Clinician survey on the value of risk prediction for immune-related adverse events induced by immune checkpoint inhibitors

Clinician survey on the value of risk prediction for immune-related adverse events induced by immune checkpoint inhibitors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Clinician survey on the value of risk prediction for immune-related adverse events induced by immune checkpoint inhibitors Bishma Jayathilaka, Jo Cockwill, Julia Dixon-Douglas, Julia Lai-Kwon, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6947226/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Introduction Identification of risk factors to predict immune-related adverse events (irAE) is a growing research area. This baseline study explores which factors oncology clinicians consider, perspectives on the value of risk prediction, and how this may impact clinical practice. Methods An electronic survey was developed to explore current practice, perceived value of risk prediction, and a vignette study where respondents selected their preferred treatment at baseline and according to proposed risk. Results Forty responses were analysed from medical oncologists (57.5%), medical oncology trainees (17.5%), nurse specialists (17.5%), and nurse practitioners (7.5%) who practice in various settings. If validated, a risk prediction tool was reported to be useful to tailor education, inform clinic review frequency and post-treatment follow-up. Perceived barriers included cost, timeliness of pre-treatment assessment, and unknown specificity of predicted risk. When confronted with higher predicted irAE risk, clinicians demonstrated a propensity to adjust treatment strategies in favour of single-agent ICI therapy compared to combination ICI. Conclusion Oncology clinicians perceive value in assessing irAE risk prior to commencing ICI, with 80% anticipating impact on patient education. Respondents perceived cost, complexity of assessments, and reliability/accuracy as important requirements for prediction tools. As predicted irAE risk increased, respondents were more likely to prefer less toxic treatment. Health sciences/Oncology/Cancer Health sciences/Risk factors Immune-related adverse events risk prediction risk factors oncology clinicians Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Immune-related adverse events (irAE) pose challenges to optimum care for patients receiving immune checkpoint inhibitors (ICI) ( 1 ). Cytotoxic T-lymphocyte associated protein-4 (CTLA-4), programmed cell death receptor-1 (PD-1) and programmed cell death ligand-1 (PD-L1) are the most widely studied checkpoint pathways and have been the focus of anti-cancer advances ( 2 – 4 ). ICI function by blocking these inhibitory pathways to promote T-cell activity against tumour cells ( 1 – 3 ). As a result of reactivated cellular immunity, auto-immune toxicities can occur. These unique toxicities, termed immune-related adverse events or irAE, can affect various organ systems, including skin, gastrointestinal, endocrine, hepatic, pulmonary and renal ( 1 , 5 ). Management of severe irAE can require hospital admission and lead to treatment interruption or cessation ( 6 , 7 ). IrAE incidence is higher with combination ICI therapy (anti-PD-1 and anti-CTLA-4) than with anti-PD-1 alone ( 5 , 8 ). Tumour histology may be associated with certain irAE types, with a higher occurrence of gastrointestinal irAE reported among patients with melanoma compared to renal cell or lung cancer ( 9 ). Clinical practice guidelines, including the American Society of Clinical Oncology (ASCO) 2021 update, emphasize the importance of assessing patient risk for developing irAE prior to and during ICI treatment. ASCO highlights the potential use of demographic factors and plasma biomarkers as predictive indicators, drawing on insights from retrospective studies ( 6 ). These studies collectively suggest that factors such as age, underlying comorbidities, and biomarkers may influence irAE risk. For instance, studies have pointed to variations in biomarkers such as T-cell characteristics, interleukin, and cytokine levels that correlate with increased irAE prevalence and/or severity ( 10 – 13 ). The association of irAE occurrence with age is not linear; younger patients may have a higher prevalence of colitis and hepatitis whereas pneumonitis and myocarditis may be more common in older patients ( 14 ). Additionally, mechanistic contributors may be further influenced by immune, microbiome, and tumour-specific factors ( 15 ). Such findings underscore the complex interplay of biological and demographic factors in irAE risk, highlighting the need for integrated predictive models to guide clinical decision-making. Despite an emerging understanding of risk factors and interest in risk prediction, definitive guidance for clinical application remains sparse. There are few validated models that can predict the likelihood of irAE for an individual patient prior to commencing ICI treatment. Lippenszky et al, 2024 implemented a comprehensive predictive model with strong performance to predict autoimmune hepatitis, colitis and pneumonitis using data from an institutional electronic medical record ( 16 ). The degree to which predictive models will impact clinical decision-making is not yet known. Baseline characteristics such as performance status, co-morbidities, organ function and tolerance to previous treatment are routinely considered prior to commencing ICI treatment ( 7 , 17 , 18 ). While these factors aid in assessing general tolerability of treatment, it does not quantify an individual patient’s risk of irAE development. Given this context, and in the absence of a widely accessible tool or systematic method to assess baseline irAE risk, we sought to understand what the value of a potential predictive tool would be and how such a tool would be accepted by oncology practitioners. The current landscape of irAE risk factors and prediction is characterized by a reliance on small-scale retrospective analyses with few prospective studies. This variability extends to clinical practice, where it is unclear how oncology clinicians assess risk of irAE prior to initiating ICI therapy and whether baseline assessments are integrated into treatment planning and discussions. There is limited guidance on how to apply predictive markers into clinical decision-making and how clinicians would want irAE risk to be reported. If validated in the cancer setting and accessible widely, predictive tools could be useful to estimate the likelihood of irAE and inform pre-emptive measures that clinicians can employ to mitigate adverse events and optimize patient experiences during cancer treatment. Our study aims to directly address these gaps by exploring the baseline perspectives of oncology clinicians in Australia on the value and application of irAE risk prediction tools. While research into irAE risk factors emerge, understanding clinicians' views on the potential use, integration, and barriers to adopting risk prediction is essential to bridge the divide between theoretical risk models and actionable strategies in clinical practice. This study seeks to inform the development of practical irAE risk prediction tools when validated evidence is available to support risk prediction and understand the implications of irAE risk in treatment selection and decision-making. MATERIALS & METHODS Ethics & consent The study protocol was approved by the institutional ethics department (Peter Mac Ethics Committee Reference 870088) for purposive recruitment of respondents. The survey was developed using QualtricsXM platform via a secure license. Respondents provided informed consent electronically by confirming their willingness to participate before progressing to questions. All methods were carried out in accordance with the ethics approved protocol and institutional guidelines for survey distribution. Study design A mixed-methods study was conducted using structured, cross-sectional questions designed to collect both quantitative and qualitative data. This design allowed the capture of comprehensive insights of clinicians' current practices and their opinions toward proposed irAE risk prediction. Survey development & pilot testing The survey was developed to include various question formats: closed-ended questions for background information, multiple-choice questions for practice-related queries, and Likert scales for assessing the relative strength of respondents' opinions/preferences. Additionally, free-text fields were incorporated to collect qualitative data, enabling participants to provide detailed insights. The survey was designed with a logical flow based on participants' answers, with certain responses prompting a cascade of follow-up questions for more in-depth exploration. All questions were mandatory. A preliminary version of the survey was pilot tested with a group of four oncology clinicians (JDD, JLK, DM and LS) to validate its content and functionality. Feedback on flow, acceptability, and usability were collected and used to refine the questions. This iterative process ensured the final survey was both comprehensive and user-friendly, facilitating collection of high-quality data. Full survey questions are available in Supplementary Tables S1-6. Participant recruitment & data collection The survey targeted a purposive sample of health professionals, including nurse specialists, nurse practitioners, medical oncology trainees, and medical oncologists involved in prescribing or managing ICI therapy for patients with cancer. The survey was distributed through professional cancer organizations, including Clinical Oncology Society of Australia, Victorian Comprehensive Cancer Centre Alliance, and Cancer Nursing Society of Australia, to reach a broad sample of the oncology community. Data collection occurred over a five-month period from February to July 2023. All responses were anonymous. No patient or identifiable information was collected. Survey participants could review their responses at any time before the close of data collection. Survey themes & structure The survey comprised four sections. The first section gathered basic demographic information on the respondent’s healthcare discipline, level of training, area(s) of oncology practice, and type(s) of healthcare or service where they mostly work, to aid in contextualizing responses. The second section sought information on current practices/conduct for assessing patients for irAE prior to commencing ICI. This included questions about use of scoring systems/tools/methods, perspective on usefulness of identifying at risk patients, regularity of assessing risk in current practice, and whether risk is discussed with patients prior to commencing ICI. The third section examined respondents’ opinions on the usefulness and purpose of irAE risk prediction assuming that a validated model is available. Respondents were asked how likely they were to use a risk prediction tool in treatment discussions and decision-making. Respondents were asked if performing simple or complex assessments would impact their likelihood to use risk prediction prior to ICI commencement. Perspectives on potential barriers to integrating irAE risk prediction, management strategies to address irAE risk, and preferred display of risk score/result were sought with the aim to interrogate theoretical and practical aspects of irAE risk prediction. The fourth section was a vignette study that contained three hypothetical clinical scenarios and was a key component to assess the implications of proposed irAE risk on treatment selection. This method used short descriptions of clinical scenarios to ascertain variation in practice/preferences ( 19 ). Each scenario contained three questions that asked respondents to select their preferred treatment approach at baseline and according to theoretical model-based risks. Proposed risks were graded ‘low’, ‘moderate’, ‘high’, and ‘very high’. Responses for each question were single-select choices corresponding to four treatment approaches: combination ICI, single-agent ICI, would not consider ICI, and would avoid ICI. Table 1 describes the vignette scenarios and questions containing model-based risks. Table 1: Vignette scenarios and questions For all scenarios, respondents were asked to assume that the patient had a cancer diagnosis that was responsive to first-line ICI therapy without considering medication funding/access. Cancer diagnosis was intentionally omitted to broaden applicability to different tumour types, especially as ICI use expands to new cancer indications. Gender was deliberately omitted to limit differences between scenarios. Overall, the vignette scenarios were designed to minimise clinical factors in the change in treatment selection so that the responses could be as closely attributed to the change in irAE risk proposed. In the first question of each scenario, respondents were asked to select their preferred treatment approach at baseline without irAE risk grading. In the second question, the patient was assigned a hypothetical irAE risk grading for colitis. In the third question, the patient was assigned a hypothetical irAE risk grading for any irAE. In questions 2 and 3, respondents were asked to consider their treatment approach from baseline. By introducing irAE risk while maintaining all other variables, the scenarios explored the influence of proposed risk on treatment selection. This method provided valuable quantitative data on decision-making patterns and qualitative insights into the rationale behind each choice. Data analysis All results were exported from QualtricsXM platform to a secure network. Basic statistical methods were used to report results. Responses to the vignette study were compared within each scenario to observe the change in treatment preference with change in irAE risk. There were no defined endpoints to this study. The study sought to gain baseline understanding and establish a benchmark for subsequent surveys of a similar nature as the role of risk factors for predicting irAE advances. Data availability All data are provided within the manuscript or supplementary information files. RESULTS Respondent demographics A total of 58 participants started the survey, and responses from the 40 participants who completed the survey were included for analysis. Respondents were medical oncologists (57.5%), medical oncology trainees (17.5%), cancer nurse specialists (17.5%), and cancer nurse practitioners (7.5%). Survey respondents were predominantly from Australia (92.5%), mostly working in metropolitan settings (42.5%), mostly treat melanoma/skin (65%) or lung (60%) cancers. Six participants worked in more than one healthcare setting and 28 in more than one cancer sub-group. See Supplementary Table S1 for summary of respondent demographics. Current practice In assessing the likelihood of irAE prior to ICI initiation, 92.5% of clinicians placed high importance on pre-existing autoimmune diseases. Other significant considerations included previous tolerance of ICI and baseline laboratory markers of organ function. A notable observation was the emphasis on patient/carers' ability to identify and report potential irAE, highlighting the importance of social factors in treatment planning such as health literacy. No respondents reported using a standardized method beyond general risk profiling, underscoring a reliance on clinical judgment. Most clinicians always assessed irAE risk and discussed it with patients before starting ICI treatment. See Supplementary Table S2 for summary of responses. Risk prediction model The survey revealed a positive inclination among respondents toward using a risk prediction tool, with 55% indicating they were likely to use a tool if it were available and validated. Most respondents recognized the potential of irAE risk prediction in informing treatment discussions (60%) and decision-making (47.5%). Respondents’ readiness to incorporate additional baseline assessments to inform risk prediction was notable, with 95% ‘certainly’ or ‘likely’ to perform simple assessments. However, the likelihood of undertaking more complex assessments, such as genomic testing, was met with divisive responses, reflecting concerns about practicality and access. Cost implications, timing of assessments in relation to treatment initiation, and uncertainties regarding the accuracy/specificity were the most cited barriers to integrating an irAE risk prediction tool into clinical practice, each noted by 75% of respondents. Other barriers included additional work for clinicians and patient access to baseline assessments. Clinicians anticipated that the most significant impact of a risk prediction tool would be on patient education and tailored instructions (80%), suggesting strong support for informed patient engagement. Frequency of clinic reviews and post-treatment follow-up were also identified as likely benefits of irAE risk prediction. Avoiding combination ICI therapy and increasing supportive care were among the management strategies clinicians would consider in response to higher predicted risk of irAE. Preferences for the format of risk prediction scores varied, with a slight majority favouring a percentage range (37.5%). Notably, some respondents expressed reservation about the quantification of risk, highlighting the need for tools that accurately reflect clinical realities and do not overstate the risk of common, less severe irAE (such as hypothyroidism). See Supplementary Table S3 for summary of responses. Vignette study Thirty-eight respondents completed the vignette study, and these were analysed. Scenario 1 : BRAF mutant disease with brain metastases In the initial baseline decision, most respondents (89.5%) expressed a preference for combination ICI therapy for a hypothetical patient diagnosed with BRAF mutant disease and presenting with brain metastases. Change in treatment selection from baseline with proposed risk of colitis and risk of any irAE are illustrated in Figure 1 for Scenario 1. Blue sections of the bars depict the number of respondents who would consider combination ICI therapy. When prompted with the introduction of predicted ‘high’ risk of colitis or ‘very high’ risk of any irAE, preference for combination ICI therapy was progressively less favoured (65.8% and 52.6%, respectively). A shift occurred towards favouring single-agent ICI therapy (orange sections), not considering ICI therapy (grey sections), or avoiding ICI treatment (yellow sections). See Supplementary Table S4 for full responses. Scenario 2 : BRAF wildtype disease with well-controlled rheumatoid arthritis For the second scenario, respondents were asked to consider their preferred ICI therapy for a patient with BRAF wildtype disease and well-controlled rheumatoid arthritis. Initially, a majority (60.5%) showed a preference for single-agent ICI therapy with combination ICI therapy being less favoured upfront (36.9%) (See Figure 2). When presented with the introduction of ‘moderate’ and ‘high’ predicted irAE risks, the number who chose single-agent ICI therapy remained the same but there was a shift in other treatment preferences. Respondents increasingly tended towards not considering or avoiding ICI therapy, as depicted by the yellow and grey sections of the bars. Overall, combination ICI therapy was less preferred in this vignette scenario compared to scenario 1. See Supplementary Table S5 for full responses. Scenario 3 : Older age with previous medication toxicity In the third scenario, respondents evaluated their treatment preference for a patient of older age with a history of medication toxicity. Without considering any proposed risk, the preference strongly favoured single-agent ICI therapy (71.1%), as shown in Figure 3, with a greater number of respondents electing to not consider or avoid ICI therapy upfront compared to both previous scenarios. As predicted risks for irAE were introduced, clinicians' preferences shifted towards further caution, with a greater inclination to either continue favouring single-agent ICI therapy (78.9%) for ‘low’ risk of colitis, or to not consider or avoid ICI therapy (10.5%, respectively) for ‘moderate’ risk of any irAE. See Supplementary Table S6 for full responses. DISCUSSION Findings from our survey demonstrate that while oncology practitioners regard irAE risk identification as beneficial to inform treatment selection, they perceive numerous limitations to adoption of risk prediction in real-world practice. The survey has drawn out key insights about current practice; while clinicians reported irAE risk as an important consideration prior to starting ICI, they do not use a standard assessment method. This reflects the lack of robust evidence on irAE risk factors and the fluctuating nature of risk with different treatments, patient factors, cancer type and stage of disease. Nonetheless, participants conveyed an interest in a structured approach to consider potential contributors to irAE risk that may potentiate serious toxicities. While clinicians recognized commonly evaluated risk factors, such as pre-existing autoimmune diseases, there was an emphasis on the role of patient/carers’ ability to recognize and report early signs of potential irAE. This highlights that the determinants of toxicities are not limited to clinical measures or discrete patient factors, and that social contributors are of high importance. Clinicians reported practical barriers to the use of irAE risk models with costs, timing, and unknown accuracy/specificity being prominent concerns. Complexity of tests and associated availability of results were unfavourably ranked if these caused delays to treatment commencement. Complex tests could be a source of inequitable care as these technologies are not accessible across different healthcare settings. These concerns point to a need for prospective risk prediction tools to be simple, cost-effective, timely, demonstrate reliable accuracy, and be equitably accessed. Validating an irAE risk prediction model in the cancer setting would be practically and ethically challenging; there is insufficient evidence about strategies to mitigate toxicities based on the presence/absence of proposed risk factors. Additionally, it is unlikely that a single tool would be effective across different cancers, treatment regimens, and irAE. The risk of a specific irAE may be of more clinical relevance than general propensity for toxicity. Patients’ acceptance of toxicities may influence treatment selection in line with their individual goals, irrespective of predicted risk. Some patient preference studies illustrate that survival outcomes were more important than quality of life ( 20 , 21 ), while patient preference for immunotherapy may also depend on presence of other co-morbidities ( 22 ). Therefore, even if a risk prediction tool was developed and validated, the influence on patient care is multifaceted. Based on participants’ responses, some clinical uses of a prediction tool could be to identify risk where there is unclear benefit of ICI therapy or to identify individuals with highest risk. Logically, when making treatment decisions, oncologists implicitly consider the risk of toxicity based on patient/social factors, disease/clinical features, and specific treatment. The value of a prediction tool may vary by healthcare setting; utility of a tool may be different in community-based or regional healthcare centres compared to specialist oncology centres, and with different experiences with managing irAE. The extent to which predicted irAE risk would influence treatment selection beyond expert reasoning and experiential evidence needs closer examination. Despite the various barriers reported, the vignette study illustrates the influence that predicted irAE risk could have on treatment approach. While the scenarios were brief, the change in treatment approach can be attributed to the change in irAE risk. Across the scenarios, a consistent pattern was noted: when confronted with higher predicted irAE risk, clinicians demonstrated a propensity to adjust treatment strategies. The marked choice of combination ICI therapy in scenario 1 compared to scenarios 2 and 3 was expected given the patient’s younger age, absence of co-morbidities, and presence of brain metastases. In scenario 2, the shift toward upfront single-agent ICI therapy is logical in the presence of auto-immune disease, which is a commonly considered risk factor ( 23 ) and was reported by respondents as a consideration in current practice. Similarly, in scenario 3, the prominent choice of single-agent ICI therapy at baseline signifies consideration of pre-existing medical history and older age. Thus, at baseline, clinicians were considering risk and adjusting treatment preference to reflect their caution with specific patient factors they deemed to contribute to risk. As predicted irAE risk was introduced and escalated within each scenario, respondents indicated a strategic shift towards minimizing toxicities associated with combination ICI therapy. These findings demonstrate the potential impact of irAE risk prediction tools on clinical decision-making, suggesting that such tools could inform treatment strategies, particularly for the choice of combination ICI therapy. This was a small study conducted in a defined geographic area. Collated responses indicate an interest from oncology practitioners in irAE risk prediction prior to ICI treatment. These results support the need for a broader, global assessment of clinicians’ perspectives into the nature of potential irAE risk models and the characteristics that oncology practitioners perceive are important in developing predictive models. This survey proposes scenarios with a hypothetical predicted risk, which may have impacted participation rate. Level of experience with ICI therapy or certain cancer types, and disease stage (metastatic, adjuvant or neo-adjuvant) may influence treatment selection and acceptability of toxicities. In the absence of a validated tool, it is essential that the usability and practicability of prediction models/tools are considered alongside new research into irAE risk factors. ICI-related toxicities are a contributor to treatment interruptions/delays, hospital admissions, long-term morbidity, and death. Understanding potential risks could support shared patient-cantered care. This is of heightened importance as ICI therapies are used to treat cancers among diverse patient groups, increasingly in early-stage cancers, and in combination with other treatment modalities. As research into the reliability of risk factors expands, this may garner research interest into how irAE can be predicted and prevented. While potential tools to predict risk could lead to personalized treatment approaches, the practical barriers to develop cost-effective, timely, and equitably accessible tests that incorporate numerous variables must be considered in tandem with emerging evidence of irAE risk factors. CONCLUSION Our study identified variability in how oncology practitioners currently assess irAE risk prior to commencing ICI therapy, underscoring a need for standardized, evidence-driven tools to guide clinical decision-making. Pre-existing autoimmune diseases and patient/carer's ability to recognize and report irAE consistently emerged, pointing to the importance of both clinical and social determinants of toxicity. Findings from this study support the development of irAE risk assessments that are simple, cost-effective, timely in relation to treatment commencement, demonstrate reliable accuracy, and are broadly accessible. The vignette studies illustrate that respondents are likely to adjust their treatment approach based on level of irAE risk, with a preference for less toxic treatment as proposed risk increases. This study demonstrates the potential of irAE risk prediction tools to influence clinical practice and contribute to patient-tailored care. Abbreviations American Society of Clinical Oncology (ASCO); Cytotoxic T-lymphocyte associated protein-4 (CTLA-4); immune checkpoint inhibitor (ICI); immune-related adverse events (irAE); programmed cell death receptor-1 (PD-1); programmed cell death ligand-1 (PD-L1). Declarations DISCLOSURE The author reports no conflicts of interest relating to this work. Author Contribution BJ contributed to the methodology including survey design & build, survey distribution for data collection, analysis, manuscript writing, preparation of figures/tables, and manuscript writing and finalisation. JC contributed to manuscript review. JD, JL, DM and LS contributed to survey design & testing, and manuscript review. LS also contributed to methodology. FF, MIJ, GA contributed to concept, methodology, manuscript preparation & review, and project administration. All authors reviewed the final manuscript. Acknowledgement The authors wish to thank the health professionals who participated in this survey for contributing their valuable time and insights, and to the organisations who kindly agreed to distribute this survey, including the Clinical Oncology Society of Australia, Victorian Comprehensive Cancer Centre Alliance, and Cancer Nursing Society of Australia. This study is part of graduate research supported by an Australian Government Research Training Program (RTP) Scholarship and was supported by resources from the Victorian Comprehensive Cancer Centre Alliance as part of the Strategic Program Plan 2021-2024. References Johnson DB, Reynolds KL, Sullivan RJ, Balko JM, Patrinely JR, Cappelli LC, et al. 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Jayathilaka","email":"data:image/png;base64,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","orcid":"","institution":"Peter MacCallum Cancer Centre","correspondingAuthor":true,"prefix":"","firstName":"Bishma","middleName":"","lastName":"Jayathilaka","suffix":""},{"id":491827925,"identity":"76c60f2d-bf18-4bba-a2d3-7f15dbdd7767","order_by":1,"name":"Jo Cockwill","email":"","orcid":"","institution":"Victorian Comprehensive Cancer Centre Alliance Cancer","correspondingAuthor":false,"prefix":"","firstName":"Jo","middleName":"","lastName":"Cockwill","suffix":""},{"id":491827929,"identity":"6a9e8be6-046f-4e60-808c-cfa93f7e8b09","order_by":2,"name":"Julia Dixon-Douglas","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Dixon-Douglas","suffix":""},{"id":491827930,"identity":"bdce2f31-cba9-47ed-be54-4a8b5d7512bf","order_by":3,"name":"Julia Lai-Kwon","email":"","orcid":"","institution":"Peter MacCallum Cancer Centre","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Lai-Kwon","suffix":""},{"id":491827931,"identity":"ad9a7aab-f6ac-4c36-a46e-f4f79eb8436f","order_by":4,"name":"Donna Milne","email":"","orcid":"","institution":"Peter MacCallum Cancer Centre","correspondingAuthor":false,"prefix":"","firstName":"Donna","middleName":"","lastName":"Milne","suffix":""},{"id":491827935,"identity":"26bc1768-efc0-4039-bc1c-d62caaf2054a","order_by":5,"name":"Lavinia Spain","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Lavinia","middleName":"","lastName":"Spain","suffix":""},{"id":491827936,"identity":"fe253af8-cd2d-481f-a379-c792c33a7aa9","order_by":6,"name":"Fanny Franchini","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Fanny","middleName":"","lastName":"Franchini","suffix":""},{"id":491827937,"identity":"efe4f922-4b24-47ab-bb50-3e0418fe0900","order_by":7,"name":"Maarten IJzerman","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Maarten","middleName":"","lastName":"IJzerman","suffix":""},{"id":491827938,"identity":"e80f871e-b0e9-411d-b0c4-3b0e8c02ed40","order_by":8,"name":"George Au-Yeung","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Au-Yeung","suffix":""}],"badges":[],"createdAt":"2025-06-22 01:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6947226/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6947226/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88000248,"identity":"1daa8887-6f1a-4139-8fb9-b8bab43a3e0e","added_by":"auto","created_at":"2025-07-31 10:19:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":618394,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure 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10:19:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46870,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryIRAEriskpredictionJayathilaka.docx","url":"https://assets-eu.researchsquare.com/files/rs-6947226/v1/7fe24a29ece847aaa0616a9e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinician survey on the value of risk prediction for immune-related adverse events induced by immune checkpoint inhibitors","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eImmune-related adverse events (irAE) pose challenges to optimum care for patients receiving immune checkpoint inhibitors (ICI) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Cytotoxic T-lymphocyte associated protein-4 (CTLA-4), programmed cell death receptor-1 (PD-1) and programmed cell death ligand-1 (PD-L1) are the most widely studied checkpoint pathways and have been the focus of anti-cancer advances (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). ICI function by blocking these inhibitory pathways to promote T-cell activity against tumour cells (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). As a result of reactivated cellular immunity, auto-immune toxicities can occur. These unique toxicities, termed immune-related adverse events or irAE, can affect various organ systems, including skin, gastrointestinal, endocrine, hepatic, pulmonary and renal (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Management of severe irAE can require hospital admission and lead to treatment interruption or cessation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). IrAE incidence is higher with combination ICI therapy (anti-PD-1 and anti-CTLA-4) than with anti-PD-1 alone (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Tumour histology may be associated with certain irAE types, with a higher occurrence of gastrointestinal irAE reported among patients with melanoma compared to renal cell or lung cancer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e Clinical practice guidelines, including the American Society of Clinical Oncology (ASCO) 2021 update, emphasize the importance of assessing patient risk for developing irAE prior to and during ICI treatment. ASCO highlights the potential use of demographic factors and plasma biomarkers as predictive indicators, drawing on insights from retrospective studies (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These studies collectively suggest that factors such as age, underlying comorbidities, and biomarkers may influence irAE risk. For instance, studies have pointed to variations in biomarkers such as T-cell characteristics, interleukin, and cytokine levels that correlate with increased irAE prevalence and/or severity (\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The association of irAE occurrence with age is not linear; younger patients may have a higher prevalence of colitis and hepatitis whereas pneumonitis and myocarditis may be more common in older patients (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, mechanistic contributors may be further influenced by immune, microbiome, and tumour-specific factors (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Such findings underscore the complex interplay of biological and demographic factors in irAE risk, highlighting the need for integrated predictive models to guide clinical decision-making.\u003c/p\u003e\u003cp\u003eDespite an emerging understanding of risk factors and interest in risk prediction, definitive guidance for clinical application remains sparse. There are few validated models that can predict the likelihood of irAE for an individual patient prior to commencing ICI treatment. Lippenszky et al, 2024 implemented a comprehensive predictive model with strong performance to predict autoimmune hepatitis, colitis and pneumonitis using data from an institutional electronic medical record (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The degree to which predictive models will impact clinical decision-making is not yet known. Baseline characteristics such as performance status, co-morbidities, organ function and tolerance to previous treatment are routinely considered prior to commencing ICI treatment (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). While these factors aid in assessing general tolerability of treatment, it does not quantify an individual patient\u0026rsquo;s risk of irAE development. Given this context, and in the absence of a widely accessible tool or systematic method to assess baseline irAE risk, we sought to understand what the value of a potential predictive tool would be and how such a tool would be accepted by oncology practitioners.\u003c/p\u003e\u003cp\u003eThe current landscape of irAE risk factors and prediction is characterized by a reliance on small-scale retrospective analyses with few prospective studies. This variability extends to clinical practice, where it is unclear how oncology clinicians assess risk of irAE prior to initiating ICI therapy and whether baseline assessments are integrated into treatment planning and discussions. There is limited guidance on how to apply predictive markers into clinical decision-making and how clinicians would want irAE risk to be reported. If validated in the cancer setting and accessible widely, predictive tools could be useful to estimate the likelihood of irAE and inform pre-emptive measures that clinicians can employ to mitigate adverse events and optimize patient experiences during cancer treatment.\u003c/p\u003e\u003cp\u003eOur study aims to directly address these gaps by exploring the baseline perspectives of oncology clinicians in Australia on the value and application of irAE risk prediction tools. While research into irAE risk factors emerge, understanding clinicians' views on the potential use, integration, and barriers to adopting risk prediction is essential to bridge the divide between theoretical risk models and actionable strategies in clinical practice. This study seeks to inform the development of practical irAE risk prediction tools when validated evidence is available to support risk prediction and understand the implications of irAE risk in treatment selection and decision-making.\u003c/p\u003e"},{"header":"MATERIALS \u0026 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eEthics \u0026amp; consent\u003c/h2\u003e\n \u003cp\u003eThe study protocol was approved by the institutional ethics department (Peter Mac Ethics Committee Reference 870088) for purposive recruitment of respondents. The survey was developed using QualtricsXM platform via a secure license. Respondents provided informed consent electronically by confirming their willingness to participate before progressing to questions. All methods were carried out in accordance with the ethics approved protocol and institutional guidelines for survey distribution.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eA mixed-methods study was conducted using structured, cross-sectional questions designed to collect both quantitative and qualitative data. This design allowed the capture of comprehensive insights of clinicians\u0026apos; current practices and their opinions toward proposed irAE risk prediction.\u003c/p\u003e\n\u003ch3\u003eSurvey development \u0026amp; pilot testing\u003c/h3\u003e\n\u003cp\u003eThe survey was developed to include various question formats: closed-ended questions for background information, multiple-choice questions for practice-related queries, and Likert scales for assessing the relative strength of respondents\u0026apos; opinions/preferences. Additionally, free-text fields were incorporated to collect qualitative data, enabling participants to provide detailed insights. The survey was designed with a logical flow based on participants\u0026apos; answers, with certain responses prompting a cascade of follow-up questions for more in-depth exploration. All questions were mandatory.\u003c/p\u003e\n\u003cp\u003eA preliminary version of the survey was pilot tested with a group of four oncology clinicians (JDD, JLK, DM and LS) to validate its content and functionality. Feedback on flow, acceptability, and usability were collected and used to refine the questions. This iterative process ensured the final survey was both comprehensive and user-friendly, facilitating collection of high-quality data. Full survey questions are available in Supplementary Tables S1-6.\u003c/p\u003e\n\u003ch3\u003eParticipant recruitment \u0026amp; data collection\u003c/h3\u003e\n\u003cp\u003eThe survey targeted a purposive sample of health professionals, including nurse specialists, nurse practitioners, medical oncology trainees, and medical oncologists involved in prescribing or managing ICI therapy for patients with cancer. The survey was distributed through professional cancer organizations, including Clinical Oncology Society of Australia, Victorian Comprehensive Cancer Centre Alliance, and Cancer Nursing Society of Australia, to reach a broad sample of the oncology community. Data collection occurred over a five-month period from February to July 2023. All responses were anonymous. No patient or identifiable information was collected. Survey participants could review their responses at any time before the close of data collection.\u003c/p\u003e\n\u003ch3\u003eSurvey themes \u0026amp; structure\u003c/h3\u003e\n\u003cp\u003eThe survey comprised four sections. The first section gathered basic demographic information on the respondent\u0026rsquo;s healthcare discipline, level of training, area(s) of oncology practice, and type(s) of healthcare or service where they mostly work, to aid in contextualizing responses.\u003c/p\u003e\n\u003cp\u003eThe second section sought information on current practices/conduct for assessing patients for irAE prior to commencing ICI. This included questions about use of scoring systems/tools/methods, perspective on usefulness of identifying at risk patients, regularity of assessing risk in current practice, and whether risk is discussed with patients prior to commencing ICI.\u003c/p\u003e\n\u003cp\u003eThe third section examined respondents\u0026rsquo; opinions on the usefulness and purpose of irAE risk prediction assuming that a validated model is available. Respondents were asked how likely they were to use a risk prediction tool in treatment discussions and decision-making. Respondents were asked if performing simple or complex assessments would impact their likelihood to use risk prediction prior to ICI commencement. Perspectives on potential barriers to integrating irAE risk prediction, management strategies to address irAE risk, and preferred display of risk score/result were sought with the aim to interrogate theoretical and practical aspects of irAE risk prediction.\u003c/p\u003e\n\u003cp\u003eThe fourth section was a vignette study that contained three hypothetical clinical scenarios and was a key component to assess the implications of proposed irAE risk on treatment selection. This method used short descriptions of clinical scenarios to ascertain variation in practice/preferences (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e). Each scenario contained three questions that asked respondents to select their preferred treatment approach at baseline and according to theoretical model-based risks. Proposed risks were graded \u0026lsquo;low\u0026rsquo;, \u0026lsquo;moderate\u0026rsquo;, \u0026lsquo;high\u0026rsquo;, and \u0026lsquo;very high\u0026rsquo;. Responses for each question were single-select choices corresponding to four treatment approaches: combination ICI, single-agent ICI, would not consider ICI, and would avoid ICI. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e describes the vignette scenarios and questions containing model-based risks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eVignette scenarios and questions\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg 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7M/vYUFXgIB06zSQdND6iEe0tRj1qSAg9Cp/zj5IPuadBSoAWqgkFId5ZCmsIx8BVBmjlUAJEEYrz95XY2iMlEOMZ577sqwAEJDhU0ITuSMEDgAyQAcTkf44AMhZOnyk/C/LIPrWw6t30VNKlPBcr0+AoCVBqxID1YIg2ve3w9SylzJ/oTB9AgQHsM8444xECpQUo19aiRLpESDpmR409Fxbv/WWx5z2Vq/ywQUoAaSX882fAGYZSu7aBDlqzpW9K62zfUGFTD/n6L0qnugjyye77m8tCVoP2d6aa66ZAvJPfvKTqTrYFSDo9UVW9PvCEATG/WX/VBBcU5Did30CBIEPjIIh8MDZqBgwRhsDzJJFbBruAYcEFtoUAjfhgrmprpRRJLBQJUCHJeYkzgS/koQx2IagAaGXXZd0Y/t8K8xgz8i8oAEJ19uvVVlbEQKtPUgbDp/uu84x4RhdAYKsPv8ruIcHBkzROqwLQCu0AZP6BAg+C8dUCZ2p8BCVsGOBDPuGD4POIOT7q9PB0xTJTjWljNFKoAQIo5Vn76sh7LJyHJ1sgPK9p3IwHBl2xqJfl/NzSEhlAVn3lABlR8YqqyjDLxqXKZAZ8H/tBw4kxeNM9Q87eIiwyzjE41F9J55wBCi0EmkxUr4XyWtfEngoRzJYhi1bIcsJZOqlS486dV8Bhr5IxisQABwABCjJfHpigSGDkPcgNglPrzGAAmz+XT/30Fvg5YNFAvNcAuwWwUe4EXxnADw9SHCAXAsKnCmQaUcOVAS08Xk3AFsVKMANDlfg7SwCUuAJaZ5W5lAzrNGmoCLJQXvQAXv1+yc84QmpzxmxYPeuye79zOe0PAkw9C8j+oiM+bBPAYqgIbfPeJKJw4cwwKFoSQ+4oTqJDJiD9iUVVVhhXU3D2SztEoIThzPjJVJIkUc4+j78KqNIYKFKAMnl69kh8s1W2ZPkmLM57JGtOq8j++/JRfw1XuA8n2SaBJ+AwPlFvh2/UEnwe4G1B4dIQOABggccw3XjULHggj0LNLQowiP/Z/PsEyZ5kAIc4O/NV5Dibz/TIih4CNKPD/g+TsNOPQTBHKyPT5fkEAzhM65hbromtEbXB4zxBCZzknj09CMdCs5owEhrLmO0EigBwmjl2ftqHBrnjvgzOIaJ0HPoiLTfySB4cghDQuo5Uv1/sgVKanH4SAsA569Mr0zHiIEIMBH5H3rooYkQqDzI+iEgAEELE6DxMwSA05dpYNScPxBxCJCjZ4j+jQT4DgP2HRkATtt6AIBWJ2vQQuARZ55oggjIblqPeyEcPu/554OGQMQ9VUPIRmVEoGE9ZRQJLDYJcJqCc4eRnc0RYAsCPGmILXrSkIEIcN6CdTaMeHOYbCXenYBQy/w7O8SeEXl2o+3PgwdkAWXkZRElJ2TfXdPBYnaKTDgLFY80RMgF9j6PgLB999f2yD7ZvQAHTqliepyxKoGkB7KgOuLJKewYNvhcHJQ0f+RDFSFvmcr3Fy5JhJBH3oLl/6qqkh+IQxlFAgtVAgIEtinQ52d1E0j6sS2+kn3z+XHmBkbABWQeJkgOIueeOoYDCJ4lE5BqiUYVRa077JaPhifs0/XZO/vjw1UNBA8+w+YlGF0Xf9ASBGskGdg1zoCXOJNgDuauyqgtGk/AY1zP79ip68AHmOTagh/tT7oTBP4CjOg0qO+j7/hMvE9JRYU8BCAwrJxFGr3mlwBh9DItVywSKBIoEigSKBIoEigSKBIoEliwEigBwoLdujLxIoEigSKBIoEigSKBIoEigSKB0UugBAijl2m5YpFAkUCRQJFAkUCRQJFAkUCRwIKVQAkQFuzWlYkXCRQJFAkUCRQJFAkUCRQJFAmMXgIlQBi9TMsViwSKBIoEigSKBIoEigSKBIoEFqwESoCwYLeuTLxIoEigSKBIoEigSKBIoEigSGD0EmgNEDwus7yZbvQCL1csElgIEvAsam/iLaNIoEigSKAugYIPRSeKBBa3BDwy27smPEbWuM1//vd93Z6f78UTj3jEIxa3BMrqigSKBBol4LnZ3jtRRpFAkUCRQF0CBR+KThQJLG4JeEmfd2iUAGFx73NZXZHA0BIoBGBokZUvFAlMjAQKPkzMVpeFTqgESoAwoRtfll0k0CWBQgC6JFR+XyQwuRIo+DC5e19WPhkSKAHCZOxzWWWRwNASKARgaJGVLxQJTIwECj5MzFaXhU6oBCY2QPjqV79a7bPPPtUb3/jGapNNNlnw2/+b3/ym2mWXXarb3va21Qc/+MHqrne964Jf02JcwLe+9a3qfe97X7XZZptVz3ve8xqX+Pe//7269NJLq3e/+92p/2+FFVaYE1FMGgH4xS9+Ub30pS+tnv70p1d77713dcc73nFO5D6KmxY8GIUUx3+NhYQHdWksBnz43Oc+Vx1//PHVAQccUD3lKU8Z/4aP8Q6XXHJJte+++1Y77bRTtfXWW4/xTuXS45QA/3/FFVdU73jHO6oXvvCF1VZbbdV6ux/+8IfVUUcdVT3zmc8cC4+ddwHCz372s+rJT35y9bvf/a7629/+Vt3jHveo7na3u1W3v/3tqxVXXDEp/gte8ILqLne5S+89+ve//52udec737m6zW1uk753/vnnV69+9avTU1qe//zn975W3w/+61//Svcclqhfd911iUC+5S1vGeq7Tps7WG59H/vYx5LMZmtQ6NNPP7067LDDqu9+97vV8ssvXx144IHVs571rLRvMezDt7/97UR6v/Od7yRA9jkE+Ha3u91sTXfO7vP1r3+9ev/735/2x6Efjw+rDzrzmc98pjrkkEOq733vexXQnyvHNV8IwFe+8pVqt912q77//e9X//znP5Nu3/3ud6/+8Y9/VGuttVb1ohe9qNpwww2re93rXkPt7e9///vqnve859R3fv7znydAXn/99VPiYBwBQv2e9Qk/4AEPqP74xz9Wf/rTn9I64Z9h3fBv9dVXT3ZubwbZzFziQb4m67jooouqT37yk9VZZ51VPeQhD0nBb4y//OUvSdaHH3741M+e9rSnVSeccEL1qEc9aqj9XGgfXmh4MJsBwh577FGdfPLJFT3mQ9j2He5wh2QPyNAOO+yQ7EFCrO9gQ3xV7pM/9alPJUyGt2ussUbfS/X+HIzi9+50pzsN/I5nw8CGL3zhCwn/+ck///nP1ZOe9KRq0003TT5VMEkubYM+4TR77rlnSnTMx4H44jdf+tKXEs4997nPrXbcccfqiU984hSekResf+9731udccYZ1eMe97jqNa95TbXBBhuMBZPnk5ysHTeCieeee2515JFHVq985Ssbp/jFL36xOvjggyvJ7jY+MdO1zbsAwYIEB7LhiBTHwSh+/OMfJ9LMeCiL7H9fQoB0f/7zn09Eou93ZirYb37zm2njzHWYIWD56Ec/mjZ81VVXHearc/JZoAvULr744mTksrAnnnhidcstt6SfL7vsslPzuuCCCxIYeDyeII/xCxCQgXXWWWdO5j/bN2VwyF2XQdN7IIEcT3qAYI84T7pz2mmnJbnst99+1U9+8pOUZeHkd9111+r1r39978QB4vHa1762Ou6442ZNBW644YaEYcccc0zrPREKDpSNcPSIi8GuzjzzzBSEC444T8FRJDxmbRFD3Mh6zV+AsMUWW1QbbbRR9YQnPGGJKyAC9iESGtajgiMIQggX+1hIeDCbAYJ7sW8E2ZMU4eXKK69cnX322cn2Ef0PfOADyQb6Dv4IF2gjXH2vM8znBMaCmo033rj1a0ghvoDTSGLCMkEyriJheuqppybMkARRVV6oAx4I7uC45JjAD3bz/4jwSiutVAmUEGQV3Gc/+9nps9/4xjeqgw46KMlH5X0SBr76kpe8JOHnIH3tix/Tldm8DBAs5u1vf3vKNL/nPe+ZEtCvfvWrREJlDQDGM57xjM51y+SfdNJJicC+853vnJUAQeQPxAADQtB3/PKXv0yABxQ8YpaRdGUe+l57XJ/761//Wl111VXVYx/72LReYCewA3Kf/vSnUzbWuOmmm1IJ7H73u1/6uQqQbLkqw4Me9KDqiCOOqJZZZplxTXPeXLevQZcA4dZbhjgjk29961urvfbaK32ArchCkauslKxi1xDUcrhA2J/ZGHSdPQt0BNCDBiKDIHOiESDE5z/+8Y9X22yzTUqakMMwldTZWGfcw+Nx7ZVnaMNwuNZE+AV7a6+9dsoOTuJYSHhQ35/ZqDCqmLHXD3/4w6nSzJ/7N/1fbbXVqvPOO6+X2vz2t79N1UGB6mwFCPw5QsuXDwoQVN1VSPlS9v3whz98iTVJGqjQCxQkDxfq+MhHPlK97nWvS1VxlUSDjAQNfi6Bizvtv//+iVN84hOfSO/hUmXEJy677LKE8fe+970Xqgh6z7sECB2iCoIkYs4NWmQtohQgbLvttp0CV3nYfPPNKy9905s/7goCgqyUrtT34he/+FYOftCEDz300Oraa6+tLr/88hRJyxje97737VzjXH7APAF47vytn5EDNE7EEBTIBgiYgGEMaz722GMT6K+55ppzuZRZufdCIgSzQQCGEbqMoax6HiCoNmo3UFmkb23nOvL7yFD5joD2y1/+8jBTmPZnP/vZz1aveMUrElHoChCUlgUIAoF6gCCrqiqnhUDAFC1I057YGL5oT+yTNWt7kDxoagXh7O0XTLcfKgzjaOsawxJHdsmFhAdzESCoLGtTiwDBHLRWsA8JQ5WEruEzglQJx3e9612zEiAgurognHHQKtUWIJgbH6iSDr+e85znNC7H5/Sas6uFOuC3Kq8gh60bKqO6CSQK/EyVQTJBIGe/VFQMHEE3hmRvH4xfqDKKeZcAYRoBAiIq24RAAwxKJALlRGXdHMxTwlaOknmgfAxOGdvQb/ywhz0sRaGcMKPceeedpwAD2RW5Hn300dU555yTIleZSoYuky/YYOxxGMjfnPSDH/zgBEAIrj5BpSGOnLPTDrDlllumloBBg2FwlObFCJAgfwsyuoasytVXX50CIOQ8Xm5l/taolUfgIuOCUCjntY04HKPiwtEr8zFqTr6rncH+AG3yF5CRbQQOyoNkZH1aCGIgdtYo4JNBaBpIHAJBpsqSAIXcVRxkaFxT5tg6ZRzIwu+Vo83Jwa3tt98+7X+UMDkK7Tt+L3sJoPQ8A2KtYX6vWmVPBXuCzEHnQuyB8rWeQC0VMsV0VWbk8Y9//FR/ZRMh8F3y1l5Gf2RXll566TSPCy+8sLQY/a9SNAUIMoPbbbddyrDBAHtJB/UVC0q1Ej30oQ9NOgwLfJ7+6IOnC+S83nrrpVZG9q2SxWbjxTA+Y8/YIsyR5XMOCi6ofAnofVZ2C264p3urlHFqdIiewRBnSmCCJMXuu++eHF5ThTACBDiikmqwX/emX9qM/HnVq161xBmfsJ02PJCJVM4nR1i5yiqrpGsOqqJMBw/YnT3xRxXBPQQI5BoO31w5+q997WvpLAlCpZVOZUemeFBvOTyXNGIbMGnddddNmAajYA+Z+TkcsPeD9s88BIwFD7q8zK1/PxsJhHqAwB7pFzxnEzfeeGPCcD9jd/aSjvEpfA6dU9FW0edj/V/VjT/W2++AMhJPT6OKBbv5UXZC1+gVneI/6Wr4B+1POAibxClcD4n3Gf5b+6JWW36HncMI+pgPlXWtQ3iM3+MS0xkqlNov2Q+fHRVW8sABnGlwb/yIz2wLWKKK4Tqqs1r/+D/yxh1gZBfetc3fGS8yhH/slb3DWzJySNze8PXsmRwdtg4cCMKMIwziL/Acb8ETllpqqdS5YMBb+4kzSNDA/J/+9Kdpj0455ZTkFxwGtm/mYfi9fbe3N998c2p3I0trgLFwGp7SN90R8FhHhACoDb/o7A9+8IOp++IbOmHoqr2pr7feYnT99dcnWZETW4ChkuhdLcvT0SnfmbctRlFBYHyi5j/84Q8pwuZ0coeAuHEuBES4Dhwjpf4vGKAsMncGAm3zbZB/c+B5hQJJowA2S/+/DJjSlv/7W0Dg34g3I0NKGaZr2FzXBAZKhtouW2VMAAAgAElEQVQDAE49A9i0UZSGoiL2Ps9p+j6wQHq6DjozAqDFoBhxBAhkd+WVV6Z1ysKQKfLUlsEEjE996lNToKK3G/iK5ikw4wrDaVqD/VFCVBb0PWVVJd3ICMr4IFiIWd5TL0Cwh3VAyO8BtBgdEFd+RbhiLgxXAAA4GD1CgzzRE4EIwmbPAIQ9NE9GxfEICgAeYPBdsicn+wi46ABSKWi0B6oibRlbcyJ/xguggas+csAcgBQGl59BADSMXfsbEEZe6RnySFblkPL/aQId1FpAxkE87S98QCrhA/uj55ylcv3973//tA9+x76RevpJH+21fTPogcqkzDzbiwABINNl+oEM2BPBKgyyZx52QN84P9+DOa6LgHDIfm9otaFzyul9KwgCS3pssE2BJ+Ii4UAXBSFNow0PZF7JSlsGxy/ZIWg3t6YxHTyAh5ws/BDwwmLYBHO1G9bnjbQJnOCAP+zang46dxMkTZAA++FlnHVy8BHuIXVIIuI1aP9gS8GD6dGH2QoQ+HC+wxkESRi4zZ7ZtT8CRfaMzGk35LfhvX0P39+UkWUH/BUfy975a/og0IcRbAUJk1yQuHGeAMFEnvkHmIAD0L04KOrnKuVGZMwHVRAiYQRP6L0E13SGQMD32XNeYXXwmt/jY3EBCSvragsQyEKFH34huwgwrCUfARBc7YN3bWuwD67Hb/PPDl/z35E4CD5QJ7yxf7iJObQNGAkXcEF4FxgpiLRf9EHiBR5p+fRv/hr2w2+fsb8eAIFLwhP3Q+TJDN7QN3+TFZ9Pxv4WSMAmgWr+8It8rriE4M1+8DWCEPrF/5C1hJLRpK+SUZKuOBn9JkN4SocnNkCQhWWEHLXKgRPtFJiQbKJMNcWltAZhi+yiJFkPEKLFqL4BHJso0B/CN/xM1GizbcBjHvOYtLlaf5BuygpQABYioOpAIYcNEDhJhJChyOrJ7lFyka0/fUtq0c4TAQIFRIhlSUTWiAMS3HbQSYZe5QXxiSfsIDXIhPUjW22DnGViOGvZHBk8RA2ZMboCBPMi27aDiYIyRB8IkjNnYTBswI2MmKd5Ax3GTz/IUgBgfvaNoVsTZyKAadKP2D+6Jfvk3gxfcNBURZEBoZOulYPXNddckwwesHA6wL9eQfAdJJCTUVKV3RI0cG5Kz6WC8H8ax+HKxNs/mCDYFYAJzjlBQG2v2BGHQP8QTlhA/9iFA49NAULcxf7mAQKHz7GqSNkbjsH1BR+ckYDAXsVTepB6uuIanB0MmW6AIOiJQ3lsV7uOAEWgQt/ca1BVr44H5Cf7ptJGDuYm2BHYN43p4AF7E6xzZhythIM9IT82Ry6SAfXBEZOhABCxl9HvSoy4PsJjXYEz5swXII4qcV37BzMKHkyHklapfTR8zfSu0P0teyOA5eP5BvfTGeD/bB6mChbgPZJuz5v8b1vLBhulcxEgIGr+rxLFxwgQBBxIP/9ErySh4Ikg1PdUBxA/AYaEEm4wbIDApmfaAq0CQV55gIC7CBq0Y8uqSzDAjLYAgW2yQ+vik+r+Ct/pg3dtOws/YZCgCUYgypKy8MjoChBwMLjbNuC+BAxfynfDHwN3xNNcHy9QUbK3uI4BswWXEnI+4z64J44QSV58StAhqYQbGGTpM3yM67kOrtU2tI1KvJCvsxS+w8fTL/cM/tCkr3BRVdT9g8fac4nMiQ0QREiUKPrcCd+fKMUADMTNxlAIygc0hg0QXEfQwYjyTLmMhayBDRBd1gMQBsMgtTjISCDRwwYIjFZQo4Ul2mC0NABARIhSdjlLClknBAg7ckzRBSAqAchs27kGMrReaxC0MGKOGuh0BQhhEJRfsARMyRG56hMgqKAgyoMIj7kh3AxVhlJ1AmBrA4p3WZAT/QgDQrZlEZEUhF1VRnafDBBF5IKsrTEAetj9s8aoGJlXPsjcfchPVrQOuDIIHA0Cla+/HFK+NcRGBYEu0wF7izQAWvsN+AFulJVlpQSTCCMgnU6AIIsmexiZKNeXufGHffl93f5zfff56QYI+RkEgS3bRXg5NYEDIqKM3jbqeCBTD+M4t3hWugTCKPGArquSIBcyYjE/eyLZYkgiBCHI5w5/YB5CyF7qhzXr64S97FaQJliA+3wAe0fy+Iiu/YtqX8GDbrJe/8RsBQjaP+h86AyM55PDJwouJQtU7GCx7DfblG0Nctc3QEDQBLgqjZGsUhHkD2X5+eIm/9BUIRymgiDgQWwH2XPXDjUFCDLknh7obySUrbCztm4ACSt/zMP1+H+yCwLaxHea8K5trpKV8b4Ze8PnytS7j+CuK0DQjsXGBw0+VQCHSwnuDAEDLiXZCrv93vryA8/2mGzMS1sVvsGv4J6y9NHuWQ8Q8oTSoHnhRvyIZAZ+GlyH7poP3XLPBz7wgbeqIOANEtL+5Ovve4apS3fafj/vW4zqh5TrCyFcSqd9QCDhbxHmsAFCGDiyOOiJGqMOEIAbMs0w6tlzhgoEKQRH1zXqhMDnRc6yjTKtDIQjlXFtIuLIDPIMjCmzrIYsdp8KQj43ZMY97QuDM1RlZCdEy6oUMRgeYgyAGE69JxtJyFsgGLp2IwEFQxIYWFs8JpFBA53IKNZlpp1L5kJWhe5o6XE92eGuAAEhk8HOh4CU3Bm4YIy88hGHsuKpW3WDbgPEEiDcWtubziDUP0WHEWmVN7Zsfzl4GaPpBAiwQGWC3jXZTF+HOZ0Wo6ZDyog2LGDXHJx3xrSNJjzgPAXVEhpIABuV+Gh6p8J08CDm5zwPG80fChEZutzB1ueubC97y16WW2651JYkoIjhPRESMX5nIHQctLK+veZAfVel2ejav4IHXV6l/fezFSDUDynXZ8THaLHRWiZwkDASSMuSDxsgwGPV3mhRblr9KAMEwawEHvtD5Pm06Y6mAEGVkF+TJEHMVdwE4W1JAYk0CT7f8dhRcxPwjyJAYMc4mnWGfQri4LT5CWD8Dr7BjnivE3moekb7pzX4LKyIodqqcyESOfyyViZJCokJekA/4EdUkAUL0Q7W5EfwL4m7qNrqQFCl6goQVGRVZvNhPSq+5qKaEy3x8ZnosKDHKlf1gDb+r0KVt6iWAKH2FKNc6Egsp/PIRz4ykVrR4HRbjAgasFAaJC+yE5yksnm8rGXUAQKwUTnQIlN/MZDMJGKCQMuKdD3SsE4IvGVP2Sraa6wL2AIAmYT6cCBLNE+BgYgsQ98Wo/q1EFyBjeyrEU8xsibXjsHYVVAEdIyHHPKBYIjmYygBe9qBjAvwA2K5kSM9SnH0IX9ZnBY14CFCBzSAynUBYn5Gxf3aKgjWIiOdD2AG3JEU+1MvfwqMfC8IT92g7QUSRF6CqnixXAkQbu0m+wQI9JuNCnSBu6zUTFqM4mkb9o9ziYEIq1CoYs1GBSHuK/iha4gxhyij1jbqeCAwlmVFijk5OsYhybJFKT6/1nTwQMJDhUfQzqnl5yQc7pbA0RbYRoRgAazg4OEPZ2svY7Bp9h2PRIbNriuxwHmyP6QgRtf+IQsFD1pVaOAv5kOAQJfjfTqqt8jsTFqMXCuSWbA9OhVkkfkOlbFRBgiBV5Jn9H7QS1vDn7dtSlOAEN9RPZccZE/4BM7R9MQw80BAJdAk8iQU8jNzfRMiTXNUteQ/87Y0eIZvSFpqAVItUuXkn/OnGAkgnD2DDyrxyLp2whgCHpWf8PmCIQEi/ua+5u0QMZzw4IpooZYczM8L2A+8UtApyahdGZeAN20tRvUKgiqW+eVDIlNVSsIzkhiBgXTLNVQQovpdDxAEJhIgEkKqY5FIndgAAVGkxIjxoDfJMSgbKBI1KIOXjiAFAgcbnRNAPXACCcQsfxEFosjBcJh6jGXvbCADo9RK+kqcwwYIsmGcmLaX/K3CoTzu5T7IYT2LR1mcSdBChaDkmfcmA6wTAoeRlNqjVA+AGIksSf4kobiWCFvFxstLZBXJSoDA+JAzZKvrSUau5Xvx5CP3MxilXj2Kjdz42+dkKCg9stLn7c8MXTYAiJGpjEPe8+ceSIKeVAaO1Onb8zkGbm2+T55kAJDoATIia6KKA3iGOWQOUOyfa9MVABDD+iLwEUDUDVoGiYzppN7PCNwiQAAaTeRtei59uG/NBgEYZkawgF7lPbb17ysTsxMZHMEnm5NdFpRyTAirtpo4pOyAOCdFHyUF6mcQBNWwiFOlywgCwmqvVNi04w0bIMja0zt73pS5R/zpn3vWH3Ig2IRFbIVuDGrDaTqDIAkR74+hizJv1hh2mstzOniAsJENXIsnqrim9WrRYBsCN4EEu0QMIhkDDwTtCD+i12doM0N6VEXggKSEpEGMrv1jrwUP+kj61p+ZDXxABpG8/DGn+UzoDDsXeCKPfBUbYT9IGdtV0c79Pd8uuSSZpNKYn0Hg71WdcAe99p5YYyBlsIFuTidAUIWERfWHXFibDLUkl3/T3/rLBN1fb7wAv+1Jfz7TFCDwh0gwfLPmwL3oga/vKkKsOkfe8AkfwkF8Ht4JmPrgXZNGIfx4hARBtIsh5MGB/A0/XN9anPPSNgiv4ZMHQaga9H03lDnjgxK/zjnEy1hxQnihUuTBIHyD+Wg98h0JO7LGOwVM+AHZSUw4B0aXtGnaz7q/6LIk94M55CCA8X3BjH2ha2Rk1AMEPgpf4XvIJfAx+ASsxXP78LOuOea/n5ctRsikxcpscfSi2aYefMqLfOlLt3kOD8mWa2NRCWD4CBenw5lSQIKmLEiDEhTFiwPOgIUTY6iIBEUkcOTPxgIjGSnEnSIhbhyd+wAaG+/AiBPyNhz5c0/OHCjlm+dayvyybcig6LAe0Uebj5K69fhcU+bfhspuMAT3YFyCDoTYz5VbEQMO34Eupb6ml5IhLoyCDICrdTAIRoyUiODrPZKUW5DG+MgIYSEbQIvM5aVM9yUHAR3izHmLtAUsTaDYpsgA1b209GgdyQfSIfMTvdVkypC1cdERGSJAJ/Oq/9iTB2RJ7JXr0QcHzugMsER22p5IkN+X7sUehjOLJ2VxLtESIYgQuMShbNdQfiRbZxGcuQFGdF7WwL4DMPKa7TEbBKDvmuLwK1tAnAWyTQEl2wSePs+22Dt98T3tYQgH/UAgAD0sgC2uySnQQzKnw6pJKk90loMWDAjyODFOg04JWP0eFrkHnYFB7B9++R5MCPvkYOmun0X1KZcBMsM+zMt1BUX0z3pk5WU3BUGID2xsO9TfhAecErnJztMnekm3ON14ekY+l+ngge+TOT1mW+wHFlmHqoV7IvJ+x8Zk1eKRjFr42EY8ia6vbrBf+0lOcDgPurr2z9oLHvSV9JKfGzc+IOKSR5I8iBrfW398JKyUhONL+Dv2y//zgfHQChlqWV37bM7+lqXmLxEyNoEPwH/kUa85fM7PsgkY4Dl9leCiu77PdxnBOfg/XAO5doYPrkg8ai9pa+2RQZbUMn++Cj6Yo2shrc4nsinzG/SUI0kzgQa+Ah/4PjYRj2qHLfy4+SDITckJyQNYZt7WoGKvDZi/FDiZQx+8a9IomMTn4my4iISj4AVGCAq0QOMv9l3C171gnIogbMBjhnlztqQvnLNn9YBIRcVczMnv4Tr56EghO/gkGMOTzAUxt0f8PH3CT30eqScbPn/QQ1xCHpKPeIg9h698vkDF/eAymbiuf6uo5PxUkpNvosM4J52UHMEf+DzcbVAFajpWPu8CBBtR76sFEJQjIsB8oZSHchG0EiOHHVEhJ80IOAFklWAZOYHm7yXglFzfIBCfZfCCBASU0G0s44oSUBC7eq8ZoHI9mw54EO16u4vv+j0gMl+D42fIER033c/nlMTrg+MFCkiuIYNmPXr7HEyMthcGTkaDnrUsG8NofRZJEe0DS38YQh2g46VIQCwMhmFZd9NBKEAtMBN8+AxwH/bldUBTdcSc4o2MuUxcW/WJXBgtsgIMomoBAIApfQJUAJ2DQMoFZfme0gEOgTF2DRkOeyroQToZsycvxfrsh//HkFVCmDg51R0BICIo62EAI0DdN5vaNb9hfz9uAtB3PnX9ju+pFNafK468h71ztuxXcMy+gHkcNLNHdCheoChYz+1RC5uDczJ+gmBJBMFdBJsIKf2uOwUJAdeig4Y2JDYF6OkYfYMPTcG2zw9yMpy9pAT9HOQo2/BAoETfEAAJCn3AMCqeCNa0H8PiQVwjHvXKMdsT8mSTkUH1e/+PTKXfC+Dq+9lHR1wf/rG3qI7k3xu0f+RQ8KCPlG/9mXHig/1EHPMReFmfiQCRniJZyCvyzg8LzvkyNh6Z6rB79siG8vY1vAB5zvGYv5dRZq+qhxJMdf+gqpDzifC/kaSLx5c3+c98LXwpMq6KEPhhzcgovR6UOa/LK/iErDMySkbww+f4lLZqvYAAf+JjyYg849wd0hpP74l5D8K7Jq0ib7gKIyVXJVrsV92/4j/Bw/jpSKANq6n0QKBjTfVB3rhB+Gvrzc8YwmvcUvLE/QWP5hsvq4Odwd/wT8Finxfb2gv+RAKJP7G//Em0HNH7/GWy9p5+uTYuBzcli1SHzFlgYL/x077Vlb5ynHcBQt+Jl89NpgRUd0TSyCFj7tOWNJmSmvmqx0kAZj67coUigf8vAUmQCL6n+xz5IsvhJVDwYXiZlW/MngQk21RiZNlncvh79mY8/+5UAoT5tydlRg0SoKgyDnoFVWSUKWV7Br1xtQhyZhIoBGBm8ivfHp8EZHZV7OgoAqBFQItGW8vV+GYyuVcu+DC5ez9fV+6sk7Yh7Vm6KPADrTplTE8CJUCYntzKt2ZRAs6k6Cd3oFTFQGlYiW7Y1qRZnPKiuFUhAItiGxflIrTqKcULDPR3a8+LQ9mLcsHzcFEFH+bhpkz4lLQUxUstnS3QylQ/GD7hIhpq+SVAGEpc5cNzIQE9oQIC5zb0ezrD0HTIei7mtpjvWQjAYt7dhb0250H0nTuv5uxInzNCC3vF82/2BR/m355M+oycQ9GT76yWp1B1vWxx0uXVtf4SIHRJqPy+SGBCJVAIwIRufFl2kUAPCRR86CGk8pEigQUsgRIgLODNK1MvEhinBAoBGKd0y7WLBBa2BAo+LOz9K7MvEuiSQAkQuiRUfl8kMKESKARgQje+LLtIoIcECj70EFL5SJHAApbAog8QPCPbCzM8HtMB11G/ac7ee/6/52l7znA8Vcc9/fGzpjcoL2Cd6Zy65y6Th2fAN73OvfMCHR/w7OGQbdPLXuLr9sVnPdnEYcYyhpPAYiUAnmdNP+nEOPQT5jg34z7xkj0/8/I+w8/GgUPD7e7sfRr2skXPovdM8lEPb1MmW3vZ9ELNuJ95wAP7whcMwo5Rz3ExXm828aGvP+V7fNbesu+FZGf00tzH5a9yLhTvKPIz7/5hOwtNXjO1qcADOgMPyhMRby3RRR0gMDgvlvCSCQbhsVejfvKNF4t4yZUX/nhZWLzp2FM1HKx1it7bWydleMur14R7IZ0XnXh50SgHY/ZCG7L10qcVVlih8fJeGuclOV5L7mBzvK21ay4IhDc+e4lK03A42tMRHH6Kl5J4eZ33MgD3+kAGvczNS3b6vMTEY9p+9atf3eqFNHFdb5mMl+i0rcWLBelcn/sNksdsEoCufRnV7z0e0wtqYIEXznj79SgHzPG0LS888hhOL20z7Km3dHJCZ599duNLBEc5j/lyLfZ6+eWXp5c/eRwpOYx6sAkvGvJCS2+IbRoCNm939zJEj0HkE9qwo/59n/dm3qaBiHrxJDzwUsUgpPCAc60P++9z8ICddhFY8vNyKYFVk+/qwqu4PzwY9YHN2cSHPv4UdnpJoJeBerkk/yNJtRAGu8AjvPh1GH/Vd23eCQCX+E4c5Zhjjklf5bu8RNQjwz38Y6HIq++62z7nkeleNuYNzWwMLvR5E/JM77vQvr+oAwROwZuAPe3C8/OR1lEHCFdffXV6Q6o/3pjoPoa3lfrjbcEcwqQMztRLiwA6MPK2v1EOGQ8vRiLbjTfeeIlXzwPYzTffPN3OPJC0N7/5zeltiH0DBJllT0gB1HTH+xa8TTLA1FsQr7322kQsPS1BxpIDF6wAGf+25+utt17FYX3rW99Kb+L1dmSvWO/KWJOZ75BfUzbUnBAta/X4V490iyFAEdwAPG+wnSngzSYBGKWODLpWBI50Yhz6CXO8mdMbwgWHCIthbwTOCCFnPCnP6xcwccQwGEGRtRv1YAfnn39+It3sNR+BCeQvmeNxyexSQqdvgCAgl2hg32zL+xa8hdr40Y9+lAJ2fsUbYb251jCfc845J73RFyHzBlYva/KGWEkr/6aDHtE6aCB25uzt9E1vVB+EV657/fXXp4AYWRZEjXLMJj60+VMERmWKHAX/3p788pe/PGGz598vFMIrmeBNvPZpGH/Vdz8FAvROwo4e8A8G/YFX9nKVVVaZmKraLbfckjiEtxgvt9xyI/GXffdiIX1uUQcINkImmBIY4wgQXJezAUp5gLCQlGDUc1VJIQ8OddQBQttcOdKnPe1plYAtBmex7rrrDg24ghAkfNddd02AeuKJJ6ZLIn8IJscjIACykYFGHFQ1ZKUD4JGja665JskCOZFNHlRNUupdeeWV03Ob6ZRXvDcN1/Lq+K9+9atLEC6tFgyao+EESoDQrC3j1k/O2J4jLhEgjNrGFtr1yINOjyNAaJMF2btnkCGfkyVF3ocJEML2Bf6C95NPPjklJwytTe7jvQxwQxAUb20VlMCk8BECEjohgWIeT3jCE6qvf/3rA7dSUAlDd95554QrTa2SbXjlwubgfRFLLbVUqmCNcsxmgNA27wMOOKBaffXVp/aD7UnOWOtCChCsT5VqnXXWGdpfDbOnEhR5gDDMdxfjZ8lbe9EoEmqLUT4lQBjBrpYAYUkhjpuA1beMg5RJ117AAceYCeByzLJ+sr0RIMR1ZVwQHo7pjDPOmLrfBz7wgWrPPfdcAuAFCd78vP/++6cs6iGHHNKqcaoWnJogRAZ67733bsw0BwHVqlInXO4nSNL2cN/73ndG2j0fCMCMFtDy5XHrZwkQbi342Q4QJAwQetnB3H5laO3/sAGCFakcsPc8QIjgwfpUGbQ1yvbHQEBgUn4/SQMkTTXw0ksvbU0awDX4I9GB2MGzqFDXJTwIryQUjEc96lEjNae5xAe4pzK1wQYbLLEfbG/99devNt100xIgNOx2CRCWFEoJEAZDwtgDBKUzZVVlViV3GRh9k0q0wC4/GKIVA7mR9deWo+QlutMuoqfT0KIhC6t06ucxvDBHj6V+VL2mSr6yN+6VVxCU9hGrGK6/4YYbpv/K8ALzpZdeOr2AJw4Y1kUo03vVVVelkqaMjh5bjicqCLLJ1q0VBol0PQN58zNtKLLRMk2ySPmb/nzX/F3LZ/LxmMc8plp++eUrbS6cjJeBkNUVV1yRPkZe9XYmclIC9zkyUYqNDFfM6corr0zzJRt9sT4XLx4CxAjr97///UpZzlrcJ2/VkqXye/uhJ9fnvaQEaW+qIJBb9Oz7fJTZ7b0suPHYxz42rZVOXHzxxelnG220USoZx5qs35ztG/JNtrLnsX/kGBkZ7T2ur/fQ/axv0KGkNofLaWtbkNFTXdCmEqMpQLCeI488stpjjz1SkCAL2DQQGk5NS9Tuu++eeo5l/vK9iu+1BQhaJ4zQt5mygXEQALpCT26++eZkx/6tTYMd+n8cngvdpOfwgxwRHHvHLugPPTPoLH2hJ+zdCPv37zhTwn5kcfMAQd863Y1zHaGPiCV5XnDBBUvoY5PO0AnrcB1zo5NaxPw8Kgj0jk2zs8AbF7YGmMU+tZRZR/5GYPZnHq7NblwzHypk5uza7gFvXA9GOC9Dpnk7Ewzye/MwV/d69KMfPSV399MP7TPsnR7e5z73SdgaeAgjzQeOuTcM8/tcNtYVWO77ev61urRVEOxn4HnYLwKdB/zs31oQQxhgfWuvvXaSnfnYc36FnMgf/qy22mrVLrvsMoUn++23X9p/dutadAv+qtzJsg8abQGCKoIA4ZRTTklY9KxnPWvqMk0BgnUKEFQbrKWtUkh3JD0EHCoUsuVwrmm04RW9GNcbpofBB7ppPWGf9JzdkkEM+0leuX/gax760Icu4U/tl2Bpt912S3brb5+jH/YiAgSYCz9gjbnyJ20PDNFqw2b8WXXVVdMc6FToRn64nt3QG7hFn2GFP7gJHYgBb/iZJr7BPqwBrsE0368ntIKPxPWsD0bkfMi6B+0vn5jjJ/zLKwi57ZgvG4Yx8IH/9HvYzK6cpWvja7lOhv2SE4IpKLHfeBJsIRN7kbfbuo+9ZNfOzlmT+eT7RZd9BoZJfsEeexXDHlqvufvdiiuuOHUmND4Ds+ASXMFJ+Vp231VB4J99j16YH19kz+JBB9ZFX/xd/z1O5+fmZl1wFL7SVWuEvzHqfAvfIascXwfhb+5fV1pppYQvdGwQnx2EeWMNEBinQyAyKzvssENSCEJUVqVwMjtheDIpMjMUCSkEJmuttVbKAjBE7R566BBOh5AIWJnWPWSKDjzwwOSolI85AYrztre9LQknDxD8X0/5F7/4xXQvWVrAb5iPFhHlScDMsdWHzXn3u99dOYxrcynchRdemLI8FJgRMQRnEpBV84m2Ek7JzwCYIOO0005LzgRpjAOlDvNpX+GwKPy73vWupFAIrnYWTs21fVepmhOluPrpZFOOP/74qSmfddZZiWQyFEZFpgjYvvvuO2VYnDYgAlIIgX1wHYZjuLbMtmDO732evP2efDydxL6RAfnrB0bU7LE2naYAgZwEEOZGD1zfsNfmssYaaySZCPDsvXtZt+sBiDh8TPkdGEbi7L89cPhUkETGgJHD0Y7DyfgumbguHdKz3DbC4T7jGc9IAYFhP4C47D5HIitorjGaAgTkTiXivPPOS2cbNttss8ZbshH7oy2LDmpXcoYCga2PpgABGLM1hGhUh+KHIQCDQCb/nUP75ARwd9xxx9R6ZTI5mtIAACAASURBVN+c69Brzdkb1uNzANl+IpD2UyCOFJOXzwNUFRrBlf2wN4gGnXnVq16VrnXwwQcnJ01HfC4PEAR5KkIqP/Ryu+22S4QSaXNv9sfps3kOuv7kG/enE2ySjSCp9NI94IM9ZRPwBmGFbe5j0CFnY1R76GcEI/rWo8edzQksOW3XY2cwRHAMp9iXe7EhyQpE1f29UZQDIpvQIfprHcgJ5wa72Cp71z/PnuGS+3PqnLD9MWfkNFpk6BnCx7G6D2wxR3rne5yKa8AtbW72lt1Zd1uAAPPsp7khduTCXuwnXQjct0Y4TZaIzpZbbpnkQVfspb22LjgK/2E8++Mg+QTzhDFbbbVV0kFYDRcQcH5i0BkhfoL+0rOwY37A/a1XcGYf8oRPPUCwftfQI+9zdL/pyU7WvMUWW6T9hFPWGsFkUwKgKUCAPXyk1tpxjGHwgR7DNgkQekwPERj7xS/yP/acn7N//u1vgZ49yf0p38i/WBc/uO222yZ7sT8IEuwXONJn92VX/Cu/QR/qg12wK2/kxif4IVgDcyQCYYn5uAZ/Ry/ptOvjITgN/YEPfCv7QOisl97RZz6EvCSW4BKSb298VwKJT6oHCHygirOggFz8n035Dt5Cf92r7SwNmzv66KMTj4p2V746AgT4g5/YA0EozoOQ8vlkYb50DccgV9eCUwa7YV/+D5+tD+44J0Nn7Qc7w0cQYJUwv7cXrilhwFcZ8MK1rROmWB/5OqwNjw3kmiwERDicfaHfbMnwbz7d92EEf+tz8C/kY6/gmLkJetwD7sCFQQGC+fm9YIXNwkxBCBmRLXx0b4PMcDH7CxOciaJT9JfP4ltcB+bAbdzMgfGoDJK9PRU8myO+hbPZe/aCfw7CXxXO8K/swjkocodZWu+GHWMNECyG4cmYOaTJUCgHh8vRWzxnT9gMDHFn3JwhpUX2CA9QcrCMhICBjM9w+g6lciIcPOfEaGwGBwBYObw8QIjoDUAz9qhMEJzPUkC93Yhn0wBYjMrfwIbzR3bjkDJg4Jgphz+ylxwncOcsbDKgMn9GiZC7H8WiaO7PoVmTQTGQRQrKYBjCUUcdlUCMfBAeAIr8INCMQISOWHNi5EJRKKWMgL0AWkAJMeFg9dOSPadEid0jDvUCPff1OdkDyikAs14/o+T2yp5YHyBxbfPjpJsCBE4S8fB5MgxixJDIyp4iAMCKDiEE5LbmmmsmwPf/IHRhWE0tDAAXgXc9wIA4mq9gkzzJrm2EwwUy8fQP/3Z/IGRvBXc5YcwDBE4AiAFRJAoZtkc5eYh70yHkiE5ao88DRg4o18/4fB4gCMoM+kTH2MR8DhBkiMjh1FNPTbaN6CKbAgPgiBQYHD9d42DsG9uADeRtbwAomXFMHKE10197G4AYjkEAgMixAzpVbzHyPQAKgwRncCoGrELi2GMTebR3cAmOIdmcHMxDBOyfAAHO0Bt2F9l+16eTdAa22UdODcnlbOCcIQnA/tkD0oO4wA1Yxxm4hwqgz7MpZFV2i7zYtX/DIIMsrYM82J15wSLrFQjROXKSzGHfvsvRIXBsnTx9Hv5x1ogBTDFn++ga5oX4WCcMsHewCP4POqQsy2at5uLeMJC9kY97mXsc0rUfbNtn7CeHTX88jSVa+NwTvvhOvcXIXsMriQ54JWiwP/BqUPUNAbV+uBkVVBlx34UF7i1BlI88QAh9IBffE0wIzppGPI2Hj4S78BiJQnhgX30EXvl5zAEpJEOyGMcYJkBwf7Lmy9k7/IXd9JTe8cv5GUH7w6bgIQJf96euRzfwiLzlK1qM7FHsh2CCzBBgOFwfZCSDzU/AW/eiGwZMxgvMl1+ldzA9gk/2gl/w+TCN/tNF8xBICP7YrCSV38Ge8Bl4A3JKd/jneoBAXgIpZNr949wLu3VP+g8TmwabJ1f6Et9zb0Q9AgTXNzf2a+300tzwCveDq7LssJg/I28+k08WNJGrpC2fJhiArz6PINNvCQFknA2QKT+KlLsGrLUnZA/vfJZMVcxl22EInIJnvo9T8A/mFu12OJjfG9aJR9IbukSW1oo3mL9rSrD6meBKsAcv+Q3BWVuAQEY4mPVE0phsyZ+vgflslEzhPozE6egZ7gPj6S8ddV/2bp04hOCEHKwpHiCAxJuP9fMlAiD3Yvv2qQt/6SE+hsfBU/InN76nqRuhCxfGGiC4uRsAEhsQQqg7aEIAljY8AJqzDMJEUSklB8mZU0wAzalE2w3Q54woJ6ceBLvtkLJNAS4iLM7N8DOfZyhNw/0QEw5U9Bij6QyCtbgOoKeEypwMg3NHHqyDAXOaPiPKtqmAJH+KgfYhGZNcfl33owzAh+HJgubtQH5GNuQNFAQMIlVAJCiiYAgLQoAIMHqElWwNWQfZVQBB0WUnAYT5RXtI3x5vRgQEBAjKaAIHgQrDNT/KjTBGwBHXb1p/W4BQP/TVpI9Ne92UkeNogb7MUDw2Nw8yACgglcFQ8SBLGQIyN4+2x46Ss2oKneJQgBk9QAAR/rwlxVybKgjsAwgjSfM5QAC09JvNs/E4SJ3vn8/ACnqWO3R2wxEJHOguW2e7QFjmjuOgS5wAQKVLAB5BlMmJcnSTfpKfJ9AAc/gCwAVdnAoCWX86TuiMPXJvOhkZsbYzCNEKEBUEeo2UcmBslLMVkOf2T7+QkOhhD0LLRkN+fe6H5HBeEhAcU4zApWiPQN6sg5OBAX6OkLN/NipLjij7neG6HL2gzPztnayaJEZe/epzBgH2wkE67PvsSIDinvRAYgKRkgjhJyKLH4855Xz7BAj5GYQ2fWzChKYWI/oGD9ipaiMczB0xu5eBhbH8B0IE9xGDtvME9C6qWKH/5izp5ToCwno7VBNeCcYFXEGiusjAsL8fNkBwfbqHVAn44Joghu+h87LSknr2nC9EjKK6UvenXQFCfgahST/a9pddhT/2mfqZFaQQzkhyGPw43LUGwQXMYqswAbeAG/bTdXwGcUVaBZWwBfbHaDozx3/DBMRR8oJN4ATk4x5N1Sf3I2M6E+dP4h71Mwj1Mxvsy97E4+HdD+YK8PEjpJcfhM98XLRQwk/+WjAb+OaeXfeTYBEEWgvfGS1MkqbwA+b4twQJ26Y/5MeX+j/SjiPAFziUV/TtBfu2D0i99jwJjTyI7zqD4NqSDHnQWNcd1xegIO9wEhbirvQ78Cjs0//9zqjvt7WRMx3EG5pGF/7Shyb/Oqxtx+fnRYDAoBgQYlvP0ilzxzN7ZRhEVhw+ABAIELIsImdrQ0RpiAFSbbQFCDIXDA/h56QNBsv51LNAISwGxynVH0PWRdhzwsa4RHRKZpxJPOmEY2QIlJEiIz2MRbmNXNwjHuHZdT9GxWA4hnpJX5QrQkUAOCn3JHtDywInSzkZYGTE/azeu8moOSyyqj8VoW+AAFA5VQRMhoxMgJOA0D4iySJvICXzFGOuAgT35+QBAdkZ9jIOA+cVBIGgzDKwQHbsXVMGGuETSAoKZK0iuyQQod8yRDJT+dNL2s4gjPow4nQIQBcQ9QkQYn2cY/3Z7WSoohWBGVIvi8yWgDPHzWYMQaZqmL/hRlcAK1iFHQJfxAyx4xjyg+j19SEx9YpFH8Ie10EUYJjqmWSBOeT4IhuJhNJ5OEBfYAPSTi+suc/9fF7gBPPyjDr8YWsIh4oXB8PZaW+DA7KPdFBGFh6RvyDD/+tDUCbYkcWzH3l2vE+A4HocPkLi+4iAvYMnHLugkh3wBxIX0e4wlwGCOdNT+wRv2TwMC7zMKwgCPVlf1SKYILnV9B4EgS9iTxeDALIbSRl/yyhH60XsQdsZBGSj6fGoXXba5/fTwQe6Si/oIT0RYCJ/MF/FQLWMvUmuCEJDjvMlQDBHa+Bj64MdIaj0kb5LaJq3rDm7hiX8Hf3AOehy+PQmwuhnAijchm/hd9iEzLAggN9tOlOB1PodX1V/iloXYY/HwvJH8AAu0SEBsPsKEGTqrY/ewy6BgkCJPguOBRcxuu6HA0QGP38Mr8QFgo+/yMiTA14keUN/4D/5ShSE/Uuy1ANnAbXAQ3AD0+p8qCtAwJfwnDqe1fdeYkXgRl44FaziQ4YJEPAA1do8QK3fpwt/VU8WZYDACIDcoHcGaB9igBQDqWQ0ImXkUXaQ0orSBRJxMG/QY04ZKtBVGpcFB8ycY9to6jH32S7CHu0PPieSRWpEgpSH8w+FAIqAX1RNHsgRwPQda4qDw1330xpAFpy1tpP8cZeRQSenyIZQamUypBQBQX45YPKRtUCUml6yAxzIfboBAtkhxYCM09daILpG0MzbmmUF/DwqGG3yno0KQugFGbpfvBArsql1/QBcyCwwt5/2vU4IkHpZA0CVV3qUkell9Lzm/YODnmLUx7H3/cx0CEDXtYcJENgIMjxo0HF6TFeAJ3kKBDgTpJduK9XmAWZbAMvJSRIIKuyljDXix6m3jaZDqH0Iu+vRDwSCbSEe9rqegOBsYRwioaVKBU8lRZbd39bc534wxtokTlQeYsR+cIDsGfGPKooAlXzhrYpk9O7mLVB1uUg2SLJMN0AgEzKAQVovBAhkIxATPCAhZAIXIpie6wCBDJA3+8GPIDTRolrXDzgAU+GeykP9QRhBCF1DJSc/FGrvYYJEjipafkZu0FOMumxyur+fDj6wMfqjYip7rj3MPtJPpNnP/GH7AuIY8yVAgMOCA0Fa24HnaEdBSO0jXoG4CuitkZ+NNtJ4sWpbgODnEmd8LNvkkyX3+JVcPvkehj0gx8MGCBKD5us+uBjsE3Cwx3hsOR1FZHEwuCsxixQjxPQ7f5xuV4Bgfr4ffCRv2aVfKm5RMcOP6Dlc8rAbgYWkhACMfCSHojUsl8cgf9kVIKgOwE3+IFo76/aCDwj46YM5+Zv/ibZDn+9TQZAUkBCVaIj3qdTvZU8H4e8wFdE+dj8vKggqAIghBURM8yHbFYeI/Vx7jM8rY4tmtRhozUCaORLtP3mZaVCAgAxzfowCqQC+cTiwSXiMwD1kiRDaeJFVF2EXIMjgcfIcrTUKYKyrXtJEaCiKIIFBAEoEUqkynEXX/XwWmaC0wCh/mzFA5oCjpzHkC7gZEjIiqwggAIWMF8Mzp8iAy/owWrLipET0rhdZxb4VBDKWOUK2/VFyt9boizYXgQECmI+5rCCYhxK4+QL5vAWoKYBExuI8DOdQPxhN52WRkdH8aTOCCgEkgkynZdlC/os9QGCzcdhWAB2BMdnLwnII8Xx5gaRSNJ3XUoGcynBxGDLdAA5e5C+dG6SfbJx9cHLsT5Ax6KVmAgifof/hnPoQdmvhQOw9DBAcNrUYcMbxoAd2aR2CA844guY+9yMTwTznBYtUTg3BgOoK8gkLYAxSEw9SkJxgh2zQWtmoIAMxyKsI9Nz1lcnpKyyVuQuH37eCQObaawRp1icQcB/7aY7mpbKYZ3DnQ4DAtq3XPuYtQPUAwfpkCmEFmdYTUjK3AiFrFCDkCYV4rwKdh4F5//lCCRDonMQKHVE1pl/xUkpBvKqJzDqfk1dc50uAAMtVs8hfNj8G21XtCXyHW9bDTumEv+0bnMIhcAGEM3+RW9tjuSXQBAVkoBVH9wNC3XagHkbGY3QlTMLWzbWLsCPkyC0Mdj/V8Sa8hBs4G57CH+oksHeSZTledt3PXPEjJBxmRJWX/atgsgGEOfBFAC5RHOepcAU8BL64r0RnHnSr8EeyDfmme2wyRleAwNeo+OFqkrUIegx+hZ8QFLkv/BTUNOFRnwCBHcAQdpHrv/WSsYP8rj8If+nEgqogILzaZ2xolEVFZf5N4CJhLQEAUWZIdBQRKBDVN2vzY4hmGRnizEgZJZDxGf/m3PJB0aKP0xzqb1Lm0AQajBnpb3u0aVyTMjIOkWtkFpEDxFb5i7JzwsqJggEKSck5VKTd5vkskk0GImHlKJsPLDhHBCUiSA7bH46WsSmRUSQAy/gBkp+RpfvJvgiQXJccrcccOFvGD1hUBMyH8Vs3h8QYXZ+jIwdr8nuOWPBAceOpMDL65gssgYQgxL5F4KNFIN5kaY1tvfdkqpRonhyjbAIwsy4ABzDrZyis1dzoS6zfdYKAcMAMyj0dwLJHStnKv9bH4FVYZEDMranE7x4yXNZgLxh+yF6ErtQqowIUZXcM30HikS3383vfQfAAn2yLQI0OcoD2NA6uA8Dc2YSuWUfsgf2yr65p3eZO7+1N19uZ+2QKmj4znQxh173Ij54KDIG+/SY7+02uwBzQA12OShZLeZjNA0n24//hiMmXrUkScGhh6wJa1yD7eDJSzI1+kh9biOpP/I5d0nPZP5mqrpdLsSVZRXsr2BMwx0FfpFwJPfYn5kwvOXm4AI+QCY4RLko+IJAwwu+U+e09/Yl2E1gQmGDerhUvZhPg0BEBZgRWdN53kIzo26Wf5GyubIkc2K8gKx6AADvgp+w9+bMh1T0ZcCV+//c0JJijdRKZV2qnp/SWA2UjcAdeceaur6LZln21HngiGy/wiyfwCJLgp2xjPFUs9gwht362HoSb35H8gaPwShBFn8mRvGEIzKOP7BUGI0JtD6ewBmvmo+ipII2c/Zwd0zuBnmsJ9g3EDgGJA8lh4+zX5+EdmdAxeMUO/J9+wvimtwBHcoR87KU9Ckyk89rIzK/+tK0uu5zO76eLD4JiOmcfBNdxxkqAad/yp0QFttb9qZ9HyzFMxRNk6ck8WlDgDBmGfrBT+tGG+WxP0oe/VKkyEHL6bT/sH77AZ9IdJC4eK8xf2Pu8A8J62KZ74jkGIown0D9tVvmgW3V/Fb9nwwILSQIy62obk1hyXzYNl9g6bIUzghP2RHfobjwWlrzYLz1noziOz8NBpNV1zA9mk7l9jIcpkGlwlNA92Az7zMGe+kzcL548CafwD/Kl9+Rp8G32Q7uZViPYrqUQzsIOfte840Et5ocvShzHG8q1B/HJ9sx12Ix99X9Yan7kSDbkAZ/rusEf+J7vwBG2yufCKO1NZMW/SLxqI4sWq6jy0gn2Sa8EGTDY7wyJGvgUfMG9PDxCEIRPRVUZ/xCksBOfHYS/8cAa663jGQzEe9hH/njYQbY/1goCkopsmhQDAYoch83wh3C0+MikMTAk18YDVEoIhDnxPGpDABktpQEylFEUK8tl85DWfCAJwAUAA24bmj+pAkGVJRckcDJdwKrfmEIgbwzchkYVwBrdn0IEWae4iGa8jwFYyRwAEs4ZEfF/nwNmAgwBFcdrMFTknmJHKY+xIBOcHRkxMo6bgVlLPBca2WfUCDHHwZkpcyIHUWVxTQofB+AoFiNl+AwHiUPUBHECI/M2Xz8zNwGbPSQX8rAvgjRzsRZEJ38ueF0ZgZb1uLb9IQOGa48RaevKB6fCUIGW9SN6HBWDYnzORiD15MVRyhpYIxBCqhBTVSjyYGz1Z5CThTXTGwbGKZAnMAXO+gwZOx0CwPE4TD/jMOLdC0iVecj42mcyByoCK8Dn+wIDTsG6yT8vNzNm1QlyEDwBCzJHYOgM+xE8IWV0F4EbFIjNJgEYdK94Ko9sMPnTDTKzdwJCf3PAcMJ6ZV4QbU6BHsdz4fN7IMX2gtOKdjpOnu5xEnl7mvsCWKCLFMt65frJlmBRPFax623U7BrGaTmghxwfnXZ9f8MBOgQLI0j1efeMR3mqdCAcbJ5O010knfOCnWQimOA44BM9oT90gkw4QX/cz9+SGPSUXbJ5wROyDUeRbBkuZws4HVVCtkfPBDfkAxvYB0LFoZOh1iG6LmCAteww3gNjbnRfNZf82GbYrfuSPwJAj/kAcs+f/V3Xl3i0sWuEfXJU8MUes/EYdIMtWLeAiG2TLVxHxuASvIqKEJ3zO9l7eE93fF+gZ6/oTP1JRmTC+dubOEDPd8FH9ycfMrUfZE3O7otYxkFS6zb/yF66HowlX3ODUXyb+dt/uuFnuU3TIboZ/sz+WgPyGHjFb7IRepjjyXTsv+s70w0QXJcNyLryYzEQX2uDpZHI4wvgQt2f0nvy5XOt03rJEWbaYzpC5oJEZJx+0EPksf7oaMTMPfEJNgSr6Y6EBKItoHct+0U37DG/Qi8QSnZE5kFeB60HnrFp1f38MLugUcAHN8Jf1d9hYl6CSn6pnuis7xUd93l2Cn8Qe76NryET65GJ5qfhLHn5W2UWD/OHjgu84ANeoJLH31onDBDoRCVXIGE9iHcEL9ZCn63Dfem1/baXsI7+4ivs3Z75m9+MpAnbjwO9eAydsS/Wg2M4DM2fsDf4AF/NiU3xuzBVAOH+7Na++bykRTzgwD7w7ci7e+WVZjKFZ+yb3sAIQQqcJC+6gaPQBbKjhzCTzsIVeGdOMQ/tmmQTCQT+37pggvXDXfsB+6Mqww/aO7Ii4y78xfn4IoklcsYr4uEYsEMiTgJM4qzPGGuAgDjHgUGToWCUKH90o82JCoFF+R0iZlGUr57Rt2GyDDYmB0DRGKWrn+onrHwQWO70GRLFkeFqekZyXYgcrvuLGJXhKF88UxdJs77IqPkuYmPjkQBOg7IBcZGutchcieb8XvYDYQlCQ9Hdz88RRg4TmHFKMUS9SEP+KDsEIQ4Iclxkaq5k6j75IWyARenIwQB25huHYuvr1S8NOOIwkGBIkMAwGQTHz4jIyH7ES9AGKSNDZ1Q+HxE8vWGs9bMPjCpfP4M0Z7IKZ4Mg0KXI7ru3eVkLZxpDD6d9yAcw5LhCHvV5B1EFnMi6+XIUQFv2JR/0OvTXepBWDqhpAI68bYJhWk+8WKjNfvxc24f9roNbHwAY9JmZEIC269btUTDKOYa8c3vh0DhEv0fG6G7T04TYBHmx38jWa8eI/tZ8LvSSzsfI8cfP6DMdk+mW6ekTdNl3+GOPYQHCbD7+bc7sK9cP+GO/6A6nQffhCEcCxH0P2TQ3tiugsRcIpGSI4Z7kI+OcP5UoXqxjnaGPHIv7wUuy4lz9PsiN+8bjd32H/pOfP37ORhCySJ6YAxyj9+Za/z2dZX+InXvAcfL0b6SkK+hiIwIXdp3rNJLCTvI9GbSf8DbeuQOHkPL8xWuCpFwXyLXuH/zMvvJlTcOaYCH55A/ZsN85/oT9xmOf7Tkymz/xJb8+H4CI5euv+1Ofr9tPXKOOJzPFgqbvzwQf4LXECfuIQb8lTPLsOF1o86f0n07wbXySvUX081Hf47q9+yx9tl98bAz2aP/q+sJH5fYu6cTe6Wq9ktu0nkgy+U6ejIyngPH1Bn+FGObXRDaRYxyhz5DEZKd0jE92PQTSvWElXxiPGM/1U+BPHuZPDgJvvov/lDiA0QJS3AJPUEmwB4FLbCGedhTroc9+Hi89jfnH+5boAwKOSwgQBAdsNiqNEqvWAVcNHApG5hWbfL2uIRDK21Nzf+L3iDl8Zb/0sK17xLrdF+m393BUMiTOAEWAidTjBXwUf0C/JXBhFCyMEU8vi7Xk+00ntEX5vHvRK3uQPx59EP7W/WvwI/eg3zAwfE0fHRprgNBnAnP9GaDrjyBhUNl7NuaJmFCmKNtxtAiLPxwZMMrPY8zGnMo9JlcCMyEAC1VqQFnVEqltOyg2m2tTvdISwIEhlRFgclrxdI5xZ4pnc73lXgtHApOID3O1O8inyjOuMupE0LBrkrDR5if7LyELk2CTIRmiSyHPXA97/fL5+SOBiQsQOFbRnShZpl2pTHDQ9pzz2doqkbPMvYyH8k8erMhq668TlUfr0WzNq9xnciUwKQRABky1BtHWMywYj3ddzOXuayWDT0r1MkHxkAIJA9l7bVD1jNFczrfce7IkMCn4MFe7iqMg4qpEsCAO487VfNxXMKCdU9VStTOq6H6nI0K7qIqPFr4+1de5XEu5d7cEJi5AkCHUh+vQjLKbLJzS3VwPJTp9bMpYyl/AV5CgHI8QyGbGGxfneq7l/pMhgUkhAErb2sG0wSi/OhMz1wkDGqZNRwleS52WA9k6LURK4rBBC1TT+wgmQzvLKudaApOCD3MlZx0Fzs9oL3F+xTmKQW/6no15SrA6xydI0NKjpUabkfYc7YZaQWFWfu5rNuZV7jEeCUxcgEDB9bfqPdOHJ2s/XyJdPXLmpuc1evr07elDc26g6wD1eFSkXHVSJTApBEAPqdK9v/XTxxNM5sO+O9ejb1eFMZ5CArecG+h64tp8mH+Zw+KVwKTgw1ztIAIeT8vR855n6+dqTu4bPfm4iqSKISBwZkCVY77wqbmU0WK598QFCItl48o6igTGLYFCAMYt4XL9IoGFK4GCDwt378rMiwT6SKAECH2kVD5TJDCBEigEYAI3vSy5SKCnBAo+9BRU+ViRwAKVQAkQFujGlWkXCYxbAoUAjFvC5fpFAgtXAgUfFu7elZkXCfSRQAkQ+kipfKZIYAIlUAjABG56WXKRQE8JFHzoKajysSKBBSqBEiCMaeM8htDzgB008qIjw8tAvCnQM9ad9K+/WGVMU5lXl/UkBm8E9ebA+kvQYqIOYzpE7mUs3pzoz1wMb4SNN3F7CVLTiMfmegyttzl7gctiGXNBANiHp/R4open9ni+tpf0HH300WkvHNRrG54A5vF73pTqUN9CGgUvRr9biwVv5wse1ndoLvBh9Fryf1cs+jJO6c7Pa9f9zfyc5f/NChZ4oAZf99rXvnbqZW3jmveiCBC8rc8bLcfxlB+PGEVShiHzV1xxRbX//vunjfTCEM8xN5z49yZCjytFkCfxtL91e7upZ7h7QlMeFHhEGsLtsZNebe6tmF6FHq9bH5cRtF33Qx/6UJqH/YtXytc/63G5AoMf/OAH6U2P8ZbE2Z7rOO43FwRgk002SaB32GGHVV4z762+Xlz2ta99rVW+Hl3sOeHeZyIgP/nkk9P7RAz2Cx88FjTe0j0OWc3kmpOGF+za46W9cIm9e3QrLPRvb3v1MkhvfvYG6GGGNz/HG+BnC2/r96zPV7DrhZeeUGeN1mp4ZrxH6Xqbq0fseuR2vOcivwbdnS94OFcBwiTpS8iYM7xMKAAAIABJREFULXgnizcfe9yp9wt4/PmGG26YHn3qzcteVJa/Sdh357O+DGPLs/XZur+ZrftO9z4SZx/96EdTEu28887rfCv9dO8T31vwAYKXiMn0ejW3Z/KOengW+Ve+8pWptxv3vf6PfvSjBP5eGx8BQt/vTtrn7KHs8Mte9rIpwJMJ5jiRvrkKEPrugyDBXEuA0Fdiw32ur3xVcV7/+tcvESB4s6eAYYstthg7mA63qiU/PWl44THO22+/fXXmmWdWb3zjG6v99tsvBQwHH3xwddppp1V77LFHtc8++yxB+AfJ1z7vu+++1bHHHjuTbRjquzLOEh6qW21DcsnL7KzP+3cOOeSQ9NEbbrghBb9eiikg9hkvxGoLYucjHs5mAmFS9IVu8IdeQibxhD+wE+9BEfxKlJxwwgmpUipwXH755RtVbz7qy1DGVT7cKAHJBknL008/vQQIfXTEs3i18BDaqAME2aEddtihWmWVVaq99tqrz3SmPjNpDn8o4WQfluG94IIL0ktgEDwvhjO8zVaGhEMtAcJ0pTuz780mARg005kECBylAEElKnRrZlIZz7cnES/YvDI57A581Wq16aabppfDwQXvgOkasqYCA8GGlzjNxlARkM1TsepKAH3hC19IyQ9JhAgQYo4f+chH0s8FD4KNvAKSr2M+4uFs48Mk6Atd9vZkwS7uQcfqnQY333xz9YY3vKHafPPNU6WtacxHfZkNu1zs9ygBwhA7/Ic//CFlBr1MCIkYdYAAkGS3/CkBwhAbM8RHb7nllmrHHXesrrnmmuqcc84pAcIQshv3R2ebALStZ7oBgoqOkvyyyy5baRcrAcK4NWa460sI7LnnnksECDLFiJG2Chn25z73uZ0X1aIFQ+5617um8yqzMWTw3JN+dQUI5557bgoCttlmm1sFCNdee21qtdRWxd94a+5CIXyzjQ+ToC/I//Oe97zUkkb/296Uftlll6VKgy6FhaIvs2GXi/0eiyZAEAkDUeVXUe66666bDuZeeeWVqf907733TmVWWRMAqjdflk8f+q677pp6ih3IuPTSS6unPOUp6TuIghKsjBNA1YvnsIZ7ybzo4eRgZHXc1+cFD8stt1z1pje9KYG573MowOa6665L2UUVCJkcJV7fQSSAtbn9+te/Tn2jziBwBG0Oy+e8ftz1ZMUZ7tlnn11tttlm6Xt6Cq1FKdlcERbD/LRF+Kw3ozpcqV1q5513Tr+nEDJj73nPeyrtTt7yqh3HoVnr1X993HHHJdndeOONydkIaNzX78nGWYiDDjoovaF55ZVXThURDktAZa76G/V8K+0bZCXj1dQDLMNnDgDMXpGzDIcyqJ+R03bbbZd+rhRq3x74wAdWxx9/fMoGyvRdeOGF1aGHHpru7xXyekyV4r2q3SFfsjAnFQR7/chHPrI68MADUwuCTJusyqDzJvZC76ZzDvo47fvb3va2ikNzv/PPPz/Jxf3oD3nY7wMOOCD1Brvntttum1rXjjjiiOTU8572OGStxKsH1BskfTZvMTKHj3/840kO9Nx99dLTLTqqf9B9kAxvxrW+733ve7daX+y/7/q9PlS2Y//Mf9j9GwZAp0MAZOydu6EL9Jys6Z/WEXu89NJLJ7ti4zvttFPS3VVXXbX64Ac/mAgeG+Ik84x/U4Bgn1QPyVCmWQn+YQ97WCKIbNC/7SG8YfPmwrb8nkzZhvt5e7JBT81Fu8cxxxyTdJ/NyNI5TK/dxTkTiQLZOweoDWuiB/aHLvm5NbC/Lh2dDl5IirgX23JvmMWOyHQ+4sUgfWsifBIGsAQ5ggPOa3mLNNw86aSTptZsb9gt/FRxYJNsgU3wNzCSPdbxtste9NzCZm+vhlVsGAY4HO/nZA2vVDXhkbMtdIteqIY0nSuLAAGeaZk04mwbXLIW+LvLLruk6w0ifF14CCNcj57TbQk0+hrnvbr8zbjxYZjr1z/bV18G+X+/G1ZfnB1kb3ABJ6Gf9huWj1pf+HpciP+HD/R5OiMqCDPVly68aZrbIP/Lj7Kf4A9kijPwZ3Qf7uMezljwyewEb+T38QbBE39Mn+H6MPaMc7gW/2s/8a0mfzNo/vgWPgcTyBiXxN9gMcx59rOfPSUSa+RTcABckz9l/zCNb4i5w/MzzjgjcRKtiPYetwr/InHKf/I/kl04B0xc0GcQALY+OoJkkEg+R0uoyJSNYmwA12eBPyDjiAkbySU4SuG7BoB+9atfvUTGyeYA4KgguKbvuYcgA+EE3DaAI0dQXOfwww9PCiLIcBhSBpsz2Xrrraf6WEXpiH5eAm8yCATQ3G0qwEdeBB1I8ZZbbpkUSguB/kHZMQYQAQI5IH3WAYh8D4mPioXAALFHXhkS5UGeKR65+D1C5HqItNKk+/i/IMLckFCkeq211kpycMjF9cgegaK05uSAIIfos+5P1vXD2QIdgYR5M3agbb/Mx73JkxzjMJ7rOLiLaNln8mE4sV9tEXEAHAdnXRy0/eS46ZW5Ng0GTIaImjkC8HhSgf0wfwfI6Rt9CN1yLXvA+Mji6quvTk4b6csPvQryBFBIsLVaE52lpxEg3HTTTcmgHTxEJOgWQow8yIxy0kCOrAFjvr6LLroo/VzvqUFP3dOaBLr2DZkmb3Y17P4N42ymEyDQBYAI6NghfY89gQV0I28ZkzggP0GWtZCHgCHP+NcDBPYlKPA9jkPQbr8EpvrC8/1iv5xsXM8+uT+9BrxxyA84r7/++inLu8YaayT95KwkIuiMgOGSSy5Je+H7+oMFGBz6pz71qWQv9tF++S67tD9NB09nghccm6DL3Miag9N/Tx+M+YYXXQGCswZ0GkbDBQEm3BE0wxnk2x4jduwXYRFY0gE4Bs8lRujNn/70p3RezGjD2y57gY/20DURehlc1+X87S9fE9fve8YsAoSVVlppSveRd7roSXee0iZBMKjC1QcPycbDFOgvMiQ5suaaayZCEkFvl78ZNz4Mc/2mAKFLX/jSQf6fHxlGX9gq/SRXeA+f+VP4zG9qHxulvvBBrmv/rLUtYOyS46j0pQtv6vPo8r8O17qmhAb+Qf/5R2dxcCXBkfZCfpKflSyVBGHvyLjgIvTZXvaxZ5hoH/l1mM13SB75d93fdM0frrheJA/ZHLvlkwUB1qIKCJN32223xENgG38DTyS16Ix1eVIj3ijhx2a1lqmo4l8wD1/AdVzbNXAe/sZn+LQFHSBQHARb1g4oIThIIxIFWGVUkUUZOyQuMtYEEMRIkADwg8Q1EfZ6gADMEQcOn9Jx7HWCATgphmw4kLaJzhtwSuaIgMf8uwIE5AH5sIk2OB7diUg4IW+deQla1JsHCJSdAVAACvvd7343PeUnMlRIOGWMXlwkBdHnUBByYKXU6P6cJcMCLOYkk0a2QeTtBULOCbseA0XgRKWuJarliJFyclQBEjzVhwjb/Owl+XHiDOLpT396WoPMHyAgU2vlnMwtSHge0HUFCNYnIyb4ogsidKQoKiz1uTF+RBSB4tztj0yE9Vk3WQigzMsa/NxA2snNXKPM33ToFaFFFFzTWuP6jJY87L890AsNSMjBZzgZAQddEFyQLfIpeMnXZ37sgs0gvf6PnABOA9AhMeutt14KGKazf13OJX4/nQAhyBP7etzjHjcVbKt+0Uf6HvYgA8sRWl9kTOqE3vWa7Jfc7IOAIwaHYl8GBQhwIZ5OJeBEoAyOBpZwWIA5HCxQ58zo709+8pNkG3Q2ssn2Ad54tKoR+CbYMLd6y8hM8UJwyxG5Ntzyb06EHpPnfMSLNn1jX7LmHF9UAxAwOCyghlcqCogZbBGERYUvP6TXRPjinnW87bIX+0WPBOl0AEZKOtFnOGzvQ8eHDRBUXiPDqEpmrZdffnn14he/OOGZA6dth5RDHwfhoXYlREPwar5G3Z4G+Zu+uDBTfBj2PvH5Pvpif7r8/zD6wo9ENhve84tsTVJAgkkSaJT6EgECfOGjpjtGpS+D8KZpbn38rwx+4D4SLVErQcQ/REAUZ7L8nCzIGTeRPFCR9jMJmD72LMGrQwTvUHmAKewcaa/bR9f88TnJGP5Hogb+G+4hKYifSQSoPlojrhGBv4DAHwkt/kSVWaApGDAkSAQOkcQW7MJAia/gZ6F/EpeLIkCoE2zGyXnaXAEAQt5ksKLy6QQIHAgSZVACm4iYAePI8LYBBEIUTt7ffSoIIkhOTuQpw4UQDnIgdYdlnRQLwXAdDkl2ifNHjCgixxGGw1kJIjgVLTSMLTKc1oocIw0RIHBESI3MKuNyH0FTlMb9e5lllpl6nq5rCZwoIiNteleB0pgsMTBDupFm3wEmyKsMgVIgEHVfIBCjHtB1BQj5IeU++yFwQp7MO1o8BDRkAsj9nny1lAAaxFWVifOXkUXec4eUPxVH+V52m14K6GLkBFY7lTlzLA95yEOmMsh0TmDKwQtCmg6R1deH7AoWtAsgF01jOvvX1+lMN0BAwoEeUFb9Il9rsC8CZ5l7eu4zsqcC3hh9AgSBIgLEblT82variSD5maDZ9yUnBHMB8OzshS98Yfp/2yE/9iXjY88F2YitADtaAegQoi44co36YwhniheuiTgIbOgwcj3f8aJN3xA+QblgWBBAbzhtMrM37BcesV167t8COLbld+EghyF8XfbCmTc9JYS+2HPYNwjfm9badAYBGYDN9EkiAS7D1PAf9ev0wQu+AWbDHQkPQQ6SIvsdFbRB/qYvLsTnposPw94nt+8ufZmJ/3efun9GCGV94yyA6/Np/ghSkdpR6ksECHy7BFZTBbKP/EalL4Pwpmkeffyv7yHpdF5VAPmVYMu7Fdoe2iAhSZ8lbeJdOYP4S5s9t/mbPvNvannN+aoAgZ+XUBIgNA0+CIYLOgUCMaw7uB8eixNKqOHLxqI5g2AxTYRu3AGC+3LCCB/A1dpCqEjdOAKEWA9CIIsfm92m4HUA4hQ5PS0ZlAaxVvkA8hwoEiQgEGU3DdUD/bru7buIvxJlBAi+w1lYv758jjXKeZSTQxLtktMwQzsUBV9xxRVTK4D7C/wAqtYmwZKst7XlTm/cAYLr22dGHG1OTeuKx8CpbCk3kjOQQvBzh5QHCE09sD6bAwYyzGEIsJB7Mm4afQAcKVGOVFaVVW0a092/Pns9EwIgw4H8CwI4dfqx9tprp2AAwdYmguDGuZc2wK7LV7tayFvFLgi9zzVVfJoCDp8VrCjvajXT0mf//Yn9bwsQBBQCbvbF5lUJBTyu0WfMFC8QFMkO2XTEhf0JyAVcZDxf8aJJNm32lH8WIRfgwyiYzmEKMjne6QQIXfbS5oBHHSDEGumDLKIKlnZFAed08YKstDeqSMJ8wTOsz1vsBvmbPvqbf2Ym+DDsvcK+64fam64zXf/vWnX/LADg5+KMT/1+o9YXeCQBIAEloTXIhw2SYR//0kdfBuFNU7Wrr/91bwk6xFd1EJ7mL8Fs4084Er3Dl3AaAcAg/tJFqOv+oc/8uwIEyUl4IXkLp5rkFOtT7W5rl9ZhIOGaV8S71jMduxr0nbG+B2EuAgSEmfIot8o0KU3VN3SUFQRArFSkzUbGXObL6BsgmKcMpGiRImi3UCaWZZahQLD8XGk4sgmylFp63Av5kqXlbF2n3mLkszJViA/HGtUUES9ypZyFIEdPdSiLbLfIPFo/mpRIBM+wzVlGQPZd+Q1RodyCEeXefIw7QADk2nvMQ4Y3jFO7kzkGAWS8ghn7J2sQ1YMckOuEU0uKdVmT70RVp6mCAPz0SEdVigzcwz7HWZz6Y1zr9mJPOHt7pZQa+y/7KABBdmayf11gMhMCAMgEiCpLMqScttIqXZN9UzrleOPw9zABgn0AwLKjCGOMYQIE++DwOh1VWZOF1+YR+98WILh3VBBkuq0h3tURtsKpImrsp95DPFO8ANiSB/ZfkMueoyQtuzaf8aKub30CBDinxM5utRaSZz1jO0wFQQJnEN6pUowyI2zNg55iBJPosr2EW3V7CJn1IXxaYyWJBAaItGpJnQAN8jfRBtqFC/H7meBD33vkn+ujLzPx/00BQmAEP5dXAwWtMt5sfpT6ooedr5dogjUettA06v6s/plR6csgvME36qOP/43zBPgZriMQYpMIfzzmt40/kY9kiOSvQKrL/7XZc5u/6TP/rgAhWrdV8HCzvPovQNcqBb8ly3Awfj7WLXCCCXCOPCR/yMnZOKMECP97yAwp5rBF1IgFUiQDPuiQsn55rT76wjxpAIElaMqESMgsiziRgfqhNsIf1GLk+5wVw80HQ7WR7oEQxlOOQsGVwwQOMeoZCuV1rTnR649sCAwAUjylRFuLjEL0uzF+5WRnF2SWrQUJs1YKpd/d95EwRFg23/f93gEg5XzZfj9zMEhfnH/L+JIPpdaLRynbSt7Ww9l4G6j5ISYyYQxYVpVzIg9Z1mECBNmueBtkF4FuAk6ysT6ZCbI1PzJgrOYiix0DAAMZGQx7mLe6+EydcCK2ruGJOUAisvoBGFqQgIF7uTfnQgedFaAnWm70zetD7gPgel/pjz8O3rkfwEM47CV9ADDT3b8uJz1TAqB6RIeUkslDZUk1xP7IfJNHvQWnT4uRzLl9Yx+uFz2e9ou87Yeg3civx2YkDGLIPOl1R/LZDD2oEzJ2kbeT0XNlcfdgG3pGBRX0x37Te73J7F/AXH8KyUzxgp3bd/Iz2Kngkb1LGMCEucQLBFxQiEAJ0AclGLTX2K9BD4GQ1IinEsEDDlLlR/XAH5XVHM+1srF35E31ro63CPQge7GXwxI++MqpIytNT65yJgtGN70HgS7TVede2HXbm9j74IXWRskElTVtn3SN/gtU2YbAUkKpzd+0tTG24cRM8aELf+q/76MvAn+ks6//79KXODCsZUS7rzXzoXw6XIuKcf3FVYMqToP0Bc9BCFWfo7JdfzeGIEjVkq7AnzY/2OU/++iLSmsb3jQ9YrWP/9V649yUrgV4TGclFSV8opMh+JPzp2Qbg29VJYW//Ml07bktQOgz/64AwZztodYp1WaJDfyOPTpjy+f5DB6gOwSe8RV0CX+CA36ntZLs6ZmWM9eIAEEiWKttnY8Oa1Ndnx9rBUEWXP85cmBTgTbSI7OorEKIzgl4fBOD4lS0Jlh0HGrk9AQKfifzKptEMUWdkb2jYAyYMwCAMsMylpw5Z+HJNwxKVgVhdFDF/WyYsq45IH8UDgEzB6TOIVGO2P04XgZbb/UQ8QF593cNSrHaaqul1gmApp9Mmdd6/d6hGxvOoXNuSDXQsR7ziKc7CTg4OEChDYZSBRFSiotHfcXPAaJKgchUkICYySRZh3vLeFNWayJPpIdjUtqWtSILWToOkhwFRM45DHLwlAvZI2v3jACN3M3DGuqtURSf8TMyhBxpsxZRtMqPAM/3BRccWTzuzb20GAhoECIybWrfEcSRIQDSN0r+EUwB+3g8pesBWuSbXtmPvBfQ7wWjAlSkjHwNpFIFwdzdh6yAqOwfB0JmAgjtM34mA2Rf6ZW95xyQe5lf1QFyBiBN60MayMD9XZMO0mcklL4AV2R1Jvs3CCBmSgCAGQeG4AF/62HHAlE2BRjzx0LSI+tkn/RDhorDpP/sABlGfPwMYCLlyECccWFvQJNe02/3hjV0nj2wm/xwe+w/PQmCXQ8Q2JxARmBoP9m3/abHyCCiAEvsic+yF0GBgIPd1cvLM8UL+sBm4Sl9Ejial/kjgHONF/ZGldCclNfbnuPOThEYuDzInmGFfdTXq3pnL53xQOzghURH4LlgEbmHYT4rIVTH2y57gT++yy840wXvIwAR8Kry8ROcN/9jv2E/v0LP6wEC7GHr7JUOxsF163dAGdZxwuYNP+pPjaOPrkHfu/AQZsIdPoV+qiiQ0/9j707AvavG/oFvU+g1D72h1BURkVKSBk3K0KBRkyYNKBoMRSpJUkKjIQkVokJoMpMkoYwZQlcUmZNMb/78r8+63vt5d9v+nd/+nfM75/kN97qu53qe55y9117rXvfwvYe1Ft3O5sTlWr3sTa87GEbBQejKL1GyNZP9J5dktgu/cHbxKdyCl8m5ABYdxg7QccPkF7SmI2Eje9AASUEM9oYthYvoOLofzplvfqG3e+mbtv2J/ewv+YDDYCQ2lS2M0+s4d/SYQEs4CLHfET/DcfAdOaLH2Y5+8kzHk0/BBFl9shutzd70Gz9njd2BOcMekU+6iGyzQcpnYT2g36ZluoEeElA1b3LIRphP6Ay8RW/JrtADxuzfZJ7sc5z8HI3oeI6FcZDrZhC2H+gf5Pfz5iAgEoUVzUQoepOqNwof8aIxsuquKX2gM+rMLAgFBgxI8VOChBUjRfqFIfce46RGF/MBxgHYKHSgrv49UUCgjvJXVqMBypwb3pt+ZC4slEVrE0jvhCdIeIEWDOzb+qZA9MEIxCkY6EEBAIgUueizRmh8S9QnjIOxAZOMEuXG4MS51pScd+IMeTWsACPGY5REtkTTOQ8UD8ViXBG18g3fRmcMb+7N38/EUBSn0ob6RmQRMw6COUZZDKNO8CnwaNYegGYoGTbgV1TfngpzjBZHfhLwaHig7ZZJv49jH/VBmaM/0NJ2IpPUPgNa3+yqDAT/1r+HH6wlZVBfD+NAM1EOygtg0czf90VGCLp30RWg4Kj0m1/wByVok5Ln4/QtIBUYjDaX9ZtpbefqIOiboyayGmsYNdD4OzJinkNrfIxfNUpP/SVDyRmMxiEVDdXoB3Sx3uSILIswoTPHD+1CPhgGa9HMiIVeoXzrLSK2lLyMmqgzvUFZ1+vErS2Z5xAwdKLmHF7fn6nNVl/4Pv1mzfFiUx80+XOh9QUHwXoBpvR/6LE6LegZ8hX6tqkP6s+aD6ec/gNIzJ8OoZfJW6ybgAydC9CTFzLapm+BrJnkpQmSGX76KPRzZIvoO4AgdLxvtZXozAS6jYWjTPf14hf07KIvQh8CH5xr4BHN6GYONj4HsmTeZrI3MzJt45fD0A9dvjcIv5DHmew/OmiD8AtQRqbZcJiCLeHUwh/D5pc6PdhWvM+JZpPwS2Rjw/436TdsfhG8mknftK3fTPYXJgl9zv7De3X+rmdjyQbsBrPAFJ6NQGf9u4PIM5kVZO5lbwRcZxo/DOOUyWhkip2hb6Kx9fACW+Z7dBaHgFPaxL+yKWSXDWmbH2yihDuOtaaP4CS0g3+GfTlwcz3nzUHoIvj5TFIgKTC6FFgoALC4KECBi8wAlPUNcsbTaw/C4hprfjcpMGoUmHT9MGr0nqbx9NqDME00GIW5poMwCquQY0gKjCAFJhEAcArcQikqJbIj4tZ2Qkk6CCPIkDmkkaLAJOqHkSLwFA8mHYTRWPx0EEZjHXIUSYGRo8AkAgDp7Uj52t/UPIbXIihrUb6kxthZ5MpHmhsFR26xckBJgQWmwCTqhwUmYX6uBwWUldkzae+RDbmDnrCVhB0OBdJBGA4ds5ekwMRRYBIBgM1qakHVt6rRbp4a06uGV01p2x6WiVv0nFBSoCMFJlE/dJx6PjaPFLB3yR6laPau0r/zuRl3Hqcz1l2ngzDWy5eDTwrMHwUSAMwfbbPnpMC4UyD1w7ivYI4/KTAzBdJBSA5JCiQFWimQACAZIymQFOhFgdQPyRtJgcmmQDoIk72+ObukwKwpkABg1qTLF5MCE0+B1A8Tv8Q5wSmnwEg4CC7DcD66m1H921nx/tQvGXLRlM2Dfu4s4vlovu2eApd59LrvYD6+m32OBwWmjT8SAIwGX9KL9J/ztulEfBj60LnhcdfIaIx2fkYRNoKdQIeYMzqgD5swF7tQ7z83pHdbw3HUD6HD8Uq/S0C7UWH0nzLn0CFxEWrXUcNDdA481Lz0sWsfk/xc0BZtgp/cxWEv26A8Nor4YrE7CIjiJmWXgbi90iUUcdtcXALhYhxXVLu0wwU5ThaZj+bWRJc3ufzCt+q3vM7H97LP4VDAJXBuVIxL6Oq9upXbZWxOrJkrkBpV/nBbpIt03MY4F5DUXI1xBADD4ajR6YVudEmOG1RdOmejND50aZNbO10m57bVSW7ADTq4NMrxs/64QMnFj+yCk6nczuoSsNk08uMiPBeMufnUJUTZ+lNgHPUDLMG+u2HdbesA86Q3Nwabq8vB3AQ+yIlAbq532aHLulzkle3/KODCsiuvvLKcigezuvAMP7kEky6K28u70mwU8cVidxAwrxvh3E6KqI4ddFuem1Hdkskzu+mmm6ozzzyz3JrKGMyXg3DbbbcVI7zCCitUG2ywQckkjHL7zW9+U7mJb6211hraMN0A7EZYt0GPS8PE559/fgFSN998c0WpudEY33AcGDLX1eOxQaIgbtt043S0heCP5je7rAFw44bQk046aahKfBwBQBd6jdMzX/3qV6sTTjihuv766ysnMDHWZN7Nru9///tLQGUUHQTjXmWVVcoNonNtHH/nortx2NzdOspBYFCBHjfcv/e97521g8DQo6Mb192Oe8MNN8x1yFPx/jjqB/btIx/5SOUmYqBuVGz8MOWlyXwqL8gMO7nrrrsO5BR96EMfKkc8b7LJJtWSSy5ZuuZQu437cY973EB99RIKMq3BXePUbrnllhLYhl/dzuyWZA6CgGUEMQbBZguBLwal72J3ECh5YO7UU0+tnvnMZ5ZSo5/+9KfVgx70oGIMA9DlxUX/ubQAIeEdpsMkwuBmWRGDcWr4Jq5xd/HVIYccUjEGAMRuu+1Wrb322iXqKpPQpYkiOgbz3HPP7fL4UJ758pe/XICOM/i7Nsqf02zOIqzPf/7zu77a97lxBAB9JzVmDwDH7mH4xCc+URyEiOJxCBkkhn/UHAQAQoBHoEf2blgNgCfP4SDolwHDp3NxEGJ8+tHSQei2YqkfutGp31O//OUvqxNPPLFEoMchSv+tb32rAOD999+/uv/9799vejP+Xong6aefXrIaO+2005z6Whwvy0jBr1tuueUiB2FxjGMi9bOFAAAgAElEQVS+vrnYHQSXYBx00EFFwfNSe7V0EO5MGUZsnXXWKaB4WA4CYZV6paSsx7g1ikapAUClFE374x//WCKDSjG++MUvlvPv+7W//e1vxcG47rrrqs997nP9Hh/K70Uxn/Oc55TIFgeta6Ogtt9++1IiwaEm0HMtpaoDpgRLXVdifp4bRwfBJUciauStec/EXKiUDsJcqDf8d9NBmDtN2Vz6/t3vfnf1sY99bOQdBLX1xx57bCn5e9e73jVnB0EUHubgbMy2RHDuqzD7HtJBWH75WVNPduDGG2+slBE94AEPqFZcccVFlw2J7l511VUFgGE0wM6FGPYdtEWdwkHAnFtttVVhULWpj3rUo6rllluupAr9H8P5ow9/K8NZddVVy3d56rITxiNdJk398Ic/fNH8gDQAS9ZCelzThzmo777vfe9bym8IiXeXWWaZnmDM/H7xi1+U9wF5QMsffTzxiU8s3//JT35S0uT2OrgERHQ7MiYUhxIZ6Tffe9jDHlatvPLKZZ5+duSRR5ayGs7Bs5/97DJe4zFf6T9zlF1Anzo9RfdEx9XvSv/7ZlwABWCLSvKIrYd3H/vYx5ZIvBIHc9HQwlpG/Sa6m4f5aIDqUkstVZ4DVm3aUSOvD+MSLTAXtND6vd+VAdscBGuqVO28884r5Wtbb711oae1sK72wKDBSiutVMbjd2effXZ19NFHF549/vjjC33QF1hr8oexoam54y/zlXqNuXvHOnJU9KEEyretjVsirYGUpAiSPzIdL3vZy4qj4E8/sK8kDiCTSTjnnHMqKeHtttuuK8lmfC4BQH8yylzZH/CrX/2qPEyuV1999aIn8J4mymbt8Rc9psX6kveQDSlmz9IPIRvDchDwB74jp3QLnieDUTZgTHQCGf3DH/5Qfi7lX9cdfo+XjZluoEc86xn6wCY9JX2HHXZYmacgAzkwd78jO/SXfUHe9Tv6RUMDujm+rf+QoViFQRyEW2+9tehBY1Y21NSv+kQL3wz9uvnmm5c9POkU9+d7T3TVD/Q7vcduCdD4G43xBN6IrO7tt99e1gTPs2U/+MEPyuZY8uTvum3zTthRPEneonk37HfIG/vpecEfPE6+8Fh9s3vocPygf7x5v/vdr9iskGe2WtkIvc0BpvvZQd9j99g6usA3/I7tYEfISNuexs985jPlxna64g1veENxEKJ/dEMDskHPkBu4g+0I/GSs0ZTWBk/7mf971jPob36bbrrpnRbX89bDWNEk5MRDaGWdrAddYW7sqP0MxnHAAQeUd+gKNNLq9KELfLdOn/rHfVuppEvQ4A24o47/ZsKPvTg0MBfepKesGxsfWI9egB2N1zN+Xm90cOgNc6nzp+foTnoMXehUusWlbnAF7OQdfaA12tT7t57eM0ZrAiPBHfrQ5oo/e8lHjDvwJrrgb7xNBmZq85JBwMzS4QAsAhF8aSlCJeINQAFMSj7U3onuulab0VRmBNA3WzgIwB5AJWJKCNXKixYDyRwOIPHaa6+ttt122wIAlYgAebITQDVBRRg1pxhXKQ2FYtEuu+yy6uSTTy4pe8aN8eDZ62OvvfYqwqZvYzZG+yLaNvwwchwfZVP6eMc73lGUiW/6DgCIMSgH4IISwGQUBWFDv0svvbTMx0Kqv8W0boBVQoLpCRUg+6xnPavUUtp4RRERXmOiaHzPmI877rhFhlg5CrpwjNDRuhBO47EOsjkUsqg0RaVvpSvmDQRQfjaTe4ejxuArjQG+fZOiEXn3HgVCYLyrth7dKTsbAa2HbxnfTO8PsomszUGgZPACpXD55ZcXI6FcQx0qPqQojIeAc7YoAOtq4xEF4GeAkveMs84feNR8bW5ES2O94ooriuF7zWteUxxefMsBNm811MZhXayBLIWfEUJraY8NGpMFfM2xnKlGlgIPxQQIAVAUvDKMYdR+dwUA3eDEZD5F0YsAcigZQ/JFRvEiuQNA6DxlbsACnr/wwgvLnhHyQ4fhH+CCTiT3Nrftt99+pcxyGA4CPqFr6SJGiwxfffXVRdcqTdOuueaawqd0IYPKuJubvTtAhXfoQeVvjIqx04Nf+MIXiqEjF2ussUYp/7EXiO6niwAz+hTPmyeZW3PNNYv+AsDIB33md+aLXnSiedOTT3/60xcxTlcHgcyTJ7pHP+TWmK0DkBXzZRfIG6cAyORcW6N0ELrJahf9ABTRr9ab3mXzgEKlrEAuXYcH2QG/syYAE/uDj+hj73nWDbv4UyBGnwCw+m/2Bq/gMXiDHpWN1Q4//PDyvpK3jTfeuDwDiO+www7FkaWzyQfga0z6ByLZLDyjTA6gIrOCk/gpStzCBuO3qIDARw44CafH7zTvtpUP4VO8DwPsvffehT933nnn8k02SFCOrdYvuTBnOoSMslPk0bvma08SXjdXcgN/hCPjWXOoOxTsBJsfdhud2B26jA1DD/RDE7rBGtVLYAWi/IyDYR6cOHZP9hAGIdt0IFxCL8J39QZ7CHB5Bh/AHXQNJ6GOH6299YCNyHCvG5XhJeOwcXjHHXcsNKTnYEU6R2WBIBo9iE6wIJoFSAawjQU90Oniiy8udj9wo7Fbe5gQJsDb+rIPQQaEg0CHwxZ4w/5FvKbBEtaAvte/9YRH4DY0Y7/1o2zcWOv4k+7GG3gVzQJ/GgedqcFknrNWdDVahHxYI3gUj5NZY6HvYVj8veAOQgBem8eAMwrYIiEYo4ARA1gPWmK03nrrFWVAkQP06vZEfiwIg2DBEZzwYzpMzvvFiDb7SQkBqv4PnFFCDDonxiJJiUt1WSCeIIZQXwbAe57gYwBMA/S2laxgLoARwPZvTGnelB3Fh+GBQ+8SdEzJgQAuGVQG01gPPPDAsogUFgZkjI2HkW0ruaJoKRUgQ3SaAvA9Cgvg0DA2IRKp5rGqs6c4Q6ESqpi/54EeYP/Vr351mT8h4qigP9qhufH727qIMlhTgBjNzJ+SQQu8YL7ABCWOoW0Gn+n9QR0ENENb3ySs/m08HEtzNR7KAw0ZKs4YXqFIKATzwiPmaJ3QW2vjDz8HKgi2vilykVGK07zMER9YFzzPGUV3TgkHi7LgHGicQPxAQXYtMQLC8DE6mgeFoB/v49m5ti4AYK7fmIT3OYl4n8KObCN+2WWXXQpf1Wvm6UGlBLGhzToB20ABcCyYwED7Gx9R5nPdgwB4ASiMD10ogEGWOQGiSmSa3FhvgAuoYPSBKN/HY36HX+kjfKo/mVvvGydnQB+ABcBDfgBt+hSoYtT0t+yyyxY6kQuyCMh4DzASIAEmYkOy9wHBAFZdHARgj9wBK+ZoLgwjPQBYCSYx6r5JXugozxqfeXg+HYRuUtlFP7AH7Cr7RV9xjOlAje0GbOhHa4Mn2HJ20nMAIT4McGd9BAL1SSbICEecQ+3kGA4v/q6XhQrIeI6uZ3vwKzuEF0MG2V68B1A7yEL/bDweMR78bEzkmSzX5RlAhwXCQXCqlueBQbzEHuB39qjNQegVAGDH2UlAmWzgaxjHvNGLvqHzzRUgZ6ONhc3Hy+YGY5kz+ebos4P+rxkXGugPTeke75hHAGO4Ay7wfT83/sjI64PTAEtxggQDfZvzVg+awnzWls5oy4a37SEaBD/WOdU8yTodhrfYeTxk7DAQmllPGAt+ohPM2dprcA+dRkcA1eiARv4PSNMb/m+uMKP50K34Ut/6wjscFHNWOoXvNHyhHzIAe9B9dD8HEN3ovdniTzSnf3vJBwwI/3GeOY94GWbwbbhuQR0EH8fcyiV46xYjGoGzWASRstcGdRAQvF5zb7EIs36Bq179MWiYAgg3RouPIZr9NQFyGwNjCgaQwZ2pnKNt05sF8T4lEse41g0qA0qhcRKMOSLBlAXggFExdpuDYDycEY4Tz5Jxlbar1+TrL7xd/yaMvFnKVWvOHxOZI6dLtgITM6oi2xQEAA8co6sIDyUiS0IRc+YoIuvu2ahHptSsIeWBT2Z6f5CjZhkLguA7gI2xKlMgrAAYxUXpeUZ0ntNAUNEHPWMTaJuDEDzcpA9jJ7ukf41Tib6AIEVhHdoAHv6x7qGwB3UQKCLGDI+Ec8cQccopMX0P4ly1KYkuAKAblJj8p8gcHsDL4YwLIDg1hI7BE9ZaBIzhEFVXGiazwKDLIGgcDAYEz1pb/DYXB4GTyzAxlKJZbWf8iyLiR/xfz95yfo3fWAQs2vRDWw1u00HwXi/+Fsmie+hvxjsaMMTI0x0RheviIDCW9Dp9BVgGGKJv6ARRWHqKHkZ3+iFablIeTE4H0Q9NfedL7ARdzW7jGbYDkGSXApB6Dn/6vWN9I2IqKAMY0+N+r9HDAjHkLpwQwBB4jkBek1/pUbwC/Ams1MEt/co26lOAq43/mnhDJl3Aib0zBvaFA0LeZdqarc1BQBf0ktVvHhZi/qLa6CPYx0kgKzABHcOJx9/NDHKT/kAx/cNBm6k116PpIMQmZfqPAwWjGIcGf6DFTPv4mvhqUPzYHHsb/mvDa019JFiCD2UWYLBo9KYAKv6jj/EanRVl2W36L445VSVB9wsI0kmwcL/DX2aDP7vIB9kQrEILYxckgpthuZna0EuMGCTMh9D1UzcMguDy3nkxwKw2VwcBsBOBIzAYdKb+MDfgLSWJqIhF6dQdjtksUC8Cz8ZB4K0DBDw8Hl+zxASzMvi9Nm0DqNJxgD/PlODWHQQMSqEwjOjGOMcejjYA4GcMK8UT5TFoLBUZm5QwHiXLKQB4jFG6kJLyLCZUy9cErBwkCnym9wcxWW0lRm3vU6qiHtJ15gQIAW+zcRBETCnsKF2ofw9PUqDz4SCQJUYh5Mh3Ra1lzWSrfHuuR9UOAgAGWadJfJZuwfeaTJ/oEmNJwZJJBoZuFOkRRdLwK/DNkW7eX+Fngh/kv4uDIGPHyEl7RwMgGAM8ASRFxLROf7Ig+ACMR8Q/fg+oSMOHfhXlaurHuToI9JEMYvOQCuBdAAEPAhpaFwcBvekaMtl2wgp9RFeR9aZ9SgdhMMkcRD+0OQi+xk7gqQiYtTkIfkduYi9LfZT1AzWAPnzuNBx6UTAIX0cgy3tNfpX9Ix8qA5qbbjmR+FMQS4lHFwcB8GLvgEiReUE1ejgcmyaF2xwE/A5QkruIQMd7ghCqIsjLuuuuW34s8yIoAfCJoMNYzVanPzvh/yLukSHvtfJdHQTzEEiMO62AbLjKeGc6fKbpIAyKH5vjnq2DEHgKPzYv0FMaZU0j01LPMHZxEMzReuKD5no2xz8b/NlFPgTrZZ7gOJkVpU3m2u9SyKE7CLylSDcBfvUIDcbm8UvHMAraXB0EdVwicoikHrtXfwQWMFYGYgwi48ZpHKPoIDDOSnhis2KTkdocBE4BYy8aqcRFdISSqTsIPHTrQJEAKqIUUnIBbpoMSoFSnIRCFER0U0SPENUVKsAtesLxMnYpTdFsqTXpYgrNt3q1Xu8PEgXv4iBwdpR4UIxqGQktZ4wzNRsHQb2sutmImnQxAJ6ZSwYBGFWiJLpRj3boV4SUwwOcMo5zuRF8EAAwGLSYzKfpIOsiEsnhRHsyyHgDGgy/kprYKIhfrRMw3Os40K57EKJMgjMSjfwBNVLZ5FYwJDbExTPkgZPOeTC+iLrH7+kDKWpzw2/DdhAYTN+nH+iqaBHhM26RzkEcBKVD+qMDmy0ioIJE6SDMTQ4H0Q+9HAQAlIzgL7XgvRwEVQlRAjrTqAF5UXH9wQIc7frG3Cagg1fIBztLb9aBPPkU7GSHZdK6OAjGxkkwVnaGbCtHEdRrCyK1yTdMA1CiRfM0QU6LvQR+HmfswzKqMoyfoxMlzXU61emv9Fb/Aon2Ns4EErs6CL5lrhwipX1kWbWDsc5kw5sOwqD4cdgOgkqItj0OoY/MZVAHwbvozZnFEzO12ToIXeQDn8RhLQJKgrkc75na0B2EMDhS6VJLdSUtms17UZ8XXu5cHQTpNBtgKARRo7b+GE2pakQBBtXN9YrAz2aBehF4NhkEDhVAzZgD8JyZaISHUeMItY1fip4ClH6l6DgCBLzuIKCXTIsogmwKxhV1oQTRpTl/mSDglyHnQRuD0hwtNi4BpYCqbwL6ojccBV4rp8Vac8SkucI7V1NpDpy7md6vnzLVz5x1cRCUICi74hwyJiKozQjtICVGxmdzZjifMUZRZD8332FnECgxfAzU1Uv4fFsWh5Jm8PBBW1q7Hx3j94MAgK59TvJzol+yfgy3DeoAOvBDXv1cdJ+eirI5ET9KmpPAoQ5njr5Smqfcp2uJEXnG274XjTwyaKJfwL+9SfVTNcgmR0YmzVil0OnJaORAdlHEHa9pw3YQ2AQlcpxs2ZTQD0CenwNoHN2uDoKSEKUhZNuY6+CH7lOvrh6cvEbpR53f/Tv3IHST0kH0Qy8HwVpxDABezmubgxCn9QHBbFe9hT2Ln7ElItacWliEHa2XqTYdBDLJFghkAcw2eUajt9lFlQawRRcHgSyZB9qI7Bs7uVI2XL90M77R5iAAlOjFlgq21Rsb6uf0CKzAjnIIgE86H3/7Yx9O3dmp01+QEIiHw9jpKFH1ncAFUaI1iIPAlsIGaGBsHCPlrjO1poMwKH5s9j3bDAKswqnxB8+E3sAfSoZkIwUelIzBtfa6aF0yCHAn+sME9Hr96Gfv2+dJDtr0a5cS9y7yEXgk9pfhSaVzSqFnOop66A6CSTJG6gMxqIFJn0etH9DCQ45Ilg1wMgo22EQdfBtDMW6UA6HF3FL4GDJOPTJhioCHJIoXexL0FQLnnYhGUSwUA+Br8aTyGSd/UwTeZwxtkqLAjM9zmmizf0s9Sk+2NYY3bgbUh778TAQAyNc/paP5N3qpg/QOBtS/aJe68o022qhsuAUi1DhLDdXpYQyx+QrNgXr0BcClW3nysiR+ZvMygBnpdx6ktJP5qb80TqDFdwl6nMbk/xQvpSdtKFLByBJomQPjthZobI0BDYrVmvi5NbBGFAY6oLvIDrBOUfV6HwAXmTA/86wfy1inuz5FIqXRInreti4ACSVg7ShbGRIZFDQxbooBX3GcwiFzKgB6+dPkD2DDvChla6u8B38o9xA1AvqMiQFCD1EkY7W+Is2UNZpZA9Fl9GBMgD2C28wAMAj6Y8hEppsbv/weoDM/Dpw59jsqtZcCHwQAdIMUk/8UR5i8qdmnp+g9/wZWGXWyFU0JRJzsQbbpI03AAx+RM/LV5B/PAAA2RwK5zah/ncq+T78o6+So4Atyjt+AbzqCEcAnQDk5jfpa0T9josONU19N/heVohcEJwSFOCT4mvz4Bn7Hy3hesAh/OyQhjnMWbRU0ImNkMwICaEWOgTalTd5HO7qzvkm0qZ85Vxwi0VTyB7CYL4c6ZN28jE/wxb/NydxijgInZD3GOPlcO7sZDqIfAqDS42EXgXk6Dwhn261xANLYk2BkQBX7qCIB/0ZwkcPgFCrObTTrz9bBFYBTs9wGv8bFVuQHvwY/6If84gd6GSDUP/uk0adshbEB1Wwy5xVPeoY9I0+yU3hPP/haECw2zTYpDXDicyWj7I+GF9kAgUK6oI53YAbzi5OMfBvIg6HYWrLn9+hkPrBX3d7oG1+TNQA+Dl0RODAfjjrMoFog1gNuaG5SRh/04MDAR4HnrCm5whvsYL+L3wIAwxfkG+Ywxq74sUnPNvzXhtdkTugjugvNBHfwIl7Di2woJ8fJbeYEr8QfdEUnWMH6wjex15OT5nn87mdRKSNoqk8ZLXMVuLPfLLAvndumX7vgzy7yIciLpgJX8AA8QgfCPTNVdsyLg8Dritt4DYhhAiwpbcYSSLUgBB7gBWil8DyHgG21o2rbAH/9APCUDK+PQiDoCMxwECrMz+AxbnGUpWcwtLo9te+ExBgxgYXjETKEDKkIoHHythzxSViNj+EBwC2wlA4GoAiaJxnZHMs7s/iUhPdF6YFG5TmIDtxhNE4BBYk2xkG4lQjxYn0nTrlhOH0/dp0rmQIgzB8zAs+UiRQW4TYfQspoYwBAFiMTWIoMoI2j0hjRcHS8S4lySigmLU4DoBRFGHi9jtHCcMZEOWI44xBxBIQpjDhGDi0oQWtIIUZdNGDNKSNEvd4nNJSheaFfs6TG+NAbD6Gfb1DsBEIkvZk+NXY/59VT3HFuMUWAX9AUb6nvR3+8hmcoPYqmzh/4BP3Rj9OLN/Unc2C++EKU2JpTBuaplIuRBPrQEJ/F6SkcF+DEGPwhC3Vwz2HEd9ZZWhyPR6QnFCWBD37Cv9F/gL5BoMAgAGCQfif5WfyHR+ghek4D4q09YNEsMwAK8Bowzbkj+2SRQ+FZAQ1yxKDpA+9yLOgd+grIZ9Ai2NBGW/IssMKRjztcyCeDF7cwCwTQFRxSOgiwMF78jz/pIZFKehTfexag9h7QxKEBgjwreIE3yREZEDkVqQRazI8+xNvGQL7pbfLAEaBn/Yy8+zadxUnC1/QNEEPOzdlzDC95CP1MHgSo6EnAib5iTxh/OpV+4vhH2aP/kzu6kC2SkaPTrEFbScgk8+6gcxtEP4SDYN2AdrqMXWCb8Lp1wksyP8AT2xlHixuXgB5dxoENG0IWlO7Vy+bwBMdAwMuJhnW9B8yyM/qm0/ERkIgf8CbHla2j6+lh+EQQLPrw7ahDZ0P9nAzoT+AL35B/8kB/c9yBN33hrbZIrWAUO0MHmA+84n30IOOqANDEz8kJG4Pf8bT5kTn6gWygA4BMftkhMmAc5kG+Yu+C+nPP0gkcKTIVR30KVADq7KNDNqyX8kfyDp8ZrwAILKVv6xeBDetk3HCaNWzb89TkMbrJGsAB1sS7+Koffmzj1cB/nCb4Ct3INX2Dxuy9n1sz/CGgaG4cTd+WUWXL6Vr8CezjBbxhnuYGV9DndCDnAoCnF60tfrWGnCn6xXc4EnQgelpPPI5/4yQj34YN6VdlabPFn/3kA0/Qi8ZozvQxfKAkfaY2Lw6CD1LqUjMGhbAUMeUdKTyRI1Edv6s3RqFZNhG/560BxoSQIZOCZ+Cixg0jxOVE3lFfHuBdFAtROCYWnUGTjiJIDJg+gczY5GcBecmUQjSE5VCIaEcjqAGk42ex2HF5CEYzFgJXny/DZD5xVrL3MZh5oZ/v+L3GqTGXiCj7PXBhfOhKGQEOFIQ5+B7nDPNSBhSasRJsStSz5u456a3o1/eA3fiebxM8kTyCyykCcCkdDoP19DvgnaLRAAMgPVKc6IgXOHToX/89hTPT+36Ph9CUgmrLIDTpHetgXZrOprH4nrX2OwrfmDg1BDVSfZQEoCPC0os/rBUaEn5KwzgiTY6/OIcRQYgxAUZ4zTsaxehn1s88g/bo16zdxP/1I1DxY5yeFP2TqTo/6YNi7HchSpuSGAQADAouJvV5IF8EiYMeJ4nQSQIBftY8lQs/hu4hk2jO8ITsNNeT/ImE0yXROAeM/kyNvsXzjCAgjucYvfohCOQa/9m8KCCBJyO6RN7r+hE48js/qzfgxnw501LnMgtowvhFo9/pjgBeaIA+9INvx2VRorfGRz5FZj0TzXy9JxhU188hD56tzwUIoasiK8CxR0MRSzKO5r5B73Psem0qnVS+nc28BtEP4SAIiuAl9gudATM6ynqxz0pueunvOAOeDQW+6Ei83GyCQOw9G1bfcOr9XvxCpoyLbdDoS05ls388BWiTjwg0sX1kRfAqztgnA4AgJ1OwrdceI98yXvLh+dgw7ed4lHwF3lEbD7s08U5dnpr6QqkV2xT2pm4P0NqczSfwFL2gNdeDrEagUDCDvZSZE2is2ylrA3TCJNanX4NV4Av4AL3D/vbDj239tuE/zzXxGp6r6yNrTR/R15xIgRf8w8mBD+p4FMYx9zjOWl94mm6BRdFNRipaHR9aT/xjrvjKfGO/Q1O/zgZ/ziQfgU84ZHTqTPJTp+28OQj9GCN/nxRICow2BQYBAKM9kxxdUiApMGwKDKIfeu1BGPaYsr/FSwHOjAyH6oA4unnxjii/PhcKpIMwF+rlu0mBCabAIABggsmQU0sKJAVaKNBVP7TVwCdBJ4cCMo+i17KbMoii6vY3ZBt/CqSDMP5rmDNICswLBboCgHn5eHaaFEgKjDQFuugHpSJKMqIG3v4o5RwznZwy0pPOwd2JAtZXvb09CspmrK1Sn37n6ycZx4MC6SCMxzrlKJMCC06BLgBgwQeVH0wKJAVGggJd9IP9Iw6AqO+5Uns908lbIzG5HEQnCsgO2dfECbQvw6ECjkvPNhkUSAdhMtYxZ5EUGDoFugCAoX80O0wKJAXGggKpH8ZimXKQSYFZUyAdhFmTLl9MCkw2BRIATPb65uySAnOhQOqHuVAv300KjD4F0kEY/TXKESYFFgsFEgAsFrLnR5MCY0GB1A9jsUw5yKTArCmQDsKsSZcvJgUmmwIJACZ7fXN2SYG5UCD1w1yol+8mBUafAmPjILhsx4Uabhx2K5wLvrINnwJOIHBkmVsmZ7rcZfhfHo0encrgtkOXNrkh04VJLtFxg7HLTNwa7eK7YTW3bLrl0aU7ToAYpTaNAMClRW6ldGmRG0LjsrNRWpeFGosLf04//fRyE3hcYNT8tsvK3FhMLtzM6pKzhWguNHITr5um3VY+m9ZlfvqdT/mfzbhH5Z1p1A/Dpr3LMV146kKuuNW8/g2XmrqYzO28LvTaZ599hj2E7C8p0JMCY+EguJ3PDYiHHnpoua3XFeOjBqYmhccOP/zwcnvjGWecUT3mMY+ZlGl1nofr0N1G6ZIXV7YDPPjPtfX+71bGtts7O3+g9iCH96STTio3LzIAe+yxx2y6mbd3pg0AAJ34/sQTTyz6xa3jzZu4543Y89gxkOEG4bhFuOun0MLxhU6hiRvpm+9ybt0W7gbRG264Yd6Or3Rainm4BZTR4riceuqp5TZm351N6zU/39KCXvMl/7MZ8yi9M236Ydi0d6GYY18FI+h/t3k32/e///3qhBNOKIHRgw8+uHrjG9847GEseKLtWSsAACAASURBVH/mtPfee5cbnAUY7nrXu1Z3v/vd7zQOsu7WaDcANx0m9y6Q3fPOO69yM7DTk9ygvtdee5XgHl1Vxy4cfLcjo7HbujU3Fb/4xS8u79Apbboxbmo/++yzyw3IbqN3q7LbrnfaaaeCSeGCSW5j4SDEAjDcolTpIEwySy7eubl+Ho+JToaDMJ8jIoAMbToI80nl7n3feuutxVG75z3vOREOgszrRz/60epJT3pS9YQnPKE7IQZ48n3ve1/lptz5dBC+/e1vl2wBkKSZ10orrVTAxWwdhLYpAgUXXXRROapxww03HIAK0/doOghzX/MLLrigOuigg3o6CL4go08nyTJMgoMQVJN15CiYG/tXb+Td/QrAeTRH5goM0DdA+rbbbluA/p///Odiq0855ZRy5Co98fjHP768xpk4+uijq5tvvrnQmUxzCFQDnHzyycXBOOaYY6r11luvusc97lHe4ZxwYgQgPPeiF72o2myzzapHPOIRJUD96U9/ujrttNPK0b0//vGP584EI9zDWDkImIPHlg7CCHPUmA8tHYT/W8BpBAB/+tOfqn333bcQYRIyCAwth/eoo44aWweBM2D897rXvcqFW9HwpzZMB+Gmm26qXvnKV1YveMELWiO6Y67ehjr8adQPQyVgVVVdHITvfe97BUTLMEySgyCD4D6MNgcBnWXYI3Op9Fepo4yLrKZSK/qg3n71q19V+++/f/XSl760OAIAvBudlY3S5Uq46pmC3/3ud9Uuu+xSngP4la37vUwyZ+JHP/pRcVDayiu/+c1vlqyFZya5pYMwyaubcxuYAukgpIMwKQ6CyBoD+Mtf/rJE3cYxgyCaJ2K33377VTvssMOdANJ8OAivetWrqg9/+MMFULSVfAysUCb4hXQQ5r646SD0dhDq1LUnUMbAvkhlRL32AV5zzTUlyr/xxhsXh0JAwV4yjlVkCOr9yjzY/8ex8IxSJ87CkUceWcqYlBK1NZlGGQiOyjD3JM6do4bbw1g6COpeeX8YQHvzm99cbbfddsX7s3DqxSz2FVdcUZjiFa94RTEwD37wg8vzNoYedthhJRW14oorVquvvnq1++67t9bZ3nHHHaXu3Dek6T3HiKhpwzzSWtLcmtpVKSe/NwZNjZvNrksvvXTryunfs/oHTKS2pdF/9rOfFU/YHPX/m9/8prr44osLU6qT987HPvaxUoe30UYblVSZ32FqNHjJS15SxqF+1u2G5mx8yy67bBEw8xYlO+uss0rq7D3veU/xlBlGfb/pTW9aBCgY6UsuuaRSM4mmhFSUbbfdditjk6JDb9995zvfWTaRKwcjZL5fjxCgD+/cN4xBnaF677Y9Jb5rA5ex+JYIgHlde+21Rehlk0T89GctKRDrEeuM4Gqj1UjHRkZjlTKMNTNmSsLei+9+97slZYkeN95446ISI7QVSfDubbfdtmgdvYveUqEEybtoRDnhReO3rp7Rn997Nn6voywxGq5C69KbNSS/jAfetI54DA9vvfXWRQ7JCr6Rnla3KpJF79i0HrIeytMeknvf+97l+V133bVkOMn9P//5z8K3ZEy9qn+LVikVkFJXPz8X/p6Jv+hHOpG8mkvU2YrE+b+yBeOlQx74wAeWVL89XnELKudCaRI5PvbYY+8kn7IS9Ccaqv+lN84///wZS4x8E13Ih/Q9PSQrIMJHVnrpt0MOOaToNdFBz5kHOlqLcBBEGsnmOeecU7mllw5bbrnlyjwBMAcMeAdNRB+tqTU29/r86ENyHnsdfO/KK6+slllmmf+Qf33TdeQbIPHdJZdcsuxfmpY2LQ6CshX89fGPf7zwFLvPtrGDg+ADfCHizL6zf/a3sc3KWXvtQfBOPYOglI+9s7m5iT9+//vfF97G8+RJ32Q8AgP42rijnGfTTTet7nOf+5RSml5NiY536Cl6k750gMnyyy//H/KlrIftV85DzsjYEkss0bPvXhkEQTp4g52MPWAAPHnzbXPs1+AO4/G3Qyfo3LYmuyAQQN9xQpSWbr/99sUu+789B9Pcxs5BAEyB9BVWWKFsFuXtYUIRssc+9rGl5oyR5xDw7mxyUa/GGPkZRgDYCYefX3XVVcVIYOy2iBEGAjqd7oPJGB7ANk5UwrSxuZSgc0YYDX1xLIB5G1495xSCZqN8CKz+DzzwwAJSKV6GCRC2kQZwYdCBWELFmAPBrjdn+KTTCCRPmNIgXJTIFltsUYAPBaNvDE9xGE80gqf+jjD6pm8BNhFxBCgYWCCaYD7xiU8sETZj2XzzzctcpfrMwTdsqEJLDZBYddVV7+QgrLvuukVxGKc0nfUE9NscBGsDWAFyQD0DDIyYH8Bv3ThSeEHkAADhOBx//PHl+2imfwrZd2308n9KBn3wDRCEh/wcTzAC+ENUwJwpqEsvvbT0SSnHBkalKPowJmBJX+Zk/Yx5tdVWKzRSZwlIcRCtmw3gIqKczXQQFl71MpKMNMOJHwBUG9rwvkABHsaf5ASvMa6cWg40h92aamQJHzGeUtPKH/1OOQ8eVLu65ZZbFr7mIGj69A0yRF6UxsyFv7vwF9miJ0KezZ/DA3QD3/QU/vRvOgXABRqMn2ybZ72kk5yZN+eD7vEc2aHrZtqDAHhz7oP36Rm6QtCD/tFPL/221lprtZZY0JPqkukHYJ4jb8xAlM3/9J6x+xnHbYMNNii6a+WVVy7gidw359eM6AIrbfLPIaLTbeAm+9afTaCbpqVNg4MAdAOMSmFgCLoAboAFOM5Ac1d8wJ6xt1F3zz76t31PXRwE+KaJPzjKdBg+NU4BCPLNbuNxthyPk202zBh8i9wYPwAd+qnJt54hmwJzZIiTTtfRaXgf1tIXXcI+L7XUUgUbkSHfZOvsE+rVwkFgd+EmLYIXcBfdyv76N5kmh5z6rbbaqq+IscOB59jxXtlTG4+VCnGA6AsBZbZZQIgujIBQfNBYBFvrTZCETpnENnYOQgBwgiWaxPABrhjVoookM+SiSKLTFvQZz3hGMdZHHHFEBdgxbN7fZpttCkMSKAIkCtTWCDIGBfBFqQBiTIKx9etbjBPBIUyMRTQGQ2rctwOUNr/BgOtfOQDjK4qHwQm86BTmBbT1a/6MnwglgWXMKQnz5CSIZBJs/XFYOBki1xwQhpyhNm+Npy9qaPyArtYEFIw+h8vYI2OgJth8RB4YSP2ZJ0XF8IeDoD+Gu55BIPDqCHfccceSzhN9ZWB7nUoVEXaZFOMnwJScPtFaX+hF0Am2deQYUJDGZU2BH40SRUcOEn7hYHIcgAfzxk9oh8Yir/VNyuiJ18JBABjNVSQoogzGBSgxILIQAIlv+rnWtocmMwiLR60Gv5IjQBX/4EPZBA6Cshy8LXosS2X9yRaZAgoYenpFsEIL/sOfghLWnT6iI+oG2M8cnYr/RBBny9++2YW/mvJsnPTMU5/61MLzImbmTIdEpjUc7DZ+ZXTRBLg2DwAEQBIImclBoGcYXPW9Wpu+aNNvshuASFsNNoAqgstxJ28a+Qai0JdxD/qaE8dfgIic0xlt8+tV8tGUf5lAzp05yQShK51IH01LmwYHgewDyfiHXm/bo9QFH3jPRldgktMakXU2VN9dHAT0buIPGEEfsIBxsnmCghrdxCHnuAqC+D+7CNyzw95hw8hvW5N955wLptCTWlN//fCHPyz4om6fbfInL3TKc5/73J7iEA6CYFpgEvZV0A8e4cA0HQSBhLbjYJsfoYMFMejjmH/bQGI9VWKEcw+LWOu2PWgCurCUACUbwQljCwRjJ7GNnYPQ3KTcVOiMN0MFzPP2CSNDAOBS4AyafyuzwcSEJrzEXrVkbZuE9F0HAGHwpCJlKqKFg8JoEW4Gr9nCQQD8MXU0kWnCC8DIJLQZNAbP9wBaRjI24RifiCVwAthjZpkGDoW5A8ci8BQHxRWtCShEykRVKTDCEC3mC2ADTV0dBPMThWTwORQi6455a6OLb7UB6F6btuo1yd7jIJpnPQrA8fIz8+HUoR0acfS0XnsQ6gABKJGdAlzQvVe77rrryq8AKk4eelOo9YhsOgiLR6324tc2ABARLM4jY2MN8a/j8+rHoDJ4QDNjo4RmEAehfopVF/5GtS781ZRn5T1rrLFGAbJ4uq4vjJd+MA8y0tQ35u1dkX7li9G6nGL085//vIBokTaRRQ6G7AH9FgGFXodQdKVHG4CZSb7m4iAIDAARIqYyRnSjYA49Py1tGhwEgJqcKSdia/E64Lv22msvApBd8AG9oQRWVBy/RJvtHoQm/mCT2DZBB86HSDvQbvwBkGXPlNHJfrB7HAq4oBfPsssCoTL0shywAGfB3JsBjrr+6oVnmnLRVmLEQVAKyF4C3U0HwVx6BXLr/VsTARz90fUyt23NvGBAGIZulGGV4ZUx4uhw/pvNO3Sn4KYgouqLSW0T5yBgCIYMuBZNFvEC2iODIPpP0IE1wBtDiH6JnkX9bXOxuygAESUOiBpb6bZoATh4ngSn7SKhXgLFgCmPUbZgjG0GLfon7IQ+auvbGNYYKQ2OCi+csiAE9dYEFP4v60Ax1kuwQkGJqvL4uzoIlI6UpMiFsRuP0oNeztlsHYQYn/pDezTaGsUge1QH7F0cBI4XgCg9G3tN2vrHWxQPnlD+RYmbezoIi1+dzsVBCH1gb4noV1trGvB4plcGYTYOQhf+asozQCLq2HasLsCH/2N/UFPfNPXRIA4CvSz6JzhD9wAl9Jro6Tg6COYuG0KHAC3qnfGDiOu0tGlwEKylSDsMIaIuqAVoi3BHhHkQfBDlzMN2EPQnE6gUSGZL2QxcwRmvR9Bdaqh6gjMhgCY4FoG1XgAarlFtYO7sV1sGdFgOQtsYYAaAXEZX9L6Or3rJGtusrAiY59DVA6/1dyKAK9gTwb6oPOEY9nKeImDYNaMxrjph4hwEwiCV7I/SHjWE9RIjZS0iWerl1CKLZIn4cRAYr7ad7l0UgJSTNByvFwCPFt4mT1gqsA0I93IQRPkIZ2yyaXMQZEw4BiKBhLceLRfxVA4Uew4AW4CGAKglFP2yj2AmB4FjoOSCYBKKcEAID+AtiiYK2NVBsFmX0HHiAGd7AGRHRFbaaD9bB0EWyTyVMklVxpFogAqaKTPgRFKQ9lgou9C6OAhoaq3VsAOCj370oxeRUIbKmovESj0q8QLKRGGyxGh01ORcHATOPgdRKlpJYP2SH7yFxxie+cwg4NMu/NV0EETtZP0EHMhcyIVIo2yiTB6e1Zr8qpxGUEHJnnJGMqT1yyCEYxypeWVAvUqM2o6xHsUMAl1K13LSADLBF6WN9lXV9cHocPzwRzINDgI7ogSVXYcPyF3zlLMu+EBZD4yhTJbsDNtBkAmAd2QuVSLAN80So7C9Ml1sEp6lq+KwgSaHyJArEQKujR126RLgmEsGoTmGGHPsYbIvTFCTfW1r1odzAPMIPsBPSs4FJNs2TJNXlRWqJOhy9LA+aCjTI2PSvMTNd9NB2HPPQii71UeldUkJA5tqAnm0NvUQcNFbTE44ATiAG8DDZLIJGEjrBeC7KAAetnpjoFzdX9zkByio4WdYgYo2ZguBElHDeBqDbS7qa+2rIPBt8+ddqznkHIjEUwoUt/IZnrPMySqrrLJoCUXsAXogQX/1DcseagIKQF4JktKCek2e8gBlNtKOgLhxUqJ+FqdLSU/aF1DfgwCIWxvCSpgpX3ONmuEmr83WQRARkEoF5gAhUVM0lO1AbyUB+uY84g3OGFqEg0CBcjbj9KlmDTIaopVIDSUiW8VxkBpVngFoxUb4OCXCOnlWBsF3OUQUYF6UtvAaZi4OghQ4kGBjs42LNs3hE7pGcABPCwjgfZv0yAp+93vPKgWcKUXfBRDTcV34ixMcm5RlL+kL44tTipROaIIl9jSQFXqnzUEQSRUpV3Mc+rXuIJC1tk2J6rjpAFFXGTWNfIlkMuJR32xDfz8HQSSXA46+Ahxa/R6ELgAmuK2fPaEjgQ1lBk35p1tlQuKwASCLjqEX6ATZUfRQ6tpWprDwHD/8L06Dg2CvGZ6LPY7ANfkRRWcPybXMQnOPTDODSF7ZHHaB7omoPTlgt/Xf66SdLviDHWZ72TE2lk31t6h4nNZItjg5sV8HDgKMjYHsNxvZVGWhbyCaffMcW0d2lM622a9hOQjk2hiVNArqwmn0B7BPZ6heqDfjE8QQ/KSn6Gnroh/jhVPq9yDY0E2P02vWMnAS3OLndCKbDVM1nYt0EEbMQbD4vFieJDAMcGtSdrxEf/sZL5CQYAwLzkME2kStlcgwSgSHMIrAiQSJwBMAHmMzii1CxGP1TJyE47vAAe+aARBBAgqVq1AeHBKC5WdSfmrbGA4blNpaCBTBk75ktGUkgA+eLOAd8xfxBsCNx1h5vBQIgRHVj9OdOCoinJy8ulNCcZm752PTb31MvkUwKCwCFacYERS08w4DTdGo1SO8kZXxvHGiIyCi7o9SFDGMU48AKaBFX/pWlykTAZi0OU9qKW1gik2GMhgUnvmLJkjri2xI90d636ZgkRTpVTRAJwoZQBHNtEYUgLW1edJcZRrQRCSGAqIk0N8aA1b4iXLiVOAlDqFTi6Rs1WRSSJwPPEcRUzrAik2g6IFmFBVla87KKryHZ+rza6PB8E17tx4nFQDgAVkjuoLyJ6NhbKyhdRVZJ4OcRgCAQfC3/TMAnyAAPYKHyBzAYI2Ba7wlus4Z4GTGEct4nx4QNIjTkuim2fA3OevCX3g5yhrIDjmjr5Quks84Bc5JQHGhUGQ5GWTZgbq+pVvJMqAA2OjT753+RZ+iHT1Qb7GHg86kP9CafAEmZIAsmAtd1dRv+hGkQH/0Ja9kSsDCO75vTTj/nB4/Vypq3PY7hP4wZn+8G61tfnQ9+STv+gkd2ZR/0VdzRl/1ysof7MsAyPCDedCZgkMA5iS2SdUP9bWir5XWkGW2hE1hZ4FPe/vwBf7rhw/oB7KGfzn2eA8v0j10iX4A/OYG3Po9IPhM1roNf5At42SbjIV9Uh9PzvSrf99SUYEvrR2MAoDXA2H1uct6GKd5qzSAHegrtp0zTDbIi+/W7bOKDHrQOCJY2OR/+ktGUSaT/iOvoXfoZ1lMuoqeir2PbCqbTKZVLqA/mggGWhN6zf/tCYjsJn0Ob6CHb8XvYAQVDBx5dKA3wvYKIrPrvi8rGAfI0CfKuAQDYE7f1M983S8zCjpjLEqMgHjgHwNEo4wxlUh5NOAaAzD6FjHS4RiBsPi9tDDmVTsGDBJci81ItZX/AAlSd9EYecImEhyNoRX9wigYCvCIoy19y59eKTF9hIMAuJqj+kCMTqEAwb3mb9xh8ABkpTQAM0cDSPXdukH0LYIpgg3c1+tlr7/++uIk1ekpxSiDRCE4YkyGw9hEzZQZAM91z9rJAWhOwAAOAEomRbmXuYmeiD5SABhPFNA69qIPwayXa9lnAZQ01wO9GWrrqdXXA6gD0GM9KKz65SfowQnzx7gpIEDEPAGKOKuaE6aZAyeVk+U5QAFAsr4Ucf2mV8oTL3qHAlXeJbrB2OAfxqe+od38jHNU2iQCALyMj+vyy6EXKbSRtllDTmc0+c0aiV6LEONfGQJyAojXT7Pwe+tLJ3CoyQzDig/U0eKvufA3GZqJv/AdZz/Gby6yqlocK8rZNnbji6hcm76h3+IUFHwLzPu++Qt8MNpxVHAb//oeHUC/0FtAATkAwsmP/pr6PfSbNaPD6TfvAd1kOuTd99GVXIrwanSxnwNf0UK+ZppfbFpUiqU/ehJAacq/yCn5jjHX9bXv0YXoTq/0umxpVOR8tuOYRP3QpIVAGzkVgRZgJOPknd6XRQIwB8EHHEl8jQcFCdh3fQGjAmXNAFEX/BH6iy5h9/Eie8jOstEAMhtM/3AYODmaygAYQzCtV+MgkH0OAhtmf5LxC3SQeQ5GXb7YtzhFyc85LGxwvUVAU/B2piZQCuDXGxtNZ3EuOCuCjOQcfqPD4LlmC/xizMYPs6ARfRYVG73GQX+wDXQ+58r6WDM6SKYwsjGzlaFRf28sHIRRJ+Jcx9c1JTfX7+T7SYFBKDANAGAQeuSzSYGkwP9RIPVDckNSYLIpkA7CCKxvOggjsAg5hP+gQAKAZIqkQFKgFwVSPyRvJAUmmwLpICzm9VXTpuRHesweC+Uy2ZICo0CBBACjsAo5hqTAaFIg9cNorkuOKikwLAqkgzAsSs6yH/WBcXOpLtQDN2v2Ztl1vpYUmBMFEgDMiXz5clJgoimQ+mGilzcnlxQo+8zsq3SQi3aXfzsW53/PeR21Y05zvZICSYGFo0ACgIWjdX4pKTBuFEj9MG4rluNNCgxGgXQQBqNXPp0UmBoKJACYmqXOiSYFBqZA6oeBSZYvJAXGigLpIIzVcuVgkwILR4EEAAtH6/xSUmDcKJD6YdxWLMebFBiMAukgDEavfDopMDUUSAAwNUudE00KDEyB1A8DkyxfSAqMFQXSQRiT5XLakdsbXebkpk6XobU1W0jcOOg5l/y0XRwyJlPOYS5mCiQAWMwLkJ9PCowwBaZRP9x+++3l8i4X8rnkz8WDSy655AivUg5toSjgEjcX1sYFjo9+9KPLRavj3NJBGIPVc1vg97///erUU08tN/q97W1vK9eftzVM6oZH16pfeOGFE30N+Bgs3VgPcRoBwFgvWA4+KbCAFJg2/eDWZLcEA31uI/72t79d7bLLLuV48gzELSDjjein3LjstmV84pZpzqNbsslJs8F0P/jBD6prrrmm723Oi3O66SAsTup3/LbswR/+8Ifq2GOPrc4666xyb0IvByEyCDfddFO18sord1Zcrn6/4oorqm222abjqPKxSafAtAGASV/PnF9SYJgUmDb9wDlwqeknPvGJ6rbbbqte/epXV1//+terr3zlKyWbkG1wCpx88snVgQceONCL3/rWt8rzq6666kDvzefD1157bbXttttWl112WfXIRz6yOAj77bdfdemll1ZrrbXWok//61//qn7yk59UH/zgB6uPfvSjFUfhhz/84XwObU59p4MwJ/It7Munn356UUozOQizHdHZZ59dcSrcy5AtKYAC0wYActWTAkmB7hSYNv1gvk9/+tNLkC7b3CnwyU9+srrgggsGoqcKiRNPPLF66EMfWu2xxx5zH8SQepApOOKII6q//vWvnXpUqiaAe9e73rW64YYbOr2zOB5KB2FxUH2W35wPB0HGQU3lXnvtVT3taU+r3vjGN85ydPnapFFg2gDApK1fzicpMJ8UmCb98OMf/7haf/31q0033XQgQDuf9B/nvgHPrbfeulpllVUGoqdS6+2337465JBDRspB2HPPPav3ve991f9eI9ZpaZ75zGdW+CodhE7kyof6USAcBBGMRzziEdUXv/jF4oFSWo9//OPL6zxsQqRG8vnPf371gAc8oPz8+uuvrz73uc9Vv/3tb6tlllmm/Nx7auZ4vjz5Jz/5ydUznvGM6klPelK11VZblfe+973vlb6UIC211FLVRhttVGrrol133XUlzeod3rM9Esstt1x5/s9//nN5zGYd/anTJEQ33nhj6WvXXXet7nOf+/Sbdv5+MVFgmgDAYiJxfjYpMLYUGAX9oPxWiYby2B133LHYlk996lOFpgAYAFpv7OCXvvSl6uabby52arPNNivRaKUeAmXf+MY3ys+1yy+/vNhD0d6Pf/zj5TbZRz3qUQXYsrdbbrlldc973rPYOrYY2GOP11577eopT3lKsXcAI5urX2XCz3rWs6rzzz+/eshDHlLeZ6vZUMG5u93tbmXfoDKUDTfcsIxdVl9Zk2889alPrTbeeONF0/n73/9evvvd73632N7HPOYxZbzmowHhatyNab311qsuueSSQp811lijODv3vve9F/XlO5///OcLWPVz41Eac4973KM8gz72XCinQg/zRzvz79XM+7Of/WyhC3tvzuuss071t7/9rTr66KOrD3/4w9VKK61U6LnuuuuWMcEMxuEd31x99dXL2O9///sXGh555JGlbAeeUGJkrcxJ07f5//SnPy3rpS/vHXTQQeX36OVdtf/WBv8+/OEPL/To1e64446Cb6666qrSn3dgIBhKu/XWWytlUtZN6dNRRx1V3fe+962e+9znFl6Zqe2+++6Fx9JBGFsVOFoD5yC86lWvql784hdXGJfCIQgUoU3JD3vYw4oS+MAHPlAYlvJZfvnlCwPav0AIORYEkOLgaFAGUn2vf/3rizDus88+1dJLL10E16YbQvyCF7ygKALg3glJnuVcqLt7+9vfXpwLytl7Um2+Q0jU4enHpmnCvMQSSxRnw1g22GCDUnt4r3vda7SInKNZRIFRAAC5HEmBpMBoUmAU9AOwr+T2Qx/6UHXOOedUJ5xwQtkfAOwDj2zhaqutVgh45ZVXFtuz7777lmfsKRDscuiH02dOOumkAmiBeAE0dott1SdgzF4Bk8p8AV6AHLAWYHvc4x5XAPzVV19dnXfeecUeeh7Y1z/nwubmTTbZpIBUgP/lL395+R7gzVnwLPDNwWHLTznllOq0004rdtNzfs+ux3zYYTXvr3zlK8t7QLcgn285SYf9PvPMM4vtZW8Bdrb/L3/5S/XOd76zAF2Ng+IAFKcempNvcEre+ta3FicA1tAXYG3vI1xx7rnnVptvvnkB7G2NEyCqzsnhLAk0mot1EjA0nxe96EWL6Gmt/Nl///3LJl/BTWtijp479NBDy/gB6p122qngIGtjnfDAcccdVzIKKiCcMHXxxRdXBxxwQHEQAoDri2Non6W/rQu888IXvrCngL3hDW8ozor9BRwMeOeBD3xg5eecJD/jPBx//PFlLQRIYSW4x3PpIIym7prIUXEQMD3F8trXvrYwIsHG6MB7bIbxb8JJMDgIhP0jH/lIER5M++tf/7oAef1QWoSXcFFeUWL0ne98pzgBZ5xxRrXFFlsUehI8ykmflBAD2bFR0gAAIABJREFUIUrh74MPPrgIDcEzLsCfUuJ1U0CRKeBgUCrGH174RC7WBExqFADABJAxp5AUmEgKjIp+CHsHXAKBjh193eteV2wcmwhAApfAs42hAKvGnrJb3mELRYo972QiNhX4F2m26bRXidFLXvKS6pZbbin2UFBM1BtYZON8154F9pY95qCwq2yh8YgwA+O+B9wby93vfvcC6vULvBobmy3wBzTHHIFnYxUxjz0RItKf/vSny3cBffb6QQ96UInMh702J4FA/QPdwL9TmDzH0WG342Sdvffeu3rpS19aADBaAvjwwj/+8Y8yDn36XdCzzuRBL7Y/gLLx+a7n2+jJATG2WA/9Gbs1CAcr8Ib/xx6ENvzi3Tg9KBwE/+foyM7E+vu7l4MA7O+www4lgMr503xLFoeTISgbp1fNpsQoMwgTqRYX36Ta9iCI3kuhERgAX2s6CKIfsg5KenjCsgiUCAXj9IWmgIlUiJLYDCRLUS8DEtXwu3e9613VzjvvXNJ7BK8usEEhypiSEZmh3KQ6jY3CpJyyjTYFRgUAjDaVcnRJgemkwKjoh6a9sxpKP5TJAMki8mwcp4HdivajH/2oRKfVtLNfwCxAWgeo8WwboAXSV1xxxerwww8vf6J99atfLQ4BkO7Eo3AQRO7Z3Xprs98Cet5nN7fbbrvyePP75sehkO1QbiNjALTL6oeD4L273OUuBUibn2YsnAoZCw6BgJ0+2Pq2kxGVLLP5+oQhIuMvSwJsi+Qrq2k2jgggDWPYLyCoKFPi/iZZmzZ6yqpwSMyDMyfYCKArAxqGg6CyAbbh7Mj4cHRkfCIAWp+D0jV4SrmSErYox7KGHCqZEA6lbIiWDsJ06sCRmvVsHQTpVKnWd7zjHcUhoCA4CrGLvukgSPFheCVCzU03hGLNNdcsEQ8gn2Lq5SBIvxE+SopiFkkg8BQO5ZlttCkwKgBgtKmUo0sKTCcFRkU/tDkIVoStAbaVIIlOy7a3nXwD5KmDH9RBAO532223/3AoBM3YT5F4QH4+HITgOFkJtflsrbIf2YxBHATvm0M9wFjnZv1ymJQmKUmKPQnxjL2HaNfWHOVpbWRLOF4wh/0EbQ5PvC8DY68kB0KZklIopTzDcBAESmVBZHFkMThfsFBbmTPHyTPmW98jwHGQXVI+Jqvz7Gc/Ox2E6VR/ozfr2ToIZiKVaIOWPQuONJWGtb+AcmxzEChS6U8pvdi0pZ9I8anFk5pzY3MvB8HzFLR6TDWCUrCEn9DbkJVttCkwKgBgtKmUo0sKTCcFRkU/9HIQlHCo91ePrnadDQL2eh2MIfg1SAaBg6AeHlhUbhItHAIRcFHz+XAQgGfAne0Wcbd/QMBObf8gDgLQbJ8Be87my/K3OQjO7hfkU9M/SIMX1OZ7V7mxfQ2Chm0ZBPsoVEPABtZOdgaIH1aJkXH/7ne/Kxu7OQp4QRlXPfsTcwsHwf85evacRAt+42igu5YZhEG4Ip+dFwrM1kHA4Jq0nRYpUJED9x601fBJC0o7qm9UShSNovX/iDi01QTWJy8bQQidlkQpqJGU3ss2+hQYFQAw+pTKESYFpo8Co6IfejkIymicLCMzoNxV/TswD1hHA1oBRvvllJIM4iAIjgme+Q57qI5fAzwBxjg2fD4cBGMGnn03SmSaexCMpV+JkbIeEXp9ybAI4kWDNxx/Dgcoz7KP0cbgaMq3lBq11fALRirPUb6koZV/23BtTdocBBkHDkK95n+YexA4ccqDNM6IwKWSLPsoll122f8QYN+2IZsjiD+0uIdBkJUzZk+Glg7C9Om/kZqx1JbaOZuI1f5FWg+jOsWAh05RKQki2G7xw9yEXxTfhhunN1BoTnjw7+c973lFuD2HwQF3m6vsOyDIHAg1d7IOduU7Su2YY44pKUNjUTJE2G1c5pGrUWxGINT8OdbN76QoCVu9URxKjoxdjWDz/ZFahCkbzKgAgCkje043KTAWFBgV/RAOgs29NvwCxbIB9r2pkWfj1MSzmYJhfu4YSs+Jtj/hCU8otg7gczKPoNlrXvOaO2W5v/nNb5YafUdpKuWJ4z09xwarwxc4YzfZS2UoMuWi4PYqiM4rN1LSUz8a1ElJItjGHnsAAGT2ma1nl7U4NMSmZ+XCwLR6fXbVPgpg3d5AgBc+4JxojhZFEzSyARodlNUA6050Yp9lHpwABUeYv9p8eAFdOB9f+9rXiqOgdAo9bSTmWHnHSVCwQbNxAByowumw/wCwVvLlb6U5MhLmImhpU7efmxeamTdacKzQAc5wqAl8ILJvczcnz94LQH+FFVYoex05ZvYGKAvyfQ5TnJxoH4Nxw0nwClr4DgdH+VQ9QxBzsVbKouyjgFEcqoJ+1lwfMjf6V4ZlnXxbhsI+i34Njc3ffPBk/SZuZVaqO2SfzLvutPXrd9i/z4vShk3ReegPSAeyefJqKXnxNigB54RNNMGJQ4SVMIv6xz0IPHJRfEqIEDjyDBNrGN1xarFpWISAIrBpiyIkOARDeZL3REgAegqX46EEybf8TSE5DpWgNhvFQtCdFGGc9WbsTgQwPgqvWeM4D+TMLjtSYFQAQMfh5mNJgaTAAlJgVPRDOAjKfYBV5Tf2xrFR9aO0ldMocWHXgHX74DgGMgvskDIbwTRHc8qgA4fKXWTNBcDYWkDOYR/KcgA8wTR2F4AHqEWUgXWg0jNsN1DNRvomG8hZ4Dg4Px/QBwQBYkE9AT421ziiTl702lGrsiAAv83F7C377TlA2fGr7LqNzWy4uxiU9nBebApms4FxgDjuUWJ3OQm+H2f5mx9Az4HifNicC7Daj8jpsalXs/cAtkDztgagC0AaL2Au8CdDY8zGCpPAC8bIgeHkoLVqA6VZHBXfCIyDNtbS2nL07LXwtzHANTIpshyOWheIRBMOAlDPUbFh2nopV/J9oNt4rHGvOZgX0I+/YB9rpjSbM2PssJM19d0ox+YkWi/0a3M69Ine1sUJkRw073A2zFdDGzypNI6j2+8+hfkU+XQQ5pO6Q+qbgHIMlOlEI8S8T55oNAqKh4tpo1EOhAKwp7hkIgg9pnOhiigKJhXlwAx+5p3YI0DRAvgEk7BLxcWxZVK3BDWaM4mjjKk+dc+JNkjvNb1rCpRyZWwoV+PJNhoUGBUAMBrUyFEkBZICdQqMin4IB0GpCPvGXrJVbFz9MjBjFwyTQQ97xkmwSZV9ZU+jAXecDcCWXfSn3thff9gr0XTR68i8OxYVUBXsEl0WiY7mZxwXdtDP/T5a7PWLi7/83NjQ2fiieVe2n+1VxgPUmyvngi01Ls2Y0UOz74Jtb17KBUyz/7CFqL7xAMP2JsYRnt7Xj3HBF5r5+WavvYSqDTgJ/tanSDu6+BMNJjF+QFtfEfmHOWL9YBnrZZ38zFj939zRJY5Kl0kA+OEJGMUf2RS0cmpjHN8K3HvWuH1X9kE2oVeLeXNaBEplInwz9rE011c/1sP61OlX7x+f1dfT7zxvPJo5oguHj5M40/jmWyOlgzDfFJ7y/jG79ClFwUtenMw+5Usx8PRHBQAMPPB8ISmQFJh3CoyKfui1B2HeCZAfSApMOAXSQZjwBV4c0+NpS0mKVshqXHTRRcU58O9s40OBUQEA40OxHGlSYHooMAr6QQAq9tzVT5WZnlXImSYF5o8C6SDMH22ntmc1dPZIqLOzmce/1SNm9mC8WGIUAMB4USxHmxSYHgqMgn6QnWZn7BOwCVY9ufrvbEmBpMDcKZAOwtxpmD00KCCqI3ugxjDq/NQgZhsvCowCABgviuVokwLTQ4FR0A+O8La3Lhp7o8Y/W1IgKTB3CqSDMHcaZg9JgYmkwCgAgIkkbE4qKTABFEj9MAGLmFNICsxAgXQQkj2SAkmBVgokAEjGSAokBXpRIPVD8kZSYLIpkA7CZK9vzi4pMGsKJACYNenyxaTAxFMg9cPEL3FOcMopkA7ClDNATj8pkBHC5IGkQFJgUAqkgzAoxfL5pMB4USAdhPFarxxtUmDBKJAAYMFInR9KCowdBVI/jN2S5YCTAgNRIB2EgciVDycFpocCCQCmZ61zpkmBQSmQ+mFQiuXzSYHxokA6COO1XjnapMCCUSABwIKROj+UFBg7CqR+GLslywEnBQaiQDoIA5ErH04KTA8FEgBMz1rnTJMCg1Ig9cOgFMvnkwLjRYF0EMZrvXK0SYEFo0ACgAUjdX4oKTB2FEj9MHZLlgNOCgxEgXQQBiJXPpwUmB4KJACYnrXOmSYFBqVA6odBKZbPJwXGiwLpIIzXeuVokwILRoEEAAtG6vxQUmDsKJD6YeyWLAecFBiIAukgDESufDgpMD0USAAwPWudM00KDEqB1A+DUiyfTwqMFwXSQRiv9crRJgUWjAIJABaM1PmhpMDYUSD1w9gtWQ44KTAQBdJBGIhc+XBSYHookABgetY6Z5oUGJQCqR8GpVg+nxQYLwqkgzBe65WjTQosGAUSACwYqfNDSYGxo0Dqh7FbshxwUmAgCqSDMBC58uGkwPRQIAHA9Kx1zjQpMCgFUj8MSrF8PikwXhRIB2G81itHmxRYMAokAFgwUueHkgJjR4HUD2O3ZDngpMBAFEgHYSBy5cNJgemhQAKA6VnrnGlSYFAKpH4YlGL5fFJgvCiQDsJ4rVeONimwYBRIALBgpM4PJQXGjgKpH8ZuyXLASYGBKJAOwkDkyoeTAtNDgQQA07PWOdOkwKAUSP0wKMXy+aTAeFEgHYTxWq8cbVJgwSiQAGDBSJ0fSgqMHQVSP4zdkuWAkwIDUSAdhIHIlQ8nBaaHAgkApmetc6ZJgUEpkPphUIrl80mB8aJAOgjjtV452qTAglEgAcCCkTo/lBQYOwqkfhi7JcsBJwUGokA6CAORKx9OCkwPBRIATM9a50yTAoNSIPXDoBTL55MC40WBdBDGa71ytEmBBaNAAoAFI3V+KCkwdhRI/TB2S5YDTgoMRIF0EAYiVz6cFJgeCiQAmJ61zpkmBQalQOqHQSmWzycFxosC6SBUVYUIF198cfXnP/+5dfWWXHLJ6pGPfGT1tKc9rfrv//7vBV3hL33pS9VXv/rVRd981ateVf79y1/+svrkJz9Z/r3FFltUD3/4wxd0XD6GZt/97ncXfRd91l9//aGN4/e//3113nnnVfe85z2rzTbbrBPtrSW6POxhD6s233zz6l73utfQxjNtHU0zAGjydnPtH/rQh1YrrbRStfrqqy8oj/3lL3+pPvShD1W//e1vy5Ce+MQnFtn4n//5n+pTn/pU9eMf/7hae+21qzXXXLO6293utuAse9xxx93pm7vvvnuRxWG1a665prr88surJzzhCUXX3OMe95ix6zvuuKOiQ7/3ve9VT3/606snP/nJwxrK1Pcz6frhW9/6VnXZZZf1XOf73//+FRqQtQc96EELxg90wCWXXFL99Kc/Ld981KMeVT3nOc+p/uu//qtgmcVp//75z39WV155ZfkTLXTUMAj073//u7r++uurz3zmM9UDH/jAaptttumrf//f//t/1dVXX13G9JjHPKZ65jOfWS2xxBLDGM7E95EOQlVVgOiXv/zl6jWveU113XXXlUXfcccdq5133rn6yle+Un3wgx+sGBqM/u53v7s4CwvVGMT99tuv+trXvlY+SUC097znPdXLX/7y8u+3vOUt1Qte8IKFGtKi73AOgJJXvvKV5Weclze+8Y1DGwfnYI899qjufve7V+9973urbbfdtm/fb37zm8s6Ut5ot8wyy/R9Jx9op8CkA4CZ1h1vf/GLX6wOOOCARY+9733vK6D7Ix/5SAGdD37wg6vtt9++OuKII6p73/veC8JGHIHTTjutet3rXlfddttt1W677VadddZZ1c9//vMCEn7wgx8U3XXqqacuKGiJyV944YVFR3KwNIb5KU95ytBos8MOO1Qf+9jHihN0wQUXVA95yENm7Pt3v/tdtd122xVwsPXWW1cf/vCHhzaWae9o0vXDL37xi+rTn/50dcIJJ1Q/+tGPynK/9KUvLY7mRRddVH384x+v7nOf+1Rbbrll0QFLL730grAEHfCJT3yi2meffapbb721evazn13so+Dl4rZ///rXv4rj8q53vat629veVv3tb38rNtz4htHM/aSTTqqOPPLISuD2iiuuqB7/+MfP2PUf/vCHsm4CK4973OOKc7WQGG4Y815cfaSD8L+UB7zf/va3Vy95yUvKTw477LAi9He5y12qt771reX//r3xxhsX73Wh2p/+9KeiCM4///zyyXAQZBV23XXX8rNzzjmnZDcWR7vhhhuqFVZYoXx62A7C97///QJ67ne/+1XAmWhtv8ZhQReg5P3vf3+JMmSbHQUmHQD0owpjJ0rNCGl4fbnllqt+8pOfVHvttVcJKnBEGcJddtmlX3dD+z39w+jKIoaDIPv5whe+sALQ6apXvOIVfSNrQxtQraO///3v1dFHH70oUDBsB+GUU06pXvva11bPf/7zK9kKUdOZmmgrvUQXcKrqDt98zH+a+pwG/QDg4ufIjL3zne+s9t1330pUeqONNio64K53vWtxIvCWYNZCNJlCuumWW265k4MwKvYPCCejf/zjH4fqIKA7HYfWcAcnrV/2hk7iOB177LHVVlttVZ1++unVfe9734VYprH/RjoItSXEOC960YvKT8JBUKIiWsAgyzSISEs9ih4uROMgUEii6Vo4CAvx7S7fwEAMxXw4CF2+n8/MHwWmAQD0ox4jrKQlHITll1+++sc//lH0gYiUEheZvGFmzvqNiYOw5557VjfffPMiB6HfOwv1e8b49a9/fTHG2rAdhIWaR36nPwWmQT80+ZmDwBHXgM7Insvyv+lNb+rrsPanarcnejkI3d6e/6fmy0GY/5HnF+oUSAehRo02BwEAEL3msf71r38t3joPNmrYfv3rX5cyJCl+wAGA2HDDDaulllpqEaD/2c9+Vn32s58t6Tb1b8qV1OWqY9ZE//ShTlYEQgpTja1vtDkIFo0Aes/41ltvverRj350KYW6/fbbS5/qo0XRlUn85je/qdZdd91qtdVWu1NdMuX3uc99rqRPfWudddYpz8zUzPHzn/98eYfzJL0X+w6aGYQu/aONuYuGisY+9alPrVZeeeXqpptuKrWU+tDUWaOJCMJ3vvOdEtU1d/sMvOt3lKYISjTOnj41qcmrrrqqlB1pUowiQA94wAPK/7/+9a+XaJC1ie+pp0Q/fchkLPT+k8WtqqYBAPSjcZuDQJ4AdDIoEiWDENk8/EmOv/nNb1bKW/DoWmutVeQq9sPgaTx/7bXXlrKENdZYo8Jr0t+xb4BccEx8S1Bigw02KDpDFrPpILzjHe8oJT3e0UTWyEtTHkTPfOcLX/hCSc/7fzP6pqwCz5OpZZddtsjITKUTAhY33nhjKbmiC/EMOVLipDUdhH79kz9yKghD38rY0If2WJkj2mp0p3IjGQTfRRNj9qyMo/nRxXSib2rmo2w0Gp1N/6Gx/mRhrYEm8jmoPu3HS5P2+2nQD70cBPZE2YpSGuWFslMHHnhgsaN0gDIbOgCP2UP32Mc+tsiSf0eLunj2hXzjbfsJo81kP9schF/96lf/Yf/gCWV1sWfJt5Tqkhc21nftoag3z5JnGVP6zbjtc5hpT5NyR1UNETylZ1796leXEqhmiVG//oN+ygLJNhtNh7LxsrfwVzR7nOgn62HMdCr9B0PAQvYc0bWxj1NJGB0QlQV1PKNP8k8P0CGaPumjaN6l777xjW8UjNdlD8Q4y306CLXVazoIL3vZywrDySaoSX7uc59bomMrrrhieQtQ9Yx62Cc96Ukl9S3lpy7ZvzGurAMmfsQjHlEMMmDN8EtbYnhKQE2xOmIKx/ME+phjjimglBPQzCAAHsD42WefXRwKYwKGZTrUH2uADWEgGITXt9TsEnQtFN9HP/rRUi7BuMa4fLdXEyk944wzqmc84xnFERJFVQ+p1R2ELv3bV2HODDca2ksBBKEdIVVnKGKjqWGkaCjRF7/4xcXh8QzhBQBEcygJkRx00Cg4IIFjpjRBbTQFbC8JegMR1tz3Pauk7AMf+EB5V3rUWll/BmD//fevDj/88AWLEI2CUpkGANCPzk0HQTkBHjnxxBMLWMfznIVwNMkyB5rhAHDxI3BgX8zznve88o4a+uOPP76UDqqhJz82z9ElnIgf/vCHxbgycN4nY76rBJJhazoI5FGqnZ6o1ySTB5H8kCH9C1ToH1Dh1HBuopEjMsapYQjJC0NJRuivtsZYkmF9KmuKGuHYQFl3ELr0r5QDfTj+ZBJt6F0ATC0xHern9Jm5cBDoCX0bM5qh10EHHVRtsskm1aWXXlrWB9DgPDD4GtBA3q0R2VZrbv+GQJCyLaVJg+jTfnw0ib+fBv3Q5iBwTGX08R3bin/CjilLxLNkbqeddiryzJYL9JFlch38h9ee9axnFTvGUSVD+LWLfW5zENrsH3uqBJn8cAgEBIzr3HPPLU6wsl16JfYJwRvG+O1vf7vMSTCBLLP7vUp8vaMMm402F3sylPSRKY5C3UHo1z/6kVH4SFAAFiGn5PzMM88sQbqDDz646Agt9Ass5J1DDjmkPEMH0KmwBIygdDwCMvr3O43OFDSkrwF+fdAxMB/8BuP5d+ypQhvl3oAz3SiDFAfHTKKMp4NQW9W6g8BIAt+8WcZ07733LgohgIDXgGQCxNCKmGFSjCjiR1j8XkRb1oGAHnroocXwAKd+x8gB6BQOYM9YEyCRfAyMyXntbSVGwDTB0TgIyhxE8wAJzd8ii5je5jyNgxAbfU8++eQCWjgMBIQgUFZAs2h62+Y/EVPGmodukzRQb4wRlas7CP36p3hlPigpURjfRgNRA8DExkJKljOghYMAQFBW6K3um0Nm3ShpisEYgC8tHATKF0gAIkQvKEn/pzht7vY9rf49ToT1pBwo7VVWWaUovGnKIkwDAOin1OsOAkeRjDF6ADP52nTTTRdF1vAX2QDi8Su5jz0MeBufchBEGhkcfCnS5W/Rf8CUkcfz5EqkjIGkFxhFPA9ktJUY9So5YMw4Jhq+x8OcG04OHUG/afSSsQIpZIw8KZmgYwD/KBlq0ov8yLBydugkGQD/tpG7bsC79C8wwjEH5NEP+KILgRffoFf9ToYiHAT0QkMGHn3obHJrHTgIHAO6Nk4xAg5kDOhzDoB50cv+LZADiNDpMrKD6NN+fDSJv58G/dB0EGQI2GQ6gK1k1wHgyFRzYsk65xUPs+1HHXVUsUl4MzLUnAPAHK8C7OSfPSb3Wj/76TttexDa7J/gJnsno0GHAbkwjbHj8Te84Q1Fv2jknX0VDIvAKPmjD3rt+aGn9C84wZkQlBOwM+/mJuV+/XseQIcDgH+VELANhwOWkm1tljCSVXrIOBwyIyCD/pwcAJ4OMD52HOgPB4HDAVvJolgr+gVN0Umgx7rSL/U9VfQO/Yk+Apz0TARDJlHG00Ho4SDEHgTpdgIkEk+4MAcjJjKA2RlCBohgAv7eAx4wHAPlmDQeNcXA8GN2afPYzIQ5MSxmFMkXAeNkqC8WwaZ4ujoIpgKAaICLKAEj29wjUC+RAEB42cBN1FaKjkgrNpsxmqNIAoNNWNr2IHTp39wIL4VL+NuyFnWHre4gUHDoTwEBVZygSH/6XWwo4yCIoFAo1lCLPRwUhjn4PfrLCtW/Z51FUCiCen+U37S0aQAA/daymUEACvANA8jZFDETPZOmB4I5uqLSeM7PyD5DAqRzhIFyDgJ+luHjZHAkOBV+Ry5WXXXVkuFj8PAnZ4KR8hwnfRAHQVBARlPzLTLctkeA8eRwcxgAB86/KB3el3Uz3ua+K8ac7NFZoS99Zzb9kz2BDLSqn8rSXJ9Yj7qD4N/0HNrTCbK5Uc7V5iDU6Rc15VEzje4ivrItXfVpPx6a1N9Pg35oyyAA9Ow+OZFBU5bCXsABsamZw8mxJjtkF+Cs259wEAB1vMZhlcmmA7rYzwCzzU3KTfvHXnGOyT3g6xuAMzzT3ERcl2fBUGWKypYAdsCcjERZdPC0Ep02+9q2B6FL/4KTvqtFgK8uP217nAQ5BSXoK1F9gYoon4SH2hwEdFH2yaGo65vQL6GvrU9dn9GT3mtzOCZRztNB6OMgYBBMATxL0TGSlANGwvB+hskwIY+XV1p3EHj6mImjwBvFXNJgGJgR43Rgel4vA1evUeR0UBizcRCCwUXomw6CVDqFYbwckHAMkMJ4REB47s1GEYow9nMQuvQPVIncy8gM4iCIlMpYqHUEZpQiAFzKnTgJTQUpairqGqVDTQfB70VtOHNtDkmbwp1ERdA2p2kAAP3Wsm0PAgdACYssmpQ4YAv80w2cBkqVUfY7kUARvJBHNb3Ag4gdA0je8bP/i4TbdyNLp/mbXohmPZQSztZBkE0gL20AXgkeXqejZPbqR5Mag/FzaOpN1hEY1/o5CP369z37swCSQRwEY+IUAPocFXuLADOBA7qtzUGgb+g/+qPpIIiCAmvWs+4gzKRP+/HQpP5+GvTDTJuUZaziVCw18kqLAGh8yE7RA/YgKN2NkpiwP8pe2CVlL3SAgBw+FlToYj/hgrYMQj8HATCmd9oAfF2ezUeQIAKZMudwSFMHcMzb7Ots+xd44XgN4iDQHWy4gCM8xkmArzg5nIc2B0GFiOxkU9+Evrd3Ae7TV1Nf0jHpIOy5Z4ksT1PEtNcpRtJ/DIrUtgiiqJroMkVAqAl5RP39vO4gxLnAIgyicDxuaSngWPYgIv6Mo01xzU2DvU4xaisx6mrQmhEFACZSpDMZMwaB0uvnIHTpPyL46Cm6D3A1WxtgZ9RFQqRBRVb9H6CRrrVhuqkggTYKDCioR3Di+yK34aCkg3DnFZgGANAPvLU5CN6JgwuUBNpYT7Y50HSBUgGROg4+vQEgBMAkZxwMWSsZSLwsiyYqCUQrAYqIP35klJttPhzwk43UAAAgAElEQVSE0CfkSQCDUezXRPtig2M/B6Ff//USKX2iZ9v55s0MggyGiKvMpjHTyXSos9I5XvZrNUuMBAvIv6xu00FoHssY+jkdhP/khmnQDzM5CLIHgY/IMB5WHaDkBC/6PyeCYx5lr+EgxGEGbF8cYY7fVSwAtPWIf5t97lVSOBcHoS7PcAycwz7P1HrhkzYHoUv/9fHbVyUDW2+9Tknzc/hMxl/gU7BQwFUwps1BiENZ7L1syyBEhpK+TgfhdYvusbjLv/+XgynQdBCOKFF+gBc97FwXAST06lUZcnsIlPMwaBhahK/uIARzq1lUMqCMCKjwvD6AWyfy6NfGObXvmmfDgRh2BiEUkKwGY8pgEibNfgTKq+1yI3stgOl+DkKX/gkxIAIsSSmKxjL2IphxcksbYA+Qz7jHpTB1ejcVpI1OFB1ebnMQ/F69I8WeDkI6CE1j2MtBkAFjjGQR1cVzGETbpOJlEWQS6YxmyjoccWoWr8t+4TtyD/grU4qDBGTFbHjW/Jyc0gXz4SBwtsk3gFyXR4Cbg2OOzeih4EeU9vVzEPr179ABulAUk85VkoF+IqXGpHHmmw5C7AmK081kXZRLctI4YDICTQcBCNO3YEfTQRDAsfdCzbWWDkJveDjtDgLnnrxrMgdsJxlRDw8LKCdWXlQHmM1jysm14JZn4AIgWnCBgzCTfaZHhp1BqMuzzKCaf/JFJvwbHonTfYIrAHMR/yjDjfm1OQhd+gfyZTk1WRX7s6KpXuCQ9TpG2bfpCnIvAAsj+Gabg6CkWCknfdPmIMQBDjIN6SCkg1A2HfHyGX0NeMX4mMxGogCzUm820zHujKYUFeMura3mn1ATXpvf1MURLEIvQq6cRimB9DVAISoFcEtpE0KbbWQaMDXFIU3mO36vllgzTkbZxSxSaBolJL3m2ShRssmXkBCOKFPwDOOnmavsBwE3fgDau5RegOmmaTAX5RQMNVAj60GACZjGYdIvw9Gvf9el21jI+SKEDLayADXI+vd/ijMMNcBF8VJEHALjACSACoAMPYEM64Q2miPRgC0OgHfVd/uZDdj6lxK2bqHc6inj+B4lDwzW++ttMifrN9MAAGZaMTKIv2QFNPxDRqXoZaVE+0XY8Ai9IYodEX9le5zu2LyHN+0BYPzxmfS9n+FdAFamS1CAkyGIoCSBwQMU7A1QN0zORBl918/pHr+jQ4yNAVeT7LsCEDbk0UlxiIA0PABNvkMPKHWQAWFYldmR/7o86ldK3RjbmvmSFZE++obu4qT7mUbX0XN+3q9/MoyunC6Ou70Asq2yLXSLDA2aCaSgA+eJfvY9+pZekIVFczrRupgXoKEMVEmBYIyyT6Uh6GGDMj3qPc4aACLqaM6D6NPJkvxus5kG/aCEhrMZewhk+fCoyDm+w9/sEFuPB51CiGfxO16VRZQ9s/FdY4sE39gp8uriVX3hWeBYEBJv97OfveS9zf7RHfqnxwTCVETEpnwONLtL9jjD9kMoizR++sb/le3YC0UHtB11qixS/+bO+YaR6CRYh9MjgMoR9232dqb+ySYHAWaq6z9yq180qzsk9FdsiqYjgH6yHvoR/qETo8ybTjRGmEjmRpCWk0d30R/eNwbYQ390QFNfRokRPFTvr5vUjNdTuQehqkokCQB3Woh/azYfiqTZLMxAMZqYhwAS4BAsghaAlbArNfI7m5MwGaEnLJQFZiNMADnF4T3KgcerHylLmQRG3O8ZRExI0NQlapSNyCLjGceLMoAAim9RZlpsnBLJAEw0oMSYRAeAC54xsCElCjRzeKTmgZhejTE1H3sUCJdov3If7xBUm439vEv/5uZdyoChd3KT/Q9oSEjROk5D4Sx5llD7JgFn/GVwOEPesXaUNyWsccoAAFEPAABoYdRs8KJY0AgNZC6MwTrEPQq+530KMu5O8H8O3LS0aQAAvdZS/SlnHyCI5pABvIO/yCODR/bwHuffxj9gGH/SFZxbxpjDSr445JxiZQVAhZ8xvgCvZxkkBpgBA+B9h5Mi2iXlz3GITcTk2O/oImCYvMXRqJ4np+QJaKEDNGMDZPwuzgXH33QHp5ye8zwnnV7StzHJDsS9L0160R2MPvkBNOyx4tyQGbTioNNf9EW//jkGnHXAXb9OFALqzZ1zQVcJlpirzCeamY9oH7rSIcYuiAJE0LMAHLrQJ0CONeDUcIRkIAUMAB97mkJnCHzQLfRmV306LTqhPs9J1w+ANbuBH+0l0Nh49pf8s7ci7PhHQIA+4PBzlskDG8WOkAMgMy5YJP/4ikwJOLBb7JFgBFnWZrKfggky7m3yTtbq9o9uYq9hG2Ojp+gjgTmg3ZjoKgEFf9NX5D3uTzI/9pzt63UfCl3BgXLcMvBMJkX72WZySu5siAb80XGm/uk3QUcOEvpyVOzNIuNsu/H52+80Tj3MILsa37I21o6+hg3ocnpOUFKGh50XMKRffMdcYQEYj45y8pRKEP05DamuL32PjrNOxqI/uER//cqxxlFHpINQVaVOFUPFpVzNhWTMgUgKoJ5iwyAEgXHFYBiE4VE2wzhzABgmhpzxQ2xMSXHUL1IjYJjXOHxLX34vva7/+jFaovwAAMNWHy9FI7VO4KMxzs0juBhNQqzFd3nZHBNGPMbVi5nNjXIRLSD4viFNR2kBPLInBK1r/2joD6OuP6DE+0AWox3HwunPxiFzRmeOm3ETYvSyLpSPes1645gZE+fMdzxDCRmv+cZ6+p26zjj2UR9t9IsTFsZR2Acd86QDgJnoQR8A722NXJIX8s34B7/jVRFAUUe6gpwCmvjY82QPKFD36m8yLMKmD8/GyTtkAX/7Pb5lpPEqviffQLD3ooXMkploZAnf0x/11uRp+kQwhF7SyAedQc7Nj65ycEGvxoADM2QnnPxI9ZNNurG+v6lf/77LwJuLsdFroa/a1gQ4I7toY64yMGiJptaJI0QHR/McgKfRH8ZtvfwcbeJbdLZ3B9Gng8rXuD8/6fqhzZ7U14ztYPPwTMgP+yG6L9hHBskPGWYjyYfgHNvD3itLggnIj5+pe6+DzF72GU/qr4u8y1aQi7q+CHnUfzTjIjNkV7/GRy7M0bjqx7u38a1nyW044mSfQ+J97/pDT3bpn/5De7Lp+bp+RKsI2BmHPmEGdLcGMe7AEnSAn9Gl0czffOlkuMzv9eu71lIGKNazib98z5p7vq5/HYNeP2Bm3GU7xp8OwqSsZM4jKTBkCkw6ABgyubK7pMBUUSD1w1Qtd052CimQDsIULnpOOSnQhQIJALpQKZ9JCkwnBVI/TOe656ynhwLpIEzPWudMkwIDUSABwEDkyoeTAlNFgdQPU7XcOdkppEA6CFO46DnlpEAXCiQA6EKlfCYpMJ0USP0wneues54eCqSDMD1rnTNNCgxEgQQAA5ErH04KTBUFUj9M1XLnZKeQAukgTOGi55STAl0okACgC5XymaTAdFIg9cN0rnvOenookA7C9Kx1zjQpMBAFEgAMRK58OCkwVRRI/TBVy52TnUIKpIMwhYueU04KdKFAAoAuVMpnkgLTSYHUD9O57jnr6aFAOghzXGuXHrlx04VIbiGtX6RW79olaGeddVa5GThuOpzjp6fm9c9//vPVy1/+8nI7qlsqezUXqLgyPm6enRoCzdNEEwAMTliX7ZBvt2u6yTP5dXAa9nvj9ttvL7eh0gtuPu11iZMb7ekDlx25Md3FVNmGR4HUD71p6ZJFt3i7TdjFor2a504++eRyqZqbwSfxsq3hcdyde3KbtBuNzznnnHILdVujj92UTB/DXy5dy9adAmPhILidz3XX5513Xrmdz/XWbtA888wzq0MOOaRcpe1mO7fmrbXWWuX67HXWWadQYe+9964uvvjicpux202bN+12J9V/Pul2QgbouOOOq9zYSyHUbw2NNzgRfveWt7yl3MJ3ww03zOWzU/eum2OtqWvjN9poo9b5u56donU1OjDAoRhm+8pXvlJAidsib7nllnJDLH46/PDDq2233bZ8ym2Ofn/JJZdUV111VbmJF0+6QRd/uCkXaMG7Rx11VHX55ZeXGyPdnrn88ssXQGmOo9JGGQBYj0MPPbT6+te/Xm4qpfjpAmux6aablhtKybwbPXfaaadiSPybLG6yySbl94Al/cDxHFZzwyk+ZLQ++MEPVtttt91i4ddhzWcU+3Hb8imnnFJkDJ3diNps5Orss88uegN4eO9731tupB5me+1rX1sCPsFrbmB18/MZZ5yx6EZmN8xyGD/1qU+VW7bd3IoP3V7tj58973nPq66++urqtNNOK86MG26XXnrpAhrf//73l5tiR7GNgn5YbbXVyq3FAnTouvbaa1cnnXRS0ctwAVoK2m288cbVu9/97vKMm8L9/qKLLqrudre7VTvvvHN1zDHHlN8No7mJ+IILLqgOO+ywYid62fvrrruuevvb314BugcffHCxL3Gb+jDGMel9kD30I+NrrLFG63Q5aHAXGbQO7OwwG3uy2WablZvvBYHdwOxGdhhkiy22KJ9y87XbtS+77LJir9zcDMOS6zXXXLPcFg1LsFurr7566Ud/eNNN3Ntss03BC4ujjYWDEEQ+99xzqz333LPacMMNi8JHVB4ixwE4EFkKwx/EBB5OOOGEQnBAYNhRJApo3333LZ/r5SDEWChULR2E+WF1wrf++uvPi4MQI2Z8KHNr/ta3vnXRlex33HFHdf7555dIBQdgl112qVZdddXiIPzsZz+rLrzwwmK4DjrooOoVr3hF6c7Ptt566wJgOJoAxii1UQAAM9GDswXgMxTW4oADDihKVRABjQH03XffvXrb2952p8gcZe33++yzz4wR/tmuxWc+85mip6x3LwdB3wvBr7Odw6S8Rx8w2vPhIKARu7LHHnuUwASHvx7JFNhicwQ4/j979wJ9W1XVD3ybYJKVGvlIRBuJCIgiIOALwsBARB4hD+WVICpvQUEUfFCiaBYCQYilpqIgIvJQjIeaYpSIPAxF1BqGVlhGZSX2sP/4rP7zutju1zm/c35n/+5da4w77r2/3zlrrzXXnN/5nXPNtRe8QFL5LAHOTTfdlGxe8oq+SCIIHo444oj0cz4LVqy11lqjXYox4MO9996bdu6RxAMPPDAFARouwP6Q7p133jnJNA8Q/f65z31utdNOOyU5I2yzbkP8/d133538hZ3GEiDMegX+rz9rD4/nESAEN2XnAhFredFFF63SJ7zg0ksvTf5J0hpW0Av69o1vfKO64oorqvPOOy/p4Mte9rI0XnxWovBRj3pUwpS2HdL5SOu+va6YAMGwAaioi8AAPgKmAeKXv/zl1WWXXZaEvv7666+ape8g7htvvHHKHN7vfvebqVxLgDBTcS6ps+UgXDJDe++9d8oO5YDu58cff3wKUGQ3ZY7yJlBFWGU9AUIQRBkEjmpeBGYpAh0DAegbv50joCxBAKQjA/e5z30uZV4QL6Uoebv55ptTdtGWftOOX98z+35fAoQ+CS3f7+cdIAT+c+o5AUFc7QjKUsMDgeLaa699n4kLcO2CwRM6rL3mNa9JO9LwAJkYcxsLPlx//fWJANoBEIxFI187t3btkcQ8e2zddt1110TQ5kXASoAwDu2dd4BgloIDfIAtK2WKhuBLUu2yyy7Vaaed9hO6JsFtfJIGAgQlZuE/7EQooVxkW1EBAkGJxE4++eQk1H333XeV7GzPAlt/lCNFU3okw+A7tm1n3UqAMGuJTt/fogIEGetDDz00ORsBKsNuakqQ3vnOd6YsoWa8JUCYfr1903assiKZmssvvzyRAe1HP/pRpfxAOZgsbWxB+znyZS2smR2HWbcSIMxaotP3t6gA4eqrr06lKxJTaqDb9MzuohIZu+IlQJhunflgAYIEobIdZRnRlBaeccYZyeb32GOPVT+3e+tsgFKjebUSIMxLspP1u8gAQVKafSsjE6w2NRz1zjvvrLbZZpuU4CoBwmTre59P33777al2U3bw7LPPTvWnyMFJJ52UyJfdA9nDIArqvmQV1ItGU5ssq4hQ2OpVA6bPo48+OgE6oqeGXUYIyIsI1TzLUupLrVhkfNsCBLsaxsc5OKuw6aabppKSrppE9fUiTs7EVrTv2mYyHtGlLLQ5mjulMga1q8apX9GrbVbkR83ahRdemEqw1LGKYAVO6nIpbFeGijzUaSrdkKFVZ0fOtsmVzjzgAQ9IY5GJdQ6EcqsBVX8rK8sQbI1bK89x7kP5h/ps/7e99ra3vS1lzaIEx89t01tL9eQMC4GzAyTbK6tmDaPeH9G79dZbq7e//e2JlEe9nu29/AyCcalDdC7A54ydbPSvNlDZmm3pAw44ID2LQxFkchzGVm9NOwgMWrZPraszCENbCRCGSqr9c9ZQZkYSgJ3YjdHYBJ33t92eWE814epWrb96T42+z9KemgKEofqazxTZMU72t+6666YyBI4GtshW0Vu7qErbJD+67JGuwUh4odQl8Mv3ZYngJvtrq9Nn7zK19N9zZGc33HDDtGsTO7nGyf5gqzMAnB27ghmyu3Aw7I29POYxj0mlNPBDuZfxOY/DDmXk4Y91UYuvTEgmnhx8ntOXFFIqEg0W252DF+zeuG644YaESzFfeGgXDzaSic8Znx1oc4dVZAyr4A0sEWT6flOdc9MOgmfYPYhDlJJTQ1vZQRgqqft+ThBAzkoO6YnGDwoKPv3pT6efwwj6FXyBbw8M8Hm7Pj5rp1fgBiuQNgmdzTbbLPlvdqeGHBfwb7bInvjUrbba6j4Z36YA4a677kp6y5b0v8UWWyS7Yw9NJUbf+9730hkVvIA/4lvpItvhz/EBeutvHEb5JN32eWchHv7whyfuoE6eDbEJZ+PYlOf6GRsOXIEP5tHW9M/28CFjs/tC7nDKDhkZ4gxsl//Htbbffvsk/yc/+ckpcWPuzulIrFkrL3mBYV4uotojAjy2KCHsXM4666yTysNxJFjBJmGJccMQWK+xPc+HjXANBtklliSKHb7gN9aTDKzJRhttlOSApxmXYNOuwCGHHJL0BZczz7Yzgk07COHfjTnWbIh2lwBhiJRaPgOQKTzgl6WhTBQIUWXUnJN/I9Ga7R1g7fByKBAA4MiQBgAgSGDwlJlxqQV3eIzSIRyxZcSZyPhS5K4AQVYTafE3UELe9Svo4CjbziBQVEGKZ+uf0QXQKV/xXArKEfs/In3CCSckIzcn/5ZJUQfPoBBhBhgBgr4YhH67AgTjQJKBF5LOoI499thEsgGIDDknrBafYcjSMVgGxjg5abWd5qHWG1ABY8ADQBg0gGPcANsY1WL6vt8DUAGG/o2F7AQUxhw13Q4dGZP1Ba7AypgFfXmAYI0ZuHHo02ds2wEiwZcSALIEBJyJgBJYCIaa3ijRFCBEhsI4jHFoKwHCUEl1f45O03s1nnRFc+4gggFBv3VZb731Elb4nUOJUV40a3tqChCG6mt9phyb4JrtIAExZrXvnP4FF1xQPfGJT+y1R8EF4g3v4EbgF9LOjuhiV4AQdqWuG1GGA/vtt18q4WIznCrHzYmzY8EyjEV42QXn6jOIgIANIUfO4ACbimQEAiMJgxgI1mENeUpseDYsRfCRdwkU2KTBZniHFMAkARMnD7/CQSNS1kGAKJFBFuyffBEYGIH4SDogN3BBQILQwIOcTOZBid/lJUawjD7CSJgZQesQbS8BwhAp/eRnJH+222675E+sl/W3QxA139bTeki8wQA+3Xpb32h8Al/Ezui47wha4YifCRIkziS2+LMg9AJW682/5CUh9QCBXvBp9JAN+Lx+EX2+uylAQKjprO/hN+aZl68edNBBKUAR5Aqm/e3MBf32XfPhn/QtecgX4jrGGfYuyWI8SDD9bwsQPJ+dmDvbx5f4TDIPAhwJAM9zHscaxPlQLxSAwWwd/mpq7wUrOBE54Fy4jGZeDvrib/gF322nzZpKxAkKJYVyfuNlAF4SwSaVkMUawqgIEHAKvAyXE3xJZuBV1hnOSYaQBZzAN8gTPipvh3FNrSlAsLZwzjPIh5yGtBIgDJFSy2dkBRBLTo0TibMHomIlBUCCsTIMPwP+HIXMsYa0c5YUD0CIehkSZ0YRZagjK4742i3Ia8rqW9ZNOwicDsV3OIUD1RgTBZMF6DqkLGo2Bgpujsbn8+bKQJBaJIgz5HxkxzTOHwDIgCANlJqCcji5AXGggKstQCBf82Xo/vY8TebAd70lggFFmQy5RUNakGuGHZkNwGecMgYyHAIAzl1w5w8igdDLwDBI/wcqsjQca77lFoc+ZQ2tK2IHIKK2F+BZ2wgQyDJIv+BKu+WWW5KjFzgARaBKHkAFqBqfNRJgNbWuAIEzEZwNbSVAGCqp7s9xHmye06OnHChnI5FAhzlI+i5opYfOIXF0GtuftT3VA4Sh+to0S85bcGweCE7oMcLMHs1TcDzUHuv4Rd/ZC8fYFiAgNmTJbjh+DQYgE3BVYIZcIdR29aLEDhnheNm/NYAL7NrOA+cuGWLnz7ysn0yiQ/vkByPYOydOBjKCSkpjZ9jn9RkBAru0puYQmX7P930EJAIifUgWwVZ2HjsIdAiJCDyiE8ahbh12w4OmA8NNOwh5gNCXka2veQkQpsMEmWPrz+fSIyRWgCnZR1etN5+E2ApSrbvgMXwHW+IzkUoBYjTkHAeg2/yF9a4fKo71piddAQI7pvey+2HHyCnSa1xth5SjTt0Y+PZ4Ow5bwhPwG3oriDA3foXOa5JzCDVfmfvbur0HwTX+tgCBvDyTvUpKanwsYo17wVJJReNzaJy9+JlMPXyyPgKDwGt/k4XqBGsEF6yHZ2j8Mn+uLzxAwlEyNw7zNvEbGMXmYYcEn+fTBziER0lICEDwJJxKwsVnYCDf7bOwBWbZ/cTjYKy1NZ+219B2BQiTJg5LgDAdBqz6lu1fQud4OSSEXhaN87ONJVLjPBgLgJdljubzlNh38kNgCCrySwkooa3DaQIEiswIAJVser5lP6QmkcOmkBwdh2WLkIFRbkZGmQGJSLnu0EXojNhOBWWfJkAQaOgfcCFTTYc4lRgwIoCq9CdvMgDAlkOWoePwKLz1sH3XREgAHKCTfQQGgrzcIdcJl6xAvM3K56PVzyAYvwBSSVMcTic/WVNEAamxzl0BU11VuwIEICTzMLSVAGGopLo/hyTalUIq6RunwjbolOwehyEgjK1wZDtsMfR9lvY0rb62zVJQa4cLeUF64B5yzU4RoEnscZoAIfonP2WM9YbAwx3khX3lL4KQLLEe1gdJk7Sp21tdXrCHfdtJgGWydnAUNkff9QAB+ZA4Qqby0sD6fPUhqImEEd2hAxIjssMyz30BUz7/vgABEVFiMbSVAGGopH7yc6oKBKsythI+kfllP/SFDiHGdhAFkXHuQ0/8NRvjN/mMaAi2nTqlNJJhgs5pAwRBAf2UiQ89HvoWozhwjV8g8VEaZEcEj0Fg+Vct7x/xNie7esi4eTfpd1+AoH8ci2+WnGhq+idzGBVv6vM5OzbkT5ZsbGhApQpDXxILkm9RBRK2W+c3cMr615+fn0EQOJk/PCLLWAcY5g9ZwpEIEMguApIuzewKELxmGx8aesdFCRCmx4D0TSe+ZftlxhmtLWfRs+0rC8F5UgJZcCQ9f092vFoSOeRUcmfGcTEoQIPIThMgGB9ion64TuCHBAi+H29lkFWwPUdJbZMDJtE6ABPEAIr84DXCyYg8XyQ8TYAQ/ftbwFF/G4/xkSmDt9MhI5I3Do7cGSXHOCRA8H3ZAYERMBfU2QK0RSnDWCcQ8X87JnnGvh4gmL9gTVDY9o7rvh2VIQGCzAx5WAsZw6EHX0uAsEQgyL5uje04CWgRUWvA4dNjemLnEC5Yq3xHcB72NK2+tkkDJslexrv/kVLP4DRlDiexx2kChOgfyaDn9aZ0yM+VQyHb+bvcw75813hlMvsCBP3L8HLi8D1q+gXg8TrKPEDIM/axoxBjrM+XfiCIsoNNbciOSv69pgDBevFPcUeOXdShrQQIQyXV/DmZbD6db7IO9C7eECOApaOSAexJ8i2agMLPkFJZ42ixvkg30igAniZAkKigi2wjryAYGiAYD1sQLBujoEXgjavgN8bnfALy7FlBon0vCLK54xHTBAjRvyoAidem83mItV14fpweRwv7dDZUcm5ogOD7Amz9qSqwAyzRE4FQnd/4Px5YvwcpDxAiASDgsqMTOyF1bZpFgICXWpN4Lf/QS9pKgLA0DEjfRkIRZSU2tqXjbQRq9BBLRgMk6iUfiISdAgBOkaKUhGNTHuOwHoIeBj1piRFiIuul9MV5CH1GGxogMCJOxeFaOx0ALS564sCUFyHmFBAgRhMw2N4DdhzoNAECOTBIxs447chEE2EDN8Crf87Y7cV5Q8LIWKCm1KAvQFAygDQIRMhOUGV7UMaCDO0q1AmXbV/PV7Il8xOtHiAIII1HMOhMShB3MrS9KaBUFrXUHQRZUcBtnaxB262Oximwi1rmEiDMAAj+fxd0066Q7W82ixjYKZTxs5Xs//SRTbD/aPOwp2n1tUsadk3tILBHWTKZQwkEjtrvhtrjNAGCoF1mTbkhe8szYWzXGOLNcjDPM6IpQSDvOD/UFJDX5SXTqCFwEi3Ki5QFONsUb6LJAwR46f9qm2Uo8/tE6vOVKIAFMn757qiyBuUHMG2pOwjGHhlpWWNkI0qj6mssQFUiFr6hBAhLwwT2LUGAjCGy4R/5UcTfOQJ2JFjNG1+j0kBSi9+NJlD1c5lnOsMvTRMgCKLhEf2SDIvE3iQBAt+MJPNjdgLYI0Ks2VWUHJQIiXs1/FxAE+ccZNjp4TQBgv7J0vwR/fxNfZ6p4V5sXULGMyNREHeFeDbeNjRACF9pl9+aqBawrnaB8Ls6v2FzfIDyYwnGCJLyAIHMVD3oz1icT8v5jRJVGDuLACHWC2dSPi2oawqscj6ixKoECB+AtVEAACAASURBVEvDgPRttePAP95SE6SMs0AK1ZAikowybyJHzo7jyWv4OTKHd2XpbFEyCFtqnIYFo+w+43dBKG1Z1s8gUFwKKdtlayrPsPseg6WEfTcmxnvcARMQiMwdgDFufdvOi4thzBGAcaJINgLhbw4VQYrL3GwPAry2MwjGp4xGAIVokQeSbp5qMwVdagWBrR0GOzJAK5oMg3VByBCJvgCBk3bAT58AzvMZry1KW5Yh//ziqairdEhRGZZtTw3hpgdKLxAWZMGhRwfZBVwCSY1uAA9zmUWAYMzGARg9y/qbT73JYDkUZ91ivOU1pzMAg//fBScvq2ad6XcQQHpEf8he3Xz+Pvp52FMT4YU5ffraJQlZdEkQtrz77rsnJxdOWoZ0qD3CDdk48qGj9B+hgkltZxBgrGSMDCQ8UN8NvxACO5VsPQ6Ky1DS88A3eCNA9zxkeUiAIMgWeMTdARys9YNHsrxavcSIXMhH0MQZx/NhERxik8gBGxVE8hHWBOFAROiIIN9nZxEg8B9wCB7bBYY/9cu4fEYyx24GvNZKgLA0PID5SlKsK98UgZkkjpJXpWR2EfPElyfSccRf4ggBjSQOnsGHsi/kkx90EBjBt3Yy0Ag/f2l9u84g0De+jr3oDw5FgBClOW1n30IqfL+DyZJbAqDcz+Aa9Efww//pP0oozc94lV+HzeIY/JUAla4KoiRH284g6FfyDgFnh8quyAvuypIbkxJj/pm9RT8CE/ihBNTvhwYIAjm+mlzhn50TWCIxKpFbDxDizWrG4fmCQS0CBLgnqSiRYKfD+VB9+lxcrCdApAezCBA8Gwdl35IWkq74TN7MSyJW0Aer8oqJcg/CErCAY2cMHH7+nmk/56CUEVCu/C0FHuf3DIMzkLVGhjkFJBy59wadyDQDAlEpR4UkUyhGJNukfIGRcGSCBoYNWHxOtsB5Af9Xf8ahqqe1paUBB+VN4RSaxIB0cmYAJ884+CxCwNg4QgeX7VjI5AMw4wM2tlkZPiUDZn7HoRuPXQZE2pyDNOdjMH4GFK9ltU2m7h+5iMN9+mLADEskLhAToNgGBcCMlMECa38jCIh8vDWF3GXcZSWUAVkTACaiF/17nuBHCYXvCrjMGUjFGiL4InLEwE6StWPYwN3aACPfNRdrHBkF2ReOG2ER5ABcAYldmj6A9iyggogAKOPTrJc5IG8ynwiIem3/5rToouf5WeiX+dFDQGqcAtIxtbFchDRUJpw/G7WW7DOaDBdCibxGxi3vc5b2xLboFkI4qb7KMHXVqXK87MNclN/k5ZFD7NGc2SdcY/dIEgevLl/ZDduWSMjfIx9yioSFDKJdUXotqBDMs2GyjzehOEzJTuEk3EGI2Ivxkg0yxebJyLPhNftGoGCAAEsfiIizDfAEyWYjbBs++SxbV99svAgQbIOX8FgShOOFkZ4Bg41DNhIJgNtkzRbZsIDLWOGTvu2EwCcJj65G7nAaaSGLHE/ttJqnvuPGXngIM2E6skjeCArZRPLKmSw448/QuuWhNjLLz40RH8iW7hhbvIQk/D6941foSFMmlw+FEfTZ+TXBBf9K5/kWhFi2N27NpZ9RooJYCzJDj6wlokm/+H4BC2Js/dkcfiDg5X/4Tz6a3kUA3LZOcbja2ATFedBptzQ4A3xgc2wE7+CbBUgavYaPSDYfKujRL/uKsuCmXXD4Qz6qK9gcn+ttQmydDyVTwYAdOjgFU/Ah6yFgV81BXspwzVXw4vPkmgdwkg3KiJBqtiExKdnDdwryycyzzIENmVucE8BZyMDzrZ3PCZzgFx4HTyQicQJYyL7gA04huYQ3Iu0CIesMS8yjrRSJPOkcGQheyBhnjMBUX5KHdBG2qM6A8/QBfjqjZW7wSfDm8wIJ2Gi307iHlibN0rajrxV3UVouBMYOaPNDyH5vy50Rimrrt1fG9+0GMHrKSulEecA9z+zLXAF4TsjvOWYLzUFSVjsIeYmPvpW1IHqejwCKVtXUUyClQUCBUdYjyabFpZiMkqLWG1JDuUTHCBCSKeioj0fWD5BQNgZD2WJHoks+AIMhAg0GHO8zz+VjjsbIoCm3wIShcrgOGjLG2H40fkDF0eeNIcSuge9owEkQJYCSscxfHSookeHT7BgAQkpMrsgZGXMEwDneeY1gIApIAYdvLchf1oQMo8Xh9aa14ABkZZQE5M3P4tWrfk4mgh/BISJD/2QGgBbQA1hIBfJoFytv9IvTGUsbIwHokw2yyRbqOzhxfwiH1dRmZU+SFbmOwxY2OFRfu+aHhHMkHG44+/zzXfYYn+PMkBg2YccUFtBVr/4TOMRb0ZrGAW9hCSJFl323vkML7zg4tiYhADcik9hkbwhAbt/kJVC384BYKR2LUgp4hXjJRrIhDdYI2CUx2CY8gEdwDt5F1o79SVpovosAIPX0gc0hbhIIdfys23cul/p8/A4xkKQJfLdm5oHIyCYicZ7Jf5BNJCRgLSw137yFP+nT+0X8foz4wJewQdgfh1pDNgIDdt712lmkjc/hW6wlguhP/opK+oPsRukd3oAcIvnIMRvL8d1602njYWfsT/+SGXFfBr3L9aFtPQUodBdBb0ruId10ng9iK3Y/BM45HrIT9kW32DvSSkdxInrZdQ+CHRV6Cj/IB0nP/R/5y9RHpt9nBA/8NluFCwhyND/jt/XHX2ohL/zJWCUa8A5zIS8k2pglh6PhSAg++8r9vfFJZjhHJSiJ8lJ6wE7ZpTH7rqSeYMHY/S5aHHxuOsdYn099PPF/3AY2Sn6QofnAcAnCPDFYxx/fN7YmDrgcNr+iA4TlENCinsG4GWvubBY1lvLcNVMCYyQAa+ZK/N+sOXHEJDJfa7IsytwXL4GCD8u/BoimAEECbUiScflHWJ64OkmgBAgjWk1bmUiA7THbzLINsmOlFQksQgKFACxC6vd9JgywMyA7qSzB7kTXIfjFj7iMYE2RQMGH5VlppUlew+48neBA2ZJywKZ7OZZnROUpa4oESoAwopW2NRjbXM4O2PpfZP3ZiERThrIACRQCsACh1x6pHEIdqtI09erK9Jrqpxc/0jKCNU0CBR/mv+JKzpQkKZ0TEJC58zFNrx+f/2jKE9Y0CZQAYUQr7l3D6uScWbCFKGNQyMCIFmgNG0ohAItfcAcYnZVSpyt50HVYbvGjLSNYkyRQ8GH+q60+Xk29V3x6qYAXs+R3H81/BOUJa7IESoCwJq9+mXuRQIcECgEo6lEkUCTQJoGCD0U3igRWbwmUAGH1Xt8yuyKBqSVQCMDUoitfLBJY7SVQ8GG1X+IywTVcAiVAWMMVoEy/SKBkCIsOFAkUCUwqgRIgTCqx8vkigZUlgdUiQFCn57WgTvt7d37+zuKVtRzDRus9wC5Bc0eCC3bctuwdvfnFScN6muxT3h3sXgPPcclKW/OuZm9fcQGIdxCPsZEh5ddcyFLOevzkKq1kAkBX2Yd1Xd1fB+iSH+8Lh39sz70lMKHtDphZ2qOLx2Cvd5e7aLKtece5P85XjRWfYao3RvEh9VuPZymz1aWvseODOyisqbcDsof8Dp/VZQ3yebgDwDv2XXzmTg/3fiyHHrt7AfaQb9f5CONzaSiccLfJvPnKNGuMFzgLGtxljGOcZl7TfmfFBwhAwIVTLtVw2YxbArsu+phWUGP5HgV2EYwbCL36zM2BLjxyA6ObeufVOE63Mrqm3SVJ+eVG9We6KMVha5fG9N0MOa/x9vWLrLh4BYG86qqrlgVIvZHCupEPAusSGZdJjfVNVWMnAG1rjBS4AAcm0FW3Y67OzUVCbFOQ4PZgf7uoyRuQ5vkqRHrsAjoXjrn9HA61NbeduhHVm9nGis/m4mI0l0y5UXY5Ggfssk746lZXB1Hd9L4cwd1S5zdmfEBab7311nSxH6w/++yzExasrs183Znk9m4XGfIz7B8OdAXuS5WHhKFXsnuRwi677NL5WnZ3OLiJ2UVp3sY2xlvCBTrG56UQLlpcjqASXnu9vRuqJXsktFzOtu666y51eZb8/RUZILipkBEAUgvoNju363pNqNsqx+qAlrxaVZUidbcVe6OJ20O95cRV4gxup512msUjVvXh+nPBl1uKZQoRArcCI/9dAQKFv+mmm1KWwI2PY2gCR7cou7lRQ9YRGxkCtyvOg0ghqG63juY2Tc91+2zcesvJekPFGJ3XmAlAU1DqZ+RI/1xv78ZdQL86BwiIgdveZQpf+cpXpluC3bTsDWhuFZ+1g8t12k3z/k++8KArQBDE+MMGx/CKRsQGMUdqopmP26LdcNp24/ZSsCzHU/3AU5ggUaEJntxm6x33blUfe5AwRnwI/YSz3/nOd9LbAPGD9773vaPE2KXoU/5duoMDeeMRkikgx4duu+22mesyG9HcABxYe9JJJ1X77rtvusixrUmKXX/99WmXAU6PYde+7qPpDQ5pjLjLrMdIXjfccMMqrmZ3S3DlOV5tD78vuOCCxEnwguArs9KTSftZkQECYJf1Zfy2gjTZKddvu059dQ4QLr300pStY/yuareDQsnWWWedmTsUgKNESECiAR6K3BcgTKqE8/68nRaZJLsZj3jEI+b9uNS/AImBy2RrQFWg9fGPf7zabLPN0vXuSOzuu++egj1B3tjaGAlAm4xOOeWUZPfkqcnGCGbJfHUOEJRRSQwgleySjXLE7FRwMMstcgRXBpBso1188cXVcccd1xsgjE23JVY4P6+NXK5Wx9N99tknlYaec845ab2Uh9FhP4PvG2ywwXINbarnjA0f4OmFF16YgoFoiOu111672gcIElECcPxHQ3QlD/CjWWIAbDnrrLOqLbfcclVCQFAroJUg6AoQplKyOX6pCc/m+LjU9ec///mElQITTQB35JFHVuedd14KuOwAkS9csivclXSZ91j1v+ICBLsHr3vd61JW4Pzzz18VIIiY/Xx1DxAozjXXXDNXwEMybM8iHjJcESBQGGCzkgIEdY/nnntu2mmxFbocAYKabNlcxnXdddclO5Y1eMELXlAdc8wxaesyGnnuuuuuCTSUHI2pjY0ANMmGrn7xi19Ml4gpDyHjNSlAoGPWif6wy3k1xAAOyADKtEdTjiNR07eDMK9xTdOvUodDDjkkkfHlIDRteLrDDjuknUQXXwUuwfcPfvCDSZ6CsTG3MeGD3QI7MZtsssl9EgLL4S8XvUaqChB0icIIEOY1JrrK3vGtIK9RUrySAoQ2PJuX3PRr98COtjOPUYGhXHz//fdPAYLfafid9RT02QVeZFtRAcL3v//9lG2xW6BOa7/99ksZbo4xAgQ1uKJm5SOaLCIgiybjfscdd1Q33nhjWrANN9ww1Xup0xN12+LxxxaYAzUEJAvpcN3Xvva1lBm2PW3rx3Z5lNCI1kXw+lZXzomqA3QgTz2r7zPkiy66aNVYHCLaa6+9UsZPlsOzXITkZw4a5s3vbB/K2BkHpXLwyrYe54III0p+ByjUtdumiq1y49PHLbfckkpcRM8O46hTzGvgZbYFB4ItZTnGoh/Gv/HGG68KENzsiCwAZjsZW2+9dcpYcoZqPu3yILxPe9rT0jRifCJm2TLPNDZlYk2NjI3DeIyXIQkKnbOI9fI96yk7b+6CR31aF/LW3ve+96USDL8Tqds6NCcNsFnrnFgZpzWWFSEzJCJqgnP92HzzzdPvEX/NGjvc6Gd2A2QB6JSAwFjIgeM3fjWOGn2gx0pCZBfnUea0FHAZEwFom4c1VGdri/3AAw9Ma2X92VHsINg9krlBDNk1/YnyDfpqHeir3yvVCXv1TKV1dMHZHxkeWUp67TP6iNpRNkxe+pYB1tiY79I5ZTV02Ofojj/s5ZJLLkk2FC3sGXaEbpmPz9ebTGkAODxgg2w0HLfnG5/+4RXZ5KVsUfLCHumoYJbzqmetnOmQ8WJHDj+SNz1GcMk9AgR4xA5zexAAsyl2BpOV9AQZJlvrAivV25IbW28K4vXhxQfkonxKtt0N09q2225bbbTRRtX973//9P977rknradnamRCNtYWhpgHXCBXThmppCvwXv85fkyiH56h9BUGwzfyoSNteGr+CN3hhx++quwKFpEHAmEtxtzGgg/s1q46e7A7yxbgtvWws4hwvetd70o6wx7ZIvvNy8jgtrX3e2vO3mTJYbLv0Q06hQv4tz8777xz4h9d/offgVH60B+bxhG85EMAiKt89atfTWOMRh933HHH9F9+RAt7Y395o+N2qZWl8E/8jTGxYTYlI61kBjcxR/bPxoJfGJ/nswu8RQDA1uL7+bOM0S68vsgY94FX/D299R07NnAOpvo9u4rmZ2wB3sT8jI8d6xMe8qHwFadoarAIhyE3n/3sZz+bgmzysaYhH3PANawnngeDYTPO1YVnPhvcxXoFpnTpBxlaU9h32GGHpef6Nx9ER+AanOaHnIVRwviiF70o4bF5wla6QHfp3kc/+tG0I2znG4Yssq2oAIFzVVpE0JoaY0ZuESJAEEAwdEoQdeeMJ2q5lHjog1NRegKIEQuGRTHUg1ns3XbbLZGDD3zgA9Wpp56aFupVr3pVAgjGzbF7Y4Dfc2gMH2mm6EcddVQyNMCA6MpsIICAye8QRcrMmGTpGTayLeDxe9nn+tsHGIGgxnhjWwq5pFickAgUWDACSi5bruyIPGJ8Sl44MJEpgLviiivSoSJzj0ZBOSgOlEwBAcOnzAySwxdUkAf5Ccj8zHNCpuTtwBTwANwapb/yyitTX9bR+IAw2dYbsAJ65gTQHNwTHDE6czniiCPSmM0PwZAFBDgM0bidfUACyEfg5WC17yI2vo8wADHg640PeUaUASNO5qtvpOmEE06oXvjCFyYgC/1wANu8OSfASV+sP4AiV/O2rnZggJP+6oey/A4I0EeB6tjaWAhAl1zoIBmeeeaZKeviXA4iT485J2RPgOtztmzpjM8G8CKdAjoOBqDbaRLY0R/OxIsP6ASypm92r2+gTjesNecoeWGnyrPoCFsUzMMH+CTQhUv0kP7BA+SUPdv5oCuyRQcffHDCCThCr5EPz2pymIIVAI5gwo0IUuka4mme7IIzNVbniTgeukweCFWUstjFor90lW3kta/wxLh9TzNPBBjRYMcCBDIQrLPd3B44fbjK1ti/vpE3hMmY9UMe5EXW/tRLROPAqbHqm4zMBQ7CMZjnDBZCCFvMgw0HRllTZXwHHXRQwkbYAtvgCnyDCdbHuiFTdqbjbMJQ/UDqzMM68gVwgfzhQhee5rptLtYL3tgRG1vCoG6HY8EHZXbskH8WPNIrAaP1YFvshK3ROeVx9JHOsgWYjODxX8gqf8N2rKHggq8TZNMXfIJNsmOBsfUlgzb/IznI7tgLX0TP+F82DUP23HPPdMifHuMyAmhYwS9E4ozt+74yPvygfi5F8G9cdDf8DfzwfeRWok9yAy7RSTt+cAz3EEhIWPKzkop77713GoM/cAnXyRsMUMLld2TMfvAPY2D/+sVbBOiIu/HgOsYCz9j/O97xjvSdyKCzd3722c9+dgo0rCOZW596Y9PRhySbgAPeCBhgtjnBwuAbcJ3Nk4Nx8M0wGU414RmcxNv4br6E7sCvLv3A34yZ/uAH+CU/g0Naa/hiHMHf4IG5SlYKoOpnD62DeahC0Gc5gzAhK0LogKfWVGIEJAAspUAIkQcLRkkpGNKArHK4nILPquunsIyIwdhKR5wPPfTQZLyIsUVGFDggRgogGIXdDNvVwIVScZjGYIwIh789z+cYlYDB9wCTsUUkL5hAeoFZPUuQi6hpy9R3TjzxxEReHHQBPkCDcYhWGVWU2lA+JS7GCCg5Npm/eiOzAw44IIFfvcSIDIEWcEQ0ZB70a/xIM8PwHcYTW/iCEg5chG183r4ENPJymxhDvJlKMMFAkAtET6ZDnwI064VIKClhwP6P3AACY0F+EAROARmzXoKFIAPAixORzYgAgcx8VzAii2FNgVkQQrJFUmT8AS8womdAT9AqW2FsQ7Z8kQH18vozj1nWiU5oUq0fHwsB6JtPbMlah3qJkSwa+0NErbOglO5H4EpnBZSCcoSMTdA7/6ebgNo6W1v6QR+RWf1yoMgpJ0dHJBqQC39gBmKBtNOh008/PdWU64P9C6bpKhKBaAgOkBIEJ5qdLnaDNLTpR1OJEacJd9glYmFeEiZIEmcO7zwH8eZY2Qmi4v8cFH1sOpwXO7H1EiPzrtsD0iGhQbawB/6xQ8RB/2QIR2AFouIziD/bqgcIZOv3+rR2nC7nLTkk+JfVFADAGvNEXAQJ8BtZIUN4a47Wr14SEf3rS8JCUBcBQp9+6B/us2eBCDwlH/KHNXRPa8PTXLdhpvHRzbEfUDbuMeFD25kj/lKyiv4h5PwBu8YFkF1JJb6B3yZ3a+Z3fDo9EYiy0dBf+kGvEEDkzr/b/A8iyN/DD1igD+SXzUpy+RlCCW/gDvIIT9hnNBl3em0O+ERTa/M3+Ers9PNL/Cq/K3vt50iqsUuG4FKeCx/tgCGvsROaP7OppDDsKfw6bBRkOGPDLvEwQQqfK4DIS5Ql2nxOMtH4jEEgA3/rze+tDdtkz4I2WX5yJE+/wyk8g80j5bAYdkogCLwkYiU2Qn/9HXhmjPDRGtEVmCZA6NMP/Xse2ZBvJJ/gtrnAdckmra9Emyzpp7lZg0XzghW1g0DAfQFCfgaBQwAQAfgMARAgZdGQC8ooAKDMFLSpppbz4Nw4dVkowO9zFC8INAeHXBpDZIQ5a4GH7SMBAQfAkXimTBYnLlABAgioLEFXqwcIHDqFkunKAw59ABvAKXDwnLo8up7TFSDkBg6cZL2AWxhUU00iR0vWMvxkw+H7f2Ql62NpA3xrB5TNRbBhV0T2lgz0aX38LGqimwKEeFad8CB8fiaQi4a8kZ/+7AQ1AaRgBEkANFFK1lUTCuhkfBDGKDfqI8GL+P2YCEDX/LsChPoh5Ryg6a4so7WIDD2CidzZMaBLAsCmhITdRXooyENY/R8OcFZh/wgyEowYh8NDwhFawQzy4fl0XV/IpcDbMxEDOi3472pNAQJs0p9xIMvR/JwjRgwEtIKAPoeVP7stQKjjZd0e9FE/IyaxIHCKVzQLEpAKpN8OUFNrwi8EXSIjDmgKhmCNjKyyPjYpSIK7kVBqq5mu9z+tfjQRtr4AwXrTia63wy0CA7qeOSZ86AoQ8jN7dX8gQGZnknIC3Sgp4ctwBbu7eYVC/YyjwL/L/7TxFZls/ovv5FM0wbHdd7qC+Gr8vYAZkW5rTfoGj3AOidE84BCwSDwgw+aHTNcTaF1r3hUg5GcQ2tajjjfmZU0EZ3ZxjQvPinr8+lia+IbPSLDgUNYDXuvP2kqcwgj4A/Nk+SMB0YRn9f5xsyH6wd/DPUFStCY+2Ie31gJuLfpwcsxhjQoQLBgFsZ1eb4gEIBABth26s5Vpy5ASIQI+C9S7AgQZSMojYxYZbJEucKB8DI4xU25K3nXRSABGDngIDYOX+ch3VMJoZC0YhzEuMkCwDUhWSgziXcixQ9AESG0AYxcBkUKygDhgd5YCqAjcAHiUYul/kgABYIj0I8OQjwsJtBsziwBBMIcQyDQoeRlrGxMBmEeAQE9k+JH8+gFxu2oCRTbaFCDEeGTA6TZssJMQdeWC4KYAgQNRD80ekQQ7HpyijJ4/glFb9RycDGWcl2mbfz1AECRLdnBqEbDGd2XUzMt8EVGlCH0Oa14Bgn7hFZnJLMJeSYa2M0k+34RfAi4ZSrKL+drllZGVCWVrbBZZmjRAmFY/pgkQJI0kcSLTOFZMqOtDvpu0yDFPGyDIELNvayZJUM/YCqgl2tpeggIfuvxPW4CgzCjwP2Qo2yyhIGnE/vl2gT5M6Ho1cJO+KZVj57Lt+VvcJBSRZnZnDALyRQYIsut4j/Iic5e9t7va5hfbAgTBjsQlDIn50gn9k6OgCzfIX4M/JECATUP0YxYBAh8iKdVUXrUo21rjAgT1g5xH2/vB297KQbk4ISUuFJjzsXWYl+A0RYwBECJ3Owa234CKTLjMFmcu0gUSFKPvveX1HYQ44xD1dfnlGhwqMOAYAd0iAwQKLpInR5G2kg0ZecA7SYAQhMhakJeSDbsSAjzrYm2VCU2zgwAwbAvnO0z1sc0iQJDBRAqRlkVvIXYBz5oQICCmQLntDpE2By/wlJESINgFE1QKWCUQunYQyDsy7JIVggGNXgtoBfp2NAQRbL3vEr2mAIFtKMHJ+491pm92KY3BNv0iAwRjUqqhhhdh0SQ06rXPMfYm/IoEgFptaxElOgIHWUiEWy25zPA0AcI0+jFpgGCsEid0YZ6XWs2aZIwJH5YaIAjo6Unb5V1tAYISoS7/04YfMV5lJNY+mgy4XUT2KaEoYZifEWxawyZ9Y/uy85Jp+ZuNwl5UVSDPyogWGSBImEiYqrzgd+PuCrucTWV2bQFC7ND5ntfd41oSMDiG8mT/FnTlO0CTBAh9+jGLAEHiN85kzdpWp+1vjQoQZJ0d/AjCHEKT3Rdl2tZr2kGwCyCK5EwZFsVt2jJuChB811mAOMyknjHIMocg48hh29UYcmK9HiCoIbQ16ewE4qnWMJrMmrk6SKzefpEBAhIgQ8aBkwlQMjZOvemAbhvgy7zbgQAmMiQOf8myIkXKC+oEfpIdBAFGHN6OLV6ydA5FU64xiwBBiZEW29nTGu+8vzcmAtA112lLjICfMi8lKna34jyQrBHnTB84sKYdBLWssv/Ox/hbuU7d/pt2ECQHlCsqTZTNioyx5yhnY7NwwHkdCYS+20abSow4QzXUAhYJgmicr/mqo46fLypAiDfEmatgmb2r+5UpjTfQ1de8bQcBsbOzqDzD4UOHT8kY3thVrK/f0BKjafVj0gDB2pMB/BpzwqC+HmPCh2kDhLhgEEmXhc4v9tSnxJ3kQVuA0Od/2gIEPkUgTFfzuziQZc9D7CUzlSF2nUm0Jk36ZrfQbhx9Yk+Bx+r65gAAIABJREFUM7GD4Pd8KHxZZICAjyH1fKKdPzsmXv4iwdd0D0hbgKB8EubBPnqpH5wKN+Jnm9ZvSIBgXMpD+/RjFgGCM48wYNaXsy2FJ6y4ACEA398O4ajxd7CGc3XGgGO3JajF4T8HfDgfxsZp2L5SPsAQ421HyCbCbnvK9ryMlK0+BsaZUQAKLICwgLb9ZOlkqykmcqFswIFgGQV9A36lLzLdMmP1LKVDUsiJbTEZw76rtSkrQJEdMKfYjpdFNwbbiZ4TJNz4zQkZQUyQIGAQ8uhyRmqfHWZiYA4RMTJ1wvpWq8mA/QzpkPm0NUj+ZAvYyEs2Pi4As9sio+dnQIkh69tcml7nF4APgI2FfAVDyI31iLc2AHRra47WhUyBgbU399hClVGwZuTgZ0pKIjBRvyzo8xkkz1kQYxMkCB7tSgAcnweq1t3f5hwHmJEQxEP9tK1C8wdmDjjRMYGbOSgBEVyanzrpMWcMx0QAukCOzZM3R0vHOGXrQi/pG+JINxB/O3TKxeivNafb1sgbPGxRy6jJCtIPNqW+2Npr7CZs1PqzRYdmraNnyvTRfeUidhTolfNHDrAJYJERJS9ejEDnEfh8Kx2mxSFl48tfEdg2fztxAldZR/hjnmwSvkkMsC/nHOg9bIu3CTlEjSwYUy6PLjmzi3htIp2WdJCYgIFd9mAt7LTCnsBn+s/xIv1wxfjsxNhl9CaRpuazvgNn4Alnqh/rZP39bX7OIMEAeEvevuOAJ2xk+8iRtbZGAjL2ipAhKw4yyt7GhXtD9ENfxmLN2DiC42cSIQJJetaEp+wLBvNL8BCO2VVcKW1M+ACn+XZloHwOX4Fg8uV8cLwCVcJIhpnNSywgznQYSaUbdFmNOr1yKF5CiI3QX3pnHcMulRb1+R/4zjcgWnDImPAIvhie0I/8JSHsi54qu/FHUrOvCSriTWrmGkkFz9AHLDAv8yAn/+fnVBXARAE2zCOPPMHY9FxEnO3xr0ogPQtuwcjc39NpiTxrYoeWfdgpkyDN+UPcDaTuHp9gQ+QET/LX08dYIkDwbPODHbDI/KyZ+XsrEjzwbC+biVePW08cgN17eUITnulL0gIngwWCsz794FM8zzzpF5nAM7gKg8k1zriRg50d+ASn4zXwxgjD4YGzsGN5ScGKCxAsAAdAQW0fIYGctu0jGTvZ+qjbZWi20WwTR02vRRRYqBvWl4Wj8AwGGDBamT19I/aCDQatLyUFCKDMokwXBWXcgINSAQIkE9mghMiFzJbP6z92D0LZ9UuJKReA6moO9clwmDsywnEJjCiTfjh/RuW5nAynx9kCCAoMAJBo8yYPGURbb21ZbAbOkIGPeZk3+ZIBmSFHiImAiTwRDkYKoK2NkgFggwxZE28ZIFdZE8ZpHA4lIdtNLQIEBIqhAQI1mvGGAxlWze4LQgHUzdszgargyPqRkTWXjWWMDoXJLAA64MEgyRRQ0SMBiDWMd6ibj34Ahn4EJkiZNUYMGTnZk69gzzojhfqhHz7H8SMcwCEySjKddefQ5wiW+/djIgBdcxeY0a94ewyy53Ch9aJrSKC/kWXrwqmzC0CNQAosZIiU6wkM/ByxQCqtN0zQOEHOlS4i/2yWM6I7yDLnZVeMDiCl7I+N0U065zV6SmqQePrY5AA9jz4PSRhIRNiZi1cX0jP2xXkKatk7HJA4ENg7N8UWYZVAxm4oeyYP9tyEUbncBSCCHzYEEyRn6L5ntdmDefoc/JXJROLJLe57Ya8wULP9Dw+i7Kq+5gIEQZjnIyMCD6We+rAu7NcOo3nAXvMW8CFySr9gJZuF+9aH7MwDkeHQ4ThcIkf6YU0lI7r0Q0CCeCBf+qYL1oVsBSL6QcQEYzmewgpyj3JTCQlrIZhcKW1M+BCH1ekQf4ED8CF8UfyOjvChgjH6Tw/5LCTQjhNiqi6fv5R8gxV0EZYEv4Dj+ond/j7/w8fRMb6PHvCTcIJOWuumy/DYCfwRsNZfg1nXDX6K/tE3RJ1e841shH3QaRwIL5DgErwisDiPYFbigr6SFXmwz65nkht8FEzBGnZvnHwmO6THEhJwAm7CHWTYvCVPBVr4A7uAiXiWYI488SP9+zcbbnrNbwQI7CZ8NtznV809EnT693wJIzwFR0LAJY2MEa+r45lyrngFMt4Qr4QW6LfpB06Bd9Eza8HX4Hswj/74W6JVIgme4F74g+SF/iMhgCf6HV6p3IqPGUNbcQECpY933wJlztd2HSIbLQ76AuhoPkdJZeE5d39EzzJ5lIrRAAJAHs2ChrEoBeIIORcGoFYMORDpIZGUm1LLMIiCPafef33BPUsWGjD1Re7m4vkyDHmL1zp6ljlxaJrAxbw4Yr9D7nN5mBsi1badZctb+Q8CbL4aJY7yGGSHDABqNKAhKs9LBDzfHBlFBC2eaXwMt+3VbREgMGSGD9gAt/WwqxANYJibQIexIQScBB0wfyDDwH1G9h7gi9YRdH9r8d5oc4oLVkKfyMmc9NOkHzIODlhFo2N0jdw8A0CGfvgM+SG0AA249G0fLxIkxkQAuuTAJuiXIDqyWtY7mvW1toKB0F+6GxchWT/fpyecJr2ky4hF3IQdfXFecWCQjvoenYvXJsME9sIZu3AP+RVgcqYwJO+/aU4cjawjR93X6vro84hA7MixmdA1YzSmONNg7Gw3t2eOresMlP7IVV8cqGxdHS/r9kAu5Bj2xE7ZpYANGUDkOW/PRabyEo/6/AUICJ3Axzw825kNY4l7Y+CWecFBv7MjY9zma73jNcf+D89kkI3HWJr8RVz8OFQ/2HSOf+wbYRN45nhqLQJ7jc/zY3x96z6W348NH6wpfYfBxoaU89fR6mvj52HPdFDwjrjjAoH77IMd5/zCm+pi977P/9BRAQIfDAckIfL+m/yvZAUcQSDr9yHV19646iV5fB8f5DnmRc9j/LCBP4tkZbxBKfrN8aNJz/hHPIRNe4b/589nx+RDltGMB/4i0HX8JVsYwB9qOR9ren4ECAIr5B9m4xBwhi1HVUT4X/yMTyBH+oB4myN/XsczfbPRnM/EblGbfsRFbzF++IbL+T8ZRQv+CZfgEx0NfuIz9ANvMRd8YSx3oKy4AGEs4Ng0jqYzCF3jte0pUy+DvZJqT5djDdpqSpfj2eUZ/yeBsRGAlbYuTWcQuubAqciMy7Qh0KXdVwKTnKEqspu/BAo+9Mu47QxC2zcRxdiRl/wrvOC+kmo7g9C/EuUT00igBAjTSK3hOzJXtpDVnKmzbdum87vYFlfiokSn6aKyGQ1rxXYjExSlBMqVxhJRr1iBTjHwQgCmEFr2FSVNdg7UPCsZaMoWcni24+MAvMzymF5ztzQJzO7bSgoQJ6UDdmiVehTyNDv5TtNTwYd+qdlRi7Ix5xqbzhnKoLurwG6TTLcEgfNJK+mVt/2SmM0n7AIrz1FC7IzAmF8TPpsZL7aXEiDMQP5KB9Q2qvezTW0nQa2sbcp6UzqgXtDvEIcoEZrBMFabLmxPqu1V32x70iEqhKDpZsfVZtIjnEghANMvitIkhEC9szMpalDVmNZLynxOTS9Hx/Gpda2fVZp+FKvHN5UCOtiv/Mo5CqUFgqiuM1Srx8zHPYuCD93ro6RW3Xu8lx8vcFaqXl+uht69SM6kKS9x5gVWlHZfCSjNkRxwzks5otr+phuXi9xmJ4ESIMxAlrYRlRPUa87U4tWbOryok1Q3VzLjP7kAlLJeQ66GTx1jacsngUIAppc1/aXH0dSlqjGvv53CwULEQL2w2tiuC5GmH83K/macoVIzHM05j64zVCt7xitj9AUfutcJH1AxkDe74nUbV8ceZ2DyM28rQwuWb5TOhzizkDeHpEubnwRKgDA/2ZaeiwRWtAQKAVjRy1cGXyQwVwkUfJireEvnRQILl0AJEBa+BGUARQLjlEAhAONclzKqIoExSKDgwxhWoYyhSGB+EigBwvxkW3ouEljREigEYEUvXxl8kcBcJVDwYa7iLZ0XCSxcAiVAWPgSlAEUCYxTAoUAjHNdyqiKBMYggYIPY1iFMoYigflJoAQI85Nt6blIYEVLoBCAFb18ZfBFAnOVQMGHuYq3dF4ksHAJlABh4UtQBlAkME4JFAIwznUpoyoSGIMECj6MYRXKGIoE5ieBEiDMT7al5yKBFS2BQgBW9PKVwRcJzFUCBR/mKt7SeZHAwiVQAoSFL0EZQJHAOCVQCMA416WMqkhgDBIo+DCGVShjKBKYnwRKgDA/2ZaeiwRWtAQKAVjRy1cGXyQwVwkUfJireEvnRQILl0AJEBa+BGUARQLjlEAhAONclzKqIoExSKDgwxhWoYyhSGB+EigBwvxkW3ouEljREigEYEUvXxl8kcBcJVDwYa7iLZ0XCSxcAiVAWPgSlAEUCYxTAoUAjHNdyqiKBMYggYIPY1iFMoYigflJoAQI85Nt6blIYEVLoBCAFb18ZfBFAnOVQMGHuYq3dF4ksHAJlABh4UtQBlAkME4JFAIwznUpoyoSGIMECj6MYRXKGIoE5ieBEiDMT7al5yKBFS2BQgBW9PKVwRcJzFUCBR/mKt7SeZHAwiVQAoSFL0EZQJHAOCVQCMA416WMqkhgDBIo+DCGVShjKBKYnwRKgDA/2ZaeiwRWtAQKAVjRy1cGXyQwVwkUfJireEvnRQILl0AJEBa+BGUARQLjlEAhAONclzKqIoExSKDgwxhWoYyhSGB+EigBwvxkW3ouEljREigEYEUvXxl8kcBcJVDwYa7iLZ0XCSxcAiVAWPgSlAEUCYxTAoUAjHNdyqiKBMYggYIPY1iFMoYigflJoAQI85Nt6blIYEVLoBCAFb18ZfBFAnOVQMGHuYq3dF4ksHAJlABh4UtQBlAkME4JFAIwznUpoyoSGIMECj6MYRXKGIoE5ieBEiDMT7al5yKBFS2BQgBW9PKVwRcJzFUCBR/mKt7SeZHAwiVQAoSFL0EZQJHAOCVQCMA416WMqkhgDBIo+DCGVShjKBKYnwRKgDA/2ZaeiwRWtAQKAVjRy1cGXyQwVwkUfJireEvnRQILl0AJEBa+BGUARQLjlEAhAONclzKqIoExSKDgwxhWoYyhSGB+Elh4gPBP//RP1Z133ln91V/9VfXv//7v1VprrVX98i//cvUrv/Ir1cMf/vDqc5/7XPXrv/7rgyTwD//wD9XHPvaxVZ/dcccdKyD2wx/+sPrCF76QnrPVVltVT37ykzv7+/73v199/vOfr7773e9W22+/ffWYxzxm0PMn/ZBxXXPNNdW//uu/Vs94xjOqxz72sdX97ne/1m7q83vKU56S5jNJ+8EPflB94hOfqMhd+7mf+7lqv/32m6SLuX/WOv3pn/7pquesv/761a/+6q9W66yzTu+z6/P76Z/+6erZz352pY9ZtB/96EfVV77yleqmm26qHv/4x6d162uT6l9ff8v1+0UQAPIFSnTgb//2byv///mf//lkxxtssEH13//939Xf//3fV0960pMGieHGG2+sbrnlllWfPeyww9K/2fanP/3p9G/6AWva2v/8z/9UX/7yl6ubb745jeOZz3xmtfbaaw96/qQf+s53vpPG9bCHPSxhD/3tavX57bHHHum7Q5u5felLX1olo1nby9BxdH2OTcODu+66a9XH4MGGG244qPul4MmQB0zjLybRvyFjWMRn5oUPdJJ8br/99oo9/Od//mfyU4961KMSBuAI//Iv/5I4wv3vf//eqeuD/gS/eOpTn5rwY1L/+8///M/VVVddVT3gAQ9I/ugXf/EXe589zQcm1ad8fp7HLoxvksa24A6ZaLjFFltsMUi+kzxnKZ/9zGc+U339619f1cUk/Ie+XHnlldV//Md/rPo+3kOvZtH+67/+K3HGv/7rv64233zzpF99ujmp/s1inJP2sdAA4brrrqvOOOOM6u67766e+MQnJrL7kIc8JP2f4yP0b37zm8kxD2nf+973qte+9rXV+eefnz5+8cUXVy94wQtSH4cffnjleYKGP/mTP7lPd9G/hdX+/M//vHrJS16SlPHEE0+sfvu3f3vI4yf+zB133JEMGSB4xtFHH53Ap62Z35lnnrlqPCeddFL1lre8ZaLn3nvvvdUf/dEfVW9961uTwxWMUeoxNcHiBz7wgertb397ks1zn/vc6j3veU/1iEc8oneY5vfxj388zY8OPfShD0197bLLLr3fHfKBf/u3f6te/epXV+9617sSWfza177W+7U+/evtYEEfmBcBaJsOB37uuedWl19+eQqUBV+CeTjwl3/5l8mOkYftttuueuUrXzlIKkCbXYWN/+///m/63vve977quOOOS/+GQQcddNCq/gTPt956a7XNNttUP/MzP1Pdc8891Ste8YrqggsuSMB/xRVXVI9+9KMHPX/SD7Fvdr3uuuumpAZS1NX+4i/+ojr55JMTtmm+M0nSgDzJ5phjjqluuOGGmdvLpPNv+jybNrazzjprVQIIHvzmb/7moO6XgidDHjCNv+jSvyHPHMNn5oEPgoGPfOQjCbOt22677ZYCgQc96EEpcQDTfUZQf+yxx1YPfOADe0Xxd3/3d8mPX3jhhSmw52vhR5//5Uc23njj9Hzt0ksvrQ488MDUx3vf+95q99137332NB+YVJ/M73d+53eqP/iDP6jYCrtgH5M0yZg//MM/rN7whjekr+FRr3vd6wbJd5LnLOWzMECAFnxsEv6DR0jG/t7v/V4i8hreg//Mon3729+unv/856dE0v7771+94x3vSFja1fr0bxbjWmofCwkQGPg73/nO6s1vfnOFcPmbUnPGP/VTP5WUXMR/wAEHpAhvEgL74Q9/uNp3332TXCJA+Md//MfqVa96VXLwnOkb3/jGVXITWb7sZS9LBDJIgsDgiCOOSGAk2Nhnn32WKufG7xsXpbIIiMFee+3VG3UiSbIo2iQGkg+AEr/4xS9OWfAxBgjGinjLoMoWTxIg+C4SSJ844VkHCKL+CNKsF0fR17r0r++7i/z9PAhA23w4KHZ47bXXpuzV7//+76ekAQKAxMIJdgs3wsEPkQ37tmsAC0I3/G1n8uCDD04/++M//uNq2223Tf+2Q/Hud7+7Qrw5kwc/+MGVDLaA0x/2Kjj083k0WS62SQYXXXRR9Qu/8Audj4GVp556anX66aenz00aIIRMPJMcZm0vs5SR4PHII49MXU4SICwVT/rmMI2/aNO/vmeN6ffzwAfElO3TQ/i99dZbr9pFY4eSB5J3AvZJCKxk0+tf//okvsCPLv/71a9+Ndn6hz70oVUBt53IXXfdNe1o0j8JhHm0afQJbuAvkhnTBAiBHTGnMQYIxrhU/iNQFExpswwQws9IHkkg+tNX8TAN/5uHvnX1uewBAvL2qU99qnrpS1+aMgSyhCI7wUG9Iecy5JMECLIPe++9d+oqAoQ2AVjU3/3d302AManDWe6FiudZMMCsTRsgyMYCkTEHCMoC7K5MEyCQDcKDvI+Z8CxKh4Y+dx4EoOnZyP8pp5ySMmCycwDcjl9T23nnnavnPOc5g3cQ2DiskTjQYgehqW+Ji+uvvz4FDnAJ/swrEBi6Bn2fEyDAL0mWcPKT7CBE/yvBXgSHL3/5y9OQJ8XrpeJJ3zqsib+fJT4oJRQQ0EOlGTLZSFZ9R52+v+lNb0qJRCR2yA6CteHnBRRaV4IBPuAldhftIMjmT2NPy60PyoYlVJcSIEiICsi0sQYIS+U/r3nNa1YlU2YZICz3ei/X85Y9QLDVYzs7Mq+2Em3JNDX1YjvssEPaXo4mgvziF7+YSk9+6Zd+KdXJ+TtaPUCwRcnpM3rNjoQdBll0GcIYB2BCCnbaaae0cxA1+urMo54v6pB/9md/NhmSSN9WZ1NDTABM1LzJCAJUgIMQ+T+Cnv9eiRPgs131Z3/2Z6ke8FnPelYqsVAjjew2GYhxRM2++swtt9yys0Y7DxCcrxCgGYvnGJdt1aixBtzGc9ttt6W6UKUfZGKstn01GV4ZF32ImtV4AnI7HQHw5Ckrq15SGRn5KdMwX82zlXWo7/dsWZpDDjkkPXPIDoJyKXoBIMkZ2RQg1gMEDkC22hor2VJHat3zum2ZKvOx3ua23nrrpfn6jGcYZzTZLE15jKygvsnHc83TujXpX3zf+RP67XtkR2abbbbZKvnTIVvI0WSJ6AZwe8ITnjDoDMS0YDJLAtA1hs9+9rOJGLBRJTX0pK2Ex2etXZQYIfU+H0kE5QC5bjYFCHRQP1GTzF6sMSwSnNB3OkzWdjHoYpxjkMh43vOelwIHz1YXqz82Y639bKONNmqcLhuNGlo7A/RanTVd8xz1/znWqZGFNXBAGZDP0ddNN9007XSYK7upBwh+b35Rs+87MKxryzsPEM4+++yku4Jzcnn6059+n0Ap7MMWOfyCqWRoPGHPbfaQr6vvk6v+6BqZm280Nd92RP7mb/4m6QVsOOGEE9Kv+wKESfCEfMmWXcOHTTbZJOFnngF0/ovesVOk1HqzU89R9jCJv2jSvzhTY13hsz8wlFzoc+wk0fMoJyMHawMvYS9sFkBbu+Vos8QHfo2vhwF2ta2vHeSmFrXoyn1ih/Eb3/hGOktDl9ghmdGZONNXDxDgdt0/+w4sEYDwidZCxQG9pNv0I2r02ROc5x/tdpM/34sP8FlwvCmDzPcrdYrmPBN8YfeeBxOs7yT6pK+mAIF98T3RyDeSi01yzQMEmXbciV2wb/7aGcncX5O53xurucI9OJA38xKc42jkyKfm5yPq/o8vzs985viOSxhDnPsbkiCFYTAk1sROC13Q6gFCHx4Zi/H7HD0zTjoDs3K8dd5RGSyZ8D9wHy8RaJoDnLfmTfoX5xaCz5Ate4Y1ga/60qcdCM3zYH7wHxxuyBmIIRix7AEC50uoMTl/q7dta8hQKASBvu1tb0uOThbRFjzhUWZEWqsHCOoEGSRDz2vuKbYAQd2dJsuozICBAyflBIwZSbD1zniOP/74ZACMhaIADIbZ1AQwSlEiayEjGgdoOVX1tBxF/D4idp+RJaNcDmeTj6DC7ylBU4AAoMgAcCrT4Ow5sLaWBwjq+s1P4KWEAzgrx4qyKop4zjnnJOMma+Ol3DIshx56aFJ24Og7nBVFJhfzB7SIFOdKns5+kANF5mhlBBk8J4vMI2ecncCAUdve5ST7AgRAJeNkrOrNkXqBHzJWDxDoH5JBzrGF7FAX+SIvHIASEzpgO9k4bRs6/Gk3y7rXa9qNUfmDDJjSNEZ+2WWXpXkgk036Z20EKEcddVTSS2BsrT/5yU8muXJgAjB1r7LryJFGp9Q3futb30pOSCD0a7/2a0NsfeLPzJIAtD3c2psPsOdsERyEq62RNWAP/aZ3akqRCQHceeedl84d0U//bwoQmmqSrZUAL+qKbbVbB07PGCU1kPlHPvKRSc/ojLIeZYtsjp5/8IMfTHri/00NliH91psts3Pj5UDtWrANdqzMKhwY8IcxnmVsnq8fa6MGW6sHCJwDWzMnBJ8s9txzz07iGAECZydzK/i0g2LOficgQ1IR/9A/TlDigA5yfvCUc+qyB/LREBd6zT7hrvFK9sSZKrYRZysQGwEUWSNwWleAMAmeIGXKVjyXTzGej370o8mOrRU8gi92aOgdO7UbRQ7wUmmq8pVJ/EVbTbx5wR4lbOTo/A3/RC7WhL0bA6wKkknXEBb+CRnmr6zD0Mz6xKCQfWGW+GA96TO/wQfD77aXgwgcfS7O51x99dXVaaedlvCbz2Qv8N2axgtJ6gECf5/75/C/8J3fohPab/3WbyXfx0/hHnxlrn9kDxvgkc9ZHz7as5vOzBk3zOcrNGVP7N+Y+S12gcROok/6aQoQ+D9zgad0GQ4MDRBe9KIXpfMWuAd9FDRLHMBD9k0v+So8wfjNBz8wH/ir4Wv8Mh9IPgIfJDtkyIbgPgyBbfCX7VpLPELDk+AL2eJ4qk9iN7gvQOC72bGg8zd+4zcSkTcX4w18jTMIfXhkzs6qeQkOXxMBKc6Dt8BseiDgyfmKtbYO5IQX4DT0HOY16R+7lQyCLfgnWeJX+CruxT/Sf8ksOkufyMa6WGe/g8vGGjJcio0ve4CAOIWTMPCuLf98YgxPdt8BZgpjF4DycFwWn6OVKWsqMWqruW/bsgY4nCIFiwBBcMBAKDdj4zgZnyClrSG5UdMH7DgSyvn+978/kW6Reb3mj6JSMIbGKXCUjI+RtQUIxslYfIah9TmHPEAAfMYkM2AnB1HnKAEAcgJgGQMjUMuNXJO7QAkocajAlzMHbBSYQgMDBMhY/B7QWitkhg7oi9Eg1j6PEAsskCxGwRD9DMHrChAEW2TF2MydAZIHcmcM9QCB7llf8ldmRZcAp7VB0GQCkABBKUIiiPN/pItRklO9pp18kDGZTsYty0xGQNWatOmfQMP86S+HyMELCoA4hynTYv7mcskllyQ1s43soD2wQDTJCOjNo82SALSND6CSu3XXwt6GzMd6kLssHGKFIABewZQ6ZrICoE0lRk01yZ4ZGUe6GSVGMAceAOAIEDgbz+XwOFFZS4f/OYG2AEH/xsfGBdlIu37pAd1lQxwqIhIODKbRT0EqJxSOgF7GId2mEiO2SMfJgY3Lbna1CBA4eboneDZOzlACJ34mIygghWcCJQQbXkhSsEOOrc8efJaM2BrCz048HykzT/3BCyRLUBLypRtBDroChEnwhK4IQGQ4PVvCAw5y6gJP6yRoowsCI/pGd2A/22Wb0/iLJv2Df0gQ2dIliShr7zlkaxzGR9c9V4PRAgLjk/UWEFqXxz3ucUNMaEmfmSU+wFRzxgf6EkL5oCWf6CpfS18RNrKAKYI3dqA1lRjl/jkCBJ9tK9njKxBOLfQvfBtugFTjKbAcWWx7qYbv0iONT/dsuifwF+jxIZPyj7YSI5wJV3rhC1/YWxOf7yDw82xZoEXX2CYcxbPYl/EhrQi7zLb/0z8cVspaAAAgAElEQVS2jBtpfChugZPR6wg2IkBAlHEwdk3H2ZG1hNv+Tx78tUAQH4G3uFDofl+A4LvkKtFH5rgI7IokQ+wgDMEjY8YB+XbrY47OIMJ9iVnEvF4SzbdJbIQ+ez49wSXoZpP+RTLKPH0Wh+J3+HnJClxCMio/o2lMfIe/4QUfZL4CvKW2ZQ8QEJ2IMA1+aICAADIkABlvpQmCDyQJBNA3BQhtNfdtAQJFp/CccBAWwODzMvsWg4FTjq7XkuYGR5kAYF7T3FTzhwRQbNkECskpIAmx9VTfQRChIgGMh9LFFmCXYvTJI9/iBS4MFWCQBUdFcQGbsSLgspsOSsoAxxthEA1jsbUHpJQS+J4AguMV/CD3CLCAiQEwjjhkObRmOO8/B4ymmmpjsHsFuGVoyEwWBEEzN3KUyZW1zwliLsumjLQskucBQGBJF2VUY4u5Sd6yGkBTNjE/VBb6JACgZzIXOcGlk7Ydl+MMySwJQFeAYH4RaA8NEBBTa0m2OaEA0ognh8iZwZemAKGtJnlogBCvOhQUGge9f9rTntb7WtIYn+/bSau/rrVeI4ucO5jLRiQU/M3JCVCMtX4Gga3KPHEqdKjvTRqxLm1nEEIe7Jt90dtI8MBJBML/kYWwvy57sB65vUfJFTyIzD1iYjwykPlLFIacQaj334cn8AwhhJ1Iv2ZnA+FCLmU+EXWJmrYzbdP4iyb9g4F8hOQLkgljQx/sKNhl4XP8jnw0shJULuIMySzxgT+NN+9MEiBIJAm0I6CmL7l8kDmya5J3k//tChCa9I/u0CFYL4kmSODju/xwPj7JRkmtvE2jT3mAAEONC1lEMAXcQ1qfPOAP0ss34yb8qSSEJKIAT0Iut1c/R2j5RAG/RFu8VjT8X7whDun3WckyOEpG5Fm3h6FnENr8ax1fJU/68MiOBn8rsdn2UpcmvoKb2P3jq8xLwoMsIlnTJG8VDuQGVwNvcn0gD7aSP08Sh+0Ilmd9hmTZAwQkShaN0DSTGvI+YRknmVLEsx4gEDgBiTznFSAgHYIEkaNIUGaYA+7axuk79NP0e9tuyI2oFGFmLAIjW6eCo9xAZB0FDmovkVvAGsCEOIn0Q85kLftp3H0BgiwUeQIWhNgzESFrJcADRhEgINuyijI2DNx4ROzIgq0vWW7lCkp3KH79wBeAYTgIiNbn0OtAp9Qm+u8LEGQBIutqG04NaTRjFRwgZIxzkgABSRNwyOQBUJls+kpfZECb5C24EnwF2Q/nGIRMFlMWpYngLtch81kSgDYHJUi0boJHjZ7XX0Pc9F3ZHwGeAKspQIgdLjYzjwAhytKMGyFnM9ZcsNlVAx4BQm4/+fzqDkzyQ8DK2QsOYQ8iKTFAf9lVnvGUlSM/mSv6E/c7sGN2m78H3HNhCPztCxAiwyqLZRwcmbJEmGC3jB2G/XXZgx2y3N6Rhvz8jwywzKPxyPRNGiCQxyR4ImFBXgIpepTXjSNX9MsYtUkChD5/0URYZV8FWk0BAj2WNURocoIpCI6y0uV+KcMs8UFQhhDGK0zhobn2NX4j3koYGeFcPv4N1+cVINiB4iusmwSZf9s94Lfb3oPftH59AUKfPuUBAh/Lpu2owA6J02j1M3R+Dkf4w74AwWcjoWtXFQmPe6zMSalQbq+SPt425RyAwEDgIuFphyz3f/H8GKN1R4Lxj7o9DA0Q2vxrHV/tHE6LR/matSU0cVKcCSbyGUpiBZFwsEnegoE63uQBAluPstT6jgWOtuIDBHXWnJJMl2bLON461AUGXQGC78WW37wCBESbw5Itj21LhihT13aGYpoAQWZREIJsyizEoUfBEUKUGwiHBoSMR9mJree4ECwAEdmNBgRsP08SIHCQDIjyMW5bdmSQExxjtJ7qdCmzIEUQaC2Qp6h7RNRkYfLW9haWoTsIbYDRRHjyDBBSE+cF8vEEQRcwyGDWL9BqeysOvabLZCMokuHl9GRGpwkQIkAxtjrBXZ0CBE6VI5Ell01CtDkxwWxXs+6IZFuAEPqJUM8rQKALSqPYGrKMPHOIUSLUNP5JAwQON3YEZLg5YWQR6VGmEu91j7cYeT67o48yy0i7lttTPq4IyIcGCJIxSgEERgJ+zo4TpJN5gN5mDwKYPKCBF7Arb334lON9XcaT4kmsB9Ii0VG/dyL3J7KgcWasj9D1+YtJAwTPayLAq0uAgADLstqpcdaCP49zhV040BcghHzmFSBYZ0SczdNleIaQS9i1XeQ3TYDQp095gKBsGUao6efH8IgoOcsJcsg1drAnCRB8VlJMQI704ggC7TxAwAvwI7+Lsh5rbG3JLN66ZBclSoDytcYb4HweMA8NENpKruoBAj8zCR7BB0nu+sWrbXxFubNEjbI3yRQJQ4GwAGiaACF22Juet1oECBRdLR+lokBIbttBXw5YhC6rhNBZ3EWVGIXiWmRZb4DDccvS20ZqatMECAiNKF3fIk/ZCP8GKpQjNxCEXeCkfIlTRYSMTVN7TX55CRfZIbxtDlgmBuCKQhmsjJZyBsaMePgdJ1kPEDzPc9TheT6yAjhkGDzTFpjsF2NUfhBlVgi7Mds2ixtuJ91BkJ2I/vt2EAQxgiwNOBmrGmPjtl1q3oJX2QdEiPwjmmfoMp0ysG2vzbTjw4kju/pw2E6NZpO88wz4JCVG5Lw6BQjWQjYaQSUz6wFM4y6Tul3BAxl6O1dKBxZVYhTOH57RHbYo+ObU1OC3tWkCBH0pOZStU+pnJ8zOJYfMlnIHx1aVVcBKZMW2OAJunLa89ZM3cpSUaAoQ6LyAze8lKNS0Ct6UFAnMZPeshf7rAYJnNNmDMoTc3mXp+QMNXklAIFjGUyccQ0uMJsETpRLKwyQ1ZFvD3gULEj9+Hlv+so3mHPhlnZHZppKQPn/RRFjhCjlPUmK0ugQI9NOuqSSLpBf5wNGmszPxdi4kLT/T2BRAzbvEyDqzMzvHxiyIt4PMf9YD39CJaQKEPn2qlxjBCc8XcLElAQxbZWPGmjfZbMS1ia/AC9iG5OqPrrN1FQP8qTmreIgzWvUSHLKxLgJrO3B8qGAQ92Pn/LeAhryU88EMFRGIuDlIHk0TIMTZIv3n/rWpxKgPj5RIR8mjOn+yjPtYjBcnxNHaXssuaYEH+A7+JjjwzCZ5251VdjpJiVHsoK8WAUI4AvXVHB3Co8aL0OIVdwTKOVlMAG67jmAtlAVH7BAIRBRhRZA5GYCeX5RGuWRi64dEZfsAf26osm2ei/zmh84QSTVqAhoLIavBUVMYV5M31RGH8eWHUGSSEfD8AHH+e3NVy8iByhIqVWC4nisbYawMkVOK0hiGKzqX5aJ8snYO88jqdV3SUX/NKadHHsgxkqN8gWzJTQZCE/n7uXHZ4QCC6j+VMjBuREP5EsW2lpx7vJbRvBzwAzaMSAStJIEjUD8qIvcdn7eOSijIBhFhfEi9NWc49dpOJVnkgDzoW8YixqCm2dam5wmuyES9eLyy0qFpgYkzEcBJIEp/AIo+fNdhIUEV8uA5yJLxkXkQN2DkEBddQaiAqPkIHO3utOmfbKq1JQPrK1Dh9GR77AbZ8akfUiZD60f/AClADn1uZaVT/mKWJQR9Q3BAk/zIks5FbSt5czICXs7eWSTgyqE5RE7O1o28OBVBA/Lq++zA9+rrRYfoRNwaKvCN12fSETggMLbDZEeJ/tEJ51UEucr//Nz36LWx2G2S1ZeJzl+DW593HFJmN3CmnrGGE7EDYV1hGpug23YskXyOmm7SLX/DFdk8TaZOyR0HDv/UvNJpJLet5MH3IkBAfmEInaTTsFUAjogKiuB27FZYBwQOsZUQsH52FgR8XfZAb60bOcE45UuwXUAOY8gEHiIf5g9n6Tm5xJr5m28QUNbbJHjCSVvreF0re49dLPqjkSUZ5vgFq+iZ9ZrGXzTpH5yga7CVbNkB2dJz86WjdozoerxeGXGLEqO4GJK/gsXzbrPGB8TV+UQEEpE0H76fTcIAmCzpZNeOrrEh/oHfsua+5/NsMl7TyUdpTfJu8r/smn4LunETyQo2xvd7bhxStgZsht0JEsnb2sBs3AWe56/zzNciP6QM/4Nsxmem0af8ojQ45BmCbfZr/AKXuIy2TS9ywqqkEbdCfMk63iponnibuZOPoM7P6KfzRGyXP4WDsI4e82d4CzvDf8KeYUysD36hD8GfNTVWGKK6hP+FSXDPrki8Wt64YER9lz/mJwBBzJURkgc5SLhaO03/SlrxmT48UtGBx+AbMNVY6R/9ICt6Exe7ep71R9j5IfoIp/xbolVSxL+b9E/AgRfQfd+DPbgHHBDg4S76kmglB3w4nicQ4wu14JN9L6zpw4hlP4MQA0KyKRrF5sRlbzkxDgcYIJAUKr9ngDLJ9nBigJRD4bBljpUbIMiAIeq5KbEF4ZBEsBwXZx719eroOAGGAZB8hmIy+tghkKmzeyH65SBEuxwDsoAAUpymhtj5HSPTKA/ljANV9d+rt+PozYFRWfTYLQHElJMzjVfq6ZNsOIi4HMW2kyDLPBDftgOKCCbnCnhlGBiyZyG9xun7CL95MjLZf0Yhu8YoBC0a40XYKDtipx8gb12sHYcWp/6RKcZtrPpG/gVj1hIhY2hICBkrzSEHcjdnAODtBvH617q8EUckQ/++iyAZE1CS9bMTQg8EGJwIkhOvYiVba+73DA8gWn9OFliREWImmLAmsibWMUgg4PJmGZk/ZS30y66S3wt8GSzH0qR/AgL9AhIOiIHHGyIc2jJ3xAQYAQRNIITg6g84eJ5XHg49iNYHCPnvZ00A+p5tjkgg/WSPZMfhcLycBp0QoMa9J0gdOSAH7D90GMmwnupwrXd9vXwWAYjblTk4BELgTY/oITLCFgA1UNYfHacjCATy5rPsA6GjewgePUd2m5qxcKhwyNg4AjodZ7DMHwHhQDXrLmuPRJgbgs4m4SDHwUHCLjovaNHghD4EFVECAKfMD3lpCxLghXmSLaeCaCEYHJ8+4530dBmJJx/9+h5CAzvUDsNNWAGnmuwBaWLvMI7tkCmiwYbYZewckRGckbBgGw4RC8I4fJhPZuQTNbe5vCfBE8/miDl05JNtmbu+4/XB9MuakzGdo5PGBK9h7KT+QpYQNjbpH93jF8kO8XTejh3wceQqaOT847t0gA9jB3EPDbkhLUMPqPfZZdvv54EPfDLsZgN4AD8PzyUKkDOYLlEW79u38yB7Tt/4FXqKU9BzyTOY3yRv8kIQwz+H/yVrO5nkJwi0BtZZYpC9Ww+NzGGI/8NvOsMm+AG+yho0yb/+1jbn9egC29Ti1vZJ+If50UHPpvtsIt6KZ9xx349AMg7iN60pLIGFsI1dkCXMZXN8JFuHDeTj3zgafsRmyYBNI7HmDqfhLLslQ9xE8o6+xh028A4G8t/wnV1bB1gVb4DCU+i6ORmD7/g9nBLIk3XbpZo+63fGCUfxS3guINA/DMFfPLcPj/BV643b4AVx94vxw3u4aKzWF9+gV8ZIFngSXUD04QfeQyY5P8z1D95YT32ZX9wfRWcFNDgUjIDLuHM8z/fYgaa/rjK3oTa/sAAhBmjx/EF2KLcAARBy1shP/S1BFA2BQOQIXUY4Lt9hCJxI3gCJn5toNIoFeCh7XHJCWeLSGRlgixBNBIx4+x6lM15KRWnjVH5d4BZXFj0/A0Apgzw0/Z5ToFR+Bww9CyExR89uml8chhK1R5N1M7+2w5KIKccMeCk7mQLauHguf9OSKFdmgPOXnUXgER0NcUbkYssXSPijH3KP5/sOELMzQq4IP3JB5rG+5otgeZbfy+gbl7F4Bnm3RcP659z1b+sSwJmjPsnceKwTpxGf9Sw6JDNpXnkmkvw5W2MWIAAWOhkHwulfNL8XNOrXXASjZGn8DJeTU67Qpn+AB0DSf/0jgb4Xc6VDnGY0RNF88v7oZ5RODTX8IZ+bBwHoey4AFSixMbLhqOiAOdLXesmBNbSW5CH7AjficjPPsh719Yq3a+QHdsnPMwTu9JvdxvvP6ZVn5PaFePgsDLJ+Pm/t6FJbY6MIXt445DjDRN+BfN4AvTlad2Og13SRYxZ8N80PCfdZ+hvNeLt2ETghASlbo/uIgbmReV5LncvHXDk6GCq4sPsFE8mVnTfZQwQosB4GmbM19pxIEMWY2QTSYv5sAkn0M/gAO4y17W0xk+CJ/gX0dEh/sImM47JItg3X4iKuHN/MY1J/IcijC036FxelkYu+PQv+RxDJLuJSTHJqwoM+/O+zwaG/nxc+wHDzFCSGb+F/ZaXhalzQGeNk9/CfnZMp3sA+BHLWs0ne7EGwkftn/tfaez4bYKv8np+xO+V7cVGaZ7MzzzM2zzdW/gZutAVndM3Odt5y/J6lPtHbOp7YSW9r+AV/T99gsDmx4fBJ8EYzB3hhpwTWsk0+ip/jR/Eo8xfkWQdVFvCELOOysBgDjGJ7fh+yy890IsDGxO593xpJ0uEDbMNn67dt5/MzD/jMrqyLscOwwJB41hA8ginmTS+tOTmxAd9lzzne4hV8kXkZHzymi8ZPnm38j67lfIkP9Cz6GhyELnqe9YrmefS8rp9DDvp32fvCA4ShYFQ+VyRQJLC8EpgXAVjeWZSnFQkUCcxDAgUf5iHV0meRwHgkUAKE8axFGUmRwKgkUAjAqJajDKZIYFQSKPgwquUogykSmLkESoAwc5GWDosEVg8JFAKweqxjmUWRwDwkUPBhHlItfRYJjEcCJUAYz1qUkRQJjEoChQCMajnKYIoERiWBgg+jWo4ymCKBmUugBAgzF2npsEhg9ZBAIQCrxzqWWRQJzEMCBR/mIdXSZ5HAeCRQAoTxrEUZSZHAqCRQCMColqMMpkhgVBIo+DCq5SiDKRKYuQRKgDBzkZYOiwRWDwkUArB6rGOZRZHAPCRQ8GEeUi19FgmMRwIlQBjPWpSRFAmMSgKFAIxqOcpgigRGJYGCD6NajjKYIoGZS2DFBwguNXL7pKu63aC6XM2thf64DdQFP33NRUJuwXXZiFsSx95cMOKGXhd9xHhdjOIWSzeMus2z64KSvvm55MPNji5icStgXMLS972V9nsXtLjYxW2tbjZ0ec+QRtZufXTxn1t6F9FWIgFwcZFbMV3OQ2/jgql5y29SfTZOl+DBD7dt5jfGz3us0/Qf46WTbgClG5qbr10e5HZTF/kspbmNVD9ugXYB5OraYKrLssiNrg5p5O9yJDgyFn0ZKz646MylXXTJjcf+LFeb1B4WxV+mlYfbpc8666x0k/huu+2WunGBnFt+X/3qV1c777zztF2n77GNF7/4xdWOO+6Ybpl3EeDq2NrwtG+uLqXEd12MthzyGV2A4Bru97znPenmWzfPub3ONdwcvRvl3Lrpdj1XTrvC2vXVrjv3HVdxL1ebJEBwI6ErsF1/bswI8ZibGyldK37ZZZela9RjvEsJEKyZICBuUp2UUI1ZXl1jC9IjQBR0Mey+5tZIOu26djc9+t4i2hgIAFmwdY6Ds3AzNd1xe6qbN+mTIMzPkFc33r773e9Ogee1116bPrMcbRJ9hmluOD3hhBPS7b3WepdddlmOYU71DLI3xje/+c1J3m7yjduVJyVEMQBrxknmN5ivCQGCW1xPPvnk6sMf/nC6FTi/FbltcRBet9IiYPR7LPqyXPjghtiXvOQlKeiPRBKddCuu23Thgttn3TD7/Oc/vzrqqKOSrn7yk59MSRmJqOVqk9iDIAbZg1vLzV+mkcd1111XnX766QlX8/EuJUBwU7Cbr6OtCQFCF552rYubv8kdl5QkEJQ98IEPnGYpB39ndAHCPvvsUx1wwAGJFJj8Rz7ykUSqQyERg0984hPpKnTBhOutXfc9dgO7884709Xs5jX2AIH2zHK8gPzss8+uDj300HTF+ZrWBIayIkMDhJBPZGnX5ADhrrvuSnb+lre8JWWoXVH/0pe+tPrKV76yKgCQQOC4tt9++5R5ev3rX5/IwXIGCNPotB1FO3FjIXx9czj44IOrq6+++j4BQt932n7/hS98IWUejz/++Gm7WLHfu/vuuxMeCBSHBAgx0bHpy3IFCBJWSLRk1ZZbbpnEQX5wlR5ttdVWKWlox9uujF25j33sY+kzcGE5A4RJlRIBI8ex85eY1yzHS//333//tFZrYpsGT2EHH2f3Zo0MEBABmbW11lor6Uw9QPCze++9NwUJO+ywQ3XPPfesCAObJeFeDmOa1Xjt+CAVMjmAe7kyussho6HPKAHCUEn95OdkmJTiKG3TZGDrAYKfSxgIRLfZZpu0+3X55ZeXAGF6sTd+cxqH1tQRYiAweshDHpJ2yda0VgKEyVZcgCAxwLZjB7oeIOjRTovyt5NOOinxA4lGfKIECJPJu+vTswwQBLwCuEUlwGYnlel6mgZP1/gAQSZAeVG0pgDB737wgx+krUVbrishAp8V4Z5OFSf/1qzGK4BjCN/61rdSoFAChP4So1itsoNQpZKiH/7wh9U666zTGSDYWVRmoJUAYXJ7H/KNaRxavV/recUVV1RHHnlkdeCBByYCt6a1EiBMtuKIv5afeWsKEHwm+EMJECaT8dBPzypAUHK75557pseWAOHHJZt967DGBwh1AbUFCPG5UFjR6CMf+cjkcCjcscceWx133HGrarRkrWxl2cpz0OYJT3hCqlPceuut01mHepO5dEDUITKlCz4j2kU+DjrooLSd6f/6tMUpU+4wqUy5bc7HPOYx1UMf+tC0DeT3dcJtjMqNBDg+Z6vdmPNDOep0nV9Qp6he97DDDksHry655JI0bocwHeYzt8985jPVueeem7Jyu+66a3XiiScmA3SgCEk3F9u05q5Uwzye9axnpXkZu/MF+rvqqqsSKdt8883Tv/fYY49UEoWkqZs+88wzqwc/+MFJjprPMnZjVCdrd2e77bZLdbZqbG0LCwyMX3BgjdQaG7M1UwqSA8S3v/3t9DxnS9SfWyclJnaLyEatt9+bK6Jx8803V6eddlrq/61vfWulRK2p5eN/0IMelORDltbPoWtrufbaa1dvetObUr+eay3JQSMjpSxnnHFGkrV1e8pTnpKITowt5OEwoUzWbbfdltZj/fXXT+c5osQo5H3xxRcnPVIm94xnPCONIYICfZUA4SdXsm0HIT5J/yJAgB10/oILLkh1rvSNLcZ6fvOb30y6dOWVV6YzDuxFRtua1Zs1Y8Pvf//7U1mDrCRdiPMiyiHr+oys6E+pEzvZbLPNUvLjnHPOSd3XS0aME/ERDKnNV6P86Ec/+j5DYTef+tSn0rOPOOKI9DvzNRdnsGCaZ9ApB2GdlYKDdJKuK2faaaedqq9//etp7nZa7Lx4yYOs/sMe9rDUJ3vxO/ghuH/Sk56U9F/Jl7KYxz3ucZXacHO48cYbU13spptumr4Lb8hU/57D3shLiSEZGqfDduZJ1vFs9uCAMjuOMxkCP4f87T7CH//3Mgq7SI9//ONTUKg23ThgmM+x6w996ENJ3vD7sY99bCMmWB/nVczz6KOPTtlp2Wc6RmaeAdPgIowT0Jhn1P0aC11QPsnW6Z5x68vYwq+Qh11EuqNOfqONNkpnT8g6SoxgvWcYP3xBivkDY4jgeE0tMWpavLYAIT4bAcIb3/jG5Iut2x133FEdfvjh1SmnnLLKz9J92By+kY+iP3xjVDHkz8/9D3vi99iZteEr6vZAR9gCX8KX0wt+kS9nL3XCzb6UTMMLGGAsynDypk82R++M0WFVtstOnve85yUfpg86dNFFF1Xnn39+2k2h76eeemryzfTcrgy9dB7G2MyZD2LbznBpxgfDYJExOZyMA0RJFPnZ4YU7r33ta6u99torfS/02bMdxOaf7QDhZ2xWDT3fr6233npp3J4DH+AZfmOMbM1zyYXPZxvf/e53k39mo3w3GeR4RxZs0Zqbs/7Mv2k9gxcYl/K1Qw45JH3emirl4cf15W9ycobQWvLX7NvYvvzlLyesw7F89pnPfGbic1tsscUqDOjD0zjTZZ50CAeBZ/iT+Xi5iTUpAUINDYYGCIyE0iBVvoPcqnEFxpTYAgN+SszAKBeHQxGCBOaPBiacCweJODznOc9Jjh7YIKEMCYllOEgH50kxGC3CeOuttyZlA/hNAQJCwiAcvJaZY5R1BQZGHDuFdThrk002SSQbKQFOMip+j9gCOMBAKb21gdJSSuOn0BzTMccck4guGXBsyCkgQXYpoYPg6rcpPYPhrM2V8iMgvqNvDj4CBM4ZOAiaGDlHSan9Wx+MqJ7RJSvjBpLmHwGCeTHkpz/96cnAjANZsJ760K9SEgCFAJibz3C0xop0ICQCmHrLx+8syO67756M3To7WIak+L8/xoHEIRichwa4jE3w5RAc8AXeCJg3iyAP1gEYmrc1BZb0wOFCpC4CBMRKUMNR+Jyf6xNwep6DyVoJEH6SGgwNEAShDjYih0g1Qs12BauaMwyCRLrszRuwgA4Lbjm1/PBsEGY6y044KH2zST/bb7/9ku3W9ZnOeo7+jIGeWPs4g1QnfIJd9qeciiOWOGjSY0TTfDyPXdNZRNMfOsou2AJyhOAi53SJDjrPRa/pKJ1Xy+rwIWzg9BAeNgVPjJOThY/khqxyUp7jcKhECD3mhBENhIeDRKgF+uTLsekD/kpsSLYoGSF3mGocvgM3fO+aa665z5kMGMveyR2uItESBuQNg5xpEljBCWTCusBI9g6jzdHYmhrZwEDjgmt8hQSGebNj44GNfs75Iwj6inK3OLgN82G/9TNnMiJHeI0YmSMChpxJFvAJdIO/igCBfK2p9Sdv8keizBWhM98SIPx4FYcGCIJJbxm0rtaPrltzuqdZG3hPv+D3K17xiupLX/pSwnV+KIhyPJkP5E/ZIL8ngKcbDkfzQ/rL7QHJY6eCEsk8pJjeeF5TgICQwwl8AO+oJwiMI/y/PgTuQZLZkGDX/+kK3kO3JSnZG0zxeyK7fNMAACAASURBVN8RdCDg/D/fg2DDMUk42On5fJ4EmDes+Qw+Qj/zl8LAN5+nu+wm3iRJn42Pj2QbuBhsklTDzZr8Gz8oySowlvyIGntY5mfGrw/8jc8OfOJ7+Vky9hy8C8bBcH48X486DsApQZTxW++nPvWpCU/hTLytiTzxFIE+TIGT5Isv+Rybhwlkqj/2Si74o/7gWx+eChAEB+QMo8kRnvi3QIUc6VcJEKYMEAiQcQNS2QMAYoEQOkrDQXE0AFpDMAUVFC4yenXlEaWLqgE1Z8vJAxEZMc6DAgP4PACgaAxN4ziAQj1AiEOUyItdhK7XhQYhQiyAFqdC4RkIx0s5OZ7YoRAgAAVk0x8KRS7AjNEIEAQXQMvPgQSHxfHJ4IV8EHHZMOQpP1QNMPMAAdH2HfKX6UKcOVcyMm+ZhLaSjzoBBigMCfEIYDRvACDbAYgZpXU1fs/Zdtttk6wFR/4gekCorRk/4h5vyrJTFA6fHMxF+Rr98TyyNgdrzcEAYXPTkDOEx7z1h0gAD8BP1/zcGtAvAAcArTlAAqB0UgbVZ6whkmNudLIECM0rOEmAAAcEYRyK9RR8xXrSNRljgK/5DIdMf6wzp9DUfA9pZS9sW7BrDeFCXZ8Ff3SEbtjBkBRAqGGEFoRPXwJpb6ngDCIT36bDSLT5IP4Cafrou8bP3mUsZeD07zwXQiP48XwkOpwmh+r/oe/mhQDBCP2H48vlAxfytxiRh/FEgCAxIZCQ/DAX9sbxkSvbkC2sBwgxzzoBjqyjwAC+kLHgRcIAcRHUsEuECSYhOQiG9eCUYaX5IRhtTV/8ABlJhhhv4OMb3vCGJGP4HHgIO2QyzQlhMTa2Hk3QIegiO38QFzsn8BqW6t+ODPmye7JEPGS27daG74B39IBe6JNulQDhx6s4NEBANK1jJG+smbWgP5JD+IFsLZzQJLxkun1HYqfpNZtxpkxykJ3BFHrms3V7QPjt2AkY+QpJu3hePUDgt2AUQm4MXa/4DH/PpxuPAAjWSHyyY5gDD2KHgq+xs8Wm4B7+Q88lSIN/8NN8kD/kQxfZDh2NhAn58Ov5oerAI4ENYgtn2BSZ6lNjjwIDcrIGbf6tToDpP/vii/GT2OFk83w/exJ0qYpgw5KfPg/vzBOfgHPGy1abWuCR8fK/5BH2h0/amSFfjS0j/+HvcQf46f8SOrAzkp/GZNyCiCF4Cqsln/AQ+hrPk9jRP/0pAUJtBYfuIHQprGgfAMvMxiEnhsK5b7DBBq0ORIAg20zJZXLyVg8QEAxZBI6Eo+G8OEMLzdDDoAF9ZLoYYFN5U/6cIER+JhMZ2XGGisCbk7KhtjMDQIASM6zYoaDQ5g6IBBNAlAEzjui/rb88QGBEiDwnTbmbWl7yUX+rTE6orAewQ8Zl/0IuABWpEuBw/qL8pkO/9fVoIwT1AKdNvuGAGLwgJd46Ur+TgCMRCNI/8zE+P0MOouXjlXk1H2CKNMY8jQNRAEACwTYAbWU6c/jFcr2lZJKhDw0Q6oeU8/Wk+/6PtOY7BX6OgNI1AV1TqxOA/DP1AAERQCgRY/YhGSCrFzsDCJ/kA4yBEzJWEaB3yaTukOOzSDnnLosJf5oIJZvlvJUFmHtkSM3d2GAAvEK44Qp8jNZ0BqEuD9lFus0eZCq7HHLsIMRn6uPlaH1G8JzjCwdt/egCktdW0z9kB45/kSgx3sgqN8m3jof0izzoCt8SjYMXCNjZkFmFDT6r/7gvpz7emI+gI39tof9bI2SO7pQA4cfaNDRAyA8p1/258jIBoF2u4AX8D7nzmUh9fSfRCLpeOlG3B1l3fdEf2WE7VYH7iGgQeEElooqws5s+XtDmn2Fa+CBj4buazmhKsElU5ffECHSMlw9CtBFw2GSHLlrTGYS6vQQh9vy2kt82/1YnwPiDHQHrgzjn1QHmIJljnci4yT6a7LuOSW0Jiyb8yNfXjjRe5dnsO7BUgAQ/YaxkgAB/CJ5KaMCPfOdYAhMu61+itAQItdWbRYDAoG0rAuz8nbt9xGSSACH6kl2S2RfNy6TZblITnEf8FlnWj+G11cdGf20ENvqLvtsAg/yMiQwodL2FwtkOzwOQIQGCrVjbh3ZE7ETUt2M9a2iAIAOqvANo+ndebhWALLsCSJc7QLj99tsTIQFSskZ5I1+BnjHZYZLBqQeU+XhlIvweGMted10QN4Tg9OnwUn6/ugYIQdDsfk36hpNJAgSyt+Mkyw9L2BjCqNaVrXBoAskoX6Q7HHNkj9rWri1A0J9nRYDT5DADT5CIqLvPnxP2qo/6a3mHBAiydbJmHFrbTt7QHQSEwBjt3srmRov143glOxYRIAiCrJWAzBjreK3UUWZTiQBciB0Wn6uPN+QBp2Wk21oJEGYbIJCnZE2cFRuKlZMECPpEGAUqAr3/x96dB89WVHcAH/8wi6ZMVRQqLmgKQ2QTIhIE2UQIKiQoIWyyaADZhCgKJuyLAiIobhSUIiCgBCKBKGuKJUYxCSVgiAuIRSrGMsYkVmJMyj9SZerT8Tz7NffO3Jm5s/x+v+6qV++9mTv3dp8+53u+5/Tp22FfVomR3SDciLRKB1lsvGDUyzza/HPcT9IO92gLEMKfsbGmFns4JD3zCoIuAYIyLoTYmGM1vOkZTf6tJMASGoJjJU+SF/n+sMC7YQmRWQYIqiVgOeIeq8AxzghgVUPgLV3wlDwEZzC0rdUAYQYBAoOTgRKdlyd0In/q8JraOAECRUb6ZIjVm3KQAJ+iWN4Lg7bs7jPZBEuMvmuqMxwVIMjuKYOwRCrj3AYYyg08S1mBpb3ITESm3gqEpUifk0/Uv3cJEGQbjFfGzHjjt/puuV8WzTJ6lxIjsuMcZdEtqSFTmoyu8QlAALkM67wDBHWk5szqAcDNsxhWp2Rujd93ADGChCB6eX9lqwCaeRGw2rQWDeAA4NiwVAOEp1plHysI9NZ8sgFOO59P2RrPsPrX1MYJEAJbYIPldWUyEgccqMRAED5/K0WxCgYPLI9HGVtTH9oCBEvsVq+GZZzZmfIBxBo+xZK95xi3jJWsHHuj70oso3UJEOg6wgxv2EKON+r5Lbt3DRAQJkkDzyWjKIVgjwiI7C4CsIgAId6zj7zIxEYzv/pLroII/YRb5GIlqSlAsApCH2VslUnmJad01SqmTG8NEPoNEJSfWD0yN/bMxSoCHLaihqQ3bWwdJ0BwL36BvaspZ6PsV3JIYil/CyNfiRwKKhH83DZLHGjzzz63UiWrDkfa3vJo1UsQYJUu5x/RX6soeIGSZQQ3Vra6BAgSh/SZ/4IluT7LkLsX2+0SIAio3Msqm1KnKPUhD1jJlyqpglPzXkHAfcyrJCmd8H8tSlpVTJBjvHp3FJ7ig+RixTFf2YHJmuR2DRBmECAwAkqGwMoYxJtMLAGpZUOgpw0QGGZsFHYvWSNZAUTZkltu0DL1HIylfhF6lJs09SEIEccu0InlJ6VBok3GaxlwGGDIUCLWMnGcFIfj9wgsEEAqlDfIFMSSYNyP4an9i1aW6Pi9uuUAPIAqeBE5Azr/zwMEdfmIGWUvAYKxuzZKlvwWQFihALIIjRWTeQcIDF6mkO5w4Hm5mX/LtAInsrEPIZZ0o5Y8+osw+p486RzZGptgiL7QC1nXOK6+BgizCRDMJ0eMxMr+COgQV6RAMIosxIFMZQ/GCRCQD3/i9Gw2Z8UCqVVPmjs0mKRPgnSBglKEJnKiPxEgsGebcjWleJbi6ZgAO97aVB7EFu+Kp88IBKJAJzkytbvuZ1kcsSUDDj5WuRBfmCnAiaRKKQ+OHGn2PfkqUWLDxi5hAIvyAIG9I8GwWYYw7y+bcH1k4ONlEoIpchTEqNtfRIBAV/RN362YRPkQf6JPyjZtWhQ8kKW+WnFAjqK/7iEJJSliPObNChCdhNcCBxleOoTE1QCh3wCBfNmdubNCFbgbB3eZx6b9geMECPGmL7bNnv2fXijv9UzBbvgLNm+O+Rgri3xE2z6E8M9sVLIpGg4CPySskPu215Iqr1JGqS7fSoOyJmW+yClbxCn8gQV4R+wbiPsJQOKlBWXCIk6Vt8/OeLwowDj8Fr4adxkgsAmYhQznB4GRmReQsB3BFQyK5qUlngUz3W/eAQL/zm7Jnzwka3EAc8zmo7oCZnbBU3LBw2CwJI7kIf4okMPb6GkNEDKPHPWyMmJAEkErSzKUDqkBs4Sjnjc24BAwQSv34RSRTkScIkV2TjTKWbdl8Cmk5wou/DZ+534+R+j0DRFmsACG8SMciD1FFwRYvVCPzCFzchQKOfc5xfIbY8uj4xBDBAh+b3MVx8NBciRIRrzhIu4PbESuUUqFOMhcAh3k2m58DSngfGS0kQrBhtIefaLYHHqUHPm9sQEz+wQAJ5CUeRUc2JgUh1QJYDg+/QIwyJjgwfjcO147xrA9R4aRkxR9AyiykIEwb4IP4IPMxGtZZT7d23z4jozNB2KhfEO03hbwRX1m3n/7HQJ0BEiWMuO4d/NE1mGY+kZWnq1vSgiALFBG9oEDMDVHwFnwYHw2pZoTjt+zyNA1Mh/AWNBkpYQ8ADsw5bw4Efoc8mmMYmf44TKWGNl7o66fXZAfGeXN54iyUjBE1pK9zwA0x+9zq4hW+Tg9wZ1MHZnTVVhCV5vKfOARWzc3yK89QJEhd59Sn801m3KtVSG19OxUJsmc0x34RWfV07q/YBEpsSrFATb1Ixyy7yQI6KyVLAG2PQAy0WzJ/a1Sea49CUF2YBV7Z7uhf2yITnJSbBlewgerlLCOvOCAt3SwA7ZsTDZgsiF667mCAfIzJljAPpFmfYRBPrPxMTYVuw9cIitYqb8hD/Nqg6DnsSefy6yxdUkGuALP6QSMhg2ww2ecNPInK6sUCd41NT7DOM2RuWeHcIDtGrcgLIIyGV9ECiGBr+ZRgifeXqdvki3GC4v8H+Ehe8SLzN3DyxUEY3DSmOgRuSJSgiFzYq6MB/YZP7mW+jJD0+9060XhQ7wqly3z+/Qhb3QZ0TIXdAph1iRmzHH4czYY2V2+LFYS6TXMjo3L+b3NA50wL3x/bCqPa0p7QIr5QYkgK2o28bMz/5eIEEDm/MX8W4GjL0hxXlqX9yMCBHaAp/BH9JGuuT/iilwHP3If/Y6VAPjAD8JIeopX0X12hsT7zP2QXsEqWxDssw2+X2DhGbDYZ/hW7BWSgHRf+KXxd+4HN5F99qLxqwIQtq96AI+AJb73b34WX8E7jEl/4AwuAvvjhRHubzwwxUptjh8wix7QARhbJl2MGQHXf28cwnu0wHP/lmCm6/CZ7uAE8EKCQGJFYMdWBV10xnf66m/8yu+64Cn+Rw50Br7EXJEjzkMWEq90LJdPJ2Od8CJ9Ma/x1sqn/cTs/vQoc4KN7NeE95/4Z0AdIHLueeNgI1uDJMeEusbOfxG1THM0CkxhNOTNpAHmiJzbNgVyEIKHaIwZKGiMTdZIM4mupSyMnoKL+oC8CBtAuU+edeZkkFhZBX2JVi73+TwCBI7GJhbj52T9HuFEbJruzyBcrwFMcgqnxOkzLlnTaLL+HDiHzLEiWQISikhB/d49AaQW8hA0WSHhrBFZjlj2IyduMobGLzhApjlv/xcNa+QR/WXo5g/RkVnzbEYZYG2Fg5FEk61ElGI+fK7P4RTiOsFO3n+yMY/GmTdGbp7jzSecBrkZj7lAJuiRfnLsgCvfyIlkmiM6BzwAj6VqwQRd4Cw0vwfs5AGk9Fl/yNOzyYcz0fxe3+ONTet1eIb/WRQBaBtSae+uE9yzm2jlnh6/oRtt8wn8OAgOMuy16fkchuAgt1fOjg5wBk36TE84xdBXmBMlJK73Tu9o9IQT1Z9o8EwGv2wRICCcgl7PsPLk3mxJgNt0//ycFU4XiUXISzwp9ZNs3E/JD/1GDNQCl/II/EJy4AVMcD3ii2DEEjw8Yo+eL0gzRxxoLg/6zibinBWEj5wFc+bJPfUBsQ7Cod/s1e8E94Evbfbjd34fLc6VyP0H/ZLwyfFbYkb/zJeEAbxAVvUtkgB5IgveITsCC6soZGG1wL9jk6w+CCYRXuSKX+J/Y+N06Y/oS3luzgyhoPHWi8AHtppvCi/9h4QYvM7fTGi+tNLeyE+An+O1kjbBeazelgMv/U/wiyZ8YA8IqQBPQow/QxjhPNywqlTyF34j12c6JbAp+xMBAr8ooKEzruV7EX+tiR+VeEKPw5/xtX6Tl1zCLjoZB5sh2QIY+id4oNdtfEtpHUxyD/gk4IFp0STN4rwACUCBs2ua8I/8BC54AUyQeJBcYCdteGesOV5HAiSf01KfyFCQxTfn+IFvwaAmvsbPk4OgTkIPLgsGA++64GmsjkYCFX7RTXIWQMET8pSQyJsAa5ZtaQOEWQ56Jd27bZPyShpD7evKlMAiCMDKlNR8e922B2G+vahPW+sSqPiwOA1oKyleXI/qk1ejBGqAsOSzWgOEJZ+gVdy9SgCWc3JrgLCc87LWelXxYXEzXgOExcl+LT25BghLPttNNfJL3uXavVUigUoAlm8i1Rw31cgvX09rj1a7BCo+LG6GJQnKPTGL60198mqVQA0QlnhmbYYpa87US9qUU1uVwKwlUAnArCU8/v3V9Zc18vYN1VYlMG8JVHyYt8T//3n2JJR7Gu1diD2Hi+lVfepqlEANEFbjrNYxVQn0IIFKAHoQYr1FlcAqlUDFh1U6sXVYVQI/lUANEKoqVAlUCTRKoBKAqhhVAlUCbRKo+FB1o0pgdUugBgire37r6KoEJpZAJQATi67+sEpg1Uug4sOqn+I6wDUugbkECN6d7wh679d1OMk0zTENDqXwx3uwvXfWZ9724zneLR2HF03znGX4rfE4tMhZBw4TWg3NwUXeaez8CGNyuNhKbubIu4udX1Ae4reSx6XvsyQA3o8fesCOp2mBCTAgf/e0s0O8c9/76ttOJZ7muYv47WrUN7rgHfF8w0rHudU4P7NcQfBeef7aWRzT+m227gwOth565DM44PC81YLPXlSAF/gbtq2WtlrwejXNz1wCBDvuHfhgg50DbqZpDqRx+qCDvRwy5RAcBM3hI472dlgXsFnpzWFrDjBxEqKDVvJNSbMYm+fZ7OigmbwJwJAuh7E46W9aEHf4iEPWbL42Z3Fa4CzGNM49OfaHHnpovUNqRv3eIUsOP3LEu0NbnOS4mtosAwSHvjjQyqmQDsyatAFj88ZWHPrl0JxoDphxSI8DbOjvSm+L0DcHE5UNJjgI0unI/kzbHKTIdviG/KCkae/b9feSV/yKgLVs8M6ppi984QuTDg0LNBcxP13HOIvr+sAHB1AiuQ6m4ssnbd/+9rfTqeJs3Um74S/Nq8Pr4iTjaf3XpP3r63eCaTzniiuuSAlR3GqWzfMcxth0IBee5SBdGNCHH182vGbPDi90Mjo/Qz8diirRLUGND8HCvM17fmY59+49lwAhyKcTeqc9mdlpmk5KdDKqk/hMmkl58MEH0wnGThF0TH1fjWIIRjiJ5z//+X3dtvE+CI5juzWn8zkJ0AmjThmcNkAQnT/yyCPpBOmm5nlOFfSGFORNP5xASaaf//znUwYmTmodRwj5mPyOYd19993J4XobU2lg49y76VrBIsIYp2d2vZ/A0gmI+pZnoYf9nlMC0EcddVQ6irwGCF2lPUhZPad/Oql2hx126P7D4kr26W1fThB1Ou1PD4JPV9FbGUqn0eang078sOyHTmV1avusM95PPvlkyoo6IXkW+lbaZykbRMSb02ADMgcXZPuRFLjrVOA4ubWLXJvw1P3MFd/gxNW+G/k5lRzeNK1gx3iM1by+8pWvTKe3axIaTkvmKJ0ca7xtmehZzE/fsujzfn0ECE45Ry7JfJrqAvMjWXPGGWes5y+///3vpzl1Ui1y1+eKtROy4zyCPuVa3sszNAGqFRE+By+APexmmjaK33je/fffn/zbrbfemlbKnTTtc1wMP5DA5Dt917U14ecs8Vq/RmFd9B1efOYzn0kBJ5njmIHzAgbcE79wCnoZGPU9P13lOavr5hIg9Nl5UTPCLFMQAUKf9y/vxXk4Rnv//fcfbLnlljN7FMIEJB977LF1zxD0UMQ+AgQKT7kd797WHGHuiHpHjV911VXJSVpRuOWWWwYnnXRSIln6JFjo0gAociZDOK8m23HNNdekDEvXxqgRTGM///zz07+7NgROBqUGCF0lNpvr6Kp5zwOE2TxpMJBZ8lpBK2DTZD279O/0009Pq1p77bVXurxPfRNQXXfddUl32xp53nzzzYOjjz568Ja3vGVw0UUXpcBAQC0w+KVf+qX0+1133bXLcFJwMQ88zTsjYXDnnXcOTjzxxNZAEfYhq/vss08K9EMmsOG73/3u4B3veEcKisw7WbS1Puenk0AXeFEfAUKf3aeT9LAPf9mlX+973/uSPiGKs2oI/OWXX54SlIi4Jmilo//zP/8zdYDQxR5hgJVYtiEIcDaTz6y4CZr93wrQgQce2KnCAAE/7rjjUsJRonAerYlfNT0Xtn3iE58YXHDBBUmPXv/61yeMs3IIC6wmXHjhhSmxaf6bkgV9zs88ZDPsGTVAGCIdTsM7hy+++OJEPmYVIKjLtyoiK8B4ovUFeIj6EUcckbJzlHtYE0gIhnLCK0sAkARkgoUuGUPGdPzxx6cs8T/8wz/MRc8Fj0qXZKWHkZ6yMzKEkYFWOgEMu64WMSCOsgYIc5ni1ofMK0DgsK+88sp0WJmM2qwChHDKAgNlPhEg9KVvVlbYinenG8uwFic3K9XI8QMOINVve9vbBpdccslIBZgXnuYdQaI4esT9Yx/72NCVJHi73XbbrRcgxL1kp2GoVY6vfOUrrWPta35GCnMJLlirAQLblEW2onbOOefMNECwKsXOJO1ihRoBhXe4wjQrCOPYY6yUyJjn/hx26JegWfA8ahWB7KzSu/6v/uqvpq4o6WIGbfyq6bcSjHvssUdKhkgWNgUAkrgSDvCgaWW6r/npMrZZXzPzAIESEpiylc0333yw0UYbpQyUpT+fyb7apKj85Yc//GEi4Zttttm6cVMov/e9aJcCyuRQrlhBQAwprWXkAw44YD2Zyf74XG2y326xxRaDTTbZZN01yocov00/auqUDXD67kkJOD/RoywB4hlRvPspbRBRyqgrn/I7S5gUMvojA+9zxNO4nv70p6/XP7K47bbbUu0txUMGXLv99tsPnnjiiXUZkYMPPjg5JiU0lsqNIcpzyM0YlQlpgFtJAuUmZ5Eu52g1QpS/8cYbt5Z1NAUIofDkgRxZTkSUyM0fQKWO1DKuTeJqeo3DmADKpZdeOnjRi140eMUrXpGu1VdzTh9iDORt/MZszjfddNM0V77n5MkTkd9vv/3SNVYljE92lZGaZ1kMBGaXXXZJAOT3o0oWjMNqiWcIDMwFsqNsqEv5U04IXve616XyJvfaaqut0pJ21LyaN/qilpMOyJzQtdAH/TAmY6ND6hvpozG0NRmchx9+OM0xOdMJz+2rzZIAsB+2x7aUGBk/WzJ+Y5cxMza6Xeq78Zlv9kDXNthgg1Qah7DHCgJ9ktlCDOl9LBHDo8CE2BjLVozVfPveCgEdtelZ2UP+vQBZGYP5PPXUU5NtWyVzf7oN0zh149pmm23SfaMZGx0WPPuda+hv2aLuVzkLIiLQJgNEAbGPgHRSfZMwkPSQmYRbsA3u+tOk820BgpphKxAcJVzQxsXTKFkib/qe6y85BSbQD5gQh0SaX/Poe/MDc4O4kzv7Mh8SO+eee27CPDZNV2QFm1pbgODe7mMVdeedd05BUVtbzXhQjnlafDA/9EXZKT2EYWyOnZhv8+p72A8T+I+8fJh+wNuwN7ZLJ/MVBLr+9a9/PdkMHYjGfn1uVb0Jj/k8OulvLwmhe/w3bGaTknlKUA4//PDBq1/96oRh7q/fbNwY+H74za5i74rnfvnLX073xRv4y+c973npT9ncQ5bdXks2SvcEsH4XAQIOFDJwL1UIuYzGtcfgN2Vf2gKE4Av4iVV7OAijyI5f4v/IQKmOfvGxVkUlDq0kGjcMhPdd8Np1uFG82MLck5P5xQPcP/e/bLeNXzW9HMO+OKVT7B2+NjX4TK7G1LT3Ig8QJp2feC4Z4r78pfk193hTW2mrvpE9v0f/lEvDw0lfBDLzAIER3nDDDYmgKuFA8CgbMmez8QknnJCUiHKILA3+4x//+DrHygkwEmTQhBi86xgt4Rs4B8ZBqZMFGtEQsve///1JQAxUDZrlIpulfSb6dm8GbqL1E8kSCVO22AxkDAySMossCd8yVNSqey6C7L6u0Uf9obT6SWn1H3ktI86o5bOJyiZhcuLELJUCL397tsDK/yk7MEL69RW42uRJpggnckMeMg6CAUtrlB0ZINt99903ybGtJKApQCBHfaCoxgKkjBnxl9lEyjhNgY1nUUyy5ZA14waexiCws3wHiAAFuXMUSDrC5hoby4Cw75E7840AAhVzoiYaGQDEghWRvuBIrSQHASRlNQQHHM+wRj5nn3126g9H4XrLpnSwS3Y4CIH5Iwf9ADDmJoI9JNcKETJD7sZGjnT/oIMOSt0jT1lKc4j8qNHUf+SmqQEhJS702m/cz33pYNdyj1GBxLQEoO3+sQGfDQF7WSjBkc3e9Agwe7bxmOtc392T/Sl1YSeCdmCt5NBcmAPyRvYQBeSCPcQmZbbJxmSCgS0M4rjokACXw7Hh3PcCPPpGn32PsAgQ2DabQBgRF/rCEbmGzrivYJqjEmwitZ7LDjhDRIZTp3v63RQgKOXTT/bIjpAMuiIoJJtp9I387W+iWzCBbdM1mNg1QNB3m0HNWxCySfDUXJsD9sY3kKFG5nBNAAgTyAMOK5H0kgPzC9vpPfIUtdQwwAqoscEWK8Bx8rQVEE42bK6UewQIsACORVkB3IFZgns2LQExKkCYZn6WFQ/6DBDgl2wtjOcbzD/SQxfMO9ujj/TshMhNQwAAIABJREFUi1/8YiKdPqdzuX7wmTL59MTcu2foIxuVNGBH9Coy8MgWfwgr2K/rAo9dwz7MN/2BG/QCFrFl/sG/YZd72FhPH2Cua32OsEp80ll+Cp64jj7hG/rqWs+B83A8VghzGcM1QYhEhH7xazvttFMi0xEg+NwzyIj/hP+RJJ3EHvGbptYWICi9wwP0kc6zW3OK08EsuCcgMif2P/o3e2Rf8BBf4r8kh9rwmkwFaHDdS1u82MKeTFhobn0GI8mXb8j9L53y/yZ+Vb7Mhv+woqrfKgImTbblAcKk88OP8B+SQ2wBz9U/Y5GUaXvpBvuBefwMbunlHXTPPHUtDc/nf+YBgkEiP8iQoECEhgDpOAdrOYfhIc6MQcYemZKV0pBcDpwSIqCM6thjj01gLUBAEiiOzwgnMoiAxb0Zi0yce7g/AklBDz300GTUBMDgkUOBAQOjcLKCGsVHMKLECCEHNvoSm1SAEEJuMmXNORGAA2SsWiC86n1d07ZZsqlEAjAhRxyejUGeyVl5LgWWCRMQAFLfy5ozmlj6Mk5KEW8JMaauJUacr/4yXGMiE86ZoSH0+oBMATikwljJDmBHli+yp7Ekad7Nm1pGeiALA4SRKUptuRYZMCeIv2sRKYrueeSMoMWmVuPXP/KNYE2GJ68hHkWCg5jLZgjiGNMDDzyQ5hsRGdUiQKB/HAFDVrN98sknD+66667UV/3TpwB6cjNH5Cb40YwPIKnNRDYRQ+Nv2zOCVKupRjDpMScHOGWZEe0+2qwCBPMp2KQHHL6xsCtzwd5LfQ/boDMycGQrs08XgbzkAt1HENg/+dFRMjaXHGUAqiwcO1BSRvc803VsxdzDHnMvCOC4yHXbbbdN9gYz2vZA0X8khh2yeasf9AcBF/RwcgJE96ej9FoWn963NfNIZ5pKjKbVt3D4e+65Z+cSI3IjBxhjvBI+HBEsR7QmwVO6gEh5yx2SFPgkWJY0oBtsSiZNIsf8wgu4D28Cp9gCnUAiJYL0U99kpGPPQNcSI6QiVoM9D6ETWBgvMjjsTTirGQ/6DBBkOtko/ZNcM1/mFOYhqfya+ZJ9RvrwB0E7H6+xT2SbLrBt94Ph7C8CBPdnc2G/MFggKRjhj3EKCQaJNxgA7+gb/IY5cJv9IrUIqOQaHxUBHDygr7EHwT2Rfb93X8/HQSQJPRP5tJJuDFZDcCN2lO8xKmXcVLIWBBSG8ImSUfwg++R7+R1tEntsw6LACyQcJsJOuIR4miPzgVjDYLJjK/DPfMJ4uI5HkAV8dQ1eYE6H4TX5mRvYz+5gQuwX5OuiJKgJD3EFmKF1KUGNBAE/Sh+7vqyklFlf80POfCT9w/34PitW/EZbgEC/+Dj952dwX7/3/7wypys/mHmAoCOxRM2ATXSbgZU19wQEkCkbY9TaHHSpAASC4AKaNlIeGQVKh6DICDDY3CGXAYIVDX1hGIi5BoQEO0BKlovDRF70AclgAIi0TEfb0lBbgFBuuioBI5RRf2Tb9Y+zZMDhECcJEGR2OWXyRto5WeUAkfFn7IIPgAzoOGpgaYUgsvZlgEBW+svoKb4AQTaVjN2LY4ilWPKU5ZTBBXxN8hHs+T4ngOMECOYFcAkiI+uCcCOKQN8qzaiayiYAL/VdFkVWxXhlaoEmQKMvUUPq/7IDQI28ORfZ83ibSmnQSKcsqes5WM8AyEG4uwLAsOtmFSB4ZhNBbdpzU8pXMCCLhtBz2NG66AdZCcDMj9WApsZ5u06yQKaOPbPh2GPShD9WjWAJEsPxR2NDMmoctkBFYsH93Q+ZlRgZtiF+WICQ73mZRN8mCRBglz+SNHSOg4Y7ga+T4ClZlfgEi80nwhGJItdJHhkrXGOvTfIRWHOK5AM3JgkQ8gSDbLA544PgHHIjMdEWJKxmPCjtpQ984N9grPkSlDXNV1nTbQXAHNFB8xGtC34IbuE73Ie5TfMo8cj+9UkCAsGS/LPaFQGsxEIZIAhwEbfcpl0fASufB1P8jj+Isho+O4jsOAFCuQeh9LeT2mMTLgZeIKkwLl6morSIjQqktFi5hXVkwR7gBJlHQrAMEOJ5pT9n6z6TREOQNVgLV3E7ssVRyj2ATXxznADBPIc+TuJH2/YgjDs/+C9Oh2ORAzsREJN1Xi6X95EO4lewkn4oh5W0nXQP7VIHCDIEDDUn7F0DBEYqQ4CEDWucHaWO8h01xMMCBAJTikBJGXjZBAeuEeUheJbVkDbBgQxJ22vWJg0QPJ9MEEQrIf7mJGVNpgkQumy6NU7LmzI8MhkUUdZlnAABgRJ8uIdMSCkf4EOGXQggWYwTIMh2CFgFhRG4yRbKEgmEANOoqLsLIdAv46Rb5IXkWpER7UeAIIsqIyqzbvMosi+zpCSurSEsfgesgYlaVeMZtem0K+j1QQDanjVpgECnAWYQwHECBGApk58HlGX/ZIytBCAFkguW7uNNPxxuE/4EwZVlLpetgTpyASs4SIkCpMgcIwXh+JrkNGmA0EXfJgkQyk3KTX0eF0+bAoRwsEomSkdopdcKE/uYR4Cgf3wImxIMWWW0itu2N2g148GyBAj8NX/AhvIV8S4BQqy4S/4Mey117BXgl/g01QdWEYYFCPCS/Tf5DAknyU74o1YfruMFxjDsfI1hKwijAgTzNYk9DgsQyk3KTdeq6ODncCr+DJ+CgeMGCOybzbH1cu+mIMV89Bkg8KPmzjzxL20kfJT/7BogdJkfq+V4iL6QgxW3YUcFSHpKauAaMB4XlHRdlQGCqAnJniRAEGHKMqpTt7TX1Bi9eyPvSkE4b8uTowIEDp4CtUX9niUTgoCaICRDVIoAjltiNGoFAUhYmWEoxkGRZJYpxawCBMu05kWGX12kJS2Zd+NDviYJEBimlQRLkE2t7wABYWdwlmRz/UAQZXvtR0Eo7U8Y1roQAhkvTsGGOcGODZRWYGKvRtwfMbUyIHiw/KpWum0Pgu/JW2ADNBCXOHhsNQcIdCQIe5QBkl8X/bAq5bfKethKU0P22RNnL3uD4EsIDFtBiACBrnD4bc2LEGS3PF8JoiSD0oO29+pPGiB00bdZBAiT4OmwAMFepyjp6BpA9b2CEM+F51adlK4Oe9XpasaDZQkQ2jbNdwkQZKAFd0qBlIQ2nZkkKFAKCFv5X0knCUelJ6MCBFg8rMQTgZPEs0oJp60Ww7SmPQjkPU2AMKk9ThMg8HFWRvEPspAYURYTFQMCjK4rCOTI//HH+cse8v51sbc2/1COkz9m4xIC+jjpXr6uAUKX+dEXlQVKLmOPIh1se6MmfyKg5d/wMKVfeO2qDBA4aobMgMJRdF1B+PCHP5x+F0tRoQwyA+qDRe3ImuUY2X7EtMkhlyVGMhBKBGTTBBP54S6yA2rnlSVoloI4FAGHLLWlxragYtIVBEGIkiKrJcYiei9rbicpMRq2ggBAlQMhUOYHyCoxkiEYN0AQbNhwZPWBAeRvmJJpV45DZl0IIJl3XUEgN1kk9aIlQYtD0zybQxm2uacLQHEGxggQAFATgMgSCFJlltSTWj2jL+pMm06rpZeCGBkaWatxCN+oDEh8v4wrCGyJLtA3WakoSeuiH0BSJk9WiuMJcqDswDyaG/ZrFQmwKnko57cJf+ip59M9gXOuL2wPKTCn5hfpQDaVNcI0AUvbW3UmDRC66Ns4+tJGyHI9iuB3XDxtChBgmOyuVRx2muu/jGi8vGFeKwj6CMclE/TNc9s2Oq9mPFiWAEF2mr3xHch2tC4BgkSaZB07hP35Cp7EjI3yuIOEF9uUzGnyn00lRlZ8+QsYEPvw9C3nA8ER4Ag9thrmWjrVlDycNECYxh6nCRDILl4MgMSSd15SPE6AAMe8MACv4D8Dr+GnFXMEmC/tq8TIuOG+0jN7/yQIm95SZN8UniPB01Qy3iVA6DI/dFuVgESJRLD9FkorrZzgLmXjhyRrJR+NgW8s+WtX3x/XzaXESA2uUhGZF8AqS4ssMXIkW+2uRgAiH5k4xFNtnmjfLm6rAQwp3mstU6RWMF5PyKEwaBFX7GaXYbRMbdItG6l3V4ahtMOmOnVxyKdMu+8IXaaXsQoebI6jnMDA5zaV2rhCcTwLQREdu44yey7ygdhREmQE+RSQ+LfIj7E0NfJByjkiBMXvAIjrkRnZDgTEZ0i0SBGZEa2TqxpZ40VEBAjAxoY9GVByJGsEBSmhnO5RZk/IwNiNK+7fVBIlonU/QKvWENiRnefJgKuzVx9qNSNeP8ixm0fzQbYyCvqiD0gfHbCxS/mIVQlvcKAb9ARpNr+ui/mlQ4I0OmBzWNRAmiP1peSBBOU14Xk20PP0o2mZ2XjI1jzaSxKbq9qA0woEHTOH5BX6LvBk0OYI4adH9q3EpjsOLjaUMWT9NnZ6FPXyVp/yoEkf6IdnKXUQIHByAgm/p8/20ghcpzmZ1HNmGSCYX301B2yJ3OiVrNMwfaff9iDQbw6dA6djAlYBE0Bjo5akORcE0x9vweBYOC3JAAG7ADfe9GCVwLOVCQkU2Lt/C1ytNNEnOkuv6SVygQjQQzon4wij2Bj8siLmtwgBrHI/mIOAeD7SISPJZnzW1OASHGST+u3Z5r4PfeO4Zcg4FH0QJCHiJSYYH10kX7ZCp5vqts3dJHgadcrwhD7EeQp0WNbWmONNdAJ2z0dC6Ka/YQhMj5UbOgGDyR0+mXPYa1+D+YFDfEq5t8g42aJMLkyHxcbpc781/57luchBW2kYzOljfpYRD/oOEPgbtiygFryzD/4P9saGVHbO5ume4IyewWeb2uEG3TVfMJO9mXPzD7eRO/6SjgU+s3e+ip9BOl3rWbLUVvVgvT0s/q9s1lt2ZGQlGpUE0QH2DpfjTXxIPgyy/46dsH0cge9R9ivAhtcwiJ/is4yVbikhkXTkb5teRQnP+FEveFHvbzz8JHkgjZ6p/54fgbQSE1xqUnssAxX9FKzDQbzE/cuSn9ANWKaP7BdmqeCwd1FfkFa+zMpJvBURVsM0nzfhNSw2TnKNxCdZ4YJ8K3sj7zb/G69tbeJXTW8plEC20gyz4YU31Bkz/SJjWGAfAPygD01YiF/1MT/kiNvBMs/DF/BjegUTyyYwYwuwgw7TdXsRzIGxsAN4Ok6beYCAwMRrrShPbKDlZJFJToFRaiYFEAMNThapZ1wGbIIoKIVyHSU1kZyseiuvPZRZ4KiVWjBsJTAcCkH7nUwrBwAYKB0gQrKQcEboeyTeJNhEJHJDEoGISeFMOWyEX/+ACOXmLCJzxnjtQ0BybKL0Zhr9o3hISdv7aDlGMvEc/VdnxpBskEP8PRcwkZmxGgewoqCMgBH7zP8BDuBgkIgwpaYwNvT6zPjKd/wKIgAX+RmfezFAjrfca8EAyAdhB45kxxFHXbg59gwEHsHXfwYjQJCZITvjRADMaSzbG6u5QqAAabyC1ufGjFRaKUHCOA0ykbEBHACc86YzCLj5MHZ7MfLGyXAe5tUYARfiF00AQnbKdxBLJJt+AjSgnrc4tRmBIwf6hFQCMA6PvtADDo3c4yA313AwiAb9lEGmG+7nPnTIdwCs7a1T7MS93QvJFvSyM3oGVMxv0x6ZccBhVgGC8iiE2RiMlc5x5v6mH236bj59x3GTCwAkO7ZO95TW0AVEnw3QQwFpPn+CNPpHz/wOLrgnwiEQpRucmyDXSiFnaT7pLifFptklxy7AgBFwCOGWdTJvbAmg+z19FRRaDUX46TtMYGN0mGNr2whP/5ACh/bZ6OdZyHof+uaZdM0eFvjBsbCr0uEh355J5hwxm5U5Q3byJsCYBE+RB/KmD/TN3LDHsFP6wMbhv2fyBb4nZ/+WndV/jpDdCxAEb+7rM32WxKBrxgsPypOQYZdleNfwN+aNvZJRnBqNJPIPcCheTlHa0mrHgz4DhPA3XhpAv9kOwgWXEXhBA12DY4gP/RDcsSekTaJQ0ozM2W+QcWSKjbF9jS7AFXPPX5hb3IFOCPDhOwyiW/wNjJdocG1gg/uwRUk/OscmESd6B3PovcQnQowr8HlxJhEiSw8lb5BqmCwwj9e1ulYA27aK6JnGhxzCebrrVefkES8H8Vw8izwEXXwKXoXnTMJv8nmm/3iOFU98Ck6TgXmJc4ry680VPISt8JEvZ6twmA+NE8lhqWvwN7ITSDXhNbnjGjgUzmZ+bPI2VvZODvC4zf/CdXhR8ivz0Vbujd/Aa6v6AhT27rn4F3nABfyiaX+gPkpc9DE/sBjukzUuAafwP/PbdL4T+3C9v8kV15Tw4b9wSjY27r6KmQcIHBDljSbKZfz5Z3Hcttc/5tfFK+UstVAQyg/gEQOk0sSJ7hiM/2sUknHEPTkQ0S8D44BkaKPOXUZK9pnSAxjRFWfoOv93nQCA09AYan5AhyVNjhFJcz0lkglFgGR0/Vafy++bSBqCoS+a/us3o8nlIYI1YdFkPckg3hEO6IyPHC3BASKf+R35ISXGRtnKDIDfeL46vLwh0DZXl80YGT4FRFwYm8ja8+L+5MvAEV1BX5wREPcCwEHO1WfrnyDAfBgbADLX+fwiCnTIfKvZjyYgESGLls2Lf+tXlKDEdQg0JxTNEiEyEc3zBAiW66IBBGBnbHkzNhmMfDx0INdt/TAncU4BZ2gpGkHVV3OE3PseqeVwAJHP2pYwPY+8BHSu59w4Pv0GxPSHzk3bZhUgkBnZRWuz/1LfQ1/oKPsgZ/ZGTgiHfwN08kP4Qj/y+XNPm9H8FpgjkGwowD7sXVDNuZNt9JWOwQQyd3+6ArxjYzLZm0d6T6/Ms3nU9NfngXHmh50My+gYJyeFLLsXm0Nu+tA394ARsEPGO/Cu1Jmynpo9Wdpvekf4JHjKjgNfPTu3R+OXFTZuzzUf/pgb8s/9BbyAl0hTjo/6CjORHH6H4yz3OelDri+lDOARu0XEyiTBWsKDUi7T4EPpbxBnOsiH5I3PzfUj9+9snn6wWcSHHvNJsND/JWFyfKYjfILG3/qeLbNBz0ZYYQgf4Tv+Cj7QOf5cQI/Ys/eoDaeb8IM9wB2YIJhEXrXwY+FvBbA4gWfwneX3pYzxFjZqHHQZVub+izyMm7+CZxp/zV95xqT8JvrR5A9jXORcnpvCZuGludEv44MzfFvwp5Cv4Cfe7NiG12RlbDgcHLRnIw5H7eJ/A09KfjXKP8IYfsKzcTyN/6ELgelN/rXkF9PMj/Hy6eZVf4yF/kpWtTWcgg+io/wL/WE/Agp8atgrmpvuOfMAYVqSUn9fJVAlsBgJTEMAFtPj+tQqgSqBeUmg4sO8JF2fUyWwGAnUAGExcq9PrRJYeglUArD0U1Q7WCWwMAlUfFiY6OuDqwTmIoEaIMxFzPUhVQIrTwKVAKy8Oas9rhKYlwQqPsxL0vU5VQKLkUANEBYj9/rUKoGll0AlAEs/RbWDVQILk0DFh4WJvj64SmAuEqgBwlzEXB9SJbDyJFAJwMqbs9rjKoF5SaDiw7wkXZ9TJbAYCdQAYTFyr0+tElh6CVQCsPRTVDtYJbAwCVR8WJjo64OrBOYigRogzEXM9SFVAitPApUArLw5qz2uEpiXBCo+zEvS9TlVAouRQA0QFiP3+tQqgaWXQCUASz9FtYNVAguTQMWHhYm+PrhKYC4SqAHCCDE7ndmpiscff3w6VXjUQRNO4XPqoFNz/a7ptL2+ZvarX/1qOj3XISMOenGybhw6Fc8Yt/999W1Z7+PgF4fxONXWHwcg1dYsgUoAqmbMWwJOynXQoZPFnSY/qjnQyum5TqJ1iuismgPYnHrrWU40fdOb3jQ46qij0qGOa7WtJXxwYJWTnc8444x0kvOohlg5Vfgv/uIv1jvobdTvxv3eQWIOksUDHNDmYLfzzjsvHVKWt3H7P24/VuL1Dlpz6JyDTJ3yXNtTJTDTAAE5daqrE0Y1Rz1feOGFrfPAoBzB7bQ4Cj/sxLi4iZP73v/+9yej8G8nyF1//fWDvfbaq5f5doLfCSeckI4Id1z1sADBEdeOdj/nnHPSqYEIaNtx3tN2zml5+uRkRn10nPuzn/3s9U7x9Yxx+j9tn5bx9z/5yU/S6ZZOfHQa6w033DBwOqwTN52C2YWELOO45tGntUQA5iHPSZ7hFHKnczpJU3OqqhNVF9WcWhunyc+iD+eff34iOh/60IfSienDmtORL7vssmTTV1999eDNb37zLLqUTqI/66yzUrIHzvr3Rz7ykYQjs3rmTAbS8037wAc+++KLLx68+93vTjjtpGrY7AT6ZWr33ntvShLiL7/3e783tGuShNdcc026lk/mZ2bVnNgsEYn/OPkZR5Es9Pw8+TVO/2fV10XfF446jVgjjw984APp1Ge69853vnPR3VvK5880QAiCGg5tVIAgOgcWAoMrr7xysMMOO3QSmmw9oL7xxht7DxA6dSC7SHbp6KOPTp/MKkAw3mOPPXbgeHpK7th3gYljwU8//fRxu7yqr5f5u+mmmwbbbrvtYMstt0zH3H/84x9P8qsBwvCp74MArGrlmsPg6O+pp546+PCHP7zwAOGxxx4bHHPMMYnALUvjwOjpLAOERx55JGUYP/WpTw222GKLwfe///3B5ZdfPnjNa14z2H777ZdFFHPvR1/48N3vfjfJV7Z9WQOESYRLPtosA4Q//MM/HPzgBz9I+v/0pz99cNdddw0kZo877riZJScnkcWifyMQPfjggwd/9md/tq4rX/jCF5IN1wChfXZmHiAEgOvCqADBtffdd99A1mznnXcePOtZz+qsVwBG1Nz3CkLnDvz0wnkECAIBAdFOO+00dEVm3L6vxusfeuihlF2RmRQgaPSEvtQAoQYIy67zkgEcmJIbbVErCLBZ4uOJJ56YKeEZdz7mESBY2YYXSjrriuPPZqivAOFf/uVfknyVftYAYTwLeN7znpdIrgChtnYJ3H333YM3vvGNg3//939fd5FSxl133bUGCEMUZ6kChGkUfC0FCByVAEEZ07CSrWnkuRp++8Mf/nCw3377pRI3QUENEMab1b4IwHhPrVfnEliGAEFQoJwRgdtoo43WXIBQEwrNNtkXPtQAYXLMe9rTnpa4QA0Q2mX4ne98J5WH3X777alcMFoNEEbr3VwDhJNPPjllvS2Z/+u//uvgiiuuSPV8lNySrc9l4NXOUXgE2BK7ZSH7DEz0//7v/w7Uwaopt1rg88MPPzxlIGIF4X3ve9/ga1/7Wro/UnjdddelTTueUzZLTzbw2Bz3m7/5m2nDylvf+tbB/vvvn8p1fHfJJZcMjjzyyGSIatopFmIum8dhbrrppum7V73qVan/eYmRPQvuZ6/AL/7iLw422WSTtC+gqRmb5UH9d43SIQTXMqLMlXu95z3vSX1Ffn/u534u1cXuvvvuqWZ3gw02WO+2xmbJMe+//inFUsrFYDj/s88+O8lWbe0RRxyRNlXJWMq+6/uZZ56ZypnUQdukd+utt6ZaXDX9J5100uBHP/pR2sBFRuTlt3/3d3+X/n/RRRcN1EmeeOKJqTRBTWbUmCr7+frXv57ubb732GOPtIeCLjBkpEQ5kP4qU1Oy9dznPjeN0ff/+I//mFYGvvSlLyV5yAZYpVIGQEdsJFRjqLbVapT+ckbh8B9//PGBOmZ9JCtyzTc6/vd//3facO56Y9hzzz3T3NgITo4Ik1I4z6VbdMIfMiN39dEPP/xwmvdDDjlkcMopp6RlXwHLZz/72aS7H/3oRwePPvpomg/3NAf2vNjPIjP0rW99Ky0d25dzyy23pHH4nayyz8mb3s6i9UUAZtG3ed5TaY0le7JWomZelNqYg2984xvpO3P7b//2bwMY56UB++yzT5pbq6HRzK/fKAdk61tttVXCGLptLjW6yi6terFJGAhz4IeWryC4Bx1kb+yY3rPV3XbbLeHjy1/+8qR/GjzznfvSTXjBPuiiGn73/+Y3v5nw9u1vf3vSYbZmTPvuu+/AnifPc182zP7V7dJ7/X3ve9+b7PFFL3pRsklYyN7Kxi4/+clPJtmwATYC39n5Nttsk2zcHgSYBbs984477kj/N8bNN988yQ22+L5cQfj2t7+dNmqap2c84xnJpvmHpsa+jZuNGzssNyaYG5uPzZ8gDb4Y9zOf+cyEMeZ8rbe+8GFYgECn6K3KAjaiskDNPZ9LF+2HMTdsBM7CZzpvHyL/6RpzBdf5aOVh/LM9JHmjZ/weXTD3NiL7HVu310TNur2FiCbb19T86xt71w96ueOOO677viwxeulLX5rwgg6xR7oZNfF5X/SbveEv+kSP7S2gm1ZYNJyAXzQe/eUXlGTzzWXjn90n7z+cseneM2AMe4Vh+Id9oHCMD4Itfvu6170u2eqGG26Y/H3cjyzgTHAluIHH8ZewhmzsB+Vj7Snye3sBlJHfc889aWw4hPI9dgbf3A//gROwBV8wr3gcrLQ/NTZhs3N+HH585jOfSfiJR+Aj8IGN4xH0wf09f+utt04lm/rBd/O59AmG4SP6T77mSaNf+kEPfA9fXGuvh7lq44/GD7OMHabBfy+6cX/9xzmUP1uhJEe+Hr+j5zDGPOg/XSQPWKufcHPjjTdOnIq+wmk8xBz23eYaIKjXpOAEIQtOOThfwKwxAs4zDxA++MEPJmFxHnacf+973xu89rWvHfz8z/98cnQve9nL0m8jQGAsu+yyS3JOyCGlUHtmcpveKCRQ4ZQ4TBNuAr0VQN8YBUP1/KhxVe/HUH3HkSDR+uj5DK0MEICW37qnJS4BSFtj8IzY/SgupWC0xqr/YRRdVxD0jRHm/Wew1157beoTI7HkJmhhjECYLAD2C17wguSYb7vttrTPgbICBX0ELAceeGAyckucCPTf/M3fJMAzn5w4g0JoPcu15gHRAED5JjRvXmLc+kM2scmbTnAGjJzzBmSCFwakMUpGBaAZELA+7bTTUoChPzbHa4xRv5tWEIw/QIN8lW4pOzL35hlwATWEyf0POuigpEP0EJHSb/3ifBj3iWiOAAAgAElEQVSnGloy8UYVzgiI+I4cECpzS0/oAsADaPqvBlfgQN6A1RgZP5m6XiCs/+QagAWIBddkOqvWFwGYVf/meV96BXc4ZPaCDHAw5pyNI6ywBAFmY8DffAu+NRsX4QUHw8HRT47TfAuI2YgEAP1HQugdPRMk0AGYqUWAgNCwLTpKL5FyOsHe/Q6x4VwC8wSYdJPThwscun04fotUsz82D08FDTBO340t8MbvJCrymmpOmzMWbLieMyYTdsFRl0kZjlJwoI/6zB987nOfSzbDebMZthX2yjZgD9tGgNgIZ2+/WluAAK8E8fxJ+Va30BnzRF4cvOCcjbFnAT7bQ5CCINQVhGZL6wsf2gIEeMxOkF/6ZC6RR0GbueKvJXjCPvgBuhQ2wxb4Jzrnt7AWgeRzyrdPCUQE24ICesY2gj/AbkG0Z/JxESCwXz6KDfIP/sYf4vs8QKDTfAwc8Qx21vQGLIkvATM/zHfhAcbIxvAA/TCmaKNWENxPUKLvef/1AzFlK5IZxkxGOJWgmQ24t6QDu4cPAjN2kd/vsMMOS1gFFwQn/sBCWILE+i3yyofiMnDLPbx9zPxEAG58cFCyz/3YOZKOx8EJyVDYK8Ei2Yav+YwvhVUCLhgEy/AduoM8l/xQIBlNotf8GT//jWiTL4w1XokW/EC/6Kj59Xv3Jz9yes5zntPKH/WfjkiKCxLcA8eBi3QBF8Kn3IcvxzsEjPiVwILOGyteBlf9XjAEq6KRtUSrN6pFkqlPvzjXAIEBA1uTQAE1DjHeCmCCOb08QPBvToISUA7RHAFzWpwJpc0DBKCOYFFcysFwTZDntr0VIxygNydwXDLlHIfJAhz5JjiTZNKQVU5do8wInsAkDxAoMSM0oRx2meHPJ5LjFnS4BwcajREhgcCCYTGKrgGCezTV6IbDA4ARdYrgEROGzAAQAwbJaAAeUqMhtf4vA4NcABEEmBMXiQNWQMb49t5772S4Mg+a+UUe8gDB/TwzVow4bkqPoOsTMCQbToBxIvuuYXxkzZiiMSpzzigZtYzfsAAhHz+d8QegAUX9Ed0DPOPROBZkwh8gb4x0i276TmaFkRqjuRLEAMqm+bIKwGGRo8CRvAETHUA+zZFsLFuh44IZJIq8BY70yjzlYNEnMLhXXwSg734t4n6CVM5aggJJYQ/whIPQZA45dSAPnzgoeihQ1gSECLRMI8CXhWIPCLY557zputUgq2+BdzJk5j9sKAIEK6TuzwY4G1hEl9jSoYcemvRfX4Kgc8Swlk6xP05FX8yxLCt9R6iNkd4ecMAB616y0BYghCw4UfqKHNFX+syZcuz0umyBP0gCYgLTXcfOSnuFX+TDWcJGz5JBY6NlgGBefCcRxS7Dbpv0hdwFCILwCLwFdbAHoSQPz9NqgNBscX3hQ1uAwM9GsGs++KIgzPCVPcXKrUy3ABs2It78A19Br5BgSUI6bS4Fom3N/diB6/gbPADG8vHsSgCZBwD6CMfpGn1ml2WAoE+R2cd1hmG2hJ1+8svsnr9H8iXw2DY/iFdEGxUgxHVN/Q//xVb5L2MQ/Hs2myNTq/ts0HPhSeBZ3M9YZcr5PXxBwC8RAs/gCz+OJ/gchrkOj2OnESDoY/hD2GSVgNwlYgQUfHKsxEoUR1IS9uJ58IN+6Ks5s/nYKpGEavCDSCCXAQKcyMcP6zzD/MMxiRlyxw1gs98H34HREhdt/FHCCL7B/njbGf3FUfXXmIJPwUrYA4v5B1UEniUgIAt47lnGo2JEw02NCxfAdWbR5hogxCblAFwDAvxhUE0BQjg4wmGIHGIECBRNJklr2oMAwAyQ48gzyKUghxHukmAjgcCJkph0fY+yF2AUAYKghnNHHBhfk6PM+yHzYSmcUebL14xOaYtnAEvg0leAkG/SbQIQ/SsBqCT0rgHE5G/lA7hqQaj0HbhpXQKEpk3e4UBkRAGIkpzYgyEyzxvjiTIiJGhYgJCPvwQoAMPw8myNzCpSJFpHBttqGOkIcLAKoq/IPX2J1+Q1AWKMgYyMMTIYSjx8Bkw4BnokI+Nv4DjqXI5pQKMvAjBNH5blt3mAwHkGwAc+BaFGYAOfEExzSaeBP4Keb8IMvOMIgwD5N9wIvDP+pk3KsmMym5IZkihWCuinlQqBg/shQtE/5F3yJX9FKeLBSdNXzo8+w1hOsUuAINOJDMjy6QPHx1nL4ApY2EjTiyaGEe7SXhEm9+IMEXr9kozxTIQm8FkSRWYT8efgh9mFLDL7Jo8oQwk9Y6cwDKn0PM+pAcL8AwTJOSsEEny5vUUAK/NL/5BEK3bsK175iWBayZZo4wsEB/xTlNsOw5Q2wt3kH9ko0isDjdApXWGPEQDAT8ksiTJBKKKJZA9rMuL4SqyYx7USDsYs+BFwIMNN/rnt3m0BQrlJl7zglzHBAs8r/a9ndOULpT9ns10CBM9oegtUTrD5QBgHH9hoYB3fG2/Fghu4U1uAUI6/5HsCGfqDBwjWNDxE0tIKg2RgGx+DLe7Hd8NlK59wxTPzACFPkMb98Rtciz5IhOCBfme8VrrokcAD5sKpWbWlDxCAgx3oJjhq75FOHVcXGK9CnVeAYCI4VBGd/vi3rLklMmAVBsGxUQrGhWiKypv2QMTEupdlf9npfMLjfggKOVCQtR4gIDXmW2aCvPKGYFh5YICChUkDBPcHCLKzba0tQAAMssAyK3TCsqLsLuIWm8rLgCSeob+ABODpP7KIyNAJ5MUKiWyGFSwOCSGjf8hnNARJECHLOk2rAcLPpDdNgADc6ROn2hQgxIop8q4USBsVIIT+uFYCwvJ9UysDmPJcFgGNIAbREXAiVV1XENgG3dMELF0PGxonQHBvDp8tWZ1gD1aOZfcEReHQBSoyuGyC/cfKTpNM8vmIfRVxXdwPOYLHCE0NEJpRpC98aFpBoLcIER/aFCAgTZGBdg08FAAgzrF3QNYeIRMwKIGBoe45rI0TILgPsobgIaJWE9iiVWONfPhvpcd8ReylG/Z8OC5AtdfPmPLmfla56LnyHW3aFYSVHCBIREgCSqzmAQK5BB9UCiT5MWmAgKTjevhEW7liGx+zAqCPkpj0QHLDXqpyBaEMEPL+Wz3hzzVjtDJGp6PaxH6bwHQVMPQwb/Rlmrb0AYI6LRn72Ogqg6suHggoa4ll5HkFCLJPHAwQENUjYgicDIIlJxmE2KRsCdSypGU1JQnIXVtmC/kDCOqcOcJoMhCiSUtIMsic4loPEOK95DIIygHyxthkWBi0bOykAQLiIyBT/5mvIuTz3xQgIDBWg9SfIzJKy4aVGMWSaowB0YsVhCD4jNSqhcCDLViCtNJk/ACAPciYRUNCZYyHLaV3AY2+CECXZy37NdMECJII5k/JmmxSlNiVKwjmLQ6HHBUgWMJXlwpv3Bs2wEKEFz6Ze9nvYQGCumgOi8OxzM5WkJmuAYLxRJml3yoxlHFkI0oLkPSmjcrjBAi5vXGWEin6J3vG4ao5jhJQwQ7MRdSQ+7ZldzJj31Yb2Hd+3o49IkgH/Iix1QBh/gECoi9Ytso7agVB75AwmEtflPEIePlnBJgOKttUnpG/NKBpVOMECMo/lLGwNzoiCQSzrWzI8EYGnD8QnNA7gXS8Ta/p+QifhJLVwXyvBJxHAPVfmWG0tRwgKMdRMmllJVZgyQUG4kyy++YmJ9xlidGoFQRzZy7oE18e/M0zkHFlpm18zIomXZR4Qexxt6YSozJAwCH03+qFlYrYC2Xlwqo02zBmvBLuRpPcEYjkbdpzsZY+QOBcOT8GwjgIBdFmZHnZzrwCBCUfSmYEAZZB9Y8CWa4HECY3f4sREAEOnL9lw/x0w3wiZVFik7NMY5BDAZHlc6VMFI1RrNQAAeBRYpkdpVcIChINNGMPQpcSI1lzBoegAAF1hNEYkCXaINB5gICwKHnwu/IchDKjL/BDmgRs7mE+9FcNpntYgm0KEOJgK4GLa+07QWwYvD04+sWJkYMsJQCKmkK/dY1sl7Kk2GBKt2QxlOghL4hYbMKeJbGuAcLPpDtNgOAu5k/wZ06tfNIn+3NkDNm3bDz99KYT9q0cDQnmkJDeWClTX0vf1TpHOZ1somvtUUFwESN6BCuHBQiynzbR00dBgt9zPMojfc4mrWrkexBk0QTOghGOy14KwYDAxj2UUyAw4cyaNs6NEyDAP8EQG0T09M3qgbGr0fb/CBCUArArsnK9JFKUBZR2IgkjSAjZR6LJ50oljT32JtQAYf4BAjJsn6L5VDFgPiRqrAZIGsJO8x/zJilE97w4IFbUcAblMjLusJMdNW0Mzkc3ToAQexoEA5I3cFmy0L4W5aURICjZ04fY0GssbedpsCV6TO8FNHEdsgnz1arnKwsrLUCAf+ZUQCdRIoknCSq4g5HkFAfrjioxwgeVlsVLEazQk4cVJf5dgGnlsQwQ6IgmqTYqQMDf6JBAgP4oe9Nib4O5auNj5kpCA98wd/y75+EE8F6SGf7DITohqafpP74A18kkb64PPhkHN86SA8w8QECU4s09DJ4TQaRlvzTELP4NrJFHAiQwBi/DpfZeBNd2sjJnFJs1ODTLc0p6LDdb4vG3+8WO9lygCD1HSeicnWXAvG5WdtbEKQ2xQsCAAQMy7/8MmSOS5XAPUaXMk+DFqoBoT0BhjFFqEsuD5cQiDpbKKZGgyG/V68mSkZmsoP5STs9iQPo+7EC5sv/IJqMxHs+juAgCQgxwrcooYQF6FB9hMX7yk5FTSqW22IqIEp8ogTE+hDre2mL5lxxkbii1sSA36vY809yaGxkAv5P1E0kjHYiKZ5Ef0gRgza9NyFYP1BRyEpZiEYDYX4JI6BsdAjyasSAMQBXZIk+fleOXsUXKkQPkTOCHJKlv9TzBINkhdgI9c0EGAhX3BgT6zinFM2WIjR/oIU3AkeFzFHSCkwNi/u3+5hyYICnlq0ujBpF+A9Y24tMnWNQA4WfSREA4IOUubJ89mLNY3hUwmkdZQgTeiqc5NFfs0/yxF/eBefBJRkpADOj9m3OjuwgwnZMo4DzZBdKh0ScOFhbSHwG238FMtoJEyYxLSAiko3/67G0ZUbvsXkrZ6KIWtbz2zOizsVmtQnqQcHprrxHCr+/sRyAuMIkXRbiHPhi3fsXb6XKdlOUP/IG1sDRvcJS9ky85clDwQj/IJYIn9gqXlG/CmcBndgsbBTGyZ+FbSrswT8ZHVvax+Y1sNZuF58YrIHGd7/ktfYrNhn3a2Uq9V1/4EC+hoF9In7/5Hb5UcEC/+X86jqD5rszEI+h8Cr/A50WpXiRi+Ld4pXqbvGG0++crFnGt+9BzuoGTaEgjW6Cb7IUt8fvwmy7x4xJ6bJ5t0B9kUNKJPoWPKvkIwqsiQZCEy8B6ukwX+Z5IjPJN/CkCywe2JR/dv6n//Bds4cMkrjxHX/lfNkfmsEtwwv8qm7Z6xzeHXHN5NPEFOMimNPiI++AsfCY+5lkwjp0bA5+LDwjkzD0b1KfAM5ijT66lB+6PO/DN5thrQJFqLyDRxyj30k/cwL0lOWAGXlKO3+p8lCabV7oHy+AL7Aw8pftIPpm38Uf9Md/BdwQkVizxVeOE38GT6CadgJ8w1XXmuuSKEtRKqtiC8TW9JrdPPJlpgMBYOaa8UUZELBrCY0Jl6EwCBxLNchtANmllQ+ZNNqeB1PkTTUCgqQOPRhFNQEmmLeOIQqMxZJ8xfkbKiUXTd+RRYCArLDhApDmaeNVaOV5KKKsQESfFl91yn6ZmUwqCiVTqC0PyJ5aZENxcfu6BeJTvdfZ5U/9lvfOyHIoqS5HLj4GQLwWP+SA/xIEx5/Lw77w/SIEgIt5S5Xv30z9KLapmqOrqzAfwiRItsgHOeQOQ+fyEvgBFjoXOyMKbB0bm2XmdtaAifu9e5i4fv+cBxrb5cX/9E/3TNZko9ze+eDe9/ub6JaDTDyDFmM0Do0fyOA/AHysWgN+StN/EOQtNQG9pFMgZd2wE7xMImu7VFwGYdT9nfX86ar5zPOFYJC5iCVsf4ENpX+whziIQDHDK9AnQm2+rCnkwiOgIEjgltoaUcH6cpKDAClJ+vXsJLOlPnN0S+sPGbPLMmwAnmv4gNTAHzuqL/nNqHC37jCwevKN/sR8AsZe9jXd5ww99gCdspO2NcQKp3P44ao7OqofvcnmSG4JA38lLcobDR9itkLDlHJ/JB6mJml3jzPG8SU9y+ZkPY8xXJMs3z+X4M2u9W/b794EP9AfhLv03f8EH0V9YCZ8Ff/RVMNekX6WvJT8+gl3KWEf5XpNc2WXOPzzb/fCFsn/hb4PcWrXjf8I/SGLBC/xAQ24jKZrbI39E55sae+MrJdUQWjapXDmCg9L/BP4gnXlrkq/+a6X/wpsi4x73YIO5PdmAjeNIZEVr4gvkx06R9xJ/4Bp5432uk1zDLYwZj1K+U8qPrNl6zj/0Db7CHUlAfhReqLaAZ3lpcOCXvniuAKMcv0Qpsh+N3pBnYBw9gE/GL3igT8P4o/vAb4kY2GKVxGtcBWPwHffQZ5gjMeyV+nQmv3+pG/rCVthemVyZBV7MNEDoo8MUySRxDk1NtF2S8j6eW+9RJTBLCbRtUm57JkMV6ACkCBZn2T/37oMAzLqP9f5VAlUCi5FAxYfFyL0+dfVIoOmtkMNGZzVXQCWoaKtE6VM6Sx8giNRknAQB8YowEaYMu8yxZZ667NunStR7zUMCXQIEr620ssYRy3oox5qnrlcCMA9NqM+oEliZEqj4sDLnrfZ6eSTQJUBQ2mUlzGqLMmqrKFZ7Z3EwWimZmQYI6mpFPNM09agEYWlZPZ+m3MKKgo29lpHyd3sPe5a61HzZaZp+1d9WCUwqATWISousjjH4qNEs76dMSUmSZWX1mrEPZdLnjvu7SgD+X2LKG+K8lXFluJavr3i7ume/D3zAD/CEeTblKsp4a6sSWLQElHQp5fRHOXL51jdlT0q4+X57GO05UJY0Lx470wChqQZ2kRMSNWuL7EN99tqWgI2jNk7mJ0C37SFBsOJAuKh5nKf0+iAA8+zvrJ6lVjXfUzOr56y2+1a8XW0zuv54+sAHNfl5jfs8JNaGt/N4dn1GlUBIoOTHsechl1C8lCbeYGgv47DN6H1Ld6YBQt+drferEqgSmJ8E+iAA8+ttfVKVQJXAPCVQ8WGe0q7PqhKYvwRqgDB/mdcnVgmsCAlUArAipql2skpgIRKo+LAQsdeHVgnMTQI1QJibqOuDqgRWlgQqAVhZ81V7WyUwTwlUfJintOuzqgTmL4EaIMxf5vWJVQIrQgKVAKyIaaqdrBJYiAQqPixE7PWhVQJzk0ANECYQtdesOlzIoRlOyZvmdVPeyOTAIocxeY3lvHanDxu2wzicYulwojgFewIxdfqJ8du465RBO/Q333zzdb9zOJS3VemDHfxtze9dS3YOeKqtHwmMQwDosIPgnIRangLdT29W3l3++Z//OR0ARiZNp6auvBG199gBPw5ZM/dNJyiPM1b46rV+DqAqD04c5z59XWujoAPinBRrY6HDombZjJ88Pc/JvvE8hzU6OMvBW06RdTJ8W3NApFckw85Z9XccfJilvJbp3k4advCmt845SHCa5jXXTmTmF5cBP+gfveKz54Hx//Vf/5V4gdOMHRCpOUjSyw+80VIfyrf+5PIex16mmafV/NsaIEwwuxTXCXkOaeMUpwHgr33ta4Prrrtu8Kd/+qfpNECnAS+yOc7dkejez8swHXs/y+Ydv8bvPAtjd3pkNK//dLqgw8GcPtzWOFJv+/H70047bZbdXVP3HocAPProo+nEW68sbDrVe00JbjBI5NYrbG+77bZ0CqpTf1dzu+OOO9JJr05FdqLspM1r/dzLSaf33XdfSh4ssiHrAt+rrroqzeXtt98++K3f+q2Zdcmp2V/5ylfSK5Bh8D333LPueU6MlZDae++908nswwioc1ac/Ao7Z9XfcfBhZgJbshvvtttug+c85znJDiQGJm1ObeeH77///mRXi8YPCUOv3PZGN/oE22bZvP72lltuSb7ECelXX311epxE1NFHH51e/e07wVNbG8deZjmWlXzvGiBMMHsySrKDAgWRfZzPMMGt0j1uvfXWwcknnzy47LLLEslaZOOgrI687W1vS91wBPosG0Lg0Duv+Prd3/3ddNx9NK/AkwX4tV/7tXXOUAZB0CCL5qhz7cc//vHAtb/8y7882HDDDWfZ3TV173EIgDngRAD2MqyCLWKiEFurgFtssUWya2TyiCOOGLz73e9euIOftTyMl0P2Cr6u59I09Qm2Wpl961vfmt73LVu5yOb5MrnItneVy+DPinAbp+eRpQDAM/PnwT4rUs961rMGz33uc1MCR7N6qkWW1b+tePjDhpGpWbRx8GEWz1/Gez755JOJD7CDYdntUX1HhAXIhxxyyFLgBz9sVevFL35xSsYFYR81jkm/509UMFiNzJ+Hn1hdpPtknK+i4Q6nnnrquke22cukfVqLv6sBwhLMuiz9H/zBHww++MEPLjxAIA4H0OmPA71mHSB4XqwAOC07DxCapkbf3vve9w6OPPLIwZZbbrkEs7d6u1AJQPe55YxkDy+99NJ1BNLq4q677roUDr77SJbjSvhjFWHRAUJIo8vJ531Kruvz6F28G32ep6wba8WHPmf8qfdaRvxAzOcRIIQ0ujwPRkgy4k8333zzbCdljd29BghLMOE1QPj/EqFRAQJnCARuvPHGRB5qgDBb5a0EoJt8rYJ96lOfGrz97W9fryRkGR18txEt/qoaILx/cOaZZ6YEzbAVi0ceeWRw4IEHptLKGiAsXm/77MEy4kcXwt6nDLo8T9IQ9vrbqktt/UmgBggTyNISMOO96667UvZGUzP34IMPDtQOOinXd3/913+dSpCc3JjvU/iP//iP9L3rLUM+85nPHFx++eWpblGN6bnnnpvuaTlP6Y1yJgREGY0SDuU/G2ywQboGORFgPPDAAynjr1Z/3333XVfmIbpW+uF5jz/+eFr+33///dfbDKzOVl+c3KvmV4mIsid9G7WC8J3vfCfV5toQZ7P2zjvvnIh+1Mci9ZbJ//Iv/zLVD5KHUiKlGNGaVhB+9KMfJfnpV9QcKsGy7G5Z0TOe/exnp4BBH/Tdsrv6XM1Ga3LxbEu+nmtJcs8995xgxtfmT8YJEOjYvffemzboqZnVzCv5v+QlL0mbx9Ww0uVXvepVSc/ZkROlbUSzqY9e/sIv/MKAPtoMR69sstx4443THh3L93vssUeae3tX1Kh+61vfSr/dZ599km47ll4Zhnb22Wencgy6H3tpDjjggMGOO+6YykbYsD+OuKe/rlM3rB+5flpel6EyFnppPK997WvTNfrKVs4555z0ogH3Z4NKA9hSrCDYbGs8yj7o6E477bSuBME97FnwfDhivPpAn5XcAGmOz/+V1X36059O5ST64BlKmciazpMXHQ+8QSBt6vM8S/Zq0tnmXnvtlYinsbEr86Ll+HL99dcn+SjdMx7jbmrlfJGvvpApufmdUgFyUj5Ujt89jZ98YvzGZI8X/GiaP/JQ0uX77bbbLvVPK/EOhtEX43/GM56xDhvgivp+8sv1J8YXNdDGoGyRftsrZRPqMMI+Co/dn6zvvPPONGewiS3oX15P3bSCoIQLHsM/44X5p59+evqMXXjBA/2jG/YxeIYStxe+8IVpWAgUe7PvjS7R9d13333d9+Oi3Dj4MO69V+L1MIVe8nPs13wqy2ED9NT+LHsK2BSMOPjgg9cr/7L/TwYch4CjL3jBC1JpohJF+HHttdcOXLPZZpul+Q9bJSvzqSwvdAhOeVY82/OUL9sfkftddkbXg6fk37vO91/60pcSz4BlfPeoFYQcz5QD/cqv/ErCGxv8oxw7/LPyODaof7/zO7+Txpy3MkAgY36GPPEcfwukyYZ977fffuvk49rcXtxX6RK/ARddT5Z4Afur7akSqAHCmFrBQG+66abBxz72sbR5TU0c4KWgCD7FR0yQcbX8FNEx2e9617vSk/z+oosuSg7nmGOOSXW7Ntu4FwKF6HCIZ511VnLMFJxSAw2gEGU/sWnXdQIOxu+NP8cff3xyiAIXDgBZAjDAiSEg1K7391ZbbZUICHL9oQ99KJUVqfdXX6gv3hIwLEBA2JT66Bcne+WVVyYnrw5QPzQOjJIdddRRCWSUEAE/RN/ztTJAMA4BhawYoAO6SJP7+D1ZnXfeeYksMXLP5bwB1wUXXJDuaYUBkBx33HHpHoijPpJFbd0k0JUAIOlXXHFFmgOk1eY6jsnGzs997nPJOQhoERsElR6wEWQLgRcIImLm8fWvf32qqUZaEcoTTzwxOVw6w+78bb+Ot3to9IQN0XeBicCCPdnk6RokXmDtBQA21tFzjhm5ZDuc0wknnJDIsz/q3m2OR9DoFnv1W45OppZ9u7fg5hOf+EQqs2D/rmf/xhxv8hH4cKo52Ufu9ck9tt5663X2x2m5P1t3L7bqfuzd2CQIYANny2lLHsAcyQIOGTmAKf4voCZfBNimQvbCmSImbAF5ZOe+QyrZBrtSq2788QYWcmW/3iDm5QGeXTb4l88XWcE1+4SMA0kx/8bjWuPIx+9+NrjDDAQBuTU++IpIwyfyNafKt/xxf4EW3YI/OWGBd+aV3BF5gaVnvuc970mbp92P3DzzsMMOSzht7PDTiyfIwFyfcsopSUY+R5Doo2uNa1iAMAqPYSb93X777ROpZydsx9zzEbFfoAwQ+BI6ArfZCIwWJAt0yBce0g/BDDwN2bt/BIKCB3IRoJszpZo2Qk+6p6IrPnRDm5V9Fduj6/TGm6dgg7mkx+zr5S9/edo3J4mBEzz00ENJlyOZIlCnF5Jd/kba2ZKkA5/1lre8JQV2CDB8Mf/8u/llo3wme/cZTKMPEgGCcToHQ+gz/NXYjnvAC/jqe/YtKQCTNToP02EJu4GfAsxhAULwCdTFLvsAACAASURBVDjHdvQHjtE3nwkS9I896jPuEP4ZL1EdwNdHywMEwRbMkkx1H3YjuQrX2A6sJnf9dy9jzO3FPeGIZIQx8Av6xp5OOumkla2AM+p9DRAmFGy5BM6Rc7YcPPIBEDTK7pV0jJEBclZICgLregblOySFMTKqtj0AiIusGtIuQOD8rFYgSQiDxlAYAOOWOdJPDj82HXM0MnwIvKwnI5UVQDAYDRIHiPwOwLUFCEiJmmuZ4XijAXDjfICeewt0ZGfJIzbQ6ZsMJyLJeJGPtj0I5XiND5GQDc5LjJp+r/+AGpkAMuQmyALgtXWTwDgEAMFHPsytAEED3N5AgZADZs4x9E/W1PxzWqFvsljsQ4u34rgnp8URcKz0SNYTwRYgykh7JsKFFLmObph/AYL75/dDBumExjGwHQ4n3rxD5wQZbFLmO5y+58lUa5yy+3NSAmk205TxbRq/38oG6iNnj+haHpfpNa4cDwQbZBf6LTuOWPgNvPFHnzjbcHDkRQZBIBBARBiRRgiRBk7xjDPOSIScQ+ag9UGfEJPY6I/Qh53aeD2sxXzl8iVbJDiff+M3Tv0jc45c39wf0dE/4yNfgULsQWi6v/7kBILsYI/ECt1yL6QCcUZEYB7yD+uQL/gr4CO/2IDsMyQfliEgyIZrrOQiH4LZNkLdBY8FbwiSe8E+GVDJEvMhUUNeWtsehDKjyoGzU/LMS4zK34d9siFEVUM69acGCN3wsMtVpf3JYsMURB9h5S/Nmbky/xJqdBxBZ5fwMTb5S5jQu/wlB+X98Q3fI/4CBLYrkcL/s33/Z9/8nkSB+ZfQwQfgAW6isRcJOf3ARQQCMMk98QfNb2HesAABHsMbAW/wCXrt+Z6BM7i3rD9+IHEQmOothIG5scG71Pd4i5HfCL7Y+rA9k+XvYQE8UtXhO33ASfCo2p4qgRogTKgVTTWygFqL7KZ/lwYNjJF5gB7ZonIPQpcAQeCBfMnGAYKmxsmF4433KDMwGQHkCpBwvFY0kIMg8V02KXP+ghlZLWVFZRNAIAIUTOZN6YiG1AhiEEHZL4HOLAIEgQS5GJtAwfM9bxneqz6hys39Z30ECOUmXbqFBAFqTsK8NOlbF0JIIE0Oo2uA0CUA5Ug4efYab2ficK2IsR9BJyfbFiCU4y8JHcLsNahWSoKYu6eASvZPtrvNPqx4cOqwCLGVKUSQYUIeICD//m9lUZOZ9xuOW4JAORLbsMrg91YLOGiERskDeY5qTfPVVENdjt9Y2SQyTieilfjaRR+QMCVWxvqGN7yhscvkKqgj78AkQaDPZXqt9uyyyy6JJAkkoiRi1KZheDcKj40dQaT3gqFoViWQKcGssiCt7wBB4Mfu6KqyFf2Ay+Y+3gY3ao7L78fBh3HvvVKvL/19F0Jrbug7u0V6ozXZz6gAwaon3bZiaxWh6Q1WVgnpl0RJlCQpQ0TgJTbhAkyCCZI3eRu1J4D+wiQ22HRWAuxExgUScCV/4xm+YHUTrvq31neAAPPxM33ExaxIS7pIbNb2VAnUAGFCrZgkQJCJAgJlBD5JgCD7IHvIkCJjWw4FwPhemU356km1hgiBLIGMQp6R7xIgWDVg6Mp/mjJQQE//7EvIAyZZSRlbICZ7ggTNIkAwJhlYy5rIk+cge/Yt1NZNAuMQgLYVhJUeIHC09DsId0hOllnwKROMVE8aIAhikUUZbtmwvMmuCcKHveULprAjqx5sjW0JuocFCGwQflmxRAKMTyZbVlEmkoNW/iejJ6MYjh7hEFDkDanQJg0QlFmQXR7AuN8kAYLVEFhnxUnZTVOTOLCaaPWifA2lBAnZ0NlRGfny3oF3w/BY/4yrfO2tUgc+AV7HuQ99Bwj6K+CTlVaGBZtlUiNo7IYI6181Dj5Mcv+V+JtJAgQlbObfimL+Fr9JAgQBrZVRhDcSMKUc+XqrVnCrDCCsmAmQ6SlyXlYQjAoQ4AX8aHuJiKSG/tlrECsA0T/lwf64R5Qo9x0gwFL3txpi/4FVVDg37CDWlaiHffW5BggTSnKSAEFNL2DuI0BgyBwaUgFIYgNePhyf23sgC8kQmhpAk9UfN0BAvhEKS/WyDWWztB8rEuoPY1nbdZHhlZ1A3mcRIHiOuk5ZRdlBpIDzRopq6yaBcQjAag0QBNax4pdnWoPAWaLmUKcJEJAChDT25JSz02YfyKQyERgg2y35wPmWJUblCkJOwNlpHOgkkFdOgKj4g9Cy88ii+8z1ebNRdpoAAWFnk30ECDKjAi2BTFspoefJUnpejkkxJgGbVYRxA4TAu2F4rH/2ASgbUdIULRIy/pbN1GYRILivftqbJbCzkjTNq7XHwYduiLPyr5okQIgEodKcPgIEdsqmJfHKpEP4XyRZcq/JBgJv4Mi4AQK9Fnzw8U0vBBEg6J9yQNUH+aