{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 1: Workflow of PyCoM\n",
    "\n",
    "In this tutorial, with a small example, you will learn how to create a workflow with the local version of PyCoM, using the steps listed below:\n",
    "\n",
    "1. [Setup](#setup)\n",
    "2. [Initalise pycom objects](#initalise-pycom-objects)\n",
    "3. [Create a query dictionary](#create-a-query-dictionary)\n",
    "4. [Save and retrieve progress](#save-and-retrieve-progress)\n",
    "5. [Analyse search results](#analyse-search-results)\n",
    "6. [Add biological features](#add-biological-features)\n",
    "7. [Some statistics](#some-statistics)\n",
    "8. [Coevolution matrix analysis](#coevolution-matrix-analysis)\n",
    "9. [Help on UniProt Controlled Vocabulary](#help-on-uniprot-controlled-vocabulary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "The assumption is that you have completed the [installation](https://pycom.brunel.ac.uk/install.html) and [downloaded](https://pycom.brunel.ac.uk/database.html) the database. For help on this please look at the quick guide [here](https://pycom.brunel.ac.uk/gettingstarted.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initalise pycom objects\n",
    "First, lets import all the libraries and classes we need from pycom, pandas, matplotlib, and numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing all usefull classes from PyCoM\n",
    "from pycom import PyCom, ProteinParams,CoMAnalysis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# matplotlib; useful for plotting later\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "#setting matplotlib parameters\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['font.family'] = \"sans-serif\"\n",
    "matplotlib.rcParams['font.sans-serif'] = \"Arial\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set the path to the database \n",
    "database_folder_path=\"/Volumes/mason/Work/Sarath/Research/pycom/\"\n",
    "#matrix file name and path\n",
    "file_matrix_db = database_folder_path+\"pycom.mat\"\n",
    "#protein database file name and path\n",
    "file_protein_db= database_folder_path+\"pycom.db\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_pycom = PyCom(db_path=file_protein_db, mat_path=file_matrix_db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a query dictionary\n",
    "\n",
    "To query the database, we need to create a dictionary object `query_parameters` using the keywords for our choice of properties. For the full list of keywords please check []()\n",
    "\n",
    "**Empty `query_parameters` will return information on all the ~457,000 proteins in the database**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "To query the database, we need to create a dictionary object `query_parameters` using the keywords for our choice of properties. For the full list of keywords please check []()\n",
    "\n",
    "**Empty `query_parameters` will return information on all the ~457,000 proteins in the database**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating empty query dictionary\n",
    "query_parameters={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we are asking for all the proteins that match the enzyme class 3 and have been associated with the disease cancer.\n",
    "query_parameters={ProteinParams.DISEASE:\"cancer\",\n",
    "                  ProteinParams.ENZYME: '3.*',\n",
    "                  ProteinParams.MIN_LENGTH: 100,\n",
    "                  ProteinParams.MAX_LENGTH: 200,\n",
    "                 }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "Executing the query with the parameters defined in the above cell using the pycom object `obj_pycom` `find()` function will return a pandas dataframe with the search results containing information about all the proteins which match our query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "entries_data_frame=obj_pycom.find(query_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save and retrieve progress\n",
    "\n",
    "We can save and retreive our progress by saving our dataframe with information on our favourite proteins by saving it to a csv file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the query to a csv file\n",
    "\n",
    "To avoid rerunning the query we can cave the progress to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "entries_data_frame.to_csv(\"output/DB_Query_Results.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read query data from csv file\n",
    "\n",
    "Retrieving our progress from the csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#entries_data_frame=pd.read_csv(\"output/DB_Query_Results.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyse search results\n",
    "\n",
    "The search returns a pandas data frame with proteins matching the query critiera with following information for each protein:\n",
    "\n",
    "* uniprot_id: Uniprot ID\n",
    "* neff: Depth of the sequence alignment $N_{eff}$ \n",
    "* sequence_length: Sequence length\n",
    "* sequence: protein sequence\n",
    "* organism_id: Organism ID\n",
    "* helic_frac, turn_frac, strand_frac: helix, turn, and strand structure fraction\n",
    "* has_ptm: Has a PTM Yes/No\n",
    "* has_pdb: Has a PDB structure Yes/No\n",
    "* has_substrate: Has a substrate for biological activity Yes/No\n",
    "* matrix: coevolution matrix column is empty because at this stage we would still want you to check the search results and if required filter them based on any of the biological properties before loading the matrices.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, look what columns we have and their names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>uniprot_id</th>\n",
       "      <th>neff</th>\n",
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       "      <td>12.817</td>\n",
       "      <td>189</td>\n",
       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
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       "      <td>0.227513</td>\n",
       "      <td>1</td>\n",
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       "      <td>P01112</td>\n",
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       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>None</td>\n",
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       "      <th>2</th>\n",
       "      <td>P01116</td>\n",
       "      <td>12.626</td>\n",
       "      <td>189</td>\n",
       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
       "      <td>9606</td>\n",
       "      <td>0.375661</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.328042</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  uniprot_id    neff  sequence_length  \\\n",
       "0     P01111  12.817              189   \n",
       "1     P01112  12.841              189   \n",
       "2     P01116  12.626              189   \n",
       "\n",
       "                                            sequence organism_id  helix_frac  \\\n",
       "0  MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606    0.349206   \n",
       "1  MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606    0.317460   \n",
       "2  MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606    0.375661   \n",
       "\n",
       "   turn_frac  strand_frac  has_ptm  has_pdb  has_substrate matrix  \n",
       "0   0.015873     0.227513        1        1              1   None  \n",
       "1   0.031746     0.359788        1        1              1   None  \n",
       "2   0.031746     0.328042        1        1              1   None  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries_data_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`describe()` function from pandas can be used to get a summary of all the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>9606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.026455</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.029141</td>\n",
       "      <td>0.009164</td>\n",
       "      <td>0.069054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "       uniprot_id       neff  sequence_length  \\\n",
       "count           3   3.000000              3.0   \n",
       "unique          3        NaN              NaN   \n",
       "top        P01111        NaN              NaN   \n",
       "freq            1        NaN              NaN   \n",
       "mean          NaN  12.761333            189.0   \n",
       "std           NaN   0.117815              0.0   \n",
       "min           NaN  12.626000            189.0   \n",
       "25%           NaN  12.721500            189.0   \n",
       "50%           NaN  12.817000            189.0   \n",
       "75%           NaN  12.829000            189.0   \n",
       "max           NaN  12.841000            189.0   \n",
       "\n",
       "                                                 sequence organism_id  \\\n",
       "count                                                   3           3   \n",
       "unique                                                  3           1   \n",
       "top     MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606   \n",
       "freq                                                    1           3   \n",
       "mean                                                  NaN         NaN   \n",
       "std                                                   NaN         NaN   \n",
       "min                                                   NaN         NaN   \n",
       "25%                                                   NaN         NaN   \n",
       "50%                                                   NaN         NaN   \n",
       "75%                                                   NaN         NaN   \n",
       "max                                                   NaN         NaN   \n",
       "\n",
       "        helix_frac  turn_frac  strand_frac  has_ptm  has_pdb  has_substrate  \\\n",
       "count     3.000000   3.000000     3.000000      3.0      3.0            3.0   \n",
       "unique         NaN        NaN          NaN      NaN      NaN            NaN   \n",
       "top            NaN        NaN          NaN      NaN      NaN            NaN   \n",
       "freq           NaN        NaN          NaN      NaN      NaN            NaN   \n",
       "mean      0.347443   0.026455     0.305115      1.0      1.0            1.0   \n",
       "std       0.029141   0.009164     0.069054      0.0      0.0            0.0   \n",
       "min       0.317460   0.015873     0.227513      1.0      1.0            1.0   \n",
       "25%       0.333333   0.023810     0.277778      1.0      1.0            1.0   \n",
       "50%       0.349206   0.031746     0.328042      1.0      1.0            1.0   \n",
       "75%       0.362434   0.031746     0.343915      1.0      1.0            1.0   \n",
       "max       0.375661   0.031746     0.359788      1.0      1.0            1.0   \n",
       "\n",
       "       matrix  \n",
       "count       0  \n",
       "unique      0  \n",
       "top       NaN  \n",
       "freq      NaN  \n",
       "mean      NaN  \n",
       "std       NaN  \n",
       "min       NaN  \n",
       "25%       NaN  \n",
       "50%       NaN  \n",
       "75%       NaN  \n",
       "max       NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries_data_frame.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get counts of categorical data in the column, for example:\n",
    "* number of proteins with a known PDB structure\n",
    "* number of proteins with a known PTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "has_pdb\n",
       "1    3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries_data_frame['has_pdb'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find number of unique elements in the column, for example number of unique organisms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['9606'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries_data_frame['organism_id'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the sequences are from the same organism, `9606` i.e. from `Homo sapiens`. Full list is available from [UniProt](https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/docs/speclist.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some statistics on the numerical column, for example `neff`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3.000000\n",
       "mean     12.761333\n",
       "std       0.117815\n",
       "min      12.626000\n",
       "25%      12.721500\n",
       "50%      12.817000\n",
       "75%      12.829000\n",
       "max      12.841000\n",
       "Name: neff, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries_data_frame[\"neff\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use other functions to get some of the information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.626"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries_data_frame[\"neff\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.761333333333333"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries_data_frame[\"neff\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add biological features\n",
    "\n",
    "Initialise the object loader class and then call each add function\n",
    "\n",
    "1. Add Enzyme Classification \n",
    "2. Add CATH Class\n",
    "3. Add Co-factors\n",
    "4. Add PTM\n",
    "5. Add Diseases\n",
    "\n",
    "For a protein entry if the requested data (EC/CATH/Cofactors...) does not exist, corresponding entry in that column will be `nan`. We can filter such rows as shown further below.\n",
    "\n",
    "**Please note the dataloader functions will not work with remote version of PyCoM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialise the object for data loader class\n",
    "obj_data_loader=obj_pycom.get_data_loader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add enzyme commission data to the dataframe\n",
    "entries_data_frame=obj_data_loader.add_enzyme_commission(entries_data_frame,force_single_entry=False)\n",
    "#add CATH data to the dataframe\n",
    "entries_data_frame=obj_data_loader.add_cath_class(entries_data_frame,force_single_entry=False)\n",
    "#add CATH data to the dataframe\n",
    "entries_data_frame=obj_data_loader.add_pdbs(entries_data_frame,force_single_entry=False)\n",
    "#get list of all cofactors for each protein\n",
    "entries_data_frame=obj_data_loader.add_cofactors(entries_data_frame,force_single_entry=False)\n",
    "#get list of all PTM's for each protein\n",
    "entries_data_frame=obj_data_loader.add_ptm(entries_data_frame,force_single_entry=False)\n",
    "#get list of all diseases for each protein\n",
    "entries_data_frame=obj_data_loader.add_diseases(entries_data_frame,force_single_entry=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get substrates for the proteins\n",
    "entries_data_frame=obj_data_loader.add_ligand(entries_data_frame,force_single_entry=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the progress to a csv file\n",
    "\n",
    "As we have added a lot of information to our dataframe, let's save our progress so that we can restart from this point, in future, if required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "entries_data_frame.to_csv(\"output/DB_Query_Results_With_Details.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Statistics\n",
    "\n",
    "Let's look at some statistics for all the columns in the dataframe. Below are some examples of how you can do some fun things with the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>uniprot_id</th>\n",
       "      <th>neff</th>\n",
       "      <th>sequence_length</th>\n",
       "      <th>sequence</th>\n",
       "      <th>organism_id</th>\n",
       "      <th>helix_frac</th>\n",
       "      <th>turn_frac</th>\n",
       "      <th>strand_frac</th>\n",
       "      <th>has_ptm</th>\n",
       "      <th>has_pdb</th>\n",
       "      <th>has_substrate</th>\n",
       "      <th>matrix</th>\n",
       "      <th>enzyme_commission</th>\n",
       "      <th>cath_class</th>\n",
       "      <th>pdb_id</th>\n",
       "      <th>cofactor</th>\n",
       "      <th>ptm</th>\n",
       "      <th>disease_name</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>ligand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>P01111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
       "      <td>9606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[3.6.5.2]</td>\n",
       "      <td>[3.40.50.300]</td>\n",
       "      <td>[2N9C, 3CON, 5UHV, 6E6H, 6MPP, 6ULI, 6ULK, 6UL...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Leukemia, juvenile myelomonocytic, Noonan syn...</td>\n",
       "      <td>[DI-01851, DI-02558, DI-03381, DI-04099, DI-04...</td>\n",
       "      <td>[GTP-binding, Nucleotide-binding]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.761333</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.347443</td>\n",
       "      <td>0.026455</td>\n",
       "      <td>0.305115</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.117815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.029141</td>\n",
       "      <td>0.009164</td>\n",
       "      <td>0.069054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.626000</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.317460</td>\n",
       "      <td>0.015873</td>\n",
       "      <td>0.227513</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.721500</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.817000</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.349206</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.328042</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.829000</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.362434</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.343915</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.841000</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.375661</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.359788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       uniprot_id       neff  sequence_length  \\\n",
       "count           3   3.000000              3.0   \n",
       "unique          3        NaN              NaN   \n",
       "top        P01111        NaN              NaN   \n",
       "freq            1        NaN              NaN   \n",
       "mean          NaN  12.761333            189.0   \n",
       "std           NaN   0.117815              0.0   \n",
       "min           NaN  12.626000            189.0   \n",
       "25%           NaN  12.721500            189.0   \n",
       "50%           NaN  12.817000            189.0   \n",
       "75%           NaN  12.829000            189.0   \n",
       "max           NaN  12.841000            189.0   \n",
       "\n",
       "                                                 sequence organism_id  \\\n",
       "count                                                   3           3   \n",
       "unique                                                  3           1   \n",
       "top     MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606   \n",
       "freq                                                    1           3   \n",
       "mean                                                  NaN         NaN   \n",
       "std                                                   NaN         NaN   \n",
       "min                                                   NaN         NaN   \n",
       "25%                                                   NaN         NaN   \n",
       "50%                                                   NaN         NaN   \n",
       "75%                                                   NaN         NaN   \n",
       "max                                                   NaN         NaN   \n",
       "\n",
       "        helix_frac  turn_frac  strand_frac  has_ptm  has_pdb  has_substrate  \\\n",
       "count     3.000000   3.000000     3.000000      3.0      3.0            3.0   \n",
       "unique         NaN        NaN          NaN      NaN      NaN            NaN   \n",
       "top            NaN        NaN          NaN      NaN      NaN            NaN   \n",
       "freq           NaN        NaN          NaN      NaN      NaN            NaN   \n",
       "mean      0.347443   0.026455     0.305115      1.0      1.0            1.0   \n",
       "std       0.029141   0.009164     0.069054      0.0      0.0            0.0   \n",
       "min       0.317460   0.015873     0.227513      1.0      1.0            1.0   \n",
       "25%       0.333333   0.023810     0.277778      1.0      1.0            1.0   \n",
       "50%       0.349206   0.031746     0.328042      1.0      1.0            1.0   \n",
       "75%       0.362434   0.031746     0.343915      1.0      1.0            1.0   \n",
       "max       0.375661   0.031746     0.359788      1.0      1.0            1.0   \n",
       "\n",
       "       matrix enzyme_commission     cath_class  \\\n",
       "count       0                 3              3   \n",
       "unique      0                 1              1   \n",
       "top       NaN         [3.6.5.2]  [3.40.50.300]   \n",
       "freq      NaN                 3              3   \n",
       "mean      NaN               NaN            NaN   \n",
       "std       NaN               NaN            NaN   \n",
       "min       NaN               NaN            NaN   \n",
       "25%       NaN               NaN            NaN   \n",
       "50%       NaN               NaN            NaN   \n",
       "75%       NaN               NaN            NaN   \n",
       "max       NaN               NaN            NaN   \n",
       "\n",
       "                                                   pdb_id cofactor  ptm  \\\n",
       "count                                                   3        0    0   \n",
       "unique                                                  3        0    0   \n",
       "top     [2N9C, 3CON, 5UHV, 6E6H, 6MPP, 6ULI, 6ULK, 6UL...      NaN  NaN   \n",
       "freq                                                    1      NaN  NaN   \n",
       "mean                                                  NaN      NaN  NaN   \n",
       "std                                                   NaN      NaN  NaN   \n",
       "min                                                   NaN      NaN  NaN   \n",
       "25%                                                   NaN      NaN  NaN   \n",
       "50%                                                   NaN      NaN  NaN   \n",
       "75%                                                   NaN      NaN  NaN   \n",
       "max                                                   NaN      NaN  NaN   \n",
       "\n",
       "                                             disease_name  \\\n",
       "count                                                   3   \n",
       "unique                                                  3   \n",
       "top     [Leukemia, juvenile myelomonocytic, Noonan syn...   \n",
       "freq                                                    1   \n",
       "mean                                                  NaN   \n",
       "std                                                   NaN   \n",
       "min                                                   NaN   \n",
       "25%                                                   NaN   \n",
       "50%                                                   NaN   \n",
       "75%                                                   NaN   \n",
       "max                                                   NaN   \n",
       "\n",
       "                                               disease_id  \\\n",
       "count                                                   3   \n",
       "unique                                                  3   \n",
       "top     [DI-01851, DI-02558, DI-03381, DI-04099, DI-04...   \n",
       "freq                                                    1   \n",
       "mean                                                  NaN   \n",
       "std                                                   NaN   \n",
       "min                                                   NaN   \n",
       "25%                                                   NaN   \n",
       "50%                                                   NaN   \n",
       "75%                                                   NaN   \n",
       "max                                                   NaN   \n",
       "\n",
       "                                   ligand  \n",
       "count                                   3  \n",
       "unique                                  1  \n",
       "top     [GTP-binding, Nucleotide-binding]  \n",
       "freq                                    3  \n",
       "mean                                  NaN  \n",
       "std                                   NaN  \n",
       "min                                   NaN  \n",
       "25%                                   NaN  \n",
       "50%                                   NaN  \n",
       "75%                                   NaN  \n",
       "max                                   NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#include=all will also include columns with 'nan' entries\n",
    "entries_data_frame.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find unique ligands and count them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ligand\n",
       "[GTP-binding, Nucleotide-binding]    3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entries_data_frame['ligand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the search results where we have a ligand interacting with the protein\n",
    "df_results_with_ligand=entries_data_frame[entries_data_frame['ligand'].notna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count columns without 'nan' entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of proteins with pdb data\n",
    "df_results_with_ligand[\"pdb_id\"].notna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coevolution matrix analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the matrix\n",
    "Lets get the coevolution matrix for the filtered dataframe `df_results_with_ligand` using the `load_matrices()` from `obj_pycom`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results_with_ligand=obj_pycom.load_matrices(df_results_with_ligand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>uniprot_id</th>\n",
       "      <th>neff</th>\n",
       "      <th>sequence_length</th>\n",
       "      <th>sequence</th>\n",
       "      <th>organism_id</th>\n",
       "      <th>helix_frac</th>\n",
       "      <th>turn_frac</th>\n",
       "      <th>strand_frac</th>\n",
       "      <th>has_ptm</th>\n",
       "      <th>has_pdb</th>\n",
       "      <th>has_substrate</th>\n",
       "      <th>matrix</th>\n",
       "      <th>enzyme_commission</th>\n",
       "      <th>cath_class</th>\n",
       "      <th>pdb_id</th>\n",
       "      <th>cofactor</th>\n",
       "      <th>ptm</th>\n",
       "      <th>disease_name</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>ligand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P01111</td>\n",
       "      <td>12.817</td>\n",
       "      <td>189</td>\n",
       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
       "      <td>9606</td>\n",
       "      <td>0.349206</td>\n",
       "      <td>0.015873</td>\n",
       "      <td>0.227513</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[[0.0, 0.5163763761520386, 0.3219393491744995,...</td>\n",
       "      <td>[3.6.5.2]</td>\n",
       "      <td>[3.40.50.300]</td>\n",
       "      <td>[2N9C, 3CON, 5UHV, 6E6H, 6MPP, 6ULI, 6ULK, 6UL...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Leukemia, juvenile myelomonocytic, Noonan syn...</td>\n",
       "      <td>[DI-01851, DI-02558, DI-03381, DI-04099, DI-04...</td>\n",
       "      <td>[GTP-binding, Nucleotide-binding]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P01112</td>\n",
       "      <td>12.841</td>\n",
       "      <td>189</td>\n",
       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
       "      <td>9606</td>\n",
       "      <td>0.317460</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.359788</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[[0.0, 0.5560339689254761, 0.34521734714508057...</td>\n",
       "      <td>[3.6.5.2]</td>\n",
       "      <td>[3.40.50.300]</td>\n",
       "      <td>[121P, 1AA9, 1AGP, 1BKD, 1CLU, 1CRP, 1CRQ, 1CR...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Costello syndrome, Congenital myopathy with e...</td>\n",
       "      <td>[DI-01437, DI-01411, DI-04532, DI-02612, DI-03...</td>\n",
       "      <td>[GTP-binding, Nucleotide-binding]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P01116</td>\n",
       "      <td>12.626</td>\n",
       "      <td>189</td>\n",
       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
       "      <td>9606</td>\n",
       "      <td>0.375661</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.328042</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[[0.0, 0.38467222452163696, 0.3104382753372192...</td>\n",
       "      <td>[3.6.5.2]</td>\n",
       "      <td>[3.40.50.300]</td>\n",
       "      <td>[1D8D, 1D8E, 1KZO, 1KZP, 1N4P, 1N4Q, 1N4R, 1N4...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Leukemia, acute myelogenous, Leukemia, juveni...</td>\n",
       "      <td>[DI-01171, DI-01851, DI-02073, DI-02971, DI-03...</td>\n",
       "      <td>[GTP-binding, Nucleotide-binding]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  uniprot_id    neff  sequence_length  \\\n",
       "0     P01111  12.817              189   \n",
       "1     P01112  12.841              189   \n",
       "2     P01116  12.626              189   \n",
       "\n",
       "                                            sequence organism_id  helix_frac  \\\n",
       "0  MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606    0.349206   \n",
       "1  MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606    0.317460   \n",
       "2  MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606    0.375661   \n",
       "\n",
       "   turn_frac  strand_frac  has_ptm  has_pdb  has_substrate  \\\n",
       "0   0.015873     0.227513        1        1              1   \n",
       "1   0.031746     0.359788        1        1              1   \n",
       "2   0.031746     0.328042        1        1              1   \n",
       "\n",
       "                                              matrix enzyme_commission  \\\n",
       "0  [[0.0, 0.5163763761520386, 0.3219393491744995,...         [3.6.5.2]   \n",
       "1  [[0.0, 0.5560339689254761, 0.34521734714508057...         [3.6.5.2]   \n",
       "2  [[0.0, 0.38467222452163696, 0.3104382753372192...         [3.6.5.2]   \n",
       "\n",
       "      cath_class                                             pdb_id cofactor  \\\n",
       "0  [3.40.50.300]  [2N9C, 3CON, 5UHV, 6E6H, 6MPP, 6ULI, 6ULK, 6UL...      NaN   \n",
       "1  [3.40.50.300]  [121P, 1AA9, 1AGP, 1BKD, 1CLU, 1CRP, 1CRQ, 1CR...      NaN   \n",
       "2  [3.40.50.300]  [1D8D, 1D8E, 1KZO, 1KZP, 1N4P, 1N4Q, 1N4R, 1N4...      NaN   \n",
       "\n",
       "   ptm                                       disease_name  \\\n",
       "0  NaN  [Leukemia, juvenile myelomonocytic, Noonan syn...   \n",
       "1  NaN  [Costello syndrome, Congenital myopathy with e...   \n",
       "2  NaN  [Leukemia, acute myelogenous, Leukemia, juveni...   \n",
       "\n",
       "                                          disease_id  \\\n",
       "0  [DI-01851, DI-02558, DI-03381, DI-04099, DI-04...   \n",
       "1  [DI-01437, DI-01411, DI-04532, DI-02612, DI-03...   \n",
       "2  [DI-01171, DI-01851, DI-02073, DI-02971, DI-03...   \n",
       "\n",
       "                              ligand  \n",
       "0  [GTP-binding, Nucleotide-binding]  \n",
       "1  [GTP-binding, Nucleotide-binding]  \n",
       "2  [GTP-binding, Nucleotide-binding]  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results_with_ligand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalise/Scale the matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scaled matrix ($S_{i}$)**: Coevolution Matrices ($C_{i}$) have to be scaled by average $\\langle{C_{i}}\\rangle$, all values < $\\langle{C_{i}}\\rangle$ are set to 0.\n",
    "\n",
    "**Normalised matrix ($N_{i}$)**: For comparing scaled coevolution scores across multiple proteins, we can normalise the values of all matrices ${S_{i}...S_{n}}$, by dividing them by the $\\max({S_{i}...S_{n}})$.\n",
    "\n",
    "These operations can be performed by using *object* from `CoMAnalysis` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialise CoMAnalysis class object\n",
    "obj_com_analysis=CoMAnalysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results_with_ligand_matrix=obj_com_analysis.scale_and_normalise_coevolution_matrices(df_results_with_ligand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>uniprot_id</th>\n",
       "      <th>neff</th>\n",
