test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a633484",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "README.md\t  data\t\t\t\t    parsed_mappings.zip\r\n",
      "analysis\t  data.zip\t\t\t    pmc_full_texts\r\n",
      "assets\t\t  dataall_uncategorized_pmids.json  preprocessing\r\n",
      "caseolap\t  kg_analysis\t\t\t    requirements.txt\r\n",
      "caseolap_lift.py  output.zip\t\t\t    scratch\r\n",
      "config\t\t  parameters.txt\t\t    text_mining\r\n",
      "config.zip\t  parsed_mappings\t\t    utils\r\n"
     ]
    }
   ],
   "source": [
    "! ls ../"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "210eb863",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests as req\n",
    "import json\n",
    "import xml.etree.ElementTree as ET\n",
    "import itertools\n",
    "import os\n",
    "import argparse\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "from utils.biomedkg_utils import switch_dictset_to_dictlist\n",
    "from elasticsearch import Elasticsearch\n",
    "from multiprocessing import cpu_count, Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "00e81895",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_disease_mesh_name_to_id(mesh_desc_file = '../data/MeSH/desc2022.xml', \n",
    "                                output_folder='../parsed_mappings/MeSH'):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Map the disease MeSH names/terms to the\n",
    "      MeSH IDs.\n",
    "    '''\n",
    "    print('Mapping MeSH disease names to IDs')\n",
    "    tree = ET.parse(mesh_desc_file)\n",
    "    root = tree.getroot()   \n",
    "    \n",
    "    name2id = dict()\n",
    "\n",
    "    for ele in root:\n",
    "        try:\n",
    "            # MeSH Tree Number\n",
    "            tree_numbers = ele.find('TreeNumberList').findall('TreeNumber')\n",
    "\n",
    "            # If Tree is a disease\n",
    "            for tree_number in tree_numbers:\n",
    "                if tree_number.text.startswith(('C','F03')):\n",
    "                    tree_number = tree_number.text\n",
    "\n",
    "                    # ID to Name\n",
    "                    try:\n",
    "                        ID = ele.find('DescriptorUI').text\n",
    "                        name = ele.find('DescriptorName').find('String').text\n",
    "                        name2id.setdefault(name,set()).add(ID)\n",
    "                    except:\n",
    "                        pass\n",
    "        except:\n",
    "            continue        \n",
    "\n",
    "    name2id = switch_dictset_to_dictlist(name2id)\n",
    "    json.dump(name2id, open(os.path.join(output_folder,'name2id.json'),'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "ed908448",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_categories2terms(output_folder,\n",
    "                         meshterms_per_cat_file='../parsed_mappings/MeSH/meshterms_per_cat.json',\n",
    "                         category_names_file='../config/textcube_config.json'):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Map disease categories -> MeSH terms\n",
    "    '''\n",
    "    print('Mapping disease categories to their terms and subcategory terms')\n",
    "    terms_lol = json.load(open(meshterms_per_cat_file, 'r'))\n",
    "    category_names = json.load(open(category_names_file, 'r'))\n",
    "    category2terms = dict()\n",
    "    for category_name, term_list in zip(category_names, terms_lol):\n",
    "        category2terms[category_name] = term_list\n",
    "\n",
    "    json.dump(category2terms,\n",
    "              open(os.path.join(output_folder, 'category2terms.json'), 'w'))\n",
    "\n",
    "    return category2terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "ad72fac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mesh_synonyms_api(category2terms, name2id,\n",
    "                          output_folder='../parsed_mappings/MeSH/'):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Get MeSH synonyms via API\n",
    "    '''\n",
    "    category2synonyms = dict()\n",
    "    total_categories = len(category2terms)\n",
    "    for category_num, (category, terms) in enumerate(category2terms.items()):\n",
    "        print(str(category_num) + '/' + str(total_categories), end='\\r')\n",
    "\n",
    "        # Terms in category\n",
    "        for term in terms:\n",
    "\n",
    "            mesh_id = name2id[term]\n",
    "            assert len(mesh_id) == 1\n",
    "            r_string = 'https://id.nlm.nih.gov/mesh/lookup/details?descriptor=' + mesh_id[0]\n",
    "            r = req.get(r_string).json()\n",
    "\n",
    "            # Add term synonyms to the category\n",
    "            for entry in r['terms']:\n",
    "                synonym = entry['label']\n",
    "                category2synonyms.setdefault(category, set()).add(synonym)\n",
    "\n",
    "    category2synonyms = switch_dictset_to_dictlist(category2synonyms)\n",
    "    json.dump(category2synonyms,\n",
    "              open(os.path.join(output_folder, 'category2synonyms.json'), 'w'))\n",
    "\n",
    "    return category2synonyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "02206494",
   "metadata": {},
   "outputs": [],
   "source": [
    "def permute_mesh_synonyms(mesh_synonyms):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Permute MeSH synonyms\n",
    "\n",
    "    PARAMS:\n",
    "    - mesh_synonyms: list of mesh synonyms\n",
    "    '''\n",
    "\n",
    "    temp_mesh_synonyms = list()\n",
    "    for mesh_synonym in mesh_synonyms:\n",
    "        if ',' in mesh_synonym:\n",
    "            mesh_synonym = mesh_synonym.split(', ')\n",
    "            mesh_synonym_permuted = [' '.join(list(permuted_synonym)) for permuted_synonym in\n",
    "                                     list(itertools.permutations(mesh_synonym))]\n",
    "            temp_mesh_synonyms += mesh_synonym_permuted\n",
    "        else:\n",
    "            temp_mesh_synonyms.append(mesh_synonym)\n",
    "\n",
    "    return temp_mesh_synonyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "5904ea2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_category_to_permuted_mesh_synonyms(category2synonyms,\n",
    "                                           output_folder='../parsed_mappings/MeSH/'):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - permute the MeSH Synonyms because sometimes they're\n",
    "      written weirdly with commas (e.g., Order, Messed, Up, Is;\n",
    "      Messed, Order, Up)\n",
    "\n",
    "    PARAMS:\n",
    "    - category2synonyms: category -> MeSH synonyms mapping\n",
    "    '''\n",
    "    print('Mapping disease categories to permuted MeSH synonyms')\n",
    "\n",
    "    # Permuted synonym->category dictionary\n",
    "    category2permuted_synonyms, permuted_synonyms2category = dict(), dict()\n",
    "    for category, synonyms in category2synonyms.items():\n",
    "        synonyms = permute_mesh_synonyms(synonyms)\n",
    "        for synonym in synonyms:\n",
    "            category2permuted_synonyms.setdefault(category, set()).add(synonym)\n",
    "            permuted_synonyms2category.setdefault(synonym, set()).add(category)\n",
    "\n",
    "    category2permuted_synonyms = switch_dictset_to_dictlist(category2permuted_synonyms)\n",
    "    permuted_synonyms2category = switch_dictset_to_dictlist(permuted_synonyms2category)\n",
    "\n",
    "    json.dump(category2permuted_synonyms,\n",
    "              open(os.path.join(output_folder, 'category2permuted_synonyms.json'), 'w'))\n",
    "    json.dump(permuted_synonyms2category,\n",
    "              open(os.path.join(output_folder, 'permuted_synonyms2category.json'), 'w'))\n",
    "\n",
    "    return category2permuted_synonyms, permuted_synonyms2category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ffaf1076",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_uncategorized_pmids(index_name):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Get the PMIDs of all the PubMed articles that\n",
    "      don't have MeSH labels.\n",
    "    - Purpose: For applying to unlabeled PubMed articles\n",
    "\n",
    "    PARAMS:\n",
    "    - index_name: The name of the ElasticSearch index\n",
    "      where all the PubMed articles are indexed/stored\n",
    "    '''\n",
    "\n",
    "    es = Elasticsearch()\n",
    "    relevant_uncategorized_pmids = set()\n",
    "    for num_pmids, entry in enumerate(es_iterate_all_documents(es, index_name)):\n",
    "\n",
    "        # Publication's MeSH (if any)\n",
    "        unlabeled = (len(entry['MeSH']) == 0)\n",
    "\n",
    "        # Save PMIDs of unlabeled documents\n",
    "        if unlabeled:\n",
    "            relevant_uncategorized_pmids.add(entry['pmid'])\n",
    "\n",
    "            # Print progress\n",
    "            if num_pmids % 10000 == 0:\n",
    "                print(str(num_pmids) + ' PMIDs processed', end='\\r')\n",
    "\n",
    "    return list(relevant_uncategorized_pmids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "e2c897bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_document_text(entry):\n",
    "    '''\n",
    "    FUNCTION: \n",
    "    - Get the full text of the PubMed publication\n",
    "\n",
    "    PARAMS: \n",
    "    - entry (dict): parsed indexed publication \n",
    "    '''\n",
    "\n",
    "    # Title\n",
    "    title = entry['_source']['title']\n",
    "    if type(title) != str:\n",
    "        title = ''\n",
    "    title.replace('\\n', ' ').replace('\\t', ' ').replace('   ', ' ')\n",
    "\n",
    "    # Abstract\n",
    "    abstract = entry['_source']['abstract']\n",
    "    if type(abstract) != str:\n",
    "        abstract = ''\n",
    "    abstract.replace('\\n', ' ').replace('\\t', ' ').replace('   ', ' ')\n",
    "\n",
    "    return abstract, title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "0991d302",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ds_label_matching(batch_id, relevant_pmid_batch,\n",
    "                      index_name, index_type, \n",
    "                      label_unlabeled_only,\n",
    "                      label_labeled_only,\n",
    "                      label_all,\n",
    "                      filter_list, \n",
    "                      stop_at_this_many_pmids,\n",
    "                      run, test,\n",
    "                      output_folder):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Using MeSH term synonyms, label a document with a MeSH term if the \n",
    "      lowercased term matches exactly within the text.\n",
    "    \n",
    "    PARAMS:\n",
    "    - relevant_pmid_batch: PMIDs to look for in this batch\n",
    "    - temp_outfile: temporary output file of PMID | MeSH Term\n",
    "    '''\n",
    "    es = Elasticsearch()\n",
    "    if run:\n",
    "        rot = 'run'\n",
    "    elif test:\n",
    "        rot = 'test'\n",
    "    else:\n",
    "        raise Except('Did not specify if running or testing')\n",
    "\n",
    "    temp_outfile = os.path.join(output_folder, rot+'_temp_labeling'+str(batch_id)+'.txt')\n",
    "    procs = cpu_count()\n",
    "    category2permuted_synonyms = json.load(open(os.path.join(output_folder,'category2permuted_synonyms.json'),'r'))\n",
    "\n",
    "    with open(temp_outfile,'w') as fout, \\\n",
    "    open(temp_outfile[:-4]+'synonym'+'.txt','w') as fout1:\n",
    "\n",
    "        # Get PubMed article's text\n",
    "        for num_pmids, pmid in enumerate(relevant_pmid_batch):\n",
    "            entry = es.get(id = pmid, index = index_name, doc_type = index_type)\n",
    "\n",
    "            # Print progress and break early\n",
    "            if batch_id == 1 and num_pmids % 1000 == 0:\n",
    "                print(str(num_pmids)+' PMIDs processed in one of the batches',\\\n",
    "                      end='\\r')\n",
    "            if num_pmids > stop_at_this_many_pmids/procs:\n",
    "                break\n",
    "\n",
    "\n",
    "            # Determine which publications to find labels for\n",
    "            labeled_meshes = entry['_source']['MeSH']\n",
    "            labeled = len(labeled_meshes) > 0\n",
    "            unlabeled = len(labeled_meshes) == 0\n",
    "            if label_labeled_only and labeled:\n",
    "                pass\n",
    "            elif label_unlabeled_only and unlabeled:\n",
    "                pass\n",
    "            elif label_all:\n",
    "                pass\n",
    "            else:\n",
    "                continue\n",
    "            \n",
    "                        \n",
    "            # Publication's text (title, abstract, full text if provided)\n",
    "            abstract, title = get_document_text(entry)\n",
    "            document_text = title + ' ' + abstract\n",
    "            title = ' '+title+' '\n",
    "            title = title.replace(',',' ')\n",
    "            title = title.replace(':',' ')\n",
    "            \n",
    "            # Only consider documents that have certain broader key words\n",
    "            # E.g., for cardiovascular disease publications, only look \n",
    "            # at publications that say \"heart\" or \"cardiac\" \n",
    "            #dont_label = True\n",
    "            #for filter_word in filter_words:\n",
    "            #    if filter_word in document_text.lower():\n",
    "            #        dont_label = False\n",
    "            #        break\n",
    "            #if dont_label:\n",
    "            #    continue\n",
    "              \n",
    "            #print(dont_label, 'dont_label')\n",
    "            \n",
    "            #fout.write(title+' | '+'ignore'+'\\n')\n",
    "            \n",
    "            # Check if MeSH synonym is in the text\n",
    "            for category, synonyms in category2permuted_synonyms.copy().items():\n",
    "                found_syns = set()\n",
    "                one_syn_in_title = False\n",
    "                                                    \n",
    "                # Each synonym in a set category\n",
    "                for synonym in synonyms:\n",
    "\n",
    "                    # Synonym in title\n",
    "                    if ' '+synonym.lower()+' ' in title.lower():\n",
    "                        one_syn_in_title = True\n",
    "                        found_syns.add(synonym)\n",
    "                        #continue\n",
    "                        \n",
    "                    # Synonym in abstract\n",
    "                    \n",
    "                    if synonym.lower() in abstract.lower():\n",
    "                        add = True\n",
    "\n",
    "                        # If similar synonym hasn't been counted already\n",
    "                        for found_syn in found_syns:\n",
    "                            fsyn = found_syn.lower().replace('\\'','')\n",
    "                            syn = synonym.lower()\n",
    "                            if fsyn in syn or syn in fsyn:\n",
    "                                add = False\n",
    "                                break\n",
    "                        if add:\n",
    "                            found_syns.add(synonym)\n",
    "                        \n",
    "                    # Categorize text with 1+ synonym per category in the text\n",
    "                    # (This could be modified to include confidence levels for\n",
    "                    #  how many synonyms were found, remove break then)\n",
    "                    if one_syn_in_title or len(found_syns) > 1:\n",
    "                        #print(pmid, category, found_syns)\n",
    "                        fout.write(pmid+'|'+category+'\\n')\n",
    "                        fout1.write(pmid+'|'+synonym+'\\n')\n",
    "                        break\n",
    "                        \n",
    "        try:\n",
    "            print(str(num_pmids)+' PMIDs processed in one of the batches')\n",
    "        except:\n",
    "            # Not enough PMIDs to require doing all the batches\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "7fe400cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def multiprocess_ds_label_matching(pmids, index_name, index_type, \n",
    "                                   label_unlabeled_only,\n",
    "                                   label_labeled_only,\n",
    "                                   label_all,\n",
    "                                   filter_list,\n",
    "                                   the_function,\n",
    "                                   stop_at_this_many_pmids,\n",
    "                                   run = False,\n",
    "                                   test = False,\n",
    "                                   output_folder='../parsed_mappings/MeSH/'):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - This takes a list of strings and splits it into input\n",
    "      for separate processes. The processes then output \n",
    "      their results to temp files which are then merged. \n",
    "    \n",
    "    PARARMS:\n",
    "    - pmids: the list to be split into input for \n",
    "      a multiprocessing function\n",
    "    - the_function: the function that will use the list\n",
    "      as input for multiprocessing\n",
    "    '''    \n",
    "    # How many processors can be used\n",
    "    procs = cpu_count() if len(pmids) > cpu_count() else len(pmids)\n",
    "\n",
    "    # List of batches for multiprocessing\n",
    "    batches = [[] for i in range(procs)]   \n",
    "\n",
    "    # Length of input list \n",
    "    tot = len(pmids)\n",
    "\n",
    "    # Create batches and send to multiprocessing\n",
    "    for i, item in enumerate(pmids):\n",
    "\n",
    "        # Add synonym to a batch\n",
    "        b_id = i%procs\n",
    "        batches[b_id].append(item)\n",
    "\n",
    "    # Create a list of jobs \n",
    "    print(\"Running jobs...\")\n",
    "    jobs = []\n",
    "    for b_id, batch in enumerate(batches):\n",
    "        jobs.append(Process(target = the_function,\n",
    "                            args = [b_id, batch, \n",
    "                                    index_name, index_type, \n",
    "                                    label_unlabeled_only,\n",
    "                                    label_labeled_only,\n",
    "                                    label_all,\n",
    "                                    filter_list,\n",
    "                                    stop_at_this_many_pmids, \n",
    "                                    run, test, output_folder]))\n",
    "\n",
    "    # Run the jobs\n",
    "    for j in jobs: j.start()\n",
    "    for j in jobs: j.join()\n",
    "    print('Done!')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "7da679bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_pmid2new_mesh_labels(run=False, test=False, output_folder = '../parsed_mappings/MeSH/'):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Merges the separate files containing PMID|category_name\n",
    "      for the imputed category_name labels.\n",
    "    '''\n",
    "    \n",
    "    pmid2imputed_meshsynonym, pmid2imputed_category = dict(), dict()\n",
    "    procs = cpu_count()\n",
    "    if run:\n",
    "        rot = 'run'\n",
    "    elif test:\n",
    "        rot = 'test'\n",
    "\n",
    "    for batch_id in range(procs):\n",
    "        ''' PMID - MeSH Category '''\n",
    "        temp_outfile = os.path.join(output_folder, rot+'_temp_labeling'+str(batch_id)+'.txt')\n",
    "        with open(temp_outfile) as fin:\n",
    "            for line in fin:\n",
    "                line = line.split('|')\n",
    "\n",
    "                # PMID, MeSH\n",
    "                assert len(line) == 2\n",
    "                pmid = line[0]\n",
    "                mesh = line[1].strip()\n",
    "\n",
    "                # PMID->MeSH\n",
    "                pmid2imputed_category.setdefault(pmid, set()).add(mesh)\n",
    "                \n",
    "        ''' PMID - MeSH Synonym '''\n",
    "        with open(temp_outfile[:-4]+'synonym'+'.txt') as fin1:\n",
    "            for line in fin1:\n",
    "                line = line.split('|')\n",
    "\n",
    "                # PMID, MeSH\n",
    "                assert len(line) == 2\n",
    "                pmid = line[0]\n",
    "                mesh = line[1].strip()\n",
    "\n",
    "                # PMID->MeSH\n",
    "                pmid2imputed_meshsynonym.setdefault(pmid, set()).add(mesh)                \n",
    "                \n",
    "    pmid2imputed_category = switch_dictset_to_dictlist(pmid2imputed_category)\n",
    "    pmid2imputed_meshsynonym = switch_dictset_to_dictlist(pmid2imputed_meshsynonym)\n",
    "    print(len(pmid2imputed_category), 'PMIDs with imputed labels')\n",
    "\n",
    "    # Export PMID-Category mappings to dictionaries\n",
    "    json.dump(pmid2imputed_category, \n",
    "              open(os.path.join(output_folder, 'pmid2imputed_category.json'), 'w'))\n",
    "    json.dump(pmid2imputed_meshsynonym,\n",
    "              open(os.path.join(output_folder, 'pmid2imputed_mesh_synonym.json'), 'w'))\n",
    "    \n",
    "    return pmid2imputed_category, pmid2imputed_meshsynonym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "4c844b50",
   "metadata": {},
   "outputs": [],
   "source": [
    "def index_imputed_mesh_categories(index_name, index_type):    \n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - index the imputed MeSH categories as MeSH terms\n",
    "      in the ElasticSearch index\n",
    "    \n",
    "    PARAMS:\n",
    "    - index_name: Name of the ElasticSearch index\n",
    "    - index_type: Type name of the ElastichSearch index\n",
    "    '''\n",
    "    \n",
    "    es = Elasticsearch()\n",
    "    for pmid, imputed_category in pmid2imputed_category.items():\n",
    "        \n",
    "        # Get current MeSH terms\n",
    "        entry = es.get(id = pmid, \n",
    "                       index = index_name, \n",
    "                       doc_type = index_type)\n",
    "        mesh_terms = entry['_source']['MeSH']\n",
    "        mesh_terms += imputed_category\n",
    "        mesh_terms = list(set(mesh_terms))\n",
    "        \n",
    "        # Update each publication's index\n",
    "        es.update(index = index_name, \n",
    "                  id = pmid,\n",
    "                  doc_type = index_type,\n",
    "                  doc = {'MeSH': mesh_terms})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "811d7d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_textcube_files(data_folder='../data/',\n",
    "                          config_folder='../config/',\n",
    "                          parsed_mapping_folder='../parsed_mappings'):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Add category names to the considered MeSH Terms\n",
    "    - Add pmid-category to pmid2category mapping files\n",
    "    '''\n",
    "    meshterms_per_cat = json.load(open(os.path.join(mapping_folder, 'MeSH/meshterms_per_cat.json'), 'r'))\n",
    "    meshterms_per_cat = [set(meshlist) for meshlist in meshterms_per_cat]\n",
    "\n",
    "    category_names = json.load(open(os.path.join(config_folder, 'textcube_config.json'), 'r'))\n",
    "\n",
    "    for i in range(0, len(category_names)):\n",
    "        meshterms_per_cat[i].add(category_names[i])\n",
    "    meshterms_per_cat = [list(meshlist) for meshlist in meshterms_per_cat]\n",
    "    json.dump(meshterms_per_cat, open(os.path.join(mapping_folder, 'MeSH/meshterms_per_cat.json'), 'w'))\n",
    "\n",
    "    textcube_pmid2category = json.load(open(os.path.join(data_folder, 'textcube_pmid2category.json'), 'r'))\n",
    "    textcube_category2pmid = json.load(open(os.path.join(data_folder, 'textcube_category2pmid.json'), 'r'))\n",
    "\n",
    "    ''' Update textcube_category2pmid '''\n",
    "    category_names = json.load(open(os.path.join(config_folder, 'textcube_config.json'), 'r'))\n",
    "    category_name2num = {name: num for num, name in enumerate(category_names)}\n",
    "\n",
    "    for pmid, imputed_categories in pmid2imputed_category.items():\n",
    "        for imputed_category in imputed_categories:\n",
    "            cat_num = category_name2num[imputed_category]\n",
    "            textcube_category2pmid[cat_num].append(pmid)\n",
    "\n",
    "    for i in range(0, len(textcube_category2pmid)):\n",
    "        textcube_category2pmid[i] = list(set(textcube_category2pmid[i]))\n",
    "\n",
    "    ''' Update textcube_pmid2category '''\n",
    "    new_textcube_pmid2category = list()\n",
    "    for cat_num, pmid_list in enumerate(textcube_category2pmid):\n",
    "        for pmid in pmid_list:\n",
    "            new_textcube_pmid2category.append([pmid, cat_num])\n",
    "\n",
    "    json.dump(new_textcube_pmid2category, open(os.path.join(data_folder, 'textcube_pmid2category.json'), 'w'))\n",
    "    json.dump(textcube_category2pmid, open(os.path.join(data_folder, 'textcube_category2pmid.json'), 'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "599acf41",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_categorized_pmids(index_name, outpath):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Get the PMIDs of all the PubMed articles that\n",
    "      don't have MeSH labels.\n",
    "    - Purpose: For applying to unlabeled PubMed articles\n",
    "\n",
    "    PARAMS:\n",
    "    - index_name: The name of the ElasticSearch index\n",
    "      where all the PubMed articles are indexed/stored\n",
    "    - outpath: Where the categorized PMIDs will go.\n",
    "    '''\n",
    "\n",
    "    es = Elasticsearch()\n",
    "    relevant_categorized_pmids = set()\n",
    "    for num_pmids, entry in enumerate(es_iterate_all_documents(es, index_name)):\n",
    "\n",
    "        # Publication's MeSH (if any)\n",
    "        labeled = (len(entry['MeSH']) > 0)\n",
    "\n",
    "        # Save PMIDs of unlabeled documents\n",
    "        if labeled:\n",
    "            relevant_categorized_pmids.add(entry['pmid'])\n",
    "\n",
    "            # Print progress\n",
    "            if num_pmids % 10000 == 0:\n",
    "                print(str(num_pmids) + ' PMIDs processed', end='\\r')\n",
    "\n",
    "    relevant_categorized_pmids = list(relevant_categorized_pmids)\n",
    "    json.dump(relevant_categorized_pmids, open(outpath + 'relevant_categorized_pmids.json', 'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1966129",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_groundtruth_pmid2categories(relevant_pmid_batch, index_name, \n",
    "                                    index_type, permuted_synonyms2category):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - This gets the ground truth labels, the MeSH-labeled PubMed documents\n",
    "    \n",
    "    PARAMS:\n",
    "    - relevant_pmid_batch: This is the list of the PubMed IDs you want to \n",
    "      get ground truth for\n",
    "    - index_name: name of the ElasticSearch index\n",
    "    - index_type: name of the type of ElasticSearch index\n",
    "    - permuted_synonyms2category: MeSH synonyms -> category\n",
    "    '''\n",
    "    pmids2real_categories = dict()\n",
    "    es = Elasticsearch()\n",
    "    total_pmids = len(relevant_pmid_batch)\n",
    "    \n",
    "    for num_pmids, pmid in enumerate(relevant_pmid_batch):\n",
    "        if num_pmids % 10000 == 0:\n",
    "            print('Getting real mappings' + str(num_pmids)+'/'+str(total_pmids),\\\n",
    "                  end='\\r')\n",
    "        entry = es.get(id = pmid, index = index_name, doc_type = index_type)\n",
    "        pmids2real_categories[pmid] = set()\n",
    "        \n",
    "        # Publication's MeSH (if any)\n",
    "        meshes = entry['_source']['MeSH'] \n",
    "        for mesh in meshes:\n",
    "            try:\n",
    "                categories = permuted_synonyms2category[mesh]\n",
    "                for category in categories:\n",
    "                    pmids2real_categories[pmid].add(category)\n",
    "            except:\n",
    "                pass\n",
    "    pmids2real_categories = switch_dictset_to_dictlist(pmids2real_categories)\n",
    "    return pmids2real_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "942376b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_label_imputation(pmid2imputed_category, pmid2real_categories):\n",
    "    tp, fp, tn, fn = 0,0,0,0\n",
    "\n",
    "    for pmid in pmid2real_categories:\n",
    "        real_categories = pmid2real_categories[pmid]\n",
    "        try: imputed_categories = pmid2imputed_category[pmid]\n",
    "        except: imputed_categories = []\n",
    "        real_and_imputed = real_categories + imputed_categories\n",
    "        \n",
    "        for category in real_and_imputed:\n",
    "            #print(real_and_imputed)\n",
    "            #print(real_categories)\n",
    "            #print(imputed_categories)\n",
    "            \n",
    "            # Real = Yes\n",
    "            if category in real_categories:\n",
    "                \n",
    "                # Real = Yes, Impute = Yes\n",
    "                if category in imputed_categories:\n",
    "                    tp += 1\n",
    "                    \n",
    "                # Real = Yes, Impute = No\n",
    "                else:\n",
    "                    fn += 1\n",
    "                    \n",
    "            elif category not in real_categories:\n",
    "                \n",
    "                # Real = No, Impute = Yes\n",
    "                if category in imputed_categories:\n",
    "                    fp += 1            \n",
    "\n",
    "    print('Precision', round(tp/(tp+fp), 4))\n",
    "    print('Recall', round(tp/(tp+fn), 4))\n",
    "    print('TP', tp, 'FP', fp, 'FN',fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b24b44cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_name = 'pubmed_lift'\n",
    "index_type = 'pubmed_meta_lift'\n",
    "undo_category_label_imputation = False\n",
    "undo_last_category_label_imputation_only = False\n",
    "label_imputation = True\n",
    "run = True\n",
    "test = False\n",
    "\n",
    "if undo_category_label_imputation:\n",
    "    label_imputation = False\n",
    "\n",
    "if test == True:\n",
    "    run = False\n",
    "STOP_AT_THIS_MANY_PMIDS = 9999999999\n",
    "filter_words = ['placeholder', 'these arent used anymore']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "76ce78ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_directory = '../'\n",
    "data_folder=os.path.join(root_directory,\"data\")\n",
    "config_folder=os.path.join(root_directory,\"config\")\n",
    "mapping_folder=os.path.join(root_directory,\"parsed_mappings\")\n",
    "output_folder = os.path.join(mapping_folder,'Output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "2d09797a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mapping MeSH disease names to IDs\n",
      "Mapping disease categories to their terms and subcategory terms\n",
      "Mapping disease categories to permuted MeSH synonyms\n"
     ]
    }
   ],
   "source": [
    "''' Label imputation '''\n",
    "if label_imputation:\n",
    "\n",
    "    '''Map MeSH ID - Name'''\n",
    "    map_disease_mesh_name_to_id()\n",
    "\n",
    "\n",
    "    meshterms_per_cat_file = os.path.join(mapping_folder,'MeSH/meshterms_per_cat.json')\n",
    "    category_names_file = os.path.join(config_folder,'textcube_config.json')\n",
    "              \n",
    "    ''' Get MeSH Synonyms '''\n",
    "    # Category - MeSH Terms\n",
    "    category2main_terms = map_categories2terms(output_folder = output_folder,\n",
    "                            meshterms_per_cat_file = meshterms_per_cat_file,\n",
    "                            category_names_file = category_names_file)\n",
    "\n",
    "    # Category - MeSH Terms' Synonyms (including terms)\n",
    "    name2id = json.load(open(os.path.join(mapping_folder,'MeSH/meshterm-IS-meshid.json'),'r'))\n",
    "    category2main_terms = json.load(open(os.path.join(mapping_folder, 'MeSH/category2terms.json')))\n",
    "    try:  category2synonym_terms = json.load(open(os.path.join(mapping_folder,'MeSH/category2synonyms.json')))\n",
    "    except: category2synonym_terms = get_mesh_synonyms_api(category2main_terms, name2id,output_folder=output_folder)\n",
    "              \n",
    "    # Category - permuted MeSH Terms' Synonyms\n",
    "    temp1, temp2 =  map_category_to_permuted_mesh_synonyms(category2synonym_terms,output_folder=output_folder)\n",
    "    category2permuted_synonyms = temp1\n",
    "    permuted_synonyms2category = temp2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e60bd913",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "106bddd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running label imputation\n",
      "Removing the labels imputed last time\n",
      "No previous label imputation\n",
      "5186823 relevant pmids\n",
      "Running jobs...\n",
      "648352 PMIDs processed in one of the batches\n",
      "648352 PMIDs processed in one of the batches\n",
      "648351 PMIDs processed in one of the batches\n",
      "648352 PMIDs processed in one of the batches\n",
      "648352 PMIDs processed in one of the batches\n",
      "648352 PMIDs processed in one of the batches\n",
      "648352 PMIDs processed in one of the batches\n",
      "648352 PMIDs processed in one of the batches\n",
      "Done!\n",
      "13 PMIDs with imputed labels\n"
     ]
    },
    {
     "ename": "AuthorizationException",
     "evalue": "AuthorizationException(403, 'cluster_block_exception', 'blocked by: [FORBIDDEN/12/index read-only / allow delete (api)];')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAuthorizationException\u001b[0m                    Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1617/2224710615.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0mpmid2imputed_category\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpmid2imputed_meshsynonym\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge_pmid2new_mesh_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutput_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_folder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;31m# Index the imputed MeSH categories into their PMID entries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m     \u001b[0mindex_imputed_mesh_categories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m     \u001b[0;31m# Update the MeSH Terms Per Category file for the textcube\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0mupdate_textcube_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparsed_mapping_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmapping_folder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_1617/1587226168.py\u001b[0m in \u001b[0;36mindex_imputed_mesh_categories\u001b[0;34m(index_name, index_type)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;31m# Update each publication's index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         es.update(index = index_name, \n\u001b[0m\u001b[1;32m     25\u001b[0m                   \u001b[0mid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpmid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m                   \u001b[0mdoc_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/client/utils.py\u001b[0m in \u001b[0;36m_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    345\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m                     \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    348\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/client/__init__.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, index, id, body, doc_type, params, headers)\u001b[0m\n\u001b[1;32m   2090\u001b[0m             \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_make_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoc_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_update\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2092\u001b[0;31m         return self.transport.perform_request(\n\u001b[0m\u001b[1;32m   2093\u001b[0m             \u001b[0;34m\"POST\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2094\u001b[0m         )\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/transport.py\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, headers, params, body)\u001b[0m\n\u001b[1;32m    464\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    467\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/transport.py\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, headers, params, body)\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m                 status, headers_response, data = connection.perform_request(\n\u001b[0m\u001b[1;32m    428\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/connection/http_urllib3.py\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, params, body, timeout, ignore, headers)\u001b[0m\n\u001b[1;32m    289\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m             )\n\u001b[0;32m--> 291\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m         self.log_request_success(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/connection/base.py\u001b[0m in \u001b[0;36m_raise_error\u001b[0;34m(self, status_code, raw_data)\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Undecodable raw error response from server: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m         raise HTTP_EXCEPTIONS.get(status_code, TransportError)(\n\u001b[0m\u001b[1;32m    329\u001b[0m             \u001b[0mstatus_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_message\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madditional_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m         )\n",
      "\u001b[0;31mAuthorizationException\u001b[0m: AuthorizationException(403, 'cluster_block_exception', 'blocked by: [FORBIDDEN/12/index read-only / allow delete (api)];')"
     ]
    }
   ],
   "source": [
    "if run:\n",
    "    print('Running label imputation')\n",
    "    '''Undo last label imputation'''\n",
    "    try:\n",
    "        pmid2imp_cat_path = os.path.join(mapping_folder,'MeSH/pmid2imputed_category.json')\n",
    "        pmid2imputed_category = json.load(open(pmid2imp_cat_path))\n",
    "        try:\n",
    "            print('Removing the labels imputed last time')\n",
    "            remove_imputed_category_mesh_terms_previous_li(index_name,\n",
    "                                                           index_type,\n",
    "                                                           pmid2imputed_category)\n",
    "        except:\n",
    "            raise Exception('Couldnt remove the last indexed imputed labels')\n",
    "    except:\n",
    "        print('No previous label imputation')\n",
    "    '''Get relevant PMIDs'''\n",
    "    try:\n",
    "        relevant_pmids = json.load(open(data_folder+'all_uncategorized_pmids.json'))\n",
    "    except:\n",
    "        relevant_pmids = get_all_uncategorized_pmids(index_name)\n",
    "    json.dump(relevant_pmids, open(os.path.join(data_folder,'all_uncategorized_pmids.json'),'w'))\n",
    "\n",
    "    print(len(relevant_pmids), 'relevant pmids')\n",
    "        \n",
    "    ''' Impute missing MeSH labels '''\n",
    "    # Impute PMIDs' Categories (i.e., Impute missing MeSH labels)\n",
    "    multiprocess_ds_label_matching(pmids = relevant_pmids, \n",
    "                                   index_name = index_name, \n",
    "                                   index_type = index_type, \n",
    "                                   label_unlabeled_only = True,\n",
    "                                   label_labeled_only = False,\n",
    "                                   label_all = False,\n",
    "                                   filter_list = filter_words,\n",
    "                                   stop_at_this_many_pmids = STOP_AT_THIS_MANY_PMIDS,\n",
    "                                   the_function = ds_label_matching,\n",
    "                                   output_folder = output_folder,\n",
    "                                   run = True)\n",
    "    pmid2imputed_category, pmid2imputed_meshsynonym = merge_pmid2new_mesh_labels(run=True,output_folder=output_folder)\n",
    "    # Index the imputed MeSH categories into their PMID entries\n",
    "    index_imputed_mesh_categories(index_name=index_name, index_type=index_type)\n",
    "    # Update the MeSH Terms Per Category file for the textcube\n",
    "    update_textcube_files(data_folder=data_folder, config_folder=config_folder, parsed_mapping_folder=mapping_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b41a1a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "if test:\n",
    "    print('Testing label imputation')\n",
    "    \n",
    "    '''Get relevant PMIDs'''\n",
    "    # Ground truth: PMIDs in your study already labeled by NIH with MeSH terms\n",
    "    #relevant_pmids = json.load(open('../caseolap/data/pmids.json'))\n",
    "    try:\n",
    "        relevant_pmids = json.load(open(data_folder+'/relevant_categorized_pmids.json'))\n",
    "    except:\n",
    "        relevant_pmids_path = data_folder+'/relevant_categorized_pmids.json'\n",
    "        relevant_pmids = get_all_categorized_pmids('pubmed_lift', relevant_pmids_path)\n",
    "        json.dump(relevant_pmids, open(relevant_pmids_path, 'w'))\n",
    "    ''' Impute missing MeSH labels '''\n",
    "    # Impute PMIDs' Categories (i.e., Impute missing MeSH labels)\n",
    "    multiprocess_ds_label_matching(pmids = relevant_pmids, \n",
    "                                   index_name = index_name, \n",
    "                                   index_type = index_type, \n",
    "                                   label_unlabeled_only = False,\n",
    "                                   label_labeled_only = True,\n",
    "                                   label_all = False,\n",
    "                                   filter_list = filter_words,\n",
    "                                   stop_at_this_many_pmids = STOP_AT_THIS_MANY_PMIDS,\n",
    "                                   the_function = ds_label_matching,\n",
    "                                   test = True)\n",
    "    pmid2imputed_category, pmid2imputed_meshsynonym = merge_pmid2new_mesh_labels(test=True)\n",
    "    # PMID - Ground Truth Categories\n",
    "    try:\n",
    "        pmid2real_categories = json.load(open(output_folder+'/pmid2real_categories.json'))\n",
    "    except:\n",
    "        pmid2real_categories = ['']\n",
    "    if len(pmid2real_categories) != STOP_AT_THIS_MANY_PMIDS:\n",
    "        pmid2real_categories = get_groundtruth_pmid2categories(relevant_pmids, index_name, index_type, permuted_synonyms2category)\n",
    "        json.dump(pmid2real_categories, open(output_folder+'/pmid2real_categories.json','w'))\n",
    "    # Evaluate results on ground truth\n",
    "    evaluate_label_imputation(pmid2imputed_category, pmid2real_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa3b3d55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8f9b594",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests as req\n",
    "import json\n",
    "import xml.etree.ElementTree as ET\n",
    "import itertools\n",
    "import os\n",
    "import argparse\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "from utils.biomedkg_utils import switch_dictset_to_dictlist\n",
    "from elasticsearch import Elasticsearch\n",
    "from multiprocessing import cpu_count, Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e6a31f7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170000 PMIDs processed\r"
     ]
    }
   ],
   "source": [
    "def es_iterate_all_documents(es, index, pagesize=250, scroll_timeout=\"1m\", **kwargs):\n",
    "    \"\"\"\n",
    "    Helper to iterate ALL values from a single index\n",
    "    Yields all the documents.\n",
    "    Source: https://techoverflow.net/2019/05/07/elasticsearch-how-to-iterate-scroll-through-all-documents-in-index/\n",
    "    \"\"\"\n",
    "    is_first = True\n",
    "    while True:\n",
    "        # Scroll next\n",
    "        if is_first: # Initialize scroll\n",
    "            result = es.search(index=index, scroll=\"1m\", **kwargs, \n",
    "                               size = pagesize)\n",
    "            is_first = False\n",
    "        else:\n",
    "            result = es.scroll(\n",
    "                scroll_id = scroll_id,\n",
    "                scroll = scroll_timeout)\n",
    "        scroll_id = result[\"_scroll_id\"]\n",
    "        hits = result[\"hits\"][\"hits\"]\n",
    "\n",
    "        # Stop after no more docs\n",
    "        if not hits:\n",
    "            break\n",
    "        \n",
    "        # Yield each entry\n",
    "        yield from (hit['_source'] for hit in hits)\n",
    "\n",
    "def get_all_uncategorized_pmids(index_name):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Get the PMIDs of all the PubMed articles that\n",
    "      don't have MeSH labels.\n",
    "    - Purpose: For applying to unlabeled PubMed articles\n",
    "\n",
    "    PARAMS:\n",
    "    - index_name: The name of the ElasticSearch index\n",
    "      where all the PubMed articles are indexed/stored\n",
    "    '''\n",
    "\n",
    "    es = Elasticsearch()\n",
    "    relevant_uncategorized_pmids = set()\n",
    "    for num_pmids, entry in enumerate(es_iterate_all_documents(es, index_name)):\n",
    "\n",
    "        # Publication's MeSH (if any)\n",
    "        unlabeled = (len(entry['MeSH']) == 0)\n",
    "\n",
    "        # Save PMIDs of unlabeled documents\n",
    "        if unlabeled:\n",
    "            relevant_uncategorized_pmids.add(entry['pmid'])\n",
    "\n",
    "            # Print progress\n",
    "            if num_pmids % 10000 == 0:\n",
    "                print(str(num_pmids) + ' PMIDs processed', end='\\r')\n",
    "                if num_pmids > 50000:\n",
    "                    break\n",
    "\n",
    "    return list(relevant_uncategorized_pmids)\n",
    "\n",
    "index_name = 'pubmed_lift'\n",
    "temp = get_all_uncategorized_pmids(index_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd1192dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29661"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c25a9198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1744764705882353"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "29661/170000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "793c674f",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "TODO:\n",
    "- run get_all_uncategorized -> PMID for unlabeled\n",
    "- run get_all_categorized -> PMID for labeled\n",
    "- use these to generate abstract & titles heatmap for overlap \n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "906aab7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_uncategorized_pmids(index_name):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Get the PMIDs of all the PubMed articles that\n",
    "      don't have MeSH labels.\n",
    "    - Purpose: For applying to unlabeled PubMed articles\n",
    "\n",
    "    PARAMS:\n",
    "    - index_name: The name of the ElasticSearch index\n",
    "      where all the PubMed articles are indexed/stored\n",
    "    '''\n",
    "\n",
    "    es = Elasticsearch()\n",
    "    relevant_uncategorized_pmids = set()\n",
    "    for num_pmids, entry in enumerate(es_iterate_all_documents(es, index_name)):\n",
    "\n",
    "        has_full_text = (len(entry['full_text']) > 0)\n",
    "        \n",
    "        if has_full_text:\n",
    "            return entry\n",
    "        \n",
    "        # Publication's MeSH (if any)\n",
    "#         unlabeled = (len(entry['MeSH']) == 0)\n",
    "#         return entry\n",
    "        \n",
    "#         # Save PMIDs of unlabeled documents\n",
    "#         if unlabeled:\n",
    "#             relevant_uncategorized_pmids.add(entry['pmid'])\n",
    "\n",
    "#             # Print progress\n",
    "#             if num_pmids % 10000 == 0:\n",
    "#                 print(str(num_pmids) + ' PMIDs processed', end='\\r')\n",
    "#                 if num_pmids > 50000:\n",
    "#                     break\n",
    "\n",
    "#     return list(relevant_uncategorized_pmids)\n",
    "\n",
    "index_name = 'pubmed_lift'\n",
    "temp = get_all_uncategorized_pmids(index_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b260ef40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pmid': '27649969',\n",
       " 'title': 'Epac2-Rap1 Signaling Regulates Reactive Oxygen Species Production and Susceptibility to Cardiac Arrhythmias.',\n",
       " 'abstract': 'In the heart, β\\nInhibition of Epac2 signaling induced early afterdepolarization arrhythmias in ventricular myocytes. The underlying mechanism involved an increase in mitochondrial reactive oxygen species (ROS) and activation of the late sodium current (INa\\nOur findings show for the first time that Rap1 acts as a negative regulator of mitochondrial ROS production in the heart and that impaired Epac2-Rap1 signaling causes arrhythmias due to ROS-dependent activation of INa\\nEpac2-Rap1 signaling attenuates mitochondrial ROS production and reduces myocardial arrhythmia susceptibility. Antioxid. Redox Signal. 27, 117-132.',\n",
       " 'full_text': \"\\n\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\tIntroduction\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tS\\n\\t\\t\\t\\t\\tympathetic control of\\n\\t\\t\\t\\t\\t the myocardium makes a major contribution to the increase in cardiac output that occurs during exercise or stress. The inotropic and lusitropic effects of β\\n\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t-adrenergic (β\\n\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t-AD) stimulation involve cyclic adenosine monophosphate (cAMP) acting \\n\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t protein kinase-A (PKA) to increase phosphorylation of multiple intracellular targets, including the L-type calcium channel, phospholamban, troponin-I, myosin-binding protein-C, and the type-2 ryanodine receptor (RyR2) (\\n\\t\\t\\t\\t\\t5\\n\\t\\t\\t\\t\\t). In addition, cAMP acts \\n\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t exchange protein directly activated by cAMP (Epac): a guanine nucleotide exchange factor for the small GTPase Rap1, which increases the level of active Rap1GTP (\\n\\t\\t\\t\\t\\t25\\n\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tInnovation\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tOur findings show for the first time that activated Rap1 acts as a negative regulator of mitochondrial reactive oxygen species (ROS) production in the heart and that impaired Epac2-Rap1 signaling causes arrhythmias due to ROS-dependent activation of INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t. This has important implications for the use of chemotherapeutic agents that target Epac2-Rap1 signaling or pathological conditions where Rap1 signaling is impaired. However, we also show that inhibition of INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t provides a promising strategy to prevent arrhythmias caused by impaired Epac2-Rap1 signaling. Coadministration of an INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t inhibitor may allow therapeutic agents that target Epac2-Rap1 signaling to be tolerated without disturbances to cardiac rhythm.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tPrenylation (geranylgeranylation or farnesylation) of activated small GTPases is then necessary to permit interactions with target membranes (\\n\\t\\t\\t\\t\\t7\\n\\t\\t\\t\\t\\t). In the case of Rap1, prenylation involves geranylgeranylation \\n\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t geranylgeranyltransferase-1 (GGT-1).\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tIn ventricular myocytes, the Epac1 isoform is present in the perinuclear region (\\n\\t\\t\\t\\t\\t33\\n\\t\\t\\t\\t\\t) and its activation induces nuclear Ca\\n\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t signaling \\n\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t a pathway involving phospholipase-C epsilon (PLCξ), Ca\\n\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t/calmodulin-dependent kinase II (CaMKII), and activation of inositol trisphosphate receptors, leading to a hypertrophic response mediated by HDAC5 and MEF2 (\\n\\t\\t\\t\\t\\t23–25\\n\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t34\\n\\t\\t\\t\\t\\t). Epac2 exhibits a subsarcolemmal/t-tubule distribution and its activation contributes to a proarrhythmic increase in diastolic Ca\\n\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t spark frequency that occurs during β\\n\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t-AD stimulation (\\n\\t\\t\\t\\t\\t33\\n\\t\\t\\t\\t\\t). The underlying signaling pathway involves PLCξ, inositol trisphosphate receptor activation, and CaMKII-dependent phosphorylation of RyR2 (\\n\\t\\t\\t\\t\\t34\\n\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tEpac2-mediated arrhythmias are benign in the normal heart, but may become of significance in heart failure where sustained β\\n\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t-AD activation occurs (\\n\\t\\t\\t\\t\\t33\\n\\t\\t\\t\\t\\t). Under physiological conditions, the overall effect of simultaneous PKA and Epac2 activation may be Rap1-dependent facilitation of Ca\\n\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t-induced Ca\\n\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t release (\\n\\t\\t\\t\\t\\t27\\n\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tInhibitors of Epac and GGT-1 are currently being investigated as therapeutics for both cancer and cardiovascular disease (\\n\\t\\t\\t\\t\\t32\\n\\t\\t\\t\\t\\t). Inhibition of either Epac or GGT-1 would be expected to reduce signaling \\n\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t Rap1. However, limited evidence from previous studies suggests that impaired Rap1 signaling may adversely affect cardiac function, for example, GGT-1 inhibitors caused sudden death in mice; an effect that correlated with reduced Rap1 geranylgeranylation (\\n\\t\\t\\t\\t\\t21\\n\\t\\t\\t\\t\\t). In a clinical study, prolongation of the QT interval, arrhythmias, and syncope were reported in patients following administration of a combined GGT-1 and farnesyl transferase inhibitor (\\n\\t\\t\\t\\t\\t47\\n\\t\\t\\t\\t\\t). These findings are consistent with the phenotype of Rap1A knockout mice, which exhibit increased arrhythmia susceptibility (\\n\\t\\t\\t\\t\\t6\\n\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tStudies on other cell types have implicated Epac-Rap1 signaling in the control of reactive oxygen species (ROS) production, for example, Epac-Rap1 signaling suppressed ROS production in T lymphocytes and retinal pigment epithelium (\\n\\t\\t\\t\\t\\t37\\n\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t38\\n\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t54\\n\\t\\t\\t\\t\\t). In kidney epithelial cells, Epac-Rap1 signaling inhibited superoxide production by mitochondria (\\n\\t\\t\\t\\t\\t46\\n\\t\\t\\t\\t\\t). As ROS are known to be involved in both physiological (\\n\\t\\t\\t\\t\\t30\\n\\t\\t\\t\\t\\t) and pathological (\\n\\t\\t\\t\\t\\t39\\n\\t\\t\\t\\t\\t) responses to β\\n\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t-AD stimulation and in susceptibility to arrhythmias (\\n\\t\\t\\t\\t\\t16\\n\\t\\t\\t\\t\\t), a regulatory influence of Epac-Rap1 signaling on ROS is likely to also be important in the myocardium, yet this is a largely unanswered question.\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tThe aim of the present study was to investigate the role of Epac2-Rap1 signaling in the heart. Basal Rap1A activation was reduced by selective inhibition of Epac2 (\\n\\t\\t\\t\\t\\t35\\n\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t50\\n\\t\\t\\t\\t\\t) in adult rat ventricular myocytes (ARVMs). This was accompanied by early afterdepolarization arrhythmias (EADs), which occurred due to an increase in mitochondrial ROS production, activation of the late Na current (INa\\n\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t), and action potential (AP) prolongation. Both \\n\\t\\t\\t\\t\\tin vitro\\n\\t\\t\\t\\t\\t and \\n\\t\\t\\t\\t\\tin vivo\\n\\t\\t\\t\\t\\t, the proarrhythmic effects of Epac2 inhibition were recapitulated by inhibition of GGT-1 and therefore Rap1 signaling (\\n\\t\\t\\t\\t\\t12\\n\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t48\\n\\t\\t\\t\\t\\t). The EADs induced by Epac2 or GGT-1 inhibition were prevented by ranolazine, an inhibitor of INa\\n\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t.\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\tThese findings show that Epac2-Rap1 is a negative regulator of mitochondrial ROS production in the heart, explain arrhythmias in patients receiving chemotherapeutics, which inhibit GGT-1, and offer a potential treatment with ranolazine.\\n\\t\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\tResults\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tInhibition of Epac2 induces EAD-like Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t oscillations in ARVMs\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tARVMs were loaded with fluo-4 and field stimulated at 0.1\\u2009Hz during confocal Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t imaging in line-scan mode (\\n\\t\\t\\t\\t\\t\\tFig. 1A\\n\\t\\t\\t\\t\\t\\t, upper). As shown in the associated line profiles, introduction of the selective Epac2 inhibitor, ESI-05 (\\n\\t\\t\\t\\t\\t\\t35\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t50\\n\\t\\t\\t\\t\\t\\t), to the bathing solution was initially followed by slight prolongation of the early descending phase of the [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t transient (\\n\\t\\t\\t\\t\\t\\tFig. 1A\\n\\t\\t\\t\\t\\t\\t, middle and lower). After 1–2\\u2009min, this prolongation developed into a distinct plateau phase comprising [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t oscillations; a characteristic of EAD arrhythmias. Resting [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t increased in the presence of ESI-05 and spontaneous SR Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t waves were often apparent between triggered events. Following introduction of ESI-05, EAD-like changes in the Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t transient occurred in 66% of cells (\\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200915; \\n\\t\\t\\t\\t\\t\\tFig. 1B\\n\\t\\t\\t\\t\\t\\t), but were absent from the control group (\\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200930).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\tFIG. 1.\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tInhibition of Epac2 induces EAD-like [Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t] oscillations. (A)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t Typical confocal line-scan recording from a field-stimulated ARVM loaded with fluo-4 (\\n\\t\\t\\t\\t\\t\\t\\t\\tupper\\n\\t\\t\\t\\t\\t\\t\\t\\t), the associated line profile (\\n\\t\\t\\t\\t\\t\\t\\t\\tmiddle\\n\\t\\t\\t\\t\\t\\t\\t\\t), and selected Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t transients on an expended timescale, both individually and normalized/superimposed (\\n\\t\\t\\t\\t\\t\\t\\t\\tlower\\n\\t\\t\\t\\t\\t\\t\\t\\t). Introduction of ESI-05 (25\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t) was followed by prolongation of the descending phase of the Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t transient, which subsequently developed a distinct plateau phase with Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t oscillations. s.p.: spontaneous SR Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t release; \\n\\t\\t\\t\\t\\t\\t\\t\\tblack arrow\\n\\t\\t\\t\\t\\t\\t\\t\\t: early prolongation of descending phase; \\n\\t\\t\\t\\t\\t\\t\\t\\tred arrows\\n\\t\\t\\t\\t\\t\\t\\t\\t: plateau/Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t oscillations. ‘*’ indicates corresponding Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t transient. \\n\\t\\t\\t\\t\\t\\t\\t\\t(B)\\n\\t\\t\\t\\t\\t\\t\\t\\t Cumulative data showing the percentage of cells exhibiting EAD-like Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t oscillations following addition of ESI-05 (25\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t), ESI-05 + isoproterenol (ISO, 20\\u2009n\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t), ESI-05 + KN93 (10\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t), and ESI-05 + KN92 (10\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t). No cells exhibited EADs in the absence of ESI-05 (\\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u200930). \\n\\t\\t\\t\\t\\t\\t\\t\\t(C)\\n\\t\\t\\t\\t\\t\\t\\t\\t Original (\\n\\t\\t\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t\\t\\t) and cumulative data (\\n\\t\\t\\t\\t\\t\\t\\t\\tright\\n\\t\\t\\t\\t\\t\\t\\t\\t) showing the effects of ESI-05 (25\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t) on the frequency of spontaneous Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t sparks in ARVMs. **\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01, n.s., not significant. ARVM, adult rat ventricular myocyte; EAD, early afterdepolarization arrhythmias; ISO, isoproterenol. To see this illustration in color, the reader is referred to the web version of this article at \\n\\t\\t\\t\\t\\t\\t\\t\\twww.liebertpub.com/ars\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIn the presence of 20\\u2009n\\n\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t isoproterenol (ISO), the percentage of cells affected by ESI-05 increased to 77.4% (\\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200931). After preincubation with the CaMKII inhibitor, KN93, the percentage of cells affected by ESI-05 was markedly reduced to 21% (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05, \\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200919), while the inactive control compound (KN92) had no significant effect (\\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200930, \\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009>\\u20090.05; \\n\\t\\t\\t\\t\\t\\tFig. 1B\\n\\t\\t\\t\\t\\t\\t). Although not studied in detail, another Epac2 inhibitor (HJC0350) had effects on the electrically stimulated [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t transient that were comparable with those of ESI-05 (\\n\\t\\t\\t\\t\\t\\tSupplementary Fig. S1\\n\\t\\t\\t\\t\\t\\t; Supplementary Data are available online at \\n\\t\\t\\t\\t\\t\\twww.liebertpub.com/ars\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tAs shown in \\n\\t\\t\\t\\t\\t\\tFigure 1C\\n\\t\\t\\t\\t\\t\\t, the changes in resting and triggered Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t release were accompanied by an increase in Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t spark frequency of 64.3%\\u2009±\\u200914.8% (\\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200926, \\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01). For detailed information on Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t spark properties, see \\n\\t\\t\\t\\t\\t\\tSupplementary Figure S2\\n\\t\\t\\t\\t\\t\\t. The increase in Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t spark frequency induced by ESI-05 was blocked by the CaMKII inhibitor, KN93, but not the inactive control compound, KN92 (\\n\\t\\t\\t\\t\\t\\tSupplementary Fig. S3A\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tESI-05 decreases Rap1GTP and its effects on [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t are mimicked by Rap1 inhibition\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tThe effects of ESI-05 might reflect inhibition of Epac2 signaling and a consequent decrease in the active form of Rap1 (Rap1GTP). To investigate this possibility, a pull-down assay was used to measure Rap1GTP in ARVMs.\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tAs shown in \\n\\t\\t\\t\\t\\t\\tFigure 2A\\n\\t\\t\\t\\t\\t\\t, active Rap1GTP was consistently detected in ARVMs under control conditions. This basal level of Rap1GTP was reduced ∼50% following inhibition of Epac2 with ESI-05 (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01, \\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u20094). Conversely, Rap1GTP was significantly increased by the Epac activator, 8-CPT (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05, \\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u20095), or by interventions that increase [cAMP]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t, including β\\n\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t-adrenoceptor (β\\n\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t-ADR) activation with ISO alone (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01, \\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u20095), or in combination with the phosphodiesterase inhibitor, rolipram (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.005, \\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u20097). These findings suggest that in common with PKA (\\n\\t\\t\\t\\t\\t\\t31\\n\\t\\t\\t\\t\\t\\t), Epac2 activation is significant under basal conditions in ARVMs. Basal Rap1 activation has also been reported in neonatal cardiac myocytes (\\n\\t\\t\\t\\t\\t\\t45\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\tFIG. 2.\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\tEpac2 inhibition decreases basal Rap1GTP and the effects of ESI-05 are mimicked by Rap1GTP inhibition. (A)\\n\\t\\t\\t\\t\\t\\t\\t\\t Original (\\n\\t\\t\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t\\t\\t) and cumulative data (\\n\\t\\t\\t\\t\\t\\t\\t\\tright\\n\\t\\t\\t\\t\\t\\t\\t\\t) showing that Rap1GTP was detectable under basal conditions (see the \\n\\t\\t\\t\\t\\t\\t\\t\\tMaterials and Methods\\n\\t\\t\\t\\t\\t\\t\\t\\t section) in ARVMS and could be reduced by Epac2 inhibition with ESI-05 or increased by the Epac activator 8-CPT (25\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t), ISO (1\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t), or a lower level of ISO (10\\u2009n\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t) in combination with the phosphodiesterase inhibitor, rolipram (20\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t). ISO was added together with the β\\n\\t\\t\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t\\t\\t antagonist CI 118,551 (1\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t). \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20094–12, *\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05, **\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01, ***\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.005. \\n\\t\\t\\t\\t\\t\\t\\t\\t(B)\\n\\t\\t\\t\\t\\t\\t\\t\\t Schematic showing that Epac activation leads to increased levels of active Rap1GTP and that geranylgeranylation of Rap1GTP by GGT-1 facilitates membrane association and interactions with effector proteins. Inhibition of GGT-1 with GGTI-298 blocks the downstream effects of Rap1GTP. \\n\\t\\t\\t\\t\\t\\t\\t\\t(C)\\n\\t\\t\\t\\t\\t\\t\\t\\t Typical confocal line-scan recordings from a field-stimulated ARVM loaded with fluo-4 (\\n\\t\\t\\t\\t\\t\\t\\t\\tupper\\n\\t\\t\\t\\t\\t\\t\\t\\t), the associated line profile (\\n\\t\\t\\t\\t\\t\\t\\t\\tmiddle\\n\\t\\t\\t\\t\\t\\t\\t\\t), and selected Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t transients presented on an expended timescale, both individually and normalized/superimposed (\\n\\t\\t\\t\\t\\t\\t\\t\\tlower\\n\\t\\t\\t\\t\\t\\t\\t\\t). Inhibition of GGT-1 with GGTI-298 (0.1\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t) mimicked the effects of ESI-05 on [Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t\\t\\t transients. s.p.: spontaneous SR Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t release; \\n\\t\\t\\t\\t\\t\\t\\t\\tblack arrow\\n\\t\\t\\t\\t\\t\\t\\t\\t: early prolongation of descending phase; \\n\\t\\t\\t\\t\\t\\t\\t\\tred arrows\\n\\t\\t\\t\\t\\t\\t\\t\\t: plateau/Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t oscillations. ‘*’ indicates corresponding Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t transient. Similar effects occurred in 8 of 10 cells from three hearts. To see this illustration in color, the reader is referred to the web version of this article at \\n\\t\\t\\t\\t\\t\\t\\t\\twww.liebertpub.com/ars\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIf the effects of ESI-05 are due to a decrease in Rap1GTP, then inhibition of Rap1 signaling should also be proarrhythmic. As illustrated in \\n\\t\\t\\t\\t\\t\\tFigure 2B\\n\\t\\t\\t\\t\\t\\t, previous work has shown that binding of active Rap1 with downstream targets requires its geranylgeranylation \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t GGT-1 and that selective inhibition of GGT-1 blocks Rap1-mediated effects (\\n\\t\\t\\t\\t\\t\\t12\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t48\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIn the present study, introduction of the GGT-1 inhibitor, GGTI-298, recapitulated the effects of ESI-05, that is, GGTI-298 induced prolongation of the early descending phase of the [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t transient, which then developed into a distinct plateau, with [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t oscillations (\\n\\t\\t\\t\\t\\t\\tFig. 2C\\n\\t\\t\\t\\t\\t\\t). As in \\n\\t\\t\\t\\t\\t\\tFigure 1\\n\\t\\t\\t\\t\\t\\t, the increase in Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t spark frequency was a relatively early event, while the sustained increase in resting [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t] (signifying Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t overload) typically occurred after the emergence of the sustained Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t transients. Similar results were obtained in 8 of 10 cells exposed to GGTI-298.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tAlthough not studied in detail, another GGT-1 inhibitor (GGTI-2147) had effects on the electrically stimulated [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t transient that were comparable with those of GGTI-298 (\\n\\t\\t\\t\\t\\t\\tSupplementary Fig. S1\\n\\t\\t\\t\\t\\t\\t). As with ESI-05, spontaneous SR Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t waves often occurred between triggered Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t release events and resting [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t, and Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t spark frequency increased during exposure to GGTI-298 (\\n\\t\\t\\t\\t\\t\\tSupplementary Fig. S2\\n\\t\\t\\t\\t\\t\\t). The increase in Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t spark frequency induced by GGTI-298 was also blocked by the CaMKII inhibitor, KN93, but not the inactive control compound, KN92 (\\n\\t\\t\\t\\t\\t\\tSupplementary Fig. S3B\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tInhibition of Epac2 or GGT-1 increases mitochondrial ROS production\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tExperiments were carried out to investigate the mechanism underlying the changes in Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t regulation induced by ESI-05 or GGTI-298. In other cell types, Rap1 signaling can modify ROS production \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t either NADPH oxidase (NOX) or mitochondria (\\n\\t\\t\\t\\t\\t\\t37\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t38\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t46\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t54\\n\\t\\t\\t\\t\\t\\t). In the present study, preincubation with the NOX inhibitor, diphenyleneiodonium (DPI), failed to prevent the EAD-like Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t oscillations induced by ESI-05 or GGTI-298 (\\n\\t\\t\\t\\t\\t\\tFig. 3A, C\\n\\t\\t\\t\\t\\t\\t). However, the reducing agent, dithiothreitol (DTT), the SOD mimetic, MnTMPyP, or the mitochondria-targeted ROS scavenger, mitoTEMPO, typically blocked the Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t transient changes induced by both drugs.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\tFIG. 3.\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\tIncreased mitochondrial ROS production underlies effects of ESI-05 and GGTI-298. (A)\\n\\t\\t\\t\\t\\t\\t\\t\\t Representative line-scan images (\\n\\t\\t\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t\\t\\t) and associated line profiles (\\n\\t\\t\\t\\t\\t\\t\\t\\tright\\n\\t\\t\\t\\t\\t\\t\\t\\t) obtained from field-stimulated ARVMs before (\\n\\t\\t\\t\\t\\t\\t\\t\\tupper\\n\\t\\t\\t\\t\\t\\t\\t\\t) and after (\\n\\t\\t\\t\\t\\t\\t\\t\\tlower\\n\\t\\t\\t\\t\\t\\t\\t\\t) introduction of ESI-05 (25\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t) following preincubation with the NOX inhibitor, DPI (20\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t), the reducing agent, dithiothreitol DTT (2\\u2009m\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t), the SOD mimetic, MnTMPyP (100\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t), or the mitochondria targeted antioxidant, mitoTEMPO (30\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t). \\n\\t\\t\\t\\t\\t\\t\\t\\t(B)\\n\\t\\t\\t\\t\\t\\t\\t\\t Cumulative data showing the effects of all inhibitors tested on the percentage of cells exhibiting EAD-like [Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t] oscillations, additionally including the mitochondrial inhibitor, rotenone (100\\u2009n\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t), and the nitric oxide synthase inhibitor, L-NAME (500\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t). \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u200915–30. \\n\\t\\t\\t\\t\\t\\t\\t\\t(C, D)\\n\\t\\t\\t\\t\\t\\t\\t\\t Show the same protocol and presentation of data for ARVMs exposed to GGT-298 (0.1\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t). \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u200910–27. \\n\\t\\t\\t\\t\\t\\t\\t\\t(E)\\n\\t\\t\\t\\t\\t\\t\\t\\t Original xy confocal images showing mitoSOX fluorescence recorded at time zero and then after 12\\u2009min, under control conditions (\\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u200915) (\\n\\t\\t\\t\\t\\t\\t\\t\\tupper\\n\\t\\t\\t\\t\\t\\t\\t\\t) or in the presence of ESI-05 (25\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t\\t\\tmiddle\\n\\t\\t\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u200917) or GGTI-298 (0.1\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t\\t\\tlower\\n\\t\\t\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u200913). \\n\\t\\t\\t\\t\\t\\t\\t\\tYellow broken lines\\n\\t\\t\\t\\t\\t\\t\\t\\t indicate cell outline. \\n\\t\\t\\t\\t\\t\\t\\t\\t(F)\\n\\t\\t\\t\\t\\t\\t\\t\\t Cumulative data showing the time-dependent change in mitoTEMPO fluorescence under control conditions or in the presence of ESI-05 or GGTI-298. *\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05, ** \\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01, ***, \\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.005, n.s., not significant, \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u200913–17. DPI, diphenyleneiodonium; DTT, dithiothreitol; NOX, NADPH oxidase. To see this illustration in color, the reader is referred to the web version of this article at \\n\\t\\t\\t\\t\\t\\t\\t\\twww.liebertpub.com/ars\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tCumulative data summarizing the efficacy of various interventions to prevent the effects of ESI-05 or GGTI-298 are shown in \\n\\t\\t\\t\\t\\t\\tFigure 3B and D\\n\\t\\t\\t\\t\\t\\t. For both ESI-05 and GGTI-298, DPI had no significant effect, while DTT, MnTMPyP, and mitoTEMPO markedly reduced the percentage of cells that responded to the drugs. MitoTEMPO was consistently more effective than MnTMPyP, which might be explained if the primary action of ESI-05 and GGTI-298 is to increase mitochondrial superoxide production. Consistent with this, the mitochondrial complex 1 inhibitor, rotenone, also markedly inhibited the effects of ESI-05 and GGTI-298 (\\n\\t\\t\\t\\t\\t\\tFig. 3B, D\\n\\t\\t\\t\\t\\t\\t). However, preincubation of cells with the nitric oxide synthase inhibitor, L-NAME, failed to prevent the effects of ESI-05 or GGTI-298 on [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t, suggesting that increased NO or generation of peroxynitrite is unlikely to be involved.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tFurther experiments were carried out to detect changes in mitochondrial superoxide production using mitoSOX. \\n\\t\\t\\t\\t\\t\\tFigure 3E\\n\\t\\t\\t\\t\\t\\t (\\n\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t) shows original data obtained under control conditions and in the presence of ESI-05 or GGTI-298. With both drugs, mitoSOX fluorescence increased over a period of 12-min exposure, while there was no apparent change in the control response over the same time period. The cumulative data (\\n\\t\\t\\t\\t\\t\\tFig. 3F\\n\\t\\t\\t\\t\\t\\t) show that with ESI-05 (\\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200917) or GGTI-298 (\\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200913), mitoSOX fluorescence was significantly greater than control at both 6- and 12-min time points (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05, \\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200913–17). Under control conditions, in the absence of the drugs, mitoSOX fluorescence did not increase significantly over the same time period (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009>\\u20090.05, \\n\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\u2009=\\u200915). Increases in cellular ROS with both ESI-05 and GGTI-298 were also detected using CM-H\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\tDCFDA fluorescence (\\n\\t\\t\\t\\t\\t\\tSupplementary Fig. S4\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tAdenoviral expression of roGFP (24-h culture) was used to detect possible changes in the glutathione redox potential within myocytes. As shown in \\n\\t\\t\\t\\t\\t\\tSupplementary Figure S5A\\n\\t\\t\\t\\t\\t\\t, mito-roGFP produced a blocky fluorescence pattern, which is consistent with mitochondrial localization (\\n\\t\\t\\t\\t\\t\\t15\\n\\t\\t\\t\\t\\t\\t). Exposures less than 15\\u2009min had no significant effect on mito-roGFP fluorescence (not shown). However, as shown in \\n\\t\\t\\t\\t\\t\\tSupplementary Figure 5B and C\\n\\t\\t\\t\\t\\t\\t, 30-min exposure to GGTI-298 resulted in a small but significant decrease in the mito-roGFP2 signal (488-nm excitation), which is consistent with a rise in ROS production.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tOne technical consideration was that during prolonged (20–30\\u2009min) drug exposures, cells often became spontaneously active, presumably due to Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t overload (\\n\\t\\t\\t\\t\\t\\te.g\\n\\t\\t\\t\\t\\t\\t., \\n\\t\\t\\t\\t\\t\\tFig. 1A\\n\\t\\t\\t\\t\\t\\t), which could change the confocal plane, leading to a relatively large variability in the measured response. Consequently, we undertook further experiments to measure the GSH/GSSG ratio in aliquots of cells using a luminescence-based system (GSH/GSSG-Glo) to quantify cellular GSH/GSSG ratios in ventricular cardiac cells. As shown in \\n\\t\\t\\t\\t\\t\\tSupplementary Figure S5C\\n\\t\\t\\t\\t\\t\\t, both drugs caused a significant reduction in the GSH/GSSG ratio measured using this method.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tAdenoviral expression of the mitochondrial Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t sensor mitycam (\\n\\t\\t\\t\\t\\t\\t15\\n\\t\\t\\t\\t\\t\\t) again exhibited an expression pattern consistent with mitochondrial localization. Exposure to either ESI-05 or GGTI for 20\\u2009min resulted in a slight decrease in the mean fluorescence level (consistent with an increase in [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t], although this was not statistically significant, \\n\\t\\t\\t\\t\\t\\tSupplementary Figure S6A\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIn experiments on HEK-293 cells, it was found that partial (∼50%) siRNA knockdown of Rap1 increased basal ROS production. Thereafter, against this background of reduced Rap1 signaling, subsequent inhibition of Epac2 caused a proportionately greater increase in CM-H\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\tDCFDA fluorescence (\\n\\t\\t\\t\\t\\t\\tSupplementary Fig. S7\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tElectrophysiological consequences of Epac-Rap1 signaling inhibition\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tExperiments were carried out to investigate the effects of ESI-05 and GGTI-298 on AP duration and INa. In whole-cell patch-clamp experiments, oscillatory EADs were induced by ESI-05 during repetitive pacing when the drug was present both in the patch pipette and in the bathing solution (\\n\\t\\t\\t\\t\\t\\tFig. 4A\\n\\t\\t\\t\\t\\t\\t). Similar results were obtained with GGTI-298 in the bathing solution and the membrane-impermeant product of the prodrug (GGTI-297) in the patch pipette (not shown). EADs were typically absent when the drugs were omitted from the patch pipette, which likely reflects cell dialysis, such that the cytosolic concentration is lower than that of the external solution.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\tFIG. 4.\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\tEffects of ESI-05 or GGTI-298 on AP duration. (A)\\n\\t\\t\\t\\t\\t\\t\\t\\t Whole-cell patch-clamp recording of APs obtained from an ARVM stimulated repetitively (\\n\\t\\t\\t\\t\\t\\t\\t\\tarrows\\n\\t\\t\\t\\t\\t\\t\\t\\t) at 1\\u2009Hz. During exposure to ESI-05, the descending phase of the AP developed a sustained plateau with oscillations in E\\n\\t\\t\\t\\t\\t\\t\\t\\tm\\n\\t\\t\\t\\t\\t\\t\\t\\t, characteristic of EADs. Breaks (-//-) indicate 2 and 4\\u2009min after initiation of recording. \\n\\t\\t\\t\\t\\t\\t\\t\\t(B)\\n\\t\\t\\t\\t\\t\\t\\t\\t Original (\\n\\t\\t\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t\\t\\t) and cumulative data (\\n\\t\\t\\t\\t\\t\\t\\t\\tright\\n\\t\\t\\t\\t\\t\\t\\t\\t) showing AP prolongation in the presence of ESI-05 (\\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20099). \\n\\t\\t\\t\\t\\t\\t\\t\\t(C)\\n\\t\\t\\t\\t\\t\\t\\t\\t Original (\\n\\t\\t\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t\\t\\t) and cumulative data (\\n\\t\\t\\t\\t\\t\\t\\t\\tright\\n\\t\\t\\t\\t\\t\\t\\t\\t) showing AP prolongation in the presence of GGTI-298 (\\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u200913). \\n\\t\\t\\t\\t\\t\\t\\t\\t(D)\\n\\t\\t\\t\\t\\t\\t\\t\\t Original (\\n\\t\\t\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t\\t\\t) and cumulative data (\\n\\t\\t\\t\\t\\t\\t\\t\\tright\\n\\t\\t\\t\\t\\t\\t\\t\\t) showing that preincubation of cells with either mitoTEMPO (30\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20096) or ranolazine (20\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20095) prevented the increase in AP duration with ESI-05. \\n\\t\\t\\t\\t\\t\\t\\t\\t(E)\\n\\t\\t\\t\\t\\t\\t\\t\\t Original (\\n\\t\\t\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t\\t\\t) and cumulative data (\\n\\t\\t\\t\\t\\t\\t\\t\\tright\\n\\t\\t\\t\\t\\t\\t\\t\\t) showing that preincubation of cells with either mitoTEMPO (30\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20095) or ranolazine (20\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20095) prevented the increase in AP duration with GGTI-298. n.s.,no significant difference. ***\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.001. AP, action potential. To see this illustration in color, the reader is referred to the web version of this article at \\n\\t\\t\\t\\t\\t\\t\\t\\twww.liebertpub.com/ars\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIn experiments to identify effects on AP duration, ESI-05 and GGTI-298 were only added to bathing solution, which allowed effects to be determined before and then after addition of the drugs in each cell. As shown in \\n\\t\\t\\t\\t\\t\\tFigure 4B and C\\n\\t\\t\\t\\t\\t\\t, both ESI-05 and GGTI-298 consistently increased AP duration. One possible explanation for prolongation of the AP is increased activation of INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t, which has been reported to occur with other interventions that increase mitochondrial ROS (\\n\\t\\t\\t\\t\\t\\t62\\n\\t\\t\\t\\t\\t\\t). Consistent with this possibility, preincubation of cells with the INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t inhibitor, ranolazine (20\\u2009μ\\n\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t), prevented lengthening of the AP with either ESI-05 or GGTI-298 (\\n\\t\\t\\t\\t\\t\\tFig. 4D, E\\n\\t\\t\\t\\t\\t\\t). Furthermore, as in experiments involving [Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t measurement alone (\\n\\t\\t\\t\\t\\t\\tFig. 3\\n\\t\\t\\t\\t\\t\\t), preincubation with mitoTEMPO also prevented the effects of both drugs on AP duration (\\n\\t\\t\\t\\t\\t\\tFig. 4D, E\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tVoltage-clamp experiments were conducted to investigate modulation of INa by ESI-05 and GGTI-298 (\\n\\t\\t\\t\\t\\t\\tFig. 5\\n\\t\\t\\t\\t\\t\\t). Both agents had a modest inhibitory effect on the peak INa (\\n\\t\\t\\t\\t\\t\\te.g\\n\\t\\t\\t\\t\\t\\t., \\n\\t\\t\\t\\t\\t\\tFig. 5A\\n\\t\\t\\t\\t\\t\\t\\n\\n\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t). It should be noted that with series resistance compensation of 60–80%, peak Na current amplitude values are likely to incur a modest systematic error, but this does not affect our overall conclusion. Much more striking was the increase in INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t (\\n\\t\\t\\t\\t\\t\\tFig. 5A\\n\\t\\t\\t\\t\\t\\t middle and right). Consistent with the idea that increased INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t accounts for the proarrhythmic actions of these drugs, augmentation of INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t (but not the inhibition of peak INa) was inhibited by ranolazine and mitoTEMPO (\\n\\t\\t\\t\\t\\t\\tFig. 5A\\n\\t\\t\\t\\t\\t\\t middle and right); mean data for these effects are shown in \\n\\t\\t\\t\\t\\t\\tFigure 5B\\n\\t\\t\\t\\t\\t\\t (ESI-05) and 5C (GGTI-298).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\tFIG. 5.\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tEffects of ESI-05 or GGTI-298 on INa\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t. (A)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t Representative recordings of INa recorded from ARVMs using whole-cell patch-clamp under control conditions and in the presence of ESI-05 (\\n\\t\\t\\t\\t\\t\\t\\t\\tleft\\n\\t\\t\\t\\t\\t\\t\\t\\t and \\n\\t\\t\\t\\t\\t\\t\\t\\tmiddle\\n\\t\\t\\t\\t\\t\\t\\t\\t) or GGTI-298 (\\n\\t\\t\\t\\t\\t\\t\\t\\tright\\n\\t\\t\\t\\t\\t\\t\\t\\t), showing that both drugs increase INa\\n\\t\\t\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t\\t\\t. The effects of ESI-05 or GGTI-298 on INa\\n\\t\\t\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t\\t\\t were markedly reduced by preincubation of ARVMs with either the INa\\n\\t\\t\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t\\t\\t inhibitor ranolazine (20\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t) or mitoTEMPO (30\\u2009μ\\n\\t\\t\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t\\t\\t). \\n\\t\\t\\t\\t\\t\\t\\t\\t(B)\\n\\t\\t\\t\\t\\t\\t\\t\\t Cumulative data showing the effects of ESI-05 on INa\\n\\t\\t\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t\\t\\t and the effects of preincubation with ranolazine or mitoTEMPO (\\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20095–7). \\n\\t\\t\\t\\t\\t\\t\\t\\t(C)\\n\\t\\t\\t\\t\\t\\t\\t\\t Cumulative data showing the effects of GGTI-298 on INa\\n\\t\\t\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t\\t\\t and the effects of preincubation with ranolazine or mitoTEMPO. \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20095–12, * \\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05, **\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01, n.s., not significant. To see this illustration in color, the reader is referred to the web version of this article at \\n\\t\\t\\t\\t\\t\\t\\t\\twww.liebertpub.com/ars\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tFurther experiments were done to investigate the possibility that ESI-05 or GGTI-298 may influence EAD susceptibility by affecting the L-type Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t current (ICa). However, as shown in the original data (\\n\\t\\t\\t\\t\\t\\tFig. 6A\\n\\t\\t\\t\\t\\t\\t) and summarized in \\n\\t\\t\\t\\t\\t\\tFigure 6B\\n\\t\\t\\t\\t\\t\\t, neither ESI-05 nor GGTI-298 had a significant effect on the time course or amplitude of ICa in ventricular myocytes.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\tFIG. 6.\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\tInhibition of Epac-Rap1 signaling does not affect ICa. (A)\\n\\t\\t\\t\\t\\t\\t\\t\\t L-type Ca\\n\\t\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t\\t currents were evoked in isolated rat ventricular myocytes before and during exposure of ESI-05 (\\n\\t\\t\\t\\t\\t\\t\\t\\tupper\\n\\t\\t\\t\\t\\t\\t\\t\\t) or GGTI-298 (\\n\\t\\t\\t\\t\\t\\t\\t\\tlower\\n\\t\\t\\t\\t\\t\\t\\t\\t). Cells were exposed to either drug for 3.3\\u2009min. Currents were evoked by step depolarizations from −30 to +10\\u2009mV (75\\u2009ms, 0.1\\u2009Hz). \\n\\t\\t\\t\\t\\t\\t\\t\\t(B)\\n\\t\\t\\t\\t\\t\\t\\t\\t Bar graph showing cumulative data (mean\\u2009±\\u2009SE) with the amplitude expressed as a percentage of the control current amplitude. Neither ESI-05 (\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009>\\u20090.05, \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20095) nor GGTI-298 (\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009>\\u20090.05, \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20095) resulted in a significant change in peak ICa. Data were obtained from three hearts.\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIn vivo\\n\\t\\t\\t\\t\\t\\t effects of ESI-05 and GGTI-298\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tTo investigate the effects of ESI-05 and GGTI-298 \\n\\t\\t\\t\\t\\t\\tin vivo\\n\\t\\t\\t\\t\\t\\t, drugs were introduced following administration of ISO (1\\u2009mg/kg ip) to induce submaximal β\\n\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t-AD stimulation, which would be expected to increase the proarrhythmic effect of both drugs (\\n\\t\\t\\t\\t\\t\\tFig. 1B\\n\\t\\t\\t\\t\\t\\t). ISO induced a significant increase in heart rate in all groups (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.001 \\n\\t\\t\\t\\t\\t\\tvs.\\n\\t\\t\\t\\t\\t\\t baseline) (\\n\\t\\t\\t\\t\\t\\tFig. 7A\\n\\t\\t\\t\\t\\t\\t). However, the heart rate was not different between the 3 groups under baseline conditions, after ISO, or after either ESI-05 or GGTI-298. During vehicle NaCl, ESI-05, and GGTI-298 (all with DMSO, 1/1000: v/v), heart rate increased further and to a similar extent (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01, \\n\\t\\t\\t\\t\\t\\tFig. 7A\\n\\t\\t\\t\\t\\t\\t). No difference was observed between the ESI-05 and GGTI-298 groups for heart rate.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\tFIG. 7.\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n            \\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tIn vivo\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\n\\t\\t\\t\\t\\t\\t\\t\\teffects of ESI-05 and GGTI-298. (A)\\n\\t\\t\\t\\t\\t\\t\\t\\t Mean heart rate calculated in vehicle (\\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20097), ESI-05 (\\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20097), and GGTI-298 groups (\\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20095) in baseline conditions, after isoproterenol injection (1\\u2009mg/kg \\n\\t\\t\\t\\t\\t\\t\\t\\tip.\\n\\t\\t\\t\\t\\t\\t\\t\\t, in sterile NaCl 0.9% vehicle), and after vehicle (DMSO/NaCl solution \\n\\t\\t\\t\\t\\t\\t\\t\\tip\\n\\t\\t\\t\\t\\t\\t\\t\\t., 1/1000: v/v), ESI-05 (1\\u2009mg/kg \\n\\t\\t\\t\\t\\t\\t\\t\\tip.\\n\\t\\t\\t\\t\\t\\t\\t\\t, in DMSO/NaCl solution, 1/1000: v/v), or GGTI-298 (1\\u2009mg/kg \\n\\t\\t\\t\\t\\t\\t\\t\\tip\\n\\t\\t\\t\\t\\t\\t\\t\\t., in DMSO/NaCl solution, 1/1000: v/v). \\n\\t\\t\\t\\t\\t\\t\\t\\t(B)\\n\\t\\t\\t\\t\\t\\t\\t\\t Ventricular extrasystoles were counted over 180\\u2009min from ECGs after vehicle (DMSO/NaCl solution \\n\\t\\t\\t\\t\\t\\t\\t\\tip\\n\\t\\t\\t\\t\\t\\t\\t\\t., 1/1000: v/v), ESI-05 (1\\u2009mg/kg \\n\\t\\t\\t\\t\\t\\t\\t\\tip\\n\\t\\t\\t\\t\\t\\t\\t\\t., in DMSO/NaCl solution, 1/1000: v/v), or GGTI-298 (1\\u2009mg/kg \\n\\t\\t\\t\\t\\t\\t\\t\\tip\\n\\t\\t\\t\\t\\t\\t\\t\\t., in DMSO/NaCl solution, 1/1000: v/v). \\n\\t\\t\\t\\t\\t\\t\\t\\t(C)\\n\\t\\t\\t\\t\\t\\t\\t\\t Typical example of ventricular extrasystoles recorded after ESI-05 injection. \\n\\t\\t\\t\\t\\t\\t\\t\\t(D)\\n\\t\\t\\t\\t\\t\\t\\t\\t Typical example of torsades de pointes initiated by a ventricular extrasystole and terminated in sudden cardiac death recorded after ESI-05 injection. NS, no significant difference. **\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01 \\n\\t\\t\\t\\t\\t\\t\\t\\tversus\\n\\t\\t\\t\\t\\t\\t\\t\\t vehicle, Student's \\n\\t\\t\\t\\t\\t\\t\\t\\tt\\n\\t\\t\\t\\t\\t\\t\\t\\t-test. \\n\\t\\t\\t\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009=\\u20095–12, *\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05, **\\n\\t\\t\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01.\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tInterestingly, ESI-05 or GGTI-298 induced a large increase in the occurrence of ventricular extrasystoles when compared with vehicle (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.01, \\n\\t\\t\\t\\t\\t\\tFig. 7B\\n\\t\\t\\t\\t\\t\\t). These arrhythmias occurred preferentially in the repolarization phase of the electrocardiogram (ECG) (into end-T wave). A typical example of such arrhythmias after ESI-05 injection is shown in \\n\\t\\t\\t\\t\\t\\tFigure 7C\\n\\t\\t\\t\\t\\t\\t.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tNo difference was noted between ESI-05 and GGTI-298 groups for the occurrence of ventricular extrasystoles. In addition, both ESI-05 and GGTI-298 triggered sustained ventricular tachycardia in four of seven animals and in three of five animals, respectively, whereas no animals exhibited triggered ventricular tachycardia in the vehicle-treated group (\\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05 for ESI-05 and GGTI-298 \\n\\t\\t\\t\\t\\t\\tvs.\\n\\t\\t\\t\\t\\t\\t vehicle, Fisher's exact test). Among these animals, one and two rats exhibited torsades de pointes in the ESI-05 and GGTI-298 groups, respectively, which degenerated into sudden cardiac death (\\n\\t\\t\\t\\t\\t\\tFig. 7D\\n\\t\\t\\t\\t\\t\\t). Ultimately, four of seven rats died within 24\\u2009h following ESI-05 injection, of which two were the result of documented ventricular tachycardia and cardiac arrest within the first 180\\u2009min after injection, and two of five died after injection of GGTI-298, with documented torsades de pointes.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\tDiscussion\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tSympathetic adrenergic drive to the heart is fundamental to the control of cardiac output during stress or exercise. While the role of cAMP acting \\n\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t PKA has been characterized in detail, the identification of Epac-mediated effects has been hampered by a lack of selective inhibitors. However, several Epac inhibitors have recently been identified (\\n\\t\\t\\t\\t\\t50\\n\\t\\t\\t\\t\\t) and independent analysis confirmed ESI-05 as a selective inhibitor of Epac2, capable of blocking its action as a guanine nucleotide exchange factor for Rap1 (\\n\\t\\t\\t\\t\\t35\\n\\t\\t\\t\\t\\t). In this study, ESI-05 was used to induce acute inhibition of Epac2, thereby revealing the influence of this pathway on cardiac cell function. As summarized in \\n\\t\\t\\t\\t\\tFigure 8\\n\\t\\t\\t\\t\\t, the findings suggest that Epac2-Rap1 signaling exerts a tonic inhibitory influence on mitochondrial ROS production, without which myocytes exhibit increased \\n\\t\\t\\t\\t\\tin vivo\\n\\t\\t\\t\\t\\t and \\n\\t\\t\\t\\t\\tin vitro\\n\\t\\t\\t\\t\\t susceptibility to arrhythmias and abnormal Ca\\n\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t cycling.\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\tFIG. 8.\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\tDiagram summarizing β\\n\\t\\t\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t\\t\\t-adrenergic signaling\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\n\\n\\t\\t\\t\\t\\t\\t\\tPKA and Epac-Rap1.\\n\\t\\t\\t\\t\\t\\t\\t The presence of a β\\n\\t\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t\\t-ADR agonist increases the production of cAMP \\n\\t\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t\\t AC. cAMP acts simultaneously \\n\\t\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t\\t both PKA and Epac pathways to influence the function of intracellular targets that control Ca\\n\\t\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t\\t signaling, force production, electrical connectivity, and cell survival. Some effects of Epac are mediated \\n\\t\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t\\t its role as a guanine nucleotide exchange factor for the small GTPase Rap1, which facilitates the release of guanosine diphosphate, thereby increasing the level of active Rap1 GTP. The GTPase-activating protein, RAP-GAP, opposes this effect by facilitating GTP hydrolysis. Geranylgeranylation of active Rap1 \\n\\t\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t\\t GGT-1 is required for interaction with downstream membrane targets. Our data suggest that Rap1GTP is a negative regulator of ROS production by the mitochondria. When Epac2 or GGT-1 is inhibited, the increase in ROS is associated with activation of CaMKII and the development of EAD arrhythmias due to increased INa\\n\\t\\t\\t\\t\\t\\t\\tLate\\n\\t\\t\\t\\t\\t\\t\\t and AP prolongation. Arrhythmias can be prevented by inhibition of CaMKII with KN93, preincubation with the mitochondria-targeted antioxidant mitoTEMPO, or inhibition of INa\\n\\t\\t\\t\\t\\t\\t\\tLate\\n\\t\\t\\t\\t\\t\\t\\t with ranolazine. AC, adenylate cyclase; cAMP, cyclic adenosine monophosphate; PKA, protein kinase A; ROS, reactive oxygen species. To see this illustration in color, the reader is referred to the web version of this article at \\n\\t\\t\\t\\t\\t\\t\\twww.liebertpub.com/ars\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tThe effects of Epac2 inhibition are caused by mitochondrial ROS\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tThe EADs induced by Epac2 inhibition occur \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t a mechanism that involves an increase in cellular ROS, CaMKII activation, an increase in INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t, and AP prolongation (\\n\\t\\t\\t\\t\\t\\tFigs. 1b\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t3e\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t4b\\n\\t\\t\\t\\t\\t\\t, and \\n\\t\\t\\t\\t\\t\\t5a\\n\\t\\t\\t\\t\\t\\t). This has similarities to angiotensin-II-induced EAD arrhythmias, which have also been linked to an increase in superoxide production and CaMKII activation (\\n\\t\\t\\t\\t\\t\\t62\\n\\t\\t\\t\\t\\t\\t). In this study, the underlying mechanism is known to involve oxidative activation of CaMKII, which leads to phosphorylation of RyR2 (\\n\\t\\t\\t\\t\\t\\te.g\\n\\t\\t\\t\\t\\t\\t., at 2814) and Nav1.5 (\\n\\t\\t\\t\\t\\t\\te.g\\n\\t\\t\\t\\t\\t\\t., at S571), thereby increasing the RyR2-mediated SR Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t leak and INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t (\\n\\t\\t\\t\\t\\t\\t40\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tThe close proximity of subsarcolemmal and myofibrillar mitochondria to the superficial surface membrane and the t-tubules means that the organelles are ideally placed to influence the function of RyR2 within the junctional SR and sarcolemmal ion channels (\\n\\t\\t\\t\\t\\t\\t41\\n\\t\\t\\t\\t\\t\\t). However, the angiotensin II-mediated increase in ROS is inhibited by DPI and absent in cells lacking functional NOX2 (\\n\\t\\t\\t\\t\\t\\t53\\n\\t\\t\\t\\t\\t\\t), suggesting a primary action \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t NOX, which then stimulates mitochondrial ROS production, that is, ROS-induced ROS release (\\n\\t\\t\\t\\t\\t\\t9\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t53\\n\\t\\t\\t\\t\\t\\t). In the present study, the lack of effect of DPI suggests that NOX is not the primary source of ROS when Epac2 is inhibited (\\n\\t\\t\\t\\t\\t\\tFig. 3\\n\\t\\t\\t\\t\\t\\t). Instead, the increase in mitoSOX fluorescence following Epac2 inhibition, the ability of mitoTEMPO to antagonize ESI-05-induced arrhythmias, and the inhibitory effect of rotenone implicate superoxide originating from the mitochondria.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tThese findings are consistent with recent work on other cell types, which suggest that Epac-Rap1 signaling can regulate mitochondrial ROS (\\n\\t\\t\\t\\t\\t\\t37\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t38\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t46\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t54\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t57\\n\\t\\t\\t\\t\\t\\t), for example, hyperglycemia inhibits Rap1 expression and activity, leading to renal tubular cell injury in patients with diabetic neuropathy and STZ-induced diabetic animal models, \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t a mechanism that involves overproduction of mitochondrial ROS (\\n\\t\\t\\t\\t\\t\\t57\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIt has been reported previously that Rap1 and other small GTP-binding proteins can interact with mitochondria (\\n\\t\\t\\t\\t\\t\\t17\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t49\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t52\\n\\t\\t\\t\\t\\t\\t) and, in some cases, modify the structure or function of the organelle (\\n\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t). Alternatively, Rap1GTP might act indirectly \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t associated pathways such as PI3K (\\n\\t\\t\\t\\t\\t\\t37\\n\\t\\t\\t\\t\\t\\t) or PLCξ/PKC (\\n\\t\\t\\t\\t\\t\\t26\\n\\t\\t\\t\\t\\t\\t). The identification of a link between Epac-Rap1 signaling and ROS production is also relevant to emerging evidence that cAMP can act \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t PKA to influence mitochondrial function (\\n\\t\\t\\t\\t\\t\\t8\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t10\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t42\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t52\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tThe effects of Epac-2 inhibition are mimicked by inhibition of GGT-1\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tRap1 prenylation occurs exclusively \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t geranylgeranylation (\\n\\t\\t\\t\\t\\t\\t7\\n\\t\\t\\t\\t\\t\\t) and previous work has shown that GGTI-298 and related drugs selectively inhibit GGT-1 and the effects of Rap1GTP on downstream targets, without affecting protein farnesylation (\\n\\t\\t\\t\\t\\t\\t12\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t48\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIn the present study, for all parameters measured at the cellular level, GGTI-298 produced effects that were strikingly similar to those of Epac2 inhibition, including an increase in mitochondrial ROS production, an increase in INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t, AP prolongation, and EADs (\\n\\t\\t\\t\\t\\t\\tFigs. 3\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t4\\n\\t\\t\\t\\t\\t\\t, and \\n\\t\\t\\t\\t\\t\\t5\\n\\t\\t\\t\\t\\t\\t). \\n\\t\\t\\t\\t\\t\\tIn vivo\\n\\t\\t\\t\\t\\t\\t, the proarrhythmic effects of ESI-05 and GGTI-298 were also remarkably similar (\\n\\t\\t\\t\\t\\t\\tFig. 7\\n\\t\\t\\t\\t\\t\\t). The similar effects of Epac2 and GGT-1 inhibition are consistent with inhibitory actions at different points on the Epac-Rap1 signaling pathway (\\n\\t\\t\\t\\t\\t\\tFigs. 2B\\n\\t\\t\\t\\t\\t\\t and \\n\\t\\t\\t\\t\\t\\t8\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tPhysiological and pathological significance of Epac2 ROS regulation\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tDuring β\\n\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t-AD stimulation, PKA-mediated effects on mitochondria promote superoxide production and the resultant modulation of ion channel function contributes to the increase in SR Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t release and contractile force under physiological conditions (\\n\\t\\t\\t\\t\\t\\t3\\n\\t\\t\\t\\t\\t\\t). However, excessive ROS generation is detrimental to cell function and, beyond a critical point, ROS release is irreversible and self-sustaining (\\n\\t\\t\\t\\t\\t\\t63\\n\\t\\t\\t\\t\\t\\t). The present study suggests that in the heart, when [cAMP] and PKA activation increases during β\\n\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t-AD stimulation, concomitant activation of Epac2 provides negative feedback, which attenuates mitochondrial ROS generation. When this mechanism is acutely interrupted by Epac2 or GGT1 inhibition, the consequence is increased ROS production (\\n\\t\\t\\t\\t\\t\\tFig. 3\\n\\t\\t\\t\\t\\t\\t) and EAD arrhythmias (\\n\\t\\t\\t\\t\\t\\tFigs. 1–4\\n\\t\\t\\t\\t\\t\\t), the incidence of which is increased by β\\n\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t-ADR stimulation (\\n\\t\\t\\t\\t\\t\\tFig. 1B\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tPrevious work has shown that heart failure is associated with reduced expression of enzymes that initiate degradation of cAMP, including phosphodiesterase (\\n\\t\\t\\t\\t\\t\\t13\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t19\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t20\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t29\\n\\t\\t\\t\\t\\t\\t), predisposing to an increase in PKA-mediated phosphorylation of some target proteins (\\n\\t\\t\\t\\t\\t\\te.g\\n\\t\\t\\t\\t\\t\\t., RyR2), despite downregulation of β\\n\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t-ADR (\\n\\t\\t\\t\\t\\t\\t11\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t19\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t36\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t59\\n\\t\\t\\t\\t\\t\\t). Depending on the model, Epac2 mRNA expression is unchanged or may decrease in heart failure (\\n\\t\\t\\t\\t\\t\\t24\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t51\\n\\t\\t\\t\\t\\t\\t), while PKA expression and activity remain constant or increase (\\n\\t\\t\\t\\t\\t\\t14\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t18\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t55\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tBased on the present findings, the development of an imbalance between PKA and Epac branches of the cAMP signaling pathway would be expected to cause increased mitochondrial ROS production and predisposition to EAD arrhythmias, both of which are features of heart failure (\\n\\t\\t\\t\\t\\t\\t58\\n\\t\\t\\t\\t\\t\\t). However, the effects of acute pharmacological inhibition of Epac-Rap1 signaling may have more pronounced effects than slower changes in protein expression, where compensatory adaptations may occur. In addition, the effects of an acute increase in ROS production in an otherwise normal heart may differ from the tonic effect of chronically elevated ROS in disease states (\\n\\t\\t\\t\\t\\t\\te.g\\n\\t\\t\\t\\t\\t\\t., diabetes or heart failure) where major functional and structural adaptations develop over a prolonged period.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tTransgenic knockout models of Epac1/2 or Rap1A might provide information regarding the long-term effects of reduced signaling \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t Epac2-Rap1. A recent patent described long-QT and increased arrhythmia susceptibility at rest and in response to ISO in a Rap1A knockout mouse (\\n\\t\\t\\t\\t\\t\\t6\\n\\t\\t\\t\\t\\t\\t). The phenotype is qualitatively consistent with the results of the present study, including the finding that arrhythmias were linked to abnormal Nav1.5 function.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIn contrast, two independent Epac2 knockout models did not exhibit increased susceptibility to arrhythmias at rest or during β-adrenergic stimulation (\\n\\t\\t\\t\\t\\t\\t28\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t33\\n\\t\\t\\t\\t\\t\\t). This might be explained by adaptive changes in other pathways, which maintain an adequate level of Rap1GTP, or possibly a compensatory increase in antioxidant capacity. However, some caution is appropriate in interpreting the findings from existing Epac1/2 mouse studies because the phenotypes of the two models differ so markedly; in one model, Epac2 was solely responsible for the RyR2-mediated increase in Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t spark frequency during β\\n\\t\\t\\t\\t\\t\\t1\\n\\t\\t\\t\\t\\t\\t-adrenergic stimulation and Epac1 was localized to the nucleus (\\n\\t\\t\\t\\t\\t\\t33\\n\\t\\t\\t\\t\\t\\t). In the other, Epac1 had major effects on both phospholamban/SERCA and RyR2, which could only occur if Epac1 was widely expressed throughout the cells (\\n\\t\\t\\t\\t\\t\\t28\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tThese authors suggested that the discrepancy might reflect the differing genetic backgrounds used in the two models. However, if this is the case, broader interpretation of the data and extrapolation to other species may be inappropriate. Another factor when considering the long-term effects of impaired signaling \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t Epac2-Rap1 is that possible adaptations in knockout models may not occur if changes in expression arise in response to disease, which simultaneously induces other forms of cellular dysfunction.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tEpac and GGT-1 inhibitors as therapeutic agents\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tThe nonspecific Epac inhibitor, ESI-09, has recently been shown to suppress pancreatic cancer cell migration and invasion and such drugs are being investigated for clinical use (\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t). In addition, there is a long-standing interest in the use of combined farnesyltransferase (FT) and GGT-1 inhibitors or selective GGT-1 inhibitors for the treatment of cancer (\\n\\t\\t\\t\\t\\t\\t4\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tInterestingly, preclinical animal studies on a range of combined inhibitors and selective GGT-1 inhibitors described a toxic effect in mice and dogs, leading to sudden death (\\n\\t\\t\\t\\t\\t\\t21\\n\\t\\t\\t\\t\\t\\t). Although the cause of death was not established, the toxic effects correlated with GGT-1 inhibition and the level of geranylated Rap1GTP, but not protein farnesylation. The authors concluded that the potential clinical benefit of this class of drugs may be limited by a narrow therapeutic index. Consistent with this, clinical trials on the combined FT/GGT-1 inhibitor L-778123 were hampered by cardiotoxic effects, including QT prolongation, arrhythmias, and syncope (\\n\\t\\t\\t\\t\\t\\t47\\n\\t\\t\\t\\t\\t\\t), which ultimately led to discontinuation of the drug.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tThe present study demonstrates that both GGT-1 inhibition and Epac2 inhibition can have lethal cardiotoxic effects \\n\\t\\t\\t\\t\\t\\tin vivo\\n\\t\\t\\t\\t\\t\\t (\\n\\t\\t\\t\\t\\t\\tFig. 7\\n\\t\\t\\t\\t\\t\\t). Importantly, however, it is also shown that coadministration of the lNa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t inhibitor, ranolazine, can block the proarrhythmic effects of Epac2 or GGT-1 inhibitors (\\n\\t\\t\\t\\t\\t\\tFigs. 3–5\\n\\t\\t\\t\\t\\t\\t). This might enable the cardiotoxic effects to be prevented, while allowing the drugs to be used at higher and more effective concentrations in the treatment of cancer, cardiovascular disease, or other pathological conditions where Rap1 signaling has a key role.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tSpecificity of Epac2 and GGT-1 inhibitors\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tWhile ESI-05 has been extensively validated as a selective Epac2 inhibitor (\\n\\t\\t\\t\\t\\t\\t35\\n\\t\\t\\t\\t\\t\\t), it is possible that the drug has other off-target effects, which explain its proarrhythmic activity. However, this seems unlikely because the Epac2 inhibitor, HJC0350, had the same effects of ESI-05, and the structurally dissimilar GGT-1 inhibitors, GGTI-298 and GGTI-2147, which act at a different point on the Epac-Rap1 signaling pathway, have identical effects to Epac2 inhibition. Direct drug modulation of sarcolemmal ion channels seems unlikely because AP prolongation, activation of INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t, and induction of EADs were abolished by the mitochondria-targeted antioxidant, mitoTEMPO.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tInhibition of hERG/IKr is a common cause of drug-induced arrhythmias. However, hERG is weakly expressed in rat ventricle. Moreover, as ranolazine itself has a modest inhibitory effect on hERG, its ability to block the EADs induced by ESI-05 or GGTI-298 is not consistent with an arrhythmic mechanism involving hERG. It should be noted, however, ROS-dependent effects on hERG may be present in species (including humans) that do express the channel.\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tAn increase in the L-type Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t current can also predispose to EADs and previous studies have reported that ROS have direct and indirect effects on the channel, resulting in variable effects on ICa (\\n\\t\\t\\t\\t\\t\\t43\\n\\t\\t\\t\\t\\t\\t). However, ESI-05 or GGTI-298 had no significant effect on L-type Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t current (\\n\\t\\t\\t\\t\\t\\tFig. 6A, B\\n\\t\\t\\t\\t\\t\\t). The effects of ESI-05 or GGTI-298 are also unlikely to be caused by an indirect effect leading to a rise in [cAMP] because ISO did not induce EADs under the same conditions (not shown).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIn summary, the present study identifies Epac2-Rap1 signaling as a novel feedback mechanism in the heart, which controls mitochondrial ROS production during simultaneous PKA activation. Any change (pathological or pharmacological) that reduces cAMP signaling \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t Epac-Rap1 relative to PKA will increase ROS and the probability of life-threatening EAD arrhythmias. This mechanism may narrow the therapeutic index of drugs that target either Epac2 or GGT-1. However, the proarrhythmic effects can be ameliorated by inhibition of INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t, which blocks the EADs, while maintaining the other intracellular effects of Epac2 or Rap1 inhibition.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\tMaterials and Methods\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tPreparation of cells and solutions\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tWistar rats (150–200\\u2009g) were sacrificed in accordance with the UK Home Office Guidance on the Operation of Animals (Scientific Procedures) Act of 1986. ARVMs were isolated by collagenase digestion as described previously (\\n\\t\\t\\t\\t\\t\\t61\\n\\t\\t\\t\\t\\t\\t). ARVMs were perfused with solutions containing (m\\n\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t) 113\\u2009NaCl; 5.4\\u2009KCl; 1\\u2009MgCl\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t; 1.0\\u2009CaCl\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t; 0.37\\u2009Na\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\tHPO\\n\\t\\t\\t\\t\\t\\t4\\n\\t\\t\\t\\t\\t\\t; 5.5 glucose; and 5 HEPES, 20–22°C, pH 7.1. Changes in intracellular calcium ([Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t]\\n\\t\\t\\t\\t\\t\\ti\\n\\t\\t\\t\\t\\t\\t) were detected by loading myocytes with fluo-4 AM (5\\u2009μ\\n\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t) for 15\\u2009min at room temperature (20–22°C). A further 30\\u2009min was allowed for washout of fluo-4 AM and de-esterification.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tUnless otherwise stated, all chemicals were obtained from Sigma Aldrich. The selective Epac2 inhibitors, ESI-05 (\\n\\t\\t\\t\\t\\t\\t35\\n\\t\\t\\t\\t\\t\\t, \\n\\t\\t\\t\\t\\t\\t50\\n\\t\\t\\t\\t\\t\\t) and HJC0350 (\\n\\t\\t\\t\\t\\t\\t14\\n\\t\\t\\t\\t\\t\\t), were obtained from Biolog and Bio-techne/Tocris, respectively. The GGT-1 inhibitor, GGTI-298 (\\n\\t\\t\\t\\t\\t\\t22\\n\\t\\t\\t\\t\\t\\t), is the ester prodrug form of the parent compound GGTI-297 and both were obtained from Bio-techne/Tocris. In some experiments, GGTI-297 was introduced \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t a patch pipette during whole-cell patch-clamp recording, while GGTI-298 was added \\n\\t\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t\\t the bathing solution. The GGT-1 inhibitor, GGTI-2147, was obtained from Calbiochem. Active Rap1 pull-down and detection kits were obtained from ThermoFisher Scientific.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\tConfocal imaging\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tThe experimental chamber was placed on the stage of a Nikon Diaphot Eclipse TE2000 inverted microscope and ARVMs viewed using a 60X water immersion lens (Plan Apo, NA 1.2). Cells were imaged in line-scan mode using a Bio-Rad, Microradiance 2000 confocal laser scanning unit. Fluo-4 was excited at 488\\u2009nm and emitted fluorescence measured at >515. For ROS measurements, ARVMs were loaded with MitoSOX (5\\u2009μm; Calbiochem) or CM-H\\n\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\tDCFDA for 30\\u2009min at room temperature. By virtue of its localization, the mitoSOX signal is assumed to predominantly indicate superoxide.\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tThere are known technical challenges and limitations associated with the measurement of ROS using dyes, such as mitoSOX or CM-H2DCFDA, in relation to specificity, photoactivation, and bleaching (\\n\\t\\t\\t\\t\\t56\\n\\t\\t\\t\\t\\t). To minimize some of these potential issues, the 20\\u2009mW laser excitation was markedly attenuated by 97–99%, the experiments were as brief as possible (10–20\\u2009min), only 3–5 xy frames were collected in each run, and suitable time controls were done for each protocol. Furthermore, we have only drawn qualitative conclusions about changes in ROS productio\\n\\t\\t\\t\\t\\tn\\n\\t\\t\\t\\t\\t.\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tExpression of mito-roGFP2 or mitycam in ventricular myocytes involved exposing the cells to the virus (MOI 300–400) for 48\\u2009h under cell culture conditions as previously described (\\n\\t\\t\\t\\t\\t60\\n\\t\\t\\t\\t\\t). Cells were then imaged using confocal microscopy at 488\\u2009nm excitation and a bandpass emission of 515–530\\u2009nm.\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tDuring imaging experiments, cells were field stimulated \\n\\t\\t\\t\\t\\tvia\\n\\t\\t\\t\\t\\t platinum wire electrodes (0.2\\u2009mm diameter; Goodfellow metals) positioned in parallel within a low-volume cell bath (\\n\\t\\t\\t\\t\\t61\\n\\t\\t\\t\\t\\t) using a Grass S48 stimulator. Square wave pulses of 6\\u2009ms duration were delivered at 1.5 times the threshold voltage for an electrically evoked contraction. Cells were only used if they responded consistently to each electrical stimulation and showed no signs of spontaneous activity under control conditions.\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tImage analysis was done using ImageJ and the Sparkmaster plug-in, which allows automatic detection of Ca\\n\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t sparks. Ca\\n\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t imaging data are presented as the mean\\u2009±\\u2009SEM and statistical significance was assessed using a \\n\\t\\t\\t\\t\\tt\\n\\t\\t\\t\\t\\t-test, chi-squared test, or ANOVA as appropriate. Differences between means were considered significant at \\n\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\u2009<\\u20090.05.\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tMeasurement of cellular GSH/GSSg ratio in cardiac cells\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tExperiments were done to measure the GSH/GSSG ratio in aliquots of cells using a luminescence-based system to detect and quantify GSH/GSSG ratios in ventricular cardiac cells (GSH/GSSG-Glo; Promega). This involves GSH-dependent conversion of the GSH probe, Luciferin-NT, to luciferin by a glutathione S-transferase enzyme coupled to the firefly luciferase reaction. Emitted light was measured from white-walled 96-well plates using a Promega Glomax system. App protocols were carried out according to the manufacturer's instructions.\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tExperiments on HEK-293 cells\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tDuring transfection, HEK-293 cells were placed in MEM (ThermoFisher Scientific) with siRNA for Rap1A (50\\u2009ng/ml; GE Healthcare Life Sciences/Dharmacon) and Rap1B (50\\u2009ng/ml; GE Healthcare Life Sciences/Dharmacon) or a scrambled sequence (50\\u2009ng/ml; AM4611, ThermoFisher Scientific), which acted as the control. After 7\\u2009h, the medium was replaced with MEM supplemented with fetal bovine serum (10%), pyruvate (1%), NEAA (1%), and Pen/Strep antibiotic (1%). Cells were cultured for 48\\u2009h before use. Lipofectamine was used as the transfection reagent.\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tElectrophysiology\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tWhole-cell patch-clamp recordings were obtained in the voltage or current clamp mode using patch pipettes (resistance range 2–5\\u2009MΩ). Stimulus protocols, recordings, and analysis were all performed with the use of an Axopatch 200B amplifier/Digidata 1322A interface controlled by pCLAMP 9.0 software (Molecular Devices). All experiments were carried out at 22°C\\u2009±\\u20091°C.\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tNa\\n\\t\\t\\t\\t\\t\\t+\\n\\t\\t\\t\\t\\t\\t current recordings were made in voltage-clamp mode with myocytes held at a potential of −80\\u2009mV. For L-type Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t current measurement, cells were voltage-clamped at −80\\u2009mV and a 50-ms prepulse to −30\\u2009mV was applied immediately before each test pulse to inactivate Na\\n\\t\\t\\t\\t\\t\\t+\\n\\t\\t\\t\\t\\t\\t and T-type Ca\\n\\t\\t\\t\\t\\t\\t2+\\n\\t\\t\\t\\t\\t\\t channels, as previously described (\\n\\t\\t\\t\\t\\t\\t44\\n\\t\\t\\t\\t\\t\\t).\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tL-type currents were then activated by successive depolarizations to +10\\u2009mV (75-ms duration, 0.1\\u2009Hz). Patch electrodes contained (m\\n\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t) CsCl 115, HEPES 10, EGTA 10, tetraethylammonium chloride 20, MgATP 5, Tris-GTP 0.1, and CaCl\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t 1; pH 7.05. The perfusate contained (m\\n\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t) NaCl 140, CsCl 5.4, CaCl\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t 2.5, MgCl\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t 0.5, HEPES 5.5, and glucose 11; pH 7.4. Na\\n\\t\\t\\t\\t\\t\\t+\\n\\t\\t\\t\\t\\t\\t currents were evoked by a 200\\u2009ms step to −30\\u2009mV and current signals sampled at 50\\u2009kHz after low-pass filtering at 20\\u2009kHz. Series resistance compensation (typically 60–80%) was applied and INa\\n\\t\\t\\t\\t\\t\\tlate\\n\\t\\t\\t\\t\\t\\t was measured between 150 and 200\\u2009ms after initiation of the depolarizing step. If a significant increase (>20%) in series resistance occurred, the experiment was terminated.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tAPs were recorded in current clamp mode and were evoked by 10\\u2009ms depolarizing current pulses applied every 10\\u2009s. For these recordings, patch electrodes contained (m\\n\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t) KCl 130, NaCl 10, EGTA 5, HEPES 5, MgATP 5, CaCl\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t 1, and MgCl\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t 1; pH 7.2. The perfusate contained (m\\n\\t\\t\\t\\t\\t\\tM\\n\\t\\t\\t\\t\\t\\t) NaCl 120, KCl 5, CaCl\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t 2, MgCl\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t 1, HEPES 10, and glucose 10; pH 7.4. AP duration (APD) was measured from the beginning of depolarization to the time when 25% (APD\\n\\t\\t\\t\\t\\t\\t0.25\\n\\t\\t\\t\\t\\t\\t), 50% (APD\\n\\t\\t\\t\\t\\t\\t0.5\\n\\t\\t\\t\\t\\t\\t), and 90% (APD\\n\\t\\t\\t\\t\\t\\t0.9\\n\\t\\t\\t\\t\\t\\t) of repolarization was completed. Results are presented as mean\\u2009±\\u2009SEM, and statistical analysis was performed using unpaired Student's \\n\\t\\t\\t\\t\\t\\tt\\n\\t\\t\\t\\t\\t\\t-tests where \\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05 was considered statistically significant.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tIn vivo\\n\\t\\t\\t\\t\\t\\t arrhythmic susceptibility to ESI-05 or GGTI-298\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tMale Wistar rats (7 weeks old, 250\\u2009g) were equipped with telemetric transmitters (CAF40; Data Sciences International) implanted under general anesthesia (Aerane, 2.5% in O\\n\\t\\t\\t\\t\\t\\t2\\n\\t\\t\\t\\t\\t\\t). All investigations conformed to the Position of the American Heart Association on Research Animal Use and European directives (2010/63/UE) and were approved by the institutional Animal Research Committee.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tAfter allowing 10 days for full recovery and acclimatization, animals were randomly assigned to three groups. Seven rats were injected with ISO hydrochloride to induce submaximal activation of the adrenergic signaling pathway (Calbiochem, 1\\u2009mg/kg ip., in sterile NaCl 0.9%, Baxter), and then injected with DMSO/NaCl solution (1/1000: v/v) as control for vehicle 5\\u2009min later. Seven rats received ESI-05 (BioLog, 1\\u2009mg/kg ip., in DMSO/NaCl solution, 1/1000: v/v) 5\\u2009min after injection of ISO hydrochloride (1\\u2009mg/kg ip., in NaCl vehicle). Five rats were injected with GGTI 298 (Tocris, 1\\u2009mg/kg in DMSO/NaCl solution, 1/1000: v/v) 5\\u2009min after injection of ISO hydrochloride (1\\u2009mg/kg ip., in NaCl vehicle).\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\tAll injections were performed at the same time slot. ECGs were recorded continuously throughout the experiments during baseline conditions, after ISO injection, and after either vehicle, ESI-05 or GGTI-298, administration, using a signal transmitter–receiver (RPC-1; Data Sciences International) connected to a data acquisition system (Ponemah Physiology Platform Pro, DSI) at a sampling rate of 2\\u2009kHz. ECGs were analyzed with ECG analysis module of Ponemah v5.2 software.\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tHeart rate was measured under baseline conditions and after injections. The ventricular ectopic beats were identified and counted by hand during the first 180\\u2009min of vehicle, ESI-05, or GGTI-298 injection. The occurrence of sustained ventricular tachycardia and sudden cardiac death was noted during the same period. Statistical analysis was performed using Fisher's exact test, where \\n\\t\\t\\t\\t\\t\\tp\\n\\t\\t\\t\\t\\t\\t\\u2009<\\u20090.05 was considered statistically significant.\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\tSupplementary Material\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\tSupplemental data\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\tSupplemental data\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\tSupplemental data\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\tSupplemental data\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\tSupplemental data\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\tSupplemental data\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\n          \\n\\t\\t\\t\\t\\t\\tSupplemental data\\n\\t\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n        \\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n      \\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n    \\n\\t\\t\\t\\n\\t\\t\\t\\n  \\n\\t\\t\",\n",
       " 'introduction': '',\n",
       " 'methods': '',\n",
       " 'results': '',\n",
       " 'discussion': '',\n",
       " 'date': \"{'Year': '2017', 'Month': 'Jul', 'Day': '20', 'Season': '', 'MedlineDate': ''}\",\n",
       " 'year': '2017',\n",
       " 'MeSH': ['Animals',\n",
       "  'Arrhythmias, Cardiac',\n",
       "  'metabolism',\n",
       "  'Calcium Channels, L-Type',\n",
       "  'metabolism',\n",
       "  'Disease Models, Animal',\n",
       "  'Disease Susceptibility',\n",
       "  'Guanine Nucleotide Exchange Factors',\n",
       "  'metabolism',\n",
       "  'HEK293 Cells',\n",
       "  'Humans',\n",
       "  'Male',\n",
       "  'Mitochondria',\n",
       "  'metabolism',\n",
       "  'Rats',\n",
       "  'Rats, Wistar',\n",
       "  'Reactive Oxygen Species',\n",
       "  'metabolism',\n",
       "  'Signal Transduction',\n",
       "  'rap1 GTP-Binding Proteins',\n",
       "  'metabolism'],\n",
       " 'location': 'United States',\n",
       " 'journal': 'Antioxidants & redox signaling'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1fcef94a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34380000 PMIDs processed (58170/34380001)\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(34383846, 58265)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_all_uncategorized_pmids(index_name):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Get the PMIDs of all the PubMed articles that\n",
    "      don't have MeSH labels.\n",
    "    - Purpose: For applying to unlabeled PubMed articles\n",
    "\n",
    "    PARAMS:\n",
    "    - index_name: The name of the ElasticSearch index\n",
    "      where all the PubMed articles are indexed/stored\n",
    "    '''\n",
    "\n",
    "    es = Elasticsearch()\n",
    "    relevant_uncategorized_pmids = set()\n",
    "    count = 0\n",
    "    full_text_count = 0\n",
    "    for num_pmids, entry in enumerate(es_iterate_all_documents(es, index_name)):\n",
    "\n",
    "        count += 1\n",
    "      \n",
    "        has_full_text = (len(entry['full_text']) > 0)\n",
    "                \n",
    "        if has_full_text:\n",
    "            full_text_count += 1\n",
    "        \n",
    "        \n",
    "        # Print progress\n",
    "        if num_pmids % 10000 == 0:\n",
    "            print(str(num_pmids) + ' PMIDs processed' + ' (%d/%d)'%(full_text_count,count), end='\\r')\n",
    "        \n",
    "\n",
    "    return count, full_text_count\n",
    "\n",
    "        # Publication's MeSH (if any)\n",
    "#         unlabeled = (len(entry['MeSH']) == 0)\n",
    "#         return entry\n",
    "        \n",
    "#         # Save PMIDs of unlabeled documents\n",
    "#         if unlabeled:\n",
    "#             relevant_uncategorized_pmids.add(entry['pmid'])\n",
    "\n",
    "#             # Print progress\n",
    "#             if num_pmids % 10000 == 0:\n",
    "#                 print(str(num_pmids) + ' PMIDs processed', end='\\r')\n",
    "#                 if num_pmids > 50000:\n",
    "#                     break\n",
    "\n",
    "#     return list(relevant_uncategorized_pmids)\n",
    "\n",
    "index_name = 'pubmed_lift'\n",
    "get_all_uncategorized_pmids(index_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8cffdee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_pmids(index_name):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - Get the PMIDs of all the PubMed articles\n",
    "\n",
    "    PARAMS:\n",
    "    - index_name: The name of the ElasticSearch index\n",
    "      where all the PubMed articles are indexed/stored\n",
    "    '''\n",
    "\n",
    "    es = Elasticsearch()\n",
    "    all_pmids = []\n",
    "    for num_pmids, entry in enumerate(es_iterate_all_documents(es, index_name)):\n",
    "\n",
    "        # Publication's MeSH (if any)\n",
    "        # labeled = (len(entry['MeSH']) > 0)\n",
    "\n",
    "        # Save PMIDs of documents\n",
    "        all_pmids.append(entry['pmid'])\n",
    "\n",
    "        # Print progress\n",
    "        if num_pmids % 10000 == 0:\n",
    "            print(str(num_pmids) + ' PMIDs processed', end='\\r')\n",
    "\n",
    "    all_pmids = list(set(all_pmids))\n",
    "    # json.dump(relevant_categorized_pmids, open(outpath + 'relevant_categorized_pmids.json', 'w'))\n",
    "    \n",
    "    return all_pmids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60aeaa25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34380000 PMIDs processed\r"
     ]
    }
   ],
   "source": [
    "all_doc_pmids = get_all_pmids(index_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "92f4eda4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pmids(index_name):\n",
    "    '''\n",
    "    Get the PMIDs of the PubMed articles that have/don't have MeSH labels or full text\n",
    "    '''\n",
    "\n",
    "    es = Elasticsearch()\n",
    "    uncategorized_pmids = list()\n",
    "    categorized_pmids = list()\n",
    "    uncate_text_pmids = list()\n",
    "    cate_text_pmids = list()\n",
    "    for num_pmids, entry in enumerate(es_iterate_all_documents(es, index_name)):\n",
    "        \n",
    "        # Publication's MeSH (if any)\n",
    "        unlabeled = (len(entry['MeSH']) == 0)\n",
    "\n",
    "        # Save PMIDs\n",
    "        if unlabeled:\n",
    "            uncategorized_pmids.append(entry['pmid'])\n",
    "        else:\n",
    "            categorized_pmids.append(entry['pmid'])\n",
    "        \n",
    "        # has full text or not\n",
    "        has_full_text = (len(entry['full_text']) > 0)\n",
    "        \n",
    "        if has_full_text:\n",
    "            if unlabeled:\n",
    "                uncate_text_pmids.append(entry['pmid'])\n",
    "            else:\n",
    "                cate_text_pmids.append(entry['pmid'])\n",
    "\n",
    "        # Print progress\n",
    "        if num_pmids % 10000 == 0:\n",
    "            print(str(num_pmids) + ' PMIDs processed', end='\\r')\n",
    "\n",
    "    return list(set(uncategorized_pmids)), list(set(categorized_pmids)), list(set(uncate_text_pmids)), list(set(cate_text_pmids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1547d32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6360000 PMIDs processed\r"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_7470/2370687499.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0muncate_pmids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcate_pmids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_pmids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_pmids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_7470/4129053749.py\u001b[0m in \u001b[0;36mget_pmids\u001b[0;34m(index_name)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0muncate_text_pmids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mcate_text_pmids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mnum_pmids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentry\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mes_iterate_all_documents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;31m# Publication's MeSH (if any)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_7470/3158173257.py\u001b[0m in \u001b[0;36mes_iterate_all_documents\u001b[0;34m(es, index, pagesize, scroll_timeout, **kwargs)\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0mis_first\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m             result = es.scroll(\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0mscroll_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscroll_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                 scroll = scroll_timeout)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/client/utils.py\u001b[0m in \u001b[0;36m_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    345\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m                     \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    348\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/client/__init__.py\u001b[0m in \u001b[0;36mscroll\u001b[0;34m(self, body, scroll_id, params, headers)\u001b[0m\n\u001b[1;32m   1568\u001b[0m             \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"scroll_id\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscroll_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1570\u001b[0;31m         return self.transport.perform_request(\n\u001b[0m\u001b[1;32m   1571\u001b[0m             \u001b[0;34m\"POST\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"/_search/scroll\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1572\u001b[0m         )\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/transport.py\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, headers, params, body)\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m                 status, headers_response, data = connection.perform_request(\n\u001b[0m\u001b[1;32m    428\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/connection/http_urllib3.py\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, params, body, timeout, ignore, headers)\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0mrequest_headers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"content-encoding\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"gzip\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             response = self.pool.urlopen(\n\u001b[0m\u001b[1;32m    256\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRetry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest_headers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m             )\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m             \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m             httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m    671\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    424\u001b[0m                     \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m                     \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 426\u001b[0;31m                     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    427\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    419\u001b[0m                 \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1369\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1370\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1371\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1372\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    702\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "uncate_pmids, cate_pmids, text_pmids = get_pmids(index_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d5244cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_groundtruth_pmid2categories(relevant_pmid_batch, index_name, \n",
    "                                    index_type, permuted_synonyms2category):\n",
    "    '''\n",
    "    FUNCTION:\n",
    "    - This gets the ground truth labels, the MeSH-labeled PubMed documents\n",
    "    \n",
    "    PARAMS:\n",
    "    - relevant_pmid_batch: This is the list of the PubMed IDs you want to \n",
    "      get ground truth for\n",
    "    - index_name: name of the ElasticSearch index\n",
    "    - index_type: name of the type of ElasticSearch index\n",
    "    - permuted_synonyms2category: MeSH synonyms -> category\n",
    "    '''\n",
    "    pmids2real_categories = dict()\n",
    "    es = Elasticsearch()\n",
    "    total_pmids = len(relevant_pmid_batch)\n",
    "    \n",
    "    for num_pmids, pmid in enumerate(relevant_pmid_batch):\n",
    "        if num_pmids % 10000 == 0:\n",
    "            print('Getting real mappings' + str(num_pmids)+'/'+str(total_pmids),\\\n",
    "                  end='\\r')\n",
    "        entry = es.get(id = pmid, index = index_name, doc_type = index_type)\n",
    "        pmids2real_categories[pmid] = list()\n",
    "        \n",
    "        # Publication's MeSH (if any)\n",
    "        meshes = entry['_source']['MeSH'] \n",
    "        for mesh in meshes:\n",
    "            try:\n",
    "                categories = permuted_synonyms2category[mesh]\n",
    "                pmids2real_categories[pmid] = list(set(pmids2real_categories[pmid] + categories))\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "    pmids2real_categories = switch_dictset_to_dictlist(pmids2real_categories)\n",
    "    return pmids2real_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fa7ecfab",
   "metadata": {},
   "outputs": [],
   "source": [
    "permuted_synonyms2category = json.load(open('../parsed_mappings/MeSH/permuted_synonyms2category.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d6a91be9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting real mappings34380000/34383846\r"
     ]
    }
   ],
   "source": [
    "pmid2cate = get_groundtruth_pmid2categories(all_doc_pmids, index_name, index_type, permuted_synonyms2category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "69cc5525",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "# sys.path.append('/home/ubuntu/InternProjects/Joanne/caseolap_lift/data')\n",
    "\n",
    "cat = open('/home/ubuntu/InternProjects/Joanne/caseolap_lift/data/relevant_categorized_pmids.json')\n",
    "categorized_pmids = json.load(cat)\n",
    "\n",
    "uncat = open('/home/ubuntu/InternProjects/Joanne/caseolap_lift/data/all_uncategorized_pmids.json')\n",
    "uncategorized_pmids = json.load(uncat)\n",
    "\n",
    "cat.close()\n",
    "uncat.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5d741936",
   "metadata": {},
   "outputs": [],
   "source": [
    "pmids = categorized_pmids + uncategorized_pmids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8a0ca7c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34440950"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pmids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "988bf46d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_ctg2pmid(cat_pmids, uncat_pmids, all_pmids):\n",
    "    '''\n",
    "    generate a dictionary with categories as keys and pmids as values\n",
    "    '''\n",
    "    \n",
    "    ctg2pmid = {'None': uncat_pmids}\n",
    "    \n",
    "    for num, key in enumerate(cat_pmids):\n",
    "        \n",
    "        # Print progress\n",
    "        if num % 10000 == 0:\n",
    "            print(str(num) + ' PMIDs processed', end='\\r')\n",
    "        \n",
    "        if key in all_pmids:\n",
    "            tags = all_pmids[key]\n",
    "            if tags:\n",
    "                for tag in tags:\n",
    "                    if tag in ctg2pmid:\n",
    "                        ctg2pmid[tag].append(key)\n",
    "                    else:\n",
    "                        ctg2pmid[tag] = [key]\n",
    "    \n",
    "    return ctg2pmid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "8a637869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29253534"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cate_pmids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "3d2674dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5130295"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(uncate_pmids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "d4a8a3d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34383829"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cate_pmids)+len(uncate_pmids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "6b808ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34383846"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pmid2cate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "42226c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "only_cvd_pmid2cate = {k:v for k,v in pmid2cate.items() if len(v)>0}\n",
    "cvd_pmids = set(only_cvd_pmid2cate.keys())\n",
    "non_cvd_pmids = set(cate_pmids).difference(cvd_pmids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "f4a13a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "967281"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(only_cvd_pmid2cate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "438ca6f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33416565"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the number of labeled or unlabled documents, which are not related to one of the CVDs\n",
    "len(pmid2cate)-len(only_cvd_pmid2cate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "2cb946f0",
   "metadata": {},
   "outputs": [
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       " ...}"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvd_pmids = set(only_cvd_pmid2cate.keys())\n",
    "non_cvd_pmids = set(cate_pmids).difference(cvd_pmids)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "e9ecf1f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# cvd_pmids = set(only_cvd_pmid2cate.keys())\n",
    "# non_cvd_pmids = set(cate_pmids).difference(cvd_pmids)\n",
    "\n",
    "print(len(set(uncate_pmids).intersection(cvd_pmids)))\n",
    "print(len(set(uncate_pmids).intersection(non_cvd_pmids)))\n",
    "print(len(cvd_pmids.intersection(non_cvd_pmids)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "a24015c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5130295\n",
      "28286253\n",
      "967281\n",
      "34383829\n"
     ]
    }
   ],
   "source": [
    "print(len(uncate_pmids))\n",
    "print(len(non_cvd_pmids))\n",
    "print(len(cvd_pmids))\n",
    "print(len(uncate_pmids)+len(non_cvd_pmids)+len(cvd_pmids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "0e7e97b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'10900171': ['OTH'],\n",
       " '19023153': ['CM'],\n",
       " '21131480': ['IHD', 'CM'],\n",
