{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92092038",
   "metadata": {},
   "source": [
    "# Multiple samples"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "95cde18a",
   "metadata": {},
   "source": [
    "In this notebook, we will work with multiple samples, and discuss batch effect issues."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c772fb6",
   "metadata": {},
   "source": [
    "# Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69e629d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2970f5d6",
   "metadata": {},
   "source": [
    "# Loading multiple samples into Scanpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3321af",
   "metadata": {},
   "source": [
    "All of the computation we have done so far has been on a single sample. However, most real world data are likely not going to be a single sample and often require us to merge multiple data. We will use Scanpy to concatenate multiple data into one unified anndata object and discuss the issue of technical effect and its correction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4962811a",
   "metadata": {},
   "source": [
    "For the purposes of illustration we will use the PBMC data (dataset 5) used in this publication: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1850-9. The authors provide the data in an easily usable format (https://github.com/JinmiaoChenLab/Batch-effect-removal-benchmarking)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7ad2fbbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path = '/Users/sara.jimenez/Documents/scWorkshop/data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a79917b",
   "metadata": {},
   "source": [
    "## batch-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82135380",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_batch1 = pd.read_csv(input_path + 'b1_exprs.txt', sep = '\\t', index_col = 0)\n",
    "celltype_batch1 = pd.read_csv(input_path + 'b1_celltype.txt', sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dece40c0",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>data_3p-AAACCTGAGCATCATC-0</th>\n",
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       "      <th>data_3p-AAACCTGGTGTCTGAT-0</th>\n",
       "      <th>...</th>\n",
       "      <th>data_3p-TTTGTCACAGGGATTG-0</th>\n",
       "      <th>data_3p-TTTGTCAGTAGCAAAT-0</th>\n",
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       "              data_3p-AAACCTGAGCATCATC-0  data_3p-AAACCTGAGCTAGTGG-0  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_3p-AAACCTGCACATTAGC-0  data_3p-AAACCTGCACTGTTAG-0  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_3p-AAACCTGCATAGTAAG-0  data_3p-AAACCTGCATGAACCT-0  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_3p-AAACCTGGTAAGAGGA-0  data_3p-AAACCTGGTAGAAGGA-0  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_3p-AAACCTGGTCCAGTGC-0  data_3p-AAACCTGGTGTCTGAT-0  ...  \\\n",
       "RP11-34P13.3                           0                           0  ...   \n",
       "FAM138A                                0                           0  ...   \n",
       "OR4F5                                  0                           0  ...   \n",
       "RP11-34P13.7                           0                           0  ...   \n",
       "RP11-34P13.8                           0                           0  ...   \n",
       "\n",
       "              data_3p-TTTGTCACAGGGATTG-0  data_3p-TTTGTCAGTAGCAAAT-0  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_3p-TTTGTCAGTCAGATAA-0  data_3p-TTTGTCAGTCGCGTGT-0  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_3p-TTTGTCAGTTACCGAT-0  data_3p-TTTGTCATCATGTCCC-0  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_3p-TTTGTCATCCGATATG-0  data_3p-TTTGTCATCGTCTGAA-0  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_3p-TTTGTCATCTCGAGTA-0  data_3p-TTTGTCATCTGCTTGC-0  \n",
       "RP11-34P13.3                           0                           0  \n",
       "FAM138A                                0                           0  \n",
       "OR4F5                                  0                           0  \n",
       "RP11-34P13.7                           0                           0  \n",
       "RP11-34P13.8                           0                           0  \n",
       "\n",
       "[5 rows x 8098 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_batch1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57f50b4",
   "metadata": {},
   "source": [
    "We see above that the rows are genes and the columns are cells. We will have to keep this in mind when we create an anndata object out of the data.\n",
    "\n",
    "Let's create an anndata object for batch1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27760320",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/my/xsqd7w190lg06fx4rlnfklshlvq6xm/T/ipykernel_62198/428804876.py:5: FutureWarning: X.dtype being converted to np.float32 from int64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n",
      "  adata1 = sc.AnnData(data_batch1.values.T,\n"
     ]
    }
   ],
   "source": [
    "# Notes: \n",
    "# We transpose the data to ensure rows are cells and columns are genes\n",
    "# We consider columns of the dataframe above as the cell names\n",
    "# We consider rows of the dataframe above as the gene names\n",
    "adata1 = sc.AnnData(data_batch1.values.T, \n",
    "                    obs = pd.DataFrame(index = data_batch1.columns), \n",
    "                    var = pd.DataFrame(index = data_batch1.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95dc6f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 8098 × 33694"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7554649a",
   "metadata": {},
   "source": [
    "Add the author provided celltype information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba03e3e3",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Sample</th>\n",
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       "      <th>data_3p-AAACCTGCATAGTAAG-0</th>\n",
       "      <td>data_3p</td>\n",
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       "      <td>0</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "      <td>10X_3prime</td>\n",
       "      <td>Monocyte_CD14</td>\n",
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       "      <th>data_3p-TTTGTCATCATGTCCC-0</th>\n",
       "      <td>data_3p</td>\n",
       "      <td>3141</td>\n",
       "      <td>1176</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>CD8 T cell</td>\n",
       "      <td>10X_3prime</td>\n",
       "      <td>CD8 T cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_3p-TTTGTCATCCGATATG-0</th>\n",
       "      <td>data_3p</td>\n",
       "      <td>5401</td>\n",
       "      <td>1379</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>CD8 T cell</td>\n",
       "      <td>10X_3prime</td>\n",
       "      <td>CD8 T cell</td>\n",
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       "      <th>data_3p-TTTGTCATCGTCTGAA-0</th>\n",
       "      <td>data_3p</td>\n",
       "      <td>6081</td>\n",
       "      <td>1802</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "      <td>10X_3prime</td>\n",
       "      <td>Monocyte_CD14</td>\n",
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       "      <th>data_3p-TTTGTCATCTCGAGTA-0</th>\n",
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       "      <td>1317</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>CD8 T cell</td>\n",
       "      <td>10X_3prime</td>\n",
       "      <td>CD8 T cell</td>\n",
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       "      <th>data_3p-TTTGTCATCTGCTTGC-0</th>\n",
       "      <td>data_3p</td>\n",
       "      <td>4027</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>CD8 T cell</td>\n",
       "      <td>10X_3prime</td>\n",
       "      <td>CD8 T cell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8098 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Sample  n_counts  n_genes  batch  louvain  \\\n",
       "data_3p-AAACCTGAGCATCATC-0  data_3p      2394      871      0        9   \n",
       "data_3p-AAACCTGAGCTAGTGG-0  data_3p      4520     1316      0        5   \n",
       "data_3p-AAACCTGCACATTAGC-0  data_3p      2788      898      0        1   \n",
       "data_3p-AAACCTGCACTGTTAG-0  data_3p      4667     1526      0        0   \n",
       "data_3p-AAACCTGCATAGTAAG-0  data_3p      4440     1495      0        0   \n",
       "...                             ...       ...      ...    ...      ...   \n",
       "data_3p-TTTGTCATCATGTCCC-0  data_3p      3141     1176      0        4   \n",
       "data_3p-TTTGTCATCCGATATG-0  data_3p      5401     1379      0        4   \n",
       "data_3p-TTTGTCATCGTCTGAA-0  data_3p      6081     1802      0        0   \n",
       "data_3p-TTTGTCATCTCGAGTA-0  data_3p      3970     1317      0        7   \n",
       "data_3p-TTTGTCATCTGCTTGC-0  data_3p      4027     1259      0        4   \n",
       "\n",
       "                                     anno      Method       CellType  \n",
       "data_3p-AAACCTGAGCATCATC-0         B cell  10X_3prime         B cell  \n",
       "data_3p-AAACCTGAGCTAGTGG-0     CD4 T cell  10X_3prime     CD4 T cell  \n",
       "data_3p-AAACCTGCACATTAGC-0     CD4 T cell  10X_3prime     CD4 T cell  \n",
       "data_3p-AAACCTGCACTGTTAG-0  Monocyte_CD14  10X_3prime  Monocyte_CD14  \n",
       "data_3p-AAACCTGCATAGTAAG-0  Monocyte_CD14  10X_3prime  Monocyte_CD14  \n",
       "...                                   ...         ...            ...  \n",
       "data_3p-TTTGTCATCATGTCCC-0     CD8 T cell  10X_3prime     CD8 T cell  \n",
       "data_3p-TTTGTCATCCGATATG-0     CD8 T cell  10X_3prime     CD8 T cell  \n",
       "data_3p-TTTGTCATCGTCTGAA-0  Monocyte_CD14  10X_3prime  Monocyte_CD14  \n",
       "data_3p-TTTGTCATCTCGAGTA-0     CD8 T cell  10X_3prime     CD8 T cell  \n",
       "data_3p-TTTGTCATCTGCTTGC-0     CD8 T cell  10X_3prime     CD8 T cell  \n",
       "\n",
       "[8098 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "celltype_batch1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f61bcd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in celltype_batch1.columns:\n",
    "    adata1.obs[item] = celltype_batch1.loc[adata1.obs_names][item]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4fc713fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 8098 × 33694\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b419f6",
   "metadata": {},
   "source": [
    "## batch-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "824dbaea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_batch2 = pd.read_csv(input_path + 'b2_exprs.txt', sep = '\\t', index_col = 0)\n",
    "celltype_batch2 = pd.read_csv(input_path + 'b2_celltype.txt', sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3b211272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_5p-AAACCTGAGCGATAGC-1</th>\n",
       "      <th>data_5p-AAACCTGAGCTAAACA-1</th>\n",
       "      <th>data_5p-AAACCTGAGGGAGTAA-1</th>\n",
       "      <th>data_5p-AAACCTGAGTCTTGCA-1</th>\n",
       "      <th>data_5p-AAACCTGAGTTCGATC-1</th>\n",
       "      <th>data_5p-AAACCTGCACACTGCG-1</th>\n",
       "      <th>data_5p-AAACCTGCACGGTGTC-1</th>\n",
       "      <th>data_5p-AAACCTGCAGATGGGT-1</th>\n",
       "      <th>data_5p-AAACCTGCAGGTGGAT-1</th>\n",
       "      <th>data_5p-AAACCTGGTAAGCACG-1</th>\n",
       "      <th>...</th>\n",
       "      <th>data_5p-TTTGTCACAGCTGGCT-1</th>\n",
       "      <th>data_5p-TTTGTCACAGGTGGAT-1</th>\n",
       "      <th>data_5p-TTTGTCAGTCCGAAGA-1</th>\n",
       "      <th>data_5p-TTTGTCAGTTGATTGC-1</th>\n",
       "      <th>data_5p-TTTGTCATCACAAACC-1</th>\n",
       "      <th>data_5p-TTTGTCATCCACGTTC-1</th>\n",
       "      <th>data_5p-TTTGTCATCGCGTAGC-1</th>\n",
       "      <th>data_5p-TTTGTCATCTTAACCT-1</th>\n",
       "      <th>data_5p-TTTGTCATCTTACCGC-1</th>\n",
       "      <th>data_5p-TTTGTCATCTTGTTTG-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RP11-34P13.3</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAM138A</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR4F5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RP11-34P13.7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RP11-34P13.8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 7378 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              data_5p-AAACCTGAGCGATAGC-1  data_5p-AAACCTGAGCTAAACA-1  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_5p-AAACCTGAGGGAGTAA-1  data_5p-AAACCTGAGTCTTGCA-1  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_5p-AAACCTGAGTTCGATC-1  data_5p-AAACCTGCACACTGCG-1  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_5p-AAACCTGCACGGTGTC-1  data_5p-AAACCTGCAGATGGGT-1  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_5p-AAACCTGCAGGTGGAT-1  data_5p-AAACCTGGTAAGCACG-1  ...  \\\n",
       "RP11-34P13.3                           0                           0  ...   \n",
       "FAM138A                                0                           0  ...   \n",
       "OR4F5                                  0                           0  ...   \n",
       "RP11-34P13.7                           0                           0  ...   \n",
       "RP11-34P13.8                           0                           0  ...   \n",
       "\n",
       "              data_5p-TTTGTCACAGCTGGCT-1  data_5p-TTTGTCACAGGTGGAT-1  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_5p-TTTGTCAGTCCGAAGA-1  data_5p-TTTGTCAGTTGATTGC-1  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_5p-TTTGTCATCACAAACC-1  data_5p-TTTGTCATCCACGTTC-1  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_5p-TTTGTCATCGCGTAGC-1  data_5p-TTTGTCATCTTAACCT-1  \\\n",
       "RP11-34P13.3                           0                           0   \n",
       "FAM138A                                0                           0   \n",
       "OR4F5                                  0                           0   \n",
       "RP11-34P13.7                           0                           0   \n",
       "RP11-34P13.8                           0                           0   \n",
       "\n",
       "              data_5p-TTTGTCATCTTACCGC-1  data_5p-TTTGTCATCTTGTTTG-1  \n",
       "RP11-34P13.3                           0                           0  \n",
       "FAM138A                                0                           0  \n",
       "OR4F5                                  0                           0  \n",
       "RP11-34P13.7                           0                           0  \n",
       "RP11-34P13.8                           0                           0  \n",
       "\n",
       "[5 rows x 7378 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_batch2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ead1e96",
   "metadata": {},
   "source": [
    "Similar to batch-1, we see above that the rows are genes and the columns are cells. We will have to keep this in mind when we create an anndata object out of the data.\n",
    "\n",
    "Let's create an anndata object for batch-2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4cdbca52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/my/xsqd7w190lg06fx4rlnfklshlvq6xm/T/ipykernel_62198/4161174250.py:5: FutureWarning: X.dtype being converted to np.float32 from int64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n",
      "  adata2 = sc.AnnData(data_batch2.values.T,\n"
     ]
    }
   ],
   "source": [
    "# Notes: \n",
    "# We transpose the data to ensure rows are cells and columns are genes\n",
    "# We consider columns of the dataframe above as the cell names\n",
    "# We consider rows of the dataframe above as the gene names\n",
    "adata2 = sc.AnnData(data_batch2.values.T, \n",
    "                    obs = pd.DataFrame(index = data_batch2.columns), \n",
    "                    var = pd.DataFrame(index = data_batch2.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8efe792c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 7378 × 33694"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cce9e245",
   "metadata": {},
   "source": [
    "Add the author provided celltype information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e4a8ce01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>batch</th>\n",
       "      <th>louvain</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>data_5p-AAACCTGAGCGATAGC-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>2712</td>\n",
       "      <td>1318</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>NK cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_5p-AAACCTGAGCTAAACA-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>6561</td>\n",
       "      <td>2164</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_5p-AAACCTGAGGGAGTAA-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>6322</td>\n",
       "      <td>2112</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_5p-AAACCTGAGTCTTGCA-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>4528</td>\n",
       "      <td>1526</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>CD8 T cell</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>CD8 T cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_5p-AAACCTGAGTTCGATC-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>3426</td>\n",
       "      <td>1332</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_5p-TTTGTCATCCACGTTC-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>6547</td>\n",
       "      <td>2044</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>Monocyte_CD14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_5p-TTTGTCATCGCGTAGC-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>3615</td>\n",
       "      <td>1397</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>B cell</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>B cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_5p-TTTGTCATCTTAACCT-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>3828</td>\n",
