Evaluating the merged model/Coverage.ipynb

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    "home = \"persistent/\" # home path for Logic Model Merger\n",
    "model1 = \"Bonzanni2013\"\n",
    "model2 = \"Krumsiek2011\"\n",
    "# model1 = \"Palma2021\"\n",
    "# model2 = \"Ikonomi2020\""
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       "    div#ginsim-toolbar:before {\n",
       "        content: \"GINsim\";\n",
       "        float: left;\n",
       "        margin-left: 1em;\n",
       "        margin-right: 1em;\n",
       "        margin-top: 0.2em;\n",
       "    }\n",
       "    div#ginsim-toolbar,\n",
       "    div#ginsim-toolbar button {\n",
       "        color: blue;\n",
       "    }\n",
       "    </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ginsim\n",
    "import biolqm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xml.etree.ElementTree as ET # for parse the SBML file\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dd0016ef-bb53-4390-ab0d-51151b016bf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SPI1', 'GATA1', 'GATA2', 'TAL1', 'ZFPM1', 'FLI1', 'ERG', 'CBFA2T3', 'RUNX1', 'SMAD6', 'HHEX']\n",
      "['GATA2', 'GATA1', 'ZFPM1', 'KLF1', 'FLI1', 'TAL1', 'CEBPA', 'SPI1', 'JUN', 'EGR1', 'EGR2', 'NAB1', 'GFI1']\n"
     ]
    }
   ],
   "source": [
    "# Get genes in each model\n",
    "def getnodes(model_name):\n",
    "    # Load and parse the SBML file\n",
    "    tree = ET.parse(home + \"LogicModelMerger/Models/\" + model_name + \".sbml\")\n",
    "    root = tree.getroot()\n",
    "    \n",
    "    # Define the namespace for SBML Level 3 Version 1 Core and Qual\n",
    "    ns = {\n",
    "        'sbml': 'http://www.sbml.org/sbml/level3/version1/core',\n",
    "        'qual': 'http://www.sbml.org/sbml/level3/version1/qual/version1'\n",
    "    }\n",
    "    \n",
    "    # Find all qualitativeSpecies in the model\n",
    "    qual_species_list = root.findall('.//qual:qualitativeSpecies', ns)\n",
    "    \n",
    "    # Extract the IDs of the qualitativeSpecies\n",
    "    qual_species_ids = []\n",
    "    for species in qual_species_list:\n",
    "        species_id = species.attrib.get('{http://www.sbml.org/sbml/level3/version1/qual/version1}id')\n",
    "        if species_id:\n",
    "            qual_species_ids.append(species_id)\n",
    "\n",
    "    print(qual_species_ids)\n",
    "    return qual_species_ids\n",
    "\n",
    "gene_1 = getnodes(model1)\n",
    "gene_2 = getnodes(model2)\n",
    "gene_merged = list(set(gene_1 + gene_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e12e5e2-ee31-481a-864f-be699a44a458",
   "metadata": {},
   "source": [
    "# BeatAML\n",
    "805 patients (942 specimens) in total. \n",
    "> For DNA sequencing, we used the 11.9 megabase custom capture library that was developed to provide coverage of all variants previously reported in AML (including all variants that were detected from exome sequencing in our prior study).\n",
    "\n",
    "## Data\n",
    "Mutation, clinical data and drug response data are obtained from the [BeatAML2 website](https://biodev.github.io/BeatAML2/):\n",
    "1. [WES/targeted Sequencing Mutation Calls](https://github.com/biodev/beataml2.0_data/raw/main/beataml_wes_wv1to4_mutations_dbgap.txt)\n",
    "2. [Clinical Summary](https://github.com/biodev/beataml2.0_data/raw/main/beataml_wv1to4_clinical.xlsx)\n",
    "3. [Inhibitor AUC values](https://github.com/biodev/beataml2.0_data/raw/main/beataml_probit_curve_fits_v4_dbgap.txt)\n",
    "4. [Inhibitor Families](https://github.com/biodev/beataml2.0_data/raw/main/beataml_drug_families.xlsx)\n",
    "\n",
    "## Patient Coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a0dfe387-a5cc-41bf-9760-4fb39a151661",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data\n",
    "mutations = pd.read_csv(\"https://github.com/biodev/beataml2.0_data/raw/main/beataml_wes_wv1to4_mutations_dbgap.txt\", sep='\\t')\n",
    "mapping = pd.read_excel(\"https://github.com/biodev/beataml2.0_data/raw/main/beataml_wv1to4_clinical.xlsx\")\n",
    "\n",
    "def patient_count(gene_list):\n",
    "    # Filter the mutations data to include only the genes in the model\n",
    "    mutations_filtered = mutations[mutations['symbol'].isin(gene_list)]\n",
    "    \n",
    "    # Merge the mutations data with the sample-to-patient mapping\n",
    "    merged_data = mutations_filtered.merge(mapping, left_on='dbgap_sample_id', right_on='dbgap_dnaseq_sample')\n",
    "    \n",
    "    # Count the number of unique patients with mutations in each gene\n",
    "    patient_counts = merged_data.groupby('symbol')['dbgap_subject_id'].nunique()\n",
    "    \n",
    "    # Calculate the total number of unique patients\n",
    "    total_patients = patient_counts.sum()\n",
    "    \n",
    "    # Print the results\n",
    "    print(f\"Total number of unique patients with mutations in the genes of interest: {total_patients}\")\n",
    "    return patient_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "64f7672d-352e-4c20-a7e4-c54efb7a60f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of patients in the BeatAML dataset: 805\n",
      "Total number of unique patients with mutations in the genes of interest: 135\n",
      "Total number of unique patients with mutations in the genes of interest: 89\n",
      "Total number of unique patients with mutations in the genes of interest: 185\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total number of patients in the BeatAML dataset: {mapping['dbgap_subject_id'].nunique()}\")\n",
    "patient_counts_1 = patient_count(gene_1)\n",
    "patient_counts_2 = patient_count(gene_2)\n",
    "patient_counts_merged = patient_count(gene_merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f97bcd3d-ffdd-4908-a9fc-41a70656481e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique patients with mutations in each model:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_65752 .row0 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "#T_65752_row0_col0, #T_65752_row0_col1, #T_65752_row4_col0, #T_65752_row4_col1, #T_65752_row9_col0, #T_65752_row9_col1, #T_65752_row10_col0, #T_65752_row10_col1, #T_65752_row12_col0, #T_65752_row12_col1 {\n",
       "  background-color: blue;\n",
       "}\n",
       "#T_65752_row1_col0, #T_65752_row1_col1, #T_65752_row2_col0, #T_65752_row2_col1, #T_65752_row3_col0, #T_65752_row3_col1, #T_65752_row8_col0, #T_65752_row8_col1 {\n",
       "  background-color: orange;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_65752\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_65752_level0_col0\" class=\"col_heading level0 col0\" >Bonzanni2013</th>\n",
       "      <th id=\"T_65752_level0_col1\" class=\"col_heading level0 col1\" >Krumsiek2011</th>\n",
       "      <th id=\"T_65752_level0_col2\" class=\"col_heading level0 col2\" >Merged Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >symbol</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row0\" class=\"row_heading level0 row0\" >CBFA2T3</th>\n",
       "      <td id=\"T_65752_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_65752_row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "      <td id=\"T_65752_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row1\" class=\"row_heading level0 row1\" >CEBPA</th>\n",
       "      <td id=\"T_65752_row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "      <td id=\"T_65752_row1_col1\" class=\"data row1 col1\" >43</td>\n",
       "      <td id=\"T_65752_row1_col2\" class=\"data row1 col2\" >43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row2\" class=\"row_heading level0 row2\" >EGR1</th>\n",
       "      <td id=\"T_65752_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "      <td id=\"T_65752_row2_col1\" class=\"data row2 col1\" >3</td>\n",
       "      <td id=\"T_65752_row2_col2\" class=\"data row2 col2\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row3\" class=\"row_heading level0 row3\" >EGR2</th>\n",
       "      <td id=\"T_65752_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "      <td id=\"T_65752_row3_col1\" class=\"data row3 col1\" >3</td>\n",
       "      <td id=\"T_65752_row3_col2\" class=\"data row3 col2\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row4\" class=\"row_heading level0 row4\" >ERG</th>\n",
       "      <td id=\"T_65752_row4_col0\" class=\"data row4 col0\" >3</td>\n",
       "      <td id=\"T_65752_row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "      <td id=\"T_65752_row4_col2\" class=\"data row4 col2\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row5\" class=\"row_heading level0 row5\" >FLI1</th>\n",
       "      <td id=\"T_65752_row5_col0\" class=\"data row5 col0\" >1</td>\n",
       "      <td id=\"T_65752_row5_col1\" class=\"data row5 col1\" >1</td>\n",
       "      <td id=\"T_65752_row5_col2\" class=\"data row5 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row6\" class=\"row_heading level0 row6\" >GATA1</th>\n",
       "      <td id=\"T_65752_row6_col0\" class=\"data row6 col0\" >3</td>\n",
       "      <td id=\"T_65752_row6_col1\" class=\"data row6 col1\" >3</td>\n",
       "      <td id=\"T_65752_row6_col2\" class=\"data row6 col2\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row7\" class=\"row_heading level0 row7\" >GATA2</th>\n",
       "      <td id=\"T_65752_row7_col0\" class=\"data row7 col0\" >34</td>\n",
       "      <td id=\"T_65752_row7_col1\" class=\"data row7 col1\" >34</td>\n",
       "      <td id=\"T_65752_row7_col2\" class=\"data row7 col2\" >34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row8\" class=\"row_heading level0 row8\" >NAB1</th>\n",
       "      <td id=\"T_65752_row8_col0\" class=\"data row8 col0\" >0</td>\n",
       "      <td id=\"T_65752_row8_col1\" class=\"data row8 col1\" >1</td>\n",
       "      <td id=\"T_65752_row8_col2\" class=\"data row8 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row9\" class=\"row_heading level0 row9\" >RUNX1</th>\n",
       "      <td id=\"T_65752_row9_col0\" class=\"data row9 col0\" >91</td>\n",
       "      <td id=\"T_65752_row9_col1\" class=\"data row9 col1\" >0</td>\n",
       "      <td id=\"T_65752_row9_col2\" class=\"data row9 col2\" >91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row10\" class=\"row_heading level0 row10\" >SMAD6</th>\n",
       "      <td id=\"T_65752_row10_col0\" class=\"data row10 col0\" >1</td>\n",
       "      <td id=\"T_65752_row10_col1\" class=\"data row10 col1\" >0</td>\n",
       "      <td id=\"T_65752_row10_col2\" class=\"data row10 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row11\" class=\"row_heading level0 row11\" >ZFPM1</th>\n",
       "      <td id=\"T_65752_row11_col0\" class=\"data row11 col0\" >1</td>\n",
       "      <td id=\"T_65752_row11_col1\" class=\"data row11 col1\" >1</td>\n",
       "      <td id=\"T_65752_row11_col2\" class=\"data row11 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65752_level0_row12\" class=\"row_heading level0 row12\" >Total</th>\n",
       "      <td id=\"T_65752_row12_col0\" class=\"data row12 col0\" >135</td>\n",
       "      <td id=\"T_65752_row12_col1\" class=\"data row12 col1\" >89</td>\n",
       "      <td id=\"T_65752_row12_col2\" class=\"data row12 col2\" >185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fffca0ac550>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the patient counts for each gene in each model and in the merged model using a bar plot\n",
    "patient_counts_df = pd.DataFrame({\n",
    "    model1: patient_counts_1,\n",
    "    model2: patient_counts_2,\n",
    "    'Merged Model': patient_counts_merged\n",
    "}).fillna(0)\n",
    "\n",
    "# Convert the patient counts to integers\n",
    "patient_counts_df[[model1, model2, 'Merged Model']] = patient_counts_df[[model1, model2, 'Merged Model']].astype(int)\n",
    "\n",
    "# Define the styling function\n",
    "def highlight_difference(row):\n",
    "    color = ''\n",
    "    if row[model1] - row[model2] > 0:\n",
    "        color = 'background-color: blue'\n",
    "    elif row[model1] - row[model2] < 0:\n",
    "        color = 'background-color: orange'\n",
    "    styles = ['' for _ in row.index]  # Default no background color\n",
    "    styles[row.index.get_loc(model1)] = color  \n",
    "    styles[row.index.get_loc(model2)] = color  \n",
    "    return styles\n",
    "\n",
    "# Apply the styling to the DataFrame\n",
    "styled_df = patient_counts_df.style.apply(highlight_difference, axis=1, subset=[model1, model2])\n",
    "\n",
    "# Display the styled DataFrame with a sum row at the bottom\n",
    "styled_df.format(\"{:.0f}\")\n",
    "styled_df.set_table_styles([{\n",
    "    'selector': '.row0',\n",
    "    'props': 'font-weight: bold;'\n",
    "}], overwrite=False)\n",
    "\n",
    "# Add a row for the sum at the bottom\n",
    "patient_counts_df.loc['Total'] = patient_counts_df.sum(numeric_only=True)\n",
    "\n",
    "print(f\"Total number of unique patients with mutations in each model:\")\n",
    "styled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2b0791c9-ac8e-40c1-a269-dd80098cb212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Increased Coverage:89\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the unique and shared patients\n",
    "patient_counts_df['Shared'] = np.minimum(patient_counts_df[model1], patient_counts_df[model2])\n",
    "patient_counts_df['Model1 Only'] = patient_counts_df[model1] - patient_counts_df['Shared']\n",
    "patient_counts_df['Model2 Only'] = patient_counts_df[model2] - patient_counts_df['Shared']\n",
    "\n",
    "# Sort the DataFrame based on the total patients for better visualization\n",
    "patient_counts_df = patient_counts_df.sort_values(by=[model1, model2], ascending=True)\n",
    "patient_counts_df = patient_counts_df[patient_counts_df.index != 'Total'] # Remove 'Total' row\n",
    "\n",
    "# Create horizontal bar plots\n",
    "plt.figure(figsize=(10, 8))\n",
    "bars_1 = plt.barh(patient_counts_df.index, patient_counts_df['Model1 Only'], color='blue', edgecolor='gray', label= model1 + ' Model Only')\n",
    "bars_shared = plt.barh(patient_counts_df.index, patient_counts_df['Shared'], color='green', edgecolor='gray', label='Shared by Both Models')\n",
    "bars_2 = plt.barh(patient_counts_df.index, patient_counts_df['Model2 Only'], color='orange', edgecolor='gray', label=model2 + ' Model Only')\n",
    "\n",
    "# Annotate the number of patients\n",
    "for bars in [bars_1, bars_shared, bars_2]:\n",
    "    for bar in bars:\n",
    "        width = bar.get_width()\n",
    "        if width > 0:\n",
    "            plt.text(width + bar.get_x(), bar.get_y() + bar.get_height()/2,\n",
    "                     f'{int(width)}', ha='left', va='center')\n",
    "\n",
    "plt.xlabel('Number of Patients')\n",
    "plt.ylabel('Genes')\n",
    "plt.title('Cumulative Histogram of Patients with Mutations in the BeatAML Dataset')\n",
    "plt.yticks(ticks=np.arange(len(patient_counts_df)), labels=patient_counts_df.index, rotation=0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "print('Total Increased Coverage:' + str(int(patient_counts_df[model2].sum())))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09dfd02-8698-4882-94e0-46d05c6d0539",
   "metadata": {},
   "source": [
    "## Drug Coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "054a0a4e-82d6-4bf8-a689-552fd14e240c",
   "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>inhibitor</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Bonzanni2013</th>\n",
       "      <th>Krumsiek2011</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [inhibitor, Symbol, Bonzanni2013, Krumsiek2011]\n",
       "Index: []"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data\n",
    "file_path_drugs = 'https://github.com/biodev/beataml2.0_data/raw/main/beataml_drug_families.xlsx'\n",
    "df_drugs = pd.read_excel(file_path_drugs, sheet_name='drug_gene', usecols=['inhibitor', 'Symbol'])\n",
    "\n",
    "# Count the number of unique patients with mutations in each gene\n",
    "drug_1 = df_drugs[df_drugs['Symbol'].isin(gene_1)].copy()\n",
    "drug_2 = df_drugs[df_drugs['Symbol'].isin(gene_2)].copy()\n",
    "\n",
    "drug_1[model1] = 1\n",
    "drug_2[model2] = 1\n",
    "combined_df = pd.concat([drug_1, drug_2])\n",
    "\n",
    "# Group by 'inhibitor' and 'Symbol', and aggregate by max to indicate presence\n",
    "combined_df = combined_df.groupby(['inhibitor', 'Symbol'], as_index=False).max()\n",
    "\n",
    "# Fill missing values with 0\n",
    "combined_df = combined_df.fillna(0)\n",
    "\n",
    "combined_df[[model1, model2]] = combined_df[[model1, model2]].astype(int)\n",
    "combined_df = combined_df.sort_values(by='Symbol')\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bdf30b8f-7956-4241-9723-9a646f3e42cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df.to_csv('beataml_coverage_drug.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc9ad13-d801-48d8-a279-7f68f08a353d",
   "metadata": {},
   "source": [
    "# TCGA - LAML\n",
    "The TCGA AML dataset, consisting of genomic data of 200 clinically annotated adult cases of de novo AML patients. \n",
    "> Fifty cases were characterized by whole-genome sequencing while for the remaining 150 only the exomes were sequenced.\n",
    "\n",
    "## Data\n",
    "Mutation and clinical data are obtained from the [NIH GDC website](https://gdc.cancer.gov/about-data/publications/laml_2012):\n",
    "1. [Supplemental Table 06: All somatic mutations with annotation and readcounts from DNA and RNA sequencing](SupplementalTable06.tsv)\n",
    "2. [Patient Clinical Data](https://portal.gdc.cancer.gov/files/c07a64a0-7588-4653-95ef-982b41a1a804?aeTable_offset=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e828c3a7-6abc-482c-9258-7edab38aadb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique patients with mutations in each model:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  background-color: blue;\n",
       "}\n",
       "#T_49b9c_row2_col0, #T_49b9c_row2_col1, #T_49b9c_row3_col0, #T_49b9c_row3_col1, #T_49b9c_row6_col0, #T_49b9c_row6_col1, #T_49b9c_row9_col0, #T_49b9c_row9_col1, #T_49b9c_row11_col0, #T_49b9c_row11_col1, #T_49b9c_row12_col0, #T_49b9c_row12_col1, #T_49b9c_row15_col0, #T_49b9c_row15_col1, #T_49b9c_row17_col0, #T_49b9c_row17_col1 {\n",
       "  background-color: orange;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_49b9c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_49b9c_level0_col0\" class=\"col_heading level0 col0\" >Palma2021</th>\n",
       "      <th id=\"T_49b9c_level0_col1\" class=\"col_heading level0 col1\" >Ikonomi2020</th>\n",
       "      <th id=\"T_49b9c_level0_col2\" class=\"col_heading level0 col2\" >Merged Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >gene_name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row0\" class=\"row_heading level0 row0\" >BCL2</th>\n",
       "      <td id=\"T_49b9c_row0_col0\" class=\"data row0 col0\" >2</td>\n",
       "      <td id=\"T_49b9c_row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "      <td id=\"T_49b9c_row0_col2\" class=\"data row0 col2\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row1\" class=\"row_heading level0 row1\" >CCND1</th>\n",
       "      <td id=\"T_49b9c_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_49b9c_row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "      <td id=\"T_49b9c_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row2\" class=\"row_heading level0 row2\" >CCNE1</th>\n",
       "      <td id=\"T_49b9c_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "      <td id=\"T_49b9c_row2_col1\" class=\"data row2 col1\" >1</td>\n",
       "      <td id=\"T_49b9c_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row3\" class=\"row_heading level0 row3\" >CDKN1A</th>\n",
       "      <td id=\"T_49b9c_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "      <td id=\"T_49b9c_row3_col1\" class=\"data row3 col1\" >1</td>\n",
       "      <td id=\"T_49b9c_row3_col2\" class=\"data row3 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row4\" class=\"row_heading level0 row4\" >CEBPA</th>\n",
       "      <td id=\"T_49b9c_row4_col0\" class=\"data row4 col0\" >13</td>\n",
       "      <td id=\"T_49b9c_row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "      <td id=\"T_49b9c_row4_col2\" class=\"data row4 col2\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row5\" class=\"row_heading level0 row5\" >DNMT3A</th>\n",
       "      <td id=\"T_49b9c_row5_col0\" class=\"data row5 col0\" >52</td>\n",
       "      <td id=\"T_49b9c_row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "      <td id=\"T_49b9c_row5_col2\" class=\"data row5 col2\" >52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row6\" class=\"row_heading level0 row6\" >ETS1</th>\n",
       "      <td id=\"T_49b9c_row6_col0\" class=\"data row6 col0\" >0</td>\n",
       "      <td id=\"T_49b9c_row6_col1\" class=\"data row6 col1\" >2</td>\n",
       "      <td id=\"T_49b9c_row6_col2\" class=\"data row6 col2\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row7\" class=\"row_heading level0 row7\" >ETV6</th>\n",
       "      <td id=\"T_49b9c_row7_col0\" class=\"data row7 col0\" >5</td>\n",
       "      <td id=\"T_49b9c_row7_col1\" class=\"data row7 col1\" >0</td>\n",
       "      <td id=\"T_49b9c_row7_col2\" class=\"data row7 col2\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row8\" class=\"row_heading level0 row8\" >FLT3</th>\n",
       "      <td id=\"T_49b9c_row8_col0\" class=\"data row8 col0\" >56</td>\n",
       "      <td id=\"T_49b9c_row8_col1\" class=\"data row8 col1\" >0</td>\n",
       "      <td id=\"T_49b9c_row8_col2\" class=\"data row8 col2\" >56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row9\" class=\"row_heading level0 row9\" >FOXO3</th>\n",
       "      <td id=\"T_49b9c_row9_col0\" class=\"data row9 col0\" >0</td>\n",
       "      <td id=\"T_49b9c_row9_col1\" class=\"data row9 col1\" >1</td>\n",
       "      <td id=\"T_49b9c_row9_col2\" class=\"data row9 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row10\" class=\"row_heading level0 row10\" >MAPK1</th>\n",
       "      <td id=\"T_49b9c_row10_col0\" class=\"data row10 col0\" >1</td>\n",
       "      <td id=\"T_49b9c_row10_col1\" class=\"data row10 col1\" >0</td>\n",
       "      <td id=\"T_49b9c_row10_col2\" class=\"data row10 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row11\" class=\"row_heading level0 row11\" >MDM2</th>\n",
       "      <td id=\"T_49b9c_row11_col0\" class=\"data row11 col0\" >0</td>\n",
       "      <td id=\"T_49b9c_row11_col1\" class=\"data row11 col1\" >1</td>\n",
       "      <td id=\"T_49b9c_row11_col2\" class=\"data row11 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row12\" class=\"row_heading level0 row12\" >MEFV</th>\n",
       "      <td id=\"T_49b9c_row12_col0\" class=\"data row12 col0\" >0</td>\n",
       "      <td id=\"T_49b9c_row12_col1\" class=\"data row12 col1\" >2</td>\n",
       "      <td id=\"T_49b9c_row12_col2\" class=\"data row12 col2\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row13\" class=\"row_heading level0 row13\" >MYC</th>\n",
       "      <td id=\"T_49b9c_row13_col0\" class=\"data row13 col0\" >1</td>\n",
       "      <td id=\"T_49b9c_row13_col1\" class=\"data row13 col1\" >1</td>\n",
       "      <td id=\"T_49b9c_row13_col2\" class=\"data row13 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row14\" class=\"row_heading level0 row14\" >NPM1</th>\n",
       "      <td id=\"T_49b9c_row14_col0\" class=\"data row14 col0\" >54</td>\n",
       "      <td id=\"T_49b9c_row14_col1\" class=\"data row14 col1\" >0</td>\n",
       "      <td id=\"T_49b9c_row14_col2\" class=\"data row14 col2\" >54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row15\" class=\"row_heading level0 row15\" >RB1</th>\n",
       "      <td id=\"T_49b9c_row15_col0\" class=\"data row15 col0\" >0</td>\n",
       "      <td id=\"T_49b9c_row15_col1\" class=\"data row15 col1\" >2</td>\n",
       "      <td id=\"T_49b9c_row15_col2\" class=\"data row15 col2\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row16\" class=\"row_heading level0 row16\" >TP53</th>\n",
       "      <td id=\"T_49b9c_row16_col0\" class=\"data row16 col0\" >16</td>\n",
       "      <td id=\"T_49b9c_row16_col1\" class=\"data row16 col1\" >16</td>\n",
       "      <td id=\"T_49b9c_row16_col2\" class=\"data row16 col2\" >16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row17\" class=\"row_heading level0 row17\" >TSC1</th>\n",
       "      <td id=\"T_49b9c_row17_col0\" class=\"data row17 col0\" >0</td>\n",
       "      <td id=\"T_49b9c_row17_col1\" class=\"data row17 col1\" >1</td>\n",
       "      <td id=\"T_49b9c_row17_col2\" class=\"data row17 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49b9c_level0_row18\" class=\"row_heading level0 row18\" >Total</th>\n",
       "      <td id=\"T_49b9c_row18_col0\" class=\"data row18 col0\" >201</td>\n",
       "      <td id=\"T_49b9c_row18_col1\" class=\"data row18 col1\" >29</td>\n",
       "      <td id=\"T_49b9c_row18_col2\" class=\"data row18 col2\" >212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f183bece4d0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data file path\n",
    "tsv_file_path_genes = 'https://api.gdc.cancer.gov/data/0d8851d7-1af0-4054-a527-5db763138400'\n",
    "\n",
    "# Mutation data\n",
    "df_genes = pd.read_csv(tsv_file_path_genes, sep='\\t', usecols=['TCGA_id', 'gene_name'])\n",
    "\n",
    "# Count the number of unique patients with mutations in each gene\n",
    "patient_counts_1_tcga = df_genes[df_genes['gene_name'].isin(gene_1)].groupby('gene_name')['TCGA_id'].nunique()\n",
    "patient_counts_2_tcga = df_genes[df_genes['gene_name'].isin(gene_2)].groupby('gene_name')['TCGA_id'].nunique()\n",
    "patient_counts_merged_tcga = df_genes[df_genes['gene_name'].isin(gene_merged)].groupby('gene_name')['TCGA_id'].nunique()\n",
    "patient_counts_df_tcga = pd.DataFrame({\n",
    "    model1: patient_counts_1_tcga,\n",
    "    model2: patient_counts_2_tcga,\n",
    "    'Merged Model': patient_counts_merged_tcga\n",
    "}).fillna(0)\n",
    "\n",
    "# Convert to integers\n",
    "patient_counts_df_tcga[[model1, model2, 'Merged Model']] = patient_counts_df_tcga[[model1, model2, 'Merged Model']].astype(int)\n",
    "\n",
    "# Apply the styling to the DataFrame\n",
    "styled_df_tcga = patient_counts_df_tcga.style.apply(highlight_difference, axis=1, subset=[model1, model2])\n",
    "\n",
    "# Display the styled DataFrame with a sum row at the bottom\n",
    "styled_df_tcga.format(\"{:.0f}\")\n",
    "styled_df_tcga.set_table_styles([{\n",
    "    'selector': '.row0',\n",
    "    'props': 'font-weight: bold;'\n",
    "}], overwrite=False)\n",
    "\n",
    "# Add a row for the sum at the bottom\n",
    "patient_counts_df_tcga.loc['Total'] = patient_counts_df_tcga.sum(numeric_only=True)\n",
    "\n",
    "print(f\"Total number of unique patients with mutations in each model:\")\n",
    "styled_df_tcga"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f8d906c2-0c2f-44e6-9fba-35c5ea71e0a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Increased Coverage:11\n"
     ]
    },
    {
     "data": {
      "image/png": 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nT560Od6sWbMMScZPP/2Ua62GkfsImHIYGTCM7M/VhQsXDEnG66+/fsN+atasaTPSlGXixImGJOObb76xWT98+HDDZDIZR48eNQzDMObOnWtIMj777DObdsOGDctxpFuSsXjx4hvWlPVanTx50pBkfPzxx9ZtWa/zf//7X5t96tata0gyVq9ebV139epVIzg42OjevfsN+9u4caMhyWbU1TAMY+XKlYYkY+HChTbnkNPodU6y2l69etW4evWqcf78eeONN94wTCaT0bBhwxz3ycjIMK5evWrcf//9Rrdu3azrc7q6Icv1o6opKSlGQECA0aVLF5t2165dM+rUqWM0atTIui7r+bz+3EeMGGF4enrajKheP5qb5e677za6du16s6cjm2eeecbw8vIyrly5YhiGYQwZMsSIiooyateubUybNs0wjL9H6nJ6Df450n2j2rKemxEjRtisnzlzpiHJiI+Pv2GNWc/P+fPnjZiYGMPDw8O4ePGikZGRYVgsFmPq1Kk59p/XkW7DMIxOnTplO5+cXLt2zbh69arx3HPPGYGBgTavTW4/xzl9VjZp0sQoW7as8ddff1nXZWRkGHfffbdRvnx563Hz+tx99NFHhiQjNjb2pudwvdxGumvVqmVkZGRY12eNqr7//vs3PN6HH36Y4++MrL5y+kyrUaOG0b59e+vjGTNmGKVKlTK+++47m3ZZ5/npp5/m+fxyGunO7+/cl156KdtVFlmyPluyln/KzMw0qlSpYpQrV876XGa9n7ds2XLTfm820m0YhtG4cWPDy8sr1+0ZGRlGenq6UbVqVWPs2LHW9d99912eR5MzMjKM5ORkw8fHx+ZKsrx87lSvXt2oV69etuemc+fOhsVisV7hcKP3DVCUmL0cwC3p3LmzzeO77rpLkqx/4f7n+luZEfV6v/76q/r166eQkBC5uLjIzc1NLVu2lCQdPny4QMfs0KGDQkJCbEaqP//8c509e1aDBg2yrlu/fr0iIyMVGhqqjIwM69KhQwdJf4823cydd96p7777LtuyefPmm+4bEBCgO++8U6+88opeffVVff/998rMzMzzeW7dulU1atRQo0aNbNZHR0fLMAzrKNeOHTvk5+enqKgom3bXf+/wn3r06JFt3blz5/TYY48pLCxMrq6ucnNzU8WKFSXl/Frl9J4ymUzW51eSXF1dVaVKlZu+p7LO5fpJqHr16iUfHx9t2bLlhvvfSEpKitzc3OTm5qbg4GA9/vjj6tChg82o5oIFC1S/fn15enpaz33Lli0Ffo/u2rVLly5d0oABA2zee5mZmYqKitJ3332nlJQUm32uH0WrXbu2rly5onPnzt20v0aNGumzzz7TxIkTtX37dl2+fDlPdd5///26fPmydu3aJenvEe22bduqTZs22rRpk3WdJLVp0yZPx8xNTucnKV+fN7169ZK7u7uWLVumTz/9VAkJCXafgHDr1q1q06aNzGaz9TNsypQpunjxYp5em+ulpKTom2++Uc+ePW0mAXRxcVH//v3122+/6ejRozb73Oy5q1u3rtzd3TV06FAtXbo0x1ne86tTp05ycXHJtc+CCgkJyfaZVrt2bZvjrl+/Xnfffbfq1q1r8/PTvn37At0hwl5iY2Otny1Zyz9nMN+xY4eOHz+uAQMGWJ/LgQMHymQyWa9guVWGYdg8zsjI0IsvvqgaNWrI3d1drq6ucnd317Fjx/L8eZacnGy9QsnV1VWurq7y9fVVSkqKzTFu9rlz/PhxHTlyxHr12T9fy44dOyo+Pj7bex1wNC4vB0qwoKAgeXt768SJE3brIyAgwOaxu7t7ruvzeyul3CQnJ+u+++6Tp6enpk+frmrVqsnb21unT59W9+7d8xwMrufq6qr+/ftr9uzZ+vPPP1W6dGktWbJEFovFetmpJP3+++/65JNPcp3VOLfbu/yTp6en7rnnngLtazKZtGXLFj333HOaOXOmnnjiCQUEBOjhhx/WCy+8ID8/vxvuf/HixRxncQ0NDbVuz/rvHXfcka1dTuskydvbO9ulv5mZmWrXrp3Onj2ryZMnq1atWvLx8VFmZqaaNGmS42uV03vH29tbnp6e2dYnJSXlfqL//xxcXV2zXa5tMpkUEhJyw8ukb8bLy8t6+aWHh4cqVqxoc/6vvvqqnnjiCT322GN6/vnnFRQUJBcXF02ePLnAoTtrpuIbzVJ86dIl+fj4WB8HBgbabM+6PDkvPydvvvmmypcvr5UrV+rll1+Wp6en2rdvr1deeUVVq1bNdb9mzZrJ29tbmzdvVlhYmOLi4tS2bVv99ttvmj17tpKTk7V582ZVrlxZlSpVumkdN3Ir55fFx8dHvXv31uLFi1WxYkXr5br28u2336pdu3Zq1aqV3n77bZUvX17u7u5au3atXnjhhQJ9hv3xxx8yDCPHr89c/7Od5WbP3Z133qnNmzdr5syZGjlypFJSUlS5cmWNGTMm2yzzeVUYr1dejpt17H8e9/fff9fx48dv6bP7RvL7OzfrqwXX/8EhIiJC3333nSRp4cKFevvtt222L1q0SNLfM6hnfaXIbDarefPmWrVqlebMmaPSpUvfwplIp06dsr5vpL8v+Z47d64mTJigli1bqkyZMipVqpSGDBmS59euX79+2rJliyZPnqyGDRtab8nXsWNHm2Pc7HMn63PwySef1JNPPpljX7f6WgKFjdANlGAuLi66//779dlnn+V5VlMPDw+lpaVlW38r4SQnWQHq+r7y8oty69atOnv2rLZv324d3ZZk/Z+PWzFw4EC98sor1u+GrVu3To8//rjNyExQUJBq166tF154Icdj/PN/VOylYsWK1v/x+vnnn/XBBx9o6tSpSk9P14IFC264b2BgoOLj47OtP3v2rCRZv1cXGBiob7/9Nlu7hISEHI+b0/cIDx48qB9++EFLlizRgAEDrOuzvvtsb4GBgcrIyND58+dtgrdhGEpISFDDhg0LfOxSpUrl+IeTLO+9955atWql+fPn26z/66+/Ctxn1msze/bsXGeuzu2PIgXh4+OjadOmadq0afr999+to09dunTRkSNHct3P3d1dzZs31+bNm1W+fHmFhISoVq1aqly5sqS/7xO8ZcuWbFc1ONKgQYP0zjvv6MCBA7nOFi3d2mdXlhUrVsjNzU3r16+3+WPSrdwrPSsE5eVnOz/uu+8+3Xfffbp27Zr27t2r2bNn6/HHH9cdd9yhPn36FLheRwgKCpKXl1euo8EFeX7+Kb+/c1u1aiVXV1etW7dOQ4cOta738vKyfrasX7/eZp/ExEStWrVKknL9/Fq+fLlGjBhR4PP49ttvlZCQoMGDB1vXZc0l8eKLL9q0vXDhQp4CfmJiotavX69nn31WEydOtK5PS0vLdt/ym33uZL1OkyZNynVOj4iIiLyeLlAkuLwcKOEmTZokwzD0r3/9S+np6dm2X716VZ988on1cXh4uA4cOGDTZuvWrUpOTi7UurJGWq/vKy+358kKd9dPKPTWW29la5vfUZS77rpLjRs3VkxMjJYvX660tDQNHDjQpk3nzp118OBB3XnnnbrnnnuyLUURuv+pWrVqeuaZZ1SrVi3t37/fuv76UZ4s999/vw4dOmTTVpLeffddmUwmRUZGSpJatmypv/76S5999plNuxUrVuS5tvy8VvaQNRHWe++9Z7N+1apVSklJuaWJsm7GZDJlO+8DBw5ku99vft6j9957r0qXLq1Dhw7l+N675557rFeb5Edu75V/uuOOOxQdHa2+ffvq6NGjSk1NvWH7Nm3aaN++fVq1apX1EnIfHx81adJEs2fP1tmzZ/N0aXleaisMTZs21aBBg9StWzd169Yt13b5+ezKrXaTySRXV1ebP+ZdvnxZ//vf//J8jOv5+PiocePGWr16tU37zMxMvffeeypfvryqVat20+PkxsXFRY0bN9bcuXMlKdvnh70Vxoh4586d9csvvygwMDDHn53CuI9zfn7nWiwWDRo0SBs2bMjz5+ry5ct1+fJl68SP1y9BQUG3dIn5pUuX9Nhjj8nNzU1jx461rs/p82zDhg06c+aMzbrcXieTySTDMLId45133rnhBKI5fe5ERESoatWq+uGHH3L9HMy64quwrqQAbhUj3UAJ17RpU82fP18jRoxQgwYNNHz4cNWsWVNXr17V999/r4ULF+ruu+9Wly5dJEn9+/fX5MmTNWXKFLVs2VKHDh3SnDlzcp1VtqBCQkLUpk0bzZgxQ2XKlFHFihW1ZcsWrV69+qb7NmvWTGXKlNFjjz2mZ599Vm5ublq2bJl++OGHbG2z7jH68ssvq0OHDnJxcVHt2rVvGEwGDRqkYcOG6ezZs2rWrFm2v5g/99xz2rRpk5o1a6YxY8YoIiJCV65cUVxcnD799FMtWLAgX/dKza8DBw5o1KhR6tWrl6pWrSp3d3dt3bpVBw4csBlBqFWrllasWKGVK1eqcuXK8vT0VK1atTR27Fi9++676tSpk5577jlVrFhRGzZs0Lx58zR8+HDr/5gPGDBAr732mh555BFNnz5dVapU0WeffabPP/9ckmxmeM9N9erVdeedd2rixIkyDEMBAQH65JNPrN/rtbe2bduqffv2mjBhgpKSknTvvfdaZy+vV6+e+vfvb7e+O3furOeff17PPvusWrZsqaNHj+q5555TpUqVbGau9/PzU8WKFfXxxx/r/vvvV0BAgIKCgnIMAL6+vpo9e7YGDBigS5cuqWfPnipbtqzOnz+vH374QefPn882sp4XtWrV0vbt2/XJJ5/IYrHIz89PERERaty4sTp37qzatWurTJkyOnz4sP73v/+padOmN525+v7779e1a9e0ZcsWLV261Lq+TZs2evbZZ2UymdS6desC12YPWVeP3EjDhg0VERGhJ598UhkZGSpTpozWrFmjr776KlvbWrVqafXq1Zo/f74aNGhgvTqiU6dOevXVV9WvXz8NHTpUFy9e1KxZs3KcmTy3n+OczJgxQ23btlVkZKSefPJJubu7a968eTp48KDef//9fM9qvWDBAm3dulWdOnVShQoVdOXKFWugu9Xv4ufX3XffLenvy639/Pzk6empSpUq5XhZeW4ef/xxrVq1Si1atNDYsWNVu3ZtZWZm6tSpU/riiy/0xBNPqHHjxrdUZ35/577++us6ceKEHn74Ya1bt04PPvigQkNDlZqaqiNHjmjFihXy9PS0XhK/aNEilSlTRk8++WS2r9xI0qOPPqpXX31VP/zwg+rUqXPDWo8dO6Y9e/YoMzNTFy9e1DfffKNFixYpKSlJ7777rmrWrGlt27lzZy1ZskTVq1dX7dq1tW/fPr3yyivZftfdeeed8vLy0rJly3TXXXfJ19dXoaGhCg0NVYsWLfTKK69YP9927NihRYsWZRspz8vnzltvvaUOHTqoffv2io6OVrly5XTp0iUdPnxY+/fv14cffiipcN43QKFw3BxuAIpSbGysMWDAAKNChQqGu7u74ePjY9SrV8+YMmWKce7cOWu7tLQ046mnnjLCwsIMLy8vo2XLlkZsbGyus5dfPwvsP2cE/qecZoaOj483evbsaQQEBBhms9l45JFHjL179+Zp9vJdu3YZTZs2Nby9vY3g4GBjyJAhxv79+7Ptm5aWZgwZMsQIDg42TCaTzazD159TlsTERMPLy8uQZLz99ts5Pp/nz583xowZY1SqVMlwc3MzAgICjAYNGhj/+c9/bnq/11u9T/fvv/9uREdHG9WrVzd8fHwMX19fo3bt2sZrr71mMytwXFyc0a5dO8PPz8/m/r6G8fd9uvv162cEBgYabm5uRkREhPHKK69ku6ftqVOnjO7duxu+vr6Gn5+f0aNHD+PTTz/NNvP4jWb+PnTokNG2bVvDz8/PKFOmjNGrVy/j1KlT2c4zP++dmz2P/3T58mVjwoQJRsWKFQ03NzfDYrEYw4cPt7lP983O4Xp5aZuWlmY8+eSTRrly5QxPT0+jfv36xtq1a3OcoXvz5s1GvXr1DA8PD0N5uE/3jh07jE6dOhkBAQGGm5ubUa5cOaNTp042sxHn9nzmdMzY2Fjj3nvvNby9vW3uhT1x4kTjnnvusd4Ht3LlysbYsWONCxcu3PQ5yszMNIKCggxJNvd+/vrrrw1JNnczyJLTc5Nbbbl9BuV2Z4Tr5fb8XC+n2dN//vlno127doa/v78RHBxsjB492tiwYUO2fi9dumT07NnTKF26tPXzJ8vixYuNiIgI6/M6Y8YMY9GiRdlem9x+jm92n24fHx/Dy8vLaNKkifHJJ5/YtMnrc7d7926jW7duRsWKFQ0PDw8jMDDQaNmypbFu3bobPmeGceP7dF/v+s+C3Lz++utGpUqVDBcXlxzv0329nN5PycnJxjPPPGNEREQY7u7uhtlsNmrVqmWMHTvWSEhIuGkNWXK7T3eWvP7ONYy/Z69/9913jbZt2xpBQUGGq6urYTabjUaNGhmTJ082fvvtN8MwDOOHH34wJBmPP/54rv0eOXIkxzuL/NP199N2dXU1AgMDjaZNmxpPP/10tvtmG4Zh/PHHH8bgwYONsmXLGt7e3kbz5s2NL7/8MtvrbBiG8f777xvVq1c33NzcbF7b3377zejRo4f13vRRUVHGwYMHs/0uzuvnzg8//GA89NBDRtmyZQ03NzcjJCTEaN26tbFgwQKbdrm9b4CiZDKM66YnBADgBl588UU988wzOnXqlF1H9AEAAEoCLi8HAORqzpw5kv6+TPzq1avaunWr3nzzTT3yyCMEbgAAgDwgdAMAcuXt7a3XXntNcXFxSktLU4UKFTRhwgQ988wzji4NAACgWODycgAAAAAA7IRbhgEAAAAAYCeEbgAAAAAA7ITQDQAAAACAnTCRmpPIzMzU2bNn5efnJ5PJ5OhyAAAAAAA3YBiG/vrrL4WGhqpUqdzHswndTuLs2bMKCwtzdBkAAAAAgHw4ffr0DW+lSuh2En5+fpL+fsH8/f0dXA0AAAAA4EaSkpIUFhZmzXK5IXQ7iaxLyv39/QndAAAAAFBM3OzrwUykBgAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANgJoRsAAAAAADshdAMAAAAAYCeEbgAAAAAA7ITQDQAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANgJoRsAAAAAADshdAMAAAAAYCeEbgAAAAAA7ITQDQAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANgJoRsAAAAAADshdAMAAAAAYCeEbgAAAAAA7ITQDQAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOXB1dAGwlJCQoJSXF0WUAAAAAgMN4e3vLbDY7uoxCQeh2MjExMfL09HR0GQAAAADgMKVKuWnMmJElIngTup3Mvn31dfBgc0eXAQAAAAAOERR0Xj16rFFqaiqhG4UvOdlX8fEWR5cBAAAAACgETKQGAAAAAICdELoBAAAAALATQjcAAAAAAHZC6AYAAAAAwE4I3QAAAAAA2AmhGwAAAAAAOyF0AwAAAACczqxZs2QymWyWkJAQmzaHDx/WAw88ILPZLD8/PzVp0kSnTp1yUMU5u+1Cd3R0dLYXzmQy6fjx44qOjlbXrl2tbePi4nJs+89l6tSpunjxoqKiohQaGioPDw+FhYVp1KhRSkpKctyJAgAAAEAxV7NmTcXHx1uXH3/80brtl19+UfPmzVW9enVt375dP/zwgyZPnixPT08HVpydq6MLcISoqCjFxMTYrAsODs7WLiwsTPHx8dbHs2bN0saNG7V582brOl9fX129elUPPvigpk+fruDgYB0/flwjR47UpUuXtHz5cvudCAAAAACUYK6urtlGt7P85z//UceOHTVz5kzrusqVKxdVaXl22410S5KHh4dCQkJsFhcXl2ztXFxcbNr4+vpaX/R/ritTpoyGDx+ue+65RxUrVtT999+vESNG6Msvv3TA2QEAAABAyXDs2DGFhoaqUqVK6tOnj3799VdJUmZmpjZs2KBq1aqpffv2Klu2rBo3bqy1a9c6tuAc3Jah297Onj2r1atXq2XLlo4uBQAAAACKpfr16+vdd9/V559/rrffflsJCQlq1qyZLl68qHPnzik5OVkvvfSSoqKi9MUXX6hbt27q3r27duzY4ejSbdyWoXv9+vXy9fW1Lr169SqU4/bt21fe3t4qV66c/P399c477+TaNi0tTUlJSTYLAAAAAOBvrVu3Vo8ePVSrVi21adNGGzZskCQtXbpUmZmZkqQHH3xQY8eOVd26dTVx4kR17txZCxYscGTZ2dyWoTsyMlKxsbHW5c033yyU47722mvav3+/1q5dq19++UXjxo3Lte2MGTNkNputS1hYWKHUAAAAAAAlkY+Pj2rVqqVjx44pKChIrq6uqlGjhk2bu+66y+lmL78tJ1Lz8fFRlSpVCv24Wd/zrl69ugIDA3Xfffdp8uTJslgs2dpOmjTJJpQnJSURvAEAAAAgF2lpaTp8+LDuu+8+ubu7q2HDhjp69KhNm59//lkVK1Z0UIU5uy1Dd1EwDEPS32+MnHh4eMjDw6MoSwIAAACAYmPatGnq27evKlSooHPnzmn69OlKSkrSgAEDJEnjx49X79691aJFC0VGRmrjxo365JNPtH37dscWfh1C93USExMVGxtrsy4gIEAVKlTIdZ9PP/1Uv//+uxo2bChfX18dOnRITz31lO69916Fh4fbt2AAAAAAKIHi4+PVt29fXbhwQcHBwWrSpIn27NljHcnu1q2bFixYoBkzZmjMmDGKiIjQqlWr1Lx5cwdXbovQfZ3t27erXr16NusGDBigJUuW5LqPl5eX3n77bY0dO1ZpaWkKCwtT9+7dNXHiRDtXCwAAAAAl04IFC3L8qu4/DRo0SIMGDSqiigrGZGRdBw2HSkpKktlsVtOmn2r37g6OLgcAAAAAHMJiidewYQs1dOjQm4ZuR8rKcImJifL398+13W05ezkAAAAAAEWB0A0AAAAAgJ0QugEAAAAAsBNCNwAAAAAAdkLoBgAAAADATgjdAAAAAADYCaEbAAAAAAA7cXV0AbDl65ssiyXe0WUAAAAAgEMEBZ13dAmFitDtZBo02K977z3k6DIAAAAAwGFKlXKTt7e3o8soFIRuJzNw4ED5+fk5ugwAAAAAcBhvb2+ZzWZHl1EoCN1OJiQkRP7+/o4uAwAAAABQCJhIDQAAAAAAOyF0AwAAAABgJ4RuAAAAAADshNANAAAAAICdMJGak0lISFBKSoqjywAAAACAEjWLuKMQup1MTEyMPD09HV0GAAAAAKhUKTeNGTOS4H0LCN1OZt+++jp4sLmjywAAAABwmwsKOq8ePdYoNTWV0H0LCN1OJjnZV/HxFkeXAQAAAAAoBEykBgAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANgJoRsAAAAAkKtZs2bJZDLZLCEhITm2HTZsmEwmk15//fWiLdKJlajQHR0dLZPJpJdeeslm/dq1a2UymSRJ27dvt3mzBAcHq0OHDvrhhx+s7Vu1apXjcSSpY8eOMplMmjp1qnXd6tWr1b59ewUFBclkMik2NtYu5wcAAAAAjlCzZk3Fx8dblx9//DFbm7Vr1+qbb75RaGioAyp0XiUqdEuSp6enXn75Zf3xxx83bHf06FHFx8drw4YN+uOPPxQVFaXExETr9rCwMMXExNjsc/bsWW3dulUWi+19tFNSUnTvvffmGNIBAAAAoLhzdXVVSEiIdQkODrbZfubMGY0aNUrLli2Tm5ubg6p0TiUudLdp00YhISGaMWPGDduVLVtWISEhatSokf773/8qISFBe/bssW7v3LmzLl68qK+//tq6bsmSJWrXrp3Kli1rc6z+/ftrypQpatOmTeGeDAAAAAA4gWPHjik0NFSVKlVSnz599Ouvv1q3ZWZmqn///ho/frxq1qzpwCqdU4kL3S4uLnrxxRc1e/Zs/fbbb3nax8vLS5J09epV6zp3d3c9/PDDNqPdS5Ys0aBBgwq3YAAAAABwYvXr19e7776rzz//XG+//bYSEhLUrFkzXbx4UZL08ssvy9XVVWPGjHFwpc6pxIVuSerWrZvq1q2rZ5999qZtL168qGnTpsnPz0+NGjWy2TZ48GB98MEHSklJ0c6dO5WYmKhOnToVSo1paWlKSkqyWQAAAADA2bRu3Vo9evRQrVq11KZNG23YsEGStHTpUu3bt09vvPGGlixZYp1HC7ZKZOiW/v5ry9KlS3Xo0KEct5cvX16+vr4KCgrS4cOH9eGHH2a7bLx27dqqWrWqPvroIy1evFj9+/cvtO8nzJgxQ2az2bqEhYUVynEBAAAAwJ58fHxUq1YtHTt2TF9++aXOnTunChUqyNXVVa6urjp58qSeeOIJhYeHO7pUp+Dq6ALspUWLFmrfvr2efvppRUdHZ9v+5Zdfyt/fX8HBwfL398/1OIMGDdLcuXN16NAhffvtt4VW36RJkzRu3Djr46SkJII3AAAAAKeXlpamw4cP67777lP//v2zzW3Vvn179e/fXwMHDnRQhc6lxIZuSXrppZdUt25dVatWLdu2SpUqqXTp0jc9Rr9+/fTkk0+qTp06qlGjRqHV5uHhIQ8Pj0I7HgAAAADYw7Rp09S3b19VqFBB586d0/Tp05WUlKQBAwYoMDBQgYGBNu3d3NwUEhKiiIgIB1XsXEp06K5Vq5YefvhhzZ49u8DHKFOmjOLj4294WfmlS5d06tQpnT17VtLftyOTZJ1OHwAAAACKq/j4ePXt21cXLlxQcHCwmjRpoj179qhixYqOLq1YKNGhW5Kef/55ffDBB7d0jJuNiK9bt87m0ok+ffpIkp599llNnTr1lvoGAAAAAEdasGCBLBZLntvHxcXZr5hiyGQYhuHoIvD3d7rNZrOaNv1Uu3d3cHQ5AAAAAG5zFku8hg1bqKFDh+YrdN8usjJcYmLiDecJK7GzlwMAAAAA4GiEbgAAAAAA7ITQDQAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANiJq6MLgC1f32RZLPGOLgMAAADAbS4o6LyjSygRCN1OpkGD/br33kOOLgMAAAAAVKqUm7y9vR1dRrFG6HYyAwcOlJ+fn6PLAAAAAAB5e3vLbDY7uoxijdDtZEJCQuTv7+/oMgAAAAAAhYCJ1AAAAAAAsBNCNwAAAAAAdkLoBgAAAADATgjdAAAAAADYCROpOZmEhASlpKQ4ugwAAAAAToRZxIsvQreTiYmJkaenp6PLAAAAAOBESpVy05gxIwnexRCh28ns21dfBw82d3QZAAAAAJxEUNB59eixRqmpqYTuYojQ7WSSk30VH29xdBkAAAAAgELARGoAAAAAANgJoRsAAAAAADshdAMAAAAAYCeEbgAAAAAA7ITQDQAAAACAnRC6AQAAAKAYmDVrlkwmk80SEhIiSbp69aomTJigWrVqycfHR6GhoXr00Ud19uxZB1cNQjcAAAAAFBM1a9ZUfHy8dfnxxx8lSampqdq/f78mT56s/fv3a/Xq1fr555/1wAMPOLhiODR0R0dHW/9C4+bmpjvuuENt27bV4sWLlZmZaW0XHh4uk8mkPXv22Oz/+OOPq1WrVtbHU6dOlclkUlRUVLa+Zs6cKZPJZG2fdczclqx2w4YN05133ikvLy8FBwfrwQcf1JEjR3I8n6FDh8rFxUUrVqy4tScGAAAAAHLg6uqqkJAQ6xIcHCxJMpvN2rRpkx566CFFRESoSZMmmj17tvbt26dTp045uOrbm8NHuqOiohQfH6+4uDh99tlnioyM1L///W917txZGRkZ1naenp6aMGHCTY9nsVi0bds2/fbbbzbrY2JiVKFCBevj7777zvrXoVWrVkmSjh49al23evVqSVKDBg0UExOjw4cP6/PPP5dhGGrXrp2uXbtmc/zU1FStXLlS48eP16JFiwr8fAAAAABAbo4dO6bQ0FBVqlRJffr00a+//ppr28TERJlMJpUuXbroCkQ2Dg/dHh4eCgkJUbly5VS/fn09/fTT+vjjj/XZZ59pyZIl1nbDhg3Tnj179Omnn97weGXLllW7du20dOlS67pdu3bpwoUL6tSpk3VdcHCw9a9DAQEB1n2vXzd06FC1aNFC4eHhql+/vqZPn67Tp08rLi7Opt8PP/xQNWrU0KRJk/T1119n2w4AAAAAt6J+/fp699139fnnn+vtt99WQkKCmjVrposXL2Zre+XKFU2cOFH9+vWTv7+/A6pFFoeH7py0bt1aderUsY42S39fDv7YY49p0qRJNpee52TQoEE2gX3x4sV6+OGH5e7ufkt1paSkKCYmRpUqVVJYWJjNtkWLFumRRx6R2WxWx44dFRMTc8NjpaWlKSkpyWYBAAAAgNy0bt1aPXr0UK1atdSmTRtt2LBBkmwGHKW/J1Xr06ePMjMzNW/ePEeUin9wytAtSdWrV882WvzMM8/oxIkTWrZs2Q337dy5s5KSkrRz506lpKTogw8+0KBBgwpcy7x58+Tr6ytfX19t3LhRmzZtsgnwx44d0549e9S7d29J0iOPPKKYmJgb/nFgxowZMpvN1uX6EA8AAAAAN+Lj46NatWrp2LFj1nVXr17VQw89pBMnTmjTpk2McjsBpw3dhmHIZDLZrAsODtaTTz6pKVOmKD09Pdd93dzcrMH3ww8/VLVq1VS7du0C1/Lwww/r+++/144dO1S1alU99NBDunLlinX7okWL1L59ewUFBUmSOnbsqJSUFG3evDnXY06aNEmJiYnW5fTp0wWuDwAAAMDtJy0tTYcPH5bFYpH0f4H72LFj2rx5swIDAx1cISTJ1dEF5Obw4cOqVKlStvXjxo3TvHnzbnqZxKBBg9S4cWMdPHjwlka5JVlHo6tWraomTZqoTJkyWrNmjfr27atr167p3XffVUJCglxd/+/pvHbtmhYtWqR27drleEwPDw95eHjcUl0AAAAAbh/Tpk1T3759VaFCBZ07d07Tp09XUlKSBgwYoIyMDPXs2VP79+/X+vXrde3aNSUkJEiSAgICbvmrtig4pwzdW7du1Y8//qixY8dm2+br66vJkydr6tSp6tKlS67HqFmzpmrWrKkDBw6oX79+hVqfYRhKS0uTJH366af666+/9P3338vFxcXa5siRI3r44Yd18eJF/sIEAAAA4JbFx8erb9++unDhgoKDg9WkSRPt2bNHFStWVFxcnNatWydJqlu3rs1+27Zts7nVMoqWw0N3WlqaEhISdO3aNf3+++/auHGjZsyYoc6dO+vRRx/NcZ+hQ4fqtdde0/vvv6/GjRvneuytW7fq6tWrBZ4i/9dff9XKlSvVrl07BQcH68yZM3r55Zfl5eWljh07Svr70vJOnTqpTp06NvvWrFlTjz/+uN577z39+9//LlD/AAAAAJBlwYIF1kvJrxceHi7DMIq4IuSFw7/TvXHjRlksFoWHhysqKkrbtm3Tm2++qY8//thm5Pif3Nzc9Pzzz9t8rzonPj4+t3RPOk9PT3355Zfq2LGjqlSpooceekg+Pj7atWuXypYtq99//10bNmxQjx49su1rMpnUvXt37tkNAAAAALcxk8GfQ5xCUlKSzGazmjb9VLt3d3B0OQAAAACchMUSr2HDFmro0KG5jnSj6GVluMTExBvOEu/wkW4AAAAAAEoqQjcAAAAAAHZC6AYAAAAAwE4I3QAAAAAA2AmhGwAAAAAAOyF0AwAAAABgJ66OLgC2fH2TZbHEO7oMAAAAAE4iKOi8o0vALSB0O5kGDfbr3nsPOboMAAAAAE6kVCk3eXt7O7oMFACh28kMHDhQfn5+ji4DAAAAgBPx9vaW2Wx2dBkoAEK3kwkJCZG/v7+jywAAAAAAFAImUgMAAAAAwE4I3QAAAAAA2AmhGwAAAAAAOyF0AwAAAABgJ0yk5mQSEhKUkpLi6DIA5BMzigIAACAnhG4nExMTI09PT0eXASCfSrmW0phRYwjeAAAAsEHodjI7tENxinN0GQDyIUhB6pHRQ6mpqYRuAAAA2CB0O5lEJSpe8Y4uAwAAAABQCJhIDQAAAAAAOyF0AwAAAABgJ4RuAAAAAADshNANAAAAAICdELoBAAAAALATQjcAFKKdO3eqS5cuCg0Nlclk0tq1a7O1OXz4sB544AGZzWb5+fmpSZMmOnXqVNEXCwAAALsjdANAIUpJSVGdOnU0Z86cHLf/8ssvat68uapXr67t27frhx9+0OTJk+Xp6VnElQIAAKAolOjQbTKZbrhER0dna+fn56d77rlHq1evth5nyZIlOe5/5coVa5v58+erdu3a8vf3l7+/v5o2barPPvusqE8ZgIN16NBB06dPV/fu3XPc/p///EcdO3bUzJkzVa9ePVWuXFmdOnVS2bJli7hSAAAAFIUSHbrj4+Oty+uvvy5/f3+bdW+88Ya1bUxMjOLj4/Xdd9+pTp066tWrl3bv3m3dfv2+8fHxNiNT5cuX10svvaS9e/dq7969at26tR588EH99NNPRXrOAJxXZmamNmzYoGrVqql9+/YqW7asGjdunOMl6AAAACgZSnToDgkJsS5ms1kmkynbuiylS5dWSEiIqlevrgULFsjT01Pr1q2zbr9+35CQEJu+unTpoo4dO6patWqqVq2aXnjhBfn6+mrPnj1Fdr4AnNu5c+eUnJysl156SVFRUfriiy/UrVs3de/eXTt27HB0eQAAALADV0cX4Izc3Nzk6uqqq1evWtclJyerYsWKunbtmurWravnn39e9erVy3H/a9eu6cMPP1RKSoqaNm2aY5u0tDSlpaVZHyclJRXuSQBwOpmZmZKkBx98UGPHjpUk1a1bV7t27dKCBQvUsmVLR5YHAAAAOyjRI90FkZaWpunTpyspKUn333+/JKl69epasmSJ1q1bp/fff1+enp669957dezYMZt9f/zxR/n6+srDw0OPPfaY1qxZoxo1auTYz4wZM2Q2m61LWFiY3c8NgGMFBQXJ1dU12+fCXXfdxezlAAAAJRQj3f9f37595eLiosuXL8tsNmvWrFnq0KGDJKlJkyZq0qSJte29996r+vXra/bs2XrzzTet6yMiIhQbG6s///xTq1at0oABA7Rjx44cg/ekSZM0btw46+OkpCSCN1DCubu7q2HDhjp69KjN+p9//lkVK1Z0UFUAAACwJ0L3//faa6+pTZs28vf3v+kswqVKlVLDhg2zjXS7u7urSpUqkqR77rlH3333nd544w299dZb2Y7h4eEhDw+PwjsBAE4hOTlZx48ftz4+ceKEYmNjFRAQoAoVKmj8+PHq3bu3WrRoocjISG3cuFGffPKJtm/f7riiAQAAYDeE7v8vJCTEGphvxjAMxcbGqlatWjdt98/vbQMo+fbu3avIyEjr46wrWgYMGKAlS5aoW7duWrBggWbMmKExY8YoIiJCq1atUvPmzR1VMgAAAOyI0J0H06ZNU5MmTVS1alUlJSXpzTffVGxsrObOnWtt8/TTT6tDhw4KCwvTX3/9pRUrVmj79u3auHGjAysHUNRatWolwzBu2GbQoEEaNGhQEVUEAAAARyJ058Gff/6poUOHKiEhQWazWfXq1dPOnTvVqFEja5vff/9d/fv3V3x8vMxms2rXrq2NGzeqbdu2DqwcAAAAAOBIJuNmQzIoEklJSTKbzaoxsYYOeR5ydDkA8sEii4ZpmIYOHSqLxeLocgAAAFAEsjJcYmKi/P39c23HLcMAAAAAALATQjcAAAAAAHZC6AYAAAAAwE4I3QAAAAAA2AmhGwAAAAAAOyF0AwAAAABgJ4RuAAAAAADsxNXRBcCWWWZZxH1+geIkSEGOLgEAAABOitDtZFqqpdqrvaPLAJBPpVxLydvb29FlAAAAwMkQup3MwIED5efn5+gyAOSTt7e3zGazo8sAAACAkyF0O5mQkBD5+/s7ugwAAAAAQCFgIjUAAAAAAOyE0A0AAAAAgJ0QugEAAAAAsBO+0+1kEhISlJKS4ugyAIdiUjIAAACUFIRuJxMTEyNPT09HlwE4VKlSbhozZiTBGwAAAMUeodvJ7NtXXwcPNnd0GYDDBAWdV48ea5SamkroBgAAQLFH6HYyycm+io+3OLoMAAAAAEAhYCI1AAAAAADshNANAAAAAICdELoBAAAAALATQjcAAAAAAHZC6AYAAAAAwE4I3QCc1s6dO9WlSxeFhobKZDJp7dq1NtunTp2q6tWry8fHR2XKlFGbNm30zTffOKZYAAAAIAeEbgBOKyUlRXXq1NGcOXNy3F6tWjXNmTNHP/74o7766iuFh4erXbt2On/+fBFXCgAAAOSsWIfuhIQEjR49WpUrV5aHh4fCwsLUpUsXbdmyRZIUHh4uk8mUbXnppZckSXFxcTbr3d3dVaVKFU2fPl2GYVj7mTp1qrWNi4uLwsLCNGTIkGz/Y//iiy/KxcXFenwAt6ZDhw6aPn26unfvnuP2fv36qU2bNqpcubJq1qypV199VUlJSTpw4EARVwoAAADkzNXRBRRUXFyc7r33XpUuXVozZ85U7dq1dfXqVX3++ecaOXKkjhw5Ikl67rnn9K9//ctmXz8/P5vHmzdvVs2aNZWWlqavvvpKQ4YMkcVi0eDBg61tatasqc2bN+vatWv6/vvvNXjwYJ05c0afffaZtU1MTIyeeuopLV68WBMnTrTj2QO4Xnp6uhYuXCiz2aw6deo4uhwAAABAUjEO3SNGjJDJZNK3334rHx8f6/qaNWtq0KBB1sd+fn4KCQm54bECAwOtbSpWrKjFixdr//79NqHb1dXV2qZcuXIaM2aMpkyZosuXL8vLy0s7duzQ5cuX9dxzz+ndd9/Vzp071aJFi8I8ZQA5WL9+vfr06aPU1FRZLBZt2rRJQUFBji4LAAAAkFRMLy+/dOmSNm7cqJEjR9oE7iylS5cu8LH37t2r/fv3q3Hjxjds5+XlpczMTGVkZEiSFi1apL59+8rNzU19+/bVokWLbrh/WlqakpKSbBYA+RcZGanY2Fjt2rVLUVFReuihh3Tu3DlHlwUAAABIKqah+/jx4zIMQ9WrV79p2wkTJsjX19dm2b59u02bZs2aydfXV+7u7mrYsKEeeughPfroo7ke88iRI5o/f74aNWokPz8/JSUladWqVXrkkUckSY888og++uijGwbpGTNmyGw2W5ewsLC8nTwAGz4+PqpSpYqaNGmiRYsWydXV9aZ/9AIAAACKSrEM3VmTnJlMppu2HT9+vGJjY22W60exV65cqdjYWP3www9auXKlPv7442zfyf7xxx/l6+srLy8v1ahRQ2FhYVq2bJkkafny5apcubL1e6R169ZV5cqVtWLFilzrmjRpkhITE63L6dOn8/UcAMiZYRhKS0tzdBkAAACApGL6ne6qVavKZDLp8OHD6tq16w3bBgUFqUqVKjdsExYWZm1z11136ddff9XkyZM1depUeXp6SpIiIiK0bt06ubi4KDQ0VB4eHtb9Fy9erJ9++kmurv/3dGZmZmrRokUaOnRojn16eHjYHANAdsnJyTp+/Lj18YkTJxQbG6uAgAAFBgbqhRde0AMPPCCLxaKLFy9q3rx5+u2339SrVy8HVg0AAAD8n2IZugMCAtS+fXvNnTtXY8aMyfa97j///POWvtft4uKijIwMpaenW0N31u3Ervfjjz9q79692r59uwICAmxqaNGihQ4ePKi77767wLUAt7O9e/cqMjLS+njcuHGSpAEDBmjBggU6cuSIli5dqgsXLigwMFANGzbUl19+qZo1azqqZAAAAMBGsQzdkjRv3jw1a9ZMjRo10nPPPafatWsrIyNDmzZt0vz583X48GFJ0l9//aWEhASbfb29veXv7299fPHiRSUkJCgjI0M//vij3njjDUVGRtq0yc2iRYvUqFGjHGcqb9q0qRYtWqTXXnvtFs8WuD21atXK+nWSnKxevboIqwEAAADyr1h+p1uSKlWqpP379ysyMlJPPPGE7r77brVt21ZbtmzR/Pnzre2mTJkii8Viszz11FM2x2rTpo0sFovCw8M1dOhQdezYUStXrrxpDenp6XrvvffUo0ePHLf36NFD7733ntLT02/tZAEAAAAAxZLJuNEwEopMUlKSzGazmjb9VLt3d3B0OYDDWCzxGjZsoYYOHSqLxeLocgAAAIAcZWW4xMTEG14lXWxHugEAAAAAcHaEbgAAAAAA7ITQDQAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANiJq6MLgC1f32RZLPGOLgNwmKCg844uAQAAACg0hG4n06DBft177yFHlwE4VKlSbvL29nZ0GQAAAMAtI3Q7mYEDB8rPz8/RZQAO5e3tLbPZ7OgyAAAAgFtG6HYyISEh8vf3d3QZAAAAAIBCwERqAAAAAADYCaEbAAAAAAA7IXQDAAAAAGAnfKfbySQkJCglJcXRZTg9JtoCAAAAUBwQup1MTEyMPD09HV2G0ytVyk1jxowkeAMAAABwaoRuJ7NvX30dPNjc0WU4taCg8+rRY41SU1MJ3QAAAACcGqHbySQn+yo+3uLoMgAAAAAAhYCJ1AAAAAAAsBNCNwAAAAAAdkLoBgAAAADATgjdAAAAAADYCaEbAAAAAAA7IXSj2Jo1a5ZMJpPNEhIS4uiyAAAAAMCKW4ahWKtZs6Y2b95sfezi4uLAagAAAADAVoke6Y6Ojs42EmoymeTp6Znj+n8u//3vf+Xi4qJTp07leOzq1atrzJgx1seHDx/WAw88ILPZLD8/PzVp0iTXfVF4XF1dFRISYl2Cg4MdXRIAAAAAWJXo0C1JUVFRio+Pt1lOnjxp8/ihhx7K1m7o0KEKDAzU0qVLsx3z66+/1tGjRzV48GBJ0i+//KLmzZurevXq2r59u3744QdNnjxZnp6eRX26t51jx44pNDRUlSpVUp8+ffTrr786uiQAAAAAsCrxl5d7eHjc9Hu+Xl5eSktLy9auf//+WrJkiZ555hmZTCbr+sWLF6tBgwaqU6eOJOk///mPOnbsqJkzZ1rbVK5cuRDPAjmpX7++3n33XVWrVk2///67pk+frmbNmumnn35SYGCgo8sDAAAAgJI/0n0rBg8erF9//VU7duywrktJSdEHH3xgHeXOzMzUhg0bVK1aNbVv315ly5ZV48aNtXbt2hseOy0tTUlJSTYL8qd169bq0aOHatWqpTZt2mjDhg2SlOPVCQAAAADgCCU+dK9fv16+vr42y/PPP5+nfWvUqKHGjRsrJibGuu6DDz7QtWvX1LdvX0nSuXPnlJycrJdeeklRUVH64osv1K1bN3Xv3t0mrF9vxowZMpvN1iUsLOzWThTy8fFRrVq1dOzYMUeXAgAAAACSboPQHRkZqdjYWJtl5MiRed5/8ODB+uijj/TXX39J+vvS8u7du6t06dKS/h7plqQHH3xQY8eOVd26dTVx4kR17txZCxYsyPW4kyZNUmJionU5ffp0wU8Skv6+euDw4cOyWCyOLgUAAAAAJN0GodvHx0dVqlSxWQICAvK8f58+fWQymbRy5UodP35cX331lfXSckkKCgqSq6uratSoYbPfXXfddcPZyz08POTv72+zIH+mTZumHTt26MSJE/rmm2/Us2dPJSUlacCAAY4uDQAAAAAk3QYTqd0qPz8/9erVSzExMfr1119VuXJltWrVyrrd3d1dDRs21NGjR232+/nnn1WxYsUirvb2Eh8fr759++rChQsKDg5WkyZNtGfPHp53AAAAAE6jxIfutLQ0JSQk2KxzdXVVUFBQno8xePBg3XfffTp06JCefPJJm5nMJWn8+PHq3bu3WrRoocjISG3cuFGffPKJtm/fXhingFwsWLCAS8kBAAAAOLUSH7o3btyYLZhFREToyJEjeT5G8+bNFRERoWPHjuV46XK3bt20YMECzZgxQ2PGjFFERIRWrVql5s2b33L9AAAAAIDiy2QYhuHoIiAlJSXJbDaradNPtXt3B0eX49QslngNG7ZQQ4cOZaQbAAAAgENkZbjExMQbztFV4idSAwAAAADAUQjdAAAAAADYCaEbAAAAAAA7IXQDAAAAAGAnhG4AAAAAAOyE0A0AAAAAgJ0QugEAAAAAsBNXRxcAW76+ybJY4h1dhlMLCjrv6BIAAAAAIE8I3U6mQYP9uvfeQ44uw+mVKuUmb29vR5cBAAAAADdE6HYyAwcOlJ+fn6PLcHre3t4ym82OLgMAAAAAbojQ7WRCQkLk7+/v6DIAAAAAAIWAidQAAAAAALATQjcAAAAAAHZC6AYAAAAAwE74TreTSUhIUEpKSpH2yaRkAAAAAGAfhG4nExMTI09PzyLts1QpN40ZM5LgDQAAAACFjNDtZPbtq6+DB5sXWX9BQefVo8capaamEroBAAAAoJARup1McrKv4uMtji4DAAAAAFAImEgNAAAAAAA7IXQDAAAAAGAnhG4AAAAAAOyE0A0AAAAAgJ0QugEAAAAAsBNCNyRJs2fPVsOGDeXn56eyZcuqa9euOnr0qKPLAgAAAIBijdANSdLu3bs1cuRI7dmzR5s2bVJGRobatWunlJQUR5cGAAAAAMVWiQ7d0dHRMplM1iUwMFBRUVE6cOCAtY1hGFq4cKEaN24sX19flS5dWvfcc49ef/11paamSpKmTp2qunXr5tjH1atXNWHCBNWqVUs+Pj4KDQ3Vo48+qrNnzxbFKRaa5cuXKzo6WjVr1lSdOnUUExOjU6dOad++fY4uDQAAAACKrRIduiUpKipK8fHxio+P15YtW+Tq6qrOnTtbt/fv31+PP/64HnzwQW3btk2xsbGaPHmyPv74Y33xxRc3PX5qaqr279+vyZMna//+/Vq9erV+/vlnPfDAA/Y8LbtLTEyUJAUEBDi4EgAAAAAovlwdXYC9eXh4KCQkRJIUEhKiCRMmqEWLFjp//ry2bdumZcuWae3atXrwwQet+4SHh+uBBx5QUlLSTY9vNpu1adMmm3WzZ89Wo0aNdOrUKVWoUKFwT6gIGIahcePGqXnz5rr77rsdXQ4AAAAAFFslPnT/U3JyspYtW6YqVaooMDBQy5YtU0REhE3gzmIymWQ2mwvUT2Jiokwmk0qXLp1rm7S0NKWlpVkf5yXgF5VRo0bpwIED+uqrrxxdCgAAAAAUayU+dK9fv16+vr6SpJSUFFksFq1fv16lSpXSsWPHFBERUaj9XblyRRMnTlS/fv3k7++fa7sZM2Zo2rRphdp3YRg9erTWrVunnTt3qnz58o4uBwAAAACKtRL/ne7IyEjFxsYqNjZW33zzjdq1a6cOHTro5MmTMgxDJpOp0Pq6evWq+vTpo8zMTM2bN++GbSdNmqTExETrcvr06UKroyAMw9CoUaO0evVqbd26VZUqVXJoPQAAAABQEpT4kW4fHx9VqVLF+rhBgwYym816++23Va1aNR0+fLhQ+rl69aoeeughnThxQlu3br3hKLf093fNPTw8CqXvwvD0009r7dq1+vjjj+Xn56eEhARJf39n3cvLy8HVAQAAAEDxVOJHuq9nMplUqlQpXb58Wf369dPPP/+sjz/+OFs7wzCsM3jfTFbgPnbsmDZv3qzAwMDCLtvuli5dqsTERLVq1UoWi8W6rFy50tGlAQAAAECxVeJHutPS0qyjtn/88YfmzJmj5ORkdenSRS1bttSaNWvUt29fTZ48WW3btlVwcLB+/PFHvfbaaxo9erS6du0qSbp8+bJiY2Ntju3r66vw8HD17NlT+/fv1/r163Xt2jVrfwEBAXJ3dy/K0y2ws2fPymKxOLoMAAAAAChRSnzo3rhxozVM+vn5qXr16vrwww/VqlUrSdLy5cu1cOFCLV68WNOnT5erq6uqVq2qRx99VO3bt7ce5+eff1a9evVsjt2yZUstWbJE69atkyTVrVvXZvu2bdus/QAAAAAAbj8mwzAMRxeBv28ZZjab1bTpp9q9u0OR9WuxxGvYsIUaOnQoI90AAAAAkEdZGS4xMfGGc3rddt/pBgAAAACgqBC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANgJoRsAAAAAADshdAMAAAAAYCeuji4Atnx9k2WxxBdZf0FB54usLwAAAAC43RC6nUyDBvt1772HirTPUqXc5O3tXaR9AgAAAMDtgNDtZAYOHCg/P78i7dPb21tms7lI+wQAAACA2wGh28mEhITI39/f0WUAAAAAAAoBE6kBAAAAAGAnhG4AAAAAAOyE0A0AAAAAgJ3wnW4nk5CQoJSUlHzvx2RoAAAAAOB8CN1OJiYmRp6envner5RrKY0ZNYbgDQAAAABOhNDtZHZoh+IUl699ghSkHhk9lJqaSugGAAAAACdC6HYyiUpUvOIdXQYAAAAAoBAwkRoAAAAAAHZC6AYAAAAAwE4I3QAAAAAA2AmhGwAAAAAAOyF0AwAAAABgJ4TuEmbnzp3q0qWLQkNDZTKZtHbtWkeXBAAAAAC3LUJ3CZOSkqI6depozpw5ji4FAAAAAG57t23ojo6Olslk0mOPPZZt24gRI2QymTRgwAC1adNG7du3z9Zm3rx5MpvNOnXqlCRp27Zt6tixowIDA+Xt7a0aNWroiSee0JkzZ+x+Lv/UoUMHTZ8+Xd27dy/SfgEAAAAA2d22oVuSwsLCtGLFCl2+fNm67sqVK3r//fdVoUIFmUwmxcTE6JtvvtFbb71lbXPixAlNmDBBb7zxhipUqKC33npLbdq0UUhIiFatWqVDhw5pwYIFSkxM1H//+19HnBoAAAAAwAm4OroAR6pfv75+/fVXrV69Wg8//LAkafXq1QoLC1PlypUl/R3M33jjDY0aNUrt2rVTeHi4Bg8erPvvv1/R0dH67bffNGbMGI0ZM0avvfaa9djh4eFq0aKF/vzzT0ecGgAAAADACdzWI92SNHDgQMXExFgfL168WIMGDbJpM2DAAN1///0aOHCg5syZo4MHD2rhwoWSpA8//FDp6el66qmncjx+6dKlc1yflpampKQkmwUAAAAAULLc9qG7f//++uqrrxQXF6eTJ0/q66+/1iOPPJKt3cKFC3Xo0CE9/vjjeuutt1S2bFlJ0rFjx+Tv7y+LxZKvfmfMmCGz2WxdwsLCCuV8AAAAAADO47YP3UFBQerUqZOWLl2qmJgYderUSUFBQdnalS1bVkOHDtVdd92lbt26WdcbhiGTyZTvfidNmqTExETrcvr06Vs6DwAAAACA87mtv9OdZdCgQRo1apQkae7cubm2c3V1laur7VNWrVo1JSYmKj4+Pl+j3R4eHvLw8ChYwTeQnJys48ePWx+fOHFCsbGxCggIUIUKFQq9PwAAAABA7m77kW5JioqKUnp6utLT03O8PdiN9OzZU+7u7po5c2aO24t6IrW9e/eqXr16qlevniRp3LhxqlevnqZMmVKkdQAAAAAAGOmWJLm4uOjw4cPWf+dHWFiYXnvtNY0aNUpJSUl69NFHFR4ert9++03vvvuufH19i/S2Ya1atZJhGEXWHwAAAAAgd4x0/3/+/v7y9/cv0L4jRozQF198oTNnzqhbt26qXr26hgwZIn9/fz355JOFXCkAAAAAoLgwGQyLOoWkpCSZzWbVmFhDhzwP5WtfiywapmEaOnRovmdRBwAAAADkX1aGS0xMvOEALiPdAAAAAADYCaEbAAAAAAA7IXQDAAAAAGAnhG4AAAAAAOyE0A0AAAAAgJ0QugEAAAAAsBNCNwAAAAAAduLq6AJgyyyzLMrfvbaDFGSnagAAAAAAt4LQ7WRaqqXaq32+9yvlWkre3t52qAgAAAAAUFCEbiczcOBA+fn55Xs/b29vmc1mO1QEAAAAACgoQreTCQkJkb+/v6PLAAAAAAAUAiZSAwAAAADATgjdAAAAAADYCaEbAAAAAAA74TvdTiYhIUEpKSn53o+J1AAAAADA+RC6nUxMTIw8PT3zvV8p11IaM2oMwRsAAAAAnAih28ns0A7FKS5f+wQpSD0yeig1NZXQDQAAAABOhNDtZBKVqHjFO7oMAAAAAEAhYCI1AAAAAADshNANAAAAAICdELoBAAAAALATQjcAAAAAAHZC6AYAAAAAwE4I3SXMzp071aVLF4WGhspkMmnt2rWOLgkAAAAAbluE7hImJSVFderU0Zw5cxxdCgAAAADc9opV6E5ISNDo0aNVuXJleXh4KCwsTF26dNGWLVusbb7//nv16tVLd9xxhzw9PVWtWjX961//0s8//yxJiouLk8lkUtmyZfXXX3/ZHL9u3bqaOnWq9XGrVq1kMplkMpnk4eGhcuXKqUuXLlq9enW22l544QU1a9ZM3t7eKl26tF3OPy86dOig6dOnq3v37g6rAQAAAADwt2ITuuPi4tSgQQNt3bpVM2fO1I8//qiNGzcqMjJSI0eOlCStX79eTZo0UVpampYtW6bDhw/rf//7n8xmsyZPnmxzvL/++kuzZs26ab//+te/FB8fr+PHj2vVqlWqUaOG+vTpo6FDh9q0S09PV69evTR8+PDCO2kAAAAAQLHm6ugC8mrEiBEymUz69ttv5ePjY11fs2ZNDRo0SKmpqRo4cKA6duyoNWvWWLdXqlRJjRs31p9//mlzvNGjR+vVV1/VyJEjVbZs2Vz79fb2VkhIiCQpLCxMTZo0UfXq1TVo0CA99NBDatOmjSRp2rRpkqQlS5YU0hkDAAAAAIq7Ao10L126VBs2bLA+fuqpp1S6dGk1a9ZMJ0+eLLTisly6dEkbN27UyJEjbQJ3ltKlS+vzzz/XhQsX9NRTT+V4jOsv+e7bt6+qVKmi5557Lt/1DBgwQGXKlMnxMvO8SktLU1JSks0CAAAAAChZChS6X3zxRXl5eUmSdu/erTlz5mjmzJkKCgrS2LFjC7VASTp+/LgMw1D16tVzbXPs2DFJumGbfzKZTHrppZe0cOFC/fLLL/mqp1SpUqpWrZri4uLytd8/zZgxQ2az2bqEhYUV+FgAAAAAAOdUoNB9+vRpValSRZK0du1a9ezZU0OHDtWMGTP05ZdfFmqBkmQYhqS/g/LN2uRH+/bt1bx582zf985rTTeq52YmTZqkxMRE63L69OkCHwsAAAAA4JwKFLp9fX118eJFSdIXX3xh/V6zp6enLl++XHjV/X9Vq1aVyWTS4cOHc21TrVo1SdKRI0fydeyXXnpJK1eu1Pfff5/nfa5du6Zjx46pUqVK+errnzw8POTv72+zFIbk5GTFxsYqNjZWknTixAnFxsbq1KlThXJ8AAAAAEDeFSh0t23bVkOGDNGQIUP0888/q1OnTpKkn376SeHh4YVZnyQpICBA7du319y5c5WSkpJt+59//ql27dopKChIM2fOzPEY10+klqVRo0bq3r27Jk6cmOd6li5dqj/++EM9evTI8z5FZe/evapXr57q1asnSRo3bpzq1aunKVOmOLgyAAAAALj9FGj28rlz5+qZZ57R6dOntWrVKgUGBkqS9u3bp759+xZqgVnmzZunZs2aqVGjRnruuedUu3ZtZWRkaNOmTZo/f74OHz6sd955R7169dIDDzygMWPGqEqVKrpw4YI++OADnTp1SitWrMjx2C+88IJq1qwpV9fsT0dqaqoSEhKUkZGhM2fOaPXq1Xrttdc0fPhwRUZGWtudOnVKly5d0qlTp3Tt2jXrSHOVKlXk6+trl+ckJ61atSrQpfYAAAAAgMJXoNBdunRpzZkzJ9v6rNtm2UOlSpW0f/9+vfDCC3riiScUHx+v4OBgNWjQQPPnz5ckPfjgg9q1a5dmzJihfv36KSkpSWFhYWrdurWmT5+e67GrVaumQYMGaeHChdm2vf3223r77bfl7u6uwMBANWjQQCtXrlS3bt1s2k2ZMkVLly61Ps4aad62bZtatWpVCM8AAAAAAKC4MRkFHBb98ssv9dZbb+nXX3/Vhx9+qHLlyul///ufKlWqpObNmxd2nSVeUlKSzGazakysoUOeh/K1r0UWDdMwDR06VBaLxU4VAgAAAACyZGW4xMTEG87RVaDvdK9atUrt27eXl5eX9u/fr7S0NEnSX3/9pRdffLFgFQMAAAAAUMIUKHRPnz5dCxYs0Ntvvy03Nzfr+mbNmmn//v2FVhwAAAAAAMVZgUL30aNH1aJFi2zr/f39c50lHAAAAACA202BQrfFYtHx48ezrf/qq69UuXLlWy4KAAAAAICSoEChe9iwYfr3v/+tb775RiaTSWfPntWyZcv05JNPasSIEYVdIwAAAAAAxVKBbhn21FNPKTExUZGRkbpy5YpatGghDw8PPfnkkxo1alRh1wgAAAAAQLFUoNAtSS+88IL+85//6NChQ8rMzFSNGjXk6+tbmLUBAAAAAFCsFTh0S5K3t7fuueeewqoFkswyy6L83Ws7SEF2qgYAAAAAcCsKFLpTUlL00ksvacuWLTp37pwyMzNttv/666+FUtztqKVaqr3a53u/Uq6l5O3tbYeKAAAAAAAFVaDQPWTIEO3YsUP9+/eXxWKRyWQq7LpuWwMHDpSfn1++9/P29pbZbLZDRQAAAACAgipQ6P7ss8+0YcMG3XvvvYVdz20vJCRE/v7+ji4DAAAAAFAICnTLsDJlyiggIKCwawEAAAAAoEQpUOh+/vnnNWXKFKWmphZ2PQAAAAAAlBgFurz8v//9r3755RfdcccdCg8Pl5ubm832/fv3F0pxAAAAAAAUZwUK3V27di3kMpAlISFBKSkp+d6PidQAAAAAwPmYDMMwHF0EpKSkJJnNZk2cOFGenp753r9UKTeNGTOS4A0AAAAARSArwyUmJt5wMuwCjXRL0p9//qmPPvpIv/zyi8aPH6+AgADt379fd9xxh8qVK1fQw9729u2rr4MHm+drn6Cg8+rRY41SU1MJ3QAAAADgRAoUug8cOKA2bdrIbDYrLi5O//rXvxQQEKA1a9bo5MmTevfddwu7zttGcrKv4uMtji4DAAAAAFAICjR7+bhx4xQdHa1jx47ZXArdoUMH7dy5s9CKAwAAAACgOCtQ6P7uu+80bNiwbOvLlSunhISEWy4KAAAAAICSoECh29PTU0lJSdnWHz16VMHBwbdcFAAAAAAAJUGBQveDDz6o5557TlevXpUkmUwmnTp1ShMnTlSPHj0KtUAAAAAAAIqrAoXuWbNm6fz58ypbtqwuX76sli1bqkqVKvL19dULL7xQ2DUCAAAAAFAsFWj2cn9/f3311Vfatm2b9u3bp8zMTNWvX19t2rQp7PoAAAAAACi28jXSffnyZa1fv976+IsvvtDZs2eVkJCgTz/9VE899ZSuXLlS6EUi73bu3KkuXbooNDRUJpNJa9eudXRJAAAAAHDbylfofvfdd/XWW29ZH8+ZM0e7du3S999/r++//17/+9//NH/+/EIvMkt0dLRMJpMee+yxbNtGjBghk8mk6Ohom/W7du2Si4uLoqKisu0TFxcnk8lkXcqUKaMWLVpox44dNn127drVZr+PPvpInp6emjlzpiTpp59+Uo8ePRQeHi6TyaTXX3/9ls+1oFJSUlSnTh3NmTPHYTUAAAAAAP6Wr9C9bNkyDRo0yGbd8uXLtW3bNm3btk2vvPKKPvjgg0It8HphYWFasWKFLl++bF135coVvf/++6pQoUK29osXL9bo0aP11Vdf6dSpUzkec/PmzYqPj9eOHTvk7++vjh076sSJEzm2feedd/Twww9rzpw5euqppyRJqampqly5sl566SWFhIQUwlkWXIcOHTR9+nR1797doXUAAAAAAPIZun/++WdVq1bN+tjT01OlSv3fIRo1aqRDhw4VXnU5qF+/vipUqKDVq1db161evVphYWGqV6+eTduUlBR98MEHGj58uDp37qwlS5bkeMzAwECFhISodu3aeuutt5SamqovvvgiW7uZM2dq1KhRWr58uYYMGWJd37BhQ73yyivq06ePPDw8CudEAQAAAADFXr5Cd2Jiolxd/2/utfPnzys8PNz6ODMzU2lpaYVWXG4GDhyomJgY6+PFixdnG4GXpJUrVyoiIkIRERF65JFHFBMTI8Mwbnhsb29vSbLeDi3LxIkT9fzzz2v9+vXcFg0AAAAAkCf5Ct3ly5fXwYMHc91+4MABlS9f/paLupn+/fvrq6++UlxcnE6ePKmvv/5ajzzySLZ2ixYtsq6PiopScnKytmzZkutxU1JSNGnSJLm4uKhly5bW9Z999plefvllffzxx4U2Q3taWpqSkpJsFgAAAABAyZKv0N2xY0dNmTIlxxnKL1++rGnTpqlTp06FVlxugoKC1KlTJy1dulQxMTHq1KmTgoKCbNocPXpU3377rfr06SNJcnV1Ve/evbV48eJsx2vWrJl8fX3l5+enTz75REuWLFGtWrWs22vXrq3w8HBNmTJFf/31V6Gcw4wZM2Q2m61LWFhYoRwXAAAAAOA88nWf7qeffloffPCBIiIiNGrUKFWrVk0mk0lHjhzRnDlzlJGRoaefftpetdoYNGiQRo0aJUmaO3dutu2LFi1SRkaGypUrZ11nGIbc3Nz0xx9/qEyZMtb1K1euVI0aNVS6dGkFBgZmO1a5cuW0atUqRUZGKioqShs3bpSfn98t1T9p0iSNGzfO+jgpKYngDQAAAAAlTL5C9x133KFdu3Zp+PDhmjhxovX70SaTSW3bttW8efN0xx132KXQ60VFRSk9PV2S1L59e5ttGRkZevfdd/Xf//5X7dq1s9nWo0cPLVu2zBrYpb9nRL/zzjtv2F+FChW0Y8cORUZGql27dvr888/l7+9f4Po9PDzsMulacnKyjh8/bn184sQJxcbGKiAgIMfZ3QEAAAAA9pOv0C1JlSpV0saNG3Xp0iVruKtSpYoCAgIKvbgbcXFx0eHDh63//qf169frjz/+0ODBg2U2m2229ezZU4sWLbIJ3XlVvnx5bd++3SZ4m81mpaenW2dtT09P15kzZxQbGytfX19VqVKlgGdYMHv37lVkZKT1cdZo+oABA3KdvR0AAAAAYB/5+k73PwUEBKhRo0Zq1KhRkQfuLP7+/jmONi9atEht2rTJFrilv0e6Y2NjtX///gL1Wa5cOe3YsUN//vmn2rZtqz///FNnz55VvXr1VK9ePcXHx2vWrFmqV6+ezW3FikqrVq1kGEa2hcANAAAAAEUv3yPdjnSz4Lh27dqbHqN+/fo2tw272S3EcurTYrHoyJEj1selS5e+6XEAAAAAALefAo90AwAAAACAGyN0AwAAAABgJ4RuAAAAAADshNANAAAAAICdELoBAAAAALATQjcAAAAAAHZC6AYAAAAAwE6K1X26bwe+vsmyWOLztU9Q0Hk7VQMAAAAAuBWEbifToMF+3XvvoXzvV6qUm7y9ve1QEQAAAACgoAjdTmbgwIHy8/PL937e3t4ym812qAgAAAAAUFCEbicTEhIif39/R5cBAAAAACgETKQGAAAAAICdELoBAAAAALATQjcAAAAAAHbCd7qdTEJCglJSUoq0TyZhAwAAAAD7IHQ7mZiYGHl6ehZpn26upTRy1BiCNwAAAAAUMkK3k+lSdp0qmf8osv7Opwdpze89lJqaSugGAAAAgEJG6HYyQe4XZfH83dFlAAAAAAAKAROpAQAAAABgJ4RuAAAAAADshNANAAAAAICdELoBAAAAALATQjcAAAAAAHZC6AYAAAAAwE4I3ZAkzZ49Ww0bNpSfn5/Kli2rrl276ujRo44uCwAAAACKtds6dEdHR8tkMslkMsnV1VUVKlTQ8OHD9ccff1jbhIeHW9u4uLgoNDRUgwcPtmlz5coVRUdHq1atWnJ1dVXXrl0dcDa3Zvfu3Ro5cqT27NmjTZs2KSMjQ+3atVNKSoqjSwMAAACAYuu2Dt2SFBUVpfj4eMXFxemdd97RJ598ohEjRti0ee655xQfH69Tp05p2bJl2rlzp8aMGWPdfu3aNXl5eWnMmDFq06ZNUZ9CoVi+fLmio6NVs2ZN1alTRzExMTp16pT27dvn6NIAAAAAoNhydXQBjubh4aGQkBBJUvny5dW7d28tWbLEpo2fn5+1Tbly5fToo49qxYoV1u0+Pj6aP3++JOnrr7/Wn3/+WSS121NiYqIkKSAgwMGVAAAAAEDxdduPdP/Tr7/+qo0bN8rNzS3XNmfOnNH69evVuHHjIqysaBmGoXHjxql58+a6++67HV0OAAAAABRbt/1I9/r16+Xr66tr167pypUrkqRXX33Vps2ECRP0zDPPWNs0btw4W5v8SktLU1pamvVxUlLSLR2vMI0aNUoHDhzQV1995ehSAAAAAKBYu+1HuiMjIxUbG6tvvvlGo0ePVvv27TV69GibNuPHj1dsbKwOHDigLVu2SJI6deqka9euFbjfGTNmyGw2W5ewsLBbOo/CMnr0aK1bt07btm1T+fLlHV0OAAAAABRrt33o9vHxUZUqVVS7dm29+eabSktL07Rp02zaBAUFqUqVKqpatapat26t119/Xbt27dK2bdsK3O+kSZOUmJhoXU6fPn2rp3JLDMPQqFGjtHr1am3dulWVKlVyaD0AAAAAUBLc9peXX+/ZZ59Vhw4dNHz4cIWGhubYxsXFRZJ0+fLlAvfj4eEhDw+PAu9f2J5++mmtXbtWH3/8sfz8/JSQkCBJMpvN8vLycnB1AAAAAFA83fYj3ddr1aqVatasqRdffNG67q+//lJCQoLi4+P17bffavz48QoKClKzZs2sbQ4dOqTY2FhdunRJiYmJio2NVWxsrAPOoGCWLl2qxMREtWrVShaLxbqsXLnS0aUBAAAAQLHFSHcOxo0bp4EDB2rChAmSpClTpmjKlCmSpODgYDVs2FCbNm1SYGCgdZ+OHTvq5MmT1sf16tWT9Pdl28XB2bNnZbFYHF0GAAAAAJQot3Xovv5+3Fn69eunfv36SZLi4uLydKy8tgMAAAAA3D64vBwAAAAAADshdAMAAAAAYCeEbgAAAAAA7ITQDQAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBObuv7dDujC+mB8rtSdH8LOZ8eVGR9AQAAAMDthtDtZD4594A8kzyLtE8311Ly9vYu0j4BAAAA4HZA6HYyAwcOlJ+fX5H26e3tLbPZXKR9AgAAAMDtgNDtZEJCQuTv7+/oMgAAAAAAhYCJ1AAAAAAAsBNCNwAAAAAAdkLoBgAAAADATvhOt5NJSEhQSkpKkfbJRGoAAAAAYB+EbicTExMjT8+iv2XYyFFjCN4AAAAAUMgI3U6mS9l1qmT+o8j6O58epDW/91BqaiqhGwAAAAAKGaHbyQS5X5TF83dHlwEAAAAAKARMpAYAAAAAgJ0QugEAAAAAsBNCNwAAAAAAdkLoBgAAAADATgjdAAAAAADYCaEbAAAAAAA7IXRDkjR79mw1bNhQfn5+Klu2rLp27aqjR486uiwAAAAAKNZKZOiOjo6WyWTSY489lm3biBEjZDKZFB0dbdP2+iUqKsq6T3h4eLbt5cuXV3p6uoKCgjR9+vQc65gxY4aCgoKUnp5ul/MsTLt379bIkSO1Z88ebdq0SRkZGWrXrp1SUlIcXRoAAAAAFFslMnRLUlhYmFasWKHLly9b1125ckXvv/++KlSoYNM2KipK8fHxNsv7779v0+a5556z2f7999/L3d1djzzyiJYsWSLDMLLVEBMTo/79+8vd3d0+J1mIli9frujoaNWsWVN16tRRTEyMTp06pX379jm6NAAAAAAotkps6K5fv74qVKig1atXW9etXr1aYWFhqlevnk1bDw8PhYSE2CxlypSxaePn52ezPTg4WJI0ePBg/fLLL9q5c6dN+y+//FLHjh3T4MGD7XSG9pWYmChJCggIcHAlAAAAAFB8ldjQLUkDBw5UTEyM9fHixYs1aNCgQu2jVq1aatiwoU0/WX01atRId999d6H2VxQMw9C4cePUvHnzYlk/AAAAADiLEh26+/fvr6+++kpxcXE6efKkvv76az3yyCPZ2q1fv16+vr42y/PPP2/TZsKECTbb33zzTeu2QYMG6aOPPlJycrIkKTk5WR9++OENR7nT0tKUlJRksziLUaNG6cCBA9kusQcAAAAA5I+rowuwp6CgIHXq1ElLly6VYRjq1KmTgoKCsrWLjIzU/PnzbdZdf1n1+PHjrZOvZR07S9++fTVu3DitXLlSgwcP1sqVK2UYhvr06ZNrbTNmzNC0adMKeGb2M3r0aK1bt047d+5U+fLlHV0OAAAAABRrJTp0S3+PQo8aNUqSNHfu3Bzb+Pj4qEqVKjc8TlBQUK5tzGazevbsqZiYGA0ePFgxMTHq2bOn/P39cz3epEmTNG7cOOvjpKQkhYWF3ex07MYwDI0aNUpr1qzR9u3bValSJYfVAgAAAAAlRYkP3VFRUdZbdrVv395u/QwePFitWrXS+vXr9fXXX+vFF1+8YXsPDw95eHjYrZ78evrpp7V27Vp9/PHH8vPzU0JCgqS//6Dg5eXl4OoAAAAAoHgq8aHbxcVFhw8ftv47J2lpadaQmcXV1TXHS9Fz07JlS1WpUkWPPvqoqlSpohYtWhS8aAdYunSpJKlVq1Y262NiYmwuqwcAAAAA5F2JD92SbniZtyRt3LhRFovFZl1ERISOHDmSr34GDRqkp59+WuPHj893jY529uzZbM8BAAAAAODWmAzDMBxdBP7+TrfZbNbROXeoWpnfi6zf+CsWLTw9TEOHDiV0AwAAAEAeZWW4xMTEGw70luhbhgEAAAAA4EiEbgAAAAAA7ITQDQAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANiJq6MLgK0L6YHyu1J0fws5nx5UZH0BAAAAwO2G0O1kPjn3gDyTPIu0TzfXUvL29i7SPgEAAADgdkDodjIDBw6Un59fkfbp7e0ts9lcpH0CAAAAwO2A0O1kQkJC5O/v7+gyAAAAAACFgInUAAAAAACwE0I3AAAAAAB2QugGAAAAAMBO+E63k0lISFBKSkqR9slEagAAAABgH4RuJxMTEyNPz6K/ZdjIUWMI3gAAAABQyAjdTqZL2XWqZP6jyPo7nx6kNb/3UGpqKqEbAAAAAAoZodvJBLlflMXzd0eXAQAAAAAoBEykBgAAAACAnRC6AQAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANgJoRsAAAAAADshdEOSNHv2bDVs2FB+fn4qW7asunbtqqNHjzq6LAAAAAAo1kp86I6OjpbJZMq2eHp65rj+n8uSJUskSW+99Zbq1KkjHx8flS5dWvXq1dPLL79s7eOnn35Sjx49FB4eLpPJpNdff90xJ3sLdu/erZEjR2rPnj3atGmTMjIy1K5dO6WkpDi6NAAAAAAotlwdXUBRiIqKUkxMjM06k8kkwzCsj//9738rKSnJpp3ZbNaiRYs0btw4vfnmm2rZsqXS0tJ04MABHTp0yNouNTVVlStXVq9evTR27Fj7n5AdLF++XBaLxfo4JiZGZcuW1b59+9SiRQsHVgYAAAAAxddtEbo9PDwUEhJywzZeXl5KS0vL1u6TTz7RQw89pMGDB1vX1axZ06ZNw4YN1bBhQ0nSxIkTC6lqx0pMTJQkBQQEOLgSAAAAACi+Svzl5bcqJCREe/bs0cmTJx1dSpExDEPjxo1T8+bNdffddzu6HAAAAAAotm6L0L1+/Xr5+vraLM8//3ye9n322WdVunRphYeHKyIiQtHR0frggw+UmZl5SzWlpaUpKSnJZnEWo0aN0oEDB/T+++87uhQAAAAAKNZui8vLIyMjNX/+fJt1eb1s2mKxaPfu3Tp48KB27NihXbt2acCAAXrnnXe0ceNGlSpVsL9bzJgxQ9OmTSvQvvY0evRorVu3Tjt37lT58uUdXQ4AAAAAFGu3Rej28fFRlSpVbukYd999t+6++26NHDlSX331le677z7t2LFDkZGRBTrepEmTNG7cOOvjpKQkhYWF3VKNt8IwDI0aNUpr1qzR9u3bValSJYfVAgAAAAAlxW0RugtbjRo1JOmWbqfl4eEhDw+Pwirplj399NNau3atPv74Y/n5+SkhIUHS3zO4e3l5Obg6AAAAACiebovQnZaWZg2RWVxdXRUUFHTTfYcPH67Q0FC1bt1a5cuXV3x8vKZPn67g4GA1bdpUkpSenm69hVh6errOnDmj2NhY+fr63vIIe1FZunSpJKlVq1Y262NiYhQdHV30BQEAAABACXBbhO6NGzfa3INakiIiInTkyJGb7tumTRstXrxY8+fP18WLFxUUFKSmTZtqy5YtCgwMlCSdPXtW9erVs+4za9YszZo1Sy1bttT27dsL9Vzs5ezZs9meIwAAAADArTEZhmE4ugj8/Z1us9mso3PuULUyvxdZv/FXLFp4epiGDh1K6AYAAACAPMrKcImJifL398+13W1xyzAAAAAAAByB0A0AAAAAgJ0QugEAAAAAsBNCNwAAAAAAdkLoBgAAAADATgjdAAAAAADYCaEbAAAAAAA7cXV0AbB1IT1QfleK7m8h59ODiqwvAAAAALjdELqdzCfnHpBnkmeR9unmWkre3t5F2icAAAAA3A4I3U5m4MCB8vPzK9I+vb29ZTabi7RPAAAAALgdELqdTEhIiPz9/R1dBgAAAACgEDCRGgAAAAAAdkLoBgAAAADATgjdAAAAAADYCd/pdjIJCQlKSUnJ935MhgYAAAAAzofQ7WRiYmLk6Zn/W4a5uZbSyFFjCN4AAAAA4EQI3U6mS9l1qmT+I1/7nE8P0prfeyg1NZXQDQAAAABOhNDtZILcL8ri+bujywAAAAAAFAImUgMAAAAAwE4I3QAAAAAA2AmhGwAAAAAAOyF0AwAAAABgJ4RuAAAAAADshNANAAAAAICdELpLmJ07d6pLly4KDQ2VyWTS2rVrHV0SAAAAANy2SmzoNplMN1yio6MlSdu2bVNkZKQCAgLk7e2tqlWrasCAAcrIyLAeyzAMLVy4UI0bN5avr69Kly6te+65R6+//rpSU1MlST/99JN69Oih8PBwmUwmvf766w44ayklJUV16tTRnDlzHNI/AAAAAOD/uDq6AHuJj4+3/nvlypWaMmWKjh49al3n5eWln376SR06dNCYMWM0e/ZseXl56dixY/roo4+UmZlpbdu/f3+tXr1azzzzjObMmaPg4GD98MMPev311xUeHq6uXbsqNTVVlStXVq9evTR27NgiPdd/6tChgzp06OCw/gEAAAAA/6fEhu6QkBDrv81ms0wmk806SYqJiZHFYtHMmTOt6+68805FRUVZH3/wwQdatmyZ1q5dqwcffNC6Pjw8XA888ICSkpIkSQ0bNlTDhg0lSRMnTrTLOQEAAAAAipcSe3l5XoSEhCg+Pl47d+7Mtc2yZcsUERFhE7izmEwmmc1me5YIAAAAACjGSuxId1706tVLn3/+uVq2bKmQkBA1adJE999/vx599FH5+/tLko4dO6aIiIhC7zstLU1paWnWx1kj5gAAAACAkuO2Hul2cXFRTEyMfvvtN82cOVOhoaF64YUXVLNmTet3wg3DkMlkKvS+Z8yYIbPZbF3CwsIKvQ8AAAAAgGPd1qE7S7ly5dS/f3/NnTtXhw4d0pUrV7RgwQJJUrVq1XT48OFC73PSpElKTEy0LqdPny70PgAAAAAAjkXovk6ZMmVksViUkpIiSerXr59+/vlnffzxx9naGoahxMTEAvXj4eEhf39/m6UwJCcnKzY2VrGxsZKkEydOKDY2VqdOnSqU4wMAAAAA8u62Dt1vvfWWhg8fri+++EK//PKLfvrpJ02YMEE//fSTunTpIkl66KGH1Lt3b/Xt21czZszQ3r17dfLkSa1fv15t2rTRtm3bJEnp6enWsJuenq4zZ84oNjZWx48fL9Jz2rt3r+rVq6d69epJksaNG6d69eppypQpRVoHAAAAAOA2n0itUaNG+uqrr/TYY4/p7Nmz8vX1Vc2aNbV27Vq1bNlS0t8zlC9fvlwLFy7U4sWLNX36dLm6uqpq1ap69NFH1b59e0nS2bNnrUFXkmbNmqVZs2apZcuW2r59e5GdU6tWrWQYRpH1BwAAAADInckgoTmFpKQkmc1mHZ1zh6qV+T1f+8ZfsWjh6WEaOnSoLBaLnSoEAAAAAGTJynCJiYk3/LrwbX15OQAAAAAA9kToBgAAAADATgjdAAAAAADYCaEbAAAAAAA7IXQDAAAAAGAnhG4AAAAAAOyE0A0AAAAAgJ24OroA2LqQHii/K/n7W8j59CA7VQMAAAAAuBWEbifzybkH5Jnkme/93FxLydvb2w4VAQAAAAAKitDtZAYOHCg/P7987+ft7S2z2WyHigAAAAAABUXodjIhISHy9/d3dBkAAAAAgELARGoAAAAAANgJoRsAAAAAADshdAMAAAAAYCeEbgAAAAAA7ISJ1JxMQkKCUlJS8r0fs5cDAAAAgPMhdDuZmJgYeXoW7D7dI0eNIXgDAAAAgBMhdDuZLmXXqZL5j3ztcz49SGt+76HU1FRCNwAAAAA4EUK3kwlyvyiL5++OLgMAAAAAUAiYSA0AAAAAADshdAMAAAAAYCeEbgAAAAAA7ITQDQAAAACAnRC6AQAAAACwE0J3CbNz50516dJFoaGhMplMWrt2raNLAgAAAIDbVrEO3dHR0TKZTHrssceybRsxYoRMJpOio6Nt2ppMJrm5uemOO+5Q27ZttXjxYmVmZtrsGx4eLpPJpBUrVmQ7bs2aNWUymbRkyRJJ0qVLlzR69GhFRETI29tbFSpU0JgxY5SYmFjo55sXKSkpqlOnjubMmeOQ/gEAAAAA/6dYh25JCgsL04oVK3T58mXruitXruj9999XhQoVbNpGRUUpPj5ecXFx+uyzzxQZGal///vf6ty5szIyMrIdNyYmxmbdnj17lJCQIB8fH+u6s2f/X3t3HpdT+v8P/HW373eLtJgoEmVINIj5DBkttskwGJKS9atshcGQXRhrImZosQxjC2NoGMq+1QgfskdmKiRKpU3n94df5+OeFpVulXk953EeD/d1Xec673Pui8e87+uc6yQjOTkZy5Ytw7Vr1xAeHo6oqCgMHz5cDmf7bt27d8eCBQvQt2/fGjk+ERERERER/U+dT7rbtGmDhg0bYu/evWLZ3r17YWZmBjs7O5m2qqqqMDY2RoMGDdCmTRvMmDED+/fvx+HDh8WZ62Lu7u44ceIEHj16JJaFhobC3d0dSkpKYtmnn36KPXv2oHfv3mjSpAm6du2KhQsX4tdffy2RyBMREREREdG/S51PugFg2LBhMrPSoaGh8Pb2rtC+Xbt2ha2trUzSDgBGRkZwcXFBREQEACAnJwe//PJLhfrNyMiAjo6OTHJORERERERE/z4fRdLt4eGB06dP48GDB3j48CHOnDmDIUOGVHj/5s2b48GDByXKvb29ER4eDkEQsHv3bjRp0gStW7cut69nz55h/vz5GD16dLnt8vLykJmZKbMRERERERHRx+WjSLrr1auHnj17IiIiAmFhYejZsyfq1atX4f0FQYBEIilR3rNnT2RlZeHkyZMVmj3PzMxEz549YWNjg9mzZ5fbNjAwEFKpVNzMzMwqHC8RERERERHVDR/N/c/e3t7w9fUFAKxdu7ZS+yYkJMDCwqJEuZKSEjw8PDB79mxcuHABkZGRZfbx8uVLuLq6QktLC5GRkVBWVi73mNOnT4efn5/4OTMzk4k3ERERERHRR+ajSbpdXV2Rn58PAHBxcanwfsePH8e1a9cwadKkUuu9vb2xbNkyDBw4EHp6eqW2yczMhIuLC1RVVXHgwAGoqam987iqqqpQVVWtcJwVlZWVhbt374qfExMTER8fD319/RKruRMREREREZF8fTRJt6KiIhISEsQ/lyYvLw+pqal4/fo1Hj9+jKioKAQGBqJXr14YOnRoqftYW1sjLS0NGhoapda/fPkSzs7OyMnJwdatW2WezzY0NCwzFnmJjY2Fo6Oj+Ll4Nt3T07PECu1EREREREQkXx9N0g0AOjo65dZHRUXBxMQESkpK0NPTg62tLYKCguDp6QkFhbIfbzcwMCizLi4uDhcuXAAAWFpaytQlJibC3Ny84idQDbp06QJBED7oMYmIiIiIiKh0EoEZWq2QmZkJqVSKW8FGsNJ7XKl9U3JN8OOj0Rg1ahRMTEzkFCEREREREREVK87hil8ZXZaPYvVyIiIiIiIiotqISTcRERERERGRnDDpJiIiIiIiIpITJt1EREREREREcsKkm4iIiIiIiEhOmHQTERERERERyQmTbiIiIiIiIiI5UarpAEhWWr4BtHMr91vI0/x6coqGiIiIiIiI3geT7lrm1ydfQS1TrdL7KSspQENDQw4RERERERERUVUx6a5lhg0bBm1t7Urvp6GhAalUKoeIiIiIiIiIqKqYdNcyxsbG0NHRqekwiIiIiIiIqBpwITUiIiIiIiIiOWHSTURERERERCQnTLqJiIiIiIiI5IRJNxEREREREZGccCG1WiY1NRXZ2dmV3o+rlxMREREREdU+TLprmbCwMKipVe093T6+45l4ExERERER1SJMumuZ3vUPwEL6vFL7PM2vh8jH/ZCTk8Okm4iIiIiIqBZh0l3L1FN5BhO1xzUdBhEREREREVUDLqRGREREREREJCdMuomIiIiIiIjkhEk3ERERERERkZww6SYiIiIiIiKSEybdRERERERERHLCpPsjc/LkSfTu3RumpqaQSCTYt29fTYdERERERET0r8Wk+yOTnZ0NW1tbBAcH13QoRERERERE/3p1Mun28vKCRCIpsd29excA8OjRIwwfPhympqZQUVFBo0aNMGHCBDx79gwA8Pr1a3Ts2BH9+vWT6TcjIwNmZmaYOXOmWHbw4EF06dIF2tra0NDQwGeffYbw8HCZ/Z49ewZXV1eYmppCVVUVZmZm8PX1RWZmpnwvRCm6d++OBQsWoG/fvh/82ERERERERCSrTibdAODq6oqUlBSZzcLCAvfv34e9vT1u376N7du34+7du1i/fj2OHTsGBwcHpKenQ1FREREREYiKisK2bdvEPseNGwd9fX0EBAQAANasWQM3Nzd07NgRFy5cwNWrV/Htt99izJgxmDx5srifgoIC3NzccODAAdy+fRvh4eH4448/MGbMmA9+XYiIiIiIiKj2UKrpAKpKVVUVxsbGJcp9fHygoqKCI0eOQF1dHQDQsGFD2NnZoUmTJvj+++8REhKCpk2bIjAwEOPGjYOjoyMuXbqEHTt24OLFi1BRUcGjR4/g7++PiRMnYtGiRWL//v7+UFFRwfjx49G/f3+0b98eenp6+L//+z+xTaNGjTB27Fj88MMP8r8QREREREREVGvV2Znu0qSnp+P333/H2LFjxYS7mLGxMdzd3fHLL79AEAQAb2a2bW1tMXToUIwaNQoBAQFo3bo1AGD37t0oKCiQmdEuNnr0aGhpaWH79u2lxpGcnIy9e/eic+fOZcaal5eHzMxMmY2IiIiIiIg+LnU26T548CC0tLTErX///rhz5w4EQYC1tXWp+1hbW+P58+d4+vQpAEAikSAkJATHjh2DkZERpk2bJra9ffs2pFIpTExMSvSjoqKCxo0b4/bt2zLlgwYNgoaGBho0aAAdHR1s3LixzPgDAwMhlUrFzczMrCqXgYiIiIiIiGqxOpt0Ozo6Ij4+XtyCgoLeuU/xDLdEIhHLQkNDoaGhgcTERPz1118VPr4gCDL9AMDKlSvx559/Yt++fbh37x78/PzK3H/69OnIyMgQt0ePHlX42ERERERERFQ31NlnujU1NWFpaSlTpqKiAolEghs3bqBPnz4l9rl58yb09PRQr149AMC5c+ewcuVKHD58GEuXLsXw4cPxxx9/QCKRwMrKChkZGUhOToapqalMP/n5+bh//z66du0qU25sbAxjY2M0b94cBgYG+M9//oNZs2aVOluuqqoKVVXV97wKJWVlZYmruANAYmIi4uPjoa+vj4YNG1b78YiIiIiIiKhsdXamuzQGBgZwcnLCunXr8OrVK5m61NRUbNu2DQMHDoREIsGrV6/g6emJ0aNHo1u3bti4cSMuXbqEDRs2AAD69esHJSUlLF++vMRx1q9fj+zsbAwaNKjMWIpn1fPy8qrxDN8tNjYWdnZ2sLOzAwD4+fnBzs5OXJGdiIiIiIiIPpw6O9NdluDgYHTs2BEuLi5YsGABLCwscP36dUyZMgUNGjTAwoULAQDTpk1DUVERlixZAuDNCufLly+Hn58fXF1dYW5ujqVLl2Ly5MlQU1ODh4cHlJWVsX//fsyYMQP+/v5o3749AODQoUN4/PgxPvvsM2hpaeHGjRuYOnUqOnXqBHNz8w96/l26dBETfiIiIiIiIqpZH9VMNwA0bdoUsbGxaNKkCQYOHIgmTZpg1KhRcHR0xLlz56Cvr48TJ05g7dq1CA8Ph6amprjvyJEj0bFjRwwfPhyCIGDSpEmIjIzEqVOnYG9vj08//RQ///wzQkJCsGzZMnE/dXV1/PTTT/j8889hbW2NiRMnolevXjh48GBNXAIiIiIiIiKqJSQCp0VrhczMTEilUtwKNoKV3uNK7ZuSa4IfH43GqFGjSn1+nIiIiIiIiKpXcQ6XkZEBHR2dMtt9dDPdRERERERERLUFk24iIiIiIiIiOWHSTURERERERCQnTLqJiIiIiIiI5IRJNxEREREREZGcMOkmIiIiIiIikhOlmg6AZKXlG0A7t3K/hTzNryenaIiIiIiIiOh9MOmuZX598hXUMtUqvZ+ykgI0NDTkEBERERERERFVFZPuWmbYsGHQ1tau9H4aGhqQSqVyiIiIiIiIiIiqikl3LWNsbAwdHZ2aDoOIiIiIiIiqARdSIyIiIiIiIpITJt1EREREREREcsKkm4iIiIiIiEhOmHQTERERERERyQkXUqtlUlNTkZ2dXen9uHo5ERERERFR7cOku5YJCwuDmlrV3tPt4zueiTcREREREVEtwqS7luld/wAspM8rtc/T/HqIfNwPOTk5TLqJiIiIiIhqESbdtUw9lWcwUXtc02EQERERERFRNeBCakRERERERERywqSbiIiIiIiISE6YdBMRERERERHJCZNuIiIiIiIiIjlh0k1EREREREQkJ0y6PzInT55E7969YWpqColEgn379tV0SERERERERP9aTLo/MtnZ2bC1tUVwcHBNh0JERERERPSvV2uS7tTUVIwbNw6NGzeGqqoqzMzM0Lt3bxw7dgwAYG5uDolEAolEAnV1dZibm2PAgAE4fvy4TD8PHjyARCJBfHy8WPby5Ut06dIFzZs3x6NHjwAAEokEampqePjwocz+ffr0gZeXl/i5MjPHP//8MxQVFTFmzJj3uxjvoXv37liwYAH69u1bYzEQERERERHRG7Ui6X7w4AHatm2L48ePY+nSpbh27RqioqLg6OgIHx8fsd28efOQkpKCW7duYfPmzdDV1UW3bt2wcOHCMvt++vQpHB0dkZWVhdOnT8PMzEysk0gkCAgIKDe2yswch4aGYurUqdixYwdycnIqcOZERERERET0MVOq6QAAYOzYsZBIJLh48SI0NTXF8hYtWsDb21v8rK2tDWNjYwBAw4YN8cUXX8DExAQBAQH45ptv0KxZM5l+Hz16BCcnJ5iYmODAgQPQ1taWqR83bhyWL1+OyZMno2XLlqXG1r17d3Tv3v2d5/DgwQOcPXsWe/bsQXR0NHbv3o2hQ4dW+BoQERERERHRx6fGZ7rT09MRFRUFHx8fmYS7mK6ubrn7T5gwAYIgYP/+/TLlt27dQqdOndC8eXNERUWVSLgBoGPHjujVqxemT5/+XucAvJnl7tmzJ6RSKYYMGYJNmzaV2z4vLw+ZmZkyGxEREREREX1cajzpvnv3LgRBQPPmzau0v76+PurXr48HDx7IlA8dOhRNmjTBnj17oKqqWub+gYGBiIqKwqlTp6p0fAAoKipCeHg4hgwZAgD49ttvce7cOdy9e7fc40qlUnF7+7Z3IiIiIiIi+jjUeNItCAKAN89Xv08f/9zfzc0Np0+fxp49e8rd18bGBkOHDsV3331X5eMfOXIE2dnZ4m3o9erVg7OzM0JDQ8vcZ/r06cjIyBC34gXeiIiIiIiI6ONR4890N23aFBKJBAkJCejTp0+l93/27BmePn0KCwsLmfIZM2agVatWcHd3hyAIGDhwYJl9zJ07F1ZWVlV+p3VoaCjS09OhoaEhlhUVFeHy5cuYP38+FBUVS+yjqqpa7gx8VWVlZcnMsCcmJiI+Ph76+vpo2LBhtR+PiIiIiIiIylbjM936+vpwcXHB2rVrkZ2dXaL+xYsX5e6/evVqKCgolJqwz5w5E/Pnz4e7uzu2b99eZh9mZmbw9fXFjBkz8Pr160rF/+zZM+zfvx87duxAfHy8zJaVlYXDhw9Xqr/3FRsbCzs7O9jZ2QEA/Pz8YGdn985V2omIiIiIiKj61fhMNwCsW7cOHTt2RLt27TBv3jy0atUKhYWFOHr0KEJCQpCQkADgzfu2U1NTUVBQgMTERGzduhUbN25EYGAgLC0tS+172rRpUFRUhIeHB4qKiuDu7l5qu+nTp+Onn35CYmKizKz4u2aOt2zZAgMDA/Tv3x8KCrK/YfTq1QubNm1Cr1693vcSVViXLl3EW/aJiIiIiIioZtWKpNvCwgJ//vknFi5cCH9/f6SkpMDQ0BBt27ZFSEiI2C4gIAABAQFQUVGBsbExOnTogGPHjsHR0bHc/qdMmQJFRUV4enqiqKgIHh4eJdro6+vju+++w4wZM2TKY2NjZfr38/MDAHh6eiI8PByhoaH4+uuvSyTcANCvXz8MHDgQjx8/hpGRUaWuCREREREREdV9EoHTorVCZmYmpFIpbgUbwUrvcaX2Tck1wY+PRmPUqFEwMTGRU4RERERERERUrDiHy8jIgI6OTpntavyZbiIiIiIiIqKPVa24vZyIiIiIiCqmqKgI+fn5NR0G0UdPWVm51DdRVRaTbiIiIiKiOiI/Px+JiYkoKiqq6VCI/hV0dXVhbGwMiURS5T6YdBMRERER1QGCICAlJQWKioowMzMrdSFfIqoegiAgJycHT548AYD3WjuLSTcRERERUR1QWFiInJwcmJqaQkNDo6bDIfroqaurAwCePHmC+vXrV/lWc/48RkRERERUB7x+/RoAoKKiUsOREP17FP/AVVBQUOU+mHQTEREREdUh7/NsKRFVTnX8fWPSXcuk5RsgJdekUtvT/Ho1HTYRERERkVzExMRAIpHgxYsXNR3Kv4K5uTlWrVpV4fZz5sxB69at5RbPP3l5eaFPnz4f7HjVgc901zK/PvkKaplqld5PWUmBz/YQERER/QtlZGQgJyfngx1PQ0MDUqm0wu29vLwQEREBAFBSUoKZmRn69u2LuXPnQlNTU15hVtqVK1ewePFinD59GmlpaTA3N8eYMWMwYcIEmXbXrl2Dr68vLl68CH19fYwePRqzZs0SZ0T37t2LkJAQxMfHIy8vDy1atMCcOXPg4uIi9nH9+nUEBAQgLi4ODx8+xMqVKzFx4sRy44uJiYGjoyN0dXWRkpICNbX/5QwXL15E+/btAbxZAKw2ioiIwNq1a3H9+nUoKCjAzs4OU6dORa9evWo6NLlj0l3LDBs2DNra2pXer7L/+BERERFR3ZeRkYGgoLUoKqr686aVpaCgjPHjfSr1/56urq4ICwtDQUEBTp06hREjRiA7OxshISFyjLRy4uLiYGhoiK1bt8LMzAxnz57FqFGjoKioCF9fXwBAZmYmnJyc4OjoiEuXLuH27dvw8vKCpqYm/P39AQAnT56Ek5MTFi1aBF1dXYSFhaF37964cOEC7OzsAAA5OTlo3Lgx+vfvj0mTJlUqTm1tbURGRmLQoEFiWWhoKBo2bIikpKRquhrVa/LkyQgODsaCBQvQp08fFBQUYOvWrXBzc8Pq1avF6/vREqhWyMjIEAAIGRkZNR0KEREREdVCr169Em7cuCG8evVKLEtOThbmzJkjtGx5RTAxSZb71rLlFWHOnDlCcnJyheP29PQU3NzcZMpGjBghGBsbC4IgCFu2bBHatm0raGlpCUZGRsKgQYOEx48fi22jo6MFAMLz588FQRCEsLAwQSqVCr/++qtgZWUlqKurC/369ROysrKE8PBwoVGjRoKurq7g6+srFBYWiv286zilGTt2rODo6Ch+XrdunSCVSoXc3FyxLDAwUDA1NRWKiorK7MfGxkaYO3duqXWNGjUSVq5cWW4cb1+HmTNnCt26dRPLc3JyBKlUKsyaNUv4Z3q3e/duwcbGRlBRUREaNWokLFu2TKb+8ePHQq9evQQ1NTXB3Nxc2Lp1a4l4Xrx4IYwcOVIwNDQUtLW1BUdHRyE+Pl6snz17tmBra1tm3OfOnRMACEFBQSXq/Pz8BGVlZSEpKUkQhP99t1FRUULz5s0FTU1NwcXFRWa8vT2eIiIiBH19fZnvQxAEoW/fvoKHh0eZMVVGaX/vilU0h+Mz3UREREREdVxamiFSUkzkvqWlGVZLvOrq6uJq0Pn5+Zg/fz6uXLmCffv2ITExEV5eXuXun5OTg6CgIOzYsQNRUVGIiYlB3759cejQIRw6dAhbtmzBjz/+iN27d4v7VOU4GRkZ0NfXFz+fO3cOnTt3hqqqqljm4uKC5ORkPHjwoNQ+ioqK8PLlS5l+3oeHhwdOnTolzmrv2bMH5ubmaNOmjUy7uLg4DBgwAN9++y2uXbuGOXPmYNasWQgPDxfbeHl54cGDBzh+/Dh2796NdevWie+lBt7cqt6zZ0+kpqbi0KFDiIuLQ5s2bfDll18iPT29QvFu374dWlpaGD16dIk6f39/FBQUYM+ePWJZTk4Oli1bhi1btuDkyZNISkrC5MmTS+27f//+eP36NQ4cOCCWpaWl4eDBgxg2bFiF4vsQeHs5ERERERF9MBcvXsTPP/+ML7/8EgDg7e0t1jVu3BhBQUFo164dsrKyoKWlVWofBQUFCAkJQZMmTQAA33zzDbZs2YLHjx9DS0sLNjY2cHR0RHR0NAYOHFil45w7dw47d+7Eb7/9JpalpqbC3Nxcpp2RkZFYZ2FhUaKf5cuXIzs7GwMGDKjI5Xmn+vXro3v37ggPD0dAQABCQ0Nlzq3YihUr8OWXX2LWrFkAACsrK9y4cQM//PADvLy8cPv2bRw+fBjnz58XnwfftGkTrK2txT6io6Nx7do1PHnyRPyhYdmyZdi3bx92796NUaNGvTPe27dvo0mTJqW+6s7U1BRSqRS3b98WywoKCrB+/Xrxu/X19cW8efNK7VtdXR2DBw9GWFgY+vfvDwDYtm0bPvnkE3Tp0uWdsX0onOkmIiIiIiK5OnjwILS0tKCmpgYHBwd88cUXWLNmDQDg8uXLcHNzQ6NGjaCtrS0mS+U9n6yhoSEmZcCbxNfc3FwmeTYyMpKZta3Mca5fvw43NzcEBATAyclJpu6fr5AS/v/CZaW9Wmr79u2YM2cOfvnlF9SvX7/M86ksb29vhIeH4/79+zh37hzc3d1LtElISECnTp1kyjp16oQ7d+7g9evXSEhIgJKSEuzt7cX65s2bQ1dXV/wcFxeHrKwsGBgYQEtLS9wSExNx7969ajkXQRBkrt0/v1sTExOZ7/GfRo4ciSNHjuDvv/8GAISFhcHLy6tWvVqPM921TGpqKrKzsyu9HxdSIyIiIqLaytHRESEhIVBWVoapqSmUlZUBANnZ2XB2doazszO2bt0KQ0NDJCUlwcXFBfn5+WX2V7x/MYlEUmpZUVFRpY9z48YNdO3aFSNHjsTMmTNl6oyNjZGamipTVpwQFs94F/vll18wfPhw7Nq1C926dXvXJaqUHj16YPTo0Rg+fDh69+4NAwODEm3+mcwWl/3zz+Ulp0VFRTAxMUFMTEyJureT8/JYWVnh9OnTyM/PLzHbnZycjMzMTDRt2lQsK+17FMpZkd3Ozg62trbYvHkzXFxccO3aNfz6668Viu1DYdJdy4SFhcks/19RykoK8PEdz8SbiIiIiGodTU1NWFpalii/efMm0tLSsHjxYpiZmQEAYmNjq/34FT3O9evX0bVrV3h6emLhwoUl6h0cHDBjxgyZBPLIkSMwNTWVue18+/bt8Pb2xvbt29GzZ89qPx9FRUV4eHhg6dKlOHz4cKltbGxscPr0aZmys2fPwsrKCoqKirC2tkZhYSFiY2PRrl07AMCtW7dk3ofepk0bpKamQklJqcRt9RX17bffIigoCBs2bMC4ceNk6pYtWwZlZWX069evSn0XGzFiBFauXIm///4b3bp1E7/j2oJJdy3Tu/4BWEifV2qfp/n1EPm4H3Jycph0ExEREVGd0bBhQ6ioqGDNmjUYM2YM/vvf/2L+/Pk1cpzr16/D0dERzs7O8PPzE2e0FRUVYWj4ZgG5wYMHY+7cufDy8sKMGTNw584dLFq0CAEBAeKM8fbt2zF06FCsXr0aHTp0EPtRV1cX/189Pz8fN27cEP/8999/Iz4+HlpaWqX+OFGa+fPnY8qUKaXOcgNvFin77LPPMH/+fAwcOBDnzp1DcHAw1q1bBwBo1qwZXF1dMXLkSPz4449QUlLCxIkToa6uLvbRrVs3ODg4oE+fPliyZAmaNWuG5ORkHDp0CH369JG5Nb0sDg4OmDBhAqZMmYL8/HyZV4atXr0aq1ateu8k2d3dHZMnT8ZPP/2EzZs3v1df8sCku5app/IMJmqPazoMIiIiIqpD6tV7WiePY2hoiPDwcMyYMQNBQUFo06YNli1bhq+++uqDH2fXrl14+vQptm3bhm3btonljRo1Elcml0qlOHr0KHx8fGBvbw89PT34+fnBz89PbL9hwwYUFhbCx8cHPj4+Yrmnp6e4cnhycrL4zm7gzYzvsmXL0Llz51Jv5S6NiooK6tWrV2Z9mzZtsHPnTgQEBGD+/PkwMTHBvHnzZFZsDwsLw4gRI9C5c2cYGRlhwYIF4sJrwJtbuw8dOoTvv/8e3t7eePr0KYyNjfHFF1+UuJ2+PKtWrUKrVq0QEhKCWbNmQSKRoE2bNti3bx969+5d4X7KoqOjg379+uG3335Dnz593ru/6iYRyrtBnj6YzMxMSKVS3Ao2gpVe5ZLulFwT/PhoNEaNGgUTExM5RUhERERENSk3NxeJiYmwsLAQH0fMyMhAUNBaFBUVfLA4FBSUMX68D++wpFrFyckJ1tbWCAoKqtZ+S/t7V6w4h8vIyICOjk6ZfXCmm4iIiIiojpJKpRg/3gc5OTkf7JhcwJdqk/T0dBw5cgTHjx9HcHBwTYdTKibdRERERER1mFQqZRJM/1pt2rTB8+fPxWfOayMm3URERERERFQnFT9vX5sp1HQARERERERERB8rJt0fmZMnT6J3794wNTWFRCLBvn37ajokIiIiIiKify0m3R+Z7Oxs2Nra1tpFBIiIiIiIiP5N6mTSnZqainHjxqFx48ZQVVWFmZkZevfujWPHjoltLl++jP79+8PIyAhqamqwsrLCyJEjcfv2bQBv7v2XSCSoX78+Xr58KdN/69atMWfOHPFzly5dIJFISmxjxowR2yxcuBAdO3aEhoYGdHV15Xr+5enevTsWLFiAvn371lgMRERERERE9EadS7ofPHiAtm3b4vjx41i6dCmuXbuGqKgoODo6ii+fP3jwIDp06IC8vDxs27YNCQkJ2LJlC6RSqczL3gHg5cuXWLZs2TuPO3LkSKSkpMhsS5cuFevz8/PRv39//N///V/1njARERERERHVWXVu9fKxY8dCIpHg4sWL0NTUFMtbtGgBb29v5OTkYNiwYejRowciIyPFegsLC7Rv3x4vXryQ6W/cuHFYsWIFfHx8UL9+/TKPq6GhAWNj4zLr586dCwAIDw+v2okRERERERHRR6dOzXSnp6cjKioKPj4+Mgl3MV1dXfz+++9IS0vD1KlTS+3jn7d+Dxo0CJaWlpg3b548Qi5TXl4eMjMzZTYiIiIion+j2rYAsJeXF/r06VNmfXh4eI0+UloVMTExkEgkJSYha4N3Xe9/qs3nUpo6lXTfvXsXgiCgefPmZba5c+cOAJTb5m0SiQSLFy/Gjz/+iHv37pXZbt26ddDS0pLZIiIiKncCbwkMDIRUKhU3MzOzKvdFRERERP9eGRkZJR6DlOeWkZFRqfiePHmC0aNHo2HDhlBVVYWxsTFcXFxw7tw5OV2RuuvttaQUFBRgZGSE/v374+HDh5XuZ+LEidUSU3E858+flynPy8uDgYEBJBIJYmJiquVYH6s6dXu5IAgA3nzx72pTGS4uLvj8888xa9Ys/Pzzz6W2cXd3x/fffy9TVt7t6O8yffp0+Pn5iZ8zMzOZeBMRERFRpWRkZCAoOAhFhUUf7JgKSgoY7zseUqm0Qu379euHgoICREREoHHjxnj8+DGOHTuG9PR0ucaZn58PFRUVuR5DHkaOHIl58+ZBEAQ8fPgQEydOxJAhQ3Dq1Kkai8nMzAxhYWHo0KGDWBYZGQktLS25f48fgzo10920aVNIJBIkJCSU2cbKygoAcPPmzUr1vXjxYvzyyy+4fPlyqfVSqRSWlpYym46OTqWO8TZVVVXo6OjIbNUhKysL8fHxiI+PBwAkJiYiPj4eSUlJ1dI/EREREdUeOTk5KCoswh7swYYP8N8e7EFRYRFycnIqFN+LFy9w+vRpLFmyBI6OjmjUqBHatWuH6dOno2fPnjJt09LS8PXXX0NDQwNNmzbFgQMHxLrXr19j+PDhsLCwgLq6Opo1a4bVq1fL7F98i3JgYCBMTU3FvODvv//GwIEDoaenBwMDA7i5ueHBgwcyffv5+UFXVxcGBgaYOnVqhSfy9u3bBysrK6ipqcHJyQmPHj0C8GbxZwUFBcTGxsq0X7NmDRo1alRu/8VrSZmYmKBDhw7w8fHBn3/+KdPmxIkTaNeuHVRVVWFiYoJp06ahsLBQvA4nTpzA6tWrxVnqt883Li4O9vb20NDQQMeOHXHr1q13nqenpyd27NiBV69eiWWhoaHw9PQs0fbatWvo2rUr1NXVYWBggFGjRiErK0usr8j1FgQBS5cuRePGjaGurg5bW1vs3r27zPgePnyI3r17Q09PD5qammjRogUOHTr0zvP6UOpU0q2vrw8XFxesXbsW2dnZJepfvHgBZ2dn1KtXT2Zl8X+2KU27du3Qt29fTJs2rTpD/uBiY2NhZ2cHOzs7AICfnx/s7OwQEBBQw5ERERERkbykIQ0pH+C/NKRVKq7ixzL37duHvLy8ctvOnTsXAwYMwNWrV9GjRw+4u7uLs6hFRUX45JNPsHPnTty4cQMBAQGYMWMGdu7cKdPHsWPHkJCQgKNHj+LgwYPIycmBo6MjtLS0cPLkSZw+fRpaWlpwdXVFfn4+AGD58uUIDQ3Fpk2bcPr0aaSnp8ssyFyWnJwcLFy4EBEREThz5gwyMzPx7bffAgDMzc3RrVs3hIWFyewTFhYGLy+vcu/cfVt6ejp27dqF9u3bi2V///03evTogc8++wxXrlxBSEgINm3ahAULFgAAVq9eDQcHB5m3L719R+3333+P5cuXIzY2FkpKSvD29n5nHG3btoWFhQX27NkDAHj06BFOnjwJDw+PEtfE1dUVenp6uHTpEnbt2oU//vgDvr6+YpuKXO+ZM2ciLCwMISEhuH79OiZNmoQhQ4bgxIkTpcbn4+ODvLw8nDx5EteuXcOSJUugpaX1zvP6UOrU7eXAm2erO3bsiHbt2mHevHlo1aoVCgsLcfToUYSEhCAhIQEbN25E//798dVXX2H8+PGwtLREWloadu7ciaSkJOzYsaPUvhcuXIgWLVpASankZcnJyUFqaqpMmaqqKvT09AAASUlJSE9PR1JSEl6/fi3ONFtaWn7QL7xLly5VusWeiIiIiKi6KSkpITw8HCNHjsT69evRpk0bdO7cGd9++y1atWol09bLywuDBg0CACxatAhr1qzBxYsX4erqCmVlZfFtQcCbNxOdPXsWO3fuxIABA8RyTU1NbNy4UbytPDQ0FAoKCti4caOY6IaFhUFXVxcxMTFwdnbGqlWrMH36dPTr1w8AsH79evz+++/vPLeCggIEBweLCXFERASsra1x8eJFtGvXDiNGjMCYMWOwYsUKqKqq4sqVK4iPj8fevXvL7XfdunXYuHEjBEFATk4OrKysZOJZt24dzMzMEBwcDIlEgubNmyM5ORnfffcdAgICIJVKoaKiUubblxYuXIjOnTsDAKZNm4aePXsiNzcXampq5cY1bNgwhIaGYsiQIQgLC0OPHj1gaGgo02bbtm149eoVNm/eLC58HRwcjN69e2PJkiUwMjJ65/XOzs7GihUrcPz4cTg4OAAAGjdujNOnT2PDhg1i7G9LSkpCv3790LJlS7F9bVKnZrqBN3/B/vzzTzg6OsLf3x+ffvopnJyccOzYMYSEhAAA3NzccPbsWSgrK2Pw4MFo3rw5Bg0ahIyMDPEXoNJYWVnB29sbubm5Jep++uknmJiYyGzF/ygAQEBAAOzs7DB79mxkZWWJs83/vKWEiIiIiOjfpF+/fkhOTsaBAwfg4uKCmJgYtGnTpsSrdt9OwjU1NaGtrY0nT56IZevXr4e9vT0MDQ2hpaWFn376qcQjlC1btpR5jjsuLg53796Ftra2OOuur6+P3Nxc3Lt3T1yErji5A978UGBvb//O8/pnu+bNm0NXV1d8FLZPnz5QUlISZ3FDQ0Ph6OgIc3Pzcvt1d3dHfHw8rly5gtOnT8PS0hLOzs54+fIlACAhIQEODg4ys+WdOnVCVlYW/vrrr3fG/fZ1NjExAQCZ61yWIUOG4Ny5c7h//z7Cw8NLnSFPSEiAra2tzJumOnXqhKKiIty6datC1/vGjRvIzc2Fk5OTzCLWmzdvLnPh6/Hjx2PBggXo1KkTZs+ejatXr77zfD6kOjfTDbwZHMHBwQgODi6zjb29vXj7Q2nMzc1LnRHesGEDNmzYIFNWkdX4wsPD+Y5uIiIiIqJSFD/z7OTkhICAAIwYMQKzZ8+Gl5eX2EZZWVlmH4lEgqKiNwvE7dy5E5MmTcLy5cvh4OAAbW1t/PDDD7hw4YLMPv98rXBRURHatm2Lbdu2lYjpn7O0VVHabeLFZSoqKvDw8EBYWBj69u2Ln3/+GatWrXpnn8VrSQFv7prdtGkTTExM8Msvv2DEiBEQBKHEcSuy4HSxt69zcfvi61weAwMD9OrVC8OHD0dubi66d+8u/hDwdhxlxVDRW+qLY/ntt9/QoEEDmTpVVdVS9xkxYgRcXFzw22+/4ciRIwgMDMTy5csxbty4Ch1T3urcTDcREREREdVtNjY2pa7RVJZTp06hY8eOGDt2LOzs7GBpaVnu636LtWnTBnfu3EH9+vVLLIpc/OpeExMTmddhFRYWIi4u7p19FxYWytzVeuvWLbx48ULm1cUjRozAH3/8gXXr1qGgoAB9+/at8DkXU1RUBABxETMbGxucPXtWZgLx7Nmz0NbWFpNUFRUVvH79utLHehdvb2/ExMRg6NChYlxvs7GxQXx8vMx3e+bMGSgoKMDKyqpC19vGxgaqqqpISkoq8Z2V97YnMzMzjBkzBnv37oW/vz9++umnajrr98ekm4iIiIiI5OLZs2fo2rUrtm7diqtXryIxMRG7du3C0qVL4ebmVuF+LC0tERsbi99//x23b9/GrFmzcOnSpXfu5+7ujnr16sHNzQ2nTp1CYmIiTpw4gQkTJoi3Yk+YMAGLFy9GZGQkbt68ibFjx5a5+PLblJWVMW7cOFy4cAF//vknhg0bhg4dOqBdu3ZiG2tra3To0AHfffcdBg0aBHV19Xf2W7yWVGpqKq5cuYKxY8dCTU0Nzs7OAICxY8fi0aNHGDduHG7evIn9+/dj9uzZ8PPzg4LCm/TO3NwcFy5cwIMHD5CWllahmeyKcHV1xdOnTzFv3rxS693d3aGmpgZPT0/897//RXR0NMaNGwcPDw8YGRkBePf11tbWxuTJkzFp0iRERETg3r17uHz5MtauXYuIiIhSjztx4kT8/vvvSExMxJ9//onjx4/D2tq6Ws65OtTJ28uJiIiIiOh/6qFerTyOlpYW2rdvj5UrV+LevXsoKCiAmZkZRo4ciRkzZlS4nzFjxiA+Ph4DBw6ERCLBoEGDMHbsWBw+fLjc/TQ0NHDy5El899136Nu3L16+fIkGDRrgyy+/FF/Z6+/vj5SUFHh5eUFBQQHe3t74+uuvkZGR8c6+v/vuOwwePBh//fUXPv/8c4SGhpZoN3z4cJw9e7ZCq4QDb9aSKp6l1dPTQ6tWrXDo0CE0a9YMANCgQQMcOnQIU6ZMga2tLfT19TF8+HDMnDlT7GPy5Mnw9PSEjY0NXr16hcTExAod+10kEgnq1St7DGhoaOD333/HhAkT8Nlnn0FDQwP9+vXDihUrxDYVud7z589H/fr1ERgYiPv370NXVxdt2rQpc8y8fv0aPj4++Ouvv6CjowNXV1esXLmyWs65OkgELnVdK2RmZkIqleJWsBGs9B5Xat+UXBP8+Gg0Ro0aJS6GQEREREQfl9zcXCQmJsLCwkJcaTojIwNBwUEoKqyemcyKUFBSwHjf8ZBKpR/smHXZwoULsWPHDly7dq2mQ6EqKO3vXbHiHC4jI0P8Eac0nOkmIiIiIqqjpFIpxvuOR05Ozgc7poaGBhPuCsjKykJCQgLWrFmD+fPn13Q4VIOYdBMRERER1WHFC4JR7eLr64vt27ejT58+Fb61nD5OTLqJiIiIiIiqGV8pTMWYdNcyafkG0M6t3KLyT/M/zMIZREREREREVDlMumuZX598BbVMtXc3/AdlJQVoaGjIISIiIiIiIiKqKibdtcywYcOgra1d6f24oAUREREREVHtw6S7ljE2Ni53uXkiIiIiIiKqOyr38DARERERERERVRiTbiIiIiIiIiI5YdJNREREREQfTJcuXTBx4sSaDqNKzM3NsWrVqpoOo1bx8vJCnz59Ktw+JiYGEokEL168kFtMbwsPD4euru4HOVZZmHQTEREREdVhGRkZSElJ+WBbRkZGpeKrbFJWm126dAmjRo0CAKSnp2PcuHFo1qwZNDQ00LBhQ4wfP77E9Xn+/Dk8PDwglUohlUrh4eEhk3BeuXIFgwYNgpmZGdTV1WFtbY3Vq1eXOPa1a9fQuXNnqKuro0GDBpg3bx4EQSg3XolEAolEgvPnz8uU5+XlwcDAABKJBDExMVW7GHJ29uxZ9OjRA3p6elBTU0PLli2xfPlyvH79uqZDqzQupEZEREREVEdlZGRgbXAQCgqLPtgxlZUU4OM7/l/55hxDQ0Pxz8nJyUhOTsayZctgY2ODhw8fYsyYMUhOTsbu3bvFdoMHD8Zff/2FqKgoAMCoUaPg4eGBX3/9FQAQFxcHQ0NDbN26FWZmZjh79ixGjRoFRUVF+Pr6AgAyMzPh5OQER0dHXLp0Cbdv34aXlxc0NTXh7+9fbsxmZmYICwtDhw4dxLLIyEhoaWkhPT292q5NdYqMjMSAAQMwbNgwREdHQ1dXF3/88QemTp2K8+fPY+fOnZBIJDUdZsUJVCtkZGQIAISMjIyaDoWIiIiIaqFXr14JN27cEF69eiWWJScnC3PmzBGuhLQUkjeZyH27EtJSmDNnjpCcnFzhuD09PQU3Nzfxc+fOnYUJEyaInw8fPizo6OgIERERgiAIwtWrVwVHR0dBTU1N0NfXF0aOHCm8fPmyRH8//PCDYGxsLOjr6wtjx44V8vPzxTbp6emCh4eHoKurK6irqwuurq7C7du3xfqwsDBBKpUKv/76q2BlZSWoq6sL/fr1E7KysoTw8HChUaNGgq6uruDr6ysUFhaK+zVq1EhYuXJlmee6c+dOQUVFRSgoKBAEQRBu3LghABDOnz8vtjl37pwAQLh582aZ/YwdO1ZwdHQUP69bt06QSqVCbm6uWBYYGCiYmpoKRUVFZfYDQJg5c6ago6Mj5OTkiOVOTk7CrFmzBABCdHS0WP6ua19YWChMmjRJkEqlgr6+vjBlyhRh6NChMt9vUVGRsGTJEsHCwkJQU1MTWrVqJezatUusj46OFgAIz58/LzXmrKwswcDAQOjbt2+JugMHDggAhB07dgiCIAiJiYkCAGHPnj1Cly5dBHV1daFVq1bC2bNnxX2Kv+vi9hKJRLh06ZJMv0FBQULDhg1LvZal/b0rVtEcjreXExERERHVcYYqaTBRS5H7ZqiSVq1x79ixAwMGDMDmzZsxdOhQ5OTkwNXVFXp6erh06RJ27dqFP/74Q5zxLRYdHY179+4hOjoaERERCA8PR3h4uFjv5eWF2NhYHDhwAOfOnYMgCOjRowcKCgrENjk5OQgKCsKOHTsQFRWFmJgY9O3bF4cOHcKhQ4ewZcsW/PjjjzKz1u+SkZEBHR0dKCm9uaH43LlzkEqlaN++vdimQ4cOkEqlOHv2bLn96Ovri5/PnTuHzp07Q1VVVSxzcXFBcnIyHjx4UG5Mbdu2hYWFBfbs2QMAePToEU6ePAkPDw+ZdhW59suXL0doaCg2bdqE06dPIz09HZGRkTL9zJw5E2FhYQgJCcH169cxadIkDBkyBCdOnCg3zmJHjhzBs2fPMHny5BJ1vXv3hpWVFbZv3y5T/v3332Py5MmIj4+HlZUVBg0ahMLCwhL7m5ubo1u3bggLC5MpDwsLg5eXl9xmz5l0ExERERHRB7du3TqMGTMG+/fvh5ubGwBg27ZtePXqFTZv3oxPP/0UXbt2RXBwMLZs2YLHjx+L++rp6SE4OBjNmzdHr1690LNnTxw7dgwAcOfOHRw4cAAbN27Ef/7zH9ja2mLbtm34+++/sW/fPrGPgoIChISEwM7ODl988QW++eYbnD59Gps2bYKNjQ169eoFR0dHREdHV+h8nj17hvnz52P06NFiWWpqKurXr1+ibf369ZGamlpqP+fOncPOnTtL9GNkZCTTrvhzWf28bdiwYQgNDQXwJsHs0aOHzK3yQMWu/apVqzB9+nT069cP1tbWWL9+vcxjBtnZ2VixYgVCQ0Ph4uKCxo0bw8vLC0OGDMGGDRveGScA3L59GwBgbW1dan3z5s3FNsUmT56Mnj17wsrKCnPnzsXDhw9x9+7dUvcfMWIEtm/fjry8PABvnqmPj4/HsGHDKhRfVTDpJiIiIiKiD2rPnj2YOHEijhw5AkdHR7E8ISEBtra20NTUFMs6deqEoqIi3Lp1Syxr0aIFFBUVxc8mJiZ48uSJ2IeSkpLM7LKBgQGaNWuGhIQEsUxDQwNNmjQRPxsZGcHc3BxaWloyZcX9liczMxM9e/aEjY0NZs+eLVNX2uypIAilll+/fh1ubm4ICAiAk5NTuf0I/38RtYrMzg4ZMgTnzp3D/fv3ER4eDm9v7xJt3nXtixfsc3BwEOuVlJRgb28vfr5x4wZyc3Ph5OQELS0tcdu8eTPu3bv3zjhLO7/Syv95zq1atRL/bGJiAgBlfm99+vSBkpKSOEMfGhoKR0dHmJubVyq+ymDSTUREREREH1Tr1q1haGiIsLAwmeSqrGQUkE0ulZWVS9QVFRWJfZTmn32X1kd5/Zbl5cuXcHV1hZaWFiIjI2X6MDY2lpmhL/b06dMSM9c3btxA165dMXLkSMycOVOmztjYuMSMdnFS+c9+SmNgYIBevXph+PDhyM3NRffu3Uu0qei1L0/xtfrtt98QHx8vbjdu3KjwbfpWVlYAIPMDydtu3ryJpk2bypS9fc2LYy3re1NRUYGHhwfCwsKQn5+Pn3/+udQfIaoTk24iIiIiIvqgmjRpgujoaOzfvx/jxo0Ty21sbBAfH4/s7Gyx7MyZM1BQUBCTsXexsbFBYWEhLly4IJY9e/YMt2/fLvOW5arKzMyEs7MzVFRUcODAAaipqcnUOzg4ICMjAxcvXhTLLly4gIyMDHTs2FEsu379OhwdHeHp6YmFCxeWOI6DgwNOnjyJ/Px8sezIkSMwNTWt8Aytt7c3YmJiMHToUJm7BIq969pLpVKYmJjIvH6ssLAQcXFxMn2oqqoiKSkJlpaWMpuZmVmF4nR2doa+vj6WL19eou7AgQO4c+cOBg0aVKG+yjJixAj88ccfWLduHQoKCtC3b9/36u9dmHQTEREREdEHZ2VlhejoaPFWcwBwd3eHmpoaPD098d///hfR0dEYN24cPDw8KjSjCwBNmzaFm5sbRo4cidOnT+PKlSsYMmQIGjRoID47Xh1evnwJZ2dnZGdnY9OmTcjMzERqaipSU1PFd0lbW1vD1dUVI0eOxPnz53H+/HmMHDkSvXr1QrNmzQD8L+F2cnKCn5+f2MfTp0/FYw0ePBiqqqrw8vLCf//7X0RGRmLRokXw8/Or8Cy0q6srnj59innz5pVaX5FrP2HCBCxevBiRkZG4efMmxo4dK/POcW1tbUyePBmTJk1CREQE7t27h8uXL2Pt2rWIiIioUJyamprYsGED9u/fj1GjRuHq1at48OABNm3aBC8vL3zzzTcYMGBAhfoqi7W1NTp06IDvvvsOgwYNgrq6+nv19y58TzcRERERUR33NL9enTxOs2bNcPz4cXTp0gWKiopYvnw5fv/9d0yYMAGfffYZNDQ00K9fP6xYsaJS/YaFhWHChAno1asX8vPz8cUXX+DQoUMlbh9/H3FxceJsuqWlpUxdYmKiOAO9bds2jB8/Hs7OzgCAr776CsHBwWLbXbt24enTp9i2bRu2bdsmljdq1EhcmVwqleLo0aPw8fGBvb099PT04OfnBz8/vwrHK5FIUK9e2d+fhobGO6+9v78/UlJS4OXlBQUFBXh7e+Prr79GRkaG2Gb+/PmoX78+AgMDcf/+fejq6qJNmzaYMWNGhWP95ptvEB0djUWLFuGLL77Aq1evYGlpie+//x4TJ06sllXGhw8fjrNnz8r91nIAkAhlPfRAH1RmZiakUqn4mgEiIiIiorfl5uYiMTERFhYW4m3MGRkZWBschILC8p87rk7KSgrw8R0vs2o1UV2zcOFC7NixA9euXSu3XWl/74pVNIfjTDcRERERUR0llUrh4zseOTk5H+yYGhoaTLipzsrKykJCQgLWrFmD+fPnf5BjMukmIiIiIqrDpFIpk2CiCvL19cX27dvRp0+fD3JrOcCkm4iIiIiIiP4lwsPDER4e/kGPydXLiYiIiIiIiOSESTcRERERERGRnDDpJiIiIiKqQ/jyIaIPpzr+vjHpJiIiIiKqAxQVFQEA+fn5NRwJ0b9H8ZsB3ucd71xIjYiIiIioDlBSUoKGhgaePn0KZWVlKChw/oxIXgRBQE5ODp48eQJdXV3xR6+qYNJNRERERFQHSCQSmJiYIDExEQ8fPqzpcIj+FXR1dWFsbPxefTDpJiIiIiKqI1RUVNC0aVPeYk70ASgrK7/XDHcxJt1ERERERHWIgoIC1NTUajoMIqogPghCREREREREJCdMuomIiIiIiIjkhEk3ERERERERkZzwme5aovil65mZmTUcCREREREREb1Lce5WnMuVhUl3LfHs2TMAgJmZWQ1HQkRERERERBX18uVLSKXSMuuZdNcS+vr6AICkpKRyvzCiysjMzISZmRkePXoEHR2dmg6HPhIcVyQPHFdU3TimSB44ruhtgiDg5cuXMDU1Lbcdk+5aQkHhzeP1UqmUf4Gp2uno6HBcUbXjuCJ54Lii6sYxRfLAcUXFKjJhyoXUiIiIiIiIiOSESTcRERERERGRnDDpriVUVVUxe/ZsqKqq1nQo9BHhuCJ54LgieeC4ourGMUXywHFFVSER3rW+ORERERERERFVCWe6iYiIiIiIiOSESTcRERERERGRnDDpJiIiIiIiIpITJt21xLp162BhYQE1NTW0bdsWp06dqumQqA45efIkevfuDVNTU0gkEuzbt0+mXhAEzJkzB6amplBXV0eXLl1w/fr1mgmW6oTAwEB89tln0NbWRv369dGnTx/cunVLpg3HFVVWSEgIWrVqJb7f1sHBAYcPHxbrOabofQUGBkIikWDixIliGccVVdacOXMgkUhkNmNjY7GeY4oqi0l3LfDLL79g4sSJ+P7773H58mX85z//Qffu3ZGUlFTToVEdkZ2dDVtbWwQHB5dav3TpUqxYsQLBwcG4dOkSjI2N4eTkhJcvX37gSKmuOHHiBHx8fHD+/HkcPXoUhYWFcHZ2RnZ2ttiG44oq65NPPsHixYsRGxuL2NhYdO3aFW5ubuL/rHJM0fu4dOkSfvzxR7Rq1UqmnOOKqqJFixZISUkRt2vXrol1HFNUaQLVuHbt2gljxoyRKWvevLkwbdq0GoqI6jIAQmRkpPi5qKhIMDY2FhYvXiyW5ebmClKpVFi/fn0NREh10ZMnTwQAwokTJwRB4Lii6qOnpyds3LiRY4rey8uXL4WmTZsKR48eFTp37ixMmDBBEAT+W0VVM3v2bMHW1rbUOo4pqgrOdNew/Px8xMXFwdnZWabc2dkZZ8+eraGo6GOSmJiI1NRUmTGmqqqKzp07c4xRhWVkZAAA9PX1AXBc0ft7/fo1duzYgezsbDg4OHBM0Xvx8fFBz5490a1bN5lyjiuqqjt37sDU1BQWFhb49ttvcf/+fQAcU1Q1SjUdwL9dWloaXr9+DSMjI5lyIyMjpKam1lBU9DEpHkeljbGHDx/WREhUxwiCAD8/P3z++ef49NNPAXBcUdVdu3YNDg4OyM3NhZaWFiIjI2FjYyP+zyrHFFXWjh078Oeff+LSpUsl6vhvFVVF+/btsXnzZlhZWeHx48dYsGABOnbsiOvXr3NMUZUw6a4lJBKJzGdBEEqUEb0PjjGqKl9fX1y9ehWnT58uUcdxRZXVrFkzxMfH48WLF9izZw88PT1x4sQJsZ5jiirj0aNHmDBhAo4cOQI1NbUy23FcUWV0795d/HPLli3h4OCAJk2aICIiAh06dADAMUWVw9vLa1i9evWgqKhYYlb7yZMnJX5BI6qK4tU2OcaoKsaNG4cDBw4gOjoan3zyiVjOcUVVpaKiAktLS9jb2yMwMBC2trZYvXo1xxRVSVxcHJ48eYK2bdtCSUkJSkpKOHHiBIKCgqCkpCSOHY4reh+amppo2bIl7ty5w3+rqEqYdNcwFRUVtG3bFkePHpUpP3r0KDp27FhDUdHHxMLCAsbGxjJjLD8/HydOnOAYozIJggBfX1/s3bsXx48fh4WFhUw9xxVVF0EQkJeXxzFFVfLll1/i2rVriI+PFzd7e3u4u7sjPj4ejRs35rii95aXl4eEhASYmJjw3yqqEt5eXgv4+fnBw8MD9vb2cHBwwI8//oikpCSMGTOmpkOjOiIrKwt3794VPycmJiI+Ph76+vpo2LAhJk6ciEWLFqFp06Zo2rQpFi1aBA0NDQwePLgGo6bazMfHBz///DP2798PbW1t8Rd9qVQKdXV18T24HFdUGTNmzED37t1hZmaGly9fYseOHYiJiUFUVBTHFFWJtra2uNZEMU1NTRgYGIjlHFdUWZMnT0bv3r3RsGFDPHnyBAsWLEBmZiY8PT35bxVVCZPuWmDgwIF49uwZ5s2bh5SUFHz66ac4dOgQGjVqVNOhUR0RGxsLR0dH8bOfnx8AwNPTE+Hh4Zg6dSpevXqFsWPH4vnz52jfvj2OHDkCbW3tmgqZarmQkBAAQJcuXWTKw8LC4OXlBQAcV1Rpjx8/hoeHB1JSUiCVStGqVStERUXByckJAMcUyQfHFVXWX3/9hUGDBiEtLQ2Ghobo0KEDzp8/L/6/OccUVZZEEAShpoMgIiIiIiIi+hjxmW4iIiIiIiIiOWHSTURERERERCQnTLqJiIiIiIiI5IRJNxEREREREZGcMOkmIiIiIiIikhMm3URERERERERywqSbiIiIiIiISE6YdBMRERERERHJCZNuIiKij9iDBw8gkUgQHx9f06GIbt68iQ4dOkBNTQ2tW7f+oMfu0qULJk6c+EGPSURE/25MuomIiOTIy8sLEokEixcvlinft28fJBJJDUVVs2bPng1NTU3cunULx44dK7VN8XWTSCRQVlZG48aNMXnyZGRnZ1foGDExMZBIJHjx4oVM+d69ezF//vz3PYUSsfbp06da+yQioo8Hk24iIiI5U1NTw5IlS/D8+fOaDqXa5OfnV3nfe/fu4fPPP0ejRo1gYGBQZjtXV1ekpKTg/v37WLBgAdatW4fJkydX+bgAoK+vD21t7ffqg4iIqDKYdBMREclZt27dYGxsjMDAwDLbzJkzp8St1qtWrYK5ubn4uXhGddGiRTAyMoKuri7mzp2LwsJCTJkyBfr6+vjkk08QGhpaov+bN2+iY8eOUFNTQ4sWLRATEyNTf+PGDfTo0QNaWlowMjKCh4cH0tLSxPouXbrA19cXfn5+qFevHpycnEo9j6KiIsybNw+ffPIJVFVV0bp1a0RFRYn1EokEcXFxmDdvHiQSCebMmVPmNVFVVYWxsTHMzMwwePBguLu7Y9++fQCArVu3wt7eHtra2jA2NsbgwYPx5MkTAG9uqXd0dAQA6OnpQSKRwMvLSzyPt28vz8/Px9SpU9GgQQNoamqiffv2MtcmPDwcurq6+P3332FtbQ0tLS3xxwDgzfcWERGB/fv3izPzMTExyM/Ph6+vL0xMTKCmpgZzc/Nyv38iIvp4MekmIiKSM0VFRSxatAhr1qzBX3/99V59HT9+HMnJyTh58iRWrFiBOXPmoFevXtDT08OFCxcwZswYjBkzBo8ePZLZb8qUKfD398fly5fRsWNHfPXVV3j27BkAICUlBZ07d0br1q0RGxuLqKgoPH78GAMGDJDpIyIiAkpKSjhz5gw2bNhQanyrV6/G8uXLsWzZMly9ehUuLi746quvcOfOHfFYLVq0gL+/P1JSUio1c62uro6CggIAb5Ll+fPn48qVK9i3bx8SExPFxNrMzAx79uwBANy6dQspKSlYvXp1qX0OGzYMZ86cwY4dO3D16lX0798frq6uYrwAkJOTg2XLlmHLli04efIkkpKSxLgnT56MAQMGiIl4SkoKOnbsiKCgIBw4cAA7d+7ErVu3sHXrVpkfUIiI6N9DqaYDICIi+jf4+uuv0bp1a8yePRubNm2qcj/6+voICgqCgoICmjVrhqVLlyInJwczZswAAEyfPh2LFy/GmTNn8O2334r7+fr6ol+/fgCAkJAQREVFYdOmTZg6dSpCQkLQpk0bLFq0SGwfGhoKMzMz3L59G1ZWVgAAS0tLLF26tNz4li1bhu+++0489pIlSxAdHY1Vq1Zh7dq1MDY2hpKSErS0tGBsbFzh87548SJ+/vlnfPnllwAAb29vsa5x48YICgpCu3btkJWVBS0tLejr6wMA6tevD11d3VL7vHfvHrZv346//voLpqamAN4k0VFRUQgLCxOvR0FBAdavX48mTZqI13LevHkAAC0tLairqyMvL0/mfJKSktC0aVN8/vnnkEgkaNSoUYXPlYiIPi5MuomIiD6QJUuWoGvXrvD3969yHy1atICCwv9uVDMyMsKnn34qflZUVISBgYF4q3UxBwcH8c9KSkqwt7dHQkICACAuLg7R0dHQ0tIqcbx79+6JSbe9vX25sWVmZiI5ORmdOnWSKe/UqROuXLlSwTP8n4MHD0JLSwuFhYUoKCiAm5sb1qxZAwC4fPky5syZg/j4eKSnp6OoqAjAm2TXxsamQv3/+eefEARBPL9ieXl5Ms+aa2hoiAk3AJiYmJS4vv/k5eUFJycnNGvWDK6urujVqxecnZ0rFBcREX1cmHQTERF9IF988QVcXFwwY8YM8VboYgoKChAEQaas+FbqtykrK8t8Ll7d+59lxUloeYpXTy8qKkLv3r2xZMmSEm1MTEzEP2tqar6zz7f7LSYIQpVWand0dERISAiUlZVhamoqnmd2djacnZ3h7OyMrVu3wtDQEElJSXBxcanUAm9FRUVQVFREXFwcFBUVZere/gGitOv7z+/qn9q0aYPExEQcPnwYf/zxBwYMGIBu3bph9+7dFY6PiIg+Dky6iYiIPqDFixejdevWJWZXDQ0NkZqaKpOgVue7tc+fP48vvvgCAFBYWIi4uDj4+voCeJMg7tmzB+bm5lBSqvr/Gujo6MDU1BSnT58WjwUAZ8+eRbt27Srdn6amJiwtLUuU37x5E2lpaVi8eDHMzMwAALGxsTJtVFRUAACvX78us387Ozu8fv0aT548wX/+859Kx/f2sUo7jo6ODgYOHIiBAwfim2++gaurK9LT08Vb34mI6N+BC6kRERF9QC1btoS7u7t4m3SxLl264OnTp1i6dCnu3buHtWvX4vDhw9V23LVr1yIyMhI3b96Ej48Pnj9/Lj4X7ePjg/T0dAwaNAgXL17E/fv3ceTIEXh7e5ebtJZmypQpWLJkCX755RfcunUL06ZNQ3x8PCZMmFBt59KwYUOoqKhgzZo1uH//Pg4cOFDi3duNGjWCRCLBwYMH8fTpU2RlZZXox8rKCu7u7hg6dCj27t2LxMREXLp0CUuWLMGhQ4cqHI+5uTmuXr2KW7duIS0tDQUFBVi5ciV27NiBmzdv4vbt29i1axeMjY3LfL6ciIg+Xky6iYiIPrD58+eXuD3Z2toa69atw9q1a2Fra4uLFy++9zup37Z48WIsWbIEtra2OHXqFPbv34969eoBAExNTXHmzBm8fv0aLi4u+PTTTzFhwgRIpVKZ58crYvz48fD394e/vz9atmyJqKgoHDhwAE2bNq22czE0NER4eDh27doFGxsbLF68GMuWLZNp06BBA8ydOxfTpk2DkZGROKv/T2FhYRg6dCj8/f3RrFkzfPXVV7hw4YI4g14RI0eORLNmzWBvbw9DQ0OcOXMGWlpaWLJkCezt7fHZZ5/hwYMHOHToUKWvJxER1X0S4V0PJRERERERERFRlfDnViIiIiIiIiI5YdJNREREREREJCdMuomIiIiIiIjkhEk3ERERERERkZww6SYiIiIiIiKSEybdRERERERERHLCpJuIiIiIiIhITph0ExEREREREckJk24iIiIiIiIiOWHSTURERERERCQnTLqJiIiIiIiI5IRJNxEREREREZGc/D/PHIOE0S/V2QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the unique and shared patients\n",
    "patient_counts_df_tcga['Shared'] = np.minimum(patient_counts_df_tcga[model1], patient_counts_df_tcga[model2])\n",
    "patient_counts_df_tcga['Model1 Only'] = patient_counts_df_tcga[model1] - patient_counts_df_tcga['Shared']\n",
    "patient_counts_df_tcga['Model2 Only'] = patient_counts_df_tcga[model2] - patient_counts_df_tcga['Shared']\n",
    "\n",
    "# Sort the DataFrame based on the total patients for better visualization\n",
    "patient_counts_df_tcga = patient_counts_df_tcga.sort_values(by=[model1, model2], ascending=True)\n",
    "patient_counts_df_tcga = patient_counts_df_tcga[patient_counts_df_tcga.index != 'Total'] # Remove 'Total' row\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "bars_1 = plt.barh(patient_counts_df_tcga.index, patient_counts_df_tcga['Model1 Only'], color='blue', edgecolor='gray', label= model1 +' Model Only')\n",
    "bars_shared = plt.barh(patient_counts_df_tcga.index, patient_counts_df_tcga['Shared'], color='green', edgecolor='gray', label='Shared by Both Models')\n",
    "bars_2 = plt.barh(patient_counts_df_tcga.index, patient_counts_df_tcga['Model2 Only'], color='orange', edgecolor='gray', label=model2 + ' Model Only')\n",
    "\n",
    "# Annotate the number of patients\n",
    "for bars in [bars_1, bars_shared, bars_2]:\n",
    "    for bar in bars:\n",
    "        width = bar.get_width()\n",
    "        if width > 0:\n",
    "            plt.text(width + bar.get_x(), bar.get_y() + bar.get_height()/2,\n",
    "                     f'{int(width)}', ha='left', va='center')\n",
    "plt.xlabel('Number of Patients')\n",
    "plt.ylabel('Genes')\n",
    "plt.title('Cumulative Histogram of Patients with Mutations in the TCGA Dataset')\n",
    "plt.yticks(ticks=np.arange(len(patient_counts_df_tcga)), labels=patient_counts_df_tcga.index, rotation=0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "print('Total Increased Coverage:' + str(int(patient_counts_df_tcga['Model2 Only'].sum())))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe9e5bf-f4e0-4a77-bfca-654cfaa44759",
   "metadata": {},
   "source": [
    "# AMLSG\n",
    "\n",
    "> Genomic DNA was extracted from peripheral blood granulocytes or bone marrow mononuclear cells for 1,540 adult AML patients ascertained through three prospective multicenter clinical trials of the German-Austrian AML Study Group (AMLSG).\n",
    "> Targeted sequencing on somatic mutations associated with 116 known myeloid and cancer driver genes.\n",
    "\n",
    "## Data\n",
    "The clinical data and summarized driver-mutation calls are available in a [Github repository](http://www.github.com/gerstung-lab/aml-multistage):\n",
    "1. [AMLSG_Genetic.txt](https://github.com/gerstung-lab/AML-multistage/blob/master/data/AMLSG_Genetic.txt)\n",
    "2. [AMLSG_Clinical_Anon.RData](https://github.com/gerstung-lab/AML-multistage/blob/master/data/AMLSG_Clinical_Anon.RData)\n",
    "\n",
    ">Gerstung, M., Papaemmanuil, E., Martincorena, I. et al. Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet 49, 332–340 (2017). https://doi-org.offcampus.lib.washington.edu/10.1038/ng.3756"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "17c7ad6e-811b-4fd3-b443-fb08e93049f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique patients with mutations in each model:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_fc3f6 .row0 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "#T_fc3f6_row0_col0, #T_fc3f6_row0_col1, #T_fc3f6_row3_col0, #T_fc3f6_row3_col1 {\n",
       "  background-color: orange;\n",
       "}\n",
       "#T_fc3f6_row2_col0, #T_fc3f6_row2_col1 {\n",
       "  background-color: blue;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fc3f6\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fc3f6_level0_col0\" class=\"col_heading level0 col0\" >Bonzanni2013</th>\n",
       "      <th id=\"T_fc3f6_level0_col1\" class=\"col_heading level0 col1\" >Krumsiek2011</th>\n",
       "      <th id=\"T_fc3f6_level0_col2\" class=\"col_heading level0 col2\" >Merged Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >GENE</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fc3f6_level0_row0\" class=\"row_heading level0 row0\" >CEBPA</th>\n",
       "      <td id=\"T_fc3f6_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "      <td id=\"T_fc3f6_row0_col1\" class=\"data row0 col1\" >138</td>\n",
       "      <td id=\"T_fc3f6_row0_col2\" class=\"data row0 col2\" >138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc3f6_level0_row1\" class=\"row_heading level0 row1\" >GATA2</th>\n",
       "      <td id=\"T_fc3f6_row1_col0\" class=\"data row1 col0\" >37</td>\n",
       "      <td id=\"T_fc3f6_row1_col1\" class=\"data row1 col1\" >37</td>\n",
       "      <td id=\"T_fc3f6_row1_col2\" class=\"data row1 col2\" >37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc3f6_level0_row2\" class=\"row_heading level0 row2\" >RUNX1</th>\n",
       "      <td id=\"T_fc3f6_row2_col0\" class=\"data row2 col0\" >137</td>\n",
       "      <td id=\"T_fc3f6_row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "      <td id=\"T_fc3f6_row2_col2\" class=\"data row2 col2\" >137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc3f6_level0_row3\" class=\"row_heading level0 row3\" >Total</th>\n",
       "      <td id=\"T_fc3f6_row3_col0\" class=\"data row3 col0\" >174</td>\n",
       "      <td id=\"T_fc3f6_row3_col1\" class=\"data row3 col1\" >175</td>\n",
       "      <td id=\"T_fc3f6_row3_col2\" class=\"data row3 col2\" >312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f182b3021d0>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data file path\n",
    "file_path_genes = 'https://github.com/gerstung-lab/AML-multistage/blob/master/data/AMLSG_Genetic.txt'\n",
    "\n",
    "# Mutation data\n",
    "df_genes = pd.read_csv(file_path_genes, sep='\\t', usecols=['SAMPLE_NAME', 'GENE'])\n",
    "\n",
    "# Count the number of unique patients with mutations in each gene\n",
    "patient_counts_1_p = df_genes[df_genes['GENE'].isin(gene_1)].groupby('GENE')['SAMPLE_NAME'].nunique()\n",
    "patient_counts_2_p = df_genes[df_genes['GENE'].isin(gene_2)].groupby('GENE')['SAMPLE_NAME'].nunique()\n",
    "patient_counts_merged_p = df_genes[df_genes['GENE'].isin(gene_merged)].groupby('GENE')['SAMPLE_NAME'].nunique()\n",
    "patient_counts_df_p = pd.DataFrame({\n",
    "    model1: patient_counts_1_p,\n",
    "    model2: patient_counts_2_p,\n",
    "    'Merged Model': patient_counts_merged_p\n",
    "}).fillna(0)\n",
    "\n",
    "# Convert to integers\n",
    "patient_counts_df_p[[model1, model2, 'Merged Model']] = patient_counts_df_p[[model1, model2, 'Merged Model']].astype(int)\n",
    "\n",
    "# Apply the styling to the DataFrame\n",
    "styled_df_p = patient_counts_df_p.style.apply(highlight_difference, axis=1, subset=[model1, model2])\n",
    "\n",
    "# Display the styled DataFrame with a sum row at the bottom\n",
    "styled_df_p.format(\"{:.0f}\")\n",
    "styled_df_p.set_table_styles([{\n",
    "    'selector': '.row0',\n",
    "    'props': 'font-weight: bold;'\n",
    "}], overwrite=False)\n",
    "\n",
    "# Add a row for the sum at the bottom\n",
    "patient_counts_df_p.loc['Total'] = patient_counts_df_p.sum(numeric_only=True).astype(int)\n",
    "\n",
    "print(\"Total number of unique patients with mutations in each model:\")\n",
    "styled_df_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c3ccaac2-bdfd-4c53-8354-4dd3c9afebe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Increased Coverage:138\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the unique and shared patients\n",
    "patient_counts_df_p['Shared'] = np.minimum(patient_counts_df_p[model1], patient_counts_df_p[model2])\n",
    "patient_counts_df_p['Model1 Only'] = patient_counts_df_p[model1] - patient_counts_df_p['Shared']\n",
    "patient_counts_df_p['Model2 Only'] = patient_counts_df_p[model2] - patient_counts_df_p['Shared']\n",
    "\n",
    "# Sort the DataFrame based on the total patients for better visualization\n",
    "patient_counts_df_p = patient_counts_df_p.sort_values(by=[model1, model2], ascending=True)\n",
    "patient_counts_df_p = patient_counts_df_p[patient_counts_df_p.index != 'Total'] # Remove 'Total' row\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "bars_1 = plt.barh(patient_counts_df_p.index, patient_counts_df_p['Model1 Only'], color='blue', edgecolor='gray', label=model1+' Model Only')\n",
    "bars_shared = plt.barh(patient_counts_df_p.index, patient_counts_df_p['Shared'], color='green', edgecolor='gray', label='Shared by Both Models')\n",
    "bars_2 = plt.barh(patient_counts_df_p.index, patient_counts_df_p['Model2 Only'], color='orange', edgecolor='gray', label=model2+' Model Only')\n",
    "\n",
    "# Annotate the number of patients\n",
    "for bars in [bars_1, bars_shared, bars_2]:\n",
    "    for bar in bars:\n",
    "        width = bar.get_width()\n",
    "        if width > 0:\n",
    "            plt.text(width + bar.get_x(), bar.get_y() + bar.get_height()/2,\n",
    "                     f'{int(width)}', ha='left', va='center')\n",
    "plt.xlabel('Number of Patients')\n",
    "plt.ylabel('Genes')\n",
    "plt.title('Cumulative Histogram of Patients with Mutations in the AMLSG Dataset')\n",
    "plt.yticks(ticks=np.arange(len(patient_counts_df_p)), labels=patient_counts_df_p.index, rotation=0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "print('Total Increased Coverage:' + str(int(patient_counts_df_p['Model2 Only'].sum())))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb342707-df7b-411d-b3ed-c7120a3b4148",
   "metadata": {},
   "source": [
    "# cBioportal\n",
    "\n",
    "This combined study contains samples from 7 studies:\n",
    "- Acute Myeloid Leukemia (OHSU, Cancer Cell 2022)\n",
    "- Acute Myeloid Leukemia (OHSU, Nature 2018)\n",
    "- Acute Myeloid Leukemia (TCGA, Firehose Legacy)\n",
    "- Acute Myeloid Leukemia (TCGA, GDC)\n",
    "- Acute Myeloid Leukemia (TCGA, NEJM 2013)\n",
    "- Acute Myeloid Leukemia (TCGA, PanCancer Atlas)\n",
    "- Pediatric Acute Myeloid Leukemia (TARGET, 2018)\n",
    "\n",
    "Mutated genes data are downloaded from [cBioportal - the combined AML study](https://www.cbioportal.org/study/summary?id=aml_ohsu_2022%2Caml_ohsu_2018%2Claml_tcga%2Caml_tcga_gdc%2Claml_tcga_pub%2Claml_tcga_pan_can_atlas_2018%2Caml_target_2018_pub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "542355af-8856-45c0-9b37-a135f46b1847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique patients with mutations in each model:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "#T_97e38 .row0 {\n",
       "  font-weight: bold;\n",
       "}\n",
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       "  background-color: blue;\n",
       "}\n",
       "#T_97e38_row1_col0, #T_97e38_row1_col1, #T_97e38_row2_col0, #T_97e38_row2_col1, #T_97e38_row3_col0, #T_97e38_row3_col1, #T_97e38_row8_col0, #T_97e38_row8_col1, #T_97e38_row9_col0, #T_97e38_row9_col1 {\n",
       "  background-color: orange;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_97e38\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_97e38_level0_col0\" class=\"col_heading level0 col0\" >Bonzanni2013</th>\n",
       "      <th id=\"T_97e38_level0_col1\" class=\"col_heading level0 col1\" >Krumsiek2011</th>\n",
       "      <th id=\"T_97e38_level0_col2\" class=\"col_heading level0 col2\" >Merged Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Gene</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row0\" class=\"row_heading level0 row0\" >CBFA2T3</th>\n",
       "      <td id=\"T_97e38_row0_col0\" class=\"data row0 col0\" >2</td>\n",
       "      <td id=\"T_97e38_row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "      <td id=\"T_97e38_row0_col2\" class=\"data row0 col2\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row1\" class=\"row_heading level0 row1\" >CEBPA</th>\n",
       "      <td id=\"T_97e38_row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "      <td id=\"T_97e38_row1_col1\" class=\"data row1 col1\" >119</td>\n",
       "      <td id=\"T_97e38_row1_col2\" class=\"data row1 col2\" >119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row2\" class=\"row_heading level0 row2\" >EGR1</th>\n",
       "      <td id=\"T_97e38_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "      <td id=\"T_97e38_row2_col1\" class=\"data row2 col1\" >6</td>\n",
       "      <td id=\"T_97e38_row2_col2\" class=\"data row2 col2\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row3\" class=\"row_heading level0 row3\" >EGR2</th>\n",
       "      <td id=\"T_97e38_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "      <td id=\"T_97e38_row3_col1\" class=\"data row3 col1\" >6</td>\n",
       "      <td id=\"T_97e38_row3_col2\" class=\"data row3 col2\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row4\" class=\"row_heading level0 row4\" >ERG</th>\n",
       "      <td id=\"T_97e38_row4_col0\" class=\"data row4 col0\" >6</td>\n",
       "      <td id=\"T_97e38_row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "      <td id=\"T_97e38_row4_col2\" class=\"data row4 col2\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row5\" class=\"row_heading level0 row5\" >FLI1</th>\n",
       "      <td id=\"T_97e38_row5_col0\" class=\"data row5 col0\" >2</td>\n",
       "      <td id=\"T_97e38_row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "      <td id=\"T_97e38_row5_col2\" class=\"data row5 col2\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row6\" class=\"row_heading level0 row6\" >GATA1</th>\n",
       "      <td id=\"T_97e38_row6_col0\" class=\"data row6 col0\" >5</td>\n",
       "      <td id=\"T_97e38_row6_col1\" class=\"data row6 col1\" >5</td>\n",
       "      <td id=\"T_97e38_row6_col2\" class=\"data row6 col2\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row7\" class=\"row_heading level0 row7\" >GATA2</th>\n",
       "      <td id=\"T_97e38_row7_col0\" class=\"data row7 col0\" >73</td>\n",
       "      <td id=\"T_97e38_row7_col1\" class=\"data row7 col1\" >73</td>\n",
       "      <td id=\"T_97e38_row7_col2\" class=\"data row7 col2\" >73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row8\" class=\"row_heading level0 row8\" >GFI1</th>\n",
       "      <td id=\"T_97e38_row8_col0\" class=\"data row8 col0\" >0</td>\n",
       "      <td id=\"T_97e38_row8_col1\" class=\"data row8 col1\" >1</td>\n",
       "      <td id=\"T_97e38_row8_col2\" class=\"data row8 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row9\" class=\"row_heading level0 row9\" >NAB1</th>\n",
       "      <td id=\"T_97e38_row9_col0\" class=\"data row9 col0\" >0</td>\n",
       "      <td id=\"T_97e38_row9_col1\" class=\"data row9 col1\" >2</td>\n",
       "      <td id=\"T_97e38_row9_col2\" class=\"data row9 col2\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row10\" class=\"row_heading level0 row10\" >RUNX1</th>\n",
       "      <td id=\"T_97e38_row10_col0\" class=\"data row10 col0\" >240</td>\n",
       "      <td id=\"T_97e38_row10_col1\" class=\"data row10 col1\" >0</td>\n",
       "      <td id=\"T_97e38_row10_col2\" class=\"data row10 col2\" >240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row11\" class=\"row_heading level0 row11\" >SMAD6</th>\n",
       "      <td id=\"T_97e38_row11_col0\" class=\"data row11 col0\" >2</td>\n",
       "      <td id=\"T_97e38_row11_col1\" class=\"data row11 col1\" >0</td>\n",
       "      <td id=\"T_97e38_row11_col2\" class=\"data row11 col2\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row12\" class=\"row_heading level0 row12\" >SPI1</th>\n",
       "      <td id=\"T_97e38_row12_col0\" class=\"data row12 col0\" >1</td>\n",
       "      <td id=\"T_97e38_row12_col1\" class=\"data row12 col1\" >1</td>\n",
       "      <td id=\"T_97e38_row12_col2\" class=\"data row12 col2\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row13\" class=\"row_heading level0 row13\" >ZFPM1</th>\n",
       "      <td id=\"T_97e38_row13_col0\" class=\"data row13 col0\" >2</td>\n",
       "      <td id=\"T_97e38_row13_col1\" class=\"data row13 col1\" >2</td>\n",
       "      <td id=\"T_97e38_row13_col2\" class=\"data row13 col2\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_97e38_level0_row14\" class=\"row_heading level0 row14\" >Total</th>\n",
       "      <td id=\"T_97e38_row14_col0\" class=\"data row14 col0\" >333</td>\n",
       "      <td id=\"T_97e38_row14_col1\" class=\"data row14 col1\" >217</td>\n",
       "      <td id=\"T_97e38_row14_col2\" class=\"data row14 col2\" >467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fffd055b410>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data file path\n",
    "file_path_genes = home + 'LogicModelMerger/Data/cBioportal/Mutated_Genes.txt'\n",
    "\n",
    "# Mutation data\n",
    "df_genes = pd.read_csv(file_path_genes, sep='\\t', usecols=['Gene', '#'], index_col = 0)\n",
    "\n",
    "# Count the number of unique patients with mutations in each gene\n",
    "patient_counts_1_c = df_genes[df_genes.index.isin(gene_1)].iloc[:,0]\n",
    "patient_counts_2_c = df_genes[df_genes.index.isin(gene_2)].iloc[:,0]\n",
    "patient_counts_merged_c = df_genes[df_genes.index.isin(gene_merged)].iloc[:,0]\n",
    "patient_counts_df_c = pd.DataFrame({\n",
    "    model1: patient_counts_1_c,\n",
    "    model2: patient_counts_2_c,\n",
    "    'Merged Model': patient_counts_merged_c\n",
    "}).fillna(0)\n",
    "\n",
    "# Convert to integers\n",
    "patient_counts_df_c[[model1, model2, 'Merged Model']] = patient_counts_df_c[[model1, model2, 'Merged Model']].astype(int)\n",
    "\n",
    "# Apply the styling to the DataFrame\n",
    "def highlight_difference(row):\n",
    "    color = ''\n",
    "    if row[model1] - row[model2] > 0:\n",
    "        color = 'background-color: blue'\n",
    "    elif row[model1] - row[model2] < 0:\n",
    "        color = 'background-color: orange'\n",
    "    styles = ['' for _ in row.index]  # Default no background color\n",
    "    styles[row.index.get_loc(model1)] = color  \n",
    "    styles[row.index.get_loc(model2)] = color  \n",
    "    return styles\n",
    "styled_df_c = patient_counts_df_c.style.apply(highlight_difference, axis=1, subset=[model1, model2])\n",
    "\n",
    "# Display the styled DataFrame with a sum row at the bottom\n",
    "styled_df_c.format(\"{:.0f}\")\n",
    "styled_df_c.set_table_styles([{\n",
    "    'selector': '.row0',\n",
    "    'props': 'font-weight: bold;'\n",
    "}], overwrite=False)\n",
    "\n",
    "# Add a row for the sum at the bottom\n",
    "patient_counts_df_c.loc['Total'] = patient_counts_df_c.sum(numeric_only=True).astype(int)\n",
    "\n",
    "print(\"Total number of unique patients with mutations in each model:\")\n",
    "styled_df_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "62d2172a-ea27-407a-b977-7356e62c9d2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Increased Coverage:134\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the unique and shared patients\n",
    "patient_counts_df_c['Shared'] = np.minimum(patient_counts_df_c[model1], patient_counts_df_c[model2])\n",
    "patient_counts_df_c['Model1 Only'] = patient_counts_df_c[model1] - patient_counts_df_c['Shared']\n",
    "patient_counts_df_c['Model2 Only'] = patient_counts_df_c[model2] - patient_counts_df_c['Shared']\n",
    "\n",
    "# Sort the DataFrame based on the total patients for better visualization\n",
    "patient_counts_df_c = patient_counts_df_c.sort_values(by=[model1, model2], ascending=True)\n",
    "patient_counts_df_c = patient_counts_df_c[patient_counts_df_c.index != 'Total'] # Remove 'Total' row\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "bars_1 = plt.barh(patient_counts_df_c.index, patient_counts_df_c['Model1 Only'], color='blue', edgecolor='gray', label=model1+' Model Only')\n",
    "bars_shared = plt.barh(patient_counts_df_c.index, patient_counts_df_c['Shared'], color='green', edgecolor='gray', label='Shared by Both Models')\n",
    "bars_2 = plt.barh(patient_counts_df_c.index, patient_counts_df_c['Model2 Only'], color='orange', edgecolor='gray', label=model2+' Model Only')\n",
    "\n",
    "# Annotate the number of patients\n",
    "for bars in [bars_1, bars_shared, bars_2]:\n",
    "    for bar in bars:\n",
    "        width = bar.get_width()\n",
    "        if width > 0:\n",
    "            plt.text(width + bar.get_x(), bar.get_y() + bar.get_height()/2,\n",
    "                     f'{int(width)}', ha='left', va='center')\n",
    "plt.xlabel('Number of Patients')\n",
    "plt.ylabel('Genes')\n",
    "plt.title('Cumulative Histogram of Patients with Mutations in cBioportal')\n",
    "plt.yticks(ticks=np.arange(len(patient_counts_df_c)), labels=patient_counts_df_c.index, rotation=0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "print('Total Increased Coverage:' + str(int(patient_counts_df_c['Model2 Only'].sum())))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d06c4a-89ce-4084-a5cf-53434f9552af",
   "metadata": {},
   "source": [
    "# Combined three datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "51c88db0-7ef8-40c3-9537-1ccde97e6b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BeatAML + TCGA-LAML + AMLSG\n",
    "pd.concat([patient_counts_df, patient_counts_df_tcga, patient_counts_df_p],axis=1).to_csv(\"counts_\"+model1+'-'+model2+\".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "7cb742f3-9c85-49d5-a0ff-f485bc553ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Increased Coverage:28\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_df = pd.concat([patient_counts_df, patient_counts_df_tcga, patient_counts_df_p]).groupby(level=0).sum()\n",
    "combined_df['Shared'] = np.minimum(combined_df[model1], combined_df[model2])\n",
    "combined_df['Model1 Only'] = combined_df[model1] - combined_df['Shared']\n",
    "combined_df['Model2 Only'] = combined_df[model2] - combined_df['Shared']\n",
    "\n",
    "# Sort the DataFrame \n",
    "combined_df = combined_df.sort_values(by=['Merged Model'], ascending=True)\n",
    "\n",
    "# Create the cumulative histogram\n",
    "plt.figure(figsize=(6, 6))\n",
    "bars_1 = plt.barh(combined_df.index, combined_df['Model1 Only'], color='blue', edgecolor='gray', label=model1+' Model Only')\n",
    "bars_shared = plt.barh(combined_df.index, combined_df['Shared'], color='green', edgecolor='gray', label='Shared by Both Models')\n",
    "bars_2 = plt.barh(combined_df.index, combined_df['Model2 Only'], color='orange', edgecolor='gray', label=model2+' Model Only')\n",
    "\n",
    "# Annotate the number of patients\n",
    "for bars in [bars_1, bars_shared, bars_2]:\n",
    "    for bar in bars:\n",
    "        width = bar.get_width()\n",
    "        if width > 0:\n",
    "            plt.text(width + bar.get_x(), bar.get_y() + bar.get_height()/2,\n",
    "                     f'{int(width)}', ha='left', va='center')\n",
    "plt.xlabel('Number of Patients')\n",
    "plt.ylabel('Genes')\n",
    "plt.title('Number of Patients with Mutations in '+ model1 +'_'+ model2)\n",
    "plt.yticks(ticks=np.arange(len(combined_df)), labels=combined_df.index, rotation=0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "print('Total Increased Coverage:' + str(int(combined_df['Model2 Only'].sum())))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "03bedbc6-bbf1-4db7-b568-66a16beeefbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cBioportal + AMLSG\n",
    "pd.concat([patient_counts_df_c, patient_counts_df_p],axis=1).to_csv(\"counts_c_\"+model1+'-'+model2+\".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "b7d1e100-37a5-4a42-93f7-fcf36b831713",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Increased Coverage:270\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_df = pd.concat([patient_counts_df_c, patient_counts_df_p]).groupby(level=0).sum()\n",
    "combined_df['Shared'] = np.minimum(combined_df[model1], combined_df[model2])\n",
    "combined_df['Model1 Only'] = combined_df[model1] - combined_df['Shared']\n",
    "combined_df['Model2 Only'] = combined_df[model2] - combined_df['Shared']\n",
    "\n",
    "# Sort the DataFrame \n",
    "combined_df = combined_df.sort_values(by=['Merged Model'], ascending=True)\n",
    "\n",
    "# Create the cumulative histogram\n",
    "plt.figure(figsize=(6, 6))\n",
    "bars_1 = plt.barh(combined_df.index, combined_df['Model1 Only'], color='blue', edgecolor='gray', label=model1+' Model Only')\n",
    "bars_shared = plt.barh(combined_df.index, combined_df['Shared'], color='green', edgecolor='gray', label='Shared by Both Models')\n",
    "bars_2 = plt.barh(combined_df.index, combined_df['Model2 Only'], color='orange', edgecolor='gray', label=model2+' Model Only')\n",
    "\n",
    "# Annotate the number of patients\n",
    "for bars in [bars_1, bars_shared, bars_2]:\n",
    "    for bar in bars:\n",
    "        width = bar.get_width()\n",
    "        if width > 0:\n",
    "            plt.text(width + bar.get_x(), bar.get_y() + bar.get_height()/2,\n",
    "                     f'{int(width)}', ha='left', va='center')\n",
    "plt.xlabel('Number of Patients')\n",
    "plt.ylabel('Genes')\n",
    "plt.title('Number of Patients with Mutations in '+ model1 +'_'+ model2)\n",
    "plt.yticks(ticks=np.arange(len(combined_df)), labels=combined_df.index, rotation=0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "print('Total Increased Coverage:' + str(int(combined_df['Model2 Only'].sum())))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}