{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1be88941-fca2-465e-a271-c77550c7e6db",
   "metadata": {},
   "source": [
    "# Spatial perturbation tasks on mouse brain data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b2d584e4-d3db-4c08-8bf5-76ab340c225a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "device = \"cuda\"\n",
    "import importlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa4a4014-6a35-415d-baa9-738cc509d53f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\dask\\dataframe\\_pyarrow_compat.py:15: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import scanpy as sc\n",
    "import squidpy as sq\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "import scipy as sp\n",
    "import numpy as np\n",
    "import multiprocessing\n",
    "import pickle as pkl\n",
    "import torch\n",
    "import gc\n",
    "import sklearn.metrics\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "matplotlib.rcParams['mathtext.fontset'] = 'dejavuserif'\n",
    "matplotlib.rcParams['font.family'] = 'arial'\n",
    "matplotlib.rc('pdf', fonttype=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d606b6a9-01be-44c4-b25a-6d001831dfb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import steamboat as sf\n",
    "import steamboat.tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2abd2d15-e58d-4fed-820f-de6b20b75eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0811bb7f-3e06-47b6-8f72-20e72aada6be",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read_h5ad(\"saved_h5ad/mmbrain_0.h5ad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9495ef8a-bcce-4faa-8839-52edb737e3e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\torch\\_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "_IncompatibleKeys(missing_keys=[], unexpected_keys=['spatial_gather.w_local._scale', 'spatial_gather.w_global._scale'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sf.Steamboat(adata.var_names.tolist(), n_heads=50, n_scales=3)\n",
    "model = model.to(device)\n",
    "model.load_state_dict(torch.load('saved_models/mmbrain.pth', weights_only=True), strict=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3c40278-24f7-4640-b78f-045be6746ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'steamboat.tools' from 'G:\\\\Projects\\\\Steamboat\\\\examples\\\\..\\\\steamboat\\\\tools.py'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import importlib\n",
    "importlib.reload(steamboat.tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb5c547e-f8ac-41bb-a04f-7cb9ccc7cdfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gbx2' 'Trdn' 'Edaradd' 'Hoxb3' 'Barhl2' 'Sec14l5' 'Mog' 'C030029H02Rik'\n",
      " 'Cldn11' 'Sox10' 'Dgkk' 'Drd2' 'Onecut2' 'Mafa' 'Gabra6' 'Cnpy1' 'Grm3'\n",
      " 'Sema6a' 'Ermn' 'Nr2e1' 'Emx2os' 'Sstr1' 'Drd1' 'Grin2c' 'Mfge8' 'S1pr1']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.features = adata.var['gene_symbol']\n",
    "sf.tools.plot_all_transforms(model, top=3, head_order=[2, 5, 24], figsize=(4, 5))\n",
    "# # plt.savefig(fig_path + \"crc_all_loadings.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a5980f-5c69-4517-a81c-2afb34e0b846",
   "metadata": {},
   "source": [
    "## Spatial perturbation scenario 1: transplanting cells\n",
    "\n",
    "### Designate cells to be \"transplanted\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f7e721d-c9b2-4d33-8a70-e8af9df07fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find OPC-Oligo in two regions\n",
    "\n",
    "adata.obs['class_region'] = adata.obs['class'].astype(str) + '@' + adata.obs['parcellation_division'].astype(str)\n",
    "adata.obs['oligo_class_region'] = adata.obs['class_region']\n",
    "adata.obs.loc[adata.obs['class_region'] != '31 OPC-Oligo', 'oligo_class_region'] = '98 Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80b9ec4b-462d-407d-8d02-05a0254a7b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confirm that there are some expression differences in OPC-Oligo in two regions.\n",
    "\n",
    "oligo_adata = adata[adata.obs['class_region'].isin(['31 OPC-Oligo@Isocortex', '31 OPC-Oligo@HPF'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f82fe27-f1f2-45b5-84d2-5a2fcbad9798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class_region\n",
       "31 OPC-Oligo@HPF          980\n",
       "31 OPC-Oligo@Isocortex    784\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oligo_adata.obs['class_region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88bba8b4-0e62-4a01-af24-20dd58376255",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\anndata\\_core\\anndata.py:1292: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n",
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\anndata\\_core\\anndata.py:1292: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n",
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:86: FutureWarning: The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\n"
     ]
    }
   ],
   "source": [
    "sc.tl.rank_genes_groups(oligo_adata, groupby='class_region', reference='31 OPC-Oligo@Isocortex', method='wilcoxon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c900f175-910b-425e-9de2-a8af8edfad48",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>scores</th>\n",
       "      <th>logfoldchanges</th>\n",
       "      <th>pvals</th>\n",
       "      <th>pvals_adj</th>\n",
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       "      <th>0</th>\n",
       "      <td>ENSMUSG00000020932</td>\n",
       "      <td>13.187178</td>\n",
       "      <td>2.604712</td>\n",
       "      <td>1.040073e-39</td>\n",
       "      <td>1.166962e-36</td>\n",
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       "      <th>1</th>\n",
       "      <td>ENSMUSG00000090291</td>\n",
       "      <td>5.466264</td>\n",
       "      <td>4.215365</td>\n",
       "      <td>4.596197e-08</td>\n",
       "      <td>6.446167e-06</td>\n",
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       "    <tr>\n",
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       "      <td>ENSMUSG00000023913</td>\n",
       "      <td>3.856600</td>\n",
       "      <td>0.769698</td>\n",
       "      <td>1.149752e-04</td>\n",
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       "      <td>3.806837</td>\n",
       "      <td>2.439926</td>\n",
       "      <td>1.407553e-04</td>\n",
       "      <td>9.870467e-03</td>\n",
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       "      <td>ENSMUSG00000053930</td>\n",
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       "      <td>2.021932</td>\n",
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       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>ENSMUSG00000058624</td>\n",
       "      <td>-6.016663</td>\n",
       "      <td>-1.100518</td>\n",
       "      <td>1.780497e-09</td>\n",
       "      <td>3.329530e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>ENSMUSG00000033730</td>\n",
       "      <td>-6.205130</td>\n",
       "      <td>-2.224680</td>\n",
       "      <td>5.465168e-10</td>\n",
       "      <td>1.226384e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>ENSMUSG00000070570</td>\n",
       "      <td>-6.513019</td>\n",
       "      <td>-0.717460</td>\n",
       "      <td>7.365549e-11</td>\n",
       "      <td>2.066036e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>ENSMUSG00000047907</td>\n",
       "      <td>-6.625102</td>\n",
       "      <td>-2.285636</td>\n",
       "      <td>3.470090e-11</td>\n",
       "      <td>1.297814e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>ENSMUSG00000005583</td>\n",
       "      <td>-10.971990</td>\n",
       "      <td>-2.906106</td>\n",
       "      <td>5.211266e-28</td>\n",
       "      <td>2.923520e-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1122 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   names     scores  logfoldchanges         pvals  \\\n",
       "0     ENSMUSG00000020932  13.187178        2.604712  1.040073e-39   \n",
       "1     ENSMUSG00000090291   5.466264        4.215365  4.596197e-08   \n",
       "2     ENSMUSG00000023913   3.856600        0.769698  1.149752e-04   \n",
       "3     ENSMUSG00000031980   3.806837        2.439926  1.407553e-04   \n",
       "4     ENSMUSG00000053930   3.645979        2.021932  2.663758e-04   \n",
       "...                  ...        ...             ...           ...   \n",
       "1117  ENSMUSG00000058624  -6.016663       -1.100518  1.780497e-09   \n",
       "1118  ENSMUSG00000033730  -6.205130       -2.224680  5.465168e-10   \n",
       "1119  ENSMUSG00000070570  -6.513019       -0.717460  7.365549e-11   \n",
       "1120  ENSMUSG00000047907  -6.625102       -2.285636  3.470090e-11   \n",
       "1121  ENSMUSG00000005583 -10.971990       -2.906106  5.211266e-28   \n",
       "\n",
       "         pvals_adj  \n",
       "0     1.166962e-36  \n",
       "1     6.446167e-06  \n",
       "2     9.146835e-03  \n",
       "3     9.870467e-03  \n",
       "4     1.758080e-02  \n",
       "...            ...  \n",
       "1117  3.329530e-07  \n",
       "1118  1.226384e-07  \n",
       "1119  2.066036e-08  \n",
       "1120  1.297814e-08  \n",
       "1121  2.923520e-25  \n",
       "\n",
       "[1122 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.get.rank_genes_groups_df(oligo_adata, group='31 OPC-Oligo@HPF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "673b1e65-4810-405e-8c4c-5eba2bddcd8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "old_adata = adata.copy()\n",
    "new_adata = adata.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c37d95be-253a-4907-9d5b-214e9686d728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Please specify a valid `library_id` or set it permanently in `adata.uns['spatial']`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\squidpy\\pl\\_spatial_utils.py:946: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored\n",
      "  _cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sq.pl.spatial_scatter(old_adata, color='parcellation_division', shape=None, figsize=(5, 5), size=1., lw=0., legend_fontsize=9, title=\"\", frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c341d343-e36b-47b0-af0f-2cf795be652a",
   "metadata": {},
   "outputs": [],
   "source": [
    "repalcement_class = '31 OPC-Oligo@Isocortex'\n",
    "to_be_replaced_class = '31 OPC-Oligo@HPF'\n",
    "new_name = '31 OPC-Oligo@Isocortex->HPF'\n",
    "\n",
    "n = 200\n",
    "np.random.seed(0)\n",
    "replacement = np.random.choice(np.where(new_adata.obs['class_region'] == repalcement_class)[0], n, replace=False)\n",
    "to_be_replaced = np.random.choice(np.where(new_adata.obs['class_region'] == to_be_replaced_class)[0], n, replace=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c566db67-a28a-4e56-9760-015baddfc0fe",
   "metadata": {},
   "source": [
    "### Reconstruct the gene expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8ddbc7ef-9c70-4216-96e1-572e659f04c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using ['global'] as regional annotations.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cca011880f1248c0a2089b909f714287",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f70897fa77f483591980cdf48adfcc9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_adata.X[to_be_replaced, :] = new_adata.X[replacement, :]\n",
    "new_adata.obs['class2'] = new_adata.obs['class_region'].tolist()\n",
    "new_adata.obs.loc[new_adata.obs_names[to_be_replaced], 'class2'] = new_name\n",
    "\n",
    "new_dataset = sf.make_dataset([new_adata], sparse_graph=True, regional_obs=['global'])\n",
    "sf.tools.calc_obs([new_adata], new_dataset, model, get_recon=True)\n",
    "new_adata.X = new_adata.obsm['X_recon']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "646e5034-7333-4f93-b506-4125a42a0b3d",
   "metadata": {},
   "source": [
    "### Perform DE analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d4f1cdf-cd96-4c24-be25-ef7163229bb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:86: FutureWarning: The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\n",
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:86: FutureWarning: The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\n"
     ]
    }
   ],
   "source": [
    "highlight = [repalcement_class, new_name, to_be_replaced_class]\n",
    "sc.tl.rank_genes_groups(new_adata, groupby='class2', groups=highlight, reference=repalcement_class, method='wilcoxon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e858db77-f69f-45b3-aa70-a5b133fef22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_df = pd.merge(sc.get.rank_genes_groups_df(new_adata, group=to_be_replaced_class), new_adata.var, right_index=True, left_on='names')\n",
    "b_df = pd.merge(sc.get.rank_genes_groups_df(new_adata, group=new_name), new_adata.var, right_index=True, left_on='names')\n",
    "ab_df = pd.merge(a_df, b_df, left_on='names', right_on='names')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2eb3e9da-dcfe-4ff7-bef4-faa3435ad561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 150x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['mathtext.fontset'] = 'dejavuserif'\n",
    "matplotlib.rcParams['font.family'] = 'arial'\n",
    "matplotlib.rc('pdf', fonttype=42)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(1.5, 1.5))\n",
    "ab_df.plot(kind='scatter', x='logfoldchanges_x', y='logfoldchanges_y', s=0.5, ax=ax, alpha=.5)\n",
    "ax.set_xlabel(to_be_replaced_class)\n",
    "ax.set_ylabel(new_name)\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "        plt.gca().spines[pos].set_visible(False)\n",
    "\n",
    "# plt.savefig(savefig_path + \"mmbrain_perturbation_logfc.pdf\", bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b58b190b-8274-43ea-9612-4d2c63a806b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SignificanceResult(statistic=0.6419909066233113, pvalue=2.277434802943895e-131)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_df2 = ab_df[~ab_df['logfoldchanges_x'].isna() & ~ab_df['logfoldchanges_y'].isna()]\n",
    "sp.stats.spearmanr(ab_df2['logfoldchanges_x'], ab_df2['logfoldchanges_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "646f8fae-dd1a-4643-ba59-826248fb88fb",
   "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>logfoldchanges_x</th>\n",
       "      <th>logfoldchanges_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>logfoldchanges_x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.641991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logfoldchanges_y</th>\n",
       "      <td>0.641991</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  logfoldchanges_x  logfoldchanges_y\n",
       "logfoldchanges_x          1.000000          0.641991\n",
       "logfoldchanges_y          0.641991          1.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_df[['logfoldchanges_x', 'logfoldchanges_y']].corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0c068778-1d48-4236-bfeb-a5fc2a771611",
   "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>logfoldchanges_x</th>\n",
       "      <th>logfoldchanges_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>logfoldchanges_x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.680731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logfoldchanges_y</th>\n",
       "      <td>0.680731</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  logfoldchanges_x  logfoldchanges_y\n",
       "logfoldchanges_x          1.000000          0.680731\n",
       "logfoldchanges_y          0.680731          1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_df[['logfoldchanges_x', 'logfoldchanges_y']].corr(method='pearson')\n",
    "# 0.475"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5ab8d6-be01-43c7-9b2c-36dcd4ace739",
   "metadata": {},
   "source": [
    "## Spatial perturbation scenario 2: changing environment.\n",
    "\n",
    "### Overview the variability of expression of genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "db220f17-38fe-4962-92db-59e7fe42fdea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\AppData\\Local\\Temp\\ipykernel_116268\\147967129.py:3: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  adata_rearrange.obs['class'] = adata_rearrange.obs['class'].astype(str)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Please specify a valid `library_id` or set it permanently in `adata.uns['spatial']`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\squidpy\\pl\\_spatial_utils.py:946: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored\n",
      "  _cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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F80o5emIeWQ4pJW1T5E2/f0tqcrjX2UZMS6gi2O12FpS7Wf67tcR1k3/eOJ8bHtpJRNX5n5Mn8tKePs6cWZT2Ofz9nRYM02LFlIKMRlqGw0bG2PwfJJmEN02Tll4/33luP7u7gjgUiceun8f192/liNpcPrOsmmOnFvP8rj5uXFBJVyDOF5bXsbGhm9o8W0qe5ferG1nf5OfuqxchadqobvRDeSymaaZ5QRtb/Dy8sYMXdvXw14tnAnDLM3tp88f4+okTmF+ZxY1HVVKTnyh5DkQ17l3fzoopBdQVDc17Gas6C6Asx8mfLp0LwCV3rGcgrNIVVLn2qFrWNfnRdJMnt/aypDqXF3f1IQpw2cJyXt/djSAI1OY5uO2sybx50E+BR2HV3j7sssiKqYkKtMo8Fw29EfrDGredPZnV+wZY0zjA8sn5vHWgn4a+KJcuKGVWeaL3Z19XiBN+v5Y5FV5uO3ta4hqzHciiSJ5TpjTLxi0rJlKbP3aJd1Qz6YvoRPVEdV5Dl48ir524bqKZFv6Yjm4ahOM6NXkuvnnSaCHRr50wgZ6QSkWuE13XMwrQGQ4LmW/V/zHC4XDKGPzxjSYe29SJAFjAeXOK6fDF6AmqrGnw8Zll1dTkOnn2s0cDieFir+9s4bv/2seciix+dPpkADY0+wnGdNr7/JTnfDx9rRllXuZUZLGkNif12OKabJ7eFqc0y44oihw/eajrftW+ft7Y309XIJ6qBJMkaVQ+yOv1Eo/HUz064XCY64+o4DerGrGAlX9cw09Pr2NXV5jTZ5Xidti47ogKSgdLi4+fks/T27tZOiEXuyJx1qwibn+7GZdN4vj6fCYMqyz78pO7ALjvytlcvricGaUe3m4I8MD61sSx6vPoD2vs6QyytztMT0ijqT9GJK7jssscPSkPuyxSkePgtb39/O3tFmaVe1OFAsPJdir86cJpOGSRtQ0D/PTlgyyqyeaxa+fSF9GoynMxscDFLi3M87t6uWKEFwWwqGboXn+Q3FuGDB+FjLH5P4CqqkQiEf7wRhOFHhsXzS/j3nVt3LuuDVGAfI+dn58zg6mFiYX1N+dOJWswOS0IQkoBAKA0y06uS2FqsSd1/F+dPQV/TP/YhgbAZZP5xoqJaUUHlyws55KFoxdJgOX1eXQHVU6ckp96bFdXmFhcZXZFduq6kz0+qqqmmkyPmlTA0tpcLrpzM53+GM0DsUFhSgtVVRGBh97roCrPSX1JNvMqs3nrwACSIPDZY2tYXp/PM9u6UfX0BfqICbmE4okkP8CPXjqAZlhcs6Sc/ohGaZadu9e2s78nwowyLyYCU4rdnHX7RpZNyuXU6UX84tWDaIbFpQvKsMsCs8qGBD07/DH+uaOHs2cVke+xU5KVuO95bg1BgNp8FyXZDu5Y08pda1q5eH4ZT2zp5JTZlcChjYmu66NEPTNk+CTIGJv/UpJy/MkEfiCm8+qePizL4pKFFTy1pQtFEvj6yVO4dElN6nXhcJgNLQF0A65eWp6qEFN1g40tAeZWZI3qXcn32MeUZxmJoihp0veHYqRnElZ1BBLGaDgeh8L1Rw7pkAmCwGV3bkQAfn7OdI6vzxt1PlVVyc7OJhaLYRgGt509hce3dHNc/ZDBsiyLZ3f00B2Ms7klwHK3jQvmFtMXinNMXR4AccNCEuG1vb28eaCfKxZX8Pe3W1hck813V9aljnXunBI6A3FOn1mUKu/+9NGJUOCaBh/9YZVt7SEGIhqv7O7DLktMKHCxttHHb19v5MWbF6aGrEHCAK5t9GFZFtcfOVThVlfk5onr56f+vb09SFQzmVjo4oenT+a21w6yqdnHb8+bNm4lXmZyZ4bDRUYb7b+E5CwWm82GpmmpijKHw4Fpmqiqyut7+7hvfRvlOQ4umlfK/t4olx1VlxLEFASBdl+ME37zJvkuhRdvXpQ6/h3vtPDCrh6Or8/n00ePTrwfTnTD5NK7twBw/5WzkaVD9+Mc9at3CcYNltbmcc81C1JqA9vaAnzrub2cMq2QL66YklZFNxZdgThb2wKcOLUwrVT7D6sbeXJLFydNLeT1fX2E4gaKJDC3IovX9/fjtUv889MLcNlkAjGNh9/r4KxZxRQOJvfjmsGvVzUypyKLEq+NhzZ28KXja+gNaXzjmT0srs7hGysm8NB7HWxsCTCp0MPK6flU5yVCdS0DUZ7c0sUF80pSXs1YdAfjRDUj9brPPLydzoDK10+sZXHN2EPgMgUCGQ4XGc/mP5RkYl2WZaLR6JAHMqJXJakGDDC3MovfvN5IX1jje6fWM7XUO0rIMscGXz9xAnkj5PGX1uawrsnH4mHx/Q/CU1u7aPNFuWlZzQd+TWNfhB+8sJ9zZpdw6owiNrcFaOqLUpptZzyVmVV7+6nJd1Kb7+SxGxfxq5cP8tUTJ6apGkQ0AxDQDItoNEowqvHWwQGOq89PiV0OnxlTnGXnxKxE4n+4Ubrj3VZMC57c0onbJlHktfHF42qYUuRmU2uAYq8Nl03m1mf2sLk1gMsmEYjpiILA2kYfnz2mmvda/OzoCHHvlbNTVWqBmIE/qvPU1i5qC1xcurAczbD43epGHt3UzmufW4wsy1TmOvncsaPvp6ob3P5OK3MqvBw5IS+tcq3dFyMUN1hYlcULO3vpCaopcdJdnUHKsh1kOxMKBMm8VoYMnyQZY/MfSigUwrKscQdqQSIkMrzvpSjHw7dPqaPYc+jF5Py5paMem1bqTVWHfRjuWtNKMKZzyfxyct3p593WHqAmz4nXkf546+DC+E7DAKfOKKInqJHnUThleiHSCJUBQRA40BPml68exK6IPH7dPErdMn+9YgF+vz8t4b24Jpf7r5yFyyYR00y+98J+GvuidATitAzE6AlrfOf06Vx79wYm5Lv4zXnTxnxPLkUkpJrIInzx+Bq6gyoPvtfBd1fWke1U8NgTP6vekIoowOQiN2fOKubv77SgGhYlWTauW1qJTRLo8MUozXHQ6otSmeNgSW0uezpDZDkk7l/fxqLqbJyyxNTiRKXdoUKQe7rDrN7Xz4YmP0dOyEv7W0N/hPDgPe0Na6ze309tgQsB+O6/9lPoUfj9+dPSiigyZPgkyRib/1CSO+1kQjcSi3PbKwcTiebZJQiCgCAIxGIxJEkiOzubcDjM/Mqs9zny+GhGYuDWSONwKD5/TDVRzRxlaNY0DPDLVw9S7LXzhwvTmyuPqM0h2ykzIT8R/jlleiGzSt0E46MXWkEQKM9xUl/kZk5FIoluWRahUGjM6/EMXvsN922hN6RSV+RmaW0uT27ZDUAsEkY3LILx0cPEYppBWDW4+4rZ3PLMHnwRjZ++dJCYbuJQBE7/ywYuXVDGp5cl8ihXLynnd6ubmF+VzaRCNz88rZ5Q3CDHpVCa5eCiOzdjAT88rZ7vP7+f2nwnZ84u5tYVE/jms3vpDqkUeGy8+9UjU0bzUGG/aSUeLphXwtSSoeKNdn+cq+7dQmmWne+eMpHndvTwr5091Be6qMlzohomTkVkV1eYn73SwC0rJo57/AwZPg4ZbbT/ML7zzA5mffdFtrYlqqtM00TTNJr6o7zTMMDDGztSz02WOBuGgd/v/9DTGEcmi7/yxG4uunMzjf1DfTPjSfwDNA/E+PWqRv61M11KX1EUqnKd+KI6e7rDbGxOD+WJosjMsizc9qG90HUP7uDye7fy5JbOtOe63W5cdplfnTuVKxZXpB5/vyKEYq8NpyJy0eDi/NMzJ/PTMydTmefi8evm8teLRqsL3PDgdq66dyt2WeTRa+fxleW1xA0LURSozHaimxb3bWhn6S/f5ektnXz/+QM09EVZ1+QDQJZEclwKAxEVmyxQmm1nIKLx1JZOuoJxesIqtzy9h5V/2sCSmhxq8pwsqMr+wOXIkihy3tzStFHU65t8DEQ09vVEqCv20BfRyHEqfP7YGtz2RPPp/5wyiWyHjEP+ZJWwM2QYTsaz+Q+j1x+myGOj3R9lVvnQojKrupCLFpQzpTjhDXzYuo+YYfLs1i5OmVqAx6HgdDpHiWsWeBQa+kXctiEjNFbDZlTVWdcUYHppops+f4RXo2kaZTkOLl1Yzr929OB1DB0vWXabDA26XC4Mw6DQa6M3rJLjTD9WNBo9pGExLYuuQJzGvih5boXJgyXbvzh7Kqf9eQO/eLWRn7ptaQu00zb6Z/HjF/fR2BelKtdBliPx91NmlFCe40BEIKIZ3PFuK019EbpDGhEtcc2LarL5+okTME2TgajOppYAv1vdyBkzi7hsUTm/fq2RnrA2qLsWRgA0w+LONa3ccelM8t3jlyH7oxqff2wXR0/MHXMyqGYkJHMWVGbxueNqcCgSPz5zCv6wmipWAPj1qkayHDI3LfvfLfzI8H+LjLH5N8U0TWKxGJqm8fbBfv7+Tjt/uXw+311ZR/NAlAnDOso9Hg/xeJxL5o/OtRyK4ZL05/5tIz2hOM/v6OEfl80acyzAt0+po7Evwm9WNfKl42vGXQh/83ojL+7qZU55Fg9cNWfc81+1pIKrllSkPabrOl6vF0mSUvNxAO65YvaYxxjLW0saWkEQ+OYze9nQ4kcUwG2TefKG+YkeHtGG264QiGmUZTt4+8AAv3qtgU8dVZlSAxjO8zt7iesmR9Tm8JMXD/D542oozXbwwHtdrG3o5/PHVvPni2YQ13Q6AnFq8t1cvKCcfV1BrrxnK92hOKpuccHcEiRBoNBj5+iJedTmOXl9Xz9b2oJMKHAyMd/F6v395LtkPA55TJHTZGVcb0jFH01I7oxlbIIxnYhqopsaU0u8xDSDa+/fhm6anDmzONW7FIzrRDUTw8wUpmY4fGTCaP+GJEuXk4bggfXtdAejPLC2iSyPi1nVhTidztQAsGAw+JEUk4cPEDt/XgmmSVq12VgL+S9ebeC9Zj9/f6d13OMurMomqprs7Ql/6GtKlmFHo9E0QdCR1zseqm5w+d1buO6BbQDMLPeQ45CZVeblnDlFWJaFYRj84vkdNPaHmVLiweOQ6QnFUQ2TNn+cZ7d10RMcOndcM/jq8lpmlmXR5o/T2B9lbaMPgGyHgCKJKekeuyJTk+9G1Q3e3NfHtQ9sZ29PBF/UIKKZ2BSJJ2+Yz5mzEpVgB3oj/OWtZiQR+sMaz2zvZiCi0eZXWb2vH7fbTSSu853n9vLstsS4g6QxnVFVwG1nT+HnZ00Z817kuW38/KzJ/PLsKTy9tZNNrQHCcZ39PVF+taqRiKrz5JbOhId0bDWN/VFCasbgZDg8ZDybfzOG7+aTO9gvHFvDe60Bzp5dSCwWG1Oe/6Mw3EBdtbiCqxZXHOLZCb6yvJZ717WxoyPAOw0DHFE71K+xtdXPa/v6ueGISv5x2UwUcXzjMHxQ2nBEUeQrj27llKl5LKlN7wVJvuf9PWF+8uIBLl5QRqHHhtuhMKnAOfgciBsmqpF47iULyrlkwWj1gdNmFPHS7l4s00TVDc6aXcKkQhe3PruXmGryRkU/vzh7Kr9d1cCre/v42gkTuPPy2fQEY6xr8rG8Pp/3mn08vbWHXJdMV0jl0js3M6fSy8aWAH0hja6QigAoImgm2GUh5YH4IioX37mZsGoQihvMLPdw+cJy/rmjm00tARRJwC4J9IZU7l7Xxo7OEC2+GKcPm+wZi8VSYqXjMbnYQ2dA5Y9vNCGLIo9eM4dX9/YTUXV2dIToDamAwI6OEK/s6UM1TFZ99fhDHjNDho9Cxtj8m6Cq6qiwlSAI3Pr0bra2B/nZmZNTUyMPdx/umkGNrauXlHPmrPQZJ7X5LqaWeNjREWJ9ky/N2Nyxpo3OQJzZ5V6Oqy8Yedg0hhua4aGi3Z1BXtzZxep9vay75Tguv3MDpV4b3105ia5AnAO9EfrDKiHVYNXeXt7YP0BENdh06zJM08SuJGbJOJ0udDWWdg5FUQiGI/QGVW58aDsx3aTVF+Ngb5QpJR52dIboCqg4FZHlUxKhNFkSMU0QLIipGvluG6dMS6hNd/hVoppBPGBw79pWekMaO7tC5LsVwoOVc7IIly0q54jaXETB4uaHt+OP6iyuyaY3rKEZFoIAF8wtZcXUwlQITzNMFEnkpy/u5L1mP7PKvFy9NLEZ8EU09nSFWDx478fbfLy1v4+HN3Zy0tQC4rqFisFPX2mgwKOQ71K47dUGjq/P4+7LZxHVDNY3+1k2KW/UcTJk+CTIGJt/A4Zrj2VnZ+P1egkGg6xt6Of1ff2YWITUjyeQaJgmP3rxALol8tuL540a4zwcf0zHssAf1VnbOEC+28akYTvos2cVM7HAxbTiocdM00QRBXKcMn0hjUc2tnPBvLLU38cbsKYoSprhmVjg5JRphcyvzKbHH2Zzi4/mwQbTbz67l5Cq86Xjarj1pImUZ9tZtW8gkduQFUw1EfqyyRK6mlAH+P7z+5hR5uXcOaWoqspNj+xMNDgODkaryXfSE1KZAty7rh3TsgjFdX7z2kFOnlrATcuqOXNmEZ97bBclXht/GqxSE0WR46bkM2GtE49NQhJF4rpJRDXoDWkcX5/H+mY/kwpdFLgV3jnYz+r9/VgItPsTnf1XLi5ja3uQ1oE4UW3o3rT5ovzy1QYkUeSyBaV0BuJcMKeEytyE93bRnZvoC2vctKyKq5ZUpr4796xt5c0DA/zszMm4bBI/fPEAvSGNgajGZ5bV8Le3mtjU4kcQBL5x4gQ8dokFVdlkOxWynQq3X/Lh+6gyZPigZIzN/ydM0yQcDmOaJp99ZAe9YY2/XDQNSJQBO51ONEJ4HDIn1OexbHLxuMbhg9Ae0nlxVy92WSQcDh+yguukqYUsqckhqhrc8ND2wWmX84DELlqWRCbkO3l8SxcrpxeS67IRjBvs7Q4TUQ1afW0IApw+sxinIvHuwX5ueWYv31gxkZOnpSffNU3DbrcTj8f585tNPLG5C8OyeGFnD3Hd4DfnTMWhJFKL580t4eXdPdQXuclxJYoT7rp8JgICujqUY0kuvgd6I7x5YIAtbUHOnVOKZVk4FJG4YfLV5bUUuBV+vaqJNl8zR0/Kw2WTyHUp9IZUekIagYhKlsuG2y5hlwV2dYU466/vUZpt47PHVPPFx3djWBb3XDGbbJeD1Xt7+PITuzAtE7dNYnKhi/UtQba2BpElEVkU+ObJE3lpdy+d/jg3HFlFmy/GH99oZlH1UP/Tt5/bx7b2IHluhYrcWqrynAwM6/uZUuJhbaOPmcPEOQHWN/nxRTW6gnGmlng5b04JTQNRLpxfzrX3bcW0LC5dUMm6pgDt/hjnzC7BLovENINNLX66gionTC0kI1iT4XCQMTb/H9B1nXB4KHke0QxCcR1j2MY/Go1yRLWHF29aCAx5AB+2VyZJZZaNX50zhRyn8oGOke1UcNskltbkUlswupfmrrVtvHVgAF9U56Zl1QRiGqXZduZW5TKlyElcN/E4bBiGQWdQRTct2vwJYxk3TF7Y0cOSmhwKs9387MW9nD+nmIfe6yAc15FEEc00eXF3LxfNL2Xp4EC3k6cVjjJW9UWeUdeW5L717eimxYJhQ8smFbjY0RHi7++0cv9Vs7l4QSn1g3NwPresmn29EQrcMpYFa5r83Lmmla+fMIHPHlvDV5/cTUN/lIb+KFvbthFSLUQBglEdm6Tx9ad2E9VMZAFiusnG1kQvlCyJaIaJQ5FYPrmA7z+/H82w+MtbzWxuDdLmj/Hsth4+e2ziOqaVJAoMLllQxv7eCGsbfezpCrF0MGz2m3PHVjb48Rn1dAVVdneF8EV1rhsm0umxS/iiOnluhc5gnIfe68TCwiGL5DgVtrUH8ToSuaevrcyE0jJ88mSMzf8i44Wu/nLRDFTDJMtpH9fjSErkfxzGE18EeGB9G395q5kvL5/A45s7qcxx8KMzJnPLSekd5Umv4dw5JUQ1g5WDi//r+/rpDWu8vLOLqSWTOHNucaqa7OzZJcyvzKYiJ9Hbcc/aNp7d2sWGZj+LqrNZtaeX1oEI3zllEuua/Hz+2Go6AyoX3LmJjS0BNnzt0Pmfkdy1poU93WFU3WJ6iZez5lZgIvL89k5ml3t5YWcvNlkgGNNZ1+jjkY2dnD2riLvWtpHnVrjhiEp2dYUwLYioBnetbaNtUEInSXiwasu04LFNnby6ty+lcKBb8MruPi6YV8rjWzqJ6yaFHoXPHz8Bl13h0oVl3LO2jUc2dlLgsVHstXPunBL8UY2fvHSAbW0BBqI633xuH8fV5XHB3FJmD1NH+P3qRipznYPjEIbwOhRU3eTv77QiiQKPXzf0eVfnOciNGdQXebhqaSVlOR7ePdCFTRR5bkcPumlRX+Tm+KmZ0dAZDg8ZY/O/RDAY5KktHThlkRMHk8BOpxPDMOjoj7BqTx+fOnYSpmkSjUb/14dY3bu+HX/M4L71bai6hWmOP10ToDLXyS0rJqUZn65AYsf89Sd3cMzE7LRS5aq8Ie/ojJmFvNfsJ9+t0OaLMbcyi2UT8zh+cn5qMFptgcwVC8vxf4hS3J0dQUqybDyxpYtWXwwBgZuOriIYjfHw+j7uWddOf0Qj3y1zyrRCagvdrGvyE9FM7ljTRlQ1KM6y888d3axp9OOySTgUkS1tAaJq+ibAZROJqiYm8OLuXjoD6WXaopjQI3PKIgHDoCekUZHn5Y0DPkq8ds6cVcSBnigDEY1LFpTitks8srGDvV1hOgMqhpUYaPduo48L55dSNDgSoDVosGpvP7IkjDI2ALkuhXNmF1OcZUfVDXZ3hZle6uGkqYU8s62bHKfMWTMTBmVRZS3huM5AVOOoibksm5SfEm3NkOGTJjNi4DCj6zqRSIRAVOXSu7dgWfDsjQuAITn3xT96BX9M43PHT+LyBSUpQzNeefAngcPhQJKkVDjv7jWt3P5OC/9zykTuW99BKK7zu/OmUTGYlB7eAJpkX3eIrz29h2Mm5fGF42rpDsS46M7NTMx3IckiS2tyuPaomnHfwxl/3QDAA1fOweOQuePdFlyKyMVjlCqPZG3DAN97fj9La3P49il1qeufVe7likVl3LWmjfdaAkwocNIb0qjJd5DnstEyEGNXVxivXeRHp09mIKrxh9XN9IZULBLVYzNLvWxqS/ckZSHhsST/GxJejShCll1ClkS8dhnDgsb+KHYJTphayJv7+gjER28cFlVlMaXUw7sHfUwv9dAdVNnXHUI1LIIxIzXirMgjAyLVeQ5+fuYUSvO93L+mibpCF7MrRuvc/fq1Bh58r53PHVODP6bzxv5+zptTDILA45s6mVuZxddPHF//TJIkPJ7xQ5MZMnxUMp7NYWR4bsbrULhicTm2YTvHpK7ZdUdU8OaBfk6enJPm0SQX6bhm0OpL76kYr7rrg+ByuVLKvsnjXLmkgisHu/kb+2Ps6AiR57al/p7s7E8WNQAEYwYD4YQuG5CoqBIE9vZEcNkldnQEKfTYWDkjsZPe0xWisT/KiZMTO+hbV0wkohp4HDKhuM7TWxNNi4cyNoqiIAgCPSGVNn+cxzd3sXJaAXetbSMUN4ipJj9/pYGBsIZNEmjoi6LpFkdPzKUy10G7P0aWXcRtV5hdkUW7L4aqGyR3XIbJKEMjAufNLaZ38JwHe6PEDQtZALskosgiPUGN7pBG5WCoMG7Am/v7yXXZCMRHh07XNQdY3xxg5fR8ir0KAxGd/khiQFyWQ8IXMxCAGaVZBFWdPV1hVvxxHVctKefFnb0E4wY3Hl3FRfPLkCQJh8NBOBxmf08Ey4IDvWFOnFLI6n39RDWT4+rzefvAAMfU5aMZJqLAKAXt5P3NkOFwkDE2hwHTNMfMsZw3JyEnk5T9T44JuGBeKRfMG19q5kcvHmBre5CvLK/lqIl5qXMkicQTUzhPmlaQ6mQ/FNFoNLWouN3u1LW2+2I09ke4YTCxbJomP31xH7kuG9cfWUksFksMYtMN9nYn+l28Dik1Frl5IIZmmBgmrJxWwKp9/bx1oD9lbL7//H5Uw6Q2z0l9sYeF1dkEYgaiKOKxy3z1hAmpUcrDeWZbF5pucvs7rURUg4hqYFqJ5LwAfOGxXQQGS8MP9IZx2mQEEQpdNnwRDUOCdxsGeHqbhmmB1y4yo8zD63v7eOvgAP6YgQhMKrQTjEPHYEhMEsCwEoOU51Rk87vVjXQNhrgAppd6ONAbpb7QTVfQB0CLL/FaWYBcp0zTQAxJAK9DIhAzGK4IY5GYYfOPNe3ohsXy+jwa+qPcc8Us9nZHeHl3L186vgbdsDj2t+vQLegOqGimRUw32dDs56L5ZanKRoDfnDuFbe1B5lRkIYoiv17VwKObOlkxtYDfnDeNdY0DnPbn9YRVgysXV6RN+oT3FzDNkOGjkjE2nzAjK80gIS5pWRbhaJxr7t9Krkvh9+dPJxDV8Nil942Tz63Moqk/SnnO2KOX//p2S6IyLKZz+aIhr2Bks1/y35ZlEY/HicfjqbyKL6Jxw4Pb0EyL6jwnvzhrCqIA7zb4EASB64+sTIXR/vhGM2safVw4t4QTJhekKsSOnpTHXy+aQWWuA7dNYkdnmJ2dYVr6I1TmubhsYRk7OkLUDOq6/eSlg7yypxeXIvGnC6djl0Q2tyZGTwOs2tdHS1+U377RDIBdSngMw3FIpAwNgC9mEFINzp9TyhNbOxGxcNoVOoMaDglMBDSDwaZUf0pU1AQiukiOS6YjEEcRIdsh0RsxUESBu9e20RPUUoYGYE9XGNW02NQaSLsmAfjWigncs6EdSBis2eVZvNPgwzTSo9bvNPiwEg4hxTluVNPii4/tZHtHmDNnFbGu0c8/1rTisUvMyvfynVPriKgGzf0xqvOTqglDx1RkiXlVOal/X7qgjKaBGIUeG5ph8oMXDtAb1pBFgdX7+0cZm8xY6AyHi4yx+QQZvsMcjiAIqKpKRDXoCqhohsVb+/v54YsHOHV6IZ8dY+ricM6eXTJmMjhJRDXoCsapGhz/m2RkOk5RlJREzf6OAf7xbisXzCtlSomHmx/dkQhDaSb9do293WEWVufw3ZV1OJV0Y1id72RLW4DpZV7OGTZozeVyMW3wn76ISpHHRndQTY1xPmV6EaXZdjY0BzhiQi5FHhuGaeGLamxq9fPghg78UZ2F1dns7grzpzea0mbYqEZiIZ9Z7k2NWIgZIAqkeQy6Cc9s78IwLQxAGawptwSBLxw/iaIsJ2sb+siywfb2EJG4gSQJXLm0mosXVXPD3esxTAO3XeLtgwOIgsDe7sTnKg7L18QGDcfIhttCj4271rfR2D9UNLB6/8CYn13SQZUFuH9dC6IAsiigGhYPvtfJoxs7Kc6yUeixcfulM4nEdVp9MUws/FE9NagtyXPbu3jovQ7+5+RJKJLA4poczpqdGB0tkSgff3JzJ5IA3zhxwqjryRibDIeLjLH5BBg+lnk4yaR6sgQ4x6Vwz+WzcNhEGvuiOBSR/PeZmvlB2Nsdpshro+R9jjX8Gl/Z3cvOzhBPbe3iGyUe6grdDER0/n7VHBo7fSyoShQvjJWEPn9eOecMM36tAxHiusWkIoGoZvDCzh7uXdfG5CIPj147Jy038IXHdxGKGzxw1WyuXFKOLFk8sL6TH794gGTB19X3bkMclpBPIgGyJLBtRE4l36Xgdcq0+RK6cbJgEdVM3HaZ0iwbdlmkO6jSG1aZXmxnTkUWJ9ZloSgKHf0h/rGmldNmFLG4viwxS6cym7vfbaI0y06B20ahR2ZzW0L+P98l0xsZv2gjxyERjGt0h8avuxlebJB8VlJAwLQYHCERxyTxvMlFbr5/ah2X3LWZgXAijCYIUJrl4PZLZvCPd1qZU5lFRY6DZ7d2E1VN9nWHuWNNG7IIPz2jnpse3cVF80q5aVk1B3oi7O0OU5M3un9qpGRShgyfFJlqtE8Av39o+JfD4Uj10nycJP4H4ZGNHTyysYPrjqqiMsvO9BEd5cOvZTiiKBKIaTyzJRHLL/AMheeSoTZFUbAs65DVcFvbAhR5bdz8yE500+KBq2bz17daeONAP+G4zmkzivncCK/t3Ns30hdWWTEln4c3dSEJ4LSJhFWTsb6JAmAbDJ85lYQkTNKLEUiUXH/zpAnYbDbWHOwjopmcOqeKF7Y0ISKkGkJ/v7qRl3f3csq0QnJdCktrc6ge5gm63UPFFx1BlRvveY+uQBSPXeLqpZX84Pl9mCYokkB8WCjMIQvMq8xiW3sozQuTRQHDtFLGxCELxAYtjMcmjvKGhhsglyJSk+dgZ1ck9fdch8hALPEaEbDJAidMyedAT5Q93WFml3vxOmT2d4e5bFE5F84r5VvP7aXArRDTTJ7Z3k1plp25FVlkOWSimkldkYuV0wpT00tlWU67DxkyfJJktjEfkWQlmSRJabmR4eXBn6ShMU0ztVtPsnp/P76oRmfQ4Nz5JakenSQjS5WTNPeFue6B7Zw1qyjN0ED6uOlD7UMaesP84IX9eGwJfa1QXMchiyyqzuaJzZ1YwOxyL76oxl1rWrloXin/2tFDXziOXZboDiW8rOROvqkvhkHCgGQ7ZHyxRGVWRY6DEybn8uB7nZwzu5j1zX72dkfId8lML/XyuWOrEUURu91OoVthc1uAaCzGkRPSu+CvO6KKPJcNhyTw0MYO1jX6+dW5U1N/T4Y///Z2M/t64/zz80fz/OZGeoJxjq/P4zevyZTnOChwy7zb6Mc0LSRR4PojKnl6WzfxwSKJZDXZHy6YzoYmH//c0c3+3hj1RS62tifOMdzQCCTKrd12CV80Yawimsnu7iFDA6QMDSTySzHd4rntvQiAJAocV5fPvevaGIgmqvBkSeSnZyZGD+zridDmj7NyWiHfe2E/hmGhyAJx3eInLx6kLCcxPO66I6pYWJcxNhkODxlj8xGJRCJjVu58UtU8SXma5IL/57daeHl3L7eumMiiwZkzvztvGi0DUSYWeYlEIliWRUwzeWRjO8sm5VGT70KSpNQ12Ww2VFWl3R/HZZPY35tY0IY/J8n7ObxFXhudgcRx/n7pzFSoLBDTCcYNBAHiusnf3m7h8U0dbGkNENFMfFEDi4SEjUhi4Wzsi6X6Sjx2CZdNRDVFcp0ylbkOvnD8RL64fBJPb+lkf0+ELx03gRNnljEh34EgCGiaRiQS4ccvHcAX1SnJso9SS3AqIpcuLMMf1Wjoj7Ji6mhVAs0weXZbNzHdJBRTOaa+KGWw3/v2iUCiOfdHL+znkU2daKbFv3b2YJrm0MA2QDMtbnv1IHs7QwzaDwRx7FyIRSLHpA16S9kOCf+IqrVDkXi9xa9WNaYee2NfP09u7WZmmZebjq4iHNf580UzCEQ16t5zUZXn5O0DA8T1RD9Pu09FIMQD69tYWFc23qkyZPhYZIzNR2SsxXi4hzPWAv5hGOmVeO0ShmkRUM1ULkgSBWryXWnnWbW3l+e2d/Pa3j4euDahq5b8u67rSJLEopocfnn2ZEqynWl//zBYQFxLqBwnb8WujiA/ffkAFglJfqcisqMjSEy32NIWxLDAqUBcSzRDCoKAaVgpQ1OSZScU15FEgdNmlbGw0sO8wZyR3W7nme3dtPtieJwKZW5GhQgvXVjG6n39TC8Zvykx26nwlRNGJ8YBFEnk1pMmYllgxKMk74rNlug3SubePntMNc9sSwiG9oZUNMNM5VycMpRn22nsi5HwzRI3Z1tbAI9NQjNN4rqFTUwYWt1MGKg8t0JYjROIGXjtUiokp0gC+R4b/SE1NaPn/XjjoA8BONATYWdHkJaBGGfNKuZzx1bzqSMr2dsdoS+osr4lUUW3fEoBr+7pRW4PfaDjZ8jwUcjkbD4kySmSn3Qu5oMORMvOziYUCvGZB7eytsnHj06rT8nfAASiGn96s4kTpxQyfzDJ/2GGre3pCvHY5k6uXVpBSZYj9fj6Rh8TCpzkD4bdDNPk1mf20uqL0BNU6Qnp5Lhk+oclz0UBSrwKPSENeXCAmCQmwjeQ6LwPxg0qcpwMRFXuuGIhiyfmp8KBI/NFwZhO00CUGaXpuanhGKbJlx7fhU0W+dmZkz+2/IooijidTkRRTDM4uzqD7G4b4L0WP9NKPHzv+QOp19QVOmnqj6UZh6SRFQZ7dxyygCAIaaMFJGCk2ffYJOKagTbOx5fsBRpJ0swl/780y8bx9fk8tKEjUaEnDlXwKZJITDeZUODmta8c++FuUIYMH5CMENKHINlDY5omgiB87IUsFNMIxhIezKGMwTsHB/jlqwcxRRumaWIYBi2+GIYJveEhD2hdo4+4bvKNFZNShub9jj2SBze0s7UtyKt7+lKP7egI8vNXDnLLM3tTj0miyJePr2VXZ4SukI4JaYYmcV5oD2hoJsiShMcuIwsCbkVkeqmHmnwnsihQnuNANy2+8+wOTNPka49u4cUdnaOuzeuQRxmaxzZ18KMX96Pqg96bYbK9I8SB3sio138UkuXsycZXWZaJxWLU5iicMr2Ib51cxzlzSrls4VD46UBPNGVopMFS6aiW8GSShqHIa+PKEZNRhxsaAVg5LZ8vL69h5KcnkCycECgc1EwDcClDWnTTShPeXfK1iiRw/6ChgYThN6zE/+KGiU0SeOVLyz7UvcmQ4cOQCaN9QEb20AzvWflIx7Msrrh3K5IgcPslM8lxjV+2/KMX99MX1kCSeW1XN6fPKOThq+fQ2B+lJMvOTQ/voCbfybsNPipy7Pzu/OljHsftdo/ZBzScm5ZV88qePs6YOaT+W5HjoCzHgdcuseIP67hwXilLa7LZ2xMdc1dtkwQ8ikD/sKR2XDfIcyqcN6+cr500DTC54h/rqcpzUleUaGb83PI6Nh3sYkPTAFvbfCyf/P5qz09s7iKmG7T5Y9Tmu/G6XTx8zVxkaWgzIAgCsizz8IY2ntrSwVdOmMisstGhNqfTmVZgkSSq6ty7vp0jJ+QyqyIHSHg8brc79b0o8tjIdsjkuWR6Q3GCgwKiwlAkLQ3DtDh6Yg6mabG7K8hbB3ypcKIkgF0ReWVPPy/v6U8rAbfLAlcsKueOd1vRTYvO4NB3sMhrJ6aZdAZVlk3Kp6U/SmAwHNc8kC4UOhzLSlzPo++1cuHCqnGflyHDxyETRnsfYqqObXDg2McJncU0g289u5dJRS5uPKoa0zQ58Q8byHcnylBPn1HIDUdVj/naNxqCvLanh+Mm5vCzVw6wsCqb751aD8CO9iDfe34/2U4pMS+lPp+zZpdwsDfMt57dx8rphVy2aHytseEhtjf39/P8zh6+vLyGV3f30TQQ5VNHVuF1yFiWxdef3s1Lu/txKiJuu0RfSBu1jjpkgR+dXs9vXm+k3RfnmLo8zppbya9e3kuBR+GuK+bi8XiIRCI09AR5+0A/Fy6uwSGLKTmch97roCrPmZLmGQ9ZllnXHCAQjnJsXf4o4VJBEPB4PCmj8/VH3hscpFbCBfNK8XoTXpKmaSiKgiiK6Lo+qqpv1d5e/vpWCyVZdn5zXmKWjMPhwG5PhBR1XScWi9EXjhOM6pz+lw1j2ZfE66REIyokvJMCt0xct4hoiSIDw0oYG0VKhBuHHyfbKfPQVbPZ1Rnim8/tpcRrp80XI1nclm0D/6DtGS+8NhYCiZDnxCIPL33xmA/2ogwZPiQZYzMOmqZx0wObeGN/H+fNKeGr4ySVPyitA1G+8PgunIrEvVfOTj3+1oF+bnu1gTNmFjGtxEOOU2HKiAR3UkvNNE18EY08rwPTMLju/m20+2N8d2UdEwtd5Lttacf9zapGZpV5+Z+Vddy/vo2uoMoXBkuFx+Ka+7bSH9a4ZGEpP3npIJYFZ88qZEdnBLssEorrHOw79OgBgCMn5GKXBQIxnT9fOAOXw8a2Vl+iSTLLidvtTutN+qA4HA4Mw0DTNGRZxulMFDjEYjEkSUoVDCQnf8KQsjZAe4+PdY39HD0xF1lK5GJstqF7Nnze0PAeqaiqc9/6do6amMvUkqEwntfrHXUvY6rO0T97DVU3CKlDPUHDf2TJKrzhjOMAoYgCcyq8dAVV+sIqNklENUxKsuw09UURRVA/gQLIsiwbd149n8mlmcFpGQ4PmTDaGJimSUuPj80tfgzDIt89fojrgzZuVuQ6+f6pdeQNO1YgqnHUxDyOmphHVyDGNfdvw2WTefTauaOuJ3mOHJeCaRg4nW42tvgxLCj0KClD88ruXra0BfncsdXcds4UyrISu+8nNncRUnWuWlxO3uBzO/wxsh0yLruMIAh897R6dncEMUwzJZnyyp5+JDFRxeWLjN23k8RtE4lqJr2hOHddvZA8l0IsFkPXdebWFCBJCR24j2JokvchWaWn63pqoXe5Es2Zyc9CFEUURRklvVJWmMNZhTmp80ejUWRZHtP4SpKUuudOmzxKQwwSZdDDDY6u67y1u42QahDXTeoLXXQF4uimRXBYb41t0LtJZlgsxjY0kLinMd0k2ynT7o8TVhOeWyCmo1sgGEPHTBqd4yfl8Np+39B7OYSXIwogCQKXLiyjKme0okCGDJ8UGWMzBqIokudSOGdOMXkuhQvnl407W+bDhNamDUtuv7y7h1++2sgF80q4ZmklBR4bx0zKpzJ3bLHN4QiCQDQa5sL5pQRjOhOLvMiyjKqq3LW2lfhgWK6+eMhD+u7KSQTjBvt7IlQZJqpu8sXHd1OcZePTR1dx/QPbOX1GIe3+OBtbAwhCYnVKxvwDMQOHMnpRtktQV+ThYG+ExdU5xHST/ojGv7Z1ct7swtT9TIapQqEPV15rs9lQFIVoLP6+OTJFUYjH40QiieKApBEayXBlhWg0muqat9vtqeIPh8ORatrVNG1MJQYYmqCaVNGeXupBGRwDfdLUQg70Rfjn9p7U8+eUudk82Nx51IQc1jf7iOmJ8CPD9NaS+GIGhXGDrlAcsFAGq/p8EY3qPAct/Ynr0o2Ex2QJ0BPW0xQJDhVOk0SB3180h88/vIVHN3fz+lePG//JGTJ8DDLGZhiBQADLsnC73YiiyKePTuRQRFHkxoe2s76hn7svn8mUkvFLbz8oOU4Fw7RQjYQ0DJrG18YQRoTRfTDJyGdyCJZpmqmQ0i0rJtLQF2FSYfpCO7M8i+0dQa69fxsxzeQLx1XTH1EpzbIRiOmIgsCWtiC+qE5k2C5cGeyH0QwrrUxXFqHQY+eYSbm8trefm4+t5ZL5JTT2RnhgYwdzytypfNB4IxfeD5vNht1u5yfP7+aed5v4xokTOHduKXa7HUmS0HWTcDhx3KSHMfJenfLbNwjHdZ751HwEEkUSdrs9VcbscDjSnp+8j8mhd8ljw+i+nuFomobNZsNlk3nyhnlsbPZzXH0+f1zdRHmOHdNIiIIWeO1AwtisbfKnvBHVsBDGOfa+viiSAFlOhWhcQyNhcMxBORwLKHAr9IQ17KKAYVmjdOVS708RiA7WUctCopn0xvs3DaoYZJaDDIePTOnzIPF4PE2qZbhGlGmaiJZBkdf2gRvrxiO5mC2szuEfl83kioVlaJqWCsX4omrKW/qf5/Zy8yM72NDs59zbN/LkltHlwEkikQiiKDK91MtpM4rHDA3V5jlTyeBVe/sxB+XxX9vbx8wyLzccWcXJU/PTXlNf5Obrg/kq00osapBo2PzdeVPw2GVsssi0IheCILCzO8rG5gC/fPXghyq5BlJDwARBSDVSxmIxyr0SsiiwuyeGy+VCVVWa+0JM/+6LfObhHYlrG7xnDocDm82WqhTrDcbxhVUimpH2PEVRUhMp/X4/wWAwLVw5nGAweEhDk0RRlIRHZMDUkoSH84Xja/nicbXkuRWWTcxlfbM/9aPTDIvk+B7TGu2BSALYB2unDQsGIlqquACg1RfHItGv1DNYAr+kNoefnjl51A9bEmBupZfrjp6AXRYp8ihpBqkqx8my+kIyZDhcZAoESCxAsVgslQ8YKw/zxOYOtrWHuGXFhA80oGz4sXd1hZlU4MKuDL1uX3eYbzyzh/LsoVLljS1+fvD8fo6amMuXl0/gojs3oeoWi2uyeHRjF1cvLeemZTVjnicpojhWPsSyLIJxgyyHTDiusb09zMQCJyf9aT26CVOL3YQ1A5dNZl9XKLXolXhtZDsl9g+WOMuiwJeX1/CzlxsAeONLR1CW502VU7vdbjp8Ub748GYuWVg2ZumyP6rhVEScdltqsQ+Hw+i6nlbhNfJ9RDWTkoLc1HN7Qhqn/nk9WU6ZbIfCypmlfGnF5NQ9T1YPtvoSkzprC1xsaAnyxzea+OX5s6ktTJxbVdVR5c4OhwNVVdO+Ax9GVHXJbe8iSwLPfXoxOQ6Bn718gHVNfpxyIv/yxeNqeHpbN5OL3dz+dkuax/hxmVzkoqk/ytdOmMAd77aS55S4eGE5wZjOiuklVBfl0N07QFTTOf73iaq5+kInJgKhuMGaW0/4xK4lQ4bh/J/2mxt7Q5RkOTC0eFo+ZqxF5bFNXYTiOtvbQ8wb1jD5fryws5c71rSytCYnTSYl2ymT5ZCZUTqUV3EqIoIgkDvYc/OH86dhmBbffm4fdkVkVtn44btkvmEsfvFKA8/v7OHTR1dx//o28lw29nWHGdSOpLEvQlS3mFTgTNtddwZVQnEpNcPFMC1e3NXPLadMRtcN8lxKWt+OpmlkKSZ3XDZrzOtoHYhyzf3bUCSRt76xPPV48rpjsRixWAyHwzEqR5acqeN0OgkGgxR6FN760hIe3tTDvWua2NTUlzJQyeT++saEEObXTqjlic2d3LuujVZfjJ+/uIvbzp6WynNBujFJGhrTNLnlmb00++L86/PLUqG199uf1eQ76QtreB0iYPHF42vZ3hZkaomb3rBGZa6TIwfLujsDMR7Z2JX2elkk9dkk37vXLrNiagGPb2pHNxPjq4d/2rkuhTsvnck5f9+IaUG7P8ZNy6qpL3JRV+RJq7zLyXLjjMf504XT+NVrjbhsEpcvKmdykQtd1zNjBjIcFv5PfqtM0+TPr+/nFy/tQxJg1/dOPKSUPsD3TqvjYG+EORXvn68JRjV2dYdYUJnNpEIXXrucmj6ZpMhr584Ri/LUEi9PXD+PSFznYG+YCQWJUN5VSyp4Yksns8rHN3LhcJioIfCvbZ2cPDUft32o6q3Ao+CyiYQHJzw29g+FhAQgOhhP2d87uqzZwmJWeRbb2oMIlsUlS6q5YEElfr9/VH7k/RL4DkUippvkuu2EQiEkScLpdI65eCfDYMNzPaZp8pPnd/OvrW2cM7uEc+eWcOmCYhySxdwKD53+GCXZjtR1ff2ZPQRjOrphsbbJT8+g0vR7jQPoup6+wRBEXt/fz3GTC3A67AnDZ4q8trcPhmVTPkgg4KGr56QVIdgkkSMnlxCNRqlx2lPXt2pvH2sa/CTrLiwrkdTXTbCJAoVeGxMLXUwt9nD0pBz+taMX1UjkaE6bWUS2XeSZbT0ossgdl86kJt/FGTOLeGlXD3u7I3z22NqUkTFNE1VVEUURh8NBPB5ncU0O588rZXqJmxllie+nYRgZY5PhsPB/4ltlmibxeBxFUfjXji4sLU59gR1JAFkSU2ObNU1LJbRHUlfopq7wg8mv//jlg+zpCnPTsiq6gir1RS6OqRu7fyEc13l0YwdnzS4mx5XYed767F6a+qP84LR6ZpR5OWJCLkdMSFcxHt5LkuTudxpZta+fnlCcG4+qZl93iK89tYclNTlcsbiCLa0+bJKQVvFkAadOK+BfO3uBRMe7TRJxKiJfXzGJ3lCc8xdUs/ZAFwtr8yjNz0m99p/bu9jUEmBuZTanziji/Sjw2HjjC0tSC7FhGDidTjweD/F4otpseChtZN4pGAzy7oFe2v0qj2/uYnd3mF+eM41LF1ex4rfvEIjpPHz1bEoHS3iPmJDDCzt6uXppOds7QiRrwkZOtwT47rO7Wb2vj+d39vCXyxckFmVR447L5+CSBc743Rsc6I3w1lePxm1XUgv4eIUPsVgsJZgKpEJ1yQKUeDxOSZYNhyJSledEHBy9fc+aVnZ2hjl/Xgl9YY2rl5Tx0MYufvZyA419UWRRYFKhi88fW02Bx85liyoGxToT9+zLx9dil0WWT07k3mw2Wyr0OBJJFLlwXmnqXpummaqqy5Dhk+a/3tgMj9+rqspXH91KoUfhnitms+HrR5Kbk5PWzDeSrW0BekLqIaVTkmGb5K73qAk5bG8PMqEoiz+s3oYoCnQEYkwqyR1lIP7wRhMbm/34YzqfP64WgLmVWbQMRCk8xOTNsZSal03KY0dHiGWDIZpkBdnG1iCPbOpEH6ZbP6PUxfaORLXVwf4oNimxo3bZRKKqyffPncWyCckQn84xdfkIQmKHr+s6rQNR/vp2Cx3+OBtbg+MaG5fLhaZpaeMSUjt+m414PJ7ycJLFE0CqXyYpsaMbJu+1+AnFNGrznVTlOjlpSgGWZaGqKpppopsWe3silOY4CUQ1Tp1exA9PS+Rx/nLRdJ7e3k1FtoOjJuaOus7lk/PY0xVkxZS8tMV5XmU2uq5jWBaWZSFKSioc9X45nLHmCSUFPXVdZ2qJl0evnccb+/p4ZU8fS2pyWFKTw+v7/fSFonz+uBoANrcGaPPFcNtEbj11EiunF6eOV5KdXk2X5VS49aRJQGJDoqrqIb12r9dLKBRKvRdd19MaXTNk+KT4ry8QGJ5oFgSB6x/cgaob3HnpDCRJwm63j6mHleTc2zfS2B9lQVU2f714BgAbmnz86MUDXL2kgjNmFY/5OpvNhqZp7OkK0hNUUzH6JFHN4GcvN7K0Jou3Dg5w+aIy6os8bGn1s6crzPnzSukNq/QGVSYWuvjZyweZUebl7MFxzO+XsN7TFWIgojG9xMPW7jifeWDz2NcpCeimRa5TJtdt42BvBMuCYyfn85tzhoaLSZKU8gCTsjL/WNNGb0hlUVUWNQWuVNhv+P3OykqEZ5IipsMZvvNPLnqWZeGP68gMleK63W6uvXsdzX0xclwyyycXcPkICZ6DvWHWNvo4b24pDpvCZx/eRosvxpeOr+XICaONSxLTNNnfG2VSoQtx0Ji+vrePNY0+PndsNTmeRPWbYQkoDideR/oGIFku/0H545st/OOdZr63chKnzUx8d7Kzs/H7/bT5Yjy5pZsntnTgkEW+fuIEjqnLp6kvwl/ebmZzS5AzZxfzpRVTUmXZhyJ53A+Doijj9idlyPBx+K/2bEYuxp1RgU3NAyiDUiXxePyQhgZgYVUWDX0RdnUOhUt6I4k58N2hscUNY5pBZyBASZaD+iIP9WNs+p/a2s26xn56QzH+cMGQcOZtrzYS0w1mlmfxs5cPEorriQmVTX52d4VZMaUAt11Oe2++iIrbLqNIibBTdzDONfdvI6oafP7YGv70ZlOa5DwMdazrhsXC6my+eHwNZdkOnt/ezSt7+/ifkyYkKtTkRNGCYRhp90oURa47ohKA6x/YRk9I5adnTmVmRTZ2u51IJJK2Q5ZlORX66w7EcCoSw7NfydJzVTe47t6tSKLAA1fNRhRFoqrOtrYgumFRmefgyS1dXLqkGtEc2rFPKHAzqciLZVm4XC4WVGcTiOlMzD90V/z969t5als3Z84s5orFCQN2z7o2OgJxplXkceb0hHFRJCFlaKLRKIZh4HK5yMrK+lB9RMlS555BhWxBEFKG43erG3nrwAAzK7LJsoksrMpGkiTuXt/BtvYgCBZtPjVVIh6ORnlsUwfTit3MPEQ+78OQ8WoyHC7+K41NMkczPGEdN0DTDbKdCpW5zg/UNwFw8zE1xAwzFZoC2NIa5Ni6PK5ZMiQRrxkmL+7oZtX+AQYiGr6oxq/OmUp5joOtrQF+/moDyyblpkqXz5hRSHcwzvL6keOLK9jREWJSoYtj6/JY3+zn4Y0dxHST6bkOlv9+HT8/czLP7+zFtCw+fXQln39sNx67xG1nTeaye7bQGx5ahJt9Cbn7pHGxSAxi0wwTSYBnPruM8iw5Fd67aGE5Fy0sZ2dHkC8+voUlNTl877T6Q96j+kIXlgXlWQlPJdnMOTL+L8sybb4op/11I+XZdp6+cSFOp5NwOEyPP8x3/rmPhdVZ5LttKBKsb/KxqCaXYDhMnsuGTRJo98UxTJNILM4TGzuozHNwXH0ixClJUkqp4NPHT+GyheMbgI3Nfn7y8gEWVmYjCUJaE+y3Tp7I5tYgZ88qwtATnldyEd7Z7mdHcw8nTilIhZxM0/xAitoAnzqykisXl1OYm53y5JLe3UXzSukOavzw9GkUOBOGyOVy8eKObjTTJN9tY11jP69ta2Z+VTa3PL2HNY0+KnOd3H/VnLTzDO8TS/J+3vBYWm8ZMnxS/NcZm/HCNcf89i0icZ2nbj6SmmxlzHj6WHgcMt9dmb7YvrF/AEUU+PRRVbjtiR/nN57ezVsHfIkKogIXmmnhdchcetdmDvZG8dglWn1DnpDbLvP5Y2tS/1Z1g6b+GEdPzOWYukRy96olFVyxqIz/+ec+4oZFVZ6TIq8d3YJdXSF0w0SWJByKiCQK3PDQjjRDs7wul68tn0C7L866Jn8qZ+OySbx081JsNhtOpzO1sxZFEa/Xmwi9CIlQX6H3/Xe6t5wyGVmWMQwjTRAzmfBXVZVIJMKre/ooy7aT41SoznOmqSs39UVoHogSjOvcceksPv/YTm56ZBdLa3P4/QXT+cdlM/mff+6jdSDG106YgG6YPL6lC1EgZWw0TTvk5zp8se0MxhNy/CGN57+wLFXqHI1GmVDgpibPyZ7OEP6Yxjee3MnFi6u4bkkZ33t6G3t7wqimwIWLPKlm0JHHPxQOWSQSiWC329M2PXMrs3no+kUYhoGqqilDdPmiMl7f38+p0wvZ0hpkSomHdn+cPd1hZEnk5mPS1cIVRUlVlA0vIR9e6j0WwWAwTbg0Q4ZPkv8qYzNWgx4kkunVuQ4CMR2PZH7s3duPTqsnpOo8v7OHoybkUp7npsBto8hr4+hJeXzpuBrkwZCWboJhWVxzRAUum41PPbSDX18wC5eYnuD/zapG1jX5uWh+KefMTigAtPRHyXMr/PD0yannJSV06oq82KWEQZtU4OT1/QOE4gYi4FAE7DaZLy2fwAu7erl6STkbmv1IwBmzivjaiRMQhMSkSL/fj81mw6/COwf7OW9+wggEozrP3riAbOfYRQrJsFgsFktNskyS1BOTZTm1gO/uCvGXt5oBeOnmhannJhfnWRXZfPH4WuoHK/7qC128c3Agdf5sp8Ki6mzCcZ1JhW5ynAqXLyojxzn+V3h4TmjkrJqV04uYW5FFSZZ91KwigLvWtvHc9h7mVmRhWhYv7ezimkUlnDOnhEc3dbBscmlaz40gCCkvDcauFhyOYRhpRR6v7+3j+V29/Oy8OdgGY53JOTw3Hl3NjUdXY7PZUsbCbZO4bFE5lTkOltSm56SGG9yRShgfZwZThgwfh/+qAoHhVURJAUdN0z7SD2xXR5Dd3WHOmlWcqsIazjPburh/fTu1+U5+euaU1OOCIKQW4dvfaebBDR38/ZKZTChwccOD29jeHuLyJVW0D4S5ZH4pUwfFOf+5vZvfrW4krpucM7uEc2YXc9MjOynwKNxxaXo/jm5anPzH9VTlOugMxOgKqqkmQEkAlyKS5ZRx2SR8ER0EAcM08dpl7rtyNlmDC7gkSRiGgaIoLP/tu/giKj88rQ5FEvnzm83UjHhvyd2ypmnc/k4LNXlOVkwtpCcY54W9Ph5Y18rVR1Zz5eLKUUY/qur84tUG+iMa588p5cjBirBXd/fyu9VNfOqoSlZOH53cGm4k1jX68EU1Vkwt5JevHmR9k5/bzplCxRhqxUmvLZkgdzgcWJaVMgDDF+7h/53k5V093P5OK19ZXoNhWSyaVE6WQ0wZk7G8mDRj4HZ/oAbQJDc8uA1/VOfapZWsmFqQOoc1WAX3YRg5OmE4wz2xJMNnGrnd7kyfTYbDwn9NgHZkj4CiKESj0Y+8k/vxSwd5cEMHuzpHqxQLgsDSmhymlXpS1WFJLMtKGbyNzQFsksBvVzUwEFG55cQJfPn4GiqyZPZ1h7lzbSuQWFROnVHET06fTJZDZkKBkyyHTK5LwTYok2+aJu81+9nU4ufSOzfRF1bZ2BKg06+mdZsnxvxalOc46Q6p9IQ1QnGDPLeNm4+pRpYEJEnC7XbjcrlSM2LOn1PMjFIPM8u8TCl2U+CxccLkAjoDMX7+ykF2doZwu904nU729ER5Zms3f3u7BYBbntnD71/bT6c/yp9X7ScQSq+UkiQJp03mU0dW0tQX5VerGnhySyc9QRVfVMe0LAbGGF/gi2hcfsc6bn874RH95KUD/H51EwNhlR0dIQIxnUB0KGw4XFQz2auTXDhVVcVmsyEIQqrUOjs7m7+t6eThjZ1savbz7ef28qMX9nHNvVupL3bzyLVzefugL6Ejp8W4850m+qI6/qg2ZrhsuNyRLMt4PB4URUl5F039Eb721G62tQdGvfbLx9dyzuxijhuRwzuUoRmrJ0YUxXENTXIw3EiGn+OD5J0yZPgo/Fd4NvF4nLa+APlu2yETtcO7ut+P1fv62NIW5MajKg+phTZ8VzicLz+5mx3tQYq9ClHN5LKF5aky6aiq88imTuZVZHHHmlYK3HZq8x2cO6cEhzL2uV7bk/AAHLLInq5wSqqk0C2jGhb+WHpYblKBk5mlXp7c1g3A2187hhvve49gzOBrKyZx88PbWFKbw58vnHHI+3DbKwd5dzAJfec1S4CE4fvCQ5uZWe7lskUV/PbVfWxo8rO7O0yuU+Hha+aMqSBsmib3rGunoS/C7q4Qkwrd/Oj0yfgi2phjsXd3BvnWc/vIc8l8/rgJvLynj3Bc59YVEzAs6AlqlGbbUh5FMj+UVItOnjMWi6US+dFoNOX1NPWFWX7b6xRl2Vg2KY93D/poGkh8P75zyiTOnFXEuX/fhGlaHD0pl8c3d6GIAvkeG9lOmbpCN986edKo6x7uHSRDdIIg8O1n97C2cYATJufz5eUfbxjfWAwvNR/Jh6mYy+RtMhwO/iv85V++tIeXdvVw4bxSLl1U8f4v+AAcU5efStSPR1LDa6wZLZ3+OIZpcf2RVQxENE4ZFiJy2mSuXFzB6v39tPlivNeUCPUoosAF88vGPJcggF0W6Y9oaZpYPWEdlyJSmWPnhiMr+fY/9yceD2lcvbSCmgInEc2kNMeF2yYTihusa+zHtGBzy+gd9khuOH4yXc/u5Asn1KGqKrFYQnn5d5fMAxKL2KeProajEyXYmmmNK1UviiLXHVXD3q4Qt71ygEsXJN7rWIYGYEqJl2+dXMfvXm/g8ns2c0J9AT8/ewqyLJPldCKLQXZ2BPFHdZZOyCUWi6VyScPPabMlPB5dT3i5qqqiKAoHO33YZBGnIvGpI6so8tj4+7ut6IZFmz+GJIr86PR6RODhTR2ouklJnoMcp4w/qtM4YmppcuMRj8cJh8OphtakN/GNE2u5f4OTc+ame8MfBUmSiGs6D6xvY/nkfMpznB8qF5n08EY2fA6/dxkyfJL8xxsbXdcHZV4SApYjvQzTNLFISHN8EK9mPE9lvOeOF6a754qZ+GM6ea7xq7mOnpCDaVbx8u5eVu3rp2HY4nWgJ8xzO7qZmO+iO6Ty7LZudMNi5bRC7lzbljZGWBAEXDaJFdOLeGRTJ4IgEIzpfPbRnTx8zRyctkSl2D3XLGR3h48vPbaD02YUcP7c0vd9j5W5Lv52xYK04ot4PJ6qbBqem8kZ9l5V3eArT+6mNNvBJQtK+cubLVyyoIyZ5V4mFjj504XTR53r1681ENUMvnPaVBp9Ko9vbOPTR5ZTledkS1uQnMHxBsnpmqIocsuze4mpBtcdUckJU/LJHaxgNk2TUCiEblos+/W7ALz71aMACKs6F935FnkuhYpcB8VeO9Ul+VzrtnH10kqW3PYuD2zo4OojJzC1xMP+ngjNAzEs4Pj6fD53bC19YRWHbcgLfXF3P9OLnVTkOlMLuKqqOJ3OVHjNoUhcuzS9GRUSvU42ZShcOpLrH9hOZyDO05+al2o8NQyD1fv6+OeOHja2BvjNudMOaWySlYZJ2abheRnTNFMCnJnS5wyHi/9YY5McERyLxbh0QSmXLhh74fzWc/vY1xPmj+dPY2NrgKW1ueNWWEmShMvlGtWjk/yRjjQswxfaqGYwEFEpy04kqwUg2zF0e202G4FQDIdt6McsiiLH1RdQlu2guT/Gqn39zKnM4qSphdy9ro1dnSGe3dadyEdJIjHd4oENbUD6GOG4biAKAs9s6+WoiXlcu7SCmx7ZQVwzkWUpNSnT7/eztcWHIgpIgsicikOHS/6xppU7313D7VcsYEr+0HtJyspsahkAXWdKiSftdT94fh87O8NENYP+iMbLuxUa+iK8sreXmeXeMY25Zlg8tbULf1RnWV0hj7zXSrs/Tm2OwndX1vHdlXWpMJHf7ycWiyFJEiunFvLq3j4e2djB3p4w3zxpUlrXvGWa1OQ5EYRETkUSRXTDQtVNoprBo9fOSxVKAIiCwNdOmkx/RKM838v1d6/njX29FGc78NplavIS1iw5htvpdPJei5/fvnaAQo+Nu6+YjaIoqUKRnzy/m8ffa+EvF01ncnGiGCRZjux2u/nmE1t5+0A/X11Rx5nzq1OhPkmSUsUM7f4olgWRuI5nmILBgqps3m3wceKgDtr7df6LopgmCTT88UwzZ4bDzX+ssUk2xEFiZ+/xePjbqt20+uJ8cVjpsX9Q9ffF3b08vbWb1fsG+MmZk8c8pmEYaXHtrzyxC39M58+XzCLb7TpkscE3nt5DU3+Un505mcnFHo797Vp0w2JqiQeHInHNknK++vQejq/L41un1KVe53Q6mVwMx08u4JGNHXQFEguMXRYxTLjp6Gr+/FYznUE1bdQvJLrRsxwKumkSUg3ufrcJpyKxuDqbv108M/U80zRTC3CB24YoClw4b3QoZ3gFlSzLHOwJ47VLrG3oZ0r+UBgwOeXyy49uZyCs8vSN8yn02Gnqj/LQhnYO9kWJaQY/PK2O8hwHNlmkNGu0JtlwL1KRBOZVZrGpJYAvHOPiBWU8s62bRdU5KWMwUiTVMAw+dXQVp84o5E9vNnP6GPpssiRy9+WJaj5pcNee7VS4+/JZ2Aa/I4ZhpMm/nDcrsXhHIhEWVmezrc3PNUuruGBRNVo0PR9omiYT8+xMKXEzuywLQRB4ff8AP/rnLk6YWszz27voj+jc+uw+nrh+fqp4IKkL1zQQozesEh6cnpnUgxteNn37JTMJxQ08DiVlGGKxGNlOJS1nlNSTG4mu62iaNsqjyZDhf5P/uG9ecuc3cnccDAZ5cksXhgWdgTgVuYkd3O/Pm4puWnQHVTY0BTh1+tA0wvcLmXUE4sR1k5iqIxPmoffaeWhDB7eunMKiyvQO7bpCF/0RDa8zsUOM6RamadHUHyXbKSPLAm6bRIHHRncwTpE3ERtPhlhyXDKTCp2cMbMY3bR4cksXpmUxf2IR/S8m8jAjR/3muRSW1OYwo9TLa3v7uWRBKQd6I8yrzsUap7lw1b4+TNNiX0+E6WVZqa7xQCBAOBxOJbf9fj+3njSRxr4oi+vL0HU95cklk+6nTCtge0eIX73aSCiuU1/kZn2zn+Pq8rh0YVlaWO3UGUUEoglVbUEQ6PTHuPK+bVx7RAUXDIbzfnrmZDZ3xjiyJiF+eeTEhPinw+FIFX2MleSuyHXy4zPSNxD7usP85KUDXL6oLNX0OZyRGmdjoWkaF84t5sK5xbzTMMCPntnK2iY/S2py+PqKSam+FX8oyqePqqauyI1lWTy3qZXesMq2lgG+s3ICP3rhIIuqc/B4Ek2ghmGkvns/OXMKp/95HfevbeK8eWWpAgZVVekJxsl3K4TiBjc9soOTp+Tz6t5+avKd3H7Z3FENrElDk/T6kp9TElVVU+KcsixnNNAy/K/yH2dsYrEYuq6ztzvEV5/cw6kzipiQ7+Soibn88LR6OgNqytDAYIhATCxIvz1/WtqxBEFAUZRxPZa/XjwDw7Tw2CTimsHuzhAWcKAvMsrY3HxMDTcDaxt9ZNtFnrhuDrs7Q/zopYOohsXs8mye+tR8bn+rmU89uJ1rllZw+szilLF7ZXcv/RGNRzZ28PbBAfRBiZmz//QOxhh2Qxbh8evmkuVMlPNeOFhYcExd/riGBuDmZdVsbAlw5MTc1MyYsXbDyUTx3JqC1G46aWzueusAzX1hPnN0FXZF4oTfr8PrkPjqCbUIAly0oDTN0IiiyAs7e7j1md2smFLAj8+YzLee20dPMM5TW7pSxsYuSxwzKT+tkiyZbD8UMc3gzf39LK3NSYWZ9naHCcV13msOkOtS+P3qJuqL3DT0RfnJGfXkDru+Nl+Ufd0Rlk3KHTdn8bvXGwnFDEQRekJxTNPkhy8c4I39fWQ7ZEzgF2fW841n9tIZVHHbJBoGovSGNJ6/aWHqPiSNjN1uT4T1ZCk1SC/pzWiaxp1rWnlwQztLavM4ojabbIfMQESjK6jSFYyPqZSgqirxePyQKgbDz5Ehw/8m/3HGJtmdvrMzhCQKvLqnl5dMi4O9EW44qor6sUWYgfSOckjvqB45GTIS1znrb++R51L4/mn1/PLVBjr8MT5zzATOX1iVtgAmF5C713fyj7cbWVqbw8rpRfx2dTPBmJFqCpUEgdr8xOySQk9isTMMg/VNPrIcMiunFfLQe+009EXJc8kMRPRUD02yIEARE1MaPXaJNU0BVgzK7AP8fnUjXUGVH5xaN2Yj6p6uELe92sBViyvI9npSIbNkVZ1hGKnFNllGnFychod1ntjUjmZaHDkhh8W1eTx09Ww03aQ0x8nNx9SkeYxJdQG7LKIZFm8dHECSJJbUZLO1LcANg2KeSZI78ZHDzQ7Fwxs7eH5HD7u6wnxuUALolGkF+OLgC0V4+2BCXeGdhgFsokiHP55mbL7//H78UR2PQ2JBVc6Y5/j0UVW0+mIcW5dHgTv5WhO3TWJqiZuuoMovX2ui1RdDERPG/6mtXbiGFREM98iT7zPXLvLCZ48EU0/zsgvcNpyDs25OnlbIjFIPRV4ba5v8GKZFOK6Pqvo7lKjs8CbUpIJ3hgz/m/xHGZtkUQDAGTOKmFzoRjVMfr2qkWllOe/7+kPt5kYubO3+GL6oTiCm87Und+OL6uS6ZOZPKEzNV0mG65KLxJIJeTy9uZ2FVdkUuhUsYHKxm08dWcl969p4bW8fPzmjnsevn5d2rgc2tLO1LUi+10lHQCWimZgWlGTZ6AioCbVmIaEOoJtQ5LVx7KQ8jpk01AAoiiL3rW9HBL50XA157tGLydb2IL6IxqObOlMd/ECanlkyTOX1DiXyR8qufOH4Wg70hJlVlsX961qZW5nF1JIhDedkqAxIlYZPKXJRkmVD1S0Mw+C6I6u47siqMT+LQ1UNBmMaf3yzmdOmFZDvsVPstTG9PJctbUGOH9YQuac7wl3vNKKZCRHVryyvZWJBYlxzXVF6QcO5c0p468AAU4s9I0+XYqwy+FtWTGQgqlPgtnHH2g7+9kYDeS6Zn589hYff6yDLoeC2Df3ExvKgTdPEaZew2YbUDizL4vSZRZw+syh1D5Pe+ryqbPrCGoo0ejMxkmR1md1uRxTFlMJGUsE7aZwyApwZ/jf4j2nqjMViKXHCj0I4rmOXxVThwKHweDyEw2FW7+slx6nw7LZu7IrIV5ZPwOv1EggE+M2qBt464OOi+SU8s62boybm8ZljatPyCxffuQnNsPjFWZP5wxvNHOyN8P3T6qgrdBPTDHJcNl7a1cPvXm9kSrGHtU0+gjGD8d6h25YoGphe6uGuK+akhUsEQWBDe4xIOMzRk/LGfL1uWvxzZz9nz6/EjA8lxLOzs4nHE6GZZFXWSC2x4Xi9XnY1d3PvujY2tPjJddr4+6UzU9eR/IySUzeTn922tgDZTpmJxdkfStlh9b6EgGddkYefvXyARzZ24rFL5LttnDunmEsXDpUT94bi/OGNZtw2iTWNicmkPztrClW5o6uw/FGN53d00x/RWTm9kJr8j5bD+PTDO+gOxHHbJE6bkaiOEwDNtPjK8homFrh56L0OqvKcHFuXj8PhQFGUVMgrOZJhvO/2oQQ+RzYq2+32tNLwkYzX3Jlp5MxwuPm39mySCs5j/diSFWhj/XAO9IR5YEMHl8wvZWKRm1ZflJsf2cmkAhe3DRsINh7JJs1lkxK72VnlQ13ZvnCMxT9/B920cCginUGdYNxgf08YwzDSPKRrl1awrztCVZ6TH5xWR1N/jOkVecz78SosCx65ZhbvNfsJxHR2dAaJaemGRiCRm5ElEVmAfI+NH51ez8zqQuyymGYMLMtifqkdGL8pz+Wwc+6cYkRLRyWxiEmSRCgUwuVypZLHpmmm5WhGoqo6V9y7hWDMoK7IzfVHDDXSulyulLFN9Zc4HGiaxowy7+Dr39/Q3LuujZ2dIa5bWpFSTrj/qjnMr8ziic1daLpJOK5TkZM+qfKdgz52d4aYWODi00dVsag6O6UFl6ShN0yhx8ada1p5cksXiiTQGYzz/VMPPUphLAzTZF3jAIYJf75wOpOKXPxqVSNum8Svz5vBrc/spizbQbs/hqqb/Oq1Br592nQWlg8Zv/cLFx4qB6MoCna7nXA4jGVZ2Gy29/VSRoaTgdRET0VRMqOhMxwW/q2NTfJHZpomgiAgiiIulytV9jxW4vjN/f28sLOHvd1hcl0KNxe5sUsiNkkg2ymnZoQkd5XvJwmfrM5K5hD6gxFkMRHC+NdnFpLnUjhndmGq72J4yOmEKYWcMCWR/H+3YYBjJubx5p5OTNPCtGBtU5B1TX7iemKksSQOJmQGsUngVGTihkm2S+HiBWXMqsjB60ossDabbcyRCuMxcpFPltAm7/Hwfot73mng+a1tyJLIr86ZkibZE4tFKHDbME2Vm5dVsXhQdThZWpuspkoqGycrow6FYZo8t72HKcVuJhd7eHl3LxHNQBYFCjw2KgeNymlzKvnrWy20+GLcfEz1qPDWiqkFBOM6yyfnU5LlGHWeHe0BvvHMXkqy7Hx6WQ2v7u1nbrmXywbVDIb33HwQbIrC0ZPy2d8d5vX9/fz17YRA6Z8umkk4rqEZFl67xNmzi9ncGqCxL8odbx7gz7pJVa6D91oCKJJA60AMhyLyymcXHVIeaSTJsQBjza8ZyXjfFUEQUhsLTdMOKeSZIcNH5d/a2CTHECcXesMwUjs4GL3ji2kGtzyzB9O0+MyyKk4b7Lso9Np55NpEnmS4kQrFdFr9MaYMxuqzs7NHhRmShib5GrdN4tsnT2RrexDPYINm+Riqw8P5y1tNDER01jX7OWpCLpcuLGN+ZTZL6op55+AA1XlONrYEkCUBTTdTJc5xAxAM8lw2uoMqBS4lZSCTu9dD5aFsNhvSoJCnLMup92Wz2WjoDvFucx+XLSpH13UikQiyLKMoCn3+ILe/eZCuQJzKXAeqbqYtgIIgpO7n8HMlGwaT50zyQYzhjo4QD2xox2WTuPOyWfz4jHo6fDGq8110B+J0+uO0+aKU50BMN8l1yiwbI1zoUCQWVeewvsnPyum2VG9N8rqLC3KJaibTSz3Mr8zi9a8cy8s7O7n+we1cdUQ11y0tT41GjkQio+5vMjQIidDuYxuasUsCXziuhjkVXq64ZytOm0SOQ+SpzT0sqs7ilOmFWBas3t/PQFRHkSV6Q3G2tgcxTQtRENBMC8000C2IqCLluU78kTiy9cGKJD4OI8N3SS25DBk+Sf6tjY2u66lxwfD+w6kcioQiCQiSyJkzi/GOoxSQ5NvP7aXVH+eWEycwryoxOTG5E09K74/FPevb6Q9rrJxeyOz36cLf0xWiJ6Rhl0U8NpEir42yLDsLa7IRTZWBiMaB3ghxPVEUMFYAZGFVFmsa/YkqARK7WVmWcTqdo7wV0zR5clsPR00upT473Qh6vYkwliiKXHr3m0Q1A5dd5vx5ZYTD4dTwsbhmcPTEXLx2ifPmluJxKGxvDxLRDBZV5wCkvBXLslLHDQQCCIKQSjhvbBrgL6v3M7HAxc3H1KSuI6YZvLSrlyMn5qY8wslFbuZXZlE9mDfJdSb6SwCWTcpjR0cQ72D11Z2XzSSmmeMqQfz05QOE4gaVuU7mVAyFQGVZpkSyeP2LR6DreqpJdPXeXmK6wRt7e7l6UQmGYaQVoyRJhpiSxl4URR7Z2ElMNyn02MhzKZwxs4jKHAfXPbCdza0B7LLIU9u68dgkSrMdRDSDP18yiz+uOsi/dnRjCYmP1bKg0GvjO8/tZdW+fmZX5LC5xcdpM4v53srRYp/Jz2C40vX7IcsyXq839d6S351k5WHytzWWykCGDB+Xf2tjM7xUVFGUDxTeuPuK2YTjBk6bTFjV06qBkiSN1hETcnlpdy8VuYkfbPLHljxPUkRxZNf1t0+eyPrmADPLvLwfVXlOjp6Yy4LqbI6ry+ekP64nqpkcNSEXmyywqzOEMWxjmeuWiWsGcR3qilx85pgJFLgkdnaF+evbLZwwpSB1jWPlq17b08ePX9iH+OJ+dn5vRdq1D69IOnNWCS/s7OaoCbmjPI8bHtpBOG5w3txifvd6I984cSK3PrsHAYHHr5+HLAppn0UwGMRut6dmr8TjcUJxg889tJm+iEZzfzTN2Dy7rZvHN3eyoyPELSdNBMCuSHztxIlD9/i5vTQPxPjmSRP54vG1addX4Dm0WORF80p5ZU8fk4tHh5ZG5kdM02RLywAV2Q5+d/5UFEVJk4pJXsuOjhA/OaOeyYNesN1uxzAMvnXyBJoH4sws89A8EOOxTZ0EVR1VT4RKI1riO+WPGVhWjLoCF0+818q3T5nE2bOLuHtNG9s6QoPFKwK5LhsCkOtK9N7s6ghy4T828fdLZpLjtg/J6ogiHs/41XMj33MsFkuVuI+c8ZQ0osnpoZmcTYbDwb+1sRke6xdF8X0b0UzT5K41rWQ5ZH728gFafTHsssh5c0u4ekll2vMAzp9XyvnzEg2FTqczJesxkpEL1IyyLOZW5xOOxugPxugLq6xr8nPh/FJcNpnuYJw93RFWzCgDohxXl89vVzfitUl47DJRTeVgb4SukIppgVsRUnIlgZiBiMW5c0r4+okTEEUR3TA5oT6f2gJXqqNe07QxE8sum4RNEslzK4TD4bSwT1JwEeCLx1XzxeOqgdGeokOR8EV1ntzchSwJtPpizK3IIscpp/JVw3l8cweNfVG+eFwN4qDgqWRZzK/KJqoZfG5wbPHmVj/f/dd+ltTkMKnQxWkzCkcdK8ni2lwGoj2UZn1wFeJkZdaJUws5cWphaoDe8IIFX1QjGjcoHcwBhSIJr1KRBRyykPJY0jrvDRNfVOP1fX3s6Q5zxsxiWnoD3PjQdrqCca5cVA71eezuCtEf0cACSRQwLQuXIjK3MouYZtAVSIxy7gqq1BW5OaYunyuXCPzPP/dSX+jih6dPRpZEvnnyJP61vZOVU/J5dHMnvWEVSPRDJavWxsrRJPOQyVL2ZPNo0nAm70NSuy0pnZMMyX5Q45Uhw0fh39rYJLusBUFI22nqhslXntxNlkPm+6cNVRBFVINNrQEkQSDPrWCXRURBoD882oAMD8klDU3y30kRRV3XU97Dq7t7+e3qRj5zdBUnTyvCsARW/GH9oO6XnZhuUpxlZ+X0Ir7xzB76QhqdgRgnTy1gIKqhGxZdQY2/XjSN6x7YQV8ojmEmwmbmMM9GG3RzyrLsqUVAlkQ+vWxozvyhusSPri/kH5cqlAzmkZLHiMfjvLS9neb+KGfNLuGBDW2cNLWA6rz0cl9VN7jj0pkookDLQJRWX4zp5dl8r2T8hejede0AXHdEJdlOkb+/08Lqff38/KzJlGYPhXkiqolhWtgkgR+cVs+969ppD6icNDVdTqYvlAid/u68qYeUlYlpBhfduZmqfA93X7Mo8dgIeZbhO/im/ghn376RLIfCE9fPJc9lQxIEHv/0EcSjEWxywqPZ2RHEbZOoznexuzNITDX44Wn1/M8/9+G2SbgVkZ+/0kBXMNED9cc3W7hzTRtzK7yYVqL51rIsSrwKpiWwpsGHU5EIqcbgfdD56csHOaYunxllXv5w/jQKPLZUWf6+7hDf/dcBIKESUeS1YZOllHhnJBIhGAymEvmxWGzcEdRjPZ7cUCWNS7K5OR6PI0lSxuhkOCz82xmbeDyeUilOyskMn34JiZ1m80Astcu+b10bEdXghqOq+PHp9UgI1A8ujr6ISpZj9Ns0TTMVv4b0RLtlWWmNjgARzcAX1Xn7oI+3Dw4QiJv4Yzo2SeSbJ01gY0uQZYONkidPLeTVPb3ct66NV3f38p2VdUwpcvObVY3IUqK/Ixk6M4GobqWNDAD4584ePnVcXUoSPrloSpKELxzHYx9dsSSKIoZhpEZNJ+VRkqOAf/VaA+G4QVdQ5Y0D/ezvDvPzs6fy2p5eHtvcyddOqOUrT+5BNUyeun4elblOKnOdqWT5eIUZ1x1ZjUsmlUPZ0REkpOp0BuMpYyMIAi6ng79fOpNCt8Ke7jD/WNNKkceWZmxM0+Qvb7ewtS1IXDO5csn484laQqDqJt3+SCqZn5T0T5YBDy/ffnBjN4ooYJME3IOS/na7HZtNQbZsyLLMq9vb+MyjuwCYW+Flf0+YYNzkrYMDfOPECcQ0CwOR3lB6rkzVTTa3BRINuCQ2EFNLPBwxIY/fvt5IVB0KO6qGxbdOGgoZlg0rMPnzGwkVgtKshGBqoSdhaO5d14ZLETl32FiI4Y2ZH4WxRkR/mEq8DBk+DP9WxiY5MgASlWHhcHjMHbzLJvOnC6clwhWSxG9fb8Rjl7lwfmlaJ/vf327mme3dfOeUScwfQ4bkUJMLh++I1zQMsK7Jz98vns6nHt5JOKbjVASKvDY+fWQVx9QVcEzd0IJ5wbxSTpqSz1ef3oNuWtz40A7mVHjZ1xth1+tNeB0yvmh6CEwRQZYEEEDVLexyovM76WElPa8r7trIrs4Qvz53Kktq01WUR96r5JTIJGfPLiUU17hkfinBmMYF8xML1+v7+ukLa+zoCOGySWRLQ1+LZEI8aYDHCjOeM7csdb/6wxqNfVHCqsGx06tSo7m3tvr5znP7qMp18PsLplNX6OLqJeWpkuYkv13dxNsHBqgrcnPi1NECmkkkSWJBTTY/PH0ypV5b6rqGhwpH7uq/tmIiU4qcHDspD7siIQjCYGWen+vu3cRARGPKsDzPptYgS6qzWNsUoCrXyblzShBFkSafytyqbHa0+0lOs/7BaXX87OUDgIUowNLabGaWZXHBvFJOnlpAQ1+ETz24Hbsi8fljqzFMi6e3dnHmrHR9pdf29RGM6Tx47SKKvIkNV+tAmL++1YRmwklTC3HZRJ7a2k1tvpO5lR+sGXNkEUDyXgF0BmI8uqmTKxaVj1t0kSHDx+XfytgML5n1+/2jmtOGN6OVZDno8MfY1x3k1lMmMxBWR/1QopqJZTGmkOWhUBSFdxr8rNrVwTVLK/n1601E4jp7usK4FJGYJiBLEt84cSLHTEpf8GOaztX3bSPbIfOH86fxi1cOsr09yKKqChZVZ/M//9xHjkMhxy7ij5sIJKqRVBMsLCpzHVy5uIKTpw3lM4aH+HpDKvqgmvRIY/N+3HzC5JQBj+sW33p2L78/fzpfXl7DlrYQR0/M5ZTB84qiiGVZPL+tnZmlXoqzHWkLlaIo6LqOJEkpQ+N0OsmW7PiiOuU5jlR4xhdR+epTu4npJvMqE+Xokihy5eJ0r+U3qxp4Zms3iiRw3REVlGWPrrQSBIFgVGVr+wDLp9s4ZlpCPcCyrJRHk2R4n5YoijyxoZVX9vZSm++msjiP/T1hCohw19uN9IRUoqqRVqwB8Iuzp3Df+g7WNAxw26sNnDunhD+91Yo/ouGx24hoKjYRfv9GM9HBvFt1rp11TQHeafCzcnoBNz2yi3ZflKhuoRo6t73aiF1OfLcX1uZR5k18bzXDpDzbQWmlnVzHkLdd5FbIddmI6yYDUY02v8G969pwKCIPXj037XqTeSpZltPynTAkrjpy6N3Nj+ykeSBKT1BNC0tnyPBJ8m9jbIb3jwzPnQxn5K7684/tIqYZ3HPlnETyeoSY5mePreHi+aU4bekhp6T45Hhxbk3TuO3lvUTVhNjhbefP4Z+bW7l7TTOqbpLllCnPcXBc/Wi9rLVNAXZ2JryJW57ZS1cgjmnB799o4psrJmKTBHoHt8OSAOfOKSbfbePxLV1E4gZx3WJjS4AzZhWz8IcvI0kCb3x5Wer4f7tkBk9t6Uol13XD5GtP7cYui/zkzCk4nc5U0tfv97OmcYBV+3xctLCSPXtbae7286+dPWTZZboCiZ1uttPBSTPLiUajaff8+Z293PluC8VZdv5wwXRe3NXDS7t6+dE5s2Dws0jllQabOfNs8OaXj0TX9ZQSw9omP70hDbsicvXSdOHN3Z0hbnlmDydPK2R9k5+wavDkTUdQPoZyTPLz/fkrDezpDtMXVrlwcW1KBWEgopE7OGI6Etf5y1vNPLChA0mE64+o5PZ3WonpJmsbNnPlEf3c824ztfkOAjEDmyRy59Uz6Y+obGz2Y1hw5swispw2trQFaPXH8ccMOgMqXz9pEo++10ZJlo2fv9LAnPIstrQFMS2LYo/CL86Zxs2P7CAQ13EoErIoYA5+3l6HTFQ1OH1GEXHTShkagL6QSvNAjM5AnNvfbmZnR4jvn1qH16lw35Wz8UV1KnOd6IbJ8sn55Hvt9IdUVMOkZNAwy7KcqkAcT0lgZOhtxdQC7l7bRk9IJazqZIRrMhwO/i2MzXh6TWMpFw9neqmHloEYz27r4qL5ZUB6aEo3TG56dCeGafHg1XNQJBF/VOOiOzczId/JHy+cMeqY3cE4TlnkF2dO5pGNHVy2sIwfPL8LuyLy6aOrWL2/n5+cMZl899jhhqMn5FBf5OJAT4SqPAe3nDqVU377Nh0BlS8/uZv4sK2zQxFRdYu71rZR4LFx3xWz+dObTZw9OzHTRjNMAjETURz6mF7c2cvr+/qxgE8fXU3cMDnYF00NAhve8CnLMt/95376Ixov7UwoENfmu4hpJl86voIlNTmpZs3hC1BSZeGIyTJ/f6eFMwYFIR/a0IEvqvHGnm5OrM/hrQMDTCrNYUpZwsOKxWKpcnEYahY8YXI+OxeWMbs8EeJ8blsXB/qi3LysimBMYyCi0RNS+cbJkxElmSnFXoLBIFFV590GH4trsslyOej2hWn1x2kZiNHcHyXPqXDx395lQ0sQEXDbRX525hSOnJjHV5/azTsHfZiAZsJf327GY5OJ6SYmsKGhD0kUONibGPdslwV+/NIBOgJxvrFiAq0DMaryEt7ZeXNLeHBDB7PLvZTnONjc7OMzR1fx6KZOsh0yfWEVQQCPXeZTR1XS1B/lX59eACQW/HuumMVf30qEdCtzHBR5bWzvCKaNxjZME4ciccNR1ZTluvjWUzvoDqnc+uxefn/BdLKdCtlOJVVxlywlv/KeLQTjBn+/ZAZ5bhuRSASPxzOqsfZQ3HBkFW8fHKAvrHGgO0xZ4ehNVIYMH5d/G2MzEpvNNspTAWjsi1DoUXDbFb6zso4bH9rOP95tpSzbzvLJ6TF+SRTIccpEVBNxsJjAMK2EYu4Yhqw3rHL9A9vJdSvcddksvnLCBEzTZGdniFDc4JsnTeSKxeMnrLe2BrhrbSt/unAalQU5PLi+hd+8uAdFElANK83QAERVk55QHN20EIHiLDvfG6bP9cT18xBlO7IsphpNl9bmsLE1wFETEgu82ybzl4umo4hDKstJL1HXdb56Qi1PbutlVomLfT1hvrNyEsHoUOnvWAPkntrSiSSKXHrkJJ4YplD97VMmcaAnzPK6bF7c3ctf3mym2GvjgRuOQNM02vuDfPXJ3SybmMtVwzwYSRTTemXuXd+OZlicN6eYv7/bSrZTodCj8JXHt/0/9t47vKoye/v/7L1PL+m9A0kIAULvIghiLyj2rohdx9EZy+iMZWbsY3fsvXexgCiK9F4DCSSkkN6T09suvz92ckgolu9XZ973/Xlfl9clOefsfcrez3rWWve6b2YPTWRTVRtPLq/BapRIsBs5emgSozKdPLS0GlnRaPGEEYAlu9spa9GzJxXwhlQcvYOfRal2Sps83H5sIbcvLCei6MQSi0Egomh4wiolGQ62NXiQNbhh5mB2NbsJKxoz8hM58+UteIIKG2pd9ARlajsDGEQ92zNJAq5AmMeW7SMtxkRQVghG9JLo6+ubQGDAtXj3okoq2nw4TAa8IQWzJBOM6BJFffjL5xWsru7mgXkllKRZuXXOYG74qIzdrd4Bv82BitgFyTZqugIHWRkcjlHW/14LRhTOfnUb6TEW/nlSITWdAUb20wH8Hb/j18R/Pdgc2MTuw6GmmFdWdvLHT3YTZzOy9Hqd6nriiDSghXE5Byf/MTExvHfVEQBRemiC3cSHl41BEgXOf20b1R1+Ppg/RmddGUUcZonsuP2zHaIo8ti8YbT7FWyHGBDtjwXvlhKSNVxBmXtOLuaxpVWIgoY/fICIaN9nB9bWurCbJF67cORBx0u0m7DbbdEGPUBhqoMnzhhoAtdfA+xAn51jh6dx+sQhhEKh6ELlMO/PyvoLZ4Ke2b22roGQokYl7vswOMnG4CS9vpWf7MRhEjllZCp+v5/NdT30+EK0ecJsqHMNCDZ9aPWEeH9zE1dOy2Z7o4d3NjXjMBtQtRBmScQTlPloayuKrCIDnpCCJ6SwvLKLN9Y36r2t3oBtEGHDPhcCMGNIPE2uIKeUpDIqK4bPtrfw/pYW7CaJrFgjj5xexFelbZS1eLGZRKbnJ3Hp1FxW7O1kc/1uDKLArMJ4zhqbilESCUUUrEYJd1BhXa2L1y4cxd2LKqjvDuIKyhQk25EkEQFIizEzJiuWHyo6aPOGObUklYw4M/XdATxBmeJ0J5Xtfqo6ApgkgQXTsrl4UiYRWcVmklhX3c3wdAcJdiNOi4FYs3515CbaSHGY8IQUXlhVxxVHHNqOob/FeB/6LAQOdQ/1DzaNrgi+kEJpk5useCtZ8dZflBH9jt/xS/BfDTaHK5/1UX0PrC0nO80YRCFawlpT3cU7Gxv463FDiOtHDvCFZK54dyeCKLLw2mkoihJVww0EAph7aa81nX7CisbmepcebEwG3r5k9IBzBiMKty3cQ08gwrHDkrlpVl609HRg5lWY4mBPm4/TRqZy3XvbcQUPrWtlN4v4QiomCTQE4u0mEux6gLvinVI6fBHeu2w0JkkkFAr9qCqw0+kcYHLWH/2H/w63iBwY6JPsuuRKZpzlsJL3ZrOZe77aTqs3Qm6ClX0dXm79bDeSAPefMpRBiYde5J5Zvo+yFi++kMK2Rg/t3jBnj02jrMXL4z/soyDFRk1nYEAxNNlhRBTAZBCYPiSBFKeJpXs6ee7sYt7c2Extp59/nFxAjHW/llerJ4wkCmiAxSBx/Ih0nl+5j+5AhJkFqfx5zhA6PUEeW1qJBmTFmbnm/V2MyHBy53FD6PSFsZsNWIMysVYD9d1+Hjx1KK+vb+DDra3UdweZkR9PSUYMuQkWTAaJPa1e/BGNjFgzRSl2/vzZbrwhhX/NHYokgMWgEy6CERVRELhn8V4kQaCq00+yw8y/zx7Orb0KCr6QTKrTxOXTcnlmRS1f7mrn/ImZfFPeTnachUGJVhIdZrY1uBGF/ark/fud4XD4kMGm/7U0JMnCk2cMI9ZqGiBl9Dt+x2+B/1qw6dM9OxQOZBX1oSjNwbo/T43+e3V1D4qqsaPJQ3G6k9XV3fQENSZk2/GFFeKsQjSYHcrf440LS/huTydzS9IO+z5FQaTZHSaiqGysc7Gn1RctNexsdLGkrB27SWRJeQf3nTqUgmQ7i3a10dBzeAMwX0jFIAkIaHx74xGkxZij/vA7W7yoqqYz6KSDRRIPen+9U+B9tgCapuH36141/XWzfmwRaXEH+fOnuylIsXPPSUVcceTgw6o19PnczCxIYGVVF1aTyM2f7iYsq5xWksKorEOXYb7d3UFpoweTQeSE4cl8Xd6BOyjz7qYmVE3fzYciKl9dM5EFb22n1RPi4blDObIgid2tXhZub+WaI3NwWozcPHswGIysrSnDKAq8tbGJ+dNyMPf2rRZMzeL00amkxdoQRZFzX9pEkzvc6xaqoigKdouBWKsJVYtgNUoIAmTHW7jsrVJUDR6cO5TKVh8b6lyMyYqhyx+h3RthcKKFiKrx6tpGJubFU5Bip6kniM0kcf2MHJ5ZUYcgwPicWDwhhave34UnpBBn1YPmDTPz6PCGqGzX1cPjbUaOHppAVbuPIcl2ml1BLnxjOxaDxCcLxjA+x0mqw8z7W5v5fEcr7d4IQ5JtPH/OcP72ZQWCIPDx5WMwSAfrBrpcrqiCRJ8Kd9/15A0q3Pb5biblxXHhxEw8Hk804PyO3/Fb4L8WbH5KCfjnWAJfe2QOm+pcTM6LQ1ZU7vyigpCs8tkVY3h8XhHJDrO+4wwrjMmOPehmzE9xkH+Aa6OsahglMbpLNBsF1v15Ghv39VDf7Y96sgC8s7GR7Y0e7CYRV1Dm1s/28KfZeaypdWOURCKHGJATBX3gL6JoXDQxgziTFi1vaZrGuttnEwiGMQty9G+HQ18wKd3XTmashbheFlofSaA/wSISiVDT4WdZZSdnj03HaTUhCLrGWY9fJhBR6fZHsNlsA7JNk8mE2WzG4/EMkKKfPzWb+VOz6fJHCEQUBidZueGo/X0ZSZIGHMsbUmjxhEiwG1lfq7POAOxmA29eWMLX5Z2cNDKZGIuRz6/Sm+uVbV7u+HwPC6Zlc+sx+4cgARZuaaDNo9PA19S4eHPDOh44dSgzChIRRZFkhxlVVanv9FHa7EHV4JzxGYzNdLCkrJ2RuQm0ekKMy47hjmOHkBFnRVVV1tXoG5hBCVYKku2c0Ksc/uHWFmq7AjR0B5FVjc88rXxW2sqWO2bz9dp6drf6cJglxuXEEFE0Zhfq6gDH/3sjogBzihKx9WbUSQ4zxw5L4r1NzRxfnMRLaxoIyyp/Pb6AZIeJdm8EgQinvbCFBdOyKUixIysqg5NsxFkjTMiNxWSQOL44GUEUMEgipY1u/rmkCqMk4rRIPHr6MCzG/XJPgUCAYEThls92MyTJzsyhyTS5QvxQ2cmFE3X6uCzLv6s9/47fDP+VYNM/o+lfivopVec+XTBBEPD7/dhMhqjBWUO3n8IUGyWZTq79oAyAdy8ZzW0L9wDw+oUlA0y0rFar7k4py/zl8wqsJpHjhiVz++cVTMhL4PmLxvfrc2hMyIlhQs7AXfv5EzIwGZq58ogc3t3UxOKyDl5YVc/eDh9zR6bwzuaWAc93miXCskpE0VCBNOf+3lCfvL+kqqjC/kB7uO+jT2Xh7fX7+NeSPRSl2nnh4gnMfngZmbEWXr2whEAgEB0IFUWRl9fWs7ddz3qunpkf/R2K0hw8dWYx8TbTgAZ0nxzKd+Wt7Ov0c9Hk7Ogmoc+zJsFm5L1LRusDqb2w2+0sL29iSdkerpuRxwur9/HWxmYAHBGVr8t16vVfji+gKNlGgsPMhZOzD5peX1TWwd4OP4t2tXHJ5Cyq2v1sbXBz7vgMvilvR1E1bEYJbyhCSFFZXtkZ9bfpkzpKj9W/YxHIiLHwzyXVADx0mgFF1djW6EEUBJZXdhCWNV46b0T0++3DN7vbeW9zI95Q39wO+CIqBlEPvkcPTWJ1dTenjEylKNXOKc9vZmu9m3PGp5MeY2ZQko3drT7aPCFmDUvhvkV7dMvxkMxTy+uiyhGDE62YDCIJNiOBiEKDK8QraxvoDkRQVI0kh4m/HDOEyYPiUVWVPW0+BODSt3YgCdDQE0RWNBRN48RnN/HRgrHkxequqKuru7BIItUdAXY2eXh/azMXT8zgh8ouzn5lK29fMvr3YPM7flP8x4NNnwIt6Lv2Z1bsY3Kuk1GZMVGF5QOzmj6tskgkEt1ZhxWVN9Y3MjYrhrE5sVz4xg68IYWjChJIjzHT5gkjCnDSiGQ6fZEB8i596reBQICIolHR5sUdVMiNt+pyI4JGOBzGaDRGlaf7Lz5b6ly8tLaeP8zM45SSVCpafVx7ZB6DU2J4dnk1npA6INAcU5TEWRNz+WZnEx9s0RddAegJRlA0DbvVqtsnh2X8Ps8AD5ZDodkdJsluRPV4KEyyIAgQY7cAInaTRFDeH6D6ymGSJHHm2HS+293BvNFpB5Uw02MtOJ3OqM3Au5ubcQVVbj6miGvf2YKqwrgcJ3mxesDur7Dw96/30uwO8cK5I1hb08O6ulraXAEaugPc+lk5WxvdgJ7VPXFWMZIgsKnew7wJg1BVlcaOHpwm7SCq+8UTM8mNt7Kr2cO5r27HbBRBg+HpTiKKRlachVcvGs33e9p5ZW0D7mCvWreiYtIE/vJlJRNz4zljTDqb61xMzovh7Y0iiqphNYqkxZixGkQWvFtKU0+IjDgLgxJt5KcMFLl8ZnkdHT79mpQEQc9OgeJetYEnf6hlV7OX+76pIjvOgiekEGs1YDEYmDQojs11bhp6Aigq/PnjMto9IVQ0DCL0qdgMS3WQl+xgQ00nUwfHsWxPB6AHEIfFQLLDiLHXABDg4e+qWbG3CwG9n5VoN3H3iQW4AxHu/6YaT0jmmvdKqWgLYDbo32ua04SsKLiCMqoKle1BwopOsRd/oV3B7/gdvxSC9lNNgV8ZfRL3rkCE5Xu7eXF1Hb6wwvc3TPpFx9lY283D39XgMBt45YISbluoz1U8f24xJdmJ2Gw2XC7XYV/fP4taW93NrQv3IIo6DTgjxszsokQcFiPf7e4gEFF45fyRUWLAo9/XsK5G38k++UMtigZfXT2BtBgTe1o8nPXq9oPOl+ow0B1QUFQtOqWeG2/m3PGZzD8yn8o2Dyc+tRabSWTNzVMHvNYXlLn+ozKy4y2cNz6TS97czuisGJ49Z0SUutxn+OX2+tA0iHXqC2GfR09/x9E+ReD+njT9NcXc/hCnPr+JTl+EnHgLQ9Njqe/y8f6CScjhgb2oYERh+mPrUFSN1y8YyVXvlxFrlbjj2CHc9OkeghGVGLNEeqyZyXlxXHlEDjd+XE5ugoU7jytgdVU3t32+h+OLk/jLsft9W6o7fLy0poFLJ2exdE8H729p4YIJ6fhCCldNz0HT4NbP9pASY+KKqdk8uLSaE4YnYxQF7viikrwkO63uAAk2E+/PH8vedj9b67vJjrOwrtZFbaefO04cTqc/yEWvbCYYUcmMM7NgajaDEm2MytI3P3vbA9R0+rnvmyocJonTRqXwxc52/jAzj1lDk2hyhznjxU34+jEOJQHG5cbT7ApQmGRjX0+Amo4AGmCUBGxmAzlxFi6ZnMnXu9rZ3uThT7MGMWdYMsc+s4Fuf4TCZDstnjBXzRhCc7eXpp4Ale1+3ps/HoOgct6rW6PDw4IAsRYD0/PjaXGHaXEFSXGY2NRweDkmUYCzxmczPtPGV7vauO/0EtLjfxfg/B2/Hf6jmU1fSQfgjJe3YhRhVGYsRT+iKHw4jM6OZWZBAuN6taEeOLUo+lgkEvnRQAMDB0Yn5MZxxpg0hqU5eGRpNXXdQf69og6nxcCgRCtd/ggLt7dx5rh0VFVl4z4X/rDKqSXJfLClGX9EJcmh7/gLUuykxZhocQ8UamzzylFpGoMoYDWKGCSRERlOXW1XiWCUBKRDzP+EVJVOX5iyZi8N7ghOixSlIPf5k/RNjQuaiqaqBIPBqDAnQGl9Nxe+tpkj8pN45LSiAZbYsbGxdHX3cOlbO4goKu9cMprZhYksLmvHapK466QiTEQOCjSg2xHMLUnhg62tXPBmKU6zRElGDMXpTiK9OkGjs2KwmSS+3NXGhLxYKtp8eHqZegk2A/E2A52BgeXCb3Z3Ut3h59vdHVxzZC5XT9+ver2twc0tn5XTE5DJirOQfGw+j5w2TB+g3NRCIKIwKMHCkYPjyIm3cP37O/lhbxcAdx2fT3mLbgXwXVkzdouJeKuBViVMQ0+IB77Zi4rAkusmsmhnG08t38fE3Fg+v3Icu5o9PL+qHndQZnuTh1lDk0iySQxPd1Lb6Sck6zNctx0zhMEpTu5cWE6i04RBEmhyhYjIKqeMTOW00Rm0uv1MyotjXa2urvD6+kbmDEvmlBEpfLajlbtPLKAoPRZBEJBlmXNe2UIgotDh8uO0Grj35KGc/fJWTJKAySASbzNy0cRMIorG9gY3723Zn1lbjSKBXl+dPsFXVYPKFhcfbqpH1eDGD0t5/4opP3LH/I7f8b/DfyzYqKoaLYGJokhKjAWDoPHX44ZgMxsQBAFBEKIL4Pd7Orj/m2rOm5DJpZMzDzqeURK55si8nzxvX2+jsrmHJbs7uWxypu410ruj37jPRVGqnRtm6seaPiSemz8pZ1V1D4GIzIvnjeTU5zfx4tp6ZhUlEm81EGczYDEImAwSo7Kc2EwG2j0h3t7YRFmL55CWBlrvfxYJRmfFcvcJhRRmJUbnjNJiLfzwh0lRJYD+SLCZePXCEq5+dxe1HV6uOXIQ80bpjetIJEJs7P4Zo75spY+l1oc9bT4iikZpo2tAoLVY9Cl5VdPtlmVFRdU07jhhKKuru6lo9TLtoR84sTiZv5889KD35grJHFuUzMfbW1FU/RhlLR5uXVjBdzdMZsu+bo4elsIJ/96IN6iwpHco0h2SuXdRJXNHpfDEGcUMShyoTzNnaCKapnHBhIyDzukORHAFZARBFz3tgyAIdHqCDEq0EpZVLp+WzQPf7I0GGgB3MEJeopWqDj+FSRau+qCMJLsRkyQSUFX0GKhx39d7KUjWs8OwoqGoGol2I62eEO6gzM7GXottg8SL541kW5Of697fwbVH5uILyVz/3g5EUWDp7g56/DIqevZx25zBvLimgcW72shLsrG9wY2mabR6w/ztywruObGAa3vVAWRZ5tW19Swu7+DOY4eQ4jBx/uvb6fFHuPuEAv4wu4Dnlu/FE5JxmA0UpDj4YGsLdd0Barv2jw1cMD6ddzY1RT2ToteVw8hRRSmsqGjnz8cc/Nv+jt/xa+I/Fmz6y5jY7XY+v3baANZTn8sjQIc3hDesYDaIdPsPYWamqHyxs42SDAdDkn88K1JVlVAoxKPf11DfHSQ33sIxw5IwGAx8uKmBF1bXMTLDyYNz9czIYpQ4qiCBNTU9GCURoyRy86zBdPrCUQvj88Zl8PB31VzyZin7Ov0kOYysr+1hX5deHw8fqObYDxPzEgnJMl3+EF6vF4fDES2F2c2H/zkSbCbuPH4IG/a5OKUkFaNRz2j6z888s2wvjy+t4NyJOdx76ghUVY1SlY8viicrfhz5CcYBPTFRFPF4PBglgTcuLOHp5bVc8lYpI9IdtHvDqOiWxVpvG9sfktHQWWS+sMI5L28lwWbgrDHpfFHaij+ssq87RKsnTLzVwOyiZDRN40+z8vistJXLp2QhiiLLKzrZsM/F5no3JoPAh/PH4gvK/G1RBa2eMO6govcozBLPrW7g2OIkajr8jM6O5/3NjUiiSKLdyKzCBHxBGYdVLyOmOHQ76RmF8RiNRiYOSebd3v6ZSYIl5R3EWgzYTBLPr2lAVlVcQYXnzxvNg0sqcFok6ruDXDghg+J0J8PSHKyp6ebcV7cRUVVsRgNzS1IYkR5Dpy9MhzfMa+sadJtug8jW3tkXu1liXHYs35S3R+3psuLNGCSRyXmxfF3WTlmTB4dZ6vU00tiwr4f67gA5/TyGdrf6CIRlPtvRitMs0dG7kSlv9bJwRyu+sIoowLhsJx2eEI9+V02oX89u2uA4FARCil46G5XpZGtvee3b8k6K0x1U/POEHyXm/I7f8WvgPzbB1d+fpm+BOxTe39TE/Ld34gspvHvpKG6ePeig52ypd/H+5mYe/q72J8+7t93HaS9sxmIUKUqzMylPzwIkSWL0oGQsRonp+QkDXnP66DQePHUoL52nT/XPLkrirHH7d9hGSUDToNUdxB9R6fbLdHjDaBoHBZqMGBN9ycqfZ+diN4nUdgZYXtmlEyS+r+Csl7ewvcF92M/Q4YsQVjRGZsQwf0o2MTYLNpuN2NhY6jwKL66s0jOkQAiH2YDLrw+C+v3+AYOxI1PNWI0ii3e1cd6r2yhv8UVnckAPtFXtfrp9Eb4obUNWVI4cEs+swkT+cmw+iqpy2dulnPfaduq6Apz7ylbcAZlWT5h3NzfjC+u6Y6IA43Ji8QRlnlmxj9JGN0cNTeKJM4YzPDeFe08s4M2LS3h8XhHD0uxMyYsDoKzVS1mLl6oOPyZJID3GzIqqHoKywpqqbrr8MnWdXgYnWilMsfPFVeN5ankd57y2jR29JIRPtrcSUTR2NPqw2WycOjoLUy9TLtZiQFY1arsC2E0Sl07NJdluxheSue2zXTS7g2ysc3PuuHRKsmIxSCLZ8RY27HMRkFUMgu5a+s3uTl5f38BNH5dz11eVrK3tYVeTC6Mk4DBL3DpnMLcePYTbjhnCdTMHRW+yija9x/L8qjq8IZkufwSzJOK0GDBJIsGwzPy3d9Le65XjCkQYkenk3HEZlDV72dbgIc5qIDfewg+VnXjDfRp0uozOKS9sZv6ULK46IoesOBMJNgM3z8rjuKIkkuxGLpuSxVlj0smMNdHrZoGqarhcLl2L7n/hjfN/Kz7++GOKiopwOBxMmDCBtWvXHvScRx99lEsuueRHj9PT08MNN9xAVlYWdrudoUOHct999w2YV5s5cyYWiwWHw4HT6cTpdDJr1iy2b9/f4/X7/dxxxx0MHjwYp9NJYWEhjz766I+eW1VVHnvsMYqLi7Hb7WRmZnLllVfS1tY24NyvvfYaAMOHD2flypU/49v5dfEfyWxUVY0qAP8U4yUt1oxBErCZRGKspkPOmYzIcDIuJ5ZpQ35aYj8s6+6QFoPI3b26Y32lNV9I4cMrJ2GR9rO2vtrZhiQIHDf80JbF3mCERbva+edJBVhNEue+th1/RC/JaQy0MzCKEGMx0O4NYzKKHDsshXmjJVZWdXP08ExMBlhd1UVE0djb4WNcXsJBTLyqdi/nvraDeJuR5TdP7/W3UXlkyW5OGJ7ERa9txRuSsZkMXDkti7PGpBBvM+Hz+Q6pGiAIAh9vb6XRFWRPm4fHl9UQiOhCjiaDxIiMGBrd7ZRkOjBKEnceN4Sk3pmVhTtaMQgQbzMQ1EQ8IZmSTCc7m/SNgwb8aU4+KyvamT85i/c2N/F1WTuV7T4en6dL7PQtaH279/69trHZMVw+NRtV1RiV6aQw1UmnN8SHW1uYlBvLtiYPe9v9PHFGMXG96s4xFomQouIO6DbZL140ntfX1OINyVz++kZeuHAcEUXDYZb4+IpxvLmhibc3NOKPqDT3+Ll4Uib/+r6GLl+EcK9I59YGDxdNFmjqCVDW4mHBlCxyE6w8vWIfnrCbFIeJnoDcK/kvE4qoXDY5k7U1LuYUJWEzGZhdosvLnD/VyUPfVhGIqByZn4DJZGJCbhw1XUES7SaSHCa2NboJyTpDLjtBZ8gBvLWxiRV7u5g2OJ4pg+IIRFQenDsUl1/mwje2DzDcCysaYUXj7DFpxNrNXHlEDoqqRpmNS67T5Z1qO/00u3VtueI0+4DN3OFUB/4bUFSNDTVdtHmCpDgtTByUgHQIS/L/DWpra7n44ov57rvvmDRpEq+++ipnnXUW9fX1gD6i8Y9//IP77ruPCy+88LDH8fl8HHnkkQwZMoRly5aRn5/Prl27uPrqq1m1ahVfffVVtHT93HPPRQOXLMvceeednHTSSdTW1qKqKkcffTRpaWl8//335OXlUVpayhlnnIHb7ebuu+8+5Pkvv/xyNm7cyPPPP8+UKVPo7OzkrrvuYvz48WzZsoWkpIGakbt27frff3n/A/zHymh9QaPPIO1wMzUzChKZUZAYFZ6Eg+dvbCYDfz568E+es6bDR5zVyJsXjxpAfVZVlS+37uPPn+5m6uA4Hp9XzGfbW3l6RS2qBnaTxNFFiVGb3v545Psavi5vZ3O9i+n5CWTFmHCFFLr8Bw+hRlTY06ZnDpKocfLzmzlrTDo3zR5ErFO/qV+6cCw76zsoSnVgt9sPIjbYzTrldXx2TJQy/tyKGt7fUMeX25s4fVQan5e2cmRhMhaLRP/wqygKmxs9/P2rCuaWpHLNrEJUVeXf543h8631jM+O4aU1DZgkIWpNPS4nhnW1PVw6OZsJuXHRY+1t9/P6hibqugPYTQZS7SbsJgOlTR48If13Mgnw5LJqbAaRvy6q7B0WVfjDzP3N/R8r10iiyLzR6Tz2fQ3vbm7hDzNzmVGQyNraHr4u7yDJbsQTUlixt5O5ozNQVZVLJmfz8bZW/rmkilnF6SSaNf5wZBZjH1iFzSRR19KJ02Ig0W7EJAoIoq4iEFJUnlhWS5zVwNySVI4pSuStTc1UtPo4c0I2yyu7uHVhOSZJ5JrpOXT6Ivz9pAI+29FGktNKrFng0e9rdJ07ASrbAzx7zn4VcZfLpdP1kXh4bhEaGlMHxaMoClceOYgb5hTR0eOlodvPjR+X0eWPMCojhufO3X+M44qS6PZHOHlkCl+WtvF1eTvlrX5ev3Ak2QlWQrLK3cfn0+UPce/iagySQEDWeGlpFb6Iwh0HDMIC5CXaGJHuZF+Xn+fOHXlIF9v/Nr7e2cw9X5TR7NpPSEmPtXDXycUcNyL9R175y5CXl0dLSwsOhwOXy0VHRweJifsVry+55BI8Hg8LFiw4SAC1P5588kmMRiMff/xxdERixIgRfPHFF+Tn5/PJJ58wb968g15nMBi48MILefDBB+nu7ubLL7+krq6OZcuWRb2HRo4cycsvv8zbb799yHOvXbuWd999l+rqatLT9e8mNTWV5557jjlz5nDPPffw1FNPHfS5X3vtNWbOnMmyZcu47rrraG5u5pRTTqGyspJ//vOfzJw5kzVr1nDTTTdRXl7OoEGDeOihhzjmmGN+2Zfc//P+j1/5CyCKIna7nWAwOOBHO5TicB/6Ak0faeBfS6uJsxuZfwiBxz48sayG1dU9zBudyhelbfQEZGwmiduOGczbG5v446w8snoteAclWEiyGynsbQLLqorFIDEyw8nUwXEYJPGQ7++0UWmsrurmggmZLN/bRaM7RPgAoYD+O04NMEsCNpOEKyDzxc5Wbuq3m7SYDFF30UMx6PxhhXljMvnDDH2nvLaylZp2j55NIdPqDbPk2glABEky8fmOVp5cXsugRCsvnldCpztIkyvEorJOrj5KVyt4Z10D721u5tHvawnLChdPyWF1rZvZBfFMHhR/SFO2wUl2MuKseEO62kBTj49mt65YbZIEnGaJVKcJb1ilONXOD3u7kFWi5bD++Ly0lRWVXdx53BDibAcPEeYlWlm+t4s11T1IgkC608TOJg+KqjJ/Sg7Hj0iJBq04q5GSDCcje1l9+ncmIwo6SyvWauD9+eP48ye7eHhpNZ/uaMNukpicF8emehcNPUF6gjKfbG/lookZ7G71cf2727EYRSwGCadZ4q2NTdR3B3GYJGRNQ9PgmiNzCMoq8TYDEQVGZzq566sKbpiRy7Or6vEEZf52QgGnPrcJBIFHTy/ihg/L8IYVbjl6ELWdAQQBThyRymdXjsckiWyp66GuK8ATP9TS6g5x9NAk5gxNZFCijdPH57C+zs11R+ZQ3eHnw/ljUFWiw7QzirPp6vGQGmNm6Z5OLEYRX1jBaTl4w/TmxaMAXVfvx9xq/xv4emczV7+1hQNXhRZXkKvf2sKzF4z9VQOOw+GgtLSU0aNHYzAY+PLLL6OPPfroo6Snp3P33XdTW1t72GMsWrSI008//SBJqLi4OE466SQ+//zzQwYbl8vF008/TXFxMUlJSXzzzTeccMIJ0UDThyOOOIIjjjjisOc+4ogjooGmP84//3zuuuuug4JNH7q6ujj99NN54YUXmDt3Lo888givv/46AK2trRx33HE89dRTnH/++SxdupR58+axefNmCgv/ZwZ7v3mwkWUZn88XXbg1TePNjU1MHxJ/EAOpD2XNHj7c2sKCqVlkxFkJywrLegfYLpqYSUJcLLd/tI1tDS7uO2Uo+b0BY2ezF3dQZku9m5CsEG8zUpBsY9GuNmo7Azz0bTXbGz3cdmw+xw9L4qPLx/DQ0hoW7WrjjDHpzClKirp99k3If7WzjUVl7dxzQj5JDjOjMmP49np9JujE4Ulc+d4u9rb56C/sHGs14A3KyL13jKppzCpMxBdSmDc67bBCowfCbrcz/8n1+MMKqXFW1lZ1sr66kwSbkfPGp9Ppj3Dx1NwBr/l4WwsdvgjuoIKiKOQn2XjlwjGMzHJG2WmqBr6wjFESCEbgtbV1ALx83ghKMmM459VtiILAe5ftd4E0SAIOk0iXL4JBFChr0wONBNwwI5fMOAtjcuK46t1Smj1h8pPtGESBu0/IZ1DS/iFJo9HIkz/sQxKhrMXH1MG6bM7nO1p5b3MTyU4TVe1+hiTZeo3fOgnL+rR+MKIxPi8ei9EQ3YwYJYEXentrwYhCZ0ijMDWWBUcOZmiiLr4qKzLNrhC7W32YJBGTJNDoCiIKAi9cNJ7LXtuIouksNwGNiKqhhBVOH5nINxWdFKbY6fZHGJ7mIM5uYlyWg06/jCeoUJhi4y/HDuFf39WwvdFDiztETaeu+PzDng5CskpE1WjsCbK3w48/rPDFznY+3daComlkx1sZlRXLrrYgN32ymwSbEatRpCegkwKW7BZ5d1A8+cl2PrlqMje+t5WKNh9/OWYIY/spnZu1cFQt4ckzi4nIKk7Lz7N4/qa8nR2NHv54dP5PP/k3hKJq3PNF2UGBBvRNmwDc80UZc4rTftWSWlFREcFgkLfeeot58+ZRVVVFcnLyIRfwQ6G1tZWUlJRDPpaWlsa2bdui/77mmmu48cYbAf1emDBhAh9//DEAnZ2dDBp0cI/6f3Pu1tbWw772yy+/ZMSIEZx55pkA3HbbbTz99NMAfPHFF4wYMYKLL74YgOOOO46TTjqJDz74gDvvvPMXvcc+/OYEgb4eRN/g4Vfl3Ty9fB/3Ltp72Nd8tqOVsmYv31V0Ajq99PY5g7ltzmBdcywSwReWiSga7n7Kys+ePZy3Ly7h7ycVcNucfN64qIS7TizEG1LoCURYU91NT0DmlTX1CIJAZbuf8hYvH23V2Ur9baX7JuS/q+ikwxtmdY17QA9kZ5OHM1/eSnmLHmhM/eRaMmMtfLxgDNcckY0EyBp8Xd7Bw/OGMyEvjt0tHibev4x7F1cCevbWR1W22+2Ioki7N8z8NzZz4og0suNtzBubpYtIqhpBWcNpNnDX8QUMz4iL1tl9Ph8t7hCaBpmxZt7Y2MQV7+3kw831aLJMJBJhWbWXl9fUU5BsZ+GV4/ho/lhdR0yDNm8Yf1ihujPA3nY/EXl/yuYORtjW4EYQBKwmid0tekNeRVdzmDU0iYgs4w/J7GzyUNHm47lzhg8INKCXUd+8aBS3Hj2Yyb1kDaPRyLYGF52+MGuqu2l2h8hLsFKcZsdulrhkUgYzCxMJRBQeWFJ5kKxNH+a+sIW5/17Ppn0ubpiVz6rqbj7d3kJOvIlzx6eTFmPGYhS5dkYegYiKSRJZUdGOJAqkOYxcNjWHty4eRU68hQSbkQ31btxBhc11buKsBkK9MyyNLl2t2m4SKW3ycM4r29hQ6+r9nmS8IRmDJHD/N1WEVY2bZg1iVmECQ1PtlGTGcMXUbBwWA4oKa2t6sFqtDMuIw2IUmTMsiZfPL+GtS0o4fXQq1x6pbyYCgQB+v5+iVAcWo0iy89CyMqIokp9sZ1j6T4tqer1ejEYjz66s4/uKTjbW/7he4W+NDTVdA0pnB0IDml1BNtR0HfY5/xMYjUaMRiOXXnopubm5rFix4he9PjU1lYaGhkM+Vl9fPyAY/Pvf/6anp4eenh7a29tZtGgRRUVF0eP0b+r3QdM0Ojp0RQmHwxH97+233/5F5z4QjY2NZGXt9+cSBIHsbL1y1N7eTm7uwI1sbm7uYc/1c/CbZjZ9tOM+mM1mRmTFEZJVSps87OsKkJtwcENy/pQslu7pZO6o1Ojfpg3ZzxgLBoPce2IBPQE5SkcGMEgiiQ59dzcuV1/INE1DECU8IZnrZgymusPLqEwH931dyU2z8rhiajb5yYfOsEAfAixr9nL0yCwURc8WBEHAF5Rp80QGNGj7sLvVy44mL8cWJzM2J5bPd3aQFWdGURR9CBMD/rBCZy+NtW+av88OwOl0cuPHu9lW10OyXeLjy8cQE2PhvSumUNfpY9a/lvPU8n1cPDkLn8+H3W5n6e4Oajr9lGQ66ajoItlp4all+1DQlatBv6mKkkwMS3dw5JB44qxG4qxGnjhjGBvrXMwemoQkitx3ciGKpmE07A+uMRYjl07OIsVp4tjhaYy9fzkANpPI8N6FLcVpoTDVQZM7TJzVSENPkIKUg6np6bFmREHj4e9qOG1UGkNTBSra/EQUjXtOyKeuK8T8qZlMe3Q9YUUlwW7kz7MHo6hVFCTbeGtjI2eMTsViHHj5psRYMEoCcUaZ819cw/ZGL6uruzltVBrnTczisilZiEYT5S0+VK2BiKKwoqINq1HiiiNySLAb+ePH5XhDMsPTY7hoYgZXvbcTTYNgRGVHkxtF1a0pEuxm7j9lKM+uqqOmI0B3IIJZEqju8CMrGgZJxChBvMXI5jo3Bck29nUF6fSGSXKauWJqNs+uqsNsNnPck2u4cGIWi6+ZgNmskzHMEfGQRn0XTcrkokkHz52FZIVbPt1NjNXI30/6eWUOTdOobnMTklU0YE7x4Rem/wTaPIcPNP+T5/0Uli5dyoMPPsi3334b/Vs4HCYuLu4XHefkk0/m1Vdf5c4778Ro7Gfz3dnJ4sWLef7553/WcY455hj+8pe/EAqFBpTSvv/+e+bOnUtLS0uUaNWHDRs28PDDD7N3717y8wdmpm+99RYnn3zyYc+XlZXFV199Ff23pmk0NjZGH/viiy8GPL+mpoahQ//n81i/aWbTvxncVzpKcfY5RILddOjTp8ZYOH9CJvYfMSvT5ywOvbvr9IZ4ZnktHV490N0+Jw+H2cC62i7uPamQL3a2s6XBzc5mH7OLkshPi8VisRySvZXgsDBlcDw+n2+AptukwfGYDIdO5RUNYq1mbvy4nH98XcWCqZlcc2Qer66t58LXtlDd7mV0VhzHjUzn2g928cjSav7wQSl+vx+Px4Oqqtx8bCEJdiMrKrt55Lvq6LFzEu1cMDGTK47Iob5bL8P5fD4e+a6Gz0vbuOqIHDbdMpUH5xYjSgIC8M3uDmJjYxFFkewEGy+dN5KLJmVhs+lBdmiqgwsmZEaZS8cMS+asSUMwmUzsavZS26V/7nPHZzB7aBKyLHPm2AyOHZbE99dP5IvSNv72ZUXvcKhGqtPEmWNSGZ6VMGCwtD8+39HGlno3n25vQdM0egIRYiwGZhYkcuX0HARBQBL199/mCbOtwc2lEzN5dV0jjyyt4foPywGdHtztCyNJEguvO4IvrhpPTyCCN6QiiQJ3nzKC1fv8nPDMBo779ybmPL6ac1/ayL6uIEkOM0/MK+aPswbhDSncu6iSHY1uuvwyeUkOSgal8vR5Y7CaJBRNQ0R/P7tbvGiaxvjcOF46byQGSUAUBCxGicw4CzMKE5k6OI5YixFZ1Vi6p53X1jVw7fQcHjpjJDabjQun5LLlr3PwhmQae/ys2tsO6Ayovj5h/+vxxywijEYjbZ4Q9T0hqtr9h31eH/qYUYIgYDFIxFmNTMyJparl180Yfin61oZf63k/hdGjR7N582bee+89ZFnm6aefRpZlpk6d+tMv7ofrr78ek8nEGWecwd69e1EUhe3bt3PSSScxfvz4Q/ZrDoVzzjmH5ORkzj//fOrr63UV+HXruPzyy7npppuim9H+mDhxIhdddBGnnHIKq1atQpZlGhoamD9/Pnv37uWuu+467PlOPvlkdu3axSeffIIsyzzxxBPRzOWEE05g9+7dvP7668iyzNdff80XX3zxsz/LofCbBpv+MxyRSIRwOIxNCLPttmms/9MUkhzmH3n1QBwo0vj8qjpOeX7TIedTXt/QxKfbW3l82T5An0Po8xMBuG3OEKYPjmd5dQ+xsbHYbDbMZjM2m+2QN7UgCDz9Qy0vrakf8PfMuMNf9OtqOjkyP4Hh6Q7SnDqFe3FZB82uIHd8upOGHj+fbG6g0xvm6/J29rb76XT7o9lgcXosZ4zPocsfodWjy/z0Be8/HpVHY3eAGz8uZ9O+HgCunDGESUOSGJxkQxAE4q0Ca26azKzCRM6fpKfGbrebB5ZUcMSj62j0aj9q4xAOh6ltdXHh69s49flNAxYogD/PHsST54zGaJBYXd3N9kYPsqrx6OnDiLebeHldI//8eu9hz1HVqfcvzh6TRlW7l3ZvCG9IxmoysLyyk7kvbOGqaVnMyE/gqRX13Pzpbl5a10BuvAWDJDAi3YEoipz9yjYuf2cngVA4+v0MTbFz5bRsXji/hAS7iYeX7MYTlOnyhaPZJEBegpXMeAtnjM3gnc3N7GjycHxxMmZJ4O31dRz32HKueXsrwYjuGJrkNKIB7d4Id31Vqat0SxJjs2MZnxvLu5eO5pMF43j09GHEWo10ByK0e8PIKmxv9GA1ieTGmRh+97dc/NoW3l9Xxco9rcwbncZNs/IGfD92u50VNW72efTAcyCLz2KxRP1n7v2ynOs+KOfqI7J5+DR953mgxUR/9AUzTdMYnJHIO5eOZnebjwte3UqPP3zI1/wnMHFQAumxFg7XjRHQWWkTByUc5hm/DElJSXz++ec8+OCDJCUl8cknn7Bo0aJfTP+2Wq2sXLmSIUOGMHv2bGJiYjjzzDM54YQT+PLLL3+2IZ3BYGDp0qUkJSUxefJknE4nl1xyCTfeeCP33HPPYV/3/PPPc/XVV3P11VcTFxfHxIkTEUWRzZs3/2gZLS4ujvfee49bb72V5ORkdu7cSW5uLiaTicTERL766iueffZZ4uPjufnmm3n33XcZNWrUL/puBny+//ErfwYOxzTrk6b53xyrrMWLrGhRja3+OGN0KhFF5cxeKZNYq5H3Lh2F1Etl/mZ3By+v09PFkux45o3VF2NRFHE6nXy1rY51VZ3Mn5pFvM1ECCOvrm9E0zSOLkwgL8nOkrJ2XIFDG4wJwBelraz98/Ro2c1sNvP3k4byt6/2EFE0CpNt3DxrEKuquxmRrqsIxFiNVLb7SYoVyZDCzJ+SxaXT8pA0JSrt3yMbeGVFNYkOPYA9uLSa00elcdXsYcBARpvJIPHovGHRRammw8dXO9sRBQ2fz0fYcfjfQNM0YmwGzEaRWIs+iOtwOAgEAtF+1u5WL9/taua5c4ZjNoqYDBImk4kZQ5Op6wowo3Agv99isVDX4cXlD9LqDmGURBw2M9+Vt6H1evxEFI3y9iDd/ggvr2tE7eVkSwIMTrTx2LximnqC+MIKi3e2oGoa6bFmWtwhHvluKxdMyCInwcLsoiTsdjsXv7oJWVWJsUhEFDhhZCpf7GghENH7XxFVw2Yzc+mUHBKtuh/Nhn26386QJCtb6t1YjBJ3HDuYB7+tif7AO5o81Hd6SYsx88CpQ3l25T5u/KicB+cOpTArmb+fXMSXO/fX370hhZfWNHDe+Aw0TVd8XlzWgQpMzYsjM27/AifLMrUdXm56fzuiKLD25inR67NvJKBvOBrobZZrpDhN0eMIghDt/x3IcuwbJeh/jKw4C2FZwWL47zl1SqLAXScXc/VbWwYwOmG/lfpdJxf/quSAI444gq1bt/7ocw4339IfMTExPProoz86gPnDDz/85HHi4+N57rnneO65537yuX0QBIHrr7+e66+//medu49Z19bWRkpKCpWVldHH0tLSSEjQg/mUKVNYt27dz34fP/k+fyvV50gkMiCzMRqNh3V/PBz6U4+/r+ik0xfhzDG6q6bBYMDtD2IzGdha7yKiakzsNxfSB1VVWVfrYmiqPVp2u+r9ctZW6+SDE0em8cz54wa87wtfXk9PQObc8RnMLUnFbrcz7h9L8YVVMuJMNPWE0FQ4VJvaIOiEAEmAjbdMw2iQcDqduN26Bla9K8w3Za2cMyYd+wEzDi2uIFe8t5Nuv8zKP04G9B1uf125J3+o5bs9nWTEmjlvfAZP/FBLcZqDe08qjHri9Ge5mUwmdrUGuOy1jSTZjdR1B8iOs/DZleMHnLv/LFOfQvR+Px9ocQe49fO9uPwR0mNMPHDKUJ5asY8t9W5OG5XChRP1/sKH29p4ZOleThmZyh3H6nMesqJS1uLl3sV76fSFcVoMPHf2cCxGEZvJgACc9cpWDKLIH2flMTgtAXcwzCWvbibRbuT+Uwr5uqwdu8nACSOSufq9XUQUFbtZwh9S+OjyMby5sYkfKrqYPCiOBVOz+GRbK8ePzsFoELnqjY26MZxRwiAJBGWNFncQQYCJeQm8edkEfD4fqqqyqtbD5FwnJklEVhSOfWYTDrOBvAQLq2u6iShw0YR0ylp9bK13MzrLyUvnjeSPn+ymptNPfpKNG+YUkRcjcs6rW2l1h7lhRi67Wrw4TCJvb2rGYZZYev1EXl/fwK5mLxeMz+C51fVcNyOXkRm6Z1JYVjnvte0kOc3MG5VCRZuP62cV8ObGRrp8YabnJzFjaEr0ugrLSlSVvD/sdjuBQGBAZqQPBssDiBb3LdnLhn0uZhenceeJxYe4sv9z+E/N2fz/HbW1tYwaNYo1a9ZQXFzMSy+9xD/+8Q9qamp+E3vw3yyzOXAI6pcGGug3CKqoPPZ9DYqqcerIZEwGSZfUN+lZzd++qsQVlPn4sjHs7fAzLicWu1X3v1lT080zK+pIdZp5/IxiXIEIz509jC0Nft7YUM/txxcRCoUQBCFqnfvX4/JZVtnF0UMT8YThT59u4sFThvLoshoq2n+crqxounXADTPzooKfHo8n+lnyEqxcPWPIAD+YPsRYDWTFWRiebuDsV7Yyf0o28ybqlOVwWC8TXTY5E4dZYurgRIal2cmOt5DRS3ntsxLQtP3un7Iss65apw8HIyqJdtMhG8/9F6NQKEQ4HCYSiaCo+utOe2Er/oiuw9UTCNPsDnLOuHRUTeOYofvVFowi2I0SS8rayY23cMHETP7x9V421LnQNF1RISfeSpLDxH3fVLGtwcOjpxdx53H5fL+ng+92d3DjR2XEWI389aRiHvq6nC5/mB8qu2j3hvmstIU2j34tXT41i56ATJzNxFlj0kGDr3a1sXR3B4qqsby6h8+uPYLBKU421XYxMS+GynY/d84q4Okfatjd6mHS4ITob/HpjlYe+KYaVYNVN03GaTWz4uYjmPHoKtbW9ETVIXY0e/GHFBRNH+ZUEHjg1CLu+7qSVVXdXPzqRgqSbRglkVvnDKKy3c/VR2Szs9nLB1tbGZRk46OtLby3uQVF1VhdVUaC3ciGWhfj8pKQJAmTorDkj0fi8/k4++XNtHnCjM1y8twPVbiDMm+sqWXlrbNItNmobenisx2tnDIylZzkGBRFIRQO88GWFmLsFuaWpA74ffuILv0xPieWVVXdTM8fmI3+N3DciHTmFKf95goC/39HXl4ejz32GCeddBIdHR2MHDmSzz777DcJNPAbZjZ98zWgZzWa9uM9ggFv6hDDlB9uacYbkrm031BnKKJQ1eFnZVU3roBMtz/MssoujhuWFJWm6fCGufiN7YQUlfE5sZS1eDl1ZApXzdAb4D6fjyeW1RBWNG6ePQixX3nv4W+rWFjaSkjWGJRo45aj87j2/V0E+92nfem+3QCjsuPYVKdrZOUl2nj0tCLSYvW+jsViiQaMw2FvZ4g31u0jwWZkWUUXaTFm3rtqGgDPL6/i0W8ruHXOIOaNTo9mH4qi0Nzt5awXN+HrFcn88LLR0fMaeq2iV1W2MSzdidO0f/drMBgwGAwHbQyMRiNtLj9oKvPf2UlNp64lFlZUFkzLYmpeAmPyEqP+N/0RUeHLHc08/sM+jilK4o7j8nlu1T5eWdvItMFxPDx3WHQQ8YYPd7G90cPz54ygKM3Bw0ur+WBLM7KqkRFrRtP032/2sBRS7BIfb23F02+C9rMFYyhMj4v+js3dAU54fjM2k8SUvFiuOCKHoalONE2jodvP7Z9X0OIOMTorli31LiYNSuC+kwuixytr9jD/7VJAD4rzp2QxPN3J7Z/vYV93EKMIiQ4TZlFAkgSGJNu5dHImw9NjaPdFuPXTcirbfQQjCrKql4Xyk+1EZBWHRaLHH+G6GblsqXfz/Z5ONCDGLLGvO0BBsp1XLxgZJUZIokizO0ibJ0IwIrOyqoebZuVx35IqKtt8JDlMPHXOGMLhIE8vr2VlVTfTh8RzXa9idFmzh798UYEoCHyyYOxhr7n+sFgsBw0U/o7f8WvhN8tsDAZD1Pmxz+v+UPi6rI1uf4Rzx++ncx4q/p059uD0+fEfatm4z8U54zK48ogc7vqqAlXT2NbgprTRzcjMGJIcuoPhvYv3Ut3hxx2QCSv6XIvP56PFFeTjba14wwqZsRbO65W0b3UFeGuT7qoZZzVwfHEywzNiMBkkgv0+i0FEn5Q3GTh9dBp/Pr6YP32wlWBEpd0bYUhGIruaPXQ0dDI+62A2iSiKmM1mDAYDz3y6h/ImN0OS9En9Pa2RqO9MMBjEbBCi9gWiKNLmDlLX5cXn09WmO30KKU5z1KnTFYjw1PK9zC5MZMrgg1UBJEk6KNDYbDZc3iBzX9hEIKxGKd0XT8xkb4efSyZmYezNLI1GI36/f8Dv9cePdtHkCvHvs4dTkhWHoiicNDyFxbvaCUZUDJIQ7Ts8eeZwZEXFIIn4QzJflLYSVnSNMEkU8AQVjipK4o45eTS7Q6ys6sHX6Y9K6zyxop6KlnLG5cVzZkkyN3+6G02DQFhhdU0PZ4/NoLEnwGvrGlhc1k5IVrEZJU4ZmUyHL8x3u9t5O83G+RP0a6843cl310/kyZV1LNrZxgtrGoi3GbAa9QAdUfXg1yeq/NIFo8hJjsPn81HR4qHVEyIz1syIjBiOGZaEKhmJMagsKetgw74eGntCrKnpYdmeTroDMvNGp3HdkTm8vr6Riydl4AkqXPX+LjJizDx51nD+srCCrkCEh04bzt9Pz9O1tI7bT28N9/oLnTE6jfIWL/G2/bTboakOhqc7OXrofvmVQ+HDLc0s3dPBX48bQm6S9Huw+R2/GX6zYNNXmuq/mB1KD+2JH/YRllVOKE4h1vbzJp4BXlpTz9Z6NwZRYGivPe/po9JodYfYVOfiT5/ujooPTsiN482LSnBajVhMJiKRSHT+RxAEnBYJf0Qmou4PIqIoYhQBAVb8cQqaplHa5KYozc6GffsZcLKq07g9QQWL0UBBgpF3F0xmX4eH0TkJqIic+swanBaJzxaMPYiBp6oqkUgEURS585hBfFHaRmmThy5/hILe+R+fz8clk7M4qjARQRT41/e1jMlN4p4vdhKSVW6aNYg7jh1CgsNKVZuby98u5czx2TiMAququun0hg8ZbPq+A28wwtI9nUzMSyBZ1XhvYwO+kL47F4CLJmXwXUUnnqDM9iYvM4ZlRLPW/tjT6qXFHcIgQpLdGN1gZMVbefHcEcT0Ds0ajfpjVe1eypq9jMmJJTPWQk6CjZ1NHhxmI03dQVSgKC2GM1/ZRnGag8fmDaO5J8hrGxopbfSwbE8HAtC0vZnPt+sbgzSniQ5fGEkQuO3z3QiCwKTcODy96aimybpS9Lh06rsDtLhDNLnCPLeylrklqYzNieW2owdz8+x83llfx7raHq47MpsPt7bS0BMkOcbKhpouClPsJNqM0et7wz4XsqIxpyiZCyZm8OQP+zhicDzFWfEUpzk595WtWIwie9v8GCSReJuBzDgzCXYTN80ejMPhoLalk4ii4uolvUzKi6W8xUdmrOGQpa8+pMVa2NbooaYzwNxRacTazFiNRp6+YELUlfVQZVtJkvj3Sl2B+oU1Ddx7YiGRSGTArMjv+B2/Fn6TYBMOh6M3YbMriNkgkmA3HbKE9KfZg3AF5J8VaCKKij+sEGs1sqqqC39E4fF5xQxNjyUSiXDv4r2EZIU4m5E4m4FWd5DUGL2ctKnezXMr67juyFxmFiZGGXEfbWvBE1KwmQx0+2ReW9fAuKwYXlnfyKhMJ3XdAWY/uQ5Z1TAbRFwBGYNIVD9DVkHQ4PyJGUwfou/kE+wWHEYnfr+f7oC+QPyYfIgsy3T5Qizc0cqJw5P5aKveRH75vJEDhrieWFbDqqpuQorGOxsbyYizUN8V4K2NjXx743SCwSCb9nWiaFDa5ObRuYWIgsa0wXE/+r2+vamZpbs7eHhpNbFWI83uUDR70IAPtrQQbzPS5Y9gEBgQaCwWC4qiEIlE+GxHK5IoMC47hoQDZqA0QcATlNnT6mNHUyNWoxDtjyTZjXxz/STeungUDyytZeH2JiwmCatR4tOtTTS5QjS5QjS4Qiy6YTqdgQjranoAGJpqpycg4wvJWAwi7181FW9I5pEle9hU24U3JLO90U2yw0SbN4wGVLT7mJCfxttDEklxGHhlXSO7W73c/nkPFqPEmxeXEGMxMndUKrOGJpIdb+XpFfVUdwZIi7fjCspMHRyPKIp0eAL0BGRGZTlZX9vD2OwYnvxhHx9sbeaHik6+umYCAG9dOpYzXtxElz/COwsmUdnYyYTewWNvMEKrp5tX1jfzzqVjsPbOb101PReXP4JJFPD7/T8aBE4dmYrJIGA3SVFHXFEUBwxVH4h7Fu1FVhTirAZm5OubkV/KEv0dv+Pn4jelPgcjChe9ocvjfzB/zCGfM3voz2tINvYEufLdnWiaxr/PGc6Dc4toc4coyohj474e9jS7OG1UKq2eEN0Bmc11Lr7a1c5lU7Kx2+2sqtqLqhH1CtE0DX8ogjsQ4fjiJI7MT2B9bQ9fl7WzcEcr3pCCKEKXT3dZVA+o7InojLM+zMiPj1oX9PVS1uxtp74nzIfzx+AwiZgN+yVp+tNRZUXjg02NfLy9lXU1PYRkjbHZsTitOjX1rY1NrKpsx2qU6FsLZFXj+5tmctcXu5hVlILJZCIUCnHm6DQm5MSSl2hDFATOHT/Q6VJVVf74yW5kReXJM4uRRJGjChOoaPVS3RnA6wodNOcQiKjEqBqSoKtYj87er8nVn2U4f0oWVe0VLC5rJ6Jq3HFsfvS9zn+7FIMA3rCKxSAiqyq9TsW0eiO4gxE6PGGqWt0Upzo4f0IG725uprFHJ2QIQIcnxKsrKrn/271RWuzuVh9FqXa+uHIcVrMRh8POde9sYW0vKcIoCajAw6cNZeKQNKY99AOeYIQFr29kypAkHpo3gitmDuXNDY24egdLld4f+9oPdtHiDnHW2HSGpzvY0eTBIAqgQUVHCEmSmPvCFnwhmflTszgyP56iNAe+kMK3uzs4bVQqkiRhsVjw+Xw8MHcoz66s47kfKkmwGnl+dT0jMpws3N5CWNF10h4JhZkyKI5jhiWzqyXAde/vYFCilVcvHHUQyebL0lbKWn3cODOXW+boKuj91dIPlX32R3mLG0UTuHXOkOh96PP5Bji//o7f8WvhNwk2fTeFUdKbndk/Mvz4c/H+Zp0gAGCWRFKdZt7f3MyeNj+vraunyx8h3mZkdKaT62bk8dbGJk4blYasqJTua2NYqo2zxqRSlLq/b7K90cO2Rt2Q6vZjEujxR1i4o42zx6RhNUm8srYBWYMR6XYaXSG6+9kIqEBfojYm28mYrJjowtKH2z7fgzugcOvReby+oYnzJ2Rw0ZQ8XC7XgJLiWa9sJRRRGJxkIy3GytYGNyMz9NkYRVH4eGsT7kAEi1HEaZbQNIVRWU4MBpF/njYSVVWRZRmn04nX62VzfRvBiErxYfSx9nXpQpF7OkK09PiZVZjII6cP45HvqkmLsbBxXw/L93YDcMKINLY39NDtDzM2K5YThw/cHNS1dLKssos5RUkkOczsbNIVqaV+O2RJgOI0Bx2+MC1NXiJGAV9oYEnoL5/vocUdorYzSKxForTJQ02nn3avfi0l2g28e+lo/vVdDcl2I+6gQjCiIggwuzg9yj5cVlpHm8vPoEQrdV0BIoqekcaYDYRDAa6fkcuj39fQ7A7xydZGftjTxvrbj+KMMal8v6eTk4YnYzEaqO300eEJ0eWLsHB7C59eMY7rZuTxty8ryIwzc/30TCKRCDEWCW9I5qU1+uT14EQbxw1P4eULRpJiN0U17wwGAykOXWR0X1eQ/CQbre4Qhck2VE03dhuUYGVRWRuLy9qZNjiBlVVduIMy+7oCh+xjvr+lhaCssLs1iZLMmOj1IisqDT1BXT6nM8CUQXGHZBi9ev4I6rqDjBmcFrX++B2/47fCbxJs+lhnkiiy9i9HD/j74XZbfQNrh6NIL5iWRWacmVmFiSTajSze1caS8nYUFay9sjeaBj1BhfRYC7fMGYKmaTyxrIYPt7YgoLtHPn7G/hmCnc0ecuItLJimM9yCEZVYqwEVGJJsi9rrVrT5OGl4CtWdfrY3eg9Spd3bEWDBuzt5e/7E6N+CwSDnj8+gvNVPR0BBAMReerX+XvcfJSfewvpaF6+dMoL81Bj+csygATTP584ezqZ6F0NT7Fz3QRm5dhOT8+JQVXWA66nJZGJdnYenlteS6jTx4fyBLKSwrPDxtlYeP2M4MWaR6z7YRaMrxO4WL4MSrWzc56YkU+OssenRYPPd7lZkRWNCbix3HDsExwGlwFfXNbB+n4suf4TLp2YzpyiJ7ys6mVW4f8pbEAQemlvEG+sbqO8O4gvJ0aymDyureqL/74+ofLWzLRpoQBc33dUSoKLN1yuro3DV9ByumJaDaDBx2Rub6fJFCMoqVqPIvNFpvLSmnpCs+/fUtPTQ7A5jMYrEWo0EIwqSKGI1imysbuO73Z2omsYzK+t5bnUDaBqpMWacFg1vWKXDGyEjTqInECEkq6yt6eHUklQWXjGORbva+ceSKhRVY2iag9JGDzd/Wq5T0GWV9+aPIzPGxDe7OxiSZEXVNG49egj1XpkkM1w7IxezJBJRNea9tIWQrPLOpkbOHZ/Li6tqEAVdObx/uBBFkb8dP4TazgAjM5yUNbu5/sNyjsyPJ9Fu4vsKvYToDemkghOGHzxJbjJI5Cfb8fv9h+0H/Y5fD5FIhPb2djIyMn76yf8P4jchVPfpYUmSFB2Wg/1KygfWhfumnX9sFsdpMXL2uAySnWaWlHfw4pp6Uhwm7jkxn+fOHsGzZ4/g3UvHRB0h+xbzJLsJf1gh1DtY+PwqXcImLCss3NHG5no3Q1JieH5lHc+srMMdkDlicBwWg0ROghWbSSSswCc72tjRtD/Q9H1xGTEmAmGZqvaBNXWj0chlU7K5cfZgvtjRyvQh8ZzVj1HXJ74J8PgZxay/ZRrpDv3f/QONJElkJ8dywvBUhmUm8NYlozl3XAZnT8jE4/EMGJwNh8OMybAzqyCR8/qVzypavdy3ZC+vrmvgtfWN3L2okgS7iZEZTtxBmYWlrWTH27AaRbr9Ee5ZtBcRmJTnBE3PUB+cW0RGvwzVH5Lxh2VOHJFMdryF2b3B5fTRaWTEWXhjQ9OA3+9vX1Xw3Mp9dHrD+Hv9GMwHbHX6SNnnjk8n2OvPYDOKlN09h4U3zGBmcToGg0SzK4QvrPJDha7l9dLqWtbVuqho99PkCnFccRInj0zlkslZzBqaxLkvbuCmT3fz168qmJ6fzLQhCXw4fyxPnTmMu07I13cp6OU+WdUwiAKyqnFUQQJjs2OItxqjdO2H5xYRZzXy1sZG9rR6EUWRk0am8u11E/j0inEMSrSRYDf09rhkAmGV8iYXiqry+rpGVlX1sLbGxb++ryLBpHHDR2Xc9MlubGYDsVYjz58zAqfZwOelbVjECEWpDj1DPSCzUVWVghQHc4Yl6xYNO9txB2WW7+2mMMWBxSBiMYqoGph/QhWgL9BIkhRVm/hvo29UIhwOI8vyYdVI/rdYtGgRw4cPJyYmhhNOOGGAqvGPPXYgfo4t9DnnnMM333wDEDUv+/8TfpPMxmq1EggEUBQlOmDYJ/oIB1ObDQbDgIzncC6eiqry9sYmbGaJxp4QPqvC6EwnZqOB1H4GXf3ndM4dn05PMEKLK8Tisg7e3NDElUfkYjLopmqdvgg2q5V/r9I9XUTgts8rcQV0Rec0p94wDyk666wv2iTYjXT4InhCMoUpDv5x0v55Db/fH73IPAF9FqWm08+Xpa0cPzw5KngpiiJV7T5+qGintMlLgs3IQ2ePG/D5FUWJKgIEg0HS4uycM9EWZScdGKCdNgv3nFQYNZ0D+Hh7C9sa3EweFEdugoVLJ2excEcri8s6GJrqJDvBiiYI7O3wU93pJxhRMBlEKtoCRFQNAej06krOoBM1LnmrFFGAty4u4ZHThkXfc06ChTirgelD4ilv9lCUEYugqZS3ePFF9C9PAJwmAU94/3VgFPX1NM1h5IaZgwiEFT7b0cYJw5O57ePtjM5N5LNtTbw5fxJnP7+WFleQ+04t4pr3d5FgM2DqFcMsyXByVEEiWxvcnD8hk/JmD6uquggG9JJbi8vPTUflcfdXe/h2TxeCAMv/OJn3Lh2DAoQxEmdUuO2z3ayo6ubJM4bR4gkz78UtnDQyhduOyefssemUNnvI66dY7rQYowSQzDgr714yike/r2FXi49xObFIosj5EzLZUNvN1gYPRal2khy61lp2/P4gPiTZznUzctlU5+L7ii5umF1Abbv7Jxv3l0/JYnu9mxSniTHZMRwzbDRdvjDl7WGm5R2svN13n5jN5mj5rL98zX8TkUiEQGBg6VAQBKxW66/KlKuurubMM8/kjTfe4JRTTuGhhx7ilFNOYfPmzdTU1Bz2sQN/i59rC93Z2fmrvff/G/GbDHX2KReDvqBardaD5E8Oh/4N1QNR1uzmr1/tBU0jP9mOomo8NHfoQTdIX5bUn+4pKwo3fFjO7KIk5o1OGxCQBEFg9P0r0YApgxIwSrBq734FXItBxGKSUFQNRdUwSSKpMWYaegJYjBI3zMjl1FH6oOVdn5dR0dzD/acOZVVVNx9sbeXBuUO5/+tKmt0hLpqUzdwSvaRhMBg4/pn1tLqCIIBREll/y/SfHH7tU3+NDs2arcz81wqcFgPf/vFIVFUd8P21eYJ8tbOd08ekEdu7IL67qZHnVtVhNkgEIyoFKTZKmzxoGnx2xVhu+mQ3qQ5TdPL/iyvHkhJj4dbPdrO2phuH2YArKGMUYWZhInefUIgnEKHDH2FQoo1Pt7fw2rpGZhYk8MdZg9je4OLKd0rpJecN0L4yCGA0iKQ7TaQ6TQxNczC7MJHKdj9vb2rCFZDp8kUwG0VmDk3imfN0eaGKhnau/WAXRknksilZfL+nk7tOyOeSN0tp9+rU+2um55IWY+IPH5VjM+m9m5NHpvDVzjYaXCFiLAa+v3Eam+p6uOqd7ZgNIm9dPJobPtyFqmk8e/Zwnly+jxV7u7AaJVbdNOWQv8kPlZ28taGR9Bgzle0+Lp2cxfOrG2j1hChOcyIKcPX0bDJjrVz29g78YV2pYmZhYnRzBnrGqCBw+gub9ftBBItR4t4TC5mUn8KuRhcfb2ni2hl5iChYemeANtR28+dP9xBWVK6bkRudHfol+D+BGHCgzNWBsNlsv1rA+fe//82nn34atRhQVZXY2FhWrVrF6tWrD/vYgWKU999/Px999BEbN24csBb19PSQn5/P888/z5YtW3jggQcwGo3ce++9pKSk8NRTT1FUVMTixYtJTU3lzTffZPx4XUbqnXfe4d5776WtrY2ZM2fy3HPPkZKSwt13383WrVspLS0lNjb2J3Xd/k/Cb5LZ9JdIdzgcqKp6SMvjQ0FRlMP2dYrSnJw+KpXBibYBcyMGg2HAAt33+j63TQCDJPHvfh7xmqYhCAKrq7r465cVpDh0Vei7TxpGYUYc76yt5h+LduOPaCQ7TUzMieOj7S0YRb0scXpJCtVdfq6clkOczYTBYEBVVZbsakEUNCpavby1oZGArNLmiTCtIIm1VZ0c2Usxren08YePykmwmoi3GmlzBwnIKj/saeOIft49h8rygsFgdJdnMpno8sv0+HVG16FsflOcFi7tZeWJokhdu4u3NjbhCigYRJVRWU5OHJ5MeYsXVVWp6vDzfq9Lp6rqXid92djqqi4Csi5i6Y+oiIC/t9l/y8I9tHvD3HtiAXnxVgySgMUosmJvFw99W4XSL8T03+H0fbopg+N5d3Mza/e5+WRbKyFZwSBJRFQVk0HXC/u2rI3Z/1rOZ1eMpazFS6LdxB9n5lGc4eTEkemENRGLQcAfVjEZBJ5bXc+QJDspThNhWaXdF+aT7a1cc1Q+ohJh6qBYLEYRk8mEqulukS+trsMXVpicF8fGOjdNPUFSnWaaXCHuX7KXnoDMmWPSGJ8bR3W7j/RYM8v2dFLTGWBzvRtNg2ZXSKchKxrbGlwMSrSyprqH0qY6Gnr0DdeD31ZR3urllhP067K2y8/1H5RhMkjMGZ6G2SAiaCreYJiiVBt//WwnC3e0IgrQ0OOjqj3AmCwnc4YlMTkvjuOLk+kORDhxRArflnfQFYhw5midEdfHVjzcdWXqnT/7b87YaJr2k+61gUAAg8Hwq1C0FUUZUHHpMzGsqqr60ccODDY/xxb69ddfZ/Xq1VxyySVccsklvPbaa2zdupVbb72VN998kxtuuIHbb7+db7/9ljVr1nDjjTeyZMkShg0bxu23387FF1/M4sWLAVi+fDlbt24lMfHHB3b/T8NvEmz6vGtMJn3W4tdqPoqCcMgdm9FojAYbs9kcvakONcjWH5qmEVZULEaRJLuRRleIq9/dxnc3zyTWIhHoLfs0uUJ8uK0FDQgpEPZHWFvbQ0WbH7NB4sajBhEOhwmHw7x1yRj2trqZlBfH308uZG+7nzGZdsZlOzlvbFr03B3eCAIQbzPw+BnFvLG+gU+3t/Lo9zX4wgrHDtP1xiRJOijYKIqC36/bEWiaRrzVxIdXTcbWrzZ/KMkfVVUxGAykxTuxGg0kOlTyk2zUdAZ4bV0joV775YeX1jA2K5Y/f1aO3WTgz8fmk2rXj31scTKLy9rxhvVAc/dJ+Zw4PAVPIEJeohVXQCYtRh9WVFSNL0vbCMtar6PnwUm0UYT3Fkyg2xNgUKKNtTU9VHcG8IUVBAFGptmZMzQJm0nk4+2tlLd4kQSN+W/voKEniCeo0OKLUAw09wQ4+bmN5CfbGJlhp9kdoaknSLsnhEGAWYUJrNvnYv6ULMqaPXywsR6nReJvx+UzZ1gyN80axOvrG9je6EbVNHY2e7hoUibvbYZ4u4lGV4ivdraT7DTx6Y5WGnoC/GNJNfnJVl46dxR525p56odaNEEvNQoCTB0cx5+PHsS2Bg8zChL5x+JKVA2sRhEEgdXVPZQ1dpMdY0DTBMKySlqMiVtm5QDw/pYmfqjoYEtviSzGIpGXaGNEupOKNj9ranr4Zncndx43hNuOHaJXECIR7l1cgaLBmKwYnGaJ5HjjgJvdZDINqDL0Xb8mk+kXS+z/Wugru/8YNE2LmhD+b3HMMcdw++23s2TJEmbNmsVjjz2G3+8nGAz+6GMH4pfYQvdHSUkJZ511FgCnnnoqV199NQCvv/46CxYsYMwYfcP3z3/+k5iYmKjF86RJk36xffT/CfhNCrQH9hH6pE1+CX6JbEb/C+BQQ2w7m9y8vbE5+u+X1tTzhw934Q/LzCxI5PKp2VS2+zFJIscM1bOK19bWE2fTL+iwohFjlnCY9N2U1Dug2B2IsKHOQ23H/rQ/I9bMtCEJ7Gnz4QnInDQiZcDC37f7mZAbx9NnDefh04YhCAIXTcriyiOyEQSBilY9MxMEXdqlbxfX/wYzmXQa7bsb6/H4fBQmWUjpZzh64E0rCEI0A5TDQe47uQC7ycDedj/d/gitnhAmSWTeqFSumZ7Dw99Vs67WxYqqLh5asl+C/MQRKSTZTYgCJDmMnDwyDVEUufL9XXxe2kZPMEJdd5BQRGFcVgw2k8QXpa0oiordePDlNjIjhhvf28F3ezqJs5n45o9HctKIFIySyAnFydx7QgEnjkzlwulDeeuSsTxxRjF/OXYI3f4IkiBw8aRMHvi6kumPrcUjCyTajaTFmMmJt9HiDkazJoMksLK6B1nRWF3j1jXT0KX/n1lRR02Hj+OLEzlzTDrZSQ6CERV3UObR72ro9IV5eO5Qpg6K44SRyYRklRHpDhbuaCOiaDR0hXBYJC6dnMXT55Zwzth0BEFge6OHNdXdZMRaOXPiICq7wozPjSXZYWJSbiwXTcxgb7uP2z7dxc5GF3/6pJxWT5g9rX6Oe2YjL6+px+WXUTWN8lYfl0/N5utrJ/DieSNZMC2HL68cy8zCRGwmiV3N+vCv2Wzmse9q8Ec0REHAF4pwxbs7OeWZNQO+98OVan9qg/Zb4sd0A/8nz/spDB06lNdff50bb7yR7Oxs3UequJjY2NgffexA/E+tmfs7gvbfMNfX1/PYY48RFxdHXFwcGRkZGI1G9u3TyU1paWmHOtz/8fhNMhtJkqITzH34pWKcPzb5fCB+ajd0z+K9BCIqY7J0Zs+a6m7cQVknB5gMWI0iiqYvyO9vaaYoI45TS1K4/5sqQO8vZCVYaHWHUTQZRQVPSGFwkpUdjW7uWVzJqxfqqbXNpjfvr/1gPXaTxOsXlgyYpu9/o2THW7l70V5Kmz0suuFITioxUJhiJyfBdlDvShRFZFmO9r9EUeTyd0sxSQIZMeYBttmHQkyMPofh9Xp7vy8Bf0jGE5SRRAGbSaLLF2F1TQ8rqrqp79GHO5NsJna3+rjls908NLeIsdmxPHJaEd0hhcm5cftLpgI4zBJGEWJsFi56ayctrgBzS1J4e2PTQVTnPhGG0mYPEUXji51tFKbYOW9yHnVdfmKtup3AGS9vAwHK7j0OgCmD9DLkY/OGkWQ3YTcbeHlNPSFF47ZPd/LlVeMRBIElZW18XdaG3DugGZQ10pwGIoqGLxjm9JJkluyMY2eTG0GAZZVdLNzRxrHFyTw5bxj/+r6Giyakc9tC3X+o2xdmZmECNqPE5jo3Ta4QNx6Vx3Or6rl+Rg6yolLV7mdqbgxTc2NYtqcTkySgqHoAqG7u5J7Py3CYDSy9XqfIT/3XWjQNpg2K5fbPK6jr0a95V1DGG5apbPdx38mFOuMvwY7bH8ZkEOi1ZUKSJP56XD6njkqjKNmKqqrc+O4WvGGZnHgLF0zIICvehiekkJs40PpctyXQeHpFLeNyYpiRv78k00ep/0/j557z13pvbrebYcOGUV5eHv33fffdx6hRo370sQPxa9lC9yEtLY27776bW265Jfq33bt3k5+fz6JFi/6vVXn4Ta4ou91ObGxsdCeuqip+v/9nB5pfG5dMymLa4Hjye7XGHp47lPtOLiQ7Xi8XTB+SwJNnjUQSBbr8Mn/5bBfL93ZFJ+k1YFezjw5fhLCskwRcAZ3hJgqQ5NwfTEKhEJe+uhFvUGZ0ZgzxNuOPXhxb6100dgeobHVht9vJT3FiEHXhUlVVoxlJ36KuKEq03n7J1EFkxlooyfxxuqokSTyyZA9HPfIDXb2GbykxJuJsRpRe07Iev86+29cVpNUTRgQKU2y8dtEILEYJf1h/P1+WtnLdB2U88PVeNE3jk23N/G3RXt67fAJLrp3IwisnsKGmgxZXEG9I5rX1hw40drMBs1HAJOr0ZqtRZEJODI9/X01Np59ARMHlj5ARa2Zcdgx7G1p5bnkN5766lRZXkNwEG3azgX+v2Eeiw4QAuHxhHA4HJpOJ0ycMYv0dx+AwS1GH1vwUJzFWXXbn0aUVlLd4UHr7NFaDiEEUKEq1YzEI3HHMYLLjrSiqRqLNwL1fV/HcyjpqOgM8dnoRN8zMY1RWLM+eM4Li9BheWF3P1e/v4o31jexu8TJ5cDw3zMjjqbOKCYVCxNuMDEq0Mj43Jvo9HDcsCatRJDfByqAkGzFm/Xa0GkSOzE/iH6cUIYoihZlJdAZUznp1K1e9twvQFTXW13azrqabl1bVEVE0ghGVnc1e9nUF+fSKccwbk06yw8TWvx3D6xeMPOi62FTXw+rqbh7/vnbg7/NfYqT1z+IPh75s/9dAa2sr06ZNo7KyEp/Pxy233MJRRx1FVlbWjz52IH6uLbTZbMbtPthZ+ECcf/75PPvss5SXl6OqKk888QTTp0//r2advwZ+U7kaIKpY/GN4a0MDL61t4Prpucw7hLpzHywWC95giNUVHUzKi8Ni+nkX3YkjUjhxxP5UNtFhJrGfIOZlb+1gT7ufP83OZ01VB+WtPlZWdRPqt0om9VKdFU3PdNq8Ya6ZnkNPQObaI3Ojz5NlGU9IJt5u5IaZOYfsnSiqiqrp7LOH5w6lKxAh26n3Zi5/ewdT8mK5dIqeuvdlJKqqsrW6lT9+vInx2TFENHAFZF65oORHP7uqqqiaxmdbGwjJChXNHiYNiqe00U1lr1/9kEQL1Z1BRAEcJn3x293m5/jiZFKcVt65eBSSKLCyxsNLaxuIqCqT8vSd8APf1hCSVU4sTiDGYsIXlPnXd7plQ58sjSAMHBMxGUVirAY6fSrpcRbMksiMggROe3FLNDDZTSIjM2P4eEEqS8raWfDuLpp6goQUjQe+reLxM4YjCAJraroJRxQsRpHCVDvzX9vAP+aNJttqYOo/lmAxiEzOjWVQsp0fKjqxm0T+dnwBl7y5g6CsMjE3FptR5LMdLRSm2Pm6rJ1JeXHYe6+tG2cN5qudbayv7SYkqyTaDCQ5TBilgQuyP6LgCcmsru5i4Y5W8hItrKnpIaJozMiP54FTi3h03kBTsmmD41m/r4fHl+3DH1bwhlXSYy10ekNsqeuhVyINn8+HWdJ/7z5lids/30NDTxCDKBBnNbJxn4uZhYncf0rhQe/tUGWnSCTCsDQ7RxUmUJIzUBVCluVfpSfyS9FHb/4xNprVav3VdvYFBQU89NBDzJo1C4/Hw5w5c3jrrbd+8rFDvaeVK1dy9913M3v2bDo6OsjMzOTCCy/k1ltvjQbvc889l+uvv562tjby8/MPeSyA2bNn87e//Y25c+fS3NzMsGHD+PLLLwcQFv5vxG9yRfnDMiJwxvNrGZRg4R8nFeIJKny4tYkTR6SR6hzYv/l2dyeBsMri8vYfDTbBYJBnltWwpqaHPW0+rjgi56Dn/FxHUE9QZnerl3HZMSAIaBrUd/n49zkj+KGyi8e+r8YdiNATUDBIAmYJTAaBsKyhAQk2I7kJNk7OdGIx6juymJgYXl5ZTW2nn+HpDpZVdqGqcM4B+mTXvF9Glz/MaxeUUJiqz0CIosi+tm7quwNUd/g4pSSVBe/s5JSRqVwyORNBEChv8dLsCvGtv4MEu4mewKEzxVBEIRBRibMZOfWFLXhDCo+ePoyN9W7G94o/Jjn2Z2OekMqgRCuNriCCINDqiSAJEG/Tn2M2SkiSxKTcGBLtRjxBmVGZTpZXdiIJevB9e2MzTa4Q0/qxBGMsIk5NQhDBaZKo7gwiACZJ5OIpecTZTXy4YR/t3hDvbW7CKIlEVJ148JdjhvDOpmbmjU5H7F08BydZKW/1R6VwLBYLD542ghs/LMUWkVnRq3pwx0fbOHFMJoGIQiAMdxyfz8LtbdjNEuOzY7HabIzPi2d1lU5ntpokqjqD1PeEcFoMfLWzDatR5OGl1YCe+VwyKYv3tjbz8rpGaruDUd23PlS2+YgoGqVN3t6+kQVZ0bPg8hYvn25v5YzRqWzcpzMBb50zmJmFuh6fTrjQSTRt7iAOs4FTRg6s89sMsPG2GdHdbYLNSGmThxSHibklKUwbEs+9iyup7w7y5BnDBrxWFMUBzEyAR7+v5Yudes+pOCNuwPN/qwHKnwOj0YjNZvuPzNkALFiwgAULFvzixw7Ez7GF7mOi9f93H2bOnBm1awa49NJLufTSSw86xs+xqP4/Fb96sOnyhpn5yDJirQZa3SFaeoUU39vcyOKyDlo9YW4/ZsiA1zx55jC+KNUH+A6VCfTHxLx4Sps8jM859EzAoQLNl6Wt+MIKZ4/bv+jftnA3Hb4IJRlOTixOxhWI8OXOVkqyY7n7yz1omsbbF5VwyxeVtHtCdPplDIJAuJe0m5tg5d8r92E2SLx76ejoTTAiMxZF03sLb/ZO0TutBnY0evjT7EEYJZGgrCD0Fuk6PCH+tmgvRxencdqIBP52XD4VHUG+LmsH4PvKDi6ZnImmaRw/PJk3NjRQkGzn6KIU6rt8RBT1oJ3spW+VUtvl5/7TRuoKzqpGeoKDEoxc/OZO7j+lkAe+0RdSgwCBiEKHL0yKQ2/8280Gkp1mTh+TjqqqfLq9mQe/rWFSXgITcuJo94aRVY2luzuQewU6d7f6sBgEFFXjsbNGsba6C4tB4JuyVhp7grRp+u+iAVvunK1vHJbXIKsqxxQls3xvJ/FWI7FWAxaTRFWHn4U7WnlvcxOXTc3h/UtHMf3x9QBsrHPx/pYWLps+hCEpDh6eN5xXVtVQ392OBqyv6WZ3q4fBiVYsBpF3NjXz2fYWVA12Nnn5dEcrT59ZTG2HD4dZIiPWQlqMmcunZNHiCXNqSRrPLNdZgbIKBlEg1mrELIl0+sIkOcyEIgpm4/7M2mKUMBtELpuSyVlj0omxGrlsciaNrhD/XFLFR1tbGJZq59s9nbR7w6zY28V5EzK58ohslu3twh1SMIkwvTCFZy8YR8B3MIU9EolgtVqJRCI09ISwGUWy4y2c2EuoqOkI0O4N4QsrWE37b+2+Xl//YBNjMRCMqJgMIk7jQPbif9tioL+YbV//6OeU2H7H/9n41YONKOospQSrkZuPzifdqd+QxxfrcxzHDUs6iOMfazVxQa+Hff8L3hsIYeydDNePLXL0iEymDIobcM7DBai+vz/xQ60+7R+UuXxaTvT9PLRU7w/EWQ3MLUnjjQ2NPPLt3ihF96zXdmAQBX3RMoqUNnkR0BefinY/CTYjQ1PtUYZKKBRiRKqFW48poKLVzYJp2dh7BT09IZnazlQKUhy8dkEJYUVlWUUnf/+6Cl9IYU11N88vN7HkD1O588vV2EwSN83OY0+rnxdX13HyiBR2NHl4/7IxxMfF8Ye3N7K71YfVJA2QwQHdNE3VoKbNxb2nDscgCAzNiGfus4tRVI0fKjtp9ejNaA1IcepZki+scMdxQ2h1h5kzMos/fFiGNxhmcKKViKKxvbGHK6ZlRQcYqzv13ackCgQiCgbRwPcVnXyxs51Nd8zC5fYQZxF5c30DnX452qzfta+dvCSbrioQVihIsVGQYsNuEpk2JBFJkmju8bG4rJ1gROGDzU0EwnoWZxAFzAaRhTuamZkfh8Uo0dbjZ2uDhyS7AW9IISRrdPtlJFGi2hug0RWiINWBAY1V1T0k2IwUpzt597Ix2Az6/MQ1vaXQ3S1e3trUTILNRJLdiC+skhln4djiRIrTY1i8u4NFO1vp9Ia49sgcYq0mvilvp6zZS7LDyHd7uli0q51nzhpOqyeEzSgxISeWZneIghQ710zP4fPSVp5eUUeTK8Sfjh7Mi+eOYN5LWwirsKKindpOH9kxJho7Pby+Xt9cHD88mfc2N1PZ7ufuEwu467ghXPDmDrY3evhwawuXT83m6bOKCUaUASXi9TXdPLZsKxdNyuaEXhHVb8vb6fJHKLv3WLp6XBglMXr//DfKZ4dCX6/yd/y/g1/914yzmfj8qonIsozD4YiyqbLirTx2xnA0TftZ1MXqNjc3fFhGfpKVv588FJvZiNlsPuTA54GBpm++p08NOSfBSpdfZlllVzTYHFeczBsbmugORLh8ahaqCqIA3X55wHS7rGq0uINYDBIaujpvitNEdYefG2bkcsaYdGRZjioWvL6ugadX7CM1xsycoUmMyoohI9ZMXXeQIUl6zVUQBD7Z1sor6xoIySqSKCAKIIoCgighiQKdvjCpdjP/2llLIKywrqYHV1DGF9G4ZHocY3LjafOGGZMVw4F48byRbKxzcXxxEiaDFFUccJglNA2OGBLHqsoOmt1hBEG3XRAFgXibEW9I4eqjCrjk9S2sqdIZVRdPzSGkwtGFiZQ2uajrDkatDkZkOHnlghIe/6GedTWdDE2xc8TgeHw+H+FeivBFkzJ5ea1uyDa7MJGcBAs9/jBXH5GDNywzfWg6Rz+2EotRxGiQGJJo5b4l1RQk2zEZRDq8Yb4sbeODy0aTaDdx9qvbKG/2cewzm3CYJU4flUq7N8yQZDtFVgO7W7wYJJG/HDOI2z6vwB9WmJoXywUTMli/z8XbG5vY2uDiiKG6CVxf01mWZe77poqaTj+Kqju0/uPkAmYWJFDW7OG2z/dglET8Ed1X6YLXd3DVtGxeXd+I3Sxxx7FDWLyrnfJW/bPfs2gvEVWjyxch1mIgGJa5/sNy2jxhfGGFNzc2UdHu4+kzh/P8uSXMf3sHEVXjs021xFiMeIIRvtvTxcLSdnY2e1lR1Y2mafzhwzLmT8vBahQRgJOGJxNRVHxhhYe+raaqM8DgRCs3zcqjst1PRNVo7NnfB3ludT2eoMzOug5y4weOGPR54fy+0P+OXxu/yRWlKMpBLp2/BCaTCVEIIIoCZa0+zn11G4+ePoxBST+PXy9JEoFAAFVV6fRFePDUoby0poHz+/VODJLIS+eNIKIo3Pt1NaururEYRTJiTIQVjXbf/nKcO7Tfe8UfljmyIJ12b4Qj+vUn+sp37d4wsVYDY7JiGJ6u92OGpTkZljaQMTYuO4YPtkhYjSLDUuxMz49nzrBkDAYDXb4Iiqbx0bYWrp6ezQdbWhibE8On21oBEbfbzWkjkzltZPIhP39GnIVT+4lmBgIBrFYrL18wmnBE5s31TWxq0Ms0TrOBQEQl1WnQjegyYgkEAgxLsbChRmBMlpOSNDsVLR6y4i18V9FJksPIzPwEHBaJyjY/oYjCTUflYDo2f0Cp5u9fV1HV4eeMMWkkOUyYDDLlbT5uWbiHijZ903DHcQVMfmAZIVkjM85MutPEOa9so9Ov9410V0udQfanTys4tSQFV0COzs/4wwrnT8zgiCEJLN3TwbYGNyeMSOamWYP5vqJTt4mOqLy2vpGZBYnsaPTQ4Q2zcZ+LiYOSiI2N5ZtdLayoaOfmmVnMG53Gt+VtlLf5uWjqIE4drw/PyY26lI/NZOCrayfz4aZ6djZ7ibEYuf2YITS5gozLjmFkhpPPdrQiCHop1WIUaOr1CLrgjR2oGqhoWAwCQVljW72bnb1l4X+dXoQ/qPDWpiaCssKwVDuBiII3JPPB1hZOGJnGnmY35S1e3t/Syq57jo0qRty9qJLSRg+SqFtXiwJ8XtrOFdOyGJ7uYETG/uvvltmDaPGEDwo0v+N3/Jb4TbcvByoHmM3m6M7px2A2m8lJdPDR/DH8ffFedjR59Inrn4AkSYiiGJW8eHtTMx9va+HiSZncdkCfCCDGaqSuK0JDT5AYq4EjhsTzt+PyeXVdA0+vqBvwXKMIAXQf+nU1Xay8aQr+UITX1jVy8ugMEi367vhPRw9m/tTsAX7wh0JhqoMbjy7gtk920uYJM6c4mSeW1bK10cu/LxjNa6tquXRyJo3uMLWdAbp8EURRYNHOZk4r+XmGc33o00rLiTPxwLcNNLuD5CXasBtFarsCmA0i188upDg7IbpByE+yY5IE2rwRnl6xjx2NHva0+fj7KcN5fX0j4UiEV9fVk2Q30eoJk5do4K7Py/BHFP5+YgGiqDPMNtb18OXONp69YBwXv7KRFneIhu4gqqYxIsPJQ9/q8i9GSeCooSlkxVvxhBWMksDdx+fT5g3jC6usrenmlJI0ilJsJNiMKIqKzSRxyaRMUp0WUp0WClNsLCxt48ThehCeOiiO44qTafNGkESBrHgL54xN5audbXy5s42zJ+SQbozw+Ld7aPWEmJrr5NSSVLY3uNnZ4ifRbmJdVSc3f7idP8zO5/NrJuG0mgkEApxaksqpJanRUu0I2cG+riDf7G7n7U3NfLWznTcvHsXjy2rY1uAmP9lOUFb5w8w85o3L5dkfKllY2sr4nBjG5SWgqQpH9xqYRVSVhaVtlLd4cQXlKJMv2WGi2iBhNWncP3f4gMA+KMHK7hYv955YgMUksq7GxdySFAJhBafFSE2Hnzu/rOS0Ual4QzKrq3uYnBc3QMAWiHrv/I7f8WvjN7+q+pcofu5gZ58tgSAI/O2EgqiOGegMpAMzpv7Nz/4EgViLAQGduXM45CTYuHFmHmmxZgYnWlm8q4Nlewaqswro2U0f6rtDaJrGx9taWFjaws5mN4+cNgxN04iNjcVsDh40lHqovtKcoiQ+zkugotVDSUYs9y+pwh2UGZeTxJwrMlBVlRRvEJOhgom5sSQ5TEztl039HPTvj7mCMp9sayHFaearq8cz7oFVyBoMTrRw2yc7+b6smftPLQLg/S3NBCO6CZc3KJOXZGf+5CxMgsJNcwr40/tbSO5VEti4z0W81cjGuh56/DKVbX6Gpjk4rjiZp5bXsrfdz5xHV5KXaCXFaaLFpf9+Rw9NIC/Bxj++3svo7FiumJ6LoClcc0QOG/b1cMzIDN5ZX8cr6+qItRj4srQNy5hUSjKdrKiM0OWXeWJ5HVceVUggECDOZuLiSVls2NfNnz4pJxBR8csaaTEWuj1hYmNieGNjBQ09QRQNjn1yDQUpdi6elMkPlV2s3NvJw99VU5zmIKKoOM0Sb66rpdsf4l/f7OHllQaePLOYtBgLkiRhNBqj1+JDS6vZ3uDBYhTxBmX2tPmobNNdRN/Z1MTamh4GJ1k5qjCRHQ1dXDVjMGeMSeOx72u55I2tvHT+yCjLTgMCYQVV04dlzSYRi0FiZ0M3u1v0TMbl9SJY91/XF0/O4uLJ+2dABifHIMsy136wi0373Jw5Jg1Z1ajrDtDqDuMJybR7Q7y9sZGyFi8PnzaMnNSE/yNUn3/H/5v4TYJN/4XVZrMN2ClZLJaDSmwHTiz3LY59xxEEIRpsDlWaO5x43yklqZxSkgpwEPWzP6bn69P3K/Z28saGRjp9YSwGgcxYC40uff7EH9FZVy9fOIaphWkYRJg6OJ4t9e5oea5PcNRgMPDnhXvo8YZ5+HTd/0TTtIMEQzVN48kz9MU9LCvE24y4gzLeQACzEGFPi5tHvqtB0zT2tPn46/EFB7/5H0Gf2kCfH0im3c6txw5lcLy+SPVJlVV36t/pyqpu/vzpbk4elc7IrHhSnEbW1ujGaLmqyh8+LueyyVmcORZOLUlhSVk7IUVl4Y5WXl7bgD8UwRNWWfDuTlb8cTIAr184io37erjt8wrqe4I8c+Yw/vzZHtxBhbc2NpNgM9Dhi7Cj0cNTy6r5qrSFbn8EWYVnlu1ldFYMoqCz3Eqb3JS3eLCZRUKygsMsMTLTiclkiqqKu3wBbv1sD129rqp2k4QnEMEbkpl433e6V5AA5t75FFXT6AlqSAYDu1o8dPkiHDM8jfPHZ7C2posbZhUwJsPOsspO9nUG+G5PJ+eMS2dHk4dbPy3nzDFpXDY1h8IUO6WNHibkxrGzyUt+spWceAsFKXZaPWFeW99Igs3IbQv3sLXBzT9PKuSR72po84YxSgI7Gtw8+n0N50/IZEZ+IruavVS2e/GEFHwhGZMksrW39Dl7aAL+kMzqqm6OHZaEoZeNuLG2m+6AwjHD9AxJEASCYV1IdU1ND6+cP5JEh5GIotHmCZERa+HKd3cSUjSOfWY9kiDywVVTGZH531d//h3/7+E3VX0GPRCIoojdbo8K7ZnNZlRVJRwOU9vp54+flDM5N45bDyh19ffa6H/MFneQF1bVM3lwHO9sbGLuqFTmlhxeL2hrvYtnVtZxw4zcqH3ugVBUlSFJOivq7KxUHvy2mkZXkL8cO4RPtrWSn2zjhtlDyUneX/suSI3hX6cPO+hYqqpS1eZFEgTaPft9YPqXFfvkaPoysT1tPuq7g0QUjcWljSza1YHFINLhizBtcLw+D4QevEOh0I+Km/YP0gAnP7OWRIeZdxZM5pxxadGAN6swge8ruqJkCE9I4ZvdHXy3pwNFA6dZ0odPDQIRVaPVE+LFNfVsrHMhoDtqGkSBW+cM5t7FexEFDU84TN++QZIk0uPtnJrg4Pu9Lva0uilKdZLiNAERzAaB8la9cd3sDvHOhoYBYp07m7ys2NvNGxeOpKozwD8W78UfUTGrkJtg49oZeTz4bRXD/7aYeWMyuHlWHmaDSFyvcdnwdDv3nTKUsKxx+ds76PCGSXeaWHnjJHa3BUi2G7j+wzLuX1JBVpxZVzYwiTz8TQWtnjCqpvHN7g5ePHcEWxpchBSVj7e1MjjRxje7O2jxhHlmZT1HDU3k7LEZnD02g5s+KUMQINlujlKjL56UxYUTMujyhXltQxM7mz1kxZux9FKX7z6piIe/qWRHk5fAmnqOX5DCA/NKeHXlXh5cWoOqgScg00vmY/qQBK7/sIxWbxirUWRWb/ntwaU6XXtYegzZcfpzHRadcBJRVFJ6S2aSqGf0EVUjM85Clz9ChzeCUdIIRn537Pwdvw1+M220vsVQVdUftRiQVQ16JVMORDAYPKTE/jflHexq8dLoCtLlj7B0d+chg00oovDKugY6vGG8QZktde6Dgk3fe53w8BoUFe46fggPLa3RzdLQyIu38sbFow/K0Ppee2BJsG9G4dULSmjzhClIsUc/Q3//HIPBgNfrjQ6hpjpN5CfbmDo4ji6fjDsok5hoxWSU6PHL/HtlHR5Z5KoZQ35SRbvvPKqq0uGTqenwUdnm5azn1vDCuSOiMjzThiTQ4dWzvb+cVMyfP9hOXU8oqpJgNUpomqYrXSsqhcl2wrJKbWeAY4oS2bjPwNySVEZmxnDPSQU8tqyO8ydkElF0xqGsqFzz4W7GZDl5+NSCaBB879Ix3P75HtbV9iAJeoYlCvrwpACkx1pocgXZ2uAmPcZMd0Dm6MJEtje4+XhrC7Ki8ZeThrG5uqPXWVNka30P/rCMzWTg/2PvLKPkOK+t/VRVMwwzg0bMTBbZkszMFHOMsWMIOTHEDvjaiR3HdhJzzMyWLIMsS7JkMY00I2mYeZqhuuD7UdOtGUl24Ca5+daavVZWEk13dXVV9Xvec84+e7979TS+qO7hoS/qWXewn0tmGedkFgVuWlTCI1828v7uTjJcRr9JEuGCablUZDl5f3cXn+/vIabqmASwmgQueH4HHX4ZTYOJeUmMy3MxMc/FjhYvgiDgHrAc7QvJLKkwspKpRYeeM0Nzr4PXt7fjCcewSCJ3f1zDVXMLOWFcFjabDYtkiJ26rVIiC351azuabsj7zChNZWezl5iqs6kpQH1fBJNoCL8+9lWDQZSYnkdDX5iC5EPltbuWj+DeVbVMKTxyk2UWBV763iS6AzI3vrmPDKeFqUUp3/lsDWMY/yz+LcFmcHD4W0OaIzKdvHr5ZCzSoYGt+r4IT3zVwFVzCxiVPdRlUJIkzp6Si80sMa80hXqfxqwiN4dcUYwAtrfNT1hW+KqmD5dF4rZjS4+qIRZfuE2iUaqxmUQskkA4ZpiCTSo0SgpHa5rabDYURRmy+MczlWS7meSBjObwYCkIAh19fj4/0Muy0RmkOq2squrFF1Fo90a5+8QKZpemMK0knSSXk8c/30enP8rEPPd3BprBgVkQDH21ZAs8ecEEfvDWXg52BWj1hCkYYKp9eaCXUEzjuFHpTCtK58lLpvDwZwdZfbCPmKpjNQuoqo43olGSakMSRfZ1BJhbmsxV84q4ZFYRTptRprt3RQ0NfWEq27yk2M2s3NeF22piR4ufum4/180vZGujh//5vI7bjy1jTU0/McUo8cwqTeVAuw9fVKUswzkw9Grcx5xkK8eNLyAYDPKz5SMozXDy3MZm7nhjNzr6wK5dpbY7xBvbO7hsoG/R1B+mOyjz7DfNXDIrnz+cNZqnN7ZS2xPijR0dAHT6jeHUDKeZsTkuEES6AjFGZzkpTDX0yl7c3JpQahCAUdlOkmyG3t3HN8xBlmV+90U9Rak2/vJ1Myl2M69dMYVUuxlJknj4i1pWVXUjCQLtvmiijxiUVXa1+jh+bCad/X66g8Zn1PSE+OpgL8eOMVGaYccTUchLtmKWRE6dkMWn1b18tb+LYyoyyHRIVGQ5uWSA5fbR96cypyRlSFabm2rnyQsnJp6Hw2ExSeQkWZlakIyqa9z8ylZ+fOI4CtKc3/qcDWMY/wz+rd1AURRJSkrC7XbjdDpxOp1IksS1r+5JKCqDMX09uGezvqaHht4Qf1nffMQxBUHAH9WIxlQcVhPHjUrHLAxdzJ/b2My9Kw9S3RVk+egMrp1fxIziFKymoVpqsqLS0GuUccbnOCnPsJHuNBOPe/W9YZxOJy7X0W11gSF6RX/PpLMgCDidTp7f1MrbOzp4Y3s7mqZx5pR8jhudwffnF+Fy2JlamIyuKvh8Pq5eMIJ3r5vNrLL079SNGhzUdF0nFosRi8UYm+PkugUlnDc9PxFoAH592jhsJoHnN7Vw6xs7GJmbyoNnjuX6Y4qwSgK9AZm8FDsXz8gnIGt8vr+H+t4wK/f14AnJWE3GZ5rNZu49qYKlozNZMCKD/lCM+l4j60x3mjlrUg4RWeEv65s40Bnk+tcruWlBEaNyXNjMIhdPzyUgq6g6PHbOaBZXpJLtNlOW7uBnS8uJRCL8eV0jc3+3EQmdhSPSWDYmg+wkG8l2w/QsHNM4YazBQjOZTJwzNZdphUkcU5HBjhY/N75ZxaZGL69ubUMUwCIJuKwG9XxklpNxuUn87os69rb56A7GOHZ0BlfOL2VSnhurScBlMeas3tnZgSAIiRLoR3u6eGlrG7/5tA5fRKG+N0SSy8giVFWlyx+lyy/T4Y8SU3WSHRZims6YHBc/XFwCwB/XNNA7kGGGZJWnN7YgyzIC4Akr7OsI8nVtL69t76AvZNDivz7Yw6dVPciqztTCJApSrKyt7eea1yt5ZuNQuXtN075zk9ITVvnqYC+f7uvhQHeId7YfXS5/GMP43+DfktnEXQEHW0PHg0lQFWn1RmnzfbuFwBkTs7FIIktGHpI9T05Oxuv1oigKf/2mmZX7utnZFuSRsw+JG8azqJJ0B7KiU5jm4NiBY8TfPxi/X93AliYvNy8q5qwpeby9s4N7VtTQN+BdPKc8/TtpoLe9sZMVe9r5w7kTmFeWit1uT3xPTdPw+Xzouo4oijzwWQ3d/hi/Pm0USaLI0jFZ9AZjLBtt1NtNgoY3HOObeg8j8w7ZBcTdC91u97c6mP4t/PCdKrY3+3jg7ImJsp0hMa+Qk+Kksj3IwU4/Pp+Pa1+tZG9HALMkMqskmWvmFTGtPJsVO5sIRBXqe8OkOc2kDOimHej088uVNZw3NZf7T65AEc3M/s2XqDpUZLl54LSRbKzrY/EfN5NqN6Fh2Gy/tKUNQYCfnDCa93Z1omGUkBQNPq/upTsQozeo8IuPD/Li9ybx5s4OwrLRU3r6oonous6vV9WQm2zjornloOvYBCM7CKoiIdXEkxdOxGKxMPfBtUQHyrThmEa2y4Ki6/SHDKWFrU0+0lLcXD6nkJV7u4gqGvMqsklxO3nuytnsavFS7IaXt7Sxf8Bu4benjqQ7IPPk143oA2XHWxYXo2gCaS4LXq9BWpmY52JLo5fSAcWEJJuEoproD8UIyiqnP7md8kwH88pSqch08OSGZvLcZl7Z2sbXdZ7EPbRIQkKBoTTDSfUALfqJtY3curiUxv4IdpOApkP+wIaiujNEfzDKvBHp6Lo+pMIgiiK72wL89tMafnVyBb89fRR9wRh72kNcu+gfI6IMYxh/D/5twUaW5QTN2WQyJbzFXSYTvzypArtZHOKquaGunwc/r+OOpWXMLU1N6JhZLJaEIkAcZ03JoqozyHXHlCR2bINLSEtHZ7B09JFKtk6nc8iCPTrbyZ42g4H05cE+ZFWnK3CIsXbLcSO/9TtGo1HQFGxmkequMItHZ6MoSuJcRVHk3Gd3IAoCz100gV2tAcKySn9Ux+L1Mj7Xyf2nHDp+uzfC7lY/O5p9XLFwJMnJyUP82OO9r38GAgKSKOAyiYnr9dAX9XxW3cNNC4vp9IU5dXwmb+1oZ1OjFx3D+rovpLCnI4TZ1IsowJ42P2ZR5LxB8jj1A66ae9r8HD82g7qufhAENE1nV4uHL/b3sKfVj6zo9IUUNv10Eb9duR9NNQLHPR/sSxxrTI6Txv4oVrOIBmiaTl/IuB/PXDSZtQe7OXVCFj94ay8VGQ4+3tuDpuv8eX0TTouJkKzgspmRFQ0BnfnlaeQlW7njuHJ+s+oAvohRtusNxRAEsJhERmQ4UAeu7ymTCzhjesmQLFsURQ52eKht17h0VgFnPLmNqKLRF4rx9o4O2v0x0hwmPGGFz/f38exFE3hgRSXVbX5kTaPDF6E7IDOnNIWnLpqHpmlsa/RQkm7nshd30+qN0h2QueuEEXxW3QPAmpp+qjpDmE0isqqiY6hmm0WDqLG/I0CWy4SmC7y/u5OXt7ThsIjkp9h556qpWEwigiBwzau7icQ03rx2DuPzko6wDH9/Zxv+cIzXt7exq8VPRZaDut4IvohMhsvGMIbxr8S/JdiI4iGtpWjUmEmJL5qKojCzLMPYWcdiPPdNC+NzXdT1hBAEgf0dAeaWHpolsVqNIbrBjfhROSk8f8nEITu1v6VUGwwGsdmG/oBOn5TD6ZNyuG/lQTp9iBF7MAAAn5dJREFUUWaXpuC2imys95KXbOWaF7awoDyVq+YWJt4bdxCNRqP8dFk5l83KZ0JpbiJrkiQp4bcRiKpYTSKKppPttiArGiOyU4740bvdbgo0HbNJpCLdmfD/iQcXq9X6v5p/CMdUst1W7JKaOGaaw4zVJFKUasdpkfjtZ3WYBGN4VdYgqmhUdfgJygqr9pq4aHouigaarjE+PzmxoRiR6eDquYVMKXBz10cH2dPu54rZBXQEYpw7KYuffLCfcEylJM0oUVr1GCWZLh77oiaxU4+jujPEj97bR0g2zvE3p4/lD6truO61Si6akcelswr4w5cNrKvpZ+3BftIcJqKKTjSm068YfjyRgc2CWYRV+7qxmiUunZlnMAJ1BV9UJabpjMw0LKyDskKW28YXe9uYVZKCJtmwS4ZnUEjRSXc7+Pn7VdgtEuNzXdx94gga+qKkOy20+wyfmuNHp7PmYB/1PSHOeWY7LZ4I4Zg+QLIwjPne3d2FJ6zwyNljmVqUzP2r6mjoM5iWbquJZza2IAowNsfF6Gwnu1r9XD2vgK2NPj7ea5jAxVUsdKAroJDmMJFkMxGQVfxRjQOdQT7Y3ck50/LQdZ0zJ2Xz9s5OvvfsJv7nrInMLDhU8vVHZBaMSKMgxUZxmo3NDV5W7+8lpup8sLOdK+b//2c7PIz/bvzbhjrjLC9VVYcMWsY9zr1eL5Vtft7c0c7KvSZeu3wS88pTKU4dGhAOX5iBREZ0+MzK38LhMzptnjA3v1XFghFpXDW3kAUjUrEM9HXWHOzjh2/vo7E3xFVzCxPvlSQJk8mEzWZDD4cZkZMy5PsGAoHE8VfeOBtBshAJh6jpDhGJGX8fPG8TD0xbW4LUdYfwhoxsZvD3iUaj3+lc+rdIGL89bRQtnghjcw8RJK6cW8gNSyqIRCJ0+aPouhFkBse0qKLjDcU4blQGucl23BZDYsVqsyfu6a1vVw3MJUn0hmLkJllp6Q+zrq6fsVkOphQk8/G+LnqCCnW9Yaq6QqTYzGS5LfijCoGIyg+OLSPVYcUbivLxnk46vBFU4OEvaunyy/QGZNq8UT7e10tliwerJDIqx82YbDsHukI09YXoGWiwm0XQdIPhpukgKCovbWkjpqoJQVdJgK5ADKtJpK43Ql1vhK1NHvJTbLR7o/zmlAoOdIf5sLKL6xeWIgCBiMIb29v5waISJhcYx/nRcaWc+fR2Vh/sZ0pBEp9UdePrUXAOGLYdNzqd86fmcfPb++jyy6yr7eeeFQe464QKZhW5eXO7UX7rC8WIKEYG0+U3vqum67y1o5Muv4xJFIkoR2a1o7NdnD8th0/29rCl2Ys/olCaaUfXdX72wX7qekPMLknmYHeIfa39Q4LNA5/WUdUZ4PJZBSwamUFpuoNeWWDNgV4unV18xGcNYxj/W/zbg42u66iqmpANH1wSG53t5LwpuRSm2ZEkidL0v88c6PDFOI7BZbm/B11+GVXXeW93J7U9IRaPNHolgiCQ7jANkAWERKYmDPxvGFre+7a5F0HXcFhEXLYk7jl5FD//oJqb3qhkbI6L/Z1Bbjm2lIrsZPx+P7MKnVwxp5A5pcmJz/p7fUWSkoxp8VW7W9jb7ueSmXk4BknMZyfZyE6yJaylVU3HJInc99E+nBaJVy6bzPZmD79f3Uh97yECgg50BmI8u7GF86Zkc9L4HEoz7LR7Qvzm4zrMokhQVvFGVBSLYRL2h3MnsPiRDURiGuvr+kh3WBKqpmYRVu7rJcVu4vHzxvHw6nqqOoI89mU9ogD3nTqa5y+egM0ssrU1zEOfHaDLH6Ukw8nx43J4YWMjTqvEU5dOZXZZOne8sZPeYIwUh4XlY7Oo6w2zoa4fqySwfGw6qw/0M6s4GX9UYWeLn6ii47CIjM12srPVj3fQLLBZFLBKRvlpZ6ufdJfFcOm0SwNeP1E+2NNFd0Dm3pMquOaVSlxWictnF2A1SXQHjIDrtEq4rSZGZzuJxlTe2t3NFz+YzRvbWvnTukZ2NPvoDcgsHZNJyqpaPGGFFLsJURDwhGJoQCSmEtOgOxDDZRFxWYYGG0mAqYVJeMIxbnt3Pw+ePpryLAdV7UEmD1D7WzwRglGVa48pJBTVhmijAUwvSqaxL8zIbIN1ZpZE7nhrN+fPKMJkGlYRGMa/HoL+b3JK0jRtSFbidruHlILizfO/B99lISAIwhD75L/HOG0wGvtC3PZONYqm8+rlk7EPMOM0TaM/JJOdkoQsG1lN3ErgcASDwUSmEi8vxc9ZVjSWPb4Fp0Ukpuo4LBK9QRldh+VjMoaoAvwjAWYwzGYzaw90cce71aQ6TFhNEuWZDh4YkJ4ZfOwLnttBbU+Ih84Yzc1vVSEKAu9fM5VgDG5/p5JITGduWQqfV/cQGChnCcA7V02hLNNYmH749j7WDSgQZ7mtKJrO4oo0fjZgKNbtj7K50UOHT+az6h484Ri+iMqckmRWH+wDoCjVxqySFHoCMp2+KDU9YSKKxrgcJwtHpPHnr5vRdMMyemFFBp9VdyOgU5ru4N1rZ/LujjY+rerGaha5bUkJucl2mvpCnPqX7egYOmJWSaDdHyXTacEsCTR7DEHMaUXJjM9x8ubODkIDE/ZWk8BNC0vIT7EypyQFu8WUUG7Y3OBhamESV7xcyXGjM7jnhHJO/NM2kmwmXr98Eu/t6WLuiCxMeoyN9R52t/nZ3OChP6ygavDN7XPwhGTuWVHDzOJkLpyRj6rrLHpkI76IhssqkWIzGRkNYJEgO8lOqydMttvCzOIUPqvuJjRQmhuT7WRKYRL7OgJUdQQZn+vCG1GIqTp/OHsM5Vlu/DGdLl+Ucfkpfxex5NOqbu7/pJZUp4U1dyz+h5/BYQzjb+HfltmIoojb7SYUCg1ho8Xxjyyq3/baOMMmrlDg9/v5Yn83GU4LkwqSafcaApbj8o6uGgDGJPpfzh9HVCURaOJ9olSHBbNZAiyIoojf78dkMmG324ccI67NFmeuxckRoiiiaCqKpuG2WfjrxZPwRFSe+rqRYFTl/GlDHTz/2bgfi8XY2+7HLInYzRJRRaehZ6iET/zY/SFD2LEnEOX4MRl0+mWafBr3fLSPVLuJ48ekcs7UXPpDMdbW9BvNaeDFza3cfZJBaPjBohLsZmPH/1FlNxYJrphzSJdLFEASRZaNSqc7ILO/K0BvMEBTXxh0SHOa8YYVFo9MZ85Af+7ujw7w3p4uqjuDiILBqipIsXHJjDze2N6ORRKwmyVuWlhMVJZ59KsGFFXjlAnZ7O0IkZtspyjNwWPnjmHlvl4WVaTy0uY2YjpMyHWysd4DMGCpEOWFzV404O3rZnPuXzYRVXT+sr6JL2+ehVkS+e2qGt7Z3YlJFEh3WnBYJHKTrEbvy2rhtatmYiLG6oN9/O6Lep7d2MraOxaiI/DyljYkUeD86YXMLk/HarWyans7+7uC9IdkTpmQhSBAcEBvT1E17j1xBPu6gtR0BRmb4+bYMZmc+qctqJrOR5VdWM0iZ0/J460drVR3BenwRzlrUg4LK9KYXZJCOKbT6okwMjeFWCyGQ9QpSTGjKAqCIHCwM8Dauj7K0x0srDAYmiFZ4c4PD5BiN1HbE6Iiy8UfLpzyTz2DwxjG38K/LbP5W4hEIolF+R+BIAhYrdYhvaD43MP+1l7OfmYnqqax+Y65zH5oI7Kqc+XsAm5bVvGtWY/bbQxLxkkMcdbaVwd7sVtMLBqTO2S+xWQyJTxiwCA9DA6ozd1eLn1+G2Nzk/j1KRWEZBWzScRhtfzNzGtwdnO4eoKmaVS2BxiR6cBhMQ15bUNviGterSTJJvHkBRMwSwJumzFU2uOPkOqyIgkCgmRmb0vfkP7N3N9vxCwKHDsqne1NPnTg2YvH88TaZt7aaQxAjs52Eowq6MBnN8/j1jf3UNnmp90bQQfG5bp58XuTALjrowMc6Apyycx8ThqfxS8+3M83Dd4E0++mhSVcu6CE1dVdhGMqy8dkElXhihd3MiHPxXu7OgkrOrNLknngtFGc9fQO+oIxJEngzEnZXD6niCte3Imi6fSGYphEgfeunkpvUGZtTT+N/VEumJbDlMJkYoKVhQ+tISjHh3eNklF4oNt+/+njOXl8BlPuX4MO/HRpKedPz+f61yvZ3OhFxBgXHpvtwhdV6PCGcVpMOG3mRDlrS5Ofc2cUcsPiEcz+1Wf0BGNMyHPx2tWzEtJM4YjMT9/bx4rKDqwmgYn5SXT5o9T2hBGAHy4p4XuzCpBVY4bs5a3tzClN5b1d7by3u4tFI9I42BNCUzV6gjFkVee0iVncO7ABeH17O4982cAfzxl7hIvt9iYPv1xZQ2dAJttt5b1rpgHQ1Bfiutf30jnga5TqMJOdbOeta+diGS6lDeNfjP+zJ8pisQxZVP9exKU8LBZL4n1xinVOihOrSSBnQAMqaWBaO9n+7eW1eB9GkqREDyYYDNLcH+YPaxr4n89qjxikHExMiMViBIPBRMkwEAjQ0W84ela1+wBw2cwkOR3fare7rcnLuc/s4N1dHUOC7+FU5y8P9nH/J7U8+Hl94nvHUZRq48RxmSwdnUma04LbZvTIvqrp49IX93DXRwexWCwkuRxMK8viwc9reXdXByaTiZsXlrBkZDq3Ly0jyWZCkkQyXDbuXF7GDccUMr8shf85ZyLNnigtnij+UISeQJS+kLFIFaRYmVeWkjiXUyZkUZbhYEaxsejddmwpV8wpwCSAzSQwPs/ND17fw23vVPGHLxsIyCpWCV6+bDKzSlJJtpsRMPTivqrppz8UwywZCg/v7uqkKDuVGxaVccvCElTNIDLcu+IgV7y0hyc3tLCqqpvrX99LSFZRdQ1F03FZRG5dXMLNi0qIqRq5SRbcVpHWXj+CqpDhMu7Nw6vr2dkRZWSWC6sksGRMFlaTyIQ8FxlOM1EFekIKIVnBG46xrraf788r4LJZ+exuMQzuzBKcNSWPa17eiSckEwgEUGJRfnF8OXazRFDW2NPm55bjRpLhNGOW4GBXkJ9/uJ9jHv6GX66sYUVlFze/tZcdLX5S7GZ8URVN01k2NpOzpmQjibChzoMvEsNkMtHSH8ZhkRJDyoOR6bZiNklMzHNzzbzCQ89MmoM7jhvBnNJkjhuVjtMisb/DT1X70aWlhjGM/w3+48YV8YwmTiEGeG5DE2/v6uAXx49gRnHKd77fbDYPYXzFEYvFSE92s/62+ShKDEkU+eIHs4a8L/46OEQm0HX9qLptBalOIjGNJJsJRdOxmKQhi398fijOJhNFkV5fEFWWGZnt4r6TR5KVYlhGR6NRVu1u5YHPa4koOk+cO5aXtrSyuzXALYuLCcoqMVVjX3uAMyZ9+3evyDSsk+ceZosd//xNDR5kReekcZkUpNpRFIVMp5neUAy7ReKptXUsHpmOZLGxqqqXr+s8nDEph/OmGXMzbrebUyfnsrfVS0gTuPDp7ag65LotvLW5KeFg+rP3q8lLtrC33aDjtniifFTZzUUz8nDbzMwoThlyH1McFt7Z1UGS3YTVJOIPG+yrJJuJiiwH25s8tHtlJhe4mVyQxIziZOaUpjC3NIWLXthtaLXpOmlOM5fMyCMWDnLqhCw6fdHEOX3T6BtyPUIxjYWPfMMXty1EFIxzmF6UzG3vVqNqhlRNhtOM0yJyoDNAUaqN7kCMiAo3vrodSRCIqjpWk4mvb53D9mYvnx/oRcPoI/3x7LH86D3DB+fP65vwRlU+quxGBxZWZPHHrxrwhWP8aU0tRSlmVlR2cUx5Cjo688tTOXdKNg+u2s/FM/L5uq6PDyu7sUgCyXYzxRluWr1RFE2nIMXGFXMyaOwL0xuUKUmz8/6eLs6YlM2EPDfJdguKonDzohKWjc1kYl5SIvuPe0eVZrpZ8YMFPLb6wBFZz6KKVMrTrfzwnWrKM+ycO72ASYX/mI3FMIbx9+A/GmwikUM+L7IsY7fbCYfDeMIxREFICET+s3j0syre3tnBS9+bhKYp/PDtKiYVJHHrklIcDgeapiWCTTQaTfwgD2eSmc1mHA4H/qhBl33xm2bCisYZE3PIHZjODgaDCeXm5ORkvqlu4ScfHMBiEnjt8snMKM9CEASi0SiRSISHv6yn1RPFJMJjaxuo7gwSljWqOkNcf0wR04uSyXJZjvhOg6Fj7FJzk4/usHj82ExaPJFEZgcwNtfNrp8v4a0tTbywuYWtTV6euGgqggCKqqNqGtJAz0sURZ5e34AvolKS4aQ7ECMcU+kLxchwWRIL++cH+jAJh9ToVB0QQDtKRfTP6xvZWOdhXI6L2p4Q4ZjGO7s6ePisMfzm01p2t/m5++ODRi8JWHvbfO49aSSXvrCLJ79ups1jPC+aDtfPL+KsKbnsaPbitppYsa8bq2koU+umBYW8tbOTdp9MmtOCpqooqtHPuOSFXagDpAObWSLdaeGcqfnc+UEVle2HNjCKqnHihGzOn11M84D9gt0kGrbWozIpS7fxwOd1FKfb0TDmqd7d1cmysVkEIjGuW1TGhU9vRtV0rl5QysVPb6bLHyHLbSGm6vjDMX78/gEsksjmJh+NA9mIruusuXU+dpuV7j4PlW1+xua4cFhNaJrGrKJkantDNPSGsJlEfn58RSK7NUkiE3Ld6LpOUlISgUAg8Vw7nU4e/eIgf15Xz46mPi6bXcD9K2u4fE4hp0zIwhcxbK5jGlx1TNl3PoPDGMY/i/9osBlMS46XwsLhMDctLOaKOQWJHkMcg9Wj41BVdcicSpxSHQ6H+fJAL1aTSGNviCS7GX9UYX9ngC3NQaaLEqI+tCz1bT2jeBb0+3Mn8tLGBp7f3IZJFPBGVH46yAZBVVWCwSD7+2Lc8V41Hb4oVrPEH9c0csuSoUNxl8zKp6ojwMQ8NyXpDt7f3cnJk/KYVmD0i8KyymUv7uGsKTmcNuDBczhW7++jqS/Mp9W9jMlx8ddNrZRnHGr4fm9WwVHfF4lEmFuWzPq6Pk4el4WAjlUSyXZbCMsqDf1h5ox04/f7yXBaUDXjuvz8pNE8saaWYFTlm3oPg6+ecthlG5vtSAiPDkZ1RxBvJMYZk0sYm+Pmshd30eaNsL62nzMnZdPhiyLHNPow7udLm5q5dqDUYzeL2C0iIVkjL9nGRXNK2d/Wxw1v7MNuFnFYjHkWt82EomrIiobNbGJKQTLRBg/LRqVT0+4hNhAF48LiT104HnSd29/bz6JHNhBTNawmEaddZGpRCgtGpHLaxBzOf24nvnCMbaPSqWwPYDdLbG7oZ+1BFUXT0THk+i2iSE1XgAOdAWwmgT5fGFnR0HSdmb/6ghnFqZw4vpSLp2Vxz4kVnPP0duPvms7D505k9d5W7l5RQ4rdzO6mXj7c20N9d4CuQIw/nD0Gh9WEKIrc80kNXT6Z4nQ7vzl1FAAxVaPbL5OXMnQ+7fDn+sRxmayuaqci00ljn8H821Dv4ZQJWYzLc/HGlVNIdtqHXTqH8W/D/8mTtbvFx182NPO9OUXMLDQo0W6bOKThfbhKbZzmHN+Bh0IhYrFYwnjN5XLx2zPG0dLnZ1KBUSp44txxrK3t456P9rFsTCY3LyoZch6H/yDjA6dxzC1JRpVzCUQV7GZx4IcdIdM99Idd295PX0jBYRbRBdjY4OGWQedtsVi4ZG45oiiy/JG1NPSEuOfEEUzOdSCKhoTMvvYAnnCMXa2+bw02503LJdNt4ZjyVJr6wnxU2YWukwg2cRwtSH/T4OXsKTlML0pBicn88uQRRBSdOz86yKYGD/edqrB0ZDKPnD2WZ79pwWWROG9mCYVpLq55cSua+t1SOV/Veuj0R3GYRKxmka6AzJ0fHqTdE8YsiTz4WT2nTcpOsM0KU228v7uTbU0+EIzmoSjCezvbmVuaxHlTc9nc6OHWY0v57ad1LBqZgSRJ9EYFIjEVm0kgKKuEYxpWHUbmuAmEDUuHRSMz+ax6C69sa+OFLW1DzjMnyUpVV5gn1zXSF5QT9gYxVSfDaeHxC6cQi8WIxBTaPRFEEb6p99AdlLloZhEVmTb++GU9BwfYfnazyJWzC/jjgI14lsvKF/s70TU9EZy3Nvbz6jWzueipjfSHYvx0WRm3v7sfTyjGDa9s539OG825U3Owm0Ue+KyO3W1+km0SdouJfR1+3t7RweVzClhckc6qqm4KU6zc/0kNMVUjpulsa/Lx/fmFXDHHCNLhcJjXtndy7Ihk0l0WNE0jw2aoRqw+0MuJ4zJ59Jwx5KfaE4xRlxVsVssRRobDGMa/Cv+xYBO3hTaZTLy24wCtngjPbWzmz2sVrpxdwPwR6dhstoTrps1mS4h5xunG8R/BW1ub6QlGuXRGXqJBDzCmIJ0896GvlJNsY3yuG6fFZEjIHwWDsyS73W4wh8JhdN3Q8rr/k1pAR9OhujNIaZqDy+cVY7fbE6SAjyu7Cckqkgguq4niQcOpVqt1SH+qsTdETNN5+MsG7GaJYwc03M6YVkhumpPxOUcfbBVFkex0N6dNsqCqKm6rxIXT8khzHZlNHB5o2j0Rnt7QgqJpfPD96QDc/0ktiqoTVTTDnCsU5ta327hxQREr9nYRljXOmF7M3R/sJTygyByHRRKIqTqFqTZUXafVE0VRNM55ejvJdjNJdhOVbUZZShQgw2mmxRPhT+uasJlEQ74/3Z4YgrSZRDKcZtJdFrY1e7n21UqsJglZ1ZhVnMINC4pRNJ1gMMi725pJdZi5dUkpB7qCVLb7mVKczi1LylEG5qHcbjdnTcvn9S2H1IuPHZlGsyfCz44fxfdf2Ul0UGqm6qCqOk2eCLog4XCY+XJ7A4KgIwgiU4qSWF/rocsX5qo5edz90YHEe8OyhqobC3lPMMaiUelsqOvHZhEJyho2k8hPTxyNKIq0eSNEYhoPrW4AAQQROnxR1tb2sWJvN56wQprDjDbg7ySHY9z54UGSrCZsFpE7jivjjuPKUDWNC57bhabruKwSOkNFNj+tbOf5jQ2s2e/gifPGAbC6xseOZh+aDtMLk/jxewdwWiVeuNRoEmqalvgdfds82TCG8b/BfyzYxB/kWCzGvadP4C9f1WEV4c3trdy/qpb1k4uHDJ/FHT7dbjfv7mjlqbW1XHdMEcvG5XLX+5XENJ0TRmeQYjM6PUeT9ZckibG5bt64cgqSJGG1WhEEgcYeP0lmo+kfDzRV7X6+3tjGjqY+ugMxnr9kAhZJxBNRcJgl+oJREAQ2NXq4fF4xoigmAtVNC4uJKgrbmv14wwqr9/cgCKPRdR1JkhIDrG63m09/MJtffFBNS3+Ybc2+RLCRZZmFo3ONXfVRrK/j5ITBwqNnDxLEPBpqu4MEoioT8lycOC6TbPehntAJYzPZ2uRld6uP/BQbWxq91PYEuWdFDUtHZVCe6WBPYxeeoGGmJgkMMM9sBGWNvqBMb1AmnvAoOngjhpqA6Dl0DiYRvOEYdotIf1AzXD9VjQc/r+Mny8q5ZGYuV75SSbvfyDJykwzjNDC8hfa1+1k/MCMjCdDujWIxiaTYzfxwSWliWFjTNNoDQTbUezl1ko2fHldCeaqF//m8DlWH5WMySbGbuOqlHRxF+QWXVcIsiZhEkUc/38+jX9YhCQJvfX8q4YhMl1/mzElZ3PDGXgY/aqoO7+3qYEphEioiVy6ooDewl4um5/K7Lxo4ZmQm35trlFTfuXoaH+7pZGOdB08ohuQ04wkrrNzblbCxnl+ewWkTMvjR+9V4wjFS7WbyUmw09oaJyAo2iwlJFHnqwvFomobdYqay3cfUgkOzZPkpNlLtZk6dkAUYv6XZxW6K0hycPTmbMbmGbYLdbGze7l1xkB0tPlIdZkZmOfntOVO/87kaxjD+GfyflNEyXDbuPGksff0ectwmJg8YlImiSHWHD7MoUJrhTGQZ6w92o2o6Ve0BFlXIXDu/iC3NPjJcRqYkCAKKogxhlVksliE7fJvNhslkYltjP9e+uIWKTCd/On98onT3848O0uKJoGg6ggA7mnzMG5HGxtvnoes6K/Z285tVB6nqCKAOLPxOpxOv18voHBe3LC7lR+/tp90XRdN0Xt3WxoljMvi8upGeQJTP9vdy0ewSlpQn8cdzxrKtycuEPDeb6vv57Wd1/GhpKQtGmr9zDudoOnHfhVvfqUYSBZ68YDxXzS0c8rer5xWRn9xFmzfKjccUM7EgiR++XU2zJ4TbbuLO40egaRoTC5L46mAvJ4/P4idLy/imvp8/fNWEJyRgMYmIAkb/YdCxBxMFbCYRX1QjGjZeEVV0anrCNPSGuXB6HrU9Ia6dV4RJEnhvdydTC5J4dXs7RSlWmj0yGxu9XDmnkE0N/fzk/QPYzAK77l6OKIp4/QF6+r009IYpSLNxxztV1PeGCEVkTp+Uw7nT8nBYJDr9UU6eVMCvVlQRJxSeMcWwad7b6qU/FCMv2cYntyzgxa9r2dPiMXTV0BmXn0YwGOThs8YSjCrsaPYRiamIA4QIm0kgN9nOU5fNBqC63Ut3MMbc0hTOvG3OEPVusyTyyOp6PBGV/BQrUwtTqGr3E1F0JAHMksAti4sQBUh3WvBHVaxmidruEFUdAfJTbNy4sARN03BbJUTRyGqnH+auee3re4mpGulOM1safYRjCgtGpPHCpRMTr3nl8kPDmw19YWRVozsg0+2XGcYw/h34jwSbwcQAp9PJ1oY+3tzazB3HFnPKhOzEa0IRmdveqcYsCbx+xRTMkjH/csfykXyyp41TxhvmWN+bXcD3ZoM3FOHh1Q0sHJHGMSMOecBIksSr2zqYlGNlxIDESrwEl2yBLLeVvGQbZrMZm83QDJtZlo6r3U9Rqo2NdX0JV894wDpzWiGPrK7FJIr4glGsA3M+cX+Y0dkuZFXDZhYxiyKPf9XImgO9dPgiNPVFkESBcQ09LB2ZiiAITB+gBt+94iAdPpnHv2piTmla4nj/G9T2hChKs7NsTAa13UHSnOaj9nF2tfnRdJ2IopHpsvDkheN5ZUsLCysyEpTuk8Zns6Gun5wUJ69sbeejyi5CsorbJnHDghJe2NSCJ6Qk9M8Ohy969F6PosMZT+1Axyi17fzZQqYUJvHn9U10+WSiMY2r5haS5jBz4vgsntrQhKbrBGWdBQ+uYc1tCzjnyS1EFA1POEZM0ZBVHVU3BCoBPtjTxT0fH8QkCVw5t4gzJ2cTiCr0hBTuO6mC4x7dSE/QuNbN/WE6en3ct/Igmq6Tn2wl2W5i6e/XcvyEXK6dm4fTauL5iyfwq1W17OsIoGk6UwuTue+UUYnvdeFTm+kLyXzvxT1MKkji8Ytm0OcP8cbWVhaOTMNuEfFFVfqDMS6bVUBRqpVkp41NtT1YzRJ2i4Sq6Tx2/mSeWV9Hhy9KTpKVtTV9HD8mE0XVuPyl3ezvCnLHspGcMynziGu7dHQmn+zroiQ7jZMfW49VErn7xAq+PNDDFbMLKMt0JjL9vU3d3HviCHKSnbR7DWLNcN9mGP8O/EeCzeDFMxQK8cTq/dT3hlm1z8HJ47MwmUyYzWbMUpSZA4uwWTIedpvNxvG/XUWqw8Ty0Rk4bYcW4/tX1rK50UNTf3hIsKnsDPLAJ9U4LSI/XFzKiEwHEwf6PtkuE69cNhlBEIa4bP7y1LGEQiFMJhMOhwNFUYjFYsjygGS92cw1C8oxoeC2mxOlLovFYuiySSK/PHkkq6o9XH1MMRc/s4X8VCeKptPtjzEq28m1xxShqip2uz1RvvvlaeP46bt7uWPpCGw221E9aw5XEvgurK/t46HP65lRnMydx49I/PvRhEJvW1LC8WMzmTwQWFNddq6aWwQcKkseNyqdk39xLBsPdnLvCmMhnlaYxPq6fv60rpG5Zak09h9Z9hsMm0lAVvUjqNGiYJShzKLA+gYfq3a3srnRgz7w+RfPzOetHe3cu+IAvzypghc2tbGmpo82T5gddZ2EYyqBqEFttpoN7xeTYARbRdNo8w4QADSIKSrXvbYXWVH57AezUVWVX55YwY/eryYka4iCwMq9xlCtpkNhsoWSDCffNHg52OmjJ5CBSRQYme3iZ8vLeeTLBna2+mjoi5Cf5kpQ6S0m47p1+GX6DvRS3eHhxW+a+WBXGy9vbWNUloug7CWm6Fz58i6mFibz2LnjmDcqB7PZzNi7V6HrEFM0RAHDdiLTwYrrZxj3UTPcO3UdnlhTe9Rg84sTR/Kbsycb929MNvXdQd7Y0Uljb5BURxc/Xl6B3W4nKKvc/FYVMVXjV6eOYmrhcK9mGP8+/MfLaLquc+H0PN7f3cncsrQhw5Z9QZlxuS6OH2v0McxmY5eV4jCTZJOwSMbrnE4nXp+fvR1+VF3n1sVDacblqRYm5rkRRIHfflZHutPM6tsWJspQ8Yzm95/uJzvZxnnT8of0lOrauvnei7s5ZXwW359vLL4+n4+zJmYcwWCTZRmHw0EoFGJuaSonTC4BYMddywC494NKdjR7OX1SdkKJeVNNJ5sbPFw+p5Alo7P5+o50zGYzwWDwqEHlHzFNc1tNCAJUZB2daOANxYiqGutq+tjT5ucny8oTgSXO+Pusqou/bm7jd2ePxy/r/OnLfaTazZgkgUUVGUzIcxtZkaazu9XHZTPzeGtnB8EBUcssl5mugLEhEIHI4Txp4IbFI3hxQ51h8a3q3PHWboJRIyBKgiGMWdnm46Ev6jGLAlMKkvjDOWN5ZHUdPcEYZRkOfnPqKO76+CAmUeCZiyawt93PMxtbKEmzcc2rBrFhdkky+Sk2Pqzsoi8kk59swyQYg0Fum4kV183g0a+a+ay6C1nVmVGczDcNXiYXpbCz2UdBioW7TxjBcY9uIqbqfH7TDEZlu3jl+/NYXd3FxLwkotEoMUVj0n2rEyXWkjQ7l84qoNAtcfnsfF7f2oKmyfSHzOi6gKxqCBqMyHTwxNpGphWn8vjaRiIxDZNokAc0DTRdx2aW6PBGqOyM4LCaWTo6nZ3NPn64xHjWfvdFHV/V9PHg6aMZle1K2IGbzWYePX+y0c/yy/z+0wNcMacARVGIRCKIuo6q63T4ZW56cx+f3DCTdLd9OKsZxr8F/5FgYzabEzYDkiQxf2Q2i8bmo+v6EHn+Jze0sKvFRzSmcuHMgsSPZu2PlqAoSiIgyLKMSRL59SkjCcgqowcxzcKywoZ6D0+cN5aQrHLsHzfTHTBEMeP9mVgsRps3yp++qkUUBE4enTLkfNu8ERxmid2t/kRW8V0aboldreXIoczXt7agaPDsxhZOnZCNIAj87ot6grLKmFwX88oOdZv/USfO+GcO7uVMKkji3QHtK4A/r2tkS5OXB08fTZrTwrWvVxJVdMySgKLq1PeGGTswDBjP1p77ppVwTGX9wR7W1nrY2uBhflkqE/PcXDQ9lxSHhWXj8/jZu3uMHhYkBDsBcpOs9IdixLRDg58m8ZCtcbrTzB3LR1GR5eKHr+8EYFSWm+3NHsCYXfGEYvx+tSG2aRIlfvFxDY+vbeKdq6diMUl8XdfHLz46QETRcFtN3PdJDR3eKP1hhbtX1KCjI0oCHT6Z3mCMkyYV4LCYuGGhQe64+pXd7Gj2MyHfzXMXT+SMqXk8trqG/BQbNpOfV7a04Y+qJNkkkp22hK30pS/u4ZMbZ+H1eplfloLJZCIYjPHy1pYhw6XeiILdKiErGrluM3edUMGO1gCf7utE1zQm5idx/fxC6vqj/GVdAztbvEzJd9HUG2ThyAw21fdjN4tMzHezs8XPNa9W0uqNIAoC+sA1LkozNhT+qIqAkPDrAaOC8OrWNr6q6eO3p44iNz2Jh8+bnBj2jFcHLp9dwB/XNFCe6cRlOVIwdxjD+FfhPxJs4vTfcDhs7KhEMdHHiWcvuq5z2oQswrLK+Dz3ERphg/s+kiQRi8UYfxQ156c3tPDlwV4+ruxmapGb5WMykUSBQCCAy+UiEAjgCcn0BBRmFKeSm2JL7Oxb+sNEFZX7VtYwLtfFDxaVfGcAaO4P8/TXzXx+oJclI9O57+SRCXbUgZZubn+3CptJICQb9sk9skR5potTJ+dT3eZhVmk6ZrMpUa77tn5NnPVms9kMqndExiwJf9NUDWBXq5/+UIzeYIzsFCf5yTaCssr1xxRT1RWkIsuJV4a+sEZJkrHQ3H3yaD7e1c4JY9NZUpHGc5usHOgMUNcbYvWBPs6cnIPVauXmRaVsb/YYSgvioYVuV9uRckKKZsjj28wiX/94CQCnT8ln8ahM3tvZxgUzixh79yfoms7iigw+re5hR4sPkyhgNtpHdPhlTvrTVmxmifF5LqKKjkkUsFskOnxReoIxJFEgyWbCaXVx/rQ8tjV5GJ3tZMmYHHbfk4OmaUQiEbLdNsDHmAE/l0/2tLO50Uuqw8xPlo/gno8NenNJmp25D67jsjlFvLipmW5/hKm/WcsVswu4aUk5FouF4x/fQqcvjNUkUJLmoDsgo2gav1p5kDX70/j58WX0BaNcOy+fhWVJPPB5Hd5wjFllaRQGFB7/qoFdbX6qOoKUZji499QxoBjPwV/WN7Gr1U9pugNJNOy9r5xbQCgmkuayoaoqdx1fTkTRcFhMiWChaVrC3mF/V4DsVGNDdnjv7uwpuZw9JfcfKtUOYxj/DP4jwSbubRP/IQz+QQw2WRub66bNW8/PPzrIH84eS0m6MWAZF9oEYxYmTiM+Gk14dI6b9/d0GrTeNh+vXj6FTJch+hnPAG54Yy9t3igvXTqJvBQbLpeLR1fX8Oz6eqYXJeENK8RUSHV8t3zMS5tb2djgISyrfNPgAQzGmMViISwr+MMKckw11E4FyE02hkGvOKZ8yHHifjiCIOB2u4eU+ywWS+I7xmIxavvCXP3iTnKSLLx46eRvvd7xa/zQGWPoDkbRNfimtoer5hby/KYWNNHEq1tbeXN7G75wDG9E5dXLJlGa4eTFb5qo6ghgt4hce0wxaXaJvR0Bxue6mFeWit1u5xfv7DTUihcbmUKq3czt7+4johz1lAC4ZHYRF88pwTbI2C3ZYeF7c0sAWHXLAmOzkZ/Mcb9bQ013kJiqYzVJOC06QVmjP6zgVHWWjc5g6ah05pal8s7OTj7Y04UkCvzm1JGMz3Pz4Bf1/HFNPRVZDhZWZCSuSV9I5verDlLZ7icvxcbZk3PY3epj9f4erCaBX544gon5bl7a5GB8npv+kEykI0h2kp1tPzuOyfd/Bui8tLWN4ybmM93pJCQrKJrBXgtGFe5cXsZjXzXS7pdxWASe/rqZtbX9HOgKUt8bwh9ROXNSDo29QW5/t5pg1Lj3Ijq+iILXH+LLA72sr+2jJNNFcZoNWdWYWpjET5ePwDQQ2ONBI6LoSKJhj2G321FVFVk2TN4a+sLMKEr+1o1JTNXY2exjUkESaSlJRx0hGMYw/hX4jwSb+KBmfOcUl+SHI62aR2U78Ua8pCU5cDjshEKhxPvjx/KEY3xc2cVJE/NIsw8VyDx7VhmnTM7nlMc34A0rHOwKkOlKG/IZUwqS6An0ElU0HvisljNnFFOS7iSqaOSlOPj58SNIdZgxm81DsrDBEEWRc6bl47KaSHWYEsQGMMp8JekOfnnyCO7+uAa7WeSGRSOGuGfGEVeUliSJyo4QT62r5JYlZZRlOBMKCYNnbGyCUTbKcB49EN710QEq2wP86fxxZLutfFXTy4q93bR6I2gazC1LobItwP0f7yMQUUixmxEFEUFQOdgdojTDiaJq+MIKEwZcH9/b00UkppLltpCdZMVkMrFibxcOi8SVcwtIsYsUpNhYPDKDdTV9CdO1xLUSYPnYbH55+oSjnnMc5ZmHyqG/O3cipz2+ER04pjyVht4QO1r8qBo4LSJFqXZ++3kDT6xrBozhVF9E5def1nLC2Cz6gjKyqrGn1U8g2oTVJFDVHWVvq5c1+w1/GKtJ4rrX93LprHzsFgmXzcwvV9YQihmaeN/U9xOSVfKSrXxZ3c7LmxqZU5rCzhYfsqJxy6u76A3KxAaGjWIaRFWdyo4Q50zNZWeLj48ru7lmXiGzS4z3HewOYxLgumOKeHxtI/W9hsLCiEwHnf4ofUGZPe1+7v+k1pDZ2d+HWRJwmCW+rvOwpdHLqpvn0djtxSKJuG0mzn92B55wjEfOHsuUAjURMApT7RSm2o+4zoGoQk1XkPf3dOEJxzjQFWRmSQp3n1CBIAgkJX27/9MwhvHP4j9WRhssyw/f3p+447j4rl9PLMSHM6le3tLKlwf6aPVE+OnxI1EGfuwmScTr9WKxWDhrcg5rDvYxZqCfE9/1BQIBbju2jDtPGst1r+5ic30f+zqCBGWNc6cXcvviQ/MosVgsQQE+HJqmMSLDzk2HSeAMxsT8ZJ65aAJOi4TVfORxBguDut1uHli1i0BEZmVlJzctMZhkUVnhrKe347JKvHnNTHKS4K0rJyeCtdPpTAzAiqLIga4gqqajDfRHvtjfS3dAZtzAdbhmXiFum5kP93TijSg4rRK/PmUkNT0hTppchByNsK8jiNUsMqssHZvNwonjsnhibSNOiynRI7rrhJG0BxS+qvEwMieJy/+6E1XVcNtMxFQNWYVMl5nR2S5+evwoxhSmH/H9vws7Gj2YBHBYJHa1+jnQNRCURUP48o73qmnxREizm0hzmilLd9IfVpAEgVe2tlGaZuNHx5bxx7VGgPjxe1UEoioFqXaiqk5BmpWZxSl8dbCXZk+UcEzj+nn53LuqBk3TSbJJuG1mJFGgKNVOUFawmkQeOG0UMVXnh+9Uc+KEXH736X70AUZdzBjO4cVNLbisEgJG+XB/ZwBBEKjvNTZNig5/Wt/EOVNyeW1bOwtGpuMPx9jbHkAH3tnRQbbbQovXULW2mUSOHZXGO7u6aPVG+cUHe9nS4EUUYE5ZKk39EVxWiftW1nDO1BwunJ4/5FrKqsZvP61lTLaL0ydlc96zO+jyy7itUkIAdfKA0vO3Pe/DGMb/Fv+RYBOnNg/uR2iaRljRSHYY8y6Ds5e/hXkjs9nTFmDZuBxUTefC5w3pjtcun4xJEpFlmfOn5Q1xwvSGYyx4eCNmEX5wbAVLK5K5eUEBH6ZaGZXt5OEvG9jZ2AsYwWbF3i6SbCbmlw/Niv7R2nbaUTKQt7Y287vPDvDAmeOYlGNPHPe+08fzztYGzpqcgyAImM1mQnKYQFShwx9NSPbEs8G4F8/g7OeVyyYhqwaDCeDO5eXsbPGzeGRaYsd706JSJuW5+LCyi0BUZWSOk5G5LiRBx+V08KtTKtB1SHbaCQaD1HWH0HR4a0cHdxxnqAIvqkhlU0uYm17dgVkSWFCRydoDXSi6saOWVZ0Ml4UHTh/N+c8bJbfblo1i+ZhMTCbT32xEp7utSJJISFZp9hgGbUlWiSS7iemFyYQUw9+lMNXOliYvDX0RLpqex7nTcjn3mR3U90V4a2cHrd4If/yqCYdFZGJBMrtbPAC09ofZaxa57+SRPPNNGyYB3t7dSXiAUefWBR49ewz7OoIsGplOTDXmkZLtxk/m1SumoigKk3Pt3PZOFQ6LxInjMvl8fx89fpksl5lmT5SJuS5avVHavFGclkMK1a9ta+fcGSXsufd4VuxooizdwkXtu+gPKvSFYqy4YSbv7O7mD6trmJjnZtNAcLGbRXJdFpr7w+hAX1jBZha5Zl4hn+/vpTzDecS1bOkPs6vVz84WH2dNySE/2UaK3URBio31dR5cVhNnTso2Mmeb7Yj3D2MY/wr8x6jPDocDWZYTQeWhL+rZUO/htuPKOXFS4Xe+N97XiasCHFORxcJROQD0ezzouoY/GudEHR2CrpFsMxGIKty/opo3Mh34oio/X17O9KJkvOEYK/b18OHuThZUpPGndcYQ4cfXDQ02/5smaly2Znt9NzaTwNvbW5h80sjEbnJ8fjJlqYajqKoa5ZCMlCReuGIGL21q5kB7Pw6LiY7eABXZrsQ80GAYwUjmno8PMn9ECo991USXX+bjG+dQmuEkEomgqipTCpLo9MvMK0tlRWU3f93cylVzCjhjcm6CeBE/9s2Litjc6OGqY8qGiKVOKXAzuySZ8kwXPz9lPBc8uYGmvjD+qIp/oA/R6gmjKhqt3ig/ems3q0alc//JI0lJSfnW6xSRFd7b0WoIm5pFooqGAEwuTGJWUTIHu0P8/pyprKxspabTx542Pw6LyBlTcslNsnLGpBwcFpHlYzK57rVKOvwyIVnjz+eOZcOAYkOnz3DJ3NroNUpNmsrZU7LZ1erDJAhEFI097UG+PNDL6GwnP/+4li5fmFcvn0yKw4Ici/FNvQf7wFzNeVNzmV6UzNaWACZJoDMQQ1F1KjsCjMlxkeYw9OHizp/esMJbO1qwSBJ/+KIGh1nk2Usm89LmZuaUptLYG8JtgafOH4/NLHLDG/vIdFlYcd0MLvrrTlTdMAd89epZHOwMsLDUxaWzChBFEU9E54WNDZwwNoPyTCdlGU5uXVxKbpIFURB48sIJ3PXxAfa2Bfj1qaM4bkx2YiMY76MOYxj/avxH52wGy5c7LEaZIdUm/c1drqqqSJKUGLAMh8OJhdBiNnaQkZjGS1vauGy2EbiCssId71azpCKNs6fm4bKZefPKyWxu9PHbz2qpyHKyvdln1MMdDnyyRiCqsLa2j1MmZnP2lBw0/bubpYMX3m/7vnEB0lgslnjt9QuKmFKYxDEV6UfUx1dW9fLz9yq57dhSzpmSi81m441tLXy8u51dzf0c7Aqi6/CLE8p57KsmfrqsjCWjMhLn43A4+P5rlWxt9PBNo0Fe0IEXNzZQnuHkrClGkP6wsot3dnawob6fhSPSEAUGXDolBEEgFoslelU5yXbW3X5M4jskBmKDQR49xxB6jEQi/PHssfSHYvQEYyTZJKxmM7nJVn51ykie2tBMVWeQyjY/5z27nWSHlWcum3mErQTAskfW0tRnbEqumF3AF/u7aeyPgqbz6NpGdB2Wjm2jO6hw9pQ88lMdHD86A6vZ8OW55/SJPPLZAf60vpn+sMLobCdd/ii/X13HTQuLGZvlwB9VOHtSNieOzeDUJ7djt4hEYip/OX8C4/JcmC12znt6E56gzFcH+1BVFW9E4Ym1jTT2G940Kyq7EdBx2808+HkdNrOI1Syx/77j+XRvB+9ua2Jrk5cZJWm8uqWFAdufBPa1eGnxGFmqDswckc0b29p46It6TKIhWXPL4hKe2dBMpz/KY+dPxiQJLByRiqzqvHP9PFIcFsbkGsOYfr8fTdN4d0cra2t6qe8N8bszxwAwr3yoIVpZmp2DXSEKkm1DSAHf5iY7jGH8b/EfDTbigElXMBjkxoUl3LiwBOCoTpmHY3DfZvBuXlVVMlxWmvvDQ9SWX9tqUFm/rvNw8dwyIpEIFpPE/PJUPiqfnnidxWLBYrFw+ax8xmQ5mZBn9Da+zRtmMOLBY1uTh59+cIBlo9O5Y+mIxL/ruo7NZjuihOi2mVk2JjMxlyPLMp9VtrK3I4isiaiazuYGLxfOLEIUReaVZfLSN810+qKYRENxuboziI5hFR0PNvHPPW9GEbtb/Zw1OY/pRUk094Z4emMz78e6uHBuOboa4/3dnbR4o9ywsJQFI1LJTbaiw1FLmnFX0zgEQRgimgpGf8tqlshJlshJPlSKcbvdHDPWyeTCZNq9Ee5fVcP6Wg8QZsI9n3LbsWUUZ7iYXpzCw6truX7hCFo9hz7/ze3tBGPGvd/Y4EHVwGISqO4I8PjaRp5YIzF3RAZv7mjn+DEZnDslF5fDxp/X1qFohhqA0yzijSisq+3nte0d6LohKpqbbEMDkmwSoijQ6Ze55e19XDizCG9E4f5Tx/PerjbOnpLLaZNy6AgL3P7GDkQBFpan8r7WBUAkpqLqOt6IilMz2IULR6TiC4bY3xXipU3NRrl3RgGvbGlBAjJcFkrTbayt6QPgpiXlRCIRFleksavVS0GaE01VGZvjorEvQkTR+d1nBxmZ5WDhiFQ+2NPF4ofWMLUolcfOG5/IhAFOnZBJf0jmxHFD1QXifT2r1coNx43mpmUi4XA4sYkDI2C53e7vfO6HMYx/Bv9xBQGTyURycvK3Bhir1ZowQ/tbJSuLxYLZbObj66YTVVTe3dNHZyDGnR9Uc9LYDEQBHBZxSFng8Gwknm2JgsDcsn/ODnf1gV56gzHe3tnJfWdNTZQLVVVNZGWHw263J4LNL96v5L1dHcQUnXH5Sbz+/TlMyEvGZDIyvqXjc7jjuFLSHWaOG5PF9a/t4dOqHk4cn821C0pwu93srOvkzR3tXDizkDOmGv8BI5DPKU2hJ6Tgk3VsFhOaJtLQF0HVdOaWpxGNKfxyZQ2qprOiJC3RB4pvDt7f2cqvV1Rxw4JizpiUgyiKiVmnwVhf28+Kql5+feoozJKIxWLhvR2t9AejnDo2jbd2dpBsNWGVhMSQ5MOrjYzggqm5fFXdzdvbWofI2gQGMjMw2F4mAXKSbOQnW9B1gyzw6d5OAPZ3BPCGFL6/oIRrFpTxzvZWuv0R9rQH0DRo9USHDJm+sb2D86bl8czlM5A0lR++WYmAwutbmwlEFN7c2ozFJHLH0gosQLS3h6cunEBfUGZMbhKaZOLNba0sG5NFQ4+fNLuZ8XluNE3j5td30eaNGpIzkoCug6YZmyQdo+m/Yl8PFsmQOrp2obFJWTQqg+UT85FlObHBykmy0BeSqeoMcKArwMq9XcYgrQbrDnbR6QkMoemnOizccpiqBhglsq0N/fzl6yZ+tryc0fnpKIqCpmmsre1ndJZzyEZhGMP4V+L/zJbPZjMG0hRFGbL4WwYJXEaj0aOWqr6u9/CLD/dz67JRXDCjEF3XeXZjCysqu9jZ6KLNE+HJDS2oA3IfzT0B0uzGri9+rIv/ugtRgFeumsXhlYO44dnRKM+D/W/iuHx2Id80eDl5okFIuP7l7XT4ojxx3jiSB4QNDw+w4XAYQRCQJImdzQaNVRJ05o9IZ0rRkUFvUkEyt769j4/3dlOYaqPZE+GkcZnkpSfT6Ytw5ct7EARjd/3YRemIooiiaKyr7WdWcRK3HDciYQwniiKbfnYcOjoWQSMS0TlhbCZRRcMycN3BWJwCgQB6TCbJZqa+x2CEKYqC1WrlQG+MK17YxonjsrhzeTk/fq+KoKzR4Q3z9vXzAfjFe3tIcZgZm2llzYE+JFHgy5tn8qP3DvB1XT+aDiFZY39XkK7AUMVhh1nkhLEZfLCni9hAlLhmfjFXzc2nPxQj02Wmd0BIUx3wgHlqQzPPb2rm0pkFPHD2RG5/cxddvuiQbp4EOC0SY/OTcLvdjLdrjL/nUxRNJz/ZQm8wRlTVKU2zYZZEBFFgwYNfEZYVvj+/iJuOHcm7uzp4Yk0d1y8ewbULyo5Q5O70y3jCMWRFIxLTkESBrU2Gn4zVJBAeoFf/5ZKpqBrc/cE+0h0mWrwR7j9l9JBM/onzxnHsHzejaIbQaFxPzmQCRYNnNrRw+3FH2jk394f5xUcHOHlcFgtHppHpsvLR3m76QzHW1vQxIjsZq9XKxoNd/HpVLXazxMqb5hxxnGEM41+B/7NgEzcU8/v9Q4JJIBAgqJlw2cwJE6dIJDJk4a9q96OoOp/v7eD08Rk4HA5G5qawsb6fsyfnMqM4melFyby8pQ1vRCHLbQxHhkIh6ntDvLurE29IRhQFwnKMN7a3Udfp5bYlJYngdrThtrg6QLzRH0eGy8LbV01FEIyp/vreME19Ic5/difXHlPEaROzCYfDRwSqOLX7V6eMojsgf2tmpWkaP32vijZvFJfVxGPnT2RXiwfTgPrCc+trcdkkdB1uXVJCJBLB4XBw+1u7WVnZjq7rmCSRNbcvIivJ2Lm6bIduvSzLXHdMMcAQZYd4j+bY0RmMyXGRnXRo9xyNRqnq8CMrGhvq+nE4HAPaarCrxctDKyq5YXE5J0/IRhR0RmY5uHBGLn9e18Qtb1fz/flF7G71EooZ5m17Ow+V5eJNdFXTuWpuIf0hJTE0u6amj5uWlCOJItcdU4xJFOiL6IzMSeKGV7aj6SCr8MzGFv66qRWbWUASjOByy+ISfvdlA4GoyuLRmdx32kQ0TaOzz4PVBIoMrd5DAa++L8LxY9LZ1+4jFI0ZPRcdbnl9F2sO9BBVddbu7+aa+SVH3LPHzx1LWNZ4e1cHH1Z2ow3YSBckW1kyOp0rZxcQiWnkp7s4+YlNBKIK4ZiG3Sxxn6jxTb2Hnx9fztTCFA52BRNmb7NLkhEEkd2tXhxmiXBMY/XBXk6bmE1F1iEmWrs3zMtbWvFHFF7f0c5LW9v4wcJirl9QzPqaXo4fm2mUPq1WRmY7GZfjIi/NOcSpdhjD+FdC0L+rw/0fwOHlNG84xnnP7iQgq+y6aykw1EUQQFZU1tX0M788NTG/kpycjKIoyLI8pLxzuDzHda9V0h2QuXx2PvPL00i2mznv2R209BsaYakOM787axwmk+mIzMZsNgY9ZVlOBI3BVOj4Zz3+VSMvbmlFUTQQBOaWpSQa6fGe1bchPg90/B/WYRIFXr5iGpc9t519nQGS7SZeuXoOGW4Lc36zGrMo8NUts1B1gee+aWZxRTpjclzY7Xai0Sjbm/r5/eoGIjEVT1jhjWtmU5zpTlzTUCiUKOXF+zQ13SGK0+xkpKUkrLfj2NPqY0O9h+/NyqcnIPOLjw+S5rTy61NGYTXB17V9PLGuke5AjEtm5HPJrKHzHoFIjBP+tI2cJCtvXjmFA51+fvBWFZ6wgiAYGc7hcA44XppFEtnNhPwkbjimkBy3lV9/Wsv2Fh8Os0i220JdT5gMl4XOAV+WeOASgDHZDpq9UfwR43mYmOfiwTPGsK8jwD0fH8Q7IH/gMAuEY0ZwkAT48fLRPL+hnvF5Lh44bRQn/2UbrZ4oJgHOnprDT5cdUtc+/P52+iJsbYvw2qYmqjr8WEwi1x1TRJbLwoNf1DGhIIUTx6Txk/f3IwpQku6gP2iQLBwWid+eNopZxcmc/cwOIjGVT2+ciSRJ3Pb2XnoCMkl2Ex0+mUfOGpNQqHhpcyvPb2ohEFU5a3IOWS4L7+7u5OwpOazY28P9p46mLM3Y7MVZovHnedilcxj/LvyfZTZxDC6TCYKAWRJIc5rJcFmMEo6u43K5cLvdCRaaxXTITjkOr9eb0FmzWCxIkpTIJAY3vM+ZlsuWBg8LRqQlmFA/WFjMrlY/n1b30B2QicaUIQFqc4OHP6xp4MKZhZw0Nj1xroNtrMEgKzy8up4vD/ZyyoRcrKLGazs62NV6qMTyXYEGDgVWh1kioqiEIlGqugKoumE5PTo3iUhExjEQZPd3BijPdPKDxeVYrVaufnE74/OSuXpOLrKik+Y0c/PCCrJcFna39PLBrg6uXWhkP3FzOrvdjtPp5K/fNPGbFVWkOS1svnNpItDE79GjXzWyq9XH/u4waTYRfyRGIKpiMxt/nz8inROnltLV24fVNLRPdbAziDeisP7WOYn7/dr2Drr88oCnjYBZBKtJIiAfuvbxrVBsUBwKRmP8ZX0Tbd4o/UHDRmBkpoMdLX50nSHluKWj0+gPqRzsDoIgcPnsQp77pplARGVPW4ArXtrFudMLCcdUjilPw2YW2N7sQ9EUitNszCpJ5ZpF5cwrdZHtNkq8/3PmeN7Z0cHn1Z3MLU3l4dX1LKpIY0phcqLPFd/03PJ2Nfs7A0wrSkJHRxB0Pqrs4szJOYRklcm5TqYXpRrPe0QhGtMM8kKSFW/E+J46xYRkFZtZ5P3dnTz3TQt9QTkRELf9eB4AHb4IaQ4zv1tdn+h7ra3t560rp5DqMLOhvt/YqB3spmyAABP3tVEUZQhbdBjD+Ffj//zpGrxgt/aH+WBPJ787YzSl2SmJzGLw8OJ3Ib44qqqa2LEJgoAuWYlFQ1hMEksq0llSMXSaXdV09rT5uXB6HsGomtCe+qa+n9e3t1OYaickq7y8uZkF5Sm4rRIXPrcDb0ThnaumYBm0sLZ6Iug6nD4hg3F5buaPSGNk5pGDdn8Lhu2vjsUkMjnfTVVniJeunMm6/Z2MzrTz7tVTeW5TM7e/t5+RmU5OHJ9FjsvMVwe6+XJ/Nz9cWsEfvtpFU1+Il7Z28Ivjy/jNp7X4oypnTEgdQjmOX//SdCeKBt0Bmb5ghI8rezl+TDoWyVi50pxmNB3WHexBEgUumF7AjceOwuUwIcsy0WiUYDBIsss5RIZIFEV+9EE1mqbz7MWTSHcan33LsSOJKhoFKTa2NnnxRxTQNfZ3D1aM0DGJho+LWTKUo8vSHext99MblLFIIqk2ExFFRxQEitNt1PWEE1nJjOJUHlnTwGWz8nljewfPbWzmgul5fF7dQ11vmFavTE1XkMq7jzOYWYrGoj98g6zq3LK4hPnl6Xi9XgpSjKzhN5/W8Ob2DlQdLJLA+joPb25v57Xt7ey7dzkhWaOho5ucJDtmSaA4zU51Z4CaboOyHpI15pWl8MqWNkrTHVw6K58b3thHks2MyyLiCav8ZPlI7v24GlnR6QnI3PvxAX6wsJicJAvPfNM64ChrXB1poNq7u9XH3R8fQBBEJOGQW2pTX5h3drTz3p4urCaRZWMyOHeA/u50Orn9zd1sru/h9EnZtHui3Lasgrz04exmGP96/J8HG4vFkljs3tzRzoZ6D76Iwl2npGC1WolEIokehNPp/FZrZE3XWbm3mzmlKaQ5LYn+SVBWOPbRzYzMdPLC9yYd9b0vbW2jtjvEjhYfuclWZhYnMy7PzQubW9nR4mfJyHQ6vFEOdAd4fVsbV80tpLY3hADU94YYlX2IKvrrU0fS4YsmtMtml6YelVQQR5c/SrrLgnRYj8g2SN7mmYuN8/7qYDd//KqRMTku/BGFNQf70IEOn8zWJi+Xzconw2khrKiIosifLpnGK980cdOxIxA1hTMn59DuixoyLJIx3zS4TDajwMGSUel4wwp3vruXL/d3UduVyw+XGMymfe0BBGBGUTImk8RNi0qwm4xMLL4rjs8VWSwWZFmmdWCO5MRxmRzoDJIxIIoKkGTR+fWpo9F1nXW1fdz6dhUxdWhVNxQbyHqBVIcZTzjGuto+kiwSIzKcHDsqjbd2dmKWjH6RL6Jw8cxcXtrcjssqUdMdJMlqIiyrBGWFoKzx4qZWxuW5aPdFsZtEbjymkHA4zMd7OnliXSMhWSMnycL88kObku1NXg72hHh9W0eCbCCrOisqOw3KuGgMgk66d5VBt7ZInDoln4fPn8o5f95AdWfAYKQB1R1BcpOtVGQ6CMdUdrX6iCoaIzIcpDsllo7N5Efv7gUgN8VOQ2+IpzY2c8LYTH6+vJz3Kns40OnHbhY5d2ouP/nwIBUZNnqDCpGYyq9OGcndK2oID6SDr25vZ2ZRCmNzXVy+cFSidB2NqfQGo+g6PLuhBUXT2dXq56nvzaB0kE7dMIbxr8D/ebABgwYci8U4e0ouqg7nTs0hEokkBswissxP39tHqtPGrYuLjmCnmc1mPtrVxlMbmvnLhha+vG0hgUDAoOgKIsVpdsymow9oCoLAr08eyZYmL7vagvQEIhSlGbTke08ZzZb6fpaNzmBUlpO3dnUkrKnPnZLD13UeVlX1Dgk2ALe9W21odF02CZvNRrNPobrNx5wiJw9+Uc/YHBdnTMphY30/D35ex5zS1IQMDEB/UObXn9Zy6oRs5pWlGOyxhn6skoDDIjExz01jfwiX9VBAiqk6E4vSuf3E8Ykh2cJUBz8+YfTAK0zccOyoxKxFHI9+Vk2m08RJ47PQNI2HB4YAv67z0NYfZHZJSuK1vzypgoNdhgz+nPIMFEVJZJ+Dg2l886BqGje+YSyar18xGYtJOoJdqOs6Hb4IozIdTMhz09Abojd0ZGDWMQKJRRKIKDq+qIpKlLd3dhKIKiiqBYskcuXcQl7f1o6OkUV8vr+XyflJ9IViRAfSgTSnGQQBt9WE1STy0d4uTp+Sy88+Opj4vKkFxrCtxWJhTXU3v19di90s4rBKZLss9ARkfFHV6CdJxsI99q5VifeHZJW9Lf3YLCaSbRKaphssNEVnY72HGcVJnDYhC4fFxJ/PG09UF5le6DLM11SFr3+8hOpOH/PKM3lvZyv/s7IKl1WiON3BLYuKEQSBr2t6+e2ntXQHZKraLJw9JZsP93TzVU0/2qBr3Nwf4fmLi0lzmvnl+7tJtpsR0PjrplYumVXI8rHZ/Ow94z41e8L8dWMj95w67oh7MIxh/G/wXxFs4nTn/BSFWwd20SaTKVE2a/OEqWzzE5I9/HCRMT9iNptZX9PLb1cd5PzpuSwsTyPFbmLhaCNQ6bpuDFWaRUZmOajuCCXeZ7FY2FxjiB3mpDjJcFs5YVwWJ088RCaQZZlsl4UTx2ViNpsZlSPys+xDu70rZheS7bKyfOzQ3pEoCKTYzQSixpCdLMvc8NJ2/JEYJ4zLZEuDh+qOIGdMyiHFbkYQhERZKb4Q72z189XBPj6t7sUiCdx1yhju/7gaWdGwmUTKM+ycNTmbGxeUYDNLhKIKTps5oUYQjUYTPavBOPz/+yMxntvQiMMiccqkPFTlUJYzryyFeWWGUOUP39rHfSePYkphMg9/2YAvovCrM2yMy7ThC8d4flMrs0rTmJLv5sPKDrr9MrNKUpiU7ybNaSbNYU6UJg8vhb6/u5O/bmph2egMnr90MnN/9zUmEbJcVhRNO+T4KUAkpiWm8K0mAW9YwTTQ59Ex5lGmFCSxal83DovIlIIktjcPMAcFgWS7CX/EsJLe2exlYp6bLLeVZzY28/w3rUPOK54VyLLMhOJ0MlzNJNtM3H78GMpTJF7e3MqITAdFGW5e3tzExnoPHT4ZAfj+vEJW7uvCaZXY19TDmBwXO1r8jMt1sbXJi6bD1mYfv/q0jucunsjkohTjudN1YorGhS/uJqpoyIpGcZqdP50/njMnLyEUCqHrOrKisb62j71tfqKKzuySFNq8UbY1+zhubBbv7mgj2W7mhoWlvPRNE93BGPs6/JRlOHj+m+YEa9Fhlli5t5O67hA2s8DkvCQmFCRz07EV3/FrHcYw/jn8VwQbODTsGZ9oHuzJUpjq4PLZ+Tjt1sTrY7EY/QMlgKa+CDlTbfz10sm4XK4hpTZN09je7MMXUfnzuiZuO34MX9f2cdULu7GYBLbfuQRRFFFVNfHfg+F2u3l1SzNvbG7k9mNLmZhveH6kOMxcNHMo2+rVrW28t7uTHy0rY1rBobr38WMyWX2wh7U1/URVnQeWG8rWY3JcvH3V1MTrXC4XoVDIKKuoh1QIJhSkkO600OqJoGg6VZ1BbnqzClnVeOqC8UwuTE54/kSj0USWYbVav1NY0W0zMyE/ifIMO5IooB7lNfd/UoMnrPDK1laumV/E0tEZ7Gr1MSLFCJDravvZUNfPFwd6afWE0XUBh1lkU4OX5y6ZyDMXTfzWz9d0nfd3d9IbjFGabiemQVjWyHBZeP/7U3lhUyt/XNs06HwlfBFjyDOuDiCrkOk2c/W8Qt7c3sHNbxnXZU5JKudPzyWiNHHu9FzmlKax5kAPP/3gQIJ1tqPFT6YryuSCJDbUeRKfc9H0XK6YY2xqojGV+m4fJ47L4s/rGvnJ27tZef0Mvj9AFQe4eVEpgt5Ask1kT0eQD/d2MyrTyfYWL79cUc2pE7JJsZs4eXwmp03M5uPKLirbA1w7v5BITOXal3YzoyiFGxcWc+6zO2nuDzMy04nFJFKSZlCRVVXFZDKM9l7d2spbOzto6osgCfDoWaO5+e0q2r1RGntDmCWBK2YVsGxUKu/uaCMU03h7ZwcPnT6K3CQroZjKpbMKyHFb+NmHB1B0KE6y47abuHxWHnbzsKfNMP71+K8JNnFYrdbE5P3gXfBpk3ITjLS4vMbxYzMZmeWgKPXQsOLg98T7Br87cxzXvrqb93Z3cs3CMsOHXhKwm6UhjWxVVdnS6GF9bT/XzCvEaTV6Lav2duCPKmxv9jIx/9u9Phr7wsRUDSU2dPd+1bxCzpmawwXP7SSmaOzvCjAqZ2hNPD6R73K5+Hx/HyYRzpiUw4+WlmE1Say4bjrNfoU11V1cOruQh74wGEfXvFbJ21dNpTDVfgTTLW6YZbPZEnNNg7Gmqp0Ndf1srOtnSUUqEwtSEn+LZ1mjs1ysr+83FIftdi6Zmc8lHAqyiyvS6PJHcdsk7vukDjB22udNyz3qNQpFFVbs62ZxRRpms4m+UIwst4X+sMIHO9uYkO9mXI4Ti0ni6Y0tifdpOgQHsdQk0Tg/TYewrLKj2Ueaw8zOqIKq6XQFDfHN2cUpPLamkRte28tZ0/Kxm0WsJoFUh5nanjDeiMLSUensbvElfHi2NXu5cIbxHe9fVcueNj9nTc5helEyS0Yd6uO8vMWwz/661sP2Fh+iACeOzaCpL8KkAjeyqlHbE+Lrun6+umNRYiNwxpT8RK9sS2M/1R0BdjT7eOrrJkNx2ibx0JmjSXeaqWzzo2qGZXe3X+b2d6uYmO/GIolG0NXBbDbR4jEGV+dXZDI+P4VzJ6bxaXUP+9r9mEwiE/PcXPriHsoyHCwYYYjLLhmVQeaXjXgiMbxRhQNdQQ50BclKGyYIDONfj//zOZuj4fDZG0EQcLlciWByOEnA4XAkJGGqO/z4AwFGZ7uGzDy8tL2bZLPOKROyAOj0RzGLAnaLiXtWHOTsydnMKE7hpjf20tQf4caFRSwdnWn0fax23tjcxEljU7FIh/odu1t9qJKZKQM2AYqq0ROQh0h+VLYHmFCUjh6LsqPZy3u7u/jBwiLSXUcu/mCU+YKRKB1BnaKkb1ffjWk6Jzy+mYii89tTRzK3LBVF04ec32CYTCacTieBQCBxzXr7vRzz8DdEFA2TCNfMK+L784uGvO+uj2tYua+Lkyfk8MuTKuj0hrn93Wom5Do5eWIef15XzxWz8xmX6+bnHx2goT/KK99CxAAj+3t/dydTC5P40dJy6ntDRGMqP37/ALKi8dnNcxKZ2ZUv72ZHky+RcY3PdVOS7mB/h88QvOwMkmKXuHFRCceNTMdiEln++Bb6QwoXTc9jS5OHZk8kMb8jAD9bXo7LIjGnLIWT/rQNRdMwiQLZbisxTafdazC9Zha58YRVnFaJqKLx42UjmJzvRlbUAYq+yOyHNpDpsnDRjFwe+Kwel0Xio2unsb3ZxzEj0mj3RXl6QzPfm5lPeZYLl8tFV5+X/Z1+JuUnUdMd5NE1jexo9SVmfwRg0cg0zKKI2yqxob4fSRR59+qp1PYG+en7B0hzmDl5fBYPfl5HVNFw2yQ8YeP9kwuSaOgLsfL6GXR7I9z8TjWzipJp8UbY1xHAYZFwWCTmlKZy3YIyqto8vL+nk2VjstE0lfnlqSQlJf1NcdxhDOMfxX99sHE6nYnduSAIQxSfgSOcBcfe9QmKqvPxjXMpzx5qsXy4lhfA81s7+dOaWpLtZj69YQa72/y8vK2T35w+FpNu6EaZTCZsNhvRaDRxDH8kxqUv7KYnKPP7M8cwrTAJSZKGNL9f2NLOi980MyHfzSNnj/tW6narJ0K224LpWwLFtyEQVXl7Rzvv7OrAZpZQNZ2/XDCe9AE23t9za896ahs1PUZD324SmF+exq9OGUmH37ApuHxOATlJNkZk2BBFkf2dAX703n78EcVozOs6Ral23rtm2t91zh3eCE9uaOacKTmMyTlErPhwTyfJdhMLRhzKHP6yrol3dnXgiyiEBrLFDKcZf1QhqugIwNMXjWdOeSZvbmthV4uXd3YZ4piZLjMXTc9jZ4uPr2r60QGrSeSnS8s4Y3IOQVnhjL9sIyirOK0mwjGVSEzDYRbwRIzPclok5pQmk+GyctqELJJsJm5+ax+iKPC9WYXU9QQYleVi+ViDNPLF/l5e397GrUtKGZ3tQtcNm+dku5nKdj9rDnrwhaPsavVz6oQsnljXhCjA6ROy2dcRICirPHPxRM5/bieSAIsq0nhjezuCIHD7ceWcMTGLup4Qj61r4mBngCy3hbHZTr482MeUgiQumF3ChU9vQcfQkDObxITV9xlPbqPTF+WKOQWsrTUcSK+aW8DxY7OG3B9RFIeFOIfxb8F/5fZlsMx5MBhEURQCgYAx9R6R+fWqWj7Y3XlUC9vROUZT+q6Pqhj9i1V83RTE7XYfNdDc/fFBNhzoYWZpGj89YQzJycnMG5XHg6eNxMyhMl788+PT9mAsRFMKkxAFgQc/r2dbs+8Iwc/JeS7SnGaOKU9F07Sj9k/e2NnJBc/t5MY3933r9ZAVlc6gdoSEjssqUZBqA+ISO4fmLkwmE+/u6uDcZ7bT4gmzqaGffe1H0sZ/d+YYUu0mJAHMkkh1Z4Alf9zM7e9WEZQVPq7sYmSWI7HTHZXt4renjWJeWQqpDjMZTgvXHfPdfkSDkZNs464TKhiT4+arml6Of3wzr21t45QJ2SwYkY6maayo7GJDXT+fVHWTZDdxztScxPt7g7GEdIsA1PWECIQi3LeyJhFo3FaJi6bn8fauTkySxB3HlWGVIKZo7Gz1sWpfN7e+XYWi6YzNdfPEeWM5e3IOTouIIIgJG4B0p5npRclsafTwhzWNnPvsTup6w0Rklce+auDTql4WVKQbdgvAnjYf3rDC9mYfAL9fXc9Fz+9iXU0fj6yuZ/X+LjKcZpwWkdI0O7quYzWJZLrNtHqjBGUVh1nkx8tH0e6N0OOXuWFBMQXJVvoCEf64pp5Up4WLZhZSmm7n5kUl3HZcOW9fNZXbjyvjsdUHGZHpwCQa5bVITOPJDUYp0hdV0AV4aUsbVpPA8WMzWTRo3mzF3i7OeGobb+/q/rvv5TCG8Y/gv65nA0av5fDgEJ+bqesxDK12tHg5fbLRF1AUJUHpfed6Y5r6pD+swywJWPQYz609wN72IHedUD6g3yWAIPBxZRepDjMfXDudjBRjN/f8uhre2tHGj5eWMblgaO16cE9EFEXuOqECi8XChpoeyjPsQzIuXdcZm+Pk5kXFTBro8xxOPgCYkOPAYhKZVvjtdfLTntxOmzfKFbMLmFmczP98XsdFM/I4e0oux47KYFZxMg6LlDgvgGte2c2Whn7cNok9rX7+vL6ZQFRhWlEK6U4TWxq9nDEpmwum5fLQGaPxhpUBdWiZS/+6m1GZDmaXpTG5KAWn04miKHy4qw1JFDl2ZCr3nTLqCOuB78KaA728u7uTHx1bSnVnkCmFSUiCoXjsDR+6bpXtAZ79pgWHReTYUenkJdtYNjqD1ft7affJmEQBUTAGcbOTLDyzoYWKTCeWgYHPLJeFwlQrK/d147ZKpDjMLBuTzqNrGhAEjTMnZvPAF/U09IaIqTohWeXrOo+RHRSmMKMoifd2d3HqhAxOHJeNzSSyuy3A9iYv5RkOXFaJJy6cxGNf1iGh88sVB1GB/zltFNfOL6Ii08G+jgCekEyqw4woCrhsEhfPyOfLg71cNDOf3W0BHlrdwH0nV3DrO/v5/epGxue6uHxWPna7nTRHBJtZoskTwT/gVPrUhhbSnGbykm2cNSWXaQPyR4IgYDVLVHUG6Aka9G5RELBKoOg6aQ5j4/abU0by3DetbG7wUNke4KkLhxI32gYsqNGO3JQNYxj/CvxXltEO78vEA0lcDv31HR2My3UxMS9pyII3WNcpHA7T4zWCQ1wx91enj2VsjouSFCND+WhfPzlOgenFKYmy0/LHNtPpl1lckcrDZ3/3rEF8WPPbSlbv7erg1W3tFKXaePCMMQk68t+zQK+o7KInKHPprALOeGobLf0R7lxejt0i8cc1jZw+KZur5h49o9AROeb3XxOUVe47eRQnjEnjgc8beGtHO7lJVjLdFqo6giTbTXxvZh572gPcfmwpGQN9JFXTkEQRQRCMQOcJ8+aVk7nutb2GL8wPZ2Mxm3E6nd9qFeF0OjGZTAQCAVRV5SfvV9PQG2ZCvptdLT5GZ7v45ckjCUYVnNZDe56wrPDwl41MKXAzvSiZfR0BHl5dT4snQnmGk4tn5vHOrg62Nx96PkziwBCsrlOR6cQbVegNxEixm2jqjyCJcEx5Gqqm44sqXDO3iNqeIM9ubDEYfRdO4IqXdqNoOjluKx98f2oiS1U1jZe3dfDHL+uxmURW3zwLm8WMy+Vif1MnN7yxj4iics6UXM6dmsMT65rY2uhl+ZgMrpxXfFQrhstf2oU3rHDqhCxe3NxGIKpikiDHbWVSYSo/W1pKf0jGbTPR6Y/y+9X17G4NMCLTyaR8F7NKUnhmQwthReUv54/HZpbQNI2N9V6qO/08sa6ZacUpdHoN+ZpR2U7ml6UyvTiZe1ccZGpRMudMMTZqqq7zzNfN5CZZ+NP6ZkrT7bx0zby/+XwOYxj/KP4rMxtRFElOTk4IQWqalihpiaLIBdMMKf/H1zawvyvMo2ePSezo44sbQJLdjKyoFKfbae2PcNf7+5BEkR13LkLXdS6YVYTf72dTfT8vbG7jR0tLUTQdUYTjB4ynfOEYn+3v5diRaaQ4LEOEPeOZzOBA4w3FCCsa5blpjMl24bJKHDMiHYvFgtVqPWo572h4coNhuHXqhGzevXpoP2RWcTIHuoJ0+6Nkuo8kGsgxBadVQhDgrGlFRKNhfra8nCnFKTR2BVg+Jp0/fd3MlkYvb+ww+j07WvwsHW0cSxq4llFFp6UviKIZU+/HjUrHJIm8vLWDHS0+HjlrTCLQrq/to6ojwBVzCojIGveu2MXJUwpZMjoLRVH40dIy9rUFKEix0hc0Zo6AIYEGwG4x8bMBavh1r1XS6Y8SUTRcVhMXTM/llAnZLB+TwcJHNiX6OKpm9K8A9nYEUFSdVIdImzcyQJM2TObiopw3vbmXW48zxDOtJhGrScQkGsOiLd4oj6xp4IeLS3noi3q2N/vo8kfRNR1NN/pE8WexJDuFHy8t49mNzby9owOXVeTcKTmYJZGTxmexp8VLeZYbm8kQmHVYRMySxDMXTgBBYPWBXm5bUoog6DzwWT01PWFqesLUdvlItpsZmeXkxoUlFKW5WF/rId1pZm1NP1uavKiqTm1PiFvfruKxc8ciiiILR2UyrzyVS2bmk5qcxL0fVaFpKl9Ud9PYF2b+iDR+c5ox5NvmiWAxCfgiKu/s6iAka/ijCp3+vy9THcYw/lH8VwabOBwOxxDl4cFZjK7rrNzbQ1dARpWsoEaPkP4HsJgkPrhuNlFZ4cY39uCNKEfIqH95sI/eoMxbOzv504WTCUejidLXi1vaWFfTR0t/mJsXlx51FmcwrnxlD0FZ5c3LJ1OYZkvMmcQFNv9escPbjy2lOxAlxXGkTe+uVj8/fn8/GU4zH103Y8jfjLKKyPIxGZhFEVWNJTKw5SNTOXtdA5/u7+W+kyoIRFWWjs7AYRZZXJF2xOe8srkFVQdJhK/r+9nZ4mPZ6Aze3dlGbzBGY2+QwgHa+R/WNNDYF2FyYRot/QF2tvqo6alm0cgMars8PPRpLcePyaAkw5mwKv5bWDYmnS/29/HDxcXkJtlwDAQmi0nir5dM4EfvH6ChNzzEq0ZWdZwWEW9EQ0AgL8lEu8+wB4i/TtXh6a8b6A8rSAIUptiIDXJse39PF1fPLWJjvYcWT4SYquO0SIgivLWjnZhgYn9HDWsOdPPC5dPJcFnY2eLnyfUtPLqmkSfOG8eq6m6eWNtEhtPCz44fxXWv7sIsCWz50TxEUaS+J8R9K2sIyQqqbgRsp0UkHNMoTnOwtyOQKC+OzrKR6TKzcEQqhSk2XDaRmUXJ3P7ufnqChzYvCat0k0QwGOTuk8fgDwTIdVmYP2ALLUkS/YEoN7yxF8sAgeD784uwmUQ+rOxi+fihs2PDGMa/Cv/VwQZIsNDMZnNioZckiS5PgJklyThtZlIcFvz+KLqu43A4iEajQwKCLBuT3Y+fOw673T5kVsdkMnHDgiI2N3pZMiYbQVMRBFsiaJ00LpNOf5QTxxmsnbjaQTzobW/ykp1sJX+A7pxsN9EfivGzjw7Q3B/h4bPGUJLuSCwEcfvev1W9nF9+5OKvaDq/WVWDzSQiCgJ2i8Sf1zVS3x/hNydXIIpi4ri3LjHkb+LXxO/3I4oiJWkOdHRG5zh54rzxiWP3BKKJMlocE/NduKwmJuS6OHtSNrta/DgsEj9ZWk5lm4/8ZCtbmzw8s7GFmcUpNPS209QfZNmodNYc6MUXUZnxq8+wSBKarqNpOseNHmpVvLnBAwK0eyOs3NvDr04dSfqArlxLf5TGvjD7O0OUH6bVNTLbzXvXTOON7e38alXtkL8FB6jOoqDT4YthNQucOSmHXS0+9ncagpjnTivkqXX1mCSj5/HCJRN5dVsbK/f1oCg6SUlJXL+4glc2NbK92YvNLOIJx/jNZ/WGA6xZIjYg4FqWnUK4spvwgNr0Q5/X0eSJEJY1cOg09gbQMQJhHFluM7KiYRJF5LiG2WWTE9bmNd1BHGaRy17cRYbTworrpiOKIje+sZcuf5RXt3bgCcfwRWK0eqMUptp5Ym0j3QGZnx9v+P30+/xsrOvntIlZpDitWCwWQ2fQImKzSGiaUSk4Y5JBwDhh4BkfPFA9jGH8q/BfH2ziGNycVxSFVVU97GsPUJ5pLKRxGZpIJIKqqjidTnRdTxiUAQkp9cGlLLPZjNumcOyoDNCMADU4EDy7sYXtLT5+unwENpuND7c3saamlx8sLKHDF+HODw+Qm2Ll+Usmo2kaz1w0EbfbzUmPrkdWdQZUWrBYLIlB1bgnjqpp3Pp2FUVpdm479kinxcPR5onw0d5uctxWXrt8MplJVhb8fiM64Fk6grSj+F7F3VDj+P1ZR2YVr25t5a+bWpk/IoOfLy9DVlR+90U9G+s9nDkpm5sXFSOKIu9//1A5b3pREjtb/by/u4sev4zDLFKSbmdbo4ezJmVT1xumO2A0yWOqRljWqOkJUd8TpDTDUMGWFZV7Vx5EEoQBerPK+to+TptoLH7FaXasJpG8lKFBUNM03t7ZSVmGnXOn5qJqOquqeugJyDR7Dg3p2kwCoZhOJKbzwe5OkuxmbGZjGPLZDQ2YTQLXzi/isa8aWLG3i6WjM8lymen0y/zyo308cOZ4dCVKpy9CabqDr+v6AQwZ/3MnEVV0FpSnEI0p7Gruoy8QpbI9wIhMF1WdISwmgUBU5ZEv6ynPcPC9Qf4+TquZe04cgQrc+cEBNB2ykyyJEp3VJHLjm/sMDbawgqYb1NFTxmfxaVU3iqZT2R7AF1Fo9Cq8tKWW1ft7iGk6Rak21tX2c0x5KquqepiY7+bqeUVc99pWThyXxRVzCnj1ssnfas8+HGyG8e/Af32wOZrKs9lsZunodLr8UZaPMbTJDg8isiwnlKLjmlLxbORAV5D7VtZw6oRMzpmad8TxB/dl6ntDSIKAN6yysrqNF75uwGIS2NrkZX5ZCqOyncwpSxkyQyPLMitvWYCmaUQiEWKxWCKTiv+92x+lrifE1/UeNtR7/mawqeoIkGI38dDpo8lyWxJioU+cN46egEymy3JEee9ofj6D0eWP0huQyUmyEY5pFA5Eq5ruEF/XeWj3RajvDR114fmmwcsf1jSQ5bLwvdn5LK5IZ3Ojhy6/zIq9XdywoAhfRGVWSTId3ihv7uiguis4RNXZYpJYXJGG02oiEFXY2uRjbI6Lh1fXcc6UXE6dmM2pE7OP+Oz63jCvb2/HYhJ55bLJXDA9jwum51HV7ufWd6pp80URBUh3Wgh5jHsekDWiiozLKnHixDx2NPbT5Y8yqSCFT/d1EJQ1nt/UigDYzCIf72nn1qUVLBuTycgsF1/UeChMsdLQF2Z3m58rnt9KdpKVD66djlkUePTssTz9dRPesILFLLL+J4tZuaeDX68wKNYXzMjntIHv0twf5pN93Zw9JZdUh5mXNrXSF1IwmyS+98JuOnxRfnbCaPpDCroOoijw8d5uTpuYzfKxmYm5Hk3TiKo6d35Uy9e1PZhFgWS7mRc2tyErGseOSifTbWFxRTpvbDP+7YPdneQkWThxXBbPrammKxDjmnkFQ+7xcKAZxr8D/5VstME4vA8Tn62RZTmxiMbnXypb+lm1r4tLZhWQn2GoJYfDYQRBQFGUxHFW7O3ilytrSHGY+eT6GUd+6AAkSaLbFyIc08hPsbPg4W9wWSWWjcng+mOKsJgk2jxhclOcuJyH5GLiTCw4Ug0BjB39uc/uBHRGZTopy3Tw/fnFR7wujvqeELe9W4XLauKFSw9N5/+9g5vfhtOf3Iaq6Tx85hhG5SahqipPb2hmc0M/C0akU5JuY1ZJGmZJOOK9Xf4od310gLllqcwuSaHVG2VuaQpnPLUdgA+vnZYgGgBUtfu486MaLpiWMyTAv7uzg7CikuG08MrWNsbmuNje7GNOaQo3Ly494nO/PNBLQ28IfzTGpPxkFg6aFdnR5OGWd6oJRlXSHGYCskJkoETlsklIgsC4vGS2NPST5rTQ5Yug6MYApCiArBn/W8fo6xw7MoN7TpvAS5sa+OvGRlLsJmRFo29AlVoS4OYlZVwxpxBBEIjFYjT0hilOs2GxWLDb7UfdLN32ThWf7+8l3WnmjcsnkeE2SrDesMzFf92NwyJx7tQcZEXHbhZZVdXDT5aVUZzmGHKcUFThh29XoeoaaU4LiyrS2NMaoLozSG9I5pTxWaQ7zfxpXfPAPI+Fht4wogBZbiuRmIo3onDyuExuHdjsSJKEyzVsLzCMfz3+67cwhz/4giAQCoUwmUyJIUdd15FlmWc2NPNpdS+Pr2tJqEbHMx5d1xN01oUjUkmymZBjKsc/voW9g4Yd44FLkgw6aZrTSn6KseP/1WmjGZHlZNmYTCwmic+qezj/+V1c9PxOTCYTbrf7qDNCAFua/Vz9yh4a+0IIA63qDKeF354++jsDDUBuspXiVDtzS1OG/Pv/dp9wyoQsyjIc5CVbE1nRlkYP1Z0hyjMdzC9PPyLQqJpGJKaS5bby5wsmcOmsAu788AC/XlVLbX+Mi2fm8YNFJUMCDUBnIEYgqrCl0Tfk35/c0MzLW9rY3OD5f+2dd2Ac9bW2n5md7UW9Wc2SXGRbbtgY2xiwKaYYCN30S0kIJQRIwiUQbgr3hlSSQGhJINxACBAbUxxCx2BjMDbuvUiyJKtabbXaPuX7YzWjXWnlmMS+X4B5/rKl1WqbfmfOOe95D8Gowpg8Nwsn5HJpGm+1PR39/OC1vby8pYMLphalBBoAu9VCptNKlkvCJomcVp3LzPIMPrj9OCqzXfRFFNbWdxORVVr8iUAjDkza5/vsPHTptIQh6cDL+t6eThb+5gPe2NaK2ybSEYihaqCP7ioa/Pb9epZuajMk8FX5HkOqr5dwW/wRYoqauHjpj7FqXzeCAB6bBY3B1/eZtS04JIHiTDu/+7CJv3zawnlTC3ns0pphgQagOxRne1s/21qDLBiXQ0mmk4/391LotXHe5AIunl5EX0TBLonkeKxce1wxi2cUYZdEwnGZcyfnEVdUnt/Qyndf3gn8658pE5OR+Lcvo4miiMPhMAwzdRm0qqqGe0A8Hicej/PVuaW8vuMgV80alTaj0AUGbruVP183i1tf2Ex9Z5Dd7UEmFSWGOiVJwul0pv35UycU8J/LdrKmfgvLbzyW0kw7GQ6JqvzBgKiXy2RZxu1243a7UVWVb/71Y2RF5f43axmT66KhO0xjdxiXy4UkSYboQBAEY0WCjsNq4dcXTeSdXZ3c+Pw27j2jipKBANjUE2Llvl7On5rYjfJZuH7O8Dmdn59XzboDIc6YVpaiBNT51rJdNPWEeeSSiRRlJB7DRccU8v6eHiYWupmYn+qSoK/qPrEqi/uBXe39ROIKDqsFi8XC4lllxGJxrp9XydraDo4rH9mX6+XN7XjtFsYXuBmVkdrHeXVLO6/vOMgTl09i2aZ2NrcEuOvUSkPBVpLl4IA/kpBIJ5XyMhwSPzxrDPPH5fKrFY2G2zYkMpxwXKUvLBOIJPomtiR3bFFI3Oipjxp5fGU9d542lvNnlBnlU0VR2NDo58dv1aJqAi99bTrdwcR77LVb+Ot1U3HYBtWGJ4/L4eP6Xva0h9A0uG5OyYjv3ft7u9A0uPPUClbu7WFmaQZdA8q0kmwn1w3MYF0zu4TzpxaQ4bTy6pZ2lm1qoyzLQXNvhDd3deG2WZBEkbquMOGYjPMzfoZMTA6Xz8UnS3csjkQiRh9Cz0Ci0aixyXNahZtxA55U6dD3rFutVqq8Ei/fPJcNjb3MLPMZJTlZllNsZ3T0YHDulDxW7usl22OlwGtl+Y0JlVAgEGB7R4Qx2TasIsiKwuMr9rGwpojKPDe3LKjiDyvruH5OCS9uakPVoCLbmbafEowp3L50B5W5Lu5eWGV8feW+bnpDcba39hvB5n/eqKM7FMNrt3DGwG6d5DXVh0tfJI7HZiHDaeXUsRkEAoG0Xm4OSUQgYUSps/iYUVwxq8xQ+kmShMViIRqNGsIIRVE4f2oBkbhqbCFVFIUb55Ub9kRzhmRuQ7l+TgkVOS7OmpQ3LCCt2NtFTyhOQ3ckxf5f51snV/D2rk7OmZzPuroevvnSbgB6wzLLNrXTE4rx5vY2BuKHgVVMDJrKWmJ4NM9jpSMYJ8cl8aerpxCIajz3aTMr9nTxw7/toiLLRlXSGvAsl5WorLKwOpdVtd28urWDxxdPpDLPbQQaVdPoCsocN24UzxRl8Mj7dVQXuDltwqByb8XeLn75Th0TCj3ce3oVty3dic0C798+2xBU5HntaT3qMpxWDvSE+cNHTfSE4ozLd+OwWmjzRynLTuxGempNM/cs38OvL5xoCgRMjgr/9j2bZJKdBZxOZ0LRNVD+0V2N9dLZSE1xXbWm91cyMjJSBkFh+KoCSJQXlmxsY1qJjxlVhfx9aytFHguTS7MJh8Osa+jlv/62l6nFXn52XjVLN7Xx5EdNjMqw8+QVU3A4HFitVgKBAM29YX72dh2XzxzF7IqsYY/xQG+Yby7ZSaZT4veX1fCTt2opzXJy0bQCdncEmV7iw2azIcsymw/08fTaZu48tYLblibWKi+/eQ7x+OBwXjo5eDIf1/fw4zdqWVSTx01pDuqhvSH9MEp+nQRBwOFwcf7jHzN3dAbfOGnwfux2O7FYLO1FQLrXWmdjk5/m3kja4DKULQf8NPREkESBpt4Ix5ZlMH0EC6BgNM4Zj66jLzL4ey2Qdp/PRVPzeWVrB3EVbjmhlKtmFdPUp1DkEfA6bEa2/J2XdlHbGeLWk8o5ZXwu65v6WLKxldvnl1PoS2R731m2k7quMOdNKeBrJ1YSjUZ54N06lm5qR1U1Hl08iRll6R/z6Y+spb0vRqZL4spjR/HIykYsgsCndx3P+kY/P327lmtml7BoUn7an4/GFX74933ke2zctqCcVbU91BR5yPHY6QhEuPOl3cytzOTr88pTeo4mJkeKz9UnKvlQikajxv8lSTL+OL6zZAtr6rpYev20YdPpMFhySyZZfQYQicV5bftBji3PZHSuB1VV+bC2k1+vqEcSRZbe5OXbS7aQ6ZRY+Z0TAcj1WPE5JCYWebBarcypyuHd3Z3Mq8zi4ic3UJnr4ZcDw4zFmU4eunjQCmfoYV6S6eRXF1ST6ZToCcX4tNHP1pYA18wuYUZZJjA4wDe1xMcDJT4skpWuYJy4ohKNR1OacaFQyBiQTYdTSgws2kdwnR4aJERRxG63I0mSEbQ1TWN/ezft/jDv7ZFTgs2a2g7e2tHJzSeW4XUkrub1XUMjBRqAn71dh6xqTC3xGcOj6VBVle//fR9oGrIGbX1R/r79IE9ePpkcj23Y7d12K6vumMuGJj/X/nkrMBhofA4LXz+hgk/quvmwtptlmzs4tTqXk8ZmcXZNQk22/2AP317awJXHjuKGkyewq6WHS2YUY7fAlFGJkurSja1sOdDHGzs6uXZOKfWd/disAqdPyGXB+DyeX99Kda7EK1vaCcUUirw2sl1WDgaivLatg3On5JPtHiwVnj4hlz3tQcbluzm3Jp819b3MGbhQ6QjEkBWN5p5B2fdQ7FYLP/nKeOP/er/rsVUNrGvw86sLJxjzTfqiNhOTI8nn6hOl91NEURw2dyPLMrFYjHd2thOTVfYeDDGtZORFZzpDsxqAD/b28ML6Vj6s7TGm3acV+1gwLofqQh85dpUTqrIoyx7sJVXkuPnr9dMNVVKx18rjl07m3b09BCIyrf4wfeEo7oGauM2WsL6JxWKGWi6Z0QPDfU6nk5vmV1LgcQwLiskocpx3bp014vMcGmj00mQ0GmVaqY9Xvz7T+H0jZYXJ6IvZYDBY5nttfOvk0VTkpDaz/7y2hfquEDP2Z3BqdaLUN5Jtjz5DMz7fncgieiPD+jNDEUWR+WOzicRVZpb5eGtnF5kuiUzXoT/ebtvwcmNfROHB92r52vFlrKpNNPJdNtEINJAwARUEsFkSpdULH/8EQYBlXzvGyPh2tPbTFogSVzXWNAa5b/kOWvuihjdcY08Yiyjic0rMLMvgjpMr+K+/7cVtE6nrTMwo3XnaYAn19vmjue+NWl7bfpAF43N44oopPLiinq/+ZSsPnF/N9FIvq2t7uOn5bfz32WOHDehCQkGY67amZIlr6nvpjyn4w3Fy3AkFXbK7uYnJkeJzFWwgcUiPZGSpqirPXD+LDY29zKrMTZltGYl0h/ex5T4+rvdwxqTEwWi32xEEgfvPGW8o4I4pzeC9PZ2GP5mqqiiawEA7AlVVae2LMr/Sh3ZqJQ+938A3l+zgySumGH0Mu90+LHAORZIkFs+qQFXVtEHA6UxY1SdvHD0cZFlO+9wP18U5GUVRaA/EeOKjJl7bfpBfnFed8v1bTixn+bYO6rtC3PnKHh65bJrRRE9GEAQ+bgzy3PoWIvGEe/FNJ5TRG4ojigJZruGHYO3BIL9asZ+bTygzRB5DXQpGYnyBh4++NZuFj6wzvNUEwGOXyHRIzK7MZkNjLxMKPCnlvjMn5bNgXA6OgU2vcyuz2NwcIMfn4ZOGbn7+1l6mFXsBjcdWNqBoDWQ6JBQNGrrCnFCVyfqmGFaLSHGWk3vPGs/OFj+BqIysikRklcbeCH3hOD5nIhP8wet1vLG9Aw1wDPTkdrYH6QnG6Q7GqMh1s7quh+5QnJW13SyamI/dOhhMv7NsJx/v7+WqY0dxY1Kp9NHFk+gNxynOdLK6rpsX1rfx0OUzyEjzWpuY/Ct87oINJPouQw8qSZKw2WxMzbAytTTLuJLXeznhcNhoVA9FFx3oZLpsfP+ssUCixKYvToNEucjhcPDMumb6wjK1nSHyvHaufHoL+7vCLL1xFqPcFp5d18qSja1cOqOIeePz8UdkFozLNrIZwBj4HImMjAyjTzVSuSldAEo+GEVRpCcY5Rt/3c7oHCf/fXailJK8G0cURSRJGlbWSh6IPRQvbWnn2XUtFGc6cEgiQ/UJY/I93LMoj6n3vYMoQFtviOw0WYemaRxX5mHW6ExsFpH39nQRV1VueG47FlHg+WunIooiL25q46k1B7jr1Ep2t/fT3hfljR0HjWAzlM7+xK6YdPJht13iv88ey2tbD9LeHyUaV3n44okUZDi4YnaZUaIdOi/jdgwO0T60eIqhYNzR3IumaeR5bTx5xWQu+MNG+qIyggCzR2dQ4LPjsVqIyBpWC/zxyqnc/eoetjX3cs/CKqoLXPz+owM09YRZ9Ph6bjy+hEtmjGLOaB87Wvq4Zk4JEwZWiv/yvPF0h2TKBoZxf3DWWF7a1Mbzn7axuy3E3acPZkar63oIx1X8kTgPvb+f4kw7F04rwm2XjHLzUx830x2K8crmFq6eM/qQ77mJyWflcxlsRFFMWfkMiQCky2xDocGpd/1A0M0300madZWaoihompZyWLtcLuLxeMrXlm9qxioKnFadywnj8onH42Q6rUAYFAWr1UF9d4jWviir63u56dSJrLvnZILBoPF4RFE0Ak0oJvP8hg4um1FIhttBLBZDVlSaD3aR5XYOCzSCICBJEvUHg3xn2XZmlmfwnYGhPEEQsNvtRhBSVZVgVKbZH+Vgf+J5OJ1O4wBVVTUlACajKAoej4doNHrILLHAY8ciCJw/pYDO/jgPvLufp6/yGbJjt9tNIBDgl+dXE4jI+GwMKxvqSKLAdwfKR7fPL0cD3t/TjdMqGu9pICKjKBrBmMKVs4opy3YyZ3TmiI/vtqU7CcdVnri8hmz34BW7/p6ePC6Xk8floqgqqoahtEvX39OxWq0p7+Wmhi6UeJTLZ44i12NjwbgcPHaJ+eOy2docYPIoL3efXoXTauHyP24EQFY0Hnyvls1NPTisiVmYshwP9y0axykPfUJfROaBFftpC8T49imVhj+fjsdhxeMYlE5nOK2cODab9/d1M710MPB+XNdNzSgvHX1ReoIyK1t7cVpFLpyWOst012kVbGkJcMn04a4aJib/Kp/LYAMMa2DqkltdoaYfBJ9FbCdJkrHSADDMP/WrWrvdTn8owtqGXiAhA3Y4Eqadj1wyEVkDq5jo2Zw9ZRQ72/qZPTqTPe0Bdrb0Mr9ysIfkcDiM7OvptS28s6uTYCTGbSdXYrPZuOWZjbT2RXl0cQ2FvtSSRmIrp4A/IhORFTY0+Y2115qmGa4JgiAgiiLFWS6WXD+dTI8Lt9tJPB4f1vPS6Q3GeHhVI5ceU8jEkoRX1z8qR84bk828MQnj0Fe3dtATjiMNDIPqjwFgbuWg8i4aV7BahEOqzPTv/eaiiSlf//pJVVw6qxyvPRGwF044dNlsRlkGB3rCiGj0hGSyBrKq5PXHuiPF4YrGkzPrSCTCXcu2EpdVnrpyMq3+qGFHU9cZQtE0bjmxnJ++Vcvquh7G5yek0WFZ5YUNbeS4JEoy7EwvSWSybf4Ix5ZnsGJPN5qm0RWKsWJPJ8eWJZbkfeelXagaPHBB9bDh2cpcN/971aDLxPYWP3e+vJuorFKR42R/d5h8j42D/TE+rO02DF8fXFFPT0jmv86sQpJM2bPJkedzG2zSlZV01VU6ma3uTza0JAaJ7EXTtJQrVf2w1tcCKIpCS08/pz30CRpw7Zxy7jqj2sgUVFXFMWCwqWkaM0u9/PWriRmck361Gp9Dosw3nspcNzabjcufXMfe9gDPXD2FE6uy2Nrcx6nVOcbhHojKdAZj2KXEoa0Hk+TnU1PkZmqxj00HAvxlbRMXTx9cn6w7L+g9oRy3DcmS3mtOD7IA//3mPlbV9rChyc+yr80YFmj+kUXO0MCQHFR1ajuD3LZkJ7NGZ/D9M8eOeF8jEY1GcYgQjyu8s7uLN3d08J1TKijwDV+7DYkZG4DLn9pEMKbwwnXTyfAMqtue/aSB371fyx8un2TIlPV5rMMRSwDMGZ1JW1+Ug/0xnv20hWBMYVKhh19fMIHecJw8r41P9vcSUzQ2H0haDAgcX5nFqeNyOOORdcyrykQUBPZ3hfnGSWU4JAthWeXhlY0cW+bj26dUsL87jCgIKKpGsoCwPxLngff2c9KYLOaPS/Qbf/h6whE7z2Pjpnll1HeHUTV4cVMbB3oj7GgN8OKmdtY19iKJIv1Rheysz+2xYPJvzOf2UyWKoqGa0TMZRVEIBAIpjgNAyiS87jywo7kXj0Umw2lNOQwdDgd2u33YTI/b7WZl3QFj+NxpFRCEhKu0w+HA6/Uiy7IRyFRVNVx1c91WuoNx8gZkuHa7nfa+CD6HRH9UoaY4g0cW16Qc5H++ZjqSJBmPRVGUlGCjZze5XgfdoW7jahkSAUF/7MlrB4aWrkRRxOv1GrNJkiRx8oRC1jX0GQvqktnbFSHX4yDr0OIwA6/XiyiKw/pr6xr8RGUVAQxboXSlwsPJSt/f20lTT4Q1+3uN4UZ/OI7XbhmWNRX4bHQF49hsUori6qnV++kMxvi4vpfzpxYiCIIh4vB6vSiKQiQSSXmMsqLy5q5OphZnUJJp546TK5AVjUuf2khMUXFZBR5e2cAvzhtPjttFR18Um0UckKcnntfEQjdXH1fM6dW5vLb9IF3BGG/v6uKJyycjCgKVOS7mVGTS2Bth7f5eThmfy8MfNCIrGrecWDZseHfDgT62t/VT2xnkvtdrGV/g4syJeRzojXDPwioki8gCEp+djU29fNrQyyf1vazZ38sZE3JZPHMUGc7EBZOpSDM50nxugw0M9mGGrpHW+zf615JFAaqqUtfSxTkPr8Fjl3jzlpkpFh2RSITusEqyq71esju1OpfrZhejahpfnVtiBCn9sLx3+W4sosAPzhyT4ukmqxo2q4AycFgFAgFe+up0eiNxct2JYJIYiky15YlEImgW6zAbGkEQCITj/MczWzi2Mod1d883mvt6sNR7U0MPcX1aXy8X6rezWq24XC4unVXOognDd+l0BKJc8ccN2CWRVXfMTvmeHtAcDkeK+7a++iE5cMRkhSc/PoDNInD3wiri8Tj3v1VLnsfGDceXGfd5uOXPOxZUsK7Bz4Jx2UiSxEubW3lydSPzx2Zz+xAjz19fONFYwBcIBIw14o9fcQzL1jewaGB7aPJqCn1dhcViob+/3/j+c5+28tuV+ynPdvLi12bidDrp7+9ndLYTq0VgV3uQj/f3smRjG619MVbVdhOKKcZOm9PGZfPLCwezwLMm5bFiTxc1o7ysa+xlVW0365v8ROJlnFKdy8OXTGLJxlZ2t/djl0Q8NguXPLmRaSVe7jl9DH3hOA+u2I+mabSFVLpCcT6q97NwQh7XpbElag/E0bQ488dmsb2tn3yvjeoCz2d67U1MPguf62CjM3SaXf+afpgAxsK0zkCUjv4YkkUkGJNp9kcYX5iolWuaxgvrW/nFu3VcNKOE7y2sNA5nVVWJRqPctqAixY4FBoPZpgN9NPVEmFqaw8XTB0sw3zhpNL98p5b/eaPOGOyULCK5SUN7uplo8h/6LUt2sKc9yO+vnM608hycTqcxU7R8Wzu1XSH2dYb47ikJKau+ehowApfFkvAgk2XZCIpv7u7hdyvr+O1FE/j79g7e2d3FjxaNY+YY14gS6gynHbfdQpHPMUy91xeOJTy2Bjzehpb7ACPLs0kWrptdgigkXgN/KM6njX4sgpASbNKRLtvJcds4Y8By/5l1zWxo9CMrGtlJG06THQySy4J6oC3NdPD144cfyJDIBtOJSjKcFkRBINNhRZZlbnh6HW19UfoiMidUZdPWF8UmiZxdk88z61pQVAhEFCQBxuS7uWJWMY+u2s/Zk/Ipy3bhD8vUdoap7w6T6bSiqBpWi8CY/EEF3QvrW1EGnAYUVUNWNfyRxPuws60fVdMoz3bisknsbAvQ6o/y2w8aOL4yi/yk9eGCIPDY4kkoWuJ1ctslaopMp2eTo8sXItjAYLM3EokQCARSAsLP3qljfYOfn583nuuf3UpfRObnX5lAhtPCmDxPSuaT5ZIo8tnJdtmMDZeRSIT/fGUvO1v9/GjRWOaNL0oroZ5XlcUH+7pZtaed8ycnsgNRFBmd58Zlkxidk8jE9EMZEo1pSZKMzKC7L4h9oDoSkzV8DgmroKRkboIgcOHMch778AALq3MIxWS+tWwXmU6Jx64+DkgcsHqg1APxkrX1FGd7eOyDOg70hFi+pY1HP2xC1WBza5jqwuEDrjtaA5TluvBYLaz45nF4vd6EWm4g2HxU18N3X93NJdMLuXV+hWGOqj/OdFfJl80cLNFluKzcd9ZYnNZ/3JrXNI3dnVHW7DvI4hlFhscagGS18cu39uGyWXj31mNTSkx6ry4cDhtqQ11MAnzmGSWAc6cUMq3EhygmtpDWdYaJxGUsYmKJ3IrbBrO/2xdUcPuCCl7f0ckL65v5ypR8vrVsFz2hOKv29fDctdPZ3x3mxKosJhZ5mV7qpcUf5ZgBu52DgSjZbiunT8jlnV2dZDgkXHaJZ66ewqYDffx9ewd/WN0ECPzmokFniluXbOdAT4SoPLy/mTcQfPojcV7Z0sHLW9p59eszja24JiZHmi9MsNHRD0E9AwAIRmRkVaMjEKM7GEcF9nWH+FoaV92FE/L4sLaHP3/SwGWzSvEOnFndoRj+sMyB3ojhM6ZnVLprwDdOKCcSU/mwvpvtrf1MKkpY3YzOtPP67ScSDoeNYUq3O9Fj0cUFoVCIu5Zu5pUt7fzsK+M5tTqXJy6vQQWkpL4LJEpeHrudlbcnAou+iM0iCoTDYTpCKo+/X8utJ5Tws7dreXdPF/dfMJnv/20PGU6J/71qKn/8pIVFUwp5fHUi2FwyvRBFSZX5bjzg58dv1FLgtfHIwArpoQIDqyhgt4j4HImAmZxdappmzDnFYjFiikowqpDlSj3MJhcPd3pYsqGF59e3ce8ZVUws8vL2zk5OHJPNncu2I6KR47Zx9uRBKbBFFLjh+FKisjqsl6Er9HT0CxO73Z5WMKGj9y1GUuO9t6eblza3M6XYi0WEC6YVcc1xxYYSbyhnTszlzIm5xGSFF9a3EYzKTCjw0B+Jc9/r+1A1jVvnl2OTLGxr6eeT/T2oGvzinTqOr8xibYOftkCMzS0BZpVnYJdEvv/aXqKyyqKaPHLdqX2WX18wgXBcMSyC0uGyWThzYi5eh2S8ViYmR4MvXLBxuVwEg8GUQ+/eM6po8UcZnePijgWj2dDcx/XHJa6u9dKTHggAWvxRMhwS/aEIBQU+BEHghRtm89bWA9y2ZBt/WtPMjfNKeWZdC49ccQzfe3k7F07N45hiLxubA/SFFVbU+plbXUwwGGTfwSA/enYbd59ZzaSChB3IrtY+bvnLRm4+aTRnTsxDURRKs+yIAtitiaxHEARsA2WwoZs+k59fntfOI1dMI98hEIvFuO25rexqD+CSNFr8UWRFo8BrY1Smk3Mm5VKcYeen59cgSRI77jszUV6TY6hK6kBokdeG02phWkl6c0iAY0dn8vats7DZbMacU11XmI6+CLMrslLKULcv3UFzb5THF0+iKDNVOTbUiqczGEdWVfZ3R1jf1MfLW9pZurmDWxaM4U+r93NcVWpfKRqN8vV5g2W4QwkMko1Eh5YEk4nH4/h8vhFXTuS6rVhEGJProrYzxKQiD0993MTy7Qe5dnYJbX1Rrj6uGN/AYb9iTxelWQ5isoqsahxT6mNrSx+PrtzPceUZvLWri7MfX89dp1Xy0Af7sQgCX59XSlNPhJ1t/eS5rfRHZYIRmYuf3MjBQAxV01A0uGxGEaNz3CmPT7KIeJPkarKiEoop+JxWNjT6+cnbtVw7uyTFUQAGM0ETkyPJ58r1+bOgN3iTHZ7ToXtB6XYwsiwTkxUismocEoIg4Ha7eXr1Xu5/sw5V1UAARYVx+W4ausPYJJEsp0R7IMa1s4v56txSekMyNknkmbUHeGFDG+MKvCy9aS6xWIynPqzliY+aOKY0g5+fV21MqlssFmMXCiR6L4cz6wKDzf8P9nbx4Ht1/PDcGiYWeekNxSjJdiGKojGHpIsrgBSHgqFKvois8qc1Bzh7WjHFnkOXuvQD/uzHPiWuqDy2eBKVSXb79/19L2/t6uQHZ43jlPHZSAOBVJedO53OFNFFb1gm222jrjPIQ+83cPnMUZwwvgBVVQ/pvGC1WnE4HEbWMlLg0YUfIwUbSHx+JEka0cQ0md5QjAue2Eh/VGZykYdgTOWqWcUsqsnn4/publuykyy3laeumMw9y/cwtdjL8xsSaw3OnZIw1+wJxZla7GNmqY9gTOH1HZ34w3FOrc7lq3NLWNfg56xJeZz28DoicRWnTcRmEVly3TRe33GQFza0ce7kfK5NIwq486Wd1HaG+fWF1exsC/L71Y2cOTGPrw3plekqQhOTI8kXLrPRcbkGG6ter3fEckmyxPOtXV2UZ0hU5blTSjGaphGPx5k9Opuq3DbOrsmjL6zw9u5OfrV4Gr99dx/n1uSyozXA37Z18ML6VjY3B2jvi2KXRB6+ZBKRuMqls0cDiYP0/CmF9EUUQwGlN/L1wOj1eolEIkQikRRrGd3+Pd2Vdiim4nPZmDvax9zrpiWcFhwSWjxMIBAwNokOJTlLGhrU/r6tgxV7umjoDnP/uQmrm7CskpvhHRbE9QP9lPG5rG3sZWNzHzkeGxkD/l7fP2ssNsnC95bv5q3deTx0SY3RuD/YH8MZlfEMmGOKomhM+1fmuvnNRRMRBOEferfp7grJS/cOtd/oH6Fp2mH5xamqSnsgRlmWg47+GA09UU6oymLB2EQGtq7BT1TRaOuL8aPX9xmDl5829rGnI8i6/b08cVkNH9X3ckxZBmVZTvZ2BHl3TzeLZxRx8wlliQHdgT1Gf7lmCrvbQ5Rk2gnGVGo7w/zl01Y6+mM0jeD+nO2yUkuIVn+UsiwHjy6eRH4aV2wz0JgcDb6wmc1QkuXRuvzVbrfjcDgIBoMc6Orn5r9ux2G18Jdrph3WfSbva9cP/yc/buKh9xso8tkoz3aR7ZL4yVcSxpT6FaOuDNOvmNOJDfS5nVAoxG0v7sQmSTxy2dQRhwx3t/dzx4s7yXDZeO6awQny5EzFbrcnMgnBwru72jmtugBQjaChm3pGo1HjgO4IRHl0ZQNfmZLP9NJM1jf6uXXJdmqKM/j9penXJDgcDn7zzh7e3d3J+HwPP7+wxghia+p7uPvVPQkXgGmJwcPO/hiL/7iR0iyHcQiny0a8Xq+xWkIPFOn24QiCYAgVdBViOgRBMJR6ejk1Ho8bAUu/f1mWU7LNdPxlXTPLNrdzyrgcnt/QSjCqcO2cYvxhmYunF+KwWjj78fUAzK3I5LFLEz0wRVXZ2dpPQ0+YNft7qS7wcMWxxSP+npEIROL89O06jin1cd6UgmHOAsmc8/inCILA89dMNSyFkklWcZqYHCm+sJnNUERRxOVyGWuXIVHn15Vg2W4rY/LcVOQ4EQQBj8dzyOYxYAgDYrEYTmfi566cO4a/7+jilhNKOHlgijv5Mei2KJD4o04+UJMPWD0I+cMyO1v76Q3HhwWa5D6HompEZJU5Be6U7+v9CV26DXD7sl18VNvNwokF/PjsMSmPb2i2ku+188NF44z/J7ZV2plYkGpqqWkaSze28b+fHODr80azcHw29Z1Brpo1ilgsZjzW2RVZrL3nZCRJMgKBw2ahKMOOXRJp9Ud4dWsHF0wrNIZgk9HLf/qgrr5yIhQKpcwrJd8+uWT4j7ZQ6tLxZCRJwuPx0NfXN2KWVJHrwmoRmFLiZWy+m/6YQn9EZtW+HlRN494zxnJOTR7N/ij/c87g62kRRWqKfaxp8PPmzk5W7evhpDFZlGQNNw3tC8e55a/bOdgf575FY5mV5AfndVj58TmJzPNX79axoy3IL84fj6xouO2WlFmtMybkEYjGcViHvw7pttSamBwJvjTBBgZ7GrqkGRKHls/nQ1H8/HzAGl/TNGRZHmb2ORSbzZZSy7dYLHgklbduPyGhvhq4otYPqOTSV21niDzZgk9KXJUnm2PC4OxOpsvK906vIsMppVzF2+12bDabcfuJRV7euXUW4sBVub61NF3AXDg+h7X1PcypyEo5PA/HmmVqSQYvfW162gN7d3s/Xf0xOvsjPLSrg8aeMEU+e8rzAVizt42Xtnbyw0WJw9Fjs3B2TT5Pf9LMXa/sIhxPuAvckNTwt9lsKb8zuUwKpAzRHop/pUSULOvW0d/TORVZxjIznf5IHKtF4JTxiUVl3zqliuue2cxtS3fw5/+YlnLb86fks6e9n3yvnaKM9M35dY1+GrrDRGWNQDSR2QUicaKymrK/pq4rTG84zp6OID9/ux63zcKfrp5ilGNvOak87f0DpjDA5KjxpSmjDSX54E+3lEwvxQSDwbS1fX2RW7rDXM9Qhq6g1jnQE+GqpzfjtVt445tzjaCUkZFhLCXT3xZ9o+VQdOl08n273W7i8bjh6wXpl8Pp/SG73T5iGU8nrqg090aMZW5D2dvRz0ub27nh+DIynBIHA1HyfQ5uen4b7YEof7xyyoAj9iA3v7CNFn+UK2aVcPG0AgRB4M2dB/njx01cNLWAhp4Il80YxajMQZ8y/fn+O6MPzUaj0bSfmWhc4Y5lOynw2vjBWeOGfb8nFCMSUyjKTN1K+sC7dbT6o/z4nLE8s66VacVeZpZnAnDVnzYRiqn88crJRm8sGJXpCsbIdFq5/cWdtPgj+MMyy746jaLM9O9jMqZAwORo8KXKbJJxOp1G81hRFKN8kKwCG+nQgERzeaQymx4o9L6Mjm7amS0LjMt3UZXrGhZI9L5KMBhEEAScTmfaYCOKomGfoj9efU1zfyjMG7u6OX/GaCOQJpuLyrJMZyDMuqY2LpxZnvI89McoiiIrtjWydGMb29uCfHdhFfOqsoeVkZ5Z28LO9n4QDnD36WPI9yWunh++ZCKKqg2beQG4elYxr2zpYFFNIZKU6JksrM5lYfVg2VEPmMkDsP/uJMupdbuhZNWc3Wrh0YF5JZ3G7hC/fm8/V84axU0v7CAqq7x767Epmcqyze2omoZgc3LDvPKUHlVFjouOQBRHklNz8o6aP145hfm/WYOsajzwXgOtfVEevGgiuWlKlCYmR5MvbbCx2WzYbLYUbzBdOKBLa2VZPmxDyHQoipIY2IwmDpycTB+SJPHalhYsosBVs1IbwZFIxAgaNzy3jZis8ODFNZTkZQ4LCPo0fPLX9EPo2XUt/O8nzby1q4snrpwGDPdIu3f5Hlr6oritQkoQUdWEYOBPa1v41bt1OCSB8mwXeW6rcZt1Db04JJG4oqFoGnMrsrh+TklKhmQRxRRH4mRmV2SxYOKolNkmHV1t93lH7xHqxOPxtPLplft62NYa4J1dXbisIrKi4nW7gMHX8onLa4jKGjmeQTl3KCbjskncd/bwDGkovzx/PBua+vjd6iYUFZZvPci1c9KLEPS1GiYmR5rP/1/1EUIQEgORunhA/4Oz2+1EIhHqO0MEYgozR+cYmcbQQCRrgKqlTJB3+fs58cG1iAJ8/O05WMJh1jX46QrGWV3XyyUzRhmBQG/gS5JETFbwR2SCkUSgSi6npXNJ1g8yr9dLaa4PaObMSYVGNqO7ROuPd25lFmvqe6kucKcNppML3QjA2Dw3f05S50XiCt99ZTceu4WxeW6aeiOcUJVlrC9O97qmu/90pTt9puWLiNVqTdsDvHh6IfleG7NHZ3LXQn2zZuprM6EwdQPpK1va+f3qRi6cWkhPOM5F0wvTbiHVmVmeRc0oH8+tbyMUU7hoegGAsTpDV+QBh5xfMjH5V/jS9mx0dElrugNct9/v6w9y7uOf0tkf48NvzcGaxo4kJiuc/4cNaBr87cYZRrCKxBXmP/gJArDqjuOQLBb84Tg72/qZUzm8LAWJnkpfKEooJlOWn5WiJNPRB1GHZgYZGRnE4wnlmiiKxmGS3EBPVsQlM1RGLKsakji4BE23o/np23XkuKycVZOfsPafnJ+2XKaTfMimO3A/Lz2ZI0k8Hjdsjz4rr27t4A+rG5lQ4KahJ8IxpT7uOq3qH/7cJ/U9/Oj1ffznaZXMH5uDy+VKm219UbJLk38vvvSfKF19M7T/YrFYjC2dFgEynDZcdgm9ND5UVCAIApG4isNqSRnCdFgtrL7jOONnILG+d/YQJVjy4KnVasVtV3DbE/Jg/THqggNIHNDJPSV9RQBgDErqgWOonDU5e7PZbClrDZLRAw0M9qFe236QtQ1+5lVmUZrlpDQr0cx+b3cnL2/p4J6FlRQOUVMlv07JgUZX4H0Zyzb6GgzA8MsbakM0EudOzufcyfn0hmK8urWDBePzjc/jz95OLEtLDj76RcTB/jiiIPD+ni7mj80ZsR/5ZXw/TI4+X/pgo6Mf9vofu6Io9PX1ARBTVJbferyRKaiqmrJ6IBAIYLWIvHPrLICUYAOHnl1I3vSpE4lEUspmkUgk7dVm8s/oPabkw9zj8aQMNepN9+T+zGd1PC7JdGCziNSMSi3tvL27i45AlF3twWHBZujv0IcmzQVdCXSrIn1Rnj4LJMvyIeXomS4bVx9XYgSamKKyqrYHAfjWySrWgaaZxWLBZrNx5qRcMl0SU4sT791Iw65msDE5Gnzpy2hDGXpgL9/awYPv7+fEcXnct2gssiwbQgIYlBsP/cP1er1GA3yopf17uztZ2+DnzlNGG+aVQ+mPytzx4k4WVudy8TFFOBwONE1D0zTj9//sjd1sPNDHry6oJt/nNA4onXT9krVNQeR4jLmVWTR0hVjb6DfKYP+KGKI3FGdHWz+zR2ekHFZD7/PLWDI7EqRTt+noZqIOh4NV+7pxWhSmlmSiaRoWi8XIeNNZHFksFkOhaL43JkcTM7MZgn6FGY1GsVqtHAwp+MNxLKJglIOS/+D1BWU6emBJXsusy6gh8cd91yu7QYCxeS4uO64cWZZZsfsgtZ0hY6vi7vZ+uoNxXt7Szjt7utjU1Mdz10ylMs9tBK36rhDdoTgN/hjFOb5hfZ10gePOF7fitll44sqp3P92HV39MWwWkfOmFhqzQf9MkzjTZWVu5eBQox5kJEkyLGCcTqe5K+WfRFe36ZlPLBYz3idFUQyLmVMnFY14H8nZsiRJOBwO0zHA5P8MM9ikweFwGH2SOxZWc8vJ4xCTPMT0MseD79UxKtPBhdMKjZ/1er0pTVer1UooFDLWI9vtdiaO8rGrNcDpk4sRRRGv18uP31yD22ZhUU0+BV4700t83HB8KVV5bu5evgeVhANzMj8/r5quYIyqwqzDLocdX5VDY0+Y0bk+zp4yig92tzN/7KBQ4XACzbaWPgp99pRZEEg9zPR5IX3mxCzNHBmGzvJEo9HDLkcOte4xMfm/xAw2h4FNEgmFUg/zVn+Yt3Z1IgoCF04rTLlK/OqfNzMuz8mdC8caJTJZlo0D/eVb5hm9nkgkgiiKXDWrhEhcIc89uF3z9Il5eL1e3ritgN6QTLbHxvIN+/nLumZuOKGCWeUZOG3SZ1I03X/OWABisQjZThFFhb5I3Jg+/0fUd4W48+XdyKrGu9+cPaJjtJ7V6M/F5Mij9/tMTD4PmKfAYWKz2VIa/3luK1fMHMVNJ5QZjsGiKNLZ28fWZj9v7+40TCGTy0d6YEg+pJ9bdwB/TOOmkyqQJImIakEZeGsMhZok8Pj7tfxudRMbD/SxdGMrbrcbn8/3T89GvL+7k8aeMCv39aT9fkweHsTy3TYqc118pSbfCChDr6xtNpvpsWViYpKCKRD4jAwVEMBg01v3UVtd14NTEjm2IjtlvkVfLRAOhw3JryzLHP/AR0RlleU3zsAliZz9u/VowMrbE3vsRVHk5r/uYN3+bo4pzWJ6eRY3nVSFxyEZtfvD2c2io6gqcUUjHFdYta+b06rzcNpSa/dPf3KAJRvbuG5OKedPLTjk/Q2dz/F4PGYvwMTEJAWzjPYZkSTJaH6LoojFYkmZg5FlmZMnFBoDe6FQCIvFYvh86U18RVGwWq30BcPcPr+c9/b1UuC1E4rKjMpwUOizpQxSXj2zkJaeEJfMLObcacUp65Y/K3e9vJv6rjAPXzKRc6cUpr2NPyKjqBo57sGPiKZpNPZEKMtypGR5ycHGNHE0MTFJh5nZ/JPokuahB6vuSJAu09BnZeLxOBaLBX8oysWPr8EuiSy54VhjJXQy6RypD/m4FJVnP23h2LIMJhZ5097m3uV7WN/k5w+XT6Ukc7AEJggCNz+/ld6wzNPXzgRNxcJgxvK9v+1jW7OfS2cUcdnMUcbP+Hw+UwRgYmJySMzM5p8knZ1HOluZZIaW3xRFxWEVsUsi8Xg8Zaum/jv0oBWIi7yzo5VFk3LTWsOIoojb7ebZNQ0s2dDG27s6h+1M0fmfc8YZaqbk8pemaXQF4/RHFWKxKE6rxcjeLBYL8yp97GnvozxnsAel92vMQGNiYnIozGBzBNElqXrW4/V6jVJXugzFahF5dsDk0u12Dzuwe4Nh7l2+l+oCNx/U9tLmj6CoKhdNHz5LoS8XO6Y8m2b/DlRt+MbJ5HUDbrc7pQwXiStIosCfrpqComl0BKJ8sLebxccU4bQnlpWdNamAMyfmAwl5uBlgTExMDhcz2Bxhhk5gezwew3VAX2U8FH0eRf+3HpR6QzIHeiP4IzL/cVwxD62op2bU8G2RMKhyq8hxUuhzUJY9KIlN7jG5XC5EUeSeZVtYU9vJDxeNpTTLwbXPbKHI5+DxyxL7Vn77wV52twcp8No5fWIekH5TpYmJicnhYAabo4xegoLhq4zToTvxiqJISRb85NxxZLus5HjsnDEhN+W2yVYwsiwTjUaJRyO8fvNMnE6n4YLgcDjo7+8nHo+jaRoOh4PGriAxRaXVH6Esy4mqQX9ssM905cxint/QyqyKbHPq38TE5F/GFAj8G6P3gNIZJn5WWxn99nowUzWNVn+E4sz0Q4H6wKBpNW9iYnIkMIPN5wh9g6iiKDgcjhFdgZNtY3SSd9robtbJg5eyLH+hl5eZmJj8/8UMNl8AkhVlqqoaSjNIWMjIsmz0akxMTEz+f2AGGxMTExOTo455qWtiYmJictQxg42JiYmJyVHHDDYmJiYmJkcdM9iYmJiYmBx1zGBjYmJiYnLUMYONiYmJiclRxww2JiYmJiZHHTPYmJiYmJgcdcxgY2JiYmJy1DGDjYmJiYnJUccMNiYmJiYmRx0z2JiYmJiYHHXMYGNiYmJictQxg42JiYmJyVHHDDYmJiYmJkcdM9iYmJiYmBx1zGBjYmJiYnLUMYONiYmJiclRxww2JiYmJiZHnf8HhjvNftCyO3gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata_rearrange = adata[np.where(~adata.obs['class'].isin(['31 OPC-Oligo']))[0].tolist() + \n",
    "                        np.where(adata.obs['class'].isin(['31 OPC-Oligo']))[0].tolist()]\n",
    "adata_rearrange.obs['class'] = adata_rearrange.obs['class'].astype(str)\n",
    "adata_rearrange.obs.loc[adata_rearrange.obs['class'] != '31 OPC-Oligo', 'class'] = '99 Other'\n",
    "sq.pl.spatial_scatter(adata_rearrange, color='class', shape=None, figsize=(4, 4), size=1., legend_fontsize=9, title=\"\", frameon=False,\n",
    "                      palette=matplotlib.colors.ListedColormap(['C0', '#f0f0f0']))\n",
    "# plt.savefig(savefig_path + \"mmbrain_KO_scheme.pdf\", bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4a9e5879-4ae3-4997-a329-2c0a01fbbeeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "wt_adata = adata.copy()\n",
    "ko_adata = adata.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "225b0470-85db-43a5-abfa-3f90715b669a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene_symbol\n",
       "Sox10      0.298431\n",
       "Nfix       0.443308\n",
       "Cldn11     0.455745\n",
       "Mog        0.514136\n",
       "Pou3f3     0.569786\n",
       "            ...    \n",
       "Lhx4      28.757689\n",
       "Npffr2    28.905586\n",
       "Fgf3      29.486641\n",
       "Scgn      29.530397\n",
       "Hoxb8     30.028955\n",
       "Length: 1122, dtype: float32"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series((wt_adata.X[np.where(ko_adata.obs['class'] == '31 OPC-Oligo')[0]].std(axis=0)/\n",
    "           wt_adata.X[np.where(ko_adata.obs['class'] == '31 OPC-Oligo')[0]].mean(axis=0)), index=adata.var['gene_symbol']).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1d1de048-b5be-4ce6-80a4-723c59fcf7a3",
   "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>var</th>\n",
       "      <th>mean</th>\n",
       "      <th>dispersion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Grm3</th>\n",
       "      <td>0.706748</td>\n",
       "      <td>1.044864</td>\n",
       "      <td>0.676402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ermn</th>\n",
       "      <td>0.840322</td>\n",
       "      <td>1.308472</td>\n",
       "      <td>0.642216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cldn11</th>\n",
       "      <td>1.662142</td>\n",
       "      <td>2.828864</td>\n",
       "      <td>0.587565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sec14l5</th>\n",
       "      <td>0.932986</td>\n",
       "      <td>1.588670</td>\n",
       "      <td>0.587275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sulf2</th>\n",
       "      <td>0.736025</td>\n",
       "      <td>1.312926</td>\n",
       "      <td>0.560599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sema6a</th>\n",
       "      <td>0.627580</td>\n",
       "      <td>1.130927</td>\n",
       "      <td>0.554925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mog</th>\n",
       "      <td>1.162848</td>\n",
       "      <td>2.097412</td>\n",
       "      <td>0.554421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zfp536</th>\n",
       "      <td>0.711731</td>\n",
       "      <td>1.330900</td>\n",
       "      <td>0.534774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gprc5b</th>\n",
       "      <td>0.698224</td>\n",
       "      <td>1.348250</td>\n",
       "      <td>0.517874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeb2</th>\n",
       "      <td>0.613312</td>\n",
       "      <td>1.227442</td>\n",
       "      <td>0.499667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pou3f3</th>\n",
       "      <td>0.565051</td>\n",
       "      <td>1.319265</td>\n",
       "      <td>0.428307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nfix</th>\n",
       "      <td>0.569929</td>\n",
       "      <td>1.702961</td>\n",
       "      <td>0.334669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sox10</th>\n",
       "      <td>0.429683</td>\n",
       "      <td>2.196494</td>\n",
       "      <td>0.195622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  var      mean  dispersion\n",
       "gene_symbol                                \n",
       "Grm3         0.706748  1.044864    0.676402\n",
       "Ermn         0.840322  1.308472    0.642216\n",
       "Cldn11       1.662142  2.828864    0.587565\n",
       "Sec14l5      0.932986  1.588670    0.587275\n",
       "Sulf2        0.736025  1.312926    0.560599\n",
       "Sema6a       0.627580  1.130927    0.554925\n",
       "Mog          1.162848  2.097412    0.554421\n",
       "Zfp536       0.711731  1.330900    0.534774\n",
       "Gprc5b       0.698224  1.348250    0.517874\n",
       "Zeb2         0.613312  1.227442    0.499667\n",
       "Pou3f3       0.565051  1.319265    0.428307\n",
       "Nfix         0.569929  1.702961    0.334669\n",
       "Sox10        0.429683  2.196494    0.195622"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dispersion_df = pd.DataFrame([wt_adata.X[np.where(ko_adata.obs['class'] == '31 OPC-Oligo')[0]].var(axis=0), \n",
    "             wt_adata.X[np.where(ko_adata.obs['class'] == '31 OPC-Oligo')[0]].mean(axis=0)],\n",
    "             columns=adata.var['gene_symbol'],\n",
    "             index=['var', 'mean']).T\n",
    "dispersion_df['dispersion'] = dispersion_df['var'] / dispersion_df['mean']\n",
    "dispersion_df = dispersion_df[dispersion_df['mean'] > 1.]\n",
    "dispersion_df.sort_values('dispersion', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1c0039ab-e646-4dc1-9c19-692a17817ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x174f6c463d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(wt_adata.X[np.where(ko_adata.obs['class'] == '31 OPC-Oligo')[0]].mean(axis=0), \n",
    "            wt_adata.X[np.where(ko_adata.obs['class'] == '31 OPC-Oligo')[0]].var(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df372284-dcff-416d-b8dc-1577b364b202",
   "metadata": {},
   "source": [
    "### Remove *Mog* from OPC-Oligo and reconstruct cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "afc48035-9fa3-449b-8baa-4207d9e80bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using ['global'] as regional annotations.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07488b130cf1407c849b6b4efd9029c6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d92173b81ada4f348e7f506110cf1963",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using ['global'] as regional annotations.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8196f604c6db4242871648717a06ae36",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8abd095368824b13a5464372394950a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cuers7NSiRYtitj/66KN64YUXoj9fuHBBu3bt0j333CNJOnDggNLS0lRUVKScnBwVFhbqrbfekiT5fD51d3eroKAgerzT6VRmZqYOHTo04ByhUEh+vz9mAQCA4WnIwZKbm6ukpCt/3lwgENDDDz+s1NRUVVVVSZJGjBihSZMmqampSSdPnlRVVZUeffRRvffeewoEApIkm80Wcz9paWnq6+sb8DHq6+vlcDiiy+VyDfVUAABAgrD8r4Q++eQTTZ06VRcvXlRLS4syMjIkSdXV1dq+fbvuuOMOjR49WiUlJbr//vu1ffv2aKgEg8GY+woGg9Hj/1NNTY18Pl90dXV1WX0qAADAEJYGy9tvv63JkyfrgQce0DvvvKPMzMzovueff1579uyJuX0oFFJqaqoyMzM1ZswYeb3e6L6enh6dO3dO+fn5Az5WSkqK7HZ7zAIAAMOTZcHy3nvvqbi4WBs2bNDzzz9/2ctGXV1dWrlypY4ePaqLFy/qlVde0f79+/XYY49JksrKylRXV6djx44pEAiosrJSRUVFGjdunFUjAgCABGXZlx/+7Gc/Uzgc1urVq7V69ero9hkzZsjj8ei5557TyJEjNWPGDPX29mrixIl6++23lZeXJ0mqra1VOBzWjBkzFAgENHPmTG3bts2q8QAAQAIbEYlEIvEewgp+v18Oh0M+n4+Xh4BhiG9r/t/49Jl58R4BXzLX+vubj+YHAADGI1gAAIDxCBYAAGA8ggUAABjPsr8SGs4S8c1+vHEOADCccIUFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxuOj+QEAUXwVCUzFFRYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYLzrDpYzZ84oLy9Pra2t0W3vv/++pkyZovT0dN1+++3asmVLzDFut1t5eXmy2WwqLCxUe3t7dN+lS5dUXV0tp9OpjIwMLViwQKdOnbre8QAAwDByXcGyb98+TZ06VZ2dndFt58+f10MPPaRly5apt7dXW7ZsUVVVlT744ANJUmtrq1atWiW3263e3l6VlJRo/vz5CgaDkqS6ujrt3r1bBw8e1IkTJ5Samqrly5dbcIoAACDRDTlY3G63lixZovXr18ds37Fjh7Kzs7Vy5UolJSVp1qxZKikp0UsvvSRJampq0uLFizV9+nQlJyerqqpKOTk52rp1a3T/mjVr5HK5ZLfb1djYKI/Ho6NHj1pwmgAAIJENOVjmzp2rzs5OLVq0KGa71+tVQUFBzLYJEyaoo6Pjqvt9Pp+6u7tj9judTmVmZurQoUMDzhEKheT3+2MWAAAYnoYcLLm5uUpKSrpseyAQkM1mi9mWlpamvr6+q+4PBAKSdMXj/1N9fb0cDkd0uVyuoZ4KAABIEJb9lZDNZou+H+ULwWBQGRkZV93/Rahc6fj/VFNTI5/PF11dXV1WnQoAADCMZcGSn58vr9cbs+3w4cPKz8+/6v7MzEyNGTMmZn9PT4/OnTsXPf4/paSkyG63xywAADA8WRYsjzzyiHp6etTQ0KBwOKyWlhY1NzervLxcklReXq7m5ma1tLQoHA6roaFBp0+fVnFxsSSprKxMdXV1OnbsmAKBgCorK1VUVKRx48ZZNSIAAEhQl78Z5TplZ2fr3Xff1fe+9z3V1tbq5ptv1saNGzVz5kxJ0uzZs7Vp0yZVVFSou7tbEydOlMfjUVZWliSptrZW4XBYM2bMUCAQ0MyZM7Vt2zarxgMAAAlsRCQSicR7CCv4/X45HA75fD7LXx66be0uS+/vf+HTZ+bFewTAUon4/0P8b/DvXWK71t/ffDQ/AAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHiWBUtzc7PS09Nj1ujRo5WSkiJJqqioUEpKSsz+zZs3R493u93Ky8uTzWZTYWGh2tvbrRoNAAAkOMuCpaSkRH19fdH1ySefKCcnR1u2bJEkHThwQJs3b465zZNPPilJam1t1apVq+R2u9Xb26uSkhLNnz9fwWDQqvEAAEACuyEvCUUiES1dulTz5s1TaWmpQqGQPv74YxUWFg54+6amJi1evFjTp09XcnKyqqqqlJOTo61bt96I8QAAQIK5IcHy+uuvy+v16oUXXpAkdXR0KBwOq7a2Vk6nU3feeaeeffZZ9ff3S5K8Xq8KCgpi7mPChAnq6OgY9DFCoZD8fn/MAgAAw5PlwdLf36+f/vSn+tGPfqSMjAxJks/n03333afVq1eru7tbr7/+ujZu3Kj/+7//kyQFAgHZbLaY+0lLS1NfX9+gj1NfXy+HwxFdLpfL6lMBAACGsDxYWlpadOrUKT3xxBPRbXPmzNHevXtVVFSk5ORkTZ48WZWVldGXfGw222XvVwkGg9HgGUhNTY18Pl90dXV1WX0qAADAEJYHy44dO1RcXBxzxWTnzp16+eWXY24XCoWUmpoqScrPz5fX643Zf/jwYeXn5w/6OCkpKbLb7TELAAAMT5YHyx//+Efde++9MdsikYiqqqq0Z88eRSIRtbe3q7GxUStWrJAklZeXq7m5WS0tLQqHw2poaNDp06dVXFxs9XgAACABJVl9h0ePHtWYMWNithUXF2vDhg166qmn1N3drdzcXK1bt06lpaWSpNmzZ2vTpk2qqKhQd3e3Jk6cKI/Ho6ysLKvHAwAACcjyYBnsjbIrVqyIXlEZSGlpaTRgAAAA/h0fzQ8AAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwXlK8BwDwv3fb2l3xHgEAhsTSKyxbt25VUlKS0tPTo2vp0qWSpPfff19TpkxRenq6br/9dm3ZsiXmWLfbrby8PNlsNhUWFqq9vd3K0QAAQAKzNFgOHDigpUuXqq+vL7pee+01nT9/Xg899JCWLVum3t5ebdmyRVVVVfrggw8kSa2trVq1apXcbrd6e3tVUlKi+fPnKxgMWjkeAABIUJYHS2Fh4WXbd+zYoezsbK1cuVJJSUmaNWuWSkpK9NJLL0mSmpqatHjxYk2fPl3JycmqqqpSTk6Otm7dauV4AAAgQVkWLP39/frTn/6kXbt26dZbb9XYsWP15JNP6vz58/J6vSooKIi5/YQJE9TR0SFJV90/kFAoJL/fH7MAAMDwZFmwnDlzRt/61re0cOFCHTlyRPv379df//pXlZaWKhAIyGazxdw+LS1NfX19knTV/QOpr6+Xw+GILpfLZdWpAAAAw1gWLE6nU21tbSovL1daWppuueUWPffcc/J4PIpEIpe9HyUYDCojI0OSZLPZrrh/IDU1NfL5fNHV1dVl1akAAADDWBYshw4d0tq1axWJRKLbQqGQRo4cqcmTJ8vr9cbc/vDhw8rPz5ck5efnX3H/QFJSUmS322MWAAAYniwLlqysLL344ov6+c9/rosXL+r48eOqrq7W448/roULF6qnp0cNDQ0Kh8NqaWlRc3OzysvLJUnl5eVqbm5WS0uLwuGwGhoadPr0aRUXF1s1HgAASGCWBcvYsWO1a9cu7dy5U1lZWSosLNSkSZP04osvKjs7W++++67eeOMNZWdna/ny5dq4caNmzpwpSZo9e7Y2bdqkiooKZWZm6te//rU8Ho+ysrKsGg8AACQwSz/ptqioSPv37x9wX2Fhofbt2zfosaWlpSotLbVyHAAAMEzwXUIAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA41kaLB0dHZozZ46ysrKUm5urZcuW6fPPP5ckVVRUKCUlRenp6dG1efPm6LFut1t5eXmy2WwqLCxUe3u7laMBAIAEZlmwXLhwQQ8++KCmTZumnp4eeb1enT17VmVlZZKkAwcOaPPmzerr64uuJ598UpLU2tqqVatWye12q7e3VyUlJZo/f76CwaBV4wEAgARmWbAcP35cd999t2prazV69GhlZ2drxYoVamtrUygU0scff6zCwsIBj21qatLixYs1ffp0JScnq6qqSjk5Odq6datV4wEAgARmWbDcdddd8ng8GjVqVHTb9u3bdc8996ijo0PhcFi1tbVyOp2688479eyzz6q/v1+S5PV6VVBQEHN/EyZMUEdHh1XjAQCABJZ0I+40Eono6aef1ptvvqm2tjb19PTovvvu0+rVq/Wb3/xGH330kYqLizVy5EhVV1crEAjIZrPF3EdaWpr6+voGfYxQKKRQKBT92e/334hTAQAABrD8r4T8fr8WLlyo119/XW1tbSooKNCcOXO0d+9eFRUVKTk5WZMnT1ZlZWX0JR+bzXbZ+1WCwaAyMjIGfZz6+no5HI7ocrlcVp8KAAAwhKXB0tnZqUmTJsnv9+vgwYPRl3l27typl19+Oea2oVBIqampkqT8/Hx5vd6Y/YcPH1Z+fv6gj1VTUyOfzxddXV1dVp4KAAAwiGXBcv78ec2aNUvTpk3TO++8o5ycnOi+SCSiqqoq7dmzR5FIRO3t7WpsbNSKFSskSeXl5WpublZLS4vC4bAaGhp0+vRpFRcXD/p4KSkpstvtMQsAAAxPlr2H5dVXX9Xx48e1bds2vfHGGzH7+vr6tGHDBj311FPq7u5Wbm6u1q1bp9LSUknS7NmztWnTJlVUVKi7u1sTJ06Ux+NRVlaWVeMBAIAENiISiUTiPYQV/H6/HA6HfD6f5Vdbblu7y9L7+1/49Jl58R4BBkvE/00Dg+Hfu8R2rb+/+Wh+AABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDzLPpof+LLiU2MB4MYjWAAACS0R/6OBrxMYOl4SAgAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgvKR4DwB84ba1u+I9AgDAUFxhAQAAxuMKyzDF1QoAwHDCFRYAAGA8ggUAABiPYAEAAMYjWAAAgPGMCpbPPvtMDz/8sG666Sbl5OSosrJSFy9ejPdYAAAgzowKlkWLFik9PV0nT57UBx98oN///vfasGFDvMcCAABxNiISiUTiPYQk/e1vf9Mdd9yhEydO6Gtf+5okaevWrfrhD3+ov//971c93u/3y+FwyOfzyW63WzobfyIMAPiy+/SZeTfkfq/197cxn8Pi9XqVlZUVjRVJmjBhgo4fP67e3l7ddNNNMbcPhUIKhULRn30+n6R/nbjV+kNBy+8TAIBEciN+v/77/V7t+okxwRIIBGSz2WK2paWlSZL6+vouC5b6+nqtW7fusvtxuVw3bEYAAL6sHA039v4DgYAcDseg+40JFpvNpmAw9krGFz9nZGRcdvuamhp9//vfj/7c39+vc+fOKTs7WyNGjLBsLr/fL5fLpa6uLstfasKNwXOWeHjOEgvPV+Ix+TmLRCIKBAIxr7AMxJhgyc/P19mzZ3X69Gk5nU5J0uHDhzV27NgBiyslJUUpKSkx2/7zKoyV7Ha7cU8yroznLPHwnCUWnq/EY+pzdqUrK18w5q+E7rjjDn37299WZWWlAoGAjh07pp/+9Kd64okn4j0aAACIM2OCRZK2b9+uixcv6vbbb9eUKVP0wAMP6Omnn473WAAAIM6MeUlIkpxOp9544414jxEjJSVFP/nJTy57+Qnm4jlLPDxniYXnK/EMh+fMmM9hAQAAGIxRLwkBAAAMhGABAADGI1gAAIDxCJYr4NujE9eZM2eUl5en1tbWeI+CK+jo6NCcOXOUlZWl3NxcLVu2TJ9//nm8x8IV7N27V1OmTJHdbldubq5WrVqlCxcuxHssXMWlS5d033336fHHH4/3KNeNYLkCvj06Me3bt09Tp05VZ2dnvEfBFVy4cEEPPvigpk2bpp6eHnm9Xp09e1ZlZWXxHg2DOHPmjObNm6eKigr19vbqo48+Umtrq5555pl4j4arWLdunf7whz/Ee4z/CsEyiL/97W9qbW3Vc889p7S0NH3961/X008/rRdffDHeo+EK3G63lixZovXr18d7FFzF8ePHdffdd6u2tlajR49Wdna2VqxYoba2tniPhkHcfPPN+uyzz/T4449rxIgROnv2rP7xj3/o5ptvjvdouIK9e/dqx44devTRR+M9yn+FYBnE1b49GmaaO3euOjs7tWjRoniPgqu466675PF4NGrUqOi27du365577onjVLiaL77bzeVyqaCgQF/96le5Kmawzz77TE888YR+9atfRb9QOFERLIO42rdHw0y5ublKSjLq8xBxDSKRiH784x/rzTffVGNjY7zHwTX461//qhMnTmjUqFFauHBhvMfBAPr7+1VaWqrvf//7uvvuu+M9zn+Nf9kHMdRvjwZwffx+v8rKyvThhx+qra1NBQUF8R4J1yA1NVWpqal69tlnNWXKFJ0/f16ZmZnxHgv/pr6+Xl/5yle0atWqeI9iCYJlEEP99mgAQ9fZ2amHHnpIt9xyiw4ePKicnJx4j4Qr2L9/v8rLy3Xo0CGNHj1akhQKhTR69OjLrkgj/l577TWdPHlSN910k6T//x/dO3fuTMi3NvCS0CD49mjgxjp//rxmzZqladOm6Z133iFWEsA3vvENBYNBrV27Vv/85z/197//XT/4wQ/0xBNPRAMG5vjLX/4iv9+v3t5e9fb2asmSJVqyZElCxopEsFwR3x4N3Divvvqqjh8/rm3btslutys9PT26YKb09HT97ne/05///Gc5nU4VFRVpzpw5fNwD/if48kMAAGA8rrAAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACM9/8ADN8vbZIOCiYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ko_gene = 'Mog'\n",
