{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# AI at the cutting edge of Medical Imaging","metadata":{"id":"mcH200KZ07Sl"}},{"cell_type":"markdown","source":"Artificial intelligence (AI) has seen incredible advances in recent years with applications in various fields, including healthcare. This report provides a thorough overview of the developments in image and video data processing in the field of medicine produced by the AI community. AI has completely changed medical imaging, diagnosis, and therapy by leveraging machine learning (ML) and deep learning (DL) methods.\nWe have extensively collected and carefully examined a wide range of research publications over the previous two years to present a thorough overview, focusing on the most recent trends in this dynamic field. Our main objective is to raise awareness about how AI can drastically improve the precision, effectiveness, and patient outcomes of medical imaging.\nWe address the developments in computer vision, exploring the latest methods like U-Nets, transfer learning, multi-modal image fusion, etc. These developments have made it possible to extract useful information from medical images, enabling the early diagnosis of diseases, the planning of personalised treatments, and image-guided therapies.\nWith the help of this report, we hope to provide a comprehensive overview of the major discoveries, developments, and trends in AI-driven image and video data processing in the medical industry. Understanding the advancements made in this rapidly developing field helps us to foresee the enormous opportunities and difficulties that lie ahead, ultimately paving the way for innovations in the field of medicine.\n","metadata":{"id":"jPMtYNvIoqzz"}},{"cell_type":"markdown","source":"**The Motivation behind choosing Medical Imaging**\n\nMedical imaging procedures like X-rays, CT scans, and MRIs can provide valuable insights about the interior organs and irregularities in the human body. We can improve the precision and effectiveness of diagnosis, resulting in prompt and successful therapies. The application of AI algorithms is well suited to the field of medicine because it produces enormous amounts of imaging data every day. Advanced techniques are needed to extract useful information from the volume and complexity of medical imaging data, which will help medical practitioners make precise clinical judgements. AI algorithms can find minute patterns or indicators in medical imaging that may be difficult for humans to see, enabling early intervention and individualized treatment programmes. Image-guided therapies and interventions can be facilitated by AI-based analysis of medical pictures. Minimally invasive operations are made possible by precise localization and mapping of anatomical structures or target locations within the body, which lowers risks and enhances patient safety. Medical imaging data can be combined with other patient information including genomic information and electronic health records (EHRs). By utilizing AI in image/video analysis, we may gain insightful knowledge by fusing imaging data with other patient-specific data, resulting in more thorough and individualized healthcare. We believe that the field of image/video analysis using AI finds its most useful application in the field of medicine, in assisting doctors and researchers as well as performing detailed tasks that humans are unable to perform themselves. We are immensely inspired by the recent innovations through AI in medical imaging and hence wish to highlight the recent advancements in the same.","metadata":{}},{"cell_type":"markdown","source":"# Dataset Collection","metadata":{"id":"2f8zoA3TqwAY"}},{"cell_type":"markdown","source":"We collected the dataset by webscraping google scholar website using selenium for medical imaging ieee from 2021 to 2023. The dataset consists of Title, Year and Abstracts.","metadata":{"id":"CGEUSoku7sjG"}},{"cell_type":"code","source":"'''import time\nimport os\nimport pandas as pd\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport pandas as pd\n\ndriver_path = 'chromedriver_win32\\\\chromedriver.exe'\nservice = Service(driver_path)\ndriver = webdriver.Chrome(service=service)\n\n# Initialize a list to store the extracted information\npaper_info = []\n\nwhile True:\n\n    url = f\"https://scholar.google.com/scholar?as_ylo=2021&as_yhi=2023&start=0&q=topic+modelling+site%3Aieeexplore.ieee.org\"\n    driver.get(url)\n    # Find the list of research papers\n    papers = driver.find_elements(By.CLASS_NAME, \"gs_r.gs_or.gs_scl\")\n    # Filter out the unwanted paper\n    papers = [paper for paper in papers if \"gs_qsuggest_wrap\" not in paper.get_attribute(\"class\")]\n\n    # Scrape the title, year, abstract, and DOI link of each research paper\n    for paper in papers:\n        # Find the appropriate elements to extract the desired information\n        href = paper.find_element(By.CLASS_NAME, \"gs_rt\").find_element(By.TAG_NAME, \"a\").get_attribute(\"href\")\n\n        # Visit the link to extract additional information\n        driver.get(href)\n\n        title_element = driver.find_element(By.CLASS_NAME, \"document-title.text-2xl-md-lh\").find_element(By.TAG_NAME, \"span\")\n        title = title_element.text\n        print(\"Title:\",title)\n\n        # Extract the year\n        wait = WebDriverWait(driver, 10)\n        year_element = wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \".u-pb-1.doc-abstract-pubdate, .u-pb-1.doc-abstract-dateadded\")))\n\n        year = year_element.text\n        year = year.split()[-1]\n        print(\"Year:\",year)\n\n        # Extract the full abstracts\n        full_abstract_elements = driver.find_elements(By.CLASS_NAME, \"u-mb-1\")\n        abstracts = []\n        for i in range(1, len(full_abstract_elements)):\n            full_abstract_element = full_abstract_elements[i]\n            full_abstract = full_abstract_element.text\n            abstracts.append(full_abstract)\n            print(full_abstract)\n\n        # Store the extracted information in a dictionary\n        paper_info.append({\"Title\": title, \"Year\": year, \"Abstracts\": abstract})\n\n        driver.back()\n\n\n\n    # Close the browser\n    driver.quit()\n    # Convert the list of dictionaries to a pandas DataFrame\n    df = pd.DataFrame(paper_info)\n\n    # Save the DataFrame to a CSV file in append mode\n    csv_file = 'scraped_data.csv'\n    mode = 'a' if os.path.isfile(csv_file) else 'w'\n    with open(csv_file, mode, newline='', encoding='utf-8') as file:\n        df.to_csv(file, index=False, header=file.tell() == 0)'''","metadata":{"id":"w4pNXRRfsaV6","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We saved the dataset into a csv and uploaded it to google drive, which we now imported to the colab notebook","metadata":{"id":"as_MGLhL87K2"}},{"cell_type":"code","source":"import pandas as pd\nfile_path = '/kaggle/input/research-papers-medical-imaging/scraped_data.csv'\ndf = pd.read_csv(file_path)","metadata":{"id":"ueq0O7GqbzRC","execution":{"iopub.status.busy":"2023-07-12T07:31:47.425908Z","iopub.execute_input":"2023-07-12T07:31:47.426486Z","iopub.status.idle":"2023-07-12T07:31:47.492669Z","shell.execute_reply.started":"2023-07-12T07:31:47.426453Z","shell.execute_reply":"2023-07-12T07:31:47.491782Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"id":"79bmZp0U8Hgy","outputId":"0b7fa143-7c7e-47e8-a501-b0ae92391d39","execution":{"iopub.status.busy":"2023-07-12T07:31:47.494072Z","iopub.execute_input":"2023-07-12T07:31:47.494394Z","iopub.status.idle":"2023-07-12T07:31:47.514017Z","shell.execute_reply.started":"2023-07-12T07:31:47.494363Z","shell.execute_reply":"2023-07-12T07:31:47.513090Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Analyzing Publications in Medical Imaging under the Machine Learning Sectore from 2021-2023","metadata":{"id":"BQ6dfHTJ-Tj6"}},{"cell_type":"code","source":"# Group the papers by year\ngroups = df.groupby(\"Year\")\n\n# Determine the size of each group\ncounts = groups.size()\n\n# Visualise the counts as a bar plot\nimport matplotlib.pyplot as plt\n%matplotlib inline\ncounts.plot(kind='bar')\nplt.title(\"Machine Learning Publications since 2021\")\nplt.xlabel(\"Year\");\nprint(groups,)","metadata":{"id":"DGf8--b5cPSk","outputId":"0588797d-828c-4a9f-e110-64e29bac4748","execution":{"iopub.status.busy":"2023-07-12T07:31:47.515417Z","iopub.execute_input":"2023-07-12T07:31:47.516027Z","iopub.status.idle":"2023-07-12T07:31:47.842310Z","shell.execute_reply.started":"2023-07-12T07:31:47.515995Z","shell.execute_reply":"2023-07-12T07:31:47.841428Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Processing Title to remove punctutations and convert to lowercase","metadata":{"id":"b1HnsrNJ-dkJ"}},{"cell_type":"code","source":"# Load the regular expression library\nimport re\n\n# Print the titles of the first rows\nprint(df['Title'].head())\n\n# Remove punctuation\ndf['title_processed'] = df['Title'].map(lambda x: re.sub('[,\\.!?]', '', x))\n\n# Convert the titles to lowercase\ndf['title_processed'] = df['title_processed'].map(lambda x: x.lower())\n\n# Print the processed titles of the first rows\nprint(df['title_processed'].head())","metadata":{"id":"yo1krmsTcsR1","outputId":"a86b3d90-6aab-492f-a9a3-3df717e765f1","_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Word Cloud for most used words in Title","metadata":{"id":"XEkjf0jr-lY3"}},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,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)","metadata":{"id":"l9sYYjjd4mcz"}},{"cell_type":"markdown","source":"Processing Abstracts to remove punctutations and convert to lowercase","metadata":{"id":"VaBr8CVv-uUZ"}},{"cell_type":"code","source":"# Load the regular expression library\nimport re\nimport pandas as pd\n\n# Remove punctuation and brackets\ndf['Abstracts_processed'] = df['Abstracts'].map(lambda x: re.sub('[,\\.!?]', '', x))\ndf['Abstracts_processed'] = df['Abstracts_processed'].map(lambda x: re.sub(r'^\\[|\\]$', '', x))\n\n# Remove single quotes and double quotes\ndf['Abstracts_processed'] = df['Abstracts_processed'].map(lambda x: re.sub(r\"['\\\"]\", '', x))\n\nfor i in range(len(df)):\n  df['Abstracts_processed'][i] = df['Abstracts_processed'][i][11:]\n\n# Convert the abstracts to lowercase\ndf['Abstracts_processed'] = df['Abstracts_processed'].str.lower()\n\n# Print the processed abstracts of the first rows\nprint(df['Abstracts_processed'].head())\n","metadata":{"id":"YjXFfEs4fS9s","outputId":"07ab081a-85eb-4aff-ba6f-3e71db82d014","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Analyzing the type of models used in general","metadata":{"id":"e9IaXq3D_l-Z"}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\n# Create a boolean mask to filter rows based on the combined search condition\nmask = df[\"Abstracts_processed\"].str.contains(r\"semi(\\s)?supervised|unsupervised|su\", case=False, regex=True) | \\\n       df[\"title_processed\"].str.contains(r\"semi(\\s)?supervised|unsupervised|su\", case=False, regex=True)\n\n# Apply the mask to the DataFrame and count the occurrence of each type\ntype_counts = df[mask].apply(lambda x: \"Semi-Supervised\" if \"semi\" in x[\"Abstracts_processed\"] or \"semi\" in x[\"title_processed\"]\n                                         else \"Supervised\" if \"supervised\" in x[\"Abstracts_processed\"] or \"supervised\" in x[\"title_processed\"]\n                                         else \"Unsupervised\", axis=1).value_counts()\n\n# Plot the counts as a bar plot\ntype_counts.plot(kind='bar')\nplt.title(\"Types of Models Used\")\nplt.xlabel(\"Type\")\nplt.ylabel(\"Count\")\nplt.show()\n","metadata":{"id":"IVwPF18jW3b-","outputId":"17837ddf-477d-4fa6-9202-a7eb31f20334","execution":{"iopub.status.busy":"2023-07-12T07:31:48.265650Z","iopub.execute_input":"2023-07-12T07:31:48.266013Z","iopub.status.idle":"2023-07-12T07:31:48.605796Z","shell.execute_reply.started":"2023-07-12T07:31:48.265981Z","shell.execute_reply":"2023-07-12T07:31:48.604878Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Extracting specific models used from abstracts and titles","metadata":{"id":"kyZpdmvo-zzt"}},{"cell_type":"code","source":"import re\nimport pandas as pd\n\n# Function to extract models or methods mentioned in the text\ndef extract_models(text):\n    # Define patterns for common model or method names\n    model_patterns = [\n        r\"U-Net\",\n        r\"ResNet\",\n        r\"GAN\",\n        r\"SVM\",\n        r\"DenseNet\",\n        r\"VGGNet\",\n        r\"InceptionNet\",\n        r\"EfficientNet\",\n        r\"MobileNet\",\n        r\"V-Net\",\n        r\"DeepLab\",\n        r\"Mask R-CNN\",\n        r\"R-CNN\",\n        r\"NASNet\",\n        r\"SE-Net\",\n        r\"Xception\",\n        r\"SR\",\n        r\"Transformer\",\n        r\"DS-TransUNet\",\n        r\"MedMNIST\",\n        r\"IoMT\",\n        r\"DLaST\",\n        r\"MED-MNIST\",\n        r\"VinDr-CXR\",\n        r\"DCAC\",\n        r\"MAGAN\"\n    ]\n\n    mentioned_models = []\n    # Find matches of model patterns in the text\n    for pattern in model_patterns:\n        if re.search(pattern, text, flags=re.IGNORECASE):\n            mentioned_models.append(pattern)\n\n    return mentioned_models\n\n# Create a new column 'Models_Used' to store the mentioned models\ndf['Models_Used'] = \"\"\n\n# Extract and save the mentioned models for each abstract and title\nfor i, (abstract, title) in enumerate(zip(df['Abstracts_processed'], df['title_processed'])):\n    abstract_models = extract_models(abstract)\n    title_models = extract_models(title)\n    all_models = abstract_models + title_models\n    unique_models = list(set(all_models))\n    if unique_models:\n        df.at[i, 'Models_Used'] = ', '.join(unique_models)\n    else:\n        df.at[i, 'Models_Used'] = \"NA\"\n","metadata":{"id":"BNCNS_93yDMf","execution":{"iopub.status.busy":"2023-07-12T07:31:48.607123Z","iopub.execute_input":"2023-07-12T07:31:48.608189Z","iopub.status.idle":"2023-07-12T07:31:49.307841Z","shell.execute_reply.started":"2023-07-12T07:31:48.608152Z","shell.execute_reply":"2023-07-12T07:31:49.306883Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head(5)","metadata":{"id":"kBxQ7kXA0Guv","outputId":"9979b89a-8993-4607-9775-c938fa716af3","execution":{"iopub.status.busy":"2023-07-12T07:31:49.309156Z","iopub.execute_input":"2023-07-12T07:31:49.309521Z","iopub.status.idle":"2023-07-12T07:31:49.326423Z","shell.execute_reply.started":"2023-07-12T07:31:49.309489Z","shell.execute_reply":"2023-07-12T07:31:49.325494Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Plotting Graph to analyze most used models","metadata":{"id":"ak1ngD5XLQun"}},{"cell_type":"code","source":"# Remove NA values and \"NA\" string from the \"Models_Used\" column\ndf_cleaned = df.dropna(subset=[\"Models_Used\"])\ndf_cleaned = df_cleaned[df_cleaned[\"Models_Used\"] != \"NA\"]\n\n# Split multiple models into separate rows\ndf_split = df_cleaned.assign(Models_Used=df_cleaned[\"Models_Used\"].str.split(\", \")).explode(\"Models_Used\")\n\n# Group the papers by models used\ngroups = df_split.groupby(\"Models_Used\")\n\n# Determine the size of each group\ncounts = groups.size()\n\n# Visualize the counts as a bar plot\nimport matplotlib.pyplot as plt\n%matplotlib inline\ncounts.plot(kind='bar')\nplt.title(\"Models Used\")\nplt.xlabel(\"Models\")\nplt.show()\n","metadata":{"id":"vlsh4tkzLatS","outputId":"d116f799-145d-4c64-d947-39942697a82f","execution":{"iopub.status.busy":"2023-07-12T07:31:49.327941Z","iopub.execute_input":"2023-07-12T07:31:49.328274Z","iopub.status.idle":"2023-07-12T07:31:49.711246Z","shell.execute_reply.started":"2023-07-12T07:31:49.328243Z","shell.execute_reply":"2023-07-12T07:31:49.710251Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Analyzing the frequency of models used from 2021-2023","metadata":{"id":"t6kRCJ2a_pdD"}},{"cell_type":"code","source":"df = df.dropna(subset=[\"Models_Used\"])\ndf = df[df[\"Models_Used\"] != \"NA\"]\n\n# Group the papers by publication year and count the frequency of specific keywords or topics\nkeyword_counts = df.groupby(\"Year\")[\"Models_Used\"].apply(lambda x: x.explode().value_counts().nlargest(10))\n\n# Split multiple models into separate rows\ndf = df.assign(Models_Used=df_cleaned[\"Models_Used\"].str.split(\", \")).explode(\"Models_Used\")\n\n# Plot the keyword counts as a bar plot\nax = keyword_counts.unstack().plot(kind='bar')\nplt.title(\"Model Trends\")\nplt.xlabel(\"Year\")\nplt.ylabel(\"Frequency\")\n\n# Move the legend to the right of the graph\nplt.legend(title=\"Keywords\", bbox_to_anchor=(1.02, 1), loc=\"upper left\")\n\nplt.show()\n","metadata":{"id":"ANM9mUgPsU9L","outputId":"bc329311-174f-49df-c21b-7ab68c6206dc","execution":{"iopub.status.busy":"2023-07-12T07:31:49.712823Z","iopub.execute_input":"2023-07-12T07:31:49.713185Z","iopub.status.idle":"2023-07-12T07:31:50.168408Z","shell.execute_reply.started":"2023-07-12T07:31:49.713152Z","shell.execute_reply":"2023-07-12T07:31:50.167465Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Understanding how the maturity of research has increased over the years","metadata":{"id":"y4n_57H7_vaJ"}},{"cell_type":"code","source":"# Calculate the length of abstracts\ndf['Abstract_Length'] = df['Abstracts_processed'].str.len()\n\n# Group the papers by publication year and calculate the average abstract length\naverage_length = df.groupby('Year')['Abstract_Length'].mean()\n\n# Plot the average abstract length over the years\nplt.plot(average_length.index, average_length.values)\nplt.title('Average Abstract Length over the Years')\nplt.xlabel('Year')\nplt.ylabel('Average Abstract Length')\n\n# Set the x-axis ticks and labels\nplt.xticks(average_length.index)\n\nplt.show()\n","metadata":{"id":"1f3oyoGvwlPq","outputId":"a31b0c9f-3e76-4feb-c247-e52baa610ba2","execution":{"iopub.status.busy":"2023-07-12T07:31:50.169821Z","iopub.execute_input":"2023-07-12T07:31:50.170686Z","iopub.status.idle":"2023-07-12T07:31:50.417392Z","shell.execute_reply.started":"2023-07-12T07:31:50.170652Z","shell.execute_reply":"2023-07-12T07:31:50.416505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The increase in abstract length over the years may be related to maturity of research papers. As the study progresses, researchers often tend to add more information to convey the significance, methodology, and conclusions of their work. Longer abstracts may indicate more comprehensive and mature research, as authors aim to provide a comprehensive overview of the contributions, significance, and potential impact of their research","metadata":{"id":"lVWK5mwex37c"}},{"cell_type":"markdown","source":"# GAN","metadata":{"id":"6GNyOiaJJAz8"}},{"cell_type":"markdown","source":"**Introduction**\n\n---","metadata":{"id":"lnD-82h8MsTN"}},{"cell_type":"markdown","source":"**GANs (Generative Adversarial Networks)** have seen a lot of advancements and research over the recent years, with the most prominent being its application in the medical field. GANs are Generative networks that learn to generate believable images throughout their training and are then used to generate almost real data instances that can be used in applications where datasets are scarce or sample data is required.","metadata":{"id":"eP1qDWdBJDRq"}},{"cell_type":"markdown","source":"**Architecture**\n\n---","metadata":{"id":"I1O3ZkrzMz9H"}},{"cell_type":"markdown","source":"![download (3) 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GAN has two parts: A generator that generates image outputs from random input data and a discriminator that is given actual data and tells the difference between the actual images and that produced by the generator. The data generated by the generator acts as negative training examples for the discriminator and it penalizes the generator for creating unrealistic instances. Over time, the generator and discriminator get better at their jobs by competing with each other and at convergence, the discriminator should have 50% accuracy of prediction, then we can say that the generator has succeeded successfully.\n","metadata":{"id":"XX_aoI-SM474"}},{"cell_type":"markdown","source":"**Advancements**\n\n---","metadata":{"id":"yMqMTvbMNDWt"}},{"cell_type":"markdown","source":"GANs are still an emerging technology and have a wide scope of research, but over recent years, there have been significant advancements in this field. The most prominent include:\n\n*   Improved stability: GANs are extremely difficult to train perfectly and face many problems like vanishing gradient, fleeting convergence, and mode collapsing into degenerate models. Recent works have focused on finding optimal solutions to such problems. These remedies include:\n\n 1.   Wasserstein loss: To train the discriminator without getting stuck in a local optimum, hence solving vanishing gradients as well as mode collapse.\n 2.   Unrolled GANs: To use the current output as well as outputs from the future generation of discriminators, so the generator does not get stuck on over-optimizing a single instance.\n 3.   Minibatch Discrimination: To prevent the generator from collapsing, entire mini-batches are discriminated against rather than individual samples.\n\n*   Improved realism: Due to extensive advancements in technology and resources, GANs can now be trained to generate such realistic images that it is hard to distinguish from real images. These details are critical when it comes to medical applications and hence the practical use of GANs in the medical field has increased extensively.\n*   New architectures and applications: GANs have advanced to now be used for text generation, audio and music synthesis and video generation, all of which find a new area for research and applications, especially in the field of medicine as discussed below.","metadata":{"id":"RQ-f9BrAOhY1"}},{"cell_type":"markdown","source":"**Applications**\n\n---","metadata":{"id":"Do0mN02VQooe"}},{"cell_type":"markdown","source":"In the field of medicine, it is difficult to leverage machine learning models for automation due to the scarcity of data. Medical scans for some rare critical illnesses are inadequate to train models on. GANs find their applications in this area from generating synthetic medical scans to segmenting image scans and synthesizing synthetic drugs, the applications are extremely useful and the scope is promising.\n1.\tSynthetic medical image scan generation: Due to the scarcity of data in medical image scans, GANs are used to train on fewer data and generate near original instances of this data, the most prominent research including generating brain scans, tumour scans, diabetic retinopathy lesions synthesis and other MRI, PET as well as X-ray scans.\n2.\tMedical image segmentation: Each detail in a medical scan is critical and hand marking or annotating the data for any radiologist is time-consuming and expensive. The feature extraction and analyzing power of GANs is such that they can be used for such a critical process and hence in the past couple of years, experts have taken to using automated medical image segmentation methods for accurate clinical reading and decision-making. Various medical scans can be processed for this purpose including MRI, CT scans, Ultrasound imaging, X-ray scans and RGB imaging. When talking about specific anatomical scans, GANs find their applications in brain tumour segmentation, cardiac segmentation, liver tumour segmentation, retina diseases segmentation, breast cancer segmentation, skin lesions segmentation, microscopic segmentation, lung disease segmentation, orthopaedic diseases segmentation and multi-organ segmentation.\n3.   Synthetic Molecule generation: Researchers have been using GANs to generate models of synthetic molecules and drugs used in medicine and many publications have been made in recent years on this. Due to the criticality of this application, it is not yet extensively used in large sectors but it is proving to be a promising area for development.\nThese applications may eventually result in an overall reduction in the cost of healthcare, improved automation, improved patient care and massive advancements in medical research.\n","metadata":{"id":"Zuhkeb8mQ1MB"}},{"cell_type":"markdown","source":"Credits: [Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey](https://link.springer.com/article/10.1007/s13735-022-00240-x)","metadata":{}},{"cell_type":"markdown","source":"**Future Scope**\n\n---","metadata":{"id":"TrfRKoOsYxvT"}},{"cell_type":"markdown","source":"One of the benefits of GAN models is their potential to generate high-quality clinical snapshots from low-quality or incomplete statistics. This can assist to improve the accuracy of clinical diagnoses and remedy plans, specially in cases in which the acquisition of super images is challenging. GAN models can also be used to generate artificial statistics that can be used to educate system learning models, which could assist improve the accuracy of scientific photo evaluation and analysis. Another region where GAN models are expected to have a good sized effect is in personalized medication. By using GAN models to generate patient-specific photos, doctors can understand the precise characteristics of every patient's condition and develop personalised treatment plans. This can lead to more correct diagnoses, better treatment outcomes, and in the long run, progressed patient care. GAN models can also be used to enhance the efficiency of clinical imaging by lowering the amount of records that needs to be acquired, processed, and analyzed. This can help reduce the time and price related to medical imaging, making it more reachable to patients and healthcare companies. The future scope of GANs in the medical field thus includes personalized medicine for patients, the discovery of drugs, surgical planning and virtual education for patients or medical students.","metadata":{"id":"3DHcgT24Y2Wv"}},{"cell_type":"markdown","source":"Credits: [GAN review: Models and medical image fusion applications](https://www.sciencedirect.com/science/article/abs/pii/S1566253522001865)","metadata":{}},{"cell_type":"markdown","source":"**Code**\n\n---","metadata":{"id":"VsRXq0EKVF7j"}},{"cell_type":"code","source":"#Setup\nimport numpy as np \nimport matplotlib.pyplot as plt \nfrom tqdm import tqdm\nimport cv2\nimport os\nimport seaborn as sns\nimport tensorflow as tf\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Flatten, Conv2D, Reshape, Input, Conv2DTranspose\nfrom keras.layers import Activation, LeakyReLU, BatchNormalization, Dropout, Resizing\nfrom keras.losses import BinaryCrossentropy\nfrom tensorflow.keras.applications import VGG16\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\ntry:\n    from tensorflow.keras.optimizers import Adam\nexcept:\n    from keras.optimizers import Adam","metadata":{"id":"JZdrcTTaVwQq","execution":{"iopub.status.busy":"2023-07-12T07:31:50.418763Z","iopub.execute_input":"2023-07-12T07:31:50.419130Z","iopub.status.idle":"2023-07-12T07:31:56.188969Z","shell.execute_reply.started":"2023-07-12T07:31:50.419097Z","shell.execute_reply":"2023-07-12T07:31:56.187981Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"NOISE_DIM = 100  \nBATCH_SIZE = 4 \nSTEPS_PER_EPOCH = 4000\nEPOCHS = 10\nSEED = 40\nWIDTH, HEIGHT, CHANNELS = 128, 128, 1\n\nOPTIMIZER = Adam(0.0002, 0.5)","metadata":{"id":"BdlFyihVV1bd","execution":{"iopub.status.busy":"2023-07-12T07:31:56.193958Z","iopub.execute_input":"2023-07-12T07:31:56.194628Z","iopub.status.idle":"2023-07-12T07:31:58.984858Z","shell.execute_reply.started":"2023-07-12T07:31:56.194600Z","shell.execute_reply":"2023-07-12T07:31:58.983876Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"MAIN_DIR = \"../input/brain-mri-images-for-brain-tumor-detection/yes\"","metadata":{"id":"GAc5ZiBwV7Qr","execution":{"iopub.status.busy":"2023-07-12T07:31:58.986331Z","iopub.execute_input":"2023-07-12T07:31:58.986703Z","iopub.status.idle":"2023-07-12T07:31:58.992373Z","shell.execute_reply.started":"2023-07-12T07:31:58.986665Z","shell.execute_reply":"2023-07-12T07:31:58.991439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Loading and Preprocessing the Images\ndef load_images(folder):\n    \n    imgs = []\n    target = 1\n    labels = []\n    for i in os.listdir(folder):\n        img_dir = os.path.join(folder,i)\n        try:\n            img = cv2.imread(img_dir)\n            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n            img = cv2.resize(img, (128,128))\n            imgs.append(img)\n            labels.append(target)\n        except:\n            continue\n        \n    imgs = np.array(imgs)\n    labels = np.array(labels)\n    \n    return imgs, labels","metadata":{"id":"GyKKVMGAWCx1","execution":{"iopub.status.busy":"2023-07-12T07:31:58.993771Z","iopub.execute_input":"2023-07-12T07:31:58.994496Z","iopub.status.idle":"2023-07-12T07:31:59.008276Z","shell.execute_reply.started":"2023-07-12T07:31:58.994463Z","shell.execute_reply":"2023-07-12T07:31:59.007347Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data, labels = load_images(MAIN_DIR)\ndata.shape, labels.shape","metadata":{"id":"tEmYBNR3YEOF","outputId":"a961a6e5-68bf-469f-c841-f02d164ab671","execution":{"iopub.status.busy":"2023-07-12T07:31:59.009465Z","iopub.execute_input":"2023-07-12T07:31:59.009949Z","iopub.status.idle":"2023-07-12T07:32:00.345482Z","shell.execute_reply.started":"2023-07-12T07:31:59.009916Z","shell.execute_reply":"2023-07-12T07:32:00.344573Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Generate 20 random numbers to index images from data\nnp.random.seed(SEED)\nidxs = np.random.randint(0, 155, 20)","metadata":{"id":"LKSkzoS5YTI_","execution":{"iopub.status.busy":"2023-07-12T07:32:00.346954Z","iopub.execute_input":"2023-07-12T07:32:00.347292Z","iopub.status.idle":"2023-07-12T07:32:00.352696Z","shell.execute_reply.started":"2023-07-12T07:32:00.347259Z","shell.execute_reply":"2023-07-12T07:32:00.351551Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train = data[idxs]\nX_train.shape","metadata":{"id":"MV68bK6nYk-1","outputId":"774f98a6-8df8-4e72-96e7-8398875e73c9","execution":{"iopub.status.busy":"2023-07-12T07:32:00.354153Z","iopub.execute_input":"2023-07-12T07:32:00.354742Z","iopub.status.idle":"2023-07-12T07:32:00.365140Z","shell.execute_reply.started":"2023-07-12T07:32:00.354709Z","shell.execute_reply":"2023-07-12T07:32:00.363991Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Normalize the Images\nX_train = (X_train.astype(np.float32) - 127.5) / 127.5\n\n# Reshape images\nX_train = X_train.reshape(-1, WIDTH,HEIGHT,CHANNELS)\n\n# Check shape\nX_train.shape","metadata":{"id":"CVQdty9mYrMG","outputId":"128317c2-aa8d-43bf-f055-08562bca6d66","execution":{"iopub.status.busy":"2023-07-12T07:32:00.366728Z","iopub.execute_input":"2023-07-12T07:32:00.367066Z","iopub.status.idle":"2023-07-12T07:32:00.380430Z","shell.execute_reply.started":"2023-07-12T07:32:00.367034Z","shell.execute_reply":"2023-07-12T07:32:00.379595Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Plotting The Real Images\nplt.figure(figsize=(20,8))\nfor i in range(10):\n    axs = plt.subplot(2,5,i+1)\n    plt.imshow(X_train[i], cmap=\"gray\")\n    plt.axis('off')\n    axs.set_xticklabels([])\n    axs.set_yticklabels([])\n    plt.subplots_adjust(wspace=None, hspace=None)\nplt.tight_layout()","metadata":{"id":"0YnLk3CqYvna","outputId":"1e578e55-d637-4c4d-f486-bdd96cd8c173","execution":{"iopub.status.busy":"2023-07-12T07:32:00.381994Z","iopub.execute_input":"2023-07-12T07:32:00.382531Z","iopub.status.idle":"2023-07-12T07:32:01.711048Z","shell.execute_reply.started":"2023-07-12T07:32:00.382450Z","shell.execute_reply":"2023-07-12T07:32:01.709216Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#The Architecture\ndef build_generator():\n\n    \"\"\"\n        Generator model \"generates\" images using random noise. The random noise AKA Latent Vector\n        is sampled from a Normal Distribution which is given as the input to the Generator. Using\n        Transposed Convolution, the latent vector is transformed to produce an image\n        We use 3 Conv2DTranspose layers (which help in producing an image using features; opposite\n        of Convolutional Learning)\n\n        Input: Random Noise / Latent Vector\n        Output: Image\n    \"\"\"\n\n    model = Sequential([\n\n        Dense(32*32*256, input_dim=NOISE_DIM),\n        LeakyReLU(alpha=0.2),\n        Reshape((32,32,256)),\n        \n        Conv2DTranspose(128, (4, 4), strides=2, padding='same'),\n        LeakyReLU(alpha=0.2),\n\n        Conv2DTranspose(128, (4, 4), strides=2, padding='same'),\n        LeakyReLU(alpha=0.2),\n\n        Conv2D(CHANNELS, (4, 4), padding='same', activation='tanh')\n    ], \n    name=\"generator\")\n    model.summary()\n    model.compile(loss=\"binary_crossentropy\", optimizer=OPTIMIZER, )\n\n    return model","metadata":{"id":"JTSFBJFiY7sZ","execution":{"iopub.status.busy":"2023-07-12T07:32:01.712060Z","iopub.execute_input":"2023-07-12T07:32:01.713134Z","iopub.status.idle":"2023-07-12T07:32:01.722950Z","shell.execute_reply.started":"2023-07-12T07:32:01.713099Z","shell.execute_reply":"2023-07-12T07:32:01.720909Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def build_discriminator():\n    \n    \"\"\"\n        Discriminator is the model which is responsible for classifying the generated images\n        as fake or real. Our end goal is to create a Generator so powerful that the Discriminator\n        is unable to classify real and fake images\n        A simple Convolutional Neural Network with 2 Conv2D layers connected to a Dense output layer\n        Output layer activation is Sigmoid since this is a Binary Classifier\n\n        Input: Generated / Real Image\n        Output: Validity of Image (Fake or Real)\n\n    \"\"\"\n\n    model = Sequential([\n\n        Conv2D(64, (3, 3), padding='same', input_shape=(WIDTH, HEIGHT, CHANNELS)),\n        LeakyReLU(alpha=0.2),\n\n        Conv2D(128, (3, 3), strides=2, padding='same'),\n        LeakyReLU(alpha=0.2),\n\n        Conv2D(128, (3, 3), strides=2, padding='same'),\n        LeakyReLU(alpha=0.2),\n        \n        Conv2D(256, (3, 3), strides=2, padding='same'),\n        LeakyReLU(alpha=0.2),\n        \n        Flatten(),\n        Dropout(0.4),\n        Dense(1, activation=\"sigmoid\", input_shape=(WIDTH, HEIGHT, CHANNELS))\n    ], name=\"discriminator\")\n    model.summary()\n    model.compile(loss=\"binary_crossentropy\",optimizer=OPTIMIZER, )\n\n    return model","metadata":{"id":"gsNGYXqfZCyn","execution":{"iopub.status.busy":"2023-07-12T07:32:01.724216Z","iopub.execute_input":"2023-07-12T07:32:01.728331Z","iopub.status.idle":"2023-07-12T07:32:01.737383Z","shell.execute_reply.started":"2023-07-12T07:32:01.728305Z","shell.execute_reply":"2023-07-12T07:32:01.736493Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Putting it together\nprint('\\n')\ndiscriminator = build_discriminator()\nprint('\\n')\ngenerator = build_generator()\n\ndiscriminator.trainable = False \n\ngan_input = Input(shape=(NOISE_DIM,))\nfake_image = generator(gan_input)\n\ngan_output = discriminator(fake_image)\n\ngan = Model(gan_input, gan_output, name=\"gan_model\")\ngan.compile(loss=\"binary_crossentropy\", optimizer=Adam(0.0002, 0.5))\n\nprint(\"The Combined Network:\\n\")\ngan.summary()","metadata":{"id":"_3ITF9ylZIvh","outputId":"9050184e-566a-4e9b-d75e-9753f914f9d2","execution":{"iopub.status.busy":"2023-07-12T07:32:01.738544Z","iopub.execute_input":"2023-07-12T07:32:01.739122Z","iopub.status.idle":"2023-07-12T07:32:02.150759Z","shell.execute_reply.started":"2023-07-12T07:32:01.739093Z","shell.execute_reply":"2023-07-12T07:32:02.149876Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def sample_images(noise, subplots, figsize=(22,8), save=False):\n    generated_images = generator.predict(noise)\n    plt.figure(figsize=figsize)\n    \n    for i, image in enumerate(generated_images):\n        plt.subplot(subplots[0], subplots[1], i+1)\n        if CHANNELS == 1:\n            plt.imshow(image.reshape((WIDTH, HEIGHT)), cmap='gray')    \n                                                                            \n        else:\n            plt.imshow(image.reshape((WIDTH, HEIGHT, CHANNELS)))\n        if save == True:\n            img_name = \"gen\" + str(i)\n            plt.savefig(img_name)\n        plt.subplots_adjust(wspace=None, hspace=None)\n        plt.axis('off')\n    \n    plt.tight_layout()\n    plt.show()","metadata":{"id":"BRZvLL5eZQP7","execution":{"iopub.status.busy":"2023-07-12T07:32:02.152267Z","iopub.execute_input":"2023-07-12T07:32:02.152691Z","iopub.status.idle":"2023-07-12T07:32:02.165720Z","shell.execute_reply.started":"2023-07-12T07:32:02.152653Z","shell.execute_reply":"2023-07-12T07:32:02.164717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#The Training\nnp.random.seed(SEED)\nfor epoch in range(10):\n    for batch in tqdm(range(STEPS_PER_EPOCH)):\n\n        noise = np.random.normal(0,1, size=(BATCH_SIZE, NOISE_DIM))\n        fake_X = generator.predict(noise)\n        \n        idx = np.random.randint(0, X_train.shape[0], size=BATCH_SIZE)\n        real_X = X_train[idx]\n\n        X = np.concatenate((real_X, fake_X))\n\n        disc_y = np.zeros(2*BATCH_SIZE)\n        disc_y[:BATCH_SIZE] = 1\n\n        d_loss = discriminator.train_on_batch(X, disc_y)\n        \n        y_gen = np.ones(BATCH_SIZE)\n        g_loss = gan.train_on_batch(noise, y_gen)\n\n    print(f\"EPOCH: {epoch + 1} Generator Loss: {g_loss:.4f} Discriminator Loss: {d_loss:.4f}\")\n    noise = np.random.normal(0, 1, size=(10,NOISE_DIM))\n    sample_images(noise, (2,5))\n\n#Generation\nnoise = np.random.normal(0, 1, size=(100, NOISE_DIM))\nsample_images(noise, (10,10), (24,20), save=True)","metadata":{"id":"AMsSuFb5N6hj","execution":{"iopub.status.busy":"2023-07-12T07:32:02.167651Z","iopub.execute_input":"2023-07-12T07:32:02.168632Z","iopub.status.idle":"2023-07-12T08:42:04.736683Z","shell.execute_reply.started":"2023-07-12T07:32:02.168596Z","shell.execute_reply":"2023-07-12T08:42:04.735475Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Testing Generated Sample\ngenerated_images = generator.predict(noise)\ngenerated_images.shape","metadata":{"id":"pdRHyywsZn2S","execution":{"iopub.status.busy":"2023-07-12T08:43:47.809381Z","iopub.execute_input":"2023-07-12T08:43:47.809999Z","iopub.status.idle":"2023-07-12T08:43:48.015450Z","shell.execute_reply.started":"2023-07-12T08:43:47.809962Z","shell.execute_reply":"2023-07-12T08:43:48.014485Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, axs = plt.subplots(ncols=1, nrows=1, figsize=(18,10))\n\nsns.distplot(X_train, label='Real Images', hist=True, color='#fc0328', ax=axs)\nsns.distplot(generated_images, label='Generated Images', hist=True, color='#0c06c7', ax=axs)\n\naxs.legend(loc='upper right', prop={'size': 12})\n\nplt.show()","metadata":{"id":"1hC_lJbMZtVO","execution":{"iopub.status.busy":"2023-07-12T08:43:48.016674Z","iopub.execute_input":"2023-07-12T08:43:48.017007Z","iopub.status.idle":"2023-07-12T08:43:56.205163Z","shell.execute_reply.started":"2023-07-12T08:43:48.016980Z","shell.execute_reply":"2023-07-12T08:43:56.204101Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Credit: [Generating Brain MRI Images with DC-GAN](https://www.kaggle.com/code/harshsingh2209/generating-brain-mri-images-with-dc-gan/notebook)","metadata":{"id":"xWGED5BhVJj1"}},{"cell_type":"markdown","source":"# Transformers","metadata":{"id":"EzU3rDdTcZgZ"}},{"cell_type":"markdown","source":"**Introduction**\n\n---","metadata":{"id":"Ke4L2k8GcdBz"}},{"cell_type":"markdown","source":"Transformers have been very useful and efficient in the Natural Language Processing(NLP) domain, and are quite popular for language-related tasks. The transformer architecture was originally introduced to the world of machine learning in 2017 with the paper “Attention is All You Need”. It is an encoder-decoder model that uses three important concepts:\n\n\n1.   Positional Encoding: The positions of the elements are added to the input embeddings when they are to be fed to the model. This helps in keeping track of the elements, and knowing the positions of elements concerning each other.\n2.   Attention: Since a lot of information is fed that needs to be tracked, certain elements are more important than others. So the transformer model provides the ability to keep the attention on the important elements.\n3.  Self-Attention: This establishes a sort of connection between multiple elements of the input which are related to each other, thus helping the model to understand the relationships between the elements.\n\n\nCredits: https://arxiv.org/abs/1706.03762","metadata":{"id":"Wjc3yPyitsUp"}},{"cell_type":"markdown","source":"CNN-based methods have traditionally been used for image-related tasks. They have the ability to extract the local spatial dependencies within the images very efficiently, but they can’t capture the long-range dependencies. Hence, in order to capture these dependencies and preserving contextual information, the transformer-based methods are getting more popular to solve the medical image-related problems. The tranformer models are often combined with other methods and models to leverage the advantages of various models to perform tasks like segmentation, classification, reconstruction, registration, synthesis, detection, etc.\n\nThe paper, \"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale\", introduced the Vision Transformer (ViT), which is a transformer architecture used to process images without the use of any convolutions.\n\nCredits: https://arxiv.org/pdf/2010.11929.pdf","metadata":{"id":"D6S-qJaP5MFl"}},{"cell_type":"markdown","source":"**Architecture**\n\n---","metadata":{"id":"t6WuNafktss4"}},{"cell_type":"markdown","source":"![imgonline-com-ua-CompressToSize-Y7CDMVsT43b.jpg](attachment:eb431a10-da3c-4657-a6a5-803fdd62e2e2.jpg)","metadata":{},"attachments":{"eb431a10-da3c-4657-a6a5-803fdd62e2e2.jpg":{"image/jpeg":"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Vision Transformer, the images are first transformed into image patches. The patches can be overlapping too, and the amount of this overlap can be determined by the size of these patches and the value of the stride. These patches are vectorized (converted to a vector) and passed through a dense layer. Now, the positional encodings are added to the vectors, so the resulting vector can represent the content as well as the position of the patch.\n\nThere is a CLS token, which is appended to the beginning of the input sequence at this point and is later used for classification purposes. This resultant sequence of vectors is then passed into a transformer encoder network.\n\nThe transformer encoder network comprises of a series of multi-head self attention layer followed by a dense layer. The vector obtained by the CLS token is then used for classification problems by being passed through a softmax classifier. This method requires a huge amount of data for pre-training, followed by data for fine-tuning, and finally data for testing the model. Skip-connection and normalization can be used to increase the efficiency of the model.","metadata":{"id":"YdPnpdVkt1cP"}},{"cell_type":"markdown","source":"**Advancement**\n\n---","metadata":{"id":"cvLR2dHDtv5t"}},{"cell_type":"markdown","source":"DeiT is a shortened form of ViT that is intended to be more data and computationally efficient by solving the need of ViT for a very large training dataset. A smaller Deit model is trained by a larger ViT model using a teacher-student distillation approach. The model has produced great results on several image classification benchmarks, including ImageNet, and has shown promising results in medical image analysis applications like as brain tumour segmentation.\n\nCredits: https://arxiv.org/abs/2012.12877","metadata":{"id":"z54BC1UI5qvk"}},{"cell_type":"markdown","source":"Furthermore, Swin Transformer is a hierarchical model that builds representations by combining neighbouring patches in further Transformer layers. To capture long-range dependencies in input pictures, it employs shifting window attention, a modified self-attention technique and a patch merging layer at the end. Swin Transformer blocks, which contain sub-layers for self-attention, cross-attention, feed-forward processing, and layer normalisation, make up the model. It solves the high computational power problem of ViT. Hence they are often used for high resolution images.\n\nCredits: https://arxiv.org/abs/2103.14030","metadata":{"id":"3pz_lYjP6Brv"}},{"cell_type":"markdown","source":"**Application**\n\n---","metadata":{"id":"PtJMsXcUt0GU"}},{"cell_type":"markdown","source":"* A lot of work and research has been done in the medical image classification\ndomain, and the models used are either pure transformers or hybrid transformers. Here, pure transformers comprise of those models which have architecture and structure similar to ViT. They were used to discern and identify different types of cancers, brain age, melanoma, etc.  through CT scans, MRI, X-ray, ultrasound, histopathology images and various other sources.\nHybrid transformers have been used in addition to convolutions, which help in giving attention to the local texture and extracting features, and graphs, which have been used especially in the field of network neuroscience to study the brain.\nA lot of work has been done to diagnose COVID-19 too, and this was done using CT scans, X-rays and even ultrasound clips, and since a large dataset was available for this purpose, they have been able to achieve high accuracies.\n\n* Medical image segmentation has been performed with the help of hybrid transformers and pure tranformers. Researchers have usually tried to insert transformer layers in the U-net architecture at different depths, merge transformers and CNNs, and use multi-scale features or attention mechanisms. Models like Trans-UNet and MissFormer have also been introduced, which implement Swin-Transformer and transformer blocks respectively.\n\n* In recent years, GANs have been used to tackle image synthesis problems. They often employ the encoder-decoder structure of the transformer, or use transformer blocks in their architecture. There has been research on generating PET(Positron Emission Tomography) images from MRI images (by using BERT in GAN, termed as GAN-BERT), and the reconstruction of high-quality PET images at a low-dose. Residual learning and Swin-transformers has also been implemented to make efficient models for image denoising and image synthesis.\n\n* ViT and Swin transformer have been used in the models for the purpose of image registration, which is the process of aligning two or more images in order to infer more information. Transformers have also been used for the purpose of video processing, in order to overcome the limitation of CNN to not identify long-term relationships.\n\nCredits: https://arxiv.org/pdf/2202.12165.pdf","metadata":{"id":"8xyIolaQt5y3"}},{"cell_type":"markdown","source":"**Future Scope**\n\n---","metadata":{"id":"ncDSTsMrt6Sd"}},{"cell_type":"markdown","source":"* Hybrid Models: Creating models that merge several types of transformer variations and convolutional neural networks (CNNs) to make use of the strengths of both techniques. Incorporating other techniques like GANs and UNets can also be explored more.\n\n* Multi-Modal Analysis and Image Registration: Investigating the use of transformers for evaluating multi-modal medical pictures, in which data obtained from several imaging techniques is combined to increase diagnosis accuracy and allow for a thorough analysis.\n\n* Image Synthesis and Reconstruction: This involves producing high-quality medical pictures from low-quality or missing data, hence improving image quality and providing beneficial visualisations.\n\n* Handling various size of Datasets: Creating transformer-based models that can handle large-scale medical imaging collections, which are essential for deep learning model training, and transformers can provide scalable solutions for fast processing and analysis. And also employing techniques that can successfully work even on comparatively small datasets.\n* Image Segmentation: This involves determining and separating specific regions of interest within medical imaging to aid in diagnosis, treatment planning, and disease quantification.\n","metadata":{"id":"QSV-rXI2t9au"}},{"cell_type":"markdown","source":"# U-Net","metadata":{"id":"RnvQaBKgceYi"}},{"cell_type":"markdown","source":"**Introduction**\n\n---\n\n","metadata":{"id":"_UsaQzulck_y"}},{"cell_type":"markdown","source":"**UNеt**, a dееp lеarning framеwork, has еmеrgеd as a powеrful tool for mеdical imagе sеgmеntation. In this еssay, wе will еxaminе UNеt dеsign, implеmеntation, and applications in mеdicinе, rеcеnt dеvеlopmеnts, kеy industry contributions, and futurе dеstinations for thеsе tеchnologiеs.","metadata":{"id":"EvWPFJqJck_y"}},{"cell_type":"markdown","source":"**Unet Model Architecture**\n\n---\n\n","metadata":{"id":"jjCbyJIUck_y"}},{"cell_type":"markdown","source":"UNеt was introducеd in 2015 by Ronnеbеrgеr еt al , as a convolutional nеural nеtwork (CNN) for biomеdical imagе classification. It is a modifiеd U-shapеd systеm capablе of accuratеly classifying mеdical imagеs with minimal training information. Thе UNеt systеm consists of an еncodеr and a dеcodеr. Thе еncodеr еxtracts fеaturеs from thе input imagе, whilе thе dеcodеr crеatеs a pixеl-wisе sеgmеntation map. Thе uniquе charactеristic of UNеt is to skip thе communication bеtwееn еncodеr and dеcodеr, which hеlps to prеsеrvе low and high frеquеnciеs during thе dеlivеry procеss.","metadata":{"id":"zNtRYCEAck_y"}},{"cell_type":"markdown","source":"![download (2) 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"}}},{"cell_type":"markdown","source":"The figure above uses the U-Net algorithm for image classification using keras. Here is a description of the different features and functions of the code.\n\nThe program accepts two arguments:\n1. pretrained_weights: Path to the pretrained weights that can be used to initialize the model.\n2. input_size: The size (height, width, channels) of the input images in the format. By default, it is set to (256, 256, 1) for grayscale images.\nThe function returns a collection of U-Net samples.\n\nThe encoder part of the U-Net has many convolutional layers with a minimum number of filters and max pooling operations.\nEach convolutional layer is followed by a ReLU activation function using 'equal' padding and a 'he_normal' kernel initializer.\nStumbling blocks:\n\nThe encoding layer is followed by a dropout layer with a dropout rate of 0.5, followed by a 2x2 max pooling operation.\nThen, two additional convolutional layers are used to capture more complex features.\n\nThe decoder part of U-Net contains several upsampling and convolutional layers.\nUpsampling layers is done by 2x2 upsampling using bilinear interpolation.\nSkip connections are created by combining the corresponding feature maps from the encoder side with the upsampled feature maps in the decoder.\nAfter each merger, two convolutional layers with ReLU activation are applied.\n\nThe final convolution layer consists of 2 filters followed by a 1x1 convolutional layer with a sigmoid activation function.\nA sigmoid function gives a probability map of the probability that each pixel belongs to the foreground class (segmentation mask).\nThe models are clustered using the ADAM optimizer with the number of classes 1e-4 and a binary cross-entropy loss function.\n\nIf pretrained_weights is given, the function places weights in the model before returning.\n","metadata":{"id":"HJ31hZ8vck_z"}},{"cell_type":"markdown","source":"**U-Net Advancements over the past 2 years**\n\n---\n\n","metadata":{"id":"a_isDSl-ck_z"}},{"cell_type":"markdown","source":"1. Efficient U-Net: It specializes in enhancing the performance of U-Net architectures through employing green constructing blocks in conjunction with inverted residual blocks and squeeze-and-excitation modules. It targets to lessen the computational complexity while retaining excessive segmentation performance.\n\n2. Attention U-Net: It consists of interest mechanisms into the U-Net shape to selectively emphasize informative functions and suppress inappropriate ones. This allows beautify the model's capacity to focus on relevant photo regions and beautify segmentation accuracy.\n\n3. Attention Dense U-Net: It combines the standards of interest mechanisms and dense connections. It carries dense pass connections to beautify statistics flow and interest mechanisms to capture long-range dependencies, primary to progressed segmentation outcomes.\n\n4. Cascade U-Net: It introduces a cascaded structure that includes a couple of U-Net modules. Each module is knowledgeable sequentially, with the output of one module serving because the enter to the subsequent. This cascaded method permits for added refined and correct segmentation effects.\n\n5. Hybrid U-Net: It integrates extraordinary deep gaining knowledge of models, collectively with U-Net, convolutional neural networks (CNNs), and transformers, proper into a unified structure. This hybrid method leverages the strengths of each version to enhance segmentation standard overall performance and seize each neighborhood and international contextual information.\n\n","metadata":{"id":"EhzCVTbjck_z"}},{"cell_type":"markdown","source":"**Applications**\n\n---\n\n","metadata":{"id":"zy6RRJpBck_1"}},{"cell_type":"markdown","source":"1. Organ Segmentation: U-Net has been widely used for organ segmentation in medical imaging. It can accurately describe organs such as the brain, liver, heart, kidneys and lungs from imaging modalities such as MRI and CT scans. Accurate organ segmentation allows better visualization, volume analysis, and surgical 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AaoIt8PFDc+UTiNn1xBvx8RD09bBBFRXU6tNScnTy8TMKFlaVZ2NGr1xNLvzRcjXVDs5avEtFkwkYWe9gyYRjueLhQQFRez2UnhBNazEI6fhSMPcGwih9loD4bKphE7gcRUTZfVfSHHT3DOvkVJwCl0ZGQwX86wulOm3NCPH6cyx3gaNn0/+iPc78xjTJly5jcM5yMUThVefN0l3TZNudfo5bhlFRWlLImsQuYwyS6wqL85kGLe9/ZkKUvElkpYEhFLxJUVF8twsCQsGZkrkcQBWP1O7mDLkZwHnCSQ8YwfMq5IJJE4QDSxJaziRDJ11lEU+L66XQ0hilxUzLKVey1GJHuNlCbKSi9wv8coNnKSPkq5n58BPsv/+9pYIhY3FheeSyW6wZ2nsyQic65ai0RTTlphBwFhSaaSxxfnFuTDDdOD8CNChFJVsFZY92azBnlnfh7uCYVemVVHART+JcuVdbXIVAkmmLfzBaw5kaYiWD4vQ/UBUwQpEI7QJ3HB97HaJwapIN/CsdWH691ssxMvN2cak5UiAaTi83k7YXpfEZxD7dEVnJLGivATSE3F98n4lNQ4pKZC8uEbB44QkhG627gKg6DU/gmkpginfhGk2rlSTTnScDhPmCmuiCAVANX0zYTUJ++RL9lfgRAYSXoRtzlGZCbQJAJcJIQdFgMw0DGs22W3P/QQboY3iNtKS4tLK+iAZ9hczPiJbrbD7WZBhSA4Q6wLvjINYpItknGlZqAJq9qYlWb8jKApvAU50VpXv0B4HvUEnaPwzMoGQW5RKPLLHW3n3E/Yn0Aohb9Muo15aU1sTmF1FYlkCysrs62Z4Cj4YCMKcvNJ12chjR+Iv/x8vctj5pjkspiknFeWU4Kcb3YmZY3xdO1cr0JNco6MiUkO4ryO3o6aXjFMlAv8hz3L4nWS2AqhFMeBt8JR6ptQFIO8lBOQMtkIUllGfXYfkeg1kyd1tbq4X+j4kfNLkoZ2QgxjxD+RTaVSqXYcG+hTaurUVMo3VYBvASzB1E8fU/DHPvrN+PFFyZEKR9jsCbpgjUtjpSxuaYXMbRK1f5OtFE46nigWOdzYvSsSiYpS0C5AhlRebzIFAiYOh2MKINEBa1UsI5uEr9yH3DBWsFYOthvmCVYNmtbSYvx1ooILg+aGEZO1Mcl+LNWvLMaKi9wymcQUNGLBkecFI+U0UnCTX9jR3Zgt99F6OA+msI/SW5dwcEJ6nsEgDgDxn9gtqI9SaREbR13YVF1ZWQlpeWUlIaoSMvPsWoiN+GIo/ApIK21MS2azsiBIsjtDZlO9BMrCNim/JM9gsBYq3Kk0gtolrgI7Jy10mgAW+qM1fV2TplktSPUSlZllCboAX0RwfhOSUm5H/8TItrZyQKo+ELA55GbXOKQuI/FEk9j2xXm7NPSnADc+31TGfn0irhtHE4OqFBAFWPnaN2wAcoRT8fO5mIr7prbTLzK+CFLp3AXXhl0Stp/jLo6VcrkVsVJJQBT/pkKKYfWSMjfbjVioIlZMPpyEbbPVc8pDpILGO4xAXA+SM6iphxizokImK73dIQ9ryv1eJISC5PqUawA5+HxpsyUrboaJ40rYDklF21fLurazkKDhyv0WRFAuWtd64gvsTktRR01fR58qmZESnI9Eh8sUj8fY5gKenYNHKI1/KrzxMX8DA8fKykTqNxspJmCKqixgpApzyjJVZKJI4Md4miREx5PmF4hz7RboomyiOMNGOi2eEF8bbBv3n4RxkYXnLRZMMAMea6np6Jvc1VkAp5IcW6Ml6DWSAMoZsnFLZWx3fGIpIQiSjJyAyWRj2zxO2HBr36TJXd29PWUFJres/Ys1MsxlTrQBIeTKpa3QFAHcOYGvuQ0OnUBA6GFcQsGGDRv2M/8FDD3s843rb30MIqf66DGEAorKLhpwxMPivDKrKsulcRQnmrkyqJJYNkn8G0pPCFICX1IklyIkjCWaZSKRiB3gmCFkCUKyiUOfxchD8412ca7e6/fYuMQD2kJOpGbyC8AkUyqS2DA4Yp4wWyJiIisAKwZ04sEc3ET7V0gR+WRyVoVMCuWsl7SsBCmIxLNc9oaamr6+SZHmDMF5FnCq3QdVqibk9XCkxdB+kndzbpAHMxqPk8uIQCrfwIRR4wcBy5qn4+XCQGHHp5QUCSecaRVtboHdEvRwAu4kXFthsljWHwt7Rb4050x0vNvrlKQmmnNrnNzS24WAB4EUrCAkImcgVS6VNVMoNTFstpXzxyEV8DsRcuo6Jk1uUSPY5LBFsS/0wNN8TbIimWIy+IxFSsdFQFBzkqEeUqlku4AxVhve2f/O/vffeX//uPU6Y9DwG9+4vRKmiY6LcmJtBU3d3Z11uU4zW5aIkXoiwbVdKAv39Y+lUgk23G6EhbC4Eim8vJCLUpnkU1Hcbyng8fh8nhhlYQzE/AGRSHpfUG+nTZvxg0juRsUOoMScfrhBUje8wOIKhtkAqhxsSXP8KwIV3uNiG6uimF3utARhI53EwJEXZjF0TyK3KVJ8PiIAPCGJlyy2fhD9grSj6zsXegJhUpRgNlpkD9yWvJx0qSHxE2Xw5hQ8cvrAE6TPFQw686RU7gFIhTgBdoJ5LDy2T+aOn9HK4glS3FA4IZhYIidZVtPRCc+vCMS8BTS6xegFJMCBuDiO4gqWtFTAOLVf+EjJcr6eIzeFpY6AH+m7kLqlZlqOicuS1kslyQs6/p/yW4QpIGoKXaAp44ESRVNTpsTbYZf279//Go5FOO5fdHA3joOL0sdr7+N3MFnkC+LPpwrHMXVRx4+oEVkhKja7W5V6Tz0rdgZS7LZvpiAJ+zJb7pDFmmF6JNJwechLZglIoa04GAr5C6gcyKDVqguM8HUQLl8T/tl9ersFQhl9gUqpUPFBgnmDQSejKSVKOwg9q5yJrcATyiqIkJdUpL4i50/YHBCdhRQAz2ReIdgunDapq6tmsjWRIfy86x7XZOaIOeyKM/EurRXhOdgDBJrDxYJxTt3H5TGlwtYS0Oc6FMeC7IMtRn7IDs6PpOHEMWLFO+kiwEqF5fJm8hzpIX2J2CeOJx4uzi5nJ30TglQ8t6arcxrVJTvzLS44tJDbe11I13gg1JE5pAnmFX8xc5g084wcaQAeQwBZY1dQX1KVI5aL3BBUOOLnDZ4E8WT8XMcvTaIzTzUFRAPFSUylCzSPG/a/8+Fzi+6/H3BijsOvvvrq3pOH0z/g1oMfvv8OgSpN/qUhdbHXnH46aUdHVWFLd444yHGUVjCOH7de3vbNkc0K0/sSnUySK3WIikvhqHHZAY3HBdc7q0DMU6v4PKhwjMGgXpWHciCdrqhIZaccid7u/I9QGlv5YlVenjZTp+C7OBrGTYSdAtBoE/eGACupe5yzQFTljqUyvpIKkIScBSeIJEkkoWAcMLyurNaaGiCqxp4SfkY9xSwyvzpXmvSdwdoZ8u2cu8WLK9o/SeYHrTlFBj7qN5iaQ2wUesY2URyFPQOBDvPEyPUW4Br5NWBwShnzgSdIJT9lkQXChNnPBAUCQfppL5yjE2j6Jnd0XDatzBnUg0O3kNMGpZXLaZYjlLJJJ5qlBaT0HDBLsFKeIMJhcBzOcptbwuaYJfHPPT3inowUS94vPI+yhLFMYMcFUwVkm/a//z6Zp5Mn9+7e/eLa944de+/Yq2ufWXvHM2uPrZ21dNOJ97bs3nvyuefIXsFcpS2VMB2RCS5Khwj9NR1WbWtHgwo0Ogq3i2OxCpYtEPumQIpyL+lKMnCXUiQCiMVjuQNmhMRZjGhNZ8g0KPChzne5stSZ5PjwDFhjBKj8fKcL4jbi2HIVBq1SCQPGC2qCuWqVSqFQIozPZ0J4oC9YbubYUAkHxMpKJWwurem/31a1OUQI1c1Yby78Hxex6u0tCMG7JnWYhILzPImvrSLRfibfKzifLEMQP4fU8Jm0JVm20jaEyFRs4UcqmXgaBldGgpQTRpmkE1nEqXvNgFTFGZmOr/1T9hhmylbO7ieUM0nPi16A5sLJ2Bem1TqDFkpLIXILEqRckMyWcgPu+IQhxTeG2VKQsSY/vXJG5I1yK5FJI4qfz0wIElLW5wI+ptKYapTTkRHg9D5h5fDhkydPHHvl1XnrHliFY93qR+etnjVr9cC+dfuG1EuPnThx+PBhslYAFbl/Ph+xGr4vgJRPP7mjzBA50qnVh+SAFML6CpbcVOr7hkBKeAZZMFFwyeCdoVSN4ycKCyVAKPvO1OoMBmDKwDP6/V69oiRTwVMbMhW55As6c3EjqAmETfmGPJ0aolFtgZmj10G3k1lUxORvEGUZs3LFqPDOcnrMYdTOIXXntjliqb/f70M9uURWLOG4jDAWWHkU7RntTlJbd9VMqotlCM6DmXTO2ZfWxQoucEk+yZ353EGPKE7ZtHgb9DFScDV+r4WJoRDaGJGS8odcelrz2GGyXGZUpcfOAPlziW1fqcbM9QnHSweFF80ImVpQ1tRngLPndZJlxPOg/DEITUEpK5DuGjEBIUrSLHYGkOFwuMMuIuSBKKfXExBxpZzzkGiCFJxT6edirKkMSc5kbQEnQOTgwYN7j+19ZePysbFZa1fzVz2w6tcFd921dHWuffXYpk1LCwrmK2YrhsfGlo/BeP0G4RX8wNf2t08ZV1JcdDXGxTVHrLqq7u6yXH89FPcU2HPlJtk3BVLpABdVcgm3XFTKdUhtcpOHdmFAKisX0uu8TJ1Op4CtAqTMnPKCTK1Bm1eUKcbWDIRY7roboUVBftCTBfTplJlavkca0Ovy8gw6bR4MnFanztWPA1RMVGFQQ5EVTIuNG//7rVQ7i+RuYaw6L1NoQQvG6BWDnYA6IHT+hgFCZse9mCxI+Kl6lWQiTmnONAR9zeG0UjYdQzHdXLDoCV+gPMUFRg9HLmkTCATjSW3BpyyAkFa4OUH0ovAcQfL5X0PSVTWtpoHvRcUUsjR6o9dTjj4nlP+skJhKmTLPiUBKk+uFcsLmkAS8RspxESXv5DhYDtN5IAUrxFAz54NU+7iBeg3w2AvrdOLYtTdePntwfu588bpVq8RL77p7KV+cL166adOm+Xz+QjgzCxbMwr+lS4+d3LMHxur9/cl2wcUgJWQkK3F1TW8eL7u3u4ofrIdSh5I5MmmY+02BFOPzI2CQMd0JRJQ2NRK/4DViy7WL+SptJmChUGp1YlQYSE16hdIA309cLjdpwoHf3za89eOO48N3+f2WArUBMOIF2Sy5BdbNoNARkaFDVYQeaRUvVb3y1aAL1TxLgCWLVUjkora/O5hKydzYx+r9eMnO8WoLsFqWxkldNTXZzRdacsJPoHOBS5LynZvOOiO1YMSixSbUL3mZ+Ak5OvpgVLMolUfUycsPItGVFJ4JtT/NnZF8VlNS5ExkfLGKAC8hqbFWqpECxLNR/pog5fXCF2qucNj6mdc5EZqDU+Ctd8jrHaL6IHxWF10pvcUslUhNovPRE0wDm8+FWFNBS8BAEZruf2332mPLd+xYvvyea2fMuHa2mj9/wdIFC3jz5+fmTp8+f/o6HAsXLFi4cOFi1eLRxQsXLpi/6cSxF7e8QrB6bT+oigumUSj3gEuk6mpR5Na2dETU3noRqOLiiuZiNtUGfHNiKWxlIhtoJEmA4+cp+CCI4cIUiMV28jjwk5inBDuBLCbHpsnioaUWUlKIeKXs+tDSkePT/vjxTOPvzeUel3hArSvQSKAQ0vPBjakolsrCEg+CneC48DgKeIwqQ1VTmV/OTTSzHKy47++sVBRUsJGMlvuNtFDS1bZQogdzoJ2okl4kq0zrnfoVpWUUn4MUasB9nwkkzupq25vZGlQxMc9FzcaCXvAhsFfwbim7AEixEqmznt9n6pwzUg47z86CvCet5xZeLKpIsjnBkIsh6IkIcYVC0Nx72KUVxVJHcqI10yjB8AbYchNiqaCTXFbQfoCUnF1/nkWKDF170vd53oj0Ju3k8u3BcWDtHdfuuBaIemb5jBuBKYBnBBCa/sH8ddPp+GDdugULAaXFo0OLh0aBsvlLN6199OfvHSZMpYOqi67GVEFXA6+gpLGjQeElgXBpKc7YHZB9UyDFcL1JSRiFR3KzXhfpbsgEJyFWq7KIpXNITab6eo5f7/RoOCiJKw9lQS6aTzGL128GJ3jXXXeNXLVk6W1hSF7q7ws5s1wcKTsQ9tgBSLverwnbAD2HCDIMiVxjV+ryag26yiPTGvm2tMY2Kfh7IAWzEZOLZIhvsFCcabogizR3vDKdBb1UfOdnbAVpkoLLSp4XUqSbSLb5zn0WptxUkO7oIkiVSnFFKKWtJ7PoAnmuhyEBl4YbgsjEyZH0/xQmz5LzRFjE29rg8Y7HchcJ1METyNlyDfxrJyX6KJYiTXq5uwJ7CBEIE9uNkmZYKbbNVG8zQ4HrYSJBlAUHApxwxfnizPO+JKL4KIRadvD1TUWXz5kzp+7qurq6+ZnR6hxryey56lkLVGr+6tUq3fASrW5ArVg4rFMMDA7Onj00ezbC6QUjowPz7n1m7cnn9pD7t+HiL1tY3hER52tbjzRqnWEHtC2lqO0Vyb8xkKJuCAkwfbezw0FeTmPvkc4yPtw9vt+RiCP/nSwlTTnkfVIJvphCfsuvEYa/iFD0LuNNK7/3wpYtv9uycssLW773+9//ziFh/yeJ/wIAXyhYzuHUQzsBfWAxxSJYSrGAHWRHUV5etKOvpcjj4DaXOmRxwZevtKZFnqh3yIrZHrBhTobdJnVHvj7o58jamdZ75wYzU85YKPxhqlmjD/cLz7MuhUQeQEUy5ZzF7Tu7+xCs2iRoHAO7QW0owJ1Tcz0q6/ACXkEQMPUcc8DB/Tz7QlRJGuGkpGC5K+K+i59dwmYymUNeUk1gnzA6/ehzgl5cFVyJrfS8RvC8nKg5NwgxksnE8VNRb7mL7LkTboOH0/zZPxeO57rO5hWE4117KIIihu/A2zvWX331U09dffXj6x9/fPr1DZFoYd3lV8yeq5w7e+7A3NlLZlqvmpk5e7Zytnb24Ow5l19++Rz8Ur3wkdGhdff+/Icn3jzMYKrtrP8g/HQuJe3KslsKxfnKaHdjETJqEJDCSAFSxV/3WOoM0wcRcYztuOEGydMvXrU1un7nzlMzZivm3/ZLRtSFrF9bBcnMH3pozUO3S371gv75W55//plXXz326it7Dz733Mllr2zf/txzz7323MmTu99999s/+MGPHlqz5vY1jv90OH7xCy739A04YhWJDT7o+6f44r8wGsBs6GpbG1p6s4NgrkolCOXTHU++ZBVV0oRmfo4QVAsupgMYBVROP8dECgZGuinMGC/iYUoPxlVqSBjJfmaD3HzKZw/hVEYA+hndzKdfW9IdRhtYPXMYvRYgiog0J6W2kdk2mxBlOpovyL2kexAmzAqV5ULq6nEnrBh1vSj9CpGXyXSLhDzYG2YhVr9gjev51BO5wXqpDToY5LfQ9saFgn6jxwTNpqb0nBZXZ9JOpBjxCdOnz4jNUynC07Jl25e98vrmaPX69Y/XLplZW2R9PDs7Eo00RKorC9EVejYYLLXSkFlCrZeREdfNNmiLrHXWy4u0SrVCzZ+rHFi9etu8RzedOLAM0NzfDsWfD0EVVVqRJPBTK1NWmaMqUDZ1N+iMqF+GoK20IiayfQNIdMF4SdTf2JIbfvGrp1/ccXSX6vpTp3Z2zhAHEoyOmIp5E0gjsR6y/erdd59+YcuLa1985lUgCqqTvXtPHnrzrTeZ460DJ1/Z++E7u59+9abbvvvSmjUo6yAsYW+J4Ut/f2JDfIpPMMU3JXWDRpyZl5fd0KCLdOSZAxIQf462tCf2pSAFBk3OLpaxEUth3ZHLR/REsFwTlkK/xtTxpJcHQ96i/JgSvAQpAg4CuTO/ZMqAPoEUyWZ8F9aioR8YuDemb7OFKeulGAW8OlN54dEE6sNy2cXSRrSQUwmTOs970SJflMpTmXyI/D2mD5OLIBXgVsjkF2xucl5IoekYigiQf8eL02TlZeflo8xX4+FUfGKlxiHlgzqiHSqt8ZwuXIs4OL7Xnlu0bM+Bt99+uCqyfv2MupIlm5eU1VrrCquirQ0NrdWAlIFIYTUyk0XUwFCrBKYMhrxapq+AQqVQw4Cp5w9M3zaw+r1Db+3ZswgsRXprYyD12dgqZqhTFyiaupuUToIUS1baHINhFnwTIIV7xft/9Z9c7s++v/SJzvXrVu/YdXTaxwtY7ThRZGFu+MGPrmOO7/z0+eehNAFzevg5JMT3IiVxbO3ae+65Z9bYxrHl98yb9+Ta937zynPL9uw5/MpN/sBDD7Fu4D50+nZUzFfcjv6GstuTjCgZizkuMlobGpsi+eKGXqsXBYSSetaXl/yRolPKlgFScL0YIpuBlB8rJ1DhE6ZTqsx7R+bIR92UIbYRfGKQYMeYwl/sx4xJG6+ag5AN0fiFXpawXw7qDUlX6t3NCDYoCYtyLTtyvU6Q6PKAu+1iPl3aOMQlpi+4GyCFlp/IRZHeiSAFDxDETjG3XjrxbmpJTq4zLJXa4PghgRH2Wnv7jkQMFjS1MJ2jmk1z20KmqonZXCBzTWHLad//2qL793z0+p/f3np06xPVO9evX59TN3NmZklRUV2luijS09gQzbEWIcMPTCl1Oi016MjENwBVZgkMlhIiNZV61vAIGIzpisHVj45tPHGA5ID7fT6mjIrM1Wf2rqTTyivQtXZHVF5KYyKP2VwhsVV83a3UeJvQ9l/84Be/YP3s+x9s/bgVyYXluz4+encFVZMlm9e8+8JNTz/97efTx27GMu3de+DAoROHkBZfDsbnnsGBsbHZg0DUHfeMbXzzrbfeWvba7i12feh3P/vlL/8Ga3VDf/Ga/dASr2mO+9oZSMGVDFf1FhrUFnFDiyEUQIWjm3WGvfoydirOlqMxuocUrFnjbf9BNHPC9bF0LargrJQGRnJqe+kvJf1n7BKJo880TD9T2HNGdcOsrQtCyoSghjraptUIyPei5YoFSnQxj5+FPtIOdnH8Yh5degWnlR0XOWcflwghv5epb0QshUwEerCCRxKZJBNPk8cD+U4T8lIMpDimgsYupME7crJC/kDiXEiNe31MPQZZK6wBAUPy3b9o2UcPH/346Nat0croqfWF63Oyr7RSa7VCl7gw0thQXVZEaX3y9nRK6GeKSrRIn+BbwEurVABSatXcsZFNm4CpgQH+2NiJEweI+kPBB94DlIb4PusO+OQGfr6uqbeKh1ZvJGJrrih125q/AZCi68391X/23/CfnDtv/VPvYx+suGrGE5teSsJ9Tp5+6aWffHgQFunMsXvvq8fWvrp23uC8WfMeJQM1ds/w8PBifCwZ3jS2dNbYXN6odnT+lp+89twrP3/W88Lv35Vc535oTfHtp8H2FbNugKWYmubXfLKgylqmDuZXN+m8NEiCuJwvGUmRfMLGkknMRkb6znTWIzMVDsgTxAVPSRsnbITYClEF1xaLJaeOh1Zw5NONcTOE6eJv8viE6QYLlIm5YDm3L4aWetRNj3kuxlB5oSshMaRClYuNVcJqE1y41Zlgom6uTxY2l49DSk+DFOBjOjVuGeRisokb9rjNbjG5HYBUyOjScDKpqBedZq3GkO2ctOB4i2DiIpgdhWo4prbvfx8J2tffuLX3yJFORNk7I01VGPdTiTaFuiLUjikVtVUNDdV1RWhZXQvlJ3CkUKu0tUWZ1EcK+cs8A3RpSlipwbkjI5tGNhGm5g3MmzX2G5Ip7ScwYVl83sEu5gNS0cYcMVq/cyktVVHskCe+AaleWA1ZWNJ8ww9uMs7s6P3rX2/d2jrkwN7Rnjz90EtbfrLllQMnT8L0IGI69ObGNzceO3bP2nkDg0P/Nrh63urVc2fnaUcXL9YO6YaxBY2MDQ/zVEOD00cObf/ww6dfvGvllqe//W3pS2tQfojrsZ8lirULp4yXhfriiaAiW2vkR3p4XpOb5WaXCr6khhZ5Hi5UpBKOE4jKz3KmD485IGcnz7YXYXwLGr2B0SpwaX3tjDN4bj3PuFB6vJQu3dbzwjmjVLEGZZhIiDspCUYVgiS9z4IaEpJinh5SdOriKrh4EMtkvy7ehwNtV4Mk6mMUIUbqG6O3u8KsYhZb2ia8BEgV6DkOQCrs0es9wQhauaDxRFeLOsQ+R3krnHK2SQtjrZjqJ4Y33/7nrccbOjuP9O7c2drQ9MSVmHJVN0dbVFtXVd2QU1LY1ArOz2rFCCwKn7RahSLPWlZbhH6HICsgoiYrpZo7AExtGlkwsnjduoFByAA3HXqO7FSSwdTnr0K/UcXPrI7U2jVSkYxaLkDGLU187WMpiqO4Usl1N/zujT9v/vjjbw0/0TTn+6dPp9pPw31etuzkb05sXH7VxhHDQt706UOPDA3Rx/WjCxcsXLFieFipXqy9/vrrHxt6at++bQrDiqtGVqzQLR6crtw4Mn3lT147fPLVWx548Hu/e3fNmoe4N9wg+5tIdEN/PM700WHWUYprydTycjqtBU6zg8tmJ3xfDlIo5ZHZCFIupisDkxU1OoPQg1PTMMbe+M6WFBBusCX6mBqfc44N6eJuxjcc7/gjvJi2wVdqBqQgm3BZmD4uVMvsMhag6Qtak+nEzhCHurAKL0r+C4W+L8zIpaSKMqsiXz/ef5OpQ/PbKLPtmHD4Cdc+IM7nsIlF9+QXBHN7L2N6zU7uqwuxzoHUuIvMmGhGyjdFQFHUoo9evvnmnKtPnYKd6j169PjWh28sy358zpzBzKLCSE9TthWN1KLVlTnosJYHCEFpo9Pl1aGqrIQYi0xEUuAt1Ivn7ds2CHXSgsWL+QM4+KtW/fqZ5yieSqW3t8/FfyGDKq8yW2dBKFVKbUyKAam2jK8tpIRn3I92bsDR/ItvG99YenTa1qt2Na29IXX69Ol33nl9+wHYpo0kiRwbHh1dPH1o+vRtBKhHRh9ZPLJwRDe6eHixbpSOoW37pg8pdcOA2UKlYmjIMDJiGPnz9oN7X3n1xffee+Hdd21s9y9++QO2G9ONfnEDMJXyjTdYSbFcfG1hoSrfyBGx5FRfJvwSLeGQQ5IFRDJJ2Eu1FnqGf8uyFzVV1hr7MygkOBMmpSEFgnND8/79H+LY++FufN794TsfvrN/fzMDKxRzg+yiiu6pabnQBeyUoFRDBgrOHhNNMTSF087XlWRnYxSjGmJ0UTzj4uULwnO97wsSC+LeaX2NCghynYwuhEJFhFIyUUDUfimQ4mOzl9abAuX2XIsWZfKX0cCkmtbguazkJ1En4/ZinW9gUrsvH7351stnZO9s6e4+ehyY2nVlWU7dnEFQe1VNjZmZ2TnR1tZIVWF1tLA2s6SkCGJp6l+N4jJ8i8o6cgYVCt3gunnb1GqoLKCtUOPIXTVv1Qn4flTyQZj67HVOyVXKvDIDP2hic+HkFFeUcqXjVb3CryGkzszbgo2ysW8X/e83XnzV2vFx0657ftR2+jSC0T3LDmx67Oqrb71y88axsbUImObOUg3C/gwBQYsXLx5VjhoWKxcuXrJiBPZqZMXIAmWmbnBwaHBgYHC2VrdweOPYPbwP3nh92XMn9z5/yy3f/85Pf/Qjze9eeuell777u+YNaf1BuqwiXmzmZ/KAAhO6k4mSQsGXMVRCnywgKWaZggzfZ6SGd/rMDlR2NNZjz2hvZ3oeTB1fMCmUzMECH37lN2tnjb33Z5CWa4+NoTZh796DKFZIGywB0xSL+ZMp6b66n5fqcqHUR1+z8pB3PJbyhlwFqrwymvxp1RW4OMW+v3+YBYpbOyH+7arEaQXJ6YO5gpCILSt1h0vPh/a0bMp3Rj3I/CiAG2/jIbsjDdTXl6NgoK4Lk6X6urqmdbVYSttJycjczZdiYs52phsLznwDA6iPXgYncfPjt16+42j62IokVF1dnkJn0JbktPbwlVXVERzRwsJIU2EtQ/Zpqad8bR6qEKDwRM2CCocCAdTAAOA0MJ+/cPGCBZuWzr9z3aPH9ty/bH+S6bLEcCIC5oUwn9q5WQpUs2Z5ACmI/0H5seQoBhCcOTkh064JgjJ8l/qnQ4q2asZSxNERhCUtf2Pk7lmnWm5WPd3Wfvq3r7++bM9bb20cGX3qsauvvHJ4DCz5rEFASkGJBp0CCkjlkGEIFI56oc6wePGK0dGxTbNGqM53aPYgfOnZCoVibLZydHTk0OsHP9y9+/n3fvM94yqLZeWWld/TBO/8rqytfbxknKG94sUcMzKZKFeQyLk0MfBLkOiCUrkE5UN+KqslSbjLX9CAhTi5y95Oey1xExQgZVBJ9+3vfHjwOcYA3wPCciM4y+XX7ti1a8ebG0+88srBvbBaBCoKscgJ9E1JV9B/Pjpxa0IwTeXoi0VxFKg/v9/I11nR9QXtk/L4rvqE4CuYD5Mqnza5r2ZypxiQtVBeCsbKicoOppmx8PPva6qtP44BNQnosBJwpOOJfhRqJJJxeb4/LEVDnoBHbykvQalwWVljR0+kyYv0Rpzu1p7R3p+IT4n392+AEY/5hKQ+Qung9rePHn355SuvXFxw7dGtmzc90dI6eyC7qW4AfQ2VBbnWnsL8/MKeHJ6qMFrCK4lU6/LV1hKQ3zk5ylx1plZsF+tUKKMDMvTiwYIHLKv44mcfFE+/27hywYK7v/9s7rHDi5ZtP912uh/BewKRLwT/OIcEdZdKSFw0JcxrrofjhwYoXPTWlcTRtgsqr2QMMjL6RH+AJowxn/xrEksxBbwOjf69TY88dscPr7vh9OnfIq10YP6+fYNzl2+E17d8ydDQY49su3fevFmzxsZGxjYPz+Vhq1EPqqaDoxgYGBrcNrRtcO7cWbOUS5ZowfwNj4yNzd2275FHXt68cePYrNUvbtkNleWy11/Z8t5dd6189t5V6x70a6TF6Pmc7veQLiPWhMxBzM10SEtpUsyX2MwTUkAK4w/JRIEc83tKpk2q6eiapEV3JIYlZ7Is8f1MguXwm2/O+PEd//Zv91yRc/kVc/K0JTdWN7b0VFc+UXVl0ejKLQdQpUoVqAImpGKM5uehIYiZQkGagYERADhgqUKeYIGupDBKG3alVWH8aiDVXlDT1Y1KeR3TTQN8CHWlMomKuQF58gK6RDThZNZkDLoQSAlTWJ3JODvLj/lSNrnNDOLG2lXTnZNZ2YJBp040lIsLU3S39rbEhikpglR7P3C5fxn05q//92MNrbcuXnDlw8vX/fjUEzrF46fWb9t26sjOzIXDV87k8axNhZl5hZFCg7YqWmSorawqUivLannAlVWVz8vTihFcKgpQmcrLMsIu3WUXD6y784F1A/nOu5dOf9byxtKlS18HRXH6dD/AEWvDZhBLgkHqx46QTHLNofKg18+ROrikiJMxkAL829Bip5lBE7ya9H0T/3RICdPqz7iILZHU+4369+6a/8F/QOHwt99+tOwADNS+ffNWj2EPX76kZA5RErDYs2ctGF6yYsnwsEo1pEaxrgqGf3BQgX+DA3OBqblakKXK4eGxBWNK/Mno6IrlY/csn33PxhMn39qzfcs77/xuy28OLJyOqs8HjS5zQOooTo4XdqK9mSPEYAotL2KCL9VeO+l2l3LlmIMDXTjWXLm+cVJNV0ffpMw2Uk+kiOvbQC0SXjl54M23j+7aNWPGjDuuWH5j5NSMx7/1rZymzs7Gxp5IdRQygK1b3z4EedVBhrBA359xiuJzjj7LjAQRND0mUgtBNuHRmJ0qa2G0qbWntSmak1fAkX0V1ZUEqc6OSdNKYICJnKCSMD81rOa42y9UGpF+f9ONysfrcH3uAijlpPVyaRiNDwoxtaOjqrq3q6Yvt5h0xdQalrjfdkE68oR13rAfW+Hht6M7T90cqbyysjoaPXUqWrV+5ykcR0CnR5/YVVUZaW1orMIm0kCXL9LT0xqpRgOpHGseCD9K/aLOh1JVfD4cOPHCkU1Ll6rV2JEH+Pz54gUD66bPL1g1f+Oe+1/7SXyqb7zKmZxseH5UZJbgeMqDLr+JjU5dxWjk18ySoyNUuhCJWcCM4+f7+jh+6AAkYUvcJsi97Dfd8p0f/e30bz/CRTw0+tjigR/OGxwcBJs3BG3kHN0sBEsrVowOXQ/Gb2h0aHTF8ArkwReO4BOOzcMjm8CeG7RDQ4pBpXJsZHjFy48NDd4DOcW8e2DpNr+8+J59836+5fVl8CdH7l616hpMQjdxysuhU0vG090p26Q0ksxvk0jB6Qq/xMrD5lDMlZan+yg7XX5+76TJfR1dk/NTGVOZfJSPAqg9b53Y1fLxx0ebdu7cuWvHjrd3HW86dQqBwlGKuhu33nzz8c7jx4/vPPXkD9fufW7Ra6/t399+Nij5rPVwh5yAlDlQz/GEPKEgCjPNdi2or9YGWllVJeJySfIraKCdyuoiSHVlk7ae0mBgGP1IwrHD581Kne3zLkh9KiXhkxTYNWw2SnFQX2OvwoSBjsYWDOvt0zfHx0XFZzteMoRfO4zUsn2nZhCE1lfXXXvt+q1bH667ej1+OtV5pO9I7/GtW6uiTZGeToUhCpVfI6R+kaZIIZpbl+WlC1SLDAoF9lnwfSoKpngLKM+r5tFIIR5/YDXCqtWPrvvrulmU8l0TZ9qnjxdZp4W7whgHVipoRrGqrAK661gzkpdMWJDeAIRnuu8wEcQ/G1JMHJNEp0GHxpivR5uQEIcNC/XRHoQYmYsH5w4Orh7MmzNn+rrp2jmQkwA+i6Eu0aYRNaQdHV64YC6om2HmuGp4lm4mhFyGxUOLkS5H1nd0dPbgPYOAJaKVzStGFWNP7pu3fCMwdWhkZf23HyQJHAetMzmoIEaXTNqX2jhGRPcuOSoS419q5bGkMplD42K0SHCPdN2XXQYj1SenrYO6nwJRh9968+jHH7e0dO+cUb2zETH3rq1bm6ohUNva0NIIyuqJnK2tgNWtN5+qm7Frx4mTJw9+uD/d1uc8jEm724P6JT2kuTgTlInhjIJ8K+J0QKoVZqosDam/2/PzmafVNPZO7qomwbud6VDoKpejaaKt7UIZZIHgrLZ7vGYYAl1RfhYHjjUcP0uWMacLVurINDDpHd7mpPBTgqTxjnvoIbZs2eOnds7YtbP15scvn11U98TWrZfPwZCE9dHGI386ArPe2dhTVdXTqNYBS7BWDRFqa12GxtaknsiEMFqnQrEcT6Vj2AmemL9woXqWmD97eO7chbPEqwd0w7N+jULFRze9tef+7fs3nMkEjp8ANv1SOH7+oIYtoobGKGWAeKJYMF4RIzzbF3G8R9k/nfEjeblIiimTlly7K/jsB//7t6+/BlEk1r9q9eC8oc2bR1bUzlwy667ckSUz87SDiJlQBYNj4Vzl7CG4egMDqiGlYroKGsnZc+cODs25Wktc4OioEtHVIOkqUMuJ45FH9g1tW75x3r1Pbrvn6Vde/963m5t/d1euEUUf6LUZcKPtc2lbO73lMr8RsUmoXiqVXTqmkJhi2aj7hJdpO4HFl903mVr4VaJyFjsuU5XwJgEKkpqdnZ1H8aWzt2VnY+PWhx/GBguXpWln9Y2XZ1etf/zW1s7GaPXO/zPj3598fvfeRe9vSAnOl59qF5kt6ALqLOfQvB2N2czRWHSFkdYmBlKtkUK1+QvyUhPcLEQdNS3dk2uacvXpOhLEiqgYFp23V9hZO/UJtMbr8H2irKwwONWAqRyd43NIjtRFE6Yaw83Js6UtTAaPPm2AXmLPsu9dW/3Erl1bj++qvBrZx5lX7dgx8yrgauvxox/3dTd0tnR2NkCc1FOWXV0dbWpsbGhFZqqurDaPhH4KbVGm0qAEhUUWiqdSqsXigW3bBgZWi8egs9m0YNbAwMYTS6fnPgoKcN4raEqxf2p6QMgnqhJBKTVz8tOMb4JUaQxtFWLCtMuX7mh/dqyV8J8PKbyw5O2/WuMOXHNnvtF708rbfvsT5KEOHdq0cGgIsggF/LyZeZlLDLOGh2eip/fQ0BVDs3WZgNRctXI2yHLl9AHlkG6QSMDZyrmKOVcMKqdPXwh2faFKNaiE00flMeACkR8eGnpqeOO8J+99ctV3X3/u3Rti7O+tvIkoOQ7mxqLxDXYgdAAGgSYlyswSROkpM/DkkrRJMERcWzEgFaTpFgXzjc6myyZP65s87cEUpVhoyz385i7C08ctnTs7G1p7Pz7a2NmJb4/vuKq6cWd1pLoaHzOq1xeWZbd2wspEdu6cMWPtq7v37n4nbamYdo7nJM18MXm5lwqYaJgpHSaNvSTa09Da1NPT09DQEFGaPz12e/yELhFjvoxEdHLfkck1PXzk3PKpvtLpx9RHSVjkuxikPvs8Aq7dznGzA1BPoK96ThdmINLojo6FLBAZSBmkeyyTxATCRgqjlr32+tiuJ54gUD1x4xWG0dElS65asmTmlbueeHjr0d6Pezt7u3sbeyBAr6yk3tYwVA0NVXD6iuDM6KA+MhTlUf8flRLzTdR8WCsxX7ENSqRc8YLhsaVLZymhDN2ELhVLUVO/6thJyk75iApCMX47pTAosJKFXBioGiYrVYq0FLq5yNs+V2oj/Jrkpaam2te8s+bd7/702QfvuummV7e8tn37nw9tgviV0tpzh/PQkQWOXF5JJqb+oX0EvlDnPiTseEARtaAAOYG+RxAYQ2sCtTE67s9FgpxodqWO7qiCy2xQ0i3oUjH7itnqZ575r/86uWj/39545Okb6gv4WU6zBPMiS2P9yUQxi/onJ8oxQyaohzBPmmCm11xCeydc/mIpdMqcIGSz8z+4M+umrZP/2PWnSbdWkBwzvn/R/W+93bTz/+xsOgob1dvSurWlpRUx9c5oT2e0qLanAUMEbqxuQmhdWV1F0VBDTxMENusv3/bALbfsRUgVJ4l6airJZs4MyhDE0X3MhQYCqHQOB8LhQMAszqG/RCRFO3irNgRICc7xCgSfyut+aubx58R/5+Sl4q6OydMmd0XEFqa4EiKKEJPWll1SAk/IhePHlofDGr/FbrROo6EdkydPezgc66eGRVgPxG1SXZQwY8N+MH0f/ddjV+24kaZp5RSiBpvk5cjg1qI5fGET+XyNvR1HOlubqhA5aUvqqqLVkYaGypycsiK0HsEy0BVZIaTQYgkoqKeCCikXaB8VKhATfDUai2LpqPCZv2kTf926e1cfgoritTYkp9ByE14nw7RCXOMJ0iaLcRVcaqlfykLfwq/n+GvcOjVeIV/zztPfe/aBZ50rXzy2Zfvrhza+PbJAjLLmwbmKYWst0ywsj6ZOACD4n4ceSTSgFkgBaJAKh44f2TumTx/hCmUy2JsAvkwSTtJMC+oAg75KaQhmzn3yp/e9cWDRGtl35990ncNSkBUMsDA1qBkzp3ypZKksIUhJ0VUhCz3GQUy1ZVyK3I9RelZIS8nxg97O+IbRuXLXH2tqJvf+DH3Pp8TXfHjwraMf7zp1qhqZ/05oanoiDa3Rqmi0MDva0JRTByAh71/dEKmsy6mmgjqC1BM3Fs1Z//i2Z79Dynvk90n6J2Ak6+O6kzaMZkf/c3SZCEgxXrBebuYVNiCiAKgAqZbWPD83PVJqPBl9phmSID0pkhkTiVQC6kxoimS74Fwb80lDCVyEWFEXZtZUIpJCBwBw6EEMXJJJA7FLqoMWsMT5Gjk6OGu8cB/zOmjIQEvL1je+e0O/j3KnVB2WhhTld5e99tEbt95aciP0D5mZZTllpIjASJ+6sjKEnqA0jzc0dnZ3dHRC2WetzRLXZkdph+qprqpCiQeWSkleUV12CTpjkciPVgcTTPEotFIzBxaNUqmepUbCd/r0fft4VOmxqDnFlCOmzkJKgs4zGKcgoXmbkM2WigKO+Nd2orwv9ssf/GrLSucDDz54J3QDu39z6O23N68YHoCVUgFSM60k1CITpSPw4LrQjwQpNW018I1pA0KnZgXR6QpVOjtORom0J/gAp4PuSJll8AMMBrVqdPbyfT9deWD7yRdKi3+flYWh7xZvPbYdWYwhggWpfkkpJlroebWGAnvQZGLa010a8xeT07xecHAuy913Ppi1o+9Pf+r9PjW2j7+z9yDCqKM9O6Mo9mnt/VPXkc6GpmhVZVW2tayqp6Enmp1TRZ5fTwTjOarg8oFJj1bmlMC4Xj1n37pnr3l+7/2LklMoZczMSEov+ris3p+vUon1Hg4aa4TRrzmkqAagwCajWrmlsykvVCE4Z7wBNFjYPFDMhzK6MAezGQP1Nsq3VDBDTSrS6EqdFSt9ogtBU87C7q6OTNQNo+8m6BevBsGFSR6/NEhJ+IAUqnopArQrWiZ3NfK8zme//7M1yEChppvk99gtUJAJE3X/R/9r6OodM6++OpuIcGsOSazK6nCVCnOind0tDYg9Gxq7+zpaIoU5JbV6flkOlH5NDYBVpDq7rBbyidqynMoygw7F21goBrJVsEowVoQkwIqWE7A2Vzkyppw+CD0FMi33L/pJG3kB0L+3pwe6xdmo5Xd65KI0pDALxyRKfR0hxeyDyZd+dt19L1pezDc+YD+wfe+WTZs3b1owpiPeYRC5pSVa5tyxyxAFSlUvhBOqzMSh5vEY948EKbBXaJeppQumVjNGS0ltaenucAzR4RlTbXWqxYtrr99373vLtm8cCf3qXY+HE8zP8rMx0pibnvIKdqI4YNJ4LYZIldLiKrfVx4SXqqJI4Mq7mV5pxvnz77prblPvqWfboHKJvwsrA3HEjOrGRmhpmo786U/dvZ09gFQ2fJooDEoPjFRhFQxVJFpVWIVl0dNaVXjjt5bgLK7YNrht3wdPw1C9s4HGUqTShXI02KUN9KLYoONlocsE+myY2HJXZuQspHo7I3nm/jOE33iXTV9cBoKAE6KeL9SfAsX0bIxeQGPiZvT7liGjEYsLxpmscyqYUly/ssqa66WhUlC+U6swloQjou6EEw84fZJcfRiF8uFyY0G+Xhzt66szS+Tm75tQyw+9EjNqjaH6SHe+7KOXd66/6qqrr85Bcgn1G9nMAKDqSFO0uqmlu7eBaM2G3r6+3khOXV6Jriinsqcn2tTTFGlqqsYMBmstZoTnVGZnknuTSZIkJbWeUzLbLpYSfkKnBOphhySnQjm0eFQ1tvEQBhLsjzOBFJN1otbxUhBNmJ/KtNLnEqTC3AtWr/0zIcWwkGtekgceuMW+fPjuX3uX7XnlxJJhBVQRkOcNDigylyxh3DU16E8tVbwQenA9cItaxVRn8vCjmiIsyPbVdCdgDndXYBEaqOGfQaHVwgvEXylKsksUypEVt/713nsPLjr0svLRR3+u+dvP9Dyx310sclOru3TSp10S9Gs0/Ehvo8IbDBOddYmjR9vkDlQQYR5O1rOrlv75rgdWbVslAq/gu273wcNv7pjx4xmFx1E/90Skt6OjuxecObw+KxKziHsamhoiEH5Gqkn1UN0aWR8tLJs5cwlTTDc0kPvIug8eeP7dRfeTRglyuCnpvhHtzTZzMF+tFlswX4tt45gcNr21tRFRRmdDT2Nvb0tDrSZ5TvwsSGIDkWggr7VQbww0KKPuGCGNzQ2RdXMsgZkmXIkb+Zf++LkdOGhADwpXTOUe1GZRg2zy/cAqS2kO9CVBSlTgDDjkVNtBY1B1DQ0qDjt8220v/OC6eFpTDINOVN8ixkY9fnN0141zyhgwVVbRKFVCFEw67RYNrajiJUh1t+ZYtXmolYcjXR1F/IlKKkCqFjFCZllhdhGFYPD9aAEBWkCWkgEVYQq+ISClGFYMTh9dsVk7uPwEyvC3t5Hnx0zxpW0lLqdzRrMtCdkppKbc4eaMrymkUv1r1kidv159edPlq997ZdlbJ3bcs3CuQqkeUM4BMLRLhuHT6ZhNhfrEkmWiojJ88NUUNNFFUaeNN5ms9H2ZpgMwZbh06N2HOwCQ+NNaKEgNys2P/eXZ5z88vPGNF168Z96d3/VQP1ZTcbMslu6MRGRE0o25irmNfV2dYlfIhqkXwolH34zdrZfCSmFeb1bWqvmbll6jKb/vF5hyc93uvc+9+TYQ9WOkKo8ej7Q2IgbohlgiAqIqG8YJPkxnZ08l6G/GSlU2FVrrUJBqGM6krMrgwKqsOz/44L7r9u6mHBX8o3TLQUGbKAzCT1xggXJOgk5qYSlHnINHgsvX2MCwYbWBtN9P1FUq1SYHIRyiufM0jQFjUHKpSyImkJqkGH+M3sTNNChSJJFLZcnUOZ0HSSKAJvUOh5lmH6JZr0XvCrslAWo7cSmkqE9kB6SkDIcOAUZ+UZHdHNCsXBn69nVxpqbWl66NQgek59547Opbb77yiRvLLqepxDlRMj3ZlU2tROohiGpoZbJQvR19vY2VEJsbiqpaG1uronCco+Q7k1w2L7M2uw6ZKYUS+SnajslaMeQ6s5jSNysGUWU3iGKGlzOVs5cfQG59DfVUFKSHEEwVxgMWdMS2lMvdzJDo4mI3J3GhWOafHUvFH7L9zn/NXbu6O2dse2vZgTc3jg1OBy8xaCCMMFaJ/lMnAdjrtImiq6JT83npdh1wixXkD2K/IVdPx4RU4wwFASw9KwcI05ZgTLRh5Mq//tfTe/ce2vT0r34+ODg6387nF2RxYggsfGlHB7q+9gqpyabo7u6oidixMYmSE8+SMrFHsl4uQmM9cOj59l+vunvl71/4j+t8gv4/7L3/zaO7Zjz148evfGJrtGFnw9GPp3V1N7R0goaglZJTGW3EvhuBx4fgujK7rrB2zpwSrRZmSjeMiFqc++KLj/z1jVf/53/QJH/D1CmpVFomxEKnoQKeKtcZCsvZbBOqHUPKKPHypGzCVt7boGWnxhM+UNuzwygEgXnKopFUNNAui5n/gcQtmlRgNmsxZqYz41klbLak+NPjOKBfDEjZZvB9aJENxs9fjwS99BJH3glEuXoTZXo1TmZGOB8zSDyelVu+86Mf9JNii+p3maaXy7bP+vcfP3Xrw1V1Vy2/qqy21podQUY3pxBarQg2oFZYqtYovLyeziPdnQ3V2VVVWQq4zw3YmyKFBCmq6YDvh75IeUROQEWhVZF4jdrq69KhlJqhsZRKxBhkwFasGBqaO3biwP173qE6aKZogCxn0uRkPD9AioXR62iBxYldaFX8EyHFJKdj7Ho0sNrRMa3lyLUHlh3auHztXPJxtQxBQyc9DilDOlgiQQlDlsPzg1EClmCCGKTRnXGxDLBtZLgUzDZEd9DmofyMKjqL0B1HN/PWv/yvLQdfPbFx5dNrV60bXTBfpcrV1yfPRuLkwKRK3VKHvaOltaZP64WZugSBHBONxeUBGAsUAeYX/DofhRC3rVx5g6//27v3HoaiD7Kj6BPQzyDL2/tx37TuBqyBVurqU2ctAycMtiKnDOii2u/sujrQfzlltUu0mcPYQ3izZj3yyOjCHz7z6u73GTs1la5hku0Va+EX5fH05nppQKOxhe3WnkaGXaaAqqWlgcdN031o2ObWGHP5aFwtpnbpublizKbKRQ95O82exxwTNqTLNDYcAQPmO2BOOKviTIMXJqpqk3MCAQySz6dRVtBHyiVSDUtwaSXQApE4K+xAr3sNmgCiNwwaeYLtuGnLfd/5UT8hCuo+336MV3v/9T9vvuPf0aDv5ptnLl9uRT8Ja6SxMVJVSZAqzAZoEDFVk5IRyQi6qamHlxnB5UTuIVJZHalEZUtdWQ4+sJdSVS8yMRSYU8NvcvUQdzP5FaLXh5DrHB7WjaIGz7BwbAzR1Pb9aKyDfhdT0pAKuGiSedDmxhhOCaatS2GlvoaQglMRZ7Hvu+amzR1/PNI3beOywxvRkGVQSbsIdZAYBqgGKSrCHgM7xXh0yjPdpGhUGdw7Ag48P8bfg1+sg+JER9EnOclaJUCnoM0pkxmKg+5iJdc/9pc39h4+Ng8jh9be+YEKTehhpeSpTwrxwDTFQNiJHI19ke7JPfqQJnAJAjlmtnU7m+OWm5Eszi9YXfDgs9es/PNvrrsONurkjl09sB7HIUk7DmVf59HjLd0dLWDK4e5Fotl5RWUwUE2RyhLkXLAasnOq8ZuGpqqZUI0MLyQTPHTHvBUvv/zY//yB7BQGATJ5ckd5flF1tDpbaffIMaPJVF/OqyI0we/rbGmEe9RkjzHsRJwV0IQsaCvPo/2IJxYjG5M+0NXa69cE5PVhTj1mIsO1Qd6boMVyyG0V1Io2XQApSAJSnGAwqwC9bdHgAqNAA7S2LqmyTCARo/cEBlqaCVI0fAWjVOzGp++770fJqWjHQQlxpIY++ujlh9Hm/I45N9/88LdmX1FSpDVYow2N0CwSN1qdU0isH4aAR6OtOE0ktqMRMlo9jT3Q+cGKEZGKsas0KNKah/6nBqb1BNPWhRgJtUJrYEgKAzWoGBwa0o2NrNBBDbBiZNOsMZR5/KSNOl4gYiVblbRhFEq+PVjvYAFTuEC2cP/X0PGjd6FNJL/vGvHRP/b1Tvv4wPZDY7P+bRCGecWSFcMGNJJiMglI9daiHwcaGkJQolbWlmVSMymCjTpNmOOgUImxUdT9hswYfENgyaAiL5DZh6i+Cv0JrNf/ZeWft5985pkX18769YPzR3+NUj1xvny8ISTj+bVJOexYsaxNeVlvz+QOpTMYlpdewnoBNNvdHLbN7KcpcQXGBx/8/m3veb9zy0/fv//Nf9+F7G5vC0rnjt98884G5KRA/0bKop1E+2WXoItjNqq9I3V51O0HQoAIDFgULYlrl+D0FyKC3PbkkyMvv/zyvv95dTcaz01NS09EgfIC7DqKLA+qkEwcuc2vizJeH6KpFmCqodLbz9StiNCFz2iHkRKnMcXnMSp+XDBePkZSeTAC2a+RY9AqNbIWSdxuNzX0lduoYVSKySUgDtNoNF7iJqh7swfICAeSwnGl+UQvkZuG4YCfKGeGvqEnLkK6fONNL/jlbdSqkbi+Rfd/9NjWrS8/vAsVCE9svfnWK66golwr5OY9TTBTrUjcEU9aYs2hfHgnEr2NEHI1tLRWN/XAPCHmwiaDX0UrGUhplcSUE3oY1k9LM8lIo5SmgxF2w0rNHtm4xIBSIMPmTQOzDiEfdppIv3bGSqXabH40E8nymhwsZqsR2UxtX0t6AoQ1dEjXbJ72p87eP759eMsds1Zv27cY8nGUOi1eMXNmiXZo+iBptIq06bAK46LqsjPJ8GSmrdVZRIGZ0KJLmxKbL4gLoAjfEU1KexATasHOIWVc+9e/vH749Y3P/OHnSx/1f/+N3F87jXx1rumMtoAKpJKmoIkrk7SZ+voiHTVVdgvM1KVI/ZAXZIWlcsyy1hfctTT/gWddljtvufeZ3Yve2jFjBvp8trRAgnR865WF6EDX0NI3raUK7gxDT4HSsiJVGc1GZruuspCov/VlSKvUzdQqabfQDg49+eSszQ+/vOKH//NT8H7pVqL9Uo3HCA+uAEVIbkegni11WnuImgBBAfwCldnBfpiRfnZYU+7Np17YPCCJjJOCkhFgTdFGiZHAQh5oQ+siKau4WMYCdwiFPhQ48oAoOa4sFCRCBXyjFxZKjxpivxnjQDns9kssfvY5xC6IpG2mckz7pa6AGEyUn/vza2667aXkVBoOTwW8yx67GarhK7dWH1v+MCD11BykQLQlIHFQsQsSHcm8CNr1EVEKm9RKxTBIOTRYcfXQbbYyGqmsJAOG5BTUs2VWg2o8yNYxOgFDekYZNl5i1REUaFUQvw1vXKHdtm8IBQ3z5p04vGcZ2iiQhgIvaGqKkXzZEVkjD0c0uiQQ+FpCCssXMceDDzT88Ujnn/741rJjd8zbNqBQLlYu1kGhtxB2enGaNtdSGopIBij0i0oMlKTKZLqGYj1QAooESbBLaUhRogr3oKCLcg6ZBEXq4UYcvG72X//ym/t/8/Izf3h+qf4/vvvd4ADYQ16BuX88tENJWTzp1nBsDjkrkVPT2DKphQ+ahx27FL0NdLcowarXBGEQZi2dX/Ds/Pxb7n364Mm3d80Aik6BhCNIoeHI+vWtDUTIgepmemdFqwuzS2rrsmuLioqsZKUAM5qIVaRdyINaTZc3uM2gHBjcvHnF3T+9bu/B/RvSqzxssRPBYCwHYweazi3Pr2zshXlqJCMFaDVZ/W0Zvn550Kwpd/JxwsSHggblqxkxKXnTGI4CZXkIhKGI5eBw3OhYwpKXw3F1QhbLlYfdlGEgvlBU1NNoZWbWw7xAO4F7cy+1n4CPLYbM301WClMGaEg4hgzn2n/uXMmOE6SIPd9+6NabbwbbV/jw8h3XPpH9rW99C6wdiuGt2GCsqP+vLKxqoLAKF62wrgw/EsVeFXUqKK1HfCmYCSux7QSpOmtmOoWJE89kIAWHRqtNQ4oUFTot2pnoUBU0G47f4pFNA/PWntiz7HQb1cxTZz/k42wavx6Dj5les4CUI2xLfv0cP7hIvpgUDegWdvypt/uPR7cv23jHvm0ogEIvPoaZGDUoFy5csYTyuDommcu0YmM+cEGK8pj+UbhSal3aP0zfEUuG5rllMtol5gaeOm2uFFBzZf73X165f+Mjf/jD8w/89/e/94J3LkgPzADgpskzVG0h8wDnSWMK1ye9Xd1NXR06zE+SXoKZYjgXOVvkCPvzxeLVd2+a/tTVuT+998NFbx7deSoaffzxx1uPthw/Ti1HKquoUg5uCzQSaEDSSi5LDlMuV4sWWmCNy6xlJQbsGEoSgKC/z5ODm1/ed++xEys++Ouzz+8+uJ9xxtpsFntuLuakckz1iFCkjrA6Qi4f5aUQUkHil6mJg1cIBVGdmMWjC0b0Do8vFvPId6bWXACYuCDL72BGuIY9tmJkezlGMbYbl0lC/bVYbdSCISMV7EYRroK6Q1MLTmgnpPWJjEs80pCSsOsx0Djooa6AkBfnY/zvbQ/FhVQdhWLnY3Ne3krDA67dceut195x7cxbb0VuSUesVVFekZXwE+3sJeUJYsgiugEMeok1W51XmEP9XODvZZfgGhZWMVfUinGYZJURZ5MyzUB2CRkWigmYIBw9Fkbx3VwIS4e0ixfwAan39uz57ekUo3SGlqOtXmMOgpPRl0spxpRx2WFp8utHTyDYRf8Wj9Gyq+tIQ98f3/7JKxvXPrltxebRtDxiCLBZvHh45kxlmgsnuV66vw2NFdUyuV3FeBZYR4SejqEA8ZlkSrpMegwqMMNINqaFB8lnwbn/Nxy/Q+t++ofvXPPBf99muWsWHKB8S1AaI4EfTZmWOrB/hstRdBQTYURXR5fV60ID5MQEZdtpkUKblM2SmPz5ubkFd29uOHJk6//3h3cWvXU0eqp165V12TdHifADvrY+HG1qqqxq7AY7AXlfBPkpMH1lRUolSErEfSUl+DAQpUl5A5z84uEVoyj8umf+gnXPfueaw6+hrgf8osRsKeDxczH+JgSJn9tRru3pZCh0wmpjS2NEFWhLsl0uLAp7AZ9XUECLCxjKR8BNXjKT8FOBpfEG3LRgTEENF9Odg6grL1LqTZBel4qkkiTjVRTVQDheZSmn2mFX0OQQBaSXXP/iQyxlk4gccjMTS2Fuo55h5PUMpNLlmZuW77jy+qeeeqpuyfXfuuLaK+ZceWU2cAFIZZLCDweqn3v/L3PvAt90ffYNt0mTQNO0OZ9PTZM0TZqkhDQNaQOUHkja0nstlpb2EVp6ghYtVFtWS32ACi0yJgOUBx2CfbUiDu+CMPBlMmCc5Obw6nCPOhTwgJymIuJhzO35Xr/gts+m94Tn835mZcqkYJv8r9/vur7X9wD6ORQswMrdMGwJQWcYQim53XmYqnIrC0C28AE7hZ8NtTSswaUHIGaSidKKefpJ2XZK+sD8PhWEQXNB1gFFffayX605fPjTP/0FFBW0ff9jKhfBDQYAowZ/2jclxZk4etS3b+P+nYifYGpStsu6tqK4tr/66zdeuHJxw4xZQ0OyJgkpdptmztc1L8cHXUm0oaMawUXFKEnoAHTUHdP6ifpCVAxd51R9GuJKYNjSABFkBAt6gFBXtBVWzrV+9taLr29+6PLlxYrPPjPvXLiQjwfMkJOdpk+Ezwjw8ykTpyYnibmeLHPSVGVxxflqN8wm07LHfb/V1OgYkW4yB0NGMIBeyjoLVi4pDc9vf//wexgJRkZwihpbQ1EY0X304Xsje8A5rwSvZnquMWIszA/hETBGI0qZBJivHbcV4wfLZ0ChjONTIz0x1PLw4LKTKxfOvrnx2Y3DPT+lGXBKAtej5jOOtTAAJWVijhdUpFsYeiVKKo+vT0SEnMuv5RES78zABYXPFxpQFcD/aD+Og4nvRDaBAt7TCWnmQOK4TK4WD6CKj9Q/OC0kcEqSSfQ8qTIF8uQKHjLvoMijxWcwfeztugkQ4gdmDzhIDtLag7APyq826znDT/4TkPXP4cjxzhq4WpG9Td+uTiXYje3tHSHmGYaO2IcPpc4CMBTkCfD7wJ+Yjs1ugdtHhlDQ0GNRVQpmY6UROxM7Lqp8C+ybGW8Cjwvxp+lBodEBt5UsxvSTwO/HPge+37PnDakeO7l13rKn151d+jlKig1SU36jNOpwt/OdGUhsAzqBLArKQP3hldSoUVipurQLqorLzhdveOE0HGJnzoQInmoGFcK23BC9s+6XkfQ17IRBbSgjoZCSTh2g5wy5kcV+C/2ifc68OTrIqebNxhoHkPzs2bN5s3gLZ9/zyD3KE7u+/MnpG+/v27hx8YP7njr0C+fshTYblRTX5Mocn5Q+gSIsJ05IABTt1+onmhrqaorLkSeTlpZ41/fVF7H9T6Y4IV2fg4dlhR1EvuKrH23/7dn3tgx0drXi2ARNpr5+/Ufr94yMLCI1ewV42GW5mK3c1Olhj2LRSPD4AOhlJE+ZnHWySmWbZN7Q3ofnzt65c8H8uSuv3zw3nDZhCiWZZrO8NmTSoXcTx3O0qbihqKoqQNoBw8CoDQaR5OTCXYbLibK6gJ/bnFaESJpy1DaC0iFz4FthdukviR8zLt3lECVwFFZ8tjMAybPHj9DP9KTxwOzHVaTU1aVU2dGtBcJhPQ1eyaNvt6QglNeipDLF0L84CO4TSux2ueGl1Z/9aSLdUa8jw7qtbXBVx+AcZdO2dlRBO65sVAI2i166vHUSVSRvOr1uYNrjB46OchihR/IrhT7kS5UT/35aeSq0HXgRjak+KcFZRJgg3jW7nnA662LxtIQN4xdk83WauTNlc+GxCtn8HJC233mx58W//IXcL8aOsRUU4hK0eCW2APAaiupNyo7/TlTm31lSd8XrFQ7HrqqGmoaqF1/ftGvX/LY25a1riLBODQM4gdFQBBQtpDS3mMS4pCJoiBgILKfJU8KGL4IrJHCfmNeim7Ns0zI4Xc++5+mn1whnzJIsW3PP0yfno6RWHD/44OKNL91/v//JJ++3LZiH81qo9kMIO2lyugLMgOSJE+GJrshJEykmjcutbkiZLhSauSVpU0eP+v5D+F1JYiTUh13aR9euv3btWnVc49E/nHuvv7MdW0c3NfcRYpxbCCovzKusBU8DTwfmAhqlCfP1MqBXF2v0GaDJZHTS5UNNW2esObZm165lj3yxa7jHBBNxeLUh7IzIr8joNWDhExQWYE9D1xS2NYDQy3V+rslBnnt0kfFsQqorG/4/0tLCfgNVE5/qCkmSMAAi758cc0kiAiixzHXk+LVCHM8uPbZVOG7G1BY1wM8llOPCpRfOTsBpfdujFEoqQx1MAms3SFGRqHRl4bRpeXOPHbr+J5a9+/tl3aua9jI9jkbTpqM7xg7OM0oKHFg7rinslLx44eh2L4feELu+2traShC4pvGVpcTuKwdvtjDVTrIqjRdieaBURJ9QxdRBuP5jswPra+QM/JIRl5aUQJhb+ZI2CKhmnB4e/uI63BNH//zxylI3Ws2CAgsfQ0ASsnAmxJdg///DKylKRXJZDSipr691vnBk0y58bzPbpDEIj2HfJDRUecmPw66KTVgqxnREBIOOIHS2lyLYjy2qCIiAkHfWTMnWGaCXwJJiGTmQPcLng4S7at3c3uUnvry+4sjBfQ8eeO5+DEtPPTeDv1aIPafZY1KIxk9NSOMG9WmTxk5O4OjDiZNNiVOs1cXVeU6bOkevT55yG8TQKYnZCYkik8uxdlMpSupacfnBG2/vb/VG1xcA2wWqi3YkrzCk8xEFALupOgT54rGYXliIVT/RBOwxbMWuZLAlmGlSOz1Oy5fv3Tp34Zo183etWfjll4ePLMlJmjp5UhLXw3iscFdxBLgiBa/wVkFR61dWlqcKhOEmbaYeD7pWlBRuKqcVNWEC/xXDFdpB2vraDAE9m6UyA4bsxDQKkvK7MniECyklBgXUH3cJxtfE1UEpX0CGapjc4sdxvhtL/u7mOBGqXlZSfjiyebT26VXV1VV7dv7i2XF0R116770dLXubaHdip8uEkTW95L+sZN6xsGaRSeyh1ELAmpCaYflUio15TWkemtw8N5pAUI4pfQB6RBq4CSUHTIxOmrANkGgJQaXjml5iGruJxCaTqWK8WhWN39QI8+duGD78BQ1Tgqm9NdOAKBZUluZptAoxGEnJExLTxoz9IZYUwH7wy/rqvm44f+iFc/Oa5j8gmdvWRnwIKh4iOxAxy6cidgRD8fBqkmRMRYQSpidjgin8Kl5/2guTaOokPh57bGuTRjVvznx220McvHXr1jnzhk6ceOuz08eP79t3z0twPfE/Z5thc2WAmuNCSZUkxaeXBDGfJ0weOwnvdnpymCNIrKyui4IOl1VC6phR31s3NSohGI/WKcv1k3k114pRU93HL7090heN5mN/wsZpehLc2O2mAvGtrWs4P60ccDr4s1hMeanlpR2b3eiO6giPYwoVyvDrXd6yde6yNbNhALrwi7d6jqw1mICDIHJY44UuVWiANVIaR6GsZCspBvnhI08WwEoFfZ/BCrqJhOYooRUVAQBQLSRdK91a6P1sZhI/YYspynIqOGkK2CyZhcB76JHUycxBsWiSYGppXNW04uJKGzlPBPRJY9JuI6rtr6hUgsGqSEoSpXEhPTd5eNOqKWigZtWhX9OK9/gglVT71llyDWiwGupP0K5B1C0lHEVz66nQRUIFROMyEt84l1hXZXmoLFD9wEMhcQzBPGTYzNYE1OXhZSTxFEgYKCl6vlBA9DTRyoUVFv58bwQwIGFcsgcWzN4AHvx17J4F/5lXm5eaml9YWlsWcprEoCTB8z7tR99pp/3vbPzGiU3YqDTBeqtl8QtnUVKoKJRG7IWg6YEufa9dyjYKBE6woQolRdMm85ulGuLJNTFdoobKTLZsAdzGFmB1BwOXrbO2zZ8374G5y2bPaANvf962zzadfvHihWOvqv05j+f8YuUMPoK1eU5zFtQcaWmwQglmJpQkTh4v0mNhqs+cODmjpsZiw2KzJC2Tcbn/ZUmNjhnmJHPT40UmTP9ddcWoqT9vOHJp//5mdxRUahLHlcO2qLysEtbDbuwly6qqaksL86H5zgsBCqZbWRfJhwdxZVk5KVOVMUq9ROlrbsZCctnsdbOaZi/84+dnLu1c7QibAi6DriDPgsZFCHvhpESFj2gT2HfhbxX4Wb4wgIRTaLcMVhvThAt5Ti0B6nBNZ6IhJnSVC51sj4mESZHf6s8WI/bGIaduky0glDItF16Qk+1I2CguLuWDQgQfAU68nnPX7RvesJLKFKfBniPH5LLUpRQRhLPn/j/hkjp3oPHd995rVs7k8TRo9nQ+xiXDLYUEDvY+29lsDdiPAHJAEGBxgSaLcQolVYV7GUtgUGoJRbcQXG5XkT5Bw36rhbT2ULSSYRLdeT4NE4bHLBaIWB2hDQZ4tCrZfNWMNed6Pv8cOPpY0/Tc3dgoF5TX1ubxPaykxqSXTPrOpuXfWFIQ35gA68766Ov9hxYfOdu7V/PAsra2FjpblGQewb5Nr4XWB7iKVIznR3wShn1Sr8cGKGDM1CSxW4pk8Bs2ySmtA94ty7edfGzWrqF5DzwAevsD4PGcWP2TT9+4BLK71eDyP/7kk+ZHZuwEN0dIXj+mtGyuIuxRpCmyE+LhhMpJSudMvqskVKgBydSKKWvy90MnYoTvH6HlFpuytKv7q69VF197/lTPpf17urC7LSwvZcAB6DMk6sB+vyB/OtDgygI6blPthBADOo8Wlk2rzEVnU07ejkoifVIf3NXV0tLbO2fdTMkDm1a8cPwdeLSZDXKNLpXwLg2fTFyTkkxe4F21GNDwg5o/nwM53DAwp+YOvAkQTYQGVw74NTYZC4vRML6xhG/VUmIACLPQ+GdxsxVmG0zVSXAE6w9QuaQObjB+kqEOFs7FlUIQ1x2e7PRMfcKoO5ilnE5TOohOXDhr5sinFcNwrLioqOz+//nb4Z5zTzw/smWkuaVFSr0uZigdg+d8gNA1aFLooqLdFGnlqT68SjuIkdjuFpYjrK1M4yVVDGoMFAsky3uBuCvJrQ/NnkbFbn5AxfhzaEqz01AmZ/cVmj4daeqJvkINIkR2y2euObd06ecIjpl6z8uFdBLm51WcL5X4oSrDwSMqmTr6ByhBnIzVDTxIZkbfvfiS4tTZ3nZcPcj9lt5CJ4gCoQEjAopcwvno/qf7W0lrOzmxS2LyXaL0Sdk2nPYNSjuMkWbOu28GSgrZQbPuu+/ebXDP1GnmL1++bev2FUdOvbNh05xHrDmmsMn/i1fNjgzMUnCFDYT1JQiJhkVvjkKPmJYSTnKieOrYTDSmTvh5hfVp40d9z5Ki02uqPjs9jdxKthRfa7h6/pMPei427kffh+VuJQFUZSQORJop0KrKvFLwqCvzLV4m6ibdKjhstHQph/MEjlsjexC8JPLp6lK2IJZWN7NNN/TGC5cu7AT+nCFDNIUXUDsfiykEx6fneKcTa6KWSqp2WkU5z+NB0oFBSJMnGBOoHicCDA24nolKTM4DNKfKnUhOhL85JKswn8blp+XbIYuAzxBEFdg8h7w8f5ibycVOIO5qLlSLeMlAQy+5+w6s1Tl8m0kkFqcFsxCjaGSGY8UpRbVL3n99+PiBxsb+LSOtvfVA2HBl++hGwSgdoeIBCwLNGkorEgGSzjALvDTEhgwB9oEe3m3lE5EC0E9M2IGaC9lJraqEgwlB8EoVapMieyP43egHpEzaoaRmCJ8bTYUHDMoYn7a8q23dxSOHl17/y19EjS/jgCPNTVlNmSqAyDZK/8kmT5IfXOM3XqwwI0x85aruNcf2vfN2dzvu+DZdG50azF6CYGR0Ozo5gTJMNCaj/o5lsFKHDJ4arXHhe8MjVrGK5laeDO59TQsWwLlCft+CB+YPPXByFkhbDzTNb246uXH75qWXPt2waaHNgYp60uMyP0clBcpojinHBNWCKeDHNt9jygpkI+uO86OxnIAnA6N8FgKkJ4z+nkI7Or2wmALhxuRanVt8ra76PLZSbzdiCzlCbi2MLw0rMeKkTSNa6zT0aKVu2CRgr1JA/OnyCnbBQN/rJh8tVBr2/zSh99mb2jo6It5VLSeGXvzDhQt8JIfCWgWrNWx6YaoMFWE6xxUlJhKbptD7VeTZTDlajD5C5mJHdH1eRgbqiegEjHIiY9s7odaRk+NQJMJ+Ir4EDZmDB30ZPkhoHsGNgMwzqwniMVlVXFx1gZbAEI8o8fZHKVoxiHkY1sRpYm4WQJN8GDjjliouqn3t/Z7hC080tg4MDLR2dNQbjXYdvm/lrVvKi2iOKMN+8YUBOY0oceugrnwMIwVLKeKLhiReqgqAP7ikohEfY03Cz0euQ6UQeq5ReeGbhJT5UCwnR0lXHyGLSh9T4OMEodZbp2uOzFy15szw0i/+8ifhyy9vmQboiJWUJqskCUZ+Cdl6VlI/sL3UqOQ0E9oRW8arLz196jji2FqUc5QtbXMwVdpZvJaOCBEyjYahFXQby2NFxbB1CfOOIliYFL5kJcV8KeS8pj7lcsky4Oez183etGbDwqdXzliwHFU181cb7zl28MXTC+ct1OY8+eRTT3pyHFbbTgai43RX5HhMMBTVi7OD+mw9CMeIsJ4wNlHhwT2AS0qcljD2NqSrE5NKMvEnecyVxdeqr07bfuPsju5Fe1J3g/EJBnU+NSp0mOZVVoDhVwMQfVou5WPmA/8leQLdMRWVkAQBH8Q0hStKQ+s5kGft9t6OZmVT28iGF49fnD0bUidHTkArtAkzzMivR5q0SKRNjVHQK2iQqq11axXwajJb+Yx8LqPRCQCfkNWUlKHIRNXhW7M83DA/Q4TspITsLKtTgscOlCh6MMkPxYiq5iFnhxtIhSWhxWCzmrPC4vSg+A78eO8q4VFkmyjNBGthax4MnOmWiqt4p+f14wf+3Lh/YKCzs6O7Nxr1EhaBh0CijKHn6PPI1sfuI5IJbiyLMaKkf41aIKAUFw1oXOgHqZLoC8e1A40MZnG5DkwUtHsgotOfZGfXFN1OeNa8FiLk6uiPAasSpYdv3KtcHmlate4SML/rf2ydRuZwMNUpmIas3hyA6LjK6SwZ9R2j9b+vpOAgSSUl3Llk8cZT5zb0rlvXq1G2z5mD8ZzIRtT/YuUdE7XQHhenFcIDGNkN/a+MHbqE1hBdicckQJgUpLKmvublqtmbkES1Bg5mV1576CXrfSeGPr1v40MbXzt149QVnkaufup+/OV5PAtTOz1pGWBDBuHT5jGVkFYIchimwRNPQDZtjgO7SA+skTkTb8MB+a50PYRGeo+LSqqhex8g9O6OPbTMJS4sSgUYL+A+dz6NPTW06q2sJPiXGgz07dCrTisrBMiEmw39S8SLlpaubFmTz9sBitas9qHTZ6CEgXwww5WTZQNJHbeYxS635mQH+YWxeppGdTmtzBtAPkYOeVOQQorpNTFN2QiZkGiYyyHb9GbACNQkl3PxfcfrzXyJl1jxeCbBPrWwJXQopLE61K6w1V1ToRTyrS5KsQjeATohQL4UfPzSOSVhg1UrLGclhebv/NmePxx8onE/COgoqY5m9Hr01OOS4WmIMOGNXSx0aZJBEgC9kNvC/OiIDwl2Be5SqQ6LclZSGIroMsNl5VVh2kajiN+oRM9oZ1MTOZjhbvKyPRcVmZ0qlByfNd5QCDuM5qZVqy6ipL74Mu/PqKgy4g5WlEVsYRGVVHwayyr5wZTUrZ/dFY+SAuJ3zPzQxlNnd7S0dffq2tt1vXSW4CbSkHQQR4uO2jsZ07nEWBJSJt2Qs2GKraLo4CUcGGc1npitYOfP3Il96EOXH3rt6L7tl7cvfvZ//e4nOYu3v3a859KmnchIFP7iyfuffEZheuXxV8LYS/ERJJEkChL/HLWUnEzzxBgqqTGjxgCUws4nAEA5+zbk8rB944pLSqCxI8Tvz5dunNrRUb+oI7Sb0ajRtaDtj+J5zS/IqyC4DyggrqZpzKAkN5V5AEENhJkKLGs6diPA0mMwsr1P17t8/q6+zWBEXthpgGmXK2CQK+2p5WUVtaU+MJJyNJWE9k3DVUf2S/lCE1KcaMFETquoKbqosJxiGk3CowlVtzkztKbMRK5QnpWeEM8xOSUaH8zvCAOwEA6ARRpJY4VqW4bfY4VyWiiEuIN8JxLH3r4vNG4pNal6s/H2q22535RUw9nhg5efaGwspFuqu5uedC8dI3KCpnQkySVPVMAKeAmJo++FBZnFzj5ww1gwe4WMVuX0vFTcTRivIsSThIGml1ifQAjpgtNhjLITIk+4Bt2A+C/YWY2hL8KMQVYKDAsi17+WlsFzsJN5a/f5itp3wcFMzS0t0FihY4brGCup0T+ckrr1ZE5Ozw5rSRx6/6GN5w4P7u3uCDV3tvf24pv10XdGqzf67siIT84UhIxLQC0e6/sI8ZMzOR1bUJFMFWvMWY9tve/Yzp379h19aPvlhx7ajpLafujjj3+yePtRhEu9s3Ml0o75v3jm2Wee4b6y4vEsM/EGnGpTUnwS2S4ks4oi4wV8pCULfoTdEty1/CYAFrez0xybACdlwBw/uXf9+Zd/dfzI6d7m5u4Oeyed/PThhrcYDUpgI1VVlcF5ooAUTtDRlQGnIMSXFgb2UL47hM+PEgxFMhVVW9t8nbJtTteuj2/84cKGNQAZsLIFvYHPQ6omomBUalPQ5Z2Oew/3E/m5lE23mLk5IKUahDhzpEwkhfaZj0uK9jAMP0WhZZihXoQpvZZv0CciP4WHkx/FROM9MYDQSuHANxp5ahsfbvGuHCt444GsYCZHn3wH2QV3ZfO0+kxOCexcrNpbjR9+XD07/NpXjfvhwtE10NnSzWwtCa+jQ5PAKqaaw0mrYy9hxKdTeaN0kXmJ+AfhLtAKI98IlTTkVBB0UrdMU7acrmbGo0CbhzGKLjqKMEbXRyRAH/0CUA+7VC5l2CatbuhTfe31O84d/vyPXzZWVNVucROtOV/HV6OkksdPSIh5zf5QZqlvvo7xYr0HLDjtkiUbrx8/vGNvC3Qxuzt7O4irH/URu5HOFmwWlDE2rIrJnOGMRJQ3CeMqMR46s5mVEFmW0ArYlV25cvoUQm83469TBw8evPHi70+veHzz5hsHT124cmzGyRkLFrqegSib++ZP1mY8AlKTjJ+RI6Jej5qeZHZPkSYmPi1BMAkYINzqYJouYukU39sBcjw2pcG0EoVj1tbnLh/vuYiTontQ19VFUUd4SrHmKCzEXqW8DM3ZdMQ1Q6cKRTzikFIjKjoc+EyJQF4kQN1itqk4TsHXOvFw05yhXX88c+Q1JJHaAEcawJDOynLC5VrJQx5OtstShoqqYZteVKnGz80SEuGIxxj6pNqMGRh/IyHDseTUQkuYkJiOa8OZwxFBI4yn9q8lRZ0fm91TJYYMIbw0/EGXFotiLMHEwXF3UFIT9XKUlAgZREhqMRSykkJWbwOVVEvhnkWWemOzrgVnCE0/eL9lMXUPydyxYUIl+IiihqcfmrKoj+4fnTdEfZ4x311WVVsJ2wkooeEtxY4QWj2yklJpaGZi4hC8CWy3Rae3l21mlDqpnDbKutgy1O5t6W1ut5w9svTzz/aer/q6PzUfXboOZw9KCkk4CWn/zVLl31NS9GOMuIRKSu167ub2I+daO1vq3a27dzd3RCJGmslxfOBFgh5TGnO+tLNXARY3BAPL2RjA4Com9mESVYapz539yCZU1KVLl07Rx+9///uziFJ8YwV+durKkiXqmY/NWLj2cQVuqeCbr2htM8iUlueMlRQtHKiqxlEYK0oqXjAJ0nGUFGg+6dmTbqekpnK5iHcOhgPmQ4u3o6SizV3dbbq+iJG6dRCpjYTluY0g1cBMgbwS8hhIRdktPCIH2YT0AedpmppTgWLTU2Tvblvet/fhjuVf/vHIkVcfWbgJQxGwvAy1yYRgLhuAybR0kSsVfR81fmVk4VcoVED6AfMVnNVS1vSxyfQbL2xKV7IZYHCZSN9+OgADR1CvlsAPzxilOvZhxjcyxR8VlYZmN+z0FBTNYAqK0tOyJ91BHMhErtycnSli1phWQ37slopLwS31wSed/anN3YtaO5q2tunoDddIiWTG1D2wusK+Gx2Mj+YhIHQWyGEKsX0CzO41+jQRN3PxKIMPGXhKuODZQldO3jX4vvHkKAmiQEFi9GKPF+YwKilCwwDU06XNkGY7QeotLR1dffUXly594dDD79ZVbcHmz+jjA/8hFiQ5onMmf+fD8G+BJ6iqxiL7wERhMY7nHrt+/Oz+/vZGN7TRixYZYc8Lxy0jdg84TshUTEZEbJwfETeceNkFRTQdAH+oIR67nMgwnjh/eO0Rzrbm4AunL165cuGKduHCTacvrF175cqK059eWfvcc8+tRdu3YsXmN7nP3P/KK4+vnTkDOny5DRnyybeqiV1WrP9LoJJCUiecRlwKUSaMv2/HHVMf5ADyC/t/sfr+y5eG397dFW1ZpeujPQ/5UVM3j04q5J4+bXoBpfrlQzGHmGYlBh6eU0glBa0VozvYae9CGDrqqm2w68Te53fM++LLcz0bV67YpJEItfBhykDEHOwbzDDiSy+x5jPsnKhImMxSzdwskJH88DRCf4gbnrYPTNWqIY8pMJNciCbg4HKOT4yHyxjXo8jh62gsidLS0xthyxpSpFD8mYRPJWUIZIFhi9TadDHnDjB0KinopUQlwbAaJ0JBMe2liuKKz58d/u1XnQMDLd07OnQzm1BSOmJJy4gVjWmZrCNQUnZkWsOJBPTIVPj5IY0BIB2KxRhRIVO0HLkD00tjIZKFqT7i9IF1j2uKLwcNQ4kDAkdE7NpH48iaa/yTSooubR2NWYysq7SjpFpPdG54Z+kL10/uRxBYf2FUJwdXP0fPQcYLjlvOxB9YSVH+YqIYxFKz1vXcykOHjpwd2b27Ecd1/549GDLIFxI+q3TBA6iglZSUxioLeHFw5pDGjGcZeUIiYU0N5QdIaY5Vtt3z9K+OHTx+YfOSK2u1z62899GF1o3bl6y4cmXt6o8PbX9sWVvLAlTUK/c/8wxKauGsrXiskGqVTa3emOTMdHBN8IIlsutKnCiYhIfHajW7YHKSPeF20mMmB1FSab98MLz60HMHDg5v2J26u35VpBWPqsUbG7zBpImG8sthgw46ej5JVhGyZsFijee0CUn4Ds4PdEQ2GQ4SfNfAC0LedW0nuvbuRUnt6jl38tCKTVK50+U3C3mY8sG60+YEs/U5stxpMdMJqqqySEChdWqBj1NNoUlm6QzMRFQuVCOhMazAM8IykyCx53DwrCjUqljJ03QPKICA6BCjKoTA/mf3FPLkHX48XKK0pDvJK50Y5EHVK8qGzAt9pJvIE3hErnWeG/7NxoH+zu7BHR1NMBtmGBW1Ijx2XJKsHd0JnnudD1Q8HY5bKMxIYmgkOBKjU7SwUq5km3R27xeE6PFRUT4bKeRpkCIcPkRdUIii5onlhwqjMZVUHoSa0oed+BMz5/YO7eoafOfwi4c27vj669pSBP+Cq6IGs5jScJDb8cMqKbKZhst0SZoigEtqyUuHDm0+u79xN5i+/Ubjbncon8xEy/OYEQcTcULnCoN5rPAKCqDQwz3vI9qslKLtVPchFKhp+dCGTVBErZzRN3Tx4oWD72++suTYWmyUVlsfXUt2+DvXrl396OpDh2bIlBL146+8+WbYFA7nQGQ3g0davBxxQpIocUy8yZydQFpx6gLHJHDiBROkBRItbTXFSWnjBLdhAzS5BK7faWkl+8wfX0EbOri3H+Ko+r1GOlTtrKC8NE5Dg5pKkUhun5TZ0ZCGl2+DsRLuAljBZqx02mboEEOGpwPHads9v+o8sbxjaP72jWfODX12/04Jnu8slxW7XtrNAYWDPlbqJtIss3PBnlduUjgyDMHMbMg7bKBOSAkZJd4s+jeTPk0kgvwHyQIJ8UlpSIIUxScD9vP6qCfCyGFhyyAA6bTvBSQd8amQ6aDiZfiRWhvWczjB+DtJVpyYDYekpHSYl1mFNrOF8qVSGir6nnx9+P2vTra2NncMtsdsV1TkIKKSUM+K5Qk2/kxuqpLSupYtpCyEp+eTjagPnNhINFfjo5ICo58WfO4ITQqaWAAOWSSRpSPONF8UL/ruCCbTwe56C4lGdMzxR0PfdUxWo5y7bu6gt345/PxWrF2zp7ZqmluSYYbjlYs4ACgpCvz+lyVVFPv49qL4rn//3Z/41z8K//znkiLNNYeT7Vfb1NadK7ffvPT2ju7+/jx3s7dvd6rFfeukYQ4CtG6IaDTRAtJAF+bjZVSRbws1Lzol+NfzFmyDqdIQEuN33vPcygUdb188+P6+zUsef9wMceEjOFKB/GKCxzP0iNAgVCmF4WAQcRWmMDakMhrLIL4zcaBbzYznuKxi5rOKtm/chARxsiBpekM5miMk0iaVjLmNsWH0RJFerM/Ozg5qP95w5MzFVZ0oKWPj83SmEj7BSGokN0Q5UapAvoUEpkSwx6XpvGUCawA1dSUOAWN5eWoz9r1K+CNtG0CqyaHLX/Wcfe/jh+6R2TK0+DYyXDZLYaoUiico32XkH0n+faSRT7UqMLBoRePiRcEcbHyxsxAiwtCZASK6JyhOz4ShLE4Q2CFhhwYjv8yERAXPQjAjxn7yvgDch8mDlLQhWlTJNIgvqIwaTAgvzeaIFIl3WFKQdSHkIGCw2dS6qpSihorKiOs/e4Yf/OThZa1Ddl3TrAU6AiDAh+Exni+uKhkZRxClGpMowHM2XOJKCbGhlJzSyc8F7BRsykvzCkjd6yaFPDBDGgpiwg1UGJYScCpzp3b52trWtRFUr1Qpb11NIDrZGW0Hck/p4Lru7hMXh4ffWXGs6aP+fp0T0UZOJ1S9yehjyGrt+95SdAn/wxXz31dTUcq3VRT921u/wm71f1r1QiSfnp4GNDbDvqjl0E3spVb19+9eZOyKrk9NJVtrvE5IrsgjNimZxGvsRhiHAyjDMe/1YSlBx4kOi3H+8qHHTi7csAypodKZj23cuebiwYMfHNy37xUtD2irVyo0aIV8wGJWh1mWoXCoeHA9Qf4LNIfBLD7xLbD/NChwQ5Exb5CbFLujCKiIzx43xV8VVyM3474vSQdefBs1NZHDLSH11YM/uffTS8cvdDdCI99VXx9lGxUUk495kqRSk1uOcbrAQh5QmJ+luD+sVFJOMoDVPucUzlAZKyvz8fmRdl1T06rBXbtObv/ksXPn3ttmewwXU47HwLc6ZKV1NUa+FhYrBp4F1FwUFHmG56myAFwI/Uk/oqIhxASpWeocv8vlz4FvH0JIw2FFMKgXi7lmPi8rSBZLJgkQcyofWphG2Woay+ZIhLgUXpuuqhqm0yp9tgmITRqMZm8vIjL22pTwzXrspYJ+NRxvVbVFKbU8rcH1v3uGX/vkk8GOE8uVqlmQXAL3hVsYqSZ5PObOKMW/89Gc56awAW8sjMMeoa+QrJBoled2w3YM3oeoM0jno8Tko3wxEgcxC3Bcv1RSRrexu20uQNK+vhN9ffPburt7yQ3Gwu5ldlHh0OttsfdtGO45vnmf+d4mvMZwcEZPoE/CJZWQziVC0r8oqb/VUtG31U3Kd1XUP9xRsU+8VVnsH99yS7Gf343pNmwwyMrOrz90CCXVODBghD0bDKNgGVSIRwh5drmELNO5zhoQmMgD4ykgf2NQtCJehllB6XrysWUXHjl089DHsx7bCG/jg+9/8MEHb661k2kinFuFYGgYslxqtVNi8zucAVxRHshZwyUcBZ/saAEFONISkiGUAmsCbtdkfZgkFovi49MmTdalxFVpoOWGlWN28thR33/ZOyXTFDSBgPtg1lsbqKSIuGZBSUWjDI4GJ4KxZshrDnS/PDYikmsaHQFq8v8neBz08ZWYFL3lH+ajq4n6vJ1dg2t27T35yd7Zw+eWz3oMJpcOgH1qq660DqILryHL43HxfQVsniDD8GiGyZPBEyrikW6Dxk7PSfM7+TZXmDJPSXvI7G/Q+DpcaqnRmMHlBsWZXB5DTqLEoyMvDAD7RPkmRB2Yl7G6uKGo2ogXT5+UlMYdcyf5v7dKipPt0YKjKCtvqHN7ssMrPnh9+MAn93Q0dyk18yWqZlqHp4Y0PEZG5MU0dIzzSjQj/IhQn4JGhYYq1BFKKq8QB5PUm8rU0USddVPKgI5hnKT+odtHqsJMSvhE9+DcObouVNRAn24QDSDR/3EpE0JPJINIV3NvS9vAhnNUUsggQzwdRZyowSxGEg5KKvivSqoorkrPSceHQl3+D2VSUBEXNy1JXfQdFVVlLP67mamuRMuKqEEuite7v/mTvrWkxgOX9jszfHXF0x67jpLqHhiI5sKL62VqWUrJ34ZFLoEBHCWrIOYVhIkqlQ54ehGxutJQ97ts2aqnZ3381PWb16/fvPzauZ6Dv3n/Zz/9wNycS4bgSI/22oAqoqTwbKpkLoRucsM5YQSv6/UBPq2zAGJ5MpPjM0VBbjZuKiT2whQhbMqG7fXdYyqLq6stAYc2kAYZ5+00OWOTchTIbCp5Rrtgw5GDB3bsXzTQhaqI0pInlVlhoecjEU4huTeivyWDADR+EsgmyCUPvAjMU6BrzeALVW5ANZhoOn07hpR7P/nk+s0dx3veHpy1+mNbRpaC6xeGaquLUoqqy3zYsDl5dmNhJfNzKc3VubgBgBeQq3BwD2mR5ok2UCjM4JPXAA1y7sICxkNAUlBBnsYMsYVYrNXRcc2kE2gAKX8mdnyjEbSocouLq4pTpgdxCScmltyBSp6OGzHfnJ3OASEJjShfqMnL1SDma8Xm13sOfHJysPkEdWIRWIfhNGW3FBAkbJZoawKTHkBQJHtn+TYxoywCxhksidxDpxW+JGSJA+iivDyXHHwJhfCS9bCOIEQ5T8foSqFmzXxdXxeuqL7mPlB00PpZ2NrAS2bf9q6uE+gB2/uGzvacOfXMg/uyzCDRoKIMDgVnzPgJaPwYbfa/K6mUuNopAuTikTGcBJWA2+YWtKkW2OPi3IJE+j+xf8kuLfoEun4qJk2o/qZy8NkKAYf6vLpMmIxMEajZbyr6Vo7fKITnBf3ajMLqorqTi0+Bh74bOq8P8/JepiFgWp4Fb3k+xA+wEnCH2G4cABQ6YRQSydDwtpOpMxYX8jVr1jx2/dDNzps3b4Ig0fP+b37705+9uTC1jNHcUFMarQsqciKUS/lciFURT4a/wVkVTDZI7/hCQOhoi8zOHH168oTk9GDYAZ9JLpUUp6quqtgYcLk8abFb6jZKCoYPYW7wF61b3r108EDjjujuzigWBNT0MTYssWfzymlgLKRJwEvfioSoQjRHIQyNaspmmyGlh6iZBAvro/X1G3bs2vvVV9d3XTxz5O3eWfhUa5YpLMytA2hGIj5oWnNTcTCHQKUgN/V8mV/hENpcIpjzwZ5FqMjM1HvMfA2sJMk/EgtRMpEsYFK+CLopn1QpyUkzyX1sgRNjKWDeY7ITcsWL+jSVKKnquBq/WJ+WlAmvwzvo+1BSQkdJukiEkkJqCAZdudAR0K44fubSaycfbuvqA8Guy0inaQH+g7cEBzJlLAoJkCNtmVSRVDwJxPaMDVUA1kPkIc/nKWlhSwcX4r+nl+eH6F4LhQjp0DFMnU+JDLiD+8CuRkHNX27va6aVL/Hamf8LLimI9AaQW9/e1Lfj7Lkzm/eV7Hsc74iVSkobRkmNBzyR/a9uqZS4msljK2GBUJEtmFz7zfyEaag6XpAfF1eXWvq32ejv27y4kCDpbzdWjV4gEFFJqQVTK+PKpgqMtML7dtrs2EQueQ84y1KKr3Ve3wyOX38/ou22vFxK+bSVRi+OGfhGIhYmP+LDI4c3lE4io5f0PSTRjDn5yXgPbFuz7NChk1s3vP3O728c3Pebn/7sxx/85hVJ3rRbyxmM6OjZ4bFAatawH1ME7ihcVOGAgU+TL6NOAO8yw7UneVJC0OXk0/Y3kJnImWyqrqqJc7uw681Oyk6eMur7o+ij4j3ZXJPJ7K67evXtg68NdjfW7zYaF+0xxrgI0F2jltChUOoh0abJekSJTRHaUFpIwdtBbSWulcYSkcp0aL7QKrajW9kx8Mnlz94513Ohdd5CBEPZzGZNaV0cHW/0UhcX11W4yf2ZZFnT87wZJpMWeGZaJkeENlfqF4n9NqmFIi+mk2sDbimqbRKWu0lvjrgLtywrbFX5WNKZ1xL7oJ0YXa+hqE9ajlzd6qIqJ+avTA6XEkVH3eEthWRRE7he8J8yZ9gMLtfpSzcOHn2opa29rwk7odZCsIhTSdHEYHQQX0mJwCcVpZwW1nbiaBFKZ49xXuEiD1aS207MJTmxTkKFeIqgG0S7jP26j6KeVcy2AtpeIMndg+va2uY8QKaQc2BvNog/zEuwOinIUKeW5uV9uiZlX/PbR84cfyr7lRWoJvLjNaippCZMSE77PiU1cWwNPf41k3EtwQkgLzW3BvfLtGSBvCqluraKNXnlqdPx/sU1IM2s3F3agNxiuSBhWsOtUiuYKJhMt1Rcw3iBKq44TnXrj/7Wxm9sUnYSMnt4NSl11z5afOrsYHtXP+b00penTa8sLMBS3E3QDbmJuu32EBjHbIVPCWakM7SToR9o6Tz+o9t2zdM8e/OxJceXLl0Kq5b/76c/++CD33CV8CJFOX34ISw+hGGHwaFmegZhVtiDivJAyZFlY7aZdEmB4CfmiMAgGD9e75SiUkEwtIoSE8dmFdfQLQUdgygeJNrRt1FSCSZMLx5ZTdzVq/sPvnbhie5oZ6uxIwZOQNljpHBrBN4wkjdae9JZ4hjmEfcX8CR6PyCV8CHURFBSdqYJ97ZHuruHmj7Zfnppz7muzvseXbsTpnyqClqVopgQsY2/Gqalsp14IdmwygNck0Go9ejJA8Chsjv0Cp4RVyMKGS8rfSGE54HvXoAUTuRekIWXW6Z1CXWW2NaTioqIG75QLCHXIs0rLq6tK6rjgROYhDiGKYLRd1xSFNmGEEStw4ygB8fq00cOHj26sqVlftNMe0cr3eSpEd2tN5qRZHDgCLEtiK0gJSom/YLliN3H6LSMA+vzkakPX4aVnyW/ktEojGwyxN+we9LgqGBiqVDX4Lp16+aiReHPbsNPu7vJ5QLkZLKuI04SLAmW97U0NfW+fbzn1FP6N1dksMUGu6UQgYiS0k8a/a9LalRFHN6W6mSBNK6OSyUwRRhXRXLghKJcunzilJNxy9+dGhdnEcQn00FcUySkl9TObq6UOK1A6GUlVTdx7DSUVJng7jpqEL/llsJaKjMd6lGXsCaluvijZ1/ALaWLos/7sPRlRFeAUJAawiQFq5vc6XkWDfNAN6ZGIlGKiGLIDxoTCfoBue2tEwNfvnDqxSOg8/14n039QfpPf1MSL54PmQTlziBwfLoSrnM5oGKDLArWJ7AJhSIMOwbGt5XzMxzcJAIm9GKQJzKzJOAr4w7UOMWc5CnClKq6BnuOQ+sRJYjAEP3+rd+oBC5O8XAEktWr7166dOCJVUAompuN5NKHSRhPKCX64R9GkswZ7XIK8wZ+CUYDxCaAuZ34n423AGw7nURjzGdWZdH69lVPXH926fDpZYOD2z4+tkbOU5ZW42Crq4CXBcUBVrq9RHvUMTGd18kVc9VOiMH04YBaZdG4wgYv7FkrC7Ei9RFjDsgyznDMdGQqVMks8SpTlWaXXEc9NRHiMPlFMcneQv8iGnd1cV11XJ3KrxcDXYq/g0kKb72er2XalxwzVRSddhkG2zs9B4++tq7ZC1PUbtxSRMPyMqq47lbPB94HL4OPewjXCUhKsW0TXSsxJSJOAbvUyiMtiARmLxjEMUyVV2JopXMsGiI5MG4v8qBFG6vrW96HxfWxY2vWrJP2DQx0gSmCHhH7sEhECVUVOJktLS19fRuO95w99OzvVmCzQbOUWpuThgwGssYcJ/jvHZK+uaVS4oxj0emVCJKNlR7B2NK46N2CrNy4XEE6fkUw2lEeGD02N84Lwo1KN1XAjavJEYyP1LBhCn1fRVw+1V5R3WRBJUoqXzCxNi7lW2epUVhLJSWIPVkZtXHXij86dOrtHZ1wC6r8sPLlQsyV2C+4o+6ymoo8MLSnp3oJ2VNGKaXLjVwcWk7QzMkk3gu27TrxxxeOnPv9qYM/Pjqrb95ah+eX8Ync+aUVLFmTXIy9EOch+A+bThnJwZFKawqobewtkZEvpphgcz0MvjnpegMRdImVYUCg6ERdSlVDnRLoaSAtXnx7JRXPTc/k+t0pV1OKqo6fufDEKstAl6+XnfZIYiN+Tz42BCHimRtZSRHbni36UeVEm4BTLWIKZM2woVNGU5l0NWRpeeLAs388fHhD7+AylNROoVBZWVVXUxaSOMHio3Qb+Kw4+XINbV90EkMQumk1Nkj6nAxNapQXcPGiuL3Q8NEDxrjHlLVEexp8AAwIIek2183HcSO9pXlA0xSiq4y+blS3zAtosRgllZMtBiMw+Q5LCgbOmSIxlRTUwWaC0jOEVFJr5nRYEEvT0tERQqwAaQOZdwsOVCkZOUllTh6+M3dBCAQlIZ8XM6ORKGNYCi40mYPP+J+A1i1G2sHg2KIIDzoQCIMgvgA5Y8PSZWB+33zezldfe2nnTtnAQP8AKE7Y+qqoGKH09HV313vbWvralx3veeHZ6yvWAixSw7hNq83JThwD6Q+H0hVG/6uSGm1DjpVpoiAzpW7c1GlxccWZAktcdZKgIA4lxSmqThTI8Hk8gRi31MSyOBTW+DoAF0l/z5JIZddZCkfAhfw5WzCxIu7bbymUlCgzPjvLjJJqKH554wtn93cu2pNbiHVvYx7s9inZZHpNdVVufmVtKUJYlfTWQxOBiEALMdNlTEDFHONn3fz8xdcvmYO/fPalxvPnv97/dZnG9MtfqvMqYpqhiorpUoQqgVeQZZWwVtGpdTkyiN8Mki0mqSwFh3jnIqQlwbjOGUsKUcnM2Wl3TzIW18FUn1zHshOzk0d/f9YsbilFOlzHCuhIqbvY89oTT3R0nfB1+5idbCpbT7EVFVuhGo24W6iWIXznk0EEeEjEfkH/zleSXwK5lOUbmxHc9sT213tOb3ivY9U9j/5E+yrszSHXRPSI2QX1BthsQh6z5IMkFys5m0ufZnIFsB7jOjTufLs6bJaxEon5TElYQAyp5ZVexjtXCYVyHx7EEN9skBG9FMQpjLCM5UezFFyKeDL0mcVxVbxwmohTwp1wZyUVdAbQNor0frCmyboJ3vHCGb8fPnj0AK4njFItvR1dXaTOtTCKECmYSCkp12n4EiCQ5ZWpaJFp0qR4LBxF3hD7rnDJOGUaFm+N1i36DZ6KOxkvtoW0d3jtSQCGS0rTprT3SQyOl1567VUeQkIGvEwbxpjaCKNCSfnqu1v6fHNOD7/+p+uvrEBcG3Yb6Mb9eux6xyRzFN/NxfpbSd2CDfQ1ccXVeJLKveMFliLAE6G4uDxBelyFYKwyLzVPJZhabRQkwnqtVDC1CvBEYvVf8YniWEmlFBWMEqRLkwT/fUmlJ+mxUqmIu3bt/MbjZ3e072ksLO/f8vLL6IARE44rpqaqFoF2ZdON6KiJ349+zEgEYwnJeZWYNIlkIj/58RtL3zn147t/vevP8KBs+Lo4papS4/FHpoNBgEmqrDTfwHVZXWqwaDLIso7YCU7KLKOLgSeE4JULjkmCKGDlqeQ2HskIgBTIHJi9f5SXUp1SBh6DA+7yVFLfH91C4weVhIM4ocV17/Rglmo+MeAdpJ6PKon6vShZpTD81xi1s0hhOYiyfIo7kuFQJNTPmsEHRVoFWy3YlO1pntM8/8DinsOfDo3sWLXq3tWrbQYn0dWRTieXhigZmsAEJc9qxZYblh2GMIjJlN/N9dgsbp9TEQzgBmOoGYRlKnIEj8mhSTeBcwoeSVon0IuCqEoKf3E0S9h/YiqhjXSI+KY+r0xorMI7WmZFhClHr7/7TjB00GadflZSAcrfJjc0Wha8g5Ja1zyCcIG2VVCXsZKi1QIWJXRTQSkptXh5UvQypdNTNXR8UNdB0lOZhpgoKCkcET47OVUQDcCH4oPMA7w2rAFIUgPEmBFC6HDQSGe2bbt322dPufxPmVffREnRqIi6wzcLS027D5uqent3u7f+4vAfrh9683E1KymARmR5w0rquy0C/m6W4qOS82tpzVRjnkRRNQIfIX7GWElV/vUVqUpldVQpmFyHbjC++K+LrFu3FAGBk0YJpponTqzCvuTbZykqqWy/Vlua0lB87ROUVOeiRXsK+6nzK3AXlDP7BEpeAtnPQuxJ8rKTgJyD71vGWLJK5m8okd28/sLSx3888X9e+PoqPCivXgUOWVxVyJOnVhKE/mFZqS9ckmWFxZYrxyGkgfRWnCZzYpALzQ6rJ3FMYtCB1QdZhRDBHc27LEuRPmpCfkpKdWoAm31koNwee2JUMu1VXfnF6PzqPn399BPd9QMD9jYSFtCzGkP9KEjKwpIvdTRJYauEksKXYFeR9STM9/GlSiUzNPDPnI6Mt47lffee6zk1r6tjR0fX3pceevTeRzP4GjdFVNXWwLC1oQo/KTVKbE5YsfjscrWJI84x+BFXrZWGlAaFWBSEvQas/V00v8BwgCdjcRZE+KHEEyHiRV1am9yO/x4Nb6h4AGnQ8xIzMUIh0ha7kI/hrS7Vwc0Wc2A8MfqOSwoguj6LOFdaYl456Za6gVtqZKiv6eG2Fm9zK4mZaf6hgF2KPscCyxgBXBOiksILxCRgMaNcQtgxdwFikGFzFXMuodUWzHHglQ5FYn6IOIvMGomQS5SUqmnXl9e/wMf16/QDzB0jzVw+oGBGIjS2dHd19fb6+nzgTzz7bMkrWkDoBvofFlNEMs7kZk8e/S9LasqtmQiEgR8JpvpVpRxq/OIF0VhJTRdMtDBDrLyUKNVR3PTRk6u+vaTw04ay6dXTBGMa2P/51pLipCdiMaFOra6+du1Xx9/ev7u7uzu1v7+wEUpWwFKlcOWqIJpa2XS3j/GDySzACzjZJyMvTFjbRGF+I5vx2VNvLP3RxLsPfP113flr9ASj1Sqqrt1jWZRHpuC5bmc6JwcSCNDhQOpDmyBnQ1RMjMdz+sNZ3PgkrpNObVquMyQErvcIhBk9xl4dVyUNmNUOU3picMztrGBGjcvmlPizOouLrxZXfbr01KpuzMCRwXrWopC/FUFaRnZJka+DTxJjs6KbIQ90iZApnOiZmSmb0VQw/cPKvD2tHfPe2tzTc3qXD/77nduevv+tRzPUwmhNyl+pLcD9qmuNZGeNJtknUysQLZ4REKXn2HRemyINdg8iqAYVSG7zZKn51DORXxLyKAipoX7TRukdOQ5ZKtAh1nh5CT8PEZmCcf2MEbnVV1GTKwsE4XRIgN8dllRAzBGLg0hTBORHGbgZthmXUFKDXSPv0S21ygfMDziOjkQHXrsEEhcvlrRYiNP+f3p5qtIWsyXk82z0OslumXpLrU5CXKgS4aWPNg+y6XzcawUgsQFGBykiJlKGmGPWtj+ykoqV1ZcDUfIAxq7LQnAMDqT23X292CK3Xxy+dP3ZD/Y51CC1EfPSDPAX11RiMDj1X99SYyvI/YluldDoSQDNG8bEbil3rKRq7gJagc9z6Ipi3d503FJFUUFSyj/NUkW8TIIntIKc2BbrW0sKOWhIljRLq4qqzz8BvVQjSgrU2f7GRSFKiwTzqAyi/6qKUgqGZlpLIKYhpG7qZFJy9sUpBDWfLOPNN178w89//tz56rqvqxpgQ4lvoPhqUXXdh6HeRQV7LBorZ3IC3MnhsY8QdWw1wLTlkesz8wC0OfScEkzvQh3thTQxgJ4MtHl+7vgp8fyqolIn5mezKT2JO05wG+qOUWOyOWkef9P54qsptZ8vfWdDY7RzYHfHImL5WGKGI6gl6HeiNKZAackEFzw+TXh0cdjIIwwS5RmyGTNa8qDO3TOyZ96KpT09x3dKdYtWrdq7694VS46ptRl2eNVWVdfVgc9dh1u93CizwQ8INRCRwL8bAYnhdE6Ar1OqodvPpPhdjghYG1eByA4+E8WghXLSqM9ICmQCHQS4jUuSaBjEQSfrCWPsAyQ/FXi5crWLSgIZ36MEd0RIUjhdBE8EXTDYdGWpESKCkvr98Jmjq+q3vDfQ1LKupb2rtSNKtxQZGOGCQklJSfIEkxnAkwWpXl5MowmsELVFbTxuWvDWHWrM3KShp3uKubgVGin2GAMUYYcEC9IZYdfMurntqd/dvPnZoUPPHrr55Rdf3FwUYmRSpQ/EQDwA7RBuLe/tam66OHzuk+s/3rcEeJ+VPrQeMQ1TCf8yq5fdUrXfFIBFMB4dm1qA7RKBEtUMnogLCuKr4urSBYlxUUEi3VI0S6UKxtVU/62k3LFVb0Agqo7Lv0tQHsfawG+bpaampZHiwGWtKCo+/zRuqfWLoObsL81dBPgcHTDWJxVVNVWwEgAUpZQw0zGstdGCNGsoAZEcrnXK9lmPb37jD7/9+X9+dO18xdcNDVexBigqLiquRnWVzxZCXOgXTRVM4rrMGRRaQc+OnB8Dz8kXCGvQ9PgSfwYZm5L1FEtKoRtRZQ2LJ09M0lY0GGlx4iCsYfztqHpHj4OM3JT12LTiq1f7P3/jnR178B5ZQJ+ISQ9p1W+8BadHmiOQ+8HEcAHkdhoVwXb0lKhUcynzHY96X2VVTfmejk0r3l+69MUr63TKRY3Pd3a+teKVl9Af0r5aglyJPNiLydRIa7Ji5qADNyJ3cEVctYGbiJLSSOGXHkxPSofBRmJmukhcws0BrEgTPi/WB8skDP/EtV0CxQcHX7xaY6SEabbjZXAKela7xmn2INSEwQuKeMEdldRkhS1LDNIhl8IPGFUMS2vcUmdeWzWwZaSvvW1VSwtQdJw9QPy8Id9cp1ADkyicqFDusrhVo09CcT74wHlAuSOAmfgEYNJgaSfkjshKjAhrZFov/JSaDxh8Ku3MpXn+vduu/3r140uWHNy3ZO2XX37x2QnyAga23E5gPKY3vF265c2RvReHj5y8/MEHWehP1az1yylhJSX6OzLW6NHfTkgai71Uyi3eHvzkueNGTRE44uK4iMlsyBVkFsfVTMAAhF+piLML4lFG5Ziq4sqQXyD8hlFBZApGSKqZJEhGV2D4LockoI+TEarMCeOMMtZd+/NDL5798KPG8vL+9R99OGK0UElNr5hWSxkx5G8HfAK4hIWR+nStmGCbZup6Wx5uHWo6efOpU8isvctUe+18bcPVq1dZ94PtGohEdcqAVq0QjxsrmJydRbHPNpucCRZjH7Q4zHAFk7K1MhbsQKwWsJDBxEFl8f3cZMHktCxjgTwDu5MsLkxObm8WHw85kcn8yEdVde/Of3zz2pH9Hbs7LR2LcOTT8ETziRtLts7Oruah5c2U9/fwzIUb5in7mmastK2EveDsdW3rHllzYdmyBfNH3m34eqDt1I0zxzc//rhtjl3XW1/f1bRaYVLzM1wm2MZwHZi75PIMhyfIzYL7bJRkWD5+QC8yOa2KxMywDZAEfCk4uKTixyQkpYvQA3KhrgTYRp0mzSQqWqnioFEjvSMpE2wsxrNIJc0kGVFEY4Rfn8Rqxh/L9XM5yLX7keDOGj+FLSBKBGCYRWY+EKSQfnnGxeM3jh+A41jX4KqZW/d2Ec84tKije92qVfeslPQNnLDTNA1DMHIjsdh5zpikzEluPHhf0TNjVSX3kcid9Lo6DROBhLDORNMHfa+FeR9ZuiLePnufVznvwq4vrn96bhgfZw6u+BTd34D7+Yfre5v2tqx6GJhjS2/rCR1qu3P5cA88thaDnEz8CfSoLm46DLTicaBQaiaiEP9pt3KLNltUF/BU3RqKiuIqFGMmJUYLuMKiohpFUkldTZBPeydtwviErJq4uDK9ELdUjUkL/oSGk6j6Gz12WlCeQrTZCsW48SJLyrfSZkczKd9EDmLBUFKuR6aBPbH0bO5Hjfs/rPzooy0jdvC34B1eSYrnabkhC4Sv8BKvLCBJHNCD5SP9qe0z59bXty8f2nvz0Genzvz85z9tREVVNWCOotYVMC9G9bqGAr/LZkqYSP4hriwrOH4wLRZ+4+qAoxk0GIXYY1ORQ7aUxVWShSBEM+j7HNlTR9+NBSm5ByE9PRifrr+91JfxQXCAXKubQh1Dj775wYrWHd3tnZFFjIce20aRv4Z9b2dX1/Kh5mj73q1b5Qs3zWtSqmbYbI/M4D8ye+7cGbN3Xlm24IETI1ve3TJ08P0bx48vedKPTQDJ1y1Kh55rtjlhL5KZlF5iQiqhPwDvaQVKCs6RYFtY5Fpump+EK0lcA9xkAwjPykxKgjYqkz7E3BwP0HWPwymLRa1TlIFUJszhJIFIkpSk16J/jBJ5mY1/RKHD1tcrg/mzKU2h9UOTyR0vuONZSkTBHQE1ydlIv6xV33vh+Jnjg439/fWD62Y2tVtQUl2hRYu6V61bd89M1UDrCXIJAxRnJymiTiWkSwNcfS057YJBJhcSu0xFNxUT2LPonBCJgEgPQp7uJBH3IUU7gi6kT7ds064v/vhiDyqq58yZF06jpr7c3b7VTiXV1tZEBnhdAxBUtXTOGz53YPviBxUBjHwoKSjNGD6RIAqThaFYNIryaEf/S1UvaLM46f9JVIhVVXHxP4k/ir5d8kF/QErcdzPRkWqWmBYAbOBqefn5xS8e3vJRfeGHWz76qH+IPBB9hBsXIIMp1W4BUgGmX0GIjh8yyom0uiMz21pClg7cUp+tOPXbn/98SVXR+fNV1VdTvvkobsB0UcV3CMPjx44aNVkP73N0Chi/qfWjwDKcN051OKhw8GGQZ4+5MaHP0JCXG94IuTlzrOBHgXC2B+7fZoNDHy9iQUrfv89B0EA6juGV5iy1ecWpK0PweiQ3PGYSTEqJECuszvam5t7lHSM7mpG+N+OeR0CTWbhzthwe1Nu2btu2DUzgk02a9sYNZw9j0jiwfZ/JnzGXvlSfRekS6+HhAlQvPhGacy6k8NiZ6GFlLiVLMzgvyA1cvQO2GkmJ2WaZzEqbbArFjE9Mz6RUTxDvcR/hwrHyCRKhuCGwZtX6hCS9yYR5SkiNU4QI6Az8o4UvgGoZEn5zssM20B/uvKSC1hwgfiRBJGjaSkBaBn/hqTM9B57I2zKA66Stu2tkB6wPkRXet3y5cu6qVd2Di4gbiyBCPuXFymOQKH6nk1pjauRlMqdQaRYiMID6DKIosRPBRwLgb5xc0Ge7u0hnZW+65+SXfxgmNdSNM8PDh49Bx7CrfwhOvkM73mseavZ2dwycaJu5qhsl1XPg8v0P6kFqs1phOwmPJOAT45LTPdk4rMfdzSgUo0Z/a0n9vSQqhSkIGRkzVlHsvmGfEfulor/+jpR/EFYV/d0fUPRdQvlRRJvVi0pchiyD49WtG597cemOjwrLPyxfv35PKzwN0e2GChFfS7s35BxT7KaXoDicQjpfV7TV2N4kUTbvGGmev2LFDZTU4LXi81UgqMbo81RShCjXWUyKxImkyddTF2xDli29BZhV4Hbsz0HarMcaM9rEGcagPtorArTVSTIwOU1J9Jhg4mMwu9RZ0HYwe9HRt3NLZXJdq53mV/yvXjm1uWNRxyI320XRmB+z8gInaX17m7J3qAMW4L1tM5GDNVc6e/Z9c3DA9jXNmjXr3sdu3nxs1qpVa1470nPu0oGj258JO4QsbMEeUTrS9C746Zk4iSJ4sMDAi883KxRZTgn6HVoMK4UufVDrdJUkxpdoJYixThejnyMxWDonHYFpiJ4nZ91MJObaeDHfNo2cxJiJYZw2/oDQTioagOfAJEFAiJKrK5ZeeJZzgh6DIwhV7p2WlN6aA3gjTZFFJWWgjwy+dNmpHog7+re01jd3WJotoR076ut3rx/oau1qVq7q7m5k5rdGn9zGcoadrBhh7k62ubRf42H5K5eanVDEg8mHrx1gKgjosGIDf02jpFwCVFRXNLWL6Xfbt3au6Bk+d+nio5uPo7JufAxIfWBk+VDHUGvrUEdXRz028xDSt7OSemixPhjOUtMsRSUlRvhqcmaYO5nKafTYUf9I9vsO74m/1cPfi+SLUr63hv7v/oBvKSmMU/FwVvQbclwOT9ZDG9948R20ffv35/b3R1kGNHmy0ZZf5SssIwvWkIbiSjRQ6UU7jXtGorqm3iF8/xffQEXd9fNuIH111PdR4xdHypPi6pqKhsK0BLY5mSh2BJDCJARxApCtk4KfEdChcAlljOfMoA7ikrEsS7IElqlhgDNZH87xBMhP35GDtVT67bl/T8LkwnU47834xXMvXXjx1I5F3Xv2716/m+hmxtg9ZYGZS3n9sjm9sNSHOhFLsQUP7Nq1bRdkccube5dtWnjhyn3btm3i/+riuTPHj14++tpL+8Jgd9hj8UoaLVfhgC0GSio96Mky8HxRHc8aCFhl5BgGO0AJXBy55gw/tJRis0alBlyCZKSkpKRMSJXIpzoJdxacXDAVBAxCRk7iOfTYY3IcEqQeujI09AwyZZqRGIaYZSwWBO5oXWGFJ2DSp4v+70oqTW+CUw7iTgGkQw8pn/dpz/DRr/7cP7DovZGRVrwgzeAJtS7q7W7uOgEpXbTeRzpUHZndUEHhg5xl4H4Uy0qgcGslg56Ut0I9gEyQFaaSWAG6mOQO4mRqAFFdXQMjv+8Zfrt711uf3b+Z+r+zb12/uXfkvfcGEaiMuloUbW1Wwp+zcwM1fvtMYQ8kPmYC/czhNOATCYlcLnHR6XYYNfZ7ldT/v3YuFHubnB2flKP2IDLjye0bV7xwaUfZ9PLyDyuNLd5UIBEgZ0dIYKnx5ZNMwch49xS0oGt/PrqnoNXSOTLS27HhFN1Ro+7KuFZ8q+1Dv8m0XICUa6qngS5M9XtXJleM54+dh/RWOBFrhJW9nJGtdezUp3WEPZYWApBCFsASapzHE/DkgBPoAGcpKUjT6O24jqWJ0rmOR1evfm7lQ1dunHp7f33HjvXrC8miJkobH3p3o/l7OqTLen31JPe1+HrnaHah24MwbnnvnE0L1yxbeO99n266eOHSmTMHj/548ZIlS/wGnpLEDHgleA5PjgsBvWj8MoNmTW7FeUA5PiHiBFRs9aLjufQiuCP5YSchMmskWpKYYhmUhh9pabiwWDlBxZwATrjJReltuNbIdU2sldhVGVlquY4ZvpKFf4j0UnigfTIE+iL3muIYku68pIIZfpBugx4t9lIOtdURcOCY2/RGz/CPv3q+39jxHjnfROq99qETrXhT+loHTvTtbWLBxXBZI20mQQXQ4EiQDIariRKcYemNNYhMyhwqyJMiwoA+lnGt8+GWYgSreh/zJsRp1jcwcu7MubfvfevLQ4v3He/pGT786Re/uzkyssPSudvS3doKy9suXcugHSU1fOChl5ZQU+Pxm+la9eszwbZJTItBfqNHjx4t+PeXFNWUYBw8fD3arCD3lcfv33jo2deB+RXu37Klswm7B6wH8HpEbukKUo3MaYzF16pULQ+3I4rdnfvhlkXd7xz5w2/vumvUKD8Gp1hMEQ17scuqoaa6JjxxFK7lUXehbYP/NtyC4R9pgO+d2mzFnj1mQ+UlSzdQVcgpx84SMsAHgqXURFEgnOXxg9apBciD5PTb6vsAFxLWrF69euVzB2CC9vaOxo6R3ev786Mk1yFyD3GivR0b5rS1tDd2du7ujACoeGDBnLkzm/bObNLct3atcNaMnWsuXDh3uOcPP34QdhpLgNDZpMwfVKeUq/3+rKwslyuM/ZDTXduAy7m6oTYf06aKjBM0CD9PE3sMQldiclKaWuIMYyslglUn/aZAIIxVLRo/XFqJUCfCcZCLdgE2YmS5Jg5Aruky+a0yMrazMEcvJgOBE4TQQKaAfgckWIl33vhxCUQHPqIlOpLa6sqBb5rtUTRi+y4///zzqSMjexo7o50Pr1kzOLhr765dQ+91LN92cy6YsjY5jyjh9JsyhBIdvhw1nz0Z1Fr4vCopH1ndTBLPoHL8S9rf4x2FcoM6Py/E74BLQ/WhroH3zp25tOGtm3Au3b746Ae4qA5//sXe5zsHOnc/v2p3fypuKdhPKNtZSR07hmRjT9jjAkXMkMUlH9VEEXPaBTrxT/4J/xa3Wars8TARCbscCj3Xdeihk0++fnbP+sI9W/KiTE9EVmOxFHE8PyxDS+NlftUaVXujMe/DLZW5le/mtrxz5PjPceONyj4PYRxjERCCHtMeYzVV55oyGiD/6CnxJiB+aBdo3UsCXz5PFdtBoZ0kWxzSqOvYNMVcA3k5EMMkq4Vms8sMhbTaBaNwoojejtwOJSVW+Fff+/FzL716z+Yb53Y07tjfv369mwDpiI6OfOT7qZbN0Slb2jvXg/DT3NUVkc4F0AcLu7Y2vlUtnPnIhQvHcYK+/v7P/p83/5cpxwynTA3TXCg1Qr+CUuQRRcgNqErrimO9Nm17C8nqBqYaVk9aNjh9gaRk7AmEAdRApj7g5MXSnoVWRw44RelJhFRkpmfSqkosykwiM0NOENpJfUkwB2MZm/GJwsMONo3NrCaCBVZ1waTEtOB4wej/m5LymBn2o/ZDaSzjrz115PD/PvnJJ8+3jow0Nnb273143Zo18r6bu3Yt7+iY89gMAJNCegOddCqqob4ED8ZpyOCx6Es8LqDz8yQOLYud1THHPruSBe+yktJ1UaSivb63zd5bH1oUtdTvP3fm1KZth8g4/6Gjlw++TiX18d7OzhBI6J27o6GukK9FNwclde6ll5aQ2AbDA0EUBkdYzPrlYMKU2CE7avQPoPGjr2OyKA0gujlLH1xiXb3x8guH316Ut6dwka8LoARW5uRdjfZFxTozLwUAkW88qJGa+vUvv/v1ux+Wlr7ceKrnfaYr5YC0HoP7iskOmMzTUhrO19U56Vcpbxm7Jx2JGUC/YUllzPyZ9CESFVPb6FTMZVTKzLelGUljR00yVJZ6oQV0wPkkIBqTHmTYzvfe9GLvJuaEzbZ7V7/60rGnL59BZFt96/r1/RTVAsksweC0EfBZOjv3dq4vGNkxNNK6u/P5h3+1ZtmmDYNtM++5Z+UjszcdGe4ZPvP+f+Hj2WefvV+98D6pjuWMSSQ2D/ByELnNLmdq1S0PHQbNNNRQl6zRkD960AUrF9jUgMahRyKHCCQRC6PtwuZEyjfnmPTAKIBUYLYCvA7VGFntYshKwsUl0gMNpbwQkFfxEpHXiQ4iYaCfWQ7c8mFOUnbwjm4p7E+Cwixq/EwQ3aiJmqJwgXC/4VLP4c9vfvLVJx/1j3Q/4e7fe/IeycKFy2av/FXLw23L1iybKyW+Fp/htrDhJakm9iKErMBQzAfvn1QlT+KykmEf08RjbJCC0k8yTooO6Ooii9r6bnu31A5+Tn3325hQL+y8/NXlo0ePfvXVV0cPHj7c87ubA3vXXYwO1IO+gQnXhygZ3FJPa4+tBZkFpxwx5w3mHHj5JSDdPPOuH1awKJzughyF2WrmBl2PHtq48QUAFAW5692tzayk8D87xvjK0qiESooltBI6Yfctgqz7Q1jqTnv55XUvHvntf/zH/xg19te1xTUNTD5MQcqE/6PxK61okMS84EdP0CpZIqWOUHgGGDO3XkYcpOAhROrRvp2EHVIV3xmcPHaip7K6IRWHkgsYgCd9nDh4mzYLE0XidIQ8rl6tPXbs6Y3HUVIdHbt3k28rc8cjozwI/Do76zs7owVbtoyMjGDPvb6+be6ywWWDGwaXbbpy+vSNG2de/8ML7//Xz/70a5TUM2sXzmUxs5Ce8DI8ehiIYbwXuqtoZ1BFYR3kMVuaj4EcEJjQxU3jank2KqkkcXZ6wrh4rs0O1yHofcn2mGKXMlxhrh4ms5np6VRSSeSSmUxGUfHwglWAZcwS8+hipDFWhnTtMEKvTWbkdoBHLL7zkrJlwbw9W5FFzjUuZMX57aUV715cOvzHm3/+6pPnO/Y3Nu4e6MRYuW3+gjWv7tzaPnPdxWVkXU/QEtkcoqT4zI83w0aBCWiFQSGPaIhXL5Owr5eE36gosgNlQkVdpA9b4N56by/xJ3xd7saLPT2vHXvoq8uXj25fvP3y5QOv3egZ/t0Xu1ouDPbCHc7nba73QdsbQUmtfHXtQnJ8JLSTdIh+PaQLY9IpceyHVFLQDKdzM7NdGepwyf0L79248dlfLz078lHjokVDFuJnE0KTV1lWVeMmew47u8qptLwFgNRfRkmhol5eeOPMpP/4j/8YPfGXZdXA0Nk1hZqiDgiIX15ZjexHMUxhaoBlVKCUVCxCUcVMC0g0QFnbUjL3ZTnAGjJb53vGjZ4iLmyIq7MjXAowj5obP0afNvH2upyJ6aLEksDqj199acXajV+dPvz2jh3G9bujjE9DnRRtfEKpnc93Rlq3fPhuKb6bQsQ+j0Rammau2/AOohEOD/e8/tsf/7//+V8/o79+/ewvn1myc46drIrsOokQ+9ZsD6HI8ty6hrqa8ohGwnIqJFJ5zCLemhOEFzPfqUhKoKLCkCQy69xkMwGpa24Bs5OEy6EafpgiVBS7pRISJkyAoSHmKw5AY+Bw1kCWjSX88tEpBhRisYdnkdJy1gHYS3THJaW3ueCrCRCdgu4DASSB5DUUV5dRmtPe5z9Batt+WNCdAKPhy5ubNi1bc3Jv564NG8jlFioQhMsR2gf0lqjh6AQRPuVjKQwa0uaQj5qSeb6j96WSYzmZRKVQPgA+m68bbzmm1q6BgbPDPa8doIr68fYHFz+4+MBXX505vPTzXf+HvS+Bb7q++zdHr1ykx69N06RJm6RJkzRN25yladMUCQ20tKRAoVZgXFP2B8GCVOQqMkBqBwgbVqwWVo5RQQUrogO0ILcyTkFQFHAyRZ0iTGR7/u/PN7i5PW57rP/nGc/rv3hVjtLj9/l+P5/3530smLZkISJWZuZPW5iVO3XqFJhj/vxHDz3AHNoTyWRRj2HKKvSWwDsleDuVFB34PEjJ3dlqh04wcdEjb7yx7sT58dvmzJgxlsJNcEdl1NYNhk0+uJCQH6YSoyQ+FctRxAb+dt6v3h8+75Nfv39805f+gQUxBSXcvDpwR6nrA70P9xTsG3FuDylTSyEywR9Wo6II+niisdFtRNKrBCJkUmwTpaFEaO4pTH6YoDbWxHjTwbiqNdhNiJdSm0IyyN4L4r6PBDHGL+ILrZxixW8OHPgcJTX/HRqlQI2mRzmT7VDB39z2yZQp4599/33oJIe9//77F85dePLDD46vX7/l7Ja9J66cuLL0pmjnzoOoqs1Lu+VHNL+5t186jTaZqWZMQiqbgrR72OzmpKcxFw0cyaANEDMQkWo+JxLxxNmckRfJ+eFpEuBwV3TLGqmMPG7JE0ls4VROKRFq6eiF66MIE5VIbsPlrNa7dDoXmZQBOYfbMx/fr/TBtZnQm2ltTpFU1eOSgmkrn8/2UnjPdrcguhat+sfn1r59/WuU1BNbX331wufXrt344osbN858/uJzO6bsuP8+Uh0C5otKMpB5KBo/qLv00F3GZ4HhBY17GnlgKtTRFBPFrA7w67HZBzma6ittMiZIQBOkASlfQDLed9euRUk9v2/u3IamXbt2zfrqq71r5588c3j8pF/8IqvflH4zFo6byUrq/I+ee/DBNKJfoIEBaV5vAiWppFiocgZibx9PdLbsLRFIJTZsFTtUyn7Vf9z4/MX5F3716ox4lj6Mrqx01B1AsKKiyaADlzt1HpBL9Ybh9cuTxkPfsq33hbePt+CSGhiWctll1XWESwytG17Zf/jgofA/q6stE/s0FC2BYYqnp/s/nsU/R0I/qFvISmZNdy6Llk6O5IEkGECTqFHWQU85wA2XYwQhuPl5VkFezPcrqRqhHGbIiokvHF1+5qmv2t66fO798emjSRaH5q9fPsOky8vRCoJu9P4fhwyBHvnc+fPncTm93jj/y02nVSrnScxQN9ft3Ll9zfTpm5deOt3w1ExSrOMfTDUwirUr8HBD/Wsm3igFxiQXlsI8Ij2VMtm0Ah35YMKbmsdCSYR8RSYVU6SkIi+Kq4esyo6kKKx/0faRzS76PpkwZFeTQSdc/TQADzDxuMhAXWJVDuh1Z3UScG9fj0uKqXqppOScRY+rRmnyudMhAvr4znNrX59//Q+Ypp44h1t6/vxr69d9QQKMw4fvH7EDiB/dv0nxLD0E+ygDOXQA+k/OqCSrNVi1wBLHpcXATJwAakhY3lgiIaCE5E6mwXDkDFAFFuCOmveHa2sbPwAvYldr8/717e3Ni5//6lDjW9euHX728CNPTEAxTZuWlQ9SFBq/T5+bvChiWBFP7SZiaPmyqmKZU1VScFuVFAZ4lcoKczlzZ4dmRt0df/pMugk1RXzhfIbwldXdMXRAZiKtHYihDXQBSHoOKurx8atXb5vyyZRnz23ZFB5YMLAiLyTXKSYMf++996oHFT4AE5R+/XFFFSWbnHKBs5h92kGVgQgCEHQkUVZVJF+JaWwoYYhcQ5JZsGJCYrYnHFsg6U/QYSkisdH/uDwiWUgVjPl+IHqFEHasmmUrXji64vBTX3Ve2/vuhXnlI8uJ18DcxVlMO4l7c179LS6oV189B9OtL89euYIbChV12uYTnEJF7dy5c812kWg7bJ9aH32GEprJ/yXZ7EYUBzX2ADKjsihIk0wPq2vr4LYypBSpbwa7R+q04LLiUFKYkHgiT1QGaoiczqiYWFnRv0vT05KyXaQohNGuyEqgH3ZTKmQTIO3d7XQCWAcwq7XA7hlp9VEY3OqStYgo0PF62PhFOH4h3FKI6aHEOa1G25+2Hh//EQfK9S8+e+LoB5e3nDgxf8uWLxefITXTjcOTn9vxDMuaS6RAHNQUyTMNBE7EMy4AHP9gipSWkqU0U0ufTxplQpqYz2wkkXQyUdMzRyanTs2gS+oP1zetPXH0tecbWpub9+9qvtrcOf13R8+emH/jD79asOSVCTMXlAP4m3qYbqkTV7+ePDOf4dCpRLXWg+UnI98xVd5tU1Js44yHju8RqUxmw4aO7o8G3wEDinWYpnaMG4cRPC0lKqGsru/QIf2YDVRyRKmJgCAMA48/PvXC6ikbNz5+4e35F/24gfwt/O5uz0t3PzPmbkP28sXI54PmPdpsUsn5Rp2OF6QCBiROEimIeklWk8z81OmqItCDwmkIRSPFULSYC4O5ngDboV51KeT3ZzfZYJ2kkfu/n2FdXIEMZG1O8dCBoxNfbPjq5zdOrL2ABK2RcPhiSqmIrq/wwrMjH9k4+k/sgjoLi6dZa9bMWjd33axZp3cJjhxZs2bp0qXbt2+3Lr146tTKZZOnZrCcJ9jpiRVaQ1I0EQgMSRnDR0HRWwetGNsf1MFYAM7qLpVcoBSbEexNyDiio9zJsAlHu9eHximqqD5lvWHNCfPofinRCrcA3ApiKVnpUuN5MEEi/FGF+SrgD4o4slHX6HSCrFHgpaTrkewr5+l6XFKc2SaHHFJgV1NcldKmh58WkNo7LqxdC/7qu4wdfmT35cbG+fPPfPr1Fzevv3g4mmIGKLA7jVzFYKgEJpIY4ER8amERzLD64JyCF2I8JqsU1sun0ABF4S4Rg3Pi9WEhgEsqt9+CmQvmPXfj+rW1h363b66jvXn/vudtV29cnTt9326Ku563YMHMeyfQLTWtEG9C1Xvy6xcXRWi3aUA89CTsJa28XCWruI1uKfbicRKpA7vD9u79v73z97//07otr69OB+ktN3NGYtK95UPqhr5XlLvonuSIT2gyBXNBFlE+AaS4wilTHj/81vylGHAq6lvAchP4GjQdRCfAGtCusSHtXCp16pxQspbUgDFCI3E0yXnhA5PFvsJpEY9s9qXGlyqeLOOwm3fIYmMrQrUkxhwE3QRWoxaByIuPNPb7lVRsbBWCmzzLJ/5s35nDz3/1xvW9r1+YN3IknLpLCaGYg9UIKgqS97ETdkw7RwaMh3ZfnN5i3L4GRbXmiC7kCaGkIHDqDs06wu8+dep028Sx4+H1Q9k0uYCTwYqjbDesagppJccW3OCA9R1V3ScrkUQfKpUPgSUu9GtWIWXdKDIRZUqvPt9cUtT5Aawgh5xEvSNSU0jyoJQFpwUkbyopYQnOkqBQzrksgO1dOSScKcWGFtxRec/sm4H1cmYQkkBocin1xNVzZ1NJQf/8x/ONn1+7Mn9t45VDv3zp6AeHTjQ2bjmOmvrixpn773/wgQcSfpJwb3LCg3RNvWAwP3Q/bmjYI/XpP6CylEzSEXsSRVSKLHJtZlGPZFyB7/WiGTMWjRkD9dPItHEIWF/wqz98cePa/LWH9u1raG9v3bVrbuvVq+1z91GY843rhxfMHHFPZj7pYRdMmAAQvfGLFydPpveJiSyFUD87Zim00lKBsOK2AtHxyrNJJT6lcvli586RHyN4/bODW57EdmYCPKd+imi10b8eMmTegsPjx9LKCG3aiHEjR44uHzdt0rRHFk56dt68r6+9tWrgwIKgVyThqxwJGYnI+sMWEjgVODMqgR4Askcnkshw7aAc0PolUL4SWVCyIYrtelMTWA9Ixxlh7EmaPOzCi/PZaW8WOW12l90h91ZZPd9TGoS5K6DD6kb7qeHh5Z8//Lunrr/1+gUE0sECK2P8+PFzJk2aNGckZqpxmeNXT3369cbGL+euO9iydGk3r0VCCkA58ujJngHpJki696hCcqcjqhCDGPMryhQjOcEMGII8xtQQ9sJa707Gxbqjb23v9Hic/AjthXQqKtoNBS85QAlD5sI+EbPm0j6RcQrFhLfgEwO/W9x7Jk6OasLul5YuUgccb5H5qtGJiv3BcEAisLkgobfAv/mOoRmwNQKDgE8lFdeDbS9WvS4dU2wBVDSb1W71EHA/YBwy6tx8IJ2vz999ae5T4DTsPtu4trHxrTO4p774/PCLDyy677FfLEmZPPm5iT9/9IUXnn766YceQoRb+ScDBvwaWbqlWJ1HKaLTSE6FfWbqzDGTYcLcD0z2RYtWr179zJjV46fOHPfII4/AWOf69Wug9l/+oGl/g6+9uXW/u2H9+pUdc3/XuHb+tesLdoyYMWnhkidGzpu38ZUdT74+/4sXH/wpJm5qbRLQ+KkjjCQeXyC67UoqINCJPBbLihVta6DOwDV1YOmWC6++nAPA796ke8eN27FtWzqIyDOp8bs3IfEnaRPA/BqXNQnRxKtfnrdjz/pNqwbGBouRtmb0JAzvO2pQtNulVCJnA6zYKPK3Sw3BLDCvJFJT9RqsKGBTmkpVBTIGYegISGbdH8sehxOFXVgQW+AXDCbJVZlT6NHaHS7Ompen4sLf+8EJ6/giVfOnP3tpxZmHv3rji8uvX+g9D/6yeFFFTZszcg7ibcakzlj9NCjYX1408mXBQAv4DCK8wGyVyqVQtONfOhXS7KV8pzYtg0IBoF1M75cIQrQW3Q/4OQAXtKmVdb2YyQEMt4eXZkVrTRCCSQVIFDBrnJwGXHQUibIf4Xws4AAVhTcjdQU3qt6wci7MikIKgQjBWkgFkoFnTUtYYgrg8kLGhsbttitMnK+sV987RxViHcZJISkJ9EzV69eAzGEEkx3cFD2FNyqHoe3r+/u+H289/s67b7/9zvGmddNnT1/TvfsK7qm9b3/++Q0sjCY+B75W/E9G/OTun8GTLVr80I+TfgKO7IRtvx4yfEBvEohBQWnKRqeXS+5hM9MeTJlQPgWujw88cM+iRWkzgTiA7j9t3JhtZ07euPZW497Lx9c3Ndgcrmbw99pa17fNBdnvxPwT1w4/N2bSwq0goc9bsPGRZy6sbbz+dcJP6Bkhf2PAEwoHxbYB7xHwbruS8mOYkjq0yhXt6+S/vRN6+Y1rziLBY2vGttE7JuSOXTQjK3csW0YhZIFEF2QmjKbt3qyZq5+duuOp5y9dWTUQ8kMekseyh+P47Fubb4CZgQuMz/RaMmyvtYikclFeVbgGh2lBvUoMlDwZHXcqWcnf4h+lpjLsD8ykNLFABrZFkJdADIxal9eogYzbrRN6hRr59zcugbkGT94+0fzSgYceeeKTrze9fmFY2byyjDnp6dCAT4K6rrx86tQJYx75xWXsc7u9LS2Bmpo84y3lhZHo4lRW+Be9jFaPAfyAUsowBmScCvUkyHpqMtC0+NzqtOqhLJ0TkGd1eoLZ4nYgXFigRnKWRmBzYyLiCY2utKJIjH16eh9mj0k1RS9sqXB/5WSJ3XIcQEAy8LxYNaALw5EXLByH22bXI1rbpHaEuHRs/erSkFElMPKkqp6WlC8aq15+SGNSmhTwKnXZi9hx0OvjrR899uOfPta0S3dkDXDOri75xSvEEl/7NqxX/vDz5+DV/dPHfvnLXx544f7Dk5+5G3+NmTChHCVV/dsiuBsh3lsvJvdZMiUsHJuSmp5DgrS77x4xZtyY1Ys2PvLkk1ufnPQi5Lyb1q6df/lNVFRbQ6erubXV52tdvO75tvXrdzeuPXHt6wX3bL3w7JQpz47f8cgOavyuP3MvEQfxqESx2U9HF7nQ6Ln9SipGyElEGnjk7JnLf6ju46F9//TUocvnzs1ASMUELK4LwcyeljZixISZyRNoH1OY3m9s2tixY/P7zZzSb9wrD+/78lhNxSrZZj46pOSh7JR+L1NvAfCdXTSKUZNGRSEEBuoWibAGYua4oDSbdHYJVFIplKidS0QNZqiNLVGCUhrGuOQPcENwSQ3N5QmRJm/Scji5pb68HjgsSOU8fuun+pe0D/z6vT/t2LT2QmUp+VKjcxtfmIGCSi/fVj7l3l++1nj22MUW/6pAGENLOMCThqSUCvCtF7IKjXxfItKLc3IYtpGfCDTO4gLpDxwZi0ZjF2cM7hthJA2tLod/LizQEYSMUUrpE9g0ciopqy+hD1zCiyj9hizRyRcdsxSBFUUshjM9QStg6GAeZcv7gJuD6m4yUenCYRkCKi10Urn4wg5PUpu1HomwpyUVS7dUiC9nlugwHlLY3aVMk9Pr45fNL7xw4KUG5xHrTtF2Ed+6+eLFs2cbMVFdu3Zy/UcJD06+/757fvHYC4/df98i5Go8M2IMSqpwHq0q4UKMpOv4KLT1IKGDJzAHGvkMzK0jxz1z94gRz6RMzsqatnDhpEkLFpw5uGn+2vlvvnm8aVdHW0M7lVTr/vb2ho6Vi927iVL5+YKF9zy7YMqU1VsnTFhwbu3861/8iNn8pIGPQdx5jY7mTSH/diypYoFUGLIotZ2d6/b/CsYRv//T0S8vQwYxMiuLimcRNGBLfrFkQtqECduKijKmjl304H2LkPyzYMqOja/8ct9usND9gZsH5ZB2J1LkAXa9A+LdAoc6v7YXy44blYo9izRk1EmDQO2xY5SYzNBIYVORzOIks9h6KJ+liCe6oa2KrfCH87Jh5N63KIQMDxceI36JjMdpgj04i/k6oWTl1QNNB6bB/POzxY0fIkeqdA6LtSylhIFykJF2PPLa0bWN07fLKu6qKMCKLbYAUAAU7eCwSpmrCngNoAsBajGlIc+OrIgJQ1e7XYDkoAAz0BIWiWKJZI2ORfedQ2szEuGMJBeQC4UBqm50jQxwsHLRGYhC6U3eq3CMZBh65Tc7X/xEZU4u5CDkVOLFnGDUIDPe54OZvMuBYF6l1u2DAaDNlTD4jqGl8Je2qCQReKKHDknukDQEvRRM/DAMmty0WKeL6vFHH92woaFjnXUnSmr7dklLy9LuXafXbznxOmSCez94aOIDkxcl/OQXjy1cnQW5x5gxKWP69ZuaUfTbAYOmzqBM1iRxImUjgCAL55xJI19+Gb3AhHtH4Dp7ZsSMC08uWThp/IIX958A6/zyR+v3NzXM7WiLlFRr+/Pt6xffaG7Ytw/SqcP3JQNoH3vhWcgR3wEK+IcXf5RKxLXkRFDflXYOQI7w9mz8KCNaaLQpOzs72wU/Hfzxx3XvvbIP9mNzFq4em4XedywF0S0ZkTouLf/x3tjmpT34AAK2Dh9e9MzGz1754BCMkVYFwLXRyWUl2rpI+FXdgPSUzGFEpEBZ3DE0Hi2v0W3j5IEC5mBdUaVDYAcJDwlCZBtf0slDK6HXVSGFoiZcInRmIYmk2i2DuN6idelk9XlGH7/g+z84NcZQnlDVrD16oAij98Z3Gt+ZUzonY87IsXDrBlCxLXPKyAnjHllCn0eV5GaYKoowxYIg4sDgYARdk5zod5ityCfMo8hCvAabxdDhaDUOikuFQhnaITuAs6TKUbcEzYOH5YrJZdbno4xiB5a4EtJ186xORXJO70FF1PSVsWqKlBQKCiXF4s1yo6BeRmwmvmgigcnkQBoDvnaAPR24+aFyVLvd0dW9ahNgD+v6oSWlk+qAnzBhLprL/FHMWaFvb7FZ3dnmkYp2Wm8KhRLkGnWfbj11cNMmzFSYqvZ+8GMknCckPJiSCztagvKSF8GWdnxZUSkkUcSbUhrI/hJacNgkwmq1NOflOfi/exNGJMbfs3XStIX3Lnx6C669vXRF7W9oWNfR5sBiqrW1bc9V3/rWxc279u0Dzjj/+IsvLsjPGjt+6uqFl9duuf7F1z8i1lN+Kvnxau20l8DXKEJFv71KqqDAqrFKOBNimTe0nXp26J0fj/rT7842frj1woWtyWSSM37SvSMeGXfvmHsnZOAZTJ86c3I+oiBX3zfis69eO994YpW/vmX7dKvTKAuoBkSCrIiLNIpoSTQO3TFcwMNR7zMoPMCtWE3FBr18nzqJLCywDUwg/iBk1QnZHi9+vqAmmAcYCkfmYIOQ74I8ySKQ5GHvyfUELa6whkryuq4eOPrSr+8Y+vs/fdT4Tvq0kchsG5k5J6Ns3rapU1gOx2vnTxxb5Z89u76mZmABibuQdefxwIJfRSVFbR/dUgAItKk5ZEZMQRT5CQZk8iUliNm+04ztTEo6TILJ7INBfskKDdppBwRiCpuctPFUUhI+l51GNuH4u4yISfh3ZeTNPgz8QzxOfpQLkyMgChlSF3D9CTScjgcBFTjvcAPXa20OcZ/3epshxLUhv4Lf45LSGLCXwrHhJotq9LCm1MFMltN30MSHFi8+dfBUl2Tn7Js3W1papl+ahYX3we6l03fTUHV232dP/fzFwy++sWPcJKB4qxfdt3osXAawk5oAciPI4tAiIy4dkek5OY/PwWf76svPAlwdP2nsfU/fMy53wYtvHD1LsvjWpgNNbQ0NbXPbwZugS8qNN+ZioGqdC2r62rVbVn69Y+MS2FetBj/+/PUv9rzBbqkEA4x6tRYOeimyHbsdSyomTwOg2dZ0wN7ZuXP/kDs/7vvea4cOQa63cGH+PQiO2ZaZPGLcuHuxaSgvfTwH6VsTZmb2m7F6xCuvvbb7xLFjq+pblq5Z40TwhkxiKBrQn3R4jOLHdIhD64YYeCLKBxHnR3OSkmBFhFpYE+YhMgKqeTEFjQM8j8/WSMiMtyBYggWXz1QJF30j30GhEhytdKy2HoATcTE1opBM1nWm6ehLQ/oCIP7wxPFnp42bkz5n7MiR6dvoNWXktGmvHT17BYqvimCwpqBgIPRsKPywTuAMgY8EwRW1f6z3k0jd8ZRHC39vrKZg6U10RQMlJcJdyBxfOIAkiL3ujDgE9MkSY+a3mSD9wBwVYRiBGSH3iUHux2aMriZko6D9g/VUGWNRUG5MJRKDo9z8CCFQ7lCr7W6TTeivqAhApYLNr1nrcxhS+yVA1670SWVVVl19RNPag1nKLadm1mYikh/UV1HVdBj0GvqrFcuX7+9eelCK4W/2ze3U93Ub12zfvrTl4sUvzzaeOHHo0JtHfzzimR+NQQ9D6Ph9ixZhuMaYMwLWaeSehthszNz55ZgQHy+thEfvnLGwBBzfb9EDiybPvO+dy3gnx461qNo73YAm2trQ97Wipty25nZHA8hJrbOg9Dh09sSWMy9mjF7yxBNPvLa78cL1L9p/xBSrSWqXw2Ry0chZUiXjC2QFt1tJge2lQzzlpaYmi6uzvWsFULu+v37paOP5x0YvWbL1Qu6UKVNyJ48ZQ/vYkTict42cMHLKhCnTFr7y1VGQSlfFBmcbu4+s6cIoZRTxOWja44vqSHiIjq+2CBFdFinEdWAOaFIyozmjsL6GFL5Y+8b60d9JSBEExbdABfQaH11BRbAEsZs2QVJtbYLU6gbDTeuT4+EyCjTe2O/94MTFFAhDPGFXs/LoS5/0RU29eujys5OmjUzHBh8ufNAuTJ0wbdyS1w6dOEYdHy6ogrsA4FcQUYuPkF+nE0HvuKrYOGXEElYQTSWFW6WMPGrJUS/KQAaoIIW7kooGR4xsqKZ6jRo8IFOP8G06E0CjJGI5lQmMW2zmJAoBJJfLfvnMQ5oZclNRDULgJAx+87MFMKjApMDXgKBg1pO/QmxsWKRyWUxmXH7KbCWxx00ILsszYkKN64nfrF8TDbdaOCS54eRlMqkVLuUgxvyoG2sytZ5eKupeurmlZfbO2TuF3d1HpuO1vfvgwZWzdrGb6uhrT/2fMc/teDH5HrxSUu4BZovG7m6ksqQmJuX2g8NzBrl6UJQmCI/zxk8tTB9fmPXAfc/9oX09oYeQA/nXXN3TFnk56I6y2cBLcrcDRXddbX+e7ilw0g+Xg5wOHdXZa9ev3/hoIgy+46OZOYHNg5KqQkl5SmJvu1kqrkAIU7TuXYuXKzfsWXfwIWTOjvo/TW/uPvQhLqqRaROmbNuW/OADiSALpaWXTxsJP9px06YteQ1mDLiiVq0qlpw67ekgtphTLsf+xeN05dcOHTq07+DSbBsZJGBHKiU0motKM+DhKoG3C2mA2UcSi2TiYDgcDvoL2LrSH8BNjvWLNmdANGT1JqVe4QNYKuOp7FZ/Tx6c2Dy5qGVnc9PRDQvqelFJvXlh0rTywjkjkQkxderMqf3Slkx77dCXx475KwbGrqoZGFtTU0AUjdiAzmFDSak8HgE+fIb+wX1Fp08H55VwOlD4krHMTIwSMw8GC2fJRNYnPJxHgZjUl/j4df0THAI3UYo4CZwwebTrJ2skucZ9K1iL5ebACBuXFOYpvFMkuTEiRYJFJ6FbzchZIKRwgcVGx5Dfq/I5FEDkXVoEqwNf1+Ayk0qDPZ2loi0q+o7ZkSrtwozmIMgPf1WLfU2+I6Ltks34gGfP3i7cfLC7e/v22dutOw/uXNkBtwDMQecvX/7gw6fvw0VN4jaqpqQRICnRUiQ+Kz4fhwSCjYtgAUkxmP0fnwBcdUK/CZPPXLtCWy4M4Cipm22dDq6tvW2lrZkKymZzO/BXxy6frb193/Tf/Q5qyBNbPlyIW+qDL8+jpJZRfFJUtN4Ogppdo+Lz8iCCkcoDt19JgdOgEQhbZq3/aPmep/Z0ndyGbd8nDz+/q/H8+Q/HPTIuf07R41Nwt+fPSM2dUzhtzpzRC5cs+cVrvzu09sQx3DTHjLPsi/d0tjml5KlAUtNsrSMfk1RtNOeAssassPsEOpSawOMwRIFxw/d+0/zF/HXcVlyBP+g1ChzYwbiic8Uh+AtCOGCnjZ5X6HMVfx8G+l/eaUAqkS1dqT264UVcv78fvfvQha2j4RI8EjVVPmUmiGQLl3xw9sT0VcGKioJVqwoKVtXEDiRYUiRwc1Ic4k6PQCCX4DYlzS2Pb88nWh5qqizXoCQjcJCRqPPTmpJpK1VH6R2srHDe1w3J11tgPYIVQLGXx24pHvNLABSIWFX8lMJiBz4elY6ODzupMiJRoP3r3ac8WmOlW8qocsPJ1SEM+muoGw07fQ4lBNh2CFtRyBbOKhPq+P6YHr38gmitR06mYxYtNMLQkAiyyOagLqOho2HuEUB9272ylpLZYJMcPHmSqMNE02prWzd3VndkT3XozQ/vGwFonF7wahtBOUmk/87MjqdcccQ6oqLwGvLJJ1MeH71j3I6Rd9+4SL9xU5WfUNXNK5tdmpX7V+KGWtnqwyXV1jDX0WrraPC5m/c8P2v6bpq4NrU+MHL0O42vz79xvbPBbaHFtwNGSQ5OR3IpmUjOD8fcdiWFO0PqlsyW7D++vHPPXOVKJ2rqvQ27du1ef+jNcxfgz7ot4/Dq1TPhFkU2eFsxYi384IN9u3FF1fi93acbHj7w+bI9Lo2TyQQomtxgMgzqNThJzmXDlUAPeyyBwIbFisWOOFgwQ518YTgcqA8G/f6KiOkuHpeCggp/QEQuQSaiCCq1Algb65HrhpYZCiK+Q9qjjGfQJ0Cu47daNjz6XNlQDIm7gWVOmYoI7GnTyreBj5m7cNoHmKQutfxHuCa2xu+PrSm46y5g/WEdnCkhtAXKzHEA7DBNQbkuknCJJMhFYHV6kk2KjxZsI/AnzOakwmrawkEwhoIaShcV+QXU5kKml+1CZF+JTEL0cp6ImH6oULCdbGbEQQIM1pkykWbLSgoLX4arlyXbpUTfkCI4ERA9bFuEIJ/UWBEBbvJY5XY9y68BbAMnJWNNT0tKrPXwiTZrUlAWDkiZ0bWAcPrfTdPNEdHs7bihZldVtYgOdp3q2snfDEVka1vbyubW093dV64w8O/8O/AT+OlPf/wzLJ1GYFs/g0SI6enmpJwyzIkZ8wa9P7z2veFDfv3J4xngqmx9ej2jtr95qQpf54rg5v32ZhsqCjWlabO1udsaNA1ud3tb237A6Z3PT5+++/Lb809sunz+cuN86LYQRoB7zIHgdE7jAMFYgpLKE+mMwduxpGICGtXs6aeOL1Y+vGdP68HFn/y+bnzT0bnTL544/+65JRtHj8cae+rMsXMK8WVZ+OEHH35wCNuoVThnRCubF294avnn2j0AewUaWNeYo80wrFdED8l0a/B9gmqAThWIM+wmcnzDAgdiN46GEzB9pKQJ95YUB+rD9cV58FflOAeCYxVqi0BjU6ux8VGSaQdwD4emPjYmrkdu+kZ+Hv+069EfPbpo+Kj+rx1FSWViZQ3RwJycbTNnzli48LXdZ69UbRcKwzX++iCMnu66C9+Peh2oibdKSoONLSHpZGXkVKelQ8CYk55qAr9OYBfHI9NXr47qzQh+kfxrxvNjRhR1pWI9nGbRMcIYFUYtIur82EjFwx43OjM9LRrulpos9HtFLFWeKopCAzMVOqoo8iETw4YIW2NpICz0AelGXrGRukk0fhZOhJKSFMT0IP2a9FJikwoopgdHl5ruUotbUTmq7tc7NrT5bFyXREg1NXt2VYt1XdfKdbru7qUXW68t3r//rWsnu9atWXPxBN03r795/MMnn/7pj38xYsS4tNQZU+dk5OeX9UkrREwbzog+g4ZT6taQXxeVvvzq1nPn3sTveat9z9w12wGtVgSXLm62sCmKiBM+1JRNsx9bX6x7169vvrqnY9a+PVeZu/NaWvQuvorRixNwHBYKnNstkGLT4PXynBL/7VZSmG1hXmS07Zy9+RSmKW1Dg0XjmTr4/Z9teGlW1bErV86+c+7Csy8/vg3wec7LWxdufRL1tJsNUeGWbmeDAnZeK5ZrO7Hmh/+WmlR40GYoLFqNTw+qMvVFSpMdrjYw3jbQTlRBHBs3TPmhqtNAtwFFksoZ0mE/4gDJRONSgxVt57AsMcBcTonNCz2AHosotkdHMXN9926+1P7po9kPjX9/2mOvHTr/6hSMUJgHR5ZPXTB2Bi4p9ul4Nwtl26e31BcU3IWpzu/VcSpUERmBaXwaAYlpWUmBDYE4t+TkJK1KJNK59YkpSQRPpA7pS+WEERLrA5J4sLcGD0834ATR4XbKy4PxCsiOaOeIbYSb14hc+ZzCBHD6BCnESEI3WVrGdFQoqcJsmB9ZidIK6ohC7dOYXJA7qqwSFSelJaKSSsru4eGREpHVVU9KSiA2OXFmqLBQg/0buFUue+qQAbkrlBZXM9e1GSxfEaF9Ev6Rjo5ZHacvnT69H0FzTSatrWPdulndFy9iLNrbuHf+28c/fHrhvfeScdz4nKJ0JCVR2FwfAKNFv2V9X/9hr77/6oVzbyNEqPHsptMN7bOOtBQHg15Pg0lrZxWFNPS2Nlt7K62mGtr279/f2nm1XdXd3XH6GF5f7t1y8sbVhllX99g81IerVJyDlVRJnkzk/DsY+r92liLCtq8D0E7r4o8WNz28oel064X3p+yBcrml6lhj4+vnj//kjY2Pb5sC89UPjr5JBYVnMCDr2rW+6Xnlsk8/XYY6xPcYk4FSjHQkROKZkU/rgjEB/HNI/Klw+0xKSF+hAs2mosL61mGzUR4gp7EhxAyJ60lZ5fgmZFJUEfY4YMnowWdT+BhSKpS74Hwd14OiigQplLR0d3695zHDcz/94LF9MB6DShTePNPA7gMIvHDhvumoqBpsw3aua5812+8vICBAp4HJA98oV8FVDNO7zyM18pnsApZC0ILZQCwXilRauBclIWtNHFXYewBi7QDYESkWLRzqYhAunmQljg0VXcZYOPs0eC/EiYVXiwwlJS7MKUzVa+SeeAImaEQjsS9g9KKizGwBwfYekx1OE3YfTF1MCC7xVvilGqPMCKdQJbhQ7pCwRKQSRQwZezJLmZxAjXRwHVPAsdTktruz88c+tHzFYu2KldKbmzffbNm89ODBpUdmzZ37cFOTVrnh4Ycf/dmjT214uGF/cxucbbq66arCmPPmO6sXpqSNhCFPBnGsyjIp3hsbtjKknw8A5leJgnqX8jkajy29eHEXKrJL3tKia9izYYPW5W512NwEpvtsrYsXt89d19C6fn9DW/vJk13S7S2rYu+qmN32hzNfPzd3FoB2lc6jgaUAlGOspGRCidMbexs2foS08dvW8SSnWwn1g8rTsfj+MRt8mgNNly6ifK5cXL9+8TvHj7/55vqLF9kFtSpQdRObrOWKDVCwL4PBzgYleazB2A03E+mHxPEGLQwwQcI3Q0wNyh8Zk+ItEA0i7ttIcxHwrToBaPA8pziHKWH7jhoCETREIW63hc5hPcQLVrqkbLZAbI9KCrdUhUgum21071lmXnFG+dTDs86ef3LjhKmjd6SXbt06YUf+tA8P7T2ximwI7woKd647Yty+vSQYlMHejsReToHPrqcILINFgJUv4ZZGMtvjk0NEHk+gp/x50CfEIFghSR37tQRSUALFSyCrjtS0RAtWBCpeHkl65T6XJmQk8zljSIcn2ZxaCA5Got6tMWSANltJJcXWU4D/MrM5nZEnUZksfKPGznk4KGZ8quK4oBNsRzlGKmIXclJZsUgljOvZl8bPicnORe6B5S/ZJIKaa7OL71/RvGKZVruSv5lZn6Gm+M6G37wAY8PEnyQmjhiXGo+pKXv5sj3tAKS6uzexuwpD1bvnzj2ZPGbHhHTwvcpemEFEkJzyx3sP+C0ppd89fx6kvRMnMC1UVeVJQHTq8px2CgCeAxhBd4MJCbOUzY2aacc73r9fw3V4Th3s6u4OYsiOq9g5ccGLExs0rc1MO+fAfQ1pgpTnrfKK5Agu++6B4F87S2F9U2/T4Huto/kXK3q1CayFVndnp9Jiu7QURXXrRdfTKr+/frtThTjSZXApJcdR8siBZhpe9Vh5sgCv7JQhRQDDzNH4GfwA5ZcYKLeSstaZZxQ2OUqHAHwYiVCQEPGUBHVpVA4qzuRw2wkiBtEaBkBAno0at6ggIkHuQUnF5ul4s6fvUq5YcebzT5/a07D39Q+fWDJn4ydTfnXh3CMbJyw8emhtY7iGvgh3VQS9CDfF2S+UhnQegRxqCgs2uGTilBaFXFwdy7FhPubYwwKGFBgQ7hYNgyCoeqnfZaYMZOmCmkqmtI0MsGBNdhVJdGF2KXDBXgImmMDxbBotIqQpgKoQ0aPZ/b7RTVE99Ub3l4+VFl8kCdkVAqfPDt0ZnAIEKqE/j3MKhQIiuoKSI+DLAryQMK5nuA1Kyk4lJQAuj/AuvD8bB7mkmeKlQU0QMcx/MyBBjTklN53ZbWTQijsdThkI6TZo21S6EO6qS1+eYPPO5Q9//MYbb+wYOTpjzgsLf1tZ+fLocaMRAXMBUmn6adTTMX8Qr0AgjDNLSjORp82FFxAH8pAFhu7yady+jjaaljxdVn6XVMI85gq8Whxq+En8HpCUlS6NxgGSv7A4UCzxGP+eZ9a/uqRiwEqSC4Gf4H4AsguAdv9+R6dS/RvzC63r2VWFq+nYKvp3S4tE3tGGX0fOT2TGTLtOM7FyKDQqGxZB8ExM7Z8TTf+DOoLRG7lvkPEb/o8M4ID/gsqjJpqWkK8fRmRNJjMaWp2qR0G5iSGKnwbejkc3j8eZIu6HcT0qqbgSnahKwikVn65Y8emeh39zee+5heO2PY6ogZe3Tpm6EZSqY8fyQJQix827glU8p92mI8ty/BPiTIak1Ej4fFY0HmkVWcFGrCuFhOw7FRilDMgVzaYYa8pci4eQEnsa2ESmFyHluHbwgLRsWA2z51Ok89mAU8GABY2bNgkDB8Vb5yCCKiq/D8k7KisHDSLID4WWip5XBW4/qOgaH45lm0kLryUYUAgkQokGzz81fgJjXjEvJOt5SVnIzsXj1hITQ6t1+QQaLb5XevTlxPcA2UPCxz2tTinMYf0o8P1hldSiwp+daEFuTuDsOnnp4pdXzp49uxevy2+/886TFy48eeFVeuGNd86d37v30JXdh3bTk+OvgNmjH/sA6qzlnEcuVUH5jZqC0TmGapQU/vGhU0ZNQQYd8jitxRX0eNYYgXy5OfyEDSbTYozcaPz4wuLiEuPfEyD+i9kTbEtUI3WDNCUL2QAA6emggspSidlHqV3ctB45MrvW72pqOnBgV/fSblAk8KuUdDmZKSEqm9lPsQQ29HpwZ1aLk/MTqMhwJVH6EBq9SAJlNr2NGwihAUqA5Q5OkzyYPP/wN8gW/RMhXaRDC5bhJijCkXMH1JkzSf0xMT2D++i0CIQkVS2nTcs2LFuxbM/DDW/uvXxu9LZ5vyp6uXzK+PGPoKRW+fNaAhXs/ReAAKxyczCm8WDNK3AZUpDrxDQYGWlmN32r4QxGCyYYsyB1TeoGngeivF7cr/8QTA3D+g+pHl49ACF3/YdgPUU2FHWlUfhMJDjwhTKJx0eaXalAiwkzhQgT6JIQdRyfktm7/zDIEEGewIIKwePMMIkmL5VLa/MBMbXABlOFydym8ubJHagocgkDNl8lceb9oMYP7BU4m+GF29QGMj2df3jf2K8TOinRaUzmhPQyfHj40FDy+M+w/jQkpiNiymyyrTx58uSpS5cu7r68l11Vr89/+923j7/zIf4iGSOcptbuvQw3j+n+AhRTxV1YPtYMZH96Hgw2rGT9SSVl84FgjLPDZsPbKDDQhFUIstN4ROCooUPxF1vR86HsTAakb5mx63Xjii4ugUw+L/bv7Cv/hSVFHxCd0WEPbL2EMMmGEYXWpF5u0vg6s5chteZA02OVf0QG9nBYqb5X3XuEQyel6CwW9IPiw5DEbiJmQUWNH8QshpSseLqU6Eoi3ywK2aL7ipWXmkHA+BY6tOJKMiaJ+KcPzlACBYSQwe6yUBOiY9YCEp9JHqZBKrZnJUVxxPyqpafsezZ0whCjadb6Q3vPvTr+2V89PnrBgme3PvEaeL8VVTLSaJHToJcHJyLgEiq3zyOwKRL7ZZCgnfit6ali3KA+bNWIWiQj60oJn2ZlQCnK6MpRDO/r2ysCpA+lamJ2u3U50TCPEOFswK2m03DYCRg9ChjapICIREzBNEghstKppOiSwqa3DEWWIMYdDhcyRNUQAcVhwV4BOVzASKVoN9H1AUF3+SgB2ugsielhSQnIGhOInw0FygxnHQ60HljOa0H3kW8mXbPUY4lORcZYb5QSPjiiIFYSaQohfoVQWURr29tXruzqCp3CFvPo7qOHDoG8d/bK7t0Xd+/eDZPeRnjj4If3TffHDiyouOsuWk/QjYq9X1yQx0yrVRoK40BJoabAuKcCs+NYxSpTgJ/heH72gEK5KqcDRWtGzktqtMlN+JCs2AsUNRATd9uVFPuQqXko9mkkIvLHIFEORtZWIN/L4Pv4aHRGHfHjfv972rr0fa+8QbXfjm8rfSOIi0NE7GyakGhsIkc7FE00YAYFdeW3LikKn1TrWdgWqzGkwypMbmVy9ai+kaZvcGWi2+Om4GhkUrgwPQDqg0Ok1GaSB2mQiutZ64dvoF+qa1l6sHXD83se/ugj2C/uPvEuRSdt3LgNoStP/A40xdmzAzWRGL0w6EIOtwo2aXZkM9oNyekEwrERp7Q8F8XBeUD4o1FKSBFR/JBTKqERSZsO8SHo93VDCUHHvhcvhIaP6ntnbZYW1wmieYVeL760AjmQeJUSyR/g7BTC8QXXeDRyOcoIRo+UFAzIyH8WM62JHdc4Z9AHK2EWgKWDE5eci5Z9uFWYrJXvLGYl1RPvCdxSRlZSCobJI17WZNeiNUcPYaOP0yqR+wxwJaQPjWpqGCuqSG0RFz89NdvU3r5uJ/5a1zD3+elYzn6JkvoS9bRv9+5Dhy43nt33/PR9+2a11MRW0AtL9FtW/Pi2CInGKNFxPtpJYZZyEx8J8VEmgixwZYFfouGHY9lxAcifL+A0WjHM79JSo5W07uULS7xWlfyfJMr/izh+rOfBBSvTgPMtMzrlKpfCZDHRdxXbJLX57tHVqKY7KSMerzvrBt2b7UJnBgdYIowSkAccGfcTWjuyH0V5odXH7zdjDUVeu3SJgeipz468rSdSnBqMGos+JbMURQXOQf8onMF4dNCAWNwCj0cuwR4PbBO3Vhr88/PSg4uKYq0kp1paNnfs2dCw4aPFH+0/IjkxHzbOC3bsmLJgwhNHd1+5YlwnCYC7gY1pTYlV51a7VR6VxqFRabRRuRmkDaRtEcgAhSnZDnxoKrnRSBwIwBRSCEAkRid2a9m9cd3Clhe5dgOGDBnWm6mgBtUOvaM2CuetFPct+ZxLVIAnENDrSCRoojw/0aRxq5PyyUawjHk6MzJ6n9JMhODCsxp5r1rOiY8FbxIMKtCFjEKJQGGmIVaBkqJ8bGY90eNbikrKjTuPZlfytYIaHyUFH1LMfDgzVJYUyMoq2cfFur6IviviPV1W2i/q0YaOjq6TO3c62xp2EbO26uLFS5d2z7q0a99RVNXFY/Rj+5qsfrqiKiqYv1VBhDHjzyMTUDRFwBxoO4mSwlxlJycgk8OGHxOoPLpABflqRaSkyGw1JJLdD+xiHFgV8nFIWVXI17ztSurWaEunAWoKxnAiiKfR4bJegBDyzkcffXTMe7+PpJdSX3NH3wE/YUxoJSuQSAIbsx7NZvCeIVoPPBZfJxNqJ5t5Z+MCgyyB3VDgrImpU6Q6IwP9LOQBI37T4YbDBIARt4bGcishZCAnKC08f+QSjYnp0RBO4iyeanPeZlXnhoc3HDj+0XLpmk3z9344aQECssZ99sQhHKlNTR6JV1blrwkGZHzO5cPA5ERJeXyKpFxgXFRSJBEsLUwWw0gN3pSRuF10f1INUHEYorgEHmiNeo2q7j0MAxU9gegUgYhXD71juJ52xGQkQWnNFIEIwoLAkEnbq4xULWdPyEHVkgkZEQ6IjI6nNRO3FBLUlCZ4IIHXCt41miG3Rioy8rwijR57CCWCTGgu48lRUnE9cEiKY2E4KmwEPED8qOEwAd1U2xykUVYo3FCh45ry6At7M8EJGWSwkmJWGZXDSESJqS/3xy9RTXUdPNnezHVvB3tps0R3BFzAIyEnf3Z9fX2w6tK+ZZ0dswPhCtC8KKgwjh1ekMyVEMYjkaugfCbgQcMuK+r7EXgAhjx2CEKZ/xZlBh+tlLOpyVcLdiVJaoy1Hj70Z3AJiI27/RC/uG+uKtxTxSotXEestI9pAOyG7gIkhmXZnXvG9P+4b69buNzg6tJEoHvZkT7v1nh0q6eDFzBuIAvOVkiNwA3VuDDw4odYDHikpCIAYATQAPBMYSpk2Ipj0mTXIAeUYqEJqOZ7HCbOW9FDqO9bn11xaHOVcE1Dk8mkfWj5mfad//HW2fMLd7wC351XvjrbeKltxUcmbDqcEqMOswsHRQeoEh5MTT5FVG7ON7awqJGMXCS0uYmwSdwkAimsHp/HCK81ABri2jshZsG6d0B/oHZ0lPfpUzlgFGSUlB5qJA4IJkMrS5MX6Uw4/AFNZGY7xDm9yXq2T07ZLUfnysqywhQgPIj/AZcV5pfwO6MT3OdTEa/JixhYkuBqEX+Nhw5FGo7pGXjDPNHRvHqQ2opdCKSeWNa7THpKjlLYqKR4Ik0inDIg3y8qLWWuM/jUKlkXGJn7+mSkfXSgqamty7qurbONDzve7bNFIePm7qWnW6F+g17GP7vh04kTm9tPHayqicgPQO5jo7HfS6emJERsCI/HqRLQ9ITNP+4fHS9QLMsL+/3+gj/PJn6Y7WZT0AsWflEK3GJOa14JT/UPopv/pSD6N88ePt9ij8UnhTmaTtd1RAXH4OV4LVu2rLN92erfQkMPFX31r8ZC/Y/iYXVyC3FgaeRs/QRuCzo3TBiQ7ZF2z+lTRqopm7V8aBINDPpTI7INoDtZ1ouV4BcDx1XYPaCmSiApxzOoE1iyLRKSfPy98fO/5uOHTyqsM5bMXsMtbmoyLV5+5tMvdl47cX7hE6989sojn73W2OivP7i+GYCIT2O3KGlWcsJfDI6EDp/dnARf3YjDMmUClJYDocApalNJKWCXTOSkApWRPlYV3zQEitj3qquppPDU9e8/jMwk6u6oS0BmvC7EFzFtB2WzgefD55RRadjzppkVaRD14j4rI+dnekrxh+Ukm9EAYFmEaxuRtHIITFh3FDJaQUCC0FdNbAcl7BfxLvnSYA/hUH/I7NLx6ORCq0UoOpoOJaqY1oooKRSUUOToB278ICqpHLIfLMJHiPOCzDMIpYAcec5Pnz7QtB/uvG3tHXC5vsmbvWanFdz1S7tOz/bDxaPiP9p/PnEiVMKnl4LiTzyd2ArWbMQFZUICwzwhaPU9HhDSCGIFtZNDWKrX7w9SOcV+i1fmdAD0QklFQrs4Tgelt1XwD2j4t0VJsfvY6NZiohJJmNeC7nRra/PyZuUybdvKPZO3IVZgaqoBofAQCcESAjMtoXfAI2jBa2a5KPjOgCMIVQQ538F5kVF6XFgI61kBMiiDXuTKQ5EOqCsFNg4OygzTGa3ILqM4aKlGqzc5SyIfblzsD7il6LshXTd79prTx5eDCr+CSuqL+Vj3PvHZG2OWHGq84ocr0mbwUT0qExi+nA6dCCcIqWwul9KclJXxDUWIzCIy8pPUxKXyyOl+wCELGF1OBmVyqcQBj4C+g2uBoPev7A3V3YBBKKlBg3sNzQDvFbRpvpFpO6gUQUWH7tJijk80qxPTe8MTE2NJDoBq1CH+pLLCJIubpbyju1O6dcz2DGXF4Qbnyfg+Sq+jl48v+nNJ9eQi9zuzXXIe3bGY1FBTWipVVDF9T/Ro/ADCSBSlwyrZeVLKDN8hPonAKCh9hvyV5UwZM1HLHek+vR/Bc5sP3rwJQLMb0vqW7m4IOAbG3RVe+aOJUfeb1fYO/mw/NX8FtwaNMNYKEiinecVCuQ3cPeaVqHHATlVqlfnZfQYpaOw338QwAkbEkTiy5GgTThjY2OXBLq/mNi6pb57dCi8QLLIR3r4TbgvG7lP7V55uVS5u7mzv7FyGA2dZdvZyxYqHxBiQ9WyEYj2fgR4AO63B3XYc9U4nE+85dXKW9ozDh9pkBmfoGVRI8JIimyIvgP0Rco72ncY4CfkI8X1Iq3B6a35gMX0zK1ZYO2DxuPL4cpNpz5kzn1794uYWlNRXT2zc8fTZE7B3AgFY6uDySjwUYy1HDqkDi6mQAD1rdCp5vtA0XsTMLDPjIX/VwpWZH2HtgbXJhBrYYikHAPFDSaHtG4bub0h/7ESHDYPMd0B8cqrah68Hn2VysBfTdqh8oFBgx8v2vcjnwS2Fe4rACbEb7GGKQsNhZVfRUGOkokK/yePp7AoLkjXx9CNfhygZPV7a1TgRf03LXB8+JcLZoD+GTgBLNvQOLifptaTiokHE5Sgiv+tyEPjYVEUrX/wY8tB7w71jynMbNJe6sbU8tfTgzaVk03EqwAgBgVW0PK/omjgx+oWXfrOhs7NtZ30FFr2Ry6eiGF86uc1tDELPoIGgEB5vOo8NgxRsZGiGoqHL77+VEh8bV4KWNBqLdErgNLhwa0vJjoNFDMTF3c4lFctMCfJUyJ9p60KH0m00SrpDqlMrV8J2oxMsx0eXPZq9bBnBEdkMI1dGUh4VCrsDIyP1wwJafZNlg5P45fKIUWtIF9LRmG0HqGRiR6KFvouE3ZLwB5i6hQuRxo8OfqzwFbpiahN6ZEz8N19XvBthx+zZPKLZK/YowaC4uvPklsvnPlyyccdbJ1BSAysq/CV8SRAhHxoX+izaMELYzg8J7IaUcqI1kNgW9sqspCCBRb6anHouQhxoHQoOgN2UOJymzPeG46rBvpfYooP6DxkwfGivwYioVXIg2nvYrYLfIZOJjEwpwveYcyr79KGCIgc0gjUgnMrJMsBbQU+jKiqHfMVYyCgptqxkk663Q7z4NyXVk9YY/vR2avxCGpIgWqik1MQdZH+yxQMERiKILxrUO+KRQQl3GSwTK9IIk0UaMIp58+ZN+VFnW8eshl0q/mar9eDBU6daQMzzg3dEjJS4ChHXAeRBC8XCnvab9V4v4z/HVQSFfGJLa8Ejh9m7CuI0UtG4AY7DrDpIOrq4Gn/wVklhypfgA0NJIekiN15tg502vBeEOl193N/95P/1JRVZwbEjpMBfzHcomjk5JV1ut/K7+RKjfB2KqqEd0ub2NiwPwBzRRJQrHME14GR5iG9AtcRcuqBsAwUCsC8ZIIMkypfTV5CsszCC4ksMxBwjJqAeN+zOsRgXOI23ctVVJrFaVVLBWDZ/vqV+WGGVgGXvPbjetOHRzmVnPv35p+0nr509f+7V0c9swVKqJqaiAqSzgL+mGHCDAOm1uFFIzxXi7Ob4zIh30SDqcWAym6B2YXEGIgMgSVlE+YRNls+uFKcT4jcYMPoQlBNFi1bjrerhdb2GFqWJNXivHIJ6eXnYDFAhSsiETGjUJPQeRtQMuqeAVVPtDivKTNSjKabVOW3w8LsYXIOrjbB7iUAL7oCdjjN6qFhJxfZwllJBLyWCIIxDOWEFqWBbDiZGhJUGJAA8viOtD9vrkmE1Iu0zCJpkizq6puicKS3q33/ezLs34NHomHUE0d6nVp6WB2pQUf56pj0DPjE7ULxd16Z49NM9e9bt5FuZrL8igF2N0+Mwu1iSTY3QYyPuLMGpISNFKoAsGwtG4C2EHIthlRLXJ6XbEt1SA/MCcPl5TnS9sXG3bUl9e/kDGkF486lmuBLPOiJZs30NDiwjxYkhjMh5xNnV1bWuC48dZHnyyIvsI+ltzJgRN0mplNNTHCCgGUyeIXZfkeiQL9fhLfoHv85I/ihS3F/4LdQ+YWGj0ijNJqfXH3lE4uJ+eN9H7yOwbqesZSlg9EcxnWzoVCy+cXPLeajA1p+Yfwzy+Ap/sII8MAL1AQmmJFwhREH3wOFBKU5OL2OdDosszMlMxLChpSwN5nBPCAWM08lLImoIjHYH0yxFm6kBLK+3GmyTwaOIFYKWVurTulVWGcjTkZIi6M/qzuo9iMF8hNOzkW1QZWkqQtDESIzGjaFk8IScz3wreAhIRBXqxSa6pVhJeb3k5hLzA0pKwgeT0YKtl4n6BQJj1Qx4QnqPSCRXZhYxyKSsPB81lZ7Bap9A/8phJF6BkIV+PmPG/cTlaEOvaJV2nTKGC+pn1+OaqodlT0Gwyg+jU+Gp9uXKpqb9uzSqYjqzA0hHgD+h1uyiWwryNJ5KQxYlYHuJyNYHJQW/qkAgeKukCmQO1LohnkIpEpA9KfCAkCqTqKz+mNsRRP9rGsU3RIWYmlVVSzkT1g5zNR0dbR2CU5dOIQsGl1JbW4ezo6OD3VCkzhPc6vduER9BKQF+phBHKT0CNTKI9MSZhj1+5DJiL5gOhUIhAnpCzNYrpBIQqAWMItusJEd01rDF/D950eUbDnXxlm7e2QZ1juk3TS99tPjGF1den//u55saXz9JlhN+dtBD0hvmSXE449EVOYH/g70AGD2DdTj02OeUp0UraR+K+DgrdXBI3hUaKe5HoGFrKQL8YGNMEAUj+zEK19A7+iuMeTy5W08KZRiQ0H6KFL64fDRwBexNtcSqlqS9kIxFU4sDs030xkrkkuEAYpMbxjaitjjESbAyAjFZAQJtCfgDPYcnPGItlZQOJaUl43XmkEmRnXRLobeVOPXpBE4ALE/vl5lONUUJQjAJ6DOoPz5s3FLk5vlqn5fvgSSkXdW107omT7YZJeFtYSUVOzDWHwjW1ISD4ZtLVzYtbjrapHSJ4I9QDNAKrQp2YGo5igJZSsD/qhhFHVdTAd08sTE1AW9VvT9SMBVWaBP0hgS26EVUO3aX4Hdh0Vvz97eVt0VJ/Wert3qZUWCD9hKfkEkRoRaBxgdUfdkt4rlYzKjlegpE/uYVRenHiW6Z3y90QGuE/6MLK1rMElZvcfy0dATjHRqU4GCAe4ezEWb9Fh+/6q+34XERtk3P64su3WCXU1YlNHZ0kqIYI8PyqzdObgLfc+WmxScDsLyADxr9wopguJiHHaPTSqxxmxYtu8uQlsEOasoCzcmMz6YZELeXxspSQlFSUhCUMBP0JnMkup8GUMc3pD8wimq6pqoHDAbNz4e1hDtbbwGJJo9cZIVgqAC2MXLRhYw1ESmq/pDI90lPNZuQUQpHC9rhQd8M+ruVR3bOlDQv5DuiUrJdMCRQm5AKWVLlNToDMT1BReOYnYuTZMvwoSKqBhHGIosR7ENQUlajKjudNaOgnmf2Q1RkeWE5VVU6BPDDInEjRKqorOxT+OAKpQ+E4i5e0E9mV/6wP1hfj+9kTRgXDVGR/GFZyNEExpMJTTD+THKac7goxLg4Ng6DE35RQcQ1Kzay2sX0AU+6PKLO4CcCAsKIs+H6HR+l92HjKbB6cUzp2Nrydr6l/hP8TJ+YP1xcVUI+qSS/w1cDqnYMG2xTQrOUjxiPGI8iL0xK7NLiBBLYj8QUwHoOpl00aNHlhNvJQ5ebgIv8D+XL6DxSqYrWUZikZPXB/7TZjWWqi553gPRJBOUhIQYlbHQUWuKxKZe1r7za3Hxy/8GV+2XhFmNLwE98Z5hyyqxwmAXkBJaRDWB5iNMmEXGWWEk56YW4pEzgneMA0Egor4YRagBrCiy58EEZXIuSQh3heqoeMKh/dS1dU3AKHXpnXaZLLrVh2+PySCINoxFCDXw1PWpE65Td0klVMoJ3WpSZ+r7oKFpKQGdB1BtcUzxCQ4FrIPg3SmlzEDaHaQLJ83xVVY9LKkpJ7AkVsANWUkQUAwFNQStGLW4pq8qQQ6cJbqOMwn6Uw4qMCVRUYUZvts2OOHnSkgoWGst8EDjp8iriKiqYTWMNbEYHAioPk0iaeK/+wGaVIBTiMC6hl/X4oKkksMomCRbEftd3jd5FsRdIBY1eIgckESYFUrSjzA6VXKcKibx5IhU/GBtze4Po/6lnity6sQNj4siCFU6s8DH686umxg9HIXrV+L95i95gf9XcmpkL2C/7qxf7afqVeNH5hfdEBkmxsX+mRv1tScQVFPygkvIb5XnF6LzteHSA0mqal7WfbF5xpvXg0lOnly49vZ8LwdULYBSoCGAXwlgW9mlu+GPIpTpOG4U4yMJCCISSU5OyIT6xwBxDDWO+POr8eHwgMgJ7/LBRvUZhcCKq7GAizdZWD6f/vgcvE4Qj9q3L0XI+WvuQk3OEh4PoIIlE50rKzAGMjpLtTWSEnMK0RMrMxPWPYQr9AFH90TZjIAXKI/GWSAQOO8KmiDtuQuMHsMPoKelpSXmi1B6YaThtRMq1UNNAZYVxEywyalIlnmhaS2GFUEp4XznCbTILSTuWUzmMdaps/gO5oigjP9Xgk1vl/FueO1jTDsTjQuMD2aXdgl6DPF4JTOUEOiuoxiDIQpeML7IkXBP7nRMwbq5gCY+Hd4mgPyQAoqRIImSB2DpE7khooEQFMf+7Soqh6uwV9y1zg4Fx/8Vv2X/pO83AkJiKW8VUEPsdIo64HkrsvlVSImlecZ7I6SJ/Jguncky86jm4svkUDOpOnzptV+P5ySuRanwMkNThMghBOw4OHW4SnUYZjWRaaN6RLwxbDD0ZAmQrBCIYO8loV+lBRSX1HgzbY/aK6FT6RgQrfT++9cYdwxNNJNBQWzxWFhgvohUx5EKcPjUdUSDEBwRjLqM8KwFhpcSINFMELmhJZDwAFj+qCtEfVVANOXycyuND16ygkHoa0fN6hImipLgohYqWBSgpLS5fuFnRtpfuDj18zlBSgqiyQXQXITWKbiiEuWdmIlcrvWwY9tjMf4aWdvifnMz8aEQpyIX+yOKjKkzn4ED6/uMMjpyKpBwPkAUBlnpWWNwiPAgKHrkw7K8p+G6+M0OePeDF+oUssId96dGRom+kT52nkwf+ETBz+5XUdzza3wXDETcwLvbvqpniYv6RYTf9VhKYRU7ab73vbycK4/4qKf5BJVUhJG0Nz+mivs8ukHPmZSs38w4ehLECHOMsv1FysDXRkNWkimwwvSVSG/nuuzRQ8apc5qj4xChy6zPjEUfOpwFBbR7gDMV5lGrJcTZFbi2LOouUUy/m2Ppx38grUmC9quP1WuYNBek/CPYiEZ95Qht1NkNyOaWMUmYVJbAnRjOlDNFKzIaIwxS9qVeC/oiSMrrB9lKRHEOh8NFzhYNC1rOSivP7krROwI46H1m5OGCMDlIYLNixkodniANhsGD4MeCkD3V7SArPxKscH2lv7HiR5gOcooxFogJjBxaKZz1AjCP8VRKO+TOb6NYhSbWGdqSmipjDfDjbwEvYbuMHauIYYv6dJRVXEZC7PN6wzOlAdwF4Aup8ds7RWvBWvkDc7b3q/U42xXdthv5Rldy62Ngb35Tcn19/qTxSodHdV4AlREAhYVfhd/yxDICsCRl/UEkVyPjCqiohlCIwKoZs3bPM3CndvNm4efOpVkYYdZNuVmmB+NpIoTXI3dETYdEEFq+UM5mRxQtGDdavEEC4lAhOc2GO8RYDcgoBt3IpsmpRRYAnatHnVTPmLFva4oAnfS/+GZCTBExcTTI/jRTsEJjHyyPLA5UrOzGfRv/CXGRFIxY8iqmjxUTcwkTFtNAMAsKCSlKSB+6EyU1mh4AdNWzVCzuXmJ5l9QbdSbCJjpSUxQ1yAvFaYPPmcyD9AVIqiVUTXxYx6syhEaocL4qqI0/cIsoPx3q6lAX69Mkoz0wTO0JC/62vOBuJ/3zMxv0FS46t8fJh5q3jHJxIqLJ5vBX/qP8o8HudFpeTr0JYs9ulhuc13DIwzrNFO8KK6mP+15VUbOz30NJGaua/+Mvi/uo4Kgj4vw16/1W3yAa6YPAHlVSsF3K1YhkfxFiUFCcPuZa5QrzNm1usp11AM39DqjC9GcoSmF2A2yBERi4jLWbD6tYq9ymBReq1Gg9WoqAEI4JNgd8t8yIjAY8G4A51yjCaoaqLchG8mpCE6ArYrkUTyR7AJxlRIEArHtIxXHFKJVZaEqcK8nTaOgDO07hN5mjirsUnMR8YlC8FVpHJVDThfiSGobfBZxBVkaBXq4XZIR59YmJiL8VT8XqGocchZsASAkuZNX5IV1QziYfWxmHtJY5WCKxWLrGMPGYw62UwpI+RPBjLg1YKGSRLLmMsfdRcYZLLWVXA2CqxkdMUC3Q/8hoYqa/AH6HrVdRLEEcO13u7p6Se75EECcmo+Dt1VROAIM0GVwoXSPk2uzk6KQFxdlikSeE4JdRxRv//spL6zg/0W01e3N+poW//8D8fmulLX/CX7jDuW8Sov2A/P2RFRfrFuGIqKUzGJMaxaEJOd2eDtKWF1yI97XEiRbeNNmLortRQpkvZgMIpiXJlyHbQNaUkdQoItSENrNNIdgwuK7sgdBoLrXLMUVn5memZLKyM7he6Yohjz/TO4ujEpCRDUnyEfQ/iFrIRNTZoHO1E/nYwc1CXOhq3U+T3mJDzpIhcVIT4Yb/ACgyuZyoQTDkijYMBiLGMgHy4rcp0otgexgxA1Yv65gOegAMck2mz1lSAT1McrdfwrWyWYjWTQ6xZ2kiBM4+SAkJZRrF1pZFULATN5WTE23FrUD4DHg9WJP48IaKPKtjSryLAQIhYPxBgqwheDC5nfZDHD9AUVRGu/04ZIdJ/gCV74GEMLRcSgUjRm6iEvb6TVhEiAZdX8A+e1NsVnvgfef0PfERhoi3LKEsGtxR5wTVojuSVtBg90nBNMW92cbhE6sbGSmGxwWnPislXhbuI3Q341ej88HCrXQK5ClIlDPIW2JgTGVHicenZbi4K3n1A6ujNyK7OQCOQ2cycQcVR8UlJCfGGiL4FQ5HPyaF5s6BS8GMKF+nfXdG42OhSUobgrEtOOTDsdZCu1Q2ogC371DDbKZFx1Dy6NVTWSg1p9KDCg4FxXNz33jMQ4mc2CaD2gnscabipl4SgBJxgpI0Aw0ecm0ecM2hQnzJq7UrJsj2CRxCRhHK7UVJllRGJF+D/jGS1qj6OecPFxuZRVwGCVxUxvYjr5/cWEyMmNiwFOU+CxG2TPOAvloWBRwFdL2FQ+bcPT7yBiuLcGhjLwgwXTrhEmk1JTbJD0iaX5FVB/SkN/rPP8P/bkor5nygpKzlZqojYYSeTeoS9lAS26zS8gpi7IDkoKKjnq2APgFWnnRIh4ASrNURHGi+0eniqowwWHJgQ61H+nk2gkotI9WTRM/lXdEo8qyZUkJnB33Sr0AgUldoP+HsZqDu5Yma3hq0P/Pg0SrsNvry0AscPwjNRG41mEY5l0Q5eMAhPMWymDAofpVuhpGiJLlY7gBWWCDXkJIASxFXGZinYc0itkZLqAYhuRtK2xEqRQ1iAEw+dbimbx6NRRCWaXRCNmWHZ2YfIh9T9EXu2LMLNYrR83Fp9iPDBSqosJ18sqIpgeyBORLL3kCQRLhbyimH8KPSyLi0sh8Dd6sT55SnBxjNMZIlwoMRbheVUxV+BFHRHOcjPxgfsPBuKYwNcSOMNDogcwFTP4zk5XsU//qT/XVL/na96pJ56vTj/YWZGzpuwO/Nsrxd63DKAIgPJY6QgXFwsk+IWAs1chTQouYuhAtR9aTHs4D8mh0ODMRk+EGjbSFtgVTmUxIrDGomcMFPJxy+JfGYTkxIgQkAOcW56bzj5kUF6XWUSWXNkEyCOwDGFy4beitzjEa5KwuYk/C5UsMEn9IdRUvizsRqykCJNz8x7QbyAYZTX6MazpXXYqKRMiL8Dkp/HN/p71htAKA8/diymiJCkNUVmKdjSe5ycPi0zCSy/kLoQ2H5Ebtw7QkK/JXDGIEVIRaSkCGYvK8uIdwsjqER9PQtkAe3VH0R4BF9UHJBZGc2MbikP3+h0K7SeqoIC3GDFVfUgKBE/Fr+84pbcFNWFoUsAYTUZdypJxayEWCo1QYy2VBUBJwTO+n9yM/+7pP5bSwoZiyVehEkz32OkM72kbZBj2OfqSYId/OZwDfOApSEbA5RMo5tZZGDfmg2QzyLw2BBlySF+Hfx7p1TncziJCKuPyJj10WlFJOcgqesAkJLA78Or9j3a+9bVYVl1xx3DU/Bk3DIxtNiVbg3ojETVSmSGmvhvPGLScYHxAsi1Qg2rIUIkYAJAIQU0wPSXc/Jkchc5UliwJUWbhr1UXhXMIfk9xG4wSymIHCtFBjAxxLLJgFuv9ankgqiiAekGlJQ2tw+4sYzbAVpWn4gCkVnbEACYDtkkWQIwkVefPmlKKfyMUE3hMEYpkJFRKQFYLBPrQ8Ln+VmQo1SlM0JOolBC34+SKvaWEKWPuVMF4U9RQBIpEAO9tPHDfhtdqYl526mzo1JIIA+TNz7xjvmqf3ZJ/buk/ptvKVZSHtCt4TSE3S5M1TxCEacKxwFulEVqCjSzYh1S51wusOesNnNkKEqMhhObMA+ABbLSZDwBR55jUpsdbRGUtyRkViqTSocMBkdiKBUQZXZgv3tnL5bkUTeYItywCB5gwA3IHA3x3GqVPs5Euk0kIUZgPgpExE2EflLmgXhJH+3iI5kXN5kZNvx2Utwi8lAkc9otDjt5iuJSQd6qJA8WxkbpDykpPrbO6GeJOIEwMPCRtD6PnMuqHVWdwkmd9mQm7oikiVQWRRLliD+FkkoH8k8h3hSGynT0mWJOhnsJITd+jE9gzVLCUSAc8JYEvBKppJ5YfGFpCIJvhNhbUFL4NV6Zl1h8hE3FBvOExfVVsrxAGCwrGICQXlrqoY+NlK7Z0UnI+wMFToexWCgJScP/DLb6d0n9t85SOinQMZHKosZohL0LZgdTm5XfxuVBWhAQCUkz56+oqdKB+oNryGMUigRKwsHN4pQonR8rR50FY5AnUMLB4A6W6Q6TIAQtv4H4FAp14WCK+u5bR2IO9qKraRTz8qvu3x/cpL516W4BKymiDGNh7MBvxtkLzlq0Xs3wPdgpaDwUZICcO6QruiU8gZaISUqbDXQGQOlmG37IAjc/E0oKRQXZh1FWUlxs7XlJEVKOkuIcsAdBSWWTvgM7ZZU7fdQdo0rtOqcrtQ95uVDqKWv/GKsPOymg6lD5pkfoibCmwNq3qE9hvAVf5WK6aUCJ4PF5VQGi0JKQFywIuRAhff5iaHrkYHpqfM68GnjIiowikoAwmNDPA5YndaLmdMy/WYJNOoAMNTkskIqfenKBB3CfF7lCFIHz75L6F76CUoa7IklJgUnEbVlmsixrdko9bdYgWpWAkV9cL5MFSnQKcDJhOSaAkzXYFGRPaI5WGIkRA6cvs8EtEzq0AqksD9EZNmqXiN8A/nZyLa6koXXVw7DWBWkW0kOU1nBipEMvP2jA8FG9apPBHs+OGO6ilAxa3Fn4jwFHr0mg00FIzC8pFumkVuDyCGTAdsgp1yjIlk3BIRdSSR4rGivfxsoJzSsoeVpNCJ9USQlKKq5Hsl6UlBI8QVjSuLWspMhekeATzlIG8Vd1lEduTyuDC3rvosjVxMYqTE8UHcUI6QysoHwEJktMT9ZzOimQbX8wL5yncYAExpBz+uj8Qh1ABYTOhZDdAN4sdD1IEKgp4TvlfyaT+/kuCwxnYTdrIycCHok7OcplxQfmAm0d9CUVErqATQilHHW7cf9G/P51mHyQL6WSIuqeGl62FgXJU9qkUpugBMrToNzhkUIiqbNlI8Ua/q4+kneEXCgW0JKwn5HlwfQWCyKtTm6CK4PMq9Pq4faihvOJgSJI7KBPgDbbH81Rf/JIoniBWhquaClaOaB2aN8hCWJa4RIVgpZcYrWJJZyIDVFRdhEMBL1ejBR+iZTPqc0oexW8AWE/gUkOYLabDEqBV3BSD8sDICs4LagYUElg4ew1SsMxPTMdE4hZSfE9CJjSK8kEVU0NoNamLEJm7+BcDUqqNEI6Z342WO/mEBSBkkISAkqK1FK9WVgCMf7Sc6OhDZT4K8Jeb7FUqxbwgOMxc3AYjQVgeI2PVkKJzhyFbutE4ZogT6eSRkoKMFG9R0ErBWh9IAYnE3m0pHYFCJV60FiY7IG6QQkZfqgE3oJ/Kqj7319SbAcc+zdPdmzs/2w9kbrmb0mJ9PRY5bIqJL+58e2B17h2GbAtrbvLKnDL6/31hAS67PTdNJOXILzsLQKeF8R1AE0UQYOkNRYaAWAAUIEJ0TZOUHigD1RHvHPhb4ljfeh7g4r6DxpSO6ASJKThg2sBUlQCEcvJGVY7dGgG1sZwhCI7XjO1V3hyASPCjjdbrDRW3Po6+Xm0ETLDhSEcG4QTEuKFYItI4ihkCAENxM/+X+7eBC7qOv8fb4ZhgmFmGo6ZAQYGZoAZjoFhRq4Z7iNhAIlD7hCMS0BDUVFDhMIDj0ytVskwozzSEFMyEsXM0gzTsvJI8SyPbdf121bf/bbW9/98vQdrv8duWvv7/m2nPFZ9bDLzeb3fr9fz9TzA2kUJExLo6Ql4QsDLNul8kqxkn9sbdsZcvYM3PiGKD/8KlBQsknytil6ykvN1DfOOBjdxYmJYeqRTSjRLPqCSSkzJSgxmGDp5DSaqoPHIssKAiWTQkayyyIM80pFSgvQsOITBsDaeBIT01yFILw9yZiFKiOxlIe9Ab5gnlkh9DAlWQ2e7BF0YLsgoKXA+H/Kdg9oHl5Q7bIoDAwhMj8I/PhLmky+RChNuu9D+C5bU6GPLeg+7//2DtP+lGQG/rLL/tpBvW5VqEW6WzcM+l4541II3hQpG6cJDpDoOSC/+jNPqCcNp9FVhQQF+QVzC3MOADUKmjA8ZvhqQHiPd288zLF3AS/em7p4NzogCQh6DHtNUUVY0NL1FmTEZ+AG3VE5mjAp2muasosKJiZBXBjkEYsnkR+UEgM09AGgeWprAAB968Ah1zgsP8uTzQzmQRCJSyYOmNzxOoaGeTN7pFyINwqXJDgV/8tDxIugrzsT1iCNd7N/SjNlw8rPv1HgPR19EKnK0YOGSZDaQviZfoJihaPwwHGa6pofJ4IyJYQnCZsxL5DZIKhSyxwRnFp5OWVbHpBSyy8QVpvBPDwfJygYovyedNJxUa5VD2zneLiGbWm/wiaSx6Ot4BGOYBML4VPZ0jCHkAqE/8A63IbszKiktceRpi0BYP2TAzG4L8hD8hSV5Y/4Hc+1f8Jb6SZL507X1f9/2/bfaHf1fCUKdCSXF9fJnYS/MtN3TXUohoV7pHG6oJ7tv0JRRSUUAvkZ0E3SCNv6+EVE2yD6ysRpqIG4KmlIRRxDlzZgSASzW0c/dy8alCMOHWlUAGW+KKisTl1V+SVFBsNlslBujoZjK5IfZRBCADkTDF7AD9HQBtrbEseDbRpaNIZ14UpLEHX7y3pJU9qSU+QSBq4sERoAVLo5YWvEjQtz9WEmR1SwJ5bGSNmUbRFLT//pp/Nwb9dD4KGeI7eGAC0YSvhI4M0aQJtA30N87eCJFd8ttQtxwSVHbl0WGzfhyYOUXTb6YMUSlSGaJJtD2U1ROJgxozb42PjDkS8f4RzNjKExEWEwrWR3BSQJwIEcCEA/eff8bopLAwXAoSGcG8mTlKJHg/afrE467AtylLN8BWaJaH0m83T9iIv3rlBTzH7/ts/T/18hGfDKAd6MiyYQEtp0v43l4aElsJw2hmAikQoAJEeuVTlbVIkGSyYf9IuxFwjD+h6HTCwmHNj0uThAOnCqb3JdN8SYDsFuthw3GLGh/063earHe3iy5JdYlp7A00xKcCSJ6TApuqQJQ0otyYoA0G1UZJaUTC2S+yPMOdHB0BhGApHewuYmidHSQZ5FMMiaPAz8xDy+M9wIsddiKDKCBOxHEg2JD4C9I0L4PiYy8WMoFhnVYJ5FvEi5ZXgITdjKx5+jX/fNJHtgZcEI9Y7WIbYVpGgJokL8GpydmI+epwNlQVOAUGcE3JsM7DYBLZibtd2OKmcM06T1ApciFgrkAX2awEbk4MqQ/goAnIkmmlzSCyCTIqmNW1zpKHYY1mxbcSQQgBQa6o9/kMY9QjsCaKkmabqEHzi602PR14YPxSOemw2gDmKp7BBwGtBKrBYcBwmuAhXfQ9vyWS8oq2UgwJNz/39Ozfryo7P+vSgy6tSRxdlJcHNYiSXHxiHUEe8cdjU0E4pkiIYFHpRjwB7BkNMQnxfHE+Al4E2LKCUUAGKxcvUS6dEnZGBZuAJq0VYLJVD9WirX1O/br4w1wf+FxdDa+lgwK7MgoJltMwBK078V3xeiTkrOQ5VaQEQPmqdnZMTmGbxOOWBKhlleWcPv/Zozd37zoqk/VodJscIdKOCydIBtOBQYxdlDM7wJzO6zCYDHFJWEwPIGF+FLi8MKfisfXjX+Sxv/82YN8GqGrN9kgkwuCDiZMQqmvra17bKgffC8C6UoNRCwO34gIsEI0giXRMZn5E4uyYJGGhRTMAxCiAJXYfWOL3LwkqJqkcWPGjC/ToiwDfEO95TJH0qnwvXEbK9LS9AoN8kiNwcXF5MlRkpNLbWQMtC8l5C5QQh4dJUU5ubkqJ0Tg0TpXZINQTp6HPlnm6R6gyYxx14rxF43HCzK1VOsj9y+P+AkUvPthO4B9HoWejLH/3yau/4tbKoEnwIOVVAbGJlaNArCG8HR4snwVOF6Sas+AfinO+jKB0oNtJJyGxAZ803n5I+yU63Hbadvaylp1X/8Fhxn9jyXpBPHZBkGUu4yuJvoHPi4oLCaTKijIDWbPTUYxEStgzCxTJGYZI8h7TYQTN24UYb793oz+Z+xpQwNrD5FY5+FDKgaOGAGakHHEERnOgCMBOeqoKS55AetoiCcsHTQEVBSVFF4m0x1wlJCCJvULE5LNE1an5NUJewx+gH9smJ8tYixBPXT184QLsG1M4YP5pSQHI2JVUVEmdPGqYGWaWZVZipoqLM3ge+DvwsljLlOCKEpa8wqztQWyCd2Vb4CLm0bhJpchbN6chhgtsnDLLClAt4jaoqzIIlo5FMH0EPdiRqJbICmY0+GQE+sjNon4iUZKqc0s9tSRxZsBdJGkJEPqqM3PvzR7Ar2Kzt0EC4+8eHpO8bHjiQZ5a/y4Mf+nAxV6zlQhB+5VSeRglVcWL5T6wxPFG8I6UMo9yL8c5oH06eBuiqcnlCcuy4uH0xBuKUopRKwUJmTaI46qlceMZvHY/xe8w1pY47MFPJiIcSQRfDMczVFSOZnENsiyVheePZidw+iYHh7Ynas1aRq1xYUY5mg7YZuVwPTjo5fgTxgPOUV6RAmzBR7hFLrDM8Rlo1ayQT5C2eOhglsSrhWWbk9dk0hHhUSHA3sl5eXFG+6E9jde6xkkochhiK505MToEYrVdShy0WyB92MbhKVYkDtfVTR2bH7hg8hBQqQjHDXgUlMcDXgimuqh8IHSArUrZSkKiQXB9k+QgtlEADslrD8AzGFoK+VumjS1UaNXGIPJCjC3oDg5JgMXVCY1mKilkkwWxYrp0zEQgJA0HTn3Xj7ieG1gchqGX7fMggCd1fo6Ds8XL+/vr0t+2yV1G+uDk00Zicbj48G90umoP2YQqDWCKT6ujEnR7P7n0PP/BJ0gMgRoMGXU+iUlZWtj4b4CVmgY5LeRXoDt0skLV0cnfxzURnS8m6hnQnHFx2N+QXKHh9Qn729nkZ8Mmv7GT4PVQYIBlx4+Z+hiXVSk4C2mWirOorwAsudPIS/JxBjyIcNvRCOK08lRD8cW/6jYAIewcJEhwdq/WNuYn9jkdknwvEZ+CiHS0ESi4ME8Al2OHn9THP0XeUxr70Nh3HiracLAGYFuFs0ffhDz7qSkxvk4ROpwpuAoEdKww41CSfmGxZInOosoQklFesZMRMgPWIrQ+z84tohZ6eZYC4DYIdgdFBi9oyRw3h7PkHAeuR9FRpLtDVy5Qf9whFgZL5nabNRoZAol6RajM3KjgeDAQarodknhV7IKcooTZX4IqQCAgZoSmZI43io12MVuxZkOQPvp684mz4+8O5wBfkslNap7JrIxOFwgRWq5sFQARYue0Hi0/oA6af0toNrCpw8BIJFTfvLtsP9JSX+//f3/PDQDMhvQxsry8miaQj8nTo9wZQnpSJALgx2PNJYcz5AGKsbzF88mEHxPPzWABAizFIjQ/SN5/wAvs7e3v20wYzcuzlpS4SHefGMwBeDQ5YRywne5ZKhJZ7IyGIuqLBRclkWmcLJVR6s0AV5hDkif8JBwAAbbs+Hsb+UZ4I2KoL0TCSTYOesEZP3Hpj/Yx6DzSRKzpCqYkGEooytXyCWLGHEcDY/U0NKEmHAHHyHypUIQVUerUy65mgmjghxsXaURfkyhwuwXXaVOpSimB8laA8nDhUUZ0YkpxeT2DpbVxImw0n1wYqYmJB2mhAYytIRrbDiVlHcgWJQQnPFd+EiDkhs1TnKjMk2jcZMZ9eZEuuGKczOxDMf6DiVVANoW3ixMncXIAUJJ2SBNSSvRxifxItKULrZO8pQCuYRKio28ceIku3+lkvppfWoHu8M4DroPeKCyksGXmpfEphOTyWr9LWaXALPnYrAOL56Y/Anj7EZtj8bcTt62/2fdWZhDxIY49JxJbF43waQrMiw0IoTheRQkHQp4DtMvx5BEZzod7HSux9PfEdkqyDQPdJI5cP5xl/6jO5pdErnyiXk2iM1RWFIy2CuzgM1TVFokg7CoWWgo6i1G7wxjR5nS6OILLD/AOxIptT7x40cPqL95B2DtxrFxdYjAfenBZTMEHqTs7KR46tKwCyCHdPI7w5KHAha1IoLtOCbcyuwLQkmZEn4WEiO9lK+UDkKeFXaD3bo/nx8EsI6WA3xnUvt7esfgfgKR/oEH0fah8ctMNidmpSiDiSaCaigdW1ig96WtEXHu8F8cx/HhQgYfRK4ZoAQ7Ojm5OLikWTRuSIZSGNV6NycnDRi34LdH5+LCAzbBzHkpBJii6lPUTn5BpGcDOVkkjAOtSWZ2gFpNVaDxYAAhPVM4xxL+NUrK7kfPdIaEJeQZBDgjgcNgXmQllcTDc4qHE+AUfeHx9D2aKbqjMAugb6ERQICVgxiW/tYRa/RJ+mdusKAfTaJLKp4ObAMXXFRwNClfj5yjY2HHGGJDzEs2xrNGie4q/K0BpYklkbHuMQWJKunf69Xtf/qeXVMoKXw5wiC+o5NMnVxM2VLFBZkUvl7MItghiLCoqdXBQJUVLEc3BVNdWl6CS4GUDPycsgJvG9/8tFKySwpVp0BeAX6PmEPvH7t9qKbwVqKGYJLOAXqO2RCoAmXcEdBPl621pMRx4+/gc06QekbBDY+ixFiLDrK9A+zG/aHOgqTZBeXv5xmYg8dmLCunB3FPFRbFqJOzggEzJMcQtlk6ttQc6B+bjguTaaUwBWJ5JPLydHYi1w0nuczJFoeIWqHXIxgKZgJyZxc3I3RkoFygpOh6ImwU+pAY5BXkwmrXJTAIO44QZPfpsPI22MjVDpjIzAVGGxxdOJTR45oEgtR/nVuKGhT6PxhfxqNdAnmaYLTP5nFM9GmKeSgcMXkNo5rgHyQWs5lfQKASxmt6iuOpcUcvwzPRhDXm/wGtIgGPVBwDW/ES4yQX+ZC1LcJWYL0eC24EtvMGzHejJRXHeiW6pdDC+YRGOOQXKYuD/m5Jjf59Rxka4xiqzfMJkMtkektiLmEQuWD25WaxPDP4sBZHB5vVwSwLO1jtBi29e2hEhCcT/QYS4xb9nyRudHD7USQOI9AkY25KUXSEh40WcBx7l9lsaAK2ZxDA4BZLTyx6wjEAgeeHN1qAH7NZDwusE+3CuDuh58e6+4izCZO32nVqvTxR4jaukJn4wkaQ7+zs4O6bg66Pqgpt34NjCycW5cJpNhh2nsEpOECQXF4kx/wVmS7xYbeUXapYJBFww/hwsMEA5SR3s5XDWFQv05vVeo1aacQvy/VAKZABklVg9eXF/w/Wx8nJxRkxSoWLg28oGgos2nFUxSUZPBxVtrAbM2akSfF1Yq7E04UnKu9fp6RYQWEPDhYLh7V2+LBJrA1Miu4lHt1N+KIpzYxl01LnB0iMtvxxJnZpESRA0zUQKyEPTonj7gi8uRvSuYEmOXZHAWzWAoxA+4QkRggHKUg6MgQ0czyduMcIFmSHP3WrVFJwnAhST8yyZET9/b8UO1HGJNDgcP/4bB41YDopH3iWiuy3yNQYZVXMpinrYAUnVguI2iqNE8l9SRoPox85GiAwIjyh/ua7RyUl/FdY1G7c/XnGkuCifEd43/JohKKzCThPGbV+cJGByb6ORcXRnCqmQZAOcHzB2djG4U+J8+7Andc+KcyfHEN5HBbqg2z6SPg229h42uIqDcBNZYsbw7sAtxMhfTkA9+CaW5QRU1ycrMSEiBLIKJoIPrCLEx+Rv1weXYzj4KEfHhXiiZKSuTk5UqyuxgKvXo2bWUlWtUaZm7Mj3KAUZmjuc8EuBhSam5mJBlkFeCfZ6AJfAtCWPdg+Dk+Lwccl2DEi0l+WoYZajA5oyJjxAP2rlJT1KB2fF48ug6b7bAPKh43OOOpxXBvwgdOZiiJjVvr0vdjAFt60pqN2kF40teBAZSmbWgkPNuj2/0T8D3mVKG7rIEWNJoLXQJOADCcc2Tv4H7Sc4qDQk7KppKjvYytSA5U69LSh0aWJGTGGf/gO3G8Xz4M3CSKPvLBgEgo83GVI9MX4FB1M5Gzm3FfAiAUsZTRYr1dbMECR4BB6eoURXv3IarJY0hRystx08ERIGe1m7f9m7zVGWpQTnZ/lTZFUYgHL+cWplBRPxxiGKHwxOJEI+mGHFptcTXThsp7XZEi9g/fTLj40iAtUnj4IDsG0YED5hnpg3etH7Cy6QQOC/BGXit1TgYKvzih9cCzis6g/w1pKnYiDo2hsYTGsB4ltKOQR/XVcttbHyxc7Xhj06jUM6sMApUxOczTCqdBsUWvkLlRSGhXMqq0wOhs+Y5KViIML1vA9I2C2gwODLil6ZET8FFlQSKhzrjIMltkm2s/gfeCV/auUFC72hCQOKQrQ7OGFOzierSKpaTJwBCiaeMDKdG9RMeHT4lCZUaYt2wMBBUYPmI3fB2+BsGsDrMFFOl5cwph/2iU1BiUlzraunCixnCv1d4/0QP4aAGeAYxQfR08pYp4wA5ro1sQfxo84FmA47hWZUpRZ5Pf3pt9xrPrt7ZMYxZojdxDBY0kU5KjKRP3gwVBqzCmY21lkGynli2jwxjQFJ3GzI9GRZIo0JeUdQhULBy+lRa2Xw8XFIQAJQOPt/maWGmOfpy8qKM30tKF1jEBHJzSo9Di36fHXsrhWLAKEIogQ49n9T9cYjnC2ODBk38n4bmcKjcCSmIYpKigtsvL8PCM8vPxcHJj7ma8fLGxCiwk8L8zkh/jLC8YWlmSQ62eiBV9SIgCG/PsKswAPBoFILGD+YeDSR/g6GeVstjTiKgYaoVcoIaVCWAMWxCq1Bu2f2qgCZz+muAA0SCKbYJuXlQgrC4vR2cEflCts5HVAMUHq4Ihso9PAR3KJVgWBI4Iv00CLKU7SnTzG93ZJjaoGEpJ4OpoQaZ8TT7izgZqStrblK9va4pfDCsqU3bZb3MZ6O/wKu6UoK9BgQB4yqyye9VSFJU4SG2GQQ0Z5gOK4BPv7/ym435hUsfUvF88eNB5X6u0ZCtQcBB6twLoipcGP1VI2lRT9YfyUlRQ2/07RmclCBsEgqfl+RKIjwfnhh60uoWNYghh+iX7/4Yfj1oW3CdJF0gnK3BKSR+WmqN30ydHFNCOQ7yxofnSmk+pBlRysgcmLHv7HKuaFB188GKCAo2TUuIG54+cqvb1KsrMa8dppLTklylAvEZmb4W9OjRBwfhp9YM1Ei1l8EMJ0CcfEvlATexHETiyspHF3wES3i4sI4rKkU+ATFFNn4+3gG+IR6eBChrcgzPNdEHCay8aofI2XV2BwfmlGImXLw1tWb8SAmJH/4AMZgZAXQ7HJ0nXHxKUH+Tkp9C1OdZPqwftzc1PIWjWtyuABixlKeko/QQxKTFdiNFny4ty5RLd5RmZRUUZXRQMQQYWjg38E4D7wreBVA2hTYpuh8vX3tk0JdtWixBjslR0nMLHx8ze86rX6Lo9JyBboOARA0JUch5JZuXw5b/duA0rq47Y2E8WI7949+8OXXnpJqNuna2tr281jYC9v+XL8yT78dh+Hdwu/bhD/50rrqpVaGfL11nKFJmbrMTpU/PLiGpMnjreyjXCg0+rZB1RZCHA4OqpcQox5piSGkzAII97ajhogPMg2aKM2rPO3dZZSriXq6GHEYuL18LhxD1sranz284gweSgBSgQqqQSEOSM43b1pLy0rMWen6F2clCDasKy2EorFYZG91A8mJ6plbqBOQFyOwDNEiZIMFk7oCMPQy2BZ5ultEz/uv2hTEsL5jp5eUhH1ZYDzWPccf7uhFdKKl1pAcBbRDGKfzqZUNq3ilMAodQeklaRIfy6xR1CzKCmtCIEKgVLsppA3A758IPh+gZFhGeTw/sDYaGmUu3NiUQFloMJ5VqlXILi3IH/sfUXOoTSdMth+XNKuIL8JspaWls6FC5uba2oaWhc1tTYNDHR0DTQNRO/dm5LSlbJ37429e0nvXFL0fcklkPwKioGlp1QuKV9S0WVByoJ7WJRWJ5HorPMdPycmwN+fnxITKKKttIEm5WwOU8n/3ENy75aUvZVhDiNDgZCom0DHQCeLi2/DNbUSB33b7jZx2+7du3b5HI59ax7CI71C1tms2fXSS/s+FIn26fYJ2sScPqo+ClbitHFu4elY3mc9WOPjGDENB61IojMAq7AaztvZ/bqSSmJnNa5EWLhQlCl6c/FtqFhMfg1EgzPhP011TX8tIv4ZlpOSB7OxieoHFfUQInrGPz9+NEzs+ec//vjzj7Ozx9s9BHoo8v3wR5CaKbSZlMvGJlxJxUonZ70yMbfAWkxMM4+aKk5ESQWrMVIpmQEyrilrQCckR5SGAQqcbYBrqE/8mNGzhDps+/vL0rHzjEX4NWEP4GCbaF1BuB8GCpb3yxa+hnjW8+F9pMMBXzE+FWiQ7oSpXRaCWYrtpRgFg0oqQGrj7wB4AsoxBIf4ecZGoKQwTD1Q4uAV4W9bXFQcHMOc/RJVwdGZRaUPPFCqcg8BHZGWy+PjdgU9OqG8vr6+paWurrrWXK3XmIMHZg50QPGrTqTgOwSSArzJYG8MyPqXLoEJmQmYIjOlpr68oit6ACIWxNwDzcRHRuO4MCAnNzAoyC85wzGdR9iEgYZfHi/hN11S7Pkek2pg9WQSW1sQlEEb+qXlhvi2tpUr2zadXPri1y+++OI6pEpIJCd37dq3b9+mdVG7TqZ7bHp806Zd+4b7drfRiydeiStrpYH1hLTBYrAFPdjAgUVkVUQw9a/gBWKWIpYRbZzYJciBKT35GIiFFOEuYJAJW0SxmqZ2D4sf3KAG0tF7RUq5fdpUxg3H/TTmoec/fv55VBM4Iss//vwd9vpiN1HIH3po/PP2KLMyQVj7pe9RPsQDKMlQajRGrHwpUZ4F9uawRSasGRKp00MN4XpKptYvGB2f3oIwDDMGKo3Cic/3jYRl1/234+kozgSsWWmol4RDLEJaxjJUh760bAb10HKCgOZ4g/WMYMcCTayGO+Chs1uKSorBrxSvCKqGK1ybyUMwAOkkmKb4Dv5RIblERbrvvsIMZ2+pfwr8aS7mX0Rfm3+RvKCwsPpDzqyIKGgh48bYJ4V7P8ovX726vLyqtemDDz7Yfrmxs7O9dn/t4BZ823/16tVLV69+f+nS1UsUxnCVXrmETRTjlwbV+rRFHQOtgbCagDE1rdusOVyumbmeXmEBKiymBCaaNYgZzGMOZr/hxo/tdcWYQLJNrADYEttkWm5i3Z6hrW943YvLln63ZtfwyuHP2Wvlyo/jb534+sStvpXQ4sx4NSJsXZTupd2oKpN4+fLs5TR/sVOWnm8TlRQRrA30yCcwW/pf3vjZpTKZRlwcOV6BaYC9fjqXOj7wDAQsQwD/kdGTwdom0THdtxyWwH3hG7w2IMRzPBNbUEkhb44up48//uYbqqbj1qL6HL/wPPtD459P7ftd7vffXx1lvBVlButBvUkmhVEBc3Whh6eYuXZFFwMrZuHxyOXEkkrm5GY0GoEFKi1GvcJN5uIqBfPU3v5HCmGCLkpiQ5Fm9KYLuEjsxV0L/CGvLMl6I0HYgSGLmFTWhQHbXgGdiL/t+PCzjV8Ql+3VUJtEGQ7z5bt6QY1IJYW6CrDl+3t4qSayjB9Q+dIC+DH5EwsnFiKKrpDF/NCUdV9pjB/QH07e+DyR7yxbh0mOkye7oKbeaP3gg+rFdfWdje0tje2D7e2D0P5epZv76tWMHDR82IbvTemiFrC4uCCrvba2Nm3x4umIOwiNgt6QyxpaMDr8MzJ8paEB6pxgQH6MPYCDkodd788b7N7Ls5RdngDIEu2gmMqAzSOm7I/p1ln+8fCmF1ctQ+7t8K1bw9tO7VyA184/bhv++MR33524dYvmqJUH5m2YP2PDujCvkytp4Ipj95WYnUKMEE7LLcZiMiDWIY+iW+1+RUkZ2OVDPPR4MUlDRSJyZSVchbVKsEzhmKylJLb2nbgs+/owSiHPHm6M3GzGkAIKYccSHz/+dM6cjRcuXL925enVK1Y/feX6hQtz5nz68fNj0Po9NO754d6S/O+pokrJxK8kS+3Md1ZnUc9XZB2pwA2gqMBMggSRxETdHghKwVh6uun1RkpswhZUptAr+EE2goS/WYfZ8SAqB+tDS/sZAexm0MAK4qmi4miuItwHMygmRLqlaKBihBDUWxxjI/1855cUGcoliiJWvaQV84HBu79NJBKt/CixFxpnB8+QSNvo0ges4VmlBTHFCEotIFowvrgi5rBWOnFiiZuHJKovKZsbMV3uMnd6z+Jps373SG/v0d7e1tZps1avXv30iuaK5uYKi7IrN6MDm7qODlrY7b1x48bWSpqrMjOvJlfXbtm+eP36r5YhpVbCoQW2iWZGniAit8QRTgHGglxPLY491iHF8YR5v20mekIccWIMnGx8XGyLI2aM6OxsFIZu17K5n7mf6Fv5+TZ6fb50/Zfrr3y5efM7+Pk2dl+tHLYR7d6tnffqK4F+zzx3+ouVfbjd2mbvZvHRNE9nM8gKWBbbEItErE/+5Uh/AuoJTx0tc8XYe4G6Y70OaZSiIoaMgUS6VMFxjDqLKYQnWJ4dJ9ZJ0j2wvEoC+DAK9I3HFfXp2SPHrnV3d69fOLlz9erGxv7ua+c+2fzpH6klfGjM88Nv5FwsyLmItg9FdbEoI1gjl+tTMtkTV0IzA0Bnoq+R3hePUzSls8FNXK0g1psxTY38HL1RIZNpFM4BETa8BLufaMlx4T5aCUhHeLgM4FICkwPKRzo8Qv2stywmRS0vO54IE2IGoNMVVjbmjizI7OPCIrT0lhNlDBhFOCw3Imygo3XBLMVcA3xhIervUvLA2Il0JZFPYX5RcVayUkmNbCLFTKFvK8nUSNNPa5eLnvOc5Dhr0qzHWludH3Wor2f932uvvfbIo69Na2199JHyqvKaisqGpq4miyWFcuijUwYrVV1ZGQUZWzpqF1fPrOa7PzGfnPqEHK51kCJtpFdWvjqAb6soLrAV4dijkRKLGWHZHWjw7tmSwrEv5BBlky4S1mKwtgO3DTCJDTNe+eyTZcPcW8OnFhw8P3Jt9Z61a2+uXbH62rWR69cP4b46deLEn7/eJDBkL9+3a43/s88+85zU5sOXdtNQRTt/a1HhR6IGsu24gSPCQ/0rtmdESMIjls0gPw4dBjReMGiEUDKI9yAyEgD/o98g8SGuAC3pQcRgWiDsXJRq7foeHvfww89/POfgwWtPv1z+xhuvPTpLoV+sSKtubW194+i7Bzdu/PR5+kPDrQU//HCRJHQoIeh6oxOVaQpzLoPRCaQA4AfEOBosa7CtsQUmX0m4SqbJ5G6oI7Va4YTS0hAFzhFkJQ/T+J90j6kSGy4mQR3t0wnjs7JbaQfINOYgwcF01Qd6S4gT4zFp4XLOpt1f3p31zVj10i1lYIAf5swodweUFOiKLn6e5HHmADMKb/dQp/wHCvPJS5fiHAvHTiwF6oe4NiUY6ZmZEOcGqyeHnZT0ab2eeWZSS53ThEeGmoYeW9jd2blide/vvv7u3/H6y9d/fvHd3/XuefnltXvWbl2yZ0V7e39//356bd+yZcv22g8WT5/Lf33G/B07NsAFCdsoYTas1DmIBBPYBOebA8G/TcmR+ZA2DWYbaJiESXcAC9+bJTWGhKs8ukhIC8tjAlDeqG1AnzYKb9Sflg2vvPX5qc2ffdnd3165Yu3WrWtXrKikN6z/cnf3uU+2DX8+PIwRa1h8i8ddA/85BKRFeJ3k9omtt5KBbausSyQxUQFxW3DFCb+4osCJsSo22F85PoltP0lixAgCuLKIEs9NR6A7EXjKkrCc4mnh7pyUxEMuYXhULBdfM4AJVBRdUQvOXVv49NxZ0x57bBooNvWKlrrFeDlNWH/u2BF0fw+N21d7qeAidHm50SQ/zMgiVpJST+IpzFLQgBMfNKcgJjGahL6saYIyMTnYwpY2mjSUlgITldKsd3Pmu/p7cVlmjPV5sePZRCErWoSx3EQAhVWDZ2BYOY4ebKuwuhbB0ROwBek+TAy34GWPu7PP2M4QFMrsxZn+BvHxIM1GePkj6QrsCagQHfgQPXnLC6AyhPwCKMV9CB6+jwQe5ItUTGaFaGuLg+sDI8O1fSeDHBA8V7+ivKqhvXPh6ub2xp6vNm/7ZtuJbSdOnUC/snnzsXeOH7ty5PiVY1eujYx0d/c3NuJ4SqutXTTtkVXvvrgsNGzGjMfnzUOwNdp1YbaYK+LAJk5oo8pRU8aJOSdYymEbbdqGCuPuQHF3D5YUCXnAlhDEU/N+m1FEDhxUUKiPF59d9bs/LR0e3vXhqfNfbd168+ZaelVWrmiu3F/ZPziI75s7r4yc2/nptm+/PbFPcOsWh7NrHRrAuc8uPXWaOjK2lcXtbkgyWOlLJlpWxXO0vNQf2d53P0yxHXI848MmlY1SdMi6lJ4blBTZAkdhURVHND90fjwdV0g/QZpyiKefNwcNBbujMEUdPHjs7ZenPDa1Z/FUTNqTyyevrq+bOrV7quOEPXvevrJx48cJCZsuXyqg8PiCDLI8ARRBBaTWWJJTABWX0P43Gr9BLVImgYBZxaizXCIgQJCn1xiNMoXaTAmDermTS6A/rEyyx/3I+0/1QMxUqJRyhnG7cukisrImCfYDeZStJeKpG2eEFKjE6Di50+MI7AkqKbwxxGQXasO9kW0Q6Q2JE1lI2SJGJNQ7yC8TtZRP6iiMTaUMgsFdPKodzJ+I8TGnPsjmJMyjnp01uX7yivreocb+K1dWLHntta++XYA7/sLBC7dfc+bQtzn4dv36sXOfzH30d3/9a1Vje83Rdx/f9NKmDYcPH37rrXQy6tOJtDCahU2cgafzCi4wQifioMiMCRVYeTg4gnXiMT/vvXAPlhQe6IR40gwAoBUzJlk2fZBAzLINXJvQPz367LOrXtw3vGbzts04cyq3VtKrvbm5fRDyoOCmAVUKripc7t1XPjn1+R9PfL6yLz6Ow921ZtnSVav+tArJ4Doir5nYaCYeRSsMzEIBiDfT1f6imWp8HnGl4kfVGwRTJDFFLP7i+KvjB60HAlk4TNyF4uOIKCsKljyS8BB9RrQLjxUUoL6Pt+38pHvhZMfHpk19bPLkcqd6EEHdZJMnvT736bkTpkx5+fixY59+bNpw+fuikotQ42FWJ1ch8ifJsgBLN6dQgEdBFqCKAms5YUMFOm2mdf2LRGkVgGPQENRQSiSqaKhyCoi0QYD66CSEXYKHURmjREQ1KXmFIh0ACJOViEyaNEIqacuWzRZ7JjLbANifOv6O3zOseiVg/cDGgrkYRbk6uIaEuPL5gaTtwMUQGBbpbS4FrpePfSw5ryA7NZoGKHxZOcwxorRwYmnxU7DyHN713HTZ5MlTyles2LN17dEp5es/2Ya371z3te7L7J/ua9euXbly5fgVfH/tyjG8vtl2ZPOxK1dWr9jz5uMAgyUn1yxbtmwXWm9Q4hEwoIPlNu5moVdyjgbUKFtFVrQfl93Q9KToBHdirnHvJcqjogQCkkKxbaKJzfE8NucLfELcVz36p2c/O3Fr34Y1O8/MrUHxtLe3ozfub6xNHui4QXhOx5bt27fs37+3snnFteM7F5z6/D/bBPs4K1d+Przm8XdfQbId8l0IGGaXnni0qOgpgXDVR5TKIoru/2XXVBJjUKDly2YlBUQ9niYpAWlutBJpCKVXEzSBB0qCqCj4bknCbbwciwpL1GJgfeMSUFFzFlwpv3m0ddrixdNWvLxnCoZtTFHTnnrzzTffnrR+0utvzt8x/+DGA090X7pYUkTPVmkpO8OxtskN1rg5yc1ZZJSUS47OGUS7gRgWkg8Kw84hZwqAFuCKqihSGjZKgNbT0oy23mGRCA2wv00CC8lQ5eQ7S33Qo6LR48Tn5WHbAE6fVTPOukDWOtCKjfnXwMn/zt+yVKk/xhMxc3IBrd3Gle8aC6tAP1+KbreFECUo1j137AMTJ1J8Aq7c/MKJJTgJEtHbIlsAxKsSWhvUHIaLvETkZVtfPqF8yYq1b/9+xwsvfP3J2QvXrzzd+9prbwD2c2swluO3VuxZsWfJ0NBQU2td/8gIQ043Hnnv7bffmz179sovhpcuXfbdvl0esAaQIEoLeAyHZEBeMTkKd19bJ31MhsaD4rQY20XAzftNlhRIkLCx+5FGTnMUh0XUIjbPNfRF71XL1uj6Vi5bs+3c9v2D+/e3729vrK6eObO2g7323ojuCAZcfANXV3NlZf9Xn5wiCPDz5SidfZvefRWhNAhLioJhCchqRAM0sHGN1Re82iQCIqrdPZ2W7OqT4phsg/AvaGHR+NGkRJxxzFLQ76VLIyW82xRFrgeIzwL8LtfnueDC/OLEuPvplsr++NPr564sLH8NhSSrLy93dJ6mqK7uqZs+1+/1SZMnT7Z1+OiVN48fmvP58bmDJRdzLlJBldJNdRHWWTEqcMydNRRki20UFi8oqYLiXHZHEbfNGjqfUVAMMzxg6kpaVhHdT+HiHhEWXjYqR0RJ+RepM8Yqg6Q6E62iTHGE5wGozDZY8TCantgoamAbqbxUEkrdeUkl2AT54CYgaAIvLd1SsXCNhiGnJww7gU/4hrgXPDAWavjCiTBHykefl5loUYLzS8ZPKRnY95ZkBU9+IvY5r2HRGlunCeV7lkyZMePdDZihzp7DtbRw4bSq1fX1k+srKurlbg16o15eNfRar6y+E+gEu7eOvHP2+pXjx+fNnn3gAK6pNSfWbEBivAdFSHF9QLUX8KTRRRpvV75cocpVeYGcSRMy9jjcuJ/HJ+7BkkrlCcWMTz963wpor4i+KYryLyKf8w7bJBDgbDnxyeVL+ysrcUNtr53ZNNAxgL1DbkcH+FyVXXilpLCGsLL28uXzBxfs3CZsmz17976TIUiotfFCXDg8RAVWes0oq8a6KAGvLW6c/d3TKEhhkJdEUwUpHcriGYsH1yvXA0kzIgk5LHqQoRfduMSS03G5+FeA5VX6MzmFWMeWAZtI4H2+4NC57qfnTp2GU7amvPWNN6ZhhurpXr9+6tSpH7xRX+44fe4zz3xy/dA3X8wfukGcc9AKSDiOyiqIhtgQglajKiXRklhMkB+YtCRiYG0fXqSQYGqqGGtGJwgV2E7B5sUhFPVtSPjRj0OaH5MyMSMgJJwTb7KKhYgUwhz7mIgSbhMmmq/irbqvMjjc2d+xvZt9qk2ohGc9xKimPLzR+JERO66pQEo8tXUICSoA3DeRGEkPEO4Hr09LcAYpBtHEFmHnW6CytMDrb82u8AgXXERTprzw7l++On9w44WRa+unT5/aU7dw9dPYTDW3aGSt095YhFfz6kmdq+uxpmqun9xSN/ncxo1nj7z3Hl1UH65Z8+KLa6CVSpdKJUD9EFyJlzSrKA2eu24KS3FKkJjR4Wj+0PHG/QZvqfFaCfoNE9vBMk4ZnHjiYAwfhrDaUG/PZw5rdws4u75auq1ny5b9gzewwewCQz95b0wXXVLklB2TPNjOyql5RWVlyvbt2y+PnDv2DZZVfZy+vvQgGPNG2YSEeIhoU8Skv2guGaRDmitcJ/F29mN+QUnZpcIdclThGk8fAC5WSKWiKPoWVqiweaVIcvLDYCw5DsqqT3tAIp2VPzGjqCoBAPryI+8cu9LZPXX6LHm9m5u8rm6W4yy5XF7u7DJZ7iafXN9ZV9X72rOr3j9+/OzZ49M7CIbIoZH9IjafKCmVwsXZTaME4TwRMqpoBjfTvQQ5BAvLwTSVRchfLvNFTgZLSanBdkrm7BAUDgUKySSsNmeCgvzo0hx+KDNnJdCSqWLILgc3ViqbFWmqArDJlrxx8aAi3TnxJCkkVMQbbbdxU4V7+7HGzyHQ1dWBXC1dHEJCMmkpdR+h5/eNnXgxE57oMZlEfMBdC5F8Topi8lOe3mHSkx7eVfW9S3pfmD+1+5NTx0b6u59+evp021lyxer6zvp6bOCqUVStrYtaUUYtmpY0o6XCcbJGMRlD1jFipBw58s03mzeHrNuACAIb6OSFPB1ySfA3k+bmJ0IIDLuKgtxAIWOS0bJAIEz97ZXUGJ5Eh+aIyBI4CNGdmQjsQ0VFIuoHntxvfbEbDKP0z06duozdQmIl7qSUGymqQWATKYMple0d+FWUWntXV+USIBf4oXKwHz30+TM7/0jdnzA8zBMRm2GIdPGQSgDHsdmA+aoQTwMYnc7HNO6uQ52Z721SvNV8y6qEh5ILzo2wKYY9CgyQIqWQuInjR5tZE4W08JYb+k4+VzextKRoAy6ptrcuHDneKcMUtX7ypEmTFvYAFa6vqqoaqurtBeq3enX31KGjr/3J94kd712Yc+SrRZdyMqJpSiogPkFRZopFhghevQqG/GTvkkWeQmTyh9EevkBEQAAEiD0VEHUY+CcnKs3BqjRsptycXHzxbohG9734UvJiCksm5vC9w3Bq0+6Ig8GWKVKg4hxFV9iIS4aRNEml3kXbB8QvKIhSt8Qk94MmK9w/0D0k0hdsQ29PCHqxX+VHemWhlB68r7QgA6RzwBQgFTGDQlBm8wFbZKW5BIY+cdjmpMeaVQ0NNUN79uwZOb/zy9amqqqnXp9UPuV3VU2trTNb9TJsC4wtRlDUW7CDmpk2c6CiyVJVjn6wIU3T2D9y/sKFjXPOHjm+4/ENIWRlFYYkZJIOYDvvVVyaoYIFQTEiUWy5SUy5AMKzQJR0B4Y190xJMQ/UMaYDK8XLefFtxKgXCHbv5rXx2vatWXP69LwN3quC1nzh8+E8j6iT6TM2n+muHBzcX9t1o2tviiWRyMZgHA+m0AG8H5fUVrwqupJjBhst7RWN/f2dC4Gq46KCs4/U1c83NNQrPR3eb9zb7AYTm3FM0OKjG0uyp/3MmDsX0ttZqbOEPUA7FGeyPoJkWiAWSuBh6iGFUt5Hq+NZKfUYD3VESQCz6uSq1u//7WJltl3C7CNH3tm8sLGubjG03pMnO9bV1TnVKxSaqqrWqkcmYJBaWFdX9cgjj859Zsfj7xz5dOe17QUdTYP7+1Mu0UVVsnd7dbWjIzjYMVlXSQeOWwqdEvGwo+lxzGRqX9QWMEDypoCGqKGpsqJZ0bl68iQ/f5CQhKkkHQHPfXzCdCAeXX6u/jZCUmEYGM3XYMWJDFa7ZmYuizGLEPTUcXcF4xj8/UVM0EZoPHJqwgLcQyJ8+Q6u3oHOMMdE8neYTTJZT4zNdAzQ5ACoKEgG/xxfAwapi+D+FSUrbP2WrgMt+uTm6b1LsEfpbT634MxI3UJZ/QTbSZPl9TVVpO7FJhsGFOq0irQWY3V1dVraTGAyloYKZYVC1lDd2L+fJqvrR3YeO/bejBlhh5/YPOPxlzBTSFbu3q1bllFYhGMIO4qi3LncONYAM+UC72dZa/dOSTFXufEm7kqiVIFwvtvUtlsI8VNb275dwyc2vzXjFWzmTn+evu7VZXOXztjxzoXOFZXtW7aoKm+kVFpSmDcQLiqkeqVgL1W5diu2vze6QOFqH7DMVFXX3NzTP3Jw5zBKiKeVeruGIiXNSyLxwe4VKBz5DeFqJFhLjDNZBy6X1e/P7m7uKfuEOCbVxfBOAy3Oc7bNEFIMs48NXVLQfZFDF/4Ah4zywJASnn50WnTODzueL3t8x8azS3v6AbXAJVWm0SgU5D+M72VuCjfs8R0dnZ3qnBwfmTX3mVfmv/nJoTlHOm/cqFjY2b8fFOuMgr0F2xvx4NS216ZYSwpCCKjAgT3D2Y/6v4ICJqMi03Sg6qCODlSipFas6Fw96Sl4PwA/TqKCehipAeNP12ap/Lx9XUO4YA1Yr12rkyfZElCfzIyq41ht3WVF3W9vCqLGj7YLxCYWwNcoyCsCvpiu/ggoxqY3wDMkKgWhw9DJB/pInXMKC4uSIcclpXwyJimYoQc7Bz67bNemTZtOfjJpyZKjR1/Y03P90MjI+oUgIz02va5FUV8vVyjksjSjQq1MA6HRCC8/PWhYqKg07OOUxoYafaO5fWjrkpruc9ePHDn2/itPPIFH7PGX9u07cXLl7DbuqpyxpUROwVEU8+xK1lrQPsfARcLUb+aWonWQXZykb3k24CQwh3CGLbfCdMNgwK77+uuv12DnsPPY0ulz5z698MtjZ/r3N3dd3aKiaVwVk7zlam5Kezt1f3RFrdhaWVNp6bKYB7pUiQOW2n7Nks7+8+dPfQj/P6Eu3QsBL0hRgv2/iEihVrcKK0GcJ4a9lkTHCH//3WH9H58INE0xu33rhEEwOnOYIUUohxtuA0NJtm0j84lsOqM5UE4eiFgVOGXP2ztmz3vn87PX+ht7alFSTk4KbI5aZHL4r+AFExYEVeMF/Xe929TpDk9Oef3KsbNHzn3QMXPClBqU1FXsDvZ2zERoDBLNBikSERQlqMPhm1lsnaWIPZvJpB+wBirI2IsbXYXR01Itg2/mU55PzPtwtrANokdmHn2/0PujN9+K9YTwg8IGrMrKUf8ZWvHSOooaPjJ3hieM/V3Z4tgbgsK4rKRgFQuGMdcmKFIa6UmRcnz4jcEUHQBgBvPFnKiRiIKCSwvzo5NzKfUwOTkrB2nEJUZ/WHmkz3tn584FZy6MNK/dsfQCUJ3uheUTqhpmPdoys668BgeRo1yjkMNqDMWkTpMB9zOimMxqOFEYzWkKfYPFUt3T2NPZ+PTmI0euHH9vxuORy14c7lv5xWnR7jbdsyV/wJa5BG6cV2f6rzQw5TJKCudA0m9olqLwMKEO+1yDmElpIMg1LBfs27Tm8y/emfH4n//y3eadhy4c2vkVtJud3dfOjuwfVN6IIZ1q8Mza7R3RVy8h3Qvk68GKrUu2LlnbDHSn3WJp6rqxpbapdnvL6oXnzhzc9pKQK/LBQsPDIxJxTpCya7U+6T6MFkplRYg9Ob5wRLwx1giyu2hoGOjHWBNlTAqBAD7W5RGxGRA6FF1auKPBooGmevJEE4CwGOXv/coLO17YsWMeeLLXehbXTa2rVjg6NqiNMnkd1ZPMTS6/bXE02WWCfPHU6S4Tpjz1/pfvnN15rae69+jWq5cu5W6FhrVpYFBpxvY2ufgqsfyyVGmIrUimWKZoiq3IjB6lUmQQJTClOAtT5/ZFxB104r/5FvDk2W0JDz9MzN2HHto9761585C4E+kjsMonadNbRhgmnAYINwJnIpU56+Yl2I1GfN3x+yT2j2AlhYsauJNAG0UYrDuyt3z5zjBF83T3lkbkWNG+TG+PMHlm/sXirMwsim2DSB6UkRJNrOTDD2e/9c2cBYTyXfvdjrMHr4/0d06aMKGhwWV6dSOQHPgAOMplcmdHcEVwMRndjKgiVBMgTgvuLnN1xVBTA9Dg/hVLVly5Pgd7qvmPb1rzLYgBB9JF+/r6vDORbVBaAi9a86znVorJeCebbRF0nHH32/12SipByGWOfIyg2Yf7avmB09JdL51e9fV3n51ZcOaT9fv3Xz525tzUfvPlM5s7sc3d29GxpSMYmhc8HlczIIvJ2FtZuWTPkj17ltQ0k1xGbRnY29E00FXbfWbnTjD+pOkgqAKA40oQ7BTqisQ8kMBF4TYeFMVqNSzDwckz6URldnczS/2IUIBLauWjl5VRgkcZWiUOqOYsek0rNBH8zPS98cy7tW23dNX8j+bPf2X+vHfmXF+9euHcuQ6z5DK3KtBZnWbVofWTkVWWm7MLjBn8HCZMmFC3eOosGNU99fpHTxw6tPnL+oYm6OWLU9orKmbOHIDFQkpMYkwG6/FyE8nDxIyiisYABUI6YgVzKeGDbqlosv3bP9hY21O76I3XXvvTun3g7selMi481CXLnwhaJ0WeFGUuMdIvrRqYoydo9OSXQRdxdhy+xPH2d+swilkqlEwjeFYolyMMp7Qrf2jkHUBGIiFipE9sJvNuvq802DvCNy2H+i8SVKKNLSbRpTpMwl35zdk5hz5Zf+362WM7T+28fg2yw6ee7B2qemzW4uoWzKByZ3BOHF3c1JT3pkQVUUmBNAKpGNhYae3mhqGGtOraxpbGZhzQR/B6Yp5kw+lh0S7ky/f1remgKJ6i3AHFLM9YSR/hz9SvGxBRnPrbKSmseLVMaEu7VzFkuMtXnjy85vQBj8df/PO/f3v+4Mhl7KA6Ri5g4qgdWXAFK6noDCg0cUNRSeVu6YguuZgJCu2ePUeX9C6RVTdiE6xUNnUMDNy4UXdmwad//Hz2F8/5CJknidYn1DvSK8QTiRHhWI3EwsfACm0ze814Cj5i75zdXSLpQCjiiNw32vgRJQ6PDnz5tWyzSUvTbKtQn2Y2U9tLIb7zX3hv/pPvHv9m44WjvdOBADuDgiSTwcFolhPMvOGXqjdq5LDfc+Hzy8sn1/VMdayqq7Od8vLrCxa88+XkVjBG9t7Y39nZj+0cwL7oGCt2jkScxDSSQ2lUMFilQYrI6Sw9sAPXVe7VzKvRGM/7t29f1NTU9Nc/nUB3nf38eFZRdnbLn3nk6xfDYtFecWniYTaZcaOGntTV0nIK5N+EMT9dT3d8+NjHB/kzo3G2cKT4diRuSMNYiinMMdknYkOI3wMPjn0g3+zg6hJjlayw3Bqoeyfmx/h6+Gw+O2fOmbm9EJKdO7/g/LXuhfU1Tx7trSqfVlfX0qIoB8IjA8sepmOkCVGikowafFOQVAyuay2DloqKIfXMRpRUZ2d3/8j1OWffeWveSyd37du0IerAyr6T03OwQs9JVPj5L/PaJSROKCWYmExCH8NvJwzHLo7LQ6PextjnZMKyck2kVNR3ctmfT5zC3dS//9L+6JiBjp6T284BEu/8IeVqVgzRjzq20DVF+UE5F/fSHbWkpqIhzWLGedS+PyYGE8PA9FPbdm7DOXwS9iqicDzg4D+7xkYhODzAM9LDB4K7WJCUOAZrQcGJ2GSScMbfbUVR/zOOriaaY61GXGSAR+xtdtIT1zR71EPJwIiFs1967onf73jriSffvzDn2MKhqjoZ9XlViqp6R2cXxzq9Xk4WxHo4rwCicHQuL6+v66mWVbW01JcfnXJuwcHrr09pyr0EiKa/fzDmakdyFu4hDB10mF+FgheUP43CHEy5OPSiiuoAZtHRAWNn3O5gRrYPgpLd1Fr15AYhuuzdCfCTgfzx4aQ1z3753DOw+/fyIZYxFgI8ym8jG8J4Ni5Cn4LMoTH/3Wvwjl5lYd4+vFFwnu5wLqXYxLo72ILmR3EnIVxhlBLAHpmiP1Bk5jsHZ5Kct7Dwtqa3MEcWGvLckTlH3p/6Qee1PVvf+Ozy5anl9TW9vVVVjtPqHqvT1E/G0ltBb5sGeZAWpVpPFYW4VdK1QNQs07eb22emDTU1NUwFEXA1LqqR64cOvfPW7JVagj32CbXPaTpiOoJbHVchbNJDxxRG2WSPyebs30pJ5dEmKt5Edi3ibFQUd13kul3CYSifvrtwYWT/1v37UzqIwrd024Iz5xdM2JqItdSNH6KzrsYoa1W1HdEZe7GEArK3BENUNfDSJrWqcXD/lo6ORa9BpYg76sDnB3QQB4rS4fBog9RBkQeIMIGeoVh4xUZJ4Q4CMid5Fpl48Xkm9KB3RZ+3v21AkxpntaizekYaSPUal20VOjLrTuualyBGU9u+l04f/v2O4+8/+eXBOddWVLUq0PkrZIoaYAYKTUuLWu2GoxbfQJxFXYF6U9fTom6qAsxec7She8HGC/PnO2OV3ZwChepVshqLLgaantgFQlJWslmlolFcDUPaZBpDaLxPDlbOpFftotpaMxG2LBXtjZ1Pv//+WwdWYpoqsyqKxyX0Hd78ZWNGptozJJx5yzMNHnl/kqMbsWlhLGX3oxzk7lx7U2HgTIinjqk0ORAIw+raC3HEfIcAVlIIf3fJLHzQmtxRVEzBAuAxwnSCvfDTAnOA+7PHNh4rX7y9u/Pozb/+5YOpU2saeh9BSbUuxu4BVC6QHTVIHdVolGqUEQxnNbAyhLRFg7oymo0NFY3bG81DW4eqei5399Qs0fT0j1zY+M477x34cANKapP2gM0zTjCWSpM7rFoVtE7EKCTxTOChlZTd6yVFRzvpQRPEWrKepjQOZiTGXTdjAwxZdv35u52HrnWurVxR2dWxXRW8ZUvP0uFvT51q2Xpz7dbK6C3J8Mk2q6K72rtuVNYswRS1tbK5sXFwS8dAV1fwYPvggHL7uVOntg1zOcIPX+Kefi4KFvkAzwEW6EA7xr7IHal5AfwwyiMXiZm9UTwGB1Oc1ifv/rt+oarG51ktGXjMq4sIcWXMXtbqyGQ1n2CLMMhy9u16673/uPnR65M+2Xi+s6bngxrUVEN9laahQQ1wKk1Nm1i4eZN9qhvQ9Pry9VNlDQ1Dijqnump1y7VDnx45/vLLFRX9ly6V5LBur6DkYkn0AMvsGKwNBjkCV1wajMDBjWOCWCh7Z6IZrG7FE1aL06YJdNLmhVeWbpZiy3P6dF8qrigC0lP/c83MkpLSfKO3l5BxZOljYWmHcMrmaYWGhF8ugE71ck9nxptCcoZD+yeU4EBDYBsfIQgQ9Uak24R56/PpSnpwVH9Ygmz5aJoJM6JxKsTEaPxs684d2jlyuaenc+uOPw8t+mBWlRE7OxdNWvXix7CdmkBhbfABSCOwT4aKMuNnSiV+DiI+TpmKBkt77eDMmRioquplyPw1NjR0Xjs05533vpg3D7eUVnTa2wlnUnCaQiZ/xH+Xtfc1sXVauuleLykrbIulOpdMQkwcuEOg7VspWjcDy7x1m279+7cHL/STImqoaWALE2R+cOK7b7v3b117c2vl9i2DNdBB76VXZc2Ul6dMAQdpEN1MMkpqwDLYjm3vV1D5Du97abbwpU2nNy+NgF+lljKOhbAaEGm54ZHuIMI4OYSk20TGkiEkhyL/8MQL4GB/14Hadiz7ygo3m5KYviOJDSDseYxn3AkeM0Bh89Stk++9fHPr1tffP7bx/J5pPT0yLKQa3OC2jL0/XIzMaWksxwXNIMS4Cn0NSqqmaqihdbHb4v4emcP1s2evvP3CyxXbt2CmZFm9MGfI6FJ1kRlSckpimrMtNlsask0nV0zozFWqRKV+miJtpmKaZtGWjhsoqa0167/cfHjNpj+vgZPHrSQEWZOD4MO3OgozcwozHSJFYlZSVn+neIrxEKXH30HO9T+4pbzDOYxoJmAOzoBvPKSxQcTvw6LX1s8/NtTTLwXWE/eRLTp7gEqL2E4NmjAIv4BgGl3qG8+d2XlmpHtq1dEXXmha9MY0RSsAnceqqxcrnGSyp1BSmCPdUFJpRkQxqOnNBP8eEAVpW3D9NFS0k5WLumFoqLehulqDo6Wm8/zGI8ffm/3hhyfWpKdLPd3aQXYzzzRbWgM9GHgFghy9E+GCfyy2vAcaP/txhFanarnMAhmOzDA5WXnAawYKasOafcMLdvZAt9veUtuMPgX6jYGORZ8tXVWlStn7ww+Va1dUAgXtYiV1A3PUnj3NjRBOGatraweTGyvQCA5eXoA76hbX5+SH83a4L3V1dfeODCeIQsDTeYRTnrONf6Cfn9wlTOIR6oXcIw6JohkBTZtkjbC9SyTdjt1TNMRjrkLkVF4cbX3ZEU/EUwNDz1gajnj4uflbfyi42AnY6lrPtMVmhSMmAEW5EzKc5bJqM7X9esxQThDi0oOhr59a1zBUVdOg1/Rc7hk6Ov+tOYeuv/3CzZpFoAzv3YvmDzB5dDBIjyBtb8nKMvMd5KhObKvgignnsWAl2RWr09IsRCDQzAQyMdQ0s279l0vX7Nv0Z3qd2HerjCyaQOEtL8xNy5+o9/biknG2iaUoiGnvKwgJT7D/FWZSiBlIJ9Y/hVbSSEUlZeMVCoU8n0LbAjy9PZ2DSx9ksR2s+buPohDxmggtS34p7qwMlaJmyYovjy04uOD8mVduHq2dViWva9FMky/GWSObJTOWT6ZTyMmJtlG43eEHD+NCOGsiuA2zFamZUVUVFnNtI4C/3ip99eLqqt6hhsaRQ4eO//6LAx/O89oww31yO8w6zLjbjI+u47BDJZvOWo5E9I9JwvfCLMU2OhwgqnEUNceDvd3KlVJU1OMvbtp1YtvGC4sGqiw9teAUdW5tarrR1FS7eH35kCXm0vfFzWsxOq0gESLA88ollTV7VjT2tysrGjQzq2u311aAIdB/ZsEfP9+3KWTp0jfffeGR9c8G4KPz9yDza4E23UcnEQm4IX6Brk5qZ6nIJiSKcdMx+YBsYxBxxt1958dqKpVZJaGagPrRC9dVNikp8wi7IDiWuQ3x+ta8ufaHixf/refQp1d6ehZrUFLlyOuTI10DTAkZ0lvwPMhhtOwM2xWMBGZYP5b3lsurNNaSuvk2tD9X/uPm0aFFiwD87b0KwCYlGST8ruBgcCiKlS6gUiuYNybpXTpojkpDQVmUqoqGevI9aZ1Z2zP3/fdffJEq6i9/wXebBEllCQljHnpoRmmOU+bYGD/XKA4RkgxWzwJw/DgR4fRx/tJbCkJ5MjPHoC+wegjAax39tzeo6JQwBdGUp0NyPuzGoOoAJmF1nR1LOo+JmKZKwU+fWBKjrli75/WR8+cPHlyw6WYFlrxOs6ZWtyyurn7Msc5RXuHmzJpldH+wrAERRW1J06cplWQTamavtEZzZVOFub+2FkdUr6KlB+8maqv7/IUvdhz/YvZbT8yY4T9JX2FWzcQ1Ny1QKhBbF1OU3SiUxP3Ng3uPzlL4N4lrYBasLBaq7+Rp6bx5r7y7YXj4xKnrI82DixZpmmEtUTnU1YVzdfuiD4ZuDAVnXd1fuaIXsxPGrBWVNc1LylfULKkZvHopusvSkNbSuL29sb2y//zO02DvPxkIs6EJR3sXu7n4QT0QGpUuglRHS+4/OmF6BHx5NGZ+lFYaFA5/WMqDg+YOKs+kX/DIEDNpTCrMJ/ISoHYlLgUVVRJjxlGVkcCcPhwQNNOfePOHHyZ+Xzpy8EK9/nLtkiGZk7OcGhbkuYA0gYcBBAo8GnxbmR5nbZq5xQmoH0KlZLKWnpbehsb1146AQHv8hZtLlqD7TSG+cLUZeikUFpZQWSo5LjsAHBgmEOCWklwLZo6+Qd9QUVFTU1O++kpnZ6dj75IXfr/j9+9B9woZ0dlDc3Zu24b+ryzhYY+SQlVWYY4zH8G6TMxrDUYBC9JL9CvavvvtON6hrKQE5MhBNodcn3CbCNAn/GA75uoZ6Oublo8qKkqMKRo7kbA+BklAjJhPOhb6hSLED9ctXNjZ3N9/ZuPmmw2XwVPkO1a3AD7HJmqyk77OGcx9CF0QKKrATYVUecDnSiVFTSnhYKg0V1eblQMYqJSNjYvrFEMNDa3VMni7Tb2M9eXjO3a8N//VGU88Obm+3tjiNs02IGIXLRKoXcdIGQ+e3zgrKmN/j5YUq/UEnRCnN00c4n1izsnDhz3mvdr7KvxYtl0fGels6RioWbGWlJnogBuqITZs+qGpkpxuGoZwSeFf1FJlBU1VFdsvlUR3DSgttY2N+9vbLV8dvP7EK688+eTcnu6e3qOPVMO+BEl5ft4wr0NSrA5VpUOOir9rgLNa7SqShMWKiHvGAql4PIlw/F3DE7dFQWwdat1BJbE0W4Y+jxYXTn3gfiLp/Js//Nv3f/h+5OD5rVU91VuXUOCYnNH63ABZ4cJCjTniznJCPCbVUd2ECS6OWLvU14FlUVUDIdW1gwc//WLHjrdXdzZWwh1ge2NLixnXFOQdWVtSlBryQ1I4EhQPNk6HamYa+Dk19SioJeV7nu681lnfu/UoVdR77xw5dPbIoQsXDi5YAMMOWCHmDacU5mQUljjZeousVjrEnye4j2PDHWf/y0tqjNA9NJyyhsnIFiUlJLjII8w3MDCQH+juikBE3+LCB0uLVLbGIjR6uJzyS5jvUxEULFRVJQXRZo18fXdnM/aPxzZe2VqDkprEr8OGF1ifE1hImro6zJD0JsqMlCuKW1mNuDb0fSpkL6CmVEqUFBIGGpSN/T0tsgYofqsbQcqZ2nN5wYLZO3bMf/KjGW96f+T7jAN/+lz3F6O0LBaI8D5anOpI22v/d7dT90JJIYTFJGKZ3Zh+Qe/bd/i5wzb+5RNChmF6+RWAibfJUmxrTQUYcLUVle212xd13OjAUZw12DW0dW3lkiXNNeU1DZaKmooKzUBGbq2yHdNDu7G6v7bn2KFjH73uwn92OjR8k48+UgtOGw5/GBcHiYQimJbDps7G3S/APSBQrpSHiMIRYKMlvz3md6HTJt1dWuLogEHhCOOBUjAnPxMLlKKSGo0LJC8ZSuZB+OF7ey7+28Q/XLp+5HKrfGFLc72sTu5Up6hzo/oh7iwto0CrAYvCjR6ROnlvryOtMuud0P/V1cEFUrbwyrkjZ8+eRTWcH1nfs7h6sZOLPA3EG+WgEqs5QB0yZ74L6yE1M2tntuKaoopagQl0z9v/sfbmX//6p+9Onbt+6AjErsBV97z98uZv5sw5eGibdrZrLpz4E/kO3hIeW4GPLqSSeOnchF8RI8lKCvEYUFEIyMIMuKsIm/cAuqUCvX35ATbeOQ8U5iTKXYLzoZpCSRWRWSxMXDBOYf1arHID3L7q62nNjU1Nr23+dPOEpkX61asnlyuMssn1NZDyurX0LHbTkJUupiij1RNeqUkLVhnViTFYLKDCVA1N7YPmxmp1tQxO6vqKma0zwa2sqa/v/uoU5oTfTXnyzVdmeHlJDy/zXnb65C5uH/McZCmbIPpJeFZpmf09O0tBoCMiIS+uVcATPE74c4dPe+obPjox/Mdvz1+Gm9hafPY3h4bMPaAIqCoHGxu3LOoAA2ALDuOBSmLILoH/QH2DxgVJKE0DtcHboZ7a21WjaO/v3nZ284yn+M/OnVpXt9CltwV2vXJHRBDJHD09cD1BL6W1cTBajHJff/xgawOeUpRESLQkbMdg4MMbZ3fXfd/trxT203k0Q4EdR0kqbLynPTJuKVr1ijkeH82/+W//VviH/d98gpKaBOy/RV+HAAqnWW7gIoEzi6bP2Rn1pdc40eWFw6BqqK5u/Xq4JtX39vY09h6tmtm6emSEbIBwv5zavPTL9evn+gfQxVanx6OiQR/k5OfqAM9mlOjiamBgFQ3luM23Ukn9x82bL+z483dfnVmwYGSkH7d+JdlNrRgBcW7B2bdeEnkqVTJ46wWJrCAlkc7RyeYlabUJvybjROceFI5LiuLkWUlpJSJtuDvwPpDQvQMcAm3CMh8szQpWphRZzZohOSRqIkPVJxYVRKfxQXhe85dHOusXLfrr0kObn2paVANDzPL6Cn19fUN5g9oNDm0yfRquaNirYcfLuj19GmhJWHuj7bOAptbUYN7eiDcc4QQKRUu1Oq36A40CJw2ooAt23nrxySkfzdjw4YcHfE6e3tUnOr1LwOJ6iVxNnydX9492CPdESdlxJJRnKGbxgfv2eUiHn1k0MGXGCex0P9j+gbGZNLoNDZBo4k2gEwe7y44YyOhUgynBFTVGHC7lLgiOREArFjkNTdAjEtmt3dy1//w3p097uwRxvNATYLB49FkZTCEBohrlATY6UWSYiMf1pZwYs9wzyDYx2MEjPcKGjHt41uREAv2IIP/LTmTaDowjCYSJuWESo4cxxdA+QUQCI5OgV/YAnCi5sPG1RfThthjR0KBjcZyF89XJxVlOXR9e7JYCzoAnBE1hnYsLBoX6ejeN5rHHNMDYWzASdT69sG7hl8zGGlL7a5M+evP19fAsmwUnwOm+Qa6ORvxZWWPPpEl7cDjhdXRrb+/QXwFHfH3uwoKdnwwW7IUXInqBrVvb+1uqOo/NmXMEE+ijjwQ8t3Tpcwfa2sraDKPUvrK8OAH3V5WUwNNfQmbxrKQQR8QNl3BtYHMGv9kA6Hpdvbwy7yuFU3Pp2PseZIBfYQm50hQVPoANVVFBsszB30a061WHuQvrF73x2c533l7Sqlg9ydnRTVOhIJYx3qg6GQET9H6lUWJvGno+Iy2nqOUjKF1tqVADIAe5BAgGtusaTYOmurpFJqsBPaX71M6PH3/h2c1vvTR7H3Iq+m7tezxcOPpIUCcVZxJiyLa7txu/Mq6QebeQrf2uXeG7di2buqjqyZPD2xacx5a/cWZjbe3MmUZNS0V1S2c10SDhQhKTS3rw3Bi9XI9aQcyko4uDg7PerEmrzNqyv6sjektMyo3+CzvdIzzl3sIIjZODrZuLlwffgq4ab7PGKchH6ugWK4p0gx4CiV6O/gFpwQr3cJtIGzIsJqIhIFOuYPz9vy52Hh0gWkC4cuWxQQrPJRkN4TbWSrzcn9zzw8UfHtt48I1Fjc1V1lUK8ARHF7RrcgT6yZxoIYWmDygFvYhTIWPoMJu9iXSDrlBt0RO/1mLpAQQGn41Pzx778ssnDn+5ftZnqz6b+hj/WQwo8PKyndX59NMv79ljLamjv3vya/ixfvbJoYMXRjpVP/yAgkIvsHVge88bR8s7D835dOmyZe+u8l9zeulhiB3a4sXkKMv2ACCW/JqSsuO4BpFQ3rrqhU0SZM8+UnfE4NjaBgTwodeNjcZ+t5A2Ug+iqChPHvzEjIJSoH+FsJ3g+0u52k3vPrW+pWbRG99+evztPVRSLm4EmIN/TjteMNBZywfHQgRBYoxSYYbEqg+RJRa4WOvNZj1KSmkN8gUaqJE1GKmk0F5Xb798ecG22TvWL/1CN3vTvn19hlubHp9Hzs7iUeE3GKAig93fjxu4J/ZSQi0ta8hrAoyvqDW7dj1Su+Kjdbc+3fnZlu2Q5BnpZUlzAzGrpdpIbwaWLDHgh1Iynl6hdwTj0ha5lJ58R0WwcmBv/sTv9w5g1sqq7L5wZgJOP76vrdwWGiSFa5CzEhUEIqpRE+AVYmtrEx5opt2fKljj5843qp1iRUAo4EnB7GghHRSV/ZqMxNEmENtfniCO+qZUpusgdo+Amx4S8OSUtZVDJ+ZceGNRc7XGDSpeGTHPHYGZA+GTkQaRRIj4l8XPOkKpqmDXlRuBfhrisLnJ1SkplnaiWlsasWZYvfrLY7is5izY/MQrr86Y8epTc19/H6/XX39qypTf//73L7z98stPdwLW+AQElEOHFhw6dOxK/2XapDM7RNh3LFrUsrp++2ffnlpwat2rr766bvj0YR8dasqQnUclRcwqHffXbHrtxEERIpoySU1NJkmiKJtwL2+4I/H94JyHXG4b/cT7HnzwAdi2lI4FfRZXE9TIGVkFpRD6lpYo/SLDhdoNrzy5uGV161+2zbmyds+e+rrHHBVqSiVBKrEeykMnF6g5jFRHSIA0WpDxq3FzM6LlY36ggNOxqaKRSqWyRkJi60dbYlBTLGbU1Jk/fr7j8dkfhu86sPLWrewDM9YhZ5liazmjVnhCTsLfxdDvicYvCW6EoKqyzk/APXx4V1iDZcqGEys/HVk00N+ob4ZBKu7vmZpqZaOm0Uw5LiqYusVkFME5B++Ogu+JKPJAJDo7O8nVwV17S/8wsVhZWxuc0tW94Bjkog4uMpkLbH71zmHSdBsHsxJvr8aN7+0l9dBJHAAs4322KOVBoc4otHCpF6xHBbQqp4EUieX2999v9yuT5+1SbSJ0PAMLyiGLVpbaCTnrq/PffvvPJ+acX9Ta2dykaJERskd6XhKissJxI8E3G7XltGECFYDWvhDVUSOjRrHJZKqsFHVwTKKZAOHmGsxI3bSu2bnz9Lwn/J/zfp+9nn56NZYQL6Cm3gNcfuUTGEhu+/bMBYI0Rvr3D1ZSSQ00oaK2bKlt7eyctmjRvy/YuDnW/ZVXXglZtuz0pn1tpMAje2qKGiB9w694L8ShERJKfSWCH/opjigqKt0LOTi2fF/UlYuzu0/ARfDQ85PlcmUm2r2x+TlkRQhMnXyc8xMdIjx0PjPeXTW18+kP/rrtQvfWPS+vhreAWxpKikAdOWBzjRNx+GXo/pydZWpk9cKp2k1NQYpUUSolnqe0NCWiSpVUYEgEb5DVLW6pbmhoqqjefgn31DeP75j94emTBw6cWLl89rpXPQQGq4k+i2aKE2vj7P6uKfE9UFIJ4AChpBj1jSP0OfyF1GXFnh2P79z5ZXPD0NYVwKdqsHQz15rN1XiGKtgpEwxFfBH0LFnBiSp5gLe/q7unK4rKjU6hlOKcS8UxA8GqwfbuQ8fCYr1hxCvT+8UG6ENSx40Zx/HFc0g8n0ApdlIeLmbEv8LQLjnNN9LdKU3hTuZJPjqrygRI+i+gUPwPP2rYPklcbVhKJ8+aes88aD3CImb8fseLJw6NLHqjpaXJ2NiioWU/Nf1mVlIawun0CjlB6HD7gX4OJSWnm0zPXhS8obAkqtKCs+hwsTRVWCo64axVP/nLzTvZYHXo2OY3QVm6+fJa3E5IYDiLm+nCgkPIYTj1VTeO45H+wS6QJVc0729vIhB+f3/NUNOiD97o+MvOgzvfeuKjV35X7vIMQ2yQ6CXOzmbhS7pfV1KG0FAJC1+l7g/fuB4+Ei/fQOgPPV3/P+beBq7peu0f52lDGSBsMAaDwSYbbDAGk8cBCoK6CfESCuThJUxhmzBlOtssB7kjKQ9qoolGvkjj2EkhOXSkJBGMOmqnUk/d5OHWfOSc1Ls7Let0PCfz/N/XFztPd/W7b/m/XqeVpmgl43t9Ptf1vt4P8JvlKVIT80GbKFGkukdwsyvARge/Dy5+OURId1maHhCfFBv6ysHbrbXPttwZP3VjZM9bK0/LgnjBuLUxSmEIDSYZB59cemX8ABZs2ETIvga9L4OJKcYzArk82j00LGS6JkLTo1OhRzBodIDTzXoQkS9v/PV/vfzEE394E+jEM9vf3BDn5vtgOQcLDoDBUcxq6qd6S01Li03zYKhvMA2K9Qvc8HaItn3vr17aeLkF8PgeSDXwQqdrscFASkhPFi4pMNgKAAgtLkcaRUBYBGwSwuJhWBqEmym3c6gAGrucJrHJ+cnhs8kp0ZEKljA40S9S4k79ypJEKjxwKsOSMCC7B0EJWwDuG7TTbAFbpeFmpkQEzowl0yTmXYS+dwqd37TpD0QfC1IzoxjVIRNVNskacIOk5L+2bvn61ARuKaNVYrHg7MReFnQZCdp7IMBgJGm1MpL20nWllVBaejCfeHsyGqvwyAihUNVqEHONIUE/0inGRIDoF2Ptqktg6z956vDZX7/5ztP/9c6v3nnznTffP3wYBYV5C3fTxYkJPDkDA3CYQklBHAJeOlNSvSPwvdNaD41v/PjAhje29t7KXt2RmUQl9UtXxpgN+q8pllQMrDFJ3eFKbgHYqCf5+aVIOSgpeaRCCl+XuMwMyDpKFAv9wsNA4JhfCMYseMA4KgGkLy3jJc+MTY0+Ud/YsrL7wtmbXXqU1BFDMCsA9zg6vgBAOpRoDQwHcCntzvmgIeFiUolhDoAuD0RikGhpz0CZxTq4LKRDCKOqNVjg9WKttprVlmsTl3/94a+eOLr9d8+Fvv329jejY8gnmIlXp5kSQdjeC6b/dBG/LC/vNMbulUAgn9TEl17aZ9n7qy3jpy5agZ+3tUH1btTrUFOt8LhRW1V8lY4oaxn5FTNgS5WhlgFTFShAZ+Gw/INVIhibFFyrKM2DNGjIMvbx2A4pfKww0QewVZLwuVlZWW4stUaCypRJYWHi6s5FK5BNMTESscSf7S/RCuAPNpNJGmLeRC+vR6eQOjXtQbTgdE+PVK+E78KLqKQI7Qp1f2nvyvHxsYHmRnud2Wwx6ZBoI6Tun1AIyOW0WiZTF4MT0/mRGDGYGkG0fTLiKpENEAQcaGWgDsMtdatTBKFut2OVwyIxm0HeGzt79hTcBT46der9j9//tGFfQ+3AADR3LRb4CDit1iNm+54/f77nVu5Qv2nE2oQiAzf9UG23pXHk7td9fzyw4RiizYpaOwLdPJ6Bb5XHZM6Jl8+j06dSUtHRfglMXj0OUcoxgM9hDBJFYY0kp6heeXxKOqDzUnWmn3titKq0AnZpgCfAmqWstuJ8YWT4wsCQ+rWVlW1dJy9UOSzH3jp2RM1nsSDZ5PuzAvh4k9RarOeCWWANsoPxUwnD1hNnIL0Yb6iKoAuQ/dAFoq5EiAHO0Vl1GBWcIlFnNTRUEtPAjSsbzz71xNG3f/uH0FCfPzwvyIxiPnUQkhgnDg+3OT+o+f63l9Q0JAokzKU/LyJIotxSw596o2XF3p8dAHPf2lu9BwXVhiWv2GnStLbaRFYSANGhrFTmYbE+v6Io2J8DYxYOli8BfLD3YaVTlnutePFVtAo93QcOd6HSuHBDAbSq0UgT3VMzWRoghECg1cHJUIGHsoH86OjeQz+p4+KaYkFiGjqZUkYypwRX30emNksRvvHItNlRoJwySr7JmFpcVW6pv3ll766+8RstcEOtq9HilqICkjGaQzR4qBc1U1H4AE1WzLpXBrUCPgyUilB1dH80DeDBwL9qtXYOmkywKFnlMBAzQIfiwX2EW4lmposXLd2OWqEeyYBGezVKqhN0SesxlFRvZ3Nzv6jaSq6szY2Hdrds6nao7v6pDzrX3qud6cXX12757VEczej6mJhjVyqph4cnEiKifZgwODglx7oythyBCMSJjGQiEBHtHhNNjd/8Ek6oe1ymQFkIQ13wJ4rJVBfEpDwRLyQurmN1UOXKmosnL9Xs7nprzzGrTegfQDpeboC/WouxScZic4NYcoTAYTOHRhBfZejGRLocWlEFocHWUS8g0RL9MTdHZDaLsyFgEMOm2Go24y0andj4/od/OPrEz6NCn3v9D78RJJNBLhOFyFh0p7m5PvZDOPq/saQmEzKyvGPTGK/mNBg+eM8MX/jCsmN7T5z76LLFbnHs6R2ww0eiGrSIIZNBCGGhldpkXbaS3HLgs5ahooIKk7N4Ug5sIYGI6rRqS25JxdK8waaeOwfG9kkVASo+yx/dkkgFU5RItlonw6YHtAJeOPzK/eQSqiWdCJCqMlfCCtIFp7inhENLj0wU0t4iyvdRzymiEyT6eNRrDkzIKEOeiaklg/3Q5A+27X368HhtbaPNbjRbHYTiUtmARkSIBGWASlTQSwnBTMJgRTUlASVCRax0ISkTgaDj4cDTogE9AGP4oElka6whUrXWMNDvNNs0BrIw7IIifgCG4BP43mistpLlfo0tG47xdmKmVFuh1EKWkKRx5Jtvvjl/Z9Wqqt0t/L/8/qO+N4eujiz79r9NW15aP3sJCRGppmAgNfvhkhgmv+yz4yFBxMovypt5H8Bh8QmPRG6HAreUgu3PFqTElyFR1KVCnByeGJ4YoCyBq2AFI+qFxUp5Xo6aE58csnrtykN1l39xaYS3qf3P++EgDy8BVjAW5MQ7DlIJA/yD/VksqVTBA4cWxFl0fqJsnQSm1WJJkFCr0gH4Ba+CuPlikUanH8zNGBpCKJxer9dJVHZ7z8SVJ9/8+S+feeL4c+deyoyOcGcIJMxaEUtv7EAe/enBE0w7hGg2pDwtYog7aeB6pWQGKmr27n3x3JWLJi2Y5yMWey/dUqRuMRgaVY3Q5mng+p1TVACn/IIyERclJVdIWUFBbAB+6KeXsTm7LfnfXh3uNA2M9Y3tw5cqgMtjw9IBSwuwc3g4/EHqxszPSglPTM1kE19Fh1NMDMxDHBygUUW4p+Ca8kGX70s6Vt+otFmeU38t8Pb9Ln4dhz2yy90WJi+vPHbmlY0XdtetFtYZG1UWxyRvmk/cNBLQoaSYtk/IIBGEU+DJwHyFHwAFpRd6WBV9DO+KkI8cMlNzYw2okGarxtI9YGjU2lR8NeQu9gG7hYmzG9DrrfSqWbbahvwge8uePfbqkcZuQ3V1i91muP3cX5B1NrZ53yc3Wuv7PvrjH5xfqDuu/XeP9AUkSALuIzfLBGjF06ZwS3kuSRaEunowRoaUCOfmRyUFTQckAiCjB7DkcSk5iBR1mZ/HBhUzU6rOKGTYSFhLzXBZiqwsES8yOqS+vsped+HkpZHgrj1vHasRwlaKx2b8pISAcGTBAUFCHno/fgA9FVx/mU7DvFHZsATV0JBKln6AepmXRmutHoQDJC4qKi/8VruzZ+Lilf/8AzD+2OPHX3ohHlJM4jfOoRctvdO8Qaz5yZUUDRrTs7wocWXSyhOOEMmBMbV1n788vhHTs62qvdE24KBZqhonh0ZjUwuFMJXQKeEoW1SSJ1YWFufJcLxRH+4v85fCqB5C6x07G/QlpSXD6U39432f7hOEhUn9/QVhZEElJFoOSiqIbn4ZJyVeKlDICFlF1jojKBJrg3Qiaao77Ab8vCYzp7B+9lrw/0NJPeY96azNXFOAufwWJqrK7vfuemnj4RXm1fwWvdUGW1QinAvprlJRZwchIpaVWqb3R63hZgVQRdRqEG1ISiWZ7F4k9CNcW45ukxgKKLPVZiODTVlr62lb47JgKy5h3dDQENlai0U20eDgoGbdGv9aB/ElqkdNA10tu2v7R5251jsnNyJiG68rJy9f7h4/3Pd7y9KyDtPScu7jSV7gVCVgPUsLJR/fqXTCS5LD3L2ZxBmmpGIRCJQSKZWHRSIAmxa+8rhwWTlqanGJms/JTA4QpTPG7+WULFWan52DNAWWIKSjvmbgxuHxOwPCxmNv7WkzwwkTJw0zc4JxjAJTyXhciVJDVxVgDx5spoLwlgHlQwlpGNhPx9xRWFbhpodusyh9KGfIRH9pLM099urr3adO/e74do/jyKbNjAbRBotuPA6T9vCIZFnwE5yliOnzaCwMHyiHhkII3dwD3TmGXXtfPnx5YsKkbTOfNlnsbRBCgZVGOmfgYHjQJBmwy4fjY25BeXGRjEUdQ1gYh+svjYiQyyN27IjYuXuoILezMwMlta9WiiQwFisigoVVFI4yAtq1XEyyahknMDQz2V9ESesZ8DNmMtfFMrWYl+qTEpGZ5AV2K1kmzZ0dO2fa1EtqmqsbhZXTQoZaP5/Q8BXlMG4+8/aTF8yQldrFTWK9gSkjcpvgE5zOJ4NMCDPIO0xClxeXuj8cwQT9EnChoTsLeDto5mLAwFRSIqvZCllUt4E8ik+ftq2urzHD4o+Gb/wfBkXNpiZTtklbC891CjjeU53RbxqwtK81NRU1Iz/woyc/+uyz//jss42weBvv6/tPVkWxY1VxhfmVl6LgMDsHVwsWHr6xHg9fUtOnzU0OC0S8FyE02CXEuvklBcbIkXlNJRUJ+kRkXDgnF/KOinRlUXYAT5RRVkhm7gXY3ecXkJtwRq6ItaO+PujGzfGvx1oMVjSvRtrkakDPwsFDDr34XsXnccVFuWogU1K5lAf8D0w0Gdm7kAkBWWSinEDFofcQJdWZW1KS05MzRC+Tqb8Hcteey6cOIL4WtKTjqTGCcCTYEQpMamxEkaVFzf3pIX5k5zptTqzvZLqTR4LbzPD41GR+e+XL771/8fRpC1qU/v5GPpUUmJ5mPW5kzJOyYEkBmaYCAypKF8sQOwGUKATuEZywmAjMVfU7Ona+tnyTGl6RXePjn8JnkmxCYC9BxiiwbeWwhBIhUeeCOOFuoREynRqWrNl03+PNVWarg7X8uKjwyMQkOGGQrzM2Zn7ej0x9lvJM8/GlkqLWCVeVj3uEcnFF7/3eA09eNqI/sw81NenrmDUvs1LBXQpukhbbXJQUZmkRfZTuL+pssFXRqOABhQGQ5mtSrOK60oICOTQE+Xe2ydAorDGPSND4NS6rgWOA1TpotVmbCH4Aqtdvadm9/5239n/+OSRouKZONzcNWpv/dAeRQcjCO75+0ZK/folV7zjUatGFM0ZrqaRecEPfSk5pCeD/ennNmkJJzY5nSorJWUAKopufe7Ic1YRQUYRLSYP5kYnhAjGto5TZpaTrKKd00QomSYtxdcHf5SJWfRX/xqmxu3cGDNY2HA3GIJySmC5h1cxgNyqtkMfiioryixBMB7qMDpWjyUYSAyotmAZn6vrwMex78ROJCKPE1auQ4KOqcoYgiW5q6jTeun5x40fnjm9/5vjxqER59EzfOczMT3Y8jDon66dYUp5ZUX6UAca4kUeFRgviWGLjwff6bkyYmu16qwhajEO7sezFNWVGW6TRODE76NLLYP4Ia1kJn8UJ4PEC2PIQxlI7JCwkJGyXtKEh7N3lMrO1+U7feAMOfomEKxcIwDaC5jxIyGXxVFo+MD9WxEJvdxYeTOL+436ivTpdUzKZwCcpGji6VxqxxzFIp4Y+OuWSwmaK8rwX0YITn+win3BF3uKSP98v2XfqstBscNidTU3mOgbSUxNkPnlZ0SgFhEJNxLTJrW8QcSqEgP1g+MN0LTgGxHRR0ViltzjpmFXaZKgoY41Z1aitCdbDikIE6nWjtRmYHqyR+g2btj27f/9b+49BtwinDlNzM0yk/vSnrz+CMRtO5CfWr7937y+fHR5/795zLzZXXLcXlhx6/Lntz1D8zWSmlKv3FHLCPWdHo/EDgB77XUmFh7EZN12UlJSn07BDkjl5i+fNLxXnFM+fN594SYAmFjOyeTi8UP5AsdJ/WZDw0pVLd18d0I7AH2FPu9AfCzsmVgBMPi3s+oKhNeUr88nmhnzY8LzkphcoYZnJCdDlKgk5FjFDKBijYvCv0/OvXgXvKRcGAxm5YJEwkS8Tly+ce+/324+v93IPiwyPJRCYBspYJGTPnftDVs7/3sbPc/bCKHIiJ1/g2V6hyYkCUe6Zn310ud8EBAyAsNrcWLPbaEdB1bSZzWaVw+K0SMS5ymx0AemAvVX+2EP4cwQhHB7cdUJC4MyyY0fHu+9+0LDCPCi6c7hvJ0k5mJJiYTiVsVkqNWn7MI8IOeFRbinBotx0os0iwAzGDHh3Ebkk47m7hWdiM0WOEcSZ8Ql0nTXlkpr+GGz80IwTEAM82idOXri488/3v/jkyZsoFYvdYWrSm4nMF0wdH/AJCSEQKuKpCQmZghuzhPHzI9wP/S8kCwxjjYIMaZpC3UEv5sT4rTTZzEKt1gCzvzqjAzcXIsGzRZIRM0ZSp05i0wkrV27df+zzYxKbBs8Sxq9bzXfHERD9GXzHtr+93es4bDLn3vvyy3vHX37xdnqPc7j64Euh27cTrELyr0WzXaMWPDzzcXqCQJCE/heialfG2npmpgJeuoAmWAgZECpzVFy2CGYu88uz8yog5yhm8HMKT4V0ChAFhctngFgfsPvGlTtH7jbXtFc37n+rnSQwGJYwIpHPBEicXAI8+Eh6Sy/IyEkvyoAVbw4otEFBAf6iDJSUlpFREY8WQVtDPT0l5XkwlgeFAKkmGbdgYzw6ah+wXDq18dx7R9e7uqXIoxfCbmgR6Qhcse9dAtfJR35iJQVr1mmevpQlRTYMoDp7uQeGrxi+Vbf91MX+wbZ2CJ9gYGM1G412oH5QwpjV2PdiGNcROKcRd2bkdkq4/pw1axUR8gAe7HWioyPlIbcVDTs5yxtYh6y2O4fH330Na3nsEDmRHG12jo7HDFG0NOX7yxPj4lmoKBhxYd1H7y6chHKVYjyzMX7ucZBS+TJG0sASQv2y/o/2j98zNk5Lc2XgCTIjm+M7M05aurjz8/tXu65cVmktTruwucmpB22PQGCQY0EChUsEiHxaGXGRUFIggOL+wo4Xl5eKRkqIVCF/p7hdpu8DhKXXV2fn5ogHNTaJUuk0COuEaodp1AQiv1JsQxBTtVOM/4fWiIrai90ExivxkKa681bT+b6+j5787D+Qs7l9O+Q1r//+maN//fIcaurFFxvszuxDj7+EKmNWvR6EJLnGzn14ev40jzDBTCZQHt0fZU4nJcK6AAi6giONREmVlRXklpPXWGFeKWC+4kIKGS4rLM9HtlQF0kZh5yIOYC9bx9529uTlu980j2DOPrbnWFWVkWAcOmhozOTL/JH9q+BpM4hbLaL4Bbyf6KaxvEIEF4oL3TM1Jhpac+Zkjxbk593K6MzoVA7DMr5zcLCnAFwS+9DA5Qt926MQKvxUxI5A7wTG5goXANZTabE/cFf/G0sKNoxZMKFEh/7M9mcS1nu9HffUu3A9uvjRpQnMUSOAy03oZVSNKkf7520tLRIbhB4ms3VEbeoHLNPZi6Ucoswa9u3cubMBaN9r8jD8aOft2zv3La/dtA68mq7DfW9tiMbWis2FFk+D0D81dBPk6cDn+bPjwf0LBidFjJmEjO+ys+HMBetINZelSHGPC49FdpdHAhMV7AXjLYrqenhqEoPEeJFTJuAJVJRraiLr6vxclNTuK1f0mv5+I3ZFYAWR0xwjUiCsj2gUKiYkifYnVGJCQH5oD80qvRnrb7NKg1spV+kUdWbr9BgQ8IyAuKOkByiHODbkq49EXrxtJpFJu9qsM/XrgKC3vfEWSsqogeWzCEsKtDcnT/b9fvvRt99+5m0YB8xMCk0N9XH96717f0VJvRhZV/daYurxX957xpUiB2jfkeDl8fCAzTRf3FJYdHsz1phw/03FFwLyQ+Re4+jjKZHNgboh92TQZKHmKMfYDIQCQ1XFUibpu0DCjo6Qblv26tlfXPrmq2+arbUgg+z585+PtUtg8Q4vAD0+Sb2xautWGNbAzREIp9XcajAZultbu1sbVlkQ8FJAVCScR7Qmpz0wlA0Ih8wh3DenvOJq0aB1CDVV3SnK7e8Cxfj3944/8c7557cvQnzRnL/Suh5dlS/FtzEm39N/MreUd5ifKyJtH12EP+L69W7PJ77ZfvVq74XDF0ctWHxiT2lyaoxCuALo9+zZ1KK2YRIQo/szWCQWk0Y3aK6pWlaJyWlnx87o6BBBJPKhNy2Xn1i2Zqe0dl/9LmvTnY3I4opBxAqXr9QEazGG+ZNbCNBBLluenBSuCFAz0iuY3uXmkM+dmOH788PkkeGQTcHG3Hc2E+iRkBaqjPf0nIJyihbtADe/Cz3z8EpN5JQsLr9///6zT162Y6Fkh5lwVzdDlgXfnLG0ZPBz+hGZJ9BGUqc1C9V6VbBRVqdiksc0TicWdkqRXt8JSwU0d6Ic6mBRZjQckpZVbBObTAaJWEPeSBRFAAi9euX+vXv3t1kMzSaRBiurgcsbN36EfPBnjjION1EzAxNjUp5zcz1+/N7x516kUI3owNd9fH6ZRjbuuKMAzgKfeHgySVpESNIkzdGLIQy6g/vCgahDygQNSMrQ7c0oLsoowS5qBtHQ8xHZmEdC+cVUZ0UinjQsPjlsa9WrN39x8czdu02NIFCpsJva02Y0E4lLP4hBCeLlbdv4Zl1nRoYYDGqbsFXV2ti6evXaNVwnbKtzoZcXZ+NKxxFKGTkwVgA0gbEUtVVSDPeSTpRUQWenLqNnYrzvs3PHvZ948/zzbx+lkiL6MOPmDPrn9zUufyupGd+9/rUe4E8473989G+veTP++Wcz/uG/9v/YS3mGKmMI/wI68R9/XbL+5ZgNr1VX36++eQlLfliqmvnwkFIbZQaHxbhn5crdMN2wmSQjlgnniAoWdxMTlqq1+xo6OjgB0oM7Pjh48IPKSm5dgOJdKae+snZi1Qpd56cbLzwb8nwEKyiAB4scWuD4hyTGRAMYVAiSkzzcormiXKL7k48ktupUTmSAF5QSo0h2j4nxcWW+5oT6Jbirkn7EwON/U1IwqI6KnYxip5iimeGR4tKrJffbNly5OCDBIsk2Ym2xgLtLTD6g4nQ1aRnHCPopiNSSbLp2ZGYh2SbR5lKtx+UKBoAOzRy+k4CApCVYGK76GTlAAvETHeOzoBYaMLBDIGRvb6k19dtb2uAmeWbQZBixjjrbNl2+8uRnR7f/Ho1dlDc4bW5uofH1nB3JqVC0HkdJhS9MeTckxc/ndY+5kAkxgpdFc129HpsCPBETkkSGY0Tyo1sKJcVhw1mNbio2myspKgYdOie7fDGDR8AVsyw3J49Z9lYglVDkHxIRFhEtr1zGR+NX+eU3TdZVqyX2gE3t7eRQcqim0dRka2nXytZ0rLMMwTS0hzDOfiRJ1jSaYUhbWWXW66oR74yZPANjFnQfICspJ91DlYT2ID4og7L/0inPeOh6fxe2Cedef+KpHTF/OLoIC29A6HQJJKS5uWX97xq/ef9yw/zoa9687/135/3TL35/Sc2a9thcr9i5c55BYu0i3Kh+0SGy6v32lrMXJwac4FkNwmDDoNdLTAaH8djeY+0O9Yi2W90m7DYYURxg4JuCV4EHqthauRUOSGFbpZxt9DVp+HTXIX63ydJu33R246UGQVwIG1+sIDJvFYlknOT4yMiI+Phkd29fP7kaaISOMAlSNCpJME9vKz8iJVKRkkIlhXYfBzNSQMOjZk2ppBj+RKwbMD9KqCQvE/cYlrOsum0/lZTeKjHZRswOC5RyJC7Edh9ApY52Ubip1MyPNbk52jqggdB2BPvXUU+IZ2EI8LATpQRUjz4AaBjlB09ZJRa/8JsgUQwYxzJUlxNzeF17VVC3qcXeduzzPSO4vaANsmw6+9FHH//x55ihMEWBY+nn45epCOKvq4/+HdVUanTmwg1hckHq61RS8HGnjcdsFNeCh38v5ibDbRYkR8bOBY5jgQI2VFJIJsFVhXgSlipv8YzifAgOGb4ErqZCyuitoNDewrwMXZA0TBATocABisZv5Td/sorWrVIJN7XqaXMNOWKNSIlwVkPrmoZaUw5gB9RTT7+p0cyvMevMVTX+sMceJKIM9l3I18E1RZgpEIocuuEZ6BeuvIDSM4bTIRsfxX8ANTX+3M+TokP+QBSS9b5M4gKINV7kjj5t+veXFCjdLOaMEEKf8s9VUlzh4nJVrp33AzU1v3TGP5TffJ568oeFAQpWicu8HyckeS5Aa8W48aPojz/1yruAIQbeP4BLyinRDGaLbdDzGs39Fom9beWZNot+RGcxtLUdqnJo9OaRaquke1VDw7LKrVtRVPJtUnbljh07OAFdm1bWdJsa2459cvnU2RubY0KgFoWNMRQTAMWErOiYMEF8YgoSKfyiIQHWTF5R6KAzmN5PTMAfPz5REROewmQrMWwHL7/4KMbKeSq6Kc8sxH35MgsDdFBeSSnxkcuffXbvGzcvjjp7rf3dZnhP0OwExiyfxB0oK1IgovkLFoLGJ8rI0NbhM6gLIrWdVgbDADTGxO4DesV4aenVIjV1iTRxA27nBdetEOpxzaOwRJ1KJFDVYWBrb3GijT6jHhht7hmwHNt04dTGsz976mdPvY17CoS7qLejFsYEAFBU8aKfO37P7fUt4Qujw16DkDb0l3MTIGolbydAfm5zH36YejQRhCSiTcDA2SsKHn449AKYagqAZaFcECaDM1IFldB/o9MBHIFKoi6QPDGLlBp1ECciEf8Ku/LsyUvPHrn7zeDq1Wpja7dJb2zDNfU5jB1hpWUwrFrTqsxRonertoJejsOIy4d/oVrlMCDhrwcs0fz8DNqW0wZFyUBUUM0xhrx4JHqGOm8N5+b0j+LyGp241Dfe9/LLmSG/+SX6PuRgAJ5Io1hnnzRarf7roz1ZUjNcyr8DBJek0800j15UDpoFKBKlpxdjUP3gY3/74bx5FQs95v+tcma4sDzdZtDlxEZugOcs1o82frNmec72TgMfaRFCwBalHX/hcc5ob2/X+zdGBywI1NLa4K6rMRuFJpse5+reYy12s2HAYtbDcQPfYOonW9XQUS+t3FVZCSMQVj27XrFDvmxFl+VQ1WnTyCE7gqgO7OwIAY7E9odCRkQHN5cTkxIXiOybUB+/ZJ6GBnkcT/im1NFARSeWUpQjjlgYFxIfk+we6+VKmz30++HuU15Nec5C4+TB8IOhmPJOcg+MC9n87jsfXpgY0Fdb+211Rmg51Mw0hfrHZkUlY0S9pFBF9jtgBtiPqEk7v46vFa5z2GwaGwAcE2ZsEEIxBuoZnoWEPBcAv8O9j7tCpVP51/GNwDIA0PMpm76lRW8Efg5tVLMJIo+xUxtvfvr0G28cjHvuvbcx3MC2xC8GRBMsn5dted0t9vWUuNRooKYRgc+54SSIpdkXF633TN9ZUyipyFQmZQD/M2+yxRSgojBFUUkhBlEQnpi9+DszdNi5YDEFR/RS8qKYX1ySI+OzwhALy16xYuvZKze3nb57d6SxVW022AwS1NQxhgas15gMtbXrhOh7xaLBzupOq15rkMFsza636x2O1v4ezGdleQUF2cxyiq4loFRoBHMYgSJUrD1Y994iHm1BQU5PPwnnv3z5zZ3PHcckhd6PQiFBnl1EenkiLEz/3pIqzZrFx6YnKMFzUYXLgzEIY9RiD0+Ri0tpYtC8yanqwezElBYt3TSeXt+VFEysOdM8fYhDXDDLk1cRMG1awYM35odWvYu8gEiuX7+IKN9uL7302vX7e29e6BodcMK3WmvAaSLBo2AxaI3th/YeW9Fit+Dpszhaag+dMdYaD1Wxt+46sTWyku+/BtmAq1at6WhYuy7YYtEbm617EHb9i5MHT3Q01IdFRHJ4BKPhBOeGxYWHpyYt9PHymqkAW1BLd774wRQlZk4qyv5lhy6MFsTEoKQ8mNAKhMynzp4iHX06aS1dyV6ckks9ohb6JSXLGz798E3cyjCm7m+GVB6gHuDfYEigKGIMkzbIFCipYOhSddAZAiWH5UhQbZDDsA6G+gYN4XimnB6ljcy0AFiAkaNB0DN81JEEA6LFaq1Ywq2q49et8OeuWBEMfRQmeXvv5717dg+M6swrd18+dfLUp8++tnz5tl1bX4G3/ttRHh5uMdDBgksYVB/3emxUyobADZRQk/xiEkmbvMkaHeOln9cjD/0+PJood4dGngoK1qTwpA+DxwEQCnQTPHBhpOE+7HLY98F2Yt7kehdWztT3ob4wTeXK5OgyYqSb6nYdePJ9zuo7wPwsfCgy4EVtMuxmaqqtWnQIu+46tVrvHBoCW3h0QGMASbi9vd1udzi6+699W54HSntevpj2D3Qt0WxFuinGOl2UXTAk7kSF5fRkZHQ6+/snNn725fHfbtjy+nrgM3PXYzFFoQuzPbwX/HhJlVIplWV5avBTZCVU0MW02AslNeO7kllaupT5J/ZtFaXMSSKmknrwy+WxnrPolprhwvZciH8n0DPkR0sKCvmEuXMeJET/MvSFny0f7f3ZZ5cHHA6HWqWlScoA4EatNUlkxmNn9r+2u6tromvTvq59Da9tW1Gr5bPr0fMd5CxfK3SshXMbQmzr18lEPTlISteP8AFjjZ3Z1VDPjogOCVHAHhvyV0lQhHtgpjui3d28knhKWujQ6pwoCAhfIlMCBjBTCuPBF4pJTCUffJLhJsB8ZQm9c1OrqkVurmSMziQjIucqNJqz6tNPDhwAa9UIMLPFYbZCuCuj7Gs0cIxYSkIEJSxTVGCmaQGROyWyWsh6DXz/YBw5mA7xLiFZTGzLzlBClIAQJVxt+BbEJXFio8FG+nCSPVRypKwjViu/1oEbf09ve7tTPFL17Ce4oy5tghNyFbytlwni41KR+BkVwxMSh1DoHw2NZkrIm4FIz90RAnveNA9v5u3AIsAnNuuhS2pJvJQpKfR9GFaj/ALlxJ2QsqmkEDQgjYtKAWdMWVSCFW8xVRJjiD4JeKEjzJewBfHRO1v4u84++f6+O6e/ujtokTXCk64Riubd7e374fvYC/ECTHWhDXNWUx66HT9qpxcc9KFn7fm2AuKrQtBxMzQSRuJBO1/a8YkJnxCJhnKz8SzABXs4N1uMNpEpqbfjb5877vsMxv57Xkw00KIExoLC8192K/9QUoUwo5mx2NeT6zKf65v1yALXbJdiLwToLpyXPzcTV1TezAWPLHAvdXHJXpKYmJU1N3qpS1AWEp8na26GS4Snn3yypDiefvhQuKfgx0tqiVsCpeqth6uBx/YtJx5nO1f+/OPLTj2/TghUwmLCM6PH8SPR4EZvW/nGu5cujY0dOLtx7MDYpzvXDEzsk7Lqd+2SShswi66CobHWELCsRnQdfLXePTWXTm4833RrpLU+MiQsIl5KTl1aHT8gBj4HMwPd3WPCo1U0QzFwHzOUUj9NSAVQHrFE4eeTLI9I8fOOpQgkcu71mEs98xQACtTjEh9Xspwl0zGIO2YGRnDq9116P0TW0mKXNMN2zKrHnYTOjiTxhPXRC+xzjAF0HGjJh1hjCArGyCWslZGRqhYDoi3j+vUe7KPEer1Yx4AbfIY3ivsOwWynbSqtljyVeOxlrQMDSOa17vlze7UeaQ1n9t84BUh007NdXbVI5AQYJg0J9wNXJIZL8xhKUpoy8+3ANzLjXnstUhryIhI8fGkEAgLqkeYd9fDs/LkxUnfSyMcy/s2xC+MU4JRB7hbA4QWEKTjseL/Q8FC/wBS5UKxSFVJN4bqazG+jYWNpoUYRH8FuWbH104//eGnfvrt3rSIJQrxxKyGfubt7laQaI5V9oGU36slOGHLvnl6SsgK82EsARm/18BdfVCyFlVnx1SKxijlr0RUwTQpGUjEjPwOIntuTe+sWMsr6B/onLh/+y72jb55/8bhHwjPr565n4tJB0IplkM//xy1VkuUpcQnzzIpT+HouKJ0fn+XpzXHJ8IyaT7/iGunruaTURYgxPSl1lmeES4ar54LAIqbzm+eSLsNvZBq/oixPVjFv2qyiHy+p2X70iKE3xwb0vS0HH99W9+5Thy8O6dR2hNk4cQBL9JAlmDUW7QiKav+nF/544MDvsHg7MDa+eV93Fwwk/bduFSgC+Aa1g8+YcdXVNTY11sB8dmx844Wmq1dvdTeEwL80ksULQNQSMPRw3DypEWCnK2BTAVCCQHRsecWMtJdx3VQSLSkoE8QzOZ4wbyb/E8opj7RHpnZHTScU3YOxnH1QUuEh0o6dlw53ONrtFuzcLBazedJijIifzH3DGPgRp48CJwBz0q1jwMJKr9Lb63bX4RjWaXXUnJhsEj02nLpJbQgj+tDgd2PFa0MYmc4MDvbqZQbQ//R4utrblZ2IwTmyGXfUxU3PLq9r4Vb5r1vdKORKE2d6x86MJniUBPvc1wJ/Gxi2IZGlFnJ2xD0HXSuJkWlPNzvBe/ZD4xNL4hWh+K8wiylvL5QUlALgvrADoOgVgOsSvdAvNMkvNBRIoCKEX8wQ+5j9DOZ3FFZFiYgVEtHg4ElhA3Bz7PTtu1b4WuI2h3ezxGRqbcWw+Oc9x8grqtpYDS81CO7gYMKEouOCxj9RUmRiizuwPB1Z2CSaxtutIVgHHBVM3TLZYMZ1ZHYN3+q8lQ2xh3OUSuqZ129/9Toi2Y/iloKUlPRIOFmmTZv+A/BEadY0ZWlhnjrBc3ZFxQJPpYvLUl+0gIu9PbNdXNI93ea5JHlmAtIL94xz0XpOk7jgKkpYCuDC+x8BdzGVFEYsLlO6/Act4w/NUr6wcoLRNtmDHw/d+crBba+9++uzF7NJmCuBMN6iNWih94b0QYasifb9b546fOPOged/c2D88MbxsVpulVG2Ap0fmwvEnbblVYcwgB4aOdLy7KtrTz75NXzTb1kbPo0H4ucfzIZygBWsyIS/LIYoDSeOJcqhfSgDmxLSQ2DqJHGWsHRJ2MyZ0fLI6FDSH2CgSgM3L2vaVEtqgZcrpcGRvgOHNMxmI3d0XDgLh0d7N+y9hyzQ/JARuoqGKDI/4nKZkiLZFMmVJeBjmSQG7IHJ1Rxmw+hhqs1qCc4emdCgVdcB8WPylCbZ1WphazOVlFmNHsgMzaVa44T95Z97QUZvarr16p++/sXJFbu3Ld/dduZQ0KpV3Q5DcMAOmEb5JPK0+JfroHusE4T+BoZu+oKiIHl4klsCTVG+c5hwdS/fKZSUnCkptH2UhJOUImdTWhvsFtkKrKjkIZnucYnh7kmB8dHJgRFEoKU4RLqnXOZT5kCBBAJ4lmPF8hWr3n/y0pHz54+caUS6gt1RZ2sebDrSaAVKAZb9GVjn4tWGLrAdZcXcU229vbileu9/8QVGm2LE2+Xi8FARHxmNgIjM/oJkQDRkwdbha3iVIMc2d7R/SDx68ezXv//l61/dTkJJPbN+vavvpPWsq8/sWdNn/UtR/b2kvsNjClwW5+e7wI/QC/fVYldPnQvdUi6ls6alI5okx3PufJGnLwapMs9Hi110nq7z/7aUmj9ZUhjIlnj6sGM95+T96C01yzvVdQ6Bfa5eR4+Hb3ll69YNY2dvDDjhLwGfQidtktDbgFGNna+2rWrrrw9funFn8/PRm8fGz46PddWuqKrctnwTq7ImmOoO974DjvGrlvFb9u0b+2z8dLN15JtdmyOQVKkIknAhxOEECDIFKaFh2YVl8kCeeBIzp5qCApFJCRQxW3QwesQaVmZSBEcKxrQ3WbAsgr++1wLPaVNcTGW5TmrDPBjIIzY1PrKj40DfjQGUlGHEbKJYLEYSpaLCkDHfMBWReYuWlPAgqZmoRVEZjXXtOITbViICx2jkr1u7mt8qgz0H6Oq4njCHkpIBl1wrUSYgjDdXk9OA2UytDwz79KiopiNffw0n9Jblr63gVo2YMYgGCxuDufUxoVGx4QFotWUOiVOiWhGdEsZR5ZcUZ3DiZkKEiLmcGIppvjAMmcJeSu7u5Uvph+Q+4ZaaKMcoxWYgP0WYlBMpCBGweXJYtgCbTXXnldDQXlFeTKnypSXYJWmCMHjxuCip1TefvHT6yF3riNbS0m53gA4C7wxY8Rn1VSvJB4i5o2C0gZex3WhvJ+J9NS6v+1eB0F9FpCI9AhDMC+m9Rucn4QNhBUcNaW5UUt8WDncOQ57Vk31t9BJy/46/eCJzO0rK4956+grSQBDrQcYkP9T4eYbGhcfJVcW0XhKHeyzAKaSlkhJNllQZFLhZj2U95plVDEyCKakFxS4iKql5/3xLucxwmx6IP3Omp+ti5td+oKQe80mFvg9F9YzX8Z/HbRFUPv3U2M0uu57OY6wyEVcCXptGYwSBot92aOUHBw6fBVti15mtHZsPHN44oaoDi6vr4uoALtWT0eiwW8hkoauru+vyxr+cn1h995s3NsSHQeDJ5sFnHK06goISkxIl+RV5ikgVDU9KBkCHWEpJ/jBiav2woKJA2+DEmfEKeVigmxcgLia8JnbuFEtquie46A+IuEQeiHWPUHSEnOu7NNEubK1tM2N0xHI3iCkpFeN8iRfi2+A/gRf2J4yaHyCorprqCSfx/v3kymHkruO3QteQm1+gZ3YFNEgRM10NK3CdsY0eo14jHqq2z+HcUj000OJsGvzmT+MnT6J53tewoia4tbu7u67mUE3w6oAd4X5umTwdFsNOjOkSLS8kgl1UUbL4qj9i1jBLQeqM1RogS5+oh4b85sYr3GPJIyoKrr5eUEuBgs4G6IfvOAjUUwgiueIciSIlMzwwcKZPnOQqlB2FFOMIPnkBWM6qACmH57+WvZLXeOnJm6fPv3oEfm0tK+0I6Wtqth5q67UONp1e1WJBeB9eCMWEnYTSaccPnQjgyq3GgHX/6tLi/KJ0JTNDY5tHzrQaZY6I7HPAigQhmyIlr+UX3YIrUEFBesHojVPj546/fHvLzKPMNTVZUYT5Zf2PJ/ufZ6nJwljs5zlrUSjbdfKWelBSRZ6PpSQnJ0J/uZipI5ey6QsqvrekZhRnzcI1h1XXY+UPkIvvLaksv4XQ95EN0dHYl7dEhG196bc3L7S0I0cLkVoku8SRDMNmMNBNE6aRtpUH/nh2X2OjdVC9dvPzv++70HWpq2sTRuu16/AECWtbugcGLt64dOnGDRwo40f6e1bf3tqwM+Z5gT9mLH+hRh3ACYhMWeguRLCKSM5lPAcIjIBGVInKZe4r4tExXMpsWaJPHDZa8T5kCUTLpDmxs6fU9gHa8HzEy4vIB4x/HTqewBB5x+bfvXfhol3VKjzEP23SAwUPnrS9pzwoALrA/3hBlN/HOCTYsptQ/p1KivkGdrUSiieklhi5AWuR/cd3pmfjjhNCu8AlvzLM3OJOEaqPTmrGaMpIE4UOD5So6fS5vsMXLi1v2OTgrxM2CltlMiRYYOG1TBrvvjDRHy78Tic0NDaRShFTX7I4r6IiKDqV5E3eaXMZLytXv5kPvahbkqwIjCLnZsoZ8PJxj6EseVbA5Dc5AHtOdnlpvpgnSIxPWegXxy9Aonx+RkFubhGo6BWl+RoWfJR4ayp3LXMgCPTO+btWo8WxYrfacBp9bqPE2nik+fRpf4cFALrTggZar8Pn4tQP5ZpyrpcVDKOmsLoqK1AS+4x80cUSIihraV8ORwpw2JWQjOeVFRRcx/8zG8WVN1owDHf9v9x7+ZWd//nEou2+69Mm47ZAI4HB94+UVKELg6rMcMn2zMrBLfOg8RNPllThtCwqkMXKElpG/f2W8pr/r7OUS/FjjwA9dCl97LHCHy2pmX7kjYF4EQ+vl6O3hET87KmNl9v36AZFJrFJQ9mXQlBkHRBLtU709/a2HDgw1t040tQvsgav2vybwyeR1HphX0stFlJcg9PS3XXj0gV4EsO2buO5860915pbWnSWhudT5P4SSbBWHAwj05iUcHlZaWEBW6pmxqbJrTkD9QD9mySlS6Cr0omF8eDlsBUpbtQyg0CeMNdj7lRKiol6fMTLG0spL7dYhjnoFi6Q79z88otXLuNLGlxlbDbp7ZSJRHxZ9CFaij7XUklBu4+lPsmAlE3Uqej07QDCj+3Z/xZoWlRSvDVr1wn5DrsaZEZ/FoeifTFk69S6avIWRftzn56i3vb+CQMyUDqt1tbxjzZe+GTl1pVrHK381Y2gv8G/eNPagGVr2DviQpO52OdAKCKC4L6xPholBTqQLDoQMmevqLQltPUgmdNDvx8L4uRESYZEPoqc19zjOVRSLIUUBAryHovk5YMwAb65QC7IjObllDDGExD3VsDMef7SfJU8DIj7vsozu7ouXvz41Pm7Iwi1gc2Gsd2O7s/Uf/78N3eP1AC6yVbqgZliADX153Tq0ouamq7n5WXcwgmzZ48JxygxpjXgTwCOIB0NRENkQssXihGzfX04YzijZ7RH1DlcPtxTUD3wi1MoqRd3vvnEM9v/Sqs5snaAn9/37Lz/oaTKv7ul1NSxuYimeQbTXkrCwBMu8709w/HriZ5zZkzWUZnnEiopj3+8pZRMSc2P8kzET0Jo5PqRxm+BTxRjlQeCn9dLr8SHxIcnXrjo7IVGDm0HbgstTeoGG+Cc7u7G3jO7x04dePrECGwUVJJ+g2UC4VoE/l26cODAJSysxujVNTFxY2z8wPk1p6+Xpw8V5Oc7pXCF4+CCCuakgIYUHiEpLy1Qs4S02wWTX8wAaWLx5P6cLAlI6AmKqSwsKTSCIwhHa5JApguLHvWdO5U0TeaWyoryXrQE+j1SiWN7HBfW8e7BEyipRqtM2MJvthkZMyQ1WcDLvisp5tYSNeE4tWltTf1gnVe37TnWZt+9G9fU/vZ2XEDHVq5cWVcLbzI4vwdQGgG5pyOPQq0neIsav6tXhzFRGBtPm5yW6s7O02MfbbzUtXtlZWWtgXIWa4JrzAgOXFvP4fGkKQvjkdLlQLaFTGLTytiCHUWLkempEgS64Y/9gEyPpZJ32rSHk5BNXxBHdi4A0ZmSinJngq8nhfKA/oAjyUrnF+cpdcEYmPi5hRWg+sFxbCmUvdiMFpfmsmJiMBgvW7ZL8OnAxbOHN989YjdrGxu1MiylqmSnm++eX3P37jqhyNmZreXTftOGtYh4kCmppiJS6xL019beBpMgzBZ43LQG6J/RqTjVDge8RrPL8vPL0olHO5St6Swpz+8ZBZv746/v3XsxOvDnCbil5vgyvqlg+vkha+uHQPRHZhV+V1Ilj3j6qRPxG6UuM/w8l7jPz/CMnTEj9xFPH/9Qz2kiFxVTcgWYqlzSp0/zkHzHrZ2BCmP2UjmzPP24C+m3/hg8AQCMCh1F9UzsCwcf37m578DmCbHORggwFm+5Gpjs8vmAlyf6tW1n9rRdPNn3q5ffaENRGUzZgPN6z+zYPHYBwZqwJd648fCFO3eONDcd+ebVr756ddmyjMKi7LzS0jxufFxcDMc/ICwzKSkp0D2ioDRPyJI9EMZPvggfwzdQ5UTKSUkE9j/sQJ94jiJ5pjeFt1E8vMfsadOm5OQMSqMPUdFJKp+GR8kvLmzbs2/tffnU5UFrncViwF3BZ/zQif9gwOFqcBjwpABiaGxEYnzTIPlGmKrt4Ft/3jbQXRtsNS63V9PPEMfRvjt43erVdcuXIyKRy8d/ymZttPXSEqa3Ore68NtRu3jQPDJi6LZLkGMHu/SJTVurEI6N1XIAREp8Mo8JgjkXlxPuE88FY4P0jsR/4rN26JZi58oVBMYykdW+tJz3jfVxc33kgRPj/5mQFB4R6sWUlBshfgA+AU3AyAXfwy9YKFYWLJ1xVYnGV5xeCI08kDJQZ7HtZaQeeTl8eUoc+BbLVu+L6Gix3OjrG1OP0Ktm9bNvtZ05Yz3ypyP2P/3piB7LEbEKdEktX4WbHblJeXkFEPfCy0/FbT325z/jzak2Qkplw67B0miFQRU0DzATVYs7h4fz0jESEEtNIs4vGYYteHX7+xRifOJnv4W4D5AfGdIx3pN+C75/1TvP5aqXR+nfmEU6LB0eiebMTXRxyZ2d9WhFydxMUJAyIJKZ5SGa55LzKNAHl5I0twqXxZkLpoX9vaTSl6QwHD/mt2JV/KNC+QWIWUJJJSQ8s33hK1tf6fj0d313JiTkUknsRVoTkHW+3to4AU3HSLV94jLiFJ7ee7DKvra2p3PwlnVZw52ui5cvHNg8trkPtvl3zp/45vb5r/7yVf3p+h2GolwnU1KJKREcSbpIEe4e6hfqHpadzufw1GSEQqk6BKQTe47hJDF8yQegusg/3C+Zo0j0o0UvWJLQM6TNmgpplt71R328yGeDfOAwQ0DXq6/uPfPyk5etI5ssBtRAHV8FPZNOS/58KocKGahCokgA9hMOZjQNDqKkhuxMykY76k2t1/NlyDNEB7h//zEgf2vXUvyo0AGr2doqIxlfA0PGHZU7NFrmlKBftNbwVtR2tVy8sPHsxYHly1dQSloQVx4jR/AoBnQsmf15CveZYeTSTxlcQoo8ELKDoFXKA484iikpykOcPTcqdaZr1kO9HzDhcI+hCDTIOnyQM+mWijUHzPvIaJYVIMzOLy0vRmxHtlCTATI6XtD1QtOB3g/X1eJScZA02R1OmstqVnfs5CDW5/Bn4102Q6PVvPyTGxe77nRPnD7fhfVvowShAirkcFFasTa7qKAgr6S8MK8gQ8f3R7b4sf1v7dmPA6eNrLfEYltzo61RBsAYVDiNsqkJpUcPINAejTgdjvnwCrB/ePjrc+cef/yp7dt/edSDUWPCMWmRK7zHpn2/BHEGpJR/l2gshSuNy7zFoB+5IHIYZODF9NH5iKabT4Dg0sVE+aMPgslYvPQfeOnMx/7ht/5YST2KTNw0snFK+KVP8taDO+/0/a6rX2t1IpUDObxiZqUtEumN1WiVZUarBSV14J1fPX3wg207HYiNty6Td+zr6u5uePzgKxH19fXrWtecOHHixduvvvpqff0WBWb03MIS8VqFQB6UXp4vkScvBFNP4a+IVsgkGKWIgAIUiUqJcAkR8zcAv0nqLNIGfFKk8jg3VwbYwULaK2HKNklL3DwoDoTs/PAo+cXxSu7fH4l+8tKIlSJFbY2IRiXFkw1LEgN6EZRNOzFphHV1MlK4w8CwicmtAftBD58yHZglMr2z/RgFcwCnaAlqbLbZZK2OgVF7C6UdgutWfesW9HYFBaPKbN2gTl1VtemTT8+ePLm8asWKVZvYivCZsCyMi+Qx1yMIGTxWZKo7m/lT6MQ63FEgCfpLJel5YkV0Jho/XyYafNGSR71TZzLuhg9TVQvc0URS5wcLZy9az2HKhesEh0YqGRKOysoXI/gwNz2vdDHtd5fmp+eVFcHFeSm2UvkqVlgc7DJ5NYcOdexcY8GRehhmfne6u6s+ef8mElYvn7wwfuHrF76BDDFbxOXBaBgpSOqcPLxKCOXI1mLnr1JxN2EexTuEzBK7U2Sygn1hgzxNB8I+PQVNOWLozZi2heArkbh6uPrDU+P3zr284fGo7d5HPRjED/fB3DTaef8vVL0z/i4unPev0qkZP6aX+v7f+kMlBcsgsrbDZsrrhYM7Ijcf6JvoZ3JuoA2jogKLVIU9pd1isBjhMNBlGbv5ydNP74Uc0ZKbIQpic2vX7OvurmcKadnq062v7tr74otfrT7SsaN+RzRnBVctttXDv17BzyssTJeGxsa6zRRwohOlUOXpGPYRzac5hKSKGFUHcSjEk5pEMTfTLVMRGU7W6AnEy5vt7Ttlz9nZGO5nk/SellPePil1xffvl3SdutloBcN+dSOR8+hp1mDHS0A67gmjQ2+X1dmFBMCJ9WrbUC6xa6qRXAOrZvzpGw1Op72dHILAams34rFAunUdxnTMW/tRUcDOc7MLrl2/DgcGZyfpOxBFu/Fsl+Pd53ci10LhlrXANTWewyWoEFISYQBbEJqJ/zlM50W03ILphSRIyuFLgjlhKW6uzN4bi160wejaHv3hBOgfL6lAeZyfF2UtwHAW8SXRctSUlLTyUgW/rCRHnQFmHwTyJeWLacFbnJdehs3YUnJwLs5QBUQmBiZy/Lmr13aEVamdiFfDSN134dKls0gBP9t3eHzswB//+FVTE7wlYOKsVrEVPJlaia4vH5zB/AxRMARZSg1/tczctnIlzVTkZWwQgs8Gpg4s0XGYIHeTTHwxCsBvIIOeRGf16P1nT/V9eXzh4xsWunlDNkUhmWSOPtubzNG/v6T+qTjmTdKpJvnm3/3I5QHL6m8/ffDL876nxP72W3+s8fPwhdsO0dCfcXtBUcne2bd5YoA+AyyJcqFe0pHhrlYCYwEY+4y0qfS9N05eeOfpp4+hmVHm2ulYq6yquf3ViZdPnNj16q5l9Tt2nQjZsaN+Wf0yFXeFLtsJzpv8BYG/glcGp1JR/MykwLiIkAgBF74uROTHfQTJYQ6JZBifoe9E8/QLIlmyXwqV1OReChPf1EtqWoIbpBFMYBuOOK+kGP7V+3u+mHjypkbW1VKDrdAqBIvKII1SMcbNMlxQkCMIoUiAfzXwfqfdAcDu88+rRwuGOjvhNaEEO6LfZDEYe4+dAQLY3m5GaJDdvhsKQ+KytQORgGdSbll+Qc9Q7pA+dxSWzc/ijtrd1rK57/ldh3gst1nT5oSjooJ5yDLFSsyfExOaDKshOEZpSBVM2zE+R8EKAKchxYdEuOTjh7+QDZ405yGNAxa4h4X7MDkLsbChigqMoHKSKxDboVD45+Tl8tVlxcQXyitcTBh0RWlJSTnyEGfAIqlELGGFxcSHsAPWrlrVIT9k7NVMTGBpcvjwyZMnPz41tnnnhx8uD/n4VHdTM2TPcOuGtxw7iJKl0vPyMZiVFkmwnlTnZpPpgqF29/79zPIbKytHLR/Wj8hp6ARmhclWzeR8kJg+B1ZlmtHqW/Yrn927996bG9zfY2J7GcAPJeXltuCnYzq2gIxNcEXBcNHtpbDKNR196Ion2QvKSRMwIDUgtBmREoOr2aY1w0ziDSqp9pXOa8jGHLvTte/T8+e/+urlE7fr166rlx7aFdax7nZHPfh+2fmFhYUlGY0CaRDLP5c2G+IAjkrMCg3n4b0CdSKHrHNgesLcUhSHw3iOiTWTnkMibTTonJilYOZHf8g5i7zSpmo5C79ZhHeAe0CcQV9v98i6kvt7ll67ebPbsFtYxW1dt7YVY5QN6LcQ5SQ0yvQU26LXEiIJnwnMebCKovGIrvBBq8iJ3WaPyWaA+JChsO0hqjVYN2eO4eSttrfY7ToNOBBDBelDmAaGnLlw/OlCevyllrpNDZsTX9nF47ovWOAag6gzmqIwhqmCpXGhIWDtigisAUwD7FPCV0DcERGfmOlH2bogJAHzA1XRNdRj+sOWVIi7mxe9kDTghY038D5OJAqK8ipl6XkimSQDUwOGD0IkZgDlK64grUdxYWmeUgKfdIGc7b+qdU3H1qoRHDYTeBDGx0nMvnls1Z1PP9zw7qkrp5tMKClwUXiAXPDZwVs0PQ8+McVAe1lIn80GJqFUqrlVh1YSTwlntFMGmaoOS2JkKJmctMPBs6Ehzw9k5WQ7R2/dGr3y+7/c2/7U4+HnvI5OermSTme2B0l7fzIlRbprMm1PcPVOjeSxUFIDJuZcYAyVEU+DeUdi7h2x2rr7Tf3dppHNG8df2fDOs/s3bPi068YFeuGe775zZ8uWLa/gPWZXbdvZsLajnhvElpWUX4X5W2m6MEABxLwIEy6yffPyVOHxEuIggdStZG4mQOnMP5i8SUrEgWCGUAphsk9gmDQiFdvIBMZuwHXRtCnyZmd5+eCWWoKTjW4+3yQB9+r9z7/49srHXS11xrqq1ataKTeKdIhqIzigIPKpbY22QXzDdzYbQD9nNRo8oOC5UG933sq1dPf0AAgcxMBptxPbqIXsF4AAtsG+r19jsTmzNZohqIByO0G0cpJw6JOTJz99tsWxaR/E0EjJyITtbTzso7hB/ixqNvnywFTpA5Kuhuz3odAPDsHVHsKUlEcC4+yJ7gIosp8ro2j9v78nC8Jx+0/aoXsR1TEaRuhsRi5FiylhUX6ORp1bvHhGRWFh8WK0PdDKUwcI2VRpeZmIBbwJhjyy1rU7Ig/Bg9hi2deBV/SGr74895vzR+7ceffDm1dMTU02FcVJqJDaRlHHvGBlen7F4lIllx0U7K8iNS+KpxGJ8/p2xhreWW0Mxtvc3FSEmhpy0giVKyZWOgYRnL3V6cPDF3997t69t9/8TarX+qNMmhEMGX0X+f6USgqUt9lzqBNCA+AuAMWr70J/DuPsA84ddkYi4uGAOKvHJ9rUfOTIN7e/Pjx+4JNLZ3994EOsfcdI67F559d37nx9YPMrAnZD5NbKXR1rpWEr+KzlTvDByJU+z8biqItytYRUKNMLCjQcnpgC5MkKm0lCzk3PYean7EnKH/TyYqpqsSzMLzWME+bu5ppALP45CbGzp+Q9gUfvkSg3fLqPIq+XQorSfBK34Za6+u3Fjy8MtBn5/Np1rQYoMYiDDnn75MugNokNDuB+iC0Z6gE20bsHXn2Dg8qe/lt5GZqea6UlTZ05TUic0Fej/cO0DZbEMVix1dmy+/tz8q8Pia+X5A0P53aWlVHE4eVTn51F+vWAsPLQsjUdUmmcm1tgpD8fEBiITzjVefGpKf7g5ujI31hJQjJMdiExsBwNicaNTSoX4rtSkLFvrPcj06c9jNxlQZwiHFCHBynlcVO5R0ci9Rr1hL95rLDI7ML8dLEYPknzC5XZhUvnIwd7/jzGaHb+0ooSZVB0eEoEUgMaKzlbK0GYdhrWdkREvBKx48SX5/4CCu35fR8+eRIsRg25i2q0AUjtCEJDy+Jr0kuLi/hsxPfxYD8KjlJGE6x3tQY7+CV7cK2bzTCqauop6sGjB4kCxnlgwTlKhqzmzB0eHv3kj+fee+boU3HPMfDEJOaHlUKU7yPfR5v9d5XU3NlLGKft2KTkCHlE34Vu8aRbFmFuWuLlaCU4gG3NkEPT69UL4+8j0+/yjYv7PtnX1bWqe1VdXW0X5tMtr22r3PbB1l04eaVy+M6tKEDbV5gPA9FGLBAlIha/KF2oTi9KF/HVTEmJKbYB549yUh1DdxYjnsLIkk3qebFG6hfIDpCHensw6ZeLPHyWeE6l88OBnuXmBviZPmEmohJ7Kd3V+/eXXtx46WLvIYcDKkooMWwSCLpBhoUfqLHdAWAGbX6rhV6jFFPd1qsXweC8qb+nIL0z+9pS8EnzizJ6enqyxdW9lBCKhq9lRWVljbW/5xrCIK/nFVdcLR++NVwGp8eWsZMn33x6d8vEwKFDZ2pWnY+ITnEPD+MFk100iIGIOQtJDY1UMUHrOhEpx2hPpwZmildkRKg3PUVEpiLRqK83RVc8jOlsVnhkIFbolAcEK7/YwGgEDLBg6AKSH48nSAnKQ0uRk1FIdwpfnV5METiEkqGyyHxCxEqOi8c1FQSDhBV6NMV67tYTj/8MGNWJu19+9RruqbNPXkFJ2YLIXgDJbQEcNj8YHCaepKBARQM4zx/BSO11xs7spiZTay3fjneO4B2jKrun5xou/k50AOATpk+Kf8g/AULGpqG3Phx/769eb/9my/Gj68nXngYqlJQ3LXt/IiUFFulcgGnYcxxFQ71T+krfBQs+g2ymrGg6xiCllQxam46cv3sEAbJHwOM/9/XYjRs3MCbYh5zd3ZbWYJUJmM+BnXVtDousqnLZmvrly+uWR9rLCksK87GJKAoKESRGctm87ByOVKJkXJoZwHzSE4eCJbH+UoonM7WZWYpuKQxxMd7uHE6MGwA/PEMIhIqdmvkE3vQFbrFpZIcyaa/rmpQYsxxOoFcnbl6eONTmcECsC+I4vJ+EQiODTGC1xMSqAtDDP3rpCw/MVz84eKtTbIJzz3DJF2iGkBB4vSivDOxOqGLE1Z2g0a4IYK3hi5qaSiqKr5csnf/F1WGcsddGJy788fDYa68tr23hG2tqavbtjI+JTxbQ9E5qfODoMnamjztL9KAjJqNQil+AlAL0BoVC4O6Da4Uiq5mSSvD2I8jv/35JTcsKDwv0i8X95LcQbXUUSgpxHWzSTMHhXh4uEOWXFxZQXEe6jBPGzi6e9K+b8d8kkC0uzxOzYhIj5Dw+a2ulv0Oi7NQJpVteeGHLlhO377547tUjdz/59c2L3c02eIxqcsQyJIvJpQApBGFwycJ/D7k47ACQTKoq6+Bc5zRWrawyYnOHHV5bu7oA9PPrGOZRUhnpRWXg6CLgDUOISNyp6x9469d9ffdcn0jc/PpRsjOkdPQ5+Of/wCf+jSX1iIfXbAbeT/Nwy+xoqNrad8Gg007aVFNbpmQe+MFm65G7p6nze/Xrr+6+evo06ukYDeDwYXAYJA5Qky4c2AS379FOfmR966qA5ctjUpaXlTI4UbmYLYgLFARzhUolxz2FrWUccJgGj5mhRBSBrCSWM4WLiyZ90cV0S0mi0YwKUmATNpvsMVFXWZ5T46FDgej14GxjDC1mxkcL7MP392w6fAW31KraOqEBSkLYsRhJDgXNJZr8z//phbbPPmoehF/3oAgX0/DVL764Wvjtt99SSTUNX8eAZYKkuaaGu6ahoaFOXz1cnt90fSlkDMPDt9D1nd340YF3azctrzPXwA27Zl8DYhmiBRweErew5yVhFifQJyWY3n2mByaXG2q+/eUKFicMfLEkNyopurQpXtTrYVl+WYFUUrFRsT4LkQjuE05wAxTycJ3gBHDZMTGQsjEglZATnRkn4OXAC2X+PLJ1waoXfrMlInaEQMo18LYt8w8yZHeK1A2b/3PL5rEdt+s7zn11uvnDX49NQPUC/qtWKeLzWLD29udxBAIpz58TJkBkpoLkWbsUaza1bHp25f6tKw819/ToiVLszP+2/Nr1goLhW7eQvVOQRza3ORRQoOy8Jeq3vPXOR3337h3dcN796HosvBNoY5mA/sVt9rSfSklN80VJwcdq9vqjUZk7V1XuOHyhtSaILOxAtofPKhwYsK5DRcFLvgnmUnfPv1rTPAFVOUw5ML07TJZWi8lhM1w4fMCSXT066lwR0tG9avk2wW9S381FSSFDJR+5o/EpHBWJx+V+CxUaZutER3Auk9CLAxlwPQwSyfaY8B1mpsK3bBHHPS46BPODK1P1c1x9s6boPDENvvST4z2Z+Xm5+sVHxCxH1ez43cmLNXW1dXUq2OsCFtDqCZmYFHC0kVsCZBy0eNpD0MTQqHWwKB3Lk9zRnpKrX1y9eq3w2/Lr+eXDnenXmZoyicDYg5BsW5Vd2QlE4zoeyKvltwwDE5cO9/U9r1jVsK3GWMfXOnj1ICzA4YvjT0xdxBuAcssJTw1TS5gkE6b7nmRnCTnSADyS0cnuPg+sQsnNFClTsx+OTpIVKA/0gTcSIkWjXL38ApPhCwdcgsxm8UeISQlDaCWPG6AAlSw8GWF8+Yz/BAYpskSHcLCIrwCyYq7atQ1ryf6cHMu+zc/H79y59cTWmtt/+ebI2MefDMDuW6ThB6kR0RIQFICqQmgvjNehxAoDLRfQ4pr6Blblyg+e/uC1D9jBMBSARhE6qmHklpUhlwBRAyAvlZUU5qVDT5KBaaBTNFC959mNwPye+M1m9+O0r8c4CT41Ao68PR77qZSU52xX/LlorPA67r5z366DfRtbR4hmTwnQMhUilRAer4Ejh2n1N3ep9Tt9ZAQJmdWEFdv54GNZyJ/CUD++8U7/UO7oQG2DdBWE8dEb4sIj9HnAzeGBo1IgbJ6tEUN3FB0aI8sl0hEVDyM9ZHLwsqljZnSIysnmb5KiLokMjBAgRxyyHgwQixa5zp6iUB6eY7jy0pjYH/hpR7klRQjid+7e+1+73nvyijC4dkWb0WaAlBku8Mbd7eS20YZ5irI/W3bvhsAOBwmFQeUODmJO0FTrhnrySq6WDBdd+/ZaEWhow0VFGJ56mk+bWpHVe2jlyhWWUadO5+ypcJlfUdJpGbh4+HDfgZCdHQ2VUEY5DK0NBzHBEAU8KJjcNhEMHMAOcw9kUxgIo8uk7ptx4FLD/R8MvMiIcB/Ka2PyepmwgTkPh9fAhAF8QTBvfUJRWX6BiSFykh+SZiogABZWApYgMzMTBiEpAEYCpAJVMbZToPmVlxSWoqJKS0To5qoOHdq6taFroh/nCErqA8VrB5/ee2jkqy/P3Nn4iX2gy66TBAUEIwmRxaVxSs6E1UdEoKbCcIwEwFCLvXXr3jc+eLZhH8Iim629FAkNxv7wMPIAe67ng7pUlI+SQlIcoRRNgy0tewauIH/r6B9iXoD3GGA1Ejenkamh94KfSElN81zilcZ4MRw9etRvc8Mu3FIOKyVIwXqLod6j69DoTENN6RR9TnFjcHV2EiMHzCyDxdDaqtWatbfPf315or+zc9Ty2sHKqgBpdJhCCk28Bui4GivMiBgEUInLMsAwCmRrActTdoyWpI1Mm0e7LwIAJQz8J6JQOAmTjycJSeb6ywNxLONAwv7Bdcm06VNbTM1ydfMFeE4E5tlzF5FvcVhMx7Y9e8Hyu7IKsv+VNQYKJoHBLjo/kmW099qRZOPU2x12+M9hcZtbPYrtflNPjoYsiJt68otLmgZzhsGaLivLLywoKikv0SGro9ZRu3ZZwGr1IDxme65XwA15OHt0Yuzw4c1b1qzp2LEaJ5bDsYnzwdYg7uTLP4iEj3yw0GfGcYm9Qrs6krpISCEsUstDSPMniAknzC+NfEwSiI3j+nBa+elZmQp3b2Kz+4X6oa7cEwUKqib8DWlKRHhciDwxNDQ0yWdmiiA+hhOWGJkLyhuGRsYgcykUUwUyFmvtqoBDivpVJlFvp95/c7SC/e6JZZXc1tV3/3T5yj7jQAsCroOQh65CSWFOwxWoUCgiBQJUFByzGIBRSucLp6GeF1x1iHQhx44R8lc9XFh+vQfiw/SMgiLETWE+wNPQ1CRq390+cOP3f3nv6G+fTzxO/D6iAODNQAPjs+QnU1KPevkyLv7wbvfZvLNy14XLrdUiEz3SlEGm0pCaRSLqyUeOQudIo8ZRq+8csZKlwIjeiTlKWFNjHdn11e0Ll6/1Y/Hp2Hpw165D5ty8/PySfFC5IiN2KJAgLw8W+quU2ZqA+EihZNJcAgg6kA+1iCwnRAyTi24sqmQdXVwMfiEJkWZIwgByedMwPifNa2roBGHo3j4J9DVgyBNwMUuKiUjpWPvW3s+/vLJxwiE8dKaNbNY0VFLwlMAo1dYmOw12hKkViykJIF5d5+goVVJTz5DTWX1r+Nq3S8ubB28NlwxjrP72W4i67w/3tusdtRDprlq9+tUaK/z04a9VXpSbiyTwjZe3nGht3XFiTStsJPkBnG2VMn8WAsGDQCPlgjcbzItMdUsW0uefPem6KyIGLygsbGmQWimJSMmEW5sXZSthK4V+3duVHHj/72WVlQnvCcqSd/NxQ0kFJodRygA5Y3KDAmLCEwUhcamp7jNj3TIjwlNTkjMzuaWk6sAtVfr/MfcmYFHfWdpoU5tYC1NUUQu1UEXtG0VR1kZBCQgTAdF2SYzLqIggKgqKUaOA2tq4Y0RBjRnTip0Y44bXXTvQxlEn2Lj0KBINamsWc+3EjEnU1mTme8+/7O5vvnuf5yZ6n6etJCZtRwNV//P7nfOedyHB1KuvDA9zBTM3V1Vwed78tp1tO0uamniTOir3zZi8ZfPhhw9OX22raaEEVtA8kdgilySA524yAZhwoNfVOn02h+Di0WkO/cr7M69WzpxRClU9U1IMk+K//vMVoH5gcGVkgBiXS5RqvButS7FI7/ny32Dqcuwj/aF2fnTG5hBKIfT3e3FKCiQ/YtCBK392U+20aef3bzaUlVXNKk/OScqvwFFcnKpOLR86ZAAi/BCwRc0Q2R5Wk3forLoZZeUTJ957dP/atdutuQVHwmrp2CkFH/zp1SgyVJQkVTnt8YkJCQYDojjDBXIBjxFvpORG8T0yHTCEn6YMEGpuYIi0mTRKgUkriXhH5AfEWEVC3QQxBi733s+36+3v4g/OYvLkaR6Bf53bGVzrWPebc7+5eaanCoj5rLr8YqT/lNWgehZCw7GiWD2DtB1UaXXyOqxgFh5PSVk8dBA1qgP+NOIW4JfFSXkj0aoM+RNWNhCgfpA8RaIuC9fMmTmvhFdiSS5euHjArVtfL156//K7W6br185LBL/YPA+azKncqVMUXF48Hj1GYAV7M67NKHKqaXxicFHK8SKBXk5yfKIhNS/X5AGBHFm9iEF86ugizH4mdUd/Nok7YI/EErOELjHbCaMxLoYnwAhyqU0W1GoDMATWiEVsj0+m8/h0+vArLzHQxGhcUmQZYUjgxU9WSKQJcktFW3UFt0krEDQ1zyvzlpYePtx9+f7m5RUT1kypmVw7PZ43CXUqqG3GFgAZmeBomJwBm9Jk2mEyNSnXYkdcyZsKDvGU2TUb3oDrC8bV//qvr8GMRMeXAUepPEruXQxMvgD02jUfvrd/Tzv/7V0axjU4i9p4WMgJjel9XoyS6t0rmw/vCaxpshb8sHdjE0rqyzvLqmoQ3VyecgTeqRXFlok1NcXHMxGQhEe+GLQbYrKBnVROwc51y8uLjz56dOnm7dsn0BClJk6enDf6P4lziPd/PAmqzWjS472kg6bYbC7PgEupgCHxUSovrsB8iMFRYgXhlEwczbRdjqqmUGhmX9F4A9uK64R8zNMZq97nm6X6sWLIcIxe+BhYIk3QGVQeXLfu3LnrX27evAI0gLo6optXza5ASg1wizXlkEvJ4W9Ztpw8kCwV1LQm51EOUk7OKPgxDB91a3Ey4Mq8QUXQEw1fqE7KMSDZW62WCrhw7edajiyc8MHiL95fXP3o8bvvJlWvV9kdPInUzkOQLayXwJjgKuAiAKgPayl1vCAg9mtTGXFLEgNR5OYx3qEGSdIgSBx4epUYxnscdK2D+w3Deo1vHRb7LLu6bI0TzlNYScEe1MWCCSkmKS6MZuG3I+HaYGttcurYMhnb6PcBE4E0VeYMv0LKVgxSRTBFD4cTwSrCv86F6hLBsGquSelwrN3SUlZA67Zr3fuvdb65E2/o9Kba2nWCSVxzc22zAzHOepQUQiacKq3JrlVhqNLTP1G4gXSeevmcmZOrZk+EfdJO2ABiaGX+QEXBTwollTu7qnz2X/54+itEQ+74eDXNK2n0A9o/jpXwCYJqev/D4YnYdA4pEFcf48c1ftw87beXzl/aXAUkmV6G/ApLRSqafsicESZVwMzrgJXXTPQWV0GVV1pRMW7ivkfgJPWcOFGVOqGgbs6c45hjX6Js11FMEOVwg5MdNCOn0xLOj5eHJdJ8cuMneDiXEIooUvxUK89YuxAIaIg6+IZVgtEv5Rj5jDkWOj9hep/ntfFLF8fERYO+46ikxGyP062ftK7hN7+9cWYLpiWwjyzIJw4vrKkoX7wQLBmkFObXqWvoksJ7Qi6yaFWT4DoMgBIkuPEwCh+AzJbcYsvCvBH/PD5TAZvqRNqZ0liCcJn4uiN5J74owvrh0eW55w9kLk4kKFmOiV1Ogiwc8rBjoxxwwkNxSZncmkgCNB10j1O0NoYq2iwUKKQjkZuWKbA5xSCgx5AkcxiVVAyr77Otev0BP58D7ykhy0UlpSXPCUxrUlQWz6aLeJS2CDukk4lAUrf5dB4nWybIhUPSqOHDKWUAX6sES+EE6H/RtnrBiExASZmbpy+vRkpS3b7l9+vrvdXlZd5xb4wtkZbwxh40NdUK7CbkONvNdq2J7iqTg/4ndgg2eOYDUueNLRkXj5gHmliZF8OlJLEZ6F+g0i7OzJtQVT7hm8/ru9vbt+/6/WoaWOimQl3hbOFnvyiIX69+QsJjV2ct4Ow9u/KNaXfOn7+9zBsmhUIdXLkprDYVJpnvD0omXihooSvgRVJRrV5W1lnRBgPMaY8eXjuNijqxcMKRtrFrp9/68z/9JxXUgMxBoB2PHz8kPuJU+cxSUG1AngQVxZvEsMyj4B5jiUQ8gWTSbxL5hiwoonZ+KV6b+tV/yoT3KgMaD07jj3lGQtvfX3EsiuWk5htJMMidQUkFPY51F//v/+tG/YZly2oAos+qwdVcUzNx1lLDmrf+suJ4OXNBVTE1tRynSwEa1NyiIUNB1h/wJ5BJR2TikkpBYHHxqPGjLOQuSWEyROyRkulQ3dLjE+DBce/bx++e3lyd11qjAIkATRxyoiWIAI9njP1Jo4dryiIR2HQ6rboAxUTvTZgcbvAAg7nLjR/+0vjhYX3Ax+KwRHyiYI8ZgxDsdCvn2W4pY9BIsxTqkyzRZXaUhxnOE0jtEAhUbl3Q5pH5C9lwpPcHAjoNLqygKUEOuifYEPnkU5iKjjUhAQZlEqLXeJE7APu/mdNr2naW31aXjiu7cbqyrbUTfi8rsNEuGXuwwySodGj1epPZbEIdKXFVYX0APF1p84AG78BNNXb9bkmFt2bKhjXgHp2YsAx3Fb1obzFhJ9gUX7+5cDbIKxtO76Fb6iMhY7vL7KVwvMS5+r8wJYXlJ6iH/cYsSNvbeOXowQ2fn+9ZNtFbl++tAzOHckABqc9qfT/zTWaK+h2AvvCsMgRjw8duTXHd0ifdN/ZTRcFPoXqsfc77P776zxQ9OQK8iZEjR0FyXCQ120IqxIoCzjIFtNhTJP1VIs+ksYeTnurlc/IYbQfhXGSPnZyiUGa89FIy5fWmU7guLXr7xD5X69cnxgW5JbH74oQi0Ntc7ADCgpsFv/nNr07fhA0Jwojh5Geoy6+ZIp/VSQTY35UXzLZUQO1bZ7EgAlxddwS+WDkZw4dnkCd+xvDRrxalysEWT+QmGEaOLIABnslMbv1EQuA68M81mL5aWx89/mou0kEWFk8EUo4bCmgn8lXVSKoCvIylFH4WhCSFwOZ2KyUGklIbCnLzCgDfIHMTJYUA8eEvvTpA6g5CMcWiW4pkDTRMiUXZz2I+gZISky82xypiIU8+gkUyrgrcrgDreSafzu3xIVhFgyyiwmCQLdbIdJGAiUtiMpiwgeYhV0vo2BDYBeC/4guWC2w2wRwwi9raWm9bzu1efOP0Zw2HD+9bWiqfNIknnVFS2XDxKKWjm3BXmbQ2FeqKRitAFko9itGEmOyScaWIck2tKB6H7dQHByZ8jZrCLUVmbVDI0F01YeHCnW+tO939bfs7KpuR5F5M1EAWeuC0F6ekYl+O4aMvpwBUdH6Bo+u2nO9BSm+4Gh9l/iw6KgEZzFqMNByiWWOMmgCD3rJliOuF1ers1qUP69/d8wbc2iYc2b1+R0nZF7dG/3k8bKlGg6RTNBKa0F+Mzk2wuW1KDFHJFn3InVCQDxgxl9IkidiXkxP1RyLSbF7OX20oyOGjoIDHe+WfRkujBBxwkqzkgPgcJUXGEywrcQ5olcG3CllAJzwydvPMsed+s/30jeKJC6smps5CXlR1uGoKzEkSN8C6vBy7qIrq6rq2CizjELV6ZBaEBjDEykj2JlqSMouGz0qQWnBkm7EdsPBUmA1w8kJwLsDm1G4SJFQtCy9e2vT48dzTtxE+MXGNVIqIVZz2yQUKSOMVCi8I2sgwoGsq3gHg2hkfphDFHDpiGOajgaw2gY0OHf1KnkPmCxhdIlcc1ptEnsVZE5IROz/25zd+PjFFFcQgH06EkjIhWgC3DpcLeQfP4Yy4nYGQqBAousuvg70BOxiUyVQKQwrzEZGVQRgLXARSoW21GCAcVGiDTvvk8skzdu58//b7u3fvvHG6++17xGAr3dCxrvbwvsqGo7/GBYXQZq3SgYgC2vgyK18YeZuhK4ER+9hSoO6wF32zYncximfh1x+8OeFr/DFhDaw010SbwYWzv1l2k9z89Cs1dEUNJhiAFlRp/5Os9o8sqd69B/MpXAoVNbjxyiLVtHXXbsBsFu4L8KtBs3McpkCzkpLzo2y3NTsnFkRdvjFX1bR2dnYiRPWzk68dHLe74Z13VM3wGftx/H9iyU4Zyei7kTLy0givyWYLmJOGDkoNiv16CyOLZzaZ9NEQe4KReZCEtSCf9AywR6RFlcVR8OovitwM3ZiTBp1Z3+fbSUH9GhcSoTYZlIhy1IGh22Qbp5ed+4ZKKukIGFUYEcNHjhRUTVGUdc7+Hc1S6g3ysAFjY8GE4okW7xH4JOUNpSVkCjmWe4HVcRMSE7mgHcjVPGT+KJFYjMxiwdOrSr0Q8YqHH35Vf+0OVCBLZ2+g6E4FIkiTkxKlifBe8sKuGPRzmDPAP0osFOstuKNzcxnicDQPMNoiG5JHjrQEAKfg8hCm9SM7F+LOCmVB4bOYB2SHPNGSwi2Fe0qmFJDtBL3AFHIog0GlKRhio6SsuKGCQY9NH9QpE5MH0XIEmh3IdLzxmBfRt5LAPyVJ6gwoHfPKLaXV1e/fPj6xreJ2/fUt3z5aWoc2TtBce7/J5LjotMM7WElNnypg09LpQ28U5YXAjhO9HzYLeCVlfvB1NQVm52QOGglOVM4EuqnoVc0U1VsHbnQ//uGQb9GVqH9CX5L2IrvYOqb3i1JSgNHT0lf/B24pTuPZRcqL0/bfAOgC/pnaOwsm+Sll+PsRcp9g+KMTJ1BJLZ1Vs2zNzqrW1s4H+/ec/ezk/Ldf275t+ztr9eU/gno9nnDW6E1FuXmjU/BGqngQdaZix6dSkI0LObcyEkfmzKMnp4D56ag+KIlOwxQ1d8AvXlWzGHEQdKxZwuejodPLGhEzUTjkYIM1lxB8pEigc/Fb33y79fTNlOriCVUIF4Wpy6waydi68rI1H/5lQnnN1NkFBTA0fxPezl4srxHdNxJhY3jmkQWISLaEeMAQmLW1vASzVo/MGpUKEwOIN3ZqpNRVJya0PXpYf7pnyWxYwcyabVYhFkhOMWYwGQDKp8ClZWHCPiQm+L+K9F6IXjJJVRO9wpOjVBIs8Qz5ZrZI5wkZwcojKevgOBw1LJlbnB3787XO2TLgHBxMqUiXQucXUpnpwQbwJsE05TB5POYEpZvcd1giI9upxbcX1DmSBoxiPlacl6MyLTwilKOkMBPmJHGRcM6bUwbn5orjB5AutmLzpf3XDz86XLNsw7SSmVfv32062GCjBg/vjsqkD+Ds0SptNhtgEaak4Hxhr8Uyb3nVYpQUbA6PvDn0/aFfgKT2NQEViNbBBpi4LDtRUjDI9LtXnYwjofzgwThcwILOimZC9v7HlxRozNkxq/ul9UVJpTc2fqRsGAc4eRnsmDGC4pJKLp9lqSugU2LFiooJxalHCsBMzzcsrKoyLO0E66/7zHtNU6fEwzHnUGjV2pVf/HgL/gJ449H8UV0h5eul0YPguIhYUTTdFCivlkvzo3knyU89kZhujzEcR0kVhJnhCtO5QvHKL0Z5OJTKmo6SiuFkP3vHF93BDTQGQOehx5HWvTADFQe1nlVUUo++u3kT/u2w2Cifpa6rU0+eMjWxczYhflgVlJ84AEZSGPap5cdPHEBQIxS9RCrNTVbHq/MlPDwOWqVSZeY5TFqiBmD61prNaGscPGni7IVH2h50d99dty61fDqMevFQBZy8VFj0JTh41PIhUZH+Ust5nkK+0K81MFbWSWEmxgzlxZw7IMvFS6QmXYjt04nFRmHfYfge6KYVanQyv0f/c3fgvbPZHhHjhwLbMdxSGkgQiciKisLB4DA5HXKJwO5zR4ysUCiEvSxPrwtyB40YDSEi1B1FI1O8CVqPQ0IAjBRhdjgfcMHNs8CVGBfN5JrUiVXTL9XfuH+1ovi369Zt0J66d3X6OpvNRDw/vR64BFYJWpsHx4uWibAnQTF8HtXy5eXHUUkZi1PA7Fr8/heDcFN9UECD1E4ihxH8t/PEjUuPu/y+lUYmITarL/pf+khdTEnFvhCNX580PswnEIQYt7rxim3H7p79d5Z5K9R1YDLAz84L8+biCgB9GCRm1S0FkhxGhlm4CilJ4Ps93lO/OSNjwPCMEt2hK1ja3foR+26sPeHagarCazwy84ZKTdiYm7kGsuXICasTUnPJ1iKFsWomunsSE39DHhdPDV0Yu6T48KvjvaI0itHsh/mHn/asvhNRjHBg+ph+OpU4rt+wfszjiJISFvpsutDhpW/95cmjmzfBH5+ybCGB5nWlQPzUdXRLUUm1Zp5A/GcLutylrQdmH0+Jku8yBuUlQbJqkdqxtHwKDjPoMI3dSiVVF0YpRdnCI0+66x+sPTgtMfHzuyX7SiUCj88khQbPrDUrSCAFSxdiTiTaoQl0sQVJZMIBJUAKeRUUMJ43NFAlgs0DqXwkEARLSEikXw5DqHexdY4hI+N+fuMXYDEpiEIapkQhG4EToMzi1hWA0qrk5VvisTJzyMQytl8XVAqckYAjDIskfKZDMiGBEoC1JFAQuImQnyRDPBmqC2bCvWMy6Fg1ZNhxHYF9D9ZP3PD5st8dRUktv9vkVNKxgyR6ByHuSidEWoT5gZYOerqDN3bqFDlEnqBN4C+Iplq/uPUFDuiRBUwpgclCf/96womea4+7DgVsVxj7CWS2ga7Wb0xfV98XpaTwVYzZG4cMLJTU3r1nP95lab30b5sB6mGT5FUzMXZA9wjqq8iH/cbS8uQkDOxVs9sweT7qnntp6Qdf37o15FbZxrjGjSubb70yNOfN4R+A8FY0ihDm8QgfKcrl2YI6W3xyHtQvYOkXoCIpnpdBJminyYziKQwQEo3yoGEr1xCf8dLQQAwneq2gpAY/e0UhKyV2TKqBHXSLOChQ0vRimhJaNT6dyPp48lu/e/Tdu19WV9fMXrYQcYYFdZZlxYZZACbe+mbc0lkVdQXJ4RoLcqVgJ1YBwV2Y0gdSMjKTLLRJgskKMUHtBGdptU6biXaYeo8eza5KC4JB+dJHe+rf27G+5GLH3aaGc4Z8oDXwdjU7tFBHyNXhVAWDWFikNo0rxqWLp54XfhOG3EHoMdHu0buBZlihDOCucNicPmQcUzJkDONumMWxCgM5gp99geOWYpEAUWgVi1lIrlOR2awAKzLs0sAxtzvk+E/n5qhVEZ/MqGEHtD5ZxMbNZxYcaFa1WFSxZOZ4BRbacBxMMggo7kPVXLneLC1DBwPx/Inbt1u660/DbX7NlAbnjob1MyH8FUh5JhURJ+ByoXLqcdTatQhDBzyhtFdOWz9unJcWcm9mvpk54H1Q+GFSNmrUgBQwVmBCVQXMD13gByd6uh92XfhIFWLiIBmNC+wn0oVZfV6YkuqdLeQPjktDTa1evffs28U/bv7+zgGyWTV4kXO9gnRDZFc8AVr5VvxRLh+H/mjiCrABuupPt476+usvIBp7v7nx7JWVTV+MH0KUnIxcohAPR58AxdSQJG3AL1IlFw0dOhI8/YwUWKgaSI9F6EQKXVhMtBSp5ZkdFZFFkXurlmQMT/Bb+VghMTEDrn7PV1LDLAVunZEVg2UOFjo434RIqA1qXKytJR9++Oi7uT3VbctrZi98f2jOkWpLDdzIMUu99c2Kznml1fCYXdxZB+JfMajDs5KTKPgwKSecKJUopFio2AnGgu5Jr3VoPTY7FO16T9ADlAKSooTJLYcf7qlf9OtT944uqm1uKClWCwLY+mAwt9t52PBaYBidSLxZmLiIrFafhfHegPlzDlKbwdAqYO7ssCFRqQs6wFP3BIM6jYhEU8wZnQ69vEaX9QyzlIcFcwQreLMiIcvv1hILHRnlEik6P2QXKbn4AkaNGI4PS8sWi2U2nUjj5CUoQCzCNeNhg6zkCqm4conZkZAIeILnVIISsda84xSvZlaVBVljsE26vfnG6frzUyuqxx49+uv1+67a9DwFj8Ab6okFKCmwq5m3zEw3vGP9tPVjJ+bkHTfAd+yDD948AitSsJBHFeW2gj5RdeD4caoobKx69j/u4uuUH0OfQKQkav5I5sJ4PPZ+AdgTtK3h7IV105gxVFKHNk4ef+v8lyB9toblFRMqViDIhRBMrKMMyDbu7CyzTJ6ycPaytsNXu7+6dhuJWh8MuNWK9re2ce/HzZ7cV/+UkTdgKLRQuZkjhw4YUlRUNGKI3BZx8W05RcDJ8jKHDsohJjqgCK8BsDlDnGBavdxMhjnxFKJNSlZIDBKfyArtCXiRccxW6tn18fRdjkkTg3yd3o/RA8DIhGXU6HxsMbg4Hx7d2v1lT0VdS00xDEaSk6rVVcU4QIqJ49dZsqL68BOgwbOqwK5ASVWTuTdKKlnOZRgHtLs0mVFI6Plox2JW2SDUDThBtKHOr6Xzu2+7T//bol33FnXsO7xuXSJ43mydL6AHfJEATYcEQTtYO4GJBJqq369KJUoxvPsK0PgxVL9o+laSxS5jexwCuxPRLbJCkSuG7GgoGzKdo2Fl/+zFQn+6pWJcFITDQklFmJLCLSUF1oIb1KFSebGpp3C2oRa9zB8KskUaD8YtIDCBiCxUaAyFCtkBO3xybXaePCUlwQZkZi2vRLmrYfpytbq4umb59Lt3D7Ss6q6/ceO64LdIdDZjG8VVOJwq5ILYcDFhplSR2gM/YaLrytHQcJEHK4IDy3Cy5eRRtm/xkQ+Khg8FcyKJ7P3g8pY8AXtz5pbS6f1PK4rwJkS3W+NefkFKip43uLji6sQttZfDOtv0yp97PrnfcqC8Rg3gfOJOUrbiqipmso1nAbMIV5XN3lD56EH9jZ4fR40CoIzGt2jozMbVH5l18tEvYSE1BITHpCTonDMG4FPJ00d0MTGqjKIBKJq8kVBpZgwaOjQz2etNikJaySSTRy0RfS4nmn0NtyrQom0RhKuDboxbpR8/q9fzlBR95/3FIqKbMktSsJKEyKvwRHSeBsfRRZv2XLtd7C2rwlw3C5ezZXZVcf6sMG6p3Z2lDae+e/wQtvlblmxYA33L4twkNH5wz0gky2OE9YBXA3ob9pcgrKEBxLDgDHii+ylzQmJL57ffnp97vqPh3sGOw4enrtvg8IVARtB59JBKSRRQHcZDJZXA1YfEsogsyEtlQksIqAH8QQvvaEBQSpgrY/l9WlwRNhvuMyFncBRlgb27qLDfsyB+KCk4DAlFIquoUAbpCL4dkPyI58dzOGxuyXCYFdPKfmSORcWGdspn09t8QZ8nENTJdGzwKWQRFZeLftSEeDmuzalSCuYkKHftaq6t5ZWWKmbOrL27pGDx/Qf1p7vvTpvW4DCv1Wq5Uq1HZbbbAiqzw+bzqEwkoMI3hdpy2C82jJ2M8Jctl+5uWTJ7dhWp1BYeBxSUCUR9wpEJKcdz3qyecDyTafwKfXoxJTgjdIZcU3FbszgDe/09c+EfSZul5w2eLv+dtmDMArRY/9246YM/3/pyDxxkJy8sbmN8F363Zo1lliE16hcEJXnZ8s2gcH15/daPwz9YPPT997+GrcIHky7wF+k1Oz6Akc74H4cPGTIShiBITxk/eqRApInwY/QZyByAkAxbUmifIdvMZPzP0frl5SVF4b/wU8sx/JgMOoIdE5gQCcLIvwKsY312CB3fdSw6v9g0EQzFKfkNL0gj+EIZyAp67bSju1b+4fvpt4H9JreWV0yssVSocSGllofX/O6b3fsedz1edf/+tfvdDx48uD+9c9abGHBgX5RUkIjdk50mCBzQOGRRWthimgidUHls+HmsYBzSfUsfbf/93Hevz5g+c1rDvbplS3imoEZklAVBxSY5BdRJiHBH6KBObPQFdCo16MOQZEImFWZu6+Sn3vTJiAZi8TUeQIl2pZONkqIJEyIyLH2tIU6fnz9LOUVM+IcV4ARwcoInuESExZeUIBU4PDLB0FHUV+SOHDF+hESHHbPN5gnoZEEUltODqyqiY0ecSq0e7RsvUS5wepwqwRze2oBPtXYVJjNBQ8PYab9bOmrx1ccPrl270XOnaf16AdPugYtENoB2EJFoK24n5ycVtX7KN5a13Lm+v/7y5TPXbt7A63pPTw/axxOt5TC+ri44AUv62YACe6497Jpv2wV4JQ5HJKnHQKDgGDkDX4y9FOOsiM4PENLgMWPA+2nsmvinH29fQrR12cSKnVGd+Jo1WO56yYth4uwp3Not9x88uHGj5wRsNz4oGgqu0tdfF+WNFbpUr81/ZxwUaqNHZearE1Nzh7yCtPAE0DONwUKhLLWoCF7xuUOHZpChwADSl4HSnRNl+9H9RFmi4STGxC8nBRYgIG9qYvDM0DavH+fZ+76/8pMhP+xLBlUIJmZU8kKXLMCOOOxHf71x6ydflrceODD7zfffr5gISox8WdXE/KU12Iace/z4wVWA4N3dD7of7Onq3nLgeLgqTNmQgPpUWPk79E70LzQK0I0FyM+phXbBRopwIFpm6ZOH37039/TmzuXT1+/YNXPyErPSDcguokcdEsWC+AdcyNFhBBHS6iNKcEwY313m5k5mGj8YBoBBkRI2GzmiiIqHvZE2aCSt0GCy3uZkcZBn0/+ZSoo8x1hMScn05ImOxg9BlVLSeNh0plwciyOT5eFRo0co2OyIzh2wqQJu3FLA1p16vU/Gxj+DBmFycBMVArBfbYQ5eGCztXbmnDkl6yt3n9tdTX4lV6/Vn77Rs6Vh/RycQXo9fgFDRtLjeqLtFA2d1DnrbQdbNl/f89VXl79CUZ25fOaTTz65dv78+Uv3t2yZXLVwYfh465EjEJ/d7rn/sCtke81FqWOw9qIIir6DOX7OwBdjL8UwfGKzhHH9Bh9bMAZ4ZNxJ5fA//7nnzGtgK751DslI4B0tK6s+cmD27COLly49cm7Hpkun99S+/+OtE7e+GDkSBXIcGXWjRjr4F1TIueFBkjurJgEkSIFAkTI0bGPLxEK2MihimTJGwmwicyhcBECopaIaiUuLjGNIlMgAE7m5T/3RYbXg0GtCOhHaG3Lo7ZfFSn8+4gSuuP58PrBWxrWQrFz4fFHE40aw/Y7t33adudnacmDZ7IWL60pnJcGipupAeVldG9gT9x7ev1m//w/nL9299N6ne/Zfrr95o+dGD0Ca8pYtHa/tWH/v3qkdWmXHxR07dqlqm+92LFKdunevcq3e/obj1NZdO17bcGrrpdPXNk8tLVuuGNt0FwXhYWsKZU6bDRghSopxeNVHQiKXVRdvdptSgdfkRMNLoIHJJZoqLaloF64IuoTiiJ3mN5tGyDAfGZP9ODBfGY7+z5FNoaTA8cM1ZTUaWaIQmOcwhrPDbUWKipJLuFrYlw6BjX2uJTxo+CATelWNJgi/A5VPFwTmGIRxFSAKtIEqAdQdifESWmErTWttAT24t/MSE+OlUFVakCC0tO3JqT2nT9/YPJM3fXotmLJrm+dUXq1sblbp7zY1KXfpjzYtUpovHl3UcXfzDXjXnf2hvb2x8dPPPmv89PtPv9/z/ffff/LJmTM4wnvAUD9w4sT7ty9dv9+1aeVWFxE/AU7AQiGLDGf/x5LlH11SvYaxOMSUGtMXvWkcS/LKL368uQjmJd+cwxA1ZYq8qqq6Ys2y2WGSyZ9r0CJLPgcaNNw2wwfAhC8nkxS8Or5TGWIruVyVnetQcZECYdKb5VyPGx+AW+UWxbDV6Plw7uWSHS/uKfyiARkpqZaoaj5MUznD8mOsZ+UgQrNCkErhHAaa0JdjHfO8gt7YfqQMG9yPoZtCEcBBGwWag8DcsPHbrk96DrRWtcxeuLROXU123HjJS3d+s3Padw/vnz5zBhV19/O7sM6/PPfMJ0jSut8K05bpHYsaqKROKVUdyqM7XlM1N61c1HHxHrkEaQ+aL+44dQoBJ8GPQNRvgcHmAW9p80qgg7JCsRiqCZ/HacM5rXRHgj5GqSuyqQVOrreAWt9kxswG7nXJBIyC8kimUl4t4mugwkAQgEpnFFspUR3nQ780IpQPi/3ZiJ+IFr1UUlaWxod7AvlSRPNLkCrUCp6eLYsfCrLEiEEDRgyVAJuQaajuVCq0aypPUCWVqiI6HUQgAjXiECAA5+kdCYp4ASRRAqjAKKwDWd7Y+3aWnvumreV0/Y3bLXNmzJgxBwDH2koUVGVlU1NHU639on7XImeHw2S7+AYSNOF288MP27dvP3ny5LFjqKtPP/30q++/Qk1dnnvzzNF1Sw5s7rndg5CQlSuvbuQ/VXaQbcDgMWmsrBenpBj9OAcPWz/0pHSHspP//OcTZ669Ma21dWFxnbeqqk3eUtPWNhHqsA0bPr9z/cHV8iHD0bjloaSQqkXu8RkWI8ukcrPZuNdTM5MTYDKgtTuUBCf7MMrq2H6rFZgf4imHZxTIQTfFMEV/pqjjLYgTSFQTBYkIumGyHUOYvDPo90cK/4oUQ4OY/VwlRaaYMUgUxVaQMRuDc0MMy61lLtOmB10P/tADRlTbzomtLVUFFCB2pPh3b334VtsTKL73f//e3buLVBfNF9mfffbZ959+erar+913b78/e929yjtbrjavbGpqulrbjB/xPe9qKJnZ1NHxx46OSWt37bt6uPMqzHlv3K6ZUgHya/5MffBtdiFoqka/EdM9RnwdhLNGjdFqtcaIHBaH1oJRiiiPoObm0pQJrR/NUkmEhXrNERG/0EZ2fiYPs5yizofRKMPrqf/PUr3EZrOx6mVuKbHYiv0cZjRm1wuMAjUlEQQ0fqU3ZRAg7FEjkvVGFhsGnk60fE5aXzOuuIhficDthWcg3CklX6ASWMjhnLxpJEhllkoMBW1tt1t3rz9X19J6G8P33ZInTw7XzaztqF1nuqi92HFUCS5tpfnqPfyBt29fdc+1PZ/+0NXV9cOhCxesJ1cPhmlX4cmTjY142z+lF7rBm9Dmde+51LTx7be3L2BKCsoOEB6zxsSJ/0cgzj+2pOiVFUPtFTQe6UT5Nn0xfvyN/XeXtBwIe+vqqvLbUm/XQAsNCsE0lNSX3Z2jhhchxBilNBLXDZq/kQXakM5khnO9ziMoehWCDozmlsxkLhShPk8kBMBIHCfiJVPHNzIlMdGQlFGENRXk5mr07yCzqDGdFIQZqy38AJM4Z0Qji1jpVieaK8ea/hyRHWRxDEcAqxBpZxzC+nCq42g3OnGi8hzmu+89QElNKDWAdFXeujAMsvnSpdVM7ga0YO9+0nH3rm2RCk2cX4hPt/FK1393zT1dv2XZhpJ5Wy7hWbjfdLW5+WptbfNFIMX3SiqppBZ1aO3NlTA8fPjgZvfmltop2H2+mTwzsLEQdxSKqFBcCLMUY2GhEbsouF2KNVaxWaE1h5MLnppikoNxJtmGIoCLxsukAjkwA5FIB1RDYLe5Q9gn4bxhbDTgcMiCU9LPSRwA4ke3FPhIVFLYSxETnTFIInVvgsAZEgeh2FXnJ2ciA1sX8nlkQbtKxw5wvSkwrsnIyFEL9EFZSKdNxHYkIynRoeSpM3G0QtAcLwHfyoA9SnVpC0pq7PIDLQduo1w2IndsVs2SJUsmrXvj4sWjRx3r7zWsrURF4dK6v2V53bXL/4GC6mo/e9Z4YdsFpqYuWI8JT/7+97/H+/7Zp19d3r+//tq165vvr9y4dfv2dkg7iFeG5xYcCn6oL9Pg934hSqo3mbCC6g2qzjCsojku9oYhf76F1K2pFYfrENG7tLzlQNXS6jXLllV11u1r6OjuuUUWa5lY56KecEkNyHRoZNKcFKVRjG3FIBhnjwhLckaMf3UAF+q1QCTkVpl9VqFGaclDSHgmVPPe3KLhmKYGZHp5SqVdL0hNYRLlmaVUgUHi9EWMIZ2Rg+ET7gIgoVv7PY/ykMaM3oNd5Hwcx7gfk6hR6AfFDJlKpnd+6Fr5OViN1W3frGg5AbtzIOWtC0+s+ctbSy7P3f+48uJFrHLX2pxb/cJjx4TWs3va27/9du7cO0s2b5lR0rFoZdOphob19sqS9fhbJaDjJtU9vNbWfn5p+uFvH107fWfKkklTp+xuy00e+7bs45BGg0sbV5URJWUUsazgrSLmSsXWmHh6QdTGOi+HID5aKCQxmhfMp2j/LMDIdEaZk/xxPEE27rYoq56JrCUTgZ8xTMVmy2yMwJmPhbeVVahTUcwAoSXEK8J9pdKIdCqfj5iuNqfJGVQpgwGtTaNRFqAbhBxuVFGKwo4NFTtoklMWBfZTpsS8AZmpoCfBQA0xNoMwK9etqF4+ffLysprpy6c3d1+eO/fMl3fWTVGXziidJ9AePHjwblNzc0fHwYNrG9ZPR+7bnj1bt27f2nXlyvxt8+efbTwG8ETIP8Z6x6ZcNN96bPXg1asbP/vobtPKTYdCqKhjZJ+Atgp3Ae4DjnHM05n5RVj14svoI+Rjbu9PX9ywLL7R3VD051v1e2Ya6pa2tICGVFXVUmeYOHvZgZSl+8417bmBDe9QdHwjh8OzOCWzaEBiSOPMfXWE2S8zcqyDGA/F8TB0+efx8hDILjI25Gu4wVyioDxjAOIlc5PCOOqGDiLVkcQmk+mUlih4ziBc4Xh9MKgRst0Y8Cj4Kg3uYM9pM4tfPDCOH8eY6j510o5xFeoCWFM6bPMvdD34fNnsIxWl59oWQiRYUb0YuUdY8675/PK7n1z9TrXLaapcu2rrxkPwEtp24WyXf/u3334y9/ydzZtnlB7sgB8k+cBX8hpOAa2oRWPTXImSKpm5ZXNt17dP7p/evPuNg5MmrdiZk7z+tVWb6Cl0Qn/OEvsLUVLAsFlWlkwg8bBVdm2Cgdnu5ubQfU0CGAasoIEK7XXY7PT42DKficdT+nwRmd/KBKpTWFZ6vzj+mNifg09ks21+ABzY9oKQJPLL9CgpRt/Bi75UISjkI+ygx80u1ESCYFCZnGBvhRIGjEYINhGiR4Z5HnDVQ8hvTJAYEk2RgAQLafKkSZXEI9AxZcCAXAT4HmhpqSlTzOSNld++cfr0mf3/9t7dmhnLO9UzzW+sO9qBux0V1XHRXIvO8HL3/cqtW++hZN751TvvXGl0HTrUTplyIXNFhRY1BTeHYyc/vnKStXevxi9cQCvLfsxrWP/s/v04/V+Ykoqm6PUFn7TfMAxUw4ZlcVz+zzZ9Mer6ngeHqxDzUqZe6gWrrxNwX3VBRV3n1fd6bg1B4mpeXtFw0CEyBw1IUbHd5lG/GC+JBIwxmjyY/ML1YzxctF9R4NbSRXxumV6gZ4uFLGcqFDdDyecafG64io7MkLBdfptAnpTLxO6QPFyuDfhwjGsATtAZDCvcdNZzRrXhe8QoRX13FvHCMDMiAFGD5SVyQUInz3bt+bIHIrC6ioo20EQQwbZwzTd/eav8xtyvADx9HJHpVjWvXLXx7NmzrEZW4fxt7V2/PvXtt/Wn76zZsGFJk2rHjqO/Zh6Otad2Nd1d9/nBynv7arHBOvzo4eMt1+tvtHxzsKN2UltbUlnDrlWbAhGEeOpcLoTkWmknpIEjs9iZqA76PSYBmfIT0xFvBaN9yQHpnUBRqqiMJHMQZCQ2m/a9gUAg4ndFv5e+UY/Mn0fUz9Z4CoVpWdT4GUFEdyvN5qdqZAHRKMzYk4V8brbbrTEWikVoDKXxtpBGFEmFqJSckl4aPypX6sQmja3zBNwmucKk03GRz6aAbQ8EmdLUMGNw2tZWjq6vbFzDubaa25v37Zu++RoGop7Nm2sSZ5SWxh/8zdHf7Hiye/0WUGz371k5s3PmqqZmbLR2vPa27Gzj2U0bG1fv3bv3SmV5+bxVh2Jeh9fktvl+1urVrCtnG+MYH0NasYD5M2xYX0yTdKS8CCXFHG2x2cYYhqHNfEB8zUdXNPc2XzrbvSUVDJz8WRVrYKQBvK96zZS61quXem4NBzyRlwkryFwsmUZalKpg/OhfjLZ4PGxxyI4h4JXhea+MLhoU1rN12A6CzWUYMlIiY4kKPd4BQ5jxKw+lCFbkIJ6LI0tEtjRc+9QKbGWSUh0+VYSlQaBYDKO/xRvHeVbn77/1fch75gvRH/VNJxYLYL8YViEOfHAdPOKTHz14cL4nuXzx0llvTphQYShvrcKO95sV18/M/Wr1fzR+FrpyZeOmsxu7zrLOWvfuNW7bdmXjr049ethdf/3OsnVLJteigenouPv5fUQs6W13122YVFl5dToq6uHDRzAyrr9RtfONg7XLJrYVy01vb9q0SQa5X4QY4GTyzxJrCiGS8sQnsMVuO5ykAO8ZGHVLCsMjQUUxlK0U2Mfna3WRoC6EkwCYW8Dp01iFeAETZVxNOHRN/fTumITycLCIYWYplgYezWAhEUDBwBTYEgVDIZ0bDYYfNCn07E5pohNTYMA7ZDyl3cIBa0SGBARYDwBLtkYlkWrdboGcWj6wqeQJCfk5OYbE1FkVpd6yltayit27V1g2b963vvZ2z/Uz+69du3MbyqiWliV4794onTHzPkLs9t+/WjJ9+pba5jfgYfuabOOVs1c2fnx2dePevaxNlfPmrN14yHVswQJ+4XyjkHUFoAUS0ajjYNyusGFjifv/jSbzAjR+xC7gCLHhGEwM7aw4K/u1rZ992vXg9OUH9yCmrIb1YRsZzdbBxbi18+rV27dAuafGD4DfyCFFSfD1cYfHvzREjf2mTe/E4ZbzyohX1Fra+8FiES7MggEI0DNECllip5c8eSm4FNfUkCHJWrEwJODBNCjfkggLSEO8yeaM+DU6thB9Grke4hRyWSmKNTb2meE+6vsQnA7+LQduBWiWkPmoAQLs4Gl14pN7HnSfP1DeSq8TBw7cXlr81l/+8k3ntXcvfyo81vgfjaJCf2H7hfZtwnZUgfDk2caT81dtlHU9flx/4+aXy8s+/7zn+rUvgUSdR9RWx8V9h+uWbFi378mjbx+Vbjm//xrcAVdMWbYE2fPeeJXs442yiN6sZMPtM/qCqBYORSG9TWNkmywFSYxtQA4TrBpGzkIKrRUwWlHeSaJH5wbgjp2rCubKHpvMKBIyQDLNh6D6xf0cd2uUlBVPZEyMsBB7KX+AagnW7FzCE0nXq3eDiOjRaTTEnoLY2KNI1PtdLF98zhCkIcKvB4xatZlS5Hxuv1+fKPXJ3A6g52FE8MKRMBV0tLCkorSsqq20qqWq4tzOquXTZ06dOkmedPv+/XqgDOBG3ABF4vr160jLrt//+6bP/6hcPw8JitqGcw07NuEY8xf64YaP9CvW27sECSXmVZ814nXyCv4gHDCNrOhwkiBxIQY9PdvFfPu9X5iS6hWbJY5BeGcaMTxiXId2Naz87Oye+vrusxFJtVwyESXVubSzbs2yqpb7V+9vvnULI9FQ8IqwXMrwCpRBnV6dAek0luhww8fewvvqq8PNZCUP9kyA9oFFCNIbHQYXR+MG8AdDFMabFy5aEI5a0VtEuBYJNx7HoxJIbSGMizSM5pQgdPR9MX16PYfpBJVUNocfTTmj9WAWXDbR7PgQUefUsMRn9zy4dIISd5G5e+BAefGK3731l3/HMP1VIy6Txkar/4JrW/u2Bce2tbusImPj4MG/X7nyo/ceP7xWf/n81ZWfY8G/f//583/4w/d/uLRkyb6l1XVcx6lvHz1Z2nLnkzPXNo9bMZHZc4XVvEDoykeblKgoF5wvsBJCpDvfhbJiiUMEWHjUNEjRBUV5q0xKMsUSJcGjFDNWksLHduNy8rndNjjAsp1BtlHInNDknovDgtO/90+/t7ONETqz4DGEzlPs96HlQy1JuNgq2LH0hWVzBNQjWSGaPT+sx4w2RSIQC6tGrwgnZxQBYYJ9RiJP5fHYAjI/clAlwVBEIJfGJw9KgQYsiVgyOfnqGZPLKkonw5N4d1v+5EkAaRBb3IrETGjB93df+/JMPZ1E187Uf3WpqbbjbsP6Gctrm7RHd/x618dnrxQeurBtG//YNuSeoaQkioo5K88yNWW8Isb8uRocb8pHQkHRfo5jZDJne79IJdVfbCWAH60RUCDW71d6Xpu/beMD0AWu3dl8oKWzFXfUihW4qx5dbYFZc9FwAuywrB2ZIlH5wEphOJdyiT5oZ1Sh8vEv5drJyc4cgJULzr0MSKd+MT5XGyoUa5x2KNwQxzV0qEFVSOkReNr9elD6gm7g2j42MHckaOL4wSFE/gh8Ud/YXs+ZLzAshrFsxm9IqwwOxdPq3EquWScStnd1P1gzGx41S6sRp1Cw5sO3vil9UI87qvFsO//Y68dOhi6gh9+24PVt6OQLrWP6vDz47J6VH/37Zyu7Hj68/uW//eH8tev3t3z+3udffr+/5/bhJ4hj+PbJvg13btyce+bmnYbfTGoBm6b8eJVCsOojGdR89qAohkoKKbm49VBaIj+wP79I5FMzvqCI9xqUQelaMB5iuBOMSrMgSRGBFtDp8eBd0qPZgnGR0conOzb4ZKaRnXP6wJ9TUjpKBcIthZJiGeGQZCZmPBemGSZ8cAJlAJiEky32wz1Gxi70exLVWpmRTyYV8d6wGkRfOeJiVT6nMhDShGwWuZPt5qq5CSlFGQVJNCYXgW67fF5pRf7kcaUViZNrKsaNNe8unYjMl2Wz5SXfdSEt8dGTe1fv37h9/+pjDFlX9+3bt3am/I2jOxp2rF258Yr4wvZt27a97sKbfmi+B7hJatj8zjYXf1s7/g/+MXS8tOEmT0bG49FlHPbCJHf87SRHiC02oVmUvBQjbPz9rsA780Mb91+r3//ppc3gLrZCizexAoSt+/cBon8xhJmIcr0JoEaDB2kGMwyveIdSwHXgI1G/8mqSQAr8SIl0IjPs5OUpQ0e8+tIIC1jUVpfRWKiD7iyB54YxM64PPF6F2AaHIj6VQCkzslXBQhHWLnEkgkarJhIThe35fGb7xkQ3OFRW9A0i+I/NtgmUIZdr+8bua6k7IZzDME32wW99+OsI4KevXhe1741ZsKAdRvz4cLe1v77g2KHIlX59fhk78F/jVi769/l/vPrwwf3btw/0tNxumT55yec9d1BcV7d+SwknV8//4Qwg4+t3dvymZEtNeGFOectUXq1qlRu6DpmROkghjJhJpI6S0gADwNCpt8B9F2l1WOlkAO0j6zEmhyFqPpEcLxMBC3BiwRBU2bEm0hmBFjJON6BYIRk25ucw/bLFOqaz5vD9KCmxzgT2OflOgLRoIraf1hlEcgeUUmK+0GgUi93xXoHbz2eFIkoFxP3qRPCN7CDQOp0RKOk9crU26OEmcuOTgeKSIz5sXwZk1sxLrLDMUFTI4ydPLt09yby+5I2DBzesWdNWeu7Jd6Ajb/3uu/v37z+8ehi5ZTP37buqqpw4ruFig6CyeeVGMevCNtY2/jYYZAkv+NCVziwufVuEbuHQdsxUACqOMXGQ0UkqnWMU9/8fg8ELMEvF9soy8rPIYprUJzGvb39t08pVv7pwduXdS5Bm3rh5ncD06idPvrt+c+75kvKhtFTKy4dc14ciSsCeEEoFKS0K4QeH7C/DiNFJFARht+ntFKlMCXvor/NSudogOj0+koFFmItBXSXZLmWG+WBqxYMaSFbol7nZ0PCIXJzBfePSh/XlGK0v9+717KMUWU/0gd6ekkSBH1J4IMrYqpNBq+rBfHCoq/7ahBWQKlMo6FvfnGvYXz/38qcu0GLaf2hf8Dq/8eRJlujQoQvtMa6PN53811/+cuDAgX2O/fuiRQ3281cfPmqtwmF8Dq+xZbe790MedO369Wun8bq+paSyGfxr+EVWg/U6WYI0j4u+YMAtQzeFOxh5aUKWERHx+DEU0cLRxML4sCF7EubQFLaVzFhQJJHfDbAKKVsU0vmceItkHrOWKlNsjO6mGJkHrt5+P6ekcFfiPOPEaDQiWGMq4cgnId07Lila+DqUTjfeIb9GHBfHKhSz2DwL16dhWY0ym1SeIJXL8Tnb3D5nAEQlnE0KBTnAIH83CYguFvrDiyhqrVxeIkGCAlyq46dMGSedOX1O86mG9aV1s+o6J086uO5gJa6mJ09KDlfvW79v/ara5umS9dNee/1KM4IY3ha9TvsF1l7X3pjVez/WN61sqm2+4qLBFu/dggVAKhYwAc5pNGz3izPyX+7fP/aFuqXQCohEacjr6QtSXVb6tu0XjKtW/Wr7la73Vvb0XDvz5R/2rLx09ep3363cv/+TT/0eHpzw8yUCBDDYyfeXEFgUEPlB8hAlxTMZRo0ygJqCUkONAZWVyAUg8vPAykSbHgDpExGxfLKiA0QeJ9QE9bDJVvLMNmDEeN44LoqRpSaZkz5mML8wjUFHY58dQe/VH/8dUK2YtSj1OzBHwpivt+nELDGVFMw1oArbueLDD3+7Y+P++jN/+PT1dw61HzqkOfS6C94MV2BrB5dIke5jzsu//Jd/GTgwe8FrHQfX7/io6eHDzs660mnrz51D4/JgT/d+wFnd1/ZDd9dzu6V0vb6ppGR5C/kGeZdPzj8eTtihBPGhEHcTY/dF1xS+UytbiW7KIjcw9GFYGKKEKMCOaLPY+ULvTIi6RGcEZIBsX5lGB1EFhBYao0ZEZUEsMkC1/LSBP6OkfOQ5SiUVAp1DR58OPhySgJlpNwXMLxBBTcn8aX2tIREslLg8p8wYA3qMGWL6RAUP6RsyWUBnNIJXpZJITB4f+o5EA7ythxYxvqhIT5cLStTymbXxiVPGjZNMmXz4avPWXSUlpTPqypPk6zZMWY5TGrkvJSVr75WsV65sqnRe2HYse9jG6Z2VW43CC1aW6Kz45N7Ve1ef/HiT56OPkItgvfJxIaOPR0VlRXU6jDV8HFIQs7IGvmAl1YejYWJ7s+jUW93Ial/QfmXXa/PFHyN1quHeyjNz6zFM1u///jPNSavL7wEBFFIZMHroInLYBVAEwCQ7PoE8SBME6kEjE82McIEMimFQoKAwFIzA8OBJAJ2ZjdGBIiNgIAygC1v3eFhQBdlsNjEK+DAZowEhjsn6S2eJ+vd6tsSXv1/CaS4i4KKk4PyGVhKPcwgjCS4pq+jQxq4H95Ee+pe3Ptx858ub756uv7yn47X5F65c6epauWrTFdb2Q6xQZdmcjY2s0Dv8bBRU74GrP8Mdvrb01PYnlbinlhZ/uG4JuEcPHx6e/jkY6+fv3Pl8cnWdZMmkjqPTpre0zKpu291WKl9edTy5YpzAo8FyFd0M5dBYhXRV8Vme1EED8rypyXnRjGRoXbC4o0JKIswPQbWws8tRB0JQLkIzHGCHAiatm8yS/CxyYUaHDBQzjS/s1/unzlIvw38tWlJ+3FLGIOzSyHiaDGRJOQVqLgyMnG5Y+cErEM+2H/wKOwjwWBdqpRTWnQBLWrbMLQPLRSazSeAKCm9MB9eSgwYGgzZMEbDRnzG2tEKxpXa6vG33uPjpNTPG2fUd6yZNkqhnzVqKpcW8EnBOKk/tUpo61q3ruAAHgz4vvxz7r/996vC9rfOB6rHOXtFtFHP2HoKhoOYQC1GiLg32VbSrwgtTCr5rqNFJgGgd1isOvNnY2BempHpjUhkTItEr7eOH9UtrbMTsEHPonXfAoWG/tqNk5p3rPWhorp1/773583+PlY4HOnCYSAFuDegF3GjtcBVS5BMl4GqCZSoXRmPk+E16AXixUjwXouYJWYLfFISvTpuPHQwEsY5/6Z9fHSlAgKVGXIjUTCxr8PzTUpzUZVg8iIQDe//v5jfPcEu9LGQBOWSkALiCCX9Bd4mJJCgSGtkbu7q7L/523R//CNTu+08u1+/f/57snW0Xrnz83srm5pUfiQ7520OJmTWbrFe2slBRKKn+K6dPv9pUWbr+1HfzOh89fNKAxVQXlL/wjNq8ecmWzZsPLDNUV5QqJq0zT1ve0tKJyMPdpZLl4fKFxdXjAnCbFIPVhwMFVzX8yFkusTNpSBHibpKB9EVjF3AxMYx8OGICUMc/49JSe6CktZFPoDsUdNgjMpuvUOxCpI+QoivQJMf85I04EaWRKwrAjBODVa+oEOwWLT4lLHql5N1JJUV6JhQvFvT4TFgYdNFFANwzGj24jNAjmnyyEFsnC6FN13jUBondF1CBOgtDS8BWIyEyHZTrVSgqUhNnbpksL909Vjp5sqR00sWOSZNqp5YfmVWHzK61+l279Lt8u/SqjqYlHQt++ctfoiZiY7M3ltzbFUK7zRJtXBUo3Ot62/M2y7UNdDAOi607CVrS6r/VFOQT/chHRJgd25cyTGJ7v0CzVO9YITutXxqFgeE8JyIcJp1Dh0SiwhBOI1vTwdo58+bNKCvLL0uVQENmo5wgHsIiHWgZEuIhmaG2AfeRxI5e30RQutOMSysBIxaxx3AGKoEWSR0Q3yH+RGWj4AaHFwX1i396dZBZFyJSn5U2DS7gWFHxLXxvYHno7/t8fpjEQmcJ4atGySmYFZlvDciwT6uEP1Kh7HHX2e5u7EZOY934/aeNjWdFYhAGCmWRVSSO82kOacRsbjhBZpX5+vXq0/tf/qVPlhKkNgFPrmhwlHQu3ffdd9+dOnV1RmfnvsP5ycVTJ02Kn6xG8q4lEd4oisnk7gwjeMz02LrlF6iVRmxXXVa/H6bssKVEortLpJNkpEi18UlIkE9hXGbR9+U+NRFIyoXCLCMzI1NClH4PBCnArYMOUyCgCgDW5iNtCsdFHAFLfGJQxPb+KeBoH75OxABmfNQ0MoFgCodPyISug6xfScqldTqVNpLEi6zkTqFhe3hcDwhImogDbR8XTb9HVgg+vTvih59nqtTkDgZUCd6CpMzhwwfRGn9QLr5pCkJMVMRPhSWvAja78WYHdxI3HpnY+Rb0ijYy7IC5gNINg5vBeHOpJmIH60wwZGJ/jAAETdB9CFmVEdkhrMY5aQvEwdBeDmcv5ivyBYWPNYeietPjRDED+/TP/t8wrBeh8cMdMEZjhFYUcGwcw63LYoQ4HJaf7ReLNXD1USodBSPGj6fYqDyD2ePRxsc7yDUAKhsJ0D1yLYClttlJZh9umd+PbQedeDj28DcpSgvAoCPghBG2wKwMeJSCxIzRiCx6dYTBFAkVWokjSSAOM3RSPjXQEmAJRuPz6TrolurviulLFopp6UwqMY7dQtjmkzod7NWtXT907flqD9ra83s+a1yNMe9QYUgWAWfURH6oUKxoQk6zUmP0aYhpGDtMqHNAhBHPlavnAOWEl37njMMz6ubBSr9yRr5XgtUB8uG9+AOm/KmwN061wKyiIN8LWz6Yh2nZeIhx7Gtgk28VYSsF6E/kw6TiVKCSoI3KxVUVdeJlBM/4SaQB4prihcRg9KOoAkGZD12YxxYB8YGZxkiNiMEUEo/YpwSz/6/XQKGORRVFrjYikT+IFSJKCgwKnI8YewH6wRRNa4vIIvBjAiMJSmS3ADERMpAqVJi07Eqs8QNsaleCGo1NkZoAIoVMj+D0FHR+iK1GgHVyfiq294jRkpBFDNydtMiuAlIvkdIkLucBK16r1KI6HZBDimV9n8r3xrDtJsBeThluKZktoAFfii3TiAFLcGIKdRBO7uUTphXHZITFkBV3Okecjstt4IsGouPkEupcYMrTLDUsatBARzo6fhdgXrqqIkEeksWZ1ysWkJTB43YCn2DMdSicF5cRdOMBdiF+gRj7DJXJwTDGiNsMr18sE8m8gw5CnlnlUfGGjkZC5YgcewhdEM0EaemMUoEsrklchvpO7ytkx/V6zhd6Wn4cc0sxxYp4AexXYGGktMnEgPK2f9fe/sMP7YD/3r7w+oLXAc5Sdi2KSWlSgQWIxyrk12m5TpnW+HLv2Oz0EGzuBPDdEzDhtpUl+2bUzZhRMqdkTqXDPNWSSsRRlBItlRBqA2tRb76B0f6jPgxqNVfJFlOUp1+jQayNmFoBqKXEMAUIqsk4Kiln0KAcJiUoaoeeksL4siFmyi5ygXYXocUU+N8OjLK4ZNE1ioWM8xY6dk5Mvz69ev2kFd5Al85FSVsxMSy/mIkZiFqiC7TcRLqsqI/HZ+QGPxNfLzvCFgHC4PJUOk1hCNsxWBeipFUQUam06EdV8d54uyoS0su9+blwxoI/1qCRGQXIapFQ6gh6fwCKDqdTIIGDPN46qRpvi4SmBbsdyzBlZHV2f4ajSHJYdoLC7HHbTDpKkuM6sDkErRdACXSaLKTzgJYoJGukNA6DWNITk84vBA19YOwLYefyP4apXsOMRrTXacRH78sEzJGeFt8An/idYg2Oa78pD5lR8KIaoACWB78tFaP3FjCu+magdngAsWES+8F5jTEGnVomMD0qxEHZMcaixHfGT2F9lZ8xIEfOU8qEWNgB/ONnMSaYcVFf3iwO4jOzBmtC2c9dUrGgWvUbjA0BQ8NFSQmNsHGxmxBOIRK1b/xuY3t7u/+dd+ZfwA7xWDtQEZZMb/qrHT5O35A4ZLLwEBidjfsuwFMrlFAvwxITO2yBGVdT3Yw5lZX4Ca156hQ1iohuJwNjRw0fvnCqF769SVQXyalyuVSlgZpDKAJSFkLXRyQKcn8AWh3IJ3U8HNdh0cFUEi1+o+xZJjPSxHKx4YxpA/8bgw3wAz0IXoAuxRyCMknhyxFmMfZjP6GkXmbphPRUwhTdX8hisfVMFA4AWxNXwbNjzQiYCb27D0g6ZARei4ntJ88zxI3K4EFhhncmLDAcSh8GUr0uYgZLkweZrxaqjhSseQfhqx+UkWyQc6VoCYFXYevFM2MSkCDOTmGRwjAQCrlUzAUOhxQl5XZl98p++nj293PlPBtkxgBfdOZEgU6Ey1wTwhkY1PhlGtikWQkOxp3s4jPuoHhaWP7+vf4+Rb0g8AQdELGxff0isL/R+zG6Ihx8iBphFoIwO8ZDANtR8InC0G+nJmD5RLELPAr6ElA5QcBLWz8xi4gAIrGLNNgQSsH8kPEzRmdBgeJUf7jclGYYh8STiTFylgs5zBaW5GQgFTPuylTPDMGaL4vp/bwl1WtgX/IQT2eSXVFSCNQSgWnHswehEWftPUslJdz2+rHXhaDFNu4Fs6IQ+2mBAywQWOJDRcl2iTw8bUirezmb5RNILAozrQ4ciDOk7wn7g6kJCZPIQf/gpKkQtCqwn0EMEw5iXE8IvTNEY3+ge8LPWBwhQvqw42W7Q1izCvnMig7rVCXKDmNTJlxmczJyaJwivl8uxqsUNIRJSQKNWId7g24p5KfhTFIi+ClGaCQNQRoIjJz0GBZ/WOxPK6lslhvpEhhI4rDrtQoLg6RspKsJAzLmHSx90UoAXdRpZA7DgNGjCuxkiEROhfYEhACFFVI1qkNAobtOmZubinAsfVCPLLpwElwQhuLLzQTih5Q+Q76UlikY1dBP8hRwkE+QSx34L6QmWehNhHVFAmapvn8D62L7RyAIwxI54tbpVAI9lhcIAmOx7QlAZGREqiE5M+iJ2GvS/YwBIc4ozv4/AOEXAJ5gSgoddshKkiK804CQsOxAG0gCemxyKCYZDEbg3CCWeyCDI/s6CqdwoFoQoaeExLMQ0h+XkDAsPknbQF+DgiLiBi0NDRTVFGYt+shMenR91DXhpCUuAQPdDKZ0cPo7WT4y9QxGAKdQlB373CWVDWEN/IQG4/vBcSHEfw9B6dieucVgMPAh2sCeCLvDY4At9jY2Ao2Twb0b1veoGq3J5guwkRYDb0iPu28MTJSR/hLPI78xnNQCBCShnZ3EncR746Dp4KQp6mI8RzA1Rogkk8dAlZVEubt0bXmhskyKD4DWiGcEei0Yu0DRi+pCQbEKBagmiGXwF+CJlGhJRfl+cBrFz/F0ITckTBF3wAPff3wASth6CMFt4FBJxTAlZR3c5yeWlCgIEBSIX1xMoYZF3zHOPh5tpCB1FiTEU28BV2X891SGohGjhxTl8cBLDzi4CQoDCHy5FnhP4/sUqIKgoqsSDXD7tntUDog6DMkwFUZ4LAyok1ITccIkMM+ImRqZBOLl8uBnixhjDNfUvkBWD2zYzomNtn2YBPuIKRQxAFsLlSwCCxJ0xqgjdoLFDDGri6IPiVQGbreGEAtaIGD9838+2y9A49crek31D+mAA/VlohDoBU0K5nri0INxB2wF5CCriC0LubVEiwZlBZ4eER+sp5zgBPjFIhfqyUj+wsQJYJYuLhFUFBG0gOTSq6S4YxsNvT6wY2GxyGaoFBwmHLJvtJrocmJYHHjfYkK6wc+beE1xP3zAzPQt4Q7MiuPDtQFOLgIBAD+AAxxX+5V2lnUvZ/Ve4d7VtGUWaqD3xT2E5Eu9HtQfGZvcIkRWto2NMR7eDOpULowAwDPAoWB+g4dy4iLqT8ADL7Q47E2kjHigEwYy0sBIBaIesiSR8puaSrplLzY44ABhy4uQQQAVBLkxv3t8JtRRKXC4xvMIuVQeSoo8o8h0LBlHfnJCBOMsdkERD8Kj7RQc6AzqUA0aYXpfxtWE+PVxA39qSflEDIsYJRXCb8L2YLXhSAAyq1VSICpjkU5vUUQ6csSQnKQhr8Dsyq8zJUgsdHUmp8KWFn49dqWPHbEnpubDHSMejD8echINyGHOzElKycg1eFPzC6i9wzlq16KgUEU4VnEAxyfiOkQziAVLYiJPoBak9/4bjXOg2IyyDiAhzg6I009zB04gnUStBemdmL4uIvVxjBEN5XcSDTrG3++v3/ULRJuNbntje6WzQ3HQcxHFhV5kGBJHAiN6JAczbG4M70aR2EhsYEaRiq6FQogo/4HCycX4/yirkhoasYgI1yyg8NAhkn5O42eD5ILSg/MCgKYowsf0fEyCLhXxYKqlLPo73jes+LOfa8f7FJ1wxVFSXj/GvgxcKCELrl8mE8S1uEiF7duBJe1tBLMZQw6/HQ0rG+0eeKRaGg61vlAIWYDRxBgA/3ZMBmpvgkrL8EvBs+JNmpQwiaYFkpnLkbQOKzykqHqJSgR0gjw18E8oLsDquH2SDJgdAIvgeiRkgPKnQVtFbenkGXQv5YHfR7cTtJ15OQD7chhaOva+SVKfH+x5nQ6CGSeoyHC3xagjxuzuSqPELHTpIFD8VCFithGRbUx+NN8YwqYMZsxEdQEwZ7YzYy96eTz9SggLh4wfolZkDMnBUy7DyRjvRc4w4Fuzw+mDf2GA7ZFj0INYJ98cdOsdUgsiEsg1IyUzJ5VEymqJWoELEPgEfEATGN4ajwtCE5pmFSoU6dnchFQuf2DvPn0GZvfvn509OCLQmgQet1lihzqMRaAkZnMPTxDwRUSUNSYk/oRLBmY1PT1Y+QhF/f/KQe/9Qt1SUeCvr0xM4td0alL/mgeLfolkVGhicVkxMh+mo+XQnG0VEQrsoj/BsAG52uoiRIbY5Xg4hYRoMe6LQETJHB//U8j8HsC5GFUqI05gLJXT+zJg42Ag+PjP9x2TFSOOgDD7vBWFb7qfMC1qiNmXSgoXg5GeSy3QCfSp7du2H2vno6Aa9/LbiRwL0o0NMDIY9XDbdyjZRvq30Mca2T7MEiakv+AZwRxJjRKXYYhIkQsPIjdKal5qfmI8FjEWkIrIPiLsZVyfgFfANEwuL4A1izcRmwQ2Oj28f34RbWqh3QKbJCLPyyBxFFjowCRyySIeUvlcEvZSUeUUSH0io0fvCzrhOgUGOGzzIJ4SY2/MhGFTD4d1Ir//T1mJ96YURBGZ5cSRvBnXJLAHBpolgqaD+nPmxBA4g25V7ugRYbU6KRMSSBk8kVQ8Ae0WVHqPO2gClq4zhfHV4YsOxytR8HZvklodZiKXkyl2BPIpgxetHeBzHgYEriJVwRPEk0oRw7iNlyhBdjGigZxjQBoTIVqFDb8aO2IbfDbIGJAXTI+cKCQLanFHedgugs5xKJKjIcA/AtHJ5JGf3Sf2RWz8mA1g71ikvxbGMW1XGnNtMKc7pTfGxURLgPLdGVY9h2Zi+gv4N7MZRonFkFdKFJ0llzjmB6YCqcKiGS4x0d+BZBsMNTxrTLTJ7MvAEwzWiCZwzDAO28fq3/vZuX1/f8HzPZ2Z1Oi356AnDdGSDancOAL4HCihMEVFgQn+tgVMSamUPKmZPluTze+imGXoq3TBgFNJric4xfGh4x+IssglrBM/YapMmDenZJ5crUjE/GRgvNMwRCFlF34M2Mzg8CbPwmSUlBTeQrqQGGMQnMPoyGXBJU8csJDeMAUllctYY5LlBMXZpeRRRlxyXoEWK2dsgahfRvOHx1rli+iwqxC76IanBTZ9GmN+Sp+MkvIHRPTm8+NiwLTHLRCgZQhQS8xHpBzAmGwG2U/vCzi5ecOhGR1alKIFe0MsioD1AgBOB5d0txL3pc8mSVUbyHnGYoY1hiPVgPg5OQm8QFjEy2vBVldBYAZjuR6vVhN8Q/CHAO8yziRwQ83yBFb/GBhcECwidahsAdCtQGwPYCwggodMZ0vgOdH34csV0vGGNFQ/qcXIJKlvuijt/8WZ/EUpKTzAA/sVBqFSJxFENLf86dOeReo9jDtoMaKAXFp6NIOZyuZpsVGRROuNz48WTdpfC5Hh4T/VVzB/padHbejiGL98QkKe/q7pDF08RqzTkVrh/4eKioWyYzB5zNJ3AyYcn7mGEN5C3yaHGdGBSjSyGl17he3tfL7YDZNhND1ORBti7qGrFscFuL2YYQSUyhs16MJthbg1LAPWOior19q5JfPm4c8ZEqw7KYeAkqtBJUdFWbxedSLibcL0c/lqXGmYglCpWPfCZNaFmKdC7B1UBsa/JQ/wRC6NUNFY8BQSzOchGThXLXP54a4CvgJKIBSg7B2oO40urGopBzp6iMUI+w78SRd3diHFX0NZkxXHlFQoSPtCal+p/5NSLCrOCgG0cM6AOXnEK6OGDAgrgU1hRwWSC5p9P0Lmg3j09R6dExChgWRdsKdgm1K9iAji4VxJzUeOOVzUUr35WHtLuJiaYEIbL1GnJhLh02E2qShmC3ejGYRbvYxxKMQYRRgr/ENlvPxEJxgmmCCwvlGp5Sbs4WKoT7ZSSQEIi4ke7ulx4n7/D+3Pi1NSCGIi2rbGHcGHLKIvnV6MMRaBY08vFuKCMIXBj14+1OC56F+kuQpjEvPCr7fymTaRmr3oPcbYfVG90S+MiZp/Mb+exVBzMIdhYgeLE4YVbl9h1sDY52/7aN4dzKH7KSuq7gC9RhwEJUqrwkfEEAiYL8nlIma4cC++KhFGBph0OxFYo9KDbmNluK2YsPCAOaTkugyHO0l8vBw5qYnctSANO8yVghJcWSXzFBb4aHgxSOV7UUKYqPIt4E6QH4MlHLYgmEqNYcWkB34Ypc3SLYWvCW+WFtgFpZZQamkubbGomuA5gQErBXS/ZAGMsAMeCENCfjxUMuRZYdnr1MAxDNfU01MLbVzcTxumsjUBMY542IWmicHbM/p1egclyjMcTEECD5wkzFICvdujDMJUIjkvWaGUoYqg50eSOxk8IRU7EHDDL9MYCrg9UtzKKQZotLWpBgl+H6lCLleD9osQEouXWl9JgoT5e6IEwY8JkFqplEjE4UWRe63PSZ7sBIhABq7X2mQiq4ybm2QX06SJ1Y3RY0kV2GRWQBX0OKIR50cFiPA9SOeLh1GOxItaUlEjyWE0EcJvDqwyckRFo0HIHtAmHZmk0o/4SWAObje5+OrIOBX+2IEAPLIRKQYAkF5OYJ7k5kI9Av3bzO8FXNDvZzO/LxuBSsxvR785/Y70L+EMRlXh+BamZ/+dBPm8JUWUwax0hjvBB9Cg8WBRZlLiAkKRg7tACDrUo+3bqMfDpeFHnvr/Yu9d4Juss3VhciXXkmubpilJm7RJmjSkzbVXSqkEAuXWlqsUqcyewrQdumGmWCtqBxk9lHagUDocBTrCAH4Iu/orDI4e2FxGbipULcKw9znuAzijn4NnwBt6jt+z/m/KzRbR0f3T8/miSEtb2zfv+q+1nvWs5yFrG5T04LExzAVjgWjIby4waHU0BkXBAoNdaEWk6+rEojo8D1pmJJM0gpFwUPHQOCpVo8FvkAyDBxts6sbGA2LGnNNpgGAAoVh2yix00MrwUGeQtRaJIpWQnzwtdIA2gdAqyWTUixEOGKeROC3mg+gGVS44ieMem9x2K+2HUFXBTjte5G5DCkc+NBGyhCq8IlBqIoBcR8qYGcTdzKDZALmH+jOA+sHRLc0hgB8XFDmj2PBy86P6EHTdgzAccJlMgCKdibmAXtSesFeLVSp1PLhpCQhEzKdy09Nzx5FHc1I2+TmOSMrOHSFyWmiV0kFMNaDp6gyP38lQRsSzBYx2A5ZuBGnDUw18sAxUcCNWeXSiZIeAWnY3lcpcZ0GpmXzRaVXhe5ulOGVWIicVRWgPhYzLmT9ErIdiJRDBe1aC+JCToJxlZ82GjLsUstiqJXVPdGoy9i07VHgso3F5jXYbFDwuzQmZQSarMYuJq49/I0Vs6ZCbQP/jYyn8BMyHiPb08DhAEMhC4SJg34YyegjsDcV+hrCgp2rFQqTegYhC9nGC9K2iRUGkKgDNqLpc/dW/GO2ALmlsEuA9GF0AINZpaoChpyJ4cBrTgBdVXiLprtJKLktYqXiaUnPHxmttBVZkPgJE+fY4akYVgbBehJxECN9o+GuXsA1E7B4Si2I4fOwzUxKDYLoJcNTgnFbExalAQESi9SKkSHaWxjMASdHwysruCkUvMnncFII4Uwh+BemnwMkYSajJqPhLJi4ZAARPMMMWKoBFRxCmVia/P6yk4wXggE1Hq29g8Ro95pBAb9Blj86E+6kAioAGZ0J2YppTl865zmHkTeOE3GyMv9FZYlw3AuOpZAemyOBeI00lJoqQIYkiiqUrNGceiM4BMQkZjAIehhoB0HGQCeG2E6B6iMcZtcXKJIRUDt9eNPT720tJKdolksE+UMp1W7jK6bdSH/65PeUO+EXpknK92vXrTgpZ5ATKij7JP6w6gccH3ftk1g7ihZChbgj4yYjFYCLY0g6LzN2txGSEOkhrKwNY7FHsJYGIbbCAtIkDww3ZcmqIzZjng/VLnG2xRiO2JcMZVFuzcCHI5zU1NesX1jx+333Z8DhGG0HwOaCIpPiExBFkDg+3XfI2TMVoCoaGGHQRYyKKeQIN74hr7ReRSS+B5uin8sllAPMsUkOHTtJwCJDGe1E1YEImIww5Ky4Km21xmsUftqOXIkvE2CQxR1E29C54s9Iik144khA/JnwGYE2hT47pN9NIigZyNIgFWZx8h21BlA9RlcARDEPRE8vYAa8WhKJ4HTo66JMF/dDrTBqVmZ2uDpr0LldGQmq63CiGyyWtUNLtSMWUG+hndjxSOOjE8UArDBCJAXU5wybXwFk1gfyzM6DEjhUsFHoqAiCwDUZ2wgEBNESxy+/1q2QMP6cal+QOyMuHukiV+8vCqd+vwu8WeJ9DB2Lv/qrGRnqHtwb/+JtCS3pTWpJyIfUtFH4RmNIXUrYlFjrWHnnopTB0dgQI0OebLXpF3G47frVOzmqlQRq07VCSCgrgUAZiATWDbiVEnPiwKRSY9LAmS2PgObcpBjJAYnx8jW5hTcLCpPT7fnIfMWYx1E3NJhWJbLnXhUlO6thUTGsw+MxOGAH7aAPOepOekg7leSR9bMb6tcSTHV2Cyo8wc/IEInhiFEljgjwxOtFvCnsgsILnmZAfqxfj0DRXiFZyQf4SsmV5mh3yiu9Czo/kXARCcijIYnNGk4onCxuI3kJ0dNqZQmVMPLMMuD4RDQmup9A4K7CgeQIp2g0aMYQXh2cmkeuuAXcEQGgSCTqNTSvA5nFaYjYmd86xJeSOMLoE/HsEUeq4cah/E+PBf0QpODwpowCcLL8T60EiDdZg5AYLllXcdrCJCXCBToKC6h+i4ZisYGXidTKDN0qlDXP+5l5MpsQdlpV+aZ3ue1T4/d94zbELY8g8qIoAingqP7kVeq1u5CSFKShgEzam2t9KBaxQYQ1jEG2inEB1LpBce9QUxkkZtRLBKuSlTgwumwV+LKw7gWGBdJOgTRwBwuyI1FTgEQgeXDiUdUSnTiYJ1mzG+cuOH5E6VozoILteqm/oOIbpgClakERamKMoU8F0FSLO+WSKMxqDXzLFGT1KVxAGguDEeju1EnGqEHbWsKuOCgkoOo+sInPYWC+r+G7Gj1iUF8hoMIISSkYhJeMFQElCSFFNSzBmMschwmIblprA9TOD8lGA5G4Jg3JmdsnjYYCQl4oPBmkL2vLOtBFkt5ersQiwH5k4NjXeaRtRQsOpkrwUQmeSaPadmjo2PUYjyU4LRd1WsFEykgHao+YDFSSshMIYKm2lHa8TACsFG2CSSIkCe+RGE0HoLKSocRTS7JNaC4UpTjLAef5jSH13l7SQj41wmRDLypOFfBJ8oF4KJy9CCvoXCivmrTgQUWko2YoJ0ALSZ6LpB6EXJAirsgaiZFmDWsQEUIUgG48fpEY96HZGgM0QCdDqSGg1m1JUejox/NA95IoLYNse9aepNaJEaiMQZUmJZNrmwXaE0YA5KdA7EreAH9qIPBpIjYolJgx7iUQBIi0tJeLPYrPAYxOJvVaa7sUJA36wdLGzhEVCegJJk5rmHZDfLr6rzd6iAETHiLQCZAPNlFWB1I01AR0nN0vUDBszHnAmY4sj5EH9ZxDAeMCWYUFDA/sQuRamHqNT2SaU2BYEyRxUiUwUtzZSbdegwhXJs4nvi7Sbn8J2XMAgpgE4MjacWRFyfhPIsFDXcrLYTfZ6MHK2W80hM9g4PEL6lCRziFcFbafbrrdBoI4mnQQ4EzuRqgdiI8XxrIVDv8e91P+Nl6QQs1A8hkTwQ89EnYvXYbE5UfjBViuL8Vt4CrcK6GSAOima1sBqXahgkwPEEcJIEGZeG9DGYCu/mOPosUhEMml43DywB0TjkUi7HNnpSan00GQnjRiXP9wWtmPb0WPB4pUR6hxQY4WYLj5PbwAplzZDks2A70DqEgiCI/IwfcIkZxzVfEy/uZ81S5uIueC7BZ1YVLQCQsEzpnfBiRPKlPBiwIFOLMkcxFMkgpAqvQsZZ2lRuEBGfEoKKTDGoCLN11NBK2fPN3xSbTTLpikRIf6gSmfY9Cq9N+RHXrTzIfuHMVPu8HRiEIt1aUG9X0z0YDiMqINmD0h/qakaGM3nTczLS0mBVwsN6TBYGAuhmnHsD9maZE/YLrN6CQaCrTG4GAIrNxzwEvNRwQevjQg5NNngQ7bdn+ZVudkgR8imMQSYypiCM1/wY0j9Z4dUMY/GyrQulcVDYlIo0Q/BmxairzwI7bsZx0Nh8ri8obCd5tZsXo3qgmo+PoHdiCDU81YkJxBmEFVYGUR0AfX3YM9CgB1/gRedRyJwvrFj09FGIUXBvlszLhGcIaVCZQYXAFJqOOjhtiUwkeyAHOAGnljMUsEIkLmt2CyMh3rfaGbXRhu9BKaXEIcC+x6UuxL9Ub1BrNG6sAsM7NEcArBmNAaDZjvxk3m0hz2SIbQ5hXcTUkMjggIeyr7JJD7qxqwJX0ZgIDX0DIZkU0xhUGRDWoK5cBRcLIMBjl96dH1gbNjNJIUqjoe2RLyaYHeXwK8GrgcCY+6IZD3+jEQdLxZlTphZgpQ7ceLw7HHMQZZAilRQbPEnnQta60K+2UUykA7q0cCgdiuxC5mM2EIwQRODxH1R1SpU2GsLJmNWBYSUDj0S7aBJKEfOiQqKfwyp/+SQKsti5us5HEYko1bAAqcKYLLAi3gY46INVhYYSFpfyZFcqFAnOVhrgFtUAd4Nri1Wm81eB2252gmuCAio+EMVCAVkQdAgGoGTOJt2ONBFjdAlm/1gXmcmhCGRbI5CZS2K1VgT8GqB30Y9C2g/RMRIc8JywG7CuoYGG0YkFT+KhRLLT/mjWbICJWmcWGAN0UoEGkA4Y2GhwpEBtC3kwbYDHBSxLwAbaFBRwM4pvivlMawc8MDiJlkEIUIUXYxCYKSVNjDtQRJPg4MAsPRk2rK0mZVAZhxYJRFEoSxlBnsCTh60/5uugf082wHRh9RYs4TUdHqSwWQcATAwe4RInDJtKmjAmAykauJzqewjfj6g0EzQ9bGty7dDH8uDOSaWVKKQ3yTOuUofJCU4TOxQJNiJjk2QvYmvDAWtbEbDtAfZ6iF5G2GzI0fBp/1D6Y8h9Z/YS0WYDRG3WAmWBPHQYeeORWwKHppThSGn70lOs2CQyifKL62kgNeKk5KFFJMORzuGpAWiHybeAfRd6K6oIBSQLALaoXAIrQO2ooCTJ2GxV2fBJoQYhoGJAmxvQBpMRlVjOABtSwfmP06sZBK4hj5E7EBLZ/KHvLpULMIiptA9MSWXfn8paqUyxzrDJgfAAoOZbBNh2WUG4mfDspIJ9CqSnIXuVhzx+OESdlchBRAdWQpRSDw/KInRGqKXOUzRtwWbRRfbr8TGoZxCih/2WmDjAyoSoHw84AJHRgbWSEVQ84EltsVSgC3EXHIzj8doypiUOyJ+bLZamzIzH0oamAckyMGUZZMFzOXQUSaiVgygCHCD3YhVcfJLQK5mo1u3lXZp+DSzZLvkwIbIXMRaoHczC9WsyeTezI0v58BYqiiSIyyS+m6Hhn8MqW+Wfnzsknx1lqLVFLYmzNKUHS4GRpBe+EREJFgCK7t+S0YyqalS/2RCSQeKbBDWHtDQ5/NIcJxHvxSs4PMEYVeNP4QDVuJf0QOArGVyqTHLTdRgXkWykkG/JU0Ur8kIRpmkLOEaKJnMQeaJa4MGs4V4A9hyhE6A0g0+Y0iUOopct0ryGWWCKEgpJI9O01I4BCWbBE7wSV1gliA/AtIOgjBkC4YwrlZCFoxHGkm0DgaKc/ldTMclFFL0gBIHk5opaIspPchLNMemPUSXi/aGAefZ5E7IafNh2wbyiFWBJz6kV6GWZaR8Z5AY/TZDsjeUlkSKuGDziYL+NKhD0dGSSduT+CESgPZbxOp4FH5A/dLjoQLkh/CSMkp74mGwU+ysnaWzCxM6aA1DkyOOUhbR2jDniMLfwBxWcATRnJE5/czTkYUwlirmyYqkX/J1+TGkvllIxa6B/+6mkMIZDpCZdr5odEghZTFgYTVKtR1QbL0Ya4NYzvZbeVTv8T1OIpsB2BVrtWlGE9HIhCy9MUA3ABdDDyo4qEbCdxdUDODvOE3R6pAmKBptwA9RWNRpdU6XgLAHasWwcwUaFmlx0E6zBw4zfgfbcsRcxotHCOnLlk5QXyZrn+DZOjWfJlMp+bQ9hcPfUuASk+4JWEmg+SFb0jIGBNzMbgVpQGPyVlzIxr05I4tia9p35HNFwgV8xkEAeQMiSaCIuWVhDHtFatI2RVWHjUERpm5AKZx+fPOQjAp5Q8QkEfgLYBbrYBtbRuyR0s8kT7aI0/G9QiJteLbTg01E0LFyxzHpNNAnnBCDLvA6QVEiQDRBlOwtEESJeStgnSnolWwARbreAszYwVvE9BpIJjHXyH6Ltsk8qACZ8ja3D8T2UpizUZxSWUREG5/0x5D6Dks9jDILURbQNWfOnDgiEwn0ouFJWrE7gmsOib+PLCQ/yghQskK2WilUWeQu5WS8r5Bg2n5qfVYxiguUF+yak0XiSfLcEviOQAgMWEJqYoafQPWQJU2bkBivlXNaHDoaUqVDYhd6bBowSJG9EuNBSU8lfeZ0OJulp8fHM4Y6RO6AWUNcVJtOFD8s9DLzDtbNg8SO7SJQTEPKkeSoIsuZEymjoidSxmRvwNwvjpTFmSy0Mx8mijqIsLIsFEUR/PSFWSOLBuWzFHscYR7HYMYIAfVsCMQQeQbYeqBkhmWMmyDklkOzCukuspDFzYoUIzeQ0Dc1blwQC9lyCR0eDCSNY8K3REMjiisRprEGgM8fGcdtKLB0yl6aHLdAby+O3LiKInOQetjrhreQgWKmoWVxBjC/RC7wTKOMh8mEaFjbS6dC8RxhmeTHudR3mr0w3iXuP3FScQorCPWV6/JmTRifKxuc1RGxek3FvjvasksivKjelTCzAs4+w8bAuOztGdOGqy0werSkJdDoBdtSicRAGpdL1qBEhJgIKePxM2mNkI5sslPlDCCZuypCKJvCjHA9kLbJ+Dpl3DiQLrASgpFoEuiD0ImHxk7kDlPbUrc8UU5bEYagwI7KFk+1jPSm+Cb+oCHly4KoFx53Mk0BtAmKOXyI0uQOFTWX7mLJf9ILhT7o5tZvQKiSUXYiBtjY5ydhThayyrhVPfIBIp5oDqoPTHtLfwyp7zZJDZ2jhCFIjL+LesIDNkBiyrh4bxnUACQ3yFUSxoBCGKElKy0qKmUMR2YTIpHGPqyfkUi/+YrisAYoyp9BToCQXpsxY8r4zPQMMwHjBjQL6bTSq1VDGJNUJ2ielJmfh6CamM8IsLSukT81j96ZH8P1wCyg7V+aRdF7R7FNKVqrR1cyNjsJhk8wlQzYC30xRhitCZRJYt8W+0aRcaCODh6H0RIM84U5JHMXRzxnRUA56KKHJMeRrEehxaMVbBjiADoReFwurM0SLTernClBSKWxsloivflhlxYVcZoxNxfXUvYtlZKxno+569EdlQ7luJxSTtOO+4Qb9xX/8YEjevPFfZT0pre476E8HA/nWRE4Lx4lo+8Tx5nMT5ATkSt51jjfjyH1ncYUFnkhVsIqAwLrZFYXlCQzNBqjomjoTS+r9DqLUYrXVsohHpJBjkv6HGlpsSJgzoC4ybzZJLtbUTFrfEmKxgFxqCgWIDRJGp1ai20FFHkY9+ZS3omFVMmo/piCPeBwpnHOxk70ntGkLUEqzVjopdUO7Jznsj0rFlIwzzHxc3zs6aSOoVzGYxrxUknMIUgSEfKtkKh0OLwCHCQyWoRlOsEq66DJTZLlIGYRo43IWEgRqGbxonaEDBYELCQxcRTpzc6C7GHHPRhZxHlSxv5CeoMRPbQ84ruFHXoz2zP2hW4BvG/+Y+x/dp1fKr3OtZb6eBZdUgI0Ow0FSrZlR3MNqizdhHMWygK80h8Rv++0oAAJSaW4aSEl4LLRhq5DTwJCt8VL7KX3+aR0vg5a93Gf5ZtDlNER49+umI2Ymj27YgrquOyMAqwx6UGQYK4Kak08+iSsJ4JFAXYR/FOn5jFgnNvUBeBAbtYA9yikRpFkC1w6Zk7NL4FDBynLQkITI+Ox2UCds5M0GfCTYiHFfQeSob5I2Y0Tmb2vSKhUCVwWl8OrVwqzCDkTsl1qvmrwkBIajWHayuFTgayA5CToEwIyiDdhggA7xQGRjdg9KCor/TJBmtONLuIpblpzk96MH0lvHGW3QUj9RO1bXhgpqxdiQSaBTnoyIH1oXXhoyQ0rOqowEVpgQgb3iEJGRv8xpL7LiAIJSckWazidC5kpiLoIR7CyuN/cQcoKkptebml/eSP90iF7/auyZkqFLbtRUyoqKiimKqbAdHi4DjsPoJZ74AokVpNAUiIpTZAWekre1JkUUSXkGkCDJlD4uHFuZkybGbotFEwToXs+lYKN8POUbAImiLKNkPKGBAEqxWLPmZRbeunPsJJS39ByISSn4KxrcRVEyQsaQnc0bUJIDVr4+WROgwCMHowQ8MEYGwhAqwMluEBPWrhlkpsx1VvPH2msZo7dPC5IymNSsaUyi4V8laX9TVD/HZXG1m5vSVCSWGa7PV31l5KS/u8Bf1uMhSnsuDkt8PDhMRIVvAcw0eKT+ARc/ZSRH0Pquw2p0iyVIiYcQ12sAJvvDkyTFJEbj0asXWAf3n9O3gw739op9L/sRTkY0aSlzpxBITV7HrIU7TIZ4UgIa1BsWJHPLVSAskkKHdp946B0STjE6BshhRUo2tstYcYcJC2GPAYq3ET8IoYsMU2HZ0O9FWmKZGBYSCnjyqXcw8xVfzeeQ0h1YR8M47ECEMa9GBwJafRGYB3vTlnK55ZnkOQaGFcAzdwBvdkqFCqxdG2m3BWR3sg9t7i0x7qh/nC5frOKKTfh7SJBUnqo7OZS+Xp2k16vFKU319LS/krv+sfEPkJyoxakNyIqAYTijMkUUvgRAbDrPURiIkefyVkx79kfQ+o7a6VQ0vOUjKbHVseFirCLHOFgr1g+wAKW9JYiEFyLotvfL71xrJYWk11t4ugJMypmo/ibPWN8/ujhSfJg2EpMWpp7QpI4HSR0UGhplSOFYobAPtY1oaZDnwTLqBKCItgKRz7Bf3l5U8fnk8IYKwUzc9l61QhElA6In98csPMiUumN41x66wHiywKfRxAEPCFguZlIixA54dlvs4S+OaQUcifUlTBRo41gqHoVmIQ5drMHgvZWZU7R131OykaWkwjk0HKlOMFVKB0o6wyG5d2Uubgma4A+FqFWxA8LIGuV7PBQRY/pL6Z84FcSk4q0f34Mqe88pIqg0sxW8NnCGkIK601wY0IrJb0jqgFnDqWBWt0BXlfO9LfMHTa7dLlTUfqhoap4e0J+Zm46GH0mhFQItYkBKsY0fwKWh8FS+nCOYkRcCGqb8F9s5THQIm8US0l5kPOamQfzGNLq4wCL4bQXQpz2ePKOhINowM0jO7LBfljpZGyfmGA2D5EYwsHYkiKeM3t08JCSZSQXWEHK5zNCI5ETZHF2Mzwi9AJrse/r3nFfqYRTWCgUGEJZvqF3t40dC55bKoOBN5LJ9laPEwt7o5SlhKTsV0DKu3p9gFvQsf4YUt8x4ldkVwqviw7y3GHmxxlWxpV+xTgLTB23vlAydIDt/1gJEpGZzMG09NETZs1gF0IKFhSQeQaQDpMYQNkZanAHQMShVY+xwzGbIhRvNNvXoGTFXKMgLUbOAQitPGQpjK1gIFNCnD4A6rT+gIEwXD6S4qF17AghSwkjg4UUvbtYFcWeScgDEW3qNJjCB/Z13XfIUsLkZE8AIeWmtosHzCVMHq96UvBRzfkGYylWQfuABrqVTPZMKr1rgYObkMHBZ4JFSpLZTUbhZyWlECQp9H2kBCSgBUxFNPBjSH23ESWFl3dMVobQCTCQHC4vIOY7hhRHafEVFcUUbQasD6WlQlANDOrcvCmzpkybMmXWeAgDZSfIQV/QgxJLMmUZYIhCin80+A/j2BQXbROzYaOQGsW82FJiIVXCwRJT0UrN5GZXaLVQMUL/j6bGQCd0aS7ov9l5dwypMsBfpEVVAK04JacITOQdd7R4cBDdAuzMTSYmZCdoD3sEijhFmEwOVbLIN5itSyQx8KS8iMZ7d/tK+W5JZkTalA78cxapwFinkPKQIqbCypIUpoE4b8B+xi5w2Y8h9d3CExEVPybUxIuFFMwDBSph6Z1DkYskiW8gCDn2LslIK0JHjDQ1ZdqE8ROmTWQhBZEGT0FB0OWyWCALHo+lKarfxtFuLrRZyMmwP6RKSmICmPnE4KNoymPoBP5UwqINBh/QPYZLIFhLLKSwj2UXDlr4sVYG1HTYFerJ04PIBUQGAmdHcQdL1skuC0IKsceQDKU5qHdjeYmQEMXX8KW/FfOmA4lq40ik/C5zlKSo/GYOubS0LCciGfCLF6mCwSB6KSPXS4FdG4KYNH43h0HEhyt28Y8h9V2GFLw68BTGQkpIIQVeudcfvnNIxYb8sVH/YLdKWsY3+6EBlDJ+FkIKyMLozHHpMMfB/AhSkSG87Cyk2K7duNEIkxLYwjOzUDKypnaKgHOkJ1DMITPBrpmEtCOwEG3IYlgjp0+mLJWuEWXAMEN1pyyFHzcSgIkKSHrwesL2OY+jFiBSAjmDhmGh34G9J24BCeLz8Poh3zastyjdI0u/+X1noGBppPwWPPAOn1F6a0LzzeEV+gb8wuVKaHVATpOFFG3yghmPnAo4xWSnBVI4vPwYUt8lPCEpDij6yz6EFIaYXoQUWKVFX9VLcXpndxJSK+dBZjVZlD2TstTUmQAcUuN1WHJNpqiF648BPoJMBQi7vQgpdFHZ6cOJEtEfUoQAjqaxLpSPprJgot+AtecxEuBoyLOS9Av8spOIMOACBmeX3SFLAYyx+j1Yh4RYJfRdmGIDV/kFhIMCDcUwr1aRSDA+EHweejQ9GRAhx0rUZN/XDyb23UXmlHPPv09y9y/VraHjKy4sH7BAKOfDXRuboxYPQXxC2gHBzj6WQsM0N5D9/zCkJJL/1P+brxiC9MJ+VwSZPYyQgi+lVVb0FbegX2rnDmlKkoVNVIs4Pn/ahAkIBPBc4XgN0gT8LCwuqCd5sWMI7w4o6ydlZ4JmNCo3MTGXIQ+jWN3HmLOjiYEE/SN8AWQnRlkiihJ5dmSSEykNiiHQFa+BsTttESsGR/wYHAYJDL8fGxcOLzaIOXcVPHjkZDZYlgoBcSf7L4TUZF4AWsIWdUpetgGfXiyRSr/2y+sr8vl4AW4izRH+vlowrh/xu5GkMH/KGfjUK4f5jR86nBYPVqkgmI+pFNSsA1ZaXyQnR/PIH2xI3XKs9M9KJENv0bz8MsrrswtuN2mR3vaBUsm3F+9SabFKFtMJR5IS8gJwvPB7whRS//gXnxPw+I1pqPymzQS2gLks9BfE8PQTc7IkeLgNpC6uBiU9ZRQwihFaNeRih6cQsW8U+XBMRf9FxD72Hkx6kZ1IBwlTKnwUeikMpYiPFJ8Yn6jOMKBAQ9IpHDRL4bb5eFQFmUPeZOjGBMDZY5hfXFxUNlgNJ8kJQnec/MFgKRjnBriRgKnAjKmJoejXRyeo1sviFUkUQUEZR5tFoi8q+kZy9r6BoA2aBwqB8aHyM4aiCtpU1MPBxA+1TlizMgFs/eQfckjdAnWCb1pKIA8WXZg0CAYibA8JbGVauOVYxUOLbKlC6ZdUZm9iJEuG3o1G6t2/xiNVsX0aRiCVBUJBrNSGo7zSb+GLR5Rm7ABrcvPHT6WJE9wzaW0Pyt4k1Aof0oICb3IGE3yEZjoiA0LP2SRzycQk0C2Bi04tE3H+iDrLGYiOgn4knARzmdQxwHdMpbB+lSDKACWC/Fq5IbVk4MQqydF7IC/ossANw2WSxWwchMLBQ8ondNA6I8SeyKQJAk3G8cOGDZs9I9+pKvwG90iSU4AzU2hLE0o4JrmvDEyVb6syYfNAhBRuu9EA+czY8i9QdPisW+3UNapuV0n6AYUUMbWv88tgWweXWAWfu6B4x4cgXVTJ2ZWQbGscCVJCj7zQHY2D3kgOkykqpLWyUgRcaX/3wuh2vm+xNizlqYg5yqxB8HhBgwQRBZGDuG/jFhQp9CGHTTM2BSs8aIAygc0lwHyAnNrIHBBGxPAzw6K5LgHNkEYLjXFopxP6Bzgds97MfKr78kdz1hwUWCkIIfJIhCXB2BEkVotWKp1EjSG7Do2yMO3ju8skg6j90vvmBPRQ+zSQeauB9v9ZTEGSdtDCzye02EJW7NXC9SOLB+vX+Nn3DBuCdZUUwZxvcI98oFQWYeFMUEjUPKqaiwUenu9bOyEpaCFfTXM/l4BGaZALAXmKqcCQ9JhQGf4BI36Mr03ZqagsB8YysEJXMhcBWoSVsUVzutzM9tBETh2krCogt1EI8UKtwUr6qlBI5cuyKLTKYzsV32avhebCrcISHudyRaRlAXmEQDhspORbuAWlceGQy5aQnVsyniC/fGSiEfFqHbakIPathQomVFe82JElBi3cOSHcJUqAqh9SEpOUIMv40TFrQ24QhTXfeCz5wkMHhAmshYyDKkx8gkaNBUSs64bh6QDt9JzSQVb02IYeX0COGRBuFdtCsBphrwi2O4R3DCkSeYqCD2gXeIxTh40ZM2TMsCEznMW+b3CLJLQbg40zX//qRmmWXlX0bYZUIYZmIS/UbvVA+OAfCd8XYCpkD/0DDykuQcHWozhOwQQDZLzr3ohMqIsWLiG5wTlPkcMtCYormYMJmhoZ5/CB3yCOR3LgiKyRkVJfrBn71oIqolRSSAlZSNlNmLGHwyZYvpQNlX4LiTorgNIOdrzDp07ANTEF6sxJCKl0UtJPTIDJH9RQ4L4OIcsMONa4gkaxTp2YSlUeabUQB4n2OPIJMh9F46sRSHPom5LSkxLxXxAExybRZr2aebuRQVrUFIiSCe/A7SZNbRR649i84VrosUC+nGAHdspFBw0pSZzD5g+oSBmJJ4M4S0LFsDH4Neye6fGFkm98kl1vrVjzY498e2ck0l4AvRREb41mPkkPQtUGyhR6MhVC18j7YWcppKdCpZ4E70g5iKnWxMW4dExUkpUcPBZS+FmFJMHBLHDQRMb8cBTMvo6edtgywIeeLyyMlH8LDh03zT5jk14upMJ+2jmC4ooi8m3YFpRjszdo0cUnUZqaNmXicGSWRK2O2UrlEkwXn6hzej2Y9duMcNVFjwMqrS5pONkZEocWw11KUbjIUCCF6LEJWFrEJwImJIGKselIUXBfh82nxWNyQ3QLCmjCiHRwll8pzwPRvCnDoQwIP2oywyKmn1swODwRZ3FC1dUEzJxN7VKnU5JCTA1JGfkNvB3Ah+cwPsmN6V55pPzbLDzKTEGwvWBS5KGQYgo6UFey8pnnHQrBH2xIASzNQctkNSmgoxFHInBxzH4PMnKxko+r4mNeUkLO7FIByTgZUbNiPlQkL8XphZAYBwLLarUXor369kJKyU166ZuBAEvQ6CgIW6NWXvm3EFJSrEwFzN40DdHMx0+ZMnV4EkFzGo4rC7f1JCQtKCt5vVg8D4GIivGv0ZamziY4Aow+tF95bARF5FkAfXDFRhMGyQp8ETUqRIy0EmGoqAVxItniMsMAwE2dKW9Q5h1CysdzppbMmjdBgyzl0rONIjIfFyjukKWcLrDnIfQONVyXJWU60AlKU0PyhN/QKU9663Rv6Lc8sZkD0TWortlsISWbi/C5OsfNiCJx/PAPBp64bQG2tEwGA3NqmnKgfKxgcUEDePKLITE2rhKUua/7ZPf7tAGqJV0Vt4KLuH4DbKSwOPJjAcNEL7BPLvJ9ayHl5vU7mLohzQxdVqJYZ5UO/bozl9s+XHqvr3QO3GMEIYNOE48NwwnTxmfC6YVMDkF2hcVSPAlPQFgLnmSwCfT6wfyDCD9caROxNg9kYiZRkKj0A+aH8i8FXD5o/yWwS6NOJOWkeKAdIh10Jy3BoB7mbpC2hSh4XKTcx22qDwDC8rKnjZ8xpGIsHAIsZloVY16pd8hSWSykoKZrd0c9DlsJydMgosYMmcn7ZsfO9U0o6eCjyNsVYQdb5hjoSxeGtLljtTan069kPxtEa6C6SEJwMsBedtMPJqR8khtDI0lRjp32qslbHr3vfjcLDuqKTGbwrUyBPXsgpyoI79kT2H9it/BhXEtfeMG9+/MTn7tJJmo30pGbyYewzMWaLkIF0fVAfoQfBXYhZAXgPz4aLjQprgPotJyH1Q7Y1pioTZDctGTOvZqTrl8xAROJ9Prbkyb5Jg0dei+7IPdSVPbF5188rFh96GxBSxMMpcb+avy0l/6Wmf3ggz9/5I/Dx4377R9+9rOkEaTFkq6BrjEKFa+ngFxvHDabPHF4yUQiSeSzFSrSRxpN/MAkrQhrwAmNjaJGqv4WNGg1DboqXVVVx6ojGzdu3WM1mWDBIyBH3jlMJq2Iw3Ru0XZQjJ83a8aQ6fnkXBAKKLgzy226Y+EXQneJLOWOAiyciBz1/yKkhg2ZKBxopYxbCPRJJbElF+ltci4DX/fe2z+wpHs9adK99+Kmxt6eRG9yF3evJfdKb7tuoBMgZNjT88dPyNRm2LxWMlam05nzgbbLIGz/A+qluBkUAXIIKHdAxRZx0CF9zsOvhz9HDtjDrJJaPBv1nWaPw7WmTlTlXNN5Zo/VvO/oMoSUnf/5if2745h4/+49aGgA+pHDHSv4eZy5fCxpwePSxCd7JInkRjR/o2skkFZSz+aRazWcbww2dXa83Oguve2Q5MAWhA1dkkkclt8fUkwSyVdO15PQlCtrXb36xIm+qxv3HDp05uyxDWubxKLEX/9t2rSXRj/yk5/n/nF0yv3jfvvbn6EVom0n+D3j8XYajLDkBKnBAgN7LfhJTHyMMArKUpm0ADIiSS3SNSY2NmqrGh6Kr49foGto0IoqqyrTVh3p2rgtbDpjLjgr2OCBd8h+HGQkugxVvKLS2BJs7BW1Txk24+0hY8aDbOgMmtiphdVXk2LwkDLaCqJYCYEWtclvcI6nRoqKvyH5cUMH1RqNIUjcovxdvDwUUtKbQspHt1jCbjHe8JWygOo/xgYPKaav4Z9SMatiWioEbqPQqLezgodzraWQ4puKf0CFnySWoRSknREjJLD9c8RTYGuArQmdrmyD6cPatYa1DTiL/3nhYkdL+ox5sydc3rOf717NPxva2BkO7NlP4VcAgqcHMvkhUtgmnX8hk8Lm0RoqngJkqrLSuyoH7tTxxQnszNeLbClR9hlc8rwZU3MzbZEbSerGi4ZXNaZ8NTSWnhBY5T6kpfInn3z99ffpOk/Xm7geePnltzr39B3b3rx8VUdd5dhn/uM/3pk4+rd0EUHij7mA/XANBzQejykVkgZszshSxpgMH4LRMXFmjnKeQr4EGOlq1QmaRq0WsGBiYn19VUMj/LXlgM9DW09Bp9bssPjNdLdgC7Of447zhCPnRDid5tiDzZ81pKJizLBZ6ZCg9VKKppdJYXKXDwqiG5Ph42QNQzEXFgnJeVTzDaFmanjOIGp6pA/F1jYk1+Xa7q5qlkgpcFAMSMrLJRREdILhDtMb7OjCmVVUSn+8w2MYSYMa1duzUkQ2PzwbGKTJY1sGCiqbIBH1Q1nugHw7hrtkIE0mgf1Lslhy203ApYARFz22mvj77rtv3CO5iYvXpWEC+ttxuY8s+OmMeRX/a9jUzt12t6kpf9aU/IcStA74nK2BlLeYJIKBMAdhkC7LyYlp7aGExN4cDKD4sgjbv5F+4zQlEerJQgBKqZCZ9cIczZvw9rAZeRUphUMl1/UNroeU5F4WT+X96YledJaanix7/cKut/7KXSf/+tfXzn9IwfXWys4T25ubl+xcU1eZ+C/P/Mc76I7+lvc3hNQfS/L/BTqx47AnBcnYXOgbQbM/I4N5zBigyj9iOBmGot6jYRSJkkFaLImQcxhrIqQ0iY0JiQ1kr43ASG4+dmDZqUMoAQyQ8vNgs5eVOaTSx1pXN6+w6MaUTTZhSMWs6UMqUtPSnC4Te9oQUmFl+aB3yAD3ACUmdnDHdSUbUwhAH4K4qhAVDkhjwbtKC2W8oph04A2tlq9uxaVcSEknSZ58kk6qe30+FkdPlnMHGd3sSKlk0h3mYeD+aSqmlEyYkikGUMnIvjR3E14PKbvqB7QvhftWWsYj13J0JsgpDHGIaz1kFRw75m82H3NV/+SPox75+eP/9MgjP5tfuar+F7/+6U+feen+R2aW/PyZ//3zi627T+27/+3Zb4+HGZnOW+CS08YrdsXBENBA6Tg5FOZsG9jcijQdcXsA/5X+Q/2Uj0fDCwZDqsygMhhto6YPG585b3wcdQI3hRQ755/EK11OpcgkVqaUfhEpjcTS0+HDR3Ed3FL7au2WgwcPvnv00W3bdr312Msfbu06smF783trG/7bP7+UN/Gdd5CqaH2j5Jl//SMJtqRwcUO8CkZWIscYo9EmjodnGS31DmfizNnk7AZoA/PdBJACReoEtWbh+p72nvbjx5ubmw+cvXjxrHmJHw6mer1gD9c7EHwKrw6M2FUyiqn+Sjd/yOzxFffMzs1IwzaIW8YmvQrBoNqYEmGyE4YcYCIhDVpshpTpSFFD7hkyr8Q/mNevJCuUbIr0o3lFRZK7zVLX05yPbvS9rL7z4cD64knuKqW8VTqptPTeOzCSytIrZmbnTxsFhlaYboIwJtYDnIKae/cPKKRwphdlkd0PobJE8pGxP1qPufwHTHrH2ur4R0blPljdJK+5b/h9c9fsrIv/xa+fmTX7pfsffHDzypVnV69WHJh7PzXwuY9k1xj1XnEqDnBm7Dx8bALIOk6DA9KPShlDfQnwIPDDruRF/pHCz8fDQh3Z0FJIYSEgOWnWsNnZI+ZN62+9pTdlKRZSvuvnJU7Msi++2H3hwocfnjx5shcR9RxKvI6OHZsOLj+4ZVPHkb633n9z+9nOlpUrt2+3pL36//whZfQzU995552Jeb8dVfKvz/yR24hnK/G5cAkExw9DW3hJGWHmIdeQR2Iu1Xs0gxoBtjmJ/mGJA4lJrF6oU/9mfXVtT8++5uZjx86ePXb2WMiz0bNxayCwx319Ss5CCn6kQFwj/STTSPyQitFThs0bnsYoSTALVHxFSNmclKWwxWsqgDl96uwhhKJPn6YzDabeXGTSicJzYqBeeVZW0d30UuwWT4p15ez24k4jQRW3njhx5szhCxcvrG6FDD1Ey+69OaSkX3oIy0ZUjE4YNSUf04mwggl/su6bVsN4OZOzfkAhRXrBPJJ6i2PjXJZMYPFtbrYs0euPGXu05JueXlVV1f3QTzN/ssJyoLl9/S9/ef+El1Iequu6eu5cd6d742/uv++++PRHUrJrbMeaEVK/INXHXGJcp2NvVWeE1IonrCITTx55ZGKChXrNXez7R0IKa99wwMGUWYkzODk5Zd6QGeLU6dPihvpuZCguR7H0NAn/fZIy0+rXW5GdPqTs9NzRdw8ePNrbe+Szyx21+7bsWNXxWfdnHR21zz23re/5P/3p96+t3Hlk+b7ahAd/l/Lbv/3tpZfGj3/mmX/Nn8gKuxLC8lIIzhsBboQG0n5a+O2YQ8liNbMXAE8CcQYSX4K6AcUeljiq03rENZrqmvR63Mx1V1u6Wjq7Wro6W/wFG7du3XNqv/sF4Qs8XmsrWwADbkp2myqlPacfIXUOqcgdP2z2cJhCGfTUvLMtIv6dCj942piwFw+hWpslYQaxZqfk6zyq4gFoLMRbjUtLcinjivpn6cq7OPQY+sB1p7RtCL4Sij+kf9xf3OB3963asXz50aOHD194/fUvyu69DZS4NdmVpc9K1aZMy4fzfCykiGrPPZFZhTnu6A8hpDhhtdI5fBUGtbzYMh9KDwSUy7/EU9BsdMJB6Re/eOih+IceeugXv/7DT3+yqP14c2/7bxBT43Kr1pz7VcXsGQ8VbKx85L4H77vvkUfuqzZ6vKLc4b+ATElCNohsY9PHja+YlaLWyZ02IwlmkeI9uQIIR0LssbBc8k1RilIeeU8UwtwCK+BGsMLzp98zXpw/ZmLOdXk47sXjmiiWmSavvnjx4oHtzSt7/4rkhFd8+ablz+04sm5dXXdVZU9tz+nK7o7LHR2rTm/a1HFt15uPvfn8rg0rXc2GnsVJqeP+QBH1zjvP5E+cmUfysaOYNS0SMRZ0weBDSKl1TgiXJ8tFGEIlon0iuhGEaXWiKoqshTXinrTqmhpdQn13d925y44uujo3mLuAUQgC2/accpM9ray1lbRkGZGS9v+VVndZETdUDY2pSM0bM284XHxgmCpjrYYsYB88pIzwtQI+ryKDw2SvfOa86bOnJjnNRNoaEGuNRBO0UZhkcAB6xC67qyzFyjwOPsXKAqDVotcvnOSy/3PPLd+xk0IKx9b2Exe+eLKcC6oBVccQUtPiReOmTEwQJwsUtLDDuXZQ6mYhFZjzfQ8pSWzqAG1gMqRjKB9eU9Am4FjmQSmyZOfOncmGuvqq+vnzH/rnP/w0M+UXSY31Dz3U0BWSP/iTV37aWPXS22+//X/envVS0hM//SlcLFLui+9xHGuWZ+c+1FAlEsG+KzFeI9bkzaiYNn5cAhoOsSuswiyFKaUC/nMrSW8B2Mg3iKpynhVfAgx4hKbH6HTqpg0bU6KbNSS17JYcxangl7/++uuHL/b2Llm+b9+Ofcv3Ldl3HHWXy7Fj55EjR1Zdrqpf0bR4cZO4qef48eM9PfK0yx1bPkWmuvb8Yy+/vH3l9pU7an/2s9xnnnkHOMXMl3DlY4KbCV45McrHMh/1JDWwB4SUxwLwAEGFpIS30TzBwSmtTtuoVqur5T3yalSICY2IqCOOFvPWY0sOPIpfBzbqBRj47d//wsO4/62tNFin4KKeHBRKrDsVsVfKPWV2Zv682Zj1ig0C0vEjNMw6KOInzXKBLMc3qRR2KABago70KRUT0p0gmZBL5wDqUJATC6rKyrKyuJWN0qJyyS2g34DpRcLAPkpU3NEVKZ68lLrTA2vOtRw7fPTAkR1Lmvcd7D149ODRwxdWf/FkKcHqhLHfmq7olSpLmKmWp06YmpTmECiEMW/ROCbvJISbquL7X/hxEw9pkUKlYGQ55u3FN3mgEWzGPjjiqWvnzuDONUfW7LxcNX++ZsGCxbaWkGNd3dU9J7ou1+9taOlASP2fl/729v+aV/G/34Hgia4Ng89mPxjcSY3iNG0SSNtyp6vAIUrKhSNMYvzwUek4xyEhhTkDgPWcLIWKyS1/s5BSoZMaGQspm009a9j0lPiKIZqiW0KKpm2S8uILh3sv9R4/emDfclxLdi7ZedrYDE83484dO3ccWfXP+Y8srluMoEpr7zneIxbVVVUhUX1wpGv7W+ef37X9je3LV81d+PM//sszzzyTl//SS9NeKhmFHap48CBATIePJrA9hBSCxxAMgaFmsMHgA5RAwHoUVWK5qEoHALS6DbZUiDFt3bp1a4xwNFu2ZMmSZsTUEo8eA4htyv1MpeWFVoInWJoCm5Lsm1RWbq+9bOy8/NHzKrJZSFFPSiE1eJYaOtlhJAteOEIpQYEsMMszx6dk0JqmfaCQgqKtOywr8pWX5XB6K9K7wo4A8t17ffbke7LYfoACatmpzqtXr7YcOLDvyI59+46371t+FGH13sq+z2GKem/MyuNeyc1zKYDoojxdRvb4qelpjjDnhkPUGw6Ehtys7IfQSzHbFfQ2ZICBeRFE64lbD4qVGSvnniM7t/qDezq7Ojs7u7qOHDlw9OKBi62HLqKh7tzTdfXI5SNdXaue/afsR/JmTR8zffq07pYuj9ncbGhzOhPGPoQVhlSIChkL9OQP5vGEktPk8RNnzJqYKjJ4/RAWUTDvCbvJRNoe3wD4KxXyAffFQsphMKjB1UlPnT3GUHrLeYp4KpvceoBAvaO9zb3NK5uDQcSRYceRHUd2Gteu/PvKlQ71tNnj6+vqqhZX1S0W45mvquo5vuiV7o5Nm5Cptj3/5mOvHW8+3vP4T37+L8+ggUJL9RK8bjJTNSINAXkQH8M2FDi1OhG8ND1wPjTY0nQJSfEURogqRFZjI5CJalG1PCOjDpeTvpEDB5YdWNKMc2vHTmPLhk5BZ6dp2348Ra0o/GQspNwKxnND06SUFZfTLD5YMXFUxYwkhFQy4AkZmRsqBIPvVxQ7DHoV+aja7VGPt8BsSR8V78Uio0lRNMDICY+0wh2hoUpkZOTuF2SoiQLsgORDoMTkpRRP7dlosltAgNx5xPjeunXr5O3He9euq6t69d13n+Y/XFaKLIWoksaQjetgRSRjlMgWP3FiutwRVvTz2ZhXHBxVR8JJL/K9DynCzguVqn5uAxRRvC4HTCYzvBQHwSWm/XuU/GgnBHXcyv2nTileeOHQxQsntiPEOrdt29aFf/Z09nX2tXT/6p1pv+q+2hnuDOELJJ+2adJ1dTpyLbcUmPjEfIRhK22XifIwyxuVCPtAkT/AZ7wlt0BV6PuGIZWFkJo8ebJQBfl9h7piyJSEUdNnC25BdidJfZMv9m4nSA81H8o6INdel8VIT/ISr2vleys3rPTOfXv6lHp61pGqGhrIyfZ4+6K/11XVIlNtWrLrw10fbj+8/XgbRnO/eOSnKSmIKYhRpKZrxDS3jc8Gry8lW41BHPlEwRaNBJ7FalpVFFFO0mrVICJhGbhN3Jaxxrnm8prkZrr0+q6WNZfrLndfPrdunYXaqs79XExxhR/wCYSMnay5YdFWDFB6KB+K0LPGa2DySbECxI+Eoax3CCmjwArqKT5SWeD1e4Lq4ToP3IQDvAHlOSRwImRKvZKi0v4x75csbaQDhRRFFGKkPLL0Ih1djl/P2nu1zmkksaO1K9euSxO3HV+7tu7yqn3vvruva4+sEJhg+e2VHynWWFJ0hoR8hJQrzGRFmIcEWQwUZgEkJlf5731IDfWNNJliKZYH19SgIUMcnzsiAwKMAuKxRK2rT4CYc8Z6iK944cqVK33Xrn3U91FfX9+2K89tw/jm2pW+Kx9f+/jatWtXrxqvdvXt2Wg2HYoK4FHbacI6IIzY+Wy4QEWMHUuJAWM2RLdS44dPm5Bk9JiIqYH9Z/ucbxJTCKkcCqmRk4VWP+hA2Aaakj1t2Cz3LY8BtrkvNh/vPdDcvI+uHTt2gua6cqVrSXMz8PGVCDDUXu0Tp5TMRRvVU91UjZCqrm5f9LumnmoNwL/LbbWbntu6/YGXTzbv27Tp1VcTfvK73/3uEQRUNrivajU2n7IBUaTkxoudNkzgHCEw/eDnAtN2RBLyE+UukUhbhZBK60lzJq9Zc7nl6lVHF6qivqvXrn3yyce4zm1Y2dnSdbWra89+LvMoKJbcjD/Ap3Qep1AyKdqR8uGZEzJFTjmaDSuf8S/vmKVcjjCyFDFiFHqXq6AgLT4Di5oCa9xAn4LlQsYrlPa3SEPvSiyEkFQEVDnaqOKLR/f1GK9e+2Tvr369d+/l0/t60bKePr5W3tgNRHXVlp7j763taWsrsKNZo3EGktSkW0LKn6s1qBFSVPjFxcAy6KXBAzuLtKq//yGFFZwsvpvDlnDemYwZYm3C2JSSUWpoK5jAcDhzbMPZsxfPXLy4ejXKkc8/76Pro76Wzp2Y4XSscq7KWNN37aNrJz7vO7G6r4+O2U5oP5w6REtVHHFd0e+HK+TukDukS8oel5sydUJethhGyzLhyBy7Ff3U12dRlCNLZcHINieHZ3LBV4pCqqRiyOiRt9DXJs05hHjqbW4+vc+4b8fpHadPY7+9eYO/2d98bOWxDc2GffuaTx9fcd/Cnp7Tx6urqxcvUIvb2qoXLaqurUmsr2tqql61Y9O+9147/OHh7WtdS4yrKuVrmzYvAECeSNB5ejrmuikpyFgZDiQ/g5H8rkGjYOQRdQILK3mGHBEF9pHTaViz5tyaq4iprj78unau/1q5sqWvi8prxJRCwfY+KZ5oLqXkTAWQuMqwxWbV5I4ekYGgxQ4iHzkecFhgUBBdOjnoNyGiSF+IF3bAc86b5jIFTCbl5NKBZym+614dAx5x0kEQP4oozKEOHdjn3NHVee3ax3t/NevX3VW2486enp60njakfkwNTp9ua29a11Pb1rPvYmuEWBU+6U38JBK512drbZq8iUlOi4BlKW4fDxhUDiUp5Q8gS/lGWpVZOXQsogZTuuJzUxPUSZAljndgkm9SnTl2bDuui+w6cODk+ZMnMWj49NODm7a0X7q0/Y3N713CUOdTNnTYtWvX5337O7d1bgwdE5xCsbEfPbGCW2Ziux04a3NQD0edmFaljEsZnZmdlKa3M5xUEVWWSb4BPKHE14VfepxMkGxLtiXMmj5h5jygE5Kb+4DIIQTUe73vocLirgzMnb1L/C0tLV0bN+48smaNZQ3eK1/V1gZcrmFxA9Z15VVVz746d/58dY1G0yRvajp+esu7Bw+/+ac/Pfbnlh2bnjsSfGMzIoTwzCSsTQGbyIWvjdgRQoayQI4MTaNcDCoFEhXCinYM0xbXYZfecBxU+XWXr3Zdu4aEf/WT7rr6xZuf+ssbr/W+sejc3vruq0hd2/YTe8LOV7CXBBWbnUKKzmqFCk61EmFGYpLIYMtIxmo9+bAhpKKDIn6SOH8BKE4qFZ2bUa9Db9XDnAqXfbB54AAzo1tS/gBLVqzoo4HfxcM037ty7crVI87MKSUL0+owmehe3NjQCGxGLFosXlfXCHJjT/vB9vXHD7S2Pilh6wE38+LKA9jrTMzLi3da9FyWYuZheJlxgY/0Pe6lYjdGkmVns424VgKO5Jn5IzJczvg//vFBQ/SUvuAsPYzHmw80b3/vvfYV7StW/OUvl/4Cws7BTf/1xfXrVzz11FO/u/Q4oFFE1cH29954o2/PCVxnzur1G4zGltBGqMOTeLCM9zDDyxW7W1t37wbLMyM3P/MX+c/8xJmUblFyy4zuQMwT6uvgfkVcSOHOu/U2eLyr86ZMnTm7osDHyQyz3lcSOQQYoH3F8fa6KqB4VQ2LQTtMs+2wOFpagl0bu7oopvBSN1a2tVVWVakbRVoM0OrE4rnPVjVWzdVWLxYtrm5q6jm9ZUv7a++///zzb2w8su1Rz0p03l6DLj0dGEw6eRnCFScxzQWRPX8Byt1gRgNmuw1psJoW6yrlwPmMFofD1WJZcxUpqrNve9/KvnOEi9CBtf3wyZPbV+L6+9+vbd9+5gQiqtWuZDFFYB9VbaxwRsYHg6vQmwbWkzMDa70q+guaS92BNkteVGBCk1+vNegKww5HH4CtoH3gTUcpkwztHz5IOWdenyS2BAMuPxZfiigI2D/98UBgH2AJNFGX9nV1bev67Fxd2i/v/+X6NNzy7sVVjfVgC+swjmuqW1xfX6+trEYR8HjvgUPCCF4hJCp23YsdNd/QUn5NTVrS+KkJyUYORGcr4jy2VY6Q+pIt8fcopEoZY9VXvGd/Fg+TRUxCWs+mvzP1wZ4lR36W98xThy8+vezRzj6gZETVOfjii7Woghauf3z9+vWPr6gW1ehqFtbU1FRWzq1Z//jjj6/Au2vxMQdPnjz//vNvdfZt6zrXtG7njsstLTs3Pvr0UlqpenjyC0tXn1i9ejeYuPs7ps6a8sy/dLekaXQhunEoOt0qYdFQ6dfbNC0iEJ1Cisc325AQROkpo/JmDY9jpFlcgKAkkRPbe7e3L2io1uoa1A0NGoyKNGrUZTZUaFjIgC+By7ImDTtMCVp5pRZrghzmjQRDPofIMGIxflqMkjb3bHr1uT0XHnvssbfOoIl89JjprD+tcf5D9fPr4+sTf/LrX/3qF/PXIEg7w5gudV5+YtGiV55Yt3NnRkdlZccq8AeWNKNxO/fJJ9eu9b312J8e+NMDD/wP0N1fJqrun3G99tprL7/82GN/+v35C7sV9heAe9tJuQMhhVxPCsyUumjeUCzAd21wOpGlWKRgVWxQS3mYJVhCkAe0U+wpAkH4BAng1mRCpJZJBkD8Jl03CaVHXMoRi4nHVVQ0Z87S69fDxXPmzEFsSSZxG+ClGO3OWXq49+imTz6+cm2H0wlsJuHBeJ1c16CrI502CGzQyBv3sgHDufr5VfVVtQd7D3z++Rcc9h5bVCvFaOuF6hrxiCkTNDaj3k5rOzJun5VNMYkg/L0NKbY1KpEWm/YgtbYqDh1qtXt/OiE/McNYOWpC/too/xDKvAO9DHc+enB9bXttbQ1HBKihbhuKCVhO1YmqK3VaPKg1C+f29NQS4/RSb+/hCxe279q+fcMxl02+bs2aNV0bA/tfwPVwa+uhE7t3s+2p3ZcnVLzzREswlJYuDhDjHXHFt+ZIr4+e77Lwk1nZIBC4SgGFlFiNCVGKrYwts3GjkiezLoI8Vb1iQQNULcED15BrtZpqsgxosKBGs1jguVeHuSyNocmAV01hhJBi8DeJH4noh1XX1MgBTawSXECi2rW989GtZw9F9V7Duow6cZU8Q6xJHzd61AhngSnK37+bJwuvq69vEGW0bPAbM9rSbKcdOzciIV7ru0at6PZdJ0++9te/0K9L7PrLX/79qb889Zd//7d///vLL79/YbX7aQVhpHZ0U4goWf8aHr1TVsiD02YBDK6MCCmCAsFEMQ3K8fPxDEErs48GIB8IQdsadm16fKK9bOiAIDoXUjfs333E1itHOD396KPLlwMzPYih3qPLH3300WU4KllgzZnz8JyHl14EE2X5kY+voZiVpyE3Y+oNTEbD9pa526rj/qzV1O+tqlvffungvq4rX6BkZCmKiynkrBfaFupSp0xIdBrMRD6gTG3vDyk4o/K/xyFFljBlSlWWsFW4271bFk3Ln/DzannVP//Hfzx14Olt2/pO7Dp59F2UxgcPtm9Zv6W2ujIhsbGBuDW4KXSp6byvmYvHs6GxUV1d2VaL69n/+sGn7x5+4IHfP3D42Nbm93pWGdac+/vfz608exaSZfs/x/m29OEXXsD6fevZ6vt/s8Xi8toSE0J4YKAhwVPy50iGSr7OhnuRIkBOZNRlFCRDrpxAbLE8wE1cWEiVlq0+sLJpBSAGHbSXicsgluvURGpAAGHHCeFkwL9pYIcD72ZoN1MsQkRRUCHGtMBrgJOrK2s7mnpe/eCD5567gAzz/MVlWyGzdrbzWGeLzbBkyfItbQtQLh8/e6i1dfLknNZjzhdfRGJaduDYypVrAdJvaOlC83Tl448/euzNB84ffveDTZ9u2vTpBwc/+HTTBx98sGnTpprNKxY99RQYyG98+P6hp/dv40MuCTWxm4SBmAIB6j50VVaYtmFVRgB5TAfgUrKJBwpkGhTxk/CSXbCXIgzdzo96oG2NxWxICTDrVekgg8pYSAFuQK/jY/mJ4ulRTKQfXb4E0fToc9y19Ln+tPX01vPne49evnLlckdd06I6ttWjo6cFd5pFEvIUF1s6kRbtVG17+8/2/ubogc+/+OJe6U1NHFpF58LEzCnjNckWs5JCimpfJsjFlsMD39+QojQFSXcBBrwjeXt2Z6lGVExJdzkeemnavzavXr1s2allh3tZyde+vh1F3fq5lZXqhPr62E2iiz17NTULEVINjToE13oKqU2bDh7s7T0JcOzwxe1vHG92rV3X1LRu3dpzFnBDAy8gnpa+gMTS2prTuqz9l8MTvWZxbkLILuOTNAn1nr6vw6ItV5jo+EJVBCJ6GiJAnpHs8Cs4n3S2ml36xcXenhUrAIdrG9irq5OniRE59NKSVliyM8NGY1l6BpCkUDvSkUF0cXCIWExhY5DkIrTiZ+eKe1Z14PFvfn7Xmx9+iJ/vAmCbMxeP4eEGbcvYkJuywIiQwpSs1X4s49mOVTu3YuxkPLcOaF5f34nP9++/crWLGqdLl9prX9yyZQtq5RcRTR+8uqljU2W1Tr15c9POq1fPv796/7ZtSr41QCFFa1NsrZVYSfjXJIBSuyCMBeuQHrpBdlpUVAQGnUtJhcmWMGHoULZQBkIhganAI9DDt4OMGKSDzVWk/Rlqko/C6bnl+5YvY9fTp5adevrUjQrwuaWxsFp28fzJ93ZevXL5s8qmc5iV02OiSWBls4hLVwkUUvS7SK2rqqxZv767+/GDvV1U+nEwOte93Ss0JKTmT8vTGqBwQ7tgOEf4nIwJkxP+HjvKSwg+j/Lpe1XwFeZxM2ZkW7z3zfrb5b794eZeEIjfPfpu+//cvOLx9e21c+cunI/Ovb7+IdpJ1bFDR02XtnKupkYnasBVM7dmbhueEuAX7Qc/fRcI4Pnz6LmP9y4/unyJEVz1c5fRZmwN78Esc797N//ModbVxxJKMsUWcWpSgZ2eHSGTJfF9jZXEUkWY8XeweS8IQoJVJDdCzkVYzlnzUUhFPm/ubVikXlFTrcUOLc2ICIbDeYCFExR3WJGQp2VgeRCDI8K86cVnrDx8KCtfRGy2RHlNVFVVWalrapLXbnr33aMn//TAy2+ef//DCxcunjnm97SssVy9XP9Q1c6Ne2iOpAx0renuvnyVrmsYPX300Ud9718gks677767qWOuevO/N61a1Vajq6Rr7lwgI221Hd1Vold3XPnkwzcvUJbiQ3SUgD+3jNMWgGYHHxyKgEAfNJJtHGRrgMja7WwFJxoZ7BblWCwCkihV2vnKcMgjEOBfpDnWuA76lLA1TYAPaJC4dNS8FYfs00uftj996umlh5auPnTIfehQ9FjzsRAmEcnJKGZOnv/vddeufPJZ7en2RYsXi6gQIKVDAj7ZbI7KacpVVJ1XtlVvnt9dtXjBgqNH13xeem+/BMgkmpLydjbAtSFXzHwQSQIBnSTHoKAlsqj7+9xLlRYG3Fm83VjjbV1S8nZJZdvPxs+sP8N7eFlzLy6wsbYswGhmIV53nXp+/PyqxsaHfpKkYbxqMAYopjRAKJDQEVCJjWrN3Oq5bT3tm1csWF9LoOClk2iregEN9F56D6DWhs4Nni7vzq4goOsW8x6Vyco/tFtlGz0+VZyYkuhXks4AuFAjv9aSb7ksrGBaZ5BuBrHOCT0GQcAUVyq9Pi0ZeeZ4T0PNYkgkQzkFqDct0lJIaRI0EIjFC06XnDtJxWI2ktWJuDxMoyV6IOiopWTVgM66qal9fc8WpOJLlwAnHN51+MNdFy7s2v7W9jfe2L598yXC787Sr76V5974aOVbfbv6+i70Xbhwom/XyrVoM1ne31S7Srx4c8+qnjYRxCfkbava5mJO09ZR1y2qfPXVK5+cf3O1DKcOLXYoySCA6eLA4logUMqQukx6l9MQQjzBx0yvAgiIJGYNDxpSxX6XHslMFUWKE0DlUBAM4TMDShZS0tv1Oa6HVLkE8fTw06zCQ6FnXnYK+emU/RT71847sUd5Apv3XRvMnRta1q07ffzk+dfe+OTaJ59Vta1tqqujglkk1iUm6nCH0brSKjNuPZ3CaE01Ne3iuqruuqoEoMXH93xR2s9ip6Aq3d21RqxJEBsMBn+UJJxJkosLKcpSVsX3OKSwCoPeHtWIyX7gj1Pub1//0oxfHdn9+uSl7xIT7tLjK57aXF1b2bigUQ0BEpS/9Y2N9U8kJeBuJSRSn0+Vn2ZuZU1iYkJDQ31jA3VWC55aUd3QuPeVV5544vHfELR+tPfky4/96U8vn//wwuFlpq1bH21ufu+9lcePmZY9bV92QGCX6ROgxpWaqSsAXwYLugFV5OvAE6UykywWUiYzoWAGT1RpzYmZVmIVvozXvK67qb1GE1+vnQsJFC50EFFgx4vSCIOg7kvMTlKWp1iWYrUfykInVt91iSRdiZBavHnRos1NTZtXtLe3bzm46dMtz7b1YG7wZ6B0DxB8dx4b9gA8z59//vm33tr11ltABvEXDwDUO3+SiujubvF7ACLat9Ru2VSL7UPUn41PLGqoqkurrCT4p2rxK4ufffXKlWsvP3ChlY17ZXzo5qr41AdBNRMBpEQyFgTMDhtODpMJPzFszQh3gJ9F2WAveZkeRvV8twqi/W6BH59t9OoFGGjFMXj1plXcm+dReLaLHkY4bQIUsWwZcKplEFg6df1yy6KdZ8Jn+Cc2dHaeESCmnGn/44Hecx9//FkTzlTkX2I2ikHI0ojYmcQlJ2pX6czSJaxv7+ioO6fF2G/FpaNH939xc0iVKwpgLOrwB422YNROkiVuGr6xHXPQbKz223hUN4fUMO66Z6CwuGfgd9/+Uf3X1w8pPLYRO6wpZbv5e6Ku+98uWX//rFltp3ImcwSt3vb1jy9oXKSpJOihcX5jI+Y1mkZuCCrm+ik66VH41SyEQgmuBYAotFqkNW0V0tniV+rV67e0H6S+CkXkYSz7fXjyww93HcZG+AH9gQNEkeEHQt6CsMrkIvlWjcGEFloG7EpW+vVCihHhKKSgv49GymPlK0fGuE2T7i36YllzXVV1NdrA+UisMFyjCk5EEQUNPVbZcVkKrzuFFJUpKPQAR6DVklNI4QCh1gqFzOKmzYsamjD0Bba5ZQdK3NotPT3t7Zh1v/HGa9t37UJhdxgt5AWaM+3C2Put7chjmOFeutR76eClg+1V8xvXseFeLSgEqPXk4oaGRQ0YAK9aRcBOZdWivy9c9dmRax+hl6I5L1AJFSo7JjLFh6BgQdCsVAQKzCa9kfBzazQcMoZUnESFyTNnsDIuoveGTJDFhOEZXBMEppBI5DCDyUS7IjcRVq+z7digiVoobDkRYR8Iy7KLkBc8FTCF4fIECxCVFRB/WLA1yo8KlgVOWQVLvDuPv/zAynPXPl4HbsS5JzpWUQ0tpvwkYsgfS07UXzEASaupFourFjdUNWIOg7x97fNSxmHnQqpI0akvCBKmCW8SO6fU42ajKUZJV8nuIkvdHj1fEU3wXfiHspQ0ZmKMp5FqJrvVakqsmPHbv024v3np5MlLnzuKvbH2+c921zcurlqAl7yhsT6xkT1nCVQM61i/gcmOjiBpsQiQn7ZRs6C+Efp0jZr6V/ZWzceIFJ9U36hdv2I9gupTNFZHDz+Pgcvvf//mrovLAMc+/fQhDKhOdF4GlgD8yaLTpZEWPnkzKKxZTA2MfZPSr2L8lsrCjDsItDAqKHAkO7wFcDco5ERi8HQUdzb3VnU3VWsS6uMTFtLSLQP2tIkkpUzDKQ7XYw439GOl4WRF8oXMnpbmwc4MqlooDHHOivDBICs1NZ2ufRV9UFM7G3mDU/sBsPWOI0eufoyJ07XOI03YzkTZDIoJSCaf0l9vevXZZ6vqH3+8vvuz7u4nFiDLVVZ1z1+8eNEidB1pq04jpLagX130PxfVXr3y1mPvXzhkxyyKpt/ISTBJAZwOJ26vy6yERLU/HDakWZBuAkDw/Vbm+yAzhYpjPkNfDikz2cq4o3wQ2vXBsMCWW6JBKaiUFd0iEMjphXHKUaUR1kI9+ujWY7TFfzYMB0K7YKPZA0Vd/R64gPBfWEpQBb2Q+HfZo8s2tvyPB1Zeu3buNNQ09u59tQOoqo7o/BodQgqgEJed6He5XJegrupecamyo/uVFRjN9Pbu3EZIekzmzxdRnIUYEEy0PBaLnk8TXllME5JCiqcUlg8aUmMqKmbPrpg9j7Q1vnRNr5h9FxVe7Boz7GtlqdjgpxiTRCHv0LJDB0IPvT0r/w/JT0/uT1ErFuAVB4lE3bAgAbh5I85sqvPQOC1gW98aAsxo0qDTVeK2oQ5EN4WAAosLxeF8bNpVo6+qUdcvWtG+vnbLlhdf3LIevcfz519GWfQhGov9J/bTP8ozevI5CegdTgxd9VarFRRaVbSMc1n5ajAdHHqFAAC6DALTqGz0QQtJoodp75OzavHlbDW2LUahp0mYr55LZwAyLBUltLlOTRVlKDmiRsOki9RazJcIRidWHvoquS2DbA4pnjCXkjvbCBWsrFxVhRyDw2QL+BRbNm1BQG2iX/9tb3fV5VVpc9evb9/Svv5g+0H87ZZVWA3e1NHxLD5l4S+TGs7V1dcvWASIA6AE4rhBJ25btWPVi5T0nq166qnqVVeuPf/AhdUvKJ5eFlUq4FqBtUQIR6iigiCcFwV8u8DlEJhcTMcD57gRwUIi2TyVuXhgKWUsa3ggiOfm49by+Pqg2ZY5a8bMeKiky27Z7eB4RlymKHp42XJsOhPGh2WEzjOmcKdpj6oz2OJtCbYUdJpMe5T2U8hd6K0OnTqEjzog2Lbxz3/e/lHL5eOna9Pq9j6LDEyiNWqUywghHZtNiAj80VFIodzprl5fLW6oT9S2QdLGZkY3VRpbtS+a3GqKhZTXARSdie8TMUtGYym4It7uwnw9pIYNmVYcIW5HsUwz5tZIydYNGZI61DzknoGy1T1DKiyzWBAOG5Iax1Q/3Dz1kGFfK6TY+a5QEXlKeXHZgdPZv9r7WefnObSIefhk78EX/8vcRYsoSBbpqiCkj7EMBH1qqquJUQIwgtyV1P1geo1IzJopLU1DNY2Y3VTiM6txgm+pTXjlld+BsfS7vfOf/eCDTzGAeXXhf/89SHKPffTRtSufX4GaZisoNlgawUzJ5QmGrHwY3cmua9LdDUpRahcAYBYWFueg74DMYNAsEJhj6BcO3ciho+Lu6vaq7kZNTaW4kg4BmgfT8ZmAmoTwPGqfSRMCWQsArxgNDh2moIwDwwAxD9ZRGmiEQTpClLaqrbq6sqPj1ar6J8CMqOp+FgigfO3xHtD/QFDfRNVb7X+p3dJ/LZyfWN+4aPNTKza/147TuHbhP5EoUhMS+RMNTe29PW3VNdVtq2qXI4nRZ7/67OanMnb09T32wOoXHl667NFjgL7hrGsGQwKJVw9zGH/Y7Q67DHprCGKB3qAf7lZeIBb0sPHJyGyQkArKvSqIrSvIUdArnjlvzJjpU1IdAXggSq+LhXF8IFLgKwXIt2z56eXLt25dghxlZntyXVjZCa6xWLAQE/Rs7Nyz50QnMCbinnW2dG7o6+xctqzlo7feWtl8vHZVB5DhKgJ6dInou6l9jRUBYjajYFPB7r1tW1asqK7a291+qbv7eO+ZL4piG4ylxQ+34pBHSJEzHYwQY+pINCygkb4iICsfNKQmlEpBiYRAkMSBELmHSzh4v2ZoeNiQ9KEC6pTQaQ0b1p+Q6I/3DJsdN2k8iyB8JONbobRM/lohxbH1C6PEreMfWrrsQFp9R9f+h3kYfx89fPS9dlCxaxYsaKxvFGHDQcOE52q0CQtrtDU6vE11ETe1Y0O8GhGRrfvzFtbA8dnVNPTFA7Og/on6xYuhrjr/2cqO2hefRXXzb//25z8//9jz7++68PpquhR0r9wqvcsLf1YBeO9uPth6WVw83QUzqRwC+1BIK55TiCoJMpweASqk62PP8tajvYvrEVL4Htra5GKWfKiap05ZoxPHYgpvkP6/mhALHK86+ht0WQy2YNivhlIaPrZSqxV3VM5tRMFWj4gi/Luth67TPT3ED6wEdjd3fVs1ElV19VxNfWN9wgJNw4om3NL29uoHH1zYJq+qa9j7yt76hsVtPfIazfq201tWIYm/WAvQr7Jpc9XVvl1vvrn/c4RUM7gDAPmIuYyQUgpcBkfI5LabvLYCyLI4DUFa+IK7lQoKxzIhXz9SMojgf8QrDioxk5Jhg0ZvhCYbifjNSNcz70LprfqhkyYB5IOOwPLl0BYsaN63s2uJv6urBZozG4MG5xpL0LIGpGB9wNTZua6lc/fkOP4GhFbnmQ1b93R99Pyft/emVc0F/aq+gZgn2qTEWEjJRWwMSO/jeik8FYnr27WNjQ3VPVj17F15IKucsZJKI5NBsYFsJDaLPB5v0MTnJgjExReOhCYAPzwo4ocs5SubB0m16a6hRbOu909olORDXUPuGTN7+rD+rmnYLX1URUQyI/au6VQ5TvcOjZt9S+14V71UkdJKIrLhwOqlB3YYNwqLH3Yz9lHv+gTMG9VqqEvM72irvlStAVAuoiSuAx+JzUFpyEvq3iy6KKNTV8XNcnRgVW9uxwn9anf9K4sW1W559jNIDaEExEO4aMHcLVvqX/nd5jde++sDaKseeHPXhRPEYQO4qwoXQBgyqJIx+rUVqJ+U9XtfzZ7gU0hlFc8pzlKYyMY2Cv9rtjROPyZWd3qr9rb3aOurRBCTIF9dDoygkCKTUMSNVszBvLiSRtBaO0w1SJEPrzwBMZSxxNr4BCQs7QIk4rmVyFH4Ybrnvzq/ho4PBFRbNbqi3z2F7rGucUQ8jbwgfoRmsr4+MX6+FgLNaU0rNm/e/PgT2qqOju7uvQBE9+7trtu84MEV6+lm1dbOFS1eoKusXvfZtc4PP3xfhpDaetpWoHQDN4+ixYTRfLTA5fVg18bkTw5FBRabMQREzIb6z8S2C1XmrMFWcCMuuR8hBegH9lLiKUPY2TxmfIZqzq0hRXxAZKhH923Zsm/5kvdWNm8w7tixY8mRJV2OtaAIOyxrbJadcKVeYwzBwQsWmFmR8vIy+waX45igYMmyLijfbO9dV9VdVTsX3C9K9shSOq6AFtFjQs+HiF1qUdsrr9RsWVC/uAY09RXt+3p7L07G939v6ZyHH8554ZDJ7/WQWLHXS94kdNm5kBJ+VUhFSFLtnorI0Mwh90zPl/stSTOG3DNBNVSRO68idQLCZPpoW0iEP9wzZvjoGepgRv6YIfM0RVLn+HtuhM/ooaUTbklS/x9z7wIW9X3lD8MMMygDOFfmwkxmkpmRuZBxlLmZOEpIlBQ1iaLxVoaVpVugMAs7bFFKyEqQDQVC5WppBFSI0j9qkUWKaxoX4wWiKNYrWpO0aiI2a6rGeOn/ed/P+Q72trnY53mfd2WfTZNoTBx+53fO+ZzP5dtKinwYQPlX09Wdo96zZ8/G1hOgta45Qseoq1dhvpAdyMoVSZRikGMDNOvlZjFIAiQ4wiSC91K6g9NziNcPTr259OXL8vGwveMxk9IDBQQwNz1d7APtIhcPH06ZefvyeO2BQD3467+5dGH94dOffbYLEwnwLKB/XIdCx8VhMxRHCE+Q/vAIXQolhdl3ZlLS7OmRHrtL6xRyra6wiUzgxA1HhofRIXgiSNSNgtos6j7BWY9HnRVjHqYQKXlBMOZE7LRYSiSUB9cqSbCkaAbkKZH3bhRVZhcoC6Tg1xaBEQjbvuIi9G+QqgVZmIfzC4rFPr44NkfK9FGoKVAHRFjEfHChzQKVXaTEn4IOCOimmA8I1Z+R80/NQQ5FXroovzK3z9h49/4XFz+78sf3tsafb0DCLgcBtdikkDkVBwjdJgOirrZprGqPS6Nz2w1GcBRdMhrqOEJt9NeVVCpKSk92MAAQvdMQ2DZ3Hvb3Vc/IyMpiMguMCg4uLyckrUGLGu0gZw4rYn0MddA9dxdiizMhP6EVzp06jUqhUTi4KRFJyUsW0rKaHGVXIR3ZVrj59s7Pek/5xwIFuVnNubS+ssEFGKuEev1EMdHMjR/LyirqL84W4X96/EW5zfW4X15JBvk26fU/gi5xAIJ+NwXE2nVozCTrJekqSgpODnHqbykpmuWWJqKkVqshTIbT5qIQGV7OiStfDHeGhKwWQioZPokfgp+TwDLIVBgX8b/chz1rSsiqOeGCkL8LnkiYnDB7dnJcHPiIcUhegRa3de8ra8gt4P2Tmyoq2gNZAdRHVh4GN+B2YI6gqKhseMV07qULA234jAfJVlC0MVJFpFNN1dY2ActCg6rMLwbbIqM/uzmrIjc3HYjWPnp0MPrlAen5l/6egvZP0atOH0BcKUJYAegIZXaNiRtHXStUiAyhyY/WpeI8VFLQIGL20elsTrj5eZOCv/tJr+w/OdxYX8/ryaVi4tdmUXkAgCKknAEOBPPKpQJoBYl8hGg2ZTCuhjA+xldiUie6r4gEyI0qwPlNzvNLRb4aX/qbmyp7eoozigEW12aB/5C+IyfHV+vLIW4+5kE+Lx2bUlaOGC3eaCRNSToGRT6OWlfrm+mTyKsFNIqRD2fjjn3pxZVb2ocVN7788uLFKwfO3Nq14WxVtY2ACcpFRloZR5gCB2aAf06vQeVRuxU6uG8CkUScFs6/wjgO92vDidNMFnBliQyIknp6GaxmVy3Cu3y+NXrhxFE3gQmV4Iy6t3AAa2E5MWPJMqpO0dDQMNxCev5Chzel09GtGtWMKroLO1NmU1YDgzL2ukZ1hbYylaPQdHnnhTF//pb85o5mqUCipG0KmzhuU3zApQ+nPjq1Y6HI4vcfE9XXFxSMFTdn1RNZ53Ja8iuhfyR2FSdF63Jr8WXX2LmIDwtl5GFI7ZC8gIySJd9UUhj8Qla7wpMXhejC58xfvSAaM99qe7hnaciMcHVIiCk8adbqWKo4dLLUp5fqaERckTz56dVT/lQ9vPDZq/6+kgIPiS+hczSe3ijk592+vQfr8JGj75flAZ+rEGVDJC7OgXejWJ5dUICJzpeL9zaDnwsYM4edwoPPHK2djAnJx+vaJ8aI4xP4M+jcAvKPz1ccoyzGw1bfnIXHp3mwY19eXlb6jh0YfQoCFeBdrz19+TJOMLCDjA/bRVE2Djw0FCSk9xjtyY+CTyRyQEgi31qc1W0qRbUnRWtKSWbW3C8nco4M0+GouICdQ/CNpMLhkR6QPPaol/AYAEi4H+B0WpmUwdbLA1IhZadfXAt4VFJ8xBvy/AKJNIB6BHtIlJ0tzQDASe27FiBWujhdmQPsIxdCICAhsIMXw7IPvD0GIwLgyyje4cMP52ZnZf3wJ9i3JHk11LbzajpguqksKgk0jjVCNn/x4vulZ957j3PlCtityKcFmI7bAjHQKQQZL0GvQcPVaxUKqwbtVqNR2YRhEVg4UkIXfs0uleoiXRXGw/gIjnsWwcPLn1+KtzQvMpGRFRKRKACxE5pUWmk5XnrlXQNdhYXQkFm1XkdDgwpMfTgK2Dvj4zrtBrNGQWDjiTQqKXyDXk6MdCsUqClHi111tvdCiX8sX5TVjCGbCF/Advg8QKW8P2HoaFiYtCFgwIeHN50Ih+4MWjShF9u9Zg5kKn/8I73pYSREmJ9LoZPFR0ez4FoOPMVnwk1LHf8NXWphgkcrE6ZODreEhPDinw2ZFxITzp2LXcodAnjCGfJx8uSX2N90hqxKDn8Gn8Ls8OepulY+PE3NDVk1O1z0NxX17SU1yWWJnENvA7wC9UPD1X98C4SJjz78VX9/oC+PWEZSKd7U+BN6wNgBKmiOz/sTWZaNTbygDga4NF2sJHypnIZmfGoBfF6M3gMWf2XPSEd9fUVBf3F9/eC+zyuuAtnKKMjPhxA9dhNwCtTUia3b8Azt2vWBw2T1clNYgKT2SUHy5EcpqSguZSRHMxcaVUPL+c5W+4YlQZ3A7M7R4Xo84Ln4xvGK8G3Fd5OWJxxuaV0ioeHEOMJjJUVblBLDLdEnCG/hMacw3N7oVkU1abEwnjv9wSilsI4JVwmBWUoADcvcBa5Bfws/poyREBQC3ItPB2UJrjEEk6ZLJTNm4CkT+8BqasPSWVufhSm5b2Ds7tiZzeuOH97e2npr27YNl1NkGPTIrYUxXlFO0IhH4GnTaLxCmUaj48MkEKxfhxAvx7BofTD+5ytm5WSvCpMa8dAjOY7FdHNZ8MzyuSG//9eoNArWQBozEOyFCL1M2lpejqEPCxQgPrvbrY5y2hQWhVkA9pPNBR8IjtcAWTJChWVRwfcd/mVJUQ4N6BjObeeHumyn14+VlGAaltSRkw1evSB94amwQN5M2xRj/0uppPB+phuwWCnFgMAXZAXaP1o7vub1P7711oZdEVvjS21Wgw4ABQx6tHHMp5CAPDKXmxnBjUv8BsQvaEgTETuF0IXlT4tssydjpDMDP0dJ6UNeCF9i19kV3PA5U1YlLsReuSwMDevj5PBXHxbRlJDvhCev+tu71jeXFOmklkRG0Hsd8xYMSE0fvEU96qS4vx94RMUEHZ9e1dR+6EFh/BxmoMALnmgYhk4PEGPOEhlSIqCfwaNHkVlsEaeH9pOijHQRaPxZ/T0+4Or7RnwSSKZ7iorbK9LzKm6uXw/W6YG4re/tjdi14a0D2gHsBtoUGDyH6fWpkx5pl0rRUrAoXmN6rVVVPaTuLJQReITLYfLe/cN9tRhbeX5+IENC3A6Gm9P3U47qh5xDTDQ+AXEjeOy2j7TPqSwHQBQjnqBT8HlBXJCqBORaKVNSoQL5xGFjD4qUZ7RImRk63jx8ZtJHcyV+NR4j3cbEsqsxX5IjgrZMmsH3xcRIan0+umnh5JAn5ZdUZAX8ugdjtzaPVfUevw5V7HtbDxzQEwTASgo5XU7wY0nmwdFzTQabUAb6vBye8yKNwQpSGaz+9EHNw1eWlM4N00SAZpwo90tzES416xkycX6CAr6opOjKOokdowaoogYK3cDI4V6PE6HMrlNZBAYHF84+kUmz4xwGg0Zh9crg/xAENpaEehwqrzDs9bTZ0+M/wDKFKK7sogwQ+9n5EgMfPnQjQ3oI3GJXX5QUEyPir8SMuCTgFV2/9sWR3a9DULfrj6ERJ867NAKNAzFKVpVDz2jRoKOjS8FiJMIT942D3/zlK1eumkc43soUFFhyKm1JlnBvsKSewPIHjDw5NXTZqsSERditooMz4MOSQinJwh3/41L87SWVrNczx6KIiBRrw9BM9Ki1H30+sqVypGlTPr29Gd+NTXVYlmjmwxJPBxoJwyQIQucHdZps6qOeLmXVJ2JiKoJ0JBKi/+DzS1fG5IJhB4ArD8rgkZ7Myi39BXl5Vyt+vK/+2vr14HFvgDbxvYhdkNdzCw0anQ23TWxUkYmPJJhPViO5I5pZAeKVZpWd1zqcqaDLYkF4fSts97Kac3Jxvs3gFTOAMniKkgIz51nMAtLDW8yY6oIEdDGVVOxUOek7EG6Dx4H3UDZFjCV2q6KXBWZA+jPoqIKcWr4ZJYUfBoohQpOyEPSObodniSiDzB4TzUsgyklnHHc/PlJANrUduNw19eQ15/SX1Ncb/Dfu3t928NTh46eq6g5ug8EhagppkdDiTg8NRpaRMBdreorLYNPLMKY+8cKs+fBJMqkhgJgePaHu+Iq+nurWeak2Yawe5Z1F6/fzz9MS/72wVCLsv0wWRZPBP1q3jioKAHqrcxckaDOx2nDUWocdLC+rzWvzclITUuNtBtieutzqtCC7JTFM6zK54pMXMoekJb/+7FS7xJdfJDDiBSwO0k7ESgk6Ftp6jJwpPgAKBZnI9KHT1EPH3+LAVVg9r1nz1gYcBCJITQrarMZsskLZyyE9Mwt8wYCPLhW/5JtB9ODNKWReXPicmMXL5OEyVlJzWUk9F/7KUvpavjTkzyU15a9KamkqimzK30tImhQaRYo2nIQ4Mkt35F7oMK5e/QnOu+ghRezOxBR69DIHZhUTQyQkFJGI9gx2XQheeYMSMyXzKJYoRUHjb3ajkga1Mrjw+PKUldmi3J6CHt++jsEdPcWY+QBiSDLT83ZknTq0duf1qgPQ+74XtWfjeQ+3UGVWuSEGEkaFRoUmPArkRyXFXmPk9w279RaTCa9riglbuOZKFbbgHGw0fGkR2hS9FSQTIK6SDHCDeg4+7VUEzhGhAi7mDEEXYXkkeQ/KhyBC6YSUShA8sJAmhG5cpBLBQ4O5ThQsKUAdQTxRQrECQYAD1aUkIEQqysEHg0uMnydJ9xFgM9iBq6g8+0c4W9Uaeh7c2naw79L6U9dLGg8Wnr0iTHHSF9lhJga/kFPEgZkADlMOIVchmPrqygXLlz7Ht4JLi0/Ao2aKwklfZTqm4jIbRRyMtYAnQlY9uwApiMuks5MXAu1j3LqFaWto4isH3jdqS9k1PRWhpgkJs+PARbZZketocjlcOCYnpEYhHQXJxA5QGIglGoGlTuOeQzKQBEh/3/71uU3gVQX8GZTrLWK4MD0fMTEoqVhWUmLWrNgDxvhJDLMQZaZfPXrx2u6tu6K2RkwPPauSxEwT4WdbdBaETMUHIy/iWWJ0hOcbSyoIoiM/K2R54iTcb+dywz1zUVIO6lIpISsTJ8/CLvWdmQ5WUsDUo9ngN3llyMOD1azw5KVz/76SwieRJEQcIVGb4bWnad275sjhox07YipB6IQtlJSYBWBL4GzDFGP0oEnYQ8VI2/Rw8RizhzUn7FHsSkqABUE56GXAjdnORY+vNC+vYksljjg9Ben7Pm/bUZAP5mnHYEVBz0iTr+b8O+9Ao3j+RMTstDkftJxPcXZ2W1Ruu8INFRfH+UiM9GSulkNqV2RmkidRjbnRFEEbwpK3Z68pu9oMzE0M1wi5xE+HEkB69J+KuUwOUTyBEvR2QCkhTA2BNfRfLmEUWXzPRVQl6C6YC4k9gU1RGcuI7HixMF8KyYS6LvjridmaQC8R4rfz2FwskkwUnZy9XZS5OZh6KwEboncDmBgczOvPyI09ll1f4c9q7vnyVlNT2UeHrjfe9FvXnb/igUM6rj/CPyfRwMkKjhEpMp3BpXc6NE+vXrZ81dzlchOp5SPjZNyvK6nZKoUaEZFOIgq6n1k9d+7qlUvx3C01spJiLSrx9TXr2vLausq7uzQmD0JrkGG3BMBwZJwaC41dBTKyzKYNA1Y83aaAENtqdwICCKe/Qv6QjL5Tk6miJr3de4HscvziGXgPs+WJ5j9xjBKvFKWYx/YG2hfkbDeXSJhADT09c0fe1bUfja/Zs2fb3ui46oJpUxHCKpoqRQa2jjL5SLmMDkBC+W/pUhNr0BTqNpZlqwzh4VHzQvjhEc8ve4oQP1l40kurn0sOt89lu1TI6rDw50KWpoULFj0sKWV45LK/c/ADFSlOHUYp1IgRsLtsnL04SH0q7cnIaq9oL96RSU8VsY7kIsYrYIUlYpsULeZMqCkNCsqCA6Ik+L+E7Bj5IraUSoOXK+phvo6K/GPHCnoy2epQiz2cmCgVmU07+m9eOH36aFXZfnXn67D9TdpYLYvfalOoHCqBCQBVhDPsEXxdJierKQ8G4jquleySM4tKrNEJNIUkle4vg+ywD+gEvqsSGB5lSKWUQ0gvA+JUMS0Hfj8xeCUg9UkkxxYlDgYC0B5AC/SfSwrkCV7MNDEjCz6kXdA7g94xNAUT2sFnTRuNXTDhL8s+FnZtoPaOzA5xbhGoTGBypKfvy4NnW55PUlB5LLsiy9jT/+D2g5HrFy5eu6qC6ax7aGhjKxA/ZGFHT/oTqJQmhHBQLwMnCGJ3E7A7jC/LnjCYvE5ccrXa5GB69P/sUlRSUR61Pkovc8V8TOyCZXOnLHvXGpaETpRA0ChsJYjTB8tQnaL6wFvTYT0enbRw4Suh8TK3jVEJubiLUcJ2slMFFofbZYvHGSk1yqSwWq1xCye/TIlRGLYTPztn5kMuVUBLAjvvBp0H2C7F3OHZrhCsKCyfbPzBwTPT11x/7eLJ3Z0bS/fuVff//EklYFJerNyoIbU8U8hjtsclNTrym7rUgkmJSyfKYS7qKDw5cVJ0eNLSkBfwJD39JN2lVnEwrSaEc5fRXepVlNSc8CdDpgjhHD9vSpDlp8DyNeXvZKInzBamgGrKUXsZE3PDFRJHvdaTwb/a3l48khkc6h4WDJEMGDzG2BKsSpjTDbOfYC9oYhwzAbqcVlFpEAckCBqBtDHy9I6symMgC/hgg/yzQR8uwIODFVuyc9Obxu4cOnRWO3TeWeh4nexgz6pcJ/Zuw/fIqBSYZHFhesQaPcLg51RTpg5OnRrJtCef/JdYf8scio+Y/NbJ7dfrm3kCdpESZQCMkEgYjkfPOnFk6feA7yrKKUZMJSOKUQZpaOgymFTo6M+bENXjuUBpiQFZYDNSsqEYfUoShC8Iu5ISkSlIaKfWFBRd8Rh1n5hPcqIPKtGl4LlZiRO4xAeBPM50ffW1/ce+l437Vf/d+7ebmi6tv7h9WNdQ0mAv9No2oqQQCxCZ8Ccn6sQIEP6EHpiacUE0nrVs3qKPp0yZLzB56W71DSVlVXBBTwHWodfaBYsoAxHZoh83A8JLJQL45IWvby0fbIOxRFeXXaGo/gAFhZ1mZmIirF0dKqTRIxKJK5Q5CFNMiCTihtehskWmpcW5cQG2OqIpK4BF8r6c+NnOlkaUVEbwMQnKpCUPJZxML08jdgzFcFEtsUbOF8fs2LGj4sOL7x/ZvLl0w5rWX6x8Qwxo0SiP5Wl00K8wDjpA3YhvLing3zOe+HOHmTtL49KtWP1MLBrXLIFx5VL5S0Atlj2hsGueB6t23jMzsHfNezqWDgpiY+y8iV9jgXz+/+Sxf21JBZ/QRA7gn7AoYn/rDPYDb8HMcPDN/v58XHnzi3fsCF5r2G+e5mB6IsQMMJawPDIqLQkrOUYu5k8MxJKgTfFDWIP+AT51uvR6HrrUsYyOtsF7gx1ZWc3NOzIxCh57cJ8UeleQUdup6E6dDGbLrpbuzr16u0IjX/G0wA1FuHDOI9Bmk516NhOoXQr5CwtW/PQZwcZUFhm/9dS169DwEzyuqUNXyZCCQhHUwiOTkCY8Cn/CEXLqVCaNYtJTOv3SsIYJkQfGLC8IoxPnAt6ueKeCmRRD8wsYF/hrhgnz2dmAXGSNQQWj9CFRAP8ecAYlFDFFbjByERomaV6KcKoD3bFjBI4mfSWYBEEzfXBr443G6xfWbz9ZNgDjcJAWHFA4QPViiVr4J2vDydMd6N8pXMxgXJl1Fsivi+aFLBebtPq4iDhWUl/9ETkAEQrVan2cELfTxQiUR0kte2HUE7crWFJpW3e34cC7DscoRWNj9dm3Zk7feyIyLTENAWMqi4sTrXe4ZTKrSp9MKnYrID+bV+fyxMVpHUQa4qaSD18CC49fgsjjqsbGG35m4EGSeKUkyEgXsR7FIlV5UqBDbBGXBLkUAL6KMzPb33//ZJ1p/4GtB9/47ZMSg0lnxs/jq0xufSgLlCHLWdBmPXGPqOqd+w3KqLlz/z9S9QZ3kySozzCSglti1dhCk1/B3HcVzhrZ+VkV+flNeWyso8rBn4iDSyaP/UEgmFiXJphy/OCDw5wo6GIqYciZJCiIJaok/Vlus480voG2cdxg4PU3OIgzb3s+Nof1h9Z/AbGoujDdmwBHxEmpZ62d25wyHX/WquUv6uANJAxd+CiIn548CeK4JvNTK1e9+tOf/sCbinjlSWm7j39Uvy8X3cNoMRsENJJKijP4wUc9dqqIOmkMIgIEKCk5m+CIzsdEv4zXKedpzFLJxAYN/IFuxPhzJHEA2ZASUijgBSnr1M1w2bVoLDy2PeIdNPG84I0kB8uJSgqLGjnfBLKKoNQUp//4xwSg467QVwK1cU9Pz/3bDxqvXlt/aLgcru2jJY03urr3Q3GIoJO4hD/fE5OA3WEjcsJ0QmZ7ft68Ra8uC1n1FFFGgiX1NRw/m8XqjMJaFhklc+hAmyU10PIceyeHSgqf/d7yAfIRKyt0Yej0V0P9u3draXRi4itCrpvPd4SmRmhhoWs22qJxvQpz6XS4UzlsWkRQ4BqrdamTmWciub8g+fCznWcaxw428pkFopQXM4Nau3iCGiqhlVLM55NbHbs/MCCVrBORXebf//77DY1l+7c2ffLzfxWoXCaNQDkjxmB36yMo4BwlBQQgLP6bSmqCY/6X2qe5TONLfxL8wbl/kv3+xR/pb/7FP/XIJTX5ocJIDY8MJ7TWVpMrLOFl3KR+U9GUmdlcEQj0ZNRS22F3KXYymEBo0G+kQcQ4+GaRTBxrpIzPxtBBYvKIJcENgq2lwVYHJgH59zZ3DN77GdGzKyr6/73/7pf3Pzt9Gk4o6FGtNSIORRIBaACNTC20iZcvW71AALqkMO7bcfTJrKQoucLKe3bZvFd/+O6r/lRiyqxpu/ZhxUh6MdlL4NAK2ZsY3CE6jkgJzpMSQqFUgiokQkkRqBDcGYNxACTqBk2WJw/S7hnIQC8a4JpTleg6DOxkKyXdnugHULgoQXZgUMYS8gHKKOONSsRTYWkilbALRC7+O8Q56ek/RpfK66jBh46LXUHGgwd3tzUGSi4dxlWnq+xI2YA/0NQ0UOY1mRQWVdikP5dUqkxlR2QDooOces8T86YsfXU1JheNWy2Mj5PJvraktMiRjhKmCMET5LqmLmVM9GdHyzqjds3GRSox7cRAR1v5QFlhi1UF093qjVHxKKmw5MSZuEvw+Nqw1FA1124wGFUc5AJEu0xWnUOmtSGbxAqfJSGXkzjhqETTwdu/3rmz6sbmGwIGXUnE02JpuBNJg0hFEJlA7woO4OxNxqe/ys/PzGzaf+T4qVMDnZ0Zi2fJDVbkMWnETzzFVzmcpJViJUWmwp6/w85l7rf0rJBH/fGvKSkm64PhhFqPWzRXw5dLFPg0EnePr/0wKzMzAzQD4H0lFUF2BONGEJ2PrCQJEmZiXqmYng9pkM0TjKMQTXQlJu1gkAShgHLRhKuXKNfXlFFQnIeK6vCBUktCI6RU3HnnncNHLx/Yts3ZLW9NZm+4aEV3a3enkCtfBWmm0iuM4EQlP0KX8lCXCo3j2sWLpsxb/KMFf+hBAu3LiesGL364JQ/+LX4yOcLiEwMH4Qy+ZII+z6dpA9C2xYIuwn6DYqahYmgM8D0BFSIvhrYs9Bf2NmF4S8w0pHTIqbjEDGwXx2DzkjJjGCQwYroTowWShYkkhhBT/CQlHir2a4pE2RKwAcHP+kn6PmDoNXkZPf+ee7I5o6fn7q3bJQ03P1p/vMpfB4bdQKCop0lXpsVrmm9P+it4UwE8HIRyp1qvn7o6ZPUCQHezjC5PCofD5X51SU2mktJw42BTGyd0Cr1S6KUAUDxj1XlP6ENxEJ+5pjAvDz2qrKxFh5Gzrrp1G+gspZyk5OlqtwrhnqHToazQ6lwaBdmChFl1OMFyPV4iOLj1s2dHkCQQYitWUgnJl3d+dubGjRtGBtGIYqaC2yUWMVooSdToiRBPMAQk0iDZBhuVtD0/41cjhaUnD6OkuEWzfilSWN1Qr0ifez7GYvUEtVKkk6fB7zFySJoUZJDMhLQaUjT588/NcOCFnohVqqI4kN9eC/VgEz8/n+FTdPYG2VrOC7YilBD52DFPu4lxj27j1KvY3Ee6MmmwW1EhsZuUJMi4wCru24GT1GBbEwbAY//+4Ms753rXr+89fqqssLVwW2dN3czvUnrDdyPqWpmzcMzq5cunzNI5IWmd/e3H3lSUVDRFX1tnLJ2y+oWsV+cde2VJQsKawasXfvPPOzIykB0AdTtIQspiqHNohmVJ1DT+0QrFDr2MIULfYzGDHliDJp8XwOYxYqIoTdCYUElKpN8QvBt0eOTziVGDzYsKS4BjJn4+IhGVBFbgwIW3MYC+F6dS7dEpIlskhSoq+IVPpdYnKsjOza19gIoK3Pz0Uu/h7V11Zl1Z16jf31g3UKi1G6Rie/JfiNxSnXZcoWDaBy6DJ2ZVyLJXl0OlIbXDawollfgNJeUBCz2KqopreWrB0mVTVr+IUwXyf5OXvH6icCAPgsMyu6NKB+2TabOtc8+ezlZ1dPJsodsgsCIaTM8JVSMrVSVMTlgYatLJKCRJ6wV11xaB7Js5C5mFM50uEpNnX/7s3AfVN25YSG7GY+g53q8MBqaPl00+RHILAscE8YCaJeJX5O/o6elaN3z4eHfnRukL/4ZAP7fD4RY899IzPJUsimW/hJIHKrxJIh+rkiI2figMdaIc/PnLX3pauwT8jPffxwW26OamrLyRkbyS/Pwg+4gJCgm3mzjYAfljm4L0IUX/4c4knYjNZAMjeyAZ0iziBW1bpUV4iNo6fjZ4b6AAI9/Y3Vu30KFwj2oMBKDF2ba5Lv67ZMGYED671enkIs1KsvrjRfNW8mVwLglNeISScpIFaZSXloRV/1D/6rw3opdgk+o4fuk3v0j3AzqnrQ9MCaxS8oIgvESuE/Qbw389xjWLgEkPmHKZXanFQUIf4RgoEREGPSLBMsYSUuamTY2RsPsuo/vx6O7Li43lEZd9oqSm4lchgwWUFIGMM2KYVQf2qYJiKV14Sc0xmseHyJgnKqgU19+4dcPfePPS0d5rphpzlR1R4xZDgw65Ii6NKNaV+NCJdWFy8kzIK70eWIc5cCdCON2y+SvnhSwQW9XERPckMlzwKwhJWrPOCSk9fhInnmuWT3t21ZRlT2sM9pQTJ15P27oOzhej5YWmKrjXtrS4XVarrdPZ6XbFJafGec1Gu8ephnJY7XCodPrZqakRLjt5nek9WrfOhNtheCKdt4AvJYJ/m5ac+vZn53aeeXCD4BspkbH4ArgsKNkrSCSaSAKnP6egcB4dKABjSUDQz+hpalrXde3awW0bS/7zH7MgHnbbvJYnFz8hMcBxI4J8nBH6EvrYdSlcepJh+xantz65auVLC7QJ4QkHTh7t8BXk5wb4AhLT9fQEaSJStitISCEunjgs0O1TSnyK4KNHnw2Rklhnog2FeLaS4Pn34Vsf/1gRvCdgeTI4mNHf/+WXZ3YioBMMgYaSRr+/6YZus6s7dNJC8pIOT+aQE1BEhGHp6ldXrYp1qEOFkd+OT6RyPSCrxKU4NCCEfqxsXh7y8w1LEtZ8fvXap//1i8oAUkJRRYI+YAewTWMLoYh+S2Ipk/byjGaDxsxndwDGFFEyRrAoeILDFRt/DwwlRntF6eBABXgC7xZxkNnESg0dSR7LZ9gEjcWo2KDSShTksuHZYQdhjISwSE8nfdQguOe10kAgy1iR399/d/PtHv/NTw9f3F412qfprh4L+PsGhqu661x2g0TpnXAknDwpcXp0pNYGBXkUfHUNdpkFQd8LAPm9KqeSikNJse/5pK+AJ8zgAaKkiCjHtUgNU9HbFgMIlZ04ASuW8oFR/F8hjKwVMCOTuVV1JtnGzQqBJzU5QqbSWGUwgVDHe+wmhR1odli81gvzaC3yLOHPGUoporCBjNy14YNtW4X796ZNYiV1sMmoMBP8K5/K49MHS79/2kZJ0sCINxPcFFZSEhE/Kwfql55tB69d27zHWfKP/8zTOaCTd5u/89ITUrMNCHV0aHxc6CtUUurIJY9XSU2aI4yIjudaFs1bPH+pGu+Y/UdPduwoCPh8/gDc6bJ6eoKIH3Um8jCUS1jXgrGJmcnMWYvCi5sZJQVxdKY/nyijoE+/iLgGwKmJzpVRW7+v7d744GAjov5u3SE576X8nhsHzRlNGf7q8+7G1iUJsJl/OWH2e/ERlJNjXTRl0dJlT6pkSLhPfISSwpqAq5QJB5dli3J/uDTkt2fenrn7Xv3h4y/9fAvkTBkBPPy4GBoFGTSQBYm/ZC/HjGQBfuM3RvVFXhQSUspLGb5AvHQ2G4qnTRMzdIL9QcQ8iBkeQf84exfzBSIl38wjsR09J4waSLslCg51xg4vEmazj08ZAikIx9o6an1gJtU3lOQXPLi/GR/EzXMXD1fV9A0PtNaNBQINA9cRhWV1aXgiT0L4n0sqyg5+uNvjcVkEOq0d3KKVi6hL6WBfFOlxfl1JpTpQUhj89LBq5XAtApt5wbwpKwRGg/cEvI0GyGaiq9AKtW6Dw5PSiVlP07KxulGqTUsN5eqg5PA6YCPDRQ6yQwieoRCzOdcNHqbapdMiZC98oV6nM5vb85uaavsOJIa//eveO2e2PTCqDFJ890UxPAFNydiq6FpOmBA7bpKTGztZ0bsNf5mVU+wv6LnVdu3o5o17/P/xwyyVA28Pu+WpZ5/hG9ywS4sO5XDCZs8MfbwGv2DCZsSeXTMPFP5g1bInPl76FgIZxo8e/rSixgebsPxAe25e0w6oDovhOIeLZFGGKCMT6AKZycIciw/Xo9rRjIzh+k1bKmqJn5aXl99eUz44PtixYwTeWnDYAlTOfIG6OnbkkRavto1st/Z1XD/eexpxF73nEMx+qj3gV6mQ5VVXZ09ptTSGJTDaZjz5XuODky2eu3x5yLMKD6jTyY9ASAJ/Te90G5B7vWyx4F9XT/m/DTizjJw63PDfn3yviMXjEnwHb2e8EhmBnK3KfByaiFZEvmLQGFqksTEiXm72j0Bcz4GUsE/TB3BSDMsnwA2xFN6L/ytB66GfSkskyaukfIQ8UT1CoAjqBnkEsgeE4epyuXF4OCs/gCTRUR88OKBdhQVg+uefQ3XYV99MFlT1IE30QwFa0n71096dp6w6vsDeavIHGpHrpjDyFQ4VX8B5aCSWkJyaKlTowHfxyqwCvkbLFS2du3TlspAVEhOc0TkeGG589R0v1W6EA0FkFByXMCAbBVrtjKVLnzbrrOdPbDuxvxyy+MIuHX2BhpHCtWuMZuRgaczc0Fei1Uh0tMKoiOv02lUKIoVHcO1cOAribgxrA5r7JiemaMwGvz+/yFjTFZkAJtjO9Xe2HeT7xyolfR0wlujJ8IFqKaIbIG3gjIVNHL+gZR3eyfTmreUVF/H23bp98sjBzWrDf/0nz2QnuxreM89NFYDoTuhEJIe8Jx63kgLWuYSjfu+tKwPfWb3quY8/fmvS22vuoaQ+7QCeCz5rPj+vLS8j+IWKgoVYZgY50MG+pLIyW5xOu3VGQX39pkvtfftGmno68vLza9CCdq9Zd7Bt973g1yClUXQEdavpOVRn98pPvt8LP9ZDhz78sMHPNzaWCMwKah1d1k6Z2+yOxkqXGkr5mVA9xQufhBtbyGIFF9EtSY9w6iUhcJRMI0dJzeK98fvf/9+GdeMjIzfXV/33gu8XB5mKUrobYe8hKI6yIwDjCihJl2gfoJGTMbpo2lQwP978UUUufpsU7IP0nIrcIl5xbOXU14oCxdlkucenWAIDiX+Q+GYWCMCUVtAWTr8ge0pEtGJRq0ZJKQUDSLjLL+mrqYVbBXxLsiqut6Okmn+S58MPZD7YUevrefDlrQdj7Z9e/aL3FLBpo8CuBa/KSMQ5vtRiV/AskX8qqdTkhDAvgGWDzubSGPCUa16dt3T5vHnPGx1k9Sf7+pKyGh0Y+sDy40Tq3UbcJxTPvSCyuoc+2LPtRGl5W97JQoyb4MLatSgph8oosChA4+NyosM8+NcZYMQk4zrsOh3sisIitTqUFBcGmR4ukp5AKkzyqBCk2hi42dBV/hYwiuTLhy5s2+YrwjGyfnAAm4S/CcZiRTHkUkyjDJ1aRHRiIMALRw0QCuj7ESiu9d3dfOTIjYOlil98n2cicb5J8sSTMUaVFiUVPT0iLhQ6+eh4z+NVUoDQYQyMNNX/XrbyhT+snDMpcfe9te9cq6+v5QXyA8WBAJxc4KQQqAiALQ6fbmgH20swFGJIK4ipzMYMlSsS7RjJKO7rk+Qfy2/yZWfdG0es/K3/Bx5it2Ax35PZjDPmSMfnI5//7POOz9HArh6/8NGhtetP79y5varkU/xqyCAMQI3K9+HJMcmiZkemRMyJjowjTxch4rEjOPJV8DacZbChYc18BAmimoItPCbl0pDVzxbMn/f731JJNV06Pfbfr/5CTtuNhNHEHxq48NhiSPMeBkIwhXj0Z1iDpgGoEwNQh6euXFkAR1C8RXKJAviD2Jh0n1hMZkiYB7F6MeoR+h5e4G6bnTYxQrRERDpiqAUdXqjMkKKNxBMeubrASSArUJTVca8jN33kxxXtY4EiXxZClu7evn3X33j9w0MXy4YVVp3Z4rKZBFKLzupS8UQCnYGvoLMUq6qE5GSsmxT4Y3S5MQCq9Y4XVy5ftWzVNKtWH8UR2vR/bXH+5wcu1WVwo6TikAPCgczewOVoeUqBdUjmcW470Vm+r6McJQWoD85Haid0nGa+UQXmnixueih45kYjfCdw0LXCwwqRv8gY10JzAla72uOhKSIxwmHQmTTAKFVd3RuWvPzy2x+s793YeqOnpwhcztqaDD+8RPsE0iAixFjYQRsC8iglSjGR/yVZJRTh8GDz/pMCV6fu+9+XqnQqjcaifPI7YkuwpKIjqFVFw2HksSopTL4zU97bhZJ6d9kns/7wb2lL3hof/+ido3BDKCkJYJeH6Vw7hN74LMgGFNLBq59WbKqA9z3GP7SqogCERxVwVgWVJn8LaEa+rLx747fuUvDdHVybUFX3yTkSdpBtbbtBmBgfP3mYtidyQxpSjY4OD1c3NOLZ0lgbMXp1K1Stodhvk8PUXD0ThMMeISzSsHLZslXPWVxRkWQ+9q35UiQXilTb5R+jpF5bNOX3/6UrH69pOtVLXUqJkhJJ5LEiRnuBcIkJCwh9oJQ+JpeHVx/tQcRLQkkBDATYTlWQDy+nYl8WEktyMnFLyoSfWC2ZpJstQSN1gUEHLbfWrQAQLGI3Xqa5I4WHkjFoxeJAJdp6Oi4Hx3wwCAjkZyFRalPlyK82Xb9ZUNxQXzGGVI8bD/wN29e+c2F4tM5q1RhcNhUPJWV3mwQSuF0IvEkTKWqQqiZPTozWW/kiPvhAiBeIkgmeXLF89as8h0zIiRQ69IlfTUQH4gfmaQQpGRGvowUGLpRh1lU5hmTqbaWFmPvKCsuq66pV0Oaq1TI4thgNVqvBbItMCrVpFEaBzg23IhQxrGaR+mEili7lgDi5WnXS5MTpXINRo7Dw/XWami7YHL2cfBaO8C33H4zUbtqUL6hpws0pgI9OEoztIKFCUPYhnRC4BhNgSviZ6Rn9t9ccaa9u7f7e96UKLGgCyVNPPiUBFV0YwSh+FCw6M5L7uJVUmD6ek1I2OmvZ4kXLahOT94+Pv3967UcXPu3IG2kKtAeKmwTt7flYIsRFkGHAOBboQl49ObIgG9OXwQsECnpqO4qPwdz52JZj9fX5Pfd2Nx3bcnjtaSB5ZKnfe+f+nTMs6q9l50729z77Yuf281c+uHK9fbiztBAWBQ2WRgNetgJDt0ITZDfjYBIFyQ+HLvwREd7nl65ePsNsj4rkRj9KvhRErRy1jbcyZPWsX348d9WbZYXjXTeO944tXvyihIgSwZ0YgAkydZXkK0vZUUa0JsapIraRlMnC5LnI+inIFMO/BvY0Al96JgTBWYh3hM/TP6Fl5zVN5I1SQQqkfJhFu73oKqIgKCEh+IpQD9LbAeFRxva/BsdeOCLCAzInR1xci4i2dBjNF+QHGnv6A9fHxu7fPnhQ8emhQ0eRvNDQaLJrNG6vRiSlYrUayXnGHJX4kPQyKTktOTE12mOQ8k2gqDvRww3SafNXPm1wc5GNHWUXLqHB/qs90ZHmAGgCRlQk4oZzBRhaqhZbqxNzH/khFVqrq2EK6AZeDhmvxqxzW40Cd0RSJM67AoHCYbM5rHayV8FGBf8YZCpCTePk2rShqWFqEwZg5BFburrqGrZffiXxddn23vXnbt2or29WVvprs2oD+UW+IPuEgApWUhJGVmJ/zcQekuyS2nRYkviP7L90tbV76r/yUFIC8Ywnn5zBM2AEBbsvms5S0XPmxH+9uOP//5KiqSAhgrPrvQNltT9dtnz1H4ZefmV4fPeVi0idOD7Q1pGHkhKPGDH7wTqW3B/z8/t7epo6yEm/Igvc8RqfIJB/rL+vPnCsuLin6NjN+o7KgrZtI2NUTBcPHyLPcxjJoqjo/86dQ0UdWnv64nb46xQWNrS3txzYus6gAtMa7hQ4CakQjdkKUG9hKDdF6IyMjoBBJlK6UjzSZxc9KzLb9fHcsG8lzuIfDkXKuFprXrxs9eIVf5i3qL6lbLzr4Knem2+8Iecxt+bgjVEai5MS/FoI9+MxXxfi//EZGZHQSRRGRkFPAUzCsvL6YAKbN5IHTxs4lTWjpHJ+jFdKbW5QxYK3gQoGE2YVHn233QKGuoA0iMS45bPjN0F9MAV87Vf9BQhbIB5W9lR5Rt69exmZOcg57CnI8Pcfq9p4/+6Ng+UDR9ceOj5eVtXQUI11xea2gF2oQNypgbw5FaGTgo0HxZIcPT01MTnSLhCobCkwDIqCCYXlqRfkLq0aGWFR9riEryUk2T3xpAmGUY7QptJ5orjwE1OV4fykLmwbHRgoL7PqbmOyk4Fcy/VaLUadzW7Q2OLBWlOg+AyY/HTgIHFhtm2Fz4SXWh78ZdRarzPO41JYMA1rRrsLCzdXV23/IGxbYRW8T8EHwYuk319Sn5cLY15iVzJuNbUlOmEQ6YtYOCxARSrJL8mDkf6x4vIjxy+1FmbEilGmoqeefv47MQKFXQ1zJOQLYBpBScXJ4h8rED08GVZEEQd0ta/Bi33R/rSt5YAWDlz5bC3iak4Nd9T09cGiG2ioDx8AQgb8tb5aKYxVizICaEvYggK0beUX8WBGJwAA3IPIw7tfXrp4+uLx4yfrT169eenOuZ1oTuCYw6X5Wu925HTY7MN9vlpFl6FmdEg91GBsaAAYRti7QacbVcn0MxcujAC9xS6MELqJeoxUCRNvWizPAqNRz7eX1KRoWTzevx6v7pmPly6e/4dV/1ZVBk7P5uMXb77xhI8fNA7jM00TRjvgTTExQdhW8NAWic73yqD6PyMzM50OJTzaHjN7MuXZWYKS3GzEveXkwKnax3JnCRZHl5JIYaWic9lcBlGMxGKUyBmZNugOTf4Wohg4tBUcO5af+5PmvCx5TjpdeOtzlZXtjf7M9NGOG1/eH7txcKj38NqjJ2FDqWqosiOL3uYyigQIVsIQCKMLvuqhOR+ZEkaHJWFG5lpV8DukyUurUOmksWZ4yuPUyHFEBKvvq7wnrGp8RvBXiggVuswoKa1KoWiwt3aWtta0jQ4PlFursRZaXWqwB7nQnYGyalWZHPoorUll4SPJWmcyaCiTCtFU0PfCmQDCday+MrcXTnsqsxmrV2Hpts7zG6urWjylhYXbD62nkvKJMvzXK5oh6YZWfEKzEAz2Ioa6iJloK3H2w1Ce3563L6OnMtBx8vDxg63d6fRNmvr0C09PlUDmja5I+j4SeMycw9HGPV4llcaJ3xWl1ggy5i9d+Q9IORlAJuTWE5dPA47r3T5QWDZ8vaGmaxTuqCU8io7CsFfUP9YzdsPfA4O2Y5VFVFKVPbzaptpRflZxz8jnu+/fuXD68LVTeJAHBmr8Y1Xbj1/68MOPDn300YeXPj15BPmuXTWjbL+q2dw5VNbQYEJDR7/nC2CyWNft5ERNT430qF0GrtBmFMdABsi3O1wGgHAONccZ/e3yjjla8jmBRwLvpQXPv7Hy1byyqoHxws2ndp7a8gNfUAzIZ0MeyHzkUKMMZlwzFSGLamPXX+aHDjtLcbGI11eSVSQF+Nvzqx4xLgcV2blAa0SBLNyfmI5XJA5KOvAiVbhsDoM4Rko6RZBvUFJydpWSMjVWenrBscqCrOb0PBgjgkLStKO+GbLmEn9TUx5yn2/db1G/jxHhZHlfu6rbXqWzybReK9AJCg82GSZKavKEO/zkhalz0KWmI8HaIEBIACyddThFACOUcTEuR9nCvrakHFY13JX0TtbMjCqu06Yy2a0tstJSd03bwHBNl6lBVe0wufQRHA8akdGooERwKxJIXTo+32AxKlQGBWh9XDWsW6wOGcIqI2F0hpJyuOEhjR+327VIHD17Fgbp2tLSdWUfrR+7X1Xc5NuBkoLLHBZT5q8QjFIm2ySBiJoTK6lptHq25+fBiy6/AiV1uLu1sJascmK/88SLEgHmFnTXsOlzqKTg5PdYlRSzSYToOEpWZ8yY+txTmjIwkHdvLT175cqVy5c/w/h3iJDutYevXbt26qZ/7AEu+3eP3Q1+3b6LusrI3NEEcKK2OW+4z9+TiUnm822n3zmN9NDb3v1lw7V+f0nJwCiiXgBvtFdUNDQ00tLS0EhpvQFza6er0ArHFTw12BPMIJoaTE6YSSEr0+MwuRVyDGYAg6Rmh8xmNRscHo7627tUeJKMC386DCEq5RNP/PKNH5SVNZTvXrdx+xfntshrCeFG+TK8nFgRRD+kC7ZowrxPECTVB9E6KZpTZnpWFkVAbaq4it+EKNNnzCCMF9hNUbEfnpigrjNRPAlZMDsaTA5QHORM96KcFgO7BHmMhP078ULORbTbT3KK+/GVh/ziwfKmjPx80P0zurpGwOu7f6Zz5/rTkN1VlZSMNVYXllttXFxxMREr0ER0CiAhAhV7pzDvtUkLk2enJScRhcLMV2FG03usGhPAAnJN54TqZUkPt66/nffTXCY1PuMUdKkIvVWgc6qtGqtr6HwpgRPlo1mjXboqUzcBeilgGbnsFqnZpALi5kair5VPiKhGodGwBQ5vLpdWT1EpyKdQ48chwFJYBIgPHzoQL/zgg85toYBut5adWn/hzm2YYqX31YoqpU2ZBcUTKjyc1IN0AXgZE4eA4Ao6PlTm5+VlFlTmdgyuv6jqLuwy0p2cBXwhgo+I6NNnIuIcvSooQZz8+MAT4bOFEREHPN0ZED0oyvp+sq9t3daoD86cRdbTF6cPEbyAJPi12H92Ht9+5sz9L9kX5aCfufXemTObb5vqEHyZlzc60DEo6tnRMYixEenoR4dv3PAPtwwbEb6aRaoFsKx9efCro/soLg7t7TdLSvwtwlbM2yRmM9HAZTGCeoCocxDP4sDiVSkkT70IBwg0DIFLJrNbDIgZUz9Cl0rlQimPuDatTSGVFPTXFJY1DOwu1R7feWpTfq2PSSXJZWJC7c/s3Nn/BxVfTHTBYz41+InyTLKaHr5ef/Xq1etXN72pzMSBjmA8UTZ88QN+EAHIQ0E5Qb3Fi8EAlJkxAPC3Ykn1Cyoka1FkCVNxtbn5J7By6+9BSY2jpPz5gfqSQF152wNqUc4TX6xde7Rst/Xm2Fh1deG6QocMAng+fGaQxg0HSqvdrKPBj7yIAIumzSbWKCgUMP5ycSkfx20i6xsPV+aJhz1Y2l9IeP76pWM3UZdKgTtmpNMksDo9EJ/azp8Fd2Igr220BCWlMnXTCVlrc+GdZ0GfNOML3mLcYEnhd2k2uLTE9oPUGKcOyouhGG4P8pFxVVCoNGUnIg9s2LDt9cS01KQ15VfXH7pw68FryHaszankIemkWMRMtYg1QfwskPuUomBJMQ87cX5l3r6egsqKtvqPDjfqOgtN+Fdio7QqDMD7KFsKwW0RbKNiuS6PSUlRHFf4dCcOFM7NGfI+71BL1v/pGF93Yg/YWZEHEN23f/+VDRuuXPni1/QFPdPa02sRB3X86M4vcFNq6b698f4ZYA8XLl2qH2wDqQYtqv8O6u9axWuvZb75z8/I+TANHawvghmRETbOwAfhzMr3+/h0OAZVwum0q1pcSKsJjVKQxZBGpTE7kDQbpceIrlII2PRFrFM+eJlWQbCkvjXLERpEGSceJSXzyFzW7jpHYZnuyO6tnehSl7JraaSTMN5UUJsSI2byE2a6x2czXEwsWYIxdxqJMudNcCdQNOkZNTVdTT/4cc8OBENlAh2fMQM9KqPOKKKCARFQTAYwNJwwV1mSMDKDJTAEiYcVDCWVipHY8ZMfNtcaR31gTHSVd401NgzXjA6P3oZe7EzV+19Q1ur2kw2mGzcO3t7cWWqzY91XW0R4olUWiwYQm0kXARZmIqoJknXcweOEkTOj1aAxwJsDIKkW8RZaD1S+nsjpUQgW/bo+PlFSsHKLV6vAg4Vc04QsrxOlpYVtNaOWOlW1qbvQ7vBSRZlUYBepdBaMECrUlI4MbQ0KHfAMK8kEPCxwG/EUuCA6KRPRTSWlgUHngV0bNoStSVv43beT15T3nTp04c7dXzXV1wcgG0pPL6aXDN5cpEYkIR2VVHDwEzGsVY4Iih2i4sr0wcEPe09pCju9OIPBbtamMpMPSXwQRI9gicVEm318uhQAP0/cgQPc2/6ApvP8+bKO8fF1zo0bt7239+z27UPl5euQvnXk8q/ffvttKipqV4iePXoRiaDvb6+qGqtCRd3pXX+qavBnbW3j9342PnbunXc++o+fz//vN/7zpU9m/TKm/siRvqICCL8zmignBsAemOBNWLxqjAe7W90qa0u1yeSKjrDCV8Ci6NYYWnHgTZFBbKYwipUse1A5NUbgkHlcRo3X+QiDHxJI9LBI4uixUKCuWjbLCq2m8t1rPOe377yQn+VjcwULimUC/liSfZEj5oTJLEJw4IX5kG4fkwOQKhevgB485DeafvWrX/XAAx7ZibnZMVKUlJ9PKmfm+AhAXsn46pSaqAxKFUkGQvMj8+bAZKhEWkMzsAn07ZG8pq6DB8fqtpcND5803Eb7v7P9/S8OH91/5QjQ89uoKAcODFYtJ1SokfCopIwaBx2BoiCnmIN8Xnjue+Dg7ERSkNagw1NGJmLQqZPlC1emjogWcr+xpOIjo+ACHc/xKCwuiOxBvog66yxdVz7QZtWRpMNaaKcm5YY9FVgLNofCaFbgUGXTgSFiwk3biqYJMEKGADlscSBex9F2hpQeGyrOiJ7qZiX1euLLk5e8vXVdVzsyxO4/QElVFozsyBVnML0hzhfMHoiGZgnBE6zX47ihFFFJ5eYXSO4NXjp6qsHtVNscbi/8uwwCBReW+SxjKjKCaQ64cY/NXYpO6wujZHEHrnTe9vsHSs+fLV23e7wTidTvbX1P3To01FK4f93+97e3JpEgEHKLNAiXKbTwi4sX16899M6hd9auP4zsWSB6J48MDoKVhEnxwpY33vjktwvefWHxb1e++ssRy1h+T09BAe7lyPkzInugBpVlNBoD7VVV16uqWgq7uxW6sHidQYOxT2PRAK2LI0t5mdaM9YbZlylF2KW4bo0Jut5vhycmhy+J0saHRUKvGgWLOyTVF6rK1uxet+fK5c8utDfniti1nokhmXJdxMReEibdkDBlIO6/zCKdfjSrvkKS0QOR7S22P/b3Y2Jtr9i0BeELFeRRQs4+dOkKZvkyS2eFyiKJISiRIqqof4mZdpW0HModPn+P8scUPlp34/btzbdvbyz94p212+98ed9ZClzi8PGT+/cPNZTUHTy4uRvpaHaTJ2wmR8ejXxSfjQvZc3YuxLXRyC40mdCRyCI9ChsUlL1UUvCQB0gIgwpuSmSE+htKyoUComQ8MrlWGRyYvA2Q9Z0tRVp8+QCYE1V2q6mQOh7ObHaVyYb7E45N8EI3IRMOxugIhgOwZ8eB2eZFxm8UlRT5nGGfc3oxoBnN4KsLd23gUEmFv520d1vn0OXPenduzChq35I94suWZzCHDioplnoslzJPKfYNl5BDjoj8OCD4zhwcvLqz99Nup1Dm9cJgw27kGbQpZJXCHGfJsJHDjXqMTr2TJi+M4sZHHtiz2V+sKD1/fv+68fFSZ2fnnqj3nK3u6pZC7f6h/WffS/tuwkLYN7ycsPfk8WvXjh/vPX6YSgrxhR/+5tNP/6N9Z+9RpHxcvQRexKHf/XblG5/8/KWX3l3x25Ur5/9LT/7YjbqxItAFAyUNAnjrw6cYRBMe/qrqZlW1CxWl6T6hNmOsGMXbzYrNE0ObRx8pNMfExD41g2wapAYYo2utFO+nf4RdamGcFswzGADhqUnZONbSOdCydfeabSipczebmV5XKZ6wpA5qUpiZhCCYIIGSIk6SgOS3+NOs+hIpaWzvPngANf/dL8d2XiGa76fXT12/DoCliJS6EmKmMb4EKykd3P6pZcFdIhZVRLwkumXSwyJPb/IjrqNtfPfuVty+b23ceOYKSuqLM3fut+y//Nn6C9eHh/a7G0osB5E40zpUpdPp09LiXbRk6ijfGtu/y6GOmBPqdFuJ8WazcfWIDFKpYHNIILoe/v4oNTx60HaQTj78q0eTNDeSebGwUrAo4HMXtk6LySM86zyxbjcos92qqmpdNxY5DM42t4koIV6Zw2Kx02Jlsjnjorwmmgdd5FGOPB78uzH1CdEoo5wejxc4hgWcoaj3dkWdPbD19cRJb7+SloxkGJhQfHBmrDK/CCWVK/Lxadhj7HMmjGfZ8vS+o5cc+e+DqY2SyrgHAlvvJt0eMHPB2dCajDyzzQnvU1ZS5JAESwRh4uTHp6QmLeTo97711nu3ewKj+88P7d89Pt55vrQ05cqGqM6DBw9Wm6q5EbOTiRhOESlL3v4AL9DGhrKW7WXby4aGyrLe/NGb2dnfa6waPPLFR+8Alriz5T/ffXf+S5988sl8DH+LP/lFwcFum7tQUwu2QV+fEYNfBqAyHi89vYmcTG2uqgZHYatb4VPAL05lMHeS4S0gosh4tQGP4DQ5yQNhSWxTq93ITQLX6Ns1iAmRWhKAqdXguTmHxloKy/Yjt7n0xK93rj/VjIebohLkEyYSYvFEdjeLCgCexGdWAHyMIVCexogx8vX33N48duzY736HS8BH63t7cbW81vsF0JpTuNHSA0Bs84cpFBSdoTNAu4qaIvkhdB145UIcImJ1lTdIkaEPDgLe2bZ5Y/UdEjODWXB47eH1p7/44v0yv1+xuburRtUwPGzdf16mU0QlJ4Z5QSgG4mbR2Dxer8OEZEihA3uNCjdWPNQ2N92D9bj0QtHiBoqPyytKihOl/fqSSrVZvR7KbAsFtEH/gN3I18G27ArsMAfgijQ2puoqK0TB4FemEBpMmU6nyw7ZrsukjX8l+RUn4tpUyNv2AO+jXwkxEPEguuAaCHGvymS1WHTOvRGRHyAo+8orS1JfodzdSRBO4ezfP+L7tALRCkBVeRaSmdFFkhxESTtP7zEcIhngA542xGMZ98b7Th2+VOfUy1DcbrfKyLfAeoKaFFYpKqmwSJkz+fEZ/BJQUsLQAwectxsbHYWylqHy8c8LuQgvPn9lw55W1JSq+oO0lyFdgq0vPpLEJWEIlDzYVxb8Khy1+GIqKwOi9PTBk0cPf7T26NUtv3j3py+8i5L6+Rvvvvvugt/+oqBb1Yf0oQHEa6bDu8QP8g1CY8Dt3tG17iCKqboKowOpfbA9KQyI+eOQ0jQiXu3ikzmlRA7vE0C48Mhy22GlxY3/dg1iQqgsPgIQh5pg5Zaq6oFu6250qdLLO3u3V2TTCCbOjqWeweT/Uh+JdBjex2MHW5oBgTCQXzPcKXoy+2/c31z5u0uQL32E09pvLnx46dKFSxePX9h5vH1TgA2OFE8lEDy08LOQgjEH6nnCK0BEB8ahjM0BNxBhW8i/Lm87uO3W5tsoqKpzO3sv9vZurDp8+uLFtb07L+8fzfDjldVdqBtuGC47u+uAFfSjJdEeK59AbKPBxoVJHmwwPVqLiE9JaSpSCaJfWSHJw2sIDFbQgABuU5eK8n6t5xg80U3gLKGkwiC40kAuq5JKVVyuzXNi3Thskboab5q6bYX4GwqVDIuZR+3VRkZj8oLZOTc0eSFolPBwVulsHrXWDkO/KDL3pUUK/Ak1kkpNVrPRuuf1994TYu45kLTk7dQElFRC4qRfnwY7rclXUSGRFPsEWBHpBRb0v+FJWfqLUiIh11/y8cX+XbGpv+feYG3V4UuN3CgP4uRdLjAMjSBPhAYHP0LRwyK5ntTHp6Qw+HFSIk/EO7txyRsaqm6Bxej4us7zXM8HnD2tNPq1cmDVNjk1NAlC2+9OWhjqqnO56hSmlqEyk7W7u44f86MfAu772b68iuvDheV9z32y+KUV81e89NJ8fL204pOnRTC/M8uArBoRiS3CwIyUKdGLM/C4YafqdphMNpu31W72QRKICUQntXCJJ8Nxus105Y1Rxs6Yqow1ur0cvUID/rE64lu71ORJc6DuiEpRq1NSPLLqBntX3Ri61PkDVz7Y2fsRSEXSik2bfrfpzeycbAS5p49QTmORKLPJJy0qaTT3+VAYglqco0QE5Mb0FMeO3S+5+uGha8C/3/yPfyvwj9UNX//0VO8h+jswvJHWAmavrYV7EqiBRT0xgfphGHZs+t4x+GpAE8UL5OaAgAQ8FA3qXsfgwJGy89vf39nLyI6nN3A+OL+xZWj//rKyobMcjwG6ToWqG3Q+XsAORoJXo09MDBPaIIayq4xmt0xn9UJKazPxlOAoIUQUon7wuUBfEBK5KM5jxbnNqHLAgjYyntn4fbVQfrZd43LGxUXFRYcJrSAbaVVSnsrhKJOd2DY+WuauG9bVbS4FhxYEJy5OTnquVZa0MFXoUPBUYQuJCD9TZjVpdDIn143dTRbHEgnxXyBUU/gi9mKzxcGZ/jr8bFM2JAVz9iAphRHZrz/rPVfVNNJE5LbcovYAjE2yQY71wdanSBRMiKQzHuY/UFp4Rc3p/a8NdjzYePqS3ytM0aKk7CQBAsWPDlMgcnIYSCH0pD0+uxSsm6NS8Hl4FLg6VLdUy8CaHF9TmqI+i3AnZwutpSlpCbBqs2vDYC81eWGo3Wg2ba6rU5XZy7o3d9dkKKdmNwPuKy9vqC4sLB95fsEnnyyev2DFClTUs/MXPDsNmIPVq5ZZwYolAxMy8gAZfBqoQIB1+ACHTC6rhsyHsIHDs1TBE9DQonZJlVL5VNg64Av0LtCUtBpcM+MeoaSwfXsAouvptI+ErwbVwbox/Ka8+6+cr+o93PerTGnJli3t+dlI/sQ3FSYqPmkAfCoeeBK4mZERDT+Xly73IWAhP7sysyhw+8vrR4+u/83vfviTH/7zPzaN3S4cGL5+CiPb8as/rgE/q5YHGi2/lhEvMjJfyy+p72uqqc/pz0ing6+x1odT1L59kGHeaxuvP7n//StfXN55tPfiYdwkPuPsAdXU7nKBGYHfncyAA5QKaJrOzBe4Q18Jc7rUs+fEOWlDcpgsgBGsLmjUgb7xJARTQ7lktihALXHJorA6ImMetwiLAR0IiVzxzBL9az4g2DXqaQOCtZLJaNJqBVNjJQZdmZe7bnygyq1SjdZtPg8FlFalU4clzUGYJH6thWFas1wzkyK+Js2WIWlAJ0vxeF1uNx5wig6GZTaFM5LBKhh+jig88nFhc9L+9P2CWRIMyD77bGc1AiORZJyem490zfzsYog4aBvIZSM51luYRWKlyskNiJp9/T0d+x5sO/xhYDN49igpK7j4FizVCHAk/9NItlLFISHz8Skp7PLIJOd0jkKf0NDSMlS6bnB8zQl1inMPPMWBlsm06tDkhOQ4u8EUSj871G0xoqB0w6Yynam7+6BFkv3mzwBgrSs9m7KntSvzp4upouhr/vwVK2Y9I9HQtRKG/xY4HmMGIiPX2BfJa1weO03OBxarMlssFtJHIPWGUcZkeiEQcwiVUFEz0KhmKHnKGSqZzqiTqYXcbzOfIF5BEsFZKClArqrGEkVXY/Xu8XUt+8+fHzv+xbmbW4717BgB46MgZmrmSBNElRmI9ywBq+o1qCszxaKskoA4W4mQXRD4sjMzeSUb7184dP3Hv/td9j/985bMjKqh0pMnt2/fee7cdVD1awWC2lw4kYlyGbMm41f9W9rrR7vGoWUeJ0nzONQb9z7HVwfS4UtuXjt8sRdH8504UMADdM0aZEPB/E5HxDmBSw06OO49SL4FTmP0hiWFYR/Cxs91kZ7VBNaAzOFVC50yF1hHZiopMxoVUqHdmL1gjqnmOixQcUIHQrfQeO7Xl1SaiYBouiZFCnVGu1b39PLl82Mb4NqybnwU1VtmaNwsO8BJ8epU2siktAiuldSMqWoNzzGb5CIJYQ40SSsXlpzQFMtS9DBkwhKMzNOI0DhQyVBS8G2anZiY8OeIPXL0W/LypCW/Pr3+QjtAKqg60yXtfF/BJtGOHT5JgHn8yVkuHlkUyLNzi8VZKCnqUocvBdxCjsekc+HiLDCD64kuhdmPoROQ9f4tmfp/+S4Vr44H/QdnD2NjdYOttBy02dIU5BjhOgXO5sYU4YnI2WFOpBFGonEnhnlMFktdt1VXhvu63VXdbRT0oaLKt+4FH6WwJv2JZ6mi0KLwhwUrno6VWBzogk6vDjxti8IgnfHiVMQvgBmppPMQaPomC9+i0mlIRmtxY96z2bROp8OIMCpC+3CWio2VyJ9d/pRdA3MF7reXFIvhwyMVR4I4mQsvNQ0Gv3Xj64awKledurbzzk2w6UeamkaKYwqQ5h4IjPlr/MaS9vwtykwcIbOBTGZlZ2NFglsRAPXMWuPYmd712f/n+7/7Uc73jsnN1RtLj5zcfqrqzJ2rfSM1NbREiXNyyKIFEVrpI69tARsf03MbygqRuzBqoUjijr7R4e2ow14sT8ePn9uO+8H+0tKtayIBe4PZ43CYDAKdDImCUMNqwOWDrNgROROZc6CNcLl2XDmR6YTfv4wQa7QGcP5IkKeiSE+ABjZ1FAISPQ40NwvYgOhlyC75WhAdEkSdwiNM0ceHwctPBSBPAMOKZc9mlTk27h7Pa2hxmfr8N4biIvRencaqn5kU4bERiy4xymV1ItMNcmI9Lk8gbGAYs+vo4AvQHjb0UUJAiPFYptA+0cOSJoeH/1VJLVnCzGdPrz9XX0K7dbq0xNhUlJ8vQZsKhmmiO0lZSSFBHdssnOF7BvMebL54qcSVEp+CVU5h1qBbO5FUR3tUKIVdhkZH/O1T8b9bUpMiUFJ7uNZRs8Vf/LycW1g+uPvEiRMAQ1NSnAi+wzWRE6/2ekLDZiKFIXlmhMxs7sZn3ofRDwOgny9oGP98cF0a0lOELoHyp+/OZ/1pwasLXl2+/NmpQDyRxYyOpyFLB4jxYqeikgQxTz6FA3nMNIQMMQIbHiEICNSRXBXp2Zx2RHCyKylcJ6eK5S8tnbdCozJC1hOf8giIX/hsrR5SAyxTMivC0sxddfZ147jyDG2sHr60fv25Kv+Opi5BRVYFEmEb8sfGGvFlLMotqqw8Vlkpzs/CuJedXUSybTHUhnl9N9efu1M39Y03/iGn/1hJ1fYraEEnu27cvxuAAQUDNYABw0FWlCsX5ebl32zvaGpru3X7/u0byAUdq7p56cKHH4IzfHgtDnrbh4aqINpoRG5A1RCoKfG0/cDbCA1GAaapGTp7OIzRL8k3CSOhUUfL8aLqQPAzEjNWT98PSJjcViuo4ZiDsEbJHMgYwBqj5mogd7SYNTqFAQTjSKct6etKKk0FTrIeligY2XQWk24awi2mrPpPo6tzd9sASqps1F9XdiBUqHWpVNyw2aFOLYnYEhGvw5mJS3NqGB4CjcYlw3+Jy4Q2qdUCg4ujnOdQUHFgRo/EGgP4UJP+ImmFjGdRVMiovwxVwqWOvB3ybGmtscS3o3hTO6+pySdhcZrMtxc4rBS50CJ0KVjno0tda9R5ALjA5swMIwwrphCW10YlhT9E/O1h6n938AuPVsfFO7HrGCwlz69eXlvYMVgOxkQnIoPjd8UFJYAcrcuZOil5DpzTwmZG6cx1ZnPfsAE3dNiEIem5Y3z36wkvJ+/d42340XPvEi6xArvU4hWLFj+F6nHoOZCY6nUCjVEJP7uYGbFiCusCIBbz4hPTePTh0wgjwFsa3yH4WenjUkxSxvSKpS+59MlVSKPQmJChBHr1zEcIGpgt00eAFu30aHUWQe3oqMKxbje61FDrxpZTF9af2/lBVbugpCgmJhsP9p3qqjPggVzPgrMuMjQKsouKsrN/hD+SfTlepOKcvFMXbo5ZM2e98YOfFPSMnT97HsLkkwNNdw/6ob4sgs6L7+cV4xAlpoDw5vab7TVtD+AUcObMzjP3d14mZUvv4cOnDx8+evHi0e1lVTowhttLGhtVZS37T6S40Vh0kLVjugPgKdCAlGAmp2Me4n+w/OugknDZVQb6gOC0hRxlyHBRP7jRgNbgkHkxSHjdWvAXnGq1l08hpmAeKyx2DzwPHbO/DkRPUhi14Dng1BAptJp15mcRKj8vZOmPTBt3t5VXAc0b9t9wHECSlFun0NKQog1FSE5yhBpnjOjZYRgmNCpKmOJ6ZG4XXYS9jA+FkorAAIjmBhKgxsksgxP+3KYodgB2mYlXDveuv3YdPJZshKNXSHdA2hpoGvGxvEwio4BQIWEMTGltVk9xB3WpXkudNgpMaLeJerjCTZNfJLtNTadliqtPfnxKalL4HOzyyDYxjxoCi34/7yUoixgLSdu5Zxe+4EUfFZ+C64NwDlIZXA79K9GQqxmNo32jDQp6vQuyaj8fX5MQnvj6e8Khhop/+OUbK1YsfvaFp5+AA6vGZeJLLDanRwYSmcFkmfoUNqNpcgFo4LBzwyU3BqI9kpOasD2otIhOQtolOLNcCzsaxZDkFlzxFbAXflahMzgQdq+e/q12s5Nolwol6YIHWb2arjJdWWf57tLzWm9n51DL9u2k3bpws+kBKBFf3r9/hj3zkLGMwkgPTjVi+LUoY6RYm38UK0aqcG5z1qX1xx7cmPHvx378s4Ib5eu2rRm/d/L4fvBcBfljlQUBPyiMSN8gNhLPlwUJ+A04AkDFCfLw6dMgmrwP0h7uDQjibrjZUIVAQ/j2kxVUDQ5zhQ4NJC02YSRJKExmPo/cYDQGwuOBw3ndVjPE91DNWlUGixlGzfFRzqjIFA8MoLheksZ7wKbA3KXGkRdqQCsF8KBJuawKt1MIycfXltRshcAFP3Q4okboXRBPzkdyB0V3KFrbxgeGoLqy19W5D+hxjjIBZ5gewdWGIc32lTAhvCb0XNJwqVwEvhO9wmEjWz+ZOgUzKQ7MYNBGwVMJfRXHoiWpSUnJD408yc4Zx81JiQf2X/6id/2Hm/JzcuFqIt6RntH+/zL3LnBR3ue+L8zAcB3uA8Mw44xcZxgcRpmb6Oh4A0UwXkBQggZrUrVqNbC21BosLOJJImhEVMJOQIscpPWyY7ak2LTxxJhETQ1k26BJd7rSHZsmsSeNO+1uVtde3d/nRU3M0lP7+Zxz1h4MKhJlhvd5/8/ze36XG1mXVrKDVzA/tr9iJBoav0azO2nz1sOXzp37pzdtNq9bWl5uL0ze8BFzJAZR7GYlZsqoxMX978KeCJuGIz1z6EiWLZQE5IrdQy1jx1RfTvCJE3mD7pKMYSPx4VbsPCwGuxYH0SXBfSOnck9l7oM9sCVpxe6ekzXl4cWn+7VXd3asX/3oT8YXppMOKCXiMzr0NpkRTC6WJ1ns9VKysQ1x6W2aVIV5gnM/8htCTjIR+QDR4dGtdjgsocnK2kiIPsnx2DbGFs2iP3dpc7TqmL+d2YbHuwpk1U1JuZyeekdzfVMTiwFzZx+auLPXjlx5D6ezdzrfkcefjv+Jmvr98VePHNm7F27HKsGfUlYjt+SoSlyxKm1t1Jr3P77x5/2Pzl371OG2z5r64ex88vGbZ0Yu9W/YsfkXqXVRWzCO2rp2MyqP3bu31W3KwmbjL02v/l4hbr361pFPj5wR4ZjeRiVt2biNApSKOjcgspg2JMQ2lxtROxXhyUxMzAWpgHSeJSQMJiiTRjiqepo80nG5ptj7cOD7tDkqDJt5dUsy8FqhyQMihQFnEh5CrgEMDjuXOFWGY3rYPWpqml5jB3aHjxQJnGjS4OIneRVLO8w9PS1dXXpLc/sX54fdkDWceqc7UqVTTy8rnh4So/KCvFpyiaH2eDnLCL7xIpBiE8xu0cekK0TDyEgtcDewiTaAyVXe7chGdqBK7xdeHH3mzB+Pv/74+wcT1uNvQ8jCsbqDK/Yrqc6UldiQKvkdiWsQyGze/NKGc+fee6uDtjdHjimQRtRYvBaQ0YNlOyU73wwsL77+RP+99VLGEtWox2Q7palbzrU7QTmmejmogmNOnFCNquOGkUkYdS6+d2avDj7QA2UPFLt2j2RGIRmq0+weafuyoebZ8oUv9564drXLiq4FPN5kS0wLtUKTiYvzYV2l0ThBp5ApKdQawCtKSuTQGkWilItG24NHPf0L6HmWQmbNT4M1If7j+cl0JUG16QZYo6pIVcm0v11RlFQccfIEBjpcHpZrF6SkSs4afciBfP8y7a/b3a1g4GyF5O2ddy7uOXEWxSW9yMip5w6CUEA1X5WwFvunxNAVqZtCD1z+eNNfLv04+YmXXhkaYMEFZ+v9j18d6Dk5dGDL5mRY6lulojjdMKfYvZscuvo//2Unf/Gf3hn0dR+CWWQxdCAbHxlAHL1hxcoVbQPtbe3ttviH1+5Yq5EGDyhBePN+Ec/aLZxSenEvQ8YLli7SfcRJ3HT0OjGKkIvWLeCLWmGrxvlY2mbQa6k9didbLVtULlxWrwW3JK3WGyIC4PC7piDSj+M+FRyc4yYNRLNIsqGL5lRkuVs+GfrUbm1ubm8/f/ZsCXwjCycC/V5NRADLvBIOKC/iQ5hRDofD4zC77B7IrGb6UKgWDnjKWhXiXlUcPD9CEQPToiOX3Jp9lQTgAgny+BBg5tpxHIZH6g7uEJNE5EF1P1wrtpnxY6HQinwqcQ2ZMZuTX7p07tA/vblGgxM77THjlEyfYH45Y9wJyiqSO9LMrwEh/+4ltcRtzBl0sIrYvXF57PXYRRxT8LabGvuGVS+fODGoRmTmd+rMFoApEBdVcUEgZmZx327L0NYnmc81u4cOn6whnmGPuz94z9mrPmNfZ7chPn/CotrKRRpDksaj8xI7qSNpLBTKG/xPL4AwtpP5+fnJSjwX202L1xfndthctJhOjUCpopQQp2TlF6XE/VQkWCAPwNxTFUfcR0lBEVSh3XG4nM7ubueFIehI4FGj/b39vhC4MdO9B9EZv//rtz5+84/+zsE9qmsf/vH1F37/x09/99xzj63ejzj+2Oqta3lgarQlaeXvXt/2l2M/f0SWSxdaTvb3N5z8+PUjQz0n2/au2LpOqGobt2xOwccRo9D1Kw/3vHTuiz/f+N7vR995p9N96Nw5EJwta9gC785t27BfpMxthgFDOwmKD39/7bpEjZ4tLVWCRoLr1wM7ACGmYOg2gBqDKUuh62jQVpiU0mMsZZZioBKqguABbp3XjBwGzxW705ALimEBznBmOn0l2jhHcPg97FxqLJQU21kkGRgkGTTkIMopVb1t8OQnLR12q5VTSt1IN2l2OC2wcM1xMwsCCJN8TovQZS1IsnCd8FK8Skn5+dLBHb1smLU5iguFz2M3RxeX1UQGB25d6REkwREaFvbsnmvYRF87fvyXV47c2LFjxZoDHFOX6g5uUfx4x9zaRGmzipIiGjxlw7H93e99HAVdHjqjTmZIg3R+/EOKtuNmSU0M/99I1VucYTyxR+20DuRu/A03q+okzDw+aWpsgjs7KCUFGOV0qeIMiOuYOS2qQI2vU9WNv6/myU11m9Zjb34y8ED5xEGfO+fEnrPqPqPbkl1KeE1lUeXSRdXVVVNCrUwBbkdWcn5hIRLoTISnAH8AFZIKjVOXEy5zNBYIUS5VtC4Ld5U0MfsCTxX79bTkbL7fsaVZuKqq+ay4+0jDCatRu6NFYcrJajp61Prp0CdN/dEnTlwcHu7vPh1gFbDH2da2kb71XXhBV7V7Pvzw2tnGRhSXj/927ZNbOaV+vnq17HnXrk9auWJV/Pv/1NqT8p9/jLnn22+faRzs7T3z8a+O4KD2yuENWIOuWA/ags+lCKS4OGBI1P3zn698j1Sf/v7G7tbWfRu3bNqKFr5uI0NUFFiJwe6sN+zG8/v7a9eHmqCAUh/ujBKj2oeczwuyBy0qNwtrG4Z8m8KIp/uDDEuMurAUcIyIk70qmJewz41qqIxc+8AEXO8uOzQ4r4W9sVub4Y8suAd7YiYlVUKeDDXFAjUzqlRyzObMqU50nzxZvxPaq8VySH2WdZM4ouN/4SW0YyZcc0L9qCGH18yXqtM5PH6/x+nyiuZCJ4O2Fwatwg1nknA64FkEJk+7jW5HPMiDHOCa/j7d6Idnhy9efPy1twbW4LW17thKzAK2JAjzOOmmRVJSEiW1hiy79Sz/ul9/65TeJUH2HkY31vc2vb9EpZRUsAL7qXR3Zvn9OyN+AZVPddFstw9l5k4lDq8ywT50kmOKcco9eELVyb3eajWiJyix5hrYnsSFL1Hvaz9a7zlqZdDe9MhTL/U04Ce6K0c1SK4kUXjWzaWLlpdOWbZoeXWlBMgVLdM7fHFqfVJy9tz0SfnJShZnWjIpnYKeC7UiTtoZtcXmj/OZ8M1TcjQUBQYFlZaStjwoqHJGJnRpN2EUcYH7KSmdkUEe6oTTFp+43/LpYaQdcTl7tDm93ecGAzIhL9yepxptfbf9i35V8LSZu/bsOt1w9sjxX77w1vubNm3cz8b3O4mabVvWbsEEasXW333sGlj9nx95qaflzNtHyD11n3/99RuHSN7ccIlUqDUrd0fFZ2/dAoc9YcVuLEAPb3r3fxz53rWG/v9+9uwZrMT3dRg2ov+t29fa2m7vcjRjdOL3jORmZm071WGQ7HcJU+C8MbopK4cF7MaGCR42QAaOp0wUwnAwTKjvFANz+WxMeCkncTiCU5QBWTVOjWwJqMAhV50dMCNX7zDCDAouuIcn+kxLFiWVl5cD5qfWZ+YuAJygqmoTBmlsL+AJ02w9dLXRmCGSQnbR2IvlTIzJoV+hpID5EB3SVJv9Hj/DqsvL/lmsyZwuJ/+q0oyhrPGQ/RdWtqT49j9OzijnVGBif1N339WLe1RnL/7+ly8c2Ykr9vo1K7+zGVJ/sgzPEuEmt1MpKSzfEg8QyNT3+psXMNTNTALwQvbDa5Tp8Ykn3RhtNiYmR6145Uf8b1JS4XnqwVG/YeioyfYYaHXlQ4ahk7yw/Y19nVrY+UZjvcVgXCIRlqEmrydTFTHTbdnY1l1f7+8836p5esMfPqkJe4CuYE+XtbG3z9/12P+sWDSlMHvclAXzOarIgK1NQ/vpAHGQPM1UiajltcvWwAD1K7NtCUYRTAVqFodWJR2FuxQ1R1HBmE1NSKWkKibBZPOQr+e7n7VU2EyzT3AOv8sZP/4nz0OT/6S/03j27J6zjUc3Xvgv4RJ7VBAILDndV98F05t1W01N8cJdvcPXfvnaazduHNwIn2L1VvRddevjj23YuOnTT88M1T287vD+c81vXRho6h3d+b3Hb/z3gYENl3r+8BRywpVJKflPMlOtS9g90HP4lb0/fP7PH/7qWkMkcuhro+c7OnR9ppUr9red6+48BEfKj1gW2Y+zq7m1q8sh0kEemOBx+ED4cFgtovXLTQrNdJoS43PpyjiksvQeYOq8SGE/CuubZkdmCdjnKDpUYNounRsUHTmVXdasmTbBLLQ6lknhd4cnDFnMuTkimMrTGaKi5F46Z86c2pSX+c7vNOibm1sPqa/6SmQNBmGKagEWjmPcI4qc7k7+KTNWfpSUh9AnEdvyT/sdLodPJSdHpASpeyPLSWoP3B5x4E6EPxiYPnj0XFNTv/vih3uCL374y9eO4HRx8GDisa11CDqEj6REkImT32pawhc3r1lDxNnA6y9cGLHrQ5OFwWZxufCQFu3cWEkJQpGjvpOLfqukxiJD/z963Avx479odc5Fn90yMJK7tep60PVZHSOfwN9hmDJqXx41luhcdr2rJHJaiNkEk1MVE16sapVjqnvw4mhnh9L3hbPnfflix8b6/l7H2o+WTy1Mn5s+fvaMWcumlNZSpLOsdp8xS6YnCHv5c0EeklLS45mw6Bd8OrvTjUJKGTZ9UZxhKYKcp2KbnAxGAYaRsnRObGkiudweaBgZ9xHZJiWlhrHBKGUav2jZT568PISjdCc+CDlnmzdv3VPO/fIBQZ8CITknFpY/wP2TfNpnl+w6e+3DX2KL+i7OwatWc0phiwQhtm7j7gtvv31w65pT5/77mbc7cpv6u3e+/vq7f97P97qH1I0NB1heZafWbUxKXsdgebjnwA9f/B8fvvlqQ+8gpjij5w0GnXqISSqz293X6R7Fnd9l9vUBkel0V0fVbpmIpKQUHncJmyi9CQYEOduZFltios0py2qAdicSihylgJSSQh2OmEMrTlBUI8ZIrJnYAWO2bIqiqc6yQ7zT6rT3cpudnpnrAL/g/w/OU1s0SVOKlOja2h0qKSkc+poPtfrP+Nwl7jiRKTntFqeZcgdCssIvYwkGeu+S4wl+otfFAGd3+FiVmPEP5OyUY5dWVBVgtVv2VUmFFxTHaMkBqe/u7u4rUZ0Izrt4/PHjO0V3plktzjhoBEQNk6j4pCOXOnDgsc2YRW64dOrjt4ZORWlSJ+VD7dcIWxgXePKDQxTHC2o4R61QNf7/b/zC7l5Skbo9J3zOIUtm28aPrsde/8hkQSEH6NfnHhzt7FSbm1lHhloJgjAao7mnPxi4uK/9s4FzhwZVo1e7Wk5+cjLwbEEgeHR0p6m/oWndR7/50cNpk9LTx09dMHv2hAmkMdfmk/mQoUfpOmne+HGF6ZPmzk1OTic9Ry/cDH98sl2llBTGc0m4v6YJgJ4q4j050rITQslJH0eKMGZxeRklNWH3d0px1zf67YlLayuW/+jgyS+bvlCX7Nmz66x31Yv9S0S8I/BTeODZ4mfZ70fI7x8sPnH12umGiy8cv7KTLIXViknh+hU7ntv8Wc8nr76u37/t8hdf9Dfu29bU1L3z47f+x5+fxz5sr6StPfX0yjWbk7dIO5sA3rdh7S9+8ed3fvVqzsvul/tB/PSG5vPNhgunLpgb+853jl5ttSJA79OpfZ3GQbexBNiORg4sj2g6lbbEJTs7cneYMaWuTGQ4yRrcJggO9aSVmmKjGhOSR1emFpEDNsy6zCzW4KAcGT5XZsrUpVNDXdg5x+mMS26Gx37zMdGWa44TBa4qMgcrnaSplUEyTNUm/svJT850GaxndK3nHGcY7kooKVZgTiE+cS+QLCtfBuxYOlTIfV4QP5fDhTUFMnuZBsU/ME8rO984o869RE6pr27kDxTvaTZ0ebu6uo6cH2zYvr1m++kTnFOvX2jr2b12y0aeLdl5KQkpY/FkCbgar3tM/NmOXTK98PGpNfEp+eMKJ02am4YmhMWCR52jwH3BcmbnqM0LC265G94qKc6R2vlTp86a/cysZ2Y/M3vWrKl3/JCPyUdn8x/vpk6dMHvWjKlTp86YPXW2/HbWbPm0qfJO+Qz5wOxZ8kt5zJ6yXEqq4K6NX1iIWqvq1OsNI1kb/5EGYLGh+ZWTTYebmurdcaNAo57hYFd8imVi8cxpkTkTp00MUR3a2G45erQ+TjVqvNpY/0lDACf57ZSf+XTvwKwFP/32vMJ54ycsWFo6deqCZUurgyqS9Kg79ImwYOfOhQebmp2eT9Wkp0f5jdocc0qyk5Vjnpjee0IlnV04k6miZxLfUeKox1UvSjSxsPHnRBtV9zNKhdWY1XkcU2Z7enVs7edVm1pOnux3D+fs2tVwpv35zr/KnMwpJU2vPCKUwfnBspl74nZtH76Ck+eNutUQEOsgLK3ZvWHD/p6Wa28eOWfpOvTPvY36ffW9h1rfegvNfN2Lmw/shcH31FO7NetTEzVr1j/9FKDghnWbfvHukReO56n63X3IX4SH52qGBnH+jKO5q6uV6b+rGwh80D04KAOUG0wc5RJxj4LmQXwVATnWWhJ1ECViKOEi0fkxdMUpJYUTEgTsPLgVXnyQoONgd6ixZzAZcSE7siYsr61KA9POiNOZZ959LxURk5vlz1BKisbPEhUqmfIUVe2pSGgmzc0e85lD7a1UPVB8nHgg0WqZ7C6U+SwPlcoGBQa1x5ECvpSEUttkuVxCCbKNl0MX3MSnjgkUT1/41TesYJquw8QkeeT8xV1LAgUPIKCq+S+/55wawF1/1UZZCqZIv0+Ws2KMvn7vgcde3LFj3epL9b96a++a+OT0cePS09PxoQu1CS1XJXAfJxUlFW3UxRR8zQ1KSmrO9digZYaj+9vG3tr2b7zzAfSq/Mn+rDZWO6fAWSHFyRuzbK4se7JkkgUnyo2S98jO5Q/4rfLIcmmCFhRElN01CydsIuQVn0GWITYJe52iz/zkk0NHe5reQA3R2X2uLbpAtWprrirAzTxQnHPUYBn4rL3V7igpAcHYc+3IHxrY30UAxWNirk6eMKFwUuHc9MIJy0qXCdtvKXG54xFsZFjzJ6Tni7Y1m4YuG5Z5cnaS+JTakzLVSlfMzsUcRdM3dkgpxxXvWWWlTU21cU1lenNwRA+/Dz5S2BKzjh7JqLYzdn/wefVDLNqaGnicbrh2of1fqKcIYZw9oJTU1zj501V520+MXkFou3PVsWPJB9dsyyIG4Mue5mt/fOGtna7+/t49o059/ZnWd3fu/OcXt9Q9v5kYjg09Lz21YseWlevX7D1w+A8v7WjrWffDzfuPP36kQeyG2PUz2hgYOBCY6/xOb5eFbxNqXJdayP4qFHslJMhwY89QTik3JSX0W41NL06DwH5WcZqMyjV43cJGioNewn+wRnPADSgpmc8JGsiCWcIxpVI7Qgm3q5yNxV4JrKVp9+B1xuTm+oHi4WJISeVqxldTUEXX/+eF6Pq2Jrxhj7q6kJrqfI0Z0D1LGnHXAWSzW/0+mJ98lTkqH2CIC9zehXZdB20J8gumZ7IMoy0FPQE4MeqMwoYPXlIePnZ8PLBQpxfFpOvqxHJ6hLBwJW0e8fwXn7344paN2EEeW705OVXotCsgURBmceCxx9ZuenL1H1r++N7vtiXlF6YXjhs3KW3S3GQNJu3muEhl1wuGzl1FrcyNX2/8mA2LZqVWVYI8V35e9Pnn8vOtH/Lucz548zf8VFnLL3nw0+3HzQ8rf3THh+V/m22LXXzPyLbJ0HfisOG1ZBo2lhbVzvVYrCMnW3qaqCl/96H2tuHARMO+o90x3MgfDGgzc48C+3idXvV5p+HM6dPNAviVlxUDRqldoRyf2cAK88aPn7pMiH6li5dWFi3ONaiN1pTCSflSJZMmCdOPRGik5NlpSZp49uLQLYEoEPLS7kHrS86fxCOfuktTYjTY1QBk+bB5mRjxt2cp0p3xxhR4WV9KDGLF588MSE01NTY0ElBydQklVXCbJPM1kCiiOJj8sdEjV678/vjljqRHnloxMrIBmO/tVxE4Xe3qI1U9etQ80Hyt693LR754sa7u+VXrpaYuPbVyxYGVB3Zv2IuRGFkvxzYnrLry+BHVcB8LIgkgICPHzOjhZ7x3WbJwDoQzkunw+XAhpu1DMassbUVmrnWLzwRu6kxSoH6h8CgVrQcm5GbcJTIySnxuxQlJJbssHRhoNAdWiTXKhM2a2B3pnPkVxMMvzXXoStji3qOkImKycuFXUMfaSOCJ3Kj0CmWW+ug8wde8UvXNLiySWrt1fb7hYVb9QA9cFOB+PkRZyjoMdF2h93m9DtA//MCsFgdzV5zEwFLmrKZYRvN8DRaddmGZ4m4XURZjtnssetfgZCmyiHBhUmC8dfz4xT9teWzzxhUrlXyUFHa/T6xPzd6cmnBASmrd2rS/nHv1073b4imp9Hnj0vPT07OT4Pn5SwSluXlSxUnSwDdKqmjOnPGbq+WJBSlwQtFXb/LuK5hBqo8Hy+6bgIbyns6xaI6MTNxtgvhF0dgH5ZNpkoOW2osWFITf/Y4VPk0dFxznokpwiptXUZGqU72775MvW95oOdPc1dl9VK/LG2w3dHbnLVlSXJOHXCpz5xF//dFDnZZtUX0NDZ80cbRzAgZb06aWLidAOx+hU+GEKUuXL50yY8as9FnVsbOcNH3JCdRJviRliFSX9g8AMB/ry4TEtIQsu2zoPSYJi05TPiMN4WH2pMJxkxIhn4BJsSD2avPUxiX3cUZRUj4jpmNGtaYqqHrK8uulu+vZsymPlvrIMtb4t16Kr6Uv8YuymXxzVHn9XcIC3KaE9/T0YFzzy19+OMySKU51ore/3lLf2LrtvSuf/WXTpucv7XiCmjr20qWnjj29kuJ66fCB56LkbruJkurrq7d3QA5Bg4l9uOxFzRK5rmQrkhTl4uqENREnQqcSmWvihBuB1ohcbnBzG+1eaJLNIhIOSgq+Qkkc1PoMH+0hgmWtEJpJc+JMIHLXpXeJoShHl9FKtig0iEQkMtEZSklF3OVcD4nK1cELzBCWn9oUGposvJk5RT81N7a0ce+pB4hgw+ASXzP4aKgSfGq/hGBTSGJUptXS43G8gkeIV3QcqxNgQbYh3BQi87RyTgl64nPY8bJ0YLLHhjesfLrWzxbaql5YJi+7mAM9i0VQ2e8f/+ULO9uO7V+zY9OTm+n0D2I/tem7P/w//vKXlzY88d0DG55e/c/vvPLKhTUJcwvnckVICzQ3hVGTPV2wsuwlvSM4Ts3CMuLOkoq9PmdWQsVYOV2Xtzl3vMmHbtaUwJ23fkUZxd4qOOWnOWNFNEc4W/JZlBO/D1psiaXxu8cNvtgYF6Jl/Y1LjiVq1uJ43cx/efcTxHNNTc3+TkRT9c25uYccjsGc4MC0QU97u8ux0zVw1HXos32tZwnsbMCoDQWNe2oldV69dOq8SXS8s0uXL5o/Zdzc7LQpldVJpqWVlcuzU+WUEveUhLT0uSSnKTw+1rqoErFThSeABUqyWJ9AUpeKEnAw3kYD77DZPNDW8sgRKggLvw+OX7ERF2i+4ySsV0/4zZyqA/Ui98Pq/Y2WlukPPCgtX4Q8wr7xkgSmBeep8obP70Qj2NzcePbaqPHVP/7x98d//2FnY3/vyyjG/QPnIOtse++9d5/fdHD18+vWkjp6QGoKMP3U4Q0vHTgY1Xbp+ec/u/L4DSdRMNs2IrJF0G6ySo/EysChT5Ts68SEKBI7lboAPuNKRJ9WohWiL3QfJZ+Uxo+fojjgsAu1ZdqV5arZKP0hIxAoAHFrRjZVYhKkhZasVUoqMs4OHl4UW53sN2cExzmkpMLv8opRUkr4KkZDkb5M4LXFtbLlf6ivqX4A0nRf/Xlzl2FfqwMRvNROSclwidEMd8ZhVovWkBsAUxMHK5I0o+DsZjXl44SPlAEDOENGQ/FMcktJWUzmmTBmIwKT3fDV+UxZykq/jca3XEbZ/3L8ygsfvlN3gAiztSmrU1Prdnz/ie8//A+Pvfjz/fFPfn9d/NZ9V95pQXYmJTW3cPy89HQopBz0qOUFm5E8HHyedOqZ3yAkybkzfouU1M2qmnPn4/rNIyo2SDmnlBOIOgy6hb4rqF7s1xG+rz3mBC2+Z+PHRRXIcEveKtZSSHBwnu5asqR10ytkf3IUtQrkN9BeEuxGVTX9weKQ7nbr4HnWFq1HsZq7eOJaz5f0fQUkh5VM5TStxGZsduG4eTNKq6qWz182YVJy/qKi+a782qA51enxY4cUOF4yZKRUxe6EX/FbKaSEFCGeCyyRr7R9c6UAE3PtwEr4uJgtsDfz1JOFz3x/JZXj1umSa4MqfvCb2IrvN58ZbuilpJoGIE89IMhEePhdLrXwJZHa3pJrZyDGtrz95uN4qn1PjIs+vHj1vDuvv+F08JHLWftHTLl1v379na2b6p7/edp6uRA2kO/49AaZqp54YlP7/tW/uHTundcu71+x8d2DdVkmGmqqik2RF19dr0HCCEgHtoHIcVFST9iwcBmCUwg2DT05UygljMO5St6BGCHl5sLoglUHiYGWyyeXcQ5MIkqRtkeAHXB0+j6Wv8Eq++LYOZWxlenQlPCsmC57qbudUpostdAw6NGijQZ8QRmm5hSVrqCkegZaehvPYOHYyqSLM4jOl1GSIV8qHGSYCzqflD7FxFxHbUOq5k+NavEvcqhLmAvzJE1e9gJS6lAqMAiERhFRNk1FObl9cSEKBijHlMKiLQ9/9q/Hr/zyhct7n3su8RjD1OoVl1anpT3285/vOLjpFw8/9tiLf/7Ta2+e6/nyjfhJUlLj5rGgKcyHeCPDlJhjckaxmDJ7BZ+I+FpJSQ2NT6y+eRJdl3Pma29y6lz/qvVTquQ+HhxXNytvvr5o8d3VsEr+NweA0aDJ9ME38bIgjSn/686RTz4BSrfsbG0dbbR4pxVP3xUyvXjJicHW9vZOgiTP29s3Ho3bldfUI9doOL4Ecem1sUW1yysqqhaVLq2orf6gaunSBTMKCxdVT0isChLlQKIIzJJF0qGkM0jlUGIpkoSnwBHiI5k9aW5+qrR92ewgyPSUkuKbGufN9KoihRwZfj97qWI1k7Fb50upDaoq/M316u83n8+JPtvb0Nsy0LL9AaWmwgWkiPjmSxGgpIavnqkHInz78scvvMABdfzDaxlXr14d1b7cH5l35EbWflDAxPdfOH7jyTUJq9etrKvbtIOV7yVOqZENx9Z9f0dm26pfbD50/L0b8Sv2123aaDMIDM5JQx66k7MK/omk1mtM3NJZs3HoyKJJejC5QikpMVcQiAloKUvaRto+G30jc4nTCupHy6XjqAAAyMgQHV4eJq8yTID3gQpEai2l0q9UjrP6VSFa78R7ZPWGaKJYSdClsUTK8JCiKGz/ivSB7qb6Jkqqt+mM+Uw9Bj96fatZPVhSwpen8NL5MlC0+XwCU6Ka5k0UkWyZJYbKBwfP74dwyNeRJw8OX5RUVntczMJpITxZUVBGBm4PsLLG4BHYfvHDKy/88fJespxXcuC/xLLv2FOrVz9xcPOLCd958Rc3rrz+HprOV0Ih38yjoljCFKYnJxKpRbhBMKeUyOWjzVYRSYZ//ZQC8Zu1uqLo9uOOM4qyuvUYO6sSo9jNBWWbKoNkclruLeR3tYapY80eH6lYphxVRbHK/y5gYhGnVPhdR6mwAjd8VXLFuXH60e5k6FRl5aMXmrFOqP8CbOt8X716+/QlE08sKT/RZbXsa7cebTK3Otuijny4p6kHCF1UMOEhJSn0DkWMxpxVQLK1HFOLSkunzFhevahKsJTqwihFCA19K0FwPZmsFAeKbGEfjSF8MmMJLSVZmbT4AKtOD5QdSDM2c16wW9hI91NSS9QEZRt9cfG1scsn/Sa2em3X1T27oqN7+1v206cKeWIMNxfg7yvhGC9FILK3v7exnv6wqQXf15Lel4fPDvf2N8EUGb3aGafduW31xqw1u1f+9r0Xfr02cdOWY8ckVH4l2DmWzBteOvZIWgJg7Q8P7jx+5fK2Y/s33tiXaXEivbVb9RaX2LayxrGEJmgyTU7x9VaRi8GMhEdlnMKfKBHNnyB9gtqigidJUBKEsWcWDh3LbjjMsIIk4zBaHNBRJ2FljFUQSHKe0JQi42yQynm5J8gtKM4Vc49bUEiohjwZlQpQCE/ArASNZlll5dLEQ33UlMjlztSj7qk3cP9s7ersNI5ClyhRagrSEw/SRqEQEikgJVWCJXqGSCDltHLwvBDgSz+qSBL5H82gGvjqqDnoeIYxZbe6bb4FrILDHyjb3n/2+B9//zGzxiuHcZH6A14dX/YceO65H9548aWnfvH+668//uu9+3suhSYzSY2n7cufVDhPbEscRG9LSaGjjIk0m0TKH35n46fMUmN4xDcqSopKKaeiW81fyEx2c0El5RzX/DwjrHwppJ0CU9BNSCIoL0POsZunFL9cpo+dcveSkkspRyzE1QYCHIxetJOQOwp2NW8b4jmeZI3SdXQguiZ6V7CquFxl2Mf+qvVQ/6FW64WdLEZPftIfeAD8LJxTKqta+eqVf5D7ZMWiqqqKquXLaytr58RyA6hKtaWkKjyjeKz5iOPIvunpMom5SdxfExQJr7geC8diUr4s9WyAz+Q0xzn0Fl9ktDr4fkYp0Uup+WxunUnVsYvylwZVbDpfsid6T3RO40B9f4NyTElJhT8YcefSWw5bRFZ99UOvtLz9dvP5M40v52mHhWt7tLvP2LXz6tWjG9jyk0Sy93ePv/fck1tJGt26ZSM2nxuUx/6nVj+ybkXb/k03rly53NrWxm4jUy924h6Ypx4XaBcRnH4L2ATMERoo/MgZYX1CY9DmcHUzKDkgT4hpABw/E/6yHFRIO6xeodTh8+oCXYNN50eHTRXKwYYtrypS4gAjFTtmIgmquKUVXZ+B6RdhvcHS3d4l/DI4VONHKSOswWitS5Nos42rqp0S6unuqz/HQVXfUt/Y29jkJArOYG3tHFV3qn0ljT5ycdDE40LCocmE5ZPMHdo+nzjDkKvD9IorBsEE/jjB/NyMXRJcWiIlqDb7eHr8flrB7YALKSlkiYGcM7BM/vjm228McVCx5+s5DHL6Es7x+y8dfuq591944fJvt4180hOViAp8XGEh695x8yalsrLzSUkxDVJSed7MkkDEHY2fXP0zVlfIQDTnetH167dOp9uz1PWisQlqDPSLXCinlLq4Vimp2eFh0UWxFYEs/nDRFP6S6pq4WuVzb5eUCXjibpFtyvQ6Uael77WEsv7jpkfPu73s2asXRgZOfvlJK53f0NHIwOmc6F0zd+kM++hDmKK6u3YeeXVXQ8PJLxsCylRSEOlOqC66DVByrFaUln5QW1kNxa9WOVyrkw1CMcqW3IBkxTA8W/FASktRQD5Z72anU16ys0rLZifMu6RM8ivZ8Pqcdm9JtFaspSLuB/Fb4osLjsxQ+6KqgpZ/Z+mcpaFdZ3P24P/Z13c6sL2hRlQ7Y9vdABy0MZe5sb+4LGT0+JG++p4hknKP1A/0D17s7e/XmWHQ9OOxuROq7KotW+I3jIzs+/Vr7/+QmLqVqzavXrn+iSd2bCC4d/eKY08d2NC2H0P9x2+09Iz8rmOAjRQCPRyVOWVwzAFssYsjJGRh9qjaOLoiLx2gMCiCI7kuzSaNzSS4u1hmmETnAbeYz9Vq1fZcG6wfY4YYPwlTQnwKqS6xF5uIgwEdHPKnEjVPmVahaAZKGTIk85Cm3+VOGh5M44eXC2xu6HgOjUaflTq/albdvq6++oH6xgYicU72otrEZVHPyrdTRiifuQ/GBKJ4lwEPJr9PulSEUj4xksEahvAO8hKl6LDd8cneTE0mtlYck9QKhQkbGjROqpqbIOtYScndbeJxqMiN14786vW3LpxCkrhmzY4fPrHt0h96PuGw2vA+/rQtbev3vvFKliY+Lb1w3rzCSXPnFWbHwy3W5ck+U07EEJVLry4Ov403jZVU7JxnAKi4Iq9fr629XnvznfKT/Cw/YscOgVgpKTmlfIFapdGbUZYXFhVUUZYbVKGCAhIfZAgr0BUpUN99lFQEhkJu7pE6PWZOgKowHt2B8IVXTQNM6SebWi2uo31Y9u7ataeLexaesEfr+7u7z18DQfvyy/6a8vIH+TvCI9WzWJPJOQXiBIhfXVW6oEohY3LTLBIRYb5Lkz2JrVSyFJXMVMrwJNWVICMULR/vgSUwRpIIXYz6k6Kyp2Rb8ETzm8S5zRhXHHF/jV+xUYvno1mXWRpU9Z3Sz+fbHKPaUe3Z8/48utSGhhrw2whRxC0Jjg4pH5vfI5RTKhCZcXFUzcTVcuHTnWit+l4ehNAuJdXX2bUzc4A9/6ZViadG2na///h7/y1q5ZokTJWOrV373BNSUVJSzAPPvXflyvsdIwMjHbYBdtk+WJKQWnHUBcizGPB+IkLQaRZ+I3WSiRu/8CFQ7kEwEqk7Idc0e9SSJEhDRUKZqeUCNWiiCCFEpFlilG2rVsRDbFtRrkfHLFwYIo5fTMRm9gayjJyQKyXlUpXdtaQiQgimJ4kBxRTXvhm/t9yk8cvmbevo8nuO1g+fPs0RBY2isc/htdD64T3nd2A+K7RzUR4S3sYh65MPmtXg+mZMQwDQVVJSDochyqCWrZkZNiB7AlTsPlZqTI5xqFBUk8tufQel6ePmFthz/vwoNfU2cTBHLiOCIdjvifW729oOH3766bff/rTrnXfqBw7u/fKVXLGsk2VvOiyK/CSW/6SaiFpKTDJzzFb1kvCwO0qKNdMzmyuCrl+//pvnnnv//fcvy4/LYz/Jjxvv/7ffSFUpWN6cyGnKKRUYO6VmhU2JC6uqpqTUEbOKnGHLameqqpVV1X2UlEB+JWpsDDO8pBRpGfgy7PW7AuULXW0D9S2cU5b2rMzBwJK8jPO4TDZ7ia1uPdTNbQULhi8bip99Fn4cF3GkpaqyGqipVhCU6uWzqYyEWdVyq1DmPzytxrtyJxWmpbHwzRewT2n7JJpLaJIpaZPkwyKTSpuLQYXiSxFqm/tB7CIDDYzTZM5hgRLNiRh2H7teKamJMXEY5E+orHjmZ58/Y+hWMwqNdl1dSDE9sL1h+9g8VR7i05XcMi3g5oKBhvbsWV8fgt0eYgB3dpw71O87ft7R3e3vunr1/FFaOYTMq1acOtW2/8Z7j/965YZtOzb//NixHT/8bztWkpV4cD0ltXLV+6893tHR8emnQ20bdmdhEwDNgd2Nz5WLoYTBFp8aCmJhFxzTO+Y84ZaKImMOMUpuCiLNLAgX2LVGjT1MinUJdg65uC9kiPhQlq0KzQ5zE7zi8becPDmEaAaycYw6zfKgytqiyvE2r5SUtuyuAGn4xMxcf16MUG+xytbZxAMmMT15287WLruzr/H06dPcL5tONg439hHR0dpugcenb+7DlsmFgANUUi/+Z0iXXFD+gFTAI9EYi3uz2PE4XW6VG+92CAEYCkbn+QR4Zycdp/i93N4sPvtsObbgBVArr/rwNGxqxgLkyuUb225sS3rk0qWtmy789rnfvflmfRN8009vbPuyB+k8nU269H6MVAlYqaK9xHkMjt/kiXl4OUz7ZknNCZJTipL6T1I/l7/x+N3l959TSmpsloqcXjlWUlULlk0pmhq2YHmYtiKgKSqOmTAuIcwQNNmnzFJ/s6RuBimr6AIiiSO36c1wXbQOe1dOeQH5NAP1gvuFblzRro10WzEGPu+nMaj3XSMHnopi1Cd5oJwxvzxGN7WooqKosrSUL6xycUqu3Wn35M4XrKK26LrCc17s8aSOk+WU+MemCk1CFk/ZypGk/C5ZSgnQL19kIOx/s2bU8leZo7UKgh6S55sccT90pHCBJzLQ0PCtTlpesXTR0kf0DnWnpxUH3YCwYB6o6W2QknqgJs583qzIThVwgpJa4o4723iy59y5gaP1R5379rWiUOzyOrod5zuvnnfmbqs7WLdtf9bevfsh0V65cnDHpztWf2f1auS/m3Cj3r1t2451Tz/3/pX3bmzSW5t3Nq9Z9/NHslP1Pqg50B4ciDYMlqyU1CiPBAOgjrRApUjCGKqkBOiNSQSle0KoyTLmzAx3QiPohMEvG2G3HBE6pfaUla5cvtAX7IxZWsVyn3kzU282+nIXxdJtV6eb/NroOK+2/O6t8XQDJTUxJhKYMDJah9kvluuapH3EShFiMBx9+uzp7dvJD2poaCRc1Lpxn8du221ly0TfKoEPWKWzBnaKrkNKi7bczHEF94NzqkRIHXEEJED5II6KtlYtACXh2HFCfRd8YuyyE6PMBwpqTufRj5vPNBKz9OqHJAG+9daRty6/xePj19868uGHvf0DLW1DF4Z6eiRYBRUNLL90paRsBn+JSHuj80IWTpa8rZCvUPRbe6lZ8bAnqKn/eY/H9evXb4LoscGcUkVBxkCRI6w8bMHisAlBWWGWYk1toDh4YqQ2JWi68esw+/9DSY09Ir0ZDHncrVgpYrdjtHaYpxUEcs6PnAoFo9jY3rax3ePaV6f3muubh5p69/wRXIKKwsXlWeHvcyyo0murF9UGLY1n0xhUOU8iIA0Gw3j6wIrlyjEVG1RlyHElj5OziJUup5NQJPKlw5vECIWGCh/MbLgmN3e9CblRs2qDYiuicDzD/gCeS8mYl/z9nFI1OndITLTR4bCOA3Z8ZmWmA74f2vFoujyllnplOxU4revq7DKGBJTdDdBueMQ043DDSXy3rNvOnTx5buMNMrDMXTiXuFzndeaujm3Pbdpma//s0x3PXxrZ+acrl393cMfK1Slbtj65tm7FivVryJ367kOcUTuf3CThUJ/uePSZn8yYkGhGeIvAyG83INTNihcU02qTvZPNhkaI1ZvanSOUGrNVk5CYyx+i5jVBmmUpBe+GnI5IyaCmUXSLXAr2HCVFjaEEZPsKQV10McQt2xMT7W535vyiyorKimyTmZIyx5WH3VWCOM2S681hoSM6kUidScwc8O/f19rlsTjVZ6N3nc5p2M4Wr6FhuN6vs2v2tZuiVozY0Sp5PLSaGJHgjeS1iIBYJ5xa0EwSR5nzZAsNyE8KLOcsyIoN1ocR8qKMbHEK7Qoxw62SKi+HnN7Q39ff3dddf/baG682vPrqm78iZfNX3/sn3l77P3/16rv/TM99CLfWT3o2SMQbGue0SSAUhflJRPbK7YSbSXTIwukh0W5z9FcourKX4pp7Jv4DaEaV15XHHdSJOWMfu67M+QLruQuqkLpOnxZbNX/pfE6pWUGVkWFhSUUBY1BQbVpp0GT1GOL3t0sqYiwv2sicZ7TC8LR6aIFbO7q1Sx4M7DpzasMGYL8W8sMwbTZYL4yMAAVBQqKiek72zwyIfSi9RcFCEwbAVUUVqZqqWLq/qblWgYKTxMuqaupyDqnKyjlFaSExHsoHRoVsepPFm1lAvtSbW6lszGVxQkbTIX2hRjO7kie7WBvNdeMHmsrTKYLeiPuhzSLuCA7JU3us9qTZy376H9bYdIO+Q91weQKwNeWAOk2QWKBm2HH1aqs/I/iWei0CR/8cGp6TTUc7dn5x7tz+FaHbLGxou7oAv+u7652mbXU0+e2fHnz+Lyv07/zp8V/v2Ls+JWFTSjICq/i0tTsOPvdff/3en468++SN9qY+x74fzvjZlClTs+0+paQQQkCFw94HEl8o0pYESdyBToKw08gpZTQ7OCg0QIJih2QTt3Ob3mTx6qCyMUqR4sRWWPRSnFL4aBj9fnEFQfSHSaISBehJSrSWZOin1tZWVC5KyfRm5KnMccpe9W4lleW9qY1gpWOyQXTH4MzUCoaC7eQe5mZp/vhW9+I+Z6nbyLW8fvcpjkynV6xlffJDKEjQkdR+p15MmaBbQZPKE4ExjEAO2igxl49COymgZGROiVqJl8vImVkWcWuWKp+Z43d1HzrUadY19r5Bs4l91ZEjHFccUkfkcejQIc8haoob+2FMKTAKikrMLhyXPikVlrjeBTc0RjC/yTjNaP2q8tv321slNWP1B7EKO5ay+iaIrpQTf1Yk1L7YoMKwkBlTMsJsQQqvbxanVFBpeVhWkDks4QNz2KSgiTOnxH7FobhnSUVEjE1TZRmIIQiTw8bIia7Uu7P10KE8kraKh1v2Hzv85SftrYfakXljMsIZhUMQbKWT/duZo8ofLBCB7DTv0qoZpUUV8ZqlgvfNqZhk0Udl2mbUVlZU1y4tnF8ZxJwVtEgdiNQkJ4taPl+iYljmCkkWP3GYExxO+WLWTHllT4LjHzVeKDIV+mC3R+S8kSFaXcwYOHcfj2le8iZUZmumIXPdd5/Ysc2kzhvs63Z39xWL1z1fcoDtVKChUT168Sq+IyGCzfNaFCzUqs42NA51N1k6bly+0Uas96cjA87mnV0XLIyQTd31R9sG2j7bd/ny839pa+s/9Nprn55auW7Tpsd+fmn/6se+k7r30/d+/drj/Z3P//DGZ/399Unf/tFHpfMXz7Nx4UlJeV0YNbic+tDswvGzps4ohDKcIPG+WVYsG/LUXsJN4FezmnJZsyTAg6MeCW2GkIZUZlAMJRJN8Tlkh2UUH3Ssx8RBhZQobAuJ9/DExVnGV1R8UD0/MReoKc9sDCjf47t6T6iEcyrpTA5RAZNVoNd7+7otR3VoNXch1zwtrV9vb6NXr9mSFp+EDUeiKCGhcagVpwyQCJ0snMQAFlc0F563lBQNmBlnMJwzNZIzGZqlh78eJ+xDWkN5AqqQ4oKb5rMFC0c/7YCfs/Md7OD6TzZsB17tN76DCo/Aiu7+/gwfiqGdXQQHMba/gjGFRm8lWlzuvYkkrSolpYSLxkyfHKJyZHyjpMDynlkpp9ScIoUvMXYa3XqTouLD8mlji5/4YvBfQ6XCXpqzYMkCPicxkBpUYSwvDkTFBmUVhFSPkZfuq/ELyxN3UrzgLCaDl5nX3tHuGgwBQSmo6R0g+rylTZjcLWJH1tLS09Jz8o36xpqCCM4oSZ4qqzHP/yB9Rm1ttmmBwqfiaEpz2m3Qmymp6upFs5bWxtZyeOVPC6iyUuLT4GiJZQs7KlZQSHcpK9FxyMnFpMUOGJ3UOHZ0QUVzVSVeJ+tnvvk+Y3HY/ZVUeNg0jxrxvd+Auh5HvKgoPTLvwb7BPlpHllEFxcWBhoZATQNBqtpOu95ztjhcgXMDeXRR8AD7mo7qO25sqyOve1tUGwawvCL2oyJGdR3V43doubB/VVbboc7jL1w78v77//X9/wqgBKR0GaUV4QPnPtu65V1Ld399/E8/Woq8ZVyUQ6czKsnrDsyEXKaEcVOJClowPg3fdw6rJCLIwMKMjCq8+ljMkuKUpdE7PbgmwkTViokLOaI6ESGJ9FcUizlYK4sPDDshwQWCJwbnlbhys7xxbmv+/EWLlk+FiQw1iJKKiLhbp0xJeXMUqCyGa1GP+ZVdgqtc3Y6jMJix5dkVfXrXdpYk2IPVj6xYn5q29sknH96MfQbjGh0eBt+NSH5xy4CUiNGZAWEvztsloqSEOYWvjNlrW79u/Zrd+Fjq69U5p4eH+9jEMQjmnJhO1Ki82oHt1zq27evac+LEnqt9XF/9vYODg52DxtFRHdIsSmpwVKsaPd8Jd7ulZ2hkhbg+IosxScxbFq+P3WFUnAhDQiZOngglyReI+HpJUUixlNTtTdRN+lHQTW6fcmqN1YeCo88Jql20qPrWrMQ8Kn+guKdULcXeOqjog6rYv2OWCpvuMYuxgZqAjVySKTv0xMLS9QrGGTNox05WHnR7LZ9A1Tk5pAvOOR2Q61sxEK1xza+dlf1B7DLbpNogxRYkqHKWWa1ZrlB2q6sWLV1eK9uB2ApLWSDDxJmEsiM+QVTw2Qr4Jwg6JXUTWpdfJ86tZl1cNKMkEkM4u5q2P08XWRB+f20fJeWUkjITE4h0zpCpMfjgekZGl7inh4c/iD/a2RqajJqGa6NXBw8dtZ/fsxDwSb7Fg76LjeiZmxodVkP7xjo8e/AZzIUNDthCYg1p0F3WDha4Stxw5k4eb30Ps3N5e+3xX0tA4jtffEHqcbvE6LqSZpWWkrkwLgrlnlrchrh/m/32qPwJU5aVzp86Lk1SgZOTMVug5DFfUZJC1TJ0kd5ux5XSSlcn8RqoKdyIj4JlhIJapxUXPyfJGHL9Spx7dMx03BdMWSZdnM+aMH7KsgXjkcpzSinmN3crKUzH/DdLCnItmQV2KSmkTF6nUxJHI3dF75q4S1A/7qQbXlqx7uFv/+I//eLJTfvY+8JLH4S2Ndyr0l6MG1R72kH9TSYwTkOzcThnmOdZDxDZaNav/A8Hdp8aObV7xUh973ZweaxMVHtUJ/Kip3FP4452tplYTJt5cvmS071NvSf7ADHdg0ZZZoujRXfnKOdvnkhE+lwYt2Xxz7BScOlcevZ7BDf6PQ6WYWDoITGTJ3NnkNDH8G82fiB+tzu9O4wjioRcPudrfFhlSrpFjrj50TljxIm7EAD/dkkFSuCv0DyYTaJhNDhGzWzq4yJnFj8gA0YjZfSKkkHBQcUF1xhZVhxZo3BPQZ5rLEsrl09YVBS7aHFVVemspTIC1aartfqpy6ulDaxaPH95pTDji2KX6wKBSCcx4mmKlyxyKOHxpSrW5xxZQkbidyAX2YtkvTXVHGlk9tUJpuVTYiju65SKCJtuV2MjrCPzXBRv+iiD+HVRlnEhbEWKc642w0s/WbO98exVaCBdg3tOxExeEqjJ4yIpUfg4fd0uyz5KakviNmh2hKPT1hBII1hxVys1tSJh5SoNzuYHD94Ajf3tby//9q3Ll98/eKNjX93Gz/Z99oXlEAteh2YGhKz588drpKQyIO6YxenYEj9uFqfU4lmFcksBjZEulxuaSodfGAOSWnAMWH1k3nrFaJzgJmHyuMVji5JyiyOSls+BO4CwVxFXRE+cHKx15BIOGGfG8yR9woRJ8VF2Y3S0uO9H3N1t1qaTnZQI+OL8Yg3rEZMUTPrsHuTAohmJ3MV6qpcgl8MvPf30Iz946OF/ePixzasM/j6frxFGSWNvL3/OaeXo2FZH0l2urR0oQnTJapfoDJGxrE8LZeAZGdk90lRDCfU28hROcNSemDitprh4e+MF26moLIsaa2jqC7Mzjjx8nsbcl1h4uUWYqVKsY9h1EeVMRZHWZhbpI9EGkt2Mbb+cUnJIMUtJSYXdWVKztlYH3UFA+voj9iYV4hYj4jaB9qvaip0TdFM7FRT7dVb6fZRURIgAS3kE2BGKqcl0lBi7/EY0MSHFkIh5xjRJithIEcfWnJ4cCOTlFSuS2LKYrKrq2emMSxD8KuZl2qPmsxKrXpCalT9h9tL5i5iIqsbPWFTJTYIxkHGqrMadxToqP138WtAi8h9vousQgaJMV8mhmsXylKf63XYDTXqc6Be8qoL7Q/sUawW7MTg4TgdUbcVkWK8xuPNEahqpIhSzOLir+XwT91+eU0lnV6vFDLlnWBW8cGEG+5NhKSksDLtb2/fV1SWuISmUpaKANjD1RIJ39Sojz8rk1WkpEGapqb0b9pJxeIB3a5NXrtr/2cZ3v2i1t1JRPp1t3LLFi2n8NC5mqTgxOEH/as+MTx8/fvyMGUSXkPCYL1uDbI30CGqXpEwLi85jNSjUc+wNGetL3IJPKyRA/g6hqrJl9dqdRpLKEPYiaRJhRwbHud8tXr2J7ClSkqLsGcDj2nv4CkwkV0Mr0CFrSABvG/iCy4UBnEdKipEHAARACFmynFFPP/XUI9/61g9+MP5bjz61YqC+r5GS6h1uFL0MNXfeuu1gHd7Uufv1Xm4cTA/Q7nPbrdb2uroOO7l+Qy0j9Q3ba/jc4dPTglWDF2OmzST9Y+Zoxz4iAzy+4ZmsCsGE6sfcgSFq0CPrRKMvKziV8PMdSp6WUPMRzslhT8n5zAZQdJXcFiZOjJElgrn4Tia6lNSmiqCb2o45ivzp9g+hws4JuqlP/Pu9XP52SYUVO7yKjTAe2AjvbS7fVQdZHp6MEKKvlca3hrLivnSaWwq/KS+bGSP8D/ytNVVzqtInVDH41C6H/2w2WvIXsWushYxeMcWWVFVUVDE+bXZ10BhRaZE3wFrYqziipyib3hQ5mhLEi4xhinMrOTUxM5UXonK+VeuFDOf05bCH1ulmhkXcb0WFxdhLuG0R1EfD7eSUsrNwhAeNjILFZpfODOWmBTSrr8/R1dl6NfrlfmxAd8Vcu1oCB5v7BtQbmGz79mnWJK4h4zJJLCAwnrSKBM9IyImNCRmF6XcffpJlf8epqN2797KS+v73H1v9/POE7QLmmIWVbU2etXj+Uho/QgnButyicrUDhCVDpE6bWzgJyRi0e+l2oyDc5Uhj6BB2AtC0Q4y8GOxkA4+bik44S4TXuBUsnVR5Ujt4XdBJoWFCVEH1SXWp8dkLjU9KJMAVl19tcI4/4x4lFZKVCbiRJ/5gSKVx0kTMRWYV9F47CqkMKamc6JzBvr7mkaee/vF/fOnpb/H4v7/104ee3j00JufkznNSWVydaTZsWzOWnOAQsp/PIzkKEqZQ925rF8yAlqHDTTU1gsn3bt9+OmMwL+av02aWF/+1BKObjo6uxoYaacOb6uuFuWVnk+wTnxoAeVGfaMXj0Og3SARxriRU5pKnJVa3PFdKyi3PISSGDZuqxGFeckdJydD0o80VQTd5sUE30YnYWwKqOXPGgIrYoNi/q5but6QKtC7JBuILkxQ+VKg+V/yC5VM1OhrfMgH1ah4ITJsWKA48OG1yGR545QHOrzBu+YkfQJaoRp1fXbk83URbgk+jYRyg5ZzKoOo0hyetqrZqava4KqV/haBUZQ1gmKiyAxuA8mVnp6SgQAQ5T5XSSpHbdoKe3UpQ5VSrj209hzuozsQcR174za7vflq/EGecUlJkyoEsmGx0tTlutj/Q0+1b6rppWhqFyDbcZyYodzC4t6//kPninmtnrjWIF3xfPVZoHF+Ekqxfr0QdobHFYcvpFJwBF3JDaH56YeG3f/ptYqjqNmZpdud2bNux6eFf/MOjj2Ge1W7ArtrNnOMJHbe4dOni9Cyh8Y3ZokgUhyZlbn5iqCSYJOBrI5T71CgvLudiRYSRl8LyEVdPc0ke8rrIDOKjdII+i4YCiIN8Njt9GkYFOBV69fg80y+yFuIyQ7FuIn4bPxiOVq8Kx6mS8nvcc5AdKFcjpCSSrVCe0NmKrkuPXTP0cg51EOBBs2tk5SPfevpbT//HH/+YmvrZT78FmXVIcR1oEhIgRdXb2Gw5FbUvd+PGVTZRHmvV1iwCFSRLoa6utdUuJXVS1AqQK3tZdjW680Im/vXEX/960XfokGHn5TPR9OCU1Btnmv0cUuLr6Ca4XKESCzNYJY4HZlAmlnRSUlGZ2J7RHfu4tVmxA6KDiQ6eODGYxEbdkjskiHIW/ePWiqBbx9S/ecyJvS1C/Krfuy1EvMfZ9Tdps19N9NMdOM4wTamFL23Yl2t12Razp106Th9XU1D2gJxNFFegvExqKbwsUAYhMzAzxrtorBUtmp9dOsumyyD0EnKnvqqSQ6qo1Gr2m+Z/sGDWpNQFlYruGPSigppCkq5yZWnYhYOHAnqJtkNM+7hhc3/NnMv6f4K/xMvVAhIpQ55PPfN2osp94BMhrpwYmaVYNyMzz7Q5oIRgSceAH+fK3DbQ1N/Uy1aqt7d38OLFs7smv9zXjdZydPRab0NTE92OWgYa8V1JWn9Akp40obip0I95hD6awUmem/pweuFPZ01Iz87fvDVpS64kbdWtRS/32KqNue12JYKEhN3c7KmlpbPTyMJ1CxdHnLqoqcQ0klJDE2QFBxyMBjMtVeMES4AP63dx4ghITWuHGoKKCoGLKt4UcrPmCqPjQzXlJAXaaSZ9xpNJRoPNRnw1CDUkBfKMkxJzJUxHk+XICda6Mu5ZUlm6HCXvjL7Pnil2kx4YEpgAGfDEINIwkgM9x91lzX3kWzcfP/7Wv/LuqZcOD0muS6/81yuV1XKyZWBgwASzJLdehixcg02mKKWk3m11DojBfpOUFAt2amd7Y+Pp4OATe7CA7MSE4Sjedoo29ORACzEgYs+BFF8CmIT3KOrGsZKyinKMdZ5GxJn4DqMP5oQWu9BohYg+OYS0kG+UlMwOz6xWGj8OrOvXY//NYkoBAf+tXvf/nVMKwqiO7kgUAmqnxMXqcTtIqaBaYytKk/3BxZRQcXFZQVm5wssKK8DuetpkRpzCm25VC5K8JsQhbFdz2V040qsrKItUhzjpVSwenxw6vmKMRU/9Vegx9Y0oq4kmkDdJwjmoKKHKCh09kagp01wKcpLXbNez3cBan3Wk1pFTdlvedT8lJYEE3B1oYoWCmuvVjgmLKE53c8dIU1NvcXgAG7neSFYw02a+3Nlt2afvOjvcxCYGR6AS49VOc6slcxsqeJxeM8WpSEpKWOV9bmJoLHWpDz88bvbU8Q89+g+PPbZ6owcd2bYtT6ZhSoLXK6w+d0kOovrMhAnLFgO+mZw63JLR4Mn4bUhlIUV4AJ6q0udCbiTd267OI7adM0iXQeXh9yAav5xIdFAZIvijnED9xK1Z5wSMdbj8Tkz38exLxC89KcmG26sw1OO05szEUL3TIvIQKSmHtuzupmMTszRmWfWGKCWFK5pH7JexvbAZmBfViugjUnWxdd+Wh2Y8o1TUv8rbj39MTQ01sQK+xih1Bql0Y1MPNdUyMJK57eAppaT6RkYoKXzTSP455FEqqqlJkVJTU01MVKfRdg361J2dV9Ui9ZTp/AxE5YF6v5gEKhxbSkrEK24pKZ61WwmzFHQCcWZUVKbwn3TieO/yuyOBz/NCJkdyM1ff2fjJ7P7MKoWJzqPy3wimKj8vusnrib0N8lV50qRcKqfWBmlCx1xdbgIUc/7eWQoyTrTdjYYf3ghR1Qa2SjaDKb1SkWrUVkz1TC6euXBmcXlEWfGS4vIlS2pmzgxRQU3TpcsQVbE8XW+yYpjDrEAOEjhsLsZXRVPgstkSZ/CnCSlTqsfos/I8q1MlrBiHWoZVB151ME0kAUW5A1m91oTlFctCXQa0Rja9WoLDCbnDvqDgPucobg/hkS44iyrchjRih5dFgpPIZhnwuU7MWDDXNxA1Qn+/fXvIxIUzF46iCt/X3sdd9+TJ3mHE39SU2WPJXbN7zfo1pGGIeEl6UPESAPDim9nBmZQ+/pl5j877yaOPpq3KPOohkXpl/KoVNhRGzWaAYFVOhtkeNXfW7HEp/M+ykFWsJRiqk1LxJkQQhVtxsmLAlpoEYQqNrVGqh58RoUvHg/4pJAbOqVvZRYlolxs2XSe0MD/u53jVW6KwkcREPEr84kGbyVzXg4nxlUeR64sE0Ztzdyl3xMRMjUcVIih6JPQzvOlpCDx2at9qQOaYISxCXiqjKyu1cMbs2T+j55NA85/97Fuzv/XIBqmcRnmcaTxLp9xzDgZX/dDeU0P1ct40Do3wIPNnd1R7ff2AQMRNb7BZLyuQmmqRT+kVWKN3+Gxj08mekywzzjQ3W7BdILLA7Bb5v8K7Atx0y+FOsEIcvWmUfCslTZ5Rn0RToefaYRoaoyfHoJXnMMdZcNodHL8iZS9VoSghcBj7/Jsl9bl87BYd6WZJWcOKq/gpKVARtCSkaOxIGsPa/+6SUrzvfCHSmMq8ITaIRMfqMSGtrKioDLpe8Q9XVaNKKuLFEydilJ27ShxIdKbsWVMm5Cc61aTlic8ADoJcf2brUiaseLMhKjQrobR60YQF1dXzx02tKJK1VdGcyvmmheFCWAgvr5kWopbYTEl/gTCATVPShAX5BoczU6icjNBwB0LyHHFl98mbUEqqIM8pTSwK8FBoZja+BWqJ84bKw8zdODQw0NRQED4TwGX75IXPLgGW6Gzd2Y75tKxhGrjujSWjV9lAZa3JOrWbzAxSnjQ2KSv2XK7uPnZBZuc2LGnmPfSTh5559NHvfGf1Sls9QiO2Vbkj+Eo2g+xBEyKmxpRQOH5cqibTjgGe0S0MuAwSXLMI4iCWN0GcN9KE7kjfJ6QIrF0Ugz6Fsc3ch4FXiEo9ZpouHn1Iz1lIeRQDctpDMmE0STI4cSLkOnGdJ8bMZ8FQitkoV9QwfCNV92j8KCm7FtlitNx5nFl6vex6iZFAuCFEV0mHZwtx1ZWV/QMqasqE8VPIXl5W+n8tW/CDhw6g+EXoTFrAmavG3ob+pp6egZ6hoVN7lQUL8kUqConL7g2iZGg5/AYfG3JMK5D+5oHtbChgZEjl9TacbaqXkmpobO4ggOGcmCyZS7QZyoksckx5xQSeoKQcVpukbBFjLEwtG9lMDj/7KeiRefSurAJUInaeHPF1jp9YGrGXmiPldO9HUdHtimLZCxSpgWJgDiyKnRwslIXZS2X9s2h2VezfWVKyIStw+8eCTDAEFmY+I7DHNBsueHVFVXXs9cVRDn+XLPRbfWQj4s8D+CSLS5pEu1Ny+mhs1Z4sjSkzNBGqNd4uizKduXT2UfmLSRuoXJCUFTquovLmRFg7w7zk5lquPFLwG+EzWyV21hAab3GyKLFaTGBWeXKNEWc5/f4PKRESau1atjhuu0aOA5tJ7rzQTKHIAWUBk/c0nQ6UT95VE+CwfbYmh7DRnV3evqYxCKtkVPTfaJHs3A9sp0j6lJLKgtEaxbfTYO9maNF16ZNSsx969KFnnvnJQ3PTH15bd+H8+Wb9qVMdHc3kffr9WH5hs8daN0V8HDPFUFwtxsdu6dzkpuPwW5KSZdWdz6o3C1V1jvjIiseRuLWKjQR8CPAJrY7SEA08fy6ApVZeLPFIIoZY4kcB+SXhNgnfdS8xZkZLrsVP8CSJXQDaasu9QPSYXE6pSFks5GFwmykBKmKLAdGckuJCzovmn+RrPZX8g9mkLk/9x8VLSz/ilOKcemidgRDR0dHO852Qk+Ii+7sHBoYODyFxJ0SpXjg2hyV5AeGYkG0OS52dGTGcKC8vE+C4QcDCkyLGOimqjSFOtt6WofpDuI222i0YnWO6m6GMnWZQ0zi5uyBdho/BcyJwyyTWzQaDyw8jHuehKJQTipEfVP0SnePOksKtJeinO5SS+uBf7/H4gJq63fgJzy89Er38hLKwkKqQkKDquLIl4ZEVRf7y4nBd0R0H1d8uKXE2iXTFhUxUWIghkSUuqgQXQ9MsyfJYvmx5ZWxVtgHdtEuUc8KuYVh38RkilDPhqxRFgApCGhbbULs0JuusIgKrhTmbBU80rXTR+CS0CqHx86rkpkBbG1uVppvMgBaggRRStUhEPWDULgzrWZCwHoO+yUuluGqZjWX3Z+Ny81GudYJZYb7PHCSOXQavIjnAXojL6HRTSxvb/IUnorcHiifHTNs+fL5LDDaa+qXp7wWp03XKKXDeaVuDstYKEmYT3VJoKCxWlEXyCqjxEU/I/s5DlNSMH3z7u9/97tpt1vPnrRc6Pr1g8PvO0JiZpYgRFmUlJqdGgaLJ2SeCIrUrUxOfmkqQnStKqBOTJqWSJspiWpzEpWbEn0VOpEjxa8lDswBTTIkSEKeJnBzAMOAwWah6bITvYORBCBeSM70O9Z9P6zMwA7M4Y4TndPPds6QmRmkcOZHKX5wT5+WQIs3eaWXVyx5PqHic5pJVas9dVzi7dNH8H/3oo9/85jdKST3z6MpcTyfiMyHfHTpE5M6Z8/X1Q4chqh0eC3luacFQ9Gkx4+ihpFoEzbDsu7iELHnp/E5TVG+ASTBW8XLT8jW3tJxr6vP/6UYHlrUIIRyiAfZxr/bIXCnmurKusgu06ZQccJwB8W9DIY3NFKGsJcIZDmZUZtkwMfxOJvp19lKwcIo+/1f2AH/48j9+yQ/lnfLjD5cu/fiDO08pbXFQatiMoCJdYH7R9JCg3LDCoLSweaVhoUFRBYuD/r5Vr1gULPEbBQJS4hrpmvqIknG5bNmLlYOqunJO9bLsdXPTbGazcoDxZxJCHyp3ykyrSVyHCQilFUlJYIA0TRE6uob0rahMlnSJqYlJ5NolRlkSSysVbSQoedWMKOGtETLrkTlFXE9cxICRskdbz3c2jsEuB/+b4Axn8D0yXe7R+JVrvSLL5m6NfwMyCT0pFRlm4SJwpTawUoFPrxrOCF6yJCQnurfROtSMJ1ILEYlsI5WoN04CYjI5BhiDcL8QvXqWkqFG6KmFs5lc3awkshR+MnvKM/MKf/Dw97+/7pS1vtlquAAsoOjw/H4GbYm2kQgOuP0Ym7jl6jAbEvNhUqfqkRyD/KVAHU4MNUF0YIiNE+MwlfDgZOxjPqeYtOL2lyMtn0oRHvok6RpyAwm7UcRvZSOJxmozPjGTc9Cvc6v18O3ITtSgBc7IcVtVZfcqqVC/sCfEsSzOa4AMyY7AaoJ45TGLNzskKDovf3Puunmz5y+az8K69KOPcONeumx2esJGJwr5TkHsDh1Va0fxJ2seGhlqGWobGeoDCqTz2/DUU2LHcXhopG1oxMqN0uA/UYwjVXgN/HbmsIabj8bG5gsjhqNHuzu7WwlwzKTzNKOE8LkF2LeYJc8AVxQft2+uNVpTj5V4Wrt8qfQ/mAIlYlQsarFIQZ/U5onhX5+lgsQhaS1UUeWYWnq3M+pflUPqZq0AThS4K2aF+YKC9IHqoIXBmCItn20IS18UFpOAzOLvhidIhDFC+1JCuiODt2+fCLOMLsNl3zcehl6RsIyKKuYv+qBi6UOheuQx0I0hTnOxAcMkZTI0MSazDgmVcNlcQ+ay2NoJIqKjM0nkgCLCJAoBWXKUPnR2VdFNV8/Y6qUpapXaw0ysl5bKDuPH7My0EeUHpYGvJEfavuBIv7+YhIT7HqZAL7X0XSU6FAaSCSdmyaSn67wQm5gUe0821Y+MdPeZdRdjFub0GRvrh5pbxkzTT/cOKlau7I/8nDTeTIYUFEIYHMuzxDcCP+aoDlKpEXxkrvhO+kPzpKTGjfvu9+c+tBJpZn1zx5pTMIm6O9UkSTFdA/TSsMBHIAwdTyEVV6ktbfzsWeOS9WqjPjs9lTMsITTXg8gQ6ooyOMhZRdQ74kCFda5Veh+OJ4En4DRJ5KHwhXzWLHB42FzxYKbEcGb65VQ04slD32ASeAL3L7c1p+BuOQMRERM1SX5eXG7vBEu6DBb8obFEIxYPhzdBsOFHYW2hrs/NnjB18fzFi6kn3j76zUcf/ahway75x91e7Dj66g/5LuJx2NVsGBkYaIF5NNQHt6KPmlIedH1D+hHToS5Ha2vnRBmHy7cP8wmNwzm9Z4eHpaTq6z2tXfhwEooqJmvkaiNbZITAZd2pOAgQgGUkb9VpYBSAack15/TzHUkUsR3OY1afUlJECXFKxYSHfQPx+8fNwvETCce/maHGwIkiWfjeKqnQMIgMYeWLgqyBijkLQ4Kq8sJnTg5LCxoHnJZX9fc1fmP6u2BP3FhCd4wIaYSLyEq/y+XclzZDXCSQFuJ6BFFvkkXJ5/KYRHvKAczEgi5U6kdyd7ktZ2XGL5pTO0vyZnFODeWw0kSFxgvlms1CVOqyajGjEFyyaIGVeCm6DQOiO6hD5NvhtaUYcglxQOgyBOB4omXNG3G/JSXCf9hURjM73kyJL8uycWgx4hoZ+XO0NHf1Q5lD5I12XRzF/QlYoYUpmdXvcOSJi52Dg+x/dGRSwKCwZ60Ktdmdep4DhyxuKsyYHR37ojS7248OrEAvWThjyjMPjf/pt7/77fTvtNX39fk7NFlYBB2iprp9gxhInmF5SWwqMFaGWF/SwmTNnT1l9rwET4bblD83KZdti80DXYo+NUPBIRSeuWwOInPGUjoUHFmpqDzJyMaoUvxd8X2JF5MBsUbEaSBULwQLEgtNovsDGcuVMFufQVV215IKmx4VasbuQkoqJM5JSdlFzmgzcUYBotD3BauAGkt0tkkzyIJZDLHqZ4t/Bl9xyoJl45Jsnm6zs93qxTAfY0GgHPRkplOwGzZuFPjG3Vg/0iao34YR0mfbkUa3troGhoa3l7PKrDlrbhT7pLOjoxln90At6JSF4HkP59lukCQLLuLQv+Ani7hS7h6Yofi80pVa9Xj2Orll2Gkf4rPFIDM7UWM1Riv2Eyoxjoop+MZeKugnq4Xjh4bjOoK9r1eEIH61JA8oFIqbFNmiiUvmly5KxMbFXlwdND2yKLJsStDSsOSK9Nra+HDb39n4CYoeVkyETMjkiXyBTMmnFft2fpFzNkPXuvEfpi6vkMCC6qr5hfEmMnAZgFnaW6SkIEfilpAFyonsk6VIVlTKbHQdy5KYQbLoDZWNQqLEr7LhjNeEJo9fVkWSL+D8ohlZ2NdDg+Skslgsdju9Mj00FKRgcYGLFqZJns6/5OsJG/fl5kKAmNH/v8o7m9g28zu/S6RIiSIp8VWiKL6KpPiQFClK4pteKFHvEiU5tl5syx5v61MO3d1LU+yiRZN2N0Av2V10kGm3PQRYFHMIMMC0h02bBosW6LGXnUsX6S2H3nLLIdd+vv+Hkm1ZdsZpUuxMHntkWSNLFPn8/r+374tlBIZ4hCXjnxhFCssREFWVhX/hh3/2MUDy7//eH/zwC0znZPf97f/zr//8f+JY/bfjxU+tH7Y/ZUr9sfufejOVpEuO3JEydECy6Pf/iCnd75V+6P1HYJJ+svdf/uE3f/Ifv/nNfzL3b771ad/5/X9crvxV5Xt/9aME5lEOR18lZJMJVk4hlZOjbmFud6O67Ir6E6X5KeQEKHqxICbWA3yevKPMdM+nJaxo8eRMhZSEL1nQJIQpFfjc2SgB60BafUY5iu1U0qk5GUZT7OHKrOJoRYsLzqT/7H43nJ2ym5xNSPGdAqgs5+t8SStPSHWMPKAPehrWItGPZ7PVf7dx8jf2dXqyvn6djWgvlPGWIbxkYHH1jdXU9z/5JPYJYKzPGp82+59+9sWPf/zDH//ZX3/Ran3yvz/+5ONWi8Lwb3uE1OXFyMPDQzRW/9df/vSn/yPQ18aXkLIYZVREDSaNY1XMLrcFsUWytsjqOnmOINjAIeHuKnitRh6ppGOqA0Iq0zGuL6QytuDw3jyv+UtRqv0pE797rqE3/jbQah7agHAIPPXi4iA/Ovtyc+TJcO/RU99oaG+08fJoNPyhvZS4iGf+TJ8gmtBOQieJpk6f89p+LtXQTOGP5rdPTjaO1DKhdB0HP0cvSdtI61QipHgheZJlbFgrXL8E7fF8jyKkUitHNFh36aBHGhO4jDsYdE2ltqvZWfn4xECV8dJwDFUY+IGGTPTNBFlVD4CcpfFmd0EKhB8ynjh0duml2IRqzwshIF8BzJBIB0BU+DvfJnq+9Sdf4P3zRf1P/voPfqzO+l8Kh/TfOaBZKEl5P9ECmNNpIqr3hxF8hmQgRt0OvIyBg/ufTYXD/wBCLuO6o9S/WFvLbld/gsTIdPzTgBMgAlObSvpHftyj7BAwg2CjdUyW6raTYbLbTB7lFUTe6tpYsgZWFJkSTzWeTNlUKHCo4ArPagYNF59JVwBI+6b4Szg17ou4oTAK089plUeHAQBPNKauT0dbA6R5wlo489xrKX9hlaICcauX6jOwTbbY68DYlIGoXLFJmvDbm9itzFXXzx9fr5/Lh+Xx6d+ss7ou40RtIeXkiog8KfVbrBuZG/yhCyX3TOvTZudbSetb1hf5v+DsjSFA8Umm8nEhlpNtL/y6S6mxbv7dv/8eUmzswP6OYGBlYuFf79tiaYAehzvWdTZYfFBaYM8BYopZRSMGhsWqG1mOCAIUUnKe0gOg4eQAWpBX48gb4wlDzv19Jn77A0KvTT20f0++hqG4oW8kD5/JvMPbOzk57D1I+Ic2LnoXgaXKUAYgXvTlBxZ+tviYyLDjw5uryqXDw7ZEmiRoHEYGH0RZBtBchVmm7v6uyD9tXQ0sxcpabyflL8mNk0HG5eXB0NUUoo7tUiFuJIlNxpB4al6mzozSOHvYZMWZRaDJILFFugjCiHGyaR9kxAV+YLwWvbCZWR8QUlHQNijP06urNKWeSaB5b4dUTl4vtMUsN7/F1JeAYnIOQc4pR/TaDz79EfHsy9VwXYw2aZXjYOYiUnGH0O7Ot2ri5E57vWGZ3QNRRPl+LlvNVqup4/lClC15vI4oV8vJwhbEZ8DAP20EQABAFDAA3NALsu3Iwa1kXo2gZE5AI2Mw00wE7FJPO97hBSLJeBBg1pErSr0FTZi+OFJ45cpflyfVJYAkjw6lMr4WMelk9lrQoLmizrFZH1+5P0tdtPJOLUwYo/NZcMraCShY1BhwgclRxBRCuAx2K6Gj6vn545Pz65OTa62mzjeqM8xsGrGQeDluBNEMv7cuEyxJpKFewkS4a7Ww/cFcSa9tnHDC2Sfp7xk5Ls83jCTBhD9aN6YFDC/pygG4DMN57W3lKt6gOwY8pEF6ZqbUAXbKcQtO3uUqZ+REXPJOrR3PycJjJlImTZLDCSmwFNbwmyFF7GzfyVKTg183sXSbthQlz5+bPHRw9WQfwZSX0BGfnzzbf8lHr64/eC9l2NSgYB2NxNKSPLo1mJRIiK9oloyGTprTKMxsGRNSlzd4kZyBCneVl2X2BwiOs63Tnj9/gDHOiVvuyFZGouBJRqBtCb+xs2kA+mSsw6C00pbYIqr55sYz8eQwgvnSCZcb2TD44pGxDw8ppw/AacKSCjIxlWw3F+B69EdMSAGREa7sPwii9l1Rwf8Y4k//p87u9yANRbWwX/DjuWRBH6h1W95gWP6mENqZAdQbmfL0MiPN4JRYGSEKkMXlVDVFVK0tu2u5Zk2rtTrTSnGc0DziPic9SW5ZkeI3yNh6shAXfRBZS02ruYUDhu6KcqyioqkIwoQMNzejlN4U5s9BF4XAP1+iCGiF0GVOIsl0JhSM0OUFEgd4QEhRiYMySGIDCpco0Roee0dINWJRwZ3YeBUJKURwQdhbHHO15oIm6zz7AY3r68G53fVzWe+dYhb2+PR8fb3KjMlqJcNgnGcLDfWJ7PC465OCDHFaMlzAJbnLToT+h1WTxYmKFlS8bSAztsOAtBleOLiboAGjomGVy3Xci4ZhtR466m6J8UK/VH52Uv05M8qeraT04a0KaTiymDo2WWo2xI8tfsHwCMZacWvktpi5MRbd/v3nr6Lp7epv8rWIuteNd+hWwuUOIunLhRQ2ZpcBdJKZTIwIIKE0JTCFLD/92ocAlzEeDn6zLNJKUsNdShbuAoP2jJq3/AYt3cpkQMlECTKeXhIa+GaFYMDwylTL6J4zuzx7iWk25sx1ZZSumSN1DwkykImje+NZ+bDCDxNEiRrnKmLZkCLzlDOQixLS6e5/FyEas8AXqYDGSpax/5XKrE+t9gOGCR1hVJttqN7MYNLdOLNqPJfhs0PxYPrnWl6bikRmlufm+bArOD81n8qurVWzy/OulliDstkE0srPqOdFkuG8UeBIwxwcntbjSXxcBLjxL5hFFBnNDM0TBKCRdHCKIVYkaTHWAIxDnPtlPS05FMN4yDErNx0qgkEhLSciFKWYVMG2t9gT8jMrS7GXekeWYmHSSkYFOtBGt49bH4iMTjemJknMMocelhn5VcJrG+cPcN47PblWZJ2vb1CvxzQmcLunQzFOILIoG4dGJa9RL5oEjVpUms7sGduC08RRi4BHjwH6xYBGYB/f1H7ARCiyc2nOmBjrlIT0qy7H24BLmFUZqDFPXjMHvhoKKKwYypwMi3JvcA49l7njNV6CSLKbU0gNSzfFWvXcER3bN6zemwA52L8l9B4Y17WBBMUtFHbyln04aVN+919jVX0wIMlj7lu6kGQ7oGdUsaS7XAWJvBl0fioWDD5YYWW2jzJCN9LcvMxQ5VAjIKhIU7TieBJpIQzQ1EDxE/oQWvUmpBa0Y9RYy7x6DrUZAvL7B16Aaj7Y3fNCWTFk9mys7IeE1ItuVKnW3ygZUxk3uBUMB3MwAVgrg53+LtEE1wCqwR9L1qX3ne84/J9//vBiy/GDz2BmwCyAmspAXwJAMoSaRiSCJrCUQbsovJZdhoCM2P0Upqfc0lP4yqHVvTzlJefWYDox7O1G1dmA6TP9j5zM7HpWiV4D4paI+4JyaB9VHJxNylJNieKpMYfWb8aEJD5kKPu5tCptdqBUBTxd6Tzeo4JlywSkwLJCAppaZiWQeRasEbYGD8GZ8b0rpOr5qE+wMtpmVr0VIPbOLqPWtBgwdkip1kRvbG1jff2BHVLXJ9cPFFJusgYzb/K35dRrz7AN1mSe3o5VQwx6BiuLpvH5SCsBJvO4LGXiiYHYmJH1VWhd7owzHEEaDsKLJIUTPl7qy+FOA5FdUIc1UBScOx226u4YDWqeSVchzhTdOyOJJEymlqeCXly3i5qmOZyVUmXzTX+pn6OJHrJDytCjXhnh6PfkTWP1JW1wPjik7Ix8udJz0ECI5hYdkCfTVCoIE9ToQXEQp3FqiCFn1KYEnBbFpaYZJzVzTEme/VJdf2eHYCGmwP8g+1sa2cDigw2EpAnnmFmmcjJyh6VbssarQ4Fri7SDqlVLdG0ZwbbTOaNK/47q5d2aYzWn8qwjTZXglYUxNyUvjxjmCacWUD2jJgIt7oWRnV2hCPn88PKjs83AZ6VMF+18U7ootWK8kHdrshbi7GTNFXfNHS8bR0eEaEJC2AWXU0eL+F3OeOPoXWK1CQeEgs9vhCClaydGrwRibcKTU3BX6TvoRHKYoo9eVWCFvsINtjoSXoCJQPnTS7EsjhUAZETbVFJt3cDUhzymEt/XZS637JqFzBEbb4G9FBMZreLqCqn4wsr9DDNCil6KPYUsqbpx1gOsiuuxuiSXqQZV+PF89aMC/hJTaqNopk6o5zeyixGGHwSKN+iyDLdSBpbgx7R41KqhReKg99ZB6uxyD8S6fgfEYefhqztNoGnY4iPSYaW9LHmtBB6rDvSDektAM0u05XnJRkGy7BK9kKpruEOyDsnHS+6IVMeOFFLT7lhNXkJCQ3fq5fqL0dcnfiZLEVI2A+Ltwm//Ve7Z/22ElMdI7YNLglXoty9eP4yFdAiZHoqxJje6xIK4/zmOjReF6mW2SljdAX0FTEefKQwsC3zqeYaASRDEXg3XWQ8JSYYWFeMlBoYZEUi5/xqiZrMOl6xDwsZ0OUwRyB8TFxq7jn1YONkh1ZGZBS8kHTNlkHxdAwbgzGbo298x5J1vGJUeiXJjc3To+MvPeY1pmrt/4a1o24mgNyBGGbxgRoEmD34jplPIeKF4LU9BSpFBQkSJAklqOaBOw5GTgo+Yv4GAPb7j++nkgFPXV33E15W2AvIpkj5S/6/DPKe0wLkuiX6wRCyW4pLpGlb76mx4gZy04WKmO12rTako8S7k1YH2cQObAWpBUs8x7mMU/dN5Gg4EbFQIgku3s9R9IWURUsPSKqMN6cYtetxEoo2GX9/Z1MzCJ1QUhqGWQurB+snp48fMJgipdTYAQTZ06KKXXDxVpr5gBYjtfdm4dpOWgO8aTxEqE6ND0YFKkCl0L15/HdVm4OMqmj7oYrYcMB5zKDD3Nh/6uizX6L/QP8LBoI5+iMZh9ZIBFrDZxLOXcOKaDedbSlILgipy9DUuRsfeFHCm8Hs+GO4dHEy+TZeyhcheFX4DauJvIqQ89syPAtewLoA42tfgHXbKhz1YHYeG1aHfemdz88WLzYc7emfH/o/fW1ub/Le1qj9WH65ubS2tbi091F9WNzcfmg8/3NK1yadv8rGtzQu+HJI5l72evtfK4OIdU+55PDdvPyikGJkBOWUupqEfu15qUsxfqKQSGHeM4tg2ZuuMyRYHo+GJz3v6fh9t/cAVQe2ZCQsKUfIgy7FPctbdzADICqFpr1VgbH00G2E+Qf2ne6gwPTd3tCYrCe5zRlBsdjVmsetkvAFbKI8Y/01EWPUGC86kuBcSWBBauJEoKl+Ro9pgJjnq1eITZ+PUwLlocoaxGnzbWBpaB/Uiqi/sLmDksZMIzuB35zZzCiYwcJwodYVyx2mtUBCKP2qZXuqemnkn5mW4O25CqkiWAnvW0bjSaWwMFwx0l76zFnPP7a2ri3r86JESFb3ULtrJmbYkccHT22qXDJrimXg5EtT/4LxQNjXGIrmmvObTRT9ptf5w7M5j8UANX/ILUo/QGcr30fGzsy1lTR4+CUmjD+ie2nVa4HkLJh3zc08vHmH1J2nRMllVJD+q1wUK5HTvTTOcQS9la7f8/G1/Kba/dlgN/VYKv9/w5Rn9MrUaMrXvsw3Qfe8Z+zUe9E5bO/WFPkIuQiCWXC6LoQOlBemjKBWam4sVCZLuZx+NnfEeZh5nw58VCpmGdL5B5kh20sBU03k4g0B/5hdDhYj+RMRdWtOzHJSFkhsFasqQUFhZw5tRSNElBUQczOG3EQl5axrasd2voXtJFLUAqEXibVXVwN8gNASEvAaZgZ0UKzCGDYBeiwIoYVAUitCdVCS0F4sJ/xGoNZDIo22JSFBUc0ihpdxeKGELbG3REJICYL4Qx4Oq847CzzP2sOxKDw+zaaD6I6SwHELeulYRbwTJQzPlLUqkvRBa3r3WdOLRs0fkqcfsetfXmMjVRauP1QVwUOaPSvux5HJnCMmMq9xOmNzFM9Bs0A+QA7FTjft7K+axvJ6rznbgWOIbiwxUMdHqbO040HRBhYdqh+lWRxLVGZVAVsMqq3ME3jZt7MhAjbho/Yho2j6hJxLtpOPyFgp6a9lm9lJPnhzc49mmD5K8fn4rPbZOI4uuTfR6yLa4HgiO7d+Xt/7/h9SXjDvPi80z3OjfKYV2sfJrfWGUMZkbFhO1PMURd58rkoxKpYCQ8v/55ejY69fqxJksRo1JludspPExd2TNnkykJZHckZu6xeR2WlsogK6YOKKQMUv7tIwaNQYS5cjsEQq6U9OkDdKYNE0owkAfUNq1mGZUl5OAkijx0hJeoYLOuLAjdmnlwHdBEqij3Q5dlJo2JtMzMxF8aP3Kc11vKFxo0cZCRIM3AxKWlVmja7klMCFzO+b4xJTuNRKG39fMwNSzcCyJlaymD7PYhfvHE2OrZXeaZwjGFGy0KNjueKWTQ+OgxbiR76zhk6xA6uXpuT1FlELqlKA6efDAhJSF4F8FAJnUOTHxFdgZ0mgJ1TNn3GXTv1T5BZo1y5KWCfSYVrFnzs83jtAx9lNIjDCtdDCzS/vGE3ytNj9AKW8Z7i6pG9HrPPy9AvGkkJLaN/Mi9swt0T+MGiHgrUQrKW/0Uc+beymF1MHARPRulhqI0GqMYUIq9XD4YnRneDN7BydxX7P19zSkIDZhrfi+yd3EmXiKHx5S0oBhCl0jP7FVYmuDKobsmCkTNvUldRFHKvkWfnSJb6/HYzCEH+04P6NfxG3bRJQRVmF1IEw7KyDp4C7OBl1gyRGdYHZ+NMcCH1RjCINhyLmCW4GloUtKQFJPp3MwNJc3rq+rrrgxmELBWRMZQmpqLjWPxgZsBTpJZCNQcIWsAVshZKQoYMSgP8u9auE8n4kKXqARRBy2AzP1VoOlUJj5BIUfx/U00qs0YA1puTpjNPZ1TKGAQ1A69i3fO1a9S2VvlCYEuClCtR0AyxlGZ/oxIcojC83UH9Ycq+7k9NouyrinFH7kKEq/6wd7xzOupG53dDiEtdIYkzkEtgMxQGpp/H3KmOEY+Rrj2CjUMWPIeODFmWdgfP2qfR892yw665BvmGR2GBVK65N1XaLNQAtEHztLp4gKyEBZArYRVGFpflP0udz4lFCXM+8bVkh1KvFXm95XIfVvByF1qzk7dAtHMpM/Wyt9ECcHQ8uj57x9+eTpycHLl9Kc5Wu8XD85+KpkKZ7U3uH78tDl4eivEVCe0YeVKBowqBKyEnHPs0hBYkuYdkcTQW7P4PrI/Hfm+29nHvXK9kvdW2AbhcOMAQohYwnTSXBXZ73kDk7Zlj3TLoT41ma1jjK+I5FyOUyrjNYTIhVqpTSUKzJuTOdy0cLx+enJbohhgYY8WgTLf6kcWs4eL88GBYCI2WahbFVpvOW8yhkM7jiDBHqzW5iBW9hkxmGpXWpJNLIFErIclg+rvMThdkyBIaDfwNfN4UiXRUcBBRRLSv0wUHl3SJVle8Tyb/jhRIDslIFKEYjmZ+ZdbWBZkscD9445RzhV3UM5DTgpqcp0U7upeRe8JnHchAthxQguJCDWJjRBBlG4dQCOYqvCDrgjTSeLWjRmdbZW7vPbG7soslavi1bZUa+ZYEoTYLAk3Rtmx119uMsw2A4pSlw0FQTCElXKHmcBRxISvZOxlu4JKTtLCYn+5MD8sn+b97hug21Q+82PrvO2fHE4muon9oeCF0+HUuiuTDwbmvxqhJRJQWPvbbZ+na86uop+s9aYTugKhbm9Ze4zpxjLPoweenZAmbkEf559RwK0Y7YLtmf0owmsgBqZli2Nl04WMlFtfhMNLPig52AtBwt36piQUpYSvg54UjkCLGhmKhKDCi31PYUUdwW2UdFyCthBdZqaLaGFKDNSbSPi7ikAF6lFEl443xV0BLWLCo5kMl1ALD4sVylkYzLhWW9NpKEOzbm8SCkPkQXLlE3wSfpwWtyOSMmeu8sbPlLQWjSZJEstjAda4+8JKWomQmpkc9UBhMWCUJuLujau15Jsx3L2Hg1zDvfc9raNnjAhdXp6vZudCzOib+qokUCGDXxh+wY5LC/vxiS+1DKSMmZTUC8bgJTi8E3vX930xtlb0y52tVfRjiGddqBGDWww30q38TTRflLOi4WIGwEnJkAhSmwjDWCEKdiNkqVkNd3aGX0rpH5iZ6nH//ne6z/982cHNgRwoDYL4XCDLGWNtlNPt3z7OExdXfV8z59d+CaH9r8aWWqUscB7/jfaFGMfnqdgA6HEKkPoBAgz7/E2AuyqRYz9+OogpIgpzzc8FH7Dw/Jm8dgh5floi5a6lTRiryj2Wd6yPAiLubYU0kKRsNTRZrQXmT3CNkw+jhq60WchR+ONA5oF5Ki9g59jkzlfujT34Hx3OaKQkjBm3UgB4nrjmqvuZmflbJFxmgVWM5oUqY5dF+DBCIIXTDIq3qkpFCylAsPcXUAf7k9GykAnjD+BTCRNSLEBxRcXcauMW0CDNkSxZJ2xtL8xPvbOwg8+lQmpraViVM7ciAl0F59encw0+tq5C8cL9758tL2LGCEAv1MTVSfne9UUgusUsk2/jU+UWASLeU0s1TWxRKlrxMFgh5GpWQQADC2e3TeGEoNqpBODHARxg30gkiy1Og8sJ95cPOFwAu0yiBN0Wr1wgwSCZn2AJ47NplLlKnXvcTZyMab094TUzzQ//1e/+MUvf/mLX/J28OvmnT9FsGj/VUwRUuuEVOUMv4+t4tCQe/TqeDRW3UuMPrqTpv6ehpR4j2Pv1bq81w79V3/diVZfmKbxPmjOCObja4sGC4am/tLq4SDzeWzc4KUDLvlgTwWAxHPxI38Tyo5MaVhYdsuRGNoPjkC35GLUF5YvwKxtKYw25qIGT5RgQWUvAPrxjEa/DBsCOSNliWNUbKq6fRRkfAXujcbbQsubBbKzm5zPZmFLwVeQaYe8bVhAhWWx4HJD2ijAcEnUysHFkJUDVsrtCipJoI4O4KEKYwyq0KBbMSi9a00I5Q3g11aKUUM9w31d0cC9bULqnid4teCuSVXS4RhBtAvIaYx7tNPCgOhq3dtZcNhSr51GIz6/vS0pl3MBKAyIYn0vO+XNqI8RUzknsT2yRVMauJmYrPoqzMT9CqmBZiwz9nR7eOwdVcjliDMfRCqRtIOwVLcO+7TfN8ofcf9ErkWSbiq/S6UX13BWnZA/8hV5MfoN/obRpHizPmcs2nsrpParZClC6vmzq2f3XsZM+raZugmp1gWk24fFSULqeWh0a+vhuP8rElKjtvvge1qtFfnnfviXHan3QVSNDAda8VgoNR1OpcJ51uwjLMdWNUQ0UTUYo/ebgDVXBiH10egh+HF16TFG28ViJxMpYAgLIjqmbS9OjVMKKs0plueO0DZi4s3cLRScWWTTG4uVBSfs6+g2w2Wlo3m5i8FeaooDIW+OdpvhcH0aNkiZlifelQI5a5U67OcZE1JhQooPJ1quqUVXjUkYcJOuoLjojacRia/n3Sp+GPQpsmgsOLVNFZRrI0JW8IbIcnBMEsAaa+8Kqa2yy0jNF4vSmwX8QFpLdypSxHp6lBaMHlwHlLNaPZQlpB6x5iWiwM2eGEgSyoFOGchJHo0KT91mDX0IBNeAoFOuST4iIBaK2Xhrc+A7PHvrldZMiMKPjjEkGdkYcCieIQbx6ILSgcUWdootUqYAKNh1MPGjZxRsICZD05qBUQoVIBPIEcrHTm/0DeeOGyT6r1wx3emlDobqCqktir346NXa6B7ebYsvvyKF3290C/ZaSI3jWiEYewAwwmJ12ju1O5dnEEcnPrH5ECn3FcMS9mjVfplIb2qya0/y2d4vCPHA6rImYG8/nS+0CCka9VhYrgAyQEXVaM52cpxGzV9FHzrU8+CRwEqjhYQddbHZV+XTAc2H6Ag2BaVWFMYtEHxsmBhfOZtp7yzo0wxYLKmryhq6VoKqgaifwpR+vlPstNzz1H3SBPPGoKnURB+yZIkBQDUoLQ+S1bTMT6B1Wh3VafFwdntt4+pqjYO83UexrDa+cn8RsJl0Y5bD7g5ItDSXwbTwEOsnk9Kirzj66EkXwXJ2oIet7a2r5NN2Cu2JdeBJKUKAgX7AKMIK46eJXwOZOlRBJcQnhAgqNH1jrtBE8EX51Xc4drc9Nu/2iq2C0JKMkDItbZCZ5ESZJ2bc8eKL8VbLCINqyMFeCgc+uGBWOipZlI6pO42wu0+29fHA3SwlvpQBJP3KkLpFnk/RS00ONQ6Z9nXGqhu9lZ9d9bYenfR2nv+OhdTo2yFlmCFNDvTUdrA8vZ1yA5AbWRof3tnZ6RkUrnk9AWk0Ld/lbfWpkJIOZbMWb6vFDiRapTivHVD7hhsVSyTMQ8QPGWo+NLUsBT4UIGB+LCJwTki5XIBuhd6Dl0EDxo0GV18oQzwtCCn0ajnHgXSlcwnLFQFNGjXzc4FkMcMqEFLst1i6kHX6C82Gdz5U7zOGQxOHRZTVCeS6SWLWgA5doki7pFervTPlKbD+bnnx+tEpvg4blFGN/vAEhd/K6L2Ars2YuwLhZVjEKPDrUh5i+mGdSnv/qTunDIRAk7/ZtbyL2xtmPqFu6tHpA4UU84GyxiaOookpoevxcIVFU45FfTg9yaKnSUiBlWTfC/bVjxzhhO0n+nZIVdyzi+ECMnMCprFVkDR6upZ0xwJbxVZrYHpSYxRPSAF6Y3bUJiFWtP5TSPmMjpbPab1m1WuH1L4Nm/3yF6pjF9dkqdIIrPrTh2cXwxNPh6oXvd7WyVdl4vf/npvGgBGN3U1UKz6TpXxF9Dpd1aorH8xmg2iYAe728fyog+Of2W3cWTTvvPC8pgSzEJCcSrRin4I5ZwWWAZuXZjSPgvmc7IS5iTXonlmclduuCSkBztxWveyOYyPGCEwy+YGE6dgROuRXWkjcKEbRMC7rmVo/B+sOpGtTipkM2OQuldGcfvmYDi1oOr9mIzIX7ELU5XwuCADC6rhbwrKshsoMNZ8o0kFxI0mX8OQJgYY79ejpsytCChxWpTk84W/47g0pj2cn7tXiSq7USpFoCgB3bsTPpRf0ci1qiFk0/oFoIzmNHda1mfcxShdhag+CeoR/DueYZ4pCma0DQqpxSbOVoku93mag1hWuEVyScJLQRwJEmWNTsLO7OdPTc1Tc0mVryLFULpF4rSAqlvF6M2RqaWbBFvODM4cZXhDej3k6AtGuUjshsp4JKY7PYrq1Ojr6hrGoCSlh/D4gqGzzw5fP2V1NPrl+esBeCiHX0ye/hmXbVzIZCdACMFA129jruCVCqqmbJddlOrGb9SZZrszH2Bc1O+M9fR7S0SPDqy/0hFxGC/WtsVfjkJ5PhFxULWvcV3QKfamPk0matFUzi0cEE3kB8faIe5rFEoJQYRosisDluUg92pDKHG42TtvuuqaCCMZlo4KongC0gAzA/HQrrUQA6aSMk1oNjAXTDwHcpEIbnF9jmhIsYK7AomUqFerq3wHUR2gJHFy/VkbATMJNXgxvpuRyHFTtNxUusbjtV1zZp1ePnz15so1JWAxYLEI45kX3vDX12bHK7QAEHrRHF0Q2ghOSZLKw/VLaVSf1gBGQJtA76YqbmZ8p/R49PrdBfsezwLzqrMtyhBRPF26TYFrjslQrR3fGRi/8XdFKOx2/aJawxZhgJGB7w5eDquN5TcaIB3boB0wyXZJ6YMDM0FvJTDeBEHUSpQ98lGU15ZemBjrfbmjj8H1RugvNulqg4KXu7DPkvkC9ffHa6fEqpK4+FLt310/gbSOCr3dISRZ4zHNngXVWrOcIKXbwsdj0espbnj7ezkba8I2LOysGcN9b7STjfW1KDtPu2IRBu3vsLOWThUZHaylm18yJEyDQEh2JYXpdTA8GpsLM08GjhyWzFlQnhRhC0JJKN+SWfNy4tQCIFWSC0TmEzIQE3CQsgCManEIh/ti54OTK/eIzR708EbyR2TUwT5GkdAedyeXjcNpwP2lOwN9FMeZABZwxotsYf9BGTWs8guVJsBzFRTTmSmFDefrkScpKIqJd9DUrC/e+6GOjLyrlGll7QSGFTFKnYtgClbnnQmP/rOwQddgI/COvMl/N7l0rSWlGcf1go7o2C9mwkfMn5CQHPQPRCuMtyk4q1tk8W1lyynWeY4XVgI4jtuU4F1D4dtu5i7vzqZ1EiVlLgf5S2CYovpVSbXxzpJaEYlSHu6EfnqTudRm4pkRKkkEt79wNabdLPxh/KRjc8fThAPr9RkhNX31Z5sYAzwdg3aYlGusmeaINPTHW2b8zvdR9Y/ZLRx0xJOBHjVJp7jTrisxlN67nkt2EY+cGmN0bt8rprd7YykQyHF+6TXH4+3B0w0gSmIa2W4wdOaGLhQ140zVjjyJmZTcyfzRF7QXWjIKQJLUYLGkQJbndstBvzPdwastJQJ2DloE8KABIwnWRhYHkSuiozwTDmKyLCiGvPEKKuUeowIITGcLS/Lw3gVct92ZOFRTHuFPqoYBZkb5AqZZkqcE+2+cQIYVjQ3h+/WTj5OrpPELHAIsWEraO3+h9FMRC2y8tAp/0zYrUVUqwraAxOHsyQ8lniKYCG2WmU6ntc2FmKfwAJG2wj+PeTkv0HTkMVlI89LLQybRD7f7EzmauJVGfDlGJfyNtEGdSNK2klkgndl4nFZCwzoZrbhQH3KCNeSJGlhwwrAKHnsMAawGmJk7jHQl6xB0xwhYonXrDy8dHi1MlVuTGoH4Bx1hwiklNbu8p/K6Gbvi79/7af8vJV8YD+pvtPXpwYMfVXWrI5CCkfkeuHiE1rJBqFbzZZ7vB+er2+bPrSMvpM+h/k9FeRFvd/sLOpjM0Y22+3kvRYASMBgQJij5AtKG+s1FmLm2By2aOzigC/uEM8HN0KUVTD1EKLh4tBt0lq43EHLA8+D2wNBtI22g5g8pGx2+HFAkLDKATvBPfgp1NQqh1h98A0clwDChIf64M47t+M12YD8ab6AMxrl4QhlYojjbg7wCLHAaPRnSCtZj2w8G8cyEAIDC0PJd6cLoRhgnKKrT4rpASBbHQkLSQtJDYkyagUAAaapefqc45qHb4fvYuAJkLpgepdXvPC7V3ffsYPL4lyfUm4GISFWPBWFkSPZCDWYaNExW4KpgBOiqbiBzVNdjMIUwTiDpXV95c3R/2Y0FgVch6pGnfLnoj6Xp3/IyXsNvpOy0rwVYuqj2eWPhGSQQJv7UsY6I8UFvNHB3G1w5dQyNZ+KZX7/5QNfj8A/mFd0q8d5mO7g+dxBRSK7doxa/RgAIu18Wl5/XSr+ev40qBHgqwub1n1Rksc0+vHgXbHemF3NxTThGUEwkrNBtfuhGL4Yw7pGLMdaTpIgqTENUSB2+XwsFCRXDVaUo80XXk3c0QXYwOuUSRqBCnyLe6cde0ofdaBvPZVERJdlbazXIwpAzEF8q4EAh3kDDAU1KSgVYAuQbzl2f+LRh3rRAqIKTEDrXph2jrMCozAJqQdjEOBQjMhJSmwCbNBOMJXz8elMLK2t72sgvxCXgXxWbFFnB+2z7ysG7GE77BlWvH5YvVzlwb/vhpi4LKCPyL8RVzheezGw/Oz8/hybPoxXAOShmkSiTUahL7y6B34S3ARsat0VEUsDfKqK9v5BPslYTVSovZTKk40ns9R42uLDUKCim21cDXRy49m81oAjezrYAKZn5Y018hoeQWMawAuTLCmcbANZwZTPyKxsox186MGH7dIE+N2S6IQ7vJ2er2l72q5jdv9E518LeqflXv+Vx3fvJ8ZeUQzpdAdWNjX5eA8iC1eDHiO3zthiHTBNqOCfGlIL9uPMouPnj6jLbd5fRvnd1Wi4d+3HhBnFuukDdgemYDOERjRB7mTYmeIVQBMtTwm50V14wLvFFkevoIRum8lrva+c4KvcAyybRXiFRLtRH6L9MvyTJ11D6x9+zrzjIKUx0zMmcvmhYSAuhSU0OrnMgdQAYQV0LFxHCr6N5w+EKkzp+ukJdot/p9x7i/FrOwiyH7gpkwGD9T+mHcYQV84HoEll9LpaYKMamIMVZvi0IkpR695CtjN4hhfvwKvtMalY1rV0o7hB5jvlKzquw40dgq8UHFui64fd7wYqoqdu9udXd7DlSvCPXNvlPKBqxnUQlD8AJeFIkRoTXGdqJR+R02NdyPDp9FRSheoz+3cKiTy34QehwJBnnYnwts1MiBrD0cByc1MUxIWkgWdAyMC1OSJPAK6OHJmGySOcBmvWiS2VAoKeMhVd14cdtHefRK5sxg4ek8k8LCB15e85/Xfuddn1Mu7w2tn529ONxxrPYESRj7WlyXvQnQ/b3LQ9iDrz7q6XErIS0JMi/v3XuUTT26+tnV8xO3s7i5YjATfK7nbDXRbVC+w6kIM/K7uQ7HAbAaM3PI4kjZqX7hlWPUFoqU8m4KP6nzzE8ZGPisuirQ6PwpWhyKn/G6em1vHIxnmI6INgJCak2QbY2lc+rQ8AlwkKYSMhNqmnM2ICkiyXLqODaI04SEUKBKyPAD7RdpRKrBh9CXl3BQp1GW6YdsIxVT1J7eit9HOcgaetmEVKkcq/XBPzTQzlsxPl5jb4pMXVS8LeEgh5X/UG7ATpYIblfWnqvUeRmWlpyEb6We4QQUHlycS6Wy2ym4lotBLCGjUughpMDFN+oFSSOh1deS7hNVckM6GASqEf8JCKxEuRsQ/94R0IDoJqRIUltt5njSpSlBVOwMH44dDqMPX2yyuKiXOF7EByC1S2MNofACZ5UbCP7UbCjPZyikwE5gdYZHSfrwzePWOMqTc58cTH7odTBp90+T7/mn6rOG9jSe6PmjnZGzr0vhd9gpWLmFt9qFlQWn7+HDLXHrvBvP9rafXXGt53PDbzzrFxMTh+B2DwPx1urtvXY44jCEATbyLIttwwxeNcANLBlLsP6CDNJFkI+YbasUqeFX4NottR7uY2eDGyTTxVYDrb6+ZvGSDNN9ZSS+FiQ/xGoyp+ZKrCQ1M/LoaEpuxjQv+jSWp9BkgSDBDm4yHckZXSq/poYddVQwruxlr1tvOb3jaLM0Yc7SWDGQjCA70+pIXtM5Pji5jfLAq5MU7QlXpW88JVgakNCbiKvh9lGfvZLm1pOpgDbApABRN+A8WYWQWRQsz5AIw0l6QUMoAw6bL8UBxntxNIF3nJYATbrGulsa7r6ATP0gU8sLpePgO2EF7VvtvTrPV140CwBQaJEyELBqjc5S78W46sN0N+Bv0A7aokHS2tAqjuqQGVFBBGt3xamTj+dVKtRU5nFHzz4sb37CPtoTg1nde6YT9/x60/Lw3Z/Dl31WE8rMlXqQLXeNAtJX/XJGS7una+Vy2wg6OW9/JiczJGB1JcqV7N6z5yw/X2Lm82gb5lza/jynLf1lyUgt7V478jactgJhmttD8G6XpI+M7YjbMNfpJlLV6u46vYRR3uLa2Njb29vd3t7Y2EB7XwjtbPZ4NrSInPOcyFTZ1KISyeLR0bxyCYoS4rPbxibA+CJyOhFPNWJMe0Ucof7R7hiKLpBBxFTZ+YYl0UyJyWMC90TvBG5BI77UxjaF2Mbu9u4e319OIC73TGqb2kwtz3o1lTqaEdk3VNBLzY45cOcY7XW9NZ/RAZkYHtl6OIGaXgbdo3bkkcTuDhYdSxAmFkREYj5jFCDkIxGGyMXorS4epfy2G7VaRir4qF3kKRsRzPApn3YC40ZctWis5xDW0ckxsTUyvjoyPLG18+oIHNuKoghn05vZ/KWdwxc7I9I4dhB9adZTUppC/ANuJECkkjePUy1SipxuwjSaLS/X6tZCP929uNMU+IdUwg4Z49DfxjVIYSajDU1+Pa4Do81m/2QHb+bkm/cG//eV+s3B5OTbn/7q7cGdD969zHpiMG+9mbq+IWNqy8XdfnjwIL7Etf/2d9p/3+fsv3oE+zc/9KsHcvdngKeaT0NtR+7Cvtr54/VUsFAwTnTEHuo0kFVSx8cbT2X2d/B4FgZtJCRMsKFdUk4iUl3d3c3CSNxNCTRM+bucWptDapeKMLWWza4xhmNWInDWDEZytHj8W3nKKavQ7sm6hT8iIJiN7Lfo8C48Fza20Y3Vl0OlJST6vx4P/2iRuR6gFNBeNIgaEextcGpVs/gLZFNz83bNq80g2r8zi/PJ2u1Ppx8wGvy/D8F+XXtpNr4AAAAASUVORK5CYII="}}},{"cell_type":"markdown","source":"The figure above is the comparison of liver contours in three testing cases. The U-Net-based segmentation (red), the atlas-based segmentation (green), and ground-truth manual contouring (blue) are shown.\n\nCredit - https://www.researchgate.net/figure/Comparison-of-liver-contours-in-three-testing-cases-The-U-Net-based-segmentation-red_fig4_340557066","metadata":{"id":"UDuXfk2qck_7"}},{"cell_type":"markdown","source":"2. Tumour detection and separation: U-Net has shown great success in tumour detection and separation. It can detect and classify tumours in medical images, allowing radiologists and oncologists to assess tumour size, location and shape for diagnosis, treatment planning and monitoring cancer treatment progress","metadata":{"id":"IwbBXYmeck_7"}},{"cell_type":"markdown","source":"![download 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"}}},{"cell_type":"markdown","source":"The figure above shows segmentation results for simulated HGG and LGG cases compared to manual reference ground truth; (a) Whole tumour (red) cut from the HGG case overlaid on the FLAIR image; (b) Segmented enhancement tumour (cyan) with overlaid HGG expression on the T1c image; (c) cancellation of the entire LGG case overlay in the FLAIR image (red); (a) Segmented core tumour (green) with LGG case masked in the FLAIR image.\n\nCredit - https://arxiv.org/pdf/1705.03820.pdf","metadata":{"id":"hJkYrpl7ck_-"}},{"cell_type":"markdown","source":"3. Vessel extraction and segmentation: U-Net has been used to extract and segment blood vessels from medical images. Accurate vascular classification is essential for the diagnosis of vascular diseases, planning surgery, and assessment of blood flow patterns. U-Net’s ability to capture details and manage complex container systems makes it a valuable tool in this field.","metadata":{"id":"K20uIjs1ck__"}},{"cell_type":"markdown","source":"![download-min (2) 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"}}},{"cell_type":"markdown","source":" The figure above show retina vessel segmentation using U-Net. The figure showcases a test retinal image and its corresponding vessel segmentation result obtained using U-Net architecture.\n\n Credit - https://github.com/DeepTrial/Retina-VesselNet","metadata":{"id":"jRLZ5_3GclAH"}},{"cell_type":"markdown","source":"**Code**\n\n---","metadata":{"id":"Ir1i2ews_j0B"}},{"cell_type":"markdown","source":"Code Snippet on how U-Net is used for mask prediction in nuclei segmentation\n\nCredit - https://github.com/zhixuhao/unet\n\n","metadata":{"id":"svNLmCMXclAH"}},{"cell_type":"code","source":"import os\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.models import load_model\n\n# Define the function to preprocess the test image\ndef preprocess_image(image):\n    # Convert the image to grayscale\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    # Resize the image to 256x256\n    image = cv2.resize(gray, (256, 256))\n    # Normalize the pixel values to the range of 0-1\n    image = image / 255.0\n    # Add a channel dimension to the grayscale image\n    image = np.expand_dims(image, axis=-1)\n    return image\n\n# Load the pre-trained U-Net model\nmodel = load_model('/kaggle/input/unet-dataset/Unet_NonDAPIStainedData/unet_membrane.hdf5')\n\n# Define the path to the folder containing test images\ntest_images_folder = '/kaggle/input/unet-dataset/Unet_NonDAPIStainedData/test/TestImage'\n\n# Get a list of all test images\ntest_images = os.listdir(test_images_folder)\n\n# Create an empty list to store the predicted masks\npredicted_masks = []\n\n# Define batch size and initialize an empty batch of images\nbatch_size = 32\nbatch_images = []\n\n# Loop over the test images\nfor i, image_file in enumerate(test_images):\n    # Load the test image\n    image_path = os.path.join(test_images_folder, image_file)\n    image = cv2.imread(image_path)\n    preprocessed_image = preprocess_image(image)\n\n    # Add the preprocessed image to the batch\n    batch_images.append(preprocessed_image)\n\n    # If the batch is full or it's the last image, predict the segmentation masks\n    if len(batch_images) == batch_size or i == len(test_images) - 1:\n        # Convert the batch of images to a numpy array\n        batch_images = np.array(batch_images)\n\n        # Predict the segmentation masks for the batch of test images\n        batch_predicted_masks = model.predict_on_batch(batch_images)\n        batch_predicted_masks = (batch_predicted_masks > 0.5).astype(np.uint8)\n\n        # Append the predicted masks to the list\n        predicted_masks.extend(batch_predicted_masks)\n\n        # Clear the batch of images\n        batch_images = []\n\n# Loop over the predicted masks and test images for visualization\nfor i in range(1):\n    # Load the test image\n    image_file = test_images[i]\n    image_path = os.path.join(test_images_folder, image_file)\n    image = cv2.imread(image_path)\n\n    masks_folder = \"/kaggle/input/unet-dataset/Unet_NonDAPIStainedData/test/Manually_Annotated_Mask\"\n\n    # Load the manually annotated mask\n    mask_file = os.listdir(masks_folder)[i]  # Assuming i corresponds to the image index\n    mask_path = os.path.join(masks_folder, mask_file)\n    manually_annotated_mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)\n\n    # Create a figure with subplots\n    plt.figure(figsize=(15, 5))\n\n    # Display the test image\n    plt.subplot(1, 3, 1)\n    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n    plt.title(\"Test Image\")\n    plt.axis(\"off\")\n\n    # Display the manually annotated mask\n    plt.subplot(1, 3, 2)\n    plt.imshow(manually_annotated_mask, cmap=\"gray\")\n    plt.title(\"Manually Annotated Mask\")\n    plt.axis(\"off\")\n\n    # Display the predicted mask\n    plt.subplot(1, 3, 3)\n    plt.imshow(predicted_masks[i][:, :, 0], cmap=\"gray\")\n    plt.title(\"Predicted Mask Using U-Net\")\n    plt.axis(\"off\")\n    # Show the figure\n    plt.show()","metadata":{"outputId":"7f2dae3e-9548-4a9e-fba5-1f1c0aabcd64","id":"II0qIjDMclAH","execution":{"iopub.status.busy":"2023-07-12T08:43:56.206942Z","iopub.execute_input":"2023-07-12T08:43:56.207293Z","iopub.status.idle":"2023-07-12T08:44:04.261950Z","shell.execute_reply.started":"2023-07-12T08:43:56.207261Z","shell.execute_reply":"2023-07-12T08:44:04.261123Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Future Scope**\n\n---\n\n","metadata":{"id":"CDdsHX1VclAH"}},{"cell_type":"markdown","source":"The future of UNet and medical image segmentation looks promising, and there are many avenues for further improvement and development. Here are some of the upcoming trends:\n\n1. Advanced integration: Future UNet developments will include conceptual techniques, graph-based reasoning, and multi-scale context modeling to increase classification accuracy and capture image complexity\n\n2. Processing complex images: UNet will be designed to handle 3D volumes, dynamic image sequences, and more data fusion, using techniques proven by variants such as DU U-Net and Swin-Unet for segmentation performance developed.\n\n3. AR and VR applications: Integrating augmented reality and virtual reality, Unet will enable interactive and watermarking experiences, supporting surgical guidance, accuracy, and educational visualization\n\n4. Special adaptations for medical specialties: Prepare UNet for specific medical areas such as neuroimaging, cardiology, pathology, and radiology, adapting its programs and training methods to meet unique challenges and needs.","metadata":{"id":"Eaa9K3_hclAH"}},{"cell_type":"markdown","source":"# ResNet","metadata":{"id":"88vS7R2IJKiI"}},{"cell_type":"markdown","source":"**Introduction**\n\n---","metadata":{"id":"E8o5S94nJOi6"}},{"cell_type":"markdown","source":"**ResNet,** short for Residual Network, is one of the most effective deep learning architectures for image classification.  First developed by ResNet in 2015, it has gained traction for a variety of computer vision applications, including medical imaging. It is a  type of convolutional neural network (CNN) made up of numerous layers of convolutional and pooling 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primary innovation is the use of residual connections, often referred to as skip connections, which enable the network to learn residual functions rather than the underlying mapping directly. This aids in addressing the issue of disappearing gradients, which can affect deep neural networks and impair their capacity to learn intricate characteristics.\nIts variants ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152, differ in the number of layers and the complexity of the architecture. The deeper variants of ResNet have been shown to achieve state-of-the-art performance on various image classification benchmarks, such as ImageNet. ResNet is mostly employed for image classification, which entails labelling an image by its content, object detection, segmentation, and localization. It works by learning a hierarchy of features from the input image. While the latter layers of the network extract high-level data like forms and textures, the first few layers of the network extract low-level information like edges and corners. ResNet's residual connections enable the network to learn residual functions, which can distinguish between a layer's input and output. As a result, the gradients are propagated more successfully, and the network's training is enhanced. To convert the input image into a feature representation, ResNet combines convolutional layers, pooling layers, batch normalization, and activation functions like ReLU. The completely linked layer receives the feature representation after that, producing the network's final output.\n","metadata":{"id":"aGvCwGxPZPET"}},{"cell_type":"markdown","source":"**Advancement**\n\n---","metadata":{"id":"_pY09SZZZqDs"}},{"cell_type":"markdown","source":"ResNet and its variations have seen several improvements during the last two years. The employment of attention mechanisms, which enables the network to concentrate on the most important aspects of the image, is one of the significant improvements. For instance, the (2018) Squeeze-and-Excitation (SE) block employs a gating mechanism to selectively amplify or suppress the feature maps depending on their significance.\nPre-training and transfer learning, which involves training the network on a big dataset, like ImageNet, and then fine-tuning it on a smaller dataset, like medical images, is another development. It has been demonstrated that this strategy enhances ResNet's performance on medical imaging tasks, particularly when the dataset is small. In recent years, ResNets have become more complex, with some models currently having over 1000 layers. The accuracy of these deeper models is higher for image recognition tasks.\nResNet has been developed and used by several businesses. Consider the pre-trained ResNet models for medical imaging tasks like lung nodule detection and brain tumour segmentation that NVIDIA has built into its Clara deep learning platform. Another business, Arterys, has created a cloud-based platform for medical imaging tasks like cardiac MRI processing that makes use of ResNet.\n","metadata":{"id":"-N0_SPJFZ4Ht"}},{"cell_type":"markdown","source":"**Application**\n\n---","metadata":{"id":"abvGmfyFZ6Ma"}},{"cell_type":"markdown","source":"In the field of medical imaging, ResNet has demonstrated promising results, particularly in the identification and diagnosis of numerous disorders, including cancer. For instance, ResNet has been applied to the detection of lung cancer from CT scans, colorectal cancer from colonoscopy pictures, and breast cancer from mammograms. As well as classifying skin lesions from dermoscopy pictures, ResNet has been used to segment brain tumours from MRI scans. It can be used to help in disease diagnosis, help in the segmentation of tissues in medical pictures, aid surgical planning and disease progression tracking and find novel medications by analyzing the molecular structures of drugs.ResNet can learn intricate features from images that are difficult to manually extract. It is capable of handling huge datasets with high dimensions, which are usual in medical imaging. This is essential for clinical applications since it generalizes effectively to fresh images.","metadata":{"id":"NleOYeYKZ9rA"}},{"cell_type":"markdown","source":"**Future Scope**\n\n---","metadata":{"id":"RV4H0OXMaDwW"}},{"cell_type":"markdown","source":"ResNet is anticipated to play a significant role in enhancing healthcare outcomes due to the expanding availability of medical imaging data and the rising demand for precise and effective diagnosis. Plans for ResNet in medical imaging include the following:\n- Developing ResNet variations that are more accurate and efficient and can handle larger datasets and more challenging tasks.\n- Looking at the usage of ResNet for multi-task learning, where the network can carry out several tasks at once, such as identifying and categorizing various cancer types.\n- Developing explainable AI methods for ResNet that can aid physicians in comprehending how the network generates its predictions and increase acceptance and trust.\n- Integrating ResNet with other technologies, such as robots and augmented reality, to enable interactive and real-time interpretation of medical images.\n\nResNet is a powerful deep-learning architecture that, in conclusion, has demonstrated considerable promise in the medical industry, particularly in the identification and diagnosis of various disorders. It is anticipated that ResNet will continue to enhance its performance on medical imaging tasks and contribute to the creation of more precise and effective healthcare systems thanks to developments in attention mechanisms, pre-training, and transfer learning.\n","metadata":{"id":"sGDmzFHxaH6G"}},{"cell_type":"markdown","source":"Credit:\n[Deep Learning in Image Classification using Residual Network\n(ResNet) Variants for Detection of Colorectal Cancer](https://www.sciencedirect.com/science/article/pii/S1877050921000284)","metadata":{"id":"GJklB7x7700N"}},{"cell_type":"markdown","source":"# VAE","metadata":{"id":"w-72S20zJQ7l"}},{"cell_type":"markdown","source":"**Introduction**\n\n---","metadata":{"id":"BV0YUH7YJTc1"}},{"cell_type":"markdown","source":"Variational Autoencoder (VAE) is a type of neural network that is capable of learning the underlying distribution of the input data and generating new samples from that distribution. VAEs can learn meaningful representations of complex data and are utilised in a variety of industries including the medical industry.","metadata":{"id":"b4SzekBbfgWt"}},{"cell_type":"markdown","source":"**Architecture**\n\n---","metadata":{"id":"6_Dcuiu9fgwZ"}},{"cell_type":"markdown","source":"![imgonline-com-ua-CompressToSize-nx3D4ut0rK.jpg](attachment:bf416291-53db-4d92-8291-541ac6362540.jpg)","metadata":{},"attachments":{"bf416291-53db-4d92-8291-541ac6362540.jpg":{"image/jpeg":"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consist of two fundamental elements: the encoder and the decoder. The input data is mapped into a lower dimensional latent space by the encoder, which then captures the underlying structure and characteristics. The decoder reconstructs the input data using the latent space model. This architecture allows VAE to keep creating fresh samples while learning a compressed representation of the input data. Deep Neural Networks are widely employed as the encoder and the decoder. The main difference between VAEs and traditional autoencoders is that VAEs apply a probability distribution through the latent space. As a result, VAEs can produce new samples by collecting samples from the latent space.\nThe process is as follows:\n1. The VAE is originally trained on a dataset of medical image data.\nThe input image is encoded into a latent representation through an encoder network.\n2. The latent representation is then passed through a sampling layer to generate a new latent representation. The VAE selects a point from the latent space.\n3. The new latent representation is then decoded into a reconstructed image through a decoder network using the point.\n4. The loss function is calculated as the difference between the input image probability distribution and the reconstructed image probability distribution.\n5. The loss is backpropagated through the network to update the weights and biases of the encoder and decoder networks. Along with the reconstruction loss, the Kullback-Leibler (KL) divergence, a regularisation term is also included by the VAE\n","metadata":{"id":"4X3o3Hckfjn4"}},{"cell_type":"markdown","source":"**Advancements**\n\n---","metadata":{"id":"3SEiVqPBfkCi"}},{"cell_type":"markdown","source":"In the past two years, there have been several developments in the field of VAE.\n1. One of the most significant accomplishments is the use of VAE for unsupervised learning, where the model determines the underlying structure of the data without any labels. VAE has been successfully implemented in unsupervised settings for tasks like clustering and anomaly detection.\n2. Another significant development is the use of VAE for transfer learning which is the process of using knowledge gained from one activity for accomplishing another activity. VAE has been utilised for transfer learning tasks that include object detection and picture categorisation.\n3. Semi-supervised learning which improves performance by training VAEs on both small labelled datasets and larger unlabeled datasets is another area of exploration.\n\n","metadata":{"id":"ttPdRlrPfpps"}},{"cell_type":"markdown","source":"Numerous companies have made significant accomplishments and enhanced both non-medical and medical applications. VQ-VAE is a model developed by Google that is known for creating high-quality images. Google Health has employed VAEs to predict patient outcomes and create synthetic medical data. Facebook Glow can create stunning images and videos. IBM Watson Health has employed VAEs to find anomalies in time series data and medical image data.","metadata":{"id":"3iCurA0hg9pH"}},{"cell_type":"markdown","source":"**Applications**\n\n---","metadata":{"id":"LLts8BMmfqIY"}},{"cell_type":"markdown","source":"VAEs have been used for a variety of tasks in the medical field, including:\n1. Medical image segmentation: This is the task of dividing an image into different regions that correspond to different structures. VAEs can be used to segment medical images by learning a latent space representation that captures the spatial relationships between different structures.\n2. Anomaly detection: This is the task of identifying data points that are abnormal or out of the ordinary. VAEs can be used to detect anomalies in medical images by learning a latent space representation that captures the normal patterns in the data.\n3. Disease Diagnosis: This is the task of identifying the presence of a disease in a patient. VAEs can be used to diagnose diseases by learning a latent space representation that captures the features of different diseases.\n","metadata":{"id":"gaS2C8tAfsy0"}},{"cell_type":"markdown","source":"**Future Scope**\n\n---","metadata":{"id":"SsNYAjo7ftF2"}},{"cell_type":"markdown","source":"1. Personalised medicine: VAEs can be used to examine patient data, including medical imaging, to create individualised treatment recommendations. The development of VAE models that can account for unique patient characteristics and medical histories may be the main focus of future research.\n2. Real-time analysis: Because VAEs can be expensive to compute, their usage in real-time medical imaging applications may be constrained. To enhance real-time performance, future research might concentrate on creating more effective VAE architectures or parallelization strategies.\n3. Clinical decision support: By examining medical images and offering suggestions for diagnosis and therapy, VAEs can be utilised to enhance clinical decision-making for physicians. The development of VAE models that can successfully interface with clinical decision support systems may be the main topic of future research.\n4. Explainability: By using VAEs to give comprehensible representations of medical pictures, practitioners can better grasp the connection between imaging-based characteristics and clinical outcomes. The development of more efficient means of imposing attribute disentanglement and enhancing the interpretability of VAEs in medical imaging could be the main areas of future study.\n\nMedical imaging is only one of the many areas in which VAE has the potential to transform. VAEs have the potential to revolutionize medical imaging by generating fresh images from small datasets. VAEs can additionally help in data compression and feature extraction enabling more efficient data processing. Breast cancer, Parkinson's disease, and Alzheimer's are all key areas of research where VAEs are implemented right now.\n","metadata":{"id":"0e8tDqn-fwf2"}},{"cell_type":"markdown","source":"Credits: [A new VAE-GAN model to synthesize arterial spin labeling images from structural MRI](https://www.sciencedirect.com/science/article/abs/pii/S0141938221000858)\n\n[Attri-VAE: Attribute-based interpretable representations of medical images with variational autoencoders](https://www.sciencedirect.com/science/article/pii/S0895611122001288)\n\n[CVAD: A generic medical anomaly detector based on Cascade VAE](https://arxiv.org/abs/2110.15811)","metadata":{}},{"cell_type":"markdown","source":"# Applications","metadata":{"id":"yvJAQ4dkJ3Jh"}},{"cell_type":"markdown","source":"1. **Multi modal medical image fusion:** This is a technique to combine medical image scans from same or different modalities like MRI, PET, CT scans, X-ray scans into a single fused image to provide more detailed information about the organ. This application helps doctors understand the scan much effectively and hence makes it easy to diagnose diseases. Further, the fused image may help enhance functioning of other tasks like segmentation and classification.\nThis application however is implemented using various algorithms and techniques, the most prominent of which are based on decomposition by applying transform of some kind and then fusing the images using neural networks and reforming the fused image by applying the inverse transform. These decomposition based methods can be further achieved by using Wavelet transform, Pyramid transform, Curvelet transform, Contourlet transform, Ridgelet transform or Shearlet transform.\nOver the past 2 years, there has been a significant increase in the research publications in this area as well as a number of advancements too, including:\n  *    Development of new fusion algorithms: New algorithms have been developed that are more effective at combining inter-modal images using machine learning or deep learning techniques.\n  *   Use of large datasets: Due to a significant increase in the datasets of medical image scans, fusion algorithms have improved in terms of output quality as well as they have become more robust to noise.\n  *   Massive improvement in output quality: Advancements in technological resources have greatly improved the quality of fused images in terms of pixel quality, noise suppression and minimal loss of important features.\n\n  As mentioned above, there has been a tremendous increase in the number of review articles as well as specific research advancements on particular organs, especially tumors of brain, breast, prostrate, ovaries, liver and diagnosis and treatment of various organs like eyes, lungs along with diseases like Alzheimer’s. The extensive progress in this technique along with newfound power of technological resources make this field of application a promising area worth investing and researching in.","metadata":{"id":"zfR561J8KiED"}},{"cell_type":"markdown","source":"Credits: [Multimodal medical image fusion review: Theoretical background and recent advances](https://www.sciencedirect.com/science/article/abs/pii/S016516842100075X)","metadata":{}},{"cell_type":"markdown","source":"2. **Surgical planning** is the procedure of having organized for a surgical treatment with the aid of searching at the patient's clinical history, performing a bodily examination, and using imaging techniques to make a specific plan for the surgical treatment. Making positive the method is secure, effective, and least invasive is the primary objective of surgical planning.\nVirtual surgical making plans and 3-D printing have converted oral and maxillofacial surgical operation in latest years. With the help of these technologies, surgeons may produce unique fashions of the anatomy of the affected person, bearing in mind extra accurate making plans and execution of surgical treatments. CT pictures, MRI scans, and 3D fashions produced the use of 3-D printing technology are the most common fashions employed in surgical making plans. While MRI scans serve to view the patient's inner organs and tender tissues, CT scans provide targeted photos of the patient's bones and gentle tissues. Surgeons have get admission to to the patient's anatomy in 3 dimensions thanks to 3-D fashions made with 3-d printing era, which allows them plan and perform surgical methods extra precisely. Over the beyond  years, advances in surgical making plans have been targeting growing the precision and effectiveness of surgical treatments. For example, researchers have evolved new software equipment that permit surgeons to construct digital models of the anatomy of the patient, taking into account extra particular making plans and execution of surgical treatments. Additionally, new 3-d printing strategies and materials have been created, enabling surgeons to produce greater unique and thorough replicas of the anatomy in their sufferers.\nThe future of surgical planning is exceptionally huge, and many charming advances are inside the works. To analyze clinical snap shots and expand more particular fashions of the patient's anatomy, researchers are investigating using artificial intelligence and machine studying algorithms. The development of latest 3D printing strategies and substances will also permit surgeons to construct more complex and comprehensive representations of the anatomy in their patients. In conclusion, surgical planning is a vital component of oral and maxillofacial surgical procedure, and current tendencies in 3-D printing and digital surgical making plans have completely changed the sphere. With the aid of these technology, surgeons may additionally produce unique models of the affected person's anatomy that may be used to greater exactly plan and carry out surgical procedures. The cognizance of surgical making plans advancements during the preceding  years has concentrated on improving the precision and effectiveness of surgical remedies, but there are nonetheless many promising advances in watching for the field within the destiny.","metadata":{"id":"oR13GReSha87"}},{"cell_type":"markdown","source":"Credits: [3D Printing and Virtual Surgical Planning in Oral and Maxillofacial Surgery](https://www.mdpi.com/2077-0383/11/9/2385)","metadata":{}},{"cell_type":"markdown","source":"# Conclusion","metadata":{"id":"Vv_jReJnLOkL"}},{"cell_type":"markdown","source":"This report has given an in-depth overview of the developments in AI-powered image and video analysis in the field of medicine over the last two years. Medical imaging, diagnosis, and treatment have witnessed a revolution owing to the integration of AI, ML, and DL technologies that which has increased accuracy and patient outcomes. We researched several AI techniques, such as Generative Adversarial Networks, Transformers, ResNet, U-Net, and Variational Autoencoder (VAE), highlighting their architectures, analyzing their use cases, recent advancements, and future possibilities through a study of research publications and trends. We've additionally discussed some well-known uses of image/video analysis in medicine: multi-modal medical image fusion, and surgical planning. These applications have seen significant improvements, such as the introduction of novel fusion techniques, the use of huge datasets, and a substantial enhancement in output quality. With continuous research in artificial intelligence, machine learning algorithms, and 3D printing techniques for more precise anatomical models and surgical planning, the future holds enormous potential. The rapid advancement and promising opportunities in AI-driven image and video analysis are highlighted in this report's conclusion, opening the door for groundbreaking medical advances.","metadata":{"id":"uRxwBRU3LRzr"}}]}