{ "cells": [ { "cell_type": "markdown", "id": "22c5adf7-a880-4885-afb0-cbb8aa48a62e", "metadata": {}, "source": [ "# Automatic CT segmentation using Nvidia Vista-3D and vessels geometry analysis and visualization with Streamlit on PCAI\n", "\n", "\n", "## ๐Ÿงฉ Problem statement\n", " \n", " Obtain **geometrical biomarkers** (e.g. cross-sectional diameters and tortuosity index) from CT exams available in Dicom format.\n", "\n", "---\n", "\n", "## ๐ŸŽฏ Target outcome\n", "\n", "- **Automatically segment** aorta, right and left common iliac arteries available in Dicom format\n", "- Availability of a **custom Jupyter Notebook (PC-AI compatible)** preloaded with Nvidia Vista 3D model and essential tools for postprocessing\n", "- Postprocessing of the segmentation output, preparing it for **geometrical analysis and 3D visualization**\n", "- Availability of a PC-AI compatible app with **Streamlite**. Within the app, it must be possible to execute geometrical analysis, visualize segmentation output and 3D renderings \n", "\n", "---\n", "\n", "## ๐Ÿ“Š High-Level Demo Flow\n", "\n", "![workflow](../docs/images/conceptual_workflow_white.png)\n", "\n", " 1. Load Dicom files and convert to Nifti format for semantic segmentation\n", " 2. Segment data using Vista-3D model\n", " 3. Postprocess the output to extract classes of interest (aorta, common iliac arteries)\n", " 4. Postprocess the segmentation output, preparing it for analysis with the created Streamlit app\n", " 5. Identify vessels' centerline\n", " 6. Measure vessels' diameter along the centerline, identifying cross-sections planes\n", " 7. Measure vessels' tortousity index\n", " 8. Prepare 3D renderings of the vessels and visualize them\n", "\n", "\n", "**Note:** this notebook walks you through the steps 1-4. \n", "\n", "Steps 5-8 can be conducted directly using the `vessels geometry analysis and reconstruction app`, for which, instructions are provided at the bottom of this notebook." ] }, { "cell_type": "markdown", "id": "dbc738bf-cf40-4329-96f4-d9e10e196cde", "metadata": {}, "source": [ "## ๐Ÿ› ๏ธ Step-by-Step Instructions\n", "\n", "\n", "### ๐Ÿ”ง Environment Preparation\n", "\n", "Install Python dependecies\n", "\n", "### IMPORTANT: ALLOCATE A COUPLE GB OF RAM TO THIS NOTEBOOK OTHERWISE IT WILL OOM AND GET KILLED" ] }, { "cell_type": "code", "execution_count": 1, "id": "3e16976e-8c54-49a7-bb53-84fb7d68bdd5", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting pydicom\n", " Downloading pydicom-3.0.1-py3-none-any.whl.metadata (9.4 kB)\n", "Collecting dicom2nifti\n", " Downloading 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"\u001b[?25hDownloading retrying-1.4.2-py3-none-any.whl (10 kB)\n", "Installing collected packages: addict, retrying, python-gdcm, python-dotenv, pyquaternion, pydicom, nibabel, narwhals, configargparse, plotly, dicom2nifti, dash, open3d\n", " Attempting uninstall: python-dotenv\n", " Found existing installation: python-dotenv 1.1.0\n", " Uninstalling python-dotenv-1.1.0:\n", " Successfully uninstalled python-dotenv-1.1.0\n", "Successfully installed addict-2.4.0 configargparse-1.7.1 dash-3.4.0 dicom2nifti-2.6.2 narwhals-2.15.0 nibabel-5.3.3 open3d-0.19.0 plotly-6.5.2 pydicom-3.0.1 pyquaternion-0.9.9 python-dotenv-1.2.1 python-gdcm-3.2.2 retrying-1.4.2\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install --upgrade pydicom dicom2nifti nibabel plotly open3d python-dotenv" ] }, { "cell_type": "code", "execution_count": 1, "id": "45a99e24", "metadata": {}, "outputs": [], "source": [ "# Load all the libraries\n", "from pathlib import Path\n", "import os\n", "import dicom2nifti\n", "import warnings\n", "import requests\n", "import zipfile\n", "from dotenv import load_dotenv\n", "import nibabel as nib\n", "import numpy as np\n", "import json " ] }, { "cell_type": "markdown", "id": "7043b22d", "metadata": {}, "source": [ "## Set-up API key and specify the URL of Vista-3D model, to be used for segmentation.\n", "\n", "### - Deploy Vista-3D model to MLIS. \n", " \n", " Here is a [how to deploy NIM to MLIS](./docs/deploy-NIM-to-MLIS.pdf) guide.\n", "\n", "\n", "### โš ๏ธ Model requirements:\n", "\n", "The model requires a path to the MLIS' endpoint and a USER TOKEN KEY, which need to be stored in a .env file locate in the same folder where this notebook resides:\n", "\n", "E.g. of the content in the .env file\n", "\n", "---\n", "\n", "```.env\n", "MLIS_TOKEN=\"$YOUR_MLIS_TOKEN$\"\n", "BASE_URL=\"$YOUR_PCAI_MODEL_BASE_URL$\"\n", "``` \n", "\n", "----\n", "\n", "Where:\n", "\n", "- MLIS_TOKEN is the authentication token \n", "- BASE_URL is the path to the model endpoint in MLIS.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "a7d93673", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using model at: https://do-not-delete--vista-3d-predictor-hugo-hpe-com-7f9ace25.ai-application.hou-pcai.hpecic.net\n", "MLIS token loaded successfully!\n" ] } ], "source": [ "# Load secret keys from .env file\n", "load_dotenv()\n", "# Specify the model endopoint within MLIs\n", "base_url = os.getenv('BASE_URL')\n", "if base_url is not None:\n", " print(f\"Using model at: {base_url}\")\n", "else:\n", " raise ValueError(\"BASE_URL was not specified, check your .env file\")\n", "\n", "# Specity the MLIs authorization token\n", "mlis_token = os.getenv('MLIS_TOKEN')\n", "if mlis_token is not None:\n", " print(f\"MLIS token loaded successfully!\")\n", "else:\n", " raise ValueError(\"MLIS_TOKEN was not specified, check your .env file\")" ] }, { "cell_type": "markdown", "id": "eed55098", "metadata": {}, "source": [ "### Prepare your data\n", "**In this example - Dicom data are organized as follows:**\n", " \n", "> dicom_data/\n", ">> PATIENT01/\n", ">>> dicom_file_CT......1\n", ">>> dicom_file_CT......2\n", ">>> dicom_file_CT......3\n", ">>> ...\n", ">> PATIENT02/\n", ">>> dicom_file_CT......1\n", ">>> dicom_file_CT......2\n", ">>> dicom_file_CT......3\n", ">>> ....\n", " \n", "Ensure to specify the parent folder i.e. `dicom_data`, when you specify the location of the data in `DICOM_DATA_PATH` below." ] }, { "cell_type": "markdown", "id": "8a24b37b", "metadata": {}, "source": [ "โš ๏ธ **NOTE:** If the dataset is available in **Nifti** format instead of **Dicom**, you won't need to create the Nifti files which are required by the Nvidia Vista-3D segmentation model.\n", "\n", "In that case you can prepare the dataset as in the following example, in which we will use the `Fast and Low-resource semisupervised Abdominal oRgan sEgmentation (FLARE) Challenge` dataset.\n", "\n", "FLARE is a dataset for abdomen organ segmentation and it has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. Although it is not designed for vessels segmentation, it contains in some scans Aorta, Right and Left Iliac Arteries, so we can use it to demo our app.\n", "\n", "- Challenge link: [FLARE22](https://flare22.grand-challenge.org)\n", "\n", "- Dataset repository: [FLARE22 dataset](https://storage.googleapis.com/ai-solution-engineering-datasets/demo-test-seg.zip)\n", "\n", "\n", "**Download** the dataset and save it so that it is organized as follows:\n", "\n", "> outputs\n", ">> nifti_data\n", ">>> training\n", ">>>> FLARETs_0001_0000.nii.gz\n", "\n", ">>> tuning\n", ">>>> FLARETs_0002_0000.nii.gz\n", "\n", ">>> testing\n", ">>>> FLARETs_0050_0000.nii.gz" ] }, { "cell_type": "markdown", "id": "83630283", "metadata": {}, "source": [ "### Specify important paths" ] }, { "cell_type": "code", "execution_count": 3, "id": "fc410e86-69cf-4037-94ef-aa05dd8cbbca", "metadata": {}, "outputs": [], "source": [ "## These are the only paths that need to be modified!\n", "\n", "# If dataset is in Dicom format - Specify the folder where Dicom data reside:\n", "DICOM_DATA_PATH = Path('dicom_data') \n", "\n", "# Specify the folder where all the outputs will be saved\n", "ALL_OUTPUTS_PATH = Path('outputs_flare')\n", "\n", "# Specify the URL where data reside so that Vista3D model can fetch it:\n", "# Please refer to https://docs.nvidia.com/nim/medical/vista3d/latest/advanced-usage.html#environment-variables for additional details.\n", "# Hint: if data are in 'https://fs2.ingress.pcai0109.dc15.hpecolo.net/califra/dicom_data'\n", "# data_path_root must be 'https://fs2.ingress.pcai0109.dc15.hpecolo.net/califra/'\n", "data_path_root = 'https://vessels-app.ai-application.hou-pcai.hpecic.net/vista-3d/'\n", "\n", "# Set the following to True if you want to convert the data Dicom to Nifti\n", "CONVERT_DICOM_TO_NIFTI = False" ] }, { "cell_type": "code", "execution_count": 4, "id": "444ea703-3d45-4e82-adba-ab03bc6bfd38", "metadata": {}, "outputs": [], "source": [ "# Assess whether the FLARE dataset has been organized correctly\n", "if not CONVERT_DICOM_TO_NIFTI:\n", " ## Check data were correctly organized\n", " assert (\n", " ALL_OUTPUTS_PATH / \"nifti_data\" / \"testing\" / \"FLARETs_0050.nii.gz\"\n", " ).is_file(), (\n", " \"Ensure the FLARE dataset is organized as expected. Look instructions above - Ignore this message otherwise\"\n", " )" ] }, { "cell_type": "markdown", "id": "c8a31ad9-74a8-40f2-8fcd-9d13136a164d", "metadata": {}, "source": [ "# Assess whether the FLARE dataset has been organized correctly\n", "if not CONVERT_DICOM_TO_NIFTI:\n", " ## Check data were correctly organized\n", " assert (\n", " ALL_OUTPUTS_PATH / \"nifti_data\" / \"testing\" / \"FLARETs_0050_0000.nii.gz\"\n", " ).is_file(), (\n", " \"Ensure the FLARE dataset is organized as expected. Look instructions above - Ignore this message otherwise\"\n", " )" ] }, { "cell_type": "markdown", "id": "b8647553-1c63-46ac-b9ad-31228fc0c16c", "metadata": {}, "source": [ "## ๐Ÿ”ง 1) Load Dicom files and convert to Nifti format for semantic segmentation\n", "\n", "**โš ๏ธ NOTE:** you can skip this step if the data is already available in Nifti format.\n", "\n", "In this section, we show how to convert Dicom file to Nifti (Neuroimaging Informatics Technology Initiative) format which is the format expected by the Vista-3D Nvidia NIM model (more details about the model are provided below).\n", "\n", "\n", "Upon completion data will be organized in the specified folder with a structure similar to what we saw for dicom_data. \n", "\n", "Eg:\n", "> dicom_data\n", ">> PATIENT01\n", ">>> dicom_file_CT......1\n", ">>> dicom_file_CT......2\n", "\n", "> nifti_data\n", ">> PATIENT01\n", ">>> 3_25mm_arterial.nii.gz\n", ">>> 601_coronal_abdomen.nii.gz\n", ">>> 602_sagittal_abdomen.nii\n", "\n", "Note that the name is self-inferred using the Dicom metadata tag.\n", "\n", "In this example:\n", "> (0008,103E) Series Description LO: '2.5MM ARTERIAL'" ] }, { "cell_type": "code", "execution_count": 5, "id": "af5e0728-987c-43eb-a3e9-e35a7a542e9f", "metadata": {}, "outputs": [], "source": [ "# If you converted your data from Dicom to Nifti using the code above, then your Nifti data will be in nifti_data folder. \n", "nifti_destination_path = Path(ALL_OUTPUTS_PATH) / \"nifti_data\"\n", "\n", "if CONVERT_DICOM_TO_NIFTI:\n", " # Convert Dicom to Nifti for compatibility with the Vista-3D Nvidia NIM model\n", " for dicom_directory in os.listdir(DICOM_DATA_PATH):\n", " input_directory = Path(DICOM_DATA_PATH) / f\"{dicom_directory}\" \n", " output_directory = Path(nifti_destination_path) / dicom_directory\n", " \n", " # If the patient's exam has already been processed, skip it.\n", " if output_directory.exists():\n", " warnings.warn(f\"{output_directory} already exists, skipping...\")\n", " continue\n", " else:\n", " os.makedirs(output_directory, exist_ok=False)\n", " print(\"-\"*10)\n", " print(f\"Saving Nifti files into directory {output_directory}\")\n", " dicom2nifti.convert_directory(input_directory, output_directory, compression=True, reorient=True)" ] }, { "cell_type": "markdown", "id": "f0137eea-6000-4677-9235-d7832a167cac", "metadata": {}, "source": [ "## ๐Ÿง  2) Segment data using Vista-3D model (133 classes)\n", "\n", "In this project we chose the **Vista-3D Nvidia NIM** model for segmenting the tissues of interest. \n", "\n", "##### ๐Ÿ“ฆ Vista-3D Foundation Model\n", "Vista-3D Nvidia model is state of the art in zero-shot segmentation of CT scans, and **HPE Private-Cloud AI** comes with Nvidia licenses, making it a great option.\n", "\n", "[Vista-3D is a specialized interactive foundation model for 3D medical imaging.](https://build.nvidia.com/nvidia/vista-3d/modelcard) It excels in providing accurate and adaptable segmentation analysis across 133 anatomies and modalities. Utilizing a multi-head architecture, Vista-3D adapts to varying conditions and anatomical areas, helping guide users' annotation workflow. This model is for research purposes and not for clinical usage.\n", "\n", "##### ๐Ÿ“ Training Data Details\n", "The Vista3D model was trained by Nvidia on a large and diverse dataset of 11454 3D CT volumes. This dataset was curated from Nvidia in-house and publicly available sources. The training data encompassed a wide range of acquisition protocols.\n", "\n", "##### ๐Ÿ“„ Reference \n", "- [Vista3D: A Unified Segmentation Foundation Model For 3D Medical Imaging\n", "](https://arxiv.org/pdf/2406.05285)\n", "- [Segment Anything](https://arxiv.org/abs/2304.02643)\n", "\n", "#### โšก HPE Machine Learning Inference Software\n", "In this notebook, the Vista-3D model has been deployed using **[HPE's Machine Learning Inference Software (MLIS)](https://docs.ai-solutions.ext.hpe.com/products/mlis/)**, which simplifies and controls the deployment, management, and monitoring of machine learning models at scale. Here is a [HOW-TO](./docs/deploy-NIM-to-MLIS.pdf) guide on deploying models to MLIs. " ] }, { "cell_type": "code", "execution_count": 6, "id": "68a2a77c-df94-477b-a2fa-74b7e6ecca4c", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Token successfully refreshed.\n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0050.nii.gz \n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0133.nii.gz \n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0134.nii.gz \n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0135.nii.gz \n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0136.nii.gz \n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0137.nii.gz \n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0138.nii.gz \n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0139.nii.gz \n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0140.nii.gz \n", "----------\n", "outputs_flare/nifti_data/testing/FLARETs_0141.nii.gz \n", "----------\n", 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"outputs_flare/nifti_data/tuning/FLARETs_0125.nii.gz \n", "----------\n", "outputs_flare/nifti_data/tuning/FLARETs_0126.nii.gz \n", "----------\n", "outputs_flare/nifti_data/tuning/FLARETs_0127.nii.gz \n", "----------\n", "outputs_flare/nifti_data/tuning/FLARETs_0128.nii.gz \n", "----------\n", "outputs_flare/nifti_data/tuning/FLARETs_0129.nii.gz \n", "----------\n", "outputs_flare/nifti_data/tuning/FLARETs_0130.nii.gz \n", "----------\n", "outputs_flare/nifti_data/tuning/FLARETs_0131.nii.gz \n", "----------\n", "outputs_flare/nifti_data/tuning/FLARETs_0132.nii.gz \n" ] } ], "source": [ "# To perform segmentation using Vista 3D model, we need to send a post request to the MLIs endpoint. \n", "# As part of the request we need to specify the path to the Nifti file to be segmented out.\n", "\n", "# Update the nvidia token\n", "%update_token\n", "\n", "def unzip_file(zip_filepath, output_dir, output_filename=None):\n", " \"\"\"Unzip the first file in a zip archive to a target folder, optionally renaming it.\"\"\"\n", " with zipfile.ZipFile(zip_filepath, 'r') as zip_ref:\n", " info_list = zip_ref.infolist()\n", " if not info_list:\n", " raise ValueError(\"Zip file is empty\")\n", "\n", " first_entry = info_list[0]\n", " original_name = first_entry.filename\n", "\n", " # Ensure output directory exists\n", " os.makedirs(output_dir, exist_ok=True)\n", "\n", " # Extract the file to the output directory\n", " extracted_path = zip_ref.extract(original_name, path=output_dir)\n", "\n", " # Optionally rename the extracted file\n", " if output_filename:\n", " new_path = os.path.join(output_dir, output_filename)\n", " os.rename(extracted_path, new_path)\n", " return new_path\n", " return extracted_path\n", "\n", "# Location where the model responses and segmentation outputs will land\n", "response_dir = Path(ALL_OUTPUTS_PATH) / \"responses\"\n", "os.makedirs(response_dir, exist_ok=True)\n", "\n", "segmentation_dir = Path(ALL_OUTPUTS_PATH) / \"segmentations\"\n", "os.makedirs(segmentation_dir, exist_ok=True)\n", "\n", "# Specify the location of the data, they need to be available and accessible online for the model to be able to process them.\n", "# Here we use the location within the hosted-trial environment\n", "\n", "headers = {'Authorization': f'Bearer {mlis_token}'}\n", "\n", "# Vista-3D model requires passing the data in a dictionary, which has a key `image` that points to a URL to the Nifti file. \n", "# e.g. data['image'] = \"https://fs2.ingress.pcai0109.dc15.hpecolo.net/califra/outputs/nifti_data/testing/FLARETs_0050_0000.nii.gz\"\n", "# Please refer to https://docs.nvidia.com/nim/medical/vista3d/latest/advanced-usage.html#environment-variables for additional details.\n", "# Note that Vista-3D model will attempt segmenting every Nifti file irrespective of the acquisition orientation (axial, coronal, sagittal).\n", "# Zero-shot segmentation quality should be state of the art on all the planes.\n", "\n", "for path in Path(nifti_destination_path).rglob('*nii.gz'):\n", " data = {}\n", " # User TODO: \n", " # Ensure 'path' does not require custom logic. \n", " # This is may be needed if ALL_OUTPUTS_PATH is not a relative path.\n", " \n", " p = data_path_root + str(path) \n", " data['image'] = str(p)\n", " print(\"-\" * 10)\n", "\n", " # Submit a post request to segment the ct scan.\n", " response = requests.post(f'{base_url}/v1/vista3d/inference', json=data, headers=headers)\n", " \n", " if response.status_code == 200: # Success!!\n", "\n", " file_name = path.name\n", " \n", " # Create the folders required to have a clean output\n", " os.makedirs(Path(response_dir) / path.parent.name, exist_ok=True)\n", " os.makedirs(Path(segmentation_dir) / path.parent.name, exist_ok=True)\n", " output_segmentation_name = Path(segmentation_dir) / path.parent.name / file_name\n", "\n", " # Zip the model response, unzip it and rename the files accordingly\n", " output_zip_name = Path(response_dir) / path.parent.name / file_name.replace('.nii.gz', '.zip') \n", " \n", " with open(output_zip_name, 'wb') as f:\n", " f.write(response.content)\n", " \n", " _ = unzip_file(\n", " zip_filepath=str(output_zip_name),\n", " output_dir = \"/\".join(str(output_segmentation_name).split(\"/\")[:-1]),\n", " output_filename=str(output_segmentation_name).split(\"/\")[-1],\n", " )\n", "\n", " print(f'{str(path)} {response}')\n", "\n", " else:\n", " print(\"skipping: \", data['image'], '\\n', response.status_code)\n", " print(f\"Reason: {response.reason}\")\n", " print(f\"Body: {response.text}\")\n", " " ] }, { "cell_type": "markdown", "id": "4b48431f-00dd-4181-b703-880ae95680e7", "metadata": {}, "source": [ "## ๐Ÿ–ผ๏ธ Segmentation output\n", "\n", "Upon segmentation, the results will be available in folder `outputs/segmentations` with exactly the same structure as saw for the Nifti data.\n", "\n", "E.g.:\n", "\n", "> outputs\n", ">>nifti_data\n", ">>> testing\n", ">>>> FLARETs_0050_0000.nii.gz\n", "\n", ">> segmentations\n", ">>> testing\n", ">>>> FLARETs_0050_0000.nii.gz" ] }, { "cell_type": "markdown", "id": "4643ed31-9b58-4087-9a6d-ab9bf4a0dc4a", "metadata": {}, "source": [ "## ๐Ÿงช 3) Postprocess the output to extract classes of interest (aorta, common iliac arteries)\n", "\n", "- a) Load the segmentation label mapping for each anatomical structure. Remember that Vista 3D model can segment up 133 (including background) tissues.\n", "- b) Post-process the segmentation output to extract only the 3 tissues we are interested on (aorta, left iliac artery, right iliac artery)" ] }, { "cell_type": "code", "execution_count": 7, "id": "5657b515-4044-44ab-8ee7-8c8bd8c5c8b3", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mapping tissue to label:\n", "background: 0\n", "liver: 1\n", "kidney: 2\n", "spleen: 3\n", "pancreas: 4\n", "right kidney: 5\n", "aorta: 6\n", "inferior vena cava: 7\n", "right adrenal gland: 8\n", "left adrenal gland: 9\n", "gallbladder: 10\n", "esophagus: 11\n", "stomach: 12\n", "duodenum: 13\n", "left kidney: 14\n", "bladder: 15\n", "prostate or uterus: 16\n", "portal vein and splenic vein: 17\n", "rectum: 18\n", "small bowel: 19\n", "lung: 20\n", "bone: 21\n", "brain: 22\n", "lung tumor: 23\n", "pancreatic tumor: 24\n", "hepatic vessel: 25\n", "hepatic tumor: 26\n", "colon cancer primaries: 27\n", "left lung upper lobe: 28\n", "left lung lower lobe: 29\n", "right lung upper lobe: 30\n", "right lung middle lobe: 31\n", "right lung lower lobe: 32\n", "vertebrae L5: 33\n", "vertebrae L4: 34\n", "vertebrae L3: 35\n", "vertebrae L2: 36\n", "vertebrae L1: 37\n", "vertebrae T12: 38\n", "vertebrae T11: 39\n", "vertebrae T10: 40\n", "vertebrae T9: 41\n", "vertebrae T8: 42\n", "vertebrae T7: 43\n", "vertebrae T6: 44\n", "vertebrae T5: 45\n", "vertebrae T4: 46\n", "vertebrae T3: 47\n", "vertebrae T2: 48\n", "vertebrae T1: 49\n", "vertebrae C7: 50\n", "vertebrae C6: 51\n", "vertebrae C5: 52\n", "vertebrae C4: 53\n", "vertebrae C3: 54\n", "vertebrae C2: 55\n", "vertebrae C1: 56\n", "trachea: 57\n", "left iliac artery: 58\n", "right iliac artery: 59\n", "left iliac vena: 60\n", "right iliac vena: 61\n", "colon: 62\n", "left rib 1: 63\n", "left rib 2: 64\n", "left rib 3: 65\n", "left rib 4: 66\n", "left rib 5: 67\n", "left rib 6: 68\n", "left rib 7: 69\n", "left rib 8: 70\n", "left rib 9: 71\n", "left rib 10: 72\n", "left rib 11: 73\n", "left rib 12: 74\n", "right rib 1: 75\n", "right rib 2: 76\n", "right rib 3: 77\n", "right rib 4: 78\n", "right rib 5: 79\n", "right rib 6: 80\n", "right rib 7: 81\n", "right rib 8: 82\n", "right rib 9: 83\n", "right rib 10: 84\n", "right rib 11: 85\n", "right rib 12: 86\n", "left humerus: 87\n", "right humerus: 88\n", "left scapula: 89\n", "right scapula: 90\n", "left clavicula: 91\n", "right clavicula: 92\n", "left femur: 93\n", "right femur: 94\n", "left hip: 95\n", "right hip: 96\n", "sacrum: 97\n", "left gluteus maximus: 98\n", "right gluteus maximus: 99\n", "left gluteus medius: 100\n", "right gluteus medius: 101\n", "left gluteus minimus: 102\n", "right gluteus minimus: 103\n", "left autochthon: 104\n", "right autochthon: 105\n", "left iliopsoas: 106\n", "right iliopsoas: 107\n", "left atrial appendage: 108\n", "brachiocephalic trunk: 109\n", "left brachiocephalic vein: 110\n", "right brachiocephalic vein: 111\n", "left common carotid artery: 112\n", "right common carotid artery: 113\n", "costal cartilages: 114\n", "heart: 115\n", "left kidney cyst: 116\n", "right kidney cyst: 117\n", "prostate: 118\n", "pulmonary vein: 119\n", "skull: 120\n", "spinal cord: 121\n", "sternum: 122\n", "left subclavian artery: 123\n", "right subclavian artery: 124\n", "superior vena cava: 125\n", "thyroid gland: 126\n", "vertebrae S1: 127\n", "bone lesion: 128\n", "kidney mass: 129\n", "liver tumor: 130\n", "vertebrae L6: 131\n", "airway: 132\n" ] } ], "source": [ "# Download from /v1/vista3d/info\n", "with open('vista3dmodelinfo.json', 'rb') as f:\n", " segmentation_dict = json.load(f)['labels']\n", "\n", "segmentation_dict = {v: k for k, v in segmentation_dict.items()}\n", "\n", "print(\"Mapping tissue to label:\")\n", "for tissue, label in segmentation_dict.items():\n", " print(f\"{tissue}: {label}\")" ] }, { "cell_type": "markdown", "id": "775d7be2-002f-4f66-bd8e-0dd813ecc771", "metadata": {}, "source": [ "๐Ÿ“ Measure diameters of vessels.\n", "\n", "We take the measurements in the axial plane scans - to do se, we identify the cross-section planes of the vessels. This is done within the `vessel geometry analysis and reconstruction` app, which we introduce below.\n", "\n", "For this reason, we create a dictionary that for each patient contains only the paths to the Nifti files that contain axial planes." ] }, { "cell_type": "code", "execution_count": 8, "id": "fb4fe305-f18c-48f2-9604-2373a8241253", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'testing': ['/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0050.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0133.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0134.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0135.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0136.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0137.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0138.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0139.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0140.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0141.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0142.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0143.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0144.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0145.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0146.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0147.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0148.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0149.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0150.nii.gz',\n", " 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'/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0187.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0188.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0189.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0190.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0191.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0192.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0193.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0194.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0195.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0196.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0197.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0198.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0199.nii.gz'],\n", " 'training': ['/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0001.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0003.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0004.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0005.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0006.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0007.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0008.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0009.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0010.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0011.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0012.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0013.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0014.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0015.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0016.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0017.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0018.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0019.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0020.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0021.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0022.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0023.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0024.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0025.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0026.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0027.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0028.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0029.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0030.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0031.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0032.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0033.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0034.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0035.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0036.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0037.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0038.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0039.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0040.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0041.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0042.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0043.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0044.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0045.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0046.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0047.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0048.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0049.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0050.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0051.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0052.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0053.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0054.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0055.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0056.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0057.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0058.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0059.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0060.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0061.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0062.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0063.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0064.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0065.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/training/FLARETs_0066.nii.gz'],\n", " 'tuning': ['/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0002.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0067.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0068.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0069.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0070.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0071.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0072.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0073.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0074.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0075.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0076.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0077.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0078.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0079.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0080.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0081.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0082.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0083.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0084.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0085.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0086.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0087.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0088.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0089.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0090.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0091.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0092.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0093.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0094.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0095.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0096.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0097.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0098.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0099.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0100.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0101.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0102.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0103.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0104.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0105.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0106.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0107.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0108.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0109.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0110.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0111.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0112.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0113.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0114.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0115.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0116.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0117.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0118.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0119.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0120.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0121.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0122.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0123.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0124.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0125.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0126.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0127.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0128.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0129.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0130.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0131.nii.gz',\n", " '/mnt/shared/vista-3d/outputs_flare/nifti_data/tuning/FLARETs_0132.nii.gz']}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patient_dict = {}\n", "for patient_paths in (Path(ALL_OUTPUTS_PATH) / 'nifti_data').rglob('*nii.gz'):\n", " patient = patient_paths.parent.name\n", " if patient not in patient_dict:\n", " patient_dict[patient] = []\n", "\n", " \n", " if \"sa\" not in str(patient_paths.resolve()) and \"co\" not in str(patient_paths.resolve()):\n", " patient_dict[patient].append(str(patient_paths.resolve()))\n", "\n", "patient_dict" ] }, { "cell_type": "markdown", "id": "d9639afc", "metadata": {}, "source": [ "## ๐Ÿ› ๏ธ 4) Prepare data for further analysis\n", "\n", "Here we process the segmentation output and prepare it for processing using the `vessels geometry analysis and reconstruction app`.\n", "\n", "The steps below include:\n", "\n", "- limiting the segmentation outputs to include only the vessels of interest (aorta, right and left common iliac arteries)\n", "- projecting the segmented voxels from voxel to the real world space, identifying (x, y, z) coordinates of each voxel" ] }, { "cell_type": "code", "execution_count": null, "id": "2b9cb297-3c74-4942-96f2-bd1a59058679", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------\n", ">> processing patient: testing\n", "processing FLARETs_0050 for patient testing\n", "After processing segmentation, the new labels mapping is:\n", "\n", "aorta: 1\n", "left iliac artery: 2\n", "right iliac artery: 3\n", "Saved pointcloud with 0 points in \n", "outputs_flare/pointclouds/testing/FLARETs_0050.npz\n", "----------\n", "Processed segmentation mask saved in \n", "outputs_flare/processed_segmentations/testing/FLARETs_0050.npz\n", "processing FLARETs_0133 for patient testing\n", "After processing segmentation, the new labels mapping is:\n", "\n", "aorta: 1\n", "left iliac artery: 2\n", "right iliac artery: 3\n" ] } ], "source": [ "# Extract the mapping between segmentation label and segmentation numerical value, using the segmentation dict provided by the Vista-3D model which we previously loaded in memory (i.e. 'vista3dmodelinfo.json')\n", "\n", "# In this study we are interested on 3 vessels, but could be expanded to any additional vessel/ tissue, provided the segmentation is available. Please refer to the 'vista3dmodelinfo.json' file, or the official Vista-3D model documentation.\n", "label_rois = ['aorta', 'left iliac artery', 'right iliac artery']\n", "vessel_labels = {k: int(segmentation_dict[k]) for k in label_rois}\n", "\n", "for idx, patient_id in enumerate(patient_dict.keys()):\n", "\n", " patient = patient_id\n", "\n", " print(\"-\" * 10)\n", " print(\">> processing patient: \", patient)\n", "\n", " for series, scan_path in enumerate(patient_dict[patient]):\n", " try:\n", " scan_path_lower = scan_path.lower()\n", " volume_name = Path(patient_dict[patient][series]).name.replace(\".nii.gz\", \"\")\n", " print(f\"processing {volume_name} for patient {patient}\")\n", " \n", " # Load the CT to memory\n", " ct_data = nib.load(scan_path)\n", " ct_scan = ct_data.get_fdata()\n", " affine = ct_data.affine\n", " \n", " # Load the segmentation to memory\n", " segmentation_path = patient_dict[patient][series].replace('nifti_data', 'segmentations')\n", " segmentation_data = nib.load(segmentation_path)\n", " segmentation_scan = segmentation_data.get_fdata()\n", " \n", " # Create a new segmentation map with only the vessels of interest in it.\n", " new_segmentation_scan = np.zeros_like(segmentation_scan)\n", " for n,label in enumerate(vessel_labels.values(), start=1):\n", " new_segmentation_scan += (segmentation_scan == label)*n\n", " \n", " # Create a new dict with the vessel labels\n", " new_vessel_labels = {\n", " k_key: id_k for id_k, (k_key, k_value) in enumerate(vessel_labels.items(), start=1)\n", " }\n", " \n", " print(\"After processing segmentation, the new labels mapping is:\\n\")\n", " for tissue, label in new_vessel_labels.items():\n", " print(f\"{tissue}: {label}\")\n", " \n", " # ----------------------------------------------------------------------------\n", " # Build the 3D coordinate grid (patient coordinates in mm)\n", " \n", " # Build coordinate grid\n", " scan_size = ct_scan.shape #(4,4)\n", " \n", " # Meshgrid 3 integer volumes holding voxel indices (i,j,k)\t(512, 512, 146) each\n", " grid_i, grid_j, grid_k = np.meshgrid(\n", " np.arange(scan_size[0]), np.arange(scan_size[1]), np.arange(scan_size[2]), indexing='ij'\n", " )\n", " \n", " # Since affine has shape 4x4, we need to stack a new column of 1s to make it the grid homogenous\n", " indices = np.stack([grid_i, grid_j, grid_k, np.ones_like(grid_i)], axis=-1)\n", " \n", " # Create a tensor, where each voxel's value is the coordinate of that voxel in the x, y, z coordinate space \n", " # This uses the affine matrix provided by the Nifti files.\n", " coords = indices @ affine.T\n", " \n", " # Undo the homogenous shape by slicing away the row that we had for math convenience\n", " coords_xyz = coords[...,:3]\n", " \n", " \n", " # ----------------------------------------------------------------------------\n", " # Now that the coordinates are available in the x, y, z coordinate we can further process the results by\n", " # extracting only the points that belong to vessels' region of interest.\n", " \n", " mask = new_segmentation_scan > 0 # convert to binary\n", " points = coords_xyz[mask] # shape (N,3)\n", " \n", " # Build colors based on segmentation label\n", " colors = np.zeros((points.shape[0], 3))\n", " \n", " flat_labels = new_segmentation_scan[mask]\n", " colors[flat_labels == 1] = [1, 0, 0] # red for aorta\n", " colors[flat_labels == 2] = [0, 1, 0] # green for left iliac\n", " colors[flat_labels == 3] = [0, 0, 1] # blue for right iliac\n", " \n", " # Save pointclouds:\n", " \n", " pointcloud_dir = Path(ALL_OUTPUTS_PATH) / \"pointclouds\"\n", " os.makedirs(pointcloud_dir, exist_ok=True)\n", " \n", " patient_pointcloud_npz = Path(pointcloud_dir) / f\"{patient_id}\" / f\"{volume_name}.npz\"\n", " \n", " os.makedirs(patient_pointcloud_npz.parent, exist_ok=True)\n", " np.savez_compressed(patient_pointcloud_npz, points=points, colors=colors)\n", " print(f\"Saved pointcloud with {points.shape[0]} points in \\n{patient_pointcloud_npz}\")\n", " \n", " # Save post processed segmentation:\n", " processed_segmentation_dir = Path(ALL_OUTPUTS_PATH) / \"processed_segmentations\" / patient_id\n", " vessel_segmentation_npz = Path(processed_segmentation_dir) / f\"{volume_name}.npz\"\n", " \n", " os.makedirs(processed_segmentation_dir, exist_ok=True)\n", " np.savez_compressed(vessel_segmentation_npz, new_seg_scan=new_segmentation_scan)\n", " print(f\"{'-'*10}\\nProcessed segmentation mask saved in \\n{vessel_segmentation_npz}\")\n", " except Exception as e:\n", " print(e)\n", " continue" ] }, { "cell_type": "markdown", "id": "03db213c-0fb0-4a6c-9caa-b40ad9e479e7", "metadata": {}, "source": [ "## ๐Ÿ” Visual inspection of the data \n", "\n", "Here we provide code snippets which are examplary of how to visualize the Nifti inputs and segmentation outputs" ] }, { "cell_type": "code", "execution_count": 10, "id": "6d96a987", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from IPython import display\n", "import time\n", "\n", "\n", "# --- Utility functions to visualize the segmentation overlayed to the acquisition scan ---\n", "def visualize_slice(image, mask=None):\n", " \"\"\"\n", " Visualize slice.\n", "\n", " Args:\n", " image: 2D np.array to visualize\n", " mask: Optional 2D np.array to overlay\n", " \"\"\"\n", " plt.imshow(image, cmap=plt.cm.gray)\n", " if mask is not None:\n", " plt.imshow(np.ma.masked_where(mask==0, mask), cmap=\"jet\", alpha=0.5, interpolation=\"none\")\n", "\n", "\n", "def visualize_scan(image, mask=None, interval=5, pause_time=0.1, show_slices=None, slice_dim=\"first\"):\n", " \"\"\"\n", " Visualize volume.\n", "\n", " Args:\n", " image: 3D np.array to visualize\n", " mask: Optional 3D np.array to overlay\n", " interval: int, interval between slices to be visualized \n", " pause_time=0.1: float, pause between slice visualization\n", " show_slices=None: Optional[List[int]]. If None, shows all slices at the interval rate, else shows the specified slices\n", " slice_dim=\"first\", whether slices are in the 'first' or 'last' dimension of the volume.\n", "\n", " \"\"\"\n", " if slice_dim == \"first\":\n", " show_slices = show_slices or range(0, image.shape[0], interval)\n", " elif slice_dim == \"last\":\n", " show_slices = show_slices or range(0, image.shape[-1], interval)\n", " else:\n", " raise ValueError(\"slice axis must be first or last. Used \", slice_dim)\n", "\n", " for n in show_slices:\n", " if slice_dim == \"first\":\n", " visualize_slice(image[n]*1000, mask[n] if mask is not None else None )\n", " elif slice_dim == \"last\":\n", " visualize_slice(image[...,n]*1000, mask[...,n] if mask is not None else None)\n", "\n", " plt.title(f\"Slice {n}\")\n", " display.display(plt.gcf())\n", " display.clear_output(wait=True)\n", " time.sleep(pause_time)\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "d556ecc4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "List of patients:\n", "0 - testing\n", "1 - training\n", "2 - tuning\n" ] } ], "source": [ "# Print the name of patients available in the dataset\n", "patients_ids = list(patient_dict.keys())\n", "print(f\"List of patients:\")\n", "for p_idx, p in enumerate(patients_ids):\n", " print(f\"{p_idx} - {p}\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "2518258c-f969-4ac7-97a5-41cf3dd4f298", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "patient 0 (i.e. testing) has the following axial series available\n", "series_idx=0 - scan = FLARETs_0050.nii.gz\n" ] } ], "source": [ "# Select a patient among those printed above by specifying `idx` below, \n", "# and the code will print the name scan series available for inspection\n", "\n", "# select one of the patients\n", "idx = 0 \n", "\n", "scans_paths = patient_dict[patients_ids[idx]]\n", "print(f\"patient {idx} (i.e. {patients_ids[idx]}) has the following axial series available\")\n", "for j, sp in enumerate(scans_paths):\n", " print(f\"series_idx={j} - scan = {Path(sp).name}\")\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "c07b932b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading FLARETs_0050.nii.gz\n", "----------\n", "CT_SCAN: /mnt/shared/vista-3d/outputs_flare/nifti_data/testing/FLARETs_0050.nii.gz loaded successfully\n", "ALL CLASSES SEGMENTAION: /mnt/shared/vista-3d/outputs_flare/segmentations/testing/FLARETs_0050.nii.gz loaded successfully\n", "PROCESSED SEGMENTATION: /mnt/shared/vista-3d/outputs_flare/processed_segmentations/testing/FLARETs_0050.npz loaded successfully\n" ] } ], "source": [ "# Select a scan series among those printed above by specifying `series_idx` below, \n", "# and the code will load the related files into memory\n", "series_idx = 0\n", "\n", "print(f\"Loading {Path(scans_paths[series_idx]).name}\")\n", "print('-'*10)\n", "# Load the CT to memory\n", "ct_visual = nib.load(scans_paths[series_idx]).get_fdata()\n", "print(f\"CT_SCAN: {scans_paths[series_idx]} loaded successfully\")\n", "seg_all_visual = nib.load(scans_paths[series_idx].replace('nifti_data', 'segmentations')).get_fdata()\n", "print(f\"ALL CLASSES SEGMENTAION: {scans_paths[series_idx].replace('nifti_data', 'segmentations')} loaded successfully\")\n", "seg_proc_visual = np.load(scans_paths[series_idx].replace('nifti_data', 'processed_segmentations').replace(\".nii.gz\", \".npz\"))['new_seg_scan']\n", "print(f\"PROCESSED SEGMENTATION: {scans_paths[series_idx].replace('nifti_data', 'processed_segmentations').replace('.nii.gz', '.npz')} loaded successfully\")\n" ] }, { "cell_type": "markdown", "id": "9a6bfadf-b35d-48dc-824c-34d42dfb8065", "metadata": {}, "source": [ "#### Example of CT scan (axial plane)" ] }, { "cell_type": "code", "execution_count": 15, "id": "aac2f6be-deb9-4de2-8de9-d1487dd1a3c2", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "visualize_scan(\n", " image=np.flipud(np.transpose(ct_visual, (1, 0, 2))),\n", " interval=5, \n", " pause_time=0.1,\n", " show_slices=None, \n", " slice_dim=\"last\"\n", ")" ] }, { "cell_type": "markdown", "id": "a1c9888c-17dc-40ec-b0ad-f4457f50d1a9", "metadata": {}, "source": [ "#### Example of all the segmented classes" ] }, { "cell_type": "code", "execution_count": null, "id": "96df2d11-8469-49e6-8836-5f5c7b68ee37", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from utils.plot_utils import visualize_scan\n", "\n", "visualize_scan(\n", " image=np.flipud(np.transpose(ct_visual, (1, 0, 2))),\n", " mask=np.flipud(np.transpose(seg_all_visual, (1, 0, 2))), \n", " interval=5, \n", " pause_time=0.1,\n", " show_slices=None, \n", " slice_dim=\"last\"\n", ")" ] }, { "cell_type": "markdown", "id": "6be39849-06fa-46c5-a150-3e0849013a1f", "metadata": {}, "source": [ "#### Segmentation example of only the classes which we are interested on (\"aorta\" \"right iliac artery\" \"left iliac artery\")" ] }, { "cell_type": "code", "execution_count": null, "id": "5bfec775-df83-4029-acdd-e525dcb776c8", "metadata": {}, "outputs": [ { "data": { "image/png": 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80DNftRjX3/t7fw87Ozv4hV/4Bdy7dw8PP/ww/uiP/mgoaD2VRO1fKUorugjFab/6jijhGVeefuczPmHrq3tUfVSg+36zQGVdgwSp8fFxTE5OIpvNolAoOK27XC5jamoKpVIpBFIK5EdHRyFXEt1LnU4H7XbbuZ2oiWtciqCkdYzqdwuyUdaJjp8VgrzPV77vu89SjVIsfNfOyyN6T5z15iPLR1quWrq2fLXeaFGTFzQ2yVgk3b+0rLPZrPu9VCo5RY6xM45/o9FAo9FAq9VCs9lEp9MJ8QOVFAtWUS76uD61FlVcn0dZNr53+Sy1KKDxjYWP4vgyCrSirHXe0+/3nTu92WyiUCigWq1ia2srti5/nXQpd4d/KiwuahVxZj/pPJpPnMvC3meB5rxana+c89TN1pF0HuuOQGXrTNBPpVLI5XIoFouYmppCpVJBuVxGuVxGoVBw7j5q2Iw99Xo9J5Ta7bZzG1Hr1jgUwYnCydaR1+JcQ9rvel/UeEVZVFF9asEuSonQOvgUmPOAVlzdrELj45E4nosS6MOUoqj72U5aaUwGsbHMQqHgLHBa4WqdDQYD50bsdDpoNpuo1+vY2dlxgKbxTAUya4n56svv5+njuPZHKX8+Oo8SEmVRndeaHkZRyooS53q/3/8rSdi4H4vraQlcClrDyDfJ47R5ey2KfEI07r4ordDnDoli7vNqcwpUpGQyiVQqhUwmg1wuh0qlgkqlglKp5MCKQDU+Po4gCBxA0YpqNptoNBoOrBSkmMWncadh2qi2WfsryjqNA+o4EBlmucQpKUoXAcMogeUjXxlx2Xm+54f1Udwc8PV5XDnqylNAY3yMbuVCoRByKefz+VBiDpNA9vf30Wq1UKvVHJjV63W0Wi3s7++Hkm20f6w1RaA97zjZ36IUBPu8TSpRnnsqKU55sXU/TzmJRMIpEE8ljYDrHGRjWko+a8j3mw8Ahlkzw8x2S8MsqSgtzCdYfRNRJ7HPomI6NAUJ3X0zMzOYmppCuVx2CRRMbNHUaLp2ms2m04YJVJparkkRFyXbB1FjplYI2+0bDxX2tgz7zmHa9nnA0r5/WPvsNW23/nZe690HRgSWuHui6LxAH/c8wUzjo1SWaJWR/4rFInK5nEvgIZB1u100Gg3UajXs7u6i0WhgZ2cHrVYrZJHZd0el22vdtE+iLGULFLYM+2wc8CldpB99YHje91iy5TCO/FTRCLhiKJFIYHJy8oyWBZxPE/a5EXxlAKcCUAUlhYEPHKMmvP3fx4hWWNlJYidH1GcikXCZY9lsFuVyGdVqFZVKBdPT0w6o0uk0xsbG0O/3XcJEu912AfXd3V0Xm9rb2wvFo+I0zGF94BMA2p8+YRml8dv3KXD57rX9HafERNV92HX7+zAw81k8w96r/R9Vn2GWnW/+xLVRf4uzSHztVCWK/Knr+YrFIsrlMkqlkgMzxs0Yu6Z7sdFoYHt7G7u7u9je3naWf6/Xw9HRkdcyUqCJUwrPaxnrPb4+GKZIR/12v9ZUFK9EKeL6P5NqngoaAVcEEbR8boDzTDjfb3o9TgCxHCto7T0X1UxZrhW6GqC2jK9/zAibmJhw2mylUsHMzIwDrEKhcEabZVyqXq9jd3cX9Xrduf2Ygm4XmKpF5YvtnFeZsEoA2xGl+Q4rT69HadZR5JvcUZpsFOheZOpdlD8sCF3k+bhyleJAJ05B8c0rC6pRCom2h7EXri+jRVYul1GpVFxCEOOsQXDiFeh0OqjX69je3sbW1hbq9ToajYbjYSpZ+s6osR7Wr/Z3q7TqtShl0/feqN/Pq7D5frPviKoH3/FUWV4j4PKQtbSiJo39Pkxr0vt9FFWGfWdUjOs8E0LLsf/7rnGyp9NpFAoFl0wxOzuLarWKUqmEfD6PiYkJBEGAg4MDdLtdtFot1Ot11Ot11Go1Z1ExPZmxKb4vSqOMqruOTZwmGfVd2xo1MS1A+qwPAqE+Y98TdS0KkM7zbFzf8JrPGoqz9qK07/PwtSUdGy3PByq++3zlnZfOY0XQKtNYGTNbaZFNT087ZYxx2OPjYxd/3dnZwebmpouTtdtt7O3tudiYvte6FHX8o8biPGJW740a67h+iKPzvt93fxzfPhXgNQIuQ4lEAul0OnaCW7rfCT5M+4577qL1i7rfrm3hZGacKp/PY3p62v1NTU05q4op6QpUtVoNjUYD9XrdaaOasaXCP87yY1vshBymIPgUjThBGFX2RRQAn8ZrF2lflIYJcqW4ONtgMAi5M89rGfrqcRGKGgcL2MPqEid8h9XtvMKfZem2VUwqYpJHtVp1XgVaZGNjY24XiVarhZ2dHWxsbLisxU6nc8Ya03rHKVXDlOUoKzNKMTuPohvVV3GK23nJzlsuLr9fGgGXEC0t3zoOn6YY9fv9UpSFFwdO5xXU+huZSN1oTEBhUsXMzAzm5+ddHCCbzTpTn64TTtCdnR00m000m02Xkq5a53msp6j6Kl3UmvRdj3qnT/P3vfc8lvUwC+u8AsI+awX/eayx89R3WN3Pe+8wsPK101KcgjIMtC7adq2/r66JxGnSBxdDMzZWqVRQrVZdZiyTjfb399Fut7G7u4utrS1sbW1hZ2cH7XbbJXloso9Plpynrdo+Xx9H9cmTsazjwC+ur32AC8Blbt4PjYBLKJPJRE5CH0UJD5IKSytcbBaWlmnfMUyrsu/0CTIrEBKJ08A1NcqZmRnMzMygUqmgWCw6d+nx8TE6nY7LumKsiot/qVWqZhkl7KMEsq1jVDsvOrniXBm+uthnh2mZ1oLw7XwRVw/f+5Q/4sYzDrjOU2dbtzhg9JV1HjC2bRtG551T56nXRd7rK1/roWvLmOyRz+ddQhKtsXK57JKRjo+P0Wq1HIjt7Oxga2vLpdwzwcPW16cAaH30/rgxthRnuV20by5yfxSfBEHgvDYXpRFw/T+ie8zSMLDwaZ3DyMd4cc9ZAaqMcF4GVrBKp9MoFovO/Tc3N4eZmRnk83k34Ziezsw/pgYzq4pWlbWo7Pvs9fP00bB7z2tt2Gei3nORuvnKOM84+H7Xd1nAi1urF6UQxb1P+yyK53xtOU+7rFI1TPBGvWtYG6IoCmz5W9wctX0Z9y4777ieLJVKIZvNolgsOktsamoK1WoV+XzeJXn0ej0HXtvb29jc3ESz2XR7Y8YpXb6+UVng4+W4Mobxgq/d5+kjW76tr+/7/cS8RsAF/+Li85jAPoqatPZ3y2znYVJ7TT/jNE+CVT6fd4kV8/PzmJ6eRi6XQzqdRr/fR7fbxe7uLmq1GjY3N9FoNLC3txfaXcCCVdT7rfC7iBYd1Vdx1obe69uo1j7j02J9dfRpiT7BHnW/j49s+Xbh9HnBeBhwRYGHzyKJEkg+cBtGFpAvwttxgHZeyy+uX84zn+P4zNenVDS4jiyTybi5Nj09Hcq4TafTGAwG6PV6zhLb2Nhw6fbdbje08HnY+Mbx4kWsMV/Zti/0+YsAmO9Z/s/vFwWvpz1w6eJipfOAVZxA9g3uMAF+3rJ9ZIURwSqXy6FarWJxcRFzc3NOCxwbG8PBwQE6nY5bS0X3H7fH4X5uh4eHZ47c8NXXV5e49seBQlRf+NocJVw0aSEqWUITQ3xkBfx5J/15QIX3+HbbH0a23XEWThwQxgkkCwZR9Ygq29YhSrDGCbe4d0QJawt8Ue2KqwP/twASxb/2OpeOcA5y/00uHZmamkI+n8f4+Dj29/fdWrG1tTVsb287S0xPJPD1YxRwxfGDbwx8/RH3fJRs8imZvnf6fr8IeD2tgSsKtEhxmrfv2nl3bY+bmHHl29/swZWaDTg9PY3FxUXMzs5iZmYGpVLJZUFxq5vNzU2XxqsMc3Bw4HYM0CQLW8+obDbbVvtbHNj7rlurIK5f7f1R747q06h7fBq9/R4F2FHviAIu+78PPOOUiDhwiivf93ucMPSNx7AYnxX2VsmIAmF9/zDrQs9aO6+oOg9PKdn2Ro0X//TMslKphGq16rweU1NTyGazSCaT7piQzc1NrK2tYWtryyU92X03+Y7zKFO2LXGyLIoPz0Nx9Rmm8ALnzzZ82gKX3TA3CjyGMa5PkMZZDcPeF/UOfRd/49/4+Diy2SympqYwPz+PhYUFzM/Po1qtYmJiAkdHR2g2m6F1J41GA71eDwDcuVXcU4yLgu16FKvd+YRQVNttP5y334YBTRRQqEDUOkdpmPbZqLJ1/OKsHl9dteyo3+x77D1R5dr6xwn/87TdV58o95UPcHz3xYHJsPZGtSlOGTqvcNY2DANqve6bj1Fl0wLjXM3lcg7MyuUypqenMTs7i+npaZck1u12UavVsLa2hnv37qFWqzlPiE2x99Xf9mGU29bKMlt3e88wWRmnQNr6+oibasfR0xK4LpKIcZGBI0UJprj7oxjGvlsnARdMzs7OYmlpCfPz85iZmUEul3NtXl9fx+rqKjY3N7G7u+s2u+QOGADcGVZ2l2xbD62DCu5hGpzVzs/TJ8PKi+qjqO9xFlCcJhg1NrSUbJ18Y3VeitrBxLYhSvjYZ3z9H0VRvO+zqoZp1D4Bb8uJusfXvjheuAidl3d9dRhWH197+MnfmFpP9+H4+MkJUYPBwC3y5040c3NzmJ6eRjabxWAwQLfbxfb2Nu7cuePWinU6HefOPw+oW9nl++4jO399fRZlaQ8jn+KXSCSGWl5PO+BKJpNIp9MAhrscLqIpRAkOqwGdZ9JFlZFMJjE5OekWBROsZmdnUS6XkUqlcHx8jHa7jVqthtu3b+P27dvY2dnB8fGxazt3Yj88PDwDWHFav62brw/0Hp/GFSXcosYhTpMfBjh6j+89PjAcVt5F6sCJHqf8+ISBFYRxAtb3XqUooDjvmMS1O+p99n/7aY+IOU+bhoF6HJ2nrr53Rt1zHl7wWafsTyrO/KMCyd1kxsfHkcvlHIDRe5LJZFxSx+bmJu7evYutrS3s7u66He31veeZa1HzLEoR0+d8Sk3U/IrqS71u63dwcBD5zNMOuHStVhTFgVPcQFuGsALKxwxRAlAFDvcGLJfLmJ+fx/LyMhYWFlCtVt0OFlxntbm5iXv37rlFwQcHB+54Ed3o9uDgIHSYnu90WF+99Deta9w9vt99feyzzHzlRgHZMIsg6n77fxRZbT0IAm+/+YDG9qt9n26obBUcy1txQMQ6JRJnY6Bx/RpnKUT1jwWS8wCnXo8ShlqPKIriHzvvop45j7U1zEKxZfvuieo3XRfGU6BpgdFVFgQBJiYmMDU1hStXruDGjRtYXFx0mYmdTgdbW1tYXV3FvXv3XDzM50oETsHfegHOM1d94xQF4Pa5OEUj6n2kqONQRsAVQcM0/TiNddjk800wWyato2KxiJmZGaysrGBpacmtt0omk+j1emg0Go55uZNFr9dDEATuDKJEIoGjo6PQrhZR7sC4tkVZJ1G+c1/7+BeV8GH7Oq4e9n4faPg0QHuvfYd+xvGAjZ/pZFbBROKWQlqW74wn23/6x+ftc7yupzurhcPf9Jwp1k/bwXHR42O0Lyzg2D7ygZkPePUe22afoIwiC+76bNTcsteG8WGc0PW13T4Xp+hxDPQ0cG45x5jz8fExMpmMU1ppiZVKJYyPj+Pw8BDNZhP37t3D7du33WJnrg+zG1YPa5evnVH9FKd4X4Si5tlgMPCucXtaA9d5tCPSeRg0rhzfZPIJWW4vUy6Xsby8jKWlJSwsLGB6ejpkXdVqNayvrzt/d7vdRr/fD+3gDsCdd6WApcLhvCCudeY1nxYfda/2g7UofP2o36M0vKj+jptc2ufWrWLHUTdG5bZYChjj4+MYGxtzgMIFqbyPa3v4fxxA8VPHiP/rPo96P0nfqfXmTuiso549tb+/j0Qi4QQllRy68fhubslDEGNd7KeekearpwXSqHkXJVD1WtTSBV/f+N6h9fEt9NZxiurzqHfcr2j07cyhO3Aw3jMxMeHi2rrEJZPJAADa7Ta2t7exurqK1dVVt1MHvSvDaJji6pOdcYrGeZV5JduPvoMon3bAxT33gGgXV5ymdB5BP6wcIOwDpyuPO1IvLS1heXkZi4uLyOVySCQS2NvbQ71ex71797C+vo7t7W3U63XH0DzanDtYW8Cyu1XH1c0HIva5KLC2zGxBx2p+PvDwra2ywo5l+cq29/jarP+zbAs+XFBKbdj3NzExEXpOwYz3KJCp1cQ2cmy4wJvJMjypl98Zh7SLwAmQeiJwJpNBOp3G5OSks/q4I0q73Uaj0cDh4WFo1wcuRueyCbqc6FbWxB3fgZ5BEDig1fvsd7XofMqJ8lLcUoEokLT8ofdGKVE+fiGdB5B8QBcFzr666P+6az1BLJVKOevj+PgYicTJZuDlchkLCwtYXFzE/Pw8yuUyJiYm0O/3Ua/Xsb6+7hI6arWad/f6qPljwcrXjqg+i1Mg4vpS5YB9l8a8nnbAZV2FwzSBKMFuadig+8ofGxtzZwLNz8/j2rVrWFxcdCmxQRCENCimxHY6HWddpVIpl9bP+JXuyK4JFz6winNjxPVHlNXm0559/eATID6K0tJ998Rp4goaBBNaJApEGjCfmJhwsUFaLuxvgpa1sAhY/G5dg1ZjZf8oENDaYfKMgoEersly1O06OTnplBgqMqwP+aPb7bqMNCYC8FTgiYkJ56YiaO3v77s/gpgCmbVi1cXIcWEd2QZtjw/YfHxjgd/2YZTmHyUMbRlKUfNCy4jiYZ+C5KvTMMVYlVpaYAQlKhaDwQCTk5MoFAqYmZlx8W9mF/f7fTSbTdRqNdy5cwd37txx27fxeVvXYZ6BYdd8bYm7N6pM+7xaXk974ALOgpd1H8RpCXEWmK8MCrZMJoNqtepiV0tLS5iamsLExISLXa2vr7tFiExlD4LACVTNDuz1ek4bs66lKGtnmFXla2vURIzri2Ealq2bBSvbDhWMPutQwUQ1V/0jSFHI8zr71oKTgpF1Gdp2+pQV+3+UkBjWv2w7rToLEhYorVWRTCYdKPniXD4hry5BWoUEMFqIBDLyIvmR92idta58H4Ux79VYnM9joG2M8xrEuVjj5rVvHHxeBVuOLdMnwHVc4ixFa3lyJw4qJZz77ONkMolsNotKpeLkyuzsLIrFIiYmJrC/v4+trS1nha2vr6PZbLqDXLV+UX0yTNG37Yzqz/OU4eufg4MDDAaDpx9w0VV4HnM1CuDOQxQqKoSpGXHd1dWrVzE3N4d8Po8gCNyCw7t37zp3IGNXFKJcf0bh4TvvyjdBz8NwdsJE3a/95wMunZQsj0LE16f2nSo8WZ4V9goiajHROqIlxRhOOp1GNpsNTXyfRaWgRIraIURPbNbfKeijtHDf6c5sk96ngt63Hsy+V8snWWBj5pqtiy8+xTrR1ajWDsvmvWoVHh4eujOqtra2UKvV0O12Q/ez3alU6kzbOA70IhDEjo6OQi5Mq6Bpu6KOJvJZZZZftf1R2r/PIoySD+ede3q/71578CUtMMbBmC0MwC1sZmIX4+Tcl1STOe7evevOD2M2o61/VD/46n/edscpB1HPBcGJy/BpB1yZTOaMJk+K0oLOA2B2wrMcuqaKxSIWFhZw5coVXLlyxW1wOxgM0Gq1sLGxgbW1Nayvr2Nrawu9Xg+DwcCBFU8YpvuIk9g3ea32Fwe457W0gNPsQeueseX4JrMFHfu73qPC1saGOGkVnGgN6ScPAszn88jn88jlcu4UW33GN7msEPdp77zPtw0PBbmSpiJHWVzaN+pm1Od8Fobv/fzU/weDgQMulm/deD6BrUkbvjid1l/7k8fhcB9MnizAmJ09i4lgROtCLV/+TguMFh7/1OUYpbhFWcK+/tJ2WbJKm0/psjRMWY56h68ctcDUCmN8W5e6UPbQjbiysuL2KwXgltCsrq7iiSeewPb2dgjAoizaqPoO48040vGJUoif1sDlE7LnofNoYCSukOdu7NeuXcPKygoqlQpSqRQODw9Rq9Vw7949B1jcHxCAm7QUGNSmGPNQ4PBZSb56DgM0370UVvZ6HNk6qfbu03q1znxOY1A27qTJD2pxsc8ymQxyuVxoax3GbqyQsn2pdVPwsc8pmNu+tsDLaz7yralRINB+8gGt7euoHU94zWZK+tqt5erY2bHU+qsVrJYarToux+CROIyz8Tw3eg/U7cU5pO5Nyyf8rvFBTXLhM3ZBsM/S530+xUyftX2qdYnqeys7ogSzljmMdK4QwDgHAISU3GQy6cITc3NzuHr1KhYWFtwepgcHB9jc3MTt27exurqK9fV1d/il8u4wMI+zsuJkhw+0fMpGEJwcD/O0A65sNgvAbxXo9SiKssY0XpDJZDA1NYXl5WVcv34dCwsLqFQqGBsbQ6/Xc+uuuPK93W7j+PjYuQMpZKMyuqKY5CIgHDc5otoYxZR2JwQVjHq/WlTWAlNrihqkxp0IUnyGgJbJZNwftU5m0lnA9SUA+FLNo9oZp7REtVevEQijEjbixkDvt++wgjPqOQXTOOEaZbH4fvO1RbMcFcBY3yAInKXU6/XQ6XTQarVQr9ddFqUqJonEqctQ47nsT435qhWpVpnG2pQHfNYuEObRqH7U/tcxsYqhKgFajn3G912vRVEQBKGsViZzWDci10NxUfP8/LyLhVUqFaTTabczx+3bt/HEE09gY2MD9Xr9DIDZdtj6+H6LAjPLGz55o3L2aQlc9pRjS1HCPE6jItNns1mUy2VcuXIFV69exfLyMqamppBInGyYyR2f79y5E8oOpLDVZAvN3IrSiuPIZ00pY/gsNSWrZVktKEoTtVqSz+1GoaSTbHJy0sWgaDFx8tksPbWu1AKzloe+n8F/XyzHWke+vvBZOz5rSX+3/RVlHVEY+qzUqPG27zgPX0QpaT6Avuh9VlhpPEYVDi6IV2JcrNPpuMxFAGcsbFpu3W4XrVYLzWbTWWdqUWv8S0GMwKcJDbrDhIKXr83KT5YImHzWPqdkQclnWZxXqbRl8E9drVziwHV8BKGJiQkUi0XMz89jaWkJ165dc7vxHB0doV6v486dO3jiiSewtrbmAIwKg5WjPqUqqi0+/vaBuO+epzVwRU3MKOFjr/E6t2Oanp528auFhQWXcNFsNrGxsYEnnngC6+vr2N3dRa/Xc8kamn6spr3diknpvC4HXvPdN0yjt+/yAZ4P+HwgS0uUk4kglc1mkc/nkclk3P9MpFCrSd1tmizAa2rJKWBZN6D+RtJ4p+UDH+AMEyzDtEz7u3XtRVmrlrS+FkC1zXGa+3m0fNtuK0Ti5ot1S+pY8je7eJp8r9ablssxJtgp8FA54fq3vb097O3tOSvOApoPyGzMMo7Pgej4YhwNs6Z8wOa7N8rS03oT1O2+iKoYU+mem5vD0tKSS+bI5/Mug299fR2PP/44VldXsbu7i729vTNtj5oTvrb6QC6K/0bAZRYgR3WUJdtcMkM+n8fs7KwLei4sLLiEi0aj4bJ2uPhvf38fg8Eg5Nbi5LFbMcVpbJaGWVfnIR/Y+d6tfUeXk3W1BUEQsoxoRRGsNGmCYGVdghb8fO9R68mCkq0PyU42X6KOFVa+vqQQtf0fBeRaZ76X9WF7fc9aa88HoPb9ts1RAmOYJeADL58CE1Wuje/52kGLSn+PAkvbTp8SoMB2cHAQWnDNE73pMtPn1SrT0759hzjad9ssTiXfuOrzcfGjqDkcpxxFWYPqRqQbPpFIhLI1E4mEi4NduXIFKysrmJ+fd4kcu7u7WF1dxe3bt3Hnzh2nhNsMWh8/RllXw5Qm256nLXBxJwofwscxBLVDppkSsJghmE6ncXh4iEaj4eJXa2traLVa7mwrCnQGnFVrJAOr791Xv4taSvpbFHPFMQqv+wQU66v30e2pVlShUEAul3PWlboBqQGqUFfhYy0kfmc/UdjxfpJNafcJ9Kg+U23VgqKN12g5vn62k9UHBsyk8wlsn9BSa8xaV3GKl9YzDtx8ZAHH1x7fM2ol2WctELBtljct8MXdSx7UNh0fH6Pb7aLZbGJ3dxe1Wu1M4gGFO8dX08tpmejaNx/A2j6J6hsff5xn3M6rgPjGgW3kH60wzdpkeweDk0XN1WrVuRC5K0cikUCn08Hdu3dx69YtPPHEE253eutCjCOf0nQeWAmCAPv7+09f4AKiEV2JlkMul0O1WsXCwgJWVlbcwj5uclmv17G2tuYOfdOV6brjgbpLVAizPkA49hHHBHGD7QNmfc4nIH3lqaBk3exkUasql8uhUCigWCyGrKpsNhva9FcFLvtIY1Csl084WjCzrkTbP7Zddvx9QsRe1zpb15y1LHzC3TeWcWDqGxv9rmVbUIhrm48ILizLAoJtk5bncxdZkNN6+vrK12f8nx4IjT3ZP9t3vsXiAFySRqPRwO7uLnZ2dpzbi1ZYMpl0fDo2NubmpwUx3xFAquz4eNf3Xett+9b2a9xc91Ec37OuuiifS2663S729vaQTCaRy+VQqVSwuLjoEs3K5TLGxsbQarWwurqKW7du4c6dO9je3vZaYPruKDlk5VFU+572Fhfg95/yOgeVZ18tLCxgeXkZc3Nzzh3IvQM5CZhw4Vsg6Ut28E1U33clH7Ofh/GjyrFl+MpVrZl106PICU6FQgHlchmlUslZV0zCYJvVmtJP/bsforvN1x8+gNDfVGGIGgdd92QVkKg+te8ZlhrvG3efpWLfFyWc9FPvi8qi9L2LbdVy4uI6PkskDjztfQr0UYLeB672nZrIY3dSYZIHz63b2dlx65cIYrTc9HnOAwKgznOfBal9GJXUYcctDoR8rml9Jk4B8/GI7SeC1/HxsbOg+Fsmk0GlUnEbJywvL6NQKCCRSKDdbmNtbQ1f/OIXXRyflpuPX3W8LKki5usLKhFPO+CyMS4LVjxaW1edz83NYWpqCuPj49jf33eLhVdXV7G9ve20NQZ8de81+x5+188oDSNKiEYJ6Pshnej8VEGmYKVuQJ4PRrCihZXJZM6smeJktzud66S+KEVlACpZDd8Xc9HyrOD03WP/98VvOOF99eD3p2IM4yzz+9HsLY/avlBgtwCj9YiyIm29fHXgtThhC4T72Bc3tOvJ1BXIjZGB0x1oGAMjkFkXP9umu6ywHrqg2gdi2l+sn/arr3/iKM768t17nnI1WYZ1ZFt0veTk5CSKxSKWl5fdvog8XmVvbw9bW1u4e/euO2bJntIcx4Nx483fn7YWl275pGCVTqdRKBRQqVQwNzcX2qgyCILQ7hZMDbV+3TirwScU9P+4yR1XlpYZ9Wwco6slYAELQGhxYy6XQ6lUQrlcDoFVLpdzi6XJ8LqLgVpXNg7lE+r8zdbfl1gR9UwUUFmBwj7TWJnPVabv9Fl02odWM7ZKB//YFyoElVgX1fbV5awC0pIVCFH3DSO1zKxAjlI8yD828SXq/T7rip9R1rJ+942/lm3HXV2JBDEqV/v7+2g0GqjX66jVaqjX6+h2u6G9GVkGLRTNiNTEDqug8M9aIjahyCfcfcp2VF/aZy4iru28UDmpCgDPCpyensbU1BSmp6exuLjoMhF3d3exvr7u1qrynEBVhuPqECf79vb2nn7AlcvlQhk2+XzeWVfz8/OYmZkJHdDG3S149hU1CJ97y+faUGY9DxD5GDPqWlQ5w4SFMoYN9JMI5rlcDsVi0bkACVgEKz6rsTydtCzTLr61MaqoCafCi6Rl+dwh+r+69CyYW7JAymQMW0efpqzCkTyhafuWrMBXYFeeomLFe2xSivap7SufdaTtV1BSisoSG+biVFC279f3+oDKZ7lp+VHKWtS742J12l/2mBqO3+HhIdrtNnZ2dtxfs9nE3t5e6Kgggp+uKQTg0vJ1aYuOlbbNFzf2tVWf8V235frGzTfPfL/F1cO6Xrnpwvz8vHMjVqtVJJNJNJvN0OkW3BNRZWicQmO/DwaDpydwLS8vO0FcrVYxOzuLqakpFAoFpFIpHB0dubVX/Gs2m866Ui3Tl1jh03TOa9r7mIl0Hm3ZCnqfBmuFuN6XTCZDrkAC1fT0tLOumBEJnJ6oq59WgJCirCKlYe4/7Ucf8NnJzUkW13f6rO0bCwx8ry6o9dVN+zNOAOsYKHDpDg8sh38+65Pgpu7eOLIWKK0/y9c6lrZPzisCfAqUCms7bj6+1HZqGee1yKJIx11PANBF0/1+H/v7+6jX6y6OXa/X0W63Q0kdwKl3QtPNOaZ2uza+XzNXfRaatsU3py/SXl/bLVlLXd9lrW6baDY2NoZsNovp6WlcvXoVS0tLmJubQzabxfHxscvoXF9fx+bmplMElN+tDPQpV0/LrMLv+I7vwPz8PCqVCvL5vFsl3u12sb29ja2tLdy7d8+5CNQ/ayfzMIq6Z5hLwAKdb4LHUZTWre4wvabbJ/FsJh5qWa1WUSgUQmnrutmpr298gsgGXK3QtnQe96HvU0HDlmVBQ7VGkv5v3TMqJIe5ZKLqFTeG7FNdVKsuW1UYCKC0GuKSFYZZQHqN1qzWpd/vnxGk/G77KKoP9Df7f5SFZeeDr02+sdVyfP9r3Ww9de9LdQMOBidbDbVaLWxvb6NWq6FWqznhy4xYLUOPyQFO4mncuYIWR1Qf2j6ydY+zmOMozuLSsqL42/6vsUReTyaT7hT3xcVFt8a1VCohkTg5FHd7e9sZBo1GA61WC/v7+15vjdY9CJ6m6fBvfvObkc1m3RYzOzs72NzcdCcKkwltRqCNP0TRMKvH13VRAt1nQdgJr89Ysi44/dQjwun20zjf9PQ0SqVS6BgVBqKpoXO7nWHCOUoAAeGtmYaRLyVa3xEFmjqxNENMsx3j+t53zQfIvnVk51E2tG8YH9E62zZq7GvYe6I0WN/9NpWbSok9lNQHkD4rz8Yy7LhZgLLAFjV3fHxklT3bvz4Q0/Vz2gf8tJs48z5uEkxFd2dnx8kOtar0FG1aYdx6ybdptu0bG/uyZOe0lTtxPKH9GtVnUeQr21pgJO4sxDVh8/PzmJ2ddXvGcn3dxsYGtre3QyCm7kTliftNzhgffsvfXOLZQNvb247h2Em6+7TtrCgt9bxai3767rW/WWa0NIzporRTaoDcxUJPVy2VSpidnUWpVEI6nUYikXBat92glJPK5+6z7bdt4HEWvhhQVDzKN1FsX/l23OCE0k1fo7R0n3BTAeKLqfE3gjrvs1sZRVl99h3aXmrv9ne1FOPcgj7lxoKDtWZ5H/uJApi7K1igiSrHN0ZRglbvsdmdvrHRjELbnii+8Skovr5T0NZ0cMav1J1eLBYxNzeHer2Ozc1Nd3xLp9Nx65n0NGsFMc4/njDtcyNy71JfDN2nEGjbzqN4+cA+qnylKNmjfcZxPDo6cmC0vr7uTmpeWFjA7OwsKpUKFhYWMD8/785wozWrLllmeJ7XgPDRpba4Hn74YXQ6HZf9Y60qBS0g3lKK0sxJKuTsMz5BEqcZ+5jMx6QWsCgMJiYmQnsD8o9WVqlUCmVdcisqu+0NJ3VcvMWSbasvPuVLfjjP83GBZwp+7tDhE3ZRE5i/W8CwlgHrYHdWoKvIttP3Dt2fj+1IJBLOPRtl8asbKypj0rbXtkn5nf2l9VXL0sZ0dZ5o2ZYH7PsUZGzfR/WRrTfrOEzQWsvY9gn/13r4wNyeqK1WWL/fd0J3e3sb29vb2N3ddZl0VNTUpZtMni7+1fnGhA4bO7d955Mz+n+UnPH17bB7fBavr899MktBjKRZiTMzMy4xrlwuI51OA4A7NaBer7tlCq1WC+12G3fv3n36uQrL5bK7pqDFz6hB4rWLuCQscEVNLp+26LtPSRlTGUOFCd2BzAwsFosoFAoOwHi4Irdf4gTSw/l0AtlJEdVPet0KMsZmrGbuc4v5/mcZvomtZamVZbdp8o1p3HctW8uxVgAtUyoKdjNgX7IGn2Ucifdw7AC4sVCg0nraJA4FMK2n/c5+1DiWWoq+bE/fWPDdPkXGApvPSlOywKZ8bvs+TgzpfPYJdtZZ60EQ4m/aPpICGDeCJpgNBgPnRuTmBFtbW25XdS2PyggtuHQ6HTodgrEw637W8feBto6VHYMoWWLLiJJxw6y4OGXEyiqVUTwZvlqtYnp6GrOzsyiXy8jn8055Ozg4cNbY+973vqefq1DdgRcFjDgQiaMobT7q92EMZsHKtoULBQuFgrOmpqamXGZgNpsNnftFN5duu2TdgTa+xP99gdS4eFXccfQ+YaE+bguCVkizXCt8fULOB3g2wSQKAKwQtJaK1ld39Ih6P5/V+wi8QRCE4or6zmFuQj5jrVmrEbOfKYB9ZUf1hS+uYV3A9hm+j5+qPOo1++6o73YuKbBHAXdUP+p4KSirS5j9Sn7UZA7uwl4sFlGtVt1ymp2dHezt7aHb7YYSYJg2f3h4iImJCbcB9+TkJA4PD50bUZULradPCTgvQF30nvPMIe0337NW4R4MBm4H/52dHTzxxBNOZjHWXqlUnHWmhsdF6W8FcCnFWVJxTBD3my3/Is/4wFSFvf7GSUoNPZvNhgaei4Xz+bybYHyOIEXBqGBlhaiv3lbA8JoVlFHWq07EKItNhZQKVYKFBS376XMv2Z0t2N++gx5tXWydrABUIaeuLOt6s2XywD8mZtBSZj/pZsy6jx5BWstU/vEpOLZ/aCEqAFugiBrLKGDX63SV6RjwPbbuUe5Hjo0FwKhxZ1m+frEAZsfEkuUhfj88PHTxP03E4P9UFufn592+iJubm6HFuKlUKrTbTrvddoon48/MRiTAWYXbN75RStZ5ZJxvrkcp+L77tBz7Pp3zdk6Q//f397G9ve36IZvNolKpYGpqCrlcbmgdouhSA1eU2RxndkeVY5ne9/8wK+08Jr0PCDT1dnJy0u0XWK1WUalUnJaSzWZDQV5aVhrotDG9uLprP9nYitbVCgNf26zLS+/RLC6fhWCtBd+ECYLTPQX1vVGZePpOq8z4+se2z/aB1exZVwo+BQkC3uHhIQBgcnISlUrFaeR7e3uhumr7rcZtXbNxlpm1CFWw2/6xrjc7bgRqO/4K5kB4gbN1S9r+tklAdG1GWfXq9rOWn1pjcckkbIcvJmpJgZUeCyqRBDG65xnP2draQq1WQ6PRwMHBQUjBYTmMdfEYEs7lg4OD0AnQHF+tq7U6fUqIkgWY81hcPuCPu9/3LrubvOUDHvfErENVvO+HLjVwxU1M3wD6hJTvu9WC7HdfPfQz7jfWWd1m3PiS+ypOTU2hUqmgWq26rEBOCDK5pvjbA/MAv5DzCSifFaGTz94bV4a2TwWZauIqpLWuCgK271g/a3HY+tlno9pg3+17n22Pks+asxYAhXIymXTnIu3v76PdbqPT6YSeYT0UFFSjHhZfs/VlLMG+I8pasb/Zd9hxU8Gq1pPynE25t/zFZ1RhURBToa08xffrPPJZA6po6HvsPZa0/nw/wUyTORhTrlQqqNfrLgW8Xq+j0+m4enFMB4NByI3I1HK6ETX9nuOofz4LjP1mlYs4eWWtNpLt42HWnH1W3816kT9sWwC4PSHvly41cAHDhZYP0Oz1KK3kInXwlWuFtE6EROLEhcSDGGk+E7Dy+bwL6Kt7iRaWbat9r2VCX5Bd6xhlefDTCiZ93q6l8sXQ9B02SM3JrYLRJ4z0WStYNf5j+yJKu7Y8Yu9TgWH70JLvvewLHg8TBIHLPNP71VJS8FIhrmUryEW1Wds4TADa67ZMC2DKc5qdqUDF7xT4er/Wk25NtkstHnoVtN38jBPsdnw1tVvb43M92/ewLPWK0Poi+BDAms0m7t27506Z6Ha7znWvMVom6GgsLJPJhNLpfeeFqfWl4xenhNtxVqU0Dth8ZBWfuGfseFjF3afAXoQuNXDFaZ2+a7YzST7Br9eHafdRA+Rz/ySTJ4dPZrPZ0HZV1WoV5XIZuVzOnaWjSRZ2EbVqhta9wPdbUFPBpHXUScH7fO1UVxCft+4hGxDX/63GrnW08RHrLuF9Pq2fFAWsCgZRPKPjFNWXug7LZg/6+o3v4mJVup0UzDUjTfvUrm+y9VbQssDt06S1nCj+9ikZti/1fh0P/c3u0zjMUtb32iNLxsfHz3gY9C9qHHU8bZ+RfLziK1O9AVQcGcfkeV/ZbNatn5ybm3O79jD1+/Dw0CV/EOx1CymeGF4qlVxsSPdHVGFvFaonS3HKXRQfnKdMlmHLjALci9ClBq4o5PcJazthz0O+Z+IGMs7SSSZPUmV1o1tm2ZRKJXeECGMjTGVXl8V5+sNXJ3stKinEWli8zyZQ+GJhvneqVRAl3PV+K9T4HgUdfYctxwpuK/h9vKB1shZEnCbrE7b2vkTidAsnuoEYp+ROArbe1g2p1p72k4196bv1Xv3d12faN9ZSi6IowLO8pkqNz9WrfzZGyr5jWzk31ONwP0LQWqnalqh5pvVStzbdm5rEkU6n3TqmmZkZbGxs4O7du6Ezwtg2PYXBxtMYSzs4OHD7KPrmgbXC7O++tth74yw03zjHzV+fvPmroEsNXMBZYRJnVfEevd9e81EcI1jQUuYng3IrJsavCFjlctm5AwlYal0pQ8Zphz6rUIWxTlCto48BLUBZ8PL1qa4bUkFgtV1fn1th5QMA33c7znZBsW/MfNq+/h81UbWe1Jptdl0UsV71eh1HR0cuw6zdbrs+i4pbWddVVL30XpIFiMHgdD/EOPLNoWECy1eGPR2b1y0vslz+by1HBTCNMXGOaHzXN6Y2mQSIX+IR1R6fG9HGv+g+tAA2PT2N9fV13Lt3D9vb26H9EGmBsZ60tJjVmE6nMTk56RI5fNtKaTq9tt1+WjkxTKZFXbO/s1y6IeOU/TgD4CJ0qYEryuKKGxDL3FbbjCM70aI0SAWsbDbrXcfAhAtdJGwnYdQEUz+1b0GuD7gtIPg0NFoPvu2HfBqyghxwKnTszhCcaLZM1Xytds532uC/naBxAGLfyX5T14+OvRW2UYLWjou1lPg9CAJncXW7XfR6PUxPT7sdBaxl5AMfn3Xoc3uq9ab32D7UTy1T+yIKqOwzUbym9faBkK89/FOXsU8B0zKtJcnlMb4sQ9se31xWigM7C7TsY7p41QLLZDJYXFx0+4bevXvXnVLBZA3dwozl8mRmWmF0SabT6RCAaRzMx9/a1ouAhR3fONnK36LkVZyFdr90qYGLFAVI+p332e++To8aYApCn+uLv+lBjRq/mp6edqvH6WIgYGkGznnbq8yobfdl5sUBOYFKN3ml9meBSlPuKYQs6aTxjYt+AnAZVrpxKd0liUQiJBhsnI8n3mqmJctXkFHBZAGQwtJmPmqdrRbp0yo1BkLXTyKRcO4g7sIwNTWFUqnkTttm/aIsJ9uP+rsKc1tvFaw6NpocoQqQCnAtK8qK9V2j4kM3GJOPOLYUvlR2NFZ4eHjoXORMnabw5vgy0UHfq/EiKk1RySV2uzNtSxTY+cj2iQU11oHW1/j4OMrlstvlplAoYH193VlfukuLLhwPgtM0cgWwYrEY2p1ed7O3yoLl17g2aXviLCwf4FzkHU8GsEiXHriiJljcffYaKUoz0O++ZAYyFbd74Rqs+fn5EGABp5qUXX9lLQOdDNZNocJKwUTJF1OwAlCTBfQ+zWjiRKJQ0L3XfHEr/d9aDPqp1p0G5DnZuYEw79W2qDVxeHiIbreLbrfr3Cxc2KnuUAs8LMf2qeUnq0xErZXSetF1Sg2Z4zkYnGwjRB6p1+uuTmqV+ix5y3/8znKjFBlr8VhFhr9ZQCRFzSlVclT54CbPuVzOubnUAtH3WQVC3XB0m/d6PXS7XbTbbbdJK4U166H8o2n4dpwI2jqmtl102/p2QNExjhLS2gbOJfbPxMQEpqen3WLkiYmJUPKGtbI479g/BCpdD0b3ogUwq5SovNL6Ryn5yhs+Rd9a5/pMnOL/VNGlBi7LnLzm0xKjtAS9x0dRmhu/kwk5aUulktshuVwuOwuLpn1UwkXU//rdCm+WYUFPNU4rVPm7bhOlFg1db3bX+UTiZHdoe9yDzz2gGrjVanWiqFtNU6cplPRe9i832FWrl/vB7e/vO/DSI9qZfuybwL6MN/abr23DrGLWmZp2JpMJxUEODg7cFkKdTiekeFBhIJhbvmQ8zAKPJsdQiFPwAQhZpgoQViHyKTrWmuI7yD8UoiqM+ZwmGLHedqNnJYIflRcuIyiVSu7IjG63i06ng3a7jf39/dCYsc853mqF6wJxzQ7V/0l2TqkMsX2m/ejjE5VR7LdisRiyTAG4k4T19Ab2g533BwcHGAwGbm9S7tjB7ZbY51p//ST5ZKelKEXfB1j66QM7e/3J0KUGLh9FaZ4+LVOv+7Ro/VMGJEPRpcX1HDMzM+6k0EwmgyAI3EK7KMEZRXbiWPcXP6My/tgmq32z7qpR6iSnK6JYLDrhx2B4s9l096q2rILT9rWPWH/tE21rKpVy7aWwp8tkcnLS1V+tx/HxcbfjCC0x7lJBobe3t3fGneizoG1do6wVH39RgAZB4IQ5gT6RSDjXV6FQQD6fR6vVcs/RCtHtohTE4urOPuDhoel02j3LE2t3d3fdbh4AzoyjFbD8s2dZ8V10g/IvCAJ3rJAuFVCLSl27qnwBCLmM6WbkCQeDwcApUew7PTpeE4So3FAJUwAjv/DTp9zpmPrG2FqoPp6xv2t7OU6Li4uuPzc2NtBut52soCtQt56iUkLXKuUL+0x35NDjVZS/WH875lHWkLWyVA5FKf4WqKKMhidjgV1q4IqzkoZpE3EDFQVYnKw834qDmMlksLS0hKtXr7pMQeu3jwru2u/qVtKJ5XNbAWePbeA1ZTJ1C/I+jR0xHsPzdXi6aRCcnt/UbrfRarXQ6/XOlK39ZskCpworCmn+Nj4+HnKlqQbOttN9pFo8BZW6HsfGxpwFwDU23NWAgk6tCFtnqwCpC8YqPqqFa1m0VOn+4a7jnU7HnQHFDDNVPmihWa3eChnWRY+5KRQKIfdkIpHA5OSks5h3d3dDFo91H6nLVkFL66YuWBLvYd3JOxxX4ERRofuUYGLdWwBCJwuz76gQ0JosFovuGXvirj2vzSY+KbAQhPT9tl98PK79Z3nCNxfVjRwEASYmJpDP50PAzxOE+S61Tulm5RpPjfmyH6lockcO3/lgvnkaBUb6/bz/+77/VdClBi4gvqOjrCxLChp6rwpGugM5eY+OjjA+Po7p6WlcuXIF165dcxupUmvS3dl97/O9304gK