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"source": [
"\n",
"\n",
"# Tumoroscope in PyMC\n",
"\n",
"[](https://www.python.org)\n",
"[](https://github.com/jhrcook/tumoroscope-pymc/actions/workflows/pytest.yaml)\n",
"[](https://github.com/psf/black)\n",
"[](https://github.com/pre-commit/pre-commit)\n",
"[](http://mypy-lang.org/)\n",
"[](http://www.pydocstyle.org/en/stable/)\n",
"[](https://www.gnu.org/licenses/gpl-3.0)\n",
"\n",
"This package builds the ['Tumoroscope'](https://www.biorxiv.org/content/10.1101/2022.09.22.508914v1) (Shafighi *et al.*, 2022, bioRxiv preprint) model with the probabilistic programming library [PyMC](https://www.pymc.io/welcome.html).\n",
"'Tumoroscope' is a \"probabilistic model that accurately infers cancer clones and their high-resolution localization by integrating pathological images, whole exome sequencing, and spatial transcriptomics data.\"\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation\n",
"\n",
"> As this package provides a model produced using PyMC, I recommend first creating a virtual environment using `conda` and installing the PyMC library.\n",
"> You can follow their instructions [here](https://www.pymc.io/projects/docs/en/latest/installation.html).\n",
"\n",
"You can install this package using `pip` either from PyPI\n",
"\n",
"```bash\n",
"pip install tumoroscope-pymc\n",
"```\n",
"\n",
"or from GitHub\n",
"\n",
"```\n",
"pip install git+https://github.com/jhrcook/tumoroscope-pymc.git\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Background\n",
"\n",
"Below is the abstract of the paper the introduced the 'Tumoroscope' package:\n",
"\n",
"> Spatial and genomic heterogeneity of tumors is the key for cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their high-resolution localization by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.\n",
"\n",
"Shadi Darvish Shafighi, Agnieszka Geras, Barbara Jurzysta, Alireza Sahaf Naeini, Igor Filipiuk, Łukasz Rączkowski, Hosein Toosi, Łukasz Koperski, Kim Thrane, Camilla Engblom, Jeff Mold, Xinsong Chen, Johan Hartman, Dominika Nowis, Alessandra Carbone, Jens Lagergren, Ewa Szczurek. \"Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues.\" *bioRxiv*. 2022.09.22.508914; doi: https://doi.org/10.1101/2022.09.22.508914\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use\n",
"\n",
"> *If viewing this package on PyPI, I apologize, but all images below are not viewable.\n",
"> Please click on \"Home\" in the sidebar or [here](https://github.com/jhrcook/tumoroscope-pymc) to go to the GitHub repository to see the full demonstration of this package.*\n",
"\n",
"Below is a simple example of using this library.\n",
"A small dataset is simulated and used to construct the Tumoroscope model in PyMC.\n",
"The \"Inference Button\" of PyMC is then used to sample from the posterior distribution.\n",
"\n",
"First all of the necessary imports for this example are below."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:00.931723Z",
"iopub.status.busy": "2022-10-25T16:53:00.931208Z",
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"outputs": [],
"source": [
"from math import ceil\n",
"from pathlib import Path\n",
"\n",
"import arviz as az\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pymc as pm\n",
"import seaborn as sns\n",
"\n",
"from tumoroscope import TumoroscopeData, build_tumoroscope_model\n",
"from tumoroscope.mock_data import generate_simulated_data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:33.312612Z",
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"shell.execute_reply": "2022-10-25T16:53:33.321495Z"
}
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"outputs": [],
"source": [
"# %matplotlib inline\n",
"%config InlineBackend.figure_format='retina'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulated data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The input to `build_tumoroscope_model()` is a `TumoroscopeData` object.\n",
"This object contains all of the data size parameters, model hyperparameters, and the observed data.\n",
"It also has a `validate()` method for checking basic assumptions about the values and data.\n",
"Below is an example of constructing a `TumoroscopeData` object with random data."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:33.326498Z",
"iopub.status.busy": "2022-10-25T16:53:33.326188Z",
"iopub.status.idle": "2022-10-25T16:53:33.346054Z",
"shell.execute_reply": "2022-10-25T16:53:33.344849Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Tumoroscope Data\n",
" Data sizes:\n",
" K: 5 S: 10 M: 100\n",
" Hyperparameters:\n",
" zeta_s: 1.0 F_0: 1.0 l: 100 r: 0.1 p: 1.0\n",
" Counts data:\n",
" D: provided A: provided"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TumoroscopeData(\n",
" K=5,\n",
" S=10,\n",
" M=100,\n",
" F=np.ones(5) / 5,\n",
" cell_counts=np.random.randint(1, 20, size=10),\n",
" C=np.random.beta(2, 2, size=(100, 5)),\n",
" D_obs=np.random.randint(2, 20, size=(100, 10)),\n",
" A_obs=np.random.randint(2, 20, size=(100, 10)),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this example, we'll use the provided `generate_simulated_data()` function to create a dataset with 5 clones, 10 spatial transcriptomic spots, and up to 50 mutation positions.\n",
"In this data-generation function, the number of reads per cell per mutation position are randomly sampled from a Poisson distribution.\n",
"Here, I have provided a relatively high rate of $\\lambda = 2.5$ to ensure the read coverage is very high for demonstration purposes of Tumoroscope.\n",
"The default value, though, provides more realistic read coverage for a good spatial transcriptomic study.\n",
"This function returns a `SimulatedTumoroscopeData` object containing the true underlying data (including a table of cell labels in each spot) and the input `TumoroscopeData` object as the `sim_data` attribute."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:33.390405Z",
"iopub.status.busy": "2022-10-25T16:53:33.389981Z",
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"shell.execute_reply": "2022-10-25T16:53:33.435571Z"
}
},
"outputs": [
{
"data": {
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" spot cell clone\n",
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"source": [
"simulation = generate_simulated_data(\n",
" K=5, S=10, M=50, total_read_rate=2.5, random_seed=8\n",
")\n",
"simulation.sim_data.validate()\n",
"simulation.true_labels.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below is a visualization of the cells in each spot.\n",
"The spots are represented by the horizontally-arrange boxes and each point is a cell, colored by its clone label.\n",
"To be clear, this information is only known because we simulated the data and the point of 'Tumoroscope' is to figure out these labels."
]
},
{
"cell_type": "code",
"execution_count": 5,
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"execution": {
"iopub.execute_input": "2022-10-25T16:53:33.440551Z",
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"shell.execute_reply": "2022-10-25T16:53:33.771612Z"
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"outputs": [
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"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 170,
"width": 567
}
},
"output_type": "display_data"
}
],
"source": [
"np.random.seed(3)\n",
"plot_df = (\n",
" simulation.true_labels.copy()\n",
" .assign(\n",
" y=lambda d: np.random.normal(0, 0.5, len(d)),\n",
" x=lambda d: d[\"spot\"] + np.random.uniform(-0.2, 0.2, len(d)),\n",
" )\n",
" .astype({\"clone\": \"category\"})\n",
")\n",
"_, ax = plt.subplots(figsize=(7, 0.7))\n",
"sns.scatterplot(\n",
" data=plot_df, x=\"x\", y=\"y\", hue=\"clone\", ax=ax, alpha=0.8, edgecolor=None, s=25\n",
")\n",
"for i in range(1, simulation.sim_data.S):\n",
" ax.axvline(i - 0.5, lw=1, color=\"k\")\n",
"ax.set_xlim(-0.5, simulation.sim_data.S - 0.5)\n",
"ax.set_xlabel(\"spot\")\n",
"ax.set_ylabel(None)\n",
"ax.set_yticks([])\n",
"ax.legend(\n",
" loc=\"lower left\", bbox_to_anchor=(0, 1), title=\"clone\", ncol=simulation.sim_data.K\n",
")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below are the mutations assigned to each clone.\n",
"In real-world applications, this information comes from the bulk DNA data.\n",
"The zygosity of each mutation for each clone is also known (not shown here)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:33.777920Z",
"iopub.status.busy": "2022-10-25T16:53:33.777561Z",
"iopub.status.idle": "2022-10-25T16:53:34.231915Z",
"shell.execute_reply": "2022-10-25T16:53:34.230840Z"
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 208,
"width": 612
}
},
"output_type": "display_data"
}
],
"source": [
"cg = sns.clustermap(\n",
" simulation.clone_mutations.T,\n",
" figsize=(6, 2),\n",
" dendrogram_ratio=(0.1, 0.15),\n",
" cmap=\"Greys\",\n",
")\n",
"cg.ax_heatmap.set_xlabel(\"position\")\n",
"cg.ax_heatmap.set_ylabel(\"clone\")\n",
"cg.ax_heatmap.set_xticklabels([])\n",
"cg.ax_heatmap.tick_params(\"x\", size=0)\n",
"cg.ax_col_dendrogram.set_title(\"Mutations in clones\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, the heatmaps below show the total read counts (left) and number of alternative read counts (right) per spot and mutation position.\n",
"This is the data gathered from the spatial transcriptomics."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:34.236769Z",
"iopub.status.busy": "2022-10-25T16:53:34.236033Z",
"iopub.status.idle": "2022-10-25T16:53:35.197646Z",
"shell.execute_reply": "2022-10-25T16:53:35.196759Z"
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {
"image/png": {
"height": 464,
"width": 686
}
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(ncols=2, figsize=(8, 5))\n",
"sns.heatmap(simulation.sim_data.D_obs, cmap=\"flare\", ax=axes[0])\n",
"axes[0].set_title(\"Total read counts\")\n",
"sns.heatmap(simulation.sim_data.A_obs, cmap=\"viridis\", ax=axes[1])\n",
"axes[1].set_title(\"Alternative read counts\")\n",
"\n",
"for ax in axes:\n",
" ax.tick_params(size=0)\n",
" ax.set_xlabel(\"spot\")\n",
" ax.set_ylabel(\"position\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model\n",
"\n",
"The model is built by passing a `TumoroscopeData` object to the `build_tumoroscope_model()` function.\n",
"This function has other arguments including the option for wether to use the \"fixed\" form of the model where the number of cells per position is assumed to be accurately known.\n",
"As in the paper, the \"unfixed\" form of the model is default (i.e. `fixed = False`).\n",
"By default, the input data is validated, though this can be skipped incase I have applied incorrect assumptions to the data (if you believe this is a the case, please open an [Issue](https://github.com/jhrcook/tumoroscope-pymc/issues) so I can adjust the validation method.)\n",
"\n",
"Below, I build the model with the simulated data and show the model structure."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:35.203083Z",
"iopub.status.busy": "2022-10-25T16:53:35.202779Z",
"iopub.status.idle": "2022-10-25T16:53:37.612684Z",
"shell.execute_reply": "2022-10-25T16:53:37.611546Z"
}
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tumoroscope_model = build_tumoroscope_model(simulation.sim_data)\n",
"pm.model_to_graphviz(tumoroscope_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the model built, we can sample from the posterior distribution using PyMC's \"Inference Button\": `pm.sample()`.\n",
"To make development easier, I have cached the posterior trace object, but sampling for this simulation originally took about 5 minutes."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:37.617792Z",
"iopub.status.busy": "2022-10-25T16:53:37.617168Z",
"iopub.status.idle": "2022-10-25T16:53:37.960951Z",
"shell.execute_reply": "2022-10-25T16:53:37.959685Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Retrieving from cache.\n"
]
}
],
"source": [
"cache_fp = Path(\".cache\") / \"tumoroscope-simulation-example.netcdf\"\n",
"if not cache_fp.parent.exists():\n",
" cache_fp.parent.mkdir()\n",
"\n",
"if cache_fp.exists():\n",
" print(\"Retrieving from cache.\")\n",
" trace = az.from_netcdf(cache_fp)\n",
"else:\n",
" with tumoroscope_model:\n",
" trace = pm.sample(500, tune=500, chains=2, cores=2, random_seed=7)\n",
" _ = pm.sample_posterior_predictive(\n",
" trace, extend_inferencedata=True, random_seed=7\n",
" )\n",
" trace.to_netcdf(cache_fp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Posterior analysis\n",
"\n",
"We can now inspect the model's results.\n",
"(Of course you would also want to conduct various diagnostic checks on the MCMC sampling process, but I have skipped that here.)\n",
"We can look at the estimates for the proportion of each clone in each spot of the spatial transcriptomics by inspecting $H$."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:37.966304Z",
"iopub.status.busy": "2022-10-25T16:53:37.965897Z",
"iopub.status.idle": "2022-10-25T16:53:38.372880Z",
"shell.execute_reply": "2022-10-25T16:53:38.372167Z"
}
},
"outputs": [
{
"data": {
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\n",
"\n",
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\n",
" \n",
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\n",
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\n",
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mean
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sd
\n",
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hdi_3%
\n",
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hdi_97%
\n",
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mcse_mean
\n",
"
mcse_sd
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ess_bulk
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ess_tail
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r_hat
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H[s0, c0]
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0.399
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0.067
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0.281
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0.527
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0.005
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0.004
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157.0
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412.0
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1.00
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\n",
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\n",
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H[s0, c1]
\n",
"
0.121
\n",
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0.051
\n",
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0.041
\n",
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0.226
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0.004
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0.003
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199.0
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309.0
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1.01
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\n",
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\n",
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H[s0, c2]
\n",
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0.267
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0.060
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0.162
\n",
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0.379
\n",
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0.005
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0.003
\n",
"
156.0
\n",
"
521.0
\n",
"
1.02
\n",
"
\n",
"
\n",
"
H[s0, c3]
\n",
"
0.177
\n",
"
0.046
\n",
"
0.092
\n",
"
0.258
\n",
"
0.003
\n",
"
0.002
\n",
"
295.0
\n",
"
561.0
\n",
"
1.01
\n",
"
\n",
"
\n",
"
H[s0, c4]
\n",
"
0.036
\n",
"
0.033
\n",
"
0.000
\n",
"
0.094
\n",
"
0.002
\n",
"
0.001
\n",
"
315.0
\n",
"
538.0
\n",
"
1.01
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \\\n",
"H[s0, c0] 0.399 0.067 0.281 0.527 0.005 0.004 157.0 \n",
"H[s0, c1] 0.121 0.051 0.041 0.226 0.004 0.003 199.0 \n",
"H[s0, c2] 0.267 0.060 0.162 0.379 0.005 0.003 156.0 \n",
"H[s0, c3] 0.177 0.046 0.092 0.258 0.003 0.002 295.0 \n",
"H[s0, c4] 0.036 0.033 0.000 0.094 0.002 0.001 315.0 \n",
"\n",
" ess_tail r_hat \n",
"H[s0, c0] 412.0 1.00 \n",
"H[s0, c1] 309.0 1.01 \n",
"H[s0, c2] 521.0 1.02 \n",
"H[s0, c3] 561.0 1.01 \n",
"H[s0, c4] 538.0 1.01 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h_post = az.summary(trace, var_names=[\"H\"])\n",
"h_post.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot below shows the posterior distribution for the estimated proportion of the cells in each spot that belong to each clone."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:38.378004Z",
"iopub.status.busy": "2022-10-25T16:53:38.377328Z",
"iopub.status.idle": "2022-10-25T16:53:38.955161Z",
"shell.execute_reply": "2022-10-25T16:53:38.954187Z"
}
},
"outputs": [
{
"data": {
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Aq9sdfC8gIiISD6ZpAu0tWwIBryQnwzCCrZoCPxfRoJBfRESGvM2bNzPcbie9H7N9nRiMUisPkUFl/Pjx1Ph8uOl/Gy0fFpWmn3HjxkWhMhGJhnXr1pFptzOmH2OMAHLtdtatWxetskREREQGHYX8IiIy5G3bsoVRfn+/xxnl97Nt8+YoVCQi0TBz5kwsoDgKY5UAPsti1qxZURhNRKJh4+efc5jfxNaPi/QGBmP9fjZ+9lkUKxMREREZXBTyi4jIkOb3+9m1ezejojDWKKC4pER9fUUGifHjxzMsP58dURhrB5CTlcXkyZOjMJqI9JdpmmzZvJmxUbhTZyywfccOvF5v/wsTERERGYQU8ouIyJBWVlaGx+tlZBTGGgn4TVOL74oMEoZhcMLJJ7Oro6dlf+y025l7wgnYbHp7LDIYHDhwgDaPJ2qv3z6/n7KysiiMJiIiIjL46FOMiIgMaXv37gVgeBTGGnbQmCISf3PmzOGA309DP2b7tmBR7vcz5/jjo1iZiPTHvn37ACiIwliBMQJjioiIiAw1CvlFRGRIKykpwWWzkRmFsdIwyLTbKS0tjcJoIhINRx99NAD9ub9mH2B1GktE4m///v0A5EZhrCzAYRjBMUVERCQ0e/fuxTAMDMNg+fLl8S5HeqGQX0REhrTq6mqybTaMfiza11k2BtXV1VEZS0T6r6CggBGFhZT0Y4x9QG5WFqNGRWP1DhGJhrq6OtLtduxReP02Oi7S19XV9b8wERERkUFIIb+IiAxpbW1tOK3+L9oX4LRMWltbozaeiPTf9JkzKetHL/0yw2DajBkYRnQuBopI/zU0NJAexX+TaRbU19dHbTwRERGRwUQhv4iIDGkpKSn4oxgS+A2DlJSUqI0nIv03bdo0ygAzgr78FhZlNhvTZ8yIfmEiEjGv14sjiuM5sPB6vVEcUURERGTwUMgvIiJDWkFBAfWmidWPRTk7qwMKCwujMpaIRMeMGTPwmCaRdNuuAZr9fmYo5BcZVPx+f1Q/rNo6xhQREREZihTyi4jIkHbEEUfQaprURGGsBiwa/X4OP/zwKIwmQ8mGDRsoKyuLdxlJa9q0adhtNoojODawYK9CfpHBJ3rN9trHUksuERERGaoU8ouIyJB21FFHYbfZ2BmFsXYCBnDMMcdEYTQZSr73ve9x++23x7uMpJWamsrUKVPYE8Gxe4FJEyaQlZUV5apEpD/S0tKIZnMdr2GQmpoaxRFFREQS0zvvvMNVV13F1KlTyc7OJjMzk2nTpnHRRRexYsUKGhoawh7T4/Hw0EMPMX/+fIYNG4bT6WTkyJGcf/75/O1vf8M0zR6PXbx4MYZhMGHCBADq6uq44447OPLII8nIyCA3N5fTTjuNv//97yHV0tLSwm9/+1vmz5/PiBEjcDqdDB8+nHPPPZdly5YN2Tv7otnmUEREZNBJT0/n2GOPZdOGDZxg9m9O4CbDYOaMGeTm5kanOBlSdu3aFe8SktrcE0/kH9u34zdN7IQ2W9fEYqfdzoITTxzg6kQkXGlpabit6M3l93SMKSIikqxaW1u58sorWbly5SHPbdu2jW3btvHCCy9w5513smTJkpDHLSoq4rzzzmPLli1dHt+/fz8vv/wyL7/8Mn/605944YUXyM/P73WsrVu3ct5557F3794uj7/99tu8/fbbvPfeezz44IM9Hv/hhx+ycOFCSktLuzxeWVnJ66+/zuuvv87/+3//jxdffJERI0aE/HdMBJrJLyIiQ97Z55zDXtOkvh83/jdjsRM465xzoleYiETNySefTKtpsjeMY0qARr+fk08+eYCqEpFI5eXl0ez3R7SgdncaTZO8vLyojCUiIpJoTNPkwgsvDAb8kydP5v777+ftt99m/fr1/POf/+S2227jiCOOCGvcpqYmzjzzzGDAf9FFF/Hiiy/y0Ucf8dRTT3H66acDsHbtWr7yla/0Oou+paWFCy64gOrqan7yk5/w5ptv8tFHH/HnP/+Zww47DIA//OEPvPrqq90e//nnnzN//nxKS0sZPnw4d955J2+88QYbNmzg1Vdf5frrr8fhcLBu3TouvPBCvN5o3jMYf5rJLyIiQ97pp5/O0t/8hs98Pk6NcIzPae/le+aZZ0azNBGJkqlTpzJ65Eg+qagg1FUzPgEK8vI46qijBrAyEYlEfn4+JtACZPZzLDcWHtOioKAgCpWJiIgkngceeIDXX38dgIULF7Jy5UpcLleXfRYsWMBdd91FRUVFyOP+7Gc/Y/fu3QD85Cc/4a677go+N3v2bP7rv/6L//7v/+bvf/877733Hg8//DDXXnttt2NVVlbi9Xp57733OPLII7uMc8YZZzBr1iza2tp46KGH+NKXvtTlWMuy+Pa3v01zczNHH300b7zxBoWFhV32Offcc/nKV77CggUL+OCDD1ixYgVXXnllyH/XwU4z+UVEZMjLyMjglFNP5TO7PeIxPrPZOOGEE9SqR2SQMgyDBV/9KptsNlpDmPnrxuJzm43zv/IVbDa9JRYZbIYPHw5AXRTGCowRGFNERCSZmKbJvffeC8CYMWNYsWLFIQF/gM1mY/To0SGN63a7eeSRRwCYMWNGty1+DMPgoYceCl5o763VDsDPf/7zLgF/wBFHHMFFF10EtLfuOdiqVav47LPPAFixYsUhAX/Al7/8ZS6++GIAli1b1mstiUafaEREJCmcdfbZVPj9HIjgtv86LPaZJmerVY/IoHb++edjGQYbQtj3M8BtWVxwwQUDXZaIRGDMmDEA1ERhrOqDxhQREUkmn3zySbBH/dVXX01mZn/vkWu3fv166urqgPbFc+09TKrLzs7m0ksvBWDz5s2Ul5d3u59hGCxatKjH882ePRuA2tra4HkDXnjhBaD97t6+7tI97bTTgPb+/UNpEV6F/CIikhTmzp1LqsvF5giO3Qw47Hb17RYZ5AoKCjj99NP50G7H6uWCnoXFOpudeSefPOQW3BIZKjIzM8nNyqIqCmNVA2kuV5+L/YmIiAxFGzZ8MQUmEHBHw8aNG4PbJ5xwQq/7dn6+83GdFRYW9tpar/PreGNjY5fnPvroI6B9AWHDMHr974YbbgDA4/FQUxON6QSDg0J+ERFJCi6XixNPPJGtEbTs2WKzMWf2bNLT0wegMhGJpgsvuogqv5+iXvYpAypMPxdceGGsyhKRCEycNIn9URhnPzBhwgQMw4jCaCIiIomlquqLS+ajRo2K2ridA/K+Js6MHDmy2+M66+vzducWmwfPwD9w4ECvx/akpaUlouMGIy28KyIiSePU007jzTVrqAdyCO2DfjMWRabFpWecMaC1iUh0HHXUUQzLz2djTQ0TethnI5CTlcWcOXNiWJmIhGvSEUfw1sZN0M9b6Q/Y7cyePDlKVYmIiCSugbrg3de4lhV+29xwBEL/efPm8f/+3/8L+bhQ1x9IBAr5RUQkaZx44ok47Ha2+P2cGOIxW2l/wzJv3ryBLE1EosRms3Hyqafyzj//CX6z23122u2cePLJOBx6KywymE2ePJln/T7cgCvEi/MH82FxwDQ54ogjoluciIhIgui8CG1ZWRlTp06Nyrid2+dUVFQwZcqUHvfdv/+Le/MGon1eQUEB+/fvp7KykpkzZ0Z9/ESgdj0iIpI0srKymH3ccWyyhf7yt8kwmDVzJnl5eQNYmYhE0zHHHEOV309zN3353Vjs9/s55phjYl+YiIRl6tSpWED3y/OFZj/gt6yoBRoiIiKJ5rjjjgtuv/XWW1Ebt3OY/sEHH/S677p167o9LlqOPfZYALZv305RUW+NO4cuhfwiIpJUzjz7bIpMk/peFuUMaMZil2Vx1jnnxKAySVSm2f1scYmfwCyi7oLBCsACBX4iCWD8+PE4U1Io68cYpbTf4XP44YdHqywREZGEcvTRRzN27FgAHnnkEZqamqIy7uzZs8nNzQXgscceO6RPfkBjYyNPPvkkADNmzIjqugABF1xwQXD7nnvuifr4iUAhv4iIJJVTTz0Vh8PB5yHsuxEwbDbOUD9+6YXX6413CXKQMWPG4ExJ6XbBzv20B37jxo2LdVkiEiaHw8GUyZMp6ccYpcCk8RNITU2NVlkiIiIJxWazccsttwBQUlLCZZddhsfj6XZf0zQpKwvt8rrL5eKqq64CYNOmTfzsZz87ZB/LsrjhhhuCi//ecMMNkfwV+vRf//VfTJ8+HYA//vGPPProo73uv3HjRl566aUBqSVeFPKLiEhSyczMZN68eXxmt/e572c2G8cff3xwdoJIdxTyDz42m41xh42lspvnDgBjRo7E6XTGuiwRicCMmTMptUe+fkap3c70mUdGsSIREZHEc/3113NOxx3qzz33HLNmzeJ3v/sd77zzDhs2bODll1/mzjvvZNq0aTz88MMhj3vHHXcwadIkAO666y6+9rWv8c9//pOPP/6YZ555hjPPPJMVK1YAcNJJJ/Gd73wn+n85wG63849//IPMzEwsy+Kqq67iy1/+MitWrOCDDz7g448/5pVXXuFXv/oV8+bNY9asWaxZs2ZAaokXrTYmIiJJ55xzz+XNNWs4AAzvYSG/WiyKTYvLzz03tsVJwulpFozE16TJR7CluOiQxXcrbTYmaQFOkYQxffp0nvT7aAYywlx8tw2LA34/M2bMGJjiREREEoTNZuP555/n8ssv5+mnn2b79u3cdNNN/R43KyuLf//735x33nls3bqV5557jueee+6Q/ebNm8eLL76IPYTJdpGaNWsW77zzDhdffDE7duzg1Vdf5dVXX+1x/+zs7AGrJR4U8ouISNI54YQTSHOlssndxvAe9tkEOB0O5s2bF8vSJAG53e54lyDdOPzww1nzxhuYWNg6gkELi/2GjdPVm1skYQRuvS8Bwl1Jo4z2NTiOPFIz+UVERNLT03nqqadYvXo1y5YtY+3atVRUVOBwOBgzZgwzZszg4osv7tLfPhQTJkzg008/5c9//jNPPfUUGzdupKGhgfz8fI499li+9a1vsWjRImy2gW8oc9RRR7F582Yef/xxnnvuOdavX09lZSWmaVJQUMDUqVM55ZRTWLhwYZcFiYcChfwiIpJ0nE4nJ518EtvWvMX8HhZN3WqzMef440lPT49xdZJoFPIPTocffjhu06QOyO94rAlo9vuDtxSLyOA3atQocrKy2NfYGHbIXwKkuVK1BoeIiEgn8+fPZ/78+SHtO2HCBCzL6nM/p9PJ9ddfz/XXXx92PcuXL2f58uV97rd48WIWL17c534Oh4PLLruMyy67LOxaEpl68ouISFI6ed48Sk0/TRz6hqUVi2LT5GTN4pcQKOQfnAJBfufFdwPbh2smv0jCMAyD6TNmUGaE16oH2kP+adOnxWTmoIiIiEg86d2OiIgkpdmzZwOwu5vn9tJ+e/+cOXNiWJEkKoX8g1NBQQGZ6emHhPzOlBRGjx4dr7JEJAIzjjySUsOG1c2F+d6U2+1MVz9+ERERSQIK+UVEJCkVFBQwbswY9nbz3B5geEGBgkAJSVtbW3A7lFtZJTYMw2DixIldQv4DwIRx4zSrVyTBTJ06lRbTT10YxzRiUef3M23atIEqS0RERGTQ0CccERFJWkcfdxzFdvshjxfb7Rw9xBbhkYHTOeT3eDxxrEQOdvjkyVTav1iC6oDdzqTJk+NYkYhEYurU9m78pWEcU9bx55QpU6Jej4iIiMhgo5BfRESS1lFHHcV+v5+WTrf/e7Ao9/s56qij4liZJJLOIX9ra2scK5GDTZo0iUrTjw8LE4sDlqV+/CIJKD8/n4K8PMrDOKYcyExPZ9SoUQNVloiIiMigoZBfRESS1syZMwHY1+mxUsAEZs2aFY+SJAF1DvY7B/4Sf5MmTcK0LCqBWsBjmsEFeUUksUyeMoWKMBbfrQAmT56MEcGCvSIiIiKJRiG/iIgkrdGjR5OTldUl5N8HpLlSGT9+fLzKkgSjkH/wmjhxItDei3//QY+JSGKZPGUKFWGsp7Hf7uAIteoRERGRJKGQX0REkpZhGEyfMYOyTrP8SoBp06Zi76ZXv0h3Wlpaut2W+MvKyqIgL48DtAf9menpFBQUxLssEYnAxIkTafD7aaXvBc69WFT7fbqoJyIiIklDIb+IiCS1qdOmdZkZuN/uYMq0aXGsSBJNS0sLdlv74q5NTU1xrkYONn7CBKqAKmDcuHFq3SGSoCZMmAC0X7DrSyVgoTt3REREJHko5BcRkaQ2ceJEGjsW3/VgUeP3qWe3hKWpqYkMVw6gmfyD0WFjx1LncFBrszNWbbhEEtbYsWMxDIOqEPYN7KPWeyIiIpIsFPKLiEhSGzNmDAA1tC/M2fkxkVA0NjaSlpIFGDQ2Nsa7HDnIyJEjqbMs6o32bRFJTC6Xi+EFBSGH/LnZ2WRmZg50WSIiIiKDgkJ+ERFJaiNGjACgoeM/gGHDhsWtHkk89XX1uBxpuFJSqa+vj3c5cpDCwkJa/H7q/X4KCwvjXY6I9MO48eOpDmG/atpn/ouIiIgkC4X8IiKS1LKysjAMg2agueOxvLy8eJYkCaa2rg5XSjqpznTq6uriXY4cJCcnp9ttEUk8ow87jDq7o8/9au12Rh92WAwqEhERERkcFPKLiEhSs9vtpKem4gbaAIfdTmpqarzLkgRSV1uLy5GOy55ObW1t3wdITHVu15GRkRHHSkSkv0aPHk2NZWJh9bpfHTBq1KiY1CQiIiIyGCjkFxGRpJfWEfJ7OrZFQuXxeGhsaiTdmYnLkUFlZWW8S5KDOJ3O4LYu4IkktuHDh+M2Tdp62ceHRaPfr5BfREREkopCfhERSXqpqal4aA/5U12ueJcjCSQQ6qelZJKWksn+igNxrkgOlpKSEty22+1xrERE+mv48OHAF2vodEfr64iIiEgyUsgvIiJJLy09HS/gRTN9JTzl5eUAZLhyyHDlcKByP36/P85VSWedg32F/CKJLRDcK+QXERER6Uohv4iIJL309HTcgLtjWyRUpaWlGIaNNGc2ma5c/H6/WvYMMjbbF293FfKLJLa8vDwAmnrZJ/Bcfn7+gNcjIiIiMlgo5BcRkaSXmZ1NG+0L72ZmZ8e7HEkg+/btIystF7vNTlZqXvAxGTw0k19k6HC5XGSkpdHYyz5NgMNu77LotoiIiMhQp5BfRESSXlZWFm12O22GQXZOTrzLkQRStLeIDGcuAOmuHOw2B0VFRfEtSrro3JPf4XDEsRIRiYac7Gxaenm+GcjJysYwjFiVJCIiIhJ3CvlFRCTp5eTk0GIYtNjs5CjklzDs3LmLnNT2vs82w0ZOegG7d++Oc1XSmavTYtpac0Mk8WXn5NDay/OtQHZ2VqzKERERkSGqpaWFe++9l7lz55Kfn09mZibTp0/nhz/8IcXFxfEu7xCaziQiIkkvNzeXZtPCabRvi4SioaGB6poqJk86MfhYlquQ7dt3xLEqOVhaWlpwWyG/SOLLyc3tdeHdViBLrfdERCTBff9/b6eiqjbeZQyIkYV53H/3/8W7jF7t2rWLBQsWsG3bti6Pb926la1bt/LII4/w+OOPc/7558epwkMp5BcRkaSXm5tLq+mnDTSTX0IWeMOXmz4i+Fhe+gg27X4bn8+n1jCDROd2PVpYWyTxZWRmUmmzgWl1+3wbMEyv5SIikuAqqmrZnn9i3zsmoqr3411Br5qamvjKV74S/Lx39dVX841vfIO0tDRWr17Nr371K+rr67nkkkt47733OOqoo+JccTt9+hQRkaQXmL1voZn8Erpt27bhdLiCC+4C5GWMwOvzsmfPHiZPnhzH6qQ7WnhXJPFlZGTgNmyAv9vn3TabFt0VERGRiP3mN79h69atANxzzz3ccsstwedOOukk5s+fz2mnnUZLSws33XQT//nPf+JVahfqyS8iIkkvu9Nt/dm6xV9CtGnTJvIyRnZZ3DEvfQQ2w8bmzZvjWJmIyNCVnp6Op5c1db02W5c2XSIiIiKh8nq9/O53vwNg+vTp3HzzzYfsc9JJJ3HllVcCsHr1atavXx/TGnuikF9ERJJeVlZWt9siPbEsi42fbyQ/fVSXxx32FPIyh7Np06Y4VSYiMrSlp6fjtrpv1QPgRq25RERE5AvvvPMOV111FVOnTiU7O5vMzEymTZvGRRddxIoVK2ho+GK1nzfffJO6ujoALr/8cmy27qPzxYsXB7efffbZgSw/ZGrXIyIiSS8jI6PbbZGelJSUUN9Qz1EjxhzyXH7aaD755NM4VCUiMvT1FfJ7LEsz+UVERITW1lauvPJKVq5cechz27ZtY9u2bbzwwgvceeedLFmyBIC33347uM/pp5/e49hz5swhIyOD5uZm1q5dG/XaI6GQX0REkl7nGX8KBiQUn332GWBQkDn6kOcKsw5jx66PqaqqorCwMPbFiYgMYWlpaXhMEwswOLRvj1shv4iISNIzTZMLL7yQ119/HYDJkydz3XXXMWfOHNLT0ykvL+fdd9/lySef7HLcli1bgtvTpk3rcXyHw8Hhhx/OZ5991uWYeFLILyIiSc/lcgW3dYu/hOKTTz4hP3M4TkfqIc8NyzoMgE8//ZSzzjor1qWJiAxpaWlpWIAXcB70nImF11TILyIikuweeOCBYMC/cOFCVq5c2eVzP8CCBQu46667qKioCD62b98+oP0O/9zc3F7PMXbsWD777DMqKytxu92HjB9r6skvIiJJz+H44pq303lwZCDSlWVZfLz+YwozDuv2+dSUDHIzCvn4449jXJmIyNAXCPA93TznBSx0wV5ERCSZmabJvffeC8CYMWNYsWJFjwG8zWZj9Ogv7s5ubGwEIDMzs8/zdG7129TU1J+So0Ihv4iISCedA3+R7pSVlVFZVcmwrHE97lOQcRjrP1LILyISbYEA393Nc+6D9hEREZHk88knn1BaWgrA1VdfHVJgH9DW1gaENvmv84WD1tbWMKuMPoX8IiIiImH4+OOPMQyDYVlje9xnRNY4yspL2b9/fwwrExEZ+gKz5roL+dsO2kdERESSz4YNG4Lbp512WljHpqa2t2P1eLq7Z7Art/uLdyODoVWgQn4RERGRMHz00XoKMkfhdPTcc3FY9jjAUMseEZEoC8zGa+vmubaD9hEREZHkU1VVFdweNWpUWMdmZWUBobXfaW5uDm4PhvceCvlFREREQmSaJh999BHDMntu1QPgcqRRkDmCjz78KEaViYgkh8CH6O5uitdMfhEREenMMIyw9j/ssPZ115qbm6mrq+t138AivcOGDYv7orugkB+A4uJifvjDHzJ9+nQyMjLIz89n7ty5/OY3v6GlpSUq59i8eTM33ngjs2bNIjs7G6fTybBhw5g/fz73339/cGEHERERGbx27dpFY2MDI7LH97nvsMxxfPjhh1iWFYPKRESSQ2ZmJoZhdBvyBx7LycmJZUkiIiIyiBQWFga3y8rKwjp2xowZwe2tW7f2uJ/P52PXrl0ATJ8+PcwKB0bSh/yrVq3iqKOO4r777mPr1q20tLRQW1vLhx9+yC233MJxxx3H7t27+3WO++67j6OPPpoHH3yQjRs30tjYiNfrpaqqijfffJMf/OAHzJo1i88++yxKfysREREZCB999BEOewoFmaP73HdE9njq6uvYs2dPDCoTEUkOdrudjLQ0upuK1QKkOByDYjadiIiIxMdxxx0X3H7rrbfCOvaUU04Jbq9Zs6bH/T766KNgu5558+aFWeHASOqQ/9NPP+XSSy+lvr6ezMxM/u///o93332Xf//731x99dUAbNu2jQULFoTUi6k7Tz75JD/84Q/x+Xw4nU6+//3vs2rVKj744AMef/zx4A9PUVERX/7yl6mvr4/a309ERESia/1H6ynMHIPd5uhz34Ks9v0++kgte0REoiknO7vHkD87KyvsW/NFRERk6Dj66KMZO3YsAI888khYme4ZZ5wRvCPwscce6/Gu7OXLlwe3Fy5cGHmxUZTUIf9NN91ES0sLDoeD1157jdtuu42TTjqJM888k4cffph77rkHaL89Y+nSpRGd46677gpuP/vssyxdupTzzz+fuXPn8s1vfpO3336br33tawCUl5fz6KOP9v8vJiIiIlHn9Xr59LPPGJ7Vez/+AIcthcKs0Xz88YYBrkxEJLnk5uXR3M3jzUCuWvWIiIgkNZvNxi233AJASUkJl112GR6Pp9t9TdPs0tLH6XTy3e9+F4AtW7bwm9/85pBj3nvvvWB+e/rpp3P88cdH+68QkaQN+T/88EPefPNNAK688kpOOumkQ/a5+eabg32Vfvvb3+L1esM6R0NDAxs3bgTabxVZsGBBt/vdeeedwe133303rHOIiIhIbGzduhW3uy3kkB+gMHMsGzZswO/3D2BlIiLJJS8/n2YOna3f3PGciIiIJLfrr7+ec845B4DnnnuOWbNm8bvf/Y533nmHDRs28PLLL3PnnXcybdo0Hn744S7H3nLLLUyZMgWAW2+9lWuuuYbVq1fz/vvv86tf/Ypzzz0Xn89HWloav/3tb2P9V+tR3/eaD1HPP/98cPuKK67odh+bzcZll13Gj3/8Y2pra3nzzTeDPyCh6HyVaNKkST3ud/jhhwe33W53yOOLiIhI7HzyySc4HS5yM0aEfMzwrHFsKn2HnTt3MnXq1AGsTkQkeeTl57PHbgO/2eXxZpudwzotticiIiLJyWaz8fzzz3P55Zfz9NNPs337dm666aaQjs3KymLVqlWcf/757Nixg4cffviQCwHZ2dn8/e9/55hjjol+8RFK2pD/7bffBiAjI4PZs2f3uN/pp58e3F67dm1YIX9hYSH5+fnU1NT0unhvYDVmIHilSERERAaXDRs2UJA5BpsR+o2Q+RkjcdgdfPLJJwr5RUSiJC8vj+6667bYDHJzc2NdjoiISNSNLMyDqvfjXcaAGFmYF5PzpKen89RTT7F69WqWLVvG2rVrqaiowOFwMGbMGGbMmMHFF1/MBRdccMixRxxxBBs2bOAPf/gDTz31FDt37sTj8TB27FjOP/98vve97zF+/PiY/D1ClbQh/5YtW4D2b5rD0fP/hmnTph1yTDi+853v8Otf/5qPP/6Yl19+mfPOO++QfQJ9++12O1dddVXY5xAREZGB5fP52LhxI5ML54Z1nN3mID9jFJ9/9jlf//rXB6g6EZHkkpeXR7NpYmFhdGrb02Sa5Ktdj4iIDAH33/1/8S5hyJg/fz7z588P+7iMjAxuvfVWbr311gGoKvqSMuRva2ujqqoKgMMOO6zXffPy8sjIyKC5uZl9+/aFfa7bb7+djz76iDfeeIOFCxdyww03cNZZZ1FYWMju3bv54x//yJo1a7Db7fz+978PrgEQjpKSkl6fLy8vD3tMERER+cLu3btpa2ujMHNM2McWZIzh008/xbIsDOPQHtIiIhKe/Px8fJZFG5DW8ZgXi1bTIi8vNrMDRURERAaTpAz5Gxsbg9uZmZl97h8I+ZuaursptHeZmZm8/PLLLF++nF//+tfcd9993HfffV32+drXvsatt97KCSecEPb4AGPHjo3oOBEREQnNpk2bsBk28sLoxx9QkDmaLeXvU15ezujRowegOhGR5BII8pv4IuRv7vhTM/lFREQkGYXeVHYIaWtrC247nc4+93e5XAC0trZGdL6PPvqIlStX9tiX/4033uCxxx6joaEhovFFRERkYG3atIm8zBE4bClhH1uQOTo4hoiI9F8gyO88BSuwrZn8IiIikoySMuRPTU0Nbns8nj73d7vdAKSlpfWx56GefvppzjjjDP7zn/8wa9YsnnvuOaqrq/F4POzatYtf/vKXeL1e/vjHP3LyySdTUVER9jn27dvX63/r1q0Le0wRERH5wuZNW8hNC38WP4DLkUZ2eh7btm2LclUSqoULF3a7LpKIJKbOM/kDNJNfREREkllStuvJysoKbofSgqe5uf0tYyitfTrbv38/ixcvxu12c+SRR/Luu++SkZERfH7SpEn8+Mc/Zu7cuZxzzjls2rSJG2+8kaeeeiqs8/S1roCIiIhErqWlhdKyEuaMPzLiMXJcw9m8aXMUq5JwfP/73493CSISRZmZmTjsdlr8/uBjgU91ubm5calJREREJJ6SdiZ/YWEh0PeitbW1tcGQP9ze90888UTw2Ntuu61LwN/ZWWedxVlnnQXAs88+S21tbVjnERERkYGza9cuLMsiN2N4xGPkZowIjiMiIv1jGAa52dmHtOvJysjA4UjKeWyD1quvvsrtt98e7zJERESGvKQM+QGmT58OwM6dO/H5fD3ut3Xr1kOOCdWWLVuC28cdd1yv+86ePRsA0zTZvn17WOcRERGRgbNr1y5sho3s1IKIx8hNG0ZrWyvl5eVRrExEJHnl5uYGW/RAe7ue3JyceJUjPfjlL3/J22+/He8yREREhrykDflPOeUUoL0Vz/r163vcb82aNcHtefPmhXWOzrNIeruQAOD1ers9TkREROJr9+7dZKcXYLdF/vqckz4MgL1790apKhGR5JZXUHBIyJ+nfvyDju5gExERiY2kDfkvuuii4PayZcu63cc0TVasWAG0zxSZP39+WOeYOHFicLuv2QtvvfUW0H7r6YQJE8I6j4iIiAycvXv3kunM69cYaSmZpDicFBUVRakqEZHklpefT4vti4+zLYZBrkJ+ERERSVJJG/LPnTuXU089FYBHH32U995775B97rvvvmDLne9973ukpKR0eX758uUYhoFhGCxZsuSQ4xcsWIBhGAD83//9H6Wlpd3W8vDDD/PRRx8BcOKJJ1JQEHk7ABEREYmu4uJ9ZKX2LzgyDIPstHz27dsXpapERJJbTk5Ol5C/1WbXorsiIgnsD3/4A/fdd1+8yxBJWEkb8gP87ne/Iy0tDZ/Px7nnnsuvfvUr3n//fVavXs0111zDrbfeCsCUKVO4+eabwx5/2rRpXHHFFQCUlpZy7LHHBnsSfvLJJ7z00kt861vf4pprrgHAbrfzy1/+Mnp/QREREekXt9tNbW0NGa7cfo+V5simrLSs/0WJiEh7T/5OrWBaDBTyi4gksH/84x+88MIL8S5DJGEldfP3Y489ln/84x98+9vfpqGhgdtuu+2QfaZMmcKqVavIysqK6BwPPfQQzc3N/OMf/6CyspLbb7+92/0yMjJ4+OGHOeOMMyI6j4iIiETfgQMHsCyLDFd2v8fKcOVQWqaZ/CIi0ZCdnU2L348J2DBoNk2ys/v/u1pEREQkESX1TH6Ar371q3z22Wd8//vfZ8qUKaSnp5Obm8ucOXO4++672bBhA0cccUTE47tcLp544gn+85//cNlllzFlyhQyMjJwOBzk5+dz0kkn8dOf/pStW7eyaNGiKP7NREREpL+qqqoASHdGdrG/s7SULGqqq7UIoYhIFGRnZ2MBbsCHhUchv4iIiCSxpJ7JHzB+/HiWLl3K0qVLwzpu8eLFLF68OKR958+fH/bCvSIiIhJfgZA/NSWz32OlOTPx+rw0NjYqiBIR6afA79FWwHvQYyIiIiLJJuln8ouIiIj0pK6uDoc9hRS7s99jpaakA1BfX9/vsUREkl1mZvvF11agreOxSFusioiIiCQ6hfwiIiIiPaivr8eVkhaVsZyO9nHq6uqiMp6ISDILBPpttAf9nR8TERERSTZq1yMiIiLSg+bmZpyO1KiMlWJ3AdDU1BSV8UREklkg0G8FAvdaBWb3i4iIiCQbhfwiIiIiPWhubsZh63+rHvgi5G9ubo7KeCIiySwtrf3uKDdgdjyWnp4et3pERERE4ilm7XpaWlpoaWnp8fkHHniAU089lenTp3P++efzz3/+M1aliYiIiHSrra0NuxGdOREOW0pwTBER6R+Hw0Gq04mb9qDfMIxg8C8iIiKSbGIS8r/00ktkZWUxevRoGhsbD3n+//v//j9uuukm3n33XbZt28arr77KhRdeyD333BOL8kRERES61dbWhi1KIb9hGNjtDtxud1TGExFJdqmpqXgAD5DmcmEYRrxLEhEREYmLmIT8r776KpZlcdFFFx2yGNLatWtZvnw50H575bHHHktqaiqWZfGTn/yETZs2xaJEERERkUN4PV7shj1q49ltdrxeb9TGExFJZulpacGZ/Gmp0Vk/RURERJJbU1MTb731Fr/5zW+49NJLmThxIoZhYBgGEyZMiHd5PYpJT/73338fwzCYP3/+Ic89/PDDAIwePZr33nuPww47jH379nHKKadQUlLCn/70J37/+9/HokwREUli11xzjWZYyyG8Ph+GEb05ETbDhs/ni9p4IiLJLDU1FW+nbRERkaHiZz/6Po3V5fEuY0BkFYzizl/fH+8yevTVr36VN998M95lhC0mIf+BAwcAmDx58iHPvfLKKxiGwY033shhhx0GwNixY7nxxhu59dZbWbNmTSxKFBGRJPetb30r3iXIIGRZVpRHVCsJEZFoSU1LwwtYKOQXGYq2bduG3+9nxowZ8S5FJOYaq8v57tih2d3k9/viXUHvOn8GzMvLY86cObz33ns0NTXFsaq+xSTkr6ysBCAzM7PL45s3b6aqqgrDMLjgggu6PDdnzhwA9u7dG4sSRURERA5hs0W7s6GlntEiIlHi6ujJH9gWkaHl2muvxefz8dZbb8W7FBFJIosWLeI73/kOc+fO5YgjjgBgwoQJCvkB7Pb2XrY1NTVdHn/77bcBGDZsGNOmTevyXF5eHtC+4J2IiIhIPDgcDkyrJWrj+U1/8H2RiIj0T2pqKq203yOlkF9k6FGLQxGJh+985zvxLiEiMVl4d8yYMQB88sknXR5ftWoVhmFw6qmnHnJMfX09AIWFhQNen4iIiEh3UlNd+M3ofMC0LAuf30daWlpUxhMRSXau1FR8hoEPcDqd8S5HREREBqF33nmHq666iqlTp5KdnU1mZibTpk3joosuYsWKFTQ0NMS7xKiISch/6qmnYlkWDz74IFVVVQB8+OGHvPLKKwB86UtfOuSYLVu2ADBy5MhYlCgiIiJyiLS0NPyWt+8dQ2BafizLVN9oEZEoSUlJwW8Y+DBwulzxLkdEREQGkdbWVhYtWsQpp5zCo48+yvbt22lsbKS5uZlt27bxwgsvcPnll7N06dJ4lxoVMWnXc91117F8+XL27NnDpEmTmDJlCps3b8bn85Gfn8/Xv/71Q475z3/+g2EYHHPMMbEoUUREROQQ2dnZeE13VMby+NqCY4qISP8FQn7Dppn8IiIi8gXTNLnwwgt5/fXXAZg8eTLXXXcdc+bMIT09nfLyct59912efPLJOFcaPTEJ+Y877jjuvfdebrnlFpqamvj444+B9jdlf/7zn8nKyuqyf319PatWrQLgnHPOiUWJIiIiIofIycnB7Y1OT363ryU4poiI9J/T6cRnGBhW+xoqIiIiIgAPPPBAMOBfuHAhK1euxHXQXX8LFizgrrvuoqKiIh4lRl3M3gl9//vf5+yzz+bpp5+moqKCUaNG8c1vfpOpU6cesu+bb77J8ccfD8DZZ58dqxJFREREuigsLKTV04xpmdiM/nU5bPU0AVBQUBCN0kREkp7D4cAP2AzN5BcREZF2pmly7733Au3rxK5YseKQgD/AZrMxevToWJY3YGI63WHWrFnMmjWrz/0uvPBCLrzwwhhUJCIiItKzYcOGYVkWrZ4mMlz9a7PT4m3EMAyF/CIiUZKSktIR8huayS8iIiIAfPLJJ5SWlgJw9dVXk5mZGeeKYiMmC++KiIiIJKJRo0YB0Oyu6/dYzW11FBYOUxAlIhIlgZn8ftoDfxGJr7179/LGG2/EuwwRSXIbNmwIbp922mlxrCS29ClTREREpAejRo3CMAya3HUMZ1y/xmpy13HY+DFRqkxERFJSUvBZFjbDUMgvMgjceeed7NmzR22XRSSuqqqqgtuBSVvJIKohf3FxcTSHCxo3rn8fqkVEREQi4XQ6GT5sOI1tNf0eq9lbx5wJM6JQlYiIgGbyiww2e/bsiXcJIiJdGIYR7xJiJqoh/8SJE6M5HND+zfD5fFEfV0RERCQU4yeMp2R7db/GMC2ThpYaTVwQEYkip9OJ37LwoZBfRERE2hUWFga3y8rKmDp1ahyriZ2o9uS3LGtA/hMRERGJlwkTJtDk6d9M/mZ3PX7Tx4QJE6JTlIiIBNv1+EHrnYiIiAgAxx13XHD7rbfeimMlsRXVd0LLli2L5nAiIiIicTdx4kQaWurw+b047JHNFG1obe8LqZBfRCR6UlJS2mfym5Zm8g9ylmUlVcsEERGJn6OPPpqxY8eyb98+HnnkEW6++WYyMzPjXdaAi2rIf/nll0dzOBEREZG4a29HaNHYVkNexoiIxqhvrSIjI5OCgoLoFiciksQCs/e9lqmZ/IOcaZrY7fZ4lyEiIknAZrNxyy238N3vfpeSkhIuu+wynnjiCZxO5yH7mqZJRUUFo0ePjkOl0aV3QiIiIiK9CMy+r2+t7FfIP2niRM1iFBGJos6z9zWTf3BTG14REYml66+/npdeeonXX3+d5557jlmzZnHdddcxZ84c0tPTqaio4P3332flypUsWrSIJUuWBI/duXMna9eu7TJeU1NT8M/ly5d3ee7LX/4yI0eOHOi/Up8U8ouIiIj0Ij09neHDRlDf0XInEk2eao47/OQoViUiIp1n7yvkH9wU8kt/+Hw+3a0jImGx2Ww8//zzXH755Tz99NNs376dm266KaRj165dyxVXXNHtc9XV1Yc8t3r1aoX8IiIiIong8MMnsXfr/oiONU0/9S01HW1/REQkWjqHfmoFM7iZphnvEiTB+Hy+4Lbb7VbIL0knq2AUv98X7yoGRlbBqJicJz09naeeeorVq1ezbNky1q5dS0VFBQ6HgzFjxjBjxgwuvvhiLrjggpjUM9Ci+ltyxYoV0Rwu6LLLLhuQcUVERERCMXHSRD7/dHNExza56zBNv0J+EZEoU7uexOH3++NdgiQYj8cT3G5rayMjIyOO1YjE3p2/vj/eJQwZ8+fPZ/78+SHvv3jxYhYvXjxwBQ2QqIb8ixcvjnqvWcMwFPKLiIhIXE2YMIGm1nq8fg8p9kMXbOpNoM2PQn4RkejqPLNXs3wHN83kl3C1trYGt91udxwrERFJDFF/J6ReeyIiIjLUBAL6htYqCjJHh3VsQ2sV2dk55ObmDkBlIiLJSyF/4tBMfglXW1tbcLtz4C8iIt2L6juhPXv2RHM4ERERkUFh/PjxGIZBQ2t12CF/fWu1ZvGLiAyAzn341ZN/cOvcX10kFJ1D/s7bIiLSvaiG/OPHj4/mcCIiIiKDQmpqKsOHjwi23glHk6eGORPnDUBVIiLJTTP5E4dCfglXS0tLcFshv4hI32zxLkBEREQkEUyaNJFGd01Yx5imn4bWGiZMmDAwRYmIJDGF/IlD7XokXJ1b9HQO/EVEpHsK+UVERERCMGHChLBD/iZ3Habp192OIiIDQO16Eodm8ku41JNfRCQ8MZ/u0NDQwNNPP817771HRUUFLS0t/OUvf+ny4besrIy6ujpSU1OZNGlSrEsUEREROcSECRNoaq3D5/fgsDtDOqahrRpAPflFRAZA52BfM/kHN6/XG+8SJMF0DvYV8ouI9C2m74T+8Ic/cPvtt9PY2AiAZVkYhkFzc3OX/dasWcO3vvUtUlNTKSkpIT8/P5ZlioiIiBwiMCGhoa2G/IyRIR3T0FpDRkYmeXl5A1maiEhS0kz+xOHxeOJdgiSYlpYWbIaB0zDUrkdEJAQxa9ezZMkSvvvd79LQ0IDT6WT27Nk97vv1r3+dUaNG4Xa7eeaZZ2JVooiIiEiPxo0bB0BjW+gtexrbqhk3bhyGYQxUWSIiSUshf+LQTH4JV2trK6k2Gy6bTTP5RURCEJOQf8OGDdx1110AfPvb36aiooJ169b1XJTNxiWXXIJlWbz++uuxKFFERESkV5mZmeTl5ocV8jd5apkwQf34RUQGgkL+xOF2u+NdgiSYlpYWnIaBEy28KyISipiE/A888ACWZXHSSSexYsUKcnJy+jzmpJNOAuDzzz8f6PJEREREQjJ23Fga22pD2teyLBrbahk7duwAVyUikpw69+G32WJ2k7pEQCG/hKulpQUX4LQszeQXEQlBTN4JrVmzBsMwuOGGG0I+ZsKECQCUlpYOUFUiIiIi4Rk3biwt3rqQ9nX7WvF42zjssMMGtigRkSSlhXcTh0J+CVdraytOy1LILyISopiE/OXl5QBMnTo15GNcLhegNwMiIiIyeIwZM4bG1losy+pz3yZ3+4x/hfwiIgND7XoGt859+BXSSrhaWlpIMU2cpklzc3O8yxERGfRiEvI7nU4gvMV2AhcGcnNzB6IkERERkbCNGTMGj8+Nx9/W577NbXUAjB49eoCrEhFJTp1b9CjkH3w6B/ttbX2/bop01tLcjMuycAEtTU3xLkdEZNCLScgfmMG2adOmkI957bXXADjiiCMGpCYRERGRcI0aNQqAZnd9n/s2eerJysomPT19oMsSEUlKmsk/uHUO+TWTX8LV0tyME9pDfi28KyLSp5iE/GeeeSaWZbFs2bKQ9t+9ezePPvoohmFwzjnnDHB1IiIiIqEJJ+RvdtcH9xcRkejrHOxr4d3Bp3Mwq5BWwtXS3Ny+8G7HtoiI9C4m74RuuOEGHA4H77zzDkuWLOl1348++ohzzz2XpqYmXC4X11xzTSxKFBEREelTVlYWaalpIYX8LZ4GRo9WyC8iMlDUrmdwC/ZRNxTyS/haWlpw0h7y604QEZG+xSTknzJlCj/96U+xLIu77rqLE044gXvuuSf4/CuvvMLdd9/NWWedxQknnMCePXswDINf//rXmgEnIiIig4ZhGIwYMYIWT0Of+7b5GvU+RkQkRhTyDz6BkN9IN7RwqoSttbUVFx3terSmQ1IxTTPeJYgkJEesTvTTn/4Ur9fLL3/5Sz788EM++ugjDMMA4JZbbgnuZ1kWhmFwxx138N3vfjdW5YmIiIiEZNToUezdvL/XfSzLoqmtgREjRsSoKhGR5KZ2PYNPU8diqWa6GdwWCVVLWxsuIAVoc7sxTVP/zpOEvtcikYnpv5qf//znvP/++3zta18jLS0Ny7K6/JeSksJ5553H22+/zZ133hnL0kRERERCMnLkSNr8jb3u0+ptwjT9jBw5MkZVicRObW0tzz//fLzLEOlCgdDg09TUBAZYaRb1DX23uRMJ8Pl8eLzeYE9+gDbN5k8amskvEpmYzeQPmDNnDk8//TQ+n4/Nmzdz4MAB/H4/BQUFHHnkkaSlpcW6JBEREZGQjRw5kqa2+uDdh91pcbe389FMfhmKHn30UV588UXOOusssrKy4l2OiAxSjY2N2F12TKepkF/CEujBH+jJD+3tn9LT0+NWkwwsy7KC2wr5Jd6Ki4tZtWoVb775Jp988gklJSX4/X4KCwuZPXs23/jGN7jkkktwOGIeq/cqbtU4HA6OOuqoeJ1eREREJCIjR47E6/Pg8bfhcnQ/OaHZ0x5mqCe/DEX79/ferkpEBNpDfsNpgBOaatSuR0IXaO+USnu7HmgP+YcNGxa3mmRg+f3+4LZC/nY/uO0HVNRUxLuMATEyfyRLf7k03mV064477uAXv/hFlwtPAaWlpZSWlvLiiy+ydOlSnnnmGcaNGxeHKrs3uC45iIiIiAxygRY8ze76nkN+dwOZGVmacSZDWncffkREAhobGzFTzPaQv1Ehv4SupaUFINiTH9DizUOcZvIfqqKmgqIji+JdxsDYFO8CelZWVoZlWWRkZLBw4ULOOussJk+eTGpqKlu2bOH3v/99cK3Zs88+m48//pjMzMx4lw3EKORva2vjySefBOC8887r8+prZWUlL7/8MgCLFi0adLc/iIiISPIaPXo00B7y52d033O/2V3H6DGjY1mWSMz11K5KRASgvr4+GPL7vD7a2tpITU2Nd1mSAHqayS9DV+dgv/OsfpFYKygo4O677+baa689pC3l7Nmz+eY3v8miRYt48skn2bFjB/fffz8//elP41RtVzFZnehf//oXixcv5vbbbycvL6/P/fPy8rj99tu54ooreOWVV2JQoYiIiEhosrOzSUtLp8ld1+M+Ld56xijklyFOM/lFpDe1dbWYThPL2f67or5effklNJ1D/tSDHpOhSe16ZLC4++67ufXWW3tcd8put/PQQw/hdLavGPL000/HsrxexSTkf+qppwD4+te/HtKsfIfDwTe/+U0sywreASAiIiIyGBiGwehRo2nuJeRv9tRz2GGHxa4okTjQTH4R6U1dfV37qqmu9q8bGhriWY4kkM4hvxMwUMg/1HUO+TWTX6LtnXfe4aqrrmLq1KlkZ2eTmZnJtGnTuOiii1ixYkXYr08FBQXBdWZ37do1ECVHJCYh/+eff45hGJx22mkhH3PqqacC8Omnnw5UWSIiIiIROWzsmODiugfzmz6aWhuCbX1ERESSUUNDQ3vA7+z0tUgIGhoaSDFspGBgwyDNbtfPzxCnkF8GQmtrK4sWLeKUU07h0UcfZfv27TQ2NtLc3My2bdt44YUXuPzyy1m6NPxFgN1uNwA2W0yi9ZDEpNl9SUkJAGPHjg35mMDst9LS0gGpSURERCRSY8eOZf26T7p9rr2Nj8WYMWNiWZJIzKldj4j0xLIsmhubu8zkV7seCVVjYyPpdhv42sPedMOgsbExzlXJQFLIL9FmmiYXXnghr7/+OgCTJ0/muuuuY86cOaSnp1NeXs67774bUQeZAwcOsGXLFgCmTZsW1br7IyYhv8/nA764yhEKj8cDfLGquogMPT6fj3379jFx4sR4lyIiEpYxY8bQ1NqAz/TisKV0ea6prQ5A7XpERCRptbS0tAd1LsABhs1QyC8hq6urI73T12mWRV1dXbzKkRgI5IagkF+i44EHHggG/AsXLmTlypW4XK4u+yxYsIC77rqLioqKsMa+9957gz+zl156aXQKjoKY3FMwYsQIADZu3BjyMZ9//jkAw4YNG5CaRCT+nnjiCS6//HJaW1vjXYqISFjaA3yLZvehgUWTuxaX00VBQUHsCxOJIS2MJyI9CQT6ltMCA2wum9qtSMhqa2tJ7xT0Zvj91NXWxrEiGWidg/3Ogb9IJEzT5N577wXaJ2etWLHikIA/wGazhdVm9YMPPuC3v/0t0P6Z8Lrrrut3vdESk5D/5JNPxrIs/vznP4d8zJ/+9CcMw+DEE08cwMpEJJ4Ca27oRVxEEk2gFU9T26EfOJva6hg9erQWJRURkaQVnLXf0Y8fF5qJLSGrra4mo1NLuAyguqoqfgXJgOucCXi93jhWIkPBJ598Emz/fvXVV5OZmRmVcffv38/FF1+Mz+fDMAwee+wx0tPT+z4wRmIS8i9atAiAjz76iO9973u99u+0LIvvfe97rF+/vsuxIjJ0qaeviCSagoICXE5XR//9rpo9dYwdF/o6RCKJSjP5RaQnwZC/Y+Kk6TQ1k19CVlVZSVanr7OA6urqeJUjMdA52FfIL/21YcOG4PZpp50WlTEbGxtZsGBBcN3ZX/7yl5x55plRGTtaYhLyn3feeZx55plYlsWDDz7I3Llz+etf/0pRUREejwePx0NRURF//etfOeGEE3jwwQcxDIPTTjuNCy+8MBYlikgcabariCQawzAYOXJUtyF/i7dei+5KUlDILyI9OSTkTzGprVO7FembZVlU19aS3emxLKC2vl692oewwLqcoJBf+q+q050/o0aN6vd4bW1tXHjhhcEJ6T/4wQ/40Y9+1O9xoy0mC+8CPPnkk5xxxhls3LiRjz/+mMWLF/e4r2VZzJo1i2eeeSZW5YmIiIiE5bCxY9i9sazLY6Zl0tRaH5U3kyIiIomqrq4OW4oNv709lLVcFjW1NXGuShJBU1MTbo+ny0z+HNovLNfW1lJYWBiv0mQAaSa/DJT+Tir1+XxceumlrF69GoCrrrqK++67LxqlRV1MZvID5Ofn88EHH/C9732PtLQ0LMvq9r/09HR+8IMf8P7775Ofnx+r8kQkjtSuR0QS0ciRI2n1NXZ5rNXThGmZCvklKWhGpYj0pK6uDiO1U7CinvwSooqKCgByOz2Wc9BzMvR0nsnfeVskEp0vBpaVlfWyZ+9M0+S///u/eemllwD4+te/zp/+9Kd+1zdQYjaTHyAtLY3777+fO++8k9WrV7Nhw4bgLRSFhYUcd9xxzJ8/n5ycnD5GEpGhRO16RCQRjRw5kqa2eizLCv4ea/G0tycYMWJEPEsTiQldpBeRntTV1WE5O/2OcEFDfUOX10yR7gSC/LxOj+V2/FleXs7MmTNjXZLEgEJ+iabjjjsuuP3WW28xf/78iMa55ppreOKJJwD4yle+wl//+ldstpjNlw9bTEP+gNzcXBYuXMjChQvjcXoRGWTU01dEEtHw4cPx+b14/G24HGkAtHgag8+JDHV6/RaRntTW1eJ3drrbxwU+r4/W1lbS09PjV5gMeqWlpThtNjI6vcakYpBht/VrRq4Mbm63u9ttkUgcffTRjB07ln379vHII49w8803k5mZGdYYP/jBD3jkkUcAOOuss3j66adJSUkZiHKjZvBefhCRpKGZgCKSiAJBfqvni5Y9rZ5G0tPSFWBIUtDrt4j0pLqmGsv1xe+IwHZtrRbfld6VlpZSYNgw6HrHR37HczI0tbW1BbcV8kt/2Ww2brnlFgBKSkq47LLLerxDxDTNQy4gLlmyhPvvvx+Ak08+mRdeeAGXyzWwRUdBXGbyi4h0ppBARBJRQUEBAG3e5uBjrd7m4OMiQ51m8ovEn9/v57333uOkk07CbrfHu5ygmpqark3VU794fMyYMfEoSRLEvuJi8vw+ODjk9/sp3rs3LjXJwHO73dgMgxTD6BL4i0Tq+uuv56WXXuL111/nueeeY9asWVx33XXMmTOH9PR0KioqeP/991m5ciWLFi1iyZIlADzwwAP87Gc/A2DMmDHcc8897Nmzp9dzTZ06dVDM8lfILyJxp4X7RCQR5efnA+3BfkCbt5mCUQr5JTno9Vsk/t577z1uu+02fvWrXzFv3rx4lwO0T+Cpq62DkZ0e7BTyi/SmaM8euuu6Pwx4v7hY6zoMUa2traQYBi6F/BIlNpuN559/nssvv5ynn36a7du3c9NNN/V53DPPPBPcLi0t5ZRTTunzmD179jBhwoR+VBsdCvlFJO40E1BEEpHT6SQ9LR23tyX4mMffQn7+6DhWJRI7uhNPJP4C7W8GUxuclpYWvB4vpHV60AmGzVDIL71qamqiuq6OYd08NwxoammhpqZGd00OQW1tbaQaNlKM9sBfYGT+SNgU7yoGxsj8kX3vFAXp6ek89dRTrF69mmXLlrF27VoqKipwOByMGTOGGTNmcPHFF3PBBRfEpJ6BppBfROJOMwFFJFHl5OTi9nUO+dvIzc2NX0EiMaSL9CLxF5jRPJhmNldXVwNgpXa6EGiALc1GVVVVnKqSRLC3ox1PTyF/YB+F/ENPa2srTgNSUMgfsPSXS+NdwpAxf/585s+fH9K+b7755sAWM4C08K70mwJa6S+FBCKSqHJysvH4vril2ONrJTs7O44VicSO3gOKDB6D6c6aysrK9o20ro9baVbwAoBId3bv3o2N7kP+AiDFMNi1a1eMq5JYaGlpwWlZpJgWLS0tfR8gIodQyA8UFxfzwx/+kOnTp5ORkUF+fj5z587lN7/5TdR/ubzxxhssXryYI444goyMDHJycpgyZQoXX3wxf/zjH2lqaorq+Qbarl27OOuss/RCK/2ikEBEElV2TjYe3xezjdq8rWRlZcWxIpHY0eu3iHQnOFv/oJDf7/Kz/8D+2BckCWP37t0U2u2kcOidKTYMhtts7N69Ow6VyUALhPxO06+QXyRCSd+uZ9WqVXzrW9+ivr4++FhLSwsffvghH374IY888gj/+te/mDRpUr/OU1tbyxVXXMELL7xwyHMNDQ3s2LGDZ555hpNOOoljjjmmX+eKpS1btmCaJlu2bOHwww+PdzmSoHw+X7xLEBGJSFZWFj6rAgC/6cPv9ynkl6ShkF9EulNZWYnNZcNv7/o7wkqzqNhfEaeqJBHs2LaN4X4/dBPyA4zw+9m5bVtsi5KYaG5uxmmapADNCTb5VWSwiGrI/4Mf/ACAH/3oRwwfPjyaQw+ITz/9lEsvvZSWlhYyMzP58Y9/zPz582ltbeWJJ57gz3/+M9u2bWPBggV8+OGHZGZmRnSe+vp6zjnnHNavXw/AggUL+MY3vsERRxyB3++nqKiIDz/8kKeffjqaf72YGEy3hUriUkggIokqIyMDn+kBwOt3Bx8TSQZqtyci3Tlw4ABGejchbRpUlaonfzKwLCvsdSIsy2LXrl3M62WfkcDrRUX4fD4cjqSfszqkNDc24gKcQENjY7zLEUlIUf2t+Nvf/hbDMLjqqqu6hPwTJ07EZrPx6quvcsQRR0TzlP1y00030dLSgsPh4LXXXuOkk04KPnfmmWcyefJkbr31VrZu3crSpUu54447IjrPjTfeyPr163E4HPztb3/j61//epfn582bx6JFi1i6dKnCTklKmskvIokqIyMDn7895A/8mZ6eHs+SRGJG71tFpDsV+yvwpXbz/j4d3K1umpqaIp5AJ4khkpC/vLyclrY2RvayzyjA6/NRXFzc724LMrg0NTZSCJrJL9IPMenJX1RUxN69e/F4PLE4XUg+/PDD4IrJV155ZZeAP+Dmm29m+vTpQPsFDK/XG/Z51q5dy1//+lcAfvKTnxwS8HdmGIauRktSUsgvIokqPT0db0e47zUV8ktyUcgvIt0pLy/HSj/0ju/AY/v3qy//UBfJnV7bt28HYHQv+wQuAOzYsSP8omRQa25uJhVI7dgWkfBFNeQPfKgNLrQziD3//PPB7SuuuKLbfWw2G5dddhnQ3lM/cFEgHA8++CAAmZmZ3HzzzWEfP9gFrs6rbY/0RyQX0EREBoO0tLRgyO/ze4OPiSQDXaQXke4cOHAAurve3dHNTiH/0BdJyL9jxw6y7XYye+jHD5CKQYHDEbwgIENHU+eQv7U13uWIJKSohvyBVjwrVqwY9KHv22+/DbTfZj979uwe9zv99NOD22vXrg3rHB6PJ7jQ7nnnnRe8JdHn81FUVERxcfGguruhP8K9FU+kM4UEIpKo0tLS8Pq8WJaF31TIL8lFPflF5GCNjY20trR2H/KngmEzKC8vj3ldElsRzeTfupWR/r6PG+nzsX3r1kjKkkHKNE1a2tpIA9Job8nkdrvjXZZIwolqb5iFCxfy2WefsWzZMl5++WUmTZpESkpK8Pkrrrgi7MXoDMPg3//+dzTLBGDLli1A+4WJ3lrkTJs27ZBjQvXpp5/S1tYGwEknnURFRQU//vGPeeqpp4K3H6WmpjJ//nx+8pOfcPLJJ4f71wCgpKSk1+f1JkoGO4X8IpKoUlNTgfaAP9CTXyG/JAu9fovIwQKfPa2Mbib9GWDLtOnzaRIIN+S3LIttW7dyDBb0MpMf2tv5vLNjB6ZpYrPFpAO1DLDm5mYsyyKV9p780H7B0OVyxbMskYQT1ZD/f//3f3nttdd47733KC8v7/LibVkWH374YchjGYYR0WItoWhrawu2FDrssMN63TcvL4+MjAyam5vZt29fWOfZvHlzl3POmjXrkFZGbW1tvPzyy7z66qvcd9993HTTTWGdA2Ds2LFhHxMtg/2ODUkMatcjIomqPeQHn+nFp5n8kmQU8ovIwYIZQA/r6vrT/Qr5k0C4a7ZUV1dT19jIqBD2HQ20tLVRVlbWZ54jiaGxsRFovwGoc8hfWFgYt5pEElFUQ/7U1FTWrFnDU089xRtvvEFpaSlut5s1a9ZgGAazZ88Oeyb/QAj8AgGCLXR6Ewj5m8Jc4bumpia4/bOf/Qy3281XvvIVlixZwsyZM6mvr+eZZ57hRz/6EQ0NDfzgBz9g6tSpnHfeeWGdJ57UpkeiQSGBiCSqwHpEPr8Hn+nFbrN3uYtRZCjT67dI/A22SVdlZWXYUmz4nd2HvGa6SXFJcYyrklgLN+QPZdHdgMCFgB07dijkj7Ndu3axffv2fmdYDQ0NQHurnpSDHhOR0EU15AdwOBx885vf5Jvf/GbwscAtVMuXL2fGjBnRPmXYAi10AJxOZ5/7B24Rag1z8Y/OK4K73W6++tWv8vzzzwf/fwwfPpxrr72WWbNmcfrpp2OaJrfeeitf/vKXwwrP+7rDoLy8nLlz54ZVu0gshfsmUERksAjM2veZXrx+D6maxS9JRK/fInKwkpISjCyj544rmVC+rXzA7tqXwSHcdj07duwg3WYnx+z7dSUDgxy7je3btzN//vxIS5QouOOOO9i3b59CfpFBIuohfyII3FoPhLTwbWDBj3Bvv+98HoB77723255xp5xyCl/72td4+umn2bhxIxs3bmTWrFkhn0dXryXRaSagiCSqwEx+r9+Dz+8l7aDXfpGhyPS3v26r3Z6IHKx4XzHe9J5/N1hZFh63h+rqarXiGMLC/Xy3fft2RloWRh/9+ANG+k12dMz+l/gJt6V1T+rr64H2kN9x0GMiErqYrFKyZ88edu/ezZQpU2Jxuj5lZWUFt0NpwROYkR9Ka5+ezjNx4kSmTp3a475f+tKXgtvhrF0Qb4Pt9lBJLIGQQCG/iCSqYLse04vP7yY9Pf5tCUUGWiDcV8gvIgcrKi6CrF526PhIXVyslj1DWbif73Zu28ZIK/TZ/yOx2KmQf8hoaGjAYRg4ATsG6Xa7ZvKLRCAmIf/48eMZP348DsfguHEgNTU1OGugpKSk131ra2uDIX+4C9x23r+v2fad9z1w4EBY54kn3WIp/RG4zV8hv4gkqi968rvxmh4yMtLjXJHIwPN62u9yDeWOWBEZWIPp81hTUxN1NXWQ3ctOmWDYDIqKimJVlsRBOO3cWlpaKD9wgJFhjD8SqKmvp66uLtzSZBCqr68nw2YL3smRbhiayS8Sgbik7uvXr+eNN95g48aNwcVp8/PzmTlzJmeffTazZ88e8BqmT5/O22+/zc6dO/H5fD1egNi6dWuXY8Jx5JFHBrf7epHr/PxguRgSikA4q56sEgl/x89PuD0bRUQGi0ArP6/fg9fvZlhWbnwLEokBd0e4r5BfJP4G053VgeDeyuqlJhvYsm3s2bMnRlVJPISTD+zevRuAEWGMH9h3165dMcmPZGDV1dWR3qlVU7pl6QKOSARimiZ//vnnfOc732HdunU97nPbbbdxwgkn8Kc//SmsvvThOuWUU3j77bdpbm5m/fr1nHDCCd3ut2bNmuD2vHnzwjrH+PHjGTduHMXFxezatavXfTs/P2bMmLDOE0+BD3eBdQtEwuHzeTv+1Ex+EUlMdrudtNQ0vH43PtMTdms/kUTkadNMfpHBYjDN5N+1a1f7gru9zeQHvJledu7aGZOaJD7Caee2Z88eDGBYGOPnAw7DYM+ePQr5h4D6+nrSOy26nOb3U1dbG8eKRBJTTNr1ALzxxhvMnTuXdevWYVkWlmXhcDgYMWIEw4cPx+FwBB9///33mTt3Lv/+978HrJ6LLroouL1s2bJu9zFNkxUrVgCQm5sb0crt//Vf/wXA/v37effdd3vc79lnnw1un3rqqWGfJ14CL976kCeR8PvbZ/BrJr+IJLL09AyF/JJU3B3tejTJQ0Q627VrF/YcO9j72DG3fd/BdBeCRFc4k7h2795Nod1BSoiL7kJ73/bhNt0RMlTU1dSQ3un3QSZQ29H1Q0RCF5OQv6qqiksuuQS3241hGFx11VV88MEHNDc3U1ZWRnl5OS0tLaxbt46rr74au92O2+3mkksuobq6ekBqmjt3bjBMf/TRR3nvvfcO2ee+++5jy5YtAHzve98jJSWly/PLly/HMAwMw2DJkiXdnuemm24iNTUVgO9+97vB/v6d/e1vf+PNN98EYMGCBX327x9MAuG+Fl6TSJgdV+sV8otIIsvIyMDraw/5Az36RYaytrY2AFpbW+NciYgMJtu2b8Ob1ffnQivXorWllfLy8hhUJfEQTshftGcPhf7w7+wu9PvZ29HqRxJbdXU1GZ2+TgfN5BeJQExC/t/97nfU19fjdDpZtWoVDz/8MMcff3yX3vN2u505c+bwpz/9iVWrVpGSkkJ9fT2/+93vBrSutLQ0fD4f5557Lr/61a94//33Wb16Nddccw233norAFOmTOHmm2+O6Bzjxo3j5z//OdC+FsHcuXN57LHHWL9+Pf/5z3+44YYbWLx4MQDZ2dncf//9Ufm7xYpm8kt/BMJ9rekgIoksIzMDr+nB63NrJr8khdaOdj0K+UXiL/B+Ot6z4n0+Hzt37oS8EHbObf9j+/btA1mSxFE4kwD37tkTVquegEJg7969cf/Zl/6rq6vrEvJnArVaeFckbDHpyb9q1SoMw+CGG27gS1/6Up/7n3vuudx4440sXbqUVatWBUPyaDv22GP5xz/+wbe//W0aGhq47bbbDtlnypQprFq1iqysrIjPc8stt1BTU8Pdd9/N5s2bg6F+Z8OHD+f5559n8uTJEZ8nHjSTX/pjsHwoERHpj8zMDBrK6/D43MGFeEWGKp/Ph8fbPuOypaUlztWISGDGdLzXuCouLsbj9mDlhfC+Pg3sGXa2bdvGGWecMeC1SeyF+vPY2tpKVW0thRGcYxjQ2NxMfX09ubm5EYwgg4HP56OxuZnO02QyALfHQ2trq95bS1ysWrWKDz/8kA8//JDdu3dTWVlJfX09mZmZTJo0iTPOOIPvfOc7TJ06Nd6ldhGTkD/QJ+2CCy4I+ZgLLriApUuXBldaHyhf/epX+eyzz/jd737HqlWrKCkpwel0csQRR3DJJZdwww03ROXW+1/96ldccMEF/PGPf+Ttt9+mvLyc1NRUpkyZwgUXXMCNN95ITk5OFP5GsRXoxaqZ/Mmjrq4uam+iAuG+2vWISCLLyMjAax7A5/eSkZHR9wEiCSwQ7KfYLJqbGuNcjYgMlpB/69at7RuhzOQHfDk+Nm3ZNHAFSVyFmg+UlpYCUBDBOQLHlJSUKORPYHV1dViW1SXkD2zX1tYmdch/xw9+QP3+/fEuY0DkjBjBz5cujXcZ3fL5fHzlK1/p9rm6ujo+/vhjPv74Yx544AF+/vOf86Mf/SjGFfYsJiF/oG9nOB98A8F6LBb0Gj9+PEuXLmVpmD9gixcv7nZWfk9OOukkTjrppDCrG9zUrie5bNy4keuuu46HHnqImTNn9nu8QLivkF9EEll6ejpuX/uaO8n8QUSSQ1NTEwDD0kyaGhviXI2IBNpexjvk37x5M/ZcO/6U0NpwWvkWW7dsxTRNbLaYdBGWGAo1HygpKQH6H/JH47OpxEdNxwK73YX81dXVjB49OuY1DRb1+/fztX0l8S5jQDwb7wL6kJOTwxlnnMEJJ5zApEmTGDVqFOnp6ZSVlfHmm2/yl7/8hfr6en784x+Tm5vL//zP/8S7ZCBGIf/IkSMpLi5mw4YNzJ49O6RjNmzYAMCIESMGsjTpp8CLt0L+5BC4K2fv3r1ReSNlme0z+dWuR0QSWWpqKm5vS3BbZChrbGyfvT8s1aS8UTP5ReJtsLRP3bhpI97c0Guw8i3aNrZRXFzMhAkTBq4wiZnO66yF+vNYVlZGms1GegSTvpwYZNltlJWVhX2sDB7V1dUAdG6QnXXQcyKx5HA4qK6uxm63d/t8oBvL7Nmzqa2t5Y477uDqq6/ucf9Yiskl81NPPRXLsvj1r39NQ0PfM34aGhq4++67MQyDU089NQYVSqQU8icXwzCiOp7fbH8jqIV3RSSRuVwu2hTyS5Ko71gIb2S6n4bGpjhXIyKDoV2P2+1m7569kB/GQR37btmyZSBKkjjoHOyHmg+UlZWRZ9gwiOxzZr5lUV5eHtGxMjhUV1dj0HUmfxrgMAyF/BI3fQX2EydO5Otf/zoAlZWVX7Ssi7OYhPzXXHMN0D4L+LTTTuPDDz/scd9169Zx2mmnBXvxB46VwUkhv/SHFt4VkaHA5XIFt51OZxwrERl4gZB/dIafNrcnJq01RaRngc9h8fy3uH37dkzTxMoP4z19Cthz7WzevHngCpOY6vwzGGjZ3Jfy0lJy/JFfoMoxTUpLhmY7k2RRXV1Nht2OvdOFHgODbLudysrKOFYmQ8k777zDVVddxdSpU8nOziYzM5Np06Zx0UUXsWLFipAmpB+sc0v6UH/nDbSYtOuZN29esI/3559/zoknnsiMGTM44YQTGDFiBIZhUFFRwQcffNDlRf66665j3rx5sShRIuT2dPTkj/PtoRIb0Q7jAyG/ZvKLSCJLSUnpdltkKKqrqyPFblCYaga/VntNkfgJzJ6OZ7ueLVu2YNgNyAnvOG+ul883fT4wRUnMdQ65Qr3oVF5Wxrh+nDMX2FJR0Y8RJN4OHDjQ7a+OLNNSyC/91traypVXXsnKlSsPeW7btm1s27aNF154gTvvvJMlS5aENe4LL7wAgM1mY8qUKdEquV9iEvIDPPDAA6Snp7N06VJM02TTpk2HXLUPBIg2m40f/vCH/PrXv45VeRKhL2byK+SX8AXCfYX8IpLIHI4v3k4p5Jehrra2ltxUg2xn+/t2hfwi8RUI9+N5Z/W2bdsw8ozw+wTkQdFnRXg8Ht0JNwR0DvlbW1v73N+yLA5UVTGrH+fMBapra/H5fF3ej0niOLB/P1l+PxzUsinb9LNfF3CkH0zT5MILL+T1118HYPLkyVx33XXMmTOH9PR0ysvLeffdd3nyySdDGs/r9QaPufvuu9m5cycAV1xxBVlZWX0cHRsx+y1oGAb33HMPl112GX/84x9544032LFjR5d9Jk+ezNlnn821116r1dETxGB4UymJy+dTyC8iia/zh0p9wJShrqamhuwUk2ynGfxaROJnMCy8u2nzJny54bdcsfIs/H4/u3fvZtq0aQNQmcRSS0tLcDuUkL+hoQGP1xvuDSBd5NAe5FVXV+uCc4KqKCtjZDeP5wA7FfJLPzzwwAPBgH/hwoWsXLmyS5tVgAULFnDXXXdR0cPP2t69e5k4cWKP5zj77LO57777old0P8X8k+jMmTP5wx/+ALS/IamtrQUgLy9PV+8T0GB4UymJK7BAmH5+RCSRdV6Yqa9FmkQSXXV1FbkpHnKcFgZoUTyROIv3Gmmtra2Ul5XD7AgOzgEM2Llzp0L+ISAQ8hsuo0vg35NAK5bsfpwzcGxVVZVC/gRkWRb7DxxgejfP5QGV1dX4/X69v5awmabJvffeC8CYMWNYsWLFIQF/gM1mY/To0WGNX1BQwIMPPsgll1wyqH4+4zrdzOl06hdxggv04ndrJr9EwKuQX0SGgM5v7Gy2cHsViCSW6soDjHdaOGyQnWpTyC8SZ4He5/FaeHfv3r1YloWVG8HaXQ6wZ9vZtWtX9AuTmAsE+1aaFdJM/miG/OrdnpgaGhpo83i6vZsjF/Cbpi7gSEQ++eQTSktLAbj66qvJzMyMaJwxY8bw+efta8f4fD5KS0t55ZVXePTRR7nuuuvYvXs3P/7xjzEMo4+RYkOfRKVfNJM/uUTzF5dlWXi97SG/2j2JSCLrHOwr5JehrqqqmlxXe6ueXFf7h28RiZ9gyB+n99O7du1qb6UdYVLry/KxY+eOvneUQa+5uRloD/kbGhv63L+6uhoDiCx6a5cGOAxDF5wTVFlZGQD53TyX1/FneXl5zOqRoWPDhg3B7dNOOy3icVJSUpg5cyYzZ87kmGOOYcGCBTzwwAO8//77GIbB7bffzpVXXhmNkqNCn0SlX7weDxYKaSV8Xq8Xs2OxbXenRZpERBJN5wugg+l2TZFo83q91DU0kh8I+VO8mj0pEmetbe0hf1uc3k8XFxdjz7RDhC9/VpZFUXFRdIuSuGhqasKwG1ipoYX8NTU1ZNjt2Il8IpmBQZbdrvVhElQg5M/r5rncg/YRCUfnSSijRo2K+vhHHXUUv/jFLwBYtmwZr732WtTPEQmF/NIvHo8HHKl4PfG5PVQSV2DWkd2waG3tu2ejiMhgpdn7kiwCMyXzUttD/nyXSeV+LYonEk+BcL+1NT4hf1FxEf4Mf+QDZEF9bX1wFrgkrqamJmwuG6RAY2Njn/vX1taSGYU7xTMtSyF/nJmmGdFx5eXlpNvtpHVzoScFgxy7I9hyRSRSA9VK58ILLwxuP/300wNyjnDpU6n0i9fjwXK48Lg1k1/CE+jZmOu0aG3Rm3oRSVyayS/JIjBrP69jJn+e2vWIxF1bW3x78hcVF2FmRhbwQftMfoCSkpJolSRxUl9fD07AGVrIX1dXR3qE4XBnaX4/dXV1/R5HIhdpyF9SUtJtq56AfMtUyC8RKSwsDG4P1N0gw4YNC24XFQ2OO9IU8kvEfD4fPp8XKyUNn8+Lr2MRVZFQBBZjyks1g4G/iEgi6hzsD5ZFl0QGQiDQz3dZHX+a1DU06D2gSBy5PfEL+S3LovJAJWT0Y5COYysqdFdQomtsbMRMMcEJLU0tfQa/tTU1UQn5M4Ba9eSPK78/srt59hUVkd/Lsfmmyb5BEp5KYjnuuOOC22+99daAnKPzBahIF/aNNoX8ErFASGs507t8LRKKpqYmoD0gaG5WyC8iiatzux7N5JehrLKyEqfdIN3RHvLnuUwsCy14KBJHbncblmGLS0/+uro6vB4vVoYV+SBOMByGQv4hoK6+DjPFxHJZWJYV/LzXk4a6OtKjcN40oKG+PgojSaQiDflL9u2joJfn84GSklIsqx+/YyQpHX300YwdOxaARx55pM/fR5F46qmngtuzZs2K+viRUMgvEQv8IzGd7dMv1Edx6Ivmi2vg52d4mkmTfnZEJIF1DvnVn1+GssrKSvLTDAI3rOR39ObX4rsi8eNuc2M5UvHEYY20AwcOtG/0J6k1wJZhY//+/VGpSeKnuqYay2WBq/3r+j6C94aGBlKjcN40QmsPJAMnknY9zc3N1DY09BryFwKt7jZqa2sjrk2Sk81m45ZbbgHa20Jddtll7WuKdsM0zS4tfZ5//nnKy8t7Hf+tt97i5z//OQAOh4NvfvObUaq8fxzxLkASVyCktVzZXb4WCUXgjdiwND8erw+3243L5YpzVSIi4es8e18z+WUoq66uJs/5RWuevI62PerLLxIflmXh9bixUnPxtMV+JnPw335a/8YxU03dETQE1NXVQQHBkL+uri44k7Y7Tc3N/f3RAdp//JpaWrAsS20T4ySSkH/fvn1Ae5Dfk8BzxcXF5Of31r1f5FDXX389L730Eq+//jrPPfccs2bN4rrrrmPOnDmkp6dTUVHB+++/z8qVK1m0aBFLliwB2kP+r3/96yxYsICzzjqLI488ktzcXNxuN7t27eKll17iySefDP7c//SnP2Xq1Klx/Jt+QSG/RKyhoQEAMzUL6PtKvSS+aL5pamhowGEzgn19GxsbFfKLSEJSyC/JonJ/BXlOb/DrDIeF024o5BeJE6/X2x4yOFIxzRq8Xi8pKSkxO39VVRUYBEPdSPldfvYf0Ez+RNfY0Aij6RLy98Q0TVrd7v7+6EDH6fymicfj0efJOIkk5C8uLgbos12PQftM7GOOOSaS0iSJ2Ww2nn/+eS6//HKefvpptm/fzk033RTSsR6Ph+eee47nnnuux33S0tK46667uPnmm6NUcf8p5JeIBV60rdS8Ll+LhKKhoYEsl0Fmihn8uvMK6CIiicLhcHS7LTLUVFVVMsb5Res+w4DcVEPtekTiJNCH30pJC34dy5C/trYWe5odvxFZP+6gVKiprYlOURIXra2teNye9sTdCRj02mKlra0Ny7KiFvJDe/sXhfzx4fP5+t7pIPv27SPbbifV3/MFAgcG+Q47RUm6+G7OiBE8G+8iBkjOiBExOU96ejpPPfUUq1evZtmyZaxdu5aKigocDgdjxoxhxowZXHzxxVxwwQXBY37zm99w/vnn85///IePP/6YiooKDhw4gM1mIz8/nyOPPJIzzzyTyy67jFGjRsXk7xGquH0SbWhooLGxMaQFOsaNGxeDiiRctbW1GDY7ljMdw2ZXnzQJS0NDAxkpFpkp7WGB7gQRkUTVOdjXbeIyVFmWRVV1DXnju34Yz3X6qalROCcSD62trQBYKe2dzVtaWsjKyorZ+evq6vo9ix8AF9SX6rNAIgtkAVaqBQbYU3vPB1paWoCo/fgAX/x7kNiLZOHd4uJiCkJY86/A50/akP/nS5fGu4QhY/78+cyfPz+kfQsLC7n00ku59NJLB7iq6ItpyP/666/z0EMP8fbbb4ccCBuGEdFVQRl41dXVGK4MMAwMV/qg/IC3f/9+iouLOf744+Ndihykvr6eLIefLKdCfhFJbJq9L4NdS0sLDz30ENdddx3p6ZGtkNnS0oLb4yXXdVDIn+KlslJtNkTi4YuZ/Oldvo6Vuro6TGf4bToO4YLW5lZ8Pp9eUxNU8K7+QOKe2vtM/kAg74zCuQP3rsT651++EEm7nqLduyk0Tdob8vSsEIu9e/ZEWJlIcrHF6kTf/e53+fKXv8yLL75ITU0NlmWF/J8MTtXV1Zgdt4aaKemDsh/rkiVLBlV/rEQXzX+P9XW1ZNp9pDssbIZCfhFJXAokZLBbs2YNL774ImvWrIl4jMD7vMBiuwG5LpNqtesRiYvAbOhAu55Yz2Suq6/Dn9LPVj2A1THpJ7DmmySeYKDfEfKbTrPXdr5utxv4IqDvj8CFAs3kj59wQ37TNCkpLe110d2AQqCishKv19vnviLJLiafSh9//HEefPBBAFJTU7nooouYPXs2+fn52Gwxu84gUVaxfz8+R/usEZ8jnYr9B+Jc0aE2bdoU7xKGlGi2oairrWGssz3gz3TZ9KZeRBJWLPsfi0QicBt9JLfTBwRC/oNn8ue5TKrLBt/dnCLJ4It2Pe2fyQKhf6zU1tVGZyp2RzDc0NBAfn5+FAaUWDs45Pc7/VRV9zwJMBDyRyOQCrwLC4wpsRduyL9//348Pl9IIf+wjvFLS0uZMGFCJOWJJI2YhPx/+tOfABg7diz/+c9/OPzww2NxWhlgFfsPYDnbF921nBlU7Net2kNdVGfy19czI7P9zUBWiqWZ/CKSsDSTX5JBdXU1ALnOg0N+i6aWVtxutxY8FImxQKhvOttD/ljPZG5sbIRoZPIdFwo06Sdx1dXVYUu14bd1XEx2QXVNdY/7ezweIDoz+QPvwgJjSuyFO4mguLgYaA/w+xK4EFBcXKyQX6QPMZlG/9lnn2EYBnfeeacC/iHCsiyqKg9gujIBMJ2ZVFdVqr3SEBe4Qt+fmYAB9Q2NwUV3M1P8vd7OKSIymGkmvySD6upq0lIMUg+6phWY2R+4CCAisRNs1+OMz0z+5ubm6KS0KZ3Gk4RUW1uLkdrpru9UqK/reRJXIJCPxjSJwBiayR8/kYT8KYZBTgj7ZgBpNlvwwoCI9CwmIX+gd9axxx4bi9NJDNTW1uLzerGc7SG/5crE6/GEvKCyJKbAItj9XQy7ra0Nt8cbXHQ3y+6jvr6uv+UBkS36IyLSHwr5JRlUVVWRl3ro43kdIX+l+vKLxFxLSwsYBthdYBgxDflN06StpS2qK6c2NjZGYTCJh9ra2q6LMLuguam5x8+NAxHyq2d7/IT7Gby4uJgCmw1bH4vuAhgYFBoGRUVFkZYnkjRiEvIHbqlpamqKxekkBioqKgCwXFkdf2Z2eVyGpmj09IUvFtnNSulo1+O0qOvlds5Q/e1vf2PxFYv7PY6ISDgU8ksyqKysJN95aFgTCPkDPftFJHZaWlqwpbQH/DaHK6Yhf1tbW/td3NF4CbQDhhZOTWQ1tTVdQn7LZWFZVo8XbqIZ8ts7/lTIHz9hz+Tfu5fCMI4p9Psp3rMn3LJEkk5MQv6vfe1rAPz73/+OxekkBsrLywEwO0L+wJ+Bx2VoCrxx6u8bqGDI7wy06zGj0pP/4YcfZu+evf0eR0QkHAr5JVEYRt8z5npSub+CvG5C/nQHpDoMzeQXiYPm5mawt0+lNxzOmLa7CZzLckShXasBdqdd7XoSWHVNNZar089CxxItPbVkDczwt3f7bHjsGBgo5I+ncGfyF+3dG9KiuwGFQPG+fWoPLdKHmIT8N998M+PGjeO3v/0tW7dujcUpZYCVl5djc6aCo+P+TIcLmzNVIf8QF612PYFAP7ujJ39WikVdvRbaEpHEpJBfEkV/PhwfOHCAfFf3H+Lz09qfF5HYag/521+DLHtKXEL+qEzFBkhRT/5EVl9fHwz2gT5D/kAgH42QH8BhGP3+jCqRC2cmf3NzMzX19WGH/M2trdTU1IRdm0gyiUnIn5OTwyuvvMKIESOYN28eDz30kHq3J7iysrJgq54Ay5VFWVlZnCqSWAjcVhn4M1KBf/+ZHbd0Zjst2tweLZYkIgnJbo/WR1SRwcnn81FdU0tBavchf4HTq5BfJA6am5sxbe0hv2l3xrQ9bltbW/tGtEJ+R6cxJaFYlkVjQ2O3IX9Pd2v7fD5shtExB7//7IbR78+oErlwQv7AArrhhvwA+/btC+MokeQTrZfkXk2aNAlo7xlYW1vLjTfeyHe/+10KCwtJT0/v9VjDMNi1a1csypQwlJSW4k3J7PKYNyWTfSUlcapIYiFa7Xrq6upwOQxSO3KxQG/+uro6RowY0a+xRURiTTP5Zairrq7GtKyeQ36Xn7Ky0hhXJSJNTU34O0J+v+FI7JDfrpA/UbW0tOD3+bsuwpwCGL3P5HcYBkSp/YrdMPq9bpxEbqBD/nzaZygXFRVxzDHHhFOaSFKJSci/d+/eLl9bVvsiLKHM+OlP71AZOMX7SjBdo7s8ZrqyKCnVTP6hLDA7or8z7mtra8l2ffFvO7ujN39tba1CfhFJODZbTG6MFIlYf3vYBtoxFqZ1H/IPSzP5uKKiX+cQkfDVNzRidfTkt+xO6hu6X+R0IAQD+SjdzGbaTS28m6ACs/W79OQ3wJ5q77Unv90wgOiE/A76f7e5RC7ckD/H7sAVxjEODPIdds3kF+lDTEL+yy+/PBankRjxeDzUVFdhTZja5XErNZuavZ/j8XhwOp09HC2JLBDuR6NdT47zixf13I4ev2rjJSIiMvgEQv5hPczkH5bmp6GpmebmZjIyMmJZmkhSa2hsxHKkAWA5XDTGcCZ/8PNAlEJ+y2YppE1QwSDfddATqT236/F6vVHrxw9g00z+uArn//2+ffsoCHOhXoB8nz94F4CIdC8mIf+yZcticRqJkbKyMrAszNScLo+bqdlYlkV5eTnjx4+PU3UykAIhf39n8ldXV5Hj+KLlT1aKhUF7OwARERGJrv7eGVtaWkpemg1nD4nM8I4Z/mVlZUyePLlf5xKR0DU1NmK52j+TWXYnjY2VMTt38PNAFEP+Nrfa9SSinkJ+02mGMJM/OuwdY0p8hDWTf88eCiwTwlyPoRCLvXv2hFmZSHLR/eUStpKOvvtWanaXx62O0L9EffmHrMBtuf0N+asO7A/O3gdw2CA71aaQX0REZBAqLS1leGrP4cmIjpC/tFR9+SX2pk+fzsSJE+NdRlw0NzdhOTra9TicNMdwJn8wUI1SomDZrH6v+yXxEZytf9DN/P4UPzW1Nd0e4/f7oxpG2ej/unESuVAvsJimSUlZWVj9+AMKgYrKSn2fRXoRk5n8MrTs27cPw5GCldJ10WQrJR3DnqI+aUNYINzv76JYVVXVHDe8a//FPJdJZWXsZh+JiIhIaEqK9zIitecP1ZkpFhlOmyZ6SFz85je/wYyg9UOi8/l8uNvawN4xfdruwt3Wis/nw+EY+I/5wVAvWpOxbQppE1VtbS02lw2//aDZ3C6oqek+5Pf5fFFt12MnvNnkEl2h/r+vrKzE4/VGFPIX0H6RoLy8nHHjxkUwgsjQF7eQf//+/WzcuDH4Sz8/P5+ZM2dq0c0EUFJSAmm5cPDtdYYB6TkK+Yewltb2cL+1LfKZ/G63m/rGJvLGdv0wlpfipfLA/n7VJyIiItFlWRb79pVwzGE9f4A3DBiZ7td7QImLrKyseJcQF00ds/Yth6vLn01NTeTm5g74+X0+H4bNiG7I71PIn4hqa2sxUrv5QUiF2tLu11zz+XzRnclvKeSPtc4XV0OdyR94n1AQwfkCFwb27dunkF+kBzEN+S3L4uGHH+bBBx9k8+bN3e4zY8YMbrzxRq6++up+9w+VgbF7z168zu7fTHud2ezesze2BUnMBGbwt7a1RjzGgQMHACg8aPG+wlQ/u8rLIi9OREREoq66upqWNjej0nufKT0y1UPxXvXKFYmVhoYG4NCQv7GxMSYhv2ma0Qv4AYz2vEAST11dHZazm++dCxrrG/H7/djtXeftR79dj6WQP8Y6B/uh3oVTUlKCzTDIjeDfehbgtOmuQZHexKwnf21tLaeeeirXXXcdmzdvxrKsbv/bvHkz1157LaeddlqPi7RI/FiWxd69ezDT8rp93kzLY+/evbEtSmKmNRDyt0bermf//vbZ+gUHhfwFqSYV+w/ozb2IiMggUlxcDMDojN7Dk9EZJsXFxXodF4mRnkL+wOMDzbKsqE/KS8a2S0NBZVUlftehrxFWanvG093PpGmaUe/Jr5+f2Ooc8od6gaWkpIR8mx17BFcIDQwKDEN3DYr0IiYhv2VZXHjhhbz77rtYlkV+fj7XXnsty5cv55VXXuHll19m+fLlXHfddRQUFGBZFu+++y4XXnhhLMqTMNTW1tLc1ISZltvt82ZaLs1NjT323pPEFlx4tx89+cvLy7EZh4b8w9JMWtrcMftgIiIiIn0rKirCboPhab2HJ6Mz/DQ2t1Bb231rBhGJruB75o5wnziE/FFlgGXqImEiqqyqxErt5nuX2v5Hd9mAaZoY0fwZshTyx1rn2fsejyekY/YVF5Pfjzsu8v1+9hUVRXy8SH+9/PLLGIYR/G/JkiXxLqmLmLTrefzxx1m7di2GYbBo0SIeeuihbnsnXnbZZfz617/m+uuv569//Str165l5cqVfPOb34xFmRKC3bt3A2Cm5Xf7fODxPXv2kJ/f/T6SuNxtbVi2lOACvJEoLS2lIN2G46BLjIHwoLS0lJycnP6UKSIiIlFSVFTEyAzjkNftg43pmOm/d+9evQcUiYH6+noALEdqlz8Djw80wzCwiG5Ia7PHrNGARFFNTQ2M7+aJtPY/qqqqOPzww7vZIXp3ghjR/FmUkHQO+UPuyV9UxFgsIv3eFwBbkmgm/23/+1NqquriXcaAyC/M5Zd33xXvMsLS3NzMtddeG+8yehWzkB/g9NNP569//Wuv+2ZmZvLYY49RXFzMmjVr+Nvf/qaQfxDZvXs3ht2Bldp9T34rNQvD7mDXrl3Mnj07xtXJQHO727BS0nC7I5/JX1paynDXoT37RqT7g8/PmDEj4vFFREQkevbu2c3otL4v7g9PM7Hb2i8KHHfccTGoTCS51dfXY3M4wdbR69xmx+ZwxjTkj3auqjX5Eo/b7aa5sTkY6HfRMZO/srIypjVJbIQ7k9/v91Nx4ABH9+OcBUBlTQ1utxuXy9WPkRJDTVUdM3PPjXcZA2Jj1WvxLiFsP/3pTykqKmL48OHBtSYHm5hcKv/4448xDIMbbrgh5GNuvPFGADZs2DBQZUkEduzYAen5YPTwo2PYID2fnTt3xrYwGXB+vx+f14uVkorX44l4YaN9RXsYmX5oyJ/ugJxULaQjIiIymOzds7vPfvwADhuMzDC0NpNIjNTX12M4D0pWnakxW9fObrdHN+S3OGRxVhn8qqurAbDSuvlhsIE9zU5VVVUPR2v2fSLrHPKHsvDu/v378fn9FPTjnIH7BMvLy/sxikj4Pv74Y37/+9/jcrn4xS9+Ee9yehSTkD/Qg23ixIkhHxPYV73dB5et27bjSe1+0d0AT2oeW7Zui1FFEiuBfvxWSjpARC17TNOkpLSMUend90scmeYLLvAnIiIi8dXY2EhNXUOwFU9fRqe5Kdq7Z4CrEhGAuro6zI4WPQGWPbYhv2Va0ctpTXDYY9JoQKIoOJu1u5n8HY93N5PfbrdjRvHODb9h6CJRjHWevR9KyF9WVgZ8EdRHInBsaWlpP0YRCY/f7+fqq6/G7/dz2223MXny5HiX1KOYhPyB/tqBf9ShCOybnZ09IDVJ+NxuN/uKizAzer/2amYUsK+4uF9922XwaW1tBcBKaX8H19LSEvYYFRUVeLy+HmcEjk73UrR3d+RFioiISNQUdSxuNyYjtMUMR2f4FfKLxEhtbS0+m7PLYz67K2Yhv8PREchHMeRXSJt4giF/evfP+1J9VOyvOORxh8NBNJfJtdDPT6x1DvlDaddTWlqKDcjtxzmzgBTDUMgvYXvnnXe46qqrmDp1KtnZ2WRmZjJt2jQuuugiVqxY0eui9ffffz8ff/wxU6ZM4X//939jWHX4YhLyz5w5E4Bly5aFfMxf/vKXLsdK/O3atQvTNDEzhvW6n5lRiGn62bVrV4wqk1gIhPqWs/0dXCD0D0cgLOg55Pezb18JphnNt3wiIrFxwgknxLsEkagqLi7GAEamhzaTf0yGn+raepqamga2MBGhqroGM6Xr9GnTkUplVXVMzh/tkN+wDJxOZ987yqBy4MABbC5bj6s9WukW5RWHtlZJSUkhtKVaQ+MzDFJSUqI4ovQlGOynhBbyl5eXk+twYO/HgssGBnl2u9r1SMhaW1tZtGgRp5xyCo8++ijbt2+nsbGR5uZmtm3bxgsvvMDll1/O0qVLuz1+79693HnnnQA89NBDg34tiJiE/BdffDGWZfHcc8+xZMkSLKvndwKWZbFkyRKee+45DMPgkksuiUWJEoKtW7di2GyY6b236zHT8zFsNrZtU8ueoSQY8ne064lkJv/evXtJTTHId3X/O2BMph+3x0tFxaGzPUREBrPHH3+cO+64I95liERVUVERhRk2XCFOjgy041PrPZGBV1NTg+XoGvJbKWkxa3cbDFQjW6brUH5wpijkTzQHDhzAyOgltE2HqspDe/KnpqbijWJLfm/HmBI7gWDfSDFCD/mjMJkv1+enTDP5JQSmaXLhhReycuVKACZPnsz999/P22+/zfr16/nnP//JbbfdxhFHHNHjGNdeey0tLS1861vf4qyzzopV6RGLSdO7q6++mgcffJCtW7dy11138cwzz7B48WJOOOEERowYgWEYVFRU8MEHH/DYY4+xadMmAKZNm8bVV18dixIlBFu2bIGMQrD18WNjc0BGIZs3b2bhwoWxKU4GXHNzMwCWM6PL1+HYs2cPh2WY2Hp4H3hYxwz/3bt3M3r06MgKFRGJg8MOOyzeJYhE3b59+xiZ2vcH94BRHTP+i4uLmTFjxkCVJZL0LMuivr4Oa+Skro+npFNfXYdlWRhR7HfenWDIH6UbcA1LM7ETUUVFBb7UXubkp0NbaxuNjY1kZWUFH3a5XHis6N297bEs0tJ6WhhABkIg2LecVmghf0lJR8jfv99NeViUK+SXEDzwwAO8/vrrACxcuJCVK1ceMhN/wYIF3HXXXd1ONH388cd55ZVXyM3N5b777otJzf0Vk5A/JSWFl19+mTPPPJM9e/awefNmbr311h73tyyLSZMm8fLLL39xG6DE3WcbN+FJLwxpX09aAZ9v3DTAFYUuFm90h7rAzH3TFXnIv2vndsak9/wGIM9l8f+z9+dRslzllTe8T0TknFWVNc/TnSdJIEC0DDKIZuhe2J+x3TZgbAazBAuwGrCQMbQHXgteLBAG3CDbNBgM3UY2yNDIvIAAI4GQjIWkK+miqzvVfGuunOeMiPP9ERlZmVU5xFRZQz6/te6qrKyIk0fKzIhz9tlnPwE3w/T0NF784hdb6yhBEARBEI4wPzuNYz7jgQpeCej0CZSVSxA7TDqdRiGfB3dvcfK7fSjk80in0wgEAjvaB8dFfpXievYji8uL4L46SQ1+7W8rKysVIn8wGEROVaECEGyKvgCQVdUd/8wTleg1GLmLG6rHuLKyguc48LodAM6tb98dQhDlqKqKj3/84wCA4eFhfPnLX64ZtSMIwjaTaTgcxnvf+14AwEc/+lH09/fvbIcdoilxPQAwPj6Op556Crfddhs6OjrAOa/6r6OjA+973/tw9uxZjI2NNat7RAPi8TiWri5ACfYZOl5p68Pi1YW6xSuaiaI4tY+0ddHzdbk7WPG7UWRZxuzsHEaDtd8LxjQ3P9VzIAiCIIjdRVVVLC6toN9vTsHr98mYn5/foV4RBAGgFMmjx2jq8GJGfzMie5yO6yEn//5kbXWtZtFdAKW/lQr0FmlrawMH0FgabkwBHAXOEQwGHWiNMErJve8GMtn69foKhQKiiQQ6HHjddgCpTMZSfDDROpw9e7ZkOrnllltMXx/e9773YXV1FS984Qvxtre9bSe6uCM01SYfCATw8Y9/HB/5yEfw2GOP4dy5c6UBSFdXF86cOYPnPe95tIK/B9EjlFSDIr9+3C9+8QvceOONO9YvoyiKQrtCbJJIJMBEFyC6wEQXEomEqfOvXr2KfEHGWB2RHwDGAnlcvEj1HAiCIAjCaczsatzY2EBBltHvMyfy93oKWLq6YLZrBEGYYGNDK667XeTXfl9fX8fo6OiO9mEnnPwk8u8vkskkMukMUM9A7wWYwLZFYeiu/jQAuyE7urzc3t5usyXCDLlcDkxgUF0qsrls3WM3NjbAOYcT75C+ULC2tobx8XEHWiQOIk888UTp8S//8i+bOveBBx7AF7/4RYiiiL/927+FIDTNH2+bXVE93W43brzxxj0h/hLGePrppyF4/OCetsYHA+CedggeP86dO7cn3mdy8tsnkUiAubTtTYLLY9rJf/nyZQCo6+TX//7Di4vI5XJ7vnI5QRAEQewnODde5XBxcREA0OczN4bq86l4emnR1DkEQZhjvRhVsT2uRxP59UWAncRpkR8qSOTfZ6ysrABA3bgeMEAICKVjdbq6ugAASQDdNvuhz0r1NonmkM1mwSQGiNrjeujmXif2WuhrSpFIhER+oibrZZFOg4ODhs/L5XJ4+9vfDgD47//9v+M5z3mO013bUcjaTBji7JNPoRDo0/JUjMAYCv4+nH3yqZ3tmEFI5LdPLBYDJC8AgEte7XcTXLlyBV0+AW3u+gLDWJsCVeWYmZnB8ePHLfeXIAiCIAjr6K7LbrNOfp+KaDyJbDYLr9e7E11ryNNPPw1FUfbdxIwgjLK2tgZBcgPilh3wohuC5K4QN3YKx0V+hUT+/UYpgqdBFL7qU+uK/HbR2+js7HSgNcIo5SJ/Jlk/ricSiQBwRuTXbafNiCUjDgZmdrL+y7/8Cy5evAhJknDq1Cncc88924555plnSo/PnTtXOuaFL3whJicn7XfYBiTyEw3J5/N49vx5yEPPM3We3NaP8+cfRz6f3/UIJlk2XjSOqE48HodcnEgoott0vYXLly5hNFC76K7OSFABA3Dp0iUS+QmCIAhil1hdXUWbR4BXNHdej1ctnb9b9bXe9a53AQB+/OMf78rrE8ROs76+DnhqKKuewP4U+cnJv+9YXV0FExjQYD1X8SlYXqmM62lvb4dLkhB3YJ4ehybidXfb3RNAmCGTyWgiv9TYya9rB3ajmQDAA0BgbM/UfyT2Jj09PaXHi4uLhrUlvYi0LMu45ZZbGh5/77334t577wUAfPGLXzxYIv+Xv/zl0uM3vvGNVZ+3QnlbRPO5cOECZLkAtc1cNWm1bQDyXAEXLlzANddcs0O9MwaJ/PYJR6JQRS0+RxE9CEeips6/dPECfqmj0PA4rwgMBBkV3yUIgiCIXWRlZQVdXvPqnX7O8vLyron8BHHQWVtbgyxVr3YqSz6sra3teB9K9c4cFPmphtr+YmVlBYJfABqZZP3A0vJSxVOMMfR2dyO2xeFvhRiAro4QfX6aTDabBZc4IAG5bP0SyslkEm5BgKQajw2sBQODTxBMxwcTrcX1119fevzjH/8YN9988y72pnk4ehV885vfDMYYGGMVwrz+vBW2tkU0nyeffBJMckMNmFsZVwPdECQ3nnrqKRL5DwDhSATcpa29c8mLDRPb46LRKDYiUYwPG4tNGgvkcOnis5b6SRAEQRCEfdZWV9Hlarw4v5Uuj6b4NcNJTBCtyvLqKhRXdZFfcfmx7IBw2oiS696hVFSuchJp9xkrKytQjUS6+YFoOApZlive4/7BQUQd+KxGAfT199luhzBHJpMBF4sify4HznlN3S+ZTMLLBDh1wfAyhkQi4UhbxMHkuuuuw+joKObn5/H5z38et912G4LBxoFRb37zm/HmN7+57jEPPPBAadHgz//8z/GhD33IgR47g+MlgjnnVYt66c9b+UfsLmeffBJKsA9gJj8uTIAc7MMTZ8/uSL/MQCK/faLRCLieye/yIRaNGj5Xd+WPtRkU+YMKrlyZou8/QRAEQewSa6vL6PSYt+hKAtDuFZriJCaIVmV1da1UZHcr3B3AymoTnfxODdcpk3/fsbi8CMVAcXbu13SdrQWhh0dGEBVNZsJVISIIGB4dtd0OYY5MJgNVVAEJUBUVhUJtY0Aul4Pbmu+3Ki5osdIEUQtBEHD77bcDABYWFvDGN76x5mdGVVUsLi42s3s7hqNL5dPT06aeJ/Y+qqri6aefhtx10tL5crAfTz99DqqqQhAcX1MyDBXetYeqqkjG4+ChopPf5UMiHjP8vl65cgVukWHAb0wsGGuTkbqSwcrKCgYGBmz1nSAIgiBaHSuL5hsbG7iu21oOR6dH3SbmEAThDIqiIBreAB87VvXv3B1AdDkMRVEgOiCg1kIX5JnCwB1Q+rnKSeTfZywvLQM9jY9DcT1qeXkZ/f2bEcBDQ0P4oQOmrihjGB4ett0OYY5kKgkuci2yB0A6na5ZizGfz8PJq5HEOYn8REPe9a534b777sP3v/99fOMb38A111yDd77znXj+858Pv9+P5eVl/Pu//zu++tWv4nd+53f2lCPfKo6K/OPj46aeJ/Y+09PTyKTTUMatCa1KWz8yC49henoahw8fdrh3JvpBIr8t4vE4VFUFd+lOfi9UVUU8HkcoFGp4/uXLlzHSxiEYXL0fC2rv15UrV0jkJwiCIIgmI8syovEEQkPWRP6QVMD6Ojn5CWIniEQi2rjcXb3wLncHoKoKIpFIReFBp3HUyc8prme/IcsyIuEIYKT0SlHkX9kSzTM8PIyMqiINwN8w2L86eXDEFAVDQ0OWziesk0wlwV1cs9VDE/lraQOFQgGig5v0RU5OfqIxgiDgm9/8Jt70pjfh61//Oi5evIj3vOc9u92tHWX3rNXEvuDcuXMAE6AGei2drwa0mJ9z58453DNzkMhvj0gkAgCbmfzFn1GDkT1XLl/EqL9+MZ5yujwcATfD1NSUuY4SBEEQBGGbSCQCzoGQhbgeQDtvfXXV4V4RBAEAq8XvVm2RP1hx3E7haCZ/8VJTywVM7D3W1ta0xaaAAeVWAkSviOXl5YqnR4sRO3YquOh7xkYprqfppFIpzTZcXJurVwiXc25xGac6TrZFHGz8fj++9rWv4d/+7d/we7/3e5icnITP50NbWxtOnDiB3/iN38A//uM/lqJ99jtNWSr//d//fTDG8OEPfxiDg4OGzllbW8P73/9+MMbwhS98YYd7SNTi6aefBgv2AKLFj4oogQW7ce7cOfzar/2as50zgapam6QSGrVE/nA4jImJibrnKoqC2dk5vHDS+AyAMWAkoJSy/AmCIAiCaB561E7Ibc12F3KreIbieghiR9DFe9VTXeRXi+L/6uoqTp06tWP9EAQBoig6M88qNkFO/v3D0tKS9qD6x3AbPMC3ZV7rETsbMLYhoBr6nWZkZMRiC4RVUqmU9v6XOfkJZ+nqCeHc+v273Y0doasn1NTXu/nmm0vFcu3y0pe+dM/Wj2zKXfRLX/oSGGO47bbbDIv88Xi8dB6J/LvH0+d+gbzf3jbPvL8XTz29u05+EvntsU3kl3wVz9djcXER+YKMkYA5m8+Iv4CpK5dM9pQgCIIgiK0wZs7zVhL5LTr5OzwckfmY5twz+doEQdRndXUVTHQBoqf6AZIHTJR23MkPAJJbQkGpXWzTMMVpgsdT47+J2HOUBPvq9Z+3IftlzC/MVzzn8/nQ09mJdQNzylqsA2gLBAxFyBLOkk6nNYG/KPLXc/Izxhyr0Q04V+97r/P/3nnHbneB2GdQXA9Rk2QyiaXFq1AD9kR+NdCDpcWr2krvLkFxPfaIRCJggggIxTu46AITREMi/8zMDABgOGjuPRgOKphfWIQsy2a7SxAEQRCEDdbX1yEwoMOik7/To0JWFMRiMYd7RhDE6uoqmDeobX2tBmNg3ramiPwet8eZuJ5iG16v14HGiGZw9epViEERhqupBoCFqwvbnh6bmLAV17MOYGzM6j4AwiqKoiCXyRkW+UVRBHdwzZ8z7GhhcYLYr+xZkT+bzQKg1fzd5PLlywAAxabIr59/6RK5svcr0WgUgse/OZlgDILHbyiTf2ZmBgE3M73lfzigQFYUXL161UKPCYIgCIKwysbGBjq8AgSLE/LO4g6ADYrsIQjHWVlZgSzVt0/Lkn9bkdOdwON1SOQvenook3//MDc3B8XMTu02ILIRQSaTqXh6bHwcG1ajgQFsiCLGJyctn09YQzdwcjcHRICJrKHI76TtUgHFexFENfasyP/Tn/4UANDf37/LPWldpqamwAQB3Buy1Q73hsAEYVeLqFJcjz2i0ShUqdJZo0pewyL/UECtaTaqxVBx0Dg3N2fuRIIgCIIgbLGxsWE5qgfYjPlZX7fjzyQIohpLyytQahTd1VFcfiwt77zI7/f5AQfSenSRPxAwGPBO7DpTM1NQ24zfJ3ibZvian6+M7BkbG8OGqkC1EMDCwbHOORXd3QUSiYT2oOjiFzzC5nNVkCTJWZGfsc3i3wRBlNiRpa+/+Iu/qPr83Xffjb6+vrrn5nI5XLlyBd/61rfAGMOLXvSinegiYYCZmRkwfycg2FwLEgQwf6gU27Ib7NWiGPuFWCwGZUvupyJ6DG3Dn52ewqA3b/o1Q26OgFvA7OwsbrrpJtPnEwRBEARhjbXVVXRK1pU7fffe2tqaU10iCKLI6uoquH+87jHcE8Tq6uyO9yUQCAC1zbvGKV5u/H6DAe/EriLLMpYXl4FrTZzUrv2YnZ3FsWPHSk+PjY1B5hxRAF0m+5EEkFVViuvZBeLxuPZAlwjcqCvyu91ux538JPITxHZ2ROT/0Ic+tK3IFuccf/M3f2O4Dc45vF4vbr/9dqe7Rxhk4epV5F1BR9rKu9qwsIuxKyTy2yMciUDdIvKrogfhBpn8nHPMLyzg+hHzt3TGgMGAgtnZnZ+gEARBEASxydrqMia81qfjkgB0eAVy8hOEw8iyjFg0Ah46Vfc47g4gthiBLMs7GmnR3tYOFmHgNstgsoKmHZCTf38wNzcHRVHAO0y87y5ADIrbdvfrAv06zIv8+h2GnPzNpyTyFxO2VJe6+VwVXC4XZAc1GRkk8hNENXYsrodzXvrHGNOqaZc9V+ufx+PBxMQE3vCGN+CRRx7Bddddt1NdJBpw9eoSuKfNkba4pw0LVxcdacvS65PIb4tIJAa+Ja6HSx5EovWd/Ovr68hkcxgKWNvyP+jNY3Zm92KeCIIgCOIgoMcWGh0Pra2to8tjb+zU6VHJyU8QDrO+vq7NmxvE9XB3AJzzHa+L0dHRAbHgQPHLPCAIAoJBZwxmxM5y5coV7UGHufOUNgWXLlfW6evr64NbkiwV312H9rkZHh62cDZhh9KO/jKRv16Ur9vtdlbk55xqeBBEFXZkWX9r/rkgCGCM4dy5czh1qr7rgNg7RCIbUHuHHGlLdfsRXbviSFtWIJHfHvF4DLytsgAzl7yIx+sv3Oh5+oN+a27AwYCCx+fnS4uFBEEQBEGYR1G0+7Asyw2PzeVyiCdT6BqzV8+o213AysqyrTYIgqhEXzhT3fXFcLW4CLC6urqjNe7a29tLLnxb5AFfwEfj/X3C5cuXIQZFKG5zczy1Q8XFSxcrnhMEAcNDQ1i3UIdtHcBAby85uneBaDQKQRKgSNpngHs4NiK1FxUdd/JzKtRNENVoSuHdsbExjI2N0ZdwH1EoFJDNZIAt7m3LSF5kMxlDk8udQJ/cEubhnCOZSIBLlXE93OVFMp6ou4AyNzcHSWDo9Vl08vtVpNJZRBrEAm2FCi0TBEEQxCb6OMjI/XF1dRUA0O21dy/t8ihYI5GfIBxFF/mNOPnLj98pOjs7gawDDWWBUCjkQENEM3j2wrOQ283P63mIIxqOIhwOVzw/NjmJDWZemtoAw9jEhOnzCPvEYjEwb9minAeIRqI1j9ed/HajvXRkrtLiDkFUoSki/z/8wz/gH/7hH8hNvY9Ip9MAAC46c+HU20mlUo60ZxYS+a2TTCahqgq45Kt4nkteqKqCZLJ2ta25uTn0BbRsXisMFXcAzJl0dtC1hiAIgiA20U0WRswWusjfZVfk96pYpbgegnCUtbU1MMkFNJqjiW4wybXjIn93dzeUrALbFTWzQF9vnyN9InYWzjkuXrwI3mlhvtWp/bh4sdLNPzY2hrBgfsIYlkSMUtFdU3DO6xbINcrGxga4t+wz4NWE/1rzcF2Qd0qVUTgnkZ8gqtAUkf/mm2/Gy172Mvz0pz9txsuZZm5uDu973/tw8uRJBAIBdHV14YYbbsBdd91VErudZmlpCaFQqFSv4KUvfemOvI5VCoWC9oA5kLFY1s5uOfl363UPAnq2HndtcfIXd3nUy96bn5vFoDdv+bX7/CoERiI/QRAEQdjBksjvsRnX49V24+3UWJogWpG1tTUwTxBoFGvDGJgnYEnkv3LlCv7sz/7MkEmqq6tYKtWmm1/KSejt6bXXCNEUlpeXkUqmrIn8AUDwCLhw4ULF0yMjI4gqMvImXN4KODYUBSMjI+b70cJ861vfwqtf/Wrkcjlb7WxsbFTENXEvh1yQaxoAnRT5VXCowI4WFSeI/UpTRP5gMAjOOa655ppmvJwpvv3tb+Paa6/FJz7xCTz77LNIp9OIRCJ49NFHcfvtt+P666/fVgHeCW699dbNYiV7kNJ2bqdyEYvt7JajvrRoQZhG307JXVuc/MXf60XpzM3OWM7jBwCXAPQGBBL5CYIgCMIGZjL519bW0OYR4Lbp89AL91LxXYJwjrW1NchbdtfWQpH8WF83X8707//+7/HAAw8YWqAr5f3bXMtjGbajtQMI5ygJ9J0WTmZaZM+Fi9tFfgAIVzunBlEAKuck8pvkoYceAgDk89aNeACwur5a6eQvXpZqXXN0Qd4JNUhvYz+K/EJxx4qiKKRZtDic89L4XLCwk6kWTcvkB7DnnDxPPvkkfvu3fxuxWAzBYBAf+chH8PDDD+OHP/whbrnlFgDaTezVr3513UgSs9x3332499570de3d7ckli6Y3KFs82I7zdxSVT6RtbtS3croIn4tkX9rpqJOLpfDyto6BmyI/AAw4M1jft6cyE+Z/ARBEASxiW52MGJ6WF9ftx3VAwCdxZ0AJPIThHOsrq5BNSzy+7Cyav77Z0b80+ezLG3DGKYCSkrZ03NjYpOLFy9C9IuAxdJ9SkjBM+efqXhudHQUAFC7bOt29GMPusivKAq+//3vO26WtCswr62tAf6yJ3xlz1dB15ecmKXvZ5Ffr1PKOSeNqsXJ5XKl76GT9WubIvK/+tWvBgD84Ac/aMbLGeY973kP0uk0JEnC/fffjw9+8IO48cYb8bKXvQyf+9zn8LGPfQwA8Oyzz+Kv/uqvHHnNZDKJd73rXQCAu+66y5E2d4LSh0x16GZSbKeZIn/5AJUuoNZZX18HE0RArIzrgeQBE0RsbFQfji0tLYFzYMBv71Y+4JOxMDdr6hxaFScIgiCITcyK/CHJ/g5IXeSvNU4gCMI8a+vrUN3+xgcCUN3WnPw6RsbTfr8fwbYgYKfsWgYABwYHB200QjSLCxcvQOmwoRF0AuH1cEXka0dHBwI+n2mR3yVJB34HyMMPP4w77rgDDz/8sKPtMhuJDblcDsl4siTsAyg91iP/tuKkk19XF/ajyB8IbBZNj8fju9gTYrcpf//LPxd2aYrI/973vhddXV341Kc+hXPnzjXjJRvy6KOP4oEHHgAAvPWtb8WNN9647ZjbbrsNJ0+eBAB86lOfciTy5YMf/CDm5+dx88034/d+7/dst7dT+HzaVZop9rZx6TAlD8YY/H5jg1InKN85kslkmva6B421tTUwb2B7dBNjELzBmqv18/PzAIB+u05+v4ql5VVT7gVy8hMEQRDEJrrxwYhDd2NtFSGbefwA4BYBv5uRyE8QDsE5RyQSBncZm09xlx/hcNiy+cWoCDg8MgzY2fRePPegO7IPChcvXoQasn6P4B3a5/HKlSul5xhjGB4aMhXXEwYw2N/vaMzFXkSPeHY66tmOKU4X8rm/rA0BEAMiVlZWqp5DTn6NYDBYeryxsaHVNtilSGtid1AUpfTe65R/LuzSlG/FwMAA/vVf/xW/+Zu/iRe96EV4//vfj9/5nd/BxMREM16+Kt/85jdLj9/ylrdUPUYQBLzxjW/EBz7wAUQiETzwwAN4xSteYfk1/+M//gOf/exn4Xa78Td/8zeW22kGoijC5w8gLzvjgGdyDl6fv6k34XKRP5WyYy9pbVZXV6G4qq8sKi5/zdX6paUluESGkNueq77Xp0BWFKyvrxt2apDITxAEQRCbmHHyR8JhHAk4cx8NeVjNWD+CIMyRTqdRyOfBDTr5ucuPQj6HTCZjyWhlVAQcGxnDpScuoQBrhjiWYBBEgeJ69gGRSASxaAz8hI35XRvARIYrV67gec97XunpkfFxTE1PA6qxtjcYw+j4uPV+tDh2RP6lpSXtwRaJgPs5lpeXq54jilqhn1Z38rvdbvT29paMkqurq1hdXYUoirZ2VxD7g/Icfp3e3l5H43qa8q04dOgQAM09lEgk8Kd/+qf40z/9UwSDQYRCodIXvhqMsYpVXqf4yU9+AkDbFlF+c9nKS17yktLjhx56yLLIL8sy3va2t0FVVbz//e/H8ePHLbXTTDo6OhAvZB1pixWy6AiFHGnLKOV1FEjkt87i0hLkGiK/LAWwuLhU9W9LS0vo89uv3dzrU0vtkchPEARBEObRHfxGRP5oPI72kDOxd0FJcdx9SBCtir5gtrVOVi304zY2NiyJ/EYFp/HxceDHADgAK+P+BDA4NLgvBbtWY3p6WnvQYaMRBrAOttlWkdHRUTzGGLQPUmMioojri1n+hHnszJeXlpa07/qWS5HskzG/MF/1HCq8u0l3dzfy+XzF+Ijc/K1JR0cHuru7HW2zKd+KmZmZit/1VcNEIoFEIlH33J1azTp//jwA4MiRI3UvDidOnNh2jhXuuusuPPnkkzh8+DA++MEPWm6nmfT09GBh0RlxnBXS6B129sPbiNJnK0B5Z3ZYWloG941V/ZvqCWJxqXpR3LW1NXS57UdcdVso3EciP0EQBEFsotcmalSjKJfLIZvLI+hyRuRvkwqIleUuEwRhHasifzgcLhU2NYNRp+/ExATUnArkYKkYq5AQcOj4IfMnEk1nYWFBE3dtJkvIARmzW2quDQ8PI6YoyANwN1gtUsARlmWKeLKBnfnywsICxDYRirBFmA4WPyNV0GszOinyN7Peo5MwxjA0NISuri5Eo1Gk02kS+VsIURTh9/sRCoXg9VqsYF6Hpoj8b3rTm5rxMobJZrOlIkSNbgydnZ0IBAJIpVKljHGzTE1N4S/+4i8AAHfffbfjb2StC6lOaTuVSfp6eyDOWS/WVI4oZ9Db09ztdPrKKA9yxOLkIrNCLpdDNBIG7zxT9e/c247oYhi5XA4eT2Vh3vXVFfS47d+svBLgczFThcNI5CcIgiCITYyK/PrOR79DIr9P4ogmyWhBEE4QiUQAmBf59fPMYtRsV4rgjcGSyC8mRExOTJo/kWg68/Pz1cVds7QBc3OVRrHh4WEAWtb+QIPTo9AiW/RzCPPYieuZX5iHEqjyGQgC8VgcqVRqWyFRXZCXLb/qJnob+1Xk1/F6vRgYaPRpJwhzNEXk/+IXv9iMlzFM+e4BIwUOdJG/PP7FDG9/+9uRyWTw2te+Fq985SsttVEPK84MI/T19UGUH3ekLbGQbnrOYjweBxMZVL9qeXDb6iwuLgIAVE971b+rnrbScZOTlYPzjY11HHYo07fTa65wH62EEwRBEMQm6Uy24mctdJHfJzoj8vsljqsWx88EQVQSjUYBJgCip+GxAADJAzCmnWcBoyLg8PAwJJcENaaC95u8dmQBJa3g8OHDFnpINJvl5WUoPgfmWQFNDC43iunmSyMiv17phUR+69iZL8/MzkCtMs/nbdr3f2FhYVs89U44+Z3MMSeIg8LBLkVeg2x2c4Jj5MKg33gymYzp1/ryl7+MH/zgB2hvb8cnP/lJ0+fvJr29veDZJGBjlRcAwDl4Lomenh5nOmaQaDQKwSsAHiAaizb1tQ8K+i4R1Vdd5OfejorjyoknkmhzyAkYlBRTkUuy7IRHgCAIgiAOBpmsLvLXH8vqY12v5Mz92yNyS+NngiC2E4lEIHh8xgteMQbB47dc/Nqok18URc3Nb2XjdPGcI0eOWDiZaDYrqytQvfZNXNyn3WPKTVyhUAg+jwdGPq1hAJIoUrFmG1gV+WVZxsrSCtBW5Y/F57bu0gBQSrOwH+a72cbWJAGCIJrk5N9rlMfl6IXI6qFvbfb5jG2N1FlfX8dtt90GAPjIRz6CwcFBU+cbpVGM0NLSEm644QbT7fb29oIrMiDnAJeNiCE5C67IhoumOkU0GgU8ADxALEpxPVaYm5uDILkBqfpnn7t8ECT3ts+gLMvIZHMIOCTyB0TZVOE+cvITBEEQxCa60N5IcNfHvB7Bmfu3WzA21t4pOOc7Vt+LIJpNJBKpOSavieS1XPzaTJzHieMnMPXTKRRMSngsyuD2uDE0NGS2e8QusLa+BjhRZq8oLWxsbJTee8YYhgYHEd5Sz7EaYQD9fX0QRdGBzrQmVuNtl5aWoChKybVfgQsQ/WJVfWonRP6dyDMniP3Oron8KysrOHfuXMlZ0NXVhTNnzjRFCG5r21x2NBLBo29dNhLtU84f/uEfYn19Hc9//vPxzne+01wnTbBTBWf0lXEhn4RqQ+QX8qmK9ppFJBKB4lYAD5DP5ZHNZulGYJL5+XlwX6i2Y4gxcF/HttX6dDoNQNum7wQ+SUUqZXy7Pzn5CYIgCGKTrEGRv1DQps6SQ3t9XQJHodDce3K5MFkoFGg7P3FgiEajkI1G9RSRRc+Ox/UAwLFjx/Dt/+/bWo6GGd01orn4SazdHySTScAJ32LxY7x1p/bI2BgWZmeBBh+9DQAjOxRZ3CpYFflnZ4sFk6tv9IcaVDePKUPXYZxY9tfbICc/QWynqSI/5xyf+9zn8JnPfAbPPPNM1WNOnTqFW2+9FbfccsuOOW+8Xi96enqwvr7esGhtJBIpifxmsu8XFxfxla98BQDwspe9DP/8z/9c9/jV1VXcc889AIDJyUm88IUvNPxaO4UuyrN8CghYj9phuyTyb4Q3oHpUcI82SgiHw+QSMcnM7CwK7mp78TYpuNsxs+VGrosIHocyfT0isJFJGz6+UWFBgiAIgmglMpkMOBORy9bP5Ncn/YJDQ3CBAYpFIcEq5Qv9uVyORH7iwBCORKBK5kQtVfQibLE2mVmRn6tci9/pMv4arrgLJ284ab5zRNNRVRX5bB5wotZpUYXSjWE6Q8PDeFoUAbn+ruyoKOEU5fHbwurO97m5OQhuAYq3+vlKUMGV6Svbnne5XHBJEnIOmPH0mb7f77fdFkEcNJom8kciEfzqr/4qHnnkEQC1Bw3PPPMM3vGOd+ArX/kK7rvvPoRCoR3pz8mTJ/GTn/wEly9fhizLkKTq/yueffbZinOMUr41+WMf+1jD48+fP4/Xv/71AIA3velNe0Lk7+rqgiCIJZHeKiyfgiCI6OzsdKhnxljfWAc6UNoOGIlESOQ3yezsHNTQsbrHqL4Q5uYuVTyni+xuh5yAboGbEu51JyJBEARBEFo9Ku72NXTy6yI/Y84s0gvMnFDoBOW1t3K5XMUOXoLYz0QiUXApYOoc7vIiEonuTIfKOHz4MARRgBpWwbsMfufzgBJTcOLEiZ3tHOEI2WxWu547oSCJAFgVkX9oCFFZLm4Iqb7azMER4WrLzeudNsBadfLPzc1p2fu1utMOLP5iEYqibNuh4/d6kTWQpNGIHACvx0M7gAiiCk0pvMs5x6/92q/h4YcfBuccXV1deMc73oEvfelL+O53v4vvfOc7+NKXvoR3vvOd6O7uBuccDz/8MH7t135tx/r04he/GIAWxfPYY4/VPO7BBx8sPX7Ri160Y/3ZiwiCgFBnJ1jeuIO6GiyfQqirC4LQvDrPnHNEI1FN4C+K/FaLTrUqsVgMqWQCarG4bi1UbweSiXjFdsvN7f7OTOwlwZxwT05+giAIgtBQFAWFfA7c5Uchn6s7sdcnzJw7Iyao3HlhohHlY4Bsg50LBLGfiMVi4C5zmfxc8iIai1p6PTMLdB6PB5OHJmGoaqpOcYOBGSMdsXvoczHuxPyOAUxk2+Z3g4ODUAHEq58FAEgByKvqjtU73Ks4vWBu1ck/PTMNOVDbjc/bOOSCjJWVlW1/CwaDcOKunAEQJBc/QVSlKarrP/7jP+Khhx4CYwxveMMbMDU1hc9+9rN44xvfiFe+8pV41atehTe+8Y34zGc+g6mpKfze7/0eOOd46KGH8NWvfnVH+vSa17ym9PiLX/xi1WNUVcWXv/xlAFq195tvvtlw+xMTE+CcN/yn85KXvKT03Je+9CVL/007QW9vrwNO/jR6e6zH/VghnU4jn8trAr8HANMK+xDG0XP2VV99kZ8XFwHKc/n1rfKiQ/N6kXFTOfu7WeSPIIidgXNzO3oIgtDQ3fvc5a/4vRq6yK84pCUoHBDF5pk8gO1OfoI4CKiqimQiDi6Zqy/GXV4k4wnLrl0zXHP6GriixrNcWJjB5/ftWH05wllKorBDl3QmsG1Csy7c1wuY0v/Wak5+p7Hs5J8vOvlrUfxbteK7HaEQ7NlHNdIAOjrqaxQE0ao0TeQHNCH7K1/5St1ts8FgEP/wD/9QEr3/9//+3zvSpxtuuAE33XQTAOALX/hCKUaonE984hM4f/48AODd7343XK7KQcuXvvQlMMbAGMOHPvShHennbtPX2wNRtrfeKsoZ9Pf1OtQjY+iCPvdygGlV3tfX1223q6qq5VXv/YZ2Y2YlEb8WutO//Eau/z9yTuQ35zZoFEdAEMT+42//9m/xute9bre7QRD7Dj0Ogbu1mA+91lQ19PjKgkN6YEFlcNWIxNwpykV+Gg8QB4VEQhPqucusyO+DqipawVSTmHUOnzp1CkpMMVxZUwgLOHnyZFN3exPWKc3FnNqcVWV+19/fD8CYyD8wMOBQR1oTMwY6nXg8jlQiVV/k92u7NKrVvuzo7HRO5G9yFDRB7Beackd9/PHHwRjDH/zBHxg+59ZbbwUAPPHEEzvVLXz605+Gz+eDLMt45StfiY9+9KP493//d/zoRz/C29/+dvzRH/0RAK2Q0G233bZj/djLdHV1QVLsTZAkJYuuLhMVmBxgbW1Ne1DcxcV93BGR///5f/4ffPCDH7Tdzn5gfn4egi8ICA0m56IEwddWVeQXHMz0beQ22FpojyCIg8VXv/pV2pFFEBbYKvJvzUAux+fTokByijMqTk5h8HnNxYvYhZz8xEEkGo0CALhkPq4H0GqTmcWsyH/69GntgZHIHg6IERHXnLnGdL+IgwGrslrgdrvRHQo1FPkDPh+CweCO9a0VsOLk14V7HqxzbWCA0CZUFfk7OzuRdiBHPyUI6GyyvkQQ+4WmWGv0LPTJyUnD5+jH7mSO+nOf+1z80z/9E373d38X8Xi8qnh77NgxfPvb327Zol2dnZ1AwaYLqpDZsQLKtVhdXdUeFMfBilfB0vKS7XZ/9KMf2W5jvzA3NwfZ027oWNndXhHXow8aBIecHkZE/vJJfT0BgyAIgiBaCd25r7o154MRkT8rO3MDzyqAz797Ij+NB4iDgi7Sm87kLx4fiUQwPj7ueL/KGRkZgT/gRzKcBB9osECQApSMglOnTu1on4j9x8DgYGlRqxoxAIPk4reNFZH/6tWr2oMG6yuyX64q8nd3dyPpQJ2elCCgu7vbdjsEcRBpipNfz8taXFw0fI5+bHu7MZHRKr/6q7+Kp556Cu9973tx7Ngx+P1+hEIhPP/5z8edd96JJ554AkeOHNnRPuxlOjo6wPMZwGqhF87B88ZF/lQqhZ/+9KfWXquMlZUViD4RKC4Ucz93RORvJaZn56C4jX3/FE87pmdnS7/rzh/nRH4OpcFApHxLPhXaIwiCIAgNM05+3dSSdEjkTxYEtHeEHGnLKDQeIA4ipShS0yK/trhnxThn1snPGMPp06chhBtLDCysXWNI5Ce2Mjg8jFidCKcoYxgYHm5ijw4mViKIFxcXIXpFoEHpDR7gmFuY2/Z8V1cXEooCDnu7/ROqqplRCYLYRlNE/jNnzgCoXeC2Gn//939fce5OMj4+jr/6q7/ChQsXkEqlEIlE8Oijj+KP/uiP4K9TtfvNb35zqViu1Ux+/fwHHnjAWud3mFAoBK6qgFKw1oBSAFdVw4VRvvSlL+EDH/hA3bxYIywvL4P7y24efmBtda0pRacOAqqqYnlxsWHR3dLxvg4sLS6W/v/qgwYHIxsbvnflogVl8BIEQRCEhj6m4kUnf70xVjAYhCAISBacEvlFtLU3tzhe+XiAnPzEQSESiYAJIiAaL2wLABBdYKK0o7vjyzlz+owm8jfS8DaAgaEBKp5JbGNgYAAxVlumioki5fE7gBWRf2lpCTxgQKAPAKsrq9vm7729vZA5t5XLnwNHRlXR29vcmo8EsV9oisj/3/7bfwPnHN/4xjfwoQ99qK4rQBfMv/GNb4Axht/6rd9qRheJGuiOLqZYyzTVzzO6I2NmZgaA9WrvOnMLc5D9mxntPMihyMpmVj9Rl9XVVchyoWHRXR3u7YBcKJRikpx38jfeTEKTeoIgCILYjr7wrTt6690jGWPo7GhDLOfMFCEmS03fUp9OpyEwBp8g0HiAODCsr6+DeYOA2agLxiB4Ao7UJjPCqVOnoOZUoEGdXyki4ZrTlMff0tT4KPf39yOmyFCqrBRxcEQUhUR+B7CitywuLUL2NS7YywOa9rJ1cVEX5mOmX3mTePFnX1+fjVYI4uDSFJH/lltuwYkTJ8A5xx133IFrr70Wn/jEJ/DQQw/h0qVLuHz5Mh566CF84hOfwHXXXYc77rgDAHDixAnccsstzegiUYOSyC9bFPnlfEU7RjG7PXQrCwsLlVlxxcelHDmiLnoRXdWgyK96tUUcPXtPHzSwJhbe1UUM5mE0qScIgiCIIul0uuQAZoLY8B7Z09ODsEMifyTL0NPT40hbRkkmk/AJAryCgGSygdJIEPuEjY0NqCaL7uooks9S4Xor87GTJ08C2Izjqd4hgEc5RfXsM0pzMae2aqP6/G5gYAAqgESV49MACpyjv7/fuU60KJYy+RevAgEDBxbDMJaXlyuedlLkJyc/QVSnKYV3XS4XvvOd7+BlL3sZpqen8cwzz+CP/uiPah7POcehQ4fwne98B5LUlC4SNSiJ80reWgPFxYFmFi5Op9OIhqNAeSmFAMAEhvn5eVx//fVN68t+ZX5+HkwQwD0NquoU4Z42gAmYm5vD85///NLzDo4BwRo4l0pxBD5OcT0EQRAEUSSTyYBJbgAAk9wN75F9A0MIP3PB9utmZSCVV5vutkskEvAxBlfxMUEcBJZXViBbFPllyYfllRXT51kR+dvb29E/2I+l8BJQq85vDOAKLy0IEPuDUryLg3ms1SJjdAE/CiC05W/R4k9y8tvHbFyPoigIr4eBIQMHFxcCVlZWKuK3u7q6IIkiYhaignSi0HQBcvITRHWa4uQHtNz7p556CrfddptWzLWYRb/1X0dHB973vvfh7NmzGBsba1b3iBoEg5rIqzvyzcKKiwOBgJEl37LzbFRdn5vTirzwtrKBqQAIbQJmy4rDErWZn58H83UAdfIQK2ACBH9HaQeAjjM+fmN1n0uFBX0c8US8wdEEQRAE0RpoIn8xx1t0NRT5h4eHsZpz237dlYwIABgaMqIIOEc8HoePc/hUFfE4jQeIg8Hy8gpUg+abrXB3EMvLzRH5AeDUyVMQY2LNv7MIgyAIOHz4sKX2id2hJAo7pSAJjUX+rUS3HENYx6zIH4lEoChKZd3DWrgAwS2Uonx1BEFAb3d31ffWKFEAXR0dcLlM1ichiBahqTb5QCCAj3/84/jIRz6Cxx57DOfOnSvldHV1deHMmTN43vOeB7fb/sSCcIaSyG/Rya+fp7djFDtxPdPT09qDLWUA5KCMqekpy+22EjOzs8i7jdVR0Cm42zFbXGARBG30p3IGJ6R+jsamkUQiASYyqF6VRH6CIAiCKJJOp8GFTZG/UVzP8PAw1lIqZBWQbIg5K2mh1F4ziUQi8CsKXAAiTSo2ShA7CeccG+vr4MO1rPH1UT0BbFx9FpxzW0Yqo5w4fgIP/uTB2gP4CDA6PgqPx7PjfSGcQ5aLWewOivyFQmHb0z6fD22BAKJVisTHALhdLirY7ABmRf4VfTeQ39jxLMC2xfUAwODwMKJbxH8zRAH0004OgqjJrmThuN1u3Hjjjbjxxht34+UJE0iSBLfHg5zFTH7IOXg8XtOxS3YGoFNTUxDbRChS5Y2Lt3NcvnLZcrutxMzsHFSvuZun4u3AzIy2U0JfWZft1U8uUVAZXK76n6FEIgHBI0BxKbQ9nyAIgiCKZDIZcEG7h3JBQjabrXv8+Pg4VK6J9MNB6zfyqykRbUE/QqGQ5TasEF5fRxCAC8BSk4qNEsROEolEIMsFcLd1J78sFxAOh00VwrZqujp27BjUQrH4bpXEViku4eQLKKpnv5HLFfWA2ps0zCEC+Xx1I+FAfz9iU9vNeREA/b29TVmsOuiYFflLgr1BkV/xKlheqSLyDw3hyaeeAhRr44uoIOBwk80DBLGfaFpcD7F/Cba12yi8m0OwzZwjHLDn5L985TLktu1V33kHRzwaRzQatdx2K5DNZrGxtgruC5k6j/tCWF9fQzabLRP5nRmAySoaLhTF43HADcAD2p5PEARBEEUymQwUpt1DFSY1dPJPTEwAABZS9pScqykRkxOTTRdjNtbX0QYgCGCDnPzEAUAX17jHWo0z/bwVk7n8VudjpRieatU1uVZ098iRI1X+SOxlSiK/UwqSWNbmFgaGhxGtcu+IARgYHHSoA62J/r02K/Kvrq5CcAvaCroBVL+KxaXFbc8PDAwgYuqVK4kJAgbpM0AQNdkVJ3+hUMDjjz9eNa7n+uuvp3ytPUZHeztWkvVdX7VgchYdHeZFfjsTwkuXL4EPVhmUhrQfly9frigOS1Si1zRQvSFT56neDoBzzM/Pw+/Xlviz1mvqVJBRGAL++nUdYrEYVLcKuIF0Mg1ZlqlwN0EQBNHypNMZqEUnvypISDUQ+UOhEDo72jGXzOCF/dujFIwyn/bgBUeOWj7fCoqiIByLoR3aJCeZTiOXy1EsCLGvWVpaAgDLmfz6eUtLSzh16pTh86yK/KFQCO2hdkSjUfCRLW0ktaK7lMe//3Dayc8FXlPk7+/vxwVRBOTKyWRMFHGkyXVeDipmRf6lpSWwADNeeNkPrF7ZHsszODiIlKIgB8BjsoqzAo6oLFNNBoKoQ1MVsGQyiTvuuANf+MIXEIlUX7/r7OzEW9/6VvzJn/wJ2tqsuRUIZ+nq6gSLWNvuzApZdHb2mD7P6qAyEokgHo2Dn6xyfhBgEsOVK1dI5K+DXpxYNenk14+fnZ3Fc57zHABAqiAAsK/0pwsMwY7614P1jXUobgXcq733sVjM1JZkgiAIgjiIJFMpQM/kFySk0/UL7wLA0ePHMTsVBmDN5JFTgKUkx9GjzRX5w+EwVFVFOzbNhmtraxgZGWlqPwjCSZaWliC4vIBkcbFK8kBweUuLBUZRVetxXYcmD+GJtSfAt9bmKm62HR+3Vl+A2D1KgrxDCpIqqrWd/AMDiCgKODhYmRAcAxXdtYuqaIkHZkX+xaVFyL7taQk1CQCZdAaJRKJC1xso5ulHAZh9J+PQSn2Qk58gatO0uJ7z58/j9OnTuOuuuxAOh8E5r/ovHA7jrrvuwjXXXIMLFy40q3tEHXq6uyEp1iZ5kpJFb495kd8qV65c0R5Uq8XDANbBcPky5fLXY3Z2FoI3AEgmC2BLHgieAGZnZ0s38kTBmS36iYKAjgaZvmvra4AX2j+gtEuIIAiCIFqZdDoNLhYz+UUXUlWKGW7l+PETmE2ZHAeUMZcQoXI0XeTX40g6sDkUNBtRQhB7jaWlJXCvPfMb97aZFvntxKdOjE9ASm1Xg1mCwef3kRFnH7ITTv5Mtvqic39/Pwqco3zfWR4cKUUhkd8megHlUiFlg1xdvAruN35N4AHt2MXFysiecpHfLLpNmER+gqhNU0T+aDSKl7/85ZifnwfnHGfOnMHHP/5xPPjgg3j22Wdx/vx5PPjggyVxn3OOubk5vPzlL0csVi3Mj2gmPT09EOT6W7trIchpdHV1mT7P6qByamoKTGJaEGsV5HYZl65cstR2qzAzMwPZE7J0ruztwMzMDDweDzragojknLnERAsu9PT21T0mHA4DPpRE/nUqtkcQBEEQSKVS4KIm2HPR3TCTHwBOnDiBSEZFOGttsX4qLsEliU2P5NAF/RCA9i3PEcR+ZX7hKgpS/djKRhRcQSxc3Z6PXQ87Tv7x8XGocRVbjfyIAyOjI1Q4dR9SKpLrlMgv1o/rASrLOsS2/I2whixrMXxmnPyqqmJleaWmxlKV4rFbRf7u7m5IomhJ5NfP6eurrwsQRCvTFJH/zjvv1DK8GMMdd9yBJ598ErfddhtuuukmHDt2DMePH8dNN92EP/zDP8TZs2fx4Q9/GIB2Qbjzzjub0UWiDn19feDZJMBNDvS4Cp5NmroR2x3wTU1NgXXUyYprB+Zm50xvT2slrkxNQ/FW2wrRGMUbwuUrUwCA3t4ebGSducRs5IS6N/N0Oo1MKrMp8jNtez5BEARBtDrpdBoQi+E1osuQyH/y5EkAwJW4tVyGK3ERR44cbnqdreXlZfgEAV4wuMDQJoqloqUEsV+5urgI1aaTX/UEcfXqVVPn2HHyDw8Pgysc2HK5EdMiJsYmLLdL7B6FQkGbYzu1PiOULRxsQdcPomXPRbf8jbCGXDDv5F9bW4NckMGDJq4JbkDwCNuuO6Ioore727KTv6ujg+rsEEQdmiLyf/Ob3wRjDK997WvxP/7H/6gr5DLG8MEPfhCvfe1rwTnHN77xjWZ0kahDf38/uKqC5RtnuJbDChlwVW3qjXhqegpyoPYNi7dzyAWZXF01kGUZS4uLpvP4dVRfCEuLi5BlGUMjY1jJ2g9tTMtAPKtiqE6RJd21z30cEADRL2J1dXuhH4IgCIJoJTjnyGTSW5z8qYbiXU9PD3q7O3E5Zu0+fjnhxanT11g61w7Ly8sIsc3pTQgwHVFCEHsJRVGwsbYG7rEZ1+Npw8b6mmn3rlWGh4e1B1vSwYSUsPk3Yl9RKBTARAd3YAhALl/dyR8KheCSJL2EAwDNyc8YQ08To4D3AnYW26ohFzP5C4WC4XNKQr3Z2t9BYGFhYdvTg8PDqF6hsz5RAP3FuB+CIKrTFJFfL+T5pje9yfA5b37zmyvOJXYPXVxluXiDIyth2XjF+TuNHvNU2p9djeL4mD5X1VlYWICqKjZE/k6oqoKFhQWMjY1hOWPfwbeU0vaEjo2N1TymtGhT3MnM/ZxEfoIgCKLlyeVyUGQZvFiwk0seKLJcMyKhnNPXXIvLcfO5/LE8w2pKi+dsNotXryKkbJo9OhUFi1UEBoLYL6ytrUFVFUdEfkVRTMVZ2hEXBwYGwBgDS5WJwgqgpBXK096ncM6djVmq0xRjDD1dXRVxPXEAne3tTd8hdtAoFHdPmHHyLywsaO+XydQw2S9jdn677jIwOIi4aD73KSYIGDS5SHjnnXfiO9/5junXIoj9SlNEfr0Ip5nsLP3YYNDsciHhNPpATMglTJ2nH29lIGdlABGPx5FOpetvI/MDTGSmt6u2CjMzMwA0sd4K+uLAzMwMxsfHsZFWkTZX02cbCykRjAGjo6M1jymJ/D7th+yVsbRMzj2CIAiitYnHiwaNosiv/yw9X4czZ67BdFyEbNLMezkqFc9vvsh/dX4e5ZWgOqEJ/wSxX9HHuKrH3pxYdQcr2jN0jg0nv8vlQkdnR2VcT/HxADlx9y9OmsobtNXX379N5O/t7XWwA/sDp+tX6A7+WlFJ1Zifn4cYFM2rh23A/Nz8tqcHBwetOfkFwbS29O1vfxsf/ehHLbwaQexPmiLyX3ONtl330iXjBU/1Y/Vzid3D4/Ggq6cXLGOuCDLLRNHV09u0zDRD28gYILRtz4YjNGZnZyG4fYDLZ60ByQvB7cPs7CyOHDkCAJhL2IvsmUuIGBroh9/vr3nM6uoqxEDZwMMPEvkJgiCIlicajQIAuOSt+BmLNR7TnTlzBnmFYyZhzm13MSaht7uz6bnJsixjZW0N5TaFLgAb0Siy2WxT+0IQTqGL8txtT+TnnkBFe0awI/IDxez08riedNnzxL5DEATHo2MEobYc1dvfj0TZ3+Ng6KNdILbRRX4zcT3zC/NQAhZqGgaBeCyOVKoyt2tgYABJRUHexKqRAo6YLNMiIUE0oCki/9vf/nZwzvGpT33K0GBBVVV88pOfBGMMb3vb25rQQ6IRE+NjELJRU+cI2RgmxmtHrDhNKXO1wTYyxafg6iKJ/NWYnp6G6g1Zb4AxqN6OkpPfJYmYMykObGUu5caRYyfqHrOysqLl8ev4gY21DduTE4IgCILYz5REfpe34mck0thDd+zYMXjcLlyMmlusvxR348y1zzF1jhMsLy9DUVV0lz2nPyZzB7FfWV1dheDybhbPtorohuDyNlXkHxoYgpDZlBtYWnMkt1qm+kHB4/FoxZSd0vkVwOvx1vxzT08PkmUif1IS6bPjALq4byS2T2d2bhZq0Pz1gLdpH5atufy6UB810VYcgAprKREE0Uo0ReT/rd/6LbzlLW/Bv//7v+M1r3kNlpeXax67srKC3/iN38DPfvYzvPnNb8ZrX/vaZnSRaMDkxATceXOZ/O58HJMTE6bOseMOWFpaguARgAbxsapfJZG/BlPTMyh4O2y1UfB0YGp6BpIk4dDkJKZtiPwqB2YSIo4fP173uMWlRcjezVwg7udQFAXhcNjyaxMEQRDEfmdjYwMAwIs79PSfRu6PkiThxPHjuBQzLi7mFWAqJu7KTtz5eS0SoFwC6tnyN4LYb2xsbACe2rtZTeH2l64JRrAr8vf29kLMlc0DMkCwPQi323ytD2L38XqLgrwFQ3c1mMzq7tTu7u5GvOwzGOcc3d3dNY8njJHLm4vrURQFy0vL5ovuAqVznBD5dWsCOfkJoj72cjQM8uUvfxkveclLcO7cOfzrv/4rDh06hFe+8pV4wQtegL6+PjDGsLKygkcffRT3338/crkcXvCCF+AlL3kJvvzlL9ds941vfGMzuk8AGB8fh5qOAaoK1NlWV0JVoKajmDAp8ttheXkZLGAgsy4ArFxYcb540D5HVVUsLMxDHXyevXZ8ISwsPAFVVXHi1Gk89qMrqAzkNM5KWkCmwHHiRH0n/9LyUmXB5eJ4cXl5mRwfBEEQRMuysbEBwe0FhOKQX5AguL2Ghb5rn/NcfOtrz4DzJIwMmWYSImSV74rIPzc3BxdjaC8zjPgB+EURs7PbC/8RxH5gY2MDsmgxRnMLsuQ1ZYCxK/L39PSAp8sMXBly8e9nfD69+BkcUZGYwuD31Rb5Ozs7kVdV5AGIANKKgq6urprHH1ScjkgyK/Kvr69DkZX6dQ9r4QYEj7CZuFCkp6cHkigiohhfMYpAq09AcV8EUZ+miPxvfvObS2IqYwzZbBb33Xcf7rvvvm3H6sLrz3/+c7zlLW+p2SZjjET+JjIxMQFwFSwbA/c3LsrKsnGAc4yPj1t6PSs3s8WlRci+xlVeeYAjn8sjFoshFApZ6N3BZGVlBXKhAO6z5+TnvhAK+TxWV1dx/Phx3PctjqwCeC0Y+vVdAMeOHat5jKqq2FjbAMp37hXHiysrK7tS+I8gCIIg9gJra2uAe0uOoTuA1dVVQ+efOXMGX/mKirWMgD5/Y8HvYlSCx+3C4cOHrXTXFrOzs+gTBAjKZj8ZGHo5J5Gf2LdshMNQpdqRJmZQJR/Wm+jk7+7uhlpQS6IwyzL0jfbZapPYPdrbi46qPAAHPpJiQURHR+15py7oJwG4oKUE0dzdHoVCofS9NlqrZnFxUXtgtSxIcHtkniiK6O/tRbhOwsdWIgC6Q6Gm1XskiP1KU+J6AE201f9t/d3o36odSzQH3ZFvNJdfyEQrzjOLlfd34eoCuN/AecW5bumGRQDY3Mqu2ozr0c+fm5vDiRMnoHJg1mJkz1RMwtBA3+agsgobGxtQFKXyvXcDgnu7a4AgCIIgWoml5WUUpEqnZEHyY9lgLvfp06cBaMV0jXAp5sKpU6cgSU3xEVUwdfkyeqq4AntVFVOXLze9PwThBNFoDFxyRtTikgexmPH4Vbvz7VK0SlFLFHMierrJyb9fKQnyxqPc65NDXZFfF/TT2KzfTCK/PXRhX2Qc2YyxnfalqG2LqWGKV8Hi0nbdZWhkBGaCdcMABoeGrHWCIFqIpozAp6enm/EyxA4SCoUQbGtHPh2B0jXZ8HghE0Gwrd3yjdjsoFJRFKytrAHXGji4KPIvLS3h1KlT5jt3QFlYWAATBHCP1WV6De4JggkCFhYWcP3118PtkjAdl3A8ZD7AcTrpxvHnnK57TMkZsKXbLMhI5CcIgiBamsXFJajuyhuk6g7i6lVjRof29naMjQzhUnQaLx6sv7Wfc+BywoP/3zVGBmOVyLKMVCpVV/Cp/9ocMzMzeFGVv/UBeHJhAbIs78riA0HYIZFIgPudiSjhkgfxuHHnrGIiSqMapWiVLIAgwHKsJeNWDgqdncXd/MYM4PXhgJpV62oFuskrDc3JD9RfFDioOBkvnMlkAADtbo5MOtXgaI3l5WWIPhGKaO16wANci9bdwsjoKB4+exaQjbUbEUVcazElgiBaiaaMdK1GthB7i4mJCUQXo4aOFTL28vjNbg9dWVnR3NxGsuLcgOgVqQjbFq5evQrm6wCYzQ0+TADzdeDq1aul4rsziXOmm9F3ALy0TlQPULYjY0sageyTsXB1YfsJBEEQBNECcM6xvLwEtf+6iudVbxuWl6cM1yY6deZaPPvIIhrV11nPCohl1ZL73wyf/vSncf/99+N73/ue6XMBLZYonc2iWhBIH4CCLGNpaQmjo6OW2ieI3SKTToO3OVOolotuZNLG62TZjeupEIU5oGbUzeeIfUcwGITb40Y2kwWHzVSFPMAVjr6+2vFNuqBfLvLX2919UHEywUIX+UNutfS4Eaurq8bSEmrhB9an17eNOUZGRhBWFKjgEFB/LMLBscE5RkZGrPeDIFqEpsX1EPufQ5MTcOeMbfF05+M4NDlh+bXM3sxKWasG7/tqm0r5rFtYWlqC7Ao0PtAAsitQctEfPX4CsynzwY2rGQFZmePo0aN1j5ufn4cYFLWKTGXwNo7ZOXqPCYIgiNYkHA4jn8uBeyoHR9zTjnwuZ7gA5+nTpzEXB3INzHaXY9qN2Mouyf/7f/+vYcGhGjMzMwBQU+QHaGcxsf9QVRX5fA4QXY0PNoLoQj6fMyze23Xyt7W1QRAEsBwDZE3UJZF//8IYQ1d3F2D9Ur1Jca2pt7e35iEejweSKCKLzc0DgYAzc9X9hJNO/nRxkS/kUUuPG7G2vgbFY/1awH0cckFGMpmseH5kZAQFzmFEXUoDyKgqifwEYQAS+QnDjI+Pg6cjAG8wMOQqeDpiaweHWZF/ZmYGgksAfMaOV4IKLk9RPms5i0vLUNz2onp0FFcAVxeLIv/Ro1hMcsgmzUBzxRz/RiL/zMwMlGCVgUcbEF4PGx7AEARBEMRBolatHf33hQVju92M1teZjkvo7+3elczkmZkZuBhDtVcOAvAJApk7iH1HLpfTatEJzmy+54IEzjlyOWOh6nad/IIgINgW1DLci2lfTsStLC0tGb5+Ec4yODAIlnJAdC5Oz+o5+QEg6PeXRH6fx9OSkWv699AJR78+L+7ycGQyxnKXVlZXwL02Xrvo9VtbW6t4emxsDACwbqAJ/Rj9HIIgatN0kV9RFNx77714xzvegZtuugmnT5/G6dOncdNNN+Ed73gH7r33XtuuAWJnGB8fB1cVsFyy7nEslwRXlaaK/FeuXAE6gAY7vTYJAQtzWj4roREOh8FdFivqbIG7/YhEIgCAQ4cOQVGBxZS54rvzSRGh9raGjp/LU5ehtm2fhPAO7TOku/sIgiAIopWYn58HGAP3bnHye9sBxgzHFh46dAguSauvU4/ppBsnTp2x3F87zM3NoUcQqm75Z2DoBSORn9h3lOYpgrkxdE2YWNmu0de3QVt7mybwF9cVnFgEfPe73413vvOdttshzDMyPAIpa19oZykGt8fdcJ7n9/uRg/bx8fsMuvkOGPr30Invoy7yd3pUpDNZQ5pLJBIxbKSsiresnTIGBgYgiaJhkZ8xhuHhYRsdIYjWoKlLod/97nfxtre9bbNQJjbFXMYYHn74YXzuc5/DyMgIPve5z+FVr3pVM7tHNEAX7YVMFIq3di6OkIlWHG8FsyL/hUsXILcbv/HxDg5FUTA7O4vDhw+b7Z7hHNv9Aucc8VgMvMN8rE7V9lw+rT3OMTmpFWpeSAkYazO+gHc1JeJQg/cmnU5jZXkFeH6VP7YDYMDly5epwDJBEATRcszOzkLwtW8XCAURgq/dsOjtcrlw+NAkpuO16+uoHJhJiLjp+HE7XbbM/OwsuhUFtdwe3aqCeVr0J/YZhUJBe2C3XpaOIFS22wAnRMVQKIT52HzJye9EpvrysvHiwYSzDA4OgiccyIhPAX39fQ3n036/H3kAKgBfi4v8Rr+39dAjc7q8KlTOkclk4PfXNvmpqopkPAkcsvGiNUR+URQxPDiINQO7ctYA9Pf2wuPx2OgIQbQGTXPyf+UrX8Gv/Mqv4OrVq9q2Q84xPj6O//Sf/hNe+MIXlgRhzjnm5+fx6le/Gv/n//yfZnWPMEBfXx/cHg9YNlr3OCEThcfrbbj9zilyuRzmZ+cBMxGPIe3HxYsXDZ9SvmU1n8+beLG9TyaTgaoq4KIzN04uuqGqCrLZLNra2tDZ0Y4lk07+pawHY+MTdY+5fPkywAEeqjLYFAGxQzT1HhMEsfeh3X4EYYyZmVkUPNWjMQqeDlM73Y4eP4G5dO0xwlpGQKbAcezYMbPddIS5uTl01/l7N4A5gzsXCGKvULrfOSXyF9tpppM/1BECKzCwvCbmtmLh1IPEyMgI1Lxa2plhFSEpYGy0cfSKr+jkzxcftyK6uO+E/pBKpeASGTrcmq6xNSd/K4lEAqqqgntsLOyIAJPYNpEfACYOH8a60Pj6tgaGiaJxkCCI+jRF5J+dncXb3vY2qKoKv9+PD3/4w1heXsbU1BQefvhhPPLII5iamsLy8jI+8pGPIBgMQlVV3HLLLZibm2tGFwkDMMYwOjpWcurXPC4bxejomC2nuxkn/5UrV7SbTzWhtxYuTQC+cOGC4VPKb6x2isPtRUrZnKJDm3sEV0W7Y2NjWEobF/lVDiynGufuPfvss2ASq1lwuRAq4NwztZ2HBEHsP0jkJwhjXJmaguINVf2b4g3h8pTxQrRHjhzB1SRq1teZS2r3eCu7I+2SzWaxEYnUFfm7AKQyGcTjRkr8EcTeYNNg1Nzdw/o8zAnncHtbO0RZBPKac9frdWbXMLE7jI6Oag/qa8MNEVMixsca7/r3+f0oACigdZ38ugbhxKJbKpVCwM3gl3jp93rEYjHtgR0fIAMEr1D1/js5OYk1A5rRuiRi8pCd7QQE0To0ReT/9Kc/jVwuh2AwiJ/85Cf44Ac/WNXl3dvbiw984AP4yU9+gmAwiFwuh09/+tPN6CJhkMOHJuHKxeoe48rFcPhQ81ZaL1y4ACYwLZPfBIX2Ap559hnDx2ezm8VpjBas2i+UFjCYQ5mfxRV5vd3h0TGs5tyGT4/kGAoKb5i794tf/AKsk9W+knUBM9MzFe8dQRDVUVUVTzzxhCOFvXYSqqVCEI1Jp9PYWF8D91Xf5sh9IWysrRouTq/X11lKV7/hLiRFdLQF0d1dT2rfGRYXFwFoQn4t9F5RsU6itTG2WCAXtPG7E87hYDCoufgLgC/gO1Bxp62IPjdjCRvvowIoSQUjIyMND/X6fCiAoQDAS05+220lEgn4JcDv4qXf61ES+Y1P46vjLmurjPHxcSQUBWnUnnvkwRGRZVtR0ATRSjRF5L///vvBGMPtt9+O5zznOQ2Pv+666/C+970PnHN873vf2/kOEoaZmJjQnPy1RCDOIWSiti/CZkSmixcvgoUYYFaf7gSuXL5i2BVafmM9aHE9QmmbnLPint7u8PAwVjPGLzerGe3NHBoaqnvc2afOQu6qLfjxbg5VUfHss88afm2CaFUeffRRvPvd78bPfvaz3e5KXQ7a9ZcgdgI9ikf1Vxf51aL4bzSyR6+vczVZfbA1nxR3bSu9Xuurnsiv/628LhhBtCqN5lm5ojnGCVNTIBAAZACF4mNiX+P1etHd0w3U14brkwTAG+/YBgCPxwNZ0ET+Vs1j18e9Tox/k8kk/JLSXCc/AMWlVBX5JyYmAGiZ+7VYh6ZQ6McSBFGfpoj8euTOy1/+csPnvOIVr6g4l9gbTE5OQi3kwPLVnV8sn4JayOGQze1UZkT+Z559BnKHeWcn7+Qo5AuYN5jRWr5l9aCJTJJUjOnhNfbhm6W4vVgX+QcHB5HMqUgbfJvWigsCAwMDNY9ZXl5GZCMC3l3ns9IBCG4BTz75pLEXJogWZnV1FQCwvr6+yz2pDzn5CaIx09PTABhUb/VtjqovBMC4yN/e3o7OjnZcrVFfZynrwcTk7mylX1hYgFsQEKxzjBcMQVEkkd8GRnd9EM7hvAlHa0cU6zujcjlnRX5e4CTyHyAmJibA4jac/MXUFiOmQLfbDZkxyILQsiK//j10Qn+Ix+MICDICkjEnfylix6aTX3WriES3Z/KPjo5CYAyrdc7V/0YiP0EYoykiv+6UbjSgKEc/trzYKbH76E4uIROu+ncho128m3URlmUZczNzpUK6piiec+nSJUOHl4v8B01k8uvbHxVnFi9YsR19MN/f3w8ACGeNXXI2sgJC7W11B3Nnz57VHvTU6wigdqs4++RZQ69LEMTe56AtshLETjA9PQ3B3w6IruoHiC4I/o7iYoAxatXXMVpHZ6dYWFhADxPAGkSRdHFu2NhBVPLtb38b/+W//JcDF1e51ylF2zgVo1dsp1Fkjh5z6UTcpd/v1wq1FgC/rzXjVg4akxOTkFLW67ixBEOgLYBQKNTw2JLIzxhcrhr3swOOk07+RDwGv8ThFgGXyAzF9Qhuwb5q6EZVkd/tdmOwv7+uk38NQE9n56ZeQRBEXZoi8uvZbQ8//LDhc/RjG8V1EM1lYGAAbo8HLLP9Ig0AQjoCj9db14FtBKOLO3Nzc1AUBbzDwuDXDYgBEVNTU4YOLxf2D5rI73a7IUoSmOyQeKbkIUoS3G5t2V+vwbFhUORfzwro799et6Ocs2fPQggJDbcPqj0qnn766QP3nhGE0+yXnFwSmQiiMVPT0yh46hcrKnhCuGJwDAQAYxOTWM5ut/OFswIKCt81kX9uZgZdSuN7fLeqYs7gzgWikgceeAAALbI2m5KT37FaObyy3RpkMhkAzoj8erFUlmMIBurttyH2C5OTk1DiCmAs8XY7MeCQwZ1fbrcbMgCFsZZ38jsx/k3EYwi6NJ0l4DYm8jOPA/MDN2oWvp84dAjrdeYga8CuxQESxH6kKSL/zTffDM45/vIv/7JUHKseCwsL+Mu//EswxvCyl72sCT0kjCIIAsYnJiCkq4v8LBPB+PhEw8FjI4zG9ZQcaO3WXkdpUzA1TSI/YwwdoU6wgjNbsVkhrbVXvGF3d3dDYAzhnLHPRTgnon+wftHdRx97FHJP4/eB93Lkc3nK5SeIAwKJ/ATRmMtXpqDUKLqro/hCuHLFuMg/PDyMlTTbpjcuFyP2jBRR3AlmZmbQa+C4XmjmkL1eXHwvQ//vmsvmLniHdrYXYzkb7a7PZLX7rBMRTV6vF4Am8uuCP7G/mZyc1NaLLObyu5IuHDl8xNCxbrcbCrSyDq3q5HdU5E8kSkV3Ay5eU3jXicfj4G4HrvseIBFPVL2HjE9MYF2ofU3aECWMk8hPEIZpish/6623QhAErK2t4YUvfCG+9rWvVS12qigK/vmf/xk33ngjVldXIQgC/uAP/qAZXSRMcOzIEbhz0ap/c+eiOHbU2E27HkaL4S4sLED0ipaLwahtKmZmZ0z36aCJ/ADQ29tbs9aCWVg+jb7ezSm3JEno6uww7OQPF1zo7a09ZV9eXsbayhp4n4FBRycguAQ8/vjjhl6bIIi9DYn8BFGfRCKBaHijVFy3FqqvE5HwBpLJpKF2h4aGkClwJAqVjruVtABBEErRfM0kHA4jlkjAyCv3A8jkclheXt7pbhGEIzju5OeNnfycc2R3QuTPs9JjYn+jx/eyqAWHtwKocdVw/T6XywWZcyhoXZG/tOiWsb+zJpFMIVjM4w+KakMnfzQahepyYJHRA8gFueruoLGxMUQUGfkqtUcUcKyryq7tFCSI/UhTRP4zZ87gjjvuAOcci4uLeN3rXoe+vj68/OUvxxve8Ab87u/+Ll7+8pejr68Pr3/960tFse644w6cOXOmGV0kTHDo0CEgHdlepJWrQCpcuvHbwYzIz4M2Br5BYGV5xdDrlQv7Rvu3nxgZGoRUMDbRb4Qrn8TIcGXUVm9vn3GRP4O6Iv8TTzxRbNRAY4IW2fPzx39u6LUJgtjbOBEfQBAHGb2YLm8g8ut/N1p8V4/QXMtU3svXMiL6e7ohSdYzmq1y+fJlAICRkEj9GP0cwjz7JdbtoLDpuG9eXE82m4VaXAxIJS1atcvQI1Z4jrds3MpBIxAIoK+/D4hZODkOcJXjyBFjpkCXywUZmpNfj4FtNZyqkZHP55HN5RHQnfxiAfFY/TcxHA1D9dgX+fXdALEqrzc6OgoA2KhyXhSAyncvDpAg9iNNEfkB4AMf+AA+85nPwOfzgXOOSCSCH/3oR7jnnnvw1a9+FT/60Y8QiUTAOYff78dnP/tZ/PEf/3GzukeYYHJyElyRwbKVAz+WjYOriuGV+XoYdcovLC5A9lt31fMAh6qoWF9fN9Wn8iK8B4WRkRGIufpb9owi5OPbtu33DQwikm8sAKRlIFPgpRz/ajz22GMQugTA4FhP7VNx7ulzlCVLEAcAcvITRH1mZmYAxqD66mfyq74OgDHTIv/qFpF/NSNgcLh+xN5OcfHiRXgEAfWXMzTaAARFERcvXtzpbhGEI+xU4d16In8qlQIAdLhVpFP2zT8l974KEvkPEMePH4cYqx/7VA0WZWCMmXbyy5y3rJPfKZFfj+ZpK4r8QRdHPFY9glknGo0anm/XpfjVryby6wJ+NZFfV2h2Kw6QIPYjTRP5AeCd73wnZmZmcOedd+IVr3iFVsTV7Ybb7cbAwABe8YpX4M4778TMzAze8Y53NLNrhAl0p76wpfiu/rsTTn6jIvry8jJgp9B68dyVlZWGh5YLxAdR5B8dHYWaTQF2i+/KeajZ1HaRv68PYQMif6To9q/l5Oec4+eP/9xQHn/pnF4OuSDj/Pnzhs8hCGLvUF6MnZz8BFGf6elpCP4QIDS45woSBF8HpgwW3w0Gg2gL+LGWqRR21vNuDA7tjsj/7PnzGAIgoLHDnIFhSFVx/plndr5jBxTK5G8uuhjPHPr/rrdTb0eGHt/V5VUNR3nVo9x9vVdF/nvuuQePPfbYbndjX3Hs6DEtrsfsRzMKDAwNGK7P4Ha7SeTXRX6bJhdd5A8U43eCbq6J+PXOicUtxyJXULwMVBP529vbEfD5qor8GwDcklR3hz9BEJU0fV9tT08Pbr/9dtx+++3NfmnCIbq6uuAPBJHPRFEeWiNkovAHgujsNOKnqo8REV1VVUTCEcDOwm5R5F9bW2t4aLl79CA6ScfHxwEAQjYKNVjbRd8IIRMFAExMTFQ839/fj42MZiKqt9t7vSjyDwxU33y/vLyM8HoY/LiJUWUIENwCzp49i+uuu874eQRB7AnKr7kk8hNEfS5fmULe3W7o2LynA1empg233d/fh/VspcljLSPg5hr37J2Ec45fPP00TqkqYEDkB4ARzvGzc+egqmpdNzNB7AWcj0cy7uTv9aq4Unxsh3KRf6/Grdx9993weDz4/ve/v9td2TccP34cak4FUgCCxs+TohLOPNd4HLPL5YLKOQpo3Ux+XdzPZe3pD7rA3l6MzmlzqYhv1I7kUlUVqWTKUSd/tUUFxhhGRkYQvnRp29/CAAYHBuh+TRAmaMq3RRAESJKEj33sY814OWKHYYxhdHQULButfD4bw9jYmCMDUiOxKrFYDKqigvtsuFskrSirkbieTCZTenwQRaaJiQkwxiCkw7baETJhMMZKiwY6fX19yMkcyUL9z8d6Vivg193dXfXvTz/9tPag+p+rwwC1S8VTTz9l4iSCaC32skOz/Jp7EK+/BOEkU9PTDYvu6qi+Tlwx6OQHgIGhEaxlN538WQVI5NSaC/M7yfLyMjaiUZhJ6h0DkMpkMDc3t1PdOtDs5fvEQWRT2HL2/3u9uZou8nd7VSRT9gvv7geRHziYBq6d5Pjx4wAAFjEx71cBHuGlc42gC/uqg07+u+++Gw888IAjbTWDfC4HLkjI5eyNf3WBXY/raXNxJJLpmjHJqVQKnHNwjwPXHxFgIqtZ6Hd4dBSRKkJ+GAwjlMdPEKZoisjvdrvBOcdNN93UjJcjmsDkxDhc+cqLtCufwMS4MxdhIyL/xkZxU5fXxgsxQPAJCIcbC9vpdBoSY/AJYmkAfJDweDwYGBqGkK6fzdcIIR3G4NDwti25g4ODALZn+W5lLSOit7uzZgG/c+fOQewQTW8dVLtVnPvFuYrYD4Ig9gfli6zljwmCqCQajSIejUD1dxk6XvV3Ih6NNNyyr9Pf349wflNoCRd33/X395vuq130RX8zI88RaJOfp56iRX8rkMh/8NEjenq9KnL5guE6abXYLyI/YY7Ozk5093ZrVmujxAGucJw4ccLwKeXCvlMi/z333IM/+7M/c6StZpDL5cBdXuTz9haiIpEIJAHwS9p1vL1OMVwAm4K8E19bBggeoabIPzQ0hGgVkT8qiRjapZo/BLFfaYrIrxfqEkXzxVmIvcnw8DCEXOVFWsglMOzQRdiIyF8S5u2I/ABUj7q5YFCHZDIJnyDAJzBHMir3IsePHoGUtefklzIRHDt6ZNvztQr2bWU1I2B4ZLTm339x/hcodJivicC7ODKpDBYXF02fSxCtgPOxAM5BcT0EYQw9X1/1G3Xyd1Wc14iBgQGsZzbrgG7sosh/9uxZ9Isi/AajegDAA4ZBUcSTTz65gz07uOzl+wThDMlkEgIDOr1q6Xc7kMh/cDlz+gzEiHF9h20wCIKAY8eOGT6nXNhvxc+Pqqoo5HPgkg+FfN7WQms0GkWbRyjF5ra7te94LbNjSZB3KiXJjfoivyxDLtu1pIIjoiglDYEgCGM0ReT/5V/+ZQDA448/3oyXI5rA4OAg1FwaUIpiq1KAmkvb3q6t37iMbJl0xMkPQPEoWN9oHNcTj8fhYww+vlm45qBx+PBhiOnw5uzdLJxDzIRx5Mh2kb+trQ3twQBW0vUHgys5d02RX5ZlTE9NA1bKPhTPuXjxooWTCaJ12IsiDsX1EIQxrly5AiZK4N4OQ8dzXweYIOLKlSuGju/v70dO5kjJ2nViPasJBrtRFO/s449jTFEaH7iFcUXB2cceI1c6QVQhlUrB5xJKbl+7u5d3wolN7A1OnzqtxfUY3SQdBsYnx+H1Gp+8lwv7rfj50Y2P3OUF59xWrNTGxgZCns03K1R08kci1Xfxl777Dv1v5xJHOl09AmxgYAAcQLnCkgIgc74rcYAEsZ9pish/6623QhRF3HXXXQdWHG01+vq0wqwsn6r4adfJJcvajcyoyC94BMDmBhHu5VhdW214XDweh0/l8KuK4W3t+40jR45ALeRK76dZWD4FtZCrKvIDwOjYGJbStS87KgeWUwxjNbL3rl69Crkgg4csTMw9gBgwLmQQRKuyF4Wv0j3BTbm5BFGPS5cugfu7AGZwiM8EINCNS1UK3lVDF/P1mJ5wVkBXqKNmxN5OsbGxgYXFRUxYOHcCwFo4jKWlJWc71QJQ8cODTzKZhN+1Gelh18lf/plpRZH2IHPq1CmoslqpzNbBFXXhmtPXmHqNVnfy68YWLnkrfrfC+vo6OqTN3fAdRcG/VqJBSeR36PauimrNRUNdyC9fbohs+RtBEMZoykjtec97Hv7n//yfmJ2dxUte8hI8/PDDzXhZYgfRJ3lbRX67Tq58UbwpFBrHsWxsbID5HHCcemuvYJcTXl9HQFXg5xwRA/E++xFdnLdafFc/7/Dhw1X/PjF5CIuZ2mH64RxDTuY1Rf6ZmRntQbul7kEJKpiZnbF2MkEQu0ZpUuMmJz9B1OP8sxdQ8Jrb7pb3duL8sxcMHdvT0wNAu19rP4VdcfHrcTsTFs6dAMDK2iAIYpNUKgWfxOFzyMlfDon8B4tjx45BEASwDQPz8QKgRBWcOnXK1Gu0+k4QvQ4Vd/kqfrfC+uoKOsuc/C4BaPMIWF+vnmhQGm87JPJzkSOTrd7/vr4+MMYQLXtOf0wiP0GYoym2m9///d8HoFVhf/LJJ3HTTTdhdHQU1157LTo7O+tm9TPG8IUvfKEZ3SRM0NWlZbiyQrriZ3d3t612c1lN5Dfq5Ffc5rdpb8MLpBIpFAqFuoOHjfV19EDbsTZzQEX+vr4++Px+5NNhKJ3miygL6TB8fn9pp8dWJiYm8IPvMagcEKqMB68mtWvB+Ph41fNnZ2cheAUoHmvvu9qm4soUOfkJYr9Rmmh4yMlPELXIZrOYm5uFOv5Lps5TAz2Yn3sE2Wy2YYxCd3c3GAMiOc0nFMmJ6DnU/An4k08+iR5RRLtiNCdiEx8YBkQBZ8+exX/9r/91B3q3N1AUxfF6aHtxpxfhLKlUCl5BdSyup5xWFGkPMl6vF+OT47gSvgJU93dtUvSP2RH5W9HJv1Xkt2N0WVtbw7U9lffMLq+KtbW1qseXaiQ6dBvhIq/Zf7fbjc72dkTLigBHAQR8PgQCAWc6QBAtQlNE/i996UuljF/GGDjnmJubw/z8fN3zOOck8u9RfD4fPB4vcgXtxsMKGXi8PlMZe9XQq8YbKby7tr4G1Wt+crcV7tMGseFwuG7c0EY4jEloIv9GjQI1+x3GGCYnJvHUSuOdDdUQMhFMTk7WzPSenJxETuZYywjo929/7+aTInxeT80V+/n5eaANMFFjr5I2YGV6ZUcmvgRB7BybmaS1JwgE0epcunQJXFWhBnpMnacGe6GqKi5duoRrrqkfpSBJEkLtbYjmtPFfTHbhUI+516vbF1U1FAnz5BNPFPP4rQ0IxhQFTz3xhKVz9wNTU1N461vfii984Qs4dOjQbneH2EekUin4BBleUZsf1crQtgKJ/AePM6fOYO6hOeRRf+7OIgwerwejo9XrrtXC49ncAd6Kn59SXI/LD8C6kz+XyyEaT6B7ZIvI7ypgZWW56jmFQgFMYNbn3VsR6xt1+gcGECsT+WMA+msYBwmCqE1TRP6xsbE9WciPsEd7RwcSBe3GwwpZtHcYK/JWD13IMeLUXFtfA+y/ZKlw7/r6ek2RP5/PI5ZIoA2ayJ/N55FKpQ7kyvLhw4dwfu4RWPHKunIxHD5U26GhTzTnkmJVkX8hKWJiYrzmBH92bhayX7bQMw0e5FAUBcvLyxgeHrbcDkEQzUW/J3A3RzZHIj9BVOOZZ54BEyWovi5T56m+LjBRwvnz5xuK/IC2mzOW15x/0RyzvYuzoi8GRP5kMonpmRn8mo3XGQfws+VlhMPh0u7Ug8QzzzwDRVHwzDPPOCry03zuYFDvfUynk/BJHC4BEAV78SBbaXbtDmLnOXHiBO771/sAGXWVJRZhOHrsqGmTVavH9eiLbHbjenS3fvcWg2S3V8HU4mLVcxRFcU7gBwAGqLy2QXNgaAizFy8BxR1jMQD9g4MOdoAgWoOm3GlLOdrEgaIj1IHFiCa8MDmLUKdzIn+jTH7OOaKRKGCvzq+Gds9EuI47X8+q68BmgfnV1VVMTk460IG9xcTEBJD+/7QbrJnJHFeBTLRm1A6gbfPvaAtiLpHBC/q2v8ezaQ+ee8OJmufPL8wD5lOENmnTfly9epVEfoLYR+RyOTCRASJI5CeIGpz7xS+gBnoBs8VRBQE80IOnz53Db//2bzc8vKu7B7G5S1A5kMip6Ow0VwOgHqraeIfm+fPnwWFvOKB7SZ955hm8+MUvttFSa0FxPc3F8UWV4vtXr91MKo02kYMxwCsJ5OQn6nLs2DGAQ8tWqbOpS4pJOPVL5qJ6AIrrKYn8bn/F72bRC833bBH5e7wqfrqwUkrQKEdVVcevQYpSO3K3r68PT4sCIGvHxEUJJymPnyBM05TCu8TBpDMUApOLTn45h1DIvshfKGgu7UZxPZlMBvlcvuTCt4UbYAKrWVkeAJaXtW1soeK/8ucOGmNjY+CqApZPmjqP5VLgilxX5GeM4fDRI5hLbl9flFVgMblZ/HcriUQCqUSqJNRbwg8wkTWMCiOIVmQvize5XA5MYg23+hJEK/PU00+jYDKqRycf6MVTTz1t6NjOrm7ECy4kC1qNHSdFflluvFvv/Pnz8AkC7Owf6ADQJoo4f/68jVYIYmcp7Wpx7P5sQOTPpOEpRvV4JGeL3ZOT/+AxOTmpFd+N1RGDC4CSUHD06FHT7ZeL/K34+dl08msiv9UaGUtLSxDYdid/n09FJpuriMnREQQBHM7ODert1Ovt7UVMUUqvGQOvWeePIIjakMhPWKajvR2iqrmxRTWPjvZ2223miw7+RiJ/JKJlxnOvAzceBgheodRmNRYXF8FQnBQCkBjDYo2tbfsdPStRyGy/2ddDyMYqzq/FkSPHMJ/e7sS4mhKhqMDhw9UrN+nCPA/aeM8ZILQJWFhYsN4GQRBNp1zkp0x+gtjOysoKIhsbUIPWtjiqwX5EwhtYWVlpeGxHRweSsoBEgZV+dwojTv6LFy5gEIBgI0eAgWFIUXHxwgXLbexl9vKiLWEcxpgmyNeJuDAF55ogW0fkz2azmyK/yB118reiE/ug43a7MTQypGWr1CKu/bASHdbqcT2pVApMEAHJDSaIlkX+q1evoscvQNqi/vX5tGtLNV1DFEXAoUsPAEAFJLH2Qk1fXx8KnCMNIA+OtKKgt7fXwQ4QRGuwqyK/LMtYW1vD2tqaIecOsbdoa2uDqGhivKjk0dZmx2KtkTfo5C8J8p66hxnHg7oi/8LCAkKiBBcYBDB0CeKBFYr7+vogiiJYLm7qPJaNQxTFusWLAeDo0aNYTXGkt3zlZxNaRmMjkd+Wkx+A7JcxMztjrxGCOIDs5azlXC4HiABEY4XZCaLVOHfuHABAabMm8ittfRXt1KOjowOJAkOyKPK3O2Dy0DEyH7h88SL6DSwGNGIAHJcvXrTdzl5kL1/PCXMIguCck5+rYA3ivHK5HDzFQ9wCd/Se24pO7Fbg2JFjEOO1s/ZZTFusGhszH7JGIn8KzKUJHkxyWxb5FxYW0O/d/l3u9yulv29FkiRwlcMxM79a/xqgC/pxAIktzxEEYZymi/y/+MUvcOutt+LkyZPwer0YGBjAwMAAvF4vTp48iVtvvdXQBIPYfdra2sCUYia/krc9yZNlueTgahTHkEgUL/0OGUJUl4pksnY8zezMDLrVzQy5bkXG7PS0My++x5AkCT19/RCy5kR+IRdHb/9Aw4JKehzPfLLyuLmkiKGBPvj9/qrnzc3NQQyItiuJ8DZOIj9B7DOy2awm8ktALktxPQSxlXPnzkHwdwDF4nymcfkh+Dvwi1/8ouGhbW1tSOVVJAvaNMJJkb9RTaZ8Po/l1VU4sYG/F8BGNFp3/EdUQosHzUeSXACvnWNtCq7WddICQC6Xh1t38guc4nqIhoyNjUFI1pGVEkBvfy88HvPuvPLPTCt+flKpFCAVBQ/JY1nkn5+dxoBv+yK6TwJCXqFqlK3XW8xFdujywxRWc54PbAr6CZQ2f5DITxAWaJrIr6oq3vOe9+A5z3kO7r77bly4cAGqqoJzDs45VFXFhQsXcPfdd+O5z30u3vve9xraskvsHm1tbVAL2sBPzWdtO/nLnSL5BoUVSxMyhxb0VUlFPFFb1J6+cgW9ZS6aPgDTU1POvPgeZHRkGCyXaHxgGSyXwOhI42K2Y2NjEEVhm8i/kHLh0JFjNc+bm5uDGnDgmtAGhNfDlOtNEFvYy/EO2WwWXOKayO/Qd/eJJ57Ao48+6khbBLHbPHXuHPI+a3n8OnlfD556unEuf1tbGxQViOQ0wTcQCNh63XIaifzLy8tQObeVx6+jt3FQ4xeJg4HkcoGpzqhsTFUgNXBD5wsyXEWFwMVUR538rejEbgVGRkagZBSgxuWbJRkmxicstV1uHmvFz08ymQQXNZGfi25Li9KKouDq4jIG/dXn0YN+GXNzc9ue9/mKpgGHAjeYwuD31Rb5u7q6wBhDHJsif3e3E3d7gmgtmrYc+rrXvQ733ntvSUQ4ffo0brjhBvT394NzjtXVVTz66KM4d+4cFEXBX//1X2NxcRH/9E//1KwuEiZpb28HlwuAUgBXCo6J/F6RI9fANVJyldQ3jRuGSxyZTKbq3zKZDJZWV/HCsuf6oLm/YrGYo1m0e4WR4WG4z0/DjJTmLiQxPPTchse5XC6MDg9hPnm54vn5lAvPqxHVAwCz87NQgvYnOTyoLSwuLS1hYmLCdnsEQew86XQaqqgCEqAqqhYnYMERVs673/1uAMCPf/xjJ7pIELtGoVDA1JUrUIZfYKsdJdiLK1d+jkKhUFdMCQaDAID1jAiXJNn+LpbTSFBcXl4GAIQceC29jeXlZRw7VttkQBC7icvtdtDJr2jt1UBRFMiKAregzdddguKoKaYVnditwMjIiPYgCaBKHXYpLWF0pH7NNiO04ucnHo9DEbT7sSK4LIn8y8vLKMgyBgPVryODvgJmprebF0sL+AUAXtMvuw1BFuqaAiRJQkcwiEQiATcAn8dT1/lPEER1mnKl/Md//Ed8/etfB2MM1113HT73uc/hBS+oPhH5+c9/jre//e144okn8PWvfx333HMPXve61zWjm4RJdFFfd3zrkz6r6IPINpfacECZz+fBRAYbNdcqEYFsjd0D09PT4JyjPOV2oPjzypUruP766x3qxN5hcHAQyMa1DFAjW7M5B88mtPMMMHn4KBafnAWgLawkCwyxrFpTdOecY2lxCXBiDl78mM7Pz5PITxBl7OUYhkQyAVVSNTc/NNHfSWGRIPYz09PTUGQZasCek18N9ECRZczMzODo0aM1j9Mn6eEcQ8BvMR6oBo3GfxsbGwBsl+cBAPihbWmuV5OJqGQv7/g6qPi8XjDFGSstUwrweWt/Z/WdNC5RF/mBAmXyEw0ozf/S2C7yc0BNqobniPUQGtSTOIjE4gmogrYwp4puRGP1KhxXZ3Z2FgAwVEPkHwoo+MnUVSiKUrFzohTF59AlgOVZw3i/rq5uJBMJuAB0dVZZMSIIoiFNuVL+r//1vwAAx44dw0MPPVRT4AeA5z//+fjxj3+M48ePg3OOv/u7v2tGFwkL6CK/nt1u19Guu/ODLo5strqrXodz7pzAX95mFS5dugQBqMh/7QHgYgIuX75c9Ry7r7nbDA4OQpXzgGzQvSPnwOW84QHc+Pg4FtObLsHFlHYpqiW6JxIJ5LI58IAD/7+8ABMZVlZW7LdFEERTSCQTWjxb8bJhNZOUIA4ily5dAsCg+rtstaP6uwGwYnu10Z114ayAgMMuu0ZO/mg0Cq8gQHJgECiAISCKJPKbYC8vBh9UfH4/oNaPsTKMKsNfZ2FO//6V4noEjlyDOZkZSOQ/mIRCIUguCSxV5fqQB7jM0dfnRCWV1iMWi4FLmqmFSx7EYuZq5gHAzMwMfC6GLk/1efRwQEFBlrG0tFTxvNMiP8/xhiJ/d28PktA2hXRRVA9BWKIpIv9TTz0Fxhje//73G8rtDAQCeP/73w8AePLJJ3e6e4RF9Iu0LvLbjevRRf52d+MiT4wx5yq9AwCv7Q64dOkSekURrrIJpQCGAaHxRLgR73rXu/DFL37RVhs7gS7WCwZz+fXjjIr8Y2NjiGVVpIvGpKW0CMbKtntuYXV1VXvghGGQAUJA2GyTIIg9TyJRFPndZb8TBAFAm8AL/nZAtJlXLLog+NsxMzNT9zBd5I/mBfgDzor8jcZ/qVQKXgfdnF7GaNHQBHvVnHKQCQb8YIozIj9T8gjU+c7qIv9mXE/jhTczlLuEiYMDYww9vT2ak38rxef6+/ur/JFoRDyeAJe0rBwueRCLWxP5hwNqzc35w0WH/9Z7fygUAgCwrAOLuwqg5lV0NnDnd3V3IyWISDGGrh57uxMJolVpisivDw6uvfZaw+foxzYqwEXsHptxPdrNptHKbCPSaW0U0OFWkWkwyfN4POAKd07oVwGvp3rY3KULFzCgbN/e1q8ouHj+vK2XPXfu3J4W+Y0W32UmRf6hoSEAwEpaG+yvpgX0dHXWjN8Ih8PaAwfyAAGAezg59whiH5FMJiuc/FYySQnioDIzO4uC294YTKfgbsd0A5FfL8YXyQnw+50rugs0Fvmz2SxqJ4qbx815afxJEHuR9rY2MMUZoZ0peXTUma/pcVlSUSFwCxy5GnGmViCR/+DS19sHVPuoFJ/r7e1tan8OCslEvMzJ70XSgsllZuoyhny1ryGdHg6/m20T+SVJQqAtUP19NUsxHKCrq/6Ow1AohLTAkBbF0iIDQRDmaIrIPz4+DkDbbmSUeHGVUj+X2Htsjeux6+TXJ1ldXhXpTLauW6hUhMWpau8Fhrbg9v4rioIrU1OoJl0PAphbWHDU4bJXaGtrg9fnM+Xk9/p8hj8Dw8PDAIC1jHYJWsmIGBoarnl8ybXrUAS34lJK1xiCIPY2nHOkU2nNxU9OfoLYxtXFJageJ1LqAdXThsXFpbrH6CJ/TmHwGdih2/A1VbX0OJOpHw1SKBQcLSgmQhvrEfWhmJ7do729HaLqzFxDUgt1x+q6yF9y8ouN62SYgT5HB5f+vn6I2e2LOCzDwBgjwdYC+XxeW2QrOvkheZDLZkyZYFVVxczsHEaCte9zjAHDARXT09Pb/tbd3a2X0LNHpqy9OnR0dCDNOdKwHwVNEK1KU0T+3/zN3wTnHPfee6/hc/RCvb/+67++gz0j7ODxeOBye8CycbjcHrjd9rxV+nbpHq8KRalffLd00Xdo3CkWxKo7Eebn55EvFGqK/IqqYmpqezX6/Q5jDIODQ6ac/IODQ4YH7+3t7fB63FjPapegcN6FgToif8ll55ABiEtcy/gmCGLPk8lkoCqqJvBLABiJ/ARRzsb6OrjbGUc9dwewsbFe9xi3212KOPT57Mf1lI/3Gon8iqKAOZgYwziJ/EZotZie22+/Hf/0T/+0290AoI2ZBcWZCQ+Tc3V3XpdE/uJ42y3wA2lmIpynu7sbQq6KtJQB2jraqB6DBXSDbLmTv/x5I6yuriKby5cieWox5MtjZmp7rcGB/gGwjAOLc8Vbe6PaDO3t7UgrClKKYjslgiBalaaI/H/4h3+IQ4cO4e/+7u/wz//8zw2P//rXv46/+7u/w+TkJN73vvc1oYeEVYLBIIR8EsFg0HZbiUQCLpEh5FFLv9eitArsUC0olmXoqZL7duHCBQCoKvIPQPsC6cccNMZGRyDmjLndxVwCozXy9KvBGENfbw82iiL/Rk6se9MvFApgInOu2LIAFGSKAiOIcvaqiKPfC7hbK7gueAQS+QmiiCzLyKRT4C4nitYA3OVDOpWq6xRkjMHr0YwduqvfDuXCfqO4Hqdh4Hv22kfsHj/72c/w2c9+dre7AQBahnXeoQlPIVM3E3urk98tcmQddPITB5fu7m7wTJVrabaxe5uoTknkd3krfpoR+fUInuGAWve4kYCCufmFip11gLZDQ8raX6BhaQa3x91w1397ezs4gAJvXKSXIIjqNEXk7+jowA9+8ANcf/31eP3rX4/XvOY1+OY3v4mrV6+iUChAlmVcvXoV3/zmN/Hrv/7reO1rX4vrr78eP/zhD2mbzh5Hv1A7IfLHYjEE3QxBlzZAqBenogvCLO2A6qsCSkqpKjI/88wz6BUl+Kqoyy4wDIgiztvM5d+rjIyMQMoby72W8nGMjhoX+QGgt68fkZwAlQORjFp1kUVn64DDNoycewSxXyjl7xfz+AU3ifwE0sKf5AAA19pJREFUoaML5Nxu0d0iXNDaaSS2e4s1dLxe+8VyykX+Rvn4giCAO5j4wRkr7UogGkNxK80nFApBLeQA1ea4VVWgFnJ1Y1P076JH1OZiXgHIZsnJTzSmu7sbakEFtqwPsyxDX0999zZRnWg0CgBlhXe9Fc8bYWZmBh6Joctbfy49FFCQyxewvLxc8Xx/f3/1gspmSQO9fb0N7yHlmpIT+hJBtCJN2TdVXmSHc4777rsP9913X83jOef4+c9/jkOHDtU8hjEGWXYokJ2wjC7yO7HSGo/HEXTxkshfb5Xa7/ejo7MDkYQDxVNTADgwOjq67U/nnnwSw4qMWhbyYUXB02fP2u/DHmR4eBhqJgGoMiDUuVSoMtRMspSzb5Su7h7MzUhIFRhUXt/l4XK5wFUHnXYq4HY5WbqPIPY/e1W82SrycxenwrsEUaQkxte7T5tB1NrJZDJ1HXc+rybyezz2i+WUi/yN4nokSYKTS/QKtDEGYQza9dB89PExK2TAPdZFL1ZIV7RXDf3755W099kjcRRkGbIsU9wKUZeSWSuL0ngNAKScVNfIRdRmm5Pfgsg/NzeHwQCH0GCIrzv95+bmMDQ0VHp+YGAASlbR6iDauASwFMPQyFDD40jkJwj7NMW6wjkv/dv6e7V/Ro6hQebeoKO9reKnHaLRKNokGW0GRH4AmJyYBIs7IEoVNwyMjY1VPJ1IJHBlehoTdU6dADC/uIhwOGy/H3sMbdGDg2XrO2a1v/Nt//8a0dnZiVheQizPSr/XwuVyARyAU4Z+lSb1BLEVfcfMXru/bhX5VUkt1XAhiFbH8cW54ve/kbvdU3TwO+HkL7n3pcZOfq/XCyctPjJjjixUEMROURL58/bueyyvfbfqCa76989b9Of5io7+RotvBFEzSjcL9Pb2Nr0/B4FoNAomiEBxhx1EF5ggmhL5Z2emMORrHLnV5VXhFhnm5uYqnh8YGNAe2Bx2SxkJQ4ONRf5AYLO+kN9vv+YPQbQiTVmS//M///NmvAyxC+gXYidWWqPRMNpcCvwShyg0XqU+euQonr7yNPKwt42URRna2tu2OVsef/xxqJyj9n4SYLL48+c//zle+cpX2urHXmOkmLEvZGNQ/LUFeCEbqzjeKKFQCMkCkCgIpd9rUbrhy9CKb9pEKAhob6OcP4IoR4+w2mtRViVBXxf5RRXJFDn5CQLAprvWbpSHDlcr262Bx7MDIr/XmMifc3AhMg9n/hsOOnt1p1croAukukhvFX2RoJHI75FYyfXrKzr6U6lUwyxtonnk83nce++9+M3f/E243fYnRk888QQ+/elP4/Of/7zlHRulxagsA0fxGs0BJa1QJr9FotEoBI8f0K+/jEFw+0yJ/PNz83hZT2OXnMCA/gAwPz9f8XzJ1Z8CYDVFmwM8yTE4WK3KYSXldX5I5CcIa5DIT9hCF1+duAhHw2EcdqlgDGjzCA2d/EeOHIHyNUXL/rNhyhaiAo4ePbptAvPII4+gTxTRqdS+MbaBYVgQ8PBPf3rgRP6uri54fT7ks7G6W+OFbAw+v7+uE78aHR0dSOVUJIpO/nqRT6WJRR6Oifw0WSGISvQIvHoFN3eDTCajJaYVnYXcxUnkJ4gi+oSYKQ59b9VCRbu18Bb/7oTApAv73MsbOoaDwSCyqgoODlYjStEMGVWl8YAB9toOr1aira0Nbo8HOYN1smrB8kl4vN66n/dkMomAe/N7VS7yE3uHn/zkJ/ibv/kb9PX14T//5/9su7177rkHU1NTSKfTliN4/X4/fH4fUpmyz0oWACcnv1VisRi4VLnTjLu8hkX+VCqFaDyBgTFjJoABTw5XFypF/q6uLkguCWpK3Vy8MUsBUAuqIZG/XFNqNA4hCKI6VGmKsIV+8XXiIhyLxUp5/G0u3vAGdvLkSe2BzVh+MSri9KnTFc/JsoyHHnwQxw04Wo+rCh55+BHkco23wu0nGGMYHR0Fy0TrH5eJYmRk1LTLq62tDRzAWla7DNXbDdLV1aU9qF8H0DAsx8hVQhBb0EX+vVbvJp1OQ3AJm6VRJBIcCELH4/HA5XYDsjNjEFbIweX2NIywcbmdy+QvF/kbLeB1dHRA5hxO/NfK4MioqiN1pQhip2CMobevH0LOnsgv5JLo7euvO15PJBLwS5tCXqD4mIrd7y30xVCnYpT0uEa7O3a6e7ori7QWu0civzUikQhkofIeKwuehkZIncXFRQBAn89Y3m2/X8XCfGVcjyAI6O3rtRfXUzzXiMhfPqYgkZ8grEEiP2ELp0R+zjniiWQpjz8oyYjH43XPGR0dhcfnAduwMSBJa9sIT5w4UfH02bNnEU+lcLrGaeWcBpDJZfEf//Ef1vuxR5mcmIArX/99cOXjmJwYN922LuqvZwT4fd6KAt1bKW0BzTiwXZwDakbdXDggCALApoN/Lzr5mavsuy9RPjBBlNPW3gEmO/OdYHIG7e2N9+TrYpBThXeZxAAXkErXVxL0e7cTkqP+SjQeMA7F9uwOI0ODEPL2PvVCPonhwYG6xyQSCQSlTYOTbr4ikX9vsVM7a+y2O9A/UDlXKwr+JPJbIxKNQt3i5FclL8IRYw7HpaUlAECvQZG/16dgbSO8zewzPDQMlrJx7Tch8pfrAVQvhyCsQSI/YQv94mv3IpzNZlGQFQRKIr+CeCxa9xxRFHHy5EkIYRsf42K93FOnTlU8ff/996NHFNG4PAzQB4YBUcQPvv996/3Yo4yPj0PIxEqF+LbBOYRMDBMTE6bb1qOe1rMCAv76i0TBYBAen6fSHWKVLMAVvllIiCAIAHvXyZ/NZktRPQAACQdu5xRB2GGgrw9CzpndLUIuhYH+PsPHOxXXI7gETeRvsEunr0/rmzEfY32ixZ/9/f0OtNYaUGzP7jA8PAxXwZ6T31VINqyfFYtFERA3xwABFwcDDDuHif2NEyK/lN1Mg2ZpBpfbhY4Oq2HurU0kEgWXKmvGcMmDaNTY93F5eRlukaHdZex97fGqUBQVGxsbFc8PDQ5BylhP+WYpBo/XY3rXXD0DIEEQtSGRH8Dc3Bze97734eTJkwgEAujq6sINN9yAu+66q2EBsEbE43Hcc889uOWWW3D99dcjFArB7Xajt7cXL33pS3HXXXeZKp6y19And3ZFft0hEnRpK80BiSMWbbxKfc3payBGRFiNiGMbDN293RXRLblcDg/+6AGcURTDea/XKgp++tBDBy5CYnx8HGohC1ao7hBkhQzUQhZjY2Om29ZF/khO2CysWwPGmCbKOxHDXXyLSOQniEp0cX+vFd7dJvKLQC5LIj9B6AwODkAsODP+EOUUBhu4fctxQuRPpVJabSWpceHd/v5+CIzZTWoEsJn2SOMBYq8zPDwMnonXNt00gnPwTBzDw8N1D4tsbKDdvfkaAgOCHmFfz1UPIju1o0aP7bFKf39/pSErDfT09tAOIIskEontmfyS8bie1dVVdJfV7W1Et1ctnVfOwMAAeNrGAlAa6O3rpc8BQTSJlhf5v/3tb+Paa6/FJz7xCTz77LNIp9OIRCJ49NFHcfvtt+P666/H1NSUpba/853voK+vD69//evx+c9/Hk888QRisRgKhQLW19fx4IMP4vbbb8eJEyfwox/9yOH/subgcrkqflpFF/l1J3/AxZFMNFZ0T506BSWjWHZ4ixER1565tuK5n/70p8jksniOiXauBVCQZTz44IPWOrJHGR/XYnhYNlr173pev36cGfTCOpGcYKhw8/jYOISU/UsWSzIwxhpOdAii1dirhXdzuRx4WUYwRCCfz+9ehwhijzE0NATRZlFOHTGfNLSlXsfu+A8oE/ldQCZdP3ZIkiQM9vdjzfarAmsAejo7DY1BCA0SaXaHkZERcEUGy1tbzGP5FLgiNxb5IxG0uyqF3nY3RzgctvS6Ou95z3tw7bXXNj6Q2FXsmjz6+/u1eXmxGZZmGBwwfj8hKkkmE1Wd/KkGtWt01tbW0OUyvju3q47Ir+ZUwOL0gKUZhgdp3k0QzaKlRf4nn3wSv/3bv60VfA0G8ZGPfAQPP/wwfvjDH+KWW24BAFy4cAGvfvWrkUyanzxtbGwgl8tBEAS86lWvwic/+Un827/9Gx5//HF861vfwmtf+1oAwMrKCn7lV34FZ8+edfI/rynog327g35d5NeLPQWkxsXXgM2YHRa28PoqgPD2qJ7vf+97GBEEdBt08QNABxgmBAE/uP9+8/3YwwwPD0MQRAg1iu8K2SgEQbQkmOuT6lhegM9f38kPaCK/mHRg214c6O7thtfrbXwsQbQQe9nJrwplooMEqIq65xYjCGK3GB4ehppNAqrNqC1VhppJmLqnS5L1Lfw6yWQSiqQAbiCfyzeMDJs8fBirDojNK2CYPHTIdjuthF2nL2GN0dFRALVNN43Qz6u385Zzjkg0hpCn0rHb4ZK3xXeY5Td+4zfwmc98xlYbxM7jiJMfKJnvxKxIIr9Fcrkc5EIBXKrcLcclD+RCQdvl2oD1tRWE3MbHBT4R8Ehs2/d96/tqFikr0Y45gmgiLS3yv+c970E6nYYkSbj//vvxwQ9+EDfeeCNe9rKX4XOf+xw+9rGPAQCeffZZ/NVf/ZXp9l0uF97+9rdjenoa3/3ud/Ge97wHN998M5773OfiV3/1V3HPPffgr//6rwFo25Nvu+02R//7moFTjh495kYX+X0SRyqdbZgN2NnZiZ6+nlK2viliWjZ7ucifTCbxs//4D1xjYZBzjari8SeeQMRgMZz9gCRJGBgcrC3yZ6IYHBqyNMkvj3gyIvJPTk5CSSmATQOvEBdwePKwvUYI4gCii/t7LZM/k8mAi5v3Av2xkQkOQbQCQ0NDADhY1l5xTP18rT1jOOHkj8VjUF0quNtYkc9jx49jSRDArWY1FlkRBRw7ccJWG3uVvVqYU2dmZgb3HzBjzE4yMDAAURS1OlkWEDIxiFJ9oS0Wi0FWFIQ8lXOgkFvG+tqKpdcl9hd2v99bxWCWZiTuWiQej2sPtjj5IWrzZyMG1PD6Bjo8xt9TxoCQd7vIr9fCQf2NdjXhab7ZBkEQO07LivyPPvooHnjgAQDAW9/6Vtx4443bjrnttttw8uRJAMCnPvUp067B1772tfjbv/3buq6JW2+9Fc9//vMBAA888IBtp0SzcWqwr9+odJHfL3HIimKouOKZU2e0XH6TsDCDIAg4evRo6blHHnkEsqLgtOnWgJPQ/n889NBDFs7euxw+NAkxW31SIWZjODQ5YaldURThcWvigM9Xv/AuABw+XBTmo5ZebvN142LFe04QhMZeLbybzqQrRH4U1xRJ5CcIDd15L+TittrRz29UnLMcJ5z8kWgEcEP7h8ZFPo8fP460otjK5U+AI6ooOHbsmI1W9i57PVbnz//8z/HhD394t7uxb5AkCUMjIzVNN40QMhEMDQ/XLWSpR3R0bRH5uzwca1viOwiiGr29Wu46SzNAAZSMQuKuRfTFbi5ud/KX/70ekWgUIbc542KHS9lmWOzu7t58X80iA2pWRW9vr/lzCYKwRMuK/N/85jdLj9/ylrdUPUYQBLzxjW8EoGUU6osCTvPSl74UgLZFbnp6ekdeY6+TSqUgCYCr+InUxX4jq9QnT54EizLzxXcjwNjEWEVsyyOPPIJhQUSHiagenSAYxgQBjzz8sOlz9zLj4+OQ8tWFAykfx8TEhOW2vR5t4GIkOmd0dBSSSwKL2Zi45gAlpWwuGBAEUUJ38u+1uJ50Jl0S9gGQyE8QW+ju7obkcjni5He53eju7jZ8jhMifzQaBTzQ/um/10E34MzbeM2F4s+tkY0HhZ1y8jtFq8537HD08GFIuailc13ZGI42GPuurGhufb34pk63V8XaRnjPjQ0I57F73XC5XGgPtWtO/qKbn0R+a5RE/m2Fd90Vf69FLpdDOpurKKRthDapgHC40nQqSRLaOtqsOfmL55DITxDNo2VF/p/85CcAgEAggOc973k1j3vJS15SerxTDu1yt7ogtOZbkk6n4XMJpervvqLIr8f41OPEiRNQCypg0sDmirpw5tSZ0u+cczz6s5/hsGp9EHtEVfHYY4/tOSesHcbGxqBmEoCyZSeLUoCaSZRyQq2gR/aUR/fUQpIkTE5OwpZ1L6r9OKjOPYKww14tvJvJZKqK/JmMxX3DBHHAEAQBfX39EHL2RH4hl0BvX78pF3g9Z7AROOeIx+KAFyWRv1HsYSgUwlB/vy2Rfw5AdyhEApRJ9vriwUFmcnISYiYCmH0POIeQiWhj6DosLS3BJTJ0bBEFe30KFEXF+vq62S4TLUhfXx+J/A6gGx23ZvLrcT2NRH59sbzdpJO/3c0RDW9Plujt7SWRnyD2Ca2pKAM4f/48AODIkSN1XUgnyrI69XOc5sEHHwSgiZhHjhwxff7CwkLdf0tLS053uYSTmfy+srdBF/nT6cYVXo4fPw4wk8V3ZUCJKiU3GKD9f4wlEqg/BK7PBIBMNnugHEp63JSwJbJH/71eHFUjdHHfaBHckydOwhWznv/LIgwen8dSoWCCOOjo4v5eW6QkkZ8gGjMyPAyWtyny55MYMZHHD9g3pyQSCSiyAu7hgAtg4vY84Gpc+9znYt7GAsO8IOCa667b87E2ewX6/7T7HDp0CGo+C1YwV/2S5dNQC1kcalBkemlpCX1+YOtb3efTRMKrV6+ael2iNRkaGIKQEcAy2geJRH5rlDL5xa1OfnMif5vLpJPfpVbdTdff1w8ha/5+z7La56Cnp8f0uQRBWKMlRf5sNltyIzTKHe3s7EQgoBUFnZ+34xmqzre//W089dRTAIBXvepVaG9vN93G6Oho3X833HCD090u4ZSjRxP5N1eafaJxkd/v92NkdMRc8d0oAF59Ecd4Eu12hqF9qXZqQWg30J36bIvIz4rFv5rl5Ac0B74SUwCrmy0iwNGjR1t2xwxB1COfz1f83CvUEvmN3B8IolUYHByAy6T4txWXnMLgoLkiiXbvpyV3sA8AAwS/YMgxfObMGSwrCnIWiu/K4LjKOc5cc43pc1sdcvLvHroRTEibmfAAQiZccX4t5ufnMODdfv/v9akQGIn8rYATi3k9PT0Q8yKQBfwBv+E5HlFJIpEAE12AsGUxWxDBRGlzEaAGdpz8sfj2BYS+3j6IOQsL6xnA4/PA7/ebP9cBVNXcfz9BHARaUukqX/kMBoMNj9dFfiP58GYIh8N417veBUDb7nzHHXc42v5+IpVKwSeWifwmMvkB4JrT10CKGs+FZRsMLrerYuvq1NQUOkUJXgt5/DouMPSIEqampiy3sddoa2tDsK0dQrZyMCFkYwi2taOtrc1y2263p/jT3eBIjWPHjmm1F6LWXs8Vd+HEsRONDySIFiSdyVb83AtwzpHL5IDyDTwk8hPENvr6+sDyjSMO65JLmXZd2hX519bWtAfF+b/qVTefq8OZM2egArAiOy4BkDnHmTNnGh5LVEKO/t1jYGAAXp/PvMif2oDX58PAQP0FvLmZaQz4t7toXALQ4xcwNzdn6nWbAdUJcBYnvt/d3d1aREsG6OzqtN+pFiWRSEBwV9/pLri8DTUS605+jly+sK3uVel9NUsG6OrqsnCiM+y13ckE0QzsV8vah5RftIyIi/oKtJPRAIqi4A1veANmZ2cBAH/yJ3+C5z73uZbaarTDYGlpaUfd/E6QTCbhFzYvwmbiegDg9OnT+M53vwPIMPSpZmGGY8eOVUQ1LSwsoMtGHr9OlyJjYQd2fewmw8PDiKxXivwsl2i4E6YRbo85kX9ychKMMbAYA+826SYrAEpcwdGjR812kyBaAv0el8nunRicbDaruXCqiPxGarYQRKvQ29sLNZ8BVBkQLAzvVRlqPmM6N9duJv/q6irAoGXyA1B8CpZWGsdMjo+Pw+fxYiGXRf0Qku0sAHBJ0oEeD5AYf/BgjOHIkaM4e9VcNr6Q3sCRI0frfiZyuRxW1tYxeKL6PGjQl8fc7Iyp190pyneTFAoF29eg/che3lHT1dUFJauAZRh6+yiH3SqxWGxb0V0dLnkRi8Wq/k0nGo3CIzG4TX49dOd/JBLB4OBg6fne3l4omeJuejNtZrSon92CFgKJVqQlRf7y/G8jsQR6YVyfz+dYH975znfiu9/9LgDg1a9+Nf70T//Uclt2hVY7ODWJSCZi6JA2ByySAHgk1jBvTuf06dOawzsMoJEJjQNSWMK1N11b8fTy4iJCnAM2nPwAEAKwuIN1EHaDsdERXFh8GuXfFlc+gdERe1vddSe/y2UsZ9/j8WBoZAjzUQuLKMU1isOHD5s/lyBaAH1RNZ3eOyK/LuTzcicSAwS3QCI/QZSh592yfBrcaz76keW1779Zkd/uOHB5eRmiX4QiFCfifu25RoiiiBMnT2Dh7FnTrzkP4Mjhw4bHHgSxVzh+7CieufJD5Eyc485FcPzY9XWPmZubA+fASLC6IDYckPHEHtmlrNcP0h8bretFNIdQKAQAYAmG7hPdjrT5S7/0S8i2WB2mWCwGWahugpNFd0ORPxwOo8Nj/v7cXiy8vVXkL2XqZwEEjLcn5SQS+QmiybRkXE95vIiROBhdSDAS7WOED3zgA/jc5z4HAHjxi1+Mr33tay3pQignHoshsGU7WdBtXOSfmJiA1+cF2zBwM8sASlrZtk07GomYuWfVJAAgVqVgzX5mcHAQQr7yuyLkExgyWaBvG0VxwMxE+9iRYxAT5r8vLMbAGMP4+LjpcwmiFUilNJEvldw74nlJyN9iSWBu5niEHkHsZ7q7NTHFbFFOHf08vR3D59kU+VdWVsD9ZeM/PxBeDxvaYn/y1CksWhg/L0sSTlFUjyn2snO4lTh27BjUdBSQDdbOkfNQUzEt7rIO09PTAIChQC2RX8Hy2tqeKHivm++2Pm4ldmqnjhPfc13kR7zssU0+/OEP4+N33eVIW/uFSDQKVay+gKWKHoQjkfrnRyJod5sXuMud/OWUovxMDjFYhpk2DzgJifxEK9KSIr/X6y2tRi4sLNQ9NhKJlEQGOwVGde6880785V/+JQDg+uuvx7/+6786ukNgvxKPxxHcKvK7eMNVah1RFHHmzBkIG40/0mxNGxhds6XgWiKZhBMlYfwAEun0gZoQDQ4OQs0mtRgAQNvWn01VrPBbgVkQ+Q8dOgQWYzBday8ODAwNUAEogqhBKpUEl7xIpYwtrjaDkpC/1czkcr5ODkHsZzZFfmsiHMtnKtoxfJ5Nsenq4lXIvk1Bn/s5VFXFxsZGw3OPHz+OmKIgaWJAkAPHmiw3FD2J6hykse1+RP/cCunG34/N47ghkb/bL8BfY4//SEAB5yjFzO4mJPLvHE4UKe3o6Kj62A6SJLXczquNjTC4q7rIz10+bGzUr80R3lhHSCrUPaYaHW4OgWHbPVgX+VnGxD2fA0pKMV3r54Ybbtim01iFRH6iFWlJkR8ATp48CQC4fPlyXbfQs88+u+0cq9x999344z/+41Jb3/ve9xy7+e1nFEVBLJ5Ex5bq7+1SYdsqcj2ec91zNJG/0fxjHRgaGapwF3DOkc3n4YT864Y2SDISBbVf0It16dv59eJ+/f3ObL8rr43QiPHxcag5Fab2KgMQ4gKOHDpismcE0RrIsoxsJg3V245sJrNnClWVhPwtcztVUimuhyDKaGtrgyiKJbHeLKyQhihJpnet2hX5l5aXKrf+Fx8biew5ckS7pzc+chP9WP1cwhwk8u8u4+PjkFwuCCljufxCah0ut7vhLtbp6SkM+2rPW4aLMT664383IZF/53BC5C+/hziVgtCKRCIRcFd1Iyh3+RCJ1tdI1tdWEfKYfz8FBrR7hW0iv9/vh8/vM+fkzwLgMC3yf/SjH8UnP/lJU+fUwonPNEHsN1pW5H/xi18MQIsCeOyxx2oe9+CDD5Yev+hFL7L8el/5ylfwB3/wBwA0J/IPfvCDzWyzFicSiUDlHCFP5cQh5FawtmJ86nbttddCzatAA/O/K+zC9c+pzKbM5/PgnDtSpELXorZWpd/PlFbvc8niz1TF83YxE1c1OTmpPYjXP27bayRFTExMmDuJIFoEXUxXi1nee8UlX0/kj8dNXgQI4gDDGEN7R8hGXE8GHR0h06K9HZFflmVENiKo2EZZfLxkoLbR8PAwPG63aZFfEs2PBxRFIYEbJPLvNpIk4fDhwxANivxiah2HDh1uaKaZvnwZI8Hai/teEegLCHtO5D9Ic629gBOu53JhPxBwIgjXWfbDNUyWZaSSiboifyqRqGvIWVtfR6fH2n9rp0fF+vr2a0xffx9gxl9TPFY3CxrF5XLB7a5ej8As5OQnWpGWFflf85rXlB5/8YtfrHqMqqr48pe/DEDLlLv55pstvda//Mu/4C1veQs45xgZGcEPf/hD+1nmBwjdrdXrq7wI93hVLJsQ+U+ePAlREktxPFXJA0pUwbXXVhbd1V33Tor85YWh9jt6lp5QdPDrP3dD5B8eHobkkrTIHqPkte2Chw4dstA7gjj46NFo3KvtLtsrAnoymdRqoW+5OHM3RzyxN/pIEHuFrq4u63E9hQw6uzpNnycI1qcSq6ur4JyDB8qECAkQfSJWVlYMvfahiQlTIv8KgNHhYdPRD29/+9vx+c9/3tQ5B5H9IJAddE6dPAl3tn5Uh447G8apkyfqHpPJZLC0uobhGnn8OsO+HKb3QPHdcmG/VUX+nfoeOrGLs3xOtxdF/r2yU7UekUhEuze6qgcJc5cfnHNEa9QAzOVySCTT6LTg5AeALlcBK1U0mOGhYQgZ4/d8ltLm6mZFfichkZ9oRVpW5L/hhhtw0003AQC+8IUv4JFH/v/s/XeUI9d55o8/FZBzo3PunpmeyOEMh5lDSlT8rWVZ1lqyLFleW2t5tV4HWassW9FW8lLyWjpfKtgiaQUvFUmKpEQNKZLD4eScu6enw3TOjdDIVXV/f6ALje5GKBQKQGH6fs6ZM2ig6uIChaq697nv+7xH123zta99DVevXgUAfPCDH1w3IXjsscfAMMlinp/73Ocyvs+BAwfw7ne/G6KY9CN74YUXaDTxGuS6CHXm1TeiBquEhUU/wmFlUWkmkwlberYAuWwqlxels4n8Wrj9yVrUzWTXYzabYbHaUhGCTDwEq82umb99ISI/z/PJ+hjKyjUkWd6WivwUSmZkUV/SmcgfDAbBmtik0J+OAfAHCrkIUCg3P3W1XjCCSpFfiKCuQD/+YklZ8qzRMYiVKIrkB4DNW7dipoAxxDTLYvPWrYq3l7l27Rp+8IMfFLxfpSiVCEhF/sqzdetWSCEfIOaZZ4hxSCEftub5vY+MjABAXpG/2SbixnDlRf704r/UrkdbtA5Q02PdwWr4zchWOcSYReRffj5TtD2QXEAHkgGTavCaRcxMrb8HNzc1gwsXUOw+BNgddlitWlQ9VAcV+SkbES0Cl6uWf/3Xf8V9992HSCSCN73pTfjUpz6FBx98EJFIBI8//ji++93vAkgWOfrwhz9ccPvHjh3D29/+dsTjcRgMBvzLv/wLEokELl26lHWf1tZWzSrRVws3btyA18rCvObX2GRNXpRHRkawbVvuKBSZvbfuRf8v+xFH5oEvM8/A5XatKxgr3/C1OCFuRpEfAGq8XgRkkT8RgaemRrO2C40E3NqzFTdO3kACygajzCIDg9GgSfFsCuVmJBhMFtsly3Y9ehL5GWOGrB0jsDSvD0shCkUveL1eGMShQkvWAAAMQrTgortAcXY92UR+wSJgfGJcURvd3d34lSRBAAG/bjVwNRIIpsHgLdSPXzVU5K88SdGegA0tQHJmj5BlQ8miu0pF/mYFIv+zN+YQjUZhNmcuCFoO0qP30wV/tdDf9ApaR7lX8neSjWrI/pidnQUASNlE/uUI/9nZ2YwaiXxvrbWoFfklTI/OghCy6h7f0tICaUlK1j9UcusPAc0tlXWvoJ78lI3Ihhb59+7dix//+Md473vfi0AggE996lPrtunp6cGzzz4Lh8NRcPvPPfdcKgo9kUjgj//4j/Pu8+ijj+LP/uzPCn6vauZ6fz9aresF8Ra7CJZJFkdWKvLv3LkT4v8TgQiADMED3AKH3bt3r5uUyjd8LSL55Ta0GHjqibpaL27cSEbOMokw6pq1qylRSCQ/kJzg/Ob53wAiACW7LgLdm7oLKvBLoWwkUpH8pqTIL4v+lcbv94MYM0zATcBScGndBEQp6ZP6eDyumfcnhVJJknY96jz5WSGCGhWL98WI/JOTk+BsHERujbhoAyYmJxS10dPTA5EQzAJoyrPtAoCYJGHLli1qultVFFsQORtUMKk86cV3c4v8c+ANhrxFd0dHR+G2sLDmGSLLwVfj4+PYtGlTwf3WCq3teuhvegWtI/n1KPJXw/x8bm4OYFiAz+7JD4bNGsk/OTkJlgFqVNr11JklRKIxBAIBuFyu1PPNzc0gIknqLAqC87kQh/bN7ar6oBXVYM9EoWjNhrXrkXnrW9+KCxcu4EMf+hB6enpgtVrhdrtx++2346tf/SrOnj2LzTTiJyvFRj8QQnCtrxedjvWDCjMHNNkZXLt2TXF7O3bsSD7IZNlDkhHdO3fsXPeSvBijhfmM3IZSm6FqodbrBScmB0acGEWtt3KR/Nu3b08OMnzKtjf4DNi1Y1fhHaNQNghLS0tgOB7gjWA4Xjciv8/ng2jIEF1oAkRBVH2dTZ/k6aXIMIVSLDU1NSCxMFDo2IwQSLGwKpG/GCYmJkCsGfpqA+Zn5xVNzjdt2gSWYaBkSUDeZiOI/KWCRj1XHp7n0d2dv/guF5pHV3f+AJfR0VE0mPOfa41WKbV9JZHv3zzDaDLXonYeK2idha6VrauWVIPIPzs7C9ZsB7It1jIMOLM9q8g/NTWFGgsLXqXSV7ecAbDWNi+VEa9wisAusRXPoqfnN2UjsuFFfiAZEfH1r38dfX19CIVCWFxcxMmTJ/Gxj30sp4fYn/3ZnyWLohCS0ZP/c5/7XOp1pf82WhT/1NQUFv0BbHZmvgB326O4fPG84vZqa2vhqfGAWcxwUwwAUkLC9u3b170kizxaxBvIbehFJNMKj8cDTkiaAHBCDB5P4QX6slFoJP+WLVuSxXfnFUSqRQExIGLXLiryUyjZWFpaAmtITsYY3oRQKFThHiWZW5gDMa0XleTnFhcXVbWbbkekF2siCqVYvF4viCQCYoGGPUIMRBLLbtczMjYCwbpeXCR2AkmSFBXftVqtaG9txZiC9xsD0FRfvyoykVIYVOTXB9u3bYUxlvv+Z4wtYIeCTOiJ8VHUm/NHcDsMBGaeUVwvo1SEw2EYWRYmltVEsK1GEbBUmTpaRz0XWuC8HFSDXc/MzAykLEV3ZUSDNeW9v5aJiQnUKTins1GfReRvbGwEy7Fgggp+fwlAjIhobW1V3Q8toJH8lI0IFfkpFeXixYsAgM2uzBfgHreAgaHhgiI1tm/bDnZx/U9bFv4zRXDJIo8W5YHMSNrU3YwiPxLLg+lERNPaEYVG8hsMBmzbtk2ZyL+c1UFFfgolO+FwGIRbnozxBv2I/HNzmVdfl5+Ti5MVit/vz/iYQqlmZJGeiRcW3Spb/KgR+dWKvoQQjI6MApncMO3J/5RGDO/cvRtjCoIFRlkOO3fvLqCXlLVQaxN9sHnzZkihBUDKImBJAqTQoqKslanJqVTkbi4YBqizrhf+yk0kEoGJYWBiGE1E/moUAeXzUOtFN63tevQu8ut10XJqehpCHpFf4K2YyrIQPjE2gnoF2TnZsBkIbEYW4+Ora+PwPI+GxgZASQLssgxS6Uj+ajy/KZRioSI/pSiKjSQ4d+4cWhwMHJk8lwFsdQuQJJKzWPFaenp6wAW4ZFGYdHxAfWM97Hb7un0CgQAMDAOjoioyuWHBwMJxN51w5HK5IMUjgCRBildW5AeAvXv2gp/n1x/nNTCzDGrra9HQ0KCydxTKzU8kEgFhk5Mxwhp0YTcmSRL8i/7Mq6/LIn+2VOV8+Hy+jI8plGqmvr4eAMDGC1ukk7eX9y8EtePAxcVFhENhEEeGm7gVYHgGw8PDitravXs3pkURkRwDgjgIJoiEW265RVV/KRQ9sWXLFoAQsOHM0fxseBEgJK/lbDwex6I/AK9Z2eKN1xjH9PRUwf3VknA4DCPDwAhtrFGrMZJfFqe1XnTT+rvQo8ifbkmk12M/NTUNyWjLuY1ksmFqKovIPzGJOoXndDbqLVLGBb2uzi6wwfzzdjnav9Iiv9YLVxRKNUBFfkpFOX/2NLY5s6fNNVkluMwszp07p7jNzZs3Q4yIwJpmOT+HrT1bM+4TCARgVSE0Z8PKMDedBYTTmSzIycSSn0vLdHc1Iv+ePXuSxzlPwgQ/x2Pf3n0qe0ahbAzC4TAIm/TtJSyvC8/ShYUFiKKY2bPbALBGNmuqspK2ZdRa/lAoesPr9YJhGDAFivxMPASGYcoayT8wMJB8kGkowQCMi1nZJg+33norCIAbObYZBSARgj179hTWUQpFh3R1dQEMAzaSReSPLAIMg+7u7pztyAvlSgt0ek0SZqYqb9djIoCJkA0r8st91lrk17q9fPUgKkF6ZLfWNQi0gBCChfl5EOP6oMRV2xltmJ+bW3cPXlpaQmAphHprcb/rWlMc4+Prs+k62jvAhRTY7AYBl8cFmy33YkWpSRf59Zq5QaFoDRX5KRVjbm4Oo+OT2O7JvsLKMMA2VxTnzpxW3G5qQLtGY2eDLDZvyhzRIkeFaIWJVEdhn0KQRX02msxQkEV/LSjUkx9I2u9wHAdmJsdxiwPSooTbbrutiN5RKDc/kUgEIpOcjImMPkT+qanlaMFMGcsMwFiZlW0KZH5+HlaOg4vjVVv+UCh6g+d5uD01YGKFFZNmYktwe2pUCTJqI/n7+vrAGtiUNc9aBJeAK71XFLXV1NSEupoaDOXYZgiA025HZ2dnoV2lpEHtevSBxWJBfUMj2PBCxtfZ8AIaGhphNueuNibf/5SK/B6TtGqRvBKEw2EYiQSjKGliLViNv+lSRfJrjd5Ffj0u8Ph8PghCAiRPJD8x2iEIiXWBKnL0fb0CC65c1FskTK6x6wGA9vZ2iEERyPPVMUEGnR2dRfVBC9JFfhrVT9koUJGfUjHOn08W1N3mye2Vts0t4Gpfn2LRqampCQajAYw/beIZTRZ/6erqyrhPPB6HlgmFPCFVUdinEFKR/CUQ+dVE8lssFmzdthXMbA6BYdnJ49Zbb1XZM4qeGRgYwK9//etKd+OmYCm0OpI/pAO7nlSacJZ5jmAVMDaupNzmeubm5uBgGDhAMDs7q7KHFIr+aG5uBhsrrCYQGwuiubm5RD3KzOUrl0E8BFldEmuAsZExRZG6DMNg3513YihHwMAQy2Lf7beXrGAlhVJutmzqBhfNHMnPRRexZfOmvG3IIr8rQ4H7TLhMEnyBYEV9rkOhEEySBBMIQkuFLWhmQu9CeSZkcVprkVrr66OaIK5Sk3689Xjs5YLzxJQnkn/59bUZrfLYuVi7njqLiOnZ+XXnent7e/JBvkz6JR5dnZl1l3KS3n8q8lM2ClTkp1SMs2fPotnOwJ1nYLnDk4AoSop9+TmOQ2tb6+pI/uUbUdkiuBh1wrWecTiS1fHYaPLLrLTIDwD7btuX05efmWFQW1erWryYnZ1NDbYo+uOzn/0svvzlL1e6G1n5zne+g0OHDlW6G4pIL7xLOANCocqL/GNjY+AsHLKtwBI7wcjoiKq2Z6an4RREOEURMyqzASgUPdLW2gI+UZjwxceDaG1Rd59Uk/5OCMH5C+ch1mQXqIiXgBDlNZn27t2LSVFEKMOAIAaCMUJw2z5q3Vcs1O5AP3R2doKPZbYG5WMBRXOexcVF8Cxg45UdV6eRgBBU1JJ0KRCACYAJQChY2IJmJvQYzZ0PWbikIv/NhzzvzOvJv/z62nnq1NQUjBwDZ5Z6h0qpM0uQJGld7auOjg4AK577GSGAFJQq7scPrBb2aRFeykbh5lIhKVXF+bOnsdWVP9q92SbBaWJTkf9K6O7sBre0MrBgAgxYlkVLS0vG7Y1GI7Rc202AgdFo1LDFyiOL/MxyhKD8txaoFflTvvxZ5hr8PI99t6mf1H/oQx/CX//1X6vefy2PPfYYjh07pll7G52REXUCb7n40Y9+hL//+7+vdDcUEQqHAW75msUZEA5X3q5nZGQEkj1HJJIDmJ2eVRWZMzE2Dg8I3AAmJyZU95FC0RttbW1gI35AqRhLCNhoYCU6r0DUiEJDQ0NYCiyB1OfoowPgLBzOnDmjqE3Zlm84w2s3kPTj37t3b8F9pVD0SkdHB6RIEBDX3APFBKRIUNE57fP54DCxUHoaOw1Sar9KsRQMwgzAjKT/eLHoMZo7H7JYqbVoqXWAmt4zp/S4aDk9PQ2G4wE+t9UWeDMYjs8o8tdaoficzkbtcibA2vadTiccTkfuSP4QQESSWhCoJDSSn7IRoSI/pSL4fD7cGB3HNnf+wQnDAFtdMZw7q9yXv729HexS2s87CNQ11MFgyBwS6na7EdLwRh8C4PF4NGtPD5hMJnAcBza2BI7nYTKZNGtb7aBy586dYFkWzFyGkUwCkBakoorsjYyMaBrJ/8gjj+ATn/iEZu1RKFoRDoXSIvmNCIWKnzgXy+DQIER7jkhfB4EkSRgbK8yyhxCCickJeADUAJienaXRPZSbhvb2dkiJKCAotAwUopAS0YIj7rZu3QpAXaTmyZMnwfAMUJtjIwZI1CVw7LiyhfGGhgY01ddnFPmHAXhcLl1EFZaLUolXehTFNiry75mNro50kf9W8nv3+/2wG5QfU8dydLDf71e8j9YsLS1RkX95zKK1aHmzZaFXI1NTU2DMjvwqPcOAMTvW1aaamZmB11j878KbReQHli17ciXzLC8A6EHkT892qcasHQpFDfRKTqkIK378ym5C29wJXL3ah1gspmj7tra2ZIT3cvNMiEFHe/YbTWNjI8KiiEg235cCSIAgIAqor68vui09wTAMLFYbmPgSLJZMlTDVo3ZQabVasaVnC5DJUns5u3D37t3qO1YCqnEyQbn5CYdDIMuR/IQzKPLBLiWiKGJ0dBTI5Qq2/NrQUK5ym+uZnZ1FNB5HLZIaoyCKmKDR/JSbBLn2EBvO7NW9Frlw56ZN+f2703nve9+Lb37zm7BaCx8PHD56GKSWAPnWBxqBwYHBdXYB2bj1ttswkmHR4QbL4tY9e3QfVVoN0DGMfpCzk5k1lj3y39myl9MJBAKw88qFL3lBoKIifygECwALgKVwuOiFp2qM7o3H4wCoyH8zMjE5iYQht1WPjGCwr9SvWmZmehIeY/FitpkHrEYm4/23s6MTfCh7UWUmyMBgNKCurq7ofhSL3gstUyilgF7JKUWhdmB1/vx51NkYeM3K9t/mEZAQBFy9elXR9ikP9uUADz7Mo601e0SLnNKqRfnFOSQt4vWweq01NpsNjCTAZlM2+FBKMZ6Ne27dA8Pi+gwNZo6B0+1Ea2trMV2jUG56BEFALBpNs+sxIRaNVHQwPD4+DiEhgLhy3CNMAGflChb5BwcHAQANy//Sn6NQqp2WlhbwBgPY8Lyi7dnwIgxGY8G1a8xms6qi9ktLS7hw/gKkpvxiMWlMFuY9evSoorZvueUWTIoi4mkBGwIIJgjBLTpb8K9WaCS/fnC5XDCZLalaWTJsNACT2QKXy5W3jUDADxunPJPNyhMwAIIaeOGrQRAEhKPRlMgvShIikeLsBatR5Jf7LIv9WkEXQivP2Ng4JJOyunei0Y7RsfFVz83PzcFj0mYx1mNOBsaspb29PRmtn+12EARaWlt0sWiULvLTrF3KRqHyZx5lQ3LuzGlscypMJQfQZhdhNTCKfflTk9UQUsVfmpqasm7f1dUFA89Di1jOcQAsw2Dz5s0atKYvZHFfa5G/mEHALbfcAnFJBNYEHnMLHG7dfSsdsFIoeZDT3QlvWv4/KfZXahIPpInueTQK0SHi+vXrBbXd398PC8vCDcAOBg6OQ39/v6p+Uih6g+M4dHV1FyDyz6Ozs6tsBRKPHz8OSZRAmhWIxSaAqWPwyqFXFLW9Y8cOECTHYTLTAARCsHPnTjXdpayBivz6gWEY1NfXg4mvtqxhYktoaGhQNP4N+H2wFWDXwzKA1chWbHwgv691+R9QfFaB0ixxPVGqSP6NMGdKD2DR2/WMEILJyUlIJrui7SWzA1NTk6nPQQiBzx+Ay6TN53LxQsbzq62tDVJCArJIOdwSh872Tk36UCw0kp+yEaEiP6Uo1AwGgsEgBoaGFPnxy7AMsNWVwNkzynz5XS4XjCYjmBADxJLFX3KJ/AaDAVs2b8YNxT3KzgiAzo4OWCyWnNvpbWChBJvNuup/rShmULljx47kg4W0J6Xk37fsuqWoflEoGwF5AL8i8ieLfQUCuQw3S8v169fBWbmk6W4OJJeEa9evFdR279WraALAIHndaRYl9CrMEruZEAQBTzzxBI1sugnZsX0bTJGF/BsCMEXmsXPH9hL3aIWDrxwEW8OuKHR5EJtEnD59WpGFWEdHB4w8j3TzggkkAwkKtSOiZIba9eiLpqbG9SJ/IoSmxkZF+wcDAdj4wuYjNkPlggDk8YqWIn80uqJUVsvvu1SR/NXy+Ysh/TPqTfSdn59HIh4DMefPwgEAYnIhHothfj65qB8KhZAQxFSB7GJxGEQsLKy360nV+8hyGWBDrG5q4KQf42rM2qFQ1EBFfkpR3HLLLXA4HNi1a5fifc6fPw9CgO01hQkL2z1xXL58WdEFWo5uQRjJaH4kffdzsXffPtzgOJAifflvcBz27tuXd7tqHEjZlyP47TqK5K+trUVtXS2Y+bSFAj9ABLKyAEChULKyIvKbV/1fSc/d/uv9EJ0KJl9uYHZ6tqDie1cuX0Zz2vW3BQRXr1ypyoXXYjh06BD+5V/+BYcOHap0Vygas3XrVkjhRUDMM14SE5DCi+jp6SlLv+LxOI4eOwqhSfn4j7QQCAkBJ06cyLstz/Po6upCehnCKQBtzc0wmUyFd7iKKVVErt5EsVJQTfeChvp6GITVdjUGIYL6emVe2KFQqKBIfgCw8pImBW/VsLiYrDViB2Bb85xa0u1+0gV/PSPPhbUWLavpt68WPdu3jI2NAYBiux7J7Fy1n3xeFnpOZ8PGEyxlCPhpbm4GwzBgljLcZwRADIm6sctNP0f0drwplFJBRX5KUXR0dOCZZ55BZ2en4n3Onj2LWiuDOnNhIvd2j4BYPIErV64o2r6pqQlMmAGWx275CuHu3bsXQVEsypd/EQQLooi9e/fm3bYaRX65wJ6aQnu5KNazb+eOneB8K1YDzCIDhmHKJlxQKNWMz+cDABCDedX/xU6ci6HvWh8klwLPbndyIjMwMKCo3dnZWcwvLiI9vqgVyeJ98iRpoyBPBkOhUIV7QtGa7du3A4SADeUe0bChWYCQ5PZl4Ny5c4hFYsqsemRsAOthFS9GdW/ejNk066EZlsWmLVsK7SolCxtB5K+mz+j1esEmVov8TCKM2travPsSQhAMhWEtNJKfEysWyb+wkMxQ0lLkT88SUpIxpAdksVJr0bKafvtqSRd9tc6EKJaRkRGAYUDMykR+YnYCDIPR0VEAK+O6Qs/pbFh5knFBz2AwoLa+NlX7cBXLz+lF5E8/R/R2vCmUUkFFfkrRFBotdObUSWx3RVFokFGHQ4TNyODs2bOKtm+obwAf48FEGPAGHk5n7hvm7t27wXMclElFmRlA0o9fichfjQMpszkp/uWzIiqUYiPOtm3bBmaRWSkAtAi0trem+kuhULKzuLgIMAywHMEP3gQwTMVE/mAwiIW5BcCtYGMHwHCMYl9+eZE4ferRuua1jcJG8N7dqHR2dsJkNoNbyi3yc0uzMJpMBQVqFMPRo0fB2bm8tTbWIjQIOHLsiKJxU0dHB2YJSWVlzjEM2js61HS3qilVRG41BqgUSjVZOni9XoixECAfb0IgxcKoqanJu28kEoEoSrAVaO1h5UQEA5XJ9Jubm4ORZWEEwIOBjeMwN7feTqQQ0oX9Yov4lgtZrNRatKzGuWmhpGdr6C1zY2RkBKzFBbAKa+SwHFiLCzduJA2H5d+vidPm+m/iSNbvqK21LXMk/7LIrxe7nng8Do7lAVTXtZ1CKQYq8lPKit/vx8DQMHZ4Co88SPryxxX78tfW1iYLwkQAT40nr6BhNptxy65dGChC+LgOYGtPDxwOR95tqzFlTBbNtRbPi43k37JlS7IA0HJAKh/gsa1nmwY9o1Bufubn58GabEitvDIsWJM15fFZbuSofOJSMElhAdbFKo7k7+3thZPj4MTKdd4CBrUcj97eXlX9pVD0Bsdx2LZtmwKRfwbbt28vW9HdI8eOIFGfAAocZpFGglAwpKhAdltbG2KShBCAKAiWRP3YBtwMaC3y63HRoJqETo/HkxT4heXisUIMICT5fB7kaPzCPflJxUT+2dlZOFk2VVPHCQazs8XkYK8W9qtF5I9RkV81erZnGhwaQsJU2Cp4wuTC4NAQgJXfg0Ejhc/AktRvbS3tbe3gw/y655klBiaLCS5Xgav5JSIWi8G4HMREI/kpGwUq8lPKyvnz5wEkrXfUsMOTwKVLlxVdpL1eL8SwCESAulpl3pR33HUXhhgGggpffgkEQyyHO+66S9H21biaXCqRv1i6u7uTD/xIRvP7gc2bN1eySzcFExMTePe7342JiYlKd4VSQubm5kAMqy24iMFWdHScWgYGBsCwDJB/rRQAkHAkFBff7bt6Fc3ielGpSRTQtwGL71JuXm7ZtQuG8OxKhO9aCIEhPIfdt5SnQP309DQmxydBGlREGHoB1sDi1KlTeTdtbm4GACwAkHORWlpaCn/PKqdUmTpaZwjoUeSvJl9yOUuZEaKr/lcisMkiv71A/267gSCQwae7HMzOzsKZJkQ7RQEz09NFtRmLxVKP9Sb6ZiMaTfY5HNG2vxtB5A+FQjAZLKnHemJgYBCixV3QPqLFjYGBweTj5ePHsdpcwzgGkKTMbTU1NYEsEayTTELLlsk6yRaNx+MpkT/9XKdQbmaoyE8pKxcuXECtlUGtRd2gfqtbQEIQ0NfXl3fbZHQLwAZY1Hrze1MCwB133IG4JGFURd8mAIQlEXfeeaei7atxNVkW9/VWwM7r9cJqs4IJJGswSAmpbPYDNzNnzpzB+Pg4zpw5U+muUErIzMwsEvxqC64Eby46Ok4tQ0NDYF2s8hGKCxgeGs4rFhFCcO3aNTRmWMRtQnJxQY+CE4Wihu3bt0OKhcDEM4sYTDwEKRYqmx//xYsXkw+UxVyshgUkr4Rz58/l3bSxsRFAUuCXRf6mpiYVb0rJxEYQ+auJFZE/tvx/dNXzufD7k9H4hYv8EgKBynjyT42Pw5X2G3QBmJ6cLKrNeDwOlkkOOKolAEu2GNIq84Asn4fVmGVeKKFQCFZj8vyoVAHpTASDQSwuzEOy5LfaSkeyeLC4ML+qToZW8nounb6lpSWZRb9GN2dDLNpb2zXqQfFEo1GYluc4VOSnbBSoyE8pKxfOnUWPU33UQbtdhJlncOHChbzbplJVA1CUtgokbV9cDgeUuTuv5joAi9mMHTt2KNq+GkV+o9G46n+tKHbSyDAMWttakz6Ay2Oc9nb9DDCAjREdQ6lOpqanM0byT04VFx2nloHBASTsyifaxEkQj8UxMzOTc7v5+XkElpaQSe5rBBCJxTBZpFhAoegFeSzCLmU+L+TnlY5ZiuXy5cvgnBygMkZAqpFw+fLlvOMFm80Gm8UCP5LJfQaeVzwGpJQfPY6NirWQLCfrRf7k3EKJbajP50tuayxsocVhJFgKRyoiCE9NT68q1+MCMJ3n3p+PRCIBnkvOa6plbiZHoGslUsfjseX/q+PzF8PS0hLMvA0Mw+oqkn9wMBmNL1kLu19J1prU/vK1SyTayPwSyZ4Vllo8X/MVchEulVGnByKRCHjWCJblqsaOi0IpluoZxVCqnng8jusDg9jiUj+g51ig2yng6tX8BRJTqaqSsrRVIDmwv/Ouu9Cvwp/2Osti37594Pn1/nSZqMaBlCzuax3Jr0VKX0dbB7gQB2aJAcuyaGho0KBn2lGNx1trFhYW8LOf/azS3aCsYW5uFsRkX/UcMdowN1eZSP7hG8NA/iDEFZa1DLnwWDbkCVRjhtfk55R6+1Mohw8fxvDwcKW7kRWv1wtvbR3YUObzmA3NwltXr6hApxZcuXoFCZf6KFniIQgthTA1NZV32/q6OvgBBADU1tToxjaAsh49ivzV9Hux2WzJB+LyGFNMirV2uz3LHiv4fD7wLGApcMrjXI78lxcJykUsFsOi379K5PcACEUiq6KYC0UURfCcIfW4GggGgyCcQTOROh5LBuBthLlKMLgEA2eCyWDSVSR/0qqSBTG7C9qPmF1gWBaDg4MwGJK/Y0GjBKmExMBoyKxryCI/E0q7XhJAWpJ0lT0XDidFfgNnpCI/ZcNARX5K2RgYGIAgiuh2Fhf50e1I4Orly3m3S09VLaT4y113341JUUSgAF/+MAhGJAn33nef4n2qMWWsVJH8WtDY2Ag2wgJhwOP1KF5sKRd0YAF873vfwze+8Y2iJmMUbYlEIgiHQpCMtlXPSyYbwqFQ2X+3fr8foWAIxFFAdo8NYDgGo6O5jdb6+/thYtlVAoGMHYCd43D9upo8LspG5JOf/CQ++clPVrobOdmxYzsM4cwFtA3heezcXp4C9YQQDA0NIePJp5TlfeXFulzUNTQgiKTIX6+zBf9qR2t7nY1gD1JKzGYzWJYFsyzyM0ICLMspCsZZWFiA28zmtOTIhNuU/A3Mz2e+tpQKOVvPnd6X5f+VLP5lQxRFcCyfeqx3RFFEJByCZHYjHFrSpM/yWG8jzFVCSyEYOCMMvFl/kfxWD1BoJhHLgbHW4Pr16ylb3ZiozUJlXMw+57fb7bDarEA47ckIQCSSss3TA6FQCDxngIE3pmyuKJSbHSryU8rGwMAAWAZotRc3GGl3CJidX8hb9Ck9VVVJ2qrMXXfdBZZhoKyMY5J+JOvO3KWw6C5QndESWkc3vfGNbwSgzaJBfX09xJAIhIDGBv0MLmTowGJlglZNRe1udlLHZI3IL/+dzwJHa1JCvfJLNsAArIPFyMhIzs16r15FEwA2g1spAwZNkoSrV/JniVEyI4oipossgFhtjI+PV7oLOdna0wMuPL+++C4h4MLz2Lp1a1n6MTs7i2gkCuIs4tpvAVgjm1wsyENtXR2WOA5BlkVtfb3696SsQ+v7tx5F/mqqE8AwDMwWKxgxmSXDSAmYLRZF4/W5uTm4TYXPyWSRf25uruB9i0G+v7jT+7LmNTUIgpAS+fX4e1xLIBAAIQSSxQ1CiCaBM3JEu54i20tFKBSCgTOBZw26mptd67+OhEmdtVzc5EL/9QFYrUnrzahGIn9EZGC32bK+3tDYsNquZ/nr1FUk//LxNnAmXS3qUCilhIr8lLIxNDSEehsDU+FOOKtoW14kyDfR49IsdwoR+V0uF3bu2IG+AsrW9AHYsmkT6uqUV5SrluJOpeRNb3oTXnzxRU3sf7xeb6rQcn2d/ib10aj6WhQ3G1Tk1w/ZRf5kqn+5i++OjY0lH+R3GliFYBUwOpY9kp8Qggvnz6Mth3jTRgguXbxYFZF8WqD1efjzn/8c73znO+m1Tkf09PRASsTAxFYLN0wsCCkRw5YtW8rSD1WLd2thkvunrhE58Hq9WGIYhFhWEzuiarxnlarPWl8f9Xi9rSaRHwDMFguwLPJDTIr8SpienkSNofC5iMtIwLHlDwKQhfx0Nz8bAJ5hihL5E4kEeDYZbFQNIr9sk0QsSUF4cXExx9bKCAST94h8AXRK+eAHP4hdu3Zp0pbWhMNh8JwRPKufyO5ktttgwX78MpK1BkNDQyn7rnBCG5E/LDCw5rD+amlqARtekRNl6556HS2uLy0ti/yskYr8lA0DFfkpZWN0ZARN5uKj1xssEhgom+jJ2HKsQmdi/wMPYIAB4gosewQQ9LMs9j/wQEHvkT6QrJYJRSkmjVrZ6ni93uQDP8rmMVwI1WjPVCqqUTC5WdFbJP/Y2Bg4KwcUeFkgDoKR0eyR/IODg1j0+9Gdo41uJL19+/r6CnvzKkXrzKyTJ08CoAvYeqK7O/mLZyMLq55nw0lRaNOmTWXpx/j4eFKkL2wotg7BKuQ8z2U8Hg+CooigRDQZD1Rj5mWp2AgifzUIvelYLJZVkfwWpSL/5CRqLYXPP1gG8FrYoixy1DA7Ows7x4FPC8JiwcDJcUVlFcRisZTIXw3nuizySxb3qr/VEg6HEYsnfz8L89pkZ/zBH/wBHn74YU3a0ppIJJyM5Gf0I/pOT08jFo2miugWimTxIBaNpOyWlgRtxndLCRYud/Y+NTQ0gIumRW+GAZvdlsooqDSEEITCSZGfY03ULpayYaAiP6VsTIyPos5S/GDeyAE1VhYTExOK9yn0ZrN//34kCIESd+YhAFFJwv3331/Qe6RPIqptQqFH3G53xsd6gQpfFD0yMzMD1mQF2DUpViwH1mStiMgv2VUsetqBuZm5rOfZq6++ChPLoiNHE60ArCyLQ4cOFf7+VUipFtvoIp5+qKurg8VqAxteI/JHFmC12VFbW1uWfkxMTICzccXPOuzKLJI8Hg8EQhCRRHg86iIj06kG4W8tpSoeq7Uor8fxrx77lAuLxQxIK5H8VgUivyAImJqZRZ1ZXZBRnTmBiYny2pXNz8/DmeF3bSekqPoA0WgUHMuD5wxVkYnm9/sBAJLFtepvtcjfXatNxNxsecd85SYejyMhJJKe/JwJwaA+7IlkdwJ54aZQ5AyA0dFR2KxmBOMaifwCv6rG4VoaGhpAQgSpmMgwUN+gnyj+aDQKURRg5M0wciYE/NpkqlAoeoeK/JSyMTs3j1qVg8m11JjEgsSnQkX+trY2dLa1QYk781UAjXV1BUfEpU+U9BjJVG2kF1cupNByuaDHmKJHZmdnU9Y8ayEme9lF/hujNyBaCz9XiJ2AEJJx8ZcQggPPPYetkgRDDhs2Dgy2SxJeOHCgarKriqFUIiBFPzAMg46ODjDR1SIQG/Gjo6O9bL+ByclJSFYNzikb4Fv05RXdtV70rzbRt5Ro/V3o8VpbbcfbarGAkZJ9ZiQBVmt+kX9qagqiKKFJxf0WABotCYzeGFa1r1oWFxdhzTCWtokiFhYWMuyhjFAoDANrhIE36ca+JReybz4xOlb9rRZ5nLfJJWC2zHUWyo0cyW3kzTDwJs3siYplZGQEDGfIOh7PBzHawXA8RkZG4Ha5EEhoI/EFBS7nQnl9fT2khAQsrzGyERZNjfrx45ePr5G3wMhbil4Qo1CqBSryU8pCJBJBJBpLFWsqFrchUVBKoVxtvhDuf+1r0c9yEHNY9kgg6OU4PPDggwVPlqsxkl/PolB6enIhNRjKBRX5V9Dz72ijMTk1hQSfWRBIcFZMljEdnxCCsdGx1Ya7Slk+5TMV37106RJGx8exV0EzewFMz87izJkzKjpBoeiP7q5OGGOrhQxDPICuzs6y9WFsYkzV4t1aiDW5mJdv8TE98jBXFKJSqmWMVg60zkrU49io2o631WIFxOU+i4KiSH75XtloU/f9N1kljI1PlPX4BXw+WDNkilkBBIrwpQ8GAjDwZhj56rDzCAaDYA0mgGXBGkxFi/xyPYOtbgGhcPSmLr4ri7xGzgKTjkTfkZERwOoC1M6PGAawuHHjxg14amoQiGkzz/JFSV6RH0Cq4C4X5dBQ36DJe2tBalGHM8PImRGogvObQtECKvJTyoLsF+gwaJPG7zQQLC4qj9pQU9h1//79CEsicrm/TgAIiiL2799fcPvpHu3V4teuZxuGdOHYnqNIUKVInwjpMXKtnOj5d7TRGJ+YBDFlXhQjJjvGJybL1pe5uTnEojEQh4rfhxlgjSxu3Lix7qWf/+xn8HJcTj9+mXYAjRyHn//0p4X3gULRIS0tLWDWiPxMNIDW1tay9WF6ejqpxBWLLa29HKQL+1os+m/0e3aS5HV5I0Ty63HhIRcWixksSR4XjoiKPPmHhoZgMTDwmtSNx1psIuIJAZOT5RsjBAMBZArZMgNFifOBQAAm3gIjZ9FNZHculpaWwPDJeS3DG4sW5aempuC2sKmsjmKKGOsduUix2WCFibfC7/frYk4yOjaOhKG4e1XCYMfY+AS8tfXwJ7j8O+QhKgAxgazUvMtAXV1d8kGyFABImKw8pwPkRRwTb4aRN2NpaUkXx5tCKTVU5KeUBXkAYuW1ubBaDRJCBQxq1Ij8W7duRY3bjVwlGPsAOGw27Nq1q+D25eI4ax9Tikfrgj9aDAiqMXODcnMjiiJmZ6YhZRH5JZMDczMzZRNhZE9SVZH8THK/VBvLTE9P4+WDB3GXKILNYdWz0gyDu0QRR44exejoqIqOVA90orMxaG5uhhSPAsJyMIEQg5SIorm5uSzvn0gkEPAFtBH5l7XLfJH86cK+Fov+ehSiy42wHMGvtQCux+tQ9Yn8FrAk2WeWCIqyl4eGhtBik1QHDrfak+83ODiorgEVhEIhZJrNmQDVNjuSJCG4FISRt8DAmlMisJ4JhUIgnAEAQLjii8dOTk6iziyibrkIcyE176qN2dlZAIDZaIfV6IAoCkUXLtaC8YkJSKbi7lWS2YGx8XHU1NTAlzAU3SdfPCkT5ork93q9YBgGTIQBBECKSboU+Y18MnNDFAWquVA2BFTkp5QF+YJq1kjkN3OFCeMGQ+E3O5Zlcc999+Eal301vJ/jcNc994Dn+YLbT4+8qIb00GpCC5E/feKpRdG99EnjRhf5qWCiD+bm5iAKAog5s6pOzE4IQiI1KSo1169fB2tgU9G6hSI4BVy7fm3Vcz/72c9gBHBbAe3cCsDGsvjpTR7NXyrbLD2KdhsZWcxnY8FV/zc1lcc3Vy7qSCwa/C44gDNzmMvjG50ucmoh8tPf9Mq4Revxix6/Wz32KRdmsxlsmie/kkj+69f60GZTP7Z1GwkcJrasIn8sFoMxw/NGADGV4/RAIABJkmDmrTAbrJifV+/tXy5CoRAkNjmvlVhD0ZH8E+OjqDPF4TISGDmmrNkZ5WZ6ehpmoxU8a4BluabBVBltKTNBCMHCXPb6WIrbMdqxMD8Hr9cLnwZ2Pb5YUiasra3Nug3P83C6nclI/mVZJtf25SYQCIBhWBg4E0zL1qR6sWiiUEoJFfkpZUH28DRopCnwLEGigImGWjHjrrvuwqwoYjGDL/8SCMZFEXfffbeqtv1+PwzL6Zb0hqMtamowrCUajaYea7HqT+16Vqi2CfTNihypLmUR+eXnyxXR3t/fD+ImUBBwnxk3MDoymrI/C4fDePqpp3CHJMFUQKMGMLhDkvCrZ5+titR9vUFrbuiLhoakPy4TW1r1f2NjY1neP1UQs/jbcqqdfEU2WXZlemM0ZpIFC2Oj37MBQFoew2gd5U6/2+Ixm81AmsifbwycSCQwMjqGdrv6Y8kwQLstgevX+1W3USixeByZQrYMAKIqRf7V9i02+Kogkn9paQnissgvaiLyj6PekszqqLcB4+PjWnRTl0xOTsJmcgEA7CY3gMpnLvj9foiiCGJUGeGyDDFaIQrJRb5gTIJQ5KVVXijIZdcDAHW1dUmBf3narKdIfp/PB7PRCoZhYDRYUs9RKDc7VOSnlAV5UsCy2oh7LABRLP3E4PbbbwfLMBjI8NpA2jZqWFxchMuSXO2ulhtOtYg3auyZ1pKe+qs2DTid9InsRp/UUpFfH9y4cQMMy+Xw5HeAYbmMPvdKOHnyJN71rncpLtR4+epliC71ogPxEEiilIos/PWvf41oNIq7VLR1JwBJEPDMM8+o7g+Fogc8Hg943gAmvizyx5fA8wa43e6yvH8qiKH42zIAQDJKBQVGaDFuqcbsO63vs5IkLv+v7fil2qxx9IjFYgHE5H2WiIm8Iv/IyAgEUUS7o7jfdZstgevXcpmaaks8kUCmvGkeyd+lmvNUFvlNBhvMBit8fp/ux6g+fwCESy5eEs4If0B9Nng4HMb8oh+N1uR53WCKY3zs5rUqHLkxApvBDQAw8maYjZaKL2rIi9bEkD8DJxfEkMxilxe2ffHi7n2LMRYmowE2W+7Fh/q6ejDRZcse5F8UKCc+nw/m5e/FzNtSz1EoNztU5KeUBW7Z8kaStBGJJQAcV/qfr91ux9aenqwif1d7O2pqalS1PTc3D6vBCZPBkkpn1zt6H/jKaCHyp3tcFhslA6z+7qrle9Saalkk2igMDQ0BVjfAZLmWMiwYq2edz71SfvrTn2JyclJRJkwwGMTUxBSg7nKaxAUwLIPe3l4QQvDUL36B7WDgUpEaYAeDnYTgqV88seEX5SjVDcMw8NTUgIknF6uZeAieZR/dcpC6fxZvEQwAkHip7Bk2Shcq9YR83dLq+iUPW7Qev1TjAoresFgsIGkifz67noGB5KymrYhIfgBod4iYmJrRJBAmH4IgQBDFrJH8gDprTVnwM/FWmHgLBEEo2uO+1AQCAZDlTHDCm4q6Ho6NjQEAGpeL7jZZRdwYVjfmqwZGR0dhN694zNtNNRWvvyQfP/mYqkXeX7YQXowWp5MsxlnU1tTkHSvU1taCj/FAFDCajJrXxSuGxcVFGLnk9VC266mGuhsUSrFQkZ9SFuRV5YRGWklCZGDgNZox5uG222/HDY4DWWPZM8xxuO2OO1S3Ozc7C7PBBovJXjUif0tLCwCUrWCfWtTUYFiL1jUT0ifGG1U03KiLG3ql//p1JJbTlbMRN7nQf/26qvYLOd59fcloQOIp4jfCAYybwdWrV3Ht2jUMj45iXwarNaXcDmByZhoXL15U3ycKRQfU19WBjSeFKzYeRn0Z0+lTi3zZyxsVBOEJwpHSi4rppNv3VQvywoRWCxTy9Vzr+3g1LqDoDYvFAiIkACKBCIm8Itvg4CBqrSyshZcTW4VcfHd4eLi4hhQg2/DlEvnVWGv6/X6wLAcDZ4RpOeJX7zZ9waWl1SJ/EXMU+dg128TU/9Oz8zdlcdJgMAh/wA+neSWaxG7yYGhouHKdwsocs3iRP6m1pET+WJEif5RFrYKxQk1NDRADEAXcHreuArrm5+Zh4pLnNctyMBstee3+KJSbASryU8qCPOAMC9pc+CMiA5utPCvFe/bsQVAUkS7D+0GwKIrYs2eP6nYXFhZgMdph4qx5i8jphb179+IHP/gB9u7dW+mu5ERNIeS1pAv7WkTyp6ekb/T0dD0NADcqkiRhcHAIkjV36LxkrcHg4FDJF2h6e3uTRXczOwcpRnALuHz1Ml566SXYOA7dRbTVDsDNcXj55ZeL65ROoYtuG4f6+jqwQlK04YQIGurLJ/LH43EwHKO+1sZaOCAWj2nUmDLSxwPVEnkuL0xoJdYRIi3/f/OL/NU2RpEj95lEdNXf2RgeHkKzRX3RXRlZGFZr6VcIcnR9JhlUfk5NRkHSzsMChmFSkb56t/MILQVXifyhJfUi/+DgILxpCz7lXLgpN/JnclpW7GQc5hqMjoxUdDy0shBeZIAamxT5k/7zPBaKFPkXEjzqG5vybldTUwMxLAIRwFujH6seAJibn4fZsGI3ZDbYqchP2RBQkZ9SFlyuZJGbQEKbn1wgzsDlcmvSVj527NgBBsBI2nPy41tuuUVVm5FIBNFYFGaDDWbehvm56ojkZxgGHR0dup8AyfZQxZAeyaNFJH+6MFAtIkGp0PvvZyMwNTWFWDSiSOSPRSOYnJws+D0KOc7Xrl1LRvEX+9PwAGMjYzhy6BB6RBFcEQ2yYNAjijhy6FCRndInej8Pp6encerUqUp346bA6/WCF5ICICdEy+qZSwjR9rfGlD8bLr0GQCH1ACqJLHhqJfJrbf8jo0eRv9qyLVdE/siqv7MxPnIDjdbix6FmDqixsCnLl1IiB9tkqjYgP6dmrO73+1MR/MZlkV/P53gsFkMiHge45aUNzoREPJ7KdCiU69f70WZdWfBptYlgmRVLp5uJkZERAMwqux6n2YtINFLRjPrUsWOKnLuyXKq9ulov5ou061mI8YqK6Ho8HoAAbJBFrbe2qPfUmsWFhVUiv4m3Vo17AoVSDFTkp5QFj8cDlmWxGNNmorcY51Bb36BJW/lwOBxob2tDumPfKIDGujrVfvyyH5yZt8LEW7FA/eE0RQtBwe/3g2cYWDlOk6ie9AG4Gt/QSlCqyJZqm0DfjFxftuCRrLnFPvn16yote5TSe623qKK7MsRNQAjB8OgoujToVzeAyZkZzM7OatAapRC+8IUv4H//7/9d6W7cFNTU1ACJ5SjXeFj12EUNDMOsszssClL+Bap0UaBaBAJZFNUiSAFYCU7QOhNRj5mNeuxTLuRsaWb5HM9l10MIweT0DOot2ozD6i1CWQqXyuPwTCVA5efUiPN+vx9GLrlMUA2R/Cn/doN51f9qPjshBNf6etHpWFloM3JAs4NJWSjeTIyMjMBhcYFnVyLmnZbkvbAc2SjZiMfjYFgOKPa+xjBgWA6JRAINTU1FifwSARYiEhobG/NuKwdyIgi43W7V76k1kUgEkWgEFqM99ZyZt2FutjrcEyiUYqAiP6UscByHxvpazES0MWWdiRpT/vC5eOc736nJDWfnLbdgPC06fJxlsUNlFD+wMkgz8haYeAuCOvd/3IgsLi7CznKwM4wmRXrSo+nKUaRMC0olpGz0TAY90N/fD9ZkBTHkjvgjBgtYkxX9/f0Fv4fSRaJYLIbpyWnAWfBbrCetjVYNmpPvMmo+/0ZFq0U8WgtBO2pqaiDFo4CYgJSIllXk53ke0HJdV0LZajLJzM/Pw7Bsj1EtIr88ztRM5E8k79taR97rcTxQLYEYMimRf7nuRq5I/kAggHhCQI1Jm5OyxpjA/OyMJm3lQj7v7BlesyGZBKjm3FyYX4CRTX5fHMvDaDDpWuSX+0b4ZZF/+bqkRuSfmZmBzx9El3P1olanLYreK5eL66gOuXHjBuxGz6rnbCY3WJZbjvKvDKIoJkV+LWBZCIKApqYWzMaMqptZjDEQJaChIX9AZUpnEfQl8su2PKvtemyYqxL3BAqlGKjITykbbR2dmAwVfxOLi8BsSEJbW1vebf/qr/4Kjz/+eNHvuW3bNkxLEhIgEEEwSQi2bdumur0Vkd8MI2/BUmiJRjfrjLm5OThAYBclTWomBAIBcMuDOL0X9So1ekzP32j09vVBsHjzRw4xDASLF729vSXry+TkJAghIA4Non15gDExYABoIWO6ABgYpix2BDcL1RYFuxHweJLCBhtJLliXcyJuNBpBJKKd0C8BJnNxBQoLZX5+Hm5LPQBUjZ+vz58cZyz6tLEeiSeSwrdaW5B00se7ehz7phda1mP/1rIi8ueP5JeDVlwaifxOI8HCQulFs5mZGZhZFqYMFnwcGDg4DjMzhS82rPfstul6IU/umxygQZathtT0+erVqwCAbufqhbZNThEDg0OanOt6YnhoGHbz6pEhy7BwWDwVFfklSSo+in8ZhmEhSRKampowW0QkvxyU2dzcnHfbVCT/mseVRj4n1p7fC4vVcQ+nUIqBivyUsrF58xaMhIufmI0ucSAANm3alHdblmVzDnaV0tPTA4kQzACYB5AgBD09ParbS4/kN/JmEEJSRaUo+mBqchJOUYSLSJiamCi6vcXFRbisSW9DPUcJlYObbeJQbRBCcOXKVQg2Zd6ZgtWLq719JbNvSk3MNaqlTjgCG8MU5ccvw4CBi+Oqpji6HqAiv/6QRX02nBT4ZNG/HJhMy+M+jX4WjMDAYsqdgaQ1yeKcNpgM5qq5f8uRvf6ANiJ/NJq8b2tx/9Z7jaL08bhWNQ1KSSF2PbKNk5XX5n5u48szf5mcnEQNk1228BCiqnbQwvw8zGvtPHR8v5+ZmQEYBsSYPMbEYAUYRtUCx9WrV+G1svCYVv8WNrkECKJYcpvGcpJIJDA1PQWHeX34h83grqhdT3JsrV3mNCEEzc3NWIpJCCXUtTsTTp5rSux6HA5HxseVRl6QtxjSzm+DDbFYtGoy6ikUtVCRn1I2enp6MB+W4CvSl38wwIPjWHR3d2vUs/x0dXWBYRhMAZhafm7z5s2q2/P7/WBZDjxrgHE55XKjR3frjcnxcXgAuAFMaiDyz87OwmZwwWgw63oCUQ5oJH9lmZqaQjDgh6RQ5JfsdQj4fZieni5Jf1J2WJkq6qmAMESrpgAAZkJSwgglP1Tk1x8rkfw+AOWN5E8JjhppuayoTfBGIQT8QRg5M4y8uWquBbJNTzCgjV1PZDm6XQvRO13Y1+P1Il20robjbbMlI1Vlu55c54e8SGPUSAEwsASxMtgbjY2Owi1mv4i4JQmjBQq1kUgEoXAIVuOKMGnm7ZiZLr39kFrGxsbAWpyAvODBsmAtTlV1Ea5euYxuW3Td8+12ETzLlDSDs9yMj49DkiQ4M4j8TnMNhocrJ/KXgtbWpGHldETdiT4VYVHnrVlZpM8Bz/Opx3Z7JkOtyjA/Pw+O5WDgVj6DLPjrOVuHQtECKvJTysbOnTsBAP1+Ps+Wuen3c9iyaZOiG49WWCwWNNXXYwbADIAalwtOp3oDab/fD4vRCoZhYOKTg3EtfN8p2pBIJDAzN4caJC0/fMFg0av+U5PTsJqcsJmcJRNLq4VqSH+/mbl8Oem1KtrrFW0vb3fp0qWS9CcSiSSDmLQakTAAp2HWAU/owlQh6DEyd6Mjp9AzUd+qv8tByh9co58FIzI5PcdLQSQSAc8ZwHPGqojsBpK1fwjLaxKxKAgCYvHkNVCLqO10z3s9Zval1zGoBpHfZDKBYRgw8RBYloXZnH2ZW87IYzUKHGYZQJJKk+WXzsjwMHKFJdQCGB0dLSjjUI5+Txf5rUYnpnQ8Rh8YGETCtPr6nTA6MTAwWFA7hBD0919Dp3P9IhvPAm0OclMV35Uj9Z0W77rXHBYv5uZmK3xt1+4cYhgmJfJPhVWK/GEObe3tBe8nLzjqgfn5eVhMjlX15WTrHiryU252qMhPKRv19fVorK9F76J6kZ8QoC9gxu49ezXsmTI6u7sxyzCYXX5cDHNzczAvrybLVd83enS3npiYmIBECGqB1KRidHRUdXuSJGF2bhZWoxNmzq4qpbgSlMqehVJZLly4ANbmAfIU3U1hsIC1eXDhwoWS9CdZdIzRLluZ0XK6lGyrVEWob0aqrWjlRsBsNoM3GMBG/OANhrKK5JqL/AKjKJL/DW94A+67915N3jMSiYBnjeBZQ9WI/NFoBMRgRSy2PlK3UGRh38QSLGlg/5P+Hab73+uF9MzaasiyZRgGZosVbDwMs8WS834lR92KGt0kBQLwvEZFQ7MQCoUwt7iIuhzb1AEIhkIF2WnJATeWdJHf5MTCwrwuM0wIIbhy9SpE62qhWrTV4srVqwUvcITCUbTbM3/ONmsMQwP9RfVXT9y4cQMmgzkVWJeOHN1fKV9+TceXhIBhGNjtdtS4naprIU5GTWjv6Cx4Pz2J/LOzs7AYVvdHPtep5kK52aEiP6Ws7Lntdlz1qzdSmImwmA8T3HbbbRr2Shlt7e1Y5DgscBzaOzqKamtycgpmPinuGzkzeM6w4aO79YQc8ZEu8hfj17iwsABRFGAzOWEzuTA1OZV/Jx1QKmGTCqaV5dTpM4hZlUXxy8Ss9Th95mxJ+sPzPIhINFXmtZyeCwxgMBg0bFEflGoRT4+RuRsdhmFgdzjBxgKwO9RnIapBa7seCLntSGQ+/elP44tf+pImbxmJhGHgTOAYY1VEdkuSBCGRADGYkYjHi86ek4XuBquIoAYif3p2gR7rUQUCAdjNbgArtQ30jsViASNEYbbkPjfkLOiYqM04LCYyMJc4s1oef+catcgLAENDQ4rbTUbyM7AaVkR+m9EJSZJ0Gel748YNhJaCkOwNq56X7A1YCgYKmqeMjY0BAJpsmUdLTTYRo2OFWwDplaGhITgttRnnH05LbWqbSsAwTDKKUcv2ALR3dGIiXLjILxFgagnoUKF1lDvLLhezM7Mwc6vtgwycEUaDCbOzsxXqFYVSHqjITykr+/btw0gA8Kv05b+0wINlGezevVvjnuWnpaUFC6KI+eWq9cUwMjICuynpkcswDBwWT1GR4hRtGRoagpXlYAdgBgM3xxU1+JMXcKxGJyxGh6oCWTcD8sBTa5Ffj/Y/es2CWFhYwNjoCERnYdcw0dmE0ZEbJbEVS3l4aiUCEiCu4W8szpTfHqQclGqxjVob6RPH8nnmLHNhvNS5o9HPgiSIovORYRiwbPHTnFgshlg8BiNvhpEzw+fTv+grZ9MQ3rTqb7XI0dGNVgk+DUTv1EIJr087HJ/PB7vJDYZhqiKSHwCs1mTEqjWPyC9bjQZVFuRcy1KCKcq+VAnDw8NggJx2PV4AHMMULPJbzXaw7IoQKkf66nGcfuLECTAsB9Gxuhiq6GgAw3I4efKk4rbkz+c1Zx4/15olhCNRXS7CqeH69QE4TeuteoCk8Gu3uDE4WJjlkVaUaizW2dWNiUjhC3AzERaCRFSJ/LmswsrNxMTkqiwdGavRiamp6gi2o1DUQkV+SlnZt28fAOCySsueSwtG7Ni2rSKFXRoaGiARggQhiqrNZyMcDmN2dgauNF9Au7EGgwX6KVJKx/X+fjSAgFn2D6mXJAxcv666PTliwGJ0wGp0IBwJV03Kv5aUSvjW4yREr5YlZ88mo/ElZ3NB+0nLiwLy/lpSU7NcCE0j1wZWYhHT8KcWJQSOMguj1YxeF7g2OrIQV2pBbi1y1D0jaCBkEEBKSGUtvCtH9JoNVpgNNsxXQZp/KpuGMwIo/n60sLAAAOhwiPD5g0VbmaSEc9tq/3u94Fv0wcRbYTZaq6Zels1mXfV/NuQi3IG4NsJiIM7C7VlfzFRLBgcH4eV5GHN4+nFgUMeyGB4eVtxu0s5j9XxStlDVY6Tvq4ePJAM0uDVzaM4A0dmEVw8fVtyWz+eDxcDAkEUJchiT9/FqyWTJRTwex9joKFzW7IZPTpMX14uY5xVDqcZMHR0dmFwCxALjoMaXLX7UiPzlrJeYC0EQMDs3A9ua+hUAYDU4MTE+UYFeUSjlg4r8lLJSW1uL7q4OXJgv3PpAkIBLi0bccdfdJehZfrzeFVG+tjZXPEluBgYGAGDVYMNtrcfA4KAuI5I3In1Xr6Ip7Vg0EYLeAv0u05mdnQXH8TBy5tSEQo9RQuVC6wGtHichevQZBpIiPWvzgBgLE8mI0QbW5sGZM2cKfs9817Xm5uUFBy20HgIgBsRBIGjg/0NAEJKksguj5UTr85HacekTl9Ox6v9ykYrs0yJTRwJAymsJsFKc0wmr0YG5+TndL2StjeQv1kJrbm4OPMugzS5CIqRo4VsW+YmdwOf3FdVWKVhYWICJt8LEWwvyeK8kKZE/zwKY1WqF3WrBXFQbCWA+bkRjU2FBA4UyeP06ahUUdK8TRQz0K/eRn5ubg4lb7dlt5MzgWF53dj3BYBAXzp9Hwp25GGrC1Y7z584rXjSLRCIw89nv1WaOpLardoaHhyFKItw5bCrd1npcu9ZfkWu7JEmAVuMmhkmNuTs7OyFIBDORws718RALq8WsSuvQi8g/NTUFURThMK9fgLSbPBgZoe4JlJsbKvJTys5dd9+Li4tmSAXeRwcCHCIJgrvuuqs0HctDKtoUK5Ewaujr6wPHcnCaV26eHms9IpEwxsdvHv/DasXn82FqdhYtac+1APAFAqqF+enpadhMTjAMA6vJmXquWtC7oKHHdHq9ivynz5xFzNaQf8MMxGwNqnz5hTyT8/r6epgtZjB+DSY5EST9/QFokd8RASARUtQ1X6/IE0Gtz28tLFIo2iNnQJY7E5JlWZjMJm1E/mXLn3KK/JOTkwCWRX6TE/F4XPfR3fL9h/DJBZZiRf6pqSnUWhnULlt7yN+JWgKBAFgjC2ImWPTp77v0+XwwG5Iiv5zFoHdkcT9fJD8ANDY2YDaiTbHc2QhbVHazEgYGBqBk1NKApN2m0nva3Ow8zIbV3xfDMLCY7Lo77ocPH4YkiRA9maOrxZoOSJKII0eOKGpPFEXwOW7V3PJwLN/4rRq4du0aAAZuS/ZIfo+1AYGAvyIFWUVR1FTklzOt5Ej88QKL704scejs6FAVsKGX+lVyRo8zg8jvtHgxNT1J60dRbmroTIxSdu655x74owTDwcJuOufmDHA77di2bVuJepabdLsGl2t9+pdSrl69CretAVyaB2SNrSn1GqWyXL58GQDQlvac/PjSpUuq2hwZGYHN6AaQFApYlquKGgzyREnvkbl69PTVY/RTMBjE+NjouqJtSpHsDRgfGy3YXiHfJJFhGGzbtg3sggZDkrTgOy1+FXIbN6PIL3vna+2hr/dFwY2KbHFTTqsbGbPFrI3Iv9xGOT/D5OQkrCY7eM4Au8kNAJiY0Heqv3z/IYbkYkh6oVs1jI+Po84UR70lKR4V+/n9fj8YEwOY9JeJF4vFEI6EYTbYYOKsmJ/Xl9ibjULO7/bObkxFihfjgnEGwZiE9vbM0eVa4PP5sOj3KxL5GwGEIhHFATmLiwswG2zrnjfrcHHn4MFXAGcDiHF9f4FktiUcDcntFCBJUg7zI4BdfvFmuJ9fu3YNbpsX/LJ9WSbc1obUtuUmKfJrs+gGhk2Nub1eL2xWMyYKFPnHIyZ0dm9S9fYcp9HnKJKBgQGYDJaMnvwuSx0IIRUrtEyhlAMq8lPKzq5du2CzWnB2trAB5rkFC+68+96KRQmmp6AVM8G8eOEiaiyro16MvBkum1e1iEzRjgsXLsDJcXCnPWcHg1qOw4ULF1S1eb1/AE5z0u6JZVi4rN6KFXgqBFko0FqwLtbPdy16LPSpx0j+3t5eAIBozx7NlAvRnkx1LnQxUsl3sefWPWDnWRTrsMPMMvDWJc81Ldx/ZJG/GIs2vSJbemhdP0KP5yNlZdxSiSLSFotFm8K7Qlp7ZWJ8fBy2ZXHfViUiv7zwTUz2VX+rZXR4EI0WAWYe8FjYooMU/H4/iIkARmApuKQrIXGlBoMNZoMV83P6sm3JhmyLpeTcaG9vx0RYXW20dCbCbKq9UiFbnCrJFZAXApR4q4uiCJ/fB7NhfWaTkdPXcY/FYjhx8iTirtzfc9zdjhMnTyiKUJYkKSXkZ4JJ267a6evtg9OUe9xrNTpgNlorIvLH43EwWmkbLJca0zEMg472DkyElLdNCDARYlT58euJ3t5euK31GYPUXNZasAyLvr6+CvSMQikPVOSnlB2e53HX3ffg7ILySdpMhMVoANi/f38Je6YctSvV8/PzmJqegtfesu41j6UJFy5cLLZrGxotbAjOnDyJDlFMFd2V6RBFnDl1quD2FhcXMTc/C491ZYriMtfjyhX9Z23INjhaR9ppnf6rx0lIejFgvQgYAwMDYDgDiFldJhIxO8FwhtSkWylKBKY77rgDUkxaFYlfMAQwzBhw3z33gWUYTSL55YWCdLu2mwVZCNBa5Ndr0emNjiz+VULkt1ltmtr12GyZo1lLwdjoGKyG5DXTwBlhMdl0b60o+8hLFs+qv9UgCALGJqfQbEveZ5stCQwPFxcBubi4CNEgAiZASAi6ynxbEfntMBvsWFjUV0R3NuRAJCWe2N3d3fBHJfhjxWVpji5x4DgWbW1t+TdWSX9/P4wsC2/+TeECYGU5RSL/4uIiJEmC1bh+3mAx2nVlqXnp0iUk4jEI7tzfs+BuRTwWUxQwlkgkwLHZx6b88mvVfj8XRREDgwPw5LGpZBgGLktdRUT+WCwGwha/6AYAYPlVizwdXd2YiCr3yV+IMYgJpKQLd6WGEILLly6jxtqU8XWeNcBtq09l7lMoNyNU5KdUhPvvvx/DfmBOYTGY0zMGGHgOd955Z4l7VlouXkyK+LWO9SJ/rb0VQ0ODurQeqRa+8IUv4I//+I9V7+/z+XDt+nVszvBaN4Abo6MF+/JfuXIFAOC1rww2vLYmDA0N6jLaO53Z2VkA0NyjUmsfRD16hqYvjBRrlaAVN27cAKxu9d6fDANY3QVHcSqx99mxYwfsTjuYiSJEhwAgBkXs378fTrtdM7ses8lUEWG01Mj3mkLtl/KxEXxO9bJwVwiy+JcqhFtGbDabppH85bTrGRsfh93sTv1tN3kwNjZWtvdXw+zsbHJB12gDwxmKuoePjIxAFCW02ZMZeC22BAavKy9umon5xXkQEwExJ88jPdU4kL8ri9EOi9GOSCSsm3t4LuTzWsn5vWlT0opjZKk4a43RJQ5tLc0wGrPboBRLX18fGgCwOc1lkjBg0EQI+pazFnOxUmtjfdCDzejE9PS0bq7zZ86cAWuyglhy2wYSSw1YkxVnz+avnbS0tAQrlz1AxsIv1zYKaVHdqHKMj48jHo/nLLor47bUo/9a/gUirYlGoyCsNl72hOVXzS3b2towGWKg9Kc8uWztU80i/9jYGHx+H2ozBFTKeK3NOHvmXPk6RaGUGSryUyrC3XffDQPP4+SMspvayTkzbr/9jop4yWrJuXPn4LB4YM3gEVfnaAUhJLUQQCmc22+/HR/4wAdU73/06FEQAFsyvLYFyQvm0aNHC2rzypUrsJrssBqdqedq7E2QJClln6JXRsfGV/2vFVpMmNOFfT0ulqRHTupFwBgbH0ciQ2p6ISQMdowV+HtQIvpyHIfXvfZ1MIwZVFv2MCMMbHYb9u3bB5fLpUnh3RAAt9OZd7tqRF6I0ipThyxn1Gi9UK3HRTytLcfKQSGRvlrjsDvACMXXdpHbKNdYMBAIIBgMwGFayeSxGd24cWOkLO+vlsnJSTAWB8AwYMyOouyFZGtBWeRvt4uYmJwu6r67sLAAmJD8J/+tE2ZmZsBzBhg5c2qsXmhwRyUo5Pxubm6G2WQsWuQfCZmwZev2otrIR+/ly2gpIFuzmUi4qiBCVw5WSF/Ak3GYaxCJRlJZHZXm4qXLSFjr8gdoMAwS1jpcvJT/88/NzsDNZ195dRoIWEb7IJ9yI1+/XDmK7sq4LHWYmZ0u+8JGOBzWLJKfsIZVc6z29nZEEgT+uLL772Q4mZ3T1JQ5Cj4bXV1dBW1fSs6cOQOGYTMGVMrUOdswPTOFqampMvaMQikfVOSnVASbzYY777wDx2fzR0fORxlcW2Tx2gcfLEPPSsvZM+fgtWW+6dhNblhNDpw/f77MvaLIvHLwINpYFo4MEUMWMOhiWRx86aWC2rx86TI81sZVvoAuSx14zqD7QsvyJGhkRBtBQ0gk035lG6BiSG9Db4X7gNV90uLzasH0zCxIhtT0QiBGG6YKFDyURsO9+c1vhhgSgVkVHZMAfpTH61/3ehgMBjhdLmhhABEB4LhJRX6fT1uRP+BfXG7Xp0l7MnrMbqtGkd9gSAZV8LxGtgAFYLVawYoaTDmWNalyZdbcuHEDAOC0rIj8TnMNRkdGdBPlm4kbIyOIG5ICddzowI0i7uHXr1+H18rCZkh+3ja7CIkQ1XWFCCHw+/yAGcl/0M9COABMTU3BbnaBYZhUlLcc9a1nZHFfSVQ9x3HY1N2NG0H1Ir9EgJEgi82bM+W+aoPf78f41BRaC9inFcC8z5d3Yeb69etwWmvAZ4igdlmSNXj0UDuLEIJr165BtCmrCyRavbh2rS/v9enG8DAarNkXTzgWqLcxqWtgtTI4OAiLKVlfIx/ycR8eHi5xr1YTCoUgMNrclwWGXyXyt7Ymz54Zhc4J0xEWTQ31BY8TvvCFL+Bzn/tcQfuUipMnTqLW0QwDl33Bs97RDoZhcPLkyTL2jEIpH1Tkp1SM17/hjbjuYzCb58ZzYtoIA8/h/vvvL1PPSkMwGMTwjSHUOTJ7KjIMA6+tBWfP5E+zpGhPOBzGyRMnsCNHxNB2ScLZc+cUi7aSJOFqby88a3wBWYaFx9aYsvLRI6FQCH7fIgRXK/y+RU0iW+Zmk5Mu2QaoGNJFAT0JBDKRSAQsw6Ye6wHf4gIkY3HiGDFYsVhg1GWmwleZ2LVrF1raWsAOqhiaTAFSSMJb3/pWAIDd4YAWpjExaFPrQ48Elm16fBotQsmiihbndzp6XMTTYx0QPWOxWLQR+QXAaDKC1apIYR4GBwfBMCwc5jSR31KLSDSi6+juwcEhSBY3AECyuDE4qN5D//r1frRbV3y52+wiWAYF12aRCYfDiMfiSYHfCDAso6tI/vQaDBajHRzL6b7QMlB4ps6WrdswElZv3TUVZhETCLZsyZT7qg3yGLkQx39523y+9JcvXYbLnNnCxWZyw2Sw6GKMPjc3h3BoCZJFWV0gyepFaGkpZwT+wsICZucX0OnMnSXXYYuh92rlv4NiGB4ehsOkpKIDlq/zDIaGiqs5UiiB4BLAaWR5xRngD6xYMLa0JAMLp8LKFvSmwxxa2gq36uno6MDrXve6gvfTGkEQcPLUKdTbcxcONvJm1DqacezYsTL1jEIpL1Tkp1SMe++9FyajAUenct/YjsxYcNfdd1e90HLx4kUQQlCXwyOu1t6Ca9eu6dJ+5Gbn6NGjiAsCdubYZgeS4s6rr76qqM0bN24gEgmv8uOXqbE24uLFS7qNBpQnteJyoa9iJ7mSJGFyalqTtoC0aGGTPkXAWCwGI5+cQOuhcFkikUA0EgH44vy4icGMWDRSks/EMAze/ra3gx1nUWgYPjfAYXPPZmzduhUAYLFaEWeKH+LEAVir/N6TjaXlhbvQUvELeIlEAov+5GLB1JS2Ua96yYRJR6/Xbb1isVi0KbwrlLemwMDAAFxWL7g0KwX3su2DkuKelSAYDGJxYT4lCkoWDxYX5lXX3rh+rQ/t9hVbDyMHNNkZ1Z9ftkAhFgIwAGthdWUJMjQ0nFrUYRkWTmtNVUQzywtfHKdMzNu8eTMmggRxlUlJstVPKSP5z58/DyfHIbcT/WocYODluJzWp5FIBNf6r2X17E4GXTXj3NlzhXW4BMjZBJJV2bcgb5dLqJY9+7e6c1+Ut7kF9Pb2VUVNimwMDgzCaVYm8vOcAQ6ru+yR/MFgEITXRuQnvAnBtOxHk8kEr8eVN6BSZi5uRHNzdp1C75w/fx6RSBjN7k15t21wdOPkiZO6mKNRKFpDRX5KxbBardh///04MmPJWhBmIsRiyM/gTW96c3k7VwIuXboEq8kOm8mddZs6RytESdS9V/vNyAvPP49WloUnR3EvBxh0siye/81vFLV57tw5sAwLr229yF9rb8XCwrxuI8TkSbdkr1v1dzHtxRMCXEYJYyPDxXYvJfITB9FlJH9S5E9Gzeth0U5eCCHFivx8MkqwEOG1kKjn3/md34HRaAQzUIB/dwDAFPCud74r9ZTZbEaCLd4DPMGyFSlUWg5CyxNBLbJ0ZmZmQAjQaBUxPantNU2PIr8e6wTko5ILE5qK/JbynY+9V3vhNK22ybAYHTAbrejr6ytbPwpBjrCXxT7J6l31fCHMz89jwRdAh2O1EtxujaG/T904NZXps5xURixE8+wftQSDQczMTq/y73YYa3HtWnGFhsuB0ow5mU2bNkEiwHhInWXPaJCD1+OG2+1Wtb8Szp05gzZRBKOg6G467aKIs6dPZ2/33DmIoogGZ2fWbeod7bhw8ULFMzEHBgbA8EYQ0/pabpkgJgcY3phzEe7IkSNocwIeU+57wi3eBARRxKlTpwrqs16Ix+MYHx+H06JM5AcAh9GLgYHy2jQthUIgGkXyE864zuKwsakZs9H8kh8hwEwYaGxs1KQvleDw4cOwmZ2KCi03uzchGovi3Llzpe8YhVJmqMhPqShvetObMRZE1uJPR6aMsFktuOeee8rcM+25ePESaqxNOQfiTkstDLwxb5opRVvm5uZw7Ngx3KpAjLxVknDm7FlFHq3Hj5+A19EMPsPgrc7RBpZhdesHKEf9SWbXqr/VMjY2BgDY7U1gfLx4EXBxcREMx4A4CGbn9SEQpBMMBmEx2sEwTNHfnRbIQqks0qtFXiQoRHgtRFy02+343bf8LvghXrEoyFxj4PK48GBa3RaLxYJEgaJHJhIMU5FCpaWGEIJgMADJZEcwGChaAJavh9s9AqampotuL11E18P5sxYlxaQpK5jNZm1EfrF8kfyxWAzXB67Da29e9TzDMPBYG3U7ThsYGADDciDLBUWJ2QWG5VSJ/P39SXG7c43I3+kU0H/9uqraFKlCh8siv2AWMKlx9o9a5IWbGtuKyOWxNaK/vx+JRPYipdWIXChzVGXx3dEQj00ljOIPh8Pou3YN3Sr27QIwODycNcvz8OHDcFjccJizR8c3ubshCELFBe6rvb0g1pr8RXdlGAbE6s26CJlIJHDk8KvY582/eNFoldDqAA4ePFhIl3XD0NAQRElUJPjKuK11uHbtWlkXxcOhkIZ2PcZke2k0NjVjPra+9sRalhIMYgIpuOiuXiCE4JVXDqHR2a1o0dNlqYXD4sYrr7xSht5RKOWFivyUinLHHXfA5bDjcAbLHkKAIzNWvOa1D1a9yCIIAnqvXkWNrTnndizDosbWhMuXL5epZxQAeOKJJ8AB2KNg21sAWFgWP/vZz3JuF4lEcOrUSTQ5M09RjLwJdc42HDp0qNDuloWlpSUwLAtwRjAsW7TQNjExAQbAzhoBS+FI0RY7i4uLYC0sYAIWF/QXyT81NQ2rwQmrya4L7+ZU5oMGdj1AYRZJhU6W3vnOd4LECJhhBZPaKMCNcHjXO9+1quCgxWKBFgm4cYaB1Zq/YFu1EQ6HkYjHIVm9SMTjRUfzT05OgmWA7Z4EwtFY0dH36f3Ro8hP07sLw2QyQRI0qGMglK/o7uXLlyEIAuoc68t+1tpbcfHiJV1mdAwODgI2DyDXLWBZwOpRVUS0t7cXdiOLOsvqY9flEBGLJ1TZ2IyPj4OzcYCsLduBkVH1hYG15Pz58zAZLatqMNTaW5BIxHWfYVvofdZqtaKhzosJlZH8ExETOrvUSPDKuHDhAkRJQpeKfeV9MkXoiqKIQ4deRZNzU04h0GGugctWW3EB8OLFS4jb6vJvmEbCWovzFzLbFZ04cQKhcAR3NSi7h91VF8HhQ69U5cL21atXwTJsQSJ/ja0JwWCgbMW2CSGIRSMgXH4RXlF7vDHZXtr1oKGhAXOx/IV055ej/RsaGjTpS7np7+/HzMw0Wt3K6oQwDIMm52a8cvAVVQvWFIqeoSI/paLwPI/XPPg6nJhdb9kzGOAwEyJ4wxveUJnOacjAwABi8di6iLBM1FibdO3VfrOxtLSEX/zsZ7hdkmBWkBJsBIM7JAm/fOqpFV/4DJw4cQLxeBwtnp6s2zS7NuPMmTO6FLEWFxfBGK0Aw4AxWnN+ViWMjo6i1sai1ZYcSI2PjxfVnt/vB0wATPoTAUVRxMjICBzmGthNNarEFa2RLY2IocjCu8uLBIVYJBUaAdnc3IzXvOY14K/zQJ7LIHOdgYE34G1ve9uq561WK6IaXEOj5OYsvJuqueFIRmwVO6GdnJyEx8KiySpp0l76IpIea27cbFG9pcZsNoOIBChW5xcBs6k8kfwnTpyAxWhbZd0i0+DsQCwWzen7XSl6+64hvsYWMm72oE+F5cylSxexyRlfF0Tc5RTAMlAVkDI0NATRniaoOICFuQVNbMOK5eSJk6izta0Sfz22BhgNZpzOYf9SrXR0dmFSYUHOdBISMBOS0NGRu7hlMZw5cwZOjkNt/k3X4QaDWo7LeMwuXLiAxcUFtNVsy9tOi2sLXnnlUMUE7qmpKSzMz0GyF2afIjoasDA/l/E+/Pzzz6PNCbTZlV2M72mMIxyN4ejRowX1QQ+cO3cOHnsjeFa5gJ6cpzM4f/586TqWRiwWAyFEO5Gf5UEIWRWIUF9fj8WIBCnPkFgW+evrlS+K6IlXXnkFJoMFdQ7lpbpbPVvg8/tocCXlpoOK/JSK8/rXvx5zYYKBwOqB5okZI9xOB/bs2VOZjmnIhQsXwLH8qhTgbNQ6WhEI+DE6OlqGnlGefPJJxKJR7C9gn3sBEEHAz3/+86zbvPrqq3Db6nKmA7d4tkAURRw/fryAdy8P4+PjkIxJcVMy2osW5UdGbqDJHEfjsshfbCE7n88HkRcBExCPxXUVZTQwMIBYLAqvvQkeayMuXrhY8UW7hYUFMBwPFDuR4IxgWA4LCwt5N41GksXa1Ig373nPeyAFJSDXz04A+EEeb/u9t8HhWO1X63A4EBFFSPlWCfIQlsSbUuSX/XrFmq5Vf6tlamoKtSYBtWYp9XcxyMU5YUh7rCOqsRBhoZ7dWpKy2CkyWI4RGVgt5cmseeWVQ2hwdmX83jzWBlhNDrz66qtl6YtSBEHA0NBgyodfRrLWYHBwoKDMA0EQcOniRfS41i9oWXigw5k5UjofV3qvQHKvCIzEnbxGy9ZAlcLn8+Fq71U0ulbHjrMMi3p7O44cPlKhnpWOtvYOTEULtwmZjbCQCNDauj7LRStOnTiBLhV+/DJdoohTJ06se/7Abw7AbnGjJkOtrLW012xHJBKumMCdLJDLQHQWFlktOhsBMOvOz0gkgsOvHsLddcrrDDRaJXS5CF54/vmC+lBpJEnC6VOnUWdTLvgCgIm3oMZeXzabplTNLjZ/pL0ilttJryVRX18PQQL88dzn0nyMBc9x8HgKKXWtH1568WU0ObvBssoXLr32FlhN9opn7FAoWkNFfkrFueWWW+By2HFqZmWgSQhwet6C++5/ADyv0Y2vgpw+dRq19mZwCm7itfYWsAx7U0YN6Q1JkvDEz3+O3YTAUcBEwgoGeyUJT/7iFxknzYQQHD92HA2O3InGVqMDHns9TmSYiFSa3r5rSCx7+ibMbvT2XSuqveHBATRbBZg5oNbKFi3yz8zOQDJLIOakQFBsYWAtOX36NHjOgBpbExoc7fAH/BWP5p+bmwNjsin3dc0Gw4Ax2xUVSpybnUm9d6Fs27YNt+y+BXx/9msmM8yAxAne8Y53rHvN7XaDACim5HECBAlCSlpYsFKcP38ejN0LYrKBsXuLjlqbmZqE15SA3UBg5JiiLapkYZ94CGbn9FdzY3p6OvW4GgX/cpOyvCrS3YYV2LLYZw0NDWFsbBStnsxp/wzDoMm1CS+9+HJBhcVLzcDAAIREApJ9dfaBZK+DkEgU5Mvf29uLcCSKHZ7MWSvb3VGcPnmioAXsiYkJLM4vgtSm7eMCWCNb8eKHR44cASFJL/a1NLs3o7evVzcFgrWiubkZM2HkjfBdy0yETe1fCvx+PwYGB1X58ct0AxibmFh1zCKRCF588UW0e7YrWvR0WrzwOprxq2d/VURP1HPq1Ckw9lqgUJtF3gzGXouTJ1cL1UePHkUsnsA9Cq16ZO6uj+Do0aNVda/r7e2FP+DPeD7no8HRhWPHjpfFwiU1h2Q0kuSW20nvuxyZv5Cn+O5ClEWt1wOWrT55cHh4GKNjIzmz5zORvJdvxssvvVzxYCwKRUuq7yym3HTwPI977tuP84srg5ipMIupJYL9+wuJr9YnsVgMp0+fRr2zU9H2Bs6IOkcrjhy5+aKG9MaVK1cwOz+P21TsexsAfzCYURwbHR2Fz+9DvbM9bzu1tjacPn1GRQ9Kh8/nw9joCCR7MnpIsjdgbHSkIIuWdGKxGCanZtCynKLfbIljeHhIdf8IIRgdGwXsSP5D8fY/WnL82HHUOdrAsTxqHa3gOUPFF3JmZ2chGbQRxyTemle4J4Rgdi4p1KYLooXwR+/6I5A5AmRKGiAAP8Dj/gfuz1gkTBbmizGAkPe9GUX+E6dOIW5Lnt9xWyNOFBm1Nj09Ba9ZAsMAXis0EfkZngGx61PkTy9qmK3AIWWFlI9+kSI/I5anRsaLL74Io8GMhhzjtvaabZibn9VVmv+ZM2fAcDykNR7ekq0WDMfjzBnlY40jR47AbmSwyZVZ6LrVm8CCz49r15QHABw5cgQMywDp3WMAsV7EoVcrW5/oxRdfQp2zBRbD+sytZvcmsCxXtQVIs9HU1ISESPJG+K5lNsKB41jU1qox08nPuXPnQICiRH45xCYZDZ/k5ZdfRiQaQWftLsXtdNbsxPETx8u+wEMIwYmTpxBzFGbVIxNzNOLkqVOrhMtXDh5Epwuotxa2MHlHfQIJQcCxY8dU9aUSvPrqqzAbrYpsctfS7N6EYDBQFju21CKxZiJ/8lzOJPLP5xP5Ywzqq9SP/+DBgzDwRjS6Ogvet9XTg5nZGd3XXaFQCoGK/BRdcNddd2E0ACxEkzeniwsG8ByHvXv3VrhnxXP8+HHE4jG0uDcr3qfJtRmnT5/Wndf4zcaFCxdgYlkUlsyZpAmAjeMyivzJgSEDb55Cy0Ayc2N2dkZXkejJBSYGoqsFACC4WwAwqheehoeHIRGS8uNvtQsY6FefGTA/P49QMATiIoAVYA1s0XYjWhGJRHDh4gU0ODoBABzLo87eiuPHKmvJNDU9A4HXRhwTDFZMTuUW7v1+P+IJASaWYGZanT/7vffei9r6WjADGQSIGUAKSHjHH6yP4ge0FfldLlcRreiPiYkJzM3MQHQmz2/R2Yy5mRnVPvqCIGB2fhF1y1Y9tcZEyvNfLSMjI2DtLGADJsYndFcU7fTpM2it2QqT0bJKRNIzlYxSS1leFVnKgEkwJbfPIoTgwG8OoNm1OWf2Za29FTazAy+88EJJ+1MIr7xyKFlnY61dActDdDbjlVeUCemEELz02xewrzYKNov+u80twGFi8OKLLyru36+f+zVIIwHWuMaRVoL+a/0YGalMAV6fz4dTp06i1b014+tG3oxGZyeeP1BdliX5qKtLrrYsxgqTAxZiDGprPOA4dUV783Hu3DnUcBzcKq16AMAGBg0ct+r6/NRTv0SjqwP2NTUrctHu3Q6O5fGrX5U3mn9wcBB+3yJElzpLJNHVAr9vEUNDyYCaRCKBY8eOYp9XuVWPTL1FQrsTOHz4sKq+lBtCCF5+6WU0OrvBqhDPa2xNsJocZVnUS0XNE40ywpbv8+nnpsvlgsloyCvyz8eMaGgsTXZOqXnpxZfQ6OxW5JiwljpHG8xG6023iEvZ2FCRn6ILbrstGUvd60tenK8u8ti+fVtZIrZKzXO/fg4eewOcFm/+jZdpq9kKURTx0ksvlbBnlOHhYdQzDFgVEwkGDBokguHh4XWvXbhwAR57HYy8KW87tfak0FauIk9K+PVzz4G4mkCMy+efwQriasJzv/mNqvZ6e3vBMkC7IynUdTsETM/Oq84MuHTpUvKBBwCTtPS4eEkfBRCPHz8OQRBWpQg3urpx/sL5ii7aTc/MQDLaNGlLMlrzRmrL0ftb3AKmVfqzcxyHt7/t7eBGuXXiIDvEorWtFbfeemvGfWWP/sKnsyvI+zqdziJa0R+nT58GGGbZt3fZv5dhVHvQDg8PQ5IkNNuSk9QWq4CBfvXR7ZIk4ejxo0h4EyC1BLFobOWc1wHhcBjXrvWhwdmBOlsrzugsE0uPpGpmFFk6hcTJuvobWtPb24vJqUm012zPuR3DMGhxbcWLv32pIK/7UjExMYHLVy4jUZPZJjDh6cTlK5cVLcBduHAB45NTuLcxu60HxwJ31UVw4LlfKfr8V65cQf+1foid6xfsSDMBa2Zz1jkqJb/97W8BgpzFWNtrtuNq71Xd1stSU3NDjsRfzCP+rWUxyqK2rnTFOS+cPYsODRZ2O0QRF5ZF/oGBAVy5chldtZnHDNkwcCa0ebbhqad+Wdbz/NSpU8msHIe6yGrJ0QiG43Hy5EkAyeCjcCSKPbXqVlr31ERw/OgR3S24Z2JoaAhj42NoLdC6RYZhGDS7NuPllw+WfHHcYFhe8dRM5E+2k251zDAMGurrMJfnPJ+LcWhsVJc5UknGx8cxODSo+nizDIsm5ya8/FLpjzeFUi6oyE/RBR6PB63NTej38SAE6A+YsfvWPZXuVtHMzs7iyNEj6KxRnhoKABajHU2ubjz5xJP0hlNCxkdH4SliwOohEsbXRJ5JkoQTx08oLvZkMdrhttVW3M5FZnh4GOfPnUOsdvVgKVbbg/PnzmVc1MjH2bNn0eUiMC0HlvS4hdTzajh8+DA4Fwcsr0GI9SJOnTq1UsCqgjz36+dQY29cVXC5taYHkkTwfAULl/kWF0A0sushBmveBRrZPmlXTQJz84uqCyO/+c1vBpEImLE0ASMBsBMsfvctv5tV2LDZkgsaxWiK8r43w2JzOi+++BKIsxGQFyF5E4izES++qG5R+fjx4zByDLqcyfN6q0fA5PSsajHs1KlTmJuZA2kngBdgHSyefuZpVW2VgmvXroEQAq+tGTW2ZvRdu6YrX3Y9IhfyY2JF1ASRACkqlbwo4AsvvACLya7Ibq/dux3+gK8gG5xS8cQTT4DlTRBqMpucCDXdYHkjfvGLX+Rv6xc/R6ONwXZPblHzwZY45hf9iiIgH33sUbBOFsgUKMoBQreAp595uuxZjYQQPPP0M2hyd8Oc4x7Z7NkMk8Fc9ohupcgZZ4VknrndbrAMA1+Bdj2+BFcykT8ajWJgeFhVhu1a2gCMTkwgGAziySefhNXkKCirWmZT/R7Mzc2W1a7m2LHjEB2N6guysjxERyOOL88tjh07BreZRYdD3ZxnT20CgaVQVViaHDp0CAbeiAZnh+o2WjxbMD8/V5AdmRrk8SUjFlYnIRtyO/IYWKaxuQVz0eyZN4IELISlqhT5X3nlFfAcjyZX5gVuJbR6ejAxOa5qjkuh6BEq8lN0w/aduzC0ZIIvzsAXlbB9e+4oqmrgqaeeAsfy6KzdWfC+m+r24PrAdV1FMN5MEEIwMjIC5fkV6/ECGBsfXyXwXLhwAfML8wUV/2lybsYrB19RLYRqyQ9/9COwJhvENdGAYk0XWJMNP/zRjwpqTxAEnDx+DLs8KwJ8jZmg1QEcO3a04P4tLi7ity/+Fom2lWgk0k4Qi8XwG5WZBloxMTGBo8eOosu7e9XzFoMdLe7N+PnPf1GRRbtoNIpEPA5iKLB4WxaIwYxEPJZzUaWvrw8eC4NtHgESIartlOrr63HrrbeCHV0ZrjATDIhI8IY3vCHrfkZjspB7MXF38r5yWzcD4+PjOH3mNOLe1QVF4zWbcfrM6YJrW0iShF898zRuq42lFvF2exOwGhg8++yzqvr4xJNPgHWzyQssAwidAl588UX4fD5V7WmN/B05zB44LTWIxaJYWMhUOEJfqIn01Qqj0Qi7w65Jao1sMVIKBEHAgQPPo9W9VZHNg8faAJfVi+eee65kfVLCwsICfvHEk4jWbQO4LKIgxyNatx1PPPlUzkXasbExvPzyQby5NZTVqkemwyFip1fEf/7ohznvbadOncLxY8ch7BCQLXGS9BBInITvfOc7ud9UY65evYqBwQF054nw5lkD2jw78MwzzyIe10aQ05J7770XX/ziF3HPPfco3ofjOLhdDvjjhckBAcFQssW2wcHBZGaYBm3JbVy+fBkHfnMAHTW7wK61slJAja0RXkcTnnrqKQ16lZ9YLIbzF84j4SzuW0g4W3Du3DnEYjEcO3IYt3giec/pbGxyirAZmarw5T98+DAaHJ2qrFtk6uytMBnMJbcoMhgMMJnMgKBRkJIQhclsXhXJDwDNzS2YiWUfy85FWRCUrph2KTn86mHUO9rBc+rH6vXOdhh4Y9VYUlEo+aAiP0U3bNmyBaNLLG4EkwOwzZsLj7YoNdu2bUNnh7LIgGg0iid+8QQ6vbfAwOW3bVlLo6sLLqsXjz/+44L3peRncnIS/mCwqIlEC4BILLZq5f+JXzwBp7UmZcOjhM7aXQiFQ8mU8QoyMDCA5w8cQKRxdwZPXw6Rxt14/sABDAwMKG7z1KlTCIbCuKNudYrwvtoIXn2l8IWNxx57DBIjgWxKExRsANqARx59BJFIMSpScTz99NMw8iZ0eHese21z/V6Mjo7g3LlzZe9X6jvhDLk3VMryQDrbd00IwaGDL2OXO4pOR3JieOiQ+qKKr33Na8HMMinLHnacxdZtW1PFxEpNJcVRrXniiSfAGkwQvKujfQXvJrAGE5544omC2jt+/DhGxyfwpraVCaqJA17TFMHTTz1Z8Pk4NzeHI0eOQOhaEQNJF4Eoijhw4EBBbZWK6elpWE12cCwPqzFp5aS2nsFGorGxEVgqooHlIhkNJSwMePjwYfj9PnQpLMzJMAw6anbi4MsH4ff7S9avfPzbv/0bBAIkmnL3O9G0CyJJbp+NRx95BC4T8JpmZffmt3WE0X99IOs1PhKJ4J8f+mcw9QxIa45FbgMg7BTwm9/8JmkpViZ+/PiP4bTWoFFBFOjm+r3w+30VzcrLBsdxuP/++9eJe/nweDwFF971xxjU1NQUtI9S5PG0Fnd3LwCOYfD8888jEo2iu+4W1W11eXfjxIkTRReVV8L58+chJBKq/fhlRFcLhEQCL7/8MoZHRlVb9QBJe65bPDEcPfJqUX0qNcFgEH19fYrO51ywLIc6eztOHC99lnVNbS3YeDEVpFZgYyF4vesLYre2tmI6BEhZLsFT4aQk2NKifO6qB5aWlnDp8iU0ujYV1Q7H8qh3tOPokcKDzygUPUJFfopu6OjoQEwguLRggMlo0GXK2Ne+9jV845vfVLTtgQMHEFxawpb621S9F8Mw2FR3G1599VDRRQwp6zl69Cg4hkFnEW20ATAwbCqyZWxsDC8ffBmb624rSBh0mD1o9mzCj374o4r5XYqiiK/+8/8BLC4I9Zl9aYX6bYDFhX/+Pw8p7ucvn3oSbQ6sSxHe3xjHUjhSUN2JkZERPPnUkxC2CsCagA1xlwif34cf/7gyi2KSJOG5Xz+HNs928BnE9DpHG5zWmopEfKZ8ZFUUIMvIcjvZ/GmPHTuGsYlJvKY5Dp4F9jdE8PQvn0I4HFb1dvfddx+IRIAZABLAzrK4f//9OfeRIy3Vx3Gt7KvHqE01xONxPPurXyFWs2W9BQDHI+rdgmd/9auCPu9Pf/JjdLsItrhWn99vaoshFA4XLMwfOHAgWWejPW0makp6dj/9zNO6sK+7cvkKnObkJNphrgHLclVhYVBpujq7wC+pPyOZIAOWY0sqQvz48R+jztkKt1W5xNhZewsIIXjyySdL1q9cXLx4Ec/+6leINt8G8HmytXgzIs234Zlnn8XFi+vr2PT19eGF376A3+9cglFhwPOOGgG7vCK+8+2HM94TvvXtb2F6ehrCbdmj+GVIFwFTz+CLX/piWWrYDA8PL4/Z9ikaszktNWjxbMYPvv8DXdRh0IIaby0CBRTelQgQjJXONmtqagpOjoOhiKK7MhwYuDkO586dR6OrAzaTciujtbTVbAPH8mVZbD579ixYkxXEUtx3TCwesCYrnn/++aRI7y2u8vme2gSu9Q9gfn6+qHZKyaVLl0AIQb0jv91aPuqd7ei71lfyLOu2lhawMW2ud2wsiLbW9YtDbW1tiIsEC1ks8yZDHExGQ9mCZ7Ti4sWLkCSpKGsmmXpHB65cvaKLrHoKpVioyE/RDW1tSQfG83MGNDc1rlSc1xEOhwNutzvvdoQQ/OLnv0CLexPs5vzbZ6PTuxNG3oRf/vKXqtugZObgSy+hG4C5iImEAQw2E4KXX3wRAPD9738fFqMdXbWFRwttb7wbo2OjePnll1X3pxh++MMforf3KsKd+9dH8cuwHMKd+3H16hX88Ic/zNtmX18fXj18BP+/1hDWzp+bbBL21An4/mOPIpFQNvH4t3/7NzAWBmRLBqHPBoibRPzoP39UEVsP2aYpW+G+ZJHGnorYMqUKe0kaiRLL7WSysREEAd/51sPo8UjYulx74Xc6oohGwvhRgVZPMo2NjahvrAczwwCLgBSXUsXasyEvKBSeQ7WCvK/axQm9cfDgQYSWlpCo35rxdaFuK0JLS4r8tYGkQHbq9Bm8qTW87vyus0jYW5fAz37yY8V+9YIg4BdP/AJSi7R+Ea9LxI3hGxUvUD4xMYFTp0+hednXmWN5NLq68MunflkVBQkryaZNmwA/ALXrNItAW3tbwZHKSjl9+jQuXb6ErQ13FLSf2WBFh3cXfvLjnyAU0iYaUynRaBRf/NKXAXsdhAZlFpdCw3bAXocvfunLqyzXCCH4xr/+X7TYgdc2F7aw+Z7NIYyPT6zz+z98+DCefOJJiLtFQEm9ZAYQbhew4F/AQw89VPJFvYcf/hZsZldBY7YdzfdiYnISTz+tnzohxVBbV49FQXmWnz/OQCIrRXu1JhgMwqph9pxJIpidnUGbJ3tRZSUYOCOaXN2qa9cUwvkLFxG3NWDdjbVQGAZxWz2uXr2KHrcEa5GXzt3eBBhANzXEMtHX1wez0VrUgo5Mja0JoihicHBQg55lp7u7C4aoT5O2DLFFdHV1rnu+Y9mFYCKUeX43EebQ2tKsS+0lF5cvX4bFZIPd5C66rVp7C0RRLHkdBgqlHFTXmVwiRkZG8JGPfATbt2+HzWZDTU0N7rzzTjz00EOaTu4ff/xxvPnNb0ZTUxPMZjM6OzvxJ3/yJ1Xhb1cO6uvrwTDAZJhDY3N1pYutZWBgAINDg+iq251/4xzwnAFtnu349a+fo4X9NMTv9+PCxYvYocEEcicIeq9dw8WLF3HgwAH0NNyhygfSa29Gk7sLjz7yWNmP9cWLF/HII48g3rwHkiN3Bo3kaES8eQ8eeeSRjJGAMvF4HP/8lS+jzQHsb8osGLxrUwgTk5P4/ve/n7ePQ0NDOHjwIIRtApBlDYJsI0gICfz0pz/N257W/OxnP4PD4slp09RZuxOhcKjsqf4OhwMcx4GJa2NlxMTD4DgODsd61eaxxx7D8I0b+JMtS6n5qddM8LvtYfznj36EK1euqHrPvbfuBb/Ig5lnwBt4bNuWe8IuR4FaVL0bVu0bCASKaEUfCIKAx/7j+5DcrSAWd8ZtiMUNyd2Kx/7jPxRFqT7yve/Ba2Vwd0Pm8/st7RHcGB3Di8uLoPl4/vnnMTM9A2lrhutfA8C6WTzy2COK2ioFhBB8/ev/AovBhk7vii3K9sa7cGPkhqKCpnqgUvZTO3bsgJSQAJ+6/Q2LBuzeVdyYKhuSJOFbD38LXkcTmlSk/W9vuhuRSAT/+Z//WYLeZefhhx/G5NQUwl0PKM/UYliEux7A5NQUHn744dTTL7zwAi5euoz3bg6CK3B22O4Q8WBLFI98799T9Snm5ubwxS99EWjGanu9fNgA4TYBL730En79618X1pECOHLkCI4dO4pbmh8AV4BPu8fagM7anfjud/8tbwH6aqC2thaLMeWffyHKpvYrBaIogtFwbSdGkveTJndxdh4A0OzejOvX+0t63AkhuH69H5JNm+9XsngRWgpip7v44BKnkaDdlcw00CsjIyNwmr2a3Oec5ppUm6Vk69atkCIBIFHkGD0RgRQJZhwfNzY2wmwyYnQp87k+GjKie7PyWnJ6YWxsDA5TjSbH22GpSbVJoVQ7G17kf/bZZ7F792587WtfQ29vL8LhMBYXF3Hy5El89KMfxW233Vb0Cm40GsVb3/pWvPvd78aBAwcwNTWFWCyGGzdu4Ic//CHuu+8+/OM//qNGn6h6MRqNcDuTolF9fek8V8vB4cOHYTSY0eDsLLqttpqtWFxcoCvLGnL27FlIhECL4YxcvvK73/0uTAYruotY2NnRdC9GRm/glVde0aBnygiHw/jc578AydGARMteRfskWvZCcjTgc5//QsaFUEIIvvGNf8XQ0BD+x/ZAVsGgzS7h7V0RfP/7/5F3sfM73/0OWDsL0plj9mcChE0CfvLTn2Bubk7RZ9GCQ4cO4ZVXXsH2xrtzDjQd5hq0e7fjWw9/qyy+rjIcx6GhsQlsRJuJKRvxJdtbE/Fz9OhR/OAH38d/7Yqgy7k6qvltXVF0OkV85h/+XtUEeevWrSA+AiwAXd1deaN5ZZG/mFLDsshfDtuIUvPoo49idHQEsdbbc24Xa70do6OjePTRR3Nud+TIEbx88CDe2bUEPsv53eMWsa8+gf/vm9/Im10Tj8fxb//+b0ArAHeGDRggsSOBc2fOldWvO52f/exnOHHiOPa2v3GVJZfX3owt9bfh29/6Nvr7+yvSt0KolOXRjh07YDQZwUyrmIyHAdEn5s3gUcsLL7yAa/3XsLvltarEAqvRgS31t+Pxx3+M6enpEvRwPUePHsWTTz6JaOudWRfuskEsbkRb78STTz6JY8eOIRaL4dsP/3+4vT6BXV51GV/v3BQFK8bwyCPfAyEEX/rylxAWwxBvF/Pa9KzrXxsB6ST4+r98vSR2leFwGF976OtodHWi1VP4SHB362sgxEV88xvK7Dv1TFNTExYjEhIKY0tml0X+UhXodLlcCGu4DhlCUrwzG6xFt1VrT9qgXL58uei2srG0tIRoJALJpCT1JT+EM0AiwCaXNpmcm+wxXOtVF6xRDnyLPhi5YsI7VuA5Iwy8seTZwbt2JYMGuEBxtX3k/eX20mFZFp2dHRgNrhf5JQKMLbHo7u5e95remZ9fgIm3adIWzxpgMlhSC9UUSjWzoUX+8+fP4w//8A/h9/tht9vxxS9+EUeOHMFvf/tb/MVf/AWAZNrXW97yFiwtqa8W9ud//ud45plnAAAPPvggnnzySZw4cQLf+973sGnTJkiShM985jP493//d00+VzUjezx6vd4K96Q4rl27Bo+1oaDooGzU2JrAMCz6+vo06BkFSGZaODgOTg08P61g4OE4XLx4EZvrbgPPqi9u6rU3o8HZjsf/3+NF90sp3/72dzA3v4BIgZGAka4HMDe/gG9/+zvrXn788cfxy18+jT/burRO7F3L27qi2FMr4HOf+XTWhawDBw7gyOEjEHYJee9aZBtBAgl85atfKUtGxMWLF/GFL/wjWmt60OHdmXf7PW2vgyQw+MiHP1JWW6G9e26FMaSN+GQMTWPvnltXPdfb24vPffYzuK02gd/riq7bh2eBv93lR2xpAZ/42EcLzpLr7u4GEQnYSRZbNm3Ju71sQ7HeUEg58pmcbmlRjRw4cAA/+MEPEG/Zlzc6ULLVIt6yDz/4wQ+yeg+Pjo7ii//0j9hbJ+C+xty2Hn/aE0I87MfnP/fZnNkBTz/9NObm5iDuzHG9aAZYL4tvf+fbZc92unz5Mh5++GH0NOxDc4aI0N1tr4HdXINP/8OnixovlpJdu3bBbDZnFADKgdFoxL59+8BNFj4uYiYYsCyLO+4ozEpHCYlEAt/9znfR6tmCOof6Qpfbmu4CzxjyLpBpQTgcxle++s+Q3G2KbXrWIjRsh+Ruw5e/8lX85Cc/wcLiIv5os/rsZbuB4Pc6QnjmmWfw4x//GKdOnkLitoRqzzRpjwSBF/DQ1x5S3adsPPLII1hcXMRtHW9UtahjNlhxa+tr8cJvX9C1dYkSWlpaIBFgNqJs/Dcd5uCwWzNm8mnVH78gIKza12uFGAiihMBh0qZ+gNXoAMtyJV3IS42NuGJGL2ksZzLUmrW5Z9ZaRMyWMYimYEqQqFbq7LeGhga0tXeA840W1Q7nG0VbR2dWX/0tPdtwI7z+gjwTYREVCLZsyT+21hs8z4MQ7caDEhFXLE4plCpmQ4v8f/d3f4dwOAyeTxbS+dSnPoV77rkHr3vd6/Dd734X//zP/wwgKV58/etfV/UeBw8eTKXvvvWtb8Xzzz+Pt73tbbjjjjvw3//7f8exY8fQ3p4sDvOxj32sIl7SesLtSaZKuVzFe+lVkkAgCBNXfNQIkPT8NfIm3QoH1Yjf74ddy5GgJIEQFBXFL9NdtwdXrl4peXookMxoePLJJxBt3Qdidha0LzE7EW3dhyeffGJV6u4zzzyDb33rW3hbZwSvbcnv68sywF/tDKLRFMVHPvyhdZ/7xIkT+MpXvwLSQUBaFUz6jEDi9gROHD+Br339ayUVA8+ePYsPf/jDcJnqcGfn7yiaCJgNVuzf/AeYmpzF3/7N35atgNm9994LEloAEy4uQoUJL4CEFnDvvfemnrt8+TI+/KG/Q7M5ir/ctQQ2y9fgNRN8ZLcfw4P9+OhHPlxQhHyrXEhMWKnfkgs50r8Yl3R532oe8B86dAhf/NKXINT1INF8a/4dACSab4VQ14MvfulLOHTo0KrXxsfH8aG/+1s4EML/3LGU1zK4xkzwNzsDOH/uHD7/uc9mrL8xPj6O7/7bd5NZOrkuQwyQ2JVAX29fWa1xZmdn8Q9//w/wWBuxu/U1GbfhWB73dL0Vc3ML+MLnv6DLopydnZ34zW9+g87Ozor14Y1veCPIHAEKHM7wozzuuPMOOJ2F3aeUcPDgQczMzmBny/6i2jFwRvQ03IHfPPebktu4/Od//id8fj+infeq9+1mGEQ778Wiz4cf/7//xF31MTRai7tfvqE1BjNH8Nh/PAa0AGgqojEDkNidwKmTp3Du3Lmi+pXOxMQEfv6zn2Nb491F+Th3eHeiztmKb37jm7ooCK4W2at7LIuNx1rGljh0tBdf5DIbe/bsAQBo4YI+vPw/ozSARQEsw5a0/orFshyFLhZWFyMryzWUJI1+opLEgCmFkq4RXq8XEUGb+XJMiCAhxEtWZDqdB+7fD1NgFJBU/rYkESb/KB7Yf1/WTXp6ejAeBOJr3mJ4Obq/p6f67HoaGuoRSvg1aSsSX0JCiKOurk6T9iiUSrJhRf6TJ0+mClz++Z//Oe65555123z4wx/G9u3JCJn/+3//r+LikOnICwUcx+Hhhx8Gx60eRNXW1uKrX/0qAGBxcRHf+973Cn6PmwnekIxcKMVErpx4a2oQEbSxeEiIccQS0bIMMjYKFosFWpY+DSNZsMfEF58i2uTqBsMwmk5qM7G0tIQv/OM/gTibIDTkj0DPhNCwE8TZhC/84z9haWkJBw4cwP/5P/+M17dG8Y5NyqOfzTzw0Vv9sEtB/N3f/g3Gx8cBJMWXj3/i4xBqBUj7JOUROk2AdLuEp3/5NL74pS+WRHA7ffo0PvKRj8JlasD+zf91lX1HPlyWWry2512YmZrHX//135TFWujuu++Gw+mCYbq4NGvD9BU4nC7cfffdAJI2Fx/8279Bs3EJH7vVD3MenaDLKeLje/wYunYF//MDf4HRUWWRS+n+v42NuetGAEBNTXLBuBg3fXnaILdVbZw4cQKf+exnIbg7EOvar1wMZBjEuvZDcHfgM5/9bCpSta+vD3/5Pz8ALjKPj+/xwWZQphrsqBHwN7cEcfjVV/Gxj35kVXHSGzdu4G8/+LeI8TFIuxUIjPWAtEXCN7/5zbIUpJ+ensbfffDvEA0ncE/374HNkZ1nN3twV9fv4viJE/inf/onxOMaiTQaUik/fpn9+/fDYrOAGSygHz5AmpPwlt95S0n6dOTIEXgdTXBZivfA7vTuhCiJOHnypAY9y4wgCHjql08jXtsDUqSlBzE5kPB0IrAUwn2NxY+KjBywxRlHOBSGuFUDIbQF4JwcfvOb3xTf1jLPPPMMeM6AnsZ9RbXDMAx2Nt2HGyOVLwheDDU1NahxO3Ejg41HJkYiJmzZWlwR21w0NjZi+9atOK2BMH+aYWC32RDWSASMCiEIYqJk9QiAZA0lp8sNNjSrSXvMss/7YECbguVDQR7tHe2atFUKdu3ahYWlKcSF4jMwZwIjqTZLzZve9CZI8ajqaH7ONwopEcWb3vSmrNts3boVEgFG1izoDQV41Nd64Xa7Vb13Jbn11luxuDSDSLz4hZ3pwDAAYPfu0tT+oVDKyYYV+Z988snU4/e9730Zt2FZFv/tv/03AEkBXl4UUMrS0hJ++9vfAgDe+MY3rkQiruG//tf/mhK1q6VwW6mxWrWJgq8Ue/buwVxwAksxX9FtjS70gmGSNzKKNmzduhULooB5DdKB/SCIE2gi8APJiFCeM6wSwkrBd77zHSz4/Ih0P1BUJGCk+wEs+Pz4/Oc/jy998Yu4vzGGP90aKbhJh5HgE3t84GOL+NAH/xZPPfUUPvOZz0BsEiHeK2YttpsN0kUg3S3hhRdewGc++xlNI69GR0fxqU9+Cl5ry7LAX3hatdPixWt73gXfnB8f//gnSh75azAY8MfveTeMc/1gouoWIJloEMa5fvzxe94NlmXx8MMP4wtf+ALurA3j43v8ikXfzS4Rn7vdB9E3gb94/5/j6NGjefdJ9/9XYufW3NwMm8WSiuRTwzAAo8GQinSsJvr6+vDJT/09Eo5mRDe9VrkVlwzDIrrptUg4mvHJT/09nn76afzNX/8VvIwfn77NB6+5sGvnvroEPrYngKsXz+Fv//qvEAgE8Nvf/hbv/x/vx3xsHon7E4q9lcitBGK3iIceeghf+cpXSmandPjwYfz39/13LM4F8cCWd8JitOfdp8nVhXu6fxcHXz6I//WX/0vxItZGwWw24/d+9/fAD/OAwrgZpp+Bx+vB/v3FRdpnw+/3w8xp4+lr5C3gOL6kWbk+nw9+3yJEV/Yi74UgWZIBJLUWbbLeOPner8XaKAMkPAkMDmsR152kr68PXltrUdaKMnWONvCcoeprZm3bsRPXg/kvwKEEg4kAyVv4vlj+4J3vxHUiYaSIMfokCHoJwWsffBCLSzMIRovPrhlbuAaWZUtWGwRILh49cP9+mBeH1Ed1y0gizIFReGvceGHCUnQ0/0yYxfl5I1774OuLa6iE3H///eA5DtdniisOTAjB9dmz2L59O5qaiklJUkZXVxc2b+mBcbZX1f7G2V5s3tKDrq6urNt0d3eD57h1Cz5DQQO2bt+h6n0rzf79+2Exm3FturhaTYQQ9M+cxr59+6reMppCATawyC+noNtsNuzblz2a4zWvWUnNfvXVVwt6jxMnTiAWi61rZy1GozEVFXnixAlVGQM3C3KUWbWL/G9+85vhdDpxbuTFotJ4o4kQrkwexgP3P4CWFm0mdJSkdYnH5cJzYCAVMYkgIHgODAwGHnOhMW0iR4IjSAjxknojxmIxPPPMM4g17tYkEjBWvxMnTxzDrbUJvH9HOKtdSz7cJoJP7PUh5J/F17/+dUhtEsS7RNV3KtJGINwj4PCrh/GTn/xEXSMZePTRR8HBhHs2/R44Vn10lN3swT2bfh/9/f2pBeFS8va3vx1utxvmkaNAodclQmAeOQq3243f//3fx5e//CX85MeP4709YXxgRwjGAhdhGq0SPnf7Irbbg/jkJz5R0CK6kqwmjuPwmgcfxDmOg6DiHBdBcI7jsH///qqz65EkCV/4x39CwuhEZNPrALW1YVgOkc2vQ8Jgx7987SF02yL41F4fnEZ118wdNQL+4TY/JkYG8Zd/+T/x+c9/HrHaGBIPJoBCbvkMQG4jkG6X8OsDv8b/+MD/0DQbJpFI4KH/8xA++clPwmFowOu3vxcOs3LFsrVmKx7c9h5Mjs3hfe97H371q19p1rebgXe84x1gBEZZNH8I4EY4vOeP3pO32LZaenp6MBcaQ0yIFN3WVGAYoiiU9P4tX4/kCF2tyFSQUQ2BxPJxVW/vvwouzMHtcmvTGJILTXFRm87FhAhESYTJpLLwgE649dY96PfxEPKs8/T5eBCUPujo9a9/PTZ1deHXLKtqjC6B4Fcsi7bWVvzlX/4lPG4PLo0fyr9jDhJiDH3Tx/HAA68pecTzu971LpBYCIbJC0W1Y5i8ABIL4QP/839hwMfit2Pqf6cSAR7ps8Pj8eCtb31rUf0qJTU1NXjr770VfdMnigq0G5m/gtnAKP7sz/5Ms77l4w/f+Q6wvjEwEV9B+zGRRbC+MbzrD9+Zczuj0Yju7k4MBVau9RJJZmfIzhXVhsPhwB+84w8wMHsWwah6O9KhuYtYDM3gT//0TzXsHYVSOUozYq4Crl69CgDYvHlzzolDerSCvE+h77G2nWzvc+DAAQiCgP7+fuzYoXxFdWxsLOfr6ZFkk5PFVW4vNcFgELFYDD6fL+/n0jvvf/+f40tf+hJYyYBtTXcXnCYviAmcGHoWIpvAH77rDwv6Pqanp+H3+zExMVFS78hq5k/f976UVdYbgII9JgkIXgZwGsD7/+T9+OEPfoiXrvwYt3e8GaxK4TccC+DY4FPo6EwWTlJ6zAs93tFoFLFYHHHfNBKOefWR/EBSLF4YBYnGsc+9hOlA8b83DxPEXNQIsU0EfEU2ZgEEq4Dfvvhb3H///UX3DQBGRkYgJQiCIV/RXq/RRBRCIoGBgYGSHe903vvH78FDDz0E0VwH0aM8Qp1bvIHEVD/e+5GP4PTp03j66WfwB90R3OqKY6oIT5w/7FjE7JINX3/on7F58+ac28qL5oFAQNF39cADD+DJJ5/EAUJwe4Hn9zkQjAP44OteV/F7UaHHOxgM4nr/NcRbb4cQLsawKInEOWGMTOCOrhAWlorLODEgAS/Hov/6AKSdUtKHX22WtRvAPqD/eD8+/4XP4+Mf+3hRfZP5j8f+A8888wx2Nu9Hu3c7wpEQwigss4oBjztafweXJg7j85//PCRJSnlN52Mj3L/vuecevHT4JSS8iZxZWsxFBkbOiNtuu61k5+E999yDxx9/HC9efhx3dPwXcJy6+3co5sPRwV9i0+ZNqK2tLen1fOeuXTh7+QTiBjfAF1GgU4jDMH4BBo7D430GNJnCMBZxSxtZYnF1VgJnkCAcFSDuK+73y0wyEMYE7HvXPs2O/549e/DCCy/gyugxNLlz33NyQQjB+bGXISQS2Lx5c1nu36WiubkZwXAUh0cl9Liz9+nwGAe7zQpRFEt+X/zjP/kT/MM//AOeIQR3F3j/Pg2CPgCffe974ff78Yfv+kN84xvfgJ33orVma8F9IYTg3MhvEYoE8fa3/37J52Mcx+G//Jf/gieffAoCY4bkaCi4z2xgGobBY/idt/8+du3ahde97nX499/8GgZRwlZPYb87QoAnhsw4OyniHz79F2WrJaWW3/md38HzB57HwSs/xV3dby04GGcpuoij15/FXffciba2trLNv3t6emA0mbE0cBSJjrsU72e4cRxGkxlbtmzJ29em5lZcOHwVk/5kQOl0hIFvKQq3213xsa5aHnzwQTzzzLM4ePVnuKf7bTltFTMRivlx/Pqv8cBr7i/o3p2uqemxDhNlY8OQaq4WpJJoNJoqbPOWt7wFzzzzTM7t7XY7QqEQ7r77bkW2AjKf+MQnUiLiyZMncfvtt2fd9qGHHsJHP/pRAMBzzz2HN7/5zYrfp9IeqxQKhUKhUCgUCoVCoVAoFMpG4cSJE7jjjjsq3Q0KJcWGtOsJBlf8iO32/B6rNlvSq3NpqbBws0LeR34PNe9DoVAoFAqFQqFQKBQKhUKhUCiUjcmGtOtJL9JmNOZPc5W9FiORwvwvC3mfdD/HQt8nX2G3aDSK3t5eNDQ0oK6urmS+pnpjcnISd955J4DkCms5CudQKgc93hsLerw3FvR4byzo8d5Y0OO9saDHe2NBj/fGgh7vjcVGPd6CIGB2dhYAcMstt1S4NxTKajaG2rsGs9mcehyPx/NuL/sAyxY/pXgf+T3UvE9ra2vebfJ5Hd/sNDU1KfqeKDcH9HhvLOjx3ljQ472xoMd7Y0GP98aCHu+NBT3eGwt6vDcWG+14d3Z2VroLFEpGNqRdj8PhSD1WYo0TCiULrimx9lH7PvJ7qHkfCoVCoVAoFAqFQqFQKBQKhUKhbEw2pMhvNptRW1sLAHkraC8uLqYE+La2toLeJ30lM9/7pFvuFPo+FAqFQqFQKBQKhUKhUCgUCoVC2ZhsSJEfALZv3w4AuH79OgRByLpdb2/vun2UsmPHjozt5Hofnuc3vLUOhUKhUCgUCoVCoVAoFAqFQqFQlLFhRf79+/cDSNrknD59Out2Bw8eTD2+7777CnqPO+64I1VwN72dtcTjcRw7dmzdPhQKhUKhUCgUCoVCoVAoFAqFQqHkYsOK/L//+7+fevzoo49m3EaSJHz/+98HALjdbjz44IMFvYfD4cDrX/96AMALL7yQ1bLnF7/4BQKBAADg7W9/e0HvQaFQKBQKhUKhUCgUCoVCoVAolI3LhhX577zzTtx///0AgO9973s4evToum2+9rWv4erVqwCAD37wgzAYDKtef+yxx8AwDBiGwec+97mM7/ORj3wEACAIAv7qr/4Koiiuen1ubg4f//jHASQXEt7//vcX9bkoFAqFQqFQKBQKhUKhUCgUCoWycdiwIj8A/Ou//issFgsEQcCb3vQmfPnLX8axY8fw0ksv4QMf+AA+9rGPAQB6enrw4Q9/WNV7vO51r8Mf/dEfAQB++ctf4o1vfCN++ctf4tSpU3j00Udx9913Y2RkBADwla98BR6PR5sPR6FQKBQKhUKhUCgUCoVCoVAolJsehhBCKt2JSvL000/jve99b8ouZy09PT149tlnMxbDfeyxx/C+970PAPDZz342azR/JBLBO97xDvzqV7/K+DrLsvj0pz+ddX8KhUKhUCgUCoVCoVAoFAqFQqFQMrGhI/kB4K1vfSsuXLiAD33oQ+jp6YHVaoXb7cbtt9+Or371qzh79mxGgb8QLBYLnn32WfzoRz/CG9/4RtTX18NoNKKtrQ3vec978Oqrr1KBn0KhUCgUCoVCoVAolP9/e3ceVVW5/3H8AyLgVRAnBHUpDuUUaDiVRUipqZg3h1JrOWCmt9TyNqh57Yp6Uyt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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 299,
"width": 764
}
},
"output_type": "display_data"
}
],
"source": [
"h_post = trace.posterior[\"H\"].to_dataframe().reset_index()\n",
"_, ax = plt.subplots(figsize=(8, 3))\n",
"sns.violinplot(\n",
" data=h_post,\n",
" x=\"spot\",\n",
" y=\"H\",\n",
" hue=\"clone\",\n",
" dodge=True,\n",
" scale=\"width\",\n",
" inner=None,\n",
" linewidth=0.5,\n",
" ax=ax,\n",
")\n",
"ax.set_ylim(0, 1)\n",
"for i in range(simulation.sim_data.S):\n",
" ax.axvline(i + 0.5, c=\"grey\", lw=0.5)\n",
"\n",
"ax.set_xlabel(\"spot\")\n",
"ax.set_ylabel(\"proportion of cells\")\n",
"ax.legend(loc=\"upper left\", bbox_to_anchor=(1, 1), title=\"clone\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And finally, we can compare the model's estimates (in red for each chain with mean and 89% HDI)v against the known proportions in each spot (blue).\n",
"In this case, the model was quite successful."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:38.962273Z",
"iopub.status.busy": "2022-10-25T16:53:38.961462Z",
"iopub.status.idle": "2022-10-25T16:53:40.058569Z",
"shell.execute_reply": "2022-10-25T16:53:40.057656Z"
}
},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 501,
"width": 788
}
},
"output_type": "display_data"
}
],
"source": [
"def frac_clone(s: pd.Series, k: int) -> float:\n",
" \"\"\"Fraction of cells in `s` that are clone `k`.\"\"\"\n",
" return np.mean(s == k)\n",
"\n",
"\n",
"fig, axes = plt.subplots(\n",
" nrows=ceil(simulation.sim_data.K / 2),\n",
" ncols=2,\n",
" figsize=(8, 1 * simulation.sim_data.K),\n",
" sharex=True,\n",
")\n",
"for ax, clone_i in zip(axes.flatten(), range(simulation.sim_data.K)):\n",
" clone = f\"c{clone_i}\"\n",
" ax.set_title(f\"clone {clone}\")\n",
"\n",
" # Plot true fraction of clones at each spot.\n",
" true_clone_frac = simulation.true_labels.groupby([\"spot\"])[\"clone\"].apply(\n",
" frac_clone, k=clone_i\n",
" )\n",
" ax.scatter(\n",
" true_clone_frac.index.tolist(),\n",
" true_clone_frac.values.tolist(),\n",
" c=\"tab:blue\",\n",
" s=8,\n",
" zorder=10,\n",
" )\n",
"\n",
" # Plot population fraction.\n",
" ax.axhline(simulation.sim_data.F[clone_i], lw=0.8, c=\"k\", ls=\"--\")\n",
"\n",
" # Plot posterior.\n",
" H = trace.posterior[\"H\"].sel(clone=[clone])\n",
" dx = np.linspace(-0.1, 0.1, len(H.coords[\"chain\"]))\n",
" spot = np.arange(simulation.sim_data.S)\n",
" for chain in H.coords[\"chain\"]:\n",
" _x = spot + dx[chain]\n",
" ax.scatter(_x, H.sel(chain=chain).mean(axis=(0)), c=\"tab:red\", s=2, zorder=20)\n",
" _hdi = az.hdi(H, coords={\"chain\": [chain]})[\"H\"].values.squeeze()\n",
" ax.vlines(\n",
" x=_x, ymin=_hdi[:, 0], ymax=_hdi[:, 1], lw=0.5, zorder=10, color=\"tab:red\"\n",
" )\n",
" # ax.set_ylabel(\"proportion of cells\")\n",
" ax.set_ylabel(None)\n",
" ax.set_xlabel(None)\n",
"\n",
"fig.supxlabel(\"spot\", va=\"bottom\")\n",
"fig.supylabel(\"proportion of cells\")\n",
"\n",
"for ax in axes.flatten()[simulation.sim_data.K :]:\n",
" ax.axis(\"off\")\n",
"\n",
"fig.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Development\n",
"\n",
"The development environment can be set up with `conda` (or faster using `mamba`).\n",
"This will handle the installation of PyMC better than using `pip`.\n",
"\n",
"```bash\n",
"mamba env create -f conda.yaml\n",
"conda activate tumoroscope-pymc\n",
"```\n",
"\n",
"The test suite can be run manually using the `pytest` command or using `tox` which also manages the test environment.\n",
"\n",
"```bash\n",
"tox\n",
"```\n",
"\n",
"This README is built from a Jupyter notebook and can be re-executed and converted to Markdown using another `tox` command.\n",
"\n",
"```bash\n",
"tox -e readme\n",
"```\n",
"\n",
"Feature requests and bugs are welcome in [Issues](https://github.com/jhrcook/tumoroscope-pymc/issues) and contributions are also welcome."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"## Environment information\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-25T16:53:40.063478Z",
"iopub.status.busy": "2022-10-25T16:53:40.063177Z",
"iopub.status.idle": "2022-10-25T16:53:40.157191Z",
"shell.execute_reply": "2022-10-25T16:53:40.156295Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last updated: 2022-10-25\n",
"\n",
"Python implementation: CPython\n",
"Python version : 3.10.6\n",
"IPython version : 8.5.0\n",
"\n",
"Compiler : Clang 13.0.1 \n",
"OS : Darwin\n",
"Release : 21.6.0\n",
"Machine : x86_64\n",
"Processor : i386\n",
"CPU cores : 4\n",
"Architecture: 64bit\n",
"\n",
"Hostname: JHCookMac.local\n",
"\n",
"Git branch: master\n",
"\n",
"pandas : 1.5.1\n",
"numpy : 1.23.4\n",
"pymc : 4.2.2\n",
"matplotlib: 3.6.1\n",
"seaborn : 0.12.0\n",
"arviz : 0.13.0\n",
"\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -d -u -v -iv -b -h -m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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