[
{
"path": ".gitignore",
"content": "*~\n\\#*\n\n*\\#\n\n# Python temp\n*.pyc\n\n# Local information\n.tox\nnlpre.egg*\n.idea\n.coverage\ncoverage.xml\ncover/\ndist/\n*.bak\n.python-version"
},
{
"path": ".travis.yml",
"content": "sudo: false\nlanguage: python\npython:\n - \"3.6\"\n\ninstall:\n - pip install -r requirements.txt\n - pip install codecov\n\nscript:\n - nosetests --with-coverage --cover-package nlpre\n\nafter_success:\n - codecov"
},
{
"path": "MANIFEST.in",
"content": "include README.md\ninclude nlpre/dictionary/*\ninclude nlpre/spacy_models/en_core_web_sm-2.1.0/*\ninclude nlpre/spacy_models/en_core_web_sm-2.1.0/ner/*\ninclude nlpre/spacy_models/en_core_web_sm-2.1.0/parser/*\ninclude nlpre/spacy_models/en_core_web_sm-2.1.0/tagger/*\ninclude nlpre/spacy_models/en_core_web_sm-2.1.0/vocab/*"
},
{
"path": "README.md",
"content": "# Natural Language Preprocessing (NLPre)\n\n[](https://travis-ci.org/NIHOPA/NLPre)\n[](https://codecov.io/gh/NIHOPA/NLPre)\n[](https://pypi.python.org/pypi/nlpre)\n[](https://img.shields.io/pypi/pyversions/nlpre.svg)\n\n## Major version update! NLPre 2.0.0\n\n+ Backend NLP engine `pattern.en` has been replaced with `spaCy` v 2.1.0. This is a major fix for some of the problems with `pattern.en` including poor lemmatization. (eg. cytokine -> cytocow)\n+ Support for python 2 has been dropped\n+ Support for custom dictionaries in `replace_from_dictionary`\n+ Option for suffix to be used instead of prefix in `replace_from_dictionary`\n+ URL replacement can now remove emails\n+ `token_replacement` can remove symbols\n\nNLPre is a text (pre)-processing library that helps smooth some of the inconsistencies found in real-world data.\nCorrecting for issues like random capitalization patterns, strange hyphenations, and abbreviations are essential parts of wrangling textual data but are often left to the user.\n\nWhile this library was developed by the [Office of Portfolio Analysis](https://dpcpsi.nih.gov/opa/aboutus) at the [National Institutes of Health](https://www.nih.gov/) to correct for historical artifacts in our data, we envision this module to encompass a broad spectrum of problems encountered in the preprocessing step of natural language processing.\n\nNLPre is part of the [`word2vec-pipeline`](https://github.com/NIHOPA/word2vec_pipeline).\n\n### Installation\n\nFor the latest release, use\n\n pip install nlpre\n\nIf installing the python 3 version on Ubuntu, you may need to use\n\n sudo apt-get install libmysqlclient-dev\n\n### Example\n\n```python\nfrom nlpre import titlecaps, dedash, identify_parenthetical_phrases\nfrom nlpre import replace_acronyms, replace_from_dictionary\n\ntext = (\"LYMPHOMA SURVIVORS IN KOREA. Describe the correlates of unmet needs \"\n \"among non-Hodgkin lymphoma (NHL) surv- ivors in Korea and identify \"\n \"NHL patients with an abnormal white blood cell count.\")\n\nABBR = identify_parenthetical_phrases()(text)\nparsers = [dedash(), titlecaps(), replace_acronyms(ABBR),\n replace_from_dictionary(prefix=\"MeSH_\")]\n\nfor f in parsers:\n text = f(text)\n\nprint(text)\n\n''' lymphoma survivors in korea .\n Describe the correlates of unmet needs among non_Hodgkin_lymphoma\n ( non_Hodgkin_lymphoma ) survivors in Korea and identify non_Hodgkin_lymphoma\n patients with an abnormal MeSH_Leukocyte_Count . '''\n```\n\nA longer example highlighting a \"pipeline\" of changes can be found [here](long_example.md).\n\nTo see a detailed log of the changes made, set the level to `logging.INFO` or `logging.DEBUG`,\n\n```python\nimport nlpre, logging\nnlpre.logger.setLevel(logging.INFO)\n```\n\n### What's included?\n\n| Function | Description |\n| --- | --- |\n| [**replace_from_dictionary**](nlpre/replace_from_dictionary.py) | Replace phrases from an input dictionary. The replacement is done without regard to case, but punctuation is handled correctly. The [MeSH ](https://www.nlm.nih.gov/mesh/) (Medical Subject Headings) dictionary is built-in.
`(11-Dimethylethyl)-4-methoxyphenol is great`
`MeSH_Butylated_Hydroxyanisole is great` |\n| [**replace_acronyms**](nlpre/replace_acronyms.py) | Replaces acronyms and abbreviations found in a document with their corresponding phrase. If an acronym is explicitly identified with a phrase in a document, then all instances of that acronym in the document will be replaced with the given phrase. If there is no explicit indication what the phrase is within the document, then the most common phrase associated with the acronym in the given counter is used.
`The EPA protects trees`
`The Environmental_Protection_Agency protects trees`\n| [**identify_parenthetical_phrases**](nlpre/identify_parenthetical_phrases.py) | Identify abbreviations of phrases found in a parenthesis. Returns a counter and can be passed directly into [`replace_acronyms`](nlpre/replace_acronyms).
`'Environmental Protection Agency (EPA)`
`Counter((('Environmental', 'Protection', 'Agency'), 'EPA'):1)` |\n| [**separated_parenthesis**](nlpre/separated_parenthesis.py) | Separates parenthetical content into new sentences. This is useful when creating word embeddings, as associations should only be made within the same sentence. Terminal punctuation of a period is added to parenthetical sentences if necessary.
`Hello (it is a beautiful day) world.`
`Hello world. it is a beautiful day .` |\n| [**pos_tokenizer**](nlpre/pos_tokenizer.py) | Removes all words that are of a designated part-of-speech (POS) from a document. For example, when processing medical text, it is useful to remove all words that are not nouns or adjectives. POS detection is provided by the [`spaCy`](https://spacy.io/) module.
`The boy threw the ball into the yard`
`boy ball yard` |\n| [**unidecoder**](nlpre/unidecoder.py) | Converts Unicode phrases into ASCII equivalent.
`α-Helix β-sheet`
`a-Helix b-sheet` |\n| [**dedash**](nlpre/dedash.py) | Hyphenations are sometimes erroneously inserted when text is passed through a word-processor. This module attempts to correct the hyphenation pattern by joining words that if they appear in an English word list.
`How is the treat- ment going`
`How is the treatment going` |\n| [**decaps_text**](nlpre/decaps_text.py) | We presume that case is important, but only when it differs from title case. This class normalizes capitalization patterns.
`James and Sally had a fMRI`
`james and sally had a fMRI` |\n| [**titlecaps**](nlpre/titlecaps.py) | Documents sometimes have sentences that are entirely in uppercase (commonly found in titles and abstracts of older documents). This parser identifies sentences where every word is uppercase, and returns the document with these sentences converted to lowercase.
`ON THE STRUCTURE OF WATER.`
`On the structure of water .` |\n| [**token_replacement**](nlpre/token_replacement.py) | Simple token replacement.
`Observed > 20%`
`Observed greater-than 20 percent` |\n| [**separate_reference**](nlpre/separate_reference.py) | Separates and optionally removes references that have been concatenated onto words.
`Key feature of interleukin-1 in Drosophila3-5 and elegans(7).`
`Key feature of interleukin-1 in Drosophila and elegans .` |\n| [**url_replacement**](nlpre/url_replacement.py) | Removes or replaces URLs
`The source code is [here](www.github.com/NIHOPA/NLPre/).`
`The source code is [here](LINK).` |\n\n\n## Citations and Acknowledgments\n\n+ He, Jian and Chaomei Chen. [Predictive Effects of Novelty Measured by Temporal Embeddings on the Growth of Scientific Literature.](https://www.frontiersin.org/articles/10.3389/frma.2018.00009/full) Frontiers in Research Metrics and Analytics, 3, 9. (2018).\n\n+ He, Jian and Chaomei Chen. [Temporal Representations of Citations for Understanding the Changing Roles of Scientific Publications.](https://www.frontiersin.org/articles/10.3389/frma.2018.00027) Front. Res. Metr. Anal. (2018).\n\n+ Galea, Dieter et al. [Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimization.](http://www.aclweb.org/anthology/W18-2307) BioNLP (2018).\n\n## Contributors\n\n+ [Travis Hoppe](https://github.com/thoppe)\n+ [Harry Baker](https://github.com/HarryBaker)\n\n## License\n\nThis project is in the public domain within the United States, and\ncopyright and related rights in the work worldwide are waived through\nthe [CC0 1.0 Universal public domain dedication](https://creativecommons.org/publicdomain/zero/1.0/).\n\n"
},
{
"path": "development/README.md",
"content": "## Current speed test (spaCy 2.1.0)\n\n```\n time frac\nfunction \nunidecoder 0.000002 0.000005\ntoken_replacement 0.000038 0.000121\ndedash 0.000354 0.001111\nreplace_from_dictionary 0.000584 0.001831\nidentify_parenthetical_phrases 0.005599 0.017542\ntitlecaps 0.058357 0.182846\npos_tokenizer 0.058928 0.184635\ndecaps_text 0.059976 0.187919\nseparated_parenthesis 0.064683 0.202667\nreplace_acronyms 0.070638 0.221324\n```\n\n#### pattern.en speed test\n \n function time frac\n unidecoder 0.000008 0.000122\n token_replacement 0.000008 0.000125\n dedash 0.000369 0.005811\n replace_from_dictionary 0.000442 0.006967\n titlecaps 0.001944 0.030632\n decaps_text 0.002502 0.039425\n identify_parenthetical_phrases 0.005824 0.091770\n replace_acronyms 0.006972 0.109849\n separated_parenthesis 0.007339 0.115635\n pos_tokenizer 0.038059 0.599663\n\n## Development notes\n\n+ Update the version number in `nlpre/_version.py`\n+ Draft a release in github\n+ Update the new tarbar location in `setup.py`\n+ Build the distribution file `python setup.py sdist`\n\nUse the following `~/.pypirc` file (with an updated username and password)\n\n [distutils]\n index-servers=\n pypi\n test\n \n [test]\n repository=https://test.pypi.org/legacy/\n username=\n password=\n \n [pypi]\n repository = https://upload.pypi.org/legacy/\n username=\n password=\n\n+ Push the release to [pypi test](https://test.pypi.org/project/nlpre/) `twine upload -r test dist/*`\n+ Push the release to [pypi live](https://pypi.org/project/nlpre/) `twine upload dist/*`"
},
{
"path": "development/time_parsers.py",
"content": "import nlpre\nimport timeit\nimport pandas as pd\n\ndoc2 = open(\"../tests/doc2\").read()\n\nkeys = [\n 'decaps_text',\n 'dedash',\n 'unidecoder',\n 'identify_parenthetical_phrases',\n 'pos_tokenizer',\n 'replace_acronyms',\n 'separated_parenthesis',\n 'titlecaps',\n 'token_replacement',\n 'replace_from_dictionary',\n]\n\n\nPOS_Blacklist = [\"connector\",\"cardinal\",\n \"pronoun\",\"adverb\",\n \"symbol\",\"verb\",\n \"punctuation\",]\n\nABR = nlpre.identify_parenthetical_phrases()(doc2)\nkey0 = (('systemic', 'lupus', 'erythematosus'), 'SLE')\nfor n in range(50000):\n ABR[(key0[0],key0[1]+str(n))] += 1\n\nn = 50\ndata=[]\nfor key in keys:\n if key =='pos_tokenizer':\n parser = nlpre.pos_tokenizer(POS_Blacklist)\n elif key == \"replace_acronyms\":\n parser = nlpre.replace_acronyms(ABR)\n else:\n parser = getattr(nlpre, key)()\n\n if key=='unidecoder':\n func = lambda : [parser(x) for x in [doc2]]\n else:\n func = lambda : [parser(x) for x in [doc2]]\n cost = timeit.timeit(func, number=n) / n\n item = {'function':key, \"time\":cost}\n print (item)\n data.append(item)\ndf = pd.DataFrame(data)\ndf = df.set_index('function').sort_values('time')\ndf[\"frac\"] = df.time / df.time.sum()\n\nprint (df)\n\n\n\n"
},
{
"path": "fabfile.py",
"content": "from fabric.api import local\n\nexclude_command = \"--exclude nlpre/spacy_models/\"\n\n\ndef test():\n local(\"nosetests --with-coverage --cover-package nlpre --cover-html\")\n\n\ndef lint():\n local(f\"black -l 80 nlpre tests *.py {exclude_command}\")\n local(f\"flake8 nlpre --ignore=E501,E203,W503 {exclude_command}\")\n\n\ndef view_cover():\n local(\"xdg-open cover/index.html\")\n\n\ndef clean():\n local(\"rm -rvf .coverage cover/ .tox *.egg-info/ docs/ dist/\")\n for tag in [\"*.pyc\", \"*~\"]:\n local(f\"find . -name '%s' | xargs -I {tag} rm -v {tag}\")\n"
},
{
"path": "long_example.md",
"content": "# NLPre \"pipeline\" example\n\nThe source text comes from the abstract of PMID [18974824](https://www.ncbi.nlm.nih.gov/pubmed/18974824), \"The effects of NR2 subunit-dependent NMDA receptor kinetics on synaptic transmission and CaMKII activation.\"\n\n| Function | Description |\n| --- | --- |\n| original | N-Methyl-d-aspartic acid (NMDA) receptors are widely expressed in the brain and are critical for many forms of synaptic plasticity. Subtypes of the NMDA receptor NR2 subunit are differentially expressed during development; in the forebrain, the NR2B receptor is dominant early in development, and later both NR2A and NR2B are expressed. In heterologous expression systems, NR2A-containing receptors open more reliably and show much faster opening and closing kinetics than do NR2B-containing receptors. However, conflicting data, showing similar open probabilities, exist for receptors expressed in neurons. Similarly, studies of synaptic plasticity have produced divergent results, with some showing that only NR2A-containing receptors can drive long-term potentiation and others showing that either subtype is capable of driving potentiation. In order to address these conflicting results as well as open questions about the number and location of functional receptors in the synapse, we constructed a Monte Carlo model of glutamate release, diffusion, and binding to NMDA receptors and of receptor opening and closing as well as a model of the activation of calcium-calmodulin kinase II, an enzyme critical for induction of synaptic plasticity, by NMDA receptor-mediated calcium influx. Our results suggest that the conflicting data concerning receptor open probabilities can be resolved, with NR2A- and NR2B-containing receptors having very different opening probabilities. They also support the conclusion that receptors containing either subtype can drive long-term potentiation. We also are able to estimate the number of functional receptors at a synapse from experimental data. Finally, in our models, the opening of NR2B-containing receptors is highly dependent on the location of the receptor relative to the site of glutamate release whereas the opening of NR2A-containing receptors is not. These results help to clarify the previous findings and suggest future experiments to address open questions concerning NMDA receptor function. |\n| [**dedash**](nlpre/dedash.py) | N-Methyl-d-aspartic acid (NMDA) receptors are widely expressed in the brain and are critical for many forms of synaptic plasticity. Subtypes of the NMDA receptor NR2 subunit are differentially expressed during development; in the forebrain, the NR2B receptor is dominant early in development, and later both NR2A and NR2B are expressed. In heterologous expression systems, NR2A-containing receptors open more reliably and show much faster opening and closing kinetics than do NR2B-containing receptors. However, conflicting data, showing similar open probabilities, exist for receptors expressed in neurons. Similarly, studies of synaptic plasticity have produced divergent results, with some showing that only NR2A-containing receptors can drive long-term potentiation and others showing that either subtype is capable of driving potentiation. In order to address these conflicting results as well as open questions about the number and location of functional receptors in the synapse, we constructed a Monte Carlo model of glutamate release, diffusion, and binding to NMDA receptors and of receptor opening and closing as well as a model of the activation of calcium-calmodulin kinase II, an enzyme critical for induction of synaptic plasticity, by NMDA receptor-mediated calcium influx. Our results suggest that the conflicting data concerning receptor open probabilities can be resolved, with NR2A- and NR2B-containing receptors having very different opening probabilities. They also support the conclusion that receptors containing either subtype can drive long-term potentiation. We also are able to estimate the number of functional receptors at a synapse from experimental data. Finally, in our models, the opening of NR2B-containing receptors is highly dependent on the location of the receptor relative to the site of glutamate release whereas the opening of NR2A-containing receptors is not. These results help to clarify the previous findings and suggest future experiments to address open questions concerning NMDA receptor function. |\n| [**titlecaps**](nlpre/titlecaps.py) | N-Methyl-d-aspartic acid ( NMDA ) receptors are widely expressed in the brain and are critical for many forms of synaptic plasticity . Subtypes of the NMDA receptor NR2 subunit are differentially expressed during development ; in the forebrain , the NR2B receptor is dominant early in development , and later both NR2A and NR2B are expressed . In heterologous expression systems , NR2A-containing receptors open more reliably and show much faster opening and closing kinetics than do NR2B-containing receptors . However , conflicting data , showing similar open probabilities , exist for receptors expressed in neurons . Similarly , studies of synaptic plasticity have produced divergent results , with some showing that only NR2A-containing receptors can drive long-term potentiation and others showing that either subtype is capable of driving potentiation . In order to address these conflicting results as well as open questions about the number and location of functional receptors in the synapse , we constructed a Monte Carlo model of glutamate release , diffusion , and binding to NMDA receptors and of receptor opening and closing as well as a model of the activation of calcium-calmodulin kinase II , an enzyme critical for induction of synaptic plasticity , by NMDA receptor-mediated calcium influx . Our results suggest that the conflicting data concerning receptor open probabilities can be resolved , with NR2A - and NR2B-containing receptors having very different opening probabilities . They also support the conclusion that receptors containing either subtype can drive long-term potentiation . We also are able to estimate the number of functional receptors at a synapse from experimental data . Finally , in our models , the opening of NR2B-containing receptors is highly dependent on the location of the receptor relative to the site of glutamate release whereas the opening of NR2A-containing receptors is not . These results help to clarify the previous findings and suggest future experiments to address open questions concerning NMDA receptor function . |\n| [**replace_acronyms**](nlpre/replace_acronyms.py) | N-Methyl-d-aspartic acid ( NMDA ) receptors are widely expressed in the brain and are critical for many forms of synaptic plasticity .
Subtypes of the NMDA receptor NR2 subunit are differentially expressed during development ; in the forebrain , the NR2B receptor is dominant early in development , and later both NR2A and NR2B are expressed .
In heterologous expression systems , NR2A-containing receptors open more reliably and show much faster opening and closing kinetics than do NR2B-containing receptors .
However , conflicting data , showing similar open probabilities , exist for receptors expressed in neurons .
Similarly , studies of synaptic plasticity have produced divergent results , with some showing that only NR2A-containing receptors can drive long-term potentiation and others showing that either subtype is capable of driving potentiation .
In order to address these conflicting results as well as open questions about the number and location of functional receptors in the synapse , we constructed a Monte Carlo model of glutamate release , diffusion , and binding to NMDA receptors and of receptor opening and closing as well as a model of the activation of calcium-calmodulin kinase II , an enzyme critical for induction of synaptic plasticity , by NMDA receptor-mediated calcium influx .
Our results suggest that the conflicting data concerning receptor open probabilities can be resolved , with NR2A - and NR2B-containing receptors having very different opening probabilities .
They also support the conclusion that receptors containing either subtype can drive long-term potentiation .
We also are able to estimate the number of functional receptors at a synapse from experimental data .
Finally , in our models , the opening of NR2B-containing receptors is highly dependent on the location of the receptor relative to the site of glutamate release whereas the opening of NR2A-containing receptors is not .
These results help to clarify the previous findings and suggest future experiments to address open questions concerning NMDA receptor function . |\n| [**separated_parenthesis**](nlpre/separated_parenthesis.py) | N-Methyl-d-aspartic acid receptors are widely expressed in the brain and are critical for many forms of synaptic plasticity .
NMDA .
Subtypes of the NMDA receptor NR2 subunit are differentially expressed during development ; in the forebrain , the NR2B receptor is dominant early in development , and later both NR2A and NR2B are expressed .
In heterologous expression systems , NR2A-containing receptors open more reliably and show much faster opening and closing kinetics than do NR2B-containing receptors .
However , conflicting data , showing similar open probabilities , exist for receptors expressed in neurons .
Similarly , studies of synaptic plasticity have produced divergent results , with some showing that only NR2A-containing receptors can drive long-term potentiation and others showing that either subtype is capable of driving potentiation .
In order to address these conflicting results as well as open questions about the number and location of functional receptors in the synapse , we constructed a Monte Carlo model of glutamate release , diffusion , and binding to NMDA receptors and of receptor opening and closing as well as a model of the activation of calcium-calmodulin kinase II , an enzyme critical for induction of synaptic plasticity , by NMDA receptor-mediated calcium influx .
Our results suggest that the conflicting data concerning receptor open probabilities can be resolved , with NR2A - and NR2B-containing receptors having very different opening probabilities .
They also support the conclusion that receptors containing either subtype can drive long-term potentiation .
We also are able to estimate the number of functional receptors at a synapse from experimental data .
Finally , in our models , the opening of NR2B-containing receptors is highly dependent on the location of the receptor relative to the site of glutamate release whereas the opening of NR2A-containing receptors is not .
These results help to clarify the previous findings and suggest future experiments to address open questions concerning NMDA receptor function . |\n| [**replace_from_dictionary**](nlpre/replace_from_dictionary.py) | MeSH_N-Methylaspartate receptors are widely expressed in the brain and are critical for many forms of MeSH_Neuronal_Plasticity .
NMDA .
Subtypes of the NMDA receptor NR2 subunit are differentially expressed during development ; in the forebrain , the NR2B receptor is dominant early in development , and later both NR2A and NR2B are expressed .
In heterologous expression systems , NR2A-containing receptors open more reliably and show much faster opening and closing kinetics than do NR2B-containing receptors .
However , conflicting data , showing similar open probabilities , exist for receptors expressed in neurons .
Similarly , studies of MeSH_Neuronal_Plasticity have produced divergent results , with some showing that only NR2A-containing receptors can drive MeSH_Long-Term_Potentiation and others showing that either subtype is capable of driving potentiation .
In order to address these conflicting results as well as open questions about the number and location of functional receptors in the synapse , we constructed a Monte Carlo model of glutamate release , diffusion , and binding to MeSH_Receptors_N-Methyl-D-Aspartate and of receptor opening and closing as well as a model of the activation of calcium-MeSH_Calcium-Calmodulin-Dependent_Protein_Kinases II , an enzyme critical for induction of MeSH_Neuronal_Plasticity , by NMDA receptor-mediated calcium influx .
Our results suggest that the conflicting data concerning receptor open probabilities can be resolved , with NR2A - and NR2B-containing receptors having very different opening probabilities .
They also support the conclusion that receptors containing either subtype can drive MeSH_Long-Term_Potentiation .
We also are able to estimate the number of functional receptors at a synapse from experimental data .
Finally , in our models , the opening of NR2B-containing receptors is highly dependent on the location of the receptor relative to the site of glutamate release whereas the opening of NR2A-containing receptors is not .
These results help to clarify the previous findings and suggest future experiments to address open questions concerning NMDA receptor function . |\n| [**token_replacement**](nlpre/token_replacement.py) | MeSH_N-Methylaspartate receptors are widely expressed in the brain and are critical for many forms of MeSH_Neuronal_Plasticity .
NMDA .
Subtypes of the NMDA receptor NR2 subunit are differentially expressed during development ; in the forebrain , the NR2B receptor is dominant early in development , and later both NR2A and NR2B are expressed .
In heterologous expression systems , NR2A-containing receptors open more reliably and show much faster opening and closing kinetics than do NR2B-containing receptors .
However , conflicting data , showing similar open probabilities , exist for receptors expressed in neurons .
Similarly , studies of MeSH_Neuronal_Plasticity have produced divergent results , with some showing that only NR2A-containing receptors can drive MeSH_Long-Term_Potentiation and others showing that either subtype is capable of driving potentiation .
In order to address these conflicting results as well as open questions about the number and location of functional receptors in the synapse , we constructed a Monte Carlo model of glutamate release , diffusion , and binding to MeSH_Receptors_N-Methyl-D-Aspartate and of receptor opening and closing as well as a model of the activation of calcium-MeSH_Calcium-Calmodulin-Dependent_Protein_Kinases II , an enzyme critical for induction of MeSH_Neuronal_Plasticity , by NMDA receptor-mediated calcium influx .
Our results suggest that the conflicting data concerning receptor open probabilities can be resolved , with NR2A - and NR2B-containing receptors having very different opening probabilities .
They also support the conclusion that receptors containing either subtype can drive MeSH_Long-Term_Potentiation .
We also are able to estimate the number of functional receptors at a synapse from experimental data .
Finally , in our models , the opening of NR2B-containing receptors is highly dependent on the location of the receptor relative to the site of glutamate release whereas the opening of NR2A-containing receptors is not .
These results help to clarify the previous findings and suggest future experiments to address open questions concerning NMDA receptor function . |\n| [**decaps_text**](nlpre/decaps_text.py) | MeSH_N-Methylaspartate receptors are widely expressed in the brain and are critical for many forms of MeSH_Neuronal_Plasticity .
NMDA .
Subtypes of the NMDA receptor NR2 subunit are differentially expressed during development ; in the forebrain , the NR2B receptor is dominant early in development , and later both NR2A and NR2B are expressed .
In heterologous expression systems , NR2A-containing receptors open more reliably and show much faster opening and closing kinetics than do NR2B-containing receptors .
However , conflicting data , showing similar open probabilities , exist for receptors expressed in neurons .
Similarly , studies of MeSH_Neuronal_Plasticity have produced divergent results , with some showing that only NR2A-containing receptors can drive MeSH_Long-Term_Potentiation and others showing that either subtype is capable of driving potentiation .
In order to address these conflicting results as well as open questions about the number and location of functional receptors in the synapse , we constructed a Monte Carlo model of glutamate release , diffusion , and binding to MeSH_Receptors_N-Methyl-D-Aspartate and of receptor opening and closing as well as a model of the activation of calcium-MeSH_Calcium-Calmodulin-Dependent_Protein_Kinases II , an enzyme critical for induction of MeSH_Neuronal_Plasticity , by NMDA receptor-mediated calcium influx .
Our results suggest that the conflicting data concerning receptor open probabilities can be resolved , with NR2A - and NR2B-containing receptors having very different opening probabilities .
They also support the conclusion that receptors containing either subtype can drive MeSH_Long-Term_Potentiation .
We also are able to estimate the number of functional receptors at a synapse from experimental data .
Finally , in our models , the opening of NR2B-containing receptors is highly dependent on the location of the receptor relative to the site of glutamate release whereas the opening of NR2A-containing receptors is not .
These results help to clarify the previous findings and suggest future experiments to address open questions concerning NMDA receptor function . |\n| [**pos_tokenizer**](nlpre/pos_tokenizer.py) | MeSH_N-Methylaspartate receptor brain critical many form MeSH_Neuronal_Plasticity
NMDA
subtype NMDA receptor NR2 subunit development forebrain NR2B receptor dominant development NR2A NR2B
heterologou expression system receptor faster open kinetic NR2B-containing receptor
datum similar open probability receptor neuron
study MeSH_Neuronal_Plasticity divergent result show NR2A-containing receptor MeSH_Long-Term_Potentiation other subtype capable potentiation
order result open question number location functional receptor synapse monte carlo model glutamate release diffusion bound MeSH_Receptors_N-Methyl-D-Aspartate receptor open close model activation calcium-MeSH_Calcium-Calmodulin-Dependent_Protein_Kinases Ius enzyme critical induction MeSH_Neuronal_Plasticity NMDA receptor-mediated calcium influx
result datum receptor open probability NR2A NR2B-containing receptor different open probability
conclusion receptor subtype MeSH_Long-Term_Potentiation
able number functional receptor synapse experimental datum
model open NR2B-containing receptor dependent location receptor relative site glutamate release open NR2A-containing receptor