/gB//EMypD8VC2NAojMrpNicnISs7OzzlLU+Fuv10O9Xke9Xkev13PvpIDQetsJYCe6ugTVLalxCp7d02633Zq3UqnkrAhq9QTRo6OjkNVJ5ULbnkqlUCqVXB80Go1QrEIBleVYi9Lyjo6hHQ8K7cnJSTf+1JxpCVIYpVKpkBWkAloFk1Va2Gd0pdESTSQSIWBXt2GhUEC73Q4JcQUr5T9aeLTIVZkjP7GdymMKdnw3FQvL2+w7bZtuf0T+0Dge30uLNp/PuzJ4XhbdbKwTwcvu6WnHLMoTYl2pqkz62qOgyE8rnwhehUIhdBjlrVu33NIJAK49dPOSxzl/OE707KhFTEuMm/r6ACyKj4fJSv1+EetpmDfmvHSpgcvHEJYseOmnalvWvUaXVTqddouDdXLkcjksLS3hxo0bmJ2dRSaTccyjmYKAHxAtWWa39fQlX/DTp0Xp1jQqiHQCJ5NJFAoFrKysYH5+Hslk0mmu1Nba7TaazSb29/fPaMacpD7ti3VWdynjUJxwWlcqCKrpt9tt7O7uuiUF09PTKJfLbtF2s9nE9vY2ut2uK1PdWAQPTma6EQ8ODkKTTUGK/aZHT0TxWZSrhNYe3XTWaib4WlL+5LixHzXtn67pUqmEYrEYAkRVCijoB4OBA5B0Oh3aRokuY+tG4lhTWFqAY2yFLj2Om/IbAYcKj+1Dm83KvqdFxgxDei/UuqBiqAqAHphKZYHlM/ZGEGM9OC80ZkqKijWz7sPkj8+yAcIucoL93NycO6Lo1q1bqNVqZ2JwdB9SgbZLRdh/JBvOsKn0VunX9WA6P6LcgHHGgPUq2eefrFV2qYFLBYJSHBPppw168rruH5ZKpUIZPclkEnNzc7h69SqWlpZQrVaRTCadYLZxG62jz3rS+ln3jQ0cK1jZuJKWo/EI4NTHr0kKExMTmJmZwfLyMqamppwwZTsZH+KqfYK8XejL+qmQ1Xdx+xpm/nHSUGCqq4UaOl17BJyJiQns7u6iVqu5HQnm5uYwMzPjNjnd3d112VkUerSceDw7XXe8z2cJW/Bg/6kiwkmp362FSS2Z11kfgov2FS0+O562jyicCN606Kw7iFYS66YWOQWZki9JQ8eEvKSxLk2ioDLQarXOHF7KTx/54rP2XQQeVS5U+VKQYmyUiqOeVqyuTCoUCtLa9/oZpTQq30f1pc/1qK56HetUKoWpqSk84xnPQD6fx1e+8hWsrq6i0+mE+IzuVl0uom5I9X5wHqpCSAA7ODgI8YxVrm2/+GRqHPhEyWb9/cnQpQYuUhyy++6NYige+sa1J/1+3wlzACiVSlhaWsK1a9cwOzvrBAddVrrOxGeNkBQwWSd1SdnJoGtvWH/eq382kAucnoujAf9cLoeFhQVcvXoVqVQKnU7HZSpR2DC+QFec7rGn2pQ+o8c06LZVVmtW4WHXGSUSCZdSra4nZrBxj8dqtYq5uTksLy9jcXERi4uL2NrawubmJprNZgik+T66DJmwYS0vFVQ2HdoCE6/Z5QEaQOf2PmxTIpE4s27PJruoELf8ybhYqVTCxMSE4zsKb+tStIJY3Ye0xKwrm6Ch1pO6+pSndOztQnrewzZEKT58P/fy4zjpuKu1py4uCm62WTd51gM7Ne6lCSv2QEefd4Z9b4FIFRtrmdg573NLkncUfOn6vHHjBsrlMgqFAu7cuYOdnZ3QxruHh4dOCWF5OpdYPutNVyFBi7FjxotVmWI7KGNYd5+nIY5oceszca7Ji9LfCuAC4tMzrV9aNWRNuNCMNVoK9BvPzc3hwQcfxOLiojtmWhMFrJZiSYWhrYsKc2vB2DYpYKjQVS2YvytgERTy+TyuXr2KK1euoN/vY3d3F71eL7QWxmqUGlcg9ft9p7VxDzZdCKw7YGsfWE0TQEjA0Hplv+ui1YmJCezv76PT6WBrawtf+tKXUKlUcOPGDVy/fh2zs7OoVqvY3NzE5uYmOp2Oi0USSFOpFKrVKnZ3d0MbmCrPUBDoBKY2r4Ke/5OX1BK2Y2eFOJ+1wMU+0D9m0k1NTSGdTjvhxQQG9p8KcR1LghD71c4XX4xQ1xApENktydTyUTelbl9GouAkn5NPms2mO+pF4z1M97YuSNZZrQvlT4Kd9q26FTnvk8mk409rRWsf+YStz2NiZQ7/VyXTygfty36/7xS02dlZN+5f+tKXsLW15VziuoZN1zoqGCufWu+Iyjsq6rorvSqmPkvMtlvlaRRZ0LL9elG61MDl60R+WleadbnRP69JFwBCbsHBYIBcLofl5WXcuHED8/PzzhKjZUFQiHIZWAYgqetJTX1LVgu0DERhQzcAha66U1i/XC6Hmzdv4vr16zg8PMTW1hZ6vV7IBcn3UCgpQGoQuNPpOEuNKdCqkWsWIOtJAcLJo5uMcv8/asZsH8uiUkEXpgajb9++jStXruChhx7CAw88gOXlZVSrVWxsbGBzc9OBLMeMO3izrGEashVEKmgIEDbuoMBjy+Qk9ykLWhdmiJVKJZTLZQfAVBIIDj43NAGL/XZ0dIT9/X1vtiKVAk2koEWgMVubLKK7NxBIycecP+wzxsU41uSlXq+HRqOBRqMR8howtlwsFt2GswQ99qEKcGvtqKWo1iHfwT7ifFbr3CeILajZPrTv1u/6rE8OqOKrCVOVSsV5H770pS9hbW0NjUbDARTlj7ofLXBZ60lPnGDZ9lgVTXJh/e2nnTNWibDzxkfnsdyi6FIDlzKsWlVKHDy1rphtQ+GhWiUFQjKZRKVScYJ+amoKAEKLWq0QIJP4NDUS3zWsXay7z0pjuTb5AEAovqAulHK5jAcffBA3btzA3t4e1tfXzxwtToEyGITX2zBZgzsY0M1Gq4QTiS4yxgJZZ3XxcAJp3yl40WpUi1FdiEEQuCQPCsp2u42NjQ2sra3h+vXrePazn40bN27ggQceQKlUwvr6Ojqdjks2yWazmJ+fR7fbxdramnOH2n7mGOj4qoAB4CY/s7cYU9CFo2oRWRBkDJW8wfGdnJxEoVBAsVhEPp9HIpFwcUI97iLOAiAgHh0duZ1FOA/4jCpxHH+CEEGLvKQ7WTA+QoWAIK4L7mmta32ohKiLjHyhGX9cTNtut5HJZFAqldxx77TAVNm080eTJ9QasVmHan1xjHzgFSe0fcqxWnBxYQtdj2it8Uwmg1wuhwcffBCFQgGFQgGPP/64W9KgoQnlNSWtD+/h3Do8PMT+/r7bU5W8TCWHCql6FZR/1ZJT/lV6MuAURxcGrv/5P/8n/vW//td49NFHce/ePfze7/0eXv3qV7vfgyDAW9/6VvzGb/wG6vU6XvjCF+Lf/tt/i6/5mq9x9xwcHOCNb3wj3ve+92F/fx8ve9nL8Gu/9mtYXl6+UF2iYkXKsOqzp0Dxuek0gymbzbp1WSsrKy7NnRq7muHWslLyDVqcX9ea5To5yXC8rseJs1x15ShDF4tFPPOZz8T169fRbrdx9+5dtFot1z9cNwUAu7u7aDabaLfbzv/dbrfRaDScpcM+0JX/rAPfTRcMwYhCSy0STYdXoKNlZYPH1PxV06R10Ol08MQTT6DRaGBnZwfb29t45jOficXFRWSzWdy7d88t8Gy328jn85ibm0Oz2US9Xj9jNasWq+1iPWmhELTUArO7exD4dTsfu8ZJhQqtk2Kx6BayUxu2rjqdA+xbFS66mFX7Wq1eupepnLDfKRwZE9zf33f/E+x9bmQl5dter3dmHaRaWbSAaCGSp9vttuPtbDbrDm9kHNRH1sJhG2lZ8RgRXYxPwPMl7rAtNvNOf7PuV9/z+oy1klkm5wUVm/HxcczPzzv5xaxDDVOo0qwubAVTXY+nvEFFheBFWZnJZJwMsLvPWAUMOHuKQdy4DLtvGF0YuLrdLp73vOfhh37oh/C93/u9Z35/xzvegV/5lV/Bu9/9bjzjGc/AL/7iL+Lbvu3b8MUvfhGFQgEA8Mgjj+AP//AP8f73vx/VahVveMMb8J3f+Z149NFHQ4kIFyHV7ik8dCEiSYU6EN4XLZVKYW5uDg888ACuXr0aSjXmXxTwqNZkg7lWQ4kiZTB1M/FZtRo5ydgm1RZJXCdy8+ZN3LhxA51OB1/+8pdRr9eRSCTcThMzMzNIpVJO4O/s7KDZbDoXDrPFWAdqrdSWOTmsa5GTiZOA15gSr1lQFF4AnAuP8Rv2CVPIqTHrmhaCGZMzarUa1tfX8bznPQ83b97EAw88gJ2dHdRqNbfIs1AoYGFhwcXNKDxYLoWIuk1U2Os+fP1+H4VCwVlLViEiIDApgnEcLhbmmPJdai3RxaNj4OMN9hOFK5NDbCxubGwMmUzGtVEtWBVme3t76HQ6aLVaztJjn9ByIhCTFFg1Nsh27O3tufvV4mI/6jE/Gu87OjpCq9Vy4Do5OekWXFO4q0UPnPXIsI81QYZ/fA/7nf0QBVLa9+oaiwIs+79aKdYyoxJERZnhDMa98vk8HnvsMayvr6Pb7Z5pr88S0v5mG3XMqFzRTUulIJ/PI5fLueUW6jr2uRJtOMPWIc51eBG6MHC98pWvxCtf+Urvb0EQ4Fd/9Vfx5je/Ga95zWsAAO95z3swNzeH9773vfiRH/kRNJtN/OZv/iZ++7d/G9/6rd8KAPid3/kdrKys4I//+I/xile84tx1UUbjhLVBXNVggDDj0PVxfHyMfD6Pa9eu4YEHHsDS0hKy2azTdDWWFefrjrrmAzX9zQ6mNcfVzUZhAYR3v1ANk4I9l8vhxo0buHnzJrrdLm7duuWSEtLpNMrlMlZWVlAsFrGxsYHV1VWsra2hVqthd3cX9Xrd7V5NYa5WBK0udf9ZBqXwTiQSDryoKfJ3Cn67VY9qh+yLsbEx5HI5JBKJkCtT+6zT6eArX/mKsx4bjQae97znYXFxEel0Gmtra87iXFxcxNHREb785S+7tWqsv7qQWX8Cjs16IxhMTk66OrRaLbTbbZckAsC5dxKJBKampjAzM4NCoeCSJ2wa+TANVS1YCm8qYxwf5Svu+ZhIJEL7O6orkFZ2q9XC3t5eKOahLj3rAqWlre9WBQuAS3pKJk8Wm5M/yNt0ubJNBFgKSo3TMB49Pj7udg6h8mNdfSxP993ku/v9vovzqacmkUiErFstT935/E37Wf/3/RYlwMlLHFu2EThxS09NTTlrf3x8HKurq25RuQVRvtsqOeo94PiwXwlQVMy4jERPerZZx2r1+cDLx7tP1oX4lMa4bt26hY2NDbz85S931yYnJ/HiF78YH//4x/EjP/IjePTRR3F0dBS6Z3FxEQ8//DA+/vGPe4FLs6EAuP3deJosBSc1f+vWscJPkwwAYGpqCg8++CCe9axnoVKpYGJiwg0IQUsHwBdX8FmKPu2HxDrrRKAQ14HnRNIFxUAYtPT9fGZiYgJXrlzBgw8+iMFggLt372JnZ8cBxOzsLJ75zGdienoaq6ureOyxx/D444+7ADCtIpL2AQUTLRROdAoC7WtOPCoV3DmcWj2FAwDnkgBwRlAwbpLP5zE5OeliLRrH00l4cHCA7e1tN86Hh4f4+q//eszOzuL4+Bjr6+toNBpuLdve3h7u3r3r3m+zH8fGxlAsFjE9PR3iO02x1q2qNjc33c7f6pZin1BAFItFPPDAA24x9fHxcSjOB8C55tSSUd5QIcRyAYS2+KI1Q4WMwKTCZ39/H81m0wGu3WPTCkACOuur48Ugv1p5dEHRzch3052cyWTc+JE3mJBBN6u6xjWVe3JyElNTU8jn886KtcDF73TVEpCV1G3IT43HWevGJiVY8NB366cPuHQ8eS/7mWNKS/OBBx5wbv4vf/nLaLVaZywg/d/KH5WX6kJUVz/nKD0M6mmYnJx091Hh0iQefb+Vwdq2+6WnFLg2NjYAAHNzc6Hrc3NzuH37truHi+3sPXze0tvf/na89a1vPXOdk9sGqq0vV+MUGsuamJjA8vIyHnroIVy7dg3FYjFkZWmgVsnGQhRoAL/GFef/5mSxLkIVjHSnAacgQiGmcbvj4+NQu5iRtLm5icPDQ7dK/7nPfS7m5ubwxBNP4BOf+AQ+//nP4969e2i1Wuj3+87NqutxfBPi4OAgpEVq3cn8wCmjcqNUxjH4G9vFxBgKF22ratgUqnQ3Wq2WVtzu7q4rt9fr4TnPeQ6uXLmCsbExrK+vo1arYWZmBjdu3MDR0RE2Njac0FSlIpvNYmFhwblVVYGghdTpdNzEppuNgt16AJLJJNrtNjY3N7G0tAQAob35gFMwpqJFUqWM/U3htre35+rL+UHLhbu70Apl5p9us8XUdCZXqIWlfKvtSSaTzp1kE4+4DyNBzrfX3sTEhHPLa+xM30+eZLv7/b7LCOX3w8NDNJtNlMtlt0m0zk0FHcZydFNgtZ70XexndRtTsVF+t+4/5W2fFeJzG2r/sWwFL/ZXJpPB9evX3aGkX/nKV9BsNkPvtnJJ+Uc/eS/d+iQqoupx0RAM98jkNnG6BEaBXgF9mMV5XvorySr0oeuwisbd86Y3vQmvf/3r3fdWq4WVlZWQ4LNCHjiN/ehaE3ZuNpvF8vIynvvc5+Lq1avIZrOh9Sc+92JUPS1z+P73PaMuTV7TgdVFk+q64qSxQMfrlUoFDzzwAPL5PO7du4ednR2Xaj4/P49nP/vZmJubw61bt/CJT3wCjz76KO7cueOyzjhJVVsH4DQuCglqZ7zXJguoW7Tf77v9BTUmooCsSgK1c2rV/J1jw77y7bCgwoZxlSeeeMJlUfX7faysrGB6ehobGxuo1WqoVCpYWVlBr9fD1taWE7gsP5fLoVqtIp/PnzkDjMKWbrhms4nbt2+j1+uF+oJto+V1eHiIO3fuYHJyEisrK5icnHR9NBgMXKICAUb7StdZ8awqKib5fN4ljNC9w13Da7UaOp2Oi1fs7e25TFGux1O3oCoNOsco6Bkb4f6CfJ68oTyhrldd5EwrVc/ZojLG+ah1AODiP1QMBoPTY+/p8iwUCm7DWcZC1ePC60w6UReojaOqVauLvjku/E35LsrbYue5kpUdCm6cd3xnKpXC0tKSU+4ef/xxtNvtkCdHP31WjnUxU65YoNW5R8+G7nLC9lNGsG80/m4zNv/GWFzz8/MATqyqhYUFd31ra8tZYfPz8zg8PES9Xg9ZXVtbW/imb/omb7k0TS3RN61Mw07m5NEJRmbL5/O4efMmHnroIaysrLh9znR1upJPa4n6jcxm79eYjtVi7Z+6ochQZAI+r2t/+F4AyOVyuHLlCmZmZrC7u4uNjQ0cH5/srVipVPDwww+jUqngS1/6Ej72sY/hM5/5DG7fvo29vT3nzuG7NOsqCIKQpcG26ASlUFX3pboR9bA89p26xVTQq2uYQlj7gCv/WS8CqLqSgiBwdW+327h9+3ZI2VleXsbs7Cy2t7fRarVQKpVw7do1t7bICibgRGAywYOusGQy6YTkwcEBbt265dx+HGMKBPKWugG//OUvY3JyEtVqFc1mE81m0/UXXXzcjFU1fx7X0mw20e/3XSYi5wpdg6lUCt1uF9vb2y5bVA9n5NlW7HPrfmQ7aPXoAl+6ZZkazzHR/meZqvDQ4uEngTMIAhfrolXFcVUrnvFICk72F0GD7atWq5iamgolo1AIJxKJkAeA7yIvWfBSAU0rTwWx8olPRgxTbq11ZsnyDnl/ZWXF1efxxx9355T5lCZ9Dy0slTEAQoqh1sVag1TYKI8U8PhHBZTv1TnMcbwfekqB6/r165ifn8dHPvIRfN3XfR2AE/fHxz72Mfyrf/WvAADPf/7zMTExgY985CN47WtfCwC4d+8ePve5z+Ed73jHhd7HLBjrZ1bA0o5KJBIoFou4efOmc5VxUaf6aIHwHnz8roNovyvZe3ygp9aMApFODuA0mM+yyARWk6JQmZubw8LCAvr9vtsqJp1OY3p6GtevX0c+n8dnPvMZfPzjH8dnP/tZ3Lt3zx0NbuN0ZFROXgoYuyOC1cyUNAapPngurtWsTU2Xp3D2BfgpUBjr0mxPFU7Wmjs+Psa9e/dcmcfHx1heXkalUsH29jaCIEC1WsWVK1fc2ieW02w2cffuXVQqFedWo7uZLpN0Ou0SW3yp6ayPFSKdTgf37t1DpVJBLpfDzs5OSCnI5/Put16vh+3tbXe+Fq2zYrHoAvcEdu6Iv7Ozg42NDWdpdTod7O7uuiUOaj2rxcB+03l1fHyy+wjjlBSSdK9zDHxWGssD4E7y5e74PG6GPK6Zmew7Ao5a3RxH5TvyEceo3+9jZmbGne3F8miZ5fN5TE1NIZVKuZgkSZMPrELJ/lB3uO0z6w70kZURmm1rPTb0quh2ZZOTk1heXnbtWVtbCwGCz2NEXqRCoQqo/q+yie9XlyvLoCKgFpvykyrBnAP9ft+dAn5RujBwdTodPP744+77rVu38Od//ueoVCq4cuUKHnnkEbztbW/DzZs3cfPmTbztbW9DNpvF6173OgAn+/398A//MN7whjegWq2iUqngjW98I57znOe4LMOLEBlTzU91b1jQunHjBr7ma74Gc3NzLuir7ghloCjtSUkZQUHPuhSp+asZrwxh3Q1af2quGsvSd/N7NptFtVpFIpFAo9FwqbQ8lXliYgKf/exn8Wd/9md47LHHnFVAMFSBpX07GAxcUJ995esHy9Rsl11ro5YI037VNUki2KhLQvuNz/Kd9PdrwgaFKIlxLFolY2NjmJ2dRT6fR7vddjHAvb09rK6uuuB/p9PB5ubmmUA2rQa6nHhYo+0ftk37R3mBSRGZTMYJAk25pytte3sb6+vrqNfr7j1TU1NOCev1ekin024T3lqt5ra/oiW5s7OD3d1dp4TQulI3O+OLmk3L+jNGpHNPgUrBxpe0RIvGBvN1B3sCF61pTZRi+ayvggctQgUv1r9arbr+ZVsImtydZGxsDM1m0y3FYLs09qV8zXlpk5LU62Ddbj7lV+WHKstaps9tyPsnJiawsrLirEYurLfvYd05Nmodk1gXra++28o5zeTmeFp+Zz01+9un9J+XLgxcn/rUp/DSl77UfWfs6e///b+Pd7/73fjZn/1Z7O/v48d+7MfcAuQPf/jDbg0XALzzne/E+Pg4Xvva17oFyO9+97svvIZLmd4KdOtaKxQKeOYzn4mv+Zqvwfz8vNNa4pIwWC4/rYWlGop+twOtZem6MhUWqpXa1Fb9zTISJxUTXrLZrHP7MOW9XC7j8PAQjz/+OP78z/8cjz/+uMu4U3cC/7RfqeXSDaMAqzEB1ptEtxjv5a4XdEdRaFFjttaaCnYKJvrQrcuS7wiCIJQppjEaChwmUTzxxBPOMkkmT3ZJOT4+RrPZxNTUFK5evYogCLC5uRkCr3a7jenpaSe4ua5uMBi4dW/Uin1CifXSNgbByc78jUYDxWIR6XQ6dCw93Vjb29vOciKwFYtFzM/PI5vNotfrOeUlm81ifX0da2tr6HQ66Ha7qNfr2NzcdO5BFba2fuoKVGKSADMBeVQGx5LPWfc9+0rL464NjHOpG1Lnrx5pouOpW5ypYGcCAa2SbreLe/fuodfrYXp62lmi7P/Dw0O0Wi1MTU25seWho9o3Ns1fXYbkNVVO7f9aRx94WcUxiqwixnpks1mXZMR4pl37x3vVgrMyioqMlYdqGJA4Ruqx0eUs6nYlb1kr/H7owsD1kpe8ZKjZ+5a3vAVvectbIu9Jp9N417vehXe9610XfX2IVLNTAaGWQiKRcGu0nvWsZ2F+ft5pplaD87n1rKmu77UaESevtbaUtK5qRSkw2fiAxrrIOHyPrjGi+0b99xMTE2g2m1hdXcVXvvIV3L5927kQfSns2n8KJrxnYmIiFHMAwqnj2jeaMEHw0vVKHB8G+wGcsWiYyKEKhmbZsW9YD3UZ+saT93Y6Hdy9e9fFOJ797GejUCig1+uh1WqhXC5jYWEBg8EA7XbbudT29/fdmq1EIuHiMYPBIASaVslh29SFwnFkm5jeT1ecuha5oLrb7SKRSLhkEd14d2xszMXatre3cffuXTQaDXS7Xezs7GBrawvNZjO0js4qYrSqVcHS31QA6tj4XKD6P9/D/lGFi0scaLlwKQazJNWaA07XY5EX2f8U0upu5rs7nY5LvgqCwO39yHofHByg2Ww6BZDzixvbcu4pf3F+cpxYTwpsVZwVvPiMTeJQvrHeFPajBTbOBc6bUqmEBx54wHlcdPMAn9VmXf62bvZ9Pr7meHLOc+5anmd96WL9a7W4/qYRmQI4dS2pAMtms1hZWcFDDz2EhYWFkHtQQUsnBnAKYgpGFDT8XRkMONUwbP20TGsuqxtNNTSN6+jAK+Px3TzMj23jxD46OkK73cbW1hZWV1exsbGB3d3dUBqx1pPBbwUuMiCBhkkR1s2jPnHrKlJwtfsQ2nqoRm034qXg2d3ddSnmKvhZls18U4tHwaLT6eDWrVtuG6IHH3wQpVLJadvj4+MoFotOu+eOGwQuTZnWTLo40FLe0XFUhUHbQMuW8ShaJ/l83p18zLbl83kUi0U0m03cuXMHzWYTe3t72NzcxPb2tksoUWEaRdSK1Q3HdnO3Cl8s2Xo+aH3Z5RXWSidIMFFEY750F7MeTEKhEkSFQWNN5BfV9JkBynZPTU2FxpC7c6RSKVQqFSSTSWxvb6PT6YQEvCp6NiHBzlGfAKeQVxmifebz1uj8UrJuQy5SvnHjhovXMZFFXb4WBOMUbfupSjY/9X9ffO48IHwRutTApR3ks7QYtHz2s5+NlZUVt3ZID3xUUiZXRlHgspqQNZ2V1AXA7wpY6hdWJqfgsgLdMheZRP3sfJbJBY1GA7u7u24HB6aWA2E3gYJ+EARn/PbsNwKkJi7QLch+USGtCS+0FPL5fCjr0yZw8H4KZZap4AXAxZZ0kTjdRKqBawamCpmjoyPs7Ozg1q1bDgzoIuQiXGZkMolAdymn9k5wYQKL9qtag+pqVR5RXtbrLI/1HR8fR6FQQKVScXzHclOpFIrFIrrdrtuz8eDgALu7u9jc3HSJHMDZ9YIcOz0Sh2VS+BPECRhMIWffE6BUidSEDHVDc2x5sjVBUBegs57K+xzbSqWCYrHo0u81RqqWG2OF1rLd3d11ZTKuxT6m5VWpVDA9PY2xsTFsbGy4WJm6Jvmnz1NhUsVJ77WKTZRHwAJalGKt81jHbXFx0S1v2NzcDLnjreXHTytvrPfI3qN1tHLRWuv6/5OxtEiXGrjYUQpY7KSJiZMTfm/evImlpSUXk9Ddjn1mO+A/soDkAyqfxaDWEoAQWKlgV6tFhdiw91lthunImqJMxuW2Powv0cpT4aeBeE2BV4WAmWDUhKm98rt1Pdh2cfJwQ18e0RDn67ZgxnK5op+xEbVW+B4dU1Vy1JVxfHzs3HCVSgVTU1OoVqvOZcj1PrRk9PA+Jg7wf1q+7E+1oKmgRLkwx8bGXGq4xlDYt8ViEYeHh+6ID1pOTBApFAoYDE53SOGJxJq6b/nVBsjHx092YqCFQ8WB8bR8Po+xsTHs7e2h2WyGLBxNoGHdtGzdbYXHlXCRMLee0qxCHWe6InnEC8/DoyuRbVO3u2aaqrVNl269Xnd9ncvlzlhe3W4XqVQK09PTTgnSI3Csp0H7kgJfwVpljs/yIlmvjl6z4GXniboN0+k0lpeXQ0or5ZLPBegjWzcfcLG9NmzD/y1oP1V0qYHLB0CcRNVqFc94xjNcCrgvK0ndgEo2SBrV6T7tAwjHaTiROKn1GctANnBqtSJ9j/r9gyCclMDgLF16TIFmiqz1wbNcdenw/0TidE9AxsW4/QuJ2qVqn1p3dYfu7e0BgEscYexBLWB1kdk0eAV6xki44wIX6tqEHT7HetDaY1/v7+9jbW3NCdNyuYxsNuv68ujoyC3qpVDkGKsAouCnJabxLr3Xjjvdf9PT065d2t4gCNwiYvKFZljmcjmk02k88cQTWF9fd+PN88isa5jWCPtElwzQ1QycbtIcBIED1b29PWfN2UQjzbJjPfledfNNTU25E8TpHuQSAi5v4RhqxmGhUEA6nXbPcC9Fq/wQnHTLKbaV7WbyQiKRwPT0tAND8h5dbIVCwa1BZYyRbVNLx55lZnnPWlokK79UubL3aihCLTES52oikXDz68aNG06R4TpJX9m+79ZSirpPwxpqZVOBs3E824b7oUsNXDro+n82m8XVq1fdNk6ckLoCPyq2xWu+dM1hZq4yBf90AaNmuall4os3+N7DZ1lH67fmBLVJDLrLA7XTIAgfOw8gBDDA6VlT3GWBn7xXY0/apxa8tP7UrAuFglv/pNo7hZYCjQa+1TVH1yBdl2qFKDHOZvteLe1Wq4WNjQ1UKhXMzMy4M7DYt+qO2t/fP6OI0OriDizJZDK0INfWSfs8lUq5dUa6eJQCIZk8PSpF3W50q6XTaXcmGV2pmoHIPmCb1RLU2JD2F60eZi0yEYJxIlXOVIhaJUStW1pxtHDoyuRmzuxHOx91xxbu8sHd9lVZUrc+habyI+c1+YqLyflOWpTsf+6xmcvlHHhxiQR5kTxP97XdqDfO2vApqPbeKDlAsokfur4tnU5jZmYG169fR6vVcstf+LvPk2OVTlsH9RRpXVRx4Vy1iW8+ul/wutTA5cvKSafTWFhYwPXr192aJt1IVF1HvvKAMAD5/MH6v0870sCyfgLhM6uoiftAyzKQMoC1NGlB2HYFQeDiALrzORncuvbUDUKw4FoizcDSozw0xuHTGCkoGEgPgsBtmcSd7rlhJxldEx6AU+Gua4p4D9f9MG6mm/Syn7S/7fixLBX2XC7BsaT7iH1Dy04BgOPMtP9UKhU6Hdq6ZghGdEMWCgW3nyKtVBsTYxs4DhSW/X4fW1tbLg6ju2Sw/+wYq2Zs+4T91Wq1MD4+7s4049EvrI/lY03qUaVAs2OpdDAln0fSk3TZg66ZOj4+OZdLFx8zlqXzxFcPbZ9NLNjf33duQ9aZmaIAnKKXyWQwMzODg4MD3Lt3z/W9D7yo0HCM+Zt1n2qfazm8br0v/G55Se8hv1O5mJycxOLioksyoSIT59lRfvOBmH2f/nEOaugmTtm/X7r0wKUawNjYGKrVKh588EEsLS2FMuCsWyxKA9KBUeEUFRj1PUttlFoyy1LQUm1ayWdBqoBUBlc3lQ+IuWaJsRoKBNZBA+FAeMd5Xa/DJAgCCI/55voudbvpjuTqjiJIEYwIdixT4zbaTn6Oj5/suUfXEMsguLKObKedcHay28A/ALfIeHNz02nfOkk1vV/PCqMQ5TXGTrLZLAqFQmgPPZaph1AylsS2sTytv/KRvndyctItfD46OnLrvXZ2dkKWkfKWWsbaz3RBqjuNm/Yydqep5wruyrOqkPFdBGlarDyclKCl7WPbGG/T0yHInxxvVXgosLV+rIcmumjCBsdd56buVB8Ep8fwFAoFLC8vOyVHsyjZj7rWiwob54ZuQmwVY2tl+eSTgleUG4/10LGvVqu4fv06arVaaD/IqPfYMm1dKbusd0Nlh62fBWB9x/3QpQYuNUtp6i8vL7tkDKK/z8eqYGRNYmUm1Xh9He7T6DROxPvV18v7VHu1zOuztvR3dc1FaTS0bLiBKOulGqIV6MDZGJ8G9mn5KEirS4b9yn6gwOfx4BRcHBu6Y9TNoJmGKlgJilyAS7cYy9HDJ4dZ14yf6XuOj4+xu7uL7e1tzM3NuYw3jd2olcrx0zFi+wnIiUTCbcyrC3N1nGnF0VrQ5Bi1kNjXx8fHzuoNgsABAC2ZnZ0dF98hsW+BU9crhaqtG8sl+BaLReTzeScMOUY6N7T9wOkiYApzLlZmcgddz+xH8hPrwB1fNI6lrlkmBhE0Oc81Y9h33AoTmLQ/BoOTtXoUyul02q2LpLVHb0GpVHLbK9Xr9TNzlnxEIn+RF/RUb31OLVO9DviTsXyywpbHpJZUKoXZ2VksLi66076tTORztmwFLgUvlTnW6oqql7blyVphlxq4FEg4ONeuXUO5XHYMpH5WPqPals9ktuUDZw+BVDeOuq0oYCgMVYhS4FHz03das1y1YwtYvjpqOxj/4NEanKzAqQCcnJwMLQbVT5skotljtMSYqKAuIQIa+4oaOi0ICkCm6FOL1kC8zzJScKQmrtowF5PzU0ktCLaBdSYY6dj2ej3s7Oyg0WigXC5jenoaqVTK9aNmL/J5O6l1zPTEZO1b3Z/Ruo/VFacWOgDXTh5hv7e3h3q97rwKzWbTuQy1HLvkgHyoiRkca4IYT8fmThN6n1X2tP20sGndKLjs7++7Rb1sl+VdTQpSxZP9Y9eW8Vldz8T1ZuPj487itVuL6fgFQeAsbsZ2i8UicrmccyXT3V6tVt1JApppaN14apFSPqiioPzgU06jvlvwsmCj85nnnRWLRVy5csXtIKLjqLLKgoq67jU0owq4GgJqaStAa7mWZ+6HLj1wUSiWy2Vcu3bNBZJ1kWwUIFmw8Gky1lXDwVMNUd2D6v6joNdAscYulHSA1TKzIKb3kFm0zqwf3UfcDZzAoi4MChfVzmmJ8L0UrPTXqzuAdbEMrXUjmDF9nRvBUrgSLOzkoEbNhdS6kJPuSrZJF9VqcF1BQCca+4nWo1qe/X4ftVoNtVrNbZ2kO8KT3zjWtCJ1SQbHijEuPQBUtVqd3OxH3UmbShctBLZNj+HY2dlxuyMwXsM1djbDywKXpr1TOdCtuLi2jRbJvXv3ziTSsJ+17TwvKghO43CJRMK5rJXHrAKnPKGZokpsF/nQej24aS5P69WNeVU5U68A28QkF2ZUcmd51r3b7bqlNr1eD0888UTII8G2+NLllX8JwKwL740S5Nba4Z8vnKFWsKbHz8zMOKur1WqFrB/rZdG6Wzlpf9M6qAykVWuVufsFK6VLD1wUQLOzs1hYWAgdaubT4JUo9H1AZt+jpJOOwlC1V7W0NCXUxuTsu3mddeN1PqsAYbPUWBaFVKfTQaPRcOnmnDgU1NQAuRaJ1o+6kxSg1LK0gMA+sRNW14XxpFSmm1erVWcRMgVb033Z7xTcvE5tm/Eh9jmD6iQddyoaCh6sv6bG0xVJq4snRlNwE0wt+FBAWMtVwVBdf6rw+CxqXWOmG6XSoqQgDoLAZddRWWG8RpU6LVv5jhaIrh+jsKGABeAOnuRhpFbxUoWF7sVMJhNKu+fv5BUlVeq0vjYura5C3zygMsasRbqO7WkG5DUdf23L7u4uUqkUSqUS5ubmMDc35zwYtGrz+TyWlpYc0GkZOqd1sbJ6KHRphvKTkp0LCl7qMbLf9VnOn+PjY2QyGSwuLoYOjFVFXMvT/1Uukj80S5djwX6mrNB+tkptnFw+D11q4AJOmLBcLmNxcRHlchnAWYYH/Nl/JN9g6aDpffwkA6p70GrSFDxWO6GgJPmsvLiBtb9RkLAcuo94uCBBitaDPexPAVeZTkmTKWg12lRqvp8TkwzNGEG323V76c3OzrqMO3Xf2DHTBBJNHqHCQjec3cFBXW8cKwoXBvZZdz3VlcS1QmpJav34brX6giBwyS8KSHY9mlp/aklS+1cLXs8woyBWV6NatroYV2OtOiYcS6sIqLaudeO6LT1CRZUotovPMDZVKpXQ7XZdP1vQinKNsXytKxM7EolESMGy7l/yBs/2YqIOx4JtJEDbrFrSwcEBdnZ2sLa2hqmpKffHPu/1etjY2MC1a9dw9epV10cKAJzrFpAo+AnErLNPiVV+seTzKviIc4igPjMzg4WFBbfZMutkLSpfOXynjo21pNSVrgqaAhnvsbG+i9ClBi5qhXNzc5ifn3epyHqGThRYWbCwWi8ZXj+B0wmqLiP1k6uGo9q8WivKBKqJaPkk695RDYf36+RggJ6bqWrdeT/fR8HHPQR18rN9VkBQcwQQ0rBUw9cYhzI194EjiKrVxX6zk1eVBQpBG8DnuV7cIcK6hOjKUmBWHlI+4FgRKGjF6UTkdkV65IZOQm4PRaVFBYO6XpUP2HYrIAhemg1G4FFLXvlO28P+ovtGY1XqMrR9RT5i3IjxHW0H+4T1oCu4Uqm4s9t0/KlAKWl2prU+ab3padj0GPgUE1qduvCYSVrkU1W6rDtPvSh0D/MMtmc961moVCruKBiCFYGACSfKs9oWK8zZXlXwrMvONwesrNKxJqlVyt+4+0kul8Pi4iJu374dijVqrNBXpo6PApACrj5jLTXKNeu5sTHp89KlB65CoYDZ2Vl3bIquT4rTRJSstmO1Ty1HfeMajLTvtsxo/dgWvFS4aMDT53dWkLPMxr3WuEuGusTsYY0UOlw7RAuCi0F1eyONJ2gyhYKBTxCyn5jS3O+fblFE8GLswGqnvvFTqySbzWJmZgblchmlUsllK7L+bKP2m104DZwKcbVQms0mdnZ23JZSnNxcHpDJZBxAqQWt1otOYgUmO446FnRzs14k5SvretMxst+pVKmQ0aUDFLZab5are1vyvWohMomBliDHY2pqym0zxjHheOr7dQy0H3idWYGsJxUtFaBWuGsavCol6inQJA5VSNWaoEtwdXUV+XweCwsL7oRqugx5avbKygrq9Tq2trZCCowFGmvZqhC3Sq4PoHyeGa27VTCV2KfcQHh2dha1Ws0pJ1onHQsLiOQ7nxzTcVA+V/5h+zgeT0vgSqfTmJ2dxezsrDvaQQeBFDXgSj6Nhf/zu/qoFWiA04Gxm8oq+OmzGofQRb4sT7es0TqpsFJGo3BqNpvOraNuGVpYNqMJOHUXcTEsd8ugVUO3C++12pS+g3XRoLz2Jyfv7Oys06R1bzrWa3z87BHx6qbiRCWQJBInBypyY1zdIYTE1GDuj+dTDPgubsDLZB+tFxeoUpPVMddMQevKYt217/jJsdB1SOxXHfuouC2XBChfsVy6p3R3h+npaYyPj7ukE5vCrctJgHByAZMT5ufnkUqlsLW1hXq97lyE4+PjLsZKZYXPKq9pogxJlQoKScZBWXdaTOpt0DgZY7eaMao8qnzG59XTQWui1+tha2sLExMTmJ2dxTd+4zdicXHRbcC8t7eHWq3mdunZ29tDq9Vy46sKjPID+cnGysnryhf2U+eSVWCtFW/nJ92u+Xwe8/PzWF9fd4qF5VULitrP6pmJcvVZT5ACmnod7pcuNXDl83l3ei2A0AD4yFpVUWa33msZS7Nm7GBo7ECf1awyG0Tl2Ut0142NjXnTuqNMbwpwgkWz2XRAo24/apk23kDm4lqbQqGAYrHoBOH+/v4ZYWkz4VSrYoIDBZRPI97d3XVuID6juyAwdsgjUFRLpeA8Pj52a5b4XcuikFSw00W/tDRILHtsbMwJW8aMuP2Tjqkdcx0bdXlZF7EvPmldhkymUQFiXY0aE2NdmKShIGmtSwAOjCqVCgC4mKcqdbQ86A5OJE529i8Wi849Vq1WHQ8PBgPnqqcbTddpKQ8TnHVZCO9TYUYrU5MrVLmjhcR6U4vX8adiwXHR+rAf2V/W40HrfXNzE1/60pewtLSEZz3rWZiennZgypOr5+bmUKvVQkeg+JRktt/yBt/LcbJuQ5IFJO1XfU7Bi38E7fHxcczMzGB6etplofJ3K++U97QOqlxE3eOz1vR6nKweRpcauEqlEiqVips41j1H8nWq/halHShTAQhNCtWCOUFUk9NJYF2FAJxgLpfL7swnCs56vX6mvtaFxnv5zsHgZKufVqsVigGQLPgoExO4ms0mJiYmUKlUUC6XcXx87M6m0rgh68R6sF0EB2rc1vXE+3q9nttUldYA096D4CRZhEesa0o4y6FQ3d/fx+3bt13sTOMb1v3Bscvlck7I+8rUTErGMqipqtXss37UXWMVI8tz2mfKhzpeai2pi1cXTbNs1pUuTT0d2NLR0RFqtZq7h0Curkbd95FJOcViMXTaAnmH7lRmY9LyZ12VR6lIsO/ojiZYqxXEMbK8rC5bLkfgmCiIMzaoFh/7VvuZ/cW4rHW5tlotPP744yiVSpidncX169fdqcr7+/vY2tpCqVTC1atX3d6LNl6nwjqZTIZOVrBeBN3ZQgW+73+f5W4VclVgedpBPp/H3NwcNjY2QgeLDnNHqsUUxdc+8nm04u4fRpcauKamplxsS/2uFoiAaN+rdcGpFmTdLZo9yGcYO2J5ms1lTW69J51Oo1KpOK2XCwXt5FFBaZlFY3kHBwdoNBouE07vY3ut9sf6q5uKWwdVKhUnGKrVKsbGxlxgmmWrIGD5zIDToC8BVoGMMbhsNhuyIoBTl54e484kDgAuUYFWZqPRcCcSE7Q1243rvrguiUJV/e7sI40zMEOt3++HjnOhcLCgxT5Qi1Y/fZqxKkdaD9ZNrWr2p7Vg1FJT68PGM1mXfv9kucT29rZTEHT9GPtGgZI7X9BNSjdcr9dDo9Fw7sZut+tO3uWzqoDoXNOYE9tKXueGztrXdPPyOBt6KJTn9E/nm/K/znXyRjabdX2j3g4qDvV6HZ///OcxOzuLb//2b8ezn/1spyi2223UajXMz89jaWnJLVHQ8VSgIc9RIdJ+53hrzEtllo65Jb2mIKPtoCKSTqdRrVZRLpddRqSS1tnWXUHZvtPSkwGnOLrUwFUsFkOape1QFSIkZQTVTlRw2MGwFhxwKlxUWFgLy6eNJJMn+75Vq1Xnaul0OgiCk+1kmNqtYGAtNjX7gVNrq9lsnjlvjMJWXVoqQLSP1N3IQyMp2Lg+TjUuK6gJXHYcCF4UeLS6GEfjOLCN1ICZBMGz1Gq1Gra2tpwrNQgCt0HvYDBAPp937WX7ksmT5RLcwomp+dw5RAUG66tWAF1C3EFBx95noWvfal18GVgEDOumUT61rkTrGaCQ0zgbLQ7LywpqwOlWXpp5yD6h8sB6sb/q9Tqy2azb1Hhra8udtsznuNiXbeQn26LClFmT/K5zSi2wdDqNXC7nACYITlxu7XbbWWY6LvYd2lccFx6VwjJpeSQSidARJRT2W1tb+OxnP4tnPOMZeN7znocbN27gsccew+HhIer1OiqVCpaWltwmvHaclA9YrnottL46P5Wv4kDLByZWbvH9TJCqVCrY2Ng4s+eoD5CiAMuCk09+WoPB99xF6FIDl2YSWg0Y8Ju9vg73ma7qFrLMT6ajALf36UJJEu9Jp9OYn59HuVx2m+AeHR2hWq2iUqlgd3c3dOgbBXgQBC4lWUGak9Fu9cN7VACyTPaVz61AN0av10O73XbaLXCqeatFxXpYd42dqLoTejabdechcSNfzaajm4iLlbl1D4H9zp07oeQPTnQ9L4s7dczNzeHatWuYm5tDMpnEzs5OaJxYZ53gmhjRarXQ7XYxNTXl6sb+si4lVU70mgKTjxSsrICnMqDAroKE42sX2eoY6Jo11k/XhUV5G3R93+HhIba2ttwGudxomWu86OrTwLtVHG1fa3/pvYx/ciEzAVE3I+Z6Ko3PaX3tOi31hHDLMK5tGgwGbocZtkOtZNbx4OAAt27dwic/+UnMzc3h5s2baDQauH37NjqdDmq1GpaWlrC0tBQ6/07H2VrfukOM8gEVGo2HqSKjZPnOvtNabOxnngFXKBRcpqSPlI9Zpo/P7DM+uWrvvV/wutTARW07aj1B1ABbDVZ/8wVM9RpwGi+yk9IyvAXKyclJTE9Po1KpOMtmMBigVCphcXERiUQCW1tb2Nvbc66RfD7vBD01a3X7ACexnkajETrLiQzLLCBqz7ooU4EMOLvbBL+32+0zqcXAqcKg1qVOHtV+eU8+n0e1WsXk5CSq1SrW1tacC1D3UNM957LZrNsRgecy3b59O7QYlW4qCif2Hd3JXAfU6XRCZzkNBoPQQlBd1EsBT0UCCCfrsB/VlWJ50JclpnEcjofyG4WwJhioguQTHNbyp5C02q9q7hZkyFMKJOoOp0C9e/cuOp2O29nexkesYkQ+0z7UxAt1EfMAxKmpKbdDP/mW46cnJmvbVDArv1IBGB8fR6FQQCaTcWVwXuk6NfKQFeSDwQCNRgOPPfYYlpeX8e3f/u148MEHXYJGs9nE9PQ0ZmZmsLa25s0KVh5iX5EHdD2onUuWLK9FgYePJzVuWSqVnLvQbpagCpKvbL3PytEo0BpZXAifvBvXIb5OtL/pdxXCNoOQk9kGyylobLCd7yRozczMONBKJBLOZTgYDLC2tubMduAk/lMqlc7EOWhR0Qrb2dkJbVprNXNeU1cJhS5/UwuK7dDMMgXMqEnoY3D2wWAwcJYa902rVCooFArY3Nx0acTcWqnT6bjUdmYDBkHgdklpNpuo1WohDf74+NhtjMs4CRMRWq0WGo0Gtra2XDxGY24+dwh/p2syk8mEeMwHXtbl53MBKj/yXdYSJj/pJq7J5Om2VtbLoHynAEiBzTHmdwowWvW6EzrrQ4VDrT1aw7RoNbNSLVX9ZBlqxbGsfr/v0td5oObVq1cxNTWFZPLkJABmyu7u7jrAstaRuq/VKqE7lqDLNhII7dpG3VXDWuJ8dmNjA5/+9Kdx/fp1PPTQQ3jwwQfx2GOPOQu0XC5jdnYWjUYjdG5XlIsPOAVXdVlzbBRQfGR/0/5mWVap4BjTXVir1VxqvJVd+v0isvUi91yULjVwaedqx/qsHfuc75pP8wTCrgwr2ChkfO5Bljs+Pu72PKOrg2dQZTIZl9a9sbHhzifKZDIu1Z+WFMtLJBJuNwMee854kWq1JKb12jqrYGGQXJNQKOwAPyipVgsgpHnrGPC+breLra0t5PN5FItFjI2dbFJbqVSQSCRcQJ6CaWtrC7lcDrlczsWdxsbGUCwWsbCw4GJQrCvfX6lUMDU1hc3NTdy+fRs7OzvuXt3tg3UlmHItIPfj4ztbrRb29/dRLBadtUBlwrp+FHyUByw/KsjpPQoSFKiaIcmx0MwuZktyMbC1tHiP1onPj4+PY2lpCfPz8xgMBqjVaqjX604j11R3ZqFqWwggyg8ci0Qi4ZKN1GK0rlC2KZ1Ou3kyPn6y1+DOzg5WV1fRarVCMSjOQ11wrOWxz6j0sO0qnLVPOSa8ZpdS6FzZ29vDrVu38Oijj6JareLq1atoNpvY3NxEp9NBsVh0GzT71iAqqfdD3Zpsk/3Nkg8QFSAUxC14jY2NIZPJuCziZrN55j33A1S++6O+Py1dhdb3ymscFO1kKzSAcCaYkgpuzXRS604noboStS4UDOVyGcvLyxgfH8fu7q57n2qRXJHP9xcKBVSr1ZAmxsy2sbExp4nu7u46l6NqawyS2zUl1HhVyGQyGaeB8h6NpfF+1dYoeAjaQRCEJhrbbl2HnU4Hd+/edW6b/f19F0cKgtNkC7r+bt++7fz9XPcFALlcDuVyOaR5M9a3u7vrgu10AzFhQBNa9JPuVCoA7BvWo9PpYGZmxvWB1UrJD1ZLVe3Xd7+6CHVMALiF1AQG1YZVkUgkTtZYFQoFl+ijYEcgViHMMvP5PK5evYqFhQXncmYZ3L6JfWlPdNZ3WGDWFHPtZwUbWjeM46TTaefWPTo6QqvVcicc0H1ugYZ8ptl4wKnlpIk6uuEuFZN0Oh1KbGE/q4KnfA+cgGK9XseXv/xl/MVf/AW+4Ru+ASsrK+6MsW63i1QqhWKxGHIzK1kwZB1t/E/JZ6nzupVlyoc++cg+4DKHUqmEdDodOt1baRjA+OSr/hZlcd4vXWrgYuerO4ZC3ufWAsLWgGq3JAUrfqomzWf4LrXSlElYfrFYxAMPPIBisYjNzU00m00XY2HmkabUk5nm5uaQz+fdmq4gCFzqLk+9peuLQWDNxrJtV1cW/fdW+Nn2qCZNQUPhoHUnMV2aAKEapk6kXq+H9fV1d+yEHhYInG7rdHh4iHa7jccffxx7e3tYXFx06714HwUfhc/+/j7u3r3rdjLX9mpKMBC2IhmP0DpwATSz1+ge0zRm1ZLZRp34VmiQx9jXVjBaQatnSFleVcUtk8mgXC5je3s7dFown+X4M4GF5REo+v2TLYzq9ToODw+Rz+dRqVQwPT3tMl1tyjvbTeWFgKhzhH1hF3xTCWMiDa2UyclJBwBUyqi08Z3WTapJNlZ5o/JhFRdVTjTJim0hqSWnSl2v18Pdu3fxuc99DsViEc94xjMwPz/vYraFQgHlctklW6ls0BikHUvypyox6k3wuemt3LE8ovfzmmY0Mu6cz+fd2FoPT1RZ5yGr5D0VdKmBSwfMp9Xyur1XQc7HCBxQLctmEMYJElIqlcLCwgIqlYo7FqJer4cCwfrJiV4oFDA3NwfgdJcDZkDx8EAeH87jCTSFluClE5BaLu/hXyKRcJtt+mJotk+4FZAKeQpHWj5sD90/g8HArclhnzFuwRgJBRjbqkkT7XYbt2/fRq/XQ7lcdtaVdQ3RjcdTn5lQwLHUGIddmKvLBZLJpDvqgwtYCa66LMDyjio5wNld+1XIqGLlUzQI3ExY4b59Cg4cL5ZXKBSQSqVC7kLV7NlO1o1W+ebmJmq1GjY2NlCv150Vzl3RCRp053JXcQpAzaRlvdjHGk/jnNDd+QeDAbLZLBYXFzE1NeUOm+x0OlhfX0ej0XDtZjk2Q5I8p4oWx0IzYDluGgZQrwJdrkEQnLHwLBAeHx9jZ2cHd+/exV/+5V+6HWd4ajN3oslms6H9Gq3VyLKVN6xbUTMkOaZKVvYNAx21uBk31SQNu11bnDXlA0zfO6Nk8v3SpQYu1YR8prMOpKK+z++qwtkyBoUw71WrRJ/X+xOJ0wPt9vb2sLq66o5Up6BX9wmfGxsbc5lwtVot5NrgokEK0p2dHbfQkW1Tbdr65xVQtA/5buBsKreCHSe5LpTWic+dK/g8g+66ZkstCn6y36nN+vzs+/v7WF9fR6vVci5DFUgK/HyeE5D7QLKfFUxtcg+1e1p2bF+73Uar1cLU1JTrR1oqJKs8RFn7/M0unVC+osVF0KKlxHarAOc4M4WcwKJ8rXxGBSOZPEl+WF1dxWAwcIvP2QdTU1NuVxdatNwZg+XrnGBMMJE4XQiuIMOYZqlUQrPZxMHBgVsesrKygnw+73bf2NraQq1WC+18of3JNjM2qTvCE8RUOVMQJ89R0WF5VGx0Q1+fAsDxZxx2dXUV5XIZDzzwAHK5XOgATC6S1pgZ55DyhJ132ma6kVUG+fhJx5j3WaXJx4vj4+NOVm1ubrpF9zYEYvk6CpiGWWO+5y9Klxq4gLPxKiULOD6kV9Cx8SoVhKrtWOCy9aF2X61WkUwmsb6+ju3t7VC8QgW3xqEymQxmZmacsKQLLZvNugygtbU1dxot40yWgTUuZ+vmYzJfe6yFxiw3ApDNcNN2cSJaoFbAIVkBZy1X1tmmvytoshzVvNmf9N9z9w1aAnQhWguIwMadQhKJhNvaZ3p6GpOTk2dOvQVOXZc2qK6TnwIyk8l4Fyezn+guY1/TgrCKh2rojKsQXNRlTotbYzd0rXLXFnW7c19CZhsSvFiWtll3e+e6Q8bEOFacE0z2qNfrSCQSWF5exrVr17CwsIDJyUl0u11sbm66DXqVp9i/2v7JyUm3tlDdrlZBsFYM60u+ppubsV7g9HQE3q9H/1Bpa7Va2N7exvr6unPx09Jjvyrw++aXlUs+y0szHXVOkBSglS+sgm0VKoIoXc20GNkHVvmxnhglO2/tb9pmXxsuQpcauFQrULIC1wZ01b9tQcsGmjXdl0yoQXh9nxIX23Y6HReHsoyrVgzfy10JeDw73QzcsohZiARClsM2kCjY2V7r02e9fa4kanyc9Npnmkau4MW+1bRiACEB71suYCei1SIp6LVcumvsOh6WS0omkygWi1haWkIul0Oz2cTdu3fRaDS8a5x0XJhKz+sHBweo1+vY3d1FoVBwwpxp+mr1aYq2pqVrn1Eo6hotfRfdtzaGat+jigldXQRW3qPjyn5Rt5taFbS29NwsthWAAzodU50j3B1lcnLSLR4m7ytQ5/N5dyQRt+tiQke/33cKm2a/UoHUGCLHiZaZAr0Kfp97WPkrn88jCE5OlFZFQOO7jMmpIOcxQq1WC1tbWwiCwAGpxq3tOKo1Z8GBpOBjvSe8V8HJJ5MsGPI3taIGg4E78blcLqNWq7l22rpp3w9T3i1Iap2GWWXD6FIDF3C2M9ScVcFLUiayAlIFvM+tY3ez1verG4EBz2QyiY2NjdD+gayDxrYAOMuJ1gHXZemk5wnPd+/exc7OjpsUWgcKTWsBWdAETpMudHKolaYgzuvsH12TxbbqXoY6oaxgZj/ZOp1nQtidvtnf9n+CxszMDK5du4ZcLufWu3FiqruG/REEgcsEo/CigsGEgenpaSfEmDLOd2q/2UMTKVR1fZy6+9ivVFh4P9v9J38yABAgCCYQBOP/7/8A3/RNR06g86QBzY60AovXVICwP+m+JHioNaVxSfa1ggn5h3xB61TP9QLgLEPGS7lcIZvNotvtuuUdtJ6t4sm20DLV9sR5XdRqISjS2komk6GdRPSUAQKzupe17GaziWaz6Sx1KjYKltbjo3POej0syFj+tIqnbbfON+tN0D5kWVRYmKmbz+fdThosm7yoipOdp1YJtaDlu36/dOmBy2dq2gmqA6eABZwCmQ0wc1AV2FiGvtO+n8HOXC7n0tzVXaQWnmqAFHTU/KhJatry1NQU7t27hyeeeMLtvq4JGCyHlo3vGJM4c17BS10V6n7jOUqaap7P5122ne66rhYqQVXBTy1CX/2B8ObJ6iK0Y833MOhPgUf3KmMPqsHbiUblgb9ROJI/jo9P9uprNBpnLE0VcnpdhRPbpK5hbWe323U7OrD/KBzDfKgTP0ChkHZWB9cGcokE+9mSKg3KHxw78i7bvre3hzt37rhdTrSNKgjJszyvjPE4va/f77ujaOhR2N7ednXpdDoOBBKJcBxKPShWEeLvtMQ1rmnbrhZzOp126yI5V1k3Fbb0VlhLmOspp6amXDINZYlm16qyoF4N2xYr4JVPrceEbfeVoaRyy1p+dOWm02lMTU2hXC67tYscO6sUW+DyyQ4dG1uv+3URki41cKnZGkVR6K4WmQbIbXnqQrG/KxNqmfSXc22WTja1hKzFwQWB/X4/tFo+lUphenoamUwGd+/exerqqveMIRWCBA0V1FpHbT+f0ViAannqNqQwUO2ah1ZyUbVunptMJt0ZWMlk0iVqUJja5AYFdo0XqetTJ7JOct2wls8PBgO3eziBXLP4VOgCZ7ezYlkTExNO42y1Ws7KpADiich8XtuhPGQtAwpJCk4KPtbxj//4CACfT0BB6+QVCXzsY0AQjOP4OIHnP/9kcTd3YCFPqMuLAMVxtBYAcLJYnJYp27y+vu72lrTjZz0VjFUlk8kzOz+Mj4+7cjguBBz2n7rG+GmFogpRdQmSVEGxihDnKReV2yUOBF0LIlRS9P200nu9novvWeXL8qyPbAhC+cfytbW6dB75wNCSzwPCJI1yuYytra0ziTE6p3zlU0aosqn0ZMFK6VIDF8k3QFGdpO41ZXh1r2l5ZMAozUjLpcDhprQ8FlutOJ1MOsAUvNlsFgAceB0dHaFYLGJubg69Xg+f+9znsLm5GYoZWDA8Pj4OnahszXPNPNR0ZoILAAc2iUQC7XbbWXjqqmAf9Psnu5FnMhkUi0WXSUjNlLtjFAoFBEHgFh3v7Oyg1WqFkikoQDX2w7rQjWbdNer20X7t9092lL916xby+Tx2dnbQ7Xa9faJAq8LBKh3HxyeLnEulErLZrKsrANfv9uwuBQTlA44x+0/jNADwx398DIJWMqmgFQBIIAgIXkAikcT4eMItXM/n89jd3QUQThqhIkMFRJcVqOt4b28P29vbbg+7vb09t/2UWk7kJ5bLtmnGpo316cGfuis5LTbWgePL52zyi1UAeI/OSRXQapnncjnnllc+JdAysUTjd3buqDu82Wyi0Wi4/RXZDoKilRk6Z32ApXNBwVyVTnVDKrj7KM7y5HN0FzKbdG9vL3RuGEMlHFdfucpHdCP/VdClBi4dSCDsFrQgZE1+Pq8xL0s2Nd4Clf7P71y7wUPstE7WjaTPckJrZhSZqVQqoVAoYG1tDX/5l38ZcteogOUk0DU0XAMDhMHSuorIlBS83AZmYmICu7u7WF9fDx2bogKFbi6CLGMG3A2DgotHSABAo9FwFgxjgCoIgPAaG/Yrx8uX/szJpX1Qq9WcNs2EF9v3Oh6ancZyKSAIxolEwgWy1aqjm4zjSIFuXX6qYOgRLWzrqWs2ibEx8mYCJ4BFXiQf8XuAZDKBP/3TMTzveSfnvLVarTOCTN3WSjZ5gy47govyl4KGnYMqPDUl3c499pneq65UnZeJRMJZ87pZNMdG3ZEUmr7sN7rweVTO2NjJadfMNh0MBq589S6wnsqf7AuCDs8lm5mZQTabDVlIp2Pmjy9aAGa7dUysp4TPWYtXedk+q7/rPQRoxvGLxaJz/+s8oyteLXafZaXgzvHku+KswIvQpQYuH9rzz+ce0462Fhfv85VlBb1OWmUOWluTk5PY2dkJHbqo7jXg7M7PfP5UaMFpgUyx/eIXv4jbt287LUjdW3THDAYD524qFAou602tFO0DZWAKALpM0uk0SqWSOzWYcTfr+lDB0el03I72DPJyI2Bm03FtCw8n1EwuHQdOTGaYEVR54i4nj1oNqqHSJbWxsRESaPoOtb7IN3rUBwCnBNBC7fV6bgcUWqUsg2uAtC50YfK9FBQqvBUETlPX6cJOIJE4ASlaWSflEcwSDsCSyaTb9YJrcvS97BdeU55MJE7S4BlnbbVaISvQJxztvAH88TO1MNTC1k/L03p4Jy0kPRn7+PhkF492ux3a8UHBhX3CNZC6Lq3dbqPZbLpd4uku41xkmjizNPWkYK0jeYZHC6XT6dA8s+497TflQ1qT6r719Q0QdpNqO8lbPsU4ilSZGh8fd0tv0um0UyrVkrLeAyuH6Y7XhDYrM3wW4EXoUgMX4Hfd+SaV1T6UURTU1EIDzp6sTPKB3eTkpDvynsLYvlvrpS4PXlOhyWMjyuUydnZ28IUvfMFlE1KT13UzGi84ODhwR3v0+/1QGq+6RinwyWAaf6L1wt3Dp6am3D268a8KXWpo4+PjmJ2dRalUcjuGMI6Ty+Vcyre6xrQc7WOCsWbxUbCw39Wq1RgRQU3vV9DmOPC6+vSVd9QiGAwG7tRbHnapv+kY6x6QbB/LpJBR0GTff+ITKUxMJJBIJHGSPcgygbD1xXoGIIhxJwSemUUtWXlY13WxPul0GtevX8fMzAzq9bpbS2YTgLQPOU5R4GWXHfDdwKngp/Wkc5PPcJuzcrmMSqXiFEMqEHSH0qWn51uRcrmcO+13bOxkn89Op+OUKroFNZatypielaXWh44513Tt7u6iVCqFnmEfqftP20ke5Jq0IDhdB6bPWGIZlE8WwHiPb9yslcc+ZMIKswt5OnKc1WYVeK2LZqmyr54saAGXHLgsEKggjRowDVbroNv7bJk6wdUSU0qn00gkEm5bJ62jb7AsiBK4qIVT40skElhdXXVn/CjoAqcWAcuk9kjGp+vSCgwr5ClIWQfuFVgqlTAYDFzqOxBen8X2UQj3ej2XwFCtVjE3N4ft7W23dRP7ie9g/9pJaq1kMj/fQ/Dmbgx0azKt2WchWHeLvlvXCPkATVPZua6Lad9cqGu1ZZ+AUGUpkUiE2nV0dCTLLljHMFBZOinn5LcgCPAnfzJAu11CNptxbmVtD+ugC1pp1dAq4TEzlggaw47a4DvoNrXXebo290GkNU8lQ6085U8VjARWn9swm8262Chd7VQ4qESpoE8mT5Y+5HI5Z1lRYaL7lxY354zduurw8BA7OzuYn5/3gp21cEnkZfYJQZRzUeeo8pG+28ad1aVrn9P/rWVGoFZ3oe5d6PNaaXlWLqrblWQt7fuhSw1cJNsBOijWx2w7Wq9bhrIgFQVCHOxUKoVGoxFKONCJpu+klqbX6RajJcaFnO12G1/+8pfPZIqRsYFTYUim1cB/oVAIAZKeNBwEp+tx1J9N7ZXrajRNnNaHjdmoBtntdrG9ve0EYrlcxuHhodtgWCeXxgvU4vEpFda1yWfp0mSsQcePfWUtHuD0LDXfzgZBEDi3pk1iYYp4rVZz76HQYb9oGjitWrpKCVgUjOSXsAUOcK1WmE4ssFN2ChAECfmecIKVafsq7KxlS4sskUi4NGgeaGo1a1rfHEf2Gd1j9gw1/rHtXJjMPwDY29tDt9sNHezocykeHh6iXq+j3++7eCV3l+FzvHcwGLilAbTquG0X+dznNaHlpu42lsff+UwymQwpmkEQoNFooFarIZvNnlnEzXmtMkj/6D3RWKK+28og1kEzYbW/oiwrH3BYd2E+n3dWO0+tsM9Zi51kFUa2R9cvPlm61MAV5d7Tieazqiis9H6fZuITdEpkjLGxMbd/Hs98skLWalyWaYBTIGJmE4Xzzs6O21lemdkKZiValIPBSeJFqVTCwcFBSJPWwK5q3wRxPb7CTiSfH13HI5k8Oeqc+81Z/7Zq+rpxr7phFBi1Xip0qRXrHmu6FZVaPjZOqdq8tcg40Xi0OQ+4VFcb/9hPpVLpTN+r9UYQ1LpT8FmhFkg8i98VmBKJU9cgY2DAieX1speNo1Y7wq1bWRSLRdc+Wg7kLcYcCRK9Xg/b29sIggDNZvPMeLFPdK9IAsT8/DwmJiawtbWFZrPpXHw8T43WOk+zpgUTBAHy+byLN9FaZtm0pniN66sU5DSJh4oFraEgCNw2X1wnR7L8oJabtcb0fsoPtfZU+avVaqhWqyiVSmdAQ5Vg6ypULwqPhAmCs/Fpflq3qnolSDaZRuviuEm+sy0MM0xOToZcgb53R5WvhoLyD3nxydClBi5LPuFDsgxi/bXq5okaYAtu/ONE3NvbC23DRL89ED7/yprTKgz39vZC62S4PmRnZ8e5H1kXW0+WbQO3QRAgl8thdnYWyWTSbSdEJuX7bMKIuiI1uKrvp/BXdycAJzjseVIESWVcAhcFjGYlRYG9xukGg4Hre7p8fBvp2iQIgiYFjk40TtyZmRnk83lngbI92n5q/DwcM51OO1eVavUqiGkR8702A+ulL03gf/0vZg0SqJiYcXLtpD9OsglVr2IZTGKhUGbSjh4VT/Dk+9k+vUcVMdadPDAxMYHFxUXcuHHjDDgzE5D9wYw+LmInL9DCZ2ILXd/kH7ruWF+1iJQv1OWXTCZD1iz5VxOfOC4aZ/UR28X+Iz9a9zvnEXfS4A4a6nFRDw7frwBGPqHCyWvWorQAqHNS46bkURurt7JNiR6kXC6HbDY7dG2WTzFUuWetRBoNyksXpUsNXHYArGVjO9lqKLZD48xqaymQmD3H9HcShbMFEkuqQXE9j7ryDg4O0Ol00Gq1Qhq/ArG2z/YFcOK+CYKT4DuPFO90Otjf33euHAUVFe42yKvuLyaHcGLTr68WhzIn66rxBU4IavJcsE0NVl0z1ASpxWvSBv3wHCMKTCoDjIfwd01VB+BifhpbOT4+xubmJra2tpwCofzAuhEUuHM9AZEb1RYKhdBkJZDo+in2BT9P+nkMJ+u3AALVyd8JgJ3w5MABGV2IR0cnfMc4BXC6/6EeSaIuSi6eDoIgtMce60z+p2JGgMjn85ifn8fMzAwAuP0cVWljnxMQ1bLQOaCxWwpvjjH7VO/nfLYWkiYp6TosnyLrUyAtiFHYqpLGe3WpA+u2v7+Per2Oubm5M8fg8Fkbk1LPBtuQSCScm5Wu6TjwomuS5SuoaJtte7VPdX5kMhkHXPpefTfllpUT1jLUpUXsT+Csp+i8dKmBSwFHr4XjBOEgPLVI28HqplENyKdp8LdMJoNqtQoAof0IFeB87igtTwPQBCpdwMfFn+122wGjtSZZJuMwOikZC+CiWQrTVCqFTqfjYhbUJqlRsk+shsrJkUwmnfsnl8uFYmCsC7V2TkIG4rmVDH9jTITrbOjSUhclBU8ul8P8/LzbXmd3d9etV2KfUshVKhW3/93a2ppbfKxtY7o4BQf/EomEO+TSp6mqhq3p+Lyv2+2i0Wig1+u5NWiq/HCcdLzIA7znZFLzd46zzSwMK0MU1Mlk0iX2AKc7nRMEON4UjgRuunXVQtAMVFpntKCy2Sz6/b5TrLifoZahwk1drBSIVCqohNANyO8aU2UZVjDqPLAeAisjqGiR/3gPSd2DbCuVIAJvInFybBEzeuk2ZFv1/DZm1SkAWvc5LTi1JFV+MCnGej60DxQQyAssX8HTgrYFLca5GI+ktamWnPKyNRBsmbZvff9flC41cAFnU5ujXH3qyrKgxHutv9iWo+Yv9w5MpVLY2toKaeMa29A4m5ahk4Okbht1yxB4OGl8A04mUtDlRBgMBi4uUCgU3ITjdjdaZ60jgYOJJ3T90P9OjUxdTjYBhNe4D1q32z0DNHwXACcQVaCwbbQ+uYsHy7JxiX7/ZFFzuVxGtVpFtVpFEAQu8UAtCZLNINQ/EhUeXVSsCSWMv7DeTEzRvfOU13TMyBMcCzumFqASCSAITq2sRCLAYBDgxS8OcHyMM1ltwInSoVl+BC3dAUTjnlZTV/ct3bGpVAq7u7toNBrY29sLZdQSROnCpftYE1Q0m5DLLDiXqPzw/Yx1qTDUeaYCnf/bBcmsF3eWIShqlrG1AqhoELyoQDIGxLFXd/vh4SE6nQ7K5bJbrkGPAfuNnhDWlUqmLqKni1TBy64z1H5QC94CoCpFKge1nRq/piKZTqfR6XTccpQ4Gan8pt/Vs6JKyP3SpQcuJZ9mDIRByboQLShFDQDTVRl05v+7u7tnrC0KUQo49eVr2dYnrBaOpmZ3Oh138B4BhJajTYnVuJTGJmjNUUBwFwLdTobxHrUCM5mM26SWf/xdAZaCgWCpIHN0dBSasM1mM5TZpdYhJzfdadY1wmQV3QeSMRM+w37MZDJYXFx0IMvjYsbGxtxJt5xAFEwUXIlEwoE960nBTzemT4hqdiCz3zS4zj6jgLOKCH871VjVFUigIq8yiYMum7C7yVqrSuQdXZqg1rV1M2nyABeO81n2E08vpqXN76pYKvArnypYqBJKwU3e8ikb7G+2i3OJAloTAlh2v98PAai6clkHXaagLl5dhK/jbTNvaaXoOkOCQTabdVtLkaj4EKjU+ue84DXrmlNSALNWnc/K0j5XXta1ZWNjY6FQhY98lpf+xj6l7NJjiS5Kf6uAy5LtZNUyfL5YILxWhM+kUil3yBo1IX4yk41lnLp4wi49Na9Vg6XQVG3bulmYDaXlK3BZtwZTf5nGqtpyv9938S0KcwoiG9xmnGZqagq5XM4Jbgo2TTTQGEq73XbaI/uKa3W4YFcTUHSser2ey3zjBOUkVIGnu3gAcAky6k5h3GlyctIJBO5+zSxLxvv4HiolyWTS7ex/eHiITCaD+fl5lwreaDRC8UAdZ/IC28zd3nXcrRXDZ9Sl89KXBvjoR08ASXkJstj4VJsd4Ju/+exu9wpAHLNEIuEEEi1zXZelWjGBwyYQ0P1LFyTrRkubgkmzLtlXGhv1He0CnO4MwX4hn6kCwPt4DwW+uv1ZF52fbItmzHFOaeIN5yOVR34nMaFIl0hoXJeKJN1tqiCRJ3XxcxAEbo5wnlnLhH2nAOvzDClP2Xij/sb7rTuVfUxZwLHyhUNUrmm5PlL5pYrHRelvBXBZt4EFJw6kzXCx/lffb6lUygWfeXzB0dGRcwFRI/c96wsGW1C0LkMVNgAcCKl2wknB7CuCAycPz/Gy1gqJDEeNGDh1XWomEdeVMG2aExuAc5nQXdfpdNxmvIyfKO3v72N7exuFQsHV14I6SS0xFfAUDHSnEohtaq1ajARPCoh8Po+VlRV3dAMtQhWgBFfGNnq9HpaWlnD16lWkUincu3cPwKkbVPuV/aQxDQpUrZe2iW0GwkeLAMBLX0plCgBOhPb/+B8AXYTf8i0DFy8KglPXmAI4QVyPS9F7+F51t+kCZY6Bpouz35gkc3R05BSA+fl5ZDIZtwZQ3b7q+lVeJKBZ0GDf2MQZACFA5TO0gtX1XCqVnHVuk4XIP+rpsKTAxnZoRquCi1ppBKpcLufWkhGkcrkcZmZm0G63QwkvXOtYLBbPeId4j83IZPv1Xl/Yw/Kakt7LPuH81wM11RqOAqhhbkM7jvdDfyuAS4WzapdW6Olg6rM+fy+F59TUFGZmZnB8fOx2ZVAftRWaViPR8q11Z4FVBQmJO6KrFsmJQg2ak4Was/rh1V1EokBVy4ZuQ9UU1QpkuTySvdfruQWdBAc9OkQtCz7faDRCGjjjRFZrpMKgiSDqAgXCx6qrhWEBwcbaOGEKhYJLUOn1eqjX66EzxgqFgktmKZVKmJubQ7lcduOQTCbP7JtHwajWAPtZ40MUfMoLbKfyn8YbVEj+f/8fXF/0euEYmVrq5CdmPdJtRd5l7InAyjqpJUneUk8C3816UcAmEgmUy2Vcu3YNmUwGW1tbzlugVpW68hS4tB90rlDYU3jSwmGf6rovBRhmqnKZAtPtFRg1WYT9xnfZRAmf18Qu/lWiVcfkJbpPCSKVSsWdjq7voXdAl1OQn9gurZ/KBR8YKF9YOWQVdy0LgOOXKKsszrrykSoV9qDVi9ClBy7tOAtYKsRsth1wNttIBSiF28LCAiYmJlCr1Vwsq1qtIplMhlxAfN7GmrSOFkwVaK1FxjJtQJqCkRqzte64jZK6BKwJT2GVTCadQDs8PHTCQN1MtMoGg5MED+6YQAFPS4Z1sNmF2qcEPv6mmXiqBbOOViujcPJZavZddF+2222MjY25ne2ZUZdIJDA1NYX9/X23bx3dsUyfb7fboQSUXq+Hvb09rK6uuuM+VFCyv0gKBGyzWl5Wa9U4qFWyfK4Z7QNVMqx1TSWBm+cSdNQNxD8LeBT2yWTSJQeoBXR4eOh2/C+VSm5PQFqaupemVdiUHzm+wKlgVtBSMGJihVqyyh/qXmZWLuM1+/v7Lo2f2XK2Pixb68d5pvOaS0CYRGKVzkTixPVcKpVC2acc63K5jKmpKezs7Lj3sN+73a7zepDIE+qxUJ6woMX6quJq3YSWrExktq/yk68MC4Janr2H4+oD+/PSpQYuq6krg+lE9oEIyQo/4DRRgYtP6/W6O7KcmXU8n0jfS5BRUk1HgcomZqjGy3tosuvZVNRWgVPBqGtfKJQ0I5ATmHEJG1RW9xutNk5ovl/dRL5+I1Dqti46NtYCUqGkfciJy8nGdmt8w6d88N2sEzVFXcMFnAi3RqOBjY0Nd+YQrVNanbQQNMmk0Wg4jVkXNVsQYr3sWiXeb/mQ42E1YRWUvEdjqcrj2pd8lxL5gBYuN97VutIq1HOoyDOsO/n74ODAJWaQ7w4ODly2Kc8WY4o8QU/brm5n64aiNcEx5btsTJQgxiUUzEhUty8X2zPOND09jWKx6DbcrdVqAOAWv7O/VBaQ/9hP7Dt12fmIrvxKpRJy1XIP0HK5jJmZGdy9e9fNN7adfEc3o84TK6+UHxRArJKs80WVZSUrHycmJpxnwSoSdh7GlaP3KHjdL11q4PJ1vM+q8oGTfcY+TxcRFxPSnGc23tbW1hmNQctWK88yE0ndJCoINOXVxrzUZUKtlIJND+UDEDqY0gZzFRzUMiWQ6XY6NlamwpbuMJvxRbejul4orBSQfGMSFWew3xUoNX7J5QqTk5PodruhY1N6vR7W1tbQaDTczgZ0xTJjFICzDNXy1Dpon9l2KBCQD3TiW6vKChyWZS15VcZ82q66qfVPrRJNcGEdSEwkoJJAHlBlR60wKjTaV0yIoStZkw9sNqzWXa18vj8IAneMzWAwcODCNrHOXKLBZCkCGIGWGxfzoNOZmRmUSqWQGxSA4wONv9m5S34PgsBtZq1z1CqyQRC4Ays3NzfdHAVOFoUz3Zz7QqqVTqCjzFGLy8bqLM+w/tbqOg9YKCAymcQq1dZjFEeWr33/X5QuNXBZ8qG/dTlZ4cNrCm7JZNL5xXlqLpm/XC4DgHf7JRXSul2LWkCqbfC9k5OT7ih4FQZ7e3tnXEb8rlaW+uqBUxcGtUwmarDNqsFb14CCle1Ty6y0ZtU9yDpSWOlEsQLBjhXrpbsr8M/WlRo3ED54UBUGxuMoRBKJhBOkjUbDpXTzd1oY1u1G1xQFmwKWClpth1qr1r2ilqOCjRUEGmPRuKbymrXWlKxFC4QtQVV+SDw7LAgCdw4beYXWFrM3NXOUZXQ6nVAsiclDupiVIKJBf/I8LSYqQ2yjgibd3LTkEomESyKqVCrO/dtut90icCqeqVQqdKSOuq6Hua8UpNin9LCocsExpeXJtXKMs3HMer2eWyS/s7MT4hMqeZy7PBOMPGyXYmgdlQdV4VV+0ft9CiT5jcBFl6XOe5WX/M1XXhRAPW2ByzbcCkEgvNGuXvcJZF7nJKMGR59yJpNxG67q+h7g9PyewSC8xxhJGUYzvsgYpVIJicTpomEu1mw0GqHUWHUXqcaoWi1jMkzS0NRxK+RUi6QgVbDRDLREIuEYmMKKrhwKFn5azYrAoRqpjyg8CP48VoGuGo1hTE5OhkDQunIoCPk765xMJt19fB/7Vl3LFPJMxtFtvQicWqbVZgeDgTtdVy0XdS1a4LIWNnlGx97ytyoEPqVNedy6NjUmp8KO9dGYnCpNfI7zgqCvyTm8F0AoQ5VWF1PymSpuwYvCmQKc7WeslPypShq9DLRkxsfHXfYoFdBut+vWLNI64/ozVb44HpoYpIJbN/JVHuAfd73v9/tu2y++Y2xszMVcuQBc41UcK4IrXa6a7UmLyCcHLaDpGFJ2RCk8yr+cKyrTWH/Wzyr+yp+kJwNSPrrUwGV9pkqqFdn7okCMxIGiZsNBLpfLyGQyuHfvXsglx1iUAplvoKLqRD89gYAThsek0MdMwaTCjmVQEHBjVa6psoLJ1kGFlVplKhDVrUNG5uSklsv05+3tbeeaU2a2FqmWaV2DdCXl83kkEgkndHgv+5ruVGrmtHyYccj6EwAHg9MkBW7Cqxsja4xI+0ctS76T8QtNaLBjzLTxWq2G+fl559JSxUV5QzVUHWOtiwoJ7VNL7HebmGRJLX/fWFg+I9BQ6NJCo4DVZB/g9JBP9TRQYeCasn6/7/iX48I1cFRAaL2QL4MgcNYIlUWuNaQFlkic7vCRTCbd7g+Mc2o2qE/psPxpPRy+WKIqZRz/vb09zM7Onll/RaHPrD11rbM8jjF5267JslaXWuTWi6TzXceVY2uVTV6nwsFnVcFTdzqfiwPEYWB5XrrUwEU6L0hYM9qnFdBq6ff77jjwo6Mj5x/n/nXK2JrtptYLf7fCRweP2wLlcjnnfmQqeC6Xc3sMUkPzuQeSyZN1IYVCAclk0vn5dSNXjWH5hJ2dvAouusURcJq9BZwIprm5OSwvL2N/fx9f+MIXsLq6emZ9k46RjXPwHeoOGwwGblNgnsOk5zZphqKOrXV30CLMZrMuNXpmZgaJRAJbW1tYXV11ZdgkHhXICuz8jfv+2ePJKXjZjl6v51xofIe6au07yaO8Rt5SZUKByfIY/6dLzbZLLTT91DYrn9n5wjlCUGAfEYDoBtNkpcFg4Kwb3fuP2XO0nCgk6Z6l4sh4JMslmLFMWneFQsHVkfUsFouubronIpUQOxdU+Ko1z3boPOC95GfWmYpVs9lEq9VyiSsKTuwrPZ+N/a18oKnvdhx9SovOgyjl3KfEW1DROU5FgNf7/X5oFxkLolHg5Jun90OXGrhsx0dpn/q7ftdP/kaGopuNzMTJxPUvvF9dODY91woG1c51QvC4B8YLeC2fz2N2dhbFYtHFqlTTsYKFk1KPX1AA17pZwaigxfIoPCiUqQXzOYIpQZKxi8nJyTMxMi2X2jZwuh5LtUrWud/vO/cscLroWYWo1SAVPPQax4cARmta1xiRLDgMBoPQpqjWMtEUftaT48R0Yu1HtW7seNiAu886JqlGznZS0PE5uplZlioHJC1D//ibfY/WRd2utILIO4VCwe3Swn6klaPZigQtxnH0pAFVCik8meFLOj4+OUqEXhKOJw+SVMuL96sixtiyjptVEpTf1OrT+WVlT79/sktNvV5HEASuLvoeq1yrMsOxVFmjY6ZjyTHSsVJesfyjn9bCt3OK1rCN06obWccjCsD02pO1vC41cFmXiR0onfwk7TTtPAU9FVjUpmjNNJvN0GJFjS/ZDDPrItEkAt4DwLkCmQLb7/eRz+ddGm21WnUT2TIZmZ5apMYe2AdWK1fXBAGJQo0BbwJQIhEOnLNdbOv+/j5u3brl1jypULEAzknKutMa0SQEAM6dNzY25mIb7XYb3W7XLRjm5KeQ5DjYZQHs9+Pjk+NNdIH29vY29vb2QnEM1Yh91omPtM913Q1Bd3p6OrTDhLXGlecsSFngUj7mc6p0Kd9RsFjt2sYYFewt3+qzKmwtj5NXmMRyfHzsstEYH6SlzBioWuXMaGT/ad3JM4xh0lpTftTvnKPMDE6n027cWUfOQ4Imecbum6huYs7fKNeiHlLJ+dXpdLC9vY1er+cyXRnnYhxPlZkoi0QBk23gfZy3PjDQMWKbbazY8pPyJhVU7leo7aVbnkqL7RMfUFmZfb90qYGLZCd11CTnb/y0GiSJAh04dYsUCgX0ej23f51v8mr5Fgit39pqcP1+3wXyuZv6lStXMDc35zKP6vX6mdgRy9UDDrV9Gozld52w6s5hGjnfR1Cj4FfXhGqcFEq6LRTfqXEgtTItwLBPNLieSCTcibpMrVa3hAoStlnL5jgxtZv9xA16dY9CTkCCv/av7mSuYxgE4ZNc6ZakgCqVSiiXyygUCshkMmfcgwpOarXZsbWavLbZjjV5j/2pwKvjb8HQ5zrW5zhudp4pUcHjjiSMSemib+B0aYaCAGNG9HbwdypSGrek9QXAjRUTJbhgnlm5TMRg/VTIqpXKMSPPM6mJv5Pv1PWpIKN8oG7FbreLzc1N1Ot1t/fn4eGhiw2qh8SnyKhFp/2uPG75wIKfVertO3xkLWxNx+d7VE5GPe+7rp/3S39rgCvuu173uUOUyLjUzghc4+PjaDabLknAV5YVSizP9x7VkOgi0wA3V/UzdpXJZByzAqdCZGxsLJS+TuI7baq2XUdFYTs+Po5yuewOv+Pec0xeYGalr784cRlTYvutlUmBQw1XNVTgNENMU5YPDg5CCRQEEQVQun7sbvfUljkuPIpmbGwM9XrdpcprPITbYOl2VHSFjY2NndnsWEFzfHzcxdB4fAxdLApCag1bflBlRwFJNVb7jFqMKkgo5JU3OEaasMFkG1ouClqqxavm7nsf+5z8ROuYpwOQb2l5JZOn2zWRN/WPoMfxUOtT5wHjWGNjY+58NYIM+YwKCGNyCo58X6FQOLM8gnOoXq+j2Wx65Qb5jPXhM0FwsgvLzs4ONjc3Ua1WXSxbt4CyCpmSHXdrEVn5EmV16bNRMsneq0qVnmxtZZ8F1aj3ax18v12ELj1w+SYWcHahm70vShtgPEMnTjabRRAELhPJDpzGZjipfaYz36Hv1rUkwOkxD7u7u6jX6w60mC3o28WbmiongsaJFKSUcSlMeEJvOp1GuVx22/M0m01sbm6i1Wq5uJIVqmrhkXxHH3AdiI2FaJ+wrweD04WmvkP+1BKm0EkkEs4lxLbzpF8+T1ApFouh3fYpaPidhz5SS+cGxxT4jN9pLIpJATMzM5ibm0O1WnU7RagAUAHE8bKWt1pT1tWtvK3PqwWo79S+sv9bL4Fq0xqDVdDU92kcyHouguD06HeOGQUfFRdaZLSwrLuSc4MZimwvAYxuNs2a5G42rLfyFxUouom5NorWINcOAqc7qDBGNjEx4fYV3N3dPXMiBP/U2udv3KNzbW3NnZbe6XTcfUwS0fKs68/nEdKYnA+8LOnvOm76jLXY9H6OH7+rfLHWu7XufNbek6VLD1w6WaM0Ux0USz6w46TQyaLbBmlZHFRq32qp2XrosxrUVJcE38+98pj0QGHKBA1qgxRQamn4NHPeq/UgWE1PT4eyuBqNBjY3N1Gr1dz6ERWyPg2QbVahRxfD9PQ0CoWCc4fu7e2FNkYl0QJi/2gfa8xJJ9f4+DhKpRKWl5cBAI1Gw6XjKwDk83lkMhkEQeAsgUQigWw265I01DrVNV52VwONBeVyOZTLZXeCANfr0NKLEkCaKGP5N0oQsM1qbZNv1BXIOip/a3/aGKS2yVpQWjf9X92GUZYZs+h07ZLGi/gbLRx1dVK48rqNNfKT5TD+qZaU9q2ul6ILk7zKOuzs7ISsIXo7uCZsenravZtzVnlB38n+Pj4+RrvdxtbWFmZmZlwSCi1CAqn2m5IFCfaP7nzDPtAsXSvzyCNa5yhQ8clEq8D43qEyTMlaiXrtfunSA5cdbNt5VggA0enzJAZ6aYkQuHRnaZbJYDI1J58WYzUZMjmFox6LTZcX3SgUmtzlwcZXlJkoWNWXb114nNiMxagLstPpYGtrC/fu3UO9XnegBYSTFlSDttq5avSZTAblchmVSgVjY2NoNBpu/zrWS5UFBXTf2Kgg56TLZrNYWFjAwsICOp2O286Jz1khzcMt6V4sFosusYUKAcdDEwm0rhynUqmEhYUFzM7OOmtV3Sk2kcHyRJxmrFaTvV/7yCYK2HsU9NVKVMHF32w8S8vRclWxsJaiCvKxsZPtmNiXqhRww1vWg/yq46t11XlNYOHvnCtqGSvfMlOXddZkDN2QOggCZ02RxzOZjDvmhuuwWJaue2Tf6P/kp1arha2tLUxPT7v5xndzcXWU3FCeiOIV5YEoIIpSXm1/67P6PHmZcsqCUZRMteD5VNGlBy7gfAE/X+faAVMNkgctUkvXo0UsQ5BJdX9BBawoLYS/c0U/FyLTUun3+07Qa4qwZVJObruuhPvusf2qdXGHjUQi4VKWd3d3UavVXCafTkQgnLTCOhG82GYCLNPO2X87OzvY2Nhw6dJc/8T4gp0AtHDoStKJopOQO2wnk0nn8qFrU4XewcEB6vW6E6BUArhuSHc50KPRrUBPJk92iqhWq1haWsLs7KzbaVzHxAp8y6tWQ1d+tDymPKSar77PvtsHdpbfffEpFVKarq08pALPpyRai5gZldyhn66wTCbjlhlw/tASUrLrCGm5sQ02SYGgw/HjJw+DJO/rnAXCMTZaNdxZnrFgACHXHutDzwtJ42PcempzcxPT09OOJ6kk+XaasXxh+1jv81lKVkb4lD99nypJvKbxYwV+5QNV6iyv+3h/mAFxXrrQSV5vf/vb8YIXvACFQgGzs7N49atfjS9+8YtnKveWt7wFi4uLyGQyeMlLXoLPf/7zoXsODg7wkz/5ky5N+FWvehVWV1cvXHnbyXZwKHTUNI4DL+1U7uDMxaPtdju0mJf36up7gocyP+un/aPab79/su0MT9ml4GeGE2NMqjkDp5NZd9qghcBdBoDT9WeML6nlRndkrVbD6uoqNjY2Qos5rTWnfaXuCTI1yy8Wiy4uyHUstVrNbZ/FMplooWOh1qjPylSrNJ/PI5/Po9/vY21tDevr626isY7Uihlr4N511LobjQYODg5CR7pwwqq1QyCdmprCtWvXcPPmTSwtLaFQKITW2rDvKOwY22P/+4RLHG/r+/k9yh2o/WfXCGm9FFQtuKoypoCl/AucbvZMALHWl5bLxfR0HdrdLtQl76u7tpdziwuQ9ZqCGOeXzlOChK7N5C4q/F2BTPmcFhKtJLXkmfSh96vrtdvtuuQOxkzpItT1bOxH6w5X0viitk8TvDTOrff4rCqNx1k+U76wGZm2rpYHlRetnI5LDjkvXcji+tjHPoYf//Efxwte8AIcHx/jzW9+M17+8pfjL/7iL1wg9h3veAd+5Vd+Be9+97vxjGc8A7/4i7+Ib/u2b8MXv/hFt5D0kUcewR/+4R/i/e9/P6rVKt7whjfgO7/zO/Hoo4+GMq2GETshyrRmR9k0Xttp1ADVJUHXGRMErFuA4KMnrpLZbIq0as3WrUM3R7fbdQkYDA4TgPQ8IW2HMgFBVYFGF9gyVqACkVmLPILCHsmi76Eg1razLQQvulaTyaRzybHvdW85ApbV3ihcabFxQam6Qijc6OocGxtz4LuzsxMK1mvAmynxFHQAXD1ZN13fwnFkf2UyGczPz+Pq1auYmZlxAlfJgoROXgAu+y9u0vqA3H63lqnyV5TwsIJQn7PLP6w16NPSOWfYbyrQ9H6+k25j7pbB57hmkMk0TECyfcfMO/Ia46GcUwpcPlezAjpwshM8lSMFX7UKOI46fjqHyYu0tBh30jkeBIE7YaLVajmllPyl+yPy3cOWHdh5rGMCIMS3PqvHlmvnoX1O+4HtV++TtdR85frq8mToQsD1oQ99KPT9t37rtzA7O4tHH30U3/It34IgCPCrv/qrePOb34zXvOY1AID3vOc9mJubw3vf+178yI/8CJrNJn7zN38Tv/3bv41v/dZvBQD8zu/8DlZWVvDHf/zHeMUrXnHmvWpBAECr1QKAMyDg+99qEDrhrelqJx7jT/q8lmutLftHskFTggsHnH5wdTlQ2POPrj9ObJuFpBo1NUCuZ7GCDkBI6GhZlrlUo8rn827tF10hujMDAKfVMomER1LononsNxXuKpAZG9EYBPtOhbCmUusOGAQtnYwaj2OMjUJLT0amq0rrlE6nsbKygmvXrmFubu7MybSW7yjQLEBZV5pa3vq75TOrvdrgulXerODwuQn5PusutL/5hI21+OL6gvVIJpMuq07jxVRC6JajkkQwZT3YTz5esv1k68x5RcWPQEdezeVyyGaz7mQG5UPunajAyTop0NgjYNgHiUTCJWhwuzJdX6jufQV/7UMLBgroPq+O5RdVUHzkG2MrC3z8pwBqwcuW5aMnY3VdyFVoqdlsAgAqlQoA4NatW9jY2MDLX/5yd8/k5CRe/OIX4+Mf/zgA4NFHH8XR0VHonsXFRTz88MPuHktvf/vbUSqV3N/KygoAhASfTzjzfwsmVpuzHR41AdQ01oCzNe1Vm1IBat/Ngaf/nTs7cGLxxGEKeboSrQBMJk93hedxEZzsOsGs+0cXayrjWVeSgiFTyun6AU7dFHt7e2i1Wm6xMOulGr1q+No3CqTcSYRWG+Nt1JC51qrVaqHRaDj3C3B2HZMKTcb06E6xJ0vTKlWNN51OO0trdnbWHU8RZVnpGintbzvmUVp0FO/6BJv+psDrAyzr0tF60A1kASjqu7Vs4sBP51YymXSuORITNagURb1LY1oaE7VrGKPkAC194DTRKAhOFwAzfZ8eEj6Tz+dRKBTOHDNPPmacShfq+wQ3Y6hUwtXlaGVYlEC3IGGBAzjdUFrboaQuTPse5ZWo3227lKd8dY+ysp4MaAFPIjkjCAK8/vWvx9/5O38HDz/8MABgY2MDADA3Nxe6d25uDrdv33b3pFIpTE1NnbmHz1t605vehNe//vXue6vVcuClTOrTKm2yhK8jrSVGQenrXP5OxlOws3VRn7sOrt7L+3iGUCqVcu/odDqh40h4fAPdKlqG7miva5R0YSQ1Tmp9moigsScbT6MgplstmTxdL6ZuE40pkdQ6Y300DV5BnYcG0n2pezeqq1JTiG2ddVIDp8F5zVojsf0qbFV45PN5LC8vY2VlBXNzc+64GB1HFSC+vrNt5HjxU12Hln/irCgt2wKE/T/uN7pHWaZaUeQh/c0CrP5uLTifRcYlGFSadD1YoVBwyw9UeKqSwLr71lFpn1hFiQBHviVQM3OXGzAPBgO02203JzqdjuMdFeos22bH2rgoaX9/Hzs7O6hWq6G1XsoTdqx9lncUb51HaWFfqAyKAxDtWxsLVRnnk61aru2LqPsuQvcNXD/xEz+Bz3zmM/hf/+t/nfnNh8xRyHuee5hg4KOoQSapFq2Dqc/pAJKhOCF1UDQQrL5prYt1N/qYQ99PhuME4vMaCAbCiQbKMErq6mDZBBQ7MXiPzehTi0gXVNK9xkxCBShf2ZwwCm6+cVLwTafTKBQKCILAJaVoW9j3dvKp9s/604WpQWqCuwo2as0WWPL5PBYWFnDlyhWXxqy7B1iQUR6L0kJt3ysv2rR9y3v6Pn3e8l8cWPnmigpduqlt/UkKHL73k6JckBynfD7vkivIm7yPLu6o5CbtKwtaqrD4YslaJueGJnuUSiVMTk6GUusJrupp0bZTJmh/K5HX9vf3UavVsLCw4Kx2vUfdj1HC3Pa7jrcqqdoP2l+8buWWlmV51ac4aX2ieMB3v77DZ2RchO7LVfiTP/mT+IM/+AN89KMfdQs/AWB+fh4AzlhOW1tbzgqbn593x1RE3XMRippEOghW6yD5Jje/69HkzNTTIxGsv9gOOHBq7VmXmDKegiL3/KP7zi4y5C7sVtsHThdN63owrRtdj9xQV7UzW2fVsGiVqNVkgY7WHF0UtEo0HkFGVaDUd9ENmEgk0O12QzsgaJxDNTzr51e3HYmTmfWgK5Xt16wx9l2xWHQxLSZi6FZTClL6pwLYApTWWfuepALXB3RqgVgwsTylFBV/sn1nrUa9V/nWftd3RrkM1fIaGzvZoqlcLruMULX26Q5X3lTAVLdhlEC1bQUQUmAAOEWR476/v49ms+niyeQRS+pFsZYN26h9y+/0oHB/zKjxsMkdth3avzr2ClQ229O6rKNAyFru+u4o2aVkFQmtt/18MqAFXBC4giDAT/zET+A//+f/jD/5kz/B9evXQ79fv34d8/Pz+MhHPuKuHR4e4mMf+xi+6Zu+CQDw/Oc/HxMTE6F77t27h8997nPunouQ1ZR9WoLV+u1zes2mLDMb7eDgIJQUYQWGvp99BYQ3o/WBm5ZBS863QJfuOcZyLJPZOJKWw/v4Xeur5aswpntErTgrjPlJ0FIAoJChRqsC2Ae8yeRJen6tVnPZiDbepO/WRBSNj2kqPIVcIpFwSxqYLq8uP/ZfMplEqVTC9evXcf36dczMzIQsLTvmPmVEKUqDtUKHfMJ+sOPj08KtsFLwVMGpoOdLIlJwtLxpFRofiKnrSpUMtYK1znwmk8lgenraJT9w/LgNGeOcygdazrBYoh0HEuumc4Fzi+sZqdypAuYT7DYJw1oRqtQAYbDUMpSPfGTlmW2zj0d8CpDtC/6mCpyPf6xya2We8oP2uTUe4qzJi9KFXIU//uM/jve+9734L//lv6BQKDjLqlQqubNmHnnkEbztbW/DzZs3cfPmTbztbW9DNpvF6173OnfvD//wD+MNb3gDqtUqKpUK3vjGN+I5z3mOyzI8L1lh4dP8FIAotOzA6L0UiqoB0crgfmbql7fJGT5Xgc9ctvVmGRZY+Ml36sp9dbNEvdMGtrkbhjXZdacNfqqFw3LperV1tK4fgp9NJrDjxnowgxAIB9J1ktsJYicv363uPDv+/F3bC5y4Eaenp3Ht2jUsLy+jVCqFAM7Xv3YCW3D3ua14rwpxX1xID6f08YtvzC3/a3/7+FI/WQ8LWradPor73cbwtO3cLovLNsir5XIZQRBgY2MDh4eHzpLXvmDmIcu3QtcqSIlEOIuPigp5in2mFo+edmDd07Z/tB99Cguv65E8OlbKCzbc4BszG5e24KRzSdtlAY111rr4youiKN48r6Fwv0B2IeD69V//dQDAS17yktD13/qt38I/+Af/AADwsz/7s9jf38eP/diPoV6v44UvfCE+/OEPuzVcAPDOd74T4+PjeO1rX4v9/X287GUvw7vf/e4LreECos1OZUQSA7Kqxer9yogap+DE0PgStXy69zR70KfFqgYaBZT2fbb+zBYiYFnt2jK8Mp66NVRz1nv5Tn1mMDjdPkcXVqqGTAGigGvBTttrhT6vMc5ETZrn/zCt3mp0VtO3gkr5Q0mFFu9NpVKYmZnBM5/5TCwuLrrdS5LJ8CJpnyDRdvoUqajr6qrUNvB/X721vVawKIDb8de6Wy1ay7f143XrHrb1ssLaZ2kpT9OCGh8fx/T0NA4PD7G9vR1SIrj0Y29vD8lk0o2J7uvHGJVdGmLbRHlgXa22Lfo7lVJffyvw2Pdoezmf9T56b3T9mPZ5nICPqrcl7Wfe6/M6sSy+27qP9f1RQKZjbee1fY+9PkwhiqMLAdd50DGRSOAtb3kL3vKWt0Tek06n8a53vQvvete7LvL6M6T+ZR/zsM4UfAogUR2obVQhYDP0+JwFAPuc1QBtHypI+r7rfblcDqlUyqXI+xjdPmtdPCrk9Bm1VthPdN1oGT7tmfEjddNpuT4Bb/ucFhrdjhSWvlgJ+4PXfBOS91DTZj1oRVFbnZycxNTUFG7evImVlRUXZ2Nf6PiqALNgajVvCyBWqYlzZ9nrtt123JXHFcRZb5L93wpD39wZJkztp1Xc4tqlLsPDw0Ps7u66ZJnx8XGXfUhw4mbWvKZjafvR8pjlkzi3H+/XBCx1Q6tFq23m/z6eVRmkcV6fkhwFIPaatdZ1bHyL43WsfUDoU7DiwMUHcFHjHSVj75f+VuxVaEk7xk5oMrwKovN0pHUvqgCiG0MFJBmQf5bJ9b02NsDfrauNqeKNRuPMJCSY6kJNS74V+ZZpbRDb556xfe1zCyho6cT0TQRuh8Sg+GBwerSMdanwnTbt3te/GqsjEGuGWCKRQLVaxfXr17GyshLa21HBxYKWT8j7LC+fEqTf+Zxe17iernezLm591mdB2ff6ANUCSxRAad2skmgFtk+JtIJZwSCROHEZTk9Puy3IWFY+n8fh4SFarZZT1FTp0P5SvtSYlI/sfPcBuAV/dcv7XHnKf7ZPrfLMMjQkYRfj27Itn1E2aF3s+7Uctkc/7Rj7lF6dA0pRcsT3u+3r88rcOLr0wGXdb3GTUQUpXUDKTMPIvgc43e5I10QpwyggqdWmZWg96LfnJNVBPjo6QqlUwr1790JWDQCXQEChri6VOCaxQk3rxLIswGt5rK+6IizTKoD7gve6JyHjHfYIdX2vTljtIxU25Atq677EjVQqhdnZWSwuLiKbzbq6axzTJ4R9gk41ZwU3CxBWwFhg1/vU5RPnRvS5oH1zwFoGPgGu9/mA2pYfJySj2q/xYV7jLv0A3FltExMTbm0VkxomJiYwOTnprC6rDPl4XV3Xvn6w7VJlV5/RHTu0zXbMVGnVd6i7koBL9z//NAtWM3J9fa3uPb1P5Y32gY19+sq27dPfojxVw5SfKFCLs+aG0aUHLp0wPm1PiVaADrrPkvJNXJ9Wq1qm3aUCOHv8hIKSHUwV/ryHRyCwPWNjY6hWq8hms24RciKRcGf88B5aI7pRLOugAlAFotWEbP9ZYODvUYIjkUiEFm5qvxK0CM7093MXAi6gtgu3fe5B9peOl8Yfg+AkRpPL5ZBMJkNbh42Pnxw1b9fVKFCzT61bxgpvX1zPZ63o70o6maM02CgB4VM87Lj57vNp58rvUXNL62OfjwIs2xZrffGYGHotuMM/Xbnj4+OhPSV5H4W71tkqR+o2U4+Cz4pIJBJnvCbal9Z7omQVYK6h1Pfyb2xszO1bqC5Cxs+5i47KE8sDUbLEN3ejxtmOpz5vQcpXbtT9cYqUjycuSpcauFSw+YLzVjMmqatBt3KhwPIFXknWdaWMRUFNgOJZXnaHCl+mmjKTPp/NZp2QLxQK2Nvbw9ramkta4MTixNAAt8/NxkSL/7+9N4/RLbuqw/dXr+a56k3V1e919+vBgLvbjmkT01b0M4kHYmFIRCQzKbIEf0AAyy1skRik2EjIbRxhBgGOIAibINJSZBwRQYgbBZoQB2G13dDdOG273cOb6o01z8P9/fG07lvfenufe+79vtftel1bKlXVveeecZ+91t7n3HP52aitaoQYODgNr2N5pEBP08ZvHLDa399va2tr5XeyPHbI4wzDx6digBjAUMGgoZzx8fHyNA60RSdcans1e1XcNxqq4XvafxEoeGsVMOpMNCKwYh3y+ozT6PoMP6tzh+vBaaM1mAiouG5af277wMBAua6F9S5c7+3ttaWlpfJdKxatHwT2QV86VzKq/cDhZV0T57TsMSrQcXr2tJD/0NBQ+cKzmZUkC1+I3t299nFMT1+4Hq1Wq7Qp3KdcJ31VJhoL/V91VvvK8/YigPLIWCeyr4ELR7V4IR2zuBMxIDDy+u4WhD0VBhVei4J7zx5MURTl2X4If/X0XN9WC+VFGagbQA5AVxRFeUo6v89y7tw5u3z5si0uLrYdZ2N23ZPq7e0tw196+Cwf7KmL1rjOOxEj8bxVPoRUQ33oS+7f3t7e8v0WsGxmtmx4+GOTPKnMrOxrzR8/YLDa5whJ8hoW64LqU+RB4D73oQcYCl5aTzzDxobrxvlrHVTHWW+1LVH9U8ATzQ8uM3XfE/UgBgYGbGJiovycEOsQCAq/y8hlKVFlfYdHzaSKDxLgMdHxUZIQjSH0EmDHJAxpBwYGyjM/p6enSzvUarXKTSmDg4PlC/hRn4KAQtgmcToGHJ3rKlXgk7ukkis5+hHJvgaupaUlm56ebvN0lO1CPObO397RrfKeK8356ToUe11sMNRosFekJz0w8AG84JXAgI+Ojtob3/hGO3funC0vL5fGHICBcnH6OX/jyMzKyYHJi1NB+JQO7gP2zjzWpAyT15EwHvz6gIZecBIJH3gLI4D88L0kPvVDjfLQ0JANDw+3fYqGQ4a885HXADiMpADheTAcKoomshoNbg/uI09du/K8B36et6bjHvRZmbmSAJQTAarWTdujazY6vzzDl2oXlw85dOjaIcsjIyPlu13srfMrIVomk0nuB17n5A0R7KnrnEc+/GKutpn/Rpl6hBrrDKIoY2NjduTIkRKguR1oPz4amzLuXv/jGtcZfRMREx1PbWNEbDw7GUk3vS2zfQ5cly9ftmPHjtnExEToaeF/TG42tBhQ3c7tMS1MCnhFeqQS3+eDZb0FTd2cwIrDGyzwSW/stDMzGxsbsze84Q124cIFO3v2rF29erXNg4KwV4KT43HsDBgoXuzEuhjAAZMZfcTrggAhPgPSzNrW1RicW61WeRo42oMwJgwGfwfM22DBBAOsFd/WQn8CkPmUdyYzqDuXMTQ0ZOPj422gr8aCDY8KA6iOo46Bjr8aCVxnffX0KwJLZvuar4IbC9/3ANszXAqaXE9+np/BdTboDDLsqfT399vExIRtbW2VO2j39q69UzgyMlLODcwX6ALGWaMEHvCoIee+0232kV7w2hS+gr2+vt72rTDMBWznx9ezZ2dnyzVXHPUGcB0bG7PFxUW3XK5vBAae/eN6R+PljSv+TpWn4JoDUrAdujs4V/Y9cC0sLJQfFGS3XIUHHAPBhgCTyNuwgR8+ZobzVeWCIWWWB+amp3JwXfinKIpy3Qc7q7a2tqyvr8+mp6ftTW96kz3zzDO2srJyw4kVzBYRMpyenrbNzU1bXFwsj45CaA6nzuMbWLxBAl6XZ8y1P4uiaFtQRt7cfjNr88C4H+HBsheCDRs8hnxiAgwO1q44fMIeFV7gBrDyxgzUJwqLqoFTw8vXWBREuA0QXeyP8vKuqYcAY8rGmfWJ03sg49VPvWTV1yojFYG6gpnOw6GhoXJNFzq1t7dXfvkaxEffV+J1Sj7lhdsBosQESdvP1/glfI0+oN4jIyM2Pj5efuUBR0cxQcN66+TkpB0+fLiMmOCTJwDt8fHxkuwxaUl5Qjp2kbcVkYuqscL6fGq8PVLk6TP+r3vgBMu+Bq6rV6/a+fPnbXp62kZHR9uUS3ctsaFlxmvWfsIzzr9DWh54j5VyPtjlhPTMoDkGzZMe5fLiKgwuFqmnp6etr6/Ptra2bHFx0UZGRuzee++1hx56yM6fP29nz54t6wfjD0Pd399vg4ODbS92cggE/TU8PGxjY2PW09Nj8/Pzbd/oYq9KF6RRphoetGNv79qny7UvYFjwDhfywoTVk/DNrK1NSIMTN1AH/vQJDMXe3l65toC6jI+P2+TkZLmjscrYw/B4E1cnPOtOjnH30qpnFRl8fZ710QPFqD08ljzGXKaWUcXkVT8iz8vrz56envL7cjgtXucy9GdiYqLtQ468bgmdUI+bjTl7T9z/DDa9vb1lSB3145ejFxcX284XbbVa5T3Ut6+vz8bGxuzo0aM2MTFh8/Pztrq6Wn6LDxEQ7IBVMuv1k46B2jWe5xGw6LgxOCM/RDIiwsPXdPy9sVXCUVf2NXCtrq7a3Nyc3X777TY6OtrGOiFVLJoHj9/T0JcDUxONhT03XgzlsnSbt8ecYdivXr1qCwsLdvTo0bLNV69etWPHjtmb3vQme/75521hYaE8GoeZIAAZH1zkk+05LDI2NmZTU1M2OTlpZmYrKytlPYuiKIE0Wh9RoEb79YgobhuPEcAI3h2/o4M1PpyqwYCPfu3r6yu/lcUL7/gQpy6O7+1d+/YTziNkneB68rilNgJ4bDICM/VUPK/N89x4AxDAXMEWZcDweJEDjI/W22Pznoem7F9ByANrz5ApmCn5wdiPjo6W36XjMzdhSHd2dkr9QZSAIx4ArvHx8fKVC2zY0M0KDFb8/+7ubnnYMsgSf3G7KIryC8dqb7hPUI/Dhw+XJGppaal8Rw26fujQobYvPahOeISBdZxFd/wqidFxZv1gHcW89MrWcY9IFv7HvgBv81au7Gvg2tnZsYWFBbt48WJ50rSGoDyg8VgrnuHB4YHldZ8IDHW9B2VpeezdcX24zggxbmxs2KVLl2xycrKceJcvX7ZWq2XHjh2zb//2b7cXXnjBXnzxxTIv9qS4PwAIqFN/f7+NjY3Z4cOHy7MkmYmyYQF4sVeDycWTAEYDbWVDy8+qEcNaBYdYenquvQIwPj5eGiYYDP7p6+uzycnJ0mM0u3bo7/z8fMnE8amYnp5r33waHR0tAZHBwwMcT3ciUSPDk5t/69/qPXkeF/cz96uClGekIu+M9UOBzaun1tdrnwfo3McsAGUV6ARCcLwF3iNoqDM2NfFxTUXRfoi02fXwH0CQdxgyEUB+W1tbbR85heHVXYlsJxC9AVgODAzYyMiIjYyM2N7enq2srNjq6mq563hgYMCGh4dL0uURHe577mP0A7/Erut43vMqHnEqiqLtPFYVLUPJDufNuvqaBS4wnfPnz9vtt99efgoh8o7YgOrk5EmgTLBuLFbX2bxwJUIIUDTeko26AqiuXr1q8/PzNjU1ZUVRlEfgnDx50h544AH7+te/bpcuXSq/9YNni+L6xgZlujgLDmHWtbU1W1hYaDulXY0f95sCG5epXqUyON1sgYVpAJGGKDc2Nsr1Pn6WxxQbUFZWVsqPUG5tbbUd88QMfGRkpCQ6uoGG28715knN9fcMAwMg6yTnz3lrOVonNvCeYVAG7ekh1zcFMMiv6r7WwfPCOI1GIXANz+jRTTDmY2Nj5WdpiqIoT88AAcRL5Zubm20v7KPfd3Z2yrmBbeZcNkJ6TPb4lJuiKGx1ddVarZZNTU2V71/19PTY6upqW2SCoy1sb1qtlg0NDZXHiuH7fvwuIl7tuXr1qq2srLT1pTfOEZBwn3N0ha/rOKqOKrEBGfDqwvl59VCbhmhMFQlMyb4GLijG8vKyLS4u2tGjR0M3Fdc4fotrujjLb7tjhxEbuIiZw/BG8WStiyqRrr1hAm1sbNjc3Fx5NNLGxoatr6/bpUuX7Pjx43b//ffbM888Yy+88EIZUmEDhnZjMuKkjbGxMevv77eVlRWbn58vdyjCKwIQoZ/VG2BQ8gw6h5XAPvGRQDBKBnU2auhz/hotsz4FZ2zY2NjYsCtXrpSTDB4X6gmPa3h4+IYtx56XyOPFaVPrXap7Hutl3eF7Xr4em+ex9cBTy+D6qR6q8dF73lzy8tC8FNx1rL05xCHEVuv6y7ojIyPl4dK4jvcbsdkIa7VYJ4U+YTctX/MMP/dNq9UqX8MACVpZWSmvYx7BqKMc1R+0BcdX4QvLy8vLbeQVYIx1bGw2Ur3jcVUSoGOt63hK6lO2iOe9mbV9jd0bc08HuE4YM5DIqo0eVbLvgcvs2jbrq1ev2vb2dvkmurJVsxs/7BZ5ZQAvsBWEozCplA0jPS/WeiEQZSt8DqCCACvb3t6ezc/P29DQkB09etT6+vpsc3PT5ufnrb+/32677TZ73eteZ1euXLErV66U5ej6EiYbALC3t9cWFxfLL7PiXSqz61uCWcHRJ2DCMBC8noB+brWuv1jZarXKrcD41tL58+fL0B2PCeqJrbJsqNWgmV0nC6g78uJjmgDkCNdMTU3Z+Ph4OaaeAY3WDCKDzIZBPRTPY9G/UwAC0Z2jXh78twcyWrbn8XF9PCbNusll5fSVVx7S8I5Abiu8EewKNbu+UUcPosb7iwg9F0XRFiL36gjButrIyIgNDQ1ZURSll4f6rq+vl+vJTLZYV7GTltuOfM2uvX+KV0IwJ+GN4evwuvNR64p+i8Yceundj4iUjjfnhfNDNZ33m3WH5ze/6qOgW1f2NXCZXesoGPHV1dW2BXx2g5FWn9VB1IHj8CGzdva+AFxs7Dyvh1kJgwEG1TNqAL/d3V27fPly+RKjmZXrXdPT03bvvffamTNnyu3xDCIAIX4J2czK0wng/TDbBzjt7u62vSfD3tPo6Gi59rSysmIbGxslqAD0GOh7e3ttamrKWq2WLSwstOXL/cqbOrh/uN/ZuJlZ6XWDHcOA9ff3lwbMzEqvj7+HpGPk6QL+5jKV7Wro1PNgIh2LdAWAC+F1G08PNY16iB6z9kDTY/iaJ/cN1wH/e6Dt1YHLwByD4cdYw/BjbHEdIGF2/R1CfHwS+od6MDHjvFEeTrQYGRlp2ymI++hDeFgcVuT/2btEe4eGhmx0dNR2d3dtfn7eBgYGrCiuHxyA9db5+Xmbn5+/YWOG6iV0jcOb3K9KWj1bp2PgjVdPT0+59sy7TVOki20f6yeTetaHJrKvgQuDu729bUtLS7aysmLT09NtC5QqykJTTJHZg3a4hhc5X2beEcNjwCuKoq3ODJJQBLC9K1eu2JEjR8rJsrm5aSsrKzY2NmYnTpyw8+fP26VLl8o8sOMKIRXE1VFP3r3EMf2iuBbegHfHxsLs+gG+o6Oj5aYItBshHOSFftzY2LCrV6+W7cEuLSUZuu0WhoDXHSDwvJaWlto8t52dHVtbWyu/AqCH66JPIjLjiQKR57V4euV5LRBda+U+4zTRepVXNzV2+J/JB+u4lq394Bm3aO5oX2h0g9vCY87jzGDLXgmiBAyiRXFtk1FRXA8LgxDBG2PiAz3m57HxCKdyYD0VL+vzOOnJGFw/EC0uw+wa6I2Pj5fAxetXKBtrdgsLC+UZhdz32u9sOxQkUA/c98bJ0yOPuIEg8nqc5qeeldYVtrOTzRgq+xq4oJR7e9d26Fy+fNlmZmZKtqUDwYCgbJlZNCsLe10cotF3XfRvzTtSHAysDmoEeCsrK+Xb9ZDV1VUzMzt27JidPHnS1tfXbX19vfR8zK4vBmPHn9l1g6GbJwDUOzs7Njo62rb1V+uEHX3Dw8O2tLRk29vb1tPTUyo68oRRuXz5cslawTiRjtmxMuJW61oIBu/icH1Q3vz8fHmuoZmVR0RxmLe/v7/8IKdnWLltChT8f+RZROOtz3qi3hGvx6h+pli0lhXdj9bpvLQRw4bwvIEuM3lR9u/lxV4hz08Yd5ymvrq6WpIYfP6DPSfog7cJgEklA4+ZlZ/UwVeKFfTY+IL04BoIIn9HDm0ZHBy06enp8gX/lZWVtnVkeF14eZnDmqpn6GuPLKM+6BOPQPH/nm5wP0Gwy1G9SJSL/uH+0LQeKHYi+xq4zK53DDYrLC8v2+HDh920VZsrWJQtR2shml7rxvc9FsusxAsZspExu2aw8UkT7KLE1t+BgQGbmZmx5eVlm5ubKycQb/dF/vxyJLw3bZd+aRaTfHBw0CYnJ21qaqoMF2K9YWVlpawrJg/yYKVmo6zxbo/B9ff3l590QZ58ziJ/w4sNMr8wCtaOfuP+RT2iscR1nYTsYUdjrXl6epRKq0AF46neQMTOGaBZPIDT9Vtum3pjXnkQ6JMXpufn1bhGcwvAxS+MIxqA458QEobOe3rFdYNAP2HwNSSmdePoAvLDqTbsVaLswcFBm5qaKr0ybK7icD5O0MC2f/ZcdLxALLVfMS/0NRUeR4+Qefnz2OFkDyUfWDLA3NE+13K47CrCVCX7HrjMrnXC9vZ2uTNuYmLihjfhWVgROT7ssWSdeGpgVBl4EuvE1Lqw8eTt8GxktFxsGcauJLzVDxCYmJiw22+/vfRueCce2B2zeeRpduOZbjj+CWnwoi9eogRoYcLAM8N1TDCwX6yB8cTmCcYTmRfq8a2m2dlZO3z4cLkRZnt729bX121xcbEtxIK+AKvFGAO08A2kaO0T/c2GSo1IFfGJ2C6LkiN+FuUyu1agYeBH/RREvY0cCrz4rfWIjKZ3L3pWPVAlCvifT7pAvc2sbSyxiaGvr882NjZKsIGO8tqut7M3+h/6C+PL/aHen4IZrjER5Lb29PTYxMREeSxdURTl53WYiGxtbZXg4BERra9Z+2eU4GlhXnljrG1XAqJp8Tfen2Tg4uPUeO2qCpC6BV77Hri4E5aWlmxubs6OHz9u4+PjN7BqNSYc/uOBZsDyWHGVd8XXmJVFkxjCsXFdQ8M1TOTd3d22N+5RxsDAgB07dqyM51+8eLEMpcArAjDgxUedKFw2jokaGBgoN2Tgw4sbGxu2tLRUhlfgbY2MjNjU1JQNDAzYxsZGuXGC+4KP5FKDq2uE2Ep87Ngxm5yctKWlpRK4sbUfgMTeHPqUXzodHBxs203IxoHJjhIR1ZFIH5CWjV1Kd7hP0HYOmSlR0lC2BxhavreW5T3PdfHSe/X2/laAZAOpmwi47Wi/9h3S9vT0lOSJd7lhO3zq3SAuS8eLN+l4hpUJHgw2H+2m7cD8wZzDu5LIc3t721ZXV0tvBeQO81F1z2sLz/lol57aO83DI1KsWxiL1dXVtjViBi32UCMbyP3Pf79mgYuNG5jMxYsXbX5+vjwCCuIppIZFvLxVvImu7FxBkAFBvT3kieu65Zm9QgjKwI4/eFMAhOHhYTt27Jitra3Z2tpa20csoXQ4VBf1RGwe+SM9b/EFGPLCtZ5mgfdrcFwPTnJHm7F2hnbzS6cK8swskW9RXDt8+PLly7a3t2d9fX3l6QMTExM2OTlpvb29try8XO6YhAC4+fSOyJvRMfcAjcc3xeYjA6SgxNe5HM/oo590MRzjpXPDA1Oth2fUtY0KTB5oa18gX89QsWfFa0raP6oL/By/Owmd4hf7vfUW9Cc8b+QLO8I7B3n+Yh7oZ3hAdvGDMoeGhmx6erpt3R3eEY8LnxYT2SQef2xsigTtS4k3priOn+3t7bZPBfEGKe13D5BS+hTpRI7sa+Aya++YnZ0dm5+ft7m5OTt69KgNDw/fYAQhCgbIy+tIva75VSkcrgM0+MBKfYbDFZgE/CIwe11Q+s3NzbbJ3NNzbXvtzMxMuT0eb/izV8cnUOM5DU1hAuKDjzjuBmtMSIN0GxsbdvnyZZuYmLCxsbFyMwUYKsJ86A/8Rlt4XFAHGJadnZ0ynDI0NFSybgDn3t6ejYyMlGcQMgMHqMLwRGPFZALPagg1AjD+X9ugnkc0oZUEcb94AMjGT8kZb0uOvDPW48jQKaimwCr6m/solb+CsQqPHYMWyA+/7tDb21sSK6zXcr2UGIJo8RmCSjS3trZu+IIB6qrf9DMzGx0dbYv+oN74zePL4Wsl2QoyvL7rjVGuHfPGgccYURWsFQPg9Qgofiayo1r2axa4PKO/vr5u586ds9tvv71cyE3FXqMBTjFkfUYnE9eNDRmzOt1FyGGHyEtDfjwJMGm5PPyMjY3ZHXfcYbu7u3bmzBlbW1trW9jnEAOvc+lJ0GZ2w+fS0Q8aHkHbcOIF1rag5LxFl0Mw0QYDbDUGKcF2Z+wwROwdXuDS0lLbyQnoG7ywiqN6uG85XKmGx5OUd6T3UI7qhQKdghf3B3sXEO53HQM8p6DJ99DuqB5aX699SMcAwG3TerCooWPP22sPh7Qh3nFGrVbLhoeHS+KEtW8cRO15XyB/e3vXvxowPDxsFy9evGE3Hc8LkEU+gLooipKkYfcvXjxmgqfer77q4QEYg5yer+jpnZIgFbU1SjparVb5agDKgb5puapnnvcVkb0msq+BSwWewJUrV+zs2bM2MTFRfiJAgcILxXnApIOp13JFJy/KjQZRF6uVrbNCMhByvL63t9emp6dLVnrhwoXy2Bz0CQMXv0gJ44my+D0u1KOvr6/Ni8N9nHqB892YFeIdL30ZkdvPYwVvc3l5ufTyzK59UHNwcLA0CngJ+ty5c2XfYXux2fUTqTlU6IlnuL31Tv2fgcYDMs9T4uve/2oIOA8FM34PUIlUioRF1yMvC7+VbXNduH6q897mEfyvZxWqcVcQZLBk6e3ttdHR0TJEh0Oar1y5Un4ihcPTAK7FxUUbGhqyI0eO2JEjR8zs2qeTQPiYCKHuqvPsiWBTUaRv3G4PWNTbwtIARzqUlCC9kglvPNQTj4AL4I326lqi6mwknXhYKvseuLQDwb5Pnz5tU1NTdurUqbaNAGY3hvbYQHvuMufvsVeuh2d4UDaMrvciHoMS/oeCMqhAMFkY4JgFYTL09/fb0aNHy4VteF7KyngicJ8g5KYvVfIJ99x2PLe+vm7Ly8slcGEBPbWGwZOMjSE+lLm8vFy+zIkT3rHOhmdwUDAW8AHSDMgeK/Q8ZTYAnj5wOtUdHVdm5KxvdYXbwvkzcfHyjTwqJWzaZm23GlPMOVzT6IbXP7r5JGdzBou2D3raarXKVzOwAxHkFWE+Xr9SEri6umpnz561nZ2d8lgwkDr+JJACJxNf/I+w9fj4eNlubMYAEeQ5xLrDIMPXAYy6tsUetK7J6fhEoqCFd+Sw+cqsPaQfjYN3PSqnE9nXwBWx2L29Pbty5Yq9/PLLNj09XZ40wYZDwyqepNgMylG2kmIdvH6lebHRZw8qaq/GxFFfXSNqta4dCnr48OFycp09e7YEFfQF/uZQHpfjMfe9vb02r4ePh9rc3LSlpaVybYrXtpQ0IC+dOAiD4mTqVqtlo6OjNjk5aUePHi0PCDa7/okVLJyzQUE/Y4MH15/HjvsRvyMjzGPGEnksel3XpPR5DVtCb9lwqhfGOsB5cDn8m71nJU4e4HD9lQB4oVDvee0jD6S89SDoKdoH3UDoDiG7VqvVtmEIxI+PPNN5ynVfXV21Cxcu2Pb2tg0NDZXzVQFbdcCLHuATJvxhVx5DbJbygIrHQMlEJNE9b7mB26JzG+Xt7Fz7lh82ZmhZnt30SBvny3rRCYDta+DyBB21vr5uZ8+etSNHjtjw8HC5UUNj3N6z3Lm4rrF0Ts8DwcChhkDLZbarbIWZGa55LydrKAAAgJeNMakHBgZsenq6fPb06dPladc6sbmP+B0wnqAAxCNHjtjhw4dtb2+vPGcNuwhxvhmvKfFORV7zg8AosYEF4OHTJZOTk+VhvcvLy+UOwp2dnfIlT7SB33GLDCj3ZdWGHQ8IosmqYOCljUBQ0/KmHC2HJQqvcdpoA4BKCpQ9MI68M48UeHOD64y07GHiuoaqcA8bFnZ3d0uPHDqMEKGZlWSK9YIJBQ7t7u/vLwHS00sGTJA7pO3p6SnXt3BaC3bFgsRhzFW/vH7Ab29ND/XW5zxy5JEYHRcI3pGMvlEY1VPLSulsU/C6pYCLjcru7q4tLCzY888/b6Ojo3bXXXeVW0iVtZrZDROB89RJGrFqfV7P1cO9iN2rQvDGEt1AoPX0drFxHB9hw8OHD5cg9MILL5RfO+Z0/I4IYvbYDIEyBwYG7MiRIzY7O2vj4+O2vLxcHg6KiYxjpnDYLb/7gvIAYsqG0X5uR19fX8li+T0yPViYj9PhNQkYK8/bjgwxj4vHSs3aQ7+RUY+IC66pcY4AE6HWFJHivtVNL+xlRcKeiOo71x1jqcac+0C9Os0n8jqVIKA+TMzgcaGemNfYtHPx4kW7ePGimVn5gi++9IB3/kCyUBb6i0+UZ08PdWOiCBBC/XZ3d21kZMSOHTtWvqKB97gAbPzFcu07JQwoQzdMaR+xnmpYvMpmcb+jXvgGGm+a8fL0CFqUv84nL7KUI/sauCImDdnd3bVLly7Z888/b8PDwzY7O+tu1IjYtjLTyHDxPUxoDdMoa9LwgJbB4Tv2IPgeC4Mke2H87KFD1z7bMDk5aXfccYdtbW3Z2bNny/c0cDoGr0Ox18YAg3Pjdnd3y8+p4Hte+I4RvyQ6ODjosmz1GNEnfBwV7vOBvjBEOJVeSYKCmNn1r9nu7e2V6w06hmp4WaLQG6f3QI7b5U187g/+nw0Ql6VrIh6IMfhw3Vn/PP2N/vf6SD0jTqPkzwNu7QPtGw8keQ1Jz/Qzs3IX6qFDh2xpaancWMAbJ+C5Y92V66HlKxgjDa8x8yYn9O3hw4ft5MmT5Ws5eOFfIwIeidCwK1750LSeJ5NDmqI+Z9uzu7tbzi0ed1460NCoZye9+kG8DSm5sq+BixWKO58n987Ojp0/f778wBzOMeRDKHUi83tTEO9vVRw1LMqe1ZNIGS4IM0woC4f1tDwYDfxm8MI6AXZOmV0z7C+88EK5VdibhPjh3Vhm188ww4vOu7u75ecZsIW2KK4fqOsZYfUU+R4z6NHRUZuYmCg/hb60tFRucUa4hj/4ibrzsU/YJLK9vd12gn1Kv3h81IBxmshT0nFEmxlYmBhwvsqk+W8FNa4n/9b2sH5E5CsiXBGDVxD1jLHWQ+ecV6aSGjzHOsr14FPezax8f5F3sLZarfJFdH4dhD0NrQMTFu4nPkINHn+r1bLx8XG799577dSpU+URZajH+Ph42/FJ2q/In20T71RUXfHAKCJkPG5m1yMFDCDIG4dW45NHyGN3d7ecOzqfo7HGb+1D7MRsIvsauFTpYbRgyNHRa2tr9vLLL9vIyEj55V/cK4riBgYbeWCeITNLnzgQgZPHXPVZFlX0oihKo6wK6DEosE70z9DQkB07dqwE97Nnz5bgw+Ed7k98CwlMc3FxsXxfCwaB36Eys7Zdiag/6oU0fPQN0vCOqN7e3nLswAS3t7fLb3rx6e/cDwiPYvKjr7zJpoyaAcZjxCmgShlt3VHIABIBogcSysp5nLkPvLA3zw0YEM+Aaru0TtxHGDM1pJ7h0jZwv6j+Yy4iPf7nkL/Wi7eN67ogk0ddY+U1XrRHIyNoI9apUG94Xv39/Xby5Em7//777fjx49bb22sLCwu2srJSfnl7cXGxLQzrkRLkqe9UemRZx4rvpbwatRdch9XVVVtYWCjnDQRA751MoqK2jevJfdtE9j1wsZHh7ywVxfWDM3d3d21pacm+8Y1v2MDAgN17773lJ+R5DYRBy8zfMuwZMTzrsVEFJ1VU5M/PeW1kgwBDDNDlfPlwWk84bDg8PGzHjx8vFfzMmTO2tLTUxqrQj/BmUB/0LerR09PT9il1Nqxs0NRQo818DBMmNdoCBo3t9DhJG6do8DZdbNDAM6gzzlv0AMKbXJ7R1jbomKWMPusD9M7zlD3w8ECM17kYsHSc+X/emcf54Z4aQ22T9gfXj3WO26FeIa4pgLNeen3mzVEFLq4/XlTH/ObncW1kZKQka948i/qIy+Hj1MzMDh8+bA8++KDdc8891tfXZ2tra7a8vGxbW1s2NTVlPT09N3gx2tfQfw5v6vijH3ksOA/VV07njSnbUTMrXzxWgoS+Za8rItuqKzwOnYCW2T4HLjakiuA9PT3lpgBMnpWVFfv6179uQ0NDdtddd7khGzzrhQh0gniM0WObZn6oUYWVzdttpBOdNxuwgvL/6j1w/lgP4C8kP//8820nbChLxjMMWtwerCOBJSoZ0PEDcwVz4/bBY8IWdnwOApMaf+NEjKIo2kIy2BBidv2AXRxdhXAh2sSAqW3mfojG0pvAkaHwwMkbZw9AuQ7YMekxa/W4+H9vrY7TKJPXdrM+seeg9YxIG9/zCALqwaSMySSH/hgkQXZ4XZMNL/d5b2+vjY2N2fr6etuXkdEP0cvxHNVhEmJ27Xin+++/3x566CGbnJy0hYUFu3r1qq2vr9vQ0JCNjIyU3pf2pwq8LfV4uH+1bpwf96/Xx97/vCQwPz9fApfqKkgD7AZvkOH8UuV4Y19H9jVwcYiPmRbCS9g9hMnQal17E/yll16yycnJcnt4ZFRV2c38EJ63TgPxjGDKIGlZOtianhmkmZWhPPZYvPR4p6Snp6dcO4K3dPr06XJRG0aKj7HhjRqYIPBydD3CY1YMnq1Wqy2UyGtqZtcnMH7z94qwoQTeFEKYy8vLtrCwYOPj4+VOSBh6EA54PAr6arBRR/ZWImPhjaV6aVVGhPvOy5frxxtXtF7q6XjRA/6NEDHrq6en2iYFWA7xcj/ys9xnyEP1O+obnqu6xsXjxd6Y9jvKwyd68JkR2A7Oj8vh/tJ29/X12W233Wb/6B/9Izt+/LitrKzYxYsXbWVlxfr6+soPv+LYMu0X1SPeMcnXveiM6hb3ieqfjqH2NebV4uJi2zFvOj/4lRZvrVXryHXgdeimsq+BSxWdBx1GEwYZO+bMzC5cuGAvv/xy2+kLXudH/7NCR/c91sPeBBt1j4Hybw8YVVl5nYuNmuc1cDweXg12CaLPXnzxxXKrPBgswrGoP4QPr2XSwO+qwJjphPHyY8XmXVv87SU+IaIoivJdGZxmjXUwvEdjds1j6+/vLz0uMyu/J8b1RJ7qwep6Efd/BAjsnXg6pEDBXp/qlvYRnsc6Im9/V6bM9VZd43VNb51U2+h5UhqCjLww7gOuIwMnl8HpuS9QJhMj9BG2jvOaqXpm0Dle8+YvfbO+al8omLRaLZucnLQHHnjAXve619n29rZduHDBlpaWrNW6dmbo0NCQLS0tlZ/3icgBSASDFvcx3gXTrfFm7R4qRJc9qjxes2tfWcf6FqfTOvOaOW9+QR28v81uJDdNZF8DF4cpMHH4KBVMhtHR0dK17evrs5WVFXvxxRdtfHzc7rnnnjaDxYOZ8hggGp7kye89w0YA5XmM1GNYHkPSvtCJaea/T4atuThvsK+vzw4fPlxuFe7p6bGvfvWrZbiAdzLCSKL+8Fz4xHiUqwyRGRfCjqgP2sjrFxxHZ8OhoMDjzJ802dnZKU+px0kA7KFhYwl7ktwuJQs6BgpS0dh44hnpiIxBF5QEqTHBj577x3XW8rWurEdee/ga56Egl2OYeL547515gA3dxvizriM9kyTuY6RfW1srTz3HLj9EDRj8lEgoEBRFYUNDQ3bq1Cl7wxveYCMjI/byyy/b/Py8FUVRng6/tbVlV65cadtFFwEyzyNv/kTesAKzjouSWAYlJgHLy8vlWreXPwTztK+vrwzBV615cf2avsNlts+BKxoM/OALuUNDQ21AZnbNZT99+nT5kqDnDahH5HkF6iWpMdP6Ij/+PwJCD6wU5FjQRuTD/2P3FE9AeCB476qnp8cmJydtaGioBIznn3++/LIwJhXXlXcS4iw4s/aPzemuKJ4keI+G1zUQruA+x85DnBmH/BA+xKddwG4R6gR77e3tLU9PwZZ4vGit6zW4xiFY9JmCjBo0BlOPkHiiOuR5W5zWG3v0HYO+Bx7euqy2Q9dXo2e1/krCtF1cfw+g9SV05IexwG5B7GRlw8db4bHuwp471xfflEPoe319vXwXEDtUvdCwgikIwtTUlN199912++23l7vx9vaufXoH7x3isN6IfOJvjjAwIUcab3cuj4/qI/dn5GVx6H9nZ8eWl5fblgp0zLgf+H1LkKUUYGH5IrUbMUf2NXCxAWQWzwu38MAwKBw2OnfunM3MzNjw8HC5FsJhRgZCXeSGeOAZhRSQXq/hbwy+WXtcnQFIn+F8Nf7PBhmTjNu1tbVlKysr5UfuMLFGR0ft9a9/vY2Ojtrhw4ftK1/5il2+fLk0BjAm+I3yYAhYQXXjBQMEGzyEIQcHB8t643Mo8PhwNiF/HBJ5wrvC15DNrBxveHbDw8Nt/cN/M6ByX8JY8ORkr4KNq8eE1YNXnfGAyCMkUb4eaKixRXsYyLjNYMC6Pskg7nl6VcQsao+GFbk/VI/51RZ8ZXt5eblck2LdwRomf+9NiSSAYX19vVznMrsOGPAg8E0v3byBumCNdHx83E6cOGF33XWX9fb22oULF2xvb8+Gh4fLMDXeOeSvJHieE+aQ7iLkH9YhzSvliUVjgTyg36urqzY/P19GIqIx5fwAXghjciSMy0QZaGsnsq+BizcHMIDxdRgehArNrn+5c3Fx0c6cOWNHjx61Y8eOlestulCc6mSPZZrFk1if5XAa2J4qK9rqhUBYYbUO7CFAoVAGBIfh4mN32FE4PDxs99xzj01MTNjo6Kh9+ctftkuXLrWdEo9QGyYxl82bKsDCYCC9cBS8N5zpBrDG+XM4Ogpl6MTd2tqyq1ev2sTERGl0UD6en5qaKkENHmjkQfGk43QMVtrn0djjnrcmqu2Irnl6pGxYy4UR1LUgLl9DOwwqDGRe/vwcl6ns2tNZNbB8X+uK9Ovr6+XXrrkcvKoBkuq1V+dUq3XtrM2xsbHS61fgAPAw4PHzw8PDdtttt9mJEydsbGysPGkCB+r29fXZ+vq6+z4Ut43HQnfjegAf6Zf2V45wP+7t7ZXrW7rTMsoP9ebXO7y2KcnNrV8k+xq4zK7vbmGjAuPM352CAWu1WuVb7tvb23bp0iWbm5srP/mu7IWF2TZEJzf+jkDF7MZ1HgZdPK8xanbnMZE8xVIjyQaY17z4a6340ivAC+G4yclJO378uD388MM2NDRkTz75pJ05c6bcdIEJDk+H64L6wRiwN6PK3dfXd8OXa7HdHfnoYjTKhhe0sbFh58+ft+Xl5XLTTU9PT2kw8E7P6OhoSWDQBoyDBxQROPD/KYMfMVzuK/2f00Tlqa6pkUM9MAZeyIz/99g99E7L9OrJZXK72FjhurfpQPPVvkdIGMCFMWMvCzrCnjuMqmcjoGss0F0csWTW/sFK/B4cHLSZmRmbnZ216elp29vbK0PqWPPBRiHvuCYPtDlMyH3q9YeXl0c0Up4YnkG/7Ozs2MLCgi0vL9+wmYPL0nmAMYUtU9vFfY8+fE0DF8ewoZzocCg0PBXsJNNJuLy8bGfPnrXjx4+X363ixV4uy+zGyQ7hwdTfXp2x2YHTYEC9XY6sALx+xIZJ03sLpfxyMIdSVldXzczKr7+ur6/blStXbGNjwyYmJuw7v/M7bWpqyv7P//k/9uKLL5ZhRQZT/M2hDn1xGX2AewMDA+WLwegTfN2VQwv8LSJMcJQHrwyLylNTU+XaAtju+vq6LS0t2cTERPmJF5TDdeK/dR1Hx1tBjo1w1cT0CI2CgZePR4I8EFMg8crnPFEf72VcBXEFHq0Le1hMyjzjqQRM+wVl8Vom73ZDeAphZTzP385iw8kRGXxRADtV4YVj/mO9Vj1GgNapU6fs2LFj1t/fX9YLbYDO6VqRtp/nvNcXKV1QQFLCy894nhuPPaIn8/PzbXPb86CisjVSxfnjukaNmsq+Bi4z/wgdjn3zCQ+81dXMSgW/cuWKXbhwwaampspwIa8psTHSwfGYkyc8cRi0mGExaEZMGoaBDQKDGIMGrqlx4+3PDO6rq6vW09NTGn6sJwC87r//fhsZGbH/+3//rz377LPli5TM5tXgwbjoeiTWJMBMsTg+ODhou7u75RoGEw9McAUIBePV1dVyBynnv7CwYENDQ23fEDNr/04a+gM/aoi9cUce2s9KGnRS8z3PG9VnPC9Mw64KOpwn7y5VXfYMFactivZXLNQQenXnfJuCNAPX+vq6bW5utvUZxhf/Y54hVI16c/9ivLVseA28To464jl8mPXkyZN2+PDhMtKwublZPgtCFO2yYyOe8rQ4nZIGJRSR6Fh5vzGXQO704OGq/JA2RdgVuDSvurKvgYvXcBgYMBC8rdQDGIDc8vKynT9/3k6ePGlTU1NtA6uDVMWAIZ4xgRHnBUoOQTBj5PwZqFjZYWS5rt7WdxUYe/X60Bd7e3s2PT1t4+Pj5Qnsa2trNjExYXfeeadNTU3Z9PS0Pfnkk3b16tW2nYutVuuG3XistPis+uDgYMlKV1dXbXNz0/r7+0uDwsYFhgiiYS/Un9/VwxmG+JwEjgC6cuVKuWuS3z3jY614/FJ96DFi9kbUQEQ6yNdYFAgjUdDkPLnvlG3req6yfZ5fXI6SKSZcCoSpsFdE+jTagdNY2NtC1AHjynXFeOp7bSgHG4ZAbFE2PB7oEPID6PX29tr09LSdOnXKTpw4Ue5WRp6oK29IitqK30jvpUW9PFKENikBwXhXAYKCf6t17TMmS0tLbSSuSj9xncPSmpZD8Z16WpB9DVxm10MNDFr4AByUsgrhsbB/5cqVcp0nx2jx4JvdePCpgpbmwQqoGyk4vBa54Gpo+JmqXTvwXvhZ1AUnu09NTZXfvlpaWipDh4cPH7a3ve1tduTIEXvyySftxRdfbDMAEOTJ611m1186Xl5eLo9oQn2wrR39ABbM/eR5CmbWRgoQGsba5cLCgm1sbJQn14+MjLSd1sHHPvHfKAOiusRrd5rWY6YeY47Sap7qxXlAxfngOl5QZhLEgKOemObJ99ij0/RVnhen89oY6SxCwboFHp44roM06edOQJgYjM2ubyBiAgg95HECaE1OTtrdd99tJ06cKAkRtwMAxCFKr634mzcw6VxWkuHd8/KNnvXy4b93dnZscXGxDG1yvpHd5PHWOaP5e0SoEwDb98CFTudYMXalMSvzJiT+3t299tmBy5cv22233VauuaRi+fxbDZvHYjkvXfPhyQRlUEbvKSIzspSnECkxgxd/xdXMys+cjI2N2ejoqE1NTZVAs7m5aVNTU/bAAw/Y4cOH7W//9m/t61//etvuKTWGAB1eU1hbWys3eqA9+oKmhiCZqCjzR3gQG3GwswtbnvG9sI2NDVtcXCzLmZ6eLt/x4rVSZopaJwhv6ojG2RuXHNDiZyOg0vzV4KIeqJsaIW8NjFl+JCn27NWd03pGNwItRE14DQn9il2EXA63k70Q9sTYk4KXDUBk4GGjPDk5aXfddZfdcccdJWgpOHJ7PBvB6QFaAK6or7Wv1MPSvD0d8fJCGtQHc4K/v6U2LLKhnI/qhL7io+DVVPY1cIEJsVE0s/JzAzBcahA9xrK5uWmXLl2yxcVFO378eGXHslFQI6EDyYIJxAzFS8Nl6D29rgoB5sl5s4KhjrwrEOl4lxV2F66vr9vk5KRNTEzYoUOHbGVlxS5cuGDj4+N222232T//5//cnn32WfvSl75kL730UrkOobuMiuLamuLS0tINYRq0GV4R7xrkwzx1QrFxYcaNEMzW1pZNTEyUaxHr6+tliBK7x3Z2dmx2dtYGBgbazmTEGCk4oc/xG8afwZp1gFmoHsuju/pUVE89xo16RJ4Q/+Aeti97XpJntLz24Lqm8+qu3g5fj074gEeFLwHgXT0mMN4P14PXKjkN5gDe6/TIXlEU5ZrWPffcYydPnrTx8XEzs7aQJfeJrrlzXvhhwFLAVuLqGXld14QwQEfeDOcJW1AURbkNHnM35TGp/gnfLwAAgIRJREFU8BKBV1/d7Yi/O5F9DVwwMDgXTw+v1OP3Uyxyb2/PlpaW7OrVq3bkyJHymYilmJk7UTzRAVP2yZ4FP8Pl6WRAer2G2D3+1npzeAi7qDi8AjKgoUMcnTQ0NFQem7W6umq7u7t2+PBhe9Ob3mRjY2P2t3/7t/aNb3yj7UOS+qNt5f5jEIXBGRgYaAMuBis8yxtxMG6bm5t29epVGxgYsPHxcRsbG2sDUfTFwsKCjY6O2pEjR9q8LeSl4TWUibw0/MeGQSeqrhNF48v9kmKo0b0qo6b9r3rMbWFdVe+Ny4p0W40gnq9qB4gObxJSneJymHxhXFA3kB8eq52dHVtbWzMzcz/W2NfXZ8eOHbNTp07ZnXfe2faKBerLQJUaI+StSxg6TtouHVO+Ht338mQd5vqiT1ZXV9vC9qyP3jhDOCrhec3RMXFmnb2EvK+BC50IxDezNmPNSoD0+jdPxrW1Nbty5Yptb2+XL7BGyhHVJZc5s9EyuzFko4qjbdGt2pwvMzmvXDyL8GCr1WpjgHhZm5nqxsaGbW9vl8fYTE5O2vr6uq2trdnc3JyNjY3ZPffcY1NTU/bMM8/Y3/3d39nFixfbXrzkshGmQf14Uw3GUEOq2g6z9lCd5tXT01Oe0g09wQaQra0tGx4etuHh4XLcsdsQoGV2fcchdkZyWAd9hbR8MgkMnIYa1eDjmo67127VHaT39JrT47cHkvy3GhL2Dtkw6e5Rr978TOQ9cH4ekAFYFhYW2rwBLlcjDex5m7UfuK2hW5AbrgvyHRgYsGPHjtndd99ts7OzNjo6WraH12qVbCo5RjugR+plecZbiU2VDfJIg9YhIhxmVr5vxlv3vTy0PO5vPRQY6XT9H3mlyFqO7Gvg4gnEb8/rZNE1JDPfQGCTxurqarnzzJucEE+BPJatwukwoVhhPLDRyR+BIyas3mdDAo+LjSx/V2dzc7PNW2VjgI/i4YSKVqtl6+vrtry8XG6GePjhh+3o0aP2d3/3d/bSSy+Vh5lCsAYVMTtex9LQGvrNi5XzkTN4qbkorr2jdujQIRsdHS1DT9jyi00ay8vLduXKlfK9MvWmWN/YAPIL5EiD+nqAxvrhGQgW9qo1rUdqonv8nO7E5Pv8Dp62ITJATBZwX+sReZnaTt04AdDCt6F4IwVvaFDQYk8GbeD0TMgYiPDcyMhI+Z7W7OxseWILzy8NQWqfcFqEBrks7gPV5SpjzkCkY835829Oo2VtbGzY0tKS+6K01z5c56gNH0iM+7x+mALoJrKvgQsfA8Qg4qgVrG+wEqVQnhUNL7EibMSSAgw2SjDyPHGV8SE/vPeh5Xh/R+IxZlYg9mBYoGjs7iNkwgDIxMDs2lbz+fl5GxkZsZGRERsdHS0/8rizs2MjIyN233332dTUlP2///f/7Ctf+YrNzc21nYShAK/XeSLyBhL+nyewSqvVajvpe2VlpTw5ZW/v2gHDAwMDNj09bYODg3bp0iWbn5+3oaGhcuxRPm+HVk/X83xZPNCKPCaPlXrrlApGnrFiUOH6MVPWNEVx45oN64qZf/iwGkN+rcMDPwVH5M8kE1+5XlhYsMXFxTbgQr9Df/k1CN5hqHqkHhGHjVG/4eFhO3HihN15550laEUgz21kwIVgHvF6s+q9AgWPlf6tY8XXmZhUEWakR1qsIYJceqRI8+CvRXB4kQkd1ss5BKttayr7Grj03R7EsdmDgNKklIMHBrsLT548Gb4Txe+HeHmqMqvoBFJWzc9GyqrlsSfAnowqnVc+H3vEx2Wh/wCufJApe198viA2X4yMjNiRI0fsO77jO+zYsWP21a9+1b7xjW/Y1atX2z59woaI/+Z+YOCCgAiA8W1tbdnGxkYb0+O1Pmy1B6BhXRRnGE5NTdnc3JzNzc1Zf3+/jY+Pl8aGGSNPWDaOTEq8RWr1Mjxj6Hkj+qwHRuppRMbQy0/JmAKW52lpyJCvMVCwfrO3gXZ4fYIwLjzgS5cu2cLCQnmag757iOOdDh06VO6ERV4MLtxODQu2Wtfer5yYmLDbbrvN7rrrLjt8+HD5KRztA29Nh183YC9PiQe3tWqNJwVqDDyRndH32FRfEc5eXFy05eXlGw5o4PLwG54mytZXFMza171AhPF8ZNvqyr4GLnbHYZzQaXzuINKm8gF7x0uqa2trNjY2dsNE9AyHJ15arbdn9LywAerGzNADR48F80YIj6nz5GGlxM4+eDj4wTeL0D6EFXEdHi+8r/7+frvrrrtsamrKjh8/bl/72tfs7NmzJeihLAYDtBH10z6GkRgcHLTx8XHb3d21hYWFcvz18xZ4hj1MfD/o6tWr5adZRkZGbGVlxebn58u2cP9zqIrry3/z+PEE5zFSrykyFJE+efe9dTL9W+uhesZGLiJmnqgeRgZZ54MaZswLgNbc3Fz5/p1uLsAPdgWura3dEH6GcWZA4XIAhIODg3bkyBE7ceKEzc7Olu/+ebsdeS2WQ4weWKlHp/2lgGrm6wP3FbfBI6isXxrhwdo1pwNJ4Pe3eKzYRoEQIooRnfaBPoLN8OrWCWiZ7XPg0gFmdgzGxYdweuCjk3pv79ruwuXl5XL7twKHrrugLhHDVobOCgKj7bEzndhQRN4liQnC3iEbHlZeZbmqPOxhwfviRWXeDYU1JLPr6xFbW1vl2iDqgQ0K4+Pj9rrXvc6mpqbs7Nmz9vWvf90uXLjQ9nlwPKN9hD70NjqYWVtdcCIGdiLycVhs8NA3eA6fV+fDXPmlVUx6DlVy+1mPGLR4F2IEUjrmbJTYICB/1d8cMuXNFfb0mRR53glvoFAPiu8pGHLZXh1Ytre3y+9ZXbhwwS5fvtz23SxeL+np6SlPPsFRUBw6RP66VsnXDh06ZGNjYzYzM2N33HGHHTt2zIaHh2/4SgTri0f6YHf0ZHdtM3s9LAowUf9UjTvK03QAE7SbxxifiuH1LW0zEzV+3YTL5KUQjJPu8vaIdVPZ18AVudJF0f4pExgvDlGxMEi0Wq1y8szMzNxg6HVSeqzJ7MZFcJ5QmhdPSM2X68jsx8zaJiq3AUrqsWYPtLUfdEMCDDQzSvzm0ypADhCK435vta6FY2677TabnJy0Y8eO2dmzZ+306dO2sLBQfvcI4Q0NFwJQeVtzURTlxzBRFv7e3d0tw578CRMG9JGRERsfHy9PEse3vDY3N21xcdH6+vpsZGSkDbx4vPgdQjaGvNMxYpie3vA11TsNxemYcV+xoVBjges67hp28gATOqDira165Xjzlde0lpaWyq81XLhwwZaXl9vCTNzX+M4ar8dirPHCObx+rX9RFOVa5uzsrN1+++02PT1d6rm2n/9mzwr5qvehbeWy1Wv0QuSe56PzksWzE946IpMos+uEE+u/mif6E33PR1NxGv6N+uvpI16dX7PApcLKop0Go6ffxfHYD97/2djYsJGRkbb4NXsTDAw86TlMyYOjrA+i75pVDSjH2tXQqFGDABA8hUFbdHKjP7HphXdG4e/d3V0bGBhoe9kYx/PgOfVQ+/r67OjRo+U62OXLl+3ixYtlCFFj7QAD9P/AwEDbwjfqogy5KIoylISTF1BnnH2Hw33R1v7+/vIF5cuXL1tRFDY8PNw2PqgT+hX9gbJ5LS7l3XsAor9TrBvieU6Rsaj6W59lAsGGm72sOsL5gJBsb2/b8vKyXbp0yS5evGiXLl0qQ8laJ/VgNUqBz5UgbMx6DeIyPDxsMzMzduedd9rx48fbxpf7xPNy1btmPUT9UiDP+fMGBgYvtRloo25yiPQoWkrQcjY3N211dbU80JrHhqNVPMfYY+Mdterd6pIHjz3XpSl43VLApcqG31BiDn0hPdLwoOzs7JRbcMfGxtqUOvJS+H8z/90U9ajMrseddYuvsmdlbV75rJSq8B5ocV2RN/5HnyBPeFDIjzdvALz4A5Bm14/rGRgYKEMOXPbQ0JAdO3bMpqambGZmxo4dO2bnzp0rTzBh1ohxUxDks97g6WCNCyHDvb298rBgDnciRDI8PGwTExPluYz9/f3lLkm8ozY5Odl2+Cq3EX3thZAhkSejaTwCwsLjr94V60PkPemaYaQT/LeCouoN1y3VHgXWvb1ruzt5Tevy5cvlCeWYj2D+KAN5eu9ZIk+cVYn6Qi/Gxsbs5MmTNjs7W27A0DCozhUeb9Z/lOfNaxbkxYSWQ3ARgdF2af96hMMDtgjgcBo8r28BkDCXdZ0K/cP11mWLqP1eu5rKLQNcEbLrxPHADX/v7e21HQC7sLBgx48fb3OHIRqXZsaP/z2F9ibazs5O6bXo4Hss2mNTaBevraAO3G72pnTXEXse3C8cBuFQG8rnbbRQas53c3OzJA66qQH5Tk9P28jIiB07dqz0vq5cuVJ+nG9vb6/8nDiPF0KIPNY65gAkPvEAZWONDR8VBIkYGBiwzc3N8uXX9fV1O3r0qA0PD7eNEUIoGpqsYu+ePnKfq27qmLD+sGg4KMdr8wyL1pHvaxhX50FUFtJj3BCSxe5BXtMCSWq1rp2cMjIyYkVRlJswzNpD/6zz8CBAWAcGBmxsbMyOHDliMzMzdvvtt5delj6Pv3VuIW9+J8sbJ8w3r2/Yy9LoAOeh13QZwRMmtqpjuM5gCd3H+1u87ADQ4tOIuP7ee3BemDRF0jqVWwK4Uh2jk5yNv6cgUKj19fVy6zbCTSjLAyGNXUdKBo+EWR2HGpDGA1xvonCd8KPH20DhtA+8ukVsj//22sc7OhkcFCwBXp5H2tfXZ+Pj4zY0NGSHDx+2K1eu2NWrV21xcdEWFhba3jXhfuf+4Bept7a2Sg8KdQdAM8jD29XPS6BdOM9wd3fXjh07Vn5BQMdNmScbfO1/D2Q9YWOvY8VpNB/OPzKkXG/N2zN+nD+THx0HjC2PvT6L3Wx4wRgnY+B5zhufwgHYIW/ecKCGFIRmbGzMjh49arfddpvNzMzY1NSUDQwM3ED+OLym4Udc4w+Z6rjomrZnI6B7DJKRIPKj+sjjwO2OvCpcU/u0s7NTRiGwBo1Dqs3MLZt3/6KPPQLN/ZYC25SdrJJbArh00LQzeBunxzbxDH56eq693wDGj1AXK2u08SECF5SF/JkJmV03/Fonz1viicvloV4c1uD8WNQjYGOTMnTeJOH6cT155xfy5C3w3m4qlDkxMWETExN28uRJW15etsuXL9vp06ft6tWr5UG5Ztdf8sTEA2DptfX19ZLNs3fKa2dg+nzd7Np61tbWll26dMk2Nzfttttus+np6bZNImgf3hdjsuPtuqsyOhFbVQ+a9YqvKXBG/eyBHpfrgSaDCv/NdUEd+DcDC0Kxq6ur5XZsfbEZ41AUhS0uLpbeO9cZHgGv8/X29tr4+LgdO3bMTp482QZYeMbzEnT+wsDzOhaH85mQpOa89meKpPC4ReDA/RwRW+8+68H6+rotLCyUa8q83qan7mMs9JSMqrZ4Oqd90VRuCeBi4Qlpdr3DoYTceZ4y8eTBickTExOuAUmxpmjCszFUEEO9kJbLUsVUg4S80AZmSx6zUU9F1040vTJTzpvLUA+EjQmf9wewYAbHgIc+6u/vt5GREZucnLTJyUm7fPmyzc/PlyEO/bgggAssEhOTv9uEuqvnBeDi4754VyXIDIMr7yxEO7D5Q/uZx5gZsGfoVKrYK49RBFQpI8Pgqh6Xehdm11/+Z0+VdUENOpMunNoPIoPQHurA44J5i/cFtZ/ZsAOwbr/9djtx4kS5WxAffIQOoI1KOpjAoW1Yp/WALTVmuKbr0bjOeqi/GeARDWDRueuNpwIpysb8w87pjY2Ntp23fNgwylLQ4vvaXrZPUd90Q24Z4NIO4gkEZfQ6XD0LZurYWXb8+HEbHBwslUnDXAxGnGdURy+Gjt8IEUT5KKNSw8ggxPnCqEdGjb09ZUs6CRhwNVyDPsZ9THooPHYZ8jV+UZfrjH7t6+srty+Pjo7a7OxseVIGDgflCYX3gfjFaGaTGAO0BxOT18o45Gh2/dQOfLcI/4+NjbWd5sBGHqFKXq/htilR8PrAY6lKWjitd0+fx2+PqStJ4jUMfYZZN79fBZ1QA4tQGwgHb6DgkK1+uoS3m/M8Rb36+vrK72XdfffddvLkSZuYmCi9XhAJ3YnKgMBjgTRsxHUsIsOtc1nHgYkbl4tn1Gvmeclzjce6itQoiG5tbZXeFr+UrKAEu+GBFutpihDdLKkFXJ/61KfsU5/6lL344otmZnb//ffbv//3/97e/e53m9m1iv7CL/yC/fZv/7bNz8/bW97yFvvN3/xNu//++8s8Njc37UMf+pD9l//yX2x9fd3e/va322/91m/ZiRMnGjfC60BmClhUZcVHeq9zsQa1s7Njly9ftuXl5ZK1oTxPKbk+bHjZWLAieqysahGWJ43n1XHdOF9+OZLrpkZOATUCYI99cr140wQrPdYM4X2pl8DvXPGz+B9ABOFXFdjAwkjCiwLj1ndRkK+uCyLf/v7+kqUPDQ1ZT09PeVYjxmtkZKQMiaKveaMNv4PGoVyMky52e6DEf7Px8nRQWa/qnHpQIGsYNwUHNt6oK8YO8wyeieoj+gj5YCcb1rPgUWOckR55w9NSQ93T01OSmZMnT9q3fuu32l133VUe1cUfn9RnoRvshaC9fJI7h+TRnx4x4GucpxdN0f7hkCuTbR6DiMygr3hcPWGbsLd3bYft/Py8ra6ulmVrGJTtp9l1UGMbqkQm0tmbIbWA68SJE/bxj3/c7r33XjMz+8xnPmP/4l/8C/vyl79s999/v33iE5+wT37yk/bpT3/aXve619kv/uIv2jvf+U577rnnbGxszMzMHnnkEfvv//2/22OPPWaHDx+2D37wg/ae97zHnnzyybbt0jmSeqGX4+PRhIJ4HggGcnl52ebn521qauoGthmFCtkY61oS/63lqkdYxaS03dGzCoZctjJ1rx1R21LAzeAFI8cGjF8DYA+NQd9j91yG2fV35sz8j/hxfaJ1JgjvHNva2iq9w7W1tXLX4MDAQBnawuGkAGE1VLzJAL/RZvZKmEToIr+2N0cHVW/Yy2TgQ1oYT+1jXt8pivZDhrWd8OgVFD3DjRAYXk9Bn2g4kD0ezm94eLj8TtbrXvc6O3HihI2Ojtre3l6565DnPJ+jubW11dZv6H/opZ6jyXVXgGAPWfWXJbWBRUU9MdWDSLer9AL6ie+bYVMGgzS3GeOj9vNmA1KutIoO/bnp6Wn7D//hP9iP/uiP2uzsrD3yyCP2b//tvzWza97V8ePH7Zd+6Zfsx3/8x21xcdGOHj1q//k//2f7gR/4ATMzO3funJ08edL+9E//1L77u787q8ylpSWbmJiw4eHhGwwbGItZO4My8z2ziLG2WtfeXRodHbV7773XHnzwQRsfH2+bzKocPOjIR+PTnJbZGdfNCwfw/ZRXBKVlxs8TRNdW1HhH6qATCmVw+crmlfGzsYR3heOaeL3LGx+dYJyPtkv72VsT8Oqs/2NthT01DlFtbW2Va2L49AXXRzeheGxWjR2fusH5qWfABpOB32uj6r+Gvzh0hfveGlKr1SrBnAGQ02s/a72xEYM3XeC9Pbx/xWE99upwPNO9995r99xzjx07dqw8VAAnr7AdgGePNR18iBL1Up2K5h10VYkXhEmLp4PIlz0p7hP0l5aroWPkFUVlPI8OZUEPFxcX7aWXXrKvfvWrdvXqVdvc3LwhPKinDanXp2PsSZUHiOf39vZscXGx/LJ0rjRe49rd3bX/+l//q62urtrDDz9sL7zwgs3Nzdm73vWuMs3AwIC97W1vsy984Qv24z/+4/bkk0/a9vZ2W5rZ2Vl74IEH7Atf+EIIXJubm21HkiwtLZmZlSeWq3FSA2OWXkDF3+qxIB5/+fJlW1xctImJiTLUo4zJCyfguhfOi5Rcjah33SsX6ZVJ4m8PtLUuntLrROM6oR94MwWu80TUEC23m9m+lqdekQfwLLqu5/UL96d6HnzP7PoZiNwOXgOFfnB/K4ngrcY8fiBVuBethyhp4b7i/vMMBY8BP8fzQseI28kGDb8Z5JAPh/J4jcus/XQXfQ7Poq1mVnq78MqGhoZsamrKbr/99vKjjpOTk9bTc+2MQuxGRB4gQ7ALm5ub5TmaXC7K4nAg9yGTDPzwu4CchsmOEk0WL8zGeXFYWfVY9UPHT9eo+Vm0b2dnx1ZWVmxpacnW1tZu8KSgi/CeIx1IibaLr+XmkSO1gevpp5+2hx9+2DY2Nmx0dNQ+97nP2etf/3r7whe+YGZmx48fb0t//Phxe+mll8zMyk9GTE1N3ZBmbm4uLPPRRx+1X/iFX7jhuq5bMTuv6iB+JvI89vaubb/FGWq8SSPypPCsV5Z6EpHbrRMArNYDJa8tzNgi46xeV9Q/qb+5rnoCgIaldEIzcGFH2fb2djlxcAJ9q9W6wTizd6Ltj4BWJ76uwSmQqfC9vr6+8kVk3hKv572xwdZwHZfL99gIsVcFT5rBxSMkOqZs1NVbVYPChl1Bj9NwfzBJ5JAw9zPagzT4Thp2FiIsxTtPEQ48efKknThxwo4ePVo+h0N1mURg7RNl4ZM1+HI3e4fR+hW3G9eYWHFUQNvqER+IAhSPq46delmsQxx257y9Mvk+xh5b4Ofn59s2NCENHz3HW941/8huaVu9tN0CsNrA9S3f8i321FNP2cLCgn32s5+1973vffbEE0+EFfVcTJWqNB/+8IftZ37mZ8r/l5aW7OTJk22KE3UIG4Ic0ecR3jh//rzNzs7a7Oxs2+nRnjeF38rEOI3nTbEhUA/O7MZT3yMPCROMPUMPmLXdKQ80FSZgY6ZGFwfe8gRQgoF2Y4Efax8DAwM39BUf/aPM0huDqM5sTNnr1HbBcHHoBHn39PSUIUK8+8LbrXmtldeHuM2qmx54qk4xeEVt1TbzGHEd8HcULlRjzv+zLmqdlCSgHGb00E18/wxrgHgH6/jx4+XGDRBIPoMQhyBjsw5Cu1jjgj7pN6MYOECuPIKFfuHx0F3KaF80vxS0mEh5c1yJB88V1YkobM5p0EbYMUSP4IEiDR875y2veB59JNF8TNW/rtQGrv7+/nJzxpvf/Gb74he/aL/2a79WrmvNzc3ZbbfdVqa/ePFi6YXNzMzY1tZWudmB07z1rW8Ny8Tn1FPiDbZnnKO/UwOzvb1tly9ftnPnztnk5KQNDw+7wKHeDhSBDZWybdSXFYQ9CWXdUduRlkMbfE+9Fg3fcZ3Us4n+5n7UtqEsXZdh4TYzg+c1DrBA7luzG9+P0n7VtkVj5dUJ13ktR3fT8Qke6rVw/Q4dOlSegwgD0dvbW75Hwy96cvsU4LVftf6sd9pO738lSOw1MePm5zT0zjrF6dT4e/cOHTpkg4OD5edncCAzvouGHZz6rh7S4oguM7sBrFBP7CzUNRz8KIhHAMPt1w1kulYVreeyKLDzePKPApvXlynhPNjbwqsiTC6xFqieFufltScij57ws55+1JGO3+MCGzp16pTNzMzY448/bm9605vM7Nr7Ak888YT90i/9kpmZPfTQQ9bX12ePP/64vfe97zUzs/Pnz9szzzxjn/jEJzqtyg3sQBWE00X3vcm2t3ft7MLTp0/b4cOH7c4772wDB+0PZfYKEp63wMqp93gScXoOz2l7eI3BY84eyKiHxZPPq4/2k663eV6FF4ZiQEBefPo8WC4mmU5qngQKAOg3Fh4P9ka5f2Gg+AQN/N7d3S1PcNCT85EHiMvw8LAdPXrUJicny92JR48etaIo7KWXXrL5+fnyNBEYU+4PGHkvNAyPgCUiVN75cmxscR0AwfmmjBJ7WCnAUoM1MDBgg4ODNjQ01HZ4MdqOsOvW1lbpZfHncoqiKEOBIDkc6oRH4YX00T7ub24P66LqupII/NYQnpbHoWvVT2/eqX7ih71cj4hzvpg38FYvXLjQdrSWWfv3ADmkGtVNxSO/0b2ozU2kFnD93M/9nL373e8uj+F57LHH7C//8i/tz/7sz6zVatkjjzxiH/vYx+y+++6z++67zz72sY/Z8PCw/fAP/7CZmU1MTNiP/diP2Qc/+EE7fPiwTU9P24c+9CF78MEH7R3veEfjRngTRSViEN7/nicErwvgNT4+foMB9hirelNe3ZAupfgRG+RQIIwPJma04MxtTjEgNeialxok1AlsHPXQOnNduRyPuYNFw1tBH+sOLQ2Hcp66HpR67UJBFsLG0TuvDuDAhAbfhhocHCzLXl1dtZ6eHpucnLQ777zTJiYmytM9sMVewZ7XuEZGRmxgYKBMv7m52bajjj+VzmDPeeHrz8yuo5A3/vY8B04X6S6PN8YMp7RPTk7a0NCQbW1ttX07S0GaXyLf27v2HhK8cp5729vbpfFl75DzUqLIdYeuq76w7mtf6T2dF5wmAq0olK+gpeE7rjvrjBLYlZUVu3z5cnmIMYe/+T0tz9NiyQEbtRGRp9ap1AKuCxcu2L/+1//azp8/bxMTE/aGN7zB/uzP/sze+c53mpnZz/7sz9r6+rr95E/+ZPkC8uc///nyHS4zs1/5lV+x3t5ee+9731u+gPzpT3+69jtcLHU72/MyPJDRQVhbWys/vcBfSk0xO+SF3x4b1t9qDLw2MKvXicPPRNvMOZ8IGDmNVx81BOhDLU8NaSSav4aqUDc+iYJ3rakRYUPBL9YyoHt1qpqgDAZctoZ48VkVeAYAm6tXr9ra2pqNjY2VnkdRXDv+aGlpqVznAxBiowoMvpmVnsba2ppdvHixBFQmC/BWkdfu7m7ptZhZuTlCQ2AMwjwm6rXz39o/nhE1u66P/K0nDa8yUGKdsyiKEqAB0hD9xA7qxh4myuY6RnU3iz8fxOVGJEeBRcmd1s0DPZ2XXA6TAe1vJptm1/TkypUrduHChXKNEHXHRhjvPdcqkFHbpvOtylHoVDp+j+vVELzHpR6FWew5qaS8j5RXNDo6at/2bd9mDzzwgB0+fLgtrs7CXoqyvEhJua6s3JESQPkAnJ7yIX4Nw6iL1F4oUyea9lmKPUVAinu8KQI/UR9FbQZgc54cTvSe48mMfLSNbBAYPNm4cEgQ4i3sm5mNj4/b6OhoCSK6doTxwRouv3sH447jrgYHB8tNCMvLy7a5uWmt1rWvGFy8eLE8OV/DZmZmIyMjpZ7Ae9nY2LDV1dW2tnCfe33FbeV+SS2yswfDYdTh4WEbHh5uO/hWD3ZFedhkAUDmUxzQXhCTqvmvRIu9K26/58Eo8TG7Ud9TfcH9hzqonuN/Dh1rGzgtk0JeV221WuXZms8//7y99NJLdvXq1bZPvWB7P3/uh+tQJZ4D4NUzAjL0xSv6Htc3g0TGLQVkHtCpZ6V58qBubGzY+fPny+9H4UOFug6S8loiZfXKTbUNE5eNM99HWTyplRVxn6jHqcrseVpcp8jz03zNrPRWGXC9MrVdbHDMrm9rh6HRd6K8urGwYVWv2QvVeMbaazMMCRtWLH5z/gj18an06C8AGEALmxlQB3zBWdkuAFHHvyiK8r0yMyuBUDfQeKFh1QMO8yppUmLGG1sg2O2H8UKeKIfXXBSM+eBbHuNIP6O5p3XitF6okMcN1/i9K29ucT9515S0RfMU9fDayHMGc2BnZ6fcRYgQId4bZA+cj1Or48NExNUjtV6+ni2uI/sauFQi0NE0nqH2nvfy2t7etvn5eXvppZdsfHzcTp48WXo0aviqACoafE94B1gk3sRkI++xOc8waV2jPlQw8IAT13GP+4iP5YoYm7ZJjSKuoX+KomgDL2XYXE+uDyY1s2++XxTtn3PQvNkoFUXRtkVb12688BCDCnulZtfIEjyz4eHhcgcY3lFSbwainhIMLZ6Jjtvi+REZIm5DT09PGyBrnzCQ830AEHsWMKYa+kO9AWCoA/LlTSlKJHSes3gAxzrCY8wbZlQH1TvjTVHeeGs/eURLbZNnP/QHdVxdXbUrV67Y+fPnyxMyzK6va6GPdXwje6Bj6/Uj244c29YUtMxuMeAy89mVp2hIw39HXo7mh0+6Dw8P2/j4uE1PT5fg5eWteeUqBU9O79nIoHDoR8EJxivyQLQePIF4LUINHNeP84RR8frGM3YeW/Ymk9YJZYGhY4JG7BveBQy6esxqlBgUIj1iHeP1R86f8+Bx0I0g3L985BSAGek4T7TPe32A67izs1Oe38d9wjvWdF0oWvNB3fVwZA0/cz/ieYAvb66BRwXQgmfFAKDjnQIljxTlMn3VNx53tTNeiBB6EIVUlUDzGHG7PNukdWH9XFtbs0uXLtn58+ft0qVLtrq62jYn9vb2XI+VJQU+EaHxbG+qXzuRfQ1cEZOKwKeqwzyw0bygVGtra3bmzBmbnp62wcFBGx4ebttppnVUxfTK8AbbY9Kog75bxF6ZZ/y9SczlRoDKz6lHov3Fz3iGU/Pzytf8PDaqeaFODEj8siqHpDxm6BkObrNHHnhjCPLl8eJDhL01SNSJd6B67BvC4bNWq1W2zyNpavSiucJgpdd5DchbB9I82IjrphkdRyYQvKmE16740yNs+NWr1LZ7EumY3vMIW2SIWe88UNENL2bt5C8C4ZT98u4xcG1ubtrCwkJbiBBjyN+ViwBfdadqbnoSOQd6LYc8RLLvgcvMVzyWqINSno8aNJ18e3vXDod87rnnbGBgwO6999627/9Ext+7HpXtCSYwDAmXpZOYy1XGzBNJNzVEwKBejj7DAK3PcBqv3WpcvTpwWg9MOb1+tgJegbemAcDRSatAw16ZN5G9l1OHh4fb2svfWsM7YHh5VvuCw5K8CUA//6EhUK4/2sUL90jLnhXAxuy6J6f9oIDB5XJ9FUz5GeTJz/AWdn3VgE9/UVLmETIFBJTH41vleTGY8/obl81jxePL7fPshoYUOS3nozoe2TUmFZubm3blyhU7d+6czc3Nle9stVqtth2EqcMAeO567eV7/GyVjfXkNQtcHktViRRC7yvbisJz/P/29rZdvHjRnnvuORsdHbUTJ06UrIYnnzKzKD8AkbeWxcaD2S8bN5bIuEdAmeo/BRwP5NQgcB96IRI1CFUTievDY5qqOxtlGEXdhYi+4ZAO56thV/bEFMCQhvsZW9kPHTpk6+vrpafE9dbXB9RIsveG+wpQCuxcd/0b//Nv1U0eO32W1009EGES4oGLjome2MCABc+Nw59arup1ROCU4GibIoOt9oDHKrI9GlbXumjaCOyjPPEbOrGzs2MLCwt27ty5cl1rfX29JEgMWto+7Q/+OwJM1ftc0bnlhVhzZF8DV+RdRMzeM7j8t064HNna2rJz587Z2NiYDQ4O2tGjR9vWIJSV4u/U4HvK44UAIzamZXHfqCHyvDAv/KR143seqKU2kvT09NzwYjK3xSMbnnFlw2t2I0CywFBi/QiTnjeIRGDLXo9uBOA02l8rKytWFNc3i+CUCNYJhHB4IZ/DnSibz/hDX/CZlCwpPYNnpZ42G3vdJad5pgiJEkAtm0GP17AwV/AMH2arc0jnAPe9rvHqPM7xDJRw6hzx0nqi9VYCmyKRKTKBv6GP29vbtri4aGfPnrXz58/b/Px8+SK7nkHoSVT/FCB59iqyYdq+TjwtyL4GLjPfdY86MmUkzeJvOnllMVNbXV21559/vvS2jhw50nZaQRU747yZfatRiYBK6+Mpv9mNL0lGddGJq8DgAT17T1xv9Q4YQKP+UAbMabxwE9+L8mQmzwYJvwEu/KN9ze3Vk+nRJj6xBN+XUpDkjRscjjLzX9bmkw4QWsP6VqvVumH9TA0k9yW32Qu/aTtZpyKD6xEcTosfPsaL17K0XLP2d/5QB07nzV3VczaSnjeoaVjvuB38yonqQ2pOoy4RuWMiyXXw1gzRRiYVAK1Lly7ZuXPn7MyZM7awsFC+7I53AXkHoSfaT9Gc5N/cF56z4JFOzasTANvXwBWBFN+PJplONjWCHiAqA+I8l5aW7Gtf+1rJoCcnJ9s8rzqDpBPHY83MetkYspHyPBBdh4mMvKeonnHjOvLzzKCRXg2ZV49IwSPi4YXJtL807Iu+4fptbW21lcFAxn3Hxl/7gdeJWPdQFpeh/cthHxgc1IE3efC6FvQAv3W3KJel60ZFcf3lc9TRC4epl6Tj7a398D0uj18c9sKynA/3i+bLY5kiQPqc1/c6r3jsvLU9vp9DJLV8/K9rj3xd81SvE2mwpnX69OkStPCKAduDFGhxfbWOVeLZV84jN58msq+BKyVeh3mKr0qNv71nPbbEk3R+ft6ee+456+npsW/5lm9p2yav4MWsWOuj15XBMBNlBmZ2Tcm9kzw4rQfeEVh67eZn1HDp/Qi4uF7aRh0DZm3K8jjPaCKp96YGDO3gMvm9Lu0//s1rjlxX7Uf+n+sIr4PHi8NAKIePSGJhYAOzRviNx4LDjFGf6HFJqg8skQ6hLzncqe9ecblRvrwWgx9v44UadYCirolpWxQwvfGCpAhbZKTxtwe8HrnitDqv0HYmUuvr63b58mU7c+aMvfzyy7a4uNhmZ2B39DinFMBzmzybkOqXSHLSNJF9D1w6QSPjlXrWU0Q1kBDeTKDGEuD1D//wD7a1tWXf9m3fVn4AD+EdCNfVUxKViP3xfTYOKIPzVOPPRkzTR5Mz9VwKmKN8qhibgguu6VhzX6bqHhktNYpIx2tieqwQAIEBjEFMjbu3W88z7Lr21NPTU34kUQkIlwtjDQCDUfe8QIwf9yvrhrcDE3mgHur94W996TrSO+4b/V/DmLq1XIkcxlSJnCfsQeqGDdWJqJ6aFv9ruty5VUVU8bOzs2Orq6t28eJFO3PmjJ07d87m5+etKK5vYEFfMWhFxC7VT97ciQiNl4/ei8a6iex74DKrViDPgHnPegxMf6cUGBN3YWHBvvrVr5bgNTMzU+4u40lu5odovHp5wsrohYm47uoRpJQmtbjNRitVNy27iq1VKTk/B4Ot7VVAw6TnycviARGXofVijwF10DUp9WqRB4yrd6yUt37DITzUSz08JhA4GUGfNbvuPXrlavlmVq7VpggA6zHvCMT6lUdYPKOpZURjEK0VeR4Cg77OXaTl98x4nDxwVzKiRDYCJc2b17OidkN45yC87e3tbVtYWLC5uTk7c+ZMeXDu3t5e6WFB//gdOO5rjyBHRIuf9fqG80wR0xShbApe+xq4PKXF9ahD1JimBiKVD4un7IuLi/bVr37VVlZW7L777rO77rrLxsbGyomOya1M0pu4ZjeenO09o/XVtvFvvc9AELUrZTiq0nN5Xhw/VWeIfpeK6+G1z8z/ojCeA6CoJ8d1ZKDSfuG6eCDB970T+tXYsu5pf/Bn1pEfexecD++UVJKk9UYYEsIbVbg/OR9ED3T3Y8SoUzqkOxRVp/goLzzj6ZWW7W2y4OdVVI+1fkpgdD56wOkRRC9yonYMhAtjia8AXLlyxc6cOWNzc3N26dIlW19fNzMrvXGAKHu83nh417R8bQvX0xsLD7wiUpsiu7myr4Erh0Vw2ojdRM/pYHhMRK/zIK6trdnLL79sKysrtrS0ZHfffbcdOXLEBgYG2k4GqFIo/M8GUw2LGiRvQVnrypOE70V9yH3C5XhsDM+3WulPdEesL2LRuK4GTvOLJpfH+jxw1byV8adYtqePzMw5nYYo1ehpHYri+udKvIN5mQjp5hI8q+VoXb05hRCgF97MISARWdFQovYxf3dN+4W92JShTdWN51JK7708vLCwiubFRInTMFjxOG5tbdnCwoJduHDBzp49W3pZ+CQNjz9Oe9fNGNHc5LZUgQ7nwbucPbJS1SfdkH0PXPp/aoAgnvFWQPAmcYodR5Nka2vLLl68WDKmU6dOlR8QHBwcbPsUBZcRGQPP41BR70lBm//2wN9jnpBoXSua9LwYr0rtGfiUaBu8fojYH/rFq6eOJ9fX0wM2xCmpAgf+P9fQMHAAwMyuHz8FA4b68SYPBQKvz1S/UJ5uqea+ge5ymDQygtq/KjrHNI9Ih3S8NL9IvHGM9EzbkZoLGmLUMvk6b8aBjm5vb7cdlDs3N1d+x41f6cBJ7wBwDUV7/ce/I/sYkUbOzyM4kUT2sansa+Ay89lt6r7HaD127hkyPIe03iTxgG9vb8+WlpZsbW3NLly4YOfPn7c77rjDbr/9dhsbGyvfteCtwpFEnpSZ3RB25PueZxIZME6jjDil8KrMGv7hsnKUPDLmHuh5JEbr6Y2Ztg1j6OXP4JEyeFX10LSRUfEIg+ar61nIl+unp+VreI5BCyEmPv5JT4HReiDvqE+qxprzggE2M9ej8fpN+yQiB56kALTK0Hphvyg/bY+SCfawVlZWSsC6dOmSXblyxdbX19t2i7Za17/lhuf44FyvPdonqbp6AFfVj5w+ut8t2ffA5UnKS4J4RpiZkqbhdJpvZMz1fxzLsrGxYXNzc3bixAm766677Pjx4zY6OlqeoIAvvEbKkwIPs/aXWCOG6ol3z/MuI1bt9Y9HLHKNAhsoXQRXENN2Rkwy1daIjcIoQz8Y0CNJga43yb1rCpieLnPb2YBy/fgcRC6Ly9F+xG/dqu+NvXoQEcBF9738mMDxeh2X54VftSx+ITxlVD39SYGm139VwsRBt7dvbW3Z+vq6XblyxS5dumQXLlwoD8nFxhuuS0/PtY9B9vb2lp+38Y5z8si1V/9Ilz2gU9BTO5AqS9M0lX0NXDrJUu5w5FmxgHHyYmcOy1Avrgoc1tfXbWNjw+bn5+306dN29OjREsAmJydtYGCgnKi8nqBt8hQnUswqhqVt4n7y7nl/K5ik6pTDhvX5orjxANeqvlbg1bHy2pKacKkxThljrY+Op65Zaru9tup1Bhgeb/awUq9VcNlKHFi89arccdS5qOuyII7e5hIGNK/+CmxcRs7mJv3fIzARcYz0Bu1lTwsh1b29PdvY2Cg9rCtXrtiFCxdsYWHBVlZWSgKrdTC75kX39/e3HZvl1c/Td60ft52vRTqskptvKn1d2dfAZeYrWhWbThkgXMPxOsrqosnMZVR5Xki7ublpFy5cKE90Pnr0qM3OztrMzIwdPnzYhoeHb/ggIRs9zj8FmGqs9Hm9zgaE260A6E2WHJalk0jz84AmB3g9Q4PrOc/x30oQOGSq7alqr+bvGT/PY2BSpv1TZYC9Pgbb17y4fI02eMaLSZ0HRp7o/EOdPFBl3Uu9t6inwPDOTZSnuzkVzJTQar45c4pBCfOT16zwN/LCd8iWlpbs6tWrdunSJbt8+bItLS3Z6upq8qXhVutaaLG/v7+0IfpByNQ4RPrmjbN3PRpnb35G+VbVMUf2NXBxZ1W5uiqe8Wal5kVtlJWqR4pha300r52dHZufn7elpSU7d+6cTU5O2vHjx+22226zI0eO2MTEhA0MDNjg4GDb+zO8s4uFD15VFo3JlFpHi5h7xPCjrfRR/3BengcVgZf2bY6ogfZAPjXptW4emKbqkwLcyCCkwCBiwB4Y4u8UyKbALzJarIPROg8Lg4T3mRI8H+22Q9uiVyH0f68vuS5R+xnYtf4QPvZL1xVxHW3lj3Xu7e2VGy7m5+dLwFpYWLClpaXyfMFoLZOlt7fX+vr6bHt72z0lhduV0r8cklnnXoqMefl0Al6tolPoexVkaWnJJiYmank/VQaEJwjix2bXzgPz2E9Ot0WGRAHAq09fX58NDQ3ZxMSEHTlyxI4fP27T09M2Pj5efr6dDQkzZA0t4pqugZjd+Hl3T5T1aniH66HX+Bk2XpwvP19FFjBWuilB61lFaBQ4ozRaN+QdSQqgPNBjz6BKp9Rb8cgE7uE+71SLvKSIYFXVBb9Vb7Qt6slD6uqel0bvV+32jKIPObtEec2Ny0df8ikqZtf6BpstFhYWSsC6evWqrays2MrKyg1rUymi0mpd25AxODhoPT09tr6+bltbW25dU7qfIxFBqppPqTy0XtC9xcVFGx8fr1W/fe1xmaVjtHWYg05EfOpaX3rtFOdzAW9ra8u2trZseXnZLly4YC+++KKNjo7a1NSUTU9PlyA2MjJiAwMDJZCxceBt0wh9MjhDUhNewSaqb8Rko/AjG46oLp6R89hztJvN+5/bAgPrGQueYCmmz/nmTl6vrBymrXWIQEvT8bjrPTzjfQdOwVW9Nwb/aI5UhaW9NCxVwBKtkeUSM72v4Uf0AUJ+Zu3rVnwAMn42NjZsbW3NlpaWbGFhoQSr5eVlW11dtc3NTfeL2Miby8Y1zNn+/n47dOhQ2y5CboM3zpp/rg3zwCoC1rqEvhNQNbsFgAsShTyqPCxPwOihyN7Oqqjs6FqOG+3VaW/v2nE+m5ubNj8/b+fOnbOBgQEbGhqy8fFxGx8ft4mJCZuYmLDh4WEbGRmxwcFB6+/vt76+vvKH8+PPSqQWrqNrkGjNjK/pDjEPmPT5VH4emHiMP/Iwte45E81b7Oc8NB9vjL3JykaR02udNCzFv6t0mQ0q141327GkQnNaT+4HJUypcHQ07lUSeWyp/HSsvA0e6AP8z++j8RjxxgpIUVxbZ1pfX7fV1VVbXl62hYWF8mdlZaX0jCKw8oTHGd4cCOrOzk5bJMhrS0RqUp6Tp68eMVOPifPX/CJg69QB2NfAlRNagXgdmHoerBwv+HmMUg1GynhUPespkjfY2KSxtrZmCwsL5eff+/v7y3WwkZERGxkZsfHxcRsbGys9M4AZFnZ1jQz584RFOmWzUaiFQSoSz9B6rN3rk+gL0ZpG+5Q3FICYRMY1WvT3zhn0QptVgAXxQohabwaQFEHyQMbzZr366/91ASUFGF55KcLTSTmeF6/rU/zb7EYvsqfn+pFXGBcdj62trTavanl52ebn5215ebn0quAR8fzyRO2RhoIZtLCLECDIEtkSb67V9bhUP7lfIt3VucAEQMlUE9nXwGWWXsg2S3tHnlHhfHAyged14XmPwVS5zd5k8NhJJMz8dnd3bXV11VZWVtpi7YiFj4yM2NjYmE1MTNjo6KiNjY3ZyMiIDQ8Pt3lmOMgTwMMneei5dN5CvPZ/Cti8NlaFyrh/orCkAoEX3uTJluOFKetGHvwT1VP1a2+vfYeqbkzRvtRJ74XFIqnyaqI+SIVFuW/qeA6egbsZ4OiRQV5v03FjT4vnDoAN8wBABa9qaWnJFhcXbWlpyVZWVmxtba18QZiJXySeffB0BvO4v7+/PKQA72x5YJSygWpjIs8rdZ3zj8h4Sjr1slj2PXApaEUuLX5H7nIU3tnd3S0Ne+rNdM1P68ISucu5CsbfX+JTDSB44XRlZcWuXr1aMjb89Pf32+DgYAlkCDmyV4YJjA8assFixsReDP/vtV+ZFk60VmDg9B5j9vKsCv2p8WTDxQYsAgdm6by5hL0hHlcGf11z0bK9v7mvcF2Bk9N4gOld94hDyitkqQs2OfnkGj2I9qV6cQAfTo/rSMfpUQf0zcbGhm1ubpabKuBRYav62tpaW5po/bBKPMPP9cSWdz4Z3jsdIwITlSovTNPVHRc8k8o7VU5d2ffAxZLyrvi+N4iR94TQFH/rKMpXpc6gVDEfKAXCGLxOlRJ4UDs7O7a+vt62XRfg1NfX17Y+Njg4aENDQyXQwTMD4DGweR+yjLwsBTbdHKEhOjNrO4KIPS4Goty+8wgMjz97Kehf7PpCH+7t7ZXMGn/ruiH/5jqCcIAM8FeOeQ1Fv++lu9X0/EEP+Fg87ywCXNZZ9HNVaM/bFKESzRG97oER6qRrctxu3jjB6RSgMc7wora3t0tvam1trfSoNjY2SqDCRinerp47t1O6qXqqByejL7DtnfVJ8+L/I/3Xeimx4Xvq9ecCUpXonGsqtxRwQVRZU2zcc9fVyOGjfPA8vE8GeIYxJaqA0SAq4+b1ljpKBe8RQMyTAu+WcDlsMOGFAcT4Bx4c/3gGGMJGRdeHvD6LvAZNC0BkD5Cf5xAon8wPEIJx2NzcvOE31hUVmDhf9X5QNoQNqgdM/Bsghx+QC/aCkU69CM6LN+ggr4GBgTbCoWtc6kl7oO95C5yfZ1xxHWnRJ57RjHSFw/a6IUTrz1+DBkhtbm6Wa1MI++EUG4AYQErHtYnkENqiKMoxxeYLzFXoXtUORCVwnh1K2SS9x/mlAK6qbaij5xB0CoT7Gri4Y7XjIdFCfmQko+s7OzulAdBJrYPgDYpei8A0GlBeNGavTxVWjYFnZNTrwRmJ7J1FouDmeREec/R2ZSmoeeIZUg01oS16OjYMgJcX/+g1MGveeelNPu1f7iO9FxkQBiDPe/LW1TitB5i4x+OCzTvqSTMBwcYdBVCtC9qAfvFCvKrfkYesG1+88Ct+4B0BlOA5MThtbW2Vu/y2trZKjwmhPfZe9ADhupIy5B7hUsONMeLXWdCWqk8eqd3LAS/9P0W2PfuSI6n8q/qsjuxr4IJEA+P937TjYMBgDLyQYVQnvoZJjYmvaT1FZyPEu/a8TRIQjyUp+4Hh4ZAhQJo3Y3j54jpegNTytW5qlDldBNZ6zTMGqee0HAYBD8S5rpwHvIWqDSR6PdVm7z4bJK6XB0zcd57XpF4g0rFnBFDjnakMYvgbXtrg4GDbMyifT4jQdrEo+eC6MpgAmHZ2dkojjr/hhbA3DK+ZPWMFJu7HOpJDVFLiEUeez0yG4e3peEf5qp56RInTptoXlVVFyKP89Bmvfp14XfsauDzvInJ5czooAjg8j7MCETL0vKWoHpBoJ1mUF+oP5qvrJgp+HoPy2sVsHSDMoUF4KxzX52eidjMb1zSeQdPPi5vduPtOy/WYpQqXp+l5/YnrrUCQAmMuR9ku/+0BOIvnMUV/c3vV42HPNmore6QRaeIf9qoZ6Dh0yfdT7Bz1Zi9WvVv+UCWHdtUr9vqoqaQMvqbj9HUNPfcp+g/vVwKw0FbPY6kqi6/VJec5/dcUsKv0oansa+DKQf9ueFvMzre3t0uWZGZtQJJ6NgJYb9Ko0eYNGd7E9YQNUFSe1hOGA8YIxgnXld1pPfU6T9aoHgrq2lcw0PqcGuioDzkP9UK07tHz3BZdQ9O+1rpxf0UApcbQIx9aF06Html/8wYaBi6PAXN9PGJS1cd6j/NU8YC421IHkLy+qCKCXn6RZwGw0i9Ws5elnj+X7dUnArWIMKue6zOqi5pXXdsZ9WlV/XNlXwNXJGCh/L9nbKKBT10HS2TGxHHyFFhWeWiR0iOUoO+IpAbfMyCeMdY0zIgxweBheoZf68+iazFVyl8FgF7e2h8s3gJ71F+6fsN9pMZL9UmBi9vAP56B4B2S2hfe/1WGx+y6F8s7YtljYu/FA9SUIde6RXMlxxhG+XppIiMeEbIqvfPy1H706sIS6QoTCPaw+P0wXsfi6IQHUF7+nmhf6v+qs9pOLTOlc1WSU8dO5JYEroiJ83UPzLx8PIMHEIFX0mq12jY0RAqg9cC9aGL19PSU+UesLKozG0aui1dHD4gwoXSnGtrvtS9ioWyg+ZoHAh6Ie2RAwUgNJJfLz+mE1a3TnlHk/Dgv7Xf+iQCB15iYiCAN776rEq//uL5mVhpH3XWoxM7sOtBXbViI9FXzUzDnOuc8q+MU5R2JRyBTxjtqW0rY0+W6Aqz4FA5eh/PmsvZV6p6ny1XPetdTZCFFqKL7nC4iFd2QWxK4zOp1Vo6yahpMbrBYXIuAwrvmKQWUAQwZoIX1Ne/5KobqlYV0VcaPQ0t6VI73twKHAr7mH/WBKn30LNKm3h/iOikTV+PKa5BV/aj5cBoO33HeHA7FqwPei+SR/nh1VfGIGXvSmh/Gljfp6PqS9rkHKlpX7XOvH1Pt9EiVV4bmq2Wrrnieko5l7vzRfgbZBKk1u77EwB+G1D7Q/kiBQvQstzlFKrU/tD+j+155WnfPlkRtqWpjSvY1cHnGliUaLL4fMWxc88qEKHjpaRZal9yB4gVvbN9Ntc2TSLlwz2NDUX9isvFuRs8AqBeUWyc1GlgPaLVa5btW0ftZEfP2JiyX43laymp1KzzfY0PlbYhQEOI+g6eleqMemBpU9ta4XM/gcj4aZvbAgPPD39ikg7S80xS/UVZEZFLjz896bdDxicgES4494PHWcDbEe+XC022ANoM+dBc7HnVDSgqYuS+9OarXor5I2UYtNwInHRtucxW4RWV2S/Y1cHmSa8S8+/q3GklP+L0nKK6ZueEWzyB59WfQQt6pCekBSEq8slOgrZPF2wkIYS/Dyz8yckVRlDsa8S4R8sM7ONErCEVR3DCZvLrhGgwWp/EAmD0UZcJqBPk51jcFSfZi0CZ+QZh31qFdyIs9NgZAlKeeD4cH+ZQS3TKP/HQDDsrktU6Mie5O1D7WOafjpvOgal6kiJinux4IAvz5XUIFey2D8/baqjsvW61Wm87qeZlVRt8j2NwmbbOSP6/PUiTOy9vL38u36pkUoUnZ1BzZ18AVsW2z2JinWAinrfOCMEIBe3t7bcf4RF8o9upq1n7ki/cOijfRozZFhqBKYTWd9kvVRIv6PAJpNrr4eObw8HD5Iiq+Jdbf31++VJoaEy0rmrRRn+kPP+v91vaycdPt9d7mFowxvyyMNkThUS/sFwkDHsrQvlEw47+xTZ1BjMPYSiZShMfrb09vWMej10dSes15q3fKBELBGvl4JAWgZ3Z9XPlFbRBY9bBSoBEBbG5/5cyD1BhEoJ3qZ05bBa659W4i+x64WFKMIwVy+qznHVQpExsUNkL8forHQHAN3hrWPPR0jKieKcXzjHXEdPSeZ5RTjC8S7Uuvb7VtPT09NjQ0VH5OBp7n0NCQra+vl8cxeS/Zan8hP+5r3UHoGfCojtFL3x5oeSFEXWPiMnG0GBtZPO+9vOvVw2sXdEnrhPzZuON5BUj+H8DF2+xzDC+LGlnvXmQEq65x/7HHqCerRP3n3UNe/D+vA/IL1PycFwngNub2lbbT011vzqfIqbbdy9/7X4GX00RkT8vItR+R7GvgYkl1QAq0UqzNSxMBBCsQgAqeV39//w3MjgeZd+yBtVWxGm/gFRS8tCkAVokUNCo3lZem9bwJfGcMzw8PD5dHFRXFtdDa8PBweRgqf/Ooqjz87dXV+6kiOh5oMThoGbxbL8W+kS418SOdrWLHCq64z0aef3No0HsxGG3lfFPGzxMuP9qtym3l6zpOWn8FfjO7IUzI97QvtT85PBq9SsDtil5k12tV/cR5efkoeY1sQ6R7Xp24XlUAqc9XAXKOXlTJvgYuZR2dSDQQOUZY62TW/mKyd5q3KpueKJ5TV8+L03ro/9omVVxWcBikXOPj1VHz9urGbUEf4JBRhA4RLkQIEWt/HiGI2u1d59+RAWLD7rWd07Ex5/rpmYlRfb38I1IRiWeQqvrFC2nr9nm0i0Pg+v0qLS8iPdE1r2+qPAn2rHSMPCOr5CDyHngO6Dh48yKyRzn2SdujHpmXRwRUkU1IzT8lUB5pyGlHjifVKWiZ7XPgMssDr8gdTikup8H91PZj/q3s1tuCzM9WMRRVKrDTFFuqIx7IaHl1nzfzJ5aXJ/cff0JkdXW1PD8Pn1PBy5tY+IbBUnDw6qbgqQbW0wMvNBgZVw23mfk7Ezkvj/VH4+GJ9nGUNicP/Z9f7+BNHnzyA0LjaLeGylLGNKUn3v98jXfz6ToWhPs9ekctsgEekEWkRiUiDpreA+pornn9yddTXlZOuVqO/p3KL6rrzZR9D1w8CFWuLyTq+Ih5eXnlGHOP1UfP4p6Co7aP64lr3oRPGanoPt/LAa2qiZ7D0qL7vDFgfX39hi3yYMwKFl7IShk6rkXpWNAHzIK9/laWjB9doI/aG41jHUm1RcdJGX1VnryZQTclgNRB6mxI8vrNqy+3g78Bx18gQP0YpDxvicv3CIxXR69Oai90DOGV6cveOcQuAskU2EUEMSIy3t98TfU20htv3kd5dUv2PXClBiUFADrIUPwqQx3lz2V4hqrKQHK+SF8FMFUAm3o++j8yxqk8orZ4il5VZ80X/2MBXPuAtyKzJ8B5qZGKQEjv48dbs4LoBgzUNWUIU+IZy5QOVOXtkZ0oTZQPjyHvgmRv16x9U4TuiE211yNI0SaS6COafKq899I0t0XLqiJZkZH2wMu7x+9uRXNPdbJqznlpU2CUIkOcj9c/Wp7qiwfGUb21b5rKvgcuSAQSKYkGM4fpegqbAgQ1lFrnHAPj1S/HGGlZ3v/R8xHoVJXHdct5ntNFearCwyhgNx5/+kXfcTJrX6DnhXN9L03Dg9HEjMDZM5YRg061n69VMeioj3IkInS4pul096Q+q9v/Iy+Y/9bdlPCulRhw+3U3H9rttUfbUCWRIfZIadQ23lFYFMUNoUzNpw7Qe3kw0GveXh9664Gp8j1inQLvqL5RH9aRWwa4qiQFKl7aaED0b6/zUwOiylVVz9w0uYYwKjtibWosvImQqmPE4CLQrwJY7xl4AtjJ6W0YYAPivTvFpxpw/gpGni5w3vyTYq5eH6T6R/PIvR4x+BRIpvLn/tST5pkQYBONt5sSRpM9KF6r0j7HD290wf/emlVKh6rIkPabPu/1VaTLXhmp8qI+98hSavy8Onptr7I1KR2M6uj1x82QfQ1cdTonMoAppoB0ucbGU8rUpPHSVv2dw7ajtlaxI06bMzG8Z1KTnfPxPBWP0Xl5aTuRjsNTMIS8zqH5Ib1u/4ZR5Dazd6V1YqPqAbAabb7vGbmoD7w+jIT72JNIV/Q+39N28A+Air+AjbT83hnqFL035r3AqwQi1Q+qW1U2IrIJ3u9oHqY8ioj0VeUZtS/K39OvOuKVk8pD5wH/9mxIyh40kX0NXCw5nZJSZs+gV3lFdRiMx7r0vpdnlWfj1TU1qaK6Rjsm60pkSLy6plinGl2dGF5f4m9slecTTLzJ5ZXBZXN9tH9QvmdcI3DTcqr6MepP7csIeHLFM9i5dcQz/DqHelJefytIeeCfa8ijNEqoqgxpDhnAsylDHJESzUfrFuXpkbTUmEfzKkVkOpHIrnrEtFvgdcsAV0q8AfX+TjEYLx/PGKbYq0pKWTVdjrAxrwLViM2nJi+Yda7UZbs57fQYtU4MPoKLj+nhPLSfvN2c2o96igS3ocqI5fYJ5xURnByD2KlUGUaP6KFfsMbYarXawrY8Phzu8/KPJCJ4Xv2rvJnoGc8GeLpWBV5RGXo/0o8cL8q7F+VxM4lple3rpm6a7XPgSg0oJNdj0TzVsKc63ys/NWlSIOaBoz5X1d4mBoAnpgfMqXbWYa5RXhG58CZFlIe2ASf1I22KgWvZ6n3hfnT+nNfOJuw2J+9OJNVnmqaKWKmBxBoh6w/GgcvTv6vaWkVqIgKUSya4DgpYrH8pQltlX6oIhwcG2j+eTfL6wGtDrkS2IyL0uYQz13bkyr4GLrNq5mOWF2ZTxcxldl45dcESz0RGResZ3a8rqXoqiNUxeFHdchU8AhQdJ8/b0nppf+UYM6/fIXosUfR8lH9O+alJrnpa1S9cH8/4VJGgyDhG6VWqSE2VzkSSMuJ1gSRVRyWUUV2856vmh25EicrgcYvanZrLqTkRzdHI1kTzTeuakipClCP5cZ9vYqkCLSiRxxbwP1/LYYCcf6csuY6BjyY9/k8ZyNSzWm7EwL3fdSQiGtHEzTWW2uaqvKI2pZ71dImfZYOSMkJV1+r2a67+1SE9nl575VTplEoqfaofI2KXm3eUHj+pkGWKbOB+BBgpUVCM8oj0zqtrVI4CXg7hrzM+msarQ5RvE7klgCslnoHh/3kwovWbKnBqYnCj57Q8zavpJGqa1qtbqg+qGFv0HKeLrisTrsqr6jru1WX5Xv9FRr4pq4x0ICqfy/bqUVWWSi5503GtA+JRXVKeUCp9VVo21hryjTySKo+mbvv4OS//VLqquR/lE3lHXhqvrBySWTXHui23HHBFkzc16J7i1jU4uQNXhxV2MuA5Sh5dSxnHqgmQKlvvV3muKWMRiTdJ67BK73pdjzoyvKxjdSZ6qi2qv01Illd/JSCRl8np6xKLOsYvV3Lnrup31Oc5Rp/zq3uvznyvsmMeyOYAb1SflB55Op4zL7sl+x64FAi8CaXpoufxDF+PBt9ja9Fg5k4mj1V7bfTSgEXyvZRHF/2fY1xSihx5HBHDjDw0FSUX3kRM5ZFqIxumlIHO7U8FXi99qt05BkOf9cAlJbmg4eXfLQOkYODptra3ah53An6e/dA8PbKlehPpl9eGOuSiiqCoPuTkGaWpO8apOaTpmpbBsq+Bq4qZ415dlpkqy1NA79nIMDQdrFwvJresFCP2GKbHQJvUPQL/Kg8xqlNOubmkpSpNBMaeQanDwKsMmZYZgWlVH6bSRM9ERDBXPEMVjUuOvlYRpFS+OfYiVR8vfZM+yZEUeeIfLT8Fnrnlpuyalz7XWcgB7VzZ18Bl1u5pqNQFrBzvy7uvZXrMJ1WXSPmicqoYmXddy4mUiI2uZ4BTxjG3rdHz0bMRsOXWTb2RiGjw9RQ4NCEhkRFoQrai8j1jVwW8VYy/E9CPxCOBLJFXqdd4LOp4LlXpcvRYJdXfVddwvQq4PVKUS5Dq6Kp6TTl2L6pvJ3qSkn0NXCkFa8o4+Nmcic3X6rr9qFcTQ6/36rCkKN8mzDEHqD0vIeVZVHmwKbLhldGNyVOVR+SJ50hdLyhXcvu8Th2a1sdLW0VGPDZf5ZF613RuVNmNlFdblXdUt7r6myJOdfSK50ATfaxbv6gOnv69pj0uSJUh1GvRs7leWtX9VJkeaFVN7LoufFXZer2OJ1HXyDGoNWXB3rVcUG9S3yq2mttHVYAR5ZczxikjX6eeuaBSdT+aR5GhztWFHC9fy4nqFc3DqvK9CEXd/s4FQK9edQA7Vb43L3I8sxQJyvUWc8vKkVsGuNRQ5HQm0nv3cpg/56MSsYu6RkzZSqR82qaccnLTec9xmU2AgY2BF1aIPDhvnL16cdrU/5Ee5BhLb4xzmH+udBqq0nucb11QTJWZey+aA95Y67jmzrOqsr3rnv5V6UPO3EkBY5UOeeBQZ15X3YvsZA5hjO7nAlg3IiC3DHCZ5TMoBYOq0EFOmZC6jCs378iQ180/5YnUYdnKEBVYUxOtDtPOnRQ5k5X/zmX+HkBy/fj5Jmzek8jw5ngXntTVuyrCFpGyOuPjeRhePXLKqAK1yDCnvJ2oPk3JXqqenTwfkfSqOtYhOl66OkSik3SRdARcjz76qLVaLXvkkUfKa0VR2Ec/+lGbnZ21oaEh+67v+i579tln257b3Ny097///XbkyBEbGRmx7/u+77MzZ87ULr+TTvKUOod9dKtOnlGuY/iq2Eunk4Tr0PQ+i9c+veZ5jKlJlAKOVD1SeeU8XwV6TfreM4h1PQd+JgJb1bmq/FL1bXI/0vM64FflDdft/xQYRfbB04MUeOTqneaf08+eHfHGtlPbplJnzvDfTcZIpTFwffGLX7Tf/u3ftje84Q1t1z/xiU/YJz/5SfuN3/gN++IXv2gzMzP2zne+05aXl8s0jzzyiH3uc5+zxx57zP76r//aVlZW7D3veU/bl2hzREM9UYfkhoM8z6FOHVJ5R+k9Y+1J1cTIKbuOkeXyUuVUMeGq9kVM3EvneV9VfRD1W2SQ6nqtqTR12HnOhK4aP54DESjVaafWLfq/Sd5soL365wKqgoPqSQ5AROl0/HJARPsJeaRALMo/Z757eub1rT6fGqtOQS03+tGJNAKulZUV+5Ef+RH7nd/5HZuamiqvF0Vhv/qrv2o///M/b9///d9vDzzwgH3mM5+xtbU1+8M//EMzM1tcXLTf/d3ftV/+5V+2d7zjHfamN73J/uAP/sCefvpp+/M///PadeFQTg4IeOlx3ZMcI6UKERmtaPJE+aTqVVc6YchV1zsx+ikjpWnqsLSo73M8kTqTOcorxwjn1D0yeF776oJlTj1yPdIqYpKab968zQXGqv7NJQJV5CY3zyhtLgDmeN2RHWni/VcRnKhuVR71zQBElkbA9VM/9VP2Pd/zPfaOd7yj7foLL7xgc3Nz9q53vau8NjAwYG9729vsC1/4gpmZPfnkk7a9vd2WZnZ21h544IEyjcrm5qYtLS21/bCkFN8DFU3vTa4cLy2XFepzmjaarCmjlTKYVc97ksMCm4YeqrylaHJWGbIIIPjZKrIQ1T2nPRFLj55JSRM9SF1rSlRy0+eSshzgrgJ5bw5XSVW53TSimm9dIGlCyOrM1xwAziE82qdRWz2S0m0iXhu4HnvsMfvSl75kjz766A335ubmzMzs+PHjbdePHz9e3pubm7P+/v42T03TqDz66KM2MTFR/pw8ebK8V2fQVXLYZEpBdCC7PThcTnQvYt5NJQrd6D0uP9fT8oxJrlLnEIluhT88YI0mqFdGDljWkahdKaKmQO7Vrwpk60QPqqTTOVEFnFyniLTkSjSHukVUm4BXBEhVHl0V8fPIfNQmLx8tK+V5dzoPWGoB1+nTp+0DH/iA/cEf/IENDg6G6Twlr4vmLB/+8IdtcXGx/Dl9+nTbM3UmhaZvojBVxqvpAOUYB60DK5wamtwwSVOpyyhz+z2XpXv55HouXnlVzL6u19hNUlNHT6PnPZD38o7+z+0/DzS9Z6Lyorqn5nqTOVelH9qGiKjq/17bU155ztipePoa6bBnDzw7kvJ6qyTX7nULvGoB15NPPmkXL160hx56yHp7e623t9eeeOIJ+/Vf/3Xr7e0tPS31nC5evFjem5mZsa2tLZufnw/TqAwMDNj4+Hjbj1nscXjiGZ06k8fLJ7qn9ak7WLlGjhUtx5Dm5BtNzByDUQWUOQxf84q8tKryvPTR39H9HGniXeWUkUMKcgAlJSlPrqqudQljnbz1fqTLHtv3jG9TDzxXv1JeT50+yvHSUkS5KgIQpa1L5r065/S5SifkuRZwvf3tb7enn37annrqqfLnzW9+s/3Ij/yIPfXUU3b33XfbzMyMPf744+UzW1tb9sQTT9hb3/pWMzN76KGHrK+vry3N+fPn7ZlnninT1JXIwDZRHi/vpqGflGGJDFPEpKJn63p4PLnriNfHdYHf68do0jf1XOuMDU+6qE/qAHPqWS8sU1X3upGEJpLyhqs8UC8frVOdKELdukZ55dS1zlg2NepIn0sEUlLHhqTy8OZVSh+bevo32/vqrZN4bGzMHnjggbZrIyMjdvjw4fL6I488Yh/72Mfsvvvus/vuu88+9rGP2fDwsP3wD/+wmZlNTEzYj/3Yj9kHP/hBO3z4sE1PT9uHPvQhe/DBB2/Y7JEjVWyC/69yg9Vz4b8jxakDap4SR6CrBrOJl1HXEOqk9+oX3YvySvVb1B9R3ppnVX2icpQgeN54JDl9w+k4f69uVdeqJOrjKl3zpEqfdY51YtC9MlJjzmmr2pUCy9QcqEsA+TNCuXnWAcwqexU9p/c8gpgi1fpsHYmIdhUprCu1gCtHfvZnf9bW19ftJ3/yJ21+ft7e8pa32Oc//3kbGxsr0/zKr/yK9fb22nvf+15bX1+3t7/97fbpT3/aDh061KjMlMHnv9lYpToyApSUodL6pAx+VH4kOYqQc61qIjT1LKskZ5LUqYeCW27YJ9crivKMvEOvjjn18eoXGcQqI9J0rHLmQ04ZOZ5V9FzUr9wnKd2NCIonOSQwd0zrzMtcY57zbBPhNilRSxGrCOiqntO0UZ0a623RLev0CsrS0pJNTExYq9Wynp6erMbnsMMU0696TvPWSVcFnJqHPp9TzxxJMdSq65Eye2VwW7TsHEOTIgtV3k40SVOTkP/f29trGyuvHiq59fHamTPRI29IdSSXUFWVW6VjdQC7Trs8sMrRFS9NU1JRBebevMwhhU3uRzqMekY6Ubd8T6rGJpUn34vmK34WFxfLfQu5sq/PKsw14NpRURrvb0+qysw1Dk0ll/lWtUOZV0653WiHgniqXvp/NFmRLjWpUmASGQftmyYGu+l9lsgr8dJ1Uq72QxNjVyURyCqx8+ZPHUmRw6hML33O+FcR45wxqXpOy65bTpM5m9K33LmQmq9NZV8DVzeMh6apy5Zxjw0cTwZv8qfqnQsimpca7VRdU5JT10jxIkOXYwzr9FHqOU+UNUZpGPByDEAqr6g9TdupUmUovXSRRDqTMjA5JJD7qRvGSvOvm3eqjfx8ji406fM6hCLVplzCrs/UIbL4XdVnqfrUBbQ6sq+BK1eqlCw1Cb17dZUg93lvEunfnE7ZYq6hqQqF5Er0vBp29dSaGMkond5Lpc3x8HK8kRzDFIFlrtfaxNvxykvdr0uq6pZd5RFGTDx6VtPl9JGOqUfwqogkC5737EY3QFnLTulkp3O1k/xUvD7uNM+U3JLAxQYmB7T4mTpeXLc8p2jQOS9NVyWRAU/1SV0jVuU5pbyCHK8wBRCeQUnVK5IoHJXrZUTGK4el1zV0TQxjCkBzdFQJTx2voe41r95VANYULHKJQW7EJUci765uubhfd752M4qR2+9VjkFTueWAqyqEkhti4fRq0DplWrnej/dcTnlV7Y6uVU2iKiLA9csFxyqQrkofXfPyrLqWul/lbfEzTeqTm6YK/FIgk/q/iQ57zzYx4impApjceVjl8efkW8f41jHsXtnqtXTqzdXx4LvhdXN5N8P7uqWAq24IR+/nMOJOO59DDV6eHqPvlJnnlKX1qjL2OXXywjRV6b1rqfAfizfR63qr2ldVXmpKcupa9XxdzyTXy62aKx658tJ5/9fR4WjO5XqEVRJ5yVrnqufr2AgvnabNIU6se9qHUZSgCSH26sllpsA1qrvXT52CL8stAVxqcHPCUvxc1QSJBqGpcnB5uRMhBaIpdp5TRy9NNMlyACEVesv1Br37TSZ+VZom97Re0bOet163DK/MHI+9ieGs8liieVLHw8/xoPl6pJNVjL7KyEeS67XlXFepY5c8wOT/c+vS1EalCIxnd3Lm+oHHRZITAvA6NdeQdquzq4xYlTQx+NFzXpoqEMxlTHXrGaWPxtNjlbll57Qt5f1GE9cro4qQeMCqbY6MQVPmqgzaM0h1SUpTo90kXZMyPI855UFXgVw3PECuVyQp8MrJO8qvjlTphVc2349IlvdMXdnXwFXVMZDIwKSMSzTQdYEy9xnvuSojlWNQUuJNnk6Mv/6vRrzKWFTVtS7IpcpIeczcr1V9nAvmdTwVr25NWH6qfnWMEte3jvGpSpsLwCmCwPVSg5kSpMkdw5S+1JE6edT16FNpI1tSh9g10ROtY24eVbKvgatuJ9QBm27Vq6rc6DllWt2UXCDlujTpu9SkqNPfKYOl6XIMjIJq7uRPXa9iz1rHqjKUbafqWtc78Axf7vhUkcRUuk4Zt9cfdfKqU15kcOvobRXA5IBr9HeKeDcFhyqyzvWOdLHOvOpE9jVwMaqbNWcx0cA0VYzcMIAnOkFy06WUKAdYtO6eB5ViT028qVxj3zR/3I8MgOfNVKVNlZMrnkHrhoHJSadtruvla/7R9ajsTjwNzUvTNjHcOREbLTP1N0tdgpjjoWq5HsGtQ25y0kWeaWpueffr1KNK9jVwQeqGO1hShsxzd+t4KF45uUZHJTek0URSkzcyfLn5epJS9twyIoPlPZs7Zl6e3nipPqTyj4xL3fpxnXJYMdJVGZam3nxVm6ukSgdS6avSNZ0nHig0rUvOPFfbU5eMRICVq2M5ZbJNrMrXA6qm+lUl+xq4mrLwVH5R5+cMcopd5HoeqfCEgnNV/pEiewDdJDSSCi3kTIgq41eHLHBdqya2/l03Xy+PiJFWiUe4Un16swxBXfEMaLc8VG+8cqIfnDZXd5r2Z1PP3MtHPdQq3WwCzE30HZLS+1dL9jVwsTQNu0CiyVInnJVzr8r99/KoY7ByJ2qVV1qnDzsNCXSrH3PzjDx0vp7rSUX14XyrjFI3vLFUPbXcTgG86ppXflWeOaE33Mvpwzo6mTuOUX2jeuT0radHuaSxE0l5pimCnCMpMnEQKhSpY9wjF72J8U0ZhjpGJ+eZHEaf44mlnmniuXmS6tO6oaRUXXLaWuUx1Q3pqLCRykmr6bz7OdINI5Ab8ssZ+ybzD8/VBda6gBfdbzLnu2mAm9gNjgxV2YTcEGGO/axrS/RaNyMG+x646g68Z2By2Yc+lxuqzFH03DBBVVlVUuWJdCOfnPucJgUo0UTsJDyTqmeuPmm6piy105BWE6LTDTKTU35OGSmJ5kQT0OzEc0iBQR3gzCnL0/1IR3O86YgoevrajTl9M6IGnux74OLBy5nEUAhPUW5GvaL/u5GnJxHbaZK39pkH+vq8hsWqjD6njQCrGxPKq1uO1PGaonrXaUuu8Fjk6EUd3alqQyrvKn2rU+8cryClk95zKW8gqmtKcrzD3Hp5cyFVbpUHkyLdXn45eVRdf6VkXwNXN5hC1eCnmHWT8rzyU//XyTdS5johtJShqmKXdbzRXGPRCcDkpvE8plT5dQDQG4um5KIJ6NV9JtKJqB9U6oZCo5BXnbp2It54VKXPvZfjqXvp9Hqd+ZtKl3oud27o9VcLwPY1cLH3FHVulXKkBINT1yvTSf9KDW5uG+uwy1wg6YS1VkldslA3/BaNcRX4pox0HWBrQoYitl43nypA6iQUFvVLrhebIgg5uuY9W9dA15FOIxu4r/XuJrBynnWBjyMv6jQ0tY1NZV8DV5WkGEsq5JAzAfTZpuyjW6EjL98qJa5jpKvKqiojl0RUsUv+P/WcV0YVwGj96npiHmGp8va1jk2lW6Ee1fVcQ5MzhlXPRRKF0FL6G42PNy8i0Kwj3SBtke2p4xWm2oe/64JNLmnItRVVnmSO3HLAletmq3iToAqMqkIcVYOZ8lI6BbQUAFQBcJXR8iZGzuRqoqg5z6hhq+rzpnWpql/KWOZ4tMqI60puOC9V16r61ZFUfnUNfR09qJIqElQnryjPupJDfLsJXnXyUy8rVdecce3GHNzXwJXLAJvc99JGbDSX3eu9OhO7jgHKlVygYWNa5YHkste64HczPMIm4o1Pt0NMdfW6TvgtR3KjB90Af/VuU0CfupbrGeZEX5pK5BnmSKrN6Jc648lztRu2UOem199VUY0m5Uayr4HLU/JcJleHneUopDLlOqHDTkIVncSMq4A4ZwLeDODMKTO3fzVtnfBc3b5VRloFcl47qgA6h+zUCb9VSR0vruqZKgIY1S3HE1EboP2cO376d53QaR29rCOeN5hDbprOzUiPcom794zWreq5KtnXwHWzmXQkUVgxJw9PsVPA2gQIc/JtIh7rqwpBpDxIDyirjE5OmCdK24RtewYx1xDWDYdVeV65rFvHpAo4U/Voqi8php6TdyceFEsTAlinbp2k6UTq6KFZ2r5F9ijVBiZp3rPR/569bCL7GrhyEN+7HzEl757mUxeU6krKOFUBUcoopVhwE8aU0+dVoJUqP8XQvfp5+aTGIxfU64xnt4xVrmFMjYUH+HytLhikwkTa91VeY9U4RnWLCI2n5xGhqtL1aM7lerN1jHFdYhM9n0PQOP86UYdc4X5SXUulbSr7GriqFDH3mZxJh2e9a9EgdSOMVuXF5IZA6oZfVLgsT0lzJpHXT1Hfe6TE89Dwf4rEeAY8ZaSi/716aro6xqiJHkYep/ZtytPKkTrG0EuXS5y6XZ8qPa87/1P17pSopOxXrh1qUocoilAlkW7r/Nvb28uqw2ve41LpJLRQp7yUoajrwjcpP5VnldGqmnxVHokHGpou1xPOBV/vGf7tgXuqvt32kCKPJteTUxDKIQM5f3v1jNJofXKlimUzyfHqUpW3lz6VR9RnOWnrglU3CGpOPTlNN7wkr9yUpIi7zoG9vb2wnt2Yd/sauDxpMqBVLm3VvYjV1Q0vVBmWFFiw5CphBLRV7a5ipVV55RrM1AStYtZRqChVpxyjVDWWKfKSo2dVY8vpIw809Xe3wLpKF3PKafJcHU80kjpjqOkj3emWF5aqRxPvzKx7oJuayzcLoCK55YCrrufTxF2PJq2yDy+fqgmVMwE8hp+TNrdOei8n3OI96/3vSdMwRVX+EUOsM6EUHOp4a3W8oSrjmDPOKYZbl9DVCZlx+rr6qJ6YV3aKwFXVO1W2l2duHerkHZVT1+Pt1MtK6X5OlKrOXItsYbfklgOuOuK5uFXKUoe9ePnlTLYqI10FWF4d64aJvLrkPluHOET51QFw/r8JkOvfdcJZOYbKy6/KUHR7wjNA1A3TNTVAdUhaHTKm+ec8W1fv6+hA9KwnN9MLyRGNBuU+k/rfzJ97TUAyV25Z4KrruaTS5g5IU4VowuK9OqcmchVLrQKbpiy36vk64Yc6HmZVPXLrV0ea9g2nU0aunol3D1IFMOqV5RCQHBKl11L55eRdJbn9nCKduXO66l4kqfmZ81yVNIkapMroJD+2X0oCo7HqFLxuKeDKDbVxekgOGOWwVc3TUxplmrl58bW6ky/KO1LiHE8rFe7xpMrgeO3J6W8t2/MsvHI6CcWkPJEcD8XzhnMMTM69HGPZxNtqEj3IlVwPJ5dURSDZ1GDWeT4qr0m0pW5963g9nY4V8vfmUzT3quqVK7cUcNUR7lDP8HoKdLPdfK+Mut5PJHWYc454obYI7Do1nl59OxEYFvU+IubYDcntA8+z8v72pAqoc+vk5ZlTpncvRQ5ySFNdSXlZuX3Zbe/Lkzoku44u3mwblSpDIz1V4NWJ3DLAlRu7jZSFjRYDWVMPzruWGsBuhhFUcsCp24qVk18uaHaj3h6jryIFOR5lFYBH/0fADh1JedtV16Nr+mxTdl8n1KRtjZ7x2tI0pKW2IGcee+V2i7w0jc40mZN1ogg5EaTc8iInIDcaU1f2NXBVueFeOr2eMhCdMq+ITSsbjkA3Ys3dAJkcY1fVD3W8Ac03x7BWeVtRPnVYe91QjTdRq9KrUczxrDUE0xS8cwxTdC9F7urkx8arW55gqp6cR673HIWzPMJTJTlRAo8gp4A6t0+6ES2oAzQe+Hrg1W3Z18DFkjt56xrYuh5SEwDNkcgY5yhZJ95c5KnkMmR+NpIUW8PvqvHNmbA5AOyJhpu60d+RKBlIEZacvveei/Soru6mDG4O6ag7HnUAqInOR8CRa4RT3mUdnVA9y9U7r7wqMu5db0L8PABL2aZOwWxfA5fH3nLd/G6zgSpmHnlVOfk1Ncip+kUssE6+dSdjk+ebehqp53ksmuTfhNU2GfebIZ4xyambpxup/mnat3XqlPNsE6PfiXgkua7xTnn1ueAdAdbNJF0K8DfD04Lsa+CKJFKauh3ZieH2WFNT6dTo1Q2bVNVFJ1QKZFmZq8JsUXkqEQOu601X3W9CGHIMfBWLz9XZOpGEnPveWOVK1J5Ovc8m8zcHNKLyUnnmlMv5KFlt4jF1mo+mz9WTmxnm0zo1kVsKuJooaTeVoOkgpwxgCmzUYKXqU2dSVj1X11Oqw9Y7lSYTIgoReX/nSGrMonxzASqSXEOTUw7+rsvutS5ReXX1p1PPPJJcI92JrqY8kCbeT9O6MCG5Ge28Gfmk5JYCLjPfTc5JWyf/3OspwKkbC2+ixN3MK5WWQRYGz/OqlNXnSE763LWK1P9aTlU4rcrbyjX8dfvDe77Kw0ldyyUtVfXsRH9y0lSNMetWE8OZ075IP14JQ6116SR9ND+0TU1185UIy+5r4GKAahpHbnItR+qARrfyjqTK26rLBFP1yTEwej/V71HdqjwXNmJRqC6qE19PGawmou3V+kVkKtUPVZ5YBLo5gJ8jUYixisx1Qqy6abxzCWwdW9JUumE3ojnSTbvRiXQj330NXFWSGxbha1UTK8XquhVPv1nPpoxYtyQnPw/MOgnN1ZFUWCgCQP6d+tsrwwMqr3wPRKJnvTqn7nWbfKXKbhKqzPHkXgkWD6kiW57ktqubnmtVHjrfqwhsnes5kiKlnbZzXwOXsuoUq2gyMBFb956LBiMHKF6JSZkb2sjtJzbOKY8mt6zckBX/702MJsw0ZyKpkfbaF3keXl5VEzoFSFqPFMjm5JHzvzfON0tvu0HqupFHHYDRvq/y4jrpuzrkM0cfc/NK5VPHS24agmTZ18Cl0sSIVYWRvPxylTJnAlQZzao21GnzzZpITUTbnQN0nnh5ROWlnk2l7TQslaqTkq9uhe9yn60b/soJ/datQ47kel43u/xcifTt1Qy/5fRFFF6vKifHbjad457se+BKMWBIN2K7VQDj1SMqN2UsUoa4jvHlZ7x8q+pWN6SYyrPKm0o9Fz2bA9CeV8Ll5epF09BRShdyvPmmXk43CErOPFIvPhWN6KSsJmHQTshibhmpZ5rm2wSAoshFlccc5Z0iU5E+RsSr07ZGsu+BKxJVzrrMku/nTI46gx1JDsvJ9TA4fRMQ4r8jpctVxtwwXDcMRI6hR907MfBenXOITW5eOcZI71f1fVOgTpXRKSls+nwuKWwa+tL70XjkiJdHRErqGnjOW/Ns6m2mwCcnGqXSxCZUyS0FXHXdW72XmgyekuVOklToMOVxePV4JUIhOXXKAbNOJrvWqW76XM87h813O7xzs8N/Kalj2Jp4Sno9BVTd7NempCeXVHUqObqkXjo/2+3IUo6tyxmrJh5i7nMp2dfApWyDr9cBBC+P6BqXy+KxklxGVZW3l5eWWVdyQTCXxTUBmSZst5Myo/y7mb5Tho88UiSAy2lisHM9zToGPpWuqXGrU1436tckjJoKj1aBEF+rEvWomkZbvDbkStP5ps/t7e01yodlXwNXFPLJCQVVSZPYOZcfTfo6EzuleDnP15EqEOsGm0ulrWvwUp5dKtSleXfDK8wBmOj/OuVU3euUCOSGxKvy9653IzLQDU+1Sjeq2tckRKmAk9KR1Fg2CSN2KyLjeX1NpBugZbbPgcusO+GzCOSarllUlVUnTa7xrluvTsNAnYTxmggbnm6HhTyPsirsq8/n6mAdA5Bi5nXqWcfYpvorRaQ69Zi863VBpFPDmksUU3XoVn1eqRCmWTOSWld3i6LoGmiZ7XPgygWWqoGp6wXVea5T1lNlgJoCd91JmpOflp07+dBHOeHDqN51yu2mMIDkePnaxhzj1iQMVqWbKVDUOqHOVXVsogNIH9W3aegyZ46n0nvPVgF7VT45ZUchxm6UE0lq3jUlCHqvm6Blts+By+xah6RiyN7/VdLNkEcOa82ZZFFI1EvnSS4g5LLzOvnnAmlUjtYtNZHqeJw5higqJxWq1LRabm55Xn29PG+WdEIGeKxuVr07ISg6hjkhujp6nLpeFSrMIQpRXp2KR1q8+3XKvxlEct8Dl1k7mueuKeSEcqqElStiTamy6wJqlHcnbc4JKUWS4/E2yd9jnU0nqHoNVWmrJi4kJ2RaZ8J2IzTUiXdfJ+qgZXZDorrnjJv3TEQyOE2qLlX5a/qqqEcu8ey2NAlLR4SY25nrQXU7sgO5JYDLzO/I1KA18VY8YxWFiJoampwJ1dRY3gwF8phrnb5oUqe6kyGHIOQax1T76ob9qohETh5VdWuSf/RcXbYdeWw5Op9Dvjpl+55nk7ILKTKWq/PaDzkRDq8M1rkmUZLceeCNVQ5p07TdllsGuMzaB9FTyhxgypmcTT0evV9n4ufm3S1pYshzlL5pOZy+aVgyNZHqGvw6/VNXqvS0CfhEXn4nQB6Fl6N+rhsarbrXjb7NqWdVmLpOhEbzzwHoqnnjEadOdMXL0yu36vmbaatuOeDa3d01s3phqdS9Kg8iBxirWFyuMc0FiIgRVgFhXZabo5jdYmVRupQ3HJGOJsCX60HntqUqbWqsukEAcqTuWOdKk3Zj3JoYRC9CwtdzIgKvRphPJZfoenYi14Z5ovl5oXzO62aDltktBlyQKP4ahfoiQ9B0gtRVMP27qi4RI+TJ7aWtw+7qGJcco1k3rJQKgdwMyfG4UhOWr+UAS1NvIdKxXC+/E6kL6t4Y5oZR69zLFSVgnehtKhLTiTee219VtqZO39ft25TuvRKgZXaLAhekCiDqGMZusdw6nmCVt9S0rJTi1QlPVoVscoHZu58TovXyyGmbF95MtaUqlBx5pB4hqDKYKKMqhJWbn9eW3DAg0vO1XJ2KQpNNytV7qfxygLHKuKb6qBtRBi6n6tkcspn7bDe8LvwdRXW6ve09klsWuHSbPCTlPlfdy51MTZhjHcBIPftKsJ1U39TxrDqNv6fuNck7BcC5/drUk4qu5wBNU3BoSsbqkK9cyQ3FdlNSJKNJ33Rz7HPT5BKYHJCvG0nJ6bObJbcscJnF4MUSGdycQbtZA+WFhKqMtSpOVf2aTphO25w7HlXihUX5Xo4XqHmlyqlbt9x0nYT/PKnjqVWV4elUbr1utp6w5BC3HI+wbrlR3jdTmoRr+dmUN1+HnHE4/5UGLbNbHLjMqt83SIUPIsNdx2BGeXRjwnAZddmWV2anYNwt5c2N69f1qJsa4Si/OqHS3Pt1jWAdbysV9qsbcmwiWtdOw985IfSqscntg7pkKCV126Bl1Q0N4zk8m/t8av4weL3SoGVWE7g++tGPtoUvWq2WzczMlPeLorCPfvSjNjs7a0NDQ/Zd3/Vd9uyzz7blsbm5ae9///vtyJEjNjIyYt/3fd9nZ86c6U5rAsmJu+qARiy4jnGvG4NXQ9g01JJi8ZxeJ22kqFXKHjFzr/y6xioF/F69ckO8TeriidfPOeTFS5sDjFF7ckOEVcDe1AhFLL6JF9mUiKXq1rQunLZpnXQeNSGt0bi9kt4el4lyX6k1LZXaHtf9999v58+fL3+efvrp8t4nPvEJ++QnP2m/8Ru/YV/84hdtZmbG3vnOd9ry8nKZ5pFHHrHPfe5z9thjj9lf//Vf28rKir3nPe8pt7HfLPE6OAIr3NNrkaTCDrnhuyoDHT2TE87LmShVIaM6EhnjukCcCwCeQepGWKsJk06NR5O61Om/HMLzarDjKukk+sB6EgF6ikhpmU37LnWviXevedf1tKrKreOde9deLU8LUhu4ent7bWZmpvw5evSomV1ryK/+6q/az//8z9v3f//32wMPPGCf+cxnbG1tzf7wD//QzMwWFxftd3/3d+2Xf/mX7R3veIe96U1vsj/4gz+wp59+2v78z/+8uy1zZG9vz3Z3d7MNd861XAXIAa06iqAhAK8+nYb+vDyrJArnVXmuOesUVffqiFfPqrBuirHnGAfv/zreYB3Sk2Osc+/nSB1vBGOf6r86hrobXrxXh7r5NPF6O5Umeov7Vfl64o3dqyG1getrX/uazc7O2qlTp+wHf/AH7Rvf+IaZmb3wwgs2Nzdn73rXu8q0AwMD9ra3vc2+8IUvmJnZk08+advb221pZmdn7YEHHijTeLK5uWlLS0ttP52Ix4CbhhFSnkEOaHnPRfVtMjG6GQ7LlbqstQq0ohALp08BwKs1yZqUGxGSqnKq0uaGeuuEhHMlp278Ez1X1xh34slVic5xzr+qL3PAq1Mv7WYI+v+boU61gOstb3mL/f7v/779z//5P+13fud3bG5uzt761rfalStXbG5uzszMjh8/3vbM8ePHy3tzc3PW399vU1NTYRpPHn30UZuYmCh/Tp48WafarqiSecqWCj3kKF5V+KCucjYJ2eWEMFLSlK3f7HCkPlsn3Fe1HtXJGlJOWta5JmGcnHqoN5Or30rmbtYaipd3J8DIQFLHE9I5qHmlyszNM3Wvqm5NvbgcwlZ3bIvilXtPq0pqAde73/1u+1f/6l/Zgw8+aO94xzvsT/7kT8zM7DOf+UyZJrXeEElVmg9/+MO2uLhY/pw+fbpOtcMyPY8rhy01vRaVX+V+57DL3FBblK4qZFYn75Qnq9fqhJi8e03CSTkgV+Xd3ezw2ishnYx1Tl7RGHXK2L05moqieETBm+/6bN05nFv3HP2J9K+OXeomeH2zeFqQjrbDj4yM2IMPPmhf+9rXyt2F6jldvHix9MJmZmZsa2vL5ufnwzSeDAwM2Pj4eNtPN6SKQeQwJr5WN5+mYBGxVa+eueJN4LrGPid/75lueB05YJhK0y0QqWvIvDrkeKZNPcMmXnuT0HNTj0TLiNrpgYaXbx2ykaM3de5FEoEl36t6NldSpLdOhOCbCbTMOgSuzc1N+8pXvmK33XabnTp1ymZmZuzxxx8v729tbdkTTzxhb33rW83M7KGHHrK+vr62NOfPn7dnnnmmTPNqSIqJRek5bXQ/kiZeQp1yIvDRZ5pMyFxj2U1vySx//aKpB+FN5gi4cydxNw1+rkedU5ec8iLJZf05bD9VL86zDrHJBb3Us3UMtRdizH0u5aWm6qnp6kodXfpmBC0zs946iT/0oQ/Z937v99odd9xhFy9etF/8xV+0paUle9/73metVsseeeQR+9jHPmb33Xef3Xffffaxj33MhoeH7Yd/+IfNzGxiYsJ+7Md+zD74wQ/a4cOHbXp62j70oQ+VocdXSzA4PT094eSrG5vmtHWl07ybKLw3iTy2XVVuVdgzp45efjl1zh0j5AnD5oFgHdLQrTHpNDSXyiOH5KTqlpuXdz/Vv1VjnFPXHN3N1bem0s2wbzS23P5O+itVHgPqN8t6lie1gOvMmTP2Qz/0Q3b58mU7evSofed3fqf9zd/8jd15551mZvazP/uztr6+bj/5kz9p8/Pz9pa3vMU+//nP29jYWJnHr/zKr1hvb6+9973vtfX1dXv7299un/70p+3QoUPdbVkD2dvbK41ZlRuv7r6mr1KwuiGCVNoq467/p9JH7ahTH82P03gMMzfM2SlApPLuptQNFzYF8boAUiWp5+rmqQRGn+9GO9SQV5EsvZaT5maLN67dDFs3AS+zV+/F4lxpFd+MfmCFLC0t2cTExE3L3wsxRAoQKV1Thhvl0c0JVQe4cu9pmpRHU5Vnbl/n3ld2j7+r2pGTrpM86o5pnX7pBLxygauJ4e9Wm5ukz5mrTQGvU7lZgFlHD1CHVxq0FhcXa+9bqOVxfbPIzcZaNrypMqOwWErJo3s56aK0TaROe6LnUqEbZnt1PKTcZ+p4mVqnyAvMGQuta+p+qk5R2CdXJ6rup/ooJ4RWx4PX/3NCyrlSx2vg9FE5Odd0PGDIc3SzTmi7ql7dkLpe16vhxzQpc18CFx8hdTOlqkPrToJU2pSBqpNnjtQ1VnXqwdeaMLcm/VX1rD7Xrf5toh9V93IAU+93Cj5Nn+lm/imp+0xT0K1K04Sg1L1/M+TVKLOOLC8v146g7ctQ4d7enj333HP2+te/3k6fPt217fG3kiwtLdnJkycP+ieQg/5Jy0H/pOWgf6qlqo+KorDl5WWbnZ21np56G9z3pcfV09Njt99+u5lZV9/ruhXloH/SctA/aTnon7Qc9E+1pPqo6V6FW/57XAdyIAdyIAdya8kBcB3IgRzIgRzIvpJ9C1wDAwP2kY98xAYGBl7tqnxTykH/pOWgf9Jy0D9pOeifarmZfbQvN2ccyIEcyIEcyGtX9q3HdSAHciAHciCvTTkArgM5kAM5kAPZV3IAXAdyIAdyIAeyr+QAuA7kQA7kQA5kX8kBcB3IgRzIgRzIvpJ9CVy/9Vu/ZadOnbLBwUF76KGH7H//7//9alfpFZG/+qu/su/93u+12dlZa7Va9t/+239ru18UhX30ox+12dlZGxoasu/6ru+yZ599ti3N5uamvf/977cjR47YyMiIfd/3fZ+dOXPmFWzFzZNHH33UvuM7vsPGxsbs2LFj9i//5b+05557ri3Na7mPPvWpT9kb3vCG8iSDhx9+2P7H//gf5f3Xct948uijj1qrde07g5DXch999KMfbfvsU6vVspmZmfL+K9o3xT6Txx57rOjr6yt+53d+p/iHf/iH4gMf+EAxMjJSvPTSS6921W66/Omf/mnx8z//88VnP/vZwsyKz33uc233P/7xjxdjY2PFZz/72eLpp58ufuAHfqC47bbbiqWlpTLNT/zETxS333578fjjjxdf+tKXin/6T/9p8cY3vrHY2dl5hVvTffnu7/7u4vd+7/eKZ555pnjqqaeK7/me7ynuuOOOYmVlpUzzWu6jP/7jPy7+5E/+pHjuueeK5557rvi5n/u5oq+vr3jmmWeKonht943K3/7t3xZ33XVX8YY3vKH4wAc+UF5/LffRRz7ykeL+++8vzp8/X/5cvHixvP9K9s2+A65//I//cfETP/ETbde+9Vu/tfh3/+7fvUo1enVEgWtvb6+YmZkpPv7xj5fXNjY2iomJieI//sf/WBRFUSwsLBR9fX3FY489VqY5e/Zs0dPTU/zZn/3ZK1b3V0ouXrxYmFnxxBNPFEVx0EeeTE1NFf/pP/2ng74hWV5eLu67777i8ccfL972treVwPVa76OPfOQjxRvf+Eb33ivdN/sqVLi1tWVPPvmkvetd72q7/q53vcu+8IUvvEq1+uaQF154webm5tr6ZmBgwN72treVffPkk0/a9vZ2W5rZ2Vl74IEHbsn+W1xcNDOz6elpMzvoI5bd3V177LHHbHV11R5++OGDviH5qZ/6Kfue7/kee8c73tF2/aCPzL72ta/Z7OysnTp1yn7wB3/QvvGNb5jZK983++p0+MuXL9vu7q4dP3687frx48dtbm7uVarVN4eg/V7fvPTSS2Wa/v5+m5qauiHNrdZ/RVHYz/zMz9g/+Sf/xB544AEzO+gjM7Onn37aHn74YdvY2LDR0VH73Oc+Z69//etLw/Fa7hszs8cee8y+9KUv2Re/+MUb7r3W9ectb3mL/f7v/7697nWvswsXLtgv/uIv2lvf+lZ79tlnX/G+2VfABdGviBaJL4u+1qRJ39yK/ffTP/3T9vd///f213/91zfcey330bd8y7fYU089ZQsLC/bZz37W3ve+99kTTzxR3n8t983p06ftAx/4gH3+85+3wcHBMN1rtY/e/e53l38/+OCD9vDDD9s999xjn/nMZ+w7v/M7zeyV65t9FSo8cuSIHTp06AZ0vnjx4g1I/1oT7O5J9c3MzIxtbW3Z/Px8mOZWkPe///32x3/8x/YXf/EXduLEifL6QR+Z9ff327333mtvfvOb7dFHH7U3vvGN9mu/9msHfWPXQlkXL160hx56yHp7e623t9eeeOIJ+/Vf/3Xr7e0t2/ha7iOWkZERe/DBB+1rX/vaK64/+wq4+vv77aGHHrLHH3+87frjjz9ub33rW1+lWn1zyKlTp2xmZqatb7a2tuyJJ54o++ahhx6yvr6+tjTnz5+3Z5555pbov6Io7Kd/+qftj/7oj+x//a//ZadOnWq7f9BHN0pRFLa5uXnQN2b29re/3Z5++ml76qmnyp83v/nN9iM/8iP21FNP2d133/2a7yOWzc1N+8pXvmK33XbbK68/tbZyfBMItsP/7u/+bvEP//APxSOPPFKMjIwUL7744qtdtZsuy8vLxZe//OXiy1/+cmFmxSc/+cniy1/+cvkqwMc//vFiYmKi+KM/+qPi6aefLn7oh37I3Y564sSJ4s///M+LL33pS8U/+2f/7JbYqlsURfFv/s2/KSYmJoq//Mu/bNuyu7a2VqZ5LffRhz/84eKv/uqvihdeeKH4+7//++Lnfu7nip6enuLzn/98URSv7b6JhHcVFsVru48++MEPFn/5l39ZfOMb3yj+5m/+pnjPe95TjI2Nlbb3leybfQdcRVEUv/mbv1nceeedRX9/f/Ht3/7t5XbnW13+4i/+ojCzG37e9773FUVxbUvqRz7ykWJmZqYYGBgo/r//7/8rnn766bY81tfXi5/+6Z8upqeni6GhoeI973lP8fLLL78Krem+eH1jZsXv/d7vlWley330oz/6o+W8OXr0aPH2t7+9BK2ieG33TSQKXK/lPsJ7WX19fcXs7Gzx/d///cWzzz5b3n8l++bge1wHciAHciAHsq9kX61xHciBHMiBHMiBHADXgRzIgRzIgewrOQCuAzmQAzmQA9lXcgBcB3IgB3IgB7Kv5AC4DuRADuRADmRfyQFwHciBHMiBHMi+kgPgOpADOZADOZB9JQfAdSAHciAHciD7Sg6A60AO5EAO5ED2lRwA14EcyIEcyIHsKzkArgM5kAM5kAPZV/L/Aw4ceq6qvp54AAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from utils.plot_utils import visualize_scan\n", "\n", "visualize_scan(\n", " image=np.flipud(np.transpose(ct_visual,(1,0,2))), \n", " mask=np.flipud(np.transpose(seg_proc_visual,(1,0,2))), \n", " interval=5, \n", " pause_time=0.1, \n", " show_slices=None, \n", " slice_dim=\"last\")" ] }, { "cell_type": "markdown", "id": "05fd55a9-23ac-4a36-8133-ac11b61dbd7a", "metadata": {}, "source": [ "## ๐Ÿš€ Next: Steps 5-8: Vessel analysis and reconstruction app.\n", "\n", "In this notebook we prepared the data, and made it ready for ingestion within the `vessel geometry analysis and reconstruction` app.\n", "\n", "**Please refer to the [README.md](README.md) for more details.**\n", "\n", "\n", "### Step-by-Step Instructions\n", "\n", "At this point all necessary data is available in `outputs` folder\n", "\n", "#### 1. Required Files\n", "\n", "Clone the app from the github repository [Vessel geometry analysis and 3D reconstruction repository](https://github.com/ai-solution-eng/hosted-trials/tree/kaiser/kaiser)\n", "\n", "The app expects files to be organized as follows\n", "\n", "> requirements.py\n", "> app.py\n", "\n", "> analysis_pipeline.py\n", "\n", "> outputs\n", ">> nifti_data \n", ">>> testing\n", ">>>> FLARETs_0050_0000.nii.gz (The Nifti file with the scan, used to extract acquisition paramenters.)\n", "\n", ">> pointclouds\n", ">>> testing\n", ">>>> FLARETs_0050_0000.npz (The npz file with 3D point cloud with real-world coordinates and colors.)\n", "\n", ">> processed_segmentations\n", ">>> testing\n", ">>>> FLARETs_0050_0000.npz (The npz file with 3D NumPy array of the final vessel segmentation.)\n", "\n", "> .... etc..\n", "\n", "and so forth for every patients in the dataset.\n", "\n", "\n", "**Action:**\n", "- Ensure the folder is structured as expected.\n", "\n", "#### 2. Set Up a Python Virtual Environment\n", "\n", "Using a virtual environment is crucial to avoid conflicts with other projects. We recommend using Python 3.12.\n", "\n", "**Action:**\n", "Open your terminal (Command Prompt or PowerShell on Windows, Terminal on macOS/Linux), navigate to your new folder, and run the following commands:\n", "\n", "```bash\n", "# Navigate to your project folder\n", "cd path/to/your/Vessel_Analysis\n", "\n", "# Create a virtual environment named 'env'\n", "# On Windows:\n", "python -m venv env\n", "\n", "# On macOS / Linux:\n", "python3.12 -m venv env\n", "\n", "# Activate the virtual environment\n", "# On Windows:\n", ".\\env\\Scripts\\activate\n", "\n", "# On macOS / Linux:\n", "source env/bin/activate\n", "```\n", "Your terminal prompt should now be prefixed with `(env)`, indicating the environment is active.\n", "\n", "#### 3. Install Dependencies\n", "\n", "Install the required Python libraries using the `requirements.txt` file.\n", "\n", "**Action:**\n", "With your virtual environment active, run the following command:\n", "```bash\n", "pip install -r requirements.txt\n", "```\n", "\n", "#### 3-b On windows \n", "\n", "May be necessary to install [Microsoft Visual C++ Redistributable](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170)\n", "\n", "#### 4. Run the Analysis Script\n", "\n", "**Action:**\n", "\n", "```bash\n", "streamlite run app.py\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }