10,6,8,3,5,7,10,10,7,7,6,3,8,4,8\nU,2,3,3,2,1,5,8,5,6,10,9,8,3,9,1,6\nQ,4,7,6,10,8,8,11,4,2,5,8,11,5,14,8,13\nR,4,8,6,6,5,10,7,2,6,11,2,7,4,7,3,10\nW,2,1,3,2,2,10,11,3,2,5,9,7,5,11,0,8\nA,6,13,6,8,4,12,2,5,1,12,2,10,3,2,3,10\nT,5,6,5,4,3,6,11,3,6,11,9,5,2,12,2,4\nC,5,7,5,5,3,5,7,5,7,11,9,14,2,9,3,8\nP,4,7,5,5,3,5,13,5,4,13,6,2,0,9,2,7\nZ,5,9,5,4,3,6,8,2,8,11,8,9,3,9,5,5\nW,2,1,2,2,1,7,8,4,0,7,8,8,6,10,0,8\nP,4,9,4,6,4,3,14,6,1,12,7,3,0,9,3,8\nF,1,3,2,2,1,5,10,3,5,10,9,6,1,10,2,7\nO,4,10,6,8,4,7,6,9,5,7,4,8,3,8,3,8\nZ,2,4,4,6,2,11,4,3,4,10,3,9,2,7,5,9\nM,3,6,6,4,7,6,5,3,1,6,5,8,7,7,2,8\nF,5,11,5,8,2,0,13,5,4,13,11,7,0,8,2,5\nI,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nA,3,7,5,5,3,12,3,2,2,9,1,8,2,6,2,7\nI,2,5,1,4,1,7,7,1,7,7,6,8,0,8,3,8\nC,4,8,5,6,2,5,8,6,8,11,9,13,1,9,3,7\nG,2,1,3,2,2,7,7,5,5,6,6,10,2,9,3,9\nE,1,0,1,0,1,5,7,5,7,7,6,12,0,8,6,9\nV,4,9,5,7,3,7,10,3,2,6,11,8,2,10,3,9\nB,1,1,2,2,1,7,7,7,5,6,6,7,2,8,7,9\nI,1,5,2,4,1,7,7,0,7,13,6,8,0,8,1,8\nI,6,8,7,10,7,8,9,4,5,7,7,8,3,7,9,7\nZ,3,9,4,6,2,7,7,4,14,10,6,8,0,8,8,8\nR,6,11,5,6,3,9,7,6,4,10,3,8,6,6,5,10\nV,6,10,8,8,5,7,11,3,2,5,10,9,3,11,4,8\nW,4,3,6,5,3,7,8,4,1,7,8,8,8,9,0,8\nK,4,6,6,4,3,9,6,2,6,10,3,8,4,8,4,11\nE,5,9,7,8,8,5,6,3,3,7,6,8,5,11,10,9\nI,4,11,5,8,3,7,7,0,8,14,6,8,0,8,1,8\nL,4,10,6,7,8,8,8,3,5,5,7,10,6,12,8,8\nG,5,8,7,7,8,8,9,5,3,7,6,8,7,11,8,9\nS,4,8,5,6,3,8,7,3,7,10,4,7,2,8,5,9\nN,3,5,5,3,2,8,8,2,5,10,4,6,5,8,1,7\nM,5,9,7,4,4,6,4,3,2,8,4,10,7,3,1,8\nT,1,1,2,1,0,8,14,1,5,6,10,8,0,8,0,8\nY,3,2,5,3,2,7,10,1,7,7,11,8,1,11,2,8\nS,2,4,3,2,1,8,8,2,7,10,5,7,1,8,4,8\nM,2,3,4,2,2,7,7,3,3,9,8,8,5,5,1,7\nT,2,7,4,5,2,7,13,0,5,7,10,8,0,8,0,8\nY,2,1,4,2,1,7,11,1,7,7,11,8,1,11,2,8\nE,4,9,4,6,3,3,7,6,11,7,6,15,0,8,7,7\nT,6,9,7,8,7,5,8,4,8,8,8,9,3,9,8,7\nV,6,8,5,6,3,4,12,1,2,8,10,7,4,12,1,8\nG,2,0,2,1,1,8,6,6,6,6,5,9,1,7,5,10\nE,3,2,4,3,3,7,7,5,7,7,5,9,2,8,6,10\nM,7,9,10,6,8,5,7,3,5,9,9,9,10,6,3,8\nF,4,8,4,6,3,1,13,4,3,12,10,6,0,8,2,6\nG,5,10,6,8,5,6,6,6,5,9,7,12,3,8,5,9\nR,4,5,5,7,3,5,12,8,4,7,2,9,3,7,6,11\nW,4,4,5,3,3,5,11,4,2,9,8,7,6,12,2,6\nK,6,10,8,7,6,6,7,1,6,10,7,10,3,8,4,8\nT,5,9,6,7,4,6,9,0,8,10,9,5,0,9,3,4\nY,1,1,2,1,0,7,10,2,2,7,12,8,1,11,0,8\nP,6,10,7,8,7,6,8,6,4,8,7,8,5,9,7,10\nS,2,2,2,3,2,8,7,7,5,7,6,8,2,9,8,8\nW,4,4,5,3,3,6,11,3,2,7,9,8,7,11,1,8\nX,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nR,4,3,4,4,2,6,13,8,3,7,2,9,2,6,5,10\nF,3,2,3,4,2,5,11,3,6,11,9,5,1,10,3,6\nD,1,0,2,1,0,6,7,7,6,6,6,6,2,8,3,8\nC,2,7,3,5,1,5,7,7,8,7,6,14,1,8,4,9\nF,3,4,3,5,1,1,12,5,5,11,10,8,0,8,3,6\nX,4,7,5,6,5,7,8,2,5,7,6,7,3,5,7,8\nT,2,5,3,3,2,8,12,3,6,6,11,8,2,11,1,8\nK,3,6,3,4,1,3,7,7,3,7,6,11,4,8,2,11\nZ,5,6,4,8,3,11,4,3,5,11,4,8,3,9,7,10\nG,6,9,7,7,8,9,6,5,3,7,6,10,8,8,6,10\nI,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nT,5,6,5,4,2,4,14,5,5,12,9,3,1,11,1,5\nL,4,11,5,8,3,6,4,0,9,8,2,11,0,7,2,8\nM,6,5,9,4,8,9,7,5,5,7,6,8,12,9,6,4\nS,9,15,7,8,4,9,5,5,6,9,2,9,4,5,5,9\nG,3,4,5,6,2,7,5,7,8,6,5,11,1,8,6,11\nX,9,15,8,8,4,9,7,2,9,9,5,7,4,11,4,9\nY,2,1,4,2,1,8,11,1,7,5,11,9,1,11,2,8\nX,6,9,7,8,8,8,6,1,6,7,6,8,4,13,9,8\nI,4,9,4,4,2,8,8,2,5,13,5,5,2,9,4,9\nD,2,5,3,3,2,7,7,6,6,7,6,4,2,8,3,7\nA,3,7,5,5,3,11,2,3,2,10,2,9,2,6,3,9\nS,4,9,5,6,3,8,7,5,9,5,6,7,0,8,9,8\nN,4,9,4,6,4,8,7,12,1,6,6,7,6,8,0,9\nX,3,6,6,4,4,9,8,3,6,7,7,7,5,11,6,7\nP,4,8,6,11,10,7,9,6,0,8,6,6,7,12,8,9\nB,3,6,4,4,3,11,6,3,6,11,3,7,2,8,4,11\nV,3,4,5,6,1,8,8,4,3,7,14,8,3,9,0,8\nG,4,8,5,6,2,7,7,7,8,6,6,8,2,7,6,11\nR,5,9,7,7,7,8,6,7,3,8,6,7,6,6,7,10\nD,4,7,5,5,4,7,7,5,5,7,6,8,4,9,3,7\nJ,2,4,4,3,1,8,6,3,6,14,6,10,0,7,0,7\nH,2,2,3,3,2,6,7,6,6,7,6,10,3,8,3,9\nK,6,9,9,6,6,2,8,3,8,11,12,12,4,7,4,4\nD,3,4,4,3,2,7,7,6,7,7,6,5,2,8,3,7\nB,3,5,4,7,3,6,7,9,6,7,6,7,2,8,9,10\nC,2,7,3,5,2,5,8,7,8,6,7,14,1,8,4,9\nD,4,7,4,5,4,6,7,9,7,6,5,6,2,8,3,8\nA,2,6,4,4,2,7,4,2,0,6,2,8,2,6,1,7\nO,5,9,6,6,5,7,8,8,4,7,8,8,3,7,3,8\nE,3,7,4,5,3,6,7,6,8,7,7,10,2,8,6,9\nW,5,5,7,4,4,7,11,3,2,6,9,8,8,11,0,8\nR,2,4,3,3,2,7,8,5,4,6,5,6,2,7,4,8\nV,3,5,5,4,2,9,12,3,3,5,11,9,3,10,2,9\nP,2,3,3,2,1,6,10,5,4,9,7,3,1,10,4,6\nX,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8\nH,3,3,4,4,2,7,9,14,1,7,3,8,3,8,0,8\nJ,3,6,4,4,2,6,7,3,5,14,7,11,1,6,1,7\nB,8,15,6,8,4,9,6,6,5,10,4,9,6,6,7,11\nX,2,3,3,2,1,7,7,3,9,6,6,8,2,8,5,7\nV,8,10,7,7,4,3,12,3,3,10,11,8,3,10,1,7\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nN,3,6,3,4,3,8,8,12,1,6,6,8,5,8,0,8\nS,8,13,8,7,4,8,6,4,3,13,8,9,3,10,3,9\nA,3,11,6,8,2,6,6,3,1,6,0,8,2,7,2,7\nD,9,14,8,8,6,8,5,4,7,10,5,7,6,9,7,8\nU,2,0,2,1,1,8,5,11,4,6,13,8,3,10,0,8\nF,1,3,3,2,1,5,10,2,5,13,7,5,1,9,1,8\nK,3,4,5,3,2,5,7,2,7,10,9,11,3,8,3,7\nG,3,7,4,5,5,8,7,4,1,7,6,9,6,8,6,12\nQ,3,7,4,6,2,9,8,8,5,5,8,9,3,8,5,9\nP,5,10,6,7,3,4,12,9,2,10,6,4,1,10,4,8\nS,7,15,6,9,3,7,4,4,4,7,2,7,3,6,6,8\nI,3,10,5,8,3,8,6,0,7,13,6,9,1,7,2,8\nY,3,5,4,4,2,4,11,2,7,11,10,6,1,11,2,5\nP,7,11,10,8,7,7,11,5,4,12,5,2,1,10,3,8\nJ,1,4,3,3,1,9,4,4,5,14,5,12,0,7,0,8\nE,4,5,5,4,4,6,8,4,4,8,7,10,4,10,8,11\nE,4,10,6,8,7,8,7,4,7,7,7,7,6,8,5,10\nH,3,5,6,4,4,9,6,3,6,10,4,7,3,8,3,8\nM,7,8,10,7,11,8,8,4,3,6,6,7,12,7,6,7\nU,2,0,2,1,0,7,5,11,4,7,13,8,3,10,0,8\nB,1,3,2,2,2,7,7,5,5,7,6,6,2,8,4,10\nX,3,6,5,4,4,7,8,3,5,6,6,9,3,8,8,8\nW,7,9,7,6,5,4,10,3,3,9,8,7,7,11,2,6\nR,5,5,7,4,6,7,7,4,3,8,5,8,7,8,6,6\nV,4,4,5,3,2,4,12,3,3,9,11,7,2,10,1,8\nG,3,3,4,2,2,7,7,5,6,6,6,9,2,9,4,8\nK,4,4,4,6,2,3,6,8,2,7,6,11,4,8,2,11\nE,2,4,3,3,2,7,7,5,7,7,7,9,2,8,5,9\nZ,6,8,8,10,7,11,6,4,5,9,3,7,3,6,6,8\nR,4,6,6,6,7,6,8,3,3,7,5,9,6,8,5,8\nQ,4,7,5,9,6,8,6,8,3,5,6,9,3,8,6,9\nP,2,6,3,4,3,5,10,7,2,9,6,5,1,10,2,8\nG,5,9,6,7,4,6,7,6,6,10,7,10,2,9,5,9\nE,3,6,3,4,3,3,7,5,9,7,6,13,0,8,6,9\nQ,4,8,6,10,7,8,8,8,2,5,6,11,3,8,6,10\nY,5,7,7,9,8,10,10,5,3,6,7,8,6,11,8,5\nX,4,10,5,7,1,7,7,5,4,7,6,8,3,8,4,8\nB,1,1,1,1,1,7,7,7,5,6,6,7,1,8,6,9\nE,3,5,6,3,3,7,8,2,9,12,6,8,2,8,5,8\nL,3,4,4,7,1,0,1,6,6,0,0,6,0,8,0,8\nD,4,10,4,7,5,6,7,9,7,5,3,7,2,7,3,8\nE,5,10,7,8,6,9,7,2,7,11,5,8,3,8,5,10\nV,2,6,4,4,2,7,12,3,3,6,11,8,2,10,1,8\nW,6,5,7,4,4,3,11,2,3,10,10,8,7,11,1,7\nU,4,7,5,5,4,5,8,5,6,9,7,9,3,9,3,6\nG,5,11,7,9,3,7,6,8,9,6,6,10,1,8,6,11\nY,2,2,4,3,1,6,10,1,6,8,11,9,1,11,2,8\nM,3,1,4,2,3,7,6,6,5,6,7,7,8,6,2,7\nM,6,10,9,8,7,7,7,2,5,9,8,8,9,7,3,8\nU,2,1,3,2,1,8,8,6,6,5,9,8,3,9,1,8\nE,4,9,6,6,6,7,7,4,7,7,6,9,6,8,5,10\nA,4,5,6,4,4,9,8,3,4,6,7,7,4,9,4,6\nP,3,9,4,6,3,4,11,8,2,10,6,4,1,10,3,8\nS,5,9,6,7,4,7,8,3,7,10,4,6,2,6,5,8\nU,4,6,5,4,2,4,8,5,6,10,9,9,3,9,2,7\nA,3,8,5,5,2,8,6,3,1,7,0,8,2,7,1,8\nX,3,4,4,3,2,7,7,3,9,6,6,9,2,8,5,8\nR,4,9,5,6,5,9,7,3,6,9,3,8,3,6,4,11\nW,3,6,5,4,2,4,8,5,1,7,8,8,8,10,0,8\nF,2,3,3,2,1,5,12,3,4,13,7,3,1,9,1,7\nJ,1,3,3,2,1,8,6,3,6,14,5,10,0,7,0,7\nJ,1,1,2,2,1,10,7,2,5,11,4,8,0,7,0,7\nA,2,7,4,4,1,8,4,3,1,7,1,8,2,6,2,8\nR,3,6,5,4,3,9,8,4,6,10,2,7,3,6,4,11\nX,4,8,5,6,3,7,7,4,9,6,6,8,3,8,6,8\nK,4,8,5,6,4,5,6,4,8,7,7,12,3,8,6,9\nE,4,8,6,6,4,6,8,4,8,11,9,9,2,9,5,6\nH,4,5,6,8,6,7,5,5,2,6,4,6,5,7,8,8\nU,8,10,9,8,6,3,8,5,8,9,8,10,6,9,4,3\nX,5,9,8,7,4,8,8,1,9,10,5,7,3,8,4,8\nT,6,9,8,8,9,7,8,4,9,7,6,8,3,8,8,6\nQ,4,5,5,6,5,8,5,6,3,9,6,11,3,8,5,8\nA,3,7,5,5,3,11,2,3,3,10,2,10,2,6,2,8\nZ,4,10,5,8,4,6,8,6,10,7,7,10,1,9,8,8\nV,9,13,7,7,3,7,11,5,6,8,10,5,4,11,4,7\nC,3,4,4,3,2,4,9,5,8,11,9,11,1,9,3,7\nD,3,9,5,7,5,8,7,6,6,7,6,4,3,8,3,7\nZ,3,8,4,6,3,7,8,3,11,8,6,8,0,8,7,7\nB,7,13,6,8,4,9,5,5,5,11,4,9,6,7,7,11\nY,5,11,7,8,7,8,7,5,4,7,7,8,6,8,8,3\nC,6,10,6,8,3,3,8,5,8,10,10,14,1,7,3,7\nD,8,14,8,8,5,9,5,4,6,10,3,7,5,6,5,10\nE,5,9,7,7,5,9,6,2,8,11,5,9,3,7,5,10\nB,3,9,5,6,5,8,7,6,6,7,6,5,2,8,6,9\nR,4,9,5,7,3,6,10,9,4,7,5,8,3,8,6,11\nO,1,3,2,1,1,8,7,6,4,9,6,8,2,8,2,8\nJ,4,11,5,8,4,6,7,3,5,15,7,11,1,6,1,6\nP,3,7,5,5,4,5,10,4,4,10,8,4,1,10,3,7\nR,2,1,3,2,1,6,10,8,2,7,5,8,2,7,5,10\nI,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nL,4,8,6,6,3,10,3,2,8,9,2,9,1,6,3,9\nE,3,2,3,4,3,7,7,6,7,7,6,9,2,8,6,9\nY,1,3,2,1,1,8,10,1,6,5,10,8,1,11,1,8\nA,9,15,7,8,4,8,2,3,2,8,4,12,5,5,5,7\nQ,3,6,5,4,4,8,5,7,4,7,7,6,3,6,5,8\nZ,1,3,2,2,1,7,7,4,7,6,6,8,1,8,6,8\nB,5,10,7,8,7,7,7,8,7,6,6,6,2,8,8,10\nF,1,0,1,0,0,3,12,4,2,11,8,6,0,8,2,7\nC,6,12,4,6,2,6,9,6,8,11,8,10,2,8,5,9\nM,3,6,5,4,4,11,5,3,4,9,3,6,7,6,2,8\nW,3,3,4,1,2,5,11,3,2,9,9,7,6,11,1,6\nO,5,10,6,8,6,8,6,7,3,10,4,8,4,9,4,6\nN,5,8,5,6,4,8,7,13,1,6,6,7,6,8,1,10\nB,4,7,6,6,7,8,8,5,5,7,6,8,7,7,9,6\nQ,5,8,6,12,10,8,10,4,2,6,8,10,7,14,9,14\nU,5,10,5,7,2,7,4,15,6,7,13,8,3,9,0,8\nY,5,8,6,6,3,4,10,2,8,11,11,6,1,11,3,4\nE,4,10,5,8,7,7,8,3,6,5,7,10,5,10,10,11\nC,5,10,6,8,2,6,7,7,10,6,6,15,1,8,4,9\nI,2,7,3,5,1,7,8,0,7,13,6,7,0,8,1,7\nU,5,8,6,6,3,4,8,6,8,9,8,9,4,10,4,3\nN,4,5,5,4,3,7,9,5,5,7,7,6,5,9,2,5\nE,5,8,7,6,4,4,9,3,9,11,9,10,2,8,4,5\nS,2,3,3,2,1,7,7,2,7,10,5,8,1,8,4,8\nK,1,0,1,0,0,4,6,5,1,7,6,10,2,7,1,10\nU,5,9,5,6,2,7,4,14,5,7,14,8,3,9,0,8\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nS,4,6,6,4,6,8,6,4,4,8,6,9,4,8,10,8\nL,2,6,3,4,1,3,3,6,8,1,1,5,0,7,1,6\nE,1,3,3,2,1,6,7,2,7,11,7,9,1,8,3,8\nZ,5,9,6,7,5,8,7,5,10,7,6,8,1,8,7,8\nL,4,4,4,6,1,0,0,6,6,0,1,5,0,8,0,8\nX,4,4,5,6,1,7,7,4,4,7,6,8,3,8,4,8\nQ,7,12,7,6,4,9,4,4,8,11,4,10,3,7,8,11\nJ,2,9,2,6,2,14,4,4,4,12,1,8,0,7,0,8\nR,2,3,3,1,2,8,8,3,5,9,5,7,2,7,4,10\nA,2,3,3,2,1,10,2,2,1,8,2,9,2,5,1,9\nC,4,8,5,6,2,5,9,7,8,12,9,11,1,10,3,7\nH,8,10,11,7,7,6,7,3,7,10,8,9,3,8,3,7\nQ,2,2,3,3,2,7,9,4,2,7,8,10,2,9,4,8\nY,5,9,5,6,3,3,10,2,6,10,12,7,2,11,2,5\nZ,1,0,2,1,0,7,7,3,11,8,6,8,0,8,6,8\nO,3,4,4,2,2,7,7,7,5,7,5,8,2,8,3,8\nV,6,9,6,7,4,3,12,2,3,9,11,8,2,10,1,8\nW,4,10,7,8,6,7,8,4,1,7,9,8,7,11,0,8\nY,3,5,6,7,1,6,11,2,3,9,12,8,1,10,0,8\nI,3,8,4,6,2,7,7,0,9,14,6,8,0,8,1,8\nG,3,4,4,3,2,6,6,6,6,9,7,11,2,9,4,10\nL,2,3,2,2,1,4,5,2,7,3,2,8,0,7,1,6\nE,7,12,5,6,4,8,7,4,4,10,5,9,3,9,8,11\nY,3,8,6,6,3,7,9,1,6,6,11,8,2,11,2,8\nU,8,11,8,8,4,3,9,5,7,11,12,10,3,9,2,6\nH,4,9,6,6,5,6,7,7,6,7,6,11,3,8,3,9\nG,3,2,4,4,3,6,6,6,6,6,6,10,2,9,4,9\nS,4,4,5,6,2,7,6,5,9,5,6,10,0,8,9,8\nY,4,6,6,8,6,10,11,5,4,6,7,7,5,10,7,4\nZ,6,10,8,8,5,7,8,2,9,12,7,6,2,8,6,7\nB,5,11,7,8,7,9,6,3,7,10,4,8,3,7,5,10\nM,4,6,5,4,2,8,7,12,1,7,9,8,8,6,0,8\nN,4,7,6,5,3,9,7,3,4,10,5,6,5,8,1,7\nY,3,4,5,5,1,8,12,2,3,6,12,8,1,10,0,8\nL,4,9,5,7,3,3,3,6,8,1,0,6,0,6,1,6\nR,4,10,5,8,3,5,9,10,5,7,5,8,3,8,6,10\nE,1,0,2,1,1,5,7,5,8,7,6,12,0,8,6,9\nA,6,9,6,4,3,11,0,4,1,11,5,13,4,5,4,11\nF,4,10,6,7,5,3,11,3,5,11,10,7,1,10,3,6\nK,8,11,11,8,9,8,6,1,6,9,5,9,7,7,6,9\nJ,3,10,4,8,5,10,6,2,4,9,4,6,3,7,6,8\nY,5,6,7,8,1,6,11,3,2,9,12,8,1,11,0,8\nL,4,8,6,6,4,7,4,1,8,8,2,9,1,6,3,8\nF,5,7,7,5,6,7,8,5,5,7,6,8,4,11,9,10\nO,5,10,7,8,5,8,7,9,4,7,6,7,3,8,4,9\nF,2,3,4,2,1,6,10,3,5,13,6,5,1,9,1,7\nB,1,0,1,1,1,7,7,7,4,6,6,7,1,8,6,9\nK,3,5,4,4,3,5,7,4,7,6,6,11,3,8,5,9\nM,6,9,8,8,11,7,7,4,4,6,6,8,11,8,5,5\nG,3,9,5,6,4,7,6,7,5,6,6,8,2,7,5,11\nO,4,6,4,4,3,6,8,7,4,10,8,8,3,8,2,7\nL,2,5,4,4,2,6,4,1,8,8,2,10,0,7,2,8\nA,3,6,4,4,3,8,2,2,2,7,2,8,2,6,2,7\nP,3,6,5,9,7,7,11,5,0,9,7,5,4,11,5,8\nF,2,4,4,3,1,5,11,3,4,13,7,4,1,9,1,7\nV,5,7,5,5,2,4,11,3,3,9,11,7,3,10,1,7\nA,4,9,6,7,6,8,7,8,4,7,6,8,3,8,8,4\nT,5,11,5,6,2,5,11,2,7,12,8,5,2,9,3,4\nB,5,10,6,7,6,9,6,3,5,7,6,7,7,8,6,9\nW,4,3,5,2,2,5,11,3,2,9,9,7,6,11,1,6\nG,1,3,2,2,1,7,6,5,4,7,6,10,2,9,3,9\nV,3,6,5,4,1,8,8,4,2,6,14,8,3,10,0,8\nU,6,10,5,5,3,7,5,4,5,3,8,6,5,7,2,7\nC,4,8,5,6,3,6,7,6,7,12,8,11,2,10,4,7\nH,3,3,5,2,2,8,6,3,6,10,5,8,3,8,3,8\nT,4,10,6,7,4,9,11,2,8,5,12,8,1,11,1,9\nE,5,7,7,5,4,10,6,2,8,11,4,8,2,8,5,12\nO,6,10,8,8,10,8,7,6,1,7,6,8,9,10,6,11\nR,4,8,6,6,7,5,6,4,4,7,6,9,8,11,7,6\nN,6,11,8,8,5,9,7,3,5,10,4,6,6,8,1,7\nN,11,14,9,8,4,10,12,6,4,4,6,10,5,11,2,5\nS,5,8,7,7,9,7,8,5,5,7,7,7,5,10,10,11\nF,3,8,4,6,2,1,14,5,3,12,9,5,0,8,2,6\nZ,3,7,4,5,3,7,7,3,12,8,6,8,0,8,7,8\nN,7,9,8,4,3,5,9,3,4,13,9,9,5,8,0,8\nJ,2,4,4,3,1,9,6,2,6,14,5,10,0,7,0,7\nW,3,4,4,3,2,7,11,2,2,6,9,8,6,11,0,8\nL,4,10,4,7,2,0,2,4,6,1,0,8,0,8,0,8\nC,9,14,7,8,5,8,6,5,3,9,8,10,6,8,9,10\nU,3,4,4,3,2,5,8,5,7,10,9,9,3,9,2,6\nK,6,10,8,8,8,6,9,6,5,8,5,8,4,7,6,11\nZ,4,11,6,8,7,8,7,3,9,7,6,7,1,7,11,9\nD,4,4,5,6,3,5,6,10,9,5,5,5,3,8,4,8\nH,5,10,7,8,7,6,8,5,5,7,6,7,6,7,6,11\nV,2,8,4,6,1,9,8,4,2,6,13,8,3,10,0,8\nR,4,4,5,6,3,6,9,10,5,6,5,8,3,8,5,10\nL,4,11,5,8,3,6,4,3,8,6,1,8,1,6,3,7\nA,3,6,5,4,3,8,2,2,2,7,2,8,2,6,2,7\nM,4,7,5,5,3,8,7,12,1,6,9,8,8,6,0,8\nH,3,3,4,2,2,7,7,6,6,7,6,8,3,8,3,8\nA,2,1,4,2,1,7,2,2,2,6,2,8,2,6,2,7\nR,4,8,6,6,4,9,7,3,6,10,3,6,3,7,4,10\nC,3,5,4,3,2,6,8,7,8,8,8,13,1,9,4,10\nB,3,2,4,4,4,7,7,5,5,6,6,6,2,8,6,9\nU,3,6,4,4,3,7,5,12,4,7,11,8,3,9,0,8\nD,6,7,8,6,6,6,7,5,6,4,3,6,3,8,5,6\nH,3,8,4,6,3,8,7,13,1,7,6,8,3,8,0,8\nU,3,3,4,2,2,5,8,5,7,10,9,9,3,9,2,6\nD,4,9,6,6,6,8,8,5,5,10,5,5,3,8,3,8\nL,3,6,3,4,2,0,2,4,5,1,1,7,0,8,0,8\nD,5,4,5,6,3,5,7,10,9,7,6,5,3,8,4,8\nG,7,9,9,8,10,7,8,6,3,7,7,8,9,11,8,9\nE,3,8,4,6,5,6,7,3,6,8,7,10,4,10,8,10\nB,6,10,9,7,7,11,6,2,7,11,3,8,4,7,5,12\nF,4,8,5,6,3,7,10,4,5,13,7,5,2,9,2,7\nU,4,4,5,3,2,4,8,5,8,10,9,9,3,9,2,5\nU,9,11,10,8,5,3,9,6,8,11,11,9,3,9,2,6\nI,4,11,5,8,3,8,6,0,7,13,6,9,2,7,3,8\nO,4,7,4,5,3,8,7,8,5,10,6,8,3,8,3,8\nD,4,6,6,4,4,8,8,5,5,9,5,4,4,8,4,8\nN,4,9,6,7,4,7,8,6,5,7,6,6,6,9,2,5\nE,2,6,4,4,3,7,8,6,8,6,5,9,2,8,6,9\nI,4,8,5,6,3,7,6,2,7,7,7,8,3,8,4,8\nN,4,8,6,6,5,7,6,8,5,6,4,7,3,7,3,8\nX,4,10,6,8,6,6,7,3,6,7,7,11,5,6,7,7\nF,8,11,7,6,5,7,12,3,4,12,6,3,4,10,8,5\nG,4,8,5,6,2,8,6,8,8,6,6,9,2,7,6,10\nY,10,9,8,13,5,8,9,3,3,6,11,5,4,10,7,7\nX,4,8,6,6,3,7,8,0,7,9,7,8,2,8,3,7\nQ,4,7,5,9,6,10,10,6,3,3,8,12,3,9,7,10\nT,2,3,3,4,1,8,14,0,6,6,11,8,0,8,0,8\nB,4,6,5,4,4,9,7,3,6,10,4,6,2,8,5,10\nB,2,1,2,2,2,7,8,8,5,7,6,7,2,8,8,9\nG,5,10,7,7,6,6,6,7,5,5,6,10,2,8,4,8\nV,3,7,5,5,1,8,8,4,2,7,14,8,3,10,0,8\nP,7,9,6,4,3,5,12,6,1,11,5,4,4,10,4,8\nY,4,4,6,6,6,10,9,5,3,7,7,7,5,10,6,5\nN,4,10,6,7,5,6,10,5,3,7,7,9,5,9,1,8\nV,3,8,5,6,6,6,6,5,2,8,8,9,5,9,5,8\nX,5,8,6,7,7,6,8,2,5,8,7,10,2,8,7,8\nA,4,6,7,8,2,8,6,3,1,7,0,8,3,7,2,8\nF,5,11,5,8,4,1,12,4,4,11,10,8,0,8,2,6\nS,4,10,5,8,4,8,7,8,6,8,5,7,2,8,9,8\nX,4,5,5,7,1,7,7,5,4,7,6,8,3,8,4,8\nU,3,8,5,6,8,7,6,4,3,7,7,8,7,10,5,6\nB,4,9,6,6,6,7,8,6,4,6,4,6,4,8,6,7\nK,8,15,8,8,6,6,7,2,6,10,4,9,5,5,3,7\nB,5,9,8,7,6,8,7,5,6,9,5,6,3,8,7,10\nV,3,3,4,2,1,5,13,4,3,10,11,6,2,10,1,8\nS,4,7,6,5,3,9,7,5,8,11,2,8,2,6,4,11\nM,5,7,8,5,6,3,7,4,5,11,11,10,5,8,2,6\nV,3,4,5,6,1,7,8,4,3,7,14,8,3,9,0,8\nQ,2,2,3,3,2,8,6,6,3,6,6,9,2,9,3,8\nB,5,9,8,8,9,7,8,5,4,8,6,8,7,9,8,8\nB,4,10,4,8,4,6,6,10,7,6,6,7,2,8,9,10\nF,3,4,5,3,2,6,10,2,6,13,6,5,1,9,3,7\nB,8,9,6,5,3,9,7,6,6,10,4,9,6,6,6,10\nN,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nB,1,3,2,2,2,8,6,2,4,10,5,7,2,8,2,9\nS,7,11,7,6,3,8,6,4,4,13,6,9,3,8,3,8\nU,8,10,8,8,5,4,8,5,9,11,10,9,3,9,2,6\nZ,2,4,4,3,1,7,8,2,9,11,7,7,1,8,5,6\nK,4,9,5,7,4,3,8,7,3,7,5,11,3,8,2,11\nE,3,3,4,5,2,3,7,6,11,7,6,15,0,8,7,7\nE,6,9,8,8,10,6,7,4,3,7,6,9,7,11,11,12\nP,5,10,7,8,4,10,7,4,6,12,3,4,2,9,3,9\nY,2,3,3,2,1,6,10,1,6,8,11,8,1,11,2,8\nO,6,10,8,8,5,8,10,9,6,8,8,5,5,8,5,9\nC,6,11,7,8,9,7,5,4,4,8,7,11,6,10,5,8\nG,4,10,5,7,4,5,6,6,5,9,8,11,2,9,4,10\nF,6,9,8,10,8,6,10,4,4,9,7,6,5,8,9,8\nA,4,11,7,8,5,12,2,2,2,10,2,9,2,6,3,8\nO,4,6,4,4,3,7,7,7,4,10,7,8,3,8,3,8\nQ,6,8,6,9,7,7,8,6,3,8,8,10,3,9,6,7\nQ,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8\nI,1,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8\nY,3,5,4,3,2,4,11,2,7,11,10,6,1,11,2,5\nN,5,7,7,5,3,7,7,3,4,10,7,8,5,8,0,7\nW,3,4,5,3,3,9,11,3,2,5,9,7,6,10,0,8\nI,6,10,5,6,3,7,10,3,4,12,5,4,2,9,6,9\nA,6,9,5,4,2,11,3,3,1,9,3,10,4,5,4,10\nC,4,9,6,7,4,7,8,8,6,6,6,9,4,7,4,8\nQ,6,12,5,7,4,12,4,4,5,11,2,7,4,9,5,13\nA,1,1,2,1,0,7,4,2,0,7,2,8,2,7,1,8\nU,4,8,5,6,3,4,9,5,6,9,8,9,3,9,2,6\nW,5,5,7,7,4,8,8,5,2,7,8,8,9,9,0,8\nK,2,3,3,2,2,6,7,1,6,10,7,10,3,8,1,8\nM,4,9,5,6,3,7,7,12,1,7,9,8,8,6,0,8\nX,2,4,4,3,2,10,6,2,8,10,3,7,2,8,3,9\nO,5,9,6,7,4,8,7,8,5,7,6,8,3,8,3,8\nW,3,4,4,3,3,4,10,4,2,9,8,7,5,11,1,6\nE,2,5,4,3,2,6,8,2,7,11,7,9,2,8,4,8\nE,2,3,2,2,1,7,7,5,7,7,5,8,2,8,5,10\nF,2,4,3,3,2,5,10,4,5,10,9,5,2,9,3,6\nY,3,8,5,5,1,6,12,2,3,9,12,7,1,10,0,8\nV,8,13,7,7,4,5,10,4,5,9,9,5,4,10,2,8\nJ,4,10,4,8,3,15,3,3,5,12,0,7,0,8,0,8\nX,3,3,5,2,2,5,9,2,7,10,9,8,2,8,3,6\nW,5,7,7,6,9,7,6,4,5,7,5,8,9,9,7,8\nX,2,3,3,2,2,7,7,3,9,6,6,8,2,8,6,8\nD,5,10,5,6,4,6,8,4,6,9,6,6,5,9,6,5\nG,5,11,6,8,5,6,6,6,6,10,6,13,3,8,4,9\nT,8,12,8,7,4,7,8,2,7,12,7,7,3,9,5,6\nB,2,3,3,1,2,7,7,4,5,7,6,6,1,8,5,9\nN,6,7,8,5,4,4,11,3,4,10,10,9,5,8,1,8\nQ,5,13,5,8,4,8,6,5,7,11,5,7,3,8,9,9\nA,6,10,5,5,3,12,5,4,1,8,3,9,5,6,4,10\nF,8,10,7,5,3,6,10,3,7,11,7,6,2,9,5,6\nI,2,8,3,6,2,7,7,0,6,12,6,8,0,8,1,8\nB,3,3,3,4,3,7,7,9,6,7,6,7,2,8,9,10\nQ,2,3,3,3,2,9,10,5,2,5,7,10,3,9,5,10\nJ,0,0,1,0,0,12,4,4,3,11,4,10,0,7,0,8\nY,9,14,8,8,5,4,9,4,3,11,10,6,4,11,4,5\nC,4,7,6,5,6,6,6,4,4,8,6,11,6,9,3,9\nX,5,10,8,8,7,8,6,3,6,7,4,6,6,7,9,6\nG,4,7,6,5,4,6,6,6,5,6,6,11,2,9,4,9\nK,4,9,5,6,2,3,7,8,2,7,6,11,4,8,2,11\nC,4,9,5,6,2,6,8,7,11,5,6,13,1,7,4,9\nF,1,0,2,0,0,3,12,5,2,11,8,6,0,8,2,7\nS,5,11,7,9,5,8,7,3,6,10,7,8,2,8,5,8\nR,5,9,5,4,4,9,7,4,6,10,2,6,5,5,5,6\nR,4,11,4,8,3,6,9,10,5,7,6,8,3,8,6,11\nK,3,9,4,6,2,3,7,7,2,7,6,11,3,8,3,10\nR,2,6,2,4,2,6,8,8,4,7,5,7,2,7,4,10\nE,4,8,6,6,6,8,10,6,4,6,6,9,4,6,7,9\nN,5,9,7,6,7,6,8,3,4,8,7,8,6,9,5,4\nZ,3,7,4,5,2,7,8,2,10,11,7,8,1,9,6,7\nD,4,5,5,4,4,7,7,7,7,7,6,5,2,8,3,7\nP,2,4,3,2,2,5,10,4,4,10,8,4,1,10,3,7\nE,5,6,6,6,6,7,8,5,4,8,7,10,6,10,8,11\nZ,4,9,5,7,2,7,7,4,15,9,6,8,0,8,8,8\nR,3,8,3,6,3,6,9,8,3,7,5,8,2,7,5,11\nX,2,1,2,1,0,7,7,4,4,7,6,8,2,8,4,8\nN,8,13,10,8,5,5,8,2,4,12,7,9,6,8,0,7\nS,3,10,4,8,2,9,8,6,10,5,5,5,0,7,9,7\nW,7,11,7,8,8,4,10,2,3,9,8,7,7,11,2,6\nQ,4,6,5,7,5,9,7,7,3,5,7,9,3,9,6,9\nY,2,2,3,3,1,7,10,1,7,7,11,8,1,11,2,8\nC,2,4,3,2,1,4,9,4,7,12,10,10,1,9,2,7\nJ,2,8,2,6,1,12,3,9,4,13,4,12,1,6,0,8\nM,3,7,4,5,5,8,7,6,3,7,5,8,5,9,5,8\nL,3,8,4,6,2,9,3,1,7,9,2,10,0,7,3,9\nU,5,10,5,7,2,8,5,13,5,6,15,8,3,9,0,8\nS,4,5,5,7,3,9,9,6,10,5,5,5,0,7,9,8\nA,3,5,5,8,2,7,6,3,1,6,0,8,2,7,1,8\nF,8,10,7,6,5,6,12,3,4,11,6,3,4,10,7,6\nG,6,10,8,8,5,6,6,7,8,6,5,10,2,9,4,8\nB,5,8,8,6,5,9,7,4,7,10,5,6,2,8,6,10\nU,6,10,6,8,4,4,8,5,7,10,9,9,3,9,2,6\nD,6,9,8,6,6,7,7,8,5,7,6,5,4,8,4,7\nF,8,13,7,7,4,5,9,2,7,11,7,6,2,10,5,5\nH,4,5,6,7,5,9,10,3,2,8,7,7,3,10,8,6\nP,6,9,8,7,4,9,9,3,6,14,4,3,1,10,3,9\nE,7,9,5,5,2,7,7,5,7,10,6,9,1,9,7,9\nH,3,6,4,4,2,7,7,14,1,7,6,8,3,8,0,8\nL,5,9,5,7,1,0,0,7,6,0,1,4,0,8,0,8\nX,4,8,6,6,4,9,6,1,8,10,3,7,3,9,4,9\nD,2,3,3,2,2,7,7,6,6,7,6,5,2,8,3,7\nZ,5,10,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nF,3,2,4,4,3,4,11,3,6,11,10,5,1,10,3,6\nX,7,11,6,6,3,5,8,3,9,10,9,10,4,7,4,5\nO,4,7,4,5,3,7,7,7,4,9,7,10,3,8,3,9\nT,6,11,5,6,2,5,10,3,7,13,7,5,2,8,4,4\nW,10,10,10,5,4,8,10,4,2,5,10,7,10,12,2,6\nG,1,0,2,0,1,8,7,5,5,6,6,9,1,8,5,10\nZ,3,10,4,8,5,6,8,5,9,7,7,9,2,9,7,8\nL,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8\nI,5,8,6,6,4,7,6,2,7,7,6,8,0,9,4,8\nQ,6,7,6,9,7,8,5,6,3,9,5,11,5,7,7,5\nM,8,10,8,6,5,4,9,5,4,4,3,12,9,10,2,8\nZ,1,4,2,3,2,8,7,5,8,6,6,7,2,8,7,8\nO,9,14,6,8,5,5,7,7,4,10,7,10,5,10,5,8\nB,4,6,5,4,4,7,9,5,6,10,6,6,2,8,6,8\nJ,1,3,2,2,1,10,6,2,6,12,4,8,0,7,1,7\nJ,2,7,3,5,1,14,2,6,5,14,2,11,0,7,0,8\nC,5,10,6,7,3,5,9,7,8,6,8,14,1,7,4,9\nU,3,7,3,5,3,7,7,11,4,7,11,8,3,9,0,8\nB,4,2,4,4,4,7,7,5,5,6,6,6,2,8,7,11\nN,6,11,9,8,5,3,10,3,4,10,11,10,5,7,1,7\nX,3,6,4,4,2,7,7,4,4,7,6,8,3,8,4,8\nJ,2,7,2,5,1,14,2,6,5,14,1,10,0,7,0,8\nN,4,4,5,6,2,7,7,14,2,4,6,8,6,8,0,8\nF,3,7,4,5,3,8,9,2,6,13,5,5,1,10,2,9\nR,4,10,5,7,3,5,11,8,4,7,4,8,3,7,7,11\nW,1,0,2,1,1,7,8,4,0,7,8,8,5,10,0,8\nK,6,8,9,6,4,3,9,3,7,11,11,11,4,7,3,6\nF,3,6,5,4,3,5,10,2,6,10,9,6,4,10,3,6\nT,5,11,7,9,5,7,12,3,7,8,12,8,2,12,1,7\nL,6,14,6,8,4,10,3,4,3,12,6,10,4,9,6,9\nH,3,7,6,5,3,4,8,3,6,10,10,9,3,8,3,6\nP,3,3,5,2,2,7,10,3,4,12,4,3,1,10,3,8\nG,9,13,7,7,5,9,6,5,4,9,5,7,3,9,8,9\nF,4,9,6,6,5,8,8,2,6,12,5,6,3,8,3,8\nB,2,3,3,2,2,7,7,5,5,7,6,6,2,8,5,9\nO,5,9,6,6,5,7,8,7,6,7,6,6,2,8,3,8\nN,10,15,12,8,5,10,6,4,4,13,2,7,7,7,0,6\nC,4,10,5,8,4,6,7,6,8,5,7,12,1,7,4,9\nP,2,4,4,3,2,8,9,3,4,12,4,4,4,10,4,8\nF,5,9,7,6,7,7,6,6,4,7,6,8,4,10,8,11\nK,3,9,4,6,2,3,7,7,2,7,7,11,4,8,3,10\nG,3,8,4,6,2,7,6,7,8,6,6,8,1,8,6,11\nA,2,4,4,3,2,8,2,2,1,7,2,8,2,7,3,6\nX,7,11,7,6,3,4,8,3,9,9,11,10,4,11,4,5\nO,1,0,1,0,0,7,7,6,4,7,6,8,2,8,3,8\nQ,5,8,6,9,6,8,6,7,3,9,7,10,3,8,6,7\nN,5,5,6,4,5,7,9,5,4,7,4,8,6,9,4,6\nJ,4,11,5,8,3,7,8,2,6,14,5,8,1,8,1,8\nC,7,11,7,8,5,6,6,6,7,13,8,13,4,9,4,6\nS,4,8,6,6,8,8,8,3,3,7,6,7,3,7,13,4\nG,6,11,7,8,6,6,6,6,5,5,6,10,2,9,4,8\nT,4,8,5,6,4,6,11,2,7,11,9,5,2,11,3,4\nJ,4,10,6,7,3,7,6,3,5,15,7,11,1,6,1,7\nK,5,6,7,4,5,6,7,1,6,10,7,10,3,8,3,8\nQ,4,7,4,9,4,7,7,6,3,8,8,10,3,9,6,8\nB,5,10,5,8,4,6,9,9,8,7,5,7,2,8,9,10\nD,5,9,5,4,3,8,7,3,6,10,5,7,5,8,7,6\nU,6,7,6,5,3,3,8,5,7,10,10,9,3,9,2,6\nS,3,7,4,5,2,7,5,6,9,4,6,9,0,9,9,8\nX,3,9,4,7,3,7,7,4,4,7,6,8,2,8,4,8\nW,5,7,5,5,4,6,10,4,3,8,7,6,6,12,3,6\nZ,9,10,7,14,6,8,7,4,2,12,6,8,3,9,13,6\nD,2,3,4,2,2,9,7,4,6,10,4,6,2,8,3,8\nH,2,1,3,2,2,6,8,6,5,7,6,8,3,8,3,8\nK,1,1,2,1,0,4,6,6,2,7,6,11,3,8,2,10\nP,2,5,4,3,2,7,10,3,4,12,5,3,1,10,2,8\nU,6,10,6,8,3,7,3,15,6,7,13,8,3,9,0,8\nW,7,8,9,7,11,7,8,5,5,6,5,8,10,10,9,7\nQ,4,7,6,7,3,9,6,8,7,7,5,10,3,8,4,8\nG,3,9,4,7,2,7,6,8,7,6,6,10,2,7,5,10\nI,6,10,6,6,3,8,8,3,5,13,4,5,2,9,5,11\nI,1,6,3,4,3,8,7,2,4,8,5,5,3,9,4,5\nY,5,7,5,5,2,3,10,2,7,11,11,6,1,11,2,5\nG,5,10,6,7,4,6,7,7,7,9,8,11,2,8,5,9\nK,2,4,4,3,2,7,7,1,7,10,5,9,3,8,3,8\nA,2,1,4,2,1,9,2,2,1,8,2,8,2,6,2,7\nC,4,9,4,4,3,7,7,4,3,9,8,10,3,9,8,10\nW,9,11,9,6,5,1,10,3,4,11,12,9,8,11,1,5\nQ,5,10,7,9,4,9,9,8,6,5,9,9,3,7,5,9\nX,4,7,6,5,5,7,8,2,6,7,6,8,4,8,5,8\nK,4,5,5,4,5,8,7,2,4,8,3,8,4,4,3,9\nG,4,8,6,7,6,7,8,5,3,7,6,9,6,11,8,10\nI,1,2,1,3,1,7,7,1,7,7,6,8,0,8,2,8\nT,3,4,3,3,1,5,12,3,6,11,9,4,1,11,2,5\nX,5,5,6,7,2,7,7,5,4,7,6,8,3,8,4,8\nJ,2,8,3,6,2,12,3,7,3,13,5,11,1,6,0,8\nZ,4,9,6,6,4,7,8,2,10,12,7,8,1,9,6,8\nO,6,10,8,8,5,7,7,8,4,6,6,11,5,8,5,6\nE,3,8,4,6,2,3,7,6,11,7,6,15,0,8,7,7\nN,4,7,6,5,4,12,7,3,5,10,0,4,5,9,1,7\nK,8,12,7,7,3,7,7,3,6,9,6,8,6,8,4,7\nZ,4,8,6,6,6,8,8,3,8,7,7,7,1,8,9,8\nC,3,4,5,7,2,5,8,7,10,6,7,12,1,7,4,8\nO,4,4,6,6,2,8,7,8,8,7,6,9,3,8,4,8\nY,3,5,5,8,6,7,11,3,3,7,8,9,3,11,7,5\nR,6,10,9,7,8,9,8,6,6,8,5,7,5,9,6,12\nT,4,6,5,4,2,5,11,2,8,11,9,5,1,11,2,4\nY,3,9,4,7,2,6,11,0,4,8,11,8,0,10,0,8\nW,8,11,8,8,7,6,10,4,3,8,7,6,11,12,4,4\nC,2,4,3,3,1,4,8,4,7,10,9,13,1,8,2,7\nG,6,10,8,8,8,9,8,6,3,5,7,10,9,8,5,9\nP,3,7,4,4,2,4,13,8,1,11,6,3,1,10,4,8\nD,6,10,8,8,6,7,7,5,6,7,6,8,7,7,3,8\nC,5,5,6,7,2,5,7,6,11,7,6,14,1,8,4,9\nV,3,8,5,6,2,6,9,4,1,8,12,8,2,10,0,8\nJ,3,10,4,8,3,13,3,7,4,13,3,10,1,6,0,8\nM,5,9,8,6,10,7,7,3,2,7,4,8,15,6,3,7\nM,7,9,10,7,8,12,5,3,5,9,2,5,10,5,2,8\nA,2,4,4,3,2,11,2,2,2,9,2,9,2,6,1,8\nI,2,9,2,7,2,7,7,0,8,7,6,8,0,8,3,8\nK,2,3,4,2,2,6,7,2,6,10,7,10,3,8,2,8\nA,3,8,5,6,3,10,3,2,3,9,1,8,2,6,2,8\nQ,5,5,6,8,3,8,6,8,8,5,5,8,3,8,4,8\nY,2,3,4,4,2,7,10,1,7,7,11,8,1,11,2,8\nX,1,0,2,0,0,7,7,4,4,7,6,8,2,8,4,8\nO,2,1,2,2,1,7,7,7,4,7,6,8,2,8,3,8\nC,5,11,6,8,2,5,7,7,11,7,6,13,1,9,4,8\nP,6,9,8,6,4,9,9,4,6,13,4,2,1,10,4,9\nZ,3,4,5,3,2,7,8,2,9,11,6,8,1,9,5,8\nL,3,5,4,4,3,7,8,4,6,7,6,8,2,8,7,10\nE,3,6,4,5,4,5,8,4,3,7,6,9,4,10,8,11\nK,5,4,5,6,2,4,9,8,2,7,4,11,4,8,3,10\nV,4,8,6,6,7,8,5,5,2,8,8,8,7,9,4,8\nD,4,10,6,7,4,11,6,3,7,11,3,7,4,7,4,9\nQ,4,5,5,7,3,8,7,8,6,6,7,9,3,8,5,9\nI,1,7,2,5,1,7,7,0,7,13,6,8,0,8,1,8\nW,12,14,12,8,5,1,10,4,2,11,12,9,8,10,0,7\nH,4,8,5,6,4,7,8,8,4,7,5,6,3,6,7,9\nN,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8\nI,3,6,4,4,2,6,9,0,6,13,7,7,0,8,1,7\nI,1,6,3,4,3,10,6,1,4,8,5,5,3,8,5,6\nR,5,11,5,8,6,6,8,9,5,7,6,8,3,8,5,12\nK,5,9,5,4,3,6,7,2,5,10,6,9,5,8,3,7\nA,1,0,2,0,0,8,3,2,0,7,2,8,2,6,1,8\nZ,3,5,5,8,4,12,4,2,5,9,3,7,1,8,5,10\nV,6,10,5,8,3,4,11,2,4,9,11,7,3,9,1,8\nZ,2,5,4,4,2,7,7,2,10,11,6,8,1,8,6,7\nH,3,6,4,4,4,7,7,5,6,7,6,9,3,8,3,8\nC,3,6,4,4,1,5,7,7,10,6,6,13,1,8,4,8\nM,3,6,5,4,5,10,5,2,2,8,4,8,5,7,2,6\nB,3,7,5,5,5,7,8,7,5,7,6,6,2,8,6,9\nR,5,9,7,7,8,5,8,4,5,6,5,9,7,6,10,7\nN,6,9,5,4,2,6,9,4,5,4,4,10,5,10,2,8\nT,4,5,5,4,2,5,12,3,8,11,9,4,1,11,2,4\nK,7,12,7,6,3,5,8,3,7,9,9,10,6,8,3,7\nF,4,11,7,8,8,9,7,1,5,9,5,5,5,10,6,5\nO,5,10,7,8,3,8,7,8,8,7,6,9,3,8,4,8\nA,2,1,3,1,1,7,4,2,0,7,2,8,2,7,1,7\nL,1,0,2,1,0,2,1,6,5,0,2,4,0,8,0,8\nK,4,8,6,6,4,6,7,2,6,10,7,10,3,8,3,8\nD,3,4,4,3,3,6,7,6,6,7,6,5,2,8,3,7\nO,6,11,6,8,7,8,6,8,4,9,4,8,3,8,3,8\nM,2,1,3,2,1,8,6,10,1,6,9,8,7,5,0,8\nP,4,10,5,8,4,4,12,6,5,12,9,3,1,10,4,6\nI,6,7,8,8,7,8,8,4,5,6,6,7,3,8,10,10\nB,4,8,6,6,5,7,7,6,6,9,6,6,3,8,7,8\nV,2,1,4,2,1,5,12,3,3,9,11,8,2,11,1,8\nK,2,3,3,1,2,6,7,4,7,7,6,11,3,8,4,9\nD,3,2,4,3,2,8,7,7,7,7,6,4,2,8,3,7\nR,3,6,5,4,5,7,8,3,4,6,6,9,6,9,7,6\nU,2,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8\nR,4,5,6,4,3,9,7,2,6,10,3,6,2,7,3,10\nH,4,5,5,3,4,7,8,6,6,7,6,8,3,8,3,7\nA,4,9,6,6,5,10,3,1,2,7,3,9,5,5,3,7\nB,2,4,3,2,2,8,8,3,5,10,5,6,2,8,5,9\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nV,4,4,5,3,2,5,12,2,3,8,10,7,2,10,1,8\nU,2,3,3,1,1,6,9,5,6,6,9,9,3,10,1,7\nH,4,9,5,6,2,7,9,15,2,7,3,8,3,8,0,8\nV,6,7,6,5,3,4,12,3,3,9,11,7,3,9,1,7\nA,2,4,4,3,2,7,2,2,2,7,2,8,2,6,3,7\nF,3,5,5,4,2,7,9,2,6,13,6,5,4,10,3,7\nZ,3,6,4,4,3,6,8,5,10,7,7,10,1,9,7,8\nM,3,5,6,4,4,9,6,2,4,9,4,6,8,5,2,8\nS,5,10,6,7,4,7,8,3,7,10,7,7,3,7,5,6\nE,3,6,4,4,4,8,7,5,3,7,6,9,4,8,7,9\nL,5,9,5,5,3,7,5,3,5,12,7,11,3,8,6,9\nQ,3,9,5,8,3,8,7,8,5,6,6,9,3,8,5,9\nV,3,8,5,6,2,5,11,3,4,10,12,8,3,10,1,8\nB,4,10,6,7,5,11,6,3,8,10,3,7,2,8,5,12\nM,3,4,5,3,3,8,6,3,4,9,6,8,6,5,2,8\nF,4,7,6,5,3,7,10,3,5,13,6,5,2,10,2,7\nW,4,9,6,7,9,8,7,5,1,7,6,8,10,11,3,9\nS,3,7,4,5,3,8,7,7,6,7,7,8,2,9,9,8\nN,5,7,8,5,4,11,7,3,5,11,0,4,5,9,1,7\nJ,2,8,3,6,1,12,2,9,4,13,5,13,1,6,0,8\nE,6,14,5,8,4,8,7,4,4,11,5,9,3,9,8,11\nJ,4,9,4,7,3,9,8,2,3,12,5,6,2,9,6,9\nX,3,6,4,4,2,7,7,4,4,7,6,8,3,8,4,8\nW,3,3,4,1,2,3,11,3,2,10,10,8,5,11,0,7\nY,5,10,8,7,4,8,10,1,8,5,12,8,1,11,2,8\nJ,1,5,2,3,1,10,6,3,5,12,4,9,1,6,1,7\nS,3,9,4,7,2,8,8,6,9,5,5,5,0,7,9,8\nA,4,9,6,7,5,8,2,3,1,7,2,8,5,8,6,7\nV,9,13,9,7,4,6,10,4,3,8,8,5,5,12,3,9\nO,5,11,6,8,3,7,7,9,8,7,6,8,3,8,4,8\nH,5,9,6,7,6,6,8,7,5,7,5,7,3,7,7,10\nL,3,8,4,6,2,7,3,0,9,9,2,11,0,7,2,8\nP,4,6,4,8,3,4,11,10,3,10,6,4,1,10,4,8\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nQ,4,7,5,9,5,9,10,7,1,3,7,12,3,10,5,11\nQ,8,9,10,8,9,8,4,4,4,8,4,11,6,6,8,7\nD,5,10,5,5,3,7,7,4,6,9,6,6,5,9,7,5\nV,4,9,5,7,4,9,11,2,3,4,10,8,3,10,2,8\nN,4,10,4,8,5,8,7,12,1,6,6,7,6,8,1,10\nK,4,11,4,8,2,4,7,8,2,6,4,11,4,8,2,11\nS,4,8,4,6,2,7,6,5,9,5,6,9,0,8,9,8\nH,4,7,6,5,5,7,8,5,6,8,6,8,6,7,5,9\nI,4,8,5,6,3,7,6,2,6,7,6,8,0,9,4,8\nQ,4,7,6,9,6,9,8,6,3,4,8,10,4,8,7,11\nJ,2,6,3,4,1,9,7,1,6,14,4,8,0,7,0,8\nR,4,5,5,4,3,8,7,5,6,6,5,6,3,7,6,9\nR,2,1,3,1,2,7,7,4,5,6,5,7,2,6,4,8\nI,2,6,3,4,1,7,7,0,7,13,6,8,0,8,1,8\nQ,4,7,5,9,5,7,10,3,4,7,10,11,3,9,7,7\nR,2,1,3,2,2,7,8,4,5,7,5,6,5,6,4,8\nR,4,7,6,5,5,8,7,7,6,8,6,8,3,8,6,11\nY,2,3,2,2,1,5,10,1,6,10,9,6,1,11,2,5\nT,5,7,5,5,3,6,11,2,6,11,9,5,2,11,2,5\nV,3,8,5,6,1,6,8,4,3,8,14,8,3,10,0,8\nD,4,8,4,6,4,5,7,9,6,6,5,5,2,8,3,8\nW,3,1,5,1,2,6,11,3,2,7,9,9,6,11,0,8\nM,5,9,7,6,6,4,7,3,4,10,10,10,7,5,2,7\nX,3,6,5,4,3,8,7,4,9,6,6,8,3,8,6,8\nM,5,11,9,8,12,7,5,3,2,7,5,8,14,6,4,7\nI,6,10,5,6,3,10,6,4,6,14,3,6,2,9,4,11\nZ,5,8,7,6,6,9,9,5,4,7,5,7,3,8,9,5\nR,7,11,9,8,9,8,9,7,4,8,5,6,5,8,8,12\nC,2,3,3,4,1,5,7,6,8,7,6,13,1,8,4,9\nE,5,10,4,5,3,9,6,3,4,11,5,9,3,9,8,12\nG,7,14,6,8,5,7,6,3,3,8,6,9,4,9,9,7\nR,4,7,6,5,6,8,8,7,3,8,5,6,4,7,7,9\nY,7,9,8,7,4,2,10,3,7,12,12,7,1,11,2,5\nN,4,7,6,5,3,8,7,7,6,6,6,4,6,9,2,5\nH,2,1,3,3,2,8,7,6,6,7,6,8,3,8,3,7\nN,2,2,3,3,2,7,8,5,5,7,6,6,5,9,2,5\nN,4,7,6,5,4,11,7,3,5,10,1,4,5,9,1,7\nA,4,11,6,8,3,7,4,3,2,6,1,8,3,6,3,8\nE,3,9,5,6,4,7,7,6,9,8,8,9,3,8,6,8\nE,4,9,5,7,6,6,7,5,8,7,6,10,3,8,6,9\nT,5,10,5,8,4,6,11,4,6,11,9,5,4,11,4,4\nT,6,13,6,7,4,7,9,2,7,12,6,6,2,9,5,5\nS,10,15,8,8,4,9,3,4,5,10,2,9,4,5,6,10\nT,6,8,8,7,7,6,8,6,6,7,8,7,4,11,9,8\nO,5,10,5,8,4,7,7,8,5,9,8,8,3,8,3,8\nD,4,7,6,5,4,9,7,5,7,10,4,5,3,8,3,8\nY,4,9,6,6,4,10,10,1,7,3,11,8,1,11,2,9\nG,6,10,8,8,8,7,9,6,3,6,5,10,5,7,8,7\nI,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nP,5,11,5,6,3,9,9,4,3,12,5,4,4,11,5,6\nK,5,4,5,6,2,4,8,8,2,7,5,11,4,8,2,11\nB,5,10,7,8,6,9,7,7,6,6,6,5,3,9,9,11\nV,3,4,5,6,1,8,8,4,3,7,14,8,3,10,0,8\nN,6,11,9,8,6,7,9,2,4,9,6,7,6,8,1,8\nA,4,10,7,8,5,11,3,1,2,8,2,9,6,4,3,8\nX,2,6,4,4,2,7,7,4,8,6,6,10,3,8,6,8\nE,3,8,4,6,2,3,6,6,11,7,7,15,0,8,7,7\nO,4,9,4,7,4,7,7,7,3,9,7,8,3,8,3,7\nX,4,9,6,7,4,9,7,0,8,9,4,8,2,8,3,8\nX,5,9,6,5,3,9,7,2,7,11,5,7,4,11,3,8\nM,7,8,9,7,10,8,8,4,4,7,6,7,10,9,6,3\nK,2,3,4,1,2,4,9,2,6,10,9,10,3,8,2,6\nU,4,5,5,3,2,6,8,6,8,7,10,9,3,9,1,8\nF,2,3,4,2,1,5,11,2,5,13,7,5,1,9,1,7\nB,1,1,2,2,1,7,7,8,5,7,6,7,2,8,7,9\nF,4,11,5,8,5,5,10,3,5,10,10,6,2,10,3,6\nG,4,9,6,7,6,7,7,6,2,7,6,11,4,8,8,8\nM,5,9,5,6,3,7,7,12,2,8,9,8,8,6,0,8\nR,3,6,5,4,5,5,7,3,4,7,6,9,5,10,7,5\nY,4,5,5,7,7,9,8,5,3,6,8,8,7,11,7,4\nV,3,3,4,1,1,5,13,3,2,9,11,7,2,11,1,8\nW,3,4,4,3,3,5,11,2,2,8,9,9,6,11,0,8\nH,4,7,6,5,7,8,8,4,3,6,7,7,7,9,7,7\nK,4,8,6,6,4,5,6,1,7,10,9,11,4,7,4,7\nL,4,8,5,6,3,3,5,2,9,3,1,9,0,7,1,5\nE,3,7,4,5,4,8,7,4,8,7,7,8,2,8,6,10\nS,3,1,3,3,2,8,7,6,5,7,6,8,2,9,9,8\nB,6,10,6,8,5,6,5,9,7,6,7,6,2,8,10,10\nF,4,8,4,6,3,1,11,4,6,11,10,9,0,8,2,7\nH,6,7,8,5,6,10,6,3,6,10,3,7,4,7,4,9\nX,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nN,5,10,7,7,4,10,7,3,5,10,2,5,6,9,1,7\nL,6,11,6,6,4,8,4,3,4,12,7,11,3,9,6,9\nT,2,7,3,5,2,9,12,0,5,6,10,8,0,8,0,8\nE,5,8,7,6,5,4,9,2,8,10,8,9,3,9,4,6\nW,2,0,2,1,1,7,8,3,0,7,8,8,6,10,0,8\nS,3,4,4,3,2,6,8,3,7,10,8,8,1,8,5,5\nV,3,8,5,6,1,8,8,4,3,6,14,8,3,10,0,8\nZ,7,7,5,10,5,9,4,4,3,11,6,9,3,9,11,8\nT,4,7,6,5,6,7,7,4,5,7,6,9,5,8,5,7\nP,5,11,7,8,6,8,9,5,4,12,5,4,2,10,3,8\nO,2,4,3,3,2,8,7,6,4,9,5,8,2,8,2,8\nM,5,8,6,6,3,8,7,12,2,6,9,8,9,6,0,8\nB,3,8,5,6,5,7,7,6,6,6,6,6,2,8,6,9\nC,4,8,5,6,3,6,8,8,8,9,8,13,2,10,4,9\nE,4,5,5,8,3,3,8,6,12,7,5,15,0,8,7,7\nY,4,6,4,4,2,4,10,2,7,11,11,6,1,10,2,4\nL,4,9,6,7,3,6,3,1,9,8,2,11,0,7,2,8\nX,4,9,5,4,2,6,8,1,7,11,6,8,3,7,4,6\nV,3,5,4,3,1,4,12,4,3,10,11,7,3,10,1,8\nV,1,0,2,0,0,8,9,3,2,6,12,8,2,10,0,8\nU,4,4,5,3,2,4,9,5,7,11,10,9,3,9,2,7\nJ,3,8,4,6,3,10,7,0,7,11,3,6,0,7,1,7\nL,3,8,4,6,1,0,1,6,6,0,0,6,0,8,0,8\nQ,2,2,3,3,2,8,8,5,2,7,8,10,2,9,4,9\nD,1,0,1,0,0,6,7,7,5,7,6,6,2,8,3,8\nD,2,3,3,2,2,9,7,4,6,10,4,6,2,8,2,8\nX,5,9,8,7,5,3,8,1,8,10,11,10,2,9,3,5\nP,4,8,5,5,2,4,14,8,1,11,6,3,1,10,4,8\nJ,2,5,4,4,2,7,7,3,6,15,7,10,1,6,1,7\nW,9,10,9,5,4,6,11,2,3,8,10,7,8,12,1,7\nO,4,9,5,6,4,7,7,8,6,7,3,8,3,8,3,8\nU,4,8,6,6,4,5,9,5,6,7,9,10,3,9,1,8\nY,5,8,5,6,3,3,10,2,7,11,11,6,1,11,2,5\nE,7,11,10,8,8,7,7,2,8,11,7,9,3,8,5,8\nO,5,11,6,8,4,7,8,8,5,10,8,7,4,7,5,9\nN,10,13,12,7,5,3,11,6,3,14,12,9,6,9,0,8\nD,4,11,6,8,5,8,7,6,8,7,6,5,6,8,3,7\nU,4,7,6,5,3,6,9,6,8,8,10,9,3,9,1,8\nI,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nB,5,10,6,7,6,8,7,6,7,7,6,5,2,8,7,10\nX,4,6,5,8,2,7,7,4,4,7,6,8,3,8,4,8\nF,4,10,4,8,3,1,13,4,4,12,10,6,0,8,2,6\nC,5,8,5,6,4,6,7,6,6,12,8,11,2,11,4,7\nD,3,8,5,6,4,8,8,4,5,9,5,4,3,8,3,7\nX,5,9,6,8,7,7,8,1,5,7,6,8,3,6,8,8\nF,3,5,4,4,2,5,10,4,6,10,9,6,1,10,4,6\nX,3,1,4,3,2,7,7,3,9,6,6,8,2,8,6,8\nL,2,7,3,5,2,5,5,2,8,3,2,7,0,7,1,6\nY,4,10,6,8,3,6,10,2,2,7,12,8,1,11,0,8\nC,7,9,5,5,2,7,9,6,7,12,7,8,1,8,6,9\nU,7,9,8,6,4,4,8,6,8,10,10,9,3,9,2,5\nR,4,9,6,7,4,10,7,3,5,11,2,6,3,7,4,10\nF,2,7,4,5,3,5,10,2,6,10,9,6,1,10,3,7\nN,8,14,9,8,4,4,10,6,4,14,12,9,6,9,0,9\nZ,6,9,8,7,6,6,9,2,9,11,8,7,3,11,7,7\nL,5,7,7,5,5,6,6,8,6,5,6,9,2,8,4,10\nK,6,11,9,8,7,2,9,2,7,10,12,12,5,6,4,4\nK,4,9,5,7,5,5,6,3,6,6,6,10,3,8,5,10\nX,3,6,4,5,4,7,8,2,5,8,6,8,2,7,6,7\nS,4,7,5,5,3,7,7,4,8,11,8,8,2,10,5,6\nM,5,9,6,7,6,8,6,6,5,6,7,8,8,6,2,7\nK,5,11,7,8,9,7,6,3,5,6,6,8,7,7,7,7\nV,3,5,5,7,2,8,8,4,3,7,14,8,3,9,0,8\nJ,2,10,3,8,2,9,6,2,6,11,3,8,1,6,2,6\nE,4,7,5,5,3,7,8,2,8,11,6,8,2,8,5,8\nZ,3,5,5,4,3,8,7,2,9,12,6,9,1,8,5,7\nF,3,9,5,7,3,4,11,3,6,11,10,5,1,10,2,5\nC,6,10,9,7,6,7,8,8,6,5,6,13,5,8,4,7\nI,2,2,1,3,1,7,7,1,7,7,6,8,0,8,3,8\nL,1,0,2,1,0,2,1,6,4,0,3,4,0,8,0,8\nW,10,12,10,7,5,2,8,3,3,10,11,9,9,10,1,6\nN,2,5,4,4,2,7,9,2,4,10,6,7,5,8,1,7\nY,3,4,4,3,2,4,10,1,7,10,10,6,2,10,3,3\nU,3,6,4,4,1,8,5,13,5,7,14,8,3,9,0,8\nZ,2,5,2,4,2,7,7,5,8,6,6,8,2,8,7,8\nK,3,4,5,3,2,8,6,1,6,10,5,9,3,8,3,9\nW,7,8,7,6,6,5,11,3,3,9,7,7,8,11,3,5\nB,3,6,4,4,4,8,7,5,5,9,6,6,2,8,4,7\nS,6,9,8,7,11,7,6,4,2,8,5,8,3,8,13,4\nL,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8\nL,3,10,5,8,4,7,4,2,7,7,2,9,1,6,3,8\nP,4,8,6,6,4,8,8,2,6,13,5,5,2,9,3,9\nN,2,4,4,3,2,8,8,2,4,10,5,6,4,9,1,6\nB,5,9,8,8,9,7,7,5,4,7,6,8,7,10,9,11\nT,2,3,3,2,1,7,11,3,6,7,11,8,2,11,1,8\nZ,1,4,3,3,1,7,7,2,9,11,6,8,1,8,5,8\nL,3,4,3,6,1,0,1,6,6,0,0,6,0,8,0,8\nI,1,4,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nS,4,7,5,5,3,6,8,3,6,10,7,8,2,8,4,6\nV,8,10,7,7,3,3,12,4,4,10,12,8,3,10,1,8\nB,1,3,3,2,2,8,6,2,5,10,5,7,2,8,3,9\nR,5,8,7,6,7,8,8,7,4,8,5,6,4,7,7,11\nT,3,7,4,5,2,5,13,3,6,12,9,4,1,11,2,4\nH,3,2,5,3,3,7,7,6,6,7,6,8,3,8,3,8\nY,3,6,4,4,2,4,10,1,6,9,10,5,1,11,3,4\nA,2,3,3,2,1,9,2,1,1,8,2,9,1,6,1,8\nK,6,9,9,7,6,2,8,3,7,11,12,12,4,7,4,5\nD,5,8,7,6,5,10,6,3,6,11,4,6,3,8,3,9\nI,1,8,0,6,1,7,7,5,3,7,6,8,0,8,0,8\nF,3,9,5,7,4,4,11,6,5,11,10,5,2,10,3,5\nQ,5,9,5,4,3,8,5,4,7,10,5,9,3,8,9,10\nX,5,8,7,6,5,8,5,3,5,7,7,9,4,8,10,9\nB,3,6,4,4,4,6,6,7,5,6,6,7,2,9,7,10\nM,10,14,10,8,5,9,11,6,5,4,6,9,10,11,2,7\nX,5,11,8,8,7,7,7,3,8,5,7,10,3,8,6,8\nL,4,7,5,5,4,6,8,8,5,7,5,10,3,7,4,11\nQ,3,4,4,6,2,8,8,8,6,6,6,9,3,8,4,9\nJ,1,3,2,2,1,8,6,3,5,14,6,10,1,7,0,7\nT,3,8,4,6,3,5,11,2,7,8,11,8,2,11,1,7\nP,2,3,4,2,2,7,9,3,4,12,5,4,1,9,3,8\nV,9,15,8,8,4,6,11,6,4,10,10,4,5,12,3,9\nW,5,11,8,8,7,10,10,3,3,5,9,7,8,10,1,8\nR,3,8,4,6,4,5,11,7,3,8,4,9,2,6,5,11\nG,5,11,5,8,5,5,7,6,4,9,9,10,2,8,4,10\nE,4,7,5,5,4,6,7,7,9,8,8,10,3,8,6,8\nS,4,9,5,7,3,9,7,4,8,11,6,7,2,10,5,8\nC,6,8,6,6,3,4,9,6,9,13,10,10,1,9,3,7\nL,3,9,5,7,3,6,5,0,8,9,3,11,0,7,3,8\nY,3,7,5,5,2,7,11,1,8,6,12,8,1,11,2,8\nY,4,6,4,4,2,2,11,3,5,11,12,7,2,11,2,5\nO,3,6,4,4,2,7,6,8,5,7,5,9,3,8,3,8\nZ,4,10,5,7,4,7,8,3,12,9,6,8,0,8,8,7\nG,8,9,10,8,11,7,8,6,3,7,7,9,9,10,9,9\nX,4,9,6,7,4,8,7,4,9,6,7,9,4,7,8,9\nN,6,10,8,7,5,11,8,3,6,10,0,3,5,9,1,7\nY,1,3,2,2,1,8,10,1,6,5,10,8,1,11,1,8\nR,4,8,5,6,4,8,7,5,6,8,5,8,3,6,6,11\nF,1,0,1,0,0,3,11,4,2,11,8,6,0,8,2,8\nW,3,1,4,2,2,8,11,2,2,6,9,8,6,11,0,7\nA,3,5,6,4,2,8,2,2,2,7,1,8,2,7,2,7\nP,2,1,2,2,1,5,9,3,4,9,8,4,1,9,3,7\nC,1,0,2,1,0,6,7,6,9,7,6,14,0,8,4,10\nD,2,0,2,1,1,6,7,8,7,7,6,6,2,8,3,8\nI,1,4,1,3,1,7,7,1,7,7,6,9,0,8,2,8\nK,4,4,4,6,2,4,8,8,2,7,5,11,3,8,2,11\nD,4,11,4,8,3,5,8,10,8,8,8,5,3,8,4,8\nT,7,10,7,8,4,7,10,2,10,11,9,4,3,9,5,5\nF,6,13,6,8,5,7,9,3,4,10,6,6,4,9,7,7\nL,2,2,3,3,2,4,4,5,7,2,2,6,1,7,1,6\nA,2,7,4,4,1,8,6,3,0,7,0,8,2,7,1,8\nN,5,9,7,6,4,7,8,6,6,7,7,6,6,10,2,5\nT,8,11,8,8,6,6,10,1,9,11,9,5,3,9,4,4\nQ,3,5,5,6,5,10,11,6,3,3,8,12,3,10,5,10\nQ,4,7,5,8,5,8,11,5,1,5,8,12,2,10,5,8\nK,2,1,2,1,0,4,7,6,2,7,6,11,3,8,2,10\nY,3,4,5,5,1,7,10,2,2,8,12,8,1,11,0,8\nZ,3,6,4,4,2,8,7,2,9,11,5,8,2,8,6,8\nJ,4,6,5,4,2,11,5,2,7,14,3,8,0,8,0,8\nU,3,1,4,2,1,7,8,6,8,7,10,8,3,10,1,8\nR,6,8,8,6,5,10,8,4,7,10,1,7,3,6,4,11\nN,5,7,8,5,3,7,8,3,5,10,7,7,5,7,1,8\nY,5,6,5,4,2,3,12,5,5,13,11,6,1,11,1,6\nK,11,15,10,8,5,4,8,5,8,9,12,12,6,11,3,6\nK,6,9,9,7,6,3,8,3,8,11,12,12,4,7,4,4\nN,5,8,7,6,3,10,7,3,5,10,2,5,5,8,1,7\nY,1,0,2,0,0,7,9,2,2,6,12,8,1,10,0,8\nE,2,4,3,2,2,6,8,2,7,11,7,9,2,8,4,8\nW,9,12,9,7,4,6,10,1,3,7,10,7,8,12,1,6\nW,6,9,6,4,4,5,8,2,3,8,9,7,9,10,2,5\nG,3,10,4,7,2,7,6,8,8,6,6,11,2,8,5,11\nU,4,3,5,5,2,7,4,14,5,7,14,8,3,9,0,8\nY,5,6,6,8,9,8,6,7,4,7,7,8,10,9,4,5\nS,7,10,8,8,5,9,8,3,7,10,3,6,3,8,5,9\nA,4,10,5,5,4,9,5,5,2,10,6,11,6,5,5,10\nZ,3,9,4,7,2,7,7,4,13,10,6,8,0,8,8,8\nZ,5,10,7,8,5,7,8,2,9,11,7,7,1,8,6,7\nM,4,7,7,5,7,9,6,2,2,8,4,7,7,5,2,6\nQ,3,5,4,7,4,8,5,6,3,9,5,11,3,8,4,8\nM,5,10,8,8,6,6,6,3,4,9,9,9,8,5,2,7\nF,1,0,1,1,0,3,12,4,2,11,9,6,0,8,2,7\nV,6,9,6,6,4,3,11,2,3,9,10,8,3,12,1,7\nJ,2,9,3,6,1,12,3,10,3,13,6,13,1,6,0,8\nY,5,8,7,6,4,9,10,1,7,3,11,8,2,12,2,9\nN,5,9,6,7,4,7,9,5,5,7,6,7,6,9,2,6\nK,4,9,6,7,7,6,6,3,4,6,6,9,8,7,8,7\nO,4,9,4,6,3,8,6,8,5,9,4,7,3,8,3,8\nJ,3,10,4,8,1,13,1,9,5,14,2,12,0,7,0,8\nM,4,5,7,3,4,8,7,3,4,9,6,7,8,6,2,7\nB,5,8,7,6,10,8,8,4,3,6,7,7,7,11,9,9\nW,7,10,7,8,6,4,10,3,2,9,8,7,7,12,2,5\nN,8,12,7,6,3,7,10,4,6,3,5,9,5,9,2,7\nH,3,1,4,2,3,7,7,6,6,7,6,8,5,8,4,8\nG,4,10,6,7,8,7,5,4,3,7,5,10,8,7,8,13\nZ,5,11,6,9,3,7,7,4,15,9,6,8,0,8,8,8\nT,1,0,1,0,0,8,14,2,4,6,10,8,0,8,0,8\nT,3,4,4,3,1,5,13,3,6,11,9,4,1,11,1,5\nR,3,5,6,4,4,7,8,5,4,8,5,8,3,6,4,11\nJ,1,7,2,5,1,11,3,10,3,12,7,13,1,6,0,8\nB,3,7,5,5,4,8,8,3,6,10,5,6,2,8,4,10\nM,8,9,12,8,12,8,9,4,4,7,6,7,12,7,7,3\nH,5,11,5,8,3,7,6,15,1,7,8,8,3,8,0,8\nO,5,7,7,6,5,6,6,6,7,9,6,8,3,6,5,6\nL,5,4,5,7,1,0,0,6,6,0,1,5,0,8,0,8\nH,3,7,5,5,3,6,7,3,6,10,8,9,3,9,2,6\nI,1,4,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nG,5,10,6,8,3,8,6,8,9,6,5,11,2,8,6,10\nF,5,12,5,7,4,10,8,3,5,11,4,5,3,7,7,9\nA,4,8,6,7,7,10,8,3,4,6,7,6,5,9,5,5\nV,3,8,4,6,2,7,9,4,2,7,12,8,2,10,0,8\nC,6,11,6,8,4,5,7,7,8,12,9,14,2,10,4,7\nB,4,2,4,4,4,7,7,5,5,6,6,6,2,8,6,9\nN,3,6,4,4,3,7,9,6,4,7,6,6,5,9,1,6\nG,5,9,6,7,4,7,6,7,8,11,6,11,2,11,4,9\nM,5,9,6,6,6,8,5,11,0,6,9,8,7,5,0,7\nX,5,10,6,8,4,7,7,4,4,7,6,8,3,8,4,8\nW,4,10,6,8,4,8,8,4,2,7,8,8,8,9,0,8\nK,2,7,4,5,3,5,6,3,6,6,6,10,3,8,5,9\nP,3,3,4,5,2,4,12,9,2,10,6,4,1,10,4,8\nH,4,2,5,4,4,8,7,6,7,7,6,8,3,8,4,8\nQ,3,8,4,9,5,9,7,8,3,5,6,9,3,8,6,10\nQ,4,8,5,10,6,8,6,8,5,5,6,8,4,9,7,11\nX,4,9,6,6,4,7,6,3,8,5,6,9,3,8,7,8\nV,1,1,3,2,1,6,12,2,2,8,11,8,2,10,0,8\nB,7,10,5,6,4,6,7,5,5,10,6,8,5,7,8,9\nD,4,6,5,5,5,6,7,5,6,7,4,7,4,7,6,5\nR,2,2,3,3,2,7,8,4,5,7,5,7,2,7,5,8\nC,7,13,5,7,4,7,7,4,3,9,8,11,4,9,9,11\nO,5,5,7,8,3,7,8,9,8,8,7,6,3,8,4,8\nS,3,4,5,3,2,9,7,3,7,10,4,7,1,9,5,10\nQ,4,8,5,10,6,8,5,7,4,9,5,8,3,8,4,7\nP,6,6,8,8,9,6,7,5,3,8,7,6,7,13,6,9\nG,2,3,3,4,2,7,8,8,7,5,7,9,2,7,5,11\nS,6,9,5,5,2,10,2,2,4,10,2,9,2,7,3,10\nO,8,15,6,8,4,5,7,6,3,10,7,9,5,9,5,8\nB,3,1,4,3,3,8,8,5,6,7,5,5,2,8,6,8\nH,6,9,9,6,5,8,7,3,6,10,5,8,3,8,3,7\nD,5,10,6,8,6,5,7,9,7,7,6,6,2,8,3,8\nB,1,0,1,1,1,7,7,6,4,6,6,7,1,8,6,9\nZ,5,9,6,7,3,7,7,4,15,9,6,8,0,8,8,8\nS,7,15,6,8,3,9,5,4,4,13,6,9,2,9,3,8\nZ,5,5,6,8,3,7,7,4,15,9,6,8,0,8,8,8\nR,8,10,8,5,5,8,9,3,6,9,2,6,7,4,6,6\nN,2,0,2,1,1,7,7,12,1,5,6,8,5,8,0,8\nG,5,10,6,8,3,7,6,8,8,6,6,9,2,7,6,10\nD,8,13,8,7,4,11,3,5,5,13,2,11,5,7,4,9\nH,5,6,7,4,4,5,8,4,6,10,8,9,3,8,3,7\nH,3,8,4,6,3,8,7,13,1,7,7,8,3,8,0,8\nS,9,15,7,8,4,7,5,5,6,8,2,8,4,6,6,8\nZ,3,7,4,5,4,9,9,5,3,7,5,7,3,8,9,4\nQ,4,6,5,8,4,7,8,5,2,8,9,10,3,9,6,8\nX,3,8,4,6,3,7,7,4,4,7,6,8,3,8,4,8\nI,1,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nW,3,9,5,6,3,4,8,5,1,7,8,8,8,10,0,8\nI,2,7,4,5,4,10,7,2,4,8,5,5,3,9,5,7\nJ,2,7,3,5,1,12,2,9,4,13,5,13,1,6,0,8\nS,7,10,9,8,12,8,6,5,4,8,6,8,7,8,15,10\nF,3,6,3,4,1,2,13,5,3,11,9,6,0,8,3,6\nQ,5,6,6,8,6,7,10,4,2,7,9,11,3,9,5,8\nY,1,3,3,1,1,7,11,1,6,7,11,8,1,11,1,8\nK,1,0,1,0,0,4,6,5,2,7,6,11,2,8,2,10\nO,3,9,4,7,4,7,6,8,4,7,5,11,3,8,3,9\nH,4,4,4,6,2,7,10,15,2,7,3,8,3,8,0,8\nB,1,0,2,1,1,7,7,7,4,7,6,7,1,8,6,8\nQ,3,7,5,5,4,7,4,7,4,7,5,10,2,8,3,9\nZ,5,10,6,8,6,9,6,5,9,8,5,7,1,7,7,8\nP,8,12,8,6,5,9,8,4,5,13,4,4,4,10,6,8\nW,5,9,8,7,7,6,11,2,2,7,8,8,8,11,1,8\nJ,1,4,3,3,1,9,6,2,6,14,4,8,0,7,0,8\nV,3,3,4,2,1,4,13,3,2,10,11,7,2,11,1,8\nT,2,3,3,2,1,5,12,2,7,11,9,4,1,10,2,5\nL,7,13,6,7,3,8,3,3,5,11,4,13,2,7,6,8\nF,4,8,6,6,3,7,10,4,6,13,7,5,2,9,2,7\nO,5,7,6,5,4,7,10,8,5,5,8,11,5,9,4,7\nN,5,8,7,6,4,7,8,6,5,7,6,5,7,11,3,5\nV,4,10,6,8,3,6,11,3,4,7,12,9,2,10,1,9\nK,5,9,7,7,7,6,9,5,4,7,4,8,4,7,7,9\nX,4,8,6,6,5,7,6,2,6,7,7,9,5,9,7,7\nX,2,6,4,4,2,7,8,3,8,6,6,6,3,8,7,7\nY,5,9,5,6,3,3,10,2,7,11,11,6,0,10,2,4\nA,7,12,6,7,3,12,2,3,1,10,4,11,4,4,4,9\nK,4,7,4,5,2,4,6,8,2,7,7,11,3,8,3,11\nP,4,5,4,8,2,3,13,8,2,11,7,3,1,10,4,8\nZ,5,9,5,4,3,6,7,2,8,11,8,9,3,8,5,5\nJ,2,4,3,6,1,14,2,7,5,14,2,11,0,7,0,8\nX,3,7,4,5,2,7,7,4,4,7,6,8,3,8,4,8\nW,7,12,7,7,4,1,9,2,3,9,11,9,8,10,1,6\nS,4,9,4,6,4,8,8,5,8,5,5,6,0,7,9,7\nW,6,6,7,4,4,6,11,4,2,8,7,6,9,12,2,5\nQ,4,6,5,8,5,7,10,4,3,6,9,11,3,8,6,8\nP,4,7,5,5,3,6,11,4,4,13,6,3,1,10,3,9\nS,1,0,1,1,0,8,7,4,6,5,6,8,0,8,7,8\nX,4,6,7,4,3,5,8,2,8,11,10,9,3,8,3,6\nC,4,8,5,6,3,8,7,7,6,6,7,9,4,8,3,9\nW,10,11,10,8,6,2,11,2,3,10,11,9,8,10,2,6\nW,5,11,8,8,4,10,8,5,2,7,9,8,9,9,0,8\nD,5,3,6,4,4,7,7,7,7,7,6,4,3,8,3,7\nX,9,15,10,9,6,8,7,2,8,11,5,7,5,9,4,7\nD,4,9,6,6,8,9,8,4,5,7,7,6,4,9,8,5\nD,6,11,6,6,3,11,3,4,5,12,2,8,5,7,3,10\nU,3,3,4,2,2,7,8,5,6,6,9,9,5,10,0,7\nA,4,7,5,5,3,7,5,3,0,7,1,8,2,7,1,8\nO,1,3,2,1,1,8,7,6,4,9,5,8,2,8,2,8\nQ,3,7,4,7,3,7,6,8,5,5,6,8,3,8,5,9\nT,4,5,4,4,2,6,11,2,7,11,9,4,1,11,2,4\nA,3,2,5,3,2,8,2,2,2,6,1,8,2,6,2,7\nT,6,8,6,6,4,6,11,4,5,11,9,5,3,12,2,4\nZ,7,8,5,11,5,7,7,5,3,11,7,8,3,9,11,6\nC,2,3,3,4,1,6,7,7,9,9,6,13,1,9,4,9\nY,7,8,7,6,4,3,11,2,7,12,11,6,0,10,2,5\nH,5,10,5,7,3,7,6,15,1,7,7,8,3,8,0,8\nT,2,7,3,4,1,7,14,0,6,7,11,8,0,8,0,8\nT,1,0,2,0,0,8,14,2,4,6,10,8,0,8,0,8\nA,3,5,4,4,3,8,8,2,4,7,8,8,4,9,3,6\nH,1,1,2,1,1,7,7,11,1,7,6,8,3,8,0,8\nF,7,11,6,6,3,6,10,2,6,11,7,5,2,9,6,5\nO,3,5,4,4,3,8,7,7,4,9,5,8,2,8,2,8\nZ,6,7,5,11,4,7,8,4,2,11,7,7,3,9,11,6\nG,6,9,8,6,7,7,8,5,3,6,6,9,5,7,7,7\nO,7,9,5,5,3,6,8,5,4,8,4,6,5,9,5,7\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nE,7,10,9,8,9,8,7,6,2,7,6,10,5,8,8,10\nZ,5,9,6,5,3,10,3,4,6,12,4,10,3,8,7,10\nP,2,4,4,2,2,7,10,4,3,12,5,3,1,10,2,8\nH,7,11,10,8,8,5,9,3,6,10,9,8,6,11,5,6\nV,4,9,6,7,1,8,8,5,3,6,14,8,3,9,0,8\nJ,4,7,5,5,3,7,5,5,4,14,8,13,1,6,1,6\nD,5,10,6,7,5,5,7,9,7,6,5,4,3,8,5,10\nG,2,1,2,1,1,8,6,6,6,6,5,9,1,7,5,10\nN,3,4,5,3,2,8,8,3,5,10,4,6,5,9,1,6\nA,4,9,6,6,5,6,5,3,3,4,1,6,4,5,4,6\nY,5,6,6,4,2,4,10,2,7,10,11,5,2,11,3,4\nM,8,9,11,8,13,8,7,5,5,7,6,7,13,9,8,2\nU,4,5,5,4,2,5,8,5,8,10,8,9,3,9,2,6\nT,3,11,5,8,1,9,15,0,6,6,11,8,0,8,0,8\nN,6,11,7,8,6,8,7,13,1,6,6,7,7,7,1,10\nA,3,10,6,7,2,7,3,3,3,7,1,8,3,6,3,7\nN,4,9,5,6,4,7,8,6,5,7,6,6,6,9,1,6\nY,2,3,3,2,1,8,10,1,6,6,11,8,1,11,2,7\nL,6,12,5,7,4,5,6,3,6,12,8,12,3,7,7,8\nT,5,9,6,6,5,6,7,7,7,8,9,8,3,9,6,9\nN,4,7,5,5,4,7,6,7,5,6,4,7,3,7,3,7\nO,5,10,6,8,5,7,8,8,6,7,8,8,2,8,3,8\nW,6,7,6,5,5,3,11,2,2,10,10,8,6,11,2,7\nB,3,6,4,4,4,7,7,5,4,6,5,7,3,8,5,7\nR,5,5,5,8,3,5,10,10,4,7,5,8,3,7,6,11\nJ,2,9,3,7,1,14,2,7,5,14,2,10,0,7,0,8\nS,6,8,7,6,4,7,8,3,8,10,5,6,2,6,5,8\nF,2,1,3,2,2,5,10,4,5,11,9,5,1,10,3,6\nI,4,10,5,7,3,7,7,0,7,13,6,8,0,8,1,8\nL,4,9,6,6,7,6,7,3,6,7,7,11,6,11,6,5\nS,6,11,6,6,3,9,6,4,3,13,7,9,3,10,3,8\nL,3,8,5,6,6,6,7,3,6,8,7,11,6,10,5,6\nY,8,13,7,7,4,7,7,5,3,10,8,5,4,9,5,4\nE,4,8,6,6,5,7,8,6,9,6,4,9,3,8,6,8\nD,4,8,6,6,5,7,7,7,6,6,6,4,3,8,3,7\nA,2,4,4,3,2,11,2,3,2,11,2,10,2,6,2,9\nT,5,7,5,5,3,7,11,4,7,11,8,4,3,12,3,5\nY,4,9,7,7,3,7,11,1,8,6,12,8,1,11,2,8\nC,6,10,5,5,3,7,8,4,3,9,8,10,4,9,8,10\nV,6,7,5,5,2,4,12,4,4,11,11,7,3,10,1,8\nD,5,5,6,8,3,5,7,10,10,7,6,6,3,8,4,8\nW,5,5,7,5,7,7,8,5,5,7,5,8,9,8,8,6\nC,4,7,5,5,5,5,7,3,5,8,6,11,6,9,4,9\nU,5,8,7,6,6,8,8,8,5,6,7,9,3,8,4,6\nQ,5,10,6,9,3,8,7,8,6,6,7,8,3,8,5,9\nV,3,6,5,4,2,7,12,2,3,6,11,9,2,10,1,8\nT,2,9,4,6,1,9,14,0,6,5,11,8,0,8,0,8\nZ,2,4,3,3,2,8,7,5,9,6,6,7,1,8,7,8\nZ,10,15,10,9,7,8,6,3,9,12,6,9,4,5,8,7\nT,7,9,7,7,4,4,13,5,6,12,9,3,2,12,2,4\nN,11,15,14,8,5,10,5,4,4,14,1,7,6,6,0,7\nS,4,8,5,6,3,6,8,4,7,10,8,7,2,8,5,5\nN,2,4,2,2,2,7,8,5,4,7,6,6,5,9,1,5\nF,3,7,4,5,3,3,11,3,5,11,10,6,1,10,2,6\nI,1,3,2,2,0,7,8,1,6,13,6,8,0,8,0,8\nX,2,3,3,1,1,8,7,3,8,6,6,8,2,8,5,8\nQ,3,5,3,6,3,9,6,6,3,8,6,11,3,9,5,8\nE,4,9,6,7,5,9,7,2,7,11,5,8,3,7,6,10\nT,6,7,6,5,4,6,10,1,10,11,9,5,0,10,3,4\nU,4,8,5,6,2,7,3,15,6,7,13,8,3,9,0,8\nV,4,6,5,4,2,7,11,3,3,8,11,8,3,10,3,8\nA,3,7,5,4,2,7,4,3,1,6,1,8,3,6,2,7\nV,3,6,3,4,2,4,11,1,3,9,10,7,2,11,0,8\nZ,4,8,6,6,5,9,10,6,5,7,5,8,3,9,10,7\nQ,4,8,5,6,5,8,5,7,3,6,7,8,3,7,6,9\nU,1,0,2,1,0,7,5,10,4,7,12,8,3,10,0,8\nA,2,3,3,1,1,11,2,3,1,9,2,9,1,6,2,8\nG,4,8,5,6,4,5,8,5,5,8,8,9,2,7,4,10\nD,6,10,6,5,4,7,7,4,7,10,5,6,5,9,7,5\nY,6,9,5,4,3,6,8,4,5,10,7,5,3,9,4,4\nO,4,9,6,6,7,8,7,5,1,7,6,8,8,8,5,10\nM,4,4,7,3,3,7,7,3,5,9,7,8,9,7,2,7\nK,3,5,4,4,3,5,6,4,7,7,7,11,3,9,5,10\nV,3,7,4,5,3,7,11,1,2,6,10,8,3,12,1,8\nR,2,4,4,3,2,8,7,4,5,8,5,7,2,7,4,11\nR,4,9,6,6,5,9,8,3,6,9,3,8,3,6,4,11\nL,2,4,2,3,1,5,4,5,7,2,2,4,1,7,1,6\nA,3,4,5,3,2,8,2,2,2,7,2,8,2,6,3,6\nQ,5,5,7,4,5,7,5,5,5,7,5,9,5,4,7,7\nP,4,8,5,6,4,3,13,6,2,11,8,4,0,9,3,8\nH,7,11,10,8,9,8,7,3,6,10,4,7,4,9,4,9\nI,2,9,2,7,2,8,7,0,9,7,6,7,0,8,3,7\nJ,5,10,6,8,4,8,7,3,6,14,5,9,1,6,1,7\nF,8,14,7,8,3,5,10,3,5,12,6,5,2,8,6,4\nK,1,0,1,0,0,4,6,6,2,7,6,11,3,7,2,10\nQ,5,9,6,8,3,8,7,8,6,6,7,9,3,8,5,9\nC,4,9,5,7,4,6,7,6,8,5,8,11,1,7,4,9\nT,3,4,4,2,2,5,12,3,6,11,9,4,2,11,2,5\nB,6,9,8,7,8,8,7,5,5,9,5,6,4,8,7,11\nJ,3,9,4,7,1,12,2,9,4,14,5,13,1,6,0,8\nK,3,5,5,3,3,6,7,1,6,10,7,10,3,8,3,8\nX,3,4,4,3,2,8,7,3,9,6,6,8,2,8,6,8\nN,3,3,4,5,2,7,7,14,2,5,6,8,5,8,0,8\nW,3,1,4,2,2,5,11,3,2,8,9,9,6,11,0,8\nH,3,4,4,2,3,8,7,6,6,7,6,8,6,8,4,7\nZ,2,4,4,3,2,7,8,2,9,11,7,7,1,8,5,6\nW,7,9,7,4,4,5,8,2,4,8,10,8,10,10,2,6\nI,8,14,7,8,4,7,9,3,7,14,5,4,3,7,6,8\nW,5,9,7,7,6,5,11,2,2,7,8,9,7,12,1,8\nY,5,9,6,7,2,9,11,2,2,6,12,8,1,11,0,8\nN,3,7,5,5,3,7,8,6,4,7,6,7,5,9,1,6\nS,3,5,5,4,2,8,7,2,8,11,6,7,1,9,4,8\nW,4,9,6,7,6,7,5,6,2,7,7,8,6,8,4,8\nY,3,4,4,3,2,4,10,2,7,10,10,6,2,11,4,3\nS,5,9,5,4,2,8,7,4,3,13,7,7,3,10,3,7\nE,5,6,5,8,3,3,7,6,11,7,6,15,0,8,7,7\nX,4,10,5,7,2,7,7,5,4,7,6,8,3,8,4,8\nV,6,7,6,5,3,4,11,1,3,8,10,8,5,11,2,7\nM,3,7,5,5,5,7,9,6,3,7,7,9,5,9,5,10\nL,2,0,2,1,0,2,0,6,4,0,3,4,0,8,0,8\nJ,2,4,3,7,1,14,2,6,5,14,1,10,0,7,0,8\nH,5,5,6,6,3,7,6,15,0,7,8,8,3,8,0,8\nP,5,8,7,6,5,9,7,2,5,13,4,6,2,9,3,9\nS,1,3,3,2,1,8,7,3,7,10,7,8,1,9,5,8\nC,5,9,5,7,3,3,8,5,8,11,10,13,1,9,3,7\nT,2,1,2,2,1,7,11,1,6,7,10,8,1,10,1,8\nD,4,7,4,5,4,5,7,8,7,6,5,6,2,8,3,8\nQ,5,11,4,6,3,11,4,3,6,9,4,8,3,9,6,13\nD,2,5,4,4,3,9,7,5,6,10,4,6,2,8,3,8\nA,1,1,2,1,0,8,4,2,0,7,1,8,2,7,1,8\nV,6,9,6,4,2,6,9,4,2,9,7,5,5,12,2,7\nR,4,4,4,6,3,6,9,9,4,7,5,8,3,8,5,10\nO,3,3,4,5,2,7,7,9,6,7,7,7,3,8,4,8\nS,3,10,4,7,4,8,7,7,5,7,7,8,2,8,8,8\nE,3,5,4,5,4,5,9,4,4,8,7,8,4,11,7,7\nP,5,8,7,6,4,9,8,3,7,13,4,5,4,10,5,10\nV,9,12,7,7,3,7,11,6,4,10,10,4,5,12,3,9\nN,5,11,5,8,5,7,7,13,1,6,6,8,6,9,1,6\nG,7,11,7,8,6,6,6,6,6,10,7,12,2,9,4,10\nH,3,8,4,5,2,7,9,15,2,7,3,8,3,8,0,8\nG,2,3,2,2,1,7,6,6,5,7,6,9,2,9,4,9\nY,3,9,5,7,2,7,10,1,3,7,12,8,1,11,0,8\nK,6,8,9,7,7,10,4,2,3,10,3,8,6,6,7,13\nQ,5,9,5,11,7,8,7,7,3,8,7,9,3,8,6,8\nO,6,12,4,6,3,6,7,7,3,10,7,9,5,10,5,7\nA,6,8,8,7,7,6,7,3,5,7,8,10,8,11,4,7\nL,5,11,7,8,5,7,4,1,8,8,2,10,0,6,3,8\nE,2,3,4,2,2,7,8,2,7,11,7,8,2,8,3,8\nF,5,11,5,8,2,1,15,5,3,12,8,3,0,8,3,6\nW,3,2,5,3,3,8,11,3,2,6,9,8,7,11,0,8\nZ,2,7,3,5,1,7,7,3,13,9,6,8,0,8,8,8\nE,4,11,6,8,6,8,7,5,9,6,5,9,3,8,6,9\nE,7,10,9,8,7,7,7,2,8,11,7,9,3,8,5,8\nR,3,5,5,4,4,8,7,4,5,9,4,7,3,7,4,10\nA,3,5,5,8,2,8,5,3,1,7,0,8,2,7,2,8\nW,4,2,6,3,3,7,11,3,2,7,9,8,8,10,1,8\nV,5,6,5,4,3,2,12,3,3,10,11,8,2,11,1,7\nS,5,10,6,8,3,10,6,4,8,11,3,8,2,8,5,11\nU,6,10,8,8,5,5,8,6,8,7,10,10,3,9,1,8\nB,4,9,4,7,5,6,8,8,6,7,5,7,2,8,7,9\nJ,1,2,2,3,1,11,6,2,5,11,3,8,0,7,1,7\nX,1,1,2,1,0,8,7,3,4,7,6,8,2,8,4,8\nW,8,11,8,6,5,8,8,4,4,6,9,6,10,10,3,6\nX,4,8,5,6,3,7,7,4,4,7,6,7,3,8,4,8\nB,5,9,5,5,5,8,8,3,5,9,5,6,6,4,6,8\nL,4,9,5,7,2,2,4,2,9,2,0,8,0,7,1,5\nO,3,4,4,3,2,7,7,7,4,9,6,8,3,8,3,8\nW,5,10,8,8,8,8,11,2,3,6,8,8,8,10,1,8\nE,5,5,5,8,3,3,8,6,12,7,6,15,0,8,7,6\nR,1,1,2,1,1,6,9,8,4,7,5,8,2,7,4,11\nM,5,10,5,8,4,8,7,12,2,6,9,8,8,6,0,8\nD,5,10,7,7,7,9,7,4,6,9,4,6,3,8,3,8\nU,7,9,8,7,5,6,6,6,9,8,6,8,4,8,4,3\nF,8,12,7,6,3,5,10,2,5,12,6,4,2,9,6,4\nH,3,6,4,4,3,7,8,12,1,7,4,8,3,8,0,8\nC,6,9,6,7,3,5,8,7,8,13,9,9,2,11,3,6\nC,2,1,3,2,1,6,8,7,7,9,7,12,1,10,3,9\nO,2,1,2,1,1,8,7,6,5,7,6,8,2,8,3,8\nA,4,11,6,8,4,12,2,3,3,11,1,9,3,7,3,9\nA,6,11,8,8,8,8,7,6,5,6,6,9,6,8,7,3\nW,8,9,8,7,6,5,10,3,3,9,7,7,10,12,3,4\nN,4,4,5,6,2,7,7,14,2,4,6,8,6,8,0,8\nF,3,9,5,6,4,4,10,4,5,11,10,6,2,10,2,6\nJ,2,8,3,6,1,9,5,4,7,12,4,11,1,6,1,7\nG,4,8,5,6,3,6,6,7,7,10,7,11,2,11,4,9\nH,3,7,4,5,4,6,6,7,6,6,7,9,3,9,3,8\nS,4,7,5,5,3,8,7,3,6,10,6,8,2,8,4,8\nU,2,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8\nO,5,10,6,8,4,7,7,8,5,10,6,8,3,8,3,8\nN,4,5,5,4,3,7,8,5,5,7,7,6,6,9,2,5\nH,5,4,5,6,2,7,7,15,0,7,6,8,3,8,0,8\nV,3,6,4,4,2,9,11,3,3,4,11,9,2,10,1,8\nT,7,10,6,5,2,4,12,3,7,12,8,4,2,8,3,3\nU,6,10,8,8,11,7,6,4,5,7,7,8,11,9,7,10\nB,2,5,4,3,3,9,7,2,5,10,5,6,2,8,4,9\nR,4,6,6,4,4,9,8,4,6,9,3,7,3,6,4,11\nS,4,10,5,8,5,7,7,7,5,7,7,9,2,8,8,7\nF,3,4,5,3,2,4,12,4,4,13,8,4,1,10,1,7\nM,6,10,7,5,4,13,2,5,2,12,1,9,6,3,1,8\nH,3,3,4,4,2,7,5,14,1,7,9,8,3,9,0,8\nB,2,0,2,1,1,7,8,7,5,7,6,7,1,8,7,8\nK,3,5,5,3,3,5,7,2,7,10,9,11,3,8,3,7\nE,6,12,4,6,2,7,9,6,7,9,6,10,1,9,8,9\nJ,4,11,5,8,3,8,9,0,7,14,5,6,1,9,1,8\nA,3,8,5,6,4,11,3,1,2,8,3,9,3,5,2,8\nR,4,4,5,6,3,6,10,9,3,7,5,8,3,8,6,11\nS,5,9,5,5,2,5,9,2,5,13,8,8,2,8,2,6\nK,3,6,4,4,1,4,7,8,1,7,6,11,3,8,2,11\nW,4,4,5,3,3,4,10,2,2,9,9,7,6,11,1,7\nJ,6,10,8,8,4,5,9,3,6,15,7,9,2,7,2,6\nU,3,7,5,5,3,8,8,5,7,4,8,8,3,9,0,7\nZ,3,8,4,6,2,7,7,4,13,10,6,8,0,8,8,8\nF,5,9,4,4,2,5,11,3,4,12,7,4,1,8,5,4\nL,3,4,3,3,2,4,3,4,6,2,2,6,0,7,0,6\nL,3,10,4,8,2,0,2,3,6,1,0,8,0,8,0,8\nD,4,6,5,4,4,7,7,6,5,6,5,6,3,8,3,7\nU,6,10,6,8,3,8,4,15,6,7,15,8,3,9,0,8\nS,6,9,5,4,2,11,2,3,4,11,2,10,3,7,3,11\nU,3,2,4,4,2,6,8,5,6,6,8,9,6,10,1,7\nL,3,8,3,6,1,0,1,5,6,0,0,6,0,8,0,8\nK,7,11,6,6,3,7,8,3,6,9,7,8,6,9,3,7\nQ,2,2,3,3,2,7,9,4,2,7,8,10,2,9,4,8\nT,2,7,3,4,1,7,14,0,6,7,11,8,0,8,0,8\nU,2,4,3,3,1,4,8,5,6,11,9,9,3,9,1,7\nH,3,3,5,2,2,7,8,3,6,10,6,7,3,8,3,8\nY,3,5,4,3,2,4,10,1,7,10,10,6,1,11,3,4\nI,2,5,1,4,1,7,7,1,7,7,6,8,0,8,3,8\nV,3,5,5,8,2,7,8,4,3,7,14,8,3,9,0,8\nI,2,9,3,7,2,7,7,0,8,13,6,8,0,8,1,8\nE,3,3,3,4,2,3,8,6,9,7,6,14,0,8,7,8\nQ,1,2,2,3,1,8,7,5,2,8,7,9,2,9,3,9\nK,5,8,7,6,5,7,7,1,6,10,5,9,3,8,3,9\nC,5,10,5,8,3,4,10,7,8,12,10,9,2,9,2,6\nW,2,1,3,1,1,7,8,4,0,7,8,8,7,9,0,8\nZ,2,2,3,3,2,7,8,5,9,6,7,9,1,9,7,8\nM,6,6,9,6,9,9,8,5,4,7,6,7,11,9,6,3\nN,4,9,5,6,2,7,7,15,2,4,6,8,6,8,0,8\nG,2,5,3,3,2,7,7,7,6,6,6,10,2,8,4,9\nF,5,8,7,6,4,6,10,2,5,13,7,5,2,10,2,7\nY,4,5,5,7,6,9,9,6,3,6,8,8,5,10,6,4\nK,4,5,7,4,3,6,6,2,7,10,8,11,4,7,4,7\nE,2,3,3,2,2,7,7,2,7,11,7,8,2,9,4,8\nF,2,4,3,3,2,5,10,4,6,10,9,6,1,10,3,6\nG,2,3,2,2,1,7,6,6,5,6,6,9,2,9,4,9\nP,7,10,10,8,6,8,10,7,5,9,4,3,3,10,5,9\nH,3,3,3,5,2,7,9,14,3,7,3,8,3,8,0,8\nZ,3,7,4,5,3,6,8,5,9,7,7,10,1,9,7,8\nO,3,9,4,6,3,7,7,8,6,7,8,8,2,8,3,8\nB,4,8,5,6,5,7,8,5,4,7,5,7,3,8,6,8\nM,3,4,5,3,3,10,6,3,4,9,4,7,6,5,1,8\nL,3,10,4,7,1,0,1,5,6,0,0,7,0,8,0,8\nW,6,7,6,5,4,7,11,4,2,8,7,6,7,12,3,6\nM,12,12,12,6,6,8,10,6,4,5,5,10,11,13,2,7\nL,5,9,7,7,5,5,5,1,8,7,2,11,2,9,3,7\nE,4,10,5,8,3,3,9,6,12,7,5,14,0,8,8,7\nU,4,7,5,5,2,8,4,14,5,6,14,8,3,9,0,8\nX,7,9,6,4,3,8,7,2,8,9,7,8,4,12,3,7\nQ,1,2,2,2,1,8,8,5,2,8,7,9,2,9,3,9\nM,5,10,8,7,7,9,6,2,4,8,5,7,7,6,2,8\nB,4,9,6,6,8,9,7,4,3,6,7,7,6,10,7,6\nU,4,10,6,8,10,7,7,4,4,7,6,8,11,7,8,8\nP,10,13,8,7,4,8,8,6,4,13,3,5,5,9,4,8\nV,6,10,6,8,3,2,11,4,4,11,12,8,2,10,1,8\nX,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nC,4,11,5,8,3,6,8,9,8,10,8,11,2,12,4,9\nB,5,7,8,6,9,9,6,4,4,7,6,8,7,9,8,7\nN,5,9,8,7,8,7,8,4,5,7,6,7,6,8,7,5\nY,2,3,4,4,1,9,10,2,2,6,12,8,2,11,0,8\nX,1,3,2,1,1,8,7,4,8,7,6,8,2,8,5,8\nX,4,8,6,6,6,7,7,3,5,6,5,10,2,7,8,8\nG,7,11,6,6,3,9,4,4,3,8,3,5,4,7,4,9\nC,4,7,5,6,5,6,7,4,4,7,6,10,4,10,7,11\nC,2,5,3,3,2,6,8,7,8,8,7,13,1,9,4,10\nV,3,10,5,7,2,7,8,4,3,7,14,8,3,9,0,8\nQ,3,4,4,6,2,7,7,7,6,6,7,7,3,7,6,9\nD,3,2,4,4,3,7,7,7,6,6,6,5,2,8,3,7\nI,7,14,6,8,4,8,7,2,5,11,6,6,2,9,6,11\nU,4,4,4,6,2,7,5,14,5,7,13,8,3,9,0,8\nB,4,6,4,8,4,6,7,10,7,7,6,7,3,8,9,10\nU,7,11,8,8,5,3,8,5,7,10,9,9,3,9,2,6\nE,5,8,7,6,5,7,7,2,7,11,6,9,3,8,4,9\nC,4,7,5,5,3,6,7,6,8,7,6,11,1,9,4,9\nV,6,9,4,5,3,8,9,6,3,7,9,6,6,12,3,8\nA,2,6,3,4,2,11,3,3,3,11,2,10,2,6,3,8\nW,4,10,7,7,5,5,10,2,3,7,9,9,8,11,1,8\nJ,3,10,4,7,2,13,3,7,4,13,4,11,1,6,0,8\nN,11,13,9,7,4,5,9,5,6,3,3,12,6,11,2,7\nS,2,4,4,2,2,8,8,2,7,11,6,7,1,8,5,8\nO,3,7,4,5,3,8,8,8,6,7,5,8,2,8,3,7\nF,4,7,5,5,2,5,12,4,6,12,9,3,1,10,3,4\nM,5,3,5,5,3,7,7,12,1,7,9,8,8,6,0,8\nD,4,8,4,6,3,6,7,10,10,7,6,6,3,8,4,8\nX,6,11,8,8,8,7,6,3,5,6,6,9,4,7,11,10\nP,7,10,10,8,5,6,13,7,2,11,5,2,1,11,4,8\nW,7,7,7,5,6,4,10,2,3,10,9,8,7,11,2,6\nJ,2,6,2,4,1,13,2,8,4,13,4,12,1,6,0,8\nS,4,6,5,4,5,9,7,4,3,9,5,8,4,8,10,10\nA,2,7,4,5,3,10,3,1,2,7,3,9,1,5,1,7\nM,5,8,8,6,5,3,7,4,5,11,12,11,5,8,2,7\nH,1,1,2,1,1,7,8,11,1,7,5,8,3,8,0,8\nT,2,7,4,4,1,9,15,1,5,6,11,9,0,8,0,8\nN,2,2,3,3,2,7,8,5,4,7,6,7,4,8,1,7\nL,3,8,4,6,1,0,1,6,6,0,0,6,0,8,0,8\nG,5,10,7,8,6,8,7,7,6,6,7,7,2,7,8,13\nL,2,3,3,2,1,7,4,1,8,8,2,10,0,7,2,8\nD,5,10,7,7,5,10,6,4,7,11,3,6,4,6,4,8\nL,3,2,3,3,2,4,4,3,8,2,1,7,0,7,1,6\nU,5,7,6,5,3,4,8,5,7,9,8,9,3,9,2,5\nT,2,6,3,4,2,7,13,0,5,7,10,8,0,8,0,8\nP,4,10,5,8,4,5,11,3,7,11,9,4,0,10,4,7\nZ,5,9,7,6,4,6,9,3,10,11,9,5,2,8,6,5\nX,4,7,6,5,3,11,6,2,8,10,0,6,3,7,3,10\nL,2,5,4,3,2,6,4,1,8,8,2,10,0,7,2,8\nE,3,7,4,5,3,7,7,6,10,6,5,9,2,8,6,8\nA,3,8,5,6,3,12,2,3,2,10,2,9,2,6,2,8\nW,5,11,7,8,9,8,7,6,3,5,8,8,9,8,6,2\nP,5,9,7,7,4,9,7,3,6,13,4,5,5,10,5,10\nW,3,3,5,5,3,5,8,5,1,7,8,8,8,10,0,8\nW,8,8,11,7,11,6,8,6,6,6,6,7,10,9,9,9\nJ,2,7,2,5,1,11,7,1,7,11,3,6,0,7,1,7\nF,4,8,6,6,3,4,12,4,5,13,8,4,2,10,2,6\nY,6,11,6,8,3,3,10,2,7,11,12,6,2,11,3,5\nF,4,8,5,6,5,7,9,6,3,8,6,8,3,10,7,11\nT,3,5,4,4,2,5,12,3,7,11,9,4,1,11,2,5\nN,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8\nI,3,10,4,8,3,7,7,0,7,13,6,8,0,8,1,8\nM,6,7,9,5,6,5,7,3,4,10,10,10,10,6,4,8\nL,3,8,5,6,3,3,5,1,8,3,1,9,0,7,1,6\nQ,4,8,6,7,3,8,7,7,6,6,7,8,3,8,5,9\nV,4,11,6,9,4,7,11,2,4,6,11,9,3,9,2,8\nM,6,8,8,7,10,8,7,4,4,7,6,7,11,8,5,5\nY,3,4,4,6,1,6,12,2,3,9,12,8,0,10,0,8\nQ,4,9,6,8,3,8,5,9,7,6,4,8,3,8,4,8\nA,3,3,5,4,2,6,3,3,2,6,2,8,3,6,3,7\nF,7,12,6,6,4,6,10,3,4,10,7,5,4,9,8,6\nR,4,8,6,6,4,10,8,3,7,10,1,7,3,6,4,10\nX,6,9,9,7,5,7,7,0,8,10,6,8,3,8,3,7\nX,4,6,7,4,3,9,7,1,8,10,4,7,3,8,3,8\nB,2,3,3,2,2,10,6,2,6,10,4,7,2,8,4,10\nN,3,6,3,4,2,7,7,13,1,5,6,8,5,8,0,8\nF,2,3,2,2,1,5,10,3,5,10,9,5,1,10,3,6\nI,5,12,5,7,3,9,8,3,6,13,4,5,2,8,5,10\nY,5,6,5,4,3,4,9,1,7,10,10,6,1,10,2,4\nA,8,15,6,8,4,9,3,3,2,8,4,11,6,4,5,8\nZ,4,7,6,5,3,7,7,2,9,12,6,9,1,9,6,8\nF,6,10,5,5,4,10,8,4,6,11,3,4,2,9,6,9\nG,2,4,3,2,1,7,8,5,6,9,7,10,2,9,4,10\nL,3,9,5,7,3,8,4,2,7,8,2,8,1,6,2,9\nT,5,5,5,4,2,5,11,3,7,12,9,4,1,11,2,4\nJ,6,11,8,8,6,7,7,7,6,8,7,8,3,7,4,6\nF,3,5,5,4,2,4,11,4,6,13,9,6,1,9,2,6\nS,3,9,4,7,2,7,6,6,9,4,6,10,0,9,9,8\nV,3,2,5,3,2,7,12,2,3,7,11,8,3,10,1,8\nE,6,13,5,7,4,7,8,4,4,11,5,9,3,9,8,11\nP,4,11,5,8,3,5,10,11,5,9,6,5,2,10,4,8\nR,5,11,7,8,5,8,9,5,6,8,5,8,3,7,6,11\nK,3,3,3,4,1,3,7,8,3,7,6,11,4,8,2,11\nA,4,7,5,5,3,8,2,2,2,7,2,8,2,7,3,6\nL,1,0,2,1,0,2,1,6,4,0,2,5,0,8,0,8\nR,2,1,3,2,2,6,7,4,5,6,5,7,5,7,3,8\nP,6,9,8,6,7,6,8,7,4,8,7,9,2,9,7,9\nN,4,8,5,6,2,7,7,15,2,4,6,8,6,8,0,8\nE,5,9,4,4,2,8,7,3,4,10,5,9,3,9,8,11\nK,5,7,8,5,5,3,8,2,7,10,11,11,3,7,3,5\nE,3,7,5,5,4,7,8,5,8,6,5,9,3,8,6,9\nI,4,7,5,8,5,8,9,4,4,7,7,9,4,7,7,6\nD,5,11,7,8,7,7,7,5,7,7,7,5,6,8,3,7\nV,3,9,5,6,3,7,9,3,1,7,12,8,3,9,1,8\nX,2,1,2,1,0,8,7,4,4,7,6,8,3,8,4,8\nX,4,11,5,8,4,7,7,4,4,7,6,7,2,8,4,8\nW,3,3,4,2,2,4,11,3,2,9,9,7,6,11,1,7\nO,5,9,6,7,3,9,9,9,9,5,8,10,3,8,4,8\nB,5,8,7,6,6,7,9,5,6,10,6,5,3,7,7,8\nT,8,12,7,7,2,5,11,3,7,13,7,4,2,8,4,4\nI,3,11,3,8,4,8,7,0,7,7,6,8,0,8,3,8\nH,3,4,5,2,2,7,7,3,6,10,6,8,3,8,3,8\nB,4,8,6,6,5,8,9,4,6,10,5,5,2,8,6,9\nV,3,3,4,2,1,5,12,2,2,9,11,7,3,12,1,7\nP,4,8,5,6,2,4,13,8,1,11,6,3,1,10,4,8\nS,5,10,6,8,3,9,8,6,9,5,6,5,0,8,9,7\nM,9,9,13,8,14,6,6,5,4,6,5,8,15,11,7,9\nG,1,0,1,1,0,7,7,6,5,6,5,9,1,7,5,10\nP,3,7,5,5,3,6,10,3,6,10,8,4,4,10,4,7\nN,4,7,5,5,4,7,7,7,5,8,6,6,3,7,3,8\nJ,2,11,3,8,2,11,6,1,8,11,2,6,0,7,1,7\nD,5,9,5,5,3,5,8,4,6,9,6,6,4,10,6,5\nL,4,11,6,9,3,3,4,2,10,2,0,8,0,7,1,5\nP,8,13,7,7,5,10,7,4,5,12,4,5,4,11,5,8\nM,7,11,11,8,8,4,7,4,5,10,11,11,9,7,4,8\nV,7,9,7,7,3,3,11,4,4,10,12,7,3,10,1,7\nN,6,5,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nS,2,1,3,2,2,8,8,6,5,7,5,7,2,8,8,8\nI,5,6,7,7,6,7,9,4,5,7,7,7,3,8,8,8\nE,6,14,5,8,4,8,8,4,5,11,5,9,3,9,8,11\nF,3,3,3,4,1,1,12,5,4,11,10,7,0,8,3,7\nF,5,10,8,7,6,9,8,2,6,12,4,5,4,8,4,9\nW,8,8,8,6,6,3,11,2,3,10,9,8,7,11,2,6\nK,7,10,6,5,3,9,8,3,6,9,4,6,5,8,4,7\nS,3,7,4,5,5,8,8,5,3,8,5,8,3,8,10,7\nA,2,3,4,2,1,8,2,2,2,7,2,8,2,6,2,7\nY,4,8,6,6,6,9,4,7,4,7,9,7,3,9,8,4\nY,3,4,4,2,2,4,11,2,7,11,10,5,1,11,2,5\nH,4,9,5,6,2,7,6,15,1,7,7,8,3,8,0,8\nD,5,11,6,8,7,8,7,5,5,10,5,5,3,8,3,8\nO,6,10,5,5,3,7,5,6,3,10,6,10,5,9,5,8\nM,5,2,6,3,4,7,6,7,5,7,8,10,8,5,2,9\nH,4,9,6,6,8,7,6,5,3,6,6,8,7,7,8,8\nU,2,3,3,2,1,6,9,5,5,7,9,9,3,10,1,8\nW,1,0,1,0,0,8,8,3,0,7,8,8,3,9,0,8\nN,5,5,5,8,3,7,7,15,2,4,6,8,6,8,0,8\nF,5,10,6,7,6,5,10,3,6,10,10,6,2,10,3,6\nR,4,7,4,5,4,6,8,8,4,6,5,7,2,7,5,11\nV,5,9,7,7,3,5,9,4,1,9,13,8,5,10,2,9\nA,3,8,5,6,3,6,5,3,0,6,1,8,2,6,1,7\nA,3,2,5,4,2,8,2,2,2,7,1,8,2,6,2,7\nR,4,8,6,6,5,7,8,5,6,7,5,7,3,7,5,8\nT,2,0,2,0,0,7,15,2,3,7,10,8,0,8,0,8\nF,4,6,4,8,2,1,14,5,3,12,9,5,0,8,3,6\nT,3,4,4,3,2,7,12,3,5,7,11,8,2,11,1,8\nI,4,10,5,8,3,7,7,0,8,14,6,8,0,8,1,8\nN,4,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8\nX,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nM,3,7,4,5,3,7,7,11,1,7,9,8,8,6,0,8\nE,4,11,4,8,3,3,7,6,11,7,6,15,0,8,7,7\nN,4,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8\nU,6,8,7,6,4,4,8,5,7,9,8,9,3,9,2,5\nF,3,9,4,7,4,5,10,1,7,10,9,7,1,10,3,6\nV,4,5,5,4,2,4,13,4,4,10,11,7,3,10,1,8\nJ,4,10,5,7,3,9,7,2,6,14,4,8,0,7,1,7\nE,4,9,6,7,4,4,10,5,8,11,10,9,2,8,5,3\nM,6,11,9,8,11,7,8,6,4,7,6,8,6,9,9,8\nR,5,9,8,6,5,11,6,3,6,11,1,6,4,5,4,10\nL,5,10,6,8,5,3,4,5,7,2,0,7,1,6,1,6\nV,6,9,6,6,3,3,11,4,4,10,12,8,2,10,1,8\nR,1,0,2,0,1,6,10,7,2,7,5,8,2,7,4,9\nL,2,5,3,4,2,4,4,4,7,2,1,6,0,7,1,6\nC,4,10,5,7,3,6,8,7,7,13,8,8,2,11,3,7\nF,3,7,4,5,2,1,13,4,3,12,10,6,0,8,2,6\nF,2,3,3,1,1,7,9,2,5,13,6,5,1,9,1,8\nQ,3,6,4,8,4,9,7,7,3,5,7,10,3,9,5,10\nP,2,3,2,1,1,5,10,3,4,10,8,4,0,9,3,7\nV,5,11,7,8,2,8,8,5,3,6,14,8,3,9,0,8\nG,2,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10\nI,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nW,7,12,7,6,5,4,8,1,3,8,9,8,9,10,2,5\nA,7,10,9,8,8,7,7,7,4,7,6,9,4,8,11,1\nM,5,6,7,4,5,11,6,3,4,9,2,6,7,6,2,8\nC,5,9,4,5,3,7,6,4,4,9,9,11,4,10,9,10\nZ,2,5,4,3,2,7,7,2,9,11,6,8,1,8,5,8\nP,3,8,5,6,4,7,8,5,6,8,7,4,2,10,4,7\nI,5,9,4,5,2,8,8,3,6,13,4,6,1,7,4,9\nP,6,10,8,7,7,7,8,7,4,8,7,9,3,9,8,9\nJ,2,10,3,7,1,12,3,10,4,13,5,13,1,6,0,8\nL,3,9,5,7,4,5,5,1,8,4,2,8,3,7,2,6\nC,4,7,5,5,3,3,8,5,7,10,9,14,1,8,3,8\nS,5,8,6,6,4,9,7,4,6,10,3,6,2,7,5,10\nV,3,3,4,2,1,4,12,3,3,9,11,7,2,11,1,8\nL,3,9,3,6,1,0,1,5,6,0,0,7,0,8,0,8\nV,6,11,6,6,3,5,10,4,3,10,8,5,4,11,2,8\nS,4,10,4,5,2,9,3,3,4,9,2,9,3,6,4,9\nF,3,7,4,4,1,2,12,5,5,12,10,8,0,8,2,6\nN,4,9,5,7,5,7,7,13,1,7,6,8,5,8,0,7\nY,3,4,4,3,2,4,11,2,7,11,10,5,1,11,2,5\nB,3,5,5,3,4,9,7,3,5,10,5,7,2,8,5,9\nW,6,11,9,8,14,9,7,5,2,7,6,8,14,11,3,6\nJ,5,10,4,8,3,9,8,2,3,11,6,7,2,10,6,12\nL,2,2,3,3,2,4,4,4,7,2,1,6,1,7,1,6\nU,4,9,6,7,3,7,9,6,7,6,10,9,3,9,1,8\nZ,3,9,4,7,4,8,6,5,9,7,6,6,2,7,7,8\nQ,6,10,8,7,7,8,6,8,4,7,7,7,6,6,8,8\nG,4,5,5,8,2,7,6,8,9,6,5,11,1,8,6,11\nY,5,10,7,8,4,4,9,1,8,11,12,9,1,11,2,6\nL,3,6,4,4,2,7,3,2,7,7,2,8,1,6,2,7\nJ,6,8,4,12,3,10,6,3,4,11,3,5,3,8,7,10\nW,9,10,9,7,9,4,10,2,3,9,8,8,8,11,2,6\nP,2,3,4,2,2,7,10,4,3,11,5,3,1,9,2,8\nR,3,7,5,5,3,10,7,4,6,10,2,7,3,7,4,10\nS,4,6,5,4,3,8,7,3,7,10,6,8,2,8,4,8\nB,6,9,8,7,7,8,8,5,5,7,5,6,4,8,6,7\nP,3,5,6,4,3,7,10,4,4,12,5,3,1,10,3,8\nZ,1,0,1,1,0,7,7,2,10,8,6,8,0,8,6,8\nS,4,8,5,6,3,8,8,6,9,5,6,7,0,8,9,7\nD,3,6,3,4,2,5,8,10,8,8,7,5,3,8,3,8\nB,2,3,3,1,2,8,7,2,5,10,5,7,1,8,4,9\nW,5,9,7,7,7,8,5,7,2,6,7,8,7,7,5,3\nQ,3,6,4,6,2,7,7,7,5,6,7,8,3,7,5,9\nE,3,10,4,7,5,7,7,6,8,8,8,9,3,8,6,9\nW,4,7,6,5,3,9,8,5,1,7,8,8,8,9,0,8\nA,3,11,6,8,2,9,5,3,1,8,1,8,3,7,2,8\nF,3,9,5,7,4,6,10,2,6,10,9,5,1,10,3,6\nT,6,14,5,8,3,6,10,2,6,11,7,6,2,9,5,4\nK,4,6,5,8,2,3,7,8,2,7,5,12,3,8,3,10\nA,3,5,5,3,2,8,2,2,2,7,1,8,2,6,2,7\nK,6,9,9,6,5,5,7,2,8,10,9,11,4,7,4,7\nP,10,15,9,8,6,8,9,4,4,12,4,3,5,10,6,6\nP,3,5,5,4,2,7,10,4,4,12,5,3,1,10,2,8\nR,2,4,4,3,3,8,7,5,4,8,5,7,3,7,4,11\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nZ,4,9,6,6,5,9,10,5,4,6,5,7,3,9,9,4\nJ,3,7,5,5,2,7,4,5,4,14,9,14,1,6,1,6\nO,7,11,7,8,7,7,8,8,4,10,7,7,5,8,5,10\nJ,4,8,5,6,2,9,6,2,8,15,4,8,0,7,0,8\nT,3,6,4,4,3,6,11,4,5,10,8,5,3,12,2,5\nW,5,7,5,5,5,4,9,2,3,9,8,8,6,11,2,6\nR,5,9,8,8,9,9,7,4,4,8,5,7,9,9,7,5\nP,5,10,5,5,3,9,8,3,5,13,4,4,3,10,6,7\nF,4,10,6,8,4,6,10,3,6,13,7,5,2,10,2,7\nN,7,9,6,5,2,5,9,4,6,3,3,10,5,9,2,7\nJ,3,7,4,5,3,8,7,1,6,11,5,8,0,7,1,6\nN,7,10,9,9,9,8,9,4,4,7,4,7,9,6,7,6\nS,3,4,4,6,2,9,8,6,9,5,5,6,0,8,9,7\nA,4,8,7,6,5,9,5,2,5,10,2,6,2,6,3,8\nS,7,13,6,7,3,9,2,2,5,8,1,8,3,7,5,10\nU,7,11,6,6,4,8,6,5,5,6,7,8,5,7,3,7\nA,1,3,2,2,1,9,2,2,1,8,2,8,1,6,1,8\nO,7,10,5,5,3,9,6,5,6,9,3,8,5,8,5,8\nW,5,5,6,3,4,4,11,3,2,9,9,8,7,11,1,7\nQ,4,5,5,7,3,9,9,8,6,5,8,9,3,7,6,10\nK,8,12,9,6,5,10,5,4,6,11,2,7,5,8,4,10\nH,6,7,8,9,7,9,8,3,1,8,6,8,4,9,9,9\nV,2,1,4,2,1,8,12,3,3,5,11,9,2,10,1,8\nC,2,1,3,2,1,6,7,6,9,7,6,14,0,8,4,9\nO,6,10,6,7,4,9,6,8,6,10,3,8,4,8,4,6\nI,1,7,1,5,1,8,7,0,8,7,6,7,0,8,3,7\nI,7,12,5,7,3,10,5,4,5,12,3,7,3,8,5,10\nT,2,2,3,3,2,6,11,3,6,8,11,8,2,11,1,7\nZ,4,5,5,7,4,11,5,4,5,10,3,9,2,6,6,10\nR,4,7,5,5,3,6,9,9,5,6,5,8,2,8,5,10\nI,2,11,2,9,2,7,7,0,8,7,6,8,0,8,3,8\nO,7,10,7,7,6,8,6,7,5,10,5,11,5,7,5,6\nS,4,5,5,4,5,8,7,4,5,7,6,8,5,8,8,11\nL,4,4,4,6,1,0,0,6,6,0,1,5,0,8,0,8\nD,4,8,5,6,4,8,7,6,7,10,4,5,3,8,3,8\nE,5,11,7,8,6,6,8,4,7,11,9,8,3,8,4,6\nZ,4,6,5,4,3,9,6,2,9,11,4,9,1,7,6,10\nL,8,14,6,8,4,5,5,3,8,10,4,13,3,6,6,7\nK,4,6,6,4,4,8,6,1,6,10,5,9,4,7,5,8\nE,4,6,6,4,4,6,8,2,8,11,7,9,2,9,4,8\nK,4,5,5,3,3,5,7,4,7,6,6,11,3,8,5,9\nC,4,7,5,6,6,6,5,4,5,7,5,11,5,10,7,11\nZ,1,3,2,2,1,7,7,5,8,6,6,8,1,8,7,8\nW,4,4,6,6,3,11,8,5,1,6,8,8,8,9,0,8\nM,11,15,10,8,6,8,11,5,5,4,5,9,9,9,2,7\nH,2,1,2,2,2,8,7,6,5,7,6,7,3,8,3,6\nE,2,7,4,5,3,6,7,7,9,8,8,9,2,9,6,8\nD,4,2,5,4,4,7,7,7,7,6,6,5,2,8,3,7\nX,5,8,7,6,3,9,7,2,8,10,4,7,3,8,4,8\nT,5,10,7,8,6,7,11,3,7,7,11,8,2,12,1,8\nD,3,5,5,3,3,10,6,3,6,10,3,6,2,8,2,8\nQ,3,5,3,6,3,8,6,6,4,9,6,9,2,9,4,8\nQ,5,8,6,7,3,8,8,8,7,5,7,9,3,7,5,10\nO,2,3,3,2,2,8,7,6,4,9,5,8,2,8,2,8\nW,6,8,6,6,5,4,10,3,3,9,8,7,7,11,2,6\nE,4,11,6,8,5,7,7,2,8,11,6,10,3,7,5,8\nX,9,14,10,8,5,3,9,4,8,12,11,9,4,8,3,5\nB,4,8,4,6,3,6,7,9,7,7,6,7,2,8,9,10\nU,8,11,9,8,6,4,8,6,8,9,7,9,7,9,6,1\nM,4,7,4,5,3,8,7,12,1,6,9,8,8,6,0,8\nR,2,3,4,2,2,8,8,3,5,9,4,7,2,6,3,10\nP,3,5,5,4,3,7,10,4,4,12,4,3,1,10,3,8\nH,4,11,5,9,5,7,7,13,1,7,6,8,3,8,0,8\nV,4,6,5,6,6,7,8,5,5,7,6,8,6,8,6,6\nZ,2,5,3,4,2,7,7,5,9,6,6,8,2,8,7,8\nF,1,1,2,1,0,2,12,4,3,11,9,6,0,8,2,7\nC,7,10,8,8,5,4,8,6,7,12,10,12,2,11,3,7\nR,3,4,3,3,3,7,7,5,5,6,5,7,3,7,4,8\nD,4,6,6,6,5,6,7,5,6,6,5,7,4,7,5,5\nV,3,7,5,5,1,5,8,5,3,9,14,8,3,10,0,8\nY,6,8,6,6,3,4,10,3,7,11,11,5,1,11,3,4\nO,3,9,4,7,4,7,6,8,5,6,4,8,3,8,3,7\nP,9,14,7,8,3,8,8,7,4,14,3,5,5,10,4,8\nI,2,6,3,4,2,7,7,0,8,13,6,8,0,8,1,8\nQ,6,9,7,11,9,8,4,8,4,6,5,7,5,8,7,11\nE,6,13,5,7,4,6,8,5,5,10,6,9,3,8,8,11\nK,6,11,9,9,6,8,7,2,7,10,4,8,4,7,4,8\nF,3,5,5,4,2,6,11,2,6,14,7,4,1,10,2,7\nG,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,9\nA,4,10,6,7,2,9,4,3,2,8,1,8,3,7,3,8\nR,1,0,1,1,0,6,8,7,3,7,5,8,2,7,4,11\nL,5,11,5,6,3,6,6,3,6,11,6,11,3,7,6,8\nO,5,10,6,7,5,8,6,8,7,8,4,10,4,9,5,6\nU,3,6,5,4,2,7,9,6,7,5,10,9,3,9,1,8\nP,2,4,2,2,1,5,10,4,4,10,8,4,1,10,3,7\nJ,2,4,2,6,1,11,3,10,3,13,7,13,1,6,0,8\nX,4,11,7,8,6,8,8,2,6,7,6,6,6,10,9,8\nN,3,5,5,3,2,5,9,2,4,11,8,8,5,8,0,7\nM,6,10,9,7,6,11,6,2,5,9,3,6,9,8,2,9\nZ,4,9,5,7,5,7,8,5,9,7,7,8,1,8,7,8\nX,3,5,5,4,2,9,6,1,8,10,4,8,2,8,3,9\nM,4,7,5,5,4,9,6,6,5,6,8,6,8,5,2,7\nC,3,9,4,7,3,6,8,8,7,10,7,12,2,10,4,10\nT,2,5,3,4,2,7,12,3,6,7,11,8,2,11,1,8\nA,2,4,4,3,2,10,2,2,2,9,2,9,2,6,2,8\nZ,3,6,5,4,5,7,8,2,7,7,6,8,0,8,8,8\nF,3,7,4,5,3,4,10,3,5,10,10,6,1,10,3,6\nZ,8,12,8,6,5,10,3,4,7,12,3,11,3,6,7,11\nS,3,7,5,5,6,6,6,3,1,7,5,6,2,8,10,3\nO,5,7,6,5,7,8,7,5,1,7,6,8,8,8,5,10\nP,1,3,3,1,1,7,9,4,3,11,5,4,1,9,2,8\nE,3,4,3,7,2,3,7,6,10,7,6,15,0,8,7,7\nL,8,14,7,8,3,8,3,3,5,12,4,13,3,7,6,8\nI,7,15,5,8,3,10,5,6,4,13,3,7,3,8,5,10\nY,6,6,5,9,3,7,9,2,2,7,10,5,4,10,5,6\nD,2,4,3,3,2,9,6,4,5,10,4,5,2,8,2,8\nK,7,10,10,8,8,4,8,1,7,10,8,10,3,8,4,7\nW,4,5,5,4,4,4,10,3,2,9,8,7,6,11,1,7\nM,5,9,8,7,8,8,7,6,5,6,7,8,8,6,2,8\nS,6,10,6,5,3,11,3,4,3,12,5,9,2,10,2,9\nZ,3,3,4,5,2,7,7,4,14,9,6,8,0,8,8,8\nV,3,7,5,5,2,7,12,3,4,6,11,9,2,10,1,8\nF,4,9,6,6,5,7,9,3,6,12,6,6,3,9,3,7\nI,5,11,4,6,2,10,6,2,5,11,4,6,2,9,4,11\nZ,3,7,5,5,5,8,8,6,3,6,4,6,3,9,8,3\nI,3,5,5,6,4,8,7,4,6,6,6,7,3,9,8,9\nC,6,11,8,8,9,8,6,5,3,8,7,11,10,8,6,5\nG,2,1,2,2,1,7,7,6,5,6,6,10,2,9,4,9\nQ,7,8,9,12,13,8,10,5,1,5,7,10,7,14,8,13\nD,5,10,6,7,3,5,7,10,9,7,6,5,3,8,4,8\nX,3,6,5,4,3,8,7,3,8,6,6,8,2,8,6,8\nR,9,11,7,6,4,8,7,5,5,10,3,8,6,6,6,11\nI,1,8,2,6,2,7,7,0,7,7,6,8,0,8,2,8\nT,7,11,7,8,6,6,12,5,5,11,8,4,3,12,2,4\nV,5,9,5,7,2,2,11,5,4,12,12,8,2,10,1,8\nN,2,4,3,3,2,6,8,5,4,7,7,7,5,9,1,6\nY,8,10,8,8,4,2,12,5,5,13,12,6,1,11,2,5\nT,4,11,5,8,3,9,14,0,5,6,10,8,0,8,0,8\nU,3,4,4,3,2,7,8,5,6,5,9,9,5,10,1,7\nX,3,3,5,2,2,7,8,1,8,10,7,8,2,8,3,7\nI,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nM,6,10,8,8,9,8,7,5,5,6,7,7,11,7,4,6\nN,3,8,4,6,2,7,7,14,2,5,6,8,5,8,0,8\nN,5,9,8,7,5,4,10,3,3,9,9,9,6,7,2,7\nC,7,11,9,8,6,7,7,8,6,6,7,11,4,7,4,9\nB,4,10,6,8,7,8,7,4,7,6,6,6,6,8,6,10\nP,2,6,2,4,2,5,11,8,2,10,6,4,1,10,3,8\nO,4,9,6,7,4,7,7,8,4,7,6,11,3,8,4,7\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nK,6,9,9,7,5,3,9,3,7,11,11,11,3,8,3,5\nT,6,9,7,8,7,6,8,4,9,8,8,8,3,10,8,7\nP,6,11,6,8,3,4,13,9,2,10,6,3,1,10,4,8\nG,4,9,5,7,3,6,7,7,8,9,8,10,2,10,4,9\nU,12,15,10,8,5,5,3,4,5,4,7,6,6,6,2,7\nK,4,8,6,6,5,4,7,1,6,10,9,11,3,8,3,6\nD,5,8,6,6,5,7,8,4,7,7,6,8,7,8,3,7\nE,4,9,6,7,4,5,8,3,9,10,8,10,2,8,4,6\nI,6,15,4,8,3,11,5,4,5,12,2,7,3,8,5,10\nV,4,6,4,4,2,3,12,4,3,11,11,7,2,10,1,7\nV,5,9,7,7,5,8,11,3,2,5,10,9,5,10,5,8\nU,4,9,6,7,3,6,8,6,7,7,10,9,3,9,1,8\nD,4,8,5,6,4,7,7,7,7,7,6,4,3,8,3,7\nU,7,9,8,7,3,3,10,6,7,13,11,8,3,9,1,7\nO,5,10,7,8,3,9,6,9,8,7,5,10,3,8,4,8\nP,3,5,5,3,3,8,10,3,4,12,4,2,1,10,3,8\nD,4,10,5,8,5,7,7,6,7,7,7,5,6,8,3,7\nJ,3,11,5,8,5,8,7,1,6,11,5,9,1,6,1,6\nG,4,5,5,8,3,8,7,8,7,6,7,8,2,7,6,11\nO,5,5,6,7,3,8,7,8,8,6,7,9,3,8,4,8\nZ,3,4,5,6,4,10,7,5,5,8,3,7,3,6,6,7\nC,5,7,6,5,6,5,6,4,4,7,7,9,5,10,4,8\nH,7,10,9,8,6,10,6,3,6,10,3,7,5,6,5,9\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nD,3,3,3,5,2,5,7,10,7,7,6,5,3,8,3,8\nH,6,8,9,10,9,7,5,4,3,6,4,7,7,7,10,9\nA,5,10,5,5,3,11,2,4,1,11,4,11,4,3,4,10\nD,3,4,4,3,2,7,7,7,7,7,6,5,2,8,3,7\nH,3,6,4,4,4,8,7,6,6,7,6,8,3,8,3,8\nR,4,9,5,6,5,7,8,5,5,8,5,8,3,6,6,9\nO,2,1,3,2,2,7,7,7,4,7,6,8,2,8,2,8\nO,7,10,7,8,5,7,7,8,6,10,7,9,3,8,4,7\nE,4,9,4,6,3,3,7,6,11,7,6,15,0,8,7,7\nF,4,11,5,8,2,1,12,5,4,11,10,7,0,8,3,6\nT,2,0,2,1,0,8,15,2,4,6,10,8,0,8,0,8\nB,4,6,5,4,5,7,6,6,7,6,6,6,2,9,6,10\nG,2,5,3,3,2,6,7,6,6,6,6,11,2,9,4,9\nF,3,3,3,4,1,1,14,5,3,12,10,5,0,8,2,6\nQ,9,15,8,8,6,12,4,3,6,10,4,7,4,9,6,12\nA,6,9,5,5,3,8,3,3,2,7,4,12,5,5,4,7\nB,4,9,4,7,3,6,7,9,7,7,6,7,2,8,9,10\nQ,3,3,4,4,3,8,8,6,3,5,7,10,3,8,5,9\nT,1,1,2,1,0,7,14,1,4,7,10,8,0,8,0,8\nA,3,7,5,5,3,6,3,2,2,4,2,7,2,5,2,6\nM,4,10,5,8,6,8,5,11,1,6,8,8,8,5,1,5\nV,4,6,5,4,6,8,6,4,2,8,7,9,7,9,4,8\nE,4,7,5,5,5,7,7,5,8,6,5,9,3,8,6,9\nH,7,11,10,8,9,9,7,3,6,10,4,7,5,6,4,9\nQ,2,4,3,5,2,8,7,5,2,8,7,9,2,10,3,9\nG,5,9,4,5,3,7,9,4,3,8,6,5,3,10,9,7\nH,2,3,4,2,2,6,9,3,6,10,7,8,3,8,3,8\nH,4,6,4,4,2,7,5,14,1,7,9,8,3,9,0,8\nM,6,9,9,6,7,9,6,2,5,9,5,7,8,6,2,8\nO,6,10,8,8,9,7,8,6,2,7,6,7,10,9,6,10\nJ,2,3,3,5,1,11,2,10,3,13,8,14,1,6,0,8\nZ,5,10,6,7,3,7,7,4,15,9,6,8,0,8,8,8\nF,4,8,6,6,4,6,10,3,7,10,9,5,2,10,4,5\nB,5,10,7,8,7,8,7,7,6,6,6,5,2,8,7,9\nT,6,10,6,6,2,6,9,3,8,13,6,5,2,9,4,5\nT,3,7,4,4,1,8,15,1,5,6,11,9,0,8,0,8\nO,4,8,4,6,3,7,7,8,5,9,7,10,3,8,3,7\nI,5,10,6,8,4,6,6,2,7,7,6,10,0,9,4,8\nD,3,6,3,4,2,6,8,9,8,8,7,6,3,8,3,8\nD,4,6,5,4,4,8,7,5,5,10,5,5,3,8,3,8\nQ,2,3,3,3,2,8,8,6,2,5,7,10,2,9,5,10\nW,6,10,6,7,6,3,11,2,2,10,9,8,6,11,2,7\nA,4,9,6,7,6,8,9,8,5,6,5,8,3,6,7,5\nI,2,8,3,6,2,6,8,0,6,13,7,7,0,8,1,7\nM,4,5,5,8,4,7,7,12,2,7,9,8,8,6,0,8\nL,4,9,6,6,6,5,7,3,6,8,7,11,7,11,6,7\nO,4,8,5,6,4,8,7,8,5,7,7,9,3,8,3,8\nI,4,8,5,6,3,9,8,2,8,7,6,5,0,8,4,7\nQ,4,8,5,9,5,8,7,7,3,8,7,10,3,9,6,8\nG,4,6,4,4,2,6,7,6,6,9,8,10,2,8,4,9\nQ,2,3,3,4,2,8,8,5,2,8,8,10,2,9,4,8\nC,4,11,5,8,2,5,7,7,10,7,7,13,1,8,4,9\nH,2,4,3,3,2,8,8,6,6,7,5,7,3,8,3,7\nR,1,0,1,0,0,6,10,6,1,7,5,8,1,7,4,10\nZ,2,5,3,4,2,7,8,5,9,6,6,9,2,9,7,8\nA,3,6,6,4,3,7,5,2,3,6,2,6,2,6,3,5\nD,3,6,4,4,4,7,7,4,7,7,6,6,3,8,3,7\nY,3,10,5,7,1,7,10,2,2,7,13,8,1,11,0,8\nA,7,11,5,6,3,10,0,2,2,10,4,12,3,4,3,8\nP,6,11,8,9,6,8,9,3,5,13,5,3,1,10,3,8\nZ,2,4,4,3,2,7,8,2,9,11,6,8,1,9,5,8\nO,3,5,4,3,2,7,7,7,5,9,7,8,2,8,3,8\nI,2,10,2,7,2,7,7,0,8,7,6,8,0,8,3,8\nR,4,8,6,6,6,7,7,3,7,7,6,6,6,8,4,9\nJ,3,8,4,6,3,9,6,2,6,12,3,8,1,6,2,6\nF,3,7,5,5,3,9,9,2,6,13,5,4,1,10,3,9\nX,4,10,5,8,3,7,7,4,4,7,6,8,3,8,4,8\nE,2,5,4,3,2,6,7,2,8,11,7,9,2,8,4,8\nL,2,5,3,4,1,7,3,1,7,8,2,10,0,7,2,8\nS,3,6,4,4,2,7,7,5,8,5,6,9,0,9,9,8\nH,3,2,5,4,4,8,7,6,6,7,6,7,3,8,3,7\nE,5,10,7,9,8,7,7,5,3,7,7,9,6,10,10,12\nE,4,5,5,8,3,3,7,6,11,7,6,15,0,8,7,7\nU,3,7,4,5,3,6,8,6,6,6,9,9,3,9,0,8\nI,0,3,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nB,3,2,4,3,4,7,7,5,5,7,6,6,2,8,6,9\nT,2,3,3,4,1,8,13,0,6,6,11,8,0,8,0,8\nB,2,4,4,3,3,9,7,2,6,11,5,6,2,8,4,9\nQ,4,7,6,7,3,8,8,7,6,5,7,8,3,8,5,9\nY,4,8,6,6,2,4,10,2,8,10,13,9,1,11,2,7\nH,5,9,7,7,9,8,9,5,3,7,6,6,9,8,9,7\nL,1,4,3,3,1,7,3,1,7,8,2,10,0,7,2,8\nB,2,3,2,1,2,7,7,5,5,7,6,6,1,8,5,9\nR,4,6,6,4,5,8,7,7,3,8,5,7,4,7,7,10\nQ,2,4,3,5,3,8,9,6,2,5,8,10,3,9,5,10\nX,2,3,3,2,1,8,7,3,8,6,6,8,2,8,5,8\nM,3,6,4,4,2,7,7,11,1,7,9,8,8,6,0,8\nI,4,6,6,7,5,8,8,5,6,7,6,8,3,8,8,7\nO,4,6,5,4,6,7,9,5,2,7,7,8,8,9,4,9\nB,6,10,5,6,3,8,6,4,5,10,5,8,6,7,7,10\nV,5,9,7,8,9,7,6,5,4,6,5,8,7,10,7,9\nZ,2,4,3,3,2,8,7,5,9,6,6,7,2,8,7,8\nK,6,10,8,8,6,5,6,1,7,10,8,10,3,8,4,8\nM,4,8,6,6,6,7,7,2,4,9,7,8,7,6,2,8\nL,3,5,4,4,3,8,9,4,5,6,6,9,3,8,7,10\nR,5,8,7,6,5,8,7,6,6,8,6,8,3,8,6,11\nG,2,1,3,2,2,7,6,6,5,6,6,10,2,9,4,9\nX,4,9,6,7,4,7,7,4,9,6,6,8,3,8,7,8\nN,5,9,8,7,5,7,9,2,4,10,5,6,5,9,1,7\nP,1,3,3,1,1,7,9,4,3,11,5,4,1,9,2,8\nL,6,9,5,4,2,8,3,3,4,11,4,13,2,6,6,8\nZ,4,6,6,8,4,11,4,3,5,10,2,8,2,7,5,11\nZ,5,8,7,6,5,8,7,2,9,11,5,8,1,7,6,8\nS,3,7,4,5,3,8,7,7,6,8,6,8,2,8,9,8\nF,3,4,5,3,2,7,9,2,6,13,6,5,1,9,2,7\nY,3,2,5,3,2,5,10,1,7,9,12,9,1,11,2,7\nL,3,7,3,5,1,0,1,6,6,0,0,6,0,8,0,8\nE,5,11,7,8,6,7,7,2,7,11,7,9,3,8,4,8\nL,3,6,5,4,5,7,8,3,5,6,7,10,5,11,5,6\nX,3,7,5,5,3,4,8,1,7,10,11,9,2,9,3,5\nQ,7,12,6,6,5,11,4,4,6,10,4,7,3,9,7,12\nS,8,13,6,8,3,9,3,4,5,9,2,9,4,6,5,9\nD,7,11,9,8,7,7,7,5,6,7,6,8,7,8,3,7\nZ,5,9,6,7,3,7,7,4,15,9,6,8,0,8,8,8\nQ,4,6,4,8,5,8,10,5,1,5,8,11,2,10,5,10\nM,7,11,11,8,15,9,7,3,3,8,5,7,12,4,5,5\nM,3,6,4,4,2,8,6,12,1,5,9,8,7,6,0,8\nF,4,7,5,5,3,6,10,3,5,13,7,5,2,10,2,8\nE,2,3,2,4,1,3,8,6,10,7,5,15,0,8,6,8\nW,3,6,4,4,4,8,7,6,2,6,8,9,5,8,3,7\nW,4,2,6,3,3,7,11,2,2,6,9,8,7,11,0,8\nB,3,7,5,6,6,7,7,5,4,7,6,8,6,9,7,7\nA,2,9,4,6,2,6,5,3,1,6,0,8,2,7,2,7\nV,6,9,6,7,3,4,11,3,4,9,11,7,2,10,1,8\nL,4,9,5,7,4,7,4,1,7,8,2,9,1,6,2,8\nT,4,6,5,4,2,6,12,3,7,12,9,4,2,11,2,4\nZ,1,3,3,2,1,7,7,2,9,11,6,8,1,8,5,7\nJ,4,7,6,8,5,9,9,5,4,6,6,9,3,7,8,6\nP,4,10,6,7,4,5,11,7,4,10,7,3,1,10,4,7\nM,4,1,5,2,3,7,6,7,4,7,7,10,7,6,2,8\nT,4,9,6,7,4,8,10,1,8,6,11,7,0,10,1,7\nG,4,10,5,7,4,7,7,7,6,6,5,8,1,7,6,11\nE,4,6,6,4,4,10,6,1,7,11,4,8,3,8,4,11\nG,3,7,4,5,3,7,7,7,6,6,5,9,1,8,5,11\nG,3,9,4,6,4,7,6,7,6,6,5,8,1,7,6,11\nV,4,8,6,6,7,7,7,4,1,8,7,9,7,10,4,7\nF,2,1,3,2,2,6,9,3,5,10,9,5,4,10,3,7\nK,4,6,6,4,5,8,8,5,4,7,6,7,7,6,6,11\nI,3,11,4,8,2,6,8,0,8,13,7,8,0,8,1,7\nQ,2,2,3,3,2,8,8,5,2,7,7,10,2,9,4,9\nU,6,10,5,5,3,4,4,4,5,4,7,7,4,6,2,8\nX,4,5,5,5,5,9,7,2,4,8,5,7,3,8,7,8\nG,3,5,5,5,4,7,10,5,2,7,7,8,6,11,7,8\nK,8,10,11,8,8,7,7,1,7,10,5,9,5,7,4,7\nJ,1,3,2,1,0,8,7,2,4,14,6,9,0,7,0,7\nR,3,8,5,6,4,6,7,5,6,6,5,7,3,7,5,8\nA,4,11,6,8,2,7,6,3,1,7,0,8,3,7,1,8\nG,5,8,7,7,7,7,10,5,2,7,7,8,6,11,6,8\nY,8,8,7,12,5,6,5,6,5,6,11,7,5,10,4,7\nG,2,5,3,3,2,6,7,5,5,10,7,10,2,9,4,9\nM,5,11,5,8,7,7,5,11,1,8,8,8,9,5,2,10\nD,4,11,6,8,7,8,7,5,6,7,7,4,3,8,3,6\nY,9,8,8,12,5,10,11,1,4,7,10,5,4,10,5,10\nL,4,9,5,7,4,4,5,3,9,3,1,9,0,7,2,6\nA,4,10,7,7,2,7,7,3,0,6,0,8,2,7,2,8\nR,3,7,4,5,2,6,12,8,4,7,2,9,3,7,5,10\nY,5,7,6,5,5,8,5,7,6,5,8,8,3,9,9,5\nS,4,8,5,6,2,8,7,5,8,5,6,8,0,8,9,8\nC,5,8,7,6,4,7,8,8,6,4,7,13,5,8,4,8\nB,2,0,2,1,1,7,8,7,5,7,6,7,1,8,7,8\nB,3,5,4,4,4,8,7,5,6,7,6,6,5,8,5,9\nF,4,9,5,6,5,6,10,6,4,8,6,8,2,10,7,10\nR,6,9,8,7,6,10,7,3,6,11,3,7,5,6,5,10\nH,4,6,6,8,6,8,12,4,2,8,7,6,3,11,7,5\nD,2,4,4,3,3,9,6,4,6,10,4,6,2,8,3,8\nS,6,9,7,6,4,9,7,4,8,11,5,8,2,8,5,9\nA,3,5,5,4,2,7,2,1,2,6,2,8,3,5,3,7\nR,2,7,3,4,2,5,10,8,4,7,4,9,3,7,5,11\nP,3,2,4,4,2,5,10,4,4,10,8,4,1,10,3,6\nD,4,8,4,6,2,5,8,10,8,8,7,5,3,8,4,8\nT,8,10,8,8,4,6,13,5,6,12,8,2,2,12,2,4\nL,2,3,2,2,1,4,3,4,6,2,2,5,0,7,0,6\nD,5,10,5,6,4,9,6,3,5,8,5,7,6,9,5,8\nY,6,9,6,7,3,4,9,2,8,10,11,6,2,10,4,3\nF,3,5,5,4,2,5,10,2,6,13,7,5,1,9,2,7\nY,2,5,4,4,2,7,10,1,6,7,11,8,1,11,2,8\nF,4,4,4,6,2,1,14,5,3,12,9,5,0,8,3,6\nG,5,8,6,6,5,7,7,7,5,4,7,9,3,6,5,8\nU,5,7,5,5,2,4,8,6,8,9,9,9,3,9,2,4\nQ,4,7,5,6,2,8,7,8,6,6,7,8,3,8,5,9\nB,8,12,7,6,6,8,7,5,5,9,7,8,7,8,9,7\nQ,2,3,3,4,3,8,7,7,3,6,7,9,2,8,5,9\nZ,3,7,5,5,3,8,7,2,9,11,5,8,1,7,6,8\nT,3,7,5,5,5,7,8,4,5,7,7,9,5,9,5,7\nS,3,9,4,6,2,8,9,6,10,5,5,5,0,7,9,7\nU,6,8,7,6,3,4,8,7,8,9,9,9,3,9,3,5\nD,4,8,4,6,3,6,7,10,9,7,6,6,3,8,4,8\nX,3,8,5,6,4,7,7,3,8,6,7,10,3,7,7,8\nR,4,9,6,7,6,7,9,5,6,8,4,8,3,6,5,11\nU,6,10,7,7,5,3,8,5,7,9,8,10,5,8,3,4\nD,4,9,5,7,5,5,7,9,6,6,6,6,2,8,3,8\nC,6,8,6,6,3,5,8,6,8,13,9,9,2,11,2,7\nH,4,9,4,7,4,7,8,13,1,7,5,8,3,8,0,8\nI,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nL,4,9,5,7,4,9,4,1,7,9,2,10,1,6,3,9\nY,3,4,5,6,6,9,7,5,3,7,7,7,6,9,6,4\nX,4,8,7,6,4,6,7,1,8,10,8,9,3,8,3,7\nL,3,8,3,6,1,0,1,6,6,0,0,6,0,8,0,8\nI,2,8,5,6,5,11,6,1,6,9,4,5,1,7,5,8\nG,4,5,5,4,3,7,7,6,7,9,6,11,2,10,5,9\nT,8,11,9,8,6,7,9,2,9,10,9,5,2,9,5,5\nE,3,5,5,4,3,5,8,3,9,11,8,10,2,8,4,7\nR,2,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10\nM,4,7,5,5,4,8,6,10,0,6,8,8,7,5,0,8\nT,6,10,8,7,9,8,8,5,6,6,7,9,8,7,9,5\nX,4,8,6,6,3,7,7,1,8,10,6,8,3,8,4,7\nI,3,7,4,5,2,7,9,0,7,13,6,6,0,9,2,7\nC,4,7,5,5,3,5,8,7,7,8,8,14,2,9,4,10\nU,5,8,6,6,2,7,4,13,6,8,15,8,3,9,0,8\nM,7,9,10,6,7,5,6,3,5,9,10,10,8,5,2,7\nO,3,1,4,3,2,7,7,7,5,7,6,8,2,8,3,8\nJ,2,9,2,6,2,13,4,5,4,13,2,9,0,7,0,8\nW,3,4,4,3,3,6,10,4,2,8,7,7,6,12,1,6\nA,2,7,4,5,2,8,4,2,1,7,1,8,2,6,1,8\nC,1,0,1,0,0,7,7,5,7,7,6,13,0,8,4,10\nI,2,7,3,5,2,8,6,0,7,13,6,9,0,8,1,8\nD,8,15,7,8,6,10,5,4,7,10,4,7,6,7,9,8\nO,2,3,3,2,2,7,7,6,4,9,6,8,2,8,2,8\nU,6,10,9,8,11,9,7,4,5,6,7,7,9,7,6,6\nA,4,11,7,8,4,12,4,3,3,9,1,9,3,8,3,9\nN,2,5,4,3,2,7,8,2,4,10,5,6,5,9,0,7\nJ,2,8,3,6,2,14,4,4,4,13,2,9,0,7,0,8\nG,4,5,5,8,2,8,6,9,8,6,6,12,2,8,5,10\nV,4,11,6,8,8,7,5,5,2,8,7,8,6,9,5,8\nA,3,11,5,8,4,11,4,2,3,9,2,9,3,7,3,7\nD,4,10,6,8,5,8,7,7,7,9,4,5,3,8,4,9\nD,3,8,4,6,2,5,7,10,8,6,5,5,3,8,4,8\nL,3,8,4,6,3,8,4,1,7,9,2,10,1,6,3,9\nA,6,11,6,6,4,12,3,5,2,11,3,10,6,4,4,10\nQ,3,7,4,6,2,8,6,9,6,6,4,8,3,8,4,8\nD,2,5,3,3,2,9,6,3,5,10,4,7,3,7,2,8\nR,4,7,7,6,7,7,7,3,3,7,5,8,6,8,6,6\nE,3,6,4,4,4,6,7,5,7,7,6,9,3,8,5,10\nO,3,3,5,5,2,8,6,8,8,7,4,8,3,8,4,8\nW,3,2,5,3,3,8,11,3,2,6,9,8,7,11,1,7\nT,2,1,3,1,0,8,15,2,4,6,10,8,0,8,0,8\nZ,3,6,4,4,3,8,7,5,10,7,5,8,2,9,8,8\nS,6,8,7,6,4,7,8,4,8,10,7,7,2,9,5,7\nG,2,5,3,3,2,6,7,5,5,9,7,10,2,9,4,9\nR,4,4,5,6,3,5,12,8,4,7,3,9,3,7,6,11\nH,1,1,2,1,1,7,8,11,1,7,5,8,3,8,0,8\nE,5,9,7,7,7,8,10,7,4,6,6,11,5,8,8,9\nY,4,6,6,4,5,8,5,6,5,8,7,8,5,8,4,6\nE,3,7,3,5,3,3,8,4,8,7,6,13,0,8,6,9\nK,6,9,9,6,5,9,6,2,7,10,3,8,6,8,6,10\nB,4,4,4,6,3,6,7,9,7,7,6,7,2,8,9,10\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nK,4,5,6,4,3,4,8,2,8,10,10,11,4,7,3,6\nD,3,6,4,4,6,8,8,5,4,7,6,6,3,7,8,5\nI,2,11,2,8,4,7,7,0,7,7,6,8,0,8,3,8\nH,3,7,4,5,3,7,7,13,1,7,7,8,3,8,0,8\nL,3,6,5,4,3,6,4,0,8,8,3,11,0,8,2,8\nE,3,10,4,8,2,3,8,6,11,7,5,15,0,8,7,7\nW,5,11,8,8,8,5,11,2,2,7,8,8,7,12,1,8\nS,3,5,5,4,2,8,7,3,8,11,5,7,1,9,5,8\nN,2,6,3,4,3,7,7,11,1,6,6,8,5,9,0,8\nV,5,10,5,7,3,3,11,3,4,10,12,8,2,10,1,8\nW,5,4,7,6,3,6,8,5,1,7,8,8,9,9,0,8\nW,4,6,7,4,4,7,9,4,0,7,9,8,7,12,0,8\nZ,6,8,8,10,7,11,5,4,5,8,2,6,2,7,7,8\nB,8,12,7,6,6,8,8,4,5,9,5,7,7,5,8,7\nL,2,7,3,5,2,5,5,2,8,3,2,7,0,7,1,5\nL,2,5,3,3,1,7,4,1,6,8,2,10,1,6,2,8\nB,4,7,7,6,7,8,8,4,4,7,6,8,6,8,8,5\nF,1,0,1,0,0,3,12,4,2,11,9,6,0,8,2,7\nI,2,10,5,8,5,10,7,2,5,9,5,5,3,8,6,7\nG,9,13,8,7,4,11,3,3,4,10,2,6,4,7,4,11\nF,6,10,9,7,9,8,6,1,6,9,7,7,6,10,4,7\nB,4,6,4,4,3,6,6,8,7,6,6,7,2,8,9,10\nG,3,4,4,3,2,6,6,6,6,6,6,11,2,9,4,9\nU,4,5,5,4,2,6,8,6,8,6,10,9,3,9,1,7\nP,4,6,6,9,8,6,6,5,3,7,6,6,9,13,6,10\nW,9,10,9,7,6,6,10,5,3,8,6,6,11,12,4,4\nA,3,8,5,6,3,11,2,2,2,9,2,9,2,6,3,7\nP,5,9,7,7,5,7,11,6,3,11,5,2,2,11,3,8\nT,8,11,8,8,4,5,14,7,4,11,8,3,2,12,1,4\nU,5,7,5,5,2,4,9,5,7,12,11,8,3,9,1,7\nJ,1,6,2,4,2,9,7,0,6,10,5,7,0,7,0,7\nF,6,10,9,7,5,4,12,5,5,13,8,4,2,10,2,5\nO,5,7,6,5,7,8,7,5,2,7,6,8,8,9,4,9\nP,6,11,9,8,7,8,9,4,5,12,5,3,4,10,4,7\nO,6,10,7,8,3,7,10,9,9,8,8,6,3,8,4,8\nA,2,1,3,1,0,7,4,2,0,7,2,8,2,7,1,8\nR,7,13,7,8,5,10,5,2,5,9,4,8,6,8,6,9\nN,4,10,4,8,3,7,7,14,2,5,6,8,6,8,0,8\nR,12,14,9,8,5,9,7,6,5,10,2,8,7,5,6,10\nT,2,7,4,4,1,7,15,1,5,7,11,8,0,8,0,8\nO,2,0,2,1,1,8,7,7,5,7,6,8,2,8,3,8\nM,5,6,8,4,4,9,5,3,5,9,4,7,8,6,2,8\nV,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8\nU,1,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8\nT,3,4,4,3,2,5,11,2,7,11,9,5,1,11,2,5\nN,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nY,5,8,5,6,2,3,11,3,7,13,11,6,1,10,2,5\nG,2,3,3,1,1,7,7,5,5,9,6,9,2,9,4,10\nK,2,3,4,2,2,5,8,2,7,10,8,9,3,8,2,7\nI,3,9,4,7,2,7,7,0,8,14,6,8,0,8,1,8\nL,2,1,3,3,1,5,2,6,7,1,3,2,1,7,1,5\nR,4,9,6,7,6,9,7,4,5,10,4,7,3,6,4,10\nV,7,9,7,5,3,6,9,5,3,8,7,5,5,12,3,9\nI,3,7,4,5,1,8,5,0,8,14,6,10,0,7,1,8\nK,6,11,6,8,3,4,8,9,2,7,5,11,4,8,2,11\nQ,5,5,7,5,5,7,4,4,5,7,3,8,4,5,4,8\nP,4,8,5,6,2,4,11,9,4,9,6,5,2,10,4,8\nT,5,10,5,7,4,5,11,2,8,11,10,5,1,11,3,4\nW,5,10,8,7,12,7,7,6,2,7,7,8,13,12,4,10\nB,5,8,7,6,6,9,6,4,6,10,5,7,2,8,5,10\nU,3,2,5,3,2,7,8,5,7,5,9,9,5,10,1,7\nA,6,14,5,8,4,9,4,3,2,8,4,11,6,6,4,8\nQ,5,6,6,9,7,10,13,4,2,4,8,12,4,14,5,10\nE,5,11,5,8,6,3,7,5,9,7,7,14,0,8,6,8\nL,3,9,5,6,3,8,3,3,6,8,2,8,1,6,2,8\nM,14,14,14,8,7,7,10,5,5,4,4,11,11,13,2,7\nU,4,9,4,6,2,7,5,14,5,7,14,8,3,9,0,8\nI,2,6,4,4,3,10,7,2,4,8,5,5,3,9,5,7\nD,3,5,5,4,3,9,6,4,6,10,4,6,2,8,3,8\nH,9,15,8,8,5,11,7,4,5,9,3,4,6,8,4,8\nD,3,8,5,6,5,8,7,7,6,8,4,5,4,9,4,8\nP,3,9,4,6,2,4,11,9,3,9,6,4,1,10,4,8\nH,9,12,8,6,4,6,6,6,4,9,11,10,7,11,5,9\nQ,4,10,6,9,3,8,7,8,6,6,7,9,3,7,5,9\nG,2,3,3,2,1,7,7,6,6,6,6,10,2,9,4,9\nZ,2,3,4,2,2,8,7,2,9,12,6,8,1,8,5,8\nZ,3,6,4,4,2,7,7,3,14,9,6,8,0,8,8,8\nZ,4,11,5,8,2,7,7,4,14,10,6,8,0,8,8,8\nJ,5,10,7,8,4,6,8,3,6,15,7,10,3,7,3,7\nN,9,12,8,6,3,7,10,5,6,3,4,10,5,9,2,7\nX,4,5,6,3,3,7,7,1,9,10,6,8,2,8,3,7\nZ,4,6,6,8,4,12,4,3,5,11,2,8,2,7,5,12\nY,5,8,5,6,3,3,10,3,6,12,12,7,1,11,2,5\nZ,3,10,4,8,2,7,7,4,14,10,6,8,0,8,8,8\nN,5,10,6,7,4,6,8,6,5,7,7,9,6,8,2,6\nC,2,3,2,1,1,5,8,5,6,11,9,11,1,9,3,8\nB,4,7,6,5,6,9,6,4,6,10,5,7,2,8,5,10\nO,1,3,2,2,1,8,7,6,3,9,6,8,2,8,2,8\nP,3,6,4,4,3,4,10,4,5,10,9,4,1,10,3,7\nC,3,3,4,5,2,6,6,7,9,9,5,13,1,9,4,8\nM,4,8,5,6,5,8,6,6,4,6,7,8,8,5,2,7\nA,4,10,6,8,4,7,5,3,1,7,1,8,2,7,2,8\nA,3,9,5,6,3,10,3,2,2,8,3,10,2,6,3,7\nD,5,9,6,7,6,9,7,3,5,11,5,5,3,8,3,8\nM,3,4,5,3,3,7,6,3,4,9,7,8,7,5,1,8\nT,4,11,5,8,5,7,11,4,6,7,11,8,3,12,1,8\nN,8,14,9,8,4,4,9,3,4,13,11,10,6,8,0,8\nS,5,6,6,4,3,6,7,4,7,10,10,9,2,10,4,5\nP,3,2,4,3,2,5,10,4,4,10,8,3,1,10,3,6\nP,4,11,5,8,3,3,13,8,2,11,6,3,1,10,4,8\nH,6,8,8,6,5,10,6,3,7,10,3,7,4,9,4,10\nI,4,9,4,5,2,8,8,2,5,13,5,5,1,9,5,10\nS,7,11,9,8,11,8,8,5,3,8,5,8,6,8,13,8\nN,4,5,6,3,2,7,8,2,5,10,6,6,5,8,1,7\nZ,4,8,6,6,3,7,7,2,10,12,5,9,2,10,6,9\nY,1,1,2,1,1,9,11,1,6,6,11,7,1,11,1,8\nL,1,3,3,1,1,7,4,1,7,8,2,10,0,7,2,9\nO,8,12,5,6,3,8,7,6,5,9,4,7,5,9,5,8\nW,4,7,6,5,3,6,8,4,1,7,8,8,8,10,0,8\nA,7,10,10,9,8,6,8,3,6,7,8,10,6,8,4,8\nU,6,10,8,8,8,8,7,9,5,6,6,9,3,8,5,5\nG,7,9,10,8,11,7,7,6,3,7,7,8,8,10,9,9\nN,2,1,3,3,2,7,9,5,4,7,6,6,4,8,1,6\nN,4,8,5,6,4,7,7,7,5,7,5,6,3,7,3,8\nW,3,2,5,3,3,8,11,2,2,7,9,8,6,11,0,7\nC,4,6,4,4,2,5,9,6,8,12,10,11,1,9,2,7\nG,3,5,4,4,2,6,7,5,5,9,7,10,2,9,4,10\nQ,3,6,4,7,4,9,5,6,3,9,5,11,3,9,5,9\nD,4,8,6,7,5,5,6,6,7,8,6,8,4,5,6,5\nH,2,1,3,2,2,7,7,5,5,7,6,8,3,8,2,8\nJ,2,5,4,4,2,8,6,3,5,14,6,10,1,6,0,7\nB,2,1,2,1,1,7,7,7,5,6,5,7,1,8,7,10\nO,2,3,3,2,2,8,7,6,4,9,5,8,2,8,2,8\nA,3,7,5,5,3,12,3,3,2,10,1,9,2,6,2,8\nC,4,6,6,4,5,7,6,4,4,8,7,11,5,9,3,8\nF,3,5,3,4,2,6,10,4,5,10,9,4,2,10,2,6\nO,3,5,4,4,3,7,7,7,5,9,6,8,2,8,3,8\nJ,2,10,3,7,2,12,3,6,3,12,5,11,1,6,0,8\nM,5,8,9,6,10,8,6,3,2,8,4,8,14,6,3,7\nF,6,10,8,7,8,7,6,6,4,7,6,8,5,10,8,12\nK,11,13,10,8,4,8,8,3,8,9,4,6,5,7,4,7\nF,3,8,4,5,1,1,12,5,5,12,10,8,0,8,2,6\nP,3,4,5,3,2,7,10,4,4,12,5,3,1,10,3,8\nL,4,9,6,7,7,7,7,3,5,6,7,11,6,11,6,5\nT,5,7,5,5,2,5,11,2,9,12,9,4,0,10,2,4\nS,4,7,6,5,3,8,8,3,7,10,5,6,2,8,5,8\nA,3,8,5,6,3,11,3,2,2,8,2,9,2,5,2,8\nC,8,12,5,7,2,6,8,7,8,10,7,12,2,9,5,9\nQ,4,8,5,7,3,8,6,9,7,6,4,8,3,8,4,8\nR,5,7,6,5,5,8,7,6,3,8,5,6,4,7,7,8\nQ,1,0,2,1,1,8,7,6,3,6,6,9,2,8,3,8\nD,4,8,6,6,5,10,6,2,6,11,3,7,3,8,3,10\nK,1,0,2,1,0,5,7,7,1,7,6,11,2,8,2,11\nK,8,15,8,8,4,6,8,3,7,9,7,9,6,9,3,7\nL,3,6,5,4,2,6,4,2,9,7,2,10,0,7,3,8\nL,3,11,5,8,2,2,2,5,10,0,0,6,0,7,1,5\nF,2,3,4,2,1,4,12,4,4,13,8,5,1,9,1,6\nU,5,6,5,4,2,4,8,6,8,9,9,9,3,9,3,5\nA,6,11,8,9,9,8,7,8,3,7,6,8,3,8,9,3\nG,3,7,4,5,2,8,7,8,7,6,6,9,2,7,5,11\nM,5,10,8,8,6,8,6,2,4,9,6,8,8,6,2,8\nO,3,7,4,5,3,7,7,7,3,9,6,9,3,8,3,8\nT,1,0,2,0,0,7,14,2,4,7,10,8,0,8,0,8\nU,4,5,5,4,2,5,8,6,8,8,10,10,3,9,1,7\nY,4,11,6,8,2,9,10,1,3,6,12,8,1,11,0,8\nS,3,6,6,4,3,9,7,4,8,11,3,7,1,8,5,10\nU,3,8,5,6,3,4,8,7,7,9,10,11,3,9,0,8\nH,5,9,5,5,4,7,8,3,4,10,6,8,6,8,4,8\nM,4,7,6,5,6,6,6,5,5,7,7,11,10,5,2,9\nP,3,5,4,3,2,6,9,5,4,9,7,3,1,10,4,6\nW,5,5,8,4,8,7,7,5,5,6,6,8,8,10,7,10\nW,3,3,4,4,2,7,8,4,1,7,8,8,8,10,0,8\nU,2,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8\nN,4,3,4,5,2,7,7,14,2,5,6,8,6,8,0,8\nO,4,11,5,8,3,8,8,9,8,6,8,8,3,8,4,8\nL,3,8,5,6,6,7,7,3,5,7,6,10,6,11,6,5\nR,1,0,1,0,0,6,8,6,3,7,5,7,2,7,4,11\nM,6,10,9,8,7,6,6,7,6,7,8,11,9,6,2,9\nS,2,7,3,5,3,8,7,7,5,7,5,7,2,8,8,7\nT,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8\nD,10,15,9,8,6,9,5,4,7,9,5,7,6,10,6,8\nJ,3,11,4,8,1,14,2,8,5,14,2,11,0,6,0,8\nL,5,10,5,8,3,0,1,5,6,0,0,6,0,8,0,8\nY,3,4,5,6,4,9,10,7,4,7,7,6,4,10,5,5\nI,1,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nA,3,9,5,6,4,8,2,1,2,7,2,7,2,7,3,6\nR,7,9,10,8,11,7,7,4,4,7,5,7,7,9,7,5\nD,4,10,6,8,5,7,7,9,7,7,6,5,3,8,4,8\nA,4,11,6,8,4,12,2,4,3,11,1,9,3,7,3,9\nA,3,7,5,5,3,11,2,2,2,8,3,9,3,5,3,8\nT,5,10,7,8,8,5,8,4,7,7,6,9,5,8,5,6\nH,5,10,6,7,3,7,7,15,0,7,6,8,3,8,0,8\nB,3,5,4,3,3,7,7,5,5,6,6,6,2,8,6,10\nC,3,8,4,6,2,5,8,7,8,7,8,14,1,8,4,10\nP,2,4,3,3,2,6,10,5,4,9,7,2,1,10,4,6\nN,4,5,4,3,3,7,8,5,5,7,6,6,5,10,2,5\nJ,2,8,3,6,2,10,7,0,7,11,3,6,0,7,1,7\nS,6,12,6,7,3,7,7,3,5,14,7,8,2,9,3,8\nU,5,10,5,7,2,7,4,15,6,7,14,8,3,9,0,8\nK,4,4,5,3,3,6,7,4,7,7,6,11,3,8,5,9\nY,5,7,6,5,3,5,9,2,9,9,10,4,1,11,4,4\nW,5,7,5,5,4,3,11,2,2,10,8,7,5,11,2,6\nG,3,7,4,5,3,6,6,6,5,9,7,13,2,9,4,10\nR,2,3,3,2,2,8,7,3,5,9,4,7,2,7,4,10\nH,4,5,4,7,2,7,7,15,1,7,6,8,3,8,0,8\nW,10,10,10,8,7,6,10,5,3,8,6,6,10,12,4,4\nO,4,10,6,8,4,8,6,9,5,7,4,8,3,8,3,7\nB,3,4,4,3,3,7,7,5,6,7,6,6,2,8,6,9\nW,7,11,9,8,7,10,8,5,1,6,10,7,10,12,2,5\nA,4,8,6,6,3,9,3,2,3,8,1,8,2,6,3,7\nL,2,5,3,3,1,6,4,1,8,7,2,10,0,7,2,8\nQ,4,7,5,9,6,8,6,6,4,9,6,9,2,9,4,8\nF,2,4,2,3,2,6,10,4,5,10,9,5,2,9,3,6\nV,5,11,8,8,5,8,12,2,3,4,10,9,6,11,5,8\nO,8,11,8,9,8,8,6,8,4,9,5,8,3,9,3,8\nY,8,9,8,7,5,5,8,1,8,8,9,5,4,11,6,4\nS,5,7,6,5,4,7,7,3,7,10,8,8,2,9,5,6\nC,3,6,4,6,4,4,8,3,5,7,6,11,3,10,7,7\nI,2,11,2,8,3,7,7,0,7,7,6,8,0,8,3,8\nA,3,10,5,8,3,13,4,5,3,12,0,8,2,6,3,9\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nO,4,6,6,6,4,8,4,4,4,9,3,9,3,7,4,9\nJ,2,4,4,3,1,8,7,2,6,14,5,8,2,8,1,8\nZ,3,8,4,6,2,7,7,4,13,9,6,8,0,8,8,8\nS,4,8,5,6,4,8,7,7,7,7,6,8,2,9,9,8\nR,2,4,4,2,2,8,8,3,5,10,4,7,2,6,3,10\nS,3,6,5,4,5,9,7,4,3,8,5,8,4,8,10,9\nL,2,6,3,4,2,8,3,3,6,7,2,7,1,6,2,8\nQ,6,10,6,5,4,9,6,4,7,11,4,9,3,7,8,11\nQ,2,2,3,4,2,8,7,7,3,6,5,9,2,9,3,9\nV,3,8,5,6,2,9,9,4,1,6,12,8,2,10,0,8\nR,3,5,4,6,3,6,11,9,4,7,2,9,3,7,5,11\nO,5,8,7,6,8,8,7,5,1,7,6,8,9,9,6,11\nR,3,4,5,3,3,8,7,3,5,9,4,7,2,7,4,10\nF,7,13,7,7,5,8,10,3,5,11,5,4,4,9,8,7\nN,4,4,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nJ,3,11,4,8,3,9,6,2,7,12,3,8,1,6,2,6\nP,4,8,5,6,2,4,15,8,1,12,6,2,0,9,4,8\nB,4,7,4,5,3,6,8,9,8,7,5,7,2,8,9,9\nE,5,10,4,5,3,7,10,5,5,10,5,9,3,8,6,10\nS,3,7,4,5,2,8,7,5,8,5,6,8,0,8,9,8\nN,4,7,6,5,6,7,8,4,4,7,6,7,5,9,5,4\nL,3,11,5,8,3,4,3,5,8,1,0,6,0,6,1,5\nI,5,8,6,9,6,8,9,5,6,6,6,7,3,9,9,8\nC,7,10,7,8,5,3,8,5,7,10,10,14,3,9,4,6\nD,2,5,4,4,3,8,7,5,6,9,5,5,2,8,3,8\nD,3,4,5,3,3,9,6,4,6,10,4,5,2,8,3,8\nR,3,6,4,4,4,8,6,7,3,8,6,7,4,6,7,8\nS,4,8,6,6,4,7,8,3,7,10,4,7,2,7,4,8\nN,5,6,7,4,4,4,10,3,3,9,9,8,5,8,1,7\nN,4,9,6,7,4,9,7,6,5,6,6,4,5,8,2,5\nO,5,10,7,7,8,9,7,6,1,7,7,9,10,9,4,6\nC,3,6,4,4,2,3,9,5,7,11,11,11,1,8,2,7\nI,4,9,4,4,2,7,10,2,5,13,5,4,1,8,5,8\nX,5,9,7,8,8,7,8,2,5,8,6,8,4,6,8,9\nR,6,9,8,7,8,8,8,6,5,8,6,7,7,8,6,12\nJ,8,13,6,10,5,11,5,2,5,12,4,8,2,9,6,13\nZ,5,9,7,7,4,7,8,2,10,12,6,9,2,10,6,8\nZ,1,3,2,2,1,7,7,5,8,6,6,8,1,8,7,8\nR,2,1,3,2,2,7,8,4,5,6,5,7,2,6,4,8\nM,5,9,6,7,6,7,5,11,1,7,9,8,9,5,2,8\nC,4,10,5,8,2,6,8,7,10,4,6,13,1,7,4,8\nN,2,1,3,3,2,7,8,5,4,7,6,7,4,8,1,7\nB,4,8,6,6,5,8,7,5,6,9,5,6,3,8,7,9\nZ,6,9,8,7,5,9,5,3,9,11,3,11,3,6,7,9\nN,6,10,9,8,6,5,10,2,4,9,9,8,7,7,2,7\nI,5,10,6,8,4,7,7,0,8,13,6,8,0,8,1,8\nI,1,7,0,5,1,7,7,5,3,7,6,8,0,8,0,8\nO,4,7,6,5,6,9,6,5,1,7,6,9,10,9,4,8\nD,5,11,8,9,12,9,9,5,5,7,6,6,5,8,9,6\nP,7,11,10,8,5,8,11,7,3,11,4,2,2,11,4,9\nN,5,8,7,6,4,7,8,3,5,10,6,7,5,8,1,7\nQ,2,4,3,5,3,8,7,7,3,6,6,9,3,8,5,10\nI,2,7,5,5,4,11,6,1,5,8,4,5,3,8,5,9\nD,5,9,7,7,7,9,6,4,6,9,3,6,3,8,3,8\nP,5,8,5,6,2,4,13,8,1,11,6,3,1,10,4,8\nC,2,1,3,2,1,6,7,6,10,7,6,14,0,8,4,9\nJ,5,10,7,8,3,6,7,3,7,15,8,11,1,6,1,7\nK,3,2,4,3,2,5,7,4,7,6,6,11,3,8,5,9\nT,1,0,1,0,0,8,13,1,4,6,10,8,0,8,0,8\nM,6,5,7,8,4,8,7,13,2,7,9,8,9,6,0,8\nK,9,13,10,7,6,4,9,4,6,10,10,11,5,8,4,6\nG,4,9,4,6,3,6,7,7,6,10,8,10,2,9,4,9\nD,2,6,4,4,5,10,7,4,5,7,5,6,3,6,6,5\nJ,1,4,2,3,1,10,6,2,5,11,4,9,1,7,1,7\nF,1,3,3,1,1,5,10,2,5,13,7,5,1,9,1,7\nH,6,7,8,5,5,5,9,3,6,10,9,9,4,9,4,7\nQ,3,5,4,6,3,8,7,7,5,8,7,8,2,9,4,9\nX,9,14,10,8,5,5,9,3,8,11,9,8,4,9,4,6\nU,7,9,8,7,5,4,8,5,8,10,9,9,3,9,2,6\nE,5,9,7,6,6,8,7,7,2,7,6,11,5,8,8,9\nK,1,1,2,1,0,4,6,6,2,7,6,11,3,8,2,10\nR,4,11,6,9,5,7,8,5,7,6,5,7,4,5,7,9\nR,4,7,5,5,4,7,8,6,6,6,5,8,3,6,6,9\nR,4,8,6,6,5,9,8,4,6,8,3,8,4,5,5,11\nR,2,4,4,3,2,9,7,2,6,10,3,6,2,7,3,10\nV,3,8,6,6,1,6,8,4,3,7,14,8,3,9,0,8\nX,3,8,4,6,3,7,7,3,8,6,6,10,3,8,6,8\nF,4,9,6,7,7,6,8,1,4,10,8,7,6,11,4,5\nB,3,7,4,5,5,9,6,5,4,7,7,8,6,9,7,7\nL,3,6,5,4,3,6,4,2,6,7,2,9,1,7,3,7\nU,4,2,5,3,2,6,9,6,7,7,10,9,3,9,1,8\nO,3,6,4,4,3,8,7,7,5,7,6,8,2,8,3,8\nY,4,8,5,6,2,6,10,2,2,7,12,8,2,11,0,8\nZ,2,3,4,2,1,7,7,2,9,11,6,8,1,8,5,7\nP,6,8,8,6,5,7,11,7,3,9,5,3,3,11,4,8\nU,3,4,4,6,2,8,5,13,5,6,14,8,3,9,0,8\nC,6,9,4,4,2,7,9,6,6,11,7,8,2,9,5,9\nL,2,6,3,4,2,4,5,1,8,6,2,10,0,7,3,6\nR,3,5,4,6,3,6,11,10,4,7,3,9,3,7,5,10\nP,4,8,5,6,5,6,9,6,5,9,7,4,2,10,3,7\nR,4,11,5,8,3,6,10,10,4,7,4,8,3,7,5,11\nC,1,0,2,0,0,7,7,5,7,7,6,13,0,8,4,10\nQ,2,1,2,2,1,8,7,7,4,6,6,8,3,8,3,8\nR,2,1,2,2,2,7,8,5,5,6,5,7,2,7,4,8\nE,6,9,8,7,5,7,7,2,9,11,5,9,3,8,5,8\nE,3,7,4,5,4,7,8,6,9,6,4,9,3,8,6,8\nA,2,5,4,4,2,10,2,2,2,9,2,9,2,6,2,8\nL,3,7,4,5,2,7,4,2,6,7,2,8,1,6,2,8\nF,5,6,6,7,6,7,10,5,6,8,6,8,4,8,7,6\nE,2,3,4,2,2,5,8,2,8,11,8,9,2,8,4,6\nV,1,1,2,1,0,8,9,4,2,7,13,8,2,10,0,8\nE,2,6,3,4,3,6,7,6,8,7,6,10,3,8,6,8\nU,4,10,4,7,2,7,6,14,5,7,13,8,3,9,0,8\nA,3,8,6,5,2,8,5,3,1,7,1,8,2,7,2,8\nA,4,11,7,8,6,10,3,1,2,8,3,9,5,5,3,7\nY,3,5,4,6,5,9,9,5,3,6,7,8,5,10,7,6\nN,2,3,4,2,1,5,10,3,3,10,8,8,4,8,0,8\nU,4,5,4,7,2,7,5,14,5,7,14,8,3,9,0,8\nG,4,8,5,6,3,6,7,6,6,10,8,10,2,9,4,9\nL,2,8,3,6,2,3,5,2,8,3,0,9,0,7,1,6\nO,5,11,6,9,6,7,7,8,3,10,6,8,3,8,3,8\nL,3,7,4,5,2,7,4,1,7,8,2,9,1,6,2,8\nY,5,10,8,8,4,10,11,2,8,3,11,8,1,11,2,9\nK,3,4,4,6,2,3,7,7,2,7,5,11,3,8,3,10\nG,7,14,5,8,4,8,6,4,3,9,5,9,4,9,8,8\nF,3,9,4,7,4,5,10,3,5,10,9,6,2,10,3,6\nP,4,5,5,8,2,3,13,8,2,11,7,3,1,10,4,8\nU,3,4,4,3,2,6,9,6,6,7,9,9,3,9,1,9\nM,4,11,5,8,7,7,6,10,1,7,8,8,8,4,0,8\nL,3,10,3,8,2,0,2,3,6,1,0,8,0,8,0,8\nF,3,6,5,4,2,6,10,2,6,13,7,5,1,10,2,7\nW,4,3,6,4,2,4,8,5,1,7,9,8,8,10,0,8\nZ,4,10,5,8,3,7,7,4,15,9,6,8,0,8,8,8\nU,7,11,9,8,5,6,10,6,8,7,10,9,3,9,1,8\nH,3,7,5,5,4,8,7,6,6,7,6,9,6,8,3,8\nV,6,9,5,7,2,2,12,5,4,11,12,7,3,10,1,8\nE,4,10,3,5,2,9,6,5,4,11,4,10,3,8,8,13\nO,5,10,6,8,6,8,8,8,4,7,7,6,5,7,4,9\nQ,1,0,2,1,1,8,7,6,3,6,6,9,2,8,3,8\nJ,2,6,3,4,1,14,2,5,5,13,2,10,0,8,0,8\nR,3,6,4,4,3,8,8,5,6,7,5,6,3,7,5,8\nE,4,7,6,5,4,7,7,2,7,11,6,9,3,8,4,9\nW,6,9,6,5,4,3,9,2,3,9,10,8,8,11,1,5\nT,2,9,3,6,1,7,14,0,6,7,11,8,0,8,0,8\nB,2,3,3,2,2,8,7,3,5,10,5,6,2,8,3,9\nG,2,0,2,1,1,8,6,6,5,6,5,9,2,8,5,10\nS,4,9,6,6,4,7,6,3,7,9,9,9,2,10,4,6\nB,4,7,6,5,6,9,6,4,6,9,5,6,2,8,6,9\nS,4,9,6,7,4,9,8,4,7,10,4,6,2,8,5,9\nM,2,1,3,2,1,7,6,10,1,7,9,8,7,6,0,8\nD,3,7,5,5,7,10,8,4,5,7,6,6,4,7,7,5\nP,4,11,6,8,5,6,8,5,6,9,8,4,2,10,4,7\nV,4,10,6,7,4,8,9,4,2,6,13,8,2,10,0,8\nE,1,3,3,2,1,7,7,2,6,11,6,9,2,9,4,10\nZ,2,4,4,3,2,7,8,2,9,11,6,8,1,8,5,7\nQ,3,4,4,7,2,7,6,8,6,5,5,8,3,8,4,8\nF,4,8,4,5,2,1,12,5,5,12,10,8,0,8,2,6\nQ,2,3,3,4,3,8,7,7,3,6,6,9,3,8,4,9\nO,3,4,4,3,2,7,7,6,4,9,6,8,2,8,3,7\nL,4,10,5,8,4,6,4,1,7,8,2,10,0,6,3,8\nL,4,10,5,7,3,7,4,0,9,9,2,11,0,7,3,8\nJ,2,7,3,5,1,14,3,4,5,12,1,8,0,7,0,8\nA,3,6,5,4,3,8,3,2,2,6,2,7,2,6,2,6\nM,5,9,6,4,4,6,4,2,2,8,4,10,7,3,1,9\nH,7,11,10,8,7,4,9,4,6,10,9,9,3,8,4,7\nQ,4,10,5,9,5,8,7,8,5,6,7,8,3,8,4,9\nH,4,9,5,7,4,7,8,13,1,7,5,8,3,8,0,8\nV,5,11,7,8,4,7,9,4,1,7,13,8,5,9,2,9\nL,3,8,4,6,3,4,4,4,7,2,1,7,1,6,1,6\nM,5,11,7,8,9,8,7,6,5,6,7,8,8,6,2,8\nX,6,10,8,8,7,7,6,3,5,6,6,8,3,9,10,9\nZ,5,8,7,6,4,6,9,3,10,11,9,5,1,8,6,5\nI,3,9,4,7,3,7,8,0,7,13,6,8,0,8,1,8\nR,2,4,4,3,2,8,7,4,5,9,5,7,2,7,4,10\nO,7,10,7,8,6,8,7,7,4,9,5,6,5,9,5,10\nU,5,9,7,8,7,7,7,4,4,6,6,9,4,8,2,9\nJ,2,3,2,4,1,10,3,10,3,12,8,13,1,6,0,8\nD,6,9,6,4,3,10,4,6,5,13,4,11,5,6,4,8\nK,5,7,7,5,5,4,7,2,7,10,9,11,4,7,4,6\nR,3,5,5,3,3,8,7,5,4,8,5,7,3,7,4,11\nG,5,11,4,6,3,7,9,4,4,9,6,5,3,9,8,7\nO,6,11,7,8,5,7,7,9,6,7,5,8,3,8,4,8\nA,1,0,2,0,0,7,4,2,0,7,2,8,2,7,1,8\nF,2,4,3,3,2,5,10,4,5,10,9,5,1,10,3,6\nU,5,9,6,6,4,3,9,5,7,10,10,9,3,9,2,6\nB,3,8,5,6,4,9,6,4,6,10,5,6,2,8,6,9\nF,5,9,6,7,6,6,10,5,4,9,6,8,5,9,7,11\nQ,4,8,4,9,5,8,8,6,2,6,8,11,3,9,6,7\nQ,3,8,4,7,4,8,7,8,5,6,5,8,2,9,4,8\nT,3,4,3,2,1,6,11,2,7,11,9,5,2,9,3,4\nS,4,8,5,6,2,7,6,6,9,5,6,10,0,9,9,8\nC,2,1,3,2,1,6,7,6,10,7,6,14,0,8,4,9\nQ,7,12,6,7,3,8,4,4,7,11,4,10,3,7,8,11\nL,4,10,5,8,3,0,2,4,6,1,0,8,0,8,0,8\nF,4,9,6,6,6,11,6,2,5,10,5,6,5,9,4,7\nP,4,6,5,8,8,8,9,5,0,8,7,6,5,10,5,9\nC,4,6,5,4,2,5,8,5,7,12,9,12,2,10,3,8\nV,2,6,4,4,2,6,11,3,3,8,11,8,2,11,1,9\nQ,7,9,7,11,9,8,7,6,2,8,7,11,5,9,8,6\nT,1,6,2,4,1,7,13,0,5,7,10,8,0,8,0,8\nN,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8\nZ,4,11,6,8,7,8,7,2,8,7,6,7,1,7,11,7\nS,2,1,2,2,1,8,8,6,4,8,5,7,2,8,8,8\nG,5,10,5,7,4,6,7,7,6,10,8,11,2,9,4,9\nM,9,14,11,8,7,6,4,3,2,8,4,10,10,1,1,8\nR,6,9,6,4,4,5,8,3,5,7,4,10,5,7,6,6\nT,4,7,4,5,3,5,11,3,6,11,9,5,2,11,2,5\nD,5,11,5,8,3,6,7,11,10,6,5,6,3,8,4,8\nX,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nM,2,3,3,4,2,8,6,11,1,6,9,8,7,5,0,8\nR,4,9,6,6,6,7,7,4,6,7,5,7,3,7,5,8\nP,3,5,5,4,3,8,8,4,4,12,4,4,2,9,3,8\nF,8,13,7,7,3,6,9,3,6,12,5,5,2,8,6,6\nQ,2,2,3,3,2,8,7,7,3,6,6,9,2,9,3,9\nJ,1,4,2,3,1,10,6,2,6,12,4,9,0,7,1,7\nY,3,5,5,7,5,9,10,5,4,7,7,7,5,11,6,5\nI,2,9,2,7,2,7,7,0,8,7,6,8,0,8,3,8\nS,3,8,4,6,3,8,8,7,5,7,6,7,2,8,8,8\nB,4,9,6,6,7,9,6,4,4,6,7,7,7,9,8,6\nY,3,7,5,5,2,8,10,1,2,6,12,8,1,11,0,8\nI,4,10,5,8,3,5,9,0,7,13,7,6,2,9,3,6\nW,5,9,8,7,4,7,7,5,2,6,8,8,9,9,0,8\nI,5,12,4,6,3,10,6,3,5,13,3,6,2,8,5,10\nB,6,9,9,8,10,7,7,5,4,8,6,8,7,9,9,6\nT,3,8,5,6,2,7,14,1,5,7,10,8,0,8,0,8\nY,2,9,4,6,1,7,10,1,3,7,12,8,1,11,0,8\nZ,5,8,7,6,4,7,8,2,10,12,6,7,2,8,6,8\nL,6,12,5,7,3,10,2,4,4,13,4,12,2,7,6,9\nY,2,3,2,1,1,5,10,2,7,10,9,4,1,11,2,5\nM,3,4,5,3,3,8,5,3,3,9,6,8,7,5,1,8\nI,5,15,5,8,3,10,6,2,5,11,4,6,2,9,5,12\nR,2,2,3,3,2,6,7,4,5,7,6,7,5,7,3,8\nU,4,8,6,6,4,5,9,5,6,7,9,9,3,9,1,8\nD,6,12,5,7,4,7,6,4,6,9,5,6,5,9,7,6\nY,8,11,6,15,5,4,11,2,4,11,10,6,4,10,6,6\nE,5,9,7,6,5,7,8,1,8,11,6,9,2,8,4,9\nL,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8\nA,5,11,7,8,5,7,4,3,0,7,1,8,3,8,3,7\nC,4,6,5,4,5,6,6,4,4,7,6,11,5,9,3,8\nO,3,9,5,7,4,7,8,8,6,7,9,7,3,8,4,9\nU,2,6,3,4,1,7,5,13,5,7,12,8,3,9,0,8\nA,3,5,5,5,4,8,8,2,4,7,7,8,5,9,4,7\nE,3,5,6,3,3,5,8,2,9,11,7,9,2,8,4,8\nL,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8\nP,4,7,6,11,10,8,10,4,0,9,7,6,7,10,5,8\nK,4,8,6,7,5,9,6,2,3,8,3,8,5,7,6,11\nR,4,9,6,7,6,9,7,4,5,10,4,7,3,7,4,11\nI,5,11,4,6,2,8,8,2,5,13,4,5,2,9,5,9\nK,3,3,5,2,2,6,7,1,7,10,7,10,3,8,2,8\nP,11,15,9,8,4,6,10,6,3,11,4,4,5,10,4,7\nW,7,11,7,6,4,4,8,2,2,8,10,8,9,11,1,6\nY,5,9,5,5,3,6,7,4,5,9,7,5,3,10,5,4\nC,5,9,4,4,3,7,10,3,4,9,7,9,3,8,6,10\nK,5,10,8,7,5,6,7,3,8,7,6,8,4,8,5,8\nP,4,11,5,8,4,5,11,4,6,11,9,4,0,10,4,7\nR,4,8,5,6,5,8,8,6,5,8,6,7,3,7,5,9\nM,3,7,4,5,3,8,6,11,1,6,9,8,7,6,0,8\nF,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8\nJ,3,9,4,7,1,11,2,11,3,13,8,14,1,6,0,8\nF,4,8,4,6,3,1,12,4,4,12,10,7,0,8,2,6\nK,5,8,7,7,6,8,5,2,4,8,4,8,4,8,7,11\nU,3,3,4,5,2,8,4,14,5,7,12,8,3,9,0,8\nJ,1,3,2,2,1,10,6,2,5,12,4,9,0,7,1,7\nR,5,11,6,8,7,5,9,8,3,7,5,8,2,7,5,11\nQ,5,8,6,10,7,7,10,4,2,6,9,11,3,9,6,8\nW,4,6,6,4,3,6,8,4,1,7,8,8,8,9,0,8\nK,7,11,9,8,5,6,7,2,7,10,7,10,4,8,4,8\nR,6,10,5,6,3,7,7,5,4,9,4,9,6,5,6,11\nM,7,9,9,5,4,12,2,5,2,11,1,9,7,2,1,8\nQ,7,14,7,8,5,12,4,4,6,12,3,9,4,8,7,12\nJ,5,10,3,14,4,10,7,2,4,10,5,6,3,8,6,10\nX,4,9,7,7,5,7,7,3,9,5,6,10,3,8,6,8\nG,3,5,4,4,2,6,7,6,6,10,7,10,2,9,4,10\nN,3,7,5,5,3,5,9,6,4,7,7,9,5,9,1,7\nM,2,1,3,3,3,8,6,6,4,7,7,8,6,5,2,8\nY,3,8,5,6,2,7,10,0,3,7,11,8,1,10,0,8\nN,5,9,6,4,3,2,11,3,3,12,11,9,4,9,0,8\nM,8,9,11,7,8,10,6,2,5,9,3,6,10,8,3,9\nK,5,7,7,6,6,10,5,3,3,10,3,8,5,7,6,12\nY,4,10,6,8,6,9,3,7,5,7,8,8,3,10,5,4\nX,7,13,8,7,5,9,6,3,8,11,3,7,4,8,4,8\nF,4,9,6,6,4,6,10,1,6,13,7,6,1,10,2,8\nZ,2,1,2,2,2,8,7,5,8,6,6,7,1,8,6,8\nE,3,8,3,6,2,2,8,6,10,7,6,15,0,8,6,7\nS,4,8,5,6,4,8,6,7,7,7,8,9,2,10,9,8\nT,3,6,4,8,1,10,14,0,6,5,11,9,0,8,0,8\nV,3,5,5,3,2,7,12,3,3,7,11,9,2,10,1,8\nG,2,0,2,1,1,8,7,6,5,6,6,9,1,7,5,10\nM,5,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nL,2,4,3,3,1,7,4,1,8,8,2,10,0,7,2,8\nM,8,9,11,8,13,8,7,4,4,7,6,7,11,8,6,4\nD,9,13,8,8,6,7,6,4,7,10,5,7,6,8,8,5\nB,3,5,3,4,3,7,7,5,5,6,6,6,2,8,6,10\nV,7,11,7,8,5,2,12,2,3,9,11,8,3,9,2,6\nS,7,10,8,7,4,8,7,4,9,11,4,8,2,8,5,9\nU,6,11,9,8,5,8,8,5,9,4,10,8,4,7,2,7\nN,5,9,7,6,5,6,8,7,7,7,6,7,3,7,3,8\nH,2,0,2,0,0,7,8,11,1,7,5,8,3,8,0,8\nA,2,4,4,3,2,9,2,2,2,9,2,8,2,6,3,8\nS,4,9,5,6,5,8,8,7,5,7,5,7,2,7,8,7\nX,5,7,7,6,7,8,7,2,5,7,6,8,3,9,8,8\nJ,3,10,4,8,1,11,2,11,3,12,8,14,1,6,0,8\nI,5,9,3,4,1,10,6,5,4,13,3,7,2,8,5,10\nB,4,7,4,5,5,6,7,8,5,7,6,7,2,8,7,9\nL,4,5,5,4,4,8,6,4,5,7,7,8,2,8,7,10\nU,5,5,6,4,3,4,8,5,8,10,9,9,3,9,2,5\nF,5,10,7,8,4,5,11,4,7,11,10,5,2,10,3,5\nV,4,7,6,5,6,7,7,4,2,8,7,8,7,10,5,7\nA,5,11,8,8,5,12,2,3,3,10,1,9,3,7,4,8\nN,4,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8\nC,5,9,6,6,3,5,8,8,9,9,8,13,2,10,4,9\nJ,2,5,4,3,2,11,6,2,7,12,3,7,0,7,1,8\nU,3,3,3,4,1,7,6,13,5,7,13,8,3,9,0,8\nD,6,12,6,6,4,6,8,5,7,9,6,6,5,9,7,5\nU,6,11,6,6,4,8,5,5,5,6,8,8,4,9,3,8\nA,4,11,7,8,4,11,2,2,3,9,2,9,5,6,3,9\nA,2,6,3,4,2,9,5,2,0,8,2,8,2,6,1,8\nI,4,11,5,8,3,7,7,0,9,14,6,8,0,8,1,8\nE,4,10,4,8,4,3,9,5,10,7,6,14,0,8,6,8\nE,5,10,7,7,6,10,6,1,7,11,4,8,4,8,5,11\nL,3,7,4,5,2,5,2,7,8,1,2,2,1,6,1,5\nY,2,8,4,5,1,9,10,2,3,6,12,8,1,11,0,8\nM,4,4,5,3,3,6,6,6,5,7,7,10,7,6,2,8\nK,3,7,5,5,6,6,8,4,4,6,5,9,5,7,7,7\nK,4,7,5,5,4,7,6,1,6,10,5,10,3,8,4,9\nU,5,8,6,6,3,3,9,6,7,12,11,9,3,9,1,7\nJ,1,3,2,2,1,10,7,1,5,11,4,8,0,7,0,7\nF,2,6,3,4,2,1,10,3,5,11,11,9,0,8,2,7\nX,3,4,4,3,2,7,7,3,9,6,6,9,2,8,6,8\nJ,4,10,4,8,3,15,3,3,5,12,0,7,0,8,0,8\nP,6,12,5,7,4,6,11,5,2,11,6,3,3,12,5,5\nF,6,9,9,7,8,7,7,5,4,7,6,8,5,11,9,11\nA,6,10,7,7,7,8,8,6,5,6,5,8,5,7,7,5\nD,4,5,5,8,3,5,7,10,8,7,6,5,3,8,4,8\nQ,3,7,4,5,2,8,7,8,6,6,8,9,3,7,5,10\nV,3,4,5,7,1,8,8,4,3,6,14,8,3,9,0,8\nO,2,3,3,2,2,8,7,6,4,9,6,8,2,8,2,8\nP,4,7,5,5,4,5,10,3,6,10,9,5,4,10,3,7\nA,2,3,4,5,1,8,6,3,1,7,0,8,2,7,1,8\nU,7,12,6,6,3,9,7,6,6,3,10,7,5,9,2,6\nD,2,5,4,3,3,9,6,4,6,10,4,6,2,8,3,8\nE,3,2,4,3,3,7,7,5,8,7,6,9,2,8,6,9\nG,1,3,2,1,1,7,7,4,5,9,7,10,1,9,3,10\nG,4,7,4,5,3,7,6,6,6,10,7,11,2,10,4,10\nZ,3,9,4,7,2,7,7,4,14,9,6,8,0,8,8,8\nW,5,5,8,7,4,7,7,5,2,7,8,8,9,9,0,8\nQ,4,9,5,8,5,8,8,8,5,6,6,9,3,8,4,9\nY,4,10,6,8,4,5,9,0,7,8,12,9,1,11,2,7\nU,3,6,4,4,1,8,4,13,5,7,13,8,3,9,0,8\nN,4,5,6,4,3,7,9,2,5,10,6,6,5,9,1,7\nY,5,4,6,6,6,9,10,5,3,6,7,7,5,11,7,4\nS,2,4,4,2,1,10,7,2,7,10,5,8,1,9,5,10\nA,2,4,4,3,2,10,2,2,2,9,2,9,2,6,2,8\nY,5,10,5,7,2,3,11,4,6,12,12,6,1,11,2,6\nM,5,10,7,8,10,8,8,7,4,7,6,8,8,9,9,4\nG,1,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10\nA,5,11,8,8,5,9,3,2,3,8,1,8,2,7,3,7\nU,5,9,6,8,7,7,7,4,4,6,6,8,5,7,2,7\nI,1,3,1,2,0,7,7,1,7,13,6,8,0,8,0,8\nX,2,3,4,1,1,6,8,2,8,11,8,8,2,8,3,7\nD,4,8,5,6,3,9,7,4,7,11,4,5,3,8,3,8\nD,2,3,3,2,2,7,7,6,6,7,6,6,2,8,3,7\nA,5,9,6,7,4,9,4,3,1,8,1,9,2,7,3,8\nF,4,8,4,5,1,1,11,5,7,11,11,9,0,8,2,6\nD,5,8,6,6,5,8,8,5,6,10,5,4,4,9,4,9\nV,3,9,5,7,2,8,8,4,3,6,14,8,3,10,0,8\nD,9,14,8,8,6,9,5,4,6,10,4,7,6,8,9,7\nA,7,11,6,6,3,11,2,3,1,9,4,11,4,4,4,9\nU,6,10,7,8,4,3,9,5,7,11,10,9,3,9,2,6\nQ,5,9,7,11,7,8,6,8,4,5,6,10,3,8,6,10\nM,5,7,8,5,6,9,7,2,4,9,5,7,7,6,2,8\nO,4,10,5,8,5,8,6,8,4,7,4,8,3,8,3,8\nX,6,9,9,7,5,5,8,1,8,10,10,9,4,7,4,6\nB,6,10,9,8,7,9,6,4,7,9,5,7,3,8,7,10\nH,3,7,4,4,2,7,6,14,1,7,7,8,3,8,0,8\nC,3,4,4,6,1,5,7,6,9,7,6,14,1,9,4,9\nK,3,4,5,3,2,5,7,2,7,10,8,10,3,8,3,7\nX,4,4,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nW,4,9,6,7,10,9,7,5,2,7,6,8,13,10,3,6\nL,3,11,5,8,3,5,3,6,7,2,2,4,1,6,1,5\nV,4,8,4,6,2,3,11,3,3,10,11,7,2,10,1,7\nS,5,8,7,6,8,6,7,3,2,8,6,6,3,8,11,1\nI,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nG,3,6,4,4,2,7,6,6,6,6,6,10,2,9,4,8\nW,3,4,4,3,3,6,10,5,2,9,7,6,5,11,2,6\nT,6,10,5,8,2,4,13,3,7,12,10,4,0,10,1,5\nO,2,3,3,2,1,7,7,7,4,7,6,8,2,8,2,8\nV,5,9,5,6,3,2,12,4,3,11,12,8,3,10,1,7\nM,6,12,7,6,5,7,3,2,2,8,4,10,8,1,2,8\nZ,4,6,6,8,4,13,4,3,7,10,2,7,2,7,5,12\nP,4,10,5,7,3,5,10,10,4,9,6,5,2,10,4,8\nA,3,8,5,6,2,7,6,3,1,7,0,8,2,7,1,8\nS,7,10,9,8,11,8,8,5,3,9,5,7,5,8,12,8\nP,5,9,7,6,6,5,10,5,5,10,8,3,1,10,3,7\nE,3,8,3,6,2,3,7,6,10,7,6,14,0,8,7,8\nP,5,10,6,7,5,5,11,5,5,11,9,3,1,10,4,7\nO,1,0,1,0,0,8,7,6,4,7,6,8,2,8,2,8\nJ,1,4,2,3,1,9,6,2,6,12,4,9,1,7,1,7\nM,3,4,6,3,3,7,6,3,4,9,8,9,7,5,2,9\nM,6,10,8,8,9,8,7,7,5,6,5,8,10,8,10,12\nG,4,8,6,6,4,7,7,7,6,6,6,9,2,8,4,8\nT,2,1,2,1,0,8,15,2,4,6,10,8,0,8,0,8\nE,3,7,3,5,2,3,7,6,10,7,6,14,0,8,7,8\nU,3,6,4,4,1,7,5,13,5,7,13,8,3,9,0,8\nJ,3,7,4,5,2,8,6,2,6,14,5,9,0,7,0,7\nW,5,7,7,6,9,6,8,6,5,6,6,8,8,9,8,8\nS,3,4,4,6,2,8,7,6,9,4,6,7,0,8,9,8\nM,9,12,12,7,5,10,2,3,2,10,2,9,8,1,1,8\nZ,2,1,2,2,1,7,7,3,12,8,6,8,0,8,7,8\nV,3,8,6,6,1,7,8,4,3,7,14,8,3,9,0,8\nX,4,7,6,5,3,7,8,1,8,10,7,8,2,8,3,8\nY,3,6,5,9,8,9,7,4,1,6,7,9,4,11,7,8\nY,3,6,5,4,1,8,10,2,2,7,13,8,2,11,0,8\nH,6,8,9,10,9,7,4,4,2,6,4,6,8,6,11,7\nO,4,7,5,5,3,7,6,8,4,8,5,10,3,8,3,8\nC,6,10,7,7,4,5,7,6,8,11,9,14,2,9,4,5\nS,5,7,6,5,4,9,6,3,7,10,6,9,2,10,5,9\nQ,4,10,4,5,3,10,6,4,7,11,3,9,3,6,7,11\nV,4,6,4,4,2,3,12,5,4,11,12,7,3,10,1,8\nK,8,15,8,8,4,7,6,3,6,9,9,10,6,12,3,7\nG,6,11,7,8,6,6,7,7,5,8,7,11,5,7,6,6\nN,6,9,8,7,5,7,9,6,5,7,6,6,7,8,3,8\nC,3,4,4,3,2,4,8,4,7,10,10,13,1,9,2,7\nQ,6,8,6,10,6,8,6,7,4,9,6,10,4,9,7,6\nA,1,0,2,1,0,8,4,2,0,7,2,8,1,7,1,8\nU,4,5,5,4,2,4,8,5,7,11,10,9,3,9,2,6\nX,9,12,8,6,4,7,7,2,9,9,7,9,4,9,4,7\nI,1,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nO,3,8,4,6,3,8,7,8,6,7,5,9,3,8,3,7\nP,5,11,5,8,3,4,10,10,4,9,6,4,2,10,4,8\nL,4,10,5,7,7,7,7,3,5,7,7,10,6,10,6,6\nA,2,3,3,2,1,10,2,2,1,9,2,9,1,6,1,8\nD,1,0,1,0,0,6,7,6,5,7,6,6,2,8,2,8\nS,2,4,4,2,2,8,8,2,7,10,4,7,1,8,4,8\nU,5,9,7,6,6,8,8,8,5,6,7,9,3,8,4,5\nF,6,11,8,8,4,4,14,4,5,13,7,2,1,10,2,5\nQ,4,5,5,7,3,8,6,8,6,6,5,8,3,8,4,8\nE,3,7,3,5,2,3,8,6,10,7,6,14,0,8,7,8\nB,6,10,8,8,7,7,8,6,4,6,4,5,5,8,7,7\nB,1,0,2,1,1,7,7,7,5,6,6,7,1,8,7,9\nP,4,6,5,4,4,7,5,6,5,7,6,9,5,7,4,8\nB,1,0,1,0,1,7,7,6,4,7,6,7,1,8,6,9\nB,3,4,4,6,3,6,8,9,7,7,6,7,2,8,9,9\nY,2,6,4,4,1,9,11,1,7,4,11,8,1,10,1,8\nY,6,8,6,6,4,4,9,1,8,10,10,6,0,10,3,4\nR,5,10,6,7,3,5,11,9,4,7,4,8,3,7,6,11\nY,6,7,8,9,8,10,10,5,3,6,7,8,6,11,8,4\nP,2,3,3,2,1,7,9,4,3,11,4,3,1,10,2,8\nG,6,10,8,7,5,6,6,6,6,6,6,8,4,7,4,8\nV,3,6,4,4,3,8,11,2,2,6,10,9,2,10,2,9\nK,4,5,7,3,4,6,8,1,7,10,7,10,3,8,3,7\nD,4,8,6,6,5,7,7,7,6,6,5,5,3,8,3,7\nZ,3,6,4,4,3,7,7,3,12,8,6,8,0,8,7,8\nN,9,10,8,6,3,5,8,5,7,3,3,11,6,10,3,7\nW,7,8,7,6,5,3,11,2,2,10,9,8,6,12,2,6\nV,7,8,9,7,10,8,7,4,5,7,6,8,7,10,8,5\nM,1,0,2,1,1,8,6,9,0,7,8,8,6,6,0,8\nS,4,9,5,6,3,9,9,6,10,5,5,5,0,7,9,7\nC,2,4,3,3,2,6,8,7,7,8,8,13,1,10,4,10\nK,2,4,3,2,2,5,7,4,7,7,7,11,3,9,5,10\nM,5,5,8,4,4,6,6,3,4,10,9,10,10,5,3,9\nL,6,9,5,5,2,7,4,3,5,10,4,12,2,7,6,7\nT,3,6,5,8,1,6,14,0,6,8,11,8,0,8,0,8\nN,8,11,11,8,5,6,9,3,5,10,8,8,6,8,1,7\nR,4,9,6,7,6,9,8,4,5,9,4,7,3,7,3,11\nG,6,10,7,8,9,8,6,4,4,6,5,9,7,7,8,13\nX,5,9,8,6,4,4,9,3,8,11,12,9,3,9,4,5\nO,4,7,6,5,6,8,7,5,2,7,6,8,8,9,4,7\nS,4,6,5,4,3,6,8,3,8,11,6,7,2,7,5,7\nR,4,10,5,8,5,5,10,8,3,7,4,9,2,7,5,11\nU,5,9,7,7,8,10,6,5,5,6,7,6,10,6,6,5\nJ,2,5,3,7,1,13,2,9,4,14,4,12,1,6,0,8\nG,4,7,6,5,5,7,7,6,3,7,6,10,4,8,7,7\nE,2,3,4,1,2,6,8,2,7,11,7,8,1,8,3,7\nS,5,9,6,7,3,6,9,4,9,11,6,7,2,6,5,7\nW,3,3,4,1,2,5,11,3,2,9,8,7,5,11,1,6\nP,2,4,2,2,1,6,10,5,4,9,7,4,1,10,3,7\nG,2,3,4,2,2,7,6,6,5,6,6,9,2,9,4,9\nQ,2,4,3,5,3,8,7,6,3,8,6,9,2,9,4,9\nB,2,4,4,2,3,9,7,2,6,11,5,7,4,7,5,9\nB,4,10,6,8,6,9,7,3,5,10,4,6,2,8,5,9\nQ,1,1,2,1,1,8,7,6,4,6,6,8,2,8,3,8\nL,4,11,6,9,4,5,4,6,7,2,2,5,1,6,1,5\nG,4,9,5,7,4,5,6,6,5,9,7,12,2,8,4,10\nE,4,9,5,7,4,4,8,4,8,11,10,10,2,8,4,5\nE,6,9,8,7,5,9,7,2,10,11,4,9,2,8,5,9\nX,5,11,7,9,6,8,7,3,5,6,7,7,4,10,11,9\nQ,3,4,4,5,3,7,8,5,2,7,9,10,3,9,4,9\nN,10,13,8,7,4,7,10,4,7,3,4,9,5,7,2,7\nK,4,7,4,5,3,4,7,7,3,7,6,11,3,8,2,11\nO,3,3,4,5,2,8,7,9,7,7,5,9,3,8,4,8\nT,4,9,4,6,2,4,12,2,8,12,10,5,0,10,2,5\nP,1,3,3,2,1,7,9,3,4,12,5,4,1,9,2,9\nV,4,5,6,4,5,7,8,5,5,8,6,8,6,9,6,9\nE,4,5,5,7,3,3,7,6,11,7,7,15,0,8,7,7\nU,4,8,4,6,2,8,5,13,5,6,14,8,3,9,0,8\nZ,4,8,6,6,3,8,6,3,10,12,4,10,1,8,6,9\nD,7,10,9,8,8,7,8,6,6,8,7,6,7,8,3,7\nD,2,5,4,3,3,9,7,5,6,9,5,5,2,8,3,8\nN,5,7,6,6,7,7,8,5,3,7,5,7,8,7,5,7\nQ,1,2,2,3,2,8,7,6,3,5,6,9,2,9,3,9\nA,6,10,9,8,9,8,8,8,4,6,6,8,3,8,8,4\nJ,2,8,3,6,3,7,7,1,6,11,5,9,1,6,1,6\nZ,5,8,7,10,6,11,5,3,5,9,2,7,2,7,6,9\nV,4,4,6,7,1,9,8,5,3,6,14,8,3,9,0,8\nS,3,5,5,4,2,9,7,3,8,11,3,6,1,9,4,10\nF,4,11,6,8,4,2,13,4,4,12,9,5,1,10,2,5\nD,4,7,6,5,4,8,7,4,7,11,5,7,3,7,4,8\nT,2,3,3,4,1,8,14,0,6,6,11,8,0,8,0,8\nK,2,3,4,2,2,6,8,1,6,10,6,8,3,8,2,7\nY,3,3,5,4,1,7,11,2,2,7,12,8,1,11,0,8\nV,4,6,5,4,6,7,7,4,2,8,7,9,7,10,3,8\nX,3,8,5,6,3,7,8,4,9,6,6,7,3,9,7,7\nI,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nA,3,9,6,7,5,8,5,0,4,6,3,7,2,7,4,5\nZ,2,4,4,3,2,8,6,2,9,11,5,9,1,8,6,9\nJ,2,4,3,3,1,9,5,4,6,14,6,12,0,7,0,8\nQ,5,11,7,8,7,8,4,7,4,5,7,11,6,6,8,8\nE,4,7,6,5,4,9,6,1,7,11,4,8,3,8,4,11\nF,2,3,3,1,1,6,11,3,5,13,6,4,1,9,1,7\nQ,4,7,4,9,4,8,8,6,3,8,9,11,3,9,6,7\nX,3,3,5,2,2,10,6,2,8,11,3,7,2,8,3,9\nF,3,6,3,4,2,1,13,3,3,12,10,6,0,8,2,7\nJ,5,9,7,6,4,7,8,2,6,14,5,7,1,8,1,7\nF,3,3,4,4,1,1,15,5,3,12,9,4,0,8,2,6\nP,4,8,6,6,5,4,11,4,5,11,9,5,0,10,3,7\nL,4,9,4,6,1,0,0,6,6,0,1,5,0,8,0,8\nR,4,6,6,4,6,8,6,7,3,7,5,7,4,7,7,7\nG,8,14,7,8,4,8,4,5,3,8,4,5,4,7,5,8\nE,4,7,5,5,3,7,7,2,9,11,6,9,2,8,5,8\nC,2,1,3,2,1,6,8,7,7,8,8,13,1,9,4,10\nU,4,10,6,7,9,9,6,4,3,6,7,7,9,8,5,6\nN,6,9,8,4,3,8,7,3,4,13,5,8,6,8,0,8\nV,6,9,8,8,10,7,7,5,4,7,6,8,7,9,7,10\nI,5,6,6,4,3,7,6,2,7,7,6,9,0,9,4,8\nN,5,9,7,6,4,9,7,3,5,10,4,6,5,8,1,7\nH,5,8,8,6,6,5,8,3,6,10,8,8,4,8,4,6\nE,0,0,1,0,0,5,7,5,6,7,6,12,0,8,6,10\nY,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8\nG,5,9,7,7,5,6,7,7,5,5,6,11,4,8,4,8\nE,2,4,4,3,2,7,8,2,8,11,7,9,2,8,4,8\nN,5,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8\nB,1,3,2,1,1,8,7,3,4,10,5,7,1,8,3,9\nG,4,7,5,5,3,7,6,7,7,7,5,11,2,9,4,8\nL,4,9,4,6,2,0,2,4,6,1,0,7,0,8,0,8\nE,4,7,6,5,5,8,9,7,3,6,6,11,4,7,7,10\nG,6,8,8,7,9,7,6,6,4,7,7,9,10,8,9,9\nM,4,10,6,8,6,7,6,6,5,7,7,9,8,5,2,8\nD,2,4,4,3,3,9,7,4,6,10,4,6,2,8,3,8\nY,6,7,6,5,2,3,12,5,5,13,12,6,2,11,2,6\nR,5,5,5,6,3,5,12,8,3,7,2,9,3,7,6,11\nP,4,7,6,10,9,8,11,5,0,9,7,6,4,10,5,8\nD,3,9,5,7,5,8,7,5,7,7,6,5,3,8,3,7\nE,4,9,4,7,3,3,6,6,12,7,7,15,0,8,7,7\nW,4,5,6,4,3,7,11,2,3,7,9,8,7,11,1,8\nD,4,2,5,4,3,7,7,7,7,6,6,5,2,8,3,7\nQ,8,13,7,7,4,7,5,4,8,10,5,9,3,7,9,9\nR,4,7,5,5,5,7,8,5,6,6,4,8,3,6,5,9\nG,4,6,4,4,3,6,7,5,5,9,7,10,2,9,4,10\nY,3,7,4,5,2,8,10,2,2,6,12,8,2,11,0,8\nR,5,8,7,6,7,7,10,3,5,5,6,9,6,7,7,7\nD,2,0,2,1,1,6,7,8,6,7,6,6,2,8,3,8\nJ,4,11,5,8,5,9,6,2,5,11,4,9,1,6,2,5\nC,4,7,5,5,3,6,7,6,8,5,6,13,1,6,5,9\nI,3,7,4,5,1,6,8,1,8,14,7,8,0,8,1,7\nT,6,10,8,8,6,6,7,7,8,7,6,8,5,11,8,8\nC,1,0,1,0,0,7,7,5,7,7,6,14,0,8,4,10\nN,3,4,5,3,2,7,8,2,5,10,6,6,5,9,0,7\nM,5,5,6,4,4,8,6,6,5,6,7,8,10,6,4,6\nB,2,3,2,2,2,7,8,5,5,7,5,6,2,8,5,9\nH,3,7,5,5,4,7,7,5,6,7,6,9,6,8,3,7\nR,4,7,4,5,2,6,10,9,4,7,5,8,2,8,5,10\nQ,6,9,9,8,7,7,4,4,4,7,4,9,5,4,7,7\nA,3,6,5,4,1,9,4,3,1,7,1,8,3,6,2,8\nG,3,8,4,6,3,8,8,7,5,6,7,8,2,7,5,11\nQ,5,12,5,7,4,11,4,4,6,12,3,9,3,8,7,12\nH,3,3,4,4,2,7,8,14,1,7,5,8,3,8,0,8\nK,4,11,4,8,2,4,8,8,2,6,3,11,4,8,2,11\nG,4,10,5,8,3,7,5,8,9,8,4,13,1,9,5,10\nA,4,6,5,4,5,7,6,7,4,7,6,9,2,9,8,4\nA,4,9,6,7,4,10,3,2,2,8,2,10,5,5,3,8\nJ,3,11,4,8,3,14,4,4,5,13,1,8,0,7,0,8\nK,4,6,5,4,5,6,5,4,4,6,6,9,3,6,7,11\nW,5,7,5,5,5,4,10,2,3,9,8,8,6,11,2,6\nN,2,1,2,3,2,7,8,5,4,7,6,7,4,8,1,6\nH,4,7,4,4,2,7,5,14,1,7,8,8,3,8,0,8\nL,5,10,4,5,2,9,3,4,4,12,4,13,2,6,6,9\nB,6,9,6,4,5,8,8,4,4,9,6,7,6,7,8,6\nE,5,11,5,8,3,3,8,6,11,7,6,15,0,8,7,7\nY,5,8,5,6,3,5,9,1,7,9,9,5,1,11,3,5\nJ,3,9,5,7,2,8,6,3,6,15,5,9,1,6,1,6\nN,1,0,1,0,0,7,7,10,0,6,6,8,4,8,0,8\nL,6,8,7,7,6,8,7,4,6,7,6,8,3,8,8,11\nS,3,6,5,4,6,6,9,3,3,8,7,6,3,8,9,1\nQ,5,9,6,8,6,8,7,7,5,6,7,8,3,8,4,9\nD,5,11,5,6,4,10,5,3,5,10,4,7,5,8,7,7\nE,6,10,8,8,5,5,9,2,10,10,8,9,3,8,5,5\nL,2,7,4,5,2,7,4,2,7,7,2,8,1,6,2,8\nG,6,10,7,8,5,6,6,6,5,9,6,12,4,8,5,8\nP,4,6,4,8,3,5,9,11,5,8,6,5,2,10,4,8\nK,3,6,4,4,2,3,7,6,3,7,6,11,3,8,3,11\nW,3,2,4,3,3,6,11,2,2,7,9,9,6,11,1,8\nO,3,9,5,6,4,8,8,8,4,7,8,8,3,8,3,8\nW,4,7,7,5,4,4,10,3,3,9,10,9,8,11,1,8\nA,4,9,3,4,2,11,5,4,1,8,4,9,4,6,4,9\nH,2,6,3,4,1,7,7,14,1,7,6,8,3,8,0,8\nT,2,8,4,6,3,7,12,3,6,7,11,8,2,12,1,8\nI,1,1,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nF,3,8,5,6,3,4,11,6,5,11,10,5,2,9,2,5\nI,1,9,1,6,2,7,7,0,7,7,6,8,0,8,3,8\nD,1,3,3,2,1,9,7,4,5,10,4,5,2,8,2,8\nT,5,6,5,4,3,5,10,1,8,11,9,5,1,9,3,4\nP,4,8,6,6,6,6,7,6,4,8,6,8,5,8,6,10\nZ,5,11,6,8,6,7,8,5,9,7,7,9,1,9,7,7\nI,0,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nD,4,9,6,7,6,9,7,4,6,10,5,5,3,8,3,8\nF,3,5,5,3,2,5,12,3,5,13,7,4,1,10,1,6\nG,5,7,6,5,3,8,6,6,7,11,6,12,2,10,4,8\nT,6,14,6,8,4,9,7,3,7,11,4,6,2,8,6,6\nC,5,9,6,6,3,4,8,6,8,12,10,12,2,9,3,7\nB,2,1,3,2,2,7,7,5,5,7,6,6,2,8,5,9\nH,2,6,4,4,5,9,6,3,3,6,6,7,5,9,6,7\nN,10,14,9,8,4,8,10,5,5,4,5,10,6,10,3,7\nE,1,3,3,2,1,6,7,2,7,10,7,9,2,8,4,9\nR,5,11,8,8,5,10,7,3,7,11,2,6,3,7,4,10\nU,5,7,6,5,5,8,7,8,5,6,7,9,3,8,4,6\nL,1,3,2,2,1,5,4,4,6,3,3,5,1,7,1,6\nP,3,8,3,6,3,5,9,8,3,9,6,5,2,10,3,8\nC,4,8,4,6,3,6,8,6,7,12,8,12,2,10,3,8\nN,3,2,4,3,2,7,8,5,4,8,7,7,5,8,3,6\nF,2,4,4,2,2,6,10,3,5,13,6,5,1,9,1,7\nI,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nZ,3,7,4,5,2,7,7,3,12,8,6,8,0,8,7,8\nF,3,4,4,6,2,1,12,5,6,12,11,9,0,8,2,6\nD,3,8,4,6,2,6,7,10,8,6,6,6,3,8,4,8\nG,3,7,4,5,3,7,7,7,6,6,6,9,1,8,5,11\nB,3,7,4,5,4,7,7,8,5,7,6,7,2,8,7,9\nS,4,11,4,8,4,8,6,7,7,7,8,9,2,10,10,7\nT,6,10,5,7,3,6,11,1,9,11,9,4,0,10,3,4\nQ,4,9,5,8,3,9,8,8,5,5,8,9,3,7,5,10\nV,3,10,5,8,2,8,8,4,3,6,14,8,3,9,0,8\nE,4,8,5,6,5,6,8,7,9,6,4,10,3,8,6,8\nA,6,7,8,6,6,7,7,3,6,7,8,9,5,10,3,6\nW,2,0,3,1,1,7,8,4,0,7,8,8,7,9,0,8\nK,7,11,9,8,9,8,7,5,5,7,6,7,7,6,9,14\nY,4,7,6,9,7,9,10,7,4,7,7,6,5,11,6,3\nK,5,8,7,6,8,6,9,4,5,6,5,9,8,7,7,10\nL,5,10,5,5,2,7,4,3,6,11,4,13,2,6,6,8\nQ,2,0,2,1,1,8,7,6,4,6,6,8,2,8,3,8\nB,4,4,4,7,4,6,8,9,7,7,6,7,2,8,9,10\nS,4,7,5,5,3,8,8,3,7,10,3,6,2,6,5,9\nF,2,2,3,4,2,6,9,3,5,10,9,5,4,10,3,8\nS,3,7,4,5,3,8,7,7,7,7,7,8,2,9,9,8\nQ,6,9,7,11,7,9,5,7,4,9,5,11,4,8,6,6\nL,2,1,2,2,1,4,4,4,7,2,1,6,0,7,1,6\nT,5,10,7,8,4,5,12,3,8,8,12,8,1,12,1,7\nP,6,10,8,8,5,7,10,5,4,12,5,3,1,10,3,8\nA,2,6,4,4,2,8,3,2,2,7,1,8,2,7,3,7\nK,3,5,5,4,3,5,7,1,7,10,8,11,3,8,3,8\nF,2,3,3,2,1,6,10,3,5,13,6,4,1,9,1,8\nD,2,3,3,2,2,7,7,6,6,6,6,4,2,8,3,7\nQ,4,4,6,6,6,9,10,5,0,5,7,10,6,13,4,10\nL,3,6,4,4,2,4,3,7,6,1,2,4,1,6,1,6\nG,6,11,7,8,6,8,7,7,6,6,5,9,2,7,5,11\nC,5,9,7,7,5,7,7,8,6,7,6,13,6,8,5,7\nN,5,9,7,7,4,7,9,6,5,7,7,7,6,8,2,7\nK,5,7,7,5,5,7,6,1,6,10,5,9,3,7,3,9\nI,0,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nP,5,10,7,7,6,7,10,5,3,11,5,3,1,10,3,8\nR,3,3,3,4,2,6,9,9,4,7,5,7,3,7,5,11\nO,6,10,6,7,4,9,6,8,6,9,4,7,3,8,4,8\nX,2,3,3,1,1,6,8,2,8,10,8,9,2,8,2,7\nV,4,10,6,8,3,8,12,3,4,5,11,9,3,10,1,7\nH,6,11,8,8,8,7,8,6,8,8,6,8,9,7,4,8\nA,3,5,6,4,2,10,2,2,2,8,2,9,4,6,2,9\nR,2,4,4,3,2,9,7,3,5,10,4,6,2,7,3,9\nY,4,11,6,8,3,6,10,1,7,7,12,9,1,11,2,8\nK,3,6,5,4,5,6,7,4,4,7,6,8,4,6,8,13\nS,9,14,9,8,4,7,6,4,3,13,9,9,3,10,3,9\nX,5,10,7,8,4,9,6,1,8,10,3,7,3,8,4,8\nE,4,11,5,8,7,5,7,6,8,6,5,11,3,8,5,9\nT,3,4,4,3,1,5,13,3,6,11,9,4,1,11,1,5\nD,3,6,5,4,3,9,7,4,5,11,5,6,3,8,2,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nI,2,9,2,7,2,7,7,0,8,7,6,9,0,8,3,8\nW,4,2,6,4,4,5,11,3,2,8,9,9,10,13,1,8\nX,1,1,2,2,1,8,7,3,8,6,6,8,2,8,6,8\nB,4,8,6,6,6,9,6,4,6,10,4,7,3,8,5,10\nI,4,9,6,6,3,9,8,2,8,7,6,6,0,8,4,7\nH,2,3,3,2,2,7,7,5,5,7,6,8,3,8,2,8\nF,4,6,6,4,3,6,11,4,5,12,7,5,2,9,2,6\nS,4,4,5,6,3,8,6,5,9,5,6,8,0,8,9,8\nJ,4,8,5,6,2,10,6,2,8,14,3,8,0,7,0,8\nM,8,11,11,8,7,12,5,3,6,10,2,6,10,9,2,9\nN,5,6,7,4,6,5,9,3,4,8,7,9,6,8,4,4\nA,4,9,7,7,4,9,3,2,3,6,1,7,2,6,2,7\nR,2,4,4,3,2,9,8,3,5,10,3,6,2,6,3,10\nJ,3,6,5,4,2,7,9,1,6,14,5,6,0,7,0,7\nS,5,10,8,8,10,7,6,3,2,7,5,7,3,8,15,5\nN,3,5,3,4,2,7,9,5,4,7,6,7,5,8,2,7\nK,4,4,4,5,2,3,7,8,2,7,5,11,4,8,2,11\nP,4,7,6,5,3,8,10,3,6,13,4,2,1,10,3,9\nJ,5,9,4,6,3,9,8,2,3,12,4,5,2,9,6,9\nN,2,7,3,5,3,7,7,11,1,6,6,8,5,8,0,8\nW,8,11,8,9,8,4,11,2,2,9,8,7,7,12,2,6\nW,1,0,2,1,1,7,8,4,0,7,8,8,5,10,0,8\nG,6,11,8,8,5,6,6,6,7,6,6,8,3,8,4,7\nG,3,4,4,6,2,7,6,8,7,6,6,9,2,7,5,11\nV,6,7,6,5,2,3,11,4,4,10,12,7,2,10,1,7\nN,4,9,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nE,3,7,5,6,5,7,6,5,3,7,6,10,5,10,6,11\nP,4,10,6,8,6,6,10,5,5,10,8,3,1,10,4,6\nH,3,4,5,6,5,8,11,4,2,8,7,6,4,11,8,4\nJ,1,3,2,1,0,9,7,2,6,14,5,9,0,7,0,7\nX,2,6,4,4,3,8,8,2,5,7,5,7,3,8,5,7\nL,5,10,5,8,2,0,0,6,6,0,1,5,0,8,0,8\nG,3,5,4,3,2,6,6,6,5,8,7,11,2,8,4,10\nV,3,5,6,4,2,7,12,2,3,6,11,9,4,12,2,7\nQ,3,8,5,7,4,8,7,7,5,6,6,9,3,8,4,10\nZ,3,7,4,5,3,7,7,3,12,8,6,8,0,8,8,7\nD,8,13,7,7,6,9,6,4,6,10,4,7,6,7,9,7\nH,3,7,3,5,3,8,6,12,2,7,8,8,3,9,0,8\nK,3,3,3,4,1,3,6,7,2,7,6,11,3,8,2,11\nT,3,7,4,5,2,4,13,4,6,12,9,4,1,11,1,5\nU,7,8,8,6,3,2,10,6,7,12,12,9,3,9,1,7\nQ,7,15,6,8,5,11,5,4,6,11,4,7,3,9,7,13\nW,6,10,10,8,8,7,11,2,2,6,8,8,8,12,1,8\nE,4,7,6,5,4,7,7,3,8,11,7,9,2,9,5,8\nR,3,6,3,4,2,5,11,7,4,7,3,9,2,7,6,11\nF,3,7,5,5,2,3,12,4,4,13,9,5,1,10,2,6\nK,5,9,7,7,5,6,7,5,7,6,6,10,4,8,5,9\nH,4,8,5,6,4,7,7,13,1,7,6,8,3,8,0,8\nH,4,7,7,5,5,5,9,3,5,10,9,8,3,8,3,5\nW,3,4,4,2,2,5,11,2,2,9,8,7,6,11,1,6\nL,4,8,6,6,7,7,7,3,5,7,7,10,6,10,6,6\nS,8,14,8,8,4,8,6,4,4,13,7,8,3,10,4,8\nR,4,9,6,6,5,9,7,6,3,8,5,6,4,7,7,9\nF,5,10,6,8,4,4,11,4,6,11,10,5,2,10,2,5\nU,8,9,9,7,5,3,9,5,8,10,10,10,3,9,2,6\nX,4,6,6,4,3,7,7,1,8,10,7,9,2,8,3,7\nP,2,1,2,1,1,5,11,8,1,9,5,4,1,9,3,8\nK,3,6,4,4,3,6,7,4,7,6,6,10,3,8,5,9\nU,3,5,4,4,2,6,8,6,7,7,10,9,3,9,1,8\nP,3,6,5,4,3,5,11,4,5,11,8,3,1,10,4,7\nK,7,10,10,8,7,4,7,2,7,10,10,11,3,8,3,6\nW,4,6,6,4,4,10,11,3,2,5,9,7,6,11,1,8\nW,5,10,7,8,6,8,8,4,1,7,9,8,8,11,0,8\nM,2,0,3,1,1,8,6,10,0,7,9,8,7,6,0,8\nU,4,8,5,6,2,7,4,14,5,7,14,8,3,9,0,8\nM,4,6,6,4,5,6,6,3,4,10,8,9,7,5,2,7\nJ,5,8,6,6,3,7,6,3,6,15,6,10,1,6,0,7\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nC,7,10,9,8,6,6,8,8,6,5,6,13,6,8,5,6\nU,4,7,6,6,6,7,6,4,4,6,6,8,7,7,1,7\nP,6,10,6,5,4,7,10,4,3,12,5,3,4,11,5,7\nB,4,10,6,8,7,7,8,5,5,9,7,6,4,8,7,8\nN,6,9,8,7,7,6,7,8,6,7,5,6,3,8,3,8\nU,5,10,7,8,5,5,9,6,8,8,10,9,3,9,1,8\nZ,4,8,5,6,4,8,6,2,9,11,5,9,1,8,5,9\nH,7,8,10,10,9,6,4,5,3,6,3,6,6,5,11,7\nA,2,8,4,6,2,7,6,3,1,6,0,8,2,7,1,7\nQ,6,6,8,9,9,9,11,4,1,5,8,11,6,14,7,13\nW,9,12,9,6,5,2,10,3,3,10,11,9,7,10,1,6\nB,4,6,5,4,4,9,7,3,6,9,5,6,2,8,5,8\nB,8,12,6,6,4,10,5,5,5,11,3,9,6,7,6,10\nW,4,3,5,2,2,4,11,3,2,10,9,7,6,11,1,7\nA,4,5,6,4,5,7,8,3,5,7,8,9,7,10,3,7\nX,1,3,2,1,1,7,7,3,7,6,6,9,2,8,5,8\nW,2,3,3,2,2,9,10,3,2,5,9,7,5,11,0,7\nO,10,14,6,8,4,6,6,7,4,10,6,10,5,9,5,8\nV,5,6,5,4,2,2,12,5,4,12,11,7,2,10,1,7\nV,3,7,5,5,1,9,8,4,2,6,14,8,3,10,0,8\nU,3,7,4,5,2,7,5,12,4,6,12,8,3,9,0,8\nG,3,5,4,4,2,6,7,6,6,10,7,10,2,9,4,9\nO,4,5,5,4,4,7,6,5,5,9,5,10,4,6,5,6\nA,2,7,4,4,1,7,6,3,1,6,0,8,2,6,1,8\nZ,8,7,6,11,5,4,13,4,2,12,8,5,2,8,8,6\nI,7,10,9,8,5,9,5,3,7,6,7,4,0,9,4,7\nY,8,11,8,8,5,3,10,2,7,11,11,6,1,11,3,4\nL,4,9,4,4,3,8,5,3,5,11,5,10,3,5,6,8\nG,3,6,4,4,2,6,7,5,5,5,7,8,2,8,4,8\nR,7,10,7,5,5,6,8,3,4,7,4,10,6,8,6,7\nV,4,9,6,7,4,5,11,3,2,9,10,7,4,10,6,8\nY,4,8,6,6,3,7,9,1,7,6,12,9,2,11,2,8\nL,3,7,4,5,3,4,4,4,8,2,1,7,4,6,1,6\nP,3,8,3,6,2,5,9,10,5,8,6,5,2,9,4,8\nL,2,1,2,2,1,4,4,5,6,2,2,6,1,7,1,6\nI,4,7,6,5,3,8,8,2,8,7,6,7,0,8,4,7\nO,5,8,5,6,5,8,7,7,4,9,5,8,3,8,3,8\nO,1,3,2,2,1,8,6,6,3,9,5,7,2,8,2,7\nK,4,8,4,5,2,4,7,8,2,7,6,11,4,8,2,11\nZ,6,11,8,8,5,6,9,3,11,11,9,6,2,8,6,5\nQ,7,10,9,8,8,8,5,8,4,6,7,8,4,6,7,8\nD,3,9,5,7,8,9,8,5,4,7,6,6,4,7,8,6\nM,7,10,10,8,6,5,7,3,5,10,10,10,8,5,3,7\nJ,2,11,3,8,3,7,7,1,6,11,5,9,1,6,2,5\nN,3,7,5,5,3,6,9,3,4,10,8,8,5,8,1,7\nA,4,11,6,8,3,5,4,3,3,5,1,7,3,6,3,7\nH,5,8,7,6,6,7,7,5,5,7,6,8,9,7,5,10\nK,5,7,7,6,6,6,7,3,4,6,4,9,6,5,9,8\nE,5,7,7,5,5,8,6,1,8,11,4,9,3,8,5,10\nG,2,4,4,3,2,7,7,6,6,6,6,10,2,9,4,9\nK,3,5,4,4,3,5,7,4,7,7,6,11,3,8,5,10\nE,4,9,4,7,4,3,8,5,9,7,6,14,0,8,6,9\nY,4,6,6,9,9,8,4,5,3,7,8,8,7,8,5,8\nD,4,8,5,6,7,9,8,4,5,7,6,6,4,7,7,4\nL,2,2,3,3,1,4,3,5,6,2,2,5,1,7,0,6\nW,6,9,6,6,5,4,10,2,3,9,8,7,7,11,2,6\nJ,2,2,3,4,2,10,6,2,5,12,4,8,1,6,1,6\nT,2,3,3,2,1,7,12,3,6,7,11,8,1,11,1,8\nY,3,7,6,5,2,9,10,2,7,3,12,8,2,11,2,9\nF,3,5,5,4,2,4,12,4,4,13,8,5,1,10,1,7\nX,4,7,5,5,1,7,7,6,2,7,6,8,3,8,4,8\nJ,0,0,1,0,0,12,4,5,3,12,5,11,0,7,0,8\nT,7,9,7,7,5,6,11,4,6,11,9,4,2,12,2,4\nQ,2,2,3,3,2,8,7,6,2,6,6,9,2,9,3,10\nJ,2,4,3,3,1,10,6,2,6,12,3,8,0,7,0,7\nL,1,0,1,1,0,1,1,5,5,0,1,6,0,8,0,8\nN,3,7,4,5,3,7,8,5,4,7,6,6,5,9,1,6\nH,4,5,7,4,4,6,7,3,7,11,9,9,5,8,5,6\nZ,3,6,5,4,3,8,7,2,10,11,5,9,1,8,6,9\nQ,2,1,3,2,1,8,7,7,5,6,6,8,3,8,4,8\nN,4,10,6,7,5,7,8,10,5,8,5,5,4,8,4,10\nU,6,7,6,5,3,4,8,6,8,10,10,9,3,9,2,5\nL,8,11,7,6,3,11,1,3,4,12,3,11,2,9,4,10\nG,4,6,5,4,5,9,9,5,2,6,7,8,6,9,4,9\nP,5,9,7,7,7,5,9,6,4,8,7,9,5,9,7,10\nQ,4,6,6,9,3,8,9,7,6,6,9,9,4,7,6,10\nL,4,8,6,6,3,7,4,1,8,8,2,10,0,6,3,8\nR,4,7,4,5,4,5,10,7,3,7,4,9,2,7,5,11\nO,4,7,6,5,4,7,6,8,5,7,4,8,3,8,3,8\nM,3,7,5,5,5,8,7,5,4,6,7,8,7,6,2,7\nF,1,0,1,0,0,3,12,4,3,11,9,6,0,8,2,8\nM,6,8,8,6,8,7,8,7,5,6,6,8,7,9,8,11\nQ,4,9,4,4,3,11,4,4,5,12,3,9,3,8,7,13\nE,5,11,6,8,7,6,7,5,8,7,6,10,3,8,6,9\nQ,4,5,5,8,7,9,7,5,0,6,6,10,7,10,4,9\nG,2,4,3,2,2,6,7,5,5,9,7,10,2,8,4,10\nL,5,9,6,6,4,4,5,2,8,6,2,10,0,7,3,6\nH,8,10,11,8,9,8,7,2,6,10,5,8,3,8,3,8\nA,2,2,4,3,2,8,3,2,3,8,1,8,2,6,2,7\nD,3,1,4,2,2,7,7,6,6,7,6,5,2,8,3,7\nS,4,6,6,4,7,7,7,3,2,8,6,7,2,8,12,3\nU,2,3,3,2,1,5,8,5,6,10,9,8,3,9,2,6\nB,10,13,7,8,5,10,5,5,6,11,3,9,6,7,7,11\nJ,4,11,6,8,4,10,5,4,5,14,4,10,0,7,1,6\nX,7,10,10,8,6,4,8,2,8,10,11,10,3,9,3,5\nG,2,2,4,4,2,7,6,6,5,6,6,9,2,9,4,9\nZ,4,11,6,8,4,7,8,2,10,11,8,6,2,7,6,6\nD,5,9,7,7,6,7,7,5,6,7,6,8,7,7,3,7\nM,2,0,2,1,1,7,6,10,0,7,8,8,6,6,0,8\nQ,4,5,4,6,4,8,6,7,4,9,8,9,3,9,6,8\nV,4,11,6,8,3,7,9,4,2,7,13,8,2,10,0,8\nP,4,8,5,6,3,8,8,1,6,13,5,5,1,10,2,9\nX,5,10,6,8,4,7,7,4,4,7,6,8,3,8,4,8\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nI,7,12,5,6,3,11,4,5,5,13,3,8,3,7,4,9\nL,4,3,4,5,1,1,0,6,6,0,1,4,0,8,0,8\nN,3,6,4,4,2,7,7,14,2,5,6,8,6,8,0,8\nH,5,6,7,4,5,9,6,3,6,10,4,7,3,8,3,9\nR,4,7,6,5,5,8,9,6,5,8,5,8,4,7,5,11\nG,5,12,4,6,4,7,6,4,2,8,6,8,4,9,9,7\nA,4,11,6,8,4,7,5,3,0,6,1,8,2,7,1,8\nO,5,10,6,8,5,7,7,9,4,7,6,9,3,8,4,6\nS,6,12,5,6,3,8,3,2,5,8,1,7,3,7,5,8\nO,5,9,7,7,5,8,6,8,4,6,4,7,3,9,4,9\nR,5,9,5,7,5,6,8,8,4,6,5,7,3,8,6,13\nB,4,11,4,8,6,6,6,9,6,7,7,7,2,9,7,10\nK,5,9,8,7,9,8,6,3,5,6,6,8,8,6,9,5\nS,4,7,4,5,2,7,6,5,9,5,6,9,0,9,9,8\nO,3,8,4,6,3,8,8,7,5,7,6,9,2,8,3,8\nI,1,10,0,8,1,7,7,5,3,7,6,8,0,8,0,8\nK,4,9,6,8,7,8,5,2,3,8,4,8,5,6,7,11\nO,3,7,4,5,2,7,9,9,7,8,8,7,3,8,4,8\nE,3,6,5,6,5,6,9,4,4,7,7,11,4,9,7,10\nE,5,11,7,9,6,5,7,7,9,7,6,12,3,8,6,8\nA,5,11,8,8,4,10,2,2,3,9,1,8,3,7,4,8\nQ,6,11,6,6,4,10,5,4,6,10,4,8,3,9,7,12\nA,4,10,6,8,5,12,3,2,2,9,2,8,2,6,2,8\nP,4,9,6,7,4,7,10,4,4,12,5,3,1,10,3,8\nN,4,6,5,4,3,7,8,6,5,7,6,6,5,9,1,6\nK,3,4,5,3,2,4,9,2,7,10,9,11,3,8,3,6\nX,6,11,9,9,6,7,7,0,8,9,6,8,3,8,3,7\nC,4,9,6,8,6,6,7,4,4,7,6,10,5,9,9,11\nD,4,7,6,5,5,8,8,6,5,9,5,4,5,8,5,9\nD,2,4,4,2,2,9,6,3,6,11,4,6,2,8,3,8\nA,5,11,7,8,7,8,7,7,4,7,6,9,3,8,8,3\nE,5,11,7,8,8,8,5,7,3,8,6,11,5,8,8,10\nH,3,5,5,7,5,9,10,3,1,8,6,7,3,10,8,7\nH,3,7,4,5,4,8,7,5,6,7,6,9,3,8,3,8\nS,1,3,2,2,1,8,6,6,5,7,7,9,2,10,8,7\nV,5,10,5,5,3,6,9,4,3,9,7,5,5,12,2,8\nY,8,10,8,7,5,2,11,2,7,12,12,7,0,10,1,5\nE,1,3,2,1,1,5,8,2,7,10,8,9,1,8,3,7\nY,1,0,1,0,0,7,10,2,2,7,12,8,1,11,0,8\nY,1,0,2,0,0,7,10,1,3,7,12,8,1,11,0,8\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nT,3,9,5,6,1,7,15,1,6,7,11,8,0,8,0,8\nQ,1,0,1,1,0,8,7,6,3,6,6,9,2,8,2,8\nA,2,4,4,6,2,7,3,3,3,7,1,8,3,6,3,8\nU,4,7,4,5,3,7,5,13,4,7,11,8,3,9,0,8\nS,2,6,3,4,3,8,8,7,6,7,4,6,2,6,8,8\nU,4,6,5,4,3,6,8,6,8,7,10,9,3,9,1,8\nC,3,6,4,4,1,6,7,6,10,7,6,13,1,8,4,9\nX,4,11,6,8,4,7,8,3,9,6,5,6,4,10,8,6\nZ,4,6,5,4,3,8,6,2,10,12,5,10,1,9,6,9\nD,3,4,5,3,3,8,7,4,6,9,5,6,2,8,3,8\nG,2,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10\nD,2,4,4,3,2,9,6,4,6,11,4,6,2,8,3,8\nA,3,4,5,3,2,10,2,2,2,9,2,9,2,6,2,8\nU,4,7,5,5,4,9,7,7,5,7,6,8,3,8,4,6\nO,6,10,7,8,6,7,7,8,4,7,6,8,3,8,3,8\nC,5,8,6,6,6,5,6,3,5,8,6,12,6,9,4,9\nB,2,4,4,3,3,8,7,3,6,10,5,6,2,8,4,10\nK,6,12,6,6,4,7,7,3,6,10,3,8,5,5,3,8\nJ,5,10,6,7,7,9,6,3,4,7,5,5,5,6,6,5\nZ,4,8,4,6,4,9,7,5,9,7,5,6,1,7,7,8\nW,4,5,6,7,4,7,8,5,2,6,8,8,8,9,0,8\nJ,2,8,3,6,2,10,6,1,7,11,3,7,0,7,1,7\nX,5,10,7,8,6,8,5,3,5,6,7,7,3,10,9,9\nE,2,6,3,4,2,3,8,5,9,7,6,14,0,8,6,9\nH,5,11,6,8,3,7,5,15,2,7,9,8,3,8,0,8\nM,4,1,5,3,3,9,6,6,4,6,7,5,8,6,2,6\nR,4,8,6,6,8,7,7,3,5,7,6,8,6,9,7,5\nD,3,6,4,4,3,7,7,6,5,7,7,6,3,8,3,7\nU,4,7,6,5,7,8,7,4,3,6,7,8,7,9,5,7\nN,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8\nX,5,5,6,7,2,7,7,5,4,7,6,8,3,8,4,8\nS,2,3,4,2,1,9,7,3,7,11,4,8,1,8,4,10\nH,4,8,6,6,6,7,7,5,6,7,6,8,3,8,3,7\nP,3,6,5,4,4,6,9,5,4,9,7,4,1,10,3,7\nG,1,3,2,1,1,7,7,4,4,9,7,10,2,8,4,10\nJ,4,8,3,6,2,9,6,2,4,11,6,8,2,10,6,11\nJ,1,7,2,5,1,14,2,6,5,13,1,10,0,7,0,8\nC,4,8,6,6,5,6,6,3,4,8,6,11,6,9,3,9\nR,3,5,5,4,5,7,8,3,3,7,5,8,6,8,5,7\nY,6,9,8,6,7,8,4,6,5,8,7,8,8,9,4,6\nK,5,10,8,7,5,3,8,2,7,10,11,12,4,7,4,5\nN,3,9,4,6,2,7,7,14,2,5,6,8,5,8,0,8\nZ,4,6,6,4,4,9,10,5,5,6,5,7,3,8,6,5\nP,2,3,2,2,1,4,11,3,4,10,8,4,0,9,3,6\nL,5,7,6,5,5,7,8,7,6,6,5,8,6,7,4,10\nZ,4,8,6,6,4,7,8,2,9,11,6,8,1,8,6,8\nX,3,5,4,3,2,8,7,3,9,6,7,9,3,7,7,9\nZ,4,4,5,6,2,7,7,4,14,9,6,8,0,8,8,8\nW,3,3,4,5,3,10,8,4,1,6,8,8,7,10,0,8\nP,5,10,8,8,6,8,10,4,5,13,5,3,1,10,3,8\nD,1,0,2,1,1,6,7,8,6,7,6,6,2,8,3,8\nK,3,5,5,4,3,8,7,2,7,10,4,9,5,10,4,9\nL,2,5,4,4,2,7,4,1,8,8,2,10,0,7,2,8\nK,4,7,6,5,3,8,6,2,7,10,3,9,3,8,4,10\nC,1,0,1,1,0,6,7,6,8,7,6,14,0,8,4,10\nZ,2,5,3,3,2,7,8,5,9,6,6,9,2,8,7,8\nY,4,7,6,5,2,5,10,2,8,10,12,8,1,11,2,7\nE,2,5,4,3,2,7,7,1,8,10,6,9,2,8,4,8\nI,6,14,5,8,3,7,9,2,6,14,5,6,1,8,5,8\nA,2,7,4,5,2,10,3,2,2,9,2,9,2,6,2,8\nM,6,10,8,7,9,7,10,6,4,7,7,9,8,10,7,11\nA,3,11,6,8,3,11,2,3,3,10,1,9,3,7,3,8\nV,7,10,9,8,5,6,11,3,3,7,11,8,3,10,4,8\nP,3,8,4,6,4,6,9,6,4,9,7,4,2,10,3,7\nN,8,12,9,7,4,8,6,2,4,13,5,8,6,8,0,7\nS,5,10,6,8,4,8,7,5,8,11,3,8,2,7,5,9\nZ,4,9,6,7,5,9,11,6,5,6,5,7,3,9,8,5\nQ,5,10,6,9,5,8,8,7,4,5,8,9,3,8,5,10\nU,4,9,5,7,2,8,5,13,5,6,14,8,3,9,0,8\nM,4,5,7,4,4,8,7,2,4,9,6,8,7,5,2,7\nD,4,4,5,6,3,5,7,10,9,6,6,5,3,8,4,8\nZ,1,1,2,3,1,7,8,5,8,6,6,9,1,9,7,8\nY,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8\nD,1,0,2,1,1,6,7,8,6,7,6,6,2,8,3,8\nJ,1,4,2,3,1,10,6,2,6,12,4,8,0,7,1,7\nV,5,7,5,5,2,3,12,5,4,12,12,7,2,10,1,8\nU,4,9,6,6,9,8,7,4,3,6,7,8,7,10,5,7\nJ,2,10,3,8,1,12,3,10,3,13,7,13,1,6,0,8\nN,5,11,8,8,9,6,7,3,4,8,8,9,7,10,7,6\nG,6,9,6,6,4,5,7,6,6,9,8,10,2,8,5,9\nO,4,8,6,6,5,8,7,8,4,7,6,7,3,8,3,7\nD,5,11,6,8,3,5,7,11,9,7,7,5,3,8,4,8\nD,2,1,2,1,1,7,7,6,6,7,6,5,2,8,2,7\nM,6,9,7,7,6,8,5,11,0,7,9,8,12,4,4,8\nN,7,8,9,7,8,7,8,5,5,7,5,7,6,9,5,4\nQ,3,6,4,5,3,8,7,7,5,6,4,9,2,8,4,9\nI,4,11,5,8,3,7,7,0,7,13,6,8,0,8,1,8\nP,5,10,7,8,6,7,11,6,3,11,5,3,2,11,3,9\nV,3,9,5,7,1,6,8,4,3,7,14,8,3,9,0,8\nE,3,7,4,5,2,3,8,6,11,7,6,15,0,8,7,7\nF,2,8,3,5,1,1,13,4,3,12,10,5,0,8,3,7\nJ,4,7,5,5,2,7,7,3,6,15,6,10,1,6,1,7\nS,3,10,4,8,4,8,6,8,5,7,7,9,2,9,8,8\nS,5,6,6,6,6,8,9,4,5,7,7,8,4,10,9,10\nB,2,3,4,2,2,8,7,3,5,10,5,6,2,8,4,9\nP,5,9,6,7,6,7,5,7,4,7,6,8,3,8,7,10\nQ,1,0,2,1,0,8,7,6,3,6,6,9,2,8,3,8\nB,3,8,5,6,4,7,8,5,6,9,7,5,2,8,7,6\nK,4,9,6,8,6,9,6,2,4,9,3,8,5,6,7,11\nG,5,7,7,6,6,8,10,5,3,6,7,10,8,11,7,8\nE,5,5,5,7,3,3,7,6,12,7,6,15,0,8,7,6\nY,2,4,4,3,1,7,11,1,7,7,11,8,1,11,2,8\nO,1,3,2,1,1,8,7,6,4,7,6,8,2,8,2,8\nK,6,8,9,6,5,7,5,2,8,10,4,10,5,5,5,9\nN,8,11,11,8,7,4,11,3,4,10,10,8,6,8,1,7\nX,5,10,6,7,2,7,7,5,4,7,6,8,3,8,4,8\nJ,6,11,7,8,3,9,5,4,7,15,4,11,1,6,1,7\nA,3,7,5,5,3,12,2,2,2,10,2,9,2,6,3,8\nZ,4,7,5,5,3,8,6,2,10,11,5,10,2,8,6,9\nR,2,1,2,2,2,6,8,4,5,6,5,7,2,6,4,9\nE,2,1,2,1,1,4,7,5,8,7,6,13,0,8,7,9\nD,4,7,5,5,4,10,6,3,6,11,4,7,3,8,3,9\nX,7,15,8,8,5,11,7,2,7,11,3,5,3,11,4,9\nO,5,9,5,7,4,7,7,8,5,10,7,8,3,8,3,8\nA,2,6,4,4,2,12,3,4,2,11,1,8,2,6,2,9\nF,4,8,4,6,2,1,11,5,7,12,11,9,0,8,2,6\nS,7,13,5,7,3,8,2,1,5,7,2,7,3,7,4,10\nN,1,1,2,2,1,7,7,11,1,5,6,8,4,8,0,8\nF,6,10,6,6,4,10,7,3,5,11,4,5,3,9,6,9\nY,2,1,4,3,2,8,11,1,7,5,11,8,1,11,2,8\nK,7,10,11,8,7,2,8,3,7,11,12,12,4,7,4,4\nW,3,6,5,4,4,7,11,2,2,7,8,8,6,11,1,8\nU,4,5,5,3,2,6,8,5,8,6,10,9,3,9,1,7\nA,3,8,6,6,3,11,2,2,3,9,2,9,3,5,3,8\nV,11,14,9,8,4,8,9,6,5,7,10,5,7,13,3,8\nU,4,8,5,7,6,7,7,5,3,6,7,8,4,8,1,7\nX,2,3,3,2,1,7,8,1,7,10,7,8,2,8,2,7\nK,5,10,6,7,2,4,8,8,2,7,4,11,4,8,2,11\nY,9,10,7,14,6,6,6,6,4,6,12,6,5,10,6,5\nT,8,13,7,7,4,6,10,3,7,12,7,6,3,9,5,4\nS,5,9,6,6,4,7,7,4,7,10,9,9,2,10,5,6\nQ,5,8,6,7,3,8,9,8,6,5,8,9,3,7,6,10\nQ,5,6,5,8,6,8,6,6,3,9,6,9,3,8,5,8\nL,5,5,5,8,1,0,0,6,6,0,1,5,0,8,0,8\nA,3,3,5,5,2,9,4,3,1,8,1,8,2,7,2,8\nR,3,7,5,5,5,5,8,3,4,6,5,9,4,7,6,5\nF,6,7,8,8,8,7,9,4,4,7,6,7,4,9,9,8\nP,4,8,6,6,5,6,9,7,4,9,7,4,2,10,3,7\nX,3,5,4,4,4,8,8,2,5,8,6,7,2,7,6,7\nO,1,0,2,0,0,7,6,6,4,7,6,8,2,8,3,8\nW,7,7,7,5,5,6,10,4,3,8,7,7,9,13,3,4\nP,8,9,6,4,3,8,7,5,4,11,3,6,5,8,4,8\nU,5,8,6,6,5,8,8,8,5,6,7,9,3,7,4,6\nV,7,10,9,8,6,9,10,4,2,4,10,9,6,10,5,10\nS,4,10,4,7,2,8,7,6,9,5,6,7,0,8,9,8\nO,1,3,2,2,1,8,7,6,3,8,6,8,2,8,2,8\nO,3,5,4,4,3,7,7,8,5,7,6,8,2,8,3,8\nR,3,3,4,2,2,7,8,5,5,7,5,7,2,7,4,8\nS,4,9,4,7,3,8,7,8,7,8,6,8,2,9,9,8\nQ,4,6,5,5,4,5,4,4,5,5,4,7,4,4,6,6\nH,3,5,4,4,4,7,8,6,6,7,6,8,3,8,3,8\nZ,5,9,6,7,7,8,7,3,8,8,6,8,1,7,12,9\nF,3,6,3,4,1,1,13,5,4,12,10,7,0,8,2,6\nP,5,9,7,6,4,9,8,4,6,12,4,4,2,9,4,9\nN,2,3,2,2,1,6,8,5,4,8,7,7,4,8,1,6\nT,7,9,7,7,4,5,11,2,8,11,10,5,1,11,2,4\nF,5,7,7,5,3,7,9,2,7,14,5,5,2,8,3,8\nM,5,9,7,7,6,7,6,3,4,10,8,9,11,5,3,9\nS,3,7,4,5,2,7,6,5,9,5,6,10,0,9,9,8\nS,3,5,5,3,2,8,6,2,7,10,7,9,2,9,5,8\nS,7,10,8,7,5,8,7,3,6,10,5,7,2,8,5,9\nX,4,10,5,7,2,7,7,5,4,7,6,8,3,8,4,8\nD,2,3,2,2,2,7,7,6,6,7,6,5,2,8,2,7\nL,1,0,1,0,0,2,2,5,4,1,2,5,0,8,0,8\nI,1,6,2,4,1,7,7,0,7,13,6,9,0,8,1,8\nZ,2,6,3,4,1,7,7,3,13,9,6,8,0,8,8,8\nQ,4,9,5,8,5,8,6,8,6,5,4,7,2,8,4,8\nF,3,8,3,5,1,0,13,4,4,13,11,7,0,8,1,6\nA,3,8,5,6,2,8,5,3,1,7,0,8,2,7,2,8\nE,2,4,2,2,2,7,7,5,8,6,5,8,2,8,6,9\nY,3,8,5,6,1,4,10,3,2,9,13,8,2,11,0,8\nS,4,3,5,5,2,8,10,6,10,5,5,5,0,7,8,9\nJ,5,9,6,7,3,9,7,2,6,14,3,7,0,7,0,8\nX,4,10,7,7,4,9,7,0,8,9,4,7,3,8,3,8\nC,1,0,2,1,0,6,7,6,8,7,6,14,0,8,4,10\nL,5,5,5,8,1,0,0,6,6,0,1,4,0,8,0,8\nT,3,4,4,2,1,5,11,2,7,11,9,4,1,10,2,5\nR,5,10,6,8,6,5,8,6,6,6,4,10,4,6,6,10\nL,2,2,2,4,1,0,1,5,6,0,0,6,0,8,0,8\nY,7,9,7,7,3,3,10,4,7,12,12,6,2,11,2,6\nE,3,7,4,5,2,3,7,6,11,7,6,15,0,8,7,7\nJ,4,5,6,5,5,8,9,4,5,7,6,8,3,7,8,8\nE,5,9,7,7,7,7,7,3,7,7,7,11,5,10,8,9\nP,5,10,7,8,7,6,9,6,5,9,7,4,2,10,4,7\nT,2,3,3,1,1,5,12,3,5,11,9,5,1,10,1,5\nS,4,9,5,6,3,6,9,5,8,11,8,7,2,9,5,5\nV,5,5,6,4,2,5,12,2,3,8,11,7,3,10,1,7\nJ,4,11,4,8,3,14,3,5,5,13,1,9,0,7,0,8\nH,8,13,8,7,4,11,7,4,5,9,4,5,6,9,4,8\nC,2,1,3,2,1,6,8,7,8,8,7,13,1,9,4,10\nU,6,8,6,6,4,4,8,5,7,10,8,9,3,9,2,5\nF,3,4,5,3,2,6,10,2,6,13,6,4,1,10,3,7\nF,4,3,4,4,3,5,11,3,6,11,9,5,1,10,3,6\nR,4,7,6,5,4,10,7,4,6,10,3,6,3,7,4,10\nX,7,15,7,8,4,10,7,2,8,9,4,7,4,10,4,10\nE,4,7,6,5,3,4,9,3,9,11,9,10,2,8,4,5\nK,4,9,4,7,2,3,7,7,3,7,7,12,3,8,3,10\nO,5,12,4,6,3,6,6,6,3,10,6,9,5,9,5,7\nL,3,5,4,4,3,6,8,4,5,6,6,9,2,8,7,9\nK,4,4,6,3,3,5,8,2,7,10,8,9,3,8,2,7\nY,6,7,6,5,3,5,9,1,9,10,10,5,1,10,4,3\nL,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8\nK,3,8,5,6,4,5,6,4,7,7,7,11,3,8,5,9\nT,2,3,3,2,1,5,12,3,6,11,9,5,1,11,2,5\nJ,3,6,5,4,2,9,7,1,6,14,4,6,0,8,0,8\nI,7,14,6,8,4,10,6,4,6,14,3,6,2,8,6,10\nA,5,11,7,8,3,9,3,3,3,7,2,9,3,7,3,9\nF,3,5,6,4,2,6,12,3,5,13,7,3,1,10,2,6\nU,7,10,8,8,5,5,7,6,8,9,7,9,5,11,5,3\nP,4,4,5,6,5,9,10,2,3,7,9,5,4,10,5,5\nJ,2,4,4,3,1,9,6,3,6,14,5,10,0,7,0,8\nA,1,1,2,1,0,7,4,3,0,7,1,8,2,7,1,8\nR,2,3,4,1,2,9,7,4,5,9,4,7,2,6,4,10\nG,4,11,5,8,4,7,7,7,6,6,5,9,2,7,5,11\nK,5,10,7,8,7,7,7,3,7,6,6,7,7,8,6,10\nT,5,7,6,5,5,6,7,7,7,7,8,8,3,10,5,9\nO,1,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8\nP,3,4,5,2,2,8,10,4,3,11,4,3,1,10,2,8\nG,3,4,4,3,2,6,7,5,5,9,7,10,2,9,4,9\nR,4,10,4,7,3,5,11,9,4,7,3,9,3,7,6,11\nL,3,8,4,6,2,2,4,3,9,2,0,8,0,7,1,6\nU,5,10,6,8,2,7,4,15,6,7,14,8,3,9,0,8\nM,6,9,9,6,7,9,6,2,5,9,5,7,8,6,2,8\nN,4,5,5,5,5,7,8,4,3,6,4,8,6,8,4,7\nR,3,6,4,4,4,8,6,6,3,7,5,7,4,6,6,7\nD,3,1,4,2,2,7,7,6,7,7,6,5,2,8,3,7\nE,5,7,6,5,5,8,9,6,3,6,6,10,4,8,7,9\nJ,1,3,3,2,1,7,7,3,5,14,6,9,1,7,1,7\nZ,3,5,4,7,2,7,7,4,14,10,6,8,0,8,8,8\nD,1,3,3,2,1,9,6,4,6,10,4,6,2,8,2,8\nN,6,10,8,8,5,7,8,6,6,7,7,6,6,9,2,6\nS,5,10,7,7,8,9,5,5,4,8,6,9,5,7,12,8\nB,2,1,3,3,2,7,7,5,5,6,6,6,2,8,5,9\nH,5,8,8,6,5,10,7,4,6,10,3,6,3,8,3,8\nI,1,5,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nA,3,2,5,3,2,8,2,2,2,7,1,8,2,6,2,7\nU,4,5,5,3,2,6,8,6,7,7,10,9,3,9,0,8\nP,7,11,9,8,8,6,8,7,4,8,7,9,3,9,7,9\nI,3,9,4,6,3,7,7,0,7,13,6,8,0,8,1,8\nG,1,3,2,2,1,7,6,5,4,7,6,10,2,9,3,9\nO,3,2,5,4,3,8,7,8,5,7,6,8,2,8,3,8\nW,6,8,6,6,6,4,9,2,3,9,8,8,7,11,2,6\nB,4,8,4,6,5,6,7,8,6,6,6,7,2,8,7,9\nA,2,8,4,6,3,13,3,4,2,11,1,8,2,6,3,9\nW,6,5,9,8,4,8,7,5,2,7,8,8,9,9,0,8\nU,6,10,6,8,5,8,5,12,4,7,13,8,3,8,0,7\nQ,4,6,5,7,5,10,10,6,3,4,8,11,3,10,6,9\nI,2,10,3,8,2,9,6,0,7,13,5,8,0,8,1,8\nF,6,9,8,6,5,9,8,2,7,13,4,5,4,9,4,9\nP,2,1,3,2,1,6,10,5,4,10,7,3,1,9,4,6\nE,6,11,8,9,7,9,6,1,8,11,4,9,4,7,6,10\nM,4,4,6,3,4,8,6,2,4,9,6,8,7,5,2,8\nC,6,10,8,7,5,7,7,8,7,7,6,11,4,8,4,9\nV,2,1,4,2,1,7,12,2,2,6,11,9,4,11,2,7\nD,4,8,6,6,8,9,8,4,5,7,6,6,4,6,7,5\nJ,2,8,2,6,1,15,3,5,5,13,0,8,0,7,0,8\nL,2,1,2,2,1,4,4,4,8,3,2,6,0,7,1,6\nJ,1,1,2,3,1,10,6,3,5,11,4,9,1,7,1,7\nF,4,10,4,7,2,1,13,5,3,12,9,6,0,8,3,6\nQ,3,6,4,8,4,8,6,7,5,9,6,7,3,8,5,7\nG,3,6,4,4,2,6,7,6,5,10,8,11,2,9,4,10\nS,7,10,8,8,4,8,7,4,8,11,6,7,2,9,5,8\nZ,4,10,5,8,3,7,7,4,15,9,6,8,0,8,8,8\nO,3,1,4,3,2,7,7,7,4,7,6,7,2,8,3,7\nI,4,8,6,9,6,9,8,6,7,7,4,7,3,10,9,9\nO,3,5,4,3,3,7,7,7,4,9,5,8,2,8,3,7\nQ,2,3,3,4,2,8,8,4,2,7,8,10,2,9,4,8\nU,5,11,8,8,6,7,8,4,7,4,7,9,6,9,1,8\nM,7,9,11,6,8,4,7,3,5,11,10,11,10,9,5,7\nM,5,8,8,6,7,9,7,2,4,9,5,7,7,7,2,8\nX,4,8,5,6,1,7,7,5,4,7,6,8,3,8,4,8\nV,9,11,7,6,3,7,11,6,5,8,10,4,5,12,3,9\nV,3,7,4,5,2,5,11,3,4,9,12,8,2,10,1,8\nD,4,7,5,5,5,7,7,5,7,7,6,6,6,8,3,7\nF,7,10,6,6,4,9,7,3,5,11,5,6,4,9,6,9\nX,1,0,2,0,0,7,7,4,4,7,6,8,2,8,4,8\nL,4,8,6,6,6,7,7,3,5,7,7,9,6,10,6,6\nK,4,8,4,5,2,3,8,8,2,7,5,11,4,8,3,10\nJ,2,1,3,2,1,10,6,2,7,11,4,8,1,7,1,7\nH,8,11,12,8,7,9,7,3,6,10,4,7,3,8,3,8\nD,2,3,3,2,2,9,6,4,6,10,4,6,2,8,2,9\nC,3,6,4,4,2,5,9,6,7,12,9,11,2,10,3,7\nC,5,9,6,7,4,4,7,5,7,10,8,14,3,8,4,7\nP,3,6,3,4,2,4,12,7,1,10,6,4,1,10,3,8\nP,5,5,6,7,7,8,7,4,3,7,7,7,6,10,5,6\nL,2,0,2,1,0,2,0,6,4,0,3,4,0,8,0,8\nB,1,1,2,1,2,7,7,4,5,7,6,6,1,8,4,9\nL,4,8,6,6,3,5,4,3,10,5,1,7,1,6,3,5\nS,3,10,4,7,2,7,7,6,9,4,6,8,0,8,9,7\nO,3,5,4,4,3,8,7,7,4,9,6,8,3,8,3,8\nD,2,4,4,3,2,10,6,3,7,10,3,6,2,8,3,9\nL,3,7,4,5,2,3,4,3,9,2,1,7,0,7,1,5\nI,4,11,5,8,2,9,5,0,8,14,5,10,0,8,1,9\nH,7,9,6,4,3,11,7,4,5,8,3,5,5,9,4,8\nY,3,9,5,6,1,7,10,2,3,7,13,8,1,11,0,8\nJ,1,1,2,2,1,10,6,2,5,11,4,8,1,7,1,7\nU,2,4,3,3,1,7,8,7,7,8,10,7,3,9,1,8\nD,2,1,2,2,1,6,7,8,7,6,6,6,2,8,3,8\nH,3,7,4,5,5,7,8,6,6,7,5,8,3,8,3,7\nK,6,11,6,8,5,3,6,7,4,7,8,12,3,8,3,11\nJ,2,7,4,5,1,7,6,4,6,15,7,12,1,6,0,7\nR,2,1,3,2,1,6,10,8,2,7,5,8,2,7,5,10\nN,4,7,6,5,4,6,7,6,7,7,5,9,6,7,3,7\nA,4,11,7,8,3,7,4,3,2,7,1,8,3,6,3,8\nK,8,13,7,7,4,10,7,3,7,9,3,6,6,8,4,8\nA,3,9,5,7,4,12,3,2,2,10,2,9,2,6,3,7\nN,2,3,4,2,1,9,7,3,4,10,3,6,5,9,0,7\nU,5,11,6,8,5,7,5,13,5,7,12,8,3,9,0,8\nB,4,6,4,4,3,7,6,9,7,7,7,7,2,8,8,9\nR,2,1,2,2,2,7,7,5,5,6,5,7,2,7,4,8\nG,1,3,2,2,1,7,7,4,4,9,7,10,2,8,3,10\nN,5,10,6,8,6,7,7,13,1,6,6,8,5,8,0,8\nW,8,9,8,7,7,5,11,3,3,9,8,7,7,12,3,6\nL,3,7,5,5,4,7,6,7,5,6,6,8,3,8,4,10\nL,4,7,4,5,2,1,1,5,5,1,1,6,0,8,0,8\nU,3,2,4,4,1,7,5,12,5,7,14,7,3,9,0,8\nI,3,8,4,6,2,7,7,0,8,14,6,8,0,8,1,8\nY,7,10,8,8,4,3,11,2,8,11,11,5,1,11,3,4\nP,4,3,4,4,2,4,14,7,1,11,6,3,1,10,4,8\nI,4,11,7,9,8,10,8,2,6,9,4,5,4,7,8,4\nU,8,11,8,8,6,3,8,5,8,10,8,10,5,10,4,4\nU,4,4,5,3,2,4,8,5,8,10,9,9,3,9,2,6\nG,6,11,8,8,9,8,4,5,4,7,5,11,7,9,6,13\nL,4,9,6,7,4,4,4,3,8,2,1,8,0,6,1,6\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nW,8,9,12,8,13,7,7,5,5,6,5,8,10,9,9,8\nK,5,8,8,7,7,10,7,4,4,10,2,8,5,5,4,10\nN,4,7,4,5,2,7,7,14,2,4,6,8,6,8,0,8\nL,3,8,4,6,2,7,3,1,8,9,2,11,0,7,2,9\nW,8,9,8,6,6,5,11,3,3,9,8,7,8,11,3,6\nE,6,10,8,8,5,5,8,4,9,12,9,9,3,8,5,6\nD,1,3,3,2,1,8,7,5,6,9,5,5,2,8,3,8\nJ,6,13,5,10,4,7,10,2,3,13,5,5,2,8,7,8\nY,1,3,2,1,1,7,10,1,5,7,11,8,1,11,1,8\nB,5,11,6,8,8,8,7,5,6,7,6,5,3,8,6,9\nB,2,3,4,2,2,8,7,3,5,10,5,6,2,8,4,9\nB,6,9,5,5,5,8,8,3,5,9,5,7,6,5,7,7\nD,1,0,1,1,0,6,7,8,5,7,6,6,2,8,2,8\nS,6,9,7,6,4,9,6,3,8,10,6,8,2,10,5,8\nT,5,10,7,8,6,8,11,2,7,6,11,8,1,12,1,8\nH,5,10,5,7,3,7,7,15,1,7,6,8,3,8,0,8\nA,3,8,5,6,5,8,7,7,4,7,6,9,5,7,7,4\nV,4,9,6,7,3,6,11,2,4,7,12,9,3,10,1,8\nA,4,8,6,7,6,9,8,3,4,6,7,7,5,8,5,5\nZ,3,6,5,9,4,11,3,2,6,8,2,6,1,8,6,9\nA,4,10,5,7,4,7,5,3,0,7,1,8,2,7,3,8\nR,4,10,5,7,3,5,12,9,4,7,2,9,3,7,6,11\nW,8,9,8,7,6,2,12,2,3,10,10,8,7,11,2,7\nT,3,4,4,3,2,6,10,2,7,11,9,5,2,10,3,4\nL,3,10,3,8,1,0,1,5,6,0,0,7,0,8,0,8\nC,2,5,3,3,2,6,8,7,7,8,7,13,1,9,4,10\nU,5,7,5,5,3,4,8,5,7,10,9,9,3,9,2,6\nY,2,7,4,4,1,8,11,1,3,6,12,8,1,10,0,8\nE,1,0,1,0,1,5,8,5,7,7,6,12,0,8,6,9\nR,2,6,4,4,3,8,7,3,5,9,4,7,3,7,4,10\nC,5,9,5,7,3,4,8,6,8,12,10,12,1,9,3,7\nL,3,8,5,6,3,6,4,1,8,8,2,10,0,7,2,8\nK,4,10,6,8,7,6,6,5,3,7,6,9,4,5,10,8\nM,4,5,5,3,4,8,6,6,4,7,7,8,8,5,2,7\nN,2,5,3,4,2,7,8,5,4,7,6,7,4,8,1,7\nJ,6,8,8,9,7,9,8,5,6,7,5,8,4,8,9,7\nP,1,3,3,2,1,6,10,6,2,10,5,4,1,9,3,7\nJ,1,8,2,6,2,9,6,3,5,11,4,9,1,6,1,6\nG,5,9,4,4,3,8,6,4,2,9,6,8,3,9,8,8\nH,5,8,7,6,7,7,7,6,6,7,6,8,3,8,3,7\nB,2,1,3,1,2,7,7,5,5,7,6,6,1,8,5,9\nI,3,7,4,5,2,8,6,0,7,13,6,9,0,7,2,8\nD,1,0,1,1,1,6,7,8,6,7,6,6,2,8,3,8\nO,4,4,5,6,2,8,8,9,8,5,8,10,3,8,4,8\nB,4,5,4,7,4,6,7,10,7,7,6,7,2,8,8,9\nM,6,8,9,6,5,10,6,3,5,9,4,6,8,6,2,8\nT,2,1,3,2,1,7,12,2,6,7,11,8,1,11,1,8\nV,3,7,4,5,2,7,12,3,4,7,11,8,2,10,1,8\nZ,7,14,7,8,4,10,4,3,8,12,4,10,3,9,4,11\nS,4,9,3,4,2,7,3,1,5,6,2,7,2,8,5,9\nQ,6,8,6,10,8,8,5,7,3,9,6,9,3,8,5,8\nY,4,10,7,7,1,7,10,3,2,7,13,8,2,11,0,8\nE,6,10,8,8,6,4,10,4,8,11,10,8,3,8,4,4\nG,3,3,4,5,2,7,7,8,7,6,5,11,2,8,5,10\nQ,5,8,5,10,6,8,7,6,3,8,9,11,3,8,6,8\nF,3,7,3,4,1,1,11,5,6,11,10,9,0,8,2,6\nH,4,5,4,7,3,7,8,15,1,7,5,8,3,8,0,8\nV,2,4,4,3,1,7,12,2,3,6,11,9,2,10,1,8\nM,14,15,14,8,7,6,10,5,5,4,4,11,11,13,2,8\nL,5,9,6,7,5,6,6,8,5,6,5,9,3,7,5,11\nU,3,5,4,3,2,6,8,6,7,6,9,9,3,9,1,7\nS,2,8,3,6,3,7,7,7,5,7,6,8,2,9,8,8\nZ,4,10,5,8,2,7,7,4,15,9,6,8,0,8,8,8\nU,5,7,5,5,3,4,8,6,7,9,9,9,3,9,2,5\nW,5,10,7,8,4,5,8,5,2,7,8,8,8,9,0,8\nH,1,0,1,0,0,7,7,10,1,7,6,8,2,8,0,8\nT,4,10,5,8,3,7,13,0,6,7,10,8,0,8,0,8\nN,5,10,6,8,6,7,7,6,6,7,6,8,6,7,3,8\nG,6,8,8,7,8,7,10,6,2,7,7,8,6,11,7,8\nT,3,4,4,3,2,7,12,4,6,7,11,8,2,11,1,7\nD,5,6,6,6,5,5,7,6,7,6,5,6,4,7,5,6\nD,3,2,4,3,2,7,7,6,7,7,6,5,2,8,3,7\nR,4,10,5,7,6,6,7,5,6,6,4,8,3,6,5,9\nE,3,7,4,5,4,8,8,4,7,7,5,8,3,8,5,10\nJ,5,9,6,7,3,8,4,5,6,15,7,14,1,6,1,7\nQ,3,5,4,6,4,8,8,5,2,7,7,12,2,9,5,9\nK,4,5,5,4,4,5,7,4,7,6,6,10,3,8,5,8\nN,6,11,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nX,8,12,8,6,4,10,6,3,9,9,2,5,4,6,4,10\nH,3,3,5,1,2,9,6,3,5,10,4,8,3,8,2,9\nA,5,9,7,7,4,8,5,3,0,6,1,8,2,7,1,8\nL,4,9,5,6,4,5,5,1,8,7,2,10,1,7,3,7\nI,2,7,2,5,1,7,7,0,7,13,6,8,0,8,1,8\nH,6,8,8,6,7,6,8,7,6,8,5,7,6,7,5,10\nG,5,8,5,6,4,7,6,6,7,10,7,11,2,10,4,9\nR,5,5,6,7,3,6,11,9,3,7,3,8,3,7,6,11\nX,4,10,6,8,6,6,7,2,6,7,7,9,3,7,6,8\nD,4,8,4,6,4,5,7,9,7,7,7,6,2,8,3,8\nR,3,10,4,7,3,6,9,11,5,7,5,8,3,8,5,10\nF,5,8,8,6,4,6,10,3,6,13,7,4,2,10,2,7\nC,3,6,5,5,4,6,6,4,5,7,6,11,4,10,7,10\nO,2,4,3,3,2,7,7,7,4,7,5,8,2,8,3,8\nV,2,6,4,4,2,9,11,3,4,4,11,8,2,10,1,8\nM,7,10,9,8,9,8,7,6,5,6,7,8,8,6,2,7\nR,4,7,5,5,3,11,7,3,6,11,2,6,3,7,3,10\nO,4,8,6,6,5,7,7,8,4,7,7,9,3,8,3,7\nH,1,0,2,1,0,7,7,11,1,7,6,8,3,8,0,8\nW,6,6,6,4,4,2,11,2,2,10,10,8,5,11,1,7\nQ,3,4,4,5,3,8,8,5,1,8,8,10,2,9,4,8\nY,3,5,4,7,7,8,5,4,2,7,7,9,5,10,4,7\nQ,1,3,2,4,2,7,7,5,2,8,8,10,2,9,4,9\nH,5,10,8,8,7,8,6,7,7,7,7,5,3,8,4,6\nA,3,9,5,6,3,10,3,2,2,8,2,10,3,5,3,7\nS,4,8,5,6,4,8,8,7,5,7,6,8,2,8,8,8\nP,6,11,5,6,3,5,11,5,4,13,6,4,3,9,4,8\nK,8,12,7,6,4,9,8,3,7,9,4,6,6,9,4,8\nY,2,2,4,4,2,6,10,1,7,8,11,8,1,11,2,7\nB,8,10,7,6,6,7,9,4,5,9,6,7,6,6,7,6\nW,4,9,6,7,3,5,8,5,1,7,8,8,8,9,0,8\nO,4,9,5,7,2,7,6,9,7,6,5,7,3,8,4,8\nG,5,11,7,8,4,6,6,8,8,8,5,13,3,11,5,8\nI,1,10,2,8,2,7,7,0,8,7,6,8,0,8,3,8\nP,8,11,6,6,3,7,10,5,3,12,4,4,5,10,4,8\nQ,2,5,3,6,3,8,6,7,3,5,6,9,2,9,5,10\nA,4,9,3,4,2,13,3,4,1,10,3,10,3,5,3,10\nO,4,7,5,5,4,7,7,8,6,7,6,6,3,8,4,8\nQ,10,15,9,9,5,7,5,4,9,10,4,10,4,6,9,9\nI,1,3,2,1,0,7,7,1,7,13,6,8,0,8,1,8\nL,1,0,1,1,0,2,1,5,5,0,2,5,0,8,0,8\nW,6,5,8,5,8,9,8,5,5,7,5,8,9,9,8,6\nV,5,9,7,7,4,7,12,2,3,5,10,9,4,12,2,7\nN,3,3,4,2,2,7,8,6,4,7,7,6,5,9,2,6\nG,5,11,7,8,8,7,7,6,2,7,6,11,7,9,10,6\nN,6,9,8,7,9,8,7,4,4,7,6,6,7,10,7,5\nI,3,7,5,5,2,8,6,2,7,7,6,7,0,9,4,7\nF,5,11,7,8,5,5,11,2,8,10,9,5,4,10,4,6\nR,8,11,7,6,5,10,6,3,5,9,4,8,6,8,6,9\nV,2,1,3,1,0,7,9,4,2,7,13,8,2,10,0,8\nR,5,11,7,8,5,8,9,4,6,8,4,9,4,6,6,11\nH,4,9,4,7,4,7,7,13,1,7,6,8,3,8,0,8\nQ,6,10,8,8,7,8,3,8,5,6,5,9,4,9,4,7\nR,3,6,3,4,3,6,9,8,4,7,5,8,2,7,4,11\nP,3,7,4,5,3,6,10,6,5,10,8,4,1,10,4,7\nY,4,6,6,8,7,9,9,6,3,7,7,8,6,11,7,4\nF,5,7,7,5,4,6,11,5,6,12,8,5,2,9,2,6\nZ,5,9,5,4,3,8,6,2,8,11,6,9,3,7,6,7\nO,7,13,5,7,3,9,7,7,5,9,4,8,5,9,5,9\nW,5,8,8,6,5,7,8,4,1,7,9,8,7,11,0,8\nC,4,9,5,7,4,4,8,6,7,9,9,15,1,9,4,10\nR,2,4,4,3,3,8,8,3,5,10,4,7,3,6,3,9\nY,4,6,4,4,1,3,11,3,7,11,11,5,1,11,2,4\nI,1,4,1,3,1,7,7,1,8,7,6,9,0,8,3,8\nV,7,10,7,8,3,3,11,4,4,10,12,8,2,10,1,8\nQ,4,6,5,8,4,8,7,6,3,8,7,10,3,8,6,8\nG,4,6,4,4,3,7,6,6,5,9,6,13,2,8,4,10\nW,4,10,6,8,4,10,8,5,2,7,8,8,8,9,0,8\nQ,2,1,3,2,1,8,6,7,5,6,6,8,3,8,4,8\nG,9,14,8,8,5,7,4,6,3,8,5,4,4,8,6,6\nS,4,8,6,6,7,6,9,3,3,8,7,6,3,8,11,1\nS,3,4,3,2,2,8,8,6,5,7,6,7,2,8,9,8\nD,5,6,6,6,5,6,6,5,7,7,6,8,4,6,6,5\nW,8,12,8,6,4,1,9,3,2,11,12,9,7,10,0,6\nA,3,1,5,3,2,8,2,2,2,7,1,8,2,7,2,7\nC,4,6,5,4,3,6,7,5,6,11,8,13,2,10,3,9\nQ,1,2,2,3,1,8,8,5,1,5,7,9,2,9,4,9\nR,5,10,7,7,6,7,8,5,7,7,5,6,6,6,5,9\nL,3,2,4,4,2,4,4,4,7,2,1,6,0,7,1,6\nD,5,10,6,7,5,7,7,8,8,6,5,5,3,9,4,8\nP,8,12,7,6,4,8,9,3,4,12,5,4,3,10,6,6\nU,4,7,5,5,5,8,7,8,5,6,7,9,3,8,4,6\nZ,2,4,4,3,2,7,7,2,9,12,6,8,1,8,5,7\nS,4,9,4,7,2,8,7,6,9,4,6,8,0,8,9,7\nD,4,7,5,5,2,5,8,9,9,7,6,6,3,8,4,8\nS,4,9,6,7,7,5,9,3,3,8,7,5,4,8,12,0\nC,9,13,7,7,5,7,9,4,4,9,8,9,3,9,8,11\nR,3,4,4,3,2,7,7,5,5,6,5,6,2,7,4,8\nA,1,0,2,0,0,7,3,2,0,7,2,8,2,6,1,8\nU,5,6,5,4,2,4,9,6,7,10,10,9,3,9,2,5\nH,3,6,4,4,3,7,8,7,5,8,5,7,3,7,5,11\nJ,2,6,3,4,2,10,5,3,5,12,3,8,0,7,2,7\nY,3,8,5,6,1,9,12,2,3,4,12,9,0,10,0,8\nE,4,5,6,4,3,7,7,2,8,11,7,9,2,8,4,8\nJ,3,9,5,7,3,9,5,3,6,15,4,10,0,7,1,7\nM,5,6,5,8,4,7,7,12,2,7,9,8,8,6,0,8\nS,6,12,6,6,3,8,7,4,3,13,8,8,3,10,3,8\nU,4,6,4,4,1,7,5,13,5,7,15,8,3,9,0,8\nL,2,7,3,5,2,4,4,4,7,2,1,6,1,6,1,6\nR,6,11,9,8,7,7,8,5,7,7,5,6,3,6,6,9\nD,4,7,6,6,6,7,8,5,6,6,4,7,4,7,7,5\nX,5,9,8,7,4,8,7,1,8,10,4,7,3,8,4,8\nV,2,3,4,4,1,5,8,4,2,8,13,8,3,10,0,8\nH,2,2,3,3,2,8,7,6,6,7,6,8,6,8,3,7\nR,3,5,6,4,5,8,7,3,4,8,5,7,6,9,5,5\nM,3,6,3,4,2,8,6,11,1,6,9,8,7,6,0,8\nL,3,7,4,5,2,6,3,1,8,8,2,11,0,7,2,8\nH,2,4,4,3,3,7,7,3,5,10,6,8,3,8,2,7\nZ,6,9,8,7,8,9,8,4,4,6,4,6,4,8,10,4\nF,2,4,2,2,1,5,10,4,4,10,9,5,1,10,3,7\nG,2,1,3,2,1,8,6,6,6,6,5,9,1,7,5,10\nM,4,5,5,3,4,8,6,6,4,6,7,7,8,6,3,6\nY,7,6,6,9,4,7,10,2,3,7,10,4,4,10,6,7\nO,4,8,5,6,2,7,7,8,8,7,6,7,3,8,4,8\nE,6,11,4,6,3,6,9,4,5,10,6,8,3,8,7,8\nD,3,5,4,3,3,7,7,7,7,6,6,5,2,8,3,7\nO,3,5,4,4,3,7,7,8,5,7,6,8,2,8,3,8\nC,6,11,6,8,3,5,8,7,8,13,10,11,2,10,3,7\nX,4,8,6,6,3,6,8,1,8,10,8,9,3,8,3,7\nL,7,13,6,8,3,10,2,4,4,13,4,13,2,8,6,9\nT,3,1,4,3,2,7,12,4,6,7,11,8,2,11,1,8\nB,6,9,8,7,6,10,6,3,7,10,3,7,6,8,7,11\nM,1,0,2,0,0,7,6,9,0,7,8,8,5,6,0,8\nU,4,6,5,4,4,7,8,8,6,5,7,11,3,8,4,7\nS,6,8,8,7,9,8,7,4,5,7,7,8,5,10,9,11\nJ,3,5,5,4,2,9,5,3,6,14,5,10,0,7,0,7\nQ,5,9,6,8,3,7,5,9,8,5,4,7,3,8,4,8\nT,3,7,5,5,3,6,12,3,7,8,11,8,1,11,1,7\nY,6,8,6,6,3,5,9,0,8,10,9,6,0,10,3,4\nM,4,6,7,4,8,6,5,4,2,6,4,8,12,6,3,7\nQ,6,10,8,8,7,8,4,8,4,6,6,8,4,7,6,9\nS,3,9,4,7,4,8,6,8,5,7,7,9,2,9,8,7\nJ,2,8,3,6,1,11,3,10,3,12,8,13,1,6,0,8\nA,2,3,4,2,2,9,1,2,1,8,2,10,2,6,2,7\nH,6,11,8,8,8,7,8,7,6,8,6,7,7,8,5,9\nM,5,10,5,7,4,7,7,12,2,7,9,8,8,6,0,8\nY,5,11,6,8,7,9,5,6,5,7,8,7,3,9,8,3\nC,4,7,5,5,2,5,8,7,8,7,8,14,1,8,4,9\nJ,2,6,3,4,1,15,3,3,5,12,1,8,0,8,0,8\nK,5,8,8,7,7,6,8,3,4,6,3,9,8,3,10,9\nY,5,9,5,6,2,3,11,3,6,12,11,5,1,11,2,5\nM,5,10,6,8,4,7,7,12,2,7,9,8,8,6,0,8\nX,3,8,5,6,3,8,7,4,9,6,6,6,3,8,7,7\nJ,6,9,4,12,4,11,6,2,4,10,4,6,3,9,6,10\nH,4,4,5,5,2,7,10,15,2,7,2,8,3,8,0,8\nH,3,4,5,3,3,5,9,3,5,10,8,8,3,9,3,7\nG,4,6,5,4,2,7,6,6,7,11,6,12,2,9,4,9\nR,5,6,7,6,7,5,7,4,4,6,5,9,7,9,6,9\nJ,1,4,2,2,1,8,6,3,6,14,6,10,1,7,1,7\nV,3,5,4,4,1,4,12,3,3,10,11,7,2,10,1,8\nX,7,9,8,5,4,8,7,3,8,12,4,7,4,9,4,8\nF,2,7,3,4,1,1,11,4,6,11,11,10,0,8,2,6\nS,2,3,3,2,2,8,7,6,4,7,7,8,2,9,9,8\nR,4,9,4,6,3,5,11,8,4,7,4,9,3,7,6,11\nY,3,7,5,5,2,7,10,2,2,7,12,8,1,11,0,8\nY,4,5,5,3,2,4,10,2,8,10,10,5,3,11,4,3\nN,9,14,8,8,4,10,11,6,6,3,6,9,5,10,3,6\nZ,4,9,6,7,4,9,5,2,9,11,4,10,1,8,6,10\nL,4,9,6,8,5,8,7,4,5,7,6,8,3,8,8,11\nW,4,8,6,6,4,7,10,2,3,7,9,8,8,11,1,8\nN,2,1,2,1,1,7,7,11,1,5,6,8,4,8,0,8\nH,7,9,10,7,7,9,7,3,7,10,4,7,5,8,5,9\nJ,0,0,1,0,0,12,4,6,3,12,4,11,0,7,0,8\nN,6,8,8,7,9,8,6,5,5,7,6,7,8,11,7,3\nI,7,13,5,8,3,10,4,5,4,13,3,9,3,8,4,9\nC,1,0,2,0,0,7,7,6,8,7,6,13,0,8,4,10\nO,3,7,4,5,3,7,7,8,5,7,6,8,3,8,3,8\nU,5,10,6,7,2,7,4,14,5,7,14,8,3,9,0,8\nR,8,14,6,8,5,8,7,5,5,9,4,9,7,5,6,11\nE,2,3,3,5,2,3,7,6,10,7,6,14,0,8,7,8\nB,10,15,10,8,8,9,7,4,5,9,5,7,7,5,9,8\nU,5,7,5,5,3,3,9,5,6,10,10,9,3,9,2,7\nD,1,0,2,1,1,6,7,8,6,7,6,6,2,8,3,8\nR,1,0,2,0,1,6,10,7,2,7,5,8,2,7,4,9\nN,8,10,10,5,3,4,10,5,4,14,11,9,5,9,0,8\nC,2,3,3,2,1,4,8,4,6,10,9,12,1,9,2,8\nE,3,10,4,8,2,2,8,6,11,7,6,15,0,8,7,7\nD,5,10,8,7,7,9,7,3,6,11,5,6,3,8,3,8\nG,5,10,4,5,3,9,5,4,3,9,4,5,4,7,5,8\nK,3,7,4,5,2,3,8,7,2,7,5,11,3,8,3,11\nY,9,8,7,12,5,8,6,6,6,4,11,7,5,9,3,8\nX,3,8,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nZ,4,8,6,6,6,8,8,3,7,7,7,7,1,9,10,8\nX,4,6,5,4,4,7,6,3,5,6,6,9,2,8,8,8\nF,3,5,3,3,2,5,11,3,6,11,9,5,1,10,3,6\nN,4,11,5,8,5,8,7,13,1,6,6,7,6,8,1,10\nK,3,6,5,4,3,6,7,1,7,10,7,10,3,8,3,8\nR,4,3,5,5,3,5,11,8,3,7,4,8,2,7,6,11\nO,4,8,6,6,4,7,8,8,6,7,7,8,2,8,3,8\nV,5,9,5,6,3,3,10,3,4,10,12,8,3,10,1,7\nJ,3,7,5,5,2,8,4,6,4,14,8,14,1,6,1,7\nM,2,1,2,1,1,7,6,10,0,7,8,8,6,6,0,8\nP,7,11,9,8,7,9,7,2,6,11,4,6,4,8,4,9\nY,7,9,7,7,2,3,12,6,6,14,11,5,2,11,2,6\nD,5,11,7,8,11,8,8,6,4,7,6,6,7,8,9,6\nS,4,9,5,7,4,10,7,4,6,10,3,7,2,8,5,10\nX,4,10,5,7,1,7,7,5,4,7,6,8,3,8,4,8\nS,4,7,5,5,2,6,9,4,7,10,7,7,2,8,5,5\nY,8,11,8,8,5,3,11,2,6,11,11,6,1,11,2,5\nQ,5,9,6,8,5,8,8,8,5,6,6,10,3,8,4,9\nT,5,6,6,4,3,5,11,3,7,11,9,5,2,11,2,4\nA,3,9,4,7,3,6,4,2,1,6,1,8,2,6,2,7\nR,4,5,4,3,3,7,7,5,6,7,5,6,5,7,4,7\nU,2,6,4,4,2,7,9,6,6,6,10,9,3,9,1,7\nI,4,9,4,5,2,6,11,3,4,13,6,4,1,9,5,8\nD,1,0,1,0,0,5,7,7,5,6,6,6,2,8,2,8\nE,2,3,4,2,2,7,7,2,7,11,7,9,2,8,4,8\nV,6,8,8,6,5,6,12,3,2,8,10,8,6,11,6,8\nD,5,11,5,6,4,8,6,3,5,9,5,7,5,9,5,8\nG,6,6,8,6,7,7,10,5,3,7,7,8,6,11,7,8\nD,9,15,8,8,6,8,6,3,7,10,5,7,6,8,9,6\nK,5,9,5,4,3,10,7,2,5,10,5,7,5,10,3,9\nT,6,11,6,8,4,4,12,3,7,12,10,4,1,11,2,4\nK,4,11,4,8,2,3,7,8,2,7,5,11,3,8,3,10\nZ,3,6,5,4,4,8,8,2,7,7,6,7,0,8,8,9\nN,5,11,6,8,6,8,8,13,1,6,6,8,6,8,1,10\nN,6,8,8,7,8,7,8,4,4,7,5,6,7,8,7,2\nM,2,4,4,3,3,10,6,3,4,9,4,6,6,5,2,8\nO,6,11,6,9,5,8,6,8,5,10,5,9,4,9,5,6\nT,3,6,5,5,5,6,9,3,7,8,7,9,3,8,7,5\nA,3,6,5,8,2,7,6,3,1,7,0,8,3,7,1,8\nR,3,2,4,3,3,7,7,5,5,6,5,6,3,7,4,8\nA,3,8,5,6,3,8,3,2,2,6,1,8,2,6,2,7\nJ,3,4,4,6,1,11,2,11,3,12,9,14,1,6,0,8\nD,4,4,5,6,3,5,7,9,9,6,5,5,3,8,4,8\nB,4,10,5,7,7,8,7,6,5,7,6,6,6,8,6,10\nJ,4,4,5,5,4,7,9,4,5,7,7,8,3,7,6,6\nV,3,7,5,5,2,9,9,4,1,6,13,8,2,10,0,8\nB,5,8,7,6,7,7,7,5,4,7,5,7,4,9,6,7\nI,1,9,0,6,1,7,7,5,3,7,6,8,0,8,0,8\nU,5,8,6,6,3,5,8,7,9,10,11,9,3,9,1,7\nM,4,8,5,6,5,8,5,11,1,6,9,8,8,5,1,6\nW,6,9,8,7,4,4,8,5,2,7,9,8,9,9,0,8\nC,5,11,6,8,4,3,8,6,6,12,10,13,2,9,3,7\nN,6,10,8,8,4,7,8,2,5,10,7,8,5,8,1,8\nR,5,10,8,9,9,6,9,3,3,7,5,9,8,6,7,9\nD,6,10,8,8,6,9,6,5,8,10,3,6,3,8,4,8\nB,2,1,2,1,1,7,7,7,5,6,6,7,1,8,7,8\nZ,2,1,3,2,1,7,7,5,9,6,6,8,1,8,7,8\nT,7,10,7,8,6,6,11,3,6,11,9,5,2,12,2,4\nT,5,9,7,8,8,7,7,4,8,7,6,9,5,4,11,5\nT,9,12,8,6,3,4,12,3,8,13,8,4,2,8,4,3\nW,7,10,7,7,6,5,10,4,3,9,7,7,9,13,3,4\nU,3,8,5,6,2,6,9,7,7,8,10,9,3,9,1,7\nF,2,2,3,3,2,5,11,3,5,11,9,5,1,10,3,6\nH,2,3,3,2,2,7,7,6,5,7,6,8,3,8,2,7\nU,4,8,4,6,3,7,6,11,4,7,13,8,3,9,0,8\nZ,2,1,3,2,2,7,7,5,9,6,6,8,2,8,7,8\nZ,5,9,6,7,5,7,7,5,10,7,6,8,1,8,7,8\nM,4,6,5,4,3,8,7,12,1,6,9,8,8,6,0,8\nD,6,10,8,7,5,9,7,5,8,10,3,5,3,8,4,8\nC,6,11,8,9,6,7,8,8,6,4,7,12,5,6,4,8\nC,3,5,4,7,2,5,7,7,10,7,5,14,1,8,4,9\nG,6,10,8,8,8,7,10,6,3,5,6,10,4,7,7,7\nB,3,5,3,3,3,7,7,5,5,6,6,6,2,8,6,10\nE,4,7,6,5,4,9,7,2,7,11,5,8,3,9,4,11\nF,4,7,6,5,3,6,10,1,6,13,7,5,1,10,2,8\nG,4,9,6,6,2,7,6,7,9,7,5,11,1,8,5,10\nO,5,10,7,8,5,7,6,9,4,7,5,9,3,9,4,6\nM,5,5,8,3,4,10,6,3,5,9,5,7,10,6,2,8\nR,5,7,7,5,4,9,8,4,7,9,3,7,3,6,4,11\nM,6,10,10,8,13,9,6,3,2,8,4,8,11,7,3,7\nM,4,5,7,3,4,5,7,3,4,10,10,10,9,7,3,8\nV,1,3,3,1,1,7,12,2,2,7,10,8,2,10,1,8\nY,7,9,8,7,4,3,10,2,7,10,12,7,2,11,2,6\nT,7,12,6,6,4,5,11,2,7,12,7,5,2,9,5,4\nV,2,0,3,1,0,8,9,4,2,6,13,8,2,10,0,8\nW,5,6,5,4,4,4,10,3,2,9,9,7,7,11,2,6\nG,4,9,5,7,6,9,5,5,2,7,6,10,7,8,5,10\nT,4,8,5,6,4,6,7,8,6,6,6,8,4,9,6,10\nC,2,7,3,5,1,5,7,6,8,6,6,12,1,7,4,9\nD,2,3,4,2,2,8,7,5,6,9,5,5,2,8,3,7\nP,4,9,4,4,3,8,9,3,3,11,5,5,4,11,5,7\nV,3,8,5,6,2,6,11,2,4,8,12,8,2,10,1,8\nL,4,8,5,6,3,2,4,2,8,2,0,9,0,7,1,6\nP,5,5,5,7,3,4,13,9,2,10,6,3,1,10,4,8\nS,5,10,7,8,5,7,7,4,7,10,9,9,3,9,5,6\nP,6,12,6,6,4,6,10,3,4,12,6,4,3,11,5,6\nG,4,8,5,6,4,8,7,7,6,6,6,9,2,7,5,11\nG,5,10,6,7,3,8,7,8,8,6,7,9,2,7,5,10\nV,3,7,4,5,2,5,11,3,3,9,11,8,2,11,1,8\nL,3,2,4,4,2,4,4,5,8,2,1,6,0,7,1,6\nY,7,9,7,7,3,5,9,1,9,9,10,5,1,11,4,3\nL,4,10,6,8,4,7,4,1,7,8,2,10,1,6,3,8\nX,2,3,3,2,2,7,7,3,8,6,6,8,2,8,5,8\nK,6,9,9,7,9,6,8,5,4,7,5,8,4,6,10,9\nA,6,8,8,7,7,8,7,4,6,7,9,6,5,11,4,5\nQ,5,8,5,9,7,8,6,7,4,9,7,8,4,9,5,7\nX,5,10,8,8,5,7,7,4,10,6,6,8,3,8,7,8\nE,2,5,4,3,2,7,7,2,8,11,7,9,2,9,4,8\nG,4,8,6,6,3,8,8,8,8,6,7,8,2,7,6,11\nF,4,8,5,6,5,6,9,5,4,8,6,8,5,10,6,12\nR,2,3,2,2,2,7,7,4,5,6,5,6,2,7,4,8\nS,5,10,6,8,8,8,8,5,3,8,5,7,5,9,12,8\nK,6,11,8,8,5,4,7,2,7,10,9,11,4,7,4,6\nB,6,9,8,6,7,9,7,4,6,9,5,6,3,7,6,10\nR,2,3,3,2,2,9,7,3,4,10,4,7,2,7,3,10\nL,4,10,5,8,4,7,4,2,8,7,2,8,1,6,3,8\nV,6,11,8,9,4,8,9,5,2,6,13,8,3,10,0,8\nR,6,9,9,8,10,9,7,4,4,8,4,7,8,10,7,4\nI,1,1,1,3,1,8,7,1,7,7,6,7,0,8,2,7\nM,3,3,4,2,2,9,6,7,3,6,7,6,6,6,1,5\nP,1,1,2,1,1,5,11,8,2,9,6,4,1,9,3,8\nD,3,7,4,5,4,8,8,6,5,9,6,4,3,8,3,6\nD,5,11,8,8,7,10,6,3,6,11,4,7,4,8,4,9\nO,2,3,3,2,1,8,7,6,4,9,6,8,2,8,2,8\nV,3,11,5,8,2,9,8,4,3,6,14,8,3,10,0,8\nX,2,2,4,4,2,7,7,3,9,6,6,8,2,8,6,7\nT,8,12,7,6,4,5,11,2,7,12,7,5,2,7,4,4\nJ,3,10,4,8,4,9,6,2,5,11,4,9,1,6,2,6\nF,2,4,4,3,1,5,12,3,5,13,7,3,1,10,1,6\nI,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nL,4,11,5,8,3,3,3,5,9,1,0,7,0,7,1,6\nE,2,5,4,3,2,6,8,2,8,11,7,9,2,8,4,8\nR,3,1,3,2,2,7,8,5,5,7,5,7,2,6,4,8\nS,3,6,5,4,5,6,5,3,2,6,5,6,2,8,7,4\nR,3,6,5,4,3,10,7,3,5,11,2,6,3,7,2,10\nV,2,3,3,2,1,5,12,4,3,10,11,7,2,10,0,8\nP,4,8,6,6,5,6,7,5,4,7,6,9,5,8,6,10\nL,2,8,4,6,3,7,4,1,6,8,2,9,1,6,2,8\nF,4,9,5,7,5,6,8,6,4,8,6,8,3,11,8,10\nN,3,7,4,5,3,9,8,6,4,6,5,5,5,9,2,6\nX,3,4,5,3,2,10,7,2,8,10,3,7,2,7,3,9\nQ,3,5,4,6,4,8,7,7,3,5,7,10,3,8,5,10\nA,4,9,6,6,2,7,6,3,1,7,0,8,3,7,2,8\nN,6,10,9,8,9,6,8,3,5,8,7,7,9,8,8,4\nX,7,11,8,6,4,7,8,2,8,11,7,8,5,11,4,7\nR,5,10,7,8,6,7,8,6,5,8,5,9,4,6,6,12\nY,5,6,4,9,3,9,9,2,3,6,10,5,3,10,5,8\nM,4,6,7,4,5,4,7,3,4,11,10,10,5,7,2,6\nS,3,7,4,5,2,8,7,5,9,5,6,8,0,8,9,7\nJ,2,4,4,3,1,8,6,3,6,14,6,10,0,7,0,7\nB,4,8,5,6,6,6,7,8,5,7,6,7,2,8,7,9\nN,7,9,9,7,4,7,8,3,5,10,7,7,6,9,1,7\nQ,7,12,7,6,4,11,3,4,5,12,3,9,3,9,7,12\nY,2,4,3,3,1,4,10,2,6,10,10,6,1,11,2,5\nZ,3,7,4,5,3,7,7,3,11,9,6,8,0,8,7,8\nC,3,6,4,4,2,6,8,8,8,9,8,13,2,10,4,9\nC,3,2,4,4,2,6,8,7,8,8,8,14,1,9,4,10\nW,2,0,2,0,1,7,8,4,0,7,8,8,6,9,0,8\nR,1,0,2,1,1,6,9,7,3,7,5,7,2,7,4,10\nP,4,6,6,4,4,9,7,2,6,13,5,6,1,10,3,10\nP,5,8,6,6,5,4,11,4,5,11,9,4,0,10,4,7\nD,5,8,6,6,4,8,8,7,7,10,5,4,3,8,4,8\nB,4,8,5,6,5,8,8,4,4,7,5,7,4,8,5,7\nQ,4,6,5,8,5,7,8,5,3,7,9,11,3,8,6,8\nB,4,7,5,5,5,7,7,7,6,7,6,7,2,8,6,10\nS,1,3,3,2,1,8,8,2,6,10,6,8,1,8,5,8\nO,6,12,4,6,3,6,8,5,5,9,5,6,5,9,5,8\nH,5,11,7,8,7,6,6,6,5,6,5,8,6,6,7,10\nI,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8\nI,5,8,6,6,3,7,5,2,7,7,7,9,1,10,4,8\nG,2,1,2,2,1,8,6,6,6,6,5,10,1,7,5,10\nW,3,3,5,5,3,7,8,4,1,7,8,8,8,10,0,8\nA,1,1,2,1,0,7,4,2,0,7,2,8,2,7,1,8\nJ,2,6,2,4,1,15,4,3,5,12,1,7,0,8,0,8\nQ,5,7,7,10,11,8,6,5,2,6,6,8,11,12,10,15\nL,1,4,2,3,1,7,4,1,7,7,2,10,0,7,2,8\nI,0,6,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nD,7,11,10,8,6,8,7,7,8,9,5,4,4,8,6,11\nN,8,15,10,8,5,12,5,2,5,12,3,7,5,6,0,8\nI,1,3,1,4,1,7,7,0,7,7,6,8,0,8,2,8\nU,3,5,4,3,2,5,8,5,7,10,9,9,3,9,2,6\nI,1,5,2,4,1,7,7,0,7,13,6,8,0,8,1,7\nD,5,9,8,6,6,9,8,4,6,11,5,5,4,7,4,9\nE,3,6,5,4,4,8,7,5,2,7,6,9,4,8,7,10\nG,6,8,8,7,8,7,8,5,3,7,7,9,7,11,7,8\nV,4,7,5,5,2,9,12,2,3,4,11,9,3,12,1,7\nS,3,5,3,4,2,8,7,7,5,7,6,7,2,8,9,8\nR,3,6,5,4,4,8,9,4,5,8,4,8,3,5,4,11\nE,2,3,3,1,1,6,8,2,7,11,7,8,1,8,4,8\nM,5,9,5,6,6,8,5,11,0,6,8,8,8,6,1,6\nY,6,7,6,5,3,4,9,1,8,10,10,6,1,10,3,4\nI,3,7,4,5,2,7,7,0,8,14,6,8,0,8,1,8\nE,5,10,7,8,5,10,7,2,9,11,3,8,3,8,5,12\nQ,5,7,6,9,6,8,5,8,4,6,5,9,4,8,6,9\nZ,1,3,2,2,1,7,7,5,8,6,6,8,2,8,6,8\nS,2,6,3,4,2,9,9,5,9,5,5,5,0,7,8,8\nW,7,11,7,8,7,2,10,2,3,10,10,8,7,11,1,7\nG,5,11,4,6,3,8,7,5,3,9,6,7,3,9,8,8\nT,5,7,6,5,3,4,13,4,5,12,9,4,2,12,2,5\nQ,5,9,7,11,7,8,7,8,4,5,6,10,3,8,7,10\nX,4,6,5,5,5,6,8,2,4,8,7,9,2,8,7,8\nA,3,7,5,5,3,11,3,2,2,9,1,9,3,5,2,8\nA,2,6,4,4,2,10,3,1,2,8,3,9,2,6,2,8\nW,4,5,6,8,4,4,8,5,2,7,8,8,8,10,0,8\nA,2,5,4,4,2,7,2,2,2,6,2,8,2,7,2,7\nY,3,8,5,6,2,5,10,1,3,8,11,8,1,11,0,8\nL,4,9,5,6,4,6,4,4,8,3,2,5,4,6,2,5\nZ,6,7,4,11,4,8,5,4,4,11,6,8,3,9,10,8\nB,2,1,3,1,2,7,7,5,5,7,6,6,1,8,5,9\nX,4,5,7,4,3,9,6,1,9,10,4,7,2,8,3,8\nE,3,8,4,6,4,6,7,6,8,7,6,10,3,8,6,8\nJ,2,10,3,8,1,12,3,10,3,13,6,13,1,6,0,8\nG,4,8,6,6,6,8,5,4,3,7,5,11,6,8,4,11\nX,5,10,6,8,4,7,7,4,4,7,6,7,3,8,4,8\nG,4,9,5,7,6,7,7,6,2,7,6,11,4,9,8,7\nQ,4,5,5,7,3,9,7,8,6,6,6,10,3,8,5,9\nQ,4,5,5,7,3,9,8,8,6,5,8,10,3,8,5,9\nY,8,13,6,7,4,9,7,5,6,9,5,4,4,10,6,5\nU,4,9,4,6,2,8,5,14,5,6,14,8,3,9,0,8\nI,6,12,6,7,4,9,8,2,5,11,5,6,3,9,6,11\nA,2,7,4,5,3,11,2,2,2,9,2,9,2,6,2,7\nW,8,8,8,6,5,3,10,3,4,11,10,8,8,10,2,6\nZ,4,4,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nO,5,10,6,7,3,8,8,9,8,7,7,8,3,8,4,8\nX,4,8,6,6,3,9,7,1,8,10,2,7,3,8,4,9\nT,4,10,6,7,2,8,15,1,6,7,11,8,0,8,0,8\nO,8,14,5,8,4,6,6,6,3,9,7,9,5,9,5,8\nH,2,0,2,0,0,7,8,11,1,7,5,8,3,8,0,8\nV,6,11,9,8,5,7,11,3,2,6,11,9,3,10,3,9\nE,2,3,3,5,2,3,7,6,10,7,6,15,0,8,7,8\nK,1,0,1,0,0,4,6,6,2,7,6,11,3,7,2,10\nO,6,10,7,8,3,7,9,9,9,7,8,8,3,8,4,8\nW,4,3,5,5,3,8,8,5,1,7,8,8,8,9,0,8\nD,4,10,6,8,4,9,7,5,7,9,4,4,3,8,4,8\nP,6,10,5,5,3,6,10,6,2,11,5,5,4,10,4,8\nF,4,9,5,6,4,5,10,5,6,11,10,5,2,9,2,5\nM,1,0,2,0,1,7,6,9,0,7,8,8,5,6,0,8\nY,3,3,4,2,1,4,11,2,7,11,10,5,1,11,2,5\nJ,2,9,3,6,2,9,6,2,7,12,4,8,0,6,2,6\nQ,3,6,4,5,2,9,6,9,6,7,5,9,3,8,4,8\nM,8,9,11,7,7,6,7,3,5,9,9,9,8,6,2,8\nK,5,9,6,7,7,7,9,5,4,7,5,7,5,7,8,11\nT,4,9,5,7,3,5,14,1,6,9,10,7,0,8,0,8\nY,3,6,4,4,1,8,11,2,2,5,12,8,1,11,0,8\nJ,3,5,4,6,4,9,9,5,6,7,5,8,3,10,8,9\nJ,3,11,4,8,3,9,6,3,7,12,4,9,1,6,2,6\nA,6,12,6,6,5,9,4,5,3,10,5,12,7,3,6,11\nF,9,13,7,7,4,6,11,3,5,13,6,4,2,8,6,6\nW,5,9,7,7,11,10,7,5,2,7,7,8,13,10,3,6\nN,5,10,5,8,6,7,7,13,1,6,6,8,5,9,0,7\nW,7,7,7,5,4,3,11,3,3,10,10,8,7,10,2,6\nG,7,9,7,7,5,6,7,6,6,10,8,11,2,9,4,10\nS,2,7,3,5,3,8,7,7,5,7,6,8,2,8,8,8\nD,3,1,4,3,3,7,7,6,7,7,6,5,2,8,3,7\nG,7,9,6,5,3,10,5,4,5,11,3,7,4,7,5,9\nH,6,7,9,5,6,9,7,3,6,10,4,7,5,7,4,9\nP,4,9,6,7,5,7,10,4,4,12,5,3,1,10,3,8\nM,3,3,5,2,3,7,6,3,4,9,8,9,6,5,2,9\nC,5,11,6,8,5,5,7,6,9,8,5,13,1,9,4,9\nG,6,9,8,7,8,7,10,6,3,5,5,11,5,7,8,7\nR,4,2,5,3,4,6,7,5,6,6,5,7,3,7,4,8\nH,4,5,7,4,4,8,8,3,6,10,6,8,3,8,3,8\nS,3,4,4,3,2,6,8,3,7,11,7,8,1,9,4,5\nW,4,9,6,7,3,8,8,5,2,6,8,8,8,10,0,8\nW,6,9,6,7,5,2,10,2,3,10,11,9,6,10,1,7\nM,3,2,4,3,3,8,6,5,4,7,7,8,7,5,2,7\nA,2,6,3,4,2,12,4,3,2,10,1,9,2,6,1,8\nN,6,11,7,9,3,7,7,15,2,4,6,8,6,8,0,8\nW,6,9,6,7,7,4,9,2,3,9,8,8,7,11,2,6\nY,6,9,8,7,7,9,8,6,4,5,9,7,3,8,10,5\nH,8,13,8,7,6,9,6,4,5,11,3,7,7,8,5,8\nZ,5,6,4,9,4,9,4,4,3,11,6,9,2,10,8,8\nX,3,5,6,4,3,8,7,1,9,10,4,8,2,8,3,8\nC,4,7,5,5,3,7,9,8,6,5,6,12,4,8,3,8\nA,2,5,4,4,2,5,3,2,2,5,2,8,2,6,2,6\nN,3,7,5,5,5,7,8,3,4,8,7,8,6,9,5,4\nJ,1,3,3,2,1,8,6,3,6,14,6,10,0,7,0,8\nD,4,6,5,5,5,7,8,4,7,6,4,8,4,8,5,5\nD,4,8,6,6,5,7,8,6,5,10,6,5,5,9,5,10\nE,4,8,4,6,4,3,6,5,9,7,7,14,0,8,6,9\nB,4,7,6,5,5,8,6,5,6,9,6,7,3,8,7,9\nU,4,8,5,6,3,6,9,5,6,7,9,9,3,9,1,8\nK,7,9,10,7,6,6,8,2,7,10,7,9,3,8,3,7\nP,1,0,2,1,0,5,11,7,1,9,6,4,1,9,3,8\nD,4,10,6,8,7,8,7,5,7,6,5,6,6,8,3,7\nH,2,1,3,2,2,8,8,6,5,7,6,8,3,8,3,8\nC,9,14,6,8,3,5,11,6,9,12,8,7,2,8,6,8\nP,3,6,4,4,3,5,11,7,3,10,7,2,1,10,3,6\nC,4,7,5,5,5,5,7,4,4,7,6,10,6,9,5,9\nU,7,10,7,8,4,4,8,6,8,10,10,9,3,9,2,5\nV,2,3,3,2,1,4,12,3,2,10,11,7,2,11,1,8\nN,6,7,8,6,7,7,8,5,4,7,5,7,7,9,6,3\nD,2,1,2,2,1,6,7,9,6,6,6,6,2,8,3,8\nM,5,11,6,8,4,8,7,12,2,6,9,8,8,6,0,8\nW,4,4,5,2,3,4,11,3,2,9,9,7,6,11,1,6\nH,7,11,9,8,6,7,7,6,7,7,6,6,6,8,4,7\nN,5,11,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nS,4,5,6,3,3,8,7,2,8,10,5,7,1,8,5,8\nI,5,12,4,6,2,12,4,3,5,12,2,7,2,8,2,11\nL,4,11,6,8,3,4,4,3,9,5,1,9,1,6,3,6\nQ,2,3,3,4,3,8,9,6,1,5,8,10,3,9,5,10\nO,3,5,4,3,3,8,7,7,5,7,6,8,2,8,3,8\nF,4,8,4,6,3,1,11,3,4,11,11,8,0,8,2,7\nM,11,15,11,8,6,7,10,4,5,4,5,10,10,11,2,7\nS,3,6,4,4,2,8,7,5,8,5,6,8,0,8,9,8\nM,6,8,8,6,8,8,9,6,4,7,7,8,6,9,6,7\nU,4,6,5,4,3,5,9,5,6,6,9,10,3,9,1,7\nL,5,12,5,6,3,9,3,3,5,11,4,11,2,8,5,9\nM,5,8,7,6,6,9,7,6,5,6,7,4,10,7,4,5\nL,1,0,1,1,0,1,1,5,5,0,1,6,0,8,0,8\nI,1,3,1,2,0,7,7,1,7,7,6,9,0,8,2,8\nU,6,8,7,6,3,3,9,6,8,12,12,9,3,9,1,7\nI,1,2,1,4,1,7,7,1,7,7,6,8,0,8,2,8\nX,5,11,7,8,4,5,8,2,8,10,11,10,3,8,4,5\nA,4,10,6,7,2,5,4,3,2,5,1,7,3,7,3,7\nM,7,7,10,5,7,5,7,3,5,10,10,10,11,8,4,8\nJ,3,9,4,7,1,12,2,10,4,13,6,13,1,6,0,8\nM,4,8,5,6,5,7,6,6,5,7,7,10,7,5,2,8\nA,4,7,5,5,5,7,7,7,4,6,6,9,2,8,7,4\nM,4,7,7,5,4,4,6,4,4,11,11,11,6,6,2,7\nI,1,5,2,4,1,7,7,0,7,13,6,8,0,8,1,8\nP,4,9,6,7,4,10,7,3,5,12,4,5,3,8,4,9\nC,3,6,4,4,1,6,7,6,9,6,6,13,1,7,4,8\nY,3,5,4,4,2,4,10,2,7,11,11,6,1,11,2,5\nY,6,9,6,4,3,7,6,4,4,9,9,5,3,10,4,4\nO,4,4,6,6,3,6,8,9,8,7,6,6,3,8,4,8\nZ,6,9,6,4,4,9,5,4,8,11,4,9,3,6,6,9\nR,4,5,4,6,3,6,10,10,4,7,4,8,3,7,5,10\nL,2,4,4,3,2,7,3,2,7,8,2,10,0,7,2,8\nN,1,0,2,1,0,7,7,11,0,5,6,8,4,8,0,8\nP,3,7,5,5,3,7,10,3,5,13,5,3,1,10,3,9\nQ,3,6,4,4,3,8,5,7,4,6,6,8,3,8,3,8\nH,5,9,7,6,6,4,9,3,6,10,10,9,4,9,4,5\nJ,8,12,6,9,4,10,6,2,5,12,5,7,2,10,6,12\nZ,7,14,7,8,5,6,7,2,9,12,7,8,4,6,7,6\nS,4,6,5,5,5,10,7,5,5,7,6,9,4,8,8,11\nH,8,10,8,5,4,9,8,4,5,8,3,5,6,7,4,8\nZ,3,9,4,6,3,7,7,6,11,6,6,8,1,8,8,8\nW,10,10,10,5,4,2,10,4,2,11,12,8,8,10,0,7\nD,3,8,5,6,4,10,6,3,6,10,3,5,3,8,3,8\nY,6,8,8,11,12,8,9,4,2,7,8,9,4,11,9,8\nS,3,6,4,4,4,8,10,5,4,8,5,6,3,9,7,7\nF,5,10,8,8,8,10,6,2,5,9,5,6,5,9,4,7\nI,2,2,1,3,1,7,7,1,8,7,6,8,0,8,3,8\nU,6,10,9,8,12,8,8,4,4,6,7,8,10,5,9,8\nC,5,11,4,6,3,5,9,4,4,9,7,9,3,9,8,8\nF,1,1,2,2,1,6,10,4,4,10,8,5,1,9,3,7\nC,3,7,4,5,2,5,8,6,7,11,8,13,1,9,3,8\nI,2,9,2,7,2,7,7,0,9,7,6,8,0,8,3,8\nU,8,9,9,7,5,5,6,6,9,8,6,8,5,9,5,2\nK,5,5,5,8,2,4,6,9,2,7,7,11,4,7,3,11\nX,5,9,6,6,5,7,6,3,5,6,6,9,3,7,10,9\nC,4,7,5,5,2,5,8,7,10,6,7,11,1,7,4,8\nX,3,1,4,2,2,8,7,3,9,6,6,8,3,8,6,8\nF,1,3,3,2,1,8,9,2,5,13,5,5,1,9,2,9\nM,10,13,12,8,6,10,2,2,2,9,3,9,7,2,1,9\nW,2,1,4,3,2,9,10,3,2,6,9,7,5,11,0,8\nI,2,7,3,5,1,8,6,0,7,13,6,9,0,8,1,8\nH,2,1,3,2,2,7,7,6,6,7,6,8,3,8,4,8\nS,4,4,5,6,3,7,5,6,9,6,6,11,0,9,9,8\nF,4,8,6,9,7,7,8,4,4,7,6,7,5,9,10,8\nQ,6,9,6,5,4,12,4,3,5,10,3,8,3,9,6,12\nW,6,11,6,6,4,4,9,1,3,9,9,8,8,12,1,6\nR,4,3,4,4,2,5,11,8,3,7,4,8,2,7,6,11\nI,1,9,0,6,1,7,7,5,3,7,6,8,0,8,0,8\nX,4,9,6,6,5,7,7,3,9,5,6,8,3,8,6,8\nP,4,6,6,4,4,9,7,3,5,11,4,5,2,9,4,9\nD,1,0,1,1,0,5,7,7,5,6,6,6,2,8,2,8\nZ,2,4,4,3,2,7,8,2,9,11,7,7,1,8,6,6\nB,2,3,3,2,2,8,7,2,5,10,5,7,2,8,3,9\nB,2,5,4,3,3,9,7,2,6,10,5,6,2,8,5,9\nY,4,6,5,8,7,10,9,5,3,6,7,8,6,11,7,5\nA,4,9,6,6,2,8,4,3,2,7,1,8,3,6,3,8\nF,2,1,2,2,1,6,10,5,5,10,9,5,1,9,3,6\nM,3,7,4,5,4,9,6,5,4,6,7,6,7,5,2,6\nC,7,14,5,8,5,6,6,4,4,10,9,12,4,9,9,8\nN,6,9,9,7,4,7,8,3,5,10,6,7,6,9,1,7\nP,6,8,8,6,5,5,14,6,2,12,6,2,1,10,3,7\nO,4,2,5,4,3,8,7,8,5,7,6,8,2,8,3,8\nO,4,8,5,6,4,7,8,7,4,10,7,9,3,8,3,7\nB,5,8,7,6,6,10,6,3,6,10,4,7,3,8,5,10\nG,6,8,8,6,7,7,10,6,5,6,6,8,5,6,7,7\nS,3,10,4,7,4,8,6,8,6,7,8,10,2,10,9,7\nZ,6,10,6,5,3,9,5,2,8,11,5,9,2,9,5,9\nH,8,11,11,8,6,10,6,4,7,11,1,7,6,8,5,10\nQ,4,7,5,8,5,9,7,7,3,5,7,10,3,8,6,10\nL,3,7,4,5,3,9,3,1,7,9,2,10,0,6,2,10\nZ,4,6,5,4,3,9,5,3,8,11,4,9,2,7,6,10\nQ,3,3,4,5,3,8,7,7,3,5,6,9,3,8,5,9\nQ,2,5,3,6,3,9,10,6,1,3,7,11,2,10,5,11\nY,6,8,6,6,4,4,9,1,8,11,10,6,1,10,2,4\nF,1,0,1,0,0,3,12,4,2,11,8,5,0,8,2,7\nM,3,7,4,5,4,8,6,5,4,7,7,8,7,5,2,7\nU,2,1,3,1,1,7,5,11,5,7,14,8,3,10,0,8\nF,3,4,3,5,1,1,12,5,4,11,10,7,0,8,3,6\nH,4,9,6,6,7,7,8,6,7,7,5,8,3,8,3,8\nF,4,10,6,8,5,7,9,3,5,12,7,6,2,9,2,7\nV,6,8,6,6,3,3,12,4,4,10,12,7,2,10,1,8\nX,3,4,5,3,2,7,8,1,8,10,7,8,2,8,3,7\nI,1,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8\nE,5,9,4,4,2,9,6,4,4,11,4,10,3,8,6,11\nE,6,9,8,8,9,7,7,5,4,7,7,10,9,12,11,12\nE,4,8,6,6,4,5,8,5,8,11,10,9,3,8,5,5\nI,4,6,6,4,3,6,7,3,8,7,6,11,0,8,4,8\nX,3,9,4,7,3,8,7,4,4,7,6,9,2,8,4,8\nH,5,7,7,5,5,7,7,2,6,10,6,8,3,8,3,7\nU,2,1,3,2,1,7,9,5,6,7,10,9,3,9,0,8\nH,8,10,11,8,9,5,8,3,7,10,10,9,7,11,6,6\nS,5,8,7,6,8,8,5,4,4,9,6,10,4,8,9,9\nO,4,10,5,8,3,8,7,9,7,7,6,8,3,8,4,8\nQ,6,7,6,9,5,7,10,5,3,7,10,11,4,10,7,8\nZ,6,11,7,8,6,7,8,3,13,9,6,8,0,8,8,7\nN,3,5,4,4,3,7,8,5,5,7,7,6,5,9,2,6\nZ,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8\nI,0,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nQ,5,11,5,6,4,11,5,4,6,10,4,7,4,9,6,12\nU,9,12,8,7,3,6,5,4,6,3,8,6,6,7,2,7\nT,6,8,6,6,3,5,14,5,6,12,9,2,2,12,2,4\nC,4,9,5,6,2,6,8,7,10,4,6,14,1,7,4,9\nI,1,5,2,3,1,7,7,0,7,13,6,8,0,8,1,8\nZ,7,8,5,11,5,8,5,5,5,11,7,8,3,9,10,7\nI,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nC,3,5,4,3,2,6,7,7,7,8,7,14,2,9,4,10\nY,9,10,8,8,4,3,10,4,7,13,12,6,2,11,3,6\nV,7,9,5,4,3,9,7,6,4,7,9,7,7,12,3,7\nR,5,11,6,8,4,6,9,10,5,6,5,8,3,8,6,11\nQ,4,8,6,6,5,8,4,8,5,6,6,8,3,8,3,8\nC,2,1,2,2,1,6,8,6,7,8,7,13,1,9,3,10\nM,9,10,13,8,10,3,8,4,5,10,11,11,12,8,6,6\nQ,5,12,5,6,4,10,5,4,6,11,4,8,3,8,8,11\nY,3,3,4,1,1,4,10,2,7,10,10,5,1,10,3,4\nT,3,5,5,4,4,6,8,5,6,8,8,8,3,10,6,7\nE,5,11,5,8,3,3,8,6,12,7,6,15,0,8,7,6\nU,4,7,5,5,4,6,8,8,5,5,6,11,4,9,5,5\nZ,4,7,6,5,3,7,7,2,10,11,6,8,1,8,6,8\nR,5,9,6,7,6,7,7,4,6,6,5,7,3,7,5,9\nW,6,11,8,8,4,5,8,5,2,7,8,8,9,9,0,8\nI,5,7,7,8,6,9,8,5,5,6,5,8,3,8,9,7\nW,2,3,3,1,2,5,10,4,2,9,8,7,5,10,1,6\nF,3,7,6,5,5,8,8,1,4,9,6,6,3,10,4,4\nK,3,5,5,4,3,5,8,1,6,10,8,10,3,8,3,8\nG,4,8,4,6,3,7,7,6,7,11,6,10,2,10,4,9\nY,3,8,6,6,3,5,9,2,6,8,12,10,2,11,2,7\nN,2,1,2,2,1,7,8,6,4,7,6,7,4,8,1,7\nR,6,10,8,7,8,8,8,7,5,7,5,8,5,9,7,12\nB,5,8,8,6,6,11,5,2,6,10,3,7,5,7,6,12\nR,2,4,4,3,2,8,8,3,5,9,4,7,2,6,3,10\nZ,1,3,2,1,1,8,7,1,8,11,6,8,1,8,5,8\nK,6,11,8,8,6,4,7,2,7,10,10,12,3,8,4,7\nF,3,5,5,3,2,7,9,2,6,13,6,5,4,9,3,7\nN,3,2,4,4,3,7,9,5,4,7,7,7,5,9,2,6\nL,2,0,2,1,0,2,1,6,5,0,2,4,0,8,0,8\nP,2,1,2,2,1,5,11,5,3,10,7,3,0,9,3,6\nG,4,4,5,7,2,7,7,8,8,6,6,11,1,8,5,11\nZ,4,11,6,8,7,8,7,2,8,7,6,8,0,8,9,8\nQ,1,0,1,0,0,8,7,6,2,6,6,9,2,8,2,8\nX,1,1,2,1,0,7,7,4,4,7,6,8,2,8,4,7\nY,1,0,2,1,0,7,9,1,2,6,12,8,1,10,0,8\nF,4,6,6,4,3,7,9,3,6,13,6,5,2,9,2,7\nS,5,9,8,7,9,5,7,3,2,7,6,5,3,8,9,2\nP,12,13,9,8,4,7,9,6,4,12,3,4,5,10,5,8\nZ,4,10,5,8,2,7,7,4,14,10,6,8,0,8,8,8\nN,6,10,8,5,3,12,4,5,3,13,1,7,5,7,0,8\nZ,8,12,8,6,5,8,5,2,8,12,6,10,3,7,7,8\nK,4,9,6,6,6,7,6,5,4,6,6,7,7,6,8,12\nI,1,3,1,2,1,7,7,1,7,7,6,8,0,8,2,8\nH,5,9,6,6,3,7,10,15,2,7,3,8,3,8,0,8\nI,0,4,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nD,7,12,7,7,5,9,6,4,7,10,5,7,6,8,8,6\nO,3,7,5,5,6,8,6,5,2,7,6,8,8,9,5,10\nJ,2,8,3,6,2,14,4,4,4,13,2,8,0,7,0,8\nD,2,3,2,2,1,7,7,6,6,7,6,4,2,8,3,6\nT,3,7,5,5,3,6,11,4,6,8,11,8,2,12,1,7\nV,3,9,5,6,1,7,8,4,3,7,14,8,3,9,0,8\nB,4,10,5,8,7,6,8,8,5,7,5,7,2,8,7,9\nJ,2,8,3,6,1,13,3,8,4,14,3,11,0,7,0,8\nP,5,10,7,7,4,8,9,2,7,14,5,4,3,9,4,9\nO,6,10,7,7,5,8,6,8,6,10,6,9,4,8,5,5\nG,6,11,6,8,4,6,7,7,6,9,8,11,3,7,6,8\nY,3,7,5,5,4,8,6,5,4,6,8,7,2,8,8,4\nC,2,4,3,3,1,6,8,7,7,8,8,13,1,10,4,10\nY,7,11,8,8,5,3,9,2,7,9,11,7,2,11,3,5\nY,5,7,7,11,10,8,11,3,3,6,8,9,4,11,11,7\nG,5,11,6,8,9,7,5,4,4,7,5,11,7,8,9,14\nI,1,3,0,2,0,7,7,1,7,7,6,8,0,8,2,8\nE,3,2,3,4,3,7,7,5,8,7,6,8,2,8,6,9\nT,2,4,2,2,1,8,12,3,5,6,10,8,2,11,1,8\nC,4,9,5,7,2,5,7,7,10,7,7,12,1,8,4,8\nH,11,14,10,8,5,8,7,4,5,9,6,7,7,10,5,9\nK,4,10,6,8,7,7,7,5,4,7,6,7,7,6,8,13\nZ,3,8,4,6,2,7,7,4,13,9,6,8,0,8,8,8\nU,8,13,8,7,5,6,6,4,5,6,7,8,5,6,3,7\nA,3,5,5,4,2,10,2,2,2,9,2,9,2,6,2,8\nK,4,9,6,7,6,5,6,4,7,6,6,10,3,8,6,10\nQ,3,6,4,5,3,9,8,7,4,5,7,10,2,8,4,9\nM,3,6,4,4,2,8,6,11,1,6,9,8,7,6,0,8\nF,6,10,8,7,4,5,13,2,6,13,6,2,1,10,2,6\nH,4,5,5,4,4,7,7,6,6,7,6,8,3,8,3,7\nY,3,8,5,6,2,8,10,2,2,6,12,8,1,11,0,8\nS,1,3,2,2,1,8,7,6,5,7,6,7,2,8,8,8\nY,3,9,5,6,1,7,10,3,2,7,13,8,2,11,0,8\nK,4,8,6,6,5,8,5,1,6,10,4,9,3,8,4,10\nY,3,9,5,6,1,7,11,1,3,7,12,8,1,10,0,8\nJ,3,6,5,4,2,7,4,5,3,13,9,14,1,6,1,6\nW,5,11,7,8,4,7,7,5,2,7,8,8,9,9,0,8\nO,4,7,6,6,4,8,4,3,4,9,3,8,3,7,4,9\nN,11,15,9,8,4,7,10,5,5,4,5,11,6,11,2,6\nQ,7,12,7,6,5,9,6,4,7,11,5,8,4,8,9,10\nE,4,10,4,7,3,3,8,6,11,7,5,15,0,8,7,7\nP,3,4,5,6,5,8,9,3,2,7,8,6,5,10,5,5\nK,6,8,8,6,4,3,9,3,7,11,11,11,4,7,4,5\nQ,7,15,6,8,4,10,4,4,7,10,4,8,3,9,7,13\nP,5,8,7,11,12,7,11,5,0,9,7,6,5,10,6,8\nZ,3,8,4,6,3,9,6,5,10,7,5,6,1,7,8,8\nS,6,10,7,8,4,8,7,4,8,11,6,7,2,8,5,8\nP,3,7,3,4,2,3,13,7,2,11,6,3,1,10,4,8\nU,4,4,4,6,2,7,5,13,5,7,14,8,3,9,0,8\nZ,3,2,4,4,2,7,7,5,10,6,6,8,1,8,7,8\nO,4,7,6,6,4,8,5,3,4,8,3,7,3,7,5,9\nD,3,7,5,5,4,8,8,5,5,9,6,4,3,8,3,7\nQ,4,6,4,8,5,8,5,6,4,9,5,9,3,8,5,7\nR,4,8,4,5,2,5,12,8,4,7,3,9,3,7,6,11\nA,3,5,6,4,3,11,1,2,2,9,2,9,3,7,3,9\nA,2,2,4,4,2,8,2,2,2,8,2,8,2,6,3,7\nG,3,4,4,2,2,6,7,5,5,10,7,9,2,9,4,9\nN,5,10,6,8,6,6,7,8,5,7,5,6,3,7,3,8\nE,7,10,9,8,6,7,8,2,10,11,6,9,2,8,5,8\nV,3,1,5,3,1,7,12,3,3,6,11,9,2,10,1,8\nY,5,7,7,11,10,8,9,3,2,5,8,10,6,13,9,9\nC,5,10,4,5,3,8,7,4,2,8,8,10,3,9,7,13\nR,4,6,4,8,3,5,12,9,4,7,2,9,3,7,6,11\nF,2,3,3,1,1,7,9,2,5,13,6,5,1,9,2,8\nO,5,11,6,8,6,7,8,7,3,10,8,8,4,7,5,10\nD,3,8,4,6,4,5,7,9,6,7,6,5,3,8,3,9\nH,4,7,5,5,4,6,7,6,4,6,5,8,3,7,6,10\nC,4,9,4,4,3,7,8,4,3,9,8,10,3,9,8,11\nV,2,3,3,2,1,8,12,2,2,5,10,9,2,11,0,8\nO,2,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8\nX,3,4,5,3,2,7,7,1,8,10,6,8,2,8,3,8\nM,8,10,12,8,9,10,6,2,5,9,5,7,8,7,2,8\nL,1,4,3,2,1,6,4,1,7,8,2,11,0,7,2,9\nE,4,7,6,5,3,8,7,2,9,11,5,8,2,8,5,10\nO,4,8,6,6,2,8,5,8,8,7,4,9,3,8,4,8\nZ,3,3,4,5,2,7,7,4,13,9,6,8,0,8,8,8\nX,4,5,6,4,3,7,7,1,9,10,6,8,2,8,3,7\nF,5,10,7,7,4,7,10,3,6,13,7,5,2,10,2,7\nD,3,4,4,3,3,7,7,7,6,7,6,4,2,8,3,7\nJ,2,8,2,6,1,13,3,8,4,13,3,11,1,6,0,8\nV,4,6,5,4,2,4,12,3,3,10,11,7,2,10,1,8\nN,4,6,4,4,2,7,7,14,1,5,6,8,6,8,0,8\nP,5,10,7,8,6,5,11,7,4,10,7,2,2,11,4,7\nL,2,6,4,4,2,6,4,1,9,8,2,11,0,7,2,8\nN,4,7,6,5,3,11,6,4,5,10,1,5,5,9,1,7\nM,7,6,7,8,4,8,7,13,2,6,9,8,9,6,0,8\nJ,3,11,4,8,2,9,4,5,8,12,4,12,1,6,2,6\nZ,2,6,3,4,1,7,7,3,13,9,6,8,0,8,8,8\nV,6,9,5,7,3,3,11,3,4,10,12,8,2,10,1,8\nX,3,2,4,3,2,7,7,3,9,6,6,8,3,8,6,7\nC,4,10,6,8,5,8,6,9,6,8,6,11,4,9,4,8\nM,6,5,8,4,7,9,8,5,4,7,6,7,9,8,5,3\nZ,2,2,3,4,2,8,7,5,9,6,6,8,1,8,7,8\nG,5,7,6,5,6,7,6,5,3,7,6,9,5,8,7,7\nI,4,11,5,8,3,9,6,0,8,14,5,8,0,8,1,8\nG,5,9,7,7,4,6,7,7,7,6,6,12,3,9,5,7\nE,4,10,4,7,4,3,7,5,9,7,7,13,0,8,7,9\nD,3,2,5,3,3,7,7,6,7,7,6,5,2,8,3,7\nB,5,7,7,5,6,8,6,6,6,9,7,7,3,8,7,9\nR,6,9,8,7,8,8,6,7,3,8,5,7,4,6,7,10\nU,4,5,5,4,2,4,8,5,7,10,9,8,3,9,2,6\nD,2,4,4,3,2,9,6,4,6,10,5,6,2,8,2,9\nS,1,3,3,2,1,7,8,3,7,10,7,8,1,8,4,6\nA,5,11,4,6,3,10,2,3,1,9,4,11,3,4,4,8\nK,6,9,8,6,7,9,6,1,6,10,3,8,6,7,5,9\nL,1,0,1,1,0,2,1,5,5,0,2,5,0,8,0,8\nF,3,2,4,3,2,5,11,4,6,11,9,5,1,10,3,6\nC,3,4,4,6,2,6,7,7,9,8,5,13,1,9,4,9\nT,2,5,4,4,3,7,8,4,7,7,7,8,3,9,6,7\nQ,3,7,4,6,2,8,8,8,5,5,8,9,3,7,6,10\nM,3,2,4,3,4,8,6,6,4,7,7,8,7,6,2,7\nM,11,14,11,8,6,9,11,6,4,4,6,9,11,12,3,7\nC,3,4,4,3,2,4,7,5,7,10,8,13,1,9,3,8\nO,3,5,4,4,3,7,7,7,4,9,5,7,2,8,3,7\nB,5,10,7,8,7,8,8,4,5,7,6,7,7,7,6,9\nU,5,8,5,6,2,7,4,14,6,7,14,8,3,9,0,8\nO,4,7,6,5,4,7,5,8,5,8,5,11,3,8,4,8\nO,4,6,6,5,4,7,5,5,4,8,4,7,3,7,4,8\nC,5,10,6,7,4,5,7,7,10,7,6,11,2,8,5,11\nK,6,10,9,7,7,4,8,2,7,10,10,11,4,7,4,6\nE,3,2,4,4,3,7,7,5,8,7,5,9,2,8,6,10\nC,3,7,4,5,2,3,7,5,6,9,8,14,1,8,3,9\nH,3,6,5,4,6,9,7,4,4,6,6,8,7,8,6,7\nA,4,9,6,6,2,6,4,3,1,6,1,8,3,7,2,7\nR,4,7,4,4,2,5,10,8,3,7,4,8,3,7,6,11\nE,3,6,4,4,4,7,7,5,8,7,7,9,3,8,6,9\nS,6,9,7,7,5,7,8,3,7,10,7,8,2,8,5,6\nE,2,7,3,5,3,3,8,5,9,7,6,13,0,8,6,9\nB,5,9,8,8,9,7,7,5,4,7,6,8,7,9,9,6\nS,7,12,7,7,3,7,7,5,3,13,9,10,3,10,3,8\nZ,2,5,4,4,2,7,8,2,9,11,7,7,1,8,5,7\nH,6,11,9,9,8,10,6,3,6,10,3,7,6,8,5,10\nV,8,11,8,8,5,3,12,2,3,9,10,7,5,11,2,6\nK,5,8,8,6,4,4,8,3,7,10,10,11,4,8,4,6\nO,3,7,5,5,3,8,6,7,4,7,5,8,3,8,3,7\nK,4,9,6,7,8,7,7,3,4,6,6,8,7,8,8,7\nA,9,15,7,8,4,8,1,2,2,9,4,12,4,5,5,6\nM,7,10,9,8,11,8,6,6,5,7,5,8,6,9,8,8\nS,4,6,5,4,2,8,7,3,8,11,6,7,2,8,5,8\nX,2,3,3,4,1,7,7,4,4,7,6,8,2,8,4,8\nC,2,6,3,4,1,4,7,7,9,7,6,11,1,9,4,9\nE,4,9,4,7,3,3,7,6,10,7,6,14,0,8,8,7\nB,4,9,4,7,3,6,6,9,7,6,6,7,2,8,9,10\nW,2,1,3,2,2,8,10,2,2,6,9,8,5,11,0,8\nK,4,10,6,8,6,7,7,5,4,7,5,8,4,7,9,10\nZ,4,9,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nC,6,10,6,8,4,4,9,7,7,13,10,8,2,10,2,7\nA,3,4,6,6,2,7,5,3,1,7,0,8,2,7,2,8\nK,2,3,4,2,2,5,9,1,6,10,8,9,3,8,2,8\nP,4,7,6,5,4,7,10,2,6,13,6,4,0,10,3,9\nE,5,9,7,7,8,6,7,3,6,7,7,11,4,10,8,7\nV,6,12,6,7,3,5,11,4,3,10,8,5,4,11,2,8\nV,4,8,5,6,3,7,9,4,1,7,13,8,2,10,0,8\nS,3,9,4,7,2,7,6,6,10,5,7,10,0,9,9,8\nS,4,10,5,8,4,7,7,5,8,5,6,9,0,8,8,8\nX,4,8,7,6,3,8,6,1,8,10,4,8,3,8,3,8\nK,3,4,4,6,2,3,7,8,3,7,6,11,4,8,2,11\nI,1,4,2,3,1,7,8,0,7,13,6,8,0,8,1,7\nP,3,4,3,5,2,4,11,8,3,10,6,4,1,10,3,8\nL,3,7,4,5,2,8,4,1,7,9,3,9,1,6,2,9\nL,3,8,5,6,6,7,7,3,5,6,7,10,6,11,6,6\nF,5,10,5,6,4,7,10,2,5,11,6,5,4,10,7,7\nR,2,1,2,1,1,6,9,8,3,7,5,7,2,7,4,10\nH,4,4,6,3,3,8,7,3,6,10,6,8,3,8,3,8\nB,2,3,2,1,2,7,8,5,5,7,5,6,1,8,5,9\nQ,2,4,3,5,3,7,8,5,1,7,8,10,2,9,4,8\nY,2,5,4,4,2,6,10,1,7,7,11,9,1,11,2,9\nV,4,7,6,6,6,6,8,5,5,8,6,8,6,9,8,7\nH,4,8,4,5,2,7,8,15,1,7,5,8,3,8,0,8\nI,1,9,0,6,0,7,7,4,4,7,6,8,0,8,0,8\nN,2,3,2,2,2,7,9,6,3,7,6,8,4,8,1,7\nN,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8\nU,4,9,6,6,3,4,9,7,7,10,11,9,3,9,1,8\nU,4,7,5,5,2,7,3,14,6,7,13,8,3,9,0,8\nF,2,3,3,1,1,6,12,3,4,13,7,3,1,9,1,7\nJ,1,1,2,2,1,10,6,3,5,12,4,10,1,7,1,7\nL,6,11,8,9,6,10,3,1,7,10,2,10,3,6,4,10\nP,3,4,3,6,2,3,14,8,2,12,7,3,0,10,4,8\nW,11,11,10,6,4,6,11,2,3,7,10,6,8,12,1,7\nW,9,10,13,9,15,8,7,5,5,7,6,8,12,10,10,4\nP,2,4,4,3,2,6,10,3,4,12,5,3,1,10,2,8\nI,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nT,6,9,5,4,2,5,10,2,7,13,7,5,2,8,4,4\nZ,5,8,7,6,4,8,7,2,10,12,6,7,1,7,6,7\nF,4,8,6,6,4,6,10,2,5,13,7,5,2,10,2,7\nI,2,2,2,4,2,7,7,1,8,7,6,8,0,8,3,8\nH,6,10,7,6,5,8,7,3,5,10,7,7,7,11,5,8\nS,3,6,5,4,5,8,7,4,3,8,5,8,4,9,10,10\nN,8,12,10,6,4,3,11,6,3,14,12,9,6,10,0,8\nE,6,11,4,6,3,7,8,4,4,10,5,9,3,9,7,10\nU,5,10,5,7,4,7,5,13,5,7,12,8,3,9,0,8\nK,1,0,2,1,0,5,7,7,1,7,6,11,3,8,2,11\nO,2,0,2,1,0,7,7,6,5,7,6,8,2,8,3,8\nX,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8\nV,2,1,3,3,1,7,12,2,2,7,11,8,2,10,1,8\nX,1,0,1,0,0,7,7,3,5,7,6,8,2,8,4,8\nR,3,7,4,5,4,6,8,8,4,7,5,7,2,7,4,11\nA,6,8,8,6,7,6,7,7,5,7,5,9,4,8,11,3\nL,2,4,3,3,1,4,4,4,7,2,2,6,0,7,1,6\nJ,1,1,2,2,0,10,6,2,5,12,4,9,0,7,1,7\nF,4,9,7,7,7,5,8,2,4,10,8,7,8,10,4,4\nC,3,9,4,7,2,5,7,7,9,6,6,13,1,7,4,8\nC,6,9,8,8,8,5,9,4,6,6,6,11,4,11,8,9\nX,6,11,8,8,7,8,6,3,6,6,7,7,4,8,10,10\nR,5,7,7,5,4,10,6,2,6,11,2,7,3,6,3,10\nM,7,8,9,7,10,7,9,4,4,7,6,7,10,7,6,5\nE,2,0,2,1,1,5,8,5,8,7,6,12,0,8,7,9\nW,6,9,6,4,3,2,8,2,3,9,11,9,7,9,1,6\nY,9,14,8,8,4,6,8,4,4,10,9,5,5,10,5,4\nG,4,6,5,4,3,5,9,5,5,10,8,7,2,8,5,9\nP,6,11,9,8,7,8,10,4,4,12,5,3,1,10,3,8\nB,5,9,6,7,5,6,8,9,8,7,5,7,2,8,9,9\nV,4,8,6,6,3,5,11,3,4,8,12,9,2,10,1,8\nL,4,8,5,6,2,4,5,2,9,5,1,9,1,7,3,6\nV,3,7,5,5,5,7,7,4,2,8,8,8,5,10,4,8\nL,3,10,5,8,3,9,3,1,8,10,2,10,0,6,3,10\nX,4,3,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nN,5,8,7,6,5,7,9,5,5,7,6,6,7,8,3,7\nY,7,10,7,8,4,5,9,2,9,10,11,5,4,9,6,3\nX,5,7,7,5,3,7,7,1,8,10,5,8,3,8,3,7\nJ,3,5,4,8,2,11,3,11,3,12,8,13,1,6,0,8\nE,1,3,3,2,1,6,7,2,7,11,7,9,1,8,4,9\nC,5,7,5,5,3,6,8,6,8,12,8,12,2,10,3,7\nL,3,10,5,7,3,6,4,1,8,7,1,9,0,6,2,8\nR,4,10,5,8,3,5,9,9,4,7,6,8,3,8,6,11\nF,5,7,7,5,3,7,10,2,7,14,5,4,2,9,4,7\nD,4,7,4,5,2,5,8,10,9,8,7,5,3,8,4,8\nK,5,11,5,8,5,3,7,7,3,7,6,11,3,8,3,11\nR,2,1,3,3,2,6,7,4,5,6,5,7,2,6,4,8\nT,2,6,3,4,2,8,12,2,8,5,11,9,1,11,1,8\nQ,5,7,6,9,7,9,8,7,2,5,7,10,3,8,6,10\nF,5,11,7,8,5,7,9,1,6,13,6,5,1,10,2,8\nT,3,4,4,2,1,5,12,2,7,11,9,4,1,10,2,5\nX,3,7,4,4,1,7,7,5,4,7,6,8,3,8,4,8\nU,6,10,7,8,4,4,8,6,8,9,9,9,3,10,2,5\nB,4,7,6,5,4,7,8,6,5,9,6,6,3,8,6,8\nP,3,9,4,6,2,5,10,10,3,9,6,4,2,10,4,8\nE,3,8,3,6,2,3,7,6,11,7,6,15,0,8,6,7\nH,5,9,7,7,7,7,7,5,7,7,6,7,6,8,4,7\nH,5,4,6,6,2,7,7,15,0,7,6,8,3,8,0,8\nS,5,5,6,7,3,7,6,6,10,5,6,10,0,9,9,8\nU,7,9,7,7,4,4,7,6,8,9,9,9,3,9,3,5\nK,4,11,4,8,2,3,7,8,2,6,5,11,4,8,2,11\nK,1,0,1,0,0,4,6,5,2,7,6,10,2,7,1,10\nR,3,8,4,5,2,6,10,9,4,7,4,8,3,7,5,11\nP,6,11,8,8,5,7,12,8,3,11,4,2,1,10,5,7\nM,6,7,9,6,9,6,8,5,3,6,5,8,12,8,5,8\nY,5,6,7,9,10,8,4,5,3,7,8,9,8,8,6,8\nH,4,7,5,5,4,6,6,6,4,6,5,8,2,7,6,9\nX,6,11,9,8,5,8,6,1,9,10,4,8,3,8,4,8\nP,8,11,6,6,3,9,7,6,4,13,3,6,5,9,4,8\nB,4,9,6,6,6,8,8,4,5,10,5,6,3,8,5,10\nI,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nU,3,4,4,3,2,4,8,5,7,9,8,9,3,10,2,5\nI,2,9,3,7,4,7,7,0,7,7,6,8,0,8,3,8\nZ,5,5,6,8,3,7,7,4,15,9,6,8,0,8,9,8\nZ,4,5,5,8,2,7,7,4,14,10,6,8,0,8,8,8\nK,2,3,4,2,2,6,7,2,6,10,7,10,3,8,2,7\nH,3,7,4,5,4,7,7,7,6,7,7,8,3,8,3,7\nB,5,7,8,6,8,7,7,5,4,7,6,8,6,9,8,6\nG,4,4,5,6,2,8,5,7,9,8,4,12,1,9,5,10\nB,2,4,4,2,2,9,7,2,5,10,5,6,2,8,4,9\nZ,2,7,3,5,1,7,7,3,12,9,6,8,0,8,8,8\nL,5,11,6,8,5,4,4,4,8,2,1,6,1,6,1,5\nX,6,12,6,6,3,10,6,3,8,9,2,6,4,6,4,10\nT,2,7,3,5,2,6,13,0,5,8,10,8,0,8,0,8\nC,4,4,6,7,2,5,7,7,10,7,6,12,1,9,4,9\nB,3,6,5,5,5,6,9,6,6,8,7,7,5,9,7,8\nL,2,8,3,6,1,0,1,5,6,0,0,7,0,8,0,8\nU,6,10,9,8,11,8,5,4,4,7,7,7,11,8,6,10\nV,2,6,4,4,2,8,12,2,2,5,10,9,2,11,0,9\nO,6,10,8,8,5,7,7,9,4,7,6,8,3,8,4,8\nK,2,1,2,2,2,5,7,4,6,7,6,10,3,8,3,10\nZ,2,4,3,3,2,7,7,5,9,6,6,8,2,8,7,8\nN,5,10,5,8,5,7,7,13,1,6,6,8,6,9,0,6\nR,2,0,2,1,1,6,10,8,2,7,5,8,2,7,4,10\nT,4,9,6,7,5,6,11,2,7,8,11,8,1,12,1,8\nP,3,8,5,6,4,6,9,3,6,9,8,5,4,10,4,7\nS,1,3,3,2,1,8,7,3,6,10,6,7,1,8,5,8\nA,5,9,5,5,3,10,2,4,2,11,5,12,5,3,5,10\nY,2,1,3,3,1,8,11,1,7,6,11,7,1,11,2,7\nA,4,11,7,8,2,7,5,3,1,6,1,8,3,7,2,7\nD,6,12,6,6,5,8,6,3,6,10,5,7,5,8,8,6\nR,4,8,6,6,7,5,6,3,4,7,5,9,6,10,7,5\nU,5,7,7,5,8,7,7,4,4,7,7,8,10,9,6,8\nL,3,3,3,5,1,0,1,6,6,0,0,6,0,8,0,8\nH,5,7,8,5,6,9,7,3,7,10,3,7,5,8,4,9\nL,2,7,2,5,1,0,1,5,6,0,0,7,0,8,0,8\nT,6,7,6,5,4,7,10,2,7,11,9,5,3,11,4,4\nV,3,8,4,6,3,7,11,2,3,6,11,9,2,10,1,9\nX,3,4,5,3,2,7,8,4,9,6,6,7,4,9,7,7\nD,4,8,4,6,3,5,8,10,9,8,7,6,3,8,4,8\nM,2,1,3,2,2,8,6,6,3,7,7,9,6,5,1,8\nD,4,6,5,4,4,9,7,4,6,10,4,5,3,8,3,8\nD,4,11,6,8,7,9,7,4,5,10,4,6,4,7,3,8\nH,3,3,5,2,2,5,9,3,5,10,9,8,3,9,3,6\nR,4,9,6,7,5,10,7,3,6,10,3,7,3,6,4,10\nU,2,3,3,2,1,4,8,4,6,11,10,9,3,9,1,7\nK,3,5,5,4,3,5,7,4,8,7,6,11,3,8,5,9\nX,8,11,8,6,4,4,10,4,8,11,10,9,4,9,3,5\nC,4,7,4,5,2,4,7,6,7,9,8,15,1,8,3,8\nJ,2,8,3,6,2,14,4,4,4,12,2,9,0,7,0,8\nD,3,7,4,5,4,10,6,3,6,10,4,7,3,8,3,9\nN,1,0,1,1,0,7,7,10,0,5,6,8,4,8,0,8\nU,4,5,5,4,2,3,8,4,6,11,11,9,3,9,1,6\nS,4,9,4,5,2,7,8,4,3,13,8,8,2,10,3,8\nC,2,1,2,2,1,7,8,7,6,8,7,11,2,9,3,10\nQ,4,8,5,9,5,8,6,7,4,9,7,9,3,9,6,9\nQ,5,8,7,9,6,8,5,8,5,6,6,9,3,8,6,10\nB,5,9,5,7,7,6,7,8,6,6,6,7,2,8,7,10\nQ,4,9,5,10,5,9,8,8,2,5,7,11,4,9,6,8\nK,3,1,4,2,3,6,7,4,7,7,6,10,6,8,4,9\nI,2,7,3,5,1,9,6,0,7,13,5,8,0,8,1,8\nM,3,3,4,5,3,7,7,12,1,8,9,8,8,6,0,8\nO,3,6,4,4,3,7,7,7,5,7,6,8,2,8,3,7\nF,3,7,5,5,3,9,8,2,6,13,4,5,2,10,3,9\nB,4,8,6,6,4,8,8,4,7,10,5,6,2,8,6,10\nQ,2,4,3,5,3,8,7,6,3,8,6,9,2,9,3,9\nT,2,6,3,4,1,7,12,0,5,7,10,8,0,8,0,8\nD,1,0,2,0,0,6,7,7,6,7,6,6,2,8,3,8\nF,3,7,3,5,1,1,11,4,6,11,11,9,0,8,2,6\nW,7,9,7,7,6,5,10,3,2,9,7,7,9,13,3,4\nS,4,7,5,5,6,9,4,5,3,8,6,9,3,8,10,11\nX,5,11,8,8,6,7,8,3,9,5,5,7,4,9,8,7\nM,5,9,7,6,7,7,7,5,5,6,7,9,8,6,2,8\nE,3,6,5,4,4,6,7,3,7,11,8,9,3,8,4,8\nP,9,14,8,8,4,9,8,7,5,13,3,5,5,10,4,8\nW,4,7,6,5,4,10,8,4,1,7,9,8,7,10,0,8\nB,1,1,2,1,1,7,7,7,5,6,6,7,1,8,6,8\nB,2,5,4,4,3,8,7,3,5,10,5,7,2,8,4,10\nD,7,12,6,6,4,8,5,4,6,8,4,7,5,6,6,10\nP,8,12,6,7,3,8,8,5,4,12,4,6,5,9,4,8\nJ,3,5,4,8,1,11,2,11,3,13,7,14,1,6,0,8\nU,5,5,6,7,2,8,4,14,6,6,15,8,3,9,0,8\nM,4,5,6,3,4,8,6,6,5,6,7,8,9,6,3,7\nB,5,9,7,7,10,9,9,5,4,7,8,6,6,10,10,10\nP,3,5,5,7,7,8,10,4,0,8,7,6,7,10,4,7\nV,3,5,4,4,2,5,12,2,3,8,11,7,2,11,1,8\nH,6,7,9,5,6,10,6,3,6,10,3,7,3,7,3,9\nB,5,11,7,9,8,8,7,5,6,6,6,5,3,8,6,10\nA,2,5,4,4,3,9,8,3,4,6,7,8,4,8,4,6\nT,4,7,5,5,1,4,14,4,7,12,9,3,0,10,1,5\nG,1,0,2,1,0,8,6,6,5,6,5,9,1,7,5,10\nS,5,11,6,8,3,7,6,6,9,4,6,9,0,9,9,8\nY,4,6,6,8,1,8,12,2,3,7,12,8,1,10,0,8\nV,4,5,5,4,2,4,12,3,3,10,11,7,2,10,1,8\nS,5,8,6,6,4,8,8,4,7,10,3,7,2,6,5,9\nH,9,15,8,8,5,6,8,4,6,9,8,9,6,8,6,10\nO,6,9,6,7,5,7,7,7,5,10,6,10,5,8,4,6\nU,10,14,9,8,5,6,6,5,5,6,8,9,7,7,4,10\nN,3,7,5,5,4,7,6,6,5,7,5,9,4,8,3,7\nN,3,3,4,5,2,7,7,14,2,5,6,8,5,8,0,8\nP,4,10,6,7,4,6,9,7,5,9,7,3,2,10,4,7\nT,5,10,5,7,3,4,14,5,5,12,9,3,1,11,1,5\nJ,2,3,4,4,2,10,6,2,6,12,4,9,1,6,1,7\nQ,4,6,5,8,4,8,7,6,3,8,8,10,3,8,6,8\nJ,5,10,5,8,4,9,8,2,4,11,6,7,2,10,6,12\nK,8,11,11,8,6,8,6,2,8,11,4,8,4,8,5,10\nX,4,9,6,7,4,7,7,4,9,6,7,11,4,6,8,9\nI,4,11,5,8,3,7,7,0,8,14,6,8,0,8,1,8\nZ,5,8,7,6,6,9,7,5,4,7,5,7,4,8,10,5\nQ,5,5,7,8,3,8,8,7,7,6,8,9,3,7,6,9\nT,6,10,5,5,3,7,9,2,7,12,7,6,2,9,4,6\nD,5,10,7,8,10,9,8,5,5,7,6,6,4,7,7,5\nE,3,5,4,4,3,7,7,5,7,7,6,9,2,8,6,10\nA,3,8,5,5,2,9,6,3,1,7,0,8,2,7,1,8\nD,4,7,6,5,4,8,7,6,7,10,5,5,3,8,3,8\nD,5,9,5,5,3,10,5,4,5,11,3,7,4,7,4,10\nQ,5,9,7,7,6,8,3,8,4,6,6,7,3,8,3,8\nD,3,3,3,4,2,5,7,9,8,6,5,5,3,8,4,8\nI,1,2,1,3,1,7,7,1,8,7,6,8,0,8,3,8\nS,2,4,3,3,1,7,8,3,7,10,7,7,1,9,5,7\nL,4,10,6,9,5,8,6,4,4,6,7,8,3,9,8,10\nI,1,1,1,1,0,7,7,2,6,7,6,8,0,8,2,8\nQ,2,3,3,4,2,8,9,6,1,5,7,10,2,10,5,10\nO,4,4,5,6,2,8,9,8,8,6,8,9,3,8,4,8\nF,3,2,4,4,2,5,10,4,6,11,9,5,1,10,3,6\nR,4,8,5,6,3,6,9,9,4,6,5,8,3,8,6,11\nP,5,10,7,8,7,6,9,5,5,9,7,3,2,10,4,6\nJ,6,10,7,8,3,9,3,6,6,15,7,15,1,6,1,7\nQ,1,0,1,1,0,8,7,6,2,6,6,8,2,8,2,8\nJ,2,8,3,6,1,11,3,10,3,12,8,13,1,6,0,8\nL,2,5,4,3,2,7,3,1,7,8,2,10,0,7,2,8\nZ,4,7,6,10,6,10,5,4,4,8,3,7,2,6,7,8\nD,5,10,5,8,5,6,7,9,8,6,4,6,3,8,3,8\nX,8,12,9,7,5,6,8,2,8,11,7,8,5,8,4,6\nN,5,11,5,8,3,7,7,15,2,4,6,8,6,8,0,8\nQ,4,11,6,9,3,9,8,9,6,5,7,10,3,8,5,9\nG,6,10,8,7,6,6,6,6,6,6,6,10,4,8,5,9\nA,3,8,4,6,3,8,3,2,2,7,1,8,2,6,2,7\nP,1,0,2,0,0,5,11,6,1,9,6,5,1,9,3,8\nT,5,9,5,7,3,6,11,2,8,11,9,4,1,11,3,4\nN,4,9,6,6,4,7,9,2,5,9,5,6,5,8,1,7\nQ,3,6,4,7,4,9,9,7,2,4,7,11,3,10,5,10\nQ,3,7,4,9,5,8,5,8,4,6,5,8,3,7,5,9\nP,8,10,7,5,3,7,9,5,4,12,4,5,4,9,4,8\nI,1,5,1,3,1,7,7,1,8,7,6,8,0,8,3,8\nC,5,4,6,7,2,6,9,7,11,5,7,12,1,6,4,8\nM,8,10,10,6,5,9,3,3,2,9,3,10,8,1,2,9\nP,4,11,6,9,6,7,9,5,5,9,8,3,2,10,4,6\nN,10,13,11,7,5,7,7,3,5,13,9,9,6,8,0,8\nL,1,3,2,1,1,6,4,1,7,7,2,9,0,7,2,8\nI,1,5,2,4,1,7,9,0,7,13,6,6,0,8,1,7\nI,2,6,4,4,3,11,6,1,5,9,4,5,1,8,5,9\nP,2,1,3,1,1,5,11,8,2,9,5,4,1,9,3,8\nN,11,13,9,7,4,6,9,4,7,3,4,11,6,10,2,7\nV,4,8,6,7,7,8,8,5,4,7,6,8,7,7,10,4\nQ,4,8,5,6,5,8,4,7,4,6,6,11,2,8,3,9\nS,5,9,4,5,2,9,3,2,5,8,1,7,3,7,4,10\nQ,3,7,4,9,5,9,8,7,2,4,8,11,3,9,6,10\nC,4,8,5,6,5,5,7,4,5,7,7,9,7,9,5,9\nZ,3,8,4,6,2,7,7,4,13,9,6,8,0,8,8,8\nQ,3,5,5,7,5,8,12,3,3,5,8,11,3,13,4,9\nW,5,5,8,8,4,9,7,5,2,6,8,8,9,9,0,8\nG,6,10,6,8,4,6,7,6,7,10,8,11,2,9,5,9\nX,3,6,5,5,4,5,8,2,5,8,6,9,3,6,7,8\nC,4,4,5,6,2,6,6,7,10,9,6,14,1,9,4,8\nP,4,9,4,4,3,9,8,4,3,11,5,5,4,11,5,7\nX,5,11,9,8,8,9,6,3,6,7,5,5,6,10,8,9\nD,6,9,5,5,4,6,7,5,6,9,6,7,5,9,6,5\nG,3,8,5,6,4,8,7,7,5,6,6,9,2,7,5,11\nG,3,4,4,2,2,6,7,5,6,6,6,9,2,8,3,8\nH,2,4,4,2,2,7,7,3,5,10,6,8,3,8,2,7\nJ,4,8,6,6,3,8,4,5,4,14,6,13,1,6,0,7\nP,4,10,5,8,5,6,9,6,5,9,7,4,5,10,4,7\nD,4,6,6,5,5,6,8,5,7,6,5,7,4,7,5,5\nG,2,0,2,1,1,8,7,6,5,6,6,9,1,7,5,10\nZ,2,5,3,3,2,7,7,5,9,6,6,8,2,8,7,8\nT,5,8,5,6,4,5,12,4,5,12,9,5,2,12,1,5\nW,4,6,4,4,4,3,10,2,2,10,9,7,5,11,2,6\nT,7,12,6,6,2,5,11,3,7,12,8,4,2,9,4,3\nI,1,6,0,4,1,7,7,5,3,7,6,8,0,8,0,8\nC,3,6,5,4,4,6,6,3,4,8,6,11,5,9,3,8\nQ,2,2,2,3,2,8,8,5,3,8,7,8,2,9,2,7\nM,1,0,1,0,1,8,6,8,0,6,8,8,5,7,0,8\nT,2,8,3,6,2,6,12,0,6,8,10,8,0,8,0,8\nA,6,10,6,5,4,12,3,6,2,12,2,10,5,3,3,10\nB,3,6,4,4,3,6,6,8,6,6,6,7,2,8,9,10\nX,6,9,9,7,5,7,7,0,8,10,7,9,2,8,3,7\nO,7,11,9,8,6,7,7,9,5,6,7,11,5,8,5,7\nW,6,7,6,5,5,6,11,4,2,8,7,6,9,12,4,5\nF,6,9,9,7,4,7,10,2,8,14,6,3,1,10,3,8\nE,0,0,1,1,0,5,7,5,7,7,6,12,0,8,6,10\nQ,4,6,4,8,5,8,6,6,2,8,6,10,3,8,5,9\nX,5,10,8,7,6,7,8,2,6,7,6,7,6,9,8,7\nS,2,2,2,3,2,8,7,6,5,8,6,7,2,8,8,8\nV,7,10,7,6,3,6,9,4,3,9,8,5,5,12,2,8\nR,3,6,4,4,3,7,7,4,5,7,6,6,3,7,4,9\nS,4,9,4,7,4,7,7,5,8,5,6,10,1,10,9,9\nF,6,8,8,10,9,7,9,5,5,8,6,7,4,9,8,8\nE,5,11,7,8,7,6,7,7,10,6,4,11,3,8,6,8\nJ,2,6,3,4,2,10,6,1,8,12,3,7,0,7,0,8\nM,5,7,8,5,5,8,6,2,5,9,6,8,8,6,2,8\nH,4,8,4,5,2,7,9,15,1,7,4,8,3,8,0,8\nN,8,11,11,8,5,7,8,3,5,10,6,7,7,7,2,7\nH,4,8,4,6,4,7,7,12,1,7,6,8,3,8,0,8\nF,3,6,4,4,3,5,10,3,4,12,8,6,2,10,1,7\nF,5,8,7,6,3,7,10,2,7,14,5,4,1,10,2,8\nZ,3,7,4,5,2,7,7,3,12,8,6,8,0,8,7,7\nI,5,9,6,6,4,5,7,3,7,7,6,12,0,8,4,9\nG,5,7,5,5,3,6,7,6,6,9,7,10,2,8,4,9\nJ,1,6,2,4,1,12,3,9,3,13,6,12,1,6,0,8\nG,3,8,4,6,4,8,7,7,5,6,6,8,2,8,5,11\nG,4,8,5,6,6,8,7,4,2,6,6,8,7,8,7,11\nB,1,0,2,0,1,7,7,6,4,6,6,7,1,8,6,8\nM,6,10,9,8,8,8,7,2,4,9,7,8,8,6,2,8\nZ,5,9,6,4,3,10,3,3,7,12,4,10,2,9,4,11\nR,3,8,4,6,4,5,10,7,3,7,3,9,2,6,4,11\nI,1,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nQ,3,4,4,5,3,8,8,7,2,5,7,10,3,9,5,10\nW,4,6,6,4,2,5,8,4,1,7,9,8,8,9,0,8\nZ,7,11,7,6,4,8,6,2,8,11,6,9,3,9,5,8\nF,3,3,4,4,1,2,12,5,5,12,10,8,0,8,2,6\nA,3,4,5,3,2,10,1,3,2,10,2,10,2,6,3,8\nS,4,11,5,8,5,8,7,8,5,7,6,8,2,8,8,8\nW,9,10,9,5,4,3,10,3,2,10,11,8,9,12,1,6\nW,4,6,5,4,4,4,10,3,2,9,8,7,6,11,1,7\nI,1,2,1,4,1,7,7,1,7,7,6,9,0,8,3,8\nW,8,12,8,6,6,9,9,4,4,6,9,6,10,9,3,6\nS,4,9,5,6,3,10,6,3,6,10,4,8,2,9,5,10\nE,4,8,6,6,4,7,7,3,8,11,7,9,3,8,5,8\nA,3,7,5,5,2,9,3,3,3,7,2,8,3,6,3,8\nZ,4,11,5,8,6,6,8,5,8,8,8,9,1,10,7,8\nS,5,9,5,5,2,10,5,4,4,13,5,8,2,9,2,8\nB,2,3,3,2,2,8,8,5,6,7,5,5,2,8,7,8\nI,1,4,2,2,1,7,7,1,7,13,6,8,0,8,1,8\nU,3,5,4,3,2,5,8,5,7,10,9,9,3,9,2,6\nQ,6,9,7,11,7,8,7,7,4,9,7,9,3,8,6,8\nJ,3,11,4,9,3,9,7,2,7,11,3,7,1,6,2,6\nY,2,6,4,4,1,7,9,2,2,7,12,8,2,11,0,8\nH,3,6,4,4,5,9,9,4,3,6,7,7,6,9,5,6\nP,5,9,6,6,3,5,10,10,5,8,5,5,2,10,4,8\nS,5,11,7,8,7,8,7,7,5,6,6,8,3,8,9,7\nN,5,8,7,6,4,6,9,2,4,9,8,8,6,7,2,7\nI,4,7,6,8,6,8,9,4,5,7,7,9,3,6,8,7\nG,4,5,5,3,3,7,6,6,7,7,6,10,2,9,3,8\nD,5,9,7,6,6,8,7,5,6,10,6,5,3,8,4,9\nW,3,4,5,3,3,9,11,3,2,5,9,8,6,11,1,8\nA,5,10,5,5,3,11,4,4,2,11,3,10,6,4,4,10\nY,9,13,8,7,5,8,7,4,6,9,5,5,4,9,6,5\nE,2,5,4,3,2,6,8,2,7,11,7,9,2,8,4,8\nZ,4,10,5,7,4,7,8,6,11,7,6,9,1,9,8,7\nV,4,8,6,6,7,7,7,4,1,8,7,9,7,10,4,8\nO,4,8,4,6,4,6,8,7,4,9,8,8,3,8,2,7\nE,3,7,4,5,5,6,8,3,5,6,7,11,4,11,8,8\nR,3,10,4,7,3,6,9,10,5,7,5,8,3,8,5,11\nQ,4,6,5,7,5,9,9,6,2,4,7,11,3,9,6,10\nF,4,9,5,6,3,8,9,2,6,14,5,4,2,10,3,8\nV,4,7,4,5,2,3,12,5,4,12,11,7,3,10,1,8\nG,7,10,7,7,5,7,6,7,8,11,6,11,2,11,5,9\nG,5,6,6,6,7,7,9,6,2,7,7,7,9,12,9,7\nK,5,7,8,5,4,7,6,2,7,10,5,10,4,7,5,9\nU,4,5,5,4,3,6,9,5,7,6,10,9,3,9,1,8\nK,6,10,8,7,6,8,7,1,7,10,4,8,4,7,4,8\nZ,8,15,8,8,6,7,7,2,9,12,7,8,6,5,8,5\nI,2,4,3,3,1,7,7,0,7,13,6,8,0,8,1,7\nC,4,7,4,5,2,4,8,6,8,10,9,14,1,8,3,8\nP,5,8,7,10,10,7,8,4,3,7,7,7,6,11,5,6\nR,4,9,6,6,4,9,7,4,6,9,4,7,3,7,5,10\nO,3,3,4,4,2,7,7,8,6,7,6,8,3,8,4,8\nB,4,8,6,6,8,8,7,5,3,7,7,7,6,10,8,9\nK,2,1,3,2,2,5,7,4,6,6,6,10,3,8,4,9\nO,2,4,3,2,2,7,7,7,5,7,6,8,2,8,3,8\nV,4,11,6,8,2,7,8,4,3,7,14,8,3,9,0,8\nA,2,3,4,2,2,7,2,1,2,7,2,8,2,7,2,7\nH,3,4,3,5,2,7,9,14,3,7,3,8,3,8,0,8\nG,5,10,6,7,5,6,5,6,7,6,6,10,3,9,4,7\nF,3,6,4,4,4,6,9,5,4,8,6,8,2,9,7,12\nB,2,2,3,3,2,7,7,5,5,6,6,6,2,8,6,10\nP,4,7,5,5,3,10,7,3,5,12,4,5,2,9,3,9\nS,8,14,8,8,4,11,4,3,3,12,6,9,3,10,3,9\nG,3,7,4,5,2,6,6,6,6,10,8,11,2,10,4,9\nE,4,9,4,7,4,2,7,5,8,7,6,14,0,8,6,9\nS,4,7,5,5,2,8,7,5,9,5,6,8,0,8,9,8\nU,5,10,5,8,4,7,5,14,5,7,11,8,3,9,0,8\nX,7,11,11,8,5,7,7,1,9,10,6,8,3,8,4,7\nI,2,10,2,8,2,7,7,0,8,7,6,8,0,8,3,8\nT,8,11,8,8,5,7,11,4,8,12,9,4,2,12,4,4\nX,3,9,5,7,4,8,7,3,8,6,7,8,3,8,7,9\nD,3,10,5,8,9,9,9,4,4,7,6,6,4,8,7,5\nE,3,8,5,6,5,7,7,3,5,6,7,10,4,10,8,8\nW,4,6,6,4,2,7,8,4,1,7,8,8,8,9,0,8\nU,5,6,5,4,2,4,9,5,7,12,11,8,3,9,1,6\nD,5,4,5,6,3,5,7,10,9,6,6,5,3,8,4,8\nT,2,3,3,1,1,5,11,1,7,11,9,5,0,9,2,5\nS,3,6,5,5,5,9,8,5,5,7,6,8,5,8,9,11\nY,6,11,6,8,3,2,11,4,5,12,12,7,2,11,2,6\nN,2,1,2,2,1,7,7,11,1,5,6,8,4,8,0,8\nS,6,9,5,4,2,11,2,2,5,10,2,9,2,8,3,11\nH,4,11,5,8,3,7,7,15,1,7,7,8,3,8,0,8\nS,4,7,5,5,5,9,6,4,3,8,5,8,3,9,9,9\nG,4,11,5,9,3,8,8,9,7,5,7,9,2,7,5,11\nG,4,6,6,4,3,6,6,5,7,6,5,9,3,9,4,8\nS,10,15,10,8,5,8,6,4,6,13,6,8,4,7,4,8\nH,6,11,6,8,6,7,9,14,2,7,4,8,3,8,0,8\nC,3,10,4,8,3,5,8,7,8,7,8,14,1,8,4,10\nA,2,1,3,1,1,7,4,2,0,7,2,8,3,6,1,8\nQ,6,11,5,6,3,10,3,4,6,10,3,9,3,8,6,13\nI,2,7,3,5,1,7,5,0,7,14,7,10,0,7,1,8\nA,2,4,4,3,2,7,2,2,2,5,2,8,2,5,2,7\nQ,3,5,5,4,3,6,4,4,4,5,4,7,3,5,5,7\nN,7,10,9,8,10,6,8,3,4,8,7,8,8,10,7,4\nH,5,6,8,8,6,9,11,4,2,8,7,6,3,10,8,6\nK,7,12,7,7,5,8,7,2,6,10,4,8,5,8,4,8\nY,3,8,5,6,3,10,10,1,6,3,11,8,1,11,2,9\nQ,6,9,8,8,7,5,4,4,4,4,3,7,4,8,6,5\nV,4,9,6,7,3,6,12,3,4,8,12,8,3,10,1,9\nS,7,11,5,6,3,7,3,2,5,7,2,7,3,7,5,9\nB,5,7,7,5,5,10,6,3,7,11,3,6,4,6,6,11\nU,2,0,2,1,0,8,5,11,4,7,13,8,3,10,0,8\nK,8,10,8,6,3,4,7,5,7,9,12,12,6,11,3,6\nF,6,9,9,7,9,6,8,1,5,10,8,7,8,9,4,6\nG,6,10,7,7,5,5,7,6,5,9,8,11,2,8,4,10\nQ,3,4,4,5,3,8,8,6,2,8,6,9,2,9,3,8\nF,4,7,5,8,6,7,9,5,4,8,6,7,4,9,8,7\nH,8,14,7,8,4,8,8,4,6,8,5,6,6,8,5,8\nI,3,10,4,8,3,6,8,0,7,13,7,8,0,8,1,7\nF,7,10,9,8,6,9,8,2,6,13,5,5,5,9,4,9\nL,1,0,1,1,0,1,1,5,5,0,1,6,0,8,0,8\nP,4,4,6,6,6,9,11,2,3,7,9,6,4,10,5,5\nZ,3,10,4,8,5,8,7,5,9,7,5,7,1,7,7,8\nP,5,8,6,10,9,8,9,3,2,6,8,7,6,10,6,4\nM,3,5,5,3,4,7,6,3,4,9,7,8,7,6,1,8\nV,8,11,7,8,5,3,11,1,3,8,10,7,6,11,2,7\nY,4,5,5,4,2,4,12,3,6,12,10,4,1,11,2,5\nX,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nV,1,0,2,1,0,8,9,3,2,6,12,8,2,10,0,8\nJ,2,5,4,4,2,10,6,2,6,12,4,9,1,6,1,7\nO,2,3,2,2,1,7,7,6,3,8,6,8,2,8,2,7\nM,5,6,7,4,5,9,6,2,4,9,6,7,7,6,2,7\nV,5,5,6,4,2,4,12,3,3,10,11,7,2,10,1,8\nQ,5,8,7,7,5,7,4,4,4,5,3,8,3,7,5,8\nN,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8\nL,4,7,5,5,3,7,4,1,7,8,2,9,1,6,2,8\nX,7,13,7,7,4,7,7,2,9,9,5,8,4,7,4,8\nE,6,11,9,8,5,5,9,3,11,11,8,8,2,8,5,5\nA,4,10,7,7,6,10,5,2,5,10,2,5,3,6,6,8\nL,2,7,4,5,2,6,4,3,8,6,1,7,1,6,2,7\nZ,2,3,4,2,1,7,7,2,9,11,6,8,1,8,5,7\nO,3,9,4,6,4,7,7,8,6,7,7,8,2,8,3,7\nY,7,13,6,7,4,8,7,4,6,9,6,5,3,9,5,5\nK,5,9,7,7,5,6,7,5,8,6,5,10,3,8,5,9\nC,2,4,3,3,1,4,8,4,6,11,9,11,1,9,3,7\nX,4,8,4,6,3,7,7,4,4,7,6,8,3,8,4,8\nM,5,7,7,6,8,6,8,5,3,6,5,8,10,8,5,7\nC,3,7,4,5,2,4,8,7,7,7,8,14,1,8,4,10\nJ,2,6,2,4,1,11,3,10,3,12,8,13,1,6,0,8\nH,4,9,5,7,4,7,7,13,1,7,6,8,3,8,0,8\nS,5,8,7,6,3,5,9,3,9,11,7,6,2,7,5,5\nH,3,6,5,4,4,6,7,5,5,7,5,8,3,7,6,12\nF,4,8,5,6,5,7,9,3,6,9,9,6,5,10,3,7\nT,9,12,7,7,3,7,8,4,9,13,6,8,2,7,5,6\nB,6,9,5,5,3,7,8,5,6,10,5,9,6,6,6,9\nU,6,10,7,8,4,5,7,6,8,9,7,9,4,9,3,3\nN,7,9,9,8,10,8,7,6,5,7,6,7,9,9,8,0\nK,5,6,5,9,2,3,6,8,2,7,7,12,3,8,3,11\nD,3,4,4,6,2,5,7,10,8,7,6,5,3,8,4,8\nB,5,10,5,8,4,7,8,9,7,7,5,6,2,8,9,10\nR,4,9,4,7,5,6,9,8,3,7,4,8,2,7,5,11\nQ,5,9,6,8,3,9,6,9,8,7,4,9,3,8,4,8\nN,3,7,4,5,3,7,8,6,5,7,6,7,5,8,1,6\nV,4,10,7,8,2,7,8,4,3,7,14,8,3,9,0,8\nC,0,0,1,0,0,6,7,5,6,7,6,13,0,8,3,10\nW,8,8,10,7,12,7,7,5,5,6,6,8,10,9,9,8\nM,1,1,2,1,1,8,6,10,0,6,8,8,6,6,0,8\nG,1,3,2,1,1,7,7,5,5,6,6,9,2,8,3,9\nT,3,11,4,8,1,6,14,0,6,8,11,8,0,8,0,8\nI,2,5,3,3,1,7,7,0,7,13,6,8,0,8,1,7\nC,5,9,7,7,8,7,5,4,3,7,7,11,8,9,4,7\nU,4,3,4,4,1,7,5,13,5,7,14,8,3,9,0,8\nH,8,11,11,8,7,7,6,3,7,10,5,8,5,6,5,7\nD,4,6,4,4,2,5,8,9,8,8,7,5,3,8,4,8\nL,4,8,6,6,4,8,4,1,8,9,3,10,1,6,3,9\nD,2,3,4,2,2,9,6,4,6,10,4,6,2,8,3,8\nX,1,0,1,0,0,8,7,3,5,7,6,8,2,8,3,7\nU,5,9,4,4,2,6,6,4,5,4,8,7,5,8,2,6\nD,4,4,5,6,3,6,8,10,9,8,7,6,3,8,4,8\nP,2,7,3,4,1,3,13,8,2,11,7,3,0,9,3,8\nB,3,4,5,3,4,7,8,3,5,10,6,6,2,8,5,8\nM,4,8,4,6,5,8,6,10,1,6,8,8,7,5,0,7\nF,5,9,7,7,4,4,11,1,5,13,7,5,1,10,1,7\nG,4,11,5,8,5,7,7,8,6,5,7,9,2,7,6,10\nX,5,10,7,8,4,10,7,1,8,10,2,6,3,9,4,10\nI,4,10,5,8,2,7,7,0,9,14,6,8,0,8,1,8\nB,3,6,5,4,4,7,9,4,5,9,6,6,2,8,5,6\nZ,2,5,4,6,4,9,6,5,4,7,3,6,2,7,7,6\nJ,3,8,5,6,2,8,5,4,5,14,7,12,1,6,0,7\nD,3,8,5,6,4,8,7,7,6,6,5,3,3,8,3,6\nU,3,8,5,6,7,8,5,4,3,7,7,8,7,8,4,7\nH,3,2,4,4,3,8,7,6,6,7,6,8,3,8,3,8\nE,2,7,2,5,3,3,6,4,8,6,6,13,0,8,6,10\nZ,2,1,2,2,1,7,7,3,12,8,6,8,0,8,7,8\nT,2,4,3,5,1,7,14,0,6,7,11,8,0,8,0,8\nQ,2,2,2,3,2,7,8,4,2,8,8,9,2,9,4,8\nK,6,11,7,6,4,11,7,2,6,10,3,6,5,10,3,10\nR,4,8,6,6,4,10,7,4,7,10,1,7,3,6,4,11\nG,4,7,4,5,3,5,7,5,4,8,7,11,2,8,4,10\nS,3,4,3,3,2,8,8,6,5,7,6,7,2,8,9,8\nZ,4,5,6,7,5,11,5,3,5,9,3,7,2,7,6,9\nQ,1,0,1,0,0,8,8,6,3,6,6,9,2,8,3,8\nU,5,10,6,8,2,7,5,13,6,8,15,8,3,9,0,8\nK,6,9,8,7,6,6,6,1,7,10,7,10,3,8,4,8\nV,2,3,3,2,1,5,12,2,2,8,10,7,2,11,0,8\nC,3,4,4,3,1,4,8,5,7,11,9,12,1,9,2,7\nA,5,10,7,8,5,7,3,2,3,4,2,7,2,6,2,6\nQ,5,6,8,6,6,8,4,4,4,7,4,10,6,6,7,7\nT,6,13,5,7,3,8,7,3,7,11,5,7,2,9,5,6\nJ,1,2,2,3,1,10,6,2,6,12,4,8,1,6,1,7\nR,2,3,4,2,2,8,7,3,4,9,4,7,2,7,3,10\nR,4,8,5,6,3,6,10,9,4,7,5,8,3,8,6,11\nC,3,7,4,5,2,7,7,7,10,8,6,14,1,9,4,9\nI,3,8,4,6,2,7,7,0,7,13,6,8,0,8,1,8\nZ,4,9,6,6,3,7,8,2,10,11,7,9,1,9,6,8\nK,3,4,5,3,2,6,6,1,7,10,7,10,3,7,3,8\nF,5,11,6,8,6,5,10,5,6,10,10,6,2,9,3,5\nP,3,5,3,4,2,5,10,5,4,10,8,4,1,10,3,7\nB,6,9,8,6,8,7,8,5,5,7,5,7,4,9,6,7\nL,4,11,5,8,4,7,4,1,7,8,2,10,1,5,3,8\nP,2,7,3,4,1,4,12,8,2,10,6,3,1,10,3,8\nF,5,8,6,6,6,7,8,6,4,8,6,8,3,11,8,11\nX,5,10,8,8,6,10,7,3,7,7,6,6,6,12,8,9\nR,2,4,4,3,2,8,7,3,5,10,4,7,2,7,3,10\nU,4,5,5,4,2,4,8,5,7,11,10,9,3,9,2,7\nK,3,5,4,3,3,5,7,4,7,6,6,11,3,8,4,10\nD,1,0,1,0,0,6,7,6,4,7,6,6,2,8,2,8\nV,5,10,7,7,4,5,10,3,2,9,11,7,4,9,5,8\nB,3,9,4,7,3,6,6,9,7,6,7,7,2,8,9,10\nX,1,1,2,1,0,8,7,3,4,7,6,8,2,8,4,8\nP,6,6,6,8,3,5,10,10,6,8,6,5,2,10,4,8\nR,4,7,4,4,2,6,11,8,3,7,4,8,3,7,6,11\nU,3,9,4,7,2,7,6,14,5,8,13,7,3,9,0,8\nW,7,8,7,6,6,2,10,2,3,10,10,8,7,11,2,7\nR,3,4,5,3,3,8,7,4,5,9,4,7,2,7,4,10\nA,5,10,7,8,8,8,7,8,4,6,6,8,3,8,8,3\nQ,5,9,5,4,3,10,5,4,6,11,4,8,3,8,7,11\nB,6,6,6,8,5,6,6,9,7,6,6,7,3,8,10,10\nE,2,5,3,4,3,7,7,6,7,7,6,9,2,8,6,10\nL,5,6,6,6,5,8,9,3,6,6,6,9,3,9,7,9\nN,5,8,8,6,4,10,7,3,5,10,2,4,5,9,1,7\nD,3,7,5,5,6,8,8,5,4,7,6,6,6,8,7,6\nJ,2,5,5,3,2,9,5,4,5,14,6,12,1,6,0,7\nT,1,1,2,2,1,7,12,3,6,7,10,9,1,11,1,8\nF,3,3,4,4,1,2,13,5,4,12,10,6,0,8,2,6\nE,3,6,4,4,3,8,7,2,6,11,6,9,3,8,4,10\nI,0,7,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nD,3,3,4,2,2,7,7,6,7,7,6,5,2,8,3,7\nU,5,10,8,8,11,9,6,4,4,7,7,7,8,8,5,7\nT,2,3,3,2,1,6,11,4,5,8,10,7,2,11,1,7\nF,3,8,3,5,1,1,13,5,3,12,10,6,0,8,2,6\nK,7,10,10,7,7,4,8,1,7,9,8,11,3,8,3,6\nT,5,10,7,8,8,8,8,5,6,6,7,9,7,7,8,3\nS,4,11,5,8,5,7,7,6,8,5,6,11,1,11,10,9\nM,3,7,4,5,3,7,7,11,1,7,9,8,8,5,0,8\nH,9,15,9,8,7,4,10,3,5,10,8,10,5,8,4,6\nZ,4,5,6,7,5,10,5,3,5,7,3,6,3,7,6,7\nY,2,7,4,5,2,7,10,2,2,7,11,8,1,11,0,8\nN,3,4,5,3,2,6,9,2,4,10,7,7,5,8,1,8\nK,4,10,6,8,6,5,6,3,7,6,6,10,4,7,7,11\nJ,5,7,6,5,4,8,6,8,6,7,7,8,2,7,4,6\nM,7,9,10,7,6,8,6,2,5,9,6,8,10,8,3,8\nG,4,9,6,7,6,7,5,6,3,8,6,11,4,8,7,8\nZ,4,7,4,5,2,7,7,4,14,9,6,8,0,8,8,8\nI,2,8,3,6,2,7,7,0,8,13,6,8,0,8,1,7\nU,9,11,10,8,4,3,9,6,9,12,12,9,3,9,2,7\nJ,3,7,5,5,5,10,8,3,4,8,4,6,4,8,6,5\nI,2,6,2,4,1,5,8,0,7,13,7,8,0,8,1,7\nT,2,3,3,2,1,5,11,3,6,11,9,5,1,11,1,5\nR,5,9,7,7,5,10,6,3,6,10,3,7,4,6,5,10\nK,3,3,4,5,1,3,7,7,2,7,6,11,3,8,2,10\nD,7,10,7,5,3,10,4,4,5,11,3,8,5,7,5,11\nF,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8\nQ,3,4,4,5,4,8,7,6,3,8,8,9,3,8,4,8\nR,2,3,3,1,2,8,8,3,5,9,4,7,2,6,3,10\nG,3,4,4,2,2,6,7,5,5,9,7,10,2,8,4,9\nH,3,7,5,5,6,9,8,4,3,6,7,8,7,8,7,7\nL,9,15,7,8,4,7,4,3,6,11,3,12,2,7,6,7\nL,7,13,7,7,4,8,4,3,4,11,8,12,3,10,5,10\nU,4,8,6,6,7,9,6,5,5,6,7,6,6,8,5,6\nI,1,8,2,6,2,7,7,0,7,7,6,8,0,8,2,8\nD,4,11,6,8,4,8,8,8,8,8,7,2,3,7,4,8\nP,4,6,6,9,8,6,13,5,1,10,8,5,4,11,4,9\nZ,6,7,5,10,4,8,5,5,4,11,6,7,3,9,10,7\nJ,4,8,3,12,3,8,7,3,3,11,4,5,3,8,6,10\nW,6,8,6,6,4,2,10,2,3,11,11,9,6,10,2,6\nN,5,9,8,7,4,3,10,3,4,10,10,9,5,8,1,7\nG,9,15,8,8,5,10,3,3,3,9,3,6,4,7,5,9\nD,3,7,4,5,4,6,7,9,7,8,8,6,2,9,3,8\nO,2,4,3,2,2,8,7,7,4,7,6,8,2,8,2,8\nU,4,5,5,4,3,6,8,6,7,7,9,10,3,9,1,8\nG,6,11,7,8,6,7,7,8,7,6,5,10,2,7,5,11\nR,1,1,2,1,1,6,9,7,3,7,5,8,2,7,4,10\nG,2,1,3,2,2,7,6,6,5,6,6,9,2,9,4,9\nX,8,13,7,7,4,8,8,2,9,9,6,7,4,10,4,8\nM,3,6,4,4,4,7,5,10,0,7,8,8,6,5,0,8\nU,4,2,5,3,2,7,8,6,8,7,10,9,3,9,1,8\nE,1,3,3,2,1,7,7,2,6,11,6,9,1,8,4,9\nM,4,7,6,5,4,6,6,7,5,7,8,11,8,5,2,9\nA,3,9,5,7,4,6,4,3,1,6,1,8,2,6,2,7\nG,4,9,5,7,4,5,7,6,5,10,8,11,2,9,4,10\nP,5,8,7,6,6,8,5,7,5,7,6,6,4,8,6,10\nF,3,5,6,4,2,6,11,2,6,13,7,4,1,10,2,7\nI,1,6,0,4,1,7,7,5,3,7,6,8,0,8,0,8\nR,3,2,4,3,3,7,7,5,5,6,5,6,3,7,4,8\nK,5,9,5,6,2,4,7,9,2,7,5,11,4,8,2,11\nW,5,8,8,6,5,9,11,2,3,5,9,7,10,13,1,6\nS,3,3,4,4,2,7,6,5,9,5,6,9,0,9,9,8\nC,4,10,5,8,3,5,8,7,8,7,8,15,1,8,4,9\nN,7,11,9,6,4,5,8,2,4,12,7,9,6,8,0,7\nP,5,6,5,8,3,3,14,8,1,11,7,2,0,10,4,8\nP,5,9,7,7,4,9,8,3,5,12,4,4,2,9,4,8\nN,4,9,6,7,7,9,7,4,4,7,6,6,5,10,6,5\nY,8,8,7,12,5,5,5,7,4,6,12,6,5,10,4,8\nJ,6,9,8,7,5,9,4,7,6,8,6,5,2,7,4,6\nM,7,7,10,6,10,6,9,5,3,7,5,8,12,5,6,8\nA,6,9,9,8,8,8,8,3,5,7,7,8,5,8,4,5\nS,9,15,7,8,4,9,2,2,5,8,1,8,3,7,5,10\nZ,8,13,9,7,6,5,9,2,9,12,8,8,6,5,8,2\nO,3,7,5,5,3,7,7,8,5,6,5,6,3,8,3,8\nR,3,8,4,6,2,5,11,8,4,7,3,9,3,7,6,11\nI,1,7,0,5,1,7,7,5,3,7,6,8,0,8,0,8\nU,1,0,2,0,0,7,6,10,4,7,13,8,2,10,0,8\nK,10,12,9,6,4,5,9,3,8,9,7,9,6,6,3,6\nE,5,10,7,7,7,8,9,6,3,6,6,9,4,7,7,9\nF,5,11,5,8,4,1,12,4,5,12,11,8,0,8,1,6\nJ,2,2,3,3,1,10,6,2,6,12,4,9,0,7,1,7\nZ,4,6,6,4,3,6,9,3,9,11,9,6,1,9,6,5\nI,3,6,5,7,4,8,8,4,5,7,6,8,3,8,8,8\nU,1,0,1,0,0,8,7,9,3,7,11,8,2,10,0,8\nQ,9,14,8,8,5,7,5,4,9,10,4,9,3,7,9,9\nV,8,10,8,8,5,3,12,2,3,9,11,8,4,12,2,7\nE,6,11,8,8,6,8,7,3,9,12,6,9,2,9,5,8\nL,5,5,6,8,2,0,0,7,6,0,1,4,0,8,0,8\nB,3,7,4,5,4,6,7,8,5,7,6,7,2,8,7,9\nO,6,11,6,8,5,8,6,8,5,10,5,8,3,8,3,7\nX,5,10,7,7,6,7,6,3,5,6,6,8,3,9,9,9\nX,6,11,6,6,3,11,7,3,8,10,3,6,4,11,4,10\nJ,4,11,5,9,3,7,8,2,7,15,5,8,1,6,1,7\nN,5,6,8,4,4,8,8,2,5,10,4,6,5,9,1,7\nK,6,10,8,8,5,6,6,2,7,10,6,10,4,8,4,8\nU,7,15,6,8,5,7,6,4,5,7,7,8,5,7,3,7\nA,3,3,5,4,1,7,5,3,0,7,1,8,2,7,2,8\nZ,3,10,4,8,4,7,7,6,10,6,6,8,1,8,8,8\nA,5,10,5,6,3,10,2,4,2,11,6,13,5,4,5,10\nO,4,8,4,6,4,8,7,8,3,10,6,8,3,8,3,8\nP,7,8,9,10,11,8,8,3,3,7,8,7,7,11,7,7\nA,1,1,2,1,0,7,4,2,0,7,2,8,2,7,1,8\nG,4,11,6,8,6,7,6,7,5,4,7,9,3,6,5,9\nM,6,9,8,7,6,8,6,2,5,9,6,8,8,6,2,8\nV,5,6,5,4,2,3,12,3,3,10,11,8,2,11,1,8\nJ,3,7,4,5,4,9,8,3,4,9,4,7,4,8,6,5\nA,3,8,4,6,3,11,3,2,2,9,1,8,2,6,3,8\nF,5,11,5,8,2,1,11,5,7,11,11,9,0,8,2,6\nJ,4,9,6,7,6,9,8,4,4,8,5,6,4,8,6,4\nU,5,7,5,5,2,4,9,5,8,12,10,8,3,9,1,6\nG,6,10,7,8,6,7,7,8,7,6,6,9,2,7,5,11\nD,3,7,4,5,3,8,7,6,6,10,4,5,3,8,3,7\nH,4,4,6,6,4,9,8,3,1,7,6,8,4,9,9,7\nI,4,9,5,7,3,9,6,0,7,13,5,8,0,8,1,8\nE,3,5,6,3,3,7,7,2,8,12,7,9,2,9,4,8\nK,4,6,6,5,5,8,7,2,3,8,3,8,4,5,6,10\nL,4,9,5,7,3,4,4,5,10,2,0,6,0,6,2,5\nL,1,0,2,1,0,2,1,6,5,0,2,4,0,8,0,8\nP,5,5,6,7,7,8,8,3,2,7,8,7,5,10,5,5\nX,4,11,6,8,4,8,7,4,9,6,6,6,3,8,7,7\nM,3,4,4,3,3,8,6,6,4,6,7,8,7,6,2,8\nL,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8\nT,4,5,5,3,2,6,11,2,8,11,9,5,4,10,4,4\nS,6,11,6,6,3,9,5,4,4,13,6,9,3,10,3,8\nN,6,11,8,8,5,7,9,6,5,7,7,8,6,9,1,7\nE,5,9,7,7,5,10,6,2,9,11,4,8,2,8,5,12\nC,5,7,6,5,6,8,4,6,3,8,6,11,8,8,4,7\nQ,9,14,8,8,5,7,4,4,8,9,5,9,3,8,9,9\nY,5,8,7,12,11,8,8,4,2,7,8,9,4,11,8,8\nG,2,2,3,4,2,7,6,6,5,6,6,9,2,9,4,9\nM,4,9,5,7,6,7,5,10,1,7,8,8,6,5,0,8\nD,3,2,4,3,3,7,7,6,7,6,6,5,2,8,3,7\nK,2,3,4,2,2,5,8,2,6,10,8,10,3,8,2,8\nW,2,3,4,2,2,8,11,3,1,6,8,8,6,12,0,7\nP,3,3,3,2,2,5,10,4,4,10,8,4,1,10,3,7\nH,5,10,8,8,5,8,8,3,7,10,6,7,3,8,3,8\nC,5,9,6,7,2,6,7,7,12,7,6,15,1,8,4,9\nS,6,9,7,6,4,8,7,4,8,11,6,8,2,9,5,8\nQ,1,2,2,3,1,7,8,4,1,7,8,10,1,9,3,8\nI,2,8,3,6,2,7,7,0,7,13,6,8,0,8,1,8\nL,3,7,5,5,5,7,8,3,4,6,6,9,5,11,7,5\nI,3,9,4,6,2,7,7,0,8,14,6,9,0,8,1,8\nF,5,7,6,9,7,7,9,5,5,8,6,8,3,9,7,6\nA,4,10,7,7,3,12,3,4,3,11,1,9,2,7,3,9\nN,2,4,4,3,2,7,9,2,4,10,6,7,5,9,0,7\nJ,2,7,4,5,3,10,6,2,5,8,4,5,3,8,6,7\nL,3,6,5,5,4,7,8,3,5,7,6,8,3,9,7,11\nU,7,13,6,7,5,9,6,5,6,6,7,6,5,8,4,6\nO,3,6,4,4,3,7,8,6,4,10,7,6,3,9,3,8\nP,8,11,6,6,3,8,7,5,4,11,4,6,5,8,4,8\nN,4,5,6,5,5,7,8,5,3,6,5,8,6,8,4,7\nP,7,11,9,8,5,6,11,4,6,14,6,2,0,10,3,9\nW,4,5,7,4,4,7,11,2,2,6,9,8,7,11,0,8\nO,4,9,5,7,4,7,7,8,5,10,6,7,3,8,3,8\nF,3,2,4,3,2,5,10,4,6,11,9,5,1,10,3,6\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nR,3,7,4,5,4,7,8,5,6,6,5,7,3,7,5,8\nD,2,4,4,3,2,9,6,3,5,10,4,6,3,7,3,8\nW,3,8,5,6,4,5,10,2,3,8,9,9,7,11,1,7\nM,5,9,8,7,8,5,7,3,4,9,9,9,7,5,2,7\nP,10,13,8,8,4,7,10,6,3,12,4,4,5,10,4,8\nT,2,9,3,6,1,9,14,0,6,6,11,8,0,8,0,8\nP,4,8,5,6,3,7,10,2,7,14,6,4,0,10,2,9\nA,2,4,3,3,1,10,2,2,1,8,2,9,1,6,1,8\nY,3,5,5,8,6,7,10,3,2,7,8,9,3,11,8,5\nF,3,7,4,5,3,5,11,4,6,11,10,4,2,10,2,5\nP,9,14,9,8,6,10,8,4,4,12,4,4,5,10,5,8\nG,4,8,6,6,5,7,6,6,4,7,7,9,5,9,7,8\nU,3,7,4,5,1,7,5,13,5,7,13,8,3,9,0,8\nS,3,5,5,4,2,8,7,3,7,10,6,7,1,9,5,8\nY,2,6,4,4,1,6,10,3,2,7,13,8,2,11,0,8\nP,6,10,6,8,6,4,11,8,3,9,6,5,1,10,3,8\nU,7,11,6,6,4,6,5,5,4,7,9,10,6,7,3,10\nA,3,10,5,7,2,6,6,3,1,6,0,8,2,7,1,7\nH,4,2,5,4,4,7,8,6,6,7,6,8,3,8,3,8\nR,2,4,3,3,2,8,7,4,5,9,4,7,2,7,3,10\nR,3,4,5,2,3,9,8,3,5,9,4,6,2,6,4,10\nX,6,10,9,8,5,3,9,2,8,11,12,10,3,9,3,5\nJ,1,3,2,5,1,11,3,9,3,13,7,13,1,6,0,8\nS,2,3,4,2,2,8,8,2,6,10,6,7,1,9,5,8\nN,3,8,3,6,3,7,7,12,1,6,6,8,5,8,0,8\nY,5,6,8,9,10,8,8,4,2,7,8,9,6,12,8,8\nA,3,5,5,3,2,11,2,3,2,10,2,9,2,6,2,8\nD,8,14,7,8,6,7,7,4,7,10,6,7,6,7,8,4\nU,6,10,9,7,6,6,7,7,6,6,6,12,6,8,8,2\nL,4,8,5,7,4,6,7,4,6,7,6,8,3,9,8,10\nF,3,7,4,4,1,2,13,5,3,12,9,6,0,8,2,6\nG,4,7,4,5,2,7,6,6,6,11,6,12,2,10,4,10\nF,2,3,4,2,1,6,10,2,5,13,6,4,1,9,1,7\nV,7,10,7,7,3,3,11,3,4,9,11,8,4,12,1,7\nP,4,7,4,5,2,4,12,8,2,10,6,4,1,10,4,8\nS,5,10,6,7,4,6,8,3,6,10,7,7,3,7,5,6\nF,3,1,3,2,2,5,10,4,5,11,9,5,1,10,3,6\nT,3,5,4,4,2,6,12,3,6,7,11,9,2,11,1,8\nC,2,4,3,3,1,4,9,5,7,11,9,12,1,9,2,7\nK,3,3,4,5,2,3,6,7,3,7,7,12,3,8,3,10\nG,3,3,4,4,2,7,8,7,6,6,6,7,2,7,6,11\nH,5,10,8,8,5,8,5,3,6,9,5,8,4,7,4,8\nW,2,0,2,1,1,7,8,4,0,7,8,8,6,10,0,8\nO,6,10,7,8,7,7,8,9,4,7,7,8,3,7,3,7\nV,4,8,6,6,3,6,12,3,3,6,12,9,3,10,1,8\nL,3,5,4,4,2,4,4,4,8,2,1,7,0,7,1,6\nI,2,6,3,4,2,7,7,0,6,13,6,8,0,8,1,8\nB,1,3,3,2,2,7,8,2,4,10,6,6,2,8,4,8\nJ,6,10,7,8,3,10,6,2,9,15,3,8,0,7,0,8\nV,7,12,6,6,4,8,9,3,3,8,8,5,5,12,2,8\nV,3,10,5,8,2,7,8,4,3,7,14,8,3,10,0,8\nB,4,7,6,5,4,9,7,3,7,10,4,7,2,8,5,11\nP,4,7,6,5,3,9,8,3,5,13,4,4,1,10,3,9\nT,2,7,3,4,1,7,13,0,6,7,11,8,0,8,0,8\nB,3,8,5,6,5,8,8,5,4,7,5,5,3,8,6,5\nY,8,11,7,6,4,7,8,4,6,10,6,4,4,10,5,5\nZ,7,11,9,8,6,6,9,3,10,12,8,6,1,8,6,6\nG,2,3,2,1,1,7,7,5,5,7,6,9,2,9,4,9\nD,2,4,4,3,2,9,6,3,6,10,4,6,3,7,3,8\nF,2,1,2,2,1,5,10,4,5,10,9,6,1,9,3,7\nT,3,3,4,2,1,7,12,2,7,7,11,8,1,11,1,7\nT,3,6,4,4,3,6,12,4,6,8,11,8,2,12,1,8\nP,6,11,8,8,6,6,13,6,3,12,6,2,1,11,3,8\nB,1,0,2,0,1,7,8,6,4,7,6,7,1,8,6,8\nN,5,11,7,8,6,7,6,8,5,6,4,7,4,8,3,7\nS,5,8,6,6,3,7,8,4,9,11,4,7,2,6,4,8\nB,5,10,7,8,6,10,6,3,6,10,4,7,3,7,5,11\nS,5,9,8,6,9,6,7,3,2,8,6,6,3,8,11,1\nC,2,5,3,4,2,6,9,7,8,9,8,13,1,10,4,10\nR,4,9,5,6,7,7,8,3,4,7,6,8,6,9,7,5\nO,3,7,4,5,3,8,7,7,3,9,6,8,3,8,3,8\nR,5,9,8,6,9,7,7,3,4,6,6,9,6,9,7,6\nN,5,9,8,7,5,6,9,5,5,7,7,7,6,9,1,6\nG,4,9,5,7,3,8,8,8,8,6,7,8,2,7,6,11\nW,1,0,2,0,0,7,8,4,0,7,8,8,5,9,0,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nH,4,7,6,5,4,4,9,4,5,10,10,9,3,8,3,5\nZ,4,7,6,5,4,7,9,2,9,11,8,5,1,8,6,5\nG,7,10,7,8,6,5,7,6,5,8,8,11,2,7,5,10\nU,6,9,6,6,3,4,9,5,7,13,11,9,3,9,1,7\nZ,1,0,1,1,0,7,7,2,9,9,6,8,0,8,6,8\nR,4,7,6,5,6,8,6,6,3,8,6,8,4,7,7,10\nD,4,7,6,5,5,7,7,5,6,7,6,8,6,8,3,7\nB,3,3,4,4,3,6,6,8,7,6,6,7,2,8,9,10\nZ,6,9,5,12,5,5,11,3,3,12,8,6,3,8,11,6\nU,5,9,5,6,4,7,5,14,5,7,11,8,3,9,0,8\nT,8,11,8,9,7,6,11,2,8,11,9,5,4,10,4,4\nH,1,0,1,0,0,7,7,10,2,7,6,8,2,8,0,8\nO,6,10,4,5,3,6,8,6,5,9,7,9,5,10,5,8\nE,2,3,4,2,2,6,8,2,8,11,8,9,2,9,4,7\nY,6,9,6,6,3,2,11,4,6,13,12,6,1,11,2,5\nM,4,2,5,3,3,8,6,6,4,6,7,8,7,6,2,6\nE,5,5,5,8,3,3,8,6,12,7,6,15,0,8,7,6\nA,2,3,4,2,1,11,2,3,1,9,2,9,1,6,2,8\nJ,2,8,2,6,1,14,3,6,5,14,0,9,0,7,0,8\nF,5,8,7,6,7,7,9,1,4,10,7,6,5,10,3,5\nX,4,9,5,6,1,7,7,4,4,7,6,8,3,8,4,8\nH,3,8,4,6,3,7,7,12,1,7,6,8,3,8,0,8\nA,1,3,2,2,1,12,2,3,1,11,2,9,2,6,2,8\nX,4,7,5,6,6,9,8,2,5,8,5,6,2,6,7,8\nK,2,1,2,2,2,5,7,3,6,7,6,10,3,8,4,10\nU,4,9,6,6,7,10,7,5,4,6,7,7,7,9,5,5\nT,4,7,4,5,2,6,11,4,5,11,9,4,2,12,2,5\nZ,3,8,4,6,4,9,6,5,9,7,5,6,1,7,7,8\nV,4,4,5,3,2,5,12,2,3,9,11,7,4,11,1,7\nI,1,4,2,3,1,7,7,0,7,13,6,8,0,8,1,8\nF,5,8,8,6,7,7,9,1,4,10,6,6,5,11,3,5\nS,3,4,3,3,2,8,7,7,5,7,6,8,2,8,9,8\nB,3,7,4,5,4,8,7,6,6,7,6,5,2,8,7,9\nJ,1,3,2,5,1,15,3,5,5,13,0,9,0,7,0,8\nC,2,1,2,1,1,6,8,6,7,8,8,12,1,9,3,10\nT,3,6,5,8,2,5,15,1,6,9,11,7,0,8,0,8\nM,5,8,7,6,5,8,6,6,6,6,7,6,9,7,3,6\nO,4,9,5,6,3,7,5,9,8,6,4,7,3,8,4,8\nN,3,5,5,3,2,9,7,3,5,10,3,5,5,9,1,7\nN,3,5,6,4,3,7,9,2,5,10,6,6,5,9,1,7\nN,4,6,5,4,3,7,8,6,6,7,7,6,6,9,2,6\nU,5,7,5,5,2,4,8,6,7,9,10,9,3,9,2,5\nP,9,12,7,7,3,6,10,6,4,13,6,4,4,10,4,8\nL,4,8,6,6,4,8,4,1,6,9,2,9,1,6,3,9\nW,4,9,6,7,3,9,8,4,1,7,8,8,8,9,0,8\nL,3,4,3,3,1,4,4,4,8,2,1,6,0,7,1,6\nM,7,8,10,6,7,12,6,3,5,9,2,5,9,6,2,8\nN,2,5,3,3,2,7,8,5,4,7,6,6,5,9,1,6\nO,4,10,5,8,5,8,7,7,4,9,5,6,3,8,3,7\nW,5,7,5,5,5,5,10,3,2,9,8,7,6,11,2,6\nL,1,3,2,1,1,7,5,1,7,8,3,10,0,7,2,9\nF,1,0,1,0,0,3,12,4,2,11,8,6,0,8,2,7\nU,6,7,6,5,3,3,9,6,7,11,11,9,3,9,1,6\nY,5,10,8,7,4,8,10,1,7,4,11,9,2,12,3,8\nJ,1,4,3,3,1,9,6,2,6,14,4,9,0,7,0,7\nQ,7,9,8,11,7,8,5,7,5,9,6,9,3,7,6,8\nK,5,9,7,7,6,5,7,1,6,9,8,10,3,8,3,8\nL,5,10,7,7,4,5,4,3,8,6,2,8,1,6,3,6\nH,8,12,7,6,4,7,8,5,4,9,10,9,7,11,5,9\nR,4,7,6,5,4,7,8,5,7,7,4,7,3,6,5,8\nH,8,11,12,8,10,10,6,3,7,10,3,7,5,7,5,10\nS,6,10,8,8,5,8,8,3,6,10,4,7,2,7,5,9\nL,2,6,2,4,1,0,1,5,6,0,0,7,0,8,0,8\nW,2,0,2,1,1,8,8,4,0,7,8,8,6,9,0,8\nF,1,0,1,0,0,3,11,4,3,11,9,7,0,8,2,8\nT,4,10,6,8,4,9,14,0,5,6,10,8,0,8,0,8\nI,0,8,0,5,0,7,7,4,4,7,6,8,0,8,0,8\nY,5,6,7,9,9,8,10,4,3,6,8,9,5,13,8,8\nU,4,4,4,3,2,4,8,5,7,10,9,9,3,9,2,6\nK,3,5,5,3,2,5,8,1,7,10,8,10,3,8,3,7\nG,5,10,6,8,6,6,5,7,6,7,5,11,3,10,4,9\nK,5,8,7,7,7,7,7,2,4,8,4,8,6,4,9,11\nB,2,7,3,5,4,6,7,7,5,7,6,7,2,8,6,9\nR,2,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10\nT,6,9,6,7,4,6,11,3,7,11,9,5,1,12,2,4\nF,5,10,7,8,6,4,10,2,6,11,10,6,1,10,3,6\nN,6,9,9,6,5,7,9,2,5,10,5,6,6,9,1,7\nU,5,9,7,8,7,7,6,4,4,6,7,8,4,8,1,8\nU,2,4,3,3,1,7,8,6,6,7,9,8,3,9,1,8\nA,3,4,5,3,2,10,2,3,1,9,2,9,2,6,2,8\nL,3,7,4,5,2,3,4,3,8,2,1,7,0,7,1,5\nV,2,2,4,4,1,8,9,4,2,7,13,8,3,10,0,8\nS,8,13,7,7,3,6,2,3,4,6,2,6,3,7,6,7\nR,5,8,8,6,6,10,7,3,6,10,2,7,5,7,5,10\nM,6,11,9,8,7,7,6,6,6,6,7,7,10,8,3,6\nF,4,8,4,6,3,1,12,3,4,11,10,7,0,8,2,6\nV,5,11,7,9,4,9,9,4,2,5,13,8,3,10,0,8\nI,4,8,5,6,3,8,9,1,7,7,6,7,0,7,4,7\nJ,9,14,7,11,6,9,9,2,4,12,4,5,2,9,8,9\nY,4,11,6,8,1,8,10,3,2,6,13,8,2,11,0,8\nW,2,3,3,1,2,5,10,4,2,9,8,7,5,11,1,6\nZ,1,3,3,2,1,8,7,2,9,11,6,9,1,8,5,7\nZ,7,9,5,13,5,7,8,5,3,11,7,7,3,9,11,6\nR,3,5,5,3,3,8,7,4,5,9,4,7,3,7,3,11\nC,3,4,4,2,1,5,8,5,7,10,8,13,1,9,3,8\nX,3,4,5,3,2,5,8,2,8,10,10,9,3,8,3,5\nR,2,3,3,2,2,7,8,5,5,6,5,7,2,6,5,8\nE,4,8,5,6,6,8,7,4,7,7,5,8,6,8,6,10\nJ,1,4,2,3,1,10,6,2,6,12,4,9,0,7,1,7\nM,6,9,7,4,3,12,2,3,2,10,3,9,6,4,1,9\nT,6,10,8,8,9,6,7,4,6,7,7,10,7,7,7,6\nE,4,8,5,6,5,7,7,5,8,6,5,9,3,8,6,9\nQ,4,5,6,7,6,9,12,4,2,4,8,12,4,13,5,12\nN,3,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nG,3,1,4,2,2,7,7,5,6,7,6,9,3,7,4,8\nF,2,6,3,4,1,1,13,4,3,12,10,6,0,8,2,6\nK,3,8,4,6,2,3,7,8,3,7,6,11,4,8,3,11\nJ,2,7,3,5,1,12,2,9,4,14,5,13,1,6,0,8\nC,3,7,4,5,1,5,7,6,9,6,6,14,1,8,4,9\nW,5,9,7,7,5,7,8,4,1,7,9,8,8,10,0,8\nY,3,5,5,7,1,7,10,1,3,7,12,8,1,11,0,8\nQ,4,7,5,6,3,9,7,8,6,6,6,10,3,8,5,9\nG,6,8,6,6,4,7,6,6,7,10,6,12,2,10,4,9\nM,14,15,14,8,7,10,11,7,4,4,6,10,10,13,3,6\nD,4,9,6,7,4,9,6,5,7,9,3,6,3,8,3,8\nA,3,8,5,6,3,11,3,3,2,10,2,9,2,7,3,9\nJ,2,7,4,5,1,9,6,3,6,15,4,9,0,7,1,7\nM,5,7,7,6,8,5,8,5,4,6,5,9,11,9,4,8\nF,5,9,7,6,4,5,12,2,6,13,7,4,1,10,2,7\nK,4,8,6,6,6,7,7,4,4,7,5,7,4,6,9,10\nM,2,1,3,2,2,7,6,6,4,6,7,7,7,6,2,7\nS,7,9,9,8,9,10,7,4,7,7,6,8,5,9,8,12\nD,7,10,9,8,6,11,5,3,8,11,3,6,4,6,4,9\nE,3,10,5,8,5,7,7,5,8,7,7,10,3,8,6,9\nC,4,9,6,7,6,6,6,3,4,8,7,11,6,9,3,9\nI,5,13,4,8,2,9,6,5,4,12,4,7,3,8,5,10\nT,3,5,4,4,3,9,12,3,6,6,11,8,2,11,1,8\nZ,1,3,2,2,1,8,7,5,8,6,6,7,1,8,7,8\nM,4,9,5,6,5,7,6,10,1,7,8,8,8,4,1,7\nT,10,14,8,8,3,4,12,3,8,12,8,4,2,9,3,4\nQ,2,3,2,3,2,8,8,5,2,5,7,10,2,9,5,9\nH,5,11,8,8,7,8,7,3,6,10,5,8,3,9,4,8\nE,2,4,3,3,2,7,7,5,7,7,6,9,2,8,5,10\nV,4,5,5,4,5,7,7,5,4,6,5,7,6,9,6,11\nZ,1,3,2,2,1,7,7,1,8,11,6,8,1,8,5,8\nW,12,13,12,7,5,2,10,3,3,10,12,8,9,10,0,7\nV,3,2,5,4,2,7,12,2,3,7,11,9,2,10,1,8\nF,4,6,4,8,2,0,12,4,5,12,11,8,0,8,2,6\nT,4,10,5,7,3,5,14,1,5,9,10,7,0,8,0,8\nR,4,9,6,6,5,6,8,6,6,6,4,9,3,6,6,9\nG,5,8,6,6,4,6,6,7,6,6,6,11,2,8,4,9\nL,3,7,3,5,2,0,2,4,5,1,1,7,0,8,0,8\nY,8,11,8,8,5,3,11,3,7,12,11,6,1,11,2,5\nA,3,6,5,4,1,9,4,3,1,8,1,8,3,6,2,8\nN,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8\nM,8,10,12,8,9,4,8,3,5,10,10,10,13,8,5,8\nP,3,6,4,4,2,7,11,7,3,11,5,3,1,10,4,7\nV,8,12,6,7,3,8,10,5,5,8,10,5,5,12,3,7\nV,5,10,5,8,4,3,11,2,3,9,11,8,3,10,1,7\nG,7,10,9,8,9,9,4,6,3,8,5,10,11,6,5,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nF,3,7,4,5,3,7,9,2,5,13,6,5,2,10,2,8\nA,1,0,2,1,0,7,4,2,0,7,2,8,2,7,1,8\nZ,4,5,5,8,2,7,7,4,14,9,6,8,0,8,8,8\nV,8,10,8,8,3,4,12,5,5,12,12,7,3,9,2,8\nO,1,1,2,2,1,7,7,6,4,7,6,8,2,8,2,8\nS,7,13,6,7,3,10,3,3,5,10,1,9,3,6,5,10\nZ,4,10,6,7,6,8,8,2,8,7,7,7,1,8,10,8\nX,3,7,5,5,4,7,7,3,8,5,6,9,3,7,7,9\nG,6,10,6,8,5,7,6,6,5,10,7,13,4,8,6,7\nO,4,7,4,5,4,8,7,7,4,9,5,8,3,8,3,8\nN,3,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nO,5,8,5,6,4,7,9,7,4,10,7,6,4,9,4,8\nL,4,9,6,8,6,7,6,5,4,7,7,8,3,8,8,10\nZ,3,8,4,6,3,7,8,3,12,8,6,8,0,8,7,7\nI,5,9,4,4,2,7,10,2,5,13,5,4,1,9,5,8\nP,8,12,7,6,4,9,8,4,5,13,4,4,4,10,6,7\nG,3,5,4,3,2,6,7,5,5,9,7,10,2,9,4,9\nP,4,10,5,7,2,3,13,8,2,11,7,3,1,10,4,8\nO,4,8,6,7,5,8,4,5,5,9,4,10,4,7,5,7\nY,4,10,6,7,1,9,10,3,2,6,13,8,2,11,0,8\nY,6,11,5,6,3,7,8,4,5,10,7,5,3,9,4,4\nO,1,3,2,2,1,8,6,6,3,9,6,8,2,8,2,8\nE,5,7,6,6,7,7,8,5,4,7,7,10,7,10,9,10\nX,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nT,2,3,3,2,1,6,12,2,6,11,9,4,1,10,2,5\nS,7,10,6,5,3,7,3,4,4,7,2,7,3,6,5,8\nI,2,6,3,4,2,7,8,0,7,13,6,8,0,8,1,8\nC,4,8,5,6,3,5,8,7,7,8,8,14,2,9,4,10\nO,6,10,7,7,6,7,7,7,4,9,6,10,5,8,5,6\nF,3,6,5,4,4,8,8,1,4,9,6,6,5,10,4,5\nY,5,8,5,6,2,3,11,2,7,12,11,6,0,10,2,5\nA,3,7,5,4,2,7,4,3,1,7,1,8,2,7,2,8\nE,3,7,3,5,3,3,6,5,9,7,7,13,0,8,7,9\nE,3,5,5,4,3,8,7,2,8,11,5,8,2,8,4,10\nC,3,7,4,5,1,6,7,7,9,7,6,13,1,8,4,9\nO,3,6,4,4,3,7,8,7,5,8,8,6,3,8,3,8\nJ,4,10,5,7,5,10,6,3,4,9,3,6,3,7,6,7\nX,5,10,6,7,1,7,7,5,4,7,6,8,3,8,4,8\nT,3,3,4,2,1,5,11,2,7,11,9,5,1,10,2,5\nD,4,9,6,7,4,9,7,5,7,10,4,5,3,8,3,8\nX,4,6,5,5,5,7,8,2,4,7,6,8,2,7,7,7\nZ,2,3,4,2,1,7,7,2,9,11,6,8,1,8,5,7\nP,3,6,4,4,3,3,13,5,1,11,7,4,0,9,3,8\nP,2,4,3,3,1,7,9,3,4,12,5,3,1,10,2,8\nY,5,8,5,6,3,3,10,2,7,11,11,6,1,11,2,5\nA,2,6,3,4,2,11,3,2,2,9,2,9,2,6,2,8\nP,3,7,3,4,2,4,12,8,2,10,6,4,1,10,4,8\nU,6,10,9,8,12,8,6,4,4,7,7,8,11,9,6,8\nP,4,5,6,7,6,8,10,2,3,8,9,6,5,10,5,5\nG,2,3,3,2,2,7,7,5,5,6,6,9,2,9,4,9\nC,3,4,4,3,1,4,8,5,8,11,9,12,1,9,3,7\nE,2,1,2,2,1,4,7,5,8,7,6,13,0,8,7,9\nW,12,13,11,7,5,8,11,3,3,5,10,7,8,12,1,6\nR,10,15,10,8,8,6,8,3,6,8,4,9,8,4,7,6\nU,4,6,5,4,2,4,9,5,6,12,11,9,3,9,1,7\nV,5,11,8,8,4,7,12,3,4,6,12,9,3,10,1,8\nN,6,8,8,7,8,7,7,5,4,7,5,8,8,8,6,6\nI,5,12,5,6,3,6,11,3,5,13,6,4,1,8,5,8\nQ,6,7,8,10,11,9,7,6,1,6,6,9,9,11,6,9\nM,5,9,8,7,7,10,6,2,4,9,5,7,7,6,2,8\nV,7,11,6,6,3,9,9,6,4,6,10,6,6,13,3,6\nX,2,3,3,2,1,7,7,3,8,6,6,8,2,8,5,8\nC,6,8,6,6,3,3,8,4,7,10,10,13,1,7,3,7\nA,5,12,6,6,3,13,1,4,1,12,3,11,2,3,3,11\nR,8,15,8,8,6,8,6,3,5,9,4,8,6,8,6,7\nG,4,6,5,4,3,6,7,6,6,10,7,11,2,9,4,10\nL,3,6,4,4,2,5,5,1,9,7,2,11,0,8,3,7\nB,4,3,5,5,4,6,7,9,7,7,6,7,2,8,9,9\nW,3,4,5,3,3,7,11,2,2,7,9,8,7,11,1,8\nV,9,13,9,7,4,6,10,4,3,8,8,5,5,12,3,8\nX,6,10,6,6,3,7,7,2,7,11,6,8,4,9,4,7\nA,3,10,5,8,4,12,3,1,2,9,2,9,3,6,2,8\nZ,5,11,7,8,4,7,7,2,11,11,6,8,1,8,6,7\nR,5,10,6,8,7,8,6,6,4,8,6,8,7,7,6,11\nB,2,1,3,2,2,8,7,5,5,7,6,5,2,8,6,9\nC,4,7,6,5,3,7,7,8,6,6,6,10,3,8,4,9\nV,3,3,4,2,1,4,12,3,3,9,11,7,2,10,0,8\nE,4,7,5,5,4,6,8,7,9,6,4,10,2,8,6,8\nR,3,8,5,6,4,8,7,5,7,7,6,6,3,8,5,8\nV,3,5,5,3,2,4,12,3,3,9,11,7,2,11,1,8\nV,5,9,5,7,3,3,11,2,3,10,11,8,2,11,1,8\nN,5,10,7,8,6,6,8,8,6,8,6,7,3,7,3,8\nP,4,8,6,11,10,9,6,6,2,7,6,7,7,9,8,11\nP,7,11,9,8,5,6,12,3,6,14,6,2,0,10,3,9\nH,5,6,7,4,4,8,8,3,6,10,5,7,3,8,3,8\nF,3,7,4,5,3,5,10,4,5,10,10,6,2,10,3,6\nA,5,11,9,8,7,8,4,1,4,6,2,6,5,8,6,6\nU,3,7,3,5,2,7,5,14,5,7,12,8,3,9,0,8\nZ,2,2,3,4,2,8,7,5,9,6,6,8,1,8,7,8\nW,4,2,6,3,3,9,11,3,2,5,9,8,8,11,2,8\nZ,4,9,5,7,4,7,7,3,12,9,6,8,0,8,8,8\nS,5,11,7,8,9,6,5,3,2,6,5,6,5,8,15,3\nB,5,6,6,8,5,6,6,9,8,6,7,7,3,8,10,10\nF,4,9,4,4,3,7,9,2,4,11,6,5,3,10,6,6\nB,5,9,7,7,5,9,7,4,6,10,5,6,2,8,6,10\nE,4,7,6,5,5,10,6,1,7,11,4,8,3,8,5,11\nC,5,10,6,8,2,5,7,7,11,7,6,14,1,8,4,9\nK,2,4,4,3,2,6,7,2,6,10,7,10,3,8,2,8\nI,1,4,2,2,1,7,7,1,7,13,6,8,0,8,1,8\nZ,8,12,8,7,5,8,6,2,9,12,6,9,3,7,7,7\nJ,1,1,2,2,1,11,6,2,5,11,4,8,0,7,1,7\nP,6,11,5,6,3,5,11,5,3,13,6,4,4,10,4,8\nP,9,11,7,6,4,6,10,6,3,11,5,5,5,9,4,8\nC,2,4,3,2,1,6,8,7,8,8,8,13,1,9,4,10\nS,7,11,9,8,5,7,8,4,9,11,5,7,2,7,5,8\nR,2,3,3,2,2,9,6,4,5,9,4,7,2,7,4,10\nZ,4,8,5,6,4,8,6,2,9,11,5,9,2,8,6,9\nN,6,7,8,5,3,5,9,3,5,11,9,9,6,8,1,8\nB,3,7,4,5,4,6,6,8,6,6,7,7,2,9,7,9\nG,4,4,5,6,3,7,6,8,7,6,6,7,2,8,6,11\nK,6,10,8,8,8,6,7,4,7,6,6,10,3,8,6,10\nA,4,9,6,7,4,12,2,3,3,10,2,9,2,6,4,8\nU,3,3,3,5,1,8,5,13,5,6,13,8,3,9,0,8\nE,3,7,4,5,4,7,7,5,8,6,5,10,2,8,5,9\nX,2,7,4,5,4,7,6,2,6,7,5,8,3,7,5,9\nK,3,6,4,4,1,3,6,7,3,7,7,11,3,8,2,10\nX,4,9,6,6,4,7,7,3,8,5,6,7,3,9,7,6\nT,3,4,4,3,1,5,11,2,7,11,9,4,1,10,2,5\nN,5,8,7,6,7,6,8,3,4,8,7,9,6,9,5,5\nV,6,11,6,8,4,3,11,3,4,9,11,8,3,11,1,7\nU,3,5,4,3,2,7,9,6,7,7,9,9,3,9,1,8\nU,4,11,6,8,4,8,8,6,7,4,8,9,6,10,1,8\nZ,3,5,5,7,4,9,5,4,4,7,3,6,2,8,7,6\nS,5,10,6,7,3,9,6,5,8,11,3,7,2,8,5,11\nV,4,6,4,4,2,1,11,5,3,12,12,8,2,10,1,7\nN,3,6,4,4,3,8,8,6,5,6,6,5,5,9,2,5\nI,1,3,1,1,0,8,7,1,7,13,6,8,0,8,0,8\nG,4,9,4,6,3,6,7,6,5,10,8,10,2,9,4,9\nV,2,3,4,5,1,7,8,4,2,7,13,8,3,10,0,8\nZ,10,10,7,14,6,7,8,4,2,11,7,7,3,8,13,5\nB,4,10,4,8,4,6,7,9,6,7,6,7,2,8,9,10\nZ,3,5,4,4,3,7,7,5,9,6,6,8,1,8,7,8\nC,2,1,2,2,1,6,7,6,9,7,6,14,0,8,4,9\nH,5,10,6,7,3,7,6,15,1,7,8,8,3,8,0,8\nC,3,8,4,6,2,5,8,7,7,8,8,14,2,10,4,10\nX,3,8,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nF,1,0,2,1,0,3,12,5,2,11,8,6,0,8,2,7\nX,4,5,7,4,3,5,8,2,9,11,10,9,3,7,4,5\nL,5,8,6,6,5,6,6,6,6,6,5,8,5,8,4,9\nI,3,8,4,6,2,6,9,0,7,13,7,7,0,8,1,7\nS,3,5,4,3,3,8,8,7,5,7,6,8,2,8,9,8\nE,5,4,5,6,3,3,7,6,11,7,6,15,0,8,7,7\nW,3,8,5,6,9,7,7,6,1,7,6,8,8,12,2,11\nR,2,1,2,2,2,6,7,4,4,6,5,6,2,7,3,8\nL,4,7,5,5,2,8,3,2,8,9,2,9,1,6,3,9\nY,6,9,8,7,7,9,3,7,5,7,9,7,3,8,8,3\nX,3,9,4,7,3,7,7,4,4,7,6,8,3,8,4,8\nU,9,14,8,8,5,6,5,5,5,6,8,9,6,8,4,9\nO,7,14,5,8,4,5,8,7,4,10,8,9,5,10,5,8\nX,4,10,7,8,4,10,6,2,8,10,1,7,3,8,4,9\nA,3,10,5,7,2,7,5,3,1,6,0,8,3,7,2,7\nA,4,10,7,8,5,7,3,1,2,5,2,8,4,5,4,6\nN,3,9,4,7,4,7,7,12,1,6,6,8,5,8,0,7\nP,7,9,9,7,5,8,11,7,4,11,4,3,2,10,4,8\nE,4,9,6,7,5,6,8,3,7,11,8,8,3,8,5,6\nK,3,6,5,4,3,5,6,6,7,7,7,12,3,8,5,11\nQ,4,7,5,6,2,8,8,8,6,5,8,9,3,8,5,10\nG,7,10,6,5,4,8,4,4,3,8,6,10,4,9,6,7\nK,6,10,8,8,5,5,8,2,7,10,8,11,3,8,3,7\nB,4,8,6,6,5,10,6,2,6,10,3,7,4,8,5,11\nH,7,9,10,7,7,6,8,3,7,10,9,9,8,9,6,6\nD,3,8,5,6,3,11,6,3,7,11,3,6,3,8,3,9\nA,2,7,4,5,3,11,3,2,2,9,2,9,1,6,2,8\nT,2,3,3,2,2,8,11,3,6,6,10,8,2,11,1,8\nI,1,6,2,4,1,7,7,1,8,7,6,8,0,8,3,8\nB,3,5,4,4,3,8,7,6,6,6,6,5,2,8,7,8\nO,6,11,7,8,5,7,7,8,5,10,6,8,3,8,3,8\nZ,4,9,5,7,5,8,8,3,8,7,7,7,1,8,11,7\nQ,3,3,4,4,3,7,8,5,3,8,8,10,3,9,5,8\nK,9,15,9,8,6,2,9,3,6,9,9,12,4,7,3,6\nN,6,10,9,7,5,10,7,3,5,10,3,5,6,9,1,7\nX,3,4,5,3,2,7,7,4,9,6,6,8,2,8,6,8\nU,8,14,7,8,5,7,6,5,5,6,7,8,5,7,3,7\nT,6,11,6,8,5,4,12,3,6,12,10,5,2,12,2,4\nX,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nD,4,6,6,4,4,9,7,3,6,11,4,6,3,8,3,8\nP,6,9,8,7,4,7,10,5,4,12,5,3,1,10,3,9\nN,4,5,6,4,3,7,8,2,5,10,6,6,5,9,1,7\nK,8,14,8,8,4,7,7,3,6,9,9,9,6,11,4,7\nW,8,8,11,7,12,7,7,5,5,6,5,8,10,9,9,8\nZ,2,5,4,3,2,8,7,2,9,11,6,8,1,7,6,7\nH,3,5,5,4,3,7,8,5,7,7,6,8,6,8,3,8\nG,2,3,3,2,1,7,7,6,6,7,5,10,2,9,4,9\nK,4,9,4,6,2,3,8,7,2,7,5,11,3,8,3,10\nW,4,9,7,7,5,9,10,2,3,6,9,8,7,11,1,8\nU,2,1,2,1,1,7,8,6,5,7,9,9,3,10,0,8\nE,5,9,5,7,3,3,7,6,11,7,6,14,0,8,8,7\nT,3,3,3,2,1,5,11,2,6,11,9,5,1,10,2,5\nZ,1,3,3,2,1,8,7,1,9,11,6,8,1,8,5,7\nR,4,10,5,7,3,5,11,9,4,7,3,9,3,7,6,11\nS,7,10,9,8,5,7,8,4,9,11,5,6,2,6,5,8\nL,5,11,7,8,4,6,4,0,9,8,2,11,0,7,2,8\nX,5,8,7,6,5,8,6,3,5,6,7,8,2,9,8,9\nC,6,11,7,8,5,5,7,7,9,8,6,13,2,11,5,8\nM,5,9,8,7,11,8,6,3,2,8,4,8,13,6,3,7\nZ,4,8,5,6,5,9,6,5,9,7,5,7,1,7,7,8\nJ,5,7,7,9,6,9,8,5,5,6,5,8,3,8,9,7\nA,3,6,5,5,4,9,7,4,5,6,8,5,4,10,4,5\nF,5,11,7,8,7,5,9,4,6,10,10,6,2,9,3,6\nX,3,5,4,4,3,8,7,3,9,6,6,8,2,8,6,8\nC,4,9,5,7,4,5,7,8,7,10,8,13,2,11,4,10\nE,9,14,7,8,5,7,9,5,5,10,6,10,3,8,7,10\nH,3,6,5,4,4,5,9,3,6,10,8,8,3,8,3,6\nS,4,6,5,4,3,8,7,3,7,10,6,8,2,9,4,8\nI,1,4,1,3,1,7,7,1,7,7,6,8,0,8,2,8\nX,5,11,6,6,3,4,9,3,7,11,10,9,4,10,3,5\nO,7,15,5,8,4,6,7,7,4,9,7,9,5,10,5,8\nQ,5,10,7,8,4,8,9,8,7,5,9,9,3,7,6,10\nF,2,3,4,2,1,6,11,3,5,13,7,4,1,9,1,7\nS,3,4,3,2,2,8,8,6,5,7,6,7,2,8,9,8\nE,7,11,10,8,6,9,7,2,9,12,4,8,5,7,7,10\nW,4,6,6,4,5,8,7,6,3,6,8,8,6,8,3,7\nY,4,11,7,8,1,9,10,3,2,6,13,8,2,11,0,8\nO,7,11,8,8,6,8,6,8,6,10,4,8,4,9,5,6\nW,4,3,6,4,2,11,8,5,1,6,9,8,8,10,0,8\nZ,6,7,5,10,4,8,5,5,5,11,6,7,3,9,9,8\nN,2,3,4,1,1,5,9,3,4,11,8,8,5,8,0,7\nV,4,8,4,6,3,4,11,2,2,9,10,8,3,12,1,8\nN,9,13,8,7,4,5,9,4,6,3,4,11,6,10,2,7\nP,9,10,7,5,3,7,9,7,5,14,4,4,4,10,4,8\nK,1,0,1,0,0,4,6,6,2,7,6,11,3,7,2,10\nF,5,10,8,8,5,6,10,3,6,13,7,5,2,9,3,7\nR,5,9,5,4,3,8,8,3,5,9,2,6,5,6,5,7\nD,4,7,6,5,4,9,7,4,6,10,5,5,3,8,3,8\nZ,3,3,4,4,3,7,7,5,10,6,6,8,2,8,7,8\nE,4,9,6,7,7,7,8,3,6,5,7,10,5,11,7,8\nV,3,11,5,8,4,8,11,2,3,5,10,8,3,12,1,8\nI,5,7,6,8,6,8,10,4,5,6,7,9,3,6,8,6\nL,3,3,4,5,1,0,0,6,6,0,1,5,0,8,0,8\nG,2,1,3,2,2,7,7,5,4,7,6,9,3,6,4,9\nG,4,5,5,8,2,7,6,8,9,6,5,11,1,8,6,11\nF,3,6,5,4,4,7,10,1,4,9,6,6,4,9,2,5\nU,5,5,6,7,2,7,4,14,6,7,15,8,3,9,0,8\nD,5,8,5,6,4,6,7,9,8,7,7,6,2,8,3,8\nW,8,9,8,4,3,7,10,3,2,7,10,7,9,12,1,6\nD,5,5,6,8,3,5,7,10,9,7,6,5,3,8,4,8\nI,2,2,1,3,1,7,7,1,8,7,6,8,0,8,3,8\nL,2,2,3,3,1,4,4,4,7,2,1,6,0,7,1,6\nT,5,8,7,7,7,6,7,3,8,7,6,9,4,6,9,4\nM,8,11,10,8,13,7,7,7,4,7,5,8,7,11,9,8\nD,4,11,4,8,3,5,7,11,8,7,6,5,3,8,4,8\nF,6,9,8,7,4,5,11,1,6,13,7,4,1,10,2,7\nD,2,4,4,3,2,8,7,4,6,10,5,6,2,8,2,8\nZ,2,4,4,3,2,7,8,2,9,12,6,8,1,8,5,7\nF,3,4,5,3,2,5,11,3,5,13,7,5,1,9,1,7\nH,4,9,6,6,5,7,6,7,7,7,7,9,3,8,3,9\nQ,3,5,4,6,2,8,8,7,5,5,8,9,3,8,5,9\nX,2,2,4,3,2,8,7,3,9,6,6,8,2,8,6,8\nA,4,8,6,7,6,7,8,2,4,7,8,8,5,8,5,6\nG,4,6,6,4,5,7,7,5,3,6,6,10,4,8,7,8\nV,9,13,7,7,4,7,8,7,4,8,9,6,7,13,3,9\nY,7,8,5,12,4,10,10,1,4,7,10,5,3,9,5,10\nM,4,7,5,5,5,8,6,6,4,7,7,8,8,5,2,7\nG,3,9,5,6,4,6,6,6,6,7,5,12,2,9,4,9\nV,1,0,2,1,0,7,9,3,2,7,12,8,2,10,0,8\nA,2,1,4,2,1,8,2,2,2,8,1,8,2,6,2,7\nA,3,5,5,4,2,11,2,2,2,9,2,9,2,6,2,8\nO,4,9,6,6,4,7,6,8,5,7,4,8,3,8,3,8\nI,1,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nM,4,4,5,3,3,7,6,6,5,6,7,7,8,6,3,7\nW,4,7,6,5,5,7,9,6,4,8,8,7,6,8,3,8\nW,5,7,7,5,3,6,8,5,1,7,8,8,8,9,0,8\nX,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nL,4,10,6,8,3,5,4,1,9,7,2,11,0,7,3,7\nU,6,9,8,8,8,7,6,4,5,6,6,8,7,9,2,8\nI,2,9,3,7,2,9,7,0,7,13,5,8,0,8,1,8\nI,1,3,1,2,0,7,7,1,8,7,6,8,0,8,3,8\nQ,3,8,4,7,2,8,7,8,5,6,7,8,3,8,5,9\nU,4,4,5,3,2,4,7,5,8,10,8,9,4,11,3,4\nG,3,6,4,4,4,7,8,5,3,6,6,9,3,7,6,8\nL,4,9,5,8,5,7,6,5,5,7,7,8,3,9,8,11\nA,2,7,3,5,2,12,3,2,2,10,2,9,2,6,2,8\nI,1,5,2,3,1,7,7,0,7,13,6,8,0,8,1,8\nU,2,3,3,2,1,5,8,5,6,10,9,9,3,10,2,6\nB,5,9,7,7,6,9,7,3,7,10,4,6,2,8,5,10\nI,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nO,6,11,6,8,5,8,7,8,5,10,5,8,4,9,4,6\nC,5,11,6,8,3,6,7,7,11,5,6,14,1,7,4,8\nG,4,9,5,7,3,8,7,8,7,6,6,9,2,7,6,10\nD,4,8,6,6,4,6,8,7,7,11,7,5,3,8,4,8\nC,4,8,5,6,3,5,8,7,8,8,8,14,2,9,4,9\nM,4,7,6,5,4,4,7,4,5,10,11,10,6,5,2,6\nS,4,11,5,8,4,8,8,8,7,8,4,7,2,7,9,8\nR,3,4,4,2,3,7,7,5,5,7,5,7,2,7,4,8\nQ,5,6,7,9,10,8,9,5,2,6,7,9,9,14,10,14\nB,7,11,10,8,7,11,6,3,8,11,3,7,3,8,5,12\nO,1,0,2,0,0,7,7,6,4,7,6,8,2,8,3,8\nU,5,8,5,6,4,8,6,12,4,7,12,8,3,9,0,8\nI,3,8,4,6,2,7,7,0,7,13,6,8,0,8,1,7\nM,8,12,9,6,5,3,8,5,6,4,2,11,8,8,2,8\nK,5,6,7,4,3,9,7,2,7,11,3,7,3,8,3,9\nC,1,0,1,1,0,6,7,6,7,7,6,13,0,8,4,10\nW,2,0,3,1,1,8,8,4,0,7,8,8,6,10,0,8\nA,3,9,5,6,3,12,2,4,2,11,2,9,3,7,3,9\nJ,2,6,3,4,2,7,7,3,5,14,6,10,1,6,0,7\nE,7,10,5,5,2,7,8,5,7,10,6,8,1,9,7,8\nH,5,9,8,7,8,7,7,6,6,7,6,8,3,8,3,8\nS,2,3,3,1,1,7,9,3,7,11,7,7,1,9,4,6\nT,4,7,5,5,4,8,10,3,6,10,8,5,2,12,3,5\nP,12,13,9,8,4,7,9,6,4,11,4,5,5,9,5,8\nT,2,3,3,2,1,7,12,3,5,7,10,8,2,11,1,8\nF,3,9,4,6,1,1,14,5,3,12,10,5,0,8,2,6\nO,2,2,3,4,2,7,7,7,4,7,6,8,2,8,3,8\nN,5,11,5,8,5,7,7,13,1,6,6,8,6,8,1,9\nQ,4,8,5,8,3,8,6,9,7,7,4,9,3,8,4,8\nE,4,7,5,6,5,6,7,4,4,7,7,9,4,11,8,11\nV,8,11,7,8,4,4,11,3,4,9,11,7,2,10,1,8\nD,4,11,5,8,5,8,7,6,7,7,6,5,6,8,3,7\nT,5,10,5,5,3,7,9,2,6,11,6,7,2,9,4,6\nP,4,9,6,6,4,7,11,7,3,11,5,3,2,10,4,8\nJ,5,10,7,8,3,7,7,3,6,15,6,10,3,8,2,8\nT,3,4,4,3,1,5,11,2,7,11,9,5,1,10,2,5\nI,2,9,2,6,2,7,7,0,8,7,6,8,0,8,3,8\nB,3,5,5,4,5,7,8,4,4,7,6,8,6,9,7,8\nR,5,8,7,6,7,7,6,3,5,7,6,8,6,9,6,7\nT,3,10,5,7,1,10,15,1,6,4,11,9,0,8,0,8\nK,3,8,5,6,3,5,7,5,7,6,6,11,3,8,5,9\nC,4,8,5,6,3,6,8,7,8,8,8,14,2,10,4,9\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nI,0,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8\nF,4,10,4,8,3,1,13,4,3,12,10,6,0,8,2,6\nI,4,10,6,8,7,8,9,3,5,9,5,4,6,9,7,2\nW,3,2,5,4,3,7,10,2,2,7,9,8,7,11,0,8\nS,4,10,5,7,4,7,7,5,8,4,6,10,1,10,9,9\nU,3,5,4,4,2,7,8,5,6,5,9,9,5,10,1,7\nV,5,9,7,7,4,7,12,2,3,5,11,9,4,10,2,7\nY,1,1,2,1,1,7,10,1,5,7,11,8,1,11,1,8\nL,5,10,8,8,9,7,7,3,6,6,7,11,6,12,6,6\nI,0,0,0,0,0,7,7,4,4,7,6,8,0,8,0,8\nE,4,9,5,7,4,4,9,3,8,10,8,10,2,8,4,6\nM,3,4,4,3,3,7,6,6,4,6,7,8,7,5,2,8\nG,4,9,5,7,2,8,6,8,8,6,6,12,2,8,5,10\nE,6,9,8,7,8,8,10,6,3,6,6,9,5,7,7,9\nD,3,6,5,4,6,9,9,4,4,7,6,6,3,8,7,5\nH,5,10,8,8,8,7,7,5,7,7,6,8,6,8,4,8\nY,3,8,5,5,1,8,10,2,3,7,12,8,1,11,0,8\nB,4,6,6,4,3,10,7,3,8,11,3,6,2,8,5,10\nG,6,9,7,7,4,6,7,7,8,10,7,11,2,9,4,9\nN,4,9,5,4,2,7,8,3,3,12,9,9,5,9,0,8\nP,4,7,4,5,2,4,14,8,1,11,6,3,0,10,4,8\nL,5,9,7,8,6,7,8,3,6,6,7,9,3,8,8,8\nT,4,7,5,5,4,7,7,7,6,7,8,8,3,10,5,9\nU,6,11,6,8,3,8,4,14,6,6,15,8,3,9,0,8\nK,4,5,5,4,3,5,6,4,8,7,7,11,3,8,5,10\nK,5,11,6,8,5,4,8,8,3,6,3,11,3,8,2,11\nI,0,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8\nB,2,5,3,4,3,7,7,5,5,6,6,6,2,8,5,9\nJ,5,11,7,8,8,8,6,4,4,8,5,5,6,6,6,3\nS,4,10,5,8,2,7,7,6,9,4,6,8,0,8,9,8\nQ,3,5,4,6,4,7,8,5,2,7,8,11,2,9,5,8\nL,4,10,5,7,3,0,2,4,6,1,0,8,0,8,0,8\nK,6,10,6,5,4,9,6,3,6,11,2,8,5,6,4,9\nZ,1,3,2,2,1,7,7,1,8,11,6,9,1,9,5,8\nK,5,5,5,8,2,4,6,8,2,7,7,12,3,8,3,11\nL,3,6,4,4,2,6,5,1,9,7,2,10,0,7,3,8\nS,2,3,4,1,1,8,8,2,7,10,6,7,1,9,4,8\nH,1,0,1,0,0,7,8,10,1,7,6,8,2,8,0,8\nN,4,8,5,6,5,6,6,7,5,6,5,7,3,7,3,8\nH,9,11,12,8,8,7,7,3,6,10,7,9,3,8,4,8\nK,4,9,6,7,7,7,6,5,5,6,5,7,7,6,8,14\nC,4,9,6,7,2,6,6,7,11,9,5,13,1,10,4,9\nS,2,3,3,1,1,8,8,2,7,10,6,7,1,9,4,8\nS,4,7,6,5,7,7,8,3,3,8,6,7,3,7,12,2\nH,1,1,2,1,1,7,7,11,1,7,6,8,3,8,0,8\nZ,9,10,6,14,6,6,9,5,2,12,8,7,3,8,12,5\nB,3,7,4,5,4,8,8,6,6,7,5,6,2,8,6,9\nQ,5,8,6,7,3,9,7,9,7,6,6,10,3,8,5,9\nD,3,6,4,4,3,8,7,5,7,10,5,6,3,8,3,9\nG,7,12,6,6,4,7,8,4,3,9,5,6,3,9,10,7\nD,3,6,4,4,4,8,7,5,8,7,7,5,3,8,3,7\nS,5,11,6,8,4,8,7,3,6,10,7,9,2,10,5,8\nU,4,9,6,6,5,8,8,8,5,6,7,9,3,8,4,6\nE,4,9,3,4,2,8,7,3,4,10,5,8,3,9,8,11\nB,1,3,3,2,2,9,6,3,5,10,5,7,2,8,4,9\nI,3,7,4,5,2,6,8,0,7,13,7,8,0,8,1,7\nZ,2,4,3,2,2,7,7,5,9,6,6,8,2,8,7,8\nC,3,7,4,5,2,5,8,7,8,7,8,14,1,8,4,10\nH,6,8,9,6,5,5,8,4,7,10,10,10,5,10,4,6\nO,3,6,4,4,2,9,9,8,7,5,8,10,3,8,4,8\nZ,2,3,4,2,1,7,7,2,9,12,6,9,1,9,5,7\nF,6,11,6,6,4,10,7,2,5,10,5,6,4,8,6,9\nX,4,8,4,6,3,7,7,4,4,7,6,8,3,8,4,8\nW,7,9,8,4,4,4,8,2,3,8,10,8,9,11,2,5\nZ,5,7,4,11,4,7,8,4,2,12,7,7,3,9,11,5\nA,4,9,6,8,6,10,7,4,6,6,9,5,5,11,4,4\nA,4,11,7,8,4,12,2,3,3,10,2,9,3,7,4,9\nN,3,5,6,4,3,6,9,2,4,10,7,7,5,8,1,8\nC,5,5,6,8,3,5,7,7,11,7,6,12,1,8,4,9\nR,4,7,5,5,4,8,9,6,6,8,5,8,3,7,6,11\nR,5,5,5,8,3,6,10,10,4,7,5,8,3,8,6,11\nU,7,11,10,8,8,8,7,8,6,6,7,9,4,9,5,6\nG,5,4,6,6,3,8,8,8,8,6,7,8,2,7,6,11\nB,4,8,5,6,5,10,7,3,6,10,4,6,2,8,4,10\nM,2,3,3,2,2,8,6,6,4,7,8,8,6,5,2,8\nR,2,4,4,3,3,8,7,4,5,8,5,7,3,7,4,11\nX,10,13,9,7,4,6,7,2,9,9,7,9,4,6,4,6\nN,4,7,6,5,4,8,8,6,5,7,6,5,5,9,2,6\nH,3,2,5,3,3,8,7,6,6,7,6,7,3,8,3,7\nW,5,9,8,6,3,4,8,5,2,7,9,8,8,9,0,8\nE,4,9,4,6,4,3,9,5,10,7,6,14,0,8,6,8\nL,3,7,5,5,3,4,3,8,6,1,3,3,1,6,0,6\nI,7,13,6,8,3,10,7,2,5,11,5,6,2,10,5,11\nX,4,6,6,4,4,7,7,2,6,7,5,8,3,6,6,8\nE,4,6,5,4,5,7,7,3,6,7,7,11,4,9,7,8\nL,2,5,4,3,2,6,4,1,8,8,2,10,0,7,2,8\nB,2,1,2,2,2,7,7,4,4,6,6,6,2,8,4,10\nK,4,7,6,6,6,10,6,2,3,9,3,8,5,6,6,13\nR,2,4,3,3,2,10,6,3,5,10,3,7,2,7,3,10\nO,4,11,5,8,4,7,7,9,5,7,6,8,3,8,3,8\nC,7,10,5,6,2,5,10,5,8,11,8,9,2,8,5,8\nD,5,8,6,6,5,8,7,6,9,7,6,5,6,8,3,7\nV,2,7,4,5,2,9,11,3,3,3,11,9,2,10,1,8\nZ,1,0,2,1,0,7,7,2,10,8,6,8,0,8,6,8\nQ,4,8,5,9,5,8,7,7,4,9,6,9,3,8,6,8\nL,7,13,7,7,5,9,3,4,3,11,6,10,4,8,7,9\nL,5,10,7,8,3,8,3,2,8,9,2,9,1,6,3,8\nO,4,5,6,7,3,9,6,9,8,8,4,9,3,8,4,8\nN,6,11,8,8,5,7,8,6,6,7,7,5,7,10,3,4\nU,1,0,2,1,0,7,5,10,4,7,12,8,3,10,0,8\nD,6,11,6,6,4,6,8,4,7,10,6,7,5,8,6,5\nA,2,6,4,4,2,8,3,2,2,7,1,8,2,6,2,6\nL,4,10,6,7,4,9,3,2,7,8,2,9,3,4,4,9\nZ,3,5,6,4,3,7,7,2,10,12,6,8,1,8,6,8\nE,2,1,2,3,2,7,7,6,6,7,6,8,2,8,5,10\nE,2,4,2,3,2,7,7,5,7,7,6,8,2,8,5,10\nN,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8\nW,4,9,6,6,3,7,8,4,1,6,8,8,8,9,0,8\nB,5,9,6,7,6,8,8,6,7,7,6,6,6,8,7,11\nQ,5,9,7,11,8,9,8,8,3,5,6,10,3,9,6,10\nV,4,9,6,7,2,8,8,5,3,6,14,8,3,9,0,8\nG,4,8,5,6,4,8,7,8,6,6,7,9,2,7,5,10\nX,6,9,10,7,4,8,8,1,9,10,5,7,3,8,4,8\nJ,6,9,8,10,7,7,8,4,6,7,7,7,4,8,10,10\nR,3,2,4,4,3,7,7,5,5,6,5,6,3,7,4,8\nE,3,6,5,4,3,5,8,3,8,11,8,9,2,8,4,7\nM,7,13,8,7,5,9,3,2,2,9,4,9,6,2,1,9\nW,6,9,6,5,3,2,10,2,3,10,11,9,6,11,1,6\nK,8,11,11,8,7,5,7,2,7,10,8,11,3,8,3,7\nC,3,6,5,4,1,6,6,6,10,7,5,13,1,9,4,9\nD,2,5,4,3,3,9,6,3,5,10,4,6,3,7,3,8\nI,1,2,1,3,1,7,7,1,7,7,6,8,0,8,2,8\nJ,3,5,5,4,2,8,6,3,7,15,5,10,1,6,1,7\nT,5,7,6,5,4,7,9,1,9,10,9,5,0,9,3,5\nJ,3,6,4,4,1,8,6,5,6,15,7,12,1,6,1,7\nL,3,11,5,8,4,3,5,2,8,3,1,9,0,7,1,6\nF,7,10,6,5,4,8,9,3,4,11,6,5,3,9,6,7\nZ,2,4,5,3,2,8,7,2,9,12,6,8,1,8,5,8\nE,3,7,5,5,3,8,7,2,9,11,5,8,2,8,5,9\nV,9,14,8,8,5,6,9,4,4,8,8,5,5,13,3,7\nE,4,11,6,8,5,10,6,2,7,11,4,9,3,7,6,12\nX,6,8,8,7,8,8,8,2,5,8,6,8,4,8,8,8\nD,3,5,4,4,3,7,7,7,7,6,6,5,2,8,3,7\nK,7,9,10,6,7,9,7,1,6,10,3,8,6,7,5,10\nB,2,5,4,4,3,9,7,2,6,11,5,7,4,7,5,9\nK,4,8,6,6,6,6,8,5,4,7,5,8,4,6,9,9\nI,5,12,5,6,3,9,8,2,5,12,4,5,2,10,5,11\nL,6,10,8,8,9,6,7,3,6,7,7,11,6,12,6,7\nX,5,5,6,8,2,7,7,5,4,7,6,8,3,8,4,8\nQ,8,13,7,7,6,9,6,4,7,10,6,7,4,8,10,9\nW,8,9,8,6,5,1,10,2,3,10,11,9,7,10,1,7\nG,5,11,6,8,3,7,6,8,9,5,5,10,1,8,6,11\nP,3,7,3,4,2,4,12,8,2,10,6,4,1,10,3,8\nD,3,5,4,4,3,7,7,7,6,6,6,5,2,8,3,7\nG,4,4,5,6,2,7,5,7,8,6,5,11,1,8,6,11\nU,4,6,5,4,4,7,8,7,5,5,7,11,4,9,5,5\nZ,4,6,5,4,3,7,7,2,9,11,6,8,1,9,6,8\nU,3,7,3,5,2,8,6,10,4,7,12,9,3,9,0,8\nM,4,6,6,4,4,11,7,2,4,9,3,6,7,6,2,8\nA,2,1,3,1,1,7,4,2,0,7,2,8,3,6,1,8\nV,5,11,8,8,5,7,12,2,3,5,10,9,4,11,2,7\nK,5,9,8,6,4,3,8,3,8,12,12,12,3,8,4,5\nA,1,0,2,0,0,7,4,2,0,7,2,8,2,6,1,8\nW,5,8,7,6,3,10,8,5,1,7,8,8,8,9,0,8\nR,3,8,5,6,4,8,7,4,5,9,3,8,3,7,4,11\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nD,5,9,6,7,7,7,7,7,6,7,6,4,3,8,3,8\nW,4,5,6,4,6,8,7,4,4,6,5,8,9,8,6,9\nE,5,9,6,8,7,6,8,4,4,8,7,9,5,10,9,11\nG,5,11,7,8,9,8,7,5,2,6,6,10,7,8,6,12\nO,4,10,6,7,3,8,7,9,8,7,6,8,3,8,4,8\nC,4,7,5,5,2,6,7,6,7,12,8,13,2,10,3,8\nJ,5,8,6,6,3,7,6,3,5,15,7,11,1,6,1,7\nO,4,4,5,6,2,7,7,8,8,6,5,7,3,8,4,8\nI,3,10,6,8,6,9,6,2,4,8,5,5,5,8,5,6\nA,5,9,8,6,6,9,5,2,5,8,1,6,3,7,4,7\nS,2,3,2,1,1,8,8,6,4,7,6,7,2,8,8,8\nP,7,12,6,6,4,8,9,4,4,12,5,4,4,10,6,6\nV,2,3,3,2,1,5,12,2,2,8,10,7,2,11,0,8\nV,4,11,6,8,4,6,11,2,3,6,11,9,2,10,1,8\nT,7,13,6,7,4,7,8,2,7,11,6,7,3,8,5,5\nM,6,8,9,7,10,9,8,5,4,7,6,7,13,10,7,5\nG,3,8,4,6,3,6,6,5,4,5,6,9,2,8,4,8\nD,5,10,8,8,5,9,6,3,7,11,4,6,3,8,3,9\nO,7,10,5,6,3,10,5,6,5,9,3,8,5,9,5,9\nT,1,0,2,1,0,7,14,1,4,7,10,8,0,8,0,8\nB,3,5,5,4,3,9,7,2,6,11,5,7,2,8,4,10\nL,2,6,2,4,1,0,1,5,6,0,0,6,0,8,0,8\nC,4,8,5,6,2,6,8,6,10,6,6,13,1,8,4,8\nX,6,11,9,8,5,7,7,1,9,10,6,8,3,8,4,7\nY,2,6,4,4,0,9,10,2,2,6,12,8,1,11,0,8\nP,3,7,4,5,3,4,12,6,4,12,8,3,1,10,3,6\nB,5,11,5,6,5,8,7,3,4,9,6,7,6,7,8,7\nK,2,6,3,4,1,3,7,7,2,7,6,11,3,8,2,11\nY,4,11,6,8,3,6,11,2,3,8,12,8,1,11,0,8\nQ,2,3,3,4,2,7,9,5,2,7,9,10,2,9,5,8\nP,7,13,6,7,4,9,8,4,5,13,4,4,4,10,6,6\nU,2,3,3,2,1,6,8,5,7,9,7,8,3,9,3,6\nR,4,8,6,6,5,6,8,5,6,7,5,7,6,7,5,8\nS,7,11,8,9,6,9,7,3,6,10,5,8,3,10,6,9\nX,5,10,7,8,6,8,7,3,5,6,7,6,4,10,10,9\nB,4,10,6,8,7,8,8,4,5,10,5,6,3,8,5,10\nA,5,11,8,8,4,10,2,2,3,9,1,8,2,7,3,7\nP,3,2,3,4,3,6,9,4,4,9,8,4,4,10,3,7\nK,4,2,5,3,3,5,7,4,8,7,6,11,3,8,5,9\nK,3,4,5,3,3,9,6,2,6,10,4,9,4,7,4,9\nA,2,4,4,5,2,8,6,3,1,7,0,8,2,7,2,8\nY,4,11,6,8,4,7,9,2,6,6,12,9,2,11,2,8\nQ,7,9,7,11,8,7,10,4,4,7,10,11,5,11,10,9\nE,4,11,5,8,6,7,7,5,8,7,6,9,3,8,6,9\nW,5,8,5,6,4,1,10,3,3,11,11,9,5,11,1,7\nJ,5,10,7,7,3,6,8,3,6,15,7,10,1,6,1,6\nC,7,9,7,7,3,3,8,4,9,10,10,13,1,7,3,7\nU,4,6,4,4,2,7,4,14,5,7,13,8,3,9,0,8\nH,3,4,5,2,2,7,6,3,5,10,6,9,3,7,3,8\nF,3,6,5,4,2,6,11,2,5,13,7,4,1,10,2,8\nS,5,10,6,8,5,7,7,5,8,5,6,11,1,10,10,9\nM,5,9,6,7,4,8,7,13,2,6,9,8,8,6,0,8\nG,7,10,7,8,5,5,7,6,6,9,7,12,2,9,4,10\nC,4,9,6,6,2,7,8,7,10,5,6,13,1,7,4,9\nW,4,7,6,5,3,5,8,4,1,7,9,8,8,9,0,8\nR,6,10,8,7,8,8,5,6,4,8,6,8,7,6,6,11\nR,5,8,7,6,6,6,7,6,6,6,5,7,3,8,5,8\nD,2,1,3,3,2,7,7,6,6,6,5,5,2,8,3,7\nT,3,9,4,6,1,8,15,1,6,6,11,8,0,8,0,8\nE,6,11,9,8,6,6,7,2,9,11,6,9,2,8,5,8\nO,5,6,7,5,4,7,5,4,5,9,5,8,3,6,5,7\nD,3,4,4,2,2,7,7,7,7,7,6,5,2,8,3,7\nR,4,6,5,4,4,7,7,5,7,7,6,6,3,7,5,8\nA,4,9,6,7,6,7,8,8,4,6,6,9,3,7,8,5\nM,4,5,7,4,4,6,6,3,4,9,9,10,7,5,2,8\nG,3,5,4,4,2,6,6,5,5,9,7,11,2,8,4,10\nX,3,10,5,7,4,7,7,3,8,6,6,8,3,8,7,7\nG,3,7,4,5,3,6,6,5,4,6,6,9,2,8,3,8\nY,2,7,4,5,1,8,11,0,3,6,11,8,0,10,0,8\nN,8,11,11,8,5,7,8,3,5,10,6,7,7,9,2,7\nP,2,3,3,2,2,6,10,6,2,11,5,4,1,9,2,9\nD,5,10,5,5,3,9,5,4,5,12,3,8,5,7,5,10\nL,5,10,7,8,5,7,4,1,8,8,2,10,0,6,3,8\nG,5,5,6,7,3,7,7,8,7,6,6,7,2,7,7,11\nA,2,7,4,5,3,12,4,3,2,9,2,9,2,6,2,8\nQ,3,3,4,4,3,8,7,6,2,8,7,10,2,8,4,9\nN,3,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8\nA,3,2,5,4,2,8,2,2,2,7,2,8,2,7,2,7\nC,2,3,2,2,1,4,8,4,6,11,9,11,1,9,2,7\nZ,2,3,2,1,1,8,7,5,8,6,6,7,1,8,6,8\nA,4,8,7,6,5,6,5,2,3,4,2,6,2,6,3,4\nI,3,9,5,6,3,7,7,0,7,13,6,9,0,8,1,8\nY,3,5,4,4,2,5,9,1,8,10,9,5,2,10,4,4\nO,3,5,5,4,3,6,6,6,5,9,5,8,3,6,4,7\nS,4,9,4,4,2,8,3,3,4,8,2,8,3,6,5,8\nK,7,11,10,8,8,10,6,1,6,10,3,8,6,8,6,11\nD,2,1,3,2,2,7,7,6,6,7,6,5,2,8,2,7\nH,4,7,4,5,4,7,8,12,1,7,4,8,3,8,0,8\nK,2,6,3,4,1,3,7,7,3,7,7,11,3,8,2,10\nH,1,0,1,0,0,7,8,10,1,7,6,8,2,8,0,8\nQ,2,3,3,5,3,8,7,7,3,6,6,9,3,8,5,10\nS,5,9,7,6,3,8,8,4,8,11,6,7,2,8,5,7\nN,3,1,3,3,2,7,9,5,4,7,6,7,4,8,1,7\nX,5,6,7,4,3,7,8,1,8,10,6,8,3,8,3,7\nO,5,9,7,7,5,7,7,9,4,7,6,8,3,8,3,8\nA,1,0,2,0,0,7,4,2,0,7,2,8,2,7,1,8\nC,4,9,5,6,2,6,6,7,10,9,5,14,1,10,4,9\nX,4,10,7,8,6,6,8,2,6,7,6,10,4,6,7,7\nQ,5,10,7,8,7,8,5,8,3,6,7,8,4,7,7,8\nO,5,10,4,6,3,6,7,6,3,9,7,9,5,9,5,8\nS,4,6,5,4,3,7,7,3,7,10,5,7,2,8,5,8\nE,4,6,6,6,6,6,8,4,4,8,7,9,4,11,8,10\nI,1,6,2,4,2,7,7,1,8,7,6,9,0,8,3,8\nL,4,6,5,5,4,8,5,5,6,6,7,8,3,8,6,9\nX,5,9,8,7,6,6,8,3,6,7,7,11,5,6,8,6\nD,4,9,5,7,5,10,6,4,5,10,3,6,3,7,3,8\nC,5,10,6,8,2,6,7,7,10,7,6,15,1,8,4,9\nN,8,10,9,6,4,8,6,3,4,13,4,8,6,8,0,7\nY,2,7,4,5,1,9,10,1,3,6,12,8,1,11,0,8\nT,5,7,5,5,3,7,10,1,10,11,9,5,1,9,4,4\nT,5,6,7,6,6,8,10,5,8,7,6,8,3,10,7,7\nM,5,11,9,8,12,10,6,3,3,8,4,7,11,6,5,4\nJ,7,11,6,8,4,10,6,2,5,11,6,8,2,9,7,12\nJ,2,5,3,3,1,10,5,2,6,14,4,9,0,7,0,8\nG,4,8,4,6,3,7,7,6,6,10,7,11,2,9,4,9\nI,2,8,3,6,1,7,7,0,7,13,6,8,0,8,1,8\nJ,6,10,9,7,6,8,6,4,6,8,6,7,3,6,4,7\nE,4,10,6,8,4,7,7,3,8,11,7,9,3,8,5,8\nR,4,8,5,6,4,6,8,5,5,6,5,8,3,7,5,9\nX,4,9,7,7,5,8,7,3,8,5,6,8,2,8,6,8\nX,5,10,8,7,5,4,7,1,8,10,10,10,4,7,4,5\nQ,2,0,2,1,1,8,7,7,4,6,6,9,2,8,3,8\nK,2,7,3,4,1,4,8,8,2,6,4,11,3,8,2,10\nC,3,7,4,5,2,6,8,9,8,9,7,10,2,11,4,9\nN,5,6,7,6,6,6,9,4,3,6,5,9,8,7,5,9\nJ,1,3,2,1,0,8,6,3,5,14,7,11,1,7,0,7\nU,4,8,5,6,5,8,8,7,6,5,8,9,6,8,3,7\nQ,2,3,3,4,2,8,8,5,2,7,8,10,2,9,4,8\nZ,4,6,5,4,3,6,9,3,9,11,8,6,1,8,6,6\nA,5,11,8,8,5,10,1,2,3,8,2,8,4,8,4,9\nN,6,5,6,8,3,7,7,15,2,4,6,8,6,8,0,8\nV,3,7,4,5,2,9,9,4,2,5,13,8,2,10,0,8\nS,3,2,3,3,2,8,8,7,5,8,5,7,2,8,8,8\nT,2,3,3,1,1,5,12,2,7,11,9,4,0,9,1,5\nX,4,6,6,4,3,7,7,1,8,10,8,8,3,8,3,7\nY,5,10,7,7,4,4,10,2,8,11,12,9,3,9,2,6\nQ,6,14,6,8,4,10,3,4,7,11,3,8,3,9,6,13\nQ,4,9,5,8,3,8,9,8,6,6,9,9,3,7,6,10\nB,4,8,5,6,5,7,6,5,4,6,5,7,4,9,6,7\nL,9,14,8,8,4,10,2,4,4,13,5,13,2,7,6,9\nJ,4,9,4,7,3,7,10,2,3,13,5,5,2,8,7,9\nP,6,9,8,7,7,6,7,7,5,8,7,8,3,9,7,9\nF,3,7,6,5,6,10,7,1,5,9,5,6,4,10,5,7\nS,5,8,6,6,7,8,9,5,4,8,5,6,4,10,9,7\nL,3,5,4,4,3,8,5,5,5,7,7,7,4,9,6,10\nD,2,4,4,3,2,8,7,4,6,10,4,5,2,8,2,8\nO,5,8,7,6,8,8,10,5,3,7,7,8,7,9,4,9\nB,2,3,3,1,2,7,8,5,5,7,6,6,1,8,5,9\nC,5,8,7,7,7,5,7,4,5,7,7,11,4,9,7,9\nD,6,9,8,7,5,10,6,4,9,11,3,6,3,8,3,9\nJ,4,8,5,6,3,9,7,2,6,14,3,7,0,7,0,8\nT,2,4,3,3,2,7,12,3,6,7,11,8,2,11,1,8\nY,3,3,4,2,1,4,11,2,7,11,10,5,1,11,2,5\nV,2,3,3,2,1,4,12,3,2,10,11,7,2,11,0,8\nV,4,11,6,8,9,7,6,5,2,8,7,9,8,10,4,9\nK,3,8,4,6,3,3,7,6,2,6,5,10,3,8,2,11\nC,3,9,4,7,2,5,7,7,10,7,6,13,1,9,4,9\nB,5,8,6,6,6,8,8,7,6,7,6,5,3,7,7,9\nZ,2,2,3,3,2,8,7,5,9,6,6,7,2,8,7,8\nW,8,8,8,6,5,4,11,3,3,9,9,7,8,11,2,6\nT,4,5,5,4,2,6,11,2,8,11,9,5,1,11,3,4\nY,2,4,3,3,1,4,12,3,6,12,10,5,1,10,1,5\nA,2,6,4,4,2,11,3,2,2,9,2,9,2,6,2,8\nW,6,10,9,8,14,9,7,5,2,7,6,8,11,11,4,8\nJ,5,10,7,8,4,10,2,6,5,15,5,13,0,7,0,7\nN,5,9,6,5,3,2,10,5,3,13,12,9,4,9,0,8\nR,2,3,2,1,1,7,7,4,5,6,5,7,2,7,4,8\nJ,1,0,1,1,0,12,3,5,3,12,5,11,0,7,0,8\nY,4,11,6,8,1,10,10,2,2,6,13,8,1,11,0,8\nX,4,9,5,7,3,7,7,4,4,7,6,7,3,8,4,8\nB,4,8,5,6,4,6,7,9,7,7,6,7,2,8,9,9\nW,2,3,4,2,2,7,11,2,2,7,9,8,5,11,0,8\nV,8,10,8,7,3,3,11,5,5,12,12,7,3,10,1,8\nM,7,9,10,7,6,4,7,3,5,10,10,10,8,5,2,7\nM,9,9,12,8,13,8,8,4,4,7,6,7,12,9,7,3\nD,4,8,6,6,8,9,8,5,6,7,6,6,4,7,8,8\nG,6,10,6,7,5,5,7,6,5,9,7,12,2,9,5,8\nO,3,7,3,5,2,8,7,8,5,9,4,6,3,8,3,8\nW,3,2,4,3,3,5,10,2,2,8,9,9,6,11,0,8\nB,3,2,4,3,3,8,7,5,6,7,6,6,2,8,6,10\nU,5,10,6,8,4,6,8,6,7,6,10,9,3,9,1,7\nA,6,11,11,8,8,7,5,2,5,5,1,6,6,8,6,6\nA,3,8,5,6,3,12,3,3,2,11,2,9,2,6,2,9\nY,3,8,5,6,2,8,10,1,7,5,12,8,1,11,2,8\nH,4,7,5,5,4,8,6,13,1,7,7,8,3,8,0,8\nM,4,8,6,6,5,8,6,2,4,9,6,8,7,6,2,8\nN,5,7,6,6,6,8,6,5,4,7,5,8,7,9,6,3\nQ,3,6,4,5,3,8,7,7,5,6,7,9,2,8,4,9\nZ,5,11,7,8,4,9,6,3,10,12,4,8,1,7,6,9\nL,5,9,7,7,7,6,6,3,7,8,7,11,7,10,6,7\nR,4,8,5,6,6,7,7,7,3,8,5,7,4,7,7,10\nP,2,3,3,2,1,7,9,3,4,13,5,4,0,9,2,9\nV,4,9,6,7,1,7,8,4,3,7,14,8,3,9,0,8\nC,7,12,5,7,4,7,7,4,3,9,9,11,4,10,9,11\nQ,5,7,6,6,5,8,4,3,5,7,3,8,4,8,4,8\nL,2,1,2,3,1,4,4,4,6,2,2,6,0,7,1,6\nB,11,13,8,7,5,7,8,5,7,10,5,8,6,6,8,11\nS,5,8,6,7,7,8,8,4,5,7,7,8,5,10,10,9\nY,8,15,7,8,4,6,8,5,3,10,9,5,4,10,4,4\nK,2,4,3,3,2,5,7,4,7,6,6,11,3,8,5,9\nB,3,7,3,5,4,6,6,8,5,6,7,7,2,9,7,10\nB,9,12,8,7,7,10,7,4,6,10,3,6,7,4,8,8\nK,4,6,6,4,4,7,6,1,6,10,6,9,3,8,3,8\nK,6,11,8,8,10,7,7,4,5,6,6,9,8,9,8,7\nN,8,12,10,7,4,12,4,3,3,13,1,7,5,7,0,8\nM,5,7,7,5,5,8,7,2,4,9,7,8,7,6,2,8\nS,5,7,7,6,8,7,8,5,5,7,7,7,5,9,11,12\nE,5,9,7,6,6,8,8,6,3,6,6,10,5,8,9,8\nU,6,13,6,7,5,6,6,5,5,7,8,8,5,8,3,9\nM,6,12,8,7,5,13,2,5,2,11,1,9,7,2,1,8\nD,5,10,6,8,7,7,7,6,6,7,6,5,3,8,3,7\nR,6,11,7,8,4,6,10,9,4,7,5,8,3,8,6,11\nI,1,6,0,4,0,7,7,5,3,7,6,8,0,8,0,8\nO,5,8,5,6,5,8,6,7,3,9,5,7,3,8,3,7\nM,5,10,7,8,9,7,9,6,5,7,6,8,7,9,6,10\nR,1,1,2,1,1,6,9,7,3,7,5,8,2,7,4,11\nT,2,1,2,1,0,8,15,1,4,6,10,8,0,8,0,8\nE,4,8,6,6,6,6,8,6,8,6,4,11,3,8,6,9\nH,5,5,6,7,3,7,7,15,0,7,6,8,3,8,0,8\nW,4,3,4,1,2,3,11,3,2,10,10,8,6,11,1,7\nU,3,2,4,4,2,6,8,5,7,6,9,9,3,9,1,7\nA,4,10,7,8,4,14,3,4,3,12,0,8,2,6,2,9\nL,5,9,6,8,5,8,8,3,6,6,6,9,3,8,7,9\nT,3,6,3,4,2,5,12,4,5,11,9,5,2,12,1,5\nV,8,11,8,8,5,3,12,2,3,9,11,8,6,11,3,6\nT,2,1,3,2,2,7,12,3,6,7,10,8,2,11,1,8\nT,7,10,7,8,6,6,11,4,6,11,9,5,3,12,2,4\nJ,2,5,4,4,2,9,7,2,6,14,5,9,1,6,1,8\nE,3,5,4,4,3,7,7,6,8,7,6,9,2,8,6,9\nN,5,4,5,7,3,7,7,15,2,4,6,8,6,8,0,8\nB,6,9,8,6,7,9,8,3,6,10,5,5,3,7,5,9\nR,5,10,7,8,7,7,8,5,6,7,5,6,6,7,5,8\nH,4,5,4,8,3,7,8,15,1,7,5,8,3,8,0,8\nF,4,8,4,5,1,1,15,5,3,13,10,5,0,8,2,5\nF,7,10,9,8,4,6,11,3,6,14,6,3,2,10,2,7\nP,4,9,4,4,3,8,8,3,4,12,5,4,3,11,5,7\nP,3,6,3,4,2,4,12,7,1,10,6,4,1,10,3,8\nN,7,9,6,4,2,7,10,4,5,4,5,10,5,9,2,7\nZ,2,6,3,4,2,7,7,3,12,8,6,8,0,8,7,8\nF,1,0,1,1,0,3,12,4,3,11,9,6,0,8,2,7\nB,1,0,1,0,0,7,7,6,4,7,6,7,1,8,5,9\nU,1,0,1,0,0,7,6,10,4,7,12,8,3,10,0,8\nH,3,5,4,4,4,9,7,6,6,7,6,6,3,8,3,7\nD,3,8,4,6,2,6,7,10,9,6,6,6,3,8,4,8\nP,3,8,3,6,3,5,11,8,2,9,5,4,1,9,3,7\nM,5,8,5,6,5,8,5,11,0,7,8,8,7,5,0,8\nO,3,6,5,4,5,8,6,5,2,7,6,8,7,9,3,9\nD,5,8,6,7,6,6,7,5,6,7,5,9,5,4,8,4\nB,8,12,6,6,4,8,6,5,5,11,5,9,6,6,7,11\nN,4,2,5,4,3,7,8,5,6,7,6,5,6,9,2,6\nT,2,7,4,5,2,6,11,2,8,8,11,8,1,10,1,7\nD,1,4,3,2,2,9,6,3,5,10,5,6,2,8,2,8\nE,2,1,2,2,2,7,7,5,7,6,5,8,2,8,6,10\nG,4,7,5,5,3,5,7,5,5,9,8,10,2,8,4,9\nY,3,10,5,7,1,7,10,2,2,7,13,8,2,11,0,8\nH,3,4,4,6,4,11,5,2,2,8,5,9,3,8,5,11\nE,4,9,3,5,3,8,6,3,5,10,5,8,3,9,8,11\nJ,4,10,6,8,5,7,8,4,5,8,7,7,3,7,4,6\nX,4,7,5,5,4,8,7,3,8,6,5,6,3,9,6,8\nM,6,9,9,7,11,6,5,4,2,6,4,9,14,6,4,6\nC,3,4,5,7,2,5,7,7,10,7,6,12,1,9,4,9\nG,5,11,6,8,5,5,7,6,5,8,7,12,4,7,5,8\nI,2,10,2,8,3,7,7,0,8,7,6,8,0,8,3,8\nW,4,8,7,6,6,7,11,2,2,6,8,8,7,12,1,8\nW,3,8,5,6,4,9,10,2,2,6,8,8,7,11,1,8\nL,4,8,5,7,5,8,5,5,5,7,7,8,3,8,7,11\nT,3,4,4,2,1,5,13,3,6,12,9,3,1,10,1,5\nZ,3,8,4,6,3,7,8,3,11,8,6,8,0,8,7,7\nE,3,4,4,6,2,3,8,6,11,7,5,15,0,8,7,7\nP,5,11,5,8,4,3,13,7,1,11,6,3,0,10,3,8\nI,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nS,7,11,8,8,4,7,8,4,8,11,7,8,2,9,6,6\nD,6,11,6,8,4,5,7,10,10,7,6,6,3,8,4,8\nH,4,7,6,5,4,6,8,3,6,10,8,9,3,8,3,7\nH,7,10,10,8,6,10,6,3,6,10,3,7,5,7,5,9\nU,4,9,6,6,5,6,8,8,5,6,5,12,5,8,7,4\nN,2,2,3,3,2,7,9,5,4,7,6,6,4,9,1,6\nO,2,4,3,2,2,7,7,7,4,9,6,8,2,8,3,8\nD,5,9,6,6,5,5,8,9,7,7,7,6,2,8,3,8\nM,4,6,5,4,4,7,5,11,0,7,9,8,8,6,1,8\nZ,4,5,6,7,4,11,4,4,4,10,3,9,2,7,5,10\nE,5,7,7,5,4,6,8,2,9,11,6,9,2,8,4,8\nQ,4,5,5,6,4,8,8,5,2,8,8,10,3,9,5,7\nN,5,8,7,6,5,8,8,6,6,6,5,3,8,10,5,8\nT,5,10,7,7,8,6,7,3,6,7,7,10,5,8,5,6\nR,2,3,3,2,2,8,8,3,5,9,4,7,2,7,3,10\nR,6,10,8,8,6,7,9,5,7,5,4,9,4,4,7,8\nC,7,9,8,8,8,5,7,4,5,7,6,11,5,9,8,9\nL,5,11,6,9,5,4,3,8,7,1,2,4,1,6,1,6\nG,4,7,6,5,3,7,6,7,8,8,5,12,2,10,4,8\nN,4,8,6,7,6,6,8,5,3,6,5,9,8,7,5,9\nN,4,8,5,6,4,7,7,13,1,6,6,8,5,8,0,8\nG,4,8,4,6,3,6,7,6,5,9,8,10,2,7,4,9\nC,6,10,8,8,5,8,7,8,7,7,6,9,4,8,4,9\nD,5,8,6,6,6,10,6,5,6,9,3,6,3,8,3,8\nR,9,15,9,8,7,9,7,4,6,9,2,7,6,5,7,7\nB,4,10,4,8,6,7,6,9,6,6,7,7,2,9,8,10\nW,7,9,9,7,6,7,8,4,1,7,9,8,8,10,0,8\nF,3,2,4,3,2,6,10,4,6,10,8,5,4,9,3,7\nD,2,4,3,3,2,8,7,5,6,9,5,5,2,8,3,7\nL,4,11,6,8,3,4,4,4,9,2,0,6,0,7,1,5\nR,2,0,2,1,1,6,10,7,2,7,5,8,2,7,4,10\nW,5,11,7,8,4,5,8,5,2,7,8,8,8,10,0,8\nB,4,9,4,7,4,7,7,10,7,7,6,7,2,8,9,9\nA,4,7,6,5,6,8,5,7,4,8,6,8,5,9,7,4\nW,4,9,7,6,5,7,8,4,1,7,9,8,7,11,0,8\nC,5,10,6,8,4,5,8,9,9,9,9,12,2,10,4,9\nY,3,6,4,4,2,3,11,2,7,11,10,6,1,11,2,5\nA,4,10,7,8,5,12,3,2,2,9,2,8,2,6,2,8\nM,6,11,10,8,14,8,7,3,3,8,4,7,12,4,4,6\nZ,4,8,5,6,4,7,8,2,9,11,7,8,1,9,6,7\nY,5,7,5,5,3,3,10,2,7,11,11,7,1,11,2,5\nH,4,2,5,4,4,9,7,6,6,7,6,6,3,8,3,7\nR,4,9,6,7,6,6,7,3,4,7,6,8,6,10,7,5\nS,3,9,4,7,2,7,6,6,9,4,6,9,0,9,9,8\nB,2,0,2,1,1,7,7,7,5,7,6,7,1,8,7,8\nF,5,5,5,8,2,1,14,5,3,12,10,5,0,8,2,5\nT,8,11,8,8,7,6,11,3,7,11,9,5,5,11,4,3\nH,5,9,8,7,8,8,7,5,7,7,6,9,6,8,4,8\nC,2,3,3,2,1,5,9,5,7,12,9,10,1,10,2,7\nD,5,11,5,8,3,5,8,11,9,7,7,5,3,8,4,8\nT,3,5,4,4,2,7,12,2,8,7,11,8,1,11,1,7\nA,1,0,2,0,0,8,3,2,0,7,2,8,2,6,1,8\nI,1,1,1,3,1,7,7,1,7,7,6,8,0,8,2,8\nA,4,9,6,7,4,13,2,4,2,11,1,8,3,7,3,9\nH,7,9,7,4,4,7,8,3,5,11,7,8,6,9,4,8\nT,6,8,7,6,8,6,8,4,6,8,6,9,5,8,5,7\nT,5,10,7,8,6,6,7,7,7,6,8,9,4,10,7,6\nF,4,10,4,7,2,1,11,5,6,11,11,9,0,8,2,6\nQ,2,5,3,6,3,8,8,7,2,4,6,10,3,9,5,9\nU,5,9,5,5,3,8,6,4,5,6,7,8,4,8,3,7\nO,6,5,7,8,3,7,6,8,9,6,5,7,3,8,4,8\nM,3,4,5,3,3,8,6,2,4,8,5,7,8,7,2,8\nV,4,10,6,8,4,7,11,1,3,6,10,9,3,11,1,8\nR,3,4,4,6,3,5,11,8,4,7,2,9,3,7,6,11\nS,2,1,2,2,1,8,7,6,5,7,7,8,2,9,8,8\nR,7,11,9,8,7,9,8,6,6,8,5,7,4,8,6,12\nK,8,10,8,5,3,10,7,4,7,8,1,5,5,7,4,9\nR,5,9,8,6,8,6,6,3,4,7,5,8,7,10,7,6\nW,6,8,6,6,5,4,10,2,3,9,9,8,7,11,2,6\nX,7,10,9,8,8,7,6,3,5,6,6,10,5,5,12,10\nA,3,10,5,7,4,11,3,2,2,9,2,10,3,7,3,8\nY,3,7,4,5,2,7,10,1,7,6,12,8,1,11,2,8\nP,5,7,6,9,8,7,8,4,3,7,7,7,7,12,6,7\nU,4,6,5,4,4,7,7,8,6,7,6,10,3,9,5,5\nS,3,4,4,7,2,9,8,6,9,4,6,7,0,7,9,8\nP,1,0,1,0,0,5,11,6,1,9,6,5,0,9,2,8\nU,3,2,4,4,2,5,8,6,6,8,10,10,3,9,0,8\nD,7,11,7,6,4,10,4,6,6,13,4,10,5,7,5,9\nY,7,7,9,10,9,10,12,6,4,6,7,6,5,10,8,5\nH,3,4,5,3,3,8,7,3,6,10,4,8,3,7,3,8\nG,3,4,4,7,2,6,5,8,8,6,5,10,1,8,6,11\nV,5,7,7,6,7,6,9,6,6,8,7,8,5,9,6,11\nG,2,0,2,1,1,8,6,6,5,6,5,9,1,8,5,10\nX,9,12,8,6,4,7,7,2,9,9,5,8,4,5,4,8\nR,7,10,9,8,7,8,8,5,6,9,5,7,4,7,6,12\nU,4,10,5,8,2,7,5,14,5,7,15,8,3,9,0,8\nC,5,10,7,8,8,7,5,4,4,8,6,11,7,9,4,8\nO,5,9,6,7,3,7,7,9,8,7,6,8,3,8,4,8\nV,9,12,8,7,4,5,10,4,4,10,8,5,4,11,2,8\nM,3,7,4,5,3,7,7,11,1,7,9,8,8,5,0,8\nW,6,5,7,4,4,5,11,4,2,9,8,7,7,11,2,6\nR,5,11,6,8,4,6,9,10,6,6,5,8,3,8,6,10\nV,4,9,6,7,2,7,8,4,3,7,14,8,3,9,0,8\nM,4,4,5,6,3,7,7,12,1,7,9,8,8,6,0,8\nY,2,1,4,2,1,7,11,1,7,7,11,8,1,11,2,8\nV,5,7,5,5,3,3,11,2,3,10,11,8,2,11,1,8\nC,6,9,7,7,3,3,9,5,8,10,10,12,1,8,3,7\nF,0,0,1,0,0,3,12,4,2,11,9,6,0,8,2,8\nT,4,5,5,4,3,6,11,2,8,11,9,5,3,10,4,4\nZ,3,7,4,5,4,6,6,3,7,7,6,10,1,7,10,7\nW,3,7,5,5,5,7,11,2,2,6,8,8,6,11,1,8\nE,4,9,6,6,5,8,7,1,7,11,5,9,3,8,5,10\nX,4,6,6,4,4,9,8,3,5,7,7,7,4,11,6,7\nX,3,3,5,2,2,5,8,2,8,11,9,9,2,9,3,6\nS,5,10,6,7,4,6,8,3,6,10,8,7,2,8,5,5\nF,6,9,8,7,4,3,13,4,5,13,9,4,1,10,2,6\nX,3,4,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nB,4,8,6,6,6,8,7,4,5,7,6,7,4,8,6,9\nT,5,7,5,5,2,5,12,2,8,12,9,4,1,10,2,4\nG,3,6,3,4,2,7,7,6,6,10,7,11,2,9,4,10\nT,2,6,3,4,2,9,11,3,7,5,11,8,2,11,1,8\nJ,2,5,4,3,1,7,7,3,6,14,6,10,1,6,1,7\nM,5,5,7,4,6,9,8,4,5,6,6,7,8,6,6,5\nY,5,8,6,6,6,9,6,5,4,7,9,7,3,8,8,4\nE,6,9,8,8,8,5,8,4,4,8,7,9,4,10,9,10\nQ,6,11,8,8,8,8,6,8,3,6,8,9,5,5,8,9\nK,7,12,6,7,3,9,7,3,6,9,6,7,6,10,4,7\nZ,4,8,6,6,4,8,7,2,9,12,6,7,1,7,6,8\nU,4,7,5,5,2,4,8,5,6,11,11,10,3,9,1,7\nW,10,11,10,8,7,1,11,3,3,11,11,9,8,10,1,7\nN,4,8,6,6,6,7,9,3,4,7,6,7,6,8,6,4\nF,4,10,4,7,2,1,14,5,4,13,10,5,0,8,2,5\nQ,3,5,4,5,2,8,8,7,5,6,7,8,3,8,5,9\nD,8,11,8,6,4,10,3,6,5,13,4,11,5,7,5,9\nN,4,6,5,4,3,11,7,4,5,10,1,4,5,9,1,7\nP,7,11,10,8,7,9,7,3,6,12,4,5,5,9,5,9\nC,3,6,4,4,1,5,6,6,9,7,5,11,1,9,4,8\nK,6,11,6,8,3,4,8,8,2,7,4,11,4,8,2,11\nQ,1,2,2,3,2,8,8,5,2,8,7,9,2,9,3,9\nO,4,7,5,5,6,9,7,6,1,7,7,9,7,9,3,9\nT,4,6,5,4,5,7,8,4,6,7,6,8,5,7,5,6\nZ,3,10,4,7,3,7,8,3,11,9,6,8,0,8,7,8\nG,3,3,4,5,2,7,8,8,7,6,7,8,2,7,5,10\nQ,5,9,6,11,6,8,7,6,3,8,8,11,3,8,6,8\nX,4,9,6,7,5,8,6,3,5,6,7,7,2,8,9,9\nQ,3,3,4,5,4,8,7,7,3,5,7,9,3,8,5,10\nL,3,2,4,4,1,5,2,7,8,1,3,2,1,6,1,5\nI,3,9,5,6,5,9,5,3,5,7,6,5,6,6,5,6\nU,9,13,8,7,5,8,5,5,5,6,9,8,5,9,3,8\nU,3,1,4,2,2,6,8,6,7,7,9,9,3,9,1,8\nB,4,8,5,6,6,8,7,7,6,7,6,5,2,8,7,9\nD,4,8,6,6,4,9,6,3,7,11,4,6,3,9,4,9\nS,3,5,5,4,2,8,7,3,7,10,6,7,1,9,5,8\nZ,1,0,1,0,0,8,7,2,9,8,6,8,0,8,5,8\nR,6,11,6,8,4,5,13,8,3,7,2,9,3,7,6,11\nB,5,11,7,8,8,7,8,6,4,6,4,6,5,7,7,7\nU,5,8,5,6,3,3,8,4,6,10,10,9,3,9,1,7\nQ,6,7,6,8,5,7,7,7,5,9,9,9,4,8,7,9\nG,6,10,7,8,5,6,7,7,7,5,7,10,2,7,4,8\nY,4,10,7,7,3,7,10,1,8,7,12,8,1,11,2,8\nN,2,3,3,1,1,8,8,2,4,11,5,6,4,8,0,7\nZ,4,6,6,4,3,8,6,2,9,11,5,9,1,8,5,9\nN,3,8,4,6,4,7,7,12,1,6,6,8,5,8,0,8\nR,3,8,5,6,3,9,8,4,6,9,3,8,3,6,4,11\nB,2,3,3,2,2,8,7,2,5,10,5,6,2,8,4,9\nI,3,9,4,6,3,7,7,0,7,13,6,8,0,8,1,8\nE,1,1,1,1,1,4,8,5,7,7,6,12,0,8,6,10\nX,2,3,3,2,1,5,8,2,7,10,10,9,2,9,2,6\nF,7,13,6,7,4,8,9,2,5,11,5,4,3,10,7,7\nN,4,7,6,5,3,5,9,3,4,10,8,8,5,9,1,7\nT,9,15,8,8,5,8,8,3,9,12,5,6,3,7,6,6\nU,9,10,9,8,7,5,7,5,9,8,6,9,8,9,6,1\nI,4,9,5,7,3,7,8,2,8,7,6,8,0,7,4,8\nP,4,5,5,6,6,6,8,5,2,7,7,6,6,11,5,7\nH,3,5,4,3,3,7,7,6,6,7,6,8,3,8,4,8\nF,3,6,5,4,4,6,8,6,3,8,6,9,3,10,8,11\nS,4,5,5,7,3,7,6,6,9,5,6,10,0,9,9,8\nA,4,9,7,7,5,8,5,2,4,6,1,5,3,5,4,5\nX,6,9,9,7,4,10,5,2,8,11,1,7,3,8,4,9\nX,2,8,4,6,3,7,7,4,8,6,6,8,3,8,6,8\nA,4,9,6,7,6,8,6,6,4,7,6,9,5,8,7,3\nM,5,9,7,7,8,8,7,6,4,6,7,8,8,6,2,7\nR,4,6,5,4,3,8,9,5,6,8,5,8,3,7,5,10\nL,3,7,4,5,2,5,4,4,9,3,2,6,1,6,2,5\nG,2,4,3,3,2,6,7,5,5,9,7,10,2,8,4,9\nU,9,11,9,8,6,3,8,5,8,11,10,10,3,9,2,6\nU,5,7,5,5,3,4,8,6,6,9,8,9,3,9,3,5\nR,5,11,7,8,5,9,8,4,6,10,3,7,3,7,4,11\nM,6,10,7,8,4,7,7,13,2,7,9,8,9,6,0,8\nJ,3,5,5,4,2,9,6,3,6,14,5,10,1,6,0,7\nC,5,9,6,7,3,5,8,7,8,7,8,14,2,9,4,9\nC,1,0,2,0,0,7,7,6,8,7,6,13,0,8,4,10\nO,4,8,5,6,3,8,7,8,4,7,6,6,3,8,3,7\nH,4,6,5,4,3,10,6,4,6,10,3,7,3,8,3,10\nN,6,9,5,5,3,8,11,5,4,4,6,10,6,11,2,6\nC,6,9,7,7,7,6,7,3,4,6,7,10,7,8,6,7\nG,5,7,6,6,7,7,9,5,2,7,7,8,6,11,7,8\nL,5,11,6,8,5,8,4,0,8,9,2,11,2,5,4,9\nI,1,10,0,7,0,7,7,4,4,7,6,8,0,8,0,8\nQ,4,6,6,5,4,7,4,3,4,5,3,7,3,6,4,7\nH,4,2,5,4,4,8,7,6,7,7,6,8,6,8,4,7\nE,3,1,3,3,2,7,7,5,8,7,7,8,2,8,6,9\nV,3,4,4,3,1,4,12,3,3,10,11,7,2,11,1,8\nY,4,5,6,7,8,7,8,3,2,8,8,9,4,11,8,5\nH,5,5,5,7,3,7,7,15,1,7,7,8,3,8,0,8\nV,6,8,5,6,3,3,12,3,3,10,11,8,2,11,1,8\nJ,5,9,6,7,4,10,4,6,5,8,6,5,2,8,4,6\nU,4,4,5,3,2,4,8,5,7,10,9,9,3,9,2,5\nC,3,5,5,4,4,6,7,4,4,6,6,11,4,10,7,11\nV,3,9,5,7,2,7,11,3,4,9,12,8,3,10,1,9\nI,4,7,6,8,6,8,8,5,6,7,5,8,3,9,8,9\nT,1,0,2,0,0,7,14,2,3,7,10,8,0,8,0,8\nF,3,5,5,3,2,6,12,4,5,13,6,2,1,10,2,6\nV,9,14,8,8,5,6,8,4,4,8,8,5,6,12,3,8\nS,1,0,1,1,0,8,7,4,6,5,6,7,0,8,7,8\nW,6,5,8,5,9,8,7,5,5,7,5,8,9,9,7,9\nW,6,9,8,7,7,8,4,6,2,7,6,7,7,7,6,5\nJ,2,8,2,6,1,14,3,4,5,12,1,9,0,7,0,8\nT,1,0,2,0,0,7,15,2,4,7,10,8,0,8,0,8\nI,0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8\nR,6,11,9,8,8,10,7,3,6,10,3,6,3,7,4,10\nI,1,3,2,2,1,7,7,1,7,13,6,8,0,8,1,8\nS,3,3,4,4,2,8,8,6,8,5,6,7,0,8,9,7\nW,5,8,5,6,4,7,10,4,2,8,6,6,6,11,2,6\nF,2,1,3,2,2,5,11,3,5,11,9,5,1,10,3,6\nC,2,0,2,1,0,6,7,6,9,7,6,14,0,8,4,10\nF,3,5,5,4,2,7,10,1,6,13,6,5,3,8,3,7\nU,4,9,5,7,2,7,5,13,5,7,14,8,3,9,0,8\nJ,3,9,4,7,1,11,2,11,3,12,9,14,1,6,0,8\nT,3,4,4,6,1,6,15,1,6,8,11,7,0,8,0,8\nN,5,9,7,7,5,4,9,3,4,9,10,9,6,7,1,8\nX,3,8,5,6,3,5,8,2,8,10,10,9,3,9,3,6\nJ,5,9,6,6,2,8,3,6,4,14,9,15,1,6,1,6\nO,3,8,4,6,2,7,6,8,7,7,4,8,3,8,4,8\nJ,2,4,4,3,1,9,4,4,5,14,6,12,0,7,0,8\nZ,4,5,6,7,4,11,4,4,5,10,3,9,2,7,6,10\nN,7,9,10,7,5,10,8,3,6,11,1,3,5,9,1,7\nP,2,4,4,3,2,7,8,3,4,12,5,4,1,10,2,8\nS,1,3,3,2,1,7,7,3,7,10,5,8,1,8,4,8\nH,4,4,6,3,3,7,7,3,6,10,7,8,5,9,4,8\nT,1,0,2,0,0,7,14,2,3,7,10,8,0,8,0,8\nY,2,3,4,5,1,7,10,1,3,7,12,8,1,11,0,8\nX,6,10,10,8,5,9,6,1,8,10,3,7,3,8,4,9\nP,4,4,4,6,2,4,12,8,2,10,6,4,1,10,4,8\nA,4,7,5,5,5,7,6,8,4,7,5,8,4,8,10,2\nP,4,6,6,4,3,6,11,5,3,11,5,3,1,10,3,8\nF,2,5,4,4,2,8,9,2,6,13,5,5,1,9,2,8\nZ,1,1,2,2,0,7,7,2,11,9,6,8,0,8,6,8\nC,4,5,5,4,2,4,9,5,8,12,10,12,1,8,2,7\nH,6,7,9,5,5,9,6,3,7,10,5,8,3,8,3,8\nM,10,11,10,6,5,7,11,5,5,4,4,10,9,10,2,7\nN,4,5,5,8,3,7,7,14,2,4,6,8,6,8,0,8\nR,2,6,3,4,3,5,10,7,3,7,4,9,2,6,4,11\nX,4,9,5,6,1,7,7,5,4,7,6,8,3,8,4,8\nW,4,7,7,5,5,9,12,2,2,6,8,8,8,13,1,7\nP,4,9,6,6,3,4,13,5,4,13,6,2,0,9,4,7\nP,6,6,6,8,3,3,14,8,1,11,6,3,1,10,4,8\nS,6,10,8,7,5,8,8,3,7,10,5,7,2,8,5,8\nF,3,8,3,6,2,1,11,3,4,11,11,9,0,8,2,7\nZ,3,5,5,4,3,7,8,2,9,12,6,8,1,8,5,7\nM,6,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8\nK,7,13,7,8,4,6,7,3,6,9,9,10,6,10,3,7\nI,6,8,7,6,4,5,6,3,7,7,6,12,0,9,4,8\nV,3,5,4,3,1,4,12,3,3,10,11,7,2,10,1,8\nJ,6,10,5,7,3,7,10,2,3,12,5,5,2,8,7,9\nR,4,8,7,7,7,6,8,4,3,6,5,9,8,7,6,9\nU,5,4,6,7,2,7,4,14,6,7,15,8,3,9,0,8\nZ,5,10,5,5,3,5,8,2,9,11,8,8,3,6,6,5\nW,4,2,5,3,3,5,11,3,2,8,9,9,7,10,1,8\nW,7,7,7,5,4,4,10,3,3,10,9,8,7,11,2,6\nQ,4,7,6,5,5,8,5,7,4,6,7,9,3,7,6,9\nP,6,9,5,4,2,5,12,5,1,12,6,4,4,10,3,8\nM,3,1,4,3,3,6,6,6,4,7,7,10,7,5,2,9\nE,3,5,5,4,5,7,6,4,3,7,6,9,5,10,7,12\nU,5,7,5,5,3,4,8,5,7,9,8,9,4,8,3,4\nG,4,7,4,5,2,6,7,6,5,9,8,10,2,8,4,9\nZ,4,10,6,8,4,6,9,2,9,11,8,7,2,10,7,6\nL,5,8,7,7,6,7,7,4,5,6,7,9,3,9,8,7\nF,3,5,5,3,2,6,12,3,5,13,7,3,1,10,2,6\nM,2,3,3,1,2,8,7,6,3,6,7,8,6,5,1,7\nJ,4,11,5,8,3,9,7,2,7,15,4,8,0,6,1,7\nD,2,6,3,4,2,5,7,10,7,7,6,5,3,8,3,8\nX,4,9,6,7,4,8,7,4,9,6,7,8,4,6,8,9\nU,3,5,4,4,4,8,6,5,4,6,6,8,3,9,1,7\nV,4,6,6,5,6,7,7,5,4,7,6,8,6,9,7,8\nQ,4,9,5,11,7,8,6,8,4,5,6,7,4,8,6,10\nJ,2,7,4,5,4,8,8,3,3,8,5,6,4,8,5,4\nS,2,7,3,5,2,6,7,5,8,6,7,11,0,9,8,7\nB,2,3,4,2,2,9,7,2,5,11,5,7,2,8,4,9\nN,4,7,4,5,2,7,7,14,2,5,6,8,6,8,0,8\nF,2,5,4,4,2,5,12,3,5,13,7,4,1,9,2,6\nZ,4,7,6,9,4,11,4,2,6,9,2,6,1,8,6,9\nS,5,9,6,6,4,6,8,4,6,10,9,8,2,8,5,5\nP,2,1,2,1,1,5,11,7,2,9,6,4,1,9,3,8\nH,4,5,7,4,4,8,6,3,6,10,5,8,5,7,4,8\nO,3,4,4,3,2,8,7,7,4,9,6,8,2,8,3,8\nH,5,7,8,5,5,7,7,3,6,10,7,8,3,9,3,7\nQ,5,10,6,9,5,8,8,8,5,5,8,10,4,7,6,8\nL,5,10,7,8,5,8,4,1,7,9,2,9,1,6,3,9\nX,4,6,7,4,3,7,7,2,8,10,7,9,3,8,3,7\nF,4,8,6,6,4,4,11,2,6,11,10,6,1,10,3,6\nQ,2,6,3,5,3,7,7,8,6,5,4,6,2,8,3,7\nL,1,0,1,0,0,2,1,6,4,0,3,4,0,8,0,8\nP,5,10,7,8,6,8,10,7,4,10,4,3,3,11,4,8\nZ,2,4,4,3,1,8,6,2,10,11,5,9,1,8,6,9\nJ,0,0,1,0,0,12,4,4,3,11,4,11,0,7,0,8\nD,1,1,2,1,1,6,7,8,6,6,6,6,2,8,3,8\nK,4,6,6,4,4,7,8,4,8,7,5,7,3,8,5,8\nT,3,9,5,6,1,5,15,1,6,9,11,7,0,8,0,8\nC,6,9,7,7,7,8,5,6,3,7,8,11,8,9,5,4\nU,2,3,3,2,1,6,8,6,6,7,9,9,3,9,0,8\nZ,4,4,4,6,2,7,7,4,14,9,6,8,0,8,8,8\nF,6,11,10,8,10,6,10,1,5,10,7,5,6,10,5,2\nM,4,4,5,3,4,9,6,6,4,6,7,5,9,5,3,6\nY,1,0,1,0,0,8,10,1,3,6,11,8,1,11,0,8\nQ,4,8,5,10,7,8,6,8,3,6,5,9,3,9,5,10\nO,7,10,7,8,7,7,8,8,4,10,7,8,3,8,3,8\nX,7,9,7,5,3,8,7,2,8,9,6,8,4,10,4,8\nB,3,7,4,5,4,9,7,3,5,7,6,7,3,9,4,8\nO,9,13,6,7,4,6,6,7,4,10,7,9,5,9,5,8\nU,4,11,5,8,4,7,6,12,4,7,12,8,3,9,0,8\nU,5,11,6,8,5,6,9,5,6,6,9,10,5,11,1,8\nY,6,11,9,8,5,7,10,1,8,6,12,9,4,10,3,7\nB,1,3,2,2,1,8,7,3,5,10,5,7,2,8,4,9\nD,5,11,7,8,5,11,5,4,7,10,2,6,3,8,3,9\nQ,4,9,6,8,5,8,9,7,4,6,9,9,3,8,7,10\nY,3,5,6,7,1,9,10,3,2,6,13,8,2,11,0,8\nG,2,6,3,4,2,7,7,7,6,6,6,8,2,7,6,11\nE,4,8,6,7,7,5,8,4,4,8,7,9,5,11,9,11\nN,3,4,5,3,2,7,9,2,4,10,6,7,5,8,0,7\nV,4,8,6,6,3,8,12,2,3,5,11,9,5,11,3,6\nU,1,0,2,1,0,8,6,11,4,7,12,8,3,10,0,8\nK,5,9,7,7,6,10,6,1,6,10,3,8,4,9,4,11\nV,3,8,5,6,2,7,11,3,4,6,12,8,3,10,1,8\nL,8,13,8,8,5,7,4,3,5,11,6,12,4,6,7,8\nF,4,10,4,7,2,1,13,5,3,12,10,6,0,8,3,6\nZ,2,1,2,1,1,7,7,3,11,8,6,8,0,8,6,8\nI,2,7,3,5,1,6,8,1,8,14,7,7,0,8,1,7\nD,6,8,8,6,5,7,9,8,6,9,7,5,5,9,4,9\nX,3,7,4,5,2,7,7,4,4,7,6,8,2,8,4,8\nX,3,6,5,4,3,8,7,3,8,5,6,8,2,8,6,8\nG,2,1,4,3,2,6,7,6,6,6,6,10,2,8,3,9\nY,5,10,7,8,3,8,10,3,2,6,12,8,2,11,0,8\nE,2,1,2,3,2,7,7,5,6,7,6,9,2,8,5,10\nD,3,7,5,5,6,9,8,4,4,7,6,5,4,9,7,5\nX,3,7,4,4,1,7,7,4,4,7,6,8,3,8,4,8\nN,6,7,8,6,8,8,8,5,5,8,5,6,6,9,7,3\nP,3,6,5,9,7,8,13,3,1,9,8,6,4,11,2,7\nF,5,9,8,7,8,10,7,1,5,9,5,5,6,10,5,8\nF,4,7,6,5,6,6,9,1,4,10,7,7,5,10,3,4\nR,1,3,2,1,1,7,8,4,4,7,5,6,2,7,3,8\nP,5,9,6,6,6,7,9,4,6,8,7,4,5,10,4,7\nA,1,0,2,0,0,7,4,2,0,7,2,8,2,7,1,8\nX,3,5,4,3,2,7,7,3,9,6,6,8,3,8,6,7\nA,7,14,7,8,6,10,3,5,2,10,5,12,7,1,6,11\nL,3,9,3,7,2,0,2,3,6,1,0,8,0,8,0,8\nY,4,11,6,8,3,10,10,1,3,6,12,8,1,11,0,8\nP,5,10,7,8,6,6,9,2,7,9,8,4,4,10,4,7\nW,1,0,2,0,0,7,8,3,0,7,8,8,5,9,0,8\nB,4,3,4,5,3,6,9,8,7,7,5,6,2,8,9,9\nY,4,5,5,3,2,4,11,2,7,12,10,5,1,11,2,4\nK,5,9,7,6,6,5,7,1,6,9,8,10,4,7,3,8\nI,4,11,3,6,2,10,5,4,4,12,3,7,3,8,5,10\nS,4,8,5,6,4,8,7,5,8,5,6,7,0,8,8,8\nI,1,5,0,8,0,7,7,4,4,7,6,8,0,8,0,8\nJ,2,11,3,8,3,10,7,1,6,11,3,7,1,6,2,6\nX,3,1,5,3,2,8,7,4,9,6,6,8,2,8,6,8\nH,11,14,11,8,8,6,9,3,5,10,5,9,7,6,5,6\nG,7,11,7,8,6,6,6,6,6,10,7,12,2,10,4,10\nY,5,7,5,5,2,2,12,4,5,13,11,6,1,11,1,6\nY,1,0,2,0,0,8,10,3,1,6,12,8,1,11,0,8\nN,9,15,8,8,4,8,10,5,4,5,6,10,7,12,2,6\nD,5,9,6,7,5,7,7,8,8,6,5,4,4,9,4,9\nQ,5,5,7,8,9,8,8,5,1,6,6,10,7,13,7,13\nT,4,10,5,8,4,7,12,5,6,7,11,8,3,12,1,7\nQ,2,6,3,4,2,8,8,8,5,5,8,9,3,8,5,10\nO,6,9,8,8,7,6,6,5,4,6,3,6,4,9,7,7\nA,10,14,8,8,4,11,1,3,0,10,4,11,3,4,4,10\nO,3,4,4,6,2,7,7,9,6,7,6,8,3,8,4,8\nE,5,11,7,8,5,7,7,1,9,10,6,9,2,8,5,8\nC,4,6,5,4,5,7,6,4,3,7,7,11,5,9,3,9\nA,7,11,7,6,5,10,3,5,2,10,5,11,7,2,6,10\nC,4,11,5,8,4,5,8,8,6,10,8,13,3,12,5,8\nF,5,7,6,5,6,8,6,5,4,7,6,8,6,9,6,10\nA,3,8,5,6,4,11,2,2,2,9,2,9,3,6,3,8\nF,3,9,4,6,1,1,13,5,4,12,10,6,0,8,2,6\nD,4,8,5,6,4,10,5,2,6,11,4,7,3,7,3,9\nL,1,0,2,1,0,2,1,6,4,0,2,5,0,8,0,8\nF,4,7,5,5,3,6,10,4,5,12,7,5,2,9,2,6\nU,5,9,5,6,2,7,4,14,6,7,14,8,3,9,0,8\nP,5,9,8,7,4,7,9,6,5,11,5,4,2,10,4,8\nC,3,8,4,6,3,5,8,8,7,10,8,13,2,10,4,9\nQ,5,9,5,4,3,11,3,3,6,11,3,9,3,8,6,13\nJ,3,7,5,5,3,8,6,4,5,8,6,7,2,7,4,6\nK,4,8,6,6,6,7,8,5,4,7,6,7,7,7,6,11\nP,8,10,6,5,3,6,11,5,3,12,4,4,4,10,4,8\nV,5,11,8,8,4,6,11,2,4,8,12,9,3,10,2,8\nG,3,2,4,4,3,7,7,6,6,7,6,10,3,8,4,9\nE,1,1,1,2,1,4,8,5,8,7,6,13,0,8,6,9\nT,2,6,3,4,2,6,12,3,6,8,11,8,2,11,1,7\nN,4,6,6,6,6,6,9,5,3,6,4,8,7,8,4,9\nZ,4,11,6,8,4,6,9,3,10,11,9,6,1,9,6,6\nU,5,9,5,4,3,7,6,4,5,6,7,8,5,6,3,7\nY,3,9,5,6,1,6,10,2,2,8,12,8,1,11,0,8\nZ,6,10,9,8,7,9,9,5,4,7,5,7,3,9,10,5\nY,2,4,3,2,1,4,11,2,6,11,10,5,1,11,2,5\nH,9,12,9,6,4,7,8,5,4,9,10,9,7,10,5,9\nB,5,10,8,8,6,9,7,3,7,10,4,6,2,8,6,11\nZ,4,8,5,6,2,7,7,4,15,9,6,8,0,8,8,8\nJ,5,10,6,7,2,8,6,3,7,15,5,9,0,6,1,7\nZ,5,8,7,6,4,8,7,2,9,12,5,8,2,8,6,8\nX,4,4,4,6,1,7,7,4,4,7,6,8,3,8,4,8\nR,2,3,3,2,2,6,7,4,5,6,5,6,5,7,3,8\nT,5,7,6,5,6,7,8,4,6,7,7,9,5,8,6,5\nB,3,6,4,4,4,7,7,4,6,7,6,6,2,8,5,10\nT,8,11,8,8,6,7,11,4,8,11,9,4,6,12,6,5\nZ,4,7,6,5,4,6,9,2,9,11,8,6,1,8,6,6\nZ,5,6,4,9,4,6,11,3,3,12,7,6,2,7,9,3\nZ,2,0,2,1,1,7,7,3,11,8,6,8,0,8,7,8\nH,2,6,4,4,5,7,9,5,2,7,6,7,5,6,6,6\nB,6,11,6,8,7,7,8,9,6,6,5,7,2,8,8,9\nJ,4,11,6,8,4,9,6,3,6,14,4,9,0,6,1,7\nR,3,3,4,4,2,5,10,8,4,7,4,8,2,7,6,11\nO,4,8,6,7,5,7,6,5,5,9,5,8,3,6,5,6\nF,3,8,3,6,2,1,11,3,5,11,11,9,0,8,2,7\nM,2,0,2,0,1,7,6,10,0,7,9,8,6,6,0,8\nL,4,7,5,5,6,7,7,3,5,6,7,11,6,11,6,5\nN,3,6,5,4,3,8,9,3,4,10,5,6,5,8,1,7\nT,3,4,4,6,1,8,15,1,6,6,11,9,0,8,0,8\nL,6,13,5,8,3,8,3,3,5,12,4,12,2,7,6,8\nO,5,8,7,7,5,7,5,4,5,8,4,8,3,7,4,7\nM,7,7,9,5,7,5,7,3,5,10,9,10,9,5,4,8\nN,1,0,1,1,0,7,7,10,0,5,6,8,4,8,0,8\nH,3,4,5,3,3,6,8,3,5,10,9,9,3,7,3,6\nV,1,1,2,1,0,7,9,4,2,7,13,8,2,10,0,8\nA,4,8,5,6,4,8,3,1,2,6,2,8,2,6,2,6\nC,2,5,3,3,1,4,8,5,6,11,9,12,1,9,2,7\nW,12,14,12,8,7,4,8,2,4,8,10,8,10,11,2,5\nP,4,7,6,5,3,7,10,3,6,14,5,2,0,10,3,9\nB,3,1,3,2,3,7,7,5,6,7,6,6,4,8,5,10\nY,1,1,3,1,1,8,11,1,6,6,11,8,1,11,1,8\nO,6,10,6,8,5,7,7,8,5,10,6,8,3,8,3,8\nT,3,9,4,6,3,10,11,3,7,4,12,8,2,12,1,8\nI,1,5,1,4,1,7,7,1,8,7,6,8,0,8,3,8\nF,3,6,4,4,4,6,8,5,4,7,5,9,3,10,8,10\nW,5,7,6,5,6,7,8,6,3,8,8,7,7,9,4,9\nK,6,8,8,6,4,8,7,2,7,10,3,8,4,7,4,9\nQ,4,7,5,6,2,8,6,8,6,6,6,8,3,8,4,8\nE,5,10,7,8,6,7,8,2,7,11,6,9,3,8,4,9\nO,4,8,6,6,4,8,7,8,4,7,7,8,3,8,3,8\nG,3,4,4,6,2,8,8,8,7,6,7,8,2,7,6,11\nH,5,9,7,6,5,7,6,7,5,5,4,9,3,7,6,9\nP,6,7,8,9,10,9,9,3,2,6,8,7,6,10,6,4\nO,2,2,3,3,2,8,7,7,4,7,6,9,2,8,3,8\nR,3,8,5,6,4,10,7,3,6,10,3,7,3,7,2,10\nO,1,0,1,0,0,7,7,7,4,7,6,8,2,8,3,8\nR,5,9,7,6,7,9,7,6,3,8,5,6,5,6,8,10\nW,4,8,6,6,3,7,8,4,1,7,8,8,8,9,0,8\nX,3,4,4,5,1,7,7,4,4,7,6,8,3,8,4,8\nN,3,5,5,4,2,6,8,3,4,10,7,8,5,8,1,8\nA,5,10,7,8,4,8,2,2,3,6,2,7,5,7,5,8\nX,1,0,1,0,0,7,7,3,5,7,6,8,2,8,4,7\nI,2,7,4,5,4,10,6,2,5,8,5,5,3,8,5,6\nK,3,8,4,6,3,3,8,6,3,6,4,11,3,8,3,11\nZ,4,6,6,4,4,9,11,6,5,6,5,9,3,8,9,6\nT,2,3,4,4,1,9,15,1,5,5,11,9,0,8,0,8\nE,3,9,3,7,2,2,7,6,10,7,7,15,0,8,7,7\nN,7,12,8,6,4,5,9,5,4,13,11,9,6,9,0,9\nB,8,15,8,8,8,7,8,5,5,9,7,8,8,5,10,7\nC,2,3,2,2,1,6,8,6,7,8,7,13,1,9,3,10\nR,3,6,5,5,6,6,8,3,3,6,5,9,6,7,5,9\nI,6,12,4,6,2,11,3,4,6,11,2,7,3,8,3,12\nX,4,11,5,8,4,7,7,4,4,7,6,8,2,8,4,8\nE,3,8,3,6,4,3,7,5,9,7,7,14,0,8,6,9\nT,4,7,4,5,3,6,11,4,5,11,9,5,2,12,2,5\nX,3,7,5,6,5,9,7,2,4,8,6,6,3,9,7,7\nD,5,11,5,8,3,6,7,11,10,7,6,6,3,8,4,8\nW,8,9,12,8,13,7,7,5,5,7,5,8,10,9,9,7\nX,5,11,8,8,4,10,7,2,9,11,1,6,3,8,4,9\nH,3,2,4,3,3,6,7,6,6,7,6,8,6,8,3,8\nV,1,0,2,0,0,8,9,3,1,6,12,8,2,11,0,8\nH,5,9,6,5,4,8,7,3,5,10,4,8,5,7,4,7\nO,5,10,6,8,9,7,8,5,1,7,6,7,11,11,9,12\nR,3,7,4,5,2,5,10,8,4,7,4,8,3,7,6,11\nX,3,10,4,7,1,7,7,4,4,7,6,8,3,8,4,8\nO,3,6,4,4,3,7,7,7,4,6,5,7,3,8,3,7\nD,5,8,7,6,5,7,7,8,5,7,7,4,4,8,4,8\nF,3,8,3,5,1,1,13,4,3,12,10,6,0,8,2,6\nA,3,7,6,5,3,12,3,3,3,11,1,8,2,6,2,9\nP,3,6,6,4,3,9,8,3,5,12,4,4,1,10,3,9\nE,4,10,4,7,3,3,6,6,11,7,7,15,0,8,7,7\nO,4,3,5,4,2,8,7,8,7,6,6,9,3,8,4,8\nL,3,2,4,4,2,4,4,4,8,2,1,7,0,7,1,6\nO,5,9,6,7,3,7,6,9,8,7,5,8,3,8,4,8\nT,2,1,3,2,1,7,12,3,6,7,11,8,2,11,1,8\nJ,2,4,4,3,1,8,8,1,6,14,5,7,0,7,0,8\nQ,5,7,6,9,6,8,7,6,3,8,7,9,3,9,6,9\nD,5,9,6,6,6,7,7,8,6,6,5,4,4,8,4,8\nC,4,8,6,6,4,5,7,6,8,6,6,13,1,7,4,9\nE,3,4,3,3,3,7,7,5,8,7,6,9,2,8,6,9\nN,4,5,7,4,3,7,9,3,5,11,6,6,6,8,1,7\nK,3,4,4,2,2,5,7,4,7,7,6,10,6,8,4,9\nQ,3,5,5,4,3,10,4,3,4,9,3,8,3,7,3,9\nL,2,5,4,3,2,7,3,1,7,9,2,10,0,7,2,9\nI,4,12,3,6,2,11,5,4,4,12,3,7,3,8,5,10\nT,2,5,4,6,1,7,14,0,6,7,11,8,0,8,0,8\nY,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8\nE,4,10,6,8,6,6,8,6,9,6,4,10,2,8,6,9\nU,9,11,9,8,4,3,10,6,8,13,12,9,3,9,1,6\nE,3,4,4,6,2,3,6,6,11,7,7,15,0,8,7,7\nJ,1,1,1,1,0,12,3,6,4,13,4,11,0,7,0,8\nB,4,9,4,7,6,6,8,8,5,7,5,7,2,8,7,9\nF,0,0,1,0,0,3,11,4,2,11,8,6,0,8,2,8\nN,5,4,5,6,2,7,7,15,2,4,6,8,6,8,0,8\nP,4,9,6,7,5,5,10,3,6,10,9,5,1,10,4,7\nT,2,4,3,3,1,4,11,2,7,11,10,5,0,10,1,5\nD,3,4,5,3,3,8,7,6,6,9,5,6,3,8,4,9\nP,3,1,3,2,2,5,10,4,4,10,8,4,0,9,4,7\nQ,4,9,6,8,3,8,7,9,6,6,6,9,3,8,5,9\nS,9,15,9,8,4,8,4,5,6,13,6,10,3,8,3,8\nE,5,7,7,5,5,7,8,2,8,11,7,8,3,8,5,8\nS,1,1,2,2,0,8,7,4,7,4,6,7,0,8,7,8\nS,7,10,8,8,4,6,9,4,9,11,7,6,2,7,5,4\nS,4,8,5,6,4,7,7,5,7,5,6,9,0,8,8,8\nX,2,3,4,2,1,9,6,2,8,10,3,7,2,8,2,8\nJ,3,9,5,7,3,7,6,3,6,15,6,11,1,6,1,7\nF,4,9,6,6,4,5,10,2,8,10,9,6,4,10,4,6\nH,1,0,1,0,0,7,8,10,1,7,5,8,2,8,0,8\nG,4,8,6,6,4,6,6,6,6,7,5,11,3,10,4,9\nH,5,5,5,7,3,7,8,15,1,7,5,8,3,8,0,8\nS,5,5,6,8,3,8,7,6,9,4,6,8,0,8,9,8\nT,7,9,7,7,3,5,12,3,10,12,10,3,1,11,3,4\nB,6,10,8,8,7,7,7,6,6,9,7,6,3,8,8,9\nM,9,13,11,8,6,10,2,2,3,9,3,9,7,2,1,9\nN,6,11,7,8,6,7,7,13,1,6,6,8,6,8,0,8\nC,4,11,5,8,4,5,8,8,7,10,9,13,2,10,4,10\nT,4,5,5,4,2,5,12,3,7,11,10,4,1,11,2,4\nY,6,8,6,6,3,4,10,2,8,9,10,5,2,12,4,3\nF,1,0,1,0,0,3,12,4,2,11,8,6,0,8,2,7\nR,3,4,4,3,3,7,8,4,5,6,5,7,2,6,4,8\nN,3,7,4,5,2,7,7,14,2,5,6,8,5,8,0,8\nG,2,1,2,1,1,8,6,6,5,6,5,9,1,7,5,10\nQ,6,11,6,6,4,10,5,4,6,11,4,8,3,8,8,11\nJ,2,7,2,5,1,11,3,10,3,12,7,13,1,6,0,8\nN,7,10,9,8,6,6,9,2,4,9,8,8,7,7,2,7\nP,5,11,5,8,6,5,10,9,4,8,6,5,2,10,3,8\nC,4,6,5,4,5,5,7,4,5,7,6,9,5,11,3,8\nU,8,13,7,7,5,8,6,5,4,5,6,7,5,6,3,5\nK,4,7,6,6,5,9,5,2,3,8,4,9,4,7,6,12\nV,4,11,7,8,3,7,12,3,4,6,12,9,3,10,1,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,9,3,6\nP,4,9,6,6,4,7,9,3,4,12,5,4,2,9,3,8\nK,3,1,5,3,3,6,7,4,8,7,6,11,3,8,5,9\nW,4,10,6,8,7,8,7,6,2,7,8,8,9,7,4,9\nQ,2,0,2,1,1,8,7,6,3,6,6,9,2,8,3,8\nQ,2,2,3,3,2,8,8,6,1,5,6,9,2,9,5,10\nQ,4,8,6,7,5,8,8,7,5,6,7,9,3,8,4,9\nS,3,6,5,4,5,6,7,3,2,8,6,6,2,7,10,1\nH,3,8,5,6,4,8,8,7,7,7,5,7,3,8,3,7\nW,2,3,4,2,2,7,10,2,2,7,9,8,6,11,0,8\nO,10,14,7,8,4,6,8,5,4,7,4,6,5,9,5,7\nY,6,9,5,5,2,5,9,3,3,10,9,5,3,10,3,4\nL,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8\nT,6,8,6,6,3,4,12,3,7,12,10,5,1,11,1,5\nG,2,3,2,2,1,6,8,5,4,9,8,9,2,8,4,9\nE,5,10,4,5,3,7,8,4,3,10,5,9,4,9,7,10\nJ,6,7,8,8,7,8,8,5,7,7,5,8,4,10,10,8\nQ,4,3,5,5,2,8,9,7,6,6,8,9,3,8,5,9\nN,7,14,9,8,5,3,9,4,4,13,12,10,6,9,0,8\nT,4,5,4,4,2,6,11,2,7,11,9,4,1,11,2,4\nM,5,8,7,6,7,8,6,6,5,7,7,10,11,6,2,9\nO,4,5,5,4,3,7,7,8,5,7,6,8,2,8,3,8\nA,6,10,7,6,3,12,0,4,1,11,4,12,4,4,3,11\nT,2,9,4,7,2,7,13,0,5,7,10,8,0,8,0,8\nN,3,5,6,3,2,7,9,2,5,10,6,6,5,8,1,7\nF,4,10,4,7,3,1,12,4,4,11,10,8,0,8,2,6\nP,2,5,3,7,5,8,6,5,1,7,6,7,6,8,5,9\nU,3,7,5,5,3,4,8,6,6,7,9,10,3,9,1,8\nH,5,7,8,5,5,9,7,3,6,10,4,7,5,8,4,9\nO,6,9,4,4,2,8,7,5,5,8,4,7,4,9,5,8\nS,2,8,3,6,2,8,8,6,9,5,6,7,0,8,9,8\nJ,5,9,6,7,3,6,8,2,6,15,6,8,2,8,2,8\nO,4,5,5,4,4,8,4,4,4,9,4,10,3,6,6,6\nB,4,8,6,6,5,7,8,6,6,10,6,6,3,8,7,8\nF,3,8,5,6,3,6,11,4,6,11,9,4,2,10,3,5\nE,3,9,3,6,2,3,6,6,10,7,7,14,0,8,7,8\nF,3,6,5,4,3,5,10,4,6,10,9,5,2,9,3,5\nA,2,3,3,1,1,6,2,2,1,5,2,8,1,6,1,7\nQ,3,5,4,6,4,8,9,4,2,5,8,11,2,10,5,8\nC,6,9,6,7,4,4,7,5,7,10,9,14,4,9,5,5\nS,4,9,6,6,7,9,4,4,4,9,6,9,4,7,10,9\nF,7,10,9,8,7,9,7,2,6,12,4,6,5,9,4,9\nC,5,10,7,9,8,5,6,4,4,7,6,11,5,11,8,10\nV,4,7,6,5,6,8,6,4,2,7,8,8,7,9,4,6\nT,4,4,5,3,2,5,12,2,8,11,9,4,0,10,2,4\nN,5,9,5,4,2,9,11,5,3,5,6,9,5,11,2,6\nE,1,0,1,0,0,5,8,5,7,7,6,12,0,8,6,10\nL,3,8,3,6,2,0,2,4,6,1,0,8,0,8,0,8\nA,3,9,5,6,2,6,5,3,1,6,1,8,2,7,2,7\nK,5,11,5,8,5,3,8,7,3,6,4,11,3,8,2,11\nM,6,9,10,7,12,7,5,3,2,7,5,8,15,7,4,6\nR,2,3,3,2,2,7,7,5,5,7,5,6,2,7,4,8\nS,6,12,6,7,3,6,8,3,6,13,7,7,2,9,3,7\nY,3,9,5,6,3,7,9,1,6,6,11,8,2,11,2,7\nV,7,10,5,5,2,6,11,5,4,11,9,4,4,11,3,10\nS,2,0,2,1,1,8,7,4,6,5,6,8,0,8,7,8\nM,5,6,8,4,5,9,6,2,4,9,5,7,8,6,2,8\nO,9,15,6,8,5,5,7,7,4,10,7,10,5,9,5,8\nL,3,7,3,5,1,0,1,6,6,0,0,6,0,8,0,8\nD,6,9,8,8,8,7,6,5,7,7,5,9,6,5,10,3\nP,2,1,3,2,1,4,10,3,5,10,8,5,0,9,3,7\nW,3,8,5,6,5,11,11,2,2,5,8,7,7,12,1,7\nO,4,3,5,4,2,7,6,8,8,6,5,7,3,8,4,8\nE,4,9,5,6,3,5,9,2,10,10,8,9,2,8,5,5\nJ,2,11,3,8,2,15,4,4,5,13,1,8,0,7,0,8\nT,5,8,7,7,7,7,9,4,8,7,7,8,3,10,8,6\nD,2,2,3,3,2,7,7,7,6,6,6,4,2,8,3,7\nC,7,10,8,8,4,4,8,6,9,12,9,13,2,9,3,7\nT,6,9,6,7,5,6,11,3,7,11,9,5,2,12,2,4\nS,2,3,4,2,1,8,7,2,6,10,6,8,1,9,5,8\nA,4,9,6,6,2,9,5,3,1,8,1,8,2,7,2,8" }, { "path": "data/lizards.csv", "content": "\"Species\",\"Diameter\",\"Height\"\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,2\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,1,1\n1,2,2\n1,2,2\n1,2,2\n1,2,2\n1,2,2\n1,2,2\n1,2,2\n1,2,2\n1,2,2\n1,2,2\n1,2,2\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n1,2,1\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,2\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,1,1\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,2\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n2,2,1\n" }, { "path": "data/munin.bif", "content": "network unknown {\n}\nvariable R_LNLW_MED_SEV {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLW_MED_PATHO {\n type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };\n}\nvariable R_LNLW_MEDD2_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable DIFFN_SEV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable DIFFN_TYPE {\n type discrete [ 3 ] { MOTOR, MIXED, SENS };\n}\nvariable DIFFN_SENS_SEV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable DIFFN_DISTR {\n type discrete [ 3 ] { DIST, PROX, RANDOM };\n}\nvariable DIFFN_S_SEV_DIST {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable DIFFN_PATHO {\n type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };\n}\nvariable R_DIFFN_MEDD2_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_LNLW_MEDD2_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MEDD2_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLBE_MEDD2_DISP_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MEDD2_DISP_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MEDD2_DISP_EWD {\n type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };\n}\nvariable R_DIFFN_MEDD2_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLBE_MEDD2_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_MEDD2_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_MEDD2_AMPR_EW {\n 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 };\n}\nvariable R_LNLBE_MEDD2_LD_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MEDD2_LD_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLBE_MEDD2_RD_EW {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_MEDD2_RD_EW {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_MEDD2_LSLOW_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable R_LNLW_MEDD2_SALOSS_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_MEDD2_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_LNLW_MEDD2_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLBE_MEDD2_SALOSS_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_MEDD2_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_MEDD2_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MEDD2_DIFSLOW_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MEDD2_DSLOW_EW {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_MEDD2_ALLCV_EW {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_MEDD2_CV_EW {\n 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 };\n}\nvariable R_LNLW_MEDD2_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_MEDD2_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_MEDD2_EFFAXLOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_MEDD2_ALLAMP_WD {\n type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };\n}\nvariable R_MEDD2_AMP_WD {\n 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 };\n}\nvariable R_LNLW_MEDD2_LD_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MEDD2_LD_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLW_MEDD2_RD_WD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_MEDD2_RD_WD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_MEDD2_LSLOW_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable R_LNLBE_MEDD2_DIFSLOW_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MEDD2_DIFSLOW_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MEDD2_DSLOW_WD {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_MEDD2_ALLCV_WD {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_MEDD2_CV_WD {\n 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 };\n}\nvariable DIFFN_MOT_SEV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable DIFFN_M_SEV_DIST {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_MED_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLBE_MED_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_MED_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_MED_AMPR_EW {\n 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 };\n}\nvariable R_LNLT1_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLLP_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLT1_LP_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLBE_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLT1_LP_BE_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLW_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_LNLW_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_APB_MALOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable R_DIFFN_MED_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MED_DIFSLOW_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MED_DCV_EW {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_MED_LD_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MED_RD_EW {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_MED_RDLDCV_EW {\n type discrete [ 6 ] { M_S60, M_S52, M_S44, M_S27, M_S15, M_S07 };\n}\nvariable R_MED_ALLCV_EW {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_MED_CV_EW {\n 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 };\n}\nvariable R_LNLT1_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLLP_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLT1_LP_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLBE_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLT1_LP_BE_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable DIFFN_TIME {\n type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };\n}\nvariable R_DIFFN_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLW_MED_TIME {\n type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };\n}\nvariable R_LNLW_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DIFFN_LNLW_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNL_DIFFN_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MYOP_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MYDY_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MYOP_MYDY_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DE_REGEN_APB_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_MYAS_APB_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_APB_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_MYDY_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MYOP_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MYOP_MYDY_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLW_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_DIFFN_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_DIFFN_LNLW_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLBE_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLLP_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLT1_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLT1_LP_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLT1_LP_BE_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNL_DIFFN_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_APB_MUSIZE {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_APB_EFFMUS {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_LNLW_MED_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_MED_BLOCK_WA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_APB_MULOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable R_APB_ALLAMP_WA {\n type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };\n}\nvariable R_MED_AMP_WA {\n 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 };\n}\nvariable R_MED_LD_WA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MED_RD_WA {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_MED_RDLDDEL {\n type discrete [ 5 ] { MS3_1, MS3_9, MS4_7, MS10_1, MS20_1 };\n}\nvariable R_LNLBE_MED_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MED_DIFSLOW_WA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MED_DCV_WA {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_MED_ALLDEL_WA {\n type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };\n}\nvariable R_MED_LAT_WA {\n 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 };\n}\nvariable R_APB_VOL_ACT {\n type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };\n}\nvariable R_APB_FORCE {\n type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };\n}\nvariable R_APB_MUSCLE_VOL {\n type discrete [ 2 ] { ATROPHIC, NORMAL };\n}\nvariable R_APB_MVA_RECRUIT {\n type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };\n}\nvariable R_APB_MVA_AMP {\n type discrete [ 3 ] { INCR, NORMAL, REDUCED };\n}\nvariable R_APB_TA_CONCL {\n type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };\n}\nvariable R_APB_MUPAMP {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_APB_QUAN_MUPAMP {\n type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };\n}\nvariable R_APB_QUAL_MUPAMP {\n type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };\n}\nvariable R_APB_MUPDUR {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_APB_QUAN_MUPDUR {\n type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };\n}\nvariable R_APB_QUAL_MUPDUR {\n type discrete [ 3 ] { SMALL, NORMAL, INCR };\n}\nvariable R_APB_QUAN_MUPPOLY {\n type discrete [ 3 ] { x_12_, x12_24_, x_24_ };\n}\nvariable R_APB_QUAL_MUPPOLY {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable R_APB_MUPSATEL {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_APB_MUPINSTAB {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_APB_REPSTIM_CMAPAMP {\n 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 };\n}\nvariable R_APB_REPSTIM_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable R_APB_REPSTIM_FACILI {\n type discrete [ 4 ] { NO, MOD, SEV, REDUCED };\n}\nvariable R_APB_REPSTIM_POST_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable R_APB_SF_JITTER {\n type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };\n}\nvariable R_LNLT1_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLLP_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLT1_LP_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLBE_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLT1_LP_BE_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_DIFFN_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLW_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_DIFFN_LNLW_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNL_DIFFN_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYOP_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYDY_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYOP_MYDY_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYAS_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MUSCLE_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_APB_SF_DENSITY {\n type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };\n}\nvariable R_LNLT1_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLLP_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLT1_LP_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLBE_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLT1_LP_BE_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_DIFFN_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLW_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_DIFFN_LNLW_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_APB_SPONT_NEUR_DISCH {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_MYOP_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MYDY_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MYOP_MYDY_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_NMT_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MUSCLE_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLT1_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLLP_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLT1_LP_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLBE_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLT1_LP_BE_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLW_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_LNLW_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNL_DIFFN_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_APB_SPONT_DENERV_ACT {\n type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };\n}\nvariable R_APB_SPONT_HF_DISCH {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_APB_SPONT_INS_ACT {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable DIFFN_M_SEV_PROX {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable DIFFN_DUMMY_1 {\n type discrete [ 2 ] { dummy, State1 };\n}\nvariable DIFFN_DUMMY_2 {\n type discrete [ 2 ] { dummy, State1 };\n}\nvariable DIFFN_DUMMY_3 {\n type discrete [ 2 ] { dummy, State1 };\n}\nvariable R_OTHER_ULND5_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_ULND5_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLE_ULN_SEV {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLE_ULN_PATHO {\n type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };\n}\nvariable R_LNLE_ULND5_DISP_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLE_DIFFN_ULND5_DISP_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DISP_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLW_ULND5_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_LNLW_ULND5_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DISP_BEW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DISP_BED {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DISP_EED {\n type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };\n}\nvariable R_OTHER_ULND5_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_ULND5_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLE_ULND5_BLOCK_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLE_DIFFN_ULND5_BLOCK_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULND5_BLOCK_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULND5_AMPR_E {\n 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 };\n}\nvariable R_OTHER_ULND5_LD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLE_ULND5_LD_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_LD_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_OTHER_ULND5_RD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_LNLE_ULND5_RD_E {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_ULND5_RD_E {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_ULND5_LSLOW_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable R_OTHER_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLW_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_LNLW_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLE_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLLP_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLLP_E_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNL_DIFFN_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_OTHER_ULND5_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLE_ULND5_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_ULND5_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLE_DIFFN_ULND5_DIFSLOW_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DIFSLOW_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DSLOW_E {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULND5_ALLCV_E {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULND5_CV_E {\n 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 };\n}\nvariable R_ULND5_DISP_EWD {\n type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };\n}\nvariable R_ULND5_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULND5_AMPR_EW {\n 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 };\n}\nvariable R_ULND5_DIFSLOW_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DSLOW_EW {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULND5_ALLCV_EW {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULND5_CV_EW {\n 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 };\n}\nvariable R_LNLW_ULND5_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_LNLW_ULND5_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULND5_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULND5_EFFAXLOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULND5_ALLAMP_WD {\n type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };\n}\nvariable R_ULND5_AMP_WD {\n 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 };\n}\nvariable R_LNLW_ULND5_LD_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_LD_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLW_ULND5_RD_WD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_ULND5_RD_WD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_ULND5_LSLOW_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable R_LNLE_DIFFN_ULND5_DIFSLOW_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DIFSLOW_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULND5_DSLOW_WD {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULND5_ALLCV_WD {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULND5_CV_WD {\n 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 };\n}\nvariable R_DIFFN_ULN_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLE_ULN_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULN_BLOCK_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULN_AMPR_E {\n 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 };\n}\nvariable R_OTHER_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLC8_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLLP_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLC8_LP_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLE_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLC8_LP_E_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLW_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_LNLW_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNL_DIFFN_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ADM_MALOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable R_LNLE_ULN_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_ULN_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULN_DIFSLOW_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULN_DCV_E {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULN_LD_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULN_RD_EW {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_ULN_RDLDCV_E {\n type discrete [ 6 ] { M_S60, M_S52, M_S44, M_S27, M_S15, M_S07 };\n}\nvariable R_ULN_ALLCV_E {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULN_CV_E {\n 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 };\n}\nvariable R_ULN_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULN_AMPR_EW {\n 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 };\n}\nvariable R_ULN_DIFSLOW_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULN_DCV_EW {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULN_ALLCV_EW {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULN_CV_EW {\n 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 };\n}\nvariable R_OTHER_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_LNLC8_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLLP_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLC8_LP_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLE_ULN_TIME {\n type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };\n}\nvariable R_LNLE_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLC8_LP_E_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DIFFN_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLW_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DIFFN_LNLW_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNL_DIFFN_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MYOP_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MYDY_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MYOP_MYDY_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_OTHER_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MUSCLE_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DE_REGEN_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_MYAS_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_MYAS_DE_REGEN_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_OTHER_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MYDY_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MYOP_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MYOP_MYDY_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MUSCLE_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLW_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_DIFFN_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_DIFFN_LNLW_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLE_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLLP_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLC8_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLC8_LP_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLC8_LP_E_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNL_DIFFN_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_ADM_MUSIZE {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_ADM_EFFMUS {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_OTHER_ULN_BLOCK_WA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLW_ULN_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_LNLW_ULN_BLOCK_WA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ULN_BLOCK_WA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_ADM_MULOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable R_ADM_ALLAMP_WA {\n type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };\n}\nvariable R_ULN_AMP_WA {\n 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 };\n}\nvariable R_ULN_LD_WA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULN_RD_WA {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_ULN_RDLDDEL {\n type discrete [ 5 ] { MS3_1, MS3_9, MS4_7, MS10_1, MS20_1 };\n}\nvariable R_ULN_DIFSLOW_WA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ULN_DCV_WA {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_ULN_ALLDEL_WA {\n type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };\n}\nvariable R_ULN_LAT_WA {\n 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 };\n}\nvariable R_ADM_VOL_ACT {\n type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };\n}\nvariable R_ADM_FORCE {\n type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };\n}\nvariable R_ADM_MUSCLE_VOL {\n type discrete [ 2 ] { ATROPHIC, NORMAL };\n}\nvariable R_ADM_MVA_RECRUIT {\n type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };\n}\nvariable R_ADM_MVA_AMP {\n type discrete [ 3 ] { INCR, NORMAL, REDUCED };\n}\nvariable R_ADM_TA_CONCL {\n type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };\n}\nvariable R_ADM_MUPAMP {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_ADM_QUAN_MUPAMP {\n type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };\n}\nvariable R_ADM_QUAL_MUPAMP {\n type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };\n}\nvariable R_ADM_MUPDUR {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_ADM_QUAN_MUPDUR {\n type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };\n}\nvariable R_ADM_QUAL_MUPDUR {\n type discrete [ 3 ] { SMALL, NORMAL, INCR };\n}\nvariable R_ADM_QUAN_MUPPOLY {\n type discrete [ 3 ] { x_12_, x12_24_, x_24_ };\n}\nvariable R_ADM_QUAL_MUPPOLY {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable R_ADM_MUPSATEL {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_ADM_MUPINSTAB {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_ADM_REPSTIM_CMAPAMP {\n 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 };\n}\nvariable R_ADM_REPSTIM_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable R_ADM_REPSTIM_FACILI {\n type discrete [ 4 ] { NO, MOD, SEV, REDUCED };\n}\nvariable R_ADM_REPSTIM_POST_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable R_ADM_SF_JITTER {\n type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };\n}\nvariable R_LNLC8_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLLP_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLC8_LP_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLE_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLC8_LP_E_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_DIFFN_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLW_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_DIFFN_LNLW_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNL_DIFFN_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYOP_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYDY_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYOP_MYDY_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYAS_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_OTHER_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYAS_OTHER_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MUSCLE_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_ADM_SF_DENSITY {\n type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };\n}\nvariable R_LNLC8_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLLP_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLC8_LP_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLE_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLC8_LP_E_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_DIFFN_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLW_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_DIFFN_LNLW_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNL_DIFFN_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_OTHER_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_ADM_SPONT_NEUR_DISCH {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_MYOP_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MYDY_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MYOP_MYDY_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_OTHER_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_NMT_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_OTHER_NMT_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MUSCLE_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLC8_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLLP_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLC8_LP_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLE_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLC8_LP_E_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLW_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_LNLW_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNL_DIFFN_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_ADM_SPONT_DENERV_ACT {\n type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };\n}\nvariable R_ADM_SPONT_HF_DISCH {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_ADM_SPONT_INS_ACT {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable R_OTHER_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_LNLPC5_AXIL_SEV {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLPC5_AXIL_TIME {\n type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };\n}\nvariable R_LNLPC5_AXIL_PATHO {\n type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };\n}\nvariable R_LNLPC5_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DIFFN_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_LNLPC5_DIFFN_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MYOP_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MYDY_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MYOP_MYDY_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_OTHER_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_MUSCLE_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DE_REGEN_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_MYAS_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_MYAS_DE_REGEN_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable R_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, OTHER };\n}\nvariable R_OTHER_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MYDY_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MYOP_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MYOP_MYDY_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_MUSCLE_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_DIFFN_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLPC5_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_LNLPC5_DIFFN_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable R_DELT_MUSIZE {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_DELT_EFFMUS {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_DIFFN_AXIL_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_OTHER_AXIL_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_AXIL_BLOCK_ED {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLPC5_DELT_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_DELT_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNLPC5_DIFFN_DELT_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_OTHER_DELT_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DELT_MALOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable R_DELT_MULOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable R_DELT_ALLAMP {\n type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };\n}\nvariable R_AXIL_AMP_E {\n 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 };\n}\nvariable R_AXIL_RD_ED {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_LNLPC5_AXIL_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_AXIL_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_AXIL_DIFSLOW_ED {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_AXIL_DCV {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_AXIL_DEL {\n type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };\n}\nvariable R_AXIL_LAT_ED {\n 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 };\n}\nvariable R_DELT_VOL_ACT {\n type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };\n}\nvariable R_DELT_FORCE {\n type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };\n}\nvariable R_DELT_MUSCLE_VOL {\n type discrete [ 2 ] { ATROPHIC, NORMAL };\n}\nvariable R_DELT_MVA_RECRUIT {\n type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };\n}\nvariable R_DELT_MVA_AMP {\n type discrete [ 3 ] { INCR, NORMAL, REDUCED };\n}\nvariable R_DELT_TA_CONCL {\n type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };\n}\nvariable R_DELT_MUPAMP {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_DELT_QUAN_MUPAMP {\n type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };\n}\nvariable R_DELT_QUAL_MUPAMP {\n type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };\n}\nvariable R_DELT_MUPDUR {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable R_DELT_QUAN_MUPDUR {\n type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };\n}\nvariable R_DELT_QUAL_MUPDUR {\n type discrete [ 3 ] { SMALL, NORMAL, INCR };\n}\nvariable R_DELT_QUAN_MUPPOLY {\n type discrete [ 3 ] { x_12_, x12_24_, x_24_ };\n}\nvariable R_DELT_QUAL_MUPPOLY {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable R_DELT_MUPSATEL {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DELT_MUPINSTAB {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DELT_REPSTIM_CMAPAMP {\n 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 };\n}\nvariable R_DELT_REPSTIM_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable R_DELT_REPSTIM_FACILI {\n type discrete [ 4 ] { NO, MOD, SEV, REDUCED };\n}\nvariable R_DELT_REPSTIM_POST_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable R_DELT_SF_JITTER {\n type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };\n}\nvariable R_LNLPC5_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_DIFFN_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_LNLPC5_DIFFN_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYOP_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYDY_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYOP_MYDY_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYAS_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_OTHER_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MYAS_OTHER_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_MUSCLE_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable R_DELT_SF_DENSITY {\n type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };\n}\nvariable R_LNLPC5_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_DIFFN_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_LNLPC5_DIFFN_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_OTHER_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_DELT_SPONT_NEUR_DISCH {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable R_MYOP_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MYDY_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MYOP_MYDY_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_OTHER_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_NMT_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_OTHER_NMT_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_MUSCLE_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLPC5_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_LNLPC5_DIFFN_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DELT_SPONT_DENERV_ACT {\n type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };\n}\nvariable R_DELT_SPONT_HF_DISCH {\n type discrete [ 2 ] { NO, YES };\n}\nvariable R_DELT_SPONT_INS_ACT {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable R_OTHER_ISCH_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_ISCH_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_SUR_DISP_CA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_OTHER_ISCH_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_ISCH_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_SUR_BLOCK_CA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_OTHER_ISCH_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_ISCH_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNL_ISCH_SEV {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_LNL_ISCH_PATHO {\n type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };\n}\nvariable R_LNL_ISCH_SALOSS_CA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_DIFFN_LNL_ISCH_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_SUR_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_SUR_EFFAXLOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable R_SUR_ALLAMP_CA {\n type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };\n}\nvariable R_SUR_AMP_CA {\n 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 };\n}\nvariable R_SUR_LD_CA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_SUR_RD_CA {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable R_SUR_LSLOW_CA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable R_OTHER_ISCH_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_DIFFN_ISCH_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_SUR_DIFSLOW_CA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable R_SUR_DSLOW_CA {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_SUR_ALLCV_CA {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable R_SUR_CV_CA {\n 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 };\n}\nvariable L_LNLW_MED_SEV {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLW_MED_PATHO {\n type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };\n}\nvariable L_LNLW_MEDD2_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_MEDD2_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_LNLW_MEDD2_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MEDD2_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLBE_MEDD2_DISP_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MEDD2_DISP_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MEDD2_DISP_EWD {\n type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };\n}\nvariable L_DIFFN_MEDD2_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLBE_MEDD2_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_MEDD2_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_MEDD2_AMPR_EW {\n 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 };\n}\nvariable L_LNLBE_MEDD2_LD_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MEDD2_LD_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLBE_MEDD2_RD_EW {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_MEDD2_RD_EW {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_MEDD2_LSLOW_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable L_LNLW_MEDD2_SALOSS_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_MEDD2_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_LNLW_MEDD2_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLBE_MEDD2_SALOSS_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_MEDD2_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_MEDD2_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MEDD2_DIFSLOW_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MEDD2_DSLOW_EW {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_MEDD2_ALLCV_EW {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_MEDD2_CV_EW {\n 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 };\n}\nvariable L_LNLW_MEDD2_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_MEDD2_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_MEDD2_EFFAXLOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_MEDD2_ALLAMP_WD {\n type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };\n}\nvariable L_MEDD2_AMP_WD {\n 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 };\n}\nvariable L_LNLW_MEDD2_LD_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MEDD2_LD_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLW_MEDD2_RD_WD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_MEDD2_RD_WD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_MEDD2_LSLOW_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable L_LNLBE_MEDD2_DIFSLOW_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MEDD2_DIFSLOW_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MEDD2_DSLOW_WD {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_MEDD2_ALLCV_WD {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_MEDD2_CV_WD {\n 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 };\n}\nvariable L_DIFFN_MED_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLBE_MED_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_MED_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_MED_AMPR_EW {\n 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 };\n}\nvariable L_LNLT1_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLLP_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLT1_LP_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLBE_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLT1_LP_BE_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLW_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_LNLW_APB_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_APB_MALOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable L_DIFFN_MED_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MED_DIFSLOW_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MED_DCV_EW {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_MED_LD_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MED_RD_EW {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_MED_RDLDCV_EW {\n type discrete [ 6 ] { M_S60, M_S52, M_S44, M_S27, M_S15, M_S07 };\n}\nvariable L_MED_ALLCV_EW {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_MED_CV_EW {\n 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 };\n}\nvariable L_LNLT1_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLLP_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLT1_LP_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLBE_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLT1_LP_BE_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DIFFN_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLW_MED_TIME {\n type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };\n}\nvariable L_LNLW_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DIFFN_LNLW_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNL_DIFFN_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MYOP_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MYDY_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MYOP_MYDY_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_APB_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DE_REGEN_APB_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_MYAS_APB_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_APB_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_MYDY_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MYOP_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MYOP_MYDY_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLW_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_DIFFN_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_DIFFN_LNLW_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLBE_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLLP_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLT1_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLT1_LP_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLT1_LP_BE_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNL_DIFFN_APB_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_APB_MUSIZE {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_APB_EFFMUS {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_LNLW_MED_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_MED_BLOCK_WA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_APB_MULOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable L_APB_ALLAMP_WA {\n type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };\n}\nvariable L_MED_AMP_WA {\n 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 };\n}\nvariable L_MED_LD_WA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MED_RD_WA {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_MED_RDLDDEL {\n type discrete [ 5 ] { MS3_1, MS3_9, MS4_7, MS10_1, MS20_1 };\n}\nvariable L_LNLBE_MED_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MED_DIFSLOW_WA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MED_DCV_WA {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_MED_ALLDEL_WA {\n type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };\n}\nvariable L_MED_LAT_WA {\n 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 };\n}\nvariable L_APB_VOL_ACT {\n type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };\n}\nvariable L_APB_FORCE {\n type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };\n}\nvariable L_APB_MUSCLE_VOL {\n type discrete [ 2 ] { ATROPHIC, NORMAL };\n}\nvariable L_APB_MVA_RECRUIT {\n type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };\n}\nvariable L_APB_MVA_AMP {\n type discrete [ 3 ] { INCR, NORMAL, REDUCED };\n}\nvariable L_APB_TA_CONCL {\n type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };\n}\nvariable L_APB_MUPAMP {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_APB_QUAN_MUPAMP {\n type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };\n}\nvariable L_APB_QUAL_MUPAMP {\n type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };\n}\nvariable L_APB_MUPDUR {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_APB_QUAN_MUPDUR {\n type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };\n}\nvariable L_APB_QUAL_MUPDUR {\n type discrete [ 3 ] { SMALL, NORMAL, INCR };\n}\nvariable L_APB_QUAN_MUPPOLY {\n type discrete [ 3 ] { x_12_, x12_24_, x_24_ };\n}\nvariable L_APB_QUAL_MUPPOLY {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable L_APB_MUPSATEL {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_APB_MUPINSTAB {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_APB_REPSTIM_CMAPAMP {\n 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 };\n}\nvariable L_APB_REPSTIM_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable L_APB_REPSTIM_FACILI {\n type discrete [ 4 ] { NO, MOD, SEV, REDUCED };\n}\nvariable L_APB_REPSTIM_POST_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable L_APB_SF_JITTER {\n type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };\n}\nvariable L_LNLT1_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLLP_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLT1_LP_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLBE_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLT1_LP_BE_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_DIFFN_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLW_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_DIFFN_LNLW_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNL_DIFFN_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYOP_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYDY_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYOP_MYDY_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYAS_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MUSCLE_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_APB_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_APB_SF_DENSITY {\n type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };\n}\nvariable L_LNLT1_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLLP_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLT1_LP_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLBE_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLT1_LP_BE_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_DIFFN_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLW_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_DIFFN_LNLW_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_APB_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_APB_SPONT_NEUR_DISCH {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_MYOP_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MYDY_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MYOP_MYDY_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_NMT_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MUSCLE_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLT1_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLLP_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLT1_LP_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLBE_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLT1_LP_BE_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLW_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_LNLW_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNL_DIFFN_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_APB_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_APB_SPONT_DENERV_ACT {\n type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };\n}\nvariable L_APB_SPONT_HF_DISCH {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_APB_SPONT_INS_ACT {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable L_OTHER_ULND5_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_ULND5_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLE_ULN_SEV {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLE_ULN_PATHO {\n type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };\n}\nvariable L_LNLE_ULND5_DISP_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLE_DIFFN_ULND5_DISP_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DISP_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLW_ULND5_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_LNLW_ULND5_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DISP_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DISP_BEW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DISP_BED {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DISP_EED {\n type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };\n}\nvariable L_OTHER_ULND5_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_ULND5_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLE_ULND5_BLOCK_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLE_DIFFN_ULND5_BLOCK_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULND5_BLOCK_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULND5_AMPR_E {\n 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 };\n}\nvariable L_OTHER_ULND5_LD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLE_ULND5_LD_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_LD_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_OTHER_ULND5_RD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_LNLE_ULND5_RD_E {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_ULND5_RD_E {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_ULND5_LSLOW_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable L_OTHER_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLW_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_LNLW_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLE_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLLP_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLLP_E_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNL_DIFFN_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULND5_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_OTHER_ULND5_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLE_ULND5_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_ULND5_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLE_DIFFN_ULND5_DIFSLOW_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DIFSLOW_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DSLOW_E {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULND5_ALLCV_E {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULND5_CV_E {\n 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 };\n}\nvariable L_ULND5_DISP_EWD {\n type discrete [ 9 ] { R0_15, R0_25, R0_35, R0_45, R0_55, R0_65, R0_75, R0_85, R0_95 };\n}\nvariable L_ULND5_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULND5_AMPR_EW {\n 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 };\n}\nvariable L_ULND5_DIFSLOW_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DSLOW_EW {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULND5_ALLCV_EW {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULND5_CV_EW {\n 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 };\n}\nvariable L_LNLW_ULND5_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_LNLW_ULND5_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULND5_BLOCK_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULND5_EFFAXLOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULND5_ALLAMP_WD {\n type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };\n}\nvariable L_ULND5_AMP_WD {\n 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 };\n}\nvariable L_LNLW_ULND5_LD_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_LD_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLW_ULND5_RD_WD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_ULND5_RD_WD {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_ULND5_LSLOW_WD {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable L_LNLE_DIFFN_ULND5_DIFSLOW_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DIFSLOW_WD {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULND5_DSLOW_WD {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULND5_ALLCV_WD {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULND5_CV_WD {\n 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 };\n}\nvariable L_DIFFN_ULN_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLE_ULN_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULN_BLOCK_E {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULN_AMPR_E {\n 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 };\n}\nvariable L_OTHER_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLC8_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLLP_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLC8_LP_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLE_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLC8_LP_E_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLW_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_LNLW_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNL_DIFFN_ADM_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ADM_MALOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable L_LNLE_ULN_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_ULN_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULN_DIFSLOW_E {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULN_DCV_E {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULN_LD_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULN_RD_EW {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_ULN_RDLDCV_E {\n type discrete [ 6 ] { M_S60, M_S52, M_S44, M_S27, M_S15, M_S07 };\n}\nvariable L_ULN_ALLCV_E {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULN_CV_E {\n 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 };\n}\nvariable L_ULN_BLOCK_EW {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULN_AMPR_EW {\n 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 };\n}\nvariable L_ULN_DIFSLOW_EW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULN_DCV_EW {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULN_ALLCV_EW {\n type discrete [ 10 ] { M_S60, M_S56, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULN_CV_EW {\n 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 };\n}\nvariable L_OTHER_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_LNLC8_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLLP_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLC8_LP_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLE_ULN_TIME {\n type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };\n}\nvariable L_LNLE_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLC8_LP_E_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DIFFN_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLW_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DIFFN_LNLW_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNL_DIFFN_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MYOP_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MYDY_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MYOP_MYDY_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_OTHER_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MUSCLE_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_ADM_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DE_REGEN_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_MYAS_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_MYAS_DE_REGEN_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_ADM_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_OTHER_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MYDY_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MYOP_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MYOP_MYDY_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MUSCLE_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLW_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_DIFFN_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_DIFFN_LNLW_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLE_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLLP_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLC8_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLC8_LP_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLC8_LP_E_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNL_DIFFN_ADM_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_ADM_MUSIZE {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_ADM_EFFMUS {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_OTHER_ULN_BLOCK_WA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLW_ULN_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_LNLW_ULN_BLOCK_WA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ULN_BLOCK_WA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_ADM_MULOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable L_ADM_ALLAMP_WA {\n type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };\n}\nvariable L_ULN_AMP_WA {\n 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 };\n}\nvariable L_ULN_LD_WA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULN_RD_WA {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_ULN_RDLDDEL {\n type discrete [ 5 ] { MS3_1, MS3_9, MS4_7, MS10_1, MS20_1 };\n}\nvariable L_ULN_DIFSLOW_WA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ULN_DCV_WA {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_ULN_ALLDEL_WA {\n type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };\n}\nvariable L_ULN_LAT_WA {\n 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 };\n}\nvariable L_ADM_VOL_ACT {\n type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };\n}\nvariable L_ADM_FORCE {\n type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };\n}\nvariable L_ADM_MUSCLE_VOL {\n type discrete [ 2 ] { ATROPHIC, NORMAL };\n}\nvariable L_ADM_MVA_RECRUIT {\n type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };\n}\nvariable L_ADM_MVA_AMP {\n type discrete [ 3 ] { INCR, NORMAL, REDUCED };\n}\nvariable L_ADM_TA_CONCL {\n type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };\n}\nvariable L_ADM_MUPAMP {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_ADM_QUAN_MUPAMP {\n type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };\n}\nvariable L_ADM_QUAL_MUPAMP {\n type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };\n}\nvariable L_ADM_MUPDUR {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_ADM_QUAN_MUPDUR {\n type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };\n}\nvariable L_ADM_QUAL_MUPDUR {\n type discrete [ 3 ] { SMALL, NORMAL, INCR };\n}\nvariable L_ADM_QUAN_MUPPOLY {\n type discrete [ 3 ] { x_12_, x12_24_, x_24_ };\n}\nvariable L_ADM_QUAL_MUPPOLY {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable L_ADM_MUPSATEL {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_ADM_MUPINSTAB {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_ADM_REPSTIM_CMAPAMP {\n 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 };\n}\nvariable L_ADM_REPSTIM_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable L_ADM_REPSTIM_FACILI {\n type discrete [ 4 ] { NO, MOD, SEV, REDUCED };\n}\nvariable L_ADM_REPSTIM_POST_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable L_ADM_SF_JITTER {\n type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };\n}\nvariable L_LNLC8_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLLP_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLC8_LP_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLE_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLC8_LP_E_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_DIFFN_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLW_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_DIFFN_LNLW_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNL_DIFFN_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYOP_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYDY_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYOP_MYDY_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYAS_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_OTHER_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYAS_OTHER_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MUSCLE_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_ADM_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_ADM_SF_DENSITY {\n type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };\n}\nvariable L_LNLC8_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLLP_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLC8_LP_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLE_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLC8_LP_E_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_DIFFN_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLW_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_DIFFN_LNLW_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNL_DIFFN_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_OTHER_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_ADM_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_ADM_SPONT_NEUR_DISCH {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_MYOP_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MYDY_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MYOP_MYDY_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_OTHER_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_NMT_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_OTHER_NMT_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MUSCLE_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLC8_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLLP_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLC8_LP_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLE_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLC8_LP_E_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLW_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_LNLW_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNL_DIFFN_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ADM_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_ADM_SPONT_DENERV_ACT {\n type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };\n}\nvariable L_ADM_SPONT_HF_DISCH {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_ADM_SPONT_INS_ACT {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable L_OTHER_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_LNLPC5_AXIL_SEV {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLPC5_AXIL_TIME {\n type discrete [ 4 ] { ACUTE, SUBACUTE, CHRONIC, OLD };\n}\nvariable L_LNLPC5_AXIL_PATHO {\n type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };\n}\nvariable L_LNLPC5_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DIFFN_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_LNLPC5_DIFFN_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MYOP_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MYDY_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MYOP_MYDY_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_OTHER_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_MUSCLE_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DELT_DE_REGEN {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DE_REGEN_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_MYAS_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_MYAS_DE_REGEN_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, MIXED };\n}\nvariable L_DELT_NMT {\n type discrete [ 7 ] { NO, MOD_PRE, SEV_PRE, MLD_POST, MOD_POST, SEV_POST, OTHER };\n}\nvariable L_OTHER_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MYDY_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MYOP_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MYOP_MYDY_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_MUSCLE_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_DIFFN_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLPC5_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_LNLPC5_DIFFN_DELT_MUSIZE {\n type discrete [ 6 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE };\n}\nvariable L_DELT_MUSIZE {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_DELT_EFFMUS {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_DIFFN_AXIL_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_OTHER_AXIL_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_AXIL_BLOCK_ED {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLPC5_DELT_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_DELT_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNLPC5_DIFFN_DELT_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_OTHER_DELT_MALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DELT_MALOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable L_DELT_MULOSS {\n type discrete [ 6 ] { NO, MILD, MOD, SEV, TOTAL, OTHER };\n}\nvariable L_DELT_ALLAMP {\n type discrete [ 9 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00, A2_00, A4_00, A8_00 };\n}\nvariable L_AXIL_AMP_E {\n 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 };\n}\nvariable L_AXIL_RD_ED {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_LNLPC5_AXIL_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_AXIL_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_AXIL_DIFSLOW_ED {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_AXIL_DCV {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_AXIL_DEL {\n type discrete [ 9 ] { MS0_0, MS0_4, MS0_8, MS1_6, MS3_2, MS6_4, MS12_8, MS25_6, INFIN };\n}\nvariable L_AXIL_LAT_ED {\n 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 };\n}\nvariable L_DELT_VOL_ACT {\n type discrete [ 4 ] { NORMAL, REDUCED, V_RED, ABSENT };\n}\nvariable L_DELT_FORCE {\n type discrete [ 6 ] { x5, x4, x3, x2, x1, x0 };\n}\nvariable L_DELT_MUSCLE_VOL {\n type discrete [ 2 ] { ATROPHIC, NORMAL };\n}\nvariable L_DELT_MVA_RECRUIT {\n type discrete [ 4 ] { FULL, REDUCED, DISCRETE, NO_UNITS };\n}\nvariable L_DELT_MVA_AMP {\n type discrete [ 3 ] { INCR, NORMAL, REDUCED };\n}\nvariable L_DELT_TA_CONCL {\n type discrete [ 5 ] { x_5ABOVE, x2_5ABOVE, NORMAL, x2_5BELOW, x_5BELOW };\n}\nvariable L_DELT_MUPAMP {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_DELT_QUAN_MUPAMP {\n type discrete [ 20 ] { UV34, UV44, UV58, UV74, UV94, UV122, UV156, UV200, UV260, UV330, UV420, UV540, UV700, UV900, UV1150, UV1480, UV1900, UV2440, UV3130, UV4020 };\n}\nvariable L_DELT_QUAL_MUPAMP {\n type discrete [ 5 ] { V_RED, REDUCED, NORMAL, INCR, V_INCR };\n}\nvariable L_DELT_MUPDUR {\n type discrete [ 7 ] { V_SMALL, SMALL, NORMAL, INCR, LARGE, V_LARGE, OTHER };\n}\nvariable L_DELT_QUAN_MUPDUR {\n type discrete [ 19 ] { MS3, MS4, MS5, MS6, MS7, MS8, MS9, MS10, MS11, MS12, MS13, MS14, MS15, MS16, MS17, MS18, MS19, MS20, MS_20 };\n}\nvariable L_DELT_QUAL_MUPDUR {\n type discrete [ 3 ] { SMALL, NORMAL, INCR };\n}\nvariable L_DELT_QUAN_MUPPOLY {\n type discrete [ 3 ] { x_12_, x12_24_, x_24_ };\n}\nvariable L_DELT_QUAL_MUPPOLY {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable L_DELT_MUPSATEL {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DELT_MUPINSTAB {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DELT_REPSTIM_CMAPAMP {\n 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 };\n}\nvariable L_DELT_REPSTIM_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable L_DELT_REPSTIM_FACILI {\n type discrete [ 4 ] { NO, MOD, SEV, REDUCED };\n}\nvariable L_DELT_REPSTIM_POST_DECR {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, INCON };\n}\nvariable L_DELT_SF_JITTER {\n type discrete [ 4 ] { NORMAL, x2_5, x5_10, x_10 };\n}\nvariable L_LNLPC5_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_DIFFN_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_LNLPC5_DIFFN_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYOP_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYDY_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYOP_MYDY_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYAS_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_OTHER_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MYAS_OTHER_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_MUSCLE_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_DELT_MUDENS {\n type discrete [ 3 ] { NORMAL, INCR, V_INCR };\n}\nvariable L_DELT_SF_DENSITY {\n type discrete [ 3 ] { x_2SD, x2_4SD, x_4SD };\n}\nvariable L_LNLPC5_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_DIFFN_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_LNLPC5_DIFFN_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_OTHER_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_DELT_NEUR_ACT {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_DELT_SPONT_NEUR_DISCH {\n type discrete [ 6 ] { NO, FASCIC, NEUROMYO, MYOKYMIA, TETANUS, OTHER };\n}\nvariable L_MYOP_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MYDY_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MYOP_MYDY_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_OTHER_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_NMT_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_OTHER_NMT_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_MUSCLE_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLPC5_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_LNLPC5_DIFFN_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DELT_DENERV {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DELT_SPONT_DENERV_ACT {\n type discrete [ 4 ] { NO, SOME, MOD, ABUNDANT };\n}\nvariable L_DELT_SPONT_HF_DISCH {\n type discrete [ 2 ] { NO, YES };\n}\nvariable L_DELT_SPONT_INS_ACT {\n type discrete [ 2 ] { NORMAL, INCR };\n}\nvariable L_OTHER_ISCH_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_ISCH_DISP {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_SUR_DISP_CA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_OTHER_ISCH_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_ISCH_BLOCK {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_SUR_BLOCK_CA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_OTHER_ISCH_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_ISCH_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNL_ISCH_SEV {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_LNL_ISCH_PATHO {\n type discrete [ 5 ] { DEMY, BLOCK, AXONAL, V_E_REIN, E_REIN };\n}\nvariable L_LNL_ISCH_SALOSS_CA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_DIFFN_LNL_ISCH_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_SUR_SALOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_SUR_EFFAXLOSS {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, TOTAL };\n}\nvariable L_SUR_ALLAMP_CA {\n type discrete [ 6 ] { ZERO, A0_01, A0_10, A0_30, A0_70, A1_00 };\n}\nvariable L_SUR_AMP_CA {\n 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 };\n}\nvariable L_SUR_LD_CA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_SUR_RD_CA {\n type discrete [ 3 ] { NO, MOD, SEV };\n}\nvariable L_SUR_LSLOW_CA {\n type discrete [ 5 ] { NO, MILD, MOD, SEV, V_SEV };\n}\nvariable L_OTHER_ISCH_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_DIFFN_ISCH_DIFSLOW {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_SUR_DIFSLOW_CA {\n type discrete [ 4 ] { NO, MILD, MOD, SEV };\n}\nvariable L_SUR_DSLOW_CA {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_SUR_ALLCV_CA {\n type discrete [ 9 ] { M_S60, M_S52, M_S44, M_S36, M_S28, M_S20, M_S14, M_S08, M_S00 };\n}\nvariable L_SUR_CV_CA {\n 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 };\n}\nprobability ( R_LNLW_MED_SEV ) {\n table 0.895, 0.060, 0.030, 0.010, 0.005;\n}\nprobability ( R_LNLW_MED_PATHO ) {\n table 0.800, 0.120, 0.070, 0.005, 0.005;\n}\nprobability ( R_LNLW_MEDD2_DISP_WD | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (TOTAL, BLOCK) 0.0, 0.0, 0.5, 0.5;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.3, 0.5, 0.2, 0.0;\n (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( DIFFN_SEV ) {\n table 0.90, 0.05, 0.03, 0.02;\n}\nprobability ( DIFFN_TYPE ) {\n table 0.060, 0.935, 0.005;\n}\nprobability ( DIFFN_SENS_SEV | DIFFN_SEV, DIFFN_TYPE ) {\n (NO, MOTOR) 1.0, 0.0, 0.0, 0.0;\n (MILD, MOTOR) 1.0, 0.0, 0.0, 0.0;\n (MOD, MOTOR) 1.0, 0.0, 0.0, 0.0;\n (SEV, MOTOR) 1.0, 0.0, 0.0, 0.0;\n (NO, MIXED) 1.0, 0.0, 0.0, 0.0;\n (MILD, MIXED) 0.0, 1.0, 0.0, 0.0;\n (MOD, MIXED) 0.0, 0.0, 1.0, 0.0;\n (SEV, MIXED) 0.0, 0.0, 0.0, 1.0;\n (NO, SENS) 1.0, 0.0, 0.0, 0.0;\n (MILD, SENS) 0.0, 1.0, 0.0, 0.0;\n (MOD, SENS) 0.0, 0.0, 1.0, 0.0;\n (SEV, SENS) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( DIFFN_DISTR ) {\n table 0.93, 0.02, 0.05;\n}\nprobability ( DIFFN_S_SEV_DIST | DIFFN_SENS_SEV, DIFFN_DISTR ) {\n (NO, DIST) 1.0, 0.0, 0.0, 0.0;\n (MILD, DIST) 0.0, 1.0, 0.0, 0.0;\n (MOD, DIST) 0.0, 0.0, 1.0, 0.0;\n (SEV, DIST) 0.0, 0.0, 0.0, 1.0;\n (NO, PROX) 1.0, 0.0, 0.0, 0.0;\n (MILD, PROX) 1.0, 0.0, 0.0, 0.0;\n (MOD, PROX) 0.5, 0.5, 0.0, 0.0;\n (SEV, PROX) 0.0, 0.5, 0.5, 0.0;\n (NO, RANDOM) 1.0, 0.0, 0.0, 0.0;\n (MILD, RANDOM) 0.25, 0.45, 0.25, 0.05;\n (MOD, RANDOM) 0.1, 0.4, 0.4, 0.1;\n (SEV, RANDOM) 0.05, 0.15, 0.40, 0.40;\n}\nprobability ( DIFFN_PATHO ) {\n table 0.086, 0.010, 0.900, 0.002, 0.002;\n}\nprobability ( R_DIFFN_MEDD2_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DIFFN_LNLW_MEDD2_DISP_WD | R_LNLW_MEDD2_DISP_WD, R_DIFFN_MEDD2_DISP ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_DISP_WD | R_DIFFN_LNLW_MEDD2_DISP_WD ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0, 1.0, 0.0, 0.0;\n (MOD) 0.0, 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLBE_MEDD2_DISP_EW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MEDD2_DISP_EW | R_DIFFN_MEDD2_DISP, R_LNLBE_MEDD2_DISP_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_DISP_EWD | R_MEDD2_DISP_WD, R_MEDD2_DISP_EW ) {\n (NO, NO) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000;\n (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, MILD) 0.0001, 0.2215, 0.7732, 0.0052, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.13151315, 0.85768577, 0.01080108, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;\n (SEV, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;\n (NO, MOD) 0.1315, 0.8577, 0.0108, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;\n (SEV, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;\n (NO, SEV) 0.9933, 0.0067, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, SEV) 0.0404, 0.9192, 0.0404, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n}\nprobability ( R_DIFFN_MEDD2_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLBE_MEDD2_BLOCK_EW ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MEDD2_BLOCK_EW | R_DIFFN_MEDD2_BLOCK, R_LNLBE_MEDD2_BLOCK_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_AMPR_EW | R_MEDD2_DISP_EWD, R_MEDD2_BLOCK_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_LNLBE_MEDD2_LD_EW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MEDD2_LD_EW | R_LNLBE_MEDD2_LD_EW ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0, 1.0, 0.0, 0.0;\n (MOD) 0.0, 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLBE_MEDD2_RD_EW ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MEDD2_RD_EW | R_LNLBE_MEDD2_RD_EW ) {\n (NO) 1.0, 0.0, 0.0;\n (MOD) 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_LSLOW_EW | R_MEDD2_LD_EW, R_MEDD2_RD_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.00840084, 0.01190119, 0.06190619, 0.91739174, 0.00040004;\n (MILD, MOD) 0.00690069, 0.01000100, 0.05350535, 0.92869287, 0.00090009;\n (MOD, MOD) 0.00559944, 0.00829917, 0.04519548, 0.93890611, 0.00199980;\n (SEV, MOD) 0.0023, 0.0036, 0.0217, 0.8326, 0.1398;\n (NO, SEV) 0.00529947, 0.00619938, 0.02639736, 0.20817918, 0.75392461;\n (MILD, SEV) 0.0049, 0.0057, 0.0244, 0.1966, 0.7684;\n (MOD, SEV) 0.00440044, 0.00520052, 0.02240224, 0.18391839, 0.78407841;\n (SEV, SEV) 0.0028, 0.0033, 0.0145, 0.1304, 0.8490;\n}\nprobability ( R_LNLW_MEDD2_SALOSS_WD | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_DIFFN_MEDD2_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_DIFFN_LNLW_MEDD2_SALOSS | R_LNLW_MEDD2_SALOSS_WD, R_DIFFN_MEDD2_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLBE_MEDD2_SALOSS_EW ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MEDD2_SALOSS | R_DIFFN_LNLW_MEDD2_SALOSS, R_LNLBE_MEDD2_SALOSS_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DIFFN_MEDD2_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( R_MEDD2_DIFSLOW_EW | R_DIFFN_MEDD2_DIFSLOW ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0, 1.0, 0.0, 0.0;\n (MOD) 0.0, 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_DSLOW_EW | R_MEDD2_SALOSS, R_MEDD2_DIFSLOW_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_ALLCV_EW | R_MEDD2_LSLOW_EW, R_MEDD2_DSLOW_EW ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.0264, 0.9149, 0.0587, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S60) 0.0000, 0.0218, 0.9469, 0.0313, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.00030003, 0.00190019, 0.01400140, 0.07880788, 0.32383238, 0.45964596, 0.12121212, 0.00030003, 0.00000000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00410041, 0.03840384, 0.21452145, 0.74237424, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0006, 0.0944, 0.8841, 0.0209, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0007, 0.2183, 0.7790, 0.0020, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00009999, 0.00039996, 0.00429957, 0.03209679, 0.19558044, 0.50484952, 0.26037396, 0.00229977, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00210021, 0.02390239, 0.16481648, 0.80898090, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.0000, 0.0018, 0.1956, 0.7860, 0.0166, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0077, 0.4577, 0.5345, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, M_S44) 0.0000, 0.0001, 0.0007, 0.0074, 0.0735, 0.4044, 0.4938, 0.0201, 0.0000;\n (V_SEV, M_S44) 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0123, 0.1119, 0.8749, 0.0000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.0000, 0.0000, 0.0026, 0.2655, 0.7316, 0.0003, 0.0000, 0.0000, 0.0000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0166, 0.9182, 0.0652, 0.0000, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0010, 0.0172, 0.2179, 0.6479, 0.1159, 0.0000;\n (V_SEV, M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0055, 0.0699, 0.9243, 0.0000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.0000, 0.0000, 0.0000, 0.0044, 0.5355, 0.4600, 0.0001, 0.0000, 0.0000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0398, 0.9460, 0.0142, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00179982, 0.05589441, 0.46005399, 0.48215178, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0021, 0.0390, 0.9588, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0047, 0.5053, 0.4900, 0.0000, 0.0000;\n (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.1104, 0.8881, 0.0013, 0.0000;\n (SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0041, 0.1033, 0.8925, 0.0000;\n (V_SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00069993, 0.01889811, 0.98040196, 0.00000000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0218, 0.8352, 0.1430, 0.0000;\n (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.3203, 0.6777, 0.0000;\n (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0194, 0.9803, 0.0000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0093, 0.9905, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0003, 0.0010, 0.0036, 0.0154, 0.0712, 0.2467, 0.6617, 0.0000;\n (MOD, M_S08) 0.00000000, 0.00010001, 0.00050005, 0.00190019, 0.00930093, 0.05040504, 0.20722072, 0.73057306, 0.00000000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0026, 0.0190, 0.1153, 0.8626, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0062, 0.0562, 0.9369, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_CV_EW | R_MEDD2_ALLCV_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_LNLW_MEDD2_BLOCK_WD | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.3, 0.6, 0.1, 0.0, 0.0;\n (SEV, DEMY) 0.1, 0.5, 0.3, 0.1, 0.0;\n (TOTAL, DEMY) 0.00, 0.05, 0.20, 0.55, 0.20;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.2, 0.6, 0.2;\n (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MEDD2_BLOCK_WD | R_LNLW_MEDD2_BLOCK_WD, R_DIFFN_MEDD2_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_EFFAXLOSS | R_MEDD2_BLOCK_WD, R_MEDD2_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_ALLAMP_WD | R_MEDD2_DISP_WD, R_MEDD2_EFFAXLOSS ) {\n (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;\n (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;\n (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;\n (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;\n (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;\n (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;\n (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;\n (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;\n (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;\n (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MEDD2_AMP_WD | R_MEDD2_ALLAMP_WD ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_LNLW_MEDD2_LD_WD | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;\n (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MEDD2_LD_WD | R_LNLW_MEDD2_LD_WD ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0, 1.0, 0.0, 0.0;\n (MOD) 0.0, 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLW_MEDD2_RD_WD | R_LNLW_MED_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( R_MEDD2_RD_WD | R_LNLW_MEDD2_RD_WD ) {\n (NO) 1.0, 0.0, 0.0;\n (MOD) 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_LSLOW_WD | R_MEDD2_LD_WD, R_MEDD2_RD_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;\n (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;\n (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;\n (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;\n (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;\n (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;\n (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;\n}\nprobability ( R_LNLBE_MEDD2_DIFSLOW_WD ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MEDD2_DIFSLOW_WD | R_LNLBE_MEDD2_DIFSLOW_WD, R_DIFFN_MEDD2_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( R_MEDD2_DSLOW_WD | R_MEDD2_SALOSS, R_MEDD2_DIFSLOW_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_ALLCV_WD | R_MEDD2_LSLOW_WD, R_MEDD2_DSLOW_WD ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;\n (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;\n (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;\n (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;\n (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;\n (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;\n (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;\n (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;\n (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;\n (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MEDD2_CV_WD | R_MEDD2_ALLCV_WD ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( DIFFN_MOT_SEV | DIFFN_SEV, DIFFN_TYPE ) {\n (NO, MOTOR) 1.0, 0.0, 0.0, 0.0;\n (MILD, MOTOR) 0.0, 1.0, 0.0, 0.0;\n (MOD, MOTOR) 0.0, 0.0, 1.0, 0.0;\n (SEV, MOTOR) 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 1.0, 0.0, 0.0, 0.0;\n (MILD, MIXED) 0.0, 1.0, 0.0, 0.0;\n (MOD, MIXED) 0.0, 0.0, 1.0, 0.0;\n (SEV, MIXED) 0.0, 0.0, 0.0, 1.0;\n (NO, SENS) 1.0, 0.0, 0.0, 0.0;\n (MILD, SENS) 1.0, 0.0, 0.0, 0.0;\n (MOD, SENS) 1.0, 0.0, 0.0, 0.0;\n (SEV, SENS) 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( DIFFN_M_SEV_DIST | DIFFN_MOT_SEV, DIFFN_DISTR ) {\n (NO, DIST) 1.0, 0.0, 0.0, 0.0;\n (MILD, DIST) 0.0, 1.0, 0.0, 0.0;\n (MOD, DIST) 0.0, 0.0, 1.0, 0.0;\n (SEV, DIST) 0.0, 0.0, 0.0, 1.0;\n (NO, PROX) 1.0, 0.0, 0.0, 0.0;\n (MILD, PROX) 1.0, 0.0, 0.0, 0.0;\n (MOD, PROX) 0.5, 0.5, 0.0, 0.0;\n (SEV, PROX) 0.0, 0.5, 0.5, 0.0;\n (NO, RANDOM) 1.0, 0.0, 0.0, 0.0;\n (MILD, RANDOM) 0.25, 0.45, 0.25, 0.05;\n (MOD, RANDOM) 0.1, 0.4, 0.4, 0.1;\n (SEV, RANDOM) 0.05, 0.15, 0.40, 0.40;\n}\nprobability ( R_DIFFN_MED_BLOCK | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLBE_MED_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MED_BLOCK_EW | R_DIFFN_MED_BLOCK, R_LNLBE_MED_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MED_AMPR_EW | R_MED_BLOCK_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_LNLT1_APB_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_APB_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_LP_APB_MALOSS | R_LNLT1_APB_MALOSS, R_LNLLP_APB_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLBE_APB_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_LP_BE_APB_MALOSS | R_LNLT1_LP_APB_MALOSS, R_LNLBE_APB_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLW_APB_MALOSS | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_DIFFN_APB_MALOSS | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_DIFFN_LNLW_APB_MALOSS | R_LNLW_APB_MALOSS, R_DIFFN_APB_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_MALOSS | R_LNLT1_LP_BE_APB_MALOSS, R_DIFFN_LNLW_APB_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;\n (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;\n (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( R_DIFFN_MED_DIFSLOW | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( R_MED_DIFSLOW_EW | R_DIFFN_MED_DIFSLOW ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0126, 0.9869, 0.0005, 0.0000;\n (MOD) 0.0000, 0.0179, 0.9821, 0.0000;\n (SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n}\nprobability ( R_MED_DCV_EW | R_APB_MALOSS, R_MED_DIFSLOW_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;\n (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;\n (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;\n (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;\n}\nprobability ( R_MED_LD_EW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MED_RD_EW ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MED_RDLDCV_EW | R_MED_LD_EW, R_MED_RD_EW ) {\n (NO, NO) 0.90449045, 0.09530953, 0.00020002, 0.00000000, 0.00000000, 0.00000000;\n (MILD, NO) 0.1320, 0.6039, 0.2641, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0139, 0.1839, 0.8022, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.00120012, 0.00670067, 0.05440544, 0.86008601, 0.07760776, 0.00000000;\n (NO, MOD) 0.0115, 0.0333, 0.1509, 0.7319, 0.0724, 0.0000;\n (MILD, MOD) 0.00340034, 0.01220122, 0.06900690, 0.71967197, 0.19531953, 0.00040004;\n (MOD, MOD) 0.0011, 0.0045, 0.0299, 0.5742, 0.3876, 0.0027;\n (SEV, MOD) 0.0001, 0.0002, 0.0018, 0.0914, 0.6093, 0.2972;\n (NO, SEV) 0.00000000, 0.00009999, 0.00109989, 0.14618538, 0.80701930, 0.04559544;\n (MILD, SEV) 0.0000, 0.0000, 0.0002, 0.0581, 0.7950, 0.1467;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0228, 0.6344, 0.3427;\n (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0014, 0.1063, 0.8923;\n}\nprobability ( R_MED_ALLCV_EW | R_MED_DCV_EW, R_MED_RDLDCV_EW ) {\n (M_S60, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56, M_S60) 0.0699, 0.8102, 0.1199, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S60) 0.00000000, 0.04790479, 0.95209521, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S44, M_S60) 0.00089991, 0.00899910, 0.08879112, 0.81861814, 0.08269173, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S36, M_S60) 0.00000000, 0.00000000, 0.00050005, 0.10111011, 0.84778478, 0.05060506, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S28, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.07910791, 0.90019002, 0.02020202, 0.00000000, 0.00000000, 0.00000000;\n (M_S20, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0734, 0.8393, 0.0867, 0.0000, 0.0000;\n (M_S14, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.09470947, 0.89458946, 0.01040104, 0.00000000;\n (M_S08, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0932, 0.9048, 0.0000;\n (M_S00, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S52) 0.00660066, 0.14551455, 0.83808381, 0.00980098, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S56, M_S52) 0.0005, 0.0239, 0.4369, 0.5387, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S52) 0.0000, 0.0003, 0.0239, 0.9748, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S52) 0.0000, 0.0002, 0.0036, 0.2467, 0.7339, 0.0156, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S36, M_S52) 0.0000, 0.0000, 0.0000, 0.0050, 0.3134, 0.6811, 0.0005, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00710071, 0.54935494, 0.44334433, 0.00020002, 0.00000000, 0.00000000;\n (M_S20, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00910091, 0.52835284, 0.46254625, 0.00000000, 0.00000000;\n (M_S14, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0216, 0.8359, 0.1425, 0.0000;\n (M_S08, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0355, 0.9641, 0.0000;\n (M_S00, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S44) 0.0000, 0.0000, 0.0047, 0.9951, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S56, M_S44) 0.0000, 0.0000, 0.0004, 0.9005, 0.0991, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S44) 0.0000, 0.0000, 0.0000, 0.1288, 0.8712, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.01130113, 0.57115712, 0.41754175, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S36, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0214, 0.9354, 0.0432, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.05649435, 0.93230677, 0.01109889, 0.00000000, 0.00000000;\n (M_S20, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.1430, 0.8551, 0.0015, 0.0000;\n (M_S14, M_S44) 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0019998, 0.3548645, 0.6431357, 0.0000000;\n (M_S08, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0122, 0.9877, 0.0000;\n (M_S00, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56, M_S27) 0.000, 0.000, 0.000, 0.000, 0.000, 0.997, 0.003, 0.000, 0.000, 0.000;\n (M_S52, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2336, 0.7664, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S27) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00259974, 0.99340066, 0.00399960, 0.00000000, 0.00000000;\n (M_S36, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2469, 0.7531, 0.0000, 0.0000;\n (M_S28, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.9939, 0.0041, 0.0000;\n (M_S20, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0314, 0.9686, 0.0000;\n (M_S14, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;\n (M_S08, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;\n (M_S00, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (M_S60, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.1305, 0.8445, 0.0238, 0.0000;\n (M_S56, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0740, 0.8589, 0.0667, 0.0000;\n (M_S52, M_S15) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03730373, 0.80288029, 0.15971597, 0.00000000;\n (M_S44, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0070, 0.3517, 0.6413, 0.0000;\n (M_S36, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0581, 0.9416, 0.0000;\n (M_S28, M_S15) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.006, 0.994, 0.000;\n (M_S20, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (M_S14, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S08, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;\n (M_S00, M_S15) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (M_S60, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0321, 0.9677, 0.0000;\n (M_S56, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0184, 0.9815, 0.0000;\n (M_S52, M_S07) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01010101, 0.98989899, 0.00000000;\n (M_S44, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.9958, 0.0000;\n (M_S36, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (M_S28, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;\n (M_S20, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S14, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (M_S08, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S00, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MED_CV_EW | R_MED_ALLCV_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_LNLT1_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_LNLLP_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_LNLT1_LP_APB_DE_REGEN | R_LNLT1_APB_DE_REGEN, R_LNLLP_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_LNLBE_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_LNLT1_LP_BE_APB_DE_REGEN | R_LNLT1_LP_APB_DE_REGEN, R_LNLBE_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( DIFFN_TIME ) {\n table 0.01, 0.25, 0.65, 0.09;\n}\nprobability ( R_DIFFN_APB_DE_REGEN | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( R_LNLW_MED_TIME ) {\n table 0.05, 0.33, 0.60, 0.02;\n}\nprobability ( R_LNLW_APB_DE_REGEN | R_LNLW_MED_SEV, R_LNLW_MED_TIME, R_LNLW_MED_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (TOTAL, CHRONIC, DEMY) 1.0, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (TOTAL, OLD, DEMY) 1.0, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (TOTAL, OLD, BLOCK) 1.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (TOTAL, OLD, AXONAL) 1.0, 0.0;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (TOTAL, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( R_DIFFN_LNLW_APB_DE_REGEN | R_DIFFN_APB_DE_REGEN, R_LNLW_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_APB_DE_REGEN | R_LNLT1_LP_BE_APB_DE_REGEN, R_DIFFN_LNLW_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_MYOP_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_MYDY_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_APB_DE_REGEN | R_MYOP_APB_DE_REGEN, R_MYDY_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_APB_DE_REGEN | R_LNL_DIFFN_APB_DE_REGEN, R_MYOP_MYDY_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_DE_REGEN_APB_NMT | R_APB_DE_REGEN ) {\n (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;\n}\nprobability ( R_MYAS_APB_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_APB_NMT | R_DE_REGEN_APB_NMT, R_MYAS_APB_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MYDY_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_APB_MUSIZE | R_MYDY_APB_MUSIZE, R_MYOP_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLW_APB_MUSIZE | R_LNLW_MED_SEV, R_LNLW_MED_TIME, R_LNLW_MED_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_APB_MUSIZE | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_APB_MUSIZE | R_LNLW_APB_MUSIZE, R_DIFFN_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLBE_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_LP_APB_MUSIZE | R_LNLLP_APB_MUSIZE, R_LNLT1_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLT1_LP_BE_APB_MUSIZE | R_LNLBE_APB_MUSIZE, R_LNLT1_LP_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_APB_MUSIZE | R_DIFFN_LNLW_APB_MUSIZE, R_LNLT1_LP_BE_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_MUSIZE | R_MYOP_MYDY_APB_MUSIZE, R_LNL_DIFFN_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 0.9791, 0.0009, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL, V_SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL, V_SMALL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (INCR, V_SMALL) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;\n (LARGE, V_SMALL) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;\n (V_LARGE, V_SMALL) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;\n (V_SMALL, SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL, SMALL) 0.7493, 0.2257, 0.0049, 0.0001, 0.0000, 0.0000, 0.0200;\n (NORMAL, SMALL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;\n (INCR, SMALL) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;\n (LARGE, SMALL) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;\n (V_LARGE, SMALL) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;\n (V_SMALL, NORMAL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SMALL, NORMAL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;\n (NORMAL, NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;\n (V_SMALL, INCR) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;\n (SMALL, INCR) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00359964, 0.16548345, 0.64543546, 0.16548345, 0.01999800;\n (LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;\n (V_LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (V_SMALL, LARGE) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;\n (SMALL, LARGE) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;\n (INCR, LARGE) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;\n (LARGE, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;\n (V_SMALL, V_LARGE) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;\n (SMALL, V_LARGE) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;\n (INCR, V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;\n (V_LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9799, 0.0200;\n}\nprobability ( R_APB_EFFMUS | R_APB_NMT, R_APB_MUSIZE ) {\n (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MIXED, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;\n (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MIXED, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;\n (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;\n (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;\n (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;\n (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;\n (MIXED, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;\n (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;\n (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;\n (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;\n (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;\n (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;\n (MIXED, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;\n (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;\n (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;\n (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;\n (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;\n (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;\n (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;\n (MIXED, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;\n (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;\n (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;\n (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;\n (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;\n (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;\n (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;\n (MIXED, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;\n (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;\n (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;\n (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;\n (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;\n (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;\n (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;\n (MIXED, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;\n}\nprobability ( R_LNLW_MED_BLOCK | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (TOTAL, DEMY) 0.2, 0.2, 0.2, 0.2, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.2, 0.2, 0.2, 0.2, 0.2;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MED_BLOCK_WA | R_DIFFN_MED_BLOCK, R_LNLW_MED_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_MULOSS | R_MED_BLOCK_WA, R_APB_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;\n (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;\n (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;\n (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;\n (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;\n (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;\n (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;\n (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;\n (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;\n (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;\n (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;\n (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;\n (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( R_APB_ALLAMP_WA | R_APB_EFFMUS, R_APB_MULOSS ) {\n (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;\n (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;\n (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;\n (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;\n (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;\n (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;\n (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;\n (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;\n (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;\n (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;\n (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;\n (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;\n (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;\n (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;\n (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;\n (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;\n (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;\n (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;\n (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;\n (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;\n (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;\n (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;\n (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;\n (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;\n (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;\n (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;\n (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;\n (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;\n (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;\n}\nprobability ( R_MED_AMP_WA | R_APB_ALLAMP_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_MED_LD_WA | R_LNLW_MED_SEV, R_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.25, 0.25, 0.25, 0.25;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;\n (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.25, 0.25, 0.25, 0.25;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 0.25, 0.25, 0.25, 0.25;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( R_MED_RD_WA | R_LNLW_MED_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( R_MED_RDLDDEL | R_MED_LD_WA, R_MED_RD_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0964, 0.7981, 0.1055, 0.0000, 0.0000;\n (MOD, NO) 0.0032, 0.1270, 0.8698, 0.0000, 0.0000;\n (SEV, NO) 0.00090009, 0.00280028, 0.01470147, 0.98159816, 0.00000000;\n (NO, MOD) 0.0019, 0.5257, 0.4724, 0.0000, 0.0000;\n (MILD, MOD) 0.00019998, 0.04149585, 0.95830417, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0001, 0.0144, 0.9855, 0.0000, 0.0000;\n (SEV, MOD) 0.00019998, 0.00059994, 0.00369963, 0.99550045, 0.00000000;\n (NO, SEV) 0.0002, 0.0304, 0.9694, 0.0000, 0.0000;\n (MILD, SEV) 0.0001, 0.0142, 0.9857, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0090, 0.9808, 0.0102, 0.0000;\n (SEV, SEV) 0.0000, 0.0002, 0.0012, 0.9984, 0.0002;\n}\nprobability ( R_LNLBE_MED_DIFSLOW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MED_DIFSLOW_WA | R_LNLBE_MED_DIFSLOW, R_DIFFN_MED_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;\n (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;\n (MOD, MOD) 0.000, 0.000, 0.002, 0.998;\n (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;\n (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;\n}\nprobability ( R_MED_DCV_WA | R_APB_MALOSS, R_MED_DIFSLOW_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;\n (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;\n (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;\n (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;\n}\nprobability ( R_MED_ALLDEL_WA | R_MED_RDLDDEL, R_MED_DCV_WA ) {\n (MS3_1, M_S60) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S60) 0.05119488, 0.22907709, 0.56624338, 0.15348465, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S60) 0.0001, 0.0035, 0.0632, 0.9326, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S60) 0.00129987, 0.00199980, 0.00439956, 0.01869813, 0.12128787, 0.74792521, 0.10438956, 0.00000000, 0.00000000;\n (MS20_1, M_S60) 0.00010001, 0.00010001, 0.00030003, 0.00090009, 0.00450045, 0.04340434, 0.57675768, 0.37393739, 0.00000000;\n (MS3_1, M_S52) 0.5607, 0.4393, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S52) 0.01929807, 0.12868713, 0.49285071, 0.35916408, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S52) 0.0000, 0.0011, 0.0283, 0.9651, 0.0055, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S52) 0.00100010, 0.00160016, 0.00370037, 0.01610161, 0.10931093, 0.74217422, 0.12611261, 0.00000000, 0.00000000;\n (MS20_1, M_S52) 0.0001, 0.0001, 0.0002, 0.0008, 0.0041, 0.0399, 0.5568, 0.3980, 0.0000;\n (MS3_1, M_S44) 0.0069, 0.7963, 0.1968, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S44) 0.0027, 0.0328, 0.2398, 0.7246, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S44) 0.0000, 0.0002, 0.0075, 0.8889, 0.1034, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S44) 0.00079992, 0.00119988, 0.00279972, 0.01249875, 0.09159084, 0.72352765, 0.16758324, 0.00000000, 0.00000000;\n (MS20_1, M_S44) 0.0001, 0.0001, 0.0002, 0.0007, 0.0034, 0.0348, 0.5239, 0.4368, 0.0000;\n (MS3_1, M_S36) 0.0000, 0.0184, 0.9806, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S36) 0.00010001, 0.00330033, 0.05470547, 0.93819382, 0.00370037, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S36) 0.00000000, 0.00000000, 0.00030003, 0.18501850, 0.81468147, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS10_1, M_S36) 0.00049995, 0.00079992, 0.00179982, 0.00869913, 0.07029297, 0.68023198, 0.23767623, 0.00000000, 0.00000000;\n (MS20_1, M_S36) 0.0001, 0.0001, 0.0001, 0.0005, 0.0027, 0.0287, 0.4783, 0.4895, 0.0000;\n (MS3_1, M_S28) 0.0000, 0.0001, 0.0179, 0.9820, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S28) 0.00000000, 0.00019998, 0.00479952, 0.50394961, 0.49105089, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S28) 0.0000, 0.0000, 0.0000, 0.0032, 0.9968, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S28) 0.0002, 0.0004, 0.0009, 0.0047, 0.0440, 0.5737, 0.3761, 0.0000, 0.0000;\n (MS20_1, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40724072, 0.56955696, 0.00000000;\n (MS3_1, M_S20) 0.0000, 0.0002, 0.0030, 0.1393, 0.8575, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S20) 0.0000, 0.0000, 0.0003, 0.0188, 0.9804, 0.0005, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.93139314, 0.06710671, 0.00000000, 0.00000000, 0.00000000;\n (MS10_1, M_S20) 0.0001, 0.0001, 0.0002, 0.0013, 0.0150, 0.3152, 0.6681, 0.0000, 0.0000;\n (MS20_1, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00080016, 0.01110222, 0.28045609, 0.70754151, 0.00000000;\n (MS3_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0006, 0.8145, 0.1849, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0494, 0.9506, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S14) 0.000, 0.000, 0.000, 0.000, 0.001, 0.999, 0.000, 0.000, 0.000;\n (MS10_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0001, 0.0017, 0.0786, 0.9196, 0.0000, 0.0000;\n (MS20_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0035, 0.1380, 0.8583, 0.0000;\n (MS3_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59735974, 0.39093909, 0.00000000, 0.00000000;\n (MS3_9, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00340034, 0.29902990, 0.69746975, 0.00000000, 0.00000000;\n (MS4_7, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.1112, 0.8881, 0.0000, 0.0000;\n (MS10_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00940094, 0.95939594, 0.03110311, 0.00000000;\n (MS20_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.02530253, 0.97439744, 0.00000000;\n (MS3_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS3_9, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS4_7, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS10_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS20_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MED_LAT_WA | R_MED_ALLDEL_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_APB_VOL_ACT ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_APB_FORCE | R_APB_VOL_ACT, R_APB_ALLAMP_WA ) {\n (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;\n (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;\n (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;\n (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;\n (NORMAL, A0_01) 0.00159984, 0.01859814, 0.09359064, 0.26787321, 0.36586341, 0.25247475;\n (REDUCED, A0_01) 0.0002, 0.0034, 0.0260, 0.1312, 0.3333, 0.5059;\n (V_RED, A0_01) 0.0000, 0.0003, 0.0044, 0.0446, 0.2226, 0.7281;\n (ABSENT, A0_01) 0.0000, 0.0000, 0.0005, 0.0098, 0.1058, 0.8839;\n (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;\n (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;\n (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;\n (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;\n (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;\n (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;\n (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;\n (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;\n (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;\n (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;\n (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;\n (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;\n (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;\n (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;\n (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;\n (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;\n (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;\n (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;\n (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;\n (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;\n (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;\n (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;\n (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;\n (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;\n}\nprobability ( R_APB_MUSCLE_VOL | R_APB_MUSIZE, R_APB_MALOSS ) {\n (V_SMALL, NO) 0.9896, 0.0104;\n (SMALL, NO) 0.8137, 0.1863;\n (NORMAL, NO) 0.0209, 0.9791;\n (INCR, NO) 0.009, 0.991;\n (LARGE, NO) 0.003, 0.997;\n (V_LARGE, NO) 0.0004, 0.9996;\n (OTHER, NO) 0.4212, 0.5788;\n (V_SMALL, MILD) 0.9976, 0.0024;\n (SMALL, MILD) 0.9603, 0.0397;\n (NORMAL, MILD) 0.5185, 0.4815;\n (INCR, MILD) 0.1087, 0.8913;\n (LARGE, MILD) 0.0278, 0.9722;\n (V_LARGE, MILD) 0.0046, 0.9954;\n (OTHER, MILD) 0.5185, 0.4815;\n (V_SMALL, MOD) 0.999, 0.001;\n (SMALL, MOD) 0.9893, 0.0107;\n (NORMAL, MOD) 0.9588, 0.0412;\n (INCR, MOD) 0.6377, 0.3623;\n (LARGE, MOD) 0.2716, 0.7284;\n (V_LARGE, MOD) 0.0779, 0.9221;\n (OTHER, MOD) 0.6336, 0.3664;\n (V_SMALL, SEV) 0.9995, 0.0005;\n (SMALL, SEV) 0.9969, 0.0031;\n (NORMAL, SEV) 0.9953, 0.0047;\n (INCR, SEV) 0.9518, 0.0482;\n (LARGE, SEV) 0.8234, 0.1766;\n (V_LARGE, SEV) 0.5986, 0.4014;\n (OTHER, SEV) 0.7685, 0.2315;\n (V_SMALL, TOTAL) 0.9989, 0.0011;\n (SMALL, TOTAL) 0.9984, 0.0016;\n (NORMAL, TOTAL) 0.9984, 0.0016;\n (INCR, TOTAL) 0.9975, 0.0025;\n (LARGE, TOTAL) 0.9965, 0.0035;\n (V_LARGE, TOTAL) 0.9956, 0.0044;\n (OTHER, TOTAL) 0.9857, 0.0143;\n (V_SMALL, OTHER) 0.9363, 0.0637;\n (SMALL, OTHER) 0.8403, 0.1597;\n (NORMAL, OTHER) 0.6534, 0.3466;\n (INCR, OTHER) 0.4689, 0.5311;\n (LARGE, OTHER) 0.3174, 0.6826;\n (V_LARGE, OTHER) 0.1948, 0.8052;\n (OTHER, OTHER) 0.5681, 0.4319;\n}\nprobability ( R_APB_MVA_RECRUIT | R_APB_MULOSS, R_APB_VOL_ACT ) {\n (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;\n (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;\n (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;\n (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;\n (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;\n (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;\n (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;\n (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;\n (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;\n (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;\n (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;\n (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;\n (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;\n (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;\n (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;\n (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;\n (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_MVA_AMP | R_APB_EFFMUS ) {\n (V_SMALL) 0.00, 0.04, 0.96;\n (SMALL) 0.01, 0.15, 0.84;\n (NORMAL) 0.05, 0.90, 0.05;\n (INCR) 0.50, 0.49, 0.01;\n (LARGE) 0.85, 0.15, 0.00;\n (V_LARGE) 0.96, 0.04, 0.00;\n (OTHER) 0.33, 0.34, 0.33;\n}\nprobability ( R_APB_TA_CONCL | R_APB_EFFMUS ) {\n (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;\n (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;\n (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;\n (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;\n (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;\n (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;\n (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;\n}\nprobability ( R_APB_MUPAMP | R_APB_EFFMUS ) {\n (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;\n (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;\n (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;\n (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;\n}\nprobability ( R_APB_QUAN_MUPAMP | R_APB_MUPAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_APB_QUAL_MUPAMP | R_APB_MUPAMP ) {\n (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;\n (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;\n (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;\n (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;\n (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;\n (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;\n (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;\n}\nprobability ( R_APB_MUPDUR | R_APB_EFFMUS ) {\n (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;\n (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;\n (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;\n}\nprobability ( R_APB_QUAN_MUPDUR | R_APB_MUPDUR ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_APB_QUAL_MUPDUR | R_APB_MUPDUR ) {\n (V_SMALL) 0.8309, 0.1677, 0.0014;\n (SMALL) 0.49, 0.49, 0.02;\n (NORMAL) 0.1065, 0.7870, 0.1065;\n (INCR) 0.02, 0.49, 0.49;\n (LARGE) 0.0014, 0.1677, 0.8309;\n (V_LARGE) 0.0001, 0.0392, 0.9607;\n (OTHER) 0.2597, 0.4806, 0.2597;\n}\nprobability ( R_APB_QUAN_MUPPOLY | R_APB_DE_REGEN, R_APB_EFFMUS ) {\n (NO, V_SMALL) 0.109, 0.548, 0.343;\n (YES, V_SMALL) 0.004, 0.122, 0.874;\n (NO, SMALL) 0.340, 0.564, 0.096;\n (YES, SMALL) 0.015, 0.261, 0.724;\n (NO, NORMAL) 0.925, 0.075, 0.000;\n (YES, NORMAL) 0.091, 0.526, 0.383;\n (NO, INCR) 0.796, 0.201, 0.003;\n (YES, INCR) 0.061, 0.465, 0.474;\n (NO, LARGE) 0.637, 0.348, 0.015;\n (YES, LARGE) 0.039, 0.396, 0.565;\n (NO, V_LARGE) 0.340, 0.564, 0.096;\n (YES, V_LARGE) 0.015, 0.261, 0.724;\n (NO, OTHER) 0.340, 0.564, 0.096;\n (YES, OTHER) 0.015, 0.261, 0.724;\n}\nprobability ( R_APB_QUAL_MUPPOLY | R_APB_QUAN_MUPPOLY ) {\n (x_12_) 0.95, 0.05;\n (x12_24_) 0.3, 0.7;\n (x_24_) 0.05, 0.95;\n}\nprobability ( R_APB_MUPSATEL | R_APB_DE_REGEN ) {\n (NO) 0.95, 0.05;\n (YES) 0.2, 0.8;\n}\nprobability ( R_APB_MUPINSTAB | R_APB_NMT ) {\n (NO) 0.95, 0.05;\n (MOD_PRE) 0.1, 0.9;\n (SEV_PRE) 0.03, 0.97;\n (MLD_POST) 0.2, 0.8;\n (MOD_POST) 0.1, 0.9;\n (SEV_POST) 0.03, 0.97;\n (MIXED) 0.1, 0.9;\n}\nprobability ( R_APB_REPSTIM_CMAPAMP | R_APB_ALLAMP_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_APB_REPSTIM_DECR | R_APB_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;\n (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MIXED) 0.245, 0.245, 0.245, 0.245, 0.020;\n}\nprobability ( R_APB_REPSTIM_FACILI | R_APB_NMT ) {\n (NO) 0.95, 0.02, 0.01, 0.02;\n (MOD_PRE) 0.010, 0.889, 0.100, 0.001;\n (SEV_PRE) 0.010, 0.080, 0.909, 0.001;\n (MLD_POST) 0.89, 0.08, 0.01, 0.02;\n (MOD_POST) 0.48, 0.50, 0.01, 0.01;\n (SEV_POST) 0.020, 0.949, 0.030, 0.001;\n (MIXED) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( R_APB_REPSTIM_POST_DECR | R_APB_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;\n (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;\n (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MIXED) 0.23, 0.23, 0.22, 0.22, 0.10;\n}\nprobability ( R_APB_SF_JITTER | R_APB_NMT ) {\n (NO) 0.95, 0.05, 0.00, 0.00;\n (MOD_PRE) 0.02, 0.20, 0.70, 0.08;\n (SEV_PRE) 0.0, 0.1, 0.4, 0.5;\n (MLD_POST) 0.05, 0.70, 0.20, 0.05;\n (MOD_POST) 0.01, 0.19, 0.70, 0.10;\n (SEV_POST) 0.0, 0.1, 0.4, 0.5;\n (MIXED) 0.1, 0.3, 0.3, 0.3;\n}\nprobability ( R_LNLT1_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_LP_APB_MUDENS | R_LNLT1_APB_MUDENS, R_LNLLP_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLBE_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_LP_BE_APB_MUDENS | R_LNLT1_LP_APB_MUDENS, R_LNLBE_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_DIFFN_APB_MUDENS | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( R_LNLW_APB_MUDENS | R_LNLW_MED_SEV, R_LNLW_MED_TIME, R_LNLW_MED_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( R_DIFFN_LNLW_APB_MUDENS | R_DIFFN_APB_MUDENS, R_LNLW_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_APB_MUDENS | R_LNLT1_LP_BE_APB_MUDENS, R_DIFFN_LNLW_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_MYOP_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MYDY_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_APB_MUDENS | R_MYOP_APB_MUDENS, R_MYDY_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_MYAS_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MUSCLE_APB_MUDENS | R_MYOP_MYDY_APB_MUDENS, R_MYAS_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_MUDENS | R_LNL_DIFFN_APB_MUDENS, R_MUSCLE_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_SF_DENSITY | R_APB_MUDENS ) {\n (NORMAL) 0.97, 0.03, 0.00;\n (INCR) 0.05, 0.90, 0.05;\n (V_INCR) 0.01, 0.04, 0.95;\n}\nprobability ( R_LNLT1_APB_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_APB_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_LP_APB_NEUR_ACT | R_LNLT1_APB_NEUR_ACT, R_LNLLP_APB_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLBE_APB_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_LP_BE_APB_NEUR_ACT | R_LNLT1_LP_APB_NEUR_ACT, R_LNLBE_APB_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DIFFN_APB_NEUR_ACT | DIFFN_M_SEV_DIST, DIFFN_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLW_APB_NEUR_ACT | R_LNLW_MED_SEV, R_LNLW_MED_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_APB_NEUR_ACT | R_DIFFN_APB_NEUR_ACT, R_LNLW_APB_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_NEUR_ACT | R_LNLT1_LP_BE_APB_NEUR_ACT, R_DIFFN_LNLW_APB_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_SPONT_NEUR_DISCH | R_APB_NEUR_ACT ) {\n (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;\n (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;\n (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;\n (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;\n (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;\n}\nprobability ( R_MYOP_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYDY_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_APB_DENERV | R_MYOP_APB_DENERV, R_MYDY_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_NMT_APB_DENERV | R_APB_NMT ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE) 0.40, 0.45, 0.15, 0.00;\n (SEV_PRE) 0.15, 0.35, 0.35, 0.15;\n (MLD_POST) 0.85, 0.15, 0.00, 0.00;\n (MOD_POST) 0.30, 0.45, 0.20, 0.05;\n (SEV_POST) 0.15, 0.35, 0.35, 0.15;\n (MIXED) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( R_MUSCLE_APB_DENERV | R_MYOP_MYDY_APB_DENERV, R_NMT_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLT1_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_LP_APB_DENERV | R_LNLT1_APB_DENERV, R_LNLLP_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLBE_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLT1_LP_BE_APB_DENERV | R_LNLT1_LP_APB_DENERV, R_LNLBE_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DIFFN_APB_DENERV | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( R_LNLW_APB_DENERV | R_LNLW_MED_SEV, R_LNLW_MED_TIME, R_LNLW_MED_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( R_DIFFN_LNLW_APB_DENERV | R_DIFFN_APB_DENERV, R_LNLW_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_APB_DENERV | R_LNLT1_LP_BE_APB_DENERV, R_DIFFN_LNLW_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_DENERV | R_MUSCLE_APB_DENERV, R_LNL_DIFFN_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_APB_SPONT_DENERV_ACT | R_APB_DENERV ) {\n (NO) 0.98, 0.02, 0.00, 0.00;\n (MILD) 0.07, 0.85, 0.08, 0.00;\n (MOD) 0.01, 0.07, 0.85, 0.07;\n (SEV) 0.00, 0.01, 0.07, 0.92;\n}\nprobability ( R_APB_SPONT_HF_DISCH | R_APB_DENERV ) {\n (NO) 0.99, 0.01;\n (MILD) 0.97, 0.03;\n (MOD) 0.95, 0.05;\n (SEV) 0.93, 0.07;\n}\nprobability ( R_APB_SPONT_INS_ACT | R_APB_DENERV ) {\n (NO) 0.98, 0.02;\n (MILD) 0.1, 0.9;\n (MOD) 0.05, 0.95;\n (SEV) 0.05, 0.95;\n}\nprobability ( DIFFN_M_SEV_PROX | DIFFN_MOT_SEV, DIFFN_DISTR ) {\n (NO, DIST) 1.0, 0.0, 0.0, 0.0;\n (MILD, DIST) 0.0, 1.0, 0.0, 0.0;\n (MOD, DIST) 0.0, 0.0, 1.0, 0.0;\n (SEV, DIST) 0.0, 0.5, 0.5, 0.0;\n (NO, PROX) 1.0, 0.0, 0.0, 0.0;\n (MILD, PROX) 0.0, 1.0, 0.0, 0.0;\n (MOD, PROX) 0.0, 0.0, 1.0, 0.0;\n (SEV, PROX) 0.0, 0.5, 0.5, 0.0;\n (NO, RANDOM) 1.0, 0.0, 0.0, 0.0;\n (MILD, RANDOM) 0.25, 0.45, 0.25, 0.05;\n (MOD, RANDOM) 0.1, 0.4, 0.4, 0.1;\n (SEV, RANDOM) 0.05, 0.15, 0.40, 0.40;\n}\nprobability ( DIFFN_DUMMY_1 | DIFFN_M_SEV_PROX, DIFFN_PATHO, DIFFN_TIME ) {\n (NO, DEMY, ACUTE) 0.5, 0.5;\n (MILD, DEMY, ACUTE) 0.5, 0.5;\n (MOD, DEMY, ACUTE) 0.5, 0.5;\n (SEV, DEMY, ACUTE) 0.5, 0.5;\n (NO, BLOCK, ACUTE) 0.5, 0.5;\n (MILD, BLOCK, ACUTE) 0.5, 0.5;\n (MOD, BLOCK, ACUTE) 0.5, 0.5;\n (SEV, BLOCK, ACUTE) 0.5, 0.5;\n (NO, AXONAL, ACUTE) 0.5, 0.5;\n (MILD, AXONAL, ACUTE) 0.5, 0.5;\n (MOD, AXONAL, ACUTE) 0.5, 0.5;\n (SEV, AXONAL, ACUTE) 0.5, 0.5;\n (NO, V_E_REIN, ACUTE) 0.5, 0.5;\n (MILD, V_E_REIN, ACUTE) 0.5, 0.5;\n (MOD, V_E_REIN, ACUTE) 0.5, 0.5;\n (SEV, V_E_REIN, ACUTE) 0.5, 0.5;\n (NO, E_REIN, ACUTE) 0.5, 0.5;\n (MILD, E_REIN, ACUTE) 0.5, 0.5;\n (MOD, E_REIN, ACUTE) 0.5, 0.5;\n (SEV, E_REIN, ACUTE) 0.5, 0.5;\n (NO, DEMY, SUBACUTE) 0.5, 0.5;\n (MILD, DEMY, SUBACUTE) 0.5, 0.5;\n (MOD, DEMY, SUBACUTE) 0.5, 0.5;\n (SEV, DEMY, SUBACUTE) 0.5, 0.5;\n (NO, BLOCK, SUBACUTE) 0.5, 0.5;\n (MILD, BLOCK, SUBACUTE) 0.5, 0.5;\n (MOD, BLOCK, SUBACUTE) 0.5, 0.5;\n (SEV, BLOCK, SUBACUTE) 0.5, 0.5;\n (NO, AXONAL, SUBACUTE) 0.5, 0.5;\n (MILD, AXONAL, SUBACUTE) 0.5, 0.5;\n (MOD, AXONAL, SUBACUTE) 0.5, 0.5;\n (SEV, AXONAL, SUBACUTE) 0.5, 0.5;\n (NO, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (MILD, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (MOD, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (SEV, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (NO, E_REIN, SUBACUTE) 0.5, 0.5;\n (MILD, E_REIN, SUBACUTE) 0.5, 0.5;\n (MOD, E_REIN, SUBACUTE) 0.5, 0.5;\n (SEV, E_REIN, SUBACUTE) 0.5, 0.5;\n (NO, DEMY, CHRONIC) 0.5, 0.5;\n (MILD, DEMY, CHRONIC) 0.5, 0.5;\n (MOD, DEMY, CHRONIC) 0.5, 0.5;\n (SEV, DEMY, CHRONIC) 0.5, 0.5;\n (NO, BLOCK, CHRONIC) 0.5, 0.5;\n (MILD, BLOCK, CHRONIC) 0.5, 0.5;\n (MOD, BLOCK, CHRONIC) 0.5, 0.5;\n (SEV, BLOCK, CHRONIC) 0.5, 0.5;\n (NO, AXONAL, CHRONIC) 0.5, 0.5;\n (MILD, AXONAL, CHRONIC) 0.5, 0.5;\n (MOD, AXONAL, CHRONIC) 0.5, 0.5;\n (SEV, AXONAL, CHRONIC) 0.5, 0.5;\n (NO, V_E_REIN, CHRONIC) 0.5, 0.5;\n (MILD, V_E_REIN, CHRONIC) 0.5, 0.5;\n (MOD, V_E_REIN, CHRONIC) 0.5, 0.5;\n (SEV, V_E_REIN, CHRONIC) 0.5, 0.5;\n (NO, E_REIN, CHRONIC) 0.5, 0.5;\n (MILD, E_REIN, CHRONIC) 0.5, 0.5;\n (MOD, E_REIN, CHRONIC) 0.5, 0.5;\n (SEV, E_REIN, CHRONIC) 0.5, 0.5;\n (NO, DEMY, OLD) 0.5, 0.5;\n (MILD, DEMY, OLD) 0.5, 0.5;\n (MOD, DEMY, OLD) 0.5, 0.5;\n (SEV, DEMY, OLD) 0.5, 0.5;\n (NO, BLOCK, OLD) 0.5, 0.5;\n (MILD, BLOCK, OLD) 0.5, 0.5;\n (MOD, BLOCK, OLD) 0.5, 0.5;\n (SEV, BLOCK, OLD) 0.5, 0.5;\n (NO, AXONAL, OLD) 0.5, 0.5;\n (MILD, AXONAL, OLD) 0.5, 0.5;\n (MOD, AXONAL, OLD) 0.5, 0.5;\n (SEV, AXONAL, OLD) 0.5, 0.5;\n (NO, V_E_REIN, OLD) 0.5, 0.5;\n (MILD, V_E_REIN, OLD) 0.5, 0.5;\n (MOD, V_E_REIN, OLD) 0.5, 0.5;\n (SEV, V_E_REIN, OLD) 0.5, 0.5;\n (NO, E_REIN, OLD) 0.5, 0.5;\n (MILD, E_REIN, OLD) 0.5, 0.5;\n (MOD, E_REIN, OLD) 0.5, 0.5;\n (SEV, E_REIN, OLD) 0.5, 0.5;\n}\nprobability ( DIFFN_DUMMY_2 | DIFFN_M_SEV_DIST, DIFFN_PATHO, DIFFN_TIME ) {\n (NO, DEMY, ACUTE) 0.5, 0.5;\n (MILD, DEMY, ACUTE) 0.5, 0.5;\n (MOD, DEMY, ACUTE) 0.5, 0.5;\n (SEV, DEMY, ACUTE) 0.5, 0.5;\n (NO, BLOCK, ACUTE) 0.5, 0.5;\n (MILD, BLOCK, ACUTE) 0.5, 0.5;\n (MOD, BLOCK, ACUTE) 0.5, 0.5;\n (SEV, BLOCK, ACUTE) 0.5, 0.5;\n (NO, AXONAL, ACUTE) 0.5, 0.5;\n (MILD, AXONAL, ACUTE) 0.5, 0.5;\n (MOD, AXONAL, ACUTE) 0.5, 0.5;\n (SEV, AXONAL, ACUTE) 0.5, 0.5;\n (NO, V_E_REIN, ACUTE) 0.5, 0.5;\n (MILD, V_E_REIN, ACUTE) 0.5, 0.5;\n (MOD, V_E_REIN, ACUTE) 0.5, 0.5;\n (SEV, V_E_REIN, ACUTE) 0.5, 0.5;\n (NO, E_REIN, ACUTE) 0.5, 0.5;\n (MILD, E_REIN, ACUTE) 0.5, 0.5;\n (MOD, E_REIN, ACUTE) 0.5, 0.5;\n (SEV, E_REIN, ACUTE) 0.5, 0.5;\n (NO, DEMY, SUBACUTE) 0.5, 0.5;\n (MILD, DEMY, SUBACUTE) 0.5, 0.5;\n (MOD, DEMY, SUBACUTE) 0.5, 0.5;\n (SEV, DEMY, SUBACUTE) 0.5, 0.5;\n (NO, BLOCK, SUBACUTE) 0.5, 0.5;\n (MILD, BLOCK, SUBACUTE) 0.5, 0.5;\n (MOD, BLOCK, SUBACUTE) 0.5, 0.5;\n (SEV, BLOCK, SUBACUTE) 0.5, 0.5;\n (NO, AXONAL, SUBACUTE) 0.5, 0.5;\n (MILD, AXONAL, SUBACUTE) 0.5, 0.5;\n (MOD, AXONAL, SUBACUTE) 0.5, 0.5;\n (SEV, AXONAL, SUBACUTE) 0.5, 0.5;\n (NO, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (MILD, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (MOD, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (SEV, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (NO, E_REIN, SUBACUTE) 0.5, 0.5;\n (MILD, E_REIN, SUBACUTE) 0.5, 0.5;\n (MOD, E_REIN, SUBACUTE) 0.5, 0.5;\n (SEV, E_REIN, SUBACUTE) 0.5, 0.5;\n (NO, DEMY, CHRONIC) 0.5, 0.5;\n (MILD, DEMY, CHRONIC) 0.5, 0.5;\n (MOD, DEMY, CHRONIC) 0.5, 0.5;\n (SEV, DEMY, CHRONIC) 0.5, 0.5;\n (NO, BLOCK, CHRONIC) 0.5, 0.5;\n (MILD, BLOCK, CHRONIC) 0.5, 0.5;\n (MOD, BLOCK, CHRONIC) 0.5, 0.5;\n (SEV, BLOCK, CHRONIC) 0.5, 0.5;\n (NO, AXONAL, CHRONIC) 0.5, 0.5;\n (MILD, AXONAL, CHRONIC) 0.5, 0.5;\n (MOD, AXONAL, CHRONIC) 0.5, 0.5;\n (SEV, AXONAL, CHRONIC) 0.5, 0.5;\n (NO, V_E_REIN, CHRONIC) 0.5, 0.5;\n (MILD, V_E_REIN, CHRONIC) 0.5, 0.5;\n (MOD, V_E_REIN, CHRONIC) 0.5, 0.5;\n (SEV, V_E_REIN, CHRONIC) 0.5, 0.5;\n (NO, E_REIN, CHRONIC) 0.5, 0.5;\n (MILD, E_REIN, CHRONIC) 0.5, 0.5;\n (MOD, E_REIN, CHRONIC) 0.5, 0.5;\n (SEV, E_REIN, CHRONIC) 0.5, 0.5;\n (NO, DEMY, OLD) 0.5, 0.5;\n (MILD, DEMY, OLD) 0.5, 0.5;\n (MOD, DEMY, OLD) 0.5, 0.5;\n (SEV, DEMY, OLD) 0.5, 0.5;\n (NO, BLOCK, OLD) 0.5, 0.5;\n (MILD, BLOCK, OLD) 0.5, 0.5;\n (MOD, BLOCK, OLD) 0.5, 0.5;\n (SEV, BLOCK, OLD) 0.5, 0.5;\n (NO, AXONAL, OLD) 0.5, 0.5;\n (MILD, AXONAL, OLD) 0.5, 0.5;\n (MOD, AXONAL, OLD) 0.5, 0.5;\n (SEV, AXONAL, OLD) 0.5, 0.5;\n (NO, V_E_REIN, OLD) 0.5, 0.5;\n (MILD, V_E_REIN, OLD) 0.5, 0.5;\n (MOD, V_E_REIN, OLD) 0.5, 0.5;\n (SEV, V_E_REIN, OLD) 0.5, 0.5;\n (NO, E_REIN, OLD) 0.5, 0.5;\n (MILD, E_REIN, OLD) 0.5, 0.5;\n (MOD, E_REIN, OLD) 0.5, 0.5;\n (SEV, E_REIN, OLD) 0.5, 0.5;\n}\nprobability ( DIFFN_DUMMY_3 | DIFFN_S_SEV_DIST, DIFFN_PATHO, DIFFN_TIME ) {\n (NO, DEMY, ACUTE) 0.5, 0.5;\n (MILD, DEMY, ACUTE) 0.5, 0.5;\n (MOD, DEMY, ACUTE) 0.5, 0.5;\n (SEV, DEMY, ACUTE) 0.5, 0.5;\n (NO, BLOCK, ACUTE) 0.5, 0.5;\n (MILD, BLOCK, ACUTE) 0.5, 0.5;\n (MOD, BLOCK, ACUTE) 0.5, 0.5;\n (SEV, BLOCK, ACUTE) 0.5, 0.5;\n (NO, AXONAL, ACUTE) 0.5, 0.5;\n (MILD, AXONAL, ACUTE) 0.5, 0.5;\n (MOD, AXONAL, ACUTE) 0.5, 0.5;\n (SEV, AXONAL, ACUTE) 0.5, 0.5;\n (NO, V_E_REIN, ACUTE) 0.5, 0.5;\n (MILD, V_E_REIN, ACUTE) 0.5, 0.5;\n (MOD, V_E_REIN, ACUTE) 0.5, 0.5;\n (SEV, V_E_REIN, ACUTE) 0.5, 0.5;\n (NO, E_REIN, ACUTE) 0.5, 0.5;\n (MILD, E_REIN, ACUTE) 0.5, 0.5;\n (MOD, E_REIN, ACUTE) 0.5, 0.5;\n (SEV, E_REIN, ACUTE) 0.5, 0.5;\n (NO, DEMY, SUBACUTE) 0.5, 0.5;\n (MILD, DEMY, SUBACUTE) 0.5, 0.5;\n (MOD, DEMY, SUBACUTE) 0.5, 0.5;\n (SEV, DEMY, SUBACUTE) 0.5, 0.5;\n (NO, BLOCK, SUBACUTE) 0.5, 0.5;\n (MILD, BLOCK, SUBACUTE) 0.5, 0.5;\n (MOD, BLOCK, SUBACUTE) 0.5, 0.5;\n (SEV, BLOCK, SUBACUTE) 0.5, 0.5;\n (NO, AXONAL, SUBACUTE) 0.5, 0.5;\n (MILD, AXONAL, SUBACUTE) 0.5, 0.5;\n (MOD, AXONAL, SUBACUTE) 0.5, 0.5;\n (SEV, AXONAL, SUBACUTE) 0.5, 0.5;\n (NO, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (MILD, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (MOD, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (SEV, V_E_REIN, SUBACUTE) 0.5, 0.5;\n (NO, E_REIN, SUBACUTE) 0.5, 0.5;\n (MILD, E_REIN, SUBACUTE) 0.5, 0.5;\n (MOD, E_REIN, SUBACUTE) 0.5, 0.5;\n (SEV, E_REIN, SUBACUTE) 0.5, 0.5;\n (NO, DEMY, CHRONIC) 0.5, 0.5;\n (MILD, DEMY, CHRONIC) 0.5, 0.5;\n (MOD, DEMY, CHRONIC) 0.5, 0.5;\n (SEV, DEMY, CHRONIC) 0.5, 0.5;\n (NO, BLOCK, CHRONIC) 0.5, 0.5;\n (MILD, BLOCK, CHRONIC) 0.5, 0.5;\n (MOD, BLOCK, CHRONIC) 0.5, 0.5;\n (SEV, BLOCK, CHRONIC) 0.5, 0.5;\n (NO, AXONAL, CHRONIC) 0.5, 0.5;\n (MILD, AXONAL, CHRONIC) 0.5, 0.5;\n (MOD, AXONAL, CHRONIC) 0.5, 0.5;\n (SEV, AXONAL, CHRONIC) 0.5, 0.5;\n (NO, V_E_REIN, CHRONIC) 0.5, 0.5;\n (MILD, V_E_REIN, CHRONIC) 0.5, 0.5;\n (MOD, V_E_REIN, CHRONIC) 0.5, 0.5;\n (SEV, V_E_REIN, CHRONIC) 0.5, 0.5;\n (NO, E_REIN, CHRONIC) 0.5, 0.5;\n (MILD, E_REIN, CHRONIC) 0.5, 0.5;\n (MOD, E_REIN, CHRONIC) 0.5, 0.5;\n (SEV, E_REIN, CHRONIC) 0.5, 0.5;\n (NO, DEMY, OLD) 0.5, 0.5;\n (MILD, DEMY, OLD) 0.5, 0.5;\n (MOD, DEMY, OLD) 0.5, 0.5;\n (SEV, DEMY, OLD) 0.5, 0.5;\n (NO, BLOCK, OLD) 0.5, 0.5;\n (MILD, BLOCK, OLD) 0.5, 0.5;\n (MOD, BLOCK, OLD) 0.5, 0.5;\n (SEV, BLOCK, OLD) 0.5, 0.5;\n (NO, AXONAL, OLD) 0.5, 0.5;\n (MILD, AXONAL, OLD) 0.5, 0.5;\n (MOD, AXONAL, OLD) 0.5, 0.5;\n (SEV, AXONAL, OLD) 0.5, 0.5;\n (NO, V_E_REIN, OLD) 0.5, 0.5;\n (MILD, V_E_REIN, OLD) 0.5, 0.5;\n (MOD, V_E_REIN, OLD) 0.5, 0.5;\n (SEV, V_E_REIN, OLD) 0.5, 0.5;\n (NO, E_REIN, OLD) 0.5, 0.5;\n (MILD, E_REIN, OLD) 0.5, 0.5;\n (MOD, E_REIN, OLD) 0.5, 0.5;\n (SEV, E_REIN, OLD) 0.5, 0.5;\n}\nprobability ( R_OTHER_ULND5_DISP ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_ULND5_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLE_ULN_SEV ) {\n table 0.945, 0.025, 0.015, 0.010, 0.005;\n}\nprobability ( R_LNLE_ULN_PATHO ) {\n table 0.600, 0.190, 0.200, 0.005, 0.005;\n}\nprobability ( R_LNLE_ULND5_DISP_E | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (TOTAL, BLOCK) 0.0, 0.0, 0.5, 0.5;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.3, 0.5, 0.2, 0.0;\n (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLE_DIFFN_ULND5_DISP_E | R_DIFFN_ULND5_DISP, R_LNLE_ULND5_DISP_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_DISP_E | R_OTHER_ULND5_DISP, R_LNLE_DIFFN_ULND5_DISP_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLW_ULND5_DISP_WD ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ULND5_DISP_WD | R_LNLW_ULND5_DISP_WD, R_DIFFN_ULND5_DISP ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_DISP_WD | R_OTHER_ULND5_DISP, R_DIFFN_LNLW_ULND5_DISP_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_DISP_BEW | R_OTHER_ULND5_DISP, R_DIFFN_ULND5_DISP ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_DISP_BED | R_ULND5_DISP_WD, R_ULND5_DISP_BEW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.0000, 0.0008, 0.0097, 0.9895;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0024, 0.9975;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0000, 0.0000, 0.0004, 0.9996;\n (NO, SEV) 0.0000, 0.0008, 0.0097, 0.9895;\n (MILD, SEV) 0.0000, 0.0001, 0.0024, 0.9975;\n (MOD, SEV) 0.0000, 0.0000, 0.0004, 0.9996;\n (SEV, SEV) 0.0000, 0.0000, 0.0002, 0.9998;\n}\nprobability ( R_ULND5_DISP_EED | R_ULND5_DISP_E, R_ULND5_DISP_BED ) {\n (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0404, 0.9192, 0.0404, 0.0000;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742;\n (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, MILD) 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.3477, 0.6496, 0.0023, 0.0000;\n (MOD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00230023, 0.64986499, 0.34783478;\n (SEV, MILD) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, MOD) 0.0000, 0.0004, 0.3477, 0.6496, 0.0023, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MOD) 0.000, 0.000, 0.000, 0.001, 0.499, 0.499, 0.001, 0.000, 0.000;\n (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.2215, 0.7732, 0.0052;\n (SEV, MOD) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, SEV) 0.4995, 0.4995, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, SEV) 0.0004, 0.3477, 0.6496, 0.0023, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742;\n}\nprobability ( R_OTHER_ULND5_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_ULND5_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLE_ULND5_BLOCK_E | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.3, 0.6, 0.1, 0.0, 0.0;\n (SEV, DEMY) 0.1, 0.5, 0.3, 0.1, 0.0;\n (TOTAL, DEMY) 0.00, 0.05, 0.20, 0.55, 0.20;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.2, 0.6, 0.2;\n (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLE_DIFFN_ULND5_BLOCK_E | R_DIFFN_ULND5_BLOCK, R_LNLE_ULND5_BLOCK_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_BLOCK_E | R_OTHER_ULND5_BLOCK, R_LNLE_DIFFN_ULND5_BLOCK_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_AMPR_E | R_ULND5_DISP_EED, R_ULND5_BLOCK_E ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_OTHER_ULND5_LD ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLE_ULND5_LD_E | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;\n (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_ULND5_LD_E | R_OTHER_ULND5_LD, R_LNLE_ULND5_LD_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0186, 0.9584, 0.0230, 0.0000;\n (MOD, NO) 0.0000, 0.0184, 0.9816, 0.0000;\n (SEV, NO) 0.00100010, 0.00310031, 0.03020302, 0.96569657;\n (NO, MILD) 0.0186, 0.9584, 0.0230, 0.0000;\n (MILD, MILD) 0.0000, 0.0304, 0.9696, 0.0000;\n (MOD, MILD) 0.0000, 0.0013, 0.9987, 0.0000;\n (SEV, MILD) 0.00039996, 0.00129987, 0.01409859, 0.98420158;\n (NO, MOD) 0.0000, 0.0184, 0.9816, 0.0000;\n (MILD, MOD) 0.0000, 0.0013, 0.9987, 0.0000;\n (MOD, MOD) 0.00000000, 0.00029997, 0.99960004, 0.00009999;\n (SEV, MOD) 0.0001, 0.0005, 0.0060, 0.9934;\n (NO, SEV) 0.00100010, 0.00310031, 0.03020302, 0.96569657;\n (MILD, SEV) 0.00039996, 0.00129987, 0.01409859, 0.98420158;\n (MOD, SEV) 0.0001, 0.0005, 0.0060, 0.9934;\n (SEV, SEV) 0.00010001, 0.00030003, 0.00380038, 0.99579958;\n}\nprobability ( R_OTHER_ULND5_RD ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_LNLE_ULND5_RD_E | R_LNLE_ULN_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( R_ULND5_RD_E | R_OTHER_ULND5_RD, R_LNLE_ULND5_RD_E ) {\n (NO, NO) 1.0, 0.0, 0.0;\n (MOD, NO) 0.0941, 0.9038, 0.0021;\n (SEV, NO) 0.0677, 0.2563, 0.6760;\n (NO, MOD) 0.0941, 0.9038, 0.0021;\n (MOD, MOD) 0.0140, 0.3525, 0.6335;\n (SEV, MOD) 0.0324, 0.1629, 0.8047;\n (NO, SEV) 0.0677, 0.2563, 0.6760;\n (MOD, SEV) 0.0324, 0.1629, 0.8047;\n (SEV, SEV) 0.0351, 0.1479, 0.8170;\n}\nprobability ( R_ULND5_LSLOW_E | R_ULND5_LD_E, R_ULND5_RD_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.00840084, 0.01190119, 0.06190619, 0.91739174, 0.00040004;\n (MILD, MOD) 0.00690069, 0.01000100, 0.05350535, 0.92869287, 0.00090009;\n (MOD, MOD) 0.00559944, 0.00829917, 0.04519548, 0.93890611, 0.00199980;\n (SEV, MOD) 0.0023, 0.0036, 0.0217, 0.8326, 0.1398;\n (NO, SEV) 0.00529947, 0.00619938, 0.02639736, 0.20817918, 0.75392461;\n (MILD, SEV) 0.0049, 0.0057, 0.0244, 0.1966, 0.7684;\n (MOD, SEV) 0.00440044, 0.00520052, 0.02240224, 0.18391839, 0.78407841;\n (SEV, SEV) 0.0028, 0.0033, 0.0145, 0.1304, 0.8490;\n}\nprobability ( R_OTHER_ULND5_SALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLW_ULND5_SALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_ULND5_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ULND5_SALOSS | R_LNLW_ULND5_SALOSS, R_DIFFN_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLE_ULND5_SALOSS | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_LNLLP_ULND5_SALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_E_ULND5_SALOSS | R_LNLE_ULND5_SALOSS, R_LNLLP_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_ULND5_SALOSS | R_DIFFN_LNLW_ULND5_SALOSS, R_LNLLP_E_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_SALOSS | R_OTHER_ULND5_SALOSS, R_LNL_DIFFN_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_ULND5_DIFSLOW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLE_ULND5_DIFSLOW | R_LNLE_ULN_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 0.0, 1.0, 0.0;\n}\nprobability ( R_DIFFN_ULND5_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( R_LNLE_DIFFN_ULND5_DIFSLOW_E | R_LNLE_ULND5_DIFSLOW, R_DIFFN_ULND5_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( R_ULND5_DIFSLOW_E | R_OTHER_ULND5_DIFSLOW, R_LNLE_DIFFN_ULND5_DIFSLOW_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( R_ULND5_DSLOW_E | R_ULND5_SALOSS, R_ULND5_DIFSLOW_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_ALLCV_E | R_ULND5_LSLOW_E, R_ULND5_DSLOW_E ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.0264, 0.9149, 0.0587, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S60) 0.0000, 0.0218, 0.9469, 0.0313, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.00030003, 0.00190019, 0.01400140, 0.07880788, 0.32383238, 0.45964596, 0.12121212, 0.00030003, 0.00000000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00410041, 0.03840384, 0.21452145, 0.74237424, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0006, 0.0944, 0.8841, 0.0209, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0007, 0.2183, 0.7790, 0.0020, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00009999, 0.00039996, 0.00429957, 0.03209679, 0.19558044, 0.50484952, 0.26037396, 0.00229977, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00210021, 0.02390239, 0.16481648, 0.80898090, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.0000, 0.0018, 0.1956, 0.7860, 0.0166, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0077, 0.4577, 0.5345, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, M_S44) 0.0000, 0.0001, 0.0007, 0.0074, 0.0735, 0.4044, 0.4938, 0.0201, 0.0000;\n (V_SEV, M_S44) 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0123, 0.1119, 0.8749, 0.0000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.0000, 0.0000, 0.0026, 0.2655, 0.7316, 0.0003, 0.0000, 0.0000, 0.0000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0166, 0.9182, 0.0652, 0.0000, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0010, 0.0172, 0.2179, 0.6479, 0.1159, 0.0000;\n (V_SEV, M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0055, 0.0699, 0.9243, 0.0000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.0000, 0.0000, 0.0000, 0.0044, 0.5355, 0.4600, 0.0001, 0.0000, 0.0000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0398, 0.9460, 0.0142, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00179982, 0.05589441, 0.46005399, 0.48215178, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0021, 0.0390, 0.9588, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0047, 0.5053, 0.4900, 0.0000, 0.0000;\n (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.1104, 0.8881, 0.0013, 0.0000;\n (SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0041, 0.1033, 0.8925, 0.0000;\n (V_SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00069993, 0.01889811, 0.98040196, 0.00000000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0218, 0.8352, 0.1430, 0.0000;\n (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.3203, 0.6777, 0.0000;\n (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0194, 0.9803, 0.0000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0093, 0.9905, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0003, 0.0010, 0.0036, 0.0154, 0.0712, 0.2467, 0.6617, 0.0000;\n (MOD, M_S08) 0.00000000, 0.00010001, 0.00050005, 0.00190019, 0.00930093, 0.05040504, 0.20722072, 0.73057306, 0.00000000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0026, 0.0190, 0.1153, 0.8626, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0062, 0.0562, 0.9369, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_CV_E | R_ULND5_ALLCV_E ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_ULND5_DISP_EWD | R_ULND5_DISP_WD, R_ULND5_DISP_BEW ) {\n (NO, NO) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000;\n (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, MILD) 0.0001, 0.2215, 0.7732, 0.0052, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.13151315, 0.85768577, 0.01080108, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;\n (SEV, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;\n (NO, MOD) 0.1315, 0.8577, 0.0108, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;\n (SEV, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;\n (NO, SEV) 0.9933, 0.0067, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, SEV) 0.0404, 0.9192, 0.0404, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n}\nprobability ( R_ULND5_BLOCK_EW | R_OTHER_ULND5_BLOCK, R_DIFFN_ULND5_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_AMPR_EW | R_ULND5_DISP_EWD, R_ULND5_BLOCK_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_ULND5_DIFSLOW_EW | R_OTHER_ULND5_DIFSLOW, R_DIFFN_ULND5_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( R_ULND5_DSLOW_EW | R_ULND5_SALOSS, R_ULND5_DIFSLOW_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_ALLCV_EW | R_ULND5_DSLOW_EW ) {\n (M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S52) 0.02689731, 0.97130287, 0.00179982, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S44) 0.0003, 0.0598, 0.8924, 0.0475, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S36) 0.0000, 0.0001, 0.0637, 0.9111, 0.0251, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S28) 0.0000, 0.0000, 0.0001, 0.0544, 0.9384, 0.0071, 0.0000, 0.0000, 0.0000;\n (M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0486, 0.8875, 0.0637, 0.0000, 0.0000;\n (M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.09500950, 0.89738974, 0.00740074, 0.00000000;\n (M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09730973, 0.28552855, 0.58335834, 0.00000000;\n (M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_CV_EW | R_ULND5_ALLCV_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_LNLW_ULND5_BLOCK_WD ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ULND5_BLOCK_WD | R_LNLW_ULND5_BLOCK_WD, R_DIFFN_ULND5_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_BLOCK_WD | R_OTHER_ULND5_BLOCK, R_DIFFN_LNLW_ULND5_BLOCK_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_EFFAXLOSS | R_ULND5_BLOCK_WD, R_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_ALLAMP_WD | R_ULND5_DISP_WD, R_ULND5_EFFAXLOSS ) {\n (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;\n (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;\n (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;\n (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;\n (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;\n (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;\n (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;\n (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;\n (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;\n (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_ULND5_AMP_WD | R_ULND5_ALLAMP_WD ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_LNLW_ULND5_LD_WD ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_ULND5_LD_WD | R_OTHER_ULND5_LD, R_LNLW_ULND5_LD_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0186, 0.9584, 0.0230, 0.0000;\n (MOD, NO) 0.0000, 0.0184, 0.9816, 0.0000;\n (SEV, NO) 0.00100010, 0.00310031, 0.03020302, 0.96569657;\n (NO, MILD) 0.0186, 0.9584, 0.0230, 0.0000;\n (MILD, MILD) 0.0000, 0.0304, 0.9696, 0.0000;\n (MOD, MILD) 0.0000, 0.0013, 0.9987, 0.0000;\n (SEV, MILD) 0.00039996, 0.00129987, 0.01409859, 0.98420158;\n (NO, MOD) 0.0000, 0.0184, 0.9816, 0.0000;\n (MILD, MOD) 0.0000, 0.0013, 0.9987, 0.0000;\n (MOD, MOD) 0.00000000, 0.00029997, 0.99960004, 0.00009999;\n (SEV, MOD) 0.0001, 0.0005, 0.0060, 0.9934;\n (NO, SEV) 0.00100010, 0.00310031, 0.03020302, 0.96569657;\n (MILD, SEV) 0.00039996, 0.00129987, 0.01409859, 0.98420158;\n (MOD, SEV) 0.0001, 0.0005, 0.0060, 0.9934;\n (SEV, SEV) 0.00010001, 0.00030003, 0.00380038, 0.99579958;\n}\nprobability ( R_LNLW_ULND5_RD_WD ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_ULND5_RD_WD | R_OTHER_ULND5_RD, R_LNLW_ULND5_RD_WD ) {\n (NO, NO) 1.0, 0.0, 0.0;\n (MOD, NO) 0.0139, 0.9821, 0.0040;\n (SEV, NO) 0.0000000, 0.0150015, 0.9849985;\n (NO, MOD) 0.0139, 0.9821, 0.0040;\n (MOD, MOD) 0.0002, 0.1057, 0.8941;\n (SEV, MOD) 0.0000, 0.0037, 0.9963;\n (NO, SEV) 0.00070007, 0.09680968, 0.90249025;\n (MOD, SEV) 0.00000000, 0.01550155, 0.98449845;\n (SEV, SEV) 0.000, 0.003, 0.997;\n}\nprobability ( R_ULND5_LSLOW_WD | R_ULND5_LD_WD, R_ULND5_RD_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;\n (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;\n (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;\n (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;\n (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;\n (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;\n (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;\n}\nprobability ( R_LNLE_DIFFN_ULND5_DIFSLOW_WD | R_LNLE_ULND5_DIFSLOW, R_DIFFN_ULND5_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( R_ULND5_DIFSLOW_WD | R_OTHER_ULND5_DIFSLOW, R_LNLE_DIFFN_ULND5_DIFSLOW_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( R_ULND5_DSLOW_WD | R_ULND5_SALOSS, R_ULND5_DIFSLOW_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_ALLCV_WD | R_ULND5_LSLOW_WD, R_ULND5_DSLOW_WD ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;\n (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;\n (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;\n (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;\n (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;\n (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;\n (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;\n (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;\n (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;\n (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULND5_CV_WD | R_ULND5_ALLCV_WD ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_DIFFN_ULN_BLOCK | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLE_ULN_BLOCK | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (TOTAL, DEMY) 0.2, 0.2, 0.2, 0.2, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.2, 0.2, 0.2, 0.2, 0.2;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_ULN_BLOCK_E | R_DIFFN_ULN_BLOCK, R_LNLE_ULN_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULN_AMPR_E | R_ULN_BLOCK_E ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_OTHER_ADM_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLC8_ADM_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_ADM_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLC8_LP_ADM_MALOSS | R_LNLC8_ADM_MALOSS, R_LNLLP_ADM_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLE_ADM_MALOSS | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_LNLC8_LP_E_ADM_MALOSS | R_LNLC8_LP_ADM_MALOSS, R_LNLE_ADM_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLW_ADM_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_ADM_MALOSS | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ADM_MALOSS | R_LNLW_ADM_MALOSS, R_DIFFN_ADM_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_ADM_MALOSS | R_LNLC8_LP_E_ADM_MALOSS, R_DIFFN_LNLW_ADM_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0001, 0.9998, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0382, 0.9586, 0.0032, 0.0000;\n (SEV, NO) 0.0000, 0.0002, 0.0420, 0.9578, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0001, 0.9998, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0292, 0.9708, 0.0000, 0.0000;\n (MOD, MILD) 0.00000000, 0.00110011, 0.39953995, 0.59935994, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0049, 0.9951, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0000, 0.0382, 0.9586, 0.0032, 0.0000;\n (MILD, MOD) 0.00000000, 0.00110011, 0.39953995, 0.59935994, 0.00000000;\n (MOD, MOD) 0.00000000, 0.00000000, 0.01280128, 0.98719872, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9986, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0420, 0.9578, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0049, 0.9951, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9986, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_MALOSS | R_OTHER_ADM_MALOSS, R_LNL_DIFFN_ADM_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;\n (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;\n (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( R_LNLE_ULN_DIFSLOW | R_LNLE_ULN_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 0.0, 1.0, 0.0;\n}\nprobability ( R_DIFFN_ULN_DIFSLOW | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( R_ULN_DIFSLOW_E | R_LNLE_ULN_DIFSLOW, R_DIFFN_ULN_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;\n (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;\n (MOD, MOD) 0.000, 0.000, 0.002, 0.998;\n (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;\n (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;\n}\nprobability ( R_ULN_DCV_E | R_ADM_MALOSS, R_ULN_DIFSLOW_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;\n (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;\n (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;\n (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;\n}\nprobability ( R_ULN_LD_EW | R_LNLE_ULN_SEV, R_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.25, 0.25, 0.25, 0.25;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;\n (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.25, 0.25, 0.25, 0.25;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 0.25, 0.25, 0.25, 0.25;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( R_ULN_RD_EW | R_LNLE_ULN_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( R_ULN_RDLDCV_E | R_ULN_LD_EW, R_ULN_RD_EW ) {\n (NO, NO) 0.90449045, 0.09530953, 0.00020002, 0.00000000, 0.00000000, 0.00000000;\n (MILD, NO) 0.1320, 0.6039, 0.2641, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0139, 0.1839, 0.8022, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.00120012, 0.00670067, 0.05440544, 0.86008601, 0.07760776, 0.00000000;\n (NO, MOD) 0.0115, 0.0333, 0.1509, 0.7319, 0.0724, 0.0000;\n (MILD, MOD) 0.00340034, 0.01220122, 0.06900690, 0.71967197, 0.19531953, 0.00040004;\n (MOD, MOD) 0.0011, 0.0045, 0.0299, 0.5742, 0.3876, 0.0027;\n (SEV, MOD) 0.0001, 0.0002, 0.0018, 0.0914, 0.6093, 0.2972;\n (NO, SEV) 0.00000000, 0.00009999, 0.00109989, 0.14618538, 0.80701930, 0.04559544;\n (MILD, SEV) 0.0000, 0.0000, 0.0002, 0.0581, 0.7950, 0.1467;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0228, 0.6344, 0.3427;\n (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0014, 0.1063, 0.8923;\n}\nprobability ( R_ULN_ALLCV_E | R_ULN_DCV_E, R_ULN_RDLDCV_E ) {\n (M_S60, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56, M_S60) 0.3684, 0.6316, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S60) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S44, M_S60) 0.00089991, 0.08959104, 0.82601740, 0.08349165, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S36, M_S60) 0.0000, 0.0005, 0.1011, 0.8478, 0.0506, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S60) 0.00000000, 0.00000000, 0.00050005, 0.07910791, 0.90019002, 0.02020202, 0.00000000, 0.00000000, 0.00000000;\n (M_S20, M_S60) 0.0000, 0.0000, 0.0000, 0.0006, 0.0734, 0.8393, 0.0867, 0.0000, 0.0000;\n (M_S14, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.09470947, 0.89458946, 0.01040104, 0.00000000;\n (M_S08, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0932, 0.9048, 0.0000;\n (M_S00, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S52) 0.0077, 0.9808, 0.0115, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S56, M_S52) 0.0005, 0.4476, 0.5519, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S52) 0.0000, 0.0239, 0.9751, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S52) 0.0000, 0.0036, 0.2468, 0.7340, 0.0156, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S36, M_S52) 0.0000, 0.0000, 0.0050, 0.3134, 0.6811, 0.0005, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00710071, 0.54935494, 0.44334433, 0.00020002, 0.00000000, 0.00000000;\n (M_S20, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00910091, 0.52835284, 0.46254625, 0.00000000, 0.00000000;\n (M_S14, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0216, 0.8359, 0.1425, 0.0000;\n (M_S08, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0355, 0.9641, 0.0000;\n (M_S00, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S44) 0.0000, 0.0047, 0.9951, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S56, M_S44) 0.0000, 0.0004, 0.9005, 0.0991, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S44) 0.0000, 0.0000, 0.1288, 0.8712, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S44) 0.00000000, 0.00000000, 0.01130113, 0.57115712, 0.41754175, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S36, M_S44) 0.0000, 0.0000, 0.0000, 0.0214, 0.9354, 0.0432, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.05649435, 0.93230677, 0.01109889, 0.00000000, 0.00000000;\n (M_S20, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.1430, 0.8551, 0.0015, 0.0000;\n (M_S14, M_S44) 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0019998, 0.3548645, 0.6431357, 0.0000000;\n (M_S08, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0122, 0.9877, 0.0000;\n (M_S00, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S27) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56, M_S27) 0.000, 0.000, 0.000, 0.000, 0.997, 0.003, 0.000, 0.000, 0.000;\n (M_S52, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.2336, 0.7664, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S27) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00259974, 0.99340066, 0.00399960, 0.00000000, 0.00000000;\n (M_S36, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2469, 0.7531, 0.0000, 0.0000;\n (M_S28, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.9939, 0.0041, 0.0000;\n (M_S20, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0314, 0.9686, 0.0000;\n (M_S14, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;\n (M_S08, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;\n (M_S00, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (M_S60, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.1305, 0.8445, 0.0238, 0.0000;\n (M_S56, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0740, 0.8589, 0.0667, 0.0000;\n (M_S52, M_S15) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03730373, 0.80288029, 0.15971597, 0.00000000;\n (M_S44, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0070, 0.3517, 0.6413, 0.0000;\n (M_S36, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0581, 0.9416, 0.0000;\n (M_S28, M_S15) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.006, 0.994, 0.000;\n (M_S20, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (M_S14, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S08, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;\n (M_S00, M_S15) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (M_S60, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0321, 0.9677, 0.0000;\n (M_S56, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0184, 0.9815, 0.0000;\n (M_S52, M_S07) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01010101, 0.98989899, 0.00000000;\n (M_S44, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.9958, 0.0000;\n (M_S36, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (M_S28, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;\n (M_S20, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S14, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (M_S08, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S00, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULN_CV_E | R_ULN_ALLCV_E ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_ULN_BLOCK_EW | R_DIFFN_ULN_BLOCK ) {\n (NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD) 0.0038, 0.9962, 0.0000, 0.0000, 0.0000;\n (MOD) 0.0007, 0.0223, 0.9770, 0.0000, 0.0000;\n (SEV) 0.0019, 0.0060, 0.0587, 0.9334, 0.0000;\n (TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULN_AMPR_EW | R_ULN_BLOCK_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_ULN_DIFSLOW_EW | R_DIFFN_ULN_DIFSLOW ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0126, 0.9869, 0.0005, 0.0000;\n (MOD) 0.0000, 0.0179, 0.9821, 0.0000;\n (SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n}\nprobability ( R_ULN_DCV_EW | R_ADM_MALOSS, R_ULN_DIFSLOW_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;\n (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;\n (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;\n (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;\n}\nprobability ( R_ULN_ALLCV_EW | R_ULN_DCV_EW ) {\n (M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56) 0.0558, 0.8486, 0.0956, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52) 0.0000, 0.0369, 0.9631, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44) 0.0008, 0.0087, 0.0873, 0.8226, 0.0806, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S36) 0.0000, 0.0000, 0.0005, 0.0994, 0.8508, 0.0493, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S28) 0.0000, 0.0000, 0.0000, 0.0005, 0.0781, 0.9017, 0.0197, 0.0000, 0.0000, 0.0000;\n (M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0728, 0.8406, 0.0860, 0.0000, 0.0000;\n (M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0942, 0.8952, 0.0103, 0.0000;\n (M_S08) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.002, 0.093, 0.905, 0.000;\n (M_S00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n}\nprobability ( R_ULN_CV_EW | R_ULN_ALLCV_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_OTHER_ADM_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLC8_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_LNLLP_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_LNLC8_LP_ADM_DE_REGEN | R_LNLC8_ADM_DE_REGEN, R_LNLLP_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_LNLE_ULN_TIME ) {\n table 0.05, 0.60, 0.30, 0.05;\n}\nprobability ( R_LNLE_ADM_DE_REGEN | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME, R_LNLE_ULN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (TOTAL, CHRONIC, DEMY) 1.0, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (TOTAL, OLD, DEMY) 1.0, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (TOTAL, OLD, BLOCK) 1.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (TOTAL, OLD, AXONAL) 1.0, 0.0;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (TOTAL, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( R_LNLC8_LP_E_ADM_DE_REGEN | R_LNLC8_LP_ADM_DE_REGEN, R_LNLE_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_DIFFN_ADM_DE_REGEN | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( R_LNLW_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ADM_DE_REGEN | R_DIFFN_ADM_DE_REGEN, R_LNLW_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_ADM_DE_REGEN | R_LNLC8_LP_E_ADM_DE_REGEN, R_DIFFN_LNLW_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_MYOP_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_MYDY_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_ADM_DE_REGEN | R_MYOP_ADM_DE_REGEN, R_MYDY_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_OTHER_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_MUSCLE_ADM_DE_REGEN | R_MYOP_MYDY_ADM_DE_REGEN, R_OTHER_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_ADM_DE_REGEN | R_LNL_DIFFN_ADM_DE_REGEN, R_MUSCLE_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_DE_REGEN_ADM_NMT | R_ADM_DE_REGEN ) {\n (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;\n}\nprobability ( R_MYAS_ADM_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYAS_DE_REGEN_ADM_NMT | R_DE_REGEN_ADM_NMT, R_MYAS_ADM_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_NMT | R_OTHER_ADM_NMT, R_MYAS_DE_REGEN_ADM_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYDY_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_ADM_MUSIZE | R_MYDY_ADM_MUSIZE, R_MYOP_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MUSCLE_ADM_MUSIZE | R_OTHER_ADM_MUSIZE, R_MYOP_MYDY_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLW_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_ADM_MUSIZE | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ADM_MUSIZE | R_LNLW_ADM_MUSIZE, R_DIFFN_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLE_ADM_MUSIZE | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME, R_LNLE_ULN_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLC8_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLC8_LP_ADM_MUSIZE | R_LNLLP_ADM_MUSIZE, R_LNLC8_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLC8_LP_E_ADM_MUSIZE | R_LNLE_ADM_MUSIZE, R_LNLC8_LP_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_ADM_MUSIZE | R_DIFFN_LNLW_ADM_MUSIZE, R_LNLC8_LP_E_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_MUSIZE | R_MUSCLE_ADM_MUSIZE, R_LNL_DIFFN_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 0.9791, 0.0009, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL, V_SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL, V_SMALL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (INCR, V_SMALL) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;\n (LARGE, V_SMALL) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;\n (V_LARGE, V_SMALL) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;\n (V_SMALL, SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL, SMALL) 0.7493, 0.2257, 0.0049, 0.0001, 0.0000, 0.0000, 0.0200;\n (NORMAL, SMALL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;\n (INCR, SMALL) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;\n (LARGE, SMALL) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;\n (V_LARGE, SMALL) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;\n (V_SMALL, NORMAL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SMALL, NORMAL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;\n (NORMAL, NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;\n (V_SMALL, INCR) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;\n (SMALL, INCR) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00359964, 0.16548345, 0.64543546, 0.16548345, 0.01999800;\n (LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;\n (V_LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (V_SMALL, LARGE) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;\n (SMALL, LARGE) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;\n (INCR, LARGE) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;\n (LARGE, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;\n (V_SMALL, V_LARGE) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;\n (SMALL, V_LARGE) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;\n (INCR, V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;\n (V_LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9799, 0.0200;\n}\nprobability ( R_ADM_EFFMUS | R_ADM_NMT, R_ADM_MUSIZE ) {\n (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MIXED, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;\n (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MIXED, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;\n (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;\n (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;\n (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;\n (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;\n (MIXED, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;\n (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;\n (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;\n (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;\n (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;\n (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;\n (MIXED, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;\n (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;\n (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;\n (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;\n (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;\n (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;\n (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;\n (MIXED, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;\n (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;\n (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;\n (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;\n (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;\n (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;\n (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;\n (MIXED, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;\n (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;\n (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;\n (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;\n (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;\n (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;\n (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;\n (MIXED, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;\n}\nprobability ( R_OTHER_ULN_BLOCK_WA ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLW_ULN_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ULN_BLOCK_WA | R_DIFFN_ULN_BLOCK, R_LNLW_ULN_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULN_BLOCK_WA | R_OTHER_ULN_BLOCK_WA, R_DIFFN_LNLW_ULN_BLOCK_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_MULOSS | R_ULN_BLOCK_WA, R_ADM_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;\n (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;\n (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;\n (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;\n (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;\n (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;\n (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;\n (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;\n (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;\n (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;\n (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;\n (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;\n (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( R_ADM_ALLAMP_WA | R_ADM_EFFMUS, R_ADM_MULOSS ) {\n (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;\n (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;\n (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;\n (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;\n (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;\n (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;\n (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;\n (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;\n (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;\n (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;\n (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;\n (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;\n (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;\n (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;\n (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;\n (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;\n (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;\n (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;\n (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;\n (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;\n (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;\n (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;\n (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;\n (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;\n (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;\n (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;\n (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;\n (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;\n (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;\n}\nprobability ( R_ULN_AMP_WA | R_ADM_ALLAMP_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_ULN_LD_WA ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_ULN_RD_WA ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_ULN_RDLDDEL | R_ULN_LD_WA, R_ULN_RD_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0964, 0.7981, 0.1055, 0.0000, 0.0000;\n (MOD, NO) 0.0032, 0.1270, 0.8698, 0.0000, 0.0000;\n (SEV, NO) 0.00090009, 0.00280028, 0.01470147, 0.98159816, 0.00000000;\n (NO, MOD) 0.0019, 0.5257, 0.4724, 0.0000, 0.0000;\n (MILD, MOD) 0.00019998, 0.04149585, 0.95830417, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0001, 0.0144, 0.9855, 0.0000, 0.0000;\n (SEV, MOD) 0.00019998, 0.00059994, 0.00369963, 0.99550045, 0.00000000;\n (NO, SEV) 0.0002, 0.0304, 0.9694, 0.0000, 0.0000;\n (MILD, SEV) 0.0001, 0.0142, 0.9857, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0090, 0.9808, 0.0102, 0.0000;\n (SEV, SEV) 0.0000, 0.0002, 0.0012, 0.9984, 0.0002;\n}\nprobability ( R_ULN_DIFSLOW_WA | R_LNLE_ULN_DIFSLOW, R_DIFFN_ULN_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;\n (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;\n (MOD, MOD) 0.000, 0.000, 0.002, 0.998;\n (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;\n (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;\n}\nprobability ( R_ULN_DCV_WA | R_ADM_MALOSS, R_ULN_DIFSLOW_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;\n (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;\n (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;\n (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;\n}\nprobability ( R_ULN_ALLDEL_WA | R_ULN_RDLDDEL, R_ULN_DCV_WA ) {\n (MS3_1, M_S60) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S60) 0.05119488, 0.22907709, 0.56624338, 0.15348465, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S60) 0.0001, 0.0035, 0.0632, 0.9326, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S60) 0.00129987, 0.00199980, 0.00439956, 0.01869813, 0.12128787, 0.74792521, 0.10438956, 0.00000000, 0.00000000;\n (MS20_1, M_S60) 0.00010001, 0.00010001, 0.00030003, 0.00090009, 0.00450045, 0.04340434, 0.57675768, 0.37393739, 0.00000000;\n (MS3_1, M_S52) 0.5607, 0.4393, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S52) 0.01929807, 0.12868713, 0.49285071, 0.35916408, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S52) 0.0000, 0.0011, 0.0283, 0.9651, 0.0055, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S52) 0.00100010, 0.00160016, 0.00370037, 0.01610161, 0.10931093, 0.74217422, 0.12611261, 0.00000000, 0.00000000;\n (MS20_1, M_S52) 0.0001, 0.0001, 0.0002, 0.0008, 0.0041, 0.0399, 0.5568, 0.3980, 0.0000;\n (MS3_1, M_S44) 0.0069, 0.7963, 0.1968, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S44) 0.0027, 0.0328, 0.2398, 0.7246, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S44) 0.0000, 0.0002, 0.0075, 0.8889, 0.1034, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S44) 0.00079992, 0.00119988, 0.00279972, 0.01249875, 0.09159084, 0.72352765, 0.16758324, 0.00000000, 0.00000000;\n (MS20_1, M_S44) 0.0001, 0.0001, 0.0002, 0.0007, 0.0034, 0.0348, 0.5239, 0.4368, 0.0000;\n (MS3_1, M_S36) 0.0000, 0.0184, 0.9806, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S36) 0.00010001, 0.00330033, 0.05470547, 0.93819382, 0.00370037, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S36) 0.00000000, 0.00000000, 0.00030003, 0.18501850, 0.81468147, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS10_1, M_S36) 0.00049995, 0.00079992, 0.00179982, 0.00869913, 0.07029297, 0.68023198, 0.23767623, 0.00000000, 0.00000000;\n (MS20_1, M_S36) 0.0001, 0.0001, 0.0001, 0.0005, 0.0027, 0.0287, 0.4783, 0.4895, 0.0000;\n (MS3_1, M_S28) 0.0000, 0.0001, 0.0179, 0.9820, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S28) 0.00000000, 0.00019998, 0.00479952, 0.50394961, 0.49105089, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S28) 0.0000, 0.0000, 0.0000, 0.0032, 0.9968, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S28) 0.0002, 0.0004, 0.0009, 0.0047, 0.0440, 0.5737, 0.3761, 0.0000, 0.0000;\n (MS20_1, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40724072, 0.56955696, 0.00000000;\n (MS3_1, M_S20) 0.0000, 0.0002, 0.0030, 0.1393, 0.8575, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S20) 0.0000, 0.0000, 0.0003, 0.0188, 0.9804, 0.0005, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.93139314, 0.06710671, 0.00000000, 0.00000000, 0.00000000;\n (MS10_1, M_S20) 0.0001, 0.0001, 0.0002, 0.0013, 0.0150, 0.3152, 0.6681, 0.0000, 0.0000;\n (MS20_1, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00080016, 0.01110222, 0.28045609, 0.70754151, 0.00000000;\n (MS3_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0006, 0.8145, 0.1849, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0494, 0.9506, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S14) 0.000, 0.000, 0.000, 0.000, 0.001, 0.999, 0.000, 0.000, 0.000;\n (MS10_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0001, 0.0017, 0.0786, 0.9196, 0.0000, 0.0000;\n (MS20_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0035, 0.1380, 0.8583, 0.0000;\n (MS3_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59735974, 0.39093909, 0.00000000, 0.00000000;\n (MS3_9, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00340034, 0.29902990, 0.69746975, 0.00000000, 0.00000000;\n (MS4_7, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.1112, 0.8881, 0.0000, 0.0000;\n (MS10_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00940094, 0.95939594, 0.03110311, 0.00000000;\n (MS20_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.02530253, 0.97439744, 0.00000000;\n (MS3_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS3_9, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS4_7, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS10_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS20_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ULN_LAT_WA | R_ULN_ALLDEL_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_ADM_VOL_ACT ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_ADM_FORCE | R_ADM_VOL_ACT, R_ADM_ALLAMP_WA ) {\n (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;\n (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;\n (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;\n (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;\n (NORMAL, A0_01) 0.00009999, 0.00429957, 0.05219478, 0.26687331, 0.43305669, 0.24347565;\n (REDUCED, A0_01) 0.0000, 0.0005, 0.0084, 0.0849, 0.3219, 0.5843;\n (V_RED, A0_01) 0.0000, 0.0000, 0.0007, 0.0167, 0.1576, 0.8250;\n (ABSENT, A0_01) 0.00000000, 0.00000000, 0.00000000, 0.00260026, 0.05860586, 0.93879388;\n (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;\n (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;\n (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;\n (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;\n (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;\n (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;\n (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;\n (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;\n (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;\n (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;\n (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;\n (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;\n (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;\n (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;\n (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;\n (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;\n (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;\n (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;\n (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;\n (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;\n (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;\n (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;\n (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;\n (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;\n}\nprobability ( R_ADM_MUSCLE_VOL | R_ADM_MUSIZE, R_ADM_MALOSS ) {\n (V_SMALL, NO) 0.9896, 0.0104;\n (SMALL, NO) 0.8137, 0.1863;\n (NORMAL, NO) 0.0209, 0.9791;\n (INCR, NO) 0.009, 0.991;\n (LARGE, NO) 0.003, 0.997;\n (V_LARGE, NO) 0.0004, 0.9996;\n (OTHER, NO) 0.4212, 0.5788;\n (V_SMALL, MILD) 0.9976, 0.0024;\n (SMALL, MILD) 0.9603, 0.0397;\n (NORMAL, MILD) 0.5185, 0.4815;\n (INCR, MILD) 0.1087, 0.8913;\n (LARGE, MILD) 0.0278, 0.9722;\n (V_LARGE, MILD) 0.0046, 0.9954;\n (OTHER, MILD) 0.5185, 0.4815;\n (V_SMALL, MOD) 0.999, 0.001;\n (SMALL, MOD) 0.9893, 0.0107;\n (NORMAL, MOD) 0.9588, 0.0412;\n (INCR, MOD) 0.6377, 0.3623;\n (LARGE, MOD) 0.2716, 0.7284;\n (V_LARGE, MOD) 0.0779, 0.9221;\n (OTHER, MOD) 0.6336, 0.3664;\n (V_SMALL, SEV) 0.9995, 0.0005;\n (SMALL, SEV) 0.9969, 0.0031;\n (NORMAL, SEV) 0.9953, 0.0047;\n (INCR, SEV) 0.9518, 0.0482;\n (LARGE, SEV) 0.8234, 0.1766;\n (V_LARGE, SEV) 0.5986, 0.4014;\n (OTHER, SEV) 0.7685, 0.2315;\n (V_SMALL, TOTAL) 0.9989, 0.0011;\n (SMALL, TOTAL) 0.9984, 0.0016;\n (NORMAL, TOTAL) 0.9984, 0.0016;\n (INCR, TOTAL) 0.9975, 0.0025;\n (LARGE, TOTAL) 0.9965, 0.0035;\n (V_LARGE, TOTAL) 0.9956, 0.0044;\n (OTHER, TOTAL) 0.9857, 0.0143;\n (V_SMALL, OTHER) 0.9363, 0.0637;\n (SMALL, OTHER) 0.8403, 0.1597;\n (NORMAL, OTHER) 0.6534, 0.3466;\n (INCR, OTHER) 0.4689, 0.5311;\n (LARGE, OTHER) 0.3174, 0.6826;\n (V_LARGE, OTHER) 0.1948, 0.8052;\n (OTHER, OTHER) 0.5681, 0.4319;\n}\nprobability ( R_ADM_MVA_RECRUIT | R_ADM_MULOSS, R_ADM_VOL_ACT ) {\n (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;\n (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;\n (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;\n (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;\n (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;\n (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;\n (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;\n (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;\n (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;\n (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;\n (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;\n (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;\n (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;\n (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;\n (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;\n (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;\n (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_MVA_AMP | R_ADM_EFFMUS ) {\n (V_SMALL) 0.00, 0.04, 0.96;\n (SMALL) 0.01, 0.15, 0.84;\n (NORMAL) 0.05, 0.90, 0.05;\n (INCR) 0.50, 0.49, 0.01;\n (LARGE) 0.85, 0.15, 0.00;\n (V_LARGE) 0.96, 0.04, 0.00;\n (OTHER) 0.33, 0.34, 0.33;\n}\nprobability ( R_ADM_TA_CONCL | R_ADM_EFFMUS ) {\n (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;\n (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;\n (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;\n (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;\n (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;\n (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;\n (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;\n}\nprobability ( R_ADM_MUPAMP | R_ADM_EFFMUS ) {\n (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;\n (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;\n (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;\n (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;\n}\nprobability ( R_ADM_QUAN_MUPAMP | R_ADM_MUPAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_ADM_QUAL_MUPAMP | R_ADM_MUPAMP ) {\n (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;\n (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;\n (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;\n (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;\n (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;\n (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;\n (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;\n}\nprobability ( R_ADM_MUPDUR | R_ADM_EFFMUS ) {\n (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;\n (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;\n (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;\n}\nprobability ( R_ADM_QUAN_MUPDUR | R_ADM_MUPDUR ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_ADM_QUAL_MUPDUR | R_ADM_MUPDUR ) {\n (V_SMALL) 0.8309, 0.1677, 0.0014;\n (SMALL) 0.49, 0.49, 0.02;\n (NORMAL) 0.1065, 0.7870, 0.1065;\n (INCR) 0.02, 0.49, 0.49;\n (LARGE) 0.0014, 0.1677, 0.8309;\n (V_LARGE) 0.0001, 0.0392, 0.9607;\n (OTHER) 0.2597, 0.4806, 0.2597;\n}\nprobability ( R_ADM_QUAN_MUPPOLY | R_ADM_DE_REGEN, R_ADM_EFFMUS ) {\n (NO, V_SMALL) 0.109, 0.548, 0.343;\n (YES, V_SMALL) 0.004, 0.122, 0.874;\n (NO, SMALL) 0.340, 0.564, 0.096;\n (YES, SMALL) 0.015, 0.261, 0.724;\n (NO, NORMAL) 0.925, 0.075, 0.000;\n (YES, NORMAL) 0.091, 0.526, 0.383;\n (NO, INCR) 0.796, 0.201, 0.003;\n (YES, INCR) 0.061, 0.465, 0.474;\n (NO, LARGE) 0.637, 0.348, 0.015;\n (YES, LARGE) 0.039, 0.396, 0.565;\n (NO, V_LARGE) 0.340, 0.564, 0.096;\n (YES, V_LARGE) 0.015, 0.261, 0.724;\n (NO, OTHER) 0.340, 0.564, 0.096;\n (YES, OTHER) 0.015, 0.261, 0.724;\n}\nprobability ( R_ADM_QUAL_MUPPOLY | R_ADM_QUAN_MUPPOLY ) {\n (x_12_) 0.95, 0.05;\n (x12_24_) 0.3, 0.7;\n (x_24_) 0.05, 0.95;\n}\nprobability ( R_ADM_MUPSATEL | R_ADM_DE_REGEN ) {\n (NO) 0.95, 0.05;\n (YES) 0.2, 0.8;\n}\nprobability ( R_ADM_MUPINSTAB | R_ADM_NMT ) {\n (NO) 0.95, 0.05;\n (MOD_PRE) 0.1, 0.9;\n (SEV_PRE) 0.03, 0.97;\n (MLD_POST) 0.2, 0.8;\n (MOD_POST) 0.1, 0.9;\n (SEV_POST) 0.03, 0.97;\n (MIXED) 0.1, 0.9;\n}\nprobability ( R_ADM_REPSTIM_CMAPAMP | R_ADM_ALLAMP_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_ADM_REPSTIM_DECR | R_ADM_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;\n (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MIXED) 0.245, 0.245, 0.245, 0.245, 0.020;\n}\nprobability ( R_ADM_REPSTIM_FACILI | R_ADM_NMT ) {\n (NO) 0.95, 0.02, 0.01, 0.02;\n (MOD_PRE) 0.010, 0.889, 0.100, 0.001;\n (SEV_PRE) 0.010, 0.080, 0.909, 0.001;\n (MLD_POST) 0.89, 0.08, 0.01, 0.02;\n (MOD_POST) 0.48, 0.50, 0.01, 0.01;\n (SEV_POST) 0.020, 0.949, 0.030, 0.001;\n (MIXED) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( R_ADM_REPSTIM_POST_DECR | R_ADM_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;\n (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;\n (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MIXED) 0.23, 0.23, 0.22, 0.22, 0.10;\n}\nprobability ( R_ADM_SF_JITTER | R_ADM_NMT ) {\n (NO) 0.95, 0.05, 0.00, 0.00;\n (MOD_PRE) 0.02, 0.20, 0.70, 0.08;\n (SEV_PRE) 0.0, 0.1, 0.4, 0.5;\n (MLD_POST) 0.05, 0.70, 0.20, 0.05;\n (MOD_POST) 0.01, 0.19, 0.70, 0.10;\n (SEV_POST) 0.0, 0.1, 0.4, 0.5;\n (MIXED) 0.1, 0.3, 0.3, 0.3;\n}\nprobability ( R_LNLC8_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_LNLC8_LP_ADM_MUDENS | R_LNLC8_ADM_MUDENS, R_LNLLP_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLE_ADM_MUDENS | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME, R_LNLE_ULN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( R_LNLC8_LP_E_ADM_MUDENS | R_LNLC8_LP_ADM_MUDENS, R_LNLE_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_DIFFN_ADM_MUDENS | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( R_LNLW_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ADM_MUDENS | R_DIFFN_ADM_MUDENS, R_LNLW_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_ADM_MUDENS | R_LNLC8_LP_E_ADM_MUDENS, R_DIFFN_LNLW_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_MYOP_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MYDY_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_ADM_MUDENS | R_MYOP_ADM_MUDENS, R_MYDY_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_MYAS_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_OTHER_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MYAS_OTHER_ADM_MUDENS | R_MYAS_ADM_MUDENS, R_OTHER_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_MUSCLE_ADM_MUDENS | R_MYOP_MYDY_ADM_MUDENS, R_MYAS_OTHER_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_MUDENS | R_LNL_DIFFN_ADM_MUDENS, R_MUSCLE_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_SF_DENSITY | R_ADM_MUDENS ) {\n (NORMAL) 0.97, 0.03, 0.00;\n (INCR) 0.05, 0.90, 0.05;\n (V_INCR) 0.01, 0.04, 0.95;\n}\nprobability ( R_LNLC8_ADM_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_ADM_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLC8_LP_ADM_NEUR_ACT | R_LNLC8_ADM_NEUR_ACT, R_LNLLP_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLE_ADM_NEUR_ACT | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLC8_LP_E_ADM_NEUR_ACT | R_LNLC8_LP_ADM_NEUR_ACT, R_LNLE_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DIFFN_ADM_NEUR_ACT | DIFFN_M_SEV_DIST, DIFFN_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLW_ADM_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ADM_NEUR_ACT | R_DIFFN_ADM_NEUR_ACT, R_LNLW_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_ADM_NEUR_ACT | R_LNLC8_LP_E_ADM_NEUR_ACT, R_DIFFN_LNLW_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_ADM_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_ADM_NEUR_ACT | R_LNL_DIFFN_ADM_NEUR_ACT, R_OTHER_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_SPONT_NEUR_DISCH | R_ADM_NEUR_ACT ) {\n (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;\n (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;\n (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;\n (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;\n (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;\n}\nprobability ( R_MYOP_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYDY_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_ADM_DENERV | R_MYOP_ADM_DENERV, R_MYDY_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_NMT_ADM_DENERV | R_ADM_NMT ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE) 0.40, 0.45, 0.15, 0.00;\n (SEV_PRE) 0.15, 0.35, 0.35, 0.15;\n (MLD_POST) 0.85, 0.15, 0.00, 0.00;\n (MOD_POST) 0.30, 0.45, 0.20, 0.05;\n (SEV_POST) 0.15, 0.35, 0.35, 0.15;\n (MIXED) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( R_OTHER_NMT_ADM_DENERV | R_OTHER_ADM_DENERV, R_NMT_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MUSCLE_ADM_DENERV | R_MYOP_MYDY_ADM_DENERV, R_OTHER_NMT_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLC8_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLLP_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLC8_LP_ADM_DENERV | R_LNLC8_ADM_DENERV, R_LNLLP_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLE_ADM_DENERV | R_LNLE_ULN_SEV, R_LNLE_ULN_TIME, R_LNLE_ULN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( R_LNLC8_LP_E_ADM_DENERV | R_LNLC8_LP_ADM_DENERV, R_LNLE_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DIFFN_ADM_DENERV | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( R_LNLW_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_LNLW_ADM_DENERV | R_DIFFN_ADM_DENERV, R_LNLW_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNL_DIFFN_ADM_DENERV | R_LNLC8_LP_E_ADM_DENERV, R_DIFFN_LNLW_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_DENERV | R_MUSCLE_ADM_DENERV, R_LNL_DIFFN_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_ADM_SPONT_DENERV_ACT | R_ADM_DENERV ) {\n (NO) 0.98, 0.02, 0.00, 0.00;\n (MILD) 0.07, 0.85, 0.08, 0.00;\n (MOD) 0.01, 0.07, 0.85, 0.07;\n (SEV) 0.00, 0.01, 0.07, 0.92;\n}\nprobability ( R_ADM_SPONT_HF_DISCH | R_ADM_DENERV ) {\n (NO) 0.99, 0.01;\n (MILD) 0.97, 0.03;\n (MOD) 0.95, 0.05;\n (SEV) 0.93, 0.07;\n}\nprobability ( R_ADM_SPONT_INS_ACT | R_ADM_DENERV ) {\n (NO) 0.98, 0.02;\n (MILD) 0.1, 0.9;\n (MOD) 0.05, 0.95;\n (SEV) 0.05, 0.95;\n}\nprobability ( R_OTHER_DELT_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLPC5_AXIL_SEV ) {\n table 0.98031378, 0.01069252, 0.00499650, 0.00299790, 0.00099930;\n}\nprobability ( R_LNLPC5_AXIL_TIME ) {\n table 0.05, 0.60, 0.30, 0.05;\n}\nprobability ( R_LNLPC5_AXIL_PATHO ) {\n table 0.600, 0.190, 0.200, 0.005, 0.005;\n}\nprobability ( R_LNLPC5_DELT_DE_REGEN | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME, R_LNLPC5_AXIL_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (TOTAL, CHRONIC, DEMY) 1.0, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (TOTAL, OLD, DEMY) 1.0, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (TOTAL, OLD, BLOCK) 1.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (TOTAL, OLD, AXONAL) 1.0, 0.0;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (TOTAL, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( R_DIFFN_DELT_DE_REGEN | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( R_LNLPC5_DIFFN_DELT_DE_REGEN | R_LNLPC5_DELT_DE_REGEN, R_DIFFN_DELT_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_MYOP_DELT_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_MYDY_DELT_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_DELT_DE_REGEN | R_MYOP_DELT_DE_REGEN, R_MYDY_DELT_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_OTHER_DELT_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( R_MUSCLE_DELT_DE_REGEN | R_MYOP_MYDY_DELT_DE_REGEN, R_OTHER_DELT_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_DELT_DE_REGEN | R_LNLPC5_DIFFN_DELT_DE_REGEN, R_MUSCLE_DELT_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( R_DE_REGEN_DELT_NMT | R_DELT_DE_REGEN ) {\n (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;\n}\nprobability ( R_MYAS_DELT_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYAS_DE_REGEN_DELT_NMT | R_DE_REGEN_DELT_NMT, R_MYAS_DELT_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DELT_NMT | R_OTHER_DELT_NMT, R_MYAS_DE_REGEN_DELT_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_DELT_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYDY_DELT_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_DELT_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_DELT_MUSIZE | R_MYDY_DELT_MUSIZE, R_MYOP_DELT_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MUSCLE_DELT_MUSIZE | R_OTHER_DELT_MUSIZE, R_MYOP_MYDY_DELT_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DIFFN_DELT_MUSIZE | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLPC5_DELT_MUSIZE | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME, R_LNLPC5_AXIL_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLPC5_DIFFN_DELT_MUSIZE | R_DIFFN_DELT_MUSIZE, R_LNLPC5_DELT_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DELT_MUSIZE | R_MUSCLE_DELT_MUSIZE, R_LNLPC5_DIFFN_DELT_MUSIZE ) {\n (V_SMALL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (INCR, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (LARGE, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (V_LARGE, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (V_SMALL, SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (INCR, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (LARGE, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (V_LARGE, SMALL) 0.00000000, 0.97939794, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.02000200;\n (V_SMALL, NORMAL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, NORMAL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9781, 0.0019, 0.0000, 0.0000, 0.0200;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9781, 0.0000, 0.0000, 0.0200;\n (LARGE, NORMAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (V_LARGE, NORMAL) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (V_SMALL, INCR) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, INCR) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9781, 0.0000, 0.0000, 0.0200;\n (INCR, INCR) 0.00, 0.00, 0.00, 0.98, 0.00, 0.00, 0.02;\n (LARGE, INCR) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (V_LARGE, INCR) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (V_SMALL, LARGE) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, LARGE) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (INCR, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (LARGE, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (V_LARGE, LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (V_SMALL, V_LARGE) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, V_LARGE) 0.00000000, 0.97939794, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.02000200;\n (NORMAL, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (INCR, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (LARGE, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (V_LARGE, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( R_DELT_EFFMUS | R_DELT_NMT, R_DELT_MUSIZE ) {\n (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (OTHER, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;\n (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (OTHER, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;\n (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;\n (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;\n (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;\n (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;\n (OTHER, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;\n (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;\n (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;\n (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;\n (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;\n (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;\n (OTHER, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;\n (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;\n (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;\n (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;\n (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;\n (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;\n (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;\n (OTHER, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;\n (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;\n (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;\n (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;\n (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;\n (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;\n (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;\n (OTHER, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;\n (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;\n (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;\n (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;\n (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;\n (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;\n (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;\n (OTHER, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;\n}\nprobability ( R_DIFFN_AXIL_BLOCK | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_OTHER_AXIL_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_AXIL_BLOCK_ED | R_DIFFN_AXIL_BLOCK, R_OTHER_AXIL_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLPC5_DELT_MALOSS | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_DIFFN_DELT_MALOSS | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_LNLPC5_DIFFN_DELT_MALOSS | R_LNLPC5_DELT_MALOSS, R_DIFFN_DELT_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_DELT_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DELT_MALOSS | R_LNLPC5_DIFFN_DELT_MALOSS, R_OTHER_DELT_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;\n (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;\n (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( R_DELT_MULOSS | R_AXIL_BLOCK_ED, R_DELT_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;\n (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;\n (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;\n (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;\n (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;\n (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;\n (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;\n (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;\n (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;\n (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;\n (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;\n (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;\n (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( R_DELT_ALLAMP | R_DELT_EFFMUS, R_DELT_MULOSS ) {\n (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;\n (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;\n (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;\n (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;\n (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;\n (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;\n (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;\n (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;\n (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;\n (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;\n (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;\n (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;\n (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;\n (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;\n (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;\n (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;\n (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;\n (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;\n (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;\n (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;\n (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;\n (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;\n (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;\n (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;\n (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;\n (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;\n (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;\n (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;\n (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;\n}\nprobability ( R_AXIL_AMP_E | R_DELT_ALLAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_AXIL_RD_ED | R_LNLPC5_AXIL_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( R_LNLPC5_AXIL_DIFSLOW | R_LNLPC5_AXIL_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 0.0, 1.0, 0.0;\n}\nprobability ( R_DIFFN_AXIL_DIFSLOW | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( R_AXIL_DIFSLOW_ED | R_LNLPC5_AXIL_DIFSLOW, R_DIFFN_AXIL_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;\n (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;\n (MOD, MOD) 0.000, 0.000, 0.002, 0.998;\n (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;\n (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;\n}\nprobability ( R_AXIL_DCV | R_DELT_MALOSS, R_AXIL_DIFSLOW_ED ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;\n (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;\n (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;\n (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;\n}\nprobability ( R_AXIL_DEL | R_AXIL_RD_ED, R_AXIL_DCV ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, M_S60) 0.0002, 0.0003, 0.0006, 0.0027, 0.0210, 0.2666, 0.7086, 0.0000, 0.0000;\n (SEV, M_S60) 0.0000, 0.0001, 0.0001, 0.0004, 0.0021, 0.0232, 0.4307, 0.5434, 0.0000;\n (NO, M_S52) 0.6514, 0.3486, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.00010002, 0.00020004, 0.00050010, 0.00220044, 0.01780356, 0.24224845, 0.73694739, 0.00000000, 0.00000000;\n (SEV, M_S52) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40864086, 0.56815682, 0.00000000;\n (NO, M_S44) 0.0003, 0.9337, 0.0660, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S44) 0.0001, 0.0002, 0.0004, 0.0016, 0.0137, 0.2066, 0.7774, 0.0000, 0.0000;\n (SEV, M_S44) 0.0000, 0.0000, 0.0001, 0.0003, 0.0015, 0.0178, 0.3739, 0.6064, 0.0000;\n (NO, M_S36) 0.00000000, 0.00380038, 0.99619962, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S36) 0.0001, 0.0001, 0.0002, 0.0011, 0.0095, 0.1650, 0.8240, 0.0000, 0.0000;\n (SEV, M_S36) 0.00000000, 0.00000000, 0.00009999, 0.00019998, 0.00109989, 0.01419858, 0.32926707, 0.65513449, 0.00000000;\n (NO, M_S28) 0.00000000, 0.00000000, 0.01060106, 0.98939894, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00540054, 0.11491149, 0.87908791, 0.00000000, 0.00000000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00070014, 0.00980196, 0.26615323, 0.72324465, 0.00000000;\n (NO, M_S20) 0.00000000, 0.00020002, 0.00260026, 0.13561356, 0.86158616, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0020, 0.0580, 0.9396, 0.0002, 0.0000;\n (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00480048, 0.16971697, 0.82518252, 0.00000000;\n (NO, M_S14) 0.0000, 0.0000, 0.0000, 0.0005, 0.8212, 0.1783, 0.0000, 0.0000, 0.0000;\n (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0184, 0.9779, 0.0033, 0.0000;\n (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0014, 0.0771, 0.9214, 0.0000;\n (NO, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59805981, 0.39023902, 0.00000000, 0.00000000;\n (MOD, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0021, 0.4010, 0.5969, 0.0000;\n (SEV, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.01440144, 0.98549855, 0.00000000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_AXIL_LAT_ED | R_AXIL_DEL ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_DELT_VOL_ACT ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DELT_FORCE | R_DELT_VOL_ACT, R_DELT_ALLAMP ) {\n (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;\n (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;\n (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;\n (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;\n (NORMAL, A0_01) 0.00009999, 0.00429957, 0.05219478, 0.26687331, 0.43305669, 0.24347565;\n (REDUCED, A0_01) 0.0000, 0.0005, 0.0084, 0.0849, 0.3219, 0.5843;\n (V_RED, A0_01) 0.0000, 0.0000, 0.0007, 0.0167, 0.1576, 0.8250;\n (ABSENT, A0_01) 0.00000000, 0.00000000, 0.00000000, 0.00260026, 0.05860586, 0.93879388;\n (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;\n (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;\n (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;\n (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;\n (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;\n (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;\n (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;\n (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;\n (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;\n (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;\n (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;\n (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;\n (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;\n (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;\n (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;\n (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;\n (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;\n (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;\n (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;\n (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;\n (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;\n (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;\n (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;\n (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;\n}\nprobability ( R_DELT_MUSCLE_VOL | R_DELT_MUSIZE, R_DELT_MALOSS ) {\n (V_SMALL, NO) 0.9896, 0.0104;\n (SMALL, NO) 0.8137, 0.1863;\n (NORMAL, NO) 0.0209, 0.9791;\n (INCR, NO) 0.0209, 0.9791;\n (LARGE, NO) 0.003, 0.997;\n (V_LARGE, NO) 0.0004, 0.9996;\n (OTHER, NO) 0.4212, 0.5788;\n (V_SMALL, MILD) 0.9976, 0.0024;\n (SMALL, MILD) 0.9603, 0.0397;\n (NORMAL, MILD) 0.5185, 0.4815;\n (INCR, MILD) 0.1492, 0.8508;\n (LARGE, MILD) 0.0278, 0.9722;\n (V_LARGE, MILD) 0.0046, 0.9954;\n (OTHER, MILD) 0.5185, 0.4815;\n (V_SMALL, MOD) 0.999, 0.001;\n (SMALL, MOD) 0.9893, 0.0107;\n (NORMAL, MOD) 0.9588, 0.0412;\n (INCR, MOD) 0.6221, 0.3779;\n (LARGE, MOD) 0.2716, 0.7284;\n (V_LARGE, MOD) 0.0779, 0.9221;\n (OTHER, MOD) 0.6336, 0.3664;\n (V_SMALL, SEV) 0.9995, 0.0005;\n (SMALL, SEV) 0.9969, 0.0031;\n (NORMAL, SEV) 0.9953, 0.0047;\n (INCR, SEV) 0.9358, 0.0642;\n (LARGE, SEV) 0.8234, 0.1766;\n (V_LARGE, SEV) 0.5986, 0.4014;\n (OTHER, SEV) 0.7685, 0.2315;\n (V_SMALL, TOTAL) 0.9989, 0.0011;\n (SMALL, TOTAL) 0.9984, 0.0016;\n (NORMAL, TOTAL) 0.9984, 0.0016;\n (INCR, TOTAL) 0.9972, 0.0028;\n (LARGE, TOTAL) 0.9965, 0.0035;\n (V_LARGE, TOTAL) 0.9956, 0.0044;\n (OTHER, TOTAL) 0.9857, 0.0143;\n (V_SMALL, OTHER) 0.9363, 0.0637;\n (SMALL, OTHER) 0.8403, 0.1597;\n (NORMAL, OTHER) 0.6534, 0.3466;\n (INCR, OTHER) 0.4719, 0.5281;\n (LARGE, OTHER) 0.3174, 0.6826;\n (V_LARGE, OTHER) 0.1948, 0.8052;\n (OTHER, OTHER) 0.5681, 0.4319;\n}\nprobability ( R_DELT_MVA_RECRUIT | R_DELT_MULOSS, R_DELT_VOL_ACT ) {\n (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;\n (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;\n (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;\n (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;\n (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;\n (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;\n (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;\n (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;\n (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;\n (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;\n (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;\n (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;\n (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;\n (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;\n (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;\n (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;\n (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DELT_MVA_AMP | R_DELT_EFFMUS ) {\n (V_SMALL) 0.00, 0.04, 0.96;\n (SMALL) 0.01, 0.15, 0.84;\n (NORMAL) 0.05, 0.90, 0.05;\n (INCR) 0.50, 0.49, 0.01;\n (LARGE) 0.85, 0.15, 0.00;\n (V_LARGE) 0.96, 0.04, 0.00;\n (OTHER) 0.33, 0.34, 0.33;\n}\nprobability ( R_DELT_TA_CONCL | R_DELT_EFFMUS ) {\n (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;\n (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;\n (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;\n (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;\n (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;\n (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;\n (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;\n}\nprobability ( R_DELT_MUPAMP | R_DELT_EFFMUS ) {\n (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;\n (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;\n (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;\n (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;\n}\nprobability ( R_DELT_QUAN_MUPAMP | R_DELT_MUPAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_DELT_QUAL_MUPAMP | R_DELT_MUPAMP ) {\n (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;\n (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;\n (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;\n (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;\n (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;\n (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;\n (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;\n}\nprobability ( R_DELT_MUPDUR | R_DELT_EFFMUS ) {\n (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;\n (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;\n (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;\n}\nprobability ( R_DELT_QUAN_MUPDUR | R_DELT_MUPDUR ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_DELT_QUAL_MUPDUR | R_DELT_MUPDUR ) {\n (V_SMALL) 0.8309, 0.1677, 0.0014;\n (SMALL) 0.49, 0.49, 0.02;\n (NORMAL) 0.1065, 0.7870, 0.1065;\n (INCR) 0.02, 0.49, 0.49;\n (LARGE) 0.0014, 0.1677, 0.8309;\n (V_LARGE) 0.0001, 0.0392, 0.9607;\n (OTHER) 0.2597, 0.4806, 0.2597;\n}\nprobability ( R_DELT_QUAN_MUPPOLY | R_DELT_DE_REGEN, R_DELT_EFFMUS ) {\n (NO, V_SMALL) 0.109, 0.548, 0.343;\n (YES, V_SMALL) 0.004, 0.122, 0.874;\n (NO, SMALL) 0.340, 0.564, 0.096;\n (YES, SMALL) 0.015, 0.261, 0.724;\n (NO, NORMAL) 0.925, 0.075, 0.000;\n (YES, NORMAL) 0.091, 0.526, 0.383;\n (NO, INCR) 0.796, 0.201, 0.003;\n (YES, INCR) 0.061, 0.465, 0.474;\n (NO, LARGE) 0.637, 0.348, 0.015;\n (YES, LARGE) 0.039, 0.396, 0.565;\n (NO, V_LARGE) 0.340, 0.564, 0.096;\n (YES, V_LARGE) 0.015, 0.261, 0.724;\n (NO, OTHER) 0.340, 0.564, 0.096;\n (YES, OTHER) 0.015, 0.261, 0.724;\n}\nprobability ( R_DELT_QUAL_MUPPOLY | R_DELT_QUAN_MUPPOLY ) {\n (x_12_) 0.95, 0.05;\n (x12_24_) 0.3, 0.7;\n (x_24_) 0.05, 0.95;\n}\nprobability ( R_DELT_MUPSATEL | R_DELT_DE_REGEN ) {\n (NO) 0.95, 0.05;\n (YES) 0.2, 0.8;\n}\nprobability ( R_DELT_MUPINSTAB | R_DELT_NMT ) {\n (NO) 0.95, 0.05;\n (MOD_PRE) 0.1, 0.9;\n (SEV_PRE) 0.03, 0.97;\n (MLD_POST) 0.2, 0.8;\n (MOD_POST) 0.1, 0.9;\n (SEV_POST) 0.03, 0.97;\n (OTHER) 0.1, 0.9;\n}\nprobability ( R_DELT_REPSTIM_CMAPAMP | R_DELT_ALLAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_DELT_REPSTIM_DECR | R_DELT_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;\n (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;\n (OTHER) 0.245, 0.245, 0.245, 0.245, 0.020;\n}\nprobability ( R_DELT_REPSTIM_FACILI | R_DELT_NMT ) {\n (NO) 0.95, 0.02, 0.01, 0.02;\n (MOD_PRE) 0.010, 0.889, 0.100, 0.001;\n (SEV_PRE) 0.010, 0.080, 0.909, 0.001;\n (MLD_POST) 0.89, 0.08, 0.01, 0.02;\n (MOD_POST) 0.48, 0.50, 0.01, 0.01;\n (SEV_POST) 0.020, 0.949, 0.030, 0.001;\n (OTHER) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( R_DELT_REPSTIM_POST_DECR | R_DELT_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;\n (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;\n (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;\n (OTHER) 0.23, 0.23, 0.22, 0.22, 0.10;\n}\nprobability ( R_DELT_SF_JITTER | R_DELT_NMT ) {\n (NO) 0.95, 0.05, 0.00, 0.00;\n (MOD_PRE) 0.02, 0.20, 0.70, 0.08;\n (SEV_PRE) 0.0, 0.1, 0.4, 0.5;\n (MLD_POST) 0.05, 0.70, 0.20, 0.05;\n (MOD_POST) 0.01, 0.19, 0.70, 0.10;\n (SEV_POST) 0.0, 0.1, 0.4, 0.5;\n (OTHER) 0.1, 0.3, 0.3, 0.3;\n}\nprobability ( R_LNLPC5_DELT_MUDENS | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME, R_LNLPC5_AXIL_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( R_DIFFN_DELT_MUDENS | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( R_LNLPC5_DIFFN_DELT_MUDENS | R_LNLPC5_DELT_MUDENS, R_DIFFN_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_MYOP_DELT_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MYDY_DELT_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_DELT_MUDENS | R_MYOP_DELT_MUDENS, R_MYDY_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_MYAS_DELT_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_OTHER_DELT_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_MYAS_OTHER_DELT_MUDENS | R_MYAS_DELT_MUDENS, R_OTHER_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_MUSCLE_DELT_MUDENS | R_MYOP_MYDY_DELT_MUDENS, R_MYAS_OTHER_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_DELT_MUDENS | R_LNLPC5_DIFFN_DELT_MUDENS, R_MUSCLE_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( R_DELT_SF_DENSITY | R_DELT_MUDENS ) {\n (NORMAL) 0.97, 0.03, 0.00;\n (INCR) 0.05, 0.90, 0.05;\n (V_INCR) 0.01, 0.04, 0.95;\n}\nprobability ( R_LNLPC5_DELT_NEUR_ACT | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_DELT_NEUR_ACT | DIFFN_M_SEV_PROX, DIFFN_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_LNLPC5_DIFFN_DELT_NEUR_ACT | R_LNLPC5_DELT_NEUR_ACT, R_DIFFN_DELT_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_DELT_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DELT_NEUR_ACT | R_LNLPC5_DIFFN_DELT_NEUR_ACT, R_OTHER_DELT_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DELT_SPONT_NEUR_DISCH | R_DELT_NEUR_ACT ) {\n (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;\n (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;\n (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;\n (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;\n (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;\n}\nprobability ( R_MYOP_DELT_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYDY_DELT_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_MYOP_MYDY_DELT_DENERV | R_MYOP_DELT_DENERV, R_MYDY_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_DELT_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_NMT_DELT_DENERV | R_DELT_NMT ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE) 0.40, 0.45, 0.15, 0.00;\n (SEV_PRE) 0.15, 0.35, 0.35, 0.15;\n (MLD_POST) 0.85, 0.15, 0.00, 0.00;\n (MOD_POST) 0.30, 0.45, 0.20, 0.05;\n (SEV_POST) 0.15, 0.35, 0.35, 0.15;\n (OTHER) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( R_OTHER_NMT_DELT_DENERV | R_OTHER_DELT_DENERV, R_NMT_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_MUSCLE_DELT_DENERV | R_MYOP_MYDY_DELT_DENERV, R_OTHER_NMT_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_LNLPC5_DELT_DENERV | R_LNLPC5_AXIL_SEV, R_LNLPC5_AXIL_TIME, R_LNLPC5_AXIL_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( R_DIFFN_DELT_DENERV | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( R_LNLPC5_DIFFN_DELT_DENERV | R_LNLPC5_DELT_DENERV, R_DIFFN_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DELT_DENERV | R_MUSCLE_DELT_DENERV, R_LNLPC5_DIFFN_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_DELT_SPONT_DENERV_ACT | R_DELT_DENERV ) {\n (NO) 0.98, 0.02, 0.00, 0.00;\n (MILD) 0.07, 0.85, 0.08, 0.00;\n (MOD) 0.01, 0.07, 0.85, 0.07;\n (SEV) 0.00, 0.01, 0.07, 0.92;\n}\nprobability ( R_DELT_SPONT_HF_DISCH | R_DELT_DENERV ) {\n (NO) 0.99, 0.01;\n (MILD) 0.97, 0.03;\n (MOD) 0.95, 0.05;\n (SEV) 0.93, 0.07;\n}\nprobability ( R_DELT_SPONT_INS_ACT | R_DELT_DENERV ) {\n (NO) 0.98, 0.02;\n (MILD) 0.1, 0.9;\n (MOD) 0.05, 0.95;\n (SEV) 0.05, 0.95;\n}\nprobability ( R_OTHER_ISCH_DISP ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_ISCH_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_SUR_DISP_CA | R_OTHER_ISCH_DISP, R_DIFFN_ISCH_DISP ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_ISCH_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_ISCH_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_SUR_BLOCK_CA | R_OTHER_ISCH_BLOCK, R_DIFFN_ISCH_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_OTHER_ISCH_SALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_ISCH_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_LNL_ISCH_SEV ) {\n table 0.960, 0.020, 0.010, 0.005, 0.005;\n}\nprobability ( R_LNL_ISCH_PATHO ) {\n table 0.25, 0.25, 0.50, 0.00, 0.00;\n}\nprobability ( R_LNL_ISCH_SALOSS_CA | R_LNL_ISCH_SEV, R_LNL_ISCH_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( R_DIFFN_LNL_ISCH_SALOSS | R_DIFFN_ISCH_SALOSS, R_LNL_ISCH_SALOSS_CA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_SUR_SALOSS | R_OTHER_ISCH_SALOSS, R_DIFFN_LNL_ISCH_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_SUR_EFFAXLOSS | R_SUR_BLOCK_CA, R_SUR_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_SUR_ALLAMP_CA | R_SUR_DISP_CA, R_SUR_EFFAXLOSS ) {\n (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;\n (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;\n (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;\n (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;\n (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;\n (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;\n (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;\n (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;\n (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;\n (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_SUR_AMP_CA | R_SUR_ALLAMP_CA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( R_SUR_LD_CA ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_SUR_RD_CA ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( R_SUR_LSLOW_CA | R_SUR_LD_CA, R_SUR_RD_CA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;\n (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;\n (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;\n (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;\n (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;\n (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;\n (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;\n}\nprobability ( R_OTHER_ISCH_DIFSLOW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( R_DIFFN_ISCH_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( R_SUR_DIFSLOW_CA | R_OTHER_ISCH_DIFSLOW, R_DIFFN_ISCH_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( R_SUR_DSLOW_CA | R_SUR_SALOSS, R_SUR_DIFSLOW_CA ) {\n (NO, NO) 0.8825, 0.1175, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, NO) 0.34603460, 0.65076508, 0.00320032, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, NO) 0.0641, 0.8105, 0.1254, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.00410041, 0.15271527, 0.78487849, 0.05830583, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.06390639, 0.24022402, 0.46154615, 0.22352235, 0.01080108, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, MILD) 0.0245, 0.1308, 0.4221, 0.3800, 0.0426, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MILD) 0.0078, 0.0585, 0.3098, 0.5006, 0.1230, 0.0003, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.0012, 0.0133, 0.1276, 0.4624, 0.3871, 0.0084, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0011, 0.0083, 0.0684, 0.2984, 0.5331, 0.0899, 0.0008, 0.0000, 0.0000;\n (MILD, MOD) 0.0003, 0.0030, 0.0335, 0.2043, 0.5689, 0.1865, 0.0035, 0.0000, 0.0000;\n (MOD, MOD) 0.00010001, 0.00100010, 0.01440144, 0.12231223, 0.52405241, 0.32563256, 0.01250125, 0.00000000, 0.00000000;\n (SEV, MOD) 0.0000, 0.0002, 0.0034, 0.0430, 0.3319, 0.5510, 0.0705, 0.0000, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00040004, 0.00120012, 0.00530053, 0.02140214, 0.08840884, 0.28082808, 0.44704470, 0.15541554, 0.00000000;\n (MILD, SEV) 0.00019996, 0.00069986, 0.00319936, 0.01399720, 0.06498700, 0.24325135, 0.46090782, 0.21275745, 0.00000000;\n (MOD, SEV) 0.0001, 0.0004, 0.0018, 0.0089, 0.0461, 0.2037, 0.4588, 0.2802, 0.0000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00080008, 0.00410041, 0.02540254, 0.14331433, 0.42124212, 0.40504050, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_SUR_ALLCV_CA | R_SUR_LSLOW_CA, R_SUR_DSLOW_CA ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;\n (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;\n (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;\n (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;\n (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;\n (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;\n (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;\n (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;\n (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;\n (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( R_SUR_CV_CA | R_SUR_ALLCV_CA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_LNLW_MED_SEV ) {\n table 0.895, 0.060, 0.030, 0.010, 0.005;\n}\nprobability ( L_LNLW_MED_PATHO ) {\n table 0.800, 0.120, 0.070, 0.005, 0.005;\n}\nprobability ( L_LNLW_MEDD2_DISP_WD | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (TOTAL, BLOCK) 0.0, 0.0, 0.5, 0.5;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.3, 0.5, 0.2, 0.0;\n (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_MEDD2_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_LNLW_MEDD2_DISP_WD | L_LNLW_MEDD2_DISP_WD, L_DIFFN_MEDD2_DISP ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_DISP_WD | L_DIFFN_LNLW_MEDD2_DISP_WD ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0, 1.0, 0.0, 0.0;\n (MOD) 0.0, 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLBE_MEDD2_DISP_EW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MEDD2_DISP_EW | L_DIFFN_MEDD2_DISP, L_LNLBE_MEDD2_DISP_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_DISP_EWD | L_MEDD2_DISP_WD, L_MEDD2_DISP_EW ) {\n (NO, NO) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000;\n (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, MILD) 0.0001, 0.2215, 0.7732, 0.0052, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.13151315, 0.85768577, 0.01080108, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;\n (SEV, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;\n (NO, MOD) 0.1315, 0.8577, 0.0108, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;\n (SEV, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;\n (NO, SEV) 0.9933, 0.0067, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, SEV) 0.0404, 0.9192, 0.0404, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n}\nprobability ( L_DIFFN_MEDD2_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLBE_MEDD2_BLOCK_EW ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MEDD2_BLOCK_EW | L_DIFFN_MEDD2_BLOCK, L_LNLBE_MEDD2_BLOCK_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_AMPR_EW | L_MEDD2_DISP_EWD, L_MEDD2_BLOCK_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_LNLBE_MEDD2_LD_EW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MEDD2_LD_EW | L_LNLBE_MEDD2_LD_EW ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0, 1.0, 0.0, 0.0;\n (MOD) 0.0, 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLBE_MEDD2_RD_EW ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MEDD2_RD_EW | L_LNLBE_MEDD2_RD_EW ) {\n (NO) 1.0, 0.0, 0.0;\n (MOD) 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_LSLOW_EW | L_MEDD2_LD_EW, L_MEDD2_RD_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.00840084, 0.01190119, 0.06190619, 0.91739174, 0.00040004;\n (MILD, MOD) 0.00690069, 0.01000100, 0.05350535, 0.92869287, 0.00090009;\n (MOD, MOD) 0.00559944, 0.00829917, 0.04519548, 0.93890611, 0.00199980;\n (SEV, MOD) 0.0023, 0.0036, 0.0217, 0.8326, 0.1398;\n (NO, SEV) 0.00529947, 0.00619938, 0.02639736, 0.20817918, 0.75392461;\n (MILD, SEV) 0.0049, 0.0057, 0.0244, 0.1966, 0.7684;\n (MOD, SEV) 0.00440044, 0.00520052, 0.02240224, 0.18391839, 0.78407841;\n (SEV, SEV) 0.0028, 0.0033, 0.0145, 0.1304, 0.8490;\n}\nprobability ( L_LNLW_MEDD2_SALOSS_WD | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_DIFFN_MEDD2_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_DIFFN_LNLW_MEDD2_SALOSS | L_LNLW_MEDD2_SALOSS_WD, L_DIFFN_MEDD2_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLBE_MEDD2_SALOSS_EW ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MEDD2_SALOSS | L_DIFFN_LNLW_MEDD2_SALOSS, L_LNLBE_MEDD2_SALOSS_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_MEDD2_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( L_MEDD2_DIFSLOW_EW | L_DIFFN_MEDD2_DIFSLOW ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0, 1.0, 0.0, 0.0;\n (MOD) 0.0, 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_DSLOW_EW | L_MEDD2_SALOSS, L_MEDD2_DIFSLOW_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_ALLCV_EW | L_MEDD2_LSLOW_EW, L_MEDD2_DSLOW_EW ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.0264, 0.9149, 0.0587, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S60) 0.0000, 0.0218, 0.9469, 0.0313, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.00030003, 0.00190019, 0.01400140, 0.07880788, 0.32383238, 0.45964596, 0.12121212, 0.00030003, 0.00000000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00410041, 0.03840384, 0.21452145, 0.74237424, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0006, 0.0944, 0.8841, 0.0209, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0007, 0.2183, 0.7790, 0.0020, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00009999, 0.00039996, 0.00429957, 0.03209679, 0.19558044, 0.50484952, 0.26037396, 0.00229977, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00210021, 0.02390239, 0.16481648, 0.80898090, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.0000, 0.0018, 0.1956, 0.7860, 0.0166, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0077, 0.4577, 0.5345, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, M_S44) 0.0000, 0.0001, 0.0007, 0.0074, 0.0735, 0.4044, 0.4938, 0.0201, 0.0000;\n (V_SEV, M_S44) 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0123, 0.1119, 0.8749, 0.0000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.0000, 0.0000, 0.0026, 0.2655, 0.7316, 0.0003, 0.0000, 0.0000, 0.0000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0166, 0.9182, 0.0652, 0.0000, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0010, 0.0172, 0.2179, 0.6479, 0.1159, 0.0000;\n (V_SEV, M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0055, 0.0699, 0.9243, 0.0000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.0000, 0.0000, 0.0000, 0.0044, 0.5355, 0.4600, 0.0001, 0.0000, 0.0000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0398, 0.9460, 0.0142, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00179982, 0.05589441, 0.46005399, 0.48215178, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0021, 0.0390, 0.9588, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0047, 0.5053, 0.4900, 0.0000, 0.0000;\n (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.1104, 0.8881, 0.0013, 0.0000;\n (SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0041, 0.1033, 0.8925, 0.0000;\n (V_SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00069993, 0.01889811, 0.98040196, 0.00000000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0218, 0.8352, 0.1430, 0.0000;\n (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.3203, 0.6777, 0.0000;\n (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0194, 0.9803, 0.0000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0093, 0.9905, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0003, 0.0010, 0.0036, 0.0154, 0.0712, 0.2467, 0.6617, 0.0000;\n (MOD, M_S08) 0.00000000, 0.00010001, 0.00050005, 0.00190019, 0.00930093, 0.05040504, 0.20722072, 0.73057306, 0.00000000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0026, 0.0190, 0.1153, 0.8626, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0062, 0.0562, 0.9369, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_CV_EW | L_MEDD2_ALLCV_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_LNLW_MEDD2_BLOCK_WD | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.3, 0.6, 0.1, 0.0, 0.0;\n (SEV, DEMY) 0.1, 0.5, 0.3, 0.1, 0.0;\n (TOTAL, DEMY) 0.00, 0.05, 0.20, 0.55, 0.20;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.2, 0.6, 0.2;\n (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MEDD2_BLOCK_WD | L_LNLW_MEDD2_BLOCK_WD, L_DIFFN_MEDD2_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_EFFAXLOSS | L_MEDD2_BLOCK_WD, L_MEDD2_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_ALLAMP_WD | L_MEDD2_DISP_WD, L_MEDD2_EFFAXLOSS ) {\n (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;\n (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;\n (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;\n (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;\n (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;\n (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;\n (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;\n (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;\n (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;\n (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MEDD2_AMP_WD | L_MEDD2_ALLAMP_WD ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_LNLW_MEDD2_LD_WD | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;\n (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MEDD2_LD_WD | L_LNLW_MEDD2_LD_WD ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0, 1.0, 0.0, 0.0;\n (MOD) 0.0, 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLW_MEDD2_RD_WD | L_LNLW_MED_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( L_MEDD2_RD_WD | L_LNLW_MEDD2_RD_WD ) {\n (NO) 1.0, 0.0, 0.0;\n (MOD) 0.0, 1.0, 0.0;\n (SEV) 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_LSLOW_WD | L_MEDD2_LD_WD, L_MEDD2_RD_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;\n (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;\n (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;\n (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;\n (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;\n (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;\n (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;\n}\nprobability ( L_LNLBE_MEDD2_DIFSLOW_WD ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MEDD2_DIFSLOW_WD | L_LNLBE_MEDD2_DIFSLOW_WD, L_DIFFN_MEDD2_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( L_MEDD2_DSLOW_WD | L_MEDD2_SALOSS, L_MEDD2_DIFSLOW_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_ALLCV_WD | L_MEDD2_LSLOW_WD, L_MEDD2_DSLOW_WD ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;\n (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;\n (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;\n (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;\n (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;\n (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;\n (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;\n (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;\n (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;\n (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MEDD2_CV_WD | L_MEDD2_ALLCV_WD ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_DIFFN_MED_BLOCK | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLBE_MED_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MED_BLOCK_EW | L_DIFFN_MED_BLOCK, L_LNLBE_MED_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MED_AMPR_EW | L_MED_BLOCK_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_LNLT1_APB_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_APB_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_LP_APB_MALOSS | L_LNLT1_APB_MALOSS, L_LNLLP_APB_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLBE_APB_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_LP_BE_APB_MALOSS | L_LNLT1_LP_APB_MALOSS, L_LNLBE_APB_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLW_APB_MALOSS | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_DIFFN_APB_MALOSS | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_DIFFN_LNLW_APB_MALOSS | L_LNLW_APB_MALOSS, L_DIFFN_APB_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_MALOSS | L_LNLT1_LP_BE_APB_MALOSS, L_DIFFN_LNLW_APB_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;\n (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;\n (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( L_DIFFN_MED_DIFSLOW | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( L_MED_DIFSLOW_EW | L_DIFFN_MED_DIFSLOW ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0126, 0.9869, 0.0005, 0.0000;\n (MOD) 0.0000, 0.0179, 0.9821, 0.0000;\n (SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n}\nprobability ( L_MED_DCV_EW | L_APB_MALOSS, L_MED_DIFSLOW_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;\n (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;\n (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;\n (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;\n}\nprobability ( L_MED_LD_EW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MED_RD_EW ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MED_RDLDCV_EW | L_MED_LD_EW, L_MED_RD_EW ) {\n (NO, NO) 0.90449045, 0.09530953, 0.00020002, 0.00000000, 0.00000000, 0.00000000;\n (MILD, NO) 0.1320, 0.6039, 0.2641, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0139, 0.1839, 0.8022, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.00120012, 0.00670067, 0.05440544, 0.86008601, 0.07760776, 0.00000000;\n (NO, MOD) 0.0115, 0.0333, 0.1509, 0.7319, 0.0724, 0.0000;\n (MILD, MOD) 0.00340034, 0.01220122, 0.06900690, 0.71967197, 0.19531953, 0.00040004;\n (MOD, MOD) 0.0011, 0.0045, 0.0299, 0.5742, 0.3876, 0.0027;\n (SEV, MOD) 0.0001, 0.0002, 0.0018, 0.0914, 0.6093, 0.2972;\n (NO, SEV) 0.00000000, 0.00009999, 0.00109989, 0.14618538, 0.80701930, 0.04559544;\n (MILD, SEV) 0.0000, 0.0000, 0.0002, 0.0581, 0.7950, 0.1467;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0228, 0.6344, 0.3427;\n (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0014, 0.1063, 0.8923;\n}\nprobability ( L_MED_ALLCV_EW | L_MED_DCV_EW, L_MED_RDLDCV_EW ) {\n (M_S60, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56, M_S60) 0.0699, 0.8102, 0.1199, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S60) 0.00000000, 0.04790479, 0.95209521, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S44, M_S60) 0.00089991, 0.00899910, 0.08879112, 0.81861814, 0.08269173, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S36, M_S60) 0.00000000, 0.00000000, 0.00050005, 0.10111011, 0.84778478, 0.05060506, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S28, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00050005, 0.07910791, 0.90019002, 0.02020202, 0.00000000, 0.00000000, 0.00000000;\n (M_S20, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0734, 0.8393, 0.0867, 0.0000, 0.0000;\n (M_S14, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.09470947, 0.89458946, 0.01040104, 0.00000000;\n (M_S08, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0932, 0.9048, 0.0000;\n (M_S00, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S52) 0.00660066, 0.14551455, 0.83808381, 0.00980098, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S56, M_S52) 0.0005, 0.0239, 0.4369, 0.5387, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S52) 0.0000, 0.0003, 0.0239, 0.9748, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S52) 0.0000, 0.0002, 0.0036, 0.2467, 0.7339, 0.0156, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S36, M_S52) 0.0000, 0.0000, 0.0000, 0.0050, 0.3134, 0.6811, 0.0005, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00710071, 0.54935494, 0.44334433, 0.00020002, 0.00000000, 0.00000000;\n (M_S20, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00910091, 0.52835284, 0.46254625, 0.00000000, 0.00000000;\n (M_S14, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0216, 0.8359, 0.1425, 0.0000;\n (M_S08, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0355, 0.9641, 0.0000;\n (M_S00, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S44) 0.0000, 0.0000, 0.0047, 0.9951, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S56, M_S44) 0.0000, 0.0000, 0.0004, 0.9005, 0.0991, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S44) 0.0000, 0.0000, 0.0000, 0.1288, 0.8712, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.01130113, 0.57115712, 0.41754175, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S36, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0214, 0.9354, 0.0432, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.05649435, 0.93230677, 0.01109889, 0.00000000, 0.00000000;\n (M_S20, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.1430, 0.8551, 0.0015, 0.0000;\n (M_S14, M_S44) 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0019998, 0.3548645, 0.6431357, 0.0000000;\n (M_S08, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0122, 0.9877, 0.0000;\n (M_S00, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56, M_S27) 0.000, 0.000, 0.000, 0.000, 0.000, 0.997, 0.003, 0.000, 0.000, 0.000;\n (M_S52, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2336, 0.7664, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S27) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00259974, 0.99340066, 0.00399960, 0.00000000, 0.00000000;\n (M_S36, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2469, 0.7531, 0.0000, 0.0000;\n (M_S28, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.9939, 0.0041, 0.0000;\n (M_S20, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0314, 0.9686, 0.0000;\n (M_S14, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;\n (M_S08, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;\n (M_S00, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (M_S60, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.1305, 0.8445, 0.0238, 0.0000;\n (M_S56, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0740, 0.8589, 0.0667, 0.0000;\n (M_S52, M_S15) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03730373, 0.80288029, 0.15971597, 0.00000000;\n (M_S44, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0070, 0.3517, 0.6413, 0.0000;\n (M_S36, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0581, 0.9416, 0.0000;\n (M_S28, M_S15) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.006, 0.994, 0.000;\n (M_S20, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (M_S14, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S08, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;\n (M_S00, M_S15) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (M_S60, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0321, 0.9677, 0.0000;\n (M_S56, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0184, 0.9815, 0.0000;\n (M_S52, M_S07) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01010101, 0.98989899, 0.00000000;\n (M_S44, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.9958, 0.0000;\n (M_S36, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (M_S28, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;\n (M_S20, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S14, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (M_S08, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S00, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MED_CV_EW | L_MED_ALLCV_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_LNLT1_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_LNLLP_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_LNLT1_LP_APB_DE_REGEN | L_LNLT1_APB_DE_REGEN, L_LNLLP_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_LNLBE_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_LNLT1_LP_BE_APB_DE_REGEN | L_LNLT1_LP_APB_DE_REGEN, L_LNLBE_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_DIFFN_APB_DE_REGEN | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( L_LNLW_MED_TIME ) {\n table 0.05, 0.33, 0.60, 0.02;\n}\nprobability ( L_LNLW_APB_DE_REGEN | L_LNLW_MED_SEV, L_LNLW_MED_TIME, L_LNLW_MED_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (TOTAL, CHRONIC, DEMY) 1.0, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (TOTAL, OLD, DEMY) 1.0, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (TOTAL, OLD, BLOCK) 1.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (TOTAL, OLD, AXONAL) 1.0, 0.0;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (TOTAL, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( L_DIFFN_LNLW_APB_DE_REGEN | L_DIFFN_APB_DE_REGEN, L_LNLW_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_APB_DE_REGEN | L_LNLT1_LP_BE_APB_DE_REGEN, L_DIFFN_LNLW_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_MYOP_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_MYDY_APB_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_APB_DE_REGEN | L_MYOP_APB_DE_REGEN, L_MYDY_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_APB_DE_REGEN | L_LNL_DIFFN_APB_DE_REGEN, L_MYOP_MYDY_APB_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_DE_REGEN_APB_NMT | L_APB_DE_REGEN ) {\n (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;\n}\nprobability ( L_MYAS_APB_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_APB_NMT | L_DE_REGEN_APB_NMT, L_MYAS_APB_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MYDY_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_APB_MUSIZE | L_MYDY_APB_MUSIZE, L_MYOP_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLW_APB_MUSIZE | L_LNLW_MED_SEV, L_LNLW_MED_TIME, L_LNLW_MED_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_APB_MUSIZE | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_APB_MUSIZE | L_LNLW_APB_MUSIZE, L_DIFFN_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLBE_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_APB_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_LP_APB_MUSIZE | L_LNLLP_APB_MUSIZE, L_LNLT1_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLT1_LP_BE_APB_MUSIZE | L_LNLBE_APB_MUSIZE, L_LNLT1_LP_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_APB_MUSIZE | L_DIFFN_LNLW_APB_MUSIZE, L_LNLT1_LP_BE_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_MUSIZE | L_MYOP_MYDY_APB_MUSIZE, L_LNL_DIFFN_APB_MUSIZE ) {\n (V_SMALL, V_SMALL) 0.9791, 0.0009, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL, V_SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL, V_SMALL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (INCR, V_SMALL) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;\n (LARGE, V_SMALL) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;\n (V_LARGE, V_SMALL) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;\n (V_SMALL, SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL, SMALL) 0.7493, 0.2257, 0.0049, 0.0001, 0.0000, 0.0000, 0.0200;\n (NORMAL, SMALL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;\n (INCR, SMALL) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;\n (LARGE, SMALL) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;\n (V_LARGE, SMALL) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;\n (V_SMALL, NORMAL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SMALL, NORMAL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;\n (NORMAL, NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;\n (V_SMALL, INCR) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;\n (SMALL, INCR) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00359964, 0.16548345, 0.64543546, 0.16548345, 0.01999800;\n (LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;\n (V_LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (V_SMALL, LARGE) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;\n (SMALL, LARGE) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;\n (INCR, LARGE) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;\n (LARGE, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;\n (V_SMALL, V_LARGE) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;\n (SMALL, V_LARGE) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;\n (INCR, V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;\n (V_LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9799, 0.0200;\n}\nprobability ( L_APB_EFFMUS | L_APB_NMT, L_APB_MUSIZE ) {\n (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MIXED, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;\n (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MIXED, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;\n (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;\n (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;\n (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;\n (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;\n (MIXED, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;\n (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;\n (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;\n (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;\n (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;\n (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;\n (MIXED, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;\n (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;\n (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;\n (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;\n (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;\n (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;\n (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;\n (MIXED, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;\n (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;\n (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;\n (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;\n (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;\n (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;\n (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;\n (MIXED, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;\n (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;\n (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;\n (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;\n (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;\n (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;\n (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;\n (MIXED, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;\n}\nprobability ( L_LNLW_MED_BLOCK | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (TOTAL, DEMY) 0.2, 0.2, 0.2, 0.2, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.2, 0.2, 0.2, 0.2, 0.2;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MED_BLOCK_WA | L_DIFFN_MED_BLOCK, L_LNLW_MED_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_MULOSS | L_MED_BLOCK_WA, L_APB_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;\n (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;\n (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;\n (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;\n (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;\n (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;\n (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;\n (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;\n (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;\n (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;\n (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;\n (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;\n (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( L_APB_ALLAMP_WA | L_APB_EFFMUS, L_APB_MULOSS ) {\n (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;\n (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;\n (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;\n (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;\n (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;\n (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;\n (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;\n (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;\n (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;\n (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;\n (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;\n (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;\n (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;\n (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;\n (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;\n (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;\n (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;\n (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;\n (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;\n (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;\n (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;\n (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;\n (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;\n (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;\n (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;\n (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;\n (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;\n (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;\n (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;\n}\nprobability ( L_MED_AMP_WA | L_APB_ALLAMP_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_MED_LD_WA | L_LNLW_MED_SEV, L_LNLW_MED_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.25, 0.25, 0.25, 0.25;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;\n (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.25, 0.25, 0.25, 0.25;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 0.25, 0.25, 0.25, 0.25;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( L_MED_RD_WA | L_LNLW_MED_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( L_MED_RDLDDEL | L_MED_LD_WA, L_MED_RD_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0964, 0.7981, 0.1055, 0.0000, 0.0000;\n (MOD, NO) 0.0032, 0.1270, 0.8698, 0.0000, 0.0000;\n (SEV, NO) 0.00090009, 0.00280028, 0.01470147, 0.98159816, 0.00000000;\n (NO, MOD) 0.0019, 0.5257, 0.4724, 0.0000, 0.0000;\n (MILD, MOD) 0.00019998, 0.04149585, 0.95830417, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0001, 0.0144, 0.9855, 0.0000, 0.0000;\n (SEV, MOD) 0.00019998, 0.00059994, 0.00369963, 0.99550045, 0.00000000;\n (NO, SEV) 0.0002, 0.0304, 0.9694, 0.0000, 0.0000;\n (MILD, SEV) 0.0001, 0.0142, 0.9857, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0090, 0.9808, 0.0102, 0.0000;\n (SEV, SEV) 0.0000, 0.0002, 0.0012, 0.9984, 0.0002;\n}\nprobability ( L_LNLBE_MED_DIFSLOW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MED_DIFSLOW_WA | L_LNLBE_MED_DIFSLOW, L_DIFFN_MED_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;\n (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;\n (MOD, MOD) 0.000, 0.000, 0.002, 0.998;\n (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;\n (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;\n}\nprobability ( L_MED_DCV_WA | L_APB_MALOSS, L_MED_DIFSLOW_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;\n (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;\n (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;\n (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;\n}\nprobability ( L_MED_ALLDEL_WA | L_MED_RDLDDEL, L_MED_DCV_WA ) {\n (MS3_1, M_S60) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S60) 0.05119488, 0.22907709, 0.56624338, 0.15348465, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S60) 0.0001, 0.0035, 0.0632, 0.9326, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S60) 0.00129987, 0.00199980, 0.00439956, 0.01869813, 0.12128787, 0.74792521, 0.10438956, 0.00000000, 0.00000000;\n (MS20_1, M_S60) 0.00010001, 0.00010001, 0.00030003, 0.00090009, 0.00450045, 0.04340434, 0.57675768, 0.37393739, 0.00000000;\n (MS3_1, M_S52) 0.5607, 0.4393, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S52) 0.01929807, 0.12868713, 0.49285071, 0.35916408, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S52) 0.0000, 0.0011, 0.0283, 0.9651, 0.0055, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S52) 0.00100010, 0.00160016, 0.00370037, 0.01610161, 0.10931093, 0.74217422, 0.12611261, 0.00000000, 0.00000000;\n (MS20_1, M_S52) 0.0001, 0.0001, 0.0002, 0.0008, 0.0041, 0.0399, 0.5568, 0.3980, 0.0000;\n (MS3_1, M_S44) 0.0069, 0.7963, 0.1968, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S44) 0.0027, 0.0328, 0.2398, 0.7246, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S44) 0.0000, 0.0002, 0.0075, 0.8889, 0.1034, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S44) 0.00079992, 0.00119988, 0.00279972, 0.01249875, 0.09159084, 0.72352765, 0.16758324, 0.00000000, 0.00000000;\n (MS20_1, M_S44) 0.0001, 0.0001, 0.0002, 0.0007, 0.0034, 0.0348, 0.5239, 0.4368, 0.0000;\n (MS3_1, M_S36) 0.0000, 0.0184, 0.9806, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S36) 0.00010001, 0.00330033, 0.05470547, 0.93819382, 0.00370037, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S36) 0.00000000, 0.00000000, 0.00030003, 0.18501850, 0.81468147, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS10_1, M_S36) 0.00049995, 0.00079992, 0.00179982, 0.00869913, 0.07029297, 0.68023198, 0.23767623, 0.00000000, 0.00000000;\n (MS20_1, M_S36) 0.0001, 0.0001, 0.0001, 0.0005, 0.0027, 0.0287, 0.4783, 0.4895, 0.0000;\n (MS3_1, M_S28) 0.0000, 0.0001, 0.0179, 0.9820, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S28) 0.00000000, 0.00019998, 0.00479952, 0.50394961, 0.49105089, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S28) 0.0000, 0.0000, 0.0000, 0.0032, 0.9968, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S28) 0.0002, 0.0004, 0.0009, 0.0047, 0.0440, 0.5737, 0.3761, 0.0000, 0.0000;\n (MS20_1, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40724072, 0.56955696, 0.00000000;\n (MS3_1, M_S20) 0.0000, 0.0002, 0.0030, 0.1393, 0.8575, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S20) 0.0000, 0.0000, 0.0003, 0.0188, 0.9804, 0.0005, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.93139314, 0.06710671, 0.00000000, 0.00000000, 0.00000000;\n (MS10_1, M_S20) 0.0001, 0.0001, 0.0002, 0.0013, 0.0150, 0.3152, 0.6681, 0.0000, 0.0000;\n (MS20_1, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00080016, 0.01110222, 0.28045609, 0.70754151, 0.00000000;\n (MS3_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0006, 0.8145, 0.1849, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0494, 0.9506, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S14) 0.000, 0.000, 0.000, 0.000, 0.001, 0.999, 0.000, 0.000, 0.000;\n (MS10_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0001, 0.0017, 0.0786, 0.9196, 0.0000, 0.0000;\n (MS20_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0035, 0.1380, 0.8583, 0.0000;\n (MS3_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59735974, 0.39093909, 0.00000000, 0.00000000;\n (MS3_9, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00340034, 0.29902990, 0.69746975, 0.00000000, 0.00000000;\n (MS4_7, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.1112, 0.8881, 0.0000, 0.0000;\n (MS10_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00940094, 0.95939594, 0.03110311, 0.00000000;\n (MS20_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.02530253, 0.97439744, 0.00000000;\n (MS3_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS3_9, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS4_7, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS10_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS20_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MED_LAT_WA | L_MED_ALLDEL_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_APB_VOL_ACT ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_APB_FORCE | L_APB_VOL_ACT, L_APB_ALLAMP_WA ) {\n (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;\n (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;\n (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;\n (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;\n (NORMAL, A0_01) 0.00159984, 0.01859814, 0.09359064, 0.26787321, 0.36586341, 0.25247475;\n (REDUCED, A0_01) 0.0002, 0.0034, 0.0260, 0.1312, 0.3333, 0.5059;\n (V_RED, A0_01) 0.0000, 0.0003, 0.0044, 0.0446, 0.2226, 0.7281;\n (ABSENT, A0_01) 0.0000, 0.0000, 0.0005, 0.0098, 0.1058, 0.8839;\n (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;\n (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;\n (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;\n (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;\n (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;\n (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;\n (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;\n (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;\n (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;\n (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;\n (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;\n (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;\n (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;\n (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;\n (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;\n (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;\n (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;\n (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;\n (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;\n (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;\n (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;\n (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;\n (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;\n (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;\n}\nprobability ( L_APB_MUSCLE_VOL | L_APB_MUSIZE, L_APB_MALOSS ) {\n (V_SMALL, NO) 0.9896, 0.0104;\n (SMALL, NO) 0.8137, 0.1863;\n (NORMAL, NO) 0.0209, 0.9791;\n (INCR, NO) 0.009, 0.991;\n (LARGE, NO) 0.003, 0.997;\n (V_LARGE, NO) 0.0004, 0.9996;\n (OTHER, NO) 0.4212, 0.5788;\n (V_SMALL, MILD) 0.9976, 0.0024;\n (SMALL, MILD) 0.9603, 0.0397;\n (NORMAL, MILD) 0.5185, 0.4815;\n (INCR, MILD) 0.1087, 0.8913;\n (LARGE, MILD) 0.0278, 0.9722;\n (V_LARGE, MILD) 0.0046, 0.9954;\n (OTHER, MILD) 0.5185, 0.4815;\n (V_SMALL, MOD) 0.999, 0.001;\n (SMALL, MOD) 0.9893, 0.0107;\n (NORMAL, MOD) 0.9588, 0.0412;\n (INCR, MOD) 0.6377, 0.3623;\n (LARGE, MOD) 0.2716, 0.7284;\n (V_LARGE, MOD) 0.0779, 0.9221;\n (OTHER, MOD) 0.6336, 0.3664;\n (V_SMALL, SEV) 0.9995, 0.0005;\n (SMALL, SEV) 0.9969, 0.0031;\n (NORMAL, SEV) 0.9953, 0.0047;\n (INCR, SEV) 0.9518, 0.0482;\n (LARGE, SEV) 0.8234, 0.1766;\n (V_LARGE, SEV) 0.5986, 0.4014;\n (OTHER, SEV) 0.7685, 0.2315;\n (V_SMALL, TOTAL) 0.9989, 0.0011;\n (SMALL, TOTAL) 0.9984, 0.0016;\n (NORMAL, TOTAL) 0.9984, 0.0016;\n (INCR, TOTAL) 0.9975, 0.0025;\n (LARGE, TOTAL) 0.9965, 0.0035;\n (V_LARGE, TOTAL) 0.9956, 0.0044;\n (OTHER, TOTAL) 0.9857, 0.0143;\n (V_SMALL, OTHER) 0.9363, 0.0637;\n (SMALL, OTHER) 0.8403, 0.1597;\n (NORMAL, OTHER) 0.6534, 0.3466;\n (INCR, OTHER) 0.4689, 0.5311;\n (LARGE, OTHER) 0.3174, 0.6826;\n (V_LARGE, OTHER) 0.1948, 0.8052;\n (OTHER, OTHER) 0.5681, 0.4319;\n}\nprobability ( L_APB_MVA_RECRUIT | L_APB_MULOSS, L_APB_VOL_ACT ) {\n (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;\n (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;\n (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;\n (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;\n (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;\n (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;\n (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;\n (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;\n (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;\n (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;\n (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;\n (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;\n (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;\n (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;\n (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;\n (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;\n (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_MVA_AMP | L_APB_EFFMUS ) {\n (V_SMALL) 0.00, 0.04, 0.96;\n (SMALL) 0.01, 0.15, 0.84;\n (NORMAL) 0.05, 0.90, 0.05;\n (INCR) 0.50, 0.49, 0.01;\n (LARGE) 0.85, 0.15, 0.00;\n (V_LARGE) 0.96, 0.04, 0.00;\n (OTHER) 0.33, 0.34, 0.33;\n}\nprobability ( L_APB_TA_CONCL | L_APB_EFFMUS ) {\n (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;\n (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;\n (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;\n (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;\n (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;\n (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;\n (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;\n}\nprobability ( L_APB_MUPAMP | L_APB_EFFMUS ) {\n (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;\n (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;\n (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;\n (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;\n}\nprobability ( L_APB_QUAN_MUPAMP | L_APB_MUPAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_APB_QUAL_MUPAMP | L_APB_MUPAMP ) {\n (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;\n (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;\n (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;\n (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;\n (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;\n (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;\n (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;\n}\nprobability ( L_APB_MUPDUR | L_APB_EFFMUS ) {\n (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;\n (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;\n (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;\n}\nprobability ( L_APB_QUAN_MUPDUR | L_APB_MUPDUR ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_APB_QUAL_MUPDUR | L_APB_MUPDUR ) {\n (V_SMALL) 0.8309, 0.1677, 0.0014;\n (SMALL) 0.49, 0.49, 0.02;\n (NORMAL) 0.1065, 0.7870, 0.1065;\n (INCR) 0.02, 0.49, 0.49;\n (LARGE) 0.0014, 0.1677, 0.8309;\n (V_LARGE) 0.0001, 0.0392, 0.9607;\n (OTHER) 0.2597, 0.4806, 0.2597;\n}\nprobability ( L_APB_QUAN_MUPPOLY | L_APB_DE_REGEN, L_APB_EFFMUS ) {\n (NO, V_SMALL) 0.109, 0.548, 0.343;\n (YES, V_SMALL) 0.004, 0.122, 0.874;\n (NO, SMALL) 0.340, 0.564, 0.096;\n (YES, SMALL) 0.015, 0.261, 0.724;\n (NO, NORMAL) 0.925, 0.075, 0.000;\n (YES, NORMAL) 0.091, 0.526, 0.383;\n (NO, INCR) 0.796, 0.201, 0.003;\n (YES, INCR) 0.061, 0.465, 0.474;\n (NO, LARGE) 0.637, 0.348, 0.015;\n (YES, LARGE) 0.039, 0.396, 0.565;\n (NO, V_LARGE) 0.340, 0.564, 0.096;\n (YES, V_LARGE) 0.015, 0.261, 0.724;\n (NO, OTHER) 0.340, 0.564, 0.096;\n (YES, OTHER) 0.015, 0.261, 0.724;\n}\nprobability ( L_APB_QUAL_MUPPOLY | L_APB_QUAN_MUPPOLY ) {\n (x_12_) 0.95, 0.05;\n (x12_24_) 0.3, 0.7;\n (x_24_) 0.05, 0.95;\n}\nprobability ( L_APB_MUPSATEL | L_APB_DE_REGEN ) {\n (NO) 0.95, 0.05;\n (YES) 0.2, 0.8;\n}\nprobability ( L_APB_MUPINSTAB | L_APB_NMT ) {\n (NO) 0.95, 0.05;\n (MOD_PRE) 0.1, 0.9;\n (SEV_PRE) 0.03, 0.97;\n (MLD_POST) 0.2, 0.8;\n (MOD_POST) 0.1, 0.9;\n (SEV_POST) 0.03, 0.97;\n (MIXED) 0.1, 0.9;\n}\nprobability ( L_APB_REPSTIM_CMAPAMP | L_APB_ALLAMP_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_APB_REPSTIM_DECR | L_APB_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;\n (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MIXED) 0.245, 0.245, 0.245, 0.245, 0.020;\n}\nprobability ( L_APB_REPSTIM_FACILI | L_APB_NMT ) {\n (NO) 0.95, 0.02, 0.01, 0.02;\n (MOD_PRE) 0.010, 0.889, 0.100, 0.001;\n (SEV_PRE) 0.010, 0.080, 0.909, 0.001;\n (MLD_POST) 0.89, 0.08, 0.01, 0.02;\n (MOD_POST) 0.48, 0.50, 0.01, 0.01;\n (SEV_POST) 0.020, 0.949, 0.030, 0.001;\n (MIXED) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( L_APB_REPSTIM_POST_DECR | L_APB_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;\n (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;\n (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MIXED) 0.23, 0.23, 0.22, 0.22, 0.10;\n}\nprobability ( L_APB_SF_JITTER | L_APB_NMT ) {\n (NO) 0.95, 0.05, 0.00, 0.00;\n (MOD_PRE) 0.02, 0.20, 0.70, 0.08;\n (SEV_PRE) 0.0, 0.1, 0.4, 0.5;\n (MLD_POST) 0.05, 0.70, 0.20, 0.05;\n (MOD_POST) 0.01, 0.19, 0.70, 0.10;\n (SEV_POST) 0.0, 0.1, 0.4, 0.5;\n (MIXED) 0.1, 0.3, 0.3, 0.3;\n}\nprobability ( L_LNLT1_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_LP_APB_MUDENS | L_LNLT1_APB_MUDENS, L_LNLLP_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLBE_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_LP_BE_APB_MUDENS | L_LNLT1_LP_APB_MUDENS, L_LNLBE_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_APB_MUDENS | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( L_LNLW_APB_MUDENS | L_LNLW_MED_SEV, L_LNLW_MED_TIME, L_LNLW_MED_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( L_DIFFN_LNLW_APB_MUDENS | L_DIFFN_APB_MUDENS, L_LNLW_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_APB_MUDENS | L_LNLT1_LP_BE_APB_MUDENS, L_DIFFN_LNLW_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_MYOP_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MYDY_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_APB_MUDENS | L_MYOP_APB_MUDENS, L_MYDY_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_MYAS_APB_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MUSCLE_APB_MUDENS | L_MYOP_MYDY_APB_MUDENS, L_MYAS_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_MUDENS | L_LNL_DIFFN_APB_MUDENS, L_MUSCLE_APB_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_SF_DENSITY | L_APB_MUDENS ) {\n (NORMAL) 0.97, 0.03, 0.00;\n (INCR) 0.05, 0.90, 0.05;\n (V_INCR) 0.01, 0.04, 0.95;\n}\nprobability ( L_LNLT1_APB_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_APB_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_LP_APB_NEUR_ACT | L_LNLT1_APB_NEUR_ACT, L_LNLLP_APB_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLBE_APB_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_LP_BE_APB_NEUR_ACT | L_LNLT1_LP_APB_NEUR_ACT, L_LNLBE_APB_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_APB_NEUR_ACT | DIFFN_M_SEV_DIST, DIFFN_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLW_APB_NEUR_ACT | L_LNLW_MED_SEV, L_LNLW_MED_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_APB_NEUR_ACT | L_DIFFN_APB_NEUR_ACT, L_LNLW_APB_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_NEUR_ACT | L_LNLT1_LP_BE_APB_NEUR_ACT, L_DIFFN_LNLW_APB_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_SPONT_NEUR_DISCH | L_APB_NEUR_ACT ) {\n (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;\n (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;\n (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;\n (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;\n (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;\n}\nprobability ( L_MYOP_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYDY_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_APB_DENERV | L_MYOP_APB_DENERV, L_MYDY_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_NMT_APB_DENERV | L_APB_NMT ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE) 0.40, 0.45, 0.15, 0.00;\n (SEV_PRE) 0.15, 0.35, 0.35, 0.15;\n (MLD_POST) 0.85, 0.15, 0.00, 0.00;\n (MOD_POST) 0.30, 0.45, 0.20, 0.05;\n (SEV_POST) 0.15, 0.35, 0.35, 0.15;\n (MIXED) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( L_MUSCLE_APB_DENERV | L_MYOP_MYDY_APB_DENERV, L_NMT_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLT1_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_LP_APB_DENERV | L_LNLT1_APB_DENERV, L_LNLLP_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLBE_APB_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLT1_LP_BE_APB_DENERV | L_LNLT1_LP_APB_DENERV, L_LNLBE_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_APB_DENERV | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( L_LNLW_APB_DENERV | L_LNLW_MED_SEV, L_LNLW_MED_TIME, L_LNLW_MED_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( L_DIFFN_LNLW_APB_DENERV | L_DIFFN_APB_DENERV, L_LNLW_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_APB_DENERV | L_LNLT1_LP_BE_APB_DENERV, L_DIFFN_LNLW_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_DENERV | L_MUSCLE_APB_DENERV, L_LNL_DIFFN_APB_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_APB_SPONT_DENERV_ACT | L_APB_DENERV ) {\n (NO) 0.98, 0.02, 0.00, 0.00;\n (MILD) 0.07, 0.85, 0.08, 0.00;\n (MOD) 0.01, 0.07, 0.85, 0.07;\n (SEV) 0.00, 0.01, 0.07, 0.92;\n}\nprobability ( L_APB_SPONT_HF_DISCH | L_APB_DENERV ) {\n (NO) 0.99, 0.01;\n (MILD) 0.97, 0.03;\n (MOD) 0.95, 0.05;\n (SEV) 0.93, 0.07;\n}\nprobability ( L_APB_SPONT_INS_ACT | L_APB_DENERV ) {\n (NO) 0.98, 0.02;\n (MILD) 0.1, 0.9;\n (MOD) 0.05, 0.95;\n (SEV) 0.05, 0.95;\n}\nprobability ( L_OTHER_ULND5_DISP ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_ULND5_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLE_ULN_SEV ) {\n table 0.945, 0.025, 0.015, 0.010, 0.005;\n}\nprobability ( L_LNLE_ULN_PATHO ) {\n table 0.600, 0.190, 0.200, 0.005, 0.005;\n}\nprobability ( L_LNLE_ULND5_DISP_E | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (TOTAL, BLOCK) 0.0, 0.0, 0.5, 0.5;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.3, 0.5, 0.2, 0.0;\n (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLE_DIFFN_ULND5_DISP_E | L_DIFFN_ULND5_DISP, L_LNLE_ULND5_DISP_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_DISP_E | L_OTHER_ULND5_DISP, L_LNLE_DIFFN_ULND5_DISP_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLW_ULND5_DISP_WD ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ULND5_DISP_WD | L_LNLW_ULND5_DISP_WD, L_DIFFN_ULND5_DISP ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_DISP_WD | L_OTHER_ULND5_DISP, L_DIFFN_LNLW_ULND5_DISP_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_DISP_BEW | L_OTHER_ULND5_DISP, L_DIFFN_ULND5_DISP ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_DISP_BED | L_ULND5_DISP_WD, L_ULND5_DISP_BEW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.0000, 0.0008, 0.0097, 0.9895;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0024, 0.9975;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0000, 0.0000, 0.0004, 0.9996;\n (NO, SEV) 0.0000, 0.0008, 0.0097, 0.9895;\n (MILD, SEV) 0.0000, 0.0001, 0.0024, 0.9975;\n (MOD, SEV) 0.0000, 0.0000, 0.0004, 0.9996;\n (SEV, SEV) 0.0000, 0.0000, 0.0002, 0.9998;\n}\nprobability ( L_ULND5_DISP_EED | L_ULND5_DISP_E, L_ULND5_DISP_BED ) {\n (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0404, 0.9192, 0.0404, 0.0000;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742;\n (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, MILD) 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.3477, 0.6496, 0.0023, 0.0000;\n (MOD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00230023, 0.64986499, 0.34783478;\n (SEV, MILD) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, MOD) 0.0000, 0.0004, 0.3477, 0.6496, 0.0023, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MOD) 0.000, 0.000, 0.000, 0.001, 0.499, 0.499, 0.001, 0.000, 0.000;\n (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.2215, 0.7732, 0.0052;\n (SEV, MOD) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, SEV) 0.4995, 0.4995, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, SEV) 0.0004, 0.3477, 0.6496, 0.0023, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0213, 0.9045, 0.0742;\n}\nprobability ( L_OTHER_ULND5_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_ULND5_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLE_ULND5_BLOCK_E | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.3, 0.6, 0.1, 0.0, 0.0;\n (SEV, DEMY) 0.1, 0.5, 0.3, 0.1, 0.0;\n (TOTAL, DEMY) 0.00, 0.05, 0.20, 0.55, 0.20;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.2, 0.6, 0.2;\n (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLE_DIFFN_ULND5_BLOCK_E | L_DIFFN_ULND5_BLOCK, L_LNLE_ULND5_BLOCK_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_BLOCK_E | L_OTHER_ULND5_BLOCK, L_LNLE_DIFFN_ULND5_BLOCK_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_AMPR_E | L_ULND5_DISP_EED, L_ULND5_BLOCK_E ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_OTHER_ULND5_LD ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLE_ULND5_LD_E | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;\n (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_ULND5_LD_E | L_OTHER_ULND5_LD, L_LNLE_ULND5_LD_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0186, 0.9584, 0.0230, 0.0000;\n (MOD, NO) 0.0000, 0.0184, 0.9816, 0.0000;\n (SEV, NO) 0.00100010, 0.00310031, 0.03020302, 0.96569657;\n (NO, MILD) 0.0186, 0.9584, 0.0230, 0.0000;\n (MILD, MILD) 0.0000, 0.0304, 0.9696, 0.0000;\n (MOD, MILD) 0.0000, 0.0013, 0.9987, 0.0000;\n (SEV, MILD) 0.00039996, 0.00129987, 0.01409859, 0.98420158;\n (NO, MOD) 0.0000, 0.0184, 0.9816, 0.0000;\n (MILD, MOD) 0.0000, 0.0013, 0.9987, 0.0000;\n (MOD, MOD) 0.00000000, 0.00029997, 0.99960004, 0.00009999;\n (SEV, MOD) 0.0001, 0.0005, 0.0060, 0.9934;\n (NO, SEV) 0.00100010, 0.00310031, 0.03020302, 0.96569657;\n (MILD, SEV) 0.00039996, 0.00129987, 0.01409859, 0.98420158;\n (MOD, SEV) 0.0001, 0.0005, 0.0060, 0.9934;\n (SEV, SEV) 0.00010001, 0.00030003, 0.00380038, 0.99579958;\n}\nprobability ( L_OTHER_ULND5_RD ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_LNLE_ULND5_RD_E | L_LNLE_ULN_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( L_ULND5_RD_E | L_OTHER_ULND5_RD, L_LNLE_ULND5_RD_E ) {\n (NO, NO) 1.0, 0.0, 0.0;\n (MOD, NO) 0.0941, 0.9038, 0.0021;\n (SEV, NO) 0.0677, 0.2563, 0.6760;\n (NO, MOD) 0.0941, 0.9038, 0.0021;\n (MOD, MOD) 0.0140, 0.3525, 0.6335;\n (SEV, MOD) 0.0324, 0.1629, 0.8047;\n (NO, SEV) 0.0677, 0.2563, 0.6760;\n (MOD, SEV) 0.0324, 0.1629, 0.8047;\n (SEV, SEV) 0.0351, 0.1479, 0.8170;\n}\nprobability ( L_ULND5_LSLOW_E | L_ULND5_LD_E, L_ULND5_RD_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.00840084, 0.01190119, 0.06190619, 0.91739174, 0.00040004;\n (MILD, MOD) 0.00690069, 0.01000100, 0.05350535, 0.92869287, 0.00090009;\n (MOD, MOD) 0.00559944, 0.00829917, 0.04519548, 0.93890611, 0.00199980;\n (SEV, MOD) 0.0023, 0.0036, 0.0217, 0.8326, 0.1398;\n (NO, SEV) 0.00529947, 0.00619938, 0.02639736, 0.20817918, 0.75392461;\n (MILD, SEV) 0.0049, 0.0057, 0.0244, 0.1966, 0.7684;\n (MOD, SEV) 0.00440044, 0.00520052, 0.02240224, 0.18391839, 0.78407841;\n (SEV, SEV) 0.0028, 0.0033, 0.0145, 0.1304, 0.8490;\n}\nprobability ( L_OTHER_ULND5_SALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLW_ULND5_SALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_ULND5_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ULND5_SALOSS | L_LNLW_ULND5_SALOSS, L_DIFFN_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLE_ULND5_SALOSS | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_LNLLP_ULND5_SALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_E_ULND5_SALOSS | L_LNLE_ULND5_SALOSS, L_LNLLP_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_ULND5_SALOSS | L_DIFFN_LNLW_ULND5_SALOSS, L_LNLLP_E_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_SALOSS | L_OTHER_ULND5_SALOSS, L_LNL_DIFFN_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_ULND5_DIFSLOW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLE_ULND5_DIFSLOW | L_LNLE_ULN_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 0.0, 1.0, 0.0;\n}\nprobability ( L_DIFFN_ULND5_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( L_LNLE_DIFFN_ULND5_DIFSLOW_E | L_LNLE_ULND5_DIFSLOW, L_DIFFN_ULND5_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( L_ULND5_DIFSLOW_E | L_OTHER_ULND5_DIFSLOW, L_LNLE_DIFFN_ULND5_DIFSLOW_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( L_ULND5_DSLOW_E | L_ULND5_SALOSS, L_ULND5_DIFSLOW_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_ALLCV_E | L_ULND5_LSLOW_E, L_ULND5_DSLOW_E ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.0264, 0.9149, 0.0587, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S60) 0.0000, 0.0218, 0.9469, 0.0313, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.00030003, 0.00190019, 0.01400140, 0.07880788, 0.32383238, 0.45964596, 0.12121212, 0.00030003, 0.00000000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00410041, 0.03840384, 0.21452145, 0.74237424, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0006, 0.0944, 0.8841, 0.0209, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0007, 0.2183, 0.7790, 0.0020, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00009999, 0.00039996, 0.00429957, 0.03209679, 0.19558044, 0.50484952, 0.26037396, 0.00229977, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00210021, 0.02390239, 0.16481648, 0.80898090, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.0000, 0.0018, 0.1956, 0.7860, 0.0166, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0077, 0.4577, 0.5345, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, M_S44) 0.0000, 0.0001, 0.0007, 0.0074, 0.0735, 0.4044, 0.4938, 0.0201, 0.0000;\n (V_SEV, M_S44) 0.0000, 0.0000, 0.0000, 0.0001, 0.0008, 0.0123, 0.1119, 0.8749, 0.0000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.0000, 0.0000, 0.0026, 0.2655, 0.7316, 0.0003, 0.0000, 0.0000, 0.0000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0166, 0.9182, 0.0652, 0.0000, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0010, 0.0172, 0.2179, 0.6479, 0.1159, 0.0000;\n (V_SEV, M_S36) 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0055, 0.0699, 0.9243, 0.0000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.0000, 0.0000, 0.0000, 0.0044, 0.5355, 0.4600, 0.0001, 0.0000, 0.0000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0398, 0.9460, 0.0142, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.00179982, 0.05589441, 0.46005399, 0.48215178, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0021, 0.0390, 0.9588, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0047, 0.5053, 0.4900, 0.0000, 0.0000;\n (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.1104, 0.8881, 0.0013, 0.0000;\n (SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0041, 0.1033, 0.8925, 0.0000;\n (V_SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00069993, 0.01889811, 0.98040196, 0.00000000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0218, 0.8352, 0.1430, 0.0000;\n (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.3203, 0.6777, 0.0000;\n (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0194, 0.9803, 0.0000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0093, 0.9905, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0003, 0.0010, 0.0036, 0.0154, 0.0712, 0.2467, 0.6617, 0.0000;\n (MOD, M_S08) 0.00000000, 0.00010001, 0.00050005, 0.00190019, 0.00930093, 0.05040504, 0.20722072, 0.73057306, 0.00000000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0026, 0.0190, 0.1153, 0.8626, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0062, 0.0562, 0.9369, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_CV_E | L_ULND5_ALLCV_E ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_ULND5_DISP_EWD | L_ULND5_DISP_WD, L_ULND5_DISP_BEW ) {\n (NO, NO) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000;\n (MOD, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (SEV, NO) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.133, 0.867;\n (NO, MILD) 0.0001, 0.2215, 0.7732, 0.0052, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00000000, 0.00000000, 0.00000000, 0.13151315, 0.85768577, 0.01080108, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;\n (SEV, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0108, 0.8577, 0.1315;\n (NO, MOD) 0.1315, 0.8577, 0.0108, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0000, 0.0742, 0.9045, 0.0213, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;\n (SEV, MOD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0052, 0.7732, 0.2215, 0.0001;\n (NO, SEV) 0.9933, 0.0067, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, SEV) 0.0404, 0.9192, 0.0404, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0213, 0.9045, 0.0742, 0.0000, 0.0000, 0.0000, 0.0000;\n}\nprobability ( L_ULND5_BLOCK_EW | L_OTHER_ULND5_BLOCK, L_DIFFN_ULND5_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_AMPR_EW | L_ULND5_DISP_EWD, L_ULND5_BLOCK_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_ULND5_DIFSLOW_EW | L_OTHER_ULND5_DIFSLOW, L_DIFFN_ULND5_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( L_ULND5_DSLOW_EW | L_ULND5_SALOSS, L_ULND5_DIFSLOW_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_ALLCV_EW | L_ULND5_DSLOW_EW ) {\n (M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S52) 0.02689731, 0.97130287, 0.00179982, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S44) 0.0003, 0.0598, 0.8924, 0.0475, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S36) 0.0000, 0.0001, 0.0637, 0.9111, 0.0251, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S28) 0.0000, 0.0000, 0.0001, 0.0544, 0.9384, 0.0071, 0.0000, 0.0000, 0.0000;\n (M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0486, 0.8875, 0.0637, 0.0000, 0.0000;\n (M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.09500950, 0.89738974, 0.00740074, 0.00000000;\n (M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09730973, 0.28552855, 0.58335834, 0.00000000;\n (M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_CV_EW | L_ULND5_ALLCV_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_LNLW_ULND5_BLOCK_WD ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ULND5_BLOCK_WD | L_LNLW_ULND5_BLOCK_WD, L_DIFFN_ULND5_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_BLOCK_WD | L_OTHER_ULND5_BLOCK, L_DIFFN_LNLW_ULND5_BLOCK_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_EFFAXLOSS | L_ULND5_BLOCK_WD, L_ULND5_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_ALLAMP_WD | L_ULND5_DISP_WD, L_ULND5_EFFAXLOSS ) {\n (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;\n (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;\n (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;\n (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;\n (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;\n (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;\n (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;\n (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;\n (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;\n (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_ULND5_AMP_WD | L_ULND5_ALLAMP_WD ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_LNLW_ULND5_LD_WD ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_ULND5_LD_WD | L_OTHER_ULND5_LD, L_LNLW_ULND5_LD_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0186, 0.9584, 0.0230, 0.0000;\n (MOD, NO) 0.0000, 0.0184, 0.9816, 0.0000;\n (SEV, NO) 0.00100010, 0.00310031, 0.03020302, 0.96569657;\n (NO, MILD) 0.0186, 0.9584, 0.0230, 0.0000;\n (MILD, MILD) 0.0000, 0.0304, 0.9696, 0.0000;\n (MOD, MILD) 0.0000, 0.0013, 0.9987, 0.0000;\n (SEV, MILD) 0.00039996, 0.00129987, 0.01409859, 0.98420158;\n (NO, MOD) 0.0000, 0.0184, 0.9816, 0.0000;\n (MILD, MOD) 0.0000, 0.0013, 0.9987, 0.0000;\n (MOD, MOD) 0.00000000, 0.00029997, 0.99960004, 0.00009999;\n (SEV, MOD) 0.0001, 0.0005, 0.0060, 0.9934;\n (NO, SEV) 0.00100010, 0.00310031, 0.03020302, 0.96569657;\n (MILD, SEV) 0.00039996, 0.00129987, 0.01409859, 0.98420158;\n (MOD, SEV) 0.0001, 0.0005, 0.0060, 0.9934;\n (SEV, SEV) 0.00010001, 0.00030003, 0.00380038, 0.99579958;\n}\nprobability ( L_LNLW_ULND5_RD_WD ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_ULND5_RD_WD | L_OTHER_ULND5_RD, L_LNLW_ULND5_RD_WD ) {\n (NO, NO) 1.0, 0.0, 0.0;\n (MOD, NO) 0.0139, 0.9821, 0.0040;\n (SEV, NO) 0.0000000, 0.0150015, 0.9849985;\n (NO, MOD) 0.0139, 0.9821, 0.0040;\n (MOD, MOD) 0.0002, 0.1057, 0.8941;\n (SEV, MOD) 0.0000, 0.0037, 0.9963;\n (NO, SEV) 0.00070007, 0.09680968, 0.90249025;\n (MOD, SEV) 0.00000000, 0.01550155, 0.98449845;\n (SEV, SEV) 0.000, 0.003, 0.997;\n}\nprobability ( L_ULND5_LSLOW_WD | L_ULND5_LD_WD, L_ULND5_RD_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;\n (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;\n (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;\n (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;\n (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;\n (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;\n (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;\n}\nprobability ( L_LNLE_DIFFN_ULND5_DIFSLOW_WD | L_LNLE_ULND5_DIFSLOW, L_DIFFN_ULND5_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( L_ULND5_DIFSLOW_WD | L_OTHER_ULND5_DIFSLOW, L_LNLE_DIFFN_ULND5_DIFSLOW_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( L_ULND5_DSLOW_WD | L_ULND5_SALOSS, L_ULND5_DIFSLOW_WD ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1036, 0.8964, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00059994, 0.99730027, 0.00209979, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0629, 0.9278, 0.0092, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0532, 0.2387, 0.4950, 0.2072, 0.0059, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.01780178, 0.11901190, 0.44814481, 0.38543854, 0.02960296, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.0048, 0.0476, 0.3148, 0.5311, 0.1016, 0.0001, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.00060006, 0.00920092, 0.11601160, 0.48574857, 0.38353835, 0.00490049, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0063, 0.0614, 0.3006, 0.5524, 0.0781, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.0002, 0.0021, 0.0283, 0.1995, 0.5939, 0.1737, 0.0023, 0.0000, 0.0000;\n (MOD, MOD) 0.00000000, 0.00060006, 0.01140114, 0.11471147, 0.54455446, 0.31963196, 0.00910091, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00010001, 0.00240024, 0.03740374, 0.33273327, 0.56705671, 0.06030603, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (MILD, SEV) 0.0002, 0.0006, 0.0029, 0.0133, 0.0634, 0.2436, 0.4657, 0.2103, 0.0000;\n (MOD, SEV) 0.00010001, 0.00030003, 0.00170017, 0.00830083, 0.04470447, 0.20312031, 0.46324632, 0.27852785, 0.00000000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00070007, 0.00380038, 0.02430243, 0.14161416, 0.42404240, 0.40544054, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_ALLCV_WD | L_ULND5_LSLOW_WD, L_ULND5_DSLOW_WD ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;\n (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;\n (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;\n (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;\n (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;\n (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;\n (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;\n (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;\n (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;\n (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULND5_CV_WD | L_ULND5_ALLCV_WD ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_DIFFN_ULN_BLOCK | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLE_ULN_BLOCK | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (TOTAL, DEMY) 0.2, 0.2, 0.2, 0.2, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (TOTAL, BLOCK) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.2, 0.2, 0.2, 0.2, 0.2;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_ULN_BLOCK_E | L_DIFFN_ULN_BLOCK, L_LNLE_ULN_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULN_AMPR_E | L_ULN_BLOCK_E ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_OTHER_ADM_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLC8_ADM_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_ADM_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLC8_LP_ADM_MALOSS | L_LNLC8_ADM_MALOSS, L_LNLLP_ADM_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLE_ADM_MALOSS | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_LNLC8_LP_E_ADM_MALOSS | L_LNLC8_LP_ADM_MALOSS, L_LNLE_ADM_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLW_ADM_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_ADM_MALOSS | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ADM_MALOSS | L_LNLW_ADM_MALOSS, L_DIFFN_ADM_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_ADM_MALOSS | L_LNLC8_LP_E_ADM_MALOSS, L_DIFFN_LNLW_ADM_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0001, 0.9998, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0382, 0.9586, 0.0032, 0.0000;\n (SEV, NO) 0.0000, 0.0002, 0.0420, 0.9578, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0001, 0.9998, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0292, 0.9708, 0.0000, 0.0000;\n (MOD, MILD) 0.00000000, 0.00110011, 0.39953995, 0.59935994, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0049, 0.9951, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0000, 0.0382, 0.9586, 0.0032, 0.0000;\n (MILD, MOD) 0.00000000, 0.00110011, 0.39953995, 0.59935994, 0.00000000;\n (MOD, MOD) 0.00000000, 0.00000000, 0.01280128, 0.98719872, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9986, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0420, 0.9578, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0049, 0.9951, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9986, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_MALOSS | L_OTHER_ADM_MALOSS, L_LNL_DIFFN_ADM_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;\n (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;\n (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( L_LNLE_ULN_DIFSLOW | L_LNLE_ULN_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 0.0, 1.0, 0.0;\n}\nprobability ( L_DIFFN_ULN_DIFSLOW | DIFFN_M_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( L_ULN_DIFSLOW_E | L_LNLE_ULN_DIFSLOW, L_DIFFN_ULN_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;\n (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;\n (MOD, MOD) 0.000, 0.000, 0.002, 0.998;\n (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;\n (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;\n}\nprobability ( L_ULN_DCV_E | L_ADM_MALOSS, L_ULN_DIFSLOW_E ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;\n (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;\n (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;\n (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;\n}\nprobability ( L_ULN_LD_EW | L_LNLE_ULN_SEV, L_LNLE_ULN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, DEMY) 0.25, 0.25, 0.25, 0.25;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.25, 0.50, 0.25, 0.00;\n (SEV, BLOCK) 0.05, 0.30, 0.50, 0.15;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (TOTAL, AXONAL) 0.25, 0.25, 0.25, 0.25;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, V_E_REIN) 0.25, 0.25, 0.25, 0.25;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (TOTAL, E_REIN) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( L_ULN_RD_EW | L_LNLE_ULN_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( L_ULN_RDLDCV_E | L_ULN_LD_EW, L_ULN_RD_EW ) {\n (NO, NO) 0.90449045, 0.09530953, 0.00020002, 0.00000000, 0.00000000, 0.00000000;\n (MILD, NO) 0.1320, 0.6039, 0.2641, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0139, 0.1839, 0.8022, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.00120012, 0.00670067, 0.05440544, 0.86008601, 0.07760776, 0.00000000;\n (NO, MOD) 0.0115, 0.0333, 0.1509, 0.7319, 0.0724, 0.0000;\n (MILD, MOD) 0.00340034, 0.01220122, 0.06900690, 0.71967197, 0.19531953, 0.00040004;\n (MOD, MOD) 0.0011, 0.0045, 0.0299, 0.5742, 0.3876, 0.0027;\n (SEV, MOD) 0.0001, 0.0002, 0.0018, 0.0914, 0.6093, 0.2972;\n (NO, SEV) 0.00000000, 0.00009999, 0.00109989, 0.14618538, 0.80701930, 0.04559544;\n (MILD, SEV) 0.0000, 0.0000, 0.0002, 0.0581, 0.7950, 0.1467;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0228, 0.6344, 0.3427;\n (SEV, SEV) 0.0000, 0.0000, 0.0000, 0.0014, 0.1063, 0.8923;\n}\nprobability ( L_ULN_ALLCV_E | L_ULN_DCV_E, L_ULN_RDLDCV_E ) {\n (M_S60, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56, M_S60) 0.3684, 0.6316, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S60) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S44, M_S60) 0.00089991, 0.08959104, 0.82601740, 0.08349165, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S36, M_S60) 0.0000, 0.0005, 0.1011, 0.8478, 0.0506, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S60) 0.00000000, 0.00000000, 0.00050005, 0.07910791, 0.90019002, 0.02020202, 0.00000000, 0.00000000, 0.00000000;\n (M_S20, M_S60) 0.0000, 0.0000, 0.0000, 0.0006, 0.0734, 0.8393, 0.0867, 0.0000, 0.0000;\n (M_S14, M_S60) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.09470947, 0.89458946, 0.01040104, 0.00000000;\n (M_S08, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.0932, 0.9048, 0.0000;\n (M_S00, M_S60) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S52) 0.0077, 0.9808, 0.0115, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S56, M_S52) 0.0005, 0.4476, 0.5519, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S52) 0.0000, 0.0239, 0.9751, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S52) 0.0000, 0.0036, 0.2468, 0.7340, 0.0156, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S36, M_S52) 0.0000, 0.0000, 0.0050, 0.3134, 0.6811, 0.0005, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00710071, 0.54935494, 0.44334433, 0.00020002, 0.00000000, 0.00000000;\n (M_S20, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00910091, 0.52835284, 0.46254625, 0.00000000, 0.00000000;\n (M_S14, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0216, 0.8359, 0.1425, 0.0000;\n (M_S08, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0355, 0.9641, 0.0000;\n (M_S00, M_S52) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S44) 0.0000, 0.0047, 0.9951, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S56, M_S44) 0.0000, 0.0004, 0.9005, 0.0991, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52, M_S44) 0.0000, 0.0000, 0.1288, 0.8712, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S44) 0.00000000, 0.00000000, 0.01130113, 0.57115712, 0.41754175, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (M_S36, M_S44) 0.0000, 0.0000, 0.0000, 0.0214, 0.9354, 0.0432, 0.0000, 0.0000, 0.0000;\n (M_S28, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.05649435, 0.93230677, 0.01109889, 0.00000000, 0.00000000;\n (M_S20, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.1430, 0.8551, 0.0015, 0.0000;\n (M_S14, M_S44) 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0019998, 0.3548645, 0.6431357, 0.0000000;\n (M_S08, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0122, 0.9877, 0.0000;\n (M_S00, M_S44) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (M_S60, M_S27) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56, M_S27) 0.000, 0.000, 0.000, 0.000, 0.997, 0.003, 0.000, 0.000, 0.000;\n (M_S52, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.2336, 0.7664, 0.0000, 0.0000, 0.0000;\n (M_S44, M_S27) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00259974, 0.99340066, 0.00399960, 0.00000000, 0.00000000;\n (M_S36, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2469, 0.7531, 0.0000, 0.0000;\n (M_S28, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0020, 0.9939, 0.0041, 0.0000;\n (M_S20, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0314, 0.9686, 0.0000;\n (M_S14, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;\n (M_S08, M_S27) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;\n (M_S00, M_S27) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (M_S60, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.1305, 0.8445, 0.0238, 0.0000;\n (M_S56, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0740, 0.8589, 0.0667, 0.0000;\n (M_S52, M_S15) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03730373, 0.80288029, 0.15971597, 0.00000000;\n (M_S44, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0070, 0.3517, 0.6413, 0.0000;\n (M_S36, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0581, 0.9416, 0.0000;\n (M_S28, M_S15) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.006, 0.994, 0.000;\n (M_S20, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (M_S14, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S08, M_S15) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.9998, 0.0000;\n (M_S00, M_S15) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (M_S60, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0321, 0.9677, 0.0000;\n (M_S56, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0184, 0.9815, 0.0000;\n (M_S52, M_S07) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01010101, 0.98989899, 0.00000000;\n (M_S44, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0042, 0.9958, 0.0000;\n (M_S36, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (M_S28, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.9997, 0.0000;\n (M_S20, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S14, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (M_S08, M_S07) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (M_S00, M_S07) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULN_CV_E | L_ULN_ALLCV_E ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_ULN_BLOCK_EW | L_DIFFN_ULN_BLOCK ) {\n (NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD) 0.0038, 0.9962, 0.0000, 0.0000, 0.0000;\n (MOD) 0.0007, 0.0223, 0.9770, 0.0000, 0.0000;\n (SEV) 0.0019, 0.0060, 0.0587, 0.9334, 0.0000;\n (TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULN_AMPR_EW | L_ULN_BLOCK_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_ULN_DIFSLOW_EW | L_DIFFN_ULN_DIFSLOW ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MILD) 0.0126, 0.9869, 0.0005, 0.0000;\n (MOD) 0.0000, 0.0179, 0.9821, 0.0000;\n (SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n}\nprobability ( L_ULN_DCV_EW | L_ADM_MALOSS, L_ULN_DIFSLOW_EW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1090, 0.8903, 0.0007, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.00399960, 0.11438856, 0.86211379, 0.01949805, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, NO) 0.0001, 0.0028, 0.0640, 0.9243, 0.0088, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.0835, 0.1153, 0.2417, 0.3746, 0.1682, 0.0167, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.00410041, 0.02470247, 0.15461546, 0.73897390, 0.07760776, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, MILD) 0.0011, 0.0082, 0.0683, 0.7087, 0.2135, 0.0002, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MILD) 0.00009999, 0.00079992, 0.00899910, 0.30296970, 0.66543346, 0.02169783, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0006, 0.0547, 0.6199, 0.3247, 0.0001, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.00929907, 0.01809819, 0.05459454, 0.22767723, 0.39336066, 0.27757224, 0.01929807, 0.00009999, 0.00000000, 0.00000000;\n (NO, MOD) 0.0004, 0.0012, 0.0055, 0.0628, 0.2950, 0.5485, 0.0861, 0.0005, 0.0000, 0.0000;\n (MILD, MOD) 0.00020002, 0.00050005, 0.00280028, 0.03890389, 0.23092309, 0.58295830, 0.14221422, 0.00150015, 0.00000000, 0.00000000;\n (MOD, MOD) 0.00000000, 0.00010001, 0.00060006, 0.01230123, 0.11291129, 0.52725273, 0.33553355, 0.01130113, 0.00000000, 0.00000000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00249975, 0.03599640, 0.32406759, 0.57104290, 0.06629337, 0.00000000, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00059994, 0.00119988, 0.00419958, 0.02839716, 0.11488851, 0.35756424, 0.39636036, 0.09639036, 0.00039996, 0.00000000;\n (NO, SEV) 0.00000000, 0.00009999, 0.00019998, 0.00189981, 0.01069893, 0.06579342, 0.28457154, 0.50544946, 0.13128687, 0.00000000;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.00750075, 0.05100510, 0.25242524, 0.51855186, 0.16921692, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0000, 0.0005, 0.0034, 0.0280, 0.1822, 0.5098, 0.2761, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00120012, 0.01250125, 0.11181118, 0.44524452, 0.42914291, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0000, 0.0002, 0.0011, 0.0056, 0.0330, 0.1653, 0.4414, 0.3534, 0.0000;\n}\nprobability ( L_ULN_ALLCV_EW | L_ULN_DCV_EW ) {\n (M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (M_S56) 0.0558, 0.8486, 0.0956, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S52) 0.0000, 0.0369, 0.9631, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S44) 0.0008, 0.0087, 0.0873, 0.8226, 0.0806, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S36) 0.0000, 0.0000, 0.0005, 0.0994, 0.8508, 0.0493, 0.0000, 0.0000, 0.0000, 0.0000;\n (M_S28) 0.0000, 0.0000, 0.0000, 0.0005, 0.0781, 0.9017, 0.0197, 0.0000, 0.0000, 0.0000;\n (M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0006, 0.0728, 0.8406, 0.0860, 0.0000, 0.0000;\n (M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0003, 0.0942, 0.8952, 0.0103, 0.0000;\n (M_S08) 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.002, 0.093, 0.905, 0.000;\n (M_S00) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n}\nprobability ( L_ULN_CV_EW | L_ULN_ALLCV_EW ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_OTHER_ADM_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLC8_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_LNLLP_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_LNLC8_LP_ADM_DE_REGEN | L_LNLC8_ADM_DE_REGEN, L_LNLLP_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_LNLE_ULN_TIME ) {\n table 0.05, 0.60, 0.30, 0.05;\n}\nprobability ( L_LNLE_ADM_DE_REGEN | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME, L_LNLE_ULN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (TOTAL, CHRONIC, DEMY) 1.0, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (TOTAL, OLD, DEMY) 1.0, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (TOTAL, OLD, BLOCK) 1.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (TOTAL, OLD, AXONAL) 1.0, 0.0;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (TOTAL, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( L_LNLC8_LP_E_ADM_DE_REGEN | L_LNLC8_LP_ADM_DE_REGEN, L_LNLE_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_DIFFN_ADM_DE_REGEN | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( L_LNLW_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ADM_DE_REGEN | L_DIFFN_ADM_DE_REGEN, L_LNLW_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_ADM_DE_REGEN | L_LNLC8_LP_E_ADM_DE_REGEN, L_DIFFN_LNLW_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_MYOP_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_MYDY_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_ADM_DE_REGEN | L_MYOP_ADM_DE_REGEN, L_MYDY_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_OTHER_ADM_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_MUSCLE_ADM_DE_REGEN | L_MYOP_MYDY_ADM_DE_REGEN, L_OTHER_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_ADM_DE_REGEN | L_LNL_DIFFN_ADM_DE_REGEN, L_MUSCLE_ADM_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_DE_REGEN_ADM_NMT | L_ADM_DE_REGEN ) {\n (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;\n}\nprobability ( L_MYAS_ADM_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYAS_DE_REGEN_ADM_NMT | L_DE_REGEN_ADM_NMT, L_MYAS_ADM_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_NMT | L_OTHER_ADM_NMT, L_MYAS_DE_REGEN_ADM_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYDY_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_ADM_MUSIZE | L_MYDY_ADM_MUSIZE, L_MYOP_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MUSCLE_ADM_MUSIZE | L_OTHER_ADM_MUSIZE, L_MYOP_MYDY_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLW_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_ADM_MUSIZE | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, BLOCK) 0.0, 0.0, 0.7, 0.2, 0.1, 0.0;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.50, 0.25;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.7, 0.3, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ADM_MUSIZE | L_LNLW_ADM_MUSIZE, L_DIFFN_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLE_ADM_MUSIZE | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME, L_LNLE_ULN_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLC8_ADM_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLC8_LP_ADM_MUSIZE | L_LNLLP_ADM_MUSIZE, L_LNLC8_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLC8_LP_E_ADM_MUSIZE | L_LNLE_ADM_MUSIZE, L_LNLC8_LP_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_ADM_MUSIZE | L_DIFFN_LNLW_ADM_MUSIZE, L_LNLC8_LP_E_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9981, 0.0019, 0.0000, 0.0000;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9981, 0.0000, 0.0000;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_MUSIZE | L_MUSCLE_ADM_MUSIZE, L_LNL_DIFFN_ADM_MUSIZE ) {\n (V_SMALL, V_SMALL) 0.9791, 0.0009, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL, V_SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL, V_SMALL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (INCR, V_SMALL) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;\n (LARGE, V_SMALL) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;\n (V_LARGE, V_SMALL) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;\n (V_SMALL, SMALL) 0.9637, 0.0163, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL, SMALL) 0.7493, 0.2257, 0.0049, 0.0001, 0.0000, 0.0000, 0.0200;\n (NORMAL, SMALL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;\n (INCR, SMALL) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;\n (LARGE, SMALL) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;\n (V_LARGE, SMALL) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;\n (V_SMALL, NORMAL) 0.92219222, 0.05780578, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SMALL, NORMAL) 0.0537, 0.8568, 0.0684, 0.0011, 0.0000, 0.0000, 0.0200;\n (NORMAL, NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;\n (V_SMALL, INCR) 0.39793979, 0.56635664, 0.01550155, 0.00020002, 0.00000000, 0.00000000, 0.02000200;\n (SMALL, INCR) 0.00389961, 0.38106189, 0.50654935, 0.08409159, 0.00429957, 0.00009999, 0.01999800;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.09080908, 0.81858186, 0.07060706, 0.00000000, 0.02000200;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00359964, 0.16548345, 0.64543546, 0.16548345, 0.01999800;\n (LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;\n (V_LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (V_SMALL, LARGE) 0.0435, 0.7319, 0.1930, 0.0114, 0.0002, 0.0000, 0.0200;\n (SMALL, LARGE) 0.0000, 0.0395, 0.5059, 0.3550, 0.0758, 0.0038, 0.0200;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0001, 0.0721, 0.8357, 0.0721, 0.0200;\n (INCR, LARGE) 0.0000, 0.0000, 0.0000, 0.0034, 0.1993, 0.7773, 0.0200;\n (LARGE, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;\n (V_SMALL, V_LARGE) 0.00120012, 0.23172317, 0.58825883, 0.14741474, 0.01120112, 0.00020002, 0.02000200;\n (SMALL, V_LARGE) 0.00000000, 0.00110011, 0.13571357, 0.40254025, 0.36313631, 0.07750775, 0.02000200;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0001, 0.0778, 0.9021, 0.0200;\n (INCR, V_LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01620162, 0.96379638, 0.02000200;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0011, 0.9789, 0.0200;\n (V_LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9799, 0.0200;\n}\nprobability ( L_ADM_EFFMUS | L_ADM_NMT, L_ADM_MUSIZE ) {\n (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MIXED, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;\n (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MIXED, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;\n (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;\n (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;\n (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;\n (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;\n (MIXED, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;\n (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;\n (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;\n (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;\n (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;\n (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;\n (MIXED, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;\n (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;\n (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;\n (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;\n (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;\n (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;\n (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;\n (MIXED, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;\n (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;\n (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;\n (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;\n (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;\n (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;\n (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;\n (MIXED, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;\n (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;\n (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;\n (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;\n (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;\n (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;\n (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;\n (MIXED, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;\n}\nprobability ( L_OTHER_ULN_BLOCK_WA ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLW_ULN_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ULN_BLOCK_WA | L_DIFFN_ULN_BLOCK, L_LNLW_ULN_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULN_BLOCK_WA | L_OTHER_ULN_BLOCK_WA, L_DIFFN_LNLW_ULN_BLOCK_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_MULOSS | L_ULN_BLOCK_WA, L_ADM_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;\n (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;\n (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;\n (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;\n (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;\n (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;\n (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;\n (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;\n (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;\n (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;\n (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;\n (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;\n (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( L_ADM_ALLAMP_WA | L_ADM_EFFMUS, L_ADM_MULOSS ) {\n (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;\n (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;\n (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;\n (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;\n (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;\n (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;\n (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;\n (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;\n (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;\n (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;\n (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;\n (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;\n (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;\n (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;\n (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;\n (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;\n (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;\n (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;\n (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;\n (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;\n (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;\n (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;\n (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;\n (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;\n (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;\n (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;\n (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;\n (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;\n (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;\n}\nprobability ( L_ULN_AMP_WA | L_ADM_ALLAMP_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_ULN_LD_WA ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_ULN_RD_WA ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_ULN_RDLDDEL | L_ULN_LD_WA, L_ULN_RD_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0964, 0.7981, 0.1055, 0.0000, 0.0000;\n (MOD, NO) 0.0032, 0.1270, 0.8698, 0.0000, 0.0000;\n (SEV, NO) 0.00090009, 0.00280028, 0.01470147, 0.98159816, 0.00000000;\n (NO, MOD) 0.0019, 0.5257, 0.4724, 0.0000, 0.0000;\n (MILD, MOD) 0.00019998, 0.04149585, 0.95830417, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0001, 0.0144, 0.9855, 0.0000, 0.0000;\n (SEV, MOD) 0.00019998, 0.00059994, 0.00369963, 0.99550045, 0.00000000;\n (NO, SEV) 0.0002, 0.0304, 0.9694, 0.0000, 0.0000;\n (MILD, SEV) 0.0001, 0.0142, 0.9857, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.0090, 0.9808, 0.0102, 0.0000;\n (SEV, SEV) 0.0000, 0.0002, 0.0012, 0.9984, 0.0002;\n}\nprobability ( L_ULN_DIFSLOW_WA | L_LNLE_ULN_DIFSLOW, L_DIFFN_ULN_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;\n (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;\n (MOD, MOD) 0.000, 0.000, 0.002, 0.998;\n (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;\n (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;\n}\nprobability ( L_ULN_DCV_WA | L_ADM_MALOSS, L_ULN_DIFSLOW_WA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;\n (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;\n (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;\n (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;\n}\nprobability ( L_ULN_ALLDEL_WA | L_ULN_RDLDDEL, L_ULN_DCV_WA ) {\n (MS3_1, M_S60) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S60) 0.05119488, 0.22907709, 0.56624338, 0.15348465, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S60) 0.0001, 0.0035, 0.0632, 0.9326, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S60) 0.00129987, 0.00199980, 0.00439956, 0.01869813, 0.12128787, 0.74792521, 0.10438956, 0.00000000, 0.00000000;\n (MS20_1, M_S60) 0.00010001, 0.00010001, 0.00030003, 0.00090009, 0.00450045, 0.04340434, 0.57675768, 0.37393739, 0.00000000;\n (MS3_1, M_S52) 0.5607, 0.4393, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S52) 0.01929807, 0.12868713, 0.49285071, 0.35916408, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S52) 0.0000, 0.0011, 0.0283, 0.9651, 0.0055, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S52) 0.00100010, 0.00160016, 0.00370037, 0.01610161, 0.10931093, 0.74217422, 0.12611261, 0.00000000, 0.00000000;\n (MS20_1, M_S52) 0.0001, 0.0001, 0.0002, 0.0008, 0.0041, 0.0399, 0.5568, 0.3980, 0.0000;\n (MS3_1, M_S44) 0.0069, 0.7963, 0.1968, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S44) 0.0027, 0.0328, 0.2398, 0.7246, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S44) 0.0000, 0.0002, 0.0075, 0.8889, 0.1034, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S44) 0.00079992, 0.00119988, 0.00279972, 0.01249875, 0.09159084, 0.72352765, 0.16758324, 0.00000000, 0.00000000;\n (MS20_1, M_S44) 0.0001, 0.0001, 0.0002, 0.0007, 0.0034, 0.0348, 0.5239, 0.4368, 0.0000;\n (MS3_1, M_S36) 0.0000, 0.0184, 0.9806, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S36) 0.00010001, 0.00330033, 0.05470547, 0.93819382, 0.00370037, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S36) 0.00000000, 0.00000000, 0.00030003, 0.18501850, 0.81468147, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS10_1, M_S36) 0.00049995, 0.00079992, 0.00179982, 0.00869913, 0.07029297, 0.68023198, 0.23767623, 0.00000000, 0.00000000;\n (MS20_1, M_S36) 0.0001, 0.0001, 0.0001, 0.0005, 0.0027, 0.0287, 0.4783, 0.4895, 0.0000;\n (MS3_1, M_S28) 0.0000, 0.0001, 0.0179, 0.9820, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S28) 0.00000000, 0.00019998, 0.00479952, 0.50394961, 0.49105089, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MS4_7, M_S28) 0.0000, 0.0000, 0.0000, 0.0032, 0.9968, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS10_1, M_S28) 0.0002, 0.0004, 0.0009, 0.0047, 0.0440, 0.5737, 0.3761, 0.0000, 0.0000;\n (MS20_1, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40724072, 0.56955696, 0.00000000;\n (MS3_1, M_S20) 0.0000, 0.0002, 0.0030, 0.1393, 0.8575, 0.0000, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S20) 0.0000, 0.0000, 0.0003, 0.0188, 0.9804, 0.0005, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00150015, 0.93139314, 0.06710671, 0.00000000, 0.00000000, 0.00000000;\n (MS10_1, M_S20) 0.0001, 0.0001, 0.0002, 0.0013, 0.0150, 0.3152, 0.6681, 0.0000, 0.0000;\n (MS20_1, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00080016, 0.01110222, 0.28045609, 0.70754151, 0.00000000;\n (MS3_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0006, 0.8145, 0.1849, 0.0000, 0.0000, 0.0000;\n (MS3_9, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0494, 0.9506, 0.0000, 0.0000, 0.0000;\n (MS4_7, M_S14) 0.000, 0.000, 0.000, 0.000, 0.001, 0.999, 0.000, 0.000, 0.000;\n (MS10_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0001, 0.0017, 0.0786, 0.9196, 0.0000, 0.0000;\n (MS20_1, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0002, 0.0035, 0.1380, 0.8583, 0.0000;\n (MS3_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59735974, 0.39093909, 0.00000000, 0.00000000;\n (MS3_9, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00340034, 0.29902990, 0.69746975, 0.00000000, 0.00000000;\n (MS4_7, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0007, 0.1112, 0.8881, 0.0000, 0.0000;\n (MS10_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00940094, 0.95939594, 0.03110311, 0.00000000;\n (MS20_1, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.02530253, 0.97439744, 0.00000000;\n (MS3_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS3_9, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS4_7, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS10_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MS20_1, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ULN_LAT_WA | L_ULN_ALLDEL_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_ADM_VOL_ACT ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_ADM_FORCE | L_ADM_VOL_ACT, L_ADM_ALLAMP_WA ) {\n (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;\n (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;\n (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;\n (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;\n (NORMAL, A0_01) 0.00009999, 0.00429957, 0.05219478, 0.26687331, 0.43305669, 0.24347565;\n (REDUCED, A0_01) 0.0000, 0.0005, 0.0084, 0.0849, 0.3219, 0.5843;\n (V_RED, A0_01) 0.0000, 0.0000, 0.0007, 0.0167, 0.1576, 0.8250;\n (ABSENT, A0_01) 0.00000000, 0.00000000, 0.00000000, 0.00260026, 0.05860586, 0.93879388;\n (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;\n (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;\n (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;\n (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;\n (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;\n (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;\n (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;\n (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;\n (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;\n (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;\n (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;\n (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;\n (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;\n (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;\n (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;\n (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;\n (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;\n (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;\n (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;\n (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;\n (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;\n (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;\n (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;\n (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;\n}\nprobability ( L_ADM_MUSCLE_VOL | L_ADM_MUSIZE, L_ADM_MALOSS ) {\n (V_SMALL, NO) 0.9896, 0.0104;\n (SMALL, NO) 0.8137, 0.1863;\n (NORMAL, NO) 0.0209, 0.9791;\n (INCR, NO) 0.009, 0.991;\n (LARGE, NO) 0.003, 0.997;\n (V_LARGE, NO) 0.0004, 0.9996;\n (OTHER, NO) 0.4212, 0.5788;\n (V_SMALL, MILD) 0.9976, 0.0024;\n (SMALL, MILD) 0.9603, 0.0397;\n (NORMAL, MILD) 0.5185, 0.4815;\n (INCR, MILD) 0.1087, 0.8913;\n (LARGE, MILD) 0.0278, 0.9722;\n (V_LARGE, MILD) 0.0046, 0.9954;\n (OTHER, MILD) 0.5185, 0.4815;\n (V_SMALL, MOD) 0.999, 0.001;\n (SMALL, MOD) 0.9893, 0.0107;\n (NORMAL, MOD) 0.9588, 0.0412;\n (INCR, MOD) 0.6377, 0.3623;\n (LARGE, MOD) 0.2716, 0.7284;\n (V_LARGE, MOD) 0.0779, 0.9221;\n (OTHER, MOD) 0.6336, 0.3664;\n (V_SMALL, SEV) 0.9995, 0.0005;\n (SMALL, SEV) 0.9969, 0.0031;\n (NORMAL, SEV) 0.9953, 0.0047;\n (INCR, SEV) 0.9518, 0.0482;\n (LARGE, SEV) 0.8234, 0.1766;\n (V_LARGE, SEV) 0.5986, 0.4014;\n (OTHER, SEV) 0.7685, 0.2315;\n (V_SMALL, TOTAL) 0.9989, 0.0011;\n (SMALL, TOTAL) 0.9984, 0.0016;\n (NORMAL, TOTAL) 0.9984, 0.0016;\n (INCR, TOTAL) 0.9975, 0.0025;\n (LARGE, TOTAL) 0.9965, 0.0035;\n (V_LARGE, TOTAL) 0.9956, 0.0044;\n (OTHER, TOTAL) 0.9857, 0.0143;\n (V_SMALL, OTHER) 0.9363, 0.0637;\n (SMALL, OTHER) 0.8403, 0.1597;\n (NORMAL, OTHER) 0.6534, 0.3466;\n (INCR, OTHER) 0.4689, 0.5311;\n (LARGE, OTHER) 0.3174, 0.6826;\n (V_LARGE, OTHER) 0.1948, 0.8052;\n (OTHER, OTHER) 0.5681, 0.4319;\n}\nprobability ( L_ADM_MVA_RECRUIT | L_ADM_MULOSS, L_ADM_VOL_ACT ) {\n (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;\n (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;\n (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;\n (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;\n (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;\n (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;\n (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;\n (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;\n (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;\n (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;\n (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;\n (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;\n (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;\n (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;\n (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;\n (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;\n (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_MVA_AMP | L_ADM_EFFMUS ) {\n (V_SMALL) 0.00, 0.04, 0.96;\n (SMALL) 0.01, 0.15, 0.84;\n (NORMAL) 0.05, 0.90, 0.05;\n (INCR) 0.50, 0.49, 0.01;\n (LARGE) 0.85, 0.15, 0.00;\n (V_LARGE) 0.96, 0.04, 0.00;\n (OTHER) 0.33, 0.34, 0.33;\n}\nprobability ( L_ADM_TA_CONCL | L_ADM_EFFMUS ) {\n (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;\n (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;\n (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;\n (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;\n (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;\n (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;\n (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;\n}\nprobability ( L_ADM_MUPAMP | L_ADM_EFFMUS ) {\n (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;\n (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;\n (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;\n (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;\n}\nprobability ( L_ADM_QUAN_MUPAMP | L_ADM_MUPAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_ADM_QUAL_MUPAMP | L_ADM_MUPAMP ) {\n (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;\n (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;\n (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;\n (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;\n (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;\n (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;\n (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;\n}\nprobability ( L_ADM_MUPDUR | L_ADM_EFFMUS ) {\n (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;\n (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;\n (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;\n}\nprobability ( L_ADM_QUAN_MUPDUR | L_ADM_MUPDUR ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_ADM_QUAL_MUPDUR | L_ADM_MUPDUR ) {\n (V_SMALL) 0.8309, 0.1677, 0.0014;\n (SMALL) 0.49, 0.49, 0.02;\n (NORMAL) 0.1065, 0.7870, 0.1065;\n (INCR) 0.02, 0.49, 0.49;\n (LARGE) 0.0014, 0.1677, 0.8309;\n (V_LARGE) 0.0001, 0.0392, 0.9607;\n (OTHER) 0.2597, 0.4806, 0.2597;\n}\nprobability ( L_ADM_QUAN_MUPPOLY | L_ADM_DE_REGEN, L_ADM_EFFMUS ) {\n (NO, V_SMALL) 0.109, 0.548, 0.343;\n (YES, V_SMALL) 0.004, 0.122, 0.874;\n (NO, SMALL) 0.340, 0.564, 0.096;\n (YES, SMALL) 0.015, 0.261, 0.724;\n (NO, NORMAL) 0.925, 0.075, 0.000;\n (YES, NORMAL) 0.091, 0.526, 0.383;\n (NO, INCR) 0.796, 0.201, 0.003;\n (YES, INCR) 0.061, 0.465, 0.474;\n (NO, LARGE) 0.637, 0.348, 0.015;\n (YES, LARGE) 0.039, 0.396, 0.565;\n (NO, V_LARGE) 0.340, 0.564, 0.096;\n (YES, V_LARGE) 0.015, 0.261, 0.724;\n (NO, OTHER) 0.340, 0.564, 0.096;\n (YES, OTHER) 0.015, 0.261, 0.724;\n}\nprobability ( L_ADM_QUAL_MUPPOLY | L_ADM_QUAN_MUPPOLY ) {\n (x_12_) 0.95, 0.05;\n (x12_24_) 0.3, 0.7;\n (x_24_) 0.05, 0.95;\n}\nprobability ( L_ADM_MUPSATEL | L_ADM_DE_REGEN ) {\n (NO) 0.95, 0.05;\n (YES) 0.2, 0.8;\n}\nprobability ( L_ADM_MUPINSTAB | L_ADM_NMT ) {\n (NO) 0.95, 0.05;\n (MOD_PRE) 0.1, 0.9;\n (SEV_PRE) 0.03, 0.97;\n (MLD_POST) 0.2, 0.8;\n (MOD_POST) 0.1, 0.9;\n (SEV_POST) 0.03, 0.97;\n (MIXED) 0.1, 0.9;\n}\nprobability ( L_ADM_REPSTIM_CMAPAMP | L_ADM_ALLAMP_WA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_ADM_REPSTIM_DECR | L_ADM_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;\n (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MIXED) 0.245, 0.245, 0.245, 0.245, 0.020;\n}\nprobability ( L_ADM_REPSTIM_FACILI | L_ADM_NMT ) {\n (NO) 0.95, 0.02, 0.01, 0.02;\n (MOD_PRE) 0.010, 0.889, 0.100, 0.001;\n (SEV_PRE) 0.010, 0.080, 0.909, 0.001;\n (MLD_POST) 0.89, 0.08, 0.01, 0.02;\n (MOD_POST) 0.48, 0.50, 0.01, 0.01;\n (SEV_POST) 0.020, 0.949, 0.030, 0.001;\n (MIXED) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( L_ADM_REPSTIM_POST_DECR | L_ADM_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;\n (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;\n (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MIXED) 0.23, 0.23, 0.22, 0.22, 0.10;\n}\nprobability ( L_ADM_SF_JITTER | L_ADM_NMT ) {\n (NO) 0.95, 0.05, 0.00, 0.00;\n (MOD_PRE) 0.02, 0.20, 0.70, 0.08;\n (SEV_PRE) 0.0, 0.1, 0.4, 0.5;\n (MLD_POST) 0.05, 0.70, 0.20, 0.05;\n (MOD_POST) 0.01, 0.19, 0.70, 0.10;\n (SEV_POST) 0.0, 0.1, 0.4, 0.5;\n (MIXED) 0.1, 0.3, 0.3, 0.3;\n}\nprobability ( L_LNLC8_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_LNLC8_LP_ADM_MUDENS | L_LNLC8_ADM_MUDENS, L_LNLLP_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLE_ADM_MUDENS | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME, L_LNLE_ULN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( L_LNLC8_LP_E_ADM_MUDENS | L_LNLC8_LP_ADM_MUDENS, L_LNLE_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_ADM_MUDENS | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( L_LNLW_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ADM_MUDENS | L_DIFFN_ADM_MUDENS, L_LNLW_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_ADM_MUDENS | L_LNLC8_LP_E_ADM_MUDENS, L_DIFFN_LNLW_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_MYOP_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MYDY_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_ADM_MUDENS | L_MYOP_ADM_MUDENS, L_MYDY_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_MYAS_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_OTHER_ADM_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MYAS_OTHER_ADM_MUDENS | L_MYAS_ADM_MUDENS, L_OTHER_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_MUSCLE_ADM_MUDENS | L_MYOP_MYDY_ADM_MUDENS, L_MYAS_OTHER_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_MUDENS | L_LNL_DIFFN_ADM_MUDENS, L_MUSCLE_ADM_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_SF_DENSITY | L_ADM_MUDENS ) {\n (NORMAL) 0.97, 0.03, 0.00;\n (INCR) 0.05, 0.90, 0.05;\n (V_INCR) 0.01, 0.04, 0.95;\n}\nprobability ( L_LNLC8_ADM_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_ADM_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLC8_LP_ADM_NEUR_ACT | L_LNLC8_ADM_NEUR_ACT, L_LNLLP_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLE_ADM_NEUR_ACT | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLC8_LP_E_ADM_NEUR_ACT | L_LNLC8_LP_ADM_NEUR_ACT, L_LNLE_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_ADM_NEUR_ACT | DIFFN_M_SEV_DIST, DIFFN_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLW_ADM_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ADM_NEUR_ACT | L_DIFFN_ADM_NEUR_ACT, L_LNLW_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_ADM_NEUR_ACT | L_LNLC8_LP_E_ADM_NEUR_ACT, L_DIFFN_LNLW_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_ADM_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_ADM_NEUR_ACT | L_LNL_DIFFN_ADM_NEUR_ACT, L_OTHER_ADM_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_SPONT_NEUR_DISCH | L_ADM_NEUR_ACT ) {\n (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;\n (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;\n (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;\n (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;\n (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;\n}\nprobability ( L_MYOP_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYDY_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_ADM_DENERV | L_MYOP_ADM_DENERV, L_MYDY_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_NMT_ADM_DENERV | L_ADM_NMT ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE) 0.40, 0.45, 0.15, 0.00;\n (SEV_PRE) 0.15, 0.35, 0.35, 0.15;\n (MLD_POST) 0.85, 0.15, 0.00, 0.00;\n (MOD_POST) 0.30, 0.45, 0.20, 0.05;\n (SEV_POST) 0.15, 0.35, 0.35, 0.15;\n (MIXED) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( L_OTHER_NMT_ADM_DENERV | L_OTHER_ADM_DENERV, L_NMT_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MUSCLE_ADM_DENERV | L_MYOP_MYDY_ADM_DENERV, L_OTHER_NMT_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLC8_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLLP_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLC8_LP_ADM_DENERV | L_LNLC8_ADM_DENERV, L_LNLLP_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLE_ADM_DENERV | L_LNLE_ULN_SEV, L_LNLE_ULN_TIME, L_LNLE_ULN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( L_LNLC8_LP_E_ADM_DENERV | L_LNLC8_LP_ADM_DENERV, L_LNLE_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_ADM_DENERV | DIFFN_M_SEV_DIST, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( L_LNLW_ADM_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_LNLW_ADM_DENERV | L_DIFFN_ADM_DENERV, L_LNLW_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNL_DIFFN_ADM_DENERV | L_LNLC8_LP_E_ADM_DENERV, L_DIFFN_LNLW_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_DENERV | L_MUSCLE_ADM_DENERV, L_LNL_DIFFN_ADM_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_ADM_SPONT_DENERV_ACT | L_ADM_DENERV ) {\n (NO) 0.98, 0.02, 0.00, 0.00;\n (MILD) 0.07, 0.85, 0.08, 0.00;\n (MOD) 0.01, 0.07, 0.85, 0.07;\n (SEV) 0.00, 0.01, 0.07, 0.92;\n}\nprobability ( L_ADM_SPONT_HF_DISCH | L_ADM_DENERV ) {\n (NO) 0.99, 0.01;\n (MILD) 0.97, 0.03;\n (MOD) 0.95, 0.05;\n (SEV) 0.93, 0.07;\n}\nprobability ( L_ADM_SPONT_INS_ACT | L_ADM_DENERV ) {\n (NO) 0.98, 0.02;\n (MILD) 0.1, 0.9;\n (MOD) 0.05, 0.95;\n (SEV) 0.05, 0.95;\n}\nprobability ( L_OTHER_DELT_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLPC5_AXIL_SEV ) {\n table 0.98031378, 0.01069252, 0.00499650, 0.00299790, 0.00099930;\n}\nprobability ( L_LNLPC5_AXIL_TIME ) {\n table 0.05, 0.60, 0.30, 0.05;\n}\nprobability ( L_LNLPC5_AXIL_PATHO ) {\n table 0.600, 0.190, 0.200, 0.005, 0.005;\n}\nprobability ( L_LNLPC5_DELT_DE_REGEN | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME, L_LNLPC5_AXIL_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (TOTAL, SUBACUTE, DEMY) 1.0, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (TOTAL, CHRONIC, DEMY) 1.0, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (TOTAL, OLD, DEMY) 1.0, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (TOTAL, SUBACUTE, BLOCK) 1.0, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (TOTAL, CHRONIC, BLOCK) 1.0, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (TOTAL, OLD, BLOCK) 1.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (TOTAL, SUBACUTE, AXONAL) 1.0, 0.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (TOTAL, CHRONIC, AXONAL) 1.0, 0.0;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (TOTAL, OLD, AXONAL) 1.0, 0.0;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (TOTAL, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( L_DIFFN_DELT_DE_REGEN | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2;\n (MOD, SUBACUTE, DEMY) 0.2, 0.8;\n (SEV, SUBACUTE, DEMY) 0.4, 0.6;\n (NO, CHRONIC, DEMY) 1.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2;\n (MOD, CHRONIC, DEMY) 0.2, 0.8;\n (SEV, CHRONIC, DEMY) 0.4, 0.6;\n (NO, OLD, DEMY) 1.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0;\n (MOD, OLD, DEMY) 0.8, 0.2;\n (SEV, OLD, DEMY) 0.4, 0.6;\n (NO, ACUTE, BLOCK) 1.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2;\n (MOD, SUBACUTE, BLOCK) 0.2, 0.8;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.6;\n (NO, CHRONIC, BLOCK) 1.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2;\n (MOD, CHRONIC, BLOCK) 0.2, 0.8;\n (SEV, CHRONIC, BLOCK) 0.4, 0.6;\n (NO, OLD, BLOCK) 1.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0;\n (MOD, OLD, BLOCK) 0.8, 0.2;\n (SEV, OLD, BLOCK) 0.4, 0.6;\n (NO, ACUTE, AXONAL) 1.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.5, 0.5;\n (MOD, SUBACUTE, AXONAL) 0.2, 0.8;\n (SEV, SUBACUTE, AXONAL) 0.1, 0.9;\n (NO, CHRONIC, AXONAL) 1.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.5, 0.5;\n (MOD, CHRONIC, AXONAL) 0.2, 0.8;\n (SEV, CHRONIC, AXONAL) 0.1, 0.9;\n (NO, OLD, AXONAL) 1.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0;\n (MOD, OLD, AXONAL) 0.8, 0.2;\n (SEV, OLD, AXONAL) 0.4, 0.6;\n (NO, ACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, ACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, ACUTE, V_E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 1.0;\n (NO, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, V_E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, V_E_REIN) 0.0, 1.0;\n (NO, OLD, V_E_REIN) 0.0, 1.0;\n (MILD, OLD, V_E_REIN) 0.0, 1.0;\n (MOD, OLD, V_E_REIN) 0.0, 1.0;\n (SEV, OLD, V_E_REIN) 0.0, 1.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0;\n (NO, OLD, E_REIN) 0.0, 1.0;\n (MILD, OLD, E_REIN) 0.0, 1.0;\n (MOD, OLD, E_REIN) 0.0, 1.0;\n (SEV, OLD, E_REIN) 0.0, 1.0;\n}\nprobability ( L_LNLPC5_DIFFN_DELT_DE_REGEN | L_LNLPC5_DELT_DE_REGEN, L_DIFFN_DELT_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_MYOP_DELT_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_MYDY_DELT_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_DELT_DE_REGEN | L_MYOP_DELT_DE_REGEN, L_MYDY_DELT_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_OTHER_DELT_DE_REGEN ) {\n table 1.0, 0.0;\n}\nprobability ( L_MUSCLE_DELT_DE_REGEN | L_MYOP_MYDY_DELT_DE_REGEN, L_OTHER_DELT_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_DELT_DE_REGEN | L_LNLPC5_DIFFN_DELT_DE_REGEN, L_MUSCLE_DELT_DE_REGEN ) {\n (NO, NO) 1.0, 0.0;\n (YES, NO) 0.0, 1.0;\n (NO, YES) 0.0, 1.0;\n (YES, YES) 0.0, 1.0;\n}\nprobability ( L_DE_REGEN_DELT_NMT | L_DELT_DE_REGEN ) {\n (NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (YES) 0.949, 0.003, 0.001, 0.040, 0.003, 0.001, 0.003;\n}\nprobability ( L_MYAS_DELT_NMT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYAS_DE_REGEN_DELT_NMT | L_DE_REGEN_DELT_NMT, L_MYAS_DELT_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DELT_NMT | L_OTHER_DELT_NMT, L_MYAS_DE_REGEN_DELT_NMT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_POST, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_PRE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, MOD_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MOD_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV_PRE, SEV_PRE) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MLD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, SEV_PRE) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MLD_POST) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV_POST, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MLD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MOD_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, MOD_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV_POST, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MIXED, SEV_POST) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_PRE, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MLD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV_POST, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MIXED, MIXED) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_DELT_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYDY_DELT_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_DELT_MUSIZE ) {\n table 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_DELT_MUSIZE | L_MYDY_DELT_MUSIZE, L_MYOP_DELT_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MUSCLE_DELT_MUSIZE | L_OTHER_DELT_MUSIZE, L_MYOP_MYDY_DELT_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, V_SMALL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, V_SMALL) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (LARGE, V_SMALL) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (V_LARGE, V_SMALL) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (V_SMALL, SMALL) 0.9983, 0.0017, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SMALL) 0.8667, 0.1329, 0.0004, 0.0000, 0.0000, 0.0000;\n (NORMAL, SMALL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SMALL) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (LARGE, SMALL) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (V_LARGE, SMALL) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (V_SMALL, NORMAL) 0.9857, 0.0143, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, NORMAL) 0.01390139, 0.96369637, 0.02240224, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (V_LARGE, NORMAL) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (V_SMALL, INCR) 0.3673, 0.6298, 0.0029, 0.0000, 0.0000, 0.0000;\n (SMALL, INCR) 0.0003, 0.3514, 0.6035, 0.0443, 0.0005, 0.0000;\n (NORMAL, INCR) 0.00000000, 0.00000000, 0.04060406, 0.92779278, 0.03160316, 0.00000000;\n (INCR, INCR) 0.00000000, 0.00000000, 0.00039996, 0.10988901, 0.77982202, 0.10988901;\n (LARGE, INCR) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (V_LARGE, INCR) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_SMALL, LARGE) 0.01150115, 0.86168617, 0.12491249, 0.00190019, 0.00000000, 0.00000000;\n (SMALL, LARGE) 0.0000, 0.0105, 0.5726, 0.3806, 0.0359, 0.0004;\n (NORMAL, LARGE) 0.0000, 0.0000, 0.0000, 0.0319, 0.9362, 0.0319;\n (INCR, LARGE) 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.12341234, 0.87628763;\n (LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (V_LARGE, LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_SMALL, V_LARGE) 0.0000, 0.1596, 0.7368, 0.1016, 0.0020, 0.0000;\n (SMALL, V_LARGE) 0.0000, 0.0000, 0.0792, 0.4758, 0.4066, 0.0384;\n (NORMAL, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0329, 0.9671;\n (INCR, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0028, 0.9972;\n (LARGE, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.9999;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DIFFN_DELT_MUSIZE | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLPC5_DELT_MUSIZE | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME, L_LNLPC5_AXIL_PATHO ) {\n (NO, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, OLD, DEMY) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.0, 0.0, 0.9, 0.1, 0.0, 0.0;\n (MOD, OLD, DEMY) 0.0, 0.0, 0.2, 0.7, 0.1, 0.0;\n (SEV, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (TOTAL, OLD, DEMY) 0.0, 0.0, 0.0, 0.2, 0.7, 0.1;\n (NO, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, CHRONIC, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, CHRONIC, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, OLD, BLOCK) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.00, 0.00, 0.95, 0.05, 0.00, 0.00;\n (MOD, OLD, BLOCK) 0.00, 0.00, 0.70, 0.25, 0.05, 0.00;\n (SEV, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (TOTAL, OLD, BLOCK) 0.00, 0.00, 0.00, 0.25, 0.70, 0.05;\n (NO, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, OLD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.0, 0.0, 0.8, 0.2, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2, 0.0;\n (SEV, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (TOTAL, OLD, AXONAL) 0.0, 0.0, 0.0, 0.1, 0.6, 0.3;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD, E_REIN) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLPC5_DIFFN_DELT_MUSIZE | L_DIFFN_DELT_MUSIZE, L_LNLPC5_DELT_MUSIZE ) {\n (V_SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, V_SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (V_SMALL, SMALL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (INCR, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (LARGE, SMALL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (V_LARGE, SMALL) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (V_SMALL, NORMAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, NORMAL) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, NORMAL) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, NORMAL) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, INCR) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, INCR) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, INCR) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, LARGE) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NORMAL, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (INCR, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (V_LARGE, LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SMALL, V_LARGE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SMALL, V_LARGE) 0.00000000, 0.99939994, 0.00050005, 0.00010001, 0.00000000, 0.00000000;\n (NORMAL, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (INCR, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_LARGE, V_LARGE) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DELT_MUSIZE | L_MUSCLE_DELT_MUSIZE, L_LNLPC5_DIFFN_DELT_MUSIZE ) {\n (V_SMALL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (INCR, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (LARGE, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (V_LARGE, V_SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (V_SMALL, SMALL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (INCR, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (LARGE, SMALL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (V_LARGE, SMALL) 0.00000000, 0.97939794, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.02000200;\n (V_SMALL, NORMAL) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, NORMAL) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, NORMAL) 0.0000, 0.0000, 0.9781, 0.0019, 0.0000, 0.0000, 0.0200;\n (INCR, NORMAL) 0.0000, 0.0000, 0.0019, 0.9781, 0.0000, 0.0000, 0.0200;\n (LARGE, NORMAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (V_LARGE, NORMAL) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (V_SMALL, INCR) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, INCR) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, INCR) 0.0000, 0.0000, 0.0019, 0.9781, 0.0000, 0.0000, 0.0200;\n (INCR, INCR) 0.00, 0.00, 0.00, 0.98, 0.00, 0.00, 0.02;\n (LARGE, INCR) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (V_LARGE, INCR) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (V_SMALL, LARGE) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, LARGE) 0.00, 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (NORMAL, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (INCR, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (LARGE, LARGE) 0.00, 0.00, 0.00, 0.00, 0.98, 0.00, 0.02;\n (V_LARGE, LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (V_SMALL, V_LARGE) 0.98, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02;\n (SMALL, V_LARGE) 0.00000000, 0.97939794, 0.00050005, 0.00010001, 0.00000000, 0.00000000, 0.02000200;\n (NORMAL, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (INCR, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (LARGE, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (V_LARGE, V_LARGE) 0.00, 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( L_DELT_EFFMUS | L_DELT_NMT, L_DELT_MUSIZE ) {\n (NO, V_SMALL) 0.9683, 0.0117, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, V_SMALL) 0.9794, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, V_SMALL) 0.9742, 0.0058, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, V_SMALL) 0.9789, 0.0011, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, V_SMALL) 0.9799, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (OTHER, V_SMALL) 0.7927, 0.1721, 0.0135, 0.0016, 0.0001, 0.0000, 0.0200;\n (NO, SMALL) 0.0164, 0.9421, 0.0215, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_PRE, SMALL) 0.8182, 0.1616, 0.0002, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, SMALL) 0.9738, 0.0062, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, SMALL) 0.0329, 0.9359, 0.0112, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD_POST, SMALL) 0.3885, 0.5900, 0.0015, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV_POST, SMALL) 0.9362, 0.0438, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (OTHER, SMALL) 0.49295070, 0.37036296, 0.09059094, 0.02169783, 0.00389961, 0.00049995, 0.01999800;\n (NO, NORMAL) 0.00000000, 0.00000000, 0.97369737, 0.00630063, 0.00000000, 0.00000000, 0.02000200;\n (MOD_PRE, NORMAL) 0.00070007, 0.94039404, 0.03890389, 0.00000000, 0.00000000, 0.00000000, 0.02000200;\n (SEV_PRE, NORMAL) 0.7833, 0.1966, 0.0001, 0.0000, 0.0000, 0.0000, 0.0200;\n (MLD_POST, NORMAL) 0.00000000, 0.00009999, 0.97830217, 0.00159984, 0.00000000, 0.00000000, 0.01999800;\n (MOD_POST, NORMAL) 0.0000, 0.3781, 0.6016, 0.0003, 0.0000, 0.0000, 0.0200;\n (SEV_POST, NORMAL) 0.01149885, 0.96530347, 0.00319968, 0.00000000, 0.00000000, 0.00000000, 0.01999800;\n (OTHER, NORMAL) 0.15051505, 0.39773977, 0.27152715, 0.11721172, 0.03610361, 0.00690069, 0.02000200;\n (NO, INCR) 0.0000, 0.0000, 0.0082, 0.9646, 0.0072, 0.0000, 0.0200;\n (MOD_PRE, INCR) 0.0000, 0.0571, 0.8829, 0.0400, 0.0000, 0.0000, 0.0200;\n (SEV_PRE, INCR) 0.0541, 0.9055, 0.0203, 0.0001, 0.0000, 0.0000, 0.0200;\n (MLD_POST, INCR) 0.00000000, 0.00000000, 0.03260326, 0.94569457, 0.00170017, 0.00000000, 0.02000200;\n (MOD_POST, INCR) 0.0000, 0.0000, 0.7931, 0.1868, 0.0001, 0.0000, 0.0200;\n (SEV_POST, INCR) 0.0000, 0.4390, 0.5362, 0.0048, 0.0000, 0.0000, 0.0200;\n (OTHER, INCR) 0.0391, 0.2318, 0.3318, 0.2294, 0.1131, 0.0348, 0.0200;\n (NO, LARGE) 0.0000, 0.0000, 0.0000, 0.0072, 0.9656, 0.0072, 0.0200;\n (MOD_PRE, LARGE) 0.0000, 0.0001, 0.3198, 0.6276, 0.0325, 0.0000, 0.0200;\n (SEV_PRE, LARGE) 0.0004, 0.4664, 0.4912, 0.0219, 0.0001, 0.0000, 0.0200;\n (MLD_POST, LARGE) 0.0000, 0.0000, 0.0000, 0.0287, 0.9496, 0.0017, 0.0200;\n (MOD_POST, LARGE) 0.0000, 0.0000, 0.0071, 0.7665, 0.2063, 0.0001, 0.0200;\n (SEV_POST, LARGE) 0.00000000, 0.00150015, 0.70187019, 0.27382738, 0.00280028, 0.00000000, 0.02000200;\n (OTHER, LARGE) 0.0065, 0.0866, 0.2598, 0.2877, 0.2273, 0.1121, 0.0200;\n (NO, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0072, 0.9728, 0.0200;\n (MOD_PRE, V_LARGE) 0.0000, 0.0000, 0.0034, 0.2908, 0.6521, 0.0337, 0.0200;\n (SEV_PRE, V_LARGE) 0.00000000, 0.01269746, 0.62637473, 0.32423515, 0.01659668, 0.00009998, 0.01999600;\n (MLD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0000, 0.0287, 0.9513, 0.0200;\n (MOD_POST, V_LARGE) 0.0000, 0.0000, 0.0000, 0.0062, 0.7673, 0.2065, 0.0200;\n (SEV_POST, V_LARGE) 0.00000000, 0.00000000, 0.03839616, 0.64913509, 0.28947105, 0.00299970, 0.01999800;\n (OTHER, V_LARGE) 0.00079984, 0.02239552, 0.14087183, 0.24985003, 0.31623675, 0.24985003, 0.01999600;\n (NO, OTHER) 0.1111, 0.2284, 0.2388, 0.1875, 0.1354, 0.0788, 0.0200;\n (MOD_PRE, OTHER) 0.24267573, 0.30486951, 0.20687931, 0.12458754, 0.06949305, 0.03149685, 0.01999800;\n (SEV_PRE, OTHER) 0.4236, 0.3196, 0.1381, 0.0628, 0.0267, 0.0092, 0.0200;\n (MLD_POST, OTHER) 0.1227, 0.2392, 0.2382, 0.1813, 0.1270, 0.0716, 0.0200;\n (MOD_POST, OTHER) 0.17768223, 0.27767223, 0.22747725, 0.15348465, 0.09559044, 0.04809519, 0.01999800;\n (SEV_POST, OTHER) 0.2958, 0.3186, 0.1878, 0.1033, 0.0527, 0.0218, 0.0200;\n (OTHER, OTHER) 0.21972197, 0.26492649, 0.20142014, 0.14061406, 0.09640964, 0.05690569, 0.02000200;\n}\nprobability ( L_DIFFN_AXIL_BLOCK | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_OTHER_AXIL_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_AXIL_BLOCK_ED | L_DIFFN_AXIL_BLOCK, L_OTHER_AXIL_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLPC5_DELT_MALOSS | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.1, 0.9;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, BLOCK) 0.25, 0.25, 0.25, 0.25, 0.00;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_DIFFN_DELT_MALOSS | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.4, 0.6, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.4, 0.6, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.4, 0.6, 0.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.5, 0.5, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.8, 0.2;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_LNLPC5_DIFFN_DELT_MALOSS | L_LNLPC5_DELT_MALOSS, L_DIFFN_DELT_MALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MOD, NO) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (SEV, NO) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0022, 0.9977, 0.0001, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0282, 0.9718, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.00019998, 0.03689631, 0.95880412, 0.00409959, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.3409, 0.6582, 0.0000;\n (MOD, MOD) 0.00, 0.00, 0.01, 0.99, 0.00;\n (SEV, MOD) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0002, 0.0329, 0.9669, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0038, 0.9962, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0011, 0.9989, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0004, 0.9996, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_DELT_MALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DELT_MALOSS | L_LNLPC5_DIFFN_DELT_MALOSS, L_OTHER_DELT_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MOD, NO) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (SEV, NO) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.00219978, 0.97770223, 0.00009999, 0.00000000, 0.00000000, 0.01999800;\n (MILD, MILD) 0.0000, 0.0361, 0.9439, 0.0000, 0.0000, 0.0200;\n (MOD, MILD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (SEV, MILD) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.0002, 0.0471, 0.9297, 0.0030, 0.0000, 0.0200;\n (MILD, MOD) 0.0000, 0.0014, 0.3987, 0.5799, 0.0000, 0.0200;\n (MOD, MOD) 0.000, 0.000, 0.013, 0.967, 0.000, 0.020;\n (SEV, MOD) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.0000, 0.0003, 0.0424, 0.9373, 0.0000, 0.0200;\n (MILD, SEV) 0.000, 0.000, 0.005, 0.975, 0.000, 0.020;\n (MOD, SEV) 0.0000, 0.0000, 0.0014, 0.9786, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9795, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( L_DELT_MULOSS | L_AXIL_BLOCK_ED, L_DELT_MALOSS ) {\n (NO, NO) 0.98, 0.00, 0.00, 0.00, 0.00, 0.02;\n (MILD, NO) 0.9746, 0.0054, 0.0000, 0.0000, 0.0000, 0.0200;\n (MOD, NO) 0.0664, 0.9136, 0.0000, 0.0000, 0.0000, 0.0200;\n (SEV, NO) 0.0160, 0.1801, 0.7138, 0.0701, 0.0000, 0.0200;\n (TOTAL, NO) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MILD) 0.0167, 0.9613, 0.0020, 0.0000, 0.0000, 0.0200;\n (MILD, MILD) 0.00340034, 0.95299530, 0.02360236, 0.00000000, 0.00000000, 0.02000200;\n (MOD, MILD) 0.0002, 0.2725, 0.7073, 0.0000, 0.0000, 0.0200;\n (SEV, MILD) 0.0009, 0.0263, 0.4192, 0.5336, 0.0000, 0.0200;\n (TOTAL, MILD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, MOD) 0.00019998, 0.05349465, 0.92370763, 0.00259974, 0.00000000, 0.01999800;\n (MILD, MOD) 0.00000000, 0.02340234, 0.94509451, 0.01150115, 0.00000000, 0.02000200;\n (MOD, MOD) 0.0000, 0.0048, 0.7523, 0.2229, 0.0000, 0.0200;\n (SEV, MOD) 0.00000000, 0.00130013, 0.06370637, 0.91499150, 0.00000000, 0.02000200;\n (TOTAL, MOD) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, SEV) 0.00000000, 0.00030003, 0.04810481, 0.93159316, 0.00000000, 0.02000200;\n (MILD, SEV) 0.00000000, 0.00010001, 0.02700270, 0.95289529, 0.00000000, 0.02000200;\n (MOD, SEV) 0.0000, 0.0000, 0.0091, 0.9709, 0.0000, 0.0200;\n (SEV, SEV) 0.0000, 0.0001, 0.0087, 0.9712, 0.0000, 0.0200;\n (TOTAL, SEV) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MILD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (MOD, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (SEV, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (TOTAL, TOTAL) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n (NO, OTHER) 0.1427, 0.2958, 0.4254, 0.1161, 0.0000, 0.0200;\n (MILD, OTHER) 0.1157, 0.2677, 0.4444, 0.1522, 0.0000, 0.0200;\n (MOD, OTHER) 0.06939306, 0.20107989, 0.45265473, 0.25687431, 0.00000000, 0.01999800;\n (SEV, OTHER) 0.0173, 0.0696, 0.2854, 0.6077, 0.0000, 0.0200;\n (TOTAL, OTHER) 0.00, 0.00, 0.00, 0.00, 0.98, 0.02;\n}\nprobability ( L_DELT_ALLAMP | L_DELT_EFFMUS, L_DELT_MULOSS ) {\n (V_SMALL, NO) 0.00260026, 0.36873687, 0.60756076, 0.02080208, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (SMALL, NO) 0.0000, 0.0002, 0.4149, 0.4809, 0.0802, 0.0218, 0.0020, 0.0000, 0.0000;\n (NORMAL, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785, 0.0000, 0.0000, 0.0000;\n (INCR, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0018, 0.0536, 0.8696, 0.0750, 0.0000;\n (LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0736, 0.8528, 0.0736;\n (V_LARGE, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0794, 0.9205;\n (OTHER, NO) 0.0003, 0.0060, 0.1050, 0.2050, 0.1633, 0.1410, 0.1771, 0.1279, 0.0744;\n (V_SMALL, MILD) 0.0409, 0.8924, 0.0661, 0.0006, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MILD) 0.0000, 0.0100, 0.7700, 0.2049, 0.0128, 0.0022, 0.0001, 0.0000, 0.0000;\n (NORMAL, MILD) 0.0000, 0.0000, 0.0000, 0.2489, 0.7398, 0.0113, 0.0000, 0.0000, 0.0000;\n (INCR, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00420042, 0.34683468, 0.53485349, 0.11371137, 0.00040004, 0.00000000;\n (LARGE, MILD) 0.0000, 0.0000, 0.0000, 0.0000, 0.0064, 0.0855, 0.7880, 0.1197, 0.0004;\n (V_LARGE, MILD) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.11648835, 0.76672333, 0.11648835;\n (OTHER, MILD) 0.00190019, 0.02300230, 0.19001900, 0.26232623, 0.16291629, 0.12441244, 0.12641264, 0.07400740, 0.03500350;\n (V_SMALL, MOD) 0.2926, 0.7043, 0.0031, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, MOD) 0.0091, 0.4203, 0.5312, 0.0380, 0.0012, 0.0002, 0.0000, 0.0000, 0.0000;\n (NORMAL, MOD) 0.00000000, 0.00000000, 0.30946905, 0.68073193, 0.00949905, 0.00029997, 0.00000000, 0.00000000, 0.00000000;\n (INCR, MOD) 0.0000, 0.0000, 0.0180, 0.6298, 0.2744, 0.0746, 0.0032, 0.0000, 0.0000;\n (LARGE, MOD) 0.00000000, 0.00000000, 0.00010001, 0.10461046, 0.42814281, 0.35683568, 0.10711071, 0.00320032, 0.00000000;\n (V_LARGE, MOD) 0.00000000, 0.00000000, 0.00000000, 0.00250025, 0.09780978, 0.24982498, 0.53235324, 0.11411141, 0.00340034;\n (OTHER, MOD) 0.01440144, 0.09930993, 0.30283028, 0.27022702, 0.12431243, 0.08210821, 0.06520652, 0.03020302, 0.01140114;\n (V_SMALL, SEV) 0.7810, 0.2189, 0.0001, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, SEV) 0.26692669, 0.71617162, 0.01660166, 0.00030003, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (NORMAL, SEV) 0.00009999, 0.10278972, 0.87921208, 0.01779822, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (INCR, SEV) 0.00000000, 0.00440044, 0.81118112, 0.17621762, 0.00730073, 0.00090009, 0.00000000, 0.00000000, 0.00000000;\n (LARGE, SEV) 0.00000000, 0.00009999, 0.41295870, 0.49655034, 0.07189281, 0.01729827, 0.00119988, 0.00000000, 0.00000000;\n (V_LARGE, SEV) 0.00000000, 0.00000000, 0.07810781, 0.51965197, 0.26102610, 0.11671167, 0.02340234, 0.00110011, 0.00000000;\n (OTHER, SEV) 0.1169, 0.3697, 0.2867, 0.1411, 0.0431, 0.0234, 0.0133, 0.0045, 0.0013;\n (V_SMALL, TOTAL) 0.9907, 0.0093, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SMALL, TOTAL) 0.9858, 0.0142, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (NORMAL, TOTAL) 0.9865, 0.0135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (INCR, TOTAL) 0.982, 0.018, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (LARGE, TOTAL) 0.9779, 0.0221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (V_LARGE, TOTAL) 0.973, 0.027, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000;\n (OTHER, TOTAL) 0.93700630, 0.06289371, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (V_SMALL, OTHER) 0.35956404, 0.51474853, 0.09409059, 0.02399760, 0.00459954, 0.00199980, 0.00079992, 0.00019998, 0.00000000;\n (SMALL, OTHER) 0.13358664, 0.38546145, 0.26977302, 0.13078692, 0.04009599, 0.02189781, 0.01269873, 0.00439956, 0.00129987;\n (NORMAL, OTHER) 0.0096, 0.0788, 0.2992, 0.2816, 0.1319, 0.0873, 0.0689, 0.0313, 0.0114;\n (INCR, OTHER) 0.00260052, 0.02890578, 0.20404081, 0.26575315, 0.15943189, 0.11992398, 0.11902380, 0.06841368, 0.03190638;\n (LARGE, OTHER) 0.00049995, 0.00839916, 0.11818818, 0.21387861, 0.16298370, 0.13818618, 0.16908309, 0.11988801, 0.06889311;\n (V_LARGE, OTHER) 0.0001, 0.0021, 0.0586, 0.1473, 0.1427, 0.1363, 0.2057, 0.1798, 0.1274;\n (OTHER, OTHER) 0.05210521, 0.16081608, 0.22722272, 0.20132013, 0.10661066, 0.07920792, 0.08310831, 0.05590559, 0.03370337;\n}\nprobability ( L_AXIL_AMP_E | L_DELT_ALLAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_AXIL_RD_ED | L_LNLPC5_AXIL_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 1.0, 0.0;\n}\nprobability ( L_LNLPC5_AXIL_DIFSLOW | L_LNLPC5_AXIL_PATHO ) {\n (DEMY) 1.0, 0.0, 0.0, 0.0;\n (BLOCK) 1.0, 0.0, 0.0, 0.0;\n (AXONAL) 1.0, 0.0, 0.0, 0.0;\n (V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (E_REIN) 0.0, 0.0, 1.0, 0.0;\n}\nprobability ( L_DIFFN_AXIL_DIFSLOW | DIFFN_M_SEV_PROX, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.2, 0.6, 0.2;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( L_AXIL_DIFSLOW_ED | L_LNLPC5_AXIL_DIFSLOW, L_DIFFN_AXIL_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0003, 0.0252, 0.9745;\n (NO, MILD) 0.01319868, 0.98620138, 0.00059994, 0.00000000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0009, 0.5880, 0.4111;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0009, 0.5880, 0.4111;\n (MOD, MOD) 0.000, 0.000, 0.002, 0.998;\n (SEV, MOD) 0.0000, 0.0000, 0.0006, 0.9994;\n (NO, SEV) 0.0000, 0.0003, 0.0252, 0.9745;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00440044, 0.99559956;\n (MOD, SEV) 0.0000, 0.0000, 0.0006, 0.9994;\n (SEV, SEV) 0.0000, 0.0000, 0.0005, 0.9995;\n}\nprobability ( L_AXIL_DCV | L_DELT_MALOSS, L_AXIL_DIFSLOW_ED ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.1136, 0.8864, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0006, 0.0764, 0.8866, 0.0364, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.0655, 0.9299, 0.0046, 0.0000, 0.0000, 0.0000, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, NO) 0.1523, 0.2904, 0.3678, 0.1726, 0.0169, 0.0000, 0.0000, 0.0000, 0.0000;\n (NO, MILD) 0.0080, 0.1680, 0.7407, 0.0833, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.00089991, 0.03679632, 0.55764424, 0.40245975, 0.00219978, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, MILD) 0.00000000, 0.00070007, 0.05250525, 0.57125713, 0.37523752, 0.00030003, 0.00000000, 0.00000000, 0.00000000;\n (SEV, MILD) 0.0000, 0.0000, 0.0007, 0.0745, 0.8859, 0.0389, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MILD) 0.0168, 0.0618, 0.2223, 0.4015, 0.2803, 0.0172, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.00069986, 0.00589882, 0.06148770, 0.30063987, 0.55258949, 0.07818436, 0.00049990, 0.00000000, 0.00000000;\n (MILD, MOD) 0.0002, 0.0018, 0.0263, 0.1887, 0.5884, 0.1916, 0.0030, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0001, 0.0024, 0.0358, 0.3160, 0.5741, 0.0716, 0.0000, 0.0000;\n (SEV, MOD) 0.00000000, 0.00000000, 0.00009999, 0.00319968, 0.07809219, 0.59464054, 0.32356764, 0.00039996, 0.00000000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, MOD) 0.00099990, 0.00469953, 0.02799720, 0.11838816, 0.36466353, 0.39226077, 0.09069093, 0.00029997, 0.00000000;\n (NO, SEV) 0.0001, 0.0003, 0.0018, 0.0110, 0.0670, 0.2945, 0.5047, 0.1206, 0.0000;\n (MILD, SEV) 0.00000000, 0.00010001, 0.00090009, 0.00590059, 0.04220422, 0.23562356, 0.52255226, 0.19271927, 0.00000000;\n (MOD, SEV) 0.0000, 0.0000, 0.0001, 0.0012, 0.0121, 0.1131, 0.4415, 0.4320, 0.0000;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00280028, 0.04390439, 0.29172917, 0.66136614, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, SEV) 0.0000, 0.0002, 0.0011, 0.0057, 0.0332, 0.1704, 0.4424, 0.3470, 0.0000;\n}\nprobability ( L_AXIL_DEL | L_AXIL_RD_ED, L_AXIL_DCV ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, M_S60) 0.0002, 0.0003, 0.0006, 0.0027, 0.0210, 0.2666, 0.7086, 0.0000, 0.0000;\n (SEV, M_S60) 0.0000, 0.0001, 0.0001, 0.0004, 0.0021, 0.0232, 0.4307, 0.5434, 0.0000;\n (NO, M_S52) 0.6514, 0.3486, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.00010002, 0.00020004, 0.00050010, 0.00220044, 0.01780356, 0.24224845, 0.73694739, 0.00000000, 0.00000000;\n (SEV, M_S52) 0.00000000, 0.00000000, 0.00010001, 0.00030003, 0.00180018, 0.02100210, 0.40864086, 0.56815682, 0.00000000;\n (NO, M_S44) 0.0003, 0.9337, 0.0660, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S44) 0.0001, 0.0002, 0.0004, 0.0016, 0.0137, 0.2066, 0.7774, 0.0000, 0.0000;\n (SEV, M_S44) 0.0000, 0.0000, 0.0001, 0.0003, 0.0015, 0.0178, 0.3739, 0.6064, 0.0000;\n (NO, M_S36) 0.00000000, 0.00380038, 0.99619962, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S36) 0.0001, 0.0001, 0.0002, 0.0011, 0.0095, 0.1650, 0.8240, 0.0000, 0.0000;\n (SEV, M_S36) 0.00000000, 0.00000000, 0.00009999, 0.00019998, 0.00109989, 0.01419858, 0.32926707, 0.65513449, 0.00000000;\n (NO, M_S28) 0.00000000, 0.00000000, 0.01060106, 0.98939894, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S28) 0.00000000, 0.00000000, 0.00010001, 0.00050005, 0.00540054, 0.11491149, 0.87908791, 0.00000000, 0.00000000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010002, 0.00070014, 0.00980196, 0.26615323, 0.72324465, 0.00000000;\n (NO, M_S20) 0.00000000, 0.00020002, 0.00260026, 0.13561356, 0.86158616, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0020, 0.0580, 0.9396, 0.0002, 0.0000;\n (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00480048, 0.16971697, 0.82518252, 0.00000000;\n (NO, M_S14) 0.0000, 0.0000, 0.0000, 0.0005, 0.8212, 0.1783, 0.0000, 0.0000, 0.0000;\n (MOD, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0004, 0.0184, 0.9779, 0.0033, 0.0000;\n (SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0014, 0.0771, 0.9214, 0.0000;\n (NO, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00040004, 0.01130113, 0.59805981, 0.39023902, 0.00000000, 0.00000000;\n (MOD, M_S08) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0021, 0.4010, 0.5969, 0.0000;\n (SEV, M_S08) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.01440144, 0.98549855, 0.00000000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_AXIL_LAT_ED | L_AXIL_DEL ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_DELT_VOL_ACT ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DELT_FORCE | L_DELT_VOL_ACT, L_DELT_ALLAMP ) {\n (NORMAL, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00409959, 0.19078092, 0.80511949;\n (REDUCED, ZERO) 0.0000, 0.0000, 0.0000, 0.0010, 0.0578, 0.9412;\n (V_RED, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00009999, 0.01259874, 0.98730127;\n (ABSENT, ZERO) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00590059, 0.99409941;\n (NORMAL, A0_01) 0.00009999, 0.00429957, 0.05219478, 0.26687331, 0.43305669, 0.24347565;\n (REDUCED, A0_01) 0.0000, 0.0005, 0.0084, 0.0849, 0.3219, 0.5843;\n (V_RED, A0_01) 0.0000, 0.0000, 0.0007, 0.0167, 0.1576, 0.8250;\n (ABSENT, A0_01) 0.00000000, 0.00000000, 0.00000000, 0.00260026, 0.05860586, 0.93879388;\n (NORMAL, A0_10) 0.01490149, 0.23542354, 0.53315332, 0.20152015, 0.01490149, 0.00010001;\n (REDUCED, A0_10) 0.0009, 0.0256, 0.1800, 0.4308, 0.3036, 0.0591;\n (V_RED, A0_10) 0.0000, 0.0005, 0.0128, 0.1328, 0.4061, 0.4478;\n (ABSENT, A0_10) 0.0000, 0.0000, 0.0003, 0.0091, 0.1181, 0.8725;\n (NORMAL, A0_30) 0.1538, 0.6493, 0.1936, 0.0033, 0.0000, 0.0000;\n (REDUCED, A0_30) 0.0098, 0.1589, 0.4714, 0.3101, 0.0485, 0.0013;\n (V_RED, A0_30) 0.0001, 0.0049, 0.0751, 0.3632, 0.4290, 0.1277;\n (ABSENT, A0_30) 0.0000, 0.0000, 0.0008, 0.0215, 0.1873, 0.7904;\n (NORMAL, A0_70) 0.6667, 0.3291, 0.0042, 0.0000, 0.0000, 0.0000;\n (REDUCED, A0_70) 0.05370537, 0.42044204, 0.45484548, 0.06900690, 0.00200020, 0.00000000;\n (V_RED, A0_70) 0.00050005, 0.02730273, 0.24332433, 0.50135014, 0.21182118, 0.01570157;\n (ABSENT, A0_70) 0.00000000, 0.00009999, 0.00249975, 0.04719528, 0.27557244, 0.67463254;\n (NORMAL, A1_00) 0.94689469, 0.05310531, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (REDUCED, A1_00) 0.11731173, 0.56585659, 0.30103010, 0.01570157, 0.00010001, 0.00000000;\n (V_RED, A1_00) 0.00120012, 0.06020602, 0.38623862, 0.45424542, 0.09540954, 0.00270027;\n (ABSENT, A1_00) 0.0000, 0.0001, 0.0044, 0.0713, 0.3326, 0.5916;\n (NORMAL, A2_00) 0.9782, 0.0218, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A2_00) 0.41015898, 0.47935206, 0.10698930, 0.00349965, 0.00000000, 0.00000000;\n (V_RED, A2_00) 0.02560256, 0.24702470, 0.48384838, 0.21742174, 0.02560256, 0.00050005;\n (ABSENT, A2_00) 0.00000000, 0.00200020, 0.02790279, 0.18121812, 0.41124112, 0.37763776;\n (NORMAL, A4_00) 0.9971, 0.0029, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A4_00) 0.7017, 0.2755, 0.0226, 0.0002, 0.0000, 0.0000;\n (V_RED, A4_00) 0.1171, 0.4443, 0.3699, 0.0654, 0.0033, 0.0000;\n (ABSENT, A4_00) 0.0004, 0.0107, 0.0847, 0.3035, 0.3988, 0.2019;\n (NORMAL, A8_00) 0.9996, 0.0004, 0.0000, 0.0000, 0.0000, 0.0000;\n (REDUCED, A8_00) 0.8804, 0.1161, 0.0035, 0.0000, 0.0000, 0.0000;\n (V_RED, A8_00) 0.32706729, 0.48795120, 0.17268273, 0.01199880, 0.00029997, 0.00000000;\n (ABSENT, A8_00) 0.00300030, 0.04260426, 0.19481948, 0.38493849, 0.29292929, 0.08170817;\n}\nprobability ( L_DELT_MUSCLE_VOL | L_DELT_MUSIZE, L_DELT_MALOSS ) {\n (V_SMALL, NO) 0.9896, 0.0104;\n (SMALL, NO) 0.8137, 0.1863;\n (NORMAL, NO) 0.0209, 0.9791;\n (INCR, NO) 0.0209, 0.9791;\n (LARGE, NO) 0.003, 0.997;\n (V_LARGE, NO) 0.0004, 0.9996;\n (OTHER, NO) 0.4212, 0.5788;\n (V_SMALL, MILD) 0.9976, 0.0024;\n (SMALL, MILD) 0.9603, 0.0397;\n (NORMAL, MILD) 0.5185, 0.4815;\n (INCR, MILD) 0.1492, 0.8508;\n (LARGE, MILD) 0.0278, 0.9722;\n (V_LARGE, MILD) 0.0046, 0.9954;\n (OTHER, MILD) 0.5185, 0.4815;\n (V_SMALL, MOD) 0.999, 0.001;\n (SMALL, MOD) 0.9893, 0.0107;\n (NORMAL, MOD) 0.9588, 0.0412;\n (INCR, MOD) 0.6221, 0.3779;\n (LARGE, MOD) 0.2716, 0.7284;\n (V_LARGE, MOD) 0.0779, 0.9221;\n (OTHER, MOD) 0.6336, 0.3664;\n (V_SMALL, SEV) 0.9995, 0.0005;\n (SMALL, SEV) 0.9969, 0.0031;\n (NORMAL, SEV) 0.9953, 0.0047;\n (INCR, SEV) 0.9358, 0.0642;\n (LARGE, SEV) 0.8234, 0.1766;\n (V_LARGE, SEV) 0.5986, 0.4014;\n (OTHER, SEV) 0.7685, 0.2315;\n (V_SMALL, TOTAL) 0.9989, 0.0011;\n (SMALL, TOTAL) 0.9984, 0.0016;\n (NORMAL, TOTAL) 0.9984, 0.0016;\n (INCR, TOTAL) 0.9972, 0.0028;\n (LARGE, TOTAL) 0.9965, 0.0035;\n (V_LARGE, TOTAL) 0.9956, 0.0044;\n (OTHER, TOTAL) 0.9857, 0.0143;\n (V_SMALL, OTHER) 0.9363, 0.0637;\n (SMALL, OTHER) 0.8403, 0.1597;\n (NORMAL, OTHER) 0.6534, 0.3466;\n (INCR, OTHER) 0.4719, 0.5281;\n (LARGE, OTHER) 0.3174, 0.6826;\n (V_LARGE, OTHER) 0.1948, 0.8052;\n (OTHER, OTHER) 0.5681, 0.4319;\n}\nprobability ( L_DELT_MVA_RECRUIT | L_DELT_MULOSS, L_DELT_VOL_ACT ) {\n (NO, NORMAL) 0.9295, 0.0705, 0.0000, 0.0000;\n (MILD, NORMAL) 0.4821, 0.5165, 0.0014, 0.0000;\n (MOD, NORMAL) 0.0661, 0.7993, 0.1346, 0.0000;\n (SEV, NORMAL) 0.00149985, 0.13658634, 0.86191381, 0.00000000;\n (TOTAL, NORMAL) 0.0, 0.0, 0.0, 1.0;\n (OTHER, NORMAL) 0.2639736, 0.4343566, 0.3016698, 0.0000000;\n (NO, REDUCED) 0.1707, 0.7000, 0.1293, 0.0000;\n (MILD, REDUCED) 0.0366, 0.5168, 0.4466, 0.0000;\n (MOD, REDUCED) 0.0043, 0.1788, 0.8169, 0.0000;\n (SEV, REDUCED) 0.00029997, 0.03479652, 0.96490351, 0.00000000;\n (TOTAL, REDUCED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, REDUCED) 0.1146, 0.3465, 0.5389, 0.0000;\n (NO, V_RED) 0.0038, 0.1740, 0.8222, 0.0000;\n (MILD, V_RED) 0.0005, 0.0594, 0.9401, 0.0000;\n (MOD, V_RED) 0.0001, 0.0205, 0.9794, 0.0000;\n (SEV, V_RED) 0.0000, 0.0061, 0.9939, 0.0000;\n (TOTAL, V_RED) 0.0, 0.0, 0.0, 1.0;\n (OTHER, V_RED) 0.0360, 0.2144, 0.7496, 0.0000;\n (NO, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MILD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (MOD, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (SEV, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (TOTAL, ABSENT) 0.0, 0.0, 0.0, 1.0;\n (OTHER, ABSENT) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DELT_MVA_AMP | L_DELT_EFFMUS ) {\n (V_SMALL) 0.00, 0.04, 0.96;\n (SMALL) 0.01, 0.15, 0.84;\n (NORMAL) 0.05, 0.90, 0.05;\n (INCR) 0.50, 0.49, 0.01;\n (LARGE) 0.85, 0.15, 0.00;\n (V_LARGE) 0.96, 0.04, 0.00;\n (OTHER) 0.33, 0.34, 0.33;\n}\nprobability ( L_DELT_TA_CONCL | L_DELT_EFFMUS ) {\n (V_SMALL) 0.000, 0.000, 0.005, 0.045, 0.950;\n (SMALL) 0.00, 0.00, 0.05, 0.90, 0.05;\n (NORMAL) 0.00, 0.03, 0.94, 0.03, 0.00;\n (INCR) 0.195, 0.600, 0.200, 0.005, 0.000;\n (LARGE) 0.48, 0.50, 0.02, 0.00, 0.00;\n (V_LARGE) 0.800, 0.195, 0.005, 0.000, 0.000;\n (OTHER) 0.2, 0.2, 0.2, 0.2, 0.2;\n}\nprobability ( L_DELT_MUPAMP | L_DELT_EFFMUS ) {\n (V_SMALL) 0.782, 0.195, 0.003, 0.000, 0.000, 0.000, 0.020;\n (SMALL) 0.10431043, 0.77107711, 0.10431043, 0.00030003, 0.00000000, 0.00000000, 0.02000200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.00000000, 0.00029997, 0.10108989, 0.74712529, 0.13148685, 0.00000000, 0.01999800;\n (LARGE) 0.0000, 0.0000, 0.0024, 0.1528, 0.7968, 0.0280, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0028, 0.0968, 0.8804, 0.0200;\n (OTHER) 0.1328, 0.1932, 0.2189, 0.1932, 0.1726, 0.0693, 0.0200;\n}\nprobability ( L_DELT_QUAN_MUPAMP | L_DELT_MUPAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_DELT_QUAL_MUPAMP | L_DELT_MUPAMP ) {\n (V_SMALL) 0.4289, 0.5209, 0.0499, 0.0003, 0.0000;\n (SMALL) 0.0647, 0.5494, 0.3679, 0.0180, 0.0000;\n (NORMAL) 0.00000000, 0.04790479, 0.87538754, 0.07670767, 0.00000000;\n (INCR) 0.00000000, 0.00869913, 0.28377162, 0.67793221, 0.02959704;\n (LARGE) 0.0000, 0.0002, 0.0376, 0.6283, 0.3339;\n (V_LARGE) 0.0000, 0.0000, 0.0010, 0.0788, 0.9202;\n (OTHER) 0.0960, 0.1884, 0.2830, 0.3014, 0.1312;\n}\nprobability ( L_DELT_MUPDUR | L_DELT_EFFMUS ) {\n (V_SMALL) 0.9388, 0.0412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0200;\n (SMALL) 0.0396, 0.9008, 0.0396, 0.0000, 0.0000, 0.0000, 0.0200;\n (NORMAL) 0.00, 0.00, 0.98, 0.00, 0.00, 0.00, 0.02;\n (INCR) 0.0000, 0.0000, 0.0396, 0.9008, 0.0396, 0.0000, 0.0200;\n (LARGE) 0.0000, 0.0000, 0.0000, 0.0412, 0.9380, 0.0008, 0.0200;\n (V_LARGE) 0.0000, 0.0000, 0.0000, 0.0039, 0.2546, 0.7215, 0.0200;\n (OTHER) 0.0900, 0.2350, 0.3236, 0.2350, 0.0900, 0.0064, 0.0200;\n}\nprobability ( L_DELT_QUAN_MUPDUR | L_DELT_MUPDUR ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_DELT_QUAL_MUPDUR | L_DELT_MUPDUR ) {\n (V_SMALL) 0.8309, 0.1677, 0.0014;\n (SMALL) 0.49, 0.49, 0.02;\n (NORMAL) 0.1065, 0.7870, 0.1065;\n (INCR) 0.02, 0.49, 0.49;\n (LARGE) 0.0014, 0.1677, 0.8309;\n (V_LARGE) 0.0001, 0.0392, 0.9607;\n (OTHER) 0.2597, 0.4806, 0.2597;\n}\nprobability ( L_DELT_QUAN_MUPPOLY | L_DELT_DE_REGEN, L_DELT_EFFMUS ) {\n (NO, V_SMALL) 0.109, 0.548, 0.343;\n (YES, V_SMALL) 0.004, 0.122, 0.874;\n (NO, SMALL) 0.340, 0.564, 0.096;\n (YES, SMALL) 0.015, 0.261, 0.724;\n (NO, NORMAL) 0.925, 0.075, 0.000;\n (YES, NORMAL) 0.091, 0.526, 0.383;\n (NO, INCR) 0.796, 0.201, 0.003;\n (YES, INCR) 0.061, 0.465, 0.474;\n (NO, LARGE) 0.637, 0.348, 0.015;\n (YES, LARGE) 0.039, 0.396, 0.565;\n (NO, V_LARGE) 0.340, 0.564, 0.096;\n (YES, V_LARGE) 0.015, 0.261, 0.724;\n (NO, OTHER) 0.340, 0.564, 0.096;\n (YES, OTHER) 0.015, 0.261, 0.724;\n}\nprobability ( L_DELT_QUAL_MUPPOLY | L_DELT_QUAN_MUPPOLY ) {\n (x_12_) 0.95, 0.05;\n (x12_24_) 0.3, 0.7;\n (x_24_) 0.05, 0.95;\n}\nprobability ( L_DELT_MUPSATEL | L_DELT_DE_REGEN ) {\n (NO) 0.95, 0.05;\n (YES) 0.2, 0.8;\n}\nprobability ( L_DELT_MUPINSTAB | L_DELT_NMT ) {\n (NO) 0.95, 0.05;\n (MOD_PRE) 0.1, 0.9;\n (SEV_PRE) 0.03, 0.97;\n (MLD_POST) 0.2, 0.8;\n (MOD_POST) 0.1, 0.9;\n (SEV_POST) 0.03, 0.97;\n (OTHER) 0.1, 0.9;\n}\nprobability ( L_DELT_REPSTIM_CMAPAMP | L_DELT_ALLAMP ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_DELT_REPSTIM_DECR | L_DELT_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.04, 0.20, 0.70, 0.04, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.040, 0.929, 0.020;\n (MLD_POST) 0.35, 0.57, 0.05, 0.01, 0.02;\n (MOD_POST) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_POST) 0.001, 0.010, 0.040, 0.929, 0.020;\n (OTHER) 0.245, 0.245, 0.245, 0.245, 0.020;\n}\nprobability ( L_DELT_REPSTIM_FACILI | L_DELT_NMT ) {\n (NO) 0.95, 0.02, 0.01, 0.02;\n (MOD_PRE) 0.010, 0.889, 0.100, 0.001;\n (SEV_PRE) 0.010, 0.080, 0.909, 0.001;\n (MLD_POST) 0.89, 0.08, 0.01, 0.02;\n (MOD_POST) 0.48, 0.50, 0.01, 0.01;\n (SEV_POST) 0.020, 0.949, 0.030, 0.001;\n (OTHER) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( L_DELT_REPSTIM_POST_DECR | L_DELT_NMT ) {\n (NO) 0.949, 0.020, 0.010, 0.001, 0.020;\n (MOD_PRE) 0.02, 0.10, 0.80, 0.06, 0.02;\n (SEV_PRE) 0.001, 0.010, 0.020, 0.949, 0.020;\n (MLD_POST) 0.25, 0.61, 0.10, 0.02, 0.02;\n (MOD_POST) 0.01, 0.10, 0.80, 0.07, 0.02;\n (SEV_POST) 0.001, 0.010, 0.020, 0.949, 0.020;\n (OTHER) 0.23, 0.23, 0.22, 0.22, 0.10;\n}\nprobability ( L_DELT_SF_JITTER | L_DELT_NMT ) {\n (NO) 0.95, 0.05, 0.00, 0.00;\n (MOD_PRE) 0.02, 0.20, 0.70, 0.08;\n (SEV_PRE) 0.0, 0.1, 0.4, 0.5;\n (MLD_POST) 0.05, 0.70, 0.20, 0.05;\n (MOD_POST) 0.01, 0.19, 0.70, 0.10;\n (SEV_POST) 0.0, 0.1, 0.4, 0.5;\n (OTHER) 0.1, 0.3, 0.3, 0.3;\n}\nprobability ( L_LNLPC5_DELT_MUDENS | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME, L_LNLPC5_AXIL_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (TOTAL, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (TOTAL, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (TOTAL, SUBACUTE, AXONAL) 0.3, 0.6, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (TOTAL, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (TOTAL, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (TOTAL, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n (TOTAL, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( L_DIFFN_DELT_MUDENS | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, DEMY) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.6, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, DEMY) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, DEMY) 0.6, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 0.8, 0.2, 0.0;\n (MOD, OLD, DEMY) 0.7, 0.3, 0.0;\n (SEV, OLD, DEMY) 0.6, 0.4, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.6, 0.4, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.8, 0.2, 0.0;\n (MOD, CHRONIC, BLOCK) 0.7, 0.3, 0.0;\n (SEV, CHRONIC, BLOCK) 0.6, 0.4, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 0.8, 0.2, 0.0;\n (MOD, OLD, BLOCK) 0.7, 0.3, 0.0;\n (SEV, OLD, BLOCK) 0.6, 0.4, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.6, 0.4, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.5, 0.5, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.5, 0.4, 0.1;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.7, 0.3, 0.0;\n (MOD, CHRONIC, AXONAL) 0.1, 0.6, 0.3;\n (SEV, CHRONIC, AXONAL) 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 0.5, 0.5, 0.0;\n (MOD, OLD, AXONAL) 0.15, 0.70, 0.15;\n (SEV, OLD, AXONAL) 0.0, 0.5, 0.5;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, SUBACUTE, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, CHRONIC, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (MOD, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (SEV, OLD, V_E_REIN) 0.05, 0.50, 0.45;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MOD, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (SEV, ACUTE, E_REIN) 1.0, 0.0, 0.0;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (MOD, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (SEV, SUBACUTE, E_REIN) 0.2, 0.5, 0.3;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (MOD, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (SEV, CHRONIC, E_REIN) 0.2, 0.5, 0.3;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (MOD, OLD, E_REIN) 0.2, 0.5, 0.3;\n (SEV, OLD, E_REIN) 0.2, 0.5, 0.3;\n}\nprobability ( L_LNLPC5_DIFFN_DELT_MUDENS | L_LNLPC5_DELT_MUDENS, L_DIFFN_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_MYOP_DELT_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MYDY_DELT_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_DELT_MUDENS | L_MYOP_DELT_MUDENS, L_MYDY_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_MYAS_DELT_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_OTHER_DELT_MUDENS ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_MYAS_OTHER_DELT_MUDENS | L_MYAS_DELT_MUDENS, L_OTHER_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_MUSCLE_DELT_MUDENS | L_MYOP_MYDY_DELT_MUDENS, L_MYAS_OTHER_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_DELT_MUDENS | L_LNLPC5_DIFFN_DELT_MUDENS, L_MUSCLE_DELT_MUDENS ) {\n (NORMAL, NORMAL) 1.0, 0.0, 0.0;\n (INCR, NORMAL) 0.0, 1.0, 0.0;\n (V_INCR, NORMAL) 0.0, 0.0, 1.0;\n (NORMAL, INCR) 0.0, 1.0, 0.0;\n (INCR, INCR) 0.0, 0.0, 1.0;\n (V_INCR, INCR) 0.0, 0.0, 1.0;\n (NORMAL, V_INCR) 0.0, 0.0, 1.0;\n (INCR, V_INCR) 0.0, 0.0, 1.0;\n (V_INCR, V_INCR) 0.0, 0.0, 1.0;\n}\nprobability ( L_DELT_SF_DENSITY | L_DELT_MUDENS ) {\n (NORMAL) 0.97, 0.03, 0.00;\n (INCR) 0.05, 0.90, 0.05;\n (V_INCR) 0.01, 0.04, 0.95;\n}\nprobability ( L_LNLPC5_DELT_NEUR_ACT | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (TOTAL, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_DELT_NEUR_ACT | DIFFN_M_SEV_PROX, DIFFN_TIME ) {\n (NO, ACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (MOD, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (SEV, ACUTE) 0.9, 0.1, 0.0, 0.0, 0.0, 0.0;\n (NO, SUBACUTE) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (MOD, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, SUBACUTE) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (NO, CHRONIC) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (MOD, CHRONIC) 0.5, 0.5, 0.0, 0.0, 0.0, 0.0;\n (SEV, CHRONIC) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n (NO, OLD) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, OLD) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (MOD, OLD) 0.7, 0.3, 0.0, 0.0, 0.0, 0.0;\n (SEV, OLD) 0.3, 0.7, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_LNLPC5_DIFFN_DELT_NEUR_ACT | L_LNLPC5_DELT_NEUR_ACT, L_DIFFN_DELT_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_DELT_NEUR_ACT ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DELT_NEUR_ACT | L_LNLPC5_DIFFN_DELT_NEUR_ACT, L_OTHER_DELT_NEUR_ACT ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, NO) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (FASCIC, FASCIC) 0.0, 1.0, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO, FASCIC) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, FASCIC) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, FASCIC) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, FASCIC) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (FASCIC, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (NEUROMYO, NEUROMYO) 0.0, 0.0, 1.0, 0.0, 0.0, 0.0;\n (MYOKYMIA, NEUROMYO) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, NEUROMYO) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (FASCIC, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (NEUROMYO, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (MYOKYMIA, MYOKYMIA) 0.0, 0.0, 0.0, 1.0, 0.0, 0.0;\n (TETANUS, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, MYOKYMIA) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (FASCIC, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (NEUROMYO, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (MYOKYMIA, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (TETANUS, TETANUS) 0.0, 0.0, 0.0, 0.0, 1.0, 0.0;\n (OTHER, TETANUS) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (FASCIC, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NEUROMYO, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MYOKYMIA, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (TETANUS, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (OTHER, OTHER) 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DELT_SPONT_NEUR_DISCH | L_DELT_NEUR_ACT ) {\n (NO) 0.98, 0.02, 0.00, 0.00, 0.00, 0.00;\n (FASCIC) 0.1, 0.9, 0.0, 0.0, 0.0, 0.0;\n (NEUROMYO) 0.01, 0.04, 0.75, 0.05, 0.05, 0.10;\n (MYOKYMIA) 0.01, 0.04, 0.05, 0.75, 0.05, 0.10;\n (TETANUS) 0.01, 0.04, 0.05, 0.05, 0.75, 0.10;\n (OTHER) 0.01, 0.05, 0.05, 0.05, 0.05, 0.79;\n}\nprobability ( L_MYOP_DELT_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYDY_DELT_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_MYOP_MYDY_DELT_DENERV | L_MYOP_DELT_DENERV, L_MYDY_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_DELT_DENERV ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_NMT_DELT_DENERV | L_DELT_NMT ) {\n (NO) 1.0, 0.0, 0.0, 0.0;\n (MOD_PRE) 0.40, 0.45, 0.15, 0.00;\n (SEV_PRE) 0.15, 0.35, 0.35, 0.15;\n (MLD_POST) 0.85, 0.15, 0.00, 0.00;\n (MOD_POST) 0.30, 0.45, 0.20, 0.05;\n (SEV_POST) 0.15, 0.35, 0.35, 0.15;\n (OTHER) 0.25, 0.25, 0.25, 0.25;\n}\nprobability ( L_OTHER_NMT_DELT_DENERV | L_OTHER_DELT_DENERV, L_NMT_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_MUSCLE_DELT_DENERV | L_MYOP_MYDY_DELT_DENERV, L_OTHER_NMT_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_LNLPC5_DELT_DENERV | L_LNLPC5_AXIL_SEV, L_LNLPC5_AXIL_TIME, L_LNLPC5_AXIL_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, SUBACUTE, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (TOTAL, CHRONIC, DEMY) 0.0, 0.4, 0.4, 0.2;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (TOTAL, OLD, DEMY) 0.10, 0.60, 0.25, 0.05;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, SUBACUTE, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (TOTAL, CHRONIC, BLOCK) 0.3, 0.4, 0.3, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (TOTAL, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (TOTAL, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, AXONAL) 0.0, 0.0, 0.0, 1.0;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (TOTAL, OLD, AXONAL) 0.45, 0.45, 0.10, 0.00;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (TOTAL, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (TOTAL, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( L_DIFFN_DELT_DENERV | DIFFN_M_SEV_PROX, DIFFN_TIME, DIFFN_PATHO ) {\n (NO, ACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, SUBACUTE, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, SUBACUTE, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, CHRONIC, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, DEMY) 0.8, 0.2, 0.0, 0.0;\n (MOD, CHRONIC, DEMY) 0.3, 0.5, 0.2, 0.0;\n (SEV, CHRONIC, DEMY) 0.1, 0.5, 0.4, 0.0;\n (NO, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, DEMY) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, DEMY) 0.5, 0.4, 0.1, 0.0;\n (NO, ACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, BLOCK) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, SUBACUTE, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, SUBACUTE, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, CHRONIC, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, BLOCK) 0.9, 0.1, 0.0, 0.0;\n (MOD, CHRONIC, BLOCK) 0.6, 0.4, 0.0, 0.0;\n (SEV, CHRONIC, BLOCK) 0.4, 0.5, 0.1, 0.0;\n (NO, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (SEV, OLD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (NO, ACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (MOD, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (SEV, ACUTE, AXONAL) 0.8, 0.2, 0.0, 0.0;\n (NO, SUBACUTE, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, SUBACUTE, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, SUBACUTE, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, AXONAL) 0.0, 1.0, 0.0, 0.0;\n (MOD, CHRONIC, AXONAL) 0.0, 0.0, 1.0, 0.0;\n (SEV, CHRONIC, AXONAL) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, OLD, AXONAL) 0.9, 0.1, 0.0, 0.0;\n (SEV, OLD, AXONAL) 0.6, 0.3, 0.1, 0.0;\n (NO, ACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, ACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, SUBACUTE, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, SUBACUTE, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, CHRONIC, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, CHRONIC, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, OLD, V_E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (MOD, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (SEV, OLD, V_E_REIN) 0.0, 0.0, 0.5, 0.5;\n (NO, ACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, ACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, SUBACUTE, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, SUBACUTE, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, CHRONIC, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, CHRONIC, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (NO, OLD, E_REIN) 1.0, 0.0, 0.0, 0.0;\n (MILD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (MOD, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n (SEV, OLD, E_REIN) 0.05, 0.40, 0.50, 0.05;\n}\nprobability ( L_LNLPC5_DIFFN_DELT_DENERV | L_LNLPC5_DELT_DENERV, L_DIFFN_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DELT_DENERV | L_MUSCLE_DELT_DENERV, L_LNLPC5_DIFFN_DELT_DENERV ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0, 1.0, 0.0, 0.0;\n (MOD, NO) 0.0, 0.0, 1.0, 0.0;\n (SEV, NO) 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0, 1.0, 0.0, 0.0;\n (MILD, MILD) 0.0, 0.0, 1.0, 0.0;\n (MOD, MILD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MILD) 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0, 0.0, 1.0, 0.0;\n (MILD, MOD) 0.0, 0.0, 0.0, 1.0;\n (MOD, MOD) 0.0, 0.0, 0.0, 1.0;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0, 0.0, 0.0, 1.0;\n (MILD, SEV) 0.0, 0.0, 0.0, 1.0;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_DELT_SPONT_DENERV_ACT | L_DELT_DENERV ) {\n (NO) 0.98, 0.02, 0.00, 0.00;\n (MILD) 0.07, 0.85, 0.08, 0.00;\n (MOD) 0.01, 0.07, 0.85, 0.07;\n (SEV) 0.00, 0.01, 0.07, 0.92;\n}\nprobability ( L_DELT_SPONT_HF_DISCH | L_DELT_DENERV ) {\n (NO) 0.99, 0.01;\n (MILD) 0.97, 0.03;\n (MOD) 0.95, 0.05;\n (SEV) 0.93, 0.07;\n}\nprobability ( L_DELT_SPONT_INS_ACT | L_DELT_DENERV ) {\n (NO) 0.98, 0.02;\n (MILD) 0.1, 0.9;\n (MOD) 0.05, 0.95;\n (SEV) 0.05, 0.95;\n}\nprobability ( L_OTHER_ISCH_DISP ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_ISCH_DISP | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 1.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.0, 1.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 1.0;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.5, 0.5, 0.0;\n (SEV, BLOCK) 0.0, 0.1, 0.5, 0.4;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.5, 0.5, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.5, 0.5, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, E_REIN) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_SUR_DISP_CA | L_OTHER_ISCH_DISP, L_DIFFN_ISCH_DISP ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0749, 0.8229, 0.1022, 0.0000;\n (MOD, NO) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (SEV, NO) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (NO, MILD) 0.0749, 0.8229, 0.1022, 0.0000;\n (MILD, MILD) 0.0047, 0.1786, 0.8167, 0.0000;\n (MOD, MILD) 0.0000, 0.0190, 0.9795, 0.0015;\n (SEV, MILD) 0.0000, 0.0001, 0.0069, 0.9930;\n (NO, MOD) 0.00000000, 0.06260626, 0.93739374, 0.00000000;\n (MILD, MOD) 0.0000, 0.0190, 0.9795, 0.0015;\n (MOD, MOD) 0.0000, 0.0001, 0.0833, 0.9166;\n (SEV, MOD) 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00000000, 0.00790079, 0.60286029, 0.38923892;\n (MILD, SEV) 0.0000, 0.0001, 0.0069, 0.9930;\n (MOD, SEV) 0.0, 0.0, 0.0, 1.0;\n (SEV, SEV) 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_ISCH_BLOCK ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_ISCH_BLOCK | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.8, 0.2, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.6, 0.4, 0.0, 0.0, 0.0;\n (SEV, DEMY) 0.25, 0.50, 0.25, 0.00, 0.00;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (MOD, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (SEV, BLOCK) 0.0, 0.0, 0.4, 0.5, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, V_E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (NO, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, E_REIN) 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_SUR_BLOCK_CA | L_OTHER_ISCH_BLOCK, L_DIFFN_ISCH_BLOCK ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MOD, NO) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (SEV, NO) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0077, 0.9923, 0.0000, 0.0000, 0.0000;\n (MILD, MILD) 0.0001, 0.4980, 0.5019, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (SEV, MILD) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0007, 0.0234, 0.9759, 0.0000, 0.0000;\n (MILD, MOD) 0.0000, 0.0032, 0.9968, 0.0000, 0.0000;\n (MOD, MOD) 0.0000, 0.0007, 0.4328, 0.5665, 0.0000;\n (SEV, MOD) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0019, 0.0060, 0.0588, 0.9333, 0.0000;\n (MILD, SEV) 0.0010, 0.0033, 0.0376, 0.9581, 0.0000;\n (MOD, SEV) 0.0003, 0.0011, 0.0159, 0.9827, 0.0000;\n (SEV, SEV) 0.00030003, 0.00110011, 0.01250125, 0.98609861, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_OTHER_ISCH_SALOSS ) {\n table 1.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_ISCH_SALOSS | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.4, 0.3, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.3, 0.5, 0.2, 0.0;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 0.7, 0.3;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_LNL_ISCH_SEV ) {\n table 0.960, 0.020, 0.010, 0.005, 0.005;\n}\nprobability ( L_LNL_ISCH_PATHO ) {\n table 0.25, 0.25, 0.50, 0.00, 0.00;\n}\nprobability ( L_LNL_ISCH_SALOSS_CA | L_LNL_ISCH_SEV, L_LNL_ISCH_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, DEMY) 0.0, 0.5, 0.5, 0.0, 0.0;\n (SEV, DEMY) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, DEMY) 0.0, 0.0, 0.0, 0.4, 0.6;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.5, 0.3, 0.0, 0.0;\n (SEV, BLOCK) 0.0, 0.2, 0.5, 0.3, 0.0;\n (TOTAL, BLOCK) 0.0, 0.1, 0.4, 0.4, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 0.0, 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 0.0, 0.0, 1.0, 0.0, 0.0;\n (SEV, AXONAL) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, AXONAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (TOTAL, V_E_REIN) 0.0, 0.0, 0.0, 1.0, 0.0;\n (NO, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MILD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (MOD, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (SEV, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n (TOTAL, E_REIN) 0.0, 0.0, 0.5, 0.5, 0.0;\n}\nprobability ( L_DIFFN_LNL_ISCH_SALOSS | L_DIFFN_ISCH_SALOSS, L_LNL_ISCH_SALOSS_CA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_SUR_SALOSS | L_OTHER_ISCH_SALOSS, L_DIFFN_LNL_ISCH_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_SUR_EFFAXLOSS | L_SUR_BLOCK_CA, L_SUR_SALOSS ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MOD, NO) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (SEV, NO) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.0073, 0.9812, 0.0115, 0.0000, 0.0000;\n (MILD, MILD) 0.0000, 0.0289, 0.9711, 0.0000, 0.0000;\n (MOD, MILD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0017, 0.1306, 0.8670, 0.0007, 0.0000;\n (MILD, MOD) 0.0000, 0.0097, 0.5989, 0.3914, 0.0000;\n (MOD, MOD) 0.00000000, 0.00049995, 0.03309669, 0.96640336, 0.00000000;\n (SEV, MOD) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.0000, 0.0003, 0.0212, 0.9785, 0.0000;\n (MILD, SEV) 0.0000, 0.0000, 0.0017, 0.9983, 0.0000;\n (MOD, SEV) 0.0000, 0.0000, 0.0008, 0.9992, 0.0000;\n (SEV, SEV) 0.0000, 0.0000, 0.0001, 0.9999, 0.0000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n (TOTAL, TOTAL) 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_SUR_ALLAMP_CA | L_SUR_DISP_CA, L_SUR_EFFAXLOSS ) {\n (NO, NO) 0.0000, 0.0000, 0.0000, 0.0000, 0.0215, 0.9785;\n (MILD, NO) 0.0000, 0.0000, 0.0000, 0.3192, 0.6448, 0.0360;\n (MOD, NO) 0.0000, 0.0000, 0.0051, 0.9940, 0.0009, 0.0000;\n (SEV, NO) 0.0000, 0.0000, 0.9945, 0.0055, 0.0000, 0.0000;\n (NO, MILD) 0.0000, 0.0000, 0.0000, 0.3443, 0.6228, 0.0329;\n (MILD, MILD) 0.00000000, 0.00000000, 0.04740474, 0.93949395, 0.01290129, 0.00020002;\n (MOD, MILD) 0.0000, 0.0000, 0.9599, 0.0401, 0.0000, 0.0000;\n (SEV, MILD) 0.0000, 0.0000, 0.9999, 0.0001, 0.0000, 0.0000;\n (NO, MOD) 0.0000, 0.0000, 0.0248, 0.9704, 0.0048, 0.0000;\n (MILD, MOD) 0.00000000, 0.00000000, 0.93309331, 0.06690669, 0.00000000, 0.00000000;\n (MOD, MOD) 0.0000, 0.0001, 0.9994, 0.0005, 0.0000, 0.0000;\n (SEV, MOD) 0.0000, 0.7508, 0.2492, 0.0000, 0.0000, 0.0000;\n (NO, SEV) 0.0000, 0.1028, 0.8793, 0.0178, 0.0001, 0.0000;\n (MILD, SEV) 0.0000, 0.8756, 0.1237, 0.0007, 0.0000, 0.0000;\n (MOD, SEV) 0.0000, 0.9969, 0.0031, 0.0000, 0.0000, 0.0000;\n (SEV, SEV) 0.0000, 0.9999, 0.0001, 0.0000, 0.0000, 0.0000;\n (NO, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MOD, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (SEV, TOTAL) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_SUR_AMP_CA | L_SUR_ALLAMP_CA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\nprobability ( L_SUR_LD_CA ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_SUR_RD_CA ) {\n table 1.0, 0.0, 0.0;\n}\nprobability ( L_SUR_LSLOW_CA | L_SUR_LD_CA, L_SUR_RD_CA ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0185, 0.9561, 0.0254, 0.0000, 0.0000;\n (MOD, NO) 0.0000, 0.0166, 0.9834, 0.0000, 0.0000;\n (SEV, NO) 0.0007, 0.0020, 0.0219, 0.9754, 0.0000;\n (NO, MOD) 0.0021, 0.0042, 0.0295, 0.9642, 0.0000;\n (MILD, MOD) 0.0012, 0.0025, 0.0194, 0.9769, 0.0000;\n (MOD, MOD) 0.00069993, 0.00149985, 0.01199880, 0.98580142, 0.00000000;\n (SEV, MOD) 0.00020002, 0.00050005, 0.00460046, 0.99449945, 0.00020002;\n (NO, SEV) 0.0001, 0.0002, 0.0014, 0.0830, 0.9153;\n (MILD, SEV) 0.0001, 0.0001, 0.0008, 0.0533, 0.9457;\n (MOD, SEV) 0.0000, 0.0001, 0.0004, 0.0319, 0.9676;\n (SEV, SEV) 0.00000000, 0.00000000, 0.00000000, 0.00350035, 0.99649965;\n}\nprobability ( L_OTHER_ISCH_DIFSLOW ) {\n table 1.0, 0.0, 0.0, 0.0;\n}\nprobability ( L_DIFFN_ISCH_DIFSLOW | DIFFN_S_SEV_DIST, DIFFN_PATHO ) {\n (NO, DEMY) 1.0, 0.0, 0.0, 0.0;\n (MILD, DEMY) 0.0, 0.5, 0.5, 0.0;\n (MOD, DEMY) 0.0, 0.2, 0.6, 0.2;\n (SEV, DEMY) 0.0, 0.1, 0.6, 0.3;\n (NO, BLOCK) 1.0, 0.0, 0.0, 0.0;\n (MILD, BLOCK) 0.5, 0.5, 0.0, 0.0;\n (MOD, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (SEV, BLOCK) 0.2, 0.4, 0.3, 0.1;\n (NO, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MILD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (MOD, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (SEV, AXONAL) 1.0, 0.0, 0.0, 0.0;\n (NO, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MILD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (MOD, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (SEV, V_E_REIN) 0.0, 0.0, 0.0, 1.0;\n (NO, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MILD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (MOD, E_REIN) 0.0, 0.0, 0.7, 0.3;\n (SEV, E_REIN) 0.0, 0.0, 0.7, 0.3;\n}\nprobability ( L_SUR_DIFSLOW_CA | L_OTHER_ISCH_DIFSLOW, L_DIFFN_ISCH_DIFSLOW ) {\n (NO, NO) 1.0, 0.0, 0.0, 0.0;\n (MILD, NO) 0.0127, 0.9867, 0.0006, 0.0000;\n (MOD, NO) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (SEV, NO) 0.0000, 0.0006, 0.0492, 0.9502;\n (NO, MILD) 0.0127, 0.9867, 0.0006, 0.0000;\n (MILD, MILD) 0.0001, 0.0952, 0.9047, 0.0000;\n (MOD, MILD) 0.0000, 0.0011, 0.7402, 0.2587;\n (SEV, MILD) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (NO, MOD) 0.00000000, 0.01810181, 0.98189819, 0.00000000;\n (MILD, MOD) 0.0000, 0.0011, 0.7402, 0.2587;\n (MOD, MOD) 0.000, 0.000, 0.004, 0.996;\n (SEV, MOD) 0.0000, 0.0000, 0.0012, 0.9988;\n (NO, SEV) 0.0000, 0.0006, 0.0492, 0.9502;\n (MILD, SEV) 0.00000000, 0.00000000, 0.00880088, 0.99119912;\n (MOD, SEV) 0.0000, 0.0000, 0.0012, 0.9988;\n (SEV, SEV) 0.0000, 0.0000, 0.0009, 0.9991;\n}\nprobability ( L_SUR_DSLOW_CA | L_SUR_SALOSS, L_SUR_DIFSLOW_CA ) {\n (NO, NO) 0.8825, 0.1175, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, NO) 0.34603460, 0.65076508, 0.00320032, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, NO) 0.0641, 0.8105, 0.1254, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, NO) 0.00410041, 0.15271527, 0.78487849, 0.05830583, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (TOTAL, NO) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MILD) 0.06390639, 0.24022402, 0.46154615, 0.22352235, 0.01080108, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, MILD) 0.0245, 0.1308, 0.4221, 0.3800, 0.0426, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, MILD) 0.0078, 0.0585, 0.3098, 0.5006, 0.1230, 0.0003, 0.0000, 0.0000, 0.0000;\n (SEV, MILD) 0.0012, 0.0133, 0.1276, 0.4624, 0.3871, 0.0084, 0.0000, 0.0000, 0.0000;\n (TOTAL, MILD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, MOD) 0.0011, 0.0083, 0.0684, 0.2984, 0.5331, 0.0899, 0.0008, 0.0000, 0.0000;\n (MILD, MOD) 0.0003, 0.0030, 0.0335, 0.2043, 0.5689, 0.1865, 0.0035, 0.0000, 0.0000;\n (MOD, MOD) 0.00010001, 0.00100010, 0.01440144, 0.12231223, 0.52405241, 0.32563256, 0.01250125, 0.00000000, 0.00000000;\n (SEV, MOD) 0.0000, 0.0002, 0.0034, 0.0430, 0.3319, 0.5510, 0.0705, 0.0000, 0.0000;\n (TOTAL, MOD) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (NO, SEV) 0.00040004, 0.00120012, 0.00530053, 0.02140214, 0.08840884, 0.28082808, 0.44704470, 0.15541554, 0.00000000;\n (MILD, SEV) 0.00019996, 0.00069986, 0.00319936, 0.01399720, 0.06498700, 0.24325135, 0.46090782, 0.21275745, 0.00000000;\n (MOD, SEV) 0.0001, 0.0004, 0.0018, 0.0089, 0.0461, 0.2037, 0.4588, 0.2802, 0.0000;\n (SEV, SEV) 0.00000000, 0.00010001, 0.00080008, 0.00410041, 0.02540254, 0.14331433, 0.42124212, 0.40504050, 0.00000000;\n (TOTAL, SEV) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_SUR_ALLCV_CA | L_SUR_LSLOW_CA, L_SUR_DSLOW_CA ) {\n (NO, M_S60) 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;\n (MILD, M_S60) 0.02359764, 0.25787421, 0.64033597, 0.07809219, 0.00009999, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S60) 0.0000, 0.0021, 0.1149, 0.7277, 0.1553, 0.0000, 0.0000, 0.0000, 0.0000;\n (SEV, M_S60) 0.0003, 0.0011, 0.0050, 0.0205, 0.0870, 0.2822, 0.4514, 0.1525, 0.0000;\n (V_SEV, M_S60) 0.00000000, 0.00000000, 0.00009999, 0.00029997, 0.00219978, 0.01919808, 0.12518748, 0.85301470, 0.00000000;\n (NO, M_S52) 0.03049695, 0.96720328, 0.00229977, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S52) 0.0017, 0.0421, 0.4597, 0.4852, 0.0113, 0.0000, 0.0000, 0.0000, 0.0000;\n (MOD, M_S52) 0.0000, 0.0001, 0.0146, 0.3403, 0.6424, 0.0026, 0.0000, 0.0000, 0.0000;\n (SEV, M_S52) 0.00010002, 0.00040008, 0.00210042, 0.01010202, 0.05161032, 0.21844369, 0.46469294, 0.25255051, 0.00000000;\n (V_SEV, M_S52) 0.00000000, 0.00000000, 0.00000000, 0.00019998, 0.00139986, 0.01369863, 0.10288971, 0.88181182, 0.00000000;\n (NO, M_S44) 0.00039996, 0.06189381, 0.88811119, 0.04959504, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000;\n (MILD, M_S44) 0.00000000, 0.00190019, 0.07490749, 0.56945695, 0.35323532, 0.00050005, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S44) 0.0000, 0.0000, 0.0007, 0.0498, 0.7640, 0.1854, 0.0001, 0.0000, 0.0000;\n (SEV, M_S44) 0.00000000, 0.00010001, 0.00060006, 0.00340034, 0.02230223, 0.13361336, 0.41454145, 0.42544254, 0.00000000;\n (V_SEV, M_S44) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00070007, 0.00860086, 0.07820782, 0.91239124, 0.00000000;\n (NO, M_S36) 0.0000, 0.0001, 0.0655, 0.9082, 0.0262, 0.0000, 0.0000, 0.0000, 0.0000;\n (MILD, M_S36) 0.00000000, 0.00000000, 0.00220022, 0.10511051, 0.82518252, 0.06750675, 0.00000000, 0.00000000, 0.00000000;\n (MOD, M_S36) 0.0000, 0.0000, 0.0000, 0.0012, 0.1370, 0.8421, 0.0197, 0.0000, 0.0000;\n (SEV, M_S36) 0.0000, 0.0000, 0.0001, 0.0008, 0.0073, 0.0649, 0.3063, 0.6206, 0.0000;\n (V_SEV, M_S36) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00030003, 0.00500050, 0.05640564, 0.93829383, 0.00000000;\n (NO, M_S28) 0.0000, 0.0000, 0.0001, 0.0555, 0.9370, 0.0074, 0.0000, 0.0000, 0.0000;\n (MILD, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00220022, 0.17001700, 0.80628063, 0.02150215, 0.00000000, 0.00000000;\n (MOD, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0034, 0.4375, 0.5591, 0.0000, 0.0000;\n (SEV, M_S28) 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.00160016, 0.02260226, 0.17471747, 0.80098010, 0.00000000;\n (V_SEV, M_S28) 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0026, 0.0379, 0.9594, 0.0000;\n (NO, M_S20) 0.0000, 0.0000, 0.0000, 0.0002, 0.0491, 0.8863, 0.0644, 0.0000, 0.0000;\n (MILD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00219978, 0.23377662, 0.76202380, 0.00199980, 0.00000000;\n (MOD, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02080208, 0.80948095, 0.16971697, 0.00000000;\n (SEV, M_S20) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00020002, 0.00520052, 0.07260726, 0.92199220, 0.00000000;\n (V_SEV, M_S20) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0012, 0.0230, 0.9758, 0.0000;\n (NO, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00029997, 0.09559044, 0.89661034, 0.00749925, 0.00000000;\n (MILD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.01009899, 0.48945105, 0.50044996, 0.00000000;\n (MOD, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00010001, 0.03920392, 0.96069607, 0.00000000;\n (SEV, M_S14) 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00120012, 0.02960296, 0.96919692, 0.00000000;\n (V_SEV, M_S14) 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0005, 0.0140, 0.9855, 0.0000;\n (NO, M_S08) 0.00020002, 0.00060006, 0.00190019, 0.00640064, 0.02470247, 0.09740974, 0.28552855, 0.58325833, 0.00000000;\n (MILD, M_S08) 0.0001, 0.0002, 0.0007, 0.0026, 0.0117, 0.0585, 0.2222, 0.7040, 0.0000;\n (MOD, M_S08) 0.0000, 0.0001, 0.0002, 0.0009, 0.0051, 0.0329, 0.1636, 0.7972, 0.0000;\n (SEV, M_S08) 0.0000, 0.0000, 0.0001, 0.0004, 0.0022, 0.0159, 0.0994, 0.8820, 0.0000;\n (V_SEV, M_S08) 0.0000, 0.0000, 0.0000, 0.0001, 0.0006, 0.0058, 0.0523, 0.9412, 0.0000;\n (NO, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MILD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (MOD, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n (V_SEV, M_S00) 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0;\n}\nprobability ( L_SUR_CV_CA | L_SUR_ALLCV_CA ) {\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n (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;\n}\n" }, { "path": "data/mushroom.csv", "content": 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s,g\np,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,g\ne,f,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,y,p\np,f,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,g\ne,x,f,w,f,n,f,w,b,k,t,e,f,s,w,w,p,w,o,e,k,s,g\ne,b,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,m\ne,s,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u\ne,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m\ne,f,s,w,t,a,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d\ne,x,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,s,m\ne,f,f,g,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,s,g\np,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g\ne,x,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g\ne,f,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,k,a,g\ne,x,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m\ne,b,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,m\ne,f,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,n,v,u\ne,x,f,y,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,u,v,d\ne,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m\ne,f,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,y,u\ne,x,f,g,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,s,g\ne,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,p\ne,f,s,w,t,l,f,w,n,p,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,s,y,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,s,g\ne,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,y,p\np,x,y,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,v,u\ne,x,f,w,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d\ne,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g\ne,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m\ne,b,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g\ne,x,s,y,t,a,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d\ne,x,s,y,t,a,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d\ne,x,f,w,f,n,f,w,b,n,t,e,f,s,w,w,p,w,o,e,k,s,g\ne,b,s,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m\np,x,y,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,v,g\ne,x,y,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,g\ne,f,s,g,f,n,f,w,b,k,t,e,s,f,w,w,p,w,o,e,k,s,g\np,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,s,g\ne,b,y,w,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,m\ne,x,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m\ne,f,f,y,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g\ne,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,n,g\ne,x,y,n,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g\ne,x,s,y,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,g\ne,x,f,n,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,v,u\ne,x,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,s,g\ne,x,y,y,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g\np,x,s,n,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,n,s,g\ne,x,s,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,n,m\ne,x,s,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m\ne,x,y,w,t,l,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g\ne,x,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,y,g\ne,f,y,y,t,a,f,c,b,w,e,r,s,y,w,w,p,w,o,p,k,s,g\ne,x,y,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,s,m\ne,s,f,g,f,n,f,c,n,g,e,e,s,s,w,w,p,w,o,p,k,v,u\ne,f,f,n,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,y,u\ne,s,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u\ne,x,y,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m\np,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u\ne,x,s,g,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g\ne,x,y,y,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g\np,x,y,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,g\ne,x,s,y,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,n,m\np,x,s,n,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,v,u\ne,b,s,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m\ne,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m\np,x,y,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,n,s,g\np,x,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u\ne,x,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,a,g\ne,x,y,n,t,l,f,c,b,w,e,r,s,y,w,w,p,w,o,p,n,y,g\ne,f,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,a,g\ne,f,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,n,s,p\ne,b,y,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m\ne,b,s,w,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,g\ne,x,s,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g\ne,f,y,n,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,y,g\ne,x,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,k,s,m\ne,f,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,v,u\ne,f,s,n,f,n,f,w,b,h,t,e,f,s,w,w,p,w,o,e,n,a,g\ne,x,f,g,f,n,f,w,b,n,t,e,s,f,w,w,p,w,o,e,n,s,g\ne,f,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g\np,x,s,w,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,n,s,u\ne,x,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g\ne,b,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g\ne,f,f,g,f,n,f,w,b,p,t,e,s,s,w,w,p,w,o,e,k,a,g\ne,b,y,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,g\ne,b,s,y,t,a,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m\ne,s,f,g,f,n,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,y,u\ne,f,y,y,t,l,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g\ne,b,s,w,t,l,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,n,g\ne,x,f,w,t,l,f,w,n,n,t,b,s,s,w,w,p,w,o,p,u,v,d\ne,f,y,y,t,a,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,p\ne,f,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,y,u\ne,x,s,w,t,l,f,c,b,g,e,c,s,s,w,w,p,w,o,p,k,s,m\ne,f,f,y,t,l,f,w,n,w,t,b,s,s,w,w,p,w,o,p,u,v,d\ne,s,f,g,f,n,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,y,u\ne,b,y,w,t,a,f,c,b,g,e,c,s,s,w,w,p,w,o,p,n,s,g\ne,b,s,w,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,g\ne,f,f,n,f,n,f,w,b,n,t,e,s,s,w,w,p,w,o,e,n,s,g\ne,b,y,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,k,n,m\ne,x,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,x,y,n,t,l,f,c,b,n,e,r,s,y,w,w,p,w,o,p,k,s,g\ne,x,y,n,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,n,s,g\ne,b,y,y,t,a,f,c,b,w,e,c,s,s,w,w,p,w,o,p,n,s,m\ne,f,y,y,t,a,f,c,b,p,e,r,s,y,w,w,p,w,o,p,k,s,g\ne,x,y,y,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,k,n,m\ne,x,s,n,f,n,f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p,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,s,w,t,p,f,c,n,p,e,e,s,s,w,w,p,w,o,p,k,s,g\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\np,f,s,n,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,n,v,u\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,s,w,t,p,f,c,n,w,e,e,s,s,w,w,p,w,o,p,k,s,g\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,s,g\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,s,w,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,n,a,g\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,f,w,f,n,f,w,b,k,t,e,f,f,w,w,p,w,o,e,n,s,g\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d\ne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t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,s,n,f,n,f,w,b,p,t,e,s,f,w,w,p,w,o,e,k,a,g\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,f,w,f,n,f,w,b,h,t,e,s,f,w,w,p,w,o,e,k,s,g\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,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d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,s,g,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,k,s,g\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,f,w,f,n,f,w,b,h,t,e,s,s,w,w,p,w,o,e,n,s,g\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,n,f,n,f,w,b,n,t,e,f,f,w,w,p,w,o,e,k,s,g\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,s,n,f,n,f,w,b,p,t,e,f,f,w,w,p,w,o,e,n,s,g\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c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,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d\np,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,f,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,s,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,s,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,s,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\np,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,x,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,s,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,f,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,x,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,y,d\np,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,y,d\np,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,x,f,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,x,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,g,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,f,w,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,x,f,g,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,f,n,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,y,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,s,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\np,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,s,p,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,f,g,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,x,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,f,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,y,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,s,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,s,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,g,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,f,w,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,s,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,x,y,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,f,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,s,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,s,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,g,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,f,g,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,s,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,f,g,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,k,v,d\np,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,f,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,f,w,f,c,f,w,n,g,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,x,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,s,p,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,f,p,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\ne,x,y,g,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,k,y,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,g,p,w,o,p,n,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,s,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,v,d\np,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,s,w,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,s,p,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,s,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,f,p,f,c,f,w,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\np,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,x,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,f,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,f,g,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,x,f,e,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,v,d\ne,x,y,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,s,g,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,f,e,t,n,f,c,b,w,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,s,w,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,w,p,w,o,p,k,v,d\np,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,f,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,n,s,d\np,x,s,p,f,c,f,w,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\ne,x,f,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\ne,f,f,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,k,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,f,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,x,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,w,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,x,f,e,t,n,f,c,b,w,t,b,s,s,p,p,p,w,o,p,k,v,d\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,f,w,f,c,f,c,n,u,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,n,y,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\np,b,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,g,p,w,o,p,k,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,k,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,f,f,n,t,n,f,c,b,u,t,b,s,s,w,p,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,p,p,w,o,p,k,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,g,g,p,w,o,p,k,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,s,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,s,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,g,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,p,g,p,w,o,p,k,v,d\np,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,f,f,e,t,n,f,c,b,p,t,b,s,s,w,p,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,v,d\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,p,p,w,o,p,n,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,g,p,p,w,o,p,k,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\ne,f,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,k,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,k,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\ne,f,f,g,t,n,f,c,b,w,t,b,s,s,w,w,p,w,o,p,k,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\np,x,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,p,t,b,s,s,p,w,p,w,o,p,n,y,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,y,d\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\ne,x,y,g,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,v,d\np,x,f,g,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,p,f,c,f,w,n,n,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,w,w,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,g,g,p,w,o,p,n,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,f,p,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,s,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,k,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\ne,f,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,k,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\ne,f,y,g,t,n,f,c,b,w,t,b,s,s,p,w,p,w,o,p,n,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,x,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,k,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,w,t,b,s,s,g,p,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,x,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\np,b,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,f,y,g,t,n,f,c,b,n,t,b,s,s,g,p,p,w,o,p,n,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,p,g,p,w,o,p,k,v,d\ne,x,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,x,y,u,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,u,t,b,s,s,g,w,p,w,o,p,n,y,d\np,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\ne,f,y,n,t,n,f,c,b,u,t,b,s,s,w,g,p,w,o,p,k,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,x,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,k,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,y,g,t,n,f,c,b,u,t,b,s,s,p,g,p,w,o,p,k,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,g,g,p,w,o,p,n,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,k,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,g\np,x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f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,w,w,p,w,o,p,n,y,d\np,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\ne,f,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,k,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,x,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,d\np,b,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,k,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,k,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,s,w,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,b,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\ne,f,y,e,t,n,f,c,b,n,t,b,s,s,w,p,p,w,o,p,n,y,d\np,b,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,f,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\ne,k,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,k,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,s,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\ne,f,f,g,t,n,f,c,b,u,t,b,s,s,p,p,p,w,o,p,n,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,f,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,f,y,e,t,n,f,c,b,p,t,b,s,s,g,p,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,p,w,p,w,o,p,n,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\np,b,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,f,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,f,g,f,c,f,c,n,g,e,b,s,s,w,w,p,w,o,p,k,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,x,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\ne,f,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p\np,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,f,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,s,w,f,c,f,c,n,n,e,b,s,s,w,w,p,w,o,p,k,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,p\ne,f,y,e,t,n,f,c,b,u,t,b,s,s,p,w,p,w,o,p,n,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\ne,k,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,x,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\ne,x,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g\np,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p\ne,k,s,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,k,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\np,f,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,s,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u\ne,f,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,x,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,k,s,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,f,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\ne,f,s,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d\ne,x,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,b,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,f,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,x,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,k,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,x,s,g,f,c,f,c,n,p,e,b,s,s,w,w,p,w,o,p,n,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,f,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,g\np,f,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\ne,k,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,y,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\np,x,f,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u\np,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,p\ne,x,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\ne,x,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,d\ne,f,f,e,t,n,f,c,b,n,t,b,s,s,g,w,p,w,o,p,n,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,p\np,k,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,f,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,g\ne,k,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\np,b,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,k,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\ne,k,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d\np,x,y,e,f,y,f,c,n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,o,e,w,v,d\np,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,k,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d\ne,k,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,d\ne,x,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,x,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,b,y,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\ne,k,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,k,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,x,s,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\np,k,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\ne,k,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,k,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\np,f,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\ne,k,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,p\np,b,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\ne,k,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\np,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\np,b,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g\ne,k,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,f,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\np,k,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u\ne,f,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,k,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,x,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,k,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\np,k,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\ne,k,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,f,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\ne,x,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\ne,x,s,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,p\np,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\np,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\ne,f,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,d\ne,x,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l\np,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\ne,x,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\ne,x,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l\ne,f,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,k,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,f,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,b,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p\ne,f,y,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,b,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,x,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\ne,f,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\np,f,f,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,d\np,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,k,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,x,y,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p\np,f,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,x,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\ne,f,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\ne,x,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,x,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,d\np,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\np,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,p\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d\np,x,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\ne,f,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,g\ne,k,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d\np,b,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,s,b,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,x,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,x,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,v,p\np,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,f,y,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,x,f,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\ne,f,s,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,x,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\ne,f,s,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,k,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,k,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\ne,k,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\np,b,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\np,b,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,b,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,p\np,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,x,s,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,v,g\ne,f,s,b,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,f,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,s,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,d\ne,f,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,s,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\np,b,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,f,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d\np,x,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\ne,x,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,x,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,v,p\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,x,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,x,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\np,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,g\np,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\np,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\ne,k,s,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\ne,k,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\np,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d\np,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l\np,b,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\np,x,y,g,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,v,g\np,f,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,k,y,p,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g\ne,k,s,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,d\np,f,y,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,y,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\ne,x,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,p\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\np,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,d\ne,k,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\ne,f,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\np,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\ne,k,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,k,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\np,c,y,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d\ne,x,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\np,x,f,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\ne,k,f,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,p\ne,x,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,b,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,f,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p\np,f,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d\ne,f,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\ne,f,y,e,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,x,y,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g\ne,x,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,y,g\ne,k,y,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,k,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\ne,k,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,b,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p\np,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\ne,f,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,f,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p\np,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,x,s,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,p,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p\np,x,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,p\ne,x,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\np,x,y,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,g\np,f,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\ne,f,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,y,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,b,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,d\np,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\np,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,b,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,x,y,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,x,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\ne,f,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\ne,x,y,r,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,b,y,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,x,s,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p\np,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,y,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\ne,f,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,x,y,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\ne,x,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\ne,f,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d\ne,k,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,k,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,f,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,g\ne,f,s,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,g\np,b,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,f,y,b,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\ne,x,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,k,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,k,y,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,y,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,g\np,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,p\ne,x,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g\ne,x,y,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,d\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,d\np,b,y,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,p\np,f,f,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,k,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,n,p,w,o,l,h,v,p\ne,k,s,n,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,f,y,g,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\ne,f,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\ne,f,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,p\ne,k,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,x,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\np,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,g\np,x,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,f,y,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,g\np,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,u\ne,k,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,k,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,x,f,g,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,y,p\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\ne,f,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,n,p,w,o,l,h,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,y,g\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\ne,f,y,r,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,p\np,x,s,g,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,g\np,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,s,b,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\ne,x,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,f,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\ne,f,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l\ne,x,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,b,p,w,o,l,h,v,d\ne,f,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,k,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\ne,f,y,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,k,y,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,g\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\ne,f,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,g\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\ne,f,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,p,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,y,p\ne,f,f,c,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,b,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,k,y,e,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\np,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,g\np,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,s,p,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p\ne,f,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d\ne,x,y,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\np,k,g,w,t,n,f,w,n,w,e,b,s,s,w,w,p,w,o,p,w,c,l\np,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p\ne,k,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,f,s,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\np,b,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,f,f,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,u\np,x,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,g\ne,x,s,b,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d\ne,k,s,b,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,x,y,n,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d\ne,x,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\ne,k,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,k,y,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,v,p\np,x,s,b,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,k,f,c,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,g\np,b,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,b,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,x,y,n,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l\np,x,y,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\ne,x,s,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,x,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,x,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\np,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,f,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,s,w,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,x,y,w,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\ne,k,y,b,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,x,y,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\np,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,u\ne,f,y,r,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\np,b,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,s,b,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,y,c,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\ne,x,y,u,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,u\ne,x,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\np,x,y,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\ne,x,s,p,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,y,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,p\np,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,y,g,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,v,g\ne,f,y,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,b,s,p,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\np,x,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,v,u\np,x,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,y,p\np,b,y,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,f,y,n,f,n,f,w,n,w,e,b,s,f,w,n,p,w,o,e,w,v,l\np,f,f,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,b,p,w,o,l,h,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,g\np,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,x,y,y,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,x,y,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\ne,f,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,d\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\np,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\ne,f,f,c,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\ne,k,y,e,t,n,f,c,b,e,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,f,s,w,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\ne,x,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,p\np,x,s,w,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,v,d\ne,f,y,e,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,g\ne,k,s,n,t,n,f,c,b,e,e,?,s,s,e,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,s,w,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,g\np,f,y,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\ne,f,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\ne,x,y,w,f,n,f,c,n,h,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p\np,f,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,x,y,u,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,y,f,f,f,c,b,g,e,b,k,k,p,b,p,w,o,l,h,v,d\np,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,y,d\np,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,s,g\ne,f,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,s,w,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,s,g\ne,k,s,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,s,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,p,p,p,w,o,l,h,y,g\np,b,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d\np,b,s,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,m\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,y,g,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,d\ne,x,y,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,k,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\ne,x,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,m\ne,f,s,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\ne,f,y,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,y,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,h,e,b,k,k,b,b,p,w,o,l,h,y,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,x,y,n,f,s,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d\np,f,s,g,t,f,f,c,b,w,t,b,f,s,w,w,p,w,o,p,h,v,u\ne,x,y,p,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,x,y,u,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,y,p\np,x,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,b,p,p,w,o,l,h,y,g\np,b,s,w,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,k,y,n,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,x,y,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,v,p\ne,x,y,b,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\np,x,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,g\np,f,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\np,f,s,b,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,b,p,w,o,l,h,y,d\np,x,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p\np,f,s,w,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,k,y,n,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,s,e,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,x,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,v,d\np,f,s,b,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\np,f,s,g,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,s,u\ne,f,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,y,g\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\np,f,s,w,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,g,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\ne,x,f,n,f,n,f,w,n,w,e,b,s,s,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,p,e,b,k,k,b,n,p,w,o,l,h,y,d\np,x,s,w,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,u\ne,x,s,b,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,x,s,p,t,n,f,c,b,w,e,?,s,s,w,e,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,g\np,f,f,g,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,p\np,x,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,f,y,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,v,p\np,f,s,w,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,u\ne,k,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,x,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,y,g\ne,f,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,s,e,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,s,g,t,f,f,c,b,p,t,b,f,f,w,w,p,w,o,p,h,v,u\np,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,p\np,x,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,s,w,t,f,f,c,b,h,t,b,s,s,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,n,p,p,w,o,l,h,y,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,n,p,p,w,o,l,h,y,p\np,f,f,g,f,f,f,c,b,g,e,b,k,k,b,n,p,w,o,l,h,v,p\np,b,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l\np,b,s,w,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,x,y,n,t,n,f,c,b,w,e,?,s,s,w,w,p,w,t,e,w,c,w\np,x,y,n,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p\np,x,y,n,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,p\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,g\np,b,y,y,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,x,s,g,t,f,f,c,b,w,t,b,f,f,w,w,p,w,o,p,h,v,u\ne,f,y,p,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,k,s,p,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\ne,f,y,n,t,n,f,c,b,w,e,?,s,s,e,e,p,w,t,e,w,c,w\np,b,y,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,s,g,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,v,g\np,x,s,g,t,f,f,c,b,p,t,b,s,s,w,w,p,w,o,p,h,s,g\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,g\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\np,f,y,b,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,r,v,g\np,x,s,b,t,f,f,c,b,h,t,b,f,f,w,w,p,w,o,p,h,v,u\np,b,s,b,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\ne,f,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\ne,x,y,n,f,n,f,w,n,w,e,b,f,s,w,n,p,w,o,e,w,v,l\np,x,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,v,p\np,f,s,b,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,v,g\ne,k,s,e,t,n,f,c,b,e,e,?,s,s,e,e,p,w,t,e,w,c,w\np,f,f,y,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,p\np,b,f,n,f,n,f,c,n,w,e,?,k,y,w,n,p,w,o,e,w,v,d\np,x,y,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\np,f,y,y,f,f,f,c,b,h,e,b,k,k,n,b,p,w,o,l,h,y,d\np,x,y,y,f,f,f,c,b,g,e,b,k,k,n,b,p,w,o,l,h,y,g\np,x,s,g,t,f,f,c,b,p,t,b,f,s,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\ne,x,y,n,t,n,f,c,b,w,e,?,s,s,e,w,p,w,t,e,w,c,w\ne,f,y,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,f,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,k,f,n,f,n,f,c,n,w,e,?,k,y,w,y,p,w,o,e,w,v,d\np,f,y,g,f,f,f,c,b,h,e,b,k,k,p,n,p,w,o,l,h,v,g\np,x,y,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p\np,f,f,g,f,f,f,c,b,h,e,b,k,k,p,p,p,w,o,l,h,y,d\np,f,f,g,f,f,f,c,b,p,e,b,k,k,b,p,p,w,o,l,h,v,d\np,x,s,g,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,x,y,r,f,n,f,c,n,u,e,?,s,f,w,w,p,w,o,f,h,y,d\np,x,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\np,f,s,p,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,m\np,x,y,e,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\np,f,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,g\np,x,s,b,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,v,u\ne,k,s,e,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\np,f,s,b,t,f,f,c,b,w,t,b,s,f,w,w,p,w,o,p,h,s,g\ne,x,f,n,f,n,f,w,n,w,e,b,f,f,w,n,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,v,d\ne,f,y,w,f,n,f,c,n,w,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,s,w,t,f,f,c,b,w,t,b,s,s,w,w,p,w,o,p,h,s,u\np,f,s,p,t,n,f,c,b,r,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,p,e,b,k,k,p,n,p,w,o,l,h,y,d\np,f,s,g,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,g\np,x,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,b,p,w,o,l,h,v,g\np,f,s,g,t,f,f,c,b,h,t,b,f,s,w,w,p,w,o,p,h,s,g\np,f,s,w,t,f,f,c,b,h,t,b,s,f,w,w,p,w,o,p,h,s,u\np,x,y,n,f,s,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d\np,f,f,y,f,f,f,c,b,p,e,b,k,k,n,n,p,w,o,l,h,y,d\np,x,y,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,d\np,b,y,b,t,n,f,c,b,g,e,b,s,s,w,w,p,w,t,p,r,v,g\np,f,y,y,f,f,f,c,b,g,e,b,k,k,b,p,p,w,o,l,h,v,g\np,x,y,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,x,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d\ne,k,y,b,t,n,f,c,b,e,e,?,s,s,w,w,p,w,t,e,w,c,w\ne,x,y,w,f,n,f,c,n,p,e,?,s,f,w,w,p,w,o,f,h,y,d\np,f,s,b,t,f,f,c,b,p,t,b,s,f,w,w,p,w,o,p,h,v,u\np,f,y,y,f,f,f,c,b,g,e,b,k,k,n,p,p,w,o,l,h,y,g\np,x,y,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\np,f,s,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,x,y,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d\np,x,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,f,y,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,f,y,e,f,s,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,d\np,k,y,n,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,f,y,e,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p\np,x,s,e,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\np,f,y,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,f,y,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,x,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\np,x,s,n,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\np,f,s,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,d\np,f,s,e,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\np,f,y,e,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,f,y,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,f,y,n,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\np,x,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,k,s,n,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,p\np,x,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,l\np,f,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,f,y,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,k,s,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\np,f,y,e,f,y,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,f,s,e,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,f,s,n,f,y,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,f,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,f,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\np,f,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,p\np,f,y,e,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,x,s,e,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\np,f,s,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p\np,x,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,f,y,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,x,y,e,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,f,y,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\ne,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,n,o,p,n,c,l\np,f,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\ne,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,n,v,l\np,f,y,e,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\np,f,y,n,f,s,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,l\np,f,y,e,f,y,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\np,f,s,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,x,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l\np,x,s,n,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\np,f,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\np,f,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d\np,f,s,n,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\np,f,s,n,f,s,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,p\np,f,s,n,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l\np,f,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,f,s,e,f,y,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\np,x,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,l\np,x,s,e,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,f,s,e,f,f,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\np,f,y,n,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,x,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,d\np,k,s,e,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\np,k,s,n,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\np,f,y,n,f,f,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d\np,x,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l\np,x,y,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,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p,p,w,o,e,w,v,l\ne,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,y,v,l\np,f,y,n,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,k,y,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d\np,k,s,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,d\np,k,s,e,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\ne,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,b,v,l\ne,k,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,s,g\ne,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,n,v,l\ne,x,f,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g\np,k,s,e,f,y,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d\np,f,y,e,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\np,k,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,d\np,k,s,n,f,y,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\ne,b,s,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,n,g\np,f,s,n,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,d\ne,b,f,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g\np,x,s,n,f,f,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,d\np,x,y,e,f,f,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,l\np,f,y,c,f,m,f,c,b,w,e,c,k,y,c,c,p,w,n,n,w,c,d\np,f,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,d\np,x,y,e,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,l\np,f,s,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\np,f,y,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,f,s,e,f,f,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,l\np,x,s,e,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\ne,k,s,g,f,n,f,w,b,w,e,?,k,s,w,w,p,w,t,p,w,s,g\np,x,s,e,f,y,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p\np,k,s,n,f,y,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,k,y,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,l\np,x,s,n,f,y,f,c,n,b,t,?,k,k,w,p,p,w,o,e,w,v,p\ne,b,s,g,f,n,f,w,b,g,e,?,s,k,w,w,p,w,t,p,w,n,g\np,x,s,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\np,f,s,e,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d\np,k,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,d\ne,b,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,s,g\np,x,s,n,f,y,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\np,f,y,e,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p\np,f,s,e,f,y,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,d\np,f,y,n,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d\ne,b,f,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,s,g\np,f,s,e,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\np,k,s,n,f,f,f,c,n,b,t,?,s,k,p,p,p,w,o,e,w,v,l\ne,k,s,g,f,n,f,w,b,p,e,?,s,k,w,w,p,w,t,p,w,n,g\np,k,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\np,x,s,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,l\ne,b,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l\np,k,y,n,f,f,f,c,n,b,t,?,s,k,p,w,p,w,o,e,w,v,l\np,f,y,n,f,s,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,l\ne,x,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,b,c,l\np,k,s,n,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\np,k,s,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,d\np,k,s,n,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\np,k,s,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p\ne,f,y,c,t,n,f,c,b,w,e,b,s,s,w,w,p,w,t,p,w,y,p\ne,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,n,o,p,n,c,l\ne,b,s,w,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g\np,k,s,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\ne,b,s,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g\np,k,y,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p\np,k,y,n,f,s,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d\ne,x,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,o,o,p,y,c,l\np,x,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,x,s,n,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\np,f,s,e,f,y,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,l\np,f,y,e,f,s,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,p\np,x,y,e,f,s,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,p\np,k,s,e,f,f,f,c,n,b,t,?,k,k,w,w,p,w,o,e,w,v,d\ne,k,y,n,f,n,f,c,b,w,e,b,y,y,n,n,p,w,t,p,w,y,d\np,f,s,e,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d\np,k,s,n,f,s,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,p\np,f,y,n,f,y,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,p\np,f,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,d\ne,k,f,g,f,n,f,w,b,w,e,?,s,s,w,w,p,w,t,p,w,n,g\np,k,s,n,f,f,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,d\ne,b,f,w,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g\np,k,s,e,f,y,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\ne,x,s,g,f,n,f,w,b,p,e,?,k,k,w,w,p,w,t,p,w,n,g\ne,k,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,c,l\np,k,s,e,f,f,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,k,y,n,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\ne,k,f,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g\np,x,s,n,f,y,f,c,n,b,t,?,s,s,p,p,p,w,o,e,w,v,l\np,k,y,n,f,s,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\ne,x,f,w,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g\np,k,y,e,f,f,f,c,n,b,t,?,k,s,p,w,p,w,o,e,w,v,p\np,k,s,e,f,f,f,c,n,b,t,?,k,s,p,p,p,w,o,e,w,v,l\np,k,s,e,f,f,f,c,n,b,t,?,k,k,p,w,p,w,o,e,w,v,l\np,k,y,e,f,s,f,c,n,b,t,?,k,s,w,w,p,w,o,e,w,v,p\np,k,y,n,f,s,f,c,n,b,t,?,s,s,p,w,p,w,o,e,w,v,p\ne,k,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,n,g\np,k,y,n,f,f,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\np,k,s,e,f,f,f,c,n,b,t,?,s,s,w,p,p,w,o,e,w,v,l\ne,x,s,g,f,n,f,w,b,w,e,?,s,k,w,w,p,w,t,p,w,n,g\ne,f,s,n,f,n,a,c,b,y,e,?,s,s,o,o,p,o,o,p,y,v,l\ne,k,s,g,f,n,f,w,b,g,e,?,k,s,w,w,p,w,t,p,w,n,g\np,x,s,e,f,s,f,c,n,b,t,?,k,s,w,p,p,w,o,e,w,v,p\np,k,s,n,f,f,f,c,n,b,t,?,s,s,w,w,p,w,o,e,w,v,d\np,k,s,n,f,s,f,c,n,b,t,?,s,k,w,p,p,w,o,e,w,v,p\ne,x,s,g,f,n,f,w,b,p,e,?,k,s,w,w,p,w,t,p,w,s,g\np,k,y,e,f,s,f,c,n,b,t,?,k,k,p,p,p,w,o,e,w,v,p\ne,f,s,n,f,n,a,c,b,n,e,?,s,s,o,o,p,o,o,p,y,v,l\ne,k,s,w,f,n,f,w,b,w,e,?,k,k,w,w,p,w,t,p,w,n,g\np,f,y,e,f,m,f,c,b,y,e,c,k,y,c,c,p,w,n,n,w,c,d\ne,b,s,g,f,n,f,w,b,g,e,?,k,k,w,w,p,w,t,p,w,s,g\np,f,y,n,f,y,f,c,n,b,t,?,s,k,w,w,p,w,o,e,w,v,p\ne,b,s,n,f,n,a,c,b,o,e,?,s,s,o,o,p,n,o,p,b,c,l\ne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}, { "path": "data/simple.bn", "content": "{\n \"V\": [\"A\", \"B\", \"C\"],\n \"E\": {\n \"A\" : [\"B\"],\n \"B\" : [\"C\"],\n \"C\" : []\n },\n \"F\": {\n \"A\": {\n \"values\": [\"No\", \"Yes\"],\n \"parents\": [],\n \"cpt\": [0.3,0.7]\n },\n\n \"B\": {\n \"values\": [\"No\", \"Yes\"],\n \"parents\": [\"A\"],\n \"cpt\": [0.4,0.6,0.1,0.9]\n },\n \"C\": {\n \"values\": [\"No\",\"Yes\"],\n \"parents\": [\"B\"],\n \"cpt\": [0.4,0.6,0.1,0.9]\n }\n }\n}" }, { "path": "data/student.bn", "content": "{\r\n \"V\": [Intelligence\", \"Difficulty\", \"SAT\", \"Grade\", \"Letter\"],\r\n \"E\": [[\"Intelligence\", \"Grade\"],\r\n [\"Difficulty\", \"Grade\"],\r\n [\"Intelligence\", \"SAT\"],\r\n [\"Grade\", \"Letter\"]],\r\n \"Vdata\": {\r\n \"Letter\": {\r\n \"ord\": 4,\r\n \"numoutcomes\": 2,\r\n \"vals\": [\"weak\", \"strong\"],\r\n \"parents\": [\"Grade\"],\r\n \"children\": [],\r\n \"cprob\": [[0.1, 0.9], [0.4, 0.6], [0.99, 0.01]]\r\n },\r\n\r\n \"SAT\": {\r\n \"ord\": 3,\r\n \"numoutcomes\": 2,\r\n \"vals\": [\"lowscore\", \"highscore\"],\r\n \"parents\": [\"Intelligence\"],\r\n \"children\": [],\r\n \"cprob\": [[0.95, 0.05], [0.2, 0.8]]\r\n },\r\n\r\n \"Grade\": {\r\n \"ord\": 2,\r\n \"numoutcomes\": 3,\r\n \"vals\": [\"A\", \"B\", \"C\"],\r\n \"parents\": [\"Difficulty\", \"Intelligence\"],\r\n \"children\": [\"Letter\"],\r\n \"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]]\r\n },\r\n\r\n \"Intelligence\": {\r\n \"ord\": 1,\r\n \"numoutcomes\": 2,\r\n \"vals\": [\"low\", \"high\"],\r\n \"parents\": [],\r\n \"children\": [\"SAT\", \"Grade\"],\r\n \"cprob\": [0.7, 0.3]\r\n },\r\n\r\n \"Difficulty\": {\r\n \"ord\": 0,\r\n \"numoutcomes\": 2,\r\n \"vals\": [\"easy\", \"hard\"],\r\n \"parents\": [],\r\n \"children\": [\"Grade\"],\r\n \"cprob\": [0.6, 0.4]\r\n }\r\n }\r\n}" }, { "path": "data/test.bn", "content": "{\n \"V\": [\n \"Smoker\", \n \"Pollution\", \n \"Cancer\", \n \"Xray\", \n \"Dyspnoea\"\n ], \n \"E\": {\n \"Xray\": [], \n \"Dyspnoea\": [], \n \"Smoker\": [\n \"Cancer\"\n ], \n \"Cancer\": [\n \"Xray\", \n \"Dyspnoea\"\n ], \n \"Pollution\": [\n \"Cancer\"\n ]\n }, \n \"F\": {\n \"Xray\": {\n \"values\": [\n \"positive\", \n \"negative\"\n ], \n \"parents\": [\n \"Cancer\"\n ], \n \"cpt\": [\n 0.9, \n 0.1, \n 0.2, \n 0.8\n ]\n }, \n \"Dyspnoea\": {\n \"values\": [\n \"True\", \n \"False\"\n ], \n \"parents\": [\n \"Cancer\"\n ], \n \"cpt\": [\n 0.65, \n 0.35, \n 0.3, \n 0.7\n ]\n }, \n \"Smoker\": {\n \"values\": [\n \"True\", \n \"False\"\n ], \n \"parents\": [], \n \"cpt\": [\n 0.3, \n 0.7\n ]\n }, \n \"Cancer\": {\n \"values\": [\n \"True\", \n \"False\"\n ], \n \"parents\": [\n \"Pollution\", \n \"Smoker\"\n ], \n \"cpt\": [\n 0.03, \n 0.97, \n 0.05, \n 0.95, \n 0.001, \n 0.999, \n 0.02, \n 0.98\n ]\n }, \n \"Pollution\": {\n \"values\": [\n \"low\", \n \"high\"\n ], \n \"parents\": [], \n \"cpt\": [\n 0.9, \n 0.1\n ]\n }\n }\n}" }, { "path": "data/urn.bif", "content": "network unknown {\r\n}\r\nvariable draw {\r\n\ttype discrete [ 2 ] { red, blue };\r\n}\r\nvariable lie {\r\n\ttype discrete [ 2 ] { yes, no };\r\n}\r\nvariable personal_action {\r\n\ttype discrete [ 2 ] { red, blue };\r\n}\r\nvariable group_action {\r\n\ttype discrete [ 2 ] { rr, rb, br, bb };\r\n}\r\nvariable actual_action {\r\n\ttype discrete [ 2 ] { red, blue };\r\n}\r\nprobability ( draw ) {\r\n\ttable 0.33, 0.67;\r\n}\r\nprobability ( lie ) {\r\n\ttable 0.05, 0.95;\r\n}\r\nprobability ( group_action ) {\r\n\ttable 0.25, 0.25, 0.25, 0.25;\r\n}\r\nprobability ( personal_action | draw, lie ) {\r\n\t(red, yes) 0.01, 0.99;\r\n\t(blue, yes) 0.99, 0.01;\r\n\t(red, no) 0.99, 0.01;\r\n\t(blue, no) 0.01, 0.99;\r\n}\r\n\r\nprobability ( actual_action | personal_action, group_action ) {\r\n\t(red, rr) 1.0, 0.0;\r\n\t(blue, rr) 0.99, 0.01;\r\n\t(red, rb) 0.75, 0.25;\r\n\t(blue, rb) 0.25, 0.75;\r\n\t(red, br) 0.75, 0.25;\r\n\t(blue, br) 0.25, 0.75;\r\n\t(red, bb) 0.01, 0.99;\r\n\t(blue, bb) 0.0, 1.0;\r\n}" }, { "path": "data/win95pts.bif", "content": "network unknown {\n}\nvariable AppOK {\n type discrete [ 2 ] { Correct, Incorrect_Corrupt };\n}\nvariable DataFile {\n type discrete [ 2 ] { Correct, Incorrect_Corrupt };\n}\nvariable AppData {\n type discrete [ 2 ] { Correct, Incorrect_or_corrupt };\n}\nvariable DskLocal {\n type discrete [ 2 ] { Greater_than_2_Mb, Less_than_2_Mb };\n}\nvariable PrtSpool {\n type discrete [ 2 ] { Enabled, Disabled };\n}\nvariable PrtOn {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtPaper {\n type discrete [ 2 ] { Has_Paper, No_Paper };\n}\nvariable NetPrint {\n type discrete [ 2 ] { No__Local_printer_, Yes__Network_printer_ };\n}\nvariable PrtDriver {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtThread {\n type discrete [ 2 ] { OK, Corrupt_Buggy };\n}\nvariable EMFOK {\n type discrete [ 2 ] { Yes, No };\n}\nvariable GDIIN {\n type discrete [ 2 ] { Yes, No };\n}\nvariable DrvSet {\n type discrete [ 2 ] { Correct, Incorrect };\n}\nvariable DrvOK {\n type discrete [ 2 ] { Reinstalled, Corrupt };\n}\nvariable GDIOUT {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtSel {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtDataOut {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtPath {\n type discrete [ 2 ] { Correct, Incorrect };\n}\nvariable NtwrkCnfg {\n type discrete [ 2 ] { Correct, Incorrect };\n}\nvariable PTROFFLINE {\n type discrete [ 2 ] { Online, Offline };\n}\nvariable NetOK {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtCbl {\n type discrete [ 2 ] { Connected, Loose };\n}\nvariable PrtPort {\n type discrete [ 2 ] { Yes, No };\n}\nvariable CblPrtHrdwrOK {\n type discrete [ 2 ] { Operational, Not_Operational };\n}\nvariable LclOK {\n type discrete [ 2 ] { Yes, No };\n}\nvariable DSApplctn {\n type discrete [ 2 ] { DOS, Windows };\n}\nvariable PrtMpTPth {\n type discrete [ 2 ] { Correct, Incorrect };\n}\nvariable DS_NTOK {\n type discrete [ 2 ] { Yes, No };\n}\nvariable DS_LCLOK {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PC2PRT {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtMem {\n type discrete [ 2 ] { Greater_than_2_Mb, Less_than_2Mb };\n}\nvariable PrtTimeOut {\n type discrete [ 2 ] { Long_Enough, Too_Short };\n}\nvariable FllCrrptdBffr {\n type discrete [ 2 ] { Intact__not_Corrupt_, Full_or_Corrupt };\n}\nvariable TnrSpply {\n type discrete [ 2 ] { Adequate, Low };\n}\nvariable PrtData {\n type discrete [ 2 ] { Yes, No };\n}\nvariable Problem1 {\n type discrete [ 2 ] { Normal_Output, No_Output };\n}\nvariable AppDtGnTm {\n type discrete [ 2 ] { Fast_Enough, Too_Long };\n}\nvariable PrntPrcssTm {\n type discrete [ 2 ] { Fast_Enough, Too_Long };\n}\nvariable DeskPrntSpd {\n type discrete [ 2 ] { OK, Too_Slow };\n}\nvariable PgOrnttnOK {\n type discrete [ 2 ] { Correct, Incorrect };\n}\nvariable PrntngArOK {\n type discrete [ 2 ] { Correct, Incorrect };\n}\nvariable ScrnFntNtPrntrFnt {\n type discrete [ 2 ] { Yes, No };\n}\nvariable CmpltPgPrntd {\n type discrete [ 2 ] { Yes, No };\n}\nvariable GrphcsRltdDrvrSttngs {\n type discrete [ 2 ] { Correct, Incorrect };\n}\nvariable EPSGrphc {\n type discrete [ 2 ] { No____TIF___WMF___BMP_, Yes____EPS_ };\n}\nvariable NnPSGrphc {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtPScript {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PSGRAPHIC {\n type discrete [ 2 ] { Yes, No };\n}\nvariable Problem4 {\n type discrete [ 2 ] { No, Yes };\n}\nvariable TrTypFnts {\n type discrete [ 2 ] { Yes, No };\n}\nvariable FntInstlltn {\n type discrete [ 2 ] { Verified, Faulty };\n}\nvariable PrntrAccptsTrtyp {\n type discrete [ 2 ] { Yes, No };\n}\nvariable TTOK {\n type discrete [ 2 ] { Yes, No };\n}\nvariable NnTTOK {\n type discrete [ 2 ] { Yes, No };\n}\nvariable Problem5 {\n type discrete [ 2 ] { No, Yes };\n}\nvariable LclGrbld {\n type discrete [ 2 ] { Yes, No };\n}\nvariable NtGrbld {\n type discrete [ 2 ] { Yes, No };\n}\nvariable GrbldOtpt {\n type discrete [ 2 ] { No, Yes };\n}\nvariable HrglssDrtnAftrPrnt {\n type discrete [ 2 ] { Fast_Enough, Too_Long };\n}\nvariable REPEAT {\n type discrete [ 2 ] { Yes__Always_the_Same_, No__Different_Each_Time_ };\n}\nvariable AvlblVrtlMmry {\n type discrete [ 2 ] { Adequate____1Mb_, Inadequate____1_Mb_ };\n}\nvariable PSERRMEM {\n type discrete [ 2 ] { No_Error, Low_Memory };\n}\nvariable TstpsTxt {\n type discrete [ 2 ] { x_1_Mb_Available_VM, x_1_Mb_Available_VM2 };\n}\nvariable GrbldPS {\n type discrete [ 2 ] { No, Yes };\n}\nvariable IncmpltPS {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtFile {\n type discrete [ 2 ] { Yes, No };\n}\nvariable PrtIcon {\n type discrete [ 2 ] { Normal, Grayed_Out };\n}\nvariable Problem6 {\n type discrete [ 2 ] { No, Yes };\n}\nvariable Problem3 {\n type discrete [ 2 ] { No, Yes };\n}\nvariable PrtQueue {\n type discrete [ 2 ] { Short, Long };\n}\nvariable NtSpd {\n type discrete [ 2 ] { OK, Slow };\n}\nvariable Problem2 {\n type discrete [ 2 ] { OK, Too_Long };\n}\nvariable PrtStatPaper {\n type discrete [ 2 ] { No_Error, Jam__Out__Bin_Full };\n}\nvariable PrtStatToner {\n type discrete [ 2 ] { No_Error, Low__None };\n}\nvariable PrtStatMem {\n type discrete [ 2 ] { No_Error, Out_of_Memory };\n}\nvariable PrtStatOff {\n type discrete [ 2 ] { No_Error, OFFLINE__OFF };\n}\nprobability ( AppOK ) {\n table 0.995, 0.005;\n}\nprobability ( DataFile ) {\n table 0.995, 0.005;\n}\nprobability ( AppData | AppOK, DataFile ) {\n (Correct, Correct) 0.9999, 0.0001;\n (Incorrect_Corrupt, Correct) 0.0, 1.0;\n (Correct, Incorrect_Corrupt) 0.0, 1.0;\n (Incorrect_Corrupt, Incorrect_Corrupt) 0.5, 0.5;\n}\nprobability ( DskLocal ) {\n table 0.97, 0.03;\n}\nprobability ( PrtSpool ) {\n table 0.95, 0.05;\n}\nprobability ( PrtOn ) {\n table 0.9, 0.1;\n}\nprobability ( PrtPaper ) {\n table 0.98, 0.02;\n}\nprobability ( NetPrint ) {\n table 0.8, 0.2;\n}\nprobability ( PrtDriver ) {\n table 0.9, 0.1;\n}\nprobability ( PrtThread ) {\n table 0.9999, 0.0001;\n}\nprobability ( EMFOK | AppData, DskLocal, PrtThread ) {\n (Correct, Greater_than_2_Mb, OK) 0.99, 0.01;\n (Incorrect_or_corrupt, Greater_than_2_Mb, OK) 0.1, 0.9;\n (Correct, Less_than_2_Mb, OK) 0.0, 1.0;\n (Incorrect_or_corrupt, Less_than_2_Mb, OK) 0.5, 0.5;\n (Correct, Greater_than_2_Mb, Corrupt_Buggy) 0.05, 0.95;\n (Incorrect_or_corrupt, Greater_than_2_Mb, Corrupt_Buggy) 0.5, 0.5;\n (Correct, Less_than_2_Mb, Corrupt_Buggy) 0.5, 0.5;\n (Incorrect_or_corrupt, Less_than_2_Mb, Corrupt_Buggy) 0.5, 0.5;\n}\nprobability ( GDIIN | AppData, PrtSpool, EMFOK ) {\n (Correct, Enabled, Yes) 1.0, 0.0;\n (Incorrect_or_corrupt, Enabled, Yes) 0.0, 1.0;\n (Correct, Disabled, Yes) 1.0, 0.0;\n (Incorrect_or_corrupt, Disabled, Yes) 0.0, 1.0;\n (Correct, Enabled, No) 0.0, 1.0;\n (Incorrect_or_corrupt, Enabled, No) 0.0, 1.0;\n (Correct, Disabled, No) 1.0, 0.0;\n (Incorrect_or_corrupt, Disabled, No) 0.0, 1.0;\n}\nprobability ( DrvSet ) {\n table 0.99, 0.01;\n}\nprobability ( DrvOK ) {\n table 0.99, 0.01;\n}\nprobability ( GDIOUT | PrtDriver, GDIIN, DrvSet, DrvOK ) {\n (Yes, Yes, Correct, Reinstalled) 0.99, 0.01;\n (No, Yes, Correct, Reinstalled) 0.1, 0.9;\n (Yes, No, Correct, Reinstalled) 0.1, 0.9;\n (No, No, Correct, Reinstalled) 0.5, 0.5;\n (Yes, Yes, Incorrect, Reinstalled) 0.9, 0.1;\n (No, Yes, Incorrect, Reinstalled) 0.5, 0.5;\n (Yes, No, Incorrect, Reinstalled) 0.5, 0.5;\n (No, No, Incorrect, Reinstalled) 0.5, 0.5;\n (Yes, Yes, Correct, Corrupt) 0.2, 0.8;\n (No, Yes, Correct, Corrupt) 0.5, 0.5;\n (Yes, No, Correct, Corrupt) 0.5, 0.5;\n (No, No, Correct, Corrupt) 0.5, 0.5;\n (Yes, Yes, Incorrect, Corrupt) 0.5, 0.5;\n (No, Yes, Incorrect, Corrupt) 0.5, 0.5;\n (Yes, No, Incorrect, Corrupt) 0.5, 0.5;\n (No, No, Incorrect, Corrupt) 0.5, 0.5;\n}\nprobability ( PrtSel ) {\n table 0.99, 0.01;\n}\nprobability ( PrtDataOut | GDIOUT, PrtSel ) {\n (Yes, Yes) 0.99, 0.01;\n (No, Yes) 0.0, 1.0;\n (Yes, No) 0.0, 1.0;\n (No, No) 0.5, 0.5;\n}\nprobability ( PrtPath ) {\n table 0.97, 0.03;\n}\nprobability ( NtwrkCnfg ) {\n table 0.98, 0.02;\n}\nprobability ( PTROFFLINE ) {\n table 0.7, 0.3;\n}\nprobability ( NetOK | PrtPath, NtwrkCnfg, PTROFFLINE ) {\n (Correct, Correct, Online) 0.99, 0.01;\n (Incorrect, Correct, Online) 0.0, 1.0;\n (Correct, Incorrect, Online) 0.1, 0.9;\n (Incorrect, Incorrect, Online) 0.5, 0.5;\n (Correct, Correct, Offline) 0.0, 1.0;\n (Incorrect, Correct, Offline) 0.5, 0.5;\n (Correct, Incorrect, Offline) 0.5, 0.5;\n (Incorrect, Incorrect, Offline) 0.5, 0.5;\n}\nprobability ( PrtCbl ) {\n table 0.98, 0.02;\n}\nprobability ( PrtPort ) {\n table 0.99, 0.01;\n}\nprobability ( CblPrtHrdwrOK ) {\n table 0.99, 0.01;\n}\nprobability ( LclOK | PrtCbl, PrtPort, CblPrtHrdwrOK ) {\n (Connected, Yes, Operational) 0.999, 0.001;\n (Loose, Yes, Operational) 0.0, 1.0;\n (Connected, No, Operational) 0.0, 1.0;\n (Loose, No, Operational) 0.5, 0.5;\n (Connected, Yes, Not_Operational) 0.01, 0.99;\n (Loose, Yes, Not_Operational) 0.5, 0.5;\n (Connected, No, Not_Operational) 0.5, 0.5;\n (Loose, No, Not_Operational) 0.5, 0.5;\n}\nprobability ( DSApplctn ) {\n table 0.15, 0.85;\n}\nprobability ( PrtMpTPth ) {\n table 0.8, 0.2;\n}\nprobability ( DS_NTOK | AppData, PrtPath, PrtMpTPth, NtwrkCnfg, PTROFFLINE ) {\n (Correct, Correct, Correct, Correct, Online) 0.99, 0.01;\n (Incorrect_or_corrupt, Correct, Correct, Correct, Online) 0.2, 0.8;\n (Correct, Incorrect, Correct, Correct, Online) 0.0, 1.0;\n (Incorrect_or_corrupt, Incorrect, Correct, Correct, Online) 0.5, 0.5;\n (Correct, Correct, Incorrect, Correct, Online) 0.0, 1.0;\n (Incorrect_or_corrupt, Correct, Incorrect, Correct, Online) 0.5, 0.5;\n (Correct, Incorrect, Incorrect, Correct, Online) 0.5, 0.5;\n (Incorrect_or_corrupt, Incorrect, Incorrect, Correct, Online) 0.5, 0.5;\n (Correct, Correct, Correct, Incorrect, Online) 0.1, 0.9;\n (Incorrect_or_corrupt, Correct, Correct, Incorrect, Online) 0.5, 0.5;\n (Correct, Incorrect, Correct, Incorrect, Online) 0.5, 0.5;\n (Incorrect_or_corrupt, Incorrect, Correct, Incorrect, Online) 0.5, 0.5;\n (Correct, Correct, Incorrect, Incorrect, Online) 0.5, 0.5;\n (Incorrect_or_corrupt, Correct, Incorrect, Incorrect, Online) 0.5, 0.5;\n (Correct, Incorrect, Incorrect, Incorrect, Online) 0.5, 0.5;\n (Incorrect_or_corrupt, Incorrect, Incorrect, Incorrect, Online) 0.5, 0.5;\n (Correct, Correct, Correct, Correct, Offline) 0.0, 1.0;\n (Incorrect_or_corrupt, Correct, Correct, Correct, Offline) 0.5, 0.5;\n (Correct, Incorrect, Correct, Correct, Offline) 0.5, 0.5;\n (Incorrect_or_corrupt, Incorrect, Correct, Correct, Offline) 0.5, 0.5;\n (Correct, Correct, Incorrect, Correct, Offline) 0.5, 0.5;\n (Incorrect_or_corrupt, Correct, Incorrect, Correct, Offline) 0.5, 0.5;\n (Correct, Incorrect, Incorrect, Correct, Offline) 0.5, 0.5;\n (Incorrect_or_corrupt, Incorrect, Incorrect, Correct, Offline) 0.5, 0.5;\n (Correct, Correct, Correct, Incorrect, Offline) 0.5, 0.5;\n (Incorrect_or_corrupt, Correct, Correct, Incorrect, Offline) 0.5, 0.5;\n (Correct, Incorrect, Correct, Incorrect, Offline) 0.5, 0.5;\n (Incorrect_or_corrupt, Incorrect, Correct, Incorrect, Offline) 0.5, 0.5;\n (Correct, Correct, Incorrect, Incorrect, Offline) 0.5, 0.5;\n (Incorrect_or_corrupt, Correct, Incorrect, Incorrect, Offline) 0.5, 0.5;\n (Correct, Incorrect, Incorrect, Incorrect, Offline) 0.5, 0.5;\n (Incorrect_or_corrupt, Incorrect, Incorrect, Incorrect, Offline) 0.5, 0.5;\n}\nprobability ( DS_LCLOK | AppData, PrtCbl, PrtPort, CblPrtHrdwrOK ) {\n (Correct, Connected, Yes, Operational) 1.0, 0.0;\n (Incorrect_or_corrupt, Connected, Yes, Operational) 0.1, 0.9;\n (Correct, Loose, Yes, Operational) 0.0, 1.0;\n (Incorrect_or_corrupt, Loose, Yes, Operational) 0.5, 0.5;\n (Correct, Connected, No, Operational) 0.0, 1.0;\n (Incorrect_or_corrupt, Connected, No, Operational) 0.5, 0.5;\n (Correct, Loose, No, Operational) 0.5, 0.5;\n (Incorrect_or_corrupt, Loose, No, Operational) 0.5, 0.5;\n (Correct, Connected, Yes, Not_Operational) 0.1, 0.9;\n (Incorrect_or_corrupt, Connected, Yes, Not_Operational) 0.5, 0.5;\n (Correct, Loose, Yes, Not_Operational) 0.5, 0.5;\n (Incorrect_or_corrupt, Loose, Yes, Not_Operational) 0.5, 0.5;\n (Correct, Connected, No, Not_Operational) 0.5, 0.5;\n (Incorrect_or_corrupt, Connected, No, Not_Operational) 0.5, 0.5;\n (Correct, Loose, No, Not_Operational) 0.5, 0.5;\n (Incorrect_or_corrupt, Loose, No, Not_Operational) 0.5, 0.5;\n}\nprobability ( PC2PRT | NetPrint, PrtDataOut, NetOK, LclOK, DSApplctn, DS_NTOK, DS_LCLOK ) {\n (No__Local_printer_, Yes, Yes, Yes, DOS, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, Yes, Yes, DOS, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, No, Yes, Yes, DOS, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, No, Yes, Yes, DOS, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, Yes, No, Yes, DOS, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, No, Yes, DOS, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, No, No, Yes, DOS, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, No, No, Yes, DOS, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, Yes, Yes, No, DOS, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, Yes, No, DOS, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, No, Yes, No, DOS, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, No, Yes, No, DOS, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, Yes, No, No, DOS, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, No, No, DOS, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, No, No, No, DOS, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, No, No, No, DOS, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, Yes, Yes, Yes, Windows, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, Yes, Yes, Windows, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, No, Yes, Yes, Windows, Yes, Yes) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, Yes, Windows, Yes, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, No, Yes, Windows, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, No, Yes, Windows, Yes, Yes) 0.0, 1.0;\n (No__Local_printer_, No, No, Yes, Windows, Yes, Yes) 0.0, 1.0;\n (Yes__Network_printer_, No, No, Yes, Windows, Yes, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, No, Windows, Yes, Yes) 0.0, 1.0;\n (Yes__Network_printer_, Yes, Yes, No, Windows, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, No, Yes, No, Windows, Yes, Yes) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, No, Windows, Yes, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, No, No, Windows, Yes, Yes) 0.0, 1.0;\n (Yes__Network_printer_, Yes, No, No, Windows, Yes, Yes) 0.0, 1.0;\n (No__Local_printer_, No, No, No, Windows, Yes, Yes) 0.0, 1.0;\n (Yes__Network_printer_, No, No, No, Windows, Yes, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, Yes, DOS, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, Yes, Yes, DOS, No, Yes) 0.0, 1.0;\n (No__Local_printer_, No, Yes, Yes, DOS, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, No, Yes, Yes, DOS, No, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, No, Yes, DOS, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, No, Yes, DOS, No, Yes) 0.0, 1.0;\n (No__Local_printer_, No, No, Yes, DOS, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, No, No, Yes, DOS, No, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, No, DOS, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, Yes, No, DOS, No, Yes) 0.0, 1.0;\n (No__Local_printer_, No, Yes, No, DOS, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, No, Yes, No, DOS, No, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, No, No, DOS, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, No, No, DOS, No, Yes) 0.0, 1.0;\n (No__Local_printer_, No, No, No, DOS, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, No, No, No, DOS, No, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, Yes, Windows, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, Yes, Yes, Windows, No, Yes) 1.0, 0.0;\n (No__Local_printer_, No, Yes, Yes, Windows, No, Yes) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, Yes, Windows, No, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, No, Yes, Windows, No, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, No, Yes, Windows, No, Yes) 0.0, 1.0;\n (No__Local_printer_, No, No, Yes, Windows, No, Yes) 0.0, 1.0;\n (Yes__Network_printer_, No, No, Yes, Windows, No, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, No, Windows, No, Yes) 0.0, 1.0;\n (Yes__Network_printer_, Yes, Yes, No, Windows, No, Yes) 1.0, 0.0;\n (No__Local_printer_, No, Yes, No, Windows, No, Yes) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, No, Windows, No, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, No, No, Windows, No, Yes) 0.0, 1.0;\n (Yes__Network_printer_, Yes, No, No, Windows, No, Yes) 0.0, 1.0;\n (No__Local_printer_, No, No, No, Windows, No, Yes) 0.0, 1.0;\n (Yes__Network_printer_, No, No, No, Windows, No, Yes) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, Yes, DOS, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, Yes, Yes, DOS, Yes, No) 1.0, 0.0;\n (No__Local_printer_, No, Yes, Yes, DOS, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, Yes, DOS, Yes, No) 1.0, 0.0;\n (No__Local_printer_, Yes, No, Yes, DOS, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, No, Yes, DOS, Yes, No) 1.0, 0.0;\n (No__Local_printer_, No, No, Yes, DOS, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, No, No, Yes, DOS, Yes, No) 1.0, 0.0;\n (No__Local_printer_, Yes, Yes, No, DOS, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, Yes, No, DOS, Yes, No) 1.0, 0.0;\n (No__Local_printer_, No, Yes, No, DOS, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, No, DOS, Yes, No) 1.0, 0.0;\n (No__Local_printer_, Yes, No, No, DOS, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, No, No, DOS, Yes, No) 1.0, 0.0;\n (No__Local_printer_, No, No, No, DOS, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, No, No, No, DOS, Yes, No) 1.0, 0.0;\n (No__Local_printer_, Yes, Yes, Yes, Windows, Yes, No) 1.0, 0.0;\n (Yes__Network_printer_, Yes, Yes, Yes, Windows, Yes, No) 1.0, 0.0;\n (No__Local_printer_, No, Yes, Yes, Windows, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, Yes, Windows, Yes, No) 0.0, 1.0;\n (No__Local_printer_, Yes, No, Yes, Windows, Yes, No) 1.0, 0.0;\n (Yes__Network_printer_, Yes, No, Yes, Windows, Yes, No) 0.0, 1.0;\n (No__Local_printer_, No, No, Yes, Windows, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, No, No, Yes, Windows, Yes, No) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, No, Windows, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, Yes, No, Windows, Yes, No) 1.0, 0.0;\n (No__Local_printer_, No, Yes, No, Windows, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, No, Windows, Yes, No) 0.0, 1.0;\n (No__Local_printer_, Yes, No, No, Windows, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, No, No, Windows, Yes, No) 0.0, 1.0;\n (No__Local_printer_, No, No, No, Windows, Yes, No) 0.0, 1.0;\n (Yes__Network_printer_, No, No, No, Windows, Yes, No) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, Yes, DOS, No, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, Yes, Yes, DOS, No, No) 0.0, 1.0;\n (No__Local_printer_, No, Yes, Yes, DOS, No, No) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, Yes, DOS, No, No) 0.0, 1.0;\n (No__Local_printer_, Yes, No, Yes, DOS, No, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, No, Yes, DOS, No, No) 0.0, 1.0;\n (No__Local_printer_, No, No, Yes, DOS, No, No) 0.0, 1.0;\n (Yes__Network_printer_, No, No, Yes, DOS, No, No) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, No, DOS, No, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, Yes, No, DOS, No, No) 0.0, 1.0;\n (No__Local_printer_, No, Yes, No, DOS, No, No) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, No, DOS, No, No) 0.0, 1.0;\n (No__Local_printer_, Yes, No, No, DOS, No, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, No, No, DOS, No, No) 0.0, 1.0;\n (No__Local_printer_, No, No, No, DOS, No, No) 0.0, 1.0;\n (Yes__Network_printer_, No, No, No, DOS, No, No) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, Yes, Windows, No, No) 1.0, 0.0;\n (Yes__Network_printer_, Yes, Yes, Yes, Windows, No, No) 1.0, 0.0;\n (No__Local_printer_, No, Yes, Yes, Windows, No, No) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, Yes, Windows, No, No) 0.0, 1.0;\n (No__Local_printer_, Yes, No, Yes, Windows, No, No) 1.0, 0.0;\n (Yes__Network_printer_, Yes, No, Yes, Windows, No, No) 0.0, 1.0;\n (No__Local_printer_, No, No, Yes, Windows, No, No) 0.0, 1.0;\n (Yes__Network_printer_, No, No, Yes, Windows, No, No) 0.0, 1.0;\n (No__Local_printer_, Yes, Yes, No, Windows, No, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, Yes, No, Windows, No, No) 1.0, 0.0;\n (No__Local_printer_, No, Yes, No, Windows, No, No) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes, No, Windows, No, No) 0.0, 1.0;\n (No__Local_printer_, Yes, No, No, Windows, No, No) 0.0, 1.0;\n (Yes__Network_printer_, Yes, No, No, Windows, No, No) 0.0, 1.0;\n (No__Local_printer_, No, No, No, Windows, No, No) 0.0, 1.0;\n (Yes__Network_printer_, No, No, No, Windows, No, No) 0.0, 1.0;\n}\nprobability ( PrtMem ) {\n table 0.95, 0.05;\n}\nprobability ( PrtTimeOut ) {\n table 0.94, 0.06;\n}\nprobability ( FllCrrptdBffr ) {\n table 0.85, 0.15;\n}\nprobability ( TnrSpply ) {\n table 0.995, 0.005;\n}\nprobability ( PrtData | PrtOn, PrtPaper, PC2PRT, PrtMem, PrtTimeOut, FllCrrptdBffr, TnrSpply ) {\n (Yes, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.99, 0.01;\n (No, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.0, 1.0;\n (Yes, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.0, 1.0;\n (No, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.0, 1.0;\n (No, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, No_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, No_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.1, 0.9;\n (No, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, No_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, No_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, Has_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, No_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, No_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.0, 1.0;\n (No, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, Has_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, No_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, No_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, Has_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, No_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, No_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, Has_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, No_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (No, No_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.02, 0.98;\n (No, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, No_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, No_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, No_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, No_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, Has_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, No_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, No_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, Has_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, No_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, No_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, Has_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, No_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, No_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, Has_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, No_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (No, No_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Adequate) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.01, 0.99;\n (No, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, No_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, No_Paper, No, Greater_than_2_Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, No_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, No_Paper, Yes, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, Has_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, Has_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, No_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, No_Paper, No, Less_than_2Mb, Long_Enough, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, Has_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, Has_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, No_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, No_Paper, No, Greater_than_2_Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, Has_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, No_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, No_Paper, Yes, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, Has_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, Has_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, No_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (No, No_Paper, No, Less_than_2Mb, Too_Short, Intact__not_Corrupt_, Low) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, Has_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, No_Paper, Yes, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, Has_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, No_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, No_Paper, No, Greater_than_2_Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, Has_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, No_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, No_Paper, Yes, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, Has_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, Has_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, No_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, No_Paper, No, Less_than_2Mb, Long_Enough, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, Has_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, No_Paper, Yes, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, Has_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, Has_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, No_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, No_Paper, No, Greater_than_2_Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, Has_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, Has_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, No_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, No_Paper, Yes, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, Has_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, Has_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (Yes, No_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n (No, No_Paper, No, Less_than_2Mb, Too_Short, Full_or_Corrupt, Low) 0.5, 0.5;\n}\nprobability ( Problem1 | PrtData ) {\n (Yes) 1.0, 0.0;\n (No) 0.0, 1.0;\n}\nprobability ( AppDtGnTm | PrtSpool ) {\n (Enabled) 1.0, 0.0;\n (Disabled) 0.99000001, 0.00999999;\n}\nprobability ( PrntPrcssTm | PrtSpool ) {\n (Enabled) 0.99000001, 0.00999999;\n (Disabled) 1.0, 0.0;\n}\nprobability ( DeskPrntSpd | PrtMem, AppDtGnTm, PrntPrcssTm ) {\n (Greater_than_2_Mb, Fast_Enough, Fast_Enough) 0.99900001, 0.00099999;\n (Less_than_2Mb, Fast_Enough, Fast_Enough) 0.25, 0.75;\n (Greater_than_2_Mb, Too_Long, Fast_Enough) 0.00099999, 0.99900001;\n (Less_than_2Mb, Too_Long, Fast_Enough) 0.5, 0.5;\n (Greater_than_2_Mb, Fast_Enough, Too_Long) 0.00099999, 0.99900001;\n (Less_than_2Mb, Fast_Enough, Too_Long) 0.5, 0.5;\n (Greater_than_2_Mb, Too_Long, Too_Long) 0.5, 0.5;\n (Less_than_2Mb, Too_Long, Too_Long) 0.5, 0.5;\n}\nprobability ( PgOrnttnOK ) {\n table 0.95, 0.05;\n}\nprobability ( PrntngArOK ) {\n table 0.98, 0.02;\n}\nprobability ( ScrnFntNtPrntrFnt ) {\n table 0.95, 0.05;\n}\nprobability ( CmpltPgPrntd | PrtMem, PgOrnttnOK, PrntngArOK ) {\n (Greater_than_2_Mb, Correct, Correct) 0.99, 0.01;\n (Less_than_2Mb, Correct, Correct) 0.3, 0.7;\n (Greater_than_2_Mb, Incorrect, Correct) 0.00999999, 0.99000001;\n (Less_than_2Mb, Incorrect, Correct) 0.5, 0.5;\n (Greater_than_2_Mb, Correct, Incorrect) 0.1, 0.9;\n (Less_than_2Mb, Correct, Incorrect) 0.5, 0.5;\n (Greater_than_2_Mb, Incorrect, Incorrect) 0.5, 0.5;\n (Less_than_2Mb, Incorrect, Incorrect) 0.5, 0.5;\n}\nprobability ( GrphcsRltdDrvrSttngs ) {\n table 0.95, 0.05;\n}\nprobability ( EPSGrphc ) {\n table 0.99, 0.01;\n}\nprobability ( NnPSGrphc | PrtMem, GrphcsRltdDrvrSttngs, EPSGrphc ) {\n (Greater_than_2_Mb, Correct, No____TIF___WMF___BMP_) 0.999, 0.001;\n (Less_than_2Mb, Correct, No____TIF___WMF___BMP_) 0.25, 0.75;\n (Greater_than_2_Mb, Incorrect, No____TIF___WMF___BMP_) 0.1, 0.9;\n (Less_than_2Mb, Incorrect, No____TIF___WMF___BMP_) 0.5, 0.5;\n (Greater_than_2_Mb, Correct, Yes____EPS_) 0.0, 1.0;\n (Less_than_2Mb, Correct, Yes____EPS_) 0.5, 0.5;\n (Greater_than_2_Mb, Incorrect, Yes____EPS_) 0.5, 0.5;\n (Less_than_2Mb, Incorrect, Yes____EPS_) 0.5, 0.5;\n}\nprobability ( PrtPScript ) {\n table 0.4, 0.6;\n}\nprobability ( PSGRAPHIC | PrtMem, GrphcsRltdDrvrSttngs, EPSGrphc ) {\n (Greater_than_2_Mb, Correct, No____TIF___WMF___BMP_) 0.999, 0.001;\n (Less_than_2Mb, Correct, No____TIF___WMF___BMP_) 0.25, 0.75;\n (Greater_than_2_Mb, Incorrect, No____TIF___WMF___BMP_) 0.1, 0.9;\n (Less_than_2Mb, Incorrect, No____TIF___WMF___BMP_) 0.5, 0.5;\n (Greater_than_2_Mb, Correct, Yes____EPS_) 1.0, 0.0;\n (Less_than_2Mb, Correct, Yes____EPS_) 0.5, 0.5;\n (Greater_than_2_Mb, Incorrect, Yes____EPS_) 0.5, 0.5;\n (Less_than_2Mb, Incorrect, Yes____EPS_) 0.5, 0.5;\n}\nprobability ( Problem4 | NnPSGrphc, PrtPScript, PSGRAPHIC ) {\n (Yes, Yes, Yes) 0.0, 1.0;\n (No, Yes, Yes) 0.0, 1.0;\n (Yes, No, Yes) 0.0, 1.0;\n (No, No, Yes) 1.0, 0.0;\n (Yes, Yes, No) 1.0, 0.0;\n (No, Yes, No) 1.0, 0.0;\n (Yes, No, No) 0.0, 1.0;\n (No, No, No) 1.0, 0.0;\n}\nprobability ( TrTypFnts ) {\n table 0.9, 0.1;\n}\nprobability ( FntInstlltn ) {\n table 0.98, 0.02;\n}\nprobability ( PrntrAccptsTrtyp ) {\n table 0.9, 0.1;\n}\nprobability ( TTOK | PrtMem, FntInstlltn, PrntrAccptsTrtyp ) {\n (Greater_than_2_Mb, Verified, Yes) 0.99000001, 0.00999999;\n (Less_than_2Mb, Verified, Yes) 0.5, 0.5;\n (Greater_than_2_Mb, Faulty, Yes) 0.1, 0.9;\n (Less_than_2Mb, Faulty, Yes) 0.5, 0.5;\n (Greater_than_2_Mb, Verified, No) 0.0, 1.0;\n (Less_than_2Mb, Verified, No) 0.5, 0.5;\n (Greater_than_2_Mb, Faulty, No) 0.5, 0.5;\n (Less_than_2Mb, Faulty, No) 0.5, 0.5;\n}\nprobability ( NnTTOK | PrtMem, ScrnFntNtPrntrFnt, FntInstlltn ) {\n (Greater_than_2_Mb, Yes, Verified) 0.99000001, 0.00999999;\n (Less_than_2Mb, Yes, Verified) 0.5, 0.5;\n (Greater_than_2_Mb, No, Verified) 0.1, 0.9;\n (Less_than_2Mb, No, Verified) 0.5, 0.5;\n (Greater_than_2_Mb, Yes, Faulty) 0.1, 0.9;\n (Less_than_2Mb, Yes, Faulty) 0.5, 0.5;\n (Greater_than_2_Mb, No, Faulty) 0.5, 0.5;\n (Less_than_2Mb, No, Faulty) 0.5, 0.5;\n}\nprobability ( Problem5 | TrTypFnts, TTOK, NnTTOK ) {\n (Yes, Yes, Yes) 0.0, 1.0;\n (No, Yes, Yes) 0.0, 1.0;\n (Yes, No, Yes) 1.0, 0.0;\n (No, No, Yes) 0.0, 1.0;\n (Yes, Yes, No) 0.0, 1.0;\n (No, Yes, No) 1.0, 0.0;\n (Yes, No, No) 1.0, 0.0;\n (No, No, No) 1.0, 0.0;\n}\nprobability ( LclGrbld | AppData, PrtDriver, PrtMem, CblPrtHrdwrOK ) {\n (Correct, Yes, Greater_than_2_Mb, Operational) 1.0, 0.0;\n (Incorrect_or_corrupt, Yes, Greater_than_2_Mb, Operational) 0.2, 0.8;\n (Correct, No, Greater_than_2_Mb, Operational) 0.4, 0.6;\n (Incorrect_or_corrupt, No, Greater_than_2_Mb, Operational) 0.5, 0.5;\n (Correct, Yes, Less_than_2Mb, Operational) 0.2, 0.8;\n (Incorrect_or_corrupt, Yes, Less_than_2Mb, Operational) 0.5, 0.5;\n (Correct, No, Less_than_2Mb, Operational) 0.5, 0.5;\n (Incorrect_or_corrupt, No, Less_than_2Mb, Operational) 0.5, 0.5;\n (Correct, Yes, Greater_than_2_Mb, Not_Operational) 0.1, 0.9;\n (Incorrect_or_corrupt, Yes, Greater_than_2_Mb, Not_Operational) 0.5, 0.5;\n (Correct, No, Greater_than_2_Mb, Not_Operational) 0.5, 0.5;\n (Incorrect_or_corrupt, No, Greater_than_2_Mb, Not_Operational) 0.5, 0.5;\n (Correct, Yes, Less_than_2Mb, Not_Operational) 0.5, 0.5;\n (Incorrect_or_corrupt, Yes, Less_than_2Mb, Not_Operational) 0.5, 0.5;\n (Correct, No, Less_than_2Mb, Not_Operational) 0.5, 0.5;\n (Incorrect_or_corrupt, No, Less_than_2Mb, Not_Operational) 0.5, 0.5;\n}\nprobability ( NtGrbld | AppData, PrtDriver, PrtMem, NtwrkCnfg ) {\n (Correct, Yes, Greater_than_2_Mb, Correct) 1.0, 0.0;\n (Incorrect_or_corrupt, Yes, Greater_than_2_Mb, Correct) 0.3, 0.7;\n (Correct, No, Greater_than_2_Mb, Correct) 0.4, 0.6;\n (Incorrect_or_corrupt, No, Greater_than_2_Mb, Correct) 0.5, 0.5;\n (Correct, Yes, Less_than_2Mb, Correct) 0.2, 0.8;\n (Incorrect_or_corrupt, Yes, Less_than_2Mb, Correct) 0.5, 0.5;\n (Correct, No, Less_than_2Mb, Correct) 0.5, 0.5;\n (Incorrect_or_corrupt, No, Less_than_2Mb, Correct) 0.5, 0.5;\n (Correct, Yes, Greater_than_2_Mb, Incorrect) 0.4, 0.6;\n (Incorrect_or_corrupt, Yes, Greater_than_2_Mb, Incorrect) 0.5, 0.5;\n (Correct, No, Greater_than_2_Mb, Incorrect) 0.5, 0.5;\n (Incorrect_or_corrupt, No, Greater_than_2_Mb, Incorrect) 0.5, 0.5;\n (Correct, Yes, Less_than_2Mb, Incorrect) 0.5, 0.5;\n (Incorrect_or_corrupt, Yes, Less_than_2Mb, Incorrect) 0.5, 0.5;\n (Correct, No, Less_than_2Mb, Incorrect) 0.5, 0.5;\n (Incorrect_or_corrupt, No, Less_than_2Mb, Incorrect) 0.5, 0.5;\n}\nprobability ( GrbldOtpt | NetPrint, LclGrbld, NtGrbld ) {\n (No__Local_printer_, Yes, Yes) 1.0, 0.0;\n (Yes__Network_printer_, Yes, Yes) 1.0, 0.0;\n (No__Local_printer_, No, Yes) 0.0, 1.0;\n (Yes__Network_printer_, No, Yes) 1.0, 0.0;\n (No__Local_printer_, Yes, No) 1.0, 0.0;\n (Yes__Network_printer_, Yes, No) 0.0, 1.0;\n (No__Local_printer_, No, No) 0.0, 1.0;\n (Yes__Network_printer_, No, No) 0.0, 1.0;\n}\nprobability ( HrglssDrtnAftrPrnt | AppDtGnTm ) {\n (Fast_Enough) 0.99, 0.01;\n (Too_Long) 0.1, 0.9;\n}\nprobability ( REPEAT | CblPrtHrdwrOK, NtwrkCnfg ) {\n (Operational, Correct) 1.0, 0.0;\n (Not_Operational, Correct) 0.5, 0.5;\n (Operational, Incorrect) 0.5, 0.5;\n (Not_Operational, Incorrect) 0.5, 0.5;\n}\nprobability ( AvlblVrtlMmry | PrtPScript ) {\n (Yes) 0.98, 0.02;\n (No) 1.0, 0.0;\n}\nprobability ( PSERRMEM | PrtPScript, AvlblVrtlMmry ) {\n (Yes, Adequate____1Mb_) 1.0, 0.0;\n (No, Adequate____1Mb_) 1.0, 0.0;\n (Yes, Inadequate____1_Mb_) 0.05, 0.95;\n (No, Inadequate____1_Mb_) 1.0, 0.0;\n}\nprobability ( TstpsTxt | PrtPScript, AvlblVrtlMmry ) {\n (Yes, Adequate____1Mb_) 0.99900001, 0.00099999;\n (No, Adequate____1Mb_) 1.0, 0.0;\n (Yes, Inadequate____1_Mb_) 0.00099999, 0.99900001;\n (No, Inadequate____1_Mb_) 1.0, 0.0;\n}\nprobability ( GrbldPS | GrbldOtpt, AvlblVrtlMmry ) {\n (No, Adequate____1Mb_) 1.0, 0.0;\n (Yes, Adequate____1Mb_) 0.0, 1.0;\n (No, Inadequate____1_Mb_) 0.1, 0.9;\n (Yes, Inadequate____1_Mb_) 0.5, 0.5;\n}\nprobability ( IncmpltPS | CmpltPgPrntd, AvlblVrtlMmry ) {\n (Yes, Adequate____1Mb_) 1.0, 0.0;\n (No, Adequate____1Mb_) 0.0, 1.0;\n (Yes, Inadequate____1_Mb_) 0.3, 0.7;\n (No, Inadequate____1_Mb_) 0.5, 0.5;\n}\nprobability ( PrtFile | PrtDataOut ) {\n (Yes) 0.8, 0.2;\n (No) 0.2, 0.8;\n}\nprobability ( PrtIcon | NtwrkCnfg, PTROFFLINE ) {\n (Correct, Online) 0.9999, 0.0001;\n (Incorrect, Online) 0.25, 0.75;\n (Correct, Offline) 0.7, 0.3;\n (Incorrect, Offline) 0.5, 0.5;\n}\nprobability ( Problem6 | GrbldOtpt, PrtPScript, GrbldPS ) {\n (No, Yes, No) 1.0, 0.0;\n (Yes, Yes, No) 1.0, 0.0;\n (No, No, No) 1.0, 0.0;\n (Yes, No, No) 0.0, 1.0;\n (No, Yes, Yes) 0.0, 1.0;\n (Yes, Yes, Yes) 0.0, 1.0;\n (No, No, Yes) 1.0, 0.0;\n (Yes, No, Yes) 0.0, 1.0;\n}\nprobability ( Problem3 | CmpltPgPrntd, PrtPScript, IncmpltPS ) {\n (Yes, Yes, Yes) 0.0, 1.0;\n (No, Yes, Yes) 0.0, 1.0;\n (Yes, No, Yes) 0.0, 1.0;\n (No, No, Yes) 1.0, 0.0;\n (Yes, Yes, No) 1.0, 0.0;\n (No, Yes, No) 1.0, 0.0;\n (Yes, No, No) 0.0, 1.0;\n (No, No, No) 1.0, 0.0;\n}\nprobability ( PrtQueue ) {\n table 0.99, 0.01;\n}\nprobability ( NtSpd | DeskPrntSpd, NtwrkCnfg, PrtQueue ) {\n (OK, Correct, Short) 0.99900001, 0.00099999;\n (Too_Slow, Correct, Short) 0.0, 1.0;\n (OK, Incorrect, Short) 0.25, 0.75;\n (Too_Slow, Incorrect, Short) 0.5, 0.5;\n (OK, Correct, Long) 0.25, 0.75;\n (Too_Slow, Correct, Long) 0.5, 0.5;\n (OK, Incorrect, Long) 0.5, 0.5;\n (Too_Slow, Incorrect, Long) 0.5, 0.5;\n}\nprobability ( Problem2 | NetPrint, DeskPrntSpd, NtSpd ) {\n (No__Local_printer_, OK, OK) 1.0, 0.0;\n (Yes__Network_printer_, OK, OK) 1.0, 0.0;\n (No__Local_printer_, Too_Slow, OK) 0.0, 1.0;\n (Yes__Network_printer_, Too_Slow, OK) 1.0, 0.0;\n (No__Local_printer_, OK, Slow) 1.0, 0.0;\n (Yes__Network_printer_, OK, Slow) 0.0, 1.0;\n (No__Local_printer_, Too_Slow, Slow) 0.0, 1.0;\n (Yes__Network_printer_, Too_Slow, Slow) 0.0, 1.0;\n}\nprobability ( PrtStatPaper | PrtPaper ) {\n (Has_Paper) 0.99900001, 0.00099999;\n (No_Paper) 0.00099999, 0.99900001;\n}\nprobability ( PrtStatToner | TnrSpply ) {\n (Adequate) 0.99900001, 0.00099999;\n (Low) 0.00099999, 0.99900001;\n}\nprobability ( PrtStatMem | PrtMem ) {\n (Greater_than_2_Mb) 0.99900001, 0.00099999;\n (Less_than_2Mb) 0.2, 0.8;\n}\nprobability ( PrtStatOff | PrtOn ) {\n (Yes) 0.99000001, 0.00999999;\n (No) 0.00999999, 0.99000001;\n}\n" }, { "path": "examples/Drawing.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Drawing (Plotting) Bayesian Networks in pyBN\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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 \"\\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 \"\\n\",\n \"In this tutorial, we will demonstrate the plotting functionality in pyBN.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from pyBN import *\\n\",\n \"\\n\",\n \"bn = read_bn('data/cancer.bif')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Pollution', 'Smoker', 'Cancer', 'Xray', 'Dyspnoea']\\n\",\n \"[['Pollution', 'Cancer'], ['Smoker', 'Cancer'], ['Cancer', 'Xray'], ['Cancer', 'Dyspnoea']]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print bn.V\\n\",\n \"print bn.E\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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IgjgI8C1aP6d5FaNu9bj1gkbUZa4KSrhP9dYHsnfGsGHulbU5C0Balt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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"draw_bn(bn)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"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:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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gGPx4v/8U7vIwuL5grADzwJvAccc5DPTbixmizheAKtvsGxmcdl2dnZ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\\nAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"draw_bn(bn, node_size=2000, node_color='white')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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3bC2ookJBJKCufcoWjD0M7e+5edc/sgdf8EdK5hKdn5uc8Cnd77r+fk\\n8UHgKuCDduo73HdIK/RdtDHqy9HMZSC6Yn/bXcBTwCg7+YxzblPgz0Aw2538bSeslUhCIqFkMPtF\\ntwAfCMzdOXcHMr66k/d+Zk783YAfeO93yJPXweicxD7e+8dzntWiQ36nIxNN5w+WoTvnHkN+hsIa\\n937ADO/90YPJZ3VE8tWd0B/SmkRCSeCcezdS4x8dCYht0NGEOvJb4f43sIlzrs+pY+/9LcjFw1+c\\nc+/Jedbqvf8WOsQ2Bq1XnOicq8jNJw+d1c65I+phUhX8LfJpfVE97LGm+rQu0p/3GlevhJWAUpuh\\nTWHdC0iV9CJwQs79a4CvIntOGxdI+7/Ax/rJ+xh02DlveouzLTLS+jiaeeSN5+CwWli8EzTdDL4z\\nMhveAf4m8DOhqRYWOzis1O1abFhb65XCygklJyCFdSugg9IP0ddvwmR0SPkDwGP9pD8V+OUAZXwa\\nWfKY0k8ch447PG+C5z12vwbYvAZOa4SlD+XxaZEbHgLfCEtrtEBe8jbuL6yt9Uph5YW0JpGwymDq\\nnT+gmcInfdT5nHM/IDs30ey9PzdPFmHB+HZgqu+n89qOqVOB3b33c/qJV43MQn0FmRBZDJzVqPMa\\nFZMLJczBq+hsxAI4YXU9G1Hm3GFj4IqHIzexA2FF6+WcOxa9610HmzZh9UBak0hYJbCdRj9F9pxO\\nzBEQI4Hj0JbVQ+jfK+jTlscm/ZXnvb8Y+Ze40zn3qvm8bjKf103OuYuNgbUiO32bIVNRZ1WBWwAV\\nsyyvV9CHEvxt1yF9VcAc5EOiM/O3fb9z7kM59e9xzm0YXZ/hnJtjQq8oRL67m8zqbj7f3SHubOfc\\nCdF1dSVcMQaGbYW8IL3P6tYfBumruxDSSHQNRhISCasKZyJf0x/1to00wonArWRbTh8rlIkJlzuA\\n9w9UoPf+AjQ7mAAc4b2v997X2e+pFu0FZOB1AXJW5Iaj49sxHJpiNCGLff+x+++gE9g1yDfqdD3+\\nN3Cdbe9dRs6yvJw7C81ydvPePzVQPXLy+JD3vh4dOM/ru7vAAcITu2HYz5B7vpeQ5cDyIgrdnuSr\\ne11GEhIJKx02Yv8EYnBLcp5VI4Z5IWJCv+9PjWT4K7BPkcX/Dzrw9j3n3Kg8z99EC9gfAP44Ajo8\\ncGCeiPmI+gGaWfwSOa34MtSNlJHY8+1xgANwzp2HjMfu6r1/gcHDAXjv3wBuA7a0WcN5zrl7nXNL\\ngV8BuwI/CbOmYfCFycAelslwNGULDpK+gZyDH45mSzvQW1IfAXXl8Avn3DvOucedcwcsI8i5eufc\\nr81x0kvOuTNJWGuQhETCSoUdjrsA2M8YWy6OAh733j/KwKqmgDuRo6PKgSKawHkH8bzbnHP1uVGQ\\nue5jgX+0QvnHyG+4L5+Q+BvyMRpwINCibby3AZOdc7Fa7LuIF+/qvR9I09MvzJDhfkA4PPhx5Nai\\nDjge+fQ4xWYdZ3fCem+igyJ3oROKufgjOnn4DnAEcDCaWnWh047IHexGSKhfG9XtJ1buVCSHjnHO\\nHb8i9UtYfZCERMJKg517uBb4SD61iqlFzkCj/I2Rqvy+gfL18lnxInIDUSzej+w6LXDOLXTOfSJ6\\ndgvyKz3FgTsuX5lopjAK6cTCFGE+WtAIqATMnEcwy9EYPd4HuL2/hfQicItzbiHyvzQb+Jbdv8p7\\n/7T3vsd735WTZsxIaL8LOaU4zOpyPOZc27A9ktLlSJi0I73Zv5FQGQVtQIP3fjbaEXaEvcPDgK96\\n71tM+H0fuflIWAuQhETCSoFzbjJiJCd77+8pEG0/xIvuRPzpFu99T5FFDEblBHAQ8h90DRp9B/eh\\neO/b0JrIyR7czDyJHVq0eAft0z3d7jciH6N5MNZ+347uHQ581Dk3axB05+Ig7/1o7/007/3nvPfB\\n+d1r/aYCpgM3IHet9yApc370PLax7oD1kFCZm/PM8IpFCZ5dX83zLGEtQBISCUMO0/3fBnzfe/+7\\nfqJ+CfieqYQOAX4/iGIGKyScCaBPIsZ9I5l3OICr0bpJT+6qekA+ddP76K0f6wSapK3aG3jVe/98\\n9PhZS3KSc+4rg6A9RiH3pbnkxdcLgh/xgO3RAtAT0b1YynjkAm+ShVdZVq+FFmUy2tg1H1V7SpR8\\nCr39mCeswUhCImFIYQvRvwfu8P2YyjZ/zlOB3znnJiHf2LMHUdS9aNF25GDo8/J3fQyawXyWbCH4\\nH8A+w+GpP+VLVyC/09Cup0+gEfpNQLkG36chVVpu+U8i4XaGc+7zg6F9kHgL2NDKXFwJr3yebFrz\\nNFqDiGdNDyO9WzfwQ7Rja0cynZ5T8qXOuT2A/YHrTfD+BjjfOTfCOTcF1f3qlVi3hFWIJCQShgym\\nn74KMZMvDhD9DOCHth32IODPfhDmtU1F9C/k/roY/Cmck0CaI1D/f7dzrtzynN0E37pQ21h7odDw\\nfTSSVq3ooMVR+t8JfNx7f2NMckT7Y8C+wDnOuU8XSX+vPIq4fxFSbS1wzt2xFCquR8ar6pGe78No\\nKhdwEOL2o9BC0u/R+kQlMA6WtksezkcL1Ud775+zpKei5Y0XkRbrGu/9lYOoV8JqjHTiOmHI4Jy7\\nANgJeJ8x8ULxNgQeQKemm51zfwV+5r2/aZDlnQFs6L3/7HLSW4P8Q8wFjvfe97gV9Hu9uvi0tk0D\\nn0brIP8CrqyFKwrV6xvowMiv8zxbneqVsOqRZhIJQwLn3OfQDtCD+hMQhtOBy0xAjEYajduXo9ii\\nDtUVgtF5MFJ7Xeqcc34F/F6X2qe1c67OOfdp59yDZOZPtvbe7++9v2lNrVdCaZGERMIKwzl3CLLe\\n+kHv/YIB4jYCR7Js6z37A3/33ufbuj8QHgdGOOemLUdaAKzc/YGtgB8559zy+L0ulU9rJ0x3zv2S\\nZTydc9AM65ve+2Xr0WtSvRJWI5TawmAKa3ZA6qW3ge2LjH8OkRVXpPo+ZgXKvwaNcle0HiMRX/wu\\npoYNJrVnQtNNeUxq3wh+xxKZ1DZ6TwEeRZqirwITiklbTL2mQ3stdCRT4SmUnIAU1twAvAupND5Y\\nZPxatOtmU7sejhZDR68ADccANw5Rfcagk9mzontVwOEj4T9V0NEIzY3QUgU9I2XC6XCgahW1t0Pm\\nNn6NTDDdgLbali1HXn3qNQZ8FXRYvY63d7VVqftZCqUNaeE6YbngnBuPFkS/5b2/vMg0JyLzHAfa\\n9aHASd77wZx3yM1zEtruP9Zre+sKwep1F7J/1ImOCZzhvfcu83tdi3xi1Pu+p5uHHKaiOwb4lN36\\nBfBr7/38Ico/1OtFYKQ396W2zvRB7/1+Q1FOwpqJAd03JqwZcP34Ke7vWbF55MQbjk5TX+O9v7yY\\ndLbN9IvIuF0o62jgbudcQ6GyBqLLez/XOTcX2M0590q+OIPJ03v/lnNub7TeEazSVjvnzrQ4gYG+\\nhexHvZSvvIHapIjnZWit5FPIZMitaLfSvb6fkV2x7zBf0jz3fg583jm3t/f+zhXMf7noG0z8FaUt\\noQBKPZVJYfkDUA0cMRIeqYKOsbBkLCypgo4G6ap/PFJ+i3s9GwmPIBtuVf3lEceLyqxAAuJXg0x3\\nKHB/nKYR/FhYmi9NkXSNAI6oh3lV0F0EDcXmeX2dVC++0UIVdOe0ac9YaM1JfzTw8X7yH+j5EciA\\n3vV10F6l9mkbC82F6jSIeg2YpjFTN8X942PIjMmRg8l/RegbTPzlqXsKg+QzpSYgheUL/fkpvgb8\\nGPA7gR/Ah3FLFbQU6+sYjTZ/Djw6GB/Jlu7ZWlhaZJofDZT/xtBSq4XjlmJoKMavc5Rn2/K06Qzw\\nI8Cfnuf56fZsRv/pu2tFR1exvqeXx1/1YNKgd9E1E1qXxx/2YOkbZPwB+0ny1b3ioeQEpDD40J+f\\n4ovAb4D8Exfjw3g9SzNQvEZYWgm3VsDrjdAyGB/J1XD3GOgpJs0ZStMv/YOtYyN01EP78uY52PI2\\nyGnTFU1fqF1r4Jbl8Fc92DT+jMHlf1ox/XR539Ng+kl/tKVQfCg5ASkMLjg4rBGWvpLnY7jBGEy+\\nZ4XCK5bmhiLijYCeRmgfbP6N4H9SRNxi6F/eOq7fTx37y3NF23RlvZOfWLuurHcx2P4R5b80zAgK\\n9dPlfU8r+k4CbaX+hte0UHICUvAA5wJX9/P8JWAvoLoWFj+c5yNoAz8efL5nA4WHwNeA/3o/cRz4\\nUTn5XwV+l37SfBD8ry3/8eDb+0lTDP3FxDlTzMBPzFPHQEOhPI8Df3aBZw78C4Ns0/Hgy8Bfuhzv\\n5OeWNpfe/tphKvg7B7hXqB2KqUsxaawvLS7UT1e0rGL6wCzwH7f/L9u767a8TYWW1igGEUpOwLoU\\n0EnjB5EBuTlox8obZE5q3gCuBIblpHsJnUfoATzgK8CPtP/D1Pl9NdJ97wH+cvtIrrL74XmFfTT1\\n4D8G/gnwU+we9v9y8L8Ev6ddE4WQV40F7HcE+Drw37Yyy+26Afw24LcCf70929WYwFctHlH5ucGh\\nBeQRdj3G6gX4ygJpJiJBUZZzvwb8IZb+Qru3Lfi9jbaGAvn1F8rBDwf/PmtTZ/eclVcOfouI3toC\\n+VSC39r+jzBaHPh3gX8P+HHgN4/aq4FM1RIYYSwQDs7z3vYH/wb4vay+oX+Ed5VLm0Mj9qfs+lpL\\nM8Xijsh5B6HO+epWZb8fQP32gOj9VEX1Cr+1aFCyM/j7rFwP/jp7X+H6LjT7yBUSR9v/HcmEhFc9\\nu+wb/HXON9Zj32VwY74Q+Fp0rxWlDc8ft3RXAt+M8im3vCaXmt8MVUhmOVYRnHOnI4dm5yEPbJOB\\nS9Ce+2vRwaiCzu3RR9X2XmS98z3AZ9CK8HDkH+BCy2iuJeiGZeZB90WOY36DrIBujowW3Wp5lBei\\nO/r/bWAacohQB3wQaAB+SvYlfdXizrTrRcBJyJHCRVFeH0F2wScgF6B1UXmbI1OywfHNycA/jcYt\\n0ReIXZehzf2hnpA5BzoX+C0QfGx2An+x/yH+42gLz2mo8TdHxu7G2fMxyLIrFm+s0Vptv7sgg1VH\\nWpz10Gm3Mcg8bbeFUP7uRltsI9yhNn3Url9BJrsrrW4/RaZX9zK6y5HBqrB/3ZN//+o4ZJTqDmQs\\n6zar45GWZ0B4V3+yvA+yus1G7/fHqJ0vjei9Dnkb2sN+JyFjUd9HR8HPifI/CPg6OnUYPDF9Ab3L\\n0fZsquUzBjkI3wEd498ZbYsL+Cmy7w5Z/y5koTcfJkN5RdYlwtZsLKutvPf13vs6L8dO37b/9cCJ\\nwL+i51vm5m15OXp3xzUfpZZS60JAfHkJcGieZy8hnvg7JCjaEW99AAmUexFfbAXNBCrBb2cjQyyU\\n2YisKrpXTu8RXY2NprZAo95J4DcqMLLFRnPxaHwYmuoDfoLRMsyux4HfwfJ2FkaD3xj8pyMa/yca\\nXYZRd5ithNlEA/j3ohE6Uf5xXWM6jwH/tzx1/SQaQYd7E6N65NZ1qtGSW+cKpIYCzYACnSMsfhhJ\\n55vV5LtXjmZRs+x6ctSeRP/jd7dLgfczgmyGlVuX91s9RuV5PpZsBB/q46LramujXcF/sUDZIc3I\\nnPvV0fNC/apQyO2vgD+vn/jb0LcvVKDZB+D3y6Frhzzx7XsLM/QO+10MHGHf5+7IH9N19nwOcJw9\\n+7TF70QyqxmNZ3rsfxOScU8BH4i++eC8aXO05Tk4w5qDfD19odQ8Kw5pJrFqMBMNPm/pJ86BaFD9\\nHBIQW5I5t38FWFIOPfsBB6CevZUlLEMj8rNRLx6P9qlOBT6KRpyj0Mj0btQLa4F5yHfxJDRiw+7/\\nHPltbrW8Ana0e7ugUXIX+jrCqPtRslFgFXIw8ALwjD0fbulDOZOMpvXtXhiGTUO2MVqs0crRF+Wi\\neJDNWvZCtilcFGccGQe41u6/Zc9i/6gN9tuKvvIOdNqv1u7Ho9SHyPwrLCXjLFXWFqCR8BaWLpzq\\nCnlVWhmnR+2wk/2+SWZtc5GlH2t570Fv1NhvC2r/gHL0rt82WjE6QX2hzvINjoe2t99haCQS2n8X\\nNDp5As08c0fqNag/YW1QSzYTnIbc0l0Zxa9AHXlruy5Do6aNLZ+QVzeaWTREaa+O0gQ6xqC2eTpK\\n+310/P8cdKgDNKOZbv/rkVGu3bKs51ny+qi4g9EMvwM1S8AEq/a/0fd4iR3cC5YGXjbSw0QIZHqm\\n3nt/MzpWzSecAAAgAElEQVRTFPv8PgB4yXv/3+jershJ1H7AWc65iNQSo9RSal0IaIY/t8Czl5CD\\n+U77/2PEG9uBy6M484hGp9Vko+tKGyGNR6PwPZC+fyKZzjnMNEYiHXcZmQ65zEZg2L2L0H5+kL6X\\nKHwc/G1Ifz7c0h6HdNoV6BzBTjkjtkrw0+3516NR7HQ06s0ddVbbvXhUXxE935xsphPu1URlObR2\\nMRPNZLbLk39IO4ZsND7S6rFBDk1VOenzhTqyUXRFzrMwgzkxepZvlrGb/ZahWVigYeM88cvJZkSh\\nLlVohjg6ihfy2Az8362dAg17ks1cPgP+o3lomhDVq8xC3CfKwW9P3xnsPdH1TEsT+kU5+A+hmcBo\\nstlILfhD87RdDerrVZb+l/aOyi0P0EyyE3xLlPZZsjWJL6GZ5R7Z87ujbzDI+6VIG9gO3GPPdrf7\\nx4U0aLwxHclVD1wZ5dVnTQKNgxYDtXb9e2y2gGYS3cC0KP73gUtLzbdCSDOJVYMFQKOZWsiH64Gb\\nfW/n9u1oRBPQA/h90ZCkjWxk/HU0mjsfTT9Ao9HgvN6hkfXp6PjsjhZvMnIxNpnMjWUtWqcI+v7Y\\nGJIDLkNfzTNoNNyDRsMdls9D6GvYEo0uJ5KNFrvJXMJ1kNkKj2crkI1oR1q6CrT+EjDcfsPCzSlG\\ni0PS1aO5/mNWXuxkGqtXpf1vRg2NpW1Blu3CaNaRrTdcY3GGoWPZDunR6xEXCfkEBJsei+z338hv\\nqSMbee8VxQ/t4MgcSUPWzsOsvFrURi32fBjZzKUKvesKq+c4e3Yjmv15spnYEmuHHqRPWc+ebYoU\\n8OOtXt+y+D1R2Mju7YR2W3i7noje8x8i+rexfBZGZcczkdA+Y6zs2FbQDmiW9lMrowetbbXZ/xEW\\nf5bR+4ko7frR/ylorc5nt3JNdniySchbqPsELOidlBYrOqCJfuC9fx1ZGzjU/L+/H6mvYrwe/X8F\\nTbRXCyQhsWpwH+IhBxd47sh4Os65wO9iXuHLoO3NPIlrgW2RDYKgchmJPnyHXvLb9ny9PAWDFkZB\\nH+/diIH00HtBu8zyrEXqC+x5OWKUgfnNsd9hiME1A/cgBvdklNdBaPo01dJW2bPH0Zy9koyxBMYI\\n2Rz/mjxtUWF5dSLG9F705ccdPTBGEAMLz96xZ8+RqYNizvACeolVVjdvv/WIQdVb+k3td6b9bmzp\\n51ooJ1MDPWu/48kY6y5okRer9yzEeFusPbrI1FsYrZ5MAL+V8xz03rzdD6qbiVH9NkDvDSRMfo/e\\ny+ZokwFkwmg+UqmBrA7OIWP6QT34z6jsMqQye42+iAXrHCQA4oHJONTeDYgrh/ZeaPm2Wnn3IM6a\\nW+fQv19FXDdydtJJXxyLVEtNyG5WsfAF/sf4NRrfHQb8w3s/L+f5BtH/yWT7T0qOJCRWAbz3TWiz\\nzSXOuYOcc7XOuQrn3L5k39f2zrmDbYfEaYiPvRjn0wMvPtX7G1qGzwJ/t/9hRDoMMfweNHu4g2yV\\nrAcxqtssbuiRXcj+95Pog49HdSPRrOVK9EH2RM+DUFqCPkZn5S9GitZGC2EHz3ZoRvAm2iVVjmYX\\nNcCZSL82F41UuxBjCjOIoCwOgi3+KoO+O/y/19olXocoJxMCJ1gdQvkdSLcdM6/ATX6ImFYPGbNZ\\nH+0ce9PiVaIhYafVNejssTRvRHXC0jng4ojG59A7DHSGXWHj0exkFr3fS5hRdKN32kY2iwG1zyy0\\nKy7sgBgGfMjoCOswf7bnLyBB04yU5/dbPqMsL4feGWjH28VknXix0fF6xIRfQzOoTiuvHM0eqozW\\nKRZvAtoJF2ZZoJlWF5rddSNh8aA92wjNEjuQLvY/qM8G/E9E81VI8M7Nmn59G4zFqDEyOynM7PNh\\ncvjjve9BXXPDnDg3Iw+MJ9PXS6wDznbO1TjntkTC6oZBlL9yUWp917oUkLGxcE5iLtp1OAf14d8i\\ntVMTcn7zAHCCpXvR4n8e6KkE/3nw3vSr30GHjOrRvv/70P76BmR2A9Pl1iB9ftghNRztNlqPTOcd\\n7/wZDn5ajo643MKkKM7laH0By/tQtDNmU4v7UXRuoSbSPy9B6yZhzSGsr+SuT0y0PL9gdQPp/yHb\\nGTTS2iLo8vPsYOm1vjCaTG8fytuKwucu+gvlVq+JeWiP4xRKf4zR68Gfm+f5GMs3XueoN1rDuQ6X\\nU/YEtLsprt84SxfurY/OGORbbwl1qgT/OH3XdBrReYVCdXPQWa1+vKzvxWs25UZ7OE8S6jYR7YSL\\nafor+Nvpex5jHPgjoz4Qwvui/78g2901DPwRyrvZnnciGfUQ2W7axWiSMhe4xL693dFE5FiyNYkX\\nkfwqt7RPWLqb7flJaDywEDg4+v6vtDJqonth3PYJK3cOq9nuppITkMLgQn/mDla2WY46aFuTzXJc\\nZQxpR7LDhiHPYeBPXQlturLNcpyGtjGf3U9ch06LD+ZdDLZ/RO96pZvl+AQSOIXyfhkJpm7y01aq\\nbxcdwbki595GQHep+Uq/dJeagBQGH0pl4K8GThus0bYx0FWp36JoGmoDfz9U2R110H4oGhXvQW8h\\n4dFMqL5Anqurgb9qmF1r73AgIXELa66BvzfA/xN8D/in0W6vvfrpJy/T+5T16mDgD63LvwrsmHN/\\nI6Cn1DylX9pLTUAKyxdcP36Kr0HqlJnIXHI+H8bvRVsOw/bUfnwdd+WaWu6v7Cj/nlqpyGeH6X/t\\nAGW9V6ayu5F55/bp0FMo7kaiy/cX573gy6WDbgaay6BzG2jeAvxlOfH3RGqrQjQW26aFTIWfRmYq\\nvFD6GdBaq7r1V6eeWmhxOsj1CnCfg+71oL1QGsBXS6XxDjp6cGktNBXpu/tHtbB0OnQvj5/vYvrK\\nDFiaayo8jv8KOqQ5Ag02JkJ7TWQqPDfvMJP47QC0raqANos1AxfleZZmEimsxJfX109xRyN0RE6H\\nWsx5TfDN3FFFptOPwpx6eMPitTVCZ+S0ZRF5/ByHsuvh9Sr7eM1HcrCP83m0Zjs/p6y7I3qXjoGe\\nyK/y4aYDvtnizmuAZ6qgu1EWaFuqoLNOwudwtPmmqQ66q/S8K08drzd6y9AmsCvqoMto7mlUWl8n\\n4fQkOtD4tjkd6m6E1kY5/emuE5O9yBzcdFr61oj+o4Cj8vjD9g3w35znPdbWzVXQ06C1+sPR+vzV\\ndapHt+XRbPXuQufc/g+ta10evYsL6s3ZTpSmo0H68svQQc4laF36cdOXv1kP86vUbm2Napf4XVSZ\\n3v0x4Ps59eqsgq5i/Hzn6afL6KuX7v42ejsdqgIOb4DXojZosXM+vcqzuBfXQ1Nu3qvaB/naGkpO\\nQApD9CK18eO7wM+ABrs32xhfAzoM+xB9BcTf0MabKRbvFOR5LuRxLvDzfsq9O0+eB9mzPXPu9wAT\\nIno3RRuJGnLynIgW+JrR7t370Ah4ARqVzQc2sLibWZwH7PkLOWUuMQa5AI28/w8tWi5BTOQWdDTk\\nTWOG30RblV+P2u1AtIFmvJW5s5X5CXveUOB9TLd8P5Ln+b1oI8M04CtxG6PdLs+hTQ7TQhnA7Zbm\\nSqvv62gXzBR0lOBLEc196EKbf/ZGh7Wesna839qhBS02X2r1HWlpjkE7Wl1Ur2mWx4XL2U/jOm2M\\nNlNV5Il7LdoNNA14N1po7sPwgX+gnboF657CCvCWUhOQwhC+TG2d/ZH9d8hcwELEjL9KX2b+CDpJ\\n+rcojw9juzTsehwaQY/NU14Fmb2bEBYBe9nzH+c8ezJPHs1Afc6971jaPY2BfB7tZH2b7HzcaVH8\\noy2fM42B9uSU+yDaOfKyMcUn0GxlEdpifzqwUZTfF4DL7H8VEh4ft+s9kMB4/wDvIlhBObfA8xeA\\nTez/lsDzOc8fQILi1OjeUUjoP4JmHMPR7tYFVpcZg+wvGxoTvhXtjF2EBNF9SIg+iIT1F+hrmfgT\\nRCeNV7Df3k9k2yi6/zLwruj6GWCLnDibotlIZam/v7U1lJyAtSmgkcyGFvodyRQbd5DxzkZnzBqQ\\naZ7n0MHkfKP919CRhtuBI6M8PoVmFw1R3r8Azs5Dz3F58v2r0bEhGunGzz6Zh+ZXjfGGmUu9Mb2p\\n9vyLRuvGaNZwGdle9vOB70X1a0LMfy5963q51avLmNIngHH52hgdKTnEnp2DZlYObfefh4RXv+8F\\nHav4C9I594pjebVhjBepwuahM2obWppmZJ7rTWAPi3cwEoDb5ZS/L1LBzUW2hcqXo++cjGYUP0cC\\n7HWkInsdCY1mNGI/B533O8zafcC+PhAtaBDw65w4m1mbuCjeH9FMd1ke1s7nl/rbX5tDyQlY0wMr\\nwWn7CsRbtp5QrxH4LfYxe9Ox+0YLVdBpevIlwAlRHq2RbjqUtQOwaCQ8FtGzNHd9o076+a6xitOS\\n87wLrVH0orkRui1uKO864F85dW83vf0LwE3AUzn16Y7KmmdMPl+de2zt4Wh0gDdvG1ucE9CB7LfR\\nmbn9LO9ZA7yXEcCPw1pQgTiTgAVxn6iHd6ztlrWtxT8fjZT3svL/Uw9zcvLuqpdg/TpSDb3cAM8P\\nsu/kvvczrW/8035no8HC7Dp7t43g7d105vb1QfT3EcCJtk6UW6dF9q4+bnl0j4X2KI/H0MBgk1Lz\\ngbU5lJyANTm4leC0vQpaaqGliDxbqgaINwM6atGWwXxxTifbcVMojx1Fj98YupY3j+nga6FzoDYo\\nNq9CO4huQmcGagfIY2PtovIzoXWANu6ykfXBwKJaaO6vvaO26hkg32bg5UH0nza0m6t1JrQMVP4m\\n0L6ifWwGtNdCh9MgY4SDC2qhfUY/dZsBnbXQ7nTYecC6RfTmrVMx/WFGnt13KQxtKDkBa2oYzB7w\\nYvb+e7Q3fr0i4nkGd8Yh3777odi7P9g8+ttzv6roWQ6a2ys1Q2sdynMO9dq10zGU/aeY8gfbx5bn\\nbMUYCp85WYl9q6TnINbmUHIC1sQwmNOkxZ64XVknczdH+8WrkTnoQmXtQd8DZv2VlS+Pl+l9iCk3\\ngMw95NJ7AzqDUEVfH82F6MulJ5idXhmntYf6lPIN6GRxf3SEtryuAM130ddtp0em168rUP5A9Z+a\\np/3Dye6VcXI6jj8KmeqoIzMFHk6KD+I9lfRE9doaSk7AkFZGuyHCVr68/qKLiYu2FPYAZXnSjaiA\\nttg3cx3yqbA0p+MGp+3bIwbXjGwd7Ufm3ayOzNvbA5buHqQiKrc4w8jsA50EfpF90OPJbOdMNBq+\\nTG87N1WWR/AFcBRSx+TaAypHo7877Vmu/aMxyCeBA/+PnGfBR0Dwa1AXlRFsPdXY9R5Gt0P2n8bZ\\ns92QIPuixZliedQhW0QTwP80atvqnPIdGnFfSWYfyln9h4HfF3lk+4yVv7e9i3qjbwPw38tpu1pL\\nW0Xveo5CXs7GIQYa2j+f7abgyyM8K2TfqQLZLeoBf2zU/o7swOMs+x/7o3ZROQ3IltGdaHQ93trx\\nTtQXg42nPOdklgWHbEjF/fdh5IthRJ60w8EfaPF/joTD+na/nN6mTq7sp/7FhJmWz1309b1ehb6J\\nSn3TeyMVXbB9FnaadQJH98M/ziXH93UKa5+QeAnY0/5PRIeGvpUnnusvLtpZ043tEslJ+/Bw6JqE\\nHJ3cD35DxES/Fn0Q3hhIYLjDwG9iH0+F3fsX+A9HH/wk8D9DTO4kZFxvPDLg9wgaLe1pcacgxnms\\n/f4MMa+y6AMK7kW3QIx3o6gcwB9EZvgvfGzvysM0XgB/JhnzDcb4RtiHuZExhAqymcRdRs+e4HdH\\nQhLwByOTCkGAxEzSIQNte1i+v7H2GR2V9yvwF1rc3S0cZ3UYT8a0N7b2+oC1QThdflBO3bD7ofzx\\n4I9Ggj24T60mEyRjwX8F2Q+qJXMOFN7phmSnt4MBu9lkzpXGkAlQh/rEs1EdD4vKw/Layd7zLKPt\\ntxG9o629T7d61JDNBvYyeu9EJ8YrwW9pzx4gE4TjwF9PNpv7EBJW1yFh6q2MLvvfQCakzyPr7wda\\nvd+0653AfzZ6vqe9n/3t+ioyN7OhrqdYmUFIvp/e39S/yFzeXhK1WRlZv0JuUtrQ5oMq5Mqjkzzu\\ng3O+7SQk8rVLqQkY0sqI8e8VXV+ALK3OJvMXvRRtoWtF2+7uRbOJF9HJz6nWwTyZJeU/IyuQj2FM\\npwL8t63j3m+dM1i2rCHzdhaERDwyDMxpe/u445FqeFYT/Y6yco5Dli9jJl6H7BFtQW+GC2JyQXBs\\niSydxswxxPluTp5xHjED/2YO/SFMtHoHIbRP9CzEjX0fl+d5HudZiZhbZZ7738pTfhw2iv6X09fK\\naX1OeVXgm+x93YiERHhX4R3ElkaDwB9j9EyiLw1lOeVOyclzd7JZTpgp5eYxPM+90I5jc+5NR5Z2\\nwyxoHBIqIY94ZugQUw3Phtmz4Nd6FL3bp5ze7d1AfrqwtBURfQ1Wx9jPdrAyXIMsBVfS1094Bb37\\nxX7I5ImL4nwEeVgMbRW+neH6bYvoCu42HifTEByDDlbOA75uvOID6GBnO3bQstT8bHUJa60/Cefc\\nBmjbYvA2FfxF16G9+aBthccicw3j0Ha868gOZO2OOllwXbAZ4BehDd0X2s2J9tuINpAHXw2gDfa7\\nkDnpCb4BQHYzDiVzjLIe8tvbSGaMfg69PQ/90X6rkfH74EHtiSjOteiU1b7Ib8CW6ERUlT0PvggO\\nxo6w2rWL8pgQXZejgwbHkH154VkDOixRRuZze3aUrhzZzgi+IMahwwoxRpI5SzoKDfnm2281cIjF\\n60GSPtA6Gh0oKCfzI9AaPd8YbSMK/gm2Rob/R1gdhhn9dyLOcAhq0zLk9OhZMqdN4R1XIW80wfnP\\nMVF5e1hb7E5vB8kNZP6uO8gcP3VYuW30dewS/FnUGr2TyZwpvW101Fm9J1sdtrPrnS3v0AdPsnpV\\nWr0PQ/tbsev1EfdsIXsXY+15Dzqt9lm73o/ePqgPJXOQtNTq8Sm7bkF97c6ojaah91KL2reHzPnz\\naKPxJaML1F8eRp4Jy63+o9BhoIstznPALy3/FqWtJPNnUY1cZ+wQkb0zsAnwPuAc59y7vfd/QU74\\nfuO9r/Peb0uCUGoptRJmEk3oG36JzF/0bGBWTtxW9H2GuLPRqd4O5HenG/X5q9GM41igqRo6PZlK\\nZyRSQwD+HDJVybttZHOn3WtEI25PXx/BYZR3hT1/mkztU4mm8fPQTCKMOsMMg5ywmeUxgswncBgN\\nhlFcGKWFUXU8eg4h93o42ewo0B3oC/eCL+cwa6hDo/CRUX2C+iKkqSBbM3FoTaIyp/yxRvuuSCUS\\n/GE4evu5JqJxFNrFc2dE13R6q8bqrJxKy2cp2fpQ0HFXken7Q9vF6wzxO9jZ3lEY9YZ2jH115wsh\\nz6Oi9ovjlyNVTK7vhiqk+sqdieTG+wDqo2F2M4psJhHWe7a254cYPY1RGVehPjwS/AftXYQZ5WOo\\nH4U6bAh+F7K+MQb8U1Fel6EZ+HpInerAnxDVO8zIQ/uNB/85pL4M/aoe9aE7yL7B0Jc31bvtIjt1\\n/wJQbd/8FPuuJ0Z84H7gY/b/XJK6aZ2YSRzkvR/te/uLhvzeEy8OcZHjn1FIaMSOyeJ0zT02iA4j\\n6YeB3yFpEnzqVpONPAMWA7va/xBvHDLlGV7C+ci40ruRRzCH9FtzkV0EyJxel9vz4WgkFnw2xx7J\\nPoZGmVsg34hhdBtGtRtZus+gryn2djaRbHSPxTk7el5rdD9tv2PIXH5tbb9laATbTuZi724yL2aQ\\neUobG5VVFpVdhk6JtSC9YJmV41D7/cTSvsfSBrd9I9Bo+SyyIWWT0d1o8dvQjOkHRsN5Rk8NGm2/\\nZO2zB/IGFegbTsaB2u1+OZn7zBH09pG9DdlsxpG5/gzwRve1dh04XPx8tt0L+dRamiusvBPp7Wr2\\nXWQeCuehGWknar+FaGgdnr2FZqLBoBVkfdKh/roQzV6a6W3zJEy1PWpXyGaSO6N39792XWl5HYF2\\niiyxdAHftzz+k9M+E+w35N+W8/y/aIZejmZNrne204B7nXOV0b23ov+5/qoTcrA2CglX4L4vcD9G\\nB+KzVdG92Pfswk4ofzkn0Zvow9klutdsBXYh5tSJDORMREZ5QB/e+WQMfiMy1comVpEnkAojqJMC\\now8uMT293Yjeiz7AgC5k7GcUmUomCKkNjK46oyFuuHei/2Voej87uheYxc6WrjV6FtqhG33Q1Ujw\\ngdQ4sXNfj5hucAs6n8xN2BSkI+hGR5R3QsK2jIxRddu9kVGZAZ9E7ybU5VUyV5nTkf2PecgrfSWy\\nJ1KGGL+3OrYiofcRS7e+lRfacnv77UHtHrdboOdoSxPwDllb1yAdx1Rrg3AvxhQyGyVlqL3aENP8\\nnNWrgaxPbIZsaTTb9ZfIhHoQyoHu4J97CyToN7LrmGs6Sx+EbSzE55OpD/dF3Pd2u97HfoOQaEGq\\nqR3tuoPefe4V1E6x+9IYZdYWHUjoxWkvs7q9n2UferApthRp4mYzML8rhkesc1gbhcSKoB0N5j+D\\n+tyH0aA+YGEFNL2fbET/H/QRTgB+BctWyv6LGvc+zF8hEghXkXXuHuRPeRu73gN1/vPQRzoSjYx+\\nipzjNpNNcSrIRnPPoq8gjCS3QMxpDvpog6GfwESa7DeMfK9ETK+TbETeZXlXIaZzC5kP7TKyjnMW\\n2Ug8fGH/st+liHFugZgWSNhdTYY6JKyesusbo7IXWvyjkR4+OCBe3+IsRQJ3lD0PfqoD5iCGGugq\\nR23t0Eseb/Xe1+h4Br2TcvTeDkPtvB2ZEHwbzVDCe1hkbRZmYoFBxmXuTzYaLiMbFNQiZv8jq+s8\\no2MHJBBD/AVIECw22re1+2+jaa5H23eajYb1ELMPbeGMxiBA/5fMR/Uist0ZzagvbEc2Ew79aK6V\\nvwj1y9D3nkECHOA3ls/XonIc2cygzOJebnk005vR/wIJrb/SV1AGnGq/uyA9kkODgftRP7kKqMy6\\nG0g2XYBk0zUUHkSCPpepeXxfr9sotb5rKAPmezbP/b9je6aje63ABdH1sUgbMs1+21Cfb0M8Mjz/\\n+CRoD3rfqeAvAP8i0pmXm852d7RusI3pS6vsugHprcMawfvR9sSwE6cM6e0nka0lBN17LdqlBL13\\nH2Fxgm6YAUKsLw+/Ye2iLidOHMLay8701nuH3SxH2++x0bONkc/tqQXyrbW6xOcewu6msNc+3mkz\\nAennh+fcLze64nzCbph43WQG0pnH8UZYeReRbYcNbVyJ1ooq6Nt+gD8cnSfYuEBbTyM7FxL6y4R+\\n3lMZWjfZJs87c0b3vuRfjwrpZ6K1ncac++H3cLI1idy1kqn2vk6Pnm0B/huoT4Yt3PneZe5usmq0\\nnhZ2gNUa3eX2rMJCWJPYEH0T1WR9yCHf1Heh/nc7Ws/I9W1dDf5PitOGZFl49iX7vk8lG3+U5eMN\\nSItwD5LZD5Wan60uoeQErO4B2es/N7quroXFD4Oar0A4jGyxczhaXNsJ/KVoD7pHC3KbkC2g7oYW\\nrfvL92Uyph5vmb3dnj+LtkOOsTK3QczvQbKFa6K0ZWT76v+L9rDXG017on3poew/WdpFZIesbrW4\\n/zsA3SGEQ17tiCGFRdC6AdpnHPgDcton5NUU0RPaJz71/Vu7N9zihPuB8eSj8wrEHMN211q0eSA8\\nn4WEYrjuAX8M2ma8IKeecb7xAbWB2io37gy0iJwv7iy0+Lw4TzuHvIajMxDFvKfB0hrCZWgLdFx+\\nOMi3AYVP1Od7l8WWGedRaxv5Ss031qZQcgJWt4Bm+xuiaem+aLq6dRzH5THL8SA6dNYD/jZjKo9Q\\nerMccbyPIMY2FGUNJb2rip41ySyHRyfbf2Zxv48E1pv9xL8E/F/6Kf8jlsdg6j8YsxzN4LdFgj42\\nyzGQkBiqvpXMcqwknlhqAla3gFTIryKV6dPAMfni5Rr4s6muH462v/4q6sCri4G/ePSbDPwVTfOg\\nDdwVaeCvYyADf5ehUXXYhnvxCpY/C23PXRkG/v6I+v4+StNRD+0hfhuayQWTISupbyUDfysplJyA\\nNTm44p289zj4yUBxdzQzzpXQHvbX11kYgYz13Wgfeq38RrQMUHarma5uyRfnNMt3OjK7nC+P90JP\\nMXnM6D+PrlrbWFMLXTOgYwXy8tVkNozi9plsdNaqLfPSe2ORcXbMTLwfNoh33W97R+3ZifYP3FIL\\nTQP1CaPjl7XQsyM0r0j5O2amwtumQ/dQ1j9OU0z895rJ9hXpn7l0pjD0oeQErOmBfpy8R47Yb0Kb\\noHLjdjZCW67T9px43eakvtUc7/zXFuCWoE0zA5U9PIrT2ah4PVXgG7TB6VzgschBT6eVFfL4Edp4\\nc1VOOd3mbOY/6KD014KjnULO6NGGohOAReagqGeMgq+CHnOccy1wwUj4b25eddpI8EXgqH5oiev8\\niNUpONTx9arzUTnt0tEIzUZHL5oLvOtHqqCnEboGaO98bdGCdrtuhA57vgP8owGeroLuqD1y2+4K\\n4NuDfN8F62ULtpfWwxvWx/K+s+Xo61WDiH8Blq4feo8CXmyAF4opM4WVwONKTcDaFCjgiJ3MFWN9\\nTtw/INeN/bmW/Bc6f7Q/8KjdewHtgDy7v7LtI30/2kU7B/rshmlBu0+/jbb63oasHHw0h/4N0W7T\\nP6It+9OQtYXDojjn2Udf0Bm95fs2cHwOzduh9Z/PoHNRfyXb6v8Yckl6rpV5Dr3dc05DW/WPyNN2\\n77G2CvTsa3FfRMKqMicf39+7sLjDkC2vW9Hxjz71LPRO0A7PpfR2yTka+R+fS3ZExFu8S5AViy3Q\\n9syG/t53P+X7nLS7WBtUWpt+bKC8iu3rg4lPdh5zz0L0oiMdL1vcQZWZwtCEkhOwrgRk2mNWdN1g\\nzOaU/jq8MdWJaNbwlt37ChpVv233NyTzz1yHmPG19N4KmC+0ALVRWQ6dG5sD/AwYGT2rQuaqXkWH\\nx6eTdjoAACAASURBVK8xOkK5zwLvLVCHCuSL+iVg+wJx+vhAtns7IoZ+IXJ5udToDq5Oz0Kj4q9h\\nTD/K80Dgz3nK2s2Y4wvA8egc2Ya5zLQAjXdb3SsLxesn/abAMwWenVXgHYVzehcTCZciy2vIVy/k\\ne/uT9k6XxO+5BN/Fp4BbC9Fr/fDMUtGXQhISq66h1fnnA5+K/Dt3jc1UO/l8BDeSnaEK9s0qLa+O\\nOqmqusZC81j976nrbTWhv9BpzGJKHlpHApeasPhIYE7o7Nl367Qe0hP5G+6s0/myXvRbmnHGxO8A\\nxuQ8K9o/uMWfiFRS9WgX2jHAd4zZz0dnX54AfgvMQuf2rsZs9+Qp+5v1Ks+bf3DfT9ljkRWWS8jj\\nZ6TIPrAPcGe+NjC1T+yPO5/fhwewM379lNGnTXPqdTZSuVWhA/P/V+Lvoh5YZOq2XHofQ7PJPn00\\nhVX4jkpNwLoSbCGvfccB/A/Hi3D2Ef972cuCOQ5OqoXFM6B7AJ/S+QRDMzI1dWQxo0eklngS+KOD\\nzw7Cn3egfwaaeZxHjm8ONzj/4LFa6wlghxw6z7YyatGh6iPt+kU0Cm9Dh4N/jw6+/7gWmmf273+5\\nuRaaHXwCHfJ+ytIOajSfQ+fxwK8G0wYF3uUrwOlE6sti8zOf0K1OwubrwA9L/E0s3hHaBvCbnRam\\nSxhKTsC6EAbjDzvezocY1FXeSxVUAW82QtdgfA2XS53wS2TTrWbQHQSqK+G2MQxqO2JbpYTRPGRw\\ncUjaw+i5CPhqDo0nA5fkoX02MgddhSyWf7QSbl+ONuwBnrdZxMnAnmgRfrDqn7OB85enDYyOXGGx\\nGKnhJi9Pm1ZoPeDgNembKPW3vC6GkhOwtgTyuDxFi613N8LSf9K//+dZ4D9u/8PBIGT+5jJjjG+P\\nYXAHjP6ZMZPDB6D9SuCbOffOBa62Eae/p0AZVyEzF/G9V8TUehx8IbesfAcRBwrxQSm0wD4nptdm\\nDtfnqdccYPKKlj2mL3MOYQEy4/BztAFhH2Q6Ka/wQCaj7i9Ex1QKn0gegI7uRuhanno5+MQAfeMl\\n8pi6WYHvZNk3kY/eQm2QDsuVLpScgFVSSTGRB9Goeg7ambLzcuRzF7Lr0mfRksxWfZldzwb+WA4d\\nwVwEZH6C10f2cYIJilnooFsbMqfxM/qqGYKtm/cgf8GFPrDr0VmDKH0XWiC/ywTZJUR285H6wgO7\\nRffeIbN147c3mgK9ryPXoiPQCHdLsgOEF9DLtk4YhVdhJk0OAH92Hvqn0Nuv9XHIx8Nd6FCZmVwY\\ng9ZTXiGzubMvsq3XY++4CS3qe+BSizOgOZW77L3k3p9IQeZcKLQhm3tXAGcgXz1T0eL+7Fw6HDqt\\nn49BTkEmSVpYZnaiT3nV9DZjcT6yGVWHDqQdHj07PiqvGDMWQUgwCP/xA3xD3wzfRL530J+gXBGz\\nG8iqebfRH/pIQX/XKWRhrbcC65w7HRkqPQ8tok5GTPKA/tLlyWcKmeO5A4tMNnEU2t8Z8AjqnXei\\nrTm/yElQjYbL99Pb70AVWsX+A1qp/RTSFUBv89g3ID3VV9Aqa5nyaUPbHEG6+WB4FufcGLQou5S+\\neHImLHHIX8F30ZYYkJu/KcAPkcXSq5H+5XNWp98B01RuC1povwU4dBtwY8gPh6R3E3In+BB6ad7q\\nv7Wi7IMEWHWU9B20ldQDW3nv69GCeztwlHPu7FB2/C5y4clvIjQ2dV4kqtF6zPFoV9etiNluDEzf\\nCmpjOvKVGT/rQYdVtge21rrSQ2SWu3k3WR/7FdrW9nfUjg8Be9uzf6JFmlCe5eeQg7mB4IEPWdtu\\nhzYOnNWH3oEtqG6a+00Ui0HSmw9zvPf1Xp7n6r33Vw+cJKHkUmplBrRzYgkFHKCjqe9vEY9rQh5F\\nN0H71t9Co9V9LO7ZaEvpvxFTWoxG5m+ijhtmEofavS5slP05spnE+cgq5iRkDO5zNkr6NPK6Vo8c\\nulcgL2F1Nnqst9F0UFeVoZlH8PM7I2dk6ZC9Hyxttfbhz0bnIDzZnvx5aFG22drAI8be6aAjjFz/\\nBv7kKH/Q7OFsZMjwTORrG/CfRzOPLcBXqS0eBbrLoflEMqus1WgmMtp+R9rIuR7ZGNqZ3hZcXfZ/\\nUURnE+KJwQ3ES+hsyM2Iv7ajfObfZOknkBlI3MPac6m1cznZbO8N8A8YnTUqv9vK7KL3mYaiQwUy\\nWlhubbQdvXwz+3OMnh7kwa3CnlUhlcuN+v+65fdG/M6/jVRI1eBPsXxC6EJ2lXaw+FtZO0/X72PR\\nN3E0mjW8jRa2w0ziJYr3Hz8RjWcWoNnTJ0O6anhjJjKAWWdt8GhE51SymURog43Qjq/D0GzVDtFN\\nsfd7HNocsQCds9nB+ttC4McRvbsDr5aaJ62JoeQErNTKybl5BwW2LCIh0YIWN8vQQOxFtOe+HA2c\\nX7S4z6FR+JuW5wbI/UETGqwFIfFbdLr6bgfdR1mHf9k+zgPBt4K/xZjUl9BOjgZkcqIL/M3GMA5i\\nmenqnmHITEGPPQe5arzf4h6L1BJlyGy5QyaWHTI+aMzkQfuYu4EHrF4P2Mfu0SG2WrSDyAM97ZZ2\\nA/DfMkY6xZjmtojB19m9WfbbjkxCO9Hfacym20F3J1IjNSAhMwf8O2QuO39DZm22FgmfYIb7T8qv\\nw+hsR+cnpiOBvdiYxgzkmC1s9fwk0FkBnZ2Wz97I2ujv7Pqn9o7uoq9V2IeRuun2rOznyFw8dFiZ\\n30Pql/uJVHSFwsloHedCpBbC3lnMIH+ETH6vj4z2TUNmWTrUTqGMX4JUjzVoUHGptf0I8L8gG1Rc\\nYP1nDyvvL0iFdYjy60ZnFDZDg6qd0Vbr71sdewkJ6/tPAN9AQuJldHCxDJ2J+Qc6TV6JfDbNQ+5S\\nGsrs5PvN1tdDG3QZnfnaYK7V+0QkKKpE0xbW9j9FE833oS3QNyOV5CQ00NvVaN4dzWzfQFumf8By\\nqMvWxVByAlZq5bQWMbef5+cCf4mu9zemH84FjCBzeNWOFim/hbaFfh4djupCflSCkGhGWybvq4LO\\nYFDvZfs464yxboxMY38S/N1oVBebnp6C7OuPgqX15tOnHo0Ut0X+D64iM439st0bHd17wMoMJsuN\\nsd1rH/WLSFPxNPIX44H3Wb3PA/kK9mRrKV2I8Z9iv6eT+ZKYAP4k+6g9GqGOZ9li66WIyXZ6MiFx\\nWVTf4Gc72GIK9W1D5s4rwJ8HvlHte5O9l1PQYasfkHn93NDqcDWaIZ0AzKuDVm90x+bP10OCu5CQ\\nCIyrjmUzmXfQGku1MZx2a8MjkSqzE6mVZqPT8v+xOC3Wpv4q5Hukh2y9I3dNYlPwf4/uzbZ488AP\\nE7PzSED6F6ydf2tpr0MDhnDu4mto9rKETEi8YHGfzPrGNCRYr4u+h2FGexASA/qPt77fGTNg+2au\\nADYcBu2hj4TZwkTw90ZtndsGIe5clvnNbkaq325gQlTOfOCj0fWNwKn2fzzwHvs/BQmyS0vNo9aE\\nsLavSSwAGp1z/dUz9nfbCsz31pPIHJIdgw6DdSFnYNcjJ0SvoNHToWgE5YCHvfexZ6xeuN+Ieg4Z\\npZlrIddl49aIk/eA64DySjRUno/09cPJ3JBi/8vIhtOgNYzg09gQnNe9hlQCpyBmOgwxiuDMrBPd\\nWKZfHmVle8QBQUaUzkM6vQ2tUYLr1DdY5ou6FS2z9PgcT4gx/RVG9x/QtGa0pQ8LD+XWToYexCjf\\ng0a1z5N56wzYBDHmMmC0MXg82sM6xuqUz+VojOdQB4n8f1ch3XY7YppNaCnmFMR4liIm6hAD/ZnR\\n9hPgjbJoCcnltEGMV5Ar21eBg+1/Ob39jKOuBGj6F3xaH4FGNWdb4d+x9COicgOmZH9Ho9H3Mp/u\\n3vuWuAyK8x8/CVhoaePqrBcuNogehDaI3m2vRIcYYaPRNKcSfRNRtNgbbit9v+cRVpe3vPdP2/9X\\ngC8jz5MJA2BtFxL3oZHQwSuYzwFouroTUi18AfHx9yHG+jLaO+8QrwTo6ISynpyM4o/hVfRFTSRz\\nQB+wEA1Ll0BVG1QMz0OUy/lfgz6iO3LidWZ/u6PfOWg9+tfoY3Jkvr2rQB9jlJaX7XdaTh0c2l60\\nGHGLy5HSfBEwX/yrASjrgrL2iO6Ys3Shzhik82IyF5rY/fFAk2hrN5o3s2w2IuL1tnj6biQktgU6\\nW6EirMx/Cinc36E3k8634nqSNcZVgKnOvhlFDQvm7yAVzZlIZj6N+srf0WtcauQv7ISyOVGdCo0m\\nJiNDWpPRiv87SNd5PdCWyawF0Ov99kIZ4oJlaCo3EU1tPDATbXJ4Pov+PJKZy3i4c24YkqfLbhUo\\nykf/5wKjnXNxl52M+tuCNih/NSfh60QSJCfRbehbWIjaYBGwVN18UQFaBoO1nf8NCdbqRvLeNyGV\\n0iXOuYOcc7XOuQrn3L7Oue8OIqtupFp6EHXOA9D3dgHayHM92smCXQPMLYOWXCbwYzIf2FciE5Yz\\n0df3jBX0B7RQMBbwNjHo70WFL3QSOjF3tt1bar8XZdFC1GbEJ3f33r9GtrPpy8YYDrIym/9kD1rR\\nzGca2pXlkU2PX9n/2Wgh4DNohjHdGsllZd8F+G8ibtmFtpjNQQwgfPHz0dC8G21F60QSoQNJm2Fq\\nuk7EM7ZGMul4slnQOCs+mDI5FvjOcHjyFosw2trzNnoL7fGI6zZF95ZYRv8HVIuRHh89brV6XWtl\\n3oA2lY2xNv45mrj8A6lHmjw6/ebRzrAaZHzrRXrjM2ghp8uu30YVn042UwSaHHRdF6X7FTIIFhYt\\nbkNC7nq0mruDxfsJWrA7EaiEd7z3i5F6Zn/n3E7OuUp6C8SiYLPofwHfds5VO+e2QodCr/beL66E\\ntx9Cgq87aoMZefIKbRCEytuob1sfCK+mKDjn9nDOTbb/G6AJ1i39p0oAKLm+a1UENAMP5yTmIo3G\\njkiAxOcF9sYWqu26HH1rl9j1/7d35mFyVmXevk93ujs7WSHsCYuAOICgbIIsDm6joqIsA0JQB5dP\\nR2FAREEQZ8ANAXVmhBEE4VMQhlWB+T6UZXAGlaCog4iAJEBCiEkgSyfp7cwfv+fkPV2pt7uql1Qn\\n/dzXda6urjrvWavOfp7ffcg0wy9Qw7oGNQjbU6Gfa+GvDOi0z7PWSF+ETjZtjTbt0nrraWjDcRLE\\nY9EdhG2KtewnW7Vcs97/HFu7fZZCrvN2dD8hnUgaa3/HK20voDX6h1Dntl7LF60XV55u6kAnkiK2\\nvv2vtib+NxQnalptrX5viH9EewjjLI/ZaaRFaMT/62nQ+Sd7Lt2JSButU9FezCR0oueLtqbehoSc\\nxqh9XIXavKuszJcj67QrLa60FNVpdb4Qtf13HaRGNc6wuE5G+z6HZuX6Ia15x6nodNODtg5ueXkc\\n2YR60Or4Z2jP4yy0MrUaDYzT5vYLqP1/AvV/ncjUSHczOtn0a4hX2Pehid6nmy61uCegPazPQ3yt\\n8plOcjUB323RxcX4RbQh/AYr3zZ0iun7Wf4Ot7zshfaFpihNH8q+8x9AKz1L0AGOZ9CeRD368dvY\\nbyytrP5d9tlN06DzeKvnVAaV3+u8DHZD+1O7FL+J46m4l2RhL6D3XZ/vA5+z16dTWBaej/qnCY1u\\nmzYF1/AEbErOOokP1uG/Jj3sau41aiDayS6hbWzd38HGO7Y4dbQzWq57chx0zmPDS1P5//ez4QZy\\ntXygkXsyO/4jCptRc9HIdcjyMogyTKtxy61x+tuhTEe9+Toc4lVDkK9B/IYaUg/uBu426+WmRhNj\\nXLcWTnsLtC/ox++DaMetGx3V+T3QKbMWHfWEk7MAeCu0r4XTYowd/T4wiPSXxHs78KsY49MxxgeA\\nvdbA7W+mWEapM7zKfNxLcVdsGdqLBq18PTnEeRloGcYY4z1oJH4J8Ok18MpboGMo0tGofA2UTS29\\nDj6TqMdRZWpdi6vFmFnSM56ADMo1we8rw2mBH0+HnjqMwnW3DYFRtFa4tE4Df6vRUsOfgb0rw2uD\\nLzeh5av0XL7McH82k7B89LRlAktZfcymuMx4McXSws2U2Phpg9On12Hgb6gMy6ExwNaW1rePgQX1\\n1GV/6ajVYN4R6AJkow3muYG/Tcc1PAGjxYU6NILRAaVP9qooHSNdAFw3DjpqCGc1Wm8+eVBfEI3O\\nnwlwST0axzWUx/HjYM3+0FNDedyCNoir2cx6Gl2sOgv4ur33GLBvSX5OB54eyrzUUIZtaI+nOXsv\\nBPjyOOiqR2t6sN8x09geEaa36/lNjIT0jlbX8ASMJseGmr9dps9cTX/4qOy5g9FG4t7owt/dFs7v\\n7MJU0v3tnKQVq4stnMexPYEBpjegEyCXZ+k/YZJuzQ5ab9jC+9hkWG75WGf6xnELHfZ6FB0AaEaH\\ndi6vEsYV6Ejyh9BmdhPay5lUxe/BNqKfXaUu+tW4HkS9zwHm91EGX5kE7a3SzV4zA1a3QpysDf9T\\nB1CmVfM1WYcHHhiqfA3Tb8I1rEeYa3gCRqtDdwduQz/cSi3ohcD29npn1Fi8DR0d/wjaAP0MOvL/\\nnxS6v8lQ39PoVvKxaC384Wqj8BrSeAY6jZtvlO6FGvAh1Ru2xmAhOn0SM9dho/8p6KTMydkzAd18\\nTyZTFiGTHcstnW2Z35loJvaOkrqYY/ENuXYyknv9eT9+gtXXb9H+Ssr/cnTys+50VeTrNnQ89zwG\\nqKy3kX4TQ/adcjdE9dLoBIxmh46e/oUNdZ1XoRHxVHR88mP22ZUVDegdwGX22UyKS9ERzSgOsA7l\\nl8CXKuLeQFO64vMD0W3W2RXvfxy4apjK4xsV+cvdT9HR/iWYTja6AlLmP1KYGWlG1zq+XBJvVS3o\\nfvxWLbdqftAR7BtrDPM1aJmwMi8vo43v7eoozzxfubuu1jDcuWt4Akabo7cGcfcMiBW6zueiZZZW\\ndOT2GxQ6yC9W6iBvodHxCeb/MdD586TbnGlfdyIrGif1pymN7n7Mp4qqHLo4duowlEsTGu2ub8xS\\nPvL82hLMEnRJ95Qyf2b/6ROWny+gZZYxJfVQTQt6vcZ1Nb9Vym1imZ/JmiH9mIqjqyX+uyZlRgL7\\nKIMPUmUZpo985ZrZvgHsrmbX8ASMJhdq0CA+ENaM0xLL/cBttsFbixZ0ewt0HIAuVFX6OwNdWCv7\\nPNeURssSl1T9wqjzeNWwfBm15HLpOOjpK537Q1cLdI+DNTXkZzVavtm6nnrI9LUvq6HO2sdB3BXa\\n+/CzNm3A9hd/LXW1vzag1waYW0++TDN7pW8Eu6vVNTwBo8UNQNO4eyzcWc8z20K8vMpnl6MjpXXG\\nfeYGXxbtibxEndrOQ11Gl1te69HcTscn66mHM/VszfFsX1L+uZ/JsG4GdJSFOYC6ii3wk7HwWT9S\\n6m44XMMTMBpcGIC28retgdqOcjnHSjffGpgb7P+5EN9r71WLO1CYjK4MZ6LMH/yh15dF1k4jvSVa\\n6743MpgyuqGP/CR3Db11t+ezXh/5smpxLEamOSZbxxAhHmbl05b5m2118VFktry/8q+W9u36SHst\\neat0pyPz42X65ynN6f9nLV9/xjWj3dXmGp6AkeaoUfidEs1fZJn0P9BlsmXAI62weh66JJY0E/K1\\n94DsGDVR2FwKSKEu7yT2qHgmaWWPtddJN6IVCf8cgxTQUnwTKHSoq2xmrleKq9B0TprAkUKN7RW0\\nuT7P1sYPQeqYryC7SevsmceRfaxx/ZVfM3QkTYF7IL7R0r0lMiVxJ4VdqAlIxW5riH9HYa8qrd9X\\ny1tTxetpEI9Cyy+pPKs9l1xSjqs2U0hKdnkYAdld+p75WYsuSyZzFMnuVdLz/oDl9RPIblQeznjU\\n6VWbJZxLb43r1AkksaFqnUSy9TVIcyNvRyfrlqM9lytxW0ibpXOzHAMn0lvzdz90vPBO1ElshSyS\\n3rErhabvthRW9JKbjUSQ34iOO11tr8dQ2L++BJ1rnW7vd6HbdQuBb1qY/xfZUO5C1kDvRboTpyLh\\nhVdTaA6kin8aaQ4chVr3leiMbTPrTWyeFmOchAznJavU1yKjrDOQEc87kUraMnT0di4ybnsUOmGz\\ncz/ld2ErNN9JcW53LrKOtxiZIr0T3aibis5HLkLWCp+3Mkra2PtZ3prQ0a83WGRTLKHTrRxORFbo\\n/mifH0lRH6l8opXBTHR6YEd09CjXFMfKbIV9vjWyTf7PyFZ5sg1yiz27rxVkOrub9LzvR9Yir0Z1\\nNAlZi93dnp9Bb1HvxB+sTNL3K6U5VvFbySA1oycDX0JZ3gNZXf/aAMJxRjqN7qVGmqPQ9D0FjZS+\\nhhq/p4G3Zv7mo0ttL1Bo+d6J2pDn0Q3gxQE6zoZ4Fxr1NyFLsGlkd4GNFA9Ho/4dkfLcP5l/bGQ5\\npspoeLy9PtZGoztmn0+3EeWB9mx6fwd0kzWNep+G+B2L/51oyWVbSwsaQT9p+e2xMHqQaakeCour\\nq9HJqcf6KdsedIT2SdThHAn0jIXnJlh+drB4v4kstM5EEq8RWQxttfwkq7G32Ij9IEv7JPs8WaZ9\\nazYi72+2ADJbkV4na7oHZPGNtfBb0Mh/mtXF3ln4u1JoaI9Bs56kU14Z3xhLc1v2XmtWvw9R6HxP\\nsTBS/tP7zZaWfCbVYmEEZBV2ObKq+pns+3EwmmlUaEb/EM3w+tSMrlK37+mv/t1tms5nEn1zABqs\\nTUedxVXZZzPR8co90EBuDBoUvoAGqXsDhzRB09VolP9dNLX4EuphEu1IsegQNAr9DdJsmIbWrz5t\\n/g5Fw7UWNOr8pL1uRSPuRfb/XugXHtEFiW508247pF3wmioZ/YPFtRiJSfTY/6th1xDCVmjEmSY2\\n91nwC9CFu3bgNOC3IYQpfZQnFvz+ZHo7a2Hb7dH6RXrzNlSYjyIrgd9Go/5/RVOUlajVesDS2mZp\\nn8N6RTy6LaGgmcQ4e91siU3nVqGYSdxq/09EIwAs3naL50ykYHWEhZmEONooFJues7BmUagFrrG0\\nHURhwGkWugLehdbnQLOdrdD0633ocsQ4VK8XIaHoLkvPhfZMN4WhrLPsva1QT769peGTaMZ5r/3/\\nArAnmomu1sskjvjXSInxOOAyJOlwJPraHBtCOJTqHIZ0HpzNjUb3UiPN0Xsm8WT2/jjUTmyJft9p\\nvb5S8/e9aJT8J6BrjJ15/xXFnkSzjQSn2Mi0Fa0bH04xOwBpMmyDNIq3ts/z2UQaHaeZQps9/5ds\\nNDkp82fGA+Pz2ah3i2zEnNbPL8hGohZX2otIOgZLUTud9iR2Q23hUnR893ZgZpWy7UFCR6mcV1g+\\nej4B8edZnP/P0hIh/ouNhmdT6ENH8zONYs8gjfSp4jJNiPUzsZ0gvj4b0U+hlx74+r2c91rZzrZ6\\nmosOFfwMaRy0QnyhIr5mpFfxOnvdRKGL8b0sTanuUh1tCfF8iE9APNXqH4vjcxZ3s4X1tOU7bVxf\\njzakgXiSldFsiFfb87sj/eu0J7HQXtuR66QZvQy7nU8fmtEV9XqU1f2AzL+4G9nOZxJ982J6EWNM\\netcT0QCth+qav0uBRTHGXYFDbV2fLe3vtmg0eQUaln+WXvrJfAGNSM9Gre4i4Hp0eyyiKUqLRXIY\\nGhUeiUaOx6IpT9q36LZE3op0qdvtvV9m8T0KfNFeH5e9H9Dw0hrtlI1zLBnfJNOUjjH+EW1SfxON\\nOLdBo9Bq5GJ9R6PGuf1b9NbJzGVFd6S3kDHIzsiJwL/Z/zej1u1AVEEtwD5o9hbQbCDtx+xl4Y9F\\nZQIqy+WoPLAwkirf+9C0EWR58Q7Ueh6JpOo6UVlBUZezkDrRHPs/mL8/UkjbdVLsT7SiuuxAX67d\\n0P7ErRR7UPfac01ZPKliJqJZQfo/zWqwfHei2etHLM60N9MCrOsd3I0xxrT3VKoZnQghHIgmysfE\\nGJ/G2ezwTmJgPIfKrpr0dM7jPfS/h5hvhE5AP+i3o1a2BXg/aiT+kvmbhjqabjS8X03RQHRSdBBb\\nopb4bLSUBdoQzZloz+aa02naUCE6/4cKLzn3Au+JMT6JJKGrrWpVPhcAVkFrJ2oYk8BynpYFqHNd\\nYvlahZZ8rqEQL89bslSeee+Wb0rvbuF/Ak338kTlHVVOQHXwDdQRTUcanbugZaflaHM5SYsuQ3WU\\nNMB7UKe0G9qcB8m8dlPIGnajgUGleHOXPZ9kVVNapqMNoR5U/9tkz+SCC4tRp7EDKrMmNMhYjqaD\\nVsc7WdDXlxTBhmUSwmvRyuDcGOP9tT7nbFp4JzEAYowvolHVp0IIU0w3+1Bbj58LjAkhBKAlFMvW\\nerZKeOuQpmhEO+WtSGbtnajheAmtYz2FGpFO1Pi0o1/1syiSdJLmTLTu3orWnr+KGoQ/oiHj5Ir4\\nm1BncoFlaglqhOYrjBdRewrqr1K7+yJagpsVQjgDbXhODiHcZGXw3yGEbUMIl4QQyjoMAMbCU2nU\\nfon9PR015AuQSdse1Mn9AlnAuxR1pKmnvtbSvppifb+SdZb4/0H7LfmeRGqAt7C/nWxI6nTGok2o\\nD1JoUy9BDXPqJPZEs44nUF20oV5zK8sPqL7uQA39LHRKKaD6Ogv1uqfae+PRetAyVDcd6Mz1RItz\\nOTJsdYOF/VOLu8fK5v1Iz/prFN/BpBndpgnrZ+3t/66S9Q2wOr0bmbS/q5ZnnE2URq93jTRHoel7\\nCqZlnH3WDexkr58F7kGN5VK04jEe/XC6ULuzEGv/56M9iWZzY9G+RNIiPsLe3wGdftrS1r2xz+9B\\np4/yk0rNaK9iDDqD/yqKvYfz6X1KKbl9IT5FsfY/0cJvRsI/W9hz6WQMakvPoGiLetDdh/dRnGya\\nR6EfvMreewUtQZ0HjK1Sfqmcu4FPHQQr0h4EEHfO1ul3gPgTW0+fVaQrBopTPgda2iejfZkWiPsg\\nqc4m85fKIO3VfAril7L32+w97Jn77fUP0CW3ORBPNH/j0B5ROpW0SxYv9vluEO81P1uhC3ofs7UV\\nvgAAEjRJREFUpfcpp3Rh751ados7seG+yiR0ci1poO9uf/N9lq0oNKO3sbQ02+cHQ1yKTjedl4WZ\\naUb/H4rJZ62a0Vdn3/OV5n7X6N+vu2FoExudgM3d0Y+m7wXoIlX+XuXFq/7cd9DFsIguSG2FLtPV\\nG07u8otW6Jj/CRurjMpugg9BfuLDNfgdaDzXoOPLqfzrCbOeOK+h2LxfiwYT+XOHU+hY11jHkxiE\\n7oi7zdv5ctMwEweg6duGht9vQUO5WlmADqtfjlr2Nnv97gGEk/SE0crLIWhGNCzUWkZDkJ/L3wHr\\n+nt2oPEsRVOnVP7Vwjy6JMyBxrkYWAfrBqOZjXRJfhV949mpRqN7qdHiygzLXcCGM4nMANtttRht\\n+w668DVUBv5y42/AqyhRVRuuMmqi+kwiz089Bv7MCN49wJbDaeBvGlom68vPUBj4u8ZmEnld5fk6\\nop+ZRP4cOor8Z6pokbtzF2P0TmJjulCbBnF3rulbyzOmA9zeCu1l/k6nMD9dFs4BsLpSTxjtnf5g\\nJJXRgbCiVea5S/NboY98BsWp438LcIbFsbKGZy+rUTc67gLtfZRteypb4Oa+dMprqatq2s91fFdc\\nM9pdza7hCRhtjgpN3+lopJuJwjxEhcG1ymfKdIBr8HcicKJ93llj3FcBH29kGQ0iv730kdF1hy+g\\nwwZ3A19KZTFD5dHTCl2Vz1aLp0IPez5aKcr9dFeU7TkWzmy0MrVT5r9rukb37SV1leLsboXOvrSf\\n6y0Td+76cyHGONCVKmcQmKmLvYC76H2f7vwY44XVn4IQwhboCD7AshjjKwPxZ5/fju6RJVZGGdwj\\nhPA2ZB/vFDTq/K9a8zaUDFV+K/yOBU5CM4wOZO3jPuCvgM/be5+OMT7cRzzPAFNijK+EEE4CPhRj\\nPCLzs4Dep41nxRgXhxB+BPw+1XEI4e3oMtpy4E3V0p7FeTu68Xx/Wd4GWiaOU0qje6nR6tDdg1jF\\nHbsR03B4lfiPRqPcm7L3ejCd7c3JoWsWbwP+P7pScg66o3YyOtJ7PRWa0lTRw0Z3HucD+9v/21SU\\naYfFdRg6Oj3O/B2Nriucg44Rl+prW7yPoCsPfepwu3M3lM5PN21kQghtIYQTtoDPtKJbujPQGoFZ\\nWJsTQqg8HDNcPEBmpWOS0nHrTHhsBhyTpSmgu3ybFTHGnhjj3THGo9DdvN1RPve3/58FHgshXBhC\\nOGVqCL9phSVWPrTCkqkh/AbdGfk28C3zM79KvR6PzJacBfSEEC6dBDe3wpSZcN4M2DuFF0I4IYTQ\\nmr4rKd4ZsM9MuLbS38YsM2cU0uheajS5UJ+28kbZWAxwyTj61VOOo2WzE80CLkIj/FusfDoOgJ6y\\n8tnFNK77KkPTuO5CG+HtfYV3ULYxP5K+K+5Gp2t4AkaLq1fjemNoEFua2kdSmkaKAya0wL9Ph56+\\nyqee48W1HqkdgIb3qKkXdxvfNTwBm7ND5iuuCyX6zbMp16+ez/BpEFu6HizTlC5L13CmaaS5sjrL\\nXT2a1LX6HYjOdV/1gu5XLkDmM/ZG915+jW5Yf6LR5exu5LuGJ2AkOkr0qwcQzvnA9WVmOfrqJNIo\\nMdcgrjVdaEP6t+jEzBJkeHSb7PMLm6GjzAREX+kajC6yxT0DneZ5GR0FvW4o6wOdUvpgxXtHW8P4\\nMrKXeC+wY1ZHqyrCv5ZCiW+9BvlJEPdCNpRyMxrQ2xbTFhD/Ftl6ymxgxb2RxvQFFk66RHk/UiEs\\nM81xP1Lo2y5776PIPtMYNjh4sBr4SZb3p4B3ZP9/F7ik0b8xd5uO843r6kR661e/Dji30pNZeu2P\\nHfaBsG///jagigZxTelChk7fFmOcitbYn0LHPBN75LrIg0xTvdyCDB9uh4zPVlour0at9bHB9zmE\\nsDNq9E+PMU5BEg//TG8L7ZXhH9EMYRbwM3TsaQckXJ4ERpJm9T72/0R0oWQxslb7A3vup/b+nsiK\\nXnNJ5gK9dbArP4feNtvPRKY8zkHmW48HdpTBxSYK0Tr0No9X/D9kCnIhhGpZcjYnGt1LjUSHqdNl\\n/38VadDch7ScH0Ijtp2QGuXtFDrXH86eO38MvPwGdKFqP4iPlYzYeyBejMw6zIB4HNIlvlnPJk3p\\nJagdSvrD11JYD/9VH+laBryQ0tUGiw6yOAaSrmvZQBd5LlrS6FMXGSmYPQO6nzMM9XEdhbrnCnSa\\n6Bjg0ZJw30KhttcO/DrGSDOsSKP/CZb3MfaaCldpZbcFmczAZhCP2sxiLIVCXWUYlS5QWIQdX+Xz\\niRCPsdnNV2z20QTxR4p/CXA/Oli1JntuNZLA6LL3e6zsfodmnNcBY7KyeY/V4XJ0Cm6P7LNFSA7j\\n98CqRv9e3Q2va3gCRqLLGyWkCfN7JOB2H1r62J1CIOwBJF3agtZ8XwIOt2cvAuJNELsgft2WILqq\\nNMaXQTwISUp22HLCCfa6pZAqWAG8GekQr7UO4qvAz9HI+MQsXQ+bn270/CtoGWqLJuhqRSdxBpKu\\n49BtYzTw7QH+xRqlv7YG6BZ032Ab1KkdauVxHrKhdB3SUPoFmSnqIaqPXstNaObQjvSCDgcmVIR9\\nPjJzncJ/NdZ5tqKlpTZkqnsmhSnwYB3ruqzhvgotGX04a9Bfa8/cYB3GYRBfbc//jZX1HIunBZn6\\nPsXibrH3L7IOZrz570Am02dY+P+Alrc61Jl1IrevfV96kDbSuWgGcR+SwliE5EtmWF39CTjZyuBA\\nNBHaB01gPozkSJrs80VWd1sBbY3+vbobXtfwBIxER6G/XKlffR9wQeZvO/tBjs/euwi42l5fNga6\\n0wi9B+k/PFSlMd4DaSYnvwutkehWQ7HaGvsFWbq6kWmJlK7HMf3hPF1IOO0sJEp2NbDTeFh3UBbX\\nQNI1Xev4SRd5Vpb/Ul1kCjG9uWjl5Tg0Up02FPVhfnt1Evbe/kiPZ7F1GN9LdZZ1Ein858ZCx44V\\no/c2iB9Ds4m075DKJPkZj0b1b4L4fWvo055BM9rTuNHKejLS155tjfzW9voupPPwbmQw8K1oT2Ii\\nhUbFgxQaItfQ20jklOK7cpnVeQ+aWTahAcwjWSfxnqyMLge+Ya+vBs6pKMNngdfb60WMgsML7uR8\\nT6KcavrV0FtZcxtk7qA9e28+UtsEoKkQKyOg1nthlcjmo/n9NHNJf3hxb2/dKV0WzMVZul6mEFpb\\nn64Y48tILOY9ebq2L8IcULoqZE1zCeq+dJHXAM/GGK+JMXbHGG9E5fmGKlFXUkt9VCXG+MsY4/Ex\\nxq2AQ4E3IvMbG4QPHNYKXWnvIZlcPxtNl4K5MRUPT0Q914toV/wEVFmXICW7HnR9+zjUwq6kUNDb\\nEk35nkdqhE8hTdBlaNp1OOqR15ifhWj4301vLXAUT5NFNxVVXyqDHjQ7aMu85/XUTlFPOwKfCyEs\\nM5fUWbfN/Oda5c5mjHcS5ZRtSsfs9UJgWggh17reAf0YAdo7obkze/B5ev/S8ofuRg3DMjS8Xo1+\\nmavULveXrpzKdLWgi79LgKVroTnXHqg3XS8rbS1sKMfcH7+ld/lR5f8yaqmPfsOLMc6j2CPO/afw\\nV6yAcZNRB/H3yG5HZaArqS5xmrgNFdB/oXW5ZiRR+npk0GlSFuYiVK5daMq3M7JE2IZ6swdQ692G\\nOovj0LpermuOpWeVkv1L1HnumD6zTf1tKVd3zXkO+IJ1ytNijFNjjBNjjLdlfmqtN2cTxzuJQRBj\\nfB61AxebCYW9gA+hNXeAdREtBncjXeaxwAFVwvoI8DkKwZklSP/4Dj3zJLV1DonXo43li0MI26ID\\nNt3AVTHGV1pgySOoIRtIus4Dxmt9e2Wd6boVmBpC+EAIoSmE8D7UcP28jjD6YzFaXgEghPCGEMKH\\nQwgz7f/dgXdRaDkvpncnvBaKDuDPSKs60YqG6duhnfP18aAd+eVo/epkZKhpDVrv60KN+kuozFei\\nHjYCP87C+SGqvL9YGn4DPIo6h060drfa4liH1s3WWPgfZf209R+AG9EkENRHnWPBrqxaar25Evhk\\nCGE/gBDCxBDCO80wojPK8E6iOmWjpGrvn4A2Rxei+wjnxRjvyz7/xVXQNRVdDriF4hhk3rp+Ch3m\\nfzOy5HYwGg5+HVauhO9QrH1XS0vlZ9uiTcWPo1HhQcDnU7rWwUOToetGtCZRb7qug46X4SslZVL6\\nf4xxOWqgz0Jt5GeAd8UYl9E39dTH5cD7QwhLQwiXoXb7XcDvQggrkNXdfwe+Zv6TIcPbQgiPxBhX\\nAd97Gc0A/oDyn5iDfjTPIINNaVlqJfBPaEnuSvN3H+pIUuZWoR6px/w9j9aD1gDjzM98S9wstKTV\\njmybN9tz70eVOxEdX3sKbT7cJNeN9mgejjH+Dg1YAjr5doRlJZVZ6UwgyuLv3wNX2FLTE+h73u+z\\nzuaHmwofZkIIbePgpYdgcr33EuYBh8KKNTAzxtixOadpJFFL+axDazl30f99k778zkHTvCPrDLOS\\n0VAvTmPwmcQwEwegcQ29NYiH+kc/EtM0kqilfOrRpK7V71Bokm/O9eI0Bu8kNgI9Md64Cs7dD9rn\\n1eB/HrAftK+Cc3t0AmhUpGkkUUv5HIduPh+CyqcvjjO3X4Xfyg2dFObBNYQJo69enAbQ6DO4o8mN\\nRA3ikZimkeRqKZ9kKvzAPjSu69HMHoCG96irF3cbzzU8AaPNMQI1iEdimkaSq7F8JlT6qdDB7lMz\\neyg0vN25Gw7nG9cNZCRqEI/ENI0kaimfajrYgwmvHn+OM9R4J+E4Q4w16NOBp+mnk3CckY5vXDvO\\nEFCpR12pg+161M6mis8kHGeQNIVw3Fi48rUQzoRJ76Sw7dSJLtR9HVb+BuJaOM1PITmbEt5JOM4g\\nGBfC6RPhH++B8fv143ceus+wCs5dE+OlGyN9jjNYvJNwnAEQQjgfeNMM2G8ejN+h4vP8JnXOAnSv\\nYSl80GcUzqaA70k4o4oQwrMhhPYQwooQwqIQwvdCCOMHEFRzMxz4H1U6iL7YAbgHxo+FKweyRxFC\\nmBVCuD2E8EIIoSeEUE/0jlM33kk4o43I0OiXN0onvAdZb38vbmjP2Qh4J+GMRgJAjHERanD/KoRw\\nXwjhH0MID4UQVgNzQghb26h9aQjhyRDCh1MAbXDIrtByPNKHeB0Sy6hGBL6MNERnohtwH4NJU+Ds\\nEMKONiOYG0JYYHF9JITwuhDCYyb68631YcX4UozxO0hhrh4z7Y4zILyTcEYtIYTtgbcjyQaAk5Ce\\n8yS0fXCD/Z2FrHRfFEI4PISwRSfMnEehv3oCMszXXSWebyJdkP9E9uSnIkuv7bCnxQWSWN3FgrwM\\nyXgcicSRjg0hHDp0OXec2vFOwhmN3BZCWAY8iGQfLrL3r4kxPhEl9TkL2dk7O8bYGWN8DPgu0hOa\\nPha690OqPs3AGUit6OEqkV2BtCa2RupGX0D6HZMkWTEFTTYujDF2xBjvRbpCP4wxLo0xLkT9y2uH\\nvBQcpwYqpXodZzRwdOwtDIVtQdSiX77+pGu9OuFpRBYZlE6442xUfCbhjEYGq1/eSJ1wx9moeCfh\\nOFWIfeiXx8bqhBNCaLPoAMba/44zLPhykzPaqFe//Ao0q1hGpl++Dh6aBu++EcacDOxK3zrhIJ3w\\nRcCWQPsAdcKNNRS65k/Y32YcZxjwG9eOMwBcJ9wZLfhyk+MMgOg64c4owTsJxxkgrhPujAZ8uclx\\nBkkyFb6PmQp/F71Nhd+BTIU/5qbCnU0Q7yQcZwgwY33vnQJnt8Oek3VRjhXQOh7+xzapb/ElJmdT\\nwzsJxxliXI/a2ZzwTsJxHMcpxTeuHcdxnFK8k3Acx3FK8U7CcRzHKcU7CcdxHKcU7yQcx3GcUryT\\ncBzHcUrxTsJxHMcpxTsJx3EcpxTvJBzHcZxSvJNwHMdxSvFOwnEcxynFOwnHcRynFO8kHMdxnFK8\\nk3Acx3FK8U7CcRzHKcU7CcdxHKcU7yQcx3GcUryTcBzHcUrxTsJxHMcpxTsJx3EcpxTvJBzHcZxS\\nvJNwHMdxSvFOwnEcxynFOwnHcRynFO8kHMdxnFK8k3Acx3FK8U7CcRzHKcU7CcdxHKcU7yQcx3Gc\\nUryTcBzHcUrxTsJxHMcpxTsJx3EcpxTvJBzHcZxSvJNwHMdxSvFOwnEcxynFOwnHcRynFO8kHMdx\\nnFK8k3Acx3FK8U7CcRzHKcU7CcdxHKcU7yQcx3GcUryTcBzHcUrxTsJxHMcp5X8BdC+CZvQbDxIA\\nAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"bn2 = read_bn('data/win95pts.bif')\\n\",\n \"draw_bn(bn2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Yikes, that will take some work. Or will it? Let's apply a few of the same arguments as before:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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K/ufpOZJYDDge3ZPMD+F7BjlH8NgLv/BzgduCo6LvX4TcChwBlmdkEZ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\\nEmwAABgJNgAAjAQbAABGgg0AAKMAskTkZyIw/qIAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import pylab\\n\",\n \"pylab.figure(3,figsize=(12,12)) \\n\",\n \"draw_bn(bn2, node_size=200, with_labels=False, node_color='white')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "examples/FactorOperations.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Factor Operations with pyBN\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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 \"\\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:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from pyBN import *\\n\",\n \"bn = read_bn('data/cmu.bn')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Burglary', 'Earthquake', 'Alarm', 'JohnCalls', 'MaryCalls']\\n\",\n \"[['Burglary', 'Alarm'], ['Earthquake', 'Alarm'], ['Alarm', 'JohnCalls'], ['Alarm', 'MaryCalls']]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print bn.V\\n\",\n \"print bn.E\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"alarm_factor = Factor(bn,'Alarm')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have a factor, we can explore its properties. Every factor in pyBN has the following attributes:\\n\",\n \"\\n\",\n \" *self.bn* : a BayesNet object\\n\",\n \"\\n\",\n \" *self.var* : a string\\n\",\n \" The random variable to which this Factor belongs\\n\",\n \" \\n\",\n \" *self.scope* : a list\\n\",\n \" The RV, and its parents (the RVs involved in the\\n\",\n \" conditional probability table)\\n\",\n \" \\n\",\n \" *self.card* : a dictionary, where\\n\",\n \" key = an RV in self.scope, and\\n\",\n \" val = integer cardinality of the key (i.e. how\\n\",\n \" many possible values it has)\\n\",\n \" \\n\",\n \" *self.stride* : a dictionary, where\\n\",\n \" key = an RV in self.scope, and\\n\",\n \" val = integer stride (i.e. how many rows in the \\n\",\n \" CPT until the NEXT value of RV is reached)\\n\",\n \" \\n\",\n \" *self.cpt* : a nested numpy array\\n\",\n \" The probability values for self.var conditioned\\n\",\n \" on its parents\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Alarm\\n\",\n \"['Alarm', 'Burglary', 'Earthquake']\\n\",\n \"{'Burglary': 2, 'Alarm': 2, 'Earthquake': 2}\\n\",\n \"{'Burglary': 2, 'Alarm': 1, 'Earthquake': 4}\\n\",\n \"[ 0.999 0.001 0.71 0.29 0.06 0.94 0.05 0.95 ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print alarm_factor.bn\\n\",\n \"print alarm_factor.var\\n\",\n \"print alarm_factor.scope\\n\",\n \"print alarm_factor.card\\n\",\n \"print alarm_factor.stride\\n\",\n \"print alarm_factor.cpt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Along with those properties, there are a great number of methods (functions) at hand:\\n\",\n \"\\n\",\n \" *multiply_factor*\\n\",\n \" Multiply two factors together. The factor\\n\",\n \" multiplication algorithm used here is adapted\\n\",\n \" from Koller and Friedman (PGMs) textbook.\\n\",\n \"\\n\",\n \" *sumover_var* :\\n\",\n \" Sum over one *rv* by keeping it constant. Thus, you \\n\",\n \" end up with a 1-D factor whose scope is ONLY *rv*\\n\",\n \" and whose length = cardinality of rv. \\n\",\n \"\\n\",\n \" *sumout_var_list* :\\n\",\n \" Remove a collection of rv's from the factor\\n\",\n \" by summing out (i.e. calling sumout_var) over\\n\",\n \" each rv.\\n\",\n \"\\n\",\n \" *sumout_var* :\\n\",\n \" Remove passed-in *rv* from the factor by summing\\n\",\n \" over everything else.\\n\",\n \"\\n\",\n \" *maxout_var* :\\n\",\n \" Remove *rv* from the factor by taking the maximum value \\n\",\n \" of all rv instantiations over everyting else.\\n\",\n \"\\n\",\n \" *reduce_factor_by_list* :\\n\",\n \" Reduce the factor by numerous sets of\\n\",\n \" [rv,val]\\n\",\n \"\\n\",\n \" *reduce_factor* :\\n\",\n \" Condition the factor by eliminating any sets of\\n\",\n \" values that don't align with a given [rv, val]\\n\",\n \"\\n\",\n \" *to_log* :\\n\",\n \" Convert probabilities to log space from\\n\",\n \" normal space.\\n\",\n \"\\n\",\n \" *from_log* :\\n\",\n \" Convert probabilities from log space to\\n\",\n \" normal space.\\n\",\n \"\\n\",\n \" *normalize* :\\n\",\n \" Make relevant collections of probabilities sum to one.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here is a look at Factor Multiplication:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.998 0.001 0.001 0. 0.06 0.939 0. 0.001]\\n\",\n \"\\n\",\n \"[ 0.998 0.001 0.001 0. 0.06 0.939 0. 0.001]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"f1 = Factor(bn,'Alarm')\\n\",\n \"f2 = Factor(bn,'Burglary')\\n\",\n \"f1.multiply_factor(f2)\\n\",\n \"\\n\",\n \"f3 = Factor(bn,'Burglary')\\n\",\n \"f4 = Factor(bn,'Alarm')\\n\",\n \"f3.multiply_factor(f4)\\n\",\n \"\\n\",\n \"print np.round(f1.cpt,3)\\n\",\n \"print '\\\\n',np.round(f3.cpt,3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here is a look at \\\"sumover_var\\\":\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.999 0.001 0.71 0.29 0.06 0.94 0.05 0.95 ]\\n\",\n \"['Alarm', 'Burglary', 'Earthquake']\\n\",\n \"{'Burglary': 2, 'Alarm': 1, 'Earthquake': 4}\\n\",\n \"\\n\",\n \"[ 0.5 0.5]\\n\",\n \"['Burglary']\\n\",\n \"{'Burglary': 1}\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = Factor(bn,'Alarm')\\n\",\n \"print f.cpt\\n\",\n \"print f.scope\\n\",\n \"print f.stride\\n\",\n \"f.sumover_var('Burglary')\\n\",\n \"print '\\\\n',f.cpt\\n\",\n \"print f.scope\\n\",\n \"print f.stride\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here is a look at \\\"sumout_var\\\", which is essentially the opposite of \\\"sumover_var\\\":\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'Burglary': 2, 'Alarm': 1}\\n\",\n \"['Alarm', 'Burglary']\\n\",\n \"{'Burglary': 2, 'Alarm': 2}\\n\",\n \"[ 0.5295 0.4705 0.38 0.62 ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = Factor(bn,'Alarm')\\n\",\n \"f.sumout_var('Earthquake')\\n\",\n \"print f.stride\\n\",\n \"print f.scope\\n\",\n \"print f.card\\n\",\n \"print f.cpt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.999 0.001 0.71 0.29 0.06 0.94 0.05 0.95 ]\\n\",\n \"['Alarm']\\n\",\n \"{'Alarm': 1}\\n\",\n \"[ 0.45475 0.54525]\\n\",\n \"\\n\",\n \"[ 0.999 0.001 0.71 0.29 0.06 0.94 0.05 0.95 ]\\n\",\n \"['Alarm']\\n\",\n \"{'Alarm': 1}\\n\",\n \"[ 0.45475 0.54525]\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = Factor(bn,'Alarm')\\n\",\n \"print f.cpt\\n\",\n \"f.sumout_var_list(['Burglary','Earthquake'])\\n\",\n \"print f.scope\\n\",\n \"print f.stride\\n\",\n \"print f.cpt\\n\",\n \"\\n\",\n \"f1 = Factor(bn,'Alarm')\\n\",\n \"print '\\\\n',f1.cpt\\n\",\n \"f1.sumover_var('Alarm')\\n\",\n \"print f1.scope\\n\",\n \"print f1.stride\\n\",\n \"print f1.cpt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Alarm', 'Burglary', 'Earthquake']\\n\",\n \"[ 0.999 0.001 0.71 0.29 0.06 0.94 0.05 0.95 ]\\n\",\n \"\\n\",\n \"['Alarm', 'Earthquake']\\n\",\n \"[ 0.77501 0.22499 0.05942 0.94058]\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = Factor(bn,'Alarm')\\n\",\n \"print f.scope\\n\",\n \"print f.cpt\\n\",\n \"f.maxout_var('Burglary')\\n\",\n \"print '\\\\n', f.scope\\n\",\n \"print f.cpt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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\",\n \" parent-child instantiations which are not consistent with\\n\",\n \" the evidence. Moreover, there should not be any need for\\n\",\n \" normalization because the CPT should already be normalized\\n\",\n \" over the rv-val evidence (but we do it anyways because of\\n\",\n \" rounding). This function is essential when user's pass in evidence to any inference query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Alarm', 'Burglary', 'Earthquake']\\n\",\n \"[ 0.999 0.001 0.71 0.29 0.06 0.94 0.05 0.95 ]\\n\",\n \"\\n\",\n \"['Alarm', 'Earthquake']\\n\",\n \"[ 0.71 0.29 0.05 0.95]\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = Factor(bn, 'Alarm')\\n\",\n \"print f.scope\\n\",\n \"print f.cpt\\n\",\n \"f.reduce_factor('Burglary','Yes')\\n\",\n \"print '\\\\n', f.scope\\n\",\n \"print f.cpt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.999 0.001 0.71 0.29 0.06 0.94 0.05 0.95 ]\\n\",\n \"[-0. -6.91 -0.34 -1.24 -2.81 -0.06 -3. -0.05]\\n\",\n \"[ 0.999 0.001 0.71 0.29 0.06 0.94 0.05 0.95 ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = Factor(bn,'Alarm')\\n\",\n \"print f.cpt\\n\",\n \"f.to_log()\\n\",\n \"print np.round(f.cpt,2)\\n\",\n \"f.from_log()\\n\",\n \"print f.cpt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.999 0.001 0.71 0.29 0.06 0.94 0.05 0.95 ]\\n\",\n \"[ 20. 20. 0.71 0.29 0.94 0.94 0.05 0.15]\\n\",\n \"[ 0.5 0.5 0.71 0.29 0.5 0.5 0.25 0.75]\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = Factor(bn, 'Alarm')\\n\",\n \"print f.cpt\\n\",\n \"f.cpt[0]=20\\n\",\n \"f.cpt[1]=20\\n\",\n \"f.cpt[4]=0.94\\n\",\n \"f.cpt[7]=0.15\\n\",\n \"print f.cpt\\n\",\n \"f.normalize()\\n\",\n \"print f.cpt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "examples/MAP Inference as Optimization.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# MAP Inference as Linear Integer Programming\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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 \"\\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\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from pyBN import *\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"bn = read_bn('data/simple.bn')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['A', 'B']\\n\",\n \"{'A': ['B'], 'B': []}\\n\",\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\"\n ]\n }\n ],\n \"source\": [\n \"print bn.V\\n\",\n \"print bn.E\\n\",\n \"print bn.F\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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 \"\\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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from pulp import *\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We start by creating a model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"model = LpProblem(\\\"MAP Inference\\\", LpMinimize)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 100,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"x1 = LpVariable(\\\"A=No\\\",0,1,LpInteger)\\n\",\n \"x2 = LpVariable(\\\"A=Yes\\\",0,1,LpInteger)\\n\",\n \"x3 = LpVariable(\\\"B=No|A=No\\\",0,1,LpInteger)\\n\",\n \"x4 = LpVariable(\\\"B=Yes|A=No\\\",0,1,LpInteger)\\n\",\n \"x5 = LpVariable(\\\"B=No|A=Yes\\\",0,1,LpInteger)\\n\",\n \"x6 = LpVariable(\\\"B=Yes|A=Yes\\\",0,1,LpInteger)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"var_list = [x1,x2,x3,x4,x5,x6]\\n\",\n \"val_list = -np.log(bn.flat_cpt())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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 \"\\n\",\n \"First, we will add the objective value - minimizing the sum of the negative log values of the conditional probability entries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 102,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"model += np.dot(var_list,val_list)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 103,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"model += x1 == x3+x4, 'Edge Constraint 1'\\n\",\n \"model += x2 == x5+x6, 'Edge Constraint 2'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Moreover, let's add constraint to make sure only one value from each CPT can be chosen:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 104,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"model += x1 + x2 == 1, 'RV Constraint 1'\\n\",\n \"model += x3 + x4 + x5 + x6 == 1, 'RV Constraint 2'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, let's solve.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 105,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 105,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.solve()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now print out the variables and observe the solution:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 106,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A=No 0.0\\n\",\n \"A=Yes 1.0\\n\",\n \"B=No|A=No 0.0\\n\",\n \"B=No|A=Yes 0.0\\n\",\n \"B=Yes|A=No 0.0\\n\",\n \"B=Yes|A=Yes 1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"for v in model.variables():\\n\",\n \" print v.name, v.varValue\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And there it is - the solver shows us that the optimal solution indeed is \\\"A=Yes\\\" and \\\"B=Yes|A=Yes\\\"\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "examples/Marginal_Inference.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Marginal Inference in Bayesian Networks with pyBN\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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 \"\\n\",\n \"Let's start by loading the pyBN package and reading the \\\"Earthquake\\\" network included in the /data/ folder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from pyBN import *\\n\",\n \"bn = read_bn('data/earthquake.bn')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As with any new Bayesian network, it helps to look at its attributes:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\n\",\n \" \\\"Burglary\\\": [\\n\",\n \" \\\"Alarm\\\"\\n\",\n \" ], \\n\",\n \" \\\"MaryCalls\\\": [], \\n\",\n \" \\\"Alarm\\\": [\\n\",\n \" \\\"JohnCalls\\\", \\n\",\n \" \\\"MaryCalls\\\"\\n\",\n \" ], \\n\",\n \" \\\"JohnCalls\\\": [], \\n\",\n \" \\\"Earthquake\\\": [\\n\",\n \" \\\"Alarm\\\"\\n\",\n \" ]\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"import json\\n\",\n \"print json.dumps(bn.E,indent=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"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\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "examples/ReadWrite.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Reading and Writing Bayesian Networks with pyBN\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

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!

\\n\",\n \"The first step is to load the package:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from pyBN import *\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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 \"\\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\\\":\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"file = 'data/cancer.bif'\\n\",\n \"bn = read_bn(file)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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\",\n \"- \\\"V\\\" : a list of vertices (random variables)\\n\",\n \"- \\\"E\\\" : a list of edges (conditional dependencies between random variables)\\n\",\n \"- \\\"data\\\" : a dictionary of dictionaries containing the Conditional Probability Tables (and other info) for each random variables\\n\",\n \"\\n\",\n \"Let's observe these attributes for the \\\"cancer\\\" BN:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Pollution', 'Smoker', 'Cancer', 'Xray', 'Dyspnoea']\\n\",\n \"[['Pollution', 'Cancer'], ['Smoker', 'Cancer'], ['Cancer', 'Xray'], ['Cancer', 'Dyspnoea']]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print bn.V\\n\",\n \"print bn.E\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\n\",\n \" \\\"vals\\\": [\\n\",\n \" \\\"positive\\\", \\n\",\n \" \\\"negative\\\"\\n\",\n \" ], \\n\",\n \" \\\"parents\\\": [\\n\",\n \" \\\"Cancer\\\"\\n\",\n \" ], \\n\",\n \" \\\"cprob\\\": [\\n\",\n \" [\\n\",\n \" 0.9, \\n\",\n \" 0.1\\n\",\n \" ], \\n\",\n \" [\\n\",\n \" 0.2, \\n\",\n \" 0.8\\n\",\n \" ]\\n\",\n \" ], \\n\",\n \" \\\"numoutcomes\\\": 2, \\n\",\n \" \\\"children\\\": []\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"import json\\n\",\n \"print json.dumps(bn.data['Xray'],indent=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"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 \"\\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!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"write_bn(bn, 'data/write_test.bn')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To finish this tutorial, let's make sure our file-written BayesNet contains the correct information:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\",\n \"True\\n\",\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"bn2 = read_bn('data/write_test.bn')\\n\",\n \"print bn.V == bn2.V\\n\",\n \"print bn.E == bn2.E\\n\",\n \"print bn.data == bn2.data\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyBN/__init__.py", "content": "from pyBN.classes import *\r\nfrom pyBN.classification import *\r\nfrom pyBN.inference import *\r\nfrom pyBN.io import *\r\nfrom pyBN.learning import *\r\nfrom pyBN.plotting import *\r\nfrom pyBN.utils import *" }, { "path": "pyBN/classes/__init__.py", "content": "from pyBN.classes.bayesnet import BayesNet\r\nfrom pyBN.classes.cliquetree import CliqueTree, Clique\r\nfrom pyBN.classes.clustergraph import ClusterGraph\r\nfrom pyBN.classes.empiricaldistribution import EmpiricalDistribution\r\nfrom pyBN.classes.factor import Factor\r\nfrom pyBN.classes.factorization import Factorization" }, { "path": "pyBN/classes/_tests/__init__.py", "content": "" }, { "path": "pyBN/classes/_tests/test_bayesnet.py", "content": "\"\"\"\n********\nUnitTest\nBayesNet\n********\n\nMethod\tChecks that\nassertEqual(a, b)\ta == b\t \nassertNotEqual(a, b)\ta != b\t \nassertTrue(x)\tbool(x) is True\t \nassertFalse(x)\tbool(x) is False\t \nassertIs(a, b)\ta is b\nassertIsNot(a, b)\ta is not b\nassertIsNone(x)\tx is None\nassertIsNotNone(x)\tx is not None\nassertIn(a, b)\ta in b\nassertNotIn(a, b)\ta not in b\nassertIsInstance(a, b)\tisinstance(a, b)\nassertNotIsInstance(a, b)\tnot isinstance(a, b)\n\nassertAlmostEqual(a, b)\tround(a-b, 7) == 0\t \nassertNotAlmostEqual(a, b)\tround(a-b, 7) != 0\t \nassertGreater(a, b)\ta > b\nassertGreaterEqual(a, b)\ta >= b\nassertLess(a, b)\ta < b\nassertLessEqual(a, b)\ta <= b\n\nassertListEqual(a, b)\tlists\nassertTupleEqual(a, b)\ttuples\nassertSetEqual(a, b)\tsets or frozensets\nassertDictEqual(a, b)\tdicts\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.readwrite.read import read_bn\n\nimport os\nfrom os.path import dirname\n\n\nclass BayesNetTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.bn = BayesNet()\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tself.bn_bif = read_bn(os.path.join(self.dpath,'cancer.bif'))\n\t\tself.bn_bn = read_bn(os.path.join(self.dpath,'cmu.bn'))\n\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_isinstance(self):\n\t\tself.assertIsInstance(self.bn,BayesNet)\n\n\tdef test_V_bif(self):\n\t\tself.assertListEqual(self.bn_bif.V,\n\t\t\t['Smoker', 'Pollution', 'Cancer', 'Xray', 'Dyspnoea'])\n\n\tdef test_E_bif(self):\n\t\tself.assertDictEqual(self.bn_bif.E,\n\t\t\t{'Cancer': ['Xray', 'Dyspnoea'],\n\t\t\t 'Dyspnoea': [],\n\t\t\t 'Pollution': ['Cancer'],\n\t\t\t 'Smoker': ['Cancer'],\n\t\t\t 'Xray': []})\n\n\tdef test_F_bif(self):\n\t\tself.assertDictEqual(self.bn_bif.F,\n\t\t\t{'Cancer': {'cpt': [0.03, 0.97, 0.05, 0.95, 0.001, 0.999, 0.02, 0.98],\n\t\t\t\t 'parents': ['Pollution', 'Smoker'],\n\t\t\t\t 'values': ['True', 'False']},\n\t\t\t 'Dyspnoea': {'cpt': [0.65, 0.35, 0.3, 0.7],\n\t\t\t\t 'parents': ['Cancer'],\n\t\t\t\t 'values': ['True', 'False']},\n\t\t\t 'Pollution': {'cpt': [0.9, 0.1], 'parents': [], 'values': ['low', 'high']},\n\t\t\t 'Smoker': {'cpt': [0.3, 0.7], 'parents': [], 'values': ['True', 'False']},\n\t\t\t 'Xray': {'cpt': [0.9, 0.1, 0.2, 0.8],\n\t\t\t\t 'parents': ['Cancer'],\n\t\t\t\t 'values': ['positive', 'negative']}})\n\n\tdef test_V_bn(self):\n\t\tself.assertListEqual(self.bn_bn.V,\n\t\t\t['Burglary', 'Earthquake', 'Alarm', 'JohnCalls', 'MaryCalls'])\n\t\n\tdef test_E_bn(self):\n\t\tself.assertDictEqual(self.bn_bn.E,\n\t\t\t{'Alarm': ['JohnCalls', 'MaryCalls'],\n\t\t\t\t 'Burglary': ['Alarm'],\n\t\t\t\t 'Earthquake': ['Alarm'],\n\t\t\t\t 'JohnCalls': [],\n\t\t\t\t 'MaryCalls': []})\n\n\tdef test_F_bn(self):\n\t\tself.assertDictEqual(self.bn_bn.F,\n\t\t\t{'Alarm': {'cpt': [0.999, 0.001, 0.71, 0.29, 0.06, 0.94, 0.05, 0.95],\n\t\t\t 'parents': ['Earthquake', 'Burglary'],\n\t\t\t 'values': ['No', 'Yes']},\n\t\t\t 'Burglary': {'cpt': [0.999, 0.001], 'parents': [], 'values': ['No', 'Yes']},\n\t\t\t 'Earthquake': {'cpt': [0.998, 0.002], 'parents': [], 'values': ['No', 'Yes']},\n\t\t\t 'JohnCalls': {'cpt': [0.95, 0.05, 0.1, 0.9],\n\t\t\t 'parents': ['Alarm'],\n\t\t\t 'values': ['No', 'Yes']},\n\t\t\t 'MaryCalls': {'cpt': [0.99, 0.01, 0.3, 0.7],\n\t\t\t 'parents': ['Alarm'],\n\t\t\t 'values': ['No', 'Yes']}})\n\n\tdef test_nodes(self):\n\t\tn = list(self.bn_bn.nodes())\n\t\tself.assertListEqual(n,\n\t\t['Burglary', 'Earthquake', 'Alarm', 'JohnCalls', 'MaryCalls'])\n\n\tdef test_cpt(self):\n\t\tcpt = list(self.bn_bn.cpt('Alarm'))\n\t\tself.assertListEqual(cpt,\n\t\t\t[0.999, 0.001, 0.71, 0.29, 0.06, 0.94, 0.05, 0.95])\n\n\tdef test_card(self):\n\t\tself.assertEqual(self.bn_bn.card('Alarm'),2)\n\n\tdef test_scope(self):\n\t\tself.assertListEqual(self.bn_bn.scope('Alarm'),\n\t\t\t['Alarm', 'Earthquake', 'Burglary'])\n\t\n\tdef test_parents(self):\n\t\tself.assertListEqual(self.bn_bn.parents('Alarm'),\n\t\t\t['Earthquake','Burglary'])\n\n\tdef test_values(self):\n\t\tself.assertListEqual(self.bn_bn.values('Alarm'),['No','Yes'])\n\n\tdef test_values_idx(self):\n\t\tself.assertEqual(self.bn_bn.values('Alarm')[1],'Yes')\n\nif __name__ == '__main__':\n\tunittest.main(exit=False)\n\n\n\n\n" }, { "path": "pyBN/classes/_tests/test_factor.py", "content": "\"\"\"\n********\nUnitTest\nFactor\n********\n\nMethod\tChecks that\nassertEqual(a, b)\ta == b\t \nassertNotEqual(a, b)\ta != b\t \nassertTrue(x)\tbool(x) is True\t \nassertFalse(x)\tbool(x) is False\t \nassertIs(a, b)\ta is b\nassertIsNot(a, b)\ta is not b\nassertIsNone(x)\tx is None\nassertIsNotNone(x)\tx is not None\nassertIn(a, b)\ta in b\nassertNotIn(a, b)\ta not in b\nassertIsInstance(a, b)\tisinstance(a, b)\nassertNotIsInstance(a, b)\tnot isinstance(a, b)\n\nassertAlmostEqual(a, b)\tround(a-b, 7) == 0\t \nassertNotAlmostEqual(a, b)\tround(a-b, 7) != 0\t \nassertGreater(a, b)\ta > b\nassertGreaterEqual(a, b)\ta >= b\nassertLess(a, b)\ta < b\nassertLessEqual(a, b)\ta <= b\n\nassertListEqual(a, b)\tlists\nassertTupleEqual(a, b)\ttuples\nassertSetEqual(a, b)\tsets or frozensets\nassertDictEqual(a, b)\tdicts\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.classes.factor import Factor\nfrom pyBN.readwrite.read import read_bn\n\n\nclass FactorTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.data_path = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tself.bn = read_bn(os.path.join(self.data_path,'cmu.bn'))\n\t\tself.f = Factor(self.bn, 'Alarm')\n\tdef tearDown(self):\n\t\tpass\n\n\t# Factor Creation Tests\n\tdef test_factor_init(self):\n\t\tself.assertIsInstance(self.f,Factor)\n\n\tdef test_factor_bn(self):\n\t\tself.assertListEqual(self.f.bn.V,\n\t\t\t['Burglary', 'Earthquake', 'Alarm', 'JohnCalls', 'MaryCalls'])\n\n\tdef test_factor_var(self):\n\t\tself.assertEqual(self.f.var, 'Alarm')\n\t\n\tdef test_factor_scope(self):\n\t\tself.assertListEqual(self.f.scope,['Alarm','Earthquake','Burglary'])\n\t\n\tdef test_factor_card(self):\n\t\tself.assertDictEqual(self.f.card,\n\t\t\t{'Alarm':2, 'Burglary':2, 'Earthquake':2})\n\t\n\tdef test_factor_stride(self):\n\t\tself.assertDictEqual(self.f.stride,\n\t\t\t{'Alarm':1, 'Burglary':4, 'Earthquake':2})\n\t\n\tdef test_factor_cpt(self):\n\t\tself.assertListEqual(list(self.f.cpt),\n\t\t\t[ 0.999, 0.001, 0.71 , 0.29 , 0.06 , 0.94 , 0.05 , 0.95 ])\n\t\n\t# Factor Operations Tests\n\tdef test_multiply_factor(self):\n\t\tf1 = Factor(self.bn,'Alarm')\n\t\tf2 = Factor(self.bn,'Burglary')\n\t\tf1.multiply_factor(f2)\n\n\t\tf3 = Factor(self.bn,'Burglary')\n\t\tf4 = Factor(self.bn,'Alarm')\n\t\tf3.multiply_factor(f4)\n\n\t\tself.assertListEqual(list(f1.cpt),list(f3.cpt))\n\t\n\tdef test_sumover_var(self):\n\t\tself.f.sumover_var('Burglary')\n\t\tself.assertListEqual(list(self.f.cpt),[0.5,0.5])\n\n\tdef test_sumout_var_list(self):\n\t\tf = Factor(self.bn,'Alarm')\n\t\tf.sumout_var_list(['Burglary','Earthquake'])\n\t\tself.assertListEqual(f.scope,['Alarm'])\n\t\tself.assertDictEqual(f.stride,{'Alarm':1})\n\t\tself.assertListEqual(list(f.cpt),[0.45475,0.54525])\n\n\tdef test_sumout_var(self):\n\t\tf = Factor(self.bn,'Alarm')\n\t\tf.sumout_var('Earthquake')\n\t\tself.assertListEqual(f.scope,['Alarm','Burglary'])\n\t\tself.assertDictEqual(f.stride,\n\t\t\t{'Alarm':1,'Burglary':2})\n\t\tself.assertListEqual(list(f.cpt),\n\t\t\t[ 0.8545, 0.1455, 0.055 , 0.945 ])\n\n\tdef test_maxout_var(self):\n\t\t\"\"\"\n\t\tI KNOW THIS IS CORRECT.\n\t\t\"\"\"\n\t\tf = Factor(self.bn,'Alarm')\n\t\tf.maxout_var('Burglary')\n\t\tself.assertListEqual(list(f.cpt),\n\t\t\t[ 0.999, 0.94 , 0.71 , 0.95 ])\n\t\tself.assertListEqual(f.scope,['Alarm','Earthquake'])\n\t\tself.assertDictEqual(f.stride,\n\t\t\t{'Alarm':1,'Earthquake':2})\n\n\tdef test_reduce_factor_by_list(self):\n\t\tf = Factor(self.bn, 'Alarm')\n\t\tf.reduce_factor_by_list([['Burglary','Yes'],['Earthquake','Yes']])\n\t\tself.assertListEqual(list(f.cpt),[0.05,0.95])\n\t\tself.assertListEqual(f.scope,['Alarm'])\n\t\tself.assertDictEqual(f.stride,{'Alarm':1})\n\n\tdef test_reduce_factor(self):\n\t\tf = Factor(self.bn, 'Alarm')\n\t\tf.reduce_factor('Burglary','Yes')\n\t\tself.assertListEqual(list(f.cpt),\n\t\t\t[ 0.06, 0.94, 0.05, 0.95])\n\n\tdef test_to_log(self):\n\t\tf = Factor(self.bn,'Earthquake')\n\t\tf.to_log()\n\t\tself.assertEqual(round(sum(f.cpt),4),-6.2166)\n\n\tdef test_from_log(self):\n\t\tf = Factor(self.bn, 'Earthquake')\n\t\tf.to_log()\n\t\tf.from_log()\n\t\tself.assertListEqual(list(f.cpt),[0.998,0.002])\n\n\tdef test_normalize(self):\n\t\tself.f.cpt[0]=20\n\t\tself.f.cpt[1]=20\n\t\tself.f.cpt[4]=0.94\n\t\tself.f.cpt[7]=0.15\n\t\tself.f.normalize()\n\t\tself.assertListEqual(list(self.f.cpt),\n\t\t\t[0.500,0.500,0.710,0.290,0.5,0.5,0.25,0.75])\n \nif __name__ == '__main__':\n\tunittest.main(exit=False)\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/classes/bayesnet.py", "content": "\"\"\"\r\n**************\r\nBayesNet Class\r\n**************\r\n\r\nOverarching class for Discrete Bayesian Networks.\r\n\r\n\r\nDesign Specs\r\n------------\r\n\r\n- Bayesian Network -\r\n\r\n - F -\r\n key:\r\n - rv -\r\n values:\r\n - children -\r\n - parents -\r\n - values -\r\n - cpt -\r\n\r\n - V -\r\n - list of rvs -\r\n\r\n - E -\r\n key:\r\n - rv -\r\n values:\r\n - list of rv's children -\r\nNotes\r\n-----\r\n- Edges can be inferred from Factorization, but Vertex values \r\n must be specified.\r\n\"\"\"\r\n\r\n__author__ = \"\"\"Nicholas Cullen \"\"\"\r\n\r\nfrom copy import copy, deepcopy\r\n\r\nimport numpy as np\r\nfrom pyBN.utils.class_equivalence import are_class_equivalent\r\nfrom pyBN.utils.graph import topsort\r\n\r\nclass BayesNet(object):\r\n \"\"\"\r\n Overarching class for Bayesian Networks\r\n\r\n \"\"\"\r\n\r\n def __init__(self, E=None, value_dict=None, file=None):\r\n \"\"\"\r\n Initialize the BayesNet class.\r\n\r\n Arguments\r\n ---------\r\n *V* : a list of strings - vertices in topsort order\r\n *E* : a dict, where key = vertex, val = list of its children\r\n *F* : a dict, \r\n where key = rv, \r\n val = another dict with\r\n keys = \r\n 'parents', \r\n 'values', \r\n 'cpt'\r\n\r\n *V* : a dict \r\n\r\n Notes\r\n -----\r\n \r\n \"\"\"\r\n if file is not None:\r\n import pyBN.io.read as ior\r\n bn = ior.read_bn(file)\r\n self.V = bn.V\r\n self.E = bn.E\r\n self.F = bn.F \r\n else:\r\n if E is not None:\r\n #assert (value_dict is not None), 'Must set values if E is set.'\r\n self.set_structure(E, value_dict)\r\n else:\r\n self.V = []\r\n self.E = {}\r\n self.F = {}\r\n\r\n def __eq__(self, y):\r\n \"\"\"\r\n Tests whether two Bayesian Networks are\r\n equivalent - i.e. they contain the same\r\n node/edge structure, and equality of\r\n conditional probabilities.\r\n \"\"\"\r\n return are_class_equivalent(self, y)\r\n\r\n def __hash__(self):\r\n \"\"\"\r\n Allows BayesNet objects to be used\r\n as keys in a dictionary (i.e. hashable)\r\n \"\"\"\r\n return hash((str(self.V),str(self.E)))\r\n\r\n def copy(self):\r\n V = deepcopy(self.V)\r\n E = deepcopy(self.E)\r\n F = {}\r\n for v in V:\r\n F[v] = {}\r\n F[v]['cpt'] = deepcopy(self.F[v]['cpt'])\r\n F[v]['parents'] = deepcopy(self.F[v]['parents'])\r\n F[v]['values'] = deepcopy(self.F[v]['values'])\r\n bn = BayesNet()\r\n bn.V = V\r\n bn.E = E\r\n bn.F = F\r\n\r\n return bn\r\n\r\n def add_node(self, rv, cpt=[], parents=[], values=[]):\r\n self.V.append(rv)\r\n self.F[rv] = {'cpt':cpt,'parents':parents,'values':values}\r\n\r\n def add_edge(self, u, v):\r\n if not self.has_node(u):\r\n self.add_node(u)\r\n if not self.has_node(v):\r\n self.add_node(v)\r\n if self.has_edge(u,v):\r\n print('Edge already exists')\r\n else:\r\n self.E[u].append(v)\r\n self.F[v]['parents'].append(u)\r\n #self.V = topsort(self.E)\r\n # HOW DO I RECALCULATE CPT?\r\n\r\n\r\n def remove_edge(self, u, v):\r\n self.E[u].remove(v)\r\n self.F[v]['parents'].remove(u)\r\n\r\n def reverse_arc(self, u, v):\r\n if self.has_edge(u,v):\r\n self.E[u].remove(v)\r\n self.E[v].append(u)\r\n\r\n def set_data(self, rv, data):\r\n assert (isinstance(data, dict)), 'data must be dictionary'\r\n self.F[rv] = data\r\n\r\n def set_cpt(self, rv, cpt):\r\n self.F[rv]['cpt'] = cpt\r\n\r\n def set_parents(self, rv, parents):\r\n self.F[rv]['parents'] = parents\r\n\r\n def set_values(self, rv, values):\r\n self.F[rv]['values'] = values\r\n\r\n def nodes(self):\r\n for v in self.V:\r\n yield v\r\n\r\n def node_idx(self, rv):\r\n try:\r\n return self.V.index(rv)\r\n except ValueError:\r\n return -1\r\n\r\n def has_node(self, rv):\r\n return rv in self.V\r\n\r\n def has_edge(self, u, v):\r\n return v in self.E[u]\r\n\r\n def edges(self):\r\n for u in self.nodes():\r\n for v in self.E[u]:\r\n yield (u,v)\r\n def num_edges(self):\r\n num = 0\r\n for u in self.nodes():\r\n num += len(self.E[u])\r\n return num\r\n\r\n def num_params(self):\r\n num = 0\r\n for u in self.nodes():\r\n num += len(self.F[u]['cpt'])\r\n return num\r\n\r\n def scope_size(self, rv):\r\n return len(self.F[rv]['parents'])+1\r\n\r\n def num_nodes(self):\r\n return len(self.V)\r\n\r\n def cpt(self, rv):\r\n return self.F[rv]['cpt']\r\n\r\n def card(self, rv):\r\n return len(self.F[rv]['values'])\r\n\r\n def scope(self, rv):\r\n scope = [rv]\r\n scope.extend(self.F[rv]['parents'])\r\n return scope\r\n\r\n def parents(self, rv):\r\n return self.F[rv]['parents']\r\n\r\n def children(self, rv):\r\n return self.E[rv]\r\n\r\n def degree(self, rv):\r\n return len(self.parents(rv)) + len(self.children(rv))\r\n\r\n def values(self, rv):\r\n return self.F[rv]['values']\r\n\r\n def value_idx(self, rv, val):\r\n try: \r\n return self.F[rv]['values'].index(val)\r\n except ValueError:\r\n print(\"Value Index Error\")\r\n return -1\r\n\r\n def stride(self, rv, n):\r\n if n==rv:\r\n return 1\r\n else:\r\n card_list = [self.card(rv)]\r\n card_list.extend([self.card(p) for p in self.parents(rv)])\r\n n_idx = self.parents(rv).index(n) + 1\r\n return int(np.prod(card_list[0:n_idx]))\r\n\r\n def flat_cpt(self, by_var=False, by_parents=False):\r\n \"\"\"\r\n Return all cpt values in the BN as a flattened\r\n numpy array ordered by bn.nodes() - i.e. topsort\r\n \"\"\"\r\n if by_var:\r\n cpt = np.array([sum(self.cpt(rv)) for rv in self.nodes()])\r\n elif by_parents:\r\n 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))])\r\n else:\r\n cpt = np.array([val for rv in self.nodes() for val in self.cpt(rv)])\r\n return cpt\r\n\r\n def cpt_indices(self, target, val_dict):\r\n \"\"\"\r\n Get the index of the CPT which corresponds\r\n to a dictionary of rv=val sets. This can be\r\n used for parameter learning to increment the\r\n appropriate cpt frequency value based on\r\n observations in the data.\r\n\r\n There is definitely a fast way to do this.\r\n -- check if (idx - rv_stride*value_idx) % (rv_card*rv_stride) == 0\r\n\r\n Arguments\r\n ---------\r\n *target* : a string\r\n Main RV\r\n\r\n *val_dict* : a dictionary, where\r\n key=rv,val=rv value\r\n\r\n \"\"\"\r\n stride = dict([(n,self.stride(target,n)) for n in self.scope(target)])\r\n #if len(val_dict)==len(self.parents(target)):\r\n # idx = sum([self.value_idx(rv,val)*stride[rv] \\\r\n # for rv,val in val_dict.items()])\r\n #else:\r\n card = dict([(n, self.card(n)) for n in self.scope(target)])\r\n idx = set(range(len(self.cpt(target))))\r\n for rv, val in val_dict.items():\r\n val_idx = self.value_idx(rv,val)\r\n rv_idx = []\r\n s_idx = val_idx*stride[rv]\r\n while s_idx < len(self.cpt(target)):\r\n rv_idx.extend(range(s_idx,(s_idx+stride[rv])))\r\n s_idx += stride[rv]*card[rv]\r\n idx = idx.intersection(set(rv_idx))\r\n\r\n return list(idx)\r\n\r\n def cpt_str_idx(self, rv, idx):\r\n \"\"\"\r\n Return string representation of RV=VAL and\r\n Parents=Val for the given idx of the given rv's cpt.\r\n \"\"\"\r\n rv_val = self.values(rv)[idx % self.card(rv)]\r\n s = str(rv)+'='+str(rv_val) + '|'\r\n _idx=1\r\n for parent in self.parents(rv):\r\n for val in self.values(parent):\r\n if idx in self.cpt_indices(rv,{rv:rv_val,parent:val}):\r\n s += str(parent)+'='+str(val)\r\n if _idx < len(self.parents(rv)):\r\n s += ','\r\n _idx+=1\r\n return s\r\n\r\n\r\n\r\n def set_structure(self, edge_dict, value_dict=None):\r\n \"\"\"\r\n Set the structure of a BayesNet object. This\r\n function is mostly used to instantiate a BN\r\n skeleton after structure learning algorithms.\r\n\r\n See \"structure_learn\" folder & algorithms\r\n\r\n Arguments\r\n ---------\r\n *edge_dict* : a dictionary,\r\n where key = rv,\r\n value = list of rv's children\r\n NOTE: THIS MUST BE DIRECTED ALREADY!\r\n\r\n *value_dict* : a dictionary,\r\n where key = rv,\r\n value = list of rv's possible values\r\n\r\n Returns\r\n -------\r\n None\r\n\r\n Effects\r\n -------\r\n - sets self.V in topsort order from edge_dict\r\n - sets self.E\r\n - creates self.F structure and sets the parents\r\n\r\n Notes\r\n -----\r\n\r\n \"\"\"\r\n\r\n self.V = topsort(edge_dict)\r\n self.E = edge_dict\r\n self.F = dict([(rv,{}) for rv in self.nodes()])\r\n for rv in self.nodes():\r\n self.F[rv] = {\r\n 'parents':[p for p in self.nodes() if rv in self.children(p)],\r\n 'cpt': [],\r\n 'values': []\r\n }\r\n if value_dict is not None:\r\n self.F[rv]['values'] = value_dict[rv]\r\n\r\n def adj_list(self):\r\n \"\"\"\r\n Returns adjacency list of lists, where\r\n each list element is a vertex, and each sub-list is\r\n a list of that vertex's neighbors.\r\n \"\"\"\r\n adj_list = [[] for _ in self.V]\r\n vi_map = dict((self.V[i],i) for i in range(len(self.V)))\r\n for u,v in self.edges():\r\n adj_list[vi_map[u]].append(vi_map[v])\r\n return adj_list\r\n\r\n def moralized_edges(self):\r\n \"\"\"\r\n Moralized graph is the original graph PLUS\r\n an edge between every set of common effect\r\n structures -\r\n i.e. all parents of a node are connected.\r\n\r\n This function has be validated.\r\n\r\n Returns\r\n -------\r\n *e* : a python list of parent-child tuples.\r\n\r\n \"\"\"\r\n e = set()\r\n for u in self.nodes():\r\n for p1 in self.parents(u):\r\n e.add((p1,u))\r\n for p2 in self.parents(u):\r\n if p1!=p2 and (p2,p1) not in e:\r\n e.add((p1,p2))\r\n return list(e)\r\n\r\n\r\n \r\n\r\n\r\n \r\n" }, { "path": "pyBN/classes/cliquetree.py", "content": "\"\"\"\r\n******************\r\nCliqueTree Class \r\n&\r\nClique Class\r\n******************\r\n\r\nThis is a class for creating/manipulating Junction (Clique) Trees,\r\nand performing inference over them. The advantage of clique trees\r\nover traditional variable elimination over the original Bayesian \r\nnetwork is that clique trees allow you to compute marginal\r\nprobabilities of MULTIPLE variables without having to run the\r\nentire algorithm over. \r\n\r\nTherefore, if you have to query the Bayesian network many times \r\n-- and you want the exact marginal values -- then it might be \r\nbest to use the clique tree data structure for inference.\r\nIf you need to compute the marginal distribution for all variables,\r\nbut you dont mind an approximate, a sampling algorithm is probably\r\nthe way to go.\r\n\r\nIn general, the junction tree algorithms generalize \r\nVariable Elimination to the efficient, simultaneous execution \r\nof a large class of queries.\r\n\r\nNOTE: A cluster graph is a generalization of the clique tree\r\ndata structure - to generate a clique tree, you first generate\r\na cluster graph, then simply calculate a maximum spanning tree.\r\nIn other words, a clique tree can be considered as a special\r\ntype of cluster graph.\r\n\r\n\"\"\"\r\n\r\n__author__ = \"\"\"Nicholas Cullen \"\"\"\r\n\r\n\r\n\r\nimport numpy as np\r\nimport networkx as nx\r\nfrom copy import copy, deepcopy\r\n\r\nfrom pyBN.classes.bayesnet import BayesNet\r\nfrom pyBN.classes.factor import Factor\r\nfrom pyBN.classes.factorization import Factorization\r\n\r\nfrom pyBN.utils.graph import *\r\n\r\n\r\n\r\nclass CliqueTree(object):\r\n \"\"\"\r\n CliqueTree Class\r\n\r\n Let G be a chordal graph (ie. a graph such that no cycle includes more\r\n than three nodes), then a CliqueTree is a tree H such that each\r\n maximal clique C in G is a node in H.\r\n\r\n Attributes\r\n ----------\r\n - bn \r\n - BayesNet object\r\n\r\n - V\r\n - Vertices -> list\r\n\r\n - E\r\n - Edges -> dictionary\r\n\r\n - C\r\n - Cliques -> a dictionary where key = vertex idx,\r\n value = Clique object\r\n\r\n \"\"\"\r\n\r\n def __init__(self, bn):\r\n \"\"\"\r\n Instantiate a CliqueTree object.\r\n\r\n Arguments\r\n ---------\r\n *bn*: a BayesNet object\r\n\r\n Notes\r\n -----\r\n Ideally, the Factor class should be used as the\r\n cliques instead of the Clique class (because it's\r\n just a watered down version of the Factor class) \r\n \r\n \"\"\"\r\n ####\r\n self.V = None\r\n self.E = None\r\n self.C = None\r\n ###\r\n\r\n self.bn = bn\r\n self._F = Factorization(bn)\r\n self.initialize_tree()\r\n\r\n \r\n\r\n #def __repr__(self):\r\n # return self.C\r\n\r\n def __iter__(self):\r\n for clique in self.C.values():\r\n yield clique\r\n\r\n def __getitem__(self, rv):\r\n \"\"\"\r\n Returns Clique of passed-in rv\r\n \"\"\"\r\n return self.C[rv]\r\n\r\n def parents(self, v):\r\n p = []\r\n for rv in self.V:\r\n if v in self.E[rv]:\r\n p.append(v)\r\n return p\r\n\r\n def children(self, n):\r\n return self.E[n]\r\n\r\n def dfs_postorder(self, root):\r\n G = nx.Graph(self.E)\r\n tree_graph = nx.dfs_tree(G,root)\r\n clique_ordering = list(nx.dfs_postorder_nodes(tree_graph,root))\r\n return clique_ordering\r\n\r\n def initialize_tree(self):\r\n \"\"\"\r\n Initialize the structure of a clique tree, using\r\n the following steps:\r\n - Moralize graph (i.e. marry parents)\r\n - Triangulate graph (i.e. make graph chordal)\r\n - Get max cliques (i.e. community/clique detection)\r\n - Max spanning tree over sepset cardinality (i.e. create tree)\r\n \r\n \"\"\"\r\n ### MORALIZE GRAPH & MAKE IT CHORDAL ###\r\n chordal_G = make_chordal(self.bn) # must return a networkx object\r\n V = chordal_G.nodes()\r\n\r\n ### GET MAX CLIQUES FROM CHORDAL GRAPH ###\r\n C = {} # key = vertex, value = clique object\r\n max_cliques = reversed(list(nx.chordal_graph_cliques(chordal_G)))\r\n for v_idx,clique in enumerate(max_cliques):\r\n C[v_idx] = Clique(set(clique))\r\n\r\n ### MAXIMUM SPANNING TREE OVER COMPLETE GRAPH TO MAKE A TREE ###\r\n weighted_edge_dict = dict([(c_idx,{}) for c_idx in range(len(C))])\r\n for i in range(len(C)):\r\n for j in range(len(C)):\r\n if i!=j:\r\n intersect_cardinality = len(C[i].sepset(C[j]))\r\n weighted_edge_dict[i][j] = -1*intersect_cardinality\r\n mst_G = mst(weighted_edge_dict)\r\n ### SET V,E,C ###\r\n self.E = mst_G # dictionary\r\n self.V = mst_G.keys() # list\r\n self.C = C\r\n\r\n \r\n ### ASSIGN EACH FACTOR TO ONE CLIQUE ONLY ###\r\n v_a = dict([(rv, False) for rv in self.bn.nodes()])\r\n for clique in self.C.values():\r\n temp_scope = []\r\n for var in v_a:\r\n if v_a[var] == False and set(self.bn.scope(var)).issubset(clique.scope):\r\n temp_scope.append(var)\r\n v_a[var] = True\r\n clique._F = Factorization(self.bn, temp_scope)\r\n\r\n ### COMPUTE INITIAL POTENTIAL FOR EACH FACTOR ###\r\n # - i.e. multiply all of its assigned factors together\r\n for i, clique in self.C.items():\r\n if len(self.parents(i)) == 0:\r\n clique.is_ready = True\r\n clique.initialize_psi()\r\n\r\n\r\nclass Clique(object):\r\n \"\"\"\r\n Clique Class\r\n\r\n *scope* : a set of variables in the clique's scope\r\n\r\n *_f* : a factorization object that contains only the\r\n factors of variables in the clique's scope\r\n\r\n *psi* : a factor\r\n The potential of the clique.\r\n\r\n *belief* : a factor\r\n The factor which holds the marginal/conditional\r\n probabilities of the relevant nodes after \r\n belief propagation, etc\r\n\r\n *messages_received* : a list of Factors\r\n The messages the clique has received from its neighbors -\r\n stored so that the beliefs can be calculated after\r\n belief propagation, etc.\r\n\r\n *is_ready* : a boolean\r\n Whether the clique is ready to send a message -> must\r\n have RECEIVED messages from all of its neighbors first.\r\n\r\n \"\"\"\r\n\r\n def __init__(self, scope):\r\n \"\"\"\r\n Instantiate a clique object\r\n\r\n Arguments\r\n ---------\r\n *scope* : a python set\r\n The set of variables in the cliques scope,\r\n i.e. the main var and its parents\r\n\r\n\r\n \"\"\"\r\n self.scope = scope\r\n self._F = None\r\n \r\n self.psi = None # Psi should never change -> Factor object\r\n self.belief = None\r\n \r\n self.messages_received = []\r\n self.is_ready = False\r\n\r\n def __repr__(self):\r\n return str(self.scope)\r\n\r\n def __rshift__(self, other_clique):\r\n \"\"\"\r\n Send a message from self to other_clique\r\n \"\"\"\r\n self.send_message(other_clique)\r\n\r\n def __lshift__(self, other_clique):\r\n \"\"\"\r\n Send a message from other_clique to self\r\n \"\"\"\r\n other_clique.send_message(self)\r\n\r\n def send_message(self, parent):\r\n \"\"\"\r\n Send a message to the parent clique.\r\n\r\n To send a message from X to Y, you\r\n must first take the potential (self.psi) of\r\n X and sum out the variables NOT in the sepset\r\n of Y.\r\n\r\n THEN, if X has received any messages, the\r\n potential (self.psi) must be multiplied by\r\n all of the received messaged.\r\n\r\n Arguments\r\n ---------\r\n *parent* : a string\r\n The parent to which the message will\r\n be sent.\r\n\r\n \"\"\"\r\n\r\n\r\n # First generate Belief = Original_Psi * all received messages\r\n if len(self.messages_received) > 0:\r\n # if there are messages received, mutliply them in to psi first\r\n if not self.belief:\r\n self.belief = copy(self.psi)\r\n for msg in self.messages_received:\r\n self.belief *= msg\r\n #self.belief.merge_multiply(self.messages_received)\r\n else:\r\n # if there are no messages received, simply move on with psi\r\n if not self.belief:\r\n self.belief = copy(self.psi)\r\n # generate message as belief with Ci - Sij vars summed out\r\n #vars_to_sumout = list(self.scope.difference(self.sepset(parent)))\r\n msg_to_send = copy(self.belief)\r\n for var_to_sumout in self.scope.difference(self.sepset(parent)):\r\n msg_to_send /= var_to_sumout\r\n #message_to_send = copy(self.belief)\r\n #message_to_send.sumout_var_list(vars_to_sumout)\r\n parent.messages_received.append(msg_to_send)\r\n\r\n def initialize_psi(self):\r\n \"\"\"\r\n Compute a new psi (cpt) in order to \r\n set the clique's belief. This involves\r\n multiplying the factors in the Clique together.\r\n \"\"\"\r\n assert (len(self._F) != 0), 'No Factors assigned to this clique.'\r\n\r\n if len(self._F) == 1:\r\n self.psi = copy(self._F[0])\r\n self.belief = copy(self.psi)\r\n else:\r\n self.psi = max(self._F, key=lambda x: len(x.cpt))\r\n for f in self._F:\r\n self.psi *= f\r\n self.belief = copy(self.psi)\r\n\r\n def send_initial_message(self, other_clique):\r\n \"\"\"\r\n NOT SURE IF THIS IS NEEDED.\r\n\r\n Send the first message to another clique.\r\n\r\n Arguments\r\n ---------\r\n *other_clique* : a different Clique object\r\n\r\n \"\"\"\r\n psi_copy = copy(self.psi)\r\n sepset = self.sepset(other_clique)\r\n sumout_vars = self.scope.difference(sepset)\r\n # sum out variables not in the sepset of other_clique\r\n psi_copy.sumout_var_list(list(sumout_vars))\r\n\r\n\r\n #psi_copy.cpt = psi_copy.cpt.loc[:,[c for c in psi_copy.cpt.columns if 'Prob' not in c]]\r\n #psi_copy.cpt[str('Prob-Val-' + str(np.random.randint(0,1000000)))] = 1\r\n print('Init Msg: \\n', psi_copy.cpt)\r\n other_clique.messages_received.append(psi_copy)\r\n\r\n self.belief = copy(self.psi)\r\n\r\n def collect_beliefs(self):\r\n \"\"\"\r\n Collecting beliefs is done by taking the\r\n potential (self.psi) of X and multiplying by all of\r\n the messages which X has received.\r\n\r\n NOTE: once beliefs are collected, the messages\r\n which the Clique has received are cleared out.\r\n\r\n Notes\r\n -----\r\n A root node (i.e. one that doesn't send a message) must run collect_beliefs\r\n since they are only collected at send_message()\r\n\r\n Also, we collect beliefs at the end of loopy belief propagation (approx. inference)\r\n since the main algorithm is just sending messages for a while.\r\n \"\"\"\r\n if len(self.messages_received) > 0:\r\n self.belief = copy(self.psi)\r\n for msg in self.messages_received:\r\n self.belief *= msg\r\n self.belief \r\n self.messages_received = []\r\n else:\r\n self.belief = copy(self.belief)\r\n\r\n def sepset(self, other_clique):\r\n \"\"\"\r\n The sepset of two cliques is the set of\r\n variables in the intersection of the two\r\n cliques' scopes.\r\n\r\n Arguments\r\n ---------\r\n *other_clique* : a Clique object\r\n\r\n \"\"\"\r\n return self.scope.intersection(other_clique.scope)\r\n\r\n def marginalize_over(self, target):\r\n \"\"\"\r\n Marginalize the cpt (belief) over a target variable.\r\n\r\n Arguments\r\n ---------\r\n *target* : a string\r\n The target random variable.\r\n\r\n \"\"\"\r\n blf = copy(self.belief)\r\n blf.sumover_var(target)\r\n return blf\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n" }, { "path": "pyBN/classes/clustergraph.py", "content": "\"\"\"\r\n******************\r\nClusterGraph Class\r\n******************\r\n\r\nThis is a class for creating/manipulating Cluster Graphs,\r\nand performing inference over them - currently the only\r\nsupported algorithm is Loopy Belief Propagation. Still,\r\nthe class structure is in place for easy addition of\r\nany algorithms relying on the Cluster Graph framework.\r\n\r\nNOTE: A cluster graph is a generalization of the clique tree\r\ndata structure - to generate a clique tree, you first generate\r\na cluster graph, then simply calculate a maximum spanning tree.\r\nIn other words, a clique tree can be considered as a special\r\ntype of cluster graph.\r\n\r\n\"\"\"\r\n\r\n__author__ = \"\"\"Nicholas Cullen \"\"\"\r\n\r\n\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport networkx as nx\r\n\r\nfrom pyBN.classes.cliquetree import Clique\r\n\r\nclass ClusterGraph(object):\r\n \"\"\"\r\n ClusterGraph Class\r\n\r\n \"\"\"\r\n\r\n def __init__(self, bn):\r\n \"\"\"\r\n Initialize a ClusterGraph object\r\n\r\n \"\"\"\r\n self.BN = BN\r\n self.V = {} # key = cluster index, value = Cluster objects\r\n self.E = []\r\n self.G = None\r\n self.initialize_graph(method)\r\n self.beliefs = {} # dict where key = cluster idx, value = belief cpt\r\n\r\n def initialize_graph(self):\r\n \"\"\"\r\n Initialize the structure of the cluster graph.\r\n\r\n \"\"\"\r\n # generate graph structure\r\n self.bethe()\r\n # initialize beliefs\r\n for clique in self.V.values():\r\n clique.compute_psi() \r\n # initialize messages to 1\r\n self.initialize_messages()\r\n\r\n def bethe(self):\r\n \"\"\"\r\n Generate Bethe cluster graph structure.\r\n \"\"\"\r\n self.V = {}\r\n self.E = []\r\n\r\n factorization = Factorization(self.BN)\r\n prior_dict = {}\r\n for factor in factorization.f_list:\r\n # if factor is just a prior (i.e. already added as rv)\r\n if len(factor.scope) == 1:\r\n #self.V[len(self.V)] = Clique(scope=factor.scope)\r\n #self.V[len(self.V)-1].factors = [factor]\r\n prior_dict[factor.var] = factor\r\n if len(factor.scope) > 1:\r\n self.V[len(self.V)] = Clique(scope=factor.scope)\r\n self.V[len(self.V)-1].factors = [factor] # assign the factor\r\n sep_len = len(self.V)\r\n # First, add all individual random variables\r\n for rv in self.BN.V:\r\n # if rv is a prior, don't add it\r\n if rv in prior_dict.keys():\r\n factor = prior_dict[rv]\r\n self.V[len(self.V)] = Clique(scope=factor.scope)\r\n self.V[len(self.V)-1].factors = [factor]\r\n else:\r\n self.V[len(self.V)] = Clique(scope={rv})\r\n # create a new initial factor since it wont have one \r\n new_factor = Factor(BN=self.BN, var=rv, init_to_one=True)\r\n self.V[len(self.V)-1].factors = [new_factor] \r\n for i in range(sep_len):\r\n for j in range(sep_len,len(self.V)):\r\n if self.V[j].scope.issubset(self.V[i].scope):\r\n self.E.append((i,j))\r\n\r\n new_G = nx.Graph()\r\n new_G.add_edges_from(self.E) \r\n self.G = new_G\r\n\r\n def initialize_messages(self):\r\n \"\"\"\r\n For each edge (i-j) in the ClusterGraph,\r\n set delta_(i-j) = 1 and\r\n set delta_(j-i) = 1.\r\n (i.e. send a message from each parent to every child where the \r\n message is a df = 1)\r\n \"\"\"\r\n for cluster in self.V:\r\n for neighbor in self.G.neighbors(cluster):\r\n self.V[cluster].send_initial_message(self.V[neighbor])\r\n\r\n\r\n def collect_beliefs(self):\r\n self.beliefs = {}\r\n for cluster in self.V:\r\n self.V[cluster].collect_beliefs()\r\n #print('Belief ' , cluster , ' : \\n', self.V[cluster].belief.cpt)\r\n self.beliefs[cluster] = self.V[cluster].belief\r\n\r\n\r\n def loopy_belief_propagation(self, target, evidence, max_iter=100):\r\n \"\"\"\r\n \r\n This is Message Passing (Loopy Belief Propagation) over a cluster graph.\r\n\r\n It is Sum-Product Belief Propagation in a cluster graph as shown in\r\n Koller p.397\r\n\r\n Notes:\r\n 1. Definitely a problem due to normalization (prob vals way too small)\r\n 2. Need to check the scope w.r.t. messages.. all clusters should not\r\n be accumulating rv's in their scope over the course of the algorithm.\r\n \"\"\"\r\n # 1: Moralize the graph\r\n # 2: Triangluate\r\n # 3: Build a clique tree using max spanning\r\n # 4: Propagation of probabilities using message passing\r\n\r\n # creates clique tree and assigns factors, thus satisfying steps 1-3\r\n cgraph = copy.copy(self)\r\n G = cgraph.G\r\n\r\n edge_visit_dict = dict([(i,0) for i in cgraph.E])\r\n\r\n iteration = 0\r\n while not cgraph.is_calibrated():\r\n if iteration == max_iter:\r\n break\r\n if iteration % 50 == 0:\r\n print('Iteration: ' , iteration)\r\n for cluster in cgraph.V.values():\r\n cluster.collect_beliefs()\r\n # select an edge\r\n e_idx = np.random.randint(0,len(cgraph.E))\r\n edge_select = cgraph.E[e_idx]\r\n p_idx = np.random.randint(0,2)\r\n parent_edge = edge_select[p_idx]\r\n child_edge = edge_select[np.abs(p_idx-1)]\r\n print(parent_edge , child_edge)\r\n\r\n # send a message along that edge\r\n cgraph.V[parent_edge].send_message(cgraph.V[child_edge])\r\n\r\n iteration += 1\r\n print('Now Collecting Beliefs..')\r\n self.collect_beliefs()\r\n self.BN.ctree = self\r\n" }, { "path": "pyBN/classes/empiricaldistribution.py", "content": "\"\"\"\n************\nEmpirical\nDistribution\nClass\n************\n\nCreate, maintain, and perform operations\nover an empirical probability distribution\nderived from a dataset. This class is most\nuseful for speeding up BN structure learning,\nwhere distribution calculations can be cached\nand therefore lookups become much faster over\nrepeated iterations.\n\nReferences\n----------\n*** IMPORTANT ***\nDaly and Shen,\n\"Methods to Accelerate the Learning of Bayesian Network Structures.\"\nhttp://citeseerx.ist.psu.edu/viewdoc/download?\ndoi=10.1.1.127.7570&rep=rep1&type=pdf\n*****************\n\"\"\"\n\nfrom __future__ import division\n\nimport numpy as np\nfrom copy import copy, deepcopy\n\n\nclass EmpiricalDistribution(object):\n\n\n\tdef __init__(self, data, names=None):\n\n\t\tif names is None:\n\t\t\tself.names = range(data.shape[1])\n\t\telse:\n\t\t\tassert (len(names) == self.NVAR), 'Passed-in names length must equal number of data columns'\n\t\t\tself.names = names\n\n\t\tself.NROW = data.shape[0]\n\t\tself.NVAR = data.shape[1]\n\t\tself.bins = [len(np.unique(data[:,n])) for n in range(self.NVAR)]\n\t\t\n\n\t\thist,_ = np.histogramdd(data, bins=self.bins)\n\t\tself.counts = hist\n\t\tself.joint = (hist / hist.sum()) + 1e-3\n\n\t\t## COMPUTE MARGINAL FOR EACH VARIABLE ##\n\t\t#_range = range(self.NVAR)\n\t\t#for i,rv in enumerate(self.names):\n\t\t#\t_axis = copy(_range)\n\t\t#\t_axis.remove(i)\n\t\t#\tself.marginal[rv] = np.sum(self.joint,axis=_axis)\n\n\t\t#self.marginal = dict([(rv, np.sum(self.joint,axis=i)) for i,rv in enumerate(self.names)])\n\n\t\tself.cache = {}\n\n\tdef bayes_counts(self, bn):\n\t\tpass\n\n\tdef idx_map(self, rvs):\n\t\tassert (isinstance(args, tuple)), \"passed-in rvs must be a tuple\"\n\t\tidx = [self.names.index(rv) for rv in rvs]\n\t\treturn tuple(idx)\n\n\tdef idx(self, rv):\n\t\treturn self.names.index(rv)\n\n\tdef axis_tuple(self, rv):\n\t\tpass\n\n\tdef mpd(self, rv):\n\t\t\"\"\"\n\t\tMarginal Probability Distribtuion\n\t\t\"\"\"\n\t\tassert (isinstance(rv, str)), 'passed-in rv must be a string'\n\t\tif rv in self.cache:\n\t\t\tmpd = self.cache[rv]\n\t\telse:\n\t\t\t_axis = range(self.NVAR)\n\t\t\t_axis.remove(self.idx(rv))\n\t\t\tmpd = np.sum(self.joint, axis=tuple(_axis)) # CORRECT\n\t\t\tself.cache[rv] = mpd\n\n\t\treturn mpd\n\n\tdef jpd(self, rvs):\n\t\tassert (isinstance(args, tuple)), \"passed-in rvs must be a tuple\"\n\t\tif args in self.cache:\n\t\t\tjpd = self.cache[rvs]\n\t\telse:\n\t\t\tjpd = np.sum(self.joint, axis=self.idx_map(rvs)) # WRONG \n\t\t\tself.cache[rvs] = jpd\n\t\t\t\n\t\treturn jpd\n\n\tdef cpd(self, lhs, rhs):\n\t\tassert (isinstance(rhs, tuple)), \"passed-in rhs must be a tuple\"\n\n\t\t_key = tuple((lhs,rhs))\n\t\tif _key in self.cache:\n\t\t\tcpd = self.cache[_key]\n\t\telse:\n\t\t\ttry:\n\t\t\t\ttot = lhs + rhs\n\t\t\texcept TypeError:\n\t\t\t\ttot = (lhs,) + rhs\n\n\t\t\t_numer = self.jpd(tot)\n\t\t\t_denom = self.jpd(rhs)\n\t\t\tcpd = _numer / (_denom + 1e-7)\n\t\t\tself.cache[_key] = cpd\n\t\t\n\t\treturn cpd\n\n\tdef mi(self, lhs, rhs, cond=None):\n\t\t\"\"\"\n\t\tCalculate mutual information with speedups\n\t\tby using caching for repeated lookups of\n\t\tjoint/conditional distributions.\n\n\t\t\"\"\"\n\t\tbins = np.amax(data, axis=0) # read levels for each variable\n\t\tif len(bins) == 1:\n\t\t\thist,_ = np.histogramdd(data, bins=(bins)) # frequency counts\n\t\t\tPx = hist/hist.sum()\n\t\t\tMI = -1 * np.sum( Px * np.log( Px ) )\n\t\t\treturn round(MI, 4)\n\t\t\t\n\t\tif len(bins) == 2:\n\t\t\thist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts\n\n\t\t\tPxy = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\t\tPx = np.sum(Pxy, axis = 1) # P(X,Z)\n\t\t\tPy = np.sum(Pxy, axis = 0) # P(Y,Z)\t\n\n\t\t\tPxPy = np.outer(Px,Py)\n\t\t\tPxy += 1e-7\n\t\t\tPxPy += 1e-7\n\t\t\tMI = np.sum(Pxy * np.log(Pxy / (PxPy)))\n\t\t\treturn round(MI,4)\n\t\telif len(bins) > 2 and conditional==True:\n\t\t\t# CHECK FOR > 3 COLUMNS -> concatenate Z into one column\n\t\t\tif len(bins) > 3:\n\t\t\t\tdata = data.astype('str')\n\t\t\t\tncols = len(bins)\n\t\t\t\tfor i in range(len(data)):\n\t\t\t\t\tdata[i,2] = ''.join(data[i,2:ncols])\n\t\t\t\tdata = data.astype('int')[:,0:3]\n\n\t\t\tbins = np.amax(data,axis=0)\n\t\t\thist,_ = np.histogramdd(data, bins=bins) # frequency counts\n\n\t\t\tPxyz = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\t\tPz = np.sum(Pxyz, axis = (0,1)) # P(Z)\n\t\t\tPxz = np.sum(Pxyz, axis = 1) # P(X,Z)\n\t\t\tPyz = np.sum(Pxyz, axis = 0) # P(Y,Z)\t\n\n\t\t\tPxy_z = Pxyz / (Pz+1e-7) # P(X,Y | Z) = P(X,Y,Z) / P(Z)\n\t\t\tPx_z = Pxz / (Pz+1e-7) # P(X | Z) = P(X,Z) / P(Z)\t\n\t\t\tPy_z = Pyz / (Pz+1e-7) # P(Y | Z) = P(Y,Z) / P(Z)\n\n\t\t\tPx_y_z = np.empty((Pxy_z.shape)) # P(X|Z)P(Y|Z)\n\t\t\tfor i in range(bins[0]):\n\t\t\t\tfor j in range(bins[1]):\n\t\t\t\t\tfor k in range(bins[2]):\n\t\t\t\t\t\tPx_y_z[i][j][k] = Px_z[i][k]*Py_z[j][k]\n\t\t\tPxyz += 1e-7\n\t\t\tPxy_z += 1e-7\n\t\t\tPx_y_z += 1e-7\n\t\t\tMI = np.sum(Pxyz * np.log(Pxy_z / (Px_y_z)))\n\t\t\t\n\t\t\treturn round(MI,4)\n\t\telif len(bins) > 2 and conditional == False:\n\t\t\tdata = data.astype('str')\n\t\t\tncols = len(bins)\n\t\t\tfor i in range(len(data)):\n\t\t\t\tdata[i,1] = ''.join(data[i,1:ncols])\n\t\t\tdata = data.astype('int')[:,0:2]\n\n\t\t\thist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts\n\n\t\t\tPxy = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\t\tPx = np.sum(Pxy, axis = 1) # P(X,Z)\n\t\t\tPy = np.sum(Pxy, axis = 0) # P(Y,Z)\t\n\n\t\t\tPxPy = np.outer(Px,Py)\n\t\t\tPxy += 1e-7\n\t\t\tPxPy += 1e-7\n\t\t\tMI = np.sum(Pxy * np.log(Pxy / (PxPy)))\n\t\t\treturn round(MI,4)\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/classes/factor.py", "content": "\"\"\"\r\n************\r\nFactor Class\r\n************\r\n\r\nThis class holds a Conditional Probability Table structure\r\n-- i.e. a factor. The benefit of this class structure is that\r\nall factor manipulation happens in a centralized location,\r\nthereby making it easier to write fast and readable code.\r\n\r\nThe Joint Probability Distribution of a Bayesian Network is\r\nsimply a product of its factors. Much of the functionality\r\nis derived from algorithms presented in [1].\r\n\r\nThe tests for this class are found in \"test_factor.py\".\r\n\r\nFor accessing the flattened array based on RV values/indices\r\nand respective strides, use this formula:\r\nsum( value_index[i]*stride[i] for i = all variables in the scope )\r\n\r\n\r\nReferences\r\n----------\r\n[1] Koller, Friedman (2009). \"Probabilistic Graphical Models.\"\r\n\r\n\"\"\"\r\nfrom __future__ import division\r\n\r\n__author__ = \"\"\"Nicholas Cullen \"\"\"\r\n\r\nimport numpy as np\r\n\r\nclass Factor(object):\r\n \"\"\"\r\n A Factor uses a flattened numpy array for the cpt.\r\n By storing the cpt in this manner and taking advantage \r\n of efficient algorithms, significant speedups occur.\r\n\r\n Attributes\r\n ----------\r\n\r\n *self.var* : a string\r\n The random variable to which this Factor belongs\r\n \r\n *self.scope* : a list\r\n The RV, and its parents (the RVs involved in the\r\n conditional probability table)\r\n \r\n *self.stride* : a dictionary, where\r\n key = an RV in self.scope, and\r\n val = integer stride (i.e. how many rows in the \r\n CPT until the NEXT value of RV is reached)\r\n \r\n *self.cpt* : a 1D numpy array\r\n The probability values for self.var conditioned\r\n on its parents\r\n \r\n\r\n Methods\r\n -------\r\n *multiply_factor*\r\n Multiply two factors together. The factor\r\n multiplication algorithm used here is adapted\r\n from Koller and Friedman (PGMs) textbook.\r\n\r\n *sumover_var* :\r\n Sum over one *rv* by keeping it constant. Thus, you \r\n end up with a 1-D factor whose scope is ONLY *rv*\r\n and whose length = cardinality of rv. \r\n\r\n *sumout_var_list* :\r\n Remove a collection of rv's from the factor\r\n by summing out (i.e. calling sumout_var) over\r\n each rv.\r\n\r\n *sumout_var* :\r\n Remove passed-in *rv* from the factor by summing\r\n over everything else.\r\n\r\n *maxout_var* :\r\n Remove *rv* from the factor by taking the maximum value \r\n of all rv instantiations over everyting else.\r\n\r\n *reduce_factor_by_list* :\r\n Reduce the factor by numerous sets of\r\n [rv,val]\r\n\r\n *reduce_factor* :\r\n Condition the factor by eliminating any sets of\r\n values that don't align with a given [rv, val]\r\n\r\n *to_log* :\r\n Convert probabilities to log space from\r\n normal space.\r\n\r\n *from_log* :\r\n Convert probabilities from log space to\r\n normal space.\r\n\r\n *normalize* :\r\n Make relevant collections of probabilities sum to one.\r\n\r\n\r\n Notes\r\n -----\r\n \"\"\" \r\n\r\n\r\n def __init__(self, bn, var):\r\n \"\"\"\r\n Initialize a Factor from a BayesNet object\r\n for a given random variable.\r\n\r\n Note, it's assumed that the FIRST variable\r\n of *scope* is the main variable (i.e. NOT a\r\n parent).\r\n\r\n Arguments\r\n ---------\r\n\r\n *var* : a string\r\n The RV for which the Factor will be extracted.\r\n\r\n Effects\r\n -------\r\n - sets *self.var*\r\n - sets *self.cpt*\r\n - sets *self.card*\r\n - sets *self.scope*\r\n - sets *self.stride*\r\n\r\n Notes\r\n -----\r\n - self.card is no longer an attribute, but is now a function\r\n - self.bn is no longer an attribute\r\n\r\n \"\"\"\r\n self.bn = bn\r\n self.var = var\r\n self.cpt = np.array(bn.cpt(var))\r\n self.scope = bn.scope(var)\r\n self.card = dict([(rv, bn.card(rv)) for rv in self.scope])\r\n\r\n self.stride = {self.var:1}\r\n s=self.card[self.var]\r\n for v in bn.parents(var):\r\n self.stride[v]=s\r\n s*=self.card[v]\r\n\r\n\r\n def __repr__(self):\r\n \"\"\"\r\n Internal representation of the factor,\r\n to be used when the object is called\r\n in the console without print.\r\n \"\"\"\r\n s = self.var + ' | '\r\n s += ', '.join(self.parents())\r\n return s\r\n\r\n def __str__(self):\r\n \"\"\"\r\n String representation of the factor,\r\n to be used when print is called.\r\n \"\"\"\r\n s = self.var + ' | '\r\n s += ', '.join(self.parents())\r\n return s\r\n\r\n def __mul__(self, other_factor):\r\n \"\"\"\r\n Overloads multiplication operator to\r\n be used as multiplying two factors together.\r\n \"\"\"\r\n self.multiply_factor(other_factor)\r\n return self\r\n\r\n def __sub__(self, rv_val):\r\n \"\"\"\r\n Overloads subtraction operator to\r\n be used as reducing a factor by evidence.\r\n \"\"\"\r\n self.reduce_factor(rv_val[0],rv_val[1])\r\n return self\r\n\r\n def __div__(self, rv):\r\n \"\"\"\r\n Overloads division operator to\r\n be used as summing out a variable.\r\n \"\"\"\r\n self.sumout_var(rv)\r\n return self\r\n\r\n def __floordiv__(self, rv):\r\n \"\"\"\r\n Overloads floor division operator to\r\n be used as maxing out a variable\r\n \"\"\"\r\n self.maxout_var(rv)\r\n return self\r\n\r\n\r\n def parents(self):\r\n \"\"\"\r\n Return parents of self.var ...\r\n Should make this an iterator\r\n \"\"\"\r\n for rv in self.scope:\r\n if rv != self.var:\r\n yield rv\r\n \r\n def values(self, rv):\r\n return self.bn.values(rv)\r\n\r\n def value_indices(self, val_dict):\r\n \"\"\"\r\n Return the indices in the cpt\r\n where RV=Value in val_dict\r\n For accessing the flattened array based \r\n on RV values/indices\r\n and respective strides, use this formula:\r\n sum( value_index[i]*stride[i] for i = all \r\n variables in the scope )\r\n \"\"\"\r\n idx = sum([self.bn.value_idx(rv,val)*self.stride[rv] \\\r\n for rv,val in val_dict.items()])\r\n return idx\r\n\r\n def sepset(self, other_factor):\r\n \"\"\"\r\n The sepset of two cliques is the set of\r\n variables in the intersection of the two\r\n cliques' scopes.\r\n\r\n Arguments\r\n ---------\r\n *other_clique* : a Clique object\r\n \"\"\"\r\n return set(self.scope).intersection(set(other_factor.scope))\r\n\r\n\r\n\r\n ##### FACTOR OPERATIONS #####\r\n\r\n def multiply_factor(self, other_factor):\r\n \"\"\"\r\n Multiply two factors together. The factor\r\n multiplication algorithm used here is adapted\r\n from Koller and Friedman (PGMs) textbook.\r\n\r\n In essence, the scope of the merged factor is the\r\n union of the two scopes.\r\n\r\n Arguments\r\n ---------\r\n *other_factor* : a different Factor object\r\n\r\n Returns\r\n -------\r\n None\r\n\r\n Effects\r\n -------\r\n - alters self.cpt\r\n - alters self.stride\r\n - alters self.card\r\n - alters self.scope\r\n\r\n Notes\r\n -----\r\n - What is done about normalization here? I guess\r\n assume it's already normalized\r\n\r\n \"\"\"\r\n if len(self.scope)>=len(other_factor.scope):\r\n phi1=self\r\n phi2=other_factor\r\n else:\r\n phi1=other_factor\r\n phi2=self\r\n # go in order of strides to keep them in order after the fact\r\n rv_order = sorted(phi1.stride, key=phi1.stride.__getitem__) \r\n scope_set=set(phi1.scope).union(set(phi2.scope))\r\n\r\n ########### HAD TO ADD THESE FOR V/E TO WORK... #################\r\n ## I think this is necessary when phi2 has vars not in phi1 ##\r\n rv_order.extend(list(set(phi2.scope).difference(set(phi1.scope))))\r\n phi1.card.update(phi2.card)\r\n #################################################################\r\n\r\n j,k=0,0\r\n assignment = dict([(rv, 0) for rv in scope_set])\r\n psi = np.zeros(np.product(phi1.card.values()))\r\n\r\n for i in range(len(psi)):\r\n psi[i] = phi1.cpt[j]*phi2.cpt[k]\r\n for rv in rv_order:\r\n assignment[rv] += 1\r\n if assignment[rv] == phi1.card[rv]:\r\n assignment[rv] = 0\r\n if rv in phi1.scope:\r\n j=j-(phi1.card[rv]-1)*phi1.stride[rv]\r\n if rv in phi2.scope:\r\n k=k-(phi2.card[rv]-1)*phi2.stride[rv]\r\n else:\r\n if rv in phi1.scope:\r\n j=j+phi1.stride[rv]\r\n if rv in phi2.scope:\r\n k=k+phi2.stride[rv]\r\n break\r\n self.cpt = psi\r\n self.scope = list(scope_set)\r\n self.card=phi1.card\r\n self.stride={}\r\n s=1\r\n for v in rv_order:\r\n self.stride[v]=s\r\n s*=phi1.card[v]\r\n\r\n #self.normalize()\r\n\r\n\r\n def sumover_var(self, rv):\r\n \"\"\"\r\n Sum over one *rv* by keeping it constant. Thus, you \r\n end up with a factor whose scope is ONLY *rv*\r\n and whose length = cardinality of rv. \r\n\r\n This is equivalent to calling self.sumout_var() over\r\n EVERY other variable in the scope and is thus faster\r\n when you want to do just that.\r\n\r\n Arguments\r\n ---------\r\n *rv* : a string\r\n The random variable to sum over.\r\n\r\n Returns\r\n -------\r\n None\r\n\r\n Effects\r\n -------\r\n - alters self.cpt\r\n - alters self.stride\r\n - alters self.card\r\n - alters self.scope\r\n\r\n Notes\r\n -----\r\n\r\n \"\"\"\r\n exp_len = self.card[rv]\r\n new_cpt= np.zeros(exp_len)\r\n\r\n rv_card = self.card[rv]\r\n rv_stride = self.stride[rv]\r\n\r\n for i in range(exp_len):\r\n idx=i*rv_stride\r\n while idx < len(self.cpt):\r\n new_cpt[i] += np.sum(self.cpt[idx:(idx+rv_stride)])\r\n idx+=rv_card*rv_stride\r\n\r\n self.cpt=new_cpt\r\n self.card = {rv:rv_card}\r\n self.stride = {rv:1}\r\n self.scope = [rv]\r\n self.var = rv\r\n\r\n #self.normalize()\r\n\r\n def sumout_var_list(self, var_list):\r\n \"\"\"\r\n Remove a collection of rv's from the factor\r\n by summing out (i.e. calling sumout_var) over\r\n each rv.\r\n\r\n Arguments\r\n ---------\r\n *var_list* : a list\r\n The list of rv's to sum out.\r\n\r\n Returns\r\n -------\r\n None\r\n\r\n Effects\r\n -------\r\n - see \"self.sumout_var\"\r\n\r\n Notes\r\n -----\r\n\r\n \"\"\"\r\n for var in var_list:\r\n self.sumout_var(var)\r\n\r\n def sumout_var(self, rv):\r\n \"\"\"\r\n Remove passed-in *rv* from the factor by summing\r\n over everything else.\r\n\r\n Arguments\r\n ---------\r\n *rv* : a string\r\n The random variable to sum out\r\n\r\n Returns\r\n -------\r\n None\r\n\r\n Effects\r\n -------\r\n - alters self.cpt\r\n - alters self.stride\r\n - alters self.card\r\n - alters self.scope\r\n\r\n Notes\r\n ----- \r\n \r\n \"\"\"\r\n exp_len = int(len(self.cpt)/self.card[rv])\r\n new_cpt = np.zeros(exp_len)\r\n \r\n rv_card = self.card[rv]\r\n rv_stride = self.stride[rv]\r\n\r\n k=0\r\n p = np.prod([self.card[r] for r in self.scope if self.stride[r] < self.stride[rv]])\r\n for i in range(exp_len):\r\n for c in range(rv_card):\r\n new_cpt[i] += self.cpt[k + (rv_stride*c)]\r\n k+=1\r\n if (k % p== 0):\r\n k += (p * (rv_card - 1))\r\n self.cpt=new_cpt\r\n\r\n del self.card[rv]\r\n self.stride.update((k,v/rv_card) for k,v in self.stride.items() if v > rv_stride)\r\n del self.stride[rv]\r\n self.scope.remove(rv)\r\n\r\n if rv == self.var:\r\n l = [k for k,v in self.stride.items() if v==1]\r\n if len(l)>0:\r\n self.var = l[0]\r\n\r\n #if len([k for k,v in self.stride.items() if v==1]) > 0:\r\n #self.normalize()\r\n\r\n def maxout_var(self, rv):\r\n \"\"\"\r\n Remove *rv* from the factor by taking the maximum value \r\n of all instantiations of the passed-in rv\r\n\r\n Used in MAP inference (i.e. Algorithm 13.1 in Koller p.557)\r\n\r\n Arguments\r\n ---------\r\n *rv* : a string\r\n The random variable\r\n\r\n Returns\r\n -------\r\n None\r\n\r\n Effects\r\n -------\r\n - alters self.cpt\r\n - alters self.stride\r\n - alters self.card\r\n - alters self.scope\r\n\r\n Notes\r\n ----- \r\n \r\n \"\"\"\r\n #self.cpt += 0.00002\r\n exp_len = int(len(self.cpt)/self.card[rv])\r\n new_cpt = np.zeros(exp_len)\r\n\r\n rv_card = self.card[rv]\r\n rv_stride = self.stride[rv]\r\n\r\n k=0\r\n p = np.prod([self.card[r] for r in self.scope if self.stride[r] < self.stride[rv]])\r\n for i in range(exp_len):\r\n max_val = 0\r\n for c in range(rv_card):\r\n if self.cpt[k + (rv_stride*c)] > max_val:\r\n max_val = self.cpt[k + (rv_stride*c)]\r\n new_cpt[i] = max_val\r\n k+=1\r\n if (k % p == 0):\r\n k += (p* (rv_card - 1))\r\n self.cpt=new_cpt\r\n\r\n del self.card[rv]\r\n self.stride.update((k,v/rv_card) for k,v in self.stride.items() if v > rv_stride)\r\n del self.stride[rv]\r\n self.scope.remove(rv)\r\n\r\n #if rv == self.var:\r\n #self.var = [k for k,v in self.stride.items() if v==1][0]\r\n\r\n #if len(self.scope) > 0:\r\n #self.normalize()\r\n\r\n def reduce_factor_by_list(self, evidence):\r\n \"\"\"\r\n Reduce the factor by numerous sets of\r\n [rv,val] -- this is done by running\r\n self.reduce_factor over the list of\r\n lists (*evidence*)\r\n\r\n Arguments\r\n ---------\r\n *evidence* : a list of lists/tuples\r\n The collection of rv-val pairs to\r\n remove from (condition upon) the factor\r\n\r\n\r\n Returns\r\n -------\r\n None\r\n\r\n Effects\r\n -------\r\n - see \"self.reduce_factor\"\r\n\r\n Notes\r\n -----\r\n - Again, might be good to check that each\r\n rv-val pair is actually in the factor\r\n \"\"\"\r\n if isinstance(evidence, list):\r\n for rv,val in evidence:\r\n self.reduce_factor(rv,val)\r\n elif isinstance(evidence, dict):\r\n for rv,val in evidence.items():\r\n self.reduce_factor(rv,val)\r\n\r\n def reduce_factor(self, rv, val):\r\n \"\"\"\r\n Condition the factor over evidence by eliminating any\r\n sets of values that don't align with [rv, val].\r\n\r\n This is different from \"sumover_var\" because \"reduce_factor\"\r\n is not summing over anything, it is simply removing any \r\n parent-child instantiations which are not consistent with\r\n the evidence. Moreover, there should not be any need for\r\n normalization because the CPT should already be normalized\r\n over the rv-val evidence (but we do it anyways because of\r\n rounding)\r\n\r\n Note, this will completely eliminate \"rv\" from the factor,\r\n including from the scope and cpt.\r\n\r\n Arguments\r\n ---------\r\n *rv* : a string\r\n The random variable to eliminate/condition upon.\r\n\r\n *val* : a string\r\n The value of RV\r\n\r\n Returns\r\n -------\r\n None\r\n\r\n Effects\r\n -------\r\n - alters self.cpt\r\n - alters self.scope\r\n - alters self.card\r\n - alters self.stride\r\n\r\n Notes\r\n -----\r\n - There are no fail-safes here to make sure the\r\n rv-val pair is actually in the factor..\r\n\r\n \"\"\"\r\n exp_len = len(self.cpt)/float(self.card[rv])\r\n new_cpt = np.zeros((exp_len,))\r\n\r\n val_idx = self.bn.F[rv]['values'].index(val)\r\n rv_card = self.card[rv]\r\n rv_stride = self.stride[rv]\r\n\r\n add_idx=0\r\n idx = val_idx*rv_stride\r\n while add_idx < exp_len:\r\n for j in self.cpt[idx:(idx+rv_stride)]:\r\n new_cpt[add_idx] = j\r\n add_idx+=1\r\n idx+=rv_card*rv_stride\r\n\r\n self.cpt=new_cpt\r\n del self.card[rv]\r\n self.stride.update((k,v/rv_card) for k,v in self.stride.items() if v > rv_stride)\r\n del self.stride[rv]\r\n self.scope.remove(rv)\r\n\r\n if rv == self.var:\r\n l = [k for k,v in self.stride.items() if v==1]\r\n if len(l)>0:\r\n self.var = l[0]\r\n\r\n def to_log(self):\r\n \"\"\"\r\n Convert probabilities to log space from\r\n normal space.\r\n\r\n \"\"\"\r\n self.cpt = np.round(np.log(self.cpt),5)\r\n\r\n def from_log(self):\r\n \"\"\"\r\n Convert probabilities from log space to\r\n normal space.\r\n\r\n \"\"\"\r\n self.cpt = np.round(np.exp(self.cpt),5)\r\n\r\n def perturb(self):\r\n \"\"\"\r\n Add some noise to avoid \"nan\" when dividing by zero.\r\n This will probably make cpt values have many \r\n decimal points (bad).\r\n \"\"\"\r\n self.cpt += 1e-7\r\n\r\n def normalize(self):\r\n \"\"\"\r\n Make relevant collections of probabilities sum to one.\r\n\r\n This function is ALWAYS going to normalize the variable\r\n for which the stride = 1, because it's assumed that's the\r\n main/child variable.\r\n\r\n Effects\r\n -------\r\n - alters self.cpt\r\n\r\n Notes\r\n -----\r\n\r\n \"\"\"\r\n self.perturb() # stops nan's from happening\r\n var = [k for k,v in self.stride.items() if v==1]\r\n if len(var) > 0:\r\n var = var[0]\r\n for i in range(0,len(self.cpt),self.card[var]):\r\n temp_sum = float(np.sum(self.cpt[i:(i+self.card[var])]))\r\n for j in range(self.card[var]):\r\n self.cpt[i+j] /= temp_sum\r\n else:\r\n for i in range(len(self.cpt)):\r\n self.cpt[i] /= (np.sum(self.cpt))\r\n\r\n\r\n\r\n\r\n" }, { "path": "pyBN/classes/factorization.py", "content": "\"\"\"\n*************\nFactorization \nClass\n*************\n\"\"\"\nfrom pyBN.classes.factor import Factor\n\nfrom collections import OrderedDict\nfrom copy import copy,deepcopy\nimport numpy as np\n\nclass Factorization(object):\n\n\tdef __init__(self, bn, nodes=None):\n\t\t\"\"\"\n\t\tInitialize a Factorization object.\n\n\t\tThe *nodes* argument allows you to make a Factorization object\n\t\tthat includes only a subset of the random variables in the\n\t\tpassed-in BayesNet object. This is mostly useful for CliqueTree\n\t\talgorithms.\n\t\t\n\t\t\"\"\"\n\t\tself.bn = bn\n\n\t\tif nodes is not None:\n\t\t\tself._phi = [Factor(bn,rv) for rv in nodes]\n\t\telse:\n\t\t\tself._phi = [Factor(bn,rv) for rv in bn.nodes()]\n\n\t\t## MAP-BASED ATTRIBUTES ##\n\t\tself.map_factors = OrderedDict()\n\t\tself.map_assignment = dict()\n\t\tself.map_prob = -1\n\n\tdef refresh(self):\n\t\t\"\"\"\n\t\tRefresh Factorization attributes.\n\t\t\"\"\"\n\t\tself._phi = [Factor(self.bn,rv) for rv in self.bn.nodes()]\n\t\tself.map_factors = OrderedDict()\n\t\tself.map_assignment = dict()\n\t\tself.map_prob = -1\n\n\tdef __getitem__(self, idx):\n\t\treturn self._phi[idx]\n\n\tdef __iter__(self):\n\t\tfor phi in self._phi:\n\t\t\tyield phi\n\n\tdef __len__(self):\n\t\treturn len(self._phi)\n\n\tdef __div__(self, rv):\n\t\t\"\"\"\n\t\tOverloads division operator for\n\t\tSum-Product Variable Elimination pass\n\t\tover passed-in var.\n\t\t\"\"\"\n\t\tself.sum_product_eliminate_var(rv)\n\t\treturn self\n\n\tdef __floordiv__(self, rv):\n\t\t\"\"\"\n\t\tOverloads floor division operator for\n\t\tMax-Product Variable Elimination pass\n\t\tover passed-in var.\n\t\t\"\"\"\n\t\tself.max_product_eliminate_var(rv)\n\t\treturn self\n\n\tdef __sub__(self, rv_val):\n\t\t\"\"\"\n\t\tOverloads subtract operator for\n\t\treducing EVERY relevant factor\n\t\tby passed-in rv_val\n\t\t\"\"\"\n\t\tself.map_assignment[rv_val[0]] = rv_val[1] # append\n\t\tfor phi in self._phi:\n\t\t\tif rv_val[0] in phi.scope:\n\t\t\t\tphi -= (rv_val[0],rv_val[1]) # reduce by evidence\n\t\treturn self\n\n\tdef sum_product_eliminate_var(self, rv):\n\t\trelevant_factors = self.relevant_factors(rv)\n\t\tirrelevant_factors = self.irrelevant_factors(rv)\n\n\t\t# mutliply all relevant factors\n\t\tpsi = relevant_factors[0]\n\t\tfor i in range(1,len(relevant_factors)):\n\t\t\tpsi *= relevant_factors[i]\n\n\t\t# Take Sum over psi for rv\n\t\tpsi /= rv # sumout\n\t\tirrelevant_factors.append(psi) # add sum-prod factor back in\n\n\t\tself._phi = irrelevant_factors\n\n\tdef max_product_eliminate_var(self, rv):\n\t\trelevant_factors = self.relevant_factors(rv)\n\t\tirrelevant_factors = self.irrelevant_factors(rv)\n\n\t\t# mutliply all relevant factors\n\t\tpsi = relevant_factors[0]\n\t\tfor i in range(1,len(relevant_factors)):\n\t\t\tpsi *= relevant_factors[i]\n\n\t\tself.map_factors[rv] = deepcopy(psi)\n\t\t# Take Max over psi for rv\n\t\tpsi //= rv # maxout\n\t\tirrelevant_factors.append(psi) # add sum-prod factor back in\n\n\t\tself._phi = irrelevant_factors\n\n\tdef traceback_map(self):\n\t\tnodes = self.map_factors.keys()\n\t\tidx = len(self.map_factors)\n\t\tfor rv in reversed(self.map_factors.keys()):\n\t\t\tf = self.map_factors[rv]\n\t\t\t# reduce factor by variables upstream\n\t\t\tfor i in range(idx,len(self.map_factors)):\n\t\t\t\tif nodes[i] in f.scope:\n\t\t\t\t\tf -= (nodes[i],self.map_assignment[nodes[i]])\n\t\t\t# choose maximizing assignment conditioned on upstream vars\n\t\t\tself.map_assignment[rv] = self.bn.values(rv)[np.argmax(self.map_factors[rv].cpt)]\n\t\t\tidx-=1\n\t\treturn self.map_assignment\n\n\tdef consolidate(self):\n\t\tfinal_phi = self._phi[0]\n\t\tfor i in range(1,len(self._phi)):\n\t\t\tfinal_phi *= _phi[i]\n\t\t#final_phi.normalize()\n\t\treturn final_phi\n\n\tdef relevant_factors(self, rv):\n\t\treturn [f for f in self._phi if rv in f.scope]\n\n\tdef irrelevant_factors(self, rv):\n\t\treturn [f for f in self._phi if rv not in f.scope]\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/classification/__init__.py", "content": "from pyBN.classification.classification import *\nfrom pyBN.classification.feature_selection import *" }, { "path": "pyBN/classification/classification.py", "content": "\"\"\"\n****************\nBayesian Network\nClassifiers\n****************\n\nImplementation of various Bayesian network\nclassifiers.\n\n\"\"\"\nfrom __future__ import division\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport numpy as np\nfrom pyBN.inference.marginal_approx import *\n\ndef mbc_predict(data, targets, classifier=None, c_struct='DAG',f_struct='DAG', wrapper=False):\n\tpass\n\ndef predict(data, target, classifier=None, method='nb'):\n\t\"\"\"\n\tWrapper for a unified interface to the \n\tvarious classification algorithms. The pyBN\n\tuser can call any algorithm from this function for\n\tconvienence.\n\n\tThe prediction algorithm works as follows:\n\t\t- For each row of data, set the observed attribute\n\t\t\tvariables as evidence\n\t\t- Pick the most likely value of the target variable\n\t\t\tby either a) running MAP inference and simply\n\t\t\treturning the chosen value, or b) running Marginal\n\t\t\tinference and selecting the value of the target variable\n\t\t\twith the highest probability.\n\t\t- Compare the chosen value to the actual value.\n\t\t- Return the actual values, the chosen values, and the accuracy score.\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\n\t*target* : an integer\n\t\tThe data column corresponding to the\n\t\tpredictor/target variable.\n\n\t*classifer* : a BayesNet object (optional)\n\t\tThe BayesNet model to use as the classifier model. \n\t\tNOTE: The user can supply a BayesNet class which has\n\t\tbeen learned as a general bn, naive bayes, tan, or ban model \n\t\tfrom the appropriate structure learning algorithm or file...\n\t\tOR the user can leave classifier as \"None\" and supply a string\n\t\tto *method*, in which class the corresponding model will be\n\t\tstructure/parameter learned IN THIS FUNCTION and used as the\n\t\tclassifier.\n\n\t*method* : a string\n\t\tWhich BN classifier to use if *classifier* is None.\n\t\tOptions:\n\t\t\t- 'bn' : general bayesian network classifier\n\t\t\t- 'nb' : naive bayes classifier\n\t\t\t- 'tan' : tree-augmented naive bayes\n\t\t\t- 'ban' : bayesian augmented naive bayes ?\n\n\t\n\n\tReturns\n\t-------\n\t*c_dict* : a dictionary, where\n\t\tkeys = 'y', 'yp', and 'acc', where\n\t\t'y' is the true target values (np array),\n\t\t'yp' is the predicted target values (np array),\n\t\t'acc' is the prediction accuracy percentage (float).\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\t\"\"\"\n\tif classifier is None:\n\t\tprint('Learning structure..')\n\t\tclassifier = learn_structure(data=data, target=target, method=method)\n\t\tlearn_parameters(classifier)\n\n\tnon_target_cols = list(set(range(data.shape[1]))-{target})\n\ty = np.empty(data.shape[0],dtype=np.int32)\n\typ = np.empty(data.shape[0],dtype=np.int32)\n\t### CLASSIFIER PREDICTION FROM INFERENCE ###\n\tfor row in range(data.shape[0]):\n\t\tif row % 100 == 0:\n\t\t\tprint('Iteration: ' , row)\n\t\tvals = [data[row,i] for i in non_target_cols]\n\t\tevidence = dict(zip(non_target_cols,vals))\n\t\ty[row] = data[row,target] # true value\n\t\tpredicted_val = max(marginal_lws_a(bn=classifier,\n\t\t\t\t\t\t\t\t\tn=100,\n\t\t\t\t\t\t\t\t\tevidence=evidence,\n\t\t\t\t\t\t\t\t\ttarget=target))\n\t\typ[row] = predicted_val\n\t\t\n\tacc = np.sum(y==yp)/data.shape[0]\n\treturn y, yp, acc\n\n\t\t\n\t\t\t\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/classification/feature_selection.py", "content": "\"\"\"\n******************\nFeature Selection\nfor Classification\n******************\n\nMany constraint-based Bayesian network structure \nlearning algorithms are actually quite useful for \ngeneral feature selection as a pre-processing task\nfor absolute any classification algorithm.\n\nThe way Bayesian network-based feature selection works\nis by essentially learning the Markov Blanket of the\ntarget variable from the data. As has been pointed out\nin numerous papers - starting from Daphne Koller's well-known\npaper \"Toward Optimal Feature Selection\" -- identifying the \nMarkov Blanket of a target variable gives you the theoretically\noptimal selection of feature variables. \n\nThe theoretical guaruntee of Markov Blanket feature selection\nstems from the definition of \"Markov Blanket\" - the set of\nnodes which, when conditioned upon, render the target variable \nindependent from EVERY other variable in the network. In other\nwords, if you observe the values of the Markov Blanket for the\ntarget variable, then it is useless to observe the values of \nany variables OUTSIDE the markov blanket - because the target\nvariable is independent of those variables.\n\n\"\"\"\n\nfrom pyBN.learning.structure.constraint import *\n\n\ndef fs_gambit(data, target):\n\t\"\"\" \n\tThe Feature Selection Gambit is just a wrapper method\n\tfor calling every possible feature selection method. This\n\tis a useful method if you want to just run every algorithm\n\tand choose the best or compare.\n\n\tIt returns a single dictionary where key = the algorithm \n\tmethod and value = the result from the algorithm \n\t(i.e. the set of variables in the Markov Blanket \n\tof the target variable).\n\t\n\tThere are currently five algorithms:\n\t\t- iamb\n\t\t- fast_iamb\n\t\t- lambda_iamb\n\t\t- gs (grow-shrink)\n\t\t\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\n\t*target* : an integer\n\t\tThe column of data that corresponds\n\t\tto the target variable.\n\n\tReturns\n\t-------\n\t*fs_gambit* : a dictionary, where\n\t\tkey = feature selection algorithm name, and\n\t\tvalue = the result from the given algorithm: the\n\t\tset of relevant features for the target (i.e. the\n\t\ttarget variable's Markov Blanket)\n\t\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\tNone\n\n\t\"\"\"\n\talgorithms = ['iamb', 'fast_iamb', 'lambda_iamb', 'gs']\n\tfs_gambit = {}\n\n\tfor algo in algorithms:\n\t\tfs_gambit[algo] = feature_selection(data=data, target=target, method=algo)\n\n\treturn fs_gambit\n\ndef feature_selection(data, target, method='iamb'):\n\t\"\"\"\n\tWrapper for Markov Blanket-based Feature Selection. This\n\tfunction provides a unified interface for using the\n\tvarious constraint-based structure learning algorithms\n\tfor feature selection instead of full Bayesian Network\n\tmodel learning. It simply calls \"learn_structure\" which\n\tis already a structure learning wrapper, but also passes\n\tin \"target\" denoting that feature selection should take\n\tplace instead of full structure learning.\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\n\t*method* : a string\n\t\tWhich feature selection algorithm to run. The\n\t\tdefault is \"iamb\", and if an unintelligable\n\t\tmethod is given, \"iamb\" is called.\n\n\t\"\"\"\n\tfeatures = learn_structure(data, method=method, feature_selection=target)\n\treturn features\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/inference/__init__.py", "content": "from pyBN.inference.map_exact import *\r\nfrom pyBN.inference.marginal_approx import *\r\nfrom pyBN.inference.marginal_exact import *" }, { "path": "pyBN/inference/_tests/__init__.py", "content": "" }, { "path": "pyBN/inference/_tests/test_map_exact.py", "content": "\"\"\"\n********\nUnitTest\nMap Exact\n********\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.readwrite.read import read_bn\nfrom pyBN.inference.map_exact import map_ve_e\n\nclass MapExactTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tself.bn = read_bn(os.path.join(self.dpath,'cmu.bn'))\n\n\tdef tearDown(self):\n\t\tpass\n\n\t#def test_map_noevidence(self):\n\t\t#p = list(map_ve_e(self.bn,target='Alarm'))\n\t\t#self.assertListEqual(p,['No',0.9367])\n\n\t#def test_map_prior(self):\n\t\t#p = list(map_ve_e(self.bn,target='Burglary'))\n\t\t#self.assertListEqual(p,['No',0.9367])\n\n\t#def test_map_leaf(self):\n\t\t#p = list(map_ve_e(self.bn,target='JohnCalls'))\n\t\t#self.assertlistEqual(p,['No',0.9367])\n\t\t" }, { "path": "pyBN/inference/_tests/test_marginal_approx.py", "content": "\"\"\"\n***************\nUnitTest\nMarginal Approx\n***************\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.inference.marginal_approx import marginal_fs_a, marginal_lws_a, marginal_gs_a\nfrom pyBN.readwrite.read import read_bn\n\nclass MarginalApproxTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tself.bn = read_bn(os.path.join(self.dpath,'cmu.bn'))\n\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_forward_sample(self):\n\t\tnp.random.seed(3636)\n\t\tself.assertDictEqual(marginal_fs_a(self.bn,n=1000),\n\t\t\t{'Alarm': {'No': 0.996, 'Yes': 0.004},\n\t\t\t 'Burglary': {'No': 0.998, 'Yes': 0.002},\n\t\t\t 'Earthquake': {'No': 1.0, 'Yes': 0.0},\n\t\t\t 'JohnCalls': {'No': 0.946, 'Yes': 0.054},\n\t\t\t 'MaryCalls': {'No': 0.99, 'Yes': 0.01}}\n\t\t\t )\n\n\tdef test_likelihood_weighted_sample(self):\n\t\t\"\"\"\n\t\tTHIS HAS BEEN VALIDATED AGAINST BNLEARN\n\t\t\"\"\"\n\t\tnp.random.seed(3636)\n\t\tself.assertDictEqual(marginal_lws_a(self.bn,evidence={'Burglary':'Yes'}),\n\t\t\t{'Alarm': {'No': 0.058, 'Yes': 0.942},\n\t\t\t 'Burglary': {'No': 0.0, 'Yes': 1.0},\n\t\t\t 'Earthquake': {'No': 0.997, 'Yes': 0.003},\n\t\t\t 'JohnCalls': {'No': 0.136, 'Yes': 0.864},\n\t\t\t 'MaryCalls': {'No': 0.343, 'Yes': 0.657}})\n\n\tdef test_lws_allevidence(self):\n\t\tself.assertDictEqual(marginal_lws_a(self.bn,evidence={'Burglary':'Yes','Alarm':'Yes',\n\t\t\t'Earthquake':'Yes','JohnCalls':'Yes','MaryCalls':'Yes'}),\n\t\t\t{'Alarm': {'No': 0.0, 'Yes': 1.0},\n\t\t\t 'Burglary': {'No': 0.0, 'Yes': 1.0},\n\t\t\t 'Earthquake': {'No': 0.0, 'Yes': 1.0},\n\t\t\t 'JohnCalls': {'No': 0.0, 'Yes': 1.0},\n\t\t\t 'MaryCalls': {'No': 0.0, 'Yes': 1.0}})\n\n\tdef test_gibbs(self):\n\t\tnp.random.seed(3636)\n\t\tself.assertDictEqual(marginal_gs_a(self.bn,n=1000,burn=200),\n\t\t\t{'Alarm': {'No': 0.9988, 'Yes': 0.0},\n\t\t\t\t 'Burglary': {'No': 0.9988, 'Yes': 0.0},\n\t\t\t\t 'Earthquake': {'No': 0.9975, 'Yes': 0.0013},\n\t\t\t\t 'JohnCalls': {'No': 0.9313, 'Yes': 0.0675},\n\t\t\t\t 'MaryCalls': {'No': 0.9838, 'Yes': 0.015}})\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/inference/_tests/test_marginal_exact.py", "content": "\"\"\"\n**************\nUnitTest\nMarginal Exact\n**************\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.readwrite.read import read_bn\nfrom pyBN.inference.marginal_exact import marginal_ve_e\n\n\n\nclass MarginalExactTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tself.bn = read_bn(os.path.join(self.dpath,'cmu.bn'))\n\t\t\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_marginal_ve_e_prior1(self):\n\t\tself.assertListEqual(list(marginal_ve_e(self.bn,'Burglary')),\n\t\t\tself.bn.cpt('Burglary'))\n\n\tdef test_marginal_ve_e_prior2(self):\n\t\tself.assertListEqual(list(marginal_ve_e(self.bn,'Earthquake')),\n\t\t\tself.bn.cpt('Earthquake'))\n\n\t#def test_marginal_ve_e_middle(self):\n\t#\tself.assertListEqual(list(marginal_ve_e(self.bn,'Alarm')),\n\t#\t\t[0.9975,0.0025])\n\n\t#def test_marginal_ve_e_leaf1(self):\n\t#\tself.assertListEqual(list(marginal_ve_e(self.bn,'JohnCalls')),\n\t#\t\t[ 0.88567, 0.11433])\n\n\t#def test_marginal_ve_e_prior1_prior_ev(self):\n\t#\tself.assertListEqual(list(marginal_ve_e(self.bn,'Earthquake')),\n\t#\t\tmarginal_ve_e(self.bn,'Earthquake',evidence={'Burglary':'Yes'}))\n\n\t#def test_marginal_ve_e_prior2_prior_ev(self):\n#\t\tself.assertListEqual(list(marginal_ve_e(self.bn,'Burglary')),\n#\t\t\tmarginal_ve_e(self.bn,'Burglary',evidence={'Earthquake':'Yes'}))\n\n\tdef test_marginal_ve_e_prior_middle_ev(self):\n\t\tself.assertListEqual(list(marginal_ve_e(self.bn,'Burglary',\n\t\t\tevidence={'Alarm':'Yes'})),[ 0.62963, 0.37037])\n\n\tdef test_marginal_ve_e_prior_leaf_ev(self):\n\t\tself.assertListEqual(list(marginal_ve_e(self.bn,'Burglary',\n\t\t\tevidence={'JohnCalls':'Yes'})),[ 0.999, 0.001])\n\n\tdef test_marginal_ve_e_middle_prior_ev(self):\n\t\tself.assertListEqual(list(marginal_ve_e(self.bn,'Alarm',\n\t\t\tevidence={'Burglary':'Yes'})),[0.06,0.94])\n\n#\tdef test_marginal_ve_e_middle_leaf_ev(self):\n#\t\tself.assertListEqual(list(marginal_ve_e(self.bn,'Alarm',\n#\t\t\tevidence={'JohnCalls':'Yes'})),[ 0.95769, 0.04231])\n\n\t#def test_marginal_ve_e_leaf_prior_ev(self):\n\t#\tself.assertListEqual(list(marginal_ve_e(self.bn,'JohnCalls',\n#\t\t\tevidence={'Burglary':'Yes'})),[ 0.17431, 0.82569])\n\n\n\n\n\n\n" }, { "path": "pyBN/inference/map_exact/__init__.py", "content": "from pyBN.inference.map_exact.ilp_map import *\nfrom pyBN.inference.map_exact.ve_map import *" }, { "path": "pyBN/inference/map_exact/ilp_map.py", "content": "\n__author__ = \"\"\"N. Cullen \"\"\"\n\nfrom copy import copy\nimport numpy as np\n\nfrom pyBN.classes.factor import Factor\nfrom pyBN.classes.factorization import Factorization\n\n\n\ndef ilp_map(bn, evidence={}):\n \"\"\"\n Solve MAP Inference as an integer linear optimization\n problem, as formulated in Sontag's notes:\n http://cs.nyu.edu/~dsontag/courses/pgm12/slides/lecture6.pdf\n\n Arguments\n ---------\n *bn* : a BayesNet object\n\n *evidence* : a dictionary, where\n key = rv and value = rv's value\n\n \"\"\"\n try:\n import pulp as pl\n except ImportError:\n print(\"You must 'pip install pulp' to use Map Optimization methods.\")\n return\n\n model = pl.LpProblem(\"MAP Inference\",pl.LpMinimize)\n\n # CREATE VARIABLE FOR EVERY CPT ENTRY\n var_dict = {} # key = rv, value = list of variables for each cpt entry\n weight_list = []\n var_list = []\n ii = 0\n for rv in bn.nodes():\n var_dict[rv] = {}\n for idx in range(len(bn.cpt(rv))):\n str_rep = bn.cpt_str_idx(rv,idx)\n #str_rep = str(rv)+'-'+str(idx)\n new_var = pl.LpVariable(str_rep,0,1,pl.LpInteger)\n var_dict[rv][idx] = new_var\n var_list.append(new_var)\n weight_list.append(round(-np.log(bn.cpt(rv)[idx]),3))\n ii+=1\n\n # OBJECTIVE FUNCTION\n model += np.dot(var_list,weight_list)\n \n # RV - VALUE CONSTRAINTS\n for rv in bn.nodes():\n model += sum([var_dict[rv][idx] \\\n for idx in range(len(bn.cpt(rv)))]) == 1, \\\n 'RV Constraint - ' + str(rv)\n \n # OUTGOING EDGE CONSTRAINTS\n k=0\n for rv in bn.nodes():\n for val_idx in range(len(bn.cpt(rv))):\n val = bn.values(rv)[val_idx % bn.card(rv)] # actual rv-val\n for child in bn.children(rv):\n # get indices of the child cpt which correspond to rv-val\n child_cpt_indices = bn.cpt_indices(child, {rv:val})\n # rv-val variable = sum(child[child_cpt_indices] variables)\n model += var_dict[rv][val_idx] <= \\\n sum([var_dict[child][i] for i in child_cpt_indices]), \\\n 'OUT-CPT Constraint - ' + str(k)\n k+=1\n \n # INGOING EDGE CONSTRAINTS\n k=0\n for rv in bn.nodes():\n if len(bn.parents(rv)) > 0:\n for val_idx in range(len(bn.cpt(rv))):\n # find which parent-vals that index belongs to\n p_cpt = {}\n for parent in bn.parents(rv):\n p_cpt[parent] = []\n for p_idx in range(len(bn.cpt(parent))):\n p_val = bn.values(parent)[p_idx % bn.card(parent)]\n if val_idx in bn.cpt_indices(rv, {parent:p_val}):\n p_cpt[parent].append(p_idx)\n model += var_dict[rv][val_idx] <= \\\n sum([var_dict[p][pi] for p in bn.parents(rv) for pi in p_cpt[p]]), \\\n 'IN-CPT Constraint - ' + str(k)\n k+=1\n \n #model.writeLP(\"MapInference.lp\")\n model.solve()\n\n for v in model.variables():\n if v.varValue == 1.0:\n print(v.name)" }, { "path": "pyBN/inference/map_exact/ve_map.py", "content": "\n__author__ = \"\"\"N. Cullen \"\"\"\n\nfrom copy import copy\nimport numpy as np\n\nfrom pyBN.classes.factor import Factor\nfrom pyBN.classes.factorization import Factorization\n\n\n\ndef ve_map(bn,\n evidence={},\n target=None,\n prob=False):\n \"\"\"\n Perform Max-Sum Variable Elimination over a BayesNet object\n for exact maximum a posteriori inference.\n\n This has been validated w/ and w/out evidence\n \n \"\"\"\n _phi = Factorization(bn)\n\n order = copy(list(bn.nodes()))\n #### EVIDENCE PROCESSING ####\n for E, e in evidence.items():\n _phi -= (E,e)\n order.remove(E)\n\n #### MAX-PRODUCT ELIMINATE VAR ####\n for var in order:\n _phi //= var \n \n #### TRACEBACK MAP ASSIGNMENT ####\n max_assignment = _phi.traceback_map()\n\n #### RETURN ####\n if prob:\n # multiply phi's together if there is evidence\n final_phi = _phi.consolidate()\n max_prob = round(final_phi.cpt[0],5)\n\n if target is not None:\n return max_prob, max_assignment[target]\n else:\n return max_prob, max_assignment\n else:\n if target is not None:\n return max_assignment[target]\n else:\n return max_assignment" }, { "path": "pyBN/inference/marginal_approx/__init__.py", "content": "from pyBN.inference.marginal_approx.forward_sample import *\nfrom pyBN.inference.marginal_approx.gibbs_sample import *\nfrom pyBN.inference.marginal_approx.loopy_bp import *\nfrom pyBN.inference.marginal_approx.lw_sample import *" }, { "path": "pyBN/inference/marginal_approx/forward_sample.py", "content": "from pyBN.classes.bayesnet import BayesNet\nfrom pyBN.classes.factor import Factor \nfrom pyBN.utils.graph import topsort\n\nimport numpy as np\n\n\ndef forward_sample(bn, n=1000):\n\t\"\"\"\n\tApproximate marginal probabilities from\n\tforward sampling algorithm on a BayesNet object.\n\n\tThis algorithm works by\n\trepeatedly sampling from the BN and taking\n\tthe ratio of observations as their marginal probabilities.\n\n\tOne sample is done by first sampling from any prior random\n\tvariables, then moving down the network in topological sort\n\torder - sampling from each successive random variable by\n\tconditioning on its parents (which have already been sampled\n\thigher up the network).\n\n\tNote that there is no evidence to include in this algorithm - \n\tthe comparative algorithm which includes evidence is the \n\tlikelihood weighted algorithm (see \"lw_sample\" function).\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\n\t*n* : an integer\n\t\tThe number of samples to take\n\n\tReturns\n\t-------\n\t*sample_dict* : a dictionary, where key = rv, value = another dict\n\t\t\t\t\twhere key = instance, value = its probability value\n\n\tNotes\n\t-----\n\t- Evidence is not currently implemented.\n\t\"\"\"\n\n\tsample_dict = {}\n\tfor var in bn.nodes():\n\t sample_dict[var] = {}\n\t for val in bn.values(var):\n\t sample_dict[var][val] = 0\n\n\tfor i in range(n):\n\t #if i % (n/float(10)) == 0:\n\t # print 'Sample: ' , i\n\t new_sample = {}\n\t for rv in bn.nodes():\n\t f = Factor(bn,rv)\n\t for p in bn.parents(rv):\n\t f.reduce_factor(p,new_sample[p])\n\t choice_vals = bn.values(rv)\n\t choice_probs = f.cpt\n\t chosen_val = np.random.choice(choice_vals, p=choice_probs)\n\n\t sample_dict[rv][chosen_val] += 1\n\t new_sample[rv] = chosen_val\n\n\tfor rv in sample_dict:\n\t for val in sample_dict[rv]:\n\t sample_dict[rv][val] = int(sample_dict[rv][val]) / float(n)\n\t\n\treturn sample_dict" }, { "path": "pyBN/inference/marginal_approx/gibbs_sample.py", "content": "\n__author__ = \"\"\"N. Cullen \"\"\"\n\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.classes.factor import Factor \nfrom pyBN.utils.graph import topsort\n\nimport numpy as np\n\n\n\ndef gibbs_sample(bn, n=1000, burn=200):\n\t\"\"\"\n\tApproximate Marginal probabilities from Gibbs Sampling\n\tover a BayesNet object.\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\n\t*n* : an integer\n\t\tThe number of samples to take\n\n\t*burn* : an integer\n\t\tThe number of beginning samples to\n\t\tthrow away for the MCMC mixing.\n\n\tReturns\n\t-------\n\t*sample_dict* : a dictionary where key = rv\n\t\tand value = another dictionary where\n\t\tkey = rv instantiation and value = marginal\n\t\tprobability\n\n\tNotes\n\t-----\n\n\t\"\"\"\n\n\tsample_dict ={}\n\tfor rv in bn.nodes():\n\t sample_dict[rv]={}\n\t for val in bn.values(rv):\n\t sample_dict[rv][val] = 0\n\n\tstate = {}\n\tfor rv in bn.nodes():\n\t state[rv] = np.random.choice(bn.values(rv)) # uniform sample\n\n\tfor i in range(n):\n\t #if i % (n/float(10)) == 0:\n\t # print 'Sample: ' , i\n\t for rv in bn.nodes():\n\t # get possible values conditioned on everything else\n\t parents = bn.parents(rv)\n\t # no parents - prior\n\t if len(parents) == 0:\n\t choice_vals = bn.values(rv)\n\t choice_probs = bn.cpt(rv)\n\t # has parent - filter cpt\n\t else:\n\t f = Factor(bn,rv)\n\t for p in parents:\n\t f.reduce_factor(p,state[p])\n\t choice_vals = bn.values(rv)\n\t choice_probs = f.cpt\n\t # sample over remaining possibilities\n\t chosen_val = np.random.choice(choice_vals,p=choice_probs)\n\t state[rv]=chosen_val\n\t # update sample_dict dictionary\n\t if i > burn:\n\t for rv,val in state.items():\n\t sample_dict[rv][val] +=1\n\n\tfor rv in sample_dict:\n\t for val in sample_dict[rv]:\n\t sample_dict[rv][val] = round(int(sample_dict[rv][val]) / float(n-burn),4)\n\t\n\treturn sample_dict" }, { "path": "pyBN/inference/marginal_approx/loopy_bp.py", "content": "\n__author__ = \"\"\"N. Cullen \"\"\"\n\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.classes.factor import Factor \nfrom pyBN.utils.graph import topsort\n\nimport numpy as np\n\n\ndef loopy_bp(target=None, evidence=None, max_iter=100):\n\t\"\"\"\n\tPerform Message Passing (Loopy Belief Propagation) \n\tover a cluster graph.\n\n\tThis is a good candidate for Numba JIT.\n\n\tSee Koller pg. 397.\n\n\n\tParameters\n\t----------\n\n\n\tReturns\n\t-------\n\n\n\tNotes\n\t-----\n\t- Copied from clustergraph class... not tested.\n\t- Definitely a problem due to normalization (prob vals way too small)\n\t- Need to check the scope w.r.t. messages.. all clusters should not\n\tbe accumulating rv's in their scope over the course of the algorithm.\n\t\"\"\"\n\tdef collect_beliefs(cgraph):\n\t\tcgraph.beliefs = {}\n\t\tfor cluster in self.V:\n\t\t\tcgraph.V[cluster].collect_beliefs()\n\t\t\t#print('Belief ' , cluster , ' : \\n', self.V[cluster].belief.cpt)\n\t\t\tcgraph.beliefs[cluster] = cgraph.V[cluster].belief\n\n\t# 1: Moralize the graph\n\t# 2: Triangluate\n\t# 3: Build a clique tree using max spanning\n\t# 4: Propagation of probabilities using message passing\n\n\t# creates clique tree and assigns factors, thus satisfying steps 1-3\n\tcgraph = ClusterGraph(bn)\n\n\tedge_visit_dict = dict([(i,0) for i in cgraph.E])\n\n\titeration = 0\n\twhile not cgraph.is_calibrated():\n\t\tif iteration == max_iter:\n\t\t\tbreak\n\t\tif iteration % 50 == 0:\n\t\t\tprint('Iteration: ' , iteration)\n\t\t\tfor cluster in cgraph.V.values():\n\t\t\t\tcluster.collect_beliefs()\n\t\t# select an edge\n\t\te_idx = np.random.randint(0,len(cgraph.E))\n\t\tedge_select = cgraph.E[e_idx]\n\t\tp_idx = np.random.randint(0,2)\n\t\tparent_edge = edge_select[p_idx]\n\t\tchild_edge = edge_select[np.abs(p_idx-1)]\n\t\tprint(parent_edge , child_edge)\n\n\t\t# send a message along that edge\n\t\tcgraph.V[parent_edge].send_message(cgraph.V[child_edge])\n\n\t\titeration += 1\n\tprint('Now Collecting Beliefs..')\n\tcollect_beliefs(cgraph)\n\t#bn.ctree = self" }, { "path": "pyBN/inference/marginal_approx/lw_sample.py", "content": "__author__ = \"\"\"N. Cullen \"\"\"\n\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.classes.factor import Factor \nfrom pyBN.utils.graph import topsort\n\nimport numpy as np\n\n\ndef lw_sample(bn, evidence={}, target=None, n=1000):\n\t\"\"\"\n\tApproximate Marginal probabilities from\n\tlikelihood weighted sample algorithm on\n\ta BayesNet object.\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\n\t*n* : an integer\n\t\tThe number of samples to take\n\n\t*evidence* : a dictionary, where\n\t\tkey = rv, value = instantiation\n\n\tReturns\n\t-------\n\t*sample_dict* : a dictionary where key = rv\n\t\tand value = another dictionary where\n\t\tkey = rv instantiation and value = marginal\n\t\tprobability\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\n\t\"\"\"\n\tsample_dict = {}\n\tweight_list = np.ones(n)\n\n\t#factor_dict = dict([(var,Factor(bn, var)) for var in bn.V])\n\t#parent_dict = dict([(var, bn.data[var]['parents']) for var in bn.V])\n\n\tfor var in bn.nodes():\n\t sample_dict[var] = {}\n\t for val in bn.values(var):\n\t sample_dict[var][val] = 0\n\n\tfor i in range(n):\n\t #if i % (n/float(10)) == 0:\n\t # print 'Sample: ' , i\n\t new_sample = {}\n\t for rv in bn.nodes():\n\t f = Factor(bn,rv)\n\t # reduce_factor by parent samples\n\t for p in bn.parents(rv):\n\t f.reduce_factor(p,new_sample[p])\n\t # if rv in evidence, choose that value and weight\n\t if rv in evidence:\n\t chosen_val = evidence[rv]\n\t weight_list[i] *= f.cpt[bn.values(rv).index(evidence[rv])]\n\t # if rv not in evidence, sample as usual\n\t else:\n\t choice_vals = bn.values(rv)\n\t choice_probs = f.cpt\n\t chosen_val = np.random.choice(choice_vals, p=choice_probs)\n\t \n\t new_sample[rv] = chosen_val\n\t # weight the choice by the evidence likelihood \n\t for rv in new_sample:\n\t sample_dict[rv][new_sample[rv]] += 1*weight_list[i]\n\n\tweight_sum = sum(weight_list)\n\n\tfor rv in sample_dict:\n\t for val in sample_dict[rv]:\n\t sample_dict[rv][val] /= weight_sum\n\t sample_dict[rv][val] = round(sample_dict[rv][val],4)\n\t\n\tif target is not None:\n\t\treturn sample_dict[target]\n\telse:\n\t\treturn sample_dict" }, { "path": "pyBN/inference/marginal_exact/__init__.py", "content": "from pyBN.inference.marginal_exact.exact_bp import *\nfrom pyBN.inference.marginal_exact.ve_marginal import *" }, { "path": "pyBN/inference/marginal_exact/exact_bp.py", "content": "\n__author__ = \"\"\"N. Cullen \"\"\"\n\nfrom pyBN.classes.factor import Factor\nfrom pyBN.classes.factorization import Factorization\nfrom pyBN.utils.graph import *\n\nfrom copy import deepcopy, copy\nimport numpy as np\nimport json\n\n\ndef exact_bp(bn, target=None, evidence=None, downward_pass=False):\n\t\"\"\"\n\tPerform Belief Propagation (Message Passing) over a Clique Tree. This\n\tis sometimes referred to as the \"Junction Tree Algorithm\" or\n\tthe \"Hugin Algorithm\".\n\n\tIt involves an Upward Pass (see [1] pg. 353) along with\n\tDownward Pass (Calibration) ([1] pg. 357) if the target involves\n\tmultiple random variables - i.e. is a list\n\n\tSteps Involved:\n\t\t1. Build a Clique Tree from a Bayesian Network\n\t\t\ta. Moralize the BN\n\t\t\tb. Triangulate the graph\n\t\t\tc. Find maximal cliques and collapse into nodes\n\t\t\td. Create complete graph and make edge weights = sepset cardinality\n\t\t\te. Using Max Spanning Tree to create a tree of cliques\n\t\t2. Assign each factor to only one clique\n\t\t3. Compute the initial potentials of each clique\n\t\t\t- multiply all of the clique's factors together\n\t\t4. Perform belief propagation based on message passing protocol.\n\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\n\tReturns\n\t-------\n\n\n\tNotes\n\t-----\n\n\t\"\"\"\n\t# 1: Moralize the graph\n\t# 2: Triangluate\n\t# 3: Build a clique tree using max spanning\n\t# 4: Propagation of probabilities using message passing\n\n\t# creates clique tree and assigns factors, thus satisfying steps 1-3\n\tctree = CliqueTree(bn) # might not be initialized?\n\t#G = ctree.G\n\t#cliques = copy.copy(ctree.V)\n\n\t# select a clique as root where target is in scope of root\n\troot = ctree.V[0]\n\tif target is not None:\n\t\tfor v in ctree.V:\n\t\t\tif target in ctree[v].scope:\n\t\t\t\troot = v\n\t\t\t\tbreak\n\n\t\n\tclique_ordering = ctree.dfs_postorder(root=root)\n\n\t# UPWARD PASS\n\t# send messages up the tree from the leaves to the single root\n\tfor i in clique_ordering:\n\t\t#clique = ctree[i]\n\t\tfor j in ctree.parents(i):\n\t\t\tctree[i] >> ctree[j] \n\t\t\t#clique.send_message(ctree[j])\n\t\t# if root node, collect its beliefs\n\t\t#if len(ctree.parents(i)) == 0:\n\t\t\t#ctree[root].collect_beliefs()\n\tctree[root].collect_beliefs()\n\tmarginal_target = ctree[root].marginalize_over(target)\n\n\t# DOWNWARD PASS\n\tif downward_pass == True:\n\t\t# send messages down the tree from the root to the leaves\n\t\t# (not needed unless *target* involves more than one variable)\n\t\tnew_ordering = list(reversed(clique_ordering))\n\t\tfor j in new_ordering:\n\t\t\tfor i in ctree.children(j):\n\t\t\t\tctree[j] >> ctree[i]\n\t\t\t# if leaf node, collect its beliefs\n\t\t\tif len(ctree.children(j)) == 0: \n\t\t\t\tctree[j].collect_beliefs()\n\n\treturn marginal_target\n\n\t# beliefs hold the answers" }, { "path": "pyBN/inference/marginal_exact/ve_marginal.py", "content": "\n__author__ = \"\"\"N. Cullen \"\"\"\n\nfrom pyBN.classes.factor import Factor\nfrom pyBN.classes.factorization import Factorization\nfrom pyBN.utils.graph import *\n\nfrom copy import deepcopy, copy\nimport numpy as np\nimport json\n\ndef marginal_ve_e(bn, target, evidence={}):\n\t\"\"\"\n\tPerform Sum-Product Variable Elimination on\n\ta Discrete Bayesian Network.\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\n\t*target* : a list of target RVs\n\n\t*evidence* : a dictionary, where\n\t\tkey = rv and value = rv value\n\n\tReturns\n\t-------\n\t*marginal_dict* : a dictionary, where\n\t\tkey = an rv in target and value =\n\t\ta numpy array containing the key's\n\t\tmarginal conditional probability distribution.\n\n\tNotes\n\t-----\n\t- Mutliple pieces of evidence often returns \"nan\"...numbers too small?\n\t\t- dividing by zero -> perturb values in Factor class\n\t\"\"\"\n\t_phi = Factorization(bn)\n\n\torder = copy(list(bn.nodes()))\n\torder.remove(target)\n\n\t#### EVIDENCE PROCESSING ####\n\tfor E, e in evidence.items():\n\t\t_phi -= (E,e)\n\t\torder.remove(E)\n\n\t#### SUM-PRODUCT ELIMINATE VAR ####\n\tfor var in order:\n\t\t_phi /= var\n\n\t# multiply phi's together if there is evidence\n\tfinal_phi = _phi.consolidate()\n\n\treturn np.round(final_phi.cpt,4)" }, { "path": "pyBN/io/__init__.py", "content": "from pyBN.io.read import *\nfrom pyBN.io.write import *" }, { "path": "pyBN/io/_tests/__init__.py", "content": "" }, { "path": "pyBN/io/_tests/test_reading.py", "content": "\"\"\"\n********\nUnitTest\nReading\n********\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nfrom pyBN.readwrite.read import read_bn\nimport os\nfrom os.path import dirname\n\n\nclass ReadingTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tself.bn_bif = read_bn(os.path.join(self.dpath,'cancer.bif'))\n\t\tself.bn_bn = read_bn(os.path.join(self.dpath,'cmu.bn'))\n\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_read_bn_vertices(self):\n\t\tself.assertListEqual(self.bn_bn.V, ['Burglary','Earthquake','Alarm','JohnCalls','MaryCalls'])\n\n\tdef test_read_bif_vertices(self):\n\t\tself.assertListEqual(self.bn_bif.V, ['Smoker', 'Pollution', 'Cancer', 'Xray', 'Dyspnoea'])" }, { "path": "pyBN/io/_tests/test_writing.py", "content": "\"\"\"\n********\nUnitTest\nWriting\n********\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\n\n\ndef suite():\n\tsuite = unittest.TestLoader().loadTestsFromTestCase(WritingTestCase)\n\treturn suite\n\n\nclass WritingTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tpass\n\tdef tearDown(self):\n\t\tpass" }, { "path": "pyBN/io/read.py", "content": "\"\"\"\n*************\nRead BayesNet\nfrom File\n*************\n\nRead a BayesNet object from a file. These\nfunctions all currently work, but support\nfor more formats should be added in the future.\n\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport json\nimport numpy as np\nimport copy\n\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.classes.factor import Factor\nfrom pyBN.utils.graph import topsort\n\n\ndef read_bn(path):\n \"\"\"\n Wrapper function for reading BayesNet objects\n from various file types.\n\n Arguments\n ---------\n *path* : a string\n The path (relative or absolute) - MUST\n include extension -> that's how we know\n which file reader to call.\n\n Returns\n -------\n *bn* : a BayesNet object\n\n Effects\n -------\n None\n\n Notes\n -----\n\n \"\"\"\n if '.bif' in path:\n return read_bif(path)\n elif '.bn' in path:\n return read_json(path)\n elif '.mat' in path:\n return read_mat(path)\n else:\n print(\"Path Extension not recognized\")\n\ndef read_bif(path):\n \"\"\"\n This function reads a .bif file into a\n BayesNet object. It's probably not the \n fastest or prettiest but it gets the job\n done.\n\n Arguments\n ---------\n *path* : a string\n The path\n\n Returns\n -------\n *bn* : a BayesNet object\n\n Effects\n -------\n None\n\n Notes\n -----\n *V* : a list of strings\n *E* : a dict, where key = vertex, val = list of its children\n *F* : a dict, where key = rv, val = another dict with\n keys = 'parents', 'values', cpt'\n\n \"\"\"\n _parents = {} # key = vertex, value = list of vertices in the scope (includind itself)\n _cpt = {} # key = vertex, value = list (then numpy array)\n _vals = {} # key=vertex, val=list of its possible values\n\n with open(path, 'r') as f:\n while True:\n line = f.readline()\n if 'variable' in line:\n new_vertex = line.split()[1]\n\n _parents[new_vertex] = []\n _cpt[new_vertex] = []\n #_vals[new_vertex] = []\n\n new_line = f.readline()\n new_vals = new_line.replace(',', ' ').split()[6:-1] # list of vals\n _vals[new_vertex] = new_vals\n num_outcomes = len(new_vals)\n elif 'probability' in line:\n line = line.replace(',', ' ')\n child_rv = line.split()[2]\n parent_rvs = line.split()[4:-2]\n\n if len(parent_rvs) == 0: # prior\n new_line = f.readline().replace(';', ' ').replace(',',' ').split()\n prob_values = new_line[1:]\n _cpt[child_rv].append(map(float,prob_values))\n #_cpt[child_rv] = map(float,prob_values)\n else: # not a prior\n _parents[child_rv].extend(list(parent_rvs))\n while True:\n new_line = f.readline()\n if '}' in new_line:\n break\n new_line = new_line.replace(',',' ').replace(';',' ').replace('(', ' ').replace(')', ' ').split()\n prob_values = new_line[-(len(_vals[new_vertex])):]\n prob_values = map(float,prob_values)\n _cpt[child_rv].append(prob_values)\n if line == '':\n break\n\n # CREATE FACTORS\n _F = {}\n _E = {}\n for rv in _vals.keys():\n _E[rv] = [c for c in _vals.keys() if rv in _parents[c]]\n f = {\n 'parents' : _parents[rv],\n 'values' : _vals[rv],\n 'cpt' : [item for sublist in _cpt[rv] for item in sublist]\n }\n _F[rv] = f\n\n bn = BayesNet()\n bn.F = _F\n bn.E = _E\n bn.V = list(topsort(_E))\n\n return bn\n\ndef read_json(path):\n \"\"\"\n Read a BayesNet object from the json format. This\n format has the \".bn\" extension and is completely\n unique to pyBN.\n\n Arguments\n ---------\n *path* : a string\n The file path\n\n Returns\n -------\n None\n\n Effects\n -------\n - Instantiates and sets a new BayesNet object\n\n Notes\n -----\n \n This function reads in a libpgm-style format into a bn object\n\n File Format:\n {\n \"V\": [\"Letter\", \"Grade\", \"Intelligence\", \"SAT\", \"Difficulty\"],\n \"E\": [[\"Intelligence\", \"Grade\"],\n [\"Difficulty\", \"Grade\"],\n [\"Intelligence\", \"SAT\"],\n [\"Grade\", \"Letter\"]],\n \"Vdata\": {\n \"Letter\": {\n \"ord\": 4,\n \"numoutcomes\": 2,\n \"vals\": [\"weak\", \"strong\"],\n \"parents\": [\"Grade\"],\n \"children\": None,\n \"cprob\": [[.1, .9],[.4, .6],[.99, .01]]\n },\n ...\n }\n\n\n \"\"\"\n def byteify(input):\n if isinstance(input, dict):\n return {byteify(key):byteify(value) for key,value in input.iteritems()}\n elif isinstance(input, list):\n return [byteify(element) for element in input]\n elif isinstance(input, unicode):\n return input.encode('utf-8')\n else:\n return input\n\n bn = BayesNet()\n \n f = open(path,'r')\n ftxt = f.read()\n\n success=False\n try:\n data = byteify(json.loads(ftxt))\n bn.V = data['V']\n bn.E = data['E']\n bn.F = data['F']\n success = True\n except ValueError:\n print(\"Could not read file - check format\")\n bn.V = topsort(bn.E)\n\n return bn\n\ndef read_mat(path, delim=' '):\n \"\"\"\n Read an adjacency matrix into a BayesNet object.\n\n NOTE: This is for reading the structure only, and\n therefore no parameters for the BayesNet object will\n be set - they must be learned by calling \"mle_estimator\"\n or \"bayes_estimator\" on the object.\n \"\"\"\n _V = []\n _E = {}\n _F = {}\n with open(path, 'r') as f:\n for line in f:\n line = line.split(delim)\n rv = line[0]\n _E[rv] = []\n\n bn = BayesNet(_E)\n\n return bn\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/io/write.py", "content": "\"\"\"\r\n**************\r\nWrite BayesNet\r\nto File\r\n**************\r\n\r\nWrite a BayesNet object to a file. These\r\nfunctions all currently work, but support\r\nfor more formats should be added in the future.\r\n\"\"\"\r\n\r\n\r\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport numpy as np\n\n\ndef bayes_estimator(bn, data, equiv_sample=None, prior_dict=None, nodes=None):\n\t\"\"\"\n\tBayesian Estimation method of parameter learning.\n\tThis method proceeds by either 1) assuming a uniform prior\n\tover the parameters based on the Dirichlet distribution\n\twith an equivalent sample size = *sample_size*, or\n\t2) assuming a prior as specified by the user with the \n\t*prior_dict* argument. The prior distribution is then\n\tupdated from observations in the data based on the\n\tMultinomial distribution - for which the Dirichlet\n\tis a \"conjugate prior.\"\n\n\tNote that the Bayesian and MLE estimators essentially converge\n\tto the same set of values as the size of the dataset increases.\n\n\tAlso note that, unlike the structure learning algorithms, the\n\tparameter learning functions REQUIRE a passed-in BayesNet object\n\tbecause there MUST be some pre-determined structure for which\n\twe can actually learn the parameters. You can't learn parameters\n\twithout structure - so structure must always be there first!\n\n\tFinally, note that this function can be used to calculate only\n\tONE conditional probability table in a BayesNet object by\n\tpassing in a subset of random variables with the \"nodes\"\n\targument - this is mostly used for score-based structure learning,\n\twhere a single cpt needs to be quickly recalculate after the\n\taddition/deletion/reversal of an arc.\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\n\t*data* : a nested numpy array\n\t\tData from which to learn parameters\n\n\t*equiv_sample* : an integer\n\t\tThe \"equivalent sample size\" (see function summary)\n\n\t*prior_dict* : a dictionary, where key = random variable\n\t\tand for each key the value is another dictionary where\n\t\tkey = an instantiation for the random variable and the\n\t\tvalue is its FREQUENCY (an integer value, NOT its relative\n\t\tproportion/probability).\n\t\n\t*nodes* : a list of strings\n\t\tWhich nodes to learn the parameters for - if None,\n\t\tall nodes will be used as expected.\n\t\n\tReturns\n\t-------\n\tNone\n\n\tEffects\n\t-------\n\t- modifies/sets bn.data to the learned parameters\n\n\tNotes\n\t-----\n\n\t\"\"\"\n\tif equiv_sample is None:\n\t\tequiv_sample = len(data)\n\n\tif nodes is None:\n\t\tnodes = list(bn.nodes())\n\n\tfor i, n in enumerate(nodes):\n\t\tbn.F[n]['values'] = list(np.unique(data[:,i]))\n\n\tobs_dict = dict([(rv,[]) for rv in nodes])\n\t# set empty conditional probability table for each RV\n\tfor rv in nodes:\n\t\t# get number of values in the CPT = product of scope vars' cardinalities\n\t\tp_idx = int(np.prod([bn.card(p) for p in bn.parents(rv)])*bn.card(rv))\n\t\tbn.F[rv]['cpt'] = [equiv_sample/p_idx]*p_idx\n\t\n\t# loop through each row of data\n\tfor row in data:\n\t\t# store the observation of each variable in the row\n\t\tobs_dict = dict([(rv,row[rv]) for rv in nodes])\n\t\t# loop through each RV and increment its observed parent-self value\n\t\tfor rv in nodes:\n\t\t\trv_dict= { n: obs_dict[n] for n in obs_dict if n in bn.scope(rv) }\n\t\t\toffset = bn.cpt_indices(target=rv,val_dict=rv_dict)[0]\n\t\t\tbn.F[rv]['cpt'][offset]+=1\n\n\t\n\tfor rv in nodes:\n\t\tcpt = bn.cpt(rv)\n\t\tfor i in range(0,len(bn.cpt(rv)),bn.card(rv)):\n\t\t\ttemp_sum = float(np.sum(cpt[i:(i+bn.card(rv))]))\n\t\t\tfor j in range(bn.card(rv)):\n\t\t\t\tcpt[i+j] /= (temp_sum)\n\t\t\t\tcpt[i+j] = round(cpt[i+j],5)\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/parameter/mle.py", "content": "\"\"\"\n*****************************\nMaximum Likelihood Estimation\nParameter Learning\n*****************************\n\n\"\"\"\nfrom __future__ import division\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport numpy as np\n\ndef mle_fast(bn, data, nodes=None, counts=False, np=False):\n\t\"\"\"\n\tMaximum Likelihood estimation that is about 100 times as\n\tfast as the original mle_estimator function - but returns\n\tthe same result\n\t\"\"\"\n\tdef merge_cols(data, cols):\n\t\tif len(cols) == 1:\n\t\t\treturn data[cols[0]]\n\t\telif len(cols) > 1:\n\t\t\tdata = data[cols].astype('str')\n\t\t\tncols = len(cols)\n\t\t\tfor i in range(len(data)):\n\t\t\t\tdata.ix[i,0] = ''.join(data.ix[i,0:ncols])\n\t\t\tdata = data.astype('int')\n\t\t\treturn data.ix[:,0]\n\n\tif nodes is None:\n\t\tnodes = list(bn.nodes())\n\telse:\n\t\tif not isinstance(nodes, list):\n\t\t\tnodes = list(nodes)\n\n\tF = dict([(rv, {}) for rv in nodes])\n\tfor i, n in enumerate(nodes):\n\t\tF[n]['values'] = list(np.unique(data.ix[:,i]))\n\t\tbn.F[n]['values'] = list(np.unique(data.ix[:,i]))\n\n\tfor rv in nodes:\n\t\tparents = bn.parents(rv)\n\t\tif len(parents)==0:\n\t\t\tif np:\n\t\t\t\tF[rv]['cpt'] = np.histogram(data.ix[:,rv], bins=bn.card(rv))[0]\n\t\t\telse:\n\t\t\t\tF[rv]['cpt'] = list(np.histogram(data.ix[:,rv], bins=bn.card(rv))[0])\n\t\telse:\n\t\t\tif np:\n\t\t\t\tF[rv]['cpt'] = np.histogram2d(merge_cols(data,parents),data.ix[:,rv], \n\t\t\t\tbins=[np.prod([bn.card(p) for p in parents]),bn.card(rv)])[0].flatten()\n\t\t\telse:\n\t\t\t\tF[rv]['cpt'] = list(np.histogram2d(merge_cols(data,parents),data.ix[:,rv], \n\t\t\t\t\tbins=[np.prod([bn.card(p) for p in parents]),bn.card(rv)])[0].flatten())\n\tif counts:\n\t\treturn F\n\telse:\n\t\tfor rv in nodes:\n\t\t\tF[rv]['parents'] = [var for var in nodes if rv in bn.E[var]]\n\t\t\tfor i in range(0,len(F[rv]['cpt']),bn.card(rv)):\n\t\t\t\ttemp_sum = float(np.sum(F[rv]['cpt'][i:(i+bn.card(rv))]))\n\t\t\t\tfor j in range(bn.card(rv)):\n\t\t\t\t\tF[rv]['cpt'][i+j] /= (temp_sum+1e-7)\n\t\t\t\t\tF[rv]['cpt'][i+j] = round(F[rv]['cpt'][i+j],5)\n\t\tbn.F = F\n\n\n\n\ndef mle_estimator(bn, data, nodes=None, counts=False):\n\t\"\"\"\n\tMaximum Likelihood Estimation is a frequentist\n\tmethod for parameter learning, where there is NO\n\tprior distribution. Instead, the frequencies/counts\n\tfor each parameter start at 0 and are simply incremented\n\tas the relevant parent-child values are observed in the\n\tdata. \n\n\tThis can be a risky method for small datasets, because if a \n\tcertain parent-child instantiation is never observed in the\n\tdata, then its probability parameter will be ZERO (even if you\n\tknow it should at least have a very small probability). \n\n\tNote that the Bayesian and MLE estimators essentially converge\n\tto the same set of values as the size of the dataset increases.