       "      <td>1480</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>CD8 T cell</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>CD8 T cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_5p-TTTGTCATCTTACCGC-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>6444</td>\n",
       "      <td>2388</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Plasmacytoid dendritic cell</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>Plasmacytoid dendritic cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_5p-TTTGTCATCTTGTTTG-1</th>\n",
       "      <td>data_5p</td>\n",
       "      <td>4457</td>\n",
       "      <td>1662</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>CD8 T cell</td>\n",
       "      <td>10X_5prime</td>\n",
       "      <td>CD8 T cell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7378 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Sample  n_counts  n_genes  batch  louvain  \\\n",
       "data_5p-AAACCTGAGCGATAGC-1  data_5p      2712     1318      1       18   \n",
       "data_5p-AAACCTGAGCTAAACA-1  data_5p      6561     2164      1        3   \n",
       "data_5p-AAACCTGAGGGAGTAA-1  data_5p      6322     2112      1        8   \n",
       "data_5p-AAACCTGAGTCTTGCA-1  data_5p      4528     1526      1       16   \n",
       "data_5p-AAACCTGAGTTCGATC-1  data_5p      3426     1332      1        3   \n",
       "...                             ...       ...      ...    ...      ...   \n",
       "data_5p-TTTGTCATCCACGTTC-1  data_5p      6547     2044      1        3   \n",
       "data_5p-TTTGTCATCGCGTAGC-1  data_5p      3615     1397      1       10   \n",
       "data_5p-TTTGTCATCTTAACCT-1  data_5p      3828     1480      1       16   \n",
       "data_5p-TTTGTCATCTTACCGC-1  data_5p      6444     2388      1       28   \n",
       "data_5p-TTTGTCATCTTGTTTG-1  data_5p      4457     1662      1       11   \n",
       "\n",
       "                                                   anno      Method  \\\n",
       "data_5p-AAACCTGAGCGATAGC-1                      NK cell  10X_5prime   \n",
       "data_5p-AAACCTGAGCTAAACA-1                Monocyte_CD14  10X_5prime   \n",
       "data_5p-AAACCTGAGGGAGTAA-1                Monocyte_CD14  10X_5prime   \n",
       "data_5p-AAACCTGAGTCTTGCA-1                   CD8 T cell  10X_5prime   \n",
       "data_5p-AAACCTGAGTTCGATC-1                Monocyte_CD14  10X_5prime   \n",
       "...                                                 ...         ...   \n",
       "data_5p-TTTGTCATCCACGTTC-1                Monocyte_CD14  10X_5prime   \n",
       "data_5p-TTTGTCATCGCGTAGC-1                       B cell  10X_5prime   \n",
       "data_5p-TTTGTCATCTTAACCT-1                   CD8 T cell  10X_5prime   \n",
       "data_5p-TTTGTCATCTTACCGC-1  Plasmacytoid dendritic cell  10X_5prime   \n",
       "data_5p-TTTGTCATCTTGTTTG-1                   CD8 T cell  10X_5prime   \n",
       "\n",
       "                                               CellType  \n",
       "data_5p-AAACCTGAGCGATAGC-1                      NK cell  \n",
       "data_5p-AAACCTGAGCTAAACA-1                Monocyte_CD14  \n",
       "data_5p-AAACCTGAGGGAGTAA-1                Monocyte_CD14  \n",
       "data_5p-AAACCTGAGTCTTGCA-1                   CD8 T cell  \n",
       "data_5p-AAACCTGAGTTCGATC-1                Monocyte_CD14  \n",
       "...                                                 ...  \n",
       "data_5p-TTTGTCATCCACGTTC-1                Monocyte_CD14  \n",
       "data_5p-TTTGTCATCGCGTAGC-1                       B cell  \n",
       "data_5p-TTTGTCATCTTAACCT-1                   CD8 T cell  \n",
       "data_5p-TTTGTCATCTTACCGC-1  Plasmacytoid dendritic cell  \n",
       "data_5p-TTTGTCATCTTGTTTG-1                   CD8 T cell  \n",
       "\n",
       "[7378 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "celltype_batch2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4bbccd54",
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in celltype_batch2.columns:\n",
    "    adata2.obs[item] = celltype_batch2.loc[adata2.obs_names][item]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa8af3c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 7378 × 33694\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa75729f",
   "metadata": {},
   "source": [
    "## Concatenate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd1cb815",
   "metadata": {},
   "source": [
    "Now that we have loaded data from both the batches, we can combine them to create one anndata object.\n",
    "\n",
    "For details on the parameters used for the function below, please see: https://anndata.readthedocs.io/en/latest/generated/anndata.concat.html\n",
    "\n",
    "For discussion/examples of concatenation, please see:\n",
    "https://anndata.readthedocs.io/en/latest/concatenation.html \n",
    "\n",
    "Special Note: Please pay attention to `join = 'outer'` parameter. We are choosing to consider any gene that is expressed in either of the data. This means there may be some genes that are expressed in one data set but not in the other. What expression counts do these genes get in cells where they are not expressed? It depends on the input data matrix: if it is in sparse format they get 0, else NaN. So to ensure no NaN, we set `fill_value = 0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a227871e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import anndata\n",
    "adata = anndata.concat([adata1, adata2], \n",
    "                       axis = 0,                        \n",
    "                       join='outer', \n",
    "                       label='batch_id', \n",
    "                       index_unique='-', \n",
    "                       fill_value=0)\n",
    "\n",
    "\n",
    "# If you have more than 2 samples:\n",
    "#adata = adata1_corr.concatenate(adata2_corr, adata3_corr, adata4_corr, \n",
    "#                                axis = 0,\n",
    "#                                join='outer', \n",
    "#                                label='batch_id', \n",
    "#                                index_unique='-', \n",
    "#                                fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "22c0c482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 15476 × 33694\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType', 'batch_id'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf4a5144",
   "metadata": {},
   "source": [
    "Below we see the `.obs['batch_id']` of the adata and see that the first batch is called \"0\" and the second batch is called \"1\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4bfbd004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data_3p-AAACCTGAGCATCATC-0-0    0\n",
       "data_3p-AAACCTGAGCTAGTGG-0-0    0\n",
       "data_3p-AAACCTGCACATTAGC-0-0    0\n",
       "data_3p-AAACCTGCACTGTTAG-0-0    0\n",
       "data_3p-AAACCTGCATAGTAAG-0-0    0\n",
       "                               ..\n",
       "data_5p-TTTGTCATCCACGTTC-1-1    1\n",
       "data_5p-TTTGTCATCGCGTAGC-1-1    1\n",
       "data_5p-TTTGTCATCTTAACCT-1-1    1\n",
       "data_5p-TTTGTCATCTTACCGC-1-1    1\n",
       "data_5p-TTTGTCATCTTGTTTG-1-1    1\n",
       "Name: batch_id, Length: 15476, dtype: category\n",
       "Categories (2, object): ['0', '1']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['batch_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dfa1d864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "270adce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for NaN:\n",
    "np.sum(np.isnan(adata.X))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab024cd0",
   "metadata": {},
   "source": [
    "As seen above, there are no NaN in the combined data, so we can proceed normally."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5cc196",
   "metadata": {},
   "source": [
    "## Basic QC and normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "522ff2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.calculate_qc_metrics(adata, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8f0f9a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2d0227490>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of library size (total counts); number of genes per cell\n",
    "fig = plt.figure(figsize = (8*2, 6*1))\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.hist(adata.obs['log1p_total_counts'][adata.obs['batch_id'] == '0'], 100, label = 'batch-0')\n",
    "ax.hist(adata.obs['log1p_total_counts'][adata.obs['batch_id'] == '1'], 100, label = 'batch-1', alpha = 0.5)\n",
    "ax.set_xlabel('Log-library size', fontsize = 14)\n",
    "ax.set_ylabel('Frequency', fontsize = 14)\n",
    "ax.legend(fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.hist(adata.obs['log1p_n_genes_by_counts'][adata.obs['batch_id'] == '0'], 100, label = 'batch-0')\n",
    "ax.hist(adata.obs['log1p_n_genes_by_counts'][adata.obs['batch_id'] == '1'], 100, label = 'batch-1', alpha = 0.5)\n",
    "ax.set_xlabel('Log-N genes per cell', fontsize = 14)\n",
    "ax.set_ylabel('Frequency', fontsize = 14)\n",
    "ax.legend(fontsize = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98bd1fe",
   "metadata": {},
   "source": [
    "If you have more than 2 batch, you can run a for loop to visualize the distribution of each batch compared to the whole data. For this, you can adapt the following example code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5ed12ee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of library size (total counts)\n",
    "fig = plt.figure(figsize = (8*2, 6*1))\n",
    "for j, item in enumerate(np.unique(adata.obs['batch_id'])):\n",
    "    ax = fig.add_subplot(1, 2, j+1)\n",
    "    ax.hist(adata.obs['log1p_total_counts'], 100, label = 'All cells')\n",
    "    ax.hist(adata.obs['log1p_total_counts'][adata.obs['batch_id'] == item], 100, label = 'batch-' + item, \n",
    "            alpha = 0.5)\n",
    "    ax.set_xlabel('Log-library size', fontsize = 14)\n",
    "    ax.set_ylabel('Frequency', fontsize = 14)\n",
    "    ax.legend(fontsize = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "703afda4",
   "metadata": {},
   "source": [
    "We see that on average batch-1 cells have higher library size and higher number of genes expressed per cell. We will keep this mind as we analyze the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "52ac4ad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 500.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of number of genes a cell is expressed in\n",
    "fig = plt.figure(figsize = (8*2, 6*1))\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.hist(np.log2(adata.var['n_cells_by_counts'] + 1), 100)\n",
    "ax.set_xlabel('Log-N cells per gene', fontsize = 14)\n",
    "ax.set_ylabel('Frequency', fontsize = 14)\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.hist(np.log2(adata.var['n_cells_by_counts'] + 1), 100)\n",
    "ax.set_xlabel('Log-N cells per gene', fontsize = 14)\n",
    "ax.set_ylabel('Frequency, trimmed axis', fontsize = 14)\n",
    "ax.set_ylim([0, 500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d08e1755",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove genes expressed in less than 64 cells:\n",
    "sc.pp.filter_genes(adata, min_cells=2**6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0ec89fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 15476 × 13386\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType', 'batch_id', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes'\n",
       "    var: 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c354fa37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing + log transformation\n",
    "\n",
    "# Store raw counts for future\n",
    "adata.layers['raw_counts'] = adata.X.copy()\n",
    "\n",
    "# Normalize with median library size\n",
    "sc.pp.normalize_total(adata, inplace = True)\n",
    "\n",
    "# Store the normalized counts for future\n",
    "adata.layers['norm_counts'] = adata.X.copy()\n",
    "\n",
    "# Take log2 of the normalized counts\n",
    "adata.X = np.log2(adata.X + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ce9148c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 15476 × 13386\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType', 'batch_id', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes'\n",
       "    var: 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'\n",
       "    layers: 'raw_counts', 'norm_counts'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "471e4171",
   "metadata": {},
   "source": [
    "## Highly variable genes, PCA, Nearest Neighbor Graph, UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "889e277c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# HVG\n",
    "sc.pp.highly_variable_genes(adata, n_top_genes = 4000)\n",
    "adata.uns['id_hvg'] = np.where(adata.var['highly_variable'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5b5e9b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCA\n",
    "sc.tl.pca(adata, n_comps=100, use_highly_variable=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1045dca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '% Variance Explained')"
      ]
     },
     "execution_count": 32,
     "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.plot(range(len(adata.uns['pca']['variance_ratio'])), np.cumsum(adata.uns['pca']['variance_ratio']) * 100, '.-')\n",
    "plt.axvline(30, color = 'r')\n",
    "plt.xlabel('Principal Component')\n",
    "plt.ylabel('% Variance Explained')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6688ca03",
   "metadata": {},
   "source": [
    "Based on the plot above, we will select 30 principal components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3f5dee32",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obsm['X_pca'] = adata.obsm['X_pca'][:, 0:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fc9d82cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    }
   ],
   "source": [
    "# Nearest neighbors on PCA\n",
    "sc.pp.neighbors(adata, n_neighbors=30, use_rep='X_pca', metric='euclidean', key_added='neighbors_30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d532193b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# UMAP using the nearest neighbors\n",
    "sc.tl.umap(adata, neighbors_key = 'neighbors_30', min_dist=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3e1b99f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clustering using leiden\n",
    "sc.tl.leiden(adata, resolution = 0.5, neighbors_key = 'neighbors_30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e3f79daf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 15476 × 13386\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType', 'batch_id', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'leiden'\n",
       "    var: 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'hvg', 'id_hvg', 'pca', 'neighbors_30', 'umap', 'leiden'\n",
       "    obsm: 'X_pca', 'X_umap'\n",
       "    varm: 'PCs'\n",
       "    layers: 'raw_counts', 'norm_counts'\n",
       "    obsp: 'neighbors_30_distances', 'neighbors_30_connectivities'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6cdf004",
   "metadata": {},
   "source": [
    "## Visualize the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "45ec29a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n",
      "/Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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RkQYpkCgiIiIiIiIiIiINUiBRREREREREREREGqRAooiIiIiIiIiIiDRIgUQRERERERERERFpkAKJIiIiIiIiIiIi0iAFEkVERERERERERKRBCiSKiIiIiIiIiIhIgxRIFBERERERERERkQYpkCgiIiIiIiIiIiINUiBRREREREREREREGqRAooiIiIiIiIiIiDRIgUQRERERERERERFpkAKJIiIiIiIiIiIi0iAFEkVERERERERERKRBCiSKiIiIiIiIiIhIgxRIFBERERERERERkQYpkCgiIiIiIiIiIiINUiBRREREREREREREGqRAokhr4qmEjy+BOU/DjsUt3RsRERGRY15qWQVnLdnItMwCUssqWro7IiIih8XR0h0QkUZUXgDrv7O+5jxRtdKAezZCSGxL9kxERETkmPRjTiELCktZUFhavS7aYWPViD4YhtGCPRMRETl4GpEo0poEx0BA+F4rTXi+N+RsbJEuiYiIiBzLRkSG1lmX6/ExcsE6Sr3eFuiRiIjIoVMgUaQ1sdnhgvfrrvdWwPd/bf7+iIiIiBzjeocGMTIsqM76jeWVzM4taoEeiYiIHDoFEkVai5yNsGoqfHhu/ds3/Qgv9oeC7c3aLREREZFj1a/5xbyzI5u5RWX1br9h9VbOW7oRn2k2c89EREQOjQKJIq3FlzfD5KvB56m93hEIVOXfyU+DT69o7p6JiIiIHHNKvV4uWLaJ+zfurLMtsCo3og/4taCU17dnN3PvREREDo0CiSKtxZgHwainfpKnHNjjKfeQ65qtSyIiIiLHqmC7nftS4qlvrGH5HiMQbcC4mL1zXIuIiByZFEgUaQ0qS2DhG2A7gELsQTFN3x8RERGRY9yaknJm5RY3uJ8PiHEewD2ciIjIEUCBRJHWoGAbrPsG6n3mvZfQhCbvjoiIiMix7tP0PP4oLsOvgf3sQKhdH8tEROTooEdfIq3B9gXW9+BYKNqx10YbxHaHAZdBZTEk9G327omIiIgca7aUVwKQHOAkrcJFgA0qfNY2O3BBQiRRfg56hwZhVOVMFBEROdIpkCjSGnQeC+1HwNZfwO4PMV0gbzMYNrj0c0gZ1tI9FBERETmmTEqOZU1pOWkVLgJtNgaHBfFbYQkJTj/mDOlOiMPe0l0UERE5aAokihyNdi2FP96Ek/8OwTEQ0Q6umAIfXwK9z7NGH4qIiIhIs/k8I48NpRU80DERwzAYFRXK/3p34KGNO3m6azLdQwJbuosiIiKHTck4RI5G67+HpR/ArmU16xz+VjBRQUQRERGRZvf+rlxe35FNmddXva5PaBBfHddFQUQREWk1NCJR5EhVtAvCkurfduLd0GU8JA9s3j6JiIiIHKPcPpNCj3efFZbf6p1CocdLsKYsi4hIK6YRiSJHouWfwr97wMrJ4PPBy0Pgo4trtjv8FUQUERERaUb3rN/Ocb+tZmeFi42lFXSZu4LXtmVVb491+tE5KKAFeygiItL0FEgUaQkbZ8KOxTXLWWvBXVGzHNcdOoy0qi0bhpUHMSiq9jkqCmHDDDDN5umziIiISCvlNU0+Ts8lvdIFgMdnsrqkvNY+x0cEMzo6lAg/O06bQbTTQeheow/TyipZUljabP0WERFpbpraLNLcvB746CJwBFhVld3lYHogeQgUp8PxN8MJN8MF78Lvr1oBxGu+q3uen5+B+S/D5V9A55Ob/3WIiIiItBK/5Zdw57rthNsNfKZJcVWaw3HRoawuqeCdPh24JDGa3iGBvLk9h5vbxfH78T3rnOeG1VtYW1rOxhP7EmjXmA0REWl9FEgUaW52h5X7sHB77fU7Flrfpz8IPc6Cz66AXYvB9MLYh61tPh/Yqm5KB1wOdj9oO7T5+i4iIiLSCvUJDcQGFHprz/SYkVsMwOXLN/HZgM5csCyVAo+PkVEhDAgLBsBnmtgMA4C7UuLZXuFSEFFERFotwzSbf15kUVER4eHhFBYWEhYW1tzNi7SsvDR4qf+B7x8UDQHR1lTmshwYdQ+MfqDJuicispuu18cO/VvLse79nTncu2HHAe/fzukATCp9JlkeH5/168SJUaFN10ERkSq6ZktL04hEkeYW1QFsTvC5Dmz/slzra7fM1U3TLxEREZFj1OiDDAJuc3lqLbuUs1pERI4RGnMv0hJ6nAE2v0M7dvSDjdsXERERkWNcnL8fSf5+2BvetQ4nMCpSoxFFROTYoECiSEu44G14OAeu+7GBHY2aHx1WHh52LW2ybomIiIgci5w2G0uG9WLn6P480CFxv/vucXdGqAEuINPlbtL+iYiIHCk0tVmkJSUPhiunwfbfYfPPsOMP8FZWbbRZxVTuXgc5G60CLakzodc5LdplERERkdbsxnaxtA10sr6knPd25eIxTYq8vurtA0KD+Kx/R1aWVBDhsLG+tJI2Ac4W7LGIiEjz0YhEkZbWcSRs+gnK8yCxHxi7J9X4rMrMQVHQbihEtIVBk8AZ1KLdFREREWnN/G02BoUH83lmHvH+fvQIDqi1/aZ2sYQ4HJwQEUKPkCDOjo9soZ6KiIg0PwUSRY4E7jKIaAfRXcD0WutCE+DEu1q2XyIiIiLHIJ9pUu4zuTAhiq0VNQXyhoUHc2pMRMt1TEREpIUpkChyJLhxLlz6KfSYWLXCgOIMWPxui3ZLRERE5FjUPtCfNSP6cFO7OIaGhwBWTqjfCktZWFjSsp0TERFpQQokihxJup8KQTFgd8KZL8KAK6Eku6V7JSIiInLMujMlHoChESE8370tnYP8KfV6W7hXIiIiLUPFVkSONGe/Aq5S6H0uvDsRdi6GezaAM7ileyYiIiJyzOkWHMDjXdowMCyYLsH+9P11NceFBfF5/84t3TUREZFmp0CiyJGm6/ian7tNgPC24Ahsuf6IiIiIHMMMw+Da5FjAyp14ZmwEvUJ0byYiIscmBRJFjmTH39TSPRARERGRKjbD4IUe7Vq6GyIiIi1GORJFpA6fz6Tk0QTMR8Mp/eP9lu6OyFGluNxNucvDgs25dHvoOz5ftB2ACndNPi2fz8TrM1uqiyIichTaWeEiYfYyEmYvw+vztXR3RI4upTng88Hc5+DJZMjZaK33VNbs43W3TN9EjjIakShypPrjLfj5GbjmOyjPh+x1MODyxm/n69us6dOj7qte5fGZ+JvWRdVXnNf4bYq0Iu/P38LyHYX42Q2mLtlJhceH3QZ/GdeNSo/Jv6avZfLiHSxIy+ORM3tyzfAOHP/kLDw+H0v+Nq6luy8iIgfhptVb2FReyfSBXfkqq4BYp4PhkaGN2ka2y83ta7dxTZsYTokJr16fUeFq1HZEWi3ThG/ugMgOsOJTyEkFnwvC2kKb46CyGGY+ChnLoHAX3DgHAiLhPwOg0xi47POW7b/IEU6BRJEjlekDTwVkrIR5/4KMFdBlHITENV4bPi+s/QaiO1mBxI0zYfbjOM99k7K/7sJVkk1orKbviNRnZ34Z7/y2hZU7ClmQlsee4wu9Pnjih/UAZBS7yagKyP9rxnqe+m4NlVWDE1/7eRPjeyXQIUbFlEREjgYe06Qkq4xfN+dw8/qtdIkJ4ech3Ru1jZ0VbubkFdMnNIhTYsL5V1oGi4tKebdPR+YO7kqcnwO7TRPLROq1aTZs/Q1WTgaPG3x7jDgs2m59Aaz/pmb9B+dZIxYxIW0eLP3QylUfFNWsXRc5WugKJHKk6jERKgph8jWQuQrOfdMKInoqradsv/0Hti04vDZsdrhtMVz5lbWctxnSl0NpNkGBgQQriCiyT1e//QdvzEvj972CiPtTUumtDiICPDd9PWe8NK9J+iciIo3vTI+TXXN3cfkbC+m+sYwXu1v3ShVuL/mlLv49cwNZRRWH1Ub/sCCWDOvFfR0SAFhVUs6y4jJcpo+uIUFE+DsP+3WItEruCisoOPcZcJXUDiLuT2k27L6b85TDVzfDV7c0WTdl3/773/+SkpJCQEAAQ4cOZeHChS3dpSPW3LlzOfPMM0lKSsIwDL788sta203T5OGHHyYxMZHAwEBOPvlkNm7c2ChtK5AocqQKjgWbnzUy0XBAYj/IWgtPtoVv74EZD8G3dx5+O0FR4KwaDTX0Rvjrdmh/wuGfV6QVMk2Tf8/cwGkvzGNjVslhn8/nM5nYP5HURjiXiIg0vT5taqYa270+kgwbHy3YRq+Hp/Ovmet5adZGXpmz6bDbSfD3w24YALzZO4XFJ/Qi2G4/7POKtEqlufDt3fBsZzC9De/fkIAISOgLZcduiievz2T+ply+WraT+ZtymyW396effspdd93FI488wpIlS+jXrx/jx48nKyuryds+bKbPGpSTscz6bjZ9HtvS0lL69evHf//733q3P/PMM7z00ku89tprLFiwgODgYMaPH09FxeE97AIwTNNs9mzvRUVFhIeHU1hYSFhYWHM3L3L0eKE/FKTVLPc4GzbOAAMrGXBUB7huFgTo90ikObw3fwsPf7W60c8b6Gdj0UOnEOx/ZGUc0fX62KF/a5ED4/H66Pzg99XL/naDkV1j+XFtFl3jQ1ifWcK5A9rw3AX9sNmMFuypyDHC64bJk2Dt141/7oR+8Ke5jX/ew9TU1+wfVqXz92lrSC+sCTglhgfwyJk9ObV3YqO3t9vQoUMZPHgwL7/8MgA+n4+2bdty22238de//rXJ2j1sWausqfKVhTXr/MOh2xkQ17tZumAYBlOnTuXss88GrMEPSUlJ3H333dxzzz0AFBYWEh8fzzvvvMPFF198WO1pRKLIkWrWP6BoZ+11a78EuwMMA3xuyNlgTX0u2espTUPPB6bcCD880KjdFWntNmYWN0kQEaDc7eO1n1NZm17E18t3NUkbIiJy+M5/bT57hgcrvSYz12YRHuTHjvxyAKYs3clzM9ZT4a49Mmp/4zdySiq56P/m89WynfvcR0TqseD1pgkiAmQsh8w1sGE6bPu9ado4wvywKp2bPlhSK4gIkFFYwU0fLOGHVelN0q7L5WLx4sWcfPLJ1etsNhsnn3wy8+fPb5I2G0XWKljxYe0gIljLKz60treAtLQ0MjIyar2f4eHhDB06tFHeTwUSRY5U2xda1cX25qkAVynVv76pP8Lro62h91lrYeOP8FgcrJ6673NvmQdpR97TNZEjWfvoYEZ2iWmy83+4YDsPf7WKP3+8lPTC8iZrR0REDt2GjMJ68+KWlLspdXmxV0UZX5mzibs+XUZJhZvUrBL+PWM9fR+dwY78snrPm1/qYvG2fJZuK2iyvou0Sj3OgIj2TXf+mY/CJ5fClzc3XRtHCK/P5O/T1tT7N273ur9PW9Mk05xzcnLwer3Ex8fXWh8fH09GRkajt9coTF/toj31Wf9Ns0xz3tvu96yp3k8FEkWORDkbIWtN/du8LuhzAThDatYV7YBnOsMrx8O6byEsCQLC6x677COY928IjofMlfBEGytgKSINcjpsdEsIbbLz55W6WL2ziH9f2I/E8MAma0dERA7Nj2syKPfU/wHaY8KfRnbAu8fm71ZlcNJzczj53z9TUukhITwAf0ftPIemafLUD+tYvbOQQD8b7/y2hdHP/kRRubspX4pI6xHZvv7PPY0ldTrEdIWJLzVdG0eIhWl5dUYi7skE0gsrWJh27OaOrCV/S92RiHurLLT2a2UUSBQ50ng98NNjUJZbsy4wCqK7gl8wJA2wRh66imofZ3NYT+Py0+D25dBpTN1zT7sdZv0d0pday64S+PCCpnstIq3MaX0SuWxIctM1YMC5xyWzq6CcWz5cwoodBU3XloiIHLDCMjePfL2mVvaY+DB/usSFEOS0cWafRN78Ja3OcVGBDqKDnYQG+jHzrlHEhvrX2r4+o5jX5mzi/qmrKK6wpkKn5Zbzly9WNOnrEWlVTrwTkgY13fkDIiFlBGz6CSZfC+X5TddWC8oqPrAiHAe638GIiYnBbreTmZlZa31mZiYJCQmN3l6j2Pvz+OHu14h2v2dN9X4qkChypFnwGqz5sma594VwwTtw2x9w/3aripgzCILjqPUr7B8MBVth82x4bSTMewF+sxLVsvoryFoPY/4Gx10JHUbD7gw/9QUcN/1kTZHebc00+OhimPl3KFL+Njk2lVZ6uOC1+Xy4cEeTtVHu8vL0D+tYvauIb1emM39TbsMHiYhIk7vzs2XsLKhJO3HTqI58fuMwZt41iu9vH8mGrBK6xoXSJiKg1nHb88vJLXXxnx83cu4rv/LOb2l8vyodl8fHRwu2khQZyLkD2nDv+G6EBVgFtxwGjOoaW6cPn/2xnY2ZxdXLz01fzz2fL+Plnzbg9jb/1DmRI8LGH+Hza2DXoqZrY9tvVjsbZsCqya1yhBlAXGhAwzsdxH4Hw+l0MnDgQGbNmlW9zufzMWvWLE444YRGb69ROA+w0M2B7teIOnToQEJCQq33s6ioiAULFjTK+3lklYcUEXCXYgX5TIjsAGumwqh7rW2mz5r2nNAbunaFpR/XHFe+xxDzjOXW1GifG5Z+CNlrrMpRg66GM1+C+S+D6YIBV4AjAKY/CKf8E2xVgcnPrraGYQdG1T7vhu9h6btw93qw+zXt+yByhAly2rl0aDumr86gqNyDy+MlPNCPvDJr+plhNFznaF8iAh0M7RjF0m2FRAc7OaVnPDPuHEnn2JCGDxYRkSbl85mUuTwA2AyIDfVn2op0/jKhBwDFFR7Sckq579RuvDt/y+67OIDqqdA+YMm2ApZU5UDs2yaMFTuLeGNeGreO7sx5A5NZsi0fm2FwxQnt+WVjNh8t2MalQ9sBVsGv+75YgcNm4O8wKHXVDhyu3VXMfy8f2NRvhciRJ2kAdBxtzdiqKABPJdSb5e8QRHWG0DjISwP/UDjl7zD4Wojp0jjnP8IM6RBFYngAGYUV9b6DBpAQHsCQDlFN0v5dd93FVVddxaBBgxgyZAgvvPACpaWlXHPNNU3S3mGLTLE+Y+9verN/uLVfEygpKSE1NbV6OS0tjWXLlhEVFUW7du244447eOyxx+jSpQsdOnTgb3/7G0lJSdWVnQ+HAokiR4qyPHixnxWN2P2nO74XBMcCNvjyFuh3MdyxAgwbTLkB7HYIaQuFW8AZDq6qP2J+QeCuSuadvcYKFlaWwK8vwsaZMOh6a2QjhpU42F0KdieMuAPm/9c6L9QOIlb3Mxf+GQOXTYEuY5vyHRE5ohiGwT/O6s0/zuoNwIzVGfzv1838vtma3rJnEDHQYaPcc2CjQ2wGFJR7mL46C7sBmUUVVLi9dI1vunyMIiJyYH5am8V17/6BifX32mdC/7YROB12lm/P573527h3fDfW/GM8DruN93/fSligH26PhzK3ib8NKqsuBw7DyqUIsGJnEX52g7ScUv7yxQpmrc1kSMdI5m3IJae4khdnWR8OQ/wdnNYngSe+W4u/3aDSa+Jx1f2I/+2qDH586Dt+f+BkIoOczfTuiBwBgqPhyi9rluc8Ays+hbzUuvsadjC9ddfvS16q9RXdFdKXQ7uhrTaICGC3GTxyZk9u+mBJrQciUD2XjUfO7IndZtRz9OG76KKLyM7O5uGHHyYjI4P+/fvzww8/1CkYcsQwbNDtDKs68750O8ParwksWrSI0aNHVy/fddddAFx11VW888473HfffZSWlnLDDTdQUFDAiBEj+OGHHwgIOPwRpYZpHur4iUNXVFREeHg4hYWFhIU1/zBPkSPSrqXw+klg87OedIXEQd+LIbwNvD0Btv4GXU+FSz+Fgp3wUl/weaw/TKEJhz7lOHmodWzRdhj9IOxcZA3b3/s5VGxPKyi5W1gy3LX6UF+tyFFv2FOz2FVQweCUSP7Ycui5cva8UYsP9SezuJJvbhtB7zZNmDj8AOl6fezQv7VI/R77Zg1v/pJGsNPOX07tToXHyzXDO+Bnt9Hjbz9Q7vby/IX9OOe4ZD74fQsPfbkaG9YIxMO5PhzfIZLNOWVkFVfy0fVDueWDJeTvVYAlOshOUYUX9x7Pra4Z3p5Hzux96C9Y5GhWWQxPVuWyThxQkxd+n/YOl+1DYCT4fHD/tsPtYaNo6mv2D6vS+fu0NbUKrySGB/DImT05tXdio7d31MtaZVVn3nNkon+4FUSMa51/jzUiUeRIUVFiffd5YNNswAeOYJhxP9XPgKI6wT9iIKabtR9Y053bj4CVnx1au9lrobIInMHw87PWdGjDZj2tc4ZBn/Nh8f9qBxEBitMPrT2Ro9QT363l3d+28Mz5fTmrfxvuGdeNZ6evZ1zP+OoPig4b7B6IGOAwqPCYOG3g2s/gxN23rw4D8korGZISWR1E/O/sVH5YlcFH1w8lNEDpBEREWkqpy8v/fk2ja3woa3cV8eWyXZhYd2i/pmbzly9W4uew7td2/8k/rl3EIQcS03KtIKLDBte+8wfle0QLh3WKotJtsnhb3XMvPowHWyJHpTfGQE4q/GmeVcG5/+WwY6E15bk6kLg7vL+nAwwiGnZrZteJd9Ws+/ACCIqGc15rnNdwhDm1dyKn9ExgYVoeWcUVxIVa05mbaiTiUS+utzXoJn+LVVjFGWZNZ26ikYhHAgUSRY4UCb0gZSRsmQs5GwATpv9ljx1s1pRlnxs8e1TK8g+H3LpVAg9YZVUVKY8bfC7rZ79gOPNF6HUWrJwMm3/aK6mwAWMfOfQ2RY5Cm7NLqfT4yCmpBKypzOmFFYQE+DH5T8dz8eu/s+ds5oqq+Wv7CyLuyWMCJmQVV1avyyyqYHt+GW5vs08eEBER4Mx+Sfy4NpMtuWXVX3sKD/RjZ0EFLq+PqGB/SiutaZMhTju/bco55HYziyoxsB5OeXzWhaRXYihPnNuXbgkhvDpnE2vTCynbI8DotBv88+w+h9ymyFEpLw1cxVW5EbEGQ+RshIs/tgKLPz5C3SAiHHAeRdNrfZXu8fucvxVcZfs+phWw2wxO6BTd0t04ehg2iOrY0r1oNpraLNKSln8Ka76C3udBYAR8cB51LmqOAPC4AB8k9of0ZVQ/QbM5rODi7mDgbmHJ4CqxEg4fLJuzJqB4yafw8UXWdGufG0ISYMIzVoBR5BhjmiYVbi+BTgefLNzG9vwy5m7IZkTnaJZuL6S00svKnftJttyAqCAHeWUe+rcN57Q+SWzNLeWxs3tjmmBroSfAul4fO/RvLVLjsW/WUOHxclb/Nny3Ip23f9tSZ5/wABuFFVZwYmD7CBZvLajeFhviJKfEVSdMMaprND9vyD2kPtkBL+B0GDx9Xj/u/HRZ9bZ+bcL590X96BSn3LpyDPK4rBzzPg/89jIUbLdmXLUfBjsXQ+bq+vO+HyhHEHjK4MS7oSQTko6DQZOq8tq3DF2zpaVpRKJIS9o826qEvP5bK0i3+5YzNBGG/dl6glY9+tCwgoiGrWo307pghsRbgcQ9kwcX7Tj0Pu0OIgIseQ/OeR1mP2YN6S/JgADdpMqxyTAMAp0OFqbl8tcpK6uT7q/eVYTPhOSI2omLQ/xtlFRl2I8J9iOn1F3faavllVnpCpZtLyS7uJKCMjc3jOzAhwu2c/7AZBVfERFpJt+uTCe3xMUHv2/Daa9ZP7FvIv5+dj5fvKM6iAhUBxF3F1PJLnERE+okp9hV67yHGkQEK4gI4PKYGJjcclIn3vt9KzYM1mQU0SYy6JDPLXJUc1QVF/rhfvjjDaoHXOxYaK0PitljZ6MmhRNAaBIUN5Bn3lM18nDev8AeAPnboM0gWPU5jPqLVc1Z5BjTeidtixzpPJXQ40y48AMrCFiSaY0GDIqC21fACTdbIwsBAsJh9P3Q71IrJ2JgZM15ygusCsxNMbg4e501WtIvGNoMhKE3Wd9FjlE/rMrg9bmbASuIaMMaCQIQFVK7Smaof82zuoaCiHsrLHcTEeTH6S/9yutzN/OfWRsPr+MiInJAsooqeOKc3kwakQKAywt2Awa1j+ClS4/jnvHdqvcd2D6SK45vzxl9EwAICqj5u98uMgi70TQftpZtL+SsAW1w2AxO7ZPAHSd3JcDP3vCBIq3Vz89A7h5VmgMiIDjBmlUVuOf0XLN24O9gc77HdIHtv8NHF8Jv/4EN0w+n1yJHLY1IFGlu+dvgowusIB1A9zPhqmkw9U/WSMKwNjVP1s57A/I2Q/vhVvVmgI6jrIvitt8hay1s/YUDzvGxm2GzApL7Y/ODyz4HuwNu+f3gzi/Syjz5/Vq+WZ5OclQACzfnExnkR36ZGx+wdIc1nXnFjtopBtKLXPWc6cCUVHopqfQSFWQVWOmT3PIVnEVEWrPvV6bz6LTVZBZZedbuOqUrz57fl39+s4biCg+DUqIAiA8L4NXLjsPPbuP4TtGE+DsorfQwOCUap8PGlpwSvl6WzpJtBQfdB7sBDaXE7RwXwgOn9cDpsLH04XEH3YZIq/LGGAiOhbR54B9spX3yeWqnd8pdX/uY3QUugYP+DJW50vruDAZsENttv7uLtFYakSjS3Ja+XxNEBFg3DaY/AP4hVj7E0Q/WbEseBH0vtCoyz37CWmeasOZL60K2dR4HfQGEfQQR98rzkTISstZDwbaDP79IK5FeWE5JpQeHzcDPboBp/cbllx3cCMNDVVhutZOWU8q3K1QpXUSkqbw8O7U6iAjw75kbWJiWR5DTQVSwk6uHdajeNqFPIiO6xPDPaWv4bmU6wf4OduSXsWBzLj9vyCa9qKK+JhpUXxBx7yxsJ3WLZcWOAnJLKuvuLHKsyNkIPh/YnVXBQzd4PVYQsUEHsk8DCrdbAz+WvA8Zqw7/fCJHGY1IFGlunU+GnYtg089UZ7zJ3WTlQhx+O3SbYK3LWgtfXA+9z4E/3gKvyzr227vA3RRVwqryiYB1Qd48y/oCmDQD2g1tgjZFjlyF5W5OenYOQztG896kIZimlVf7gkFt+eyPbSzcUtDkfdj9ofLjhdtZsaOA0/smNnmbIiLHmkqPl5tO6sR/Zm1kfWbNaKU/0vLILK7gg2uHkhBu5cGdsmQHb/+axphucXy+eDtlbi/pBeW8OS/tUB7tNmjPc/rZDd6cl8ZHC7ZhtxksfugUnA6NC5FjzIYZ1uyuk/8Ol3wMPzwAJ90P4e3gu/ug4tBzkR4wb9Wsk4X/B34BkNC76dsUOYIokCjSnKb8CVZ8vMeKqhp8pg9uXQThybBpNqSMgNIcyFwFWWusac2XfQ1fXHtoQcSeZ8HaaQ1MZ/ZZORl7ToR+l8Anl1ojIt0VEJly8G2KHOVC/B2ce1wb+iZH4PH6ePe3LYQEOAj2s7M5tymC+fs3qF1kwzuJiMhBMU2Tk//1M9vzy6vX2auKacWFB/D1n0fg85ks3prHwPZRbM0tY21GMSt3FnF630QuHtyWWz9ackhBxGGdovhtU8PVZPskhnHpCe2w22w8P3MDwzvH4GevGikvcqyJ7wndz7A+L2VvgOUfQWI/62dPecPHN7YBVzV/myItTIFEkeaSscKaklxL1YjExL6wajIERcM3d8CpT8HxN8GD6TD1Jqsyc9Y6K6h4sGx+cMrjkLHSyre4P0U7YPG7UFkKPi/0OhecQfD51daIxG2/gzMELp988P0QOcrYbQZPntu3enn6nSMZ9/xc8lpoOlmJy9vwTiIiclA++H1brSAi1IwG75kQyvcr01mwOY8pS3fywx0ncucpXblsaFv+9MESTu2VwNfLdx1SuosO0UG8dMkAhj7xE17f/sOQK9OLeGteGl0TQvGzGdw+tgvfrEjnsjcXML5XPK/O2cxVw1K46aROB90PkaNOeDJc/GHN8rlvwpTrqJsIoJm4ihreR6SV0Vh4keYy73nrKZkjADqMgvYjarbtXAI//RN+fhp6nQ+bf7byE35yBayZCgtfg0VvHlq7UR3gpb7Q+3yI6wGOwJpthl/tfUMSwV0KS9+F4l2wagrMfQ62/Qa/PA8elzW3U+QY4vOZXPP2Qt6bv5Wvbx3BlJuHt0g/Vu/SjaqISGN7efZGAMICHFw8uC1RQTXVjz/+Yzt/+WIlW3JLOb1PIm/M3UxWUQXjX5jHkm0F3PP5cr5YvL16/wO9QzKAuLAAhjw+i0fO7EFUsB/7m6EcF+pPanYp363MYHt+OU9+v5aPF27jt025vDpns27N5NiUvwX+byTY/aw0TKc+0zL92PJLy7TbCs2dO5czzzyTpKQkDMPgyy+/bOkuHdEaer+mTJnCuHHjiI6OxjAMli1b1mhta0SiSFNa/A6s+BxCE60kNz0mwthHYMs8azj+Z1dCzgYrz0ZoElQWw46FULgNtv4GlVY1WOz+4C6HuJ5QuLNm/X7ZrIpiARHWqMS5z8D4p6yg4PrvrGTEQZFQmlW1uxNKsqy2vFUjrtZMtb5PmmH1scOJjfv+iBwFPD6TNelFmFjVMgvLXNiABuqeNyoDq+CKiIgcvoemrmRHfjmRQX70bxtBYrg/V5zQgSVb8zmzfxJ3fLyUgjI3lR4fcaH+lLm8zFqbSYXHx7cr06lwW1eA5KhAvF4Tf4eNrbllVHj2f2Vw2sDlg06xwZRWWgUfHvlqDR9dP5Qnvl3Lyl1FRAQ6KKn0sPtUIf52yird1dcdE/huZQaxIf58cO0Q4sMC6BIf2nRvlsiRqrwAMtdYhSF7nQ0lmS3Tj7SfYdhtLdN2U/J5rc+jJZnW7Lj2w8Bmb/i4w1BaWkq/fv2YNGkS5557bpO21dh8po+Mih2UeUsJsgeTEJCMzWjacXsNvV+lpaWMGDGCCy+8kOuvv75R21YgUaQpbZgOW/d6SrXuWzC9ENcL8tKsgF54WxjzMMx4wKoCBnsFC03YtQSCY6HLKdY0aHsAeOupCuiMAGcAlGRYAcEdC2u2zXgITI9VTCU4HkozqS6yEtPZmjrt3WP6ZHRX6HuBCq3IMc3psDHvvjE4bNaQj8Xb8vEBwU47pc003fjy49txRt+kZmlLRKQ18/pMpi7dWevvt82Az/7YSZnby6iuMWSXWIUUJvZLJCLIybRlO6nw+LAbVAcRAXKKKiis8DK+Vzw+EzZmlWCryq+4t5hgP3JL3RhAVnElRRU1lWMveWMBALEhfuSVufHuEY8M8LOTU9Wf3fq3DeeW0Z0Z0SW2Ed4RkaNUUn+4fzv4Vc222r6g+ftwyj+sfI2tzZqv4Ye/QNGumnVhSXDq01Y+/SYyYcIEJkyY0GTnbyqbSzfwW94sSr01xbqC7SEMixpLx+CuTdZuQ+/XFVdcAcCWLVsavW1NbRZpSue9Bf0vt362+YFhq/nKWg2x3SG2B9idMPV6KM2GvdN1h8TDac9BfB8oL7SCiFB/ENGwg6vACiJ2GguBUdb6sGTodZ4VRAQreFmaafVjd3u5m6zvwbHgV/VkO3cj/P4KTLujcd4PkaPYjDUZlFV6yCt10SspjK7xIfvct7Fnmf2yMYe2UUGNfFYRkWOP3WYw865R9G4TVr0u0M9OmduL02Hj1405HNc2gvZRgfyxJZ/35m8lv9y6f/KaVtARYEDbCO49tTvxof7MWZfFxizrA+S+0h3mlLoxgRtHdqCyarhhYpg/5/SreUiUU2IFEfe8huRVBRHbRgZWf3BLzSrhns9W8O2KPT7kixyLKgph44/gqpq10W64NRuruexaBhHtm6+95rDma2vWXNFef1+K0q31a75umX4doTaXbmBm9le1gogApd4SZmZ/xebSDS3Us6alQKJIU7I7weGEkfdaF5noLhAYCf2tpwOkL4XstZC3qeYYv2Ar8NdxrJXPsOtp8P1fIHMl+Fzgt+/gBWFtrDZsTuhxJpTlWhfToh2w+oua/WxVuRH3rOLsrYQu4+CiD8FdXLO+PN+qJC1yDPtiyQ7+9MESbvloCfd8voLVu4r489gunHdcG647MYXooNoD/Pf8HLl3Uc3B7SPqnN/PBqf3SSAu1L/e9tNyy7jpg8UUVRx8Qn8REanNBJIjg7hnXFdiQpy0jw4iPsyfEzpG4TFhyfYCtuaVk15Y89A22GmnS1wIg9pHEhrgoE9yGI98tZrM4koqvSaBfvv+WNU5NoTE8ABC/O20iQqi0uMjyM9GelElX+0RDLTbavq3+9LhA+48pTPXjuhYnVKjtNJLYYWbJVvzG/NtETn6TH8APjwP3jnDGvyQuQIueBsGXwddJtTODV/N2Ot71c8dTqq7a2gSdB4HAZH1t796Cnx/72G9hCOKz2uNRKy3Dn3Vuh/+au0n+Ewfv+XN2u8+v+X9hM9szoRIzUOBRJGmVJ4Pi9+z8iTmpULOeisn4c5Fe+3ogHFPwon3WsVOojrAcZdbU6CXvGMVafELsi6G7pJ6Gqq6EFYWQmmOVRBlx0JodwJUFNTsZqsKdviqghF2J4x7HNoOtfIvVpbC/8ZZeRKB6gtGE+d3EDnSndIznptP6sQ5A5I5sUsMb189mAq3jy+W7CTY6cc7k44HrJEqe96eJoT51xqdEh3ixx9bC6qXd3/udPvg+1UZpEQHEx5Qf/6ZFTsKySysZySyiIgclJU7CvhhVQZfLt1JTomLNenFZBZVsjW3rNZ+8aFOnjq3NyO7xFDq8nL2gCT6tY2guMLDe/O34TUhLsQPmwHl7rofFHc/SMorc+Hx+qj0+IgM9MNpt+Guujj42WqCGbvzIsaEOHn4zJ50TwilZ2IYHy/YztPfr8VRtevuy0pMmLNR3xeRo87xt8Doh6w89D3Ogut/ttJI/fEmDL8Nhlxn7efcYyCG3Q8iO1gzuaqZVq7DalW/bMW7IHUG9D5v331Y+21jvZqWt/W3uiMRazGhaKe1n5BRsaPOSMS9lXqLyajY0Uw9aj7KkSjSlLbOB0wo2FJ7fdbqvXb0wM9PQmVVVdYt86yvPfm8Vi7FvNS67TiqAn/uippCKSsmWyMYwbpQRnWCMQ9ZQcYf/mpdBLwu2PGHVTU6qkNNbpHd56hu24PIsSwmxJ/7Tu0OwMT+1jQ0r8/kjSsHMaxTNIF+dk7tFU+lx8fPG7KJDPSjoNzNY2f3IS7Mn4kv/0pEoIM7xnYlr9TF63OtUcif3DiMq976nbwyDz4TFm7JqzWC0QDG9Yzn5J7xDOkQRfvo4OZ+6SIirYrH62P5tgIAUrNrF7HatlcgMbPYxSNfr6meivzs9LpT1CKC/ckrc9c7pdluM4gM8COn1IXDZuDxmdz52TLcXnBUPXia0CeRswe0YXN2Cc/N2ECZy0tOiYuVOwtZl1FMu6ggckpceOppILvIVbdRkWNJ8kDra0+jH4Qu4yFlOMR0gW2/gzMUdv4BrjJr9tflU2H9tzDjQSsn/NAbrRliv79iTY8eeBVM/RPVpfVWfFq7DZsfDL/dytPY7oTmeKXN40AL1rRUYZsjTJn3wAohHuh+RxMNMxJpTD6fVUHsp8fho4vh8yusUYUHYncQcV+8lVZVsvp4KqyvlBFw72a48MPa202vFcz8/EqI7gwJfaz1ARGwcTpEd4TjroagqLrn7nkOXPXVgb0GkWOI3WZwSs94gv0d2GwGi7cVsGRbAWv/eSqXH98enwnXvbeIjZklPH1eHz658QSuOCGF20/uSmxoAB4fXP/uIi4/PgWAMH87of52vCb4OwwGp0RiYo08uWBQWwURRUQOUYXbS1ZRBfd8voyx/57Dq3M317tffZPPdgcR905TsduGzBLstvo/Urm8JjmlLu4Z15WVj47jTyM7Vte081TNX/5mRTp3f7acSSM64jOtYGGvpFCmLNnJ+QOT6d82nLCAumM/bh/TiXvGd9vv6xY5JgVFQddx1s/l+dagCf8Q6zNSXA+oyIeXj4OOo2D8k3DdDGvk4pAbrNlaOxbAplnQdoh1joAI63MWQFA0BMVY6aEi2lqppIJjWuRlNomQ+Mbdr5ULsh/YvfmB7nc00YhEkcZSWQLPdgRPZcP7HiqfyxpduGdw0i8IIjtahVE6jYUf7oeVn1q5GO/eCe4y+PYuWD/dyqH44flwyScQkgDLPoTYbjDkehh4NQy9Ad47B7buMRoyqgNEdWy61yTSSrx99WB8pom/w05SRCDhgQ5iQvzpFBfMP6atYc76bF693Hpq3iUuhK15ZQT525m7MRvDgKJKL9HBfoQAJZVeduSXEeLvwLmvT68iItKgX1OzuezNhYd9Hu8+iqiAFWy026hVbTk62A/TNDAMSAwP5PK3FrB4awGn9IznXxf0wwBOeWEugX528stcXPrG77xzzRBe/imVX1JzOL5jFBcPbsuglChySioZ9/xc8kprRiCO7ZlAkFMf5UT2K7YbXPo5xPey8tbH9bSm7sb3Aq/bmgLt88HwWyEwAkIToGA7GA7IWA7+YVaaKHsAhCdA4XaI6WZ9vjL380fhaNV+mFWduSid+vMkGtb29sOapPmSkhJSU2tm36WlpbFs2TKioqJo165dk7R5OBICkgm2h+x3enOwPZSEgOQmab+h9ysvL49t27axa5c1XX39+vVWvxMSSEhIOKy2NSJRpLF8c3fTBhGjq5467z3C0V0G7YZCTFdY+JoVRLT5Qfvh1gXT4Q+5qdDhROtCuHtKc3m+lSux88lWEHHTTzD7Cbjsc5j4X7joYyvpcLfTmu41ibQiRRVuOsdZOXguHtKOp87ty66Ccn5en82SbQV8vyqDxVvzAAj2d2CasCW7lGXbC7loYBsSwvwpc/l46aL+BDvttI0KZtXfx/PyZQP316yIiOzHLR8uadLzt40MAGoHEQGKKzx0SwhleOdo7p28nMVbC/B32DihUzRhgX5syyuz0lf0iKOw3M3CtDyGdohi1c4C7IbBveO7Myglind+TWPW2kym3jyMVy8bwEOn9WBM9zjaRQU16esSaRUqS8AvEMLbWMvnvQF9LoDsDfDHW9Z05pkPWUHBwEgrXzwmLP8Y3OVwyj/B5ocvpgfFg18DDOv4B9Nh0DUt+cqahs0Opz5dtbD3g+yq5VOfsvZrAosWLWLAgAEMGDAAgLvuuosBAwbw8MMPN0l7h8tm2BgWNXa/+wyLGoOtieoNNPR+ff311wwYMIDTTz8dgIsvvpgBAwbw2muvHXbbeowl0hi8Hlj5WdOd3xEIuetrfvaUgzMMXMWACSs+s6YrZ64EDCtAaNhg8rXWw6T05dYXwPE3Q/IgGP849DkPep5lrf/jLVj3TVWBmHfghtma0ixHvTfnbaZbQigp0cE8N309P63PItDPzvkDk6tzHjaGOeuzuPrtP7jppE78peq8/n42bDaD53/cyIUDk6n0+nA6bPT7+3QC/ewYQKCfjVK3j6iQADKKrAcRk95bTJDTzpXHH3lPXkVEjiZLtuZTUN40eZ4NIMBhY3u+NeXRblijFuPD/MksqsTlNdmUXUKAX1h1/sRKj481Owv5yxcr+Hl9FhlFlbz5SxqmCW9cNQjDMPjfNUMwTRjY3qoS+6+ZGwgP9OOjBdvYllfG4odO4bqRmikiRy93icn6T01STjUo2QmpX/nIWAj+ETDwDhtJwxtxJsbPT8NvL8EVU6HTGGudM9hKKbXiU+h9PsR0Y+v3laz6v2LGHx+Aw+5f9VnKz8of73Oz/JcLqfhpJ0MGt8Xe/5LG69+RqOdEuPA9q3rznoVXwpKsIGLPiU3W9EknnYR5lI307BjclVM4i9/yZtUamRhsD2VY1Bg6BndtsrYber+uvvpqrr766iZpW4FEkcPlccHMh6k/s05jtVFe++eoTnDaM/DlLda6kgzY9hvE9oTsNVaQce3X1rbkIbXPFRwLqbOg81grt8duZ7wAw++A8jwozrAKu4gchZ6fuYH/m7sJA4Nyt5chKVGM6xXPV8t3ERfqT2mlh/RGrn48oF0kVw9LwTRNfliVwam9ExjTPZ5RXWP5LTWXoR0iOfu4tvxj2moKyz0UlnvoGBvE5uwywgIcTOidyB9peSzbXoDbZ1Lm8vJLai5n9GvTqP0UETlWbMst4/9+3tRk5zeBck/NvZ/XhFFdYzi5RzzPz9xAudtLVnEl2euzCQ90UFLhIcjfzuQlOwGqRxR6TQhy2knNKqF9dBDHtYus1c7Um4fhsNn4ZsUudhVWYLMp3YUcnWbf7qVwszVxqjLfytaUt84kY4EVRKzIh8qiquShjaXP+XjKTdbOPIF2NpPwDgbeEx9mzoc34W/Pos8VPQiMsVH29/dwlU6kJDeMiGQHFGyF2B74htyCZ9MqcouH0ibmK+xl2yB/K4Q3zVTVI0bPidD9dKs6c0mmlROx/bAmG4l4tOsY3JWUoM5kVOygzFtKkD2YhIDkJhuJeCRQIFHkcBTtgjfGQHE6OEPAtf/y740mbxP88qIVQIztbn13hkLOuqoRi2Uw4RnrCl1ZDDuqcgONfxJm/A1CYuGk+62KZLuFxFpfAF3HN8/rEGkkf/54CeGBTv55dm8qPV7sBpS6vCSE+XNi1xg+Xrgdh81gxp0jCQvwa/QPYuGBfjxwWg96PfIDnWKzObW3lXdkS04pBeVu7p68kqenbyCr2Bp1GBfqx/a8cmxAUYWH2z9ZwuYcq1poRICdggovXeNDGrWPIiLHil9Tc7j67YW4vSYxwU5yShu/urFB3QxiP2/IwWGzkVfmJiHMn3J3JQF+NirdPrwmtIkI4rQ+iSSGBzBjdQbb8qy/+0+c3Zs7PlvOrHWZ/HlMF4Z1rine0DkuFIBbx3Rp9Ncg0pQq8k1+fdhHxzOgw3g7pg98HnCXQNxgcJWYZC2G8I4w7i07ps/EaOxAeWI/cpP7su4FH16vSf9bDHxuKNgVhc8VTfokCIzz4c0bT0r8BwR2TMbM/JGCku648yPY+kQeJTsfJTRwLeWObpRUJBMSGNlwu62BzW6lxpIDYjNsJAUeO7OJFEgUORyZa6wgos0PTnkMirbDvH81bhvOMHDtrui8x21raY5VPCV7PfgFW1WdTZ8V0CzLgSXvg6sU8jdbowsDIyFvM/gFQEA4TPszdD8DgqMbt78izWR7Xhl3fLKMTdnF1VPXPlqwtVZC/IyiSv41YwMBDhtto4LYlF3CwPZWdXKfz2zUgKLTYePzPw0jPNAPgLxSF+syiqu3ZxVXEh7goLDCQ3axm46xwWzKLgWoDiICuE3wsxuM7BrXaH0TETmWzN2QjdtrEuy08+Utw7nq7QVsyi5r+MCD0DsxlJXpxXXWe00fBuD2+nA6DFweX/W1JrfExeeLtmMCO/LLiQr2Y3zPRF6evYmU6CAWpuXxj2/W8MMdIxu1ryLNaedvXla8BuU54C2HvFWw5N9efHvE87P+sL78I61CyZWFJv7hRvU0TcNovPuzuONgxJM2ontby7YtM5k4+BpmLJ5LWUUKriLw+mJJ3XUzW6ffjOn24Sm3UfOowCAocDtlWW2p6NKDYZEpjdY3kaOVAokih6o4AyZPgn6XwJn/gfJcWDUFsAPeho4+cK4ia7Sh12NdjXfLXlPzs9sKRmDY4JKP4N2zqvIlAm2HWklz2wyAef+G6M5w2nPWFGYFEZuX1w2fXwvFmXD99JbuzVHrzx8v5evlu+rdtq+qmm0iAtiUU8qPa7IY2D6Kh75cyTcr0vnp7pOICnY2Wt/6t42gwu1lxNM/MbBdJAPaRVJQ5iIu1J8d+eU4HTYKKzz0SApjfUZRvecY3jGay05oX124RUREDtyPazJ5Y95m/nlWLy4/vj2rdxUxsmscm7K3NGo76zJL8AN8Ru1rz5z1Ofg7DHJL3dXrUqKC+PPYLtz+ybLqddeN6MCNozoSGxrAde/+QVigH/84qzfRIY13TZIDU5aTw7xHHiGqV28G33xTS3fnqOR1+Zh+nUnp9vq3+/YxKDgkCXJXQ2GaSVx/g+lX+wiIgpOeb7wptIZhkHg85K03mXOHlyHXJRIX1Y2ErrsoC0ohf731Uas8G+L6w865u6ej1gQzXY62tD8Vul8yAJxKLyCiQKLIoXL4Q1SKlTvisRjodDJs+tHKH7F1ftVOjZQs1rXHE++gOCjLqn+/YX+G7+61go67bf8D3hgNtyyAE++yvqRl7FgE66oK2DwaDqe/DIOvaNk+HWXWphftM4i4P6Uua7rz0m35vDd/Cx6vSbuoIPzs9d8MztuYTWmlt3qK8oHYllvGkm35jOgSQ1iAH5HBTh47pzfH/XMmBgbb88tJCPPnhYv6c3rfRCYv2s5z0zeQW1Zzd223Gcxal8VT5/c76NcoIiIQHuRHSnQwj3+3lse+XUtkkJPSSg8h/g7CAh3sKmicHLnu3RVUqr5FB/tVBw8rPTX3f342GNM9lrfmba51/IcLtjJtxS4WPHAyb141uFH6JIdmxetvUJq2hdK0LWz/5htO//QT/ENDW7pbR5W1H+w7iLg/jkDre+Zik5zlPgKiITh+34G6zd/6iOxqENnlwIN52ctNTBNMr4l/GJhRvchNmsXmaT7COlj5GpOGw8C7bSQMgUXP+dg6E8yaZwF4iqEgFcLaKYgoAmCYLVAWp6ioiPDwcAoLCwkLC2vu5kUah9dt5UScdjus+x66TYCAUBj5V9gyF7qMg0X/gzlP0mgBxYb4h1lVyPZkOKHrKVbexIS+cMo/6h5XmqvRic3l0fDay/elQVBUy/TlKLR0Wz4XvPYbnoOobeTvMHj0zN7sKCgjOSKI+6euZGiHKD698YR9HjP8qZ/ILqlkw2MTDrid2z5eyrTlu4gI9KOw3M2CB8YSFxbAxsxiQgIc/L4pl5O6xbE9v4wHpqxk1a76RyS+fGl/Tu6RQIBfyye01vX62KF/a2ktSis92G0GY//1M+UuL33bhnNilxj6t42g3OUlKtjJq7NTmbYyo9n6ZBiw9yeupHB/hneOZXNOKdeN6MCEPom1tru9PsrdXsIC/Jqtn8eqstxcfrjiyuplv7Awzvzk4xbs0dFn/Wc+Vrx6cJ93AmJh4B0G6fMhOAlWvm7S62qDnlfVX6CiZJfJ95f5SBgKJz514PdIX5/rxee2JnA5w+CsL61jc1ebBMSY5Kw0aTfGRtq3JhunmBRt2fNoN+CHYYcJHxoERBrYj4ARibpmS0trvWVkRJra51fB0x1g1zK45ltY+xWUZMMH58JXN8OGH6DXWTRbEBHqBhEBTJdV0XnTbPj1RchLq739j7fg2Y6Q+mPz9PFY92ghOKuK2gQcI8maG9GAdpGkPnE66/4xnm9vG169fnBKJO2iAus9xuM1SS8sZ1BKFJcMbce7k4bw5Ll99tvOS5f0562rBh1wvz77YzvdE0L551m96NMmHH8/GyOfmc2PazK45/Pl/LEljwHtItieX0Zaduk+g4gOILu4ku5/+4HPFx3Co30RkWPcCU/NotfDP9ApNpi/TujOnPXZZBdVcs3bf3DF/xYSHxZAdGhAs/ZpzyDi7hDErsJKZq3LZPHWfO6bvKLOMXd8soxhT/5EQVnjF4qR2oKiozn3u2+rlyM6dmzB3hydul1o44LZds74Aob+vWb94L+yzyLMNjtkLoZuFxt0vcBg6N8Mupy/7yBdSJLB8X8z6HfzgYUwfG6T1W/76HaxwaB7DQKiwPTCd5d62T7bx7KXfVCUSWK/Ioq2Qu46k6Jte5/FQWLUNALiYNUbJl+e4aM08yCeZou0UpraLHLIqpLwFmyFt06xVqUvtyoog1V0JWU4JPSDjOXWui7jYeN06q/1t/ucPrA5951M5FCU59f8PO/fMO5xCAyzKjrvXAxJAyD82Kky1eIeSG3pHhz1ApwOerWJ4KWL+/P3aWu48+SudE8MI7u4gls/WsrGrJoK6nYDXvoplcRwf968cjCjusY2eP7dBVkO1BPfr8Vpt7HwwZO54oQUvlq2k3/P3EC528eqXUU88MUKSlzWjef+qjF7gHkbcwBYk15/sFFERPbNz27Da8LcjTnM25iDDfh88XaKKqy0L+e88htXntCeIKedMpeXAAckRQaxeT/FWAxqRhUe7uPhPY/Pq5oKXVLp4X+/bGbSCCuAtSGjiFKXh8EpUQQ6W350+rFiz2CiHJrAKDvtRkLWGV6yFkPiUBunvgeuEh8/3wnePTILVBRB6hSTwi0mxz9k0G5MwwHCtgewz25FW2HNeybtTjbodqGN5JGw5EUfBRtNSnaZVs7EP5dRVhEL+AhpA9QTI0zPOxOAAn/wuaE8C4LjD7gbIq2SpjaLHIrVX8GXfwL3fioA+gWC4QB3OZhVOQvbDoH+l4PPCz/81aq0DFaxloFVUyqm3ACbfoL2IyDlRJj50P7bOSxVAc2UkXD1tCZqQ6T5fbFoB3+ZsoL20UHVlZF3+/WvY2gTUf/oxUO1ZlcRdptBt4S6OZWWbsvnnFd+O+Bz3T+hG+GBTs7ol0SIf8s+79P1+tihf2tpDR7/dg1v/ZKGbx+fbvxs4DMNMEwchkFlVZWUsT3i+NPIjrz/+1a+Xp4OQFigg09vOIEeiWFUeryc8u+fCQvwo3tiKPGhAbwyZ1OTzzl5/OzeXHZ8+yZuRaR5mD6TRf/yseU7CEmGkh012wKi4czJjR80z1xsEt4RAiLrjnRc+aaXdR9WLVSN5dizUvPeRj5nUFkA7ca2/KROXbOlpbX8b4HI0cZVBl9M2n9wb3cA0eeGuJ7gFww2B+xcCt/fBys+AYcTekwEmx/M/0/Nsd3PgNJsaDcUfn4SwpLgmh/g/LfB7gRnCPQ6BwKj4KS/wo2/wMUfwwm3gt3fOofdCR1Pgs5VIyVt+8qvU3WxHH774b4rIkeU8wYlM/OuUbx55SAiAmsH4y55fT6/b85ttLa8PpPkqEC6JYRSVOFm1tpMbnhvEV0f/I57Pl8GwLiecQT4Hdgl9/W5m+kcF9LiQUQRkaPJzxuyeGPevoOIAG4feE2TsAA/OsQG4++wEeJvZ9baLC5+YwF2mw0DuGxoW4rKPXy80Jrn6O+wM6prHKvTizi5RwKv/LyJiwa35ePrj+fGkR3wsxvEhDgZkhJJQqiTf57di+l3nMj/rh7Eqb3iq0MSwf52rji+3X5Hpu9mM+CMvkmH/8aIHCEMm8Hge+2c/JqNgffW3laRCz/e6KWyoPHC854Kk5g+VhCxJMPHjrlevrvcy1cTvax800fCYIOonlU712m2bj9Wv28S26/l8yOKHAn0KUXkYJXng8+z7+2OALD7WdOGPRWQuRJr1N8ImPgyfHs3hCVaoxKTB8HwO8C/ahRTeQHE97QCjO2HQWkOOIOgfVVRiNJsKxDZ50K44J2aNhP7QPfT4JR/WvkQU0bUbNs4AwKj4bMrwT8CslfV7m/Pc6HLyYf9togcaWwG3PjBEiaN6MCb89Kqp7Vtyyvn6v8toFtCGFNvHsarP2+md5vwOlOe0wvLiQh0kl9Wyf9+2cKJXWPrnRb9ly9W8M3yXcy9bzST3v2DVTtrpiRPXryTCreP168cTMpfD2zKVG6pm+vfW8TxHaN59fKBh/EOiIgcO9Jy9j97Iyk8gIzCCnxAfpmb/DJrWvF9p/akS1wwT32/juTIQDrFhXBmvzaM7RHPce2sXMa5JZWM6R5HUYWbfsnhnNUviXG94jmhUzQndIomLaeMnzdk89oVg4gKdla32S0hjDHd48krrWRXQQW921gF18pdHn7ekEN+aSX/nrmBQD+DbfmVtfr73qQhhAep0Iq0PqUZsOY96HoRbPic6unE+Rtg2kU+Op0JPa+wse4jk04TDULa1A7eFe8wCWkDOStMts8x6Xq+rc4+AD9c5cM/DEa/ZOOHK8zqCWIA6z40CY436HE5/PoAe8QNd5+nbiAxdznMuNZHzysMupyv8ViN7cknn2TKlCmsW7eOwMBAhg0bxtNPP023bt1aumtHrLlz5/Lss8+yePFi0tPTmTp1KmeffTYAbrebhx56iO+++47NmzcTHh7OySefzFNPPUVS0uE/pFIgUeRgZa7a/3ZPhfVVzYSY7nDZF1aA8ZzXICSu7nElWfBiP+h5Flz0vlXE5cS7rUDibkNusKpBR3Wov22bzQoiZqyC9yZCdFfI2wS3LYbz3oS3TwXDZiX56TYBznwBQpTkQ1qnonIPm7JLCHI6WPHoeF7+aSPPzdgAQIXHZPmOQqatSOe5GesZ2C6iVpBw9rpMbnh/MWO6x7E5u5SNWSWs3FlYvU9RhRun3UaAn50Kt5cKj4+7P1tGbnElBjCqazSrdxVTWO5mc3YJXR888LxLx7ULZ1teOTsLyhv1/RARac3Sskv2u31XYUWddRcOSubaER0orfTwyY0nEBbgx93jan9onbE6gxveX8xzF/TjuQv6sWpnIS9cPKDWPi9ePIDiCnetIOKeooL9iQr25+OF23h2+nr8HTa6J4Ty9jVD2FVYwX9+snInO+0Gt43pzNXDOxCqas3SSpVlW0VNBt1no9+fDGbd7iWvqt6Q6YLULyCis48Nn4HNYdLn+popzyvf8LHuI5Pe18GqtwATAiJNel5pBQAr8k38I8AwDCuV/SZY8rLPSkHvB21GwvafrKnUK/9n4irYVy/rBiaTR8OuX6E8r9kzw7UIr8/HosxUsssLiQ0MZ1B8Z+y2pgug/vzzz9xyyy0MHjwYj8fDAw88wLhx41izZg3BwcFN1m6j8fkgIx3KyiAoCBISrc/mTai0tJR+/foxadIkzj333FrbysrKWLJkCX/729/o168f+fn53H777UycOJFFixYddtsKJIocrM6nWCP7KgsObP8BV1jBuue6QkxX2LUEbvoN4rrX3s8ZYgUB2wyEHYvhzTEQGAn3VVVZNgzra19BxD3lp4Fhh8AIiGhnTauO6gihSdbPN8yB4OgDf80iR6E+yeGs+vt4/B3WDeitY7rgtNl48od11c+Zl2zJo11UIIu2FjB58Q6Gd47mrV/SePfXNOx2GwPbR3Jyj3gWb83n1jGdAXB5fIx6ZjbdEkL55IYT6Bhr3dzMS83FbrOeYe/IL2dUt1gmL97JmvTig+r3km2FAHi9Pnw+E5tN02hERBpy9oAk3p2/9YD2DbDD5Sd0oKjCTfeHvicuLACXx8fvD4yts2+76CAGp0TSMTaYl2Zt5D8/pTK6WyxvXTW4+u9zoNPeYFGUcpeXgjIXdptBfJg/SVW5ege1jyTQaWdMt1j+fVH/6muWSGvV9XwbnSaa2J3W78/YF+38fK+XrD1iG6UZ4B8J6z+DlNN8uEtg3QcmO3+x1kf3gq4Xgs1uVXwGyFtrMutWH72uMuh5pUFgLJRnw9bvwBECnhJrQlh4ByjcfPD93jHb+l63snPrM2PLUp5Y8BkZZQXV6xKCInhg6IWMSxmw7wMPww8//FBr+Z133iEuLo7FixczcuTIJmmz0WzeDL/9AqV75GUPDoZhI6AJq8BPmDCBCRMm1LstPDycmTNn1lr38ssvM2TIELZt20a7dodXaFXFVkQORUkWvNjXyoPYoKrsvY5A6HGmNdIwb5MV2Ot/KfiHwZDrah9SUQSvj7K2+QVBeR7csqDhpjbNhh8fhvQV0PNsuPDdg39tIq3Y9NUZ3Pj+4jrr7QY4bDYqvdb8mu7xIazLLCEu1J+FD9ae+m+aJnd+uowOMSHcfnIXTNPkq2U7mb85l9Hd4nhpVmqjVVzuEhfCzLtGNcq5DoWu18cO/VtLa7BoSx7nvzb/gPZ12sDlg1B/O6O6xbF0Wz67Cio4tVcCiREBnNwjnmGdY+qc/9p3F3Fm30SmrUjn7P5J/P2s3vttxzRN3pu/hZdmpZJb6uLlSwZwRj/lPhTZ08InfWydUTcsYQ8Ar4vq6c/BbaB0J7Q9CY5/pHbQvTzbZOFTPrqebyPxBAN3qUnaDz6K0qD9eIO5d5v43I3T3y7nQ/9bWi7o35TX7BlblnL77NfrTO7e/Vj7xdE3NFkwcU+pqal06dKFlStX0rv3/v/OtqjNm2Hm9H1vP2V8kwYTdzMMo9bU5vr8+OOPjBs3joKCgsP+f6MRiSKHIiQO/vQrvDocPA0FE6uufJ5yWPlZzercjTDr79bPO/6wpjwbVX+iA8Lgz0utn6f+CQLCa5/yq1uhcDvEdofQBNg4E0oyoSgd3KXW9OVQ3aSK7M1mGAT62bl1dEeenbGxer3XtEYAWvvAoJQo1mWWkF1cyfbcUtpG10ypMAyD5y7ox6R3F/HU92u5eXRnzh6QzNkDkgHYWVDOmm8aJ5BY4d5PPlYREallUEoUT5zdmwe/XNVgRWVX1e1ZcaWXb1akV6//fnUGAO/8toUnzunDxUNqRm0MSoli+SPjKKpws3xHIfHhATXn8/i48P/mMyQlgm15FYzpEcerczYRF+rPgrQ8AOyGQVigpiyL7M0/wvoKToa8PbJIeffISBAQA7aq2F3msrrnCIw1OO5OG7//w4er1KDtKIOu51kHeMpN4gaaZPzeOP3dX7r8o5nX5+OJBZ/V+/fTxAomPrHwc8a269ek05x9Ph933HEHw4cPP7KDiD6fNRJxf377FVJSmnyac0MqKir4y1/+wiWXXNIowWeNSBQ5XKYJT7YD12EGDvyCrOnIAeGQ2M8qttL1VIhMgQWvWVOi10yDq76Gd0+3/nBhWgk/fG6rUvPpz1v5GXtOrD8Po4gAsCO/jBFPz97ndn+7gdtn4jOhXWQgb08aQqfYmiqbFW4vI57+CW9VedBrR3TgowXbOKFTNF8s2dlo/YwOdrL4b6c02vkOlq7Xxw79W0tr09Df+QMV6m/DMGzEhvgzvHM0yVHBXDq0HWUuD+/P38r8zblsyy3l2fP7cdXbfxDib6ek0kt0sJPcUhcp0UFcO6IDwf52Tu+bpKnLIvux7FUvGz/b93ZHMHiqZo+mnAZ9rrUREFWTAiZ3tcnsP/sw7BB3HDhDIXu5NRV6x5zG62fySXDCI61vROKC9A1c9cPzDe737ql3MjSxa6O1u7ebbrqJ77//nl9++YXk5OQma+ew7doJ075ueL8zJ0JSmybtyv5GJLrdbs477zx27NjBnDlzGuX/jEYkihwuw4DLP4dfnoeoTrD8Y6v68u6RiAfKXVVt0FUMRTtg/beQ+iOMfhBmPGht8w+ziq+c/rw16jB7A8R2hbAkSB4CAaGN+cpEWq3/+3kzBvXV5LNUemu2bM8vZ+y/fubNqwZxcg+rOFGAn51f/zqGf0xbTU6Jq7qIy9fLdhIT4iSnxFXveZPCAzhnQBL/nXMIyXlEROSAJUcG8c+ze7M4LRePafLjmgwqDmEUUXGlD/BRVOFhU44VwQh02ikqd1cXSekQE8SAdpHcNKoTwzpF8/niHVw2tB1ur4/hnWOswg8i0qCMBjI57Q4iYsCW72DbLB8TJ9vwC7F+x6J7GZwxGRY8Bv7hsHWGtXvW0qpp0nvXXLIDXkg4HvJToTLnwPpptNIoSnZ5YaPudyhuvfVWvvnmG+bOnXtkBxHBKqzSmPs1AbfbzYUXXsjWrVv56aefGi3w3Ep/BUSaWbvj4dJPrZ9PfcL6vuZr+P0V2PYHsMedq+EPke3AGQw56yAgEiLaQvpy8FZC2xMgNxX8g+GkByB5EEz8L+RsgMHXQmR7a8SiiBy0xVvzuO2jpQzuEEWwv4OSyoY/Ve4OKT43fT0D2kZQWO6mY2wIH8xP48MF27lmeAoBfjYq3D7cPsgpce0zSLmrsOKggojdE/VwQETkUF1xfHuuOL599bJpmvx75gZ+WpfBml0ltf5ORwY6iAh2YgO25JYxsmssm7JL2Z5vfQA8vkMU6zKKaB8dzFn9k9g9p8vnM7l+ZEcC/Oz8ZYJVSO/ErrHN9ApFWocNn/nYNM3EGYoVoWjo9mz3718lbJjso93JNpwh4AgyWfQcZC2BwQ/WBBJdu+NeVYHDalU/H+yU5+heB7f/0SI2MLzhnQ5iv4Nhmia33XYbU6dOZc6cOXTocAAFRltaUFDj7tfIdgcRN27cyOzZs4mObrxiqwokijSVnhOtr90yVkFQNIQl1qxzV4Dps0YZ+nzgKrHyI+7tuMubvr8ix4BKt4/8MjcTeieydlchG7Jq7lR7JISwNqNkn8euzyjm6rf/YE16IXGhAaQXWo+1t+aU0jMxtLracu+kMNILK8gttUYlHtcugiXbCg6pv0u35B/ScSIiUpdhGNw9rht3j+tWvW7B5lz6tY0gwK9mmmJRhZtgpwO7zaDS48U0qbV9t1tGd26Wfou0du5yK0vU0AcMfrrdrB3k7w756/Z97MYpsPZDHwHRUJEDZlVw0FsBtiDwVQ0GSx5dU3n5QM67P1lLocvZh3bskWxQfGcSgiLILCuo94G4AcQHRzIovvH/9t1yyy189NFHfPXVV4SGhpKRYeWrDQ8PJzAwsNHbaxQJiVZ15j2rNe8tOMTarwmUlJSQmppavZyWlsayZcuIiooiMTGR888/nyVLlvDNN9/g9Xqr39OoqCicTudhta0ciSIicszJLq5k8OM/Vi93iQvm3AHJRIU42Zxdyv/NrX/UYJi/naJKb611u/NgdU8I4dXLBtIhNoRXZqfy0qyNnNEvgUuHtOfcVw+siujeAv1srP3nhEM6tjHoen3s0L+1iIi0tLRvfSx6riY8EdMXOpxuYBiw4TOTgtT6jzMcYO41inF3oDBpOAx5wMAvyMbPd3vJWwd9bgLTBcv+c2j9TJkAg+9rfTkSoaZqM9SeXdPUVZv3lQLi7bff5uqrr2709hpNC1ZtnjNnDqNHj66z/qqrruLRRx/d56jO2bNnc9JJJx1W2xqRKCIix5zQAAfhgQ4Kyz1cMqQt363M4LPFO5h9z0lkF1fy1i+bCfX3I7/cTd/kcMBkza6iOkFEgEkjUvB32DmpWxwdqgqy3Dy6M6f3TeTv09Zw/mvzCfKzUenx4T3IR3cJYf6N8GpFREREjnzhna3ak9igy3mw4RMIaw8D77Jh9/cx/xETexB4y6DntbDhUyuAWCf3oQG9r4OCDQadzjLwC7KCVKP+ZWfXbybrP/GRsxIMZ9UIxrq3d/vlbMWZZ8alDODF0TfwxILPyCgrqF4fHxzJA0MuaJIgIlhTm49KHTtawcLffqk9MjE4BIYNb7IgIsBJJ5203/etKd9TBRJFROSY47TbWPq3cRRVuIkIcnLrmC742aybzNAAB2O6xzO8SzSf/rGDVTsLGd8rnjW7iokO9quesuxnA7cPylxebhndpU4bK3cW8tO6LEZ1jeHiwe2Y0CeRJ79by//N3Uy3hBDaRwUzY01mvf3bnXMxp9TddG+CiIiIyBEksrPBOd8ZmF5wBBqknGISnGRtC21rEDvApPPZsOgZ2DINnNbzW8oyqR4+54wAVwEY2Oh+Sd1Rbum/m+SsgrajoccVNkLbwZw7fOSugshuVmXo7CX76GBV/saCTY36so8441IGMLZdPxZlppJdXkhsYDiD4jtjt9laumtHpo4dISUFMtKtwipBQdZ05lb8fmlqs4iItHplLg9+dhullR7+OzuV//26hX9f2I+z+rcBYNXOQu74dBm3ju6E22uSXVRJbJg/oQF+zFidwcNn9uSntZlMWbKDBVvySQwP4Me7TiKzqILE8AAc9vpvFFKziukUG1I9XaPM5WH66gxemb2JjVn152M0gC9vGUZyVBCBDjtB/i33zE/X62OH/q1FRKS5uUpM/IKhLNNkyQsmOSvglDdthCRZ902bp/nY9LVJr0kGmCbpC6DDBIP0Bdb9UsoEKzC47jMoz4TkUTD0ARulGVSfY29el0l5NoS0sbabpo/ybMhZBQv+ue/QiCMITv/EhqfMJCi+ZQNEumZLS9OIRBERadUq3F5OfHo2CeEBrN5VBIDdMJi3Ibs6kFhY7iYtp5TPFu3gt025ADjtBhseP41Teyfwx5Y87vp8BQDRIU7m3jcGgLZR+6/C1jmuZu7LXZ8uY9baTDDA5fHVu39EoIPzByXTr23k4b1oERERkSNY7hqTn27zEdMbcldZ9ScNO+SvM6uDgGXZULwDlv7HpCwDMCFvnckp/2flJ1zzro/V71jna3MiHP+QtT4kad/t2p0GIdbtH6bpY878v2BuGkjBJxfUrepcxS8Yhjxo4Ay1vkSOdQokiohIq+ZntzEoJZLwQL/qQKLXNFm8NZ8nvltL+6ggFm/LZ9Wj41iXUcw3y3eRUVRJ/7YR1efonRTOiV2i+WVjLpXu+oOADSksd+PFpKR834l45t9/MoHOlkveLSIiItIcAqIgugf4h1tBRLDyFaZ+ZeJ1+8hdYxKcYHD2Nza2TDepyDHJXg49r6oJ5CWPhi0zoHQXuPdTOHdffB5wzTkHv40evGU+oO5IQ0cgnP2N7s1E9qRAooiItGp2m8H/XTGINbuK+GzRjur1mcUVvPVLGj6ficNu8NDpPXnhx43M25jN/PvHEh8WUL3vyp2FzNtojVSMDnEeUj+W7yigwrX/IKTvaE00LSIiInIQghMMxrxsZ8X/1X7AmrMS8lNNvOUQ2cWk20UGy14yCU6A8e/UDuilfWtSusv6OTDm4PtQssOgaOZQ2sZ9QaG9BI+37jTh0LYHf16R1q71BxJzN0FwDNj9ITcVgmOrHj2UgM0BYUngF9jSvRQRkSZ2/9SVtZbLqoJ6CWH+vHDxAMICHFx5Qnv6tAknNqSmWnJmUQUBfjbaRQVx4aC2XHdihzrnLq5wE+R0sGx7AX+ftprHz+6Dy+tlydYCooP9eOy7tcSGBtAzIYy5qTkEOAwqPLWDhn87owfBLZgPUaQ5ZW3ZTHRyWzwuFwUZ6US1SaYgI53AsHDKi4uIbZfS0l0UEZEm5i4zWf8p1kDA3c9aTasqc1RPOOHvYNgMel1t4L931pf8LYSltCM4yaTfzdBmeN1Rg65iE2eowZYds8jMXsLAPreSmb0Uu8Ofne/3Z+c8iB8I7tJB+AqCrGnNBtWFWwwn9P1T6y2YIXKoWtcnFq8bMGDVZJh2B3jK69/PsNWMnw5NgsS+cNYrEBzdXD0VEZFmdt/4bvx75gYWb82vXpcQ6uSLm4fxp/eXUOnxMuPOUYztEV+9fXteGaOfm8M5A9rwyQ3Hc8cny+iWEMopPWv2ySyq4KRn5zCxXxLDOkezYkchuwrLeW/+Fn5NzSXIaafM5SWv1M16ikkMD6BdVBDjesby+Lfr8QF2AyYNrxugFGkN3K5K/Jz+TP+/l1j98yxMbz3T+2028NWM2I1L6Uhil+6cfN3NzdhTERFpTn5BBgPvNFjxhom7uGZ9VA844WGD6VeapJzqY8Cf9wrmLf8Ept5Ih3NeJ/ilHmza8i2R5dcRFBhbvcvOeSa/Pexj6IMGxW22U1C0GY+3kpXr3sUsDaPkm/5gmuQsK8frTSa8ox17sFWwZcXL1jmiu0HcAOVEFNlb6wokvjzICiYWp9cECuuz57biXVCSDovfgaE3wvrv4etboe9FMPGlJu+yiIg0j+GdY/hmRXqtQGJGsYvVO4volhBKZT0FUKJDnJzSM54RXWLILq5k8bZ8RmYU4bAbhPg7GJwSRYi/g0EpkfRuE8aIzjF8c9twpizZyfzUXAyg0u2tfrgd7G/nwQnduPWT5SzfXsCDp/Xgu1Xp/PPsXtWVnUVak7xdO3jvvtsIiYyiMCtz3zv6av/+ZW3ZTEFmBu37HkfnwUOZ/tqLbPj9V8ZdfyvdR4xq4l6LiEhz6XimjXUfe2sFEivyoLIIwjpQXRillvje0PlkSOhNSel28gtTqajIIzd/LTGRPQkMjCEowRrVGJxk0Db5VHrYE8j44794y21UTLuYoItfxWi/itiZiVAxFmefE9jwiTUasu1YMD3UDWCKCACGaTZ/QqYmK1f+yWWw7pvDO4fND3xuayr037Iap18iInJEME2Tkc/8xPb8CgASwwPIKq5k+SPjCDmAacUFZS7CAhx0feh7AvwczLn3JCIC/bAZBmszipj48q8E+tkoqaw74ioh1ElxpRevz8Tl8WECZw9ow9SlO/nuzyfSM6kRr4eNpMmu13LEaap/69KCfD55+D4KMtMP70SGAaZJXIdOXPHUi43TOREROSKU5/r45vzaYYmoHjD2lQMrcuJyl1BSuovflzxFaHBbRgx5FNMDNj8Dfn8NfvgLXsPO9OFngs2OaVqXFUwISr2cgq3lBCUaFE4ZjzPcCgV4K+Csr47MIiu6P5OW1rpC7Bd/CLE9D+8cPrf13VsJK7+Auc8dfr9EROSIYBgGN4zsXL2cW1LJyC6xBxRELCxz8eqcTUxevJPT+iRRUulh8dZ8Rj07hyv+t4CVOwoZ1D6SfT2eyyx2UeryUuHxMbF/Emf0TeSiwW25dkQHOsYGN9ZLFDmiBEdEcvlTL2D38zu8E1X9YhVkZvD7lE/ZsODXRuidiIgcCQKjbUR2t342HIAN2pzY8EwN0/RRWp7N2o2fYGAjKDCekrKd5KWWMvV0H6s+ySczNBRX28Gs6jYIw+Ox2jCwrisGlHX8FL8R32H0mkVsf+hxOfS4zKDnlZopIrIvrWtqM0BIHGSvaZxzfTEJDLs15dk/tHHOKSIiLeq0Pgn8sCqdmBA/5qzPoWdSw3/fP/1jG3+dshKHYdC3bQQfXT+UK05oz6D2kXy8cBten8lfp6wkMsiPUlc9+d8Auw1uHNWR0/sk0TMpvHr98R2Vn1daN/+gYLye+n8vDparrJRfP32fuJROdB06vFHOKSIiLa//LTaWv+IjJBl2/QpR3Rs+ZvmaN8nMXoLX5yIwIIoTBv6VSlcx9pJgwtr7KIqdwrbMX6FDe6orqFD1o2GAz4szMILO3U8jPvY4AsccmSMQjwWvvvoqr776Klu2bAGgV69ePPzww0yYMKFlO3YEmzt3Ls8++yyLFy8mPT2dqVOncvbZZ1dvf/TRR/nkk0/Yvn07TqeTgQMH8vjjjzN06NDDbrv1BRJThkPanMY7n2nC/42EmO5QtAPOfR3iejTe+UVEpFlFh/gTEeTkq+XWVMuyfQT+9tQ+Oph+yRHcPrYz/dpGYjcMgvysqTHvXDOEzMIKrnl7IWsyivd5Do8PvD7onqApKHLsiWnbnpxtaY12vqKcLN66/QYiExLxeb2cde9D+PkHNNr5RUSkecX0NijZCQWp1iRBT1nDIwLDQtvjdpfQqcNEIkJT8Hpd2Aw7wQkGp7xhJzP7OFauX4XLVVj7wN0jEm123K4igoPiCQyIapoXdpQyvV7KFi3Gk52NIzaWoEEDMexNF2hNTk7mqaeeokuXLpimybvvvstZZ53F0qVL6dWrV5O121hM06QotwxXhQdngIOw6KAmz39eWlpKv379mDRpEueee26d7V27duXll1+mY8eOlJeX8/zzzzNu3DhSU1OJjY2t54wHrnXlSAQrWfcXk2D11MY9726jH4Stv0J4Wzjr5aZpQ0REmtSWnBKuf28xG7NK+Oa2EfRuE97wQUBqVjHPTl9PdLCTjxZuZ3BKJJ//aRhv/bKZf36z9oDO8cVNwxjYPvJwut9slIPn2NHU/9blJcV8/NA95KfvbPRzA4z7059ZPuM7eo8+hf7jTm+SNkREpGllLPIy7y8QGAOnvmfg8D+wTGzpWYvIyllKUfEOiku3073zRXRsN575i58kv3DjAZzBxmlj3jy8zjejpr5mF82YQeYTT+LJyKhe50hIIP6B+wkbN67R29uXqKgonn32Wa699tpma/NQ5O4qIm1lBq4KT/U6Z4CDDn0SiG6mHOiGYdQZkbi33f9vfvzxR8aOHXtY7bWuHIkANhuc9pxVyakpLHkfNs+Bpe/DV7fBS8dBSRb4vLBhOrjKoDgDPJVN076IiByWLTmlnPbSL/RJDqd7QigXv/47BWWuAzp2ybYCpq/OJCzQSUyIE5fHx+hnf2La8l0NHmszIC7UnwFtIw7zFYgcfQJDQplw293Ym2jU4Iz/+w+Zm1OZ896bTH78YT7/54OAFcDcsmIpAIVZGfs7hYiItKCtM338cj90OhM8ZTDv3gMf75SZvZRdmQuIjuiGnyOY9KyFzFvwKCWlB/J33yA2us+hd7yVKZoxg52331EriAjgycxk5+13UDRjRpP3wev18sknn1BaWsoJJ5zQ5O0djtxdRaz/Y0etICKAq8LD+j92kLurqIV6VpvL5eL1118nPDycfv36Hfb5Wt/UZoDgGLjpV1j7LUx/EAoOYypNYCyUZ4MzFOwOGH47rPgcdvwOqyaDuww+ugj6XQLf3wtdxsPGHyE4yhq12OY4CIiAsX9rtJcnIiKHLsDPTnJkIP2TI1izq4j4MH8C/BqeqpFVVME5A9qwIaOYmWsyGNohim9XVt1k5ZZjw0q5s6/b3rP6J/H8RQMa62WIHHUSO3Xljvcm8+tnH7Domy/xVFYc8rmcwSG4SksIDA/Hz+nP0HMuZP7kjynJy2XriiUALPjyc3asWcmW5UvoM2Y8K3+aTmBoGMk9+4Bh0P2EEXQ9fkRjvTwRETkMzjCD4HiThDH55LR/k5CSiUDDhVTLK/Lo0/1KKl0FlJRn4ecIprDowD//9+91I0nxQw6j562H6fWS+cST1Fs5sKrUdeYTTxI6dmyTTHNeuXIlJ5xwAhUVFYSEhDB16lR69jzMYrpNyDRN0lbuP1idtiqDqMTQJp/mvC/ffPMNF198MWVlZSQmJjJz5kxiYmIO+7ytM5C4W4/TIS8VZj58cMcZDjA94AyBDiMgoTd0PdX6DtDrHHh5IHSfCNvng6cC3OXWtp1LAC+UZltfu6ybWeZVVX9OGgQ3zGqUlyciIgcvITyAGXeO4utlO9mQWczwzjH1BhLX7CrC6bDROS6EjxZs4YGpqzm9TyJzN2ZTXOFha25prf397LCvdIuJYU6eOq9vU7wckaPO8AsvJ33DOrauXHZQxxk2O6bPS1SbtiR27U5UQhJ9T55AQEgIAMERUUx7/kkGnXkuS7+fRmlBHl6vNUJg5WxrBEV5cREbqyo+b/z9l+pzj7j0aoaedX4jvDoRETkUiUMNEofaWbZ6Cp7wDcQM2kl9gcScvNWEhbbHzxHEklWvkpm9mG4dzyM3fy1gB2rfjAUFtKGsov60GglxgxVE3EPZosV1RiLWYpp4MjIoW7SY4KGN/75169aNZcuWUVhYyOTJk7nqqqv4+eefj9hg4u6ciPvjKvdQlFtGeExwM/WqttGjR7Ns2TJycnJ44403uPDCC1mwYAFxcXGHdd7WN7V5b+1HQFTHmmWjgdip4YCoTla1Zrs/rJkKqbNqgogAwdHwly1w1ktw6x9w02/W6ESApN3DRB1g1PP27loEU245nFckIiKNYGeBNRrqtjGdASit9PDTukx8Pusp7AWv/cY17yxkbXoRD0xdDcDqXYUUV3iwG1bxFLsB4YF+AFR66x+N2DEmkPMGtsPf0fhPbr0+k/unrODjhdsa/dwiTanbsJE4A4MOeH+7nx8h0VaF8/LiIlbPnond6awOIgJ0GjiEOz6YyoiLruDWtz9l5KXXsHPtGkKiowkM3SNHUT2jAn756B2K83IO/QWJiEijcLmL8XME067NSQCUleeQX5gKQEFRGguX/Yt1qZ+zZccsMrMXA7B5+49VR1tBRMOw4+ewAjf7CiKGBnciMW5wk7wGt7uY9PSvqahIb5LzNxVPdnaj7newnE4nnTt3ZuDAgTz55JP069ePF198sUnaagwNBREPdr+mEBwcTOfOnTn++ON56623cDgcvPXWW4d93tYfSEweCOe+AZFVwcS+F4DDv/Y+xh4f7kwP5K4H0wvleRDWBpIH7b8NTwVkrLQClpdNhsunwP1b4ZF8+NNvdfdf8QF4W+4/k4jIserX1BwemLKSwnIX147owN/O6MnNHy5hY2Yx//fzJia9s4gZazIBuP+0Htx9SjeSIwMZ3yuevsnh7MgvA2BivyTAikcUlrvrbSsq2I/bxnTmp3vGcM/4bk3yekoqPUxZspN/fL2SH1c3nKdR5EjRZ8w4Rl0xicDQMAybjfZ96077N2w192det5vi7CwAKkpKCImKJixm3xUHDcNg++oV+Lwe2vfuz7UvvsF5D/6Tuz6Zxt2fTGPoORfWOWbyE480wisTEZGDlbZ9JhvTvsbn8zKwz23ERPfmt0X/xOfzsnzN68xf/CRudymhwcl0an867dqMIjKsIzFRvXH6heJ2F2Iz/AgNageAaXpxe0rrbSs4KJHj+tzKiUMfJDGugc/5h8jlyqG0NJUdO76oHhl/NHAcYCXfA93vcPl8Piorj9zaE86AA5vge6D7NYfGek+PnFfUlJIHwU2/wLbfYfkn4NnjQ19oEkR3hp2Lwb33HxsTBlwJo/9qLRZnwvpvYdCk2rv5BcLlkyEk3vpU2XmPCjgJvSAwygpKVjOgsgiCVGJeRKS5VLi9XP32Qtxek4+qRvD5O2w4bNbopLMHtKHS4+OETtEs2pJHTIiTHglhPPzVKqavzsRpN/D4rHN9u9IK2nmrRiV66xmKOO3W4bSJbNppDJ8s3Ibp8VEOXPf+UrY8ldSk7Yk0pr5jTyWl73GUFubz9b+eqLUtIj4B/9AwsjanYvp8tQ80fZzzl0eIS7EeEqdvXE9ZUSGdBtaeZtW+3wAm3Ho3KX0H4B8URMoewcqeo8ay8KvJtc7tsDX+qGEREdm/vPwNrN34MQAb074EDGw2B/7OCAA6tT+dktJd+PkFszPjd2Kj+2EYdlasf4fi4u2A9XfcZ7opKWu4uMrIoY81ab460/RRUrK+aslFWtpLdO58V5O115iCBg3EkZCAJzOz/jyJhoEjPp6gQQMbve3777+fCRMm0K5dO4qLi/noo4+YM2cO06dPb/S2GktYdBDOAMd+Rxw6Ax2ERR/4DIyDUVJSQmpqavVyWloay5YtIyoqiujoaB5//HEmTpxIYmIiOTk5/Pe//2Xnzp1ccMEFh932sRFIBMhLg6UfQNJxkDoTyvOt9cW7rK+wNtZoRI8LMMHutCoxL/+oJpD4wbmQuQq+vQecwXD6CxCeBHHdofPJ+277jOfhi+vA54bw9jDpewURRUSa2S+p2bj3ivhVenxUAjsKyhndLY77T+uBaZqc/9p8APzsRvUxrj2O3Z0L0aT+ICLAf35K5anzDr8q2v78sDqDA6s3LXJk2rl+DTvWrqLjgMGs+Gl69QeXgswMyMwgLqUjWVs2Y9jtmF4vwRFRlBbksWzGd4y74VYAPnnkPnxeLxgGodExnPPXR6ksKSGxS1d6nji63najEtvQd+ypLJ/5HQD9xp3OiZdc2TwvWkREqqXt2Lt+gInP56a8Ihefz01cTD/iYvqRk7eW5Wter9rHoL6EMuYB3BVlZC8hMa7xA2G7+XyVlJRs2GONX5O11dgMu534B+5n5+13WAOk9gwmVgVf4x+4v0kKrWRlZXHllVeSnp5OeHg4ffv2Zfr06ZxyyimN3lZjMQyDDn0SWP/Hjn3u06F3QpMFrhctWsTo0TX3OXfdZQWsr7rqKl577TXWrVvHu+++S05ODtHR0QwePJh58+bRq1evw2772AkkbpkHq6eA11UTRKxmwIi7rIIoSQOsoGJJpjWSsHgnZK6G+F7Q4SQrkGh6rRGFU/YYmfi3HLBX/ZFY85U1wnHso7BxBky5wQoiGja45XdwNk1EWkRE6uf2+rju3cW11sWFOMkqcREX4s9xbSPJLKpgwovzMPe4aTLqexp7AAxg7oamz7f23qQhVHp8RAY5sdtaphqcyOFYP38eaUsXEZ3cvs7oB7ufk6HnXsT3L/+bYRdcxpYVS4jv2IWMjeswfV7Ki4sIDA0jLDaegoxdYJoU52Tz3j1WLuqwuHiu/09NHqD5kz8mNDqG3qNPYfrr/2HVT1YBluDIKE6+9qbme9EiIgJAUfE2MrP/qLXO6ReOy11IdEQ3HI4A0rMWsXzNW5jmnqPT6w8kHojM7MVNGki02wNp334SNps/hmHHqK9uwhEsbNw4ePEFMp94slbhFUd8PPEP3G9tbwKNkbevJUQnhdFtcDJpKzNqjUx0Bjro0DuB6KSw/Rx9eE466aRan1v2NmXKlCZr+9gJJA65EVJGQHQXeDx+r42mFdwLioaI9lC4HdZ9A9fOhLdOgYoiOP9/sOwD6H4GJA+BH/eqBP3ZVXD+W9Y0519fgl1LYdifrZGI3qo56KZPQUQRkRawJbtunpysEhcBDhuJEQGEB/lRkOsir7T2k2yXr85hB2RYpyh6tYk4tIMPQmiAH6FN3opI05lwy92UFxWSnrqO7/7zr5oNhoHX4yYoPJKAkBDCYuPI3LSR3O1bGXPNjUx7/ilCIqOI69CJ4twc+o07jcqyMtb9Mqf6FEVZmSz8ajJDzjofV0UFC778jKg2bek9+hRWzaqZKlVaWNB8L1jk/9m77/iq6vuP469zZ272XoSw995TEUQFFWfdWhStdVUt1Z+2VatVa61t1bptVdxWreKeCILsKXsEwggkZO911++PC4GQAAncmxvI+/l45EHOOd9zvp97r97zzed8h4jUKSje0mBfrbMEMBET3Q2AsvIsPJ5D53Q7lgaaQXRUVxLjAztaBMBiCT96oVYs8swziTj9dN8qznl5WBISCB06JCA9EU8GcamRxKZE1K3ibAvxDWcO5BD6YGs7iUSTCZL7wa4ljR+f9QgMvBKG/NL3RHzgldB+OJz/nG8Oxdl/AZMZOoyGUbfCwmehIvfA+Zu+gH+fDlFpsHsZ9L4AwuJ9Q6Bry3xlwo5viW0RETk2z85u2FA1DHj6ikGc1ScZALvFjMkATxMfcJvwNWNtZqPesGeAy4Z14LyBmq9Q5GjsoaHYQ0NZM+e7+gf2PWFf8N83GXjWuXQZMpyI3z+IYTIRn9aBU6+6jo4Dh/Ddy89gczjoNXY8EbFx9RKJAPPemUHOts2UFxTidjrpP3EyzprqemUGnXluIF+iiIgcxqat/2uwzzAsnDbqcRwhMXi9XiLC2jX5eoZhxuvdN/9MXUtt/zETg/r8GkeIphhrCsNsJmzE8KMXFMA3zDkqPrBzo7cmbSeRuF98N7CGNVxYpWw3zHsCNn8FN8+HnQvhHz2h/+Uw8Cp47yrfkOdRt8L3D/qGR4engtcJlcW+fwsyIHcdWEJg2A2+6172JuRt8q0M3e/iln61IiJtntPtIcTS8IlghN3CtrwKHvtyAxuyS5m7JZ/DPWeND7cRYjWRVVSN2eRbZMUDtI9x0CM5gu83+B4sdUkII9Rm4dz+KYF7QSInoe7DR7Pk4/cb7N+1fg271q+huryMcVdP49uXn2HH6pWcNvVXeD0ecjK2MPic82nXoxdv/3E6IWHhWGw2aqur8XjcuGpq2LJoAQBRScl0HTIcqz2Es2+/G7PZjBfoMXJsC79aERGpqSnBZg2n+pCHO3ZbJHvzV1JUsoXa2lIKijYc9hoh9jg8Hie1zlLAqEsiJscPJb9oAy63729+R0gC8bG9lUQU8ZO2l0h0xMCIm+CnfzR+fO9ayPwJ5jwGZdkw/0nf4izj/wB9L4bCbRDT0de7cch1vuNjp8OsP4OryncNt/PAfInth/t+REQkKL5am8N/l++p2452WHBYTQxMj+XZH7ZQ5XTX9UJ0H+Ya+eW1XDm8PR+v3E2o1URBpQuzAV0Swpm14UDv9K15FYRZTZg0X6FIsyR17kpkYhKluXsbPb7s84859arrWPPDt+D18t1LzxIeG8sv7nuYxI6dKS8qJD6tA5EJSUTExZO5chljLr2az558DADDZKK2shKbwwFArzHjWuy1iYhIQxu2fEB1zYH5pK3WCCymEBLi+7F+81scfR5EE9U1BXTpcC5bd3yOzRJJrasEs8mO011Zl0QEqKrOo6oq8HNXi7QVbS+RCDDxAVj0/IHE36HeuWzfXIZmGHA5rP0Qts6GOX/1DWeO6wpF2yF/ky+x+NM/YcAVsPxV3/yJFblg1VyIIiKtQXmVs952z5QoFm0rIHttDhaTcdihzMlRdiyGQaTDSk5pNWO7JfBzVgnr9pQCYDYZZBVVNWjiVjg9zM/IJyHCjtVsolMbGuYgcjyuevQfvPCrqxs9ZphMPDvtMiw2Gx63h55jT2XVN1+w8uvP2bVuNW6XC6/XS2hkJMWRURTu3kXG0oXEt+9A/q4dpPcdQE1FueZ3EhFpJaprCup+t5jD8HhqqXKWsXP3DxwpiWi3xoHhxhGSSFV1LqlJI9i64wtqXSUAhDoSKSha3+Aa+UXrqXWWU1a+m4jwdtisJ/Y8hiLB1DYTiRmzDp9EBHCW+37At9pz2nDY+NmB44WZvpWbN34B076FkChfL0VnJXQ7A/r9IqDhi4hI0zjdHt5cvKNuO9RmZsOekrpt1xEmRLz1tK7sKqritfmZACzJLKR3aiQZe0vxYNA7NYpTuyfw7A9bOLtvMp+vydlXh0GozczFLywgymHlp3smBOjViZxcFnzw3mGPed1uaisr67azN28kLCaOjKUL65UrK8in67BRjLnsapK7dKe6vIy5b89g7GVXE5/eMVChi4hIMzhdlRSXbavbdoTEU1ax86ASh2+fjRn2R5aveYbyil14PC725q0kMX4AufmrMAwzifEDsVrDKCndRmx0D/IK1+yrI4Hy8t0sXvk4aSlj6d9rWqBenshJr9mJxKqqKpYvX05sbCy9e/eud6y6upr333+fX/7yl34LMCB2L2962ZKdvp+D7Z/AtSjT9zPgct/2RS/5Jz4REfGLBz9dx4bssrrtytrGBy9bTQbhdjNFVS4sJuicEE5VrYv3l+7EuW8hlZkrsyiucvHRzaPpmhhOfnkNW/aWkRrt4MLBaWzMKWNCzyT+cE4vAO46swcOm3o/Scs4GdpnhVk7jl5on5ytDRdQ2m/l158x7LyLCY2MIjQyigvuvs8f4YmIiJ/MX/pnPJ4DI0bKKhr//rfbY6mpKQTAbA4nPCyRwuLNlJRl1i2ssmX7J1gtoUwY808Mw+y7ruHF6Swnvd1plJZn0bv7laQkDsHtdtI5fRJJCYNb5HWKnKwMr9fbxPUpYfPmzZx55pns3LkTwzAYO3Ys7733Hikpvknl9+7dS2pqKm734WaZ8iktLSUqKoqSkhIiIyOP7xUcC48HNn8NC/4FpXuheNvRzzlYZHso3eX7/cKXDiQSjyZ/C3z8axh8LfQ6F0I12auISCCd+c85bM6tOOxxA9/qzXaLiSqnp9HjXuD+c3rx8BcbGh1oc8tpXViwNZ+fd5Xw63GduXdyLz++guAK+v1amsQf7bPW8Fk7a6pZ9e2XbF22mJyMTbhdrmadH52cSnGObz7UW155F0d4RJPO27hgLiu/+owRF15KhwGDMJvb5oAdEZGWUOssZ9ZPvz1odeXDqb/q8qEMzHRIm8D2rO8aLTtswN2sWPsMHnctQwbcTmJc/+MNvdVoDfdsadtMzSl8zz330LdvX3Jzc9m0aRMRERGMGTOGnTt3Hv3k1sRkgp5nw7Sv4TdLfPMbmqxNP788B6wOuPSNpicRAcpyYM9K+Ow38LdO8PRAWPOhb3EWERHxqy17yxokEWNCrXRJ8M1ZaMKXFPR4oXdqJEM7Rje4xv6k4bo9pfRICsdoZA2V5+dsZdUu33Dp8wak1jtW7XTjch++ESziDydL+8xqD2HYlIu4/KHHmfbUy6R079ms84tz9hCX1p5rHv9Xk5OIAAVZu8jO2MTHjz/EU1dewDcvPs32n1c0N3wREWmCLZmfNkgihtjjsJh97TPDsO3b66Fj+zPr9h/Kbo+itHwnoSGJNJZwXPrzP3G7qzFMFuJi6j/kdbmqG5QXkaZrVo/EpKQkvv/+e/r16weA1+vllltu4csvv2T27NmEhYWdGD0SG+P1wqq3Yf1M2PLdkcsOvAZOnQ6xnZtfT2UhvDrJt1CL2QHufXM1XvcNdBjZ/OuJiEijbnt7ed28hQAOq0HnhAhKq5zsKqo/T25kiIWbx3Xh8W82NbhOqNVEpdODyYC+qVGszy7BdZjc4Ce3jmFA+2jAl8ic9PQ8Qiwmlt43kVDbidfLqVXer6UBf7TPWutnXVNZyfIvP2HjwrkUZe06fEHD4PRpN9Nj9CnNSiKC7/0q3JPFW/fegau2FpPVisfpe8j7u/9+fjzhi4jIIb764Sa81NZtW8xhxER3Ia9gdYOykeGdsFpCKCje0OCYxezA5a7CYY/D5a7G6Wp8BIrZZGPiKU9jNtsB2LbjazZufZ/I8I6MHf6An15Vy2qt92xpO5rVI7GqqgqL5cAfQoZh8MILLzBlyhTGjRvH5s2b/R5gizEMGHQ1XPUh3L4Kxu+bT8cWBZHtABOMvA0eLIELnj22JCL4hjPftgQeKILJfz2w/91m9GwUEZEjKiiv4YuDkogAVU4v6/aUsquoCvMhPQtLq11EhVpJjGiY7KvcN+T5kqFpFFTUkhrt4MzeiQxKiyLUcuBCozvHcsXLC1mzu4RnZm1h4dZ83B4vNS4PTleTn9lJC3ruuefo2LEjISEhjBgxgiVLlgQ7pGNyMrfP7KGhjP7FFUz7xwtc9tDj9Bk3EYDwuHhCIiPBMPjF/Y/wu/c+Y+CZZzc7iQi+9yuuXXvuePMjpr/7KVEJyXXHtixZeIQzRUSkOYpKttZLIgK43BWNJhEBSssz6dm18b+TXfs65HRsfyYudzVxMb1IThiKIySJ/UtBWMyhWK1RzF38ACVlu9i282uKSzPrnS+t01//+lcMw+DOO+8Mdiit1ty5c5kyZQqpqakYhsHMmTMPW/amm27CMAyeeuopv9TdrO4RPXv2ZNmyZfTqVb9r8LPPPgvAeeed55eggi62E4y7G9JHQEQKxHfzfx0mEwy9Fpb8G3LXww3f+78OEZE26sfNeUdY7w/cXhjXLZ6SKie1bi/nD0zl4iFplFW7eOyrjdhMULuv12G4zcyEnknMXLkHl8dDXJidb9fn1l0rJSqEnJJqFmzzTQb+wpwMvlyTQ3SIQUyohSuHdyAqtBnTZ7RBbo+XJZmF5JZVkxgRwvBOsZhNjYwj96P//ve/TJ8+nRdffJERI0bw1FNPcdZZZ7Fp0yYSExMDWre/tZX2WVrPPqR270nnwUNJ7zeQkLBwv9dhmExMe/IFnrr6IgzDoMvQ4X6vQ0SkrdqR9cMRjxuGlZjo7tTWlmCzhtEx7QyiItsTF9ObgqL19crabTFERqSzIeM9DMNCSdkO3O6aumHTFnMYLncFLnclAJsyPiS/aA1mUxg2awy9ul4WmBd5kvC6veStgeoCLyFxBgn9wDj0SXyALF26lJdeeon+/U+ceS29Hg+le7OprazAFhpGZFIKhqlZ/faaraKiggEDBjBt2jQuuuiiw5b7+OOPWbRoEampqYct01zNGtr82GOPMW/ePL788stGj99yyy28+OKLeDxHng9KXXFFRCSQXG4P367LoUtiOB+vyOLFuZmNlnvson78/qM1PHnZQC4c1I5qp5sPlu3i33O3sbOoiqRIOy6PFwPIL/c9QQ+3mxmQFsX2vApySms4dLBopN1EaU39++CLVw9mUt+UALzSwGqJ+/XXa7N56LP1ZJccmK8oJSqEP03pHdD3bMSIEQwbNqwu2ebxeGjfvj2/+c1vuPfeewNWbyD4o32mtpmIiARabW0Z+UUbiQxrz9pNb1JY0nDIstUaSVxMD/bmLWf86CcIscdQWZXP3rwVbMz4EC8uIsLSqazKwWoNp7qmCPASFpqKyWShsjIft6cGGrTQ6jObQhk19B4iw9sH5sUGUKDv2Vlzvax61kNV3oF9jgQYeJuJtFMDm0wsLy9n8ODBPP/88zzyyCMMHDjQb73oAqVg+1a2LZpHbcWB4fW2sDA6jzyFuI5dWiQGwzD4+OOPueCCC+rt3717NyNGjOCbb77hnHPO4c477/RLL89mJRL9RY1VERFpSdklVYz72w/UHtKmfPTCvvzx47V0jA1haMc4uiVFsGBrPj9uzvd7DOseOosw+4k1T2Kg79dfr83m5rdWNOg9ur+J+kKAErC1tbWEhoby4Ycf1mtwTZ06leLiYj755BO/19naqW0mIiItraBwE4tXPV5vn4GZ9mkT2Jn1HZHhHUiMG4hhwM49c6mpLfJr/Y6QBMaPfvzoBVuZQN6zs+Z6Wfinwz94HPVQYJOJU6dOJTY2lieffJLTTjut1ScSC7ZvZeOsrw97vOfpk1okmdhYItHj8TBx4kTOP/987rjjDjp27Oi3RGKz/6LZvn073333HbW1tYwbN46+ffsedxAiIiKBlBLlYPOj5/DV6myW7yzk05+zyS2rweX2pbC2F1azvXB3wOpPiLCfcEnEQHN7vDz02fpGh6B78SUTH/psPWf0Tvb7MOf8/HzcbjdJSUn19iclJbFx40a/1tVS1D4TEZETTVxsDyaP/w+ZWT9QUrKVvILVuNw1uF2+4cil5TsoLd8RsPojw9MCdu0Tkdft64l4JKue9dBujCkgw5zfe+89VqxYwdKlS/1+7UDwejxsWzTviGW2LfqJ2PROAR/m3JjHH38ci8XC7bff7vdrN+uvmtmzZ3PuuedSVeWbmNRisfDqq69y9dVX+z0wERERf5vcP4XJ/VO479w+7CqspH1sKPO25DF7Q+5hB8CYDPAclO0y8CW62sWEkBBmY83uUtxeSIm0U1nrpqTahQGM7xlPv9Rofjm6A6E2zZF4qCWZhfWGMx/KC2SXVLMks5BRXeJaLrATkNpnIiJyojIME53bT4T2E3G7nbhclZhMVoqKt1JZncOBllfThIe2x+WuorrGN7ok1JFCVXUeXq8Lw7CRFD+Q+NjepCQOxWIJCcyLOkHlraHecObGVOX5yiUO9G/du3bt4o477uC7774jJOTE+FxK92bXG87cmNqKckr3ZhOV0q6FovJZvnw5Tz/9NCtWrMAw/J/0bVYi8f777+eMM87ghRdeICQkhPvuu4//+7//U0NVREROOO1jQwH4z9Rhdfu+WL2H2DAbeaU15JXXcMnQ9kQ6rGzPr6C0ykmnhDBCbZYj9pBbnVWMw2qmW1LzV49tS3LLDp9EPJZyzREfH4/ZbGbv3r319u/du5fk5OTDnNV6qX0mIiInA7PZitkcBcBpo/8CgMfjZteeHwkPb09VdT4edy3tU0/BMEwUlWRgtUYQYo/BbLJgGIfv9ZVbsJroiE7YbGqfHU51QdMStr5y/k1OLV++nNzcXAYPHly3z+12M3fuXJ599llqamowm81+rfN41VYeOYnY3HL+NG/ePHJzc0lPT6/b53a7+d3vfsdTTz3F9u3bj+v6zUokrl27lgULFpCS4puv6IknnuCll16ioKCAuDj1FhARkRPbOf0bX82sY3xYk6/RPy3aT9Gc3BIjmva0uanlmsNmszFkyBBmzZpVN5eMx+Nh1qxZ3HbbbX6vL9DUPhMRkZOVyWSmQ9qEfVvd6h2Liera5Oskxp04KwAHS0hc03p/+sr51+mnn86aNWvq7bvuuuvo2bMn99xzT6tLIgLYQpv290FTy/nTNddcw8SJE+vtO+uss7jmmmu47rrrjvv6zUoklpaWEh8fX7cdGhqKw+GgpKREDVURERFpsuGdYkmJCiGnpLrRJqsBJEeFMLxTbEDqnz59OlOnTmXo0KEMHz6cp556ioqKCr80rlqa2mciIiJyvBL6+VZnPtLwZkeCr5y/RURENJjfOSwsjLi4uFY773NkUgq2sLAjDm+2hYUTmeT/hQPBt8J1RkZG3XZmZiarVq0iNjaW9PT0Bm1Aq9VKcnIyPXr0OO66mz3z+zfffENUVFTd9v4n+GvXrq3bd9555x13YCIiInLyMpsM/jSlNze/taLB7Ef7n3P/aUpvvy+0st9ll11GXl4eDzzwADk5OQwcOJCvv/66wQIsJwq1z0REROR4GGaDgbeZjrhq88DbArPQyonIMJnoPPKUI67a3Hnk2IAttLJs2TLGjx9ftz19+nTAt/L1jBkzAlLnfobX623yzKWmJrwBhmHgdh9uynqfQC5XLiIiIv7REvfrr9dm89Bn6+stvJISFcKfpvRmUt/APME92fijfaa2mYiIyIkh0PfsrLm+1ZsP7pnoSPAlEdNOVRLxUAXbt7Jt0bx6PRNtYeF0HjmWuI5dghhZ4DSrR6LHc+SlwEVERESaY1LfFM7oncySzEJyy6pJjPANZw5UT8STkdpnIiIi4i9ppxq0G2Mib41vYZWQOIOEfqgn4mHEdexCbHon3yrOlRXYQsOITEoJWE/E1qDZQ5uPxOPx8OWXX3Luuef687IiIiJyEjObDEZ10Vx+gaL2mYiIiDSHYTZIHAj+Xp35ZGWYTESltAt2GC3GL4nEjIwMXn31VWbMmEFeXh5Op9MflxURERGRY6T2mYiIiIj42zH3tayqquKNN97g1FNPpUePHixYsIAHHniArKwsf8YnIiIiIk2k9pmIiIiIBFKzeyQuXbqU//znP7z33nt06dKFq666igULFvD888/Tu3fvQMQoIiIiIkeg9pmIiIiItIRmJRL79+9PaWkpV155JQsWLKBPnz4A3HvvvQEJTkRERESOTO0zEREREWkpzRravGnTJk499VTGjx+vp9siIiIirYDaZyIiIiLSUpqVSNy2bRs9evTg5ptvJi0tjbvuuouVK1diGFrJR0RERCQY1D4TERERkZbSrERiu3bt+OMf/0hGRgZvvvkmOTk5jBkzBpfLxYwZM9i8eXOg4hQRERGRRqh9JiIiIiIt5ZhXbZ4wYQJvvfUW2dnZPPvss/zwww/07NmT/v37+zM+EREREWkitc9EREREJJCOOZG4X1RUFLfccgvLli1jxYoVnHbaaX4IS0RERNoMjxsy58GaD33/etwBrW7u3LlMmTKF1NRUDMNg5syZAa0vGNQ+ExERkWPl8bjZtW41G+b/yK51q/EEuG324IMPYhhGvZ+ePXsGtE5/8nq9uAvKce8pxl1QjtfrDXidR2vPXnvttQ3e00mTJvml7mat2nw0AwcO5F//+pc/LykiIiIns/Wfwtf3QOmeA/siU2HS49D7vIBUWVFRwYABA5g2bRoXXXRRQOpoTdQ+ExERkabasngBP8x4mfLC/Lp94bHxTLj2RrqNGB2wevv06cP3339ft22x+DVdFTDunBKcG7Kh2nlgZ4gVa68UzMlRAau3Ke3ZSZMm8dprr9Vt2+12v9TdrE9mwoQJRy1jGAazZs065oBERESkjVj/Kbz/S+CQp7al2b79l74RkGTi5MmTmTx5st+vGyxqn4mIiIg/bFm8gE//+ZcG+8sL8/n0n3/hvOl/CFgy0WKxkJycHJBrB4o7pwTnyp0ND1Q7ffsHpQcsmdiU9qzdbg/Ie9qsROKcOXPo0KED55xzDlar1e/BiIiISBvhcft6Ih6aRIR9+wz4+l7oeQ6YzC0c3IlF7TMRERE5Xh6Pmx9mvHzEMrNff5kuw0ZgCkDbbMuWLaSmphISEsKoUaN47LHHSE9P93s9/uL1en09EY/AuSEbU1IkhmG0UFT1zZkzh8TERGJiYpgwYQKPPPIIcXFxx33dZiUSH3/8cV577TU++OADrrrqKqZNm0bfvn2POwgRERFpY3YsqD+cuQEvlO72let0SouFdSJS+0xERESO1+4N6+oNZ25MWUE+uzeso30f/y7iNmLECGbMmEGPHj3Izs7moYce4pRTTmHt2rVERET4tS5/8RRW1B/O3JhqJ57CCsxx4S0T1EEmTZrERRddRKdOndi6dSt/+MMfmDx5MgsXLsRsPr5EcLMWW7n77rtZv349M2fOpKysjDFjxjB8+HBefPFFSktLjysQEZG2pKC8hgc+WcvvP1rNGwu2sSO/jCe/28xv/7sKj8fXQ2v/vyInpfK9/i3Xhql9JiLiH0U1BXyT8zGLCn5kQ8nPVDgr+C73U1aXLAN8PZBaYhEFkWAoLy7ya7nmmDx5Mpdccgn9+/fnrLPO4ssvv6S4uJj333/f73X5TY3Lv+X87PLLL+e8886jX79+XHDBBXz++ecsXbqUOXPmHPe1j2n2ylGjRjFq1CiefvppPvjgA5577jnuuusu9uzZQ2Rk5HEHJSJyMvN6vQx55PtD9m6o++37tbsp2/dw668X9CUi1MLE3snYLWbyymqID7cFrXu8iN+EJ/m3nKh9JiJyHHZWbuOr3P/5Nqr37Sz6FoBtlZtYVbSYKiqxYGV0zASibXEkhaRgYFDlqSTUHBacwEX8JDw6xq/ljkd0dDTdu3cnIyMj4HUdM3sT02lNLRdgnTt3Jj4+noyMDE4//fTjutZxvaIVK1bw448/smHDBvr27at5eUREmqC06shPpcoO6iF/78y1db+H2y2U17g4s3ciszfm4vT49i+85zRSYtR4lRNMh9G+1ZlLs2l8nkTDd7xD4FYHPFmpfSYi0jxer5cK55F7cFdRCYALJ3OLvgHAhAmLYaXWW0N6SBd2Vm/dt9/M9R3uxGQ0awCgSFC169WH8Nj4Iw5vjoiLp12vPgGPpby8nK1bt3LNNdcEvK5jZYoNgxDrkYc3h1h95VqBrKwsCgoKSElJOe5rNfubbc+ePfzlL3+he/fu/OIXvyA2NpbFixezaNEiHA7HcQckInKyWr6jiOtfX8qAP397TOeX7+sWP2vDgSQiwKjH5/ghOpEWZjLDpMf3bRzaw3bf9qS/BmShlfLyclatWsWqVasAyMzMZNWqVezc2ciqeycItc9ERI7NxrI1fJrzLnOLvmv2uR481HprAOqSiL79bj7c/brfYhRpCSaTmQnX3njEMuOn3hiQhVbuuusufvzxR7Zv386CBQu48MILMZvNXHHFFX6vy18Mw8Da68hJOWuvlICNJDtSe7a8vJy7776bRYsWsX37dmbNmsX5559P165dOeuss4677mb1SDz77LOZPXs2Z555Jk888QTnnHMOFkvr6KYpItLa/W9FFrM25B72uNkAtxccVjPDOkQzf2sh43rEc9eZPahyutmSW878jHwGpEXz/rIdbN7rezLeJaF1POUSabbe58Glb/hWbz544ZXIVF8Ssfd5Aal22bJljB8/vm57+vTpAEydOpUZM2YEpM5AUvtMROTYrSheQJn76PPJRlliMWOiyFXAqOjxJIak4PF6yK3NZm/NHtrZO/DTQcnI7qGB77Ul4m/dRozmvOl/4IcZL9frmRgRF8/4qTfSbURgRopkZWVxxRVXUFBQQEJCAmPHjmXRokUkJCQEpD5/MSdHwaB03+rNB/dMDLFi7ZXiOx4gR2rPvvDCC6xevZrXX3+d4uJiUlNTOfPMM3n44Yex2+3HXbfhbcZssSaTiZSUFBITE4+YVV2xYsURr1NaWkpUVBQlJSWas0dE2oy8smqGPTqrwf5O8aFEh9p46ZohZBdX0zUxnLBWMpeGtG0tdr/2uH2rM5fv9c2J2GF0QHoinqz80T5T20xE2qqFhT+yunRJg/0OUxg9IvrSLaw3BgaR1mjMhu5NEnwtcc/2eNy+VZyLiwiPjqFdrz4B6Yl4svB6vb5VnGtcYLdgig07qee0b9Zfqg888MBJ/WaIiARSQkQIsaFWCit9T6sM4OVrhjChVxJmk++7NTEiJIgRigSJyQydTgl2FCcstc9ERI7dgKih9RKJoYQxKeUiEuzJQYxKJLhMJjPt+/QPdhgnDMMwMMeFBzuMFtOsHon+oqfeItJWrd9TyryMPE7rnkCPZH3/Seum+3Xboc9aRNqy7ZUZlDtL6BLWC4clNNjhiByR7tkSbM3qkRgTE9PoE++oqCi6d+/OXXfdxRlnnOG34ERETja9UyPpnaobvoj4j9pnIiLHp2No12CHICJywmhWIvGpp55qdH9xcTHLly/n3HPP5cMPP2TKlCn+iE1EREREjkLtMxERERFpKc1KJE6dOvWIxwcOHMhjjz2mhqqIiIhIC1H7TERERERaismfFzv33HPZuHGjPy8pIiIiIsdB7TMRERER8Re/JhJramqw2Wz+vKSIiIiIHAe1z0RERETEX/yaSHzllVcYOHCgPy8pIiIiIsdB7TMRERER8ZdmzZE4ffr0RveXlJSwYsUKNm/ezNy5c/0SmIiIiIgcndpnIiIiItJSmpVIXLlyZaP7IyMjOeOMM/joo4/o1KmTXwITERERkaNT+0xEREREWkqzEomzZ88OVBwiIiIicgzUPhMRERF/8nq81GSW4CmrxRRhw94pCsNkBLTO3bt3c8899/DVV19RWVlJ165dee211xg6dGhA6/UHr9dDVdVu3O4KzOYwHI52GIZfZxJsYO7cuTzxxBMsX76c7OxsPv74Yy644IJ6ZTZs2MA999zDjz/+iMvlonfv3vzvf/8jPT39uOpuViJRREREREREREROTlVr8yn+bCvuktq6feYoG9FTuuDoGx+QOouKihgzZgzjx4/nq6++IiEhgS1bthATExOQ+vypvHwLeXmzcbvL6/aZzeEkJIwnPLxbwOqtqKhgwIABTJs2jYsuuqjB8a1btzJ27Fiuv/56HnroISIjI1m3bh0hISHHXbfh9Xq9x32VZiotLSUqKoqSkhIiIyNbunoRERFpAt2v2w591iIiIieGQN6zq9bmU/DWhsMej7u6V0CSiffeey/z589n3rx5fr92IJWXbyEn57PDHk9OnhLQZOJ+hmE06JF4+eWXY7VaefPNN/1eX2D7WoqIiIiIiIiISKvm9Xgp/mzrEcsUf7YNr8f/fdE+/fRThg4dyiWXXEJiYiKDBg3i3//+t9/r8Sev10Ne3pGnl8nPn4PX62mhiA7weDx88cUXdO/enbPOOovExERGjBjBzJkz/XJ9JRJFRERERERERNqwmsySesOZG+MuqaEms8TvdW/bto0XXniBbt268c0333DzzTdz++238/rrr/u9Ln/xzYlYfsQyLlcZVVW7WyiiA3JzcykvL+evf/0rkyZN4ttvv+XCCy/koosu4scffzzu62uORBERERERERGRNsxTduQkYnPLNatuj4ehQ4fyl7/8BYBBgwaxdu1aXnzxRaZOner3+vzB7a7wazl/8nh8vSDPP/98fvvb3wIwcOBAFixYwIsvvsi4ceOO6/rqkSgiIiIiIiIi0oaZImx+LdccKSkp9O7du96+Xr16sXPnTr/X5S9mc5hfy/lTfHw8FoslYO+pEokiIiIiIiIiIm2YvVMU5qgjJwnNUXbsnaL8XveYMWPYtGlTvX2bN2+mQ4cOfq/LXxyOdpjN4UcsY7FE4HC0a6GIDrDZbAwbNixg76mGNouIiIiIiIiItGGGySB6SpcjrtocPaUzhsnwe92//e1vGT16NH/5y1+49NJLWbJkCS+//DIvv/yy3+vyF8MwkZAw/oirNsfHn4ZhBKb/Xnl5ORkZGXXbmZmZrFq1itjYWNLT07n77ru57LLLOPXUUxk/fjxff/01n332GXPmzDnuug2v1+v/JXeOIpDLlYuIiIh/6H7dduizFhEROTEE+p5dtTaf4s+21lt4xRxlJ3pKZxx94/1e336ff/45v//979myZQudOnVi+vTp/OpXvwpYff5SXr6FvLzZ9RZesVgiiI8/jfDwbgGrd86cOYwfP77B/qlTpzJjxgwAXn31VR577DGysrLo0aMHDz30EOeff/5x161EooiIiDRK9+u2Q5+1iIjIiaEl7tlej5eazBI8ZbWYImzYO0UFpCfiycLr9exbxbkCszkMh6NdwHoitgYa2ixtm9sJu5bB6ndh5C2Q2DPYEYmIiIi0abVVlZTm7qVo1w46jxiN2er/if1FROTwDJNBSJfoYIdxwjAME6Gh7YMdRotRIlHartpK+Es7wLc0OitehwdLghqSiIiISFtWUVTIqo/erdsuytrJ8Mt/GcSIRERE5GAnb19LkaP51yDqkoj7PRgFK94KSjgiIiIibZnH466XRARwVpQx/5XnqK2qClJUIiIicjAlEqXtMpkb3//prbD+k5aNRURERKStO8LM7cs/fIvK4qKWi0VEREQapUSitF1Hmvz0/V9C9s8tF4uIiIhIG2cyH+YhL+CprWX1Z/9rwWhERESkMUokStsVltD4fpMVBk+F+B4tG4+IiIhIG+ZxuzFbrY0es4SE0n7g0BaOSERERA6lxVakbakshB8fh01fgsftSxp6nPXLeJwQngjWkODEKCIiItKGbCzMYtbqeQzf68TtcmGYzXjd7nplXNWVhMXFBylCERER2U89EqVtWfEGLH4RindCfHe4azO0G3LguNkOg66BUbcGL0YRERGRNuT++W/zTOZcdpTn4Rnan6GXTQXDAJPvTxWz3U76kJFEpbQLcqQiIiKiRKK0HT8+AeW5+zYM6DIeQmMhuiOEJYLJAl43nP8sOGKCGamIiIjISa/KVcu9816na3QyAA9bdhCalIzN4SAkIpKQiAgAbCEO2g8cgmEYwQxXREREUCJR2pLFL8Ki5+HCl+DONTDmDt/+PSugIhfO+ivcvNA35HnbHHhxLOSsDWrIIiIiIiervMoSZmYs4tsdq3hp4q0svPIJhiR1xevxUF1WSnVJCX3PuZC+51wIQNbPy1n75UzcLleQIxcREX/r2LEjhmE0+Ln1Vo0WbMzcuXOZMmUKqampGIbBzJkz6x1v7L00DIMnnnjiuOtWIlFOfh43rH4fxk739TSM7wbR7Q8cn/YNXPgyDLse3r4Y3jgfSrIgZw28NA6qSoIXu8hJZE1WCV+vzQl2GCIi0goUVZezKm8b53UeTqIjimHJ3Qi12gEwTCb6n3sRfSafj9UewrL/vsGulUspL8inJHs3y95/K8jRi5w8ysu3UF29N9hhSCvj9XooKNrInpxFFBRtxOv1BLzOpUuXkp2dXffz3XffAXDJJZcEvO7j5fF6ya6sYVtZJdmVNXi83oDXWVFRwYABA3juuecaPX7we5mdnc2rr76KYRhcfPHFx123FluRk9/W2fDRr2DkLXBPZsPjEUkw4DLf70l9ITIVNn7h2/a64PF0OOcfMOyGlotZ5CT0x5lrWJ3lS8ynR9uZe+/EIEckIiLB8p813/LK2u94/vSb+du46xocj0j0DXeuqSgnNCYWs9VKSc4eAFxVFcx/5Tn6n/cLIhKSWjRukZOJx1NLTs5ndduRkcNITDwliBFJa5CTu5z1W96huqaobl+IPYbe3a4kOXHIEc48PgkJCfW2//rXv9KlSxfGjRsXsDr9YXt5FYvzSqh0HUi2hlpMjEiIomO4I2D1Tp48mcmTJx/2eHJycr3tTz75hPHjx9O5c+fjrls9EuXk13EsnPFnGP6ro5e94l046y+wa3H9/ZnzAxObSBvy4Hl9sJp981vtLK6h471f8OAna9iaWxbkyEREpKVd0XMcvxtyAaNTex6xnD0snIHnX0pEYjKuqsp6x2orKwIZoshJz2SykZg4qW67tHQpGRlPU1i4DJerPIiRSbDk5C5nxdrn6iURAaprilix9jlycpe3SBy1tbW89dZbTJs2rVXPj7u9vIrZ2UX1kogAlS4Ps7OL2F5eFaTI6tu7dy9ffPEF119/vV+up0SinPysIb75EGObmHmvLIQJD0Cv8w/sGzs9MLGJtCGD02PY9PDkejeeGQt3cvo/59Lnga/weAI/BEBERFqHtIg4ftX/LEIstqadYBj0mDCp3q64Dsffq0KkrYuM7E16+sEdLtwUFs5l+/aXyc7+PGhxScvzej2s3/LOEcus3/JuiwxznjlzJsXFxVx77bUBr+tYebxeFucdeRq0JXklLTLM+Whef/11IiIiuOiii/xyPSUSRQ419wn4/A5oPwwi20HHcTDrT1Bd6t96vF5Y9Ta8dxXMeRy+vAe+fQC2zgFP4L+cRYLBZDJY9+dJDfZX1Hp4ee7WIEQkIiIngi0/fs+m2d/QftAwbGHhhCcls23xT/6vyOmEpUvgqy9h40b47ltYuxby8/xfl0grYbNFEBt7doP9FRWbqa7W/NZtRWHx5gY9EQ9VXVNIYfHmgMfyyiuvMHnyZFJTUwNe17HaW1XboCfioSpcHvZW1bZQRIf36quvctVVVxESEuKX62mORJFDDb8R4rr4/k0eAG9M8e3/3/Vw1Qf+qWPrbHjzImDfF8/Gg572LXja929oPNyxCqqKYe9a6HH4+Q9ETiQOm7nBvtN7JjKsU1wQohERkdbO6/XSacRYXM5aEjp3o6ainNzNGyjfm4MjIoqU3v2OvxKPB1at9CUR99u5w/fvtoMedLVrB5POhoICMBmQkHj8dYu0ApGR7SgsrL8vLKwbVmtUcAKSFldTU+zXcsdqx44dfP/993z00UcBred4Vbndfi0XKPPmzWPTpk3897//9ds1lUgUOdjy1+GL6TD1czBbIW0oxHeHou1QtMM/dTw/GnLXHb1cZT48lgbRHaB4B5z9DxiuBV/k5DDj2mF8sioLm8VEr9Qorh3dKdghiYhIK7V5zneU7MliyKVXA5DYrSdFO7fjrKmmojD/+Ctwu+C1V6Epf+zt3g2v/BvM+x6KXXo5REYefwwiQWaxRBATMwaXqxqXq5C4uJGEhKQEOyxpQXZ7tF/LHavXXnuNxMREzjnnnIDWc7wc5oadI46nXKC88sorDBkyhAEDBvjtmkokihwsPAniuoEj2rdtC4VL34TnR0DhMQy79Hohaxm0GwzVJfDdA1Cy68BxiwM8bkjoAXvX4euhaAAHzaNQvC+B+eXvYNGLEN8FznrMV6yp8z6KtDKn9UzktJ7qxSEiIkcXEhlFbWUFhsn3x1hUcipJPfuQtWoZZbl7m3298toqdpcX0CM2DfJy4eefDyQRzWbf76GhYDJD+WEWBNtf/t23ISEBYuNh+DDA8J0rcgKKixsR7BAkiGKjuxNijzni8OYQeyyx0d0DFoPH4+G1115j6tSpWCytO12V5LARajEdcXhzmMVEkqOJcwE3U3l5ORkZGXXbmZmZrFq1itjYWNLT0wEoLS3lgw8+4B//+Idf627dn4xIS+sxyfdzsMSeMOJmKM2CnLWQ3Lfp1/v6Xlj8Ioy5E7qeDivfhBE3QbuhMOcx+MUrkDoInh4IeCCpH2CCvT83fr3CLb6fzV/7ts99EoZOa/7rFBERETlBdBjSMLnRrt9AKgrzsTrCqK2qxOZoWvKu1u3iV98+y8q8bbw88TZOzamArRkw/nQoKoQ9u+Hc88BqhZdf9J0UF+/rtVhc3PhF8/J8P5s2gGHAVddAWNgxvloRkeAwDBO9u13JirXPHbZM725XYBiBW2rj+++/Z+fOnUyb1vr/xjUZBiMSopidffjE6/CEKEwBWnV62bJljB8/vm57+nTfArFTp05lxowZALz33nt4vV6uuOIKv9ZteL0tv4RMaWkpUVFRlJSUEKmhANIalWTB+1Nh9G+gzwXw4fWw9kMIT4GeZ8OZD4PtCA3EwkyY9WdY9xGYLOBxwZUfQFg8JPb2rSQN8PN/fcnFtGGw8i2oyG1enOc8CcNa/5esiJyYdL9uO/RZy4ngx11reWbV5/z91Gl0jEpk8Vv/wVVTQ2RyKjFpHUgbMPiI58/NWsvr635g/p4NmA0TXq+XxZf/jYiyCkhMOlBw/k/grIXKSti7F2qbOVH+5VdAVHTzX6CISBME+p6dk7uc9VveqdczMcQeS+9uV5CcOMTv9Z3otpdXsTivpF7PxDCLieEJUXQMdwQxssBRj0SRxlQWQPYqKNgClYWQOc83V2JUe1j2iq8XYGM9Ewu2wud3Qm0F7F4OXc6AzDlgskJFPvz8DtijoNsZULYHZv8Fqopg+7zmxWdY4MY5kOKHycVFRERETgA7ynJZX7CTwuoyoitq8bjcRCanUl1Wyu41Kw+bSPxuxyre3zSP1XnbKamtZFRyDxbmbCIlNAZLZibszIKICOjYEXbtgrVrji1Amw1+ee2B+RNFRE5AyYlDSEoYRGHxZmpqirHbo4mN7h7Qnognso7hDtLDQthbVUuV243DbCbJYQtYT8TWQIlEkcakDIA7foaoNN9CK7Yw6H0+9DoP9qyCnQt8iURXrW/hlP9eDaU5vpWVM+dCZBo4YmDrdweu+cnNB35f8ToYJvC6IX2073pHkjoILnzJN4fj/vkbRURERNqQq3qexqQOg0kMi2bXzq2YbVbSBgxmYekunl/5BX8r2EXvuPaU1FSwNHsLd899FTDon9CRxTmbibSFkh4Rz8KcTQBkVxRhnjeXcryEYz6QQLTZwGSC6uojBzRgAHTtBjGxSh6KyEnFMEzExfQMdhgnDJNhkBJqD3YYLUaJRBHwJQR3zIekPhAa7xvG/NGvoNskyPgOTvs9DL4GZt4C1cXw1b2QPAC+/j3sWX7gOhs/8/1bmgWWkMbrcsRB+2G+RVfK9sJlb8ArZ/kWc4loBwMvh/ajfCtIV+bD2X+DhJ5gCcwkrSIiIiKtUX5VKbvLC0gLjyPOEcmfFrzNp9uWMDypGwuyN/Lm5Ols9tTwSsY8MmqLmfbN03x14YNM/PA+Kl01dUvXLc7ZDEBpbSVmDupRY8A0SyZVeLgppjdnWBOhsMA3J+LIUfDpTHA6ISkZunb1JRg3bPDNfzhyJIRHtPh7IiIiEmyaI1FaXtle30IjI2/2rVa8X2UhhET75hVMGQjxXeuft3MRzH0CJtzvW1nZvm+OQmf1gTkH9/9evAuylkDfi30rJ5fu9vUuBF/S8LVJvoRhj0ngqoHFL0FRpu+42Q7uGupWTzbbIbkflO7xDUfeP+dhXblGGGZfb0OzA66ZCYuegW5nQXiib9EVs9Uvb6WISCDpft126LOW5Xsz+CpzOdOHXECo9UCviqLqcmxmC19sW8bZnYcSbq3/oPSdDT+yJGczD4+5GrNhItRqx+v14vS4sZkteLwe3F4vVpOZVbnbcHrcDEvuRrWrlkpXDbEhvmTclqI93Pz98wxK7ML5XYbzw641fL5tCaW1VQA4zDaq3AfmKoywOegYkURuVTF7K4sxGQYer7dBucYMTerK9X3P5O2Nc7i+70QsJgvDkrv5660UEQko3bMl2NQjUQInezXsXQu9L4CybIjr4tu/+r+w/DXYtRg6jIF2Q3xDd18YDQOvgpVvgMUBU/4Fvc71Jd3euxoyvvElBTO+913HHulL6lUVwtTPYc37sOItCIuDpP6wbRZUl0JtOXx7H1z+DiT3h5/fhd0rfMOVt3wLHJJLr0sOeg9s71524LjHdWC/NRyc5Q1fu9ft+zckAjqO9P2IiIiIBNmsHT8TbnPQJToZgHhHJC6Pm9fXzeLbHatYk7+DYcndGN++HxsKsnhk8X+5quc43t74I0+t+IT/nvt/RNvDCbeGMO79P1BYVYrL6+Hr7SsASHHEUONx4sHL5xfcz6+/f54tRXtIi4invLaKktpKPjn/Ph5b8gFLc7bwvyn3EuuI4I8/vcnu8gKyygtYsGcDBdVlWA6aj+vQ5GBZbRVrCrbXbXv29Y2octdiNcw497fFGpEcGs349H6MT9dc0yIiIs2lHokSGBu/hHl/9y040vsC2PAp9DzXN0R37t8OlDPM4PXA+D/C7Ed8+0LjfUN6DROYbWCx+xKChyb8DlwETrkL5j1xYNfgqbDxc9+iKfHdfb0OvV7fkOOJf4ZFz0F0OuRt8i2MkjbcN9S4ZOe+C1gB55FfozUMnBWNH7NF+BZUOfX/IKnXUd8uEZHWSPfrtkOf9cnP6XHzecZi7l/wDinhMbi9HpxuN8OSuxJqtvNhRv35mvvFd8ButrJsbwYG4LDYqXTVYMLAg5e4kAgKqssOW1+YJYSzOg7io4yFdft+N+QC/rF8JlaThU4RCVjMFjYX7SYpLIYhSV1YlZtJclgMy/ZuwWaycG6noXy8dTFur+ew9Rxqf3yNSQmL4dS0Ptwz7Bf1el2KiJxIdM+WYFOPRPG/r/7PN1QYoNuZ4Kr2LRKy4VPfz8FMFl/Pvv1JRPCtYhwS7ZuL0O3ynX9EXl8Scf9w4rG/9SUNLfuWWs/3zYuDI9a3erKrGiryfL0kk/tBzjrYOf+Qax4liQjgrGq479R7fEOoO4450ANTREREJMjO+eghdpblAXBx19H8L2MBDpuNLzOXNyhrNcysyd+BCd+Kk17A7XVjHJSkO1ISEaDCVc3nGYvrtp8a9yvm7VmHzWSh1uNic0k24Ev8hVnslNVWsassj/yqEhId0eRUFvHhQUnIpmisJ6IFEw+OvhKnx815XYcTZj3MHNYiIiLSJEokiv+V5hz4fcu3B37fn+g7WGNzDBomXy9FOKj8vvkKDQvgOXD8YF63r9zifzccbmwJgfieMOJXMPsx3/DkPhf7eg06K2HFm5C70dcD0l19+LkP6/FARDJc97VvuPWeVXDK9APzNYpIi6p2ulmcWcgpXeMxmYxghyMi0qqU1BwYRfHUSt+D3XBL473y9ifjDu7Z5/F6MRsmXPuO7WuZARBmtVPhbNh2qsXXXrMaZu79aQbV7voPaiOsDgYndeH6vhOZ+vVTmA0TF3cbzVkdB/PRlgVkFO1hfUEW4TYHxbWHGQXSSNyjU3ry5zFX8db62cQ6IvlFjzFHPVdEAsNVUoO7pAZ7unrOiZwsNLRZ/OvVyVCaDcWZgbl+Y8OJD048HthJXfP24ONRab5kZPH2xhObzdH3EjjzzxCZeuzXEBG/+desLfzzu808f9Vgzu6XEuxwTgq6X7cd+qxPXrvLC7jmy39QVltFmfNoozwOz4QJD40PMXaYrFR5jj6a4+Dk48FDkNtHxFNWU1WXLGzKgimHu/7NA87mpgGTsGlhO5FWIe/l1dRklpBy30jMYfr/0h90z5ZgU49E8Y9lr8PaD2DnEsAVuHoam5PQtK8XYT0H5ce9Ht+QZnuUb4GVvH1DnY81iWiLhCvf8w1fFpFW49z+KZRUORnVOe6wZUqqnBRW1NIpPqwFIxMRaXler5c/L/ovu8vz2VNR1OzzD51r8HBJRKDRJKIVE85Dzjm494JhGJi8XrpHp7G9bG+93opHSiKagcZacB0iEnjvnLuJcUQc9lwRaXnhp6Zh7xKNKfTwqQdXYTWGzYQ53NaCkYnIsTIdvYjIURRsg89vh+3zCGgS8XAaJBE5MD/ifh4nVOX7FlcxzMdRmQEjblQSUaQV6pwQzv3n9iYmrGEj1Ov18tOWPCY9+SPj/z6HbXmNrLYuInISeXP9bN7d+CNzs9Yd0/mHW7CkqQ5NIgKYD1qF2e31pSY3Fmc1GPJ8JIcmEc2YMGPikbHXKIko0go5esYSeXo6htFw2hlPjYuKdfnk/G0pOf9YFoTopLVwu93cf//9dOrUCYfDQZcuXXj44YcJwgDaE8bcuXOZMmUKqampGIbBzJkz6x0vLy/ntttuIy0tDYfDQe/evXnxxRf9Urd6JMrx+fm/8PGNwY6iIVcjC6Hs592X7DRZfQnGIzJ8C7+YLZDcH/I2+hZoEZFW7YvVe3jxx62YTQY7CyoorKz/kOPp7zfzwJQ+xIXbqap147AdzwMGEZHW5Y7ZL/PN9pXBDqMBowl/DzZlWLPFMGE2mUmPSCDBEcnu8gKSw6L9E6SIBEzxl5lUZRRimEy4suo/1PVWualcnYejT7yvu5PLg2FV+yxY3F4vi4rLya11kWizMDI6HHMjyWB/efzxx3nhhRd4/fXX6dOnD8uWLeO6664jKiqK22+/PWD1+ovX7SZ/3TqqCwsJiY0lvk8fDHNg//utqKhgwIABTJs2jYsuuqjB8enTp/PDDz/w1ltv0bFjR7799ltuueUWUlNTOe+8846rbiUS5fiU7t73y8Gz3pwgPE5fz0WrA6oKDzpgAqt936rMXuh9Pkx5CgL4xSki/nXHeytxHX4UHp/8nE1GXgV3ndmDaa8v5clLB3LBoHYtF6CISABlH8NQ5pbgOsLw6P2q3LWkhsWSW1lSt7AL+FqaNpOZGo8bl9fDyxN/w6iUHo32chKR1qd2Tznlc7OOWKbwnY2En9IODKhYmE3Sb4dgidVCli3ti7xi7tuym+yaA51uUuxWHunWjnMSogNS54IFCzj//PM555xzAOjYsSPvvvsuS5YsCUh9/rR7/nxWv/QyVfn5dfsc8fH0//WNtBsTuJGMkydPZvLkyYc9vmDBAqZOncppp50GwI033shLL73EkiVLjjuRqKHNcnx6n+/rqReZFuxImqCRhqY9ypdENB88FNKzL4m4T1EmPN0f3myY5ReR1qWq1s3ekmps5qPf3txuDwkRdnolR5IcpUaqiJw8pvWZSK/Ylm+b+esPi1h7OC6vG8dB7TMvUONx76vH4L8b5zLozTv4z5pv/VSriASKu7wWQprYO8tsYIl3YE0Jw7CrR2JL+yKvmBvWbq+XRATIqXFyw9rtfJFXHJB6R48ezaxZs9i82beewc8//8xPP/10xERZa7B7/nwWP/qXeklEgKr8fBY/+hd2z58fpMh87+mnn37K7t278Xq9zJ49m82bN3PmmWce97XVI1GOT1wXuGkerHoPZv462NEcRSM9Jity9h3y+FZz9h40/NEWAT3PgdXvQVx3rc4scgK45MUFrN1T2qSyLo+Xj1fu5ovbx7Itr5zC8hqyS6tZk1XCz7uK+Skjn0l9k7l+bCdW7SxmUr8UyqqdLNxaQFy4jT99so4JPePpkhjBK3O3MalvCp0Twskurea0Hola0EVEgmZSpyFM6jSEaV8/zYLsjS1Wb2P9DU2H2X8kawt3ApAQGsXOsrx6x9qFx9IpMplvd6wiLTyOGHv4McUqIi3DU+sm+7El4G7C6DUDqtcWEHlmBxJ+1R9XQRV4wV1ZS+XiHDAZVCzbS+wvumOEWzAAe4coXPlVOItrcJdVU/5DFuGTO+DcWUbVthIihidjtltwF9cSNiIZk6azOSy318t9W3Y3Os7Qi69bzv1bdjMpPsrvw5zvvfdeSktL6dmzJ2azGbfbzaOPPspVV13l13r8yet2s/qll49YZvVLL5M6cmTAhzk35plnnuHGG28kLS0Ni8WCyWTi3//+N6eeeupxX1uJRPGPgZfDzJtpflOxqYx9PwG6fsoAmPIvWPQ8rHobrGG+lZm9Xuh1HvSYDCZ14BVpzT5YtqvJScR7J/fg8a83kZGXicVk8NLcbYRYTFS7PITbzFTWuvEA7y3Zyas/ZeL2QqTdhN1iIa/iwNxdB9e3ek9Z3e8/bspjxrThfnttIiLH4o8jL+Wcj/8csOtbDHO94ceNOZaW2/4Voy/qNorhyd24Z97rZJXlMzihM/eNuoJtJTncMXgK/RI6HlPcItIyvF4vBe9taFoSEYg4qwNlX++g9JvtlM7ehSu7AiPMgrfC5XsqYTdBlYeSubtw7djX7go3Q40XnAe+bYrfOPAApWRnxsEREXHKiTCSLjgWFZc36Il4MC+wp8bJouJyxsT4d3Gr999/n7fffpt33nmHPn36sGrVKu68805SU1OZOnWqX+vyl/x16xr0RDxUVX4++evWkdC/fwtFdcAzzzzDokWL+PTTT+nQoQNz587l1ltvJTU1lYkTJx7XtZVIFP+xh0NN0/6IbzZrGDgPXWXVD/MyDrsBOp4Cjlh4cayvB+L0DfDT0zDDNz8DtnA47xlwxEBcN4jWzUektbnjvZV8smpPk8s/+0MGg9pH+4Y073ugGumwUF1WS3ntgT+Ky2oO/F5a44GaIy8AsN+8zXm8t2Qnlw9Pb3JMIiL+Fu+IrPs9ELNZh5itlLsOfE9aMDVpHsSjeeGMm8mvLKPG4+TKL//B/w29iIu7j+aqL//BhZ8+CkBaeBz/PO168itLGZ3aixCr7ShXFZGW5PV4yX58CZ6SprWdsBlULMrBnBRK6JBEKlf5eiPXrdrrAap83y91SUSA8iM/zDhYyfwsbOmR2DtEHr1wG5Rb6zp6oWaUa467776be++9l8svvxyAfv36sWPHDh577LFWm0isLiw8eqFmlPOnqqoq/vCHP/Dxxx/XzTvZv39/Vq1axd///nclEqUVuXmBrzdfv0vhueFNWBG5GRokEeGYmsP7hy87YgEDYjtDnwvA7YRuZ/oWVAlPgp0HzWVQWw4fXk/dM/W7t/mSiuqhKBJUbo+Xtxdt51+ztpBf0fTvm+6J4RRV1rIh2/fg45krBjOuewK9UyJ5bX4mT8/yPbk2GeA5xr+63cD9n6zh45W7efdXIzDp+0JEgiDKHsabk6ezJn8Ho1N6csG+JJy/lLuq620faxJx/0rNaeGxlDtriLSFMS6tH1llBfSOa4/T4ybKHsb2kr1152SVF3Dp53+rO3/FNU8BaPEVkSDzON0UfpJB9apcaEa+yZoajqekFvfeSrxVbhJv6IczvwpzmJWiTzKo2VzsK2g2mtzDsYFiJ3mvrCF8ZApRkzvp++IQibampYeaWq45KisrG7SXzWYzHk+gRjwev5DYWL+W8yen04nT6QzYe6pEovhPdHs47V7f78n9fYuUGBaozA1uXAfzuny9Cgu2wK1LIKGHb7/ZCh4XbPweKgvg8nfgk1uhtgIKtkJ18YFr/Pg3+PldOGU6mMww8hbfvyISUNvzK4gJs/HcrE38e/4OvIdpQ5qAUJu5Xs/Cg1XWuhmYHsMjF/Tli9XZPPfDFtrHhbI1t4L/rdjNGb0S6Zkcwe0Tu/Pnz9bx5qKddefeNr4Lz87eSrjdRHmNh9N6xDNnU+NDGpxuWJxZSOc/fAVAWlQIP/3+9ON6D0REmmtYcjeGJXejuKaCMIudrtGprM7P9HvvxOPh9LhwWGxUu5wsuvLvdftTwmLYVpzDF55l/HrAJF6ceAv/XD4TwzCRVZZPSW0lALVuJ5d8/jger5dLe0xgZEpfOkZqnlqRllC7uxxrShg5z67Avafy8AXtJqg5fALDU1RD+Oh22HvFUrEkm6qNhXhdHir3VuAurcXWJYqwkSmE9oln779W4Mrx1WU4LFjTwqjdUuIb5lzuxpwehntnxWEC9lA+dzflc3cD4BiaRNwvuh/z6z+ZjIwOJ8VuJafG2eg9wsC3evPIaP/PTTtlyhQeffRR0tPT6dOnDytXruSf//wn06ZN83td/hLfpw+O+PgjDm92xMcT36dPQOovLy8nI+PA0P3MzExWrVpFbGws6enpjBs3jrvvvhuHw0GHDh348ccfeeONN/jnP/953HUrkSiBkdQLHFFw+bvwaFKwoznAFgbFO+CUuyC2S/1jF70MZTkQnujbnvrZgWMlu2HtR1BbCiHREBYH3/9p37E9kNQbukwArxuiNZRRxN+KKmo57e9zjlou3GZmXI9EdhdXsn53CZgMal31m0LlNS7yympIigzhvaU72VlQSbXLw6D0aPLLa9hZZGFxZiG3T+zOhYPTWLO7hIpaNxl7y4ly2Lh5XGesJhPPzsmgosbNwPbRZOSWUr6vcXy44YNZJdXc+d4Knrp88PG/ISIizWQxmekUncwpab25a9gFXPPVk8EOCfB9Z7q9HtLDYrix/6R6x8wmEx+d/wdCLXYAxqb1YWzagT/IthZl8/bGHxme3I2vtq9ga3E2Dy6YwQU9LubibsNIDw/BZjIRZjFhC8JE9yInu6p1BRS8uf7IhQzAaiJyYgdKf9gJNe5GJ0/1VLnwVLkwh1iomL+HmngHrrwqbB0i8FQ6cRdUU2UzE9YvgfCRKVSuzKU2rwoMCOkQhb1jFO7iGiqX7sXRIZryPZWYomx4CmqO/BqW7cU5oT3WWMexvxEnCbNh8Ei3dtywdnuD9uz+vpsPd2vn94VWwDef3/33388tt9xCbm4uqamp/PrXv+aBBx7we13+YpjN9P/1jSx+9C+HLdP/1zcGbKGVZcuWMX78+Lrt6dOnAzB16lRmzJjBe++9x+9//3uuuuoqCgsL6dChA48++ig33XTTcddteL2H69MROKWlpURFRVFSUkJkpOYnOOnNvA3WfgiuqmO8wHHM6mOygWffvBw9JkPPKVC4FU4/zi8kVw3MeggsDlj6b7BH+pKQHqdvdefbloMt9PjqEJE6q3YWccHzCw57/OJBqaTFhvGvH7Zw9YgOfLZ6D8WVTswmcB/SWO0cH8rnt59CqM1CTkk1FbUuFmwtYFTnWLomRvDCnK3sLKxgQs8keiZH0D42lM9+3sP/VmSRVVRFZn4Fbo8XA/jtGd25emQ6u4ur+XpNNs/N2Up0qJXiSt9Q60O/vR6/qB+XnUDzJup+3Xbos25b3B4PF37yKDtKc6nx+H+uq6MxY+De9+3459FXsqM0j56xaUzpcnyLVO0uL+D19XM5s9OprCmuoX2YnV0VviRCeqid09vFHXfsInJA3tvrqVlTcNjj4Wd1wFtSS8WibKIv7Erxx/t6TzWynHvY6BSiJnXCZDPjzKkAs0H1xkLChiVjWE0UfbgFa1o4plArjj5xmGxmir/KxFNWS+XKXEyhVjwVTjBB/K8HYHJYwOul6OMtOLeXQYQFylxgMeCQh8ztHh6NYT1xHjYE+p79RV4x923ZXW/hlVS7lYe7teOchGi/13ei2z1/Pqtferlez0RHfDz9f30j7caMCWJkgaNEorScv6ZDdUnL12uywZl/hv6XQWgA5ifI3egb2vzelZC/2bcvphM4K30Jyz4X+npCisgx+/Wby/hm3d56+4Z1jGFy3xT6p0Xxn3mZdE8OZ3t+JVP6p/DF2mxmrmx88ZWeyRFs3lvGIxf0o6zaSbsYB+f2T6WoopbLXl7IBYPaMalPMhP+8SMJ4XauGpnOnRO7s7Ogko9X7mZLbhlhdjMzV+7hjtO7sb2ggveXZTGkQzQrdhTXJQ4fPK83neLCmPraUgBiQq2sfODMQL5Nfqf7dduhz7pt2liwiws+PXxPikBKDY9l+uALOLvzEEyG/+eRzamqIdxi5n/bc+vyFSFmE2EWM0PiIkgJtWPS/GgixyXrwQVQXX8qGWv7CKImpuM1GZTN2knogARqMooJG5dG8Udb6oYkH8rWJYra7aUk3NSfqp/zCR2ciC01nJptxRS+v5mYS7rjyquieGYG5mg70Rd0xdEzltrd5ZTNy8IcYaM2qxxnVhkJNw0g/811eMpd2DtHHphf0QRJdw6hfMFuKhblABDSK4b4qX0D+Tb5XUvcs91eL4uKy8mtdZFoszAyOjwgPRFPFl63m/x166guLCQkNpb4Pn0C1hOxNdDQZmk5g6+FJS8fuWdiWAJU5B1nRYZvjsZOp0GPs3zzIIbFH+c1jyCxp+/f25bCijdh5dsQ1Q7WfeSbZ/GTW33He18Ml74auDhETmIXD0pj+Y4iOsU52FtaTXpcOG/dMBKA0ionP2XkUev28OyVg3h9QSYzV+4hLsxKp/hwKmqdbMgux2oycHq87CiopFtiBEmRdv44cw12s4mz+6ZQ63aTkVvOP77ZzIB2Ufzh7J48+0MGP+8qxuv1cva/5uH2enG6PPztF/15f1kW7y7ZSUqUg8QIO9vyKjDtm/+7W0I4Fwxsx+i//oDdYsLt8XJq9wB+D4mIHIO0iHhOa9+PObvWHLFc9+h2bC7efVx1WQ0Tp6cPJC0yjimdR9AhMoEQS+BWWk52+IZD/7JrCgtzi6lyeyiudVFQ4+TbPQdW0Dw3NZqEMI0iETkW4UOTqVidiynOjju/msgRqURO7ABA9aYiareX4ugTR8wl3cl/bS2unEosyaG+lZhNBu7syrrFU9wF1dhSw3HtraD8p93UbCsm6fbBOAuqcRfXUPDGehJvG0jo8GQql+Tgyq2kNsxK7nOrwGRgTQnD1jGS2l2l5L22FltSGDXlpXhrD3R9dPRPwFPp9CURzYZvWHRvtc8aYzYMxsREBDuME4ZhNpPQv3+ww2gx6pEoLas8D57sAyYLOPdNgGuLgNqyA2VM1mNf8dni8CUqT70bJtx3/PEej11L4cNpULKz/v4+l8Al/wlOTCInkcpaF6/M28a/52VSWu3id2d046W5mYzpGseW3HJuOKUj989cj9vjxWIyiHJYKKp0EuWw8s1vT2VrbgW/emMZcWE2Zt91GgCn/G02e0urefXaYZzaPYHKWhdWswmr2cQzs7Zgsxic3iuZtxdtZ8aCHXW9D/91+UB6JEfw7pKdvLVgBy6gQ2wog9KjGdIxlmtGdgjW23RcdL9uO/RZt22fb1vCXT++RpjFToXLNxQ43GKn3HVgbjETBp5jnGom0R5Fbk0JMybdyciUHn6J+VhtKalgfm5Jg1dyRmIkaVH+X0BApK1x5lVSvjibivl7wGQQPaUzxTO3Ejo8iZptJUROSKfofd8oLsNmwuvygBds6REk3jyQ0lk7Kf1hJ6GDE4m9uDvOvEr2Pr0Sw2yQfPdQzOE2PFUuTA4LnhoXJV9kYu8cia1TNPlvrMO1t9I3dNmApN8NxV1cQ/nC3VSv8z1AsKZHYImyEz46FXunqGC+VcdM92wJNvVIlJYVngDXfQU1ZbD5a+gwFnqfC6vfh2Wv+xKKSX18qyLjBcxgMh1ILNr3PRWpKQMscOEL0Hkc7F7um/tw6PVQtB3iuwXn9R2s/TD47Rr4302w5t0D+9d94PtJmwA3fBy8+EROcHM35/GP77YQZvcNG1i2vYiB7aNIiQrh/nN7Ex9u57+Ld7Ihu4xaj5eCCt/3SFm1iwi7ld3FVVTUunjuqsGYTL6hGvPvnVCvjlDbgdvkb073fa9s3lvGjAU7iAixEBtuI9pho0+7KJ77IYOPVh7osbOjsJKckiq+WZfDmC5xdE7QH6gi0jqd23k4oZYQDGBxzmYu6DKCHrFp/H3pR6zJ30mlq4ZIm4MF2RsBSHZEk1NVXHd+amgse6uKcXs9dIpI4M6hFzIiuRuzdq3GYpiZ3GkI20v30j2mXXBe4EG6RYXRNTKUdzOyOXgJhu9ySyG3lEmxdlLiNJeiyLEqX7CHioXZvnkQ3V5chdVY2oVhiXMQfW4XXIVVEGWFEme93oLufe00V34VhtVE1KROAFgTQkl7pP48cyaHr31msluIuahbXb2u3RWYo+1gNrAmhWGJspP38mo8pbV15zp3luE0lVGzq5SUu4djmDVcV6S5lEiUlpc2xPdvl9MO7Ot/qe8HYOts2PIdnPEQDLoKlvwbdi2BxN4w8EqISPItduJxHZh7sOfZB66V1LtFXkaTXfyi7+fT6bDilQP7s36A16fUXx1aRJpsUHo0kSEWKmrdJEbYKaioZXdxFfMzCtiQXcbVIzvw8+6yBue5PF6WbC/gF0PSmNQ3mXB7826F7aId/GJIGmf3S8FhM/PJqj0kRYaQHGlvULbG7QW3lx835SqRKCKt2oR035Cs8ekHhmbdPfziut+fXfk5O0vzeH7izXSJTuHRxe/j9XroHJ3CZT3GYjNbKa+tIsRiw2LyPeC5uNvouvNbQxJxP8MwuLJbKgBvbdnDweNgvi6sYbSpnB4x+s4WORa29hFULMkBtxd712gqFuVgOMyUfrUdnB5q86ugpOHoM3dRNZ5qFzGXdCfa5cFka978cvYuUTgGJBB1VkeqNxXiqXRhWE2YI6z1EokAeMBTXIvX6cYwKyUi0lz6v0Zany7j4f8yDmwP/5Xv52AWO9Dwj/ZW7bx/+n7+3gPKfZP7sv2n4MYkcgLbUVBFabWLiBALY7vGU+Nys25PKXHhNi4c1I7Hvtxw2HMzcisY151mJxEBwuwWnrhkAAD3z1zLu0t2UlxZw7o9DZOW+8WGnWDfVyIih7ht0LncNujcuu0HRl3eoEy4zdGSIfnF1fsSiq9tObBA19riCiUSRY6RM6cC3F5MMXasKWG4iqtx51djTQ3DFGGj+oedjZ/oBlehb55Eo5lJRABrUhhxV/jmri+bvxt3UQ2u8hpfPIfjafFZ3kROCkokirS0uzb5/v3+Yeg6PrixiJzAhneK5fvpp7Ixu4zb3l1JYoQdL5AcGcKDn66l2nX4xuGkPkkN9pVWO1m+vYjTeiRgNHFVuqtHpvPmoh18tXbvEcttyz9CI1ZERILuum6p1LrdLM8vo3+skogixyrqrE6EDUmifGE25fN2Y9h8q7IboRaKP844/IlmsCaHNdjtyq/CXenEnt70uQDDhyVT8tV2KhfmHLGcM6cSe+cTc55EkWAyBTsAkTZr4v3QcWywoxA5oXVNjGBcjwRun9CV/HLfbFdr95QeMYkI8NevNjXY98ysLVw3Yylzt+Q3uf4eyZE8ckFf7pnUg2euGIT1kAfo/VIjmHnrGG4d37XJ1xQRkeCwmc2MSoomzKq+FiLHytg3P2H4qBQcgxLr5kGszSg58oluqFzZ8MFswTsbyHtxNV6nu8kxhI1IIeL0dKIv7EL4hPYNj49vR+JtA5VEFDlGukuKiMgJLSLEyvQzexDlsPDwFxuPWj421MLyHYUN9l8ytD0Ws4khHWKaVf/VB63IPGVAKqXVTsJsFswmTd4tIiIibZM1KYy4y3qQvacU997qo59gQM3WEsKGJNfbHXl6Oq6iGoxDn9YegSnEQtQZB9pn0Wd2xFPpxBRqbfI1ROTwlEgUEZGTwvieSUdNJN51RjeuHtWx0SlxuidFcM+knscdR2SIGqkiIiIiAI7e8ZTvzTpimfgb+2OJtWMOszU8v0+8X+JQElHEfzS0WURETgqdE8L58KaR2C2+W5t1X4/AEIvv326J4dx2eneiQ23ENtJQFRERERH/ij6rExFndID9AzX2/7svExF+SjtCOkdhiQ7BsCo90ZaVlZVx55130qFDBxwOB6NHj2bp0qXBDksaoR6JIiJy0hjaMY5vf3sqn6/OZtqYTjhsZp6bncGibQX8Z+rQYIcnIiIi0uZEnZ6OPT0Cd3ENYcOS8Xq9FLy5AUt8CNFndw52eNIIr9dLfqmTaqebEKuZ+EhrkxcjPFY33HADa9eu5c033yQ1NZW33nqLiRMnsn79etq1axfQuqV5DK/X2+JrnpeWlhIVFUVJSQmRkU1ffUlERERaju7XbYc+axERkRNDoO/ZuwurWb29jOp9C+UAhNhM9O8YQbvYEL/XB1BVVUVERASffPIJ55xzTt3+IUOGMHnyZB555JGA1CvHRn2HRURERERERETauN2F1SzZXFIviQhQXethyeYSdhc2YeGcY+ByuXC73YSE1E9UOhwOfvrpp4DUKcdOiUQRERERERERkTbM6/WyenvZEcus2V5GIAa1RkREMGrUKB5++GH27NmD2+3mrbfeYuHChWRnZ/u9Pjk+SiSKiIiIiIiIiLRh+aXOBj0RD1VV6yG/1BmQ+t988028Xi/t2rXDbrfzr3/9iyuuuAKTSWmr1kafiIiIiIiIiIhIG1btdPu1XHN16dKFH3/8kfLycnbt2sWSJUtwOp107qwFeVobJRJFRERERERERNqwEKvZr+WOVVhYGCkpKRQVFfHNN99w/vnnB7Q+aT5LsAMQEREREREREZHgiY+0EmIzHXF4s8NmIj7SGpD6v/nmG7xeLz169CAjI4O7776bnj17ct111wWkPjl26pEoIiIiIiIiItKGGYZB/44RRyzTr2MEhmEEpP6SkhJuvfVWevbsyS9/+UvGjh3LN998g9UamMSlHDv1SBQRERERERERaePaxYYwvDus3l5Wr2eiw2aiX8cI2sWGBKzuSy+9lEsvvTRg1xf/USJRRERERERERERoFxtCaozdt4qz002I1Ux8pDVgPRHlxKNEooiIiIiIiIiIAL5hzglRtmCHIa2U5kgUERERERERERGRo1IiUURERERERERERI5KiUQRERERERERERE5KiUSRURERERERERE5KiUSBQREREREREREZGjUiJRREREREREREREjkqJRBERERERERERETkqJRJFRERERERERETkqJRIFBERERERERERkaNSIlFERERERERERESOSolEEREREREREREROSolEkVEREREREREROSolEgUERERERERERGRo1IiUURERERERERERI5KiUQRERERERERERE5KiUSRURERERERERE5KiUSBQR8YNal4cvV+/hh417gx2KiIiIiAAetxtnZj6esupghyIictKwBDsAEZETWVm1k+tfW8KSHcV1+9rHOPjx7vGYTEbwAhMRERFpo9ylVTgXZoBn3zZgpEZhH5Ae1LhERE4GSiSKiDRTbmk1K3YWER1q5fKXFzc4vquoij9/vo4Hz+sbhOhERERE2h53SSW4vThLK2BDwxEi3j0luNuXY44ND0J0IiInDyUSRUSa4eu1OUz/7woqnd4jlnt9wQ7undyLEKu5hSITERERaXu8Xi+ubXm4Nx99ehnn6izMp/VsgahERE5emiNRRKQZ7vpg1VGTiAA2s4HJ0NBmERERkUDyVjmblEQEQA94RUSOmxKJIiLNMK5bQpPKRYXasFn0FSsiIiISSKZQW5MThKaIkABHIyJy8tNfuSIizfDwhf2Y0j8Fq/nwvQ1tZoMrhrdvwahERERE2i7rkA4YMaFHLmQyMLWPa5mAREROYkokiog0w73/W81nq7O5bEgalwxOw7YvoTiyUyyXD0ujQ2wotW4vczblsbu4KsjRioiIiJz8nCt24C2qxNw5HiMlqm6/kRSBkRoJFhN4vHiyC3AVFQUxUhGRE58SiSIizbCrsBKA6DAbeRU11Lq9JEXY+c3pXbFZzNx8WhfOH5hKSlQIYx//gbW7S4IcsYiIiMhJzuUBwFmRS/Wa1b594XaMBCv5bzwDCWBKjiT78QfZNvlsvN6jz3ctIiKNUyJRRKQZXrh6CPec1YNuSRHM2ZRHp/hQ/jN1GD2TI3l3yU6+WZfD05cP4pKh7TmjVxLtoh3BDllERETkpGYZnI6lXztKPptJ8Wcf4rV7sI/sQvWGdZR+8QUVK5ZgG9SBsJFDiTznHAwtiCcicswMbxAex5SWlhIVFUVJSQmRkZEtXb2IyHHzeLws2FrA0I4xhOyb4Ht7fgUxYTaiHNYgRyfiH7pftx36rEXkZOAuK6N6wwbChg+v21e9fj32Hj0wzFqxWU4OumdLsKlHoohIEyzaVkBhRU3dtslkMLZbfF0SEaBjfJiSiCIiIiItwFNTQ/n8+Xiczrp95oiIeklEgJDevZVEFBHxI0uwAxARae0ueXEBS7f7JuY+vUc8r1w3IsgRiYiIiLRdXq+XzWNPwVtWBkDqs88QNXFikKMSEWkb1CNRRALutfmZnPuveXy5ek+wQ2m2X72xtC6JCDBrUz5vLtwevIBERERE/GDzsiw2LN5FWVFlsENpti2jx9QlEQH2/OZ2anfuDGJEIiJth3okikjA7S6qYu2eUm55ZyW8s5Ih7SO4eXwPJvZOCnZoR7S3tJrv1ucCEG4zUV7rWxFw7R6txCwiIiIntqryWipKqinK8SXkopND6dg7hdAIe5AjO7LSr7/BXbTvIW90NJSUgNdL9fr12NLTgxqbiEhboESiiATcfef25tv12ewsrAZg+a4ybnhjGVYD2kWH0CctmvvP7UNyVEiQIz1gxvxM9pbVMCQ9muU7ixndNYFZG3O5fUJXbjqtS7DDExERETku/cd1YuGnG+q2i3MqWZWzFQwICbUSkxROhz7JmEytY4Vjr9PJ3sf+irV9e0wJCXjy8ogcO5bSr7+m3VNPEqmhzSIiLUKrNotIixn80NcUVrkPe7xHUgTf/PbUFoyocTkl1Vz20kLyK2pY/cCZ7Cmp5tOf95AUGcIvhqQFOzyRFqP7dduhz1qkbfJ6vCz8bMMRyyR1jKbLgNQWiujwyhcuZNf1NxA2ZjTtX36Z6vXrKf30M8LPOpOwwYODHZ5Ii9E9W4JNcySKSItZev9Z3D3x8L35Nu0tO+yxlnTta0vILq2mosbNP77bTI3LzT+/3czzszO4872VbMsrp8blptp5ICnq9nhZsaOQP3+2jlW7ioMXvIiIiEgTGSaDEef2xLAevkzeruBP6eLKz2fXDb/CmppKxbyfKJs1i+p16yl8/XUKX32V7Af+hKe6Gnd5BV6Pbyoar9eL1+ul6KOPyHn8cVz5+UF+FSIiJwf1SBSRFjVnUy43vr4Uu9WgrKbh10+3hFDe/NVIkiMdjZ6fmV+BxWSQFBmCzeL/ZyFuj5c/f7aOpdsL2V5Qidfjpcrla5B2jHWwvbAKkwGeg0I3AZ5DrjOiQzSD0qPp1z6WyX2TAKPVDA0SaSrdr9sOfdYibVvmmhyytxVimMHbyOCRhI7RdO6TjPkwba+qteuwJCZgTUwMSHyuklL23H03tTt24MrLw1tdDSYTuFwQGQmlpTRooBkGHPynrtlM2GnjCBkyhPABA3AMHIhhNgckXpFA0j1bgk2JRBEJmhqXm+XbC7nvo9Vs2zd/4n7xYRb+dF4/hneKJSnSN3diSZWToQ9/hwcY1z2BV68dVlfe6/Uyd0s+g9OjiQixsiSzkNgwK10TIw5bf7XTjckwcLo9fLB8Fx8syyKnpIqCCqffX2u0w8qqP53p9+uKBJLu122HPmsR2c9V6yZnRyE71+c1OBaTEkF8SgRx7aLqHpCWzZlD1k03A5B4zz3EXXdtXXl3eQVVK1cQNnYseL2Uff89YcOHY46OPmz97vIKTGGhuPbuJX/G61QtWkTNli3gPvz0OMcqfMJ42j//vN+vKxJIumdLsCmRKCKtwicrs3hx7lY2ZJfX228A8/7vNNJiw/B4vDz8xXqW7yhiUt9kbjmta125n7bkc/Uri7lhbCfuOqsHff70Dd2TInj/1yM5/R8/0j8tijmb8rhsWHtiQm3cfFoXJj89j+hQK/3Tonhr0c6Av8ZeyRG8Pm04iZGtZ1EZkSPR/brt0GctIodyuzxkbysgO7MQZ3X9JJ7JajB8Uk9MJgNXXh45Dz1E7Z5sEn83nfAxY+rK5f7zSQpefpm0F1/AMJvZ9asbib32WiImns7O628gbNw4yn/4gfjbf4M5NIywMaPJPP8CYqf+ktJvvsW5M/Dts/BzziHtib9hmDTrl5wYdM+WYFMiUURaFafbw6s/beOj5bvYlFtZtz/MZuaW8Z3ZmlfJn8/vS7i9/qLz5TUunpm1hQsGtaNXSiRvL95BarSDEZ1iufSlhewurKSoykW43UJ5jYtXrx3KZz9nExFi4ex+KTzy+XrKql1cMzKdR77cCEDPpHDaRYewYlcJRZVOIuxm/nhOb+79aM1h4w+3mSivPXSgc32JEXa+++04okKPMCGRSCug+3Xboc9aRI7EWetmx/oc8rNK8bgP/PloD7MQnxqFYTJI79lwWHNNRgbFH31M/M03YVgsFLzyKpFnn42nspKs3/wGV14euFyYIiPxVFTQ5btv2XP3/xF9yS9wl5VR9PY7mBwOHEOGUPzWWwCETpiA1+Omat5P4HZj69oVx6BBlHzwweFfgNl85B6NZjOhI0fQ4ZVXjvk9EmkpumdLsCmRKCKt1u8//Jl3l2U12P/Xi/oytGPsEYct71dR48JkGEx5Zh7bCyqxmw0qnB6euWIgvVOj6BgXhvmguQudLjeTnp5Hu2gHc7ccmJR7cp9klu4o4ux+yXz28x5So+wUVTrZU1ID+HpOeoGzeiXxzYa9ANhMcLicos1scNWIDgztGMs5/VOa/qaItCDdr9sOfdYi0hRer5dVc7ZSVVrb4Fj/cZ2whViwhRz9QamrqAhPVTXbJk0CqxVvRQUAXef+iKeiAnunTvXKV61Zw66bbsYxeBDl330PgL1bNzxVVVg7dcRTVo5z506s7dvjzMnBvXdv3bmW1FQ8lZV4iouPGpcpKoq4G64ndOhQQgcNOmp5kWDQPVuCTf23RaTVeuwXA9jy6GTGdYvHetC31b0frWXiP+eyI7/iqNc488m5XPj8fL797TjO6J1ErcdLt6Rw7FYzp//jR56fnVGv/Jdrc9iaV8HA9BhGdY4jMsTC5L5J/P3SASz94+kUVTqxW8wM7xTHnpIazuufQkyoBS9w8eB2XDO6A/vTklPHdCTUemAS7/E94hnVORaAWreX1xZs59Z3VvDxiobJUhEREZHWxjAMBo3vyuCJXQkJq58wXD1nGyu/3nDUa9Rs28aWU06l6PXX6bZoISaHA8NuJ2LSWRS9/Q7bJp9N5YoV9c4pfv993AUFRJxxJrbOnTHHxxMx6Sw6f/sN7Z97DlfuXkKHDsXrrMWdm0voqadiRPgeOCfccQdRl/wCAHNsLBFnHjRntclE/G/vxAgNBcBTUkLeP/5J1q23UbNt2/G8VSIiJy3L0YuIiASP1Wzi9etHAHDRcz+xYldJ3bHFmQU8/s1GRneO4/Lh6VjMDZ+NnNo9gXC7GZPJoGN8GHFhdn4xOI0BadGc2TuJUV3i6pUf1z2Bm8Z14ZIhaUw/ozuj/zqLr9buJa98CR/eNJpnrhiE1+tlR0El0aE2HFYzd5zRnfyyGoZ2jMXj9fLBTaNYtauYM3on8fXavVQW+VZ67pYUyfQzuvOPbzcREWKhvNrFq/O38/dvN3Ph4DS8Xi+GoZWdRUREpHULCbMxeGI3PG4PS77ehKfGBbXVmNYuojQ8n5JPZhJ95ZVEjBrZ4FxzTAxhI0YQ0r8fJpsNW8eOuPLzibnsMrxA7bZt2Dp2rHdO9OWXY0lIIHLSWYSPHsWWsaeQ/8yzmOx24m64ga6zZmEYBuXz51O9cSOGyUzqQw/iKigkpE9votxuQrp0wbBYCB0xgrLvvvOt6Ozx4OjZk04ffkjhm29iio7GlZ1N6cyZFPznFVL/8qjaZyIih1AiUUROGB/dOhaAf83aQnZJFf+atYWs4mq+XJPDK/MyuXVCF/q2i6J9bBhh++ZQfOyifoBvheZJfZJ4c8F2vliTTdfEcL7bsJexXePpnhxBZIiVO95byeLMAi4d0p72sb4n05cOTeOp7zPo3y6aNxfu4MUfM7jv3N5M7pvC0A4xXPXKEv7+7WaW3jeRd5bs5KFP1/H+TaPYmFPKI19s4M/n9WZPSTVTR3cgOdKBxwtOt5cIu5W3Fu3k4iHtiA+38/T3m3ny+y1cO7ojD57XJzhvsIiIiEgzmMwmRp7TC6/Xy/bX/4fjjOHs/vW1AJR/9x2OMWOIvvQSQgcMwBoXh2G1YomJIf2V/wDgKigg5vLL2HPX3VTMn4/X5aZ83jyqN28mbOhQvLW1bLvoYkw2G/G33orJZsMUH49j0CCqfv4Ze+/e7L7nXqrXr6f9C88TPmYMZd99T/F771H03nt0/eZrsu64k8oVy+n67bdsm3IersJCku67D2xWIk49FWtSEs69e/GUl2NLb0/x228Tc911WGJj2DHteioXLiT9jdcJGzYsiO+0iEjroUSiiJxwbj+9GwAfLtvJXR/6Fj7JLKys+91kQOf4MK4YkU7n+HC25Zfz9282U+V0ExNq4eLBaSRGhNAzOZJHv9zA52uyef/Xo/B4obDcyYwF2+nbLooJPRPpmRzJC1cPZnLfFHrd/zVVTjf/98HPPP39Zgorawm1mXG6PdS43BRV1NA+NpTIECvZxdUA7CysZGB6DKMfm81Tlw+kXZSDNxZsx2QCvFBU7uS/S7NIj3UA8Pai7WwvqODh8/sQHx7Cbe8sJzLEyuO/GMDCbQV0jg+rS3KKiIiItAaGYdDpWt/w4drTT6di1iwAqubPp2r+fF+Z0FBC+vcn+sILsCQlUfLJp5R++im43dj79CF01CicWbuxpqWx69rriL/tNuKuuxacTmp27KD4ww8wx0QTOmQIEZMmkXT/fRiGQeknnwCQ88CfqN6wAUu7VNi3ArPH6cTrdmPr2BHDYsFdUoK3spLQ4cMoeuttcv/yGJ0+/ojKhQsp/ewzSr/6CgDXnj0UvfYa5thY8HrJmv47Ik49lZSHHqQ2M5M9991P+OkTiL/2WspmzSJ83DhMoWqfiUjboMVWROSEll1Sxb9/3MqrC3Y067zbxnfh2dlbMRnwx7N78c26vYTazKzZXcK47gl8tHI3T18+kHv/t4aoEAuL/jiRV3/K5LUFmaRFOViYWQjAkj+eTlZhBRe9sKju2iM7xZKRV05+eS3dEsO4aVwX7v5gNWYTOA9ZfCXUagLDoLLW3WC/xWRQWuPbbzbA7fWt+LzkjxOP4Z0SaT7dr9sOfdYi4k/VW7aw+//uoXbD0edM3M8UE4O9c2eqli/HiIgg5f77yH/+Bazp6dTu2IE5LpbqlatIe/EFsn59E5HnnEPKo4+Q/ehfqPhxDpb4eGrWbwCbjR7Ll1Hwn/+Q//S/fBc3DMImjKfix7ngchE2diy2zp0peuMNsFjA5aoXizkpEXd+Qf2Vng3DNxzaboca32J72GxQW0vExImkPfvM8b5tIk2ie7YEmxKJInJSeG/xDu79eO0xnRsZYsZuMRNmt5BXVsPdk7rz5HdbSI8NpaTSyc6iKp69chBfrcnmizU5dedFh1j4xdD2/OenzEav2y46hN3F1XRLDGdLbvkxxdaY9Q+dRahdHcol8HS/bjv0WYtIIOy8+RYqZs8+pnPtffpQs2kTtq5d8RQWEnnB+RS/8y6ho0dTuXAh3poaOrz3HjtvvBFPfn7deaHjx+Peu5ea9esbXtRqxZqWhjMzk5BBg6heufJYX1o9puhoeixa6JdriRyN7tkSbEokishJ46Y3l/H1ur2EWAyqXc37amsXHcKe4mrYNyx6a96BFaHNBiRHOdhdXOXvkJvNABb/4XQSI0OCHYq0Abpftx36rEUkUDaPGYu7sBDM5gY9/47I4YCqKswxMbiLijDHx+M+KGGI1erbl51d/7x9vQRbkrV9e7p+922L1iltl+7ZEmzq0iIiJ43pZ/ZgYHoM14/txMR//siOgsomn7t735yGhhcKymvqHXN7aVIS0QAC+WTm9F6J/OncPkoiioiIyAkj5ZGH8ZRXEDZqJFsmnN70JF+Vr+3lLioCqxV3xSGjO5zOhklEqH/9/cORA6j9m28Q0rVrQOsQEWlNlEgUkZNGx7gwZm/MJbe0mnbRDqpr3ewtq6FTXCiZTUwqeoHiqmY8LT/k3EB6/KJ+xEcoiSgiIiInDlt6Oll33IErLxd7167UZGRAbS3W9HScO3c27SJOp++nuQI9+M5sJlyrOYtIG6NEooicNNweL9vyK7BbTSzaVsB5A1IZ0jGW/u0i+fVbK8gpqQ52iMesc1yokogiIiJywvGUlVG7fQdVa9dRs3498TffRMiAAbhyctj718fxVp+47bOYa6cGOwQRkRanRKKInDQcNjOLfn86ZpPBnuIqEiLsWM0mABb9/nRKq52c8/RcdhWdeA3WzonhwQ5BREREpNkcAwfSc9VKDIuF2t/eia19+7pj0ZddRvX69Wy/9LL6KySfIOwdOgQ7BBGRFmcKdgAiIv5kNhkApEY76pKIy3cU8fdvNhFiMTPn7gn88eyeGMEMspkGp0fzn6kaNiMiIiInJsPi679ycBKx+KOPKf7gAxx9+tBj5QrCJk4MVnjHJOG3dxJz6aXBDkNEpMWpR6KInLCKK32TaYfZLbw8dxun9UigT2oUAHllNWzNK2dDdik/7ypm5qo9nN0vhd6pkbw2fzs2i8HpvZLIKammuLKWbflNX5jlYJF2M6U1gXmC3jc1gkuGtGfqmE4Bub6IiIiI31UXgy0caitg9xJIHwPWUN+xykJKf1qCYbaS99RTeGtqiLn0UlzZ2VR8/z2hw4aB2YTX46F63Xq8FRXHFsO+FZ+P6BgXYom68goiTz+d8DFjji02EZETnBKJInLCcbk9zNuSz+3vrcRmNiiscOIFZizIpKC8lrP6JDFrYx4utwePFz749UguHdae3qmRADxz5SBqnB5Gd42vu+bKHUXc8d+VdE4IZ9XOoiYtuGJAwJKISRE23v/1aELt+poWERGRE0BtBexdC5tmQkQqlO3x7c9eCTUl0HECZH6PPa+cHS+sosOM1/B6PADYOnSg3VNPYu/RA3sn3wNUr8dD8Ycfkvfsc0RMPJ3id95teixHSyLCMSUSw049ldQHHmjWOSIiJxvD6w30UlYNlZaWEhUVRUlJCZGRkS1dvYicoKqdbv7x7SY+WLar0USf3WxQ4/aSFh1ChMPKrsIq/nx+Hy4anNaset5YuJ3X5m/nnRtG8MHyLHYVVvLpz3uocXnqyoTZzLi9XiJCLIzuEkd6TBivzM+k1unG5fUlGc0mA5enaV+x7aPthNgsbMmt4PqxHZl+RnfC7NZmxS3ib7pftx36rEXkmFUWQOZsyF7e+HGTFTxOSBkKuWtwE0ZN9ERCBw1qVjW7f/c7XMUlpD75T/Ke+Dtep5PSmTPrlbEkJ+MuKcGSlETYqFE48/KoWrgQz/6ejTYbhmHgrak5eoU2G7YuXXDl5+OprCTprt8Re8UVzYpZJBB0z5ZgUyJRRFqd373/Mxm5Zbx0zRCSIkMorXayJbec37yzkuxGVl6OdlhIirDz3q9Hk1dWQ3pcKCFWs19jqqxx8dnqPZzVJ5noUNsRy2bkltEuOhS7xcTqrGKufW0pN57aieQoBy/OySCnpIqkqFC6JobTLTGMXilRTO6XAkBJlZMohxKI0jroft126LMWkSNy18LSFyCyPXQ/B0xm8LghZzVs/ARoZISGEQKxHaH/VVCZD+FJvl6AflSblUXV8uVEnncexhGu7fV4qNm0iZBevfB6vRS88iqFr75Kyl8epTYzk9xnn8MaF4c1ORkjPBzHwIGEn3oKjp498TqdeJ1OTKGhfo1d5Fjpni3BpkSiiLQKTreH95fu4vGvN1LrdlPtrP/VZLcY1LgO7DMBlw1L49rRHemREtXC0R4ft8dbtyiMSGum+3Xboc9aRBrlccG6D6FoB9QWH3LQAA75U9ISBgm9oOd5YD5xHox63W4Ms38fQosEiu7ZEmyafEtEgm7NrmLOf34+IRYTlU5Pg+NWs8EZvZMpqqzll6M6cFaflCBE6T9KIoqIiEirt+U72PEDYKbRHof2CHDEgSMW0kZAVPuGZU4QSiKKiDSdEokiEjTDHvmavPIDDdODk4iD2kfSJSGCCT0TObt/ajDCExEREWlb3C6Yff+hOw/8GtsdQhMgdRBEtmvR0EREpHVQIlFEWsz2/Aru/O8KVu0qbfR4VIiF/1w7BKvZwsD20S0bnIiIiEhblL8F1r4HrsrGj8d0hq6TwBICYQktG5uIiLQ6SiSKSMA5XR6en5PBk99vafR4ZIiFlfefgdlsauHIRERERNqomnJY8y4Ub2v8eExXGHJ9y8YkIiKtnhKJIuJ3e0urue2dFezIryC3vPaw5R67oDdXjOzUgpGJiIiItFEFW2DL11CeTYNFUg427DaI0rBlERFpnBKJIuJ3eWU1LN1e1OgxA7hlfGfuPqtXywYlIiIi0pZV7IXyPY0fM9uh9y8gqW/LxiQiIiccJRJFxO/6tovCZIDnoIfdozvHcNdZPRncITZ4gYmIiIi0VeljYfMX9fe1GwUdxkKo2mciItI0SiSKSED8/Kczmbc5Tysui4iIiLQWI6dDdRnEdw52JCIicoJSIlGkBfywMZcXZmdgMuDqkR2YMvDkn3cmIsSqJKKIiIi0XpmzIXcd2CKg65kQkRLsiAIvPMH3IyIicoyUSBQJMKfbw7QZS+u2F28vYmy3BGLCbEGMSkRERKQNK9gMW789sF20DSY8FLx4REREThCmYAcgcrJ7b8nOettxYVYlEUVERESCKWtJ/e34HsGJQ0RE5ASjHokiATahVxJb8yrYvGsvj142lE7xkcEOSURERKRtaz8K7FGQuxvG3ABm/VkkIiLSFLpjigRYu2gHD57XB+gT7FBEREREBCC2i++nZ7ADERERObFoaLOIiIiIiIiIiIgclRKJIiIiIiIiIiIiclRKJIqIiIiIiIiIiMhRKZEoIiIiIiIiIiIiR6VEooiIiIiIiIiIiByVEokiIiIiIiIiIiJyVEokioiIiIiIiIiIyFEpkSgiIiIiIiIiIiJHpUSiiIiIiIiIiIiIHJUSiSIiIiIiIiIiInJUSiSKiIiIiIiIiIjIUSmRKCIiIiIiIiIiIkelRKKIiIiIiIiIiIgclSUYlXq9XgBKS0uDUb2IiIg0wf779P77tpy81DYTERE5Mah9JsEWlERiWVkZAO3btw9G9SIiItIMZWVlREVFBTsMCSC1zURERE4sap9JsBjeIKSxPR4Pe/bsISIiAsMwWrp6ERERaQKv10tZWRmpqamYTJoN5WSmtpmIiMiJQe0zCbagJBJFRERERERERETkxKL0tYiIiIiIiIiIiByVEokiIiIiIiIiIiJyVEokioiIiIiIiIiIyFEpkSgiIiIiIiIiIiJHpUSiSBtz2mmnceeddzbYP2PGDKKjowF48MEHMQyDSZMmNSj3xBNPYBgGp512WoNjWVlZ2Gw2+vbt22jdhmHU/URFRTFmzBh++OGHuuNz585lypQppKamYhgGM2fOPJaXKCIiInLCUNtMREROJEokikijUlJSmD17NllZWfX2v/rqq6Snpzd6zowZM7j00kspLS1l8eLFjZZ57bXXyM7OZv78+cTHx3Puueeybds2ACoqKhgwYADPPfecf1+MiIiIyAlObTMREWkNlEgUkUY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      "text/plain": [
       "<Figure size 1455.6x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = ['batch_id', 'leiden'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f9c728",
   "metadata": {},
   "source": [
    "## Visualize some genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8a90dbfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "genes = ['CD3E', 'CD8A', 'CD4', 'IL7R', 'MS4A1', 'CD19', 'LYZ', 'FCGR3A', 'NKG7', 'CST3', 'PPBP', 'PTPRC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a694e92d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/plotting/_dotplot.py:749: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored\n",
      "  dot_ax.scatter(x, y, **kwds)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1280x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 623,
       "width": 1036
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.set_figure_params(scanpy=True, fontsize = 14)\n",
    "\n",
    "fig = plt.figure(figsize = (8*2, 6*1.5))\n",
    "ax1 = fig.add_subplot(1, 1, 1)\n",
    "ac = sc.pl.dotplot(adata, genes, groupby = 'leiden', log=True, \n",
    "                   layer = 'norm_counts', \n",
    "                   show = False, ax = ax1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e23cf98",
   "metadata": {},
   "source": [
    "# Batch Effect Correction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca67f20",
   "metadata": {},
   "source": [
    "Batch effect is a technical effect that can confound biological interpretations. We mostly design experiments in such a way that there is as little batch effect as possible. But often times, we wish to compare our data at hand against some other published data. It is natural to expect some batch effect in such cases. Therefore, we need to find computational solutions to correct such technical confounder. As such, this is one of the busiest areas for computational methods development. \n",
    "\n",
    "While there exists many methods to perform such correction, there is a common underlying assumption in all the methods: there must be some sub-populations with the same phenotype (cell type or state) across batches. Under this assumption, a majority of the methods begin by computing nearest neighbor cell or mutually nearest neighbor (MNN) between batches to identify the similar cells. While we have covered nearest neighbor cell in the previous sessions, we have not discussed mutually nearest neighbor cells. We define mutually nearest neighbor cells as follows: If a pair of cells from each batch is contained in each other's set of nearest neighbors, those cells are considered to be mutual nearest neighbors. The methods interpret these pairs as consisting cells that belong to the same cell type or state despite being generated in different batches."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d433121",
   "metadata": {},
   "source": [
    "Here, we will discuss some of the most popular methods for batch effect correction.\n",
    "\n",
    "Scanorama: https://github.com/brianhie/scanorama\n",
    "- Scanorama uses randomized singular value decomposition (SVD) - similar to PCA - to compress the gene expression profiles into a low-dimensional embedding.\n",
    "- It then searches for MNNs in the dimensionality reduced spaces and uses them in a careful way (weighted by similarity) to guide batch integration.\n",
    "- Note: Scanorama also corrects the expression of the genes in all the batches (i.e. outputs a batch corrected gene expression matrix) that can be utilized downstream for further analysis such as visualization, differential expression etc.\n",
    "\n",
    "FastMNN: \n",
    "- FastMNN uses principal component analysis (PCA) to compress the gene expression profiles into a low-dimensional embedding.\n",
    "- It then searches for MNNs in the dimensionality reduced spaces and uses them to compute a translation vector to align the datasets into a shared space.\n",
    "- More details can be found here: https://marionilab.github.io/FurtherMNN2018/theory/description.html\n",
    "- Note: The original version is `mnnCorrect`, but it is more computationally demanding in terms of CPU and memory as it uses the full high dimensional gene expression space. (https://rdrr.io/bioc/batchelor/man/mnnCorrect.html)\n",
    "- Note: FastMNN also corrects the expression of the genes in all the batches (i.e. outputs a batch corrected gene expression matrix) that can be utilized downstream for further analysis such as visualization, differential expression etc.\n",
    "\n",
    "Harmony: https://portals.broadinstitute.org/harmony/articles/quickstart.html\n",
    "- Harmony uses Principal Component Analysis (PCA) to compress the gene expression profiles into a low-dimensional embedding.\n",
    "-  In the PCA space, Harmony iteratively removes batch effects present. At each iteration, it clusters similar cells from different batches and maximizes the diversity of batches within each cluster. It then calculates a correction factor for each cell to be applied. It is good for visualization.\n",
    "- Note: Harmony _does not_ correct the expression of the genes in all the batches.\n",
    "- Note: Harmony has also been noted to mess up the structure of the data post-correction, so we recommend to use with caution and ensuring that biological interpretation remains consistent.\n",
    "\n",
    "Seurat Integrate: https://satijalab.org/seurat/articles/integration_introduction.html\n",
    "- Seurate Integrate uses canonical correlation analysis (CCA) to compress the gene expression profiles into a low-dimensional embedding.\n",
    "- It then searches for MNNs in the dimensionally reduced spaces and uses them as \"anchors\" to guide batch correction.\n",
    "- Note: Seurat Integrate _does not_ correct the expression of the genes in all the batches."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e18b7c",
   "metadata": {},
   "source": [
    "We provide example code to run four different batch correction methods."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59461d9b",
   "metadata": {},
   "source": [
    "# Batch effect correction - Scanorama"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26bdcc59",
   "metadata": {},
   "source": [
    "While Scanorama is integrated into Scanpy, the current implementation on Scanpy does not correct the gene expression matrix. Therefore we will use the original stand-alone implementation of Scanorama (i.e. we will not call the function via Scanpy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6e8bbb5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scanorama\n",
      "  Downloading scanorama-1.7.4-py3-none-any.whl (12 kB)\n",
      "Collecting geosketch>=1.0\n",
      "  Downloading geosketch-1.3-py3-none-any.whl (8.1 kB)\n",
      "Requirement already satisfied: scikit-learn>=0.20rc1 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from scanorama) (1.2.2)\n",
      "Requirement already satisfied: matplotlib>=2.0.2 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from scanorama) (3.7.1)\n",
      "Collecting intervaltree>=3.1.0\n",
      "  Downloading intervaltree-3.1.0.tar.gz (32 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hCollecting fbpca>=1.0\n",
      "  Downloading fbpca-1.0.tar.gz (11 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: annoy>=1.11.5 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from scanorama) (1.17.3)\n",
      "Requirement already satisfied: scipy>=1.0.0 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from scanorama) (1.11.4)\n",
      "Requirement already satisfied: numpy>=1.12.0 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from scanorama) (1.26.4)\n",
      "Collecting sortedcontainers<3.0,>=2.0\n",
      "  Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from matplotlib>=2.0.2->scanorama) (4.39.4)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from matplotlib>=2.0.2->scanorama) (3.0.9)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from matplotlib>=2.0.2->scanorama) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from matplotlib>=2.0.2->scanorama) (1.4.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from matplotlib>=2.0.2->scanorama) (1.0.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from matplotlib>=2.0.2->scanorama) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from matplotlib>=2.0.2->scanorama) (9.5.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from matplotlib>=2.0.2->scanorama) (2.8.2)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from matplotlib>=2.0.2->scanorama) (5.12.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from scikit-learn>=0.20rc1->scanorama) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from scikit-learn>=0.20rc1->scanorama) (1.2.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib>=2.0.2->scanorama) (3.15.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib>=2.0.2->scanorama) (1.16.0)\n",
      "Building wheels for collected packages: fbpca, intervaltree\n",
      "  Building wheel for fbpca (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for fbpca: filename=fbpca-1.0-py3-none-any.whl size=11373 sha256=8a94821a87ee66a0211e56ab18ee5926dc8a9e6b477abcedffc7962b823c79a7\n",
      "  Stored in directory: /Users/sara.jimenez/Library/Caches/pip/wheels/f5/60/60/28df6c25f4d22b73d1a2b1c7e4842a5e2178e35e47b62e8e9a\n",
      "  Building wheel for intervaltree (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for intervaltree: filename=intervaltree-3.1.0-py2.py3-none-any.whl size=26098 sha256=17d2921060b808a31206ba813de6462470362bd69a129f9bc790d0a147fc501c\n",
      "  Stored in directory: /Users/sara.jimenez/Library/Caches/pip/wheels/ab/fa/1b/75d9a713279796785711bd0bad8334aaace560c0bd28830c8c\n",
      "Successfully built fbpca intervaltree\n",
      "Installing collected packages: sortedcontainers, fbpca, intervaltree, geosketch, scanorama\n",
      "Successfully installed fbpca-1.0 geosketch-1.3 intervaltree-3.1.0 scanorama-1.7.4 sortedcontainers-2.4.0\n"
     ]
    }
   ],
   "source": [
    "!pip install scanorama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1f2e7954",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanorama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ec3cd1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 15476 × 13386\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType', 'batch_id', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'leiden'\n",
       "    var: 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'hvg', 'id_hvg', 'pca', 'neighbors_30', 'umap', 'leiden', 'batch_id_colors', 'leiden_colors'\n",
       "    obsm: 'X_pca', 'X_umap'\n",
       "    varm: 'PCs'\n",
       "    layers: 'raw_counts', 'norm_counts'\n",
       "    obsp: 'neighbors_30_distances', 'neighbors_30_connectivities'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c03c426",
   "metadata": {},
   "source": [
    "First, we need to divide the data into respective batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "074365df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our batch variable is stored under 'batch'\n",
    "batch_id = 'batch'\n",
    "\n",
    "adatas = []\n",
    "for c in np.unique(adata.obs[batch_id]):\n",
    "    idx = adata.obs[batch_id] == c\n",
    "    adatas.append(adata[idx, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c716a1e",
   "metadata": {},
   "source": [
    "Now we can run Scanorama to obtain corrected expression counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "edb2d951",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 13386 genes among all datasets\n",
      "Processing datasets (0, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages/scanorama/scanorama.py:237: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n",
      "  adata = AnnData(datasets[i])\n"
     ]
    }
   ],
   "source": [
    "corrected = scanorama.correct_scanpy(adatas, \n",
    "                                     return_dimred=True,\n",
    "                                     verbose=True, \n",
    "                                     dimred=30, \n",
    "                                     knn=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e08244",
   "metadata": {},
   "source": [
    "A note on the code above (you can also view the help page for scanorama):\n",
    "1. The first input is a list of the adata, or batches we want to correct. Note: Scanorama directly acts on `.X`.\n",
