{ "cells": [ { "cell_type": "markdown", "id": "d485603c", "metadata": {}, "source": [ "# Few-shot Prediction of Perturbation Effects" ] }, { "cell_type": "markdown", "id": "1c2b07c1", "metadata": {}, "source": [ "In this tutorial, we will learn a universal embedding of perturbations and use it for few-shot prediction of perturbtion effects in a new cellular context." ] }, { "cell_type": "code", "execution_count": 1, "id": "ff372620", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import scanpy as sc\n", "\n", "import torch\n", "\n", "from scmg.model.contrastive_embedding import CellEmbedder, embed_adata\n", "from scmg.model.perturbation_prediction import TrainConfig, train_model, plot_history, get_category_embeddings" ] }, { "cell_type": "markdown", "id": "457a1c61", "metadata": {}, "source": [ "Load the trained SCMG model." ] }, { "cell_type": "code", "execution_count": 2, "id": "ca8b3379", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CellEmbedder(\n", " (encoder): MLP(\n", " (layers): ModuleList(\n", " (0): Linear(in_features=18108, out_features=2048, bias=True)\n", " (1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", " (2): LeakyReLU(negative_slope=0.01)\n", " (3): Dropout(p=0.0, inplace=False)\n", " (4): Linear(in_features=2048, out_features=2048, bias=True)\n", " (5): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", " (6): LeakyReLU(negative_slope=0.01)\n", " (7): Dropout(p=0.0, inplace=False)\n", " (8): Linear(in_features=2048, out_features=512, bias=True)\n", " )\n", " )\n", " (decoder): MLP(\n", " (layers): ModuleList(\n", " (0): Linear(in_features=576, out_features=1024, bias=True)\n", " (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (2): LeakyReLU(negative_slope=0.01)\n", " (3): Dropout(p=0, inplace=False)\n", " (4): Linear(in_features=1024, out_features=2048, bias=True)\n", " (5): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", " (6): LeakyReLU(negative_slope=0.01)\n", " (7): Dropout(p=0, inplace=False)\n", " (8): Linear(in_features=2048, out_features=18108, bias=True)\n", " )\n", " )\n", ")" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the autoencoder model\n", "model_path = 'models/embedder'\n", "\n", "scmg_model = torch.load(os.path.join(model_path, 'model.pt'),\n", " map_location=torch.device('cpu'))\n", "scmg_model.load_state_dict(torch.load(os.path.join(model_path, 'best_state_dict.pth'),\n", " map_location=torch.device('cpu')))\n", "\n", "device = 'cpu'\n", "scmg_model.to(device)\n", "scmg_model.eval()" ] }, { "cell_type": "markdown", "id": "de8f960a", "metadata": {}, "source": [ "Load the example perturbation dataset." ] }, { "cell_type": "code", "execution_count": 3, "id": "d36911aa", "metadata": {}, "outputs": [], "source": [ "adata_pert = sc.read_h5ad('data/pseudo_bulk_perturbation_database.h5ad')\n", "\n", "# Mask out the direct target genes\n", "for i in range(adata_pert.shape[0]):\n", " pg = adata_pert.obs['perturbed_gene'].iloc[i]\n", " \n", " if pg in adata_pert.var_names:\n", " adata_pert.X[i, adata_pert.var_names.get_loc(pg)] = 0" ] }, { "cell_type": "markdown", "id": "db79f5e9", "metadata": {}, "source": [ "## Learn a universal embedding of perturbations" ] }, { "cell_type": "markdown", "id": "d6a752a0", "metadata": {}, "source": [ "Let's train a universal embedding of perturbations from multiple CRISPRi datasets. We will leave out the dataset for RPE1 cells from the training and use it to test few-shot prediction." ] }, { "cell_type": "code", "execution_count": 4, "id": "726986e3", "metadata": {}, "outputs": [], "source": [ "adata_train = adata_pert[adata_pert.obs['condition'].isin([\n", " 'ReplogleWeissman2022_K562_gwps', 'ReplogleWeissman2022_K562_essential', \n", " 'JiangSatija2024_IFNB', 'JiangSatija2024_TNFA', 'JiangSatija2024_TGFB', 'JiangSatija2024_IFNG', 'JiangSatija2024_INS',\n", " 'FrangiehIzar2021_RNA', 'TianKampmann2021_CRISPRi', 'AdamsonWeissman2016_GSM2406681_10X010',\n", "])].copy()\n", "\n", "adata_rpe1 = adata_pert[adata_pert.obs['condition'].isin([\n", " 'ReplogleWeissman2022_rpe1',\n", "])].copy()" ] }, { "cell_type": "markdown", "id": "66b80c88", "metadata": {}, "source": [ "Because perturbation effects depend on the starting cell states. We need to provide the cell state information to the model such that it can decompose the intrinsic properties of perturbations from the context-specific effects. We will use the SCMG embedding of the unperturbed cell states to define the cellular context." ] }, { "cell_type": "code", "execution_count": 5, "id": "7c5c5b26", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b698550356d9478a9ed85438c662a8b2", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2 [00:00 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 01 | train_loss=0.0012 | train_corr=0.0485 | \n", "Epoch 02 | train_loss=0.0011 | train_corr=0.1642 | \n", "Epoch 03 | train_loss=0.0010 | train_corr=0.2042 | \n", "Epoch 04 | train_loss=0.0010 | train_corr=0.2392 | \n", "Epoch 05 | train_loss=0.0009 | train_corr=0.2657 | \n", "Epoch 06 | train_loss=0.0009 | train_corr=0.2867 | \n", "Epoch 07 | train_loss=0.0009 | train_corr=0.3058 | \n", "Epoch 08 | train_loss=0.0009 | train_corr=0.3159 | \n", "Epoch 09 | train_loss=0.0009 | train_corr=0.3317 | \n", "Epoch 10 | train_loss=0.0009 | train_corr=0.3398 | \n", "Epoch 11 | train_loss=0.0009 | train_corr=0.3509 | \n", "Epoch 12 | train_loss=0.0009 | train_corr=0.3590 | \n", "Epoch 13 | train_loss=0.0009 | train_corr=0.3672 | \n", "Epoch 14 | train_loss=0.0009 | train_corr=0.3747 | \n", "Epoch 15 | train_loss=0.0009 | train_corr=0.3773 | \n", "Epoch 16 | train_loss=0.0009 | train_corr=0.3836 | \n", "Epoch 17 | train_loss=0.0009 | train_corr=0.3883 | \n", "Epoch 18 | train_loss=0.0009 | train_corr=0.3907 | \n", "Epoch 19 | train_loss=0.0009 | train_corr=0.3938 | \n", "Epoch 20 | train_loss=0.0009 | train_corr=0.3972 | \n", "Epoch 21 | train_loss=0.0009 | train_corr=0.4013 | \n", "Epoch 22 | train_loss=0.0009 | train_corr=0.4041 | \n", "Epoch 23 | train_loss=0.0009 | train_corr=0.4084 | \n", "Epoch 24 | train_loss=0.0009 | train_corr=0.4103 | \n", "Epoch 25 | train_loss=0.0009 | train_corr=0.4116 | \n", "Epoch 26 | train_loss=0.0009 | train_corr=0.4129 | \n", "Epoch 27 | train_loss=0.0009 | train_corr=0.4157 | \n", "Epoch 28 | train_loss=0.0009 | train_corr=0.4172 | \n", "Epoch 29 | train_loss=0.0009 | train_corr=0.4183 | \n", "Epoch 30 | train_loss=0.0009 | train_corr=0.4214 | \n", "Epoch 31 | train_loss=0.0009 | train_corr=0.4247 | \n", "Epoch 32 | train_loss=0.0009 | train_corr=0.4232 | \n", "Epoch 33 | train_loss=0.0009 | train_corr=0.4257 | \n", "Epoch 34 | train_loss=0.0009 | train_corr=0.4270 | \n", "Epoch 35 | train_loss=0.0009 | train_corr=0.4270 | \n", "Epoch 36 | train_loss=0.0009 | train_corr=0.4291 | \n", "Epoch 37 | train_loss=0.0009 | train_corr=0.4278 | \n", "Epoch 38 | train_loss=0.0009 | train_corr=0.4274 | \n", "Epoch 39 | train_loss=0.0009 | train_corr=0.4284 | \n", "Epoch 40 | train_loss=0.0009 | train_corr=0.4319 | \n", "Epoch 41 | train_loss=0.0009 | train_corr=0.4331 | \n", "Epoch 42 | train_loss=0.0009 | train_corr=0.4324 | \n", "Epoch 43 | train_loss=0.0009 | train_corr=0.4328 | \n", "Epoch 44 | train_loss=0.0009 | train_corr=0.4319 | \n", "Epoch 45 | train_loss=0.0009 | train_corr=0.4347 | \n", "Epoch 46 | train_loss=0.0009 | train_corr=0.4360 | \n", "Epoch 47 | train_loss=0.0009 | train_corr=0.4378 | \n", "Epoch 48 | train_loss=0.0009 | train_corr=0.4320 | \n", "Epoch 49 | train_loss=0.0009 | train_corr=0.4366 | \n", "Epoch 50 | train_loss=0.0009 | train_corr=0.4361 | \n", "Epoch 51 | train_loss=0.0009 | train_corr=0.4343 | \n", "Epoch 52 | train_loss=0.0009 | train_corr=0.4370 | \n", "Epoch 53 | train_loss=0.0009 | train_corr=0.4376 | \n", "Epoch 54 | train_loss=0.0009 | train_corr=0.4373 | \n", "Epoch 55 | train_loss=0.0009 | train_corr=0.4382 | \n", "Epoch 56 | train_loss=0.0009 | train_corr=0.4399 | \n", "Epoch 57 | train_loss=0.0009 | train_corr=0.4395 | \n", "Epoch 58 | train_loss=0.0009 | train_corr=0.4370 | \n", "Epoch 59 | train_loss=0.0009 | train_corr=0.4404 | \n", "Epoch 60 | train_loss=0.0009 | train_corr=0.4404 | \n", "Epoch 61 | train_loss=0.0009 | train_corr=0.4403 | \n", "Epoch 62 | train_loss=0.0009 | train_corr=0.4375 | \n", "Epoch 63 | train_loss=0.0009 | train_corr=0.4394 | \n", "Epoch 64 | train_loss=0.0009 | train_corr=0.4389 | \n", "Epoch 65 | train_loss=0.0009 | train_corr=0.4409 | \n", "Epoch 66 | train_loss=0.0009 | train_corr=0.4413 | \n", "Epoch 67 | train_loss=0.0009 | train_corr=0.4413 | \n", "Epoch 68 | train_loss=0.0009 | train_corr=0.4416 | \n", "Epoch 69 | train_loss=0.0009 | train_corr=0.4416 | \n", "Epoch 70 | train_loss=0.0009 | train_corr=0.4425 | \n", "Epoch 71 | train_loss=0.0009 | train_corr=0.4404 | \n", "Epoch 72 | train_loss=0.0009 | train_corr=0.4380 | \n", "Epoch 73 | train_loss=0.0009 | train_corr=0.4416 | \n", "Epoch 74 | train_loss=0.0009 | train_corr=0.4409 | \n", "Epoch 75 | train_loss=0.0009 | train_corr=0.4414 | \n", "Epoch 76 | train_loss=0.0009 | train_corr=0.4422 | \n", "Epoch 77 | train_loss=0.0009 | train_corr=0.4410 | \n", "Epoch 78 | train_loss=0.0009 | train_corr=0.4391 | \n", "Epoch 79 | train_loss=0.0009 | train_corr=0.4420 | \n", "Epoch 80 | train_loss=0.0009 | train_corr=0.4401 | \n", "Epoch 81 | train_loss=0.0009 | train_corr=0.4403 | \n", "Epoch 82 | train_loss=0.0009 | train_corr=0.4407 | \n", "Epoch 83 | train_loss=0.0009 | train_corr=0.4430 | \n", "Epoch 84 | train_loss=0.0009 | train_corr=0.4419 | \n", "Epoch 85 | train_loss=0.0009 | train_corr=0.4386 | \n", "Epoch 86 | train_loss=0.0009 | train_corr=0.4406 | \n", "Epoch 87 | train_loss=0.0009 | train_corr=0.4423 | \n", "Epoch 88 | train_loss=0.0009 | train_corr=0.4430 | \n", "Epoch 89 | train_loss=0.0009 | train_corr=0.4416 | \n", "Epoch 90 | train_loss=0.0009 | train_corr=0.4406 | \n", "Epoch 91 | train_loss=0.0009 | train_corr=0.4429 | \n", "Epoch 92 | train_loss=0.0009 | train_corr=0.4429 | \n", "Epoch 93 | train_loss=0.0009 | train_corr=0.4407 | \n", "Epoch 94 | train_loss=0.0009 | train_corr=0.4411 | \n", "Epoch 95 | train_loss=0.0009 | train_corr=0.4414 | \n", "Epoch 96 | train_loss=0.0009 | train_corr=0.4432 | \n", "Epoch 97 | train_loss=0.0009 | train_corr=0.4435 | \n", "Epoch 98 | train_loss=0.0009 | train_corr=0.4429 | \n", "Epoch 99 | train_loss=0.0009 | train_corr=0.4432 | \n", "Epoch 100 | train_loss=0.0009 | train_corr=0.4437 | \n" ] } ], "source": [ "cfg = TrainConfig(epochs=100, batch_size=256, lr=1e-2)\n", "\n", "cats = list(adata_train.obs['perturbed_gene_name'].values)\n", "X = adata_train.obsm['X_ctl_ce_latent'].copy()\n", "Y = adata_train.X.copy()\n", "Y_mask = adata_train.layers['measure_mask'].copy()\n", "\n", "result = train_model(cats, X, Y, Y_mask, config=cfg, emb_dim=16)" ] }, { "cell_type": "markdown", "id": "d7b7504a", "metadata": {}, "source": [ "We can plot the training loss and the correlation between the predicted and measured perturbation effect vectors as functions of the epochs." ] }, { "cell_type": "code", "execution_count": 7, "id": "dacb1122", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_history(result['history'])" ] }, { "cell_type": "markdown", "id": "5677617d", "metadata": {}, "source": [ "The loss function is converged after the training.\n", "\n", "Let's extract the perturbation embeddings from the model." ] }, { "cell_type": "code", "execution_count": 8, "id": "c7835517", "metadata": {}, "outputs": [], "source": [ "pert_emb_df = get_category_embeddings(result[\"model\"], result[\"vocab\"])" ] }, { "cell_type": "markdown", "id": "7c002403", "metadata": {}, "source": [ "## Few shot prediction\n", "Now we can use the learnt universal perturbation embeddings to perform few-shot prediction for RPE1 cells." ] }, { "cell_type": "code", "execution_count": 9, "id": "f12a2c86", "metadata": {}, "outputs": [], "source": [ "# Assign the perturbation emebdings to the RPE1 dataset\n", "adata_rpe1 = adata_rpe1[adata_rpe1.obs['perturbed_gene_name'].isin(pert_emb_df.index)].copy()\n", "adata_rpe1.obsm['pert_emb'] = pert_emb_df.loc[adata_rpe1.obs['perturbed_gene_name']].values" ] }, { "cell_type": "markdown", "id": "fae5379b", "metadata": {}, "source": [ "Let's define simple functions for few-shot prediction" ] }, { "cell_type": "code", "execution_count": 10, "id": "54a6e1a8", "metadata": {}, "outputs": [], "source": [ "import scipy\n", "from tqdm import tqdm\n", "from sklearn.cluster import KMeans\n", "from sklearn.linear_model import Ridge\n", "\n", "def kmeans_find_centroid_indices(X, k):\n", " '''Use KMeans to find k centroids in the dataset'''\n", " kmeans = KMeans(n_clusters=k, random_state=0).fit(X)\n", " centroids = kmeans.cluster_centers_\n", " indices = []\n", " for centroid in centroids:\n", " distances = np.linalg.norm(X - centroid, axis=1)\n", " index = np.argmin(distances)\n", " indices.append(index)\n", " return np.unique(indices)\n", "\n", "def few_shot_prediction(adata_ref, adata_query, alpha=3):\n", " '''Few-shot prediction using Ridge regression'''\n", " model = Ridge(alpha=alpha)\n", " model.fit(adata_ref.obsm['pert_emb'], adata_ref.X)\n", " adata_query.layers['predicted_X'] = model.predict(adata_query.obsm['pert_emb'])\n", "\n", "def sim_func(v1, v2):\n", " return 1 - scipy.spatial.distance.correlation(v1, v2)" ] }, { "cell_type": "markdown", "id": "3f20da8b", "metadata": {}, "source": [ "As a baseline for comparison, we assume the perturbation effects are context-independent, such that the perturbation effects in RPE1 cells are the same as those in K562 cells." ] }, { "cell_type": "code", "execution_count": 11, "id": "0f217276", "metadata": {}, "outputs": [], "source": [ "adata_target = adata_rpe1.copy()\n", "\n", "# Create a dictionary to store the prediction accuracy\n", "comp_dict = {\n", " 'k' : [],\n", " 'gene_name' : [],\n", " 'sim': [],\n", " 'true_effect_size' : [],\n", " }\n", "\n", "# Use the K562 dataset as the baseline reference dataset\n", "adata_k562 = adata_pert[adata_pert.obs['condition'].isin([\n", " 'ReplogleWeissman2022_K562_gwps',\n", "])].copy()\n", "\n", "# Iterate over each cell in the RPE1 dataset\n", "for i in range(adata_rpe1.shape[0]):\n", " pg = adata_rpe1.obs['perturbed_gene'].iloc[i]\n", " \n", " # Find the corresponding cell in the K562 dataset\n", " if pg in adata_k562.obs['perturbed_gene'].values:\n", " v1 = adata_rpe1.X[i]\n", " j = list(adata_k562.obs['perturbed_gene']).index(pg)\n", " v2 = adata_k562.X[j]\n", "\n", " comp_dict['k'].append(0)\n", " comp_dict['gene_name'].append(adata_rpe1.obs['perturbed_gene'].iloc[i])\n", "\n", " # Compute the similarity between the perturbation effect vectors\n", " comp_dict['sim'].append(sim_func(v1, v2))\n", " comp_dict['true_effect_size'].append(np.linalg.norm(v1))" ] }, { "cell_type": "markdown", "id": "48dcf80f", "metadata": {}, "source": [ "Now perform the few-shot prediction using different numbers (K) of few-shot samples as training datasets." ] }, { "cell_type": "code", "execution_count": 12, "id": "2c0d3523", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 6/6 [00:06<00:00, 1.03s/it]\n" ] } ], "source": [ "k_values = [5, 10, 20, 50, 100, 200,]\n", "\n", "for k in tqdm(k_values):\n", "\n", " # Select k centroids from the RPE1 dataset\n", " few_shot_ids = kmeans_find_centroid_indices(adata_target.obsm['pert_emb'], k)\n", " \n", " # Split the RPE1 dataset into a reference and query set\n", " adata_ref = adata_target[few_shot_ids, :].copy()\n", " adata_query = adata_target[~adata_target.obs.index.isin(adata_ref.obs.index)].copy()\n", " adata_true = adata_target[~adata_target.obs.index.isin(adata_ref.obs.index)].copy()\n", "\n", " # Perform few-shot prediction\n", " few_shot_prediction(adata_ref, adata_query)\n", "\n", " # Compare the true and predicted perturbation effect vectors\n", " for i in range(adata_true.shape[0]):\n", " comp_dict['k'].append(k)\n", " comp_dict['gene_name'].append(adata_true.obs['perturbed_gene'].iloc[i])\n", "\n", " v1 = adata_true.X[i, :]\n", " v2 = adata_query.layers['predicted_X'][i, :]\n", "\n", " comp_dict['sim'].append(sim_func(v1, v2))\n", " comp_dict['true_effect_size'].append(np.linalg.norm(v1))\n", "\n", "# Convert the comparison dictionary to a pandas DataFrame\n", "comp_df = pd.DataFrame(comp_dict)" ] }, { "cell_type": "markdown", "id": "b5e5caef", "metadata": {}, "source": [ "Compare the performance of the baseline and the few-shot prediction results." ] }, { "cell_type": "code", "execution_count": 13, "id": "3bbc1174", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_756038/2975183287.py:16: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " ax.set_xticklabels(['baseline \\nfrom K562'] + k_values, fontsize=12)\n" ] }, { "data": { "image/png": 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8de/hrLPOwp133pntruUE3d3duPTSSyHl16B71NmAIsG3+u8YO3YsHnvssWx3Lyf485//jLlz56LjmAvBXIWwN++Be8cXuPHGG3H55Zdnu3umZ8mSJfjNb34D/6ATEKw8FgDgqP8Grv1r8PDDD2PChAlZ7mHvtLa2ora2Fvn5+X1eZzkxXFVVhYMHD0YdO3jwIAoKCuJahQHA5XLB5XLFHC8oKCAxTEBRFNjt6p9SMBikZ4IzwWAQdrsdSkEl7F0N6OrqojHmSF1dnTq+vhKIvlLYHU4cOXKExpgT+/fvDz2/ZbC51HeM6C3CgQP1NMacqKurg93phphfAQgiUFgNu92O+noaYx4cPHgQdrsdgYKq8DNcUAX7ofU4dOiQKca4P5dW02eTSJZJkyZh3rx5Ucfmzp2LSZMmZalHhNlpbW2N+3+CD4cPHwYAKJ4iQLShoaEhux3KMfbv3w8AUFz5gCBCceahLnSMSJ9t27YBAGRvqX5M9pagqekwjhw5kq1u5QySJGHXrl2Q3UWqEAbAXPmAzYHt27dnt3M5wo4dOwAAiqdYPyaH/q+dMzumF8Pt7e1YvXo1Vq9eDUDdUl29ejX27NkDQHVxuP766/Xrf/7zn2PHjh34f//v/2HTpk147rnn8Pbbb+OOO+7IRveJHKC5uRkAwCDgSOj/BD/q6+sBAIrLB8WRF7OzQ6SHNlcq7kL936bDh9HR0ZHNbuUMmiBTPCX6McVbEnWOSJ26ujoEAgEo3rBQgyBAdhdj1+7dCAaD2etcjrB161YwuwvMmacf0xYcW7duzWLP+GF6Mbx8+XKccMIJOOGEEwAAM2bMwAknnIAHH3wQAHDgwAF9sgeA4cOH4+OPP8bcuXMxduxYPPHEE3jppZcwZcqUrPSfMD+adUfxFKH5yBEqhsAZTfwqTh8Ulw9NTU1R2V2I9Ni1axcAQHEXqf96iqKOE+mxfft21eIeGlcgLIY1qzGROps2bQIQbXkHADmvFLIk0YIjTdrb27F37151fCNdDQQBkrcUu3bvRldXV/Y6yAnT+wyfccYZfYqPeNXlzjjjDKxatcrAXhFWorm5GRDtUFz5CHYdQWdnJ/Ly8vr9HJEYmmWYOfOgOH0AgIaGhj4jg4nE2bVrl2r1cai+gNr25+7du3HMMcdks2umR5ZlbN26DbK7EBBt4eMh4bZly5ZsdS1n2LhxIwBA9lVEHZfzygFsxMaNGzF69Ogs9Cw30J5ROa8s5pySVw7WVo+tW7fi+OOPz3TXuGJ6yzBBZJvDTU1QHG5dTDQ1NWW5R7nFgQMHwJxeQLSBuVQxvJ98WrkQDAaxb9++kL+lavXRLMRkGU6fXbt2oaurE7KvPOo4c/rAHB6sX78+Sz3LHTZs2ACItig3FAD6mGtimUgNzfKu5JXHnNMEsnaNmSExTBBpIMsyDjc2QnH6dH+qQ4cOZblXuYPf70d9fT1kV9ifFVBzRhLps2vXLsiyHOVvqW7nCznjC5hNNLEr50VbLSEIkPLKcfDgQT1AlEgev9+P7du3Q/KWAj0KKjBXAZjdhfUbNmSpd7nBhtD4yXHFcHnUNWaGxDBBpMHhw4ehKEpoC5/EMG/q6urAGIsK7gJIDPNi8+bNAADZG7EFanNA9hRiy5YtUBQlSz3LDTQxrPTYwo88Rtbh1NmwYYO6mPNVxp4UBMh5Fajbt49261KEMYZv1q6F4spXd+d6ng+99775Zq3pY2VIDBNEGmjCV3HmkWXYAPRMBx5NDBdEHSfSQxPDSg9/QMVbho6ODnJHSZN169aB2V36Ii4Szcd13bp1me5WzqBlkZLyq+Ke146vWbMmU13KKfbs2YPWlpYYf2wdQYDsq0BT02HTzxUkhgkiDbRMB5HBXZT6ix+7d+8GELYIa4GKO8mflQubN28O+VsWRR3PJV/AbHHo0CHU1dVB8lVGR+GHkPPKAdFGwdxpoIphAXJ+HMswALmgOuI6IlnWrl0LAJB7WWxEntOuNSskhgkiDerq6gAAiqtAtQwLgn6MSB8t9ZSWigpQsx0caWqirc806erqwvbt20Mpk6JfBZovIG3hp44mcuWCmvgXiDZIvkps27YNLS0tGexZbuD3+1U3CW8pYHPGvUbxlgA2B1aRGE6Jb775BgAgx3NDCaGd0641KySGCSIN9u3bByBkuRRtUJz55M/Kka1bt4I5PGCOsL+aEkpLRTla02Pjxo2QZVm1XPZA8ZYCot301p5ssnLlSgBh62Q85PxqMMZoGz8F1q5di2Aw2Of4QhAh+aqwZ/duqlyZJIwxrFy1Cszhievmo6F4isHsLqxcae4dDhLDBJEGe/fuVVN+hfyFFXcBmpqa0NnZmeWemZ+2tjY1k0ScZPoAieF06XMLVBQh5ZVj+/btaG9vz3DPzA9jDCtXakKiqNfrpJCQ04QzkTjLly8HAEiFg/Rj7u0L4F3/PtzbF+jHtPPa9URi1NXVobGhAVJ+te7mE298IQiQ8qtRX38ABw4cyE5nOUBimCBShDGGvXv3QXblR+RopWwHvNDEruyNzh+q5ROl1F/pEd4CjR8cI+dXgjFGrhIpsHv3bjQ0HOpXSCh5ZWB2J5YuXZqlnpqXpUuXAqI9agtf7G6BrfMwxO6w24kmhmmMkyPezka88Y28xsz+7ySGCSJFDh8+jPb2tijLjxaItHPnzux0KofQclf2TPbOnHlUsCBNAoEA1q1bp1abs7viXqNZjCn4KHk04SUVhqskxhUSgggpvwb79+/XXa6I/jl8+DB27NihZouIqOwXD+YqgOL0Yfny5ZBlOUM9ND+asJXy+3BDCSHlq37xZt7hIDFMECmi1bzvGdwVeY5IHb1gQU/LpSBA8lXg0KFD5AeYIhs2bIDf7+89uAuhcRdttL2cAkuWLAEAyBFb+L2hCWayXCaOvtgo6H98IQiQCgehra1NTyVI9I2iKFixYoVaTMqV3+/1zF0A5vRixYoVps03TGKYIFIk3ja+QmKYC4wxrFu/Xk32HipzHYlW0Yusw6mxYsUKANH+ljGIdsp2kAJdXV1Ys2YNZG9Z3Ge3J3JIDGsCmuifxYsXAwCkotqErpcKa6M+R/TNtm3b0NraCqmgJm5awBgEAVJ+DY4cOWLaXVESwwSRIrpl2FMS9gfc9TUUVwG2bd9u2hXyQKCuri4q2XtPf0uFChakxYoVKwBB7DNlEqCmBWOMmdoXMNOsXLkSkiRBKhrc/8UAmNML2VuClatWobu72+DemR9JkrBs2TI1nWWoCE9/yAXVgCCSGE4QbTdILux956gnZg9UJDFMECmyefNmMLsLzJkX5Q8o55WitaWFim+kgeanqom1nv6Wcl4ZINooJVUKtLa2YuPGTZB8FYDN0ee1UsiNYtmyZZnoWk6wcOFCAGFrZCJIhbUIBgKm9rnMFGvXrkVnZ2fCVmEAgM0BKb8aW7ZsweHDh43rXI6gZ+row42qJ5rLFYlhgrAQLS0tqKurU4sT9NhG0goWbNy4MRtdywn0Mqu95RAVbZDyKrBt2za0tbVlrmM5wPLly8GYom/Pa8TNduAtBXN4sGTJEtrpSABFUbBo0WI1pVqPEtd9oQm7RYsWGdW1nEF3kShMzPKuoVnqyR2lb/x+P9auXaumtLS7E/4cc3gge4qxZs0aBAIBA3toDCSGCSIFNKEr98h0EHlMy4ZAJEc4R6sXzNX7NqhcQAULUkETAz3FRPxsBwKkgkFobGzEjh07MtlNU7J161Y0NR1GsHBwYr6WIZS8cjC7GwsXLqRFRz8sWrRITanWR4ngeJDfcGJoxUySsQpryAU1emVAs0FimCBSQBfDcXK0KnlqeVuyDKfG3r170dR0OCpHazy0lyH5syaOoiiqldfp1YM9+0OiAK+E0Vwk5GS28AF10VFUi8OHD1P+7D7Yv38/9uzZg2DBoH5TqvWEuQuguAuxdNkyBINBg3pofrTg2r4yzfSGJqC1NswEiWGCSAG9ele8rVDRDtlbgk2bNsHv92e4Z+ZHz3TQV5lVhCzwot2UE2+22LJlC5qbmxEsSNxyqQbGCGRRS4BFixapuYMTSfnVA81ySa4SvaM9g0kvNkJIhbXoDmX7IOKzfPlyQLRBzu87uDYecn4VIIim9BsmMUwQSRIMBrFu3Xo1pVofBQskSTLldlG2CUcy9yMoRBuk/Grs2rWL8g0niCa0khITdhckXwXWrVuH1tZWg3pmfhobG7FlyxZ1R6OfwMR4SIWDAEHUrctELKn6C2tovtm0sItPa2srtm3bpgbXivbkG7A5IPkqsGnTZtOVcScxTBBJsmnTJgQC/j591rSqPWSBSI5gMIgVK1dC9hSBOfP6vd7s6XwyTdhymdwWqFxUC0VRKKtEHySb+zaGUMaDzZs3U8aDOPj9fqxavRqytwTM6U2pDTmUQWUpPcdxWbNmDRhjkPOTd5HQkPOrwZiil3s3CySGCSJJNIHblxhWU4IJJIaTZMOGDeju6oKc4DazJoapelf/HD58OGXLJWU76B9tbFIWwyDLZV+sXr0awUAgZaswAEC0IZhfjT27d6O+vp5f53IEPYtPksGJkZi1jDuJYYJIEi1gS/b1MWHYnZC9JVi3bh35DSeBXmY1gTK2AMBcBVBcPixfvhyyLBvZNdMTtlwmLyYUdxEUpw+LFy+hcY5DIBDAihUr1B2NBMrX9gaVZu4dbUx6pgRMFu3ztMsRy6pVqwDRDiVOlqRE0cq4my2wmcQwQSSB3+/HN998A9lbCuboOwejVFCDYDCoB9sR/bN48eJQ8EaClglBgFQwGG1tbdi0aZOxnTM5YX/LFCyXggCpaDDa29vIDz4O69atQ3d3d8I7Gr3B3AVQXAVYvnw5JEni1LvcYNmyZYDNoZdiTxVacMSnpaUFO3bsgOSrBMQ0pKFJc8CTGCaIJPjmm28SzsEokz9rUjQ0NGD79u3qNn4SwRuUTL9/AoEAli1fDsVdmHAJ255Qntbe6S13cypIhYPR0dFBi44IGhsbsWfPHnX7Ph2hBoC5fFBcBVi1ajUUReHUQ/Ozfv16AEgpi0RP5PxKMMZMlV6UxDBBJEHCmQ4Q3i4iMZwYYReJ5ASFnF8NCCKJtD5Yu3Yturu6UrMKh5AL1EUK+Q3HsnTp0pQKQcSD/OBj0bbcpTQCuyKRCqrQ3t6G7du3c2kvF1i3bh0ALd4lPbQ2tDbNAIlhgkiCZcuWqS+9RCYM0Q7JV4Vt27ahqanJ+M6ZnJStazYHpPwqbNmyhca5F/SxTcFfWEe0Q8qvxo4dO3Do0CFOPTM/TU1N2LlzJ6T8yqQLQcRDy9W6cuVKDr3LDfQ4jYL0FxsA9GwJNMZh1q9fDwhC/Nz5SaJVYSUxTBA5SENDg+pTlV+V8EtPs/JQsEbfSJKE5cuXQ3EVpLSNL1FQTJ/olss0rT6Uyi6WcAQ+H6slbA5IeeXYtGkTOjs7+bRpclatWgVmd0HxlHBpTw4V9DFbkJdRSJKEjRs3QvaUpJQjOwa7E7KnGBs2bDBNwC2JYYJIkFS28WUqZZsQ69atQ2dnZ8qWS/Jn7Z2Ghgbs2rUr5IudnuWSFnexaGJY7qdiYjLIBdVQFIWCb6Fa3g8cOKAGdiVYNbE/mMMDxVWA9evXgzHGpU0zs3v3bgQCAd2iywM5rxzd3d3Yt28ftzaNhMQwQSRIKmJYcRdCcfqwbNky06yQs0G6AUhqFH4+li2jKPyeaFbcRNPV9QWlsotl1apVYDYnFC8fqyUQ8oMHWS4B6EFY6aT7iofsK0dbWxvq6uq4tmtGtmzZAgBQ8kq5tam1pbU90CExTBAJkPI2viBAKqTUX/2xZMmS9AKQBAFS4SC0t9M494SnGFZT2Q1CW1sbtm7dmn57Jqe5uRl79+5Vg2UFfq9T2VcOCKKpfC6NQsuqIfvSS6nWE80KSlk7gG3btgEAZC8/May1ZZZ5gsQwQSTAhg0b0NHRkZLlkvJa9s3hw4cjfLETT6nWE/IbjoUxhlWrVoM5vWCu1FKq9URzBzBbhSkj0BZevIUaRDtkT4laMdDiOx2qWOUT2BWJ9jsjMRyy3goCFE8RtzYVTzEAEsMEkVOEt/GTt67JBWrqL/Ibjs+KFSsAAFKaBQu0FGsU3BVm3759aGo6DMlXxc3fUrPeU6nx8BY+b6EGALKvDIFAADt27ODetllgjGHbtm2Q3QV8ArsiUDzFgCBaPr0aYww7d+6C7CpMyxgRg80BxV2AnTt38mvTQEgME0QCqNH4Nt2XLylsDki+SmzevBnNzc3c+2Z2NEuuXJhmNL7NAclXgQ0bNpqq8pGRaIKVR/5bDebwQnEXYs2aNZb3Gw6LYb7+rJFtmqlwAW+amprQ1tamWxm5ItoguwuwY+dOSwfRNTc3o729DYqnkHvbirsQzc3NppiPSQwTRD8cPnwYW7duVa1rttRWzlLhYDDGaAu/B4wxLF++HMzpheIuSrs9uaAGjCkUeBQinPaLX6YDtb0qdHZ2WtpqCQCbN2+B4soH7C7ubWsBY5s3b+betlnQrIqGiOFQux3t7WhsbDSkfTOwd+9eAKpw5Y02p+/Zs4d727whMUwQ/aBv46dRalVLsUZb+NHs2bMHR44cUXO0ctjG18pkkz+rysaNG8HsrpRLMPcG+VuqFrWWlmbIRgk1dwEgiNi9e7ch7ZsBbbFlpBiOvI8V0YSqMWJYbVMT3AMZEsME0Q/hEsypb+MrniIwhxfLli239JZcT8JlVvls4yveMkC0k2UYQGtrK+rq6lR/Vk7+whpktYQuUnkGHUUhiJDdhdi1a5dl5wwt7ZkRW/hAWKxZOb2aPsaGiGF1EW6GXMMkhgmiD9Rt/BUhP8mi1BsSBEgF1WhqOoxdu3bx6p7p4V6wQBQh+Sqxc+dOy/tna0LVCH9WxV0I2ByW9mfV/o6NsloC6jZzR0cHDh8+bNg9BjL19fUAAMXpM6R9xZUfdR8ropVWZwaMsfZ7a2ho4N42b0gME0Qf7Ny5U43GL0h/G1/LlkCuEiqMMaxevRqK08d1IpYLKNsBECmG+Wc6gCBA8pZh165dli0ZHN5eLjLsHppF1KquEvX19WB2N/dMEhravGN5MSyIYA4397aZwxu+xwCHxDBB9IEegMTBcimTP2sU+/fvR3Nzc6hgAb9tfNmniuH169dza9OMaCmjFI6J9CNRvCVgjJkiOMYIdKulyxirpdq2ark8ePCgYfcYqDDGcKC+3jCrMAAwuwsQ7ZYXw4rDy7VojI4ogjm8JIYJwuyEU1OlL4aZ0wvFVYA1a76Boihpt2d2jKssVQoIoqWDuwDVmshsTt06wxvNPcAseUR509DQoFosbU7D7qFZLs0gJnjT3t6OgN8PxWnM8wsAEATIzjzLZpNQFAWNjY2GjrHi9KKhoWHA+73nhBh+9tlnMWzYMLjdbpx00kn9Vvp66qmn8O1vfxsejwe1tbW444470N3dnaHeEmaBMYY1a9ao2/icrD9SfhXa29vIbxjGiWG1elexpat3SZKEPXv2qL69nIPnNORQ4JhVt/APHjwE2ZFn2PgCgOLMA2BNMdzS0gIAqpuEgTC7Cy2trQNerBlBR0cHZFkGs3sMuwezexAIBNDV1WXYPXhgejH81ltvYcaMGZg5cyZWrlyJsWPHYsqUKb1OHm+88QbuvvtuzJw5Exs3bsTLL7+Mt956C/fee2+Ge04MdPbu3atu4+dXcmtTa8vq/qxASAyLNkMCkGRfBQKBgGWrS+3fvx+SJOmC1Qi06HMrLuz8fj9aWprBQmLVKFjIYmeGACTetLa2AsiEGHZDliRL+r6HFxz882RraG1rv8+BiunF8JNPPombb74Z06ZNw5gxY/DCCy/A6/XilVdeiXv9woULccopp+Caa67BsGHDcO655+Lqq6/u15pMWA/N51T28RTDqj/runXruLVpRiRJwvbt29UcraKNe/ta0NjWrVu5t20G9u/fDwDc8wtHYXeBOTz6vayELiIMCDqKQrQDNqclM6OEx9g4oQaExbZ2PyuRiQWH1jaJYQMJBAJYsWIFJk+erB8TRRGTJ0/GokWL4n7m5JNPxooVK3Txu2PHDsyePRvnn39+r/fx+/1obW2N+iJyn02bNgEAZB+/1FTM6QOzu7DJwvlZATXvpCRJkL0lhrRv9WT6WsCVkcFHAKA48lB/8KDltpjb29sBAMxmrFADAMXmNEU5W95oYwyjx9iu+nxbcYwzaRke6IsNU4vhxsZGyLKMyspoy11lZWWv0aHXXHMNHn74YXzve9+Dw+HAyJEjccYZZ/TpJvHYY4+hsLBQ/6qtreX6cxADk82bN6vb+G6O2/iCADmvDHX79lly8tXQMx14jBLDRQAEy7pJaG5ihothlw8Bv3/Av+h4o/3tMrtxwXMazO6y5Fzh9/sBAMyAnaNImGiPup+V0F1DDHyOtb+Rge6GYmoxnAoLFizAo48+iueeew4rV67EP//5T3z88cf47W9/2+tn7rnnHrS0tOhfZigtSKRHMBjEtm3bIHtKAJHvn4nsVbfwt2zZwrVdM6GXWTXIMgzRDtldiO3bd1jOagmELcO8Aj97Q/OZtVrqL10MZ8AyzGxOPdDJSujiNCRWDSMktgOBgLH3GYBoQW3MwDHW2h7oAXQGP2XGUlZWBpvNFjMRHzx4EFVV8cu7PvDAA7juuuvwk5/8BABw3HHHoaOjAz/96U9x3333QYwjfFwuF1wu4yc9YuCwa9cudRu/hH/BAiXCn/XEE0/k3r4Z0DIQGBrg5SlC+5FdaGpqQmmpMbl2BypaxTLmMC5KHICekslqFdJ0EWFQMYhItHt0d3cjL8/YgL2BhJbhyUihBgBMsK5lWM+iZeQYh9oe6Bm7TG0ZdjqdOPHEEzFv3jz9mKIomDdvHiZNmhT3M52dnTGC12ZTV4a5akFqa2vDs88+a+nE4smiiTXFALFm9ZRUAFBXVwdmcwIGBm4ooeAxKwZ4tba2qr56RiTSj8AswTG80VP2GbyFDwAQbNH3tAjBYFD9j8HPsJUtw/qCw8BFXeRibiBjajEMADNmzMCLL76I1157DRs3bsQtt9yCjo4OTJs2DQBw/fXX45577tGvv+CCC/D888/jzTffxM6dOzF37lw88MADuOCCC3RRnGu8++67eOedd/Dss89muyumQRfDBpRaZa58QBAtK4YZY9i/fz8Ul4GZDgAwl3XFcHNzs6FBMRpmSZvEG104GS3UAN1NSxeHFkE3ThmYxznu/SxE+Dk2UPuE2h7olndTu0kAwFVXXYWGhgY8+OCDqK+vx7hx4zBnzhw9qG7Pnj1RluD7778fgiDg/vvvR11dHcrLy3HBBRfgkUceydaPYDhajkqrBhOlgpGWYQgiZHehWiGMMQgZmuwHCk1NTfD7/VBKagy9j1bKtq6uztD7DDQYY6pl2Mk/f3PMvSyalkq30mZADDPB4mLYaELzrxXFsPZMMSOf41DbA31nw/RiGACmT5+O6dOnxz23YMGCqO/tdjtmzpyJmTNnZqBnhFnZs2cPmN1lWP5FxV2EjiM7LenPqrnrKM58Q++jieEDBw4Yep+BRiAQQDAYBPNmINNBKIBMT4NlEbQXu6EiQiN0D6sF0IXFqbWMBZlEf6YMfI6ZScSw6d0kCII3jDHUHzyopqUyyGqrhKL8rRaFD6gpEYFwdS2jYA5rBneFU1JlwNYhmmMLlDfh3cZMWBPVe1htBylzWNcynJEdDpMs5kgME0QP2tvb0d3VBcXAUqtaSqreyobnMuFMB8aKYYgimN1tOTGckQjxEMxmzUh8XZhmTgvHzXSUy+gxPEwx9kah9u32nNgoT4qwZdjAhVaobRLDBGEyNIHKDBTDWjEEK4thxeC0X9o9Gi0mhjNrGTZH2iTeaMJUyIAaFixqGdbFaYbEcK4G0PeFZg1nhrqimMPyTmKYIHqQCTFsZcvwkSNHABifA1e9hxcd7e2WSpuk/6wZTPtlpfEFIi3DGXjBh+5hNcswiWHjCWfsMPAmJlnDWeuviyASoLm5GYCxlktNCGr3shLhUrbG5RjW0FJ/WSnAS1E08ZCBt1BIFIbvaQ0cjlBeVpaBrd/QPfR7WoSwGDZ2wSFY2E0ik5BlmCBMhpYz1cg8rVbNzwpECNOMVO9yRt/TAujCNGPb6oLlxLDTGcrUoWRADIfuod/TImjiXzB6wWHR8Y2G3CRIDFuAgf4QDjTCYthAy6VoA0S7JcVwW1tbqDqa8WKN2Z36Pa2CJkwz9lcvWFcMGy7UAAghseZyGV9EZSCh/7xGLzgsOr7RGDlbmMPNZ2D3jiCyQCYswwCg2F1oabGeGG5vbwcTM7Tla0HLcDbys1ptwR22DGcgdyqTIQiC5XxaM7Xg0Nq3omVYF6jGa+EBHwBKYtgCDPSHcKDR2dmp/sfgbXxmc4bvZSG6u7v1lFxGo2VUsFLqr8zmwFXvM9CtPrxxu9VdIyEDbhKCIsHlclluHs/YgiPUvtV8soGwNjA2K4o5sqFYawYjiATQ0kQZbr0UbfD7rZWSCgD8/gAgZChYxcJiWMiUtZZZVwxDzoBlWJbg8RifeWWgobktGL3g0NrXf6cWIjMLZxLDBGFKdOFkcGoqJtrh9/stt8UcCAbAMpH2C9DvY6XUXxm1DFs07ZcmTgUlaPi9BCVoaTGcKcuwFX2GM5Ii0CRzxMDuHUFkAb/fr5aQNLJEJQCIdjDGEAwa/0IdKMiyDFmSMpMDF7BkueCMVe5SbxJ9T4sQdpMw3jIsKJIlrZZkGTae8FxBYpgS6xFEDwKBQEbEGosQalYJ3pCkkHgweqERgoXuM9BLgfIknAM3A2I4lEXCav6WuqVW7nsh6133HoSg6golSOq/YmcT8lb9HczhRuexl/R7L0GxtptEpizDVpmDI8mkm8RAXzCTGCaIHsiyrIsoY7FewQJdlGYwB27UfS1Axip3AXpBCKsVLPB6vQD6d5MQgt0Qpa7oY2AQpC4k9NtRFECR9ftZiUTFcLoLDkGR4HQ6B7xPqxGEs0kYN1doRU3IMkwQJkMVTpmo3mU9q2U4B26GXjwWHONwsQLjxbB2D6tZhp1OJ2x2O6R+LMNpExLbVrYM9+cmkf6CQ7akvzAQttYaGmxrEjeJgd07gsgCiqJkxnJpwVK24epoGZp6BAtbhjNRHc3CpWy9Hg8Eg8Ww1j5Zho1DYJLlxXAm3CQG+hwxsHtHEFlAluXMWC4taLXMfOYM6219ZtZn2LoFC7xeL1qaOwy9h0CWYeNzOZNluM+5Im2/91Db5DNMEGbEgv5jRG6gV+5KQESk7W8Z8hm2mpsEoApU4XCLsTexsGXYbrerW+tGV6BTZEsu5oDEskmk7YbCKICOIExJpq2XVssznFnMkfCdJzabTf15ExARPPwtAWuKYa/Xa3ieYSu7SQDqwi5odGo1RpbhTATQDXQxTD7DBEFkjGyJUiuJYUEQ4HA4M1Iq2OpiGIpsrJCQresmAYR2OTLgJmHF5xeIzDxjfADdQPcZJjFMEFnGSpbhsCjNXKng6PtaA4fTkRGfYc3qY8Vt5kRzDaeFhX2GgdBzZaSbBFMApljy+QUis0kYOFeQZZggiESwklAL57XM1ALAHGl9eJMRixpg6YIF4ZLMxmU70Nq2spuEoVX+LFo0RiMjOclNIoYHtt2aIIicQhOlieS15FG9yyw5LnnjcjohdLQbfh8tgM7KYhiygWI4ZHW2YqlgQBWpxlotrfv8Ahkq3U5imCDMSaYttVayDCeT1zLt4K6I+1hNDKsWNSrHbCSaQBWUoHFOPyGrqFXdJOx2e0aCuwa6P6tRZDKAbqCPsbXeEASRKBby480kmSj/GYlZIpl543A4DE9JBcDSljXdTcJAn2GrB9A5HA5jA0EtHAAKhAVqJnyGSQwThBmxkLU2kySS15IrJslxyRvVMmy8GBYsXHRDd10w1KeVLMOZ2MK3qhjOSIEeEsMEQRDRCIKgCtMMWYbNMhHzxvAofA0LW9bCbhLGB9BZNQ+uusOhGLZ4NssWvlGQm0QYEsMEQWQUm81u7LZcJBZ1k1DFMDN80WHlCnSZtAxbNYAuLKAM2kkyiVAzCu3vVjA6fR0G/hxBYthCWClQKx2oAp2x2O2ZswwLJkn4zhvdbSEDBQsAa1ouM2kZtqoYNtxySWJY/Y+RwbYm2T2y5hNgUawmulJFHaf+Fw7ppv5ioXtY7fdidziAQKYsw+aYiHkT9gWUARj4s1s4gE5fABhqGZYhiKJlxZr+cyuKMaY7EsPqf8hnmMQwQfSEMZaIFk4/9ZcQcT8L4bDbAb+BAiISi6b+0sSpoCiG1voTTGL1MQJNDBsZqCgoElwul2V39cICyhixZtVsMxqZcJMwSy5ycpMgiB4oioKE1HDaCBH3sw6GJ9KPxCRWCd6Etz8NXnSY5EVnBJmyDLuc1nNB0TA89ZdF5weNzLhJmGOMSQwTRA8SdZNIH2u6STgcjsyUCkbYKjHQJ2Le6JZhozNKWDi1Wtj6brRl2Hpjq6FbbI0Saxa3DOt/t8xAv3dmjrgCEsME0QNZljOkhYXw/SyEw+GAYNC2ZwwmiWTmTaYC6KycZzhsGTZSDMsDXkQYib6IJcuwIWRiQaftnAz0OYLEMEH0QJZlMCEDfxqChd0kMmUZtqhPa8aySZCbhMFpqUgMA6A8wwaRkXnCJAtmEsME0QPVUpuJPw0x4n7WIZNuEmaZiHmTKTcJsgzDUJ9hQZEtObYamvuCYTtJFq1QqZGZIFBzzBEkhgmiB6qbhPF+EsyibhJqQQjjqkpFYVHLZabzDFttfIEMjDFjgCJZcmw1yE3CWDLhMwwmQxTFAb87R2KYIHqQKTGMkCuGJGUozdgAIRO+lhqCSfzVeJOJghBa+06nNVN/Ge5vGRJqVnt2I8lUAJ1VxbAoinA4nRAMNMgIsgSXa+AXjSExTBA9kDLsM2w1y7Auho20RmhYtEJaJi3DVs12YLfbIYiivvvAHYvuakQSzoNrbJ5hq4phAHC7XIaXFHe7B/78S2KYICJgjEGWJN1qaygWtwwLsvE/t2BRMRz2BTTYMswky41tJE4Dg0HN4mtpJJlykxjoW/hG4nK5DC8pboY5IifE8LPPPothw4bB7XbjpJNOwtKlS/u8vrm5Gbfddhuqq6vhcrnwrW99C7Nnz85Qb4mBjG6lJTFsGGHLcAYs4hZ3kzC86IbFU385nU7jrO8khnuUFTcAkxSEMBKPx2O4GNbnowGM6Z+At956CzNmzMALL7yAk046CU899RSmTJmCzZs3o6KiIub6QCCAc845BxUVFXj33XcxaNAg7N69G0VFRZnvPDHgyKQY1lwxrOYmEenPanQInebTKoo5se5PmIxZhk3yojMKp9MJBA0aY2bNtICR6CLVIJ9hqxbliSQTYtjj8RjWPi9M/wQ8+eSTuPnmmzFt2jQAwAsvvICPP/4Yr7zyCu6+++6Y61955RU0NTVh4cKF+iQzbNiwTHaZGMBoVtrM+Axb0zKsTYyCHDT+ZiaZiHmTKcuwWV50RuFyuSC0+Q1p26ouPpGEfYaNDVK08oLD7XYDSlDNXsI7EDaUEcUMc4SpzSWBQAArVqzA5MmT9WOiKGLy5MlYtGhR3M988MEHmDRpEm677TZUVlbi2GOPxaOPPmo56xwRH12Ykhg2jIxt4UMV3B6P9SyXYeu7kQUhFHKTcDqNE2rkJmF8IKhF3agicbvdqmg1wi87NL5m2D0ytWW4sbERsiyjsrIy6nhlZSU2bdoU9zM7duzAv//9b1x77bWYPXs2tm3bhltvvRXBYBAzZ86M+xm/3w+/P7z6b21t5fdDEAOKTIphZlExHLYMZyKAzhxWCd7oLx8jre8hgWKGF51RGCmGBcomYXw2CYXS13m9XvU/ShAQ+RYfEZRg9D0GMKa2DKeCoiioqKjAX//6V5x44om46qqrcN999+GFF17o9TOPPfYYCgsL9a/a2toM9pjIJGGf4czlGbbaroQuThXj3SQEJWhJsaYvOIz0BQwJbSsuNjQogM5YwpZh8ss2CkONEyaaI9K2DEuShM2bN6O5ubnXl/ppp52W7m3iUlZWBpvNhoMHD0YdP3jwIKqqquJ+prq6Gg6HI6r84tFHH436+noEAoG4E88999yDGTNm6N+3traaShDrfrCZqPhlcrRnOJM+w1YTw5qVwHCfYUXdxvf5fMbeZwCSEb9sxTwvOqPQxbAR/pYKCbVwhTRjFxxWdvUJL5yD3AOaNYFthjkiZTHMGMODDz6Ip59+Gm1tbX1ea9TL3ul04sQTT8S8efNw8cUXA1Atv/PmzcP06dPjfuaUU07BG2+8AUVR9AjzLVu2oLq6utcVuMvlMvUfS0dHBwBYskpUsmQ2tZo1i25kTgybZ4uON263W/17N9D6rv3+rDi+GlFiTeDrdSiEitKY+d2TLkb7vlu1QmUk+t+vAfOxYKIFc8p/vb/97W/xyCOPoKioCNdffz0GDx6clfQkM2bMwA033IAJEyZg4sSJeOqpp9DR0aFnl7j++usxaNAgPPbYYwCAW265Bc888wx+8Ytf4L/+67+wdetWPProo7j99tsz3vdM0dnZCcB6oisVFC2FT0bcJIToe1qEvLw89T9ywND7CKH29ftZCEEQ4HZ7IBm44BBMFBxjFFEBXiLn9x9ZLY13k1BowWHoLpIV3CReeeUVDB06FMuXL0dpaSnPPiXFVVddhYaGBjz44IOor6/HuHHjMGfOHD2obs+ePVE5Rmtra/Hpp5/ijjvuwPHHH49BgwbhF7/4Be66665s/QiG097eDgDo6OjMck8GPuEFQwYC6GB1NwmjxbC1LZderwcdHQZa3y282NDQxJqgyPy3mMlnOAOWYQoCNVIMm2kOTlkM19fX45ZbbsmqENaYPn16r24RCxYsiDk2adIkLF682OBeDRw0N5aOzo4o9xAiFt2vOhMeJUKPe1oEzYfXeDEciLqf1fB6vRBaDxvWvpledEZhqOWSxHDYYmugZVgURUsX3YjKJsEZS2STGD58OKUYMwmaGGaKortMEPEJC9PMqWESwwYhWVsM5+Xl6S8jIyAxHGEZNiDAi3yGIyzDBqVhtHoFRcDgGA4TzREpi+FbbrkFH330EQ4dOsSzPwRnlB4CWHOZIOKTDf9dq/kMO51OOJ0uCJLRlmE1N7hVxbDX61VfRgblaDXTi84owpZLA7bxyTIcUaDHoEWdIsFtAn9WIzFSDJsp/WLKewMXXXQR/vOf/+Dkk0/Ggw8+iPHjx6OgoCDutUOGDEm5g0R6dHR0RFke29raek07R2QYwZqWYUAVqF2dBothydo+reEocQmw8xdUZtoCNQpNDBuRz5l8hiN9ho2zDHvc1lwsaxiZ991Mc0TKYnj48OEQBAGMMT1zQzwEQbBcha2BRE9LMFmGBxAhEWzFlHcFBfk43Haw/wvTQLMM97ZIz3W0RYCaP9QAMRyy+lh1sQFkxjJsZTcJu90Oh8MBmdwkDIPcJFRSFsPXX3+9JV/iZkMTv0wQITCFxHA/ZCO40Ip/Rz6fD4K0x5hiBSE0y3B+fr4h7Q90IrN2MBggWEM+32Z40RlFVGo13lDaLwCqdbhbMsZNQpCDln5+gcy4SZhhjFMWw6+++irHbhBGoRXcYE4fBH+r/j0Rn7AwzYTrgnoPK2b3KCgoUH1ZFQmwGVNhiyzDWj5n44QEYI4XnVEYuY0vkBgGoG7jt7R08W9YkQGmmMKf1UgykU3CDGNsvbewxdDEr+JUH3jKJtE3ujDNnBa2pGVYs9YKkt+we2htk2XYWDFMbhIwplwwuUkACKUINCR1nXlKBRtJOM+wAWMsS6ECQAPfFYVLcr2vv/4aq1evRmtrKwoKCjBu3DiccsopPJom0kQTv8yhvhjJMtw3YTFsfIYHIXQPy1qGoVpvGYwJYBEkP5xOc5dSTwfdZ9igFHaCHIDT6bJ0jlZjA+jIMgyoYs3ILXwzCDUj0f2yDbIMu9xuU7zj0prFFi5ciGnTpmHbtm0A1Kh4zco1atQozJo1C5MmTUq/l0TKdHWp20vMoa7+uru7s9mdAY/DEdqyz4AY1u6h39NCZMQyLPtRUGBNqzBgvGUYchBer7WtaoYWhWAyRFG05PwQicfjUceXKYDAT1SZKdOB0Xg8HnQHjQmgM8sckbIYXr9+Pc4991x0dnbinHPOwZlnnonq6mrU19dj/vz5+OyzzzBlyhQsXrwYY8aM4dlnIgk08auJYU0cE/Gx2WwAwlZbQwndw4qWNd0ybLCbREFBmWHtD3TCPsMGWYaVILzeQkPaNgthy7ABRTdkCS6Xta2WQGTqLwmwccyKQj7vOl6vF81H+AfXC4oEj8ccqetSfgs//PDDCAQCmD17NqZOnRp17q677sKcOXNw4YUX4uGHH8abb76ZdkeJ1NDEsGJXJ1W/3zjxkQuELcMG+AD2hCzDxolhxiBIAcv6CwPGW4ZFOWjZgiYaxlqGJbhc1s0xrBF+jiUwjmJYIJ9hHbfbDUFp4d6uKobNMb4p7zksWLAAl19+eYwQ1pg6dSouv/xyzJ8/P+XOEekTCISsQnZ10iYx3De6lTYDVeG0Eq5WtAwXFqoWRcPEsBwAwPT7WBFDxTBjoS1Qa1vVjM4mYXV/ViBCrHJ+js1UHc1ojPTLNssznLIYbmlpwfDhw/u8Zvjw4Whp4b/aIBJHE7+MxHBC6C83loFCMaGtVbNMFjzRLcOyMc+j1TNJAMamTFItoczyYthIy7CgyJYPngMiFnW8n2Nyk9Bxu90hv2yOaZQUxVSp61IWwzU1NVi8eHGf1yxZsgQ1NTWp3oLggC6GQ9tLJIb7xm63QxBFY5Lo98DK0eJ67l+DLMOaGLZqjmEguugGbyjHsIqhPsMkhgFEpv7ibBkmNwkd3SDDc1GnmCtbR8pi+MILL8SCBQvwwAMPxGQo6O7uxsyZMzF//nxcdNFFaXeSSB3NTYLEcGIIggCXy2VMXsuekBg2zE1Cszhb2ac10teSOxSJD8Bgn2FykwDQI4COJ+QmoWOEu49gsp3PlJ0VH3jgAXz00Ud49NFH8Ze//AUTJ05EZWUlDh48iGXLlqGhoQEjRozAAw88wLO/RJLo4le0A6I97ENM9Irb5UKH33gxbGXLsNfrhSiKxuXAJcuwuqgTRWMqS5GQAGBgnuHQFrMV54aeGGcZpmdYI7yo47jDYbL3W8qW4dLSUixevBg33HAD2tvbMXv2bMyaNQuzZ89GW1sbpk2bhsWLF6OkpIRnf4kk0cSwZ/McMKaQZTgBvF6vcblZI7HwVrMgCPD5fAZahlWRbWWfYUEQ4HG7DS1YYMVnNxKHw6Hm1uftJsHMJSSMxKggRVrQhTHGMmyuZzitMPaysjK88sor+Mtf/oJNmzbpFehGjx5tyXRRAxEtr7CtqwlMECnPcAJ4PB4IymHD72P1yTg/Px/NDc2GtE0BdCoejwftHUYE0Fn72dXQ3Kok3kLNZELCSMJuEpyf49AYm2Ub30iMsQxbxE0iEofDgeOOO45HUwRnossvC+gIlWcmeke3DDMGhCoqGoHVKyDl5+dDPHDQkLYFiSzDQOhZbuW/sNP8kK0uhgHA6XKhI8DZMhwSEiSGI6yWnH3fBRLDOka4+2htOZ3myJU98AtGE2nR0dEBhpCgEwT1e57pU3KQvLw8tSCG0YU35CBsdrtpJgve5Ofnq9YZIzJ3hNwkrBxAB2i7HMbkwNXatzpGBNySZTiMYZZhmcSwhv4O4vnOY+Za0CVsGT7rrLMgCAJee+01DB48GGeddVZCnxMEAfPmzUu5g0R6tLa2qvXcmQwIAmQpiK6uLstaIxNBK2MryEEw0biCGIIchC/PumJNE6qCHAAT+Yoq8hlW8Xg8qojgvcthcRefSNwuFwSFcylbsgzrhP1Z+S6aaUEXxoisKGazDCf8pl+wYAEEQUBnaJt9wYIFCX1OMHCbmegbWZbR3t4OCDaAAQwiBADNzc0khvsgslQwcxg3UQqSHwVlpYa1P9DRrbZyAOA8zlpqNas/5263WxXCTAYEfgs7wWQ5RI3E5XJx30Uiy3AYw9LXKRJsNpslK4D2RBOsXBccobZyTgwrPcrT9vyeGHg0NzerLhFiyBtGUP89cuQIFUPpgyiRZiCiHLD0Nr5uGZYC4O24I0gBeL1e2Gw2zi2bi6hk+jx3OcjfUsfpdBqQWs1cQsJIDMsmoUi02AihP2cWFsPkM5zDNDQ0AACYEC2GteNEfMIizcA0dIoCKJLukmFFIt0keCPIAeRZeKGhYVjwEQXQ6bhcLvXFzzMWg5lLSBiJcZZhqvCnoVuGOe5wCCZ7hlMWwzfeeCM++OCDPq/56KOPcOONN6Z6CyJNDh06pP5HE8MhC/HBg8ZE8OcKupuEgZZhbRvfykUhIn2zeaP6Y1t3oaGhFywwYIsZIMswECHWeAoJcpPQMarktUAV/nT0VLhkGU6eV199FatXr+7zmjVr1uC1115L9RZEmuzbt0/9j6huFTNB/beuri5bXTIF4VLB3f1cmTpa21YWw7o/L+9FB2OqZZjEcLSbBEfIZziMkVvMlK/fuMImApNNI9SMxohsEppl2CzPsKFuEt3d3eScnkU0MayJYM0yrItkIi5hMWycmwSVCzbQMsxkgCkkhhHpJsG7lC1ZhjUM2WKmbBI6giDA4XAaYBkmNwkNTbAaEUBnCTHcW6YIxhj27NmDTz75hAK1ssju3btVFwlRCyISoLjysWvX7qz2a6BDYjgzhMUwX8swlQoOY5RlmNwkwoQtwxyDyk3mb2k0TqdTL1HNDbIM6+iClXF8hkN/D2YZ46TEsCiKsNlseoT2Qw89pH8f+WW32zF8+HCsXLkSP/zhDw3pONE3iqJg+44dkN1FUcdlTzGamg6jubk5K/0yA4WFhQAy4yah3cuKGGYZDrVn5UwdGoZF4ssSnE4nRJFisI3wtxRMZlUzGqfTyddNgjFAITGsYYSrj9ncJJLyYTjttNN0a/CXX36JIUOGYNiwYTHX2Ww2lJSU4KyzzsLNN9/MpaNEcuzfvx/dXV1QSgdB7DqiH1e8JUDzHmzbtg0TJkzIYg8HLrplOGigGA61XVRUZNg9BjrhylK8LcOB6PYtjJGWYbIKq4TdJCR+KQJNFnxkNE6nA0IHx526kAXULELNaHQ3CZ6WYWYuy3BSYjiy0IYoipg2bRoefPBB3n0iOLBx40YAgJxXFiWG5bwyAMCmTZtIDPeC3W5HQUEBjhgphiUSw0ZZhrX2yGfY4BytblpsAJGWYXKTMAqHwwHBgPElMaxiZDYJs4xxytFtVHRjYBMWw+VwNG7Vjyt55VHnifgUFRWh5YBx+ZjJMhz26eXuJqGQz7CGcdkkZHjIMgwg0qoWX0g899xzcY///PZf9dqmJvzMIiSMRvUZ5u+GQosNlbDPMMdn2GQLDkr1kKOsXbsWEG2qW0QEzOGB4srH2rVroSgK+fz1QnFxMfbs3av6lhlQUlyQugBY22fY6XTCZrNBMsgyTG4SiRXdSE2skZuEhiGp1UwmJIzG4XD0uYWf9DNMbhJRhMsx8w+gM8sYpyWGZVnG22+/jc8//xz79++H3x/r0yMIAubNm5fObYgkaW9vx7Zt2yD5qiIySYSR86vQ2rgVu3btwogRI7LQw4FPUVGRmq9W6gZz8BdVQrALeT6fpS0TgiDA4/EiYJAYJsuwQZZhxgAlSGI4RH+R+Lfeemv8D9r7mFdIDEdht9s5ZzqQw+0S/VqG03mGzfKOS/lJ6OjowLnnnovFixeDMQZBEMAiylFq3/eWfo0wjm+++QaMMcj5lXHPS/lVcDRuxerVq0kM90JxcTEAGCaGRakbJWVl3Ns1G16vB60tXb2eT8VqCRLDOon4DCf9omMKwBiJ4RD9uUmkgmCytFRG43A4wiWv42iKZJ9hgSzDURiRZ9hsGVFS3iP/3e9+h0WLFuE3v/kNGhsbwRjDQw89hAMHDuCtt97CiBEjcMUVV8S1FhPGsmLFCgCAXBA/x7OcXx11HRFLSYnqXiIEexdqKROyOGv3sDJer9eAghAkhjUMCaCjHMNRGBNAp7ZFlkuV8DhwytdB4xtFf5bhlGDmsr6n3Mt//vOf+O53v4v7778/6nhlZSWuuOIKTJo0CWPHjsWf/vQn3HPPPWl3lEiclStXAqIdcihYrifM5YPiLsTq1ashSZJpHtZMoluGDRDDgtQNMGbp4DkNr9eri9d4pLI9R24SYXTBynHBQT7Z0ejzJ1chQZbLSPQxVmTAxiHOhcRwFHa7HYIocnVFERQlXErbBKT8VO3Zswff/e53ww2JYpQVePDgwfj+97+P1157Lb0eEknR0NCAnTt3QsqvjusvrCEV1KCjowObNm3KYO/MQ1gM80+vprVJluGQYFVkvlY1EsM6mmDlaRmmUszRGJGjVTCZVc1ouFdII5/hGJwOB99yzCar8Jfyk5CXlxeViaCwsBAHDhyIuqaqqgp79uxJvXdE0ixfvhwAIBUO6vM6qXAQnIc2YtmyZTj22GMz0TVTEXaT6OzzupQi8UNtkhiOEKxKEBBdXNoky3AYp9OpWnzITcIwjCxlS5ZhFa3qrcAULo4S5DMci8PhBGS+eYYdbvOMb8qW4aFDh0YJ3WOPPRb//ve/deswYwzz5s1DdXV1+r0kEmbp0qUA+hfDcn41IIj69UQ0mlAVDSjJrBXcIDEcYbnkuY2v0Da+hiAIcLlcfaZWS7pNEsNRhLfw+foMC4Kgi0CrE3ZFIZ9ho3A6HXxzOVvFMnz22Wdj1qxZus/pDTfcgJ/85CeYNGkSzj77bCxcuBCrV6/Gr37VR9Q3wRVZlrFs2XIornwwV0HfF9sckHyV2LRpE1paWiyd7zYeYTeJvi3DKfm0htrU7mFl9MIbSpBfKVs5CJvNZqqJ2Eg8Hg86OjlahslnOAojskmAyaofp0n8LY2Ge4BXSAzTYiOM0+mE0NHOr0GmmGoOTtkyfPPNN+POO+9EQ4NapevGG2/EbbfdhjVr1uCJJ57AokWLcOmll+Khhx7i1ddeefbZZzFs2DC43W6cdNJJCVs733zzTQiCgIsvvtjYDmaITZs2ob29DVLBoIQKRciFg8AY010riDAulwt5eXmG+AyL5DOso5dM5hzg5fF4SUiE8LjdfQYpJotmGSYxrBK2WvL0GVZgI6ulji5aOY0xuUnE4nQ6uRbdEBRzWYZTFsOjRo3CXXfdFeUG8fTTT6O+vh6LFi3C/v378c477xjut/fWW29hxowZmDlzJlauXImxY8diypQpOHToUJ+f27VrF+68806ceuqphvYvk4RdJAYndL12HblKxKekpASiEdkkyGdYxyg3Ca+XhJqGx+Oh1GoGYoQYhqLAbiMxrKGNMbcgRXKTiMHh4O8mYabFBvdavOXl5TjppJNQVVXFu+m4PPnkk7j55psxbdo0jBkzBi+88AK8Xi9eeeWVXj8jyzKuvfZa/OY3v8mpohNLliwBBBFyQWJ+2oqnGMzhxZIlS6Dw9HfLEUpKStSyybz81EJo6dootVqEmwRnyzAFz4XxeDzcxxegAEUNo/IMO5zmERJGw33BQW4SMaiWYZ6LZouL4UwSCASwYsUKTJ48WT8miiImT56MRYsW9fq5hx9+GBUVFbjpppsSuo/f70dra2vU10DjyJEj2Lx5MyRfJWBL8AEUBAQLB6G5uRlbt241toMmpLi4WC+QwRMh2AWfL99UW0hGEZVNghOqZZiEmobH41Ff/pzEmuZyQZZhlXCmA55WNYWslhFwD1Iky3AMTqczXOUvXZgCMAUuF58MQZkg4SfhrLPOSukGgiBg3rx5KX22PxobGyHLMioro8sOV1ZW9po/96uvvsLLL7+M1atXJ3yfxx57DL/5zW/S6arhLFu2DIwxSEW1SX1OLqwFGrdiyZIl+Pa3v21Q78xJZK5hniWZRakbJeXxC6JYDe6WYUUBFDnsi0yEfXt5pa+TyWc4EkNSqzEZDhJqOvx9hlXBR2I4jC5cmQIIaVrMQ/mKc1IML1iwIKUbDKQglra2Nlx33XV48cUXUVZWlvDn7rnnHsyYMUP/vrW1FbW1yYlOo9Es4VJhcv2SCmsAQcTChQtx/fXXG9E106KLYakLAKfMD0yBEOymTBIhuPsMU1q1GCLHmNnTfzlRarVojAmgYyTUIiCfYePRdyqZDCBNMRzaJTHT7mfCT8JA9CktKyuDzWbDwYMHo44fPHgwrs/y9u3bsWvXLlxwwQX6Me3nstvt2Lx5M0aOHBnzOZfLNaBXOIFAAIsXL4HiLgTzJJkizeaElF+FTZs24fDhwygtLTWmkybEiJLMguQHwEgMh9DdGTiJYfJnjUUTrYIi8UlfR6nVouAu1ADVMmwif0ujIZ9h49GEq6DIYGkOi1bJzkzPsKl9hp1OJ0488cQoNwxFUTBv3jxMmjQp5vrRo0dj7dq1WL16tf514YUX4swzz8Tq1asHnLU3UVavXo2urs6kXSQ0pKIhAICvv/6aZ7dMjxElmTX/YwqeU4nMM8wDEsOxhN0k+ATHUGq1aIxwkyCf4WiMEsM0xmF0Ky6Pksy57CbRH01NTejo6Mi4oJwxYwZuuOEGTJgwARMnTsRTTz2Fjo4OTJs2DQBw/fXXY9CgQXjsscfgdrtjSg9rosTMJYm/+OILAECwaGhKn5eKhwJ7FuPLL7/EhRdeyLNrpkZ7NlQ3CT5owposwyr8fYZJDPeEvysKuUlEYlQFOjNZ1YwmbMHlk9lHIDEcQ7SbRHoIJnSTSMsy3NLSgl/84heorKxEeXk5hg8frp9bsmQJzj//fKxYsSLtTvbFVVddhccffxwPPvggxo0bh9WrV2POnDl6UN2ePXtw4MABQ/uQTSRJwpdffgnmzIPiq0ipDebMg+yrwMqVK9Hc3My3gyYmLIb93Noky3A0UcFdHBBoCz8GXbTysgzLlE0ikrDVkld1NAYoMm3hR6C7olA2CcMIu0lwmCcU84nhlJ+EpqYmnHzyydiyZQvGjx+P8vJybNy4UT9//PHH4+uvv8brr7+OE088kUtne2P69OmYPn163HP9Bf69+uqr/DuUQZYtW4a2tjYEK49JqOpcbwRLhsPWfgjz58/HJZdcwrGH5kUrUW2EmwSVv1YJWy15beGTGO6JPsa8FhyKBJfLBVE0tZcdN/j7DKvWT7IMhyGfYePh6SahCWozieGUZ7OHHnoIW7ZswZtvvonly5fjiiuuiDrv8Xhw+umn49///nfanSR6Z86cOQCAYNlRabUjlYwABBGffvopj27lBHl5eRBFkWueYc3KTGJYxW63w+Fw8NvCp7RfMegWXE4LDigS3G4aXw3yZzUeGmPjiQygSxsrBdB98MEH+MEPfoArr7yy12uGDRuGffv2pXoLoh+am5vx9ddfQ/aUQPGmlwWCOTwIFg7Gpk2bsGPHDk49NDeiKKKgoMAQN4mCggJubZodt9vNz02CLMMxhC3D/NwkPB5ykdCw2WyqlZyXUAu5AphJSBgN/zzDJIZ7EvYZ5jDGoTbMFECXshg+cOAAxowZ0+c1LpcLHR0dqd6C6Ic5c+ZAkiQEy7/FpT2tnQ8++IBLe7lAfn4+RJmnGPbr7RIqHo+HXxlQmYK7ehJOrcbPTYLGNxq73cHHooZw8BEJtTDhKn+8LMMsql2Ct5uE+XyGUxbDpaWl2Lt3b5/XbNq0CdXV1anegugDRVHwr3+9D9jsCJam5yKhIRcOBnP68Omnn6Kzs5NLm2anoKAAghzgU6ISgCAF9HYJFZ5imApCxBIOoOMk1hSJLO89cDjs3LfwyTIcJrww4DMPk5tELLqbBI9AUCtlkzjttNPw/vvv9+oGsWHDBsyZMweTJ09OuXNE7yxatAj19QcQKBkJ2Dk9cIKIQMW30dXVpfsiW538/HxVRPASErIfdrvdVNtHRuN2u/lZhkkMx6Bbhnn4DDMW8hmm8Y3EbneQP6uBcE9fRwF0MeiLL44+w2Z6hlMWw/fddx9kWcYpp5yC119/HY2NjQCAjRs34uWXX8ZZZ50Fl8uFX//619w6S4R5++23AUDNIsGRQPloQLTjnXfegSxzShVkYnw+HwCo1mEeSAH4fL4BVaY826g+w2QZNgquqdWY+ZLpZwK73cbdZ9hMQsJowmNBYtgoNDHMwxVFa8NMluGU/9qOO+44vPXWW7juuutw/fXXAwAYYzj22GPBGEN+fj7efvttjBo1iltnCZXNmzdjzZo1kAoHQ/EU8W3c7kKg7CgcOLAJX3/9NU477TS+7ZuMvLw89T+8ygUrQfh8VHAjEqfTGbI4KkC66bpMWPnIaLSx4OEzLFC2jrg4HA4ICp/YAm2bmtwkwoR9hnkV3SCf4Z6EKynyc5Mw0zOc1tLzwgsvxM6dO/Haa69hyZIlaGpqQkFBAU466SRMmzYNZWVlvPpJRPDmm28CAAJVxlTNC1QeA+ehTfj73/+OU0891dJWzHCFtAAXbzVRDlJ1tB7owpVJANKzJJgxv6XR6OPLZfuTxjceDgd/NwkSamF4Z5OgMY4l7CbBYYxNuLuRck//93//F5WVlZgyZQruuOMOnn0i+mD//v1YsOALyN5SyPnGBCcydyGCxcOwceNGrF27Fscff7wh9zEDmmWYSyQ+Y4AcIDHcg7DlUgZL990UEmtkGQ4TOb5pQ5b3uNjtdgicg7towRFGL/DCyTJM2SRiCReP4VeO2UyW4ZT3JG+66SYKssoC7777LhhTEKg6Lq2Kc/2hWZ3feustw+5hBsKWYQ5iOCTUSAxHwzNwQ6BI/BjCKZPS9xkWGC024mG327mVYxbIahlDWAzzrfJHYxyGa2ETEwaBpiyGq6urIUmcIsCJhGhtbcXHs2dDcfkglQwz9F6KrwKSrxILFy7Enj17DL3XQCa8xcxBSFBwV1y4+qqZsPKR0YiiGPJpJcuwUdjtdgicMx3QMxwmXPqbr/WdSoqHCYthDmNsQst7yk/ChRdeiLlz58Lv51eQgOibjz76CP7ubgQqjgEE4/+Ig1VqMOQ//vEPw+81UAkXLOCw8KMt/LiEy4DysUjY7HZ6yfXA6XJxWWwItNiIi2oZptRqRhH2GeYUQBcS1TRPhOHql23C3Y2Un4RHHnkEeXl5uPTSS7F+/XqefSLiIMsy/vWvfwE2B7eKc/0hFdVCceVjzqefor29PSP3HGhw3WImMRwXnpZhgclwkIiIwelw8AmgM2Ey/Uygi2EuVjXzCQmj0UQrr2wS2u+JxHAYnmLYjK4+Kb81TjjhBPj9fqxevRpz5syB2+1GRUVFTOYBQRCwffv2tDtqdRYtWoRDhw4hUHE0YMuQVUYQESgfDXHfMsyZMweXX355Zu47gOBrGaYt5njw9VVjagEEIgqHwwGhsyvtdsgyHJ/oCmnpxXIIJozENxrubhJkGY5BT1/HZYwt5CahKAqcTieGDBmCIUOGoKKiAoCaazjyS+HlR2VxPvroIwBAsGJ0Ru8bLP8WINrw0UcfgfFalZsIvulmyKoWD65WH6aYagLOFGouZx6LDXqG42H1LWajCRvZeFmGe7ZL6GPBZR5W/zHTYiPlpeeuXbs4doPoi8bGRixZshRyXjkUT4YLNthdCBYNwa5dO7F582aMHp1ZMZ5twlv4/LaOyKoWDV8hwWCzmWcCzhRqgBePADp6huPB16fVfFY1owkLNV4tsuh2Cc5jYb7xTfmtceONN+LPf/4zz74QvfD555+DMQXBsuxU8wuWqT7KVkylF869aM10M5mA5xaoQJbhuKjilZ5hozDCMkxjHIa/qDKfWDMavtZ3841vymL4jTfewKFDh3j2heiFf//734AgIlgyPCv3lwuqwRweLFiwwHLp9HjmwNW2mOklFw3fhPrMVBNwpuCV+ksgoRYXruWCKbgrBu5uEjHtElyt7yb0qEz5r23kyJE4cOAAz74Qcairq8OWLVsgFQwC7FkKvBJEBIuHobm5GWvWrMlOH7IEX6slbX9mAnrBxWKz2chqaSC8dzcAmiciCcer0N+2UehjzHGIzRRnlJabxMcff4y6ujqe/SF68MUXXwBA1qzCGlLo/l9++WVW+5FpuFotyeITF97bcySGY+GWB5cWdHHhWyGN5gnjUecIM4k1o+G64DDhFJzyX9tll12Gk046CSeffDKeffZZLF26FLt378aePXtivojU+fLLLwFBhFRUm9V+yL4KMIcHX/7nP5bKEMI3pQ+95OLBW7ySGI6F36KOKnfFgxbNxkKi1Xj4jrH5Fhsp73WNGDECgiCAMYbbb7+91+sEQbCcnykvDh06hE2bNmXXRUJDEBEsGoIjDZuxbt06HH/88dntT4agl5zx8BavZpqAM0X0oi718RboGY6LEQswWtQZiTq2iqLQsxzCCDFsJsNZymL4+uuvpz9Wg9FcJKSSYdntSAipeBicDZvx5ZdfWkYM00vOeMITJo9xEUgMxyFqUZfWMJMYjofVI/GNRpZDAcy8xkRPqUtzhYY2xkzg8Lcd+j3pvzcTkLIYfvXVVzl2g4jHggULAEGAVDQ0210BEMoqYXdjwRdf4NZbb6UXIsGFcOAGHzGs0AsuBm7uPmQZjgtX4UoFIWLgO0cAmoeomSyXRhNecKT/t60JajOJYZrRBih1dXVYv349pPwaMIc7291REUQEi4eisaHBclklCOPgaRlmggBGLzjDIaHWCxwXYjTGYbQ5gvGKzBKi2yX4imFLWYYj+frrr7F69Wq0traioKAA48aNwymnnMKjacsyd+5cAECw7Kgs9yQaqfQoOBs249NPP8UJJ5yQ7e4QOQBvXzWZXnBEtuAoYGkLPwxvNwlmQp9Wo9Fju3iMsQktw2mJ4YULF2LatGnYtm0bAPWPV1vNjho1CrNmzcKkSZPS76XFkGUZs2fPBmyOAeMioSH7KqC4CjB//nzcdtttyM/Pz3aXDMWIFxJNwNGEJ2E+FgkzTcBEbsB1niB/1hi4Wi0j2qHg/jDBYBAAwAQOaRNDbQQCgfTbyhApi+H169fj3HPPRWdnJ8455xyceeaZqK6uRn19PebPn4/PPvsMU6ZMweLFizFmzBiefc55Fi1ahEOHDiFQcTRgG2DJ7QUBgfJvQ9y3DJ999hkuu+yybPfIUHThymW1bL50M5mAb+CGCFkOpt8O0Sf0DPcGH793gMY4Eq4L5oh2SAyH0YWrmL4YZqE2NIFtBlJWWg8//DACgQBmz56NqVOnRp276667MGfOHFx44YV4+OGH8eabb6bdUSvxz3/+EwAQrDg6yz2JT7D8W3DvX4l//vM9XHLJJTkdTMM700F0mwTAeQuULMNx4ZdQn4RaPIz4m6YxDmOUZZjmijC6cOVklIhq0wSk/FMvWLAAl19+eYwQ1pg6dSouv/xyzJ8/P+XOWZHNmzdj5cqVkAoGQfEUZbs78bG7ECg9CnV1+/D1119nuzeGwjWKmSzDceHrJiFCJmtPDNx2OARa0MUjPL78hASNcRhtjmC8fIbJMhyDZhlmHCzDEO1RbZqBlP9yW1paMHx43yWChw8fjpaWllRvYUk0K3qg+rgs96RvAlXHAgBef/31nBZ3YcsBn0wH0W0SAF8xzAQRkiTl9DOZCryeYwo8io8R7lQ0T4QJzxGcyoCL5rNcGo3f71f/I6bvmqkJar1NE5DyT11TU4PFixf3ec2SJUtQU1OT6i0sx549e7BgwQLIeWWQ86vTasu77j0IwW4AgCCp/4qdTchb9Xcwhxudx16SVvvMXYhg8TBs2rQJK1aswIQJE9Jqb6DC9SVHQiIueuAGD4uEYANjDLIsw24fYP72WYS3ZZiEWjR66i8O8wQtOGLRLYzc3CTUuYYsw2G6u1WdwDiIYU1Qa22agZSfrAsvvBALFizAAw88EPMDd3d3Y+bMmZg/fz4uuuiitDtpFTQrq796XNovLSHYDVHqgih1QdAqGoGp3wf5PKCBmrEAgL/97W9c2huI8PZnjWqTAMDfTSKqTQJA6JmzaP7QTMBzB4nGOBbdTYLHghlhNwmyDIfhaxk2nxhO+ad+4IEH8NFHH+HRRx/FX/7yF0ycOBGVlZU4ePAgli1bhoaGBowYMQIPPPAAz/7mLAcOHMDcuXMhe4ohF9VmuzsJoXhLIRXVYs2aNfjmm29yskRzONk7+QIaBU+rD4vY/nS7B0ixmgGAJEmcxDBZ1OLBdRufxjiGcHAXLzcJ86X+MhojLMNmcpNIeXYsLS3F4sWLccMNN6C9vR2zZ8/GrFmzMHv2bLS1tWHatGlYvHgxSkpKePY3Z3nrrbegKAoC1WO5Jm43Gn+1ah1+4403stwTY+Cb7F1tg15y0fB2kwDoJdcTfpZhsrzHw4hIfBrjMDzTfgHQ5wmyDIfp6upS/8MhnSsLtaG3aQLS+qnLysrwyiuv4C9/+Qs2bdqkV6AbPXo0HA4Hrz7mPEeOHMHHs2dDceVDKhmW7e4kheKrgJRfhcWLF2P79u0YOXJktrvEFb4lKskyHI+wZZhHFDOJ4XhIksQlj7PmE0tCLRqei2Ztd4PGOAzXTAcR7dA8EUYTrkxMX7tpbZhJDCc9Oz7yyCO49957o1ZUDocDxx13HE455RQcd9xxYIzhvvvuw+9//3uunc1VPv74YwQDATVDA68AgQwSqFIzX7z33ntZ7gl/uL7kyBcwLjytaowsPnEJBoOciprQ+MYjGAyqzy/HUrY0xmG4LpgBWjTHQRfDNg6GTFuOi+HPP/8cDz74IEpLS/u0/DqdTpSWluK+++6jPMP9IMsyPvjgA8DmQLD0qGx3JyXkwsFQXPmYO3cu2tvbs90drlBwl/EEg0H15cRDSNBLLi6BQIDPFrMJK0tlAv0Z5gEtOGLgbhkWzJf6y2h04coxgC5nxfD//u//ori4GNOnT+/32ttuuw0lJSWYNWtWyp2zAqtWrVJLL5eM1FdTpkMQECz/Nvx+f84tfniXCgZIDPfE7/fT9qfBBAIBXQCkgxnLrGYCXuML0DMcj3CmA7IMG0VnZ6f6juJYdKOjoyP9tjJEUm/4hQsXYvLkyXC5XP1e63K5MHny5JyvUJYuX375JQBAKh2R5Z6kR7BE7f9XX32V5Z7wxQjLMLlJROP3+/ltf1IAXVwCvCyXNL5x4eaGApCbRBw0Mcwl0wFgymwHRtPZ2Qlmc/JpTBAAmzN3LcP79+/HiBGJi7bhw4fjwIEDSXcqWZ599lkMGzYMbrcbJ510EpYuXdrrtS+++CJOPfVUFBcXo7i4GJMnT+7zeiNhjOGrr74Gc3gg+yqy0gdeMJcPsrcUK1asUFeYOQLPADpGYjgufC3D9JKLR8Dv5yKGyWoZHz+n8QVAVss46H/PnK3vNE+E6ejo0LNA8ECxOXLXMiyKYlKr1WAwCFE0NiDsrbfewowZMzBz5kysXLkSY8eOxZQpU3Do0KG41y9YsABXX3015s+fj0WLFqG2thbnnnsu6urqDO1nPPbv34+mpsOQ8mtMGTjXE6lwMCRJwsaNG7PdFW6Qz7Dx+P1+MIGXxYeERE9kWVYtlxy3P82UTD8TBAIB7v6s9AyHCadWI8uwUXR0dIKJnCzDUDNK5KwYrqmpwbp16xK+ft26dRg0aFDSnUqGJ598EjfffDOmTZuGMWPG4IUXXoDX68Urr7wS9/rXX38dt956K8aNG4fRo0fjpZdegqIomDdvnqH9jMemTZsAAHJeWcbvbQRK6OfYvHlzlnvCD73yEccocRLD0fj9nIK7AHrJxSEcic8jMIYsavHw+wNcxhcAPcNxCLtJ0ILDCBhj6Ozs4JNJQiOXLcOnnnoq/v3vf2PXrl39Xrtr1y78+9//xmmnnZZq3/olEAhgxYoVmDx5sn5MFEVMnjwZixYtSqiNzs5OBIPBPouD+P1+tLa2Rn3xYPv27QAAJa+US3vZRhP127Zty3JP+MG3shSJ4XgEAn5uvoAk1mLhKiRIqMVFfYYpgM4o9J0IXpZhGz3Hkfj9fjX/PS+fYQDM5kQwGDSN73tSYvi2225DMBjE5ZdfjsbGxl6vO3z4MK644gpIkoRbbrkl7U72RmNjI2RZRmVlZdTxyspK1NfXJ9TGXXfdhZqamihB3ZPHHnsMhYWF+ldtLZ9yyQcPHgQAKK58Lu1lG+bwAoLYq4uKGeHpJsFIDMcgSZLqQ83Z35JecmF0IcHB6sPITSKG8DNMlmGj0FMDcqrOqlmG6TlW0Sy4PC3DWltmiSFK6g0/fvx4/PKXv8TKlSsxZswYPPjgg5g/fz62bt2KrVu3YsGCBXjggQcwZswYrFixAnfccQfGjx9vVN/T5ve//z3efPNNvPfee3C73b1ed88996ClpUX/2rt3L5f7Hzp0CBAEMIeHS3tZRxCgOL04mINimPHwfRcpSrwn4fyhvCzDJCR6or3wuYwxieEYeOfA1Rbe9AyH6e7u5je+gP4ck/VdRROs3LJJRLRlFjGc9Oz4xBNPwO12409/+hMeeeQRPPLII1HnGWOw2Wy455578Lvf/Y5bR+NRVlYGm82mW1g1Dh48iKqqqj4/+/jjj+P3v/89Pv/8cxx//PF9XutyuRJKJ5cszc3NUOyenAie02AOL5qPHAFjDAKnVXw2oQA6Y+GfP5TEWk+4iuFQlTUa3zDhZ5iTZVgQANFOYjgCNf0iv0wHtGiOxgjLsLYTZZZCXEk/XYIg4NFHH8VNN92EWbNmYeHChbpLQlVVFU455RT8+Mc/xsiRI7l3tidOpxMnnngi5s2bh4svvhgA9GC4vgqD/PGPf8QjjzyCTz/9FBMmTDC8n73R3NwMZucvsrMJs7sQbA+iq6sLXq83291JG56lggEhuk2Cr1CLaIcsPmG4+lsKAiA6SAxHEH6G+VkuGYnhKHimXwRA7lQ90K23BrhJmCXXcMqz48iRIw23/CbCjBkzcMMNN2DChAmYOHEinnrqKXR0dGDatGkAgOuvvx6DBg3CY489BgD4wx/+gAcffBBvvPEGhg0bpgt5n88Hn8+XsX5LkoT29nYwX98WbLOh2FV3kyNHjuSEGOZrGRYA0UZ5hiPgblUTyRewJ7pY45RDlIl207zgMgH3ZxiqsCahFoZn6joAoflcoDEOEbYM83eTMEtGCX5/vVniqquuQkNDAx588EHU19dj3LhxmDNnjh5Ut2fPnqhcx88//zwCgQAuv/zyqHZmzpyJhx56KGP9bmxsBGMMzJmXsXtmAu3naWhoMDytXibQrLi8Sq1CsJFlOAKjLMP0kgujC1eRj9WHiXZabERghBiGaKMxjkBNXcfRuBIyTNAOkkrYZ5i/m0TO+gwPRKZPn96rW8SCBQuivk8kLVwm0DIuKDkqhnv6cZsV3TLMqXgME0TyGY6AfIaNh/uCw2ZHVxeNrwbvHLhqW3ZVABIAgEAwAObmm3WJrO9hdOstpwUzoBbdAMzjM5w7kVsmY+vWrQAAxVOU3Y5wRvYUA8idXMPhggWc/lQEkawREfC2qpFlOJawZZijm0Q3uUloGGIZFuzwB+gZBtQKirIk8Vswh2C0S6ejzRFGpFYzi0sVieEsoZUslvPKs9wTvijeEkAQc6YkM283CZZkSfNcJ2xV4+szTGI4DG+fYYgOyJJEOxwhDLEM22wIBgJqIQSLEw5i5iuGQZZhHSPEMEgME/0hSRKWL18O5vCA5UjBDR3RDtlbio0bN6KlpSXbvUmbsJsEvzKgJIbD8N7CV1N/ieQmEQF3n+GQqDbLS85ojLIMA5QVBTAgj3MIRj7DOrznCCDsJmGWeYLEcBZYuXIlmpubESwezq2izkAiWDIcsizjyy+/zHZX0sYINwkSw2G4+wwDgGinl1wE/C3DJIYj0cUaR8sllWQOE56DOVuGBRLDGuEAOo4ZUUy2aCYxnAU++eQTAECwdESWe2IMUon6c82ePRuMsSz3Jj24Z5MQbQiQGNahtFTGo2+BcssmobZD1ncVXVBRHlxD0OdgTkHMOoIISaI0lwDnXOQaZBkm+mLfvn1YsOALyN5SKDnmL6zBnF4Ei4di48aNWL16dba7kxa6FZdbNglyk4iEu88wVDFMQi0M9xcdZeyIwohtfLIMhwnneufvJiFJNBcDke5qPN0kzBXMTGI4w/z9738HYwoC1WNz0kVCI1A9FgDwt7/9Lcs9SQ/uwRuiCFmSKDAmhBFuEkyg6l2RcC+6YSMxHIkh2/jkM6zDtfBRJIIIRVFoLoZx7mqAeeYJEsMZZPfu3fjkk0+geIogFQ/NdncMRckrg1Q4GCtXrsTy5cuz3Z2UCQQCoaAsTguX0AuTrMMqRliG1ShxEhEa+otO4GsZpgWHipFuEiSG+xfDzOGGYvdAsXvAQiXvGQT1e4e794ZD7dFcHPpbFm18DXSi+t40yzNMYjiDvPDCC1AUBd2Dv5PTVmEN/+AJAAQ899xzpi1BHAwGOVstSQxHYoSQYKIdgaA5JuBM0N3drb74ebn6mMziYzSaWOMaQBcSapS+Dvq7g/Xyzuw89hJ0nHA1Ok64Wk3tCTXFZ8cJV6Pz2Et6bzjUHlmG1b9lrgYJDRNVqyQxnCFWrFiBRYsWQSqogVw4ONvdyQiKtwSBslHYsWOHHjRoNoLBINeXnCZISAyrGBGJD9EGWZJMuwDjjWr14Wl5JzEcCe+4AgC61ZLEcFgM83aTYCH5Q/NE6Dnj7YYCc1VcJTGcASRJwtPPPANAgL92oiWswhqBweMBmwMvvfxyuOSjiVDFMM+XHFmGIzFki1mgLeZI/H4/BXcZSDiuIHaeoC389AmLYc7vTbIM63B/z2mYKGDcALs40ZPZs2dj186dCJR/W9/GyQTPPfdc3OM/v/1XGesDc3jhrz4ezftW4PXXX8dPf/rTjN2bB4FAgPMWPgmJSIyMxPf7/fB4PNzaNSuBQICv5V2gtF+RhN0kYsVE5Da9d/37sHUehuItQecxF/XZJqMdJJ2wWDVGDJNlOPSc8c7jDPVvwizPMFmGDaa7uxuzXn0VsDkQGDQ+293JCoHKY8CcPrz77j/Q2NiY7e4kRYC3mwRZLaMwpNRqSAybZXvOaLr9fu6p6wB6hjWM2cZX2yKrJcK56rnvqArR7VsYwyzDomiaeYIswwbzj3/8A0eamuCvGQfmyKyV6tZbb41/wp5ha5loR/egEyDs/A/+93//FzNmzMjs/dMgGAhyLVGp+RWSUFMxIi0Voy3mKNTdDVfcc8zhhm53k7ohgIFBALO7e9/GFyntVySGWC5pC5/IIIqi9DoHpzRHaJ+FCMUkiw0SwwbS1dWFN998E8zuRqDq2Gx3J6tIpSOhHFiLjz/+GD/60Y9QUVGR7S4lRDAYAOx9/8EnA2WTiCacNilWSKQ8CdMYRyEFg70ugFPZxqfFRjS6YOVpuaQtfCKDKArr9flN1dUHACAAzCQLOnKTMJDZs2ejra0NgcoxgM2Z7e5kF0GEv/p4yLKMd955J9u9SZhgMMjVn5Xyh0ajp66LMxGnnDJJJDEciVHPMO1uqBjj00qW4UxBbhIAYwq4+2QDAATTWIZJDBuEJEl4++23AZsdgYqjs92dAYFUMgLMmYcPP/wQbW1t2e5Ov8iyHNo+4p8yiYSaihEpfShHaxh6hs2NYKHMQ/3CXVSp7Yk8U+KZFEVhMEoMk2XY4ixduhQHDx5EoHQUYI/vr2c5RBH+ijHo7u7G3Llzs92bfglv4ffhS5Vk2iTNTYKEmoosy/wDN2iLWceIUrbk6kNkEptNm385i2FGYlhDnTKNsOCawyoMkBg2jA8//BAAECz/dpZ7MrCQykYBgogPP/powG9PaS971stkmdI2PqVMikKWZQPyh1IyfY1w9S6elmHawo8kbL3lOZ+xHm1bF10M835fkBjWEUQRRolhkaeLloHQU2AAhw8fxuLFSyDnlWc0r7AZYA43gsVDsXPHDmzevDnb3ekTI6xqIMtwFKpY420ZJjGsYWSmAxpfFRKsxhIWw7wXXyy6fQtjE0UD3FAAMAabzRwy0xy9NBmff/45GFMQLBuV7a4MSLRx+eyzz7Lck74xIgcuReJHoyiKYRtpZLk0qnoXieFIdMsiTzHBSKhpaGMgcBbDAlOfX7udkmqJBolhgTHTWN7N0UuT8dlnnwGCiGDJ8Gx3ZUAiF9SAOTz4fN68AW0hDVeW4mlVo+CuSNTgLkqmbxTGpP2ighCROByhPOSM4+KAhJqO0xnKxMTbMhx6fvXfn4Wx2+3cFxsqimkWdCSGObNjxw5s374dwaJaCpzrDUFEsGQEWltasGzZsmz3plcMqSxFW8xRGClYSQwTmcAIy6XWFonhCLGq8J0zBSarIpDcXNQFB8/FXAhBUeBymUMHkRjmzLx58wAAUsnILPdkYBMsVcfn888/z3JPescYMUz+rIZD7zYig+iCladlLbSQIzEctgwLvMWaIoetzhbH6XQaYxlm5hljEsMcYYypYtjmhFQ0ONvdGdAo3lIo7kJ89dVX6OrqynZ34mKEGGYkhgkipzDEchlqi7bwEbYscrYMkxgO43Q6ISj8XfcERTLNGJMY5siGDRtQX1+PYPFQQKQVfZ8IAoIlI+D3+7Fw4cJs9yYuYcHKPxKffIZVDAmuYAa2TRA9cLvVnOICR7GmCROtbSsTHl++QceCIsHr9XJt06y4XC51scE7CNRECw56W3BEc5EIlozIck/MQbBUHSdt3AYahgQfUXBXFIIgQDCoshT5AhqV6UCJbtvihC2XHBe4JIZ1HA4HbHY7IPM1IAhKEB6Ph2ubZsXr9ap/1zxdUULPcF5eHr82DYRmM04oioIvvvgCzO6GXFCd7e6YAuYuhOwtwbJly9DZ2Znt7sSgCVZDUpGTGAagBR/xTqavRLRtbcL+rPwLQtD4qmiCiuc2s9aWWYKPjMbtdnPfxhcUiRYbITQLuSDzs75rbZnF+k5imBMbN27E4cOHESwawjfgKseRiochGAxi0aJF2e5KDLpg5ZqWiizDkdhsNv4pk6iylI6e6QA8g7so00EkZBk2Hq/XC0EO8GtQUQBFJstwCN16y1UMB6LbHuDQ24ITWoowqXhIlntiLqQidbyWL1+e5Z7EYoxgpVK2kTgcDq6+lkA46pyCj4yp3iXQYiMK7WXPU6xpbZnFqmY0+T4fZ6ulX203P59bm2bGCMuwJqzNsuCg2YwT33zzDQBA9lVluSfmQvEUg9ldWLNmTba7EoMxPqckJCJxOByGJdM3S+CGkejWW56Lr9Big8ZXxefzAeC8xSwF4fZ4yBUlhM/nUxcIvAwUocWG9ruzOtqiQFsk8EBrq6CggFubRkJvZA4wxrB+/XrInmLATi+IpBAESL5K7N+/H83NzdnuTRS6GDagzCoFd6moyd4VzmNMlmENQRDgcDr5Wt8VEsORGGIZVgLwmWR7ORP4fD51nuDkiiJIJIYj0QSrIHVza1OQzGV9JzHMga6uLvj9fjAX/WGlAnOq49bU1JTlnkSjCVYjZCuJYRVdUHFNS0ViLRLe1aVofKPRfSIlnm4SQRJqEejWd4mP5VIgy3AUYTHM8RkO/a4KCwu5tWkkJIY50NLSAgBgdgp2SAUWKlutjeNAwZC0VOQmEYUhOURD1iOz+KoZjcvpMiS4izIdqOhCgtcWM2MQJL9ptpczgSaoeI2xZgE1i1AzGmMsw91RbQ906I3MAd1CwjPa1UJoq/SB9nI1pswqReJHogtWnmmpQr6bFImv4na7DEn7ReOr4vP5IIoiN6sl5CDAFBJqERQVFQEAhCAfsaa1o7VrdXiPb2RbZhljEsMcKC0tRXFxMWydA2ub3yyInU0QBBEjRgysYiW6zynPSPxQIBP5s6roOVp5RjErQdjtdhrjEF6vl6/l3WRR4kYjCAIKCgr4CTWTWdQygW4Z5mS5JMtwNMXFxQAAIdjFrU2tLa3tgQ6JYU58+9vfhuhvgzhABDFzuKHYPVDsHjCt6hkE9XvHwLHoCMEu2DsaMHTokAFnaQpbhjkGH4XaIsuwii6oOCd7J6EWxuv1QpCC3Nx9zJZMPxMUFRVB5LyFT2I4jC6GOS84SAyrFBYWQhBFCBI/MSxKXbDZ7RRAZzUuv/xyAIBr34os90Sl89hL0HHC1eg44Woo3hIAgOItQccJV6Pz2Euy3Lswzv2rAEXSx28goVkWBa5pqchNIpJwWiq+OVp9PnNMwJlAFa2MW5CiZmU2SzL9TFBUVKQKNQ67SJrgKykpSbutXEEbC16WS60dGmMVm82GosJC7pbhkuJi0wSLkxjmxIQJEzBhwgTYW/bCfmRXtrtjCsT2Q3A2bEHtkCGYOnVqtrsTg+4LzqjMqlEYIYZFOYD8fIoS1+A9xlQQIhZVVDEufsNisDOiTQIIj4XIyXIpBrvgdLpoQRdBaWkpbKFnL20YgxjsMtUzTGKYI7feeivcbg88O76AraUu290Z0Iidh5G39TOIAvCL228fkJZSPdOBzDESX6bgo0j0ZO+8go+YAshB02zNZQLuCfUlcyXTzwRhy2X6YkLbwjeTkDAafXwDfMSaEOxEaWmJaayWmaC8vFx1V+PhsiYHAEVCRUVF+m1liJwQw88++yyGDRsGt9uNk046CUuXLu3z+nfeeQejR4+G2+3Gcccdh9mzZ3Ppx4gRI/DYY4/CYbfBu20ebK0HuLSba4hdR+Dd8hkEOYj77rsPEyZMyHaX4uJwONQKUBSJbxi8xbCWJ5PEcJhw2iReY2yuZPqZgOc2vkCW4RhcLtWKy2OxYUarZSYoKysDAIiBjrTb0trQ2jQDphfDb731FmbMmIGZM2di5cqVGDt2LKZMmYJDhw7FvX7hwoW4+uqrcdNNN2HVqlW4+OKLcfHFF2PdunVc+nPCCSfgd7/9LWwC4N3yKZwHvuGcp9bc2Bu3Im/DhxCCXfjVr36Fs88+O9td6hO32801LRVIDEcRjhKn4COj0EUrp4T6ghyAKIq0xRwBT8ul1gaJtWjKy8t1F5J0ECTVt7u8vJxDr3IHbTwEDmJYa8NMY2x6Mfzkk0/i5ptvxrRp0zBmzBi88MIL8Hq9eOWVV+Je/9///d+YOnUqfv3rX+Poo4/Gb3/7W4wfPx7PPPMMtz6ddNJJ+POfn0RZaSlc+5bDs+Uzro7ppkQOwr3jC3h2/gd5Hjd++9vf4gc/+EG2e9UvXq+Xa9ovgdJSRaHnt+TkC2i2dD6ZQFsYiLzSUgW74cvPpy3mCLSXPg+xJgY74XK5qDpaD8rLy9VFc5rGCW2xYSahlgl4imHt74AswxkiEAhgxYoVmDx5sn5MFEVMnjwZixYtivuZRYsWRV0PAFOmTOn1+lQ5/vjj8fLLL+Hkk0+GvbUOeev/BXvjVktaiW3Ne5C3/l9wHN6OMWOOwcsvv4RTTz01291KCJ/Pxz3TAUBbzBqFhYUQBIF7yiSyqoXhnUNUlNQocSKM9tLnIiQCnarwo8VGFJr/abrWdyHYEdUeoVJZWQmAj5uE4G8HAFRVVaXdVqYYeFFLSdDY2AhZlvVfokZlZSU2bdoU9zP19fVxr6+vr+/1Pn6/H35/eBu3tbUVALB69eqo1XtxcTGGDx+O7u5ubNiwAQBw2WWXobi4GP987z14dv4HcsNmNOePhOSItgw6C8pg9/gQ7GxFsC06V7HodMNdXAWmKOhq2BPTP0/ZYAg2O/zNByH7o194Dp/60goGg+hsOYLug7v0c4LdCU9pDQCg8+BuaKWCNdwlNRAdTvhbGiF3t0eds3sL4MwvgRzogv/IwahzgmiDp7wWQncr5HVzgNYD6LbZMGXKFFxwwQX69mpdXR0OHoz+bGlpKYYOHYquri5s3Lgxul1BwAknnAAA2LhxI7q6on/W4cOHo7i4GAcPHkRdXXQAY2FhIUaOHIlgMIi1a9fGjOHYsWNhs9mwdetWtLW16cc7OzsR7GoHGIPU3YFAa2PU50SHC+6SagBAW1sblKAYNcbu0hqIdif8zQ2Q/R2QD9fD1t6GLVu2YOjQoaiurkZrayu2bdsW1a7L5cIxxxwDAPjmm28gSdHWkG9961vw+XzYt29fjEtQWVkZhgwZgs7Ozpi/A1EUMW7cOADAhg0b0N0dLUJHjBiBoqIi1NfXY//+/VHnioqKMGLECAQCgbhuRePGjYMoitiyZQva26OflyFDhqCsrAyNjY3Ysyf6GdbyWzKmoOtQ7PPtLhsMAOjq6kIgGP0MO3xFcOQVqb+blgbYD++E1NaGQ4cOYcOGDRgzZgwA9W9V6ZEib/To0fB6vdizZw8aG6N/rxUVFRg8eDDa29uxZcuWqHN2ux3HH388AGD9+vVRcwMAHHXUUSgoKMCBAwdw4EB03EC8OSKS8ePHAwA2b96Mjo7ol9KwYcNQUlKChoYG7N27N+pcfn4+Ro0aBVmWsWbNmqhz+/fvh6IoEKTuXucIR14hpGAAnT2e4Zg5gsnAkUYEK8uwcuVKHH300fB4PNi9ezcOHz4c1W5lZSUGDRqEtrY2bN26NfqeDgeOO+44AMDatWsRDEbvvowaNQr5+fmmmCO0PgGA3H4EnRHPJ5DkHNHVBjQdBKsoxsqVK1FdXU1zBFSjRHl5ORhj6Nq/BUpetMXRXTYYos2O7s4OKD3GOGaOaNoJua0NTU1NNEdEoBkQAk370WmPXuwmNUeAQTq4C4G2Nhw8eBBdXV1ZnSNWr14d87PGhZmYuro6BoAtXLgw6vivf/1rNnHixLifcTgc7I033og69uyzz7KKiope7zNz5kwGVSn2+XXttdcyxhjbunVr3PMPP/wwO/3001l+fkHMuaHn/5SNv/M1Vnv2dTHn8ocdy8bf+Rob+18vxG33uFufZuPvfI0VjhwXc27QGVez7513KRszZkzMOU/FUDb+ztfY+DtfY4LNHnP+6B8/wsbf+RorPe60mHOVE7/Pxt/5Ght15d0x5xy+IvbdH/4XO/2MM5nT6Yw5P3/+fMYYY3ffHfvZm266iTHG2Lp162LOOZ1O/XdywgknxJx/++23GWOMPfHEEzHnLrjgAsYYY4cOHYo7hi0tLYwxxs4999yYc0cddRQbf8fLbOj5P405l1c9ko2/8zX2vfMujdvumJv+yMbf+RorPnpSzLmZM2cyxhibM2dOzLmRI0fqP2tZWVnMee2Zv+OOO2LO3XrrrYwxxlasWBH7LOXn6+3Geybef/99xhhjjz76aMy5yy+/nDHG2N69e+P+rN3d3Ywxxk4//fSYcy+++CJjjLEXX3wx9lmqrGSnnX0uG/fLl+K2e+zP/sy+d96lcceh5tTL2fg7X2MjLv5F7NiPGaP/rPn5+THnV6xYwRhj7NZbb405d8cddzDGGFu4cGHMubKyMr3dkSNHxpyfM2dOr/NGf3OExne/+92Yc3/7298YY4w988wzMefOPfdcxhhjLS0tcdudNGkSO/mSab3OEePvfI2NHjcx5lx/c8S6desYY4zddNNNMefuvvtuxhhj8+fPj73noEH6zzpo0KCY82abI5555hl2/vnns28df2LMOZoj0p8jTj/9dDZ79mx26qmnxm332J/9WX1XVcU+SzRHJDZH1NXVsTPOOIMVV9bEnMuFOUL7++0NgTHz7tsHAgF4vV68++67uPjii/XjN9xwA5qbm/H+++/HfGbIkCGYMWMGfvnLX+rHZs6ciX/9619xV0tAfMtwbW0tvvjii34tw5GMHz8eq1atwsMPP6yufG02BEuOQrB0JJzF1YZZhgt3LYDSUo9OuNA98gz9HHfLsCLD3rQLrqZt8LkdqKiowHnnnYfjjz8+asvPTFafl19+GatWrUJgwo8QVNCrZdi7/n10HtwFxV0YNcY9LcOebZ+jyG3H448/TlafEM8++yy2b9+O1vE/Qldj9D0B1erj2/QxAof3IWDPixrfnlYfZ90q2Jv34NFHH0VtbS1ZfQBIkoRf//rXkAtq0Fw1vlfLsHP1Owg07Y96hnvOEUJXMzw7FmDy5Mm46qqryDIcora2FnfddRc2bd+FI0PPiDqXzBzBmvbAvesrnH/++bjkkktojgjh8/nQ3t6OO+64A0c8gxGs+HbUec0yLC57A0pbQ9QYx8wR+5bD3lKHP/3pT6iqqqI5IsRxxx2Hq6++GrsPNqF9+BlR55KZIwAGz5bPUFbgxe9///uszxHLli3D6aefjpaWlj4Dq00thgE1WG3ixIl4+umnAQCKomDIkCGYPn067r777pjrr7rqKnR2duLDDz/Uj5188sk4/vjj8cILLyR0z9bWVhQWFvY7uL0hSRLmzJmDWbNm4fDhw2B2N/zVYxGsGA2ItqTb6w/v+vdh6zwM2VuKzmMu4t4+mAL74e1w162CEGhHXp4P1157DS699FLTZ0147rnn8Pbbb6NjzIUxW3ORJDrGvlWvY+SQQb0GeFqRP/zhD/jkk0/QftzlYO74f0+Jjq9n86ewt9bhs88+CxdNIXDppZeisVNCx3G9V3pMZIxtzXvg3fo5fv7zn+OHP/yhUd01JXfffTcWL16MtvHXATZHzPlExtd+eDs8O77Ar371K1xwwQVGd9lU7N+/H9dccw2CZaPQPTx+zEkiY+zd8CHcwRZ89umn5Jfdg+nTp2Pd+g1oO/F6QIgNKUtoHlYU5K94DePGjcVTTz1lbIcTIFG9ZmqfYQCYMWMGbrjhBkyYMAETJ07EU089hY6ODkybNg0AcP3112PQoEF47LHHAAC/+MUvcPrpp+OJJ57A97//fbz55ptYvnw5/vrXv2asz3a7HT/4wQ8wefJk/POf/8Trr78BYe8SuA6uR3fNOEhlR8V9EAccjMHevBuuupUQu5rhcDpx+dVX45prrsmZALFw6i8OAV6KAkHy6xkUCBU9v2WwE3IvYjhRhGAHCgoLSQj3oLy8HE1btwOMAWkIAJEi8XtFCxYSA+1QPKkFGIqhwKOecS0E9KBCLTgrVcRAOyorK0kIx6G6uhrr1q2DEOgEc6WWzUQItANgqK6u5ts5gzG9GL7qqqvQ0NCABx98EPX19Rg3bhzmzJmjTyZ79uyBKIaF5cknn4w33ngD999/P+69916MGjUK//rXv3DsscdmvO9utxvXXHMNLrjgArzxxhv4xz/+CWHXV1Dq18I/aDyk4mFpvbiMxNZSB1fdCtg6GiGKIs7/wQ9www035NxLUk/9xSHbgSB3R7VJqHBN6RPoRHlNbdrt5BplZWXYvHmzWhnKnnopcDPmD80UerYDfzuQohhWhQSJ4Xg4HA6UlZXjYEtb/xf3hixBCHahuto8WQ4ySU2N6u4g+tsgpyiGRb/6+yExnAWmT5+O6dOnxz23YMGCmGNXXHEFrrjiCoN7lTj5+fn42c9+hssvvxx/+9vf8OGHH0LcPh+ytxT+2u9ALqjJdhd1xI5GuPYug71N9XM6++yzMW3aNAwePDjLPTMGPS0Vhzy4mqCmHLjRhFP6pGfxgRSAIAdMlc4nU+h5cAOdUNIQw2bMH5opIi3DcoptkGW4b2pqqtHQ8A2gKICY/O6pGGgLtTNw3qkDCe0ZFtKYi7V5nMQwkTKlpaX45S9/iSuvvBKvvvoq5s6dC9vmOZAKa+Gv/Q4UT1HW+iYEOuDatwKOw2oQx6RJk3DTTTfhqKOOylqfMoGWMkkM8CuzqrVJqGgvfsGfnmVYJKtar0S95LypL8YEfzsEQSDLcBy0l7+Yxja+GGhDcXGx6WMtjKKmpgZr1qyBEGjvNb6gLwSTWi0zRaRlOFWEbnOOMYnhAUhNTQ3uvfdeXHHFFXj++eexcuVK2Fv3IVA+Gv5BJwD2DE6UchDO+rVw1a8DFAmjRo3Cbbfdpkcb5zp6Mn0eZUADZFWLBy/LMG0x907kGKdqtdQ+X1ZWBocjNkDM6mgvfyFVIcEYRH87qkeM5tir3CK84GhLKb5AE3lkGY6PPr7drSm3IfrVz5ptjEkMD2BGjRqFJ554AosXL8Zzzz+PvXs2wtG0E91DToJUMsJwf2Jbyz54di+E4G9HaVkZfvbTn2Ly5MlRPti5TlFREURR5CKGRbIMx8Xj8aCgsBBH0g2MoS3mXtG38NMZY6ZADHSgsnIop17lFoWFhXC73ZBTFMNCsAtgCrn59EGUT2sKnxdNarXMFNpCN9VnGFB/N26323TugNZRNSZFEARMmjQJs155BdOnT4fbDnh2fAHP1rlpR9X2itQN944v4N3yGexSF6677jq8/n//h3PPPddSQhgAbDYbSkpK9Cj6dCDLcO8MqqmBLdCGdMqVk9Wnd3Q3iXS2P0ms9YkgCKiurk55h8OsFrVMMmjQIADhsUoWbY7Q2iGiEUVRfYbTFMM1NTWmy9ZhLWVjYux2Oy6//HK89uqrmDhxIuwt++Bb/0/YG7f2/+EksLXsg2/dP+E4vB2jR4/Giy++iJtuusnSPmwVFRUQgx1pCTUg7AagRZ0TYaqrqwFFVgVXipA/YO8UFRXB7Xan95LrJrHWHzU1NRAkPyD5+7+4B5pxg57f3tFErJDiNr7gb0VRURG8Xi/PbuUU6jPcrWaeSRbJD0EOmHKOIDFsMqqqqvCHP/wB9913H3weNzw7/wPXroWAko4nIADG4Ny/Gt4tn8EpKLjtttvw7LPPYsSIEXw6bmIqKytVoZZmrmEh0IGCggJ4PB5OPcsdwtuf6fiqtSE/Pz9nclzzRBAE1NTUpBcYQ5b3fknHHYV2NvqnoKAAeT5fas8xU2Dzt5FVuB/C1vfkx9jMC2YSwyZEEAScc845ePHFF3HUUUfB2bAJ3k2f6NvwSSMH4N42D666laisqsJzzz6LK664AjYb/2p4ZiSc7SAdf0sGWyjZOxELj+Ajm7+NrGp9UFNTA0EOpGS1BGgbPxHSicY3a37WTDN40CDY/Mm7VAmBDoApJIb7QX+Gu1N/hs04xiSGTUx1dTWeeeYZnHvuubB1HIJ38+zkBbEcgHfzp3A078F3vvMdvPjXv2LUqFHGdNikaG4NaWU7kAOAHCQXiV7QrRGpbn8GuwBFytl81zwIv+TI39IowtkOkh9jwd8Km91Oaev6YfDgwYAiJR3UrD33NEf0TTq7dGZeMJMYNjlutxv33HMPbrjhBojdrfBu/iRxv0s5CM+WubB1NOC8887D73//+z5rd1uVyHQ+qUJWn77RXlApB8Z0twAgodYX6Y9xK9xuN0pKSnh2K6fQfVpTmCts/jZUV1XRjlw/pLpw1p57miP6JvwMpyKGzevqQ2I4BxAEAT/+8Y9x7bXXQuxugXfLp4As9f0hpsCz7d+wtx/EOeecgzvvvJMm4V7Q/rDTicQ38ySRCUpLS+FyudK2WpLVp3d0MRxaOCQFY7D5WzF48GDTRYlnkrDPcJJzhV4mmBbL/ZHqoo4sw4lRVVUFQRBScpMQulths9lM6Q5IYjhHEAQBP/nJT3DxxRdD7GyCa+/SPq931q+DvbUOJ598Mu666y4Swn2Q8gsuAhLDfSMIAgYNGqSOUwpZOwSyDPdLWAynsIUvdQFykIREP7hcLpSVlSU9V5ALSuKkbBnuJstwIjidTpSXl6f0vrP521BZWQm73XwlLEgM5xCCIODWW2/Vg+psR/bEvU7saISrbiVKy8pw9913m/LBzSSpvuAiobRf/VNbWwtBDqSUtYOsPv1TVlYGpzM16zuNb+IMGjRIjS9QlIQ/o1k5aX7on1TTqwn+VhQWFsHn8xnRrZxCfYY7kstSFfLjNutig8RwjuF0OvHAAw/A4XTCs2dx7MPMGFx7FgNMwX333ks+wgmiWi3bU05hJ3a3QhAEKljQB7W1tQBS28YX/S3w+fJRWFjIu1s5gyiKGDx4kCq8krS+a78TEsP9U1NTAzCmlwdPBEpblziFhYWh9GpJzBOhtGq1tfT8JoL6HCb3DJt995PEcA4ydOhQXHLxxRAC7XAc3hZ1zta6H/b2QzjttNMwfvz4LPXQfKir3eQmh0hEfysqKirgcrn4diyHSHkbP+JFR/6sfTNkyJCQ9T254iaaGNYWLETvpJJejYK7EkcQBAyprU0qvZrgbweYQou5BEkl84zQTWKYGIBcddVVcDpdcB74Juq488AaAMD111+fjW6ZlnT8LaFIEAMdNBH3Q6qWYSG0nTdkyBAjupVT6GPcldwYa78TGuP+SSU1lSacaecoMQYNGqQWQgp0JHQ9LTaSI7UFHYlhYgBSWlqKc86ZDNHfBkEOqgeZAntbPcaPH4+jjjoqux00GenkwdWicmki7htNqAlJimFKq5Y4KS84ulV/S6ru1z+pFC0Qu9tQXFxMZYITJNkqaeTznhzp7G6QGCYGHGeeeab6n1CNcSFUeeqss87KVpdMiy4ikvFTC6F9hibivikoKEBBQUHKUeJkteyflMSworqhDBlCLhKJkHQqRqZADLTTYi4Jkk0TSJbh5EglnajZc+mTGM5hxo0bh/z8fLUEK6D6CgoCTj311Cz3zHwMGjQolHsxBTFM/pYJM2TIENj8rQBLIhKfxjdhUhHDgr8NYAotNhKkoKAAeXl5CVvVhEAnwBTTWtSyQfKWYXNv4Wea/Pz8UJBi4mJY8LehsLDItLsbJIZzGLvdjuOOOw5CSFgIioThw4dTxH0KuFwuVFRUpJWWisRa/9TW1gJMUQNeEoQyHSSOz+dDcUlJUmKYFhvJIQgCampqYAskFuBldotaNginV0vsORb8rSgoLKS0akkwqKYm8SBFxmALtKOmxrzPMInhHOfYY48FACgODwDgmGOOyWZ3TE1tba2ae1HzwU4QsbsFNrudgmMSIJUqaWJ3C2XqSIIhtbVJpQmk4LnkqampAeRgQjmzqeBG8iRlfQ9lmxlM45sU1dXVau7gBJ5hIdgFKLKpLe8khnOckSNHAgCYXRUKFDiXOmG/4WR9WlswqKaGipskgCa4ErbAhzJ1kFBLHHWsWMLPMVmGkycZn0sh9HugxXLiaBUrE7FckhtKaiTzDOdC0RgSwzmOJhJsXc1R3xPJk1JaKqkbguQnIZEgSQfGUJR40oT9hhMXwzabzdQvukyTTAl3s6ekyhY1NTUhy2XfObPJ8p4a4We4f5c1za3NzAs6EsM5TkVFBWw2m/49TQipE7ZaJrGF30VbzMlQU1OTVKAibeEnT7ILDpu/FdXVtLORDNrCIREhIfrb4XA4UFJSYnS3copEU9jRYiM1dDEcSGBBFypGZeYFM4nhHMdms6GktBQAIIgiTbhpkEokPm0xJ4fL5UJlZWUSW/iUMilZklrUSX4IwS5Kq5YkyW0xt6G6uhqiSK/jZEh0jIUc2MLPBpoYTiSYOReCQOmvzwKUl5UBAIqLisi6kwbl5eVwulwkhg1m8ODBoUBFqd9raXyTp6qqCjabDUICbhLkhpIalZWV6g5Hf2I4FGRn5u3lbBG2XPYt1jTrvJmFWjZI1k1CEEWUl5cb3S3DIDFsAbRUam63O8s9MTeiKKJ28OBQHtwE0s2A0qqlgr6Nn4B1WPS3wma3o7Ky0uhu5Qx2ux3V1dXqc9wPVKwgNRwOB0pLS/sXajmwvZwtwq4o/btJaL8PInFcLheKi4sTdJNoQ1lpKRwORwZ6Zgwkhi2AlluRJSjgiN4ZMmSIas0J9h20oSF2tyA/Px9FRUXGdiyHCPu0Jma5HFRTE+UXT/TP4MGD1Wc4VJCnNyiHc+pUV1erOxx9FJARyGqZMpWVlRBEsV83CTHQjqqqKnJDSYGqqqrQM9yHdmAKxECn6Xc36OmwAGZerQ00kgo+Ygps/layCidJ2DLczxhLfghSNwm1FNArePWz4CA3idSpqqpSC8gEOnu9RrNqml1IZAO73Y6y0rK+re+KBCHYReObIpWVlYAi95lrWAh2AUwx/e4ciWELoFmEBUHIck/MTzJBdELIKkRiODnC1aUSixKnLfzkCZez7UcMh7aYzewLmC0S8WnVzpldSGSLqqpKiKE8wvEQAh0AaHxTRRu3voLoNJ9is48xiWGCSIJkcrRScFdqVFVVQRDF/oUaZZJImbAY7n/BQZkOUkMXEiFBFg/BT2ItHSorK1Xrey9ua7ki1LKFNm59LeiE0LmKioqM9MkoaIazAGQR5oe2XSwkEnxEW8wp4XA4QunV+hNq6vhS/tDk0XxU+/S3lAMQpG4a3xTRhURfVrWAmmO4uLg4U93KKfobY5Esw2mhCdy+FnS5MsYkhgkiCfLz81FQWJiQmwSJ4dQZPGiQOskqvadXo2T6qVNVVRUqbtK7GKbxTY+wkOhLDHfoadiI5OlPrGljT24+qRG2DPexuxE6R5ZhgrAYanq1tj6jxIGwmwRt4ydPIhW8BH8bBFGk4JgUcDqdKCsv79P6nguJ9LNJv1vMigwh2Gl6EZFNNJErBHsTw2rwIo1xaujjS2KYIIieDBo0KBQl3vsEAajb+CUlpfB4PBnqWe6QyDa+6G9DRXk5FZJJkeqqKojBPoKPKO1XWrjdbuTn5/eaTUIIklBLF02sib2MsWbRLAsVniKSo7CwEE6nq58g0A54PF49hatZITFsAU444QQAwNSpU7Pck9wgoeAjRYEY6MDgwWQVTgVta77XMVZkiIFO2sJPg/5Sf2lWebK8p05FRQXEXqyWYo5Y1LJJf5ZLIdCBwsIiuFyuTHYrZxAEAeXlZeqiuRfEYCcqKszvhkImFQtw9tlnY+jQoRgxYkS2u5IT6EKtuxVyQXwxJgTaAaaQWEuRcCnQ+GJYffkxslqmQWTqL9kVa9URKO1X2pSXl2P79u2AHIw5pwk48mdNncLCQtjtdsi9LeiCnSgbRNl80qGsrAx1dfvj7yApMoRgV05Y3skybAFEUcS3vvUt2k7mRCI5WikHbnpoQq03i4+2bUdWy9TpL4eo6G9HXl4e8vPzM9mtnCK8jR/7HJNlOH0EQUBpaanuchKFLEGQAzkh1LKJ+gyzuOnrtHHPhQUdiWGCSBLN2tu3Pyul/UqHwsJCuFyuXgPoBMofmjaaCOstUlwMdpBQS5NwgFesWNPcU0pLSzPap1yjrCy0jd+jZLA25jS+6dGXK0ou+WSTGCaIJCksLITb4+k7fyj5W6aFIAioqqrqNXCDKnelT59pqWQJguQnMZwmmkiI55etibVcEBLZpLS0VN3Cl/1Rx0USw1zQns94QYrac50LzzCJYYJIEkEQUFNd3WcAnUBpqdKmsrISgtQd19+SKkulT19b+LmSLinbaEIsXgCSGOyEzW5HYWFhpruVU5SUlAAAxED0Nr62rU9iOD208Yu7u5FDY0ximCBSoLq6GoIcACR/3POivw0ul4sqS6VBuJxtPItEBwRRzAlftWzh9XqR5/PFLQqhZUCg8U0P3TLci5tEWWkpFdxIE00MC1J8MUxzcHqExXDvPsNkGc4yTU1NuPbaa1FQUICioiLcdNNNaG/vfeu6qakJ//Vf/4Vvf/vb8Hg8GDJkCG6//Xa0tPRfTYwgIomMxI+HGGjXq3wRqaFbLuOkphIDHSgpLqag0DQpKy2FGO8ll0Pbn9lEFxI9F3SMQZS6csKilm00sdtTrAlSd9R5IjX6292IvMbMmFoMX3vttVi/fj3mzp2Ljz76CF9++SV++tOf9nr9/v37sX//fjz++ONYt24dXn31VcyZMwc33XRTBntN5ALhSPx4/pZBCJKftvDTpNfADcZCuS1pCz9dysrKVNGgyFHHc+kll00KCgogiqIuzHTkAKDIulWTSJ2wGI4eY00c0xinR99uEuqxXFhwmNassnHjRsyZMwfLli3DhAkTAABPP/00zj//fDz++ONxo/iPPfZY/OMf/9C/HzlyJB555BH86Ec/giRJZGUiEiYcid8Oucc5zQeTxHB69OrTKvsBRaItfA6Et/F7bjGTZZgHoiiiqLgYjR2dYKIjfJyEGjd6tQyHvi8qKsp0l3IKl8sFr9eLth6LDUBdgBQUFMDhcMT5pLkwrfpbtGgRioqKdCEMAJMnT4YoiliyZAkuueSShNppaWlBQUFBn0LY7/fD7/dHfQYAWlt7zzNL5DY+nw+SJEHpaILs74IUDIJJEuRgEEp7IyRJQkFBAT0jaeB2uyFJEuSOZiBifFl7EyRJgs/no/FNE4/Hoz/HUc9wRzMkSYLD4aAxTpN8nw+HGvdCdvoixvcIJEmCx+Oh8U0Tm82mzhNdrdHzhNwGOwBZlmmM0yQ/Px8th5qg2D36+Mr+LihdbfAVVwzo8dX6xnqk3ouBmZRHHnmEfetb34o5Xl5ezp577rmE2mhoaGBDhgxh9957b5/XzZw5kwGgL/qiL/qiL/qiL/qiL5N97d27t0+dN+Asw3fffTf+8Ic/9HnNxo0b075Pa2srvv/972PMmDF46KGH+rz2nnvuwYwZM/TvFUVBU1OTWvnGBAFSra2tqK2txd69e1FQUJDt7uQkNMbGQuNrPDTGxkLjazw0xsZixvFljKGtra3fAlgDTgz/6le/wo9//OM+rxkxYgSqqqpw6NChqOOSJKGpqanfQgdtbW2YOnUq8vPz8d577/Xr7+JyueByuaKOmdEPqaCgwDQPsFmhMTYWGl/joTE2Fhpf46ExNhazjW8iubwHnBguLy9PKDBm0qRJaG5uxooVK3DiiScCAP79739DURScdNJJvX6utbUVU6ZMgcvlwgcffAC3282t7wRBEARBEIS5MG1qtaOPPhpTp07FzTffjKVLl+Lrr7/G9OnT8cMf/lA3h9fV1WH06NFYunQpAFUIn3vuuejo6MDLL7+M1tZW1NfXo76+HrLcMycAQRAEQRAEkesMOMtwMrz++uuYPn06zj77bIiiiMsuuwz/8z//o58PBoPYvHkzOjvVNEErV67EkiVLAABHHXVUVFs7d+7EsGHDMtb3TOJyuTBz5swYVw+CHzTGxkLjazw0xsZC42s8NMbGksvjKzDWX74JgiAIgiAIgshNTOsmQRAEQRAEQRDpQmKYIAiCIAiCsCwkhgmCIAiCIAjLQmLYIB566CEIgoDGxsZsdyUuZ5xxBs444wz9+127dkEQBLz66qtZ6xNBEARBEESmITFMECmyYMECCIIQ92vx4sXZ7p7paG9vx8yZMzF16lSUlJT0uTjbuHEjpk6dCp/Ph5KSElx33XVoaGjIbIdNxLJlyzB9+nQcc8wxyMvLw5AhQ3DllVdiy5YtMdfS2KZGMvPBwoUL8b3vfQ9erxdVVVW4/fbb0d7enqWeD0yMmA8URcEf//hHDB8+HG63G8cffzz+/ve/G/yTDEyMmBPMPL6mTq1G8GPo0KHo6urqtxofEcvtt9+O73znO1HHeqbuI/qnsbERDz/8MIYMGYKxY8diwYIFca/bt28fTjvtNBQWFuLRRx9Fe3s7Hn/8caxduxZLly6F0+nMbMdNwB/+8Ad8/fXXuOKKK3D88cejvr4ezzzzDMaPH4/Fixfj2GOPBUBjy4P+5oPVq1fj7LPPxtFHH40nn3wS+/btw+OPP46tW7fik08+yXR3ByxGzAf33Xcffv/73+Pmm2/Gd77zHbz//vu45pprIAgCfvjDH2boJxsYGDEnmHp8GWEIM2fOZABYQ0NDtrsSl9NPP52dfvrp2e6GqZk/fz4DwN55551sdyUn6O7uZgcOHGCMMbZs2TIGgM2aNSvmultuuYV5PB62e/du/djcuXMZAPaXv/wlU901FV9//TXz+/1Rx7Zs2cJcLhe79tpr9WM0tqmT6Hxw3nnnserqatbS0qIfe/HFFxkA9umnnxrdTdPAez7Yt28fczgc7LbbbtOPKYrCTj31VDZ48GAmSZJxP8wAhPecYPbxJTcJg2lsbMSVV16JgoIClJaW4he/+AW6u7ujrpk1axbOOussVFRUwOVyYcyYMXj++edj2lq+fDmmTJmCsrIyeDweDB8+HDfeeGPUNYqi4KmnnsIxxxwDt9uNyspK/OxnP8ORI0f67Gc8n+Ef//jH8Pl8qKurw8UXXwyfz4fy8nLceeedMRX7Ur1vrtDW1gZJkrLdDVPjcrlQVVXV73X/+Mc/8IMf/ABDhgzRj02ePBnf+ta38PbbbxvZRdNy8sknx1h1R40ahWOOOQYbN27Uj9HY8qG3+aC1tRVz587Fj370IxQUFOjHr7/+evh8PhrjCHjPB++//z6CwSBuvfVW/ZggCLjllluwb98+LFq0iO8PMMDhPSeYfXxJDBvMlVdeie7ubjz22GM4//zz8T//8z/46U9/GnXN888/j6FDh+Lee+/FE088gdraWtx666149tln9WsOHTqEc889F7t27cLdd9+Np59+Gtdee22ML9rPfvYz/PrXv8Ypp5yC//7v/8a0adPw+uuvY8qUKQgGg0n3X5ZlTJkyBaWlpXj88cdx+umn44knnsBf//pXQ+9rJqZNm4aCggK43W6ceeaZWL58eba7lLPU1dXh0KFDmDBhQsy5iRMnYtWqVVnolTlhjOHgwYMoKysDQGPLi77mg7Vr10KSpJgxdjqdGDduHI1xkiTzzK5atQp5eXk4+uijY67TzluddOYEs48v+QwbzPDhw/H+++8DAG677TYUFBTgueeew5133onjjz8eAPDFF1/A4/Hon5k+fTqmTp2KJ598ErfddhsANeDiyJEj+Oyzz6IezN/97nf6/7/66iu89NJLeP3113HNNdfox88880xMnToV77zzTtTxROju7sZVV12FBx54AADw85//HOPHj8fLL7+MW265xbD7mgGn04nLLrsM559/PsrKyrBhwwY8/vjjOPXUU7Fw4UKccMIJ2e5iznHgwAEAQHV1dcy56upqNDU1we/352S5UN68/vrrqKurw8MPPwyAxjZdEpkP+hvj//znP5nutqlJ5pk9cOAAKisrIQhCzHUAsH//fuM7PMBJZ04w+/iSZdhgNDGr8V//9V8AgNmzZ+vHIoVwS0sLGhsbcfrpp2PHjh1oaWkBABQVFQEAPvroo14tre+88w4KCwtxzjnnoLGxUf868cQT4fP5MH/+/JR+hp///OdR35966qnYsWOH4fcd6Jx88sl49913ceONN+LCCy/E3XffjcWLF0MQBNxzzz3Z7l5O0tXVBQBxBZnb7Y66huidTZs24bbbbsOkSZNwww03AKCxTZdE5oP+xpjGNzmSeWa7urro2e6DdOcEs48vWYYNZtSoUVHfjxw5EqIoYteuXfqxr7/+GjNnzsSiRYvQ2dkZdX1LSwsKCwtx+umn47LLLsNvfvMb/PnPf8YZZ5yBiy++GNdcc43+AG7duhUtLS2oqKiI25dDhw4l3X+3243y8vKoY8XFxVG+wEbc16wcddRRuOiii/DPf/4TsizDZrNlu0s5hbZw9Pv9Mec0X/zIxSURS319Pb7//e+jsLAQ7777rv6M0tjyp+d80N8Y0/gmRzLPrMfjoWe7F3jMCWYfXxLDGabnFsL27dtx9tlnY/To0XjyySdRW1sLp9OJ2bNn489//jMURdE/9+6772Lx4sX48MMP8emnn+LGG2/EE088gcWLF8Pn80FRFFRUVOD111+Pe++eojYREhFzRtzXzNTW1iIQCKCjoyMqSIZIH23LTdu+i+TAgQMoKSmhbfw+aGlpwXnnnYfm5mb85z//QU1NjX6OxtYYIueD/sY48vdB9E8yz2x1dTXmz58PxljUe1j7rFXHntecYPbxJTFsMFu3bsXw4cP177dt2wZFUTBs2DAAwIcffgi/348PPvggKlqzN9eC7373u/jud7+LRx55BG+88QauvfZavPnmm/jJT36CkSNH4vPPP8cpp5yS0VVYtu47UNmxYwfcbjd8Pl+2u5JzDBo0COXl5XGDFJcuXYpx48ZlvlMmobu7GxdccAG2bNmCzz//HGPGjIk6T2NrDJHzwbHHHgu73Y7ly5fjyiuv1K8JBAJYvXp11DGif5J5ZseNG4eXXnoJGzdujHr2lyxZop+3GjznBLOPL/kMG0xkRggAePrppwEA5513HoCw5ZUxpl/T0tKCWbNmRX3uyJEjUdcA4YdL25q48sorIcsyfvvb38b0Q5IkNDc3p/6D9EG27ptt4lXgWbNmDT744AOce+65EEX68zKCyy67DB999BH27t2rH5s3bx62bNmCK664Ios9G7jIsoyrrroKixYtwjvvvINJkybFvY7GNnUSmQ8KCwsxefJk/N///R/a2tr06/72t7+hvb2dxjgFEn1mL7roIjgcDjz33HP6McYYXnjhBQwaNAgnn3xyRvudbXjPCWYfX7IMG8zOnTtx4YUXYurUqVi0aBH+7//+D9dccw3Gjh37/9u776ioru0P4N+hzFCkiIABgaGIRKMhNlBBYPRRFQSliOWBaHRZnvKeYFfEXrFGn+IDS6zEiI0IQUFR8CmxdwRsWECQqqF5fn9kzf05zoCDgLyE/VmLlcy+5567z3UcN3fOPRcA4OLiAj6fD09PT0yYMAHl5eWIjo6Gvr6+xFcTu3btwpYtW+Dj4wMLCwuUlZUhOjoampqa8PDwAAA4OjpiwoQJWL58Oa5duwYXFxcoKysjKysLcXFx2LBhA3x9fZt8jC113JYWEBAAVVVV9OvXD/r6+rhz5w62b98ONTU1rFixoqXT+1PavHkziouLuTuPjx8/jmfPngH44+ZTLS0tzJkzB3FxcRCJRJg2bRrKy8uxevVqdOvWDWPGjGnJ9P9nTZ8+HceOHYOnpyeKiorw448/SmwfNWoUANC5bQR5Pw+WLl2Kfv36wdHREePHj8ezZ8+wdu1auLi4wM3NrQVH8L+nKT8PjIyMEBoaitWrV6O6uhq9e/dGfHw80tLSsHfv3lZ3f0dTfyb86c9vyz3v469N/AS6O3fuMF9fX6ahocHatm3LpkyZwt69eyfR9tixY+zbb79lKioqzNTUlK1cuZLFxMQwACw3N5cxxtiVK1dYYGAgMzExYQKBgOnr67PBgwezzMxMqWNv376d9ezZk6mqqjINDQ3WrVs3NmPGDPb8+XOuzcdPoMvNzZV6wk9QUBBTV1evc2yfc9y/kg0bNjAbGxumo6PDlJSUmIGBARs1ahTLyspq6dT+tIRCIQMg80f8d4Exxm7dusVcXFyYmpoa09bWZiNHjmQvX75sucT/xzk6OtZ5Xj/+u0zn9vM05PMgLS2N9evXj6moqDA9PT02efJkVlpa2gJZ/29r6s+D2tpatmzZMiYUChmfz2fffPMN+/HHH7/giP53NMdnwp/5/PIY++i7d0IIIYQQQloJmtRICCGEEEJaLSqGCSGEEEJIq0XFMCGEEEIIabWoGCaEEEIIIa0WFcOEEEIIIaTVomKYEEIIIYS0WlQMN4HLly+jX79+UFdXB4/Hw7Vr11o6JUIIIYQQIgcqhhupuroafn5+KCoqwrp167Bnzx4IhcKWTqtOO3fuBI/Hk3rWeElJCWxsbKCiooJTp05JtJX18/LlS6m+y8rKMGPGDJiZmUEgEKBDhw7w9fXF27dvuTanT59GSEgIOnXqBDU1NZibm2PcuHEST9sjhBBCCPlS6HHMjZSdnY3Hjx8jOjoa48aNa+l0PktpaSlcXFxw48YNHDlyROqRoIsWLYKZmZlETFtbW+J1SUkJHB0d8ezZM4wfPx4dO3ZEQUEB0tLSUFlZCTU1NQDAzJkzUVRUBD8/P1haWiInJwebN2/GiRMncO3aNXz11VfNOlZCCCGEkA9RMdxI+fn5AKSLQ1kqKiqgrq7ezBk1TFlZGVxdXXHt2jX8/PPPcHd3l2rj7u6OXr161dvP7Nmz8fjxY1y5ckWicJ45c6ZEu6ioKNjb20NB4f+/lHBzc4OjoyM2b96MJUuWNHJEhBBCCCHyo2kSjRAcHAxHR0cAgJ+fH3g8HpycnLhtbdq0QXZ2Njw8PKChoYGRI0cC+KMonj59OoyNjSEQCGBlZYU1a9bg4ydj83g8TJkyBXFxcejSpQtUVVXRt29f3Lx5EwCwbds2dOzYESoqKnBycsKjR48alH95eTnc3Nxw5coVHD58GIMGDaqzbVlZGWpra2VuKy4uRmxsLMaPHw8zMzNUVVWhsrJSZlsHBweJQlgc09HRwd27dxuUPyGEEEJIY1Ex3AgTJkzAnDlzAABTp07Fnj17MHfuXG57TU0NXF1doa+vjzVr1mDYsGFgjMHLywvr1q2Dm5sboqKiYGVlhfDwcPzrX/+SOkZaWhqmT5+OoKAgLFy4EHfv3sXgwYPxww8/YOPGjZg0aRLCw8ORkZGBkJAQuXOvqKiAu7s7Ll++jLi4OAwePLjOtiKRCJqamlBTU4OXlxeysrIktp8/fx6///47OnbsCF9fX6ipqUFVVRV2dnZy3UxYXl6O8vJy6Orqyp0/IYQQQkiTYKRRUlJSGAAWFxcnEQ8KCmIA2KxZsyTi8fHxDABbsmSJRNzX15fxeDz28OFDLgaACQQClpuby8W2bdvGALCvvvqKlZaWcvHZs2czABJtZYmNjWUAmFAoZMrKyiw+Pr7OtgcPHmTBwcFs165d7MiRI2zevHlMTU2N6erqsidPnnDtoqKiGADWrl07ZmNjw/bu3cu2bNnC2rdvz9q2bcueP39eb06LFy9mANjp06frbUcIIYQQ0tToynAzmzhxosTrhIQEKCoqYurUqRLx6dOngzGGX375RSI+cOBAmJqacq9tbW0BAMOGDYOGhoZUPCcnR668Xr16BRUVFRgbG9fZxt/fH7Gxsfj73/8Ob29vLF68GImJiSgsLMTSpUu5duXl5QD+mNZx+vRpjBgxAhMnTkR8fDzevHmDH374oc5jnDt3DpGRkfD398eAAQPkyp0QQgghpKlQMdyMlJSUYGRkJBF7/PgxDA0NJQpZAOjcuTO3/UMmJiYSr7W0tABAqogVx9+8eSNXbtu2bQOfz4ebmxvu378v1z4AYG9vD1tbWyQnJ3MxVVVVAICnpyfatGnDxfv06QMzMzOkp6fL7OvevXvw8fFB165dsWPHDrlzIIQQQghpKlQMNyOBQCB1s1hDKSoqNijOProJry5dunRBQkIC3r17B2dnZzx9+lTunIyNjVFUVMS9NjQ0BAC0b99eqq2+vr7MAv3p06dwcXGBlpYWEhISpH45IIQQQgj5EqgY/sKEQiGeP3+OsrIyifi9e/e47V+KjY0N4uPjkZ+fD2dnZxQUFMi1X05ODvT09LjXPXv2BADk5eVJtX3+/LlEWwAoLCyEi4sLKisrkZiYCAMDg0aMghBCCCHk81Ex/IV5eHigtrYWmzdvloivW7cOPB5P5jq/zWngwIHYv38/Hj58CDc3N5SWlnLbZBXHCQkJ+O233yQezGFlZQVra2scPXoUr1+/5uJJSUl4+vQpnJ2duVhFRQU8PDyQl5eHhIQEWFpaNtPICCGEEEI+jR668YV5enpCJBJh7ty5ePToEaytrZGUlISjR48iNDQUFhYWXzwnHx8fREdHIyQkBF5eXjh16hRUVFTQr18/dO/eHb169YKWlhauXLmCmJgYGBsbc0vKia1btw7Ozs6wt7fHhAkTUFJSgqioKHTq1EniJsKRI0fi0qVLCAkJwd27dyXWFm7Tpg28vb2/1LAJIYQQQqgY/tIUFBRw7NgxLFiwAAcPHkRsbCxMTU2xevVqTJ8+vcXyGjNmDIqKihAWFgY/Pz8cOXIEAQEBOHnyJJKSkvD27VsYGBjg+++/R0REhNT8YJFIhFOnTmH+/PmYM2cO1NTU4O3tjVWrVkncVCdedzgmJgYxMTESfQiFQiqGCSGEEPJF8Zi8d1wRQgghhBDyF0NzhgkhhBBCSKtFxTAhhBBCCGm1qBgmhBBCCCGtFhXDhBBCCCGk1aJimBBCCCGEtFpUDBNCCCGEkFaLimFCCCGEENJqUTFMCCGEEEJaLSqGCSGEEEJIq0XFMCGk1QgODgaPx8OjR49aOpUmkZSUBDs7O7Rt2xY8Hk+ux5lnZWXBx8cHBgYGUFBQgLa2drPn2RxSU1PB4/GwcOHClk6l0UJDQ6Grq4uysrJG9dO/f3/Y2to2UVaEtB5UDBNCGuzRo0fg8Xjg8XhwdXWV2ebixYvg8XgIDg7+ssm1Eo8ePcKQIUOQk5ODMWPGICIiAsOHD693n9raWnh7eyMhIQGDBg3CggULMGvWrC+U8Z8Pj8eDk5NTsx4jKysLW7ZsQVhYGDQ0NLj4zp07wePxsGLFCql9qqurMXz4cPB4PPztb39DeXk5AGDhwoW4dOkSDhw40Kw5E/JXo9TSCRBC/tySkpJw5swZDBgwoKVTaVWSk5Px+++/Y+3atRgxYoRc++Tm5uLOnTv4/vvvsX379mbOkMhj8eLFUFZWxuTJk+Vq//btWwwdOhSJiYkYOnQo9u3bB4FAAAAYOHAgevTogYiICAQEBIDH4zVn6oT8ZdCVYULIZzM1NYWCggJmzpwJxlhLp9OqPH/+HABgaGjYrPuQ5lNYWIhDhw7Bx8dH4qpwXYqLi+Hs7IzExESMHTsWhw4d4gphsVGjRuHBgwc4c+ZMc6VNyF8OFcOEkM9mZWWF0aNHIzMzE4cOHZJrH1NTU5iamsrc5uTkJHU1a+HCheDxeEhNTUVsbCy6desGVVVVmJmZYePGjQAAxhjWrl0LKysrqKiowNLSErt3764zh/fv32PVqlWwtLSEiooKzMzMsGjRIlRXV8tsf+7cOXh6ekJXVxcCgQCWlpaYN28e3r59K9Huw3ms6enpcHFxgba2ttxX6G7dugV/f3/o6+tDIBDAzMwMoaGhKCws5NqIp6hEREQAAEQiETdlJTU1tc6+TU1N4ejoCACIjIzk9vlwzm1VVRWioqLQo0cPqKurQ0NDA/3798exY8ck+tqwYQN4PB5++ukniXhoaCh4PB7s7e0l4uKcx4wZ88lz8P79e+zYsQM2NjbQ0dGBqqoqjIyM4OnpWef4MjMz4ezsDA0NDWhpacHHx6fOeeEXLlzAoEGDoKOjAxUVFXz99deIiIiQ+LMU/zkCwNmzZ7lzxePxsHPnzs/O82P79+9HZWUl/Pz8Ptn2xYsXcHBwQHp6OsLDw7Fjxw4oKipKtRP3Jc6TEPJpNE2CENIoixYtwoEDBzBv3jwMHToUysrKzXKc9evXIzU1FUOGDMGAAQNw+PBhTJs2DWpqarh69SoOHz6MwYMHY+DAgThw4ACCgoJgamoKBwcHqb5CQ0Nx4cIF+Pv7o02bNjh+/DgiIiJw48YNqQJv69atmDx5MrS1teHp6Ql9fX1kZmZi6dKlSElJQUpKCvh8vsQ+6enpWLZsGUQiEcaPH48nT558cnznz5+Hq6srqqqq4OvrC1NTU2RkZGDDhg04ceIELl68CF1dXWhrayMiIgKpqak4e/YsN04Adf6SIR7ztWvXsGvXLjg6OnJzYcX/rayshJubG1JTU/Hdd99h7NixqK6uxsmTJzFkyBBs2rQJU6ZMAfBHAQ4AKSkp8PX15Y6RkpICALh06RIqKiqgrq4uERfvV5/Zs2dj1apVsLCwwIgRI6ChoYG8vDycP38eycnJUnN4L1++jFWrVkEkEmHChAm4evUq4uPjcfPmTdy6dQsqKipc27i4OAQGBkIgECAgIAD6+vpISkrCokWLkJiYiNTUVKioqMDU1BQRERGIjIyEUCiUmPf+3XfffVaespw+fRoA0KdPn3rb5eTkwNnZGTk5OVi5ciVmzJhRZ1sjIyMYGxtzfRNC5MAIIaSBcnNzGQDm6urKGGMsLCyMAWCbNm3i2mRkZDAALCgoSGJfoVDIhEKhzH4dHR3Zxx9LERERDADT0dFh2dnZXPzJkyeMz+czLS0t1qlTJ5afn89tu3jxIgPAPD09JfoKCgpiAJienh57+vQpF6+srGQODg4MAPvpp5+4+O3bt5mSkhKztrZmr1+/luhr+fLlDABbs2YNF0tJSWEAGAAWExMjc4yy1NbWMgsLCwaAnTp1SmJbeHg4A8BCQkJknpeUlBS5jyPOLyIiQmrbnDlzGAA2f/589v79ey5eWlrKevXqxfh8PsvLy2OMMfb+/XvWrl071rlzZ67d69evGY/HYwMHDmQAWGJiIrdt9OjRDAB78uTJJ3PU0dFhhoaGrKKiQmpbYWGh1FgAsAMHDki0Ex9v//79XKykpIRpaWkxgUDArl+/zsVra2tZQEAAA8AWLVok0Q8A5ujo2Kg866Onp8c6dOggc1tsbCwDwEaOHMkMDAyYoqIii46OlqtfHx8fBoDl5OTI1Z6Q1o6mSRBCGm3OnDnQ1tbG4sWLuTvbm9q0adNgbm7OvTY2Noa9vT1KSkowd+5c6OnpcdtsbW1hbm6O69ev19mXkZER95rP52Pp0qUAJL9e3rZtG2pqarBp0ya0a9dOoo8ZM2ZAT08P+/fvl+q/R48eck0JELtw4QKys7Ph7u4utTrHggULoKOjg3379qGqqkruPhvi/fv32Lp1KywsLLgpFGIaGhpYsGABqqqq8PPPPwP4/1UW7t69i5cvXwL4Y2oBYwzz5s2DQCCQmLOakpICc3NzGBsby5UPn8+XOQVAR0dHKubg4ICAgACJWEhICIA/rhqLHT16FCUlJQgJCcG3337LxRUUFLBq1SooKSk1eGpBQ/L8WFVVFQoKCtC+fft62+3duxcvXrxAeHg4xo0bJ1de4j6fPXsmV3tCWjuaJkEIabS2bdti1qxZmDVrFtasWdMsa7+Kv57+kIGBQb3b/vvf/8rsq3///lKxvn37QklJCVevXuViFy9eBAAkJibK/NpZWVkZ9+7dk4r37t1b5nHrIj6mrK/W27Rpg169eiEpKQn3799Ht27dGtS3PO7fv483b97A0NAQkZGRUtsLCgoAQGKsIpEIhw8fRkpKCgIDA5GSkgINDQ3Y29ujT58+3NSIhw8f4tmzZxg7dqxcuQwfPhxbtmxB165dMXz4cIhEIvTt2xeqqqoy2/fs2VMqJv5Fp7i4mIvVd45NTExgbm6OBw8eoKysTK6b2Rqa58fE88A/tc6zk5MTLly4gPXr10MkEsHFxeWTfYuL8devX8uVCyGtHRXDhJAmMXXqVGzevBlr167FpEmTmrx/TU1NqZiSklK922pqamT2JetqnKKiItq1a4eSkhIuVlRUBADcVWN5fepq38dKS0vr3U9c9IvbNTXxOG/fvo3bt2/X2a6iooL7/w/nDYuLYQcHBygpKUEkEmHx4sUoLS1t0Hxh4I+b88zMzBAbG4slS5ZgyZIlUFFRgb+/P9auXQtdXV2J9vW9L2pra7mYPOf4wYMHKC0tlasYbmieHxMXzb///nu97VxdXfHPf/4Tfn5+8PLywpEjR+Du7l7vPu/evQMAqKmpfXIchBBaTYIQ0kRUVVURGRmJ8vJymVcXxRQUFOosUj8sRJvTq1evpGK1tbUoLCyElpYWFxMXWqWlpWCM1fnzsYau7yo+jqy8AHBTEWQVfk1B3O+wYcPqHWdsbCy3T5cuXdC+fXukpKQgPz8fd+7c4QpekUiE2tpapKWlcSsryFsMKykpISwsDLdv30ZeXh727duH/v37Y/fu3Rg5cmSjx9hU57ixeWpra0NZWZn7RaQ+Xl5e3BQVHx8fnDx5st724j4/nDpECKkbFcOEkCYTFBSEb775BtHR0Xj48KHMNm3btkV+fr5UQVxRUYGsrKwvkSbS0tKkYhkZGaipqUH37t25mPjRtuLpEs1FfExZS3JVVFQgMzMTqqqqsLKyapbjd+7cGZqamsjMzKxzeTlZnJyc8PDhQ+zZswcAuAev9OnTB6qqqjhz5gxSUlJgaWn5WWsbGxoaIjAwEKdOnULHjh2RnJzMXfVsqPrO8dOnT5GdnQ1zc3OJq8IKCgoSV5ebOs+uXbsiNzdXrrnggwYNwpEjR8Dj8TB06FCp5e4+dP/+fSgrK+Prr7/+ZL+EECqGCSFNSFFREcuWLUN1dXWd84Z79+6N6upq7N27l4sxxjB79myJr+Gb04YNGyRuLqqqqsLcuXMBQGIZrUmTJkFJSQn/+Mc/ZC6PVlxcLDHH+HPZ2dnBwsICv/zyC5KTkyW2LVmyBIWFhQgMDJRawq2pKCkpYeLEiXj8+DHCwsJkFsS3bt1Cfn6+REx8tXflypXQ0dGBtbU1gD9uLLOzs8OePXvw4sULua8KV1ZWIj09XSpeUVGB8vJyKCsrQ0Hh8/7ZGjJkCLS0tBAbGysxFYQxhpkzZ6Kmpkbq0eE6Ojoyb0JrqjwdHR1RWVlZ542eH3N3d8fRo0ehoKAAX19fxMfHS7WpqqrC1atX0atXL5omQYicaM4wIaRJeXl5wd7eHufPn5e5fcqUKYiNjcW4cePw66+/Qk9PD2lpaSguLoa1tbXchUFj9OnTB9bW1ggICIC6ujqOHz+O+/fvY+jQoRg2bBjXrmvXrtiyZQsmTpwIKysreHh4wMLCAmVlZcjJycHZs2cRHByMf//7343KR0FBATt37oSrqys8PDzg5+cHoVCIjIwMpKamwsLCAitWrGjssOsVGRmJK1euYOPGjTh58iQcHBygr6+PvLw83Lx5E9evX0dGRgb09fW5fcRFbkFBAXx8fCQKQJFIxBX28hbD7969g52dHTp16oSePXvCxMQE5eXlOHHiBF6+fImwsDCpJ67JS1NTE9HR0QgMDIStrS0CAgKgp6eH5ORk/Pbbb7CxsUF4eLjEPgMGDMChQ4fg7e2N7t27Q1FREV5eXjAxMWmSPH18fLB+/Xr8+uuvct906eLiguPHj8PLywv+/v7Yv3+/xHs2LS0NlZWV8Pb2btD5IaRV++KLuRFC/vQ+Xmf4YxcuXODWgP14nWHGGDtz5gyztbVlAoGAtWvXjo0ePZq9evWq3nWGZa2nK143ODc3V2qbrL7E7bOzs9mKFStYx44dGZ/PZ0KhkC1cuJBVVlbKHM+lS5fY8OHDmaGhIVNWVma6urqsR48ebNasWezu3btcu/rW8ZXHjRs3mK+vL9PV1WXKyspMKBSyadOmsYKCAqm2Tb3OMGOM1dTUsG3btjE7OzumqanJBAIBMzExYW5ubmzr1q2svLxcap8OHTpIrTHNGGPp6ence+DFixdy5VdVVcVWrlzJXFxcmJGREePz+ax9+/bMwcGB7du3T2L94/rGIn5/ynrvnTt3jrm7uzNtbW3G5/NZp06d2Pz582WO7cWLF8zf35/p6uoyBQUFBoDFxsY2KM9P6dKlC+vSpYtUXLzO8PLly2Xud+bMGaampsaUlJTYwYMHuXhwcDDj8/kS624TQurHY0zG3R+EEEIIaXb/+c9/MG7cOJw/fx52dnaN6uvNmzcQCoXw9fVFTExME2VIyF8fFcOEEEJIC6mtrYW1tTUMDQ2RlJTUqL7mz5+PqKgoPHjwAB06dGiiDAn566Mb6AghhJAWoqioiJiYGNjZ2aGsrKxRfeno6GD37t1UCBPSQHRlmBBCCCGEtFp0ZZgQQgghhLRaVAwTQgghhJBWi4phQgghhBDSalExTAghhBBCWi0qhgkhhBBCSKtFxTAhhBBCCGm1qBgmhBBCCCGtFhXDhBBCCCGk1aJimBBCCCGEtFr/B2bHJesEMlIfAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_df = comp_df.copy()\n", "\n", "fig, ax = plt.subplots(figsize=(8,4))\n", "sns.violinplot(data=show_df, x='k', y='sim',\n", " order=[0] +k_values, \n", " #inner= 'quart', fill=False, #color='tab:green',\n", " width=0.9,\n", " ax=ax, )\n", "\n", "ax.axhline(0, color='black', linestyle='--', linewidth=1)\n", "ax.set_ylim(-0.2, 1.0)\n", "ax.set_xlabel('Number of few shots (K)', fontsize=14)\n", "ax.set_ylabel('Correlation', fontsize=14)\n", "ax.set_title('RPE1 prediction', fontsize=16)\n", "\n", "ax.set_xticklabels(['baseline \\nfrom K562'] + k_values, fontsize=12)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "c873a61d", "metadata": {}, "source": [ "The few-shot predictor outperforms the baseline even when only K=5 samples are used for few-shot training.\n", "\n", "For a given K, we can futher investigate how the prediction accuracy depends on the perturbation effect size. " ] }, { "cell_type": "code", "execution_count": 14, "id": "c6711c4e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'RPE1 prediction')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "k1 = 0\n", "k2 = 100\n", "show_df = comp_df[\n", " (comp_df['k'].isin([k1, k2]))\n", "].copy()\n", "show_df['color'] = 'deepskyblue'\n", "show_df.loc[show_df['k'] == k2, 'color'] = 'red'\n", "shuffled_df = show_df.sample(frac=1, random_state=42, replace=False)\n", "\n", "fig, ax = plt.subplots(figsize=(4, 4))\n", "ax.scatter(shuffled_df['true_effect_size'], shuffled_df['sim'], s=2, alpha=1, rasterized=True, color=shuffled_df['color'])\n", "\n", "ax.set_xlim(1, 24)\n", "ax.set_ylim(-0.2, 1)\n", "\n", "# Add legend to the plot\n", "handles = []\n", "handles.append(plt.Line2D([0], [0], marker='o', linestyle='', color='deepskyblue', markersize=2))\n", "handles.append(plt.Line2D([0], [0], marker='o', linestyle='', color='red', markersize=2))\n", "plt.legend(handles=handles, labels=[f'baseline', f'K = {k2}'], title='Categories')\n", "\n", "ax.set_xlabel('True Effect Size')\n", "ax.set_ylabel('Similarity')\n", "ax.set_title('RPE1 prediction', fontsize=16)" ] }, { "cell_type": "markdown", "id": "5545bbb4", "metadata": {}, "source": [ "We can see a strong dependency of the prediction accuracy on the perturbation effect size, suggesting that the low accuracy predictions may be partially due to low signal-to-noise ratios for perturbations with small effects." ] }, { "cell_type": "code", "execution_count": null, "id": "bfd7aa07", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "scmg", "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": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }