2-Cell-Morphology-VIP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f896ed2",
   "metadata": {},
   "source": [
    "# cmVIP\n",
    "This notebook runs the cell morphology-based Variant Impact Phenotyping (cmVIP) analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "20ff9676",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import umap\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "from plotly import graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from plotly import express as px\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"notebook_connected\" # Set to \"svg\" or \"png\" for non-interactive plots\n",
    "\n",
    "from zca import ZCA\n",
    "import correlations as corr\n",
    "import mvip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddde24b2",
   "metadata": {},
   "source": [
    "## Load Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ad3bc84",
   "metadata": {},
   "outputs": [],
   "source": [
    "platemaps = pd.read_csv('inputs/metadata/cytodata/barcode_platemap.csv')\n",
    "pmaps = pd.concat([pd.read_csv('inputs/metadata/cytodata/platemap/DOC45.46.47.48.txt', sep='\\t'),\n",
    "                   pd.read_csv('inputs/metadata/cytodata/platemap/DOC49.50.51.52.txt', sep='\\t')],\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0836d87b",
   "metadata": {},
   "source": [
    "### Filter quality control status\n",
    "\n",
    "All of our TP53 alleles (including our allele annotated as \"wt\") actually harbor an inactivating p53 variant. Thus, we flagged such alleles (`qc_status=False`) and discarded them in this analysis because they are wrong perturbations for the purposes of this study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "de22d55e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pmaps = pmaps[pmaps.qc_status]\n",
    "metadata = pmaps.merge(platemaps, how='inner', left_on='VirusPlateName', right_on='Plate_Map_Name')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32a4f662",
   "metadata": {},
   "source": [
    "## Load Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2d647c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "key_cols = [\"Metadata_Plate\", \"Metadata_Well\"]\n",
    "deep_features = pd.read_parquet('efn_pretrained_profiles.parquet')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc8e4692",
   "metadata": {},
   "source": [
    "## Define controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b557e43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trt        4552\n",
       "control     320\n",
       "Name: Metadata_broad_sample_type, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "morphology = metadata[['Assay_Plate_Barcode', 'well_position', 'broad_sample', 'pert_type', 'x_mutation_status']].copy()\n",
    "morphology.columns = ['Metadata_Plate', 'Metadata_Well', 'Metadata_broad_sample', 'pert_type', 'x_mutation_status']\n",
    "control_def = 'EMPTY'\n",
    "if control_def == 'EMPTY':\n",
    "    morphology = morphology[morphology.pert_type!='ctl_vector'].copy()\n",
    "    morphology['Metadata_broad_sample_type'] = morphology.pert_type.apply(lambda x: 'control' if x == 'EMPTY' else 'trt')\n",
    "elif control_def == 'NOT_TREATMENT':\n",
    "    morphology['Metadata_broad_sample_type'] = morphology.pert_type.apply(lambda x: 'control' if x != 'trt_oe' else 'trt')\n",
    "elif control_def == 'CTL_VECTOR':\n",
    "    morphology = morphology[morphology.pert_type!='EMPTY'].copy()\n",
    "    morphology['Metadata_broad_sample_type'] = morphology.pert_type.apply(lambda x: 'control' if x == 'ctl_vector' else 'trt')\n",
    "morphology['Metadata_broad_sample_type'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc27c61",
   "metadata": {},
   "source": [
    "## Keep only `MUT` with `WT` and `WT` with `MUT`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c70e218c",
   "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>name</th>\n",
       "      <th>wells</th>\n",
       "      <th>controls</th>\n",
       "      <th>alleles</th>\n",
       "      <th>total_mutants</th>\n",
       "      <th>total_wild_types</th>\n",
       "      <th>agg_wild_types</th>\n",
       "      <th>wt_no_mut</th>\n",
       "      <th>wt_with_mut</th>\n",
       "      <th>mut_no_wt</th>\n",
       "      <th>mut_with_wt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>metadata</td>\n",
       "      <td>6144</td>\n",
       "      <td>320</td>\n",
       "      <td>593</td>\n",
       "      <td>434</td>\n",
       "      <td>159</td>\n",
       "      <td>125</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>23</td>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no_ctl_vector</td>\n",
       "      <td>5632</td>\n",
       "      <td>320</td>\n",
       "      <td>589</td>\n",
       "      <td>430</td>\n",
       "      <td>159</td>\n",
       "      <td>125</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>19</td>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no_tp53</td>\n",
       "      <td>4872</td>\n",
       "      <td>320</td>\n",
       "      <td>501</td>\n",
       "      <td>344</td>\n",
       "      <td>157</td>\n",
       "      <td>124</td>\n",
       "      <td>74</td>\n",
       "      <td>50</td>\n",
       "      <td>19</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name  wells  controls  alleles  total_mutants  total_wild_types  \\\n",
       "0       metadata   6144       320      593            434               159   \n",
       "1  no_ctl_vector   5632       320      589            430               159   \n",
       "2        no_tp53   4872       320      501            344               157   \n",
       "\n",
       "   agg_wild_types  wt_no_mut  wt_with_mut  mut_no_wt  mut_with_wt  \n",
       "0             125         74           51         23          411  \n",
       "1             125         74           51         19          411  \n",
       "2             124         74           50         19          325  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def choose_wild_type(options):\n",
    "    # Choose one WT id out of a list of options\n",
    "    # If multiple WT, closed WT is preferred \n",
    "    wtypes = [opt for opt in options if opt and 'WT.' in opt]\n",
    "    if not wtypes:\n",
    "        return None\n",
    "    if len(wtypes) == 1:\n",
    "        return wtypes[0]\n",
    "    # Get first closed wildtype\n",
    "    return next(filter(lambda x: '.c' in x, wtypes))\n",
    "\n",
    "def get_allele_stats(s, name):\n",
    "    wells = len(s)\n",
    "    controls = (s=='EMPTY').sum()\n",
    "    s = s[s!='EMPTY']\n",
    "    alleles = s.str.split('_', expand=True, n=1)\n",
    "    alleles.columns = ['gene', 'suffix']\n",
    "    wild_type_map = alleles.groupby('gene')['suffix'].unique().apply(choose_wild_type)\n",
    "    wild_type_map = wild_type_map.index.astype(str)+'_'+wild_type_map\n",
    "    wild_type = alleles.gene.map(wild_type_map)\n",
    "    df = pd.DataFrame({'x_mutation_status': s,\n",
    "                       'wild_type': wild_type,\n",
    "                       'gene': alleles.gene})\n",
    "    \n",
    "    wt_with_mut = df.wild_type[~df.x_mutation_status.str.contains('WT')].dropna().drop_duplicates()\n",
    "    wt_no_mut = df.wild_type[~df.wild_type.isin(wt_with_mut)].drop_duplicates()\n",
    "    mut_no_wt = df.x_mutation_status[df.wild_type.isna() & (~df.x_mutation_status.str.contains('WT'))].drop_duplicates()\n",
    "    mut_with_wt = df.x_mutation_status[~df.wild_type.isna() & (~df.x_mutation_status.str.contains('WT'))].drop_duplicates()\n",
    "    return {\n",
    "    'name': name,\n",
    "    'wells': wells,\n",
    "    'controls': controls,\n",
    "    'alleles': s.nunique(),\n",
    "    'total_mutants': (~s.drop_duplicates().str.contains('WT')).sum(),\n",
    "    'total_wild_types': s.drop_duplicates().str.contains('WT').sum(),\n",
    "    'agg_wild_types': df.wild_type.nunique(),\n",
    "    'wt_no_mut': wt_no_mut.nunique(),\n",
    "    'wt_with_mut': wt_with_mut.nunique(),\n",
    "    'mut_no_wt': mut_no_wt.nunique(),\n",
    "    'mut_with_wt': mut_with_wt.nunique(),       \n",
    "    }, wt_with_mut.append(mut_with_wt)\n",
    "\n",
    "metadata_stats, metadata_alleles = get_allele_stats(metadata.x_mutation_status, 'metadata')\n",
    "no_ctl_vector_stats, no_ctl_vector_alleles = get_allele_stats(metadata[metadata.pert_type!='ctl_vector'].x_mutation_status, 'no_ctl_vector')\n",
    "morphology_stats, morphology_alleles = get_allele_stats(morphology.x_mutation_status, 'no_tp53')\n",
    "pd.DataFrame([\n",
    "    metadata_stats, no_ctl_vector_stats, morphology_stats\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5703ca8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trt        3504\n",
       "control     320\n",
       "Name: Metadata_broad_sample_type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "morphology = morphology[morphology.x_mutation_status.isin(morphology_alleles) |\n",
    "                        (morphology.x_mutation_status=='EMPTY')]\n",
    "morphology['Metadata_broad_sample_type'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4097ce28",
   "metadata": {},
   "source": [
    "## Merge features and metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17b8b7f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "morphology = morphology.sort_values(by=key_cols)\n",
    "deep_features = deep_features.sort_values(by=key_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "397fb567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3824 entries, 0 to 3823\n",
      "Columns: 6402 entries, Metadata_Plate to 6399\n",
      "dtypes: float64(6400), int64(1), object(1)\n",
      "memory usage: 186.8+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3824, 6400)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "morphology = pd.merge(deep_features[key_cols], morphology, on=key_cols)\n",
    "deep_features = pd.merge(morphology[key_cols], deep_features, on=key_cols)\n",
    "\n",
    "morphology.reset_index(drop=True, inplace=True)\n",
    "deep_features.reset_index(drop=True, inplace=True)\n",
    "deep_features.info()\n",
    "\n",
    "deep_feature_matrix = deep_features.select_dtypes(include=[\"float64\"])\n",
    "deep_feature_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb548ab3",
   "metadata": {},
   "source": [
    "## Sphering data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5511abec",
   "metadata": {},
   "outputs": [],
   "source": [
    "lambda_ = 1e-2\n",
    "spherer = ZCA(regularization=lambda_)\n",
    "controls = morphology.Metadata_broad_sample_type == \"control\"\n",
    "spherer.fit(deep_feature_matrix[controls])\n",
    "new_feature_matrix = spherer.transform(deep_feature_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce0490bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save spherized features with metadata useful for plotting.\n",
    "dffeats = pd.DataFrame(new_feature_matrix)\n",
    "dffeats.columns = list(map(str, dffeats.columns))\n",
    "\n",
    "dffeats['x_mutation_status'] = morphology['x_mutation_status']\n",
    "alleles = dffeats['x_mutation_status'].str.split('_', expand=True, n=2)\n",
    "wild_types = alleles.groupby(0)[1].unique().apply(choose_wild_type)\n",
    "wild_types = wild_types.index.astype(str)+'_'+wild_types\n",
    "dffeats['wild_type'] = alleles[0].map(wild_types)\n",
    "dffeats.to_parquet('outputs/spherized_morphology_features.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "312b565f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S = spherer.S\n",
    "lambda_loc = (S>spherer.regularization).sum()\n",
    "fig = px.line(S, log_x=True,\n",
    "              title=f'Eigenvalues of spherized data, {spherer.regularization:0.2e}')\n",
    "fig.add_vline(x=lambda_loc, line_width=3, line_dash=\"dash\", line_color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4727b2bf",
   "metadata": {},
   "source": [
    "## UMap visualization of Sphered data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7dd65c77",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #271: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "UMAP(random_state=0, tqdm_kwds={'bar_format': '{desc}: {percentage:3.0f}%| {bar} {n_fmt}/{total_fmt} [{elapsed}]', 'desc': 'Epochs completed', 'disable': True})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reducer = umap.UMAP(random_state=0, n_components=2)\n",
    "reducer.fit(new_feature_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d5850d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(reducer.transform(new_feature_matrix), columns=['x', 'y'])\n",
    "df = pd.concat([df, morphology], axis=1)\n",
    "aggregate_per_allele = True\n",
    "if aggregate_per_allele:\n",
    "    df = df.groupby('x_mutation_status', as_index=False).agg(x=pd.NamedAgg(column=\"x\", aggfunc='median'),\n",
    "                                                             y=pd.NamedAgg(column=\"y\", aggfunc='median'),\n",
    "                                                             count=pd.NamedAgg(column=\"x\", aggfunc='count')\n",
    "                                                            )\n",
    "df['Gene'] = df['x_mutation_status'].str.split('_', expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ff4ca591",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perturbation(s):\n",
    "    if s == 'EMPTY':\n",
    "        return 'EMPTY'\n",
    "    if '_WT.' in s:\n",
    "        return 'REF'\n",
    "    else:\n",
    "        return 'VAR'\n",
    "df['perturbation'] = df.x_mutation_status.apply(get_perturbation)\n",
    "df = df.copy()\n",
    "df['x_mutation_status'] = df['x_mutation_status'].str.replace(r'WT.[oc]', 'REF', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8745a1cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap_type = {\"EMPTY\": '#1f77b4', 'REF':'#2CA02C', 'VAR': '#FF7F0E'}\n",
    "\n",
    "fig = px.scatter(df, x='x', y='y', color='Gene',\n",
    "                 title='', #opacity=0.7,\n",
    "                 category_orders={'perturbation': ['VAR', 'REF', 'EMPTY'],\n",
    "                                  'Gene': ['EMPTY'] + df.Gene.sort_values().tolist(),\n",
    "                                 },\n",
    "                 hover_name='x_mutation_status',\n",
    "                 color_discrete_map=cmap_type,\n",
    "                 width=800,\n",
    "                 height=800\n",
    "                )\n",
    "fig.update_traces(marker_size=8)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "faca0e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hp87fYGZQHy4EJCoyjHSWpUKzCrgE8ca+arQqtrjt/b2PGp9WdarfGfWANb0DpmBpWhiF3cIULIVwNU2NKViaFkZhtzAFSyFcD1JjCpYHsJw0hYDw+EFAmPys4VGfP0EhcXtdZhu8tzr971xR+b5RAcEakDA6F5+AERAfPaNmSAMBMUOVfNtHCIhveZohGwREjypBQHh1gIDw+EFAmPys4emdAbLvSk4aeaAavVD4EI08UN2wgGT0s0AwAuKjh9NEaSAgJiqWj7oKAfERSBOlgYDoUSwICK8OEBAePwgIk581POu+pRT548A02Q5dy0Gv7q1J39T8Sb731emi1Lrgf4bueqH5x5RQqqmhtoHYCAISiFVN/zNBQDJezSEgGa/mEBA9ag4B4dUBAsLjBwFh8rMPz7+wOgVfOWl76fj1bNRrd236odY6j++SnKMIxXbd7nFcIAVAQAKpmsY+CwTEGKdAagUBCaRqGvssEBBjnFS3goDwCENAePwgIEx+9uH2oyCnEsKp81/16cdH1nh1h0sNZ9D18u28ig2UIAhIoFTS+OeAgBhnFSgtISCBUknjnwMCYpyVypYQEB5dCAiPHwSEyS91uFgLcm3XKmrz5+P0W+0fvMqe0dd+WKFBQLx6fEwdBAExdfm86jwExCtspg6CgOhRPggIrw4QEB4/CAiTX+rw+CtXqXvPwbS59iqvMmf0na/soUFAvHqETB0EATF1+bzqPATEK2ymDoKA6FE+CAivDhAQHj8ICJOfffj1GwnUrscw+ubTGZTerliubomRD0cyEBAfPpwmSQUBMUmhfNhNCIgPYZokFQREj0JBQHh1gIDw+EFAmPys4YlJSfT0C6/S98tm2TJmidlEEVujKUvMlnTvIkY94msNocTCdXzUm8BIAwEJjDp68ikgIJ7QCoy2EJDAqKMnnwIC4gktdW0hIDy2EBAePwgIk581vNGzvWndl7OdZguOP0FhR/5HmeNP2A4rTMpbgW5FFKOEUk0oOaKYj3oRWGkgIIFVTyOfBgJihFJgtYGABFY9jXwaCIgRSurbQEB4jCEgPH4QECY/Ed6kbR9a/dlMypw52AfZkMJKAAKS8Z4FCEjGqzkEJOPVHAKiR80hILw6QEB4/CAgTH4tOwygLxZMpfCwUGYmhKcmAAHJeM8EBCTj1RwCkvFqDgHRo+YQEF4dICA8fhAQBr/WnQfT4g/GU47s2RhZEOqKAAQk4z0bEJCMV3MISMarOQREj5pDQHh1gIDw+EFAvOT3XPehNO+dkZQrMsLLDAhzRwAC4o5Q4L0PAQm8mrr7RBAQd4QC730IiB41hYDw6gAB4fGDgHjBr0PPN2jGm0MoX97cXkQjxCgBCIhRUoHTDgISOLU0+kkgIEZJBU47CIgetYSA8OoAAeHxg4B4yK/zK6PordH9qVCBKA8j0dxTAhAQT4mZvz0ExPw19PQTQEA8JWb+9hAQPWoIAeHVAQLC4wcB8YBf9/7jaOzQnlSsSEEPotDUWwIQEG/JmTcOAmLe2nnbcwiIt+TMGwcB0aN2EBBeHSAgPH4QEIP8Xn51Ig3t35VKlyhiMALNuAQgIFyC5ouHgJivZtweQ0C4BM0XDwHRo2YQEF4dICA8fhAQA/z6DH2L+vdsR/eXKWGgNZr4igAExFckzZMHAmKeWvmqpxAQX5E0Tx4IiB61goDw6gAB4fGDgLjhN3BENPV44WmqWL4MkzTCPSUAAfGUmPnbQ0DMX0NPPwEExFNi5m8PAdGjhhAQXh0gIDx+EJB0+L02Zjp1aNOMqla6n0kZ4d4QgIB4Q83cMRAQc9fPm95DQLyhZu4YCIge9YOA8OoAAeHxg4C44Dd8wix6psXjVKNqBSZhhHtLAALiLTnzxkFAzFs7b3sOAfGWnHnjICB61A4CwqsDBITHDwLihN+oybOpWcPa9EiNyky6COcQgIBw6JkzFgJizrpxeg0B4dAzZywERI+6QUB4dYCA8PhBQFLxG//2PHq0bnWq/0g1JlmEcwlAQLgEzRcPATFfzbg9hoBwCZovHgKiR80gILw6QEB4/CAgdvw+W/EDlS9bkqo+WI5JFeG+IAAB8QVFc+WAgJirXr7oLQTEFxTNlQMCoke9ICC8OkBAePwgIEx+CFdHAAKijq2umSEgulZGXb8gIOrY6poZAqJHZSAgvDpAQHj8ICBMfghXRwACoo6trpkhILpWRl2/ICDq2OqaGQKiR2UgILw6QEB4/CAgTH4IV0cAAqKOra6ZISC6VkZdvyAg6tjqmhkCokdlICC8OkBAePwgIEx+CFdHAAKijq2umSEgulZGXb8gIOrY6poZAqJHZSAgvDpAQHj8ICBMfghXRwACoo6trpkhILpWRl2/ICDq2OqaGQKiR2UgILw6QEB4/CAgTH4IV0cAAqKOra6ZISC6VkZdvyAg6tjqmhkCokdlICC8OkBAePwCQkCuXrtOYWGhlDk4mEXjVnIy3U6+TVmyhBjOk5CQSGFhWQy3R0PjBCAgxlkFSksISKBU0vjngIAYZxUoLSEgelQSAsKrAwSEx8+vApKUdIvGR89L02PxhT9njuyUKzKCalStQOXuK+HQJlMQUVRkGMVeTCAhCdv/+od+/3M3HTh0nGJOnaUbCQmyfcH8eala5fLU4dmmlC9vLrdk4q9eoy3bdtG/R07Q/oPH6MDh4zImPCyMFswaS3lz53SaY8++w/Tdug20d/9hOh0bJ9tXrVSWaj1UiZo1qus05qf12+jXTdvle6MGv+SR5Lj9IAHaAAISoIVN52NBQDJezSEgGa/mEBA9ag4B4dUBAsLj51cBEaMFLTsOcNvj+0sXp4kj+lBkzhyyrb2AbNuxh0ZMet9tjimj+0sZcXUdPnaSxk2dKwXC2bX4gwlSaFJfv23+kyZOm+8y79PNHqVeXZ+j4OBMDm0WLV1NS5Z/J1/7ZskMCg8PdfsZMnoDCEjGewIgIBmv5hCQjFdzCIgeNYeA8OoAAeHxu2cCIiSj5kMVZe9v3LhJcRcv0a8bLSME4ipVvDDNmDSEwsPDXApI8yfrUdlSxShfVG45irF+y1906MgJGS9GJRa9P06OqqS+xH0mTf/I9nKjRx+WJ6AXLhBF8Veu0c7dB6hL+5ZpYoW09Bo8ScYJOXrhueZUtnQxKTHfrllPe/Ydku+93OVZatOqIQSE+WxCQJgATRgOATFh0ZhdhoAwAZowHAKiR9EgILw6QEB4/O6ZgHRo05S6tW/l0Psr167T8HEzbVOhJo3oQzWrVXQQkIOHjtPWP3dTyyYNbCMk9knmLFxBX377k3xp5OAe1KD2Qw73uHgpntq++Lp8LW+eSBoxqAdVLF/aEMXZC1fQyju5Z00ZRuXKFLfFiXUofV+fQjGnz8qRk0Xvj6egoCDb+xgBMYTYoREExHNmZo+AgJi9gp73HwLiOTOzR0BA9KggBIRXBwgIj18aAdm4dSclJydTyeKFqUjB/LRz7wE68O8xOnT0P4rKk4vKlSlBdWpVoZCQzB7f2X4KljMBEQnFOoy+Q9+SuTs+14y6tmvpICDubnr8v1PUY+AE2czZPeZ/8hUt+3qtfD+1RKSXO+FmIrXt/rpcb1K/9kM0anCPNM3X/LyZot//RL4+feIQB7GBgLirXNr3ISCeMzN7BATE7BX0vP8QEM+ZmT0CAqJHBSEgvDpAQHj80ghIo2d7y4ziS/aFi5dt04rsb1O5Qlka/VpPisiRzaO7GxGQuAuXqf1Lw2ResaB7UK+OHgnIyVNnqVu/MTK+8/MtqFPb5rY+2o9+PNW0AfXt0c5w/8XUrt6vTZbt+/dsTy0b108TK0Y/uva13LtHp9b0/NNP2tpAQAyjtjWEgHjOzOwREBCzV9Dz/kNAPGdm9ggIiB4VhIDw6gAB4fFzKSD2acV6jWzZs9K+A0dtO07VqVWZxg7t5dHdjQjIPweP0oDhU2Xe7h2fovbPNPFIQH78bStNmblQxr/evys1bFDL1scNv/9F49+27MI1ZewAuq9UMToZE0tnYs/L13LnipBrOsS6k9TX9p3/0PAJs+TL0ycOporlyzj97FaBe7blE9SraxsIiEdPiGPj/LnC6NylBLqdwkiCUFMRgICYqlw+6SwExCcYTZUEAqJHuSAgvDpAQHj80hUQMYVJ/BU/650v5GIEYdSbH9jWaMx/dzQVL1LQcA/cCcjt2yk0fOIs2rFrn8w5Z9oIKl2iiGEBEdv89h7yJh0/eVrGf7kw2mGURqwNEWtExCXERMhK6kssXu/Qpon83PZrOOzFZnb0G1SmZFGnn7tVx0FS0h6vW4OGD+oOATH8dKRtCAFhwDNpKATEpIVjdBsCwoBn0lAIiB6Fg4Dw6gAB4fFzKSBd2rWQuzylvsTZFwNHRMuXXbVx1SV7ARE7WLV9qhGlpKTInafETlLLvlpDR47HyHAhAGIak7jst+FN7+O+/9Ey+vr7X2UTMXIiRlDsL/tF5NbXxW5WefPkorjzF+nS5Su25mLqlpjCZb1WrPqR5i7+Uv5z4XvjqHDBfE670vmVUfKziGlq0eMH2dpgCpbnDyoExHNmZo+AgJi9gp73HwLiOTOzR0BA9KggBIRXBwgIj59LAbFOf3KW/rnuQ+WX9caP16YhfToZ7oGRc0DECMTQ/l2obq0qtrxGBOTr73+h9z/6QsaIKWPvTByc5rC/MW/Nps1//C3bvNS5NTVq8LDDVru79hykUZNn26aZiWla1SqVk+0/X/k/WvDpKvmzqzNCxHsQEMOPg9uGEBC3iAKuAQQk4Erq9gNBQNwiCrgGEBA9SgoB4dUBAsLj55WAiF2qxG5VVSreT2+PG2i4B0YExNnIhTsBWb12A7079zPZDzEy8c6EVyl3rrSnmIs1HGIth9h+9/N5lgXlqa+//t5PQ8e9K1/u3LY5dbozCvLDj5vondlL5OvzZ4yi4kULOY23ylm9h6vKhfrWCyMghh8TW0MIiOfMzB4BATF7BT3vPwTEc2Zmj4CA6FFBCAivDhAQHj+vBKTf8Km0/+BRloCIRdrWHarE9Kd+w962jTxMGP4KPVy9ku2TpScgYsqVmHolLnH+RvSEVylfnlxOqcyc97k8MFBca5Z/QJlE4lRXcvJtatK2j3y15kOVaNIbr8ift2z/m0ZPni1/fm/qMDnK4uyyLkJPvcsWBMTzBxUC4jkzs0dAQMxeQc/7DwHxnJnZIyAgelQQAsKrAwSEx88rAWnfczjFnb9k2ybXaBfSW4QuThEfNHKaLdXst4dTmVLF5L+dCYhYO7Jw6bf02YofZBvR9s0RfZyefG5NuuLbn2junUXon3/4FuXNnXaURLS1jmJUKFdansYuLvvduUYOfoka1K6W5mOLaWkiVlyp18dAQIw+JXfbQUA8Z2b2CAiI2Svoef8hIJ4zM3sEBESPCkJAeHWAgPD4eSwgZ86ep069R8q7vtzlWWrTqqHhHrjbBWvtr7/T27MWyXxicfj7bw+XoxmpBUTsdvXuvM9JHPwnLjEVbOzrL1O2rOHp9kWs/xDrQMQ1aURfqlmtQpr2t5KTqWnbvvL15g3r0sDeHeXP9nLxdLNHqc+Lz6eJ3bxtF42ZMke+nvoUdgiI4cfE1hAC4jkzs0dAQMxeQc/7DwHxnJnZIyAgelQQAsKrAwSEx89jAREnfVu/+M99ZySVKl7Y1gOxTe8nX3wn/x0RkV2eYm5/uRMQ0fajJV/T0q/WyDCRWywmz5EtnKIiwyj2YgLFX71GE97+kHbuOSDbiO10xWGFWbKEuCUh+telzxg51UtsHzx72ggKyRzsEGd/mvmAlztQiyfr2d63LmIXcvTRjNHyM1ovMSIzdsocuchdLKT/4qMpFBaWxfY+BMRtedI0gIB4zszsERAQs1fQ8/5DQDxnZvYICIgeFYSA8OoAAeHxcykgYkpT21aNqFKF+yhreCidOn2Wlq/6kX7e+Ie8Y4vG9WlAz/YOd/8v5gx17z9Ovia+pC9fYD8YBucAACAASURBVDlQ0HoZERCxBmPCtHm0aesuGSZGKSYM700F82STAjJ26hzbe2IdRvdOTzuc15EaR+lihR1EYdUPv9F785fKZvVrV6NXuj1HeXJHkhj52LD5L5o+51MpKGIxuziHJCz0rkT8/sffNOrOCEq1yuWpz4ttqWih/HJ05KvvfpE7ZYnr6WaPyffsL3sBeWPgixQa6lqYckZkJzH9K6NfEJCM9wRAQDJezSEgGa/mEBA9ag4B4dUBAsLjZ+gk9NS3ECMTU8YMkJJhf/lCQES+GzcS6NXR0+nQkRMyfasm9Wni0G5SQKw7WRn92OOG9abaNR60NReiMXD427bDFK2yZH8GiBjBiB43kMqWcVxoLmKnz/6U1v6yxeXtBZtJI/ulWV9iLyDu+p76DBF37QP1fQhIoFbW9eeCgGS8mkNAMl7NISB61BwCwqsDBITHL90RECECMafP2u4gvpg3a1SHunV4ikKdTHk6eeosdes3RrZ3ttVtws1EatlhgHxfHHIoFmq7us6ev0h9XptsOxxwzOCuVLd2LRox6T3atmOv4U89YVhvethOQETgzcQk+uLrtbR42eo0eerUqkx9e7R3uUBdTLVa8sV3tPybn2y7dlmTiNghfTpT9mxZ0+T9ZNlqWnxnepq7znu6vbG7fGZ9HwJi1sp5328IiPfszBoJATFr5bzvNwTEe3a+jISA8GhCQHj80l0D0q51YxLrJsSOV9myhVOhAlHpTndidsVluLtzQLy9b2JiEp06c45OxcZR7sgIKlakAGUNDzOUToiIiD0Rc4aickdSsSIFDa1DMZQcjSQBCEjGexAgIBmv5hCQjFdzCIgeNYeA8OoAAeHx83gROvN2XoWrEhCvOoMgvxGAgPgNtTY3goBoUwq/dQQC4jfU2twIAqJHKSAgvDpAQHj8ICBMfghXRwACoo6trpkhILpWRl2/ICDq2OqaGQKiR2UgILw6QEB4/CAgTH4IV0cAAqKOra6ZISC6VkZdvyAg6tjqmhkCokdlICC8OkBAePzSCMjAEdFykfbTTRtQ48drM7P7JhxTsHzD0WxZICBmqxi/vxAQPkOzZYCAmK1i/P5CQPgMfZEBAsKjCAHh8UsjIMx0SsIhIEqwap8UAqJ9iXzeQQiIz5FqnxACon2JfN5BCIjPkXqVEALiFTZbEASExw8CwuSHcHUEICDq2OqaGQKia2XU9QsCoo6trpkhIHpUBgLCqwMEhMcPAsLkh3B1BCAg6tjqmhkComtl1PULAqKOra6ZISB6VAYCwqsDBITHDwLC5IdwdQQgIOrY6poZAqJrZdT1CwKijq2umSEgelQGAsKrAwSExw8CwuSHcHUEICDq2OqaGQKia2XU9QsCoo6trpkhIHpUBgLCqwMEhMcPAsLkh3B1BCAg6tjqmhkComtl1PULAqKOra6ZISB6VAYCwqsDBITHDwLC5IdwdQQgIOrY6poZAqJrZdT1CwKijq2umSEgelQGAsKrAwSExw8CwuSHcHUEICDq2OqaGQKia2XU9QsCoo6trpkhIHpUBgLCqwMEhMcPAsLkh3B1BCAg6tjqmhkComtl1PULAqKOra6ZISB6VAYCwqsDBITHDwLC5IdwdQQgIOrY6poZAqJrZdT1CwKijq2umSEgelQGAsKrAwSExw8CwuSHcHUEICDq2OqaGQKia2XU9QsCoo6trpkhIHpUBgLCqwMEhMcPAsLkh3B1BCAg6tjqmhkComtl1PULAqKOra6ZISB6VAYCwqsDBITHDwLC5IdwdQQgIOrY6poZAqJrZdT1CwKijq2umSEgelQGAsKrAwSExw8CwuSHcHUEICDq2OqaGQKia2XU9QsCoo6trpkhIHpUBgLCqwMEhMcPAsLkh3B1BCAg6tjqmhkComtl1PULAqKOra6ZISB6VAYCwqsDBITHDwLC5IdwdQQgIOrY6poZAqJrZdT1CwKijq2umSEgelQGAsKrAwSExw8CwuSHcHUEICDq2OqaGQKia2XU9QsCoo6trpkhIHpUBgLCqwMEhMcPAsLkh3B1BCAg6tjqmhkComtl1PULAqKOra6ZISB6VAYCwqsDBITHDwLC5IdwdQQgIOrY6poZAqJrZdT1CwKijq2umSEgelQGAsKrAwSExw8CwuSHcHUEICDq2OqaGQKia2XU9QsCoo6trpkhIHpUBgLCqwMEhMcPAsLkh3B1BCAg6tjqmhkComtl1PULAqKOra6ZISB6VAYCwqsDBITHDwLC5IdwdQQgIOrY6poZAqJrZdT1CwKijq2umSEgelQGAsKrAwSExw8CwuSHcHUEICDq2OqaGQKia2XU9QsCoo6trpkhIHpUBgLCqwMEhMcPAsLkh3B1BCAg6tjqmhkComtl1PULAqKOra6ZISB6VAYCwqsDBITHDwLC5IdwdQQgIOrY6poZAqJrZdT1CwKijq2umSEgelQGAsKrAwSEx48uXU1kZlAfHhRElCM8hOKvJ6m/Ge6gDYGIrCF05UYSpaRo0yV0RDGBbGGZKfHWbUq6dVvxnZBeFwJhIZmIgoIoITFZly6hH4oJZA4OotCQYLqWcEvxnZA+PQKR2bMAEIMABIQBT4Sa4X8AgogoLDSYbtzEf6CY5TZVeHhoMCXcTKaM7h/BJzZScr6KRGGRpqqfN50Vfxm9lZxCybczetW9oWfOmJDMmWTHIZ3mrJ83vQ7OFERCQm4m4Q8N3vDzVYz4gw8u7wlAQLxnJyNPnb/BzKA+PFMQUVRkGMVeTFB/M9xBGwIZdQpW1n1LKezI/+T/pb6SoipSQskmdLVKD0oJDTwhwRQsbX79/NYRTMHyG2ptboQpWHqUAlOweHWAgPD4QUCY/BCujkBGE5CwIz9Qzg2jKTj+P7dQU0Jz0tXKL9GVWkPctjVTAwiImarlm75CQHzD0UxZICB6VAsCwqsDBITHDwLC5IdwdQQykoBk2zlPyoenV2Lh2nS++YKAGQ2BgHj6BJi/PQTE/DX09BNAQDwlpqY9BITHFQLC4wcBYfJDuDoCGUVAcmyNphzbor0GKaZlxbVeERASAgHx+jEwbSAExLSl87rjEBCv0fk0EALCwwkB4fGDgDD5IVwdgYwgIGLaVe7vurEhXi//PF1q+C47z71OAAG51xXw//0hIP5nfq/vCAG51xWw3B8CwqsDBITHDwLC5IdwdQQCXUCCbl6ifEsbGVrzYYRy3DNfUmLhOkaaatsGAqJtaZR1DAKiDK22iSEgepQGAsKrAwSExw8CwuSHcHUEAl1AvF334Yq4mIp1rt2P6grih8wQED9A1uwWEBDNCuKH7kBA/ADZwC0gIAYgpdMEAsLjBwFh8kO4OgKBLiBRnz9BIXF7fQowtss2So4o5tOc/kwGAfEnbT3uBQHRow7+7AUExJ+0Xd8LAsKrAwSExw8CwuSHcHUEAllAxPSrgvPK+Rze5Xrj6VqVnj7P66+EEBB/kdbnPhAQfWrhr55AQPxFOv37QEB4dYCA8PhBQJj8EK6OQCALSJaYTZR35bM+h3el5hBTnw0CAfH5I6F9QgiI9iXyeQchID5H6lVCCIhX2GxBEBAePwgIkx/C1RGAgFjYxt8KoQNXIuTPMQlZKUfmW/RE1Gmn4BNKNqYLLRapK4rizBAQxYA1TA8B0bAoirsEAVEM2GB6CIhBUC6aQUB4/CAgTH4IV0cAAmJhu+1iXuq2q578OXtwIi2qupHKZb/sFPz1cm3pUqOZ6oqiODMERDFgDdNDQDQsiuIuQUAUAzaYHgJiEBQEhAfKVfSp8zfUJPZh1kxBRFGRYRR7McGHWZFKdwIQkNQCkkKTy/1JrQr857J0mIKl+1ON/qUmAAHJeM8EBESPmkNAeHXACAiPH0ZAmPwQro5AIAuIoFZwblkKSox3C3D/1Zz07PbHaeL922nZqVLUpeghapovxmncheYfU0Kppm5z6toAIyC6VkZdvyAg6tjqmhkCokdlICC8OkBAePwgIEx+CFdHINAFJPfqLhR2dI0hgF+dLkqtC1pGPgbvrUHlc1yiHsX+TRN7uud+SgmNNJRTx0YQEB2rorZPEBC1fHXMDgHRoyoQEF4dICA8fhAQJj+EqyMQ6ALC2QnrncMV6EpyCI0pu9NWALOv/xAfBAKi7vdJ18wQEF0ro65fEBB1bD3JDAHxhFbathAQHj8ICJMfwtURCHQBEeTyrmxNWWK2eAVx2akS9FNcIZr34GYZb/ZDCCEgXj0Gpg+CgJi+hB5/AAiIx8iUBEBAeFghIDx+EBAmP4SrI5ARBEQcSFhgYU1Da0Gckd54IR+9dehBWvJGC7pevp26YvgpM0ZA/ARao9tAQDQqhp+6AgHxE2g3t4GA8OoAAeHxg4Aw+SFcHYGMICCCXsi53fJQQiML0p3R3l26P3VafJo+nTOJckVazgsx6wUBMWvlvO83BMR7dmaNhIDoUTkICK8OEBAePwgIkx/C1RHIKAJilZDIHwdSSNxej4BeajhDjnwkJiZRx14jaPyw3lS+bEmPcujUGAKiUzX80xcIiH8463QXCIge1YCA8OoAAeHxg4Aw+SFcHYGMJCCCopiOlX3nfMq+c57b0RBx4vnl+hMoOaKYQwH6DZtCz7Z8gh6tU11dYRRmhoAohKtpagiIpoVR2C0IiEK4HqSGgHgAy0lTCAiPHwSEyQ/h6ghkNAGxkhQiEhqzhcIO/0CZr9w9dPB2lgi6WaQOJZRqkkY87Kswcdp8uq9UUXq+dWN1xVGUGQKiCKzGaSEgGhdHUdcgIIrAepgWAuIhsFTNISA8fhAQJj+EqyOQUQXEF0Q//OQrunb9Bg18uYMv0vktBwTEb6i1uREERJtS+K0jEBC/oU73RhAQXh0gIDx+EBAmP4SrIwAB4bFdvWY9bdq2iyaP6sdL5MdoCIgfYWtyKwiIJoXwYzcgIH6Enc6tICC8OkBAePwgIEx+CFdHAALCZ/vHX3tp9sfLacHMsfxkfsgAAfEDZM1uAQHRrCB+6A4ExA+QDdwCAmIAUjpNICA8fhAQJj+EqyMAAfEN2xMnz1CvIZNMsU0vBMQ3NTdTFgiImarlm75CQHzDkZsFAsIjCAHh8YOAMPkhXB0BCIjv2Jplm14IiO9qbpZMEBCzVMp3/YSA+I4lJxMEhEOPCALC4wcBYfJDuDoCEBDfs9V9m14IiO9rrntGCIjuFfJ9/yAgvmfqTUYIiDfU7sZAQHj8ICBMfghXRwACooatztv0QkDU1FznrBAQnaujpm8QEDVcPc0KAfGUmGN7CAiPHwSEyQ/h6ghAQNSx1XWbXgiIuprrmhkComtl1PULAqKOrSeZISCe0ErbFgLC4wcBYfJDuDoCEBB1bEVmHbfphYCorbmO2SEgOlZFbZ8gIGr5Gs0OATFKynk7CAiPHwSEyQ/h6ghAQNSxtWbWbZteCIj6mut2BwiIbhVR3x8IiHrGRu4AATFCyXUbCAiPHwSEyQ/h6ghAQNSxtc+s0za9EBD/1Fynu0BAdKqGf/oCAfEPZ3d3gYC4I5T++xAQHj8ICJMfwtURgICoY5s6sy7b9EJA/FdzXe4EAdGlEv7rBwTEf6zTuxMEhFcHCAiPHwSEyQ/h6ghAQNSxdZX5Xm/TCwHxf83v9R0hIPe6Av6/PwTE/8yd3RECwqsDBITHDwLC5IdwdQQgIOrYppf5Xm7TCwG5NzW/l3eFgNxL+vfm3hCQe8M99V0hILw6QEB4/CAgTH4IV0cAAqKOrbvMYpve2jUepArlSrtr6tP3ISA+xWmKZBAQU5TJp52EgPgUp9fJICBeo5OBEBAePwgIkx/C1RGAgKhjaySzWJxerEgBI0191gYC4jOUpkkEATFNqXzWUQiIz1CyEkFAWPggIDx8BAHhAkS8MgIQEGVotU0MAdG2NMo6BgFRhlbbxBAQPUoDAeHVASMgPH4QECY/hKsjAAFRx1bXzBAQXSujrl8QEHVsdc0MAdGjMhAQXh0gIDx+EBAmP4SrIwABUcdW18wQEF0ro65fEBB1bHXNDAHRozIQEF4dICA8fhAQJj+EqyMAAVHHVtfMEBBdK6OuXxAQdWx1zQwB0aMyEBBeHSAgPH4QECY/hKsjAAFRx1bXzBAQXSujrl8QEHVsdc0MAdGjMhAQXh0gIDx+EBAmP4SrIwABUcfW28zixPSkW7coW9Zwj1OkpKRQQkIihYeHuoz1h4DcSk6m5avWkehPzWoVqUzJoh5/ltQBl+Ov0HfrNsqXG9avRfmicrNzZpQEEJCMUum7nxMCokfNISC8OkBAePwgIEx+CFdHAALiHdukpFs0PnqeLThLlhB6rU8XCgvL4jThtA+W0KXL8fRoner0RP2aDm1iz56njVt30u/bd9Ox/07RpctXbO+XKVWMmjeqK79wu8otvuSv+20rrfvldzpw6DjdSEigyJw5qGzpYtTmqUZUteL9DvcTAhJ36TqNmXq3/0YovNanM0VEZDfSlG7cSKBWLwySbV/t/QI1bVjHUFx6jY4cj6GXX50om0wdNzDN52LfIIATQEACuLguPhoERI+aQ0B4dYCA8PhBQJj8EK6OAATEO7ZilKFlxwEOwS93eZbatGroNOFz3YdKsXjhuebUpV0LhzatOg6S0pDeVfG+IjSreQjlillLIef22JpeTgqhV480ot9Pux7xaP5kPRr4cgdbjBCQM+fiqXlHiyAYvT6dM8nwqAMExChV/7SDgPiHs053gYDoUQ0ICK8OEBAePwgIkx/C1RGAgHjH1pmAhIeF0afzJlGObFnTJDUiIJUrlKXqVR+g4kULUrbwcDp+8jQt/3oNnT57QebrVvQgDSm91yH36/seotWxxeRrjQvEUstHq1D4Q+3o6Jkr9NGSr+l0bJx8b+Tgl6hB7WryZyEgF+Jv0OyFK91++H8OHqX9B4/KdhAQt7i0bQAB0bY0yjoGAVGG1qPEEBCPcKVpDAHh8YOAMPkhXB0BCIh3bO0FpH7th2j95j9lok5tm1Pn5x1HOMTr6QnI+x8to8fq1aQHypZ06EzIud2U8nkHar6hFl2/HUJls12ir2r8Ymuz6UI+6vm3ZWpTm4JHadz9O+XPSVEVKa71ChKjIy/2HydHXsSUrIXvjZPrSjxZAzJwRDTt3X9Yxn8yeyKFhTqfYpaaIkZAvHuuVEVBQFSR1TcvBESP2kBAeHWAgPD4QUCY/BCujkAgC4hYV5GcnEwlixemIgXz0869B+jAv8fo0NH/KCpPLipXpgTVqVWFQkIyewzYXkCG9u9CP/62jXbs2ifzLJv/FuXOldMhZ3oC4uzmQj7yftWGgm5epq4769Ifl6Ioa6Yk+qP+alvzmUfL0dzj5eW/f3nkB8oXencaV0poTjrTZSut/X0/TZ25SLZ5vX9XatiglmEB2bL9bxo9ebaMdbWOQ6w/+XPXPtp38CgdPvYfZc+WjYoVyU81qlagnoMs6zVcxYqF6tv/+oeOnoghsb4jNEsWKlG0IFWpWJbE2pfUF9aAePyY2gIgIN6zM2skBESPykFAeHWAgPD4QUCY/BCujkAgC0ijZ3tLcGKE4sLFy7Rn36E0IMW0p9Gv9aSIHNk8gpxaQIoWLkj9Xn9L5nimxePUu9tzXgtI0M1LVGBRLSkfN5KDqf6mpnIE5JFcsTS/8mZb3t5/P0LrLxRIIybWBmIkZN+TX1L7l4bJl1o2rk/9e7Y3JCBCDnoMGE8xp89S4YL5aP67oylzcLDDZ4q/co2mvb+YNv/xd7rsnAmIyPvWzIW26V2pEzz/9JPUpX0rCsl8954QEI8eUYfGEBDv2Zk1EgKiR+UgILw6QEB4/CAgTH4IV0cgIwiIPb37SxenbNmz0r4DR20Lv+vUqkxjh/byCHJqAWnU4GGaMO1DWr95h8yz+IMJVDB/XltOT0ZAItf1p8x7l9Oeq7nooxNl6dfzBWWez6r9SpUjLtpy9thVm7ZczC8FZFu9bykoKCjNZ7hcfTDVjj4iX3+4eiWaMPwVQwLy/bqNNH3OpzJu1JCeVP+Rqg65haD0eW2yHLkQV948kfRg+fsoPGsYnYyJpV17D9rapxaQs3EXqePLb9jef7rZo1S8aCG6eu06/bZ5Bx06ckK+JyROyJz1goB49IhCQLzHFRCREBA9yggB4dUBAsLjBwFh8kO4OgIZRUA6tGlK4q/qWcPDJMyLl+Jp1Jsf0IHDx+W/xV/4ixexfNE3cjkTkBMxZ+SaC3E9+dgj9FrfzrZURgVk99aNtGzedNp6KZ8tNndIAk0tv50eyX3OoWvTjzxA809YtthdXWMtlcx2LU3XxVSsx/5oTecuXKYK5UrTjElD3AqIWL/Ruc9ouXZETIV6f8owypTJUW7sBaV5w7r0yottSWxFbL3E4vfOr4yS/0wtIBOmzZdrZsS6EtEfMcJivYTYTJ6xQIqcWNS/ZO5EishuGZ2CgBh5Mp23wQiI9+zMGgkB0aNyEBBeHSAgPH4QECY/hKsjkBEERGx7K7a/TX2JxdVikbW4XLVxRd6ZgIi2YtRAfDkX17zpI6lkscLyZ6MCsmnuEBq71lEkXim+j14ocoRyhiQ6dGft2UI06J9a8rXy2S/SwiobKXvmWw5tDlyNoBf3N6WLVxOpWuXyNGV0f7cC8umKH2jh59/IPKK9iEt9WT+PGOVZMGtsmulZrhah20vEhGG96eEaD6bJ/V/MGep+R+SmjX+VHqxwHwSE+T8BEBAmQBOGQ0D0KBoEhFcHCAiPHwSEyQ/h6ghkBAHp3vEpav9ME6cQrV+kGz9em4b06WQYtCsBsZ9eVLvGgzRumGUdilEBuTynKW08dI3ib4XQv9ciaMOFAjJeTLNaUm093Z893tbHWylEA/Y8bJuiJdq0KPAfRYXcpLikUNobH0l7rt49Ldw6KpPeLlhirczzPSxrRqzCkhpKfPxVerbba/Llvj3a0VNNG6Th5kpAftmwnd6c8ZFsP2lEHwqxGzWxJbmdQkPHvSv/KWoiaiMujIAYfjzTNISAeM/OrJEQED0qBwHh1QECwuMHAWHyQ7g6AhldQPoOfUtOw6pS8X56e9xAw6BdCYhIMG/xSlq+ap3M9e7koXJ7XaMCUmiWRTislxjBeGFHfbkIvXj4FVpd80eynw117VZm6renlsOULfv4/KHXKfam5VwSsT2w2CY4PQERWwJ//f2vsv2sKcOoXJniaZiIs0EGDJ8qX588qh9Vr/JAmjauBOSTL76jxcvu7uTlDri9PEJA3NFy/T4ExHt2Zo2EgOhROQgIrw4QEB4/CAiTH8LVEcjoAtJv+FS5E5MvBeRy/BVq022oLJrYZSt6/CCvBUTksN9ud02tNVQk/HqaB+Kvy7lo44X8dOZmVkpOCaJiYdeofI6Lsu3Tf1hOZ3+tXxd68tGHXQrIyVNnqVu/MbJt/drVaNTgl5w+eOu3/EUToufJ98T6kLJOJMWVgMyY+xl9t3aDjH20bnW3D/ZjdapT7ZqVZTsIiFtcLhtAQLxnZ9ZICIgelYOA8OoAAeHxg4Aw+SFcHYGMLiDtew6nuPOXqFmjujSoV0fDoNMbARFJ7NdRiFGCKTMXykXdYi2KWG/i6ko9AiLafXOmCA3bX4MoJcVhp6u9j37ltr8rThWnMQctJ6DPmTaCSpco4lJA7HfxWjBzDBUt7DgaY73Znn2HadBIy9oZT0dAvli1lj5cbOn3qiXTbZsCuP0gEBAjiCAgLEqBFQwB0aOeEBBeHSAgPH4QECY/hKsjkJEF5MzZ89Sp90gJ9+Uuz1KbVpaRAiOXOwG5fiOB2vUYLrf6FTtJxZ2/6LWAzD12P8089gClEFHxsCt0IiGH7KI7Abl6KzM13dqILiSFUc2HKtGkN16Rcc6mYNlPqxK7Wg3s7VrGYs+epxes3Lq2oTYtn0iDzNUIiP3hhlPHDKCqD5Yzglu2wQiIYVRpGmIExHt2Zo2EgOhROQgIrw4QEB4/CAiTH8LVEcjIAhL9/ie05mfLwX5z3xlJpYpbdqwSl9imV6xXEFdERHbq2q6lQxHcCYhovOqH3+i9+Usd4lKPgIhTwG8kJMp1IuKK+vwJConba4u5nBRCLbY1lBJhuVKoVf4T9E1s8XQFJCE5E409WJW+jbWcKD5z8lAqf+ceqQVEnGY+aOQ0EruCievTeW9Svjy5XD50YqvcZzoPkXIlttD9cMZohwMDReCuPQdpyJjpMof9NrxxFy7bDkYUsXOiR1BYWBan9xI5ihTKR3lyR8r3ISDe/+8ABMR7dmaNhIDoUTkICK8OEBAePwgIkx/C1RHICAIiRiDatmpElSrcR1nDQ+nU6bO0fNWP9PPGPyTYFo3r04Ce7R0g228FK86rWL7AsujaehkRkJuJSdS172g5xct6pRaQ79ZtoBlzPqOK5cvQU00a0AM3NtEDe9+im7eD6c/LeeidIxXp8PUIGR6aKZl+fuR/FBmSSG/sq0Zvlt9BQjTWny9ANSPPUY7MSXT6ZlY6cDUnTTtSgY7fsIyUNHmoCA1+Y4StD6kFRJxkPuat2fJ9cVZKj06t3T5wn6/8Hy34dJVsV7NaBerWvhXlzpWTjp08TWKnq//9tMmWI/U5ICtW/UhzF38p3xdnr/Tq1oYq3F9aiogYlTpyLIa+/2kTbftztzwnRJxfIi4IiNuyuGwAAfGenVkjISB6VA4CwqsDBITHDwLC5IdwdQQygoCkR0+MekwZM0Aeimd/+UJARL51v/1OU2cucisg7iqcJSiZlj/0M5XJftWh6dmbYfTYlqYuw18tf4KaTFjtsHbEXkDEaEaPAeMp5vRZy8F/syfIER9315Vr1+VJ6OLAQXdXagER95zw9jwS4uPugoC4I2TsfQiIMU6B1AoCokc1ISC8OkBAePwgIEx+CFdHICMIiBgBEWsSxJds6yW+bDdrVIe6dXiKQp2cRWG/I1TePJH0+bzJDkVIuJlILTsMkK+93r8rNWxgORAw9WX/BV+8MdW0MwAAIABJREFU17ltc+r0/N1F6OIv/qvXrKefNmxzGCmx5hFne4gRj6kP/EFVc15Mk9+ZgIiT06tEXKAuRQ9RmTaj6Hr5dg5x9gLy0/pt9Na7H8v30zsvxdlnE7t9TZz2Ee3cc8Dh7YerV6IeLzxNPQZOkK/bn+Vh31AIyLxFXzrUxfr+/aWLU8NHH6ZGj9aibFnD5cvHTpyilwZZckaPG0SVK5ZV94sRYJkhIAFWUAMfBwJiAJIfmkBAeJAhIDx+EBAmP4SrI5ARBER8sW7XurFc1yGmQ2XLFk6FCkQ5jAqoI2w8s/hCfy7uIsXFxlC+rW9SqcQ9NPZgFepU5AjVyxPrNNHtFJIHFp69GU4hmZKpTNZ4yhtqOTH9erm2dKnRzDRx6Z0DYry3d1sKroePnaSw0CxUqkQRj3a2ElmEzJ2JjaMLl+MpT66clC9PbgoPD/WmK4hxQQACkvEeDQiIHjWHgPDqAAHh8YOAMPkhXB2BjCIgrk5CV0eWlzk4/gRFDx9Aj+b6j5rnP+lxsqS8FSjumS8pJdSygNv+8rWAeNw5BPidAATE78jv+Q0hIPe8BLIDEBBeHSAgPH4QECY/hKsjAAFRx5aTedaHS6lY/pzU41q0w65YRnImlGxMFxu961Q+RDwExAjFwGoDAQmsehr5NBAQI5TUt4GA8BhDQHj8ICBMfghXRwACoo6tt5kXLf2WMmXKRJ3aNqegm5co+875lH3nPApKjE83ZXKOInSl1pA0az5SB0FAvK2MeeMgIOatnbc9h4B4S863cRAQHk8ICI8fBITJD+HqCASygAwcEU1iK9ynmzagxo/XVgfRh5lXrv6ZzpyNo1e6t3XIKkQk/Mj/KOzwD5QpMZ5Czu2l26ERlBxRlG7lKEoJpZtSQinXu2HZJ4OA+LBgJkkFATFJoXzYTQiID2EyUkFAGPCICALC4wcBYfJDuDoCgSwg6qipybzu199px9/75a5aKi8IiEq6euaGgOhZF5W9goCopGs8NwTEOCtnLSEgPH4QECY/hKsjAAFRx9aTzL9v302r166niW/08STMq7YQEK+wmToIAmLq8nnVeQiIV9h8HgQB4SGFgPD4QUCY/BCujgAERB1bo5n37j9M8xavpHfffM1oCKsdBISFz5TBEBBTlo3VaQgIC5/PgiEgPJQQEB4/CAiTH8LVEYCAqGNrWbexhoLj/6PQmM3yRklRFeh2lpyUUKoxJUVVohMnz9DYqXNowcyx6jqSKjMExG+otbkRBESbUvitIxAQv6FO90YQEF4dICA8fhAQJj+EqyMAAfE9W3GGR84NoynsyP/STX42tCS13liDli9Oe1ig73t1NyMERCVdPXNDQPSsi8peQUBU0jWeGwJinJWzlhAQHj8ICJMfwtURgID4lm3WfUsp8seBbpMm3Q6i6hta0a4GqyihVBO62HCGy3M73CbzsAEExENgAdAcAhIARfTwI0BAPASmqDkEhAcWAsLjBwFh8kO4OgIQEN+xjVzXn7Lu/8JQwuobWtL62t9T1uBk2T4pqiLFtV7hFwmBgBgqUUA1goAEVDkNfRgIiCFMyhtBQHiIISA8fhAQJj+EqyMAAfENW6MjH+JuT2xpTEuqrqeCYTccbi4k5Fy7H33ToXSyQECUI9buBhAQ7UqivEMQEOWIDd0AAmIIk8tGEBAePwgIkx/C1RGAgPDZhpzbTVFLGxlK9Mz2x+jNcjuoXPbLTttfqTlEnmau8oKAqKSrZ24IiJ51UdkrCIhKusZzQ0CMs3LWEgLC4wcBYfJDuDoCEBA+27wrW1OWmC1uE3XbWZd6Fd9PtXLFpds2tss2So4o5jaftw0gIN6SM28cBMS8tfO25xAQb8n5Ng4CwuMJAeHxg4Aw+SFcHQEICI+t2PEq/6KabpMM3FOTmuU/SU9GnXLb9lrlHnS5/kS37bxtAAHxlpx54yAg5q2dtz2HgHhLzrdxEBAeTwgIjx8EhMkP4eoIQEB4bLPtnCe33E3vGnOgKlXMcYGeK3Tc0M1UrwWBgBgqQ0A1goAEVDkNfRgIiCFMyhtBQHiIISA8fhAQJj+EqyMAAeGxzb26C4UdXeMyyTuHK1BESCL1KPavRzc61e+MR+09aQwB8YRWYLSFgARGHT35FBAQT2ipawsB4bGFgPD4QUCY/BCujgAEhMc2vfUf80/cR/FJWejV0ns9vonKdSAQEI/LYfoACIjpS+jxB4CAeIxMSQAEhIcVAsLjBwFh8kO4OgIQEB5bVwKy/FRxWnKyNOXKkihvEHsznKZX2OZy96vUvYh75ktKLFyH1zkX0RAQJVi1TgoB0bo8SjoHAVGC1eOkEBCPkTkEQEB4/Oh2Sgozg3/Cg4jIHD31D4+McBdRc3FZ6279d0b47L74jEELmxMd3+SQ6tfzBWjduUL0epnd1PWvunTgWqR8/+PKG6immx2wrIlSxlzyRfeQAwRAwCCBQPtvX+r/bTeIAc18TCBTEP6rykEKAeHQI6LT5x0PHGOmUxKeKYgob2QYnb2YoCQ/kupJIF+uMDp3KYGsjhxo/xFWTT3n+pGUbdd8l7d5/Z+H6I9LeSk2MathAUnJEkGnXz6orOsYAVGGVtvEGAFxX5pA+5qYJSQTZQvLTBevWEZhcd0bAgXzhN+bGwfIXSEgzEKeMomAREWGUSwEhFltc4VjChavXmFHfqDc33VzmmTYvoeoRmQcNYo6JUdChpXZbWgEJKFkY7rQYhGvY+lEQ0CUodU2MQRE29Io6ximYClD61FiTMHyCFeaxhAQHj+sAWHyQ7g6AhAQPtuCc8tSUGK8Q6Ih/9Sg+rnPUKsC/8nX42+F0JVbIVQ47LrbG15qOIOul2/ntp23DSAg3pIzbxwExLy187bnEBBvyfk2DgLC4wkB4fGDgDD5IVwdAQgIn22OrdGUY1u0LdGAPbWocb6T1CxfjMfJk3MUodiu2z2O8yQAAuIJrcBoCwEJjDp68ikgIJ7QUtcWAsJjCwHh8YOAMPkhXB0BCAifbdDNS5R35bMUEreXXtn9MLUucJwaRZ32KrHK3a+sHYKAeFUaUwdBQExdPq86DwHxCpvPgyAgPKQQEB4/CAiTH8LVEYCA+IZtcPwJGjlkCHUsdJAezePdIYKqp15BQHxTazNmgYCYsWq8PkNAePx8FQ0B4ZGEgPD4QUCY/BCujkAgCEjSrWQKyRysDpKBzENGT6eOj5elJsfGUPCVk24jbqUQ3bqdicKCb8u2/pIPcS+MgLgtT8A1gIAEXEndfiAIiFtEfmkAAeFhhoDw+EFAmPwQro6A2QQkISGR1v66hY4cj6FjJ07RkWMxdCMhgSJz5qDCBfNR8aIFqcEj1ejBimUpc7AxKbmVnExzF35JZ87GUWTOCBr8yguGgG/+42/64uu1dDo2joYP7EZVKt5PYjpWzvWjKev+L9LkuJEcTB+fuI9+O5+f9lzNLd8vkjWBHqhQnjp06UaFC0Y5xNy+nULjo+dScvJtqlCuNLVr3dhpv37bvIN+/O13+V7H55pTuTLFnbabOe9zOnf+IlV+oBS98FxzSkhMNvQ50cj8BCAg5q+hp58AAuIpMTXtISA8rhAQHj8ICJMfwtURMJOAbNuxl6bPWUJx590f0ieE5PnWjalNyyfcwpu9cAWt/PYn2S5vnkj6fN5klzFCVm7dSpYnNw6bMJP27j9MJYsXpnnvjHSIyRKzibL+s4zCj/xP7pC1/0oEDdhbi04mZHeZ+7V+XejJRx92eL/v0LfowOHjUrCWL5jqNHbs1Dm0aesu+V7n51tQp7bN07S7cu06PdN5sHz9pY6tqHO7FhAQt09G4DSAgAROLY1+EgiIUVJq20FAeHwhIDx+EBAmP4SrI3AvBeT4ydM0/5Ov5Idr2bg+1axW0ekHXbxsNa39ZQvFnrtge79+7WpU/r6SVKJYIRInzcbEnqMvvlpLcRcuWQSBiCpXKEvR4we5hLd67QZa9cOvciTFejkTEDHqsnzVWtr4+1905E7b4EyZKPm2ZfpUxfJlaPpEy5d767X/0HH6dPl38p/JSYn09z+H6GaSpV9ipEMIRaZMmSj+yjU6/t/dBeufzJ5IBfLloZ/Wb6NfN22n/2JiKeb0WRm38L1xcpTH/hKjI607D5ajQK76Il7ftmMPjZj0vmwzZ+prVKH8fRAQdb9W2mWGgGhXEuUdgoAoR2zoBhAQQ5hcNoKA8PhBQJj8EK6OwL0UkKvXrssvz+J68rFH6LW+ndN8UPEFu1XHgZSYlCTfy54tnEYNfomqVS7v0Nb6BTuFUui5Vo3ox9+2UvEiBV0KiBgxGDt1NhE5nn+cWkAOHf2Pxr89T06zEqfFBzk5Lrlwwfy08L2xDv3Zsv1vGj1Z5CdKSUmhIGugOGo+VQ77l2rXeJDGDetNi5aupiXLv3O455A+najx47Ud7iP613vImw6vffvpuxQWlsXhtY+WfE1Lv1ojX9v4zWxKCQqGgKj7tdIuMwREu5Io7xAERDliQzeAgBjCBAHhYXIdjZPQVZFFXi4BXwjIxq07KTk5WU5FKlIwP+3ce4AO/HuMxJfjqDy5qFyZElSnVhUKCcmcprsDR0TLaUyuphjt2XeIBo6MpqA739qfbv4Y9eneNk0e+y/Y3yyZQYlJifTHX/9Qwwa10rQVoxN9h062SYEQi8frVqdfNm13mIJ148ZNennwRCkf4mr+ZD0pE79t+pNKFitEu/cfkv0S8UvmWEYurNfREzH0v582y3/+snE7XbwUT9myhcspVjYZIaKfN/xBly5foSxZQigx0SJZYhRkzc9b7gjIXXkRn+X1/l0dPs+qH36j9+YvlWtEBEdxvT1uEFWpWNahnXUql1in8tE7Q+n6zWQICPeXx0TxEBATFctHXYWA+AgkMw0EhAcQIyA8fhgBYfJDuDoCvhCQRs/2lh2sX/shunDxMglpSH2J6VCjX+tJETmyObz1yRffkZhiJS5nU4wmRM+n9Vv+lO+LL/qVHkg73Um8Z/2C7W7aVezZ8/TK0LfocvwVKQJydIKIHqtfk37Z8IeDgHz8+Tf02Yof5L2HDehGT9SvSS/0GkFvjR5ARQrlo37DptD+f4/JftWpaRm5SH2JEZwmbfvIlx+tW51GDHrR1iQ+/iq90HuUnD5VvmwJ2nfwmHxv5OAedOzEaSkg4qpaqRz9tXu/lLQvPpriIDBidGbD73/Ry12epd+27KD9B4+mWQdiP9LUveNT1KdLKwiIul8pLTNDQLQsi9JOQUCU4jWcHAJiGJXThhAQHj8ICJMfwtURMCIg7kY4vryzgNu+l/eXLk7ZsmelfQeO2tYn1KlVmcYO7eXwYYSsDBo5Tb7mbIpRp94j6czZ8xYBuTN7KfUUI/sF1t06tKIOzzZ1Ckx8Ee8/bCqdiDkjv8SLL/RiZEL8/Fi9GmkEpN/wqfILvVh38fGssdTupeH07puv2UY6hk+YRdt3/mObJmVdv2F/c7FFcLPn+8qX6j1cVUqY9RI7b6349kf5zxGvvkiT3vlI/tyraxu6eu2GTUC6d3iKFny2Sr63YOYYKlq4gPzZfv3HnFeb0Lbff6cFmy9R1ahEmtuxEN3OkpNuFq5NG8/mpBGT3pMx704eSnUfuh8Cou5XSsvMEBAty6K0UxAQpXgNJ4eAGEbltCEEhMcPAsLkh3B1BIwIiJERDmsPO7RpSs8//SRlDQ+TL4kv+KPe/EDu5CSu+e+OlmszrJeYdtS8fX/5z9RTjMQX7KbP95WjFNmzhtPV6zdku9RTjMTuWNYv2DMmDZHTkVJfQgRGvvke/blrn5w2FRYaSkJWZn+8XDZNLSCpRy527Nond7rKkzvSljq1gIiRiwa1H0pzb+vojFhfIiRFbA986sw56tJntGzb5Ik61Om5ZtSx1wj5785tm9NtMa3rzgjI9AmDadAoi6S92vsFatqwjvz5yMF99PLwmfLnnfW/pu2X8lKPv+vKf2+v9w2FB1sWvc84XpU+PFpC/vz9svcof65wCEiaKvnnBbGL2u3k23LKndFLbIKQek2P0VhrOyEgt27fpusJyQ4jaJ7mQXvzEICA6FErCAivDhAQHj8ICJMfwtUR8ERA7HvhbISjaJH8tOBdx8XYIkasTRBrPcTVpV0LeQaF/TXizQ9o25+700wxsl9g/VDl8nTtRkKaKUbiC51Y/7Him3Uy5ZcLoykks2WtSabgYAq980VvxtzPaPWa9bYvX2IkIDb2PL05wzLqkFpA7EcugoMz0bL5UyhnhOMWuqkFRIxcPOtk29/3P1pGX3//q7yPWEfS/6X2NOXdj+nnjX/I1xZ/MJEOHj5OE6d9KP8tpkklJt6yCcjKRdOo48sj5EjS43Vr0PBB3Snk3G5a+/7r9Obu4lQv9xma8+AWup4cTDU2tJI5FlZeTzVyWUaOnt/eQJ470qDQVRo3eijlKPVQwApIUtItGh89L80vjPjCnzNHdsoVGUE1qlagcvdZhMzTS+yctvXP3TJMyKDIl94Vf/Uabdm2i/49coL2HzxmE/HwsDBaMGss5c2d02n4nn2H6bt1G+TvjliDJNpXrVSWaj1UiZo1skimu+ufg0fl7mfHTsTQseOnKObMORkiRvQKF8pPVSvdL4U5Ks9dqXaXE++bhwAERI9aQUB4dYCA8PhBQJj8EK6OgKcC4myEo+2Lr9s6mHqEw/rGc92HysXWYhcnMdXK/lq5+mfbSIT9FKOV3/1CsxdYDvSrWa0ClS1dQn4pt1/nYV2E7YyQdXvc5avW0dxFK207WI0c/BI1qF2Nftmw3aWAiHzWkYvcuXLSp3MnpTnYMLWAiJGLTs+3SNMVsS6mz+tv2c4vyZs7Uh4IKNaelChWmK5ev+5wtok4D+T0mTibgIhF9dEffELrN/8pv4h+P70bRX39HA3eUZbWnCtCg0vtpu7FLOtuXthRn/6Kz0N9S/xDvUscoKu3MlOtjS3le2+U2UUdSp+nxBdW0bXICgG5CF2MFrTsOMDtL4wQ6Ikj+kjpNXpZdk6bY2u++IMJVDB/Xpfhh4+dpHFT59o2MUjd0FX8b5v/pInT5rvM+3SzR6lX1+dIiLGzS0jPx599I4XbyCV2lBv4cod0P4uRPGijFwEIiB71gIDw6gAB4fGDgDD5IVwdAU8ExNnoheiZdYqW+NlVG/tdmN4eN9DhA9mPdAzq1dH2F94J0z6UO06JNRpi+tLQvl1o6Lh3Zax1Hciyr9fazhJJTUlMxRIjEmKhtvUSp4m3e8ZyoviGzX9R9AeL5c9ifYZY6yK+kIrF8GLal3WLYPG+deQiU6a7e+gOGz9TTumybs8rRi7aP9PEabHEWSPDJ85yeYiiEAvrWR5iitmuPQcdBOSXjX/Q9Dmfytyr6vxOpTKfplobWtD12yH0WbVfqXLERfneB8fK0fvHylONyHO0sMpG2nQhH/X82zJla2X1n+j+7PFEYTnp8nNf0bXIB9Q9WPcos72ACMmo+ZDlbBmxo1ncxUv068bttp6VKl6YxJS98DvTBdPrstg5rd/rbzk0SU9AxH0mTbeMrsnfkUcfpvJlS1LhAlHy7Jeduw9Ql/Yt04ygCGnpNXiSjBHPohgtLFu6mJSYb9est23wIDYdaNOqYZoui+fstbEzpOyLS/zePFGvJpUvXZQKFMhLp89dIrERw5Y//qZdew/a4qPHDaLKqXZOu0clxG19RAAC4iOQzDQQEB5ACAiPHwSEyQ/h6gh4IiCuvmDbC4izEQ7Re+uCbrENbGoBuX07hZ7u9KrDFCPra9dv3LBNmxIngYuRFHFZvzCJbWzfnL5AviZGVooUyk/R7y+Wh/eVKl6Ejhw/6TW8jm2a0ZpfNtukQfy1u3bNynJ9izgUUUiBmPJjFRBnJ5nb31xMF1u09Bv6/Mu1ckn9/WWKU92Hq8pdro4cO2kTjIXvjZfnmFjXgIgRkPMXL1O3fmNkujFl/6IqEReo9XbLKe9i/UdIJrFEn2jrxbzUfVc9+bNYB/Lhifto7vHylDVTEm2tt5qs/pScryLFPm9ZAB9Il72AiNG6bu0tU9Ksl9iwYPi4mbapUJNG9HF5AKY1Rnxh7ztsiu1LvfV1VwIi1j1ZRwWFAIwY1IMqlk+7LskZ99kLV9DKO5s6zJoyjMqVKW5rJjZR6Pv6FPlsi2dx0fvjHdZziOdr4PC3bZ9NrC3q3a2NfF6dLUKPOX2OFnz2Na3fvMP2+xRIz0JG/ywQED2eAAgIrw4QEB4/CAiTH8LVEfC1gDgTDNH79j2Hyy/yYv66GOVIfU2evkCuiRAjAauWvENHjsfY/hJsbTvg5Q7yhHCxc5Z1pGXBp6vo85X/k3FfLZ4mp6VYzxbJni0riS9t9mdkeELy5c7PUvWqD7gcubAeMGgVEGfnb9jfT3xB7PXqJBInwIv+Lps/2fbXd/vP/+WiaLn9r72AhIeHUsfuA+js5URqmu8/qp7zPE34twrVyXWG5lXeYrvNjeRgqn5nHciiKutp2pGK9Hd8bhkT/cDdv/6LgEsNZ9D18u08QeKTtu52VUvv3Bh3HXAnICJebIggRuTE1fG5ZtS1nWWKmrNLPD8D34iWNRMjJi0a16eZ8z6XTV0JyPxPviIxMieu1BKRXv8TbiZS2+6vSxEXW1qPGtwjTfM1P2+m6Pc/ka9PnzjEQWy+/v4Xev8jy5RFMWInplVZr/R2wdq8bZc8wye96WTuuON9/QhAQPSoCQSEVwcICI8fBITJD+HqCPhDQMQ2umI7XXG5mjpi/8Vq/oxR9Pc/h+QXvQfKliSxmFZc4kt7yyb16Iuv19nWgVhHVurXriZPSBdXagERrwlZSH0CuQNVecq5ZXrVio+nyv8fFhYmF7ELcdi0dScdPHSC4s5fpLDwULkV75Ztf9O+f4/aRkDEyEXhglEui7X219/p7VmL5Pv9e7anlo3ry5/tvxAL6WnzVEPbSejifTECIgRkzpD29OXRSDmaUSf3WVoXV5gGldxDPYr/63DPrjvr0h+XoqhrkX9p4cn75Hvjyu6gNoUsO5FZr6SoinSunf9HQYzsqubq3Bh3vwlGBCTuwmVq/9IwmcqVEIv3xEYEoyd/ILdaFtOh3p86nPbuO2xbN+RMQOxHP55q2oD69jAueIeOnKDer01O83zYf2Yx+tG1r2UkrEen1nLHOXHZjyKK35PFH4x3WN+CbXjdPTmB9z4ERI+aQkB4dYCA8PhBQJj8EK6OgK8FxNkXOvEXWyEY4pr7zkj5l2TrJb6wicMIr11PoJ82bJVb5IqRDnHwnpga8uILT9O1azdo6VdrZIhYtH30+EkpC8vmv0XP97B8kRQxLZ60TD1yJiDuCFpHM8Rp7d8vneWuuXzffhG6mOYiRi5CMgc7jRVfjDu9MlJO4xHTcha/P0GeDH/u/CUaOek9OeJjPyqyaOlqhxGQ7EmxtGNqaxqyr6ZD/iVVf6WqOS3rP6zXnGP306xjjus7vq+5lopnvZamb7FdtlFyRDFDn9dXjeyn7FlzGj03xl0fjAiIENoBwy2Smd66HbFz2ndrN8h2s98eTmVKFXPYuMCZgIhDIa1rjqaMHUD3lSpGJ2Ni6UysZUey3Lki5JoOZ+tOhOiIZ0pc0ycOJrGJgrPLyk+sbxI7r4lLTBN74Y7kCykRcmJ/QUDcPTmB9z4ERI+aQkB4dYCA8PhBQJj8EK6OgC8FRHyJb1i/FvXo/AxlDQ+lU6fP0vJVP9q2mxXTVwb0bO/wYf6LOUPd+4+Tr4kBCDFQIUYz/tjxj5yKIhYJly5RlHoNmSTnvlsvea8GteRaCTG08e7kIRQUlIn+3PmPvKfldPGSNGF42tPJrTnWb/nLNp1GyIBYz5End05a+qHjYmNrezESIs4tsV5Dxkynfw+fkAckdm/fip5u/ph8KzRLljQ7FH34yUrLtJwUoqH9ulDZ0sVp975D9OHir2yLz+0PYkwtILlO/0zJK3vRo1uaOfD7q97XlCXYsv7Dev15KQ913mkZXRFX7pAE2lDHcqJ76ivumS8psbBlkbq/LnsB8fTcGHd9dCcgYqRAbAYgznUR15xpI6h0iSJp0oqd0+YtXilfnzCsNz1c40H5s/3Oac4ERBzKOWfhCtn27vPpmF6IZoc2TeTohXXUTbQQz/KUmQtl49nRb1CZkkWdftxWHQc5rJcSjezlZWj/LtSowcMOsRAQd09O4L0PAdGjphAQXh0gIDx+EBAmP4SrI+BLAUmvlzmyZ6UnH6tNvbo+69DMXkDEWQ32X/BFw+8+nykPbTsbd5HEl3j7XYzcUXmifk0aNqCby2butuFNHWjfPr17jxvWm2rf+cIq2l2Ov0Ider5BiUm3nIaJL6SjX3uJqle5O2qRWkDy/T2LcmyLplbbnqDD1y1nTzySK5bmV7aMLNlf129lohobn7K99EyBYzSh3F9O732l5hC6UmuIO5Q+fd8qIK52THN3bkx6nbEXELEOou1TjeT0O7HzlNhJatlXa+Rok7icjRSI1+1HMfq82JaebmYRS3G5ExD7ReTWGDF9K2+eXHL6nnV3KvFep7bNqbPdts0rVv1Icxd/KcPETmzivA5nV+dXRsnPYr8dtb34ODuMUwjIjZtJdOXaTac5Q0NDyX6HN58WHMnuCQEIyD3BnuamEBBeHSAgPH4QECY/hKsj4ImAiCkobVs1okoV7nM6whGRIzsJ0bAfqRBfbBISEuTwRq6cOUjsZGV/nTx11ra7U44c2ejKlbvThMT5BFNGW05Jt167/zkk11Gcio2zneth/7740vZo3er0WN3qDieuOyMoRkrm3fnC93j9mvTz+m1yetTn8yzz8MUldgo6e/4ClStdQq4DmTLL8hfq9K7xw3vTI9UtfzEXl/iL+JffirUWd7fwFa+LvooF8l2eb0H5onI7pLQXkDcGvkiRx1dT+MGv6eszxein85YpbC3yHafGUacoV5abDtOwlp4sQdFHKtH15Mz6rapAAAAgAElEQVTyls8VOELjyu1y2uV7KSDpTX9K79yY9NgbOQdECJ8YJahbq0qaVPv/PUb9hk2Rr4vzNvq8+LxDG3cCMuat2bT5j79lzEudW8uRCPvDCsX2yqMmz7aNeolpWtUqlZPtxWYKYlMFcaW3xa8zAbHfjnrm5KFy9M/+EgLycDPLGiln19RxA6lqxfvTfa7xprkIQED0qBcEhFcHCAiPHwSEyQ/h6gh4IiDuetHh2SbUtX0rEus6xI5X2bKFU3JyMr04YLwMFX8JTi0g9jnFX4et2+yK1119QRUS8uqoaXK6lvyC3aoRNWtYm/JH5ZGjJUYuse3tGxPfk9vbpveFL71D4ZyNXKS+96kz56hLn9Hyr/Biqk+3Dk9RRER2KlG0oNwe1dVlLyDuPo/1zA9ru/ePlqMPjpeX/5x4/3ZqXfA/lynuxU5Y1hGQ9AQkvXNj0uNhREDEWS3i3s6uEW9+QNv+3C3FcPyw3mmm0olzaaznsYhpUmLnqODgYAoLzSLTWdcFpRZZ+3v99fd+23k29odX/vDjJnpn9hLZVGzEULxoIad9tMqZOLtm9Gs9ZRshPUJ+ZB8GdqfH69VwiHUnIPYi5O55w/vmIAAB0aNOEBBeHSAgPH4QECY/hKsj4ImAiBGQGzcSHEY47A/Qc/aF0n6EI70vZdZPKHYBErsBicvVQtybiUnUov3dkZElsydS/nx5DEMSJ5MPmzCLOjzTxHZYnKsczgQkvZGL1J0QX1a/X7dRvuxsaoyrTn+ybDUt/uI7Q5+pVuRZWlBlk63tV6eL0sgD1alkeDzVyxNLr5fZ4zLPvVwDkp6ApHduTHpQ7AVELNIW05zEJaY/9Rv2tm3kYcLwV+jh6pXSpLIKiCHwdxrVfKgSTXrjFfkvsXObODBQXGuWf+B0WlNy8m1q0raPbGMfu2X73zR6skUi3ps6jMTCfGeXVeDsd9myn8poLzXWeCEgew4cpSvXEm0p9/97lOYuskz5goB4UnFztIWA6FEnCAivDhAQHj8ICJMfwtUR8ERAxBdGcZK4/QhHoQJR9GQby5ev9L5QqvsEnmde+8sWevKxRwwFirMZjh6PocvxVw2NXBhK6kWjoJuXqOA8y1Qdd9e2i3nlVK03y++gRf+VoR2X89C7FcVi/bTXqX5n3KXz+ftGRkDcnRvjqlPpLUIX58cMGjnNFmrd2co+F1dAVnz7E829swj98w/fory5czrtqnUUQ4y0CDEVl/3uXCMHv0QNaldLE2s/Smi/hkZsGdzs+b6yvRD9j2eOo7Awy6iMuJwtQt+xez+9PvZd+T4ExOeP+T1PCAG55yWQHYCA8OoAAeHxg4Aw+SFcHQFPBURMX0l9GflCqe4TZJzMuVd3obCjlu2I07tiErJSjsxJFJHZsmPXunMFae7xcrSi+i8OYQklG9OFFpZzSfx5uXtejJwb46q/7nbBsj+LRZ7t8fZwypcnly2dGOFLTLq701nq+9jvnCYOGSyYLzdlCQmxbatrPxVq0oi+VLNahTRdFbupNW1rkYXmDevSwN6Wgznt5cLZ+hPRRhwaOGbKHNl+5OAe1KD2Q7b8YvtfsYBeXGJxu3X0R/wbAuLPJ1yPe0FA9KgDBIRXBwgIjx8EhMkP4eoI+EJAxLkbYlrU000bUOPHa6vrbAbPHHJuN0UtbeQVhX+u5KQXd9WlHx9eQ9kyW3bjuhdngIj7uhMQI+fGiDxiLU3qU8zdCYiI+2jJ17ZzZcSZNO9MHEzZsoYb4upuEboYHezSZ4yc6lW8SEGaPW1EmrNh7A/dtD+/RnTAuohdyNFHM0bLz2i9xDqisVPmyPUeYurjFx9NcRjlEAcsdu831jbNTBx22bxRPTkNDAJiqLwB1ej/7F0FVBVbG910gxIqKnaLit36nt3d3d3d3d3dXc/WX58dGNjdgQqKgCIgEgL/+s44l7nFvZfhKk/Pt9ZbC+6c75wz+wy+2feLzQlIyjhOTkDknQMnIPLw4wREJn7c3XgIJAcBMd7u+MyqCDidHwO7O2uSBMyXGAtUuVIdWwufR6byzfGp3OQkzSPXSSQgurqq6dKN0dTUQB8CQjUYk+eugvdVoTMYRSmodbK5mWYRSen96iIgNPbA/85hyZodzI00bXp1bAoX51SgyMeFS7dYETsRFKolIh0SsYCdxl+5dhdjfxSTUxc4agPskT4ti47sO3KGdcoio9bAdE3VpOSGrnl55kajupWQN3sGuKdzw+fQSJZC+ejpK5w6fxU+Nx+wKXgKltynOuX5cwKSMs6EExB558AJiDz8OAGRiR93Nx4C+hAQHuEwHv5Jmdlte2VYBAkvjkmxBrfronPf/ijq5ZkUd9k+mpTQVSelyMTM8f1Z5zSpSYutk0pAaD5KtRo0br6i4UHd6hVAEQNdpg8BIaIxYORsPHnhq5iO9irVAKEIxpyJA5Arh3KhOfnOX74VVKekzQibqWP6aqwvoSjJqfM+rLhcup6u+1o2awRyail61+XLr6dMBDgBSRnnwgmIvHPgBEQefpyAyMSPuxsPAX0IiPFW5zMnBQEqSHc50hGWftpfUrXNG52hNOKab0XfyRtRuGAeJZG9pOwlKT7SCIimrmq1qpZl7YqtNLRU1tVVjZoG1G3Vn22rTdPaoEJtbfYx+DN6D52ueFEf2KM1alUtl+gtkRDm1Plr2ZjEuq9RSuKu/f9i087DavOVLVkIfbq01FqgTiRiy64j2H3wlCKdSpyEfIf0bgd7O9tE9xnxLRI79h7HWe/rTLRQ1YgAFfLMydTaSxYpoJTKlZQz5T4pDwFOQFLGmXACIu8cOAGRhx8nIDLx4+7GQ4ATEN3YkpaHhYU53CTFyrq9jD/C4eocpo6ur4mig84OloiIisWcZdtgbm6GHh2a6DtFsoyT1oBo6qpmYqIs2Jgsi/6iSaKjY0DPDwlnOqdyRKaM6RLVf5Fuk4gI+b7x+wA351TIlNFdb50b6Ty0h/DQEPgFfIKZhSXc07goiSP+Imj4skZGgBMQIwOs5/ScgOgJlJZhnIDIw48TEJn4cXfjIfAzCQi1CrUw151nr+luQ8O/wsLMHDY2VgaDQd+yf4+Lg4OOb401TUwvbzHfv2PGwg1M7LB08UIGr08OlFpjamKqURciSRP+cDILfQP726tg/fIYzMLeqU0V65ARkdlqINyrG2IdM7HrIgGJjI7FnoMncf/xC0wY1l3NlyItVn5XYBF4HxZB92EaFYrvjh6IdfBAVIYyiM6YtIYDuorQ5eDBfTUjoKkInWP1eyPACUjKOF9OQOSdAycg8vDjBEQmftzdeAgYi4BQMfC/Zy/jpa8fXr/xx8vXfiydhHLhqfg2s4c7KpYugoKeudSKf+ml/+rN+7hy/R7L0ac5RKPUkYpli6J+jQqgImZVo2+NHzx+wQqM7z95Ad8375XSWKjguG71iihRxFMjGQj4GIyLV2+ztV+/9VfKo7ewsECzBlXRokF1nSkrlAJz7NQlvHj9ju1fFFck5WyPDGlRokgB/F22qFKXI7mnTGTELCxB9ZyIgkg6pHNLCQh9fvHKLWzd8z+QsjcZEQ+nC+Nh+2hnoluKt3JCaIkh+OrV1aCtcwJiEFzJMpgTkGSB8T81CScgKeO4OAGRdw6cgMjDjxMQmfhxd+MhYAwCQp115q/YgqDgEJ0bJ0LSvGF1NKlbWTGWlMOpU5AumzCsBygnXmqUrtK530RdriCVbE2pR/VaD1TLu1edLG+urJg1foBWEnL99kPMXbZZr/unHPzh/Tro3G9yDlAlIDT3s5dvMHziQuwZ/hcyXBwCk6gvei8Z4+aJT7XWaSQ7mibhBERvaJNtICcgyQblf2YiTkBSxlFxAiLvHDgBkYcfJyAy8ePuxkMguQkItR+lNqSiURvSvDmzIkum9DA1MUFwyBc8fe6Ls943FNGFQvlzYc6kgWoEhKId1SuVRo6sGeGe1g0UVfD2uYNjp7wVY0kLIXUqR8XvUgJSqVxx5MuTHR7p04DqCkgJe9OuI4qxk0f0RKniBZXAFQkI7alY4XwsUmNnYwPfd++x+8AJRUFv0/pV0a1dI7WDoUjChu0HFZ8TQfLyzINMGdLiS+hXvHrjx1qg3r7/RDHmxD/LjXfAGmbWREBo2Pcbm9F45jnWpjeXfahBe6JoSFDDPYhxK6DTj3dV0wlRsg/gBCTZIU3xE3ICkjKOiBMQeefACYg8/DgBkYkfdzceAslJQHxu3sfoqUvZZimyMbJ/R5CWgSajmohT565izZb9TLBNSkAogvDa1x81q5bVKBC3busBhR7CpJE9UbpYAokIDA7BnkMn0bDW30iXxkVtaZp75OTF7PMmdauge4fGSmOWrt2Jv8uXQL5cWdV8gz+FoGPfiSxCYmlpgSPbFymNIZLSpf8k9hmRp2H92qNcSS+N93/r7mMW5aEORSmBgFj6ecN1r4BFg2uV0D/rQ/zt+sGgB49IyIf2VxFvlcogv18xmFrUqrb4/RX7+FlrcgLys5BOOetwApIyzoITEHnnwAmIPPw4AZGJH3dPHgSotiE2NhZZM2dARve0uP3gCfzevcOdh6/g6pIaeXJkQdmSXqzjk6FG0YnO/Scq0o42Lp2E9OncdE7zJTQM1249ZO1A9bW7D55h8Lh5bDhpN5CGg75GhfC1mvdhwyuUKYqxg7vo68rGDRk3H3cePAXVg5QtURCjBwn+VHsydPwCdo1s6ujerM4kMaNWrWcvXvvp6vGqERCq+Ui3saRS2lXve6VQJvVHtM740iB8ojOUQVCjvQb5/OzB5y/fhEtqJ+TPk/1nL/3L1uME5JdB/8sW5gTkl0GvtDAnIPLOgRMQefhxAiITP+6ePAiIuff04v3p8xeWkqRqlHo0bmg3ODrYGbSoVP1Zl/6CQRNrGLz/6BksXbuLXVkwdYhBL5JUDN5z6HTm26l1fbRsVEPv7VBhfbPOw1kEhCI7Xp65EBERic5tGuDxc1/0HT6DzUWF7lNHCyQnJZoqAdHWznfqs4IwN4nD8Bz3DbqNoEb/IDpDWYN8ftbgoye9cf3WA/aM/0nGCcifdNrCvXICkjLOnBMQeefACYg8/DgBkYkfd08eBDQpUHvmzgora2s8fPJKUXxNdQtU4G2IUToRFY+T7d00N0ktb3Wt9zHwE27efYS5y7YIL/pFC2DqqF663Nh1auP78NELLFu/m6U9UfrN2kXj4Wivm2hRV66nL3yxc/+/rDsW2cLpw1ia1rxlm5EnVzZYWphj5qIN7Jo+0Q+9Nq1jEEVdKIpibWVp0HSqBMR9ZS6YRGuu+dj4NgdufnHBQs+req8RmbU6PtXZqPf4nzWQanhISX1Qr7Y/a8kUsw4nICnmKH7aRjgB+WlQJ7oQJyDyzoETEHn4cQIiEz/unjwISAlIqyY10bxBNWRNnwqBIZEI/hyKsdOW4ckLX7bYmoXjWG2GvtZ35Cw8fvqKvdjvXjdLXze9xhG5oZoJqaJz/ZoV0aFlPZ2K0BSZOXHmsuK+aMFSxQqwAnKPDOkSXf/2/afYuvuoUsG4WNtC16ignOzhk5csbS34k9A5impfKlUoofPeqF3wjn3H2bjGdauwiEpiFhsbh0vX7rAi/qcv3+CRhDRqKqjXNNfmnYdZmhhpmlSqUArWL/8H5yMdFUNffLXHvJdC6lgVVz80dH+LE4HuWOmbB3uKndG4vag4Uwx5UBxxMEHxVIHo4PEC/n0Nqx/RCZbMAeu2HUBMzHd0b69c8yNz2v+MOycg/5mjSraNcgKSbFDKmogTEFnwgRMQefhxAiITP+6ePAiIBKR9izqgNCkyaRE6vRBThyIy6Rhdq9M38dWaCJGIYl75MH1sXzUXKjqPiY7ROJWpmRmsLC20LtO00zAlPQ5KE2vRqDpbS5fNWboZx09fUgxzdUmF9s3roMpfpdT0R1TnOnHuCmYtUv4mv22z2mhQ62+Mn7lCKYUtHvEwgaDgPbRvB1T7K/GaFr/3H9FzyHRF1Ila8SZWBxMaGo6ZizeAWhxrMl3+5EMErvugqWzNTq3qoWXjmlBNv4qMNUV571qIiLNAZpswHC15ki33MMwJne+Uw8lSx2Fn/l1pC6cD06Hvg9Lssxl5rqFuundISWlYi1fvYDUfRLr/VOME5M87eWsLU9hYm+NzWPSfd/Mp6I45AZF3GJyAyMOPExCZ+HH35EFAk/6Cahcs8WW/eqUyGNJbv1QVIhc1mwk1D9rqH6Q1Iqp345k3B+ZPGaz1Jg8dP88IyKeQUCaaRz+T1apaDgN7tE4UHOrM9ezlW5aCRcXroiAgiRjOnTQQtjbWWv0pwkEpV+Hh3/D6rZ/i5Z86XPXp0hyO9rY4e+kGTp33YUXo1OqXbOvK6Ujjqr0TVGjYV/QbOQtEQkRLjECQmOGYaUsVBf5EwEoW9WQRnLj4eBZ5EmtSVG/m8bPXeP7qLfvv9PlrCsKjjYCQ/6SnhbDTPxubanfR08jnIER2vsRYoMqV6mptegc9KI7jgRnZGJ9yhxhBSSkEZPqCdciXOxvq1/wref6I/qOzcALyHz04GdvmBEQGeMnoygmIPDA5AZGHHycgMvHj7smDgD4EpM+wGSxdycszN2ZPHKD3wt0HTWGK3xRh2L5KKPKW2v6jZ0EtbjUZdSOiYnJ9jMgOfaMt1puMGdwFFcsU1ceVjaFoCEVFyKh7FnXR0teICAwcPZe9xJOa+7pFE7D74Ams2bxPmYCsmIo0bs4ap6V6klFTlii6ZYmDtBEQIjZ9h89UpJAN7tWGdc0SyY6uvS9YuQ1H/r2gNiwxAnIzxBltb1dkPh09nmJIdiHq0vdeCYR+t8KDsFTIaPMVM/LegLtVBMp41xHwTPuGfUaWEggItYSuVL44KuuRDqcLx//6dU5A/usnaPj+OQExHDNjeHACIg9VTkDk4ccJiEz8uHvyIKAPARFrOQwlIDMWrmeRALJD2xaqFUaThsb7gGClG5mzdBOLAhhCQGgCavlbv40gXFijclnQS7km09R2+Mmz19h94CTCwr/Cwd4WO9fMNKjt8PrtB7Ftz//YcpuWTQa1dFUlINkzZ8SKeaPVtkRkYvaSTThx9gq7NnlkL4ydvoz9rI2ASLVVundooqQYr89TQRGcF6/eKoZu2HGI/ZwYAYmLB4t0BETZwtkiEmfK/A/mJkD7W+Vw/YvQWjm33Wc0dn8DW7MYjHlSjH22qqA3yjoLUZ1fTUAGjJqNlo1roGRR3cKI+uD4Xx/DCch//QQN3z8nIIZjZgwPTkDkocoJiDz8OAGRiR93Tx4E9CEgLbuNZKk++qQ3SXe1fe8xkEAgWf/urVCnWnmdm6Z6E6o7MZSA0MRiqlju7JmxZNYIjWtpazssTZfyzJMDE0f00LvtsLQuhFrtUpoWIyCSGhBKh0rjkhqDVVLYtuw+go07DrO9Up1MpozuaN19lFYCIo1+UEMAIjXmZmY6cU1sgOIZ+FEDYnd7FZwujFNzWfY6D5a+FkQk1xa8iFLOgQoCYmHyHd5l/4cFr/LhdJA7PqgQFfIJaO+DWMdMsvaaVOcuAyZhQI/W8PyDdD50YcUJiC6Efr/rnICkjDPlBETeOXACIg8/TkBk4sfdkwcBXQTkw8dgtO05hi1G3YKa1Kui98JSfQ2qkdi8fBKcHB0S9U8qAaFuRrVaCIXuibUM1tR2mAiL/4dAhH2NQHy8sL1ypfRvO7x1z/+wYftB5rd8zijcuPPoBwEh9XMrREZGsWs1KpeBe1o3tGos6IycvnANVI9ANqBHK9SuWh4fgz6jdfeRAEw0RkBIq6V5F4FctWtWG80bVsdb/wB8CAhGROQ3uKRyQiaP9HB1dtL7nFQJiFnoG6TdqN6x69VXO9S5Vo3N2yjda0zOc4sRkAbpfFlnLK9z9XCg+EnU8qnOxnTyeIrBP1K1Yh0yIqDDdb33lJwDW3QdgRlj+yFLpvTJOe1/fi5OQP7zR2jwDXACYjBkRnHgBEQerJyAyMOPExCZ+HH35EFAFwGRdoxaOW8MsmXOoFj4c0goNu86wn53dLRHhxZ11Ta1dst+RVtZKiwf1rc93NO6at28NgJy9uJ1FC+SH3a2Nhp99xw6hZUb9rBrHVvVQ6vGCd2Nbt55xOoz0qZxgaa2w75v37MCcDJbaytE/CAM1HY4Li4O3yKjmb6HJqNC9s79JiqK4I/uWIy9R04zAkLWu3MzhUAiFbm7pHZEpfIlkMY1NQaOmcvGNKtfDV3bNWRF8cvW7capc4K+hqYULGq32/uHuCFFiShdTSzAl+6PolUkhqiPpokqAaF5XPc2hKXfZbVbbn69Iu6HC7Us18sfhO83e+SxFwrSyfKfbcDIE1lWm1AcLnmK/RxWYgjCSupX06P14TDwQmxsLBq0HYT1iyeyOiRuyghwAqL9iQj/GgFra6skRxdJoNTa2jAtnp/xfHIC8jNQ1r0GJyC6MUpsBCcg8vDjBEQmftw9eRAQXz7p5bhZvaookD8nsrg74c7jN9i1/yROX7zGFqpTvQL6qxRnk4Bbp34T2XVtWh/0P+IeQ6YqdXcivY38eXMgY/o0sLe1RVh4OPw+BOHG7YesFoMKulVTsOq1Fuo7GtWthCIF8jBfIj0BH4NBHbH+OSS86FKkZc2icSzdSbTp89ex+6j6VymcOHuZRTnaNa+NOtUqgGpC1mzer+gE1bVtI6zevJe5Utvh1KkcsWDFNhB5ql+jIrJmTs+iGNExMbj/8DnWbNkP33fv2XjSUOnStiETJxQJyM41MzBt/jqlAnPnVE6s3iQ65jtKFsmP+rX+xkvfd+wepGRCEwHxvnoHE2atUDp8uucM6dPg27dIJZwp7WvKyF4661k0ERBLP2+47lXXx9jllwUTnxVm68/LdxXV0/gr7aWBz994FpEK2W1DMSPvdZiaALmd4/Ghgw/irX4eCfgSGs4U6vdvnseiUNzUEfgdCAhFPifNWaV2c5aWFnBysGd/v8UL50eenFm0PgLUxOL6rYe4cuMenjz3hZ//R8W/B/RlCf0d0Rca9KVBYkbNLC753MGte0+ZP/nSv2PUIEKXno/qvPTlzpK1O0ENKooWyocGtdQ7tlF93Vlv5aiiqYkJrK2sWMOLQp65UKRgXpjSH+EP4wQkZfxLwAmIvHPgBEQefpyAyMSPuycPAppSklRnpqjHzPH9GcmQmj4EhMZTWhG91FMUQ1+jLkUj+ieI4REBof+p67KxQ7qhQmnhBVk0kYDo8m1UtzJ6dmiiqCWhF4e8ubIwAqLLKI1r3pTBoBcfKQHZtXYmc121aS9O/ohs6JpLrEfRRED2Hz2jiKjUrlYeDWv9hcweCalFFBGZsWgDa8NLRmlabZsLHam0mSYCQmOdD7eH9StBFFG0T9GWKH9J0Iup5OKPxQUS1NCffXVAg2tCip6FSRxuVzyA2HggrOoCRORtoeu2k+06YUARrX82CPo13DQj8DsQEPqCo27r/jqPmP4+p4zurfZvGDlKmzokNtHMcf0YGVG1uLh4rN60F3sOCfo4mmzkgE6s+5o+Rvc0dOICxd+wtqYaVDtGNWSJGZGg0YO7gO6fjBMQfU7A+GM4AZGHMScg8vDjBEQmftw9eRCQRkBUv0Gnb9ZrVS2Ljq3qaxQFfOf/ER37jmcb0dZqV7rLew+fY9POw+xbRk1kgtKk/ipXDH+XK6amuE4v7yfOXQWlU2ky0hqhjlCZNCiZ33/0AkdOXNBKAGjdXp2aMb0SMmnbYSoaP3z8PE5d8FFobkjXJ4wo1al2tXKKdA1VAkLfwpKRcvvc5VtY1EaTFS2Uh31jSWSFWupSO+Aa2WNhYmIKK7esrICbVNIprY1sq5bWvlQn0qHPBI2RJE3raiMgJlEhLApiEaQsdNjvfgmcChJS8bzLHEYqS0FMctGrPFjpK7ygHSh2grXtPdXFDiFVFyXPw6rHLJSiNnX+WmxcOkmP0X/2kN+NgNBLdominuxQv32LQtDnEKUvPeiLFGrtbaOi8yMlIETqc2XLxCIIz16+wfnLtxQ6QfS3vnHpRBZVkdrBY+dYG3CyPLmyskgp/ZtCOjv0750Y1Vw8cwTy5BCIgDYjMkPP7/lLQutqMn0ICEVf6cuPyKhoUPTk+u2HinVp34tnDmP/pnICkjL+5jkBkXcOnIDIw48TEJn4cffkQUBaA9KiYXX2P6+4mG+IiTNFurRuemtLGLob+p+yf0AQwsMj4J7WBWndXNj/QHVZVHQMIwJBwZ/xNeIbS4twT+emtTZEOh+lWtRo2hswMUGV8iVQt0YFlibhnFq5YFtb2+EvoWEIDPrM1reyskT6dG5wdUkNMzNTpW1rIyA0aM+Bk1ixUahVSTBKkUgQLdSEwYO/hJqSQya1MOKMkFKU2AsNtfKldrtk/9u1JNFcdm0EhHw1kZCTge7o/6AUm3tCrptomt4X0ja9pVMHYE2hS3ibqSlaHLDB7nVCfY2qWQTeg/XL47DyuwST6FBYBN5HrKMHYh08EOOWHxF5miHGTf+WuTfvPmIpcctmURE/N10I/G4EhFTtO7asp3Tb1Fhi5MRFCs2cqaN7o0QRgaSIRqT16o17qFujosYIyYoNexQpnpo0hrr0n8TSMOnfkiUzRyh1zyMS0nPINLaUpjRW1TOiroHUPVBq+hCQg1sWwMYmIdWQUtPWbt2v2Hf5UoUxbmg3TkB0/VH8pOucgMgDmhMQefhxAiITP+6ePAjoKkJPnlVSziya7ld1d0ltOyzOo4uArNz0j8GAiATkflgqNL/xN/Of1KYoSjfsonGuucu24Ngpb3ZNkwaL1CkxAiKOczo/BnZ31rBfI2NNUd67FiLiLFDYMRhbipzHzS/OaHtLECqc7nkfFVr0ZmlXH4M+YcCoOdi2SngJI6MuW6lPDoCl3yWdOERnKIPQkoMRnaFsomNJe+Xw8QuYNUF3Oo7ORf+QAT+LgGjT3qGXczeX1MiTIwvKlvTSWcdogigAACAASURBVKuk6VikKViaCAj5kIgqRTXJWjetpbFZRmJH7vvWH10GTGZDVNd4/Ow1+o4Q0iy1tRqfuWiDIvqqShSk60oFUYf1a4/NO4/gfUCQXhEQTfNSNKXbwMmMHIk1ejwCkjL+uDkBkXcOnIDIw48TEJn4cffkQYATEGUc5bQdFmdKjIBQBCdSpZbl6OHDCLl5AIPSe+NDpC2a3KjEphqd4w5qpn0Liq84WQhpTiHRFih7Sajp6JrpCTq2b4mvXl3VHobhkxaxdDV9UuP0ISAicbC/vQrWL49h6g0X7PTPxtY9XvI41r/NgR3+2dnvB9dOgE2qtIo9UYvjEZMWMZFG20c74HRhPEyiEjpn6fMkfyk/WeN9ku/Rk964fusB+4aXm/4I/CwCok17R7rTQvlzsfNzdLDT/waIDEtqQLQRkKBPX9Cyq9C62lAtI/KRppq2a14HbZsJNVBk0pos1S6B4hgpsVg4fZjGjnqUnjls4kLmQvPTOu16jZVFQGguEjn994zQzW7P+llI6+IEG2tzfA6LNghnPjh5EeAERB6enIDIw48TEJn4cffkQYATEGUc5bYdptkSIyCqp0YpTml2VMWsG6mQ0SYCVVz9UeWKoBMyI8811E33Tu2gW96siLuhQivcYyWPw7HaCHz1Snj5fuP3gbUGJqtQpijGDtYcJREn1peASDdy/+5tDJy4kn3UsVZB7DgtdP6pUrEkax+sam/evcekKXNwNP/WJD+4Xwt1wZcKU5T8dx84AWqGMKhX2yTP+6c6/mwCIsWZ6jXs7G3x6MkrRT1YYvo92s5IHwLy8Okr9P/RZrtT6/po2Uj4+9LXqP6Mohhkqo0hpClTx3YtVUvHJB9pBGbiiJ4oU7yg0tIUYek7YjbDgZpv0BpUA5YcBGTy3DWKepIj2xfB0c6KExB9D96I4zgBkQcuJyDy8OMERCZ+3D15ECDdDfpWvkHNiqxdJFna1NYIDIlkef2/m2lqO2xrYwX/9x9ZC2A5bYepAJR0Q/45eBKbfuijbFgiFK1SfYsmxXK37ZUVRd6975VinaXGPS3KYNdGQB6FOSmiJGK723QtFiAyW01QSsiMhesV7XjXLhqvVphPrT2pHka0+m2EFsdtmtRA0waCiCCZrUqxrvRZoPSO1j1GqRXma+sURG19gzZ1xKhHxbC3+OkkP1YhVRI6aq3bdgCU604CmZqM7tPUzDRJWg7UkOF7XBwc7GyTvNeU7vgrCAhFKahgWny2qOZs7LRlihoN0t6hYml9TRcBoed05JTFiuYVK+aORvYsGfWdnj1fVMMhttqmzmrSKI001fHEP8s1ziv9QmBgj9YsCiMaNYwgXR+qK6OWvdRt0OpHLZxcAiKN5lIBPkVoeAqW3kdv1IGcgMiDlxMQefhxAiITP+5uPAT+BAKSGHpJbTtMwoL3Hz3XOPWoAZ3xd/liStcoHSnVyQFKn9W6WgW+34R2x9oICF2b/dwTG97lTCALZrGIt7BT6i4mqqurbmjBym048u8FnQ/QP+tnM60VTUYvZt0GTcFb/w/ssglMWJ759tXTNb7wp1lfFBOup2HChc/CHdEq4wv0y/pYaer3kTaY8qxQ4vsyNUeURwV8i45F4QJ5WE6+1D4Gf8aufcfx4MkrRfciSu8pkC8ne/EVxeFivsfCwtyMuVLb4wePX4A0Vu4/eQHfN++VcKTuaHWrV2TFy1JNBXFd0mWJjY1DyaIFUKdaea37J92G0xd8GEnNlyubzvbImiZKToG7n01ASFenTdOE9CXx/gh7+iKETNsYbaBKCQh1sGpWvyo7z9Cwryx9aee+43jp68fcRZ0enQ++ZMDStTux/+hZ9glFTiiCIrXR05bB58Y9rTpINJbakLfuPoq5kVgr1aGQSdvtUgH74hnD4OSY0OpcDgG5ee8xlq3ZpSBOYuSGExBDTt94YzkBkYctJyDy8OMERCZ+3N14CPwJBISEF5O77fCQcfOVBAelJ6SJgKTdUAxmYcopVgGR1qh0RXipnpnnOuqke4vQ7xZwNBdqQKR2NCAjxj/xYsXgUiMCNbBnG63iawtXbWethXWZ6re90vGq+gsk7ti4TiX07NRUbVqHq3Nw/9hGdLmb8M2vvVkMrpY/rDT2abgjGl6vrGtb7Hrhgrkxa7wyeXv1xg/DJizUqAxPPhSJovaqb95+YASDCBO1S3V2dsKFSzd1rtu4bmX06NCEjaMI0soN/+DDxyBFt7H6NSuiTxd1vRPSJZm3bCvuPnzKfOkFmShb3txZ0bReVZQr6aWR2IgbMkTgjr7xnzRnJSNE9I06dbbTZOcu3cTZiz7sUvNGNbW2h120ajsCgz8jV/bMSrUPOsGSDNCn8UPTTsPYuVEUdkhv/dPp9NEBoTa0VNRNOBti0voOqc6PdA59aq2kNSgiARHa7VJ61E0mnrp01nB4qLQQN4SAkFAqRVgpoiRGa8R9StMwOQEx5Akw3lhOQORhywmIPPw4AZGJH3c3HgJ/AgGhbzLFtsOU/mBnZ8Pa6lLu9c8wakHrtqOqxqWCoy2ZoN+Fskcx6lERZLCOQG+VaIHoSGly/pG2eBXhAGQogtRNFqi1FdbnfpwdLBERFYvI6ITUrMT8NL349e7cXKNiM6WZ9TzlhMufEwrTc9uFsML69V4XFctICUjZ1B+QzS5cbQunAt2R1f4bqnUZgzIlEqIlFJFp1X2UgnyQAGPB/Llw58Ez7DrwL6KihKJbevdXPWKBDgiWI6sH6zrkkT4NexYooiWm09H1ySN6olTxgli+YQ/2HjqltD9NBOTEuSuYuXCD4rli5MOE4kUJRjUB44Z2V6sfSKrAnahjI3Y+0nSOFLWhiA+ZamG1OJ5a2DZqN5j9SpELik4kxfQhIFLtndkTlYmloc+h6nhNkQtd93H43wtYuFIQICWSOm/yII1/V5TuSJEtIhEHt87XOK20iJ0IKj0nj5/7ou9woTMXPVMF8idEM8VJugyYxFKzKpUrjn7dW7KPaR0xCqdLiJDG9uvWgtWViP+ucQKi6+R/znVOQOThzAmIPPw4AZGJH3c3HgJ/CgExtBg1ORGnqICDj3al7kuf3DD0YXGEfLdCr8yPtBIQ1T359xVSogy15CAg9MJL4ohSPRdquRu0uBqa3RRaB4tWPFUgumR6ik1vc2BVIaEdr5SALM5/GZXclO+lzc0K6Jr5CSq6BOBT7fWs5kU0epmml2oy8SVvyZodOPC/cz8iDvTeL7z2D+vXAS6pHBEc8gWkAXHq/DWEhn9l17zy58KcSUJNjGgk6jZy8mL2a5O6VeCezlUhPCcdp0pAXrx+hx6DpigYj4WFBWaN78dqEG4/eMqiUD43BZFHTcXRSRW4k76YUg0SvUBLjaIjDdsNVqSZ0bfn86cIRENqUoG+WRMHoLBnbrUx+nygDwHRpr2ja34pEaYIldihinSCxMJummPyyF4oVUw/TRlKuaLUKzJKjZozeRDSuKTWuBWKgokK6NpqQJ6/fIOeQ6czf1FHRNq+V9c9Sq9LtX+k50wk0srKApYWFkibxgXp07oibRpXWFtZKk3PCYghaBtvLCcg8rDlBEQefpyAyMSPuxsPAU5AjIetOLPz4fawfnVc40KUctXxdjk8Dk/Frq8vdAElUgfptamA9j5MMZ3MEP0FOQSEIkmk0E42uFcbFkEQjYrPp02egeOBGfGXy3sERFnjUXhqEAHZ4HURZ4LSYc/7LFha4EqiBKSeT2VMyH0LRZw+sanDSgxBWMkhinXEXHz61vfAlnm4dusBRk9dyq4TMerapiFmL93Efld92ad0qlPnrjIRQyqAViUgVC9Sq3kf5ksv69rqfFQJyKxFG/Hv2Sss4mJtZYUF04YoFUBTkTx9y021CpQSuHy2soBiUgXubt9/iqHjhW/jKZ1JbC4hgiUVxxM/O7R1oaI+Rvxs7Zb9inPVdF2vBxKAPgQkqdo7iRWh0zlRXZZohC/hrM0oOrVhxyFs2/M/NoTGThvdW035XOpP0bXVmwSR0B2rp8PFWfiblZqUHM+ZOBCFPHOxZhGifoi+ONK4xTOGK1IrpQQkMX0R6fycgBiCtvHGcgIiD1tOQOThxwmITPy4u/EQ4ATEeNiKM7vubQhLP6E/v6rte+8B/0g7PA53QmC0NbpmforKru/12lRQo38Uon2G6C9kSZ86ySlYlF9P3+SfvXidfWO8bvEERSH6p4vr0Hz+Nbb3bUXOYvLTQoyA5LD9gl5ZHiOnfSgrSt/ilx05bEOx672gLdIhw1MMyP4QFqbx+PtSDawu5I0cdmEKDCLyt0VIpdmK3xPutQgG92qLzv0nKjp0bVw6iaXXiXUGxbzyYfrYvmp4ktL9tVsPWSthqUm/waaXVIqkEMkyMzHBkZMJKWRSAkKkolO/Cfj+XUhpmzd5MArky6G25uS5q1kdAJn0G3Q5AnfUja1uK0GQUVNbZIoKUXSoYL4cuPtQaJowe+JAeHnmUtpfUtOiVG9SFwGRo72jqwsWEcDZizeyLRERXTp7pMZoBqXwUW0U1duQeXnmxoTh3WFna5Po392la3cxfobQ/WrskG6oULqw2nhput62VdPh5pKK1eiEfxWibtqMxA+pLuavcsXQp3MzNszezk6RqscJSKLwpeiLnIDIOx5OQOThxwmITPy4u/EQ+J0JiKa2w8ZDUvvMiREQOfvRRECk82nTX1g0uZ8sApLVI70izURacL9i9mT8c8VfEfFocv0vRkDEWgxzkzh8jyepRaE4W0yTouuFHIPxJNwJh0qeQAbrSCVYYlzyI7CVUINBzQTq/Wgl3LFVPdjZ2rIXbDJp7cLY6ctYwbjYklQXzpSW9fDRCyxbv5tFKSiSQXUZ1H1r2pg+WLpul1I3MZGABH8KYd+8k494X2MGdWEvkqrWoc941jJZVQNDrsAdpYxR6hi9dO9aO1OptmnS7FW4cOUW+nVpilMXb+DB45dqdSDhXyNYmhZZUrQzpPepi4DI0d7RRUBoH9JIDp39vCmDlYgFnfPk2atx+/4Ttm0ibdQuV5pKqO1ZoShWk47DWDobdUubPLK3UkMBmps0eYhI0PWpo4VImj5mSBE6j4Dog2jKGcMJiLyz4AREHn6cgMjEj7sbD4HfmYAYDzXDZnY6PwZ2d9YY5qTHaGkNiPjiR27UrrZBzb8U6SSq+gu710xGurRpk1SEThGQqhVLYfTUJSwSQmlMq+aPRciXUDTvIihQryrojbLOH6FKQMQXdCIetqbRiIhTzlkXb9nZIhIN0/miWfrXTLARZlbw7+XLLtOLPr2skVH9x0vfdzh6QohM7N00V6HlMX3+OqbzkljBMPlQhODEmcsKbQr6zNbWBl+/RiBj+rRYNGMYHO3toNrOmAhIuxZ1MXTcfEXrV2nRe/d2jVG/1l+wsDBne5PWliS3wN2eAyexctM/bJ11i8YrOixJ6z/Wzh8F72t3sW7bYZZaJq0DoXOk8yTTpt6tx+PIhhhVe0cPJXS658lzVymK7okIkCCgqMsjLcgngt6pbYNEm1Fkz5RBqT31qk17QYKYZNTqt2Htv1kqFhHLJWt2snMmI6X38qXUIyTacOQERN8n7L83jhMQeWfGCYg8/DgBkYkfdzceApyAGA9bcWZNGiByV411yIiADtcV04gvfqK2AulPtOk5hhWK0wu/VH+hR/v6aN2ktiwCcvfBMwweN4+tTy94lLq0edcR5LX/jN1Fz7A1NREQSsf6EGWLWmneKlKwEsNiR5GzKOD4CUGN9rJ0M2m6EtU8HD5xEY+fvlLTZpAShqM7lyh0QFTXkn4jT9csLc0RFRXDiMuq+WNYmhmZKgGhb86JDBGuZF3bNWREhcTqRKOIRL3qFVg7YFqHrFHdyujatqGSfopcgbtnL3zRa5jQZWlQzzaoWUWoy2GF8YOnsp8vHFyBW/eeot9o4cykdR5She/EsNLnmZUSYW3jk6q9o08EhNakKNmgcfMV2jB1q1dAv25CZykxWqTPvYjPtlTNnMj8qKlLFXPTGHpWKCoiGnWyGtK3vdZnTtPanIDoeyL/vXGcgMg7M05A5OHHCYhM/Li78RDgBMR42IozU3eotBtLJOtCXwt1wZcKUxRzakp9+Rj4Ca17jMamZZPZi7RYF1GvejkM7dNOFgGhhcVuRjR3yJdw9hI2v9BdVEstvJSrEpDCjkHYUuQCq3c5F5gG10Nd4f1JaNerqLdwe4dP361xLcSNfW5rGoP/lfwXaLmdEZD7j15g4Biho9jwvu0x80fOv2qtB2laHPqhf0IaJ6IQofQQTM3McOf+Ezx7+ZZ1xjp1zgdUG0JG0Y+ls0YoVLx1CTq6uqTC4hkjcPnaHdDamozIR88f2iLS63IE7mgeKqxv1G4Iw59efkcO7MSmFztrURRg0ZQBiIiMQqVGQlqQWCBNP4v1H9Q5ijpIyTFpBCS5tXek9S66WgWTSGXvodMVrZpFVXIxcqfvPYqtmKXjI75FYu6yzYp6HvEaEZFGdSuhXbM6iWq9aFpbJCCknE57VbXNOw8rWkTr2ySAF6Hre8rGHccJiDx8OQGRhx8nIDLx4+7GQ4ATEONhK5051Yl+sH28K9kWk3bAokkTy71v23MMBvduizWb9rFUo+JeeZjWQVJ0QMQULFrzyrW7GPujKJd+pxawByreg80HobhXlYAMyPqAFdmLdvC9B95G2uFOqDOufHZFLMzQMN1rTMlzCxOeeGH3+6xs6Px8V1Gkx2JGQN76fUCnfhPZ5327tcTiHy/7qjn3MxdtwMlzVxPFW5qKJBZrS2tTpN+cayIg9MKZP082RdoNRToCPn5i9SPUIpZemA8fv6D07Ti1UG3RqIYSIUqqwJ305ibPJaG7G+zbeOoMRhEoseidojOdW9RiLYI7D5zOojZipOxrxDc0aDuITdW7czM0qKXcQtnQB1b6HP5K7R1D952U8VTMTkKAJN7okT4da34g6nYkZb7k9uEEJLkRTdp8nIAkDTfRixMQefhxAiITP+5uPAQ4ATEettKZkzMKEpGnGUKqLlLauK7iX1Jup2+F338ITDYCQkXa3QZOVqgxD+3bHo0tTsDhxhL2squLgEhvoNKl6giItkXJVB+xzssbbyLsUNOnGhvSNdMTtBkymRGQ0LCvaNxBaMnbvUMTVr/x0tcPFIHYvkrQXyATc/2dHO3xJVRd5JDGkHr4gqnCXCJ+9NJ8895jphni4uyEtQvHs+tL1+7CibNXFPNbW1uxblLZMqVnLYDFomYgHoN6t0PNSmXYWHpJ9b56m9VokNAcmWqkIakCd1L8qA5m/oqt7KM1C8exF2IiFhQVobqOkoVysjNZtmE/Nu08jEI/NFCktSkr5o5Wah2clL8MXc9hUubkPklDgBOQpOGW3F6cgMhDlBMQefhxAiITP+5uPAQ4AZGHLRELs9B3iknirRwQ46ZZBC05akFiXPODul/FWylrEOjz4lenVX+mEt6wVgVWKyA3AkI3TcrQ9AJN9Q7bVk5Dqgfr4HSBisRNUN67Jj7FWCu6YKlGQKTIt7xZEXdDnZHe6itOlP4X0bEmKHyhARvSKN1r9Jswi+FKpKd6UyFNiArBw79+Y3sgO7RtoUKMTdTVyJc7G7q2baR0yHOWbmJFw5oICA1U7dClqqYujunRoQma1KuCz6FhaN5xOOJBOutgWhykkSJ2+aLPqNPU4LHzFAXrUpG5pArcSW9KqsDdv3sr0H13J2FEAEd3LIazozUjIOd97mPY+AUCXlsXYvu+Y0wLgyIn+zfPk/0Nvj7Poby/OO6tLwKcgOiLlHHHcQIiD19OQOThxwmITPy4u/EQ4ATEcGxNokLg6DMX1i+PwSz0rdoEsY4eiMpQBmElBiuEAsVBcjpiUeE5FWOL4oPShXW9+En1F/LmyoxVc0cnCwGh+oM37z7A3t6WaS6IkZ5pzwrgRGB6fIy21YuAVLpcHQFRtshjH4Kqbv6o6vIO9a4LEZABed+i9pTDitulF2uKelDKV/VKpUFF1GT04l2nWnmW90/1LmTSNCpxAmrPTGlIuggIdbUi00pA2jdBk/pVWDF6215jKPihIB3tmtVG2+Z1lJ4Nqeo2RYuq/VWKXU+qwJ3qgycK/FEL4IL5crJaFLE2xsHGnN1I4KevqNta0A2ZO2kQVm/Zx4r4yWf0wM6G/zGoeOh6DmUvwCfQGwFOQPSGyqgDOQGRBy8nIPLw4wREJn7c3XgIcAJiGLYOV+fA/s5qmER90cvxq1dXfCk/WWlsUiIh2iIf4sS6Xvyk3Z56tm+ISzfuY85EIfefOvtQBysyR0d7dGhRV2m/0u5D0hoQbQCsHtQY2S3eY/+HTEo6INIIyIXgtCzdytIsHn6Rtqh2pTqbbmj2u6jk8h6zXnjiTHAG9tmS2mbI3UloE0sm1c2gTlhihyn6Fn/z8kk4cc4HKzfsYWNV28revPOIqaA/e/lGiYBQEXp8XByoo9TIKYKqeoG82TF+WHf28/L1exSRFvq9ZuUy6N2lBawsLSCSO4qAmJuZIzZWECRUVYq/5HMH42euYNcG9GiF2lXLs5+TKnCniv+C5VuZWCLhUMwrL9P/6NymASitTCQgYRExoHS8Ow+eokndKthz6CSbRizS1nam+n6eUrR39N3v7zyOE5CUcbqcgMg7B05A5OHHCYhM/Li78RDgBEQ/bCnq4XRhPGwf7dTPQTIqxs0TQQ33KKVNUaSAyIyuwnSKeoSVHIKIvC0SXdcQ/YWJg9vj0vVHmDxvHbaunKpU2E2pVLvXzVJayxACMnfpZnjaBaJj2KxEa0CKn68Da7NYdPR4hushrjj7yZ1FEBZ6XsaRgEw4ESSQj4IOwchctApg5aAgR9IIB+23aKG8OHmO0rDimQbGO/8A5ksaJVQPITVRH4TSrDJ7uGPKqN5I4+rMul9dvHobazbvVxSNU0coqtcg06QDQjokZDRX/TZCvQURuFBJzQmJ0VGBPNmazfuwc/+/7GdpvUVyCdyROv3U+WuV7pdqXCjSIyUgW3cfxYYdh5TGrV88ERnTpzH42eYOKRcBTkBSxtlwAiLvHDgBkYcfJyAy8ePuxkOAExD9sHU+3B7Wr47rN1jDKE0khIYREbHyuwSLwPuwCHrAPOMsHVm9Q1TG0qzwWh8zRH8hW0YXpoTu6/cRbXqMwczx/TF84kK2jBwCQp2nCubPiZqVy8Jte2W0POamFAEhjZBOGZ+hWOpg1L5aBRFxFoneGgkSjq/lhv4HhNa40r0dOXEBC1Zs0+pvbm6OXp2boWKZIrC3tUVYeDj8PgRhwfItePXGXyekqi1zEyMgNNnyDXuw95Cg1p4zmwdr7SvakpkjGMkjfMhIAG/JLEG0UbTkELgjVfYWXUcqzXtk+yKm8i0lIPcePsegsXMV4zSduU6AUvCA12/84ZEhLczMzFLwLo2/NU5AjI+xPitwAqIPStrHcAIiDz9OQGTix92NhwAnILqxlVO3IZ09Im9zhFQRXvST2wzRX3B2sGQERCxCb9FlBII/Cyllqt2k6DOp/oKqird4H1PnrUGJIp6o+qOugSJGfbv3xqMwR6WibnE8RQ2oUF1TfQWNaZb+JXqVscPLCssUbXdVX5QpdWn+8i0KrQfluRPqMaRYC8vG08oaj4DqSnp1aqaIWoiDVAkItaultrWiRUXHgNKPSJCRTFrIbmpigtj4OLYm3cOy2aPg5qLcRCC5BO7E4nvaQ5FCeTFzXD+2HykBkZ4nXatRuSxLF/sdrEXXEVgyYwR7jv904wQkZTwBnIDIOwdOQOThxwmITPy4u/EQ4AQkcWyTs30urUQdrPSNahhy6oboL6gSEFqHOjRRQXe1v0sbsiwbO3H2SlQoXRR/lyum5GsReA/ORzqi4KHi7HOKgETEmsP3m4NinLVJDKqm8UcNNz9GCTLZhCOjTQTiPUohuPZ6vA2ORse+QitcTeSIPv/0+QsrSreytGQq2LsPnsST575K+hvigkQwqOC6fOnCcLS3ZxoOnz+HIpWTPRNrdE7tpPH+F67ajsPHz2vdAzlRqtq+o6cVRfGqEzWtXxUtGlVniumaLDkE7lZv2seK2sk6tqqHVo1rsp+lBIR+Hzp+gaJ18Ij+HVG5QvIKZRr8ECWDA+ndzJowQKFenwxT/qen4AQkZRwfJyDyzoETEHn4cQIiEz/ubjwEOAFJHNvkFhCMzlCGdbJKbtNVhC5dTxMBoeuzFm9kL28koqevjZ2+nHVzohd6TUaRkCqthLQgKkLvkukJgmOsERBpAweLGHhYhyu1q6Vxqirv+u5FdRzVivgHBCE8PALuaV2Q1s2FpSMZ2yjCEPAxCO8DghEd8x3p07kiQzo32NhY67W0MQTuVAmIXhv5Dw3q0Gc8pozqxRTsuQkIcAKSMp4ETkDknQMnIPLw4wREJn7c3XgIcAKSOLbuK3PBJDo0WQ/gfbfHajoechdIDgJCe9i44xA+Bn4CtYnVZSMnL0a9GhVQunihRIeKe+tTNBo9HYRuW6oWb+mIb9lqsIJ7TW2Gde2FX08cgd+ZgHQdMAmjBnVB1kzp+WMgQYATkJTxOHACIu8cOAGRhx8nIDLx4+7GQ4ATEO3YWvp5w3Vv42QHP6TKAp1drQxdNLkICK17/Mxl/HvmMtOJ0GZDJyxAs/pVUbyw0OEpMVPdG+FKZhH4ADFu+UHkQ5t4o6659b1OeiXfv8cqhAr19VMjSvHxoHoPayvLpE7xS/x+VwLSY8hUDOnVDjmyefwSXFPyopyApIzT4QRE3jlwAiIPP05AZOLH3Y2HACcgP5+AhJUYwr7pT04zRH9BWwqWdD/3fC5g1uoD2Dm+EaIzllHa6sAxc9CuWR0ULphHr1vQlxxR6tGkOavYnBXLFEWViiW1zk+K6BNnrUBcfDzKlfRi6uOqRjUZuw/8i6s37uPJC192mVLMqC1tm6a1kcHdTef+Y2PjcOnaHTx9TnSt4AAAIABJREFU7ounL9/g0ZNXCW16R/REqeIFleYgVfaz3tfZZ2MHd/0pKV86b0JDDYg+Pil9TN8RM1nDgLy5sqb0rf6S/XEC8ktgV1uUExB558AJiDz8OAGRiR93Nx4CnIBox5Z0Ohx85mgc8DjcCacD3eFgHo089l+YpkUBxxC9DsoYBESvhX8M0kZASCCR1N3pPzJRIPBkqWNIk8YVkdlqoP1BS3Tt2BIF8uVgY6jGw+blcVj6XYK5iir8t2w1EZmtOvrO3MWiBg1qVtRIFMS9S/VGWjWpiY4t62m9LYpo1GzWh12n4u5u7RopjX3+6i0mzV7FVMq1mVSNXNMY0vOYuXgDfG4K7ZFVTVNHsI07DmPLbiHN7OCWBbCxsTLkaIw29neLgBDh7tKmATzzCs8hN3UEOAFJGU8FJyDyzoETEHn4Iezbd5kzGN+dOtDYWZsjPDLl79X4aPw5K9hbm+Nr5HfSgOOmgoD5syOw2dtOIy6h3y3Q4VY5PPkqtPvsnukx+mV7pBeG0WWHIarccL3GGmOQjaUZYmLj8D1WOHW6T+tTY2DyRWghq2qVL1fHzLzXMeN5QYzNdRv5ytZAZNmhsLy3E1Y3VgCRiavCx2Yqi8hKUxCXVjlaoLrOt8ho/N2wF/u4Y8va6N6uodbbJwJSro6gUt66SXX07dxUMTbiWxTa9poAvw+B7LNOreqgbPGCLBpx8+4TzF+5QzF274YZSJ/WVW0d0vEYPH4RPgZ9ZteKFMzN5sji4c5a6j54/BIlCudH0UK5lXxXbz6AtdsEkb/Te5fCNoUQECtzU+p6jKiYOGM8Uj91zt4j5qBji9pM7Z2bdgTMTU1gYW6Kb9GxHKZfiAAj/9ySjAAnIEmGTnAMi4iROYPx3akfvy29jP4HyJLx0fhzVrCzMUcEERDOQNQO3ezNRdjuaKD1YSASUt67Jr7Hm6FX5kfonfWxXg9OVKUpiC7WQ6+xxhhkY2WGmO/x+B4bB8vrK2B1eozOZYqer4vOmZ6iV5YnTHEcZpZArGH/rkXWWowYz5Za12IEpFFvdp1eMLu30449IyB1BQxbNyYC0kQx78pN+7F+hxCFmDCkC2pUSkjlOnPxBtxcU6PLoOnseoVSXpg1TlhTNNLw6DxgGh4+e80+Gj2gPepULavWrUvTjazefBBrt/8gIP8sSZSAvHv/EQtW7WLT5M6WCV3bao/40Jhrtx9h5wFB7LBetbKooKXz2Pkrt7Flz3HkzpEZg3sIau2WFqZMdyU6Rr+X0bsPn2PTbiES1rJBVTWipeneCbehk5YiNOwrhvRqhVyJ1GUcO30VJy9c0/nc0QDSTBneR9Ap6T9mPlo2rIZSRXXXHuk1+W88yNzMBObmpoiM0u/Mf2MofumtOdgav/PeL71BIy/OCYhMgP2Dv8mcwfjupiaAWyprBHyONP5ifIUUgwBPwdJ+FJRe5L5Kc50DBQ9KXayDrYXPYcSjYqjs+l5vAhLY4oTRi64Te8DEFCwzn+VwujBO57NYx6cK5uXzwfq3ORETZ4Kqbn7IaR+GLDbh8Alxxb1QZzwKd0I6q28o4PAZVdz8YWGqmdEm1mI3uVKw+o6chcdPX4E0P9YvnsCIw9eIb+jQexxWzhvDtD7mLN2M46cvsXvfvHwK0qVxUeDgc/M+Rk9dyn7v3qEJmtStrBMjcYChKVijpy2Dz417zH3OxIEo5JlL41qETbdBk1lKGYkZrls8AQ52tmpjadyIyYvw4PELlp40f8pgNsaQFCy/9x/Rc8h0Ra2LNvFJ1cUvX7+LcdOXs49nTxwILy33QtelOOkCl+p2Ni2bjLHTl6FWlXIorVJ3o8v/T73OU7BSxsnzFCx558AJiDz8eA2ITPy4u/EQ4AQkcWxd9zaEpd9lpUEhMZYo710LV8odhp35d1Ak5NpnV1R2e6/zoGIdMiKgg1Ck/KuMCEjMg0NwOKC71W71K9WwvOAlZLMNZ5GP/GeFWovqbu8QGGWNm6Hq6UvFUwViYX4fOFlEa7xFbV3AkoOAUNF4jWZCRIMEB0cP7IynL3yZ8N6GpZOQ2kkQQTx36QamzF3Dfh4zuAsreiejb/H7Dp/JitYzZ3THinmjYW5mpvdRGUpA/D8Eon1vgQQSYVo9fywsLNRTNjbtPIzNu4SozpjBXVGxTBHFngI+BjNRQb/3gawAXqx7SQoBoehFv5GzQCREtMQICKnRfwwMxr1Hz3H+0k2FjyEEpFbVcrAw146xk6MDXrx+hyoVSqBcKc16M3of0B80kBOQlHHYnIDIOwdOQOThxwmITPy4u/EQ4AQkcWytX/6PqXmL9uabHVrcqIhL5Y4m6VC+lJ+Er17dkuSbXE5EQCyXFIKpStG46vxU+7HB6wI8bCKES/HxyH9OudibPva0/8REBe98cUZEnJBuUNnVD4s8fbRuOaC9j5reR3IQkJjvsajVXChOL1+qMCqUKYJd+09g2WxBDFG0x89eg7ookfXo0ASNf0Q5SFW9eZcR7PN2zWqjecPqeOsfgA8BwYiI/AaXVE7I5JEers6aFdMNJSC0zva9xxTq6V3aNkTzBtWU9vrG7wM695vIPitRtACmjhLqZEQ7c+E6pi1Yq4a1oQQkOjoGo6YswZ0HT5XmSoyAtOs1VmOhvyEERFex/tR5a1CmhBf+LldM6/PEL6gjwAlIyngqOAGRdw6cgMjDjxMQmfhxd+MhwAmIbmzFKMiDsFQY/KA4jpU6odtJw4iUEP2gbbm92gOLw8JLujar4F0TO4uehbu1mD5KKVUmyH82oTDc3iwap0sfZ1EgsuBoS/S6Wxr3w53Z7weLn0B2O4qcqFtEnmYIqbpI6YKUgNAFVxehwF+bBQULXcdUu2D1GTaDRTBsba1RtFBejBuiTvhIbLF1j9HMn4hG2+Z12M/Ubrf38BnsZ2rXS5EAUlRXtRJFPZEjqwfe+QfAzSU18uTIgrIlvbDtn2MGd8GiF/9ug6Yoog7SlDBWVzF+gYIUqKaL0b5ev/GH99Xbii0ePnEBhI0hBITWmb1kE06cvcLmmTyyF0t5IkuMgBw8dg5hYV/ZuPcfgxVpbclFQGYsXI9iXvkSbcmc6EPyB1/kBCRlHD4nIPLOgRMQefhxAiITP+5uPAQ4AdGNLdWCvJpbHfOfZMXuYmd1O2gYQWJ7QY3++aW1H+K20h3rCNNn/9N6H6Uv1sahEifhahmVMIa6FJgkEJA+WR6ibtq3qH61Gk6WOs6ICrUm3uvvga3+OZkfjenJitY1m6oivCoB0RdoVQKydO1O7D8qnFPtauXRr2tLmFKR2w+jNK2LV29jytzV7JNOreujZaMa7Gfvq3cwYdYKpaVtrK0R8U0gYlRPIlo84mHCeJnwWaH8uZAre2bsPigQVF3f7EsXuXX/CYaNX8A+KlO8ICaO6Ml+PnnuKmYu2sB+7t6+MZrUq6ITFlKov377oUEEhFoHU/SGbPrYvsiU0R2tu49iv+tbA/Lw6Sv0HzmL+SQHAZm7dDPy5cmGmpXL6rxnPkAdAU5AUsZTwQmIvHPgBEQefpyAyMSPuxsPAU5AdGN7/vJNHD38P2zMtBEm0aG6HVRGEPn4UmFSsqufG7yRHw7pF6fT6lrsQl2cKnUMThaaO1yJEZABWR+ga+anrHtalSvVMT7XLQx7VBxtM7zADv+s+BRjjYbpXmNKnlta1/pUez0is9VUXJcSEC/P3Inm+8fGxmL5+t3MV5WA9Bo6HR8+BiEsXEgdoyLmMiUKwdbGGgGBn3Dz7iMWIRBNqgey/+gZLF0rdKYi8tKw1l/I7JEeopgi1cHEw4Q62irmDvkSrijY9kiflqVskRlCQGg8EQ0iHGSTR/REPhJM7DGGzZ0tcwYsnT1Sr3oUQwnI6QvXMH3BOrbugB6tULtqedZ++FcSkAUrtyFHloyoU71CUh/zP96PE5CU8QhwAiLvHDgBkYcfJyAy8ePuxkOAE5DEsT160hvXbz3AuKHdYBb6htWDWARpFqbTNFNKinzQ/uge0m4sofGmvc7Vw6VyR2Brptq2U0i/IlMlIPQZFeFXulQD3+LMWTvi88FpWRpWyVQfsc7LWyvAqoKMcmtAvn79hvZ9xmHi8B6ws7XByCmLlYiGdCMU1aAXezLpt/U79h3H2i372edbV0xFGjchnSyBgAANa/+N/526jMjISJamNX5oN4ydtoylff0IFCWJgFD9SYc+E9i+KP2sYN6cOH1RaFW7eMZw5MmZRa9/CAwhIPcfPcfAMXPZvM3qV0PXH9orP5OAENm0tEwovPd96w8rKyvWmax4YU80qPWXXvfNBykjwAlIyngiOAGRdw6cgMjDjxMQmfhxd+MhwAmIdmx37f+X5fgP6tVWaZDd7VVwvDon0WgIEY9wr24I9+qCeKvEaxmMd7rqM1v6ecN1b2OlC9RSuNC5BjhU4gSCoqwV1xwsvjOVd6b7kQgBaX+rHK5/cWN+1d3e4n2UHe6G6iYgqi155RCQv8oWxdAJC7BxySTWppaMtEKoNuLp8zcICv4MaxvhpbZwgTx4+fod5q/YysZtWDIJGdyF/Uu7Yy2eOQJ5cmRWIiDtW9RBm6a1WX3EletC+9z/7VqCJ89eg9S55RAQmuvwvxewcOU2pfNpUOtv9O7cTO/HRF8CIm23SwX7owd1gZkZ6YXgp0ZAErux2lXKYUDP1nrfOx+YgAAnICnjaeAERN45cAIiDz9OQGTix92NhwAnIJqxXbd1PxPq69ZOveuT6GEReA/WL4+zX638vBHjmh9xVqkQ45ZfKbXIeKdn+MyqBCQqzhTFztfDnYr7ER6rrO7uYR3+o+BeOwGhug+/b7ZwMBdStr7Hm2DMkyIIiLJFE/dXmJg7oThadbeqhehJJSAlixXAp09f1DpdJYbO9PnrWHSBIiH/bJyjaANLUQwqYieTtucVIyBivcjcZVtw7JQQ3Tm0bSGsrSzRtNMwfA4JE0tCDE7BormoPoXqKGgfZESmSMvEXoPmh7b704eAhIaGo8+ImayDVe7smTFn0iBYW1sqpvyZERBKdbOysgRFYyzMzZFbEunJlyurokWy4U/7n+3BCUjKOH9OQOSdAycg8vDjBEQmftzdeAhwAqKO7aJV2+HqkhqtGguFyb+biTUg9G19kQv1cKvCQcUtUjpV1ztlcD/MGcWcArGx8EWdERApPn6Rtqh2pTr7aGj2u+jg8UIrfMmVgpUxQ1qsXzRB72OSkozu7RqjSf2Ewm56MW/ccSibiwrTiXCQqRKQ4ZMW4eadRyxVavsqQVWdiMvj576yCAjNI02L0rfwXHrz+hCQPQdPYuXGfxgBWzprOBNnlNrHoE/oNnAK+6hft5aoVL44TE1MYGOTECFTBVxOEfquA//C1NQUbZvV1vsc+cDEEeAEJGU8IZyAyDsHTkDk4ccJiEz8uLvxEOAERBnbafPXwTNvdtSrUdF4oP/imd03FYfJl7f4EmOhVmy+zS8rXn51RESsGYhMGEpAxjwujH0fhFqFfcVOIZd9QtE+teld9lpQlk9lEY127VopaaIkNQKiWoSeGLyBwSEYM3UJXvr6sZfvnWumq71Ui0rqNM/GpZOQPp2bEgGhdruiLkeFMkUxdnAXtiT5PXrySjYBIUHBDn0EcUJ6+a9rYCG2XgTkwEms3PSPwU/iiX8EpXNNJiUg8wfWR6GC+dS0XkQ/qV5Kmya1EBsfh06tBLLHLXkQ4AQkeXCUOwsnIPIQ5AREHn6cgMjEj7sbDwFOQBKwJRG2KhVLoFJ5zUXaxjuFnztz2svjYXZ9pdqiu/2z4H5YKkXa1L73Hmjo/lYYp9KGN6/9Z3TyeI5iqYJgZ/YdJNC4/m0OHPmYiQ1vnv4lxuW6o7TGq692qHNNENlztojEnlUTlV5QDSEglD5EdRdkmggIvcR/DP6EPNmzsLoGUhwnte7Vm/Ypis+H9G6L6pXKqOHw/OUb9BwqRDVIDX14v/bo9SMti8jAzbuPFZodaxeNR6YMQlexlt1GIjAoREFARg3oDCsrQZhRkzk52rMidlX7LxIQSu17cXYHOu0VNGHWF7qAEqmD2M+xjh6IzFYD37LVQHQGoaWulIA0rPU3ehlQ4/Jz/1r+u6txApIyzo4TEHnnwAmIPPw4AZGJH3c3HgKcgAjY9hs5C22a1kKJIp7GAzuFzOwS+x5Wywor7Wb/h0zwCXHFtDw3texSXYhQ2+3ksgvB2kLecLaMVhqiRECsYrFz2yql6/oQEOp01a73WEwY1h2Dxs5j/poIiLSYXHWfFPkYN7QrE7jTZis3/IM9h04qLhP/IpPIgCha1tLnHz4Go23PMUpF6LqOm3RD5kwaqDbsZxCQqOgY1sVLmwUGfVaQsD5dWuCvskVgYmoKR3s7JReqg3K6MB6WfpdwJzQ1Wt0UOlZJCYjUISJvc3wpPxEb9l00WLBRF578ujICnICkjCeCExB558AJiDz8OAGRiR93Nx4CnIAAnfpNBH0bni93NuMBnYJmdnawhMn+nrB6uJPt6nBARpwPTodZ+a4nvsv4eOQ/JxTlUwQkItYcvt+EjlNktqYxaJr+Nfpnewgr0zi1uXwj7FDLR4iAuKWyxba1QvtX0SKjolG3VX/2K3Waoo5TUiOV8qETF2DT0smws7NBzWaCmnvzBtXQpW2CQjt9pomAZHBPwyIO7ZvXUbTXTeyGz1y4zjplie16xbGkyTGwZxultrhzlm4WVMClbbB0nDm1n509cYDaqJ9BQHQ9jvoUoVPUw+VIJ5hEUac06EVAaBxFRLo8bwSfh2+Yn6F6Kbr2zq8LCHACkjKeBE5A5J0DJyDy8OMERCZ+3N14CPzpBKRZ5+GYM3EAU37+U4wIyLcvwbDf2QCnHobg38D0mJdf0JtI3OKR/6xAQEiIsEumJwiOsUZApA0cLGJAXbOkSuHa5orMUhVhpYYZpApPYnkUkVg2a6SuTbLrRGZe+frhS2g4HB3tkcXDnQkRGmpxcfEICKToxliQ7HmWTBnQulFNFMifE7Y2VvB//xG7D5xU6HWQcF7/bi2Vlnnr94GRXDLqarV7naAWrs0MJSDUbjg6OkE4ctz05bjz4Cny5MqKmeP6sWXsrc1hbW2FiChVjRfNu9BFQCjy4bajKr7FmiEuXtCIuRuaGl3ulmM/L/W8hOKpgtnPpibxsJFoy+z2z4yd73PgUZgju84JiKFPpX7jOQHRDydjj+IERB7CnIDIw48TEJn4cXfjIfCnEpDomBg0aDsIW5ZPUesAZDy0U8bMREDoRfTMv8dxZvcaLMlzVu+NaRIi1O38I3+JDRQ1xAWveCsnVhsQkbeZoj5Adb4tu4/izbv3GDWws+6ljDRCKkSobQmKjMwc31+hQyKOMzYBoUjNtAVrdd75rPF9ULhgfp3jaEBiBITELNPsqMoiH9WvVMW7SPtE56SUvH3Fz7AxYqpfeqsILPfNywmIXqeRtEGcgCQNt+T24gREHqKcgMjDjxMQmfhxd+Mh8CcSkM8hoWjdfTT2b54HS0vtRcLGQ/3XzkwE5OTFW/jn8FlMG9KaCRPqq+5uGAERiYcy6dB299EZyrD6gBi3AoohMxauZ+KBHVrW+6WgiQQkR7ZM+PYtUlGETpuimpJaVcuiY6v6sNLwPL3z/4iOfcez/Uvb9mq7IdLmaNdrLLvcv3sr1KlWPtF7P3vxOqbO101AZk/oA68C+hEQ6hbWqpsQbRrRvyNIqNDUzBTmZmZIdaIfbB/vYtf0IiC2IdhR7BxOBqbH2eB0mJ3vOpa9yoOlPwjIoa0LlTRIfulB/0aLcwKSMg6TExB558AJiDz8OAGRiR93Nx4CfxoBefPuAwaPm6czDcZ4iP/6mZ88fYaNO49h2ti+is3YPtoBh6tzYBb2jn0WE2eCqDgz2JsLXY1EEwlI17pe6IvVivHqd2UY+RD9KSLyucoCJuTYd8Qs1K9ZEVUqlvzloEl1QFo0rA4isUHBIawWhdr06pN6lpSbiIn5jklzhGL9imWKJhkLe2szDJ20FJ+/hKNPl+bIkdVD53Y+Bn/Grn3H8eDJK1BnMDKvXBlQOuokOmd6ppRWpToZCVxSy+U7oc64FiKozDuYRaN22rfo6PEcGW0iFC4B7X1YNzTpvUrns7O1gUvqVKxup3wprz8uYqnzoLQM4AQkqcglrx8nIPLw5AREHn6cgMjEj7sbD4E/iYA8evoSs5dswjoDROv0Rf7O/adKXZP09aNx1A2pSb0EMTyp76Vrd7Fr/7/ImS0TeiexVemDxy+wY5+g2O7lmQfXbt7FgqmDERmdUA8Q9OkLLl+7g8sXL8L3rT8+hgnXbM3jkNPNEo3K50TZ6vXRf/ZuUAelBjUrsha2RFxOnb6I0/c+AHHfYYp4TM97/Qdx0Rz5uPLJDZv9EtrPDsz6ADnswxS3TWKINW41xuQx/ZA3V8poDKAqRGjI+coZq09nMH3mv333PoZOXMqGzp44EF6euRJ1e/XGD8MmLETIl4RzkToUdgzG0gKX1XRkaAzpy/R/UFJBPFQXohbMawp5I/cPjZiQKgsQkbcFpPea2OYa1amEbu0as/bKf5qFxYTi3qdbePnlqeLW7SwcUNC5MLI75VaCgxOQlPF0cAIi7xw4AZGHHycgMvHj7sZD4E8hINduPcCmnUeweMYwo4B56rwPKF0oKVapXHGMHNhJzZVeyEZMXgQiEJ55c2D+lMEGT+/3/iN6Dpmu6OSUIZ0bDm6awWpApASEdC/Eb7m1LVKkUF5MHd2bpeFITarpQJ8PKhiIzs6koK7ZWt6siLuhzoqLG73Oo9iPguV7oanQ9W5ZHC1/FTHtjmoVsjMYCJkO/0UCQuT1Y2Aw0z85fymhvbIuAkKRiFbdRynIR7tmtVEwfy58i4zCtW3TcdBXUE2v4PwBywteVkN20IPiOB6YkX1uYxYDD6twjMl1j7V5XvJaaH1MHdPOl/0fi6JEZq2OT3U2KhGQ3Nkzo0RRT8TGxSE07Cve+QXg9v0nirUoMkbtgf8UuxN8A/tf7cClD+e03nJaW3dUy1gHbXJ1ZWM4AUkZTwcnIPLOgRMQefhxAiITP+5uPAT+BAJCHZROnL2C6ZKUo+RG9MkLX5w4o/4yFhYWoeiQRC1gc2QVXsykRtENURAv4GMwe9GiTkhnva+D6gHIkkJA6MWN9E2IhIg2vF8HNKtTXisBoRqHCqULI1MGd3yP/Y4r1+/B++odBYGh1rjUIldqqgSEvuE+Ueo4rM3UW/HeDHFG29vKKvMiATkSkBGb3uXAzqJCUTzVhAQ12pvcR5Wk+f6LBITqSMTnR3rTuggInfeEWSuYC73k08u+aO6L0mLE42I4HCAITp4sdQzu1t8U14OirVDxUq0fv8cjPt4EeR1CsN7rIhzNY7DHPzPGPy3Crs/J64Oaaf0QnaE0ghrtUyIgrZrUREeVuh8i4iMmLUZkVBTzXzF3NLJnUf97StIB6+kUHx/PIoDWVpZ6esgfNvv2RJx8d0TviYiIjC86C/ld88LG2hyfw5T1ePSeiA9MFgQ4AZEHIycg8vDjBEQmftzdeAj87gTk0PHzuPfw2S/roGRoS1VtHY0MJSDUlpWU3akdq9S0EZD12w+iQN4cKFoor1o9A5GirgOnMBJCWhoblggtZUVTJSD0+bict9A8g6BULrXe90rhbLByy2MiINdCXPEywoEVKEstsMUJg9r1GusvZcDoOUqpZ9J1Ll69jdjYWGTNnAEZ3dPi9oMnTKX9+au3cHNJjTw5sqBsSS9YWJgbvD05KVgHj51DWNhXtmZQ8CccPuHNftZFQEZPWwafG/dYcf2BLfOUnof0i9NBqudC7Zi7Zk54xja8yYHZL4UmApmsw+BqGYkXEY6wN49BTjtK54qH96e0iI43hbNFFAo5fmZjI7NWA7U89rl5j3VKa96oOjxzZ8eVG/fw5Lkv/Pw/KkgwkQCybu0aoVkDQVdGX5OmIzauW0VnKlpsbBwuXbsD0qB5+vINHj15pdjH5BE9Uap4QaWlKRJKXxyQjR3cVVaTC0q3Gnall1K6lb73aW/hgD4FBqFR7oacgOgLmpHGcQIiD1hOQOThxwmITPy4u/EQ+J0JyPa9x/Ax8BPrJPSzXhhVT8pQAvL6jT+8r95WTHP4xAVW7GwIAaEXNKp1oagPmZtrapCyNZk2AqLrCaMuS9RtiezglvmwkWhqiAQktZ0FKjo8x/4PmZHWKgLHS/4LC9OEFrzPwx1Q/7pQ6+Ju9RXvowRV7dKpA9iLaN+sj9S28bVQF3ypMEXX9n7pdTE6UqFMUXz6/AX3Hz1X2w/V+Ywb2g2ODspK4ro2LoeASOf29X2DLoOms490EZCE+ynCXqKlRgSErLx3TXyKsUbZ1B+wqlBC5K/vvZI4HZyepVhdLX+YNTIodkH/DmaijuNf5YopnrfEMCKdE0oN1MdU0xHpbyGxBgehoeGYuXgDfG4+0Di9Jn8pGZerbzL0cg/cDU5IndPnHlXHbK6xA2nMU0YdVVL2/zv4cAIi7xQ5AZGHHycgMvHj7sZD4HclIKs37YWpqSk6t2mgBp4xXxhVFzOUgKj6j5y8GNdvPzSIgGzZfQT0IkTmktoJi2YMR+vuo9jvSSUgMxdtwMlzV9kch7cvUmo3K750OVvHYUOBU6h3rSobNz3PNdRLJ3TVIhvzuDD2fcjCXloDomzwPEKoJeiW6TH6Z1MnH3SNVLMD2usjkmi8vxFdM2vSCKEaBjt7W6VvzMuWLIQJw3romk7p+vL1u7H38Gn2maa0JH0n05eAUIvhem0Gsmk7tqqHVo1rKi3hvjIXTKJDIUaypBofNLD59Yq4H+6Mkqk+Yp2XNxMqFAkIjS2WKgiPw5xw44srm7eJ+2tYOKbBt+y1QNEGilhSRzEpAaldrTxyZcvEumD53LzP8BC7jlGUZuPSiUidShA1lFrM91hYmAv1StrSEbURkLsPn2Ha/HUI/hTFxAgaAAAgAElEQVTC/IlAlizqCY8M6RAXH4/HT18x4qNazK9KQKhQnvZqaPRr89PV2PJ0tb7HqzYu/kf2o6O1A9b/vQ8OFur4JHly7mgQApyAGASX2mBOQOThxwmITPy4u/EQ+B0JyLzlW1iqUHMt6RnGfGFUPamfTUCo3mX6gnVsGw72tti7ca6aqJymGpDEnjB6MSQdC6on0KRjoSAgVrG4UPog+t0vgVNBGZDZJgyHip8ENSt6H2mDKldqsGU2FDqPyc+8WGoO2bICl1DRJUDrFvz7fjDeH0AyzCx9nogk0HMnqq5Tu96x05aBaoTI1iwch8wZlVPQtG2BulBRx6ex05exIT+DgEg1SFTrP2gPzofbw/rVcQx7WBRHPmZikY5rFQSyS1bpcnUERNmisqsfFnn6KBGQ7pkfoV/Wx0p1IIdKnEC6IrUQUnWRUg0I1USldU2NujUqKgk7Ll27C/uPngFF+UQSMmZwF5QsUgD/nv0/e1cBHsX1fU/chRAjWHALDsGd4FDcoaG4u7u7W2mhUCiluLsUd7fgECBOQgTiyf6/+yYzmd2dlbAs0P9v7vf1a9h5782bO7M777x77zlX8CowCBRFfPUmiKVKkfJ8Dg9XhEVEseiU2PgIBqUrXrv9kNU7ERHDyzcEmk1gkkHiRtHHAT3aolCBvDqfllW/b8f+Y1wNE52Xr8Gh+q9ls0fp7E8NQuOD0X1bFygecNEmkxLhMMkhzUbGD6hINoXieXYgyhaKKBuYRGVE2uyTkDOnK1rVaIo61crD0VG7aKReE5QbZckDMgDJkrvUGssAxDD/yQDEQP/J3Y3ngf9vAIRyvAPfhaCJX3WNTjPWglHqhN8SgFDqz/BJi9k0rK2scPDvZexvVVXrrAIQcV2K1CJYACCWybhQ9TDuxWZD59u12bmXFr+GBu7BWPyyBP54VxilHKOwrdw51L/cACHJ3CKJagWOVj6l8X79VwCIVIE+XRQ9k1RDQqapjerFE/jo2LoBihcugOZdhrLDxgIg4hqWT58SMGT8fLa4L1IwL4u8iWtYiHbZ+dQwTH9WGjuCudSeuzX3sVQ7AgU+51qzz37yCMScYrclAciRsFwYHVCRtaP6n/xdFjHdF23pZnGf47F9z3Fs33eC9XN3zcaea7La1Srg4ZMXLFVR1fh6ER6skKbI53iuaJ4HIEdOXsTSX7eKulLaIIc+Mv8Ci15RFEuTnbt8CzMXrReAixgk6SNAyY+78PxcHF/9GiapXPTGpMZrmBSM0nje9CAHKC7lhclnK41t+AMU8aHrlu3beUAGIIb5WgYghvlPBiAG+k/ubjwP/H8DIOEfouDumknxKuU5HoB8zQWjpjv0rQCIOL/d3Nwch/5eLugkGAJAxONSusvGlVOR3cVZ6XIzIyCpuFDlIDvmf7c604GgtJuNpS+i2uVm7PM1PpdRyzUM5z54YMDDquwzMQ2vlB//KwDkly4/oVNrLsqjau1+GcNobWlnf9TAblq/0Dz4KFeqmE5mKH1/GbSlYIlTEt8FheF1YGbaHD8+X8PiZJkCz02+mPXQG/8Ec1ouPABJVQClz7Vin7X2fIOZRe9IApDj4V4Y8ZgTl/yjagByj+QiBmIAQpEL79xeSElNZSxuqnokk0b2xqzFXIoSXzdCf9esWg7FCuWDdx4vmJqY4MCJ87h0laupyqhdFwCCKgCh57tBncq4dvMBQiMi4Z49G8qUKooTIna7HRvmq6V7EdBY9+du7D54Wmkuw/p1Rg53V9Bv0ss3QXrp+FCqWPuhQ5EWYyHcWm0AJP2eJ3A7Z+ZjkPcj4BkHE6dEKJLMgSgbIMIOCM1MwTq5e62+j43c7it4QAYghjlRBiCG+U8GIAb6T+5uPA/8fwMg+nhKH0rVrCwYtZ3zWwAQKpYdNG4+S/egRdf+v5bB2jqTJvRLAQilrIyYvESg8Z0+th+q+qrvAKumYJE/LkW5o8/9asw1PvZRrC6ggG0s9lU8DVMT/M8BkEFj5rE0rDI+RbBw+jCNj4wYfKguyo0VAZFKSaTFep7cnvjwIVpgfeJrWByuLcLsP//FwQwq3ke19wrXU/F8M8SnW6Cx+zssKn5TEoDsDcmDSU/Lsz5/DiwPr7q91ACIJgfRHAb27Ih7D5+C6pI4YKFgoGD80B5KBenidMQhfTvB0twc6zbvEZjBeABCNVZvAoPR2K8akpKS0aHXODYu6Z90aNUQK3//B0dPXwZMFPDv2ByN6lWHqwtXv0RGdSkTZ3Mijx5u2REWEcn+zmoROqWCjZm5DI8ev1K6fE0AJD3aCtjrw/nAPA2mNd/AJK96FIiO1zPtiIeHPrLfCBmA6POW+HptZABimC9lAGKY/2QAYqD/5O7G84AMQKR3rPVdMOq6M98CgOw6cIrtwNKikSIULtkyF0c0P9qB7TOcY5Ia0qcT2jSuhsSUdJiaZe6yql5HTGwcRk5eisD3IexQ/x7tWD2ClAksWDYmuFiJ0+2gRWG7W3UQ8Cmb0GVhsRto4sHtrusbAeFF6nT5+Xse1wfQDh6/gBUuawMgquCDrulbsGCJAUib5vWw68BpFiXo698W9WpUlKxhmTPMH/++s2F1PkcqZabPtbpRB88+O6N29hC08gxEfKoZxj/l0q2yWSTCyTwFEclW+JTKPXtUxG2VoakxpE9ndO03kX1OqV9VKnJ0vs5OjvDycIOnhytb+JNQYt+Rs0DRGu5hA/5cMwNenm7CYyBOR2z/UwP07s5FZogauf+oOexvKRYrotsdOHYeO051G1LRFzpGKZ5EcOFgZ4vBY+czcElRmwa1q2DHfi5NLCsARJW5zqT+CyhOFWTjSAEQaq84VgQIdeDa+D2HSa5YjV+D+rmaYkjxCTh78YagOfQ9vzP/S+eWAYhhd1sGIIb5TwYgBvpP7m48D8gARBqA6LNg1OeufAsAsn3vCaz/K3MXWp95URtNO6EfomIwbvpyAXzQ4o0WcZpMACD2lrhYYbvQTJxqQ7S8Jyofh3lGYa++ACSmxgx8LtNH30v6Lu30ASCd+oxnNQq0cB3er4vaPKXABzX6lgCEUhI7t2mChu0GsPnxauNSNSy9hkxFYFA4KjpHYFOZTNX7fver4EKUJwMmgQkOSilJum7OH8un4pehnMaMtmjP6g3bse8Il7ZFC/Fu7Zux2hrexGmDNSqXxcQRvTSmI6qyYIlFGPnxLMzNkZySwi30+cp0gEVbfmpUC1Pnc6KNlBZGBfDEQkeWFQAiZq5Dg2cwcUqCYicHwHgAQsXligvegGUa0pPMYPrRlptirmiY+r3U6t5S2cthYRVunrJ9Ww/IAMQwf8sAxDD/yQDEQP/J3Y3nARmASAMQXQtGfe/ItwAgfq378zWz+k6LtZMCIO+DwzF62lKhoJcWy9oK+mkcHoDQDvD5mmdgFsdFOagmoOk1P7xPtMe0wrfRzotjgiLTB4AoLB0R6n8dCivlmpMsXeQ3aKwLgISGR6Jb/0lsJn1/boO2LTgtFN40gQ86/i0BCF/D0nfELMYmJRadFKck9uraEvRvsnZFUjAtRyYL1oIXPvjzfSHh2sT1GQQWXC2TEJ1qhdR0E+GZpchFlYql0K65Hzr14dKfNAEQYsAiJiwyrqZDgb1blrBIBJk4HZGokBfNGKE1HVEVgIjHr1vTF3m83LEpg9Lat3xJxoY1b8UmFs0iK1rIG0+ec4KbB7cuBwl67jl0mv175IDuLI2N5mEd9QgWHx7DLPYdO5Zu5YQU1xJIzlUV4lSxtt2qY4/5cig+WaoBEHa1gc5QnCmgxAKmK/pB/SgCMrrM1G/wbZBPoeoBGYAY9kzIAMQw/8kAxED/yd2N5wEZgKgDEF0LxqzcDWMDkEbtB2L3psVITeV2aKWMRAj7j+ZE6IhatWXDykhKUcDSylqpOe10j5+5Ssj5nzyqD2pWKavzcsUA5OCkOnA53EPoE55kjegUS+SzjVMSJdQHgMT5jkJcJf2oS3VO0ogNdAGQRau34PiZy2wG65ZMQv68mUXDIyYthp2dDVNMJ4pUqjEQ2/cAIOJFOFHHUiqSOCWxUoWSWLdpF5vm8rljUCHtGqguhIBnQJwT2t7KTNUTAxD//MGo0ron+i46yhbQRBYREckxWRE9sYdrdq2MX4dOXMDydX+z9qSrQTUTVPux848Fgsv4dEQqKF+9YKzOdMS6NSqyuileWPOfvcex4a993HiiyVM0yL9TC9jb2TI6X/9B09j3hP796XM8a165QklcufFAjQUrr+1njM5/D3Vc1ammb8XnQffrXD0MRRk7dqqHtsfrawYgj9yRHuAKxFoL0RiTNg9g4pis9QnvWrg3uhVWFpXU5ytBFNypaWlKuj/69JPbZHpABiCGPQ0yADHMfzIAMdB/cnfjeeBrABCx4JfxZvr1RjZkwUi6Dlt2cCkWUgtG1VkaE4A06zQEuzYtgnVGDr0mD+lThE4q56R2TkaLtzmTBsGnGMdypMvEAIQWg84nh8D2CbdLrcl0AZDknFXwoXXW08p0zdUYx/nnqWD+PGjfwg8lSxSCrY0VgkPCsXP/KZy5yAkpNmtYE0P7dBKmQJGPejUrggAKGUWQxItp+kwMQKpWLKUzf59qJ3i1deqbnqFIF/jmLYZMWsrOM3Ncf5TyKcz+NjUxFRb9fASEGKf4CAfNadqYvli7aTeePn8N79w58OYdVxdEeiYEHHizDLoEq/dX0O23J3gezdV4iAFIw7pVcO3WQ4HRatrYfpiWkb5EKVRtm/tpBCCUckWpV2SkrxEcFkFKHahQpjjmTh4szGHX/lNYt3l3lm8zHw0kKt1Zi9crzZ0YwDq2bsjOxRtps5BuCLtGKNhcVK/X2jQVienmQp/1pS6icrZwTH5aDh9TrFiB/q2Y7EhVmMHPPRyTB7ZFkk8n9DvfBa+CA4UICKq/AgJdODYrr1iY1nqDtB0+Au2uSffbMDFj4SBJU9zJgbxxZdG2UQOdzw8NkJScgh37TuDew2e49+gZG5Oe7WKFvNHuJz/mf1X7Y+t+vH4bJHn+di38UKpEZlQsyzfnP95BBiCG3UAZgBjmPxmAGOg/ubvxPJBVAEKLGk2CX5SykTd3DtSqUo4tcMzNOB57XUY7bOs27UZo+AdWcDpyQFddXdjxyzfusxdlofx5tFJc0u4zaR3wxi8cSCyO0j9owUaKxXRtwaERiMwQLOMXjOu37BXqIUgp+t6j52woSwtztGleH8WK5EOFMiUE1WXx5LMKQMgXtLPL25S5a9kioGjhfJg/ZYjwecfe47Bt3Vy2e67LdAEQEl/jIyQ0FhWq58rpoXFY8hullfCmCkDoc16wTtMg2gAIpaZ8aL37h0+94q9NikVK9bop6jF/6lBBVI9Pu6LIxy9DuLoHXQBE132m44tnjBAWe90HTBaE8DT1pXlRuhWZmEb48MkLWPYrF20g4xfZ/P+1gVRKSaLrowiBagoWX0PBixyKU7sG9eygBkAoUrLpn4P4e9dRNg9aCM8Y1x+d+4xn//YtVwKzJw4S5mkoAKFicor2kJUrXZQJGvIUwOL6ncVr/sKx05c437CL5Irhzc3MkZaexj6/XPUgjkfkwvTnmVHEZcWvYNjjKlyfjJoSYonbVPYibMzS8LlMb6zyyIMtt/8UAIiieChMHmeIEjZ6xkQJ048VAkI4al2Tn2/BxFT6DiviLKHYVwJINUX3Ds3QrX1TrY9R7KfPmLHgNwF4qDamZ3TelCEo4J1L6RDpD1Hhv5SNH/YLKNL0v2oyADHszssAxDD/yQDEQP/J3Y3ngawAkOu3H2Hpr39JCn5JvaiIwrJt83o6J7920y7sOcjlTesr2EVgYdzMFUzkjXZ9l84aqfE8BG52HdQsdCfVUbxgJBE5Og+/2OAXUWKhMXoxzxw/gOWEiy2rAEQs+qfNcWMG+8OvNqeloMt0ARBxkbGuseg4+YZSiXiTAiB0TFskRBMAIdarj37L/zPgg65THAEhgEpF0LzRQr2JXzX06PyTkMYirvmgmhtSmdf07CcmJaN5Z06IUB+j7wF9H8gMASDUnwD+0rV/4WNMXMb+PrfYpsXnhBE9tSq6vwsKZcBKFYBQyhQBXCoOJxOnds2cMEC41q7tmqJzm0ZY/ts2IX2NGMSmje0LEhPk61RUfy9o9z4xMRFHTl3Chq0ZqVRixxFWyCgkJxBUu1o5mJiawtGeE8WkGpI2PUazv0nTpXvHZoyGl8QKyUh1vVbV8hg7YwVu3wvIBGZsXOWIz43qB2BrnobVr4tiTWAx1r+YfRQCPrkINRymUOBAxZNws0oSZhlUaRQ6XjmH5Msc6MgEK4BJo+ccALmWC3jMbRKYtLsPE/vMTQtFBKmh2wKRtlC8dBEEDfUBIDMX/47zl2+zcak+hjaT7Oxsce/RU1brRUbPNOmhiKm+6TckLu4zzl6+hdPnr7N2pHfj5GCPggXyKtEW6/Mc/39qIwMQw+6mDEAM858MQAz0n9zdeB7QF4CsWv8P9h89J0xEVfArMjoGRGF59tItYceQUhcWzRiudfIHjp1jL3jetAGQsPBI3H34FLSoP3vpprC7qwuAXL5+T2lHb8+hM+x0To4OSE1LxefPnDIy97IH8ufxwsoF44QFIw9ACGTQ4mP/UY6Bh+hDbaythOulF/OyOaOUcvyJd58WgmRD+3ZGswY1tPpDnAqlreGM8f1RpUIpvR6MiMhoYcd43NAeaNe0OuKT0pCYzO3UPn72GkPHZ+bR6xpUFYBs2X4Im3cclgSPlJbjeG0RLIOuKA17IdID/R5wQoRbyp5D6Vy2rN4jvlhHXaf/4Y6LU/o6tmoIStMjxiuKTlGETcycpK3g3JgX5mBjzlbIcfHqtUK6UhJnL9nAvm9kS2ePgk/RzNQ8bSmJNK4YgLRqWgcDfmmvdJma2OZoJ37mwt/Z952MFsNEiGBpyaV2zVu+UVjoHvx7uVoaYmRUNELCOD0O3hat3gwCfDyRlRQNL7Xl50R//7l6BotM/dSV+x1rVK8a2v1UHz0zolZWlpZISk4WrlN8vTwAeRjnjA636nDT4KXVxRLrWbjxJhkREMUTVyiucFFIk4bPYOIVJ4ySfjkP8DSTkpg/oAuAUG0Lr4FCv+8Th/eCKYn2ZJhYMX7i8J6oXb2C2sxJqZ4ixmRSoo1ZuNT/N01lAGLYrZQBiGH+kwGIgf6TuxvPA/oAELHQFr2MVQW/xLOjFKLT565h/V/72A6pNgAiHpcfQxsA0RQd0AVAVL0ntWC8dO0uVvy+jS0kCCg0b1hT6MYDEEqDWjmXY//hja731407BXDWoWUD9OrGaQ58bevSdyKWzBrBxM4MMRcHSyUAYshY+va1iHgA61fHYRXEpa3wlpSzGpJyVUFyTk608L9ouhbw/DV9L/BB59cHgGS1hoXG5SMd9LdqCpkqAJFittLENjdtwa8gSlwySvf7pVtLJSB35vx1HM1IgWrTrD66tGnEarKkjNIv09LS8M+eE3j+6q0AQIoUzMuiRUULeqNapTKssJ1MnJJIv2Fjh/yMCbNXs42GPDk9kZaeLkS5ShYvhAePn7MoxS8lPuOPR/bC+KR7Q3UgkSlWmPaUi/hQu5KO0XiXYMvYwMTgVJ9nnwcg6SH2AOmAkJUIg6lvpnq94p0TFJE2sDSzRIv8rbBrBxe90QVAxOlrvy+dzNTkxUZzb99zLPMDURCLU0L5djIAUb+LMgDR58nW3EYGIIb5TwYgBvpP7q7dA/wLNl/enMiVwwN3Hz3F0+dvmOgW5ZirvmDFo+kCIPEJieg5dLqQdkU