    "2. return_dimred = True implies that the method will return the reduced dimensions (like PCA) where the two batches have been co-embedded or co-aligned. \n",
    "3. verbose = True indicates whether to output the intermediate comments.\n",
    "4. dimred = 30 is the number of dimensions on which Scanorama will perform the cell nearest neighbor mathching. I set it to 30 because based on our analysis above, we find that 30 principal components capture enough variance.\n",
    "5. knn = 30 is the number of nearest neighbors to estimate. This number should be smaller if you know the size of your sub-populations that are shared across batches is smaller.\n",
    "\n",
    "Note: There is another parameter called `hvg` to specify the number of highly variable genes (automatically estimated by scanorama), which we welcome you to play with. The only catch is that if you specify this number, the output matrix will only be corrected with highly varying genes (i.e. not all the genes)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5110134b",
   "metadata": {},
   "source": [
    "The output is stored in `corrected` which stores output `anndata` as lists. For example, `corrected[0]` contains the corrected data for batch-0 and `corrected[1]` contains the corrected data for batch-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2160729b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 8098 × 13386\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType', 'batch_id', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'leiden'\n",
       "    var: 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'hvg', 'id_hvg', 'pca', 'neighbors_30', 'umap', 'leiden', 'batch_id_colors', 'leiden_colors'\n",
       "    obsm: 'X_pca', 'X_umap', 'X_scanorama'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrected[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "70c96455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 7378 × 13386\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType', 'batch_id', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'leiden'\n",
       "    var: 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'hvg', 'id_hvg', 'pca', 'neighbors_30', 'umap', 'leiden', 'batch_id_colors', 'leiden_colors'\n",
       "    obsm: 'X_pca', 'X_umap', 'X_scanorama'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrected[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea1a051",
   "metadata": {},
   "source": [
    "We can combine the results and create a new anndata called adata_corrected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f20738f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_corrected = anndata.concat(corrected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c144a5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 15476 × 13386\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType', 'batch_id', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'leiden'\n",
       "    obsm: 'X_pca', 'X_umap', 'X_scanorama'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_corrected"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cd1d8610",
   "metadata": {},
   "source": [
    "Note: In the above combined adata_corrected data, we have X_pca and X_umap, which were computed previously and should not used for the corrected combined data. So we need to recompute them. But first let's save them as something else so they don't overwritten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9ac31083",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_corrected.obsm['old_UMAP'] = adata_corrected.obsm['X_umap']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "56f68128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/sara.jimenez/Documents/scWorkshop/data/'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "07aae168",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save for later:\n",
    "adata_corrected.write_h5ad(input_path + 'processed/batch_data_scanorama_corrected.h5ad')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "71e28a07",
   "metadata": {},
   "source": [
    "##  Nearest neighbors and UMAP"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "99343e03",
   "metadata": {},
   "source": [
    "Note: Corrected reduced dimensions - PCA analogous - have already been computed (they are stored in `.obsm['X_scanorama']`) so all we need to do is run nearest neighbors algorithm followed by UMAP for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "45a5ba69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nearest neighbors on corrected PCA\n",
    "sc.pp.neighbors(adata_corrected, n_neighbors=30, use_rep='X_scanorama', metric='euclidean', \n",
    "                key_added='neighbors_30_corrected')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dabb6ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# UMAP, FDL using the nearest neighbors\n",
    "sc.tl.umap(adata_corrected, neighbors_key = 'neighbors_30_corrected', min_dist=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7181f69d",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "22bac2b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'UMAP of un-corrected data')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 398,
       "width": 399
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (3*2, 3*2))\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "c0 = adata_corrected.obs['batch'].astype(int) == 0\n",
    "c1 = adata_corrected.obs['batch'].astype(int) == 1\n",
    "ax.scatter(adata_corrected.obsm['X_umap'][c0, 0], adata_corrected.obsm['X_umap'][c0, 1], s = 1, label = 'Batch-0')\n",
    "ax.scatter(adata_corrected.obsm['X_umap'][c1, 0], adata_corrected.obsm['X_umap'][c1, 1], s = 1, label = 'Batch-1')\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_title('UMAP of corrected data')\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "c0 = adata.obs['batch'] == 0\n",
    "c1 = adata.obs['batch'] == 1\n",
    "ax.scatter(adata_corrected.obsm['old_UMAP'][c0, 0], adata_corrected.obsm['old_UMAP'][c0, 1], s = 1, label = 'Batch-0')\n",
    "ax.scatter(adata_corrected.obsm['old_UMAP'][c1, 0], adata_corrected.obsm['old_UMAP'][c1, 1], s = 1, label = 'Batch-1')\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_title('UMAP of un-corrected data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "84eedd92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function correct in module scanorama.scanorama:\n",
      "\n",
      "correct(datasets_full, genes_list, return_dimred=False, batch_size=5000, verbose=2, ds_names=None, dimred=100, approx=True, sigma=15, alpha=0.1, knn=20, return_dense=False, hvg=None, union=False, seed=0)\n",
      "    Integrate and batch correct a list of data sets.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    datasets_full : `list` of `scipy.sparse.csr_matrix` or of `numpy.ndarray`\n",
      "        Data sets to integrate and correct.\n",
      "    genes_list: `list` of `list` of `string`\n",
      "        List of genes for each data set.\n",
      "    return_dimred: `bool`, optional (default: `False`)\n",
      "        In addition to returning batch corrected matrices, also returns\n",
      "        integrated low-dimesional embeddings.\n",
      "    batch_size: `int`, optional (default: `5000`)\n",
      "        The batch size used in the alignment vector computation. Useful when\n",
      "        correcting very large (>100k samples) data sets. Set to large value\n",
      "        that runs within available memory.\n",
      "    verbose: `bool` or `int`, optional (default: 2)\n",
      "        When `True` or not equal to 0, prints logging output.\n",
      "    ds_names: `list` of `string`, optional\n",
      "        When `verbose=True`, reports data set names in logging output.\n",
      "    dimred: `int`, optional (default: 100)\n",
      "        Dimensionality of integrated embedding.\n",
      "    approx: `bool`, optional (default: `True`)\n",
      "        Use approximate nearest neighbors, greatly speeds up matching runtime.\n",
      "    sigma: `float`, optional (default: 15)\n",
      "        Correction smoothing parameter on Gaussian kernel.\n",
      "    alpha: `float`, optional (default: 0.10)\n",
      "        Alignment score minimum cutoff.\n",
      "    knn: `int`, optional (default: 20)\n",
      "        Number of nearest neighbors to use for matching.\n",
      "    return_dense: `bool`, optional (default: `False`)\n",
      "        Return `numpy.ndarray` matrices instead of `scipy.sparse.csr_matrix`.\n",
      "    hvg: `int`, optional (default: None)\n",
      "        Use this number of top highly variable genes based on dispersion.\n",
      "    seed: `int`, optional (default: 0)\n",
      "        Random seed to use.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    corrected, genes\n",
      "        By default (`return_dimred=False`), returns a two-tuple containing a\n",
      "        list of `scipy.sparse.csr_matrix` each with batch corrected values,\n",
      "        and a single list of genes containing the intersection of inputted\n",
      "        genes.\n",
      "    \n",
      "    integrated, corrected, genes\n",
      "        When `return_dimred=True`, returns a three-tuple containing a list\n",
      "        of `numpy.ndarray` with integrated low dimensional embeddings, a list\n",
      "        of `scipy.sparse.csr_matrix` each with batch corrected values, and a\n",
      "        a single list of genes containing the intersection of inputted genes.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(scanorama.correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08f31895",
   "metadata": {},
   "source": [
    "## True celltypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ef237d3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n",
      "/Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 960x320 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 299,
       "width": 971
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata_corrected, color = ['batch_id', 'CellType'], wspace=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f65c3bf",
   "metadata": {},
   "source": [
    "# Batch effect correction - Harmony"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34cc09bf",
   "metadata": {},
   "source": [
    "Harmony is another popular method for correcting batch effect in single-cell RNA-sequencing data. As discussed in the beginning, Harmony iteratively removes batch effects in the PCA space. In each iteration, it clusters similar cells from different batches and maximizing the diversity of batches within each cluster; it then calculates a correction factor for each cell to be applied. While it is a fast method, there are a couple caveats: 1) Since it operates only on the PCA space, any non-linear association between features could be missed or altered; and 2) Harmony does not provide a corrected gene expression matrix. Therefore, the output of Harmony can only be used for visualization and clustering but not for differential expression analysis or any gene centric analysis, since one does not get a corrected expression matrix back. One would need to be careful to perform downstream analysis with Harmony."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e5ebe0",
   "metadata": {},
   "source": [
    "Harmony is implemented in Scanpy: https://scanpy.readthedocs.io/en/stable/generated/scanpy.external.pp.harmony_integrate.html. \n",
    "\n",
    "Let's first make a copy of `adata` for application of Harmony. Note we do not simply do `adata_harmony = adata` but specify `adata_harmony = adata.copy()` because of the issue of mutable variables in Python. See more here: https://www.geeksforgeeks.org/mutable-vs-immutable-objects-in-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e3d97afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_harmony = adata.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "68676bd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting harmonypy\n",
      "  Downloading harmonypy-0.0.10-py3-none-any.whl (20 kB)\n",
      "Requirement already satisfied: pandas in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from harmonypy) (1.5.3)\n",
      "Requirement already satisfied: scipy in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from harmonypy) (1.11.4)\n",
      "Requirement already satisfied: numpy in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from harmonypy) (1.26.4)\n",
      "Requirement already satisfied: scikit-learn in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from harmonypy) (1.2.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from pandas->harmonypy) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from pandas->harmonypy) (2023.3)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from scikit-learn->harmonypy) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from scikit-learn->harmonypy) (3.1.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas->harmonypy) (1.16.0)\n",
      "Installing collected packages: harmonypy\n",
      "Successfully installed harmonypy-0.0.10\n"
     ]
    }
   ],
   "source": [
    "# Please run this step if we did not install harmonypy\n",
    "!pip install harmonypy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fca2946d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-08 00:24:58,334 - harmonypy - INFO - Computing initial centroids with sklearn.KMeans...\n",
      "2024-11-08 00:25:01,665 - harmonypy - INFO - sklearn.KMeans initialization complete.\n",
      "2024-11-08 00:25:01,729 - harmonypy - INFO - Iteration 1 of 10\n",
      "2024-11-08 00:25:04,518 - harmonypy - INFO - Iteration 2 of 10\n",
      "2024-11-08 00:25:10,266 - harmonypy - INFO - Iteration 3 of 10\n",
      "2024-11-08 00:25:18,058 - harmonypy - INFO - Iteration 4 of 10\n",
      "2024-11-08 00:25:25,918 - harmonypy - INFO - Iteration 5 of 10\n",
      "2024-11-08 00:25:33,910 - harmonypy - INFO - Iteration 6 of 10\n",
      "2024-11-08 00:25:41,468 - harmonypy - INFO - Iteration 7 of 10\n",
      "2024-11-08 00:25:50,139 - harmonypy - INFO - Iteration 8 of 10\n",
      "2024-11-08 00:25:57,992 - harmonypy - INFO - Iteration 9 of 10\n",
      "2024-11-08 00:26:00,988 - harmonypy - INFO - Converged after 9 iterations\n"
     ]
    }
   ],
   "source": [
    "sc.external.pp.harmony_integrate(adata_harmony, \n",
    "                                 key = 'batch_id', \n",
    "                                 basis='X_pca', \n",
    "                                 adjusted_basis='X_pca_harmony')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7809e67c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 15476 × 13386\n",
       "    obs: 'Sample', 'n_counts', 'n_genes', 'batch', 'louvain', 'anno', 'Method', 'CellType', 'batch_id', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'leiden'\n",
       "    var: 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'hvg', 'id_hvg', 'pca', 'neighbors_30', 'umap', 'leiden', 'batch_id_colors', 'leiden_colors'\n",
       "    obsm: 'X_pca', 'X_umap', 'X_pca_harmony'\n",
       "    varm: 'PCs'\n",
       "    layers: 'raw_counts', 'norm_counts'\n",
       "    obsp: 'neighbors_30_distances', 'neighbors_30_connectivities'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_harmony"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1b90ff97",
   "metadata": {},
   "source": [
    "Now we just need to recompute the neighbors and UMAP on the above computed X_pca_harmony:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bc1a7e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to prevent overwriting\n",
    "adata_harmony.obsm['old_UMAP'] = adata_harmony.obsm['X_umap']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c59b2aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nearest neighbors on PCA_harmony\n",
    "sc.pp.neighbors(adata_harmony, n_neighbors=30, use_rep='X_pca_harmony', metric='euclidean', \n",
    "                key_added='neighbors_30_harmony')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8aa367dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# UMAP using the nearest neighbors\n",
    "sc.tl.umap(adata_harmony, neighbors_key = 'neighbors_30_harmony', min_dist=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a3bf437c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'UMAP of un-corrected data')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 398,
       "width": 430
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (3*2, 3*2))\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "c0 = adata_harmony.obs['batch'].astype(int) == 0\n",
    "c1 = adata_harmony.obs['batch'].astype(int) == 1\n",
    "ax.scatter(adata_harmony.obsm['X_umap'][c0, 0], adata_harmony.obsm['X_umap'][c0, 1], s = 1, label = 'Batch-0')\n",
    "ax.scatter(adata_harmony.obsm['X_umap'][c1, 0], adata_harmony.obsm['X_umap'][c1, 1], s = 1, label = 'Batch-1')\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_title('UMAP of Harmony corrected data')\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "c0 = adata.obs['batch'] == 0\n",
    "c1 = adata.obs['batch'] == 1\n",
    "ax.scatter(adata_harmony.obsm['old_UMAP'][c0, 0], adata_harmony.obsm['old_UMAP'][c0, 1], s = 1, label = 'Batch-0')\n",
    "ax.scatter(adata_harmony.obsm['old_UMAP'][c1, 0], adata_harmony.obsm['old_UMAP'][c1, 1], s = 1, label = 'Batch-1')\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_title('UMAP of un-corrected data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3eebd5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sara.jimenez/miniconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 320x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 296,
       "width": 485
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata_harmony, color = ['CellType'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "296.797px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}