    "\n",
    "plt.hist(ko_adata.X[np.where(ko_adata.obs['class'] == '31 OPC-Oligo')[0], \n",
    "         np.where(ko_adata.var['gene_symbol'] == ko_gene)[0][0]])\n",
    "ko_adata.X[np.where(ko_adata.obs['class'] == '31 OPC-Oligo')[0], \n",
    "           np.where(ko_adata.var['gene_symbol'] == ko_gene)[0][0]] = 0\n",
    "ko_dataset = sf.make_dataset([ko_adata], sparse_graph=True, regional_obs=['global'])\n",
    "sf.tools.calc_obs([ko_adata], ko_dataset, model, get_recon=True)\n",
    "\n",
    "wt_dataset = sf.make_dataset([wt_adata], sparse_graph=True, regional_obs=['global'])\n",
    "sf.tools.calc_obs([wt_adata], wt_dataset, model, get_recon=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "968bbd14-cd6e-425f-b143-f4796e80e31f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_rmse = ((ko_adata.obsm['X_recon'] - wt_adata.obsm['X_recon']) ** 2).sum(axis=0) ** .5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "95f9b4a9-c625-42a2-b016-36509db8c731",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_rmse = ((ko_adata.obsm['X_recon'] - wt_adata.obsm['X_recon']) ** 2).sum(axis=1) ** .5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "72ede593-e186-4f25-81d7-3a2e67773665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01820022, 0.04508448, 0.04385628, ..., 0.03686631, 0.04018443,\n",
       "       0.02311902], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "77d05c5e-5f1f-4c9b-a3e5-a30f0908a145",
   "metadata": {},
   "outputs": [],
   "source": [
    "ko_adata.obs['cell_rmse'] = cell_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8da3bd29-fc4f-404c-bfef-0fd8d22b0d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 80x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(.8, 3))\n",
    "my_obs = ko_adata.obs.copy()\n",
    "my_obs['class'] = my_obs['class'].tolist()\n",
    "my_obs = my_obs.sort_values('class')\n",
    "class_count = my_obs['class'].value_counts()\n",
    "chosen_classes = class_count[class_count > (class_count.sum()) * 0.005].index.tolist()\n",
    "chosen_classes.remove('31 OPC-Oligo')\n",
    "sns.boxplot(my_obs[my_obs['class'].isin(chosen_classes)], y='class', x='cell_rmse', \n",
    "            fliersize=.0, linewidth=.5, ax=ax)\n",
    "#ax.set_yticklabels(ax.get_yticklabels(), rotation=30, ha='right', va='top', rotation_mode='anchor')\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlabel(\"RMSE(WT, KO)\")\n",
    "for pos in ['right', 'top']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.xlim([0, .2])\n",
    "# plt.savefig(savefig_path + \"mmbrain_perturbation_cell_mse.pdf\", transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "acf00c79-3a96-4a53-a6b5-1e648ec4068b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\AppData\\Local\\Temp\\ipykernel_116268\\945811404.py:13: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha='right', va='center', rotation_mode='anchor')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 0.2)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x80 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4.5, .8))\n",
    "my_obs = ko_adata.obs.copy()\n",
    "my_obs['class'] = my_obs['class'].tolist()\n",
    "my_obs = my_obs.sort_values('class')\n",
    "class_count = my_obs['class'].value_counts()\n",
    "chosen_classes = class_count[class_count > (class_count.sum()) * 0.005].index.tolist()\n",
    "chosen_classes.remove('31 OPC-Oligo')\n",
    "sns.boxplot(my_obs[my_obs['class'].isin(chosen_classes)], x='class', y='cell_rmse', \n",
    "            fliersize=.0, linewidth=.5, ax=ax)\n",
    "#ax.set_yticklabels(ax.get_yticklabels(), rotation=30, ha='right', va='top', rotation_mode='anchor')\n",
    "ax.set_ylabel(\"RMSE(WT, KO)\")\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha='right', va='center', rotation_mode='anchor')\n",
    "for pos in ['right', 'top']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "plt.ylim([0, .2])\n",
    "# plt.savefig(savefig_path + \"mmbrain_perturbation_cell_mse.pdf\", transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7011bb6c-d5d3-4ac2-9dde-0d43d3f5e1ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:86: FutureWarning: The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\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>names</th>\n",
       "      <th>scores</th>\n",
       "      <th>logfoldchanges</th>\n",
       "      <th>pvals</th>\n",
       "      <th>pvals_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pif1</td>\n",
       "      <td>16.773153</td>\n",
       "      <td>0.083190</td>\n",
       "      <td>3.835997e-63</td>\n",
       "      <td>4.303989e-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Diaph3</td>\n",
       "      <td>14.955308</td>\n",
       "      <td>0.079347</td>\n",
       "      <td>1.438155e-50</td>\n",
       "      <td>8.068052e-48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clspn</td>\n",
       "      <td>14.882693</td>\n",
       "      <td>0.067352</td>\n",
       "      <td>4.269589e-50</td>\n",
       "      <td>1.596826e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Slfn9</td>\n",
       "      <td>14.656121</td>\n",
       "      <td>0.064221</td>\n",
       "      <td>1.231072e-48</td>\n",
       "      <td>3.453157e-46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nfib</td>\n",
       "      <td>13.375875</td>\n",
       "      <td>0.069715</td>\n",
       "      <td>8.366315e-41</td>\n",
       "      <td>1.341001e-38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>Sec14l5</td>\n",
       "      <td>-11.687506</td>\n",
       "      <td>-0.152907</td>\n",
       "      <td>1.476599e-31</td>\n",
       "      <td>8.283721e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>Cldn11</td>\n",
       "      <td>-12.559337</td>\n",
       "      <td>-0.239797</td>\n",
       "      <td>3.532823e-36</td>\n",
       "      <td>3.303190e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>Zfp536</td>\n",
       "      <td>-13.351248</td>\n",
       "      <td>-0.143543</td>\n",
       "      <td>1.164816e-40</td>\n",
       "      <td>1.633655e-38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Sox10</td>\n",
       "      <td>-13.862960</td>\n",
       "      <td>-0.142347</td>\n",
       "      <td>1.062029e-43</td>\n",
       "      <td>1.985995e-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>4921534H16Rik</td>\n",
       "      <td>-14.482137</td>\n",
       "      <td>-0.034744</td>\n",
       "      <td>1.571361e-47</td>\n",
       "      <td>3.526134e-45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1122 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              names     scores  logfoldchanges         pvals     pvals_adj\n",
       "0              Pif1  16.773153        0.083190  3.835997e-63  4.303989e-60\n",
       "1            Diaph3  14.955308        0.079347  1.438155e-50  8.068052e-48\n",
       "2             Clspn  14.882693        0.067352  4.269589e-50  1.596826e-47\n",
       "3             Slfn9  14.656121        0.064221  1.231072e-48  3.453157e-46\n",
       "4              Nfib  13.375875        0.069715  8.366315e-41  1.341001e-38\n",
       "...             ...        ...             ...           ...           ...\n",
       "1117        Sec14l5 -11.687506       -0.152907  1.476599e-31  8.283721e-30\n",
       "1118         Cldn11 -12.559337       -0.239797  3.532823e-36  3.303190e-34\n",
       "1119         Zfp536 -13.351248       -0.143543  1.164816e-40  1.633655e-38\n",
       "1120          Sox10 -13.862960       -0.142347  1.062029e-43  1.985995e-41\n",
       "1121  4921534H16Rik -14.482137       -0.034744  1.571361e-47  3.526134e-45\n",
       "\n",
       "[1122 rows x 5 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = wt_adata.obsm['X_recon'][wt_adata.obs['class'] == '31 OPC-Oligo', :]\n",
    "y = ko_adata.obsm['X_recon'][ko_adata.obs['class'] == '31 OPC-Oligo', :]\n",
    "cmp_adata = sc.AnnData(np.vstack([x, y]), var=ko_adata.var.copy())\n",
    "cmp_adata.var_names = cmp_adata.var['gene_symbol']\n",
    "cmp_adata.obs['grp'] = ['WT'] * x.shape[0] + ['KO'] * y.shape[0]\n",
    "sc.tl.rank_genes_groups(cmp_adata, groupby='grp', method='wilcoxon')\n",
    "cmp_df = sc.get.rank_genes_groups_df(cmp_adata, group=\"KO\")\n",
    "cmp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e5d0d46b-2403-4aa6-9bdc-a3949f8f7589",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:86: FutureWarning: The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\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>names</th>\n",
       "      <th>scores</th>\n",
       "      <th>logfoldchanges</th>\n",
       "      <th>pvals</th>\n",
       "      <th>pvals_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Slc35d3</td>\n",
       "      <td>0.756400</td>\n",
       "      <td>0.013487</td>\n",
       "      <td>0.449409</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kirrel</td>\n",
       "      <td>0.742567</td>\n",
       "      <td>0.014942</td>\n",
       "      <td>0.457744</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sfrp1</td>\n",
       "      <td>0.733180</td>\n",
       "      <td>0.011120</td>\n",
       "      <td>0.463449</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Satb1</td>\n",
       "      <td>0.711935</td>\n",
       "      <td>0.017550</td>\n",
       "      <td>0.476505</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Il1rap</td>\n",
       "      <td>0.702054</td>\n",
       "      <td>0.010552</td>\n",
       "      <td>0.482645</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>Tspear</td>\n",
       "      <td>-0.681304</td>\n",
       "      <td>-0.015128</td>\n",
       "      <td>0.495679</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>Tnc</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.024466</td>\n",
       "      <td>0.486350</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>Pappa2</td>\n",
       "      <td>-0.729721</td>\n",
       "      <td>-0.018365</td>\n",
       "      <td>0.465561</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Pax3</td>\n",
       "      <td>-0.729721</td>\n",
       "      <td>-0.021977</td>\n",
       "      <td>0.465561</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>Irx5</td>\n",
       "      <td>-0.852247</td>\n",
       "      <td>-0.019905</td>\n",
       "      <td>0.394077</td>\n",
       "      <td>0.999606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1122 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        names    scores  logfoldchanges     pvals  pvals_adj\n",
       "0     Slc35d3  0.756400        0.013487  0.449409   0.999606\n",
       "1      Kirrel  0.742567        0.014942  0.457744   0.999606\n",
       "2       Sfrp1  0.733180        0.011120  0.463449   0.999606\n",
       "3       Satb1  0.711935        0.017550  0.476505   0.999606\n",
       "4      Il1rap  0.702054        0.010552  0.482645   0.999606\n",
       "...       ...       ...             ...       ...        ...\n",
       "1117   Tspear -0.681304       -0.015128  0.495679   0.999606\n",
       "1118      Tnc -0.696125       -0.024466  0.486350   0.999606\n",
       "1119   Pappa2 -0.729721       -0.018365  0.465561   0.999606\n",
       "1120     Pax3 -0.729721       -0.021977  0.465561   0.999606\n",
       "1121     Irx5 -0.852247       -0.019905  0.394077   0.999606\n",
       "\n",
       "[1122 rows x 5 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "celltype = '12 HY GABA'\n",
    "x = wt_adata.obsm['X_recon'][(wt_adata.obs['class'] == celltype), :]\n",
    "y = ko_adata.obsm['X_recon'][(ko_adata.obs['class'] == celltype), :]\n",
    "cmp_adata = sc.AnnData(np.vstack([x, y]), var=ko_adata.var.copy())\n",
    "cmp_adata.var_names = cmp_adata.var['gene_symbol']\n",
    "cmp_adata.obs['grp'] = ['WT'] * x.shape[0] + ['KO'] * y.shape[0]\n",
    "sc.tl.rank_genes_groups(cmp_adata, groupby='grp', reference='WT', method='wilcoxon')\n",
    "cmp_df = sc.get.rank_genes_groups_df(cmp_adata, group=\"KO\")\n",
    "cmp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09e9da0-cfc0-459d-8213-6289951f01a5",
   "metadata": {},
   "source": [
    "### GSEA on DEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "942ab2f9-fe44-46bf-9584-8d6a3fd0b510",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\gseapy\\msigdb.py:20: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  d = pd.read_html(resp.text)[0]\n",
      "C:\\Users\\lshh\\miniconda3\\envs\\py311_torch211_cuda121\\Lib\\site-packages\\gseapy\\msigdb.py:72: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  d = pd.read_html(resp.text)[0]\n"
     ]
    }
   ],
   "source": [
    "import gseapy\n",
    "from gseapy import Msigdb\n",
    "\n",
    "msig = Msigdb()\n",
    "gmt = msig.get_gmt(category='c5.go.bp', dbver=\"2024.1.Hs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "875f0436-b937-4ecc-b12d-435d098dca99",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-03-03 17:52:50,598 [WARNING] Duplicated values found in preranked stats: 36.36% of genes\n",
      "The order of those genes will be arbitrary, which may produce unexpected results.\n",
      "2025-03-03 17:52:50,598 [INFO] Parsing data files for GSEA.............................\n",
      "2025-03-03 17:52:50,688 [INFO] 5396 gene_sets have been filtered out when max_size=1000 and min_size=5\n",
      "2025-03-03 17:52:50,689 [INFO] 2212 gene_sets used for further statistical testing.....\n",
      "2025-03-03 17:52:50,690 [INFO] Start to run GSEA...Might take a while..................\n",
      "2025-03-03 17:52:55,533 [INFO] Congratulations. GSEApy runs successfully................\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gene_df = cmp_df.copy()\n",
    "gene_df.index = cmp_df['names'].str.upper()\n",
    "\n",
    "gsea_res = gseapy.prerank(rnk=gene_df['scores'], # or rnk = rnk,\n",
    "                          gene_sets=gmt,\n",
    "                          threads=5,\n",
    "                          min_size=5,\n",
    "                          max_size=1000,\n",
    "                          permutation_num=1000, # reduce number to speed up testing\n",
    "                          outdir=None, # don't write to disk\n",
    "                          seed=0,\n",
    "                          verbose=True, # see what's going on behind the scenes\n",
    "                          )\n",
    "\n",
    "grea_res_selected = gsea_res.res2d[(gsea_res.res2d['NOM p-val'] < 0.01) & (gsea_res.res2d['FDR q-val'] < 0.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c02acc6e-f5a5-42e7-9291-f0234f437cc2",
   "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>Term</th>\n",
       "      <th>ES</th>\n",
       "      <th>NES</th>\n",
       "      <th>NOM p-val</th>\n",
       "      <th>FDR q-val</th>\n",
       "      <th>FWER p-val</th>\n",
       "      <th>Tag %</th>\n",
       "      <th>Gene %</th>\n",
       "      <th>Lead_genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>prerank</td>\n",
       "      <td>GOBP_TELENCEPHALON_DEVELOPMENT</td>\n",
       "      <td>0.482072</td>\n",
       "      <td>2.151512</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>0.048</td>\n",
       "      <td>33/58</td>\n",
       "      <td>31.37%</td>\n",
       "      <td>SLC32A1;ARX;ERBB4;DLX2;LHX6;BCL11B;OXTR;NR2E1;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>prerank</td>\n",
       "      <td>GOBP_PALLIUM_DEVELOPMENT</td>\n",
       "      <td>0.546905</td>\n",
       "      <td>2.149917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>0.049</td>\n",
       "      <td>19/34</td>\n",
       "      <td>23.71%</td>\n",
       "      <td>SLC32A1;ARX;DLX2;LHX6;NR2E1;PROX1;DRD1;EGFR;AL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>prerank</td>\n",
       "      <td>GOBP_REGULATION_OF_ORGAN_GROWTH</td>\n",
       "      <td>0.725254</td>\n",
       "      <td>2.047186</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.106388</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7/11</td>\n",
       "      <td>7.13%</td>\n",
       "      <td>ARX;ERBB4;COL14A1;IGF1;ZFPM2;PROX1;RBP4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>prerank</td>\n",
       "      <td>GOBP_FOREBRAIN_DEVELOPMENT</td>\n",
       "      <td>0.419623</td>\n",
       "      <td>2.040435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.086937</td>\n",
       "      <td>0.172</td>\n",
       "      <td>45/91</td>\n",
       "      <td>29.06%</td>\n",
       "      <td>SLC32A1;ARX;ERBB4;DLX2;LHX6;BCL11B;SOX2;LHX8;O...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>prerank</td>\n",
       "      <td>GOBP_CEREBRAL_CORTEX_CELL_MIGRATION</td>\n",
       "      <td>0.676108</td>\n",
       "      <td>2.031769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>0.196</td>\n",
       "      <td>8/13</td>\n",
       "      <td>22.37%</td>\n",
       "      <td>ARX;LHX6;NR2E1;EGFR;RELN;FGF13;SLIT2;POU3F3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>prerank</td>\n",
       "      <td>GOBP_FOREBRAIN_GENERATION_OF_NEURONS</td>\n",
       "      <td>0.572954</td>\n",
       "      <td>1.984345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.131596</td>\n",
       "      <td>0.355</td>\n",
       "      <td>11/21</td>\n",
       "      <td>18.81%</td>\n",
       "      <td>DLX2;LHX6;BCL11B;LHX8;NR2E1;PROX1;SOX1;GATA2;N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>prerank</td>\n",
       "      <td>GOBP_HIPPOCAMPUS_DEVELOPMENT</td>\n",
       "      <td>0.591436</td>\n",
       "      <td>1.956173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163665</td>\n",
       "      <td>0.457</td>\n",
       "      <td>11/19</td>\n",
       "      <td>18.63%</td>\n",
       "      <td>SLC32A1;DLX2;NR2E1;PROX1;DRD1;ALK;RELN;FGF13;Z...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name                                  Term        ES       NES  \\\n",
       "0  prerank        GOBP_TELENCEPHALON_DEVELOPMENT  0.482072  2.151512   \n",
       "1  prerank              GOBP_PALLIUM_DEVELOPMENT  0.546905  2.149917   \n",
       "2  prerank       GOBP_REGULATION_OF_ORGAN_GROWTH  0.725254  2.047186   \n",
       "3  prerank            GOBP_FOREBRAIN_DEVELOPMENT  0.419623  2.040435   \n",
       "4  prerank   GOBP_CEREBRAL_CORTEX_CELL_MIGRATION  0.676108  2.031769   \n",
       "5  prerank  GOBP_FOREBRAIN_GENERATION_OF_NEURONS  0.572954  1.984345   \n",
       "6  prerank          GOBP_HIPPOCAMPUS_DEVELOPMENT  0.591436  1.956173   \n",
       "\n",
       "  NOM p-val FDR q-val FWER p-val  Tag %  Gene %  \\\n",
       "0       0.0    0.0786      0.048  33/58  31.37%   \n",
       "1       0.0  0.039896      0.049  19/34  23.71%   \n",
       "2  0.001608  0.106388       0.16   7/11   7.13%   \n",
       "3       0.0  0.086937      0.172  45/91  29.06%   \n",
       "4       0.0    0.0786      0.196   8/13  22.37%   \n",
       "5       0.0  0.131596      0.355  11/21  18.81%   \n",
       "6       0.0  0.163665      0.457  11/19  18.63%   \n",
       "\n",
       "                                          Lead_genes  \n",
       "0  SLC32A1;ARX;ERBB4;DLX2;LHX6;BCL11B;OXTR;NR2E1;...  \n",
       "1  SLC32A1;ARX;DLX2;LHX6;NR2E1;PROX1;DRD1;EGFR;AL...  \n",
       "2            ARX;ERBB4;COL14A1;IGF1;ZFPM2;PROX1;RBP4  \n",
       "3  SLC32A1;ARX;ERBB4;DLX2;LHX6;BCL11B;SOX2;LHX8;O...  \n",
       "4        ARX;LHX6;NR2E1;EGFR;RELN;FGF13;SLIT2;POU3F3  \n",
       "5  DLX2;LHX6;BCL11B;LHX8;NR2E1;PROX1;SOX1;GATA2;N...  \n",
       "6  SLC32A1;DLX2;NR2E1;PROX1;DRD1;ALK;RELN;FGF13;Z...  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grea_res_selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5f6792f8-b93e-4358-ad3d-8257671abda1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\AppData\\Local\\Temp\\ipykernel_116268\\2450731809.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  grea_res_selected['Term2'] = grea_res_selected['Term'].apply(func)\n"
     ]
    }
   ],
   "source": [
    "def func(x):\n",
    "    x = x[5:]\n",
    "    return x.replace('_', ' ').lower()\n",
    "grea_res_selected['Term2'] = grea_res_selected['Term'].apply(func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "68f0a772-9f57-4003-a4c3-d865a6f6d947",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lshh\\AppData\\Local\\Temp\\ipykernel_116268\\2706694812.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  grea_res_selected['-log(q)'] = -np.log10(grea_res_selected['FDR q-val'].astype(float))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 180x125 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grea_res_selected['-log(q)'] = -np.log10(grea_res_selected['FDR q-val'].astype(float))\n",
    "fig = sns.relplot(grea_res_selected, y='Term2', x='NES', size='-log(q)', legend=False)\n",
    "plt.gcf().set_size_inches(1.8, 1.25)\n",
    "plt.grid(axis='y', zorder=0)\n",
    "# plt.savefig(savefig_path + \"mmbrain_perturbation_go.pdf\", transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca25173d-30b7-4b1a-bdac-08664bd89f29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}