\n\n\tAlso note that, unlike the structure learning algorithms, the\n\tparameter learning functions REQUIRE a passed-in BayesNet object\n\tbecause there MUST be some pre-determined structure for which\n\twe can actually learn the parameters. You can't learn parameters\n\twithout structure - so structure must always be there first!\n\n\tFinally, note that this function can be used to calculate only\n\tONE conditional probability table in a BayesNet object by\n\tpassing in a subset of random variables with the \"nodes\"\n\targument - this is mostly used for score-based structure learning,\n\twhere a single cpt needs to be quickly recalculate after the\n\taddition/deletion/reversal of an arc.\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\t\tThe associated network structure for which\n\t\tthe parameters will be learned\n\n\t*data* : a nested numpy array\n\n\t*nodes* : a list of strings\n\t\tWhich nodes to learn the parameters for - if None,\n\t\tall nodes will be used as expected.\n\n\tReturns\n\t-------\n\tNone\n\n\tEffects\n\t-------\n\t- modifies/sets bn.data to the learned parameters\n\n\tNotes\n\t-----\n\t- Currently doesn't return correct solution\n\n\tdata attributes:\n\t\t\"numoutcomes\" : an integer\n\t \"vals\" : a list\n\t \"parents\" : a list or None\n\t \"children\": a list or None\n\t \"cprob\" : a nested python list\n\n\t- Do not want to alter bn.data directly!\n\n\t\"\"\"\n\tif nodes is None:\n\t\tnodes = list(bn.nodes())\n\telse:\n\t\tif not isinstance(nodes, list):\n\t\t\tnodes = list(nodes)\n\n\tF = dict([(rv, {}) for rv in nodes])\n\tfor i, n in enumerate(nodes):\n\t\tF[n]['values'] = list(np.unique(data[:,i]))\n\t\tbn.F[n]['values'] = list(np.unique(data[:,i]))\n\n\tobs_dict = dict([(rv,[]) for rv in nodes])\n\t# set empty conditional probability table for each RV\n\tfor rv in nodes:\n\t\t# get number of values in the CPT = product of scope vars' cardinalities\n\t\tp_idx = int(np.prod([bn.card(p) for p in bn.parents(rv)])*bn.card(rv))\n\t\tF[rv]['cpt'] = [0]*p_idx\n\t\tbn.F[rv]['cpt'] = [0]*p_idx\n\t\n\t# loop through each row of data\n\tfor row in data:\n\t\t# store the observation of each variable in the row\n\t\tfor rv in nodes:\n\t\t\tobs_dict[rv] = row[rv]\n\t\t\n\t\t#obs_dict = dict([(rv,row[rv]) for rv in nodes])\n\t\t# loop through each RV and increment its observed parent-self value\n\t\tfor rv in nodes:\n\t\t\trv_dict= { n: obs_dict[n] for n in obs_dict if n in bn.scope(rv) }\n\t\t\toffset = bn.cpt_indices(target=rv,val_dict=rv_dict)[0]\n\t\t\tF[rv]['cpt'][offset]+=1\n\n\tif counts:\n\t\treturn F\n\telse:\n\t\tfor rv in nodes:\n\t\t\tF[rv]['parents'] = [var for var in nodes if rv in bn.E[var]]\n\t\t\tfor i in range(0,len(F[rv]['cpt']),bn.card(rv)):\n\t\t\t\ttemp_sum = float(np.sum(F[rv]['cpt'][i:(i+bn.card(rv))]))\n\t\t\t\tfor j in range(bn.card(rv)):\n\t\t\t\t\tF[rv]['cpt'][i+j] /= (temp_sum+1e-7)\n\t\t\t\t\tF[rv]['cpt'][i+j] = round(F[rv]['cpt'][i+j],5)\n\t\tbn.F = F\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/__init__.py", "content": "from pyBN.learning.structure.constraint import *\r\nfrom pyBN.learning.structure.exact import *\r\nfrom pyBN.learning.structure.hybrid import *\r\nfrom pyBN.learning.structure.naive import *\r\nfrom pyBN.learning.structure.score import *\r\nfrom pyBN.learning.structure.tree import *\r\n\r\nfrom pyBN.learning.structure.mdbn import *" }, { "path": "pyBN/learning/structure/_tests/__init__.py", "content": "" }, { "path": "pyBN/learning/structure/_tests/test_chow_liu.py", "content": "\"\"\"\n********\nUnitTest\nChowLiu\n********\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.structure_learn.chow_liu import chow_liu\n\n\nclass ChowLiuTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tpath = (os.path.join(self.dpath,'lizards.csv'))\n\t\tself.data = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')\n\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_chow_liu1_V(self):\n\t\tbn = chow_liu(self.data)\n\t\tself.assertListEqual(bn.V,\n\t\t\t[0,1,2])\n\n\tdef test_chow_liu1_E(self):\n\t\tbn = chow_liu(self.data)\n\t\tself.assertDictEqual(bn.E,\n\t\t\t{0:[1,2],1:[],2:[]})\n\t\n\tdef test_chow_liu1_F(self):\n\t\tbn = chow_liu(self.data)\n\t\tself.assertDictEqual(bn.F,\n\t\t\t{0: {'cpt': [], 'parents': [], 'values': [1,2]},\n\t\t\t 1: {'cpt': [], 'parents': [0], 'values': [1,2]},\n\t\t\t 2: {'cpt': [], 'parents': [0], 'values': [1,2]}})" }, { "path": "pyBN/learning/structure/_tests/test_grow_shrink.py", "content": "\"\"\"\n********\nUnitTest\nGrowShrink\n********\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.structure_learn.grow_shrink import gs\n\n\n\nclass GrowShrinkTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tpath = (os.path.join(self.dpath,'lizards.csv'))\n\t\tself.data = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')\n\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_gs1_V(self):\n\t\tbn = gs(self.data)\n\t\tself.assertListEqual(bn.V,\n\t\t\t[0,1,2])\n\n\tdef test_gs1_E(self):\n\t\tbn = gs(self.data)\n\t\tself.assertDictEqual(bn.E,\n\t\t\t{0:[1,2],1:[],2:[]})\n\t\n\tdef test_gs1_F(self):\n\t\tbn = gs(self.data)\n\t\tself.assertDictEqual(bn.F,\n\t\t\t{0: {'cpt': [], 'parents': [], 'values': [1,2]},\n\t\t\t 1: {'cpt': [], 'parents': [0], 'values': [1,2]},\n\t\t\t 2: {'cpt': [], 'parents': [0], 'values': [1,2]}})\n\n\tdef test_gs_data(self):\n\t\td = np.loadtxt(os.path.join(self.dpath,'gs_data.txt'),dtype='int32')\n\t\tbn = gs(d)\n\t\tself.assertDictEqual(bn.E,\n\t\t\t{0: [], 1: [], 2: [0, 1, 4, 3], 3: [], 4: []})\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/_tests/test_orient_edges.py", "content": "\"\"\"\n********\nUnitTest\nOrientEdges\n********\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.structure_learn.orient_edges import orient_edges_gs, orient_edges_pc\n\n\nclass OrientEdgesTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tpass\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_orient_edges_pc(self):\n\t\te = {0:[1,2],1:[0],2:[0]}\n\t\tb = {0: [], 1: {2: (0,)}, 2: {1: (0,)}}\n\t\tself.assertDictEqual(orient_edges_pc(e,b),\n\t\t\t{0: [1, 2], 1: [], 2: []})\n\n\tdef test_orient_edges_gs(self):\n\t\te = {0: [1, 2], 1: [0], 2: [0]}\n\t\tb = {0: [1, 2], 1: [0], 2: [0]}\n\t\tdpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tpath = (os.path.join(dpath,'lizards.csv'))\n\t\tdata = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')\n\t\tself.assertDictEqual(orient_edges_gs(e,b,data,0.05),\n\t\t\t{0: [1, 2], 1: [], 2: []})" }, { "path": "pyBN/learning/structure/_tests/test_pc.py", "content": "\"\"\"\n********\nUnitTest\nPC\n********\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.structure_learn.path_condition import pc\n\n\nclass PCTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tpath = (os.path.join(self.dpath,'lizards.csv'))\n\t\tself.data = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')\n\t\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_pc1_V(self):\n\t\tbn = pc(self.data)\n\t\tself.assertListEqual(bn.V,\n\t\t\t[0,1,2])\n\n\tdef test_pc1_E(self):\n\t\tbn = pc(self.data)\n\t\tself.assertDictEqual(bn.E,\n\t\t\t{0:[1,2],1:[],2:[]})\n\t\n\tdef test_pc1_F(self):\n\t\tbn = pc(self.data)\n\t\tself.assertDictEqual(bn.F,\n\t\t\t{0: {'cpt': [], 'parents': [], 'values': [1,2]},\n\t\t\t 1: {'cpt': [], 'parents': [0], 'values': [1,2]},\n\t\t\t 2: {'cpt': [], 'parents': [0], 'values': [1,2]}})\n" }, { "path": "pyBN/learning/structure/constraint/__init__.py", "content": "from pyBN.learning.structure.constraint.fast_iamb import *\nfrom pyBN.learning.structure.constraint.grow_shrink import *\nfrom pyBN.learning.structure.constraint.iamb import *\nfrom pyBN.learning.structure.constraint.lambda_iamb import *\nfrom pyBN.learning.structure.constraint.path_condition import *" }, { "path": "pyBN/learning/structure/constraint/fast_iamb.py", "content": "\"\"\"\n*********\nFast-IAMB\nAlgorithm\n*********\n\n\nFor Feature Selection (from [1]):\n\t\"A principled solution to the feature selection problem is\n\tto determine a subset of attributes that can \"shield\" (render\n\tindependent) the attribute of interest from the effect of\n\tthe remaining attributes in the domain. Koller and Sahami\n\t[4] first showed that the Markov blanket of a given target attribute\n\tis the theoretically optimal set of attributes to predict\n\tits value...\n\t\n\tBecause the Markov blanket of a target attribute T renders\n\tit statistically independent from all the remaining attributes\n\t(see the Markov blanket definition below), all information\n\tthat may influence its value is stored in the values\n\tof the attributes of its Markov blanket. Any attribute\n\tfrom the feature set outside its Markov blanket can be effectively\n\tignored from the feature set without adversely affecting\n\tthe performance of any classifier that predicts the\n\tvalue of T\"\n\nReferences\n----------\n[1] Yaramakala and Maragritis, \"Speculative Markov Blanket \nDiscovery for Optimal Feature Selection\"\n\n[2] Tsarmardinos, et al. \"Algorithms for Large Scale \nMarkov Blanket Discovery\" \n\n\"\"\"\nfrom __future__ import division\n\nimport numpy as np\nfrom pyBN.utils.independence_tests import are_independent, mi_test\nfrom pyBN.utils.data import unique_bins, replace_strings\n\ndef fast_iamb(data, k=5, alpha=0.05, feature_selection=None, debug=False):\n\t\"\"\"\n\tFrom [1]:\n\t\t\"A novel algorithm for the induction of\n\t\tMarkov blankets from data, called Fast-IAMB, that employs\n\t\ta heuristic to quickly recover the Markov blanket. Empirical\n\t\tresults show that Fast-IAMB performs in many cases\n\t\tfaster and more reliably than existing algorithms without\n\t\tadversely affecting the accuracy of the recovered Markov\n\t\tblankets.\"\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\n\t*k* : an integer\n\t\tThe max number of edges to add at each iteration of \n\t\tthe algorithm.\n\n\t*alpha* : a float\n\t\tProbability of Type I error\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\t- Currently does not work. I think it's stuck in an infinite loop...\n\n\t\"\"\"\n\t# get values\n\tvalue_dict = dict(zip(range(data.shape[1]),\n\t\t\t[list(np.unique(col)) for col in data.T]))\n\t# replace strings\n\tdata = replace_strings(data)\n\n\tn_rv = data.shape[1]\n\tMb = dict([(rv,[]) for rv in range(n_rv)])\n\tN = data.shape[0]\n\tcard = dict(zip(range(n_rv),unique_bins(data)))\n\t#card = dict(zip(range(data.shape[1]),np.amax(data,axis=0)))\n\n\tif feature_selection is None:\n\t\t_T = range(n_rv)\n\telse:\n\t\tassert (not isinstance(feature_selection, list)), 'feature_selection must be only one value'\n\t\t_T = [feature_selection]\n\t# LEARN MARKOV BLANKET\n\tfor T in _T:\n\t\tS = set(range(n_rv)) - {T}\n\t\tfor A in S:\n\t\t\tif not are_independent(data[:,(A,T)]):\n\t\t\t\tS.remove(A)\n\t\ts_h_dict = dict([(s,0) for s in S])\n\t\twhile S:\n\t\t\tinsufficient_data = False\n\t\t\tbreak_grow_phase = False\n\t\t\t\n\t\t\t#### GROW PHASE ####\n\t\t\t# Calculate mutual information for all variables\n\t\t\tmi_dict = dict([(s,mi_test(data[:,(s,T)+tuple(Mb[T])])) for s in S])\n\t\t\tfor x_i in sorted(mi_dict, key=mi_dict.get,reverse=True):\n\t\t\t\t# Add top MI-score variables until there isn't enough data for bins\n\t\t\t\tif (N / card[x_i]*card[T]*np.prod([card[b] for b in Mb[T]])) >= k:\n\t\t\t\t\tMb[T].append(x_i)\n\t\t\t\telse:\n\t\t\t\t\tinsufficient_data = True\n\t\t\t\t\tbreak\n\n\t\t\t#### SHRINK PHASE ####\n\t\t\tremoved_vars = False\n\t\t\tfor A in Mb[T]:\n\t\t\t\tcols = (A,T) + tuple(set(Mb[T]) - {A})\n\t\t\t\t# if A is independent of T given Mb[T], remove A\n\t\t\t\tif are_independent(data[:,cols]):\n\t\t\t\t\tMb[T].remove(A)\n\t\t\t\t\tremoved_vars=True\n\n\t\t\t#### FINALIZE BLANKET FOR \"T\" OR MAKE ANOTHER PASS ####\n\t\t\tif insufficient_data and not removed_vars:\n\t\t\t\tif debug:\n\t\t\t\t\tprint('Breaking..')\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tA = set(range(n_rv)) - {T} - set(Mb[T])\n\t\t\t\t#A = set(nodes) - {T} - set(Mb[T])\n\t\t\t\tS = set()\n\t\t\t\tfor a in A:\n\t\t\t\t\tcols = (a,T) + tuple(Mb[T])\n\t\t\t\t\tif are_independent(data[:,cols]):\n\t\t\t\t\t\tS.add(a)\n\t\tif debug:\n\t\t\tprint('Done with %s' % T)\n\t\n\tif feature_selection is None:\n\t\t# RESOLVE GRAPH STRUCTURE\n\t\tedge_dict = resolve_markov_blanket(Mb, data)\n\n\t\t# ORIENT EDGES\n\t\toriented_edge_dict = orient_edges_MB(edge_dict,Mb,data,alpha)\n\n\t\t# CREATE BAYESNET OBJECT\n\t\tbn=BayesNet(oriented_edge_dict,value_dict)\n\n\t\treturn BN\n\telse:\n\t\treturn Mb[_T]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/constraint/grow_shrink.py", "content": "\"\"\"\n********************\nGrowShrink Algorithm\n********************\n\n\"Our approach constructs Bayesian networks by first identifying each node's\nMarkov blankets, then connecting nodes in a maximally consistent way. \nIn contrast to the majority of work, which typically uses hill-climbing approaches \nthat may produce dense and causally incorrect nets, our approach yields much more \ncompact causal networks by heeding independenciesin the data. Compact causal networks\nfacilitate fast inference and are also easier to understand. We prove that under mild \nassumptions, our approach requires time polynomial in the size of the data and the number \nof nodes. A randomized variant, also presented here, yields comparable results at\nmuch higher speeds\" [1].\n\nThis algorithm relies on the \"Markov Blanket\", which is for a given random variable\nthe set of other random variables which render the given RV conditionally independent\nfrom the rest of the network. That is, if you observe any of the variables in a given\nRV's markov blanket, then observing the values of any OTHER variables in the network\nwill not change your beliefs about the given random variable.\n\nThe Markov blanket of a node X is easily identifiable from the graph: \nit consists of X's parents, X's children, and the parents of all of X's children.\n\nThe runtime of this algorithm is O(|V|) [1].\n\nReferences\n----------\n[1] Margaritis and Thrun: \"Bayesian Network Induction via Local Neighborhoods\", \nNIPS 2000.\n\n[2] https://www.cs.cmu.edu/~dmarg/Papers/PhD-Thesis-Margaritis.pdf\n\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nfrom pyBN.utils.independence_tests import mi_test\nfrom pyBN.utils.orient_edges import orient_edges_MB\nfrom pyBN.utils.markov_blanket import resolve_markov_blanket\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.utils.data import replace_strings\n\nfrom copy import copy\nimport numpy as np\nimport itertools\n\ndef gs(data, alpha=0.05, feature_selection=None, debug=False):\n\t\"\"\"\n\tPerform growshink algorithm over dataset to learn\n\tBayesian network structure.\n\n\tThis algorithm is clearly a good candidate for\n\tnumba JIT compilation...\n\n\tSTEPS\n\t-----\n\t1. Compute Markov Blanket\n\t2. Compute Graph Structure\n\t3. Orient Edges\n\t4. Remove Cycles\n\t5. Reverse Edges\n\t6. Propagate Directions\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\t\tData from which you wish to learn structure\n\n\t*alpha* : a float\n\t\tType I error rate for independence test\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\n\tSpeed Test:\n\t\t*** 5 variables, 624 observations ***\n\t\t- 63.7 ms\n\n\t\"\"\"\n\tn_rv = data.shape[1]\n\tdata, value_dict = replace_strings(data, return_values=True)\n\t\n\n\tif feature_selection is None:\n\t\t_T = range(n_rv)\n\telse:\n\t\tassert (not isinstance(feature_selection, list)), 'feature_selection must be only one value'\n\t\t_T = [feature_selection]\n\n\t# STEP 1 : COMPUTE MARKOV BLANKETS\n\tMb = dict([(rv,[]) for rv in range(n_rv)])\n\n\tfor X in _T:\n\t\tS = []\n\n\t\tgrow_condition = True\n\t\twhile grow_condition:\n\n\t\t\tgrow_condition=False\n\t\t\tfor Y in range(n_rv):\n\t\t\t\tif X!=Y and Y not in S:\n\t\t\t\t\t# if there exists some Y such that Y is dependent on X given S,\n\t\t\t\t\t# add Y to S\n\t\t\t\t\tcols = (X,Y) + tuple(S)\n\t\t\t\t\tpval = mi_test(data[:,cols])\n\t\t\t\t\tif pval < alpha: # dependent\n\t\t\t\t\t\tgrow_condition=True # dependent -> continue searching\n\t\t\t\t\t\tS.append(Y)\n\t\t\n\t\tshrink_condition = True\n\t\twhile shrink_condition:\n\n\t\t\tTEMP_S = []\n\t\t\tshrink_condition=False\n\t\t\tfor Y in S:\n\t\t\t\ts_copy = copy(S)\n\t\t\t\ts_copy.remove(Y) # condition on S-{Y}\n\t\t\t\t# if X independent of Y given S-{Y}, leave Y out\n\t\t\t\t# if X dependent of Y given S-{Y}, keep it in\n\t\t\t\tcols = (X,Y) + tuple(s_copy)\n\t\t\t\tpval = mi_test(data[:,cols])\n\t\t\t\tif pval < alpha: # dependent\n\t\t\t\t\tTEMP_S.append(Y)\n\t\t\t\telse: # independent -> condition searching\n\t\t\t\t\tshrink_condition=True\n\t\t\n\t\tMb[X] = TEMP_S\n\t\tif debug:\n\t\t\tprint('Markov Blanket for %s : %s' % (X, str(TEMP_S)))\n\t\n\tif feature_selection is None:\n\t\t# STEP 2: COMPUTE GRAPH STRUCTURE\n\t\t# i.e. Resolve Markov Blanket\n\t\tedge_dict = resolve_markov_blanket(Mb,data)\n\t\tif debug:\n\t\t\tprint('Unoriented edge dict:\\n %s' % str(edge_dict))\n\t\t\n\t\t# STEP 3: ORIENT EDGES\n\t\toriented_edge_dict = orient_edges_MB(edge_dict,Mb,data,alpha)\n\t\tif debug:\n\t\t\tprint('Oriented edge dict:\\n %s' % str(oriented_edge_dict))\n\t\t\n\n\t\t# CREATE BAYESNET OBJECT\n\t\tbn=BayesNet(oriented_edge_dict,value_dict)\n\t\t\n\t\treturn bn\n\telse:\n\t\treturn Mb[_T]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/constraint/iamb.py", "content": "\"\"\"\n******************\nIAMB STRUCTURE\nLEARNING ALGORITHM\n******************\n\nThe IAMB algorithm - Incremental Association Markov Blanket - proceeds\nsimilar to the Grow-Shrink algorithm, where the growing phase adds\nrandom variables to a given node's markov blanket, and the shrinking\nphase performs a second pass of conditional independence tests to\neliminate any variables from the markov blanket for which the given\nnode is conditionally independent given any other node in the markov\nblanket (hence doesn't belong in the markov blanket).\n\nIt significantly reduces the complexity compared to PC/GS, and is\nnormally used in conjuction with the mutual information test for\nindependence (I do so here) [2].\n\nLastly, it can also be used as a feature selection algorithm for\nclassification or general dimensionality reduction - simply return\nthe markov blanket of a given node, which has been shown by Daphne\nKoller to be theoretically optimal for prediction [3]. \n\nIAMB for Feature Selection (from [1]):\t\n\tBecause the Markov blanket of a target attribute T renders\n\tit statistically independent from all the remaining attributes\n\t(see the Markov blanket definition below), all information\n\tthat may influence its value is stored in the values\n\tof the attributes of its Markov blanket. Any attribute\n\tfrom the feature set outside its Markov blanket can be effectively\n\tignored from the feature set without adversely affecting\n\tthe performance of any classifier that predicts the\n\tvalue of T\n\nReferences\n----------\n[1] Yaramakala and Maragritis, \"Speculative Markov Blanket \nDiscovery for Optimal Feature Selection\"\n\n[2] Tsarmardinos, et al. \"Algorithms for Large Scale \nMarkov Blanket Discovery\" \n\n[3] Koller \"Toward Optimal Feature Selection.\"\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport numpy as np\nfrom pyBN.utils.independence_tests import are_independent, mi_test\nfrom pyBN.utils.orient_edges import orient_edges_MB, orient_edges_gs2\nfrom pyBN.utils.markov_blanket import resolve_markov_blanket\nfrom pyBN.classes.bayesnet import BayesNet\nfrom copy import copy\n\ndef iamb(data, alpha=0.05, feature_selection=None, debug=False):\n\t\"\"\"\n\tIAMB Algorithm for learning the structure of a\n\tDiscrete Bayesian Network from data.\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\n\t*alpha* : a float\n\t\tThe type II error rate.\n\n\t*feature_selection* : None or a string\n\t\tWhether to use IAMB as a structure learning\n\t\tor feature selection algorithm.\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object or\n\t*mb* : the markov blanket of a node\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\t- Works but there are definitely some bugs.\n\n\tSpeed Test:\n\t\t*** 5 vars, 624 obs ***\n\t\t\t- 196 ms\n\t\"\"\"\n\tn_rv = data.shape[1]\n\tMb = dict([(rv,[]) for rv in range(n_rv)])\n\n\tif feature_selection is None:\n\t\t_T = range(n_rv)\n\telse:\n\t\tassert (not isinstance(feature_selection, list)), 'feature_selection must be only one value'\n\t\t_T = [feature_selection]\n\n\t# LEARN MARKOV BLANKET\n\tfor T in _T:\n\n\t\tV = set(range(n_rv)) - {T}\n\t\tMb_change=True\n\n\t\t# GROWING PHASE\n\t\twhile Mb_change:\n\t\t\tMb_change = False\n\t\t\t# find X_max in V-Mb(T)-{T} that maximizes \n\t\t\t# mutual information of X,T|Mb(T)\n\t\t\t# i.e. max of mi_test(data[:,(X,T,Mb(T))])\n\t\t\tmax_val = -1\n\t\t\tmax_x = None\n\t\t\tfor X in V - set(Mb[T]) - {T}:\n\t\t\t\tcols = (X,T)+tuple(Mb[T])\n\t\t\t\tmi_val = mi_test(data[:,cols],test=False)\n\t\t\t\tif mi_val > max_val:\n\t\t\t\t\tmax_val = mi_val\n\t\t\t\t\tmax_x = X\n\t\t\t# if Xmax is dependent on T given Mb(T)\n\t\t\tcols = (max_x,T) + tuple(Mb[T])\n\t\t\tif max_x is not None and are_independent(data[:,cols]):\n\t\t\t\tMb[T].append(X)\n\t\t\t\tMb_change = True\n\t\t\t\tif debug:\n\t\t\t\t\tprint('Adding %s to MB of %s' % (str(X), str(T)))\n\n\t\t# SHRINKING PHASE\n\t\tfor X in Mb[T]:\n\t\t\t# if x is independent of t given Mb(T) - {x}\n\t\t\tcols = (X,T) + tuple(set(Mb[T]) - {X})\n\t\t\tif are_independent(data[:,cols],alpha):\n\t\t\t\tMb[T].remove(X)\n\t\t\t\tif debug:\n\t\t\t\t\tprint('Removing %s from MB of %s' % (str(X), str(T)))\n\n\tif feature_selection is None:\n\t\t# RESOLVE GRAPH STRUCTURE\n\t\tedge_dict = resolve_markov_blanket(Mb, data)\n\t\tif debug:\n\t\t\tprint('Unoriented edge dict:\\n %s' % str(edge_dict))\n\t\t\tprint('MB: %s' % str(Mb))\n\t\t# ORIENT EDGES\n\t\toriented_edge_dict = orient_edges_gs2(edge_dict,Mb,data,alpha)\n\t\tif debug:\n\t\t\tprint('Oriented edge dict:\\n %s' % str(oriented_edge_dict))\n\n\t\t# CREATE BAYESNET OBJECT\n\t\tvalue_dict = dict(zip(range(data.shape[1]),\n\t\t\t[list(np.unique(col)) for col in data.T]))\n\t\tbn=BayesNet(oriented_edge_dict,value_dict)\n\n\t\treturn bn\n\telse:\n\t\treturn Mb[_T]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/constraint/lambda_iamb.py", "content": "\"\"\"\nLambda IAMB Code\n\nReferences\n----------\n[1] Zhang et al, \"An Improved IAMB Algorithm for Markov\nBlanket Discovery\"\n\"\"\"\n\nimport numpy as np\nfrom pyBN.utils.independence_tests import are_independent, entropy\n\ndef lambda_iamb(data, L=1.5, alpha=0.05, feature_selection=None):\n\t\"\"\"\n\tLambda IAMB Algorithm for learning the structure of a\n\tDiscrete Bayesian Network from data. This Algorithm\n\tis similar to the iamb algorithm, except that it allows\n\tfor a \"lambda\" coefficient that helps avoid false positives.\n\n\tThis algorithm was originally developed for use as a\n\tfeature selection algorithm - discovering the markov\n\tblanket of a target variable is equivalent to discovering\n\tthe relevant features for classifications.\n\n\tIn practice, this algorithm does just as well as a feature\n\tselection method compared to IAMB when naive bayes was \n\tused as a classifier, but Lambda-iamb actually does much\n\tbetter than traditional iamb when traditional iamb does\n\tvery poorly due to high false positive rates.\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\n\t*L* : a float\n\t\tThe lambda hyperparameter - see [1].\n\n\t*alpha* : a float\n\t\tThe type II error rate.\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\t\"\"\"\n\tn_rv = data.shape[1]\n\tMb = dict([(rv,{}) for rv in range(n_rv)])\n\n\tif feature_selection is None:\n\t\t_T = range(n_rv)\n\telse:\n\t\tassert (not isinstance(feature_selection, list)), 'feature_selection must be only one value'\n\t\t_T = [feature_selection]\n\n\t# LEARN MARKOV BLANKET\n\tfor T in _T:\n\n\t\tV = set(range(n_rv)) - {T}\n\t\tMb_change=True\n\n\t\t# GROWING PHASE\n\t\twhile Mb_change:\n\t\t\tMb_change = False\n\t\t\tcols = tuple({T}) + tuple(Mb[T])\n\t\t\tH_tmb = entropy(data[:,cols])\n\t\t\t# find X1_min in V-Mb[T]-{T} that minimizes\n\t\t\t# entropy of T|X1_inMb[T]\n\t\t\t# i.e. min of entropy(data[:,(T,X,Mb[T])])\n\t\t\tmin_val1, min_val2 = 1e7,1e7\n\t\t\tmin_x1, min_x2 = None, None\n\t\t\tfor X in V - Mb[T] - {T}:\n\t\t\t\tcols = (T,X)+tuple(Mb[T])\n\t\t\t\tent_val = entropy(data[:,cols])\n\t\t\t\tif ent_val < min_val:\n\t\t\t\t\tmin_val2, min_val1 = min_val1, ent_val\n\t\t\t\t\tmin_x2,min_x1 = min_x1, X\n\t\t\t\t\t\n\t\t\t# if min_x1 is dependent on T given Mb[T]...\n\t\t\tcols = (min_x1,T) + tuple(Mb[T])\n\t\t\tif are_independent(data[:,cols]):\n\t\t\t\tif (min_val2 - L*min_val1) < ((1-L)*H_tmb):\n\t\t\t\t\tcols = (min_x2,T)+tuple(Mb[T])\n\t\t\t\t\tif are_independent(data[:,cols]):\n\t\t\t\t\t\tMb[T].add(min_x1)\n\t\t\t\t\t\tMb[T].add(min_x2)\n\t\t\t\t\t\tMb_change = True\n\t\t\telse:\n\t\t\t\tMb[T].add(X)\n\t\t\t\tMb_change = True\n\n\t\t# SHRINKING PHASE\n\t\tfor X in Mb[T]:\n\t\t\t# if x is indepdent of t given Mb[T] - {x}\n\t\t\tcols = (X,T) + tuple(Mb[T]-{X})\n\t\t\tif mi_test(data[:,cols]) > alpha:\n\t\t\t\tMb[T].remove(X)\n\n\tif feature_selection is None:\n\t\t# RESOLVE GRAPH STRUCTURE\n\t\tedge_dict = resolve_markov_blanket(Mb, data)\n\n\t\t# ORIENT EDGES\n\t\toriented_edge_dict = orient_edges_Mb(edge_dict,Mb,data,alpha)\n\n\t\t# CREATE BAYESNET OBJECT\n\t\tvalue_dict = dict(zip(range(data.shape[1]),\n\t\t\t[list(np.unique(col)) for col in data.T]))\n\t\tbn=BayesNet(oriented_edge_dict,value_dict)\n\n\t\treturn bn\n\telse:\n\t\treturn Mb[_T]\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/constraint/path_condition.py", "content": "\"\"\"\n*************\nPathCondition \nAlgorithm\n*************\n\nPseudo-Code\n-----------\nStart with a complete, undirected graph G'\ni <- 0\nRepeat:\n\tFor each x \\in X:\n\t\tFor each y in Adj(x):\n\n\t\t\tTest whether there exists some S in Adj(X)-{Y}\n\t\t\twith |S| = i, such that I(X,Y|S)\n\n\t\t\tIf there exists such a set S:\n\t\t\t\tMake S_xy <- S\n\t\t\t\tRemove X-Y link from G'\n\ti <- i + 1\nUntil |Adj(X)| <= i (forall x\\inX)\n\nfor (each uncoupled meeting X-Z-Y)\n\tif (Z not in S_xy)\n\t\torient X - Z - Y as X -> Z <- Y\nwhile (more edges can be oriented)\n\tfor (each uncoupled meeting X -> Z - Y)\n\t\torient Z - Y as Z -> Y\n\tfor (each X - Y such that there is a path from X to Y)\n\t\torient X - Y as X -> Y\n\tfor (each uncoupled meeting X - Z - Y such that\n\t\tX -> W, Y -> W, and Z - W)\n\t\torient Z - W as Z -> W\n\n\nReferences\n----------\n[1] Abellan, Gomez-Olmedo, Moral. \"Some Variations on the\nPC Algorithm.\" http://www.utia.cas.cz/files/mtr/pgm06/41_paper.pdf\n[2] Spirtes, Glymour, Scheines (1993) \"Causation, Prediction,\nand Search.\"\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport itertools\nimport numpy as np\n\nfrom pyBN.utils.independence_tests import mi_test\nfrom pyBN.classes import BayesNet\nfrom pyBN.utils.orient_edges import orient_edges_CS\n\n\ndef pc(data, alpha=0.05):\n\t\"\"\"\n\tPath Condition algorithm for structure learning. This is a\n\tgood test, but has some issues with test reliability when\n\tthe size of the dataset is small. The Necessary Path\n\tCondition (NPC) algorithm can solve these problems.\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\t\tThe object we wish to modify. This can be a competely\n\t\tempty BayesNet object, in which case the structure info\n\t\twill be set. This can be a BayesNet object with already\n\t\tinitialized structure/params, in which case the structure\n\t\twill be overwritten and the parameters will be cleared.\n\n\t*data* : a nested numpy array\n\t\tThe data from which we will learn -> will code for\n\t\tpandas dataframe after numpy works\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object\n\t\tThe network created from the learning procedure, with\n\t\tthe nodes/edges initialized/changed\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\n\tSpeed Test:\n\t\t** 5 vars, 624 obs ***\n\t\t\t- 90.9 ms\n\t\"\"\"\n\tn_rv = data.shape[1]\n\t##### FIND EDGES #####\n\tvalue_dict = dict(zip(range(n_rv),\n\t\t[list(np.unique(col)) for col in data.T]))\n\t\n\tedge_dict = dict([(i,[j for j in range(n_rv) if i!=j]) for i in range(n_rv)])\n\tblock_dict = dict([(i,[]) for i in range(n_rv)])\n\tstop = False\n\ti = 1\n\twhile not stop:\n\t\tfor x in range(n_rv):\n\t\t\tfor y in edge_dict[x]:\n\t\t\t\tif i == 0:\n\t\t\t\t\tpval_xy_z = mi_test(data[:,(x,y)])\n\t\t\t\t\tif pval_xy_z > alpha:\n\t\t\t\t\t\tif y in edge_dict[x]:\n\t\t\t\t\t\t\tedge_dict[x].remove(y)\n\t\t\t\t\t\t\tedge_dict[y].remove(x)\n\t\t\t\telse:\n\t\t\t\t\tfor z in itertools.combinations(edge_dict[x],i):\n\t\t\t\t\t\tif y not in z:\n\t\t\t\t\t\t\tcols = (x,y) + z\n\t\t\t\t\t\t\tpval_xy_z = mi_test(data[:,cols])\n\t\t\t\t\t\t\t# if I(X,Y | Z) = TRUE\n\t\t\t\t\t\t\tif pval_xy_z > alpha:\n\t\t\t\t\t\t\t\tblock_dict[x] = {y:z}\n\t\t\t\t\t\t\t\tblock_dict[y] = {x:z}\n\t\t\t\t\t\t\t\tif y in edge_dict[x]:\n\t\t\t\t\t\t\t\t\tedge_dict[x].remove(y)\n\t\t\t\t\t\t\t\t\tedge_dict[y].remove(x)\n\t\t\t\t\t\t\t\n\t\ti += 1\n\t\tstop = True\n\t\tfor x in range(n_rv):\n\t\t\tif (len(edge_dict[x]) > i-1):\n\t\t\t\tstop = False\n\t\t\t\tbreak\n\t\n\t# ORIENT EDGES (from collider set)\n\tdirected_edge_dict = orient_edges_CS(edge_dict,block_dict)\n\n\t# CREATE BAYESNET OBJECT\n\tbn=BayesNet(directed_edge_dict,value_dict)\n\t\n\treturn bn\n\n\n" }, { "path": "pyBN/learning/structure/exact/__init__.py", "content": "from pyBN.learning.structure.exact.gobnilp import *" }, { "path": "pyBN/learning/structure/exact/gobnilp.py", "content": "\"\"\"\nGOBNILP Solver for Exact Bayesian network\nStructure Learning with Integer Linear\nOptimization.\n\nThis code relies on the \"pyGOBN\" package, which\nimplements a Python wrapper for the GOBNILP\nproject - which itself builds on the SCIP\nOptimization engine for building Integer Linear\nPrograms to solve the BN structure learning problem\nEXACTLY.\n\nTh pyGOBN module - which must be installed from source - allows \nusers to create/alter a vast number of global parameter settings \n(i.e. wall-time, parent limits, sparsity, equivalent \nsample size, etc), along with various network constraints \n(i.e. required edges, disallowed edges, and independence\nrequirements).\n\nNOTE: You must download and install pyGOBN to use this functionality.\nVisit github.com/ncullen93/pyGOBN to get the source code, then run\n'python setup.py install' in the main pyGOBN directory to install the\nmodule for your local python distribution.\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\ndef ilp(data, settings=None, edge_reqs=None, nonedge_reqs=None, ind_reqs=None):\n\t\"\"\"\n\tLearning the EXACT optimal Bayesian network structure from data using\n\tInteger Linear Programming from the GOBNILP project.\n\t\"\"\"\n\ttry:\n\t\tfrom pyGOBN import GOBN\n\texcept ImportError:\n\t\tprint('You must download & install pyGOBN to use this functionality.\\\n\t\t Visit github.com/ncullen93/pyGOBN')\n\n\tgobn = GOBN()\n\tgobn.set_settings(settings)\n\tgobn.set_constraints(edge_reqs=edge_reqs, nonedge_reqs=nonedge_reqs, ind_reqs=ind_reqs)\n" }, { "path": "pyBN/learning/structure/hybrid/__init__.py", "content": "from pyBN.learning.structure.hybrid.mmpc import *" }, { "path": "pyBN/learning/structure/hybrid/mmhc.py", "content": "\"\"\"\nMax-Min Hill Climbing Algorithm\n\nReferences\n----------\n[1] Tsamardinos, et al. \"The max-min hill-climbing Bayesian\nnetwork structure learning algorithm.\"\n\n\"\"\"\nfrom pyBN.structure_learn.hybrid.mmpc import mmpc\n\ndef mmhc(data, alpha=0.05, metric='AIC', max_iter=100, method='hc'):\n\t\"\"\"\n\tMax-Min Hill Climbing Algorithm for\n\tlearning a Bayesian Network structure\n\tfrom data.\n\n\tArguments\n\t---------\n\t*data* : a numpy ndarray\n\n\t*alpha* : a float\n\t\tProbability of Type II Error for\n\t\tindependence tests.\n\n\t*metric* : a string\n\t\t*metric* : a string\n\t\tWhich score metric to use.\n\t\tOptions:\n\t\t\t- 'AIC'\n\t\t\t- 'BIC'\n\t\t\t- 'LL' (log-likelihood)\n\n\t*method* : a string\n\t\tThe type of hill-climbing algorithm to run\n\t\tOPTIONS:\n\t\t\t- 'hc' : normal hill-climbing\n\t\t\t- 'rr' : hill-climbing with random restarts\n\t\t\t- 'tabu' : tabu hill-climbing\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object\n\n\t\"\"\"\n\t# GET EDGE RESTRICTIONS FROM MMPC\n\tPC_dict = mmpc(data)\t\n\trestriction = []\n\tfor y, pc in PC_dict.items():\n\t\tfor x in pc:\n\t\t\trestriction.append((y,x))\n\n\t# RUN HILL-CLIMBING WITH EDGE RESTRICTIONS\n\tif method == 'tabu':\n\t\tbn = tabu(data=data, metric=metric, max_iter=max_iter, restriction=restriction)\t\t\n\telif method == 'rr':\n\t\tbn = hill_climbing_rr(data=data, metric=metric, max_iter=max_iter, restriction=restriction)\n\telse:\n\t\tbn = hill_climbing(data=data, metric=metric, max_iter=max_iter, restriction=restriction)\n\n\treturn bn\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/hybrid/mmpc.py", "content": "\"\"\"\nMax-Min Parents and Children Algorithm\nfor determining the Parent-Children set\nfor all nodes. This returns an unoriented\ngraph that then must be oriented using\nsome set of rules or other orientation\nalgorithm.\n\nUsing a version of hill climbing to orient\nthe result of MMPC results in the MMHC -\nMax-Min Hill Climbing algorithm. In other\nwords, MMPC can be viewed as a subroutine\nof MMHC.\n\nReferences\n----------\n[1] Tsamardinos, et al. \"The max-min hill-climbing Bayesian\nnetwork structure learning algorithm.\"\n\n\"\"\"\n\n\ndef mmpc(data, alpha=0.05):\n\t\"\"\"\n\tMax-Min Parents and Children Algorithm\n\tfor determining the Parent-Children set\n\tfor all nodes. This returns an unoriented\n\tgraph that then must be oriented using\n\tsome set of rules or other orientation\n\talgorithm.\n\n\tArguments\n\t---------\n\t*data* : a numpy ndarray\n\n\t*alpha* : a float\n\t\tProbability of Type II Error for\n\t\tindependence tests\n\n\tReturns\n\t-------\n\t*CPC_dict* : a dictionary, where\n\t\tkey = rv, value = list of rv's children AND parents\n\n\tNotes\n\t-----\n\t\"\"\"\n\n\tnrow = data.shape[0]\n\tncol = data.shape[1]\n\t\n\tnodes = range(ncol)\n\n\tCPC_dict = dict([(n,[]) for n in nodes]) # rv -> children of rvs\n\tvalue_dict = dict([(n, np.unique(data[:,i])) for i,n in enumerate(nodes)])\n\n\t# LEARN PARENT-CHILD SET FOR EACH NODE\n\tfor T in nodes:\n\t\t\n\t\t# GROW PHASE\n\t\tCPC = []\n\t\tchanged=True\n\t\twhile changed:\n\t\t\tchanged=False\n\t\t\t# MAX-MIN HEURISTIC\n\t\t\tmin_assoc = 1e9\n\t\t\tmin_node = None\n\t\t\tfor x in nodes:\n\t\t\t\tif x!=T:\n\t\t\t\t\tcols = (x,T) + tuple(CPC)\n\t\t\t\t\tpval = mi_test(data[:,cols], test=True)\n\t\t\t\tif pval < min_assoc:\n\t\t\t\t\tmin_assoc = pval\n\t\t\t\t\tmin_node = x\n\t\t\t# only add node if it's small but still dependent\n\t\t\tif min_assoc > alpha:\n\t\t\t\tCPC.append(min_node)\n\t\t\t\tchanged=True\n\n\t\t# SHRINK PHASE\n\t\tfor X in CPC:\n\t\t\tcpc = [c for c in CPC if X!=c]\n\t\t\tfor i in len(cpc):\n\t\t\t\tfor S in itertools.combinations(cpc,i):\n\t\t\t\t\tcols = (T,X) + S\n\t\t\t\t\tpval = mi_test(data[:,cols])\n\t\t\t\t\t# if I(X,T | S) = TRUE\n\t\t\t\t\tif pval_xy_z > alpha:\n\t\t\t\t\t\tCPC.remove(X)\n\n\t\t# ADD CPC TO CPC_dict\n\t\tCPC_dict[T] = CPC\n\n\t# REMOVE FALSE POSITIVES\n\tfor T in nodes:\n\t\tfor X in CPC_dict[T]:\n\t\t\t# If X is in CPC[T] but T is not in CPC[X], remove X.\n\t\t\tif T not in CPC_dict[X]:\n\t\t\t\tCPC_dict[T].remove(X)\n\n\treturn CPC_dict\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/mdbn.py", "content": "\"\"\"\nLearn a Multi-Dimensional Bayesian Network Classifier\nfrom data, and use it to predict a vector of class\nlabels from a vector of feature values.\n\nAn 'MBC' consists of two subgraphs:\n\t- G_c : the class subgraph,\n\t\twhich includes edges only between class variables\n\t- G_x : the feature subgraph,\n\t\twhich includes edges only between feature variables\n\nAdditionally, there is a collection of \"bridge\" edges:\t\n\t- E_cx : the class-feature bridge,\n\t\twhich includes the \"bridge\" edges between class and\n\t\tfeature variables.\n\nIn an MBC, both the class sugraph and the feature subgraph can\nhave their own form of BN structure - e.g. empty, directed tree,\nforest of trees, polytrees, or general DAGs. These 5 types of\nnetwork structures over two subgraphs means that there are 25\ntypes of MBC structures.\n\nTo learn MBC's from data, the algorithm typically proceeds by\nfirst learning G_c and G_x seperately using whatever structure\nlearning algorithm the user chooses, and then looks to optimize\nthe selection of \"bridge\" edges through further optimization\nprocedures.\n\nTo actually perform classification using an MBC, the traditional\nMost Probable Explanation (MAP inference) procedure is employed.\nThe result is a vector of class predictions, which can then be\ncompared with the ground truth class labels to measure accuracy,\nand so on.\n\n\nReferences\n----------\n[1] Bielza, C., Li, G., Larranaga, P. \"Multi-dimensional\nclassification with Bayesian networks.\"\n[2] de Waal, P., van der Gaag, L. \"Inference and Learning in\nMulti-dimensional Bayesian Network Classifiers.\"\n[3] van der Gaag, L., de Waal, P. \"Multi-dimensional Bayesian\nNetwork Classifiers.\"\n\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nfrom pyBN.learning.structure.score.hill_climbing import hc\nfrom pyBN.learning.structure.score.random_restarts import hc_rr\nfrom pyBN.learning.structure.score.tabu import tabu\n\nfrom pyBN.classes.bayesnet import BayesNet\n\n\ndef bridge(c_bn, f_bn, data):\n\t\"\"\"\n\tMake a Multi-Dimensional Bayesian Network by\n\tbridging two Bayesian network structures. This happens by\n\tplacing edges from c_bn -> f_bn using a heuristic \n\toptimization procedure.\n\n\tThis can be used to create a Multi-Dimensional Bayesian\n\tNetwork classifier from two already-learned Bayesian networks -\n\tone of which is a BN containing all the class variables, the other\n\tcontaining all the feature variables.\n\n\tArguments\n\t---------\n\t*c_bn* : a BayesNet object with known structure\n\n\t*f_bn* : a BayesNet object with known structure.\n\n\tReturns\n\t-------\n\t*m_bn* : a merged/bridge BayesNet object,\n\t\twhose structure contains *c_bn*, *f_bn*, and some bridge\n\t\tedges between them.\n\t\"\"\"\n\trestrict = []\n\tfor u in c_bn:\n\t\tfor v in f_bn:\n\t\t\trestrict.append((u,v)) # only allow edges from c_bn -> f_bn\n\n\tbridge_bn = hc_rr(data, restriction=restrict)\n\n\tm_bn = bridge_bn.E\n\tm_bn.update(c_bn.E)\n\tm_bn.update(f_bn.E)\n\n\tmbc_bn = BayesNet(E=m_bn)\n\ndef mdbn(data, f_cols, c_cols, f_struct='DAG', c_struct='DAG', wrapper=False):\n\t\"\"\"\n\tLearn the structure of a Multi-Dimensional Bayesian Network - \n\ttypically used for Classification.\n\n\tNote that this structure does not have to be used for classification,\n\tsince it simply returns a Bayesian Network - albeit with a more\n\tunqiue structure than tradiitonally found. If there are any other\n\tapplications of this bipartite-like BN structure learning, this\n\talgorithm can certainly be used.\n\n\t\"\"\"\n\tf_data = data[:,f_cols]\n\tc_data = data[:,c_cols]\n\n\tf_bn = hc_rr(f_data)\n\tc_bn = hc_rr(c_data)\n\n\tmbc_bn = bridge(c_bn=c_bn, f_bn=f_bn, data=data)\n\n\treturn mbc_bn\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/naive/TAN.py", "content": "\"\"\"\n**************\nTree Augmented \nNaive Bayes\n**************\n\nTAN is considered to be quite useful\nas a Bayesian network classifier.\n\n\"\"\"\n\n\ndef TAN(data, target):\n\t\"\"\"\n\tLearn a Tree-Augmented Naive Bayes structure\n\tfrom data.\n\n\tThe algorithm from Friedman's paper\n\tproceeds as follows:\n\n\t- Learn a tree structure\n\t\t- I will use chow-liu algorithm\n\t- ADD a class label C to the graph, and an edge \n\tfrom C to each node in the graph.\n\t\"\"\"\n\treduced_data = data[:,:-target,:]\n\ttree, value_dict = chow_liu(reduce_data,edges_only=True)\n\ttree[target] = [tree.keys()]\n\n\tbn = BayesNet(tree, value_dict)\n\treturn vn\n" }, { "path": "pyBN/learning/structure/naive/__init__.py", "content": "from pyBN.learning.structure.naive.naive_bayes import *\nfrom pyBN.learning.structure.naive.TAN import *" }, { "path": "pyBN/learning/structure/naive/naive_bayes.py", "content": "\"\"\"\n***********\nNaive Bayes\n***********\n\nLearn both the structure AND parameters\nof a Naive Bayes Model from data\n\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport numpy as np\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.learning.parameter.mle import mle_estimator\nfrom pyBN.learning.parameter.bayes import bayes_estimator\n\ndef naive_bayes(data, target, estimator='mle'):\n\t\"\"\"\n\tLearn naive bayes model from data.\n\n\tThe Naive Bayes model is a Tree-based\n\tmodel where all random variables have\n\tthe same parent (the \"target\" variable).\n\tFrom a probabilistic standpoint, the implication\n\tof this model is that all random variables \n\t(i.e. features) are assumed to be\n\tconditionally independent of any other random variable,\n\tconditioned upon the single parent (target) variable.\n\n\tIt turns out that this model performs quite well\n\tas a classifier, and can be used as such. Moreover,\n\tthis model is quite fast and simple to learn/create\n\tfrom a computational standpoint.\n\n\tNote that this function not only learns the structure,\n\tbut ALSO learns the parameters.\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\n\t*target* : an integer\n\t\tThe target variable column in *data*\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object,\n\t\twith the structure instantiated.\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\n\t\"\"\"\t\n\tvalue_dict = dict(zip(range(data.shape[1]),\n\t\t[list(np.unique(col)) for col in data.T]))\n\n\tedge_dict = {target:[v for v in value_dict if v!=target]}\n\tedge_dict.update(dict([(rv,[]) for rv in value_dict if rv!=target]))\n\n\tbn = BayesNet(edge_dict,value_dict)\n\tif estimator == 'bayes':\n\t\tbayes_estimator(bn,data)\n\telse:\n\t\tmle_estimator(bn,data)\n\treturn bn\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/score/__init__.py", "content": "from pyBN.learning.structure.score.bayes_scores import *\nfrom pyBN.learning.structure.score.hill_climbing import *\nfrom pyBN.learning.structure.score.random_restarts import *\nfrom pyBN.learning.structure.score.info_scores import *\nfrom pyBN.learning.structure.score.tabu import *" }, { "path": "pyBN/learning/structure/score/bayes_scores.py", "content": "\"\"\"\nVarious Bayesian scoring metrics for evaluating\nthe fitness of a BN structure during score-based\nstructure learning.\n\nBayesian scoring functions:\n\tBD (Bayesian Dirichlet) (1995)\n\tBDe (\"'e'\" for likelihood-equivalence) (1995)\n\tBDeu (\"'u'\" for uniform joint distribution) (1991)\n\tK2 (1992)\n\nReferences\n----------\n[1] Daly, et al. Learning Bayesian Network Equivalence Classes \nwith Ant Colony Optimization.\n\n\"\"\"\nfrom __future__ import division\n\nimport numpy as np\nfrom scipy.special import gamma, gammaln\nfrom pyBN.learning.parameter.mle import mle_estimator, mle_fast\nfrom pyBN.classes.empiricaldistribution import EmpiricalDistribution\n\n\ndef BDe(bn, data, ess=1, ed=None): \n\t\"\"\"\n\tUnique Bayesian score with the property that I-equivalent\n\tnetworks have the same score.\n\n\tAs Data Rows -> infinity, BDe score converges to the BIC score.\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\t\tNeeded to get the parent relationships, etc.\n\t\n\t*data* : a numpy ndarray\n\t\tNeeded to learn the empirical distribuion\n\t\n\t*ess* : an integer\n\t\tEquivalent sample size\n\n\t*ed* : an EmpiricalDistribution object\n\t\tUsed to cache multiple lookups in structure learning.\n\n\tNotes\n\t-----\n\t*a_ijk* : a vector\n\t\tThe number of times where x_i=k | parents(x_i)=j -> i.e. the mle counts\n\n\t*a_ij* : a vector summed over k's in a_ijk\n\n\t*n_ijk* : a vector prior (sample size or calculation)\n\t\t\"ess\" for BDe metric\n\n\t*n_ij* : a vector prior summed over k's in n_ijk\n\t\n\t\"\"\"\n\tcounts_dict = mle_fast(bn, data, counts=True, np=True)\n\ta_ijk = []\n\tbdeu = 1\n\tfor rv, value in counts_dict.items():\n\t\tnijk = value['cpt']\n\t\tnijk_prime = ess\n\t\tk2 *= gamma(nijk+nijk_prime)/gamma(nijk_prime)\n\t\tnij_prime = nijk_prime*(len(cpt)/bn.card(rv))\n\t\tnij = np.mean(nijk.reshape(-1, bn.card(rv)), axis=1) # sum along parents\n\t\tk2 *= gamma(nij_prime) / gamma(nij+nij_prime)\n\n\treturn bdeu\n\n\ndef BDeu(bn, data, ess=1, ed=None):\n\t\"\"\"\n\tUnique Bayesian score with the property that I-equivalent\n\tnetworks have the same score.\n\n\tAs Data Rows -> infinity, BDe score converges to the BIC score.\n\n\tNijk_prime = ess/len(bn.cpt(rv))\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\t\tNeeded to get the parent relationships, etc.\n\t\n\t*data* : a numpy ndarray\n\t\tNeeded to learn the empirical distribuion\n\t\n\t*ess* : an integer\n\t\tEquivalent sample size\n\n\t*ed* : an EmpiricalDistribution object\n\t\tUsed to cache multiple lookups in structure learning.\n\n\tNotes\n\t-----\n\t*a_ijk* : a vector\n\t\tThe number of times where x_i=k | parents(x_i)=j -> i.e. the mle counts\n\n\t*a_ij* : a vector summed over k's in a_ijk\n\n\t*n_ijk* : a vector prior (sample size or calculation)\n\t\tess/(card(x_i)*len(cpt(x_i)/card(x_i))) for x_i for BDe metric\n\n\t*n_ij* : a vector prior summed over k's in n_ijk\n\t\n\t\"\"\"\n\tcounts_dict = mle_fast(bn, data, counts=True, np=True)\n\ta_ijk = []\n\tbdeu = 1\n\tfor rv, value in counts_dict.items():\n\t\tnijk = value['cpt']\n\t\tnijk_prime = ess / len(nijk)\n\t\tk2 *= gamma(nijk+nijk_prime)/gamma(nijk_prime)\n\t\tnij_prime = nijk_prime*(len(cpt)/bn.card(rv))\n\t\tnij = np.mean(nijk.reshape(-1, bn.card(rv)), axis=1) # sum along parents\n\t\tk2 *= gamma(nij_prime) / gamma(nij+nij_prime)\n\n\treturn bdeu\n\ndef K2(bn, data, ed=None):\n\t\"\"\"\n\tK2 is bayesian posterior probability of structure given the data,\n\twhere N'ijk = 1.\n\t\"\"\"\n\tcounts_dict = mle_fast(bn, data, counts=True, np=True)\n\ta_ijk = []\n\tk2 = 1\n\tfor rv, value in counts_dict.items():\n\t\tnijk = value['cpt']\n\t\tnijk_prime = 1\n\t\tk2 *= gamma(nijk+nijk_prime)/gamma(nijk_prime)\n\t\tnij_prime = nijk_prime*(len(cpt)/bn.card(rv))\n\t\tnij = np.mean(nijk.reshape(-1, bn.card(rv)), axis=1) # sum along parents\n\t\tk2 *= gamma(nij_prime) / gamma(nij+nij_prime)\n\n\treturn k2\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/score/hill_climbing.py", "content": "\"\"\"\n**********************\nGreedy Hill-Climbing\nfor Structure Learning\n**********************\n\nCode for Searching through the space of\npossible Bayesian Network structures.\n\nVarious optimization procedures are employed,\nfrom greedy search to simulated annealing, and \nso on - mostly using scipy.optimize.\n\nLocal search - possible moves:\n- Add edge\n- Delete edge\n- Invert edge\n\nStrategies to improve Greedy Hill-Climbing:\n- Random Restarts\n\t- when we get stuck, take some number of\n\trandom steps and then start climbing again.\n- Tabu List\n\t- keep a list of the K steps most recently taken,\n\tand say that the search cannt reverse (undo) any\n\tof these steps.\n\"\"\"\n\n#from scipy.optimize import *\nimport numpy as np\n#from heapq import *\nfrom copy import copy, deepcopy\n\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.learning.parameter.mle import mle_estimator\nfrom pyBN.learning.parameter.bayes import bayes_estimator\nfrom pyBN.learning.structure.score.info_scores import info_score\nfrom pyBN.utils.independence_tests import mutual_information\nfrom pyBN.utils.graph import would_cause_cycle\n\n\ndef hc(data, metric='AIC', max_iter=100, debug=False, restriction=None):\n\t\"\"\"\n\tGreedy Hill Climbing search proceeds by choosing the move\n\twhich maximizes the increase in fitness of the\n\tnetwork at the current step. It continues until\n\tit reaches a point where there does not exist any\n\tfeasible single move that increases the network fitness.\n\n\tIt is called \"greedy\" because it simply does what is\n\tbest at the current iteration only, and thus does not\n\tlook ahead to what may be better later on in the search.\n\n\tFor computational saving, a Priority Queue (python's heapq) \n\tcan be used\tto maintain the best operators and reduce the\n\tcomplexity of picking the best operator from O(n^2) to O(nlogn).\n\tThis works by maintaining the heapq of operators sorted by their\n\tdelta score, and each time a move is made, we only have to recompute\n\tthe O(n) delta-scores which were affected by the move. The rest of\n\tthe operator delta-scores are not affected.\n\n\tFor additional computational efficiency, we can cache the\n\tsufficient statistics for various families of distributions - \n\ttherefore, computing the mutual information for a given family\n\tonly needs to happen once.\n\n\tThe possible moves are the following:\n\t\t- add edge\n\t\t- delete edge\n\t\t- invert edge\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\t\tThe data from which the Bayesian network\n\t\tstructure will be learned.\n\n\t*metric* : a string\n\t\tWhich score metric to use.\n\t\tOptions:\n\t\t\t- AIC\n\t\t\t- BIC / MDL\n\t\t\t- LL (log-likelihood)\n\n\t*max_iter* : an integer\n\t\tThe maximum number of iterations of the\n\t\thill-climbing algorithm to run. Note that\n\t\tthe algorithm will terminate on its own if no\n\t\timprovement is made in a given iteration.\n\n\t*debug* : boolean\n\t\tWhether to print(the scores/moves of the)\n\t\talgorithm as its happening.\n\n\t*restriction* : a list of 2-tuples\n\t\tFor MMHC algorithm, the list of allowable edge additions.\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object\n\n\t\"\"\"\n\tnrow = data.shape[0]\n\tncol = data.shape[1]\n\t\n\tnames = range(ncol)\n\n\t# INITIALIZE NETWORK W/ NO EDGES\n\t# maintain children and parents dict for fast lookups\n\tc_dict = dict([(n,[]) for n in names])\n\tp_dict = dict([(n,[]) for n in names])\n\t\n\t# COMPUTE INITIAL LIKELIHOOD SCORE\t\n\tvalue_dict = dict([(n, np.unique(data[:,i])) for i,n in enumerate(names)])\n\tbn = BayesNet(c_dict)\n\tmle_estimator(bn, data)\n\tmax_score = info_score(bn, nrow, metric)\n\n\t# CREATE EMPIRICAL DISTRIBUTION OBJECT FOR CACHING\n\t#ED = EmpiricalDistribution(data,names)\n\n\t\n\n\t_iter = 0\n\timprovement = True\n\n\twhile improvement:\n\t\timprovement = False\n\t\tmax_delta = 0\n\n\t\tif debug:\n\t\t\tprint('ITERATION: ' , _iter)\n\n\t\t### TEST ARC ADDITIONS ###\n\t\tfor u in bn.nodes():\n\t\t\tfor v in bn.nodes():\n\t\t\t\tif v not in c_dict[u] and u!=v and not would_cause_cycle(c_dict, u, v):\n\t\t\t\t\t# FOR MMHC ALGORITHM -> Edge Restrictions\n\t\t\t\t\tif restriction is None or (u,v) in restriction:\n\t\t\t\t\t\t# SCORE FOR 'V' -> gaining a parent\n\t\t\t\t\t\told_cols = (v,) + tuple(p_dict[v]) # without 'u' as parent\n\t\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\t\tnew_cols = old_cols + (u,) # with'u' as parent\n\t\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\t\tdelta_score = nrow * (mi_old - mi_new)\n\n\t\t\t\t\t\tif delta_score > max_delta:\n\t\t\t\t\t\t\t#if debug:\n\t\t\t\t\t\t\t#\tprint('Improved Arc Addition: ' , (u,v))\n\t\t\t\t\t\t\t#\tprint('Delta Score: ' , delta_score)\n\t\t\t\t\t\t\tmax_delta = delta_score\n\t\t\t\t\t\t\tmax_operation = 'Addition'\n\t\t\t\t\t\t\tmax_arc = (u,v)\n\n\t\t### TEST ARC DELETIONS ###\n\t\tfor u in bn.nodes():\n\t\t\tfor v in bn.nodes():\n\t\t\t\tif v in c_dict[u]:\n\t\t\t\t\t# SCORE FOR 'V' -> losing a parent\n\t\t\t\t\told_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent\n\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\tnew_cols = tuple([i for i in old_cols if i != u]) # without 'u' as parent\n\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\tdelta_score = nrow * (mi_old - mi_new)\n\n\t\t\t\t\tif delta_score > max_delta:\n\t\t\t\t\t\t#if debug:\n\t\t\t\t\t\t#\tprint('Improved Arc Deletion: ' , (u,v))\n\t\t\t\t\t\t#\tprint('Delta Score: ' , delta_score)\n\t\t\t\t\t\tmax_delta = delta_score\n\t\t\t\t\t\tmax_operation = 'Deletion'\n\t\t\t\t\t\tmax_arc = (u,v)\n\n\t\t### TEST ARC REVERSALS ###\n\t\tfor u in bn.nodes():\n\t\t\tfor v in bn.nodes():\n\t\t\t\tif v in c_dict[u] and not would_cause_cycle(c_dict,v,u, reverse=True):\n\t\t\t\t\t# SCORE FOR 'U' -> gaining 'v' as parent\n\t\t\t\t\told_cols = (u,) + tuple(p_dict[v]) # without 'v' as parent\n\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\tnew_cols = old_cols + (v,) # with 'v' as parent\n\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\tdelta1 = nrow * (mi_old - mi_new)\n\t\t\t\t\t# SCORE FOR 'V' -> losing 'u' as parent\n\t\t\t\t\told_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent\n\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\tnew_cols = tuple([u for i in old_cols if i != u]) # without 'u' as parent\n\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\tdelta2 = nrow * (mi_old - mi_new)\n\t\t\t\t\t# COMBINED DELTA-SCORES\n\t\t\t\t\tdelta_score = delta1 + delta2\n\n\t\t\t\t\tif delta_score > max_delta:\n\t\t\t\t\t\t#if debug:\n\t\t\t\t\t\t#\tprint('Improved Arc Reversal: ' , (u,v))\n\t\t\t\t\t\t#\tprint('Delta Score: ' , delta_score)\n\t\t\t\t\t\tmax_delta = delta_score\n\t\t\t\t\t\tmax_operation = 'Reversal'\n\t\t\t\t\t\tmax_arc = (u,v)\n\n\n\t\t### DETERMINE IF/WHERE IMPROVEMENT WAS MADE ###\n\t\tif max_delta != 0:\n\t\t\timprovement = True\n\t\t\tu,v = max_arc\n\t\t\tif max_operation == 'Addition':\n\t\t\t\tif debug:\n\t\t\t\t\tprint('ADDING: ' , max_arc , '\\n')\n\t\t\t\tc_dict[u].append(v)\n\t\t\t\tp_dict[v].append(u)\n\t\t\telif max_operation == 'Deletion':\n\t\t\t\tif debug:\n\t\t\t\t\tprint('DELETING: ' , max_arc , '\\n')\n\t\t\t\tc_dict[u].remove(v)\n\t\t\t\tp_dict[v].remove(u)\n\t\t\telif max_operation == 'Reversal':\n\t\t\t\tif debug:\n\t\t\t\t\tprint('REVERSING: ' , max_arc, '\\n')\n\t\t\t\t\tc_dict[u].remove(v)\n\t\t\t\t\tp_dict[v].remove(u)\n\t\t\t\t\tc_dict[v].append(u)\n\t\t\t\t\tp_dict[u].append(v)\n\t\telse:\n\t\t\tif debug:\n\t\t\t\tprint('No Improvement on Iter: ' , _iter)\n\n\t\t### TEST FOR MAX ITERATION ###\n\t\t_iter += 1\n\t\tif _iter > max_iter:\n\t\t\tif debug:\n\t\t\t\tprint('Max Iteration Reached')\n\t\t\tbreak\n\n\t\n\tbn = BayesNet(c_dict)\n\n\treturn bn\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/score/info_scores.py", "content": "\"\"\"\nVarious metrics for evaluating\nscore-based search for Bayesian\nNetworks structure learning.\n\nGenerally, score-based structure learning\ninvolves finding the structure which maximizes\nsome function of the likelihood of the data, given\nthe structure and parameters of the learned BN.\n\nIt is important to take advantage of the decomposability\nof the scores. That is, if you add/delete/reverse an edge,\nthen you only need to re-calculate the score based on that\nlocal change (usually just the child node's CPT) to get the\ndifference from the original graph. \n\nHere are a few which are (or can be) implemented:\n\nInformation-theoretic scoring functions:\n\tLL (Log-likelihood) (1912-22)\n\tMDL/BIC (Minimum description length/Bayesian Information Criterion) (1978)\n\tAIC (Akaike Information Criterion) (1974)\n\tNML (Normalized Minimum Likelihood) (2008)\n\tMIT (Mutual Information Tests) (2006)\n\nReferences\n----------\n[1]\nhttp://www.lx.it.pt/~asmc/pub/publications/09-TA/09-c-ta.pdf\n\n\"\"\"\nfrom __future__ import division\nimport numpy as np\n\nfrom pyBN.utils.independence_tests import mutual_information, entropy\n\n\ndef info_score(bn, nrow, metric='BIC'):\n\tif metric.upper() == 'LL':\n\t\tscore = log_likelihood(bn, nrow)\n\telif metric.upper() == 'BIC':\n\t\tscore = BIC(bn, nrow)\n\telif metric.upper() == 'AIC':\n\t\tscore = AIC(bn, nrow)\n\telse:\n\t\tscore = BIC(bn, nrow)\n\n\treturn score\n\t\n\n##### INFORMATION-THEORETIC SCORING FUNCTIONS #####\n\ndef log_likelihood(bn, nrow):\n\t\"\"\"\n\tDetermining log-likelihood of the parameters\n\tof a Bayesian Network. This is a quite simple\n\tscore/calculation, but it is useful as a straight-forward\n\tstructure learning score.\n\n\tSemantically, this can be considered as the evaluation\n\tof the log-likelihood of the data, given the structure\n\tand parameters of the BN:\n\t\t- log( P( D | Theta_G, G ) )\n\t\twhere Theta_G are the parameters and G is the structure.\n\n\tHowever, for computational reasons it is best to take\n\tadvantage of the decomposability of the log-likelihood score.\n\t\n\tAs an example, if you add an edge from A->B, then you simply\n\tneed to calculate LOG(P'(B|A)) - Log(P(B)), and if the value\n\tis positive then the edge improves the fitness score and should\n\ttherefore be included. \n\n\tEven more, you can expand and manipulate terms to calculate the\n\tdifference between the new graph and the original graph as follows:\n\t\tScore(G') - Score(G) = M * I(X,Y),\n\t\twhere M is the number of data points and I(X,Y) is\n\t\tthe marginal mutual information calculated using\n\t\tthe empirical distribution over the data.\n\n\tIn general, the likelihood score decomposes as follows:\n\t\tLL(D | Theta_G, G) = \n\t\t\tM * Sum over Variables ( I ( X , Parents(X) ) ) - \n\t\t\tM * Sum over Variables ( H( X ) ),\n\t\twhere 'I' is mutual information and 'H' is the entropy,\n\t\tand M is the number of data points\n\n\tMoreover, it is clear to see that H(X) is independent of the choice\n\tof graph structure (G). Thus, we must only determine the difference\n\tin the mutual information score of the original graph which had a given\n\tnode and its original parents, and the new graph which has a given node\n\tand new parents.\n\n\tNOTE: This assumes the parameters have already\n\tbeen learned for the BN's given structure.\n\n\tLL = LL - f(N)*|B|, where f(N) = 0\n\n\tArguments\n\t---------\n\t*bn* : a BayesNet object\n\t\tMust have both structure and parameters\n\t\tinstantiated.\n\tNotes\n\t-----\n\tNROW = data.shape[0]\n\tmi_score = 0\n\tent_score = 0\n\tfor rv in bn.nodes():\n\t\tcols = tuple([bn.V.index(rv)].extend([bn.V.index(p) for p in bn.parents(rv)]))\n\t\tmi_score += mutual_information(data[:,cols])\n\t\tent_score += entropy(data[:,bn.V.index(rv)])\n\t\n\treturn NROW * (mi_score - ent_score)\n\t\"\"\"\n\treturn np.sum(np.log(nrow * (bn.flat_cpt()+1e-7)))\n\ndef MDL(bn, nrow):\n\t\"\"\"\n\tMinimum Description Length score - it is\n\tequivalent to BIC\n\t\"\"\"\n\treturn BIC(bn, nrow)\n\ndef BIC(bn, nrow):\n\t\"\"\"\n\tBayesian Information Criterion.\n\n\tBIC = LL - f(N)*|B|, where f(N) = log(N)/2\n\n\t\"\"\"\n\tlog_score = log_likelihood(bn, nrow)\n\tpenalty = 0.5 * bn.num_params() * np.log(max(bn.num_edges(),1))\n\treturn log_score - penalty\n\ndef AIC(bn, nrow):\n\t\"\"\"\n\tAikaike Information Criterion\n\n\tAIC = LL - f(N)*|B|, where f(N) = 1\n\n\t\"\"\"\n\tlog_score = log_likelihood(bn, nrow)\n\tpenalty = len(bn.flat_cpt())\n\treturn log_score - penalty\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/score/random_restarts.py", "content": "\"\"\"\n*******************\nHill Climbing with\nRandom Restarts for\nStructure Learning\n*******************\n\nHill Climbing with random restarts proceeds\nas the traditional hill climbing algorithm\ndoes, except that when the hill climbing algorithm\nreaches a local maxima or a plateau (or the actual\nglobal maximum), it makes a K random moves - arc\nadditions/deletions/reversals - and continues the\nalgorithm. \n\nThe hyper-parameters in this algorithm are the number\nof random moves to make per random restart (m), and\nthe number of random restarts to make (r). It is\nintuitive that the algorithm is more likely to get out\nof local maxima when m is large and r is large, although\nthere it depends on how isolated the local maxima is and\nalso depends on how long you want the algorithm to run.\n\n\"\"\"\n#from scipy.optimize import *\nimport numpy as np\n#from heapq import *\nfrom copy import copy, deepcopy\n\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.learning.parameter.mle import mle_estimator\nfrom pyBN.learning.parameter.bayes import bayes_estimator\nfrom pyBN.learning.structure.score.info_scores import info_score\nfrom pyBN.utils.independence_tests import mutual_information\nfrom pyBN.utils.graph import would_cause_cycle\n\n\ndef hc_rr(data, M=5, R=3, metric='AIC', max_iter=100, debug=False, restriction=None):\n\t\"\"\"\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\t\tThe data from which the Bayesian network\n\t\tstructure will be learned.\n\n\t*metric* : a string\n\t\tWhich score metric to use.\n\t\tOptions:\n\t\t\t- AIC\n\t\t\t- BIC / MDL\n\t\t\t- LL (log-likelihood)\n\n\t*max_iter* : an integer\n\t\tThe maximum number of iterations of the\n\t\thill-climbing algorithm to run. Note that\n\t\tthe algorithm will terminate on its own if no\n\t\timprovement is made in a given iteration.\n\n\t*debug* : boolean\n\t\tWhether to print(the scores/moves of the)\n\t\talgorithm as its happening.\n\n\t*restriction* : a list of 2-tuples\n\t\tFor MMHC algorithm, the list of allowable edge additions.\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object\n\t\"\"\"\n\tnrow = data.shape[0]\n\tncol = data.shape[1]\n\t\n\tnames = range(ncol)\n\n\t# INITIALIZE NETWORK W/ NO EDGES\n\t# maintain children and parents dict for fast lookups\n\tc_dict = dict([(n,[]) for n in names])\n\tp_dict = dict([(n,[]) for n in names])\n\t\n\t# COMPUTE INITIAL LIKELIHOOD SCORE\t\n\tvalue_dict = dict([(n, np.unique(data[:,i])) for i,n in enumerate(names)])\n\tbn = BayesNet(c_dict)\n\tmle_estimator(bn, data)\n\tmax_score = info_score(bn, nrow, metric)\n\t\n\n\t_iter = 0\n\timprovement = True\n\t_restarts = 0\n\n\twhile improvement:\n\t\timprovement = False\n\t\tmax_delta = 0\n\n\t\tif debug:\n\t\t\tprint('ITERATION: ' , _iter)\n\n\t\t### TEST ARC ADDITIONS ###\n\t\tfor u in bn.nodes():\n\t\t\tfor v in bn.nodes():\n\t\t\t\tif v not in c_dict[u] and u!=v and not would_cause_cycle(c_dict, u, v):\n\t\t\t\t\t# FOR MMHC ALGORITHM -> Edge Restrictions\n\t\t\t\t\tif restriction is None or (u,v) in restriction:\n\t\t\t\t\t\t# SCORE FOR 'V' -> gaining a parent\n\t\t\t\t\t\told_cols = (v,) + tuple(p_dict[v]) # without 'u' as parent\n\t\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\t\tnew_cols = old_cols + (u,) # with'u' as parent\n\t\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\t\tdelta_score = nrow * (mi_old - mi_new)\n\n\t\t\t\t\t\tif delta_score > max_delta:\n\t\t\t\t\t\t\tif debug:\n\t\t\t\t\t\t\t\tprint('Improved Arc Addition: ' , (u,v))\n\t\t\t\t\t\t\t\tprint('Delta Score: ' , delta_score)\n\t\t\t\t\t\t\tmax_delta = delta_score\n\t\t\t\t\t\t\tmax_operation = 'Addition'\n\t\t\t\t\t\t\tmax_arc = (u,v)\n\n\t\t### TEST ARC DELETIONS ###\n\t\tfor u in bn.nodes():\n\t\t\tfor v in bn.nodes():\n\t\t\t\tif v in c_dict[u]:\n\t\t\t\t\t# SCORE FOR 'V' -> losing a parent\n\t\t\t\t\told_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent\n\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\tnew_cols = tuple([i for i in old_cols if i != u]) # without 'u' as parent\n\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\tdelta_score = nrow * (mi_old - mi_new)\n\n\t\t\t\t\tif delta_score > max_delta:\n\t\t\t\t\t\tif debug:\n\t\t\t\t\t\t\tprint('Improved Arc Deletion: ' , (u,v))\n\t\t\t\t\t\t\tprint('Delta Score: ' , delta_score)\n\t\t\t\t\t\tmax_delta = delta_score\n\t\t\t\t\t\tmax_operation = 'Deletion'\n\t\t\t\t\t\tmax_arc = (u,v)\n\n\t\t### TEST ARC REVERSALS ###\n\t\tfor u in bn.nodes():\n\t\t\tfor v in bn.nodes():\n\t\t\t\tif v in c_dict[u] and not would_cause_cycle(c_dict,v,u, reverse=True):\n\t\t\t\t\t# SCORE FOR 'U' -> gaining 'v' as parent\n\t\t\t\t\told_cols = (u,) + tuple(p_dict[v]) # without 'v' as parent\n\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\tnew_cols = old_cols + (v,) # with 'v' as parent\n\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\tdelta1 = nrow * (mi_old - mi_new)\n\t\t\t\t\t# SCORE FOR 'V' -> losing 'u' as parent\n\t\t\t\t\told_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent\n\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\tnew_cols = tuple([u for i in old_cols if i != u]) # without 'u' as parent\n\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\tdelta2 = nrow * (mi_old - mi_new)\n\t\t\t\t\t# COMBINED DELTA-SCORES\n\t\t\t\t\tdelta_score = delta1 + delta2\n\n\t\t\t\t\tif delta_score > max_delta:\n\t\t\t\t\t\tif debug:\n\t\t\t\t\t\t\tprint('Improved Arc Reversal: ' , (u,v))\n\t\t\t\t\t\t\tprint('Delta Score: ' , delta_score)\n\t\t\t\t\t\tmax_delta = delta_score\n\t\t\t\t\t\tmax_operation = 'Reversal'\n\t\t\t\t\t\tmax_arc = (u,v)\n\n\n\t\t### DETERMINE IF/WHERE IMPROVEMENT WAS MADE ###\n\t\tif max_delta != 0:\n\t\t\timprovement = True\n\t\t\tu,v = max_arc\n\t\t\tif max_operation == 'Addition':\n\t\t\t\tif debug:\n\t\t\t\t\tprint('ADDING: ' , max_arc , '\\n')\n\t\t\t\tc_dict[u].append(v)\n\t\t\t\tp_dict[v].append(u)\n\t\t\telif max_operation == 'Deletion':\n\t\t\t\tif debug:\n\t\t\t\t\tprint('DELETING: ' , max_arc , '\\n')\n\t\t\t\tc_dict[u].remove(v)\n\t\t\t\tp_dict[v].remove(u)\n\t\t\telif max_operation == 'Reversal':\n\t\t\t\tif debug:\n\t\t\t\t\tprint('REVERSING: ' , max_arc, '\\n')\n\t\t\t\t\tc_dict[u].remove(v)\n\t\t\t\t\tp_dict[v].remove(u)\n\t\t\t\t\tc_dict[v].append(u)\n\t\t\t\t\tp_dict[u].append(v)\n\t\telse:\n\t\t\tif debug:\n\t\t\t\tprint('No Improvement on Iter: ' , _iter)\n\t\t\t#### RESTART WITH RANDOM MOVES ####\n\t\t\tif _restarts < R:\n\t\t\t\timprovement = True # make another pass of hill climbing\n\t\t\t\t_iter=0 # reset iterations\n\t\t\t\tif debug:\n\t\t\t\t\tprint('Restart - ' , _restarts)\n\t\t\t\t_restarts+=1\n\t\t\t\tfor _ in range(M):\n\t\t\t\t\t# 0 = Addition, 1 = Deletion, 2 = Reversal\n\t\t\t\t\toperation = np.random.choice([0,1,2])\n\t\t\t\t\tif operation == 0:\n\t\t\t\t\t\twhile True:\n\t\t\t\t\t\t\tu,v = np.random.choice(list(bn.nodes()), size=2, replace=False)\n\t\t\t\t\t\t\t# IF EDGE DOESN'T EXIST, ADD IT\n\t\t\t\t\t\t\tif u not in p_dict[v] and u!=v and not would_cause_cycle(c_dict,u,v):\n\t\t\t\t\t\t\t\tif debug:\n\t\t\t\t\t\t\t\t\tprint('RESTART - ADDING: ', (u,v))\n\t\t\t\t\t\t\t\tc_dict[u].append(v)\n\t\t\t\t\t\t\t\tp_dict[v].append(u)\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\telif operation == 1:\n\t\t\t\t\t\twhile True:\n\t\t\t\t\t\t\tu,v = np.random.choice(list(bn.nodes()), size=2, replace=False)\n\t\t\t\t\t\t\t# IF EDGE EXISTS, DELETE IT\n\t\t\t\t\t\t\tif u in p_dict[v]:\n\t\t\t\t\t\t\t\tif debug:\n\t\t\t\t\t\t\t\t\tprint('RESTART - DELETING: ', (u,v))\n\t\t\t\t\t\t\t\tc_dict[u].remove(v)\n\t\t\t\t\t\t\t\tp_dict[v].remove(u)\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\telif operation == 2:\n\t\t\t\t\t\twhile True:\n\t\t\t\t\t\t\tu,v = np.random.choice(list(bn.nodes()), size=2, replace=False)\n\t\t\t\t\t\t\t# IF EDGE EXISTS, REVERSE IT\n\t\t\t\t\t\t\tif u in p_dict[v] and not would_cause_cycle(c_dict,v,u, reverse=True):\n\t\t\t\t\t\t\t\tif debug:\n\t\t\t\t\t\t\t\t\tprint('RESTART - REVERSING: ', (u,v))\n\t\t\t\t\t\t\t\tc_dict[u].remove(v)\n\t\t\t\t\t\t\t\tp_dict[v].remove(u)\n\t\t\t\t\t\t\t\tc_dict[v].append(u)\n\t\t\t\t\t\t\t\tp_dict[u].append(v)\n\t\t\t\t\t\t\t\tbreak\n\n\t\t### TEST FOR MAX ITERATION ###\n\t\t_iter += 1\n\t\tif _iter > max_iter:\n\t\t\tif debug:\n\t\t\t\tprint('Max Iteration Reached')\n\t\t\tbreak\n\n\t\n\tbn = BayesNet(c_dict)\n\n\treturn bn\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/score/tabu.py", "content": "\"\"\"\n******************\nTabu Search for\nScore-based \nStructure Learning\n******************\n\nTabu search in this case proceeds by the traditional\nhill-climbing algorithm, with the additional feature\nthat it does not allow the algorithm to reverse any\nmoves that happened in the last K steps. The idea\nbehind tabu search is that it may give the algorithm\na better chance of escaping out of local maxima or\nplateaus. Still, it is a local search technique.\n\n\"\"\"\n\nimport numpy as np\nfrom copy import copy, deepcopy\n\nfrom pyBN.classes.bayesnet import BayesNet\nfrom pyBN.learning.parameter.mle import mle_estimator\nfrom pyBN.learning.parameter.bayes import bayes_estimator\nfrom pyBN.learning.structure.score.info_scores import info_score\nfrom pyBN.utils.independence_tests import mutual_information\nfrom pyBN.utils.graph import would_cause_cycle\n\n\ndef tabu(data, k=5, metric='AIC', max_iter=100, debug=False, restriction=None):\n\t\"\"\"\n\tTabu search for score-based structure learning.\n\n\tThe algorithm maintains a list called \"tabu_list\",\n\twhich consists of 3-tuples, where the first two\n\telements constitute the edge which is tabued, and\n\tthe third element is a string - either 'Addition',\n\t'Deletion', or 'Reversal' denoting the operation\n\tassociated with the edge.\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\t\tThe data from which the Bayesian network\n\t\tstructure will be learned.\n\n\t*metric* : a string\n\t\tWhich score metric to use.\n\t\tOptions:\n\t\t\t- AIC\n\t\t\t- BIC / MDL\n\t\t\t- LL (log-likelihood)\n\n\t*max_iter* : an integer\n\t\tThe maximum number of iterations of the\n\t\thill-climbing algorithm to run. Note that\n\t\tthe algorithm will terminate on its own if no\n\t\timprovement is made in a given iteration.\n\n\t*debug* : boolean\n\t\tWhether to print(the scores/moves of the)\n\t\talgorithm as its happening.\n\n\t*restriction* : a list of 2-tuples\n\t\tFor MMHC algorithm, the list of allowable edge additions.\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object\n\t\n\t\"\"\"\n\tnrow = data.shape[0]\n\tncol = data.shape[1]\n\t\n\tnames = range(ncol)\n\n\t# INITIALIZE NETWORK W/ NO EDGES\n\t# maintain children and parents dict for fast lookups\n\tc_dict = dict([(n,[]) for n in names])\n\tp_dict = dict([(n,[]) for n in names])\n\t\n\t# COMPUTE INITIAL LIKELIHOOD SCORE\t\n\tvalue_dict = dict([(n, np.unique(data[:,i])) for i,n in enumerate(names)])\n\tbn = BayesNet(c_dict)\n\tmle_estimator(bn, data)\n\tmax_score = info_score(bn, nrow, metric)\n\n\ttabu_list = [None]*k\n\n\n\t_iter = 0\n\timprovement = True\n\n\twhile improvement:\n\t\timprovement = False\n\t\tmax_delta = 0\n\n\t\tif debug:\n\t\t\tprint('ITERATION: ' , _iter)\n\n\t\t### TEST ARC ADDITIONS ###\n\t\tfor u in bn.nodes():\n\t\t\tfor v in bn.nodes():\n\t\t\t\t# CHECK TABU LIST - can't delete an addition on the tabu list\n\t\t\t\tif (u,v,'Deletion') not in tabu_list:\n\t\t\t\t\t# CHECK EDGE EXISTENCE AND CYCLICITY\n\t\t\t\t\tif v not in c_dict[u] and u!=v and not would_cause_cycle(c_dict, u, v):\n\t\t\t\t\t\t# FOR MMHC ALGORITHM -> Edge Restrictions\n\t\t\t\t\t\tif restriction is None or (u,v) in restriction:\n\t\t\t\t\t\t\t# SCORE FOR 'V' -> gaining a parent\n\t\t\t\t\t\t\told_cols = (v,) + tuple(p_dict[v]) # without 'u' as parent\n\t\t\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\t\t\tnew_cols = old_cols + (u,) # with'u' as parent\n\t\t\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\t\t\tdelta_score = nrow * (mi_old - mi_new)\n\n\t\t\t\t\t\t\tif delta_score > max_delta:\n\t\t\t\t\t\t\t\tif debug:\n\t\t\t\t\t\t\t\t\tprint('Improved Arc Addition: ' , (u,v))\n\t\t\t\t\t\t\t\t\tprint('Delta Score: ' , delta_score)\n\t\t\t\t\t\t\t\tmax_delta = delta_score\n\t\t\t\t\t\t\t\tmax_operation = 'Addition'\n\t\t\t\t\t\t\t\tmax_arc = (u,v)\n\n\t\t### TEST ARC DELETIONS ###\n\t\tfor u in bn.nodes():\n\t\t\tfor v in bn.nodes():\n\t\t\t\t# CHECK TABU LIST - can't add back a deletion on the tabu list\n\t\t\t\tif (u,v,'Addition') not in tabu_list:\n\t\t\t\t\tif v in c_dict[u]:\n\t\t\t\t\t\t# SCORE FOR 'V' -> losing a parent\n\t\t\t\t\t\told_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent\n\t\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\t\tnew_cols = tuple([i for i in old_cols if i != u]) # without 'u' as parent\n\t\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\t\tdelta_score = nrow * (mi_old - mi_new)\n\n\t\t\t\t\t\tif delta_score > max_delta:\n\t\t\t\t\t\t\tif debug:\n\t\t\t\t\t\t\t\tprint('Improved Arc Deletion: ' , (u,v))\n\t\t\t\t\t\t\t\tprint('Delta Score: ' , delta_score)\n\t\t\t\t\t\t\tmax_delta = delta_score\n\t\t\t\t\t\t\tmax_operation = 'Deletion'\n\t\t\t\t\t\t\tmax_arc = (u,v)\n\n\t\t### TEST ARC REVERSALS ###\n\t\tfor u in bn.nodes():\n\t\t\tfor v in bn.nodes():\n\t\t\t\t# CHECK TABU LIST - can't reverse back a reversal on the tabu list\n\t\t\t\tif (u,v,'Reversal') not in tabu_list:\n\t\t\t\t\tif v in c_dict[u] and not would_cause_cycle(c_dict,v,u, reverse=True):\n\t\t\t\t\t\t# SCORE FOR 'U' -> gaining 'v' as parent\n\t\t\t\t\t\told_cols = (u,) + tuple(p_dict[v]) # without 'v' as parent\n\t\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\t\tnew_cols = old_cols + (v,) # with 'v' as parent\n\t\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\t\tdelta1 = nrow * (mi_old - mi_new)\n\t\t\t\t\t\t# SCORE FOR 'V' -> losing 'u' as parent\n\t\t\t\t\t\told_cols = (v,) + tuple(p_dict[v]) # with 'u' as parent\n\t\t\t\t\t\tmi_old = mutual_information(data[:,old_cols])\n\t\t\t\t\t\tnew_cols = tuple([u for i in old_cols if i != u]) # without 'u' as parent\n\t\t\t\t\t\tmi_new = mutual_information(data[:,new_cols])\n\t\t\t\t\t\tdelta2 = nrow * (mi_old - mi_new)\n\t\t\t\t\t\t# COMBINED DELTA-SCORES\n\t\t\t\t\t\tdelta_score = delta1 + delta2\n\n\t\t\t\t\t\tif delta_score > max_delta:\n\t\t\t\t\t\t\tif debug:\n\t\t\t\t\t\t\t\tprint('Improved Arc Reversal: ' , (u,v))\n\t\t\t\t\t\t\t\tprint('Delta Score: ' , delta_score)\n\t\t\t\t\t\t\tmax_delta = delta_score\n\t\t\t\t\t\t\tmax_operation = 'Reversal'\n\t\t\t\t\t\t\tmax_arc = (u,v)\n\n\n\t\t### DETERMINE IF/WHERE IMPROVEMENT WAS MADE ###\n\t\tif max_delta != 0:\n\t\t\timprovement = True\n\t\t\tu,v = max_arc\n\t\t\tif max_operation == 'Addition':\n\t\t\t\tif debug:\n\t\t\t\t\tprint('ADDING: ' , max_arc , '\\n')\n\t\t\t\tc_dict[u].append(v)\n\t\t\t\tp_dict[v].append(u)\n\t\t\t\ttabu_list[_iter % 5] = (u,v,'Addition')\n\t\t\telif max_operation == 'Deletion':\n\t\t\t\tif debug:\n\t\t\t\t\tprint('DELETING: ' , max_arc , '\\n')\n\t\t\t\tc_dict[u].remove(v)\n\t\t\t\tp_dict[v].remove(u)\n\t\t\t\ttabu_list[_iter % 5] = (u,v,'Deletion')\n\t\t\telif max_operation == 'Reversal':\n\t\t\t\tif debug:\n\t\t\t\t\tprint('REVERSING: ' , max_arc, '\\n')\n\t\t\t\t\tc_dict[u].remove(v)\n\t\t\t\t\tp_dict[v].remove(u)\n\t\t\t\t\tc_dict[v].append(u)\n\t\t\t\t\tp_dict[u].append(v)\n\t\t\t\t\ttabu_list[_iter % 5] = (u,v,'Reversal')\n\t\telse:\n\t\t\tif debug:\n\t\t\t\tprint('No Improvement on Iter: ' , _iter)\n\n\t\t### TEST FOR MAX ITERATION ###\n\t\t_iter += 1\n\t\tif _iter > max_iter:\n\t\t\tif debug:\n\t\t\t\tprint('Max Iteration Reached')\n\t\t\tbreak\n\n\t\n\tbn = BayesNet(c_dict)\n\n\treturn bn\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/learning/structure/tree/__init__.py", "content": "from pyBN.learning.structure.tree.chow_liu import *" }, { "path": "pyBN/learning/structure/tree/chow_liu.py", "content": "\"\"\"\n*************\nChow-Liu Tree\n*************\n\nCalculate the KL divergence (i.e. run\nmi_test) between every pair of nodes,\nthen select the maximum spanning tree from that\nconnected graph. This is the Chow-Liu tree.\n\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nfrom pyBN.utils.independence_tests import mi_test\nfrom pyBN.classes.bayesnet import BayesNet\nimport operator\nimport numpy as np\n\n\ndef chow_liu(data,edges_only=False):\n\t\"\"\"\n\tPerform Chow-Liu structure learning algorithm\n\tover an entire dataset, and return the BN-tree.\n\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\t\tThe data from which we will learn. It should be\n\t\tthe entire dataset.\n\n\tReturns\n\t-------\n\t*bn* : a BayesNet object\n\t\tThe structure-learned BN.\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\n\t\"\"\"\n\tvalue_dict = dict(zip(range(data.shape[1]),\n\t\t[list(np.unique(col)) for col in data.T]))\n\n\tn_rv = data.shape[1]\n\n\tedge_list = [(i,j,mi_test(data[:,(i,j)],chi2_test=False)) \\\n\t\t\t\t\tfor i in range(n_rv) for j in range(i+1,n_rv)]\n\t\n\tedge_list.sort(key=operator.itemgetter(2), reverse=True) # sort by weight\n\tvertex_cache = {edge_list[0][0]} # start with first vertex..\n\tmst = dict((rv, []) for rv in range(n_rv))\n\n\tfor i,j,w in edge_list:\n\t\tif i in vertex_cache and j not in vertex_cache:\n\t\t\tmst[i].append(j)\n\t\t\tvertex_cache.add(j)\n\t\telif i not in vertex_cache and j in vertex_cache:\n\t\t\tmst[j].append(i)\n\t\t\tvertex_cache.add(i)\n\t\n\tif edges_only == True:\n\t\treturn mst, value_dict\n\n\tbn=BayesNet(mst,value_dict)\n\treturn bn\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/plotting/__init__.py", "content": "from pyBN.plotting.plot import *" }, { "path": "pyBN/plotting/plot.py", "content": "\"\"\"\n********\nPlotting \n********\n\nCode for plotting BayesNet objects, built on\nthe graphviz framework.\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport networkx as nx\nfrom graphviz import dot\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom networkx.drawing.nx_agraph import write_dot, graphviz_layout\nimport subprocess\nimport sys\nfrom PIL import Image\n\t\t\ndef plot(bn, save=False):\n\tplot_gv(bn, save=save)\n\ndef plot_nx(bn,**kwargs):\n\t\"\"\"\n\tDraw BayesNet object from networkx engine\n\t\"\"\"\n\tg = nx.DiGraph(bn.E)\n\tpos = graphviz_layout(g,'dot')\n\t#node_size=600,node_color='w',with_labels=False\n\tnx.draw_networkx(g,pos=pos, **kwargs)\n\tplt.axis('off')\n\tplt.show()\n\ndef iplot(bn, h=350, w=450):\n\t\"\"\"\n\tInline Plotting of a BayesNet object\n\t\"\"\"\n\tdef execute(command):\n\t\tcommand = ' '.join(command)\n\t\tprocess = subprocess.Popen(command, \n\t\t\tshell=True, \n\t\t\tstdout=subprocess.PIPE, \n\t\t\tstderr=subprocess.STDOUT)\n\n\t\toutput = process.communicate()[0]\n\t\treturncode = process.returncode\n\n\t\tif returncode == 0:\n\t\t\treturn True, output\n\t\telse:\n\t\t\treturn False, output\n\n\tcmd = ['mkdir' , 'pyBN/plotting/images']\n\tp = execute(cmd)\n\n\tG = nx.DiGraph(bn.E)\n\twrite_dot(G,\"pyBN/plotting/images/bn.dot\")\n\n\tcmd = ['/usr/local/bin/dot', '-Tpng' , \n\t'pyBN/plotting/images/bn.dot', \n\t'>pyBN/plotting/images/bn.png']\n\tp = execute(cmd)\n\t\n\tim = Image.open(\"pyBN/plotting/images/bn.png\")\n\tout = im.resize((w,h))\n\n\tcmd = ['rm' , '-r', 'pyBN/plotting/images']\n\tp = execute(cmd)\n\n\treturn out\n\ndef plot_gv(bn, save=False):\n\tdef execute(command):\n\t\tcommand = ' '.join(command)\n\t\tprocess = subprocess.Popen(command, \n\t\t\tshell=True, \n\t\t\tstdout=subprocess.PIPE, \n\t\t\tstderr=subprocess.STDOUT)\n\n\t\toutput = process.communicate()[0]\n\t\treturncode = process.returncode\n\n\t\tif returncode == 0:\n\t\t\treturn True, output\n\t\telse:\n\t\t\treturn False, output\n\n\tcmd = ['mkdir' , 'pyBN/plotting/images']\n\tp = execute(cmd)\n\n\tG = nx.DiGraph(bn.E)\n\twrite_dot(G,\"pyBN/plotting/images/bn.dot\")\n\n\tcmd = ['/usr/local/bin/dot', '-Tpng' , \n\t'pyBN/plotting/images/bn.dot', \n\t'>pyBN/plotting/images/bn.png']\n\n\tp = execute(cmd)\n\tplt.figure(facecolor=\"white\")\n\timg=mpimg.imread('pyBN/plotting/images/bn.png')\n\t_img = plt.imshow(img, aspect='auto')\n\tplt.axis('off')\n\tplt.show(_img)\n\n\tif not save:\n\t\tcmd = ['rm' , '-r', 'pyBN/plotting/images']\n\t\tp = execute(cmd)\n\telse:\n\t\tcmd = ['rm', '-r', 'pyBN/plotting/images/bn.dot']\n\t\tp = execute(cmd)\n\n\n\n\n\n\n\n\n\n\n\n\t\t\n" }, { "path": "pyBN/utils/__init__.py", "content": "from pyBN.utils.class_equivalence import *\nfrom pyBN.utils.data import *\nfrom pyBN.utils.discretize import *\nfrom pyBN.utils.graph import *\nfrom pyBN.utils.hybrid_distance import *\nfrom pyBN.utils.independence_tests import *\nfrom pyBN.utils.markov_blanket import *\nfrom pyBN.utils.orient_edges import *\nfrom pyBN.utils.parameter_distance import *\nfrom pyBN.utils.random_sample import *\nfrom pyBN.utils.structure_distance import *" }, { "path": "pyBN/utils/_tests/__init__.py", "content": "" }, { "path": "pyBN/utils/_tests/test_independence_tests.py", "content": "\"\"\"\n****************\nUnitTest\nConstraint Tests\n****************\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.independence.constraint_tests import mi_test\n\nclass ConstraintTestsTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tself.data = np.loadtxt(os.path.join(self.dpath,'lizards.csv'),\n\t\t\tdelimiter=',',\n\t\t\tdtype='int32',\n\t\t\tskiprows=1)\n\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_mi_two_vars_value_a(self):\n\t\tself.assertEqual(mi_test(self.data[:,(0,1)]),0.0004)\n\n\tdef test_mi_two_vars_value_b(self):\n\t\tself.assertEqual(mi_test(self.data[:,(0,2)]),0.0014)\n\n\tdef test_mi_two_vars_symmetry(self):\n\t\tself.assertEqual(mi_test(self.data[:,(1,0)]),mi_test(self.data[:,(0,1)]))\n\n\tdef test_mi_three_vars_value_a(self):\n\t\tself.assertEqual(mi_test(self.data),0.0009)\n\n\tdef test_mi_three_vars_symmetry(self):\n\t\tself.assertEqual(mi_test(self.data[:,(0,1,2)]),mi_test(self.data[:,(1,0,2)]))\n\n\tdef test_mi_random_three(self):\n\t\tnp.random.seed(3636)\n\t\tself.data = np.random.randint(1,10,size=((10000,3)))\n\t\tself.assertEqual(mi_test(self.data),0.0211)\n\n\tdef test_mi_random_four(self):\n\t\tnp.random.seed(3636)\n\t\tself.data = np.random.randint(1,10,size=((10000,4)))\n\t\tself.assertEqual(mi_test(self.data),0.0071)\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/utils/_tests/test_markov_blanket.py", "content": "\"\"\"\n**************\nMarkov Blanket\nUnit Test\n**************\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\nimport pandas as pd\n\nfrom pyBN.readwrite.read import read_bn\nfrom pyBN.independence.markov_blanket import markov_blanket\n\nclass ConstraintTestsTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tself.bn = read_bn(os.path.join(self.dpath,'cmu.bn'))\n\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_markov_blanket(self):\n\t\tself.assertDictEqual(markov_blanket(self.bn),\n\t\t\t{'Alarm': ['Earthquake', 'Burglary', 'JohnCalls', 'MaryCalls'],\n\t\t\t 'Burglary': ['Alarm', 'Earthquake'],\n\t\t\t 'Earthquake': ['Alarm', 'Burglary'],\n\t\t\t 'JohnCalls': ['Alarm'],\n\t\t\t 'MaryCalls': ['Alarm']})" }, { "path": "pyBN/utils/_tests/test_orient_edges.py", "content": "\"\"\"\n********\nUnitTest\nOrientEdges\n********\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.structure_learn.orient_edges import orient_edges_gs, orient_edges_pc\n\n\nclass OrientEdgesTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tpass\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_orient_edges_pc(self):\n\t\te = {0:[1,2],1:[0],2:[0]}\n\t\tb = {0: [], 1: {2: (0,)}, 2: {1: (0,)}}\n\t\tself.assertDictEqual(orient_edges_pc(e,b),\n\t\t\t{0: [1, 2], 1: [], 2: []})\n\n\tdef test_orient_edges_gs(self):\n\t\te = {0: [1, 2], 1: [0], 2: [0]}\n\t\tb = {0: [1, 2], 1: [0], 2: [0]}\n\t\tdpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tpath = (os.path.join(dpath,'lizards.csv'))\n\t\tdata = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')\n\t\tself.assertDictEqual(orient_edges_gs(e,b,data,0.05),\n\t\t\t{0: [1, 2], 1: [], 2: []})" }, { "path": "pyBN/utils/_tests/test_random_sample.py", "content": "\"\"\"\n********\nUnitTest\nMisc\n********\n\n\"\"\"\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport unittest\nimport os\nfrom os.path import dirname\nimport numpy as np\n\nfrom pyBN.utils.random_sample import random_sample\nfrom pyBN.readwrite.read import read_bn\n\n\nclass RandomSampleTestCase(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.dpath = os.path.join(dirname(dirname(dirname(dirname(__file__)))),'data')\t\n\t\tself.bn = read_bn(os.path.join(self.dpath,'cancer.bif'))\n\n\tdef tearDown(self):\n\t\tpass\n\n\tdef test_random_sample(self):\n\t\tnp.random.seed(3636)\n\t\tsample = random_sample(self.bn,5)\n\t\tself.assertListEqual(list(sample.ravel()),\n\t\t\t[0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, \n\t\t\t1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0])" }, { "path": "pyBN/utils/class_equivalence.py", "content": "\"\"\"\n****************\nEquivalence Code\n****************\n\nThis code is for testing whether or not\ntwo Bayesian networks belong to the same\nequivalence class - i.e. they have the same\nedges when viewed as undirected graphs.\n\nAlso, this code is for actually generating\nequivalent Bayesian networks from a given BN.\n\n\"\"\"\n\ndef are_class_equivalent(x,y):\n\t\"\"\"\n\tCheck whether two Bayesian networks belong\n\tto the same equivalence class.\n\t\"\"\"\n\tare_equivalent = True\n\n\tif set(list(x.nodes())) != set(list(y.nodes())):\n\t\tare_equivalent = False\n\telse:\n\t\tfor rv in x.nodes():\n\t\t\trv_x_neighbors = set(x.parents(rv)) + set(y.children(rv))\n\t\t\trv_y_neighbors = set(y.parents(rv)) + set(y.children(rv))\n\t\t\tif rv_x_neighbors != rv_y_neighbors:\n\t\t\t\tare_equivalent = False\n\t\t\t\tbreak\n\treturn are_equivalent" }, { "path": "pyBN/utils/data.py", "content": "\"\"\"\nThese are functions for dealing with datasets\nthat have strings as values - as those values\nmust be converted to integers in order to be \nused in many structure learning functions, for\nexample.\n\nThere is, of course, the issue of how to get those\nstring values back into the underlying representation\nafter the learning occurs..\n\"\"\"\nimport numpy as np\nfrom copy import copy\n\ndef replace_strings(data, return_values=False):\n\t\"\"\"\n\tTakes a dataset of strings and returns the\n\tinteger mapping of the string values.\n\n\tIt might be useful to return a dictionary of the\n\tstring values so that they can be mapped back in\n\tthe actual BayesNet object when necessary.\n\t\"\"\"\n\tdata = copy(data)\n\ti = 0\n\tvalue_dict = {}\n\tfor col in data.T:\n\t\tvals,_,ret, = np.unique(col, True, True)\n\t\tdata[:,i] = ret\n\t\tvalue_dict[i] = list(vals)\n\t\ti+=1\n\n\tif return_values:\n\t\treturn np.array(data,dtype=np.int32), value_dict\n\telse:\n\t\treturn np.array(data, dtype=np.int32)\n\ndef unique_bins(data):\n\t\"\"\"\n\tGet the unique values for each column in a dataset.\n\t\"\"\"\n\tbins = np.empty(len(data.T), dtype=np.int32)\n\ti = 0\n\tfor col in data.T:\n\t\tbins[i] = len(np.unique(col))\n\t\ti+=1\n\treturn bins" }, { "path": "pyBN/utils/discretize.py", "content": "\"\"\"\n**************************\nDiscretize Continuous Data\n**************************\n\nSince pyBN only handles Discrete Bayesian Networks,\nand therefore only handles discrete data, it is\nimportant to have effective functions for \ndiscretizing continuous data. This code aims to\nmeet that goal.\n\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport numpy as np\nfrom copy import copy\n\ndef discretize(data, cols=None, bins=None):\n\t\"\"\"\n\tDiscretize the passed-in dataset. These\n\tfunctions will rely on numpy and scipy\n\tfor speed and accuracy... no need to\n\treinvent the wheel here. Therefore, pyBN's\n\tdiscretization methods are basically just\n\twrappers for existing methods.\n\n\tThe bin number defaults to FIVE (5) for\n\tall columns if not passed in.\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\n\t*cols* : a list of integers (optional)\n\t\tWhich columns to discretize .. defaults\n\t\tto ALL columns\n\n\t*bins* : a list of integers (optional)\n\t\tThe number of bins into which each column\n\t\tarray will be split .. defaults to 5 for\n\t\tall columns\n\n\tReturns\n\t-------\n\t*data* : a discretized copy of original data\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\t- Should probably add more methods of discretization\n\t\tbased on mean/median/mode, etc.\n\t\"\"\"\n\tif bins is not None:\n\t\tassert (isinstance(bins, list)), 'bins argument must be a list'\n\telse:\n\t\ttry:\n\t\t\tbins = [5]*data.shape[1]\n\t\texcept ValueError:\n\t\t\tbins = [5]\n\t\n\tif cols is not None:\n\t\tassert (isinstance(cols,list)), 'cols argument must be a list'\n\telse:\t\n\t\ttry:\n\t\t\tcols = range(data.shape[1])\n\t\texcept ValueError:\n\t\t\tcols = [0]\n\n\tdata = copy(data)\n\n\tminmax = zip(np.amin(data,axis=0),np.amax(data,axis=0))\n\tfor c in cols:\n\t\t# get min and max of each column\n\t\t_min, _max = minmax[c]\n\t\t# create the bins from np.linspace\n\t\t_bins = np.linspace(_min,_max,bins[c])\n\t\t# discretize with np.digitize(col,bins)\n\t\tdata[:,c] = np.digitize(data[:,c],_bins)\n\n\treturn np.array(data,dtype=np.int32,copy=False)\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/utils/graph.py", "content": "\"\"\"\nCollection of Graph Algorithms.\n\nAny networkx dependencies should exist here only, but\nthere are currently a few exceptions which will\neventually be corrected.\n\n\"\"\"\n\nimport networkx as nx\nimport numpy as np\nfrom copy import copy\n\ndef would_cause_cycle(e, u, v, reverse=False):\n\t\"\"\"\n\tTest if adding the edge u -> v to the BayesNet\n\tobject would create a DIRECTED (i.e. illegal) cycle.\n\t\"\"\"\n\tG = nx.DiGraph(e)\n\tif reverse:\n\t\tG.remove_edge(v,u)\n\tG.add_edge(u,v)\n\ttry:\n\t\tnx.find_cycle(G, source=u)\n\t\treturn True\n\texcept:\n\t\treturn False\n\n\n\ndef topsort(edge_dict, root=None):\n\t\"\"\"\n\tList of nodes in topological sort order from edge dict\n\twhere key = rv and value = list of rv's children\n\t\"\"\"\n\tqueue = []\n\tif root is not None:\n\t\tqueue = [root]\n\telse:\n\t\tfor rv in edge_dict.keys():\n\t\t\tprior=True\n\t\t\tfor p in edge_dict.keys():\n\t\t\t\tif rv in edge_dict[p]:\n\t\t\t\t\tprior=False\n\t\t\tif prior==True:\n\t\t\t\tqueue.append(rv)\n\t\n\tvisited = []\n\twhile queue:\n\t\tvertex = queue.pop(0)\n\t\tif vertex not in visited:\n\t\t\tvisited.append(vertex)\n\t\t\tfor nbr in edge_dict[vertex]:\n\t\t\t\tqueue.append(nbr)\n\t\t\t#queue.extend(edge_dict[vertex]) # add all vertex's children\n\treturn visited\n\ndef dfs_postorder(edge_dict, root=None):\n\treturn list(reversed(topsort(edge_dict, root)))\n\ndef mst(edge_dict):\n\t\"\"\"\n\tCalcuate Minimum Spanning Tree\n\tfor a weighted edge dictionary,\n\twhere key = rv, value = another dictionary\n\twhere key = child of rv, value = edge weight\n\tbetween rv -> child edge.\n\n\tArguments\n\t---------\n\t*w_edge_dict* : a dictionary, where\n\t\tkey = rv, value = another dictionary, where\n\t\tkey = child node of \"rv\", value = integer/float\n\t\tedge weight between the \"rv\" -> \"child\" edge\n\n\tReturns\n\t-------\n\t*mst_edge_dict* : a non-weighted edge dict\n\t\tThe minimum spanning tree from w_edge_dict, stored\n\t\tas a dictionary where key = rv, value = list of\n\t\trv's children\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\ttest:\n\t\tinput:\n\t\t\tg = {0:{1:3,3:2,8:4},1:{7:4},\n\t\t\t\t2:{3:6,7:2,5:1},3:{4:1},4:{8:8},\n\t\t\t\t5:{6:8},6:{},7:{2:2},8:{}}\n\t\toutput:\n\t\t\tg = {0:[3,1,8],1:[7],2:[5],3:[4],4:[],\n\t\t\t\t5:[6],6:[],7:[2],8:[]}\n\t\t\n\t \n\t\"\"\"\n\tmst_G = dict([(rv,[]) for rv in range(len(edge_dict))])\n\tnrv = len(mst_G)\n\treached = [0]\n\tunreached = range(1,nrv)\n\n\tfor k in range(nrv):\n\t\tsource, sink = None, None\n\t\tmin_cost = np.inf\n\t\tfor rn in reached:\n\t\t\te = edge_dict[rn].items()\n\t\t\te.sort(key = lambda x : x[1])\n\t\t\tfor sn, weight in e:\n\t\t\t\tif sn in unreached and weight < min_cost:\n\t\t\t\t\tmin_cost = weight\n\t\t\t\t\tsource = rn\n\t\t\t\t\tsink = sn\n\t\t\t\t\tbreak\n\n\t\tif source is None or sink is None:\n\t\t\tbreak\n\n\t\t# Add e to the minimum spanning tree\n\t\tmst_G[source].append(sink)\n\t\t#if undirected == True:\n\t\t#\tmst_G[sink].append(source)\n\n\t\t# Mark newly include node as reached\n\t\tunreached.remove(sink)\n\t\treached.append(sink)\n\t\n\treturn mst_G\n\ndef make_chordal(bn, v=None,e=None):\n\t\"\"\"\n\tThis function creates a chordal graph - i.e. one in which there\n\tare no cycles with more than three nodes.\n\n\tCan supply a v_list and e_list for chordal graph of any random graph..\n\n\tWe start from the moral graph, so if that is already chordal then it\n\twill return that.\n\n\tAlgorithm from Cano & Moral 1990 ->\n\t'Heuristic Algorithms for the Triangulation of Graphs'\n\n\n\tParameters\n\t----------\n\t*v* : a list (optional)\n\t A vertex list\n\n\t*e* : a list (optional)\n\t An edge list\n\n\n\tReturns\n\t-------\n\t*G* : a network Digraph object\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\tWhere is this used? Do we need to use networkx? \n\n\n\t\"\"\"\n\tchordal_E = bn.moralized_edges() # start with moral graph\n\n\t# if moral graph is already chordal, no need to alter it\n\tif not is_chordal(chordal_E): \n\t\ttemp_E = copy(chordal_E)\n\t\ttemp_V = []\n\n\t\t# if v and e is supplied, skip all the rest\n\t\tif v and e:\n\t\t\tchordal_E = copy(e)\n\t\t\ttemp_E = copy(chordal_E)\n\t\t\ttemp_V = copy(v)\n\t\telse:\n\t\t\ttemp_G = nx.Graph()\n\t\t\ttemp_G.add_edges_from(chordal_E)\n\t\t\tdegree_dict = temp_G.degree()\n\t\t\ttemp_V = sorted(degree_dict, key=degree_dict.get)\n\t\t#print temp_V\n\t\tfor v in temp_V:\n\t\t\t#Add links between the pairs nodes adjacent to Node i\n\t\t\t#Add those links to chordal_E and temp_E\n\t\t\tadj_v = set([n for e in temp_E for n in e if v in e and n!=v])\n\t\t\tfor a1 in adj_v:\n\t\t\t\tfor a2 in adj_v:\n\t\t\t\t\tif a1!=a2:\n\t\t\t\t\t\tif [a1,a2] not in chordal_E and [a2,a1] not in chordal_E:\n\t\t\t\t\t\t\tchordal_E.append([a1,a2])\n\t\t\t\t\t\t\ttemp_E.append([a1,a2])\n\t\t\t# remove Node i from temp_V and all its links from temp_E \n\t\t\ttemp_E2 = []\n\t\t\tfor edge in temp_E:\n\t\t\t\tif v not in edge:\n\t\t\t\t\ttemp_E2.append(edge)\n\t\t\ttemp_E = temp_E2\n\n\t\n\tG = nx.Graph()\n\tG.add_edges_from(chordal_E)\n\treturn G\n\n\ndef is_chordal(edge_list):\n\t\"\"\"\n\tCheck if the graph is chordal/triangulated.\n\n\tParameters\n\t----------\n\t*edge_list* : a list of lists (optional)\n\tThe edges to check (if not self.E)\n\n\tReturns\n\t-------\n\t*nx.is_chordal(G)* : a boolean\n\tWhether the graph is chordal or not\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\tAgain, do we need networkx for this? Eventually should\n\twrite this check on our own.\n\n\t\"\"\"\n\tG = nx.Graph(list(edge_list))\n\treturn nx.is_chordal(G)" }, { "path": "pyBN/utils/hybrid_distance.py", "content": "\"\"\"\n****************\nBN Hybrid\nDistance Metrics\n****************\n\nCode for computing the distance between \n2+ Bayesian networks that takes into account\nboth structure AND parameters. \n\nAny structure-based distance metric can be\ncombined with any parameter-based distance metric\nto compute the hybrid distance.\n\nAlso, we support a type of supervised approach to\nhybrid distance measurement where the relative importance\nplaced on the structure and distance metrics are varied\nsuch that maximum (or minimum) closeness is achieved.\n\n\"\"\"\n\ndef hybrid_distance(x,y, alpha, s='hamming', p='euclidean'):\n\tstructure_score = hamming(x,y)\n\tparameter_score = euclidean(x,y)\n\n\thybrid_score = structure_score*alpha + parameter_score*(1-alpha)\n\n\treturn hybrid_score\n" }, { "path": "pyBN/utils/independence_tests.py", "content": "\"\"\"\n******************************\nConditional Independence Tests \nfor Constraint-based Learning\n******************************\n\nImplemented Constraint-based Tests\n----------------------------------\n- mutual information\n- Pearson's X^2\n\nI may consider putting this code into its own class structure. The\nmain benefit I could see from doing this would be the ability to \ncache joint/marginal/conditional probabilities for expedited tests.\n\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport numpy as np\nfrom scipy import stats\nfrom pyBN.utils.data import unique_bins\n\ndef are_independent(data, alpha=0.05, method='mi_test'):\n\tpval = mi_test(data)\n\tif pval < alpha:\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef mutual_information(data, conditional=False):\n\t#bins = np.amax(data, axis=0)+1 # read levels for each variable\n\tbins = unique_bins(data)\n\tif len(bins) == 1:\n\t\thist,_ = np.histogramdd(data, bins=(bins)) # frequency counts\n\t\tPx = hist/hist.sum()\n\t\tMI = -1 * np.sum( Px * np.log( Px ) )\n\t\treturn round(MI, 4)\n\t\t\n\tif len(bins) == 2:\n\t\thist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts\n\n\t\tPxy = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\tPx = np.sum(Pxy, axis = 1) # P(X,Z)\n\t\tPy = np.sum(Pxy, axis = 0) # P(Y,Z)\t\n\n\t\tPxPy = np.outer(Px,Py)\n\t\tPxy += 1e-7\n\t\tPxPy += 1e-7\n\t\tMI = np.sum(Pxy * np.log(Pxy / (PxPy)))\n\t\treturn round(MI,4)\n\telif len(bins) > 2 and conditional==True:\n\t\t# CHECK FOR > 3 COLUMNS -> concatenate Z into one column\n\t\tif len(bins) > 3:\n\t\t\tdata = data.astype('str')\n\t\t\tncols = len(bins)\n\t\t\tfor i in range(len(data)):\n\t\t\t\tdata[i,2] = ''.join(data[i,2:ncols])\n\t\t\tdata = data.astype('int')[:,0:3]\n\n\t\tbins = np.amax(data,axis=0)\n\t\thist,_ = np.histogramdd(data, bins=bins) # frequency counts\n\n\t\tPxyz = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\tPz = np.sum(Pxyz, axis = (0,1)) # P(Z)\n\t\tPxz = np.sum(Pxyz, axis = 1) # P(X,Z)\n\t\tPyz = np.sum(Pxyz, axis = 0) # P(Y,Z)\t\n\n\t\tPxy_z = Pxyz / (Pz+1e-7) # P(X,Y | Z) = P(X,Y,Z) / P(Z)\n\t\tPx_z = Pxz / (Pz+1e-7) # P(X | Z) = P(X,Z) / P(Z)\t\n\t\tPy_z = Pyz / (Pz+1e-7) # P(Y | Z) = P(Y,Z) / P(Z)\n\n\t\tPx_y_z = np.empty((Pxy_z.shape)) # P(X|Z)P(Y|Z)\n\t\tfor i in range(bins[0]):\n\t\t\tfor j in range(bins[1]):\n\t\t\t\tfor k in range(bins[2]):\n\t\t\t\t\tPx_y_z[i][j][k] = Px_z[i][k]*Py_z[j][k]\n\t\tPxyz += 1e-7\n\t\tPxy_z += 1e-7\n\t\tPx_y_z += 1e-7\n\t\tMI = np.sum(Pxyz * np.log(Pxy_z / (Px_y_z)))\n\t\t\n\t\treturn round(MI,4)\n\telif len(bins) > 2 and conditional == False:\n\t\tdata = data.astype('str')\n\t\tncols = len(bins)\n\t\tfor i in range(len(data)):\n\t\t\tdata[i,1] = ''.join(data[i,1:ncols])\n\t\tdata = data.astype('int')[:,0:2]\n\n\t\thist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts\n\n\t\tPxy = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\tPx = np.sum(Pxy, axis = 1) # P(X,Z)\n\t\tPy = np.sum(Pxy, axis = 0) # P(Y,Z)\t\n\n\t\tPxPy = np.outer(Px,Py)\n\t\tPxy += 1e-7\n\t\tPxPy += 1e-7\n\t\tMI = np.sum(Pxy * np.log(Pxy / (PxPy)))\n\t\treturn round(MI,4)\n\n\n\ndef mi_test(data, test=True):\n\t\"\"\"\n\tThis function performs the mutual information (cross entropy)-based\n\tCONDITIONAL independence test. Because it is conditional, it requires\n\tat LEAST three columns. For the marginal independence test, use \n\t\"mi_test_marginal\".\n\n\tWe use the maximum likelihood estimators as probabilities. The\n\tmutual information value is computed, then the \n\tchi square test is used, with degrees of freedom equal to \n\t(|X|-1)*(|Y|-1)*Pi_z\\inZ(|z|).\n\n\tThis function works on datasets that contain MORE than three\n\tcolumns by concatenating the extra columns into one. For that\n\treason, it is a little slower in that case.\n\n\tFor two variables only:\n\n\tThis function performs mutual information (cross entropy)-based\n\tMARGINAL independence test. Because it is marginal, it requires\n\tEXACTLY TWO columns. For the conditional independence test, use\n\t\"mi_test_conditional\".\n\n\tThis is the same as calculated the KL Divergence, i.e.\n\tI(X,Y) = Sigma p(x,y)* log p(x,y) *( p(x)/p(y) )\n\n\tNOTE: pval < 0.05 means DEPENDENCE, pval > 0.05 means INDEPENDENCE.\n\tIn other words, the pval represent the probability this relationship\n\tcould have happened at random or by chance. if the pval is very small,\n\tit means the two variables are likely dependent on one another.\n\n\tSteps:\n\t\t- Calculate the marginal/conditional probabilities\n\t\t- Compute the Mutual Information value\n\t\t- Calculate chi2 statistic = 2*N*MI\n\t\t- Compute the degrees of freedom\n\t\t- Compute the chi square p-value\n\n\tArguments\n\t----------\n\t*data* : a nested numpy array\n\t\tThe data from which to learn - must have at least three\n\t\tvariables. All conditioned variables (i.e. Z) are compressed\n\t\tinto one variable.\n\n\tReturns\n\t-------\n\t*p_val* : a float\n\t\tThe pvalue from the chi2 and ddof\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\t- Doesn't currently work with strings... \n\t- Should generalize to let data be a Pandas DataFrame --> would\n\tencourage external use.\n\n\t\"\"\"\n\t\n\t#bins = np.amax(data, axis=0)+1 # read levels for each variable\n\tbins = unique_bins(data)\n\tif len(bins)==2:\n\t\thist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts\n\n\t\t#Pxy = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\tPxy = hist / data.shape[0]\n\t\tPx = np.sum(Pxy, axis = 1) # P(X,Z)\n\t\tPy = np.sum(Pxy, axis = 0) # P(Y,Z)\t\n\n\t\tPxPy = np.outer(Px,Py)\n\t\tPxy += 1e-7\n\t\tPxPy += 1e-7\n\t\tMI = np.sum(Pxy * np.log(Pxy / (PxPy)))\n\t\tif not test:\n\t\t\treturn round(MI,4)\n\t\telse:\n\t\t\tchi2_statistic = 2*len(data)*MI\n\t\t\tddof = (bins[0] - 1) * (bins[1] - 1)\n\t\t\tp_val = 2*stats.chi2.pdf(chi2_statistic, ddof)\n\t\t\treturn round(p_val,4)\n\telse:\n\t\t# CHECK FOR > 3 COLUMNS -> concatenate Z into one column\n\t\tif len(bins) > 3:\n\t\t\tdata = data.astype('str')\n\t\t\tncols = len(bins)\n\t\t\tfor i in range(len(data)):\n\t\t\t\tdata[i,2] = ''.join(data[i,2:ncols])\n\t\t\tdata = data.astype('int')[:,0:3]\n\n\t\t#bins = np.amax(data,axis=0)\n\t\tbins = unique_bins(data)\n\t\thist,_ = np.histogramdd(data, bins=bins) # frequency counts\n\n\t\t#Pxyz = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\tPxyz = hist / data.shape[0]\n\t\tPz = np.sum(Pxyz, axis = (0,1)) # P(Z)\n\t\tPxz = np.sum(Pxyz, axis = 1) # P(X,Z)\n\t\tPyz = np.sum(Pxyz, axis = 0) # P(Y,Z)\t\n\n\t\tPxy_z = Pxyz / (Pz+1e-7) # P(X,Y | Z) = P(X,Y,Z) / P(Z)\n\t\tPx_z = Pxz / (Pz+1e-7) # P(X | Z) = P(X,Z) / P(Z)\t\n\t\tPy_z = Pyz / (Pz+1e-7) # P(Y | Z) = P(Y,Z) / P(Z)\n\n\t\tPx_y_z = np.empty((Pxy_z.shape)) # P(X|Z)P(Y|Z)\n\t\tfor i in range(bins[0]):\n\t\t\tfor j in range(bins[1]):\n\t\t\t\tfor k in range(bins[2]):\n\t\t\t\t\tPx_y_z[i][j][k] = Px_z[i][k]*Py_z[j][k]\n\t\tPxyz += 1e-7\n\t\tPxy_z += 1e-7\n\t\tPx_y_z += 1e-7\n\t\tMI = np.sum(Pxyz * np.log(Pxy_z / (Px_y_z)))\n\t\tif not test:\n\t\t\treturn round(MI,4)\n\t\telse:\n\t\t\tchi2_statistic = 2*len(data)*MI\n\t\t\tddof = (bins[0] - 1) * (bins[1] - 1) * bins[2]\n\t\t\tp_val = 2*stats.chi2.pdf(chi2_statistic, ddof) # 2* for one tail\n\t\t\treturn round(p_val,4)\n\ndef entropy(data):\n\t\"\"\"\n\tIn the context of structure learning, and more specifically\n\tin constraint-based algorithms which rely on the mutual information\n\ttest for conditional independence, it has been proven that the variable\n\tX in a set which MAXIMIZES mutual information is also the variable which\n\tMINIMIZES entropy. This fact can be used to reduce the computational\n\trequirements of tests based on the following relationship:\n\n\t\tEntropy is related to marginal mutual information as follows:\n\t\t\tMI(X;Y) = H(X) - H(X|Y)\n\n\t\tEntropy is related to conditional mutual information as follows:\n\t\t\tMI(X;Y|Z) = H(X|Z) - H(X|Y,Z)\n\n\t\tFor one varibale, H(X) is equal to the following:\n\t\t\t-1 * sum of p(x) * log(p(x))\n\n\t\tFor two variables H(X|Y) is equal to the following:\n\t\t\tsum over x,y of p(x,y)*log(p(y)/p(x,y))\n\t\t\n\t\tFor three variables, H(X|Y,Z) is equal to the following:\n\t\t\t-1 * sum of p(x,y,z) * log(p(x|y,z)),\n\t\t\t\twhere p(x|y,z) = p(x,y,z)/p(y)*p(z)\n\tArguments\n\t----------\n\t*data* : a nested numpy array\n\t\tThe data from which to learn - must have at least three\n\t\tvariables. All conditioned variables (i.e. Z) are compressed\n\t\tinto one variable.\n\n\tReturns\n\t-------\n\t*H* : entropy value\n\n\t\"\"\"\n\ttry:\n\t\tcols = data.shape[1]\n\texcept IndexError:\n\t\tcols = 1\n\n\t#bins = np.amax(data,axis=0)\n\tbins = unique_bins(data)\n\n\tif cols == 1:\n\t\thist,_ = np.histogramdd(data, bins=(bins)) # frequency counts\n\t\tPx = hist/hist.sum()\n\t\tH = -1 * np.sum( Px * np.log( Px ) )\n\n\telif cols == 2: # two variables -> assume X then Y\n\t\thist,_ = np.histogramdd(data, bins=bins[0:2]) # frequency counts\n\n\t\tPxy = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\tPy = np.sum(Pxy, axis = 0) # P(Y)\t\n\t\tPy += 1e-7\n\t\tPxy += 1e-7\n\t\tH = np.sum( Pxy * np.log( Py / Pxy ) )\n\n\telse:\n\t\t# CHECK FOR > 3 COLUMNS -> concatenate Z into one column\n\t\tif cols > 3:\n\t\t\tdata = data.astype('str')\n\t\t\tncols = len(bins)\n\t\t\tfor i in range(len(data)):\n\t\t\t\tdata[i,2] = ''.join(data[i,2:ncols])\n\t\t\tdata = data.astype('int')[:,0:3]\n\n\t\tbins = np.amax(data,axis=0)\n\t\thist,_ = np.histogramdd(data, bins=bins) # frequency counts\n\n\t\tPxyz = hist / hist.sum()# joint probability distribution over X,Y,Z\n\t\tPyz = np.sum(Pxyz, axis=0)\n\n\t\tPxyz += 1e-7 # for log -inf\n\t\tPyz += 1e-7\n\t\tH = -1 * np.sum( Pxyz * np.log( Pxyz ) ) + np.sum( Pyz * np.log( Pyz ) ) \n\n\treturn round(H,4)\n\ndef mi_from_en(data):\n\t\"\"\"\n\tCalculate Mutual Information based on entropy alone. This\n\tfunction isn't really faster than calculating MI from\n\t\"mi_test\" due to overhead in the histogram/binning from\n\tnumpy, but it's mostly here for entropy validation or\n\tif \"mi_test\" breaks. BUT, calling \"entropy\" once is much\n\tfaster than calling \"mi_test\" once, so that is where the\n\tspeedup occurs.\n\n\tThis has been validated with both 2, 3 and 4 variables.\n\n\tEntropy is related to marginal mutual information as follows:\n\t\t\tMI(X;Y) = H(X) - H(X|Y)\n\tEntropy is related to conditional mutual information as follows:\n\t\t\tMI(X;Y|Z) = H(X|Z) - H(X|Y,Z)\n\t\"\"\"\n\tncols = data.shape[1]\n\n\tif ncols == 1:\n\t\tprint(\"Need at least 2 columns\")\n\n\telif ncols==2:\n\t\tMI = entropy(data[:,0]) - entropy(data)\n\n\telif ncols==3:\n\t\tMI = entropy(data[:,(0,2)]) - entropy(data)\n\n\telif ncols>3:\n\t\t# join extra columns\n\t\tdata = data.astype('str')\n\t\tncols = data.shape[1]\n\t\tfor i in range(len(data)):\n\t\t\tdata[i,2] = ''.join(data[i,2:ncols])\n\t\tdata = data.astype('int')[:,0:3]\n\n\t\tMI = entropy(data[:,(0,2)]) - entropy(data)\n\n\n\treturn round(MI,4)\n\n\ndef chi2_test(data):\n\t\"\"\"\n\tTest null hypothesis that P(X,Y,Z) = P(Z)P(X|Z)P(Y|Z)\n\tversus empirically observed P(X,Y,Z) in the data using\n\tthe traditional chisquare test based on observed versus\n\texpected frequency bins.\n\n\tSteps\n\t\t- Calculate P(XYZ) empirically and expected\n\t\t- Compute ddof\n\t\t- Perfom one-way chisquare\n\n\tArguments\n\t---------\n\t*data* : a nested numpy array\n\t\tThe data from which to learn - must have at least three\n\t\tvariables. All conditioned variables (i.e. Z) are compressed\n\t\tinto one variable.\n\n\tReturns\n\t-------\n\t*chi2_statistic* : a float\n\t\tChisquare statistic\n\t*p_val* : a float\n\t\tThe pvalue from the chi2 and ddof\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\t- Assuming for now that |Z| = 1... generalize later\n\t- Should generalize to let data be a Pandas DataFrame --> would\n\tencourage external use.\n\n\t\"\"\"\n\t# compress extra Z variables at the start.. not implemented yet\n\t#bins = np.amax(data, axis=0)+1\n\tbins = unique_bins(data)\n\thist,_ = np.histogramdd(data,bins=bins)\n\n\tPxyz = hist / hist.sum()# joint probability distribution over X,Y,Z\n\n\tPz = np.sum(Pxyz, axis = (0,1)) # P(Z)\n\tPxz = np.sum(Pxyz, axis = 1) # P(X,Z)\n\tPyz = np.sum(Pxyz, axis = 0) # P(Y,Z)\n\n\tPx_z = Pxz / (Pz+1e-7) # P(X | Z) = P(X,Z) / P(Z)\t\n\tPy_z = Pyz / (Pz+1e-7) # P(Y | Z) = P(Y,Z) / P(Z)\n\n\tobserved_dist = Pxyz # Empirical distribution\n\t#Not correct right now -> Pz is wrong dimension\n\tPx_y_z = np.empty((Pxy_z.shape)) # P(Z)P(X|Z)P(Y|Z)\n\tfor i in range(bins[0]):\n\t\tfor j in range(bins[1]):\n\t\t\tfor k in range(bins[2]):\n\t\t\t\tPx_y_z[i][j][k] = Px_z[i][k]*Py_z[j][k]\n\tPx_y_z *= Pz\n\n\tobserved = observed_dist.flatten() * len(data)\n\texpected = expected_dist.flatten() * len(data)\n\n\tddof = (bins[0] - 1) * (bins[1]- 1) * bins[2]\n\tchi2_statistic, p_val = stats.chisquare(observed,expected, ddof=ddof)\n\n\treturn chi2_statistic, p_val\n\n\n\n\n\n" }, { "path": "pyBN/utils/markov_blanket.py", "content": "\"\"\"\n**************\nMarkov Blanket\n**************\n\nReferences\n----------\n[1] Koller and Sahami, \"Toward Optimal Feature Selection.\"\n[2] Yaramakala and Margaritis, \"Speculative Markov Blanket Discovery \nfor Optimal Feature Selection\"\n[3] Pellet and Elisseff, \"Using Markov Blankets for Causal Structure Learning\"\n\n\"\"\"\nfrom __future__ import division\nfrom copy import copy\nimport itertools\nfrom pyBN.utils.independence_tests import are_independent, mi_test\n\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\n\ndef markov_blanket(bn):\n\t\"\"\"\n\tReturn the Markov Blanket dictionary from a fully\n\t(structurally) instantiated BayesNet object.\n\n\tThe markov blanket for a given node is just\n\tthe node's parents, children, and \n\tits children's parents (i.e. spouses)\n\n\tArguments\n\t---------\n\t*bn* : BayesNet object\n\n\tReturns\n\t-------\n\t*mb* : a dictionary where each key \n\t\tis a node and the value is a list of\n\t\tthe key-node's markov blanket\n\t\"\"\"\n\tmb = dict([(rv,bn.parents(rv)+bn.children(rv)) for rv in bn.nodes()])\n\n\tfor rv in bn.V:\n\t\tfor child in bn.children(rv):\n\t\t\tfor c_parent in bn.parents(child):\n\t\t\t\tif c_parent != rv:\n\t\t\t\t\tmb[rv].append(c_parent) # add spouse\n\treturn mb\n\ndef resolve_markov_blanket(Mb, data,alpha=0.05):\n\t\"\"\"\n\tResolving the Markov blanket is the process\n\tby which a PDAG is constructed from the collection\n\tof Markov Blankets for each node. Since an\n\tundirected graph is returned, the edges still need to \n\tbe oriented by calling some version of the \n\t\"orient_edges\" function in \"pyBN.structure_learn.orient_edges\" \n\tmodule.\n\n\tThis algorithm is adapted from Margaritis, but also see [3]\n\tfor good pseudocode.\n\n\tArguments\n\t---------\n\t*Mb* : a dictionary, where\n\t\tkey = rv and value = list of vars in rv's markov blanket\n\n\t*data* : a nested numpy array\n\t\tThe dataset used to learn the Mb\n\n\tReturns\n\t-------\n\t*edge_dict* : a dictionary, where\n\t\tkey = rv and value = list of rv's children\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\t\"\"\"\n\tn_rv = data.shape[1]\n\tedge_dict = dict([(rv,[]) for rv in range(n_rv)])\n\tfor X in range(n_rv):\n\t\tfor Y in Mb[X]:\n\t\t\t# X and Y are direct neighbors if X and Y are dependent\n\t\t\t# given S for all S in T, where T is the smaller of\n\t\t\t# B(X)-{Y} and B(Y)-{X}\n\t\t\tif len(Mb[X]) < len(Mb[Y]):\n\t\t\t\tT = copy(Mb[X]) # shallow copy is sufficient\n\t\t\t\tif Y in T:\n\t\t\t\t\tT.remove(Y)\n\t\t\telse:\n\t\t\t\tT = copy(Mb[Y]) # shallow copy is sufficient\n\t\t\t\tif X in T:\n\t\t\t\t\tT.remove(X)\n\n\t\t\t# X and Y must be dependent conditioned upon\n\t\t\t# EVERY POSSIBLE COMBINATION of T\n\t\t\tdirect_neighbors=True\n\t\t\tfor i in range(len(T)):\n\t\t\t\tfor S in itertools.combinations(T,i):\n\t\t\t\t\tcols = (X,Y) + tuple(S)\n\t\t\t\t\tpval = mi_test(data[:,cols])\n\t\t\t\t\tif pval > alpha:\n\t\t\t\t\t\tdirect_neighbors=False\n\t\t\tif direct_neighbors:\n\t\t\t\tif Y not in edge_dict[X] and X not in edge_dict[Y]:\n\t\t\t\t\tedge_dict[X].append(Y)\n\t\t\t\tif X not in edge_dict[Y]:\n\t\t\t\t\tedge_dict[Y].append(X)\n\treturn edge_dict\n\ndef mb_fitness(data, Mb, target=None):\n\t\"\"\"\n\tEvaluate the fitness of a Markov Blanket dictionary\n\tlearned from a given data set based on the distance metric\n\tprovided in [1] and [2].\n\n\tFrom [2]:\n\t\tA distance measure that indicates the \"fitness\"\n\t\tof the discovered blanket... to be the average, over all attributes\n\t\tX outside the blanket, of the expected KL-divergence between\n\t\tPr(T | B(T)) and Pr(T | B(T) u {X}). We can expect this \n\t\tmeasure to be close to zero when B(T) is an approximate\n\t\tblanket. -- \n\t\tMy Note: T is the target variable, and if the KL-divergence\n\t\tbetween the two distributions above is zero, then it means that\n\t\t{X} provides no new information about T and can thus be excluded\n\t\tfrom Mb(T) -- this is the exact definition of conditional independence.\n\n\tNotes\n\t-----\n\t- Find Pr(T|B(T)) ..\n\t- For each variable X outside of the B(T), calculate \n\t\tD( Pr(T|B(T)), Pr(T|B(T)u{X}) )\n\t- Take the average (closer to Zero is better)\n\n\t^^^ This is basically calculating where T is independent of X given B(T)..\n\t\ti.e. Sum over all X not in B(T) of mi_test(data[:,(T,X,B(T))]) / |X|\n\t\"\"\"\n\tif target is None:\n\t\tnodes = set(Mb.keys())\n\telse:\n\t\ttry:\n\t\t\tnodes = set(target)\n\t\texcept TypeError:\n\t\t\tnodes = {target}\n\n\tfitness_dict = dict([(rv, 0) for rv in nodes])\n\tfor T in nodes:\n\t\tnon_blanket = nodes - set(Mb[T]) - {T}\n\t\tfor X in non_blanket:\n\t\t\tpval = mi_test(data[:,(T,X)+tuple(Mb[T])])\n\t\t\tfitness_dict[T] += 1/pval\n\treturn fitness_dict\n\n\n\t\n\n\n\n\n\n\n\n" }, { "path": "pyBN/utils/orient_edges.py", "content": "\"\"\"\n******************\nOrient Edges from\nStructure Learning\n******************\n\n[1] Chickering. \"Learning Equivalence Classes of Bayesian\nNetwork Structues\"\nhttp://www.jmlr.org/papers/volume2/chickering02a/chickering02a.pdf\n\n[2] Pellet and Elisseff, \"Using Markov Blankets for Causal Structure Learning\"\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nfrom pyBN.utils.independence_tests import mi_test\n\nimport itertools\nfrom copy import copy\n\ndef orient_edges_MB(edge_dict, Mb, data, alpha):\n\t\"\"\"\n\tOrient edges from a Markov Blanket based on the rules presented\n\tin Margaritis' Thesis pg. 35. This method is used\n\tfor structure learning algorithms that return/resolve\n\ta markov blanket - i.e. growshrink and iamb.\n\n\tAlso, see [2] for good full pseudocode.\n\n\t# if there exists a variable Z in N(X)-N(Y)-{Y}\n\t# such that Y and Z are dependent given S+{X} for\n\t# all S subset of T, where\n\t# T is smaller of B(Y)-{X,Z} and B(Z)-{X,Y}\n\n\tArguments\n\t---------\n\t*edge_dict* : a dictionary, where\n\t\tkey = node and value = list\n\t\tof neighbors for key. Note: there\n\t\tMUST BE duplicates in edge_dict ->\n\t\ti.e. each edge should be in edge_dict\n\t\ttwice since Y in edge_dict[X] and\n\t\tX in edge_dict[Y]\n\n\t*blanket* : a dictionary, where\n\t\tkey = node and value = list of\n\t\tnodes in the markov blanket of node\n\n\t*data* : a nested numpy array\n\n\t*alpha* : a float\n\t\tProbability of Type II error.\n\n\tReturns\n\t-------\n\t*d_edge_dict* : a dictionary\n\t\tDictionary of directed edges, so\n\t\tthere are no duplicates\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\n\t\"\"\"\n\tfor X in edge_dict.keys():\n\t\tfor Y in edge_dict[X]:\n\t\t\tnxy = set(edge_dict[X]) - set(edge_dict[Y]) - {Y}\n\n\t\t\tfor Z in nxy:\n\t\t\t\tby = set(Mb[Y]) - {X} - {Z}\n\t\t\t\tbz = set(Mb[Z]) - {X} - {Y}\n\t\t\t\tT = min(by,bz)\n\t\t\t\tif len(T)>0:\n\t\t\t\t\tfor i in range(len(T)):\n\t\t\t\t\t\tfor S in itertools.combinations(T,i):\n\t\t\t\t\t\t\tcols = (Y,Z,X) + tuple(S)\n\t\t\t\t\t\t\tpval = mi_test(data[:,cols])\n\t\t\t\t\t\t\tif pval < alpha:\n\t\t\t\t\t\t\t\tif Y in edge_dict[X]:\n\t\t\t\t\t\t\t\t\tedge_dict[X].remove(Y)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif Y in edge_dict[X]:\n\t\t\t\t\t\t\t\t\tedge_dict[Y].remove(X)\n\t\t\t\telse:\n\t\t\t\t\tcols = (Y,Z,X)\n\t\t\t\t\tpval = mi_test(data[:,cols])\n\t\t\t\t\tif pval < alpha:\n\t\t\t\t\t\tif Y in edge_dict[X]:\n\t\t\t\t\t\t\tedge_dict[X].remove(Y)\n\t\t\t\t\telse:\n\t\t\t\t\t\tif X in edge_dict[Y]:\n\t\t\t\t\t\t\tedge_dict[Y].remove(X)\n\treturn edge_dict\n\ndef orient_edges_gs2(edge_dict, Mb, data, alpha):\n\t\"\"\"\n\tSimilar algorithm as above, but slightly modified for speed?\n\tNeed to test.\n\t\"\"\"\n\td_edge_dict = dict([(rv,[]) for rv in edge_dict])\n\tfor X in edge_dict.keys():\n\t\tfor Y in edge_dict[X]:\n\t\t\tnxy = set(edge_dict[X]) - set(edge_dict[Y]) - {Y}\n\t\t\tfor Z in nxy:\n\t\t\t\tif Y not in d_edge_dict[X]:\n\t\t\t\t\td_edge_dict[X].append(Y) # SET Y -> X\n\t\t\t\tB = min(set(Mb[Y]) - {X} - {Z},set(Mb[Z]) - {X} - {Y})\n\t\t\t\tfor i in range(len(B)):\n\t\t\t\t\tfor S in itertools.combinations(B,i):\n\t\t\t\t\t\tcols = (Y,Z,X) + tuple(S)\n\t\t\t\t\t\tpval = mi_test(data[:,cols])\n\t\t\t\t\t\tif pval < alpha and X in d_edge_dict[Y]: # Y IS independent of Z given S+X\n\t\t\t\t\t\t\td_edge_dict[Y].remove(X)\n\t\t\t\tif X in d_edge_dict[Y]:\n\t\t\t\t\tbreak\n\treturn d_edge_dict\n\ndef orient_edges_CS(edge_dict, block_dict):\n\t\"\"\"\n\tOrient edges from Collider Sets. This is a little\n\tdifferent than orienting edges from a markov blanket.\n\tThe collider set-based algorithm is used for the \n\tPath-Condition (PC) algorithm.\n\n\tSee [2] for\tgood full pseudocode.\n\n\tThe orientation step will proceed by looking\n\tfor sets of three variables {X, Y,Z} such that\n\tedges X - Z, Y - Z are in the graph by not the\n\tedge X - Y . Then, if Z not in block_dict[x][y] , it orients the\n\tedges from X to Z and from Y to Z creating a\n\tv-structure: X -> Z <- Y\n\n\tArguments\n\t---------\n\t*edge_dict* : a dictionary, where\n\t\tkey = vertex and value = list of its neighbors.\n\t\tNOTE: this is undirected, so the edges are duplicated.\n\n\t*block_dict* : a dictionary, where\n\t\tkey = X, value = another dictionary where\n\t\tkey = Y, value = set S such that I(X,Y|S)\n\n\tReturns\n\t-------\n\t*d_edge_dict* : an directed version of original edge_dict\n\n\tEffects\n\t-------\n\tNone\n\n\tNotes\n\t-----\n\t\"\"\"\n\td_edge_dict = dict([(rv,[]) for rv in edge_dict.keys()])\n\tfor x in edge_dict.keys():\n\t\tfor z in edge_dict[x]:\n\t\t\tfor y in edge_dict[z]:\n\t\t\t\tif y!=x and x not in edge_dict[y] and y not in edge_dict[x]:\n\t\t\t\t\tif y in block_dict[x]:\n\t\t\t\t\t\tif z not in block_dict[x][y]:\n\t\t\t\t\t\t\tif z not in d_edge_dict[x]:\n\t\t\t\t\t\t\t\td_edge_dict[x].append(z)\n\t\t\t\t\t\t\tif z not in d_edge_dict[y]:\n\t\t\t\t\t\t\t\td_edge_dict[y].append(z)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif x not in d_edge_dict[z]:\n\t\t\t\t\t\t\t\td_edge_dict[z].append(x)\n\t\t\t\t\t\t\tif y not in d_edge_dict[z]:\n\t\t\t\t\t\t\t\td_edge_dict[z].append(y)\n\n\treturn d_edge_dict\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/utils/parameter_distance.py", "content": "\"\"\"\n****************\nBN Parameter \nDistance Metrics\n****************\n\nCode for computing the Parametric distance \nbetween 2+ Bayesian Networks using various\nmetrics. Parametric distance only involves a\ncomparison of the conditional probability values.\nFor structural distance metrics, see \"structure_distance.py\"\n\nSince a Bayesian network is simply joint probability distribution,\nany distibution distance metric can be used.\n\nMetrics\n-------\n*euclidean*\n*manhattan*\n*minkowski*\n*kl_divergence*\n*js_divergence*\n*hellinger*\n\n\"\"\"\nfrom __future__ import division\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport numpy as np\n#from pyBN.classes.bayesnet import BayesNet\n\n\n\ndef euclidean(x,y):\n\t\"\"\"\n\tEuclidean distance is a well-known distance metric.\n\n\tIt is defined as follows:\n\t\tSum of ( sqrt of (x - y)^2 )\n\t\"\"\"\n\tassert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'\n\tassert (x==y), 'Passed-in BayesNet objects are not structurally equal.'\n\n\tdistance = np.sum( np.sqrt( ( x.flat_cpt() - y.flat_cpt() )**2 ) )\n\treturn distance\n\ndef manhattan(x,y):\n\t\"\"\"\n\tManhattan distance is a well-known distance metric.\n\n\tIt is defined as follows:\n\t\tSum of ( |x-y| )\n\t\"\"\"\n\tassert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'\n\tassert (x==y), 'Passed-in BayesNet objects are not structurally equal.'\n\n\tdistance = np.sum( np.abs( x.flat_cpt() - y.flat_cpt() ) )\n\treturn distance\n\ndef minkowski(x,y,p=1.5):\n\t\"\"\"\n\tMinkowski distance is a metric where the user\n\tcan specify the exponent to which the Parameter\n\tdifference values is raised.\n\n\tIt is defined as follows:\n\t\t(Sum of (x-y)^p)^(1/p)\n\n\tNote that when p = 1, this is equivalent to Manhattan Distance.\n\tNote that when p = 2, this is equivalent to Euclidean Distance.\n\n\t\"\"\"\n\tassert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'\n\tassert (x==y), 'Passed-in BayesNet objects are not structurally equal.'\n\n\tdistance = np.sum( ( x.flat_cpt() - y.flat_cpt() )**2 )**( 1/p )\n\treturn distance\n\ndef kl_divergence(x,y):\n\t\"\"\"\n\tKL-Divergence is a well-known metric, although\n\tit is not technically a valid distance measure\n\tbecause it is NOT symmetric - i.e. KL(X,Y) != KL(Y,X)\n\t\n\tIt is defined as follows:\n\t\tSum of x * log(x/y)\n\t\"\"\"\n\tassert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'\n\tassert (x==y), 'Passed-in BayesNet objects are not structurally equal.'\n\n\tdistance = np.sum( x.flat_cpt() * np.log( x.flat_cpt() / y.flat_cpt() ) )\n\treturn distance\n\t\ndef js_divergence(x,y):\n\t\"\"\"\n\tJensen-Shannon Divergence is similar to KL-Divergence,\n\tbut it is symmetric and therefore is a valid distance metric.\n\n\tIt is defined as follows:\n\t\tJSD = 1/2 * KL(X,Z) + 1/2 * KL(Y,Z),\n\t\t\twhere Z = 1/2 * (X + Y)\n\t\"\"\"\n\tassert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'\n\tassert (x==y), 'Passed-in BayesNet objects are not structurally equal.'\n\n\tz = 0.5 * ( x.flat_cpt() + y.flat_cpt() )\n\tdistance = 0.5 * kl_divergence(x,z) + 0.5 * kl_divergence(y,z)\n\treturn distance\n\n\ndef hellinger(x,y):\n\t\"\"\"\n\tHellinger is a well-known distance metric.\n\t\n\tIt is defined as follows:\n\t1/Sqrt(2) * Sqrt(Sum of ((sqrt(x) - sqrt(y))^2))\n\t\"\"\"\n\tassert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'\n\tassert (x==y), 'Passed-in BayesNet objects are not structurally equal.'\n\n\tdistance = ( 1 / np.sqrt( 2 ) ) * np.sqrt( np.sum( ( np.sqrt( x.flat_cpt() ) - np.sqrt( y.flat_cpt() ) )**2) )\n\treturn distance\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "pyBN/utils/random_sample.py", "content": "\"\"\"\n******************\nRandom Sample Code\n******************\n\nGenerate a random sample dataset from a known Bayesian Network,\nwith or without evidence.\n\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nfrom pyBN.classes.factor import Factor\nimport numpy as np\nfrom copy import deepcopy\n\ndef random_sample(bn, n=1000):\n \"\"\"\n Take a random sample of \"n\" observations from a\n BayesNet object. This is essentially just the\n forward sample algorithm that returns the samples.\n\n Parameters\n ----------\n *bn* : a BayesNet object from which to sample\n\n *n* : an integer\n The number of observations to take\n\n *evidence* : a dictionary, key=rv & value=instantiation\n Evidence to pass in\n\n Returns\n -------\n *sample_dict* : a list of samples, where each sample\n is a list of values in bn.nodes() (topsort) order\n\n Notes\n -----\n\n \"\"\"\n sample = np.empty((n,bn.num_nodes()),dtype=np.int)\n\n rv_map = dict([(rv,idx) for idx,rv in enumerate(bn.nodes())])\n factor_map = dict([(rv,Factor(bn,rv)) for rv in bn.nodes()])\n \n for i in range(n):\n for rv in bn.nodes():\n f = deepcopy(factor_map[rv])\n # reduce_factor by parent samples\n for p in bn.parents(rv):\n f.reduce_factor(p,bn.values(p)[sample[i][rv_map[p]]])\n choice_vals = bn.values(rv)\n choice_probs = f.cpt\n chosen_val = np.random.choice(choice_vals, p=choice_probs)\n sample[i][rv_map[rv]] = bn.values(rv).index(chosen_val)\n\n return sample\n\n\n\n\n\n" }, { "path": "pyBN/utils/structure_distance.py", "content": "\"\"\"\n****************\nBN Structure \nDistance Metrics\n****************\n\nCode for computing the structural Distance\nbetween 2+ Bayesian networks. Note, this\ncode compares only structure - i.e. the\nconditional (in)dependencies. For a comparison\nof conditional probability values of 2+ BNs,\nsee \"parameter_distance.py\"\n\nFor computing distance metrics that include\nboth structure AND parameters, see \"hybrid_distance.py\"\n\nMetrics\n-------\n1. Missing Edges: \n\tcounts edges that are present in the original structure but\n\tare missing in the learned structure. (lower is better) [1]\n\n2. Extra Edges: \n\tcounts edges that are found in the learned structure but are\n\tnot present in the original structure. (lower is better) [1]\n\n3. Incorrect Edge Direction: \n\tcounts directed edges in the learned structure\n\tthat are oriented incorrectly. (lower is better) [1]\n\n4. Hamming Distance: \n\tDescribes the number of changes that have to be made\n\tto a network for it to turn into the one that it is being compared with. It is the\n\tsum of measures 1, 2, and 3. Versions of the Hamming distance metric have\n\tbeen proposed by Acid and de Campos (2003), Tsamardinos et al. (2006), and\n\tPerrier et al. (2008). (lower is better) [1]\n\n\nReferences\n----------\n[1] Martijn de Jongh and Marek J. Druzdzel\n\t\"A Comparison of Structural Distance Measures\n\tfor Causal Bayesian Network Models\"\n\n\n\"\"\"\n\n__author__ = \"\"\"Nicholas Cullen \"\"\"\n\nimport numpy as np\n\ndef missing_edges(x,y):\n\t\"\"\"\n\tCounts edges that are present in the original structure (x) but\n\tare missing in the learned structure (y). (lower is better)\n\t\"\"\"\n\tmissing = 0\n\tfor rv in x.nodes():\n\t\tif not y.has_node(rv):\n\t\t\tmissing += x.degree(rv)\n\t\telse:\n\t\t\tfor child in x.children(rv):\n\t\t\t\tif child not in y.children(rv):\n\t\t\t\t\tmissing += 1\n\treturn missing\n\ndef extra_edges(x,y):\n\t\"\"\"\n\tCounts edges that are found in the learned structure but are\n\tnot present in the original structure. (lower is better)\n\t\"\"\"\n\textra = 0\n\tfor rv in y.nodes():\n\t\tif not x.has_node(rv):\n\t\t\textra += y.degree(rv)\n\t\telse:\n\t\t\tfor child in y.children(rv):\n\t\t\t\tif child not in x.children(rv):\n\t\t\t\t\textra += 1\n\treturn extra\n\ndef incorrect_direction_edges(x,y):\n\t\"\"\"\n\tCounts directed edges in the learned structure\n\tthat are oriented incorrectly. (lower is better)\n\t\"\"\"\n\tincorrect_direction = 0\n\tfor rv in x.nodes():\n\t\tif x.has_node(rv):\n\t\t\tfor child in x.children(rv): # child is rv's child in x\n\t\t\t\tif rv in y.parents(rv): # child is rv's parent in y\n\t\t\t\t\tincorrect_direction += 1\n\treturn incorrect_direction\n\ndef hamming(x,y):\n\treturn missing_edges(x,y) + extra_edges(x,y) + incorrect_direction_edges(x,y)\n\t\n\n\n\n\n\n\n\n\n" }, { "path": "setup.py", "content": "#!/usr/bin/env python\n\nfrom distutils.core import setup\nfrom setuptools import find_packages\n\nsetup(name='pyBN',\n version='0.1',\n description='Bayesian Networks in Python',\n author='Nicholas Cullen',\n author_email='ncullen.th@dartmouth.edu',\n url='sites.dartmouth.edu/ncullen',\n packages=find_packages()\n )" } ]