7gmLBL00zIyG7G3ces51LKRMLgtGuopmJCQ6fuqg1BYs49gko8Hbo5AU2L0MACKVZpKcrmPgYLTwIAP25aoaw00rn0gZA6DiBkMbtuTx0KZDyNZ5f/0FTMGvCIOTycjd4uO8BQAye9A88gD4A5HuCD3KdPgBEm4tVa1j4toYAEG1sc+NnrgQplOtrY4b4w0/Dbw1/fxwd7BET+0mNpYrOQdHaKaP7CAX8qmmbGSUbQl/2u9CvM1NQ35cREZ1c6Da2BRXAi3hlIVBN10AL+rJOkVhV8qpSk2EPK6GYQzS28HTGJcKgeOTOoiemjbkULEWyGdK3lxTSq0x+egwTl8xIrp25PZbXWIdSniVQoUFPNj4BEKrFoFo9KZs6by1Lu6NI7r4tS5SiH3z7BSv/xMmz3HyP71yj1kYGIOqelQGIvt9i6XYyADHMfzIAMdB/cnftHuBfsDWrlmcUkVLFgKovWH5EXQCE0q4o/YqM8rLFgl9fel9I6K7/qNlsJ43yuolxafUfO3D4xAW9a0Do3PwixVAAIo6sSKVl6AIg7OWeUfCrbw1LVny3cOWfLBfd0MgHf04ZgGTF+7rb6gIg3xt80BXoA0AoAqJPDYvYI9pqWLgIiELY5Vf9/dBGTzxm2jLcecClX+ljeXLlwKwJAyQZmvj7w+uG8FEH79xeCIuIQnwCt3CvXrkMpo3pJ5yOfheW/rpVoKXmDxAYG96/K6v3unrjASbPW8MOVXIOR16bT9gRkl+fKTPf+Gb7gE1lLrD2H5Kt0epGPcwvdoPRVte/2ph9XqquJ+6fCWV/8xEQ+jv9hQtwIR/7XOHyGSZ1XsHUMRkkOjiy9BR4O+WCjbU5AyB0riKFvFmarCYBUnE9zsLpwySvYdfB0wIF8+6NC9WiTjIAUXebDED0+jpobCQDEMP8JwMQA/0nd9fuASkWHkpbsLO3RcDT18ILtFql0kovWBpVFwChFzBfgLln82JB8OtL7wktcEZMWcqiDbQTt2LeGFYAumzd398FgLRuVg8UXaBICqVbUHE1MWKJTRcAIRriJh24CIim1IQv9Zcx+skA5Ot6lZ4PKn5u2biWGs3pjwA+6Gr1ASDEgqWrhiUrnvtSemI6h7bICj8HIqLoN2q2UtF/n+6tUaJYQRYptLe1RdwnKiofw9VeMNYpoFjhfFiRkUpJNSxU90BRUJgoMHawP8qXLsYW1lRzduDoOexmwoImrCZs6ZxRKJw/j+AGmkOzLkMEGt5B3o9xO8YFlz96wtsmFg3dg7AuowCdGMR+L3UZ1VzC0fZmbQR8yiYoyV+McseEJ+Wxt8JpZLdMRkiiDepf5TSK+hUOwq/POO0Ym6bvkOTOkRwQqFBQGlaogzCfBq3KoGWVRsidMwdSkhIQFBqOQeMXZ1w7V8+hCYDw6XBS7wn+BOI6kA0rprKUNLHJAET9GyIDkKz8aqi3lQGIYf6TAYiB/pO7a/eAGIBQnjXVIRBVKhm9YCfPWQOilyQj3n5aaPOmC4DwBZlSFKFZvS+UqjR9wTrGn09h/nVLJgo7lt8SgIgXjJ/jE7F24052KcRiRWJiqqYLgGzddRSbth1g3YjhR1w/klUffYv2MgD5Fl4Go6Lt2LoBypXiGJC+p+kLQCgl8WvZl9AT8+fWB4BQW2J4+33LHhB5g75Wr6YviIyBt2adhrJCcl0mJczJAEhnosc2UUrPEmuD0LgEFgrbx2BvhTMMrLS9lQlAariE4dpHN/xWmhOrJBMDkAF5H2NNIKdBsnD6cDjkVuBT6ifcj7wN+9RsuHo4ELevSlPgql4TaXhsXjNT7VJpfg3aDmCf+9WujDGDf5Z0hzhaLKZ85hvLAETdbTIA0fXN0n5cBiCG+U8GIAb6T+6u3QP8i57SoyjNQdXENKuqbbQBEPFLSVXwiz8HgYoUkW6F+NymZmZKCrq00OcZqEhBuXhhLn2A7FsCEP6cxLTTte8kFiEitWdSfZYyHoAQcCP2KbL4+ARQKtmxM5eEYllKBSNRNF3CgN/7eZYBiPHvwI8EPuhqvyYAuX0/gC2iy5YqqtWRWaUnFg+mi55Y9cQPHr/A5u2H8PRFoFrKFLWl37IyJYtgcO+OavTBp85dw6I1W5CWyrHCqRppjfT1b6u220/teKFIGr+U40c8iHNR62+OVKQozLDc5xpqZAvFurdFsSckLyKSreFonox2XoEYWeCRUr+wRGvUzUjBGpj3MVZnAJCNpc/DNxvH7qWwckJC/kZIzlkVl1JK48/thwS6cPWrUKBGlXIYN6SHUn2b+Hecr2OjmjxNWkxioUYZgOj3OyIDEP38pKmVDEAM858MQAz0n9xduwd05aBTb7HgF/Gz86YNgIiLq1UFv/j+4hoR1VmKazPE7SaN7I1aVcspNf8eAGTDX/vwz97jbB5i/QTV6xDrgGi6E1Tg2alNI73FF7/nMy0DEON6/0cDH3S1XxOA0HikSu7l6Q6i1tVkWaEn/pp3hGrLgsM+4NOneOTwyI4eg6exOpTezcugZ7FMulo6Z5pjbiTkb4iBk3/Fk5eBKFYoH3p1bYnP8Qlwd82GHJ5uTHeEzCz2LWwDdsD69TFYRDxkn5GaeYULLdjfc4veQF3XELyOd8SZDx4MaJgwul0T5LWJw8GKJ2FmaoISZzNZ8lwt4nGuGvcbpMnuxWZD59u12eGNpS/AN1sEi7iIja4jpsYMJORrxDZGQkIjYGEGeLg5o33vyez6dbFgtegynIE3otclml0pI1FXqt0h+3XxRDVBQjkCou41GYAY9u2WAYhh/pMBiIH+k7tr94A+AERTgaGuFCxNgl/8jPYdOcsWI1Imjirwc6Qc846tG6o1X71hB2NXoVSvTas4ZWhLS0tJdXG+85cWoVP/8IgodOk3kQ1FaVeUfqXJdAEQus4lM0dKssb8iM+uDEB03xVabBJtsGlSDKyCuNSYpJxVkW7lhMT8DZHmmFkHIB7tRwQfND9tAERbDYs2T5EWB0VGNTHd6fO7pPtOGNbCJCka9TtzqunD8j1C77zPJAfs+LAZHnywYKQYqgXYNIbThamwDVD/nVMFIC08iQ5XASopaXmjPl7Gc2rlc4rexE+ebxlwIADCiQuaMDCxqQxHk6vJ1AHIB5GgiHKvON9RTE+HzNrCVChCp3/rAiD8b72239x30cYAACAASURBVEPx7/223+epCQzKAET9LsoAxMDvsIKX4jRsnP/Z3sGRmfR4P6oTSG/IzdkaYR8Tf9QpyvPS4AF9XvSaBL90AZAvFfwiNWgpAJKVm9j35zZo26K+xi6GAJCFqzbjxL9XmO7H6oXjmNaAJuMBCLEELZo+DKRObWFmholz1+DJs9esmzH1P7LiM33aygBEs5c44cTFsMwAHZpaUtpLbKWRSvolPyr4oGvQBkD0eWY0tZk0ZzWaNagpWTulz++SIefW1ZfuZfbDv8DneF3WVBsAqXulIcKSbNHSxwoDpy8Thib9GufTw4WIh+o5pQEItVLgUGhujH1SES4WiThT+SiLSCx+UQKb3hdkbFmvExxRzP4jdlXghE01mSQAESseqnSML9YB0fWXZxmATJy9CtdvP2LkIPwmkOqc1m7ahT0HqSgfOLJ9ldoGkQxA1O+iDEB0fVO1H5cjIIb5T46AGOg/ubt2D+jzotck+KULgGzbcwx/bN3PJqCPkjd70U9cxHKRDQUgvbq0xMOnL9UuntSQnRzscfNeAEJCwuFTvBBLodLHHga8ZKrO+47+y5hxeEpOKr4lFiBVI4ar0VOXMmpjB3s7pKWlszQFKuYsmD837j18jti4T6wbKTU38auucRq0jzNl3lrEfYrHoF4dUDBfbsm2f/5zCC9ev4WttTXGD/9Fss3boFD8vnkPO1a9Uhk19iW+06ETF3Dt1gNYmJtj8qje7HplACJ9i5xPDoHtkx36PEZCm89leiOmxswfquBc6gKMBUDY933CQvTq3lpJIZ0+1+d3KUvOzkJj24B/4HyKo5LlU540AZBxAeVxMIyLaI0ucB9dytngQ6tdMI99B9e9bWGSFKPxzJoBCJCqUODVZ0c4WqTA0zIBc1+UQlCiHQblC8CkJ2UZC5aFSRqOVz4BDyvNG39ZiYDwE42uvwzppTpnKQLy66Zd2J0BLv75fS6yuzirXXevoTMQ+D6E/f5JFbPLAET9UZEBSBa+uBJNZQBimP9kAGKg/+Tu2j2g60WvTfBLFwARqwITc9WWtTPg5JhJ+yg1MykAQgXfivR0jReyduMunD5/naVgrV82mWtnYoq2/tKF4eKBbKyssHntTNZXk1E9y5/bDgo1H6rtqPBy4C/tceLsFbwKDAKJHr56EwQSYjQ1MQGx2mgyMeMNqQMTFS9vdF4SNQsN/8CoWu/cf8IOEZtNGZ/CkkMS4CPgR0bpLaRNomrimhp7O1v4FCsgORYV51I+O9+mYtkS8G/nJyihr9+yly0o9DECfpNH9tan6X+ujeueVrAMuvJF8+7xrAXa9B7+Q7BdaboAYwIQOieJlU4Z1Qd5c2cy7H1patcX3QRRJ+tXR+FyOJPlShcAGfu4HA6FcxFQosEtbB+LRO96MPschtigZ1jzhiu2d7ZIxuB83PeXN20AhGtDvxsK+N+tiXquIeiWi9tQEdPw3oh2w6PaezVetjQA4cdWrgXhB6EC9eieN2Dt7KokRNitvTpJCd9H/Fvfq1srFtUVGzEpUiovWe/urdD+J+Xj9LkMQNRvowxADPtGywDEMP/JAMRA/8ndtXtAFwDRJvhljmQs/X03S0Ui7nv/js3VTiYu1qbCcqJopB0wTSYFQHTdQ6kidJ5hhvpSipRveR82TEJCEj58jFai3iRxMGKxssmgHxafj8DPzIW/4+5DTtiMz7+uVN4HPzWujbfvQhAcFoErN+8Liu/Ci1wkpMYgiEIBB3tblClZlEVFqOA142OBhvO3pZOQLw/H2z953lpcvXEvY7jMxYI2ACIWN/PK4caU2VVt2oJfBfYt1fMLcxeVqvIZG03rV8eMMT0EAKKrvkX1vJo0BHTd3x/5+JdEPvjr6XmvGnrleYbSVeuytJcf1YwNQOi6O/QcizWLJiB7Nv2UwI3hK6rdcf/HTylqwQMQSnf6JfcLVHD+ADuzVLxNsMPGdwVxOJyLfjiYJWNRiRuo7sLpbBB2eB1vh2Y3uIU2pVJdqHZUadq6AAjR6ba+WZcxYPk6U+0GZ2IAMr/YTXS8VRv/VuU2Hcho3HQF93txPzYbet3nIqurfS6jojPHgmVqkg4bM/VNneQ0E8a6lVjaHwq/GajVciBrT/V3RJTBG0/VLr4gvg6EPhs/7BeWWkcbDy9evcPkuWuE3zvV+g9KS01PT8fuA6ewecdhNiSlcWVzdmT9zc3MlPz2v/QPGYAYdrdlAGKY/2QAYqD/5O7aPWCI4Fd83Ef85D+BnUCT1oe+gl9BoR9w6+5j7Nx/Sie1reoV6QIgpG9Cu3fJIsrfCbNWgVKqSFyMbMroPkxEzMrSUklMkHb5aWeOjKI3BBqoz/b18+CSzYkpvVNEgbeaVcsxNhzvPF5YtnYrwj5EMdBiamYORTpH1UnK8v16tEX/UXPYv4sXKYDHGelipIa+at5YXLp+Dyt+2yakeYmvWRWAkOgZAaSgkAicuXADYRHcIkPqnlAaWKvuI5XoRqtXLstYe8RGka/L1znwwytSawIgFN1qUKeS1gfNzNwc/f3b/r/6OopTdbJ6YTz4qMJYiQBKe4kv1jGrw3yT9t8CgNCFNOk4GPs2L5Gkev0WFyoFJsWsU5rmUNguGhtKX8K4gArwcwtGO683DIG8/mz/xQDkbKQnZj4rjT3lT8PJMpWdeu2bIujv/VRNiPBBbDbMel4Kf5U9jzsx2TH6cXlEJNtI/nbw10Bz3lvxX7VLmv6sNHYE61Zjl1Iyf/L8DUsnpDRTNSPhQxOwzRVP9+xKhynlljZ6lCwjxXXCsJ6oU6OC2nD0+0Q1hpQWSoX/WQUpW7Yfwq37T9C4XlWNKajM5xt3IjiU+47qY+1a+KFUiUJCU9rsod/cSuVLolmDGhqHoAg+pfeSkVhltw7N2N8yANHH65rbyADEMP/JAMRA/8ndtXvAEMEvfQAInV2T4BeXfkSvapMMtWFFxktTgdw5PdG9fTNUq1QGFhbmWi9CHwDincsLc5ZtUHnH0Zk5BMKnQk0f1x9VK5Zin5EQY/ueY9nfVLNBO3lk3ds3ZS+I67cfYuLs1ewzWuyPH9pDSKGil/HgcfO58ykUKFo4P5o1qI71f+1jWgKLZgzH/BWbQDoCZA3qVsWJMxxjEtF3Bodk7KRmzNjKylIAUKoARCzwxZ2O8yOpsh/bwc2Pt5dv3qPfyNkZ15SHqcpLMdyIU7n48Ugo8ee29dUiIEUL58PKDHXo/6Xvm8emCjCLI+airJkq+KDeRIUa9vONrA30jVp/KwBCGwQtu4/AkX9WfqMryzwNsVXl+E1dm0QcAYlPM0dgQmaqpq1pCgMbQ/M/hpUpF02Y+rQMS7canv8xAj/boUlGBMTDKh5nqihT5iammaL8Ba52bF7RG2jOWLCAdYGFcS/WBWtKXlVirDoQmhv7QvMgNtWC1YBUcg7HH2UusT6nInJg2avirDhd/BugyZGaAMiMZ6WxXR8AsmkRHB3s1IYnEcg5yzay3xWxOTrYC/Vu+txc/vdYEwD5bfMe7Nx/kg1Fz4uud4T4nCFhH9B3xGwGlPRl99JnztSGIj91a1QUmvPv158a18KgXtIbDJeu3QMBFTLKDlgya5TAECYDEH09L91OBiCG+U8GIAb6T+6u3QOGCH4lfo5B8+7j2Alo537bb3O1nkyT4BdjleQo79WMogUUnZB62fGNl/+2DYeOn1eagzgFq1nDGihZrBDmLvtDaXwxGQz/wiOxwCoVOAAiTh9zdnZEdHQsU2H/+7fZMDU1ZbnrHyKjWds/V8+Al6ebMP6+I/+C6IHJ6DxUZ0FpXjGxcbhx5zGrzxDz4i+ZNRKb/zmklOrVuH51mJmY4PCpi3ByskdMDFewrgpAqObk0rW7wrl37D/J6k/I/lgxlYE53vj6D9/yJVEof25s3XkEYs0Vvh1PvUxpaQkJicy3lM7lmd1OBiBEU6pSK6Dtwd8bkhutcnDgVQp88H2jmm5EYv7GP9xP1rcCIHThkR9jMGDUHGzfkAHev5E3NEWzxDUgvfI8RWSKNcISbeBgkYLc1p8kowy/BxbGs8+OWFicAKV0nYWmyxr+yBfeNp8YqOGMo9zl7epHN4wNqICDvqfgaJ6iNEy/+1VwIcoTpkjHzgpnUdRecwG86rhS80luuwUfcvh90R2g3x++Di6/dy7Y29qgeZehbCxKeS1ZvKDWcb3z5FSKGNDv+cVrdxAR+ZGxbVEKK2/6ABDaEHrx+h3778z5G0KURhcA2bH/BCKjtPkRiI37LGwkZRWA3H7wBGOncemXtIm1ct5YpQiRDEC+6PETOskAxDD/yQDEQP/J3bV7wBDBL11F6NrOTKlMJHCoalSvYWdvi4Cnr4WXRLVKpTFtTL8s3UoxAMmdy5MxX4lfWqqvdmKxIjYrsfkPmgqeEpiYst4HhyEkLBIfo2Pw8MkrHDt1kaEmUpAnlXixiSMIFIWgaISqiQszKfJSuEBe9B81m6V5kabAnEmDsPqPHTh84oJWAKI67pBxCxDwnKP4JVViKpLnbcbC33Dh6h307d4G+b1zYuyMFezQwa3LYW1tyf7+9DmepWlxPuKiRFTMTqBJzILF14D8L0ZAslL7QYvYWUVu4lB4Hlbzwaddqd63+KLtEe3H3Y8fyb4lAKHrfvs+BNMW/MbA87cyp/OTYHdvvdrpdBWha5rf4bBc+DsoP7aWO6/XJSSlm6LVjboYmu8xGroHi/ooAxA6EJFkheY36uPXkldQxilKaEtAd/qzsqjjGoznn51xyPcUghJtEZzAiSGSlXWOgjlTOFQfV3WiqdXHIrzscL3mr6uR+PeYUmJ7dOIEGPU1iqJ36cul+6qaPgCEj5Kr9tUFQPSZ3+bth7Alo3ZFVWBRWwREHCWnja3VC8YqbRbRuWUAos8d0NxGBiCG+U8GIAb6T+6u3QO6itC19TYEgNC44vQveikRcwpf3EjpT5PnrAEt0snWL5/CUpf0NfELj08hor48wHn85DUSkhKEFCzVFwf1adCWExiksLiNtRVjuFI1WqAvnzMGJYoo500vXvMXjp3m0iM0FV8THW7PIZxw4qCeHXDs3yssdYG49FfMGwNHezvwL05tERDVOY2dsRy37j5hEaV6NX0xbijH6iOu/1gxdwy8c+dAi67cAmPR9OEoncGsRTuMxOvPTKFA3txeWLdkEgNRMgDh3JIV5it+Eeuf6ylGF+R3ttWf5OScVfChtWZGI32f/a/d7lsDEJo/1QVQms3yOaO/9uVIjqfpfn4pAKGT3IlxwYhHvjhZ5XjGol/51O8TbBCSaIvQJGtMeloe+yuegbctF+VUMg26HZ1v10SbHIHsP7LrH10Rl2qBkx+8UNA2FpvfF8S28ufQ+kYdfErjNhf65w3AIO8AyXAzpXGlKUxQyD6WRWGu5BqEq6jAogZu2bOhaEFvvVJipRxsKAChiMreQ2eEoe88eIp7jzhhSH0AyNWbD/DyNReFJNv0z0H2f0MBCCnHd+rNZQGIf2v582gCIBS1HjJ+IdtkI/CxdPZINWV4GkMGIIZ9/WUAYpj/ZABioP/k7to98CMAEIoeUBRB1WgRQrvsZJraaLo6VQDSonEtNPWrjuTkVFAO8NZdR/D2fSjrTmBjw4qpSgBHU4SGB0Fi+lkKna9eOB7u2TMLuSfOWYPrtx5oLM6n84p39XJ5ebAIC72M1i2ZKDCFfQkAIZHFG3cfMXBFc9uxYT5LFRHXf/BCYHwUQ+xfpfoPKDBr/EBBLE4KgNC1UIqWNqPifk0CZf/F76jXysy0Nl3zFxcyUySET8eS6hc8mHsmfyT7HgCErp80aA4cO4/ZEzkmJmNaVgDIvOc+cDTnCsMDPjthoPcTjelOYUlW8LvaiEUj8tgoF1qLn4uxBe6he+5X0peoJVgx+UlZ2JunYGzBh0p9e9ytjk5eLzE6wBd/ljmPrndqsVgmO0+uV5KZYfx8Grq9R0SSNW7HqrMV6pMSK3URhgIQ1THFlL36ABDV/vx7z1AAIo6sbFo1AzlzZKbh0jmlAAi9f4aMXyCwglHaVdFC3pL3XgYghn3rZQBimP9kAGKg/+Tu2j3wIwAQqfQnftaUpkVgoGHdqhg1sJvet1P8wtPUiRbFiclJbKGuCnDEvPb00u3crjHKlCgCU1MTBlgatBnApSdlFK6IhRPpfGOmL2e6HdldnLBxxTSlKZiamcHK0gLi3TM+SrN87hgUL5xPaP/FAOTOI2FuBK7y5PTEgWPnsPL3f+BbrgRmTxzEzkFsMER9SddIhfFkxM5Fu54U/ShRrCCrXeFNEwDR58b8f6LhzQoAoZ1psflmy6RUVfWbDECUPUIkDTfvPhaiePo8Z1/SRhMA6Xq7JhLTTdEl50sBOHa9XQN3MhbnugAln+rU6KofphW5i8oZrGc0R37BX94pApvLXtQ47RTXEjBNitFIePDnu4K49NEdv5XiSCx4a3qtPhYWu452t+tiauE7mP6sHDaWOg9fF3r+1GtTpBi/jJES+yUpWKrO+REASOC7YPQaNpNNrWWT2hjYs4PaPVQFIJFR0Rg+aTHbBCNbPGOEEmuW6gAyAPmSb3NmHxmAGOY/GYAY6D+5u3YPGCL49bVSsLQBEL4YmmoiiG5RX9MHgFDNx9HTlyQBjjj6olpYSAKBjdsPYgDE0d4ecRkUkuI6ioFj5+HZi0BWgK5aXM8Xfb8PDkePwVyeO7UjtfFaVcspXeIXA5C7j4UlxrB+ndHUrwZmLv4d5y/fVhICu/PwKcZMXcbOSfNPS09Dy24jhDktmz2SFanzJgVAKMoiFcESXwilb2mjodT3vv4o7XKsKwyT5NivOp00h1wI8+eoOH8k+14REN4HROhA35VBvdQXeF/LT5pqQKTG//lOddyMcYOLRQIuVMvU39A1l1/uVkMzj/donZEyRalRVCieojBFEfsY9MrzXHKIxHwNEVdpFNz+0VwQfjHKHdOelmXF6TZmHN33+wRbNLzWAKcqH0OXO7XRLscrlHeKhG82jqZb1cQApE+hIHRZuA/JaVzt2tdMiaXxJCOmokhPpbI+GNa/i0aXagIgtJGTlp6uk5b3a0RAps5bi8s37rM5alJ/FwOQ7h2bY/SUpUIqL20C0WaQNpMBiK5vlfbjMgAxzH8yADHQf3J343ngWwCQweMX4Mmz16wo+0sBCEUn+DSOD5EfMXgcl3tLltPTHUGh4Wrji6MTfX9ug7Yt6is5khfdsrW1QXx8AjsmDqWPnrpMYLRSe9EX5RixxFGWqr6lMGawumo5MWmdPHuVqZFTcTjZvClDUb60OmUofx6WgnXnEczNzVjdR+3qFTB+6C8MWNB1i6MsYqBGu3HhH6IYPTBZyeKFsGQmB0Z4k2tAOE9kpQZE32+gXAOi2VNU5EtidT9LiJ3q619t7bLCakYApKVnIMo6fUTv+1VxsjKnE6RmErsPk56UhYdVIgbnC8Dc5yUxMN8Txma15GUJxKebYVIhbkFL9ijOGSUcogWNGDFTV0q6CYY9UtbeSUwzw/VoV9R3DYa9eSouRHkwQFL/SiNcq3EILW/UxaRC91AhQ4xQ7XfpbCv20QDvx4jMXgXBFt6ws7NnRBZsPqKU2Kq+pdn90Nfo9/vXTbv0bc6YsqjuTJOJAcjYQf548vI1Xrx+z5i36DeOAA79tvsUL4j6NSshl5e70lCGAhBidBwxeTEbU4qEhD8Zfx4i8KCoB/mQTJMavOr1ygBE70dGsqEMQAzznwxADPSf3N14HvgWAKRTn/GM6raJX3UM76d5R0z1KsUL69I+hbBoeuZCmtiwKAzOG+2alS1ZVAngpKcr0LAdV4QuxeFOIlgkHsWzRFG7uZMHo0KZ4qzPpm37sXUXtztKDF5OjvZYtHqzwKpFAETM/y4ulJe6Y+I08GYNamJo304abywBEEpbocgEpa/xRY68/sfhbSuUxN7IF+QTUrI/d+U2XgcGsYjM2kUTGFWv2GQAwnnD4doiOFzn6pO+lsX5jmI73T+afe8ICO+PNX/shKe7C1o3q/fVXUQ6IJ6bfPWKaj355CTUfBDLFKVpiZXIMycnXbxBgoJvE+wxt9gtpesg1qzLUe5YVfIq6l9piAO+p2Brlo4PrXcjOWdV1paAUraTQ5GY8BkVLuhmkvKxj8LYAvcx9Xk5HPQ9je53auDn3C9QzzVEzYd8BKSsSxzuRHF6J6r06nxKLBFTUAqSvtawdhUcP3uFNScwQuKnz1+9xbHTymlj4vFaNa2Nfv7tJBkExQBEMsysMjGK4k4c0UvQ1zAEgNBv9eCx8xlBCv22bv1tNhzsbCVdoUlni/y6ct44YT6a/CgDEH2fMOl2MgAxzH8yADHQf3J343nA2ACE1G679ed2waSiENquTBsAoX4nzl7FwpV/siHohVK3pi8mDPtFaUg+ykF50KsWcEwnvG3bcwxUrC0GDpvXzBSKxyk0TyF6ssmj+qBmlbKsoJ52wPh6kbWbdmHPwdPCHPh6EqnrUgIgfjUxtJ9uAMIXttN4pGJOeiIEkAgoie2vnYfx5z+HGA0wvVQpO9zTLTu2/DpLbSoyAOFcYhHxQGtKzJd868J+vo40xzxf0tWofX4UAEIXuWDlnyjjUxgN6lT56tf8paAyLMkabW7WxcVqRzLnxKIf9E9pHZD9obmxO8Qbm8teULqOUxGeGPaoMk5UPg4va4qsKqCwckZMjemIL8YJ2ZnFvoX5xUWouYHTASGQUTN7GPv7ZrQr3ibaITQpc0FMooND8gVga1ABrC99CYMfVkLd7CFolUNZLFCqBkQVgPApsaRoXrZUEa33gDZxKH2OTAxAqAakZpVygiAqn8JZuEAeFiU4ePy8QJmu6Xd/6dqtOEI06GQKBYoQQ1flMsiX24sprtMmCpFuUMopb7ShUjAft6FiCAA5f+UOZi76jY3T178t2jbXDIjFAITASomi+dnmEFnB/HmweMZwgflRypkyADHsay4DEMP8JwMQA/0ndzeeB4wNQBat3sLE+sgoHE9hed4oJ5nnXnd0tGe792LTBUCoLS9gSH+7ZHPEHyumwc42kzOfqBpJqI9s4vCeLJWJN0qf6jd6Lnv5EXCwtrbCgb+WCoXfpOrctscYlg5Aeb4zxw9kIXsegJDgIVHwUoSCXuQ0vpSt3biLRVrEKVhzJg1GxbJcpEXK+AhI4YLeePbijVITqXqb+4+eY+SUJUrtenVtiQ6tGqoNLwOQTJdkRQtE17fwR9UAoXn/SACE5jN57lo0qV8VVSqWlnQrgUPLoKuscJss3coJVMidnIuLImgyioK4b6v/Rer2UcmWaHLdD1erH84YXrxloBDJbmQCkpvR2TEuoAJOZSik0xi1LjfBxjIXMPxRJZypfAQWZpmz5SIhnKaP+PetZ+lkDPKJwrIblkhJTcfE3OcRm2aBltfrISyZAyK/lryMyBQrlqI1p+htTHxSDoXtYlk0hDd9AEhWUmKJhIPIOMj692iHtRt3sr8JgCQmJQubLyvnj0PRgnmFeVCq6aCx81m0mCjQSeRVvDlDfTv3GY+4T1xKatd2jdGlXVPJug8aY9X67WzR/zUASEpKKhOgJaBEwGnL2lmwtuIojqWMByAEPpbMGsHIQCbOXi2k59K7YdrY/rAwF91o0UAyANH6ldV5UAYgOl2kvUFwJJdf/iObqQng5myNsI9cXr1s/xse+FoAhHaC2rfwQ8kShWBrY4XgkHDs3H8KZy6SkjDQrGFNDO2jvOP/LigUv2RoaNCLYOcfC5Scrg8AWbByM06cvSxogdDLgAQBzc3MWNHlxr/34/SFGyAwQS+QkQO7oUblsowJKyIyGqOmLEFwaAQ7L2l3UGSBXpi8kY7Bzv0n2T9J44S465+9DGRpTU6ODsJOGCm907iqRtewasN2lqbg5GjHFHfJZo7rj1IZmh2mJqaCgCDfnwcglHZA10EvYd6WzhrFVNnFRi/05p05lWLeiDKXrknVZACS6RHaiaYFq6HF6ApLR4R3OvVDRj/oan80AEJzGj5pEX7p3FJQ1CbwYH93Pezv/Q6TDOCh+uwqrJyQkL8R4nxHavQ1gRfXPW2+6J7Gplqg7uVGuFnjAGOeUNXVuB6dHQ9iXRDwyRmeVgko6fARxR0+osn1hthY+gJGPPYVoiiRSZaoe7UxDvmeRG4bbqFN8w/veJLNXZXWNjTsA9ORaE9aSgH/wPnUMDyMc0aHW3VY3955AjAs/xOsCyyMxDRzprQ+/4UPK1in6EhAYg60vVqZtaVIaNECeXDgxAW1FCx9U2Ip+kFEHLRRQ1GUdYsnoY0/l15Iczx47DzbnKlZtTwmj+yl9jtDG0+0AUWm+ptFG08UseXJPfSh4b147S7Tasrm7MjG/NIICM8kSGMQKyOxM2ozKRreuM/xGD5hEXgqdxqD6mykIuAyANHqXp0HZQCi00XaG8gAxEAHyt2N5oGvBUC0TZCiHvOnDmW7TWLLCgChl05//3YaAI4Cjg4OiI3jBMCaN6yJIX06QRhfopCUr63g5yNOw+rTvTWjrqWix5TkVIyftZKlA2S2VWbFqlu9IkYN/llyB6z7gMkCXaMmH5F/SIOEQBJvU+auZSJdpFBOaQ0Hjp4Tjh3atoJRAIuNFgstuw1HQmIS+9glmxO2r58neUopAEKgq59/W63PmampqaAlYrQH8jsMbBl0iS1YDTHxzrYh4xir748IQOhaew+bgQkjeqHE5/NwujBVI/BY8rI4XsZzC09mpuZIdysK+5zF4ORgj2JF8qFCmRLCd1AVhEgVfGvydZoCuBTliQe19wk0u24WCSCl89g0K7VuTuZJyGvzCQ/iXFDaMQojCzxEOcdIIX2r4dUGmF30llA4nujth8SCzZD8IRB1lr1m43lks0OP7u2ZEB5vBI5TDgxHw73cJoIJFNhd4QyK2MdixrPSLPrRMedr/BpYBK8tSuBiiA2i4zigQ9Hkj9Ex2H/sUcMz2AAAIABJREFUvBIAyUpK7L8XbmLOsg1svLFD/FG9Ulk078JtcjSuVxVHM2o/6LeWfnNVjTZN/AdxDIG9urViGzhkRKrRqP1ApbIPfQCI6vhfAkAoMtO5z0QGnGhzhsRxabNKm2kSIgyP/IiBo+cKWiDd2zdFtw7N1IaSAYhW9+o8KAMQnS7S3kAGIAY6UO5uNA98LQBCEZCEhESlnXqKODTxq4YenX9SWzDTBYkpbFXzlOm4eFdfg5Aw8wst4EcP/hkUNaB0KDIqdi9ZvKAQYaH0J08PV7abJzaaY4/OLfD42SucvShNn8qdW70Ylfq2bl4X3ds3YxEVKdMXgHRs1Uh42auOI752ytv+bak6s8yZCzcwZ+kGYQdOW8G/rAOifqf4wuCsRkIo8vHRbzkS8zc22nf0awz8owIQurbOPw/C9pKHGLOUJqMi8Tux2bW6gjYVZo4fIAjCEQhxujAFlkFXkJBmplfBN3+Cmi4hOB+VQ/J89H0E/R4oTIQdfDGRRYccLzGlSCYTFg1ChePtc7xGM09S8uZ+K8RzauYRiHllXqpFdx4/e42h47nI8ODSsTgWaINe+d+iQTE7DDvrjCbVisOnTkv8PGkjiB2QN0rTjAwPw/6TV+HmbItd0zmAPWfPYxw/xxXOa0uJtbOzAem3EHkICbdSW0pd4gFIneoV8G/G7+XSWco032Kn8Yv3Ns3rCRscPAgS/659KwCyefshIe136pi+qF6pjM6vlyYAQh1fvw3C0PGLBEZGioI0qsel2PEmAxCdLtbaQAYghvlPrgEx0H9yd+N54GsBEHrhdWzVkKUL0UuLXmBenm6SIemsXA3/4/+1AE7Uxxi8CwpDUnIyqMDb091VAA9Ey0gvqKcvAoUXitKLxMMNpUsWRoF8uVC+VHF2fZqAh7gfX6ciBbL4dgR+Zi/ldhu1GdWdVKlQSqkJRW86953A/E6g6O/fZrN6E00mAxBpz4gXrLruAx0nyt2YGjOQ4lZSn+bftc2PCkD4VKPy55uz1CVe/0LVWTwAcbFIREcvLmqQrDBFVLIV/o3Nj4/xHJ0sPf/L5oxSqjWjc5jc34GqOzxYGyroruAsEpIkQEEm2kMoZBeH9l5vhAgIHabvWUG7GFTKEKFMSDPFyfBc+JTO1Q80cQ+Es0UK3ifYoZTjR/T3fpoxMHeCMY8rorBIKyQy2RI1Lzdlx/rmDcCQfE+EyyY2tZiKI1n09fa9APb5r4snshSt2UvWs917/04tmOAo1a4RA56FhQVSUrgoalHnRDgjFlc+usPdKh5rS17FxncFcTicI0ho6euFgWMnK7lZHJG2sbYSoqkE6ipXKKmUMkYpqM9fcps5/Xq0U0pb5QelqPXY6SvYbylFiccP/wXxCYlM3HXq/HUsosMjuN0bF8Lcwpx1tTA3h0XG3+IJUoSY9Jt4+6krJ7pK751ObRoJn9vaWCtdF/8PMS07kZKsnD9Wr/eTNgBCY6vW36lqg8gARPJ26P2hDED0dpV0QzkCYqAD5e5G88DXBCAkCvi1TazybgyAo2m+FEkJDvuAT5/ikcMjOzzcsivR3n7t6/yW44kByLc873/lXBQNsX38D6yCrqjVEVDEIylnFcQX7/jDRz3E/v4RAYg49Y1SpIiS9l6t/ZKPCQ9ASjlGYVu5zHREapyqAKanj8WeC89YX0r1oZQfsYnrLbrXL4wxWAGTVC5dSRPLFR3hi7r5CIcYKBDl7pkIT1yJ5oDNIO/HAuhY+boYiFlrVlFicMpENkwrJM0cXXO9RNubdZCQzi2623u9wi+5n0MBE0QnW+Bdoj1+C6uIF1HcDFWv6a+dR/DyzTu4u7pgz6EzrM3qMW0wcMFunV8zAmAbSl+Ck6sHopr8IQBoMQDhB+HZ/ujf+gjDik9OoCXwXQhLQS1dojDGDPVHlz4TdM5PHC0RN+YFXXUNQGCGSE1UbdnarYxFkGz+tKEoV1KzDpO4ry4AQm3PXb6FWYvXC93WLBiHQgW4onwZgOi6Y9qPywDEMP/JERAD/Sd3N54H/ksAxBgAx3ie/XFHlgGI/veGoiJ8WhaBj/9CtEPq6n5EAKIqBPkh2Qqtb9TF+WpH1S5BGwChxkn2uVHuEMdwR3VTK+eOURpDvHjuUcUZo6w26vUQqLJK8QCEdECIkYpEB2tcaoyoFGu08nyDWUXvCOPuCcmDg2F5sLFMBtVsxhESLyT63gvVjuhMC7O1AEYPl04VWrzmLxw7fYmNSsXcDxqcgc/xuuzfliZpsDRNw6e0THYnW9MUtPN6w4rXrUwzBQij6y9j9MDilFgecInTq6SILrQ5sWrFUnj9NlgAIGOH9mDMV7qsbfP66OuvXpMlZjzUNsbuTYvg6GCn1IRAEKXDkknRmGsbjwcgLZvUwcCe7TU23X3wtCDUSOmA65dNZkQlMgDRdcdlAGKYh3T0liMgRnWvPLgBHpABiAHO+492lQHIf/TGGTBtQwFIUnIKE5PTVbCr7xQ1abC8irfHkIeVccj3FBuKBAPJZj0rzWpApCIg/Dn97ndBcFS8GusTHRcDkN5FP2CYp7J2h6Z5qwKQrjmfs7QdO7NURn+75V0B7Ar2RniKDVwtEnCuGidcynH2Atc+umHq07I4Vplj0jsclgs7Q7zRPdcLrH5TDE8+OWt1We88T9B98BjJaJtYn2JVicuY+aIMwjK0Qzp7vcSuEG8kK8zgaJaEP8pcRC7rz7A2S4eFKZ9zlnlqHoSER0ShS7+J7ABFMCj9SsqOnrqEJWv/YodooU2ihlLGix4SQyAxBZIRm9X0BevY3+OH/YK6NSrq+9j8J9vJAMSw2yZHQAzznxwBMdB/cnfjecBQAELCfLQ4adm4lk46wy+5CnEKlhwB+RIPqveRAcjX8WNWR6EUksioaNbNO7cXenZtmaUhqAZhyry1TDthUK8OgiCbpkHmLv0D8YlcYbe5mSn7f2pa5s43388rhzv6SzCgEcvPlu2H8eDxc4FcgggfKpYtwXLuxXo7/FhUqDwjQ+BNPC9qmz2bM9zdXFCDxOYeLITdvcyUFXHbuzEuWPDSB1MK34P/3epYUeIaVrwurhWAUApXmfOcP8uVLob5U4YouUUMQFTrLbTdBFUAYmaSjt55nmJwRr3G3pDcmPi0AkuyogjDjZqHuOFEFdZvE+zQ9Jofeud9gncJ9lhYnCO7uB3tgm53a7G/f871HAO8uRoQSt3qeKs24tM5prtVvs9RdNguJmYoNk0K3dquZ3SB+/DP/VKtCdEDf2i1C3O238GJfzm1c77mRGq8Kzfvg5j62PwWjAPVVEiZVPoSFW73Gc4JpHZr3xTdJZijtF3Df+2YDEAMu2MyADHMfzIAMdB/cnfjecBQAGK8mXEjGxvgGHv+P+L4MgD59ncl8F0weg2bqXTiHRvmC5oG+sxIvOhbOH04UxPXZi26EC2zbl0nAhXEciQ2MQWr1DkoxWTOxIFCnjvfRt86gc5FPmOcxwlk4CK1U/weWAgr3xRHmsIUV6ofwoD7VbQCkHVvimDFG07YU4oWVnVeHlZ8/Ye0B2u6hGFakbtKRejU0tE8CUnp5ijpECUUzF+M8mC1GxYmafi36lFks0hWqyuhtKs9oXmxrtRllHPiCjuOhuXEqACOdreT1wtMKvxAmMztGBd0u8OBE7KNA8ohV73eSpP9IgCS/wH882QKF4oHfGhbEx2OcExj9WtVYtS7mkzMzjVpZG/UqlpOrSnV0VEEhOznjs3QtR1XcC9O5eIpyL9WZE3b9+F7HZMBiGGelwGIYf6TAYiB/pO7G88DPzoAMd6VG3dkEiskMbAf0WQA8u3vComu/bWTV9jmzj+4d0e0aJS5yJSa1eUb9xEeEYkHAS9w/jIVNHOWFQBCNKqVK5Rg/UhlW9VcXZzR7ic/4eMXr98xdiUyYpXq598GRQp5M52aMxeuY9+Rs+yY1OJRvNCnXXHf8j5IS09nApzvg8IE9Wjq3znnC0wslLno5idAUYVJT7l6DrJHtfeCrwEpYBuL1SW5HfpPqRYIT7LG7tC8OP0hJ/uMhDtJTFRV2VpfYMSfs3b2EKwueVUNgKj6TkkrXaHA6SrHkcOawE1m4fnIRxVZ6tjPuV+y6+iY8xXcLRPR414NYTgH82Ss8rkqaIXQgf2huTHhCecHF+t0rFwxD+7Zswl9Et7cgMue1kpTqn6J06HwcYiCt+0nHArLw0CTtWk6FhS/jlKO0Ur1H+LO/e5XwYUoT/YRKZcTy58mE4OLlk1qY2DPDmpNL1+/h6nzf2WfTxrZC7WqlhfajJ2xQmD3Gtq3M5o1yPSFpnMSi5W9rY2aaKvGSf4gB2QAYtiNkAGIYf6TAYiB/pO7G88DMgAxjm9Jk4QWl1UqKlPmGudsWRtVBiBZ85ehrUl4rWv/iYwmmQpzafeYFnC0QKf0FW2mSUcmKwCkdfN6GNO/I6tUjovPFLvUdN5BY+bh6ctAdnjTqumM8lVs2/edwPote9lHJF5JrEW8qap79+jUQqmvqm7CngqnmbCe2K5/dMWTT46IS7VklRT9vJ/C/04NnTogA70D0GzBack6FfG8KjmHo75riNrlhyXZYEtQASSlc8J0xe0/4vGnzAU/fUYMUpUzaHi3vs+PbBZJ+JBiw9pXcIrAypLX4GiWzHxNNLutb9ZFYbsYRsvLp23RQv96tJtwHupLaWE3o93Q3us1mnm8F+a29FVxrH9bhP2bAN+SWSOF1DeHa4vgcH2R0nUQwKEUro8plihkH4t7MdlZMfrdWgdYgf/YgvcFGmFxx6tRbuh5vzr7qHWzuujfo53Ox37qvLUggEzRsA3LpigxT1G64LT5v7LjBGIp2mdtnVkQr0z5a41p4/pqZKUi6t1Dx89j9YYdWLtogs7UQ50T/8YNZABimMNlAGKY/2QAYqD/5O7G84AMQIzn28lz16Cabxk0qlfVeCf5gpFlAMI5jYph09LSkC9vTuTK4YG7j57i6fM3oAiAW/ZsKFrQG9UqlZHUJMiK28U6AdPG9MPTF2+wbQ9XrLxhxVTkycntOkvZgWPnEBf3mR0KCY/E8TOX2d/GAiCf4xPQstsIdg5St6Z0JlWjOo+eQ6czdiNVbRtdAITGOn/lDmZm1InMLHILrXMoi4NK+UGXEGFZx0hsLnseoUNC1bqTDkjamyuouZ5So9Q1N8QdqOi9y+2aSEw3Z4DiZgwXBaAFNUU1uuV6zvQ62t2qBS+rBIGCt4PXK1azktGYAZB6VxoiNKMonPRLLlQ7ithUCzS42oCBK7HxdSljHldQ0gqhkp3hj32FCI9vuRKYPq4/A1mqLGLK1+GINjfrstQwYsQiAELW4251dM75En5umQAsXQG0v1UbARlga/v6eXDJxhX/a7OrN+5j8jyuDoTqboghysnBDunpCuw9/K/wjGtij9qy4zDTXeKNnjcqSM+TKwcLIL19F8qE/o6cuiQIyMoARNdd+f93XAYgBt5TmQXLQAfK3Y3mARmAGM21bOBFqzcjt5cHOrRqaNwTZWF0GYBwzuJz6GtWLQ8SqCQxN1Uj/QJi71Gl9cyCu7H01604cvIi2wneuXEB3geHod/I2WyIrBThivPujQVAHga8xPBJ3K66ttSYVev/wf6jnB7Htt/nwdWFW7DqA0DElKjEBjW2oHoalqp/eQBSzP4jNmXQ2qamm6D/w6q4H+vCmvcoHInOc3cIXUnPhZTQzWLfKamOaytCvxPjgtnPS7Hak+efHdkCXpdZmaZiafFrqOUaztivYlMsUPtKExAzVe8HXGoRD0CoFiQ40RZF7WPwJt4ehyNyszOI58RrhUzKADTxaWZMSZ0HCDww9NhUAWZxmdES8TyjUyzQ+FoDxGYAHUpj423ow0qomT0UbXJwUS5i5hoTwDFRDcwbgJZLuBQ7XUaRiaVrtwpF60L7jAJ8Am1MOHHSYOH5EI9JQHbH/pPYtI0DR/rYxpXTkctLOSKnT7/v2UaOgBjmfRmAGOY/OQJioP/k7sbzgAxAjOdbfuTft+xFeloa+kowDRn/7OpnkAEI5xOpIl5Ki7Kzt0XA09dCAXe1SqVBkYsvMfGCvGmDGhjWtzMbptfQGQh8H8IiCFt/nQNTU90L3S8FIHTeAT//hNT0dKSmmWoFU4+evGTED2SDenXET42la1R27T+FdZs50bvV88ehcEGu3kkfACJmQRrs/ZilWOkyTTogH5Is0f52HYF+dni/LmjiVx1O5ycpsWwlpJkJmht98wRgSP5M1XH+3MGJNuh+pyb+LneORSna5niDbcEF2GEuAkKBjcz7JOhq5HuEX+7VwNB8j5DdMplFUM5WPQpK6WpyvQHrR0XvZ6ocV7rMxDRTlL/wE/usf94A9M37FMMeVWL/Dkq0ZSrvJR0/wt48BXamqdj/sRgSE5PYcbrOXk/aCuPFpZjj1IccrIaDojgEbsQ2PP8jdMn5CrbmnJL45Cdlkd8uDv65nqPiheaITzeHGRSo6hKO1PyZ9UBS96VdCz+UKlFI8MtfOw7jr11HkZaextiHxT7y8nRF8wa1QM+gjY2V2nCUykfpfm/fhyA65hM3RoaZMK10BQrnz4t6NX3hV6uSpMCgrmfnex+XAYhhd0AGIIb5TwYgBvpP7m48D8gAxHi+FY+888ApvHrzXiuzzLeZCSADEM7TYgDSuW1jpjZta2PNjn2MjsXkOWuEWoj1y6eAirmzamKF5MUzRggLN/ECXvy5tvG/FIBIjVm3ekX81LQOihfOp3RYnIKljQnp9PnrmLecE/MTq0rrA0Aol3/fkX9Z31U+l1HHNUynW7UJET775IhWNzPrUFY1NUOdz7uUxlQCIHkDWBqV2Hgl9l0VzqD3vWo4W5VLkRPT8BIIaef1Gu1zBMLBIgW5rT9xi+0McNL6Zj3EpJrjdOXjnDJgFk08R21dqUajT/c2yLW+qCCSueRlcWx4x9WKaDKKwuyu8C/crThmtIUvfdDE/T0mPSmLZ5+165GIx1TV7qB6t3uPniMlRXN9EQHtNQvGq7G+Edgl0KvNqMaE9EiKFvLOokd/jOYyADHsPsgAxDD/yQDEQP/J3Y3nARmAGM+3qiOfPHsV/168iTmTBn27k0qcSQYgnFN4ACKmCBW7SxwN0NRG142kOqCrNx+wQt3t6+cLkQ6x4FvT+tUxrH8XXUOx4vWh4xdwi8evRMMrxcTlP2iqoPsxbmgPtvssNgIZxOhFxehkS2eNgk8xLlKgDYDEfY7H9j3HhX55HFKwr+wRjaxM4nPqUkI/raiNIecyC8b3VTyFQnZxwhBqAMQ7QAkkVL/UBDOL3May1yWwv+JpoZ8mJXShAUs3UmB/aF4cCMvD0qm653qOmtkpHStrJp6jj30UamYPYwXlB8PzoJQ7cDOUK44no8jUNJftsAziGMF4AEIgg1KrCtrGwckiGVc+uuFYRE4hQlTROUJIYaN+VOx/Ocod4clcIX26dXYkFFVXIScWs1PnrrE2YgAyafZqBtKJVIGM2LBIadzTIzuePHsDAuA82JTSZ+EBCH0/KLWMjFKzomM/4erN+8K4lL64bM4oVoj/XzMZgBh2x2QAYpj/ZABioP/k7sbzgAxAjOdbqZGv336ITf8cZLuB38tkAMJ5Xh+RS17JuWHdqhg1sFuWbhnVlXToxbFcUYRFlRFq1JSluPfomSRLkNSJsgpAqEbDzTUb3Fyc4ObigOSUFLwNjgRFL/jFJJ1nwfRhKOuTuXtOtTDDJy0WpkDUtj5FC7Dd/qDQCNy4/UhJX2TTqhnImYMr1hYDEFpUkuBiSmoqAzT8IpUfePWk7qh1/WdhF1+bc3UBkA+td2P75RCs+YcDD5TytL3cWbhZcSlL4sV93ezBaOX5JuN0Jpj7ohQqO4fjbqwLhud/zD53tUxEKadoNRpe9foRBda/LYyXnx0xt9gt1pdYrtp5vUE9CaYtbdeoKUpDbFp+N5pj5YIJGDp+keD7rZ2dUCZ4ExuSKHvTFCZo6vFeDdBRdIf89/ATVyvzb5WjQhREdT7xRdsj2m+F2jSpWJyKxsl4gcIJs1ahRNECQg0HfT/oe6JqC1dtFupE9mxeDAc7W6EJD0CKFs6HlXM5zRDeqMbk1407hVojilD26tZKmwt/yGMyADHstsgAxDD/yQDEQP/J3Y3nARmAGM+3mkZ+9iIQ0xf+hq3ruELkb20yAOE8rg8A4Slpy/gUwcLpw7J0qwgAULE22cxx/ZHfO5dS/2NnLguLOk1ibuIOWQUg4r4ONuZKNLziFCopHYezF29iydqtegkZHt62ApaWnGq3PnobVFMzsGdHuGV3BjFUOZ/S7VdtACSmxgx8LtMHzieHYOGBp9gRnJ/NhQrWN5e5wOoe9E1v4n1GOiBUHE+F3GJr4PYOjdyCGcWtW6WOGH0iBQlhr5DdIhGeVgko6fAR9d2CMeKRL5p5vEND92C9nxlNACTNIRfC/Dn1dDGD2Lh2pdEtYppe4+8I9sb0Z2VZ299KXUI1F+kITVTTjUjM31hpTNLf6NSbA9IUDaOo2Jhpy9G2RT2EhEUKz7imVEJiclv5O/c9WLtwPArmzyOMrw2AUCMCIY3bcxFjKZCi18V/50YyADHsBsgAxDD/yQDEQP/J3Y3nARmAGM+32kYOi4hEzyHTsWvTIjXRNGPPSAYgnIf1ASCDxy/Ak2ev8SUApP/ouQJ9qK57WrlCSZbnrs2+JgCh8/DRHU16JJQyRUDk7ftQREXHwsXZgWmCVK1YGhv+2oczF2+w1LKdf3BpYWRiAEKRkyoVS7LPnZ0c4eXhBk8PVzVGJH1ACA9AiG73r3LnhfPF+Y5CXKVR7N9eKz1Bu/1971fFtWiOKamp+1ssKH4L4oJvXfeCjvs6RSAk2QauFkkMvAR8clIqrq7r44Ybb+IR94mjSBYbpTktL3EN056WQT23ECVdD23n1gRAxKBAzCBG+i5TrNbC4sMjnZe0NyQPJj3lhAD/KH1BUgtEDHTEAy5b9zcOn7jAPqJo1+LVm9G1fROUK1WMMcfx0bJmDWtiqARt8+oN25l4JaVR7dm8SEmnRRcAoXPyWjiqlM86L/oHaSADEMNuhAxADPOfDEAM9J/c3XgekAGI8Xyra+TEpGS09R/FtCA83LLrav7VjmcVgJjFvoXFh8ewiHgozCHNMTeSclZBmmPmbuZXm+A3GkgfANKpz3gmIEjMSsQ8pK8FvgtGr2Ez9W3O2pFYWzZnR419vjYA4Rd/WV3Y0a50p97jWUpVjcplGU0xb/oUoUtdIFHmZjs5VK90LOqvsHRETM0ZiC/WkQ1H/V0O99DL36tfF8WawGIoYBuDai4RTJyPKR5mFI7fjnHBrOelsacCVygvNtWaEDpG9RpUlH4vxgXx6VwkqJ5rEFb4XMOYxxUZs1RLT91aJ1IA5HPpXoipOUuYgphBzL9jc/So6gTXPeo1G6rzJtarPaFcEfeJyseRk6m1K5tU9EP8HFOk7MnzQPTu3gqlinMsWPQstO4+SoiUEXEBiRjytNVh4ZEYNG4+e1Ya1auGkQO6Kp2UfwaLFPTGqvlj1eaUkpqGJh24CIhUDYlqB2pvYZ5ZK6PXA2HkRjIAMczBMgAxzH8yADHQf3J343lABiDG862+I3fpOxFTR/cRqEz17fel7fQFIJZBl+B4bTEsgzjxOylLcfPB59K9EV+sw5dO57v10wVAQsMj0a3/JDa/vj+3QdsW9fWeK9X5bN15hLVfOX8csjkq06LyA919+BSLVm9h/5QqCBef8GsCEBKLI8HBhMREVjQ8d/Jgva9tz6EzWLtxJ3dt88YqsRN9KQChsUySomF/dz1Ly9Kkb0G79AQ6PpXpBYVVJnOTlCq4pgsaeL8yzkblgJVpGqPcJU0OzhS4EOmBdYFF8FdZLsqS4lYCCisnJORvjOScVVC73xph2G4NfTD481zYmaeyz6hWY8D9KkKtxYGKJ1HALg4TnpQHRW7aeXG6G5pMDEBquQRjUfu8SvUYVJzdY/A0UPSUrHDBPHBxdoJd3Gs4xjxEDqsEFLSLRXWXcGFO1O59gi0aXuN0iEo5RmFbOU6/RWw80CFAsW7TboSGf2BRq9jYOKZmTpYvjxeG9++Cs5duITg0QuhOUaCnLwKRmppJoevu5gInR3sQgElOToVv+ZIYN9RfqP+g52Tn/hPYsf8UEpOSGP7z8nBlNSVd2zUVaoq27joq1JiQKCZfqM7ulkLBUtICnr1C4LsQJiBKQIciLTm93EEUwBStq1KxlMBup/UGGOmgDEAMc6wMQAzzH0KiEgwcwfjdiYbe1cka4dEcRZ9s/xsecHe2xoeYRJAarmzfzwMDRs+Ff6fm8C3nY/RJZLO3REJSGhJTMhcM/8feVcdHkTTRF/cACQmEJEhwd9fD3R0OOfxwd3d3d5fDDnd3OByCe4AQg7jn+1VPZjK72d1sMlnI3df1z21m2qZ6l+s3VfWefFI6CDoc/QPmn7QDD/VFRjsVgn+DDf+qiEjN5n3YY3Tr0ATtWtRN5Pe5y7ZKyuNrFo5TYeAhml6xKNfezpbtnWgxMbGs+JwOQ5SGtGj6UK17SlGwVl1HMiBAqVDL5wq59pqMAMiAUUK60zxiwSqcR2vbR56vkdXVGens7VgbW0uhBiQ4TKBKJWFEqvEg69iqvsr6dX0B5WsgVW51Rjc6WDZsP5AN0YEK79s3TtH3maJuJCBo8UlgeYpwK49YC3tEOwkpXepmd2MebG8J+iW67JJfJhbd8Aq3Yc3sTCMZKxSBkJPfsmD/12xYXUSYkyywilBfIpr4nencriG6lU8PR7Xow70fGdDxXjXWvF/2p+jDNE7iMOxJaabn0cBZEA60M4uWAR9hdDkAaVfJDd2GjJXmpXS47XuOYe9hocie6ZIYkVKGZiNl9h5ZXyCDWQQ63qsiiRiuKnwNlR0F2mNSWSeLLNAa32sLhecrN+7Fvvg5CED8CAxm19MZ/2fGAAAgAElEQVTb22HG+L7IkzMbeg6ehjfvvVQmpvXI9T/kN10yZcTGZZOk1CsCCpNmrcIXAlJxRkLgKY4UPxKsZaOa+Orji6s3BXV5+h3NmtCfpasGh4Tim28AiM75weMXWjygerlmtbIY3rcTTEyM9Wqfmo1cHASGMW4p8wAHICnzm9QrniZc4SiG7y6jNDf8ZHyGNOEBvudpYhvYInqPnI/6Ncqhce2KBl0U23OaQRPo/P4eRrs7AF+TVqdOtEjLdIjrfATIXMSg60+twQv91oUNlT93NnRtUx+liuaFjbUlPnh5Y9Pu4zh69ga736ZJdYwf1Ell2rcfvqBRZ4HJjGojLh1YKt2/ff8Zug6exf6eNLQrWjbULOYndpg0fyP2HhHeSv+9aQZyZssijRUWHonYWOGk+PDpa/QYPpd9XjZ9EEoXy8c+Gxsbw8rSXGV9Qycvx8kLt/F7y9qoVqE4PLK6wDGDPbx9v2PPoXNYu/0Ia0+6J8e2zU5Ul3Hv8Ut2j/pFREbh7cevOH3xNjbsEqI6dG/z4tHMd3Kj9ZauJxzYe/3eCP3/SDo9KDX20+jCTOCi4HNtRgJ9Y56VYNENaxNV8E01Epf8M2NhwVsq3eOarACKJaTeid+Zgd1bokfFdDDa3DDRdJWv1oN/lCVj2pqW7x67fysgI7o+EFTRbU0isbm4AHrk9vdnN4x5ISiS03cqdw43REZF4z2rwUmgFKb7tC91fysDSjn6ERQCv4AfeP7kCT75C6xfZFksgpDHNhgX/AT9mt9dX6J8Bh/s/ZIdr0Pt8D5MAKc0Vza3zGysx89ei7ImMDM1ZSlWBCy2LBmD4oWEtCv6DhAAuHDtPvtu5PZww92HzxEeEYVsbpnY9+zFm08IDkl48VqnWmn07NgIu/4+hwPHryA6WogamZmZsggJmSbpFIIlDWpUgLWVBZ48f4v3n7wRGia8JBXFIQvm9cBvFYsxf6WzswEVzdP39eiZa3j38SsYSoszwv0z639JelYKJGG0f4n/D+9wAKJw0z/7/TsiIE7pLeEdwCMgCrf7X9Wdp2Clre0icbdcOdyTle6T3CfQloJFkY+MB1qq1Hokd2xKV/nW9vS/IhKiSQld/XlJd2D2xIGs2FpuH72+4o8Bk9kl9ULshau2swgD2d6Nc6QohDZf3n30DCMnLWa3Kf2ENEdEEwtwde0DrXH1AiFVTLSp89fi0rW7SW7f+GE9UaW8wI4kN2LvIhYvTUbzTRjeS0qTkbdRkoKV5GJ1NEgqBcs/0hyNb9fElYoCgJLb9k8eeBqcHtPzJfYX0ftGuia8EJCn7bVvUA4uawQQKLc2d6qyNKyy6b9hQ7Gr7FYCAIlDN/cXGJLzCTsUMzOKw7K3+UGCimf9dOtcsDhBHNChVf1EtM50IH9x/wb2bt+K828E0CpGJao6eME30gpP4ql4k/J1bFwcjONPzm2a1UH3jk1Vuly/8xATZq5k1+KDMSA9m/4927Eow8Ydh7B97zH2joPGoXWz/2qJtAvXE0dRsrplZiQISdnsCQNYjYi69Rg8Fe8+fGbrOL572S8BIDwFK6nd032fAxBl/uM1IAr9x7sbzgMcgBjOtykdedWmvTA1NUH3jobhvNcGQByOdIbl25MpXbbUj+pCfNqeUTyOoQcQD5NECxoWFi6J79G8lEdev1ZFdG3fBBbxFLPy9Xz6/A1d+09kl+RF3PKiXH2Yrai/vKib0lW2rEgoXk8pACG9mcMnLzERRE1GrF4DeraFu2tmjfc1ARBKEaNDXvsW9WCpFnERB6GUskbxKVjqYMqQ+0n1SrqKsYtcbIK7VQ7B1Ej1BLz2fR58i7TE2NxCnYO6eXe+pQKm1euGMm0qpVKvcsbHBfPfFMSHMDuUTfcNPbM/x6NAB1zxd8aDQAf0y+6JxW8LsLWYGQtrGfykDHLbBKKr+0uUuiykrNnaWMMhg0BIQJGIdOnskMnREcfOXmGRAk26MpSadPXmfRA4uHH7IWLi82oJTFiZGiEiPuiT3t4GZUoWQfHCeWFhYY77j57j7+MXCK5IUQiKulFkjcAEsV3Rf/0DAhEZFYnAoFCQxo1oIgBZu2gCLly9g9Pnb+Cbrz+7TfesrS0QGhqekKIVF4eMjhkwsFd7zF+xVdKHyZ/HA/T9p+9sSFhYfHoZYwdgYxXMnwvBQcEsqkE+IOIOsR6Gfq+bl0/WqrSeydkRW5ZPlYRADfldVB+bAxBl3uYARJn/OABR6D/e3XAe4ADEcL5VMvKu/SfZgXhoMsXv9JlTEwBJ6gCnz7jyNt9rLpIYipLb92e1lx8m2zarA6rrIMYrGxsrZMnspDWn/WetLzXmoYiEj58/wkNDEBwaBjs7e7i6ZIKVlYXO4Un5+u17LwbMnJwckNU1M0uXScvmsjqPRhatmtfrYEvxS8hiqZqJsOhNAfZmXBQfVH+2qIwF4dMuQRWd7qsDkHSXxsHmwTqpK7Fk0dt+qs5wNAuDX1Ti/H+i6b393QlPqh1Ak9s18Gc2T6YXIq8B0VVTQZMRa9TiGcNVahrWbT2A3QdPxkckhEM7jSNGIehv0mvZuHQKnDMKBfyUrtRn2HT23RcP+kJabuJohLY1Echwd3WGn3+gRt2YuNg4GFGRKSmtx0dWqlUqhbGDu2HgmLl48uy19FsTwYxQ4yKkTlH8hLCUcfwYdI9qQhZOG4rVm/dJNSvjhnZH1QoC1bBo+tD8Gvo7zQGIMg9zAKLMfxyAKPQf7244D3AAYjjfKh35+NmruHbrIaaOFoqlU8s0AZDUin6IaySaXu/Ot1NryQYZJykWLINM+osGVRci/EXLSNG0VNRPllQBMQkRWj/bozJHh7tVMCznYxRPJ7yRF23GyyJM7bxnNu1FzKLAobyf+neGiuUzbS4jNSlwoZlUGC4e2DXR9JZL740b351xsPRZ5LYJAkVOCICMfFaapVjFIRbOjg4MDFtZWsDKyhJRkVGsRkO0JvWqol93gYaYbMmaXTh04oJ0mC+QOweevKD2FNkQAQlQrFAezJsymIHLIRMWMq0aos2tXL4EWztFzsioD1HvUiSEjIDCs5eCgnzRgnnw8OlL9pmwAlHfUiRP+FuIWjSpVwW/VSqDGQs3MBBMl2PjYmFibCzRNxNAeOxJACTBy0UK5MKDJ6/igVPCddGfNM6McX0ZYYecJlhTVIgDkBT95NJUJw5AFG4HrwFR6EDe3WAe4ADEYK5NlYGv336AnftPYsnMEakyHg2iCYCQiFtqm3r6SmqPr3Q8DkCUetAw/emgefOfxzh+5gorwKZIIBnV2pAQIqXpUHpbyaL5WaqSaFTDlHlTGSkKMuhJGdRz+ohD3tnwI8oc43LfRz67QIx7VpwVgHd0e6P1AeSifHJqWjGl7Q9iTmsuMKfJoyAFzjeVDvstXd5gRM4nOPrNDRf9MiM42gQvg+3xPdqCvd3fU/wcOj+oihsVD6PYZSHdUqyR0FS4bGdrg8AggZVKBBSr5o9FzuxueP3uE4aMmy8VaBMwqVOtFBp1HCZFZMTxaextq6YzBXN6HkpfWr1gLPNr3db9EBMPJOzsrLF/03zJR1t2H5GY30YM6II5SzZJaxajJlS/Rmsh27V2Jhwd0ktRI4JVAjYxgpNjemxdOQ0DR8/Fi9fvpYhLscJ5GVtVh94CA1j1SqWZ4KVosYiFUZwxtqyYwqKU8lTITm0a4vfWDVT2lAMQw/xGf+aoHIAo9DYHIAodyLsbzAMcgBjMtak28NPnbzB7ySZsXj5F45iUPmXtuQcWXtcYdancwj3qItyjHsI86ki6CeoAJLXTr8T5Nb1BTjWnpMJAHICkghNTeQiqLaDi5uevdWtmiNOuWzwB2dwElicyUVV95svCcLMKhZtlMPo9rsDubSx6GTs/e6Cygzeau+ge36ftaUTFU/6u3LQX++OpacV5Mjs7onG9aqhaoRRevfsAzz0zcPGjOb5ECvS+BWz98Vepizjr44IBT8qxa/IUJvrcP4cnOri9Qc3rtRESo8piJrQXCtQlqlpWph2HuFgjxAclmLAfCfzJCRWa1v8NFcoWhbmJMSbOXcPSq4yNhCgGpUBRaKFi2SK4fluoD1o8cwQK5MmBuq37Qow2UbOMjumwa63ALEapWu16CBTRNaqUYc8+cLRACa2SNhUPjsT6nwdPXmDYhIXS84v+IwDVoHZlRqP76bO3BJJIa6RDqwaYNn8ta0pAz9zMDFQXx+Zizx+Hk3tXMFrfMxdvsn8byUb274yKZYtJ3wV2bcoSeL54izy5smO5BqFDlcYG+oOnYClzLAcgyvzHU7AU+o93N5wHOAAxnG9Tc2R6C/zniJnYt3EeK1AnM/N5hHSXJ+oUChTXQOxUgWWGIaRYj0QREEMBkKAywxBUdlhquiFVx6K3o0Qj2rReVdSpLhxS/6v2b0jBonqVYRMWSOCDIh6UZiMWSxMb0ruPn3HzzmOp1mDlvDGMNU5ui8YNgu+Xj0hvFomTPm7SLUqF+iPrS1Zvocvk9UuHTlzE0rW7kvW1IHBB9SZT895F94eVpL7qAIRAENH0eoVZofbNBB0aVrcRF8cEHvPnyQEq7Kdn/h4oUPHGxAppTGTNG9VAny4tVQBIosWKCCH+hoBBqMYD6Nq+KRrXqYLmnYeiXvUKOH5O0P6h+Z0yZsDONTPZ34tW78DRU5fZ503LpuBHULAEQArnz41HnkI6FhmBi/Kli8LPLwDPXr2TojUEfqwtLWFjYwk/f6GI3cTEhEVcEnzDCj+ksYb374za1cqpzE9tD25ZCAsLM/QZNgPvP31h7UkbZ9hEAeyIJoEj0r7ZuYTVwPxs4wBEmcc5AFHmPw5AFPqPdzecBzgAMZxvU3vkkJAwtOg6DNtXTYe79wmkPzMo2VOQYrlxvZkINbJDeKSQs62LwtQr3Bqfw6xY0SxZTpvAJA9w4qLSOgBJtvP+xR3+DQDk/OU7mLFoPfNy/VqVMLBne42sRaQDceDoeaaQrQ5Azl66xQ6r4eEJehjitg3O8RjdsyUclNW3M87cHgG1FrOIIRkxiY2dvlzjrosaFHLxPYoysPQlKpaOi4O5cSyi4oSXBWRyHEAAoFx6H0bTq1J8Hl/ATm07NvsNXX5vzfpS/QUBZrJYorSNrzTp0rYROjaugJrtBU0abZZUUTultlE6k5FRHBwzpGe6IgQANy2bjI+fvqJ/fLSDakL6dmsDEqQUIyCzJgzAuOnLEEWaIerSiLKHpjXUq1kJLRpWx+hpSxnhg3hbTvtraWHB1NHJ5pLgZqE8TKOk85/j4eMXwK6TwrmzUwYcPEbsXWDpcEQn3We4AJjUfU6Q5ujOJRoZ7XQ6LhVucgCizIkcgCjzHwcgCv3HuxvOAxyAGM63hhq5XZeBWJ33OPLZEnNN8i02W0V8b3lQAiC6IiCB0Wbo/6gs7vwQAEj59N5YV0w/hXQOQJK/N4bqIQKQG3efMV0StyzOKZrqys377JCdI5sr3Fwy4f6T53j+8h1I3drJMQPy5crO0mBSwpglp/7duXZWInFE9QUHBofA0txceqtN6Txb9hxhTEhBQSGgAnHfZ9fx9xuhToRSsMpk8NX43KH5WrNoXYx9VnafnoferpNRipOJkRGOnhG0XRIOt3EsAlGpTDE4OqRjNQmUCkWHakbYZGTEUsC+hFshBgIQOVn2JIZ7lsbDwASdEDkAMTOKRlScwDbWrWgk2k4QABlZy67DmTJ5bGycBMym/1kPTV4Ph1GEqqghtfcMSoeW/1RnfSs7fMElv/g6LyNWlq51/0UWL+k5ZSxWYl2HCgAZPwBzl22Gj38AG1fgraI0r6LI6uaCXftPsKE8srux4nc7G2tWsE6Uwc9evAORbRCFsAjmcmR3xZt3Xiz7bMmskcifJzvrf/zMVSxYuY19blyvKg7F69QQPfSCaUOZX2jf5DZpzmr8CAxi6+EAROuWp+kbHIAo3B5eA6LQgby7wTzAAYjBXGuQgUWw0Px2dQzP9YgpG6fEIgq2hV/1RayrPilY1a7VhU+kFUql82EqzvrYv4GKV5/n+C+0kUdARkxajLbNazN9h+SaWGtQpUJJpgXx2PNVoiGIIWnC8J6MWSk5Rrn8lNNPtnT2KOTLpaqyrmssqiMYN2MFe2MvN/lBeU0NfwZAzHyfINK1PGsW5lGPfRbrPeiaSE37/UcQSC9lxrh+WL5hj5SCJI5PgpFU6yC3ms16C/Sx8ZlEJsZxyGMTCFJhJ2DSye01K0onpfSWLm8xOe99lQhIQVt/SSywVzZPdG3TUEpj7D1kOl6//ySwTBkBWTJY40jJ47CMFqICZKTofsY3C/v8LtQWb8NsWUaTlUkMwmLjozFCGQgzOuQL5SGqgCQ2lq4LDQkYCFS7mdCqcU0WnZL7tVmD6jhw9Fwi6l6W6kW0u/H0uRnS2yO3hzvy5c6B8qUKI2cOdzY2CQVShEVT1IpWRjo7ZYsXgkcOdyxdu1MlkkSRmwVTh8AhQzqNX4/2PcewqAkHIMn5JaatthyAKNwPDkAUOpB3N5gHOAAxmGsNMrBc+Kzbg4ponvk9GmQSWGeSa3KVZ20aCjTmpOfFUMDuO3wiLHHre0a9AUhaZ8FKrr/+ze3VU7C69JuAaWP6JTsSokk9nt5A29haw/P5W6k2g95+TxrRO1ku27n/BDZs/5v1IYAzvF8nWFokLs5WHzQsPAKt/xiBwzsENXm5yQ/KYjqPrkXJqWnpcLtk1gjY29qo1CCI/eVMWOK1Gs166yV2R4fzvtmfoG+OFyoAhAC+GG10MAtHbrtQBGcsDi9vfwR8D2J1G2I61aKiD1ArgyqT19xXhbDpU269/V67bF707N2NiQHuPnAKp+MBoHoURD4gpWFVrVgKg+NTwszNzBERGQkTYyNGs6sCZhJ0BBkIkMOcWtXKYUT/zmzo8TNXMEYuKiwX6Xw1PQSluQlzgIGOpbNHwtkxg9bn5QBE769Cmm3IAYjCreEARKEDeXeDeYADEIO5NtUHFhl+5AMPf1oKRe39VShFKW3K3jQqyfkjXSvAt/l+4X/mWlTQ133IjcAocwzJ+YS1oze5RGGalGkScUuqD79vOA9oqgEh1qMjO5ZIpAb6zC4HIKS70KZpbVhbWbKuxLY0fsYKqYhcnaEqqfG/+fhL9KvUluhhf6tcCnlyZkWObG7I4Z5Fo4Bik98HY8eqGUwzQ92SA0Do4Dt5zup4aloLbBhRD1kc7diQ8w4+xuHz91SGVwcgf/19Gqs375c0LQhkUApWAbsfOO3jKhzAZSfwbFZB2FvqPDuUiwropkYxiI4TCszlFi+tEZ/eJAgdUjoXMX3J7ZS3C/76mh1X/TMlGiObdTCK2fvhtI8bwmKFNK+WWX3Qa6HAMEUpbS06C6QRcs0NCoPUqFoWtD9ixIuYtg4eO8/aUrSEauLV60xILNDNxQknzl2XxjQ1NZVofuUgtfvAKayYnAAnAUoLc3N069AEbz9+wY07D+OFEhPoh2mudPa2GD+sJ6sR0WYcgCT1q0v79zkAUbhHHIAodCDvbjAPcABiMNem+sBOO2uw9BF1I1E1W9MoDMjhiQNf3PE53AZ9czzTa34xSqEuqEadT37LghM+blhY8JZeY8kb+TfYKBXzJrsz75DqHtAEQKjQ+M9hM7B7/Wy95xMBiKb0IxpEXiytrY2uyS7fuIcpc9dobeKRzRUVyhRDzaplmCbIHwMmY9KInqzWQJMlB4CsXLcN+49fZcPsKHEBRe0TUpsmvyiKPZ89YGYMRMYICUzdOjaVtEDEdasUmsfFoUQ6P7TI/B7jXggK3VRszaok4oHIqsLXUCq9rwRAND2Dupq5eNCfm/8mHM0jmcZJQJQ53ofZ4q/P2REaa8Yq3gXAI6RP1XTyQv/snuj3uDwm57mHbvHsXONz30fdzgMRml8QNGzYbgBjhhP7UmcqRifNjujoaPQbOZvpslDNi8hkJamWC5KHDDgunD6U6ZOQ9Rw8FW8/fJYASo2qZfD6rRcyOztg/NAejClr6PgFrK34bMP6/i4x0wWHhGHMtKWMTldsw+ps4lO7SJCxYplicHdzgWMGe6aF4uMbgAdPXmLtlv0sOsNTsPT+iae5hhyAKNwSDkAUOpB3N5gHOAAxmGtTdWASWXNZk0/rmKve58WzoHS48d0Jv7u+1huAyOs05IJqj4PSY8qLYthTUmCZSY5RTr1v8wPJ6cLbGtgD2liwPF+8wYoNe7F0ln5Cl/pop7T6YwRL6SFqYzpIJte+ePvi2OkrOHPpJmNK0mZU9D2wVzudtSyaAIimQvrD29biyquQ+DqNVxia8wnMqAYi3kQAkskiFN4RQlG7GAEhdfD+owQQp061K9SCJAAOup/VKggfw+1Z+97ZPNE960sJgAiHeQGdFLHzh5N5GC76uSCajZGgZi5PxRIZuVgnVmAOWBjFIJxqPoyMkN40Am6WofCPNMep8qfQ+p9q8AzOAHqWE2VOwShzQfi0PQPyC0WAyOSsVHIwcPLcNcxbvlUlnUodVDWqUwUDeraTfEeRkuXrBYV6WmvRQnnw8MnLhCiLLDXL3Nwck0f2QqliBVh7ispMnbsW9x8/F/oLMvFo2bgm9qlps2j6nojuNDE1weFti1JEjpDc7696e86CpcyDHIAo8x9nwVLoP97dcB7gAMRwvk3NkZMqFKfUqA53q7JDx5/ZPPUGIOpMVRRlCf3yAnVv1sb1SkeT/QikIP2t3RlJ9DDZA/AOBvGALhre81fu4Nqt+xg7pHuSc+sDQPqNmMXSsKiAe+7k5FNF0yKoFiM6NhakDfL+42e8fe/F3pRfunZXZY1TR//JVNHlRn3oFE7pPJoAiEohvZ8fHj9/l+i5S6Tzxax8d5DOTEhlpCjj397ZQHUZfpEW7FrfQn5oPnYt+k1cDQIh+XPnwNPnb+OLuumwHRtfMJ0AHho6v8cf7i/R/J+abAwqRB+V65EqAImvlOjs/hwPfjjifqCjTEsjnmFLJU2KND0EcGJuFA0XizC8D7djURAH8wjYmkThA/0NoLidL+4GOjL/TMl9F/UzeeFLhBV2OU/AjqNiqpTAGWxkbMSiTJRKR7UZ5NdPX7zReyixgyXMSZokUdGxUlSnbKnCmD7mT8mnBEYJlJIRAMmbOxuev/wg6H2wInhh7TbWVlizYBycnRykvpPmrMLVmw/Y3/Z2tozRitY+bUxfEC35yfPXmZI6sWgJEySkaVHkhiIyAT8C4eKcEb27ttT5/aYie3fXeKawJH8J+jfgAER/X2lqyQGIMv9xAKLQf7y74TzAAYjhfJuaI+sCIAQ+utyvhKBooWC3kJ0/dpe8qNf0RD/6vdYSqS1FWpp0GoFz5Y7B1jRarzHERlT3QREVOaNQsgbgjQ3mgaR0QHYfOInA4FD0+L2ZzjWoA5CoqGhMmaeaMvXI8zVCQkKR0SE9Y0wqXbwgE9XTZnQopdQtOmg+fv4a7z98kYrZqU+ZEgXRqE5VJkpIB9DRU5fi9TuBeIHAB0Uirty4j38eeLL0IDrwktEB1D1LJjyKZ+oSi9A1FdJrUx3XRVeb1+Y7Ykys8DbEWqprUE/BkuuEiM8vL8YelOMxq98Sa0B0OZ+ojT2cbfHcS4gKiTUgb0Ns0PB2bXbNKF4lhD5XzvAVlwMyw4jSseTFJ0l8y4QojjDaxBG9UCleXbzTn+NB0SldJhXITx+GgvlySk2Xrtsl0eZq6k9q56QnUrhALpXbtNd37j/V+3fRvkU9VC5XDC6ZnRigIe0U+m7pY6Sp0qFVfX2aJqsNByDJcleixhyAKPMfByAK/ce7G84DHIAYzrepOXJSERD5XCHRprDREzyEFO2OH1WmSd17DZmGEQO6oMSHVbB5sE7vR6C0K78GG3nkQ2+P/dyGSQEQWs3i1TuYVgOl0GgzdQBCb8UbdRio0lxkUJIzKRFT1rSxfRkoULcPXl/RbYAqfa6m+Vs0qgGXTBmZ1sOJs4IWTZbMGfH5q/ZDsfywrwmAENVtNstgjHlemo0npDOppkxpAhHU1gSxUsG4eL5PCoCIQEcENvtLnUVWqxAJgDiahcM30kKKaNA8lE5VunBOeAfHwij4K158i2RrFQHI+1Ab1L8lABDR5x1cXyMwyhSHv2VjURgnizCYGcXgS4Stxq21NjNGwUL5cevuE4lpizRdiGVKfP7kABCahABIqWL5YW5hgV37TuBHUIigj6JmYgpZ/rweqFmlLLK7uyB7VhdWS0K1H1TLoa9NHdUH5UoXkZonB4B0bd8YBGBS2zgAUeZRDkCU+Y8DEIX+490N5wEOQAzn29QcOTkAJDnzylOwJs1ehZrVyklvPM18HjGVdAuv6zCK1Cx6GJ6jDoKL90Ska8XkTMvb/mQP6ANAaEljpi0D0axStEGT6QIgBDIK5PNgKuVkHjnc8OZtAkU0FZAvmj4MVvGsWeL4IgChg3KNyqVRIF9OuGdxZgdfYl3asichFZAiKUtnjWQpPRTpyJMrG168es+GqlC6CIoXyccEAb/5BuD67Qe4SQfq+Il6dWmFlo2qM7FAsn7Zn6J6xi9ofqcGAx4ZzCLQ1f0VTI2iMfdNMdaGVMepfwHbAHgGOwgpWFFCClaCnJ9Q8E0miBAKM1JtBEECc+M4RMUas9ShjKy/wNZlimg8qHY4EQ3vrQBHGBsbs7H6ZPdE6XQ+WBLTnYn4rZ8xCrv+EVjo1FmwCl5QjV6xNcUvTKwtYSUp8WUmiwtcR2arCLhahsCs0gBs/FoMq7fsE58EsycNQonCievORCBCei+0JjKq8SAtEDFyoum7Q/MWyp8TtX8rD4f09ixCQWKE+46c1fhd03Sx++/NGPPav8k4AFG2WxyAKPMfByAK/ce7G84DHIAYzrepPXKWpamfnywWoa/atJelzFBxpyYjAGQcEQgznyeIsXdDtL07KOUqziJ9aj8mH88AHtAXgNDU3QZOxoRhPRRhISIAACAASURBVJHNPTGzlC4AQrS8xIxEhcpkqxeMg1PGDBg9eQmrCaFDcLmShVmOv729LSjlhczH7zv2Hj6Df+57Ir29LXp2as6AhWiUgkOpOGQtG9VE8SJ5MXb6cunvsIgINKtfDdncBQE+ue0+eBrrtgpU0xXKFMXkkb0lADIoxxP4R1lgyych7WdXiQsoHM98VflqPSYWaGMcieBYc9iYRCE0xowVhZMgJ1lGM/psqRKtEClpdW0hwyWIY8x1tyofVQEg+W0C4Bki6FoUtvPH2NwPMPVteSxat5Fdu7ZtNiYeEGpWkgIg8jWoUuQKUZ5rFY9INS7078Ci2+YgKmEyotBdOG2oxsfQBEBo/EvX72Llxr0ScYCurC8CoxOG90ImZwc8fPqS1facu3RbJfVOPjlR9nZp2xjZsybeY12+Tgv3OABRtgscgCjzHwcgCv3HuxvOAxyAGM63qT2yNq0OJfN86fkMB8/dx4dPX9G/h0DFye2/5QFiNzI3iWPK0xkyOOL+k+d4/vIdS2VycswASrWpWLaYCkNQo/YDsWfDHFhZim/7BZ+IACSXR1a0blwLeXJmQ+d+E9iBmg6H7z9+Ye0a1qmCgfFMSAQ+qDBdfgimVKy/NsxRcXSf4TPx6g0VJwPVK5VGiaL5kD2bK1wyOaF5pyEsEuHm5oJPXl9ZG0rRWbtwHDI5O6qMQ2lholjdsxdvMXKKUOPkmiUTVswZhSYdhbf2FAHZ8CE3o62t4/QJCwrelsZpc6cqHgc7IKtlMD6E20qsUPIai7Lpv+FGgBMDIFuKXkRc50OYsXIf860u65H1GQZ5eEqaOmExJlIKVjrTCPyItkBxez8M83iEMc9LYm2/iggt0oUNefXobszdJajFV3H4grkF7sDUKBaWJoI4nyb7Em6FmjfqslvVHL9geeEbiZqRKOnfnsCCldvYvXWLxmsEdHRPjD5VLlecKd7LjbRUiAL3q7cvvvl+ZwX54WER8PL2wT/3njKtD9EoYrZ45giYmCTonlAxOenJ+AcEsigQ7a1jhnQqbXQ6Nw3e5ABE2aZwAKLMfxyAKPQf7244D3AAYjjfpvbIlm+Ow+Fo11QblgrQTzr0xKETFzB9bL9UG5cPlLY8IIIGSm/y9g2QxOTkq6R0GjpM2tvZsMs/AoPxx4BJ2LdpnsrDaCrgVn9aers9e+JAqd7D1/8H2vUYlSQAEdmz9PXe7EkDdaYIJTVOe9fX2OElFEqTHkZbV0Fngqzd3ap4GOiAnFY/8DosnQRA5DS8BEBufndm7W9XOoTIuvPhn6O5VBPT2e0lq+c4/NWd1T4wBZD4fLAVha6hakZv1lcTAElq7fL7VRy+YmURgcFKk419VgIHvwoRJao5yWurmk4ZZ26PL71e4Pqdh5gwcyVrt2zOKBBA0GTid4D0N/p11/+lBQHQ2/eeYO6yLRJRgK55kuODtNyWAxBlu8MBiDL/cQCi0H+8u+E8wAGI4XxriJEz7m8Gcy/th43kzHm79nGMX/43NiyZlJxuvO2/zAOaQAMdLm1sreH5/K2U9iJXpqZHJHrTRat2YMXc0dITyyMgRJVLrFOikco1HUq7tm8CC3Mz6bpIhSsv0M7omB4718xU8SQJyNEB9fyV2xL1qrqrLS0t0LBWZbRpVltjQTu116dYmto1y/wOB74K7Fxbi19EiXT+0nTVr9dheh9VHb7gor+LpHuhC4DEVBgMn6IDJABCBe7vQu1w4ptrfJqWvCQe2FvyHPLb/UB4jDFKXGrCokhUhO4fnVjRXddXzsUiFGfKn9TY5HmwPatxIWuU6QNm5f8nUTuRCU9OWTxuaA9UrVAiUVs5pW5KhCZpwA3b/8bO/SfY2AR6KZLyXzYOQJTtLgcgyvzHAYhC//HuhvMAByCG860hRqbC8Iz7W2gtCtd3zoCai/DbpKs4tlvIref23/WAHIBQnQYV8VrHF4JTusv4GStYjQYZaT5kk6mKX7lxD2cu3cKkEb3YfXkNSNtmdfDV2w+//zkeRkZxaNeiHv5oTwfpBIuNjcPoaUtx94Enu7hq/lhJIVubx/8+fhGnzl2X1kQH88L5c4EKkOXUrvrs2KLVO3D01GXWdOb4/kzgTnwGSrs66SOode8reRb57ITIgFe4NWrfqMM+N830Dge9BSYpSrWalvcOxj0vxe6pR0DUAUg79w/Y+TEra5vH5jsOlD6Pv7+6Y8wzoT8VtP9V6jwyW4RLj/IjyhSVr9XHxQrHEZqrKQKrkeZGgl26fg9L1uxkF6hmxc0qGPu/ZMO3CCuMzv0okUt6PyyPy/5C7diJsifhbhWvlyFr6d35FmLss7KohKjXQUQEfbu1STTetVsPMHH2KnZ93NDuqFpBUHgnwcDYmFitoFA+kChmSNdmTxiAEkXzJ5rnv3SBAxBlu8kBiDL/cQCi0H+8u+E8wAGI4XxrqJGpINzxSNcUg5CYwm3x23ZLLJ89Co4OvIjcUPuUVsYVD9w9fm+C1k2FWgC5kU4C0ZWSaXqrvffQGfj6f0fvLi1VAEi75nWZOJ1Iw9ugdmW0blKLpVoFBoUwzQjSF3nz3ouNTcCHQERSRkrbYiE7taVoSec2DRlDGwni6Wt0WJ8ar1GSL08OLJkxnIEI0R+VHb5Kh/PjZU8xOlx2sH5WXIqMHCh1Fv0fl8WncFuUTu+D+QVuofnt6kyZPJ1pJN7HK5pTCpY6ALG2NEVouKClMyvfbTTKLDCCLXxTAOs+5GWfCZhsLX5Z0twperEJ7lQ+hKgCrVT0ecRnPn/5DmYsWs/+lBehUy2Lf5Q5huV8Irnnhr8Tuj2sxP7+3e0VEzxUN3Ua7omzVuLa7YcMSKxfNIGRBYhG+0pMeXSf6m/2rJ8NS0tBe+jW3ceMGID2t0ndatJ19fkiIqMwasoSKQ3wwJb5sLURlOX/q8YBiLKd5QBEmf84AFHoP97dcB7gAMRwvjXkyCmNhPyoPAV99gWhV+fmyJ0rhyGXyMdOIx4QD9x9ujRH80a1NK5KLCyuU70ChvX9PVGb5et3I0tmZ6zYsIfdI/E/dQCi7XHpsDpiQGeJ3jkpt9Bh9uWbj+yt+sMnL6XCdCp8nz9lsBS90TWOHFTR/KsXjGUaImSiP4jRiiIHZAdLn0FMnDE2fsyFo9+EqEWbLG8wIc8D1LlRSwIgE3Pfk0T/rIyjEBYrpJqVsPeFcXp3eEenw8fPQpG8WHRfyzUIi3KfkZYbEwsMfloGZ31d2TUCQssKX0flqw1wouwpGFcciKCywzQ+nhyAHG7yDR4/rkrt1rzPg9AYUwzyeIrYOKD1P9XgGSwwal0sfxQZLQT9ENFIu8e3+QGVazduP8T4WUIdCEUm+nZrzcQcKTpC9Mpi6lTT+r+xe6KJAIT+JvDSvGF15MjqihxZs8DRMT0CA4Ph+eIddh08CSIGYP7VE5Dq2ud/wz0OQJTtEgcgyvzHAYhC//HuhvMAByCG862hRybV8nSXJsD6mXAo1GV02AgsOwxT9r5AtfJFUK1SGYRHxiTVjd//D3hAHwAiFoAXK5QXcycP0vjUE2atxPXbD9m95AAQAiotm9SEmYkprKxUWbX0ce/hkxexeM1OKuFmIokD4tm1tPV99uo9+o+cxW6TQsfcSYNQXKZnoU8hPUUm1he9CgfzSIiUvLUyemFgjicaAYi4Fnmdi4mJCQNzNaqUgc2z3bC5vxZmvkKEIjTGBJ3uVZYAgpVxNP5qbwnrGsNZOpQ2kwOQrYuGo8CDybB8m1D/sextPlbwns0qGCM8BXHFvtk88WeOZypDkn5PQK3FiWi0icVq4crtOHVee50ZkQxMH9cfGR3SSWOSgOHY6cv02U7WhjRbxg/vmayIlt6Dp7GGHIAo2xAOQJT5jwMQhf7j3Q3nAQ5ADOfbnzWySeAHWHhdg+Xr4zCODIRJ4EfE2Luz6SNcKyLcow6inApj867DjNpyYLdmCI2I4QDkZ23QL55HLwAycjaevXqLdHa2KJDXQ1qxubk5o+J1zpiB6YLMWboZmZwc0aZpLVC0RJ6CRUrlv7dugKjIKJy7chtrNh9AdEy0ik4GRSOqViyJJnWrgCIa2oyK27f9dQykAUJv30UzMTXBmEHdWDTFWIOsNgkXjp66DOHh4SqsUxT9oPqRjq0aoAujDQbcLIPxMcxGZX3WxlFoleUdBno8hYWxQG0rCvy1d32FBs6f0OFeNXadlMWj4oSUsD55vRBdth8ba81mQXekbbO66NZRtSaGfqsmQR+Zno6Prx+6bPVGQHAEaz+4dwfUryWkTGmzC1fuYPpCIQVr28ppjKaWxEJt76+RUjIXvs6PXZ89EBxjDnqec+VPwM5MSAUjxqvgYj21RlhYm7g4bNtzFH8dOptIl4OICob17aQxber1u09MWPDMxZss/U6T0Z53bddIq9DlL/6pGGR6DkCUuZUDEGX+4wBEof94d8N5gAMQw/k2LY188vx1PHz8AsP7d4aDnTkHIGlpcwy8Fn0ASJvuo+AfIChsJ2UmpqZYOWcUcmRzVQEgVODetV1jHDt9BQtXbU9qGEwa0Rt0oFW30xdvYM6SzTr70xt0ErKTa0gQjSwV1JMREEjKKMVql1cOFllh/FRGceib7Sn+zPFC6uofaY7K1xqwv0k35F2YLY54C8BJzqJ1blAOxFQegeETF6J9y/ooXlio8UjK+o6YhQE92yJvLoGNK6VG0VD7m/MYQx5FWU75ZIGNSTQqOggsZSQaKkZB9RUPJSDy+asPSKneySE9srq5wFzGbqZrrQRM/QK+M2HKmJgYOGV0YCBW3/4p9UNa7McBiLJd4QBEmf84AFHoP97dcB7gAMRwvk0rI5PS8KYdh7AgXtmYA5C0sjM/Zx1JAZCv3/zQsfc4SaOCKHrLlCyE6OgYVofx/Ucg3rzzUnmrTYf2HWtnwtbaSipCVwcgFO2g4u97Dz3ZW3UqNi5VvCAuXr0jPTgVMmdIby/9TW/Rew+dzv4mNi4akw7zU+etxUPPlzAxNgYxa5GJaWD0+cipy1gUD3pE8NGhVX2ULVGIHXofPH6BlRv/UnF4t4LBWP/EljFcpTeLxI9ooaBaXpB+zicz+j8pz673zuqJVR8ExiaKhqQzjcLK98LfhzdMxvJtJ1Egnwfq1aio18YOm7AQHVvXB6W9pbYRUYW5iTEszE3g51AmtYfn4yXDAxyAJMNZGppyAKLMfxyAKPQf7244D3AAYjjfpoWRv/n6Y9CYedixJoHOkwOQtLAzP28NSQEQYp06cfaaBEBEIEErJJrerXuOMgARGR2DkJAQSaPDzNQUm5dNRfvegk6I2I/Spt69/4x6tSrCxtoK67cdxK4DQp0C1Q8UL5IP+w6fZX9PGd0HFmZmcHVxZulEqzftw97DQsH2pmWT2XXPF28xYLSgml6kQG74+AUwMETpPMtmjcS6rQdYH7nS+qiBXVnthdzkOhd0vVubOnh7bivO+TjDziQCQTFCfUrFDF+xpuh1Nt6Ax+Vwzi8LrIyiWD2IV4QNo889WvYMtnzMKQGQjq3qIyY2NhENsbZdHj9zBerXrITypYsY7ItgaWYMK0tTBASpFp8bbEI+sEYPcACi7IvBAYgy/3EAotB/vLvhPMABiOF8mxZGrtOqL47tWgIqiBWNA5C0sDPJX8OVm/dZOgulPrm5ZML9J8/x/OU7vHr7EU6OGZAvV3ZULFsMZmamKoOLACRvrmxo0bAmChfMDWsrC3z+8g1//X2G1WvIi6flAOSj11f8MWAyG4/YjXaunYm5SzazPmQ2VlYIDgtlaUzyfnSP1TsEfkJMTCwmbrqEyw8/sj75cmfHs5fv2GcqKH/89BUbr1a1cqD5xHtbV04FFTev23pQqkWYOvpPUIrWpWt3WX9S4l62bhf7LMr8EW0vpRpqSsM6fPwiLt+4x9oTZXAxVzOMWXGM/U2sWD6RAivW5Dz/MOartfF0uUXs/fAw0JHdW1jgJmo7fwYVfIsRkOq5zDG/7EdE27sjxs4dUU6FEO6RmPKY+k9fsA4VyhTDb5UEPRBDGQcghvJs8sblACR5/lJvzQGIMv9xAKLQf7y74TzAAYjhfPurR6Zi22lj+sEti7PKUjgA+dU7k7L5RSBRpUJJVq9BBdfqVrRgHqYubW9nI93Sh/Upu3sWvPv4mfXRBUD+2jAHUdExmDJ3NW7cEXQlRPAi9rP23AWbB+tg5vNYWoMq61OCIvii6cNw6PhFCdDIoxjyzzRQ80Y10KdLS3TpN5EpsFP9SL7cOViERbUf1XNo9nHZkoVw4w6tKw6N6lZhYnuLlqzGySuP2HOQUV8aDwSrjIDsVoF4FyakiVVx+IqVRa4DxhZY9jYvVr7Nya6TDoi1qSqrXJxFOgQX7YHgYt0ltqkFK7YiXx4P1K+pX5pWyr4pQi8OQJR4L/X6cgCizJccgCjzHwcgCv3HuxvOAxyAGM63v3JkEvtq2bgmU35WNw5AfuXOpHxuTUCC6jVsbK3h+fytFCWggzkVeIsmj4AEh4Sxw7toVKdRv1ZFpmLesougPSEHIJ8+f0PX/hPZdYos7Fwzk30mZfORU5awz+Lhv3OdwhhqsZGxsGmyBz/So8eDigiJFWotctuFYu3cYbjna42jpy8z9iR10EFhjSyZndC3exuUKVGQsWKNnrqU9R85oAv8v//A2i0HVCI48miO+jooLevspVvsMgkrEnOXyPq09+BxhEYKzFfsuRCHGo5ejE3q1ncBxJ+qdBEu9iYwDvXBvFcFsfFTHnadxAOtTDTTWhMQ8W22F4sPP0NmZwc0b1gj5V+CZPTkACQZzjJgUw5AlDmXAxBl/uMARKH/eHfDeYADEMP59leNvGj1DuTK7oaGdapoXAIHIL9qZ5TNKwcgBBJIyM3aypINSrUaxAD1/PV79ve6xRNYETeZvAakWcOarK2v33fY2Fixwz2lKsnpdNVTqTSt+kdgEFp2HcFuEQhysQxBExxFbSchiiK3ic+L4UaAE0tpEo2KuAdk94StrQ1+VJ6M0PxtQRoUAd+DQLofO/edkNpS6lfjOlXg7OQAqlcho2hIj9+bqehIiFomBJS2rpymUWPim48/OvQey8bo1LoBfm/TUJqHgAgBrskzFuP91wChTf5gbPEU1j2gdhb0NNoGowiBLazEpca4VvEILE0SQIu2HV7yNj9ic9dF6z4jlX0JktGbA5BkOMuATTkAUeZcDkCU+Y8DEIX+490N5wEOQAzn218x8u6Dp5jqcI9OzbVOzwHIr9gZ5XOKQKJz24ZMz0Ld5Orf8jZJFaHTOMkFINSnXc/RDMhksDHDldJ70OafahiX+z4K239XWZoo5CdeLJ3eBz2yvpAoYum6b/N9iHRNSEs6fPISlqzZqdFpYiqW+k1Saz947AK73KB2ZQzo0U5FK4RqUaiOZtr8tayNnEVLPpaciUu87uHmhP35d8MsSgAfjW/VwIKCt5DLJkGjRNsOr/uQG4FR5hhc4BOLhJAmz8+w5AKQ4JBQWFpa/HRxQErpi4yMZIQF/0XjAETZrnIAosx/HIAo9B/vbjgPcABiON/+7JEvXb8LEiqjGgBdxgHIz96Z1JlPBBLaDs40S6s/RjDhPhIJJBVuMkMBELEWg8Tublc5wuaqdLU+DpU+wxijRNvtlR1+UZbwjbTAaZ8s8I8SojYtXd5ict777DOlKX1re5qpgJ+6cANzlwo6ICRsGB4RiSMnL6uI4nVq0xBtm9eFmWkCuQLVxfQdOYuBIjISH6xQpiiLEnn7+OPuQ0/pHt2nQvXa1cpp3BxK69rz9ynp3ta6figRfon9/eejcmid5R2qOX5NcmN3eOXAmxB7jMvzgLUlgVDvzkIBf0otKioaU+atSdSd6IZJSJJojUsXL4iiBTxgY2kKfw0sWBRtunPvKW788wjPX72H1+dvkn/JbyWK5kf7FvWYdoe6nTx3jQE5fY3W0rhuVam5r/8PXL/9ANduPWB1R+J+UTqgR3ZXNK1XDVTnpEloktLnLshonGlQYyMjWFpYsAhZ0UJ5UKJIfo199V1varfjAESZRzkAUeY/DkAU+o93N5wHOAAxnG9/5sgvXr/HolU7sGKuQImqyzgAScpDafO+PgBETEMibYm5kwexBzEEAImIjELDdgPY+HlsvuNA6fPsc3iMMSpcbYi7VQ5pdGJ0HDD1RTHs/ZKD3V9Q4CbqOAtpW6H5WmNLTAssXSuwWo0Y0Bm1qgoAgQ7dpLK9ess+6cBarlRhECuW3N59+IzR05aqAA35fTrkhoWHs0tzJw9GsUJCDYe6hYaFo0nHwexy+XyZsC7zKvZ5+ssiyG4VjA5ub5L8khz8mhW3AjJiRn6BsUu0oDLDdCqRJzWwPFqlqy2xoi2dMQgwEeiF5Xbr7mOMnb48qakwe8IABkbkJqdKTnIAikbVrIRBfTpITfsMn4lXbz7o7EpzTh/bN1E0ZvOuI9j211GdfQlAjR3anaUGpgXjAETZLnAAosx/HIAo9B/vbjgPcABiON/+rJEjo6Jw6tx1rTUf6uvgAORn7UzqzqMPAOk/eg6evXjLxO1EADJo7DxER0WhVePqqFqprMZFJTcFiw6QdJAkq+P0CQsKJrzVfxNqy/QzjpQR9DwCo80QFG0GV8tQ9ndItCnKXGnEPjfP/A5T8wm0uAROyt1ojbCIKBa92LJiaqK1UprQ0PEL8Oa9F7u3dPYo5MuletCkt/sEVl68+gBfvwBYWlkgs7MjihfOhzfvPkkq7ZuWTYGri5PWTRKjSbWyhmORx3Fs+ZQTn8OtMSqXwP6lbmd9XFA6gy/sTaOYEvkxbzcsKiQUvMtNaRREvleiaCSNHxYWAd+A7ywKKlruHG6YP3UorOJrhcTrcgBC6Wp5PLKyCMLLNx9w6fo9CSAQYNu8fLKKWCRFLh48SVCL1+QLEos8eEwApdoACOm4VClfHFldXRAdE81Y1a7efCABRE2phnIAQjVQFPWhCBnVNRFBAUX/yGjdS2ePkOqgtG7yT7jBAYgyJ3MAosx/HIAo9B/vbjgPcABiON+m1ZE5AEmrO6N7XfoAELEuo36tShjcO+Gts52VKeOXDQqN0jhJcgHI1t1HsGWP8CZa1MWQD3zF3xlbPuXCEI8n6P+4LKbnvYsyGXylJmJdSCFbf+wudZFd/xRmjTo367DPxE5FLFWajOo8qN6DTFcalaa+MxduYJS/dEDdt3meSgqXenspnc3pExpm+oj9X7JjWeEbWjdp+dt8OOfngpYu73De1wVril7T2tan7ekU14IktVdBIaEYPXmJREhAkYQyJQqprOXFq/e4+c8jNKpblem7qNuqTXslschxQ7ujaoWSyfrR3Hv4DCMmL2Z9SOulkYwQY+POQyicPxdKFs2fSKvF+5sfegyexkAIiVCSGKXc5ADk0LZFsLJKiO5QlGz99oPSuiuXK55kOmqyHiqFjTkASaHj4rtxAKLMfxyAKPQf7244D3AAYjjfptWROQBJqzuje11JAZCv3/zwe59xbJBenVswGmbRUhOAnL98BzMWrWdDkyr4mfInYWGcmAmKmJ82fMiDqDhjPKl2QFpLVKwRil1qyv6ukdELS+KjBF7h1qh9QwAgrZrUQk8tRAr0Bn7ibCElalDv9mhQq7JeG0oMYZSixvzTqQVaNknwj6YBRABSIYM3fCMtcaD0OZ3zEABZ8T4/TIziMMTjEVwswpDbNpClbN36nhGPAh3gGZwOmS3CkKt4RZTsMD6RaKQ+D5IUAKEx5M/aoVV9dGkrRJz0tfcfP6P7ICECpQ8rmnxcin5QLQ5FyYiRbPOyKSxSoa9NX7heiuIc2rZQJXqjC4DQ+DR3z8FT8f7TFwasSLfmVxsHIMp2gAMQZf7jAESh/3h3w3mAAxDD+TatjswBSFrdGd3rSgqAEEUtFQiTrV4wDh7ZXKUBo8JDsX7HYURGx8Le3jbRgVSfQ21kZBQuXPtHKhCnwVcVvobKjt4qCz/u7YrvUaaY9qqEdF0OQDZ/zIk5r4uwewNzPEHPbEI6D9HglrnaFKHRxuzgumHxxESpQ9Ru3dYDILY3Nv/8sciZ3S3JDfXx+45x05ex1C2KfuxeN1Pj2PKBRABijFg8qvZ3knNMe1EEOz8LwoSi1CKlp/lEWOJuYMZE/TWJRiY5iYyxjHRKWjeprRGoUaF3ux6j2HDq0TD5HNqYr+T6L1TwT2QA6kYRh6DgEKSzt4OJibF0Ww5QSaulZlXNaX/annX2kk1ME4bsyM4lsJCBl6QACPWZu2wLTp2/zvrv3TiHre9XGgcgyrzPAYgy/3EAotB/vLvhPMABiOF8m1ZH5gAkre6M7nWJAIRy51s3roXCBXPD2soCn798w19/n5HUxEn/ZWDPdiqD+fv6oE2vCeyapjfDcgBSrVIpNKpdBTFxsQgKCgVpfpB44bHTV1WYqIZ5PELXrInV2EtfasiiHkXs/eFuGQJToxgMyvkUQdEW2OWVA5s/5WbrIPasw2XPILOFUBRONvtVYZa6RUZF5pRG5pAhHfub3m6fu3wLdEAlo/qHZXOEQ7ZoXl988M3PH/lyZmeH4s9fffDI8xUTKxSLz4kdjFjCkjIRgPzm+BnLCgsHYl1GERyvMGvWpMv9yonU2CndzM4sCg9+OCA0VogIqItGJjXHrbtPGAPVsdNXpPGpXiZ3zqygovOGtauwtKSnL95i4Gjh7b/ImpYc5isCAKKf5SAiMDgE+4+cw7lLt/DFOyGljtKlGtaqjJq/lUOfYdMZCQDp0BAQloMT9eej2o1l63eDwG3JogVYqhYJX9LYovClnPkqh7srdh4QNGLUU7DEsafOX4dL1/5hfx7duSRZ0Zek/J+S+xyApMRrCX04AFHmPw5AFPqPdzecBzgAMZxv0+rIHICk1Z3RvS5NSujqPSjqMXviwER5/ckBIEl5hw68ff5ohRbXa2tsSgBEPGDrGktT7UiotTvaPWuun76hpAAAIABJREFUwpJEjEi2NlZ4+fqDdOglELVi7hg4OaZXmeLitX8wbf46jdNS5GPC8B4oVaxAUo/I7tdt1RcxsbGJiuz16VzwQjOpWa9snujm/go2ptHsml+kOXq/aY6nXyPY33LRSG1jh4SGYdWmfThx9qrO6WlvRg7oii17jjC1ejIxSqQv89X0MX2xZst+lsZEtm/TPNjb2eCD11f0GzFbBYSqL4bELYODQ1ltBzGUEYjUZgR6h09exEgTmL9rVESJwvmk9D4x9Uu18LwOdh88ydprAiDyNET6LRAA+tXGAYiyHeAARJn/OABR6D/e3XAe4ADEcL5NqyNzAJJWd0b3uuQRkLCwcBaVEI0O1/VrVUTX9k1UUlbE+9/9/dCqh3AYE98sy2cjJqFG7QdqXACN7ZrFGblyuKNmlTIoUjA3O2Bm2lQKJkGfEvU5/NUNf3tnxfWATBrHq+zwFSNyPYKHdXCi+5Gu5fGp/m4cOHYOG7ZrTnui+pC2zevA3tYmUX9NAITezhfMlxOd2zRkTE/6GIGYu488WQSonvNHzCuQwCylT38RgPTL/hR9sj9P1OVS7snos1bQ0tAmLCl2or3uNXS6BL7S2dvie2AQEGeEsqUKsX158PgFSIhSsrg4RjrQuXU9dGzTmF3Wl/nKxMQE0dHRbI/bNa/LIigEFnoOmSqtoW+3NgzIZc7kiGcv3oH8LrJeUWpYoXy5sGj6MK2uomgW1XqIkQpqWLl8CaZNQpEq+s5tXDoRjg7poS8AufvoGVas2yMBp5Skf+mzt8ltwwFIcj2m2p4DEGX+4wBEof94d8N5gAOQ1PEtqfnKRdFSZ9SkR0mJenFqAxBK7TA2Mk5T4l9Je+7f10JeA9K2WR1GPUqpLvTWOUtmp0SMQvInTKoIPSXeSH96AKyf7dHaNSLWGN7hVvCOsGQ0vC6WYXC3CoFtfCRAU8eQot3xo8o0dotAkfc3X3zx9kNkVDSyZM4I18xOOms3qM/b9174ERjMal2yu7swIcLk2LptB2Brbc1ADpnL6jwwigxMzhAQAcigHE/QI77GRT7Al57P0LLPjESikZom2brnKLbsFoQeiV55SJ+O6NR3vEpTqp8hwGEkXjUCJgzpjiZ1yktChMlhvqIqlrw5s2PBtKEshYkAzrCJC9no2lLYqPj77QdB00UT85Z8wQQud+4XUqlEI6pgAltkk0f2ZiKSZNoASKH8uZhOCP0OxGiNOBYJGY4f2j1Ze2aoxhyAKPMsByDK/McBiEL/8e6G8wAHIMn3Lb0NPHXhOitoJeGzN++82Fs7Sguht63Z3F1QtXwJFCmUJ5GQFs1GVKCh8WJoumbP4uKMPjIq0uTkcOsaVx2A0JvTXQeEtIYWjWpqFWcTxySRthNnr+H1u0/MB6KoGKV/uLtmQpkShfFbxZLsAMgt9TyQVBG6rpkMAUCsPXch/RlB7DC1zLf5PkS6Vkyt4ZI9DumEBPwIYhSxoiUFtM74uCAmzkiF8arbA2LmikMJez+0d32Dmk6fYWZMpekARXl8mx9gjFzEViXXbFFf8De/AHToOYZdFhmlKHrQqIPmaBXigQi179mpKXp1bKRRCV2TYzbvOoxtfx1jt2xtrLF+8QSp/ubv4xexbJ0gEDl/yhAWBZPbNx9/tO81RgLBK+eOBtUqaTIiSiDCBDISm9y88zBTqheX3qdrKzRvWF3qqg2AaBqbIicDerZFjSpldALyZH8xFHTgAESB8wBwAKLMfxyAKPQf7244D3AAkjzfUhHowlXbtCoty0cjQNKmWR20bFRDZZLGHQbrzKMWG6vnMOubw61JvVi+ADkAoTSePsNmSutJKm2BxL7mr9iq1/MT+w2Nxy11PJDWAAg9lbY0rJQ8sXgwT0lfQ/YxCfyATJvLaJ1CjHboYrwqnd4HiwveQjqzSIggS5NopPok2/86hk27DrPLFPmoV7MiS4cSAQjppYgMVSS62HfkHISHR7ACdXNzc1w/slIvAELaKsvW7ZYK21s0ronenVtIy3ns+QqDx81nf2siORCYp4iBzQgWFuY4uHWBxpcvcn0QWnf9WpXRsfdYxMTEMADSs3MzxuwlN20AhNi5LCzMYG5mhkzOjsiSKSMyOWeEpYW5Ib8OyR6bA5Bku0ylAwcgyvzHAYhC//HuhvMAByD6+5beANKbQNGqVCiB/LlzIHvWLDA2MoLf9x+gNIcLV/+RFHmJanPelMEaAQixxJQslvCmVX0lGR3SMz0E0fTN4dakXqwJgHzzC8SA0XNUagl0AZDte49j085D0lDE4FOsUD5kdc2EH4EhePvBC54v3uL+44Sc99P7VurvYN5SpwfSIgCxfHMcDke7psrO/eroh66HsLs5D3a35mlsIi84FxvQYRpGcTA3ikVUnAm7TJons1pmx/daS9jf2kQj5ZPMWrwRxAJFdmzXUqYboosymYACKd9T/QbZ6rnD4eHhofXRKHWLAM6OvccZDbLYT/3fAYq+Nu80THpRQS8XKFJBxekUCe01REibo/5UTD70z46J5iRtkf6j5rIxKELRoWV9jJi8CD6+Aawf1eloqhvRtwYkVb6EBhiEAxBlTuUARJn/OABR6D/e3XAe4ABEP9/KD/8U2Rg9sCuInUeT0f+sz168iXXbDjIqSm0ApHmjGiopVkmtJDk53LrUiykC8j0oHEMmLMaDJ4IGg2jaAAjlWHcfOIU1I4BDqROVyhbTuGR6y7lw1XZWsMoBSFK7qv99OlhGREahab2qetHIykc2RAqWOH5SKUr6PGFQmWEIKqu9aFmfMQzdRttzamK8KnNFEP7r4f4M1wOc8ThYKH5fP3cwsnrkgS7RSPlz9Bk+k6U4UnrjlhWCMGBSmi0zFq7H+StC0by9rRVWLhgPZ8cMidxDOh6L1+wUtGPi4mBpYYHwyEjWTmS+kneilwsTZq2UXq7QvQa1K+P5q3d4+eYDqAKFXrhMHNkLdjYCHbFo/gE/mDgh1SwR0CBhxAmzVjEwIgIfbcCFAxBDf7PT9vgcgCjcn89+YQpHMHx3YyPAKb0lvAMSONkNPyuf4Vd74L8CQIgbn8L4ObK5ws0lE+4/eY7nL9/h1duPcHLMwDjyK5YtliLlYap56DZwspR2tHn5FFb0m5SRfsLte08TCXGJKVjJBSBJzaevenEGWzOMm70eJ84JYl1Elzl+5gr2WRMAoQPC8ImLJLCSVIEpjUMH5QtXbif7oJzUM/L7KfOAIQEIrUgJCAnN11qKCqTs6X5OL6OI70h3aUKiwntNjFfyIvRS6X3Q8V41tkiR8UqXaCQVVVPhOf3ujpy6zPoR49TM8f3Z56QACP1bOHnO6ninxMEjmxsrJrextpIcRXoeU+eulaKVVLv10UsQlBSZrzR5laIdvYdO1+jwDOnssGPtzESpV3K6XQJSbZvVxsJVO9gY9DKDtGz8An5ojZxwAPJzvt9pdRYOQBTuDAcgCh3IuxvMA/8VACKmpxD7Cb1to1QEdUup8rC8ALNjqwbsEKHEDAVA9FEvpnXvO3QCqzYLFKd0qMnq5oIOvYRCV00A5Nmr9+g/cha7X6ZEQUwf20/J4/O+v8ADhgYg9Ei60pS0PfL3mosQmr/tL/BIyqek57S9v0ZixtLEeCVey28bgA6V3DHnahwCg0JQvnQRWFlY6BSN/Oj1FX8MmAyisxV5rSqULoLJo/qwRScFQK7dfoiJs1RTH+l3S/2JNYps0pxVuHrzAfvsnDEDvvkGsM9uWTLhzz9awdTMVMVBObO64vM3P4yfsZxFQCj9NE/ObNiy6zBjKBONrg/o0VZSHxfodkkY8K4QOe3fCZPnrpHaD+jZjhW+07/ZpUsUVEk5pUbEYHbj9iNs++so69OmqW4dkJTvquF68hQsZb7lAESZ/3gKlkL/8e6G88B/DYDIPUVKyTa21vB8/lbKXU6u8jCNR+lEpDxMtn/L/ETpBcndHRGAUPpCp9YNEB0bC0tzc5ZPrcS0qRfLxzx3+TZmLtrALg3q3R4NalVmhw9dAEQ+rj7RDyXPwPsaxgM/A4DQyqlgO92l8bB8K7CqaTOKelDKVYy9ZqYkw3gh9UalaIjt/XWgGphie/MIvycZ5a56XYjI8CSvs9AmGikCEBqTaiOoD6VykmAhWVIAhKImi1cLEQZi5RP1YkhlnA78ZKOnLgURSshIs9g84pzqnurZuSXWbN7LLjeoWQn9e7Zj2h/0bwn1c3ZyhI+vP7tPKaobl05iTFrylxdTCQCZmWLUFKEGRjIjKl0XnlN+kcpYyEcVyhTjACT1vrr/upE4AFG4ZTwCotCBvLvBPPBfBCCkoNumaW2J/59SGsbPWMEoL8n0UR6WO1xkq6H/sf61YY7ivdDFglW9Umk0afAbCuTJkax5KJ+7z7AZidSL5YPImWzaN6+Drh2asttJAZCNOw+xIlWyTcsms0MNt3+XB34WABG9Qgd0qzcnYBKoKlQY5VQQEa7lEWehqmD+M7xJB1w6vFtZWaRourCwCFhamieidxWjrz0aFUPHWoUQlbEgarYfzeYgKlrStvj05RvT6KA1WFtZ6RSNlEcyzcxNERUpRBhO7V3B5paLRmqKyK7fdlCi1V42cwjGzVor1W0M7t0B9WtVwtjpy3Dzn8fSswg6IgIQ0GSF8uXE43ihw51rZ8Exgz2j3aWaDopsbFs1Deu3H5Re1PTq3AItG9fEs5fv0H/U7GT7WwRG6gCkXbM62BlPGX54+2K2H2ndeARE2Q5xAKLMfzwCotB/vLvhPPBfAyDaVIVJ64KKeMmSUh6We5v+x1y75Z/skjwPW96Gis6jIqM0bpKxiUkiZWp9aHj792iLxnWr6r3xy9fvBlFpkmnK4ZbT7daoVBLjh/VAVIzwxjEpACJP1zi2e9kvEVzU2xG8oUYP/GwAktJtoN/S1LlrEBsXx4Bub5kOjq4x1209wMA3vXWXUz/T7/f0xZs4ff4Gnr96L+n15MmZFS2b1ELxQnl1LpX+3dhz8BSevnjLDvF02PbI7srquurVqAgTE2Oos5MxVqihAitU03rVQKrhbXuMZmlGVSuWxNjB3fTWqKDn2n3wFBtrzuRBSa6X/EdkEWLU48qhlQhT+6dJznxF4xJImjG2LzKkt9fqC5GxS50aXN6BiuWpaJ6s9m/lMbxfpxQDkKWzRiJf7uxsLHkNyKFti1IMIFP6nVTajwMQZR7kAESZ/zgAUeg/3t1wHvivAZA/OjRhB3BN1uqPEXopD8v70v/Q67UWah601T/Ia0TU5yW13oXThqpcpvZOGTPAySEdrK2tERkVCT//H4xuk9KdRNPnwEFtDx47j+XrBUVqSjsT1YvFcQIDg9Fv1GzGSkX31y8chTgjE4RHxrAmSQEQMV2D2p78awVXPDfcz9FgI/9bAAg5YOyMFbj1zyPmix1rZsLJUXe0hA72bbqPYu3lTEpUaD19wXrcfeCp1a+UBjmoV/tE9+mQvnrzPuw7fFZr33x5cjDF7xadBfYu8d8emm9kfJoRHcRJ7+L3PuNYGzEyoO9Gy6OWNN/SmSN0dpWnS7Zo+BvGDOioogOiwnwVr6w+aWQvlQJ1TRPoA0CCQ0LRrJPwb53o15iYWASHhOhcc/dBU9m/y9UqlUK/bq1ZW1sbGwbuyDgA0ffb8t9sxwGIwn3lKVgKHci7G8wD/08ARB/lYU2OJo57UvwmJeKda4Q3fHKjyANFIDSZNm57bRtKIIS4/8ma1hfenuoy9XzvBVOHSOrFYr+9h86wwxS9vV0+ZyRyujshLCIG4VEiAPFHz8HCG1vKEa9euTTTNbGysmTXVm78C/uPnGOft6+aDmcngVKU27/HA/8mAHLhyh1MX7ieOVddFVuTxw+duIilawWV7rmTB6NYIaEmQ66hQcXRJKDnkN4e7z58BqUpESAnGze0B6pWKKEytPx3RW/9O7drDFcXJ/j7/8Cug6ckUNOkXlVJG0gbAKG0KUZ1S7ocC8axugbRRMYr+tve3pbR06rb1PlrWRE3GYnvtW9RTzqcy9sSTffY+CJxur5p6QQUzptNAiDqzFcUxaGULHNzsyS/yFPnUyH5P6zdqvljkTO7W6I+N+48ktj0xFSvJAemZ/pzPNsLfWh4eQREH4/+t9pwAKJwPzkAUehA3t1gHvh/AiD6KA9rcrT8IHN4x+JESrt+/t/xxdtPpeu85VtYGkRyAQgNIkZqKFqxbI7wZleTyYEP0VvOmzpEI9//3r/PYPWWfcn+DokaHodPXsKSNTtZ/zkTB6J4kXzJHot3+LUe+DcBEKq1aNxxEHOYrpQf0aPiiwWq0dq1dhY7nFOBNUXuyKjmgQ7EcqPDeLcBk9mbd+pHtU0iTS1FDFt0Hc6aUxoYgQYL2SGdoiPjZizHrbtPVMakVKbWjQXhUAFAxcElkxO+fhOAjiYFcXnBubYas89ffdC5r1CATkapoG2a1WG+oZcENMad+0+wdssBqU2/7m3RuvFvsLE0lQCIPJWS/m354/emOlPBiPmKQBHZtVsPMHH2Kskn44Z0Y6lbolGxuciQRS86Ni6dCEcH/ep8kgNAxgzqxtTPtVk6e1v2b25aMp6CpWw3OABR5j+egqXQf7y74Tzw/wRA9FEe1uTpnftPYMN2gbZ2YK/2aFi7cpIbQvUmlD+eEgAi9tUWcUluDrdSAHLv8XOMmLiIPXNqa5ck6UjeIFU8YAgAYkjtnfkrtuHE2avs2dctGo9s7lk0+oFAfpd+E9k9Ip/o2q4x+ywnTqCi6YwO6RL1P33xBuYs2cyuy+mnCVhQkTaZNkHPb08uo8MEgWlKH9OH8UoXyQUVc5PAoBi10TWn6AcLM2MVACJPpdRnzUTbS/S/olGaJ6V7ikYpYc4ZHeDt7SsRfNC9eZMHo2h8FEqfeZIDQJIaj6jW1YVfk+pj6PscgCjzMAcgyvzHAYhC//HuhvPA/wsA0Vd5WJOn5cWV9HZv68opEs+9tp1JKQAh3vymvw9hxbKait5TksNNooDh4QkCo+ltzREWGYOI+BoQH98AqXiU3pxWq1gCRsbGsLcVaIGDQkLRoedYicpYPY1Emw/ocMgZswz3203OyIYAIIbU3pGDXqqhoNQjTbZ973Fs2nmI3Vq7cDyyZxWAilhHQr/XQ9sXauzr6/8D7XoIEUY5Ra38hYMIfqw9d8HyzQlYeF2HUcQP1qf+zZp4H2YnjZ0rexaERURLBeB0w9zcHI3qVEbX9k0SkVHQfTnjlbYXDuIExOC1adchRn9L7FPqRgBm5rh+UmRCHYAQqFKP2uj6DhFtbjkZAKEXH5eu38WqTXs1zk9gpfvvzeDumjk5X00pBUtTpIoG2rr7CLbsEXRAkrJihfJi7mQhepZWjAMQZTvBAYgy/3EAotB/vLvhPPD/AkD0UR4mL2vLw5ZTW1Jh+Yj+nUFpT9pMGwB57Pka7q7OWgEM6Y2Q7ghZh1b1VXLCleRwy9fpYGeOUKoB0bMInfqeunADc5cKb4sp9WLckO4sJ16T0To37jiEIycvQUzjMtw3mI+sjwcMCUDk86eW9g4VL7ftMYqlSNHBfPuqGYnID+hATKlJFBWg7+TKuQL1LRkVgVMxOAGQv7ct0JhqRGC/TiuB4a5cqcKYOlr4LAcg60c2QGnPyTAJ/JjIzX0elscl/4TD9sDcr9G2aS0cjfkNU+evh5FRHGaM64/SxQvqs0XJakO/sQ8fvyDgRxDWbtnPfEDPemDLfKk+RB2AJGsCHY2JmINeWnz19sX3H8HIlMkRWTJlZKls3BJ7gAMQZd8KDkCU+Y8DEIX+490N54H/GgAR87ALF8wNaysLfP7yDX/9fUYv5WHysrY0CHr72HvYdJW3mz07NUfB/LnglsUZttbWCAoOhtdXX/xz/ymbk6IY6ilYYkFp84bV2dtF9yyZkSGdHfwCfrADOx1+yOgwsWnZJJWCciU53PJvUEoACB32hk9chAdPXkhD9erUAkUL5Ya7qwuLkrz/+Bmv3n5kzEF0cCTjAMRwv93kjGxoAJLa2jv0bJT2KP4eFk0flii3Xy5yR5E7KggXTf7CYMOSiVrfyotpmfLf6a27jzF2+nI21NS8/6C5yweNrp74vBj2fknQ6xGFCC9a1MefJwWtkZnj+7NIpiFt0crtOHpGEEqlGonfKpdin81MjBAdFY4445Tpnhhyzf9PY3MAomy3OQBR5j8OQBT6j3c3nAf+awBEl6eU5mETXe3arftBLD36Wo0qZTBqYFepuZzRRtcY44f1RJXyxVWaKM3hFgdLCQChvsTYs2bLfhWqYF3PQBGiLSum6usq3s6AHjAkAElt7R3RDcRW1WOw8P0hcEEgQ26UCiTS5O5eN0sFrF+6fg9T561hzemlxLzJgxJRzZJeB6lyE1guUTQ/Zk8YwNoTqQTpdpBZG0dhR8mLyG0jAGrRfCIsMPpZSVwPyCRdEwHIdX8ndH9YiV3/GQCENEoGjk4QSKW0R9L0oOfL4+GOeVOGGPCbxYdOygMcgCTlId33OQBR5j8OQBT6j3c3nAf+awBEVB4WhbjIcxRNqF+rYqrkYdN4j56+wpbdRyRhM/XdoQMAcdr/VqkUsrm5qNymt6vEKkWUlZqMcpgH9Gyr8Y2t0hxucT51AOLj9x3tewoHLgJLBJp02b2Hz7B59xFWZK/JSC+lVtVyqFCmqF4Un4b7dvORRQ8YEoCktvaOfNdI2I5qsOg3vG/TXJiZmbLblAbUvNMwFmUsU7Iwpo8R0qdEo/uT56yWfmfUv3qV0nBMbw//H0F4+eq9SuG0KJxH/S3fHMfBldMx/01habw6Tp/gYR2EkBgzvA21xWVZ6pXY6FcBEJpfThss90PRgrk5APnF/wxwAKJsAzgAUeY/DkAU+o93N5wH/msAhA5DbZvVYW/rqVDTxsYKWTI76a08nFxP09vTz96+CA4OhUsmR2RyctTr0E0pXT5+/vAPCERIaBicM2aAq0umn6Ly+7/2zjM+qqrr2yuVFAi99yKCgGADBQFvJdKkCIIUqQKC9CqRLiC9g1QREQURUVQUERsCAvIgKEiRIiWU0ENJSAh5fvvEGSfJTOacWbPCTvifD+8LM3uts8614nPnYp+9d0oBsfrMtvHqtSy1kPfsuQuk/qy2wFSszZwr4Ok9EecZgXslIJ6evWN7SsetptUaDbVWQ12O2+y62qnqVkwsjZq4gPbsO+QUmlpbYlvMrRa5q8Xu6sq/7HHyu36aph6tQO+dSjpTJOWlZkZu3U3aDjbUL56KBd+gtoWP0osFT5HjDMiUHs9SlTotPGuaxaioS1do956/KPLsBUq4e5dyZc9Kj1Z8gEqV/u81MYspMdwLBCAgPIgQEB4/CAiTH8LlCGRGAXF1ErocxYyV2VsCkrGe+v6u9l4JiKdn79i65fg61LNPP0ER/TsbX02Zu5w2/vir8ecvP5xFQUGBLhusZup+2/OXsXD67t27xoLpMqWLUYF8ue0HcA7u3YGef+ZJCt2ziLL/8t+ZG6diQmhDVBE6czuYrt8JoEJZblHJkOsUnvcs/W9bPUNCmhY4QePLJR0UqC5HAZn3vytUtlfSIYkSl1p87mozDKlF6BLPkZlzQkB43YWA8PhBQJj8EC5HAAIix1bXzBAQXTsjV9e9EhBPz95xJGHbUld99vkH08nXx9d+UKGr07PNkHTccc52unfelc9RwMXkBww6y3XsVlZqtDPp0MFhZfZSmyLHnArIooe3Upn+X1FC2H+H9pmpzcwYdQK82vL2kUoPOh0OATFDUX4MBITHGALC4wcBYfJDuBwBCIgcW10zQ0B07YxcXfdCQDhn7ziSUJs+JJ0snnRgoFoHMm7aEuPvU8b0pyoWDr2z5VWvPaoDDNUrlLY1JH7RJyn/+2mvf7LF9/6zGv1wqZCxSP3nGt9QiF+CSwGp2KQX3azSzavNfX/Vl3Qt+gb16dbaZV4IiFeRe5wMAuIxOiMQAsLjBwFh8kO4HIHMIiDq3A114F7T+rWp7rPV5YBlgswQkEzQRIuPcC8EhHv2ju0RY2Ju22c81Ja2/v5+xuJytWX2qsUT7edemEUSezuOZi38yL6b2+wJQ6h82ZIUGLmV8qxtnmYatdZpZWRpGn+ksjFuUKk/qVOxI8liHF/BUjMgD9drT9erDTJbnttxsxetNHa5sq1ZcRUAAXGLMl0GQEB4mCEgPH4QECY/hMsRyCwCIkco82WGgGS+nrp7IkkBkTp7x/GZpr2zgjZ8vzXZY6Y8qDMlAyUa6uTvKhUeoNDQEIq6eJmO/RNpbKVt2yUv/JknjUNF1ZVSQH64WIDKhV6j/Fli6MqdLHT8ZlZaeqqscfhgIhEVCLxJYx78nYJ87xrxT+S8ZPz/jgLSvfgBKlOuIt2s0tVpi3x8fejJxyqZ3iRj7NTFVLliWWpc778zTyAg7n767+33EBAefwgIjx8EhMkP4XIEICBybHXNDAHRtTNydUkKSFpVc8/eseXes+8wDR41I9mtlswcQcWLFnJ5e7VDW+uuQ11+rw4SfalxHfsv/ykFpMGOOnQixvnp3lWyXaQ91/PYc497cJexA1ZKATHT0W9WzyV/Pz+3QweOnG6IR+3qj7kdqwZgBsQUJvFBEBAeYggIjx8EhMkP4XIEICBybHXNDAHRtTNydUkKSHqcvZOQcJdadR1qrNlQl7rn/ClJZ9e4upwJiHpt66GyJal5ozr0cIUHkoW6ExC13qNS2BWqn+80FQ++SZ321jTiHeVD/X37lTz06r/fmemoGQF5te8Y6tO1tTH7YfaCgJglJTsOAsLjCwHh8YOAMPkhXI4ABESOra6ZISC6dkauLkkBSe+zd8xSuns3kY6fjKRLl6+Rf4AflShSMNlp6c7yFJpTwP5xZGwInbwVSncSfalEyHUqHHSLfH2Svt55JY8hIL1L7KNvLhSjdU9877Ksyw3fo9hS9c2WnWrcS50G07S3BlDxosk14GvcAAAgAElEQVQPNXWXEALijlD6fA8B4XGGgPD4QUCY/BAuRwACIsdW18wQEF07I1eXtIBklrN3cn3VgYKOf+u2EUpAImODjdeu/r6ZjQb9VdWlhJztdpASs+RwmzPlADXb06LzEPpk6WRjwb3VCwJilZjMeAgIjysEhMcPAsLkh3A5AhAQOba6ZoaA6NoZubogIObYmtkJy1kmVxJyq1xLuho+29zNHUadOHWW1JqPNe9NsRxrC4CAeIzOq4EQEB5OCAiPHwSEyQ/hcgQgIHJsdc0MAdG1M3J1QUDMs82z9kUKjEw6Zd3K5UxCznfYafkQwr37D5PaavfdWaOs3D7VWAgIC5/XgiEgPJQQEB4/CAiTH8LlCEBA5NjqmhkComtn5OqSEJDMevaOOpAw38o65BMXbbkhjhJytc5MulW+laUcm3/dTeu+/ommjR1gKc7ZYAgIG6FXEkBAeBghIDx+EBAmP4TLEYCAyLHVNTMERNfOyNUlISBy1d77zAEX/jQOJfRUQgYcq0sLF8219CBfbPiZ9vx5iEYO9s6p6RAQS/jFBkNAeGghIDx+EBAmP4TLEYCAyLHVNTMERNfOyNUFAbHOVklIrvWdyO/6aUvBauZjf0hNenvmUlo8Y4Sp2A9Wr6crV6OpT7fWpsabGQQBMUNJfgwEhMcYAsLjBwFh8kO4HAEIiBxbXTNDQHTtjFxdEBDP2PrcvkpZ9yyhrHsWuZ0NiSv8FF2pM8u+5uP4iUhTEjJn8SoKyxZKHVo18qxIF1EQEK/i9DgZBMRjdEYgBITHDwLC5IdwOQIQEDm2umaGgOjaGbm6ICA8tkpEgo9toMDT28j/etKJ57YrplR9ii1Vz+lic3cSMnbaYnr4oQeoSf1neAU6iYaAeB2pRwkhIB5hswdBQHj8ICBMfgiXIwABkWOra2YIiK6dkasLAiLH1l1mVxKittltXLc21a7xmLsUHn0PAfEIm9eDICA8pBAQHj8ICJMfwuUIQEDk2OqaGQKia2fk6oKAyLE1kzmlhHTp9xb17tKKKlcsaybcozEQEI+weT0IAsJDCgHh8aObsXeYGeTDfYgoKIsfxdxOkL8Z7qANgeAsfhR7O4EStakIhUgTUL+Y3ElIpIS76Lo0a13yB/j7GqXE37mrS0kZug7f83+Q38lfySf6BPme32c8S0Kxpykxe1FKKFadErMXT/V8R46fphGTFtPlK9E0f/IgKlW8sCgDP18f8vfzodvx6LkoaDfJQ4P87+XtM/y9ISDMFl67Gc/MIB/u40OULTiAom/pX6s8jfvnDmEhAXQ9Jp4S8bvofdN09T+IcXfu4pfR+6bjREEBvkQ+PhQbh39g4rQ94O/1FPTjCPK9djLNNHeK1qCY+rPproOIXL12nRq0GUDFixSglQvHcsowFavkI0uAX4b4B1BTD5RBB2UPDcigletRNgSE2Yczl2KYGeTDfX2I8uYIovNXYuVvhjtoQwCvYGnTinQrBK9gpRtqbW6EV7B4rVCL0HNu6kdBxzZYSnSt5li6WaUrnTx9lvoPn0Zr3ptC/5w8Y2p3LEs3cjIYr2BxCXonHq9g8ThCQHj8sAaEyQ/hcgQgIHJsdc0MAZHvTGDkVqJEH4rP+xAlZskhf0M3d4CAeN4CJR95PnuJAi4kvWpl9dqSvQWN25WHls4eZQ899s9pmjh7GS2aPtxqOtPjISCmUYkOhIDw8EJAePwgIEx+CJcjAAGRY6trZgiI9zujhCPkwGrKErmN/KKTb9OamCU73S78FMWWqk+3yr/s/ZubyAgBMQHJxZA8a1+kwMhfPUqw8UIhWnG6NM3pE043qyQ/4VxaQiAgHrXM60EQEB5SCAiPHwSEyQ/hcgQgIHJsdc0MAfFeZ9S/jmf/ZRSFHPjYVNK4wtXpWs0xFJ+3kqnx3hoEAfGMZLYdUynbzqkeBa+KLEnbr+almRV2GvEXm31KcYVrJMslKSEQEI/a5vUgCAgPKQSExw8CwuSHcDkCEBA5trpmhoB4pzMBF/6kXF93TjXj4S67mhFREnKrfCt3Q732PQTEOkollwXer0Y+t69ZDp7/z4N0IS6IRpbda49V8nmx2dpUuaQkBAJiuW0iARAQHlYICI8fBITJD+FyBCAgcmx1zQwB4XdGyYdaF+DJL6e2u1+tMzPdJAQCYr3noXsWUfZfRpoKXH6qFLUveswYO+7vhym7fzz1LnkgVeyFVt85nf2SkBAIiKnWiQ+CgPAQQ0B4/CAgTH4IlyMAAZFjq2tmCAivM9xFyba7q5mQiy+uSZfXsSAg1nued+VzFHBxv6nACj+9SOMe3EW/XC5Aj+e4RG0KJ8lIyutazbdSrQWxjfG2hEBATLVOfBAEhIcYAsLjBwFh8kO4HAEIiBxbXTNDQHid4awLSHlnV6/l8CpMHQ0BsU600JwCpoOUgKjr5YJHaeSDf7iMiyv8FF1s9pnL770pIRAQ0+0THQgB4eGFgPD4QUCY/BAuRwACIsdW18wQEM87w1kX4OquzhYne16h80gIiHWiZgUk+k4APbXlBfsNPn38ByqX1fm6EXcCopJ4S0IgINZ7LhEBAeFRhYDw+EFAmPwQLkcAAiLHVtfMEBDPOxNyYBXl2NTP8wROIm+Va0lXw2d7NWfKZBAQa3j9ok9S/vermgpSArL1Uj7KHXibDt4IM2Js60FSJjAjICrm6D+nafLsZbSQcU4IBMRU+8QHQUB4iCEgPH4QECY/hMsRgIDIsdU1MwTE887k+K4PhRxc7XkCJ5EJYUXpfIffvJoTAsLHaXYGxMqdzAqINyQEAmKlM3JjISA8thAQHj8ICJMfwuUIQEDk2OqaGQLieWc4h9Klddczvc95XpSJSMyAmICUYkjBhWXJJy7aemAaEVZnuzgzIRAQr7bO42QQEI/RGYEQEB4/CAiTH8LlCEBA5NjqmhkC4nlnrAjIzit5yIcS6Ymcl9zeEALiFlG6D5CY7fJk62VPJQQCku4/Mk5vCAHh9QECwuMHAWHyQ7gcAQiIHFtdM0NAPO+MFQGx7Yyk7vZg6FVa9sgWCvOPd3pzCIjnPZGKDDr2DeVa38lr6RMDw+hcx52UmCWH5ZyeSAgExDJmkQAICA8rBITHDwLC5IdwOQIQEDm2umaGgHjeGU8E5NncZ2hOpR1p3hQC4nlPJCOt9NtdHderDqLr1Qa5G+bye6sSAgHxGLVXAyEgPJwQEB4/CAiTH8LlCEBA5NjqmhkC4nlnsm8eTqF7l5hKoGZAmuQ/QT9cKkgbn9zocvYjPk8FutD6e1M5PR2ENSCekVMn3uddFe5ZsEOU6rHabtmT2Q/Hm1uREAgIu21eSQAB4WGEgPD4QUCY/BAuRwACIsdW18wQEM87Y+UX0uWnShnbscYm+FL1rS/Q7lpfOL1xWqdje15p8kgIiOckuVsvq1evlHzE563keREOkWYlBALiFdzsJBAQHkIICI8fBITJD+FyBCAgcmx1zQwB4XXGk9dyTsaEUre91WnDk98luzlnXYCVp4CAWKGVeqynEuJt+bBVdvL0OVr64ec0+o3uLh8MAsLrubeiISA8khAQHj8ICJMfwuUIQEDk2OqaGQLC60xg5FbKs7a55ST/dzU3zTz+EH3wyC/2WO66ALNFQEDMknI9TvU953d9ye/6aVPJYkvWpSvhs9ivXZm6mZNBEBBPyXk3DgLC4wkB4fGDgDD5IVyOAAREjq2umSEg/M5YWQvieLdvogrTpguFaFqF3yg91n7Y7g0B4ffclkHNhgQd/YaCjn+bKqma8YgpVY9uPfQyxRWu4b2bepAJAuIBNIEQCAgPKgSExw8CwuSHcDkCEBA5trpmhoB4pzOenhPx/qnSFOlblDqNWZBu/zoOAfFOz1NmUWuCbIcVJmQrSglhxWRu5EFWCIgH0ARCICA8qBAQHj8ICJMfwuUIQEDk2OqaGQLivc5k2zGVsu2caimhejVnYlRdypk7L7Vowt9hyczNISBmKGWuMRAQPfoJAeH1AQLC4wcBYfJDuBwBCIgcW10zQ0C82xm1NiBsx1QKjPw1zcQJ2YoY50DcKt/KGPf2jKX05OMV6dmaVb1bkJNsEBBxxNrdAAKiR0sgILw+QEB4/CAgTH4IlyMAAZFjq2tmCIhMZ/yiT1LQsQ0UcGEf+V8/ZdzkbmCYsf1qbKm6TrdhHThyOrVv+QJVrlhWpqh/s0JARPFqmRwCokdbICC8PkBAePwgIEx+CJcjAAGRY6trZgiIXp3p2GsUjXvzdSpSKL9YYRAQMbTaJnYUkPg7CRQXF0ehIcHsemNj48g/wI/8/fxM5YqJiaW4+HjKHpbN1PjMNggCwusoBITHDwLC5IdwOQIQEDm2umaGgOjXmUZt+tLqpZMpOCiLSHEQEBGs4knj4+/QW1MXGfepXf0xqlO7mql7Xrx8jX7b/Qf9+tte+vt4JF28dNWICw4KolIlClPT+s9QreqPka+vj9t8+w4cpT/+OkyHj56g/QeP0dVr142Y1s3qUee2TZzGR56NohWffE279vxlH68Glitbklo0Dqenq1VJdW/HZ3VMqqQpd84clC9vLqr5ZBXKlTO725p1GgAB4XUDAsLjBwFh8kO4HAEIiBxbXTNDQPTrzM1bMdSm25u0bsUMkeIgICJYxZOq2YZGbfsa92nzUn3q1LqxqXv2GDyBjhw7mebYRyuXp/HDerqcybiTkEDvr/ySVn2WerthlbjeczVo4OuvpLrHdz9vp8mz30/z3tWfeJhGDn6N/Px87eMcnzWt4GYvPEvd2jdPFmsKyj0aBAHhgYeA8PhBQJj8EC5HAAIix1bXzBAQPTujTrcePXkBLZ092usFQkC8jjRdEnIFpFyZ4lS9WhUqVrgg3Um4Q9t3/Ulbd+ylmNhYo/4OrV6gV1o0TPUs0Tdu0tgpi2nPvkPGdwXz56HwZ56k4kULUVCWQDp56iwFZgmgxvVqJ4s9+s9p6j5wvPFZ8SIFDWl6pNKDRsye/Yfpq283087d+43v1eyJmkWxXY7P+mDp4lT1sYqUcPcuRV+/Sacjz9trUeOb1K9Nvbokbeag+wUB4XUIAsLjBwFh8kO4HAEIiBxbXTNDQHTtDNHvfx6ij9Z8TVPG9PdqkRAQr+JMt2SeCsh7K7+gRyuVpWeerERXbsQnq/d81CXq2n+cISGFC+ajZXPHpHqeJR98Rh9/vtH4XM04dGrdhIKCAt0+98Jln9KaLzcZ41Reld/xiouLpy793qKz5y9SmVLFaP6UCKcC4my25/jJSOobMdUuTwumDaPSJYq4releD4CA8DoAAeHxg4Aw+SFcjgAERI6trpkhILp2JqmuTT/vMN6dH9q3k9cKhYB4DWWqRFt27KGEhAQqWbwwFSmYn/bsP0SH/v6Hjhw/RXlz56RyZUpQjWpVKCDA33IRngqIulFau2CNn/Eu/bRll1HPFytmUHBwkL22K1ejqeWrbxh/f/LxSjQ24nXTdY+Y8I4xy6Kur1bOpiyBAalix05bTJu37TY+/+7T+aYFRA3c/OvvNPbfNTHq9S/1GpjuFwSE1yEICI8fBITJD+FyBCAgcmx1zQwB0bUz/9Wl/vU5OvoGdW3fzCvFQkC8gtFpkvDmPYzP1aLuy1eu0b4DR1KNq1yhLI0c3I3CsoVaKkRKQCbNXmaIrjNReHfF5/Z1Hx8ufJvy5clpuuaPPv2G3vvoC2P8sP6v0jNPP54qVu36phap16hWmUYP6W5JQNTMSfvXRxgxzRo9Rz06vmS6tns1EALCIw8B4fGDgDD5IVyOAAREjq2umSEgunYmeV3vLF1NBfLlMV6B4V4QEC5B1/E2AXEcodYwhGYNoQOHjttfGUr5C7eZiiQEJCHhLnXqPcp4DSpP7hy0ctGEZKXYBKFCudI0Y9xAOn3mPJ09f4muXL1GISHBVKxwfmMtiLNLzfr0GPS2/avX2jenJg2esc/+qJm9iLFzjO/f6NMx2a5eZp5VvYbVrf84I75jq0bUtkUDMxjv6RgICA8/BITHDwLC5IdwOQIQEDm2umaGgOjamdR1jZ26mGpVf9TYgpVzQUA49NKOdRQQtXbh5abPU8i/rzSp15lGvP0OHTp6wkiyZNZIY3G22cvML+Wucrl6BevHX3bR2zPfNcJSrrVITEyk519KeuVKLTxX20IfOxGZ6hZqBy01+1CiWGoR2fD9Vpr2zgp7TI7s2ahx3VrGNrpT531gfK5mL7q2ezHZDlxmnnXeu6vp869/NHK8FdGDnnr8YbMo79k4CAgPPQSExw8CwuSHcDkCEBA5trpmhoDo2hnndfV7cwp1ad+MKpYr7XHhEBCP0bkNtAmIqx2l9h88Sv2GTTXyuBrj6iZmfim3IiDq1acegyYYszLqTJD35oyi3Lly2FOoMz5adB6SKqVNmk6cPptMLOZNiaB8uVO/ovXlt5tp9qKVTktz9epUWs96/eYt+njtt/aF8Wpx+8Lpw52uMXHbsHQeAAHhAYeA8PhBQJj8EC5HAAIix1bXzBAQXTvjuq52PYbT5NH9jH+V9uSCgHhCzVyMTUBSbivrGK1+qVe/3Nd9tjoN6tnOXGIi8qaAqPUpA0ZMN9ZfqGvMG92petXKyWpRZ4eoM0TUpdattGlRn6pUeNB+aKDaEnfpR+to/cZfjDHqNa2Z4wcly7Hxp+00ZU7SOSDtWjak2Ntx9NW3v9hfRVOft3/5BWrVrB4F+P93mrrjs6pZkxJFC1H8nTtGvbbDD203mj1hCJUvW9I0x3s5EALCow8B4fGDgDD5IVyOAAREjq2umSEgunYm7boavNybPl8xnQIDUu8s5O6JICDuCHn+vRkB6TVkovEaVpWKD9KUMf1M38xbAnIt+joNHDGDbDMYPTq1cLq2yHG2JqJfZ3q25hOpalUHFA4dM5v27j9sfPflh7PsW/R+seFnmrN4lfH5kD4dKLz2k8af1SnnW3fsoYXLP7Wfyp5yhy0zBxGqdTQ9X21FeXP/N2tjGuY9GggB4YGHgPD4QUCY/BAuRwACIsdW18wQEF07k3Zd6l+fO/YaSWvfn2b5ASAglpGZDjAjIL0jJtPBw8fviYAcPnGBho6ZZZePru1fpJZNnnf6fBcvX6PWXYca373WoTm91LiO03FrvvyeFi5bY3w3Z+IbVO6BEqTEpFn7QcZMh5qpW/7O2FSxN27eooEjptvXlcyZNJTUYYnqchSQiuXL0FNPVDI+z5E9jArlz0sF8uehPLmym+6LLgMhILxOQEB4/CAgTH4IlyMAAZFjq2tmCIiunXFf1/GTZ+jt6Uto8cyR7gc7jICAWMJlabAZAWndLcL4l/8G4U9T/+5tTefnzoBcunSJug6abJ91UPdWNbi67t5NpLotkhahp3Xa+Hc/b6fJs5Nes5owojc9XuUhY1ct2xa5zRs9R91dbJH7+dc/0bx3PzZiB/fuQM8/kzRLwnlW00DvwUAICA86BITHDwLC5IdwOQIQEDm2umaGgOjaGXN1qa1M13yxiSaO7GMugIggIKZRWR7oTkDORV0itYZHXWnNKji7MeeX8sNHjtHg0XPoVkyskXrEoG5U66lH3D7fawPGGTMUaivhuZOTZkNSXivXbqClH64zPlYzHWrGw/E5WzQJp24uzrDZtnMvjZq0wIjt170NNQyvCQFx25X7dwAEhNn7M5dimBnkw319iPLmCKLzV5L+jxWu+4MABOT+6LPjU0JAMn7Pv/3xV/pj32HjX5DNXBAQM5Q8G+NOQNTWs9/+sM1IrnZuKlW8sP1GapveD1avN/4eFpbVONvC8fJUQNQp5+q0c3Wp3a7eHt6LKpY3t4vaslVf0oeffG3EOjtM8HZcPHXt95Yx46Fyr1sxnXx8fEht4dvklQHGK1jqfJGls0YlO2Hd9lxLPvjMvpvVgmnDqHSJIhAQz3707osoCAizzRAQJkCEixGAgIih1TYxBETb1lgq7KNPN1BsbCx1btvUbRwExC0ijwfYBKRMqWLUsnE4VarwAIUEZ6EzZ6Pok3Wb6Ictvxm5X6hbi/p2a53sPqciz1HnPmOMz9TOT58snexSQKo/8bCxi1Zal1o7EXXhkn0nKzW2T7fWVKRwfpdh6swSNdthu2JiYql9z5HGzlNKMAb2bEc1n3zE2AnrwqWrNHvRR7R915/GcLWjl2NN85etobVffm98pxaZq1e+cuVMWrehXu/64ZedpE5hV1fKGRZPZcvjxqVTIF7B4oGGgPD44RUsJj+EyxGAgMix1TUzBETXzlivS+04VKxIAWpS/5k0gyEg1tmajXB2EnrKWDXrMWlUX0MyHC8rAmKmnmlvDSA/P1/7uSNmYlRtambG8XJ8Tcr2uardcTvc52pVpaF9OyWLU7Mj6swTtZ2v7VKHFmYNDaa/j540Zk3UpXK9M+XNZLtZQUDMdOv+GwMBYfYcMyBMgAgXIwABEUOrbWIIiLat8agw9T69Wshbo1oVl/EQEI/QmgpynAFRswe2czZUsJpBaBBegzq1aeL00LzTZ6KoU+9Rxn3Ua0srFyWdwWG71Bkajdr0NVWHGjRj3EDy9fOjvhHJZ1LSSuBMQNT4w0dO0IyFHyWTCdsz9X2tNSkBcXYpkfjs6x/sa0RSjlHrQ1o1q0thWUNdPusrLRoahzZmhgszILwuQkB4/DADwuSHcDkCEBA5trpmhoDo2hnP6+r1xkTq1aWVsR2qswsC4jlbd5GOa0BavViX1LoOteNVaGgwFSqQ11gfcS+uLAG+FBrkT5evx7Furw4wPBV5nm7HxVGRQvmpQL489oMJ00qs5Ol81EU6e/4SxcXfoUIF8lDhAnmdrgthFah5MASE1yAICI8fBITJD+FyBCAgcmx1zQwB0bUzvLpad42g2ROHUN7cOVMlgoDw2KYV7W4Rutyd087sLQG5V/VnlvtCQHidhIDw+EFAmPwQLkcAAiLHVtfMEBBdO8Ova8DwaTRt7IBU/+oOAeGzdZUBAiLHNjNkhoDwuggB4fGDgDD5IVyOAAREjq2umSEgunZGri4IiBxbCIgc28yQGQLC6yIEhMcPAsLkh3A5AhAQOba6ZoaA6NoZubogIHJs1a5PavenpvVru90mV66K1JnxClZ60nZ9LwgIrw8QEB4/CAiTH8LlCEBA5NjqmhkComtn5OqCgMix1TUzBESPzkBAeH2AgPD4QUCY/BAuRwACIsdW18wQEF07I1cXBESOra6ZISB6dAYCwusDBITHDwLC5IdwOQIQEDm2umaGgOjaGbm6ICBybHXNDAHRozMQEF4fICA8fhAQJj+EyxGAgMix1TUzBETXzsjVBQGRY6trZgiIHp2BgPD6AAHh8YOAMPkhXI4ABESOra6ZISC6dkaurpQCcuPmLQoKykL+fn6Wbxoff4eu37hJ2cOykZ+fr+V4dVJ2UFCg5TgEWCMAAbHGS2o0BIRHFgLC4wcBYfJDuBwBCIgcW10zQ0B07Uzadalf/N+aush08S83fZ4qli9DdxIS6K+/DtGWnX/QnwePU+SZKIqJjTXyFMyfhx6tXJ7aNK9P+fKkPsDQdrPoGzdp7Vc/0A+bd9LZ8xftNRQumI9eCK9JDZ+vScHBWVzWtu/AUVr/3S+0/+BRIz44KIgeqVSWqj1WiRqEP50s7qNPv6EDh48bn3Vp9yIVL1LQZd67dxNp1qKPSJ3WHRgYSG/26+yRFJmGmoEGQkD0aBYEhNcHCAiPHwSEyQ/hcgQgIHJsdc0MAdG1M2nXpWYOGrXta7r4N/u9Sv+r+Tjt3L2Pho2f5zZu0sg+hoykvE5GnqNeQybZpcVZojy5c9A7kyMoZ46wVF//vO3/aNy0JS7v37TBM9S9Ywu7OCj56BMx2RhfuUJZmjKmX6qDFW3Jvtm0labPX2H8tVv7ZtSiSbjb57xfBkBA9Og0BITXBwgIjx8EhMkP4XIEICBybHXNDAHRtTPmBaRU8cJU6aEyaQbU/d9T9EDp4skERM1UlC1VjPLlzUV/HztJm3/9nY4cO2nkUbMS788bk0wilPR0GzDWPuvR89WX6fEqD1GB/Lnp4OF/SMnF51//aMQreVES43gd/ec0dR843vgoR/Zs9EqLhlS2dDEj35ffbqZ9B44Y373WoTm91LiOPXTWopX01bebjb8P7duJnqtVNdWzXrkaTR16jjLESPGYOzmCAvytv1KWMX8a3FcNAXHPKD1GQEB4lCEgPH4QECY/hMsRgIDIsdU1MwRE186YF5A2L9WnTq0bm3qQw0dO0O9/7KfmLzxL/oFBqWIWLFtDn375vfH58IFdqHb1x+xj9u47TINGzTD+PqhnO6cH7U2Zu5w2/virMWbt8mmULTTEHj9/2Rpa+2/uOZOGUrkyxe3fqXUovd6YRJFno4xXwd6f95Z9pkO98vXKa8MNuVBitGLhOArLGpqsdsf7zp4whMqXLWmKx/0yCAKiR6chILw+QEB4/CAgTH4IlyMAAZFjq2tmCIhcZ7bs2EMJCQlUsnhhKlIwP+3Zf4gO/f0PHTl+ivLmzknlypSgGtWqUECAv+UiHF/BsiIg6kZp7YJ14tQZ6tJvrFFPyrzrvvmZ5i5ZZXw37a0B9HCFB1LV/cWGn2nO4qQx86dEUJlSxYw/x96Oo5ad3zAkolb1x2jEwC6pYr/9YRtNnfeB8fmMcYOoYvnS9jGbft5Bk2YvM/7epH5t6tWllf07RzFSr3CpmZnMePlFnyS/6NPJHi0+70OUmCWH28eFgLhFlC4DICA8zBAQHj8ICJMfwuUIQEDk2OqaGQIi15nw5j2M5OoXbrUw2vaKkeMd1bqGkYO7UVi25P+i764qKQE5fSaKOvUeZdy+/csvULuWDe2lqPr7D59m/P2FurWob7fWqcqc9+7H9PnXPxkzFWuXT7XvrKVe7eoxeIIxvk+31tSobq1UsWr2o2OvpHurBedq4bztSkxMpMGjZtLe/YeNj+ZNGkplyxSnuLh46jZgnBQlnwMAACAASURBVDFzol7rem/OaMrqMOvijqPu3/vcvkpZ9yyhkIMfk1/0KaflxpaqRzeqdKW4wjVcPg4ERI9OQ0B4fYCA8PhBQJj8EC5HAAIix1bXzBAQuc7YBMTxDg+WLk6hWUPowKHj9oXcNapVptFDulsqREpAHGca3ujTkerUrmavS+2g1az9IHvd6rsenVrY5el81CXqNXQSXb12neo9V4MGvv6KPXbXnr8oYuwc4+8zxg00duRydtmYNW/0HHXv+FKyIY6zM4rjrAlDaPW6jbT0w3XGuBGDulGtpx6xxFHnwSEHVlH2X0aRz+1rpsqMK1ydrtSZSQlhSbNOjhcExBRC8UEQEB5iCAiPHwSEyQ/hcgQgIHJsdc0MAZHrjKOAqNeZ1L/ohwQnrbtQi6ZHvP0OHTp6wvj7klkj09xiNmWVjgKidp1SC69dXUN6dzDO6bBdrl7BUlv79hj0Np04fdYY+umyqalmZtSuVCMnzjckw3apxexqMbqa/bh46SpVfawSDe3bMdn6D0exmT/1TSpTsqjTchu37W8IzrNPP0ER/TunGvPeyi/oozXfGJ83a/ScfU3Jk49XorERr8s1M50z5/iuD4UcXG35rolZstPFF9dQfN5KEBDL9OQDICA8xhAQHj8ICJMfwuUIQEDk2OqaGQIi1xmbgHRo9YKx41PKS52D0W/YVONjV2NcVWdlG94PF4w3drpyJyC216fUuNbN6lHntk2c3t5xN6uUA9TMxMwJg1Mdarhm3SZauPxTY/iyuWNInRni7Gr/+ghjVyz1atrUt/qnGqKeu1OfUYboOF4r5o+j/PlyyzUzHTNn3zycQve63qrYXSnOJAQzIO6opc/3EBAeZwgIjx8EhMkP4XIEICBybHXNDAGR64xNQNQv8uoXemdXi85DjNmEus9WN3aWMns5Coia/ajycDmXoa+0aJBsNsLZDIjaPnfeu0n/4q4kYvq4gRQYGJAq58EjJ2jE2/OMmmtVf5QeLFOcNv6w3T5rogLU5326tko267Jy7Qb7q1LL3xlr7HTliYComO27/qQRE96xh6tXtdQrW5nhCjr2DeVa34n9KPF5KxozIbYF6hAQNlKvJICA8DBCQHj8ICBMfgiXIwABkWOra2YIiFxnzAhIryETjdewqlR80Dhkz+zlzTUgX238hWYt/Mi4tZqZmD52AOXKmT1VKWoL355vTDQ+b1jnaerdrbX9wEA1m7Nkxef2hfYpF4Q7HhK4ZOYIKl60kNNHtQlZzScfMRbnu7rUgvQ9+w4ZX3+zem6qGRezHHUapxac51sV7nKxudVar1cdRNerDTLCICBW6cmMh4DwuEJAePwgIEx+CJcjAAGRY6trZgiIXGfMCEjviMl08PDxeyYgascq9eqVutSsxNSxAyhf7pxOoYydtpg2b9ttfLdy8UTKkyu5pNy9m0izFn1EX3+3xRjjeKDgr7v+oJET5hufz5081JhlcXbZmKXcajflWNu5H2q3rS8+TDqbJKNfoXsWUfZfRnrtMdSrWGe7JUkaBMRrWFmJICAsfAQB4fGDgDD5IVyOAAREjq2umSEgcp0xIyCtu0UY6xkahD9N/bu3NV0MdwYkkYjmLF1rX9Ctzut4e1jPZCefpyzGVqt65Wvh9OFOa3Xcbvf5/z1Fg3u1N8b9dfg49Y2YbPx5+MCuVLv6o6ni1WtdagZEXe7WxGRGAcmz9kUKjEw6xNFb1+WG71FsqfoQEG8BZeaBgPAAQkB4/CAgTH4IlyMAAZFjq2tmCIhcZ9wJyLmoS9SuR9Iv8o6zBWYq4ghIkD/RpHkf0lcbk2Yq1Otfo994jUJDgtO8tRkBUSeav9h+oJFH7Y7V77U2xp8d5cLVYYHbdu6lUZMW/CspyU9hT1lYZhSQQnMKmGm9pTG217AwA2IJm9hgCAgPLQSExw8CwuSHcDkCEBA5trpmhoDIdcadgKhTv9Xp3+pSMwqOW+mqbXo/WL3e+C4sLCt1bNUoWaGeCkj0jZs0YfoS2rX3oJFPneWhZl6cLThPSWbstCW0edv/GR8vmDaMSpcokgqe4wJxlVfN7NiuURPn07bf/jAODHx35kjjuWyXOmhw9KQFxvfqtarV706ioKBAl83JbAKiTjnP/35Vr/8wxhV+ii42+wwzIF4n61lCCIhn3GxREBAePwgIkx/C5QhAQOTY6poZAiLXGZuAqNebWjYOp0oVHqCQ4Cx05mwUfbJuE/2w5Tfj5s5OFT8VeY469xljfK9+Yf9kadLrS7bLUwEZPXkBbd2x10ij1mF0bteUfHx8XEIoXaywXRQcZyjUYvXhA14l9Wy2y3GHLCUR780ZRblz5bB/v/23P2jExKR1II9WLk89X21JRQvlN2ZHPlv/I6mdstTVtMH/jO/SujKbgARGbqU8a5ub+mGMvhNAh66HJRv7RM5LTmMhIKaQptsgCAgPNQSExw8CwuSHcDkCEBA5trpmhoDIdcbZSegp76ZmPSaN6mtIhuMlJSDqNHJ1KrnZa8zQHlT9iYftw9VWvWrLXttVrmxJypcnF50/f9F+qKL6buqY/lS5Ytlkt1Enqc+Y/yFt/NH1OgfFY/zw3qkWuKesN7MJSMCFPynvqnBTbdl5JQ912lvTGJvVL47mVNxBVXNehICYondvB0FAePwhIDx+EBAmP4TLEYCAyLHVNTMERK4zjjMgMTGxFHk2yn4zNUPQILwGdWrThLI4OW/j9Jko6tR7lDFenXS+ctGEZIXG3o6jRm36Gp+pQw7Vom0z17Dxc2nn7v1mhhpjxg7tQU86CIh6VWrzr7tpwbI1qQ4DVOOVrHRp9yIVLex8PYOKX7F6PX3yxffGieeOV41qlWlQz/aUNTTEbX3T3llBG77faryulVl2wTK7BsQmIImUSB8/+jNVCrviktfNyl3oWq1xeAXL7U9U+gyAgPA4Q0B4/CAgTH4IlyMAAZFjq2tmCIhcZxzXgLR6sS6pdR1qx6vQ0GAqVCBvmq8+yVVF5OwgQqv3U7MZFy5eoXPnL9LVazcof/7cVCh/nlQzOa7yKhE5c+4CnYw8R3lz5aBiRQqaWoditc6MND7vyuco4KJ7OVQC0ntfNVr08DZjJmR3rS9cPubVOjPpVvlWEBBNfhAgILxGQEB4/CAgTH4IlyMAAZFjq2tmCIhcZ9wtQpe7c9qZvSEg96r2zHzf7JuHU+jeJW4f8eCNpPNXymW9RlG3g6jl/z1DP1VPWj+T8jrb7aBxGjp2wXKLNV0GQEB4mCEgPH4QECY/hMsRgIDIsdU1MwRErjMQEDm2mTGz2gkr38o65BMXbenxjtzMRgP2V6Uvqn6fLO5WuZZ0NXy28RkExBJSscEQEB5aCAiPHwSEyQ/hcgQgIHJsdc0MAZHrDAREjm1mzZxtx1TKtnOq5cfbfS0XTT9WkVY8stmITQwMo6jWmyghLGmXMgiIZaQiARAQHlYICI8fBITJD+FyBCAgcmx1zQwBketMv2FT6XZcPDWtX5vqPltd7kYWM+MVLIvA0nm4pyei/3ixAK05W4LmVdpOthPQbaVDQNK5iS5uBwHh9QECwuMHAWHyQ7gcAQiIHFtdM0NAdO2MXF0QEDm23sjsc/uqcSaImQXpKe/3+blitCWoAQ0YMTrZVxAQb3SGnwMCwmMIAeHxg4Aw+SFcjgAERI6trpkhILp2Rq4uCIgcW29lVhKSffNICjm42nRK9drVlfBZ9NFfgXQu6jK93rmFPRYCYhqj6EAICA8vBITHDwLC5IdwOQIQEDm2umaGgOjaGbm6ICBybL2dWZ2QHrZjKgVGuj68UYnHjSrd6EaVLsaOV+p6f9WX5OvrS+1aNjT+DgHxdmc8ywcB8YybLQoCwuMHAWHyQ7gcAQiIHFtdM0NAdO2MXF0QEDm2UpnVDllZIreRX/TpZLeIz1uBYkvVd3rbuUs+piKF8lHTBv+DgEg1xmJeCIhFYCmGQ0B4/CAgTH4IlyMAAZFjq2tmCIiunZGrCwIix1a3zBNnvUePV3mIGtZ5ikKD/Ony9TjdSryv6oGA8NoNAeHxo/NXYpkZ5MN9fYhyhWWhi9duy98Md9CGQJ7sWehy9G26m6hNSShEmECO0ACKiUug2/F3he+E9LoQCA3yI/LxoZsxd3QpCXUIEogYN5eav/AM1anxCF29GS94J6R2R0D9Ix8uzwlAQDxnZ0TeScgY/0Pv5+tDCfhNlNntjBWOnmesfnmjWl9fH0pMJEpU/w+u+4KAr4+P8Zx30fP7ot/qIdv3Hk+DerxMDz9U5r55Zh0f1N/PV8eyMkxNEBBmq85cimFmkA9XMyB5cwRliNkaeRr3zx3wCtb902vbk+IVrPuv53gF6/7ruVqE3r7naBoxqCsVK1Lw/gOgyRPjFSxeIyAgPH5YA8Lkh3A5AhAQOba6ZoaA6NoZubogIHJsdc1s2wWrTsv+tGDqm5QrZ3ZdS83UdUFAeO2FgPD4QUCY/BAuRwACIsdW18wQEF07I1cXBESOra6ZHbfhbfByb/p8xXQKDAjQtdxMWxcEhNdaCAiPHwSEyQ/hcgQgIHJsdc0MAdG1M3J1QUDk2Oqa2VFAbt+Oo2YdB9H6lbN1LTfT1gUB4bUWAsLjBwFh8kO4HAEIiBxbXTNDQHTtjFxdEBA5trpmTnkQ4cVLV6nX0Im0avFEXUvOlHVBQHhthYDw+EFAmPwQLkcAAiLHVtfMEBBdOyNXFwREjq2umZ2dhH785Bl6e/oSWjxzpK5lZ7q6ICC8lkJAePwgIEx+CJcjAAGRY6trZgiIrp2RqwsCIsdW18zOBETV+udfR2jpR5/TjHGDdC09U9UFAeG1EwLC4wcBYfJDuBwBCIgcW10zQ0B07YxcXRAQOba6ZnYlIKreX3/bS19v2kZjI3roWn6mqQsCwmslBITHDwLC5IdwOQIQEDm2umaGgOjaGbm6ICBybHXNnJaAqJo3/vgr7dl3mIb07qDrI2SKuiAgvDZCQHj8ICBMfgiXIwABkWOra2YIiK6dkasLAiLHVtfM7gRE1b32q+/pXNRler1zC10fI8PXBQHhtRACwuMHAWHyQ7gcAQiIHFtdM0NAdO2MXF0QEDm2umY2IyCq9vdXfUm+vr7UrmVDXR8lQ9cFAeG1DwLC4wcBYfJDuBwBCIgcW10zQ0B07YxcXRAQOba6ZjYrIKr+uUs+piKF8lHTBv/T9XEybF0QEF7rICA8fhAQJj+EyxGAgMix1TUzBETXzsjVBQGRY6trZisCop5h4qz36PEqD1Gd2tV0faQMWRcEhNc2CAiPHwSEyQ/hcgQgIHJsdc0MAdG1M3J1QUDk2Oqa2aqAqOcYNn4eNa5Xi6o9VknXx8pwdUFAeC2DgPD4QUCY/BAuRwACIsdW18wQEF07I1cXBESOra6ZPREQ9SwfrfmGatd4jAoXzKfro2WouiAgvHZBQHj8ICBMfgiXIwABkWOra2YIiK6dkasLAiLHVtfMngqIrs+TUeuCgPA6BwHh8YOAMPkhXI4ABESOra6ZISC6dkauLgiIHFtdM0NA9OgMBITXBwgIjx8EhMkP4XIEICBybHXNDAHRtTNydUFA5NjqmhkCokdnICC8PkBAePwgIEx+CJcjAAGRY6trZgiIrp2RqwsCIsdW18wQED06AwHh9QECwuMHAWHyQ7gcAQiIHFtdM0NAdO2MXF0QEDm2umaGgOjRGQgIrw8QEB4/CAiTH8LlCEBA5NjqmhkComtn5OqCgMix1TUzBESPzkBAeH2AgPD4QUCY/BAuRwACIsdW18wQEF07I1cXBESOra6ZpQUk+sZNCvDzp+DgLJYRxMbGEfkQBWUJtByrAlR8UJBnsR7dkBEEAWHAIyIICI8fBITJD+FyBCAgcmx1zQwB0bUzcnVBQOTY6prZJiDnL9+it6YuSlVmYGAAZc+WlXLmCKMnHqlA5R4o4fJR4uLiacfufbR915905NhJOnYi0j42OCjIODekSb1aVKZUMac5jp+MpC3b99D/7T1AkWej6Oq168a4HNmz0UNlS1KrZvWofNmSaaLcd+Aorf/uF9p/8CidPX+R1H0fqVTWODSxQfjTTmO/37yTftq6y/jO18eHhvTpSKEhwS7v8/sfB2nt+h/s37/atimVKFaI1WIICAsfBISHjyAgXICIFyMAARFDq21iCIi2rRErDAIihlbbxDYBOXPhBjVq29dtnQ+WLk7jhvU0pCDl9fV3W2jGgg/d5hg9pDvVqFY52bgbN2/Ri+0Huo3t0OoFeqVFQ6fjft72fzRu2hKXOZo2eIa6d2xBfn6+yca8v+orWvHJevtnr7VvTi81qeMyT++IyXTw8HH799PeGkAPV3jAbe1pDYCAsPBBQHj4ICBcfoiXIwABkWOra2YIiK6dkasLAiLHVtfMzgRESUbVxyoaJcfE3KaLV67ST1uSZgjUVap4YZo5fhAFBwcleyybgKhZh7rPPkVlShahgvnz0q2YWNq6cy9t+H6rffzqdycZsyq2y1FAqj/xMD3ycDkqVCAvRV28Qr/+tpd27t5vHztlTH+qUrFssnsf/ec0dR843vhMyZGSlLKlixmzIF9+u5n2HThifPdah+b0UuPkcpFSQFT8B/PHOX31S82w9B8+Ndm9ISD3/qcbr2Axe3DmUgwzg3y4rw9R3hxBdP5KrPzNcAdtCEBAtGlFuhUCAUk31NrcCAKiTSvSrRBnAtLmpfrUqXXjZDVcv3mLIsbMpkNHTxifjx/Wk6o+miQptmvXnr/onxNnqH54DaevMC39cB2tXLvBGP5WRA966vGH7bFqvcaC99fQiw2eoeJFU7/OtO23P2jUxPnG+Cb1a1OvLq2S3Xv+sjW09svvjc/mTBpK5coUt3+v5KbXG5OM17oK5s9D7897i3x8fOzfpxQQ9UWfbq2pUd1aqfowYsI7xitmjhcEJN1+XF3eCALC7AEEhAkQ4WIEICBiaLVNDAHRtjVihUFAxNB6JfGWHXsoISGBShYvTEUK5qc9+w/Rob//oSPHT1He3DmpXJkSVKNaFQoI8Dd9P7MCohIq+eg1ZKKRu22LBtSxVSPT91ED/9j/Nw0cOd2IcfULfloJG7ftTzGxsVSubEmaM2GIfWjs7Thq2fkN47ta1R+jEQO7pErz7Q/baOq8D4zPZ4wbRBXLl04lIGrmQ0nVxh9/pTy5c9Dyd8ZRgL+ffdw/J89Q1/5j7c//4SdfG3+GgFj6MRAZDAFhYoWAMAEiXIwABEQMrbaJISDatkasMAiIGFqvJA5v3sPIo37Jvnzlmv21IsfklSuUpZGDu1FYtlBT97QiIBcvX6PWXYcaedWC7v7d25q6h23Q51//SPPeXW38Vb3CVaHcfxJgJlGLzkOMhekVy5ehGeP+Wy+iFrz3GDzBSOFKbNTsR8deo4wxXdq9SC83fd6pgEwd04+69EuSjCF9OlB47Sft45TAKJF5vMpD1KT+M6RmQ9QFATHTPdkxEBAmXwgIEyDCxQhAQMTQapsYAqJta8QKg4CIofVKYpuAOCZT6zVCs4bQgUPHjRkAdakF3mqht5nLioD8dfg49Y2YbKTt3LYJtW5Wz8wtKOrCZdr9xwGa9s4KY3zVxyrR+DdfNxVrG6TWcrR/fYTx13rP1aCBr79ij1evfkWMnWP8XYmJEhRnl41f80bPUfeOL9mH2F7BUjMgnyydTKMnL6CtO/ZS4YL56N1Zo4xF62otStvX3jRipo5RMzG3ISCWOig7GALC5AsBYQJEuBgBCIgYWm0TQ0C0bY1YYRAQMbReSewoIGqdhvpX/JB/F4JfuRpNI95+x75GY8mskVS8SEG39zUrIHfvJlLEuDm0e+8BI+eCacOodIkiaeZXO2KpLWuVPNgutX6jY+vGlDU0xG1tjgNmLvyI1m/8xfhowojexiyE7dr08w6aNHuZ8df5U9+kMiWLOs1te4Xr2aefoIj+nV0KiKNojRjUjWo99QgtXv4ZrV630f76l1oHghkQSy0UHQwBYeKFgDABIlyMAAREDK22iSEg2rZGrDAIiBharyS2CYirrWjV2Rf9hiXt0JTWdrWOxTgTkIbP16SWTcIpMTGRoq/fNATi48++tZ/rocRHvcbk7rK9MmUbp14Pa9WsbjJ5cJdDfb/5199p7L9nlKj1H7PfHpxsEfmadZto4fJPjVTL5o4xZi6cXWoGRT2LqmPqW/1dCoj6YtDIGbR3/2Fjx68po/tR806DjfFjI16nJx+vZCxEh4CY6V76jIGAMDlDQJgAES5GAAIihlbbxBAQbVsjVhgERAytVxLbBCSt159sv/TXfbY6DerZzu19rZwDorbXVesinq5WxW1eNUBtf6vWbFy+Gk1btv9uP1jQyvoRR6lS9184fZixk5XjpXbWUjtsqWv5O2NTfW8ba0VAHF/rUq+5qQX4akZp0YwR5OvrAwEx9ROQfoMgIEzWEBAmQISLEYCAiKHVNjEERNvWiBUGARFD65XEZgRE7VKlflmuUvFBmjKmn9v7WhEQteZDyY8n152EBJqzeBWps0LUNXxgF6pd/bE0Ux08coJ6v5G065a6XC1c/2bTVpo+P2l9yZKZI5xu46u+s8lZzScfMRbq266Ua0DU52r25/UhE40T3W3Xm/1epf/VfNz4K2ZAPPkpkIuBgDDZQkCYABEuRgACIoZW28QQEG1bI1YYBEQMrVcSmxEQ2yndHAFRi7TbtUw6bfzipSvUe+gU+wJ32ytInjyQOpCwyStJrz6lXEieMp86OPDNcfPs93V2+KAt5tddf9DICUlnhMydPJTUjIWzy8Yv5TkizgRExf+8bTeNm7bYSKW25VWHE/r7JW3LCwHx5CdALgYCwmQLAWECRLgYAQiIGFptE0NAtG2NWGEQEDG0XklsRkBad4ugi5eumt4m18widCUD/YdPsz/D/CkRVKZUMY+eyTYLoSRByYKzy1Eo1GtXk0f3pXIPlHB5P8dF48MHdqXa1R9NNVa9Cqbura6U62NcCYiatence7SxbqRf9zbUMLymPS8ExKP2iwVBQJhoISBMgAgXIwABEUOrbWIIiLatESsMAiKG1iuJ3QnIuahL1K7HcONer3VoTi81ruP2vmYERCXZ+NN2mjLnfSOf2q523pQIypc7p9v8jgPi4+9Qg1a9jY9cbRX81cZfaNbCj+z3UYvF3e3m5SgXTRs8Qz1ffTlVXdt27qVRkxYYn6d8/cuVgKix6uyT6Os3qGih/MkOeISAWGq9+GAICBMxBIQJEOFiBCAgYmi1TQwB0bY1YoVBQMTQeiWxOwGxHZSnbrZw+nBjByfbpbbp/WD1euOvYWFZ7aeYmxUQFffuis9p1WffGjlU7unjBlJoSLD9Hj9t2UVPPFoh2WeOD77my+9p4bI1xked2jSmNs3r279OSLhLSz74jNZ8ucn4TM2wqNe98uTKbordqInzadtvfxhy9O7MkcYz2i61nmP0pAXG92pGZfW7kygoKND+fVoC4urmEBBTbUm3QRAQJmoICBMgwsUIQEDE0GqbGAKibWvECoOAiKH1SmKbgKhfzls2DqdKFR6gkOAsdOZsFH2ybhP9sOU34z4v1K1Ffbu1TnbPU5HnqHOfMcZntgP31J+tCIiShLHTFhmH9Kmr6qMVaMzQHvZ1EeqcDXU1a/QsPVqpHBUplM8QgfNRl4wdsT798nvjeyUBS2aPTDaDsu6bn2nuklX2mlXe4OAsLrmp2RfH7Xa3//YHjZiYtA7k0crlqeerLY1ZCzU78tn6H0ntlKWupg3+Z3zneEFAvPLjeU+TQECY+CEgTIAIFyMAARFDq21iCIi2rRErLKMLSGDkVvKPPk1+0acMRvF5K1JCtsIUn7eSGLP0TOzsJPSU91czE5NG9TUkw/HyhoCofDExsTRg5Az77lCN6taiPv/Kju2gP3dMbIf7OY5TMytqhsXslfI0c7VeY8b8D2njj7+6TKHYjB/eO9WsCgTELHV9x0FAmL2BgDABIlyMQP4cQXQhOpbu3hW7BRJrRgACollD0qGcjCggftEnKdvOaRR8bAP53L7mlFJCWFG6Ve5lulGlCyVmyZEOJGVu4TgDokQg8myU/UZqVqFBeA3q1KYJZQkMSFXA6TNR1Kn3KONztaPTykUTjD/bZ0Au3qBGbfoan73SoqGxUNvVFXXpCvUcPMF+rkf/7m2NRe/qRPLvft5hPy09ZbyaMXmt40tUrHCBVKnVKePqtHGzV4sm4dStfbNkw9WrVitWr6dPvvjevnuWbYBaczKoZ3unJ7B/8PFXtHz1+mRc3NWxc/c+GjZ+njFsxriBVLF8GXchaX5fKPd/r7KxEt2nwRAQZuMhIEyACBcjAAERQ6ttYgiItq0RKyyjCUjonkUUtnOaS/FICSoxS3a61HApxRWuIcZQMrHjGpBWL9Ylta5D7XgVGhpMhQrkTXY6uNk6bAJy+Xqc2RC3427HxRt1qS18b96KoXx5clLBAnldrg1xm9DiACUiZ85doJOR5yhvrhxUrEhBCnQiZRbTig6HgPDwQkB4/AgCwgSIcDECEBAxtNomhoBo2xqxwjKSgOT4rg+FHFztEYurdWbSrfKtPIq9l0HuFqF7UpuEgHhSx/0eAwHh/QRAQHj8ICBMfgiXIwABkWOra2YIiK6dkasrowhI9s3DKXTvEhaIjCghEBBWy7UOhoDw2gMB4fGDgDD5IVyOAAREjq2umSEgunZGrq6MICBBx76hXOs7sSGo17GiWn1HCWGeHajHLsCDBBAQD6BlkBAICK9REBAePwgIkx/C5QhAQOTY6poZAqJrZ+TqyggCkn/Z4+R3/bRXINwq15Kuhs/2Sq70SNJv2FRS6yua1q9NdZ+t7pVb4hUsr2BkJ4GA8BBCQHj8ICBMfgiXIwABkWOra2YIiK6dkatLdwEJObCKcmzq51UA5zvszFCzIF59eIddsLy5CN3bNd4P+SAgvC5DQHj8ICBMx7S93QAAHVxJREFUfgiXIwABkWOra2YIiK6dkatLdwHJ9VUHCjqedBK3t65rNd+im1W6eStdhsuDGRA9WgYB4fUBAsLjBwFh8kO4HAEIiBxbXTNDQHTtjFxdugtIwYVlyScu2qsA4go/RRebmT9/wqs31yAZBESDJhARBITXBwgIjx8EhMkP4XIEICBybHXNDAHRtTNydekuIIXmpD7AjksDAuJLoUH+hFewuD9JvHgICI8fBITHDwLC5IdwOQIQEDm2umaGgOjaGbm6MpOAvPHXY1Q8+KYBK6t/HLUveswpuPg8FehC6+/loGqeGTMgejQIAsLrAwSExw8CwuSHcDkCEBA5trpmhoDo2hm5ujKTgFT46cUk+fCLo/cf2ULlsl5zCg4zIJgBkfsvynxmCIh5Vs5GQkB4/CAgTH4IlyMAAZFjq2tmCIiunZGrS3cBsbIGRAlIgE8CtSl8jIaU2ecSGgQEAiL3X5T5zBAQ86wgIDxWTqPPXIoRyOrdlL4+RHlzBNH5K7HeTYxsWhOAgGjdHpHiICAiWLVOqruAWNkFq9ovDY2Zjy/OFaVcgbepS7G/nbLHLlgQEB3+o4SA8LqAGRAeP8yAMPkhXI4ABESOra6ZISC6dkauLt0FxMo5IAdvZLe/djX9WAUK9r1DPUocSgUP54BAQOT+izKfGQJinhVmQHisMAMiwA8p5QhAQOTY6poZAqJrZ+Tq0l1A1JN7ehL6nOPlKZGI+pQ8YAeY0U5Cl+g8FqFLULWeEwJinZljBGZAePwwA8Lkh3A5AhAQOba6ZoaA6NoZuboygoAEHfuGcq3v5BGEBScepFt3/GlA6f2UGBhGUa033denoCuIEBCPfpS8HgQB4SGFgPD4QUCY/BAuRwACIsdW18wQEF07I1dXRhAQ9fTZNw+n0L1LPAKx5OQDdCkuC3XtO5BiS9X3KEdmCoKA6NFNCAivDxAQHj8ICJMfwuUIQEDk2OqaGQKia2fk6sooAqII5PiuD4UcXO0RjAXBA+lEYlHq3bWVR/GZKQgCokc3ISC8PkBAePwgIEx+CJcjAAGRY6trZgiIrp2RqysjCYiioBalZ988knziok1BSchWhK6Ez6K4wjVo3Tc/0fETkdSve1tTsZl1EAREj85CQHh9gIDw+EFAmPwQLkcAAiLHVtfMEBBdOyNXV0YTEEXCL/okZdsxlYKPbXApIko8bpVvRTeqdKHELDnsAL/a+Asd/Ps4DerZXg6q5pkhIHo0CALC6wMEhMcPAsLkh3A5AhAQOba6ZoaA6NoZuboyooA40giM3Er+0afIL/q08XF83gqk5CM+byWX0DZ8v4327DtEQ/t6trBdrhvpkxkCkj6c3d0FAuKOUNrfQ0B4/CAgTH4IlyMAAZFjq2tmCIiunZGrK6MLiKdkNv28g7bv+pOGD+ziaYoMGwcB0aN1EBBeHyAgPH4QECY/hMsRgIDIsdU1MwRE187I1XW/Cogi+tPWXfTT1v+j0UNekwOsYWYIiB5NgYDw+gAB4fGDgDD5IVyOAAREjq2umSEgunZGrq77WUAU1S3bf6dvf/yVxka8LgdZs8wQED0aAgHh9QECwuMHAWHyQ7gcAQiIHFtdM0NAdO2MXF33u4Aosr/+9gd9seFnmjCitxxojTJDQPRoBgSE1wcICI8fBITJD+FyBCAgcmx1zQwB0bUzcnVBQJLY/vb7flr9+UaaMqa/HGxNMkNA9GgEBITXBwgIjx8EhMkP4XIEICBybHXNDAHRtTNydUFA/mP7+5+HaPnHX9KMcYPkgGuQGQKiQROICALC6wMEhMcPAsLkh3A5AhAQOba6ZoaA6NoZubogIMnZ/vnXEVq0fC3NmThEDvo9zgwBuccN+Pf2EBBeHyAgPH4QECY/hMsRgIDIsdU1MwRE187I1QUBSc32wOFjNGfxx/TOlAg58PcwMwTkHsJ3uDUEhNcHCAiPHwSEyQ/hcgQgIHJsdc0MAdG1M3J1QUCcsz189ARNmbucFs8YIQf/HmWGgNwj8CluCwHh9QECwuMHAWHyQ7gcAQiIHFtdM0NAdO2MXF0QENdsj5+IpLHTFtPS2aPlGnAPMkNA7gF0J7eEgPD6AAHh8YOAMPkhXI4ABESOra6ZISC6dkauLghI2mxPnj5Hw9+eR8vfGSvXhHTODAFJZ+AubgcB4fUBAsLjBwFh8kO4HAEIiBxbXTNDQHTtjFxdEBD3bM+cu0CDRs2gjxa+7X5wBhgBAdGjSRAQXh8gIDx+EBAmP4TLEYCAyLHVNTMERNfOyNUFATHHNurCZeo1dBKtfneSuQCNR0FA9GgOBITXBwgIjx8EhMkP4XIEICBybHXNDAHRtTNydUFAzLO9fOUadek3lta+P9V8kIYjISB6NAUCwusDBITHDwLC5IdwOQIQEDm2umaGgOjaGbm6ICDW2EZfv0HteoygdStmWAvUaDQERI9mQEB4fYCA8PhBQJj8EC5HAAIix1bXzBAQXTsjVxcExDrbm7diqOWrb9D6lbOtB2sQAQHRoAk4CZ3dBAgIE+GZSzHMDPLhvj5EeXME0fkrsfI3wx20IQAB0aYV6VYIBCTdUGtzIwiIZ624fTuOGr/Sjzasnkc+Pj6eJblHURCQewQ+xW0xA8LrAwSExw8zIEx+CJcjAAGRY6trZgiIrp2RqwsC4jnbhIS7VK9lT/p61RwKCPD3PFE6R0JA0hm4i9tBQHh9gIDw+EFAmPwQLkcAAiLHVtfMEBBdOyNXFwSEz7b+y73oixUzM4yEQED4PfdGBggIjyIEhMcPAsLkh3A5AhAQOba6ZoaA6NoZubogIN5hu/TDz6lz26beSSacBQIiDNhkegiISVAuhkFAePwgIEx+CJcjAAGRY6trZgiIrp2RqwsCIsdW18wQED06AwHh9QECwuNHMbcTmBnkw9X6OvV/sGLj7srfDHfQhkBQoC/djr9LiYnalJT5CtFs7Wqgvy8lJCRSApqecX/WLP736u/nQ+RDdOeOxcCMS+i+r9zXl8jfz5fi4vG/6ffyhyE4i9+9vH2GvzcEhNnCy9dvMzPIh6vfkcJCA+nazTj5m+EO2hDIHhJI0TFxEBDJjmj2O1/WYH+6fecuxeMXE8muy+a2KLXBgeqXIB+KibsjWxeya0MgwM/X+EfFG7Ho+b1sSq5sWe7l7TP8vSEgzBZiG14mQISLEcArWGJotU2MV7C0bY1YYXgFSwyttonxCpYercErWLw+QEB4/LAGhMkP4XIEICBybHXNDAHRtTNydUFA5NjqmhkCokdnICC8PkBAePwgIEx+CJcjAAGRY6trZgiIrp2RqwsCIsdW18wQED06AwHh9QECwuMHAWHyQ7gcAQiIHFtdM0NAdO2MXF0QEDm2umb2hoAkJiZSwt275O+HhdSe9hkC4im5pDgICI8fBITJD+FyBCAgcmx1zQwB0bUzcnVBQNJmGx9/h96auijVoMDAAMqeLSvlzBFGTzxSgco9UMJyk+4kJNDCZZ/SuaiLlCN7GA18/RVTOc5FXaKJs94jHx8fmjKmX5oSEHXhMs1ZsipZXl9fH/L39aG4O8l3wXq6WhWq+2x1pzXcvZtIP2/9P9p/6AgdOX6ajv0TSTGxsZQndw4qXCAfVXyoDNWpVY2KFMpn6hkwiAgCwvspgIDw+EFAmPwQLkcAAiLHVtfMEBBdOyNXFwQkbbaxsXHUqG1ftw14sHRxGjesJ+XIns3tWNuA+cvW0Novvzf+qn6RX7logqnYRcvX0ifrvjPGfr1qTponsB87EUmvDRhnKm+LJuHUrX2zVGNPRZ6j6fM/pH0HjrjNU7F8GRo2oAvlyZXd7dj7fQAEhPcTAAHh8YOAMPkhXI4ABESOra6ZISC6dkauLgiIeQFRklH1sYpGQEzMbbp45Sr9tGWXPUGp4oVp5vhBFBwc5LZhX2z4meYs/m9mIi0BURK0ZcfvdOHSFdq5e38yEbAiII9XeYiKFSlAfr4+pLbijY1Pfg5Z5QplqXrVyslq//2PgzRkzCz7Z4pBjSerUMmihSjRh+j4iUg6+s9p2rxtt33M/KlvUpmSRd0yuN8HQEB4PwEQEB4/CAiTH8LlCEBA5NjqmhkComtn5OqCgJgXkDYv1adOrRsnC7h+8xZFjJlNh46eMD4fP6wnVX00SVJcXTt376Nh4+cl+zotAYm6eIXavvam03RWBGTMG90NwTC7BiT2dhx16z+Wzp6/aNy7Q6sXqFWzek5f+Yo8G0Vzl3xMu/b8RRAQc/+9QkDMcXI1CgLC4wcBYfJDuBwBCIgcW10zQ0B07YxcXZlBQLbs2EMJCQlUsnhhKlIwP+3Zf4gO/f0PHTl+ivLmzknlypSgGtWqpPmqkivCjq9gORMQFafko9eQiUaKti0aUMdWjVw2TNXUY9Dbxvf1nqtBfj4+tH7TljRfwboVE0ufffWDPefvfx6ivfsPG3+XFJAPVq+n5R9/Zdyn1Yt16dVXmrr9QVS9qPBgKWNtDK60CUBAeD8hEBAePwgIkx/C5QhAQOTY6poZAqJrZ+TqygwCEt68hwGoVvXH6PKVa07XKqjXi0YO7kZh2UItwTQjIBcvX6PWXYcaeRuEP039u7d1eg81rseg8XT12nWqUvFBent4L5q3dDWt3/iLpTUgH3++kZZ88JmogCQk3KV6LXsa9wgOCqKPFo2nrKEhlthhMARE8mcAAsKki5PQmQARLkYAAiKGVtvEEBBtWyNWWGYSEEdIaq1CaNYQOnDouLFbk7pqVKtMo4d0t8TSjID8dfg49Y2YbOTt3LYJtW5WL9U9YmJiacDIGXTk2EkqXDAfzZ44hMKyhtLMhR+lm4AM7t2BHn24PAUF+lKhfDko+lbyNSCORaudttr1GG58ZHb2wxJYDMYuWMyfAQgIEyAEhAkQ4WIEICBiaLVNDAHRtjVihWU2AVGvSb3c9HkK+Xch+JWr0TTi7XfsazSWzBpJxYsUNM3TnYCo7Wkjxs2h3XsPGDkXTBtGpUsUSZZfbbc7ZvJC2r7rT2M2YeH0YVQwfx5jTHoKSMqHVjt21f3fU9Tw+Zr2emxj1FqOiLFzjL8O6tnO5fa8pkFiYCoCeAWL90MBAeHxwytYTH4IlyMAAZFjq2tmCIiunZGrKzMJiFok/UqLhqlg7T94lPoNm2p87mqMK8KOAqJ+UW/ZJJzUIXzR128ai7M//uxbUlvdqkuJT5d2L6ZKNf+9T2jtv2s4Zk0YQg+VLWkfcy8FxLHQOZOGUrkyxe0frfvmZ5r77/kh08cOpEoPlZH7IbxPM0NAeI2HgPD4QUCY/BAuRwACIsdW18wQEF07I1dXZhIQV68/KXotOg8x1l6og/bUv+ibvcycA6JmNYb06UDqIL+Ul+Mv8sMHdqXa1R9NNkRaQKKjb9APW36jYoULUPbs2Sg4KAvFx92mCxcu0qp1P9oXs6tnWDZ3NOXKmXR+x6rPvqV3V3xu/DmlnJhlh3FpE4CA8H5CICA8fhAQJj+EyxGAgMix1TUzBETXzsjVdb8IiNqlSu1WpRZ/q9PDzV5mBESt+VDy4+yyLZBX6yhaNaubasi8d1fTdz9tNw4wXDZ3jPF9YGAgBfj7uSzRyiJ0Z0kct+GdOf9DYxcudY0Y1I1qPfWI8We1m5V6bUxdEf0607M1nzCLDONMEoCAmATlYhgEhMcPAsLkh3A5AhAQOba6ZoaA6NoZubruFwHpHTGZDh4+zhKQ5o2eo3Ytk17xunjpCvUeOsW+wH1sxOv05OOVUjXKJiBWOvhah+b0UuM66SIgkWcvUMdeI417OYrU8ZOR1K1/0gnq6pnbv/yClUfAWBMEICAmIKUxBALC4wcBYfJDuBwBCIgcW10zQ0B07YxcXfeLgLTuFkEXL11Nc5tcZ5TTWoS+78AR6j98mj1s/pQIKlOqWLI0HglI++b0UpP0EZC4uHhq2LqPUbM6l2Tg668Yf1aHEDZq09f4szrhfd6UCKcHEMr9ZGb+zBAQXo8hIDx+EBAmP4TLEYCAyLHVNTMERNfOyNV1PwiI45ay7mYXUpJ2twvWxp+205Q57xth6jUq9Yt6vtw57Wmib9ykxLt3XTZw/ntr6PvNO43YJTNHGOOCgoIoS2CAyxhvvoJ14tQZ6tJvrHEvtYBeLaS3XW+8Ndu+u1ff19rQC8/XdPuDqM46yRoSTEFBgW7H3u8DICC8nwAICI8fBITJD+FyBCAgcmx1zQwB0bUzcnXdDwIydd4H9O0P2wyIC6cPN/5F33apbXrVid/qCgvLmuoUc3cCouLUYm21aFtdKvf0cQMpNCTYVNOkF6Hv3L3PeO0s0EFoHNeATJix1Fikrq5JI/vQo5XL2+s+FXmOOvdJWpeiFqmPHvoaPVqpnNPnUlsNf/XtZlJrWuZPfZPKlCxq6vnv50EQEF73ISA8fhAQJj+EyxGAgMix1TUzBETXzsjVlZkERL3+1LJxOFWq8ACFBGehM2ej6JN1m+y/YL9Qtxb17dY6GUzHX7LVLMQnS5MOFLRdZgREnRo+dtoi2rpjrxFW9dEKNGZoD1OvLJkVkFsxSYcpquvTLzbR8n+l6dP3ppB/gL/xeYC/PwX8+2fb2MZt+1OWLAHUokkdqlj+ASpaOD9lzxpMFy9eoLlLP6dftv9uDK1YvgxNe2sA+fr6JHt+JWfLP/7K/lmjurWMBenF1FkqPkQnT50jtV7k601bjUMW1QUBMfffKwTEHCdXoyAgPH4QECY/hMsRgIDIsdU1MwRE187I1ZWZBCQtSmpmYtKovsarTo6XNwRE5XM86Vz9Xf2i3ieF7Dirz4yARF26Qm27ven2h0Atku/e8aVk45SA2E6Cd5VAMZkxbhAVKZQv1ZD4+Du0et13tGzlF27vbxvw3pwxTnOZTnCfDISA8BoNAeHxg4Aw+SFcjgAERI6trpkhILp2Rq6uzCQgagZEiUDk2Sg7MPXqUIPwGtSpTROn6ypOn4miTr1HGePz5M5BKxdNSAbbcTG2OuRQHWTo6lKi0HPwBOO8EXX1797WWPSe1jVr0Urj1SVn97bFXbh0ldp0i3D7Q/BSozr0WsfmycapAxB/2LzTfhJ8yiTqcEW1hXBY1tA08yum6lWzXXsOOBWaB0sXp//VfILCa1czXmXD5Z4ABMQ9o7RGQEB4/CAgTH4IlyMAAZFjq2tmCIiunZGrKzMJiPpFWp23odZ1qB2vQkODqVCBvOTjk/y1Ijma+mZWi+EVk8tXrpEPJdIDJQpQcNYcaZ434upp1CnwSkiU7IVlC6X8+fNQttAQfR9e08ogILzGQEB4/CAgTH4IlyMAAZFjq2tmCIiunZGrK7MJiDrLAlfaBBwXoYPVvSMAAeGxh4Dw+EFAmPwQLkcAAiLHVtfMEBBdOyNXFwREjq2umSEgenQGAsLrAwSExw8CwuSHcDkCEBA5trpmhoDo2hm5uiAgcmx1zQwB0aMzEBBeHyAgPH4QECY/hMsRgIDIsdU1MwRE187I1ZUZBKTfsKl0Oy6emtavTXWfrS4HK5NkhoDo0UgICK8PEBAePwgIkx/C5QhAQOTY6poZAqJrZ+TqygwCIkcnc2aGgOjRVwgIrw8QEB4/CAiTH8LlCEBA5NjqmhkComtn5OqCgMix1TUzBESPzkBAeH2AgPD4IRoEQAAEQAAEQAAEQAAEQMACAQiIBVgYCgIgAAIgAAIgAAIgAAIgwCMAAeHxQzQIgAAIgAAIgAAIgAAIgIAFAhAQC7AwFARAAARAAARAAARAAARAgEcAAsLjh2gQAAEQAAEQAAEQAAEQAAELBCAgFmBhKAiAAAiAAAiAAAiAAAiAAI8ABITHD9EgAAIgAAIgAAIgAAIgAAIWCEBALMDCUBAAARAAARAAARAAARAAAR4BCAiPH6JBAARAAARAAARAAARAAAQsEICAWICFoSAAAiAAAiAAAiAAAiAAAjwCEBAeP0SDAAiAAAiAAAiAAAiAAAhYIAABsQALQ0EABEAABEAABEAABEAABHgEICA8fogGARAAARAAARAAARAAARCwQAACYgEWhoIACIAACIAACIAACIAACPAIQEB4/BANAiAAAiAAAiAAAiAAAiBggQAExAIsDAUBEAABEAABEAABEAABEOARgIDw+CEaBEAABEAABEAABEAABEDAAgEIiAVYGAoCIAACIAACIAACIAACIMAjAAHh8UM0CIAACIAACIAACIAACICABQIQEAuwMBQEQAAEQAAEQAAEQAAEQIBHAALC44doEAABEAABEAABEAABEAABCwQgIBZgYSgIgAAIgAAIgAAIgAAIgACPAASExw/RIAACIAACIAACIAACIAACFghAQCzAwlAQAAEQAAEQAAEQAAEQAAEeAQgIjx+iQQAEQAAEQAAEQAAEQAAELBCAgFiAhaEgAAIgAAIgAAIgAAIgAAI8AhAQHj9EgwAIgAAIgAAIgAAIgAAIWCAAAbEAC0NBAARAAARAAARAAARAAAR4BCAgPH6IBgEQAAEQAAEQAAEQAAEQsEAAAmIBFoaCAAiAAAiAAAiAAAiAAAjwCEBAePwQDQIgAAIgAAIgAAIgAAIgYIEABMQCLAwFARAAARAAARAAARAAARDgEYCA8PghGgRAAARAAARAAARAAARAwAIBCIgFWBgKAiAAAiAAAiAAAiAAAiDAIwAB4fFDNAiAAAiAAAiAAAiAAAiAgAUCEBALsDAUBEAABEAABEAABEAABECARwACwuOHaBAAARAAARAAARAAARAAAQsEICAWYGEoCIAACIAACIAACIAACIAAjwAEhMcP0SAAAiAAAiAAAiAAAiAAAhYIQEAswMJQEAABEAABEAABEAABEAABHgEICI8fokEABEAABEAABEAABEAABCwQgIBYgIWhIAACIAACIAACIAACIAACPAIQEB4/RIMACIAACIAACIAACIAACFggAAGxAAtDQQAEQAAEQAAEQAAEQAAEeAQgIDx+iAYBEAABEAABEAABEAABELBAAAJiARaGggAIgAAIgAAIgAAIgAAI8AhAQHj8EA0CIAACIAACIAACIAACIGCBAATEAiwMBQEQAAEQAAEQAAEQAAEQ4BGAgPD4IRoEQAAEQAAEQAAEQAAEQMACAQiIBVgYCgIgAAIgAAIgAAIgAAIgwCMAAeHxQzQIgAAIgAAIgAAIgAAIgIAFAhAQC7AwFARAAARAAARAAARAAARAgEcAAsLjh2gQAAEQAAEQAAEQAAEQAAELBCAgFmBhKAiAAAiAAAiAAAiAAAiAAI8ABITHD9EgAAIgAAIgAAIgAAIgAAIWCEBALMDCUBAAARAAARAAARAAARAAAR4BCAiPH6JBAARAAARAAARAAARAAAQsEICAWICFoSAAAiAAAiAAAiAAAiAAAjwCEBAeP0SDAAiAAAiAAAiAAAiAAAhYIAABsQALQ0EABEAABEAABEAABEAABHgEICA8fogGARAAARAAARAAARAAARCwQAACYgEWhoIACIAACIAACIAACIAACPAIQEB4/BANAiAAAiAAAiAAAiAAAiBggQAExAIsDAUBEAABEAABEAABEAABEOARgIDw+CEaBEAABEAABEAABEAABEDAAgEIiAVYGAoCIAACIAACIAACIAACIMAjAAHh8UM0CIAACIAACIAACIAACICABQIQEAuwMBQEQAAEQAAEQAAEQAAEQIBHAALC44doEAABEAABEAABEAABEAABCwQgIBZgYSgIgAAIgAAIgAAIgAAIgACPAASExw/RIAACIAACIAACIAACIAACFghAQCzAwlAQAAEQAAEQAAEQAAEQAAEeAQgIjx+iQQAEQAAEQAAEQAAEQAAELBCAgFiAhaEgAAIgAAIgAAIgAAIgAAI8AhAQHj9EgwAIgAAIgAAIgAAIgAAIWCAAAbEAC0NBAARAAARAAARAAARAAAR4BCAgPH6IBgEQAAEQAAEQAAEQAAEQsEAAAmIBFoaCAAiAAAiAAAiAAAiAAAjwCEBAePwQDQIgAAIgAAIgAAIgAAIgYIEABMQCLAwFARAAARAAARAAARAAARDgEfh/EphRk1HmG+AAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_to_plot = 'KEAP1'\n",
    "plotable = df[df.x_mutation_status.str.contains(gene_to_plot) &\\\n",
    "              ~df.x_mutation_status.str.contains('WT.o')].copy().sample(frac=0.8)\n",
    "plotable['x_mutation_status'] = plotable['x_mutation_status'].str.replace(r'WT.[oc]', 'REF', regex=True)\n",
    "fig = px.scatter(plotable, x='x', y='y', color='perturbation', #opacity=0.7,\n",
    "                 title='',\n",
    "                 category_orders={'perturbation': ['EMPTY', 'VAR',  'REF'],\n",
    "                                  'Gene': ['EMPTY'] + df.Gene.sort_values().tolist(),\n",
    "                                 },\n",
    "                 hover_name='x_mutation_status',\n",
    "                 color_discrete_map=cmap_type,\n",
    "                 width=800,\n",
    "                 height=800\n",
    "                )\n",
    "\n",
    "fig.update_traces(marker_size=22)\n",
    "\n",
    "for i, row in plotable.iterrows():\n",
    "    fig.add_annotation(\n",
    "            x=row['x'], y=row['y'],\n",
    "            ax=np.sign(np.random.choice([-1, 1]))*np.random.randint(40, 50),\n",
    "            ay=-np.sign(np.random.choice([-1, 1]))*np.random.randint(40, 50),\n",
    "            text=row['x_mutation_status'].split('_', 2)[1],\n",
    "            showarrow=True,\n",
    "            font_size=24,\n",
    "            arrowhead=1)\n",
    "fig.update_yaxes(showticklabels=False, title='')\n",
    "fig.update_xaxes(showticklabels=False, title='')\n",
    "fig.update_layout(showlegend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef9a5e8b",
   "metadata": {},
   "source": [
    "# Replicate correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6d8b0bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = new_feature_matrix #new_feature_matrix deep_feature_matrix\n",
    "corr_matrix = corr.correlation_matrix(A, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ba175b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:28<00:00, 34.60it/s]\n"
     ]
    }
   ],
   "source": [
    "treated_samples = morphology[morphology.Metadata_broad_sample_type == \"trt\"]\n",
    "null_treatments = corr.null_distribution(treated_samples.index, corr_matrix, 8, repeats=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c219eed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 375/375 [00:00<00:00, 1164.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treatments tested: 375\n",
      "At 95th percentile of the null\n",
      "Fraction strong: 83.20%\n",
      "Null threshold: 0.4381\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "treatments = treated_samples[\"Metadata_broad_sample\"].unique()\n",
    "treat_correlation = corr.fraction_strong_test(treated_samples, treatments, corr_matrix, null_treatments, \"Metadata_broad_sample\", 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5adeb257",
   "metadata": {},
   "outputs": [],
   "source": [
    "null_th_treatment = null_treatments[int(0.95*len(null_treatments))]\n",
    "fraction_strong = np.sum([m > null_th_treatment for m in treat_correlation.values()])/len(treat_correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6bd94f9a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Histogram(x=pd.Series(treat_correlation), \n",
    "                           nbinsx=20, \n",
    "                           histnorm='probability density',\n",
    "                           name='True replicates'))\n",
    "fig.add_trace(go.Histogram(x=pd.Series(null_treatments),\n",
    "                           nbinsx=100,\n",
    "                           histnorm='probability density',\n",
    "                           name='Null distribution'))\n",
    "fig.update_layout(barmode='overlay')#, title='Median correlation distribution of treatment samples')\n",
    "fig.add_vline(x=null_th_treatment, line_width=3, line_dash=\"dash\", line_color=\"black\",\n",
    "              annotation_text=f' Null threshold:{null_th_treatment:0.2}', annotation_position=\"top right\")\n",
    "fig.update_traces(opacity=0.75, marker_line_width=1)\n",
    "fig.update_layout(height=400, margin=dict(l=0, r=0, t=0, b=0))\n",
    "fig.update_layout(legend=dict(\n",
    "    orientation=\"h\",\n",
    "    yanchor=\"bottom\",\n",
    "    y=1.05,\n",
    "    xanchor=\"right\",\n",
    "    x=1\n",
    "))\n",
    "fig.update_layout(\n",
    "    font=dict(\n",
    "        size=22,\n",
    "    )\n",
    ")\n",
    "fig.update_yaxes(showticklabels=False)\n",
    "\n",
    "fig.add_annotation(text=f'Percent replicating: {fraction_strong:0.1%}', \n",
    "                   x=0.2, y=2.8, showarrow=False,\n",
    "                   bgcolor=\"#ffffff\",\n",
    "                   xanchor='left')\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ac9288b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:02<00:00, 369.19it/s]\n"
     ]
    }
   ],
   "source": [
    "control_samples = morphology[morphology.Metadata_broad_sample_type == \"control\"]\n",
    "null_controls = corr.null_distribution(control_samples.index, corr_matrix, 8, repeats=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2ceecb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34/34 [00:00<00:00, 1556.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treatments tested: 34\n",
      "At 95th percentile of the null\n",
      "Fraction strong: 76.47%\n",
      "Null threshold: 0.0295\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "controls = control_samples[\"Metadata_Well\"].unique()\n",
    "controls_correlation = corr.fraction_strong_test(control_samples, controls, corr_matrix, null_controls, \"Metadata_Well\", 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e8c8b4f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "null_th_control = null_controls[int(0.95*len(null_controls))]\n",
    "\n",
    "fig.add_trace(go.Histogram(x=pd.Series(controls_correlation), \n",
    "                           nbinsx=20, \n",
    "                           histnorm='probability density',\n",
    "                           name='control'))\n",
    "fig.add_trace(go.Histogram(x=pd.Series(null_controls),\n",
    "                           nbinsx=100,\n",
    "                           histnorm='probability density',\n",
    "                           name='null'))\n",
    "fig.update_layout(barmode='overlay')#, title='median correlation distribution of control samples')\n",
    "fig.update_traces(opacity=0.75, marker_line_width=1)\n",
    "fig.add_vline(x=null_th_control, line_width=3, line_dash=\"dash\", line_color=\"black\",\n",
    "              annotation_text='null th')\n",
    "fig.update_layout(height=400, margin=dict(l=0, r=0, t=0, b=0))\n",
    "fig.update_layout(legend=dict(\n",
    "    orientation=\"h\",\n",
    "    yanchor=\"bottom\",\n",
    "    y=0.90,\n",
    "    xanchor=\"right\",\n",
    "    x=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445ff03b",
   "metadata": {},
   "source": [
    "# Variant for single mutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8bde75e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "allele_mask = (morphology[\"x_mutation_status\"] == \"ARAF_p.S214F\")\n",
    "wild_type_mask = (morphology[\"x_mutation_status\"] == \"ARAF_WT.c\")\n",
    "controls_mask = (morphology[\"pert_type\"] == \"EMPTY\")\n",
    "allele_index = morphology[allele_mask].index\n",
    "wild_type_index = morphology[wild_type_mask].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "33a2a811",
   "metadata": {},
   "outputs": [],
   "source": [
    "allele_corr_matrix = corr.sample_rectangular_matrix(allele_index, allele_index, corr_matrix)\n",
    "wild_type_corr_matrix = corr.sample_rectangular_matrix(wild_type_index, wild_type_index, corr_matrix)\n",
    "cross_corr_matrix = corr.sample_rectangular_matrix(wild_type_index, allele_index, corr_matrix)\n",
    "control_allele_matrix = corr.allele_to_control_matrix(allele_index, morphology, \"Metadata_Plate\", controls_mask, 20, corr_matrix)\n",
    "control_wild_type_matrix = corr.allele_to_control_matrix(wild_type_index, morphology, \"Metadata_Plate\", controls_mask, 20, corr_matrix)\n",
    "\n",
    "matrices = [control_allele_matrix, allele_corr_matrix, cross_corr_matrix, wild_type_corr_matrix, control_wild_type_matrix]\n",
    "samples = [\"MUT_CTL\", \"MUT_MUT\", \"MUT_WT\", \"WT_WT\", \"WT_CTL\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ba7ac489",
   "metadata": {},
   "outputs": [],
   "source": [
    "row = np.median(cross_corr_matrix, axis=0)\n",
    "col = np.median(cross_corr_matrix, axis=1)\n",
    "cross_corr_median = np.concatenate([row, col])\n",
    "dots = pd.DataFrame()\n",
    "dots = dots.append( [{\"Sample\":\"WT_CTL\",\"Correlation\":k} for k in np.median(control_wild_type_matrix, axis=1)] )\n",
    "dots = dots.append( [{\"Sample\":\"WT_WT\",\"Correlation\":k} for k in corr.correlation_median_row(wild_type_corr_matrix)] )\n",
    "dots = dots.append( [{\"Sample\":\"MUT_WT\",\"Correlation\":k} for k in cross_corr_median] )\n",
    "dots = dots.append( [{\"Sample\":\"MUT_MUT\",\"Correlation\":k} for k in corr.correlation_median_row(allele_corr_matrix)] )\n",
    "dots = dots.append( [{\"Sample\":\"MUT_CTL\",\"Correlation\":k} for k in np.median(control_allele_matrix, axis=1)] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fc00e458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xtFCArtrHbOMJsSahWF9OvAAmKqayj1mxMNfhFTZi6sK8r37C1JffNwaq+RG7zoiXMmTQKGzEz8dfLlmOVV+/gZBgvY0nzGOtXLvFWDvY3FbYbBdJQby5KF48qPgR2+Y9+tx/8da0e9D3lM5ef/t9zWZj+9iqPnNeegC9uv2965HujaxPfnUxdsz7/OITk3Buf+vcPHF/xFbBT734Lirfc7GJgIgfsUKI+REPzIfvvMprBy3VmKtPsVDd11IXY8vUwHzHoTJMib+L+BF5qKr3H8w+CLzVHVHe/Ynql6iQVQZMYWV+KRHbDJ9xalfpiYgcIN5VETuNiqkRR/sxn4uV37o/mHIYgy77p9G9mOMrtoCVfar7fI72euqzf22KHcFer779mbFNdUUOEwU/Aa2Cgypzna/cYubdqu5fRQY8lvf4mAHv0Z60+HaaejgDGZk5iG/c0PiGUdOf4uISHEg5DLFCWeO4BkcN0jV9/ifq8WpD7OhoLzYNOJh6GLExUcaSUuLFTn5qlwJ1NbZql4o8GypABeqKAumZOTickYWYqAjpUod15TrEedZa4K0OETOycjDpQbUlxu699XLj50B+qIBQgLHDODhWCjC2jpWy9bff5X9sxCuzFyhd4FvT7obLFaRkS6P6rwBj5+97XK+BVyzu/fNv65QiukfXdlVOvVDqgEb1SgHGTr26nbXqYhhbtep21ImTEfMpN2zZpXSuA07vgYAAfyVbGtV/BRg7Jwjw1v9Q5hVSASpABagAFaACVIAK2ClQryu8dhfPv1MBKkAFqAAVoAJUgArUfwUIvPX/HvMKqQAVoAJUgApQASpwQivgF9RjQpkTBQKDw6TmV951nbR9895MS/vy946sJan6Seg9VGo6dpR8aZfZs5ZI7dN3rJa2PzF9irT92Wc/trSHxsZLbbP3b5G2lxb9vbRaRYP3Xj+y9mrlz/I9GdL2ZRtSpe0X9Em0tHeMi8DILk1V5T1qu0MHDuHBhCO75zn99Bh3IUbPeMqpG1zZyYhIk2tu11lJRhqKk3bamUn/7hcQAP8gvRdCCuI7IqfLeY6P6wrwQ3iQ3py8gOxkBKdsdXxM4VDWcQCCQkK0fHWcdqblIq+kVMcVzQPyEebv3NcvYz/8iuVj1O5E0hq0R6FLvuSine+qg9nILCixM5P+vaS0DI6S9l+9dGkcgegQ7x37VE+gqNSDMo2Duvz9cHJCjOphjtqusLAYt4fU/DKPgWIpH41PoB8Q6wrQ8AQiQgLRu0NDLd8cdzFWbk137BvmCkCPZnoxX1hcin2H3I6PKRwGf/IW2gw6Q8tXx+mHP/fjyucWW1zbdYxH85ZxlvZduzKQnJxraS8qcKO0qMjSPrBva/TqluDV/uPvSVizOc16un6QrlEbHBoIV7B1PDeMC0N0jHWZufT0fORkF1r6T922GRlJey3t14w9FyMHn2pp/3H7IaxNyvJq37slCSn7rOdemJOOEneOpY/mJ3dB03atLe1pSRnISrPqeFqvRHTp4L0RmHD+efk2rNuQZOknomEDhEVHWdoJvAAIvDop4W8fAq+afgRe3zoReNXiiMBbtU4EXrUYIvD61onAKzbeIvCCFV5WeCunCgKv2kOGwEvgNRVghVdtzDi1IvCqKUbgJfCaCrDC6yMWCLwEXgIvpzSoPVadWbHCq6YXK7ys8AoFOKVBbbzoWLHCywqvETcEXgIvgZfAq/MQsfMh8NopdOTvBF4CL4FXbazoWhF4CbwEXgB8ac2aQjilQS2tckoDpzRwSoPaWNG14pQGNeU4pYFTGjilQWGssMLLCi8rvKzwKqQKxyas8KpJxgovK7ys8KqNFV0rVnhZ4WWFt45UeLs0jsTwTk10x7pjv/SUdExp2suxn3DgsmT2sh2vZcnQaQACg2tuWbKkjHxkFBTbCyKx4LJk9rIdj2XJQgL90TU+2v7kqsmiqKgYk4K5LJmdnHWtwjt03iy0Oqef3WVV299/3ZSMS/+9yNIflyUDuCwZAK7DW3vW4e3aJBLDOtYc8GakpON+Aq9tsq1rUxrQcQACa3Ad3gNZbhzOt65ZaSsswHV4FUQ6HsAbGhSALk2t62EqnK6WCYFXTba6BrzD5s1CyxoE3uWbU3DJUwsJvFyHlxtPyFJKbdp4olOjCJzfuQY3njh4GA82O0Ut01ayYoXXXrbjVeGt6Y0ndh/KQ06x3mYMrPDax9HxAN7gAH90a1ZzFV7O4bWPA2FR14B3yCdvoXUNbjzx0/oDGPfMNwReAi+Bt7YDL3da8530udOa2kOxpoGXc3jV7gvn8FatE4FXLYbqGvBypzXrTn7caY07rRmjnVsLc2thX2mfwKv2UCTw+taJWwvbxxG3FvatEbcWto8hYUHgJfBya+FRXaWjhcBL4CXwHlEgIDsZwSlb1Z4qlawIvAReU4GiUg/KypyHEYGXwGsqUFhcin2H3M6DiMALPz8CL4GXwIsL+iRaEginNHBKg6kAgdd3LPhl7IdfcYHWQ5gVXnvZCLwEXgKv9UXcgX1bo1e3BK/g+PH3JKzZnGYNGD8QeHslgsBL4CXw2j9zLRac0qAmGiu8rPCywqs2VgIlFTgVT05pUFGJUxpY4SXwYiyBl8Crli+9rAi8aqIReAm8BF61sULgtdeJUxr+1ogV3i5o2q61JWjSkjKQlZZraT+tuiq88ZfOkM7Mim7mXW43z2DH9wukkd1j1OXS9jXzP7C0j518i9Q2yN86T0UYNoqSL36/YtshaT+bf1knbS/MktufdfEQqf0d57S1tF/1+FdS2yk3nyVtf2bWSml791NPkrZv2ZgibR9wenNp+wtDrO0lCER4VM2tfZnnLkZekd76qavfX4D5Nz9gny0rWXTr0gQjhnVw7CccyjxiPqLGhMS/jib7tq1yIuEdOyHu3MEqpl426b//gaSP5jn2Ew7BMWGITGio5dto8vMIrsE4WrgxBWtTsrXOdWzjHDQPcb6kmcedC3hKtY65LqQtsgIitXwz3cUo9ujFYHJOAQpKPI6Pe0GHODSLDHbsJxx+25+D9ALn+ka6AjC0BtcEP16rNGiJCkBM+TgpVG/nxrDgAHRr00Dr0LnuEqzekeHYt8hThpRC53EgDuTyBxI1r/Wi+bPR/rwzHZ+vrsMPfx7A+GmLLe6JrRuhSUKspT03vwgFhdY8kp3uRn52ocW+MDcLxe58r/YzTm+P7t2sz/M9h/KQnGWddrVvyz6k7ku19D1xdG+MHtjJ0j7nh51YtPqApT1l81pk7N1uaW/WtScatbE+Yyf0b4mRPZt52U+buxxzl2y09BGb2ATRja3Pn26tYtGmmZVRYsOCEB1iHQ8ff/k7vvtlk6X/McN74+x+1mvdlpqLpAzrfHE/Ai9A4NVNC0f8CLxq+hF4fetE4FWLIwJv1ToReNViiMDrWycCL0DgBcAKLyu8lVMFgVftIUPgJfCaCrDCqzZmnFoReNUUI/ASeE0FWOEFwCkNAKc0qCVPAq+aTgReAi+BV22s6FoReNWUI/ASeAm8FWKAwEvgVUudnNKgqhOBl8BL4FUdLXp2BF413Qi8BF4CL4HXaxSwwquWPFnhVdOJwEvgJfCqjRVdKwKvmnIEXgIvgZfAS+BVy5deVgReNdEIvAReAq/aWNG1IvCqKUfgJfASeAm8BF61fEng1dCJwEvgJfBqDBwHLgReNbEIvAReAi+Bl8Crli8JvBo6EXgJvARejYHjwIXAqyYWgZfAS+Al8BJ41fIlgVdDJwIvgZfAqzFwHLgQeNXEIvASeAm8BF4Cr1q+JPBq6ETgJfASeDUGjgMXAq+aWAReAi+Bl8BL4FXLlwReDZ0IvAReAq/GwHHgQuBVE4vAS+Al8BJ4Cbxq+ZLAq6ETgZfAS+DVGDgOXAi8amIReAm8BF4CL4FXLV8SeDV0IvASeAm8GgPHgQuBV00sAi+Bl8BL4CXwquVLAq+GTgReAi+BV2PgOHAh8KqJReAl8BJ4CbwEXrV8SeDV0InAS+Al8GoMHAcuBF41sQi8BF4CL4GXwKuWLwm8GjoReAm8BF6NgePAhcCrJhaBl8B7QgJvpzs+LZPd+h0/fCaNiLIyj7S9Vf8Lpe1JK5da2ovdOVLbCfdPkrbP/veLaqP4L6sO542R2m9Z/D9H/ciME/sMl1/nb1856jvAFSq1Ly1yS9td4THS9oReAyztF516Ep4e38vR+RyNcX6eG64t32l1kbdzFzJWrHDsu259Mr5ctNWxn3Bof1IkBvZsquUbe2ovNB93iZavxxUGT1gDx77pP/6IpJkzHfsJh8CQAARHBWv5tpr+DkJi5HGn1aGN08KNKViTkq3Vde+EaMSGuRz7tgorRXigYzfDYeGefCTnl2o5L15zACmZBVq+A7o2RaPoEMe+rWJDEeHSu9gNyTnILixxfMy4MBcmnt7SsZ+ugwDef4a213IvKyuD3t3UOly5U5C/n1YHUa4AnHVSlJZvZmEJvturN9a0DniUTjctnIPuQ/sfZS/q7kt+34YL7pxhcSgtdMNTUmRpj23dBdEJbSzt7ds0RIvEaEu7x1OGyuC1fPEP2PD7Gottqz59kNC5i6V977oNSN2x29Iu+EqcZ+VPo/Yno0Hztpb21C1rkblvh6U9KDwaQaERlvai3EyUFOQpiRnTojMi41tbbDN2b0Bu8i6lPoSRf1AwAoKsz7GOp/VCyy4dLP1sXbUB+7Za+/cj8CprbhgSeK16EXjVYojA61snAq9aHBF4q9aJwKsWQwRe3zoReAECLwBWeFnhrZwqCLxqDxkCL4HXVIAVXrUx49SKwKumGIGXwGsqwAovAE5pkA8IVnhZ4eWUBrWHqlMrVnjVFGOFlxVeoQCnNKiNFx0rVnhZ4TXihhVeVnhZ4eUcXp2HiJ0PgddOoSN/J/ASeAm8amNF14rAS+Al8HIOrzR/cEqDWlrllAZOaeCUBrWxomvFKQ1qynFKA6c0cEpDhRjglAb5gOCUBqsuBF61hwyBl8BL4FUbK7pWBF415Qi8BF4CL4HXNlvUBeC9uG9zPDmup+21VJdBkbsA/pusy8+p9M9lyexVOl7LkrV+4R0ER9fcsmRLt6dhxb5Me0EkFlyWzF6247EsWZOIYFx/Wgv7k6smi6KiEtwV0k6rNy5LpiVbjTjd+vW76Dr4zBo5ljjIT6t3YOhtL1uOx2XJAC5LBoDr8NaedXhH9TkJU6+suXV43XluBHEdXttkzAqvb4kWbUrB6mS9tUEJvLbhh+MBvI3CXLiR6/D6vDlch9c+dm9eOAcn1+g6vNtxwZ2vEHi5Di83npANzyRuPGGftSQWrPDay3a8KrzceML3veHGE/axy40n7DUi8NprxI0nuPEEN56oYpxwpzXutOYrhXKnNfsHjLAg8BJ4TQW405ramNGxIvDaq0bgJfASeAm84NbC9smysgWBV00zAi+Bl8CrNlaOxorAa68egZfAS+Al8BJ47XOlxYLAqyYagZfAS+BVGytHY0XgtVePwEvgJfASeAm89rmSwBsVrKESpzTYicY5vHYKAZzDa68RgddeIwIvgZfAS+Al8NrnSgIvgbfKKGkVVorwQI0gAkDgtdeNwGuvEYHXXiMCL4GXwEvgJfDa50oCL4GXwCteQowNRYRLj+750ppGolF0IfDaC0XgJfBWC/B+tHZ/mSzcXly0RRqFKz6YI23vcdFYafuVg6yLgL/zzVapbVZavrQ9Nj5C2j6od6K0ff4326Tt279fIG3vfqH83Pv3bGaxn/HES9I++l4xTtq+e/0Baftr95wrbZ/4jHwDh2vGyjeSuL1fc0s/ZWV+iIkIsc8i1WSRl5UDz4LntHorSM9Gzr5Ux75F2W7kp+U49hMOW/Zm44c1KVq+HVvH4Lwz5HFn12F0186IHzHUzszy96z1G5Gy8BvHfsJh4/Z0LP45Sct3avIqxDRpqOWr47Rqbwb25xbquOLUJsGIDQlw7BuQlQy/Er1jlkQnoFSl5lgAABSnSURBVMwV6viYwmFvdjHcJdLUa9tfSm4hCkvly0P6cg4NCkCgv59t/zKDhEgXQoP8HfuKoyU0kOdvx50pOIid1p6O6aRgaTUR5xoS4PwaizxlSC4s0Tqm2OxCMwwQ1yAEY4e31TpuWrobHy7c4dg3NMAPnSP1pkjll3qwKafI8TGFQ00D74qdh/HAV5ss51pYUIKSolJL+/71a5G206qnn38g/AOsXzJdEQ3gCo/y6ic4Mgau8EhL3yUFBSgtLra0uzNTUJSbYWlvf0o3tOjYxtK+afkq7NloZa/S4kJ4Sq3xG9awGUKiG1n6Oal9Ipo2b+zVvm/bAaQmHbLYRsbFIryB93UKo4ioYIRHuCz2MWFBiAoNsrT//sNv2Lx6o6W9Za/uiO9gZczgIH8EB1qfB34EXoDAq5WDyp0IvGr6EXh960TgVYsjAm/VOhF41WKIwOtbJwIvQOAFwAovK7yVUwWBV+0hQ+Al8JoKsMKrNmacWhF41RQj8BJ4TQVY4QXAKQ0ApzSoJU8Cr5pOBF4CL4FXbazoWhF41ZQj8BJ4CbwVYoDAS+BVS50AgVdNKQIvgZfAqzZWdK0IvGrKEXgJvAReAq/XKGCFVy15EnjVdCLwEngJvGpjRdeKwKumHIGXwEvgJfASeNXypZcVgVdNNAIvgZfAqzZWdK0IvGrKEXgJvAReAi+BVy1fEng1dCLwEngJvBoDx4ELgVdNLAIvgZfAS+Al8KrlSwKvhk4EXgIvgVdj4DhwIfCqiUXgJfASeAm8BF61fEng1dCJwEvgJfBqDBwHLgReNbEIvAReAi+Bl8Crli8JvBo6EXgJvARejYHjwIXAqyYWgZfAS+Al8BJ41fIlgVdDJwIvgZfAqzFwHLgQeNXEIvASeAm8BF4Cr1q+JPBq6ETgJfASeDUGjgMXAq+aWAReAi+Bl8BL4FXLlwReDZ0IvAReAq/GwHHgQuBVE4vAS+Al8BJ4Cbxq+ZLAq6ETgZfAS+DVGDgOXAi8amIReAm8BF4CL4FXLV8SeDV0IvASeAm8GgPHgQuBV00sAi+B94QE3qAeE8pkt75xlzOlEXHpJX2k7f5+8gB68ZHnLX9Y/dV0qXGfix+Wtl9043hp+4LX50jbOw0aLm1v06qBtH3pvO+l7T2HnGFpjwoNktrGx4RI25et2i9tH3N2G2n75H4J0vZvduVI20+Kth430hWIto0j1DJfNVjlZ+cAC1/W6smdloHsXQcc+4Z37Iy4cwc79hMOmWvW4OCCBVq+m3Zk4Ntf5PfUrsM+Q3riintH25lZ/u7Jz4En85BjP+HgPpSF7N3JWr7NHp2BkOhoLV8dp9d+2onPN6bouOLGfi3QJi7csW+Eyx+BVSUvm94+25SGgzmFjo8pHJpFhyA0KEDLd/nOdKTnFTn2Hdg+Do0jgx37CYfFm1KRqnGtLWJCMX3MyVrH1HEqKizG8gEDdVyBsjJA+jT03Z2n1IPCHOf3Q/QaFBaIxl0ba52vLrSKg0WHBGBw21jHx/UL8ENIFc86u878g/wR0dj5GBX9xk9+Ag179rI7RLX9fcWOw7jv8w2W/rokRqN1E+uzNdwVgLCgQIv9zkO5OJhVYGnftS8Tyal5Xu3Xnt0aI09JtNg+OWMe3pn/g/K1uSJi4QqPstiHx8UjrEEjS3tu2gG4sw5b2kcM7Y0z+naytLuLSlFY4vFqX/rdGvzxx3aLbVBYFAJDrPe8b++W6Nop3mK/fOUebNhsfQb07J6IDu2t42TLrgzsPWhlo/N6NUOfDtZr9SPwAgRe5bEkNSTwqulH4PWtE4FXLY4IvFXrROBViyECr2+dCLwAgRcAK7ys8FZOFQRetYcMgZfAayrACq/amHFqReBVU4zAS+A1FWCFFwCnNACc0qCWPAm8ajoReAm8BF61saJrReBVU47AS+Al8FaIAQIvgVctdQIEXjWlCLwEXgKv2ljRtSLwqilH4CXwEngJvF6jgBVeteRJ4FXTicBL4CXwqo0VXSsCr5pyBF4CL4GXwEvgVcuXXlYEXjXRCLwEXgKv2ljRtSLwqilH4CXwEngJvPUCeKOCA9GmUc0tS+bOyUXZVy+pZdpKVlyWzF62E2VZspk/78aC9QftBZFYcFkye9mOx7JkLRuE4fnR3exPrposCLxqQtY14G12978Q26On2sVVg9XK3Rm4e8E6S09clgzgsmQAuA5v7VmHNzokCK011iTVzRMFubnwfEngtdOvrlV4Ex6bgeComluH961fd2PenwReuziqS8uStYoNw7RRNQe8xUXF+PUsrsNrF0N1DXgT7vkXGnSvOeBdtScDk+cTeLkOLzeekOYSbjzBjSfsHjJ1DXi58YTvO8qNJ+wiHuDGE7414sYT9jEkLLjxBDee4MYT3GkN3GlNLWFWtOJOa2qaEXgJvKYC3Gmt6ljgTmtq+YQ7rf2tE3da405r4NbCALcWVkue3FpYTSduLexbJ24trBZHBF4Cr1CAWwtza2HO4eUcXnBKA6c02KEDpzT4VohbC9tF0JG/16U5vJzSwCkNpgKs8LLCayrQtzcrvKzwghVetUc+wAqvmlKs8LLCaypwPFZpIPASeAm8BZYg4JQGAi+Bl8CrRnEg8KoKReAl8BJ4FUdLWRlQpmhbwYxzeNU0Y4WXFV5WeCuMFc7hZYVXLXUSeFV1IvASeAm8iqOFwGsrFOfwcg5vnZ7DmzBupvQ7bU7yLmnwu9Pl62T2GHW51N5fskDvvo3yvtM2/VrFgPOTtkcntpe256btrcK+g7Q9tnkraXvOocOW9pR1P0pt43sOkrYfXP2ttL1Zr8HS9sDgYGl7XLNIaXvyrkOW9jH9WmLatX1tk1d1GeS6i3E4v1Cru7CiTDR0O1971eMKgycsVuuYfvmZ8M9yfkxxMI87D56cTK3jrlj0B95/Zr5j3x6nJOKiSzTXoDQe4hplKwANbp2K4Eh53Dm+CAWHd3/bi+92WsecgivObheHxpHysePLv0m4CyGB/iqHsNgs3paGlNwiLd/0vCIUl+rdl7TsAhSWeBwft2l0CEJdAY79hIPIwH5+8jzsq8P4yGBMOU+ed7VOxMapsLAYU2M6aXcdqHGNRZ4ypBSWaB9T11F3LV1xvEx3CRbvyNA6tI5G4kCBfkCj4ECtY45ZMBsdB5+p5avjtHTVToy5/22La0mhG54S65gvLSqQtkef1BERTVpa+jHsS4u92gNcoQh0WXNYbNMIRDUMt/ZRUgqPJIekJ2chJz3PYh/fPBZx8dZ11bMy3MiT5LGDm9Yifc8OSz9lnlLLM+WkHqegSfuOFlvBf/4B1vy6e+XvSN6yyWIf1jABIdGNLO3hDRsiNNp67jkpB5GfkW6xd0XGwhVutfcj8AIEXp2U8LcPgVdNPwKvb50IvGpxROCtWicCr1oMEXh960TgBQi8AFjhZYW3cqog8Ko9ZAi8BF5TAVZ41caMUysCr5piBF4Cr6kAK7wAOKUB4JQGteRJ4FXTicBL4CXwqo0VXSsCr5pyBF4CL4G3QgwQeAm8aqkTIPCqKUXgJfASeNXGiq4VgVdNOQIvgZfAS+D1GgWs8KolTwKvmk4EXgIvgVdtrOhaEXjVlCPwEngJvAReAq9avvSyIvCqiUbgJfASeNXGiq4VgVdNOQIvgZfAS+Al8KrlSwKvhk4EXgIvgVdj4DhwIfCqiUXgJfASeAm8BF61fEng1dCJwEvgJfBqDBwHLgReNbEIvAReAi+Bl8Crli8JvBo6EXgJvARejYHjwIXAqyYWgZfAS+Al8BJ41fIlgVdDJwIvgZfAqzFwHLgQeNXEIvASeAm8BF4Cr1q+JPBq6ETgJfASeDUGjgMXAq+aWAReAi+Bl8BL4FXLlwReDZ0IvAReAq/GwHHgQuBVE4vAS+Al8BJ4Cbxq+ZLAq6ETgZfAS+DVGDgOXAi8amIReAm8BF4CL4FXLV8SeDV0IvASeAm8GgPHgQuBV00sAi+Bl8BL4CXwquVLAq+GTgReAi+BV2PgOHAh8KqJReAl8BJ4CbwEXrV8SeDV0InAS+Al8GoMHAcuBF41sQi8BN4TEnhb3fBJmezW71+5UBoRZR6PtL3t2aOk7alb11vaPcVFUlt/V7C0PTtpq7Q9MDhM2p7Q61xp+55fP5e2+/kHSNtH3nKdpf3b9+V9NOncW9rH7p+/kLa37He+XN8qxuG4kZ2kf1n650FL+5BOTfDACLm9Wjp0ZpXnLkapp8SZ01/WOT8tRdrMFx37eko98BRLQ9e2r8DgAARHyWPNzjk8viEadm1lZyb9e3FeAQoychz7rlm1Dws+XuPYTzg0Dg5Auwi9ax21cRkiGsdqHVfH6Y1lu/DlphQdV4w7JRHNY+X5wFeHcWGBCA701zrm67/swc7D+Vq+6TmFKCqR51K7Dts2jUBESJCdmeXvnZtGIirUuZ/oaOnmVKTmFDo+ZosGoXhhTHfHfroORYXF+KHH6VrumQUlWLI7U8tX1yk0wA+dI/XGZ0hIINp00BufGblFWPiH87FWVlaGUs2LDfH3Q9sIl5b30E9moe15Z2j56jgtXr4ZI256xuIaGBoBGXf4BwTBP9A6tlp2a4/4ti0s/SRt3Y+0pDSv9tzUvSjItN6T5r16I76D9Xl+eP8hZB/KsvR92eAuGHRaa0v7ojUHsGL7YUt7dFQwIiX3ZffWg0jeZ7UfPrAD+vXyvqYP5y7Ekm+XW/oOcIUiwBViaQ8Kj0ZQaISlPTYxHtGN4yzth/bsRVZyqrWfMNFPpKW9RdtGiE+0jg0/Ai9A4NVJCX/7EHjV9CPw+taJwKsWRwTeqnUi8KrFEIHXt04EXoDAC4AVXlZ4K6cKAq/aQ4bAS+A1FWCFV23MOLUi8KopRuAl8JoKsMILgFMaAE5pUEueBF41nQi8BF4Cr9pY0bUi8KopR+Al8BJ4K8QAgZfAq5Y6AQKvmlIEXgIvgVdtrOhaEXjVlCPwEngJvARer1HACq9a8iTwqulE4CXwEnjVxoquFYFXTTkCL4GXwEvgJfCq5UsvKwKvmmgEXgIvgVdtrOhaEXjVlCPwEngJvATeegG8w7s2xX1DO6plvmqwKiwqRWEVS83Zdc9lyewUAo7XsmSjNy1DeCO9ZY/sr8pq8faKPZgnWWZPpS8uS2av0vFYlqx1wzBMG3Wy/clVk0VxUQm+795XqzcuS2Yv2/FalmzE/NlodU4/+xOsJosfV23FoOumWnrjsmQAlyUDwHV4a886vEM7N8GU4TW3Dm++uxglXIfXNtWywutbopk/78IXG52vDSp6JfDahh+OB/ByHV7f94Xr8NrHrbAY9skstKnBdXi/XbEZwydyHV6uw8uNJ6QjlBtPcOMJu9RN4PWtENfhtYugI3/nOrxV68QpDWoxxCkNvnXiOrxch9eIEK7Dy3V4K6cKzuFVe8gQeAm8pgJch1dtzDi1IvCqKUbgJfCaCnAdXq7Da8QCV2lQS54EXjWdCLwEXgKv2ljRtSLwqilH4CXwEngrxADX4SXwqqVOrsOrqhOBl8BL4FUdLXp2BF413Qi8BF4CL4HXaxSwwquWPFnhVdOJwEvgJfCqjRVdKwKvmnIEXgIvgZfAS+BVy5deVgReNdEIvAReAq/aWNG1IvCqKUfgJfASeAm8BF61fEng1dCJwEvgJfBqDBwHLgReNbEIvATeExJ4+z/7XZns1i9/7x1pRJw2bry0fcX7c6TtvS+90tIeFBwgtQ0PC5K2px7MkbbnZRVK2w/v3i5tz9yzXtref8IEaXvfdnGW9v8t2iq1bRgfKW3fvVZ+LuFxjaX2yet+ket+yQXyaz2Ya2k/v0czPH5xzS32np/nhmvbj2qZtpKVe89uZK363bFvcZ4bBenyuLDrbOP2dCxelmRnJv17186NMXxwOy3fgBAXgqPCHPseWH8QG77e7NhPOKQUlGB7XpGW79TkVYhp0lDLV8dp8eZUrE+zxrNKXyPax6JZZLCKqZeNK+lP+LuzHPsJh13RHZEXFKXlu2x3Og7nF2v59mwWjQah8lzpq8PY0EAEB/prHbOotAzSB4VNb4F+fujQRJ4btU7ExqmwsBizmnTT6rq0rAz5pR7HvoWlZdhfoLcso+ODVXCIiwnB2OFttLrIyizA99/tdewroicySC+G3KUe7MjTi/mbFs5B96H9HZ+vrsOfSVmY+dsei3teYQkKikst7amH8pAp4ZED61fj0E4rM5zU4xQ0ae+9OZTgoiBXoKXv/JxCFLqt8ZWVegj5GdkW+5j4OEQ1amBp37dmFdK2b9GVpNyvaafuaNiqvVc/qVs3IGPfLkvfAa5QBLhCLO3hjRIR2qCJpb0oLxslBfmWdndmKopyMyztUQltENEo0dKevms9cg5az8ePwAsQeI9uDBB41fQj8PrWicCrFkcE3qp1IvCqxRCB17dOBN6q9SHwAmCFlxVetVTrbcUKr71qrPDaa8QKr71GrPD61ogVXvsYYoWXFV4CL4EXnNLAKQ12jwtWeFnhNRXglAa70aL3d1Z41XRjhZcVXrVIsVoReAm8BF7O4bXNHwReAi+B13aYHJUBgVdNPgIvgVctUgi8Up04pYFTGnQGEKc02KvGKQ32GnFKg71GnNLAKQ2mAgReAq99xpBbsMLLCi8rvKzw2uYPVnhZ4WWF13aYHJUBK7xq8hF4CbxqkcIKLyu8EgU4h5dzeO0SCIGXwEvgtRslR/d3Aq+afgReAq9apBB4CbwEXi8FuCyZWuog8BJ4CbxqY0XXisCrphyBl8CrFikEXgIvgZfAq5EtCLwEXgKvxsBx4ELgVROLwEvgVYsUAi+Bl8BL4NXIFgReAi+BV2PgOHAh8KqJReAl8KpFCoGXwEvgJfBqZAsCL4GXwKsxcBy4EHjVxCLwEnjVIoXAS+Al8BJ4NbIFgZfAS+DVGDgOXAi8amIReAm8apFC4CXwEngJvBrZgsBL4CXwagwcBy4EXjWxCLwEXrVIIfASeAm8BF6NbEHgJfASeDUGjgMXAq+aWAReAq9apBB4CbwEXgKvRrYg8BJ4CbwaA8eBC4FXTSwCL4FXLVLqF/D+H7Jbf2GTbW3SAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = make_subplots(rows=1, cols=5, subplot_titles=samples)\n",
    "zranges = {'zmin': dots.Correlation.min(),\n",
    "           'zmax': dots.Correlation.max()}\n",
    "\n",
    "for i, matrix in enumerate(matrices, 1):\n",
    "    hmap = go.Heatmap(z=matrix, colorscale='RdBu_r', **zranges)\n",
    "    fig.add_trace(hmap, row=1, col=i)\n",
    "fig.update_traces(showscale=False)\n",
    "fig.update_xaxes(showticklabels=False)\n",
    "fig.update_yaxes(showticklabels=False, autorange='reversed')\n",
    "fig.update_layout(height=160, margin=dict(l=0, r=0, t=30, b=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "062859f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PYPLT_COLORS = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',\n",
    "                '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',\n",
    "                '#bcbd22', '#17becf']\n",
    "fig = px.box(dots, x='Sample', y='Correlation', color='Sample',\n",
    "             points='all', color_discrete_sequence=PYPLT_COLORS,\n",
    "             category_orders={'Sample': samples}\n",
    "            )\n",
    "fig.update_layout(showlegend=False,\n",
    "                  margin=dict(l=0, r=0, t=0, b=0)\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02737544",
   "metadata": {},
   "source": [
    "# Example Kruskal-Wallis test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48d91d2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H_0:  There is no difference between replicate self correlation and control correlation\n",
      "pvalue 4.3661279526873136e-17\n",
      "Reject H_0\n"
     ]
    }
   ],
   "source": [
    "# Using all values of the matrices to compute the distributions\n",
    "self_corr_upper = corr.sample_upper_triangle(allele_index, corr_matrix)\n",
    "print(\"H_0: \", mvip.H0)\n",
    "pvalue = mvip.kruskal_wallis_test(self_corr_upper, control_allele_matrix)\n",
    "print(\"pvalue\",pvalue)\n",
    "if pvalue < 0.05: print(\"Reject H_0\")\n",
    "else: print(\"Accept H_0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "185fdc72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H_0:  There is no difference between replicate self correlation and control correlation\n",
      "pvalue 0.0007637037391315315\n",
      "Reject H_0\n"
     ]
    }
   ],
   "source": [
    "# Using the median rows of the matrices to represent the distributions\n",
    "self_corr_upper = corr.sample_upper_triangle(allele_index, corr_matrix)\n",
    "print(\"H_0: \", mvip.H0)\n",
    "pvalue = mvip.kruskal_wallis_on_medians(allele_corr_matrix, control_allele_matrix)\n",
    "print(\"pvalue\", pvalue)\n",
    "if pvalue < 0.05: print(\"Reject H_0\")\n",
    "else: print(\"Accept H_0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcfcd48a",
   "metadata": {},
   "source": [
    "# Cell morphology VIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1e266468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3824 (3824, 3824) Alleles: 376\n",
      "Number of mutants: 326\n"
     ]
    }
   ],
   "source": [
    "print(len(morphology), corr_matrix.shape, \"Alleles:\",len(morphology[\"x_mutation_status\"].unique()))\n",
    "alleles = morphology[\"x_mutation_status\"].unique()\n",
    "alleles = [a for a in alleles if a.find(\"WT\") == -1]\n",
    "\n",
    "print(\"Number of mutants:\",len(alleles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8877a0c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:28<00:00, 34.51it/s]\n"
     ]
    }
   ],
   "source": [
    "treated_samples = morphology[morphology.Metadata_broad_sample_type == \"trt\"]\n",
    "null_treatments = corr.null_distribution(treated_samples.index, corr_matrix, 8, repeats=1000)\n",
    "null_treatments = null_treatments[0:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a07ccda7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No wild type for: EMPTY\n"
     ]
    },
    {
     "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>wild_type</th>\n",
       "      <th>wt_samples</th>\n",
       "      <th>mutant</th>\n",
       "      <th>mut_samples</th>\n",
       "      <th>impact_test</th>\n",
       "      <th>strength_test</th>\n",
       "      <th>directionality_test</th>\n",
       "      <th>power_test</th>\n",
       "      <th>is_sig_impact_test</th>\n",
       "      <th>adjusted_impact_test</th>\n",
       "      <th>is_sig_strength_test</th>\n",
       "      <th>adjusted_strength_test</th>\n",
       "      <th>is_sig_power_test</th>\n",
       "      <th>adjusted_power_test</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>STC2_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>STC2_p.N59I</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.006323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.777281e-07</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>True</td>\n",
       "      <td>0.014172</td>\n",
       "      <td>True</td>\n",
       "      <td>2.416805e-07</td>\n",
       "      <td>LOF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RBM45_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>RBM45_p.R81S</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.653723e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>True</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>True</td>\n",
       "      <td>2.419621e-11</td>\n",
       "      <td>LOF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.K754I</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.002712</td>\n",
       "      <td>0.004574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.613815e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>0.004429</td>\n",
       "      <td>True</td>\n",
       "      <td>0.010696</td>\n",
       "      <td>True</td>\n",
       "      <td>2.419621e-11</td>\n",
       "      <td>LOF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PIK3R3_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PIK3R3_p.P127L</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.033015</td>\n",
       "      <td>0.035692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.593887e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>0.042078</td>\n",
       "      <td>False</td>\n",
       "      <td>0.057711</td>\n",
       "      <td>True</td>\n",
       "      <td>2.419621e-11</td>\n",
       "      <td>COF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KEAP1_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>KEAP1_p.A159P</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.981598e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>True</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>True</td>\n",
       "      <td>2.419621e-11</td>\n",
       "      <td>LOF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>MET_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MET_p.F1341L</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.003643</td>\n",
       "      <td>0.006323</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.923367e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>0.005692</td>\n",
       "      <td>True</td>\n",
       "      <td>0.014172</td>\n",
       "      <td>True</td>\n",
       "      <td>2.133427e-02</td>\n",
       "      <td>GOF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>RIT1_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>RIT1_p.Q40L</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.420098e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>True</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>True</td>\n",
       "      <td>3.729973e-02</td>\n",
       "      <td>LOF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>ERBB2_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>ERBB2_p.S418T</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.207578</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.751288e-10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>False</td>\n",
       "      <td>0.275359</td>\n",
       "      <td>True</td>\n",
       "      <td>1.473662e-09</td>\n",
       "      <td>COF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>ATF2_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>ATF2_p.A481V</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.293622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.402556e-10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.004517</td>\n",
       "      <td>False</td>\n",
       "      <td>0.369872</td>\n",
       "      <td>True</td>\n",
       "      <td>6.319033e-10</td>\n",
       "      <td>COF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>MDM2_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MDM2_p.S221C</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.366638e-01</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>True</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>False</td>\n",
       "      <td>2.457372e-01</td>\n",
       "      <td>GOF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>325 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       wild_type  wt_samples          mutant  mut_samples  impact_test  \\\n",
       "0      STC2_WT.c         8.0     STC2_p.N59I          8.0     0.000008   \n",
       "1     RBM45_WT.c         8.0    RBM45_p.R81S          8.0     0.000062   \n",
       "2      EGFR_WT.c         8.0    EGFR_p.K754I          8.0     0.002712   \n",
       "3    PIK3R3_WT.c         8.0  PIK3R3_p.P127L          8.0     0.033015   \n",
       "4     KEAP1_WT.c         8.0   KEAP1_p.A159P          8.0     0.000151   \n",
       "..           ...         ...             ...          ...          ...   \n",
       "320     MET_WT.c         8.0    MET_p.F1341L          8.0     0.003643   \n",
       "321    RIT1_WT.c         8.0     RIT1_p.Q40L          8.0     0.000007   \n",
       "322   ERBB2_WT.c         8.0   ERBB2_p.S418T          8.0     0.000016   \n",
       "323    ATF2_WT.c         8.0    ATF2_p.A481V          8.0     0.002794   \n",
       "324    MDM2_WT.c         8.0    MDM2_p.S221C          8.0     0.000005   \n",
       "\n",
       "     strength_test  directionality_test    power_test  is_sig_impact_test  \\\n",
       "0         0.006323                  0.0  1.777281e-07                True   \n",
       "1         0.000778                  0.0  6.653723e-12                True   \n",
       "2         0.004574                  0.0  6.613815e-12                True   \n",
       "3         0.035692                  0.0  7.593887e-12                True   \n",
       "4         0.000778                  0.0  6.981598e-12                True   \n",
       "..             ...                  ...           ...                 ...   \n",
       "320       0.006323                  1.0  1.923367e-02                True   \n",
       "321       0.000778                  0.0  3.420098e-02                True   \n",
       "322       0.207578                  1.0  8.751288e-10                True   \n",
       "323       0.293622                  1.0  3.402556e-10                True   \n",
       "324       0.000778                  1.0  2.366638e-01                True   \n",
       "\n",
       "     adjusted_impact_test  is_sig_strength_test  adjusted_strength_test  \\\n",
       "0                0.000043                  True                0.014172   \n",
       "1                0.000191                  True                0.002717   \n",
       "2                0.004429                  True                0.010696   \n",
       "3                0.042078                 False                0.057711   \n",
       "4                0.000359                  True                0.002717   \n",
       "..                    ...                   ...                     ...   \n",
       "320              0.005692                  True                0.014172   \n",
       "321              0.000043                  True                0.002717   \n",
       "322              0.000059                 False                0.275359   \n",
       "323              0.004517                 False                0.369872   \n",
       "324              0.000034                  True                0.002717   \n",
       "\n",
       "     is_sig_power_test  adjusted_power_test prediction  \n",
       "0                 True         2.416805e-07        LOF  \n",
       "1                 True         2.419621e-11        LOF  \n",
       "2                 True         2.419621e-11        LOF  \n",
       "3                 True         2.419621e-11        COF  \n",
       "4                 True         2.419621e-11        LOF  \n",
       "..                 ...                  ...        ...  \n",
       "320               True         2.133427e-02        GOF  \n",
       "321               True         3.729973e-02        LOF  \n",
       "322               True         1.473662e-09        COF  \n",
       "323               True         6.319033e-10        COF  \n",
       "324              False         2.457372e-01        GOF  \n",
       "\n",
       "[325 rows x 15 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_dir = 'outputs/images/morphology/'\n",
    "os.makedirs(images_dir, exist_ok=True)\n",
    "mvip_test = mvip.Morphology_VIP_CNN_Features(morphology, corr_matrix, treatment_samples=8, \n",
    "                                             perturbation_field=\"x_mutation_status\",\n",
    "                                             control_samples=20)\n",
    "results = mvip_test.test_allele_set(alleles, create_images=True, null_distribution=null_treatments,\n",
    "                                    images_dir=images_dir)\n",
    "results = mvip_test.adjust_pvalues(results, Q=0.05)\n",
    "results.to_csv(\"outputs/benchmark_results.csv\", index=False)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0878f983",
   "metadata": {},
   "outputs": [],
   "source": [
    "signal_strength = {}\n",
    "treatments = morphology[\"x_mutation_status\"].unique()\n",
    "for trt in treatments:\n",
    "    index = morphology[\"x_mutation_status\"] == trt\n",
    "    median = corr.median_correlation(morphology[index].index, corr_matrix)\n",
    "    signal_strength[trt] = median\n",
    "\n",
    "with open('outputs/signal_strength_cp.json', 'w') as fp:\n",
    "    json.dump(signal_strength, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1530909",
   "metadata": {},
   "source": [
    "# False positives analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "be5e7d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_wild_types = ['EGFR_WT.c',\n",
    " 'EGFR_p.L858R',\n",
    " 'EGFR_p.T790M, p.L858R.o',\n",
    " 'KEAP1_WT.c',\n",
    " 'KRAS_p.G12V',\n",
    " 'MDM2_WT.c',\n",
    " 'NFE2L2_WT.c',\n",
    " 'STK11_WT.c',\n",
    " 'EMPTY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3a2c2c3a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:25<00:00, 39.77it/s]\n"
     ]
    }
   ],
   "source": [
    "fp_treated_samples = morphology[morphology.Metadata_broad_sample_type == \"trt\"]\n",
    "fp_null_treatments = corr.null_distribution(fp_treated_samples.index, corr_matrix, 8, repeats=1000)\n",
    "fp_null_treatments = fp_null_treatments[0:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "67b57898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36fe43947cfa4620850e46324ce94991",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp_results = []\n",
    "fp_mvip_test = mvip.Morphology_VIP_CNN_Features(morphology, corr_matrix,\n",
    "                                                perturbation_field=\"x_mutation_status\",\n",
    "                                                treatment_samples=8, control_samples=20)\n",
    "for i in tqdm(range(1000)):\n",
    "    fp_results.append(fp_mvip_test.test_allele_set(fp_wild_types,\n",
    "                                                   create_images=False,\n",
    "                                                   false_positives=True,\n",
    "                                                   null_distribution=fp_null_treatments)\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a5f5f2c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EGFR_WT.c\t7.7%\n",
      "EGFR_p.L858R\t7.8%\n",
      "EGFR_p.T790M, p.L858R.o\t8.0%\n",
      "KEAP1_WT.c\t6.6%\n",
      "KRAS_p.G12V\t7.1%\n",
      "MDM2_WT.c\t5.4%\n",
      "NFE2L2_WT.c\t7.6%\n",
      "STK11_WT.c\t7.9%\n",
      "EMPTY\t2.7%\n"
     ]
    },
    {
     "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>wild_type</th>\n",
       "      <th>wt_samples</th>\n",
       "      <th>mutant</th>\n",
       "      <th>mut_samples</th>\n",
       "      <th>impact_test</th>\n",
       "      <th>strength_test</th>\n",
       "      <th>directionality_test</th>\n",
       "      <th>power_test</th>\n",
       "      <th>is_sig_impact_test</th>\n",
       "      <th>adjusted_impact_test</th>\n",
       "      <th>is_sig_strength_test</th>\n",
       "      <th>adjusted_strength_test</th>\n",
       "      <th>is_sig_power_test</th>\n",
       "      <th>adjusted_power_test</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.062539</td>\n",
       "      <td>0.035692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.113369e-07</td>\n",
       "      <td>False</td>\n",
       "      <td>0.219632</td>\n",
       "      <td>False</td>\n",
       "      <td>0.119238</td>\n",
       "      <td>True</td>\n",
       "      <td>5.611550e-07</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.661664</td>\n",
       "      <td>0.528612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.149285e-05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.813966</td>\n",
       "      <td>False</td>\n",
       "      <td>0.657750</td>\n",
       "      <td>True</td>\n",
       "      <td>3.254514e-05</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.093670</td>\n",
       "      <td>0.045999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.383933e-09</td>\n",
       "      <td>False</td>\n",
       "      <td>0.277953</td>\n",
       "      <td>False</td>\n",
       "      <td>0.140100</td>\n",
       "      <td>True</td>\n",
       "      <td>2.946361e-09</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.758294</td>\n",
       "      <td>0.674424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.503611e-06</td>\n",
       "      <td>False</td>\n",
       "      <td>0.871535</td>\n",
       "      <td>False</td>\n",
       "      <td>0.771064</td>\n",
       "      <td>True</td>\n",
       "      <td>1.617365e-06</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.023906</td>\n",
       "      <td>0.015714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.391101e-09</td>\n",
       "      <td>False</td>\n",
       "      <td>0.128613</td>\n",
       "      <td>False</td>\n",
       "      <td>0.075349</td>\n",
       "      <td>True</td>\n",
       "      <td>5.298996e-09</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8956</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.005212</td>\n",
       "      <td>0.003276</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.593460e-07</td>\n",
       "      <td>False</td>\n",
       "      <td>0.060145</td>\n",
       "      <td>True</td>\n",
       "      <td>0.040892</td>\n",
       "      <td>True</td>\n",
       "      <td>2.889113e-07</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8965</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.966472</td>\n",
       "      <td>0.916359</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.344699e-04</td>\n",
       "      <td>False</td>\n",
       "      <td>0.981971</td>\n",
       "      <td>False</td>\n",
       "      <td>0.934213</td>\n",
       "      <td>True</td>\n",
       "      <td>3.394102e-04</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8974</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.081351</td>\n",
       "      <td>0.027423</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.647480e-04</td>\n",
       "      <td>False</td>\n",
       "      <td>0.257639</td>\n",
       "      <td>False</td>\n",
       "      <td>0.103009</td>\n",
       "      <td>True</td>\n",
       "      <td>4.704985e-04</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8983</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.024499</td>\n",
       "      <td>0.035692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.317579e-07</td>\n",
       "      <td>False</td>\n",
       "      <td>0.130572</td>\n",
       "      <td>False</td>\n",
       "      <td>0.119238</td>\n",
       "      <td>True</td>\n",
       "      <td>3.678026e-07</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8992</th>\n",
       "      <td>EGFR_WT.c</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EGFR_p.L858R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.215361</td>\n",
       "      <td>0.115184</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.468998e-05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.445678</td>\n",
       "      <td>False</td>\n",
       "      <td>0.245594</td>\n",
       "      <td>True</td>\n",
       "      <td>1.541266e-05</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      wild_type  wt_samples        mutant  mut_samples  impact_test  \\\n",
       "1     EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.062539   \n",
       "10    EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.661664   \n",
       "19    EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.093670   \n",
       "28    EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.758294   \n",
       "37    EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.023906   \n",
       "...         ...         ...           ...          ...          ...   \n",
       "8956  EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.005212   \n",
       "8965  EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.966472   \n",
       "8974  EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.081351   \n",
       "8983  EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.024499   \n",
       "8992  EGFR_WT.c         8.0  EGFR_p.L858R          8.0     0.215361   \n",
       "\n",
       "      strength_test  directionality_test    power_test  is_sig_impact_test  \\\n",
       "1          0.035692                  0.0  5.113369e-07               False   \n",
       "10         0.528612                  0.0  3.149285e-05               False   \n",
       "19         0.045999                  0.0  2.383933e-09               False   \n",
       "28         0.674424                  0.0  1.503611e-06               False   \n",
       "37         0.015714                  1.0  4.391101e-09               False   \n",
       "...             ...                  ...           ...                 ...   \n",
       "8956       0.003276                  0.0  2.593460e-07               False   \n",
       "8965       0.916359                  1.0  3.344699e-04               False   \n",
       "8974       0.027423                  1.0  4.647480e-04               False   \n",
       "8983       0.035692                  0.0  3.317579e-07               False   \n",
       "8992       0.115184                  0.0  1.468998e-05               False   \n",
       "\n",
       "      adjusted_impact_test  is_sig_strength_test  adjusted_strength_test  \\\n",
       "1                 0.219632                 False                0.119238   \n",
       "10                0.813966                 False                0.657750   \n",
       "19                0.277953                 False                0.140100   \n",
       "28                0.871535                 False                0.771064   \n",
       "37                0.128613                 False                0.075349   \n",
       "...                    ...                   ...                     ...   \n",
       "8956              0.060145                  True                0.040892   \n",
       "8965              0.981971                 False                0.934213   \n",
       "8974              0.257639                 False                0.103009   \n",
       "8983              0.130572                 False                0.119238   \n",
       "8992              0.445678                 False                0.245594   \n",
       "\n",
       "      is_sig_power_test  adjusted_power_test prediction  \n",
       "1                  True         5.611550e-07         NT  \n",
       "10                 True         3.254514e-05         NT  \n",
       "19                 True         2.946361e-09         NT  \n",
       "28                 True         1.617365e-06         NT  \n",
       "37                 True         5.298996e-09         NT  \n",
       "...                 ...                  ...        ...  \n",
       "8956               True         2.889113e-07         NT  \n",
       "8965               True         3.394102e-04         NT  \n",
       "8974               True         4.704985e-04         NT  \n",
       "8983               True         3.678026e-07         NT  \n",
       "8992               True         1.541266e-05         NT  \n",
       "\n",
       "[1000 rows x 15 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_predictions = {}\n",
    "fp_adjusted_results = pd.concat(fp_results, ignore_index=True)\n",
    "fp_adjusted_results = fp_mvip_test.adjust_pvalues(fp_adjusted_results, Q=0.05)\n",
    "for r in fp_adjusted_results.iterrows():\n",
    "    try: fp_predictions[r[1][\"mutant\"]].append(r[1][\"prediction\"])\n",
    "    except: fp_predictions[r[1][\"mutant\"]] = [r[1][\"prediction\"]]\n",
    "\n",
    "for k in fp_predictions.keys():\n",
    "    truep = len([1 for p in fp_predictions[k] if p in [\"NI\",\"NT\"]])\n",
    "    falsep = len(fp_predictions[k]) - truep\n",
    "    print(\"{}\\t{:3.1f}%\".format(k, 100*float(falsep)/len(fp_predictions[k])))\n",
    "    \n",
    "fp_adjusted_results.sort_values(\"mutant\")\n",
    "fp_adjusted_results[fp_adjusted_results[\"mutant\"] == \"EGFR_p.L858R\"]"
   ]
  }
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