       "      <th>sequence_length</th>\n",
       "      <th>sequence</th>\n",
       "      <th>organism_id</th>\n",
       "      <th>helix_frac</th>\n",
       "      <th>turn_frac</th>\n",
       "      <th>strand_frac</th>\n",
       "      <th>has_ptm</th>\n",
       "      <th>has_pdb</th>\n",
       "      <th>...</th>\n",
       "      <th>enzyme_commission</th>\n",
       "      <th>cath_class</th>\n",
       "      <th>pdb_id</th>\n",
       "      <th>cofactor</th>\n",
       "      <th>ptm</th>\n",
       "      <th>disease_name</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>ligand</th>\n",
       "      <th>matrix_S</th>\n",
       "      <th>matrix_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P01111</td>\n",
       "      <td>12.817</td>\n",
       "      <td>189</td>\n",
       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
       "      <td>9606</td>\n",
       "      <td>0.349206</td>\n",
       "      <td>0.015873</td>\n",
       "      <td>0.227513</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>[3.6.5.2]</td>\n",
       "      <td>[3.40.50.300]</td>\n",
       "      <td>[2N9C, 3CON, 5UHV, 6E6H, 6MPP, 6ULI, 6ULK, 6UL...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Leukemia, juvenile myelomonocytic, Noonan syn...</td>\n",
       "      <td>[DI-01851, DI-02558, DI-03381, DI-04099, DI-04...</td>\n",
       "      <td>[GTP-binding, Nucleotide-binding]</td>\n",
       "      <td>[[0.0, 2.4990853333930714, 1.5580765172867141,...</td>\n",
       "      <td>[[0.0, 0.18836677642207234, 0.1174389073708615...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P01112</td>\n",
       "      <td>12.841</td>\n",
       "      <td>189</td>\n",
       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
       "      <td>9606</td>\n",
       "      <td>0.317460</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.359788</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>[3.6.5.2]</td>\n",
       "      <td>[3.40.50.300]</td>\n",
       "      <td>[121P, 1AA9, 1AGP, 1BKD, 1CLU, 1CRP, 1CRQ, 1CR...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Costello syndrome, Congenital myopathy with e...</td>\n",
       "      <td>[DI-01437, DI-01411, DI-04532, DI-02612, DI-03...</td>\n",
       "      <td>[GTP-binding, Nucleotide-binding]</td>\n",
       "      <td>[[0.0, 2.4841366766214805, 1.5422925960913767,...</td>\n",
       "      <td>[[0.0, 0.18724003206873668, 0.1162492055567066...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P01116</td>\n",
       "      <td>12.626</td>\n",
       "      <td>189</td>\n",
       "      <td>MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...</td>\n",
       "      <td>9606</td>\n",
       "      <td>0.375661</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.328042</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>[3.6.5.2]</td>\n",
       "      <td>[3.40.50.300]</td>\n",
       "      <td>[1D8D, 1D8E, 1KZO, 1KZP, 1N4P, 1N4Q, 1N4R, 1N4...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Leukemia, acute myelogenous, Leukemia, juveni...</td>\n",
       "      <td>[DI-01171, DI-01851, DI-02073, DI-02971, DI-03...</td>\n",
       "      <td>[GTP-binding, Nucleotide-binding]</td>\n",
       "      <td>[[0.0, 1.8285180440964264, 1.4756510916226846,...</td>\n",
       "      <td>[[0.0, 0.1378232447662731, 0.1112261496390277,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  uniprot_id    neff  sequence_length  \\\n",
       "0     P01111  12.817              189   \n",
       "1     P01112  12.841              189   \n",
       "2     P01116  12.626              189   \n",
       "\n",
       "                                            sequence organism_id  helix_frac  \\\n",
       "0  MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606    0.349206   \n",
       "1  MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606    0.317460   \n",
       "2  MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVI...        9606    0.375661   \n",
       "\n",
       "   turn_frac  strand_frac  has_ptm  has_pdb  ...  enzyme_commission  \\\n",
       "0   0.015873     0.227513        1        1  ...          [3.6.5.2]   \n",
       "1   0.031746     0.359788        1        1  ...          [3.6.5.2]   \n",
       "2   0.031746     0.328042        1        1  ...          [3.6.5.2]   \n",
       "\n",
       "      cath_class                                             pdb_id cofactor  \\\n",
       "0  [3.40.50.300]  [2N9C, 3CON, 5UHV, 6E6H, 6MPP, 6ULI, 6ULK, 6UL...      NaN   \n",
       "1  [3.40.50.300]  [121P, 1AA9, 1AGP, 1BKD, 1CLU, 1CRP, 1CRQ, 1CR...      NaN   \n",
       "2  [3.40.50.300]  [1D8D, 1D8E, 1KZO, 1KZP, 1N4P, 1N4Q, 1N4R, 1N4...      NaN   \n",
       "\n",
       "   ptm                                       disease_name  \\\n",
       "0  NaN  [Leukemia, juvenile myelomonocytic, Noonan syn...   \n",
       "1  NaN  [Costello syndrome, Congenital myopathy with e...   \n",
       "2  NaN  [Leukemia, acute myelogenous, Leukemia, juveni...   \n",
       "\n",
       "                                          disease_id  \\\n",
       "0  [DI-01851, DI-02558, DI-03381, DI-04099, DI-04...   \n",
       "1  [DI-01437, DI-01411, DI-04532, DI-02612, DI-03...   \n",
       "2  [DI-01171, DI-01851, DI-02073, DI-02971, DI-03...   \n",
       "\n",
       "                              ligand  \\\n",
       "0  [GTP-binding, Nucleotide-binding]   \n",
       "1  [GTP-binding, Nucleotide-binding]   \n",
       "2  [GTP-binding, Nucleotide-binding]   \n",
       "\n",
       "                                            matrix_S  \\\n",
       "0  [[0.0, 2.4990853333930714, 1.5580765172867141,...   \n",
       "1  [[0.0, 2.4841366766214805, 1.5422925960913767,...   \n",
       "2  [[0.0, 1.8285180440964264, 1.4756510916226846,...   \n",
       "\n",
       "                                            matrix_N  \n",
       "0  [[0.0, 0.18836677642207234, 0.1174389073708615...  \n",
       "1  [[0.0, 0.18724003206873668, 0.1162492055567066...  \n",
       "2  [[0.0, 0.1378232447662731, 0.1112261496390277,...  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results_with_ligand_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`matrix_S` column contains the $S_{i}$ matrix and the `matrix_N` column contains $N_{i}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the matrix and save it\n",
    "\n",
    "Lets plot the matrix for the first `(index is 0)` protein in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(df_results_with_ligand_matrix.loc[0,'matrix_S'],cmap='Blues')\n",
    "plt.colorbar()\n",
    "file_name=\"output/%s_Scaled_Matrix.png\"%(df_results_with_ligand_matrix.loc[0,'uniprot_id'])\n",
    "plt.savefig(file_name,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1qsZilEaIDG3rjFYdZzZkrNyyoArsq8dr7A+nVJDgip8UApQD3nt7hJfuLnFzmvlt5zbLuZCwKmoM0sjnxgRvlSeG8pMVRTKLTY1xHuNzr82xP4yxN4jJQbk2Gd+3ONiijhjLN0wjD/icb+pzCfacc41y/8QRjdKokxuyfPyuiEUiaIAi6zwxqBuHhtMYADgHSfd7S9qpA0kJf9Nz1wbnPptGGttuN0Wg6FRUZBEUfGHj3Dm4SmgCV9EK5xY6gPJ2RXWFjvAxjcAeqQMjFDbhchrrsCxqHC1L7A8SmpyN40kSIQkS32VtcbwoMS9qWDiMkxjD1CBjGMWQJ9+moq1nzNVNmfRSio8jjTjSPi8TPqKitjBGYZxH3jHI5O/nXHY929jorTmIyGhkgAdRjrMIBTvw2BBoVqqYOQNaxWT7BQhCnrYxAhKtGCMXc9XWOaqeXZ+kdC6tqITfuHO5Fs1V1b5DzzhqirSCVUCu6BokmX9rmiKN21yU4a26h08E90PwZ+H5xQkpYKvT6yew+8eJKbX9+PZ9ykn2zxNB+XOF1ybVvaRXcRSsnXyWPnP+OfexbeHvkfEoJvdSUgIAQV6kqCX5vqK2aKzzYwMgZ3zuOxQ9y6a3GDycqSc5sG1mLYNCHW0jIqMxZudjOflb1BZJRVW0kYp8haaxDrHWDIGgQTZMaYJGPEnF8UjUAFDpOYuNL2NrDUQRRRZFbdEE24bY0GSuGnJ42yZHY50HgPZNBmi85T0xwZq1Dkz5hVmikhrkhAvuVmgdGF2PXJeU56W9KbymNCJ8WNXQicOJJTkwcajWOWQxXduf+wdfxL/z0Rd850NZ0cqexgZxRPANSUyHJoWT/m+9CFqptUKyLezB9vssUIWIo/ZdjiS0mKu9ghGMzAXXs7PY8NYBopTHBVdaW+iH5cVHUhpAFzPXNAKRoH8nXF0uGHJSW0f5Yq2glMblGp7uYwpPqpBbTQFlY/12apwRVGKcRR7jsipqzNcUhe0PE8zWlUfYC65I8GMUrVBkM1tRJUpAlkVFwNBh2kYKs3UF6yhKsNbhZFki5xB+msdoLB23qSyUgi8iiEOSkv+uFjZxJBLg1431k1AgC0lE+TlJzK/KBkZbWGd8rk2BnCmBWQkKIX2L8lskbyI9deH3AlTVlK1ZYjTSWHu4wumqwiiLPIRAYCBaafzVn/o8fv+3PEOLAxSKymLGVT4AHrCpFP1Wa2lLtiwJAtN33jIxW8AuXbfmaEXek2vz99KGYGbLnyFc1S5Hs8tKxgJuOyz8XoGe9K9lGwj3YUwwYOTA+udWHjYSRsoy5hVjAJNII+P2sSymCK0G5Uibxm3dBj+4PYnAtpqCVJIq1I3jKIqgElopxnDRJdaNw2xd4WCU4AuvL3Bzmvp+MQFAFrVFysBYCrupHzBhLNiyaDBb19gLciQ+98a5sUFiqI3JUdJZmr/jSFPLjtGY5oTxKTjJ3+9nlC1APwJZFgS8PRwljPCn7787LzxY9cV7Szw1zTtb2thQsSPSCndmBQ5HiXe0I6N97keikTTWW0rxAJyATh1qxrlNcu2jkTzRHvsmOKlP/pnf4RHolp1vmI8J0elFRb+vceTsZxu61xIJy3NclQ3GGW3bxLEmDAhelw0meew/I/2RcaQRowuj2AaGvYyF198BffKYmQ66BaKKQc3hlvUiOMhF74UWGY3RAyLs+85MTO4FjcW3IVq6Gk945fbIcWCSwJeE7N1ZgZjzC4KLKWsLpciR/PrrC3zz81N89WhNFR4QsHGQRhjEVGmSCpe0+wBgJL/C2bryiWLrgCGjvqX5uagtpoMY1jmM0ph7JV0LGViUOFvT9w447wbQoD3h6lmIIQtNrg2gxLjkOyQaOBwliLTqgCitJccdaYX9Yey3y/KHJjQ5dnHA27ask5ya0NNIn0Oq35uXPmfVjy7CqqxEj2GSPHQGUqGdb2rEXPW7xxCLUdrmeCZ55DFVkhTflA2WHP3N1pWv2p2uKCoOr+ut5pOlL3aUSasaTfx58L1isvjNeu+tisZXVfs2W9fn8GVXbdIPuy2Bf+X2BAe23WrrfKJ5wXgdoN1q3Z2XAJfNZft3Y5riq0drPH9tgL/+yy9jXja4OUzxzCj3TBDjLMLBKMGbZwXePNsg4mbrSR57CMDZqsI4a7eTWWzwvqfaPvQjZlJQaKubQDuhjxZlpxQvJoN9lEZbHcnpsiRQbh57GMbeIMbxssLRonUkm7LBhhk29nZg1wB0SvQ1QxLErCVskeCmytoi4uR0UVvvfD74zMQfs9jUWBU10th0HNVFE0WwXZGhSRtO3Gvj1DNeJIHz7FcKs8QAiia/2NG86ODt6oaqxn0Yx0UmINMwf3k4Tv1vjYzCgK8pvM9Hi9IvbkAXzX9vXmCSc3piiyN5J3BbUoDaZsKEMUjM1VH0PKlCnjej21YLafMRjI917fZuXtSItcYkj3xl8q//8sv43vffxk/+6qu4ty4wTWJuddHeEWqtoK1CHlNFTvJch+MUVWPh3AUVK4DL1OfbRwBgfxB3nqlSqvPari3EdBBz4aLbhqIVMB0kntkh486CB7H9QYwlN7m3fYrt+wfDxLfWbCs6ALRI+DL/JW0cMDRss9F93gcoAmusw8Ew9k5MYBWE4qfG60n+YBOpT5MU2jDd/t58XWGat83i/e8Nt/cO2/tLH8aEyWRb21CINXTBmBHHKblhQKAqZmvP6MOZepLE32bWtb1yRU35E2k9kdxEYx0sKBnpy9YA5mWDv/mZV/Gdzx7izrzAvU2BcUJbAll1GkvsCZvKQq3rNsHP27ddz3e5IUBnZDSGyfl8EnC+hL/rtb5Jq4hWwLq0HpQqnE9hBHWZPEr/+0PYhdZd/q02D9WyPmy7vgddbO93nZfqgQwq0ZOcrnmfHa48d+m26HOKPex373ovZBfZ9r3TPOpwyWl1NZNbYC4AbRFr2z4j59DCKHjeWBCvmEPbs0vzSftrvjJ7soU8b86BE+3t9oeqjwoKzMpqNMZJm/tprEMaa9wcpri3LnBnXmAvj3E4TFA1jvsrgTfOCs9mGjOSn3JpxBAhjcYVk9ZJ5U4ak61rKVOIDI+ueV1ZJExity0y2/47yYnmicFyU/tzrcrGgw0jo5FGDlC41HnFCUkUN2LSwNi0yWapcoWN2fJ7yVHunnjOORSV3eq8pWx/EeYqtE3Z+E4Ky1CN/nmlWrwsaoAZRMrGMoi1XbjiC2AP26zkQstFUJa+SSO5h2f0vrdmHJZEsxdBMXZZxc8g3G7L9zrXwkYAcA5Q+zkSGYVIt79nw89JrkJ4znYBrB/Y6Idezbmu2B5tFVJR5UqwW1KZG3BZuOYJOUyNh0oIwvuZUY5pEuPOeoPDYYLDcYpN2Xi2Uapi0dbDcAVOtmuNo4d+tq6R85Zzwa1BdWMRDWKGTHAVqnHIeAIsuLJGEaLyrTD342uyznmEtVEKYMaGmBlbpRwOkHNyFp4CqJ8Mrhv6jYIdE4dXVA2cIzyUvBZGdEXVBPTW9x/cuyhn3AXvAa3z051rCCp4/F/53YCAgy3WJf22OCKWBSNVZE43pA+4lXFSxnwA23A1NWbAcWR053ud624bw98reTHrnO8q2Xpd2LH1dC0/nbB7AIQTWzM0Rf7IuQVC0bc01lfnd55EYOdN8ktaUzUnV0SJkzK/Vd04aEUPZ5gSzquoLYapQTmg0vY4YYoabuQ+XlZYbGi7KJOsahwaS1tCcFWxqBqcLBySaYosNlhXtFK3SWNyEMIvNsyIVSBltlO/HeOIYhRglwgp3W5RhN1VqLBlTg1V5DsQJLo0WqHi360VwQzS2PhzW3aoFeOHQqYCxRAJ0xAXmFLKM6HGkcadWXGppm0512AHK4V0O+wy6wgdLtHJMIhWZZJLtBtp1XGGsdFw4IWNcYKRUedIBy+ykGZHWpsE3Hk/E+cl2DxyJF2uLoneZQFbV5S7pQUz8Wh56uFtjwvP089ByjVvi3jDY6RDIIysdz0npdTVsFHQya7mPFdsj9StSog8SIxnMRVKHAGqAi14Tyva1skETJk1NWLE/vGywu29zJfn84TAoIPEYH+Y+Oqk1gq39jJ83Y0hpoMYaaxxa5ri+WsDaKV8T5+1xGIwYuc139S4MUm3TgTBJwEUpYWc5mJCq7MoahTc8qMV2kosc/BLCT4OfuvZmhqdpf9tkkcdWAZAEWPeqwICIJyZdfj5l488l1r/2rfZg3BJhZ81mprLHei3hVviuqHG6QX392lN+LsTrsK+erLGJI+5F1Ftpcu537UJzY+YdFJc5jd85e4K0zyGA8Fk+o76ZFnhH33pLs7WbU/lV++t8JXjlT/HKKNnI85oW2P8tmu+ny0LoopK4+1kk2+fqSfN3Fu/nCEC83WFsnFImM2zbog54r/7tdfwv/zAUxiwIxLWiekgxpiR40JUWNbUKyncV7/80ilu72ewzuHuvEBREVfVdBAzypwGhPgZrdrczr1F2ZLFlcStJfmKV49neP9T4w6VjqymAlcQp7KpGjRMXQNQaT7SCkeLkgRMEgMwzs1ohRuTFEfzAlCqU8IHCL4hEJFNZTFbl9Badah/xtwY39jzfY4A8K9/87Nbn8OZiKcEjkbwZ31cVB+CIceLEEnGjn/GuLHQJLoRsRH/2xjWUPXyasLZlsbnI5Zt1xweF1r4jOTYMIoWU0p5KI1Qivft+iTF933wtv93Fhvc3stwY5L6e0XX3BIIaK38bwwhJyVDMC4bFV/ECfa2msJjW4V8LDa2xLPehviKUfj/6296GoPEtGoxTINyxtim65MUt/cyaE0kepS0tvjll07xjc9OPIpeKXr/S8cLOBCO53RV4dWTjV+thZliwyh+qTQZQxz6wniRRCRIIa074jgvY9fGKRyounZ9kuJwnHpHLHg3B3jQ5/Gi9LASAJy0B+PIonMJbQGq9p2XAGWXAeuHAEaBlk7nbFXheFFiyQygfecF0JZskBhPD0TXRY6naVpCSIc28S1VXwVatEJamz41Tf/3iIhJv2FZrhmghahPaSRA5m02HWx3fAC4C8Nhf5h4Lrf+8w2vOY60Vz8Ko6xlUeN0WWIZHHu2qphlpF38HplTeiB7EoFttYqdkWMktPT+0RYp5mSm8lXA6SD2clZvnhWeG98yIFZaUm7vZ7gzK3AwTJiehrapSaHx3/zKK/j4+2/j5dMVDnICjcZGY5RpnCwrxJHGtXGCkyVNRAVKpt6YpFgVNV64PvR9mkDb27fN0kijDzWTAbspGyybGoPU4NZUVuca0zz2Va1+5exg2II6t02/Pr2MmDRb54zvEiqWPshRCinpfSp2kdEd4Ob+MOGmevq30FwDYBR+xCkU5aXihGFXPg8IxIVwbNMB0WQvNrUXEAnhDLN15fFn+ZYE9rYI6zI2HcR444wIMTdMGLnY1Pj0vVN83/ufwnQQU2RtSeJNhGRCRyT0QPSLL74mYZq4DKJ+zg5dekWLyj50O9UD22OaA3u0QFZFqkQCdBToQsUqPPJwpHInkl8OwJtnG2jbCuD6cJ2bnLUibq00KMM/qwe4PkgRRxqHg9TTOkeGkp2i70j86wTHkAlN4h2EMxukBp99dYbDUYIsMb65eb6u/ECU1qT+1sVoYsqIOd/XL8Nrzuf5pG6vzB6a42S55Gl2TdgwgV5UjY/CRinBOuQ+y/VcNPGrmrBrkjgW2TulFKqGmSy08d8pLLc55/SESQSgXOCIz1PWFtY67A9ibPj6lFK+6b2xDlVDAiYAOvCKhhvIQ9GOh3Fecq9EaSpPSKBjnEeIzH5ne7ssak95NFtXuDunlqfnDnPaEWzpAFhXTYcOCWg1HMWpA+jQ+DhHaRPpEd5UFqqm6qwwa4iuhAO4cBAsrleFZn1ShTxvWlHkRWVpgiJoRavWprZQTHsilCFV4zpl90FiPK+XUbTFGiQGd7n/rmpoEsjW73RZ4fokRcMAQWkglskTClFQdYzhD1x5qq3Dm2cb3JxmfhLGpuWmD3MmmpuDRVlHSuAVs28opZA4eKofrRSzTDC7gvyXLWQHjbTyoMWqdkDa5tuUgketO0c5vdAJikyYUqKg057XOkBz3LntWADMyhH+TppU0jMoxwruTWvl6XsE/iGEh0YrD6UQrBapENG500h71tmaK6+DtGWK6FzTW6CN6fOjiSNRAJKIdSHR5uGGaYTGVu13bwM1bwEEV7VFn+FQClNux7FhJVEi45rB3anRsLZdnOBEFd4Gkfpv7CrkQzkwpVQM4K8AeAFACuA/BPAygL8D4Nf5Y/+Fc+6/ud+5Jjn1L8YMGJSGYQcCuYb4LymtS2+jbEEiQw+vsYQRK1iXTxyLqGOv68bnY2SQ1tah4R5BrRQaEPhTa4Wmtj7vJM7naFlibxDjBhMExoYqblVtvYBDZChpvGbZN8ewkMNxitNl6XNDjXNQAZhXEr0KzgM/xZrGeSCjFDW8viBvJ8QpWAdkYM6pxiHk1QlZHeqmTZrXHPWKc1RgcV1t/feI8wuBsQOGhzjGwgl/WskJeYkOJYKyDFKtG+c560WkJDwn0GVeqJqmhcUwLCQYj9DbUVVbrQ9pcI7yeBlafrXlpmYZNO0r5GJCPRQbWiimeezHj1IUYQt2LbRtwNJtIh791IG8L/8NJdv8e7zQbJg2yt+Xq/A7Sj22EdjDXtW/AeDIOfebAfzPAfw/AHwbgD/vnPsY/7mv85IWCNlWLYoay6Lx/PgyJKUkbnmyW+vofQevjbip20FptMKbqzVem69RVBZvnhV4/WyDDz41wem6wq/cPfWS9GH+SrZtwnElOTCJLtJIYxBTU3hkiISwqKzv2QPa65PrFscgK5himMYkj/yKqrnCKjbf1L7sL+cSRyPjUfQBpIAhLTcKxCa7LmkbsitHYh060ALC3VmUjfXg4hGrl0tEtasxeJS1iemaIQuyuEiOUoojokouC8iwJ1Ab/mYxEm5pVaxmLKwiJpxtl7V573hJxIc2zCKKwrlFZ5sytoxLrYmCWp7RpmrHVQjnECGV/m8Nr+Uy0JU0Njs5ybKg+nmVprS+1J932h72G/86gH+f/64A1CAH9n1KqX+slPrLSqnxzqPZhA8+jw32hglGDHgU6bOERU3FKQj1tLx/sixZ5ILyKiSnRonYb33mAL96d45N1VCJO4/x2dfm+PZ37ePDtw9IYYcbiAVQGxvlwayCu8oTYmWgogKV0WfrCq+dkFCGOAhZ6foVLurti71OpABlNxVHPEbjYBh3Bl3ECtllbXHKla1ThiqEVDuSPJY/jq/ncJTcNync59kf5zEOxyn2h0kHGCkc8SPuariMicIOQIwS9xalz2vdzwSCEdqKtQksV5QPR0nHCfXpwO9nl/2sUhSZ3Z0V+OKby857r52s/W8UO1u3z4g6CRosi934s7K2OFlVnd+72Dx+oh4CmL7Mn3faHmoL6ZxbAAA7qf8WwL8H2kr+Jefcv1RK/WkAPwrg/9g/Vin1gwB+EACefoZwSfNN7bm1QoHYvWHSYoyYc12UewDacklJu3GUA7s3JwzXvXmBj7//NvGCcy7qC6czTN+M8fW3x3jjdIP5RiS3uLXDASX3F4qYiDwUzeDMu7MCtXW4Oc2gFUEyBOV9tqo8z5hEDUopzxN2zBQ9zhFIUmS1lGrl3uZrgopo3lJLXu0gwHutOSoKHc3ZqqJ2G75egR5sg0L07XhR+s4BEcMVuzMrcH2cnBuczjkcLyscMg+ZMN3GkcZeIDN2Y5oR8629HMlfH74h2p3S9C+qROG5lP+/3SZUPCH9dSjntsvS2HiHdLZqmWhv7WXnqrX7wwRHi9IzWWSx6WzfAXTulQKJFof2jvB7PagpXN7rv8P20El8pdSzAH4SwI875/4rpdSec+6U3/5JAH9x23HOuU8A+AQA/KZv/jZnNDExVI3FybLsgBxna3rY67LptJtYR/CLipPC1JZCW59be+RYkkjj5dMVDgeUqxplET7yzDWsihpvnG5wbZzAOufzVEZTRXR/mODuvMSK23v6OB0Bjp4J5koIDUER4cEwYck0w8pIO+8fSu7bDNkVfAKZD5xsaRTOttDsCIODHBdHGlPT3sdtlDfSPbAX0ABtKtupTF5jByXkfwDlqJRSOGBVcSGe3GXjHdvYbVY3FnfnJbKYFivLzh6O2orE2SvV/lbn/w/+94bPjeTtnL9Hcr2DHXQ6M3aarx2vMUgN3n1zRKLIQcQVm/M5LoAojcLX68ay0C/pbwpmDOg+o8fbHk10dRl72CT+TQA/C+CPOOf+Hr/8M0qpP+qc+wUAvxXAv7zvl/NWac0d+EArMDvOY6IPYUEP51pOJOuAcWbgnOHIyeFsXeNk4TDNiRNrWTQ4yBOP1QG4SdoZT1sz5e3RqqhheRu6LIhN9Po49Y3hAgVQoBVSFGKsIyiARBdTpkIeMMPrhtH/SlGu53RZehmxqm48/q13bwG0jdfb+tyKinJVYU/ibF17gshQQg5olYxW/Nvi4P1hajwUQ2vlc3+hRJm0dYUtSqHcl5y74raGSHdzb4LlA1pRDYk0FqyPabkim8Yah6PER4TD1DC9Utvc3JdOE+iNON6+dFzMva2zdd25tl2TUo63e1mnnU3UxqVNTYEi3YIXoRAKISatbECr49CK7epLM3o8avsN5cAA/CkA+wD+faWU5MJ+GMD/XSlVAXgDvE28yOSWCGIekLwDTwSjsSwIJqE1YB1FLcNef1xVW+SxRjJNicfetYMliylUlwS0NBJLS0saaVhrcLIsffUQACvzEO6scZSEHmaRFx5tGACbRBoN87xHhvJoa2F9AOAacm7CdTaKjU9IA+0WsT/4+5JkoUWGqkKyXVxz0aEq6q1J4JILHNuihoIreoI9i0V81rm2bM8QBueIotvwVlOeU3svlYebiC0Lot/WjHcL4QjLTe2rd4CDluqsdUh8ddR5GiTFFegk6ioLNQyxkXt+rijA2/qE81LglMXu+6v9fast3YchFyriyDG0gT4bdhzERnm6JLkDmvNzkWnPK9g2BQI0X1VfY0hbdNWmH0GC/jL2sDmwPwbgj2156197kPNYRw+9aoDaORgFH20F30UXalo5sf4Dl21kmMsYpsbrEJaNRRPoLkaGkfBFDeuokicQCFHF2VSWetYiBWXBQhUKK8ZjVcxeIeVy2cakMUV4kv9yIGzVqmxa7T8GsorzIk4v53+L8FBZS6wZ27jVFcLqLK3mmidzWdtOt4BskcV5E5MB6z86qlzJNt2hrZZmLBwiugCeZUMiuPAZ8UQVdhGA+yZZ0Yk41iiCCiXEBrHpsD4457i1q/Eq3iQVZj2wVyug5jFSN+RkQscI0KIWmXYRUIDXWBRKpsaSY9zFvybRfc3PQanz+TIiGqDX0tigalrtUsXjreYcKUXqLV+XfMdV2VWeq2O/EXNgV2HWMeMkwwDCkLqlLOHPcrlZclXhQKot8XmtK4tb0xRaAUXtMMpIWNYyMBWgweMAVFrBNpQQHqQRbk5THC9KHM1L3JimWJc0cRtHDKfCRgGI6ky7Qgt6exjggxoerAqsTckMqMfcKD5MAa20x+xIzqaoGujEwFn4HFnI/RRivgCaNMOUtjYAGIMlat7a9+xJNUyqrpGBh1kIHiyx+hz1TMGdEo63gJFpuwuE+yqJtG/xkmilbqgFR7arxKtG5xiqbjtSWTdcgHB+MZGuhTwxUBWYWqklh5QtZFmTc857W7eiFkohdLBrwywi/J0Cyooi010ObH8Ye8e4ZBWncGHYZm0UXftOjFVR+w4IwTD2FcGvwt6u7aj6jZYDu7Iv521SP89jOQ8muCzBNp0sS6q2KdUR4JCog6ISeAT8ybLy7UESnRj+jEQcAtI8XpS4MUnxpXtLDDYGL50u8cLeEJvKomInIoBWYYWVnJFcPyl/U35tzJWrULbrbFVBKUpqR0Z1aF5kQsqkPlmW2FQW18cJztY1DoaUa7o7Lz29dhiFjbII9+YlBimxQQjD7PGixI1phqN5gUXReAXz0CSRT7qTFonRPk8kbV4y0dZlg/mmxv4wwSiL8OrxGrGhyqG0vtQN5Zscunz5m7LBoqgxWzui2+HK4jCLsCkbrKvGf48oSw1TnKvmSTIe2M2FtYvvno6PO8eGMmjh31tnr73UWj8Rv8tCxyTPNDx39LWR+vL2xIFd0hrbMhoIMh0AGlbGFl7w0EiFhVblo0Xpk+8iz9ZYhwZtq1DZ0AQKiQSP5iW+dG+Jj73vOn7lq2f40FN7THdCrS8pr7rHixLXGFYgxIMAy6otqw7cQXBAjXW4MUkpF8M4J3F+w9ScwxPN1xWKyrIICTm91083uDXNKPrhXFRtCTYSs36l9JXqIEcjAy+8l31KHJGf28Y1f7ysvPME4HsExZ4+yHG6LL2m4zqg3z435Hvbp+Nl5at22/JA4sSyuN12Oq5A3w8eEnLKbaoGVW23QhSExVcpcmz35vR8pZLcx75ZB5/IvygSkwgyvKcCw3hcncFF9rhe82PjwCQikipYyHMFAFBUfTtZlp0K1GJTUwMrA0T3BjHuMe/WtXHi8yoiqDplOui789K3dkRG48Y0xWBj8CtfPcMHn5ngs6/O2pyL1pxYJmod64C9HrBTKeWdl1C55Ik59zuMaZuRJce0qSxePlrhmYMcpysS+RWRC+scIs7TCHhTc/SlFXz0Izxoq7Lh/E+Xy10gKg23IzkHzEBwg7K2Hmohk1KqkIejxEvBbZuwQj/jIApA5DwMlRGJe4sjlqyHIBeOMzHpKw2rjHLuzlDYMZmkgr03TDpjpP+9oWU93rXrE3L0kzzGqiCJuZBdQ57xYlMjstupnAEpwnRfuwwm73G1Jw5si9W2BShmMVXClkXjlbNFZkoiLnndOkLwy6rvIEh4ylMZTRilk2XFAgiK4QsGR4sSh6MEq6LG9XGKxhEX2Lq0eOl0iQ89tYfPvjrDc9cG+Mdfuot13eCD16a0lQLw9H7u8yq7jGAT51fvSQAdgKLVf7ahKPBoXuDzry8wHcTYG5CIiXD77w0Tz9g67Ynmyt8OR0QNNEoVXNrm3iTq2MY7JcfenKbnktma8XkALtwyiaOQt9NIdyI2oK209pks+tflrznrvi+nEkzVLlxZv7vgQcxa0iblegSGKUElhJm3XyW+n/TcrveF0JA48M4XpEKrufH/QVSYBM4i+dcrYaNQgLrPlvlR2SOPwET3T7YWUqUB2uqYMconxiOjMEppUK+YiSLlXkXqTWw8/ELQ9dYBDW8rwY4gYehAFmuYxMA6hxf2hj5p/o+/dBfPT4Z+26kUOdzTVYUk0himATSD1WRkYg4S43nrk6h98Juy4fJ924couayU4RXSiyl9f3XjPE/asmg8x33JyXlrHfZYDBege2CtI4R5QGtzUc5GGq/7pi841jmHVUHQkmXBUbBpIQ15ojuTfsMRtqeI4WNDQZKKf6tcizSJZ7FBzPdRIlDB4vUjg8sqRW37rTlXPRVfj7B6UDCpz31eTKi+t0nY9S2PjVdkD2mUQlqk8LdsAy1fZEmkEQaF+goipydJ/B2mFIX9MlikAga0+KFV2WCcR760LipAisnxhon2zoVK/0QD7dBSoAgMQKEVOpWtk1YKUaTQOINNRSteFhusa1HFjj2+aF02qAxtaYQLynNtqbY6GhkN66yPSoTTSfM2kNhfa58jsY7Q9dTYTKDPknECWsG3KDnQCts4eBZX6REcJMavtkKtIttymRgiogGgowtZMm2OUl1YQhobzzcFoCNBJ0UMgHBhbRK8dYidSFEBkW65qvqORrBgoTOVwkltHcALi+8DDSYUAZS3S6dJFfYy2KiYGURyBiJLhbjP2bbN+v6q4ryapDCEXijm8RhWlvu/R+wy39u3/u+8KrdzFQ5M0Srw4wA+BKAA8Iecc18M3v93Afw+ABbAf+Sc+8n7nfORotOEpJBUgkQFhrZO1CrkPD9Y1dAgsNzzWDMSPWM+sYo59YUznuhdKFHeIrmpYiitRbEhkKpUmyoR2tAKH7w29dp8+xzh3FtQi1HdWM8SKziwNNYebV43bUW0ZgoZocw5W1Us30ZO1fJ3UKUrRsJlfQfC9RijveNOIo1VSbTXggAXauhKrgk0mRzO0103DUV0BTeS++cQRhPsgITdoWqcvy+Ncz7KlRwfQNug8BxSiYyC19LYINLKO5SMtQ/qxvrmZaICarc8eWK4bYm2QiLcK8cL1Ma63VslqcZe1qrA4V02mBPoR2iNa1XXnaOFRyyNzwuVvLMiHQ9h6pJ/LrYfAJA55z4K4E8C+DF/eqX2QNjSjwL4HQD+08tc1iPfQuaxwdLWWBS150ufrWusiga39zKizNHKV7eSSHcStACt7lmAP7K84t2YpL6Z2/CWLMQ4ObCkPTsaoxVS3qoSfIDOL3mL15Yr3FlrfIOZULTXOFjDIEXXAgmXReNzexE7N6OIUWJTWUzzCOkw9ttbOOfFRRbcZgIwwNVSQ/Sds01HZ1Gc4AHnvgZpBOtqT3sTszMPaXlku1cwyFMslLP3KHmecPQ8Kgx1hCw2XvQ3HKtyTwWQa12byJ8EYNLakjPUSvE2MCRTdD7/1LddiXKJlqhQsX329BuuBQW/a4snObltW+rLSrOF1yyYwatEx4fQnAe5poc2dWVJ/O8C8NMA4Jz7eaXUh4P3lgC+AmDIfy5FyfHIHdh8U3vnAdCg3B/G/iGdsfPIE+MjmF0mYNM0pkG9KmooRAyiJE3H01ULdVAgYKNWtLWTRTxJ2vajddng3qLEa8sV/hff+DR+5rNvMDKc8mGb2mJvEHO+ho5XChgkhAMTTJQ09N45K0ioomiQxtRzeWdW+GhCK4VRToWLBbMlABSJCWiUWCOc7xwwWnmRjXEee2qhyGjc2sv8fR6mFK0mke7ka+asVxk6sSRglAgT7auy4f5F4xeSY1ZLcq5ljrXO4WDQrboJr9psS6VQqI0eJGHt6bvLZicodFk0/jlbS8WfxKidika7zFqHk1V1vjp+gYl+45Bl1q7KaCdCucHFpr7Sc++yB3Bg15RSnwr+/QkmcACACYCz4L1GKRU55yQ8fRnAr4Ek4//jy3zZI3dg4c0va4tVUeNsVRHVjevmFrQilSCpJIqtK+vhFDcmKV49niGJNF64Tkn5N882OFqWGMQG1ycp7s4Kz5m1KmrfHmS0YmS+wdP7BGmojMMki3BnrfEzn30Dv/ODt/GlNxdtHkMrL9cmFk542RpK/+X1SYrDURJUYIUCmtk8uRom2CzZ0oijsg5Qjgn+fIIZOBhR+X+5qTkvRni3xabG89cGwT08/wz6W6xQBWhv2MIoBCx8c5p5PFZREdMsicE2PgneWIcTZq/1QNaq8Uynx4uyg5m7yBbcX7gLtX7R9kuqcQBFXTcm5+XNLmN9CbuLjLBrpO2ZxpfHrl3W+tJzb7cpqAfphbznnPvwjvdmAEKeQB04r+8FcBvAu/jfP6OU+iSTQ+y0R+7AZusKq6LxhILCsCATNsQfxZHGNI+8qKhYYojlVKKI9z819hxXg9Tg5jRjFlTaDkn1cFm05HGiLkQ0O/CORZp1v8FMUFuHL725wLOHA9ydFVhwQr7ifI/wl8nWraitz0X9q9dO8K3PHNBWUFFEJQIMB6PEM20YRdvKsHvgaEFg1UlOwiFrn+Oj7dwhAz4lHwTQtYtk2+my9BHHtirdfg/2EEcaIxX5/JmAL40mwRHBjFnncG2cYr6pUXHez1c9QbCXEFAqW7dUd6XVJMKWLf6iqHsqPy2biESyl7VtlcqjeREosD+4hepAIRtFn8ZHrvlsVXlMnFD5XBQ1PpZ2NbvUTwL4fgA/oZT6CIBPB++dAFgDKJxzTil1CmDvfid85HdvkBifyCZ1GcByWEz9Z+erVQoEoJSJN0gjn3wHKDLZHxKI9bOvzjwSfr6p8fopiXLI1rTRihqzVavKs8dOc5iCq4JtvsE6h7szmgCxUThZVniDz3lnVoDp+QHw+UCD+IM3pwCAd10fsgCHhZXtIWOflgwpCX9ymBcShopJ1lYVEVQAB2nE/YLw+Dbh7BKlHHHeoryz2LTUNlIZpYbp9iJCqEvCDfJKKb9g0Na0xT1ZR8eEdDjyOtC2JwHknDaVJVWh2Pj8nkAoBmmo0E7jZRttzTYL4R2dMaTVpeAFm7IBFN2rddn4+2Qd8KU3l9gfJbg+TgBonC5LH1UPU+oWcRWxhIzz2G/h5XfL4njVJsWlbQQAD21XlwP7SQC/XSn1c3RW/AGl1A8D+KJz7m8ppX4bgJ9XSlkA/xTA373fCR+5AyO2A5r0kW67/YnzvbvlkR5H6fcTowHevcFSUj9kiXtBvd+YpJ7f3rpWaKFibE6ofCTVyWEWIWso52W08k3KaWywNwROlxXLbGlkMWHCakt9kQUr9szXteeFchDBhfaaIy4eyCQQ6pu6sX6LtGQeM0n4R4ak2QAwVITgEI21aBoSygijDDonHSxle3FoQokTVg7lDosoikR9oTKU5K6yWHtapJjbnRbMylHUlH8kxaHuti0yGkkvVUBtWtbnPUsGwGpFTjfme7stuhIjjn+Hi+ZxwSIoAnVYl02nmhg6Pulble3bdBD7748MOYyGt910Pt6+KoVIt5V0o51XiZdeWYFYXNYqjuy3UXw/KOzisnYVDsw5ZwH84d7Lnwve/1EQk/Ol7RGzUaDdDvEDVYG3p0Q+/V0S08D2qpQ0MAPdCSehfszaj8Mswr15QQlQ7oVMuVJJgNjWMTrA09BY40g4RCsUlcUp53cGCUUr0qqjAMahUeuT8G1VjfVbSgC+aieyYgDT71jbWZnDv29qi8S0jk/p1hElhmAc4pBlC4zeuUIaH7lXcq+1DvBcgC9KiCpUWTs4WN8lIMcVwW+QJyacYlAKysErPYUOUkzztlmKOXlM0UkWG9+3WQcg1zQ2WLKWwrZ5VVQNLwLKUxJppTo4sZKxYzKSnMOFERGBb2vOPVLKIo60H3dZrHG2ojNIxwE5R/joXnjbCsa0CWRExgEA39cKtHJy2/Btclz//beDCwx40kq01Zog2hCGy1XZeErooraelpl6+HZThthgEIgDE4zUIDE4GCVIY6oKSvRmrUPDE0ugCKElASW01jRojabo4PWTDU5Xld8mlTVVDq2DR9LHvH1JjMLtvcw3CCslArwUKcjkL2vbsoE6ujehE4qN6mypGuv8liFLDJasLJT0Ii+pSIqzFIcRMroO0ogT99bzW4UTOjYkhUbXRlFV1ZB2QNVUpGepyfForVgwWPvJuCpqVCX9nlFvkgmuj2hvdOc5SIRHEWt7jKD+Q9yY5OmWRcMsGHydrMM54uesQIuLcNNLv2U/HyXgZymwlLXFuqJzLwvKYXknY4zH5RXcApaYNuc4TCOsioLuK0ddZU1AVcltAkCkI7rP/FtXZeOv2z8LjtyEhWWXg7tKe9JKtMWkbUgGEoCOqEeYEA0ncoiDEYsMTYyQXgZoqzRVTRALmmjKb+NWHC1JZBBSnojgbMqUx1KyjrTCrb0My6LGybJEWROf2JtnBe7NC7w8W+G56RCn6xJnRYVvf+4ADiT6sD+M/RZUthVics3LTe0jhNjs7mckp2twsqx8EaRgEGrEODCllKfjCZWfS2ZnCH+voGflHnrWUY4+RLNTcnbHy8q/7xywqWnrPEgMZusaByyMArSI+P40kCi5jYW6ZrTy22ohW3SOZPWOF2XQBwuvp6AUcLKqEBty0JFW2HClWsaDFBYAAUmfLw6Ei+XeIMbpqvKiMtY5bEpyHpM89gpWQrM9SAzWVYOTJTGPHAxb2IaMPXGm4bMXIsnwnm2DStSN88WDzjN8G0ypJ61E27/8ksnU0ASPA2wXUJCJIzZfVz66CtkiTpYlCZJy1n3IAyGkxBmkETZV4+l9ZLHfGya4c7ZhGAM5jTfPCjx9kOPv/LM38N3PHaJuLA4GCZ7eyzFnKINg2AS+AZynsJEKl/RgbtN1FHCqtEUdMGPENMA2iSTb/jDZWf53zvnPiImEmVJt0j2sJG4zgQeULLJSNc47v/uV+YnzK9oZRXhsGEd9IkQyHcREicRYOa2Uvy/7W+AKYVJ7b5j4yFy6IfrOa76u/Da4bhwJiwCe+oh2x5SWqBvinqOtKh3z2ukGr5ysAQDvuzXGvXmJ2brCNI+2Xl84NuQ+hzxkfYujVttA7sfbaU8c2A6bDih8jqPd1CTbTPBPoQkkITSJXkJZLescDoYJNYMzB7wIevSP30bFIlxPGa+g1pG82v/w82/gD3778/iVl8+4X5CQ641ruaDuzArsD2LvHAXuIU5Arvciwrv7JXxFpFZUzwF08FiET6JzyGQ6W3UdZxpr3JvTsdsmh1LncVEK8EpOgn2a5ATHqDl662/T+tgogY0khqIpB4pIM9bnlOjwdFn6LW6uDZJYb01qr4qa8qFoIzQAftsWM3ljKGICdOXNUv5rODYWLMknFbrIKERW+2jlmYMcZ6sKn3rjBB94aoL9YYw3zzZ45XiNW3uZz++tSlLCeisYsccMyPqO2iPthawZ7CgTHWgBkLtMclHbTMgQt1lktKdhkTK/JFSP5kU7IEGTebaufAgvWB7Bao2zCHvDxDuSVdng5dkK3/XsIX7l5TO85+YIN8ap7wwwXE08WxE5oFBFS35FVv+zVXVOlXqbkepSFwsXIssV4BvNRfT2fgNQsGvLspWtj4zCjMValwwwlshRHJTYiluUqoY4uQSUK72fTdBrFR4b3mcAngpJ7sIkj7E3THxuUOAbbXmHrKqtzyOFlie0AEWcqgg/I98hqvCXsTnfj0FCdEmCfZvkMTndPPKMIofjFP+zd93whanrk5TgKIogLdJKJdckmqBAC+oOv3e2rjBfVz7XG74HkKOfrytP8Bn2wb5lU5f88w7bW9GF/EUQshYAvgzg/wXgL4BUun/WOfcfXOY80upBCVyqUt1PmZh4r85rEWqFc5JakseSpmsAnqJlE6gHNdZ5B9dYwvJUjYN10pjtMEhaWmWA+cz4ep6bDj2Tw/GixGQQI6+NR6gLhquoLTZchZKIYcH4qzwxvu9TK7C4CPPIc5Jd+hGrxnUoWBYMyvURp3WeSSLUeQSYcaJxPtcyW1cYMYYsvKNaKa70tRi4UPQjvNfUf6n9pAbIKYaSdCJ9phQ5liUvIpJGqBvr8VbyfI0C4+acb97X/PwBWjyEUVW2+ELdPWShlpILN8K6ISYwCAXqye2bCHmEkV3G92MjHPea7o3w0sn2L400RqlBYZS/rnXZ4NY046qq9WNPfosQQgKSXul+r1h/Ky/vpbH2vcNabdcPfVh7XCOwh9WFzAAo59zHgtd+GcD/CsCLAP57pdS3OOd+6eLz0INqHF2IDORQUEJK/4Iyl8G0LWeilEIcqVbwgieHH2CS/IdIbFl/nFLwcvA5r67ChiH5DvnOorZIY40lb0+MVjhdlzgYJEjZMea14Uqc4u9ucWXS9Cx5NaOVZ+YAyBELJENz9Ut0JtOoVW0KKViMVnDcWmQhuLD2vQ1XO32UFoxHKSQopRAb+EprzhQ9Md+vpmz1O+m4tgImTl4Fz0YWIvkqao+y/v4uNjXypJvH1Lp9fnFwnDSgZzBdURfhVVMKNV+cwBSkt1QBvjoajhvCZaFzX0KTTggBtBIvWSuuYhnmI8UoyUlumD1EM65RmEIEOiFdJoI7DIVPPD2TVtDBcnJRpVHe60MorgoTptTu5vdHbQ/roj8EYKCU+lml1N9XSn03gNQ59yVHXuJnAPy2+51EQfmyfmOJqcC6lvSN5Kxo9RQHcBlrGoJHSOOy1lwCLxtmq6SHEVaBxLEsi8YPRIEjiKOVrY5EFEXV0s6cFZRDmuZENS06kIOAEobAmOQo5PdK+T6sPAkdsafYZlS9jEdR1RFYCEBOPmyPCUGpGTOAVhyJhDxmdWORRS1cxEvRMS/Xhh1/ypGH0N9UvF0Ui7mtKdQrKBsLKOXhEYKAb8HKbYWR/q09iWXD5xauL4CxWq4VQhEjGbqWOyth6Ipgv+LgngC0MAL0PPpjKtzOxjJ22AGFOwPR2TQMscgTgyTQfZyta++0AHJOAp2JNBF0yr2W7wz1UR/Uwuu+elM+t3e/P++0PawDWwH4TwB8DwhZ+//m18TmAKbbDlRK/aBS6lNKqU/dvXvH54PuzQu8cbbxSPvZuvKYKYAG067ktXNdCbZhFhHLBA+e2BAuScr/8knJTYQFgf6AlhJ1GmvM1rXH3syZ1XWcRUgjjW9/7oB5qSgBLJHXurKYcGtSw4li8CQsAvI+yX1sKsIQLYvGsw5YnrjTQezFeKnthrZQlnNNnoYHDE/pUSDLoiALg9BUD7Pz23FZMGrG38n1SYeB4KGkDSxLWgpt+ez+MGEog8GY1axDIOskjzvbeLGQa0xEUSS94BgUKzJ74gRr2/YV7g2TDkWQbC8l7yb3apLH5+7/fNMVB04i3SqbB9REIsgSbu1EcTvnKPx0VTGusW3bymLNmpTtrkAccnjNl7HwOmUcvF2m1OX+vNP2sDmwL4D6lxyALyilzgAcBO+PAZxuO5CpNT4BAL/pW77NxUbh3pyYCURQAQCujVNSu7nEXZGJvqsxVtRyoJTXdgQEXFhjXTlYjsxuBNcAwDsTo+jvjXX4V6+d4IM3p96h3ZqmcACevzbA6bIM+MdotT+xzp/3q/dWHfS7/PdsTRijEFZR1RZn67qD8yG6IHJg4pBJaq69Zht0LYhJL55EaMKAu23Iyy0/WZZwjH8D4CenQE5OVxU7FFKEytlJnq1baIZU+xquyvZhIX34ggJFc7N1xQ6bqqahc9QKvqAyyiIY5c4RGor4C7VbGQ9wBaiRvl8AWDMObFsE1IczzNYVGgcMe4Ig0szvHEE/ZKE54T5JkQIkAkpygg9CHxRaxWmFizQPrtJ+Q+XAAPxBAN8E4P+glHoKwADAUin1blAO7HsA3DeJH2uFw3GKQ+bEeunuEiOuhj17ODhXWg6hEKHJxFnwJD1alLjGMmICeQhlxY4XVOWUBtwsJjYEoW4GODpgGuhpHuF0RUwD67LBtz5DvvoWS7yJTqJQppwxQr/hbZtsWQBgnEe4Oyt8M+/qZE2qSJMUR4vSo/2BNkc4HZB6z8mSYAllbXG0KL3Qb4hvGyQGUOTUXzle45mDHEBbapfK1oCT5PvDxGPiZIjuwh8tC9oWlQ1xoDnXQguWRQ1XNTCqi+07Ya6wiJllw0Zsud+jAAemdZeayPE1N5Y43k5Xld8uiwnkpLHkVA9HSed5+3PxsxX4R4ghE8cVLgQikCGsKHLNfS6xxroO2HScx/gXXz5GHhl86Pk9HI5TvHm28ffZBtvmUEkptA1zie1ycCEO7G23RxRdXcYe1oH9ZQB/VSn1T0Fj7A+Ccsf/XxCk+medc//8fieR/MeLd5YA4BPnt/cy3J0VXn8x/PyapdFOeYVVSmG5qdG4tooYaepNnORt649Ec4LUBmiyjFjI4XhRUt7JOi99NkgJWX3GTKp3zgpcn6S4c7bBu64PPe1zFhvsM+vDnVmBg2GMQphHFUU/X723wjiPOCKpiaxQK1wbp3jtZA2jaMslE2NV1FSN1ApHi5ILAARaBODZOyKjPMZMdC4VyKlvU++RNp2Gq5Rj1gZw/Hv7K+3ZqqIevlj7fN6yqHF3XpLo7orK9tNB7LdTQ0WLw94w8WSPaSyRlPMwCpnw4XeGE1rAplLBrRtqLQuFbUMTCMY2i8x5Jt/w94r8XKjb6Bw8LblQ5QgdTv975Zrk/Q+/sN95/9Y081FjeB0yJvuWxQYuevu2hA9iCo9vEv+hHJhzrgSR7/ftIw9yHqNpSzfKIhbDaFs+9gYxXj3Z4NY0bbc9RvvVc5RFWBUNMi49G15FF0WNo0XptzAiTRUbhb2BKDHT9w9TkkorG8tqR5EfZEKiV1TkiKZ55HUeFZf2w1ya0NLsD2JK7jNqXMr0SaRxd1Zgvq6xP4wxSClZfXdWeELEkBlVqp45N507BYqSFPxvEDFb4cuSvlFJhVw06MLezwPm/Bf4RohaH6YUnUqeRooQin/bMDU+QS2mFLVqzdaVT9pLgl0m/6iXdxN+LMntAdhKOGjv0zZzv61OyNklFUN6xhQ5LYvGc3slAgtR6ODQAFpghJmjqi1ePVnjhetDUo7qXbeIuoyCvCw1nNO4CPnPVrwIUfHo8XEav6Ec2FUZld7h8wXivIT9U3rYMv689OLN1ySWal0Q8ruWgSGJSIBW2BnCquPaS5spVHXjK0IqYaUhTdgnKMqdLYsadeOQDmNUBVUWI6PRNNQgLJW1ONIedyVJdCmZr8u2yXyxqTFIDXKeqCJVL/k0B1bOZkoecVQCjoz4vJZfJxhHg3FOFTORjIuNImCkO4/UrhvbUSXyZXh9XoxVPiersFTjBEwcGcI7CX5PpNMGifGMHCHLhdi2kr9xyrNb9D8TXnNIa/Og1i8YRFoBcZsTk0gs4eplyzHXvX5ZLIDutpcog85/rzDrDvlehGmCkLc/5vY0+b2WoSmDNOpUX0McIYAOTKjzex+2rBnab8At5JWZOByjFVAJXQ45szGH5UKTIw5PKpdUwbF+qzVU5AgHjL5eNpTwTlz7IKl5OOLj6RoMgwYlsZoxfEDeoyqgAAVJ/ciy5qLg0+T5bqrGR1wOlGy1cdAgzU5UjskT4/nshd+LqINoq1XYxnP8V0036e4AjyeT+7ZkDi5RMNpmAkfom3D4CzOE3C8xcWIJ3yMxidjEOVDVVkQtLgfQlu8Liw8ityfOTynaWkkeTxa5B4kO+u1GBPFoKW3kuhUEwqHOOVugi8syWvmIfxeRYGJ0hzkjPGd4jJzX8vOHkx2C7T0zGnshfx1wnhLoivzXYxUNhvbIHVi4kiaRpu1VYnwo3kYZytPkaK1IaszRyrZh0kDJE4HBnxJpWeugOGKRbVNfu9A6WsWGqUFVW8w2gW4jQwmuj1NKrAeDT/Blwu5wvCj9OXn8+dX5tZO1F/E4XZasqKPx2nzto0nBNMXMYUY4MdqmDYwmeTFF2DnRb8xikigbppEHbipF7S2hQ5D7Qzml83Q7AEVcjXWe8sbfr8YiAUEAwmcm0SHAgNyeuwo/K86+jyQXWiV5DpK7I1YNUnQSRyPXVHNxRCcG+gIXKdACiQJ3VaqlYqxVS9QIvrfb+lLlXm6b2P33lGohLY1tG90vMsHOETaRxtmwl6OU7oIkgBiFv1c0F966Pb5sFI+0F/KixWHFMu9HXDFMIo2isrgzK3x1ZpJHuDZO8cxBjj1OYgP0sEXpB6Co6GRZeUgC0KVTCVe201WFN2fE26QAj+1yzuHOrMDJsupsQ0TmTcrZWtEELGqiSZaBJWymr56sYZ3DOKfk9q+9McN3vfsaskhjP01Q8LWfsi6mYMe+cneFN2cFXj5a482zDY6XlU/ov3G6wb15iZrR85Z7FxvrOvoBG3a0a2HY4N89W9dbMUQhzCNkjAjvV9hDWjZ07l0mzr4f/S2LGmfrGqerCmerqkOFtIsBQ1gs+tFR/9wbJkS86DMAmBo7wnFQ3R0EAOO+CY5v2/mkQ2Obia7oZU0pWkT7OUO5ZrlXcg3r3u/dJpj7MPa44sAebTM3AxKXm9pvFySZOWX+JWm4PmWlIsu5ruNlhdNV2wgcHkvc7e3dlGpg3VhPhyK22NS+4Xo6iHFtnMI5h6M5ORGBQIgatjQ47xqEe1xJlPYgAF6C7cYkxd4gxjMHuW8JapzDv3jpBL/5vdfx+mqDF08WyGKDSR61pH2WaHVEuPdwlGCYGp8zC1tzqtpikFK1UwEd4CjpZlJYKPqbfRPRX8tocgCd+7zc1LjDDl6e04Kl8bLYXChSkcbUXhVi8QBW9gblgq73cHjbmpcBbJ0t4TWLSbU2tG0OW9gobnC1UKK8PupfTApPZW3Pfeckj32vat8I5Hz1005yqDJevJjIVZji1qZL/Hmn7ZETGgLw4bV04MtK01fLIbl3YgE9WpSYDhJmlXBeqXmxqXFjkuJ4UfqBlcV6Ky4IOM/HBQDPHOT4/OsL0hNMW02/EFC6ZGcq1x2eR6hpfG+jJgYNqY7OOWqJmOY6jwz+h8+8ju95/0185pUZb48sKwpVeOYwx9P7GV45XuPGJPU5Eyrv137S35kVaBgLl8XmHKZK7rUob8t728ZdeGzIXxWZbhO3qDCFoiEXgSojozHJ6b4cM+6tZM3OIasoCYxC5Ny2Tfit1NQMZ5CJO8qiDi3ORSbHHjG9kVSjgYCTbItJDrdvSqFTmd1moaLRWzV5VuHCCbT547diT3JgO8y5LkJcpMPE5hvCS0k+aFU2+PLR0qsOCTOmpEAkZ3U0Lyj3xIDD/lifr6tOSF7WlE+RwX7KGCXD+aiqJroVrahtxShmOuDcjUxoqY6GIrEC4xhlEfaYgFHamaTSVdQWWAGfeWWGr7sxxJ1ZwdQ0lKRfcTtN3RB+SuuaQbIcwRY1bVWdwzAhgdmQqSI04evP4raaGwIzN2VDhQhFOZQ+wLJqnI+Ep4PYFwoEbuEc/DX1nUdRUS9quEVcsDiHdV2nMeNr27WF3NbDWFQWwyzyOTSv6A7qB5XWqfD5iImcW0jV9FambJ8VZZvFkd75+x7W6sbB6bb4dFXnf0z916N1YFLFExNWCDFp6s4ZbKqVxa1xhhFLdckgEb6tqiFxBSiFcUrYIwGUKtDEi1joIbRIK+jACdWNwx5Pzk1FjcuNdRjlERZFu40NW3GWzHcPtBNHEuZSfQqZBgqOJqnFpcbzTMdzd04A3U1lPeGecK0rRQ4k5jK8QClS3j7emKSwznn4hYjICmnkgPNYFW+NyqZN+IpJA7NY6OxXRY1NRVAOufexaZ+hONQk0r5nMnSiJCjS3nvDkJXItE3O/fERmvQPSk4oZJ9tLLE69CXXbFAiVoxJ28o8wddyVRO+qKxnsLjIrnrbNUhazYVRFl0ZRdeTCGyLacVMCY1D3RAUImYwK8D5mDTyGBzraPClMcEcwgSrs4BWDK7kSACAZ82kyh5NfM3YsI6slqW2l4L7KmNuHi5rC8mCiCORQS4sB6SCbWGM8hQwOthChDneFUdy0ioiLKWTPPIqRiLFNsoinDLvvCjfCGreaOVbUiKtAkk2eGdXNs4zRgjzBjlPh3VlMUrPTy4TOAUAHD3SeyS+QVtfwb0Jw0LBsBQR2qCqnsUgoUpZxGpIzlJ06CXSuLIsifPIkOpT+GzlWkQFHejCIbQCFB/b2HaiCZYuxHDdj5amQ9XzkCapjujyatZXZuHvS67QOT6m/uvRJvEtRJ2HWQ94AgoWCWBwqWrBfyHe6hxGie9ylhjqzucQWrOTkgRqqOojWx7JC0kiVHBmYrL9qJqW8iRMXDoHTwFD191+j9G0Da195GMD/UfKjxH6Wvn8S1ER/itLjI9y6qYVABZCPKOVjwJXRYOqsRCRjdhoD5IcdNgZuipMIU1M4+9Fey4xWQAER1azs5LXFP9WgUV073Nb0ZR7EX5nLQloh3P4NUlMa622Algjo1mST52DeEgUHNpFhJlNc/7Zb7NQ3WrbOfpUOQ9qkogPj6/5eTS998L/9q/7CmBgENrsx5FO59HqQnLSfZRFne1DmDsh0jvhrKd8hqzWoQkebNpbYcPVVL7PaIWJaSewqPRcHyc4WVKD8YqFUbUi9gJrAWNUh0MqFBiNTZe+xisaRSR2e7auPegyjylCLKzQN7MIB393bDROlxWyxOKFawOcLEtUDXHFT5hvTLbfzoG3tK0jc4Df5mmJ1NBO5BDLZS391jFfmziviB2cFBMEHxUb68WIR2mEu2WBvYHhRYa+XxRzxlkE68h5Cu2OVupcXi10OsI20X/fudaJXdb6Mnliy6LGRHcbsuXcQ2b3MFnUwWv1v7tg5adUm3PvyTgQ1tZR2q3QhapaUhjqn6O2zoOix1z02FQWWUzbc6EkH+cEWJ7kBDNK41aJXIgz36opPJoK42XskasSifTWsqFJYVRX6IFQ8go1kxnHDGPoT4JQqALAOXEOgCKmba8L3crZmvofI55kRlOjdFE1uDHN/OcFmxZuc7ZVu6Q4YJ3weVH1dMEYrTQ2xNO+obL+M4c5VkWbRI8NNaVfG6eYrZcYMKcUAO4ZbJWwx1mEqrGoG9pSlc56kYr5pkbFDBL9Hr3+vZTIaZASA8eMqXDiSOOVoxWsA57az3CyLBEz4PNsVfkuAoByb9fGKc4YfkGN5hTRpZG+sDKo9XkHB9AWtaztldDG9M8v+DSp5G1rvSIn0b4eVs7DY0OTrpHTpuqMu1XR+J2FVCFDQRFge3VTcnuR6S7Mcl19qiKi5H6SxH/bTSh0tnF6yWAXZs3TVbW1hA6w+Ma6PqeWI5zqeWJwuqq2OrHIaBwMY7x+uvHRzbJoE+13zjYwTEoYm/s/zSGrDhWVhQatsifMFeZ4WxMbEkW9c1ZgmEV4ej9rt4i8ylaNw2y9xHtujfDinSWh71XbZykV2qM58fBrrQDnOqtvODEEmzRkOp3+ZCa2Vro+uY9GMe7p1si374w4UpFWnwFv24URA6Dc4MEowbKg7b7mKNFah3vzgiNbKjJIZLvLQW1ThzpaEFVPf6ILj5lzhJ+TToU5t1mFv3lZ0OIhv2ebIwqhH33rOxoB4kp+8FoA3zlalNgfxB1HIzkr+d45E3nejyqnzwfWN6Geuip7ksTfYpIHktU5JPo7XpQd7FZRESlhpJWnrulbZDT2B7Hfch0M24ijZqDiRZGwUkR7IvCNZdH4LaLmPJYo0BhNND6b2p6DfwDMncU00DI4T5ZEPDjNqcJJzcnag1RfOV6jbhz3/JES9tGixCAxePHOEs8e5HjtdAPLEAppARItTIFTxEZzn2R7PUeLEtM8uhBoClBUWdVtU71Cq3gkyuMNO9HpIPYFA4Ci4zSiKvCdWQGj6HulYuwcIcUXBUVocpxQ5YSPJqRL2mUHw+3vd6AQ/H5kdOd1aXQXmiBqvereG2Ekud89C/nAhE5HCAnCSqxc75Ij4thQT6l8zySPL52zEtV3MS/MotvfO8mvyOmoJxHYVhOepzwxUCV8xlEmh+CCRMKqaqidJ1SrDs3nc7KIuZzaVUy6+neBBgXCkERtVDBgxlZJKicR8b6LbJjlxu5tuRZRDwJavNskjzCTbSon360jaECURbgxoW1XxYlkikhaXcXXTjc4GBIgdsbRTxJpvHa6RmyITXbBW9CMt6fHi/JcxCmrt+Yevfm6OkdnHOKsZPDmsQEcsBGxDqXw6ddmeO/1Uet8CF+CSHcdgiT6AeBsXXH+JvYkjFlsUDbW092EOL0+lksob3Y5t8u8XlvneewlByWUN2KXZbwwWnlKo7D/MYk0QkoveS9LDFKnfTFlGFSDh6wOdRkLf4/kOzcsSiwaBldhCk8isK0mt6TivkEAfitZN8Rm6huUFfHaH46SrchnwYEJDCMciJLQVkr5Rl0RrJBVTDih4JxvBaLrAifKmdvLOc9z37hWf1GuPU8MlkXdaRVKI+LUD1tiZHunFSX5J3mENDbQukbMWy2lWqYBmWRHi9L3X0ofYh61Te9w7RZTOPABApqKwpHQEsmgFBESKGJNEIUdOU4+V3O0lcftPcy4oECwCtpCEnEhRWz9wW8dEU96+bFY+2puDA24dpsbGe2jkzS4D+Hz3zALbH/LtSxq5DswXwBN+JAuR6nzTBWG8XqbcrtCemiRIViJYART5vQyinYUm6rVP/CwEaNgdJc77GFxaHKOq1Ii6tvj6sAeKYxCbMPkbpqxWUBL+BaCRQU71KcsqVnSXjrz14wmF14tgVP0uegFziDEgCEkQd73jcp1+/f7ldgBxkzZttqUxQbrynqsluU/mldLOWNY+pf/Zkyn4wAvFJvFxudaRPZ+JaK0/P3rqnWY5Eja6wsdQcr8Z2Jad51ECCsQDjSxSdpueyJWOZcodlNZlo5z/reRVF0bjWUBeNQ/o+D2Oq7KhpN82wJ27v7XdmsFLqT3Dq3vGLeZLEBV3UIoZOzcz6SgA1DF/CpafLZZ1LtXV2VPeiEvMBk8WgF1Q6tjwYN/WZBjMdr4qKI/0Cou28vtEzgA0HJfRUYj7+GfpMIpA11B5K+Ck3MyvA5yTiI4GxkNzQ3X3aiFaJcNwyjkfYESyB+HtldttqaoTUCfgomTZLxgf6RpG6AexbuzAnGkPTeUJMrF2QoWqeSOhJg7EYj5M/G/SfI0co3iVDasXjTOY2YEIbYDoeoR51k3JPgRqoxL7kuix5o904q7FLYxPRBQuZ2EeWIu7CncBTqNgq2hnFcS22lMWgYOQORoDOyafFTdbvnChGctizWMUx4yksXGj6tQJi10znJ8xLjEyzji0HZR+Mj3hHi7K7UnObDtJrdaciXC9SV/Fz57eSaR0RgxR1L4EMPoQCnVqUIuixpV7XjwdqtcYXI2iym/cm9eEqsm2pxaY6maJgPIMngrjTWMorxDn09+GAxmycsJ0lzwQZKXE4T5bF370rvAGRxvYSUKEd6zVVHj7qzAC9cHeO1kg01F6tSSK0q5ncdoheNlhetjgqvEjo4vmTBPBEnEROZMcozTQRxQ77TXI8Dep/Zzj29bFbVP/u+x9oDPY3H0sY1TPnyOQgEkz3C2pl7JXbQ2u0xSCMuiJXiUxZCug3Ksa0utWIM0OjeuQpPEvFznivNycXSea39ZNC07Bx8TpjR24dMu+n75LURX3f2MFBDWZVeibpfq1IOaeoz5wB6pA5MV42xFMlWUC4qp5M6T+tXjwiPSAaFM6WKXZIu4rQQ/TCMgbR1J31ZF7cVlAaqkybn3holvMjZ8PUoBh73qmFJUYdwFzxilhCc7GCXMY2VgtDRVG7xxukGeGFyfpD5/1ib5BUJCDu610zXyyGCQUqn9tZMNnrs2wJfvLJEnxsMRBonBJNa4Ny9xay/j62ylxSQy2u/BKLZNLolQl0WNOdO2TAcx9oYJfvyTL+J73nMDT+/nGKRUcPjSm0QJNM7FkdGCUNYW8x1QicWm9jzz4QK07Z4+iIWL1CRXHTql8Doa6/wzup/tDRMsua92GwNr6NB2KWlts9manH/BBZa+05br7UOO5Nyhk5Tv7Te9P6w9pv7r8dhCGq1QMyBQMDmk0hNjfzj1zqKqCcvTH2T3oyMRPJY8BJLGivy2TWvlm5YbS5XOw1Hit0AxVxpPlyUORqmna6EqJJXCjVaeGnqSE8sCYb10R7exld7S/jd5Sm3AC6CG9MzjLMIRa2fGvGUF2uTwl+8s8fy1Ae4tStSlwyChrauojHfudTAQCchK3yXN65GmMG7OND2CXZJ2LMPgS7Ef+MBt3Ji0wivCCHJ7L4NSCkfzAlAKo9R4iMY2kwbti0ywXKEUnFTeGkuUSiIkIucUxHpkyHn3Hba/LztAzrvsoqT+2apCFpMe5TaJOnF+SqmtANpdEZqoNAGsGdpsx60BrTTeLszkg9r9qIEelT2UA1NK/dsA/m3+ZwbgmwH8b0Fq3S/z6z/qnPtHF52n3TbRVmq+qXG3bPD+p8bQvPXRjMPxCXjQAKmt8xNrXVKyXinl1WVCXEzTtEn6u7OipbqxDhUDDgHg3rzEpmpwey9r+/g492FdS04nDKXSdrPiauZUWFm5gumMxiDp6jYui9pXv7KYck/XeHt3Z1YAjkRwq4aVm0FJ3wkzzt6YpMSbxjgypcjp3WNuLbBTEqgB0DJK0OBvHZo4R4lKRL1btswAPAWQWMoK5wJ3EOe14u4C66gCJxqZjQOmmfHYsSw2HWWgB7HIaH+PQwog6QBQqn0G8hyy4L1x9mCTcM7OMOWq6y7QaIgDm60pwiYAMt1XgYyI082TVnFep8bTGt3PZDwbTdte61znWJEXdI7SEvernF7WlLoa1gxFPOY/DuBDAAoAf8g598Xg/e8F8KOgW/YvAfyQu09S72Fl1f4qgL/KX/qfA/grAL4NwI845/7GZc9jXasyk/HEIB5wUrB+3+2xdwaWsUUCiRDa5EEacbOz9iK1i023hK5Uy8YwyltYxpgVpqU6Nki5slc2GKVtpJFxUWGcx57IUHESWoQwiiB/B9CAlYSrRD2CZ1OKtkzLTcvKMEwjIiNMIr9lk1xH3cA3Mi+KBuD8mPDfV41FXVJyapBSrm1TWU+lfXOawvL2RKABkogXfjLJYVWN67A+bMrGT4SianxSf8ASdFTdJDCvYPWkLxWAr6RGRnsCvyw2WG5qf9512fiqa8mMsrss1M0EwBTaNJa2UeUISLZu7LktWfi9q5LgGOGkF/43VdtOJLtiqIinR1It/1fD1VchrEwC9SIpKBTczaAU/f2yeLOwGV8pQLkuVk0cuXNXv+W7okDuBwBkzrmPKqU+AuDHAHwcAJRSYwB/DsDHnHP3lFI/AuAagLsXnfAtuWil1IcBfNA590NKqZ8C8C1KqT8O4BcA/Ann3HlO3cCca6XSqoYwV3lisGYgZhpr3/BqHUVMadwqElvHDAhccTKNRcxJ7g6tCANQZfKErwMt0R41EnP0lLYNvFoBS67OhXm0kM5aJqyALgFi26ht4xkvoFqefRGgVYCXYxumhFYXaIcCmAeNblbM1U/JKTnAM9EOEopmjVaMJ1OYBxoA0vVQ1QTvCLcWkjeTIopUNYXeR5eNr6YCbU5uvqHrFEJKOYc0ZBOGixYCQui38mJ9lgUFDdPjIgtN2DeESskEk1nDoeUp2W7heTcs1KsVfFGmsQ5J3N3qJZGG4d9MNEL0esiKItcQR/SK0ExHWnksmLROSTqKHLzzsJGHhT0QrrG9krcDPhF+1xXYdwH4aQBwzv08+w+x7wTwaQA/ppT6OgB/yTl3ofMC3joO7E8B+A/4738XwB8F8N0ARgD+8LYDlFI/qJT6lFLqU0dH97CpGt8TKA/+dFnhcJxQL2BtPTUJXLdsniftiisTFzhfno8MgUWFJ7+fh/Gc+VbUrjU7RuWl3OgaORI0tDILvY0MpJJpYhpLpfU1594owrOYr+uWU0u3fFgCpchi4+ETUsELdS0l6S+YsIYd0Sg1vr1pU1lUDU+gmCpkNcvFVQ3xlim0XGYCdpVSfMrofnGkknNz/P2xaZV9pIIqRICen8xoGKN983a4CagbhxPOFS6L1sEKfk3Atv1nJNg5iZgHaeSjTaHT2WV9yILcb8lRaQVPZxRaHFF+UyrHAPwCEtIaLQOO/zwx1PPKFUXpevj1NxeAo3FglPLPOeUI/nE3pS73B8A1md/85weD00wAnAX/bpRSEkRdA/BbAPwJAN8L4I8rpb7+ftf10A5MKbUH4H3OuX/AL/0V59yLvGf9/wH4lm3HOec+4Zz7sHPuw9euXfP5J+FaT2Nq2BaxhBm30chgErPW+ZDcuYvxLwJJ8LmCYOIAzOaQxxiyCo1sqTIGmMp2S/jExiyOEXP53fA2TLa0w4y2tVop3/i8N0xQW0KhSw/hiiOJIdPNAJQclugoYZaJJOIJ6ijaSTnpb7TyLTHSBqM4Ab8qGxyOEoz4uiJDordSV5d7sNhQr96Q+eNlEdC8VR+kkRe2FWdOfF6UfyP2WBK7jSOqIk9y2ppPc5LIE1575yj6kNaou7OCxVvpmogyiaAYs03g3HjREMpq2eZtE+a4jI241UwsYt60XQIeQIuQn61JBGZZUD/jqmzwxtmm89lffuOUW8KoF3JZNPjC6RxxROy6KWs0TAfxVv6zx83o2V/ufwDuyfzmP58ITjUDMA7+rYNd2hGAf+Gce8M5twDwj0G59QvtrWwhvxvA3wMARSHCryilvtM59wqA3wpKwl1oRrXCEQotrOD9T43x628sSNTDbUe+n3Jlb8TOxDq3M3dS1BZN07aDbKNrqWqL42VJTse0DdvWUQFgXTa4tZf5CmHYKM73wFfHJnm89VqeOcj932WuCYNGpNt7UXNOTsFC66hTaTpelD6CkFzVvXnpcyHHTPUzVdRqtD9MoJXCl+8s8dy1AQACp0ofqkA7+jzxAlcR9gvJ8yyLBl89WmGYRniOucp+6vOv48O3D3AwShhlT2Ik0mYV5t6EEvqN0w1ucuM8Fz4RRxr7UdL5HQBvyxmEG+LWpHk9MtoLwmx7ttts4TFVlJtbV83OwgKRYdKzvtFTTcpi4zn4xT7+jU+1fY+8IPxbH34BJ8uy03AN7Ib/PG52RTmwTwL4fgA/wTmwTwfv/SKAb1RKXQNwCuAjAP7L+53wrTiw9wF4EQCcc04p9YcA/HdKqTWAX7vMl9fWeeUZiY6qmnQRb01TvHy0xntvj7ZibcKSt+Qmqtri7pwkv7LYeGGOLDZAb4xISVr4nKAUbgacX2LDtMvg8Py1gYdL+N+xhcZnycSJuwCYoVPq0/+E74ncl7wW/u66sTheVh7nNVu3fxcmC2mMXxY1PvPqDAeDBDenaafqta0Uv42XixSjgKf2c0zyyNPW/N5vfq4VBWHNyf0Rofz3h6mfzHliPCD42cMBvnpvhaf2M3zutTmmgxi3ppmniBHHKngn2QKGMIh+5e5BEE9yf0ZpRESVDB6+MytwY5JizhoH0v0ROplw7NyZFXhjtsE33J6QFOCyxGdfn+Pd14cdDjm5doHg1MEOIoTGTAex13HYldzfMA7somLHldoFnQoPaD8J4LcrpX4OtGb9AaXUDwP4onPubyml/s8AfoY/+xPOuV+93wkf+g445/5c798/C+BnH+QcEgWEFkeaMUQ0QF+6u8T1SYqUE+VKYeeKFUcat6YC2rw48ThmNgOhfl4WDY7mBQ7HqY+iTC8iET3DsIQPtFxiADxwc5hF2JQNFhta6fuEdRfZnIGzgzTyMAqgJVIcpsYDQ6+PgwmdEbattg6jzGBvGOPLd5YefpFxC00f3HjEEAzKoTVYFtQpIJW1H/3ZL+CPfvR573yOVyX+yYt38X0fvI07swK/9urM47ikUtxYx5Q5IQTD+E6Cz702x3tvjfDm2QYHo8RTyYQ9gtKh4ZvJA1CoLCKh7Fofq3Q0L/wWWDBklnOcm6rBwTBB40j1SSipb0y61yysIs45DzeRsREZjecODZ45yP1YmQ5ifPu79nc2ZSuOZI2m4saqaDgSjv1vlevrC5QAFHXWTZeTbhePmdB+v1WT3ORbNeecxfnc+OeC9/9rAP/1g5zzkbNRDFNzzlGsWL35dFXh5jTzCHPrHMDI+LK2OGARWYAqiXdmBX7+5SP869/8LJZMRb1r5VCKYANxkOBdMEo9fFhChjjMIk97IpAD4d0KdSHDwZ9EGqoRTqjtq2ljXQeqABCQUc5itPI8VlOe0ELfrJXylT4APt8kfZHzdY3nrg3wmVdnvvk7YijEnVnhsUSyhVsWdQsKjbW/5j/1W96NySD2uTlpqFdK4WCY+O1nE2zlZ+vqXOVPVHq0Ike/2NS+YKAU6Vr+j196Ex//wFOIgpylVHbT4JrkfrcCKwqmN9mng9g/S8lTAtTqI6wma8bwDbmV6LWTDZ7azzBbE8QlZO0Y975Tnt+C81zPHORUdWWRZgfH/aLtdQldjlLKd1xICxwA7lGlnNzWNk9O/F+mj/Iq2yIfUxzro2WjaDi5rhShqdfMZRQbhaqhlhNZrcSEZ17kowB42bNJHuNbbu0DAK+oDJHYgf4WQKlUlGRyhBGK5JmEoFD6FGvr/PXvGkykstQisgW71PmMYpQ4R3cbTuj2I7wlt9lERvscmagDFYFIhvRPWtfqVR4MEhRcDYu5IltUDT73+pwre/Q9UnlcFg3uzgrKDzGTbRx+dxD5SeQSM6W0VEn7kUNk2shsXTbYG8Ye71ZbhzdON5itKnznM4fIY9O5p7FRXnothKtQXo0qllLx+/kvHfn7XDMYWCqHcv/EAS+LBp9/fYEvvL4AwAyv3Ku5N4g9hU/4HMK8Z8UV3JyV1P31MhxEOjFC619zKP5Cv1V3RGz6JjCicHzkiSHeuuDPZRgyHsTEyd7vzzttj7yVqKgtkl4yPI1Jak2ikrqxgCOuqlqwQDwohWkBXBGTUDqJKDkr7A7bNm9Seq+ZxSGLjddrDE1gEdsclQAhhSpF8nXiUHaF3jU3qUslEYCXTKsai6Ki5Hk/D1IFW0BhjujcT5YSUwCgaMLfnKYeYAmAnZjBoqrwl37hK/hdX38Taaw93k6ERbZV5aQRPOfvShn35Jzz1VLprAjNM+M2FmVjsZ8lVEl2gNEWq4Im57uuD7Eo6k6aQI6V3tn+5Kytg7oPFMEylCXtOcdId4WPJ3mMomo6PGgXmSxgGbr4QnJQ7Rjrs6g0fM2hQ5TITWAadUNOLmGWEilmibZpFfzmkI0FuFq9yQAi8djZI9aFVB771U92h1sqKdmnsUZdCF+Y9dgm6xzingZfCDpVCn5yblvVaGWlv4u+oZhQ9US9qCLSrWCCdS1XVOvAmGpZt/2OgzTyeKuK2SCU0thwP5yozqzLBmuuFIYUPQBtaWWQr4oaZdNGWnL9Ea/8S+7HlK3YnVnBfXpUOXvPtTF+///t7+EDP/wxXB8nyBgukScGh6MEq5J6B6u6VUAyutXLBFoHXjUOWhPYUxg15F73q7WyYIVbKxkLZWNxsqy8UK/cSwEUW0dbwFEqBJUU0RSWOim+5bm9rXhAWehS5seXSOz9T415O06tOgA89ZBSbT+q/JY6cDoXYc/KxnJFu0vFQ/eDrrm2zlM8AeCWNCJBFFBw6MCEvURSCKKZIOkEGdoSVYsTvQq7KnGQq7ZHzkYxTKNzTqVPKyIDkVYkek0irbzX3ye2LDiZnRo/aMN+tvA7wr9P8hgzVH41CyXIQgtfEycRwj2y3vvyHYK6D48/GCWYrdteuqqh65YJHl6fbC+ITjrx78l/w8hlarqsGWmk8eW7SyyqCu+5NsbT+xl+7s9+HJOctnMpdwfMNzVOV5V3IMdLYoEVXnut2grg0bzw1V4xzfdDKm7DtHViWm1vhD5elDhbVXju2gCDxOCP/eSv4j/73d8I64gfP+f83i985RgffnYfd2YFbu9lOFtTYn6QUNT++ukGL1wfnnu+XRxgg3FGaYtlUft2qpeP13jPzSHGOeXnWm1S7RevxabutIntsmHw7IAu4cCyaDzvnA0onsJoPTYKsWnPIbnN8DfliUHNqYcxYwCBVoBZ2Iyvwh7F9vAy9khzYJEmFoi+yOlsvb0DiYQKLlfJm+QxDkeJz4OIsK3YbF1zVavGqqfe0q8ybrMFT3KhPw6rVKHVjcXZqvLvjXvVTaDVAAivW5yXtc47AoAmfxK12zE574wnslhZUyQTmnUO77k5wqdem+GjP/I38crxGi9cH+K1k7UHDp8sK6wKan+S53BzmmF/mPgtXAivOBynWBYNgY7Xtb8fQLulX5eNZyTdpZSzP4xxY0pCH5uqwY//nm/C4SjBfF35HOK6bPDBW1NSFWcpskkW4fokxZALCVLwEEm3VVHjbFX5CrJcl2hA3phmmOYEXXjvrZHnPhsF9zlcmPaG1Hh/tqrum2cSSEl/PEeaePD7YiGyEKwKupdnq6pz/JydqkjZSYS8P0ww52uarQmsLODoq6DToajvcn/eaXvkOTDx7IQMp5VqkhN1TThRTpclTlcVMS/MS3zwmcnW8wm/mBy7a7XfY2xUHGkvuyXUMmVNeSOhqRa0/JxBrPvD+BwGSYCsYhJJZZH213KyLH31NA6ckAxQrbrOs2LWifA+zNck5iGTWr6zseRAvT6kdZ6McDogIRCpNv6ur7+JD/zwxzDJY/zqy2f4wNMTvHqyxuunK/zim6fIY43vfd9tzuvFnsOsCpTHxWHvDxPSnlyTNmSkVYdtoiVDbNlNt9neMMHekP8+iD2Q9do49U3KFTc+f/nuCk/vE12PMDAINXXI5QbQojdIz3+fPKOYt5LyPMJ7Te1K54+7LBymLwV3vCh9lKQa+M4FAJ3xvg3fFYKNIwMPeQlNKeqeaJzzzfJXQqfziBL0l7FHu4XklU7k0tKkpT7pR1qhUs0uTqddxwLobCE1JyWHaUQ9iyxQsTcgLNKMVYfypB2Ass2Q7wCoEibMDGKnjCGTfEr43KeMdQojsL7cVxj5xZHGuDcAhyndr9rWnRVcziFVVaO60IG9Qey3xWmscX2ceN3GV0/WuD5JoZXCb8mvwzqHr95b4b//4h380e98F0ZZq4yUBXkfaVLeHyZwaNXIw98s1EPSMJ5EVI0NF4BV0Tahh/dXjvfP0DocLyv80hsn3lEfjhI0jcM/fPEu/vw//BL+P//Gt3YArQKCrZkhI9zKjVLadm3TWJyxPuOuCrPIsgEtB5qYbHkd6Nxy3r1BjOMlaVnGAUvFLgtJOPudEkLBLovFOIuw4Og3jTRq5Tz1+FXYY+q/Hn0SXyiMYyZ4C7mVQmUZaTjOE701Ed85L0cBRlFDs3XkbGSr6vNJuuURb6z2bS6hak744MIBtGQeMc0QC5k1A47UNCdWw8mkAAxSog8iaIA+d95tv+Xcv7d8fFU2JC5b1J0EetjsLlGuc87TTs/WNV4/XUErhXEeYZASdVBsNJ6ZJnjjdIP33Bp55HjZEDuF8GyNs8i3GQnMRL5bekM3VYOSG8q1avOHy03tG8TFAWY9JyZ0SgDdz3Fm8Jtu7BGOjKElWgHvuznGn/5tX48B6xFYzgE11qFgLjh5/gvus8wTg0hrWEfXvdzUAC9sOcu8CSGimO/DDJhU5F7I78kYdlKy2rzkPTVDhmTckJKW9XRKIXZRIBpeTzOgLQ+rvjnfL2FwEaLEyABEv3U19iQC22LWuXOMpAotD5YIIcRchVkxTkw4vy4qddeNQ5xQtU6DWBuso88Kqj9lWhgHxeDPNmnbnsd6+pvQpBpH32/9IBNYhAIQG5zDAUlfoZxbtmUyqTclocJFpEEGqzSXA/BVPFL+aTBII18ZrLhi17/eTdVgU1Ly2Dpp0KVr/qU7p/hYfh2D1Hhg6aps8OFb+zTpQEyucUxJ/rqxUFzckCgkMhoZulQzAjEgeh3CrVFDt/PbIHqm/PydtCspzxPH2GWfiE8jjYNRwgWayPPxXx8n2B/EzH1P97NxzleDE8bjyTNaFY2vfIrTEbZZ56hRXxg/BJIiq5EwqIhAh9HKn9fx+FJKIXIKYBohsQGDsmVBaJxDDhNgIoO50VgoRSkOcsTWdzLUjfWLYwyBArGOgqUxGzN27q2awsPLvb3d9shzYALcs64tUcvWKIspogiVeSRRveJm3F0LQxprTxcNFiVQnNPOEuNXS9m2WOs6eDSxmp0I0OJ7JG8mZjQJgoioggAghTECoEhOHGOIFesnWTdV0xE2ESZZg5YGyPCkFQAufUFLrxNvQWoLP/qqaDyR5HxD0VpqCJFfVKLkRBPh1l5Gk5zxSJJPqRqKamKjMWfdRsNwgCym66oC7rWwIivUQrqi1xfcGkNfDBYioVzobN3mzwQYWvP2+HRV4fZeBuscnJXqbPus8sTgq0drguiw4yVmjsZTTdMC6rihOvKQHlkU+/ewYDGWpnH+Nwm8pA4wZnVjfQQeme6CmAdRMnVB0HdtONcZHmtU2yOZ8XGSgrBWdcYOAX2dvx5rHWAUrkbWY2vQ/1jYI99C5onBSUBpHAWIBWvb/FIa0eT0/45b5xWqEslxYX7IcbtMp6Ujizx/leKQPqRp8eyXIEzNkmXIlkWNkeqWp2WICFZNKRaxaIjlQNI4fW50UVnqfyfQOq+ICwhaq1bINjFIeEJLfmW+Jq0AgWzI75LzkpakRrWqvHDG6Yqopn/n+2/j5XsrxEZ7ip+n9zPfi3fM1UxhhgWAs3VN7Tua+kiNgmcpleRyuH2VZ2S0wiiNcG9e4MaUIjjBvlWNw/WA7WGSU3FAsHNSiQQYsMt5LWE3na1rZDEtiI11eOV0hWuDFEmUeB1OyVVJIl7uqXXUStZvrA/H4mxNkAuhMhchWyEjOF2WSKIEm8p2ONtky2it84wrSlmmQZexozzYOGVAsCfu5GfolZO0QrxDBUvuc8yR71XgtwRn9jjaI8eBicz8IO1WbAAqGwsVS2w0jG5Xk7ASVFTW9+CRUEVXtahPpxOeP+3xjMnrkjSlyln3fUmO0taG/msUTZB78xIRc4HlCRENNrYl8dtmzrWME+F6KQ77ZFV5bq9dFk58oTQesMjF3jDBIDEYpgb7wwTrknj8b0xSzNY1iqrB3/niHTwzTfDhW/ueNmg6iPHGWUG5LwZFCp+ZRG83JikWTLNdsFamFFGk+XyaRx4pLouIRDeCNXMOiKPz0cI4p1zoumxQKOsT4QejhGEF3JXA20Vh4ziaF/iOdx2g4Epyzc/g2rhbVpRtm9Fqp/MC4FvcwnEljk9M3hMONml7k2c750JImMZsFzeq9o6yLi4yHBsAsGIMmQjN9G1dNp4Sahdk5WHsMfVfj34LKSXzbXiV0En9X//Br+Ov/fSv45/+6G8/N9BCB6TUeRqYLDaotfUVmzuzAtdGiT+/YJQk2gjPLzzx8tnL8E3N1hXymPIap8vSq3ED6EASpIIZ4sD2h4R9Eu75TWU74hBKtfzrm8r644RRYlMS371EAH02AolgpJJLx8f4Ix99AW+cFT6nM8oivHFGYNFXjtdQiq79eEFVVmmUPl6UxDmmCRi6KltWj0OmxFGKosiCcVH9+zhnOMOwV82VSZ9G2lerF0V9zgktNzXWVYNBcn6WDdMIkaboLlwAhKonj8/jscLzSlI/PE60LbVWUDt2aEMuXoSqQ87RLkKKKFLEEtsGzwjHBtAWEarabhUDCZ1aP+J/K/Ykib/FDG+LpoMuOWBocy5n/9BH34V/61ue7dCIPIgRkyode32c+NWubiisF4jGwbC7Eg7SCFXdOr9wYiVBLkwkyqaD2FP1xAAzPrRRm0A2QiR+iKkCRGKM3pM8l2GMmFLoVKJkIsq2ZphF0IHAqQy8/+Dv/jr+5Me+zn9vVSscL0vcnGaEMcpbbFvFPGPWOrxyvMataYp/+dIp9gexvx9KUYS4qazHn2XMqNtYQsQPAnZbcZyTPIJ1wJtnRGj4Nz/9Kj50cw97wxhV00IOpkxLJPcSoDxbCJQ9WhAkYZgRp5cATKcD4hP7zCsz3NrLcG2cIonoPn7l3gpfd2Poq6Vr7h3NE4PTVXfxCntxQ21HuR5hsNhlQsUjFjoiOYeMZ7nuBTtsy4uXVl3es8WGtu6SQgnxY7JwWiYbGCTmSuh06Hqv5DRXbo+8CimMmrs/Q1u0cUBnc1kZqtBCHJBSLUto1TifUB1yNNSn+ImMwlC3CssSUUieShqf5fpWrG8pgFMVDPI0ZmEO/nfFdCshq2e42mlF+YxRDwfUZ3zoSJ9xBU2hRUf/0EeewySPfV5FyvAAURoJzsuo9vtl2/iLXznFu64PvQJSGmvM17Ste8+tETezU75PhdfgnB/4MpEF0nIwTHC6LPGtt/dxOEp87lBstq59pCNJb82RoTz/pmkbnOm3tvfkeFnhK7OVdyLHywp/+/Nv4LuePsDJsvRbx5SfkdHK0xWFJhgxGyxQoXWvufJgbKmgS4RFiw8Bb4nlliqjVDVt6ZbyxPAzEyHlLnRH1I6AbouT3AOAtuex6/ZwvhW7qvO8HfaI+cBa8japLooicdgzKKVyDfXQ7RHC7SQmjb1S3QxXmD5liiC95TipLlKTLuVe6sYi8is2V03LxjNACN+7bP/E4cTQ575Ttq2CFXvlaIXs1ogkuOJ2VZXfI4rWAOWhRIRDAR4bdjBKEJl2IDrXvlc1zpf145igATWR1FJXQ04g2Cwm2InoYDrQ+wk7K9kqJxHxdi0KIuXTSqFxxHARs/gJnY/EbhOmNaLWn8Y3vctvK5iDPwJDa5pudCl/D6u+zjm892CE/SFBK/YGMX7rC9c8xquorYfNbGs69+OG86CJu389zzfwswnUJuS8F2xYFml/D0VIJrwWYR3JGNy9DKq1oSuR44RHLFSO6n/2rdiTLeQ2C+6JDP40JsewriwGLG1mFVCW1lPa3I/MTWhegLYjPzIaybZVxLXahbK9C62srZcFWzGxnlAARUbDoYVYiLXYMor8hDNqU9Lk1IrK5qL1CLRqzRWL8NaNQ6kVYmM924V1dL2yjSyqtmdRBFhbyhzF1VOqdMnAFuCw8IaJZZH2ikVA23lwvChp29hY71DTmK5XigSy7Yp0OyFDOEmIoRIiPyjKXRLI1cLV9Kx9xBZEZKRK1VZppRsgT7rajOF2LU8Mnj7IvbRZypxd67Ihnc/aekLKkGEitFA27zKWcgVU7qHg4GoG04ZUPnJdYSTcN6GzFviO3L/aOqBq/L9Fj1Noppo6qERe6srvb4+0afoCe7RbSIYxZLEBXCsbJrAHgCZcrmjASR6qH8ZLeVoGc8FRUsTSXoLHCU2SnVorpNp4wYi+A6sZNCh5rJvTDLN1hYgHp4AFwxA7TKRWNTOyZhFKbh4X8r001qy047zIxHxDAh8C+ixri6f2M08GuKkt8pjJGpn6R/jKJCoUtaSqtvjq0QpP7ec4XlF7S1VblCzrplU3ma65W0HAtdNBjEke+2tdlQ3SGEiMwgICOKaI+XhRkopRYnDGyj03p9lWokVRugbonhLIGF6kBaD8UshmKs9Y6LoB+qxgA8PtVt1Yal8K7o9WLUVNEtGiJJQ8oeZjaG3vp73UNmpbMcBo6k9cl+TA+p/ZxvkGtKSIQp8UGWHPpV1IGeQ4BQiuQHnCyhItEY2ht54DU3gSgV1oUpUJLaySCcnhribaIshhAfDJ9vEOdaCQgkbaQMLvDD+XxhTqy5bHOddJLEuEFvZJ9umANDuWvUGMu/MSiv8uv1mpVlDE8tZOiAGtoybwUfCdglCfMIWP3JfZugqUymngDlNqjv8nL95FGhtcHyfI+bpDaiGAtogqIVJHcUJ0/wlnJsyxCwC39zK8dHeJd98kBodffPUEL0yH+LobQz/gpcgAcEKcEf7OOfzaGzN813uuwbqWolqqlIfjFM45bo9qOzJI3q57n4US2nBFj2AXRJdzvCz9PUgjjZNVhX/85bv47nddx8miRFVH2BvGPhnff3Y534tl0XjnGtLZiF30GqUOqABwkTPpn0McpozpPp1O38TxZ4npkCteFY/XY5oCezwc2HxTIzHaq0wvAtzLfEP9kBeRx21bwS6COxwvKxwMqZJI8mW0TejjrFYFbfNkEAlWx7ITk21H1TgP9jxkbi9pGaka+uzpihK818fJhatZi/epfbtMbLSvUoUrds0ai/JbJznxic03zkMuRPrs+z54239vUXXxQaeryucgPX00Ow3RtlyXJHtHYGOFl+4u8fW3x7hztsHhOMU33drDvXmBX39jQfJxDLFoOLJaFQ3j4ggN/5vfex1AV6YsxNwdLwjrdbKsfASltcLxknCDwqEmquBAm/cR0Y/DUeIZRQrOs37nc4e+y0CODZu5ZWzIvZJr6rOcLIvGF3DkmR0vSkApGIZeWHawcq6TZeUXJfk94uxDYK3lPGJ/XM83rRTcO2mCk3sc7VIOTCn1HQD+rHPuY0qp9wD4q6Ad368C+CHnnFVK/SiA7wNQA/jjzrlfuO+Xc9jfZ48IVyp5T8CXsXkwHb0FN1aHkZjkbMLzlLXFq8drPH2Q43hBUIltVDxF1eDaOPXQAOfcTlm0EEntHLxUWDjpQoctHE8iimudw92ywLpsfIL206/NkEUakzTGIDV4aj/Hj3/yRfzAB27j5jSDQttSNMljL312Z1Z4Gh+puglea3+Y4N68wJi3ZHOWhNsfpjhelNhUFu9hrizCpCm8++YId842uD5JcXdeIo8JwLrc1MwtT9xW1xhwKnz5QvN9PztkrNfBKNmKxxLWj12N8HssYZbGLZ2RtQRXOeg9rxD1fhGYNYyfRIVpUzU4WRIW7nB8nnsnBEFbxoFNg8UPaCmgpoPzNE2hXWbcC4B2lyTbw9pj6r/un5tTSv0IgL8EQETu/jyAf88595tB2+OPK6W+FcD/FMB3APi9AP7zy3x5wy0vszWRw5WcLxrnMY5Y3/Fzr81xuiyRxRr7g5amJuyT69vZqsK9eeGFOpRSHWqRkKNqXTZYFeTkYqNwuiz9e6LSA7TUMYfj1BPLNZaKDWer6pxWZGiSKAaY5I7R70BLxSJ8TwOuugKUvD4ckepPxqwc770+wguHQ+yPEuIE0wrf854buDFJ/So5TIkaqKga/NTnX6et66xAFZDhCcBStnj7w8Sv+oOUKp0ijCvULUlE13N9kvqt3t15if1Bq1h+fZIiNtQPaBRwb0G5tzTSOJoXeOneCtNBjM+/Nu88P9k+huSNAEvMsWp73VgczQsGk15MOjnjFqTwEyHh4dGixOmyPBeN7jKlFKY9ByLV8/7ri03tk+51Q4BTuceT/DzmUfBuoYlQ7y6rhD2Dbb6uPDNtmBa5KqPC0f3/vNN2mQjsSwB+N4C/xv/+NgD/iP/+UwB+B4DPA/hZRyPyq0qpSCl13Tl396ITCz0ybbccamu9xLuE3fvD2Et4hTeIGrnP3zHpAxSaF0lyhpNFnNqKVysH2hLsDRNfMJOqVkjrMmY0OJXfaaDI5A59qWz/FFhAIzGeHkgQ7CHRHOU6DG9XaeWUBD0VILSHXyj0xEi0wtP7uVc9koSvKCJ9+PaBx0/JayK7Jg5vvq6C7aGmxmxF+Tj53UXVYq6OFyV+8dUTfNOtPeSxZhFYyhVumF2CoBM0EefrCkVtUTDnvwLwxZMFvv72CAs+Vpr6xaRgI6wUm5K2a0O+FulFFNhGmGIQMslhStvhgqXklCLnkicGw8RgXTUPBMkR6qQ6OLekm4RAEKBIKzxr1bQRVnjNShF+TqLLkE6HiiznHdGGe1WJP61bdQ2JMBtrd4oqP6gpPL69kPd11c65vwEgXBaVa73BHMAUwATAWfAZef2cKaV+UCn1KaXUp+7dI/8WMT5INBqpB5Bu/h5TpADwklxyzI7zI2N+dKniOec6nw9zVwB886+UvEU0I5S8ki7/IgBrCpeYVgqVp6euvbIyAE+YKNs2IckTxgahxBEHZLgi51zbOiQVQMEtrVh4NmVyQBmoAo8QrUmtld8ujbIIDbcvEcC2ez+kkbhurCcXdM55ep/GtSKpg8TghekQ9+YFzta17zUUlow5Nz07R05cuNwi7t/TWuF9h2MULFwibTnhVq7ma40Y6uAA3xCdxtr/vsbR/VsF0bIsDoIfdI6en6gx1Y3jhef+TKN1Yxmu4vzziY1ih9x4DKHAcFYMNnWOqtd1I2Bt5cfchjFe1sHfN6DtywT/1l24NKXOv98VTrlaVSKAHMVl/rzT9jDfGSYwxgBOAcz47/3Xz5lz7hPOuQ875z58/RolckUjTwYvKd20SVR5wEQHU9837M8CsKflpKlz5DD6GpGyAgtyXHQEZQDKMYKqlolEv6XtFKjZuQrflrQZhbxeYUWIrst1rkGwcALWDVdxaWhuHLxGZcz4n8WG3iN4UPsbgJY0kqAKdP6IJzI5aVntWz4xmZARs1OII3L8W/PE4OtuDBFppnRmhync9w7w2xtpbE8j7YVcjVZ4/toAjes2Nu8yo5TH/5HoLvWZppFGYloZMlkURBhYwLmCx4oNOfSMYSghDx3QbvNDC+mUav5u4UwjJ+T8ornhSE+eXWOdb1OSbXXEDlCpVhNUHG62Y1cRmkTYF1lkLid8+yD2uG4hH+ZX/pJS6mP89+8F8E8AfBLA9yiltFLqOQDaOXfvsidc8cAHyCGsysbnMGpuF/GcYA4dgQYx51qEvrXO9+CJ0GrVUFk+7KWLNIECHZheh0Giwo0fHrMsGk8EKNcZ86QUrI8UJBKGXUgL0CAxFJ0xzipsMwqrnJKklclGTtR5BWso2quGgMhRFuG1kzVOlqUXKSnY0cp9O1vRvUyZsdO5lldtmEbUAqRaMsKawbRSupf7KhP3bN0qUQ9S4yOwo3mJ2brG7b0MR9ygL1v0xrUSdXKdGRMMhts4ua5wMsiCptD2BUrbV8ZqRWPebgrq3WiSLmus8/xows5AsAlyxHIfbRDdd67FgdMXyudf18xIO87jDoMKbbX485Dj4KM3isSp2CGaBjnfg8fZwi6H+/15p+1hYBT/LoD/UimVAPgsgP/WOdcopf4JgH8Gcoo/9LAXJORy9+alV37Z59zUsiQ8zjahgqKiSUHldcrpTLgStqmobUTgDGJd7UmL+YbyE7KtyRNKaovwhrQdGcAnZtOYcmGzdQ3Dg7VfSdK65fE/XZY+39Sn6QFaOTjZ9hS19ewLd2YFA2iVVyvaG9Akemo/x+my9A3A1hE84nRZYX+UsNoTNakrdHFg/epWGKUejlO8frpByu1BUqVUvOSO85hJ+BT2uWL4xTcX+Obnp/jnXz7Gu6+N/DbLOudVqIxWqBgqEbbPCJwhxORlCXUxhLQy20zrFo5w0efOuGdxnEU+6hcpuNCEGlwk8+T7L6oUimK65F7FQYYN14MkkJnb8r2Poz2uVchLOTDn3EsAPsJ//wKo4tj/zJ8B8Gce9AJEfUZW3MbSIJdKl7SLSLg/C3BPoQnf+DHjhkLWijwxyDnb2i+hA1RNEoZWFVzMguEEsu05GCQ4WRJtjVQ2JU8j5z1bVag5AiTUu+1Mxr1h62C3mfBfFZX1lT3BnhEjBXFrbXz+CBgzuFPuy2JTo2ospoMEWlGPoUA/xIxWPrEsJkK4UvE8WhCWauC7BRqkkcbNaQbnHI4X1BSdGIOGHe4gNUjjDP/8y8f4195zDV++szzHICuRmQU9awGQxj3pOwCe0DCJdOdaBUaxKGqvLCRGFD9dTUyAHKhUt6Go82IbD53cwzBCk+NPliWm/L018/yHC+G1AOcn1EaTPPKN4A0vPDLWQmWiy9iCW862XfPbZY9zEv+RA1lVr4InTcEAOqhrgB72NCc2zz4nFED5hD6rw3xd+W1aY2kA35hm/jPiLOJI+2OllB8CWyfsEPcGsQdQirML6VYmOTkcidCgWpkxcX5aKa7qbQfhDtMIg0RyeBQdHowSTxCoQKh52bbuDQh2IqBR328IeDzXRbkVgZUM0+gcYFjgHVLBA1qty8YB10YJ7i1K36AdcTHm3ddG+PKdJZ49zPHS3RUc4NXJM6bcEQ75YdKKUYTfO8kJa7UqKLdU28pH0bKICAK/sS3f1zbnBbQ5PMXPUe5VaHdnBa6NE3+fw2OrxuH6OPGLrvS1ykIgNEuW86CK83sCYhZZtZoduDHqXP4vVDsCWhFhGZOEJbvYeRVMEbSL5+xh7DH1X4/egYnTML3oANjefyW5kFDUQ/A4khcBaCAM04giHR4Q4nBm68q3A0mlL/y+Pitm+J7Q7URGE8uo616nUBTLRIk4Pzbn7yTYgPEo7F0mzdu5h2A03fsStJXIdm6aUbTqIRgMn9h2H0l7gCEU7LzEEWRJl15GIjctM9I/C9pm7bEupExMSWBrrfDS3RWuT1LM1hXnjliYhBPyCsCC1aciozzlUYjTqxpqFbO2xUxJBCznCH/h6arqUHFTlZLu3ySlBUbGUMyV5nbrSvc6i3WHEkcBHqGv+DcSJbTybK/7wwRFRRFXp/BUtq1IszX1fso9LqouMeGAZfOUapvTZ2sCP6/KBhqkUYCyJfKUKHbAUAqh07kyU1/jW8i3y6gETrJSMskkIb7Lwkkpx0Sa7nDodARnkzPxm1TKDJfiAWaE4FxFVyBBt+/r8wo/Xpwj0qgbStBKBasOnJe/Zq6SCZNDP9rYZsJUYHkCCEix/Y30O4SDS/JTkdEdVobwnmy4b1CuX64HIJ6vkpVtgDb/JjqUKUMp4AS3FnFzt/awAmGgaFzbflKi1Vg0mtSAMkHtBxAQqYTKvRamWknCR46uUXBUUmlrK78WKGriLKstEGm/TadqsYPiApE8I/ndDtrnIyOjkQR5UKPhW6mUUlCV9dJzNeNdNO8iVkXtFb2FxlwqzYM08kWFihWF8tj4Zn6xivt6Y6394tzS7TQ+ZREFKZJWT5NHhqKK80VA2Ae1i5fbR2ePPAID2gEngyaNSTVIQuzY6HO4ltCp9MvKIWFgLJirxrJD013nZxS0U6ibhkGYNCBkIAkHmeDD5Nw0iSgHFSpWJ5EGeLJJmb1kxZqQ5keMOMWsf13UfsjxtBAMrbr0N411qHkalnW7CFS19d6zahov5pHGZideQRx4GnUnhkK3b1Ik4uQ6Mm4POpoXnLOjY61tVaRi00qOxUYjMhavnWyoV9A6zwXmr4X/LhM/5vvpFKCgfZK8ffYklyfXJJXJKIhwI61IP1EJZY/yeR2HlvZG7n+oGgXQeAjpiPYTpg9qSBZNtsNA2wAeMuIqOKa8sX77DGDr4ggl9EHtNcpnEqNhtTsHpdgFmbgqBgnaSVzJqa7cHi2ltFKe69wGg8A5EvuQVhmjicxQYAXhhAfa0ruQ4onSdyi1ZR3ONWvHkWZnRMcvAoXosrGes0nwYFoRvknAkYPUeFodGSqSd6CiADkeERSZrysPkvXX7lqSRKL/cTCapMIEJCsMq2t2JpaR9ADlCefs5LKAqK9unG8dOhglHpcm0BSliE5H+KbCjgKZkAPebgkNtIgPW0dR4SSnQsXxsqKcHOs0QlmffKc8EUVekaHvfXmxwsFCALYW2jJhpWoxcUVFuLkw+kkiDcdRoYjCxtw5ALRbqhxUORbHGBkFrdvo1brKY7T6/HJFMLbqhnAzadT2FnqZs9gAMTm8xaZGwr8foChP2qeKymJdEh+Y5GE3FT2rbfnPfnI+/MyDIOuNvtp+yCd0OhdYv3dL9vMO3XyU5BMm+fm+MUkSCwPoOI/bfFQPiS8mrTdpbDBUwGzd6vSFfOcRt7nMOM+TalKxlnOOeucWKuU86Va4pBwfmvRCzjc1JnnLiLHkSuLeMME4j2EtcYadcS7NEyyqtrldqrYAsNiUXhatrfzRNrJsiJBxyA3Jci9ybumqGusxT0BLAy3o/zSm7e2SOwJeuDYAEGg3Ntb3isqzzWKN1042eHmxwve8/xa+em9FHO7M2z5MiZl1tqmRGIJD3JuXjAGj7VFV2/b+sL7jkquQ/TG0KhsMUzpWGF3D1MSM25u0ioCAfiasCIrDqGqLBbed9WlxBC94OA5l/Oi/ZU3QnJNliU3V4NnDAVZFjdm6glLwbWPA/R3ERVQ6b7dRJPhIvvq+9lg4sONFiSw2nXK0VNdCE2GOPjd52EoTUpPIYBeG076FJfsQCiEmid+Mk6N7w8QzVVw0mGbr8yV4MREM6bZ+nGfY0FrBuPY7pPKq0JID9gfVdEBsEDVj3vLE4I3TDZ49HOBzr82xN4ixN4yxn52Hksi9iAygK/q+JCImhzfPNjhgLB4UObNfe2PmKXE+/9ocXzxZ4H2HYzx/beDhGGGL1iQn3q2DRYKv3lvhPbdG+MLrc7/VXhZAFbWc/MeL0leVw37B8LeerSrfrhPywQGtI1pyXiyNNO4tShbc1fjcm3M8uze4L6pdHH4IhQmf4SCNznHOhePoemw6kndjltlrHDxVUB8iss0u08D+tpl6fKuQj8XO9nCcnqOu8dxbtut4QrBiaMJkIe/JsYLo36WpuNzUHvLgnPNQBYCArhkDWaWMfTC6mM9Lrr2qLY4XpZdEk2u6SIEplJfLE9NxsMZoxnwRijtLzFY83DCLsMe0OcIg+9V7K7z31sg3Ru9K7i43NTacA7oxzfz5b04z75SGKaHev+s91/xxX397hN/5gVt45iBHycpCmlHowiRL7U+0BZ6tK3zh9Tnee2vEXF1tt8WaQccSdZ+tKRcq28Y+64fwoyXMmXayLJlCqO000Irwg2mk8fRBjsNxiu989yE2VYM3Tjf40psL/MPPU1/unVnhuxfKmhqiB4npsGTsDRP/DM9W9FvEvvD6HJ97be4/v9zU+MwrM//+CQOZ4Wibvg2XuM32hgljz1qQsWDK3gnTSl3qz0XGnTr/T6XUP1NK/UOm5tr2mZ9SSv3hS13XQ/6eKzGh09ll0g4kJgR+2yyJdGfbIzZKo3NyXGICVBXnphSpLp8st+tUisnKH5pzzk+u2bpCZJRHyYsTsxf8VoCwScveIBUTRy7dCfezQULofKMVntrP8ObZJigsbD/GgaLOe/MCd2aFf/1vfvpVat9SFM3emxe4O28diVIkoupADmu+Js6tNNKeWSFPSFT35jTFs4cDjLIIX7m3wq29jLdllHPKYu1TAuuywSg1vutgIR0AbOMs4ntMTeDD1MA5YMoKQC/dXeLXXpnh9dMNwMn2l+4uAQA/89k3MGHYxDCN8I1PTQC0MmdjriaKibM4XpSdRXWcRfi6G0P/bPaHpDfqE++Rxq1pG4FNRSCFaW8kzQG0lDhhY3poo7QbuRt9nobn7TCK+i/35z72AwAy59xHAfxJAD+25TP/IYD9y17bI91CXsQGsNzUflsQOrGLnIB1DifzkiK6tKUXSSK99TiRoafCQEOailtC9P6hg8RgU1kkvI2rmHFAtjCDxHg5+VgBWtEWd5Sex2RJdGIU4XsEKlFUjceBAS21tmB9dtma+y3pflDo/7nX5jgYJeQkrIPR5x3kqiC9QQXNTcs0GufrCh+6uYeDYYLEkPCHOKT+vfTVvbSVBBsmBgtePLRW0FZ5doq6cZivCQ0vbB57g9gzO7QJb2EPafNGc26LEmqdJFK+91Duz/VJyjnQ9rkJC8e3PXPgaZaqxuJkWeNwlOCN0w2mva2aOAqlWoohsddON/iFV47wW99z02sCiCMHqIAQ7hg6MCB+bVE0XjLNAVtbzOTYba+RLFubLukrcL11oyLaFdh3AfhpAHDO/bxS6sOdb1Hq94DIIn76sid8pA5M+v22OTFjFKrSdlpu5MFsWFlmU5JeX1hWlsVR/i0I8W2U1JZRrjLo5bxiBQ8G+mzb7JsnxjMpCFIeaGmv+/ktKfvL+yEeS7i5ajgvgsvwok4UWHFiXECwoUmyHpCG7BYDpdCKcygFvHG6wapAoKodY2+YeFS8McrnuZyjkv7eMCbFHa1IPch1J5lAPzzgNagmR4YgDFHw3jA1WBYt170IrwzTCCXT6IhyuVRnjXZwoK18HJH60iCl+xJDe6qls1WFlBH/wzTCpmxaSbeArmeck/NbFKTaPWbF9r1hci7fQ0wp9KJ39KqFTbx7OiI6qGCMlbWFEgxbT4w5ZOO1TrjtAMOiK3GPJWObCTRI8HViRqkrz1cpPFAO7JpS6lPBvz/hnPsE/71Pu9UopSLnXK2U+kYAvw/A7wHwf7nslz1yYVvHUYJUrQDG8sTG08ootDxIAFWYoFo6E4IUWH9Oz4ZpWetwx2pkrYNmR6KV8zCF2GgWcXUAWgkyuT6vpIT7S3Jte69xrpOgF46uYRZ5xaP+AJb7UPKEDSEnAqAEWCKOYR0CAr01zeBA+Z2iIoWhsrYB5TK8JJeQb1WNQxxRBFwx7EKYNLTq4qWogVx1HGvFsmXiwGUh0IrwTFUknFnORxCCNj9ZUXVYMGgK0n0AX8ARnJT0gwprhoBfw3utcB4rJb9D7v2taYrXTogiu24c7i0KTPLIV6MFtrJmCISMxf1BjGk+OfecreuywYpjCk3yfmERQGjK70cyFDKKhJXui7QjHtrUA1Uh7znnPrzjvT7tlnbOSW7n3wTwNIC/D+AFAKVS6iXn3IXR2COvQspAED1E6SO7PkmRxcZLnSW6JeojZoUaB0NKiBNXFk0YacIFaLDvD+JOY7flFpeaFaYBxltp1d0CshORCEGptqopQE2g5c0SEwzVmlH8vt0H8A3Nom4kcm3OtdUpuSfSAC3XK1W12boiihfOL7UobTKRV9Oc99uPEqrCWof/8Utv4jufOcS7rg9RNhbPXRt0VtYsNp4No185Ayg/JveBnN/2rY6QNMpxm4qxZpq267MNMXfI9nBvEKNk6iJSDypxc5LhzUWBYRrBMF22wC2mvM0EuloH00FbzZP7tqsfUI6LDXUYREbj2cMcrxyvMUgj/NOX7uLbnz7A4JrxPHQAOOp2fkvVWOpYCKE0lkHB8uydo/sRR9pfFwBG1HcdzmWxW3LstiJXeB1X1VF0Rc3cnwTw/QB+Qin1EQCfljeccz8if1dK/RkAb9zPeQGP2IFFuhuKC3WNVALnnAfznzfaK+8Arfw8XIsFe/fNkf+8c8CGhW0HjPyerUlZ5u6cmBYEJxZOhPmm9jCEbQNqwVL3SinkAYARIK71gyFRrmyqltdM1GukWbfgKFF45On3VB7lv6kIhKpVV2FJWnCEW+psXXeEKGTCltwPCbQO6OMfeAp5TDmpk2XFubzGN0YDNFB3JWM7wihBM32/KtyniBl14DGkK6mUQoYWxOucw8mqxsmqxEfefYDTVYW/9+ICDpRfvDZOkAxiWBvhZFluFdAIbc7b6vttxcKUgeOtcVlb/E+eOUTO+pWDlCq7UkSSLXvU25aKLYuaOdt48VLt/ZhvukpKb8WEuSVMwYgknxAMPAhl9i57wC3kRfaTAH67Uurn+LR/QCn1wwC+6Jz7Ww9zwkcegQHweYuMOdlFI7E/MerGYlU25+ADMpD6TK3bJpYcSwl8IElaPcLLcEkB5ESEsqY/PBIWyJWKaGI08rytRF4k97bPVDsAtjrPU9aHBAAo+t33U9EReTJAFoz2+v/YT/4qfvz3fFNAaUTJ+dDhnC4J9yYkj2ms/bbqeNE6ktm68li70OkK04UsIEVtMR3EhPPSCoahHRXTPN+cZDjlat7Hv+E2NEfBr55sEGnigQ+/U5L40tYkfG+ScxNFo5RhJSFOULoFANoBLIsakzzG9XFCUnMO3mGN8xj/p7/zWfyRjzzvCyLrskFRNb6SLfRDofMOmUrEqFnc+V7HEIhMUnrnueKkwh1iCE0wnvsWGY3pQG/lznsYuwqyQuecBdCHR3xuy+f+zGXP+Vg4sElOVRnp/N/FjyRAVgBEH2Md9ocJysZ6Ij9xRDMuSQu1TL8NI4w6hLZaaG/EBGaxLQpzzp1D2gOt81NKIQaAuPs6EdoxmwVTEMt7Isa7a7BIJfIyq3cSaRwM24kxyaPOxLoxSfGf/e5vPOes12XTYUeQ7wypjWQhCTFMu8r5fUokoxXuzQmkKlAJiYKLqsGbiwJ/78UFPv6Bp3A4TvGZV2a4NU0xyWOmt6bz3psX2B8mGDMzkpxffrO8FuIL+7+13xlxMErw8tHKVwSJGbdlpfix7/8GZtV1nlpbqq8ASbL1C1K0a2hfO0cRZba/VzD4epAan/cLmYir2nr2WfltEt1LT+uyaDw11VsxhccEMLrFHgsHFj5MhRY0uC0Scg44XZX+gSpFXf0hDGHOTiKktglNHvQwNZ3BOEhaLrAxU7rsQm0IF1RRNb5ZWb5ryhU/oF2BNeWgO8KkUj11zvltyUUrndwnIVLME8oR9hXF+5/fBT1xDnjxzhLXWAk7FMAQJybbaaWUzwMN0giDhJDkB6MEc24J6jtWOYcIXcRRy4k/ziK//RVyRoBbuEAJ9s+8MsPTBzkiTUDUuqGm9aN5wcwWtYeKiDDyLlhOGAmF4OI0apkojAKe2s+Zvob6OiteHK0TSm7tq6NLZsYIcYRifX3Gs1XVafiu6uYcH1x4PDlPem0bJVLEUnMiQyiCuGmsfYP8KLsimmf1pBfyUkbRBW3vHGgQ5D3YgFLwk2WYRh5vJIytQhFTVISilkoVwBU73q4MUuNX1to6qIqS2FnMdDiqFQfZVC2P1ZJZPOVYoZGpGe0vzK5CKie+I42NV7UJf68swDI5xEo+bz/6E66oJCKsWX4BM6eIlAjx4ZpFc6lBnTisSHm6hZyICT5NktGithQH25fGOiw3NWKmtXFoSJJNK6+yLj2dwioheb2isp4CiCTZKC1uJimyhPJQN6epF8EoamC+rn2RZpBG/t4LU4c4alH6yfn5VfyMvdCLdf47HNrURM1MFyPuNd1U9HyJDTeM0AlyU1R0vCpqz5SoILJ87RZWtrjCYZYy+4e1DtYSw0m/HUkwbXKvV6whEHLCRYYWE2HTWJd0/2W+iMbBVdjj6b4eOYwCLFkFyul4B0STUiYrAP/AY2ZVALqlca3bByfUJYkhACsxM9C5vL6hawGqlkGViLv4pjhSQG2JB2uLCeuDUsqfU5xmzQIWAK2WRFnTRgh9mMU24CEl9Ok88psV4FkygO0MBSSE0vKOpcGx4f0KoybFfyxHYoZxcbIFK7khOtL0HEpm55D80qYkJL6JaGu4YTVxiV4ix5Q4MjF5gZCI12i6j5FWuD5O8OrJBgd5jNmmRlEDcLToeBEQ53wF1zEcwkExjTf1VAovmkjCZbH2XQwJO/Gmoecmieqypj5SUXcqa+ufXyi+UbHYjDgJpwL+OKOhdDsGIk15N6mgl4x7UxpeCQpoqYvC0SYOsagt5wPb6rcUP8Lcrzhk564OUqHwhFJ6q1nXUt0oBSDSiIL5GEYfwqggm0rBX4U4sdCEuqRkSTRBRwvPVtlYpllRnvEgNHEwRivEuh0IwyxiUQr46I62h8onqOU4WS2F7iU2yp9X5MuiLQFUY0mCXnPZvQwc2C5KlRCPJgDJONI+x1I31kdFwkwxzoiUUCIUrVp1mchoFMr6VV1WfK1VZ0JJj6IAWYVaSAQwaluhsTThFYi6pgoocQhjBThYgDsFBOclKk5C4X1jkmJdNrgzK7CuLGJ+fuR8yPmtmq4eQjvR4Rk8Gtfi6jbsYIapwb15iQGz/MqCJxFrHhOJYcVVba0IuCpbVwCety1jQGrdWPjVme9ZZAgak8RCWNhun6WyTc6YTAW/IxRbEYaUUPJNTMDVcUDq+Fbt8XRfj8EWMtzfS0JV0PkhSl8iFPnMsmgwydttUbqFJ1zkryQ8759XaHYElxWeX1hQ0y28TSvelvXDfgEmCqlexmybzlGUd7qqEHNUuK0AEF63FA9CDFj4G/o239S+BUZEaa11Pke2LBoMU4JnOI5e/vlXjvHBW1PkicHxssIkM17cd10SvGJR1FgW3fycJPWPlxQ9na0qkqVjvNQ4j70jD/NnVePgAkocqQoOU4rExFFZGzFlDy1ionsplNSzdY0XTxb44M0pbcM1RcpF1WBVNnie6X3kvlTsbCUHdudsgxnn9uabGiteVP7TT34Z//HvfD8AylUOg3EDkBOhZ0iOO0TYO+c6uUjN2/J+oj0ce0CXUmdbLq3/zPvPfxziAwHfdSKyfPfrv72cqXMBwuNil3JgSqnvAPBnnXMfU0p9M4C/CKABUAD4N51zbyql/gKo10la8z/unDu737kFiS9UwSlLlB2MEpwsqw5oUVZjpeAHyy6Wif57ws10OEo8tEDAmFrRxBZBDqXUuUby0ASGELODo99B4FqgbULfcIK/bijiiY32FVeZkNsqnBf9pmXRnCPhA9CZPLJVGWaRf91TDBnaRi2LBh9+dh/ryuLLd1f4pTdO8KEbe9gfJT4vd7wsvXjKbF2hahyySJ9jDqGKKgGD+079lEVBZEsbAktDCEDMlDJTbqYXqMS9eUGtW9bhzqzAbF3jw1+3j3/1lVPP8qpVKyL7/LUBjhelj8yERck5QEAvtXWAoubsJNIY5xGy2OBPfuzdXjBksSEsl6QgRlnkGVImeYRl0WC+IYCuONk+VKLkaC2sdB4vSE2pbKxXJpfnc8pc/aG4iowrOfeCc479xS9c5KSCOcyijpjyw9rXdBVSKfUjAH4/gCW/9BcA/FHn3C8rpf73AP4EgB8G8G0Avsc9gKCtSL8DXZCklOf7VCPbOLv6JlQoIl+Wx8YLm+7zAJQVbG+YEJ+9ayd4OKmkyXrbtk3I9JbMWzXhqON0WeJsXWGURlwUMB5AGw5wcVyeqoe3QI11Hb3Cbd+7zUTuy9Pe9D4nGDLJ44xSgzuzAqMswtP7GUZZhP1BjJojxaLqwiT60BalFA5HiZc3k17SsLH4NJCgk+2tJMzD883XFTaV9Vv80HkJMyy4APPlkyX+1VdO8U3PTvHLXznF3pBYL4R99t68xHOHOTHdcgUx5ucj9y7SdO1hQaWqLc7WNZ6/NsDRvOjSHgU/vc81V9a2VaAK7jVhGo1nkxWT30bsGd1nuK2aHOLWgC6H3S6jwgw8FfpV2NdyFfJLAH43gL/G//69zrnXg+M3SikN4L0APqGUugngLzvn/splLqBfgpfqlyDuR4GyjFAzX2QhOaAcK6Z1Sz8SdgDsskHgZNZlc27wDBKDnGmaJaq7fxdbayVve/oDd1vkJwj/XQMpZFDw8Am+l9tQ8Uop3N7LvEht3RDr6CiNcGuaegFb+e6G+yEF6CqRgTivVUHVtkEQGUzyVj9TIsaqofxXCFcZZREGafe+zdYEZpXqp3WURvjAzQmMVvjlr5zifbfHmG1qvHy8wpfOFtAAftONPZ/Ej4zGSCuv9l5w4UGm9JI1FpVSvmXtdFn65nYxcbrym+TYknN/oTMWskPDW9ZNZf04EhqmvuqW5WLAxMMhWqcjOxTnWryk6E0qrnpuc2rhNV+FPZ7u6xIOzDn3N5RSLwT/fh0AlFLfCeCPAPhuAEPQtvLPg/h5/4FS6lPOuV/pn08p9YMAfhAAnn3uuXPcVKEqUZ865rJrSZ8VYdt7wfWgbizKenspm65pu6OTiprlpOuSJ7wD5z54AElpvm+RVtD3occRk1wSAF9JC7cRco5Q4UkqjUBLzV1UbWX0bF1jwtCPwxGR9EWaOO83VdtcL5g4uhfKMz+ElEWO27mkEguwxoBqPy+akcJ5JtdZBCSG8gyKymKcUUJaKHGIN18xQ0aC2abGKDW4OcmQGsHUAf/Fz7+Ef+c7nkccKa5UEgOqAqA4yhOw6nzD4i1K4bNvzvAtz+x1CjrSCyu8bkLjI0QEdWOxrmjbd7wofb8mERJoKERYliScPEwN5humzGF4StVYDJM2MpbcZRxpz04SQmGAgNWk6kZ3IUxG5tETHNgWU0r9bwD8aQDf55y7q5QyAP6Cc27F7/99AB8CcM6BMbXGJwDgm7/12xxVorrOIZT98q/p+zM+hiYYMmIoaNHNSilfmk45YR2qyvRNGq5jc55uJzStiIom52KCh1BoamA2yp2rOGq9nWdJIAq7lGe25TU23BtpHZhMhz7Tv5fSFF1U1k+Uxv3/23vTWEu26zzsWzWc8Y49vNfsN4rTk0xRFsVHkZZkiQ6tULKUWFEC2ECsHxYSwZKAIHZgZ1AA2n8cBIpkQAJigbJgQ4kDJ45CyBlkKoBCi4NoiqJsayBFc3gT+V6/7tt3PEOdGnZ+rPXt2lWnzr2n+12+203eRVy+vvecqtpVtWvV2mt96/sUThCJVRmrZitOO+qtKteQZ+P42DROS+NaxUmlz1SINjUIw9BoZKJIkCJq9NvV0aSpB0ndEB8ZROKFvSlubA8w7scYpgPPfX99nOLWYYYnr40QxYJSFVJ89CWAb3sSAdJIOfUfGQ+WwLBkNxE777x0JlASwUHhCoQzEN5BS+IapR/HYhRB8OIpLPDkKwgsCa+g2HNpUBVS9zibK5yXRVkhiWJPs63bLu36nk3QPeceBLvn3JyI/BVo5PVe59yX7M9vBfBxEYlFJIUm8z9z1r5481naP82oA7iucX+lxwvpf6tKpbB0gijEgtXGVWNkSL8KD0ZjCZ0UL4pr05+ztqWRVui0r3c1EPN8GRno2GsYiTZ3uwa0gpHuII3xu8/v487xQiMj6INdVkoxNM+1529h0ACR5QlNDqswYT3sxcrDZQ8yP1KZt8g38/fsfMKXCIGfPbvvPcNiMU+YlxW+dHSCg4kypA57sYGBBX/u6Wt4dTr3LUpwztML0VgIUYps5bV/bHewlByPLX+nUB/D8zmFaiSR/ii9znLE46E/iSonVVXNcVY657FkfCHk1tgeOqfKKREA5214L/k8cG4lRjvuDI6SFRXOi49C1vx5ve2eIjCLtH4BwAsA/g97u/wL59wHROR/AvBJADmAX3XO/dGZB4+0GfVkXgBlhbgDClEFEdS9GPMSg2CfbfWgibUa9TucASdimkQ+h7uKmkVEo8OZB28qOLdwum7hdqdFeqRZyfKqkwTvLGPkED6Azll1F8YyMUiQ2/JzZG1UHNPPf+SL+Jk//1ZLumt+S5V8xCs9xZH4pQ/pYUJjpBRSxvils4lrMIJlHmxs95YA1e0RVYxq3FtlWCoy1RLmEcEqoEUFGBzj2Iol5DsjKn+QRigqYBhrZbJnjpw4RGfL5rbxZUHMWpgKIN8c79/YlpaRwSSY49T5obmqwpwto2CgzmFRTZ4N7EVZLaU12JaVlw79RPfL+dVsS9LK+nlFTg9oALaeA3POPQfgPfbrlRXf+VkAP3s/gzgNNuD5wJLzv4KrCgLs4Vs3fxBH61WKDqZ5ZyLeOWeyagnGg8R6OO9N1+9oli9RIYfsG7TfffEAAPCOJ3fw8sHclIp6+NW/8h0Y9ZUHnmK4N3a0UzpMTJ92jgRwHrcgBUBAShhYZYUAEfjqsG9sb10rItaJ87pzvMC3PbJjeSgHQJvQn74+xpdfneD6Vh+3jzJ85IU9PLndx3c/dQ2x8cVV1l50dbOHz798gsNsgWefuoLn96b+nEPrYpQAaidbVs7DKGruNC2OtK8/95HE6vBDfYPwmnZBZQAsObRVcy2Jo6UX9v2awigeTA924UDWvZMFYoFfApDjaKeFX1rHwol0lh1O85W8TKdR3rQtM0xViE3j/rOiQi+uJ/Eq6hs+uIeGmepy6PsBJKHL1s0Pftvj2wC01P709bH/u4PmDSfZ8v66aI3aDzSZZEf9BNv24JGy+mCaq5xZL8YQTae8MzLwbRKtTCMUlcOidOjFgievKs7ryatDLEqHv//J53B9nOLPPX0N26MUX351gjff2MCLe1Nc3+rjh9/6KD76wh38vY8/h7/7g8/YySro9NWjDNc2e74a+8SVIQC91psD7bUkX9lmQJdz90Q/X9hynxCavRNVQ9oZpRgZnfXeyQIChQTdtc8d4KOyLrqbSVZ4RluyUQBoYAjbMnJfa3uoI7CvlRVVrQNIa0cz92JaitZTojNYVeELj5FbU/Y4oC5Zx0j/uzlIsN+StxKp21iqyq1FwneahTkxTvAkjk6NXrssfIOHmDkAPpdE4OjesXqzKxs93J3kngFj3E+wORAPOSDIVVl1F/4e7loEleiTagWNGgcWOmSOi90D4YMdiza964OvkWXllD3iJ9/9FG4dZvjq8QyLosL1rT5e3Jvi5u5QpfUc8K43XMHNjSn+6b9+Ce9786PIigp3Jwp23ewnDcFboBabUbIAhe9o3k8/3x0r3GHRpol2TRppxYIpnOJgos5cX5xK051ZVBlKo20MjMtfSszyEsVM82ZkX+E8SGPxL+zG/e3F59YDWZvg/NrCz9cu1IHFUa2gzeezqnSZcK8PJo0PxMYpzqttSSyII80hrXtchQI4E3hQIsPQSPpHrvh1nGI45izXnFDYBkUbpnGDsri9bZcRnMnqFHNm4fmO+wlK5/D8nSm+cjjDu79JswV/9NIRnj+a4i1XNvDYlaFqXk40GjmcKinguK/CHWFup+2cWEUc98V//txtXe4xmqBqT7j90NSE+LsA1ssaIU0ET14b4bFKHdbtowzXt/p+2TsxEY43XdnA/iTH//qvX8KPvu0mjmYFRrbkzUS8jub2KG0sl/tJBGe0NlyuiWikOew123rYYjXPK/StF1VfMvX5UtthkMZ+CTnux55OaZ6XXv08dER0qP731rWifS06fh7kKuSFOjBBjfsKy+a9JGogurtsmhUe88SqTFhJvBf8S5u6JLSi1EbnfhL5peKop0KqaQzPMsqHlOMqjHEgiptwhraRP76sVBlnZj2YhTVc08YBDozwCwJVx/3E561WLTFHJvBaVs1E9cIiOTZXK6VMhGujvkWlCW7saHVud5zqA+3qbfupZkfYzB2JW5rs7WsaCfy4p0Z7TPUgUiSF8A1CM2LRnGjpNEFNkGoU63KsqBw+8sIefvitj6JyNbXzwARLtoYJ3v7IJiZGCT5Itbl7nld+yRjqh5L1o2tuaHO1Q1nVxadxXyl60ljxXZX12qqTskR7yiq1Os0oEq/wzTnCCLBdx+ma0+eC8zrL5HIJudLaklxaqq5ZG1aZSFMwA1b5ITXxusetghI0gAb3lz9W8F8moymzleUlcmuDAWpGgoXBEYhDWwXTqM8lOJZYVBjMmlWN39L67yo1JBYE4lYD8qJQeTJqJBaVwzCN0E966tyiEtc2++hbUpkMIHSaXH5meQlXwMMAQmNVlrCSzHBVxC85V1+npRODLvFLgx+QkSGJQpBq5ZWun9ju42Mv3sG7blxBEou1c6kTWhQVnr4y1gjJIqBIb6hqJ0CdBzU59Z4aXxx0CVtWDqNebFqilZ8//YB7jPeqcmThgJcAFKlVkxhTEyun9wTIqwooa8aLNlU6gaqkWhKsnl/n1El06cBW2WxRwvWAHqLGEugsJxRW6PjAsLp01rZFSQ4mo/JBTWsTTkCA6i/6766qJd+WfCgcdNJSEaaoSo934/4a20fNauM6dDnhtjxXtsQUZdW5lGQLC3sz+Tu/SzhFz4gFK8MS5aVDPwXuTrTJmc3v/daDRZaLXhItTfZppg+vklPqw99PIhzNC2xbzjKJpfEQEtrCCJjkkGSN3R6lnhqHuadBGuF7nrqGn//Yl/GG8RRvurLhW25Y1WNL0rAXG8eYMutOrbF+2NPrM/dkh4qFS+PIO6xhGgdkirCXpvNkhbkxxxKPNexpbjWz6AxA4xorE7HpZAYgVDp8AlopIzjsAXEU++6HSAT9FH48IXPF+eHAHkwPduFN5uztWrQirklWt1asa1G03MTcZUotE3vcDxlJgdObZdsSVoA6zzAPQRphPiTkJZtkpaeCDn/atup8j23b0FgNdE73T6bRLgd+HEjYs/E57BudZoXPRW5YJZS4p7Jy+Gefexlf3Z95xPn+VBP3vCaMMnu21Fa2hgJHs6LmRpM6ZbA/zT2QddRbFrHgtSKv+yCNtHXJolXmqZJI0LMlZOWAOI7w3/2Fb8ZBlmN/kmOaFTic5jic5ri+2ced48xzrFUOPpGeWkHEOcVz9ahlaWMeGwvvtc2+d/p9u/dsHSJgl8vB1OYAq8z9RPsm05hzI/YdJiLKfzZM45rPzJwR74ddwnquRc0mdbKQ0HQZ+todj0am6/283nbhDmzbcEirxDHI53Tex2R1bJ6XmGZnN72WlcMdU+9pW1g53R6l1pOmlCe9JML1rb4/TzYOH0zzBsMn7cS439vWnhtZXvry+oEJmZyWxA+dbz+Nlxz15jD1y8FJVvqHFQCevzPFn338KsZ97dk7mRf47S/fRuWUuaKyapj2O9a0ONTP5MMfjiE38sejWb708gLqKGJ7lHrmiNmitOqmjjMy5wIRxHFNa3N3kuN9b34Un3p5H7dO5j7y+qOvHOGd37QLEeBDf/xVPLc3AazCF+LPnNN8ZM9oa9iUHhZiNgYJnH2f98E5h63hal2D7VFdvXSG7OfcoXp6RCjKKF3KAfMerUonjAPFovO2yMDaZ/283nbhS8jT8FhheXlqidetYXomJCGUSKMVpQIsmcMiaNTNCxXUtUm7ygR1pXHvOGvy9Nt/6QDC/VCyLQ4k0NrUNF1cYoA+5JOswM64t4RNU8qU7ok8X5Seo5+mAsAlhj1dQnVhuU4MvtCubL3xkTH2JwsM09givArf+03XEUfiKXeGlhcimvwwINg7tGhN4Q/694FFvSEWLbRQxYmOr3TAq4dzFJUy1jo0ed9LaxWD5YZ+9G03MckK391wZdzDc7cneOraCD9YPmrU4w63jzK/9Lqy0cOd48xj2kIiTc5HOpqQRYIWcthlueov8HoSlrJlIsbsktifLOocmF2zvkViXQBaQOdGKB7Ma0X6qPO2B3UJeeE4MCZBJ0YjMuonNap5kODEqkIhg+lZlZcuia8kjrAd3Gy+4cf92Dr9Tx8rw3XyevFtQ2GPjUGyJMsG6ES8unk6jpkEijwOLU0ibEVN3qzCksiALn+6JOj6aYQ+9GE+MbjHPC9xdaMX5MxqiToal2whNfbhNPdgzszOdVFUOJgs8IgJ8u6dLDDuxegNNf/le5pFFOsFNGAyR7Mcn7t1jO/a6OHDn30F73z8CjaHyui6zHKrecFerI33x/MCEOdfBoSzMMeVFxV2xz3cnSxwNCusUqq5rSwv8eufewU/UD6Cx68O8eVXJ8jLCLvjnm/03jtZYHfcW6Ipd04d5G4Q6bbxYz4ytGgRULzWneMM1zb72B33vJJWae1WGysq7SKcF817pLAQFlSan4WMJedpXEI+iHbxODDLZYR5KFoU1Q2yYaWO6j/kL+/arsv493BbMWjBIl/uO1s1HkaLWa7sAFwiUcqtcupkFNxZNTik7nfMgDoGNkgD+sC1nSZVf/pp7CEfyiWmzovAx2Ev9uV98q4Ne5pjCvmoGGWIaO4sjTUXlRV1x0AswCxXx8fcH+maI4E/ZiTim4yf2Bnh375ygm+/uYteooyu4SVgOxerh0WpifrNQeIlyiZBXg9QZ3F1UxH2vFcqYCyANUU/e2MbvSTCc7enuGr8bUcms0dnGybCF0XlnVEs9f2YZoWSMMZ1BHY8L/Dc7QnesDPQsUNfALcO1YFNDfjMvs/K6XU7DS7UnheDNK4rz6159LWjvHlwgawXmgMTwFeXEuNziqReXgBaBQpz5yz9x7Hc9w1rb8tcBKDLr9MsXOoqpXFNK5OXzmTAIr/PNPx+zIpleSZMpG2Kaao1/6KoZnBtnFvU1AJM4ggbBlCdLUp84ZUTPHd7ohgycyypYZ2IeSospzW3pLJY1XFkyHIRwe64Ziwd9mIPswib4MlhlcbKruCgy9fNQYLrW33LE0a+F/RgmvvrTyYJ7legAhx3Txa1WEosnksMUGfzb18+0bybOS9AHdLQEOpveWTDN3LDxjMyDNwnnlMy4aNZ4TnLKiu29BIV71Dqc+cb21PTiZwbFxcLOKmxkAx6Ma5umjK6wTrIvMFIDNAXz9wc3NyYP4BaGg/QuRlGhjwujeBnGhlYXrMJI8Kzf15vu/Ac2MJUYbjIEqnL6cT/JI6ZAbXKocH8eZaFkmzAMqZKH/rY42pWQRnaxgeY+DMyEiiIFYiDXERu5W5AyfqQNhWYTjNS/oSkdoBGNO2KI6txhF3k5uyVw0ojIE62yHBVQ0OkV67yzBXEZY37iQJ5DYBJsOu4n2i/XhKtzMXxOsaR8n1RvxDWD+mg8Iu8ZCVwmXaon8am7gNTHaqwadALRotJpMupygH78wVu7Aww6seY5wpvgb1MEgdsj3q4faSq3kezHGNbvpeVw3OHc7zDpNFUFEPHwJeRcw57Jwtct0pkFMFTMpHvvyhdY/5sDTU57++/1PcupOgkJCU1oHBe1Q6M8KB5rveQz0rlDLdo92+eVxgFEIo4knOl03kQ7cId2GmVCy5xmsK298bSAKjz8OKqp1i4TA0pYVYZCekcdAJuDlPfCqXJVPH7mhmYM7WSvwTbR1ahC/9Lqyp9A1O9KaQXoqxZSNMzteUJHdssL72jGfcTvOPpHQDwsmozo8XhW3/Qi7E1TBSSYPscBjxXxMUxUui6pu1zmOelh1iE9sL+FN96cwv7E6W9vrHdb0SztKlFHlTwGaQ164WK+9aR0LuevuKpsBntzQvtv2Q0zJJ/aT2qReWwO07xo3/qDb4AVJTASVl4ZzYzibf9iVGgR+ocqc799HVVQvrI83fw2JUhSouwqOQ9y0u7d83iD52kp5lGvbwnTTdzr6cxDJNAEzCBE6PiOS8YxWUr0Qo7rfewqypYVU22irZ1yY4NejEGWM/pMZ9xYoyupzm92YI5sKTmdGo11wJ1AYDG75IvatxPPJ1O+7jso6QaznRBVaL6TXs4zX01MBZBL9WIZGZN06GU155BQTb6CV68O8Nbbmz4MU3mSgG9KCqcZKVPlB9Mc2wPmyX6sDcwtC79gFXV3fc+cx2AsnS8epThq/tzPHF1CGdtQFdMISk3ttjbxwv8wieew3/5fW/E4Uxbtj576wiPjAd4bHeAeV7h+b2pZ5U4mRceqU4uf47dQZPun3juDl44nONH/tQbcGN7gLJy+Or+HOO+fn+nn/oX0/G8wNuf2MKBNWTvjCOf0nj1KMPN3SF+6rvfCEDH7ztFRDqLLaG1rxEFYWhd24cV0PZzdC/kn2vZg+m/Lt6BtS2kxNk3xZtQVm2SlSud1zwv8fztKZ65uYm7J4uGJNs65pzDneMFrm/1fWQ1W5SNRHxoXUn/VfAN5/Stvz1KMclMy9ISsneOMzyyPcDeiVLmxJE0pNHC/bYnKkv/tBDIuznUSfzqUYZHtvr46v4cm8PEn8ubHx17RgndGbySdEj900UD5JzD3Um+9FkSR/64AGy5lvqewLyo/MP66lGGNBa8cjDHzriH61t9vHR3hr5hr8gqQeLBcT/G3/3BZ3AwzfHUtREOJgu84/EdfUHZwx7yeem1Ljy42Blu7cpGz9PcfNfT1/Dti9I3pT9xdYT9SY4ji+64VN6f5ugnEV7cm3k0/iIr8PL+HJ/fO8bbbyhN0cFk4c8vMzZbMuxet6otsX6MikM7nuk4WDklxIaMFF10Oo17aHb+oh4Ppge7cAd2NMt9ZS8vmhe9zX+lIMn64QgVjajByIhid5zilcPM45ray84u/JmI4JolXJmuGHZwWN2LJXGEnVETvhE6oTSJcG2zj4PJooG0HvRizyp6v/RCtEe2+hAR3NwdgNoAC3Mk4b7JoLFOcUREcGV89riubaqobs8tRxXX7VpvW7WxKBVGsSgqXN/sYZIVOJopLVIIEdgZpdg7zrBj8If2ePcnC0TBdc5s+TYeJNgZpbhznHnoBDsFtkcpvro/x/4kx7fc3MTHvngHe/sZposSm8a0ujPu4Q9vHeKJzRF6iRYTNgYJvv3mjk+6i4g6IQcMEuXzahsR/13XeTOImLm/UBAXWBaYISC4rcLFVqbzsAd0BXnxSPxxP0GW61KK6j2ceHwrdtneyWLJeWV5hVtHmQIDRVtMmDeZtJZ2q24sl4DkbDqedSPmO7dt6QPSCNfgBDya5Z5n/mCyWJI+o6Ur1Izu1bSlJ8dX9ude4myQxl4Zm2MKlYLWsbO+ezIvMMlUHd23xwSmkQWMYkZw52SB3/rCLcWZeZqexANOJ1YBZIROeE3bSArJ6LtyWsE8nuW+A0Ns/KqUJBZtxZjlJT72xTt42xu28eYrWrHsp5Fv5Xnr1U2M7MWXGPp/d9zzOTu2XpVl5YkLw3u/zrXr+uys77dzxeSKOy+2Clnz5/W2C4/AFkXVALSGFa1BTxuHs7z0nfm0cYDHInRB3+CacD4ysdRhL/ZwjYmBCAHgq3dnqHYGPvm5O1Z09CCNPSUOW1C65sDcZMIAzdGQUZbWNWaqj/dM6ILl/34aLzEOAOxlq38/MXrtVZPyeJb7ymR4XEY9kdSc9iKa6J1mBUYmwhvqL9LaqkRshD+tkMJ8Ea89HQ0rmKzSDVuSclvDBO967AqGad10nRgodGY4sXE/MfGO1e9e0gABRoljqH0WjEbBw056oJOyMCcl2NvPvGBvUSlUYWOQmPiLsnEMjbpIAA+fADSNMeonDapo51TJiNdxtih1zrbmBu9VxGqvzcd1bBXVzrk5lQc0AnsgHFgS1w2tofHm8e/kySImh5bEEQaAL/sDtbS7PggCh8g38fbT2N6igulCcT6CGpYwNR6pU/vKZPmfYbM1aVNcwI6Rl6o+0zMeKOfE90uepcoE1GE8nV0bvkCgaFy5UEwalXP+OG0L6XQI1+g6z7xoslxkVlkMjQ8d8308niL5m/vrkqgb9xOMrsW+35JOfp5XRrHMiKoeIxki4uBloccrPZ8XoI6zMkhBOC7uK4oEO33tP5wutAWITBGLSPBHrxzixeMZ3vv09UZ7kUQ6d3dGqcdpcS6JUB6t8veO3HWLsuZ8Y6S2KCqIVJ7brKxqhleeMXGAVGaihY6uDRt6rSayPmX5621rnaGIvFtEPmL/foeIfEVEPmI/f8n+/gER+ZSIfEJEvnOd/epbUd+CijDv5gFjREGNPn2T6/d4s0jKR+sb6nwewBcGvdhLzb/p0Q3sjnu4ttHDFWux4XKNcIbTmDCSRrjOIkP9fU7yELRaq/pwu6gRjaQrHCa3J9lfUbrGsfw5B0u18BrxoQlNl09NKTPx/1cbiQ5nloxOYj1Gl4JPaXglkjPSuFwWiwDzojLamMqr/QDwTBcsjsQGVVBiQPGQBJ67RoOVV6rm3+JIUJa1vFxhMBbmqWgEj5bmqDaHqc+3bQ2V036S6f374zsn+NWPvqCEj5HgJCsxXZRe2amfRDgOnG4k5CxTtSMPa1mUhimz4+eV18hMIs1PZoXi3mJRKp3ZQosfeeBwy0qZbxf299D4oj9Pe2iXkCLytwD8GICJ/emdAH7eOfdzwXe+A8D3AXg3gCcA/BqAd521b+Y4+ECzZ29j0J3bUKS+Lg+1NC1ejbjfkmTbGCT4gzsHSOJd4/vSFpiDaY40iXy1bzxIvDAHCwSUeBdp9puF+KaZvaWZXFUhh1a4b29g7pv9kkS0t60r30Vc2VZQ2eNSpJ0InhmnVVFqnojKNIRShMYE9Coj3gzQaJaIdMAwaILG70DNuUXIho5RJ7ZWzrTL4iQrsDuu6ag3BwnSREw0VpvXN4dpI7rqGbI/vE7TRelbawjenWSlJfzrcekyL/L5UOLvAHiALplw96e5b5/aGCT4o1cO8cd3TvDjzz6Ft1wZIbNCE5k3GAkRVEuOM49nM+cS3sOZjTvtx76PlveVnGKpEUwCwOG08jhAmkAdZBd9VCgFeG52Dt5JRCIA/yNU9DoD8J84574QfP7XAfxl+/X/cc79nbP2uU4E9kUAPxr8/k4APyQivy0ivyIim1Ah2990ai8ASETk+nonpQ9IZqDIUT/xzAydA45U5Ycl5Y1BshIJ/kPffBNvenQDVzeVziaJI3zzzU0kkahmYBDttf3lRouahJObtjlMfd6DSef9STOJP0jjpTL51jA9fWnacb5dEI5FUXnOtHBMjOp2xr2Ggzpone9Kc7o83DtZeB6traHmhugC+XAfTnNPpwPog3lssmVHM20NumPFFkJGgHo5Uifim0Moygp/8//6rE+Q08HsT/KluXE0yz2VD+l29m3cIfPpoqj8tofTHPtGmyPmiAl36ScRdqxvNBLgxeMZPvSpr+CjX7qN93/LDZxkBQZpjEe2B14lKI60taodQc8XJY4s78p7SLHbWa4RXHvJvjFIPAurUpiLCacUje+mSbQW9935mKz9vzPsRwAMnHN/BsB/BSAMgt4I4D8G8F1QCcd/V0S+7awdnnkFnHO/JiJPB3/6FIB/4Jz7PRH5GQAfAHAAYC/4zjGAbQC3Tz24JXfDBy3k1urSIzzNiJQm3mYV/MBzjc1ypInmh+JIVXYqhwauiha1sFEAfJJ6lpfYGal8GPfLnrc0ibzMGJ3BeTTdrtINpM2NL4yMEW0rygon86KBH+IDQfaJHYuSgOVryQeLNskUjrDVr3Fr+5MFrm8qB5rk2gpTVK6xLzY1T7PCJ+pP5gV++t1P2j5T//Dn9qIgtGRrmOLYFJHCFED7PpWlFomujOq/k1VimhXYO1lgf5Lj7U9s4cW9Gf7w1iGeubaJXhLhvU9fx/c8cQ1ZUeF3v7yP73zjLv7k5ROMejGe35/geFHg7Te2cTQr8NS1kQcKs9kbzsG5+n5f2eh14sD2Jwvf3jQwZl+SXtLxskJOaTag+TJo31+SQZ6HnVMK7HsA/HMAcM59UkSeDT57EcAPOOdKPZ6kAOZn7fB+XPiHnHMH/DeAXwTw6wA2g+9sQp3akonITwD4CQB4/Ikn18IdsaUiTFTuHWeACLaHiU8cl5XDp168ix96m7aEsBI07ndrLYZhuQIw05VQiNCOjb1AtxPPvNC1X6CmkCEx3nkYMXPO8Em0qTkSKmkDNWNFu8l7c5gu4czmucqIEbu2LkUL2SJCJP72MMXdE4W0jPoxdns6zlDOjbqQqTU6VwaTuLrZR1Fy2WpLQdFcZkiXtDEwYZBgiUw9So571I8bVVPyeQH6Erq+2cewp+mFyjk8saltQWzJGvWULfXqRg9/8vIJnr42wtG8wFO7Y9/4/sLhFLtjlYqjIvggjTBIVRkpvM7jfgznmhRO29YuVFrrGNlenS2NvWYE4AsyvFa8xxt9bQ0L86PnAaMQ3JMDuyYinw5+/6Bz7oP27y0Ah8FnpYgkzrnCOZcDuCN6034WwO875z5/1sHup0zx4SBJ/z4Avwfg4wDeLyKRiDwJIHLO3ena2Dn3Qefcs865Z69fv+6plrtYV0lT00+VD8q2x2SuuKkti856xo80SCO858krflvymB/Ncr8c+uxXjnAwWRjtTfN4XBpRvXmVDXsxXjmYY56XiEVJ54i2nhjqfrYoGxiy0jWpWHi+Xdp+tKKslvBr4VgHqbI8hN9RIjy9VkyzjVZAL8I81rFdI9JSc6yr+kGda4573I99czadBbnctFdUE/dRK+oOxVyo0LMxSLBrLKXsSjie5cjyyrPCws77ZF546hzaplc3Z7Jf84i8TuF9WFh1NYmUm+vR7QGubmoO7upGTxu97X6SYeNoXtTnaxCLp3fG+PCfvKKMrnbti7JmkgivM7nASGoI1DnVkVFNM5fIa7M5SDyv2sIayEXEz2Eqew2MCFEhJ/F5pK50DOsvIe/w+bafDwa7OUIz0Imcc34SicgAwD+27/zUOuO6nwjsJwH8oojkAF4B8BPOuSMR+SiA34E6xZ9eZ0cC+ApQl4dnrqhRKTPwYbh8qrcVz9TKpD2T5awabo/Sms6kY0xJHCGp3KlvnCSOsDtOjftcUDrBfKETinxXTECLGKtDC/bB/WuBoftgkZ1r52eRIHaytL2nmkHNBkHpNKBOKqf2gPA7lUWwFNOYZoVvEl/Fk6YFF8U8pV4MxPljEVmf2dIxKR2YXT4tD5jEkS9ITDPtZyQsIIkFx/MKY+iDrC1fFYAIRaXJcT7gw55DXih0hufGNpyRVaT7SYQoUse7M46wyGoOfybmI9ExFWWFF/aneHJ3pJoCALZGqTbGDxPkVYVbB3M8fnUIQCuoWVFhmGpecm5Vx8oBSVSLqswWJeJYMIqajov3mcBZgrMrB5ROoTJhASyOnB+7s2b88ypGntPC4eMA/j0A/5uIvAfAH9T7F4Gu5H7LOfffr7vDtRyYc+45aGINzrnPAPjuju/8bQB/e90D09JYGohqlslPw7D0jY0AqNV4gLpixyoOpbNE6gcmfBhzU55pRydngQeLUhWgyQ5BKbFYRCuPzpnDrM8txDyFzjeKBINI24aWqHEira52faaMA24lnQ2bjPtJhFePMjx5VdW2i4C5on2u5PMij1TlcKouVxpHvk0HsVWJo/r7fUuEi2bKUTrnJcuGvdirp798MMegF2N3pGBiJrEHBholMwajcGLWUnMqzjWrptyHYvEq74T6aYQDI0MEdO5EkWo4EuX/8v5cCzhBBDpIYz+Ow4UuD1kwINxiuijxtqvbXpGK56yyb/qiOskKo/1RHFjs9JyyXPt726sQPgtkgmBhIXK1Q+FLrrLKqgiMRsf28WDR6XwIwPeLyCdsl39VRP4GgC9A37nfB6AvIj9o3/+vnXO/c9oOLxzIOuon1rnv/BtjkhXYMV3CdAVx4dzYUEeiURYpS17cmyojZpR6RH4UCbaHy6IXlLmKo26HFVLXNI9dGUhSMUZF6RS1ba04FLBob8uIhqBQPkgkretKypNOp5dEDRgHpenbCXE6jHle4fZRhs1BgleO5nj8ytAzXXQdh0tSvr3PamFij97+ZOGpuvVeNeEezAdFUi+XieS/dTjHuD/G77y4h7fsbGB7uKX3xBP+KTZrkqmWQYiZO7a83izn2GN/DcKqLe8tW30iqau6hJdQZGNRVPj83jHe8diuJyWMRFDGCpWII8G33dj2PPWCGpC7d7LAY1eGyPLK91emSYR+ovf72Cln3HWrhhO7lgUA4axQkdzQETOPS4WjaVZ4LB7nU1jp5gvVr17OI3QSnIsHc85VAP5a68+fC/49wD3ahfdCAvD9bjQ+9C/cmTZ4oULbNKK4mt5Z2Tr3pguUlVLuVJXD7eMFbh1mDcVl2mkQDAAe79O13cSqTKn1W1aVszK/5jio+BwapcK+sj/DUVAsoDPoMtLpAFqFIhRikMZL22h1q+7Je9OjGxARfMsbtpSCOVuWrwvPiUu3rmu1ynbHPb8MyjrArXzQFZ2vjoaMEW99wyaSOML3v+VRvOXGhs9/AS16GVcj0Zdzb0ktJLsin9hPYy991r5mmd3Dp69rBfHtN7aR5SVeujvDK4eZJxcg2r5ywPMHE9+WNl2U+MrdGb755qbvvlgUFfYnCxyZ8ybpYS8WX4Ec9OKGGhHvQbufcXOYNq7FKOgNJU9caLMVc/a12jnBKM7dLjwCa1sIo3izMUusY1Gkic5vvbnVKP8zoRzZcu9wlnvxBW1XkZVObFUUcmTiGmVW4HCqOS+KRiRVhKpawNmxQ2UhntcqJZ6zjNsz8dvOTZ0FG3nceLJW2WSukUOSRksULcRV9dPIs1bcPanVocaDxLBai8Y03h6luLLRWznmw6kCe9spgzvHGa5uKAQDopHOwgQ7wvO8e7LwatnjgcqR7Z0sVOxW9OXCSiFJDwF4gQ1SSgPAzd3l6zNflL46uT9RGp/dcYoP/8kryKsKb7u6jceuDD210N5x5ns8Z4sS86PMd3rkRo9+OM0BYUM5fHJ+t4MmijCK8JyZFxTUClyEHI0tJ8n5fx4wCsGlqMdKO5rlcIbQPi0aOssUF9XzE5Q3/MmrOikZ1ZGK5cpGz4uDFGVxT6DANrK9rJyxg9pb3rQBeczNKA0kuZIGH9i6FmLIVs3JQ3vQBmkTNnAwWXjg72lWOYcI4q9l25Z64lr7c0EeJi8drm/1cWTVwy5Ko8+/fIzdcW9J3QdA7bxgD1C0rDvI5Smca0jjsbd1a5j4Vhs2sassn96L525PfOfCR56/g5/67jfiwJhMWJwQ6Jw5MtFfcrb9O29+FLcO5rqUz7VIsHec4Y2PjHF3kqOX6Crgc3eP8NRijGdubPpOE2olMOEezqcQiR8KF4f3X6wzIVw6bo9Sjz8EapR+cl6e59KBddu4n3jQn1ZnBLeOspXRApkO2tFGXtTyZsRyhRQjhTUFb9hbui5R457SnHRE5BLLreIYMp96gRDrHxymSky3aTiwUMZNVbuLJbQ98Vh0rCG2rK481dqO5DebW3/cKwdz/JtbB/j3v/UmPvvyMd71Tbu+26ErB3YwWWBg4hzhtaJ5NtNgGxY/qcSkiW4Hh7p6qjmpxJSHSsOuKaCzcibqEuyzsusRLpsctIl50HLKVMWmRcFLSq9h5fFluVEscxkZRYIbOwP0jRjgMZtvxAIeTBb6fbsX28P6WnAOPH51aOmKwqqeMe5O1NEwanrX8CriSLB3vLAKeOKT84R/UIGJ0I/pooS02Eno4KZZ4dMqbaYQVnxJ4HiedklouMLmuSaopwtrJxokndxRtMiSzW3jAwRozmNioqFTY+TsJZGHCIQlfAcTkEWhgqxWpSKuhsIifLPx2JklfvOywsRK/l890GT5JNO3aGWT+/ZRho1Bgt/98l08+/Ru4/iR1I3nIornyayBOXQ006zwTqwf/L3OiUUeLpDEytL6HfEuRARvuj72gho8p0VZNd7gbCZnn6OKqhb+O+1KLTmoAKOkQY0QF9QRL8v5hEKED9YbdgaeMoewk36rqkpGVYmMESNVgd5Bb5ktRGlrCr8k7Nv9EXugPcQkqum2OdfKSHzur3R1Aj2y68FjTbNCK7kCADVgeJ6rGEgv0SVfP4kAFxsXvyDLnSdnjONa0IU9ouF1bFxn1C9YRl7ctv2U8B6LtX2dZy7snGAU524X7sCK0iat3bxIaqT1PC996B9a19KLTA8AjBJH/y4Go1AApam4dGzPtzd78zxOS+BVYEIKGZeXvmTdiwWzvMRL+zMcTnNc3exjox97hPkk03McdsgQiehSsqiKeokkQBpF/sFgApxwCj5M5C/jNTma5UhivQ6DtNbafGRbk+aMdcKlIK8HAZKJE9/3GIn4z9leFDoyMiMwf1WUlakZ6YM0X5R1pGlRQ98gEEAzZyeV846PfP/hfSHrA0BcmxIyDtLIN1UT3MnKIBlMbJXpjXRE4axixTjLSwySCM7OSaTGEBLHNUgjWw5qq1CaKEEkwcvboxRwNbCUvF5Tg1vsjmMAyryyKOv9h3nALkocKnitwgYCtaOtKneuMdMD6r8utgrpAC+HRZbQKABOFgElylmWtJYYBFa2iQZDKhbnnJIOxvWySpclzRajXtKkcWFOJYk1WmA7CwB8+pV9v5x1TsP6nXEPgzTGn35qZyWAc2zCsiK1dmBlJf5JpkuFomrq/JFNI2zS7sX1A93WuCwMo0Qm0VBXMC80l1NY3yABxrlR8ZASh9eNLUPcL8kKKeYLwCea66WlVv3YdjWxDgznlvFsPKdhTyu8mdEoTc0psnGc+Sell9FIeLKoKYxCrUsaRUKOrFMiiTWRP+ypIx+ZoMfGIMHQ+mTzQiNt5+reWY+rEwWyOufwubtH2ttpVDublofii+VoVvixsAOA1zO0LkqcvKzWTndEkawtD7iWyZo/r7NdqANjBJGXyv3OEjgfknZZeR1jC9C4Y9uwhYXyYwKd3NMV0AHq8nGfWhXKvQ4kcxNRJHjmxib+g7c9hh1jJlAyPmXf3J8sanYEAziexjcG1KK0jELIBxZu75zzrBSsalWV88ypoY36iX+jt6lwlMlCS2JJHGGYxkpNE/RvMoLQvF3p/z5baOO4g74AmJTfGia6jF+UhpdT53XXGp6fuzP1sJPwegPwyWsyRiRRzV+mVTFd5k+M0ys8F7HtuQysKtdIO2xY7+qtw6zRIA1gaSy55Rm5FEziCJOsMGEPdbp5qS+aKxs9PLVVL9cLy9fujlNMM80/vnQ8xa2DuW9+J7wjZBZxTtMA7TzWuJ/49q9Vcyf87Kz5ta4xYl/n5/W2C11CJpHiYsb9GF9+dYJ5XuFbn9jCl25NcHN3sFJ9aJUV5ghZ2j/NmDRPYnU+XZTOgCaCCR5kshwiOJ4ruJKwgivjFHeOm03Ec3twhz1dRtw6nOPG9sAYPyv04rMpUaJIltgxmKhn7yaXYkx83z7K8IVXT/CFgxP82LNPLe1zYYWHcFsAjZxYiD8Lr1eW17Q1NDaFM2Ikp5fnvJcav0SlHgB42+NbjXFRvDUUGzmc5hik0cp2psm8UMVz1KX+Kxs9HEwW/pzm5mDDMV/b7OOazZO8UMJGLcTU+55mGu2N+7HxedVjck57RgXAsXOonDqtZ25sYu94gSzXl3MlwIt7M7z5xga+9OoE335zx0fRWa4RKgHBLAJNF6sT8SRYDOEfoU2zWpj3fFWJHky78BwY7ZGtvlH4RvflvACNHOi82KbihV1bzoJVrDB/ErIk0Ia9JiSBGLNxP/ZgwmmWITLU/63DOa5v9T3zZiSCr9yd4ZmbmwpunOU+Cqzu4Q25d7LAFXOOQ9MKaDOM7k8WGhUCeNP1Mb75DZuepiW0NhXPXYMGtAV1j+dNOS9Ac219q6ruT9RphdgsMqIC+mDePdHrnJkC97if4Imro85zJP1QaL6zwJDroRMK78OirOXa2mwUldMlKDFTe6ZKNDVdA0EduV8P6IfC+RISSnIM80WJE6sK9g3nVZTOqwSxrW3Qi/GlVyf4pusjvHwwt2KHeKrpTaiDOJhqd4ECd+soinQ6W0Nd1s6tn/MsO1dm1gfUg124AyO25dpm3/clvlYZMVZ7PEtBh6TaZkAvwypTiNQ+muUrhW1rIjvXqAhuDxO8dHeGSAQ3tgc+gfzotvGT2QMWRYJxVMMMpitUiULbbeG4upSut4N9sNVowyqrGkl0c6uJAHcN/zQyh+2gS2smmLUFqGrgj5z1fArq8n4SqYPl0lBE1YdKW8aFdDtsQ+pynOF1JkkiW8YIoNUlqamjO+e5xvgiiiLBMNUlGhWeCIGgc06iGBJg9tq2KOpcX1k5Hw0fm2hMEguuWbtT5WBq5UpLPbXI76XjKb795g5ePpjj+mYft48zzTFGWqneO8788YiL1GMrP50WVKjipHNKsXb5UioAAFxZITubFeoe7GJQ9uvYhTswEfFLtJ6v/IjPhxHOcC8WbtM1MUnTMwkYF4gn8hxSpstYObdU9eR3spzJeueR1Dd2BohFYQOlOQDSYFdVTebHfcSyrPMXmkIDVNFmMi+8XmReakWKy4jJvMDIcGB5WSEWaVyHkUVtzBclseZd6KA2TZEnN6WiQRo36HbYzBxa5YBte1E4w3SJvQxGvRpomkS6X81JVY28FyNSFjciuxfMCQ0Nm7YImqS5lGPTciRAbIUJAF4tnVViHmtm+bst06GcLcqlyOJkXvgIeeaT9pH/N1Mew542eDto5LghNQTHOYc4jrA7jjW3GIlVQh1uH2t/6qKoPIFjiB2s6J3smsNgHP531BVyHp99ppEAQ+NUy4o12HfvwR5UGMWFJvErB+spEwNPlr4ylpgaM8UkTjOCSWlnUTaHMAQmhNsiGeRs6opY5kYFrEtENKqDLEoUJuxAzBFVbgD4at7649Ux5Fbc0OJBc7s4oA0i4tvT2thY+aCwGlhUzueP0jjyDA556XwFcWCVUREVau0FxwzxUb0kwticDc+JUIyBSdQxMb01TFE6eJhA+yWTxMrtJuZQF8Y5P7Cla6jG009Uos4hfMDFGsjryErsOo7M8bB6SbEMWigywmuVl64hd8ZrT961uVUyIbW8H/eRxoKdsVLuRFL3SvIc+d2y6kjMB9eFBRqRoOgntf8V1EK26yhc3Ys1jn3Gz+ttFxqBVU4pVvpxhKKELQVgfE2JOhVHtRpWZ7pBrOEty43RoMsW9mCmNvF5zcvW5NFJKp3HK1395q2cQgfo6DRJXfnIzkHhBOFSc1X6omvcElR3CDSNY/1p0PLwQRXYUqu5LCOGLE0iSFlhbm9saleK1DCLyjlf1OB+FZ7RvB5hZMxoJ5zEAnWssS3lIltejvsx9ie5AjptCcoHmPsPKZKqykGi+m/zXKXg+D069XleeSAu24B6hi+sDEsHqRl+Kwe4ygGoMVOhQ6Xc3SwvkVqEHl6rOFJOtqyofEKfHF8waERkhRbmANMowiyvMLD72U8DeTUxvCL7scCILLjIqB0KjfOJc40fnacbu1xCrjDKoSdx3UvHZVHYyjI3qtyNDofSXuLNrGWGCW2G6cyvxAJsxyrKMV+UfskR2mkYmjAHBMAnggF4nBIALEqHymkkRsVmgjy73lZZUZPc0YjBSi0ZrGDS5bHNFiUiU7lh10FIvzPJSqNZUSc0FEGV6b4953ypXQdhOZyqT3QEIQV4eAqM6KjxGUXmGO0zCgXrOanD2h6lPlXAJRYdGDs0Rv3EO+B5XvnlPwAfoYUvBIrG5haFRsRZVbr840uF9Mtk0m3fWxqjaDoGfr4oDHkfR365Ric+6imodVE6D/eg06nMWU2zEv3UYXuYeiUkQOAIQLWQqnSaZqgcEEXwTeq8RfR1PIdI4B32eUZED+oS8sIdWHhhuPTpSmgzbF7H+EAyscpKEICG0jVwuqM6y+a5LjfCPsbdcQ/Hxk/OhlwmgYnSPrFWo7bj7WK/ICcWAJ9/6YoK2cw77NU9moQGkFMtXKqxiTi0YS/GEN3Xg/nCXlLnmhj9kDGin2pExV5G7l8T07WXENHohr1/J1mJUS9uwALCsc3zshHl0irnMO4lRiLYpOBhhTKJNUomtxchC8Cys+oyFpba92Zk1NaZOUkA1kCees2E2HKI7FfdhGo5iAiSSJ3q/jTHI1t9PH9nWt8nACL19SohPremg3Lg08CkPuEqZfiUnGMI9oD6r4vHgYUPVWq9esTZ7AQqLVq+P31/JMwLcVEnxiIA1JJs69okKxDLakTz0BLqx5aIJgQjfJCU00kdDo/dJZO2ysLm9bPeguFxQ0iCVs9Ws1GEmCmCP8OKbOgMaPyccJXQGWwOmw62fb5tXi4ltFx9Xqtog5hfBLppuU/mhW/IDj8N/00nFEey9OKcZkUD/AzAV05nlqcN5xMj1HA/bLgPaYa0sKAOp3IOz9+Z4o2PjPGlVydIY6s6orkcDxlnfSSMjkgrSKecxT6ytl1Qfmsdu9AkftGRuNTIIAkI7FZvfzxrah12QTC6aFzWtXE/ua8IjeOa5/dGDthlIaC0S1OSkIF7sSzXB4rb7Yx72Bn3fCGEmpKhpNqqQgOZHSbzYql1aR07nuWdgi58iYVjPrZIjhHUlgFet0cpenG0pCi1MVDwLNu97uchXBRNYZXdcQ8nWYF5oewpeVH5ZfDuuOcr6DXluQKgd0ZpIyDS/KPlIGPBl1+d4KlrI89KTIpoLq1rto9aW9JSxJo7RtD0bf89LyR+vdd1fl5fu/AlZJexVH480zc/QabtJRfpcuZ5Cbizl4Msg5+FuboXS2NBEiWNpV5Ywu+aQ9Os8FW/dY0P56iF5WpHCF1GICqPGzaEhzY0mp82nc7JvGiwOQDL15Lbto3b5kUFyLLewLifeEYQCnkA3UR87dwbGW4BLFErk88LUCdAEC3QXFkNUlMTt99JbaOfxRCpkOWlh1cQArLRr3N6YU6Sy+L26DmnCacB9OXkLC+axMCtwzmu2KpjuihRgjlW7cVtLCPRbAezlJl3ZMTonYcJHlxCwwunlGZvHWAPjr3FyXPuZa86HjjmHpxbriLSMkPiA3qjzxLsuFdjWZxj5riYc+rCsFE8416My+iuzVa1QfnPbXw8LuEUcSSNHlCOOxJtPOYYKc7RttDHcNu2sal80Uq200LO95BGacN6BGlhAYGOyEciLNb4gkXh2UkiYbO062ShoIIUtw3hDJHBZKKoZjoZmFBMvOIe9zteSpoH0whV01e6JmvHLXmpfbZ9U3SvU141Fq/rGmrSPsgxghVXObc02IMKo1jLgYnIu0XkI/bvfyIiH7Gf50Tkn9jff11EPm5//411BxBiZsLqClDnxIDV4XBugNFTxr60z/O29o1bGPdV2/iwMrHcZiBoWxU4RxIRdjmJs5poSYvDCmfYOhU2fCsjRelVffw47NqHmClWU087X2+ikIGuiCCkMQpfUv1UKbobEYf9l2MTgRcACV8keaEvwnZFl+faT+tokdJpvDf9tHacRWlYOYOaZMb6SsfYZYRZAPA4Mx5naRM6MmGztL7QFSdWg6tD49IyNNfxN3+sc/JgDy0nvoj8LQA/BmACAM65v2x/3wXw/wH46/bVtwB4m3sNC+8uiMAgVaiDEvUtb0Pg3iqOpC720XWMOJ+zEqGMUoqy1l5UEGuEqHVDi9LBJQ4Ei67CtdH4YJ11DmctnYe92DjhKbhameJT4h0EH1ai+EMHpi0zlDirAb5+ubfifAF9uPpJ7Jc59zL2onRI49Pf7OxBXBjdTs/wfYTSxOYckkh8qBMqXfHasvsgzJdybsUWxfGehey4BKGG957GXkQqCgHaHkSEve0AgP4aQVCiRvuP+okfAyu+zilejEvGRmIf9nf7gyDAkL1We4iXkF8E8KMdf/87AH7ROfeyiDwKYAfA/ykiHxORH77fAbWpQjT3tZovn/iiVdf3fv0pwahnWVFqmTykQ1HB2+VLG058vknPGt95zZuNQZ07a0MqAHi6mF4S+YP6pZT9ThbUeWvJuup8gdrBz/PynttbRv24AVFoJ6eZ5AYMZW9Ly1E/bkRIaRJh1I/90nOSlT5i5HzrivQHadxgSSVHmHN183dbfVtpgODB0qFDJOQhBKmGDSB19RDWR5l7xoywlYv7CVmIw4CLSf8qzOy/RnswU/hrODDn3K8BaJR3ROQRAO8D8I/sTz0APwfgR6DO7u/Zd5ZMRH5CRD4tIp/e27ujhG9xvRQ5nOa++x7Qptuj2ekVrmELQxTaNOCLuhcL+/NOszSJzkyid5kAnmtqlVFW67ytF4y57cgGln+pqpoFIWx8b4tJnGWbw9Qvi+6FfQOom8ABBFXpelwiNQwifFDZ9EzL8uYLhpVTQF9U1PXktm0jTRM/358scDDNlxw5j8ve2K7zcQgrkBoR+1yawHOecVl863COJ66OLOJqvtg1kGMurpXUP8eE1Lr5rwc2B9Zh/xGA/8U5xzv4CoBfcs4VzrlXAfw+gGe6NnTOfdA596xz7tmrV681tA6Bbh6qVcvDo1mOw2l+ahL7fqEQ92Jt+MYqaEBoo36y0ul+rW1hpf8Qy7XRTzDP6yJK+z4Amhw/mCw6tRdXmWLIEuyOe40OBmLPVtnBZIEtU3eaZoU2U6OJIXNOm9S7ovPtUbpWoYQRPHU6l1YAqO8vnVhkUB8y5/Ils47yE4fEb1UOnqSRx6eJKKTihTtTPHlNKYjCRD6dWghmDfcdnaNTqR3l6T+vt92vA/vzAH6j9fs/BQAR2QDwrQA+u86O1lnijXrxSic07seoXM00EArGHpuDu58I7LXYOnHGzGiPuxK1tKKsTnUW4fmG5fkzx2dLDI1mauohjqWLnXYSiOaG265jk6zA0SxvMKe2IQVdptF37imw24/H1jDx7Urhh1tDZVwNUwAUO27bvCXku8oJiahWA+frybzA4TRfEs7YGipJZM+4/8N9bw0Tq4raHLEKplZX9TttqAqg1/u52xPc3B1iYFRHlWvOs6X6gN/H0qnclz2oS8j7DQGeAfAl/uKc+w0Reb+IfBJABeC/cc7dOY8BAlrqLq2VpO3I5qbgw7A71Bgc9uIldezXw+7lRp721lIoyep3TCTw53sW7dAkKwAHL/Qhduw29ZBvfq40tzMeJB7bxO+Q5SG81vMFVXnq8XI7T4Md3Dpyta0ac7gsjyPBoqhzVsSf+UQ6NBdFRSKRZfxdu3o3saQ9WTJo7fvRC6qnHipiS8RebJXDQAmJeDuqLcVR7HNjG4ED9NaKuJxzvjLKXJqILif3ThbYMp6xOu/GbUmG4HykWoSN4K/RLmJ5uI6t5cCcc88BeE/w+9s6vvOf388A1nlB8OFpLwmI/0qM8SBUDQKMpLDQ6hrZGO7XtKm6aiRl8wDiEFo/jddavpxllTNSwRVBmgIg63aaOHKdDnFuAhNUeGLDNaDQAu6fsmxxpFU7gkm1AVyXOUVVawQ06HyiZZhAEguSKFa1o7iJ82pv37aljoPgnHm954vS48eca/J+hRZWTGlkIokiaVRPQzAtOj5XfjA9p8oBkWtKrrHCnMR1e5MzyMzcJNREgqglcIh1Dksrki7Ia6kTV1ETkgFMskbnY6eTOR+/c0loeN+WF5XH0PAGhbQzijjWxGeb06swSEAFd88J5C5r7yNMLBdljZ3qcpRtqhx1cKdPCufU8Q7S+FSKoMq5QH26+3NWGKNIfF+dc873AfaSGptWOUEax16VqAH+zUssWsu+vINFAwhQ94vV1ES0Np0O95sY5CGJBIhrMClQA3SJkcuDJW4aMIQkgQ4j97vqZda+xwTKsqroUN/f0pZ+qXHXZafsl9c3vD/1nJalyIxOrkK9xHSWKtFjRhikzogWxW+j/5XGf1+rCR7cCOzCkfjhddE52rxSUwMkisCDL1n9iaS+SW1ZNUBpXMhB9VoR+F0o/p69/cvqdAbMcMztbc/KW/FqzFugztAoA7fKSbNgQAeTG0MGSRvnRrBIckCe76if+M9Os7MqjF0itEBTASgva1JK/p3SZGyjaTsHEb0u1IMkCn7cT7RrIYBGhNc5vJbEcfGY7Qorr1VImFm5WguS+LjM+MfG/cRznNXjlPoehDmw8DtYdjhcEoYJekAjr3leqkhuJB5O8bVMpH+9VSHPzcILnsTLJXr6s34a+/wGqz6k611l1EzMziEPVhnTRdvyUtk9T4NShGNub3uackwIowhzPl22PUp9YWAdy0tludgy2hvf0tU6BBk9uN+uh2S8JuSkbSfGdQ9oVEXur+OgIjgxrv12QhyoH3pWVcPPw7xdVlQ4CT4Lr2WWK5vIaU33IrXARyTiE/hHM5UCnOdVY5/jQbIyWmaOyrnwZ5mum8cF6uV5WMGcLUrsnSxwc3foW5tCZ0wiyHPK4T+8SPzX08hm2pbsmmQ60bsqkQ416LQLlnBeUIUuWAFQK/wQn0ReLpbYT7O2OlDbKOS6in6niz5olRECUHN5NT8PZebaFlLGnDXmLju2/r4lEZJRzdvG3lcdW70EIgC3HcEC9fnOFyVK51bi0wSr260GrQr3wWSxVIns2pLORFt+ksb5nGXclkh6D8w3XFeYD2M3yHLuXyOvVw7meOzKEC/dnYHsxe32uddsFxRdrWMXHoGFRjBfl2Udb+DDqdLW9E/RDZxmRefkP28L8Umbw9Q7r6KsOuEGs0YlqZsSJ4mjhvM6WoM+aJWRdoZOMRLBtuk5Hk4VzjDJNLJYFY1M+XkrEm3TGrXtrGXNuJ90LvEZVebB0rILCjHoxaeCa0mns8pCSpydca8BwRikcePFQBDsxkDHHJ7ZOrRGxHbRITUiMfBvIZVO9zKS2wLAS3dnuLkz8KSN/CyJ5FyQ+KfBJto/r7dduANbt9UnjZVXvW2reuz8/v3/dds8cCTnZZOsXnJRXXtpXK7ZxN71gBVlk4tq3GrZKVvL2kkLFLzKNCJh6d35MZNqp23HsxzHs9wzk3YpRp+2hBz14qUK5Dp2bNQ100WJLK8anFe02UJ5ws66h2Xl/LUMGWKzvGwwovB8Tqsib9j59tOo4Xg3Bokf82nmI7Dgd/7o7zVbSNhKBTRhE2wcFwB3jjNsjRT0zWV56c4xcnpAPdgDtYQE0JkLAOChEO1l5FkOsJ+oxPs8Lzvf8qtQ/h7Ls+LBpHJSp8hIi/ZkFdbJ462MAYKVVLKfxpF43BagEVd4XOY8aKsohU4zEfEgUcJR6FxnixKjIDpisppKQ4A6vtEZnQ5nyeLN89I3kHstT+hc6KdN5of2nlKDarQfVMrI+TFIXT0cBIwT1AcINz8LAjOzucTcVIiZGxiLRjh3nFNpPNowVaX2rKhqGmjn6kbs4FyJ6eL5ted7ZRFaXgJH09zT/aziJbtfe1BhFBcfgYX/PuUBbKsMz3NtPj4Lc5UEyjcrP+9wQso8sHrcp73ZQhqgdSwSQcyfYDvFbC1X3lYZlyLrWOVqtaTKsHQh4wHHpd91gXNDQ9bM48mMDaJtbUqcLmtTzXD53U9Uxq0X8JGR1oamgsTL592WawvxY2GUyUhn1QhDShwaucUoj8cKHOl2Qjk3f45R3a/ITobwG671X/7bR+loOu/2uTEXTCqeVQST92thlHjaz+ttF+7Azup+KUwTsO0QitJhaEuTs/iwNNy/NxjFWTe/n3ZDAwCYmO16xyHMo58qIjwkEgyNzqHruPysC0zadTxP3ldqYaQsnYcrVA6+JM/otLCIknir8F4MTTeyS/hWx6bbhlCFtrUj3Xr5LQEUotZsJN4v/D1b4UDXMQJNuc8uSpz22MpKj1vYtRpZL2leOu8guZQTkZrpA/ri4D7DS9IGs3Y5uHYVmOkAQO//ybzAoqiwOUjOlwjgHJaQIhKJyC+JyO8Yb+CbW5//p0b08Ml1GW0ufAmZtCKOtmV5Zf1uUeMBGPViD+xLLR/RtX2YZ7oXey0vk3leYZBGK5enoYW9dYBO6M1BgjQR/7mIqmhvDZff7GxT2R6dzorB4xArlRcVxtbawnYhEUPLWx6F+x316iVXmnSf18peVRsTUepn6ROEx9X2G42QKFLC8TqnuTEuN6n63SW7x/NfNT/iqKbimRprauO4wfdDxfCuF02o6i12XTjWNuZPl4eoq4/OaKBZhfUMX7WTKqru+dyLBaWr86Iigivj9NwWfue0hPwRAAPn3J8RkfdAGWz+IgCIyA0A/xmAZwEMAHxMRP5f51x22g4vPALbGjYTwO05MR6obJZD/TAAmog9muU+v7UqicsE8L3aa8kdrEvFkxcVjm2y7ZggxM6oWcE8NsfW1WRMwYh17GhW+IrjvKP6yTFneYVpVjZgIyGtjeKx7r3oMerfu7hKmxkivB5Hs6LhEJJ4tTZAm04ntHleNbB4ZylGOec8BVSWV415F451GMAz2LzOpR5Nl11SU+tY3jOy6IvEkfxhr6ourQW9WHOkZOrtmQr49kiVwI/nxbnw4gvODcj6PQD+uV2rT0KdFe07AXzcOZc55w4BfAHAt521wwuNwP7Nv/rMnVEvmgA4t8bvB9Su4ev/HIFvjPN82M7xqde6g8985vc+PEzl2ppfH4jIp4PfP+ic+6D9ewvAYfBZKSKJc67o+OwYwPZZB7tQB+acuy4in3bOPXv2tx9e+0Y4R+Ab4zy/Ec6xbc65HzinXR0B2Ax+j8x5dX22CeDgrB1e+BLy0i7t0r5h7OMA/gIAWA7sD4LPPgXgz4rIQES2AXwLgD88a4cXnsS/tEu7tG8Y+xCA7xeRT0BTa39VRP4GgC845/6ZiPwCgI9CA6ufcc7Nz9rhg+DAPnj2Vx56+0Y4R+Ab4zy/Ec7xa2LOuQrAX2v9+XPB578M4JfvZZ+ybivPpV3apV3ag2aXObBLu7RLe2jtQhzYWYjch91E5DNSq5f/QxF5j4j8S1Hl8g9c9Phei0lTpf3NojqgHxWRvy8ikf39AyLyKRH5hIh854UO+D6tdZ7vEJGvBPf0L9nfH/rzfNjtonJgP4IViNyH3URkAF2avzf4278C8B9ChVD+bxF5h3Pu9y9mhPdv0lJpB/DzAP5b59xHROSXAPxFEXkewPcBeDeAJwD8GoB3XcR479c6zvOdAH7eOfdzwXe+Aw/5eX492EUtIU9D5D7s9qcBjETkN0Xkt0TkewH0nXNfdJpw/DBUhu5htLZK+zsB/Av7929Az+t7APymU3sBQCIi11/fYb5m6zrPHxKR3xaRXxGRTXx9nOdDbxflwDoRuRc0lvO2KYD/AcD7oRWXf2h/o62FMH4QzS2rtIurq0A8r/tCVD9I1nGenwLwN51z3wuNoj+Ar4Pz/Hqwi3JgpyFyH3b7PID/2d7Mn4dO8ivB52shjB8SC5tMeV73hah+wO1Dzrnf478BvANfn+f50NlFObDTELkPu/04NKcHEbkJYARgIiJvEu3Gfj8UrPf1YL8vIu+1f/8g9Lw+DuD9Vqh5Evpyeph6B7vsw0GS/n0Afg9fn+f50NlFLduWELkXNI6vhf0KgH8kIh+Dklr8ODRS+ccAYmje5F9e4PjO0/4LAL8sIj0AnwXwvzvnShH5KIDfgb4gf/oiB3hO9pMAflFEcgCvAPgJ59zR1+F5PnR2CWS9tEu7tIfWLoGsl3Zpl/bQ2qUDu7RLu7SH1i4d2KVd2qU9tHbpwC7t0i7tobVLB3Zpl3ZpD61dOrBLu7RLe2jt0oFd2qVd2kNrlw7s0i7t0h5a+/8By5Pa7YS9fFwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(df_results_with_ligand_matrix.loc[0,'matrix_N'],cmap='Blues')\n",
    "plt.colorbar()\n",
    "file_name=\"output/%s_Normalised_Matrix.png\"%(df_results_with_ligand_matrix.loc[0,'uniprot_id'])\n",
    "plt.savefig(file_name,dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the list of top coevolution pairs\n",
    "\n",
    "Residues with scores close to 1 have the strongest evolutionary relationship, and close to 0 may not be covarying during evolution.\n",
    "\n",
    "The top scoring residues are sorted in descending order and saved to an ASCII file for further interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved file : output/P01111_Pairs.txt\n",
      "Saved file : output/P01112_Pairs.txt\n",
      "Saved file : output/P01116_Pairs.txt\n"
     ]
    }
   ],
   "source": [
    "obj_com_analysis.save_top_scoring_residue_pairs(df_results_with_ligand_matrix,data_folder=\"output\",matrix_type=\"matrix_N\",res_gap=5,percentile=95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Help on UniProt Controlled Vocabulary \n",
    "\n",
    "For each biological feature category, UniProtKB/Swiss-Prot has a curated list of [keywords](https://www.uniprot.org/help/controlled_vocabulary). To search using those keywords or ID's and help you find them we have some helper functions that will help you find them:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function `get_cofactor_list()` from `pycom` will get you list of cofactors. You can either use the `cofactorId` or the `cofactorName`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>cofactorId</th>\n",
       "      <th>cofactorName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEBI:597326</td>\n",
       "      <td>pyridoxal 5'-phosphate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEBI:18420</td>\n",
       "      <td>Mg(2+)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEBI:60240</td>\n",
       "      <td>a divalent metal cation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEBI:30413</td>\n",
       "      <td>heme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEBI:29105</td>\n",
       "      <td>Zn(2+)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>CHEBI:61721</td>\n",
       "      <td>chlorophyll b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>CHEBI:73095</td>\n",
       "      <td>divinyl chlorophyll a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>CHEBI:73096</td>\n",
       "      <td>divinyl chlorophyll b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>CHEBI:57453</td>\n",
       "      <td>(6S)-5,6,7,8-tetrahydrofolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>CHEBI:30402</td>\n",
       "      <td>tungstopterin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cofactorId                   cofactorName\n",
       "0    CHEBI:597326         pyridoxal 5'-phosphate\n",
       "1     CHEBI:18420                         Mg(2+)\n",
       "2     CHEBI:60240        a divalent metal cation\n",
       "3     CHEBI:30413                           heme\n",
       "4     CHEBI:29105                         Zn(2+)\n",
       "..            ...                            ...\n",
       "109   CHEBI:61721                  chlorophyll b\n",
       "110   CHEBI:73095          divinyl chlorophyll a\n",
       "111   CHEBI:73096          divinyl chlorophyll b\n",
       "112   CHEBI:57453  (6S)-5,6,7,8-tetrahydrofolate\n",
       "113   CHEBI:30402                  tungstopterin\n",
       "\n",
       "[114 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of cofactors\n",
    "cofactors = obj_pycom.get_cofactor_list()\n",
    "cofactors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function `get_disease_list()` from `pycom` will get you list of diseases. You can either use the `diseaseId` or the `diseaseName`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>diseaseId</th>\n",
       "      <th>diseaseName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DI-04420</td>\n",
       "      <td>Intellectual developmental disorder, autosomal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DI-00085</td>\n",
       "      <td>Alzheimer disease 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DI-00097</td>\n",
       "      <td>Cerebral amyloid angiopathy, APP-related</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DI-00262</td>\n",
       "      <td>Chanarin-Dorfman syndrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DI-01042</td>\n",
       "      <td>Spastic paraplegia 42, autosomal dominant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6039</th>\n",
       "      <td>DI-05800</td>\n",
       "      <td>Wieacker-Wolff syndrome, female-restricted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6040</th>\n",
       "      <td>DI-01041</td>\n",
       "      <td>Spastic paraplegia 33, autosomal dominant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6041</th>\n",
       "      <td>DI-05703</td>\n",
       "      <td>Neurodevelopmental disorder with dysmorphic fa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6042</th>\n",
       "      <td>DI-06050</td>\n",
       "      <td>Intellectual developmental disorder, autosomal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6043</th>\n",
       "      <td>DI-04662</td>\n",
       "      <td>Paget disease of bone 6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6044 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     diseaseId                                        diseaseName\n",
       "0     DI-04420  Intellectual developmental disorder, autosomal...\n",
       "1     DI-00085                                Alzheimer disease 1\n",
       "2     DI-00097           Cerebral amyloid angiopathy, APP-related\n",
       "3     DI-00262                          Chanarin-Dorfman syndrome\n",
       "4     DI-01042          Spastic paraplegia 42, autosomal dominant\n",
       "...        ...                                                ...\n",
       "6039  DI-05800         Wieacker-Wolff syndrome, female-restricted\n",
       "6040  DI-01041          Spastic paraplegia 33, autosomal dominant\n",
       "6041  DI-05703  Neurodevelopmental disorder with dysmorphic fa...\n",
       "6042  DI-06050  Intellectual developmental disorder, autosomal...\n",
       "6043  DI-04662                            Paget disease of bone 6\n",
       "\n",
       "[6044 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of diseases\n",
    "diseases = obj_pycom.get_disease_list()\n",
    "diseases\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function `get_organism_list()` from `pycom` will get you list of diseases. You can either use the `organismId` or the `nameScientific` or `nameCommon` or any categories in the `taxonomy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>organismId</th>\n",
       "      <th>nameScientific</th>\n",
       "      <th>nameCommon</th>\n",
       "      <th>taxonomy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>561445</td>\n",
       "      <td>African swine fever virus (isolate Pig/Kenya/K...</td>\n",
       "      <td>ASFV</td>\n",
       "      <td>:Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10500</td>\n",
       "      <td>African swine fever virus (isolate Tick/Malawi...</td>\n",
       "      <td>ASFV</td>\n",
       "      <td>:Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>561443</td>\n",
       "      <td>African swine fever virus (isolate Tick/South ...</td>\n",
       "      <td>ASFV</td>\n",
       "      <td>:Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>561444</td>\n",
       "      <td>African swine fever virus (isolate Warthog/Nam...</td>\n",
       "      <td>ASFV</td>\n",
       "      <td>:Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10498</td>\n",
       "      <td>African swine fever virus (strain Badajoz 1971...</td>\n",
       "      <td>Ba71V</td>\n",
       "      <td>:Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14316</th>\n",
       "      <td>31581</td>\n",
       "      <td>Rotavirus A (isolate RVA/Pig/Australia/TFR-41/...</td>\n",
       "      <td>RV-A</td>\n",
       "      <td>:Viruses:Riboviria:Orthornavirae:Duplornaviric...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14317</th>\n",
       "      <td>31579</td>\n",
       "      <td>Rotavirus A (isolate RVA/Pig/Australia/BEN144/...</td>\n",
       "      <td>RV-A</td>\n",
       "      <td>:Viruses:Riboviria:Orthornavirae:Duplornaviric...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14318</th>\n",
       "      <td>10918</td>\n",
       "      <td>Rotavirus A (strain RVA/Pig/Russia/K/1987)</td>\n",
       "      <td>RV-A</td>\n",
       "      <td>:Viruses:Riboviria:Orthornavirae:Duplornaviric...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14319</th>\n",
       "      <td>31580</td>\n",
       "      <td>Rotavirus A (isolate RVA/Pig/Australia/BMI-1/1...</td>\n",
       "      <td>RV-A</td>\n",
       "      <td>:Viruses:Riboviria:Orthornavirae:Duplornaviric...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14320</th>\n",
       "      <td>47664</td>\n",
       "      <td>Populus tremula x Populus tremuloides</td>\n",
       "      <td>Hybrid aspen</td>\n",
       "      <td>:Eukaryota:Viridiplantae:Streptophyta:Embryoph...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14321 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      organismId                                     nameScientific  \\\n",
       "0         561445  African swine fever virus (isolate Pig/Kenya/K...   \n",
       "1          10500  African swine fever virus (isolate Tick/Malawi...   \n",
       "2         561443  African swine fever virus (isolate Tick/South ...   \n",
       "3         561444  African swine fever virus (isolate Warthog/Nam...   \n",
       "4          10498  African swine fever virus (strain Badajoz 1971...   \n",
       "...          ...                                                ...   \n",
       "14316      31581  Rotavirus A (isolate RVA/Pig/Australia/TFR-41/...   \n",
       "14317      31579  Rotavirus A (isolate RVA/Pig/Australia/BEN144/...   \n",
       "14318      10918         Rotavirus A (strain RVA/Pig/Russia/K/1987)   \n",
       "14319      31580  Rotavirus A (isolate RVA/Pig/Australia/BMI-1/1...   \n",
       "14320      47664              Populus tremula x Populus tremuloides   \n",
       "\n",
       "         nameCommon                                           taxonomy  \n",
       "0              ASFV  :Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...  \n",
       "1              ASFV  :Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...  \n",
       "2              ASFV  :Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...  \n",
       "3              ASFV  :Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...  \n",
       "4             Ba71V  :Viruses:Varidnaviria:Bamfordvirae:Nucleocytov...  \n",
       "...             ...                                                ...  \n",
       "14316          RV-A  :Viruses:Riboviria:Orthornavirae:Duplornaviric...  \n",
       "14317          RV-A  :Viruses:Riboviria:Orthornavirae:Duplornaviric...  \n",
       "14318          RV-A  :Viruses:Riboviria:Orthornavirae:Duplornaviric...  \n",
       "14319          RV-A  :Viruses:Riboviria:Orthornavirae:Duplornaviric...  \n",
       "14320  Hybrid aspen  :Eukaryota:Viridiplantae:Streptophyta:Embryoph...  \n",
       "\n",
       "[14321 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of organisms\n",
    "organisms = obj_pycom.get_organism_list()\n",
    "organisms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acetylcholine receptor inhibiting toxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Actin-binding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Activator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acyltransferase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allosteric enzyme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Viral short tail ejection system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Viral exotoxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Chloride channel impairing toxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Proton-gated sodium channel impairing toxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Translocase</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            name\n",
       "0        Acetylcholine receptor inhibiting toxin\n",
       "1                                  Actin-binding\n",
       "2                                      Activator\n",
       "3                                Acyltransferase\n",
       "4                              Allosteric enzyme\n",
       "..                                           ...\n",
       "191             Viral short tail ejection system\n",
       "192                               Viral exotoxin\n",
       "193             Chloride channel impairing toxin\n",
       "194  Proton-gated sodium channel impairing toxin\n",
       "195                                  Translocase\n",
       "\n",
       "[196 rows x 1 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#full list of helper functions to get searchable terms on other biological categories\n",
    "obj_pycom.get_biological_process_list()\n",
    "obj_pycom.get_cellular_component_list()\n",
    "obj_pycom.get_developmental_stage_list()\n",
    "obj_pycom.get_domain_list()\n",
    "obj_pycom.get_ligand_list()\n",
    "obj_pycom.get_ptm_list()\n",
    "obj_pycom.get_molecular_function_list()"
   ]
  }
 ],
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