       " '30625100': ['CM'],\n",
       " '3413762': ['ARR', 'CCD'],\n",
       " '5701309': ['IHD'],\n",
       " '25760988': ['IHD'],\n",
       " '10325756': ['CHD'],\n",
       " '29466183': ['CM'],\n",
       " '7343368': ['IHD'],\n",
       " '3122317': ['IHD'],\n",
       " '34045667': ['CHD'],\n",
       " '18097070': ['IHD', 'CHD'],\n",
       " '20662236': ['IHD'],\n",
       " '282461': ['VD'],\n",
       " '22300785': ['CM'],\n",
       " '12613441': ['IHD'],\n",
       " '6233892': ['IHD'],\n",
       " '17200451': ['VD'],\n",
       " '23257536': ['ARR', 'CCD'],\n",
       " '10478813': ['IHD'],\n",
       " '12142190': ['CM'],\n",
       " '4468125': ['CHD'],\n",
       " '18609044': ['IHD'],\n",
       " '9609817': ['IHD'],\n",
       " '1618218': ['IHD'],\n",
       " '15794112': ['CM'],\n",
       " '2284717': ['IHD'],\n",
       " '10976155': ['ARR', 'CCD'],\n",
       " '4251819': ['OTH'],\n",
       " '5597558': ['IHD'],\n",
       " '28510240': ['ARR'],\n",
       " '22381430': ['ARR'],\n",
       " '2465472': ['IHD'],\n",
       " '31395251': ['IHD'],\n",
       " '21324629': ['ARR'],\n",
       " '11603072': ['IHD'],\n",
       " '1043104': ['IHD'],\n",
       " '28593660': ['CM'],\n",
       " '26056239': ['IHD'],\n",
       " '3917445': ['CM'],\n",
       " '1751150': ['OTH'],\n",
       " '3929730': ['ARR', 'CCD'],\n",
       " '14066053': ['ARR', 'CCD'],\n",
       " '30533038': ['IHD'],\n",
       " '18487852': ['IHD'],\n",
       " '15370660': ['OTH'],\n",
       " '25466856': ['CHD'],\n",
       " '14809633': ['IHD'],\n",
       " '9841309': ['IHD'],\n",
       " '1080447': ['IHD', 'CM'],\n",
       " '22676938': ['ARR'],\n",
       " '28622942': ['CHD'],\n",
       " '14357462': ['IHD'],\n",
       " '21901576': ['IHD'],\n",
       " '1858931': ['IHD', 'ARR'],\n",
       " '21994105': ['IHD'],\n",
       " '33627595': ['CM'],\n",
       " '22471738': ['OTH'],\n",
       " '29694948': ['IHD'],\n",
       " '3183534': ['OTH'],\n",
       " '26806174': ['OTH'],\n",
       " '3605893': ['ARR'],\n",
       " '9659935': ['CHD'],\n",
       " '12403893': ['IHD'],\n",
       " '3967458': ['CM'],\n",
       " '2791146': ['ARR', 'CCD'],\n",
       " '7549991': ['IHD'],\n",
       " '15172429': ['VD', 'VOO'],\n",
       " '25925978': ['IHD'],\n",
       " '13599700': ['IHD'],\n",
       " '11720060': ['IHD'],\n",
       " '3342490': ['IHD', 'ARR', 'CCD'],\n",
       " '3155679': ['CCD', 'ARR', 'OTH'],\n",
       " '30132751': ['IHD'],\n",
       " '17453626': ['CM'],\n",
       " '23109772': ['IHD'],\n",
       " '3789832': ['IHD'],\n",
       " '32169829': ['OTH'],\n",
       " '4955702': ['VD'],\n",
       " '10970007': ['ARR'],\n",
       " '5293505': ['IHD'],\n",
       " '5907588': ['VD'],\n",
       " '8729299': ['ARR', 'CCD'],\n",
       " '35346355': ['IHD'],\n",
       " '18462576': ['IHD'],\n",
       " '22421176': ['IHD'],\n",
       " '27019883': ['VOO'],\n",
       " '8037144': ['IHD'],\n",
       " '13594847': ['ARR', 'CCD'],\n",
       " '14749500': ['OTH'],\n",
       " '14230443': ['IHD'],\n",
       " '12694774': ['VD'],\n",
       " '954020': ['IHD'],\n",
       " '34293106': ['ARR'],\n",
       " '7327321': ['CM', 'IHD'],\n",
       " '15291967': ['ARR'],\n",
       " '9301767': ['IHD'],\n",
       " '10645580': ['VD', 'CHD'],\n",
       " '23398824': ['CM'],\n",
       " '33181307': ['ARR'],\n",
       " '31691756': ['CM'],\n",
       " '7313641': ['IHD'],\n",
       " '16107827': ['CM'],\n",
       " '15477415': ['CM'],\n",
       " '19112782': ['IHD'],\n",
       " '31522912': ['CM'],\n",
       " '1165960': ['CHD'],\n",
       " '15550477': ['CM'],\n",
       " '12860878': ['IHD'],\n",
       " '24616336': ['IHD'],\n",
       " '30318622': ['VD'],\n",
       " '3258930': ['CM'],\n",
       " '17682517': ['IHD'],\n",
       " '19163634': ['OTH'],\n",
       " '29226427': ['CM'],\n",
       " '8548426': ['IHD'],\n",
       " '6844635': ['IHD'],\n",
       " '18100831': ['ARR'],\n",
       " '11040100': ['CM', 'IHD'],\n",
       " '33418127': ['ARR', 'CCD'],\n",
       " '1874648': ['VD', 'ARR'],\n",
       " '11930820': ['IHD'],\n",
       " '31894303': ['IHD'],\n",
       " '9008909': ['VD'],\n",
       " '22169422': ['IHD'],\n",
       " '1117574': ['VD'],\n",
       " '6605943': ['IHD'],\n",
       " '17727476': ['IHD'],\n",
       " '27357216': ['ARR'],\n",
       " '7311236': ['VD'],\n",
       " '27438117': ['OTH'],\n",
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       " '4499160': ['IHD'],\n",
       " '29499204': ['CM'],\n",
       " '10931403': ['IHD'],\n",
       " '12052020': ['IHD'],\n",
       " '7279828': ['IHD'],\n",
       " '20005976': ['CM', 'OTH'],\n",
       " '7702429': ['IHD'],\n",
       " '33413945': ['CHD'],\n",
       " '1551248': ['ARR'],\n",
       " '18326504': ['CM', 'OTH'],\n",
       " '31775067': ['IHD'],\n",
       " '21959857': ['CM'],\n",
       " '11877372': ['IHD', 'CM'],\n",
       " '16385847': ['IHD'],\n",
       " '18828643': ['IHD'],\n",
       " '19395952': ['IHD'],\n",
       " '20357744': ['IHD'],\n",
       " '29693431': ['ARR', 'CCD'],\n",
       " '35703074': ['CM'],\n",
       " '13548040': ['IHD'],\n",
       " '27456178': ['OTH'],\n",
       " '21048056': ['IHD'],\n",
       " '3559798': ['OTH', 'ARR', 'CHD', 'CCD'],\n",
       " '1088759': ['VD'],\n",
       " '9138820': ['IHD'],\n",
       " '32080960': ['CM'],\n",
       " '23230307': ['CM'],\n",
       " '2563784': ['IHD'],\n",
       " '2883512': ['IHD'],\n",
       " '29807890': ['CM'],\n",
       " '32646699': ['IHD'],\n",
       " '3543093': ['CM'],\n",
       " '29727706': ['OTH'],\n",
       " '11751653': ['OTH'],\n",
       " '25083976': ['CM'],\n",
       " '17215709': ['IHD'],\n",
       " '8510295': ['IHD'],\n",
       " '23092826': ['IHD'],\n",
       " '14558305': ['CHD'],\n",
       " '33155956': ['CM', 'OTH'],\n",
       " '22925077': ['ARR'],\n",
       " '27014962': ['IHD'],\n",
       " '15583723': ['IHD'],\n",
       " '14376720': ['CHD'],\n",
       " '18378025': ['IHD'],\n",
       " '21372403': ['CM'],\n",
       " '7611150': ['CHD'],\n",
       " '9368452': ['VD'],\n",
       " '5973317': ['ARR'],\n",
       " '13827387': ['OTH'],\n",
       " '29685202': ['IHD'],\n",
       " '12464785': ['IHD'],\n",
       " '27633970': ['CM', 'IHD'],\n",
       " '756116': ['IHD', 'CM'],\n",
       " '32241367': ['ARR'],\n",
       " '31000139': ['ARR'],\n",
       " '34234096': ['ARR', 'CCD'],\n",
       " '20391423': ['ARR', 'CCD'],\n",
       " '1347848': ['IHD'],\n",
       " '31350028': ['VD', 'VOO'],\n",
       " '8296728': ['ARR', 'CCD'],\n",
       " '29961835': ['VD'],\n",
       " '27237410': ['CM'],\n",
       " '11216033': ['ARR', 'OTH'],\n",
       " '4063083': ['IHD'],\n",
       " '27277903': ['IHD'],\n",
       " '7082413': ['IHD'],\n",
       " '14470194': ['IHD', 'ARR', 'CCD'],\n",
       " '8198664': ['ARR'],\n",
       " '5910761': ['CM', 'VOO', 'CHD', 'VD'],\n",
       " '13512141': ['VD', 'VOO', 'CHD'],\n",
       " '9522045': ['CM'],\n",
       " '17391160': ['IHD'],\n",
       " '14413658': ['CM'],\n",
       " '4276257': ['CHD', 'OTH'],\n",
       " '2528015': ['OTH'],\n",
       " '3779764': ['IHD'],\n",
       " '5976519': ['CHD'],\n",
       " '19193677': ['OTH', 'ARR', 'CCD'],\n",
       " '23782856': ['IHD'],\n",
       " '19931541': ['CM'],\n",
       " '23527914': ['IHD'],\n",
       " '7860770': ['CHD'],\n",
       " '1427939': ['IHD'],\n",
       " '30045821': ['ARR', 'CCD'],\n",
       " '2833226': ['IHD'],\n",
       " '19195517': ['ARR'],\n",
       " '20148889': ['CM'],\n",
       " '18611833': ['IHD'],\n",
       " '10213285': ['CM'],\n",
       " '1761264': ['IHD'],\n",
       " '4546500': ['IHD'],\n",
       " '5769099': ['CHD'],\n",
       " '9251546': ['IHD'],\n",
       " '26377711': ['CM'],\n",
       " '130902': ['VD', 'VOO'],\n",
       " '18085836': ['IHD'],\n",
       " '13711211': ['OTH'],\n",
       " '9228361': ['IHD'],\n",
       " '16429308': ['CM'],\n",
       " '160905': ['VD'],\n",
       " '28452401': ['ARR'],\n",
       " '3401087': ['VOO', 'OTH'],\n",
       " '540586': ['IHD'],\n",
       " '31532456': ['CM'],\n",
       " '14447045': ['OTH'],\n",
       " '13160928': ['IHD'],\n",
       " '21367468': ['CM'],\n",
       " '24214652': ['ARR'],\n",
       " '7088299': ['CM', 'IHD', 'OTH'],\n",
       " '22241241': ['VD'],\n",
       " '32711993': ['VD'],\n",
       " '21872439': ['IHD'],\n",
       " '21092538': ['CHD'],\n",
       " '30107931': ['VD'],\n",
       " '5711338': ['VD'],\n",
       " '29753507': ['VD', 'CHD'],\n",
       " '22343262': ['IHD'],\n",
       " '28753643': ['IHD'],\n",
       " '9671913': ['IHD'],\n",
       " '7882503': ['IHD'],\n",
       " '31882777': ['IHD'],\n",
       " '1622689': ['VD', 'CM'],\n",
       " '12538748': ['CM'],\n",
       " '385638': ['ARR'],\n",
       " '32475904': ['CM', 'OTH'],\n",
       " '21915473': ['CM', 'IHD'],\n",
       " '33334447': ['VD', 'ARR'],\n",
       " '9092406': ['IHD'],\n",
       " '4804050': ['IHD'],\n",
       " '16649238': ['CHD'],\n",
       " '20286826': ['ARR', 'CCD'],\n",
       " '20978569': ['CM'],\n",
       " '8020044': ['IHD'],\n",
       " '34650192': ['CM'],\n",
       " '15313958': ['CM'],\n",
       " '12555339': ['IHD'],\n",
       " '22400408': ['OTH'],\n",
       " '19276489': ['IHD'],\n",
       " '20498433': ['ARR'],\n",
       " '16410529': ['CM'],\n",
       " '2797667': ['IHD'],\n",
       " '10149318': ['IHD', 'ARR', 'CCD'],\n",
       " '29046971': ['IHD'],\n",
       " '34749668': ['IHD'],\n",
       " '12645705': ['IHD'],\n",
       " '31782766': ['IHD'],\n",
       " '27570159': ['VD'],\n",
       " '15391774': ['IHD'],\n",
       " '11603446': ['ARR'],\n",
       " '16883029': ['IHD'],\n",
       " '28329309': ['ARR'],\n",
       " '7834726': ['IHD'],\n",
       " '19699047': ['IHD'],\n",
       " '1806012': ['OTH'],\n",
       " '1709402': ['CM', 'ARR', 'CCD'],\n",
       " '3226403': ['VD', 'VOO'],\n",
       " '19631182': ['CM', 'IHD'],\n",
       " '2277244': ['IHD'],\n",
       " '28577683': ['IHD'],\n",
       " '5979408': ['ARR'],\n",
       " '637987': ['OTH'],\n",
       " '14400615': ['CM'],\n",
       " '2401083': ['IHD'],\n",
       " '4996159': ['IHD'],\n",
       " '4200943': ['IHD', 'VOO', 'CM', 'VD'],\n",
       " '25363093': ['IHD'],\n",
       " '4712784': ['VD'],\n",
       " '6622418': ['IHD'],\n",
       " '29538159': ['VD', 'VOO'],\n",
       " '28846805': ['CM', 'IHD'],\n",
       " '31189435': ['CM', 'ARR'],\n",
       " '8077542': ['ARR'],\n",
       " '24286443': ['IHD'],\n",
       " '7484247': ['ARR'],\n",
       " '4540102': ['CHD'],\n",
       " '35178699': ['CM'],\n",
       " '6689650': ['ARR', 'CCD'],\n",
       " '31658150': ['IHD'],\n",
       " '33736772': ['IHD'],\n",
       " '1433817': ['ARR', 'CCD'],\n",
       " '4553319': ['VD'],\n",
       " '2901501': ['IHD'],\n",
       " '4952080': ['IHD'],\n",
       " '10956322': ['CM'],\n",
       " '13861956': ['VD', 'VOO', 'CHD'],\n",
       " '11447535': ['OTH'],\n",
       " '19751953': ['IHD'],\n",
       " '32563523': ['VD', 'VOO'],\n",
       " '13956932': ['VD'],\n",
       " '14196896': ['CHD'],\n",
       " '4399632': ['IHD'],\n",
       " '16286351': ['IHD'],\n",
       " '21413871': ['IHD'],\n",
       " '16508591': ['IHD', 'OTH'],\n",
       " '12381643': ['CHD'],\n",
       " '4758173': ['ARR', 'CCD'],\n",
       " '8353922': ['VD', 'VOO'],\n",
       " '15538070': ['IHD'],\n",
       " '13272809': ['VD'],\n",
       " '29295673': ['ARR'],\n",
       " '35692373': ['IHD', 'CM'],\n",
       " '8105196': ['IHD'],\n",
       " '28498792': ['IHD'],\n",
       " '3870621': ['IHD'],\n",
       " '7634472': ['VD'],\n",
       " '30677272': ['IHD'],\n",
       " '5047098': ['ARR', 'CCD'],\n",
       " '31271517': ['IHD'],\n",
       " '11412778': ['CM'],\n",
       " '2561166': ['ARR', 'CCD'],\n",
       " '3358897': ['IHD'],\n",
       " '11195622': ['IHD'],\n",
       " '2541572': ['IHD'],\n",
       " '24183565': ['IHD'],\n",
       " '3342420': ['IHD'],\n",
       " '27248622': ['ARR', 'CCD'],\n",
       " '31079700': ['CM'],\n",
       " '5936965': ['CHD'],\n",
       " '1411951': ['OTH'],\n",
       " '23176958': ['IHD'],\n",
       " '30632777': ['OTH'],\n",
       " '20124049': ['IHD'],\n",
       " '17483234': ['CM', 'IHD'],\n",
       " '6149761': ['CM'],\n",
       " '33752676': ['CM'],\n",
       " '35399034': ['OTH'],\n",
       " '33855328': ['VD', 'VOO'],\n",
       " '24485580': ['IHD'],\n",
       " '22587991': ['CM'],\n",
       " '416768': ['IHD', 'OTH'],\n",
       " '32125713': ['IHD'],\n",
       " '8672023': ['CM', 'IHD'],\n",
       " '24856032': ['IHD'],\n",
       " '14916292': ['OTH'],\n",
       " '25240782': ['VD'],\n",
       " '9196857': ['IHD'],\n",
       " '26901907': ['VD'],\n",
       " '23552575': ['IHD'],\n",
       " '1778255': ['IHD', 'VD'],\n",
       " '4546085': ['IHD', 'VD'],\n",
       " '3964510': ['IHD'],\n",
       " '10171191': ['VD', 'OTH'],\n",
       " '15449492': ['ARR'],\n",
       " '16159803': ['IHD'],\n",
       " '3190069': ['VD'],\n",
       " '5938382': ['ARR', 'CCD'],\n",
       " '6167005': ['ARR', 'CCD'],\n",
       " '28818197': ['OTH'],\n",
       " '26391298': ['IHD'],\n",
       " '29762917': ['VD', 'VOO'],\n",
       " '453229': ['CM', 'IHD'],\n",
       " '12475448': ['IHD'],\n",
       " '19608027': ['VD', 'VOO'],\n",
       " '27932414': ['IHD'],\n",
       " '14578976': ['VD'],\n",
       " '4045601': ['CM'],\n",
       " '16449724': ['IHD'],\n",
       " '25499131': ['CM'],\n",
       " '31050670': ['IHD'],\n",
       " '6157082': ['IHD', 'ARR', 'CCD'],\n",
       " '12624477': ['CM'],\n",
       " '3159504': ['IHD'],\n",
       " '22102630': ['ARR'],\n",
       " '3157268': ['IHD'],\n",
       " '3745781': ['IHD'],\n",
       " '385410': ['ARR', 'CCD'],\n",
       " '29955839': ['ARR', 'CCD'],\n",
       " '18137226': ['OTH'],\n",
       " '9852933': ['ARR'],\n",
       " '11781935': ['IHD'],\n",
       " '13911610': ['OTH'],\n",
       " '27172093': ['CM'],\n",
       " '2287151': ['ARR', 'CCD'],\n",
       " '3927591': ['IHD'],\n",
       " '4743330': ['CM', 'IHD'],\n",
       " '2090675': ['ARR', 'CCD'],\n",
       " '8629615': ['IHD'],\n",
       " '19786267': ['IHD'],\n",
       " '30488944': ['CM'],\n",
       " '24014680': ['CM', 'IHD'],\n",
       " '25560114': ['ARR'],\n",
       " '20845041': ['IHD'],\n",
       " '15891755': ['IHD'],\n",
       " '15231371': ['IHD'],\n",
       " '712859': ['IHD'],\n",
       " '9296312': ['VD', 'VOO'],\n",
       " '15850140': ['IHD'],\n",
       " '17651108': ['VD', 'CM'],\n",
       " '30458719': ['OTH'],\n",
       " '33902079': ['OTH'],\n",
       " '15878556': ['VD'],\n",
       " '10941538': ['VD'],\n",
       " '8613602': ['IHD'],\n",
       " '18398241': ['VD'],\n",
       " '5708919': ['IHD'],\n",
       " '27302392': ['ARR', 'CHD'],\n",
       " '28120034': ['CM'],\n",
       " '13753499': ['CM'],\n",
       " '4482025': ['CM'],\n",
       " '10424817': ['CHD'],\n",
       " '16829112': ['VD'],\n",
       " '25715476': ['CHD'],\n",
       " '9413380': ['ARR'],\n",
       " '6232407': ['IHD'],\n",
       " '6018952': ['IHD'],\n",
       " '1642435': ['CM', 'IHD'],\n",
       " '11805966': ['VD'],\n",
       " '8546548': ['CM'],\n",
       " '2139804': ['IHD'],\n",
       " '30545953': ['IHD'],\n",
       " '17541138': ['IHD'],\n",
       " '20121997': ['OTH'],\n",
       " '16095170': ['CM', 'VD'],\n",
       " '4210258': ['ARR', 'CCD'],\n",
       " '5742105': ['ARR'],\n",
       " '31291602': ['CM', 'OTH'],\n",
       " '26897812': ['VD', 'VOO'],\n",
       " '3280640': ['IHD'],\n",
       " '30327287': ['VD', 'VOO'],\n",
       " '16705118': ['IHD'],\n",
       " '34381126': ['CM'],\n",
       " '6986074': ['OTH'],\n",
       " '26808075': ['IHD'],\n",
       " '25466854': ['VD'],\n",
       " '16275862': ['IHD'],\n",
       " '4815408': ['VD'],\n",
       " '5400056': ['OTH'],\n",
       " '2502960': ['IHD'],\n",
       " '24937432': ['CM', 'OTH'],\n",
       " '12440044': ['IHD'],\n",
       " '6477384': ['IHD'],\n",
       " '13354106': ['IHD'],\n",
       " '4713113': ['CHD'],\n",
       " '16270294': ['IHD'],\n",
       " '3724221': ['VD', 'CHD'],\n",
       " '19028812': ['IHD'],\n",
       " '22291127': ['IHD'],\n",
       " '8149384': ['IHD'],\n",
       " '12134544': ['CHD'],\n",
       " '22056820': ['ARR'],\n",
       " '28127030': ['IHD'],\n",
       " '20287247': ['IHD'],\n",
       " '14925361': ['VD'],\n",
       " '12195087': ['IHD'],\n",
       " '23735367': ['VOO'],\n",
       " '9665223': ['CM', 'IHD'],\n",
       " '5584331': ['CHD'],\n",
       " '22790162': ['VD', 'VOO', 'CHD'],\n",
       " '593813': ['IHD'],\n",
       " '2041017': ['ARR'],\n",
       " '1150739': ['IHD', 'VD'],\n",
       " '16203075': ['IHD'],\n",
       " '1182715': ['VD', 'IHD', 'VOO'],\n",
       " '27765670': ['CM'],\n",
       " '24169932': ['IHD'],\n",
       " '17966822': ['ARR', 'CCD'],\n",
       " '11755301': ['IHD'],\n",
       " '4171029': ['VD', 'VOO'],\n",
       " '21492813': ['IHD'],\n",
       " '21643756': ['IHD'],\n",
       " '20580517': ['IHD'],\n",
       " '22776806': ['IHD'],\n",
       " '15034958': ['ARR', 'CCD'],\n",
       " '27738076': ['ARR'],\n",
       " '21249647': ['IHD'],\n",
       " '19469059': ['IHD'],\n",
       " '3656926': ['IHD'],\n",
       " '31977539': ['ARR'],\n",
       " '25585031': ['IHD'],\n",
       " '27269184': ['VD', 'VOO'],\n",
       " '28911514': ['ARR', 'OTH'],\n",
       " '17541387': ['OTH'],\n",
       " '17187462': ['CM'],\n",
       " '20398862': ['IHD'],\n",
       " '1957771': ['CHD'],\n",
       " '32078212': ['VD', 'VOO', 'CM'],\n",
       " '30223140': ['IHD'],\n",
       " '1026527': ['CM'],\n",
       " '25209741': ['OTH'],\n",
       " ...}"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_cvd_pmid2cate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "28d667d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29250000 PMIDs processed\r"
     ]
    }
   ],
   "source": [
    "ctg2pmid = generate_ctg2pmid(cate_pmids, uncate_pmids, pmid2cate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "31ac441f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctg2pmid_path = '../text_mining/ctg2pmid.json'\n",
    "json.dump(ctg2pmid, open(ctg2pmid_path, 'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "fd3dd844",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_num_of_publications(all_pmids):\n",
    "    '''\n",
    "    plot a bar plot of the number of publications of different categories\n",
    "    '''\n",
    "    \n",
    "    # ctg = list(all_pmids.keys())\n",
    "    ctg = order.copy()\n",
    "    ctg.append('None')\n",
    "    count = [len(all_pmids[val]) for val in ctg]\n",
    "    \n",
    "    fig = plt.figure(figsize = (10, 5))\n",
    "    # c = ['#ceb78b', '#caa072', '#b68e6b', '#907659', '#945c37', '#66472d', '#463111', '#2c1504','#2e1f09']\n",
    "    g = sns.barplot(x=ctg, y=count, log=True)\n",
    "    \n",
    "    for index, row in enumerate(count):\n",
    "        g.text(index, row, row, color='black', ha='center')\n",
    "    # plt.bar(ctg, count, color ='blue', width = 0.4)\n",
    "    plt.xlabel(\"Categories\", fontsize=15)\n",
    "    plt.ylabel(\"Number of publications\", fontsize=15)\n",
    "    plt.title(\"Amount of publications per category\", fontsize=15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "05802a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = order.copy()\n",
    "c.append('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "51e973e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CM', 'ARR', 'CHD', 'VD', 'IHD', 'CCD', 'VOO', 'OTH', 'None']"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "bdd495cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plot_num_of_publications(ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "c2756deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_overlap = {}\n",
    "for key in ctg2pmid:\n",
    "    if key != 'None':\n",
    "        number_of_overlap[key] = {}\n",
    "        for key2 in ctg2pmid:\n",
    "            if key2 != 'None':\n",
    "                l1 = ctg2pmid[key]\n",
    "                l2 = ctg2pmid[key2]\n",
    "                number_of_overlap[key][key2] = len(set(l1).intersection(l2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "c55383e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OTH</th>\n",
       "      <th>CM</th>\n",
       "      <th>IHD</th>\n",
       "      <th>ARR</th>\n",
       "      <th>CCD</th>\n",
       "      <th>CHD</th>\n",
       "      <th>VD</th>\n",
       "      <th>VOO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>105852</td>\n",
       "      <td>8895</td>\n",
       "      <td>10466</td>\n",
       "      <td>7406</td>\n",
       "      <td>3335</td>\n",
       "      <td>1690</td>\n",
       "      <td>6739</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CM</th>\n",
       "      <td>8895</td>\n",
       "      <td>200895</td>\n",
       "      <td>38856</td>\n",
       "      <td>11175</td>\n",
       "      <td>5441</td>\n",
       "      <td>4946</td>\n",
       "      <td>8042</td>\n",
       "      <td>1824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IHD</th>\n",
       "      <td>10466</td>\n",
       "      <td>38856</td>\n",
       "      <td>448068</td>\n",
       "      <td>17036</td>\n",
       "      <td>8971</td>\n",
       "      <td>4015</td>\n",
       "      <td>9630</td>\n",
       "      <td>2626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARR</th>\n",
       "      <td>7406</td>\n",
       "      <td>11175</td>\n",
       "      <td>17036</td>\n",
       "      <td>155414</td>\n",
       "      <td>71593</td>\n",
       "      <td>2077</td>\n",
       "      <td>5518</td>\n",
       "      <td>1332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCD</th>\n",
       "      <td>3335</td>\n",
       "      <td>5441</td>\n",
       "      <td>8971</td>\n",
       "      <td>71593</td>\n",
       "      <td>71759</td>\n",
       "      <td>1593</td>\n",
       "      <td>2175</td>\n",
       "      <td>856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHD</th>\n",
       "      <td>1690</td>\n",
       "      <td>4946</td>\n",
       "      <td>4015</td>\n",
       "      <td>2077</td>\n",
       "      <td>1593</td>\n",
       "      <td>73942</td>\n",
       "      <td>11182</td>\n",
       "      <td>4587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VD</th>\n",
       "      <td>6739</td>\n",
       "      <td>8042</td>\n",
       "      <td>9630</td>\n",
       "      <td>5518</td>\n",
       "      <td>2175</td>\n",
       "      <td>11182</td>\n",
       "      <td>115057</td>\n",
       "      <td>36260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOO</th>\n",
       "      <td>1986</td>\n",
       "      <td>1824</td>\n",
       "      <td>2626</td>\n",
       "      <td>1332</td>\n",
       "      <td>856</td>\n",
       "      <td>4587</td>\n",
       "      <td>36260</td>\n",
       "      <td>39051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        OTH      CM     IHD     ARR    CCD    CHD      VD    VOO\n",
       "OTH  105852    8895   10466    7406   3335   1690    6739   1986\n",
       "CM     8895  200895   38856   11175   5441   4946    8042   1824\n",
       "IHD   10466   38856  448068   17036   8971   4015    9630   2626\n",
       "ARR    7406   11175   17036  155414  71593   2077    5518   1332\n",
       "CCD    3335    5441    8971   71593  71759   1593    2175    856\n",
       "CHD    1690    4946    4015    2077   1593  73942   11182   4587\n",
       "VD     6739    8042    9630    5518   2175  11182  115057  36260\n",
       "VOO    1986    1824    2626    1332    856   4587   36260  39051"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "matrix = pd.DataFrame(number_of_overlap).T.fillna(0)\n",
    "# cols = matrix.columns.tolist()\n",
    "# cols = cols[-1:] + cols[:-1]\n",
    "# matrix = matrix[cols]\n",
    "# np.log(matrix)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cefe51f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_overlap(all_pmid):\n",
    "    number_of_overlap = {}\n",
    "    for key in all_pmid:\n",
    "        if key != 'None':\n",
    "            number_of_overlap[key] = {}\n",
    "            for key2 in all_pmid:\n",
    "                if key2 != 'None':\n",
    "                    l1 = all_pmid[key]\n",
    "                    l2 = all_pmid[key2]\n",
    "                    number_of_overlap[key][key2] = len(set(l1).intersection(l2))\n",
    "                    \n",
    "    matrix = pd.DataFrame(number_of_overlap).T.fillna(0)\n",
    "    mat = np.tril(matrix) - np.diag(np.diag(matrix))\n",
    "    plt.figure(figsize=(10,8))\n",
    "    sns.heatmap(matrix, norm=LogNorm(), annot=matrix, mask=mat, fmt=\".0f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "ae24c89b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LogNorm\n",
    "import numpy as np\n",
    "                    \n",
    "mat = np.tril(matrix) - np.diag(np.diag(matrix))\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(matrix, norm=LogNorm(), annot=matrix, mask=mat, fmt=\".0f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "6957c416",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_proportion(all_pmid):\n",
    "    CVD = sum([len(all_pmid[key]) for key in all_pmid if key != 'None' and key != 'OTH'])\n",
    "    non_CVD = len(all_pmid['OTH'])\n",
    "    none = len(all_pmid['None'])\n",
    "    \n",
    "    features = ['None', 'Non-CVD', 'CVD']\n",
    "    amount = [none, non_CVD, CVD]\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "    ax.pie(amount, labels=features, autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "8749ffe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_proportion(ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ab31744a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pmid2cat = '/home/ubuntu/InternProjects/Joanne/caseolap_lift/parsed_mappings/MeSH/pmid2real_categories.json'\n",
    "f = open(pmid2cat)\n",
    "data = json.load(f)\n",
    "not_CVD_pmids = []\n",
    "for key in categorized_pmids:\n",
    "    if key not in data: \n",
    "        not_CVD_pmids.append(key)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "f0e2c5a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running label imputation\n",
      "Removing the labels imputed last time\n",
      "No previous label imputation\n",
      "5130312 relevant pmids\n",
      "Running jobs...\n",
      "641288 PMIDs processed in one of the batches\n",
      "641288 PMIDs processed in one of the batches\n",
      "641288 PMIDs processed in one of the batches\n",
      "641288 PMIDs processed in one of the batches\n",
      "641288 PMIDs processed in one of the batches\n",
      "641288 PMIDs processed in one of the batches\n",
      "641288 PMIDs processed in one of the batches\n",
      "641288 PMIDs processed in one of the batches\n",
      "Done!\n",
      "17 PMIDs with imputed labels\n"
     ]
    },
    {
     "ename": "AuthorizationException",
     "evalue": "AuthorizationException(403, 'cluster_block_exception', 'blocked by: [FORBIDDEN/12/index read-only / allow delete (api)];')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAuthorizationException\u001b[0m                    Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2102/293688223.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0mpmid2imputed_category\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpmid2imputed_meshsynonym\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge_pmid2new_mesh_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutput_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_folder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;31m# Index the imputed MeSH categories into their PMID entries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m     \u001b[0mindex_imputed_mesh_categories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m     \u001b[0;31m# Update the MeSH Terms Per Category file for the textcube\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0mupdate_textcube_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparsed_mapping_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmapping_folder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_2102/1587226168.py\u001b[0m in \u001b[0;36mindex_imputed_mesh_categories\u001b[0;34m(index_name, index_type)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;31m# Update each publication's index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         es.update(index = index_name, \n\u001b[0m\u001b[1;32m     25\u001b[0m                   \u001b[0mid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpmid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m                   \u001b[0mdoc_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/client/utils.py\u001b[0m in \u001b[0;36m_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    345\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m                     \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    348\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/client/__init__.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, index, id, body, doc_type, params, headers)\u001b[0m\n\u001b[1;32m   2090\u001b[0m             \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_make_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoc_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_update\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2092\u001b[0;31m         return self.transport.perform_request(\n\u001b[0m\u001b[1;32m   2093\u001b[0m             \u001b[0;34m\"POST\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2094\u001b[0m         )\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/transport.py\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, headers, params, body)\u001b[0m\n\u001b[1;32m    464\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    467\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/transport.py\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, headers, params, body)\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m                 status, headers_response, data = connection.perform_request(\n\u001b[0m\u001b[1;32m    428\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/connection/http_urllib3.py\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, params, body, timeout, ignore, headers)\u001b[0m\n\u001b[1;32m    289\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m             )\n\u001b[0;32m--> 291\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m         self.log_request_success(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/elasticsearch/connection/base.py\u001b[0m in \u001b[0;36m_raise_error\u001b[0;34m(self, status_code, raw_data)\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Undecodable raw error response from server: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m         raise HTTP_EXCEPTIONS.get(status_code, TransportError)(\n\u001b[0m\u001b[1;32m    329\u001b[0m             \u001b[0mstatus_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_message\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madditional_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m         )\n",
      "\u001b[0;31mAuthorizationException\u001b[0m: AuthorizationException(403, 'cluster_block_exception', 'blocked by: [FORBIDDEN/12/index read-only / allow delete (api)];')"
     ]
    }
   ],
   "source": [
    "if run:\n",
    "    print('Running label imputation')\n",
    "    '''Undo last label imputation'''\n",
    "    try:\n",
    "        pmid2imp_cat_path = os.path.join(mapping_folder,'MeSH/pmid2imputed_category.json')\n",
    "        pmid2imputed_category = json.load(open(pmid2imp_cat_path))\n",
    "        try:\n",
    "            print('Removing the labels imputed last time')\n",
    "            remove_imputed_category_mesh_terms_previous_li(index_name,\n",
    "                                                           index_type,\n",
    "                                                           pmid2imputed_category)\n",
    "        except:\n",
    "            raise Exception('Couldnt remove the last indexed imputed labels')\n",
    "    except:\n",
    "        print('No previous label imputation')\n",
    "    '''Get relevant PMIDs'''\n",
    "    try:\n",
    "        relevant_pmids = uncate_pmids\n",
    "    except:\n",
    "        relevant_pmids = get_all_uncategorized_pmids(index_name)\n",
    "    json.dump(relevant_pmids, open(os.path.join(data_folder,'all_uncategorized_pmids.json'),'w'))\n",
    "\n",
    "    print(len(relevant_pmids), 'relevant pmids')\n",
    "        \n",
    "    ''' Impute missing MeSH labels '''\n",
    "    # Impute PMIDs' Categories (i.e., Impute missing MeSH labels)\n",
    "    multiprocess_ds_label_matching(pmids = relevant_pmids, \n",
    "                                   index_name = index_name, \n",
    "                                   index_type = index_type, \n",
    "                                   label_unlabeled_only = True,\n",
    "                                   label_labeled_only = False,\n",
    "                                   label_all = False,\n",
    "                                   filter_list = filter_words,\n",
    "                                   stop_at_this_many_pmids = STOP_AT_THIS_MANY_PMIDS,\n",
    "                                   the_function = ds_label_matching,\n",
    "                                   output_folder = output_folder,\n",
    "                                   run = True)\n",
    "    pmid2imputed_category, pmid2imputed_meshsynonym = merge_pmid2new_mesh_labels(run=True,output_folder=output_folder)\n",
    "    # Index the imputed MeSH categories into their PMID entries\n",
    "    index_imputed_mesh_categories(index_name=index_name, index_type=index_type)\n",
    "    # Update the MeSH Terms Per Category file for the textcube\n",
    "    update_textcube_files(data_folder=data_folder, config_folder=config_folder, parsed_mapping_folder=mapping_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "0bbbfc09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'35615696': ['VD'],\n",
       " '34401699': ['ARR', 'CCD'],\n",
       " '29467831': ['ARR', 'CCD'],\n",
       " '27747715': ['ARR', 'CCD'],\n",
       " '16943976': ['ARR', 'CCD'],\n",
       " '28770754': ['ARR', 'CCD'],\n",
       " '19083349': ['ARR', 'CCD'],\n",
       " '18650979': ['ARR', 'CCD'],\n",
       " '28374333': ['ARR', 'CCD'],\n",
       " '27134440': ['ARR', 'CCD'],\n",
       " '29211605': ['CM'],\n",
       " '30555606': ['ARR', 'CCD'],\n",
       " '25147639': ['CM'],\n",
       " '20169139': ['ARR', 'CCD'],\n",
       " '32477709': ['ARR', 'CCD'],\n",
       " '30532877': ['ARR', 'CCD'],\n",
       " '18782505': ['CM']}"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmid2imputed_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "f116d649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('35615696', ['VD']), ('34401699', ['ARR', 'CCD']), ('29467831', ['ARR', 'CCD']), ('27747715', ['ARR', 'CCD']), ('16943976', ['ARR', 'CCD']), ('28770754', ['ARR', 'CCD']), ('19083349', ['ARR', 'CCD']), ('18650979', ['ARR', 'CCD']), ('28374333', ['ARR', 'CCD']), ('27134440', ['ARR', 'CCD']), ('29211605', ['CM']), ('30555606', ['ARR', 'CCD']), ('25147639', ['CM']), ('20169139', ['ARR', 'CCD']), ('32477709', ['ARR', 'CCD']), ('30532877', ['ARR', 'CCD']), ('18782505', ['CM'])])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmid2imputed_category.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "d727b4b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_ctg2pmid(all_pmid, imputed_cate):\n",
    "    \n",
    "    for key, cates in imputed_cate.items():\n",
    "        for cate in cates:\n",
    "            all_pmid[cate].append(key)\n",
    "        all_pmid['None'].remove(key)\n",
    "    \n",
    "    return all_pmid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "5914d194",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctg2pmid = update_ctg2pmid(ctg2pmid, pmid2imputed_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "4236ef55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_num_of_publications(ctg2pmid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf2763bf",
   "metadata": {},
   "source": [
    "## libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0dc5070c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "import matplotlib.ticker as mtick\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542b8e9c",
   "metadata": {},
   "source": [
    "## Functions to create figures\n",
    "1. heatmap of overlapping between categories\n",
    "2. bar plot of number of publications of categories\n",
    "3. venn diagram showing proportions of publications: unlabelled, CVDs, non-CVDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61b6ecb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "order = [\"CM\", \"ARR\", \"CHD\", \"VD\", \"IHD\", \"CCD\", \"VOO\", \"OTH\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "96c745ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_overlap(all_pmid):\n",
    "    '''\n",
    "    plot a heatmap to display the amount of overlap between each pair of categories\n",
    "    '''\n",
    "    \n",
    "    number_of_overlap = {}\n",
    "    for key in order:\n",
    "        if key != 'None':\n",
    "            number_of_overlap[key] = {}\n",
    "            for key2 in order:\n",
    "                if key2 != 'None':\n",
    "                    l1 = all_pmid[key]\n",
    "                    l2 = all_pmid[key2]\n",
    "                    number_of_overlap[key][key2] = len(set(l1).intersection(l2))\n",
    "                    \n",
    "    matrix = pd.DataFrame(number_of_overlap).T.fillna(0)\n",
    "    mat = np.tril(matrix) - np.diag(np.diag(matrix))\n",
    "    plt.figure(figsize=(10,8))\n",
    "    \n",
    "    # tick = mtick.StrMethodFormatter('${x:,.0f}')\n",
    "    ax = sns.heatmap(matrix, norm=LogNorm(), annot=matrix, mask=mat, fmt='d')\n",
    "    # ax.yaxis.set_major_formatter(tick) \n",
    "    for t in ax.texts:\n",
    "        t.set_text('{:,d}'.format(int(t.get_text())))\n",
    "\n",
    "def plot_num_of_publications(all_pmids, text_pmids):\n",
    "    '''\n",
    "    plot a bar plot of the number of publications of different categories\n",
    "    '''\n",
    "    \n",
    "    # ctg = list(all_pmids.keys())\n",
    "    count = [len(all_pmids[key]) for key in order]\n",
    "    text_count = [len(text_pmids[key]) for key in order]\n",
    "    proportion = [np.round(text_count[i]/count[i],2) for i in range(len(count))]\n",
    "    \n",
    "    df = pd.DataFrame({'ctg': order, 'total': count, 'only_full_text': text_count})\n",
    "    \n",
    "    # fig = plt.figure(figsize = (10, 10))\n",
    "    \n",
    "    ax = plt.subplots(figsize = (20, 10))\n",
    "    ax = sns.barplot(x=order, y=count, color='#ADD8E6', log=True, label ='titles and abstracts')\n",
    "    \n",
    "    for index, row in enumerate(count):\n",
    "        ax.text(index, row, row, color='black',fontsize=15, ha='center')\n",
    "    \n",
    "    # for t in ax.texts:\n",
    "    #     t.set_text('{:,d}'.format(int(float(t.get_text()))))\n",
    "        \n",
    "    ax = sns.barplot(x=order, y=text_count, color = '#44a6c6', label='full text')\n",
    "    \n",
    "    for index, row in enumerate(proportion):\n",
    "        ax.text(index, text_count[index], row, fontsize=15, color='black', ha='center')\n",
    "    \n",
    "    for t in ax.texts:\n",
    "        if float(t.get_text()) < 1:\n",
    "            t.set_text('{:,.0f}%'.format((float(t.get_text())*100)))\n",
    "        else:\n",
    "            t.set_text('{:,d}'.format(int(float(t.get_text()))))\n",
    "        \n",
    "    # plt.bar(ctg, count, color ='blue', width = 0.4)\n",
    "    ax.set(xlabel=\"Categories\", ylabel=\"Number of publications\",title=\"Amount of publications per category\")\n",
    "    ax.xaxis.label.set_size(20)\n",
    "    ax.yaxis.label.set_size(20)\n",
    "    ax.title.set_size(20)\n",
    "    # plt.xlabel(\"Categories\")\n",
    "    # plt.ylabel(\"Number of publications\")\n",
    "    # plt.title(\"Amount of publications of categories\")\n",
    "    leg = ax.legend(loc =\"upper right\")\n",
    "    plt.show()\n",
    "\n",
    "def plot_proportion(cat_pmid, uncat_pmid, pmid2cat):\n",
    "    '''\n",
    "    plot a pie chart to show the proportions of unlabelled, non-cvd, and cvd\n",
    "    '''\n",
    "    \n",
    "    only_cvd = {k:v for k,v in pmid2cat.items() if len(v)>0}\n",
    "    count = 0\n",
    "    for key in cat_pmid:\n",
    "        if key in pmid2cat and len(pmid2cat[key]) > 0:\n",
    "            count = count + 1\n",
    "    cvd = count\n",
    "    non_cvd = len(cat_pmid) - cvd\n",
    "    \n",
    "    features = ['Unlabeled', 'Non-CVD', 'CVD']\n",
    "    amount = [len(uncat_pmid), non_cvd, cvd]\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "    ax.pie(amount, labels=features, autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5707fa0c",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "583e016e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pmids(index_name):\n",
    "    '''\n",
    "    Get the PMIDs of the PubMed articles that have/don't have MeSH labels or full text\n",
    "    '''\n",
    "\n",
    "    es = Elasticsearch()\n",
    "    uncategorized_pmids = list()\n",
    "    categorized_pmids = list()\n",
    "    uncate_text_pmids = list()\n",
    "    cate_text_pmids = list()\n",
    "    for num_pmids, entry in enumerate(es_iterate_all_documents(es, index_name)):\n",
    "        \n",
    "        # Publication's MeSH (if any)\n",
    "        unlabeled = (len(entry['MeSH']) == 0)\n",
    "\n",
    "        # Save PMIDs\n",
    "        if unlabeled:\n",
    "            uncategorized_pmids.append(entry['pmid'])\n",
    "        else:\n",
    "            categorized_pmids.append(entry['pmid'])\n",
    "        \n",
    "        # has full text or not\n",
    "        has_full_text = (len(entry['full_text']) > 0)\n",
    "        \n",
    "        if has_full_text:\n",
    "            if unlabeled:\n",
    "                uncate_text_pmids.append(entry['pmid'])\n",
    "            else:\n",
    "                cate_text_pmids.append(entry['pmid'])\n",
    "\n",
    "        # Print progress\n",
    "        if num_pmids % 10000 == 0:\n",
    "            print(str(num_pmids) + ' PMIDs processed', end='\\r')\n",
    "\n",
    "    return list(set(uncategorized_pmids)), list(set(categorized_pmids)), list(set(uncate_text_pmids)), list(set(cate_text_pmids))\n",
    "\n",
    "def generate_ctg2pmid(cat_pmids, uncat_pmids, all_pmids):\n",
    "    '''\n",
    "    generate a dictionary with categories as keys and pmids as values\n",
    "    '''\n",
    "    \n",
    "    ctg2pmid = {'None': uncat_pmids}\n",
    "    \n",
    "    for num, key in enumerate(cat_pmids):\n",
    "        \n",
    "        # Print progress\n",
    "        if num % 10000 == 0:\n",
    "            print(str(num) + ' PMIDs processed', end='\\r')\n",
    "        \n",
    "        if key in all_pmids:\n",
    "            tags = all_pmids[key]\n",
    "            if tags:\n",
    "                for tag in tags:\n",
    "                    if tag in ctg2pmid:\n",
    "                        ctg2pmid[tag].append(key)\n",
    "                    else:\n",
    "                        ctg2pmid[tag] = [key]\n",
    "    \n",
    "    return ctg2pmid\n",
    "\n",
    "def update_ctg2pmid(all_pmid, imputed_cate):\n",
    "    '''\n",
    "    update the ctg2pmid dict with imputed categories\n",
    "    '''\n",
    "    for key, cates in imputed_cate.items():\n",
    "        for cate in cates:\n",
    "            all_pmid[cate].append(key)\n",
    "        all_pmid['None'].remove(key)\n",
    "    \n",
    "    return all_pmid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d042d9c8",
   "metadata": {},
   "source": [
    "## All PMIDs with titles and abstracts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54df163",
   "metadata": {},
   "source": [
    "### Before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "946b8def",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34380000 PMIDs processed\r"
     ]
    }
   ],
   "source": [
    "uncate_pmids, cate_pmids, uncate_text_pmids, cate_text_pmids = get_pmids(index_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "d872bc25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29250000 PMIDs processed\r"
     ]
    }
   ],
   "source": [
    "ctg2pmid = generate_ctg2pmid(cate_pmids, uncate_pmids, pmid2cate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "590716f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "ctg2pmid = json.load(open('../text_mining/ctg2pmid.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6eb73cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P9254EYByNSvRsFtzAGo0q0Oz3m259LHrCCsVjnVZMtIyWDXtD+IOxLBz1RYAVk1dQvciSlKqd2pEwu7DpMS6X4tbpi+ncsu6bJj0W/Y+yUcSCC8fRdKhOMLLR5F8xF1+r9C4Jr1H3wNAWHRJanZriivTxdaZfxIcEUa/Tx5i8avfcmDlVu8PLItNSiTjr5UEt25LYK3aZG5wT8ulzZtD5Euv5to/tGcvUiZ8BYBr316cB/bjqFadzI0bcMW4q5KuA/vJWL2KwDp1Sd+/D+fhw6QtWgBA+qKFlHzIu0lKQtbvfmJMAmtnLKNa09psW7oBgJCIUCqeW407J7jXdJQsF8nNHz3EJ7e+RvVmdWjSuy2XPHYtYaVKYF2WzLQMfvt8plfj/y8uv7Yvdw4cCsDqP9cSEhJM6TJRxB45SoVK5Rg17mWeuPd/7NnpXiDdtFUjGjQ9j2nLJhLocBBdtjQfT3yXwf1zJ6yeVrVTI47tPkxq1u/e9mnLqdiqLpuO+92rd2VnFj3zBQBbf1lCt1fca6IyElOYO2xs9n6DFo8iYZf7xIKkA+7XQ0pMAtun/0mFZrXZv2SjV8ZUKHxkeqaw5VdJeRL3FE9T4CVghTFm9T+3U3Wy1o611ray1rbKL0Gx1vLs+FnUrBjN9Re2zG7v0rgWk5e45w4nL/mbrk1qudub1OKXJeux1rJ6+34iwoKzp4Uue+5TACqVLsnq7ftJSc/AWsuSjbuoVdFd0vvnLKL0jEw+nbmMqzo3AeBIfFL2tMSaHQew1hIVHprPP49nhVUsTbdvHivQvtUvacu+X1fiOm7uPLRcZPbP0c1qYQKMVxIUgMmvfM3T7e/i2U738um9b7Fp8Vq+GDqavg8PpEnP1gBElHFPfxhj6HlPf377chYAb109gmc73cuzne5l/ripzHp3Egs/n8Gxw/HE7YuhfK1KANTr2IgDJ6wJ8JZje2Oo1KIOgaHBAFTv2JDYLXvp+MjV1O7pLidvm7WCBle6X/8NruzMtlnuAuS4Tg8yruNQxnUcyuapS5nz5KdsnfknAUEO+n74AOsnLmTz1GVeH5OJjMSEZyX8wcEEt2iFc9dOTHg4jipV3c0t3W0AwR07E36L+xOq69Ahgpq3cB8nqjSOatVw7t+PiYiAIPeaBlMqksCGjXHu3AFA+uJFBDd19wlq0gznHu89l8FhIYRk/X4Hh4VwbucmHNi0h4439KDjDT1IPZbCMy2G8GKn+3ix033sWrmFT259jT1rtvHe1c9mty8cN43Z7/7oFwnKgFuuZMAtVwJwYO9B2nZ2v05r1j2H4JBgYo8cpWSpCEaPf523XniPVcv+/RP/7WeTuKjZpfRq3Z8b+93Ozm27iiRBAffvXoXm//7uVenYkKOb99LukaupebF7TMkHj1K5Xf3s7XHb3VP8waVKEBDkAKD+wK7sX7KBjMQUAsNCCMp6PQSGhVDt/EbEbCyavy2+xBjT1xgz1hjTt6hiyO/snnpABWD3Ce3VgAOFEcCqrfv4Zel66lYuy9Uvukur917akVt6tObhj6cwafE6KkeX5JXBfQDo3LAmi9btoO+ITwgNDuTZQT0AOJqYkl0Zb1yzEhc1r8vAl7/EERDAeVXLcUXHxgB8Oms5C9dux2UtV3VuQpt61QH4deVmvl34F4GOAEKCAnn5lt45yp+FpcN7d1O+fX1CokvSb/k7rHn9e9KPJtHy+RsJKVOSLl8M5+i6ncy7diRh5aOwzn+z4wsnPUWpOpUJLBFKv+XvsGTYWA7Md58dUr1fO9aPzjlFVa1PG+recBGuTCfO1AwW3zmaolapXjXW/LocgJaXdqTz9e7n768ZS/nju3n59v9+xCfc8Oa9OIICidl9iC8fet+T4Z7SgVVb2Tx1KddNfR6X08nhdTtZ89Vc+oy5n22zVgCw7L3JXPL+vTS8pgvH9h7hlzvfyfOY5/ZpR5U29QiNiqDBlecDMHPYGA7/vcvj4wEIiC5DyYcfxwQEgDGkLZhH+pLfOTbqNUo98z9wuXAlHuPYa+4pAEelyriS3Ul/8pefUXL4Y5Qe615UmfTRGGxCPIENGlLygYfc89sBAaRM+DI7yUme8BUlH3uSsCuuwqakcOyNV04emAdElI3kprHu6c8Ah4OVP/3Gxvl/0eDZm9j+56YzPu51b99L7Xb1CS9dkid/H83MUd+z9Nt5hRR1wY18/1ladWhBVHSUe93Jqx9Rs845rFrqTjxeG/E2z7z2GNcPGYC1lqfufx5wJzLVa1bl9gdv4fYH3Wcb3jHgAWKPHD3lY3nboVVb2Tp1KVdNc//uHVm7k3VfzeXiMfezPet3b+4jH9NpxPUEBAbgTMtg/qMfA1C6TmUuHHU71sLRTXuYO/xDAEqUK8XFHz4AuF8Pm39azO55p/wc7ps8sIbEWjsZKNK1D+ZUixoBjDG/4J7iWXNCe2PgRWttvtlVyq8feGVSfcGabew5Es+1WVMG3vLjDQvz3+kM1b25O8l7Y9g7c4XHHiM/vwfnO7t3Wu78/HHez5ri8QU1XUGFerzLv3iYSdd77802L4PqnfjZonCVfOQJEj8YjY2P9+jjnMrIzZUL/Zi3fDycz+54A2dG0V9zYmbqjkI93jtfvMbQWx4lM6Nwf6fP1B1BtQv1eH3GP8wvg3zjdw/grt3jC/9Tbh5S/5pa6O+1oU17e3UMJ5NfkrLMWtv6FNvWWGsb5/cA3kpSioonkxRfUNhJiq8p7CTFl3g6SSlqnkhSfElhJym+prCTFF+jJKVw5DfdE5XHNv9YUSoiIlLcnaULZ5cbY247sdEYcyseOA1ZRERE5B/5VVIeACYZY67j36SkFRAMXO7BuERERKSgfOTia4UtzyTFWnsQ6GCM6QY0ymqeYq09+XXORURERApJfpUUAKy1c4G5Ho5FREREzkQxXZNSoCRFREREfJir6E+b94QCfXePiIiIiLepkiIiIuLviul0jyopIiIi4pNUSREREfF3Z+MpyCIiIuIHNN0jIiIi4j2qpIiIiPi7Yjrdo0qKiIiI+CRVUkRERPxdMa2kKEkRERHxc9bqirMiIiIiXqNKioiIiL8rptM9qqSIiIiIT1IlRURExN/pYm4iIiIi3qNKioiIiL8rpmtSPJ6khF10h6cfokhd9uahog7Bo0Y9MLuoQ/Co0ODKRR2Cx2xYU66oQ/CoEdenFnUIHjXh42NFHYJHJYYUdQTFjKZ7RERERLxH0z0iIiL+zgPTPcaYvkBfYLK1dnKhP0ABKEkRERGRXLISkyJJTv6hJEVERMTfFdM1KUpSRERE/F0xPbtHC2dFRETEJ6mSIiIi4u9USRERERHxHlVSRERE/J0WzoqIiIhP0nSPiIiIiPeokiIiIuLviul0jyopIiIi4pNUSREREfF3WpMiIiIi4j2qpIiIiPi7YromRUmKiIiIv9N0j4iIiIj3nFElxRhT3Vq7q7CDERERkTNwNlZSjDHtjTFXGmPKZ91vYoz5CvjNK9GJiIjIWeuUSYox5lVgHHAFMMUY8zwwE1gC1PVOeCIiIpIvawv/5gPymu65BGhurU01xpQGdgONrLU7vBKZiIiIFEwxne7JK0lJtdamAlhrjxpjNitBOX3PTPqdBRv3Eh0eyg/39gHg/Tmrmbh8C6XDQwG4t3tTOp9bhb1HE+n/9i+cU7YUAE2qleHJS9ue8tgjpyznxxVb+f2pawD4c8dBXp36J5sPxvHyVZ3o3qh6jv0TUzPo/85kutWvxmN9WntiuNnKVy7HiLeeILpcabCWSeMn883HP+TYJyg4iBFvP855jc8l/mgCT9zxLPv3HKDn5Rdx/V0DsverU7821/e8jb079jL2x3f+fYxK5Zj2wyxGPTPao2M5lUcXvU1aYgrW5cKV6eLtS5/Isb3lledzyWPXkXAwFoDFn81k6TdzARj82aNUb16HHcs28sngV7P71OnQkEsevw4TYEhLSuXbhz4gZudBr4yn7qi7iO7ekowj8azo+iAA540ZSljtygAERoaTGZ/EyouG5+pb/uouVH/gSgB2vfk9h76dD0DDr54guEJpTKCDhD/Ws+Wxj7L/mFYe3ItKN12MdbmI/fVPdvxvvMfGZspVJnTQQ9n3A6IrkD7ja1wJsQR3v4aA8lVJeedhXHu2nrR/+MjvcR1wL8OzRw+T+ulLADhqNya4z40QGIRrz1bSvhsNLheOhm0I7jnQ/WnU6STt53G4dqz32PhO9No7/+OiHudz5EgsF3W8PNf2QTddzU23DsDpdJGUlMwjQ0eweeM2mrVoxMhRIwAwxvDGyPeYPmU2lapU5K33XqRs+TJYa/nqs+/5eIznnq+8RNeqRN/R92Tfj6penkVvfM+f42bk2vfcXq257IP7+bzPUxxYsx2Atnf1pck1XbFOF7+O+JwdC9YAEFKqBBePvJWy51YFLNOGf8i+FVu8MiY5tbySlFrGmJ+Pu1/z+PvW2ks9F1bxcWnzWgxoW48nf1ico31Qh/O4sVODXPtXjY7g27t753vcdXtjSEhJy9FWMTKc5/q35/NFJ/9j+O7sv2hxTvnTiP7MOTOdvPXcu2xcs5kS4WF8Pv1Dli5YzvbNO7P3uXTgJRyLO8YVHa+je78LuOfJ23nijmeZMelXZkz6FYDa59Xi1XHPs3md+4/FoO63Zvf/bPpY5k1d4JXxnMqYgc+TfPTYKbf/9cvv/PTMp7na54+ZTFBYCO2uvTBH++XPD+az217j0NZ9tB/UnQvvvZxvH/qgsMM+qYPfzGXfuGnUe+fe7LYNt4/K/rnmiBtwJiTn6hcYFUH1YVezqucjYC3NZr5C7IzlZMYnsWHIGzgTUwCo/9FDlOvbnsM//UZkx4ZE92zNiguHYdMzCcpKzD3FHt5Hyih34oUJoMRTH5G5dgkEh5D6+UhCr7gz7wNkpP/b/x/GEDLgPlLGPIM9so/gHgMJbNmNzGWzcW5eTcq6pQAEVDqH0EEPkfzqvSc5sGd899WPfPrhV7z5/osn3f7jD1MY/+m3AHS/uCvPPP8wg666gw3rt9D7gmtwOp2Ur1CWmQt+YNb0eTgzM3nuqVdZu3o94RElmDbnWxbMW8zmjdu8NqZ/xG7bz2e93R8ITIDhziXvsHnG8lz7BYeH0vLmnjkSjTJ1K1O/bzvGdX+EiAqlufrLR/mo60NYl+XCZ65n+/zV/HTn2wQEOQgKC/HamApFMa2k5LVwth/w+nG3E+9LAbSsUYFSYcGFekyny8WoGSt5oGeLHO1VSkdwbsXSmACTq8/fe2OITUylfZ1KhRrLqcQcimXjms0AJCelsH3LTspVKpdjny49OzLlO/ennzm/zKd1pxa5jtPjsguZ9dOcXO3Va1UlumxpVi5Z7YHoPW/L4nWkJaXk3mAtISXDAAgtVYKEg0e9FlPCH+vJjEs85fZyfTtwaNKiXO2luzYlbv5fZMYlkhmfRNz8vyjdrRlAdoJiAh2Y4EAs7nnuSjf2ZM87k7DpmQBkHEko5NGcmqNuY2zMAWzcYeyhPdjD+87sQCVKgjMTe8TdP3PzKgIbt3dvS0/9d7/gUPDy9P6S3/8k7mj8KbcnHkvK/rlEiTBs1vqD1JRUnE4nACEhIdnLEg4dPMLa1e4PP0mJyWzetI2KlSp4KPqCO6djQ+J2HSJhb0yubZ2GXcmSD34hMy0ju61O95asn/wHzvRM4ncfJm7HQSo1q01wyTCqtq3H6gnzAHBlOEk7SUIu3nfKSoq1dv6pthljOnomnLPHhCWb+GXVdhpUiWbYxS0olZW17z2ayDXvTiUiNIi7L2xKixq5Kx8Tlmyiy3lVKJf1ZpYfl8vy+vQVvHhlR/7Yur9Qx1EQlapWpF6juqxb8XeO9nIVy3Jw3yEAnE4niQlJREZHEh/77x/X7pd246Gbc06jAHTvdyGzfs6dvHiVtdz2xWNYa1ny1WyWfJ07nsa92lCrTX0Ob9/P5P99Tvz+2DwP+d2jY7nlk0fISE0nLTGF0Zc/7anoT0updvVJPxJP6vYDubYFVypD2r5/3yTS9scSXKlM9v1GXz9JRPM6HJ2zkiOT/wAgrFYlSrWrzzmPXYtNS2fbs5+TuOrkUy2FLbBpZzJXLjzNTsGE3fcquJykz52Ic91SSEqAgAACqtbGtWcrgY07YKLKZndxNGpLSK9BmIhIUsa9UMij+O9uHDyA2+66keDgIK7pd0t2e/OWjXntnf9RtWpl7r/zseyk5R9Vq1WmUZP6rPyz6D8gnHdpe9b//Huu9gqNalCycjTb5qyizZBLsttLVizNvpX/vs6OHYglomJpMlPTSYk5Rq/XhlC+QXUOrtnB7BFfkHFCtdqnFdMrzuZ1do/DGDPQGPOQMaZRVlsfY8xiIM9FAMaYIcaY5caY5WPHji3kkP3f1W3q8svQS/nmrt6UjQjj9ekrAChXMozpD13ON3f3ZtjFLXjsu99ITM3I0fdQQjKz1u5iYNt6BX68b5duotO5lakQWaJQx1EQYSXCePmj53jj6XdISjy9TyYNm9cnNSWNbRu359rWvd8FzJw0u7DCPCPvXTmCt/o8zsc3jaT9DT2o2ea8HNvX/7qClzrdx6hej7B50Rquef2ufI/ZeXBvxt08khfb38Py7+bT98lBngr/tJS/vBOHT1JFKYi1A59nSdPbCAgOIqpTI8BdWQmKiuCv3o+x7bkvqD/2wXyOUkgcgQQ2bE3m6sX573uc5BeHkPL2cFK/GkXIpYMxZSoCkPrlG4T0vYWwe1+BtJQcbxTOtUtIfvVeUj592b0+xcd89vEEOrXsxYsj3uC+Ybdnt6/8cw0XdriMSy4awD0P3EpIyL+V4BLhYYz9bBQjHh+ZoxpTFAKCHNS5qAUbpyzJucEYuj15HXOf/6rgx3I4qNCoBqvGz+az3k+SnpxG27v6FnLEHuZyFf7NB+Q13fMxcCtQBnjbGDMeeA14xVrbPK+DWmvHWmtbWWtbDRkypPCiLSbKRIThCAggIMDQv1Ud1u5xfwoNDnQQVcJdUWlQpQxVoyPYGZOzDL5h/1F2xx6j75s/0+v1H0nNyKTvqJ/yfLy/dh/hmyWb6PX6j4yasZJfVm3jrZkrPTO44zgCHYz86DlmTPyVedNyf3I9fOAIFSq7K0UOh4OIUuE5qig9+l3AzB9zJyJ1G9Qm0OFgw5pNngu+AP6ZikmKSWDdjGVUa1o7x/bkuEScWdMZSyfMoUqjmnkeLzy6JJXrn8PurIrCX7/8zjktz/VA5KfJEUCZ3m05/NPJL4+Uvj+GkMr/Vk5CKkWTvj9n+d2mZRAzYxllLnYv2E7bF8ORqe43l8SVW7AuS1AZz65LAXCc1wLn3m3YxFNPhZyMTXBXwGzsQZzb1hJQ2f1cunZuJOX9J0h552Gc29fhOsnUkWv73wREV3BPD/mgnyZOo+clF+Rq37JpG0lJydSr777iRGBgIGM/e5NJ309h2i+/ejvMXGp1bcrBtTtIPmGqMDgilLL1qjJwwhPcvmgUlZvXpv/HD1KxcU2OHThKyUrR2fuWrBhN4oGjHDsQy7H9sezP+t3bNHUpFRrV8OZw5BTySlJaAd2ttY8BvYE+QEdr7Y/eCKw4O3zs37UIc9bvpk75KABik1JxZmWve2KPsSvmGFVLRwDw5PeLWbPnCOfXq8LsR65g2rDLmDbsMkKDApk8tF+ej/fSVR2Z/tDlTBt2GUN7NqdPs1rc3yPPPLNQPPX6I2zfvJOvxn6b3XbVzZdz1c3usw0WzPyNS67qCcAFfbqwfNG/iZMxhgv7dmPmT7mTlB6XXciMk7R7U1BYCCFZZ2cFhYVQt3MTDmzaQ4cbetDhhh4AlCwXlb1/g+4tObR1b57HTIlPIrRkCcrWdH9Kr9upMYe25N3HG0qf34SULXtJP26qKrhiNI2/ewaAo/P+onTXpgRGhhMYGU7prk05Ou8vAkqEEpT12sYRQPRFLUjOGk/M9GVEdXRXVcJqVSIgKJCMGM+vSwls1qlAUz2mVDShQ5513wkLB0fWzHiJkjjOOQ/Xwd3u/cIj3e2OQIK69ifzd/caq38qLQABVWpBYBAkn3qBtTfcdOtAbrrVXdGpWevfM/8u7HE+27e6z1yqVr0KDocDgCpVK1G7bk1273I/Z6+9/RxbNm3jw/c+93LkJ1f/hKme5jd2p/mN3Uk/lsLo5ncyptNQxnQayr6VW5k4+A0OrNnOllkrqN+3HY7gQCKrlaN0zYrsX7WVpMPxJOyPJbqWe83eOR0bErO56H/3TstZeJ2UdGvdtcusa6Vss9bmXp0keXr020Us336QuOQ0erw6kTsvaMLy7QfZuP8oxhgqR4XzZD/3acYrdhzivdmrCXQEEGDgyUvbEJlVWdl0MI7yJfOerlm7J4YHv55PQko6CzbscZ/qfF8fj4/xZJq2aUzvq3qy+e+tjJ/1EQDvvfQh59SpzuplawH4+eupPPv2E/zw25ckxB3jiTufze7fvF1TDu47xL5dudfQXNS3Gw9c/4h3BnIKJctGckPWFEWAw8Gqn35j0/y/qP/sTez8013h6XjzxTS4qCUup5OUuMQcZ+nc+e0zlKtdmZDwUB7/fTTfPzKWTQtW8/1jY7n+/aFYa0mJT+K74WO8NqZ67z9AVIeGBEaXpM2KMex89RsOfj2Hcpd15NCknFWU4AqlsZnutQqZcYnsGvUDzaa/DMCuN74nMy6RoLKRNPz8UQKCgyDAEP/bWvZ/NhOAg1/P4dxRd9Fi3hvY9Ew23ueF08iDQgis24y0H/59HhyN2hLS71ZMRCShtzyJa992Uj96DlOqNLjc4wsoX5WQK+50T+WYANLnTsQe2uM+ZNfLCKzfCowh4/fpOLe6T2cNbNyewJZd3cfISCd1vHfPNRj94Su079ia6DJRLFv7K6+//B516tZk2RL3B4GbbruWTl3akZmRSXxcAkPvfhyANu1acNcDg8nMyMTlcvHE8Oc5GhtH67bNuXLApaxft4kZ878HYOT/3mLOr6e5tqeQBIWFUKNzI2Y8Pi67rUztSuxdvjnPfjGb97JhyhJu+XUkNtPFrKc+xbrcb8azn/mMPm/dSUBQIPG7DjH1IS1V8AXGniJbMsYkA/+cu2WA2ln3DeCy1jYt4GP4RjrmISnfPufxx0hMzWDEj3/w2oDOHn+sE3V5oHArFm989hIP3/oUmRmZhXrcM9U1uHKhHu/mj4fz+R1v4Mxw5r+zh/U9YT1TYap0y8Wk7TlC7Mzcp356S/PrPfdvHNShF664Izj/Xuaxx8jPeR/n/YZ7uj79+l1uu+F+Mnzkd+++ks0K9XhXjBvGpNvfxOUDv3sAD+8cn/s0Sw9K+eThQn+vDbv5Fa+O4WTyqqTUP0mbAaoBj3kmHDmZiNCgIklQPOHBG4v3S+f4C7MVZ/vHTS/qEDwqY/G0og6h0N008O6iDsGjfrhFV8YojvI6BTn7qlvGmObAtcBVwHbgh1P1ExERES/zkbNxCtspkxRjzLnAwKzbEeAb3NND3bwUm4iIiBREMb1OSl7TPRuAhUAfa+0WAGPMUK9EJSIiIme9vJKU/sAAYK4xZjowAfeaFBEREfEh/5ylVNyc8jop1tofrbUDgPOAucADQHljzPvGmB5eik9ERETOUnldzA0Aa22StfYra21foCqwEijai1SIiIjIv4rpZfHzmu7JxVp7FBibdRMRERFfUEwXzuZbSREREREpCqdVSREREREfdLYtnBUREREpSqqkiIiI+DsfWeha2JSkiIiI+LtimqRoukdERER8kpIUERERf2dt4d8KmTGmqzFmoTHmA2NM14L0UZIiIiIiZ8QYM84Yc8gYs/aE9ouNMRuNMVuMMY9mNVsgEQgF9hTk+EpSRERE/F3RXXH2U+Di4xuMMQ7gXaAX0AAYaIxpACy01vbCfdX6ZwtycCUpIiIickastQuA2BOa2wBbrLXbrLXpuL+guJ+12ZfFPQqEFOT4OrtHRETE33ngYm7GmCHAkOOaxlprC/K1OFWA3cfd3wO0Ncb0B3oCUcDogsSgJEVERMTfeeC7e7ISkkL7rj5r7URg4un00XSPiIiIFKa9QLXj7lfNajttqqSIiIj4O9/67p5lQF1jTE3cyckA4NozOZAqKSIiInJGjDFfA78D9Ywxe4wxg621mcA9wAxgPfCttXbdGR3feuCCLSfwqfROTk/iI/2LOgSPmvxFWFGH4DEzg1OLOgSP+i1pZ1GH4FEXhtcq6hA8al7yjqIOwaPWH1pqvPl4SS/dWOjvteGPfebVMZyMpntERET8nW9N9xQaTfeIiIhILsaYvsaYscaYvkUVgyopIiIi/s4zpyBPBiYX+oFPgyopIiIi4pNUSREREfF3xXRNipIUERERf1fwLwT0K5ruEREREZ+kSoqIiIi/K6bTPaqkiIiIiE9SJUVERMTfeeAUZF+gSoqIiIjkoou5iYiIyH/ngTUpvnAxNyUpIiIifs7qFGQRERER71ElRURExN/pFGQRERER71ElRURExN8V00pKvkmKMSYQ6AWcl9W0Hphurc30ZGAiIiJSQGfjdVKMMVWAdcAwoDJQBRgOrDPGVPZ8eCIiInK2yq+S8gLwvrX2zeMbjTH3AS8BN3ooLhERESkoD0z3ZF3ErS8wOeuaKV6XX5LSzlp704mN1tq3jTEbPROSiIiIFDV/uJhbSh7bkgszEPF/pmxlQq8bln0/ILoC6bMmYEqUxNGgNViLTYwn7dt3sMeO5uzsCCSk/x0EVKkN1pI++WOc29bl2CX0xscw0RVIGfUAAMG9byCwfiusMxMbc5DU796BVM+9LNu+cRuVL2pO6pEEpl3wKADV+rSh8bArKFW3MjN7P03s6u0n7dv1y4cp06IOh5duYsGNr2W3V+jUkGZPDcQEBJCZlMofD4whccdB6g3pRe1ru2EznaTGJLDkwQ9J3nvEY2M7FRMQwDOTR3L0QCxvDX4px7bwyAjuHP0gZauW58ieQ7x39+skJyRRsXYVBr96N+c0rMXE175i+oc/Z/d5ddH7pCam4HK5cGY6ee7SR7w9JABefOtpunXvRMyRo/Q5/5pc2y8f0IdHnrmfgwcOATD+42/5bvxPADz01L107d4JgPfe+IipP84C4IU3n6Jx0/pgDDu27eLRe0eQnJTXn1DPMgGGxya/TNyBWN4bPDLHtkseuIpOAy7kWGwCAD+98jXr5q0kINDB9SPvoFrDmgQEBrBk4gJmvPcjFWpVYvDoodn9y1Yrzy+jvmXOuKleHdM/nn/zSbp270TskaNc2mXgSfe5+NKLuHv4rWBhw7rNDL/zKQCGPXUPXS7qCMD7b3zMtJ9+BaBK9cq8PuZ5oqIj+fuvDTxy9zNkZPjP0kt7li6cjTTG9D9JuwFKeSAe8WP2yD5S3spKUkwAJZ74kMy1S7ApiTDzawCCOvQm+KKrSZs0JkffoDYXAZDy5lBMeCShtzxJyuiHwbp/8RwN22LTUjDH9XFu/ov06ePB5SK41/UEd7uC9GlfeGx8275ZyKZPZtHurTuy2+I37GHhrW/SeuQtefZd//4UHGHB1Bl0YY72Vi/dzMKb3yBhyz7q3HgRDe+/jCVDx3B07U5m9HoSZ0o6dW64kGZPDWTxHe94ZFx56X7zJezfspfQiLBc23rfeTl/L17D1Pcn0fvOy7nkrsv57uXxJMUd46sRH9O8R9uTHnPkwGdIPHrM06HnaeKEyYz/+BteGf3cKfeZ+tMsnnv0lRxtXbt3pGGT8+jX7VqCQ4IY/+MY5v+6mKTEJF588g2SEpMAeOy5oQwafDVj3/7Mo+PIywU39+bAKZ47gNkfT+HXD3N+SG7Zux2BwYE8f/FDBIUG88yvb7Ds5984uG0/L/Z+GHAnPy8tGcOqGUs9PoZT+XHCFL76+DteHj3ipNvPqVmN2+6/kev63EZC/DGiy5YGoMtFHWnQpB6XXzCI4JAgPpv0AQtm/05SYhLDnrqHz8d8zdQfZ/HMq49yxXX9mPDpD14c1X9UTJOU/K6TMh/3fNSJtz7AAs+GJv7MUacxNuYgNu4wpB33aTI4NDvxOJ4pXw3nljUA2KR4bGqSu6qS1Se486Wkz/k+Rx/n5r8g61LQzl2bMJFlPDOYLIeXbCD9aGKOtoQt+zi2dX++fQ8uWkdmYupJtliCSrrfRIJLliDloLvCdGjx3zhT0gGIWbGFEpWi/1vwZ6B0xWiaXtCCBRN+Pen25t1b89v3cwH47fu5NO/eBoBjMQlsX70VZ6bvfgpd/vtK4o8mnHa/2ufWYtnvK3A6naQkp7Lh7y2cf2F7gOwEBSAkNORkL3OviaoYTaMLWvDbhNmn1c8CwWGhBDgCCA4NJjM9k9RjOauT53VszJGdB4gtgsreP5b/sZK4uFM/f1ddfxlfj/uehHh3Mhx7xP17VbteTZb/vjL7+dv09xY6X+B+/tp1asWMyXMA+OmbKVzYq4uHRyEFkWclxVp7s7cCkeIlsGknMlctzL4f3PNaAlt0xaYmkzL26Vz7u/bvILBBazL/WoiJLIujSm1MVFnYs4XgHgNJX/gzZKSd8vGCWl1A5urfPDIWT1o67CO6fDEcZ2oGGYkpzOzzTK59ag3syv45f3k9toFP38K3L31xyk/ikeWiiD8cB0D84Tgiy0Xle0xrLQ998TTWWuZ9NYv5X88qxIgLV48+F9CqXXN2bNvFi0++wYF9B9mwbhP3DB/CuPfHExYWSruOLdm6cVt2n5fefpouF3Zky6btvPzMqCKL/aqnb2LSS+MJOcVzB9D1xp607X8+u9Zs44fnPyc5IYkVU/+gafdWvLx0LMFhwXz/v89Ijk/K0a9V344s+9m3f9fOqV0dgC9/+RBHQACjX/2QRXP/YMO6zdz90K188v6XhIaF0qZTS7Zs2kZUdCQJCcdwOp0AHNh3kAoVyxXlEE5fMf3unjyTFGPMg3ltt9a+UbjhSLHgCCSwQWuSp4/Pbkqf8RXpM74iqGt/gjv0In3WNzm6ZC6fTUD5qoTd+yr26GGcOzeAy0VApRoElKlI+i+fYEqf/I9GULcrwOUic6X/FffqDenF/OtfJWblVs678xJajLiOpQ99lL29Rv+ORDepxewr/ufVuJpe0JJjMfHsXLuNeu0aFqiPLUDp4MUrnyTuYCwly5TiofHPsH/rXjYt/fu/hlvo5s5YyC8TZ5CRnsE1N/Rn5OgR3Nj/Tn6bt4TGzRvyzdRxxB6JY+XyNTiPe3N47L7nCAgI4KmXhtP7sh5M/Nr7aw4bXdCCYzHx7Fq7nbrtGpx0nwXjZzL17e/BQt9h13DFkzfwxcPvU6NpHVxOF4+2vZ3wyHCGffscGxat4chu99ocR5CDJhe15MdXvvLmkE5boMPBObWqceNld1ChcgW++GkM/boMZPG8JTRu1oCvpnzM0ZijrFq+BpezeL65Fxf5TfeUPO720An3S56qkzFmiDFmuTFm+dixYwsrVvETjnrNce7dhk2Mz7Utc9UCHI3a5+7kcpH+yyekvDWM1M9fxoSF4zqyj4Bz6hFQtTYlHvmAsDtfJKBsJcKG/LuOILBlNwLrtyJ1QtF9aj1TIdEliWpQnZiVWwHY9fMflG11bvb2Cp0b0uD+fiy46XVc6d6dOqnb6jyaXdSaVxe9z53vDKV+h8YMGXVfjn2Or55Elosi4Uju5/tEcQdjAfeU0IoZS6jVtE6hx14Y4o7Gk5GeAcB343+kUdP62ds+GDWOft2u4+ar7sYYw46tu3L0dblcTPlxJj37XODVmP9Ru1U9mlzUiucXjWbwOw9Qr0Mjbhp1b459jh2Jx7os1loWTZhNjabuqdU2/Tqxbv4qXJlOjsUksPXPjVRvUju7X8Ouzdm1djvHCvBcF6UD+w8xZ/oCMjOd7N21jx1bd3FOrWoAjHnzE/pfMIjBV92Lwf38xcXGU6pUSRwOBwAVK1fg4IHDRTmE0+eyhX/zAXkmKdbaZ/+5AQePv5/Vdqp+Y621ray1rYYMGVLoQYtvC2zWmcy/FmXfN2Uq/butQRvs4b0ABFStQ8jVWW98QcEQFAKAo25TcDqxh/aQ+ccMkl+4leSRd5Dy/uO4juzPni5ynNuc4C6XkfLZS5CR7qXRFUxYxdJ0++axPPdJj08iuFQJStaqCEDF8xuRsNn9b1O60Tm0HjmYBTe9TlrM6a+d+K++f+VLhrUfwvBOd/L+vaNYv3gNY4e+zZUPX0eLnu61J6t+XU7HK7sB0PHKbqyctSzPYwaHhRAaHpr9c6POTdmzaVeefbxp0OCrGTT4agDKVfh3fdOFF5/P1k3us7YCAgKIKh0JQL0GdajXoC6L5v4BQPWaVf/t0/N8tm3e4aXIc/rpla95vP2dPNnpHj6+9002Ll7Lp0Pfod/DA2naszUApY6bmmvWsw37Nu0GIHbfEep1aAS4n6OazetycOve7H1bX9qR5ZN9c6rn2luu4tpbrgJg9tR5tOnYEoCo6Ehq1K7Onp37cjx/5zaoQ70Gdfht3hIAlvz2Jz37uhPLftdcwpzp84tgFHKi0/nuHt9Iq8S3BYUQWKcpaRM/yG4K6TUIU64KWBf26OHsM3tM6XKQ6U4uTEQkYYOfBmtxxceQ+s3b+T5USL9bITCIsFvd6zhcuzblOmuoMHV4727Kt69PSHRJ+i1/hzWvf0/60SRaPn8jIWVK0uWL4Rxdt5N5144krHwU9rgy8oWTnqJUncoElgil3/J3WDJsLAfmr2HpQx/R6cMHsC4X6fFJLHnQXXls9tS1BIWH0mns/QAk7T3CwpuKfna1Sr3qrPzVnYxMeX8id707jPOvvpAjew/z/t2vA+43wGd+foWwiDCstXS/pQ9PdL+fkqVLcc9Y9xkiDoeDP35ayNr5q4pkHG+MeYE2HVtSOjqKBX9N4e1XxlKrbg3+XOJe+3PDbQO4oOf5ODOdxMUl8Oi9IwAIDArkq8kfApB4LInhdz2F0+nEGMPI0c8SERGOMYYN6zbxzPCXi2Rsp1KlXnVW/7ocgP6PDaJqgxpYa4ndc5gvH3e/7uZ/Pp3rX72Lp2a+jjGG37+by94N7kQyOCyE8zo1yd63KL32wf9o07ElUdFRzF01mdGvfEituuewYulqABbN/YOO3doxeeEEXE4Xrz37NnFH4wkOCeaLn91/I5KOJfHw3U9nr0N5/X/v8PqYF7jvsTtYv2YT33/58ykf3yf5SOWjsJmCzCMDGGNWWGtbnMFjFM9/ubNE4iMnOwO9cAT3voHMFfNxHdjpscfIz+QvTr2w8L+oe3N3kvfGsHfmCo8cvyBmBp/sbKL/ZtjnT/H6Dd5dH3MqvyUV7utmzJejuOem4T5zbYwLw2sV6vHu/fxx3rnhxUI95n8xL3lHoR7v/fFvcN/ND/vM87f+0FKT/16FJ+H2noX+XltqzAyvjuFk8kxSjDFr+DfJqANsOX67tbZJAR5DSYof82SS4gs8laT4Ak8kKb6ksJMUX1PYSYqvKewkxdcUhyQlcuzMS/Hxy+L3ByoAu09orwYc8EhEIiIicno8MN3jC5fFz+/snlFAvLV25/E3ID5rm4iIiIhH5FdJqWCtXXNio7V2jTGmhmdCEhERkdNSTBfO5pekROWxrfhO5ouIiPiR4voFg/lN9yw3xtx2YqMx5lbgT8+EJCIiIpJ/JeUBYJIx5jr+TUpaAcHA5R6MS0RERAqqmFZS8vuCwYNAB2NMN6BRVvMUa+0cj0cmIiIiZ7UCXXHWWjsXmOvhWERERORMFNPvSTydy+KLiIiIDzpbF86KiIiIFAlVUkRERPydKikiIiJytjDG9DXGjDXG9C2qGFRJERER8XceWDjrD9/dIyIiIlIkVEkRERHxc8X17B4lKSIiIv6umF4nRdM9IiIi4pNUSREREfFzxXW6R5UUERER8UmqpIiIiPi7YromRUmKiIiIn7PFNEnRdI+IiIj4JFVSJE8RIycWdQge1a/GvUUdgsckvnC0qEPwqA3BJYs6BI8aUeNQUYfgUWO31y3qEIoXVVJEREREvEeVFBERET9XXNekKEkRERHxd8U0SdF0j4iIiPgkVVJERET8nCeme4wxfYG+wGRr7eTCf4T8KUkRERGRXLISkyJJTv6hJEVERMTPaeGsiIiI+KTimqRo4ayIiIj4JFVSRERE/J01RR2BR6iSIiIiIj5JlRQRERE/pzUpIiIiIl6kSoqIiIifs67iuSZFSYqIiIif03SPiIiIiBepkiIiIuLnrE5BFhEREfEeVVJERET8XHFdk6IkRURExM8V17N7NN0jIiIiPkmVFJE8jJi5lgXbDxNdIpjvr+8IwKxNB/jgj61sj03ii4FtaVghEoB98Sn0//w3zikdDkDjSpE8eWGDXMfcG5/Mo1NXE5+aQf3ypXj+4sYEOQL4ed1eRi3aRPnwUACuaVaN/o2qsmx3LK/N35jdf8fRJF7u1YRudcp7dOwNB/fkvIFdMcaw4au5rP14Rq59avVpS4sH+4O1xKzfxdx73iOiShm6fzQUE2AICHSw7pOZrB8/B4CyjWvQZdTtOEKD2T1nFb8//YVHx3Aq5SuX4+m3HiO6bGmshZ++/IVvP/4hxz7N2jbhgWfvpnb92jx913PMnbIAgBYdmnH/iLuz9zundnWevus5Fsz4jSdHPULzdk1JPJYEwPNDX2bzuq2eH1BwMNFvvQVBQRiHg9T580n69FMAwgcPJrRrV3C5SP7pJ1ImTszZtWVLIoYMgaAgyMjg2AcfkLFypXtjYCAl77+f4GbNwFoSP/qItAULICiIyMceI7BePWx8PHHPPYfrwAHPjzNLSKkS9B15G+XOrQpYfh4+lr0rtmRv7/7UIGq0d//uBYUFE16mFK82GUJklbJcNfYBjAnAEeRg6aczWfHlbAAa9GlHp3v6EeAIYPPslcx+eYLXxlMYrC3qCDwjzyTFGHMjcD9QL6tpPfC2tfZzTwcm4gv6NqjMNc2q89SMNdlttctG8HqfZjw/++9c+1eNCuObQe3zPOZbizZzXYtzuLheJZ6f/TeT1u7l6qbVAOh5bkUe7VY/x/6tq0VnHzM+NYNLP1lIu3PK/Neh5al0vaqcN7ArP/Z5BldGJr3GP8yu2atI2HEwe59SNSvQ9J6+/Hz5s6THJxNaphQAyYfi+KnfCFzpmQSWCOHK2S+zc9YKkg/G0fGlm1n48EccWrGVi78YTtVuTdgzd7VHx3Iyzkwnbz/7PpvWbqZEeBifTB/D0gXL2bF5Z/Y+B/Ye5H9DR3LdHdfk6Lti8Spu7HEbAKWiSvLdovEsmb88e/vo5z/ITmi8Jj2dow8+iE1JAYeD6HfeIX3pUhzVq+MoX56YG24AazFRUbm6uuLjiXv8cVwxMThq1qT0K69w5KqrAAgfNAhXXBwx118PxmBKuZ/jsN69cSUmEnPddYRccAElhwwh/rnnvDbcns9cz5b5f/H9nW8REOQgKCwkx/ZZ/xuf/XPrm3pQseE5ABw7dJRPLh+BMz2ToBIh3DFzJJtm/YkzPZOLHh/IR32eJDn2GJe+fjs1OjZkx2/rvDYmOblTTvdkJSgPAMOAykAV4GHgfmPM9V6JTqSItawaTWRIUI62WtER1IgOP6PjWWtZtjuWi+pWAKBv/crM23qowP1/3XyQjjXKEhbkOKPHL6ioOpU5vGorztR0rNPF/j82UKNXqxz7nHdtN/7+7FfS45MBSI1JAMCV4cSVngmAIzgIE+CeKw8rH0VwRBiHVrgrC5u/X0SNnjmP6S0xh2LZtHYzAMlJKezYvItyFcvm2OfAnoNsXb8Nl+vUKxK7XdKF3+cuJS01zaPxFoRNSXH/EBgIgYFYaynRrx9Jn3+e/THbxsXl6pe5ZQuumBgAnNu3Y0JC3FUV3MlI0pdfZj2AxcbHAxDSsSOp06cDkDZ/PsEtW3pwZDmFlAyjetvzWDVhHuB+vaUlJJ9y/4aXtmftT79n7+vMem0GHvfajKpentgdB0iOPQbA9kXrqN+rtQdHUfisyxT6zRfkVUm5E7jcWrvjuLY5xpgrgAlA0dRpRXzY3vgUBnz5O+HBgdzdoQ4tqpTOsT0uNYOSIYEEBrg/H1QoGcqhpNTs7bM3H2TF3qNUjyrBQ13Oo2LJ0Bz9Z2zcz6AWNTw+jqMb99D6kasIiYogMzWdahc05cjq7Tn2iaxZEYC+k57GOAJY8cZE9sxzV0XCK0XT8/OHiKxRgSXPf03ywTjKNqlJ0v7Y7P5J+2MJr5jz36coVKxagXMb1WHdyvWn3feift2YMPa7HG23PzKYWx64geW/reC9Fz8kIz2jsELNW0AA0WPH4qhShZRJk8hcvx5H5cqEdutGSOfOuOLiOPb22zj37j3lIUK6dCFj82bIyMBERAAQccstBDdrhnPfPo699Rauo0dxlCuH8/BhdyenE1diIiYyMjuJ8aSoauVJjjnGpa/dToUG1dm/ZjszRnxBRkruRDGySlmiqpVjx+J/KyKlKkUz4JPhRNeowK8vfk3ioTgyU9MpU6sykVXLkrA/lno9W+II8q/VEJ5IKowxfYG+wGRr7eRCf4ACyGvhbKkTEhQAstpKeSogEX9VNjyEaYPPZ8J17Rl2fj0en7aaxLTMAvc/v1Y5ptxyPt8O6kC76mV4+rgpJoDDSWlsjkmkvYenegDituzjr/d+oddXj9Br/MPErNuJy5mzohAQ6CCyZkV+ueoF5t79Lp1fGUxwqRKAOwGZ2P1xvuk0jLpXdSasrG/+yQgrEcpLHz7Hm8+8S3LiqT+Nn0yZ8tHUPq8Wf8xblt32/ksfMuD8G7nlkjspFVWK6+8aWNghn5rLReytt3LkqqsIql8fR82aEByMTU8n9vbbSfnlF0o98sgpuztq1CBiyBCOvf56VoMDR/nyZKxbR+yQIWSsW0fEnXd6aTCnFuAIoFKjGiwf/ysf9n6C9OQ0Ot7V96T7NuzbjvVTl2Jd/y7YSNgfy9iLH2P0+Q/S5IrOhJctRWpCMlOfGMcVo+/lpu+fJm7PEayzmJ7TexqstZOttUOKKkGBvJOUlDPchjFmiDFmuTFm+dixY88sMhE/ExwYQFRYMAANKpSiamQJdsYl5dgnKjSIY2mZZGZNIRw8lpq9UDYqLJjgQPev5OWNqrL+0LEcfWdtOsAFtcsT5PDOSXkbJ8znx95P8cuVz5MWn0z8tpwLI5P2x7Jz5gpsppNjuw8Tv+0ApbKqK/9IPhjH0Q17qNi2HkkHjhJeKTp7W3ilaJIOHPXKWE7GEejgxQ+fY8akX5k/beFp97+wbzfmT1uEM9OZ3RZzyF0pykjP4JdvptGg+XmFFm9B2cRE0leuJKRNG1yHD5O6wL0+Jm3hQgJr1Tppn4By5Yj63/9IeOklnPv2uY8TH49NSXEvlAVS580jqG5dAJyHD+MoV87d2eEgICLCK1UUgIQDsSTsj2XfKve04fqpS6nYqMZJ9214aXvW/fz7SbclHorj8KbdVG/jfo42z17JuMue4ZPLRxCzdR8x2723ELgwWFv4N1+Q11+7+saY1caYNVn//ee2BsjzN89aO9Za28pa22rIkCGFG7GIj4pNTseZ9YltT3wyu+KSqRrpriw8OWMNaw/EY4yhVbVoft3sXoA6ef0+utZ2/7E/nPRvuXr+tkPUPGHdy/SNB7i4XiVvDAUgeyFseOUy1OzViq0/LqbBTd1pcFN3AHbM+JNK7d2LfENKRxBZqyLHdh4ivFI0jlD3mobgyBJUbHMucVv3k3IojvTEFMq3qA1A3Ss7sXPmn14bz4meeP1hdm7ZmWO65sqbLuPKmy4rUP/ul13ArJ9m52grU/7fJKzLxZ3YumH7id08wkRGZk/PEBxMcKtWZO7aRdqiRQQ3bw5AULNmOPfsASDwvPMo9dhj7r4REUS99BKJY8eSsXZtjuOm/f47Qc2auQ/bsiWZO90Li9MWLyb04osB9xRR+ooVnh5itqTD8STsj6FMLffvQs2ODTm8eS+tbuxOqxu7Z+9XpnYlQkuFs+fPzdltJStGE5i1xiy0VAmqtapHzNb9AJTIer2HlipBq+u7s3LCXG8NSfKQ16TbPOBFYA/gIzmViHc9OnU1f+6JJS41g54fzeeOdrWJDA1i5LwNHE1J576fVlKvbEne69+SFXuP8v7vWwgMCCDAwBMX1icy68168+FEyoW7z0C4v1NdHp26mvcWb6Fe+VJc1rAqAF+v3MX8bYdwBBgiQ4N4tkej7Dj2xadw4FgqLat6bw1H97H3E1I6AldmJr898RnpCclE1anEwWXuP/p75q2m6vmNuXLOSKzLxZLnvyYtLpGyjRvR9ulr3R/FjGH1mKkc3eB+c/zt8U/p8sYQAkOD2T3vL3bP+ctr4zlek9aN6HVlD7b8vZXPZn4IwAcvf8Q5daqzepn7jbp+03q8/PH/KBkZQafu7bl12M1cd8HNgHsdS4VK5Vj5e874R4x+gtLRUWAMm9dt4ZVH3/DKeBxlyriTjoAATEAAqXPnkv7772SsWUPkE09Q4qqrsCkpJLz6qnv/ChWwae6kuMTllxNYpQrhN95I+I03AnD0oYewcXEcGzOGyMcfx9xzD664OBJGjgQgZepUIh9/nDJffolNSPDqmT0A05/5nMveugtHUCBxuw7x80Nj6PrQVexevil7n4Z927Nucs4qStk6len+5HXZr83fx07h0MbdgPuMoQoN3GcBLXxrIrH+VknxkYWuhc3YU9R0jDH3AwOASsC3wNfW2pVn8BhKcMRnJb9/r8cfIzEtk2d/XcerlzT1+GMd78sXCn8qpeenw5h125u4Mpz57+xh4+ypF4Ceqdc+e5FHb32azIyCryXylB/reu5PZ8Qdd5A6cyaZ27Z57DHyM3Z7lUI93jXjHuK720f5xGsT4KmdX3o1a9jWuEehv2BqrZlZ5JnPKZOU7B2MOQd3sjIACAO+xp2wbMqz47+UpIjP8kaSUlQ8kaT4Ek8kKb7Ek0mKLyjsJMXXeDtJ2dqoZ6G/YGqvnVHkSUq+K/CstTuttSOttc2BgcBluC/qJiIiIj7Augr/5gvyTVKMMYHGmL7GmC+BacBGoL/HIxMREZGz2ikXzhpjuuOunPQGluK+gNsQa23SqfqIiIiI97lskc/MeEReZ/c8BnwFDLPWFu/JbREREfE5p0xSrLUXeDMQEREROTP2LKykiIiIiB8ortdJ8c71tUVEREROkyopIiIifs5XvmunsKmSIiIiIj5JlRQRERE/V1zXpChJERER8XPF9Topmu4RERERn6RKioiIiJ8rrtdJUSVFREREfJIqKSIiIn5OpyCLiIiIeJEqKSIiIn6uuJ7doyRFRETEz2nhrIiIiIgXqZIiIiLi57RwVkRERMSLVEkRERHxc55YOGuM6Qv0BSZbaycX+gMUgJIUOauVuPOdog7BY266altRh+BRzuZPF3UIHnXd1sNFHYJH3VBMvxCvqHhi4WxWYlIkyck/NN0jIiIiPkmVFBERET9XXK+TokqKiIiI+CRVUkRERPxcMT0DWUmKiIiIv9N0j4iIiIgXqZIiIiLi5/TdPSIiIiJepEqKiIiIn3MVdQAeokqKiIiI+CRVUkRERPycpXiuSVGSIiIi4udcxfRCKZruEREREZ+kSoqIiIifcxXT6R5VUkRERMQnqZIiIiLi57RwVkRERHySrpMiIiIi4kWqpIiIiPi54jrdo0qKiIiI+KR8KynGmECgF3BeVtN6YLq1NtOTgYmIiEjBFNc1KXkmKcaYKsAcYD+wEjBAH+B1Y0w3a+0+z4coIiIieTkrkxTgBeB9a+2bxzcaY+4DXgJu9FBcIuIlTqeTawbfR/lyZXnv1Wez218c9T6Tpsxk2a+TANh/4BCPP/86xxITcbpcDL3jZs7v0CbX8X6aOosxn00A4PYbB9Cvd3cAMjIyeOGN91i2cg0BxnDfkBvp3q1TgY9bmJrcejHnDegKWGI27GHesLE40zKyt5epX53OL91MUHgox3YfZvZ975ORmELdyzrQ9I5LjtuvGt/3epKYv3fR5uGrOPeKToREhvPxebd6NP68lKtUjkfeHE7pslFYC1O+msqkcT/m2q9Ln/O5YeggrIVt67fx4r0vU7tBLe5/8V5KRITjcjn56p0JzJs8P7vPzQ/fRJdLOuN0upj8xS/8+MlPXhlTh9dvo8pFzUg9ksDkCx8DIDgqnPPfv4eIauVI3H2YBXe8Q3p88kn7h1cuw6XzRvLX6xP5e8xUAIJKlaDDa7cSVa8q1loWD/uQI39uAeC8m7tT76buWKeLPbNXseKFCV4Zp+SWX5LSzlp704mN1tq3jTEbPROSiHjT+O9+olaN6iQm/fsHfu36TSQcS8yx35jPvqbnhZ0ZcHkftm7fyZ0PPc3ME5KJ+IRjvP/JV3zz8dsAXDP4Prp2akdkqZKM+WwC0aWjmDLhI1wuF/EJxwp83MIUXrE0jW7uwTcXPoIzNYPu791LnUvbsfG7hdn7dHn1Vn5//iv2/7GBetecT7M7LmHZa9+z+cfFbP5xMQDR51Wl50dDifl7FwA7Zq1g7aezGLjgNY/FXhBOp5MP/jeWLWu3EBYexvtTR/PnwhXs2rwre58qNSoz8O5ruL//gyTGJxJVJhKA1JQ0Rj7wKnt37KNMhWjemzKaZfOXk5SQRM+re1C+Ujlu7nor1trsPt6w5dsFbPhkFh3fuj27rdHdfTmw6G/WvjuZRnf3pdHdfVnx4jcn7d9qxHXsnftXjrY2z13P3rmrmT/kbQKCHDjCQgCo0KE+1Xq2ZHL3x3GlZxJappTnBlaIztaFsyl5bDt5yioifuPAocMsWLyUK/r2zG5zOp28/u7HDLtrcI59jTEkZSUyx5KSKVe2TK7j/bbkT9q3bk5kqZJElipJ+9bN+W3JnwBMmjKTW6+/BoCAgABKR0UW+LiFLSDQQWBoMMYRQGBYMEkHj+bYHlmzIvv/2ADAngVrqdmrda5j1OnXga0//5F9/9DKrSQfivNo3AUReyiWLWvdFYGUpBR2bdlN2Yplc+zT+9pe/PTZZBLj3YloXEw8AHu372XvDvcsfszBWOJi4omKdj9Pfa/vwxdvfYm1Nkcfbzi0ZCNpcTmT5mo9W7I1K7Hc+t1Cql3c6qR9q/VsSeKuw8Rv3JvdFlQyjPJt67Hl63kAuDKcZCS4X4P1briIte9OxpXuXnaZGpNQ2MOR05BfJSXSGNP/JO0G8I/0UkROaeRbY3jwrsEkJf/7eeSrHybTrVM7ypWNzrHvXbcMYsjQJ/jq+59JSU3jwzdfzHW8g4ePULF8uez7FcqV5eDhI9lVmdEffs6ylaupVqUSjz94F2WjSxfouIUp6cBR/hozlUF/vEVmajp7Fqxhz4K1OfY5umkPNXq2ZMeMP6ndpy0RlaNzHad237ZMHzzKo7H+VxWqVqBOw9psWLkhR3vVWlUBeHPiGzgcAXw+ajzL5i3PsU+9ZvUIDApk3879AFQ+pxJd+3ah48UdiI+J592n38tOaIpCWNlSpGQlhSmH4ggrm/stKbBECI3u7sOsAS/T8Lhpuojq5UiLOUaHUUOIblCdmNU7WPb0F2SmpFGqVkXKt6lHs4evwpmWwZ//+5qYv7Z5a1hnzFU8Cyn5VlLmA31PcusDLPBsaCLiSfN+W0J06Sganlc3u+3Q4Rhmzl3ItVdemmv/qb/Oo1/vi5j943jee+05Hvvfq7hcBVuu53Q6OXjoCM0a1+e7T0bTtFF9Xhv90X8+7pkIjixBjR4t+LLDUL5odS+BJUKoe3nHHPvMe+hDGt5wEVdM+R9B4aG4MnKezFi+WW0yU9I5unGPx+L8r0JLhPLMmKd4b8QHJCfmLHw7HA6q1KzCsKuH88I9LzF05AOElwrP3h5dPppH3xzOa8Nez66cBAUHkZ6Wzt2X3MvUr6bx0OvDvDqe/GSFmUPTYf35+8PpZCan5WgPcDiIblyDTZ/P5peeT5KZnEaje/oCYBwBhERFMK3vCP58/mvO/+Aeb4Qvp5BnJcVae/OZHNQYMwQYAjBmzBiGDBlyJocREQ9aufpv5i36g4W/LyMtPYOkpGQuu/4OgoKC6H3NLQCkpqbR6+pbmPbtOCZOnsEHbzwPQLNG9UlPz+BofAJlSkdlH7NCubIsW7k6+/7Bw0do3bwJUZGlCAsN4aIu7mSgR7fOTJw8A6BAxy1MVTs1ImH3YVJj3Wtitk9bTsVWddk86bfsfeK27mfKdSMB99TPORc2y3GMOv3aseWn3z0SX2FwBDoYMfYpZv84h0XTf8u1/fD+I2xYtQFnppMDuw+yZ9seqtaswsa/NlEiogQvfPoc4175lPXHVWAO7z/CommLAFg0/TeGF3GSknIkgbDyUe4qSvmok07LlG1eh3MuaUPLJwYQXKoE1mVxpmWwc8pSkvfHcmTlVgB2TlmanaQk7z/KzmnLAIhZtQ1clpDokqRlvV581Vn5LcjGmAfzup2qn7V2rLW2lbW2lRIUEd809M6bmf3jeGb+8BmvPvsobVo2ZfH075g/+Stm/vAZM3/4jNDQEKZ9Ow6AShXLs2T5KgC27thFWlo60VGRHDx8hMH3PQpAx7YtWbx0BfEJx4hPOMbipSvo2LYlxhi6dGybncAsWb6K2jWr53lcT0ncG0OF5nUIDA0GoErHhhzdvJc2j1xNjax1DdmLJY2hxX39WDd+9r8HMIbafdqy5WffTVIeevVBdm7ezQ8fTsxu63fjpfS70V0hWzxzMU3bNQGgVOlSVK1Vlf079xMYFMiID59m1g+zWTh1UY5jLp6xmGYdmgLQtF0T9mwv2irSnpkrqH1VZwBqX9WZ3TPca5/KNKuVvcB2Rv//MbHdUCa2G8r6j2aw5p2f2fjpLFIPx5O0L5ZStSsBUKlTQ+I3udes7J6xnIodGgBQslZFAoIDfT5BAbAeuPmC/NaklDzu59uBMR6MRUR82PB7buWZkW/z+beTMBief+JBjDEcPhKLw+EAILJUSW6/aSADbr0fgDtuvpbIUu4/Iw/edQuPPfcaL781huioSJ5//ME8j+sph1ZtZdvUpVwx7Xms08mRtTv5+6u59BxzPztnrQCgbr/2NLzxIsBdadn4zb+z25XbnkfivliO7Tqc47jtHh9Ancs6EBgWzKClb7Ph63ksHzURb2vUuiHdr7yIbeu38cH09wAYN/ITqtWpxrrl6wBYNm85Lc9vwcezx+JyuRj7wockxB3jwssvoEnbxpQqXYoeV7lPHX/1wdfY+vc2vn7vGx5/+xGuuLU/KUkpvD78Ta+NqfO7d1OhfX1CoyO4Yvnb/PXaD6x9dzLnf3AvdQZ2IWnPEebf8Q4A4VXK4EzNyOeIsPSpz+j0zp04ggI5tusQix8cC8CWCfPp8PoQ+s5+CVeGk98e0NteUTL2ZBN5J9vRmJXW2uZn8Bi+kpCJnFUyjnhnsd9X3/9MpQrl6da5nVce7x8fN3+6UI93yfiHmTLolUI95n/xPYfz3+k0PP/Jc4wY8hyZGb5xsfAbXOXy3+kMtHxyIFt/WETc+t0eOX5B3bB3vFfnXyZWvLbQ32v7H/iqyOeQTucLBpVsiEguJ1tk6498KUHxhCdvLtykzlf9+fzXRR2CFCJ9C7KIiIifc3lwirQo5ffdPWv4t4JSxxjzz7J9A1hrbRNPBiciIiL5K65THflVUvp4JQoRERGRE+R3nZSd3gpEREREzsxZ+S3IxphjnLyK9M90jy6NLyIiIh6RXyWlZF7bRUREpOgV1+/u0dk9IiIifu6svCy+iIiISFFRJUVERMTPFddTkFVJEREREZ+kJEVERMTPuUzh3zzBGBNujFlujCnQddiUpIiIiMgZMcaMM8YcMsasPaH9YmPMRmPMFmPMo8dtegT4tqDHV5IiIiLi51weuBXQp8DFxzcYYxzAu0AvoAEw0BjTwBjTHfgbOFTQg2vhrIiIiJ8rqoWz1toFxpgaJzS3AbZYa7cBGGMmAP2ACCAcd+KSYoyZaq3NMx9SkiIiIiK5GGOGAEOOaxprrR1bgK5VgN3H3d8DtLXW3pN13JuAI/klKKAkRURExO95YqFrVkJSkKTkdI/7aUH31ZoUERERKUx7gWrH3a+a1XbaVEkRERHxcz72LcjLgLrGmJq4k5MBwLVnciBVUkRERPxcUZ3dY4z5GvgdqGeM2WOMGWytzQTuAWYA64FvrbXrzmRcqqSIiIjIGbHWDjxF+1Rg6n89vpIUERERP2eL55cga7pHREREcjPG9DXGjDXG9C2qGFRJESmmgsrWKuoQPGrQgKSiDsGj7nlrbf47+bGoSi2LOgSPusHLj+eJhbPW2snAZA8cusCUpIiIiPg5Hzu7p9BoukdERER8kiopIiIifq6ovrvH01RJEREREZ+kSoqIiIif88R39/gCVVJERETEJ6mSIiIi4ud0do+IiIj4JE98d48u5iYiIiI+SRdzExERkf9MpyCLiIiIeJEqKSIiIn6uuJ6CrCRFRETEz+nsHhEREREvUiVFRETEz2nhrIiIiIgXqZIiIiLi51weqKVkXcStLzA565opXqckRURExM95YuGsL1zMLd/pHmNMN2PMRGPMuqzb98aYrp4PTURERM5meSYpxphLgHG4M6lrgeuAqcA4Y0xvz4cnIiIi+bEeuPmC/KZ7hgOXWWv/Oq5tlTFmOfAO7oRFREREpNDll6RUPCFBAcBau9oYU8FDMYmIFCpTrjKhgx7Kvh8QXYH0GV/jSogluPs1BJSvSso7D+PaszVXX0ftRgRfesu/fctVIfXL13GuW0rINffiqNUQm5oMQNo3b+PatwPCwgm9+h5MmYqQkUHat6NxHdzl8XHm5f77buOWWwZirWXt2g0MvvVB3nv3Zc7v3I74hGMADL51KH/9tQ6ALue35/XXnyUoKJCYI7FccNGVRRl+tqCQIJ779iWCgoNwBDr4fepvfDvq6xz7DBh2Ha27t8XlcpEQE8/oYW9x9FAsrbu3ZcCw63C5XLicTj559iM2LF8PwDfbJrFrw04Ajuw7zMhbX/D62P6L4noxN2PtqYs6xpg/rbUtT3fbCXylaiQixUji8MvPrKMJoMRTH5Hy9iMQHALWRegVd5L2y6cnTVJyCIsg/NH3SHr+VshIJ+Sae8n8eznONb/n2C34khux6SlkzPoWU64KIZcPIXXsM6cVZtRbS093ZKdUuXJF5s+dROOm3UhNTeXrrz5g2rQ5dOnSnilTf2XixCk59o+MLMXCBT9xSZ/r2L17H+XKleHw4ZhCiwfgskoFefs4udASoaQmp+IIdPD89y8z7tmP2LxyY/b2sIgwUhJTAOh9Ux+q1q3G2Cfez+4HcM55NXjw3Ye5/8K7APji72+4vsE1/2FEOX2/82evXqh+xDnXFfp77YidXxb5xfbzq6TUNsb8fJJ2A9TyQDwiIh7lqNsYG3MAG3f4tPsGNmlP5oYVkJGe534BFaqSPnciAPbwXgKiy2MiIrGJ8WcUc2EIDAwkLCyUjIwMSoSFsX//gVPuO3DA5fz44zR2794HUOgJyn/1T6LhCHTgCAqEEz5s/5OgAISUCM3e/E8/d3sIthh9hj5bv7unXx7bXivMQEREvCGwaWcyVy48s77NOpOxIOfntpCLr8N2vxrn5tWkT/0CnJm49u0gsFE70revJ6BaXUxUOUxkmSJLUvbtO8Aboz5g+9alpKSkMuvX+cz6dQEDBlzO/557hCefeIC5c37jsSdeJD09nbp1axEUFMjsWd8RUTKCd0Z/zPjx3xdJ7CcTEBDAyF/eoGKNSsz4fCqbV23Ktc/A4YPo0r8byceSGTHgiez2Nj3bcd3DN1CqbCQv3fxcdntwSDAjJ7+OM9PFpPe/Z9nMJV4ZS2HxxHVSfEF+pyD/DRy21s4//gYcztomIuI/HIEENmxN5urFp93VlCyNo2J1nBtXZrelTx1P8qv3kPLWcEyJkgR16+9unzsRExZO2NA3COrYG9e+bWCLbtVAVFQkl/btSZ1z21HtnBaEh5fg2mv788STL9Gw0fm0a38JpaOjeHi4e+ojMNBByxZN6NvvBnpfci1PPPYAdev6TvHc5XIxvPcD3N7uFuo0q0u1c6vn2ufrV8dzR/vBLPxxPhffeEl2+9IZf3D/hXfxym0vMmDYddntd3YYzCN9h/Hmfa9x89O3UqF6Ra+MRfKWX5LyDlD2JO1lgLdO1ckYM8QYs9wYs3zs2LH/JT4RkULjOK8Fzr3bzqiiEdi0I5lrl4DLmd1mjx11/+DMJHPZbBzV6rrvp6WQ9u1oUkY9SNqEtzDhkbhiDhbGEM7IhRd2ZvuOXRw5EktmZiaTfpxG+3atOHDgEADp6el89tk3tG7VHIC9e/czc9Y8kpNTiIk5ysJFf9CkSYMii/9UkhOSWLt4Dc27tjjlPgt/nEe7Xh1yta9fuo4K1StSsnRJAGIPxgJwaPdB1v2xlpqNfCcpK4jiegpyfklKHWvtghMbrbULgSan6mStHWutbWWtbTVkyJD/GqOISKEIbNapQFM9plQ0oUOezd13Vc6+pmTp7J8djdriOpB1Bk9oCXC4Z9MD23THuX0dpKVQVHbv2kvbti0ICwsF4IJundiwYTMVK5bP3ufSSy9m3d8bAPh58gw6dmiDw+EgLCyUNm2as2HD5iKJ/USloktRolQ44J6iadq5GXu37OHah2+gTc92AFSsUSl7/9Y92rJ36x53+zn/ttdsVIvA4CCOHT1GeKlwAoPdz1fJ0iU5r1V99mze7a0h+SxjTF9jzNisy+MXifzWpJTMY1tQYQYiIuJRQSEE1m1G2g8fZDc5GrUlpN+tmIhIQm95Ete+7aR+9BymVOkcFRNTuhwmqizObetyHDLk2qGY8FJgDK5927OPHVChGqHX3AfW4jq4m9TvRntnjKewdNlKJk6cwrKlM8jMzGTVqnV8+NGXTJk8nrLlojHG8Ndf67jr7kcB2LBhCzNmzmXlil9xuVyMG/c169ZtzOdRvKN0+WjueeMBAgICMAGGxb8s4s85y+kxqBfLf3WvIxn06I1UrlUF67Ic3nuIsY+/B0C7Xu3pcsUFZGZkkp6Wzqi7XwGgat1qDHnxLqzLYgIMk97/we+SlOJ6Wfz8TkGeArxrrZ16Qnsv4D5rba8CPIavVI1EpBg541OQCyCoQy9ccUdw/r3MY4+Rn8I8BdkX/ZdTkE/myc9H8PwNIwr1mP+Ft09BfqTGwEJ/rx254+siP2cov0rKA8AUY8zVwJ9Zba2A9kAfD8YlIlJkMhZPK+oQ5DT5UoIihSe/JMUCtwB1gUZZbfOBDwHnqTqJiIiI9xTXKYv8kpQ3gcestZ8c32iMaZy1rcgW04iIiEjxll+SUsFau+bERmvtGmNMDc+EJCIiIqejuH53T35JSlQe28IKMQ4RERE5Q2frFWeXG2NuO7HRGHMr/y6kFRERESl0BTm7Z5Ix5jpynt0TDHju/D8REREpsOJZR8knSbHWHgQ6GGO68e/ZPVOstXM8HpmIiIic1fKrpABgrZ0LzPVwLCIiInIGiuvC2fzWpIiIiIiPsx74nz98d4+IiIichXzhu3uUpIiIiPg5TfeIiIiIeJEqKSIiIn7ubL2Ym4iIiEiRUCVFRETEzxXPOoqSFBEREb+n6R4RERERL1IlRURExM/pFGQRERERL1IlRURExM/ZYromRUmKiIiIn9N0j4iIiIgXqZIiIn4p4tVJRR2CR+1f1aWoQ/CoXmuPFnUIxUpxne5RJUVERER8kpIUERERP+fywM0Y09cYM9YY09ergzmOpntERET8nMsW/nSPtXYyMLnQD3waVEkRERERn6RKioiIiJ8rnstmVUkRERERH6VKioiIiJ/TtyCLiIiIeJEqKSIiIn6uuF7MTUmKiIiIn9N394iIiIh4kSopIiIifk4LZ0VERES8SJUUERERP6eFsyIiIuKTtHBWRERExItUSREREfFz1gPfguwLVEkRERERn5RnJcUYUx64G2iY1bQOeM9ae9DTgYmIiEjBnHWnIBtjOgLLsu5+nnUDWJK1TURERHyAywM3X5BXJeV14DJr7crj2n42xkwCxgBtPRqZiIiInNXyWpNS6oQEBQBr7SqgpMciEhERkdNiPfA/Y0xfY8xYY0zfohpXXpUUY4wpba09ekJjNFpwKyLiO4KCiXrjbUxQEDgcpC2cT/LnnxDUrDnhQ+7CBAaSuXkTx15/BVzOXN3DBlxH2MW9sS4Xie+9TcZy90x/9BcTsCkp4HJinU7i7r4dgODzuxJ+/U04qp9D3L13kLlpo1eH+/Qbj9KpeweOHjnKNd1uzLW9YtUKPP3GY5QuE0VCXAJP3fM/Du0/DMB9T95Jx4vaExBgWDJ/Oa899RYAgUGBPPziUFq2b461Lt57+UPmTJnv1XH5GmvtZGByUcaQV5IyCphpjHkIWJHV1hIYmbVNRER8QUY6ccOHQmoKOBxEjRpN+vKllBz+OPEPD8W5dw8lbryF0B49SZ0+NUdXR/VzCO16AbG33URAmTJEjXyD2JsHgcu9KiHuoQewCfE5+jh3bCfh2aeIeGCY14Z4vMnfTuObTyby3NtPnHT7A0/fzZTvpjPlu+m06tiCex6/nafvfZ4mrRrRtHVjBl5wEwAf/fQuLds348/fV3HL/Tdw9MhRruh0LcYYSpUu5cUR/Xdn3cJZa+1Y4Fngf8COrNtzwPPW2jHeCE5ERAooNcX938BA983lgswMnHv3AJDx53KCO3fJ1S24QydS582BjAxcBw7g3LeXwHr183wo566dOPfsLvQhFNTKP/4i4WjCKbfXPLcGy39zf7Ze/tsKzu/ZCXBfSyQ4NJig4ECCQoIIDAok5oh7suDSAb355O3x2fvFx8af/ODiVXmegmyt/QX4xUuxiIjImQoIoPR7Y3FUrkLKzz+SuWE9OBwEnluPzE0bCT6/C45y5XN1c5QtS8b6v7PvOw8fJqBsWfcdC5EvvwbWkjplMqlTi7TyX2Cb122hW+/zmfDR93TrfT4RJcOJLF2KNX+uY/lvK5i+6keMMXz7yUR2bN5JRKkIAO585FZadmjOnh17eeXxUcQeOZrPI/mOs/JibsaYXsaY+caYI1m3+caY3vkd1BgzxBiz3BizfOzYsYUXrYiInJzLxdE7biVm4FUE1quPo0ZNEl54jog77iHqnQ+wycknXY+Sl7ih9xB3123EP/EwYZdeRlDjJh4KvnC9+dy7tGjfjC9nfkyL9s04uO8QTqeLqjWqULNuDXq3uIJezfvTqmMLmrVtgiPQQcUqFVi9bC2DegxmzZ/reOCZu4t6GKflrDsF2RhzG3A78DCwPKu5FfCyMaZq1nTQSWVt+2d78UzvRER8kE1KJOOvlQS3akPK998Q9+C9AAS1bIWjarVc+zuPHCHguAqLo1w5XEeOAOCKcf/XxsWR9ttCAuvVJ2PNai+M4r85cjCGhwc/CUBYiTAu6N2FxIRELr+uL2tWrCMl2T01tnjOEpq0bMSqJatJSU5hzlT3QtlfJ8/l0oGXFFn88q+8KilDgR7W2jnW2oSs2xygV9Y2ERHxASYyEhPunrIgOJjgFq1w7t6FiYpytwUFUeKaa0n95ScAAuudR8mHHwcg/fffCO16AQQFEVCxIo4qVcncuB5CQzFhYe7+oaEEt2xN5o7tXh5ZwV19c3+uvrk/AJHRkRhjALj5vkH8PMG9WPjA3oO0aNcMh8OBI9BBi/bN2L55BwALZy6mZYfmALTu1JLtm3Z4fQz/hSdOQfYFeZ6CbK2NPbHRWhvzz5MvIiJFLyC6DCUffhwTEADGkLZgHulLfif8tjsIbtcBjCF18k9krHJf+iqgfAVsWhoAzp07SFswl+iPPsM6nSS+8ya4XARElSZyxPPuB3A4SJv7KxnLlwIQ3LEzEXffR0BkFJHPv0zm1i3EPzbca+N94b1naNmhOVHRkUz58wfGvjaOGnWq89eyNQC0at+cux8fgrXuRbYjH38DgNm/zKN1pxZMmPsp1sLvc5ewcNZiAN5+4X2ee+dJhj13H0dj4nh26IteG4+cmjnVYhtjzBJgiLX2rxPamwIfWmvbFPAxfCMdExHxI4e75z4Tp7CE33YHqb/OxLl9m8ceIz+91hbuqodRn49k+OAnyMzILNTjnqnl+xd69dP8RdV6Fvp77a+7ZxR5RSKvSsqDuC+D/wnwZ1ZbK+BGYJCnAxMREc9I+vCDog6h0A294ZGiDqFIFdeze/JKUq4FrgN64E5MDPA30M5ae8ALsYmIiMhZLK8kZRPwKlAZ+Ab4+mTf5SMiIiJF62y84uxb1tr2wPlADDDOGLPBGPOMMeZcr0UoIiIiZ6V8vyjQWrvTWjvSWtscGAhcBqz3dGAiIiJSMGfjKcgAGGMCcV8bZQBwITAPGOHRqERERKTAXGfbwlljTHfclZPewFJgAu5TkpO8FJuIiIicxfKqpDwGfAUMs9b6z7csiYiInGWKZx0ljyTFWnuBNwMREREROV6+a1JERETEt511pyCLiIiIFCVVUkRERPxcca2kKEkRERHxc8X1u3s03SMiIiI+SZUUERERP1dcp3tUSRERERGfpEqKiIiIn/OV79opbEpSRERE/JwWzoqIiMhZwxjT1xgz1hjTt6hiUCVFRETEz3li4ay1djIwudAPfBpUSRERERGfpEqKiIiInyuua1KUpIiI+KBys+YXdQgeteDN24s6hGJF10kRERER8SJVUkRERPxccb1OiiopIiIi4pNUSREREfFzrmK6cFaVFBEREfFJqqSIiIj4ueK6JkVJioiIiJ/TdI+IiIiIF6mSIiIi4ueK63SPKikiIiLik1RJERER8XPFdU2KkhQRERE/p+keERERES9SJUVERMTPFdfpHlVSRERExCepkiIiIuLniuuaFCUpIiIifs5aV1GH4BGa7hERERGfpEqKiIiIn3OdjdM9xpj+eW231k4s3HBERERE3PKrpPQ94efJx923gJIUERGRImaL6SnIeSYp1tqb//nZGLPy+PsiIiIinnQ6a1KKZ5omIiI+J7j7DThqNsYmHyN1/HPuxpAShPS+DVOqDDYhhrSpH0JaMgFVzyWk713YhCMAZG5ZSeaSKbmO6ajXmqDWvcBabFI8adM/htQkgtr1wdGoE6QkApD+24+4dqwloHp9gjteDo5AcGaSvvAHXHs2eu3f4HSclWtSREREikLm37+TsWouIT3/LeAHtb4Y5+4NZC6fQWCrngS1vpiMRe5VB669m0n7+d1TH9AEENzlalI+H+FOTDr1J6hZNzL++MX9eCtmk7liVo4uNiWRtJ/fxSbFY8pUJuTy+0j96NHCHmqhKK7TPXmegmyMmWyM+dkY8zNQ65+fj2sTEREpdK69myEtOUebo1ZTMv/+HXAnMY5aTQt+QOP+PxMU4r4fHIorMT7PLvbwbmySex8bsw8TGOyuqojX5Pev/dpxP7/uyUBERETyYsJLQXKC+05ygvt+loBKtQi97klsUjzpC77Hxu7P2dnlIn3OV4QOehoy03EdPUTG3K+zNwc260pg/Xa4Du0kfcH3uROkOi1wHdoFzkyPje+/KK7f3ZNfknKztfYmbwQiIiJyWrLemF2HdpEy7nHISCOgRiNC+t5J6mdP59w3IIDAJl1I/ep5bPwRgroOILB1LzKXTiVj9XwylkwBC0EdLiX4/CtJn/V5dlcTXYmgTv1Jm/SmFwcnkP8VZ5ucyUGNMUOMMcuNMcvHjh17JocQERHJwSYlQIms6kmJUtjkY+6f01MhIw0A14614HBAaHiOvgHlqrmPEe9eXOvc/CeOSrXcG5OPZSU8lsy1iwioUCO7n4mIIqTvnaTP+CS7ry+yHvifL8ivklLCGNOcrNm8E1lrV5yifSzwT3biGyMVERG/5ty2msAG7d0LZxu0x7ntL/eGEv9OA7kTjABITQIgpP9Qd4KRGEdAmUoQFgEpiTiq18cVeyBXf0ftZrhi9rnbQ8II6XcPGYsm4dq/1YsjPX3FdeFsfklKFdxrUU6WpFjggkKPSEREznrBvQbjqFoPQiMIHfwyGX9MJmP5dEJ6DyGwYUfssVjSprg/CwfWbUFgky7gcmIzM0if9mHWUQwmqhw2NQmcGWT88QuhVz0ETqe7/8xP3Y/V+Qp3pcVaXAkxpM8e7z5u026YqPIEtbuEoHaXAJA68S1IOebtf46zlskr+8q6gFvz//gYxTO9ExGRM5b85u0efwxTpjKBDTuSseA7jz/WiUo8MOakMxCeUi6yXqG/1x6O3+jVMZxMgc6lMsaEAnWy7m6x1qZ6LiQREZH/zsbsK5IERQpPfknKI8aYkcBgYCfuaZ9qxphPgCestRmeDlBERETyVlzXpOR3dk9voAxQ01rb0lrbAqgNRJHzGioiIiJSRFzWFvrNF+SXpFwC3GatzV4lZK1NAO7EncCIiIiIeER+0z3WnqSGZK11GmN8I80SERE5y52t0z1/G2NuOLHRGDMI2OCZkERERETyr6TcDUw0xtwC/JnV1goIAy73ZGAiIiJSMK5ierWPPJMUa+1eoK0x5gKgYVbzVGvtbI9HJiIiIme1Al0nxVo7B5jj4VhERETkDPjDmhRjTH3gfqAsMNta+35+ffJbkyIiIiI+rqhOQTbGjDPGHDLGrD2h/WJjzEZjzBZjzKMA1tr11to7gKuBjgU5vpIUEREROVOfAhcf32CMcQDvAr2ABsBAY0yDrG2XAlOAqQU5uJIUERERP2c98L8CPa61C4DYE5rb4P4KnW3W2nRgAtAva/+frbW9gOsKcvwCrUkRERGRs4sxZggw5LimsdbasQXoWgXYfdz9PbhPwukK9AdCKGAlRUmKiIiIn/PEZeyzEpKCJCUFPd48YN7p9FGSIiIi4ud87OyevUC14+5XzWo7bVqTIiIiIoVpGVDXGFPTGBMMDAB+PpMDKUkRERHxc0W1cNYY8zXwO1DPGLPHGDPYWpsJ3APMANYD31pr153JuDTdIyIiImfEWjvwFO1TKeDi2LwoSREREfFzPrYmpdAoSREREfFznkhSjDF9gb7AZGvt5EJ/gAJQkiIiIiK5ZCUmRZKc/ENJioiIiJ8rnpM9OrtHREREfJQpbottjDFDCnjZXr+k8fm34jy+4jw20Pj8XXEfX3FVHCspQ/Lfxa9pfP6tOI+vOI8NND5/V9zHVywVxyRFREREigElKSIiIuKTimOSUtznHDU+/1acx1ecxwYan78r7uMrlordwlkREREpHopjJUVERESKAb9NUowxFY0xE4wxW40xfxpjphpjzjXGWGPM88ftV9YYk2GMGV2U8RaUMeayrDGcl3W/hjEmxRizyhjztzHmc2NMUNa2rsaY+KxtG4wxrxVt9KeWx/O19oT9RhhjHsr6+VNjzHZjzF/GmE1ZY69aNCMoGGPMXGNMzxPaHjDGTMt6HlcaY9YbY5YaY24qojDPiDEmMeu/NYrh83aq1+e5WT9vNsasMMZ8a4ypcNzv3kpjzEZjzAJjTJ+iHseJ8ng9vm+M6ZT1OtyQdRtywn5Djtu21BjTybvRF5wxpqox5qes52mrMeYtY0zPrL+Nq4wxiVnP06qs12NXY8wvJxzjU2PMlUU1Bjk5v0xSjDEGmATMs9bWtta2BB4DKgDbgUuO2/0q4Iy+IrqIDAQWZf33H1uttc2AxkBV4Orjti3M2tYc6GOM6eilOAssn+crP8OttU2BesBKYI4xJthz0f5nXwMDTmgbALyE+3lsbq2tn9X2gDHmZm8H6CV+87zl8/qcArxvra1rrW0BvAeUy+q6MOv5rAfcB4w2xlxYBEPIy6lej18DXwF3WGvPAzoBtxtjLgHISrhuBzplbb8D+MoYU9FrkRdQ1vM3EfjRWlsXOBeIAC6y1jbL+vu4HLgu6/4NRRetnC6/TFKAbkCGtfaDfxqstX8Bu4FkYL0xplXWpmuAb70f4ukzxkTg/mMxmNx/WLDWOoGlQJWTbEsBVp1smw/I6/kqEOs2CjgA9Cr8EAvN98Al/7whG2NqAJU5YazW2m3Ag7jf3IotP3neTvX6rAv8fvwXq1lr51lr1554AGvtKuA54B7Ph3taTvV67A58aq1dAWCtPQI8DDya1e8R3InmkaztK4DPgLu9Gn3BXACkWms/gey/k0OBW4wxJYo0MvnP/DVJaQT8mcf2CcAAY0w1wAns80pU/10/YLq1dhMQY4xpefxGY0wo0BaYfmJHY0xp3H9UF3gj0NOU1/NV+7iS7Crcn9jysgI4rzCDK0zW2ljcieQ/b8gDcCfJJ1uh7tNjyUdxet5O9frM7+/MiXxujHm8HhuSe2zLs9opwHZfkitWa20CsAuok0e/zie8hi/1XIhypvw1ScnPdNyfFAYA3xRxLKdjIO4Ei6z//jPlUzvrl+ggsN9au/q4Pp2NMX8Be4EZ1toD3gq2kGz9pySbVZb9IJ/9jRdi+q+OL7H/U1o/GX8Yy6kUx+ftv/LVMRb09Xi2WXjCa/jnog5IcvPXJGUd0PJUG6216bgz62G4y50+zxgTjbts+ZExZgcwHPfaE8O/a1JqAy2NMcdn/Auz5v4bAoONMc28GXcB5fl8nabmwPpCOpan/ARcaIxpAZSw1p7q07g/jKWw+PJYT/X6PN3Xra+O8WSvx7/JPbaW/Lt+L7/tviRXrMaYUkB1YEuRRCSFxl+TlDlAyPGr0Y0xTYBqx+3zOvBIVrnTH1wJfGGtPcdaW8NaWw33IuDsMWXNDz+Ke1FfDtba7cDLuOeSfU1Bnq88Gbf7gEqcZLrLl1hrE4G5wDhO8ak1a23Aa8A73ovM+/zkeTvV63MT0OGfxaRZ7ecbYxqdeICs/Z8C3vVCvKflFK/Hd4Gb/vlQY4wpA4wEXsna/gowMqudrP1uwr1w2NfMBkoYY24AMMY4cP/9/9Ram1ykkcl/5pdJinVfge5y4KKs083W4T574sBx+6yz1n5WVDGegYG4zzA43g/kTkh+xP0L2fkkx/gAOD/rDdBnFOT5ysOrWdNZm4DWQLesSpmv+xpoSs4kpXbWKavrca8LePufxX7FkN88b/m8PvsA92ad2vo3cBdwOKtr539OQcb9pn+ftXZ2EQyhIHK8Hq21+4FBwIfGmA3AYmDcP4uErbU/405qFmdt/xAYlNXPpxz3/F1ljNmM+zWXCjxepIFJodAVZ0VERMQn+WUlRURERIo/JSkiIiLik5SkiIiIiE9SkiIiIiI+SUmKiIiI+CQlKSIiIuKTlKSIiIiIT1KSIiIiIj7p/5ijXa1eY7MuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_overlap(ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "3508e228",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_num_of_publications(ctg2pmid, text_ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "545c4d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_proportion(cate_pmids, uncate_pmids, pmid2cate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2351e96a",
   "metadata": {},
   "source": [
    "### After"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "8cb68202",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctg2pmid = update_ctg2pmid(ctg2pmid, pmid2imputed_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "8bfa3873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_overlap(ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "868cd537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_num_of_publications(ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "5c3812d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_proportion(cate_pmids, uncate_pmids, pmid2cate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d794566",
   "metadata": {},
   "source": [
    "## PMIDs with Full text only"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a264a6",
   "metadata": {},
   "source": [
    "### Before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0b06d828",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 PMIDs processed\r",
      "10000 PMIDs processed\r",
      "20000 PMIDs processed\r",
      "30000 PMIDs processed\r",
      "40000 PMIDs processed\r",
      "50000 PMIDs processed\r"
     ]
    }
   ],
   "source": [
    "text_ctg2pmid = generate_ctg2pmid(cate_text_pmids, uncate_text_pmids, pmid2cate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "6f9e88e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_overlap(text_ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "3b393ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_num_of_publications(text_ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "fb79512e",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "plot_proportion() missing 2 required positional arguments: 'uncat_pmid' and 'pmid2cat'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2102/1016170360.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_proportion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_ctg2pmid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: plot_proportion() missing 2 required positional arguments: 'uncat_pmid' and 'pmid2cat'"
     ]
    }
   ],
   "source": [
    "plot_proportion(cate_text_pmids, uncate_text_pmids, pmid2cate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9cfd29",
   "metadata": {},
   "source": [
    "### After"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "48ab155a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running label imputation\n",
      "Removing the labels imputed last time\n",
      "No previous label imputation\n",
      "1183 relevant pmids with full text\n",
      "Running jobs...\n",
      "147 PMIDs processed in one of the batches\n",
      "147 PMIDs processed in one of the batches\n",
      "147 PMIDs processed in one of the batches\n",
      "147 PMIDs processed in one of the batches\n",
      "147 PMIDs processed in one of the batches\n",
      "147 PMIDs processed in one of the batches\n",
      "147 PMIDs processed in one of the batches\n",
      "146 PMIDs processed in one of the batches\n",
      "Done!\n",
      "0 PMIDs with imputed labels\n"
     ]
    }
   ],
   "source": [
    "if run:\n",
    "    print('Running label imputation')\n",
    "    '''Undo last label imputation'''\n",
    "    try:\n",
    "        pmid2imp_cat_path = os.path.join(mapping_folder,'MeSH/pmid2imputed_category.json')\n",
    "        pmid2imputed_category = json.load(open(pmid2imp_cat_path))\n",
    "        try:\n",
    "            print('Removing the labels imputed last time')\n",
    "            remove_imputed_category_mesh_terms_previous_li(index_name,\n",
    "                                                           index_type,\n",
    "                                                           pmid2imputed_category)\n",
    "        except:\n",
    "            raise Exception('Couldnt remove the last indexed imputed labels')\n",
    "    except:\n",
    "        print('No previous label imputation')\n",
    "    '''Get relevant PMIDs'''\n",
    "    try:\n",
    "        relevant_pmids = uncate_text_pmids\n",
    "    except:\n",
    "        relevant_pmids = get_all_uncategorized_pmids(index_name)\n",
    "    json.dump(relevant_pmids, open(os.path.join(data_folder,'all_uncategorized_pmids.json'),'w'))\n",
    "\n",
    "    print(len(relevant_pmids), 'relevant pmids with full text')\n",
    "        \n",
    "    ''' Impute missing MeSH labels '''\n",
    "    # Impute PMIDs' Categories (i.e., Impute missing MeSH labels)\n",
    "    multiprocess_ds_label_matching(pmids = relevant_pmids, \n",
    "                                   index_name = index_name, \n",
    "                                   index_type = index_type, \n",
    "                                   label_unlabeled_only = True,\n",
    "                                   label_labeled_only = False,\n",
    "                                   label_all = False,\n",
    "                                   filter_list = filter_words,\n",
    "                                   stop_at_this_many_pmids = STOP_AT_THIS_MANY_PMIDS,\n",
    "                                   the_function = ds_label_matching,\n",
    "                                   output_folder = output_folder,\n",
    "                                   run = True)\n",
    "    pmid2imputed_category, pmid2imputed_meshsynonym = merge_pmid2new_mesh_labels(run=True,output_folder=output_folder)\n",
    "    # Index the imputed MeSH categories into their PMID entries\n",
    "    # index_imputed_mesh_categories(index_name=index_name, index_type=index_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "80cb4c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_ctg2pmid = update_ctg2pmid(text_ctg2pmid, pmid2imputed_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "4c3c6679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_overlap(text_ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "d77b2d51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_num_of_publications(text_ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "cc6592ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_proportion(text_ctg2pmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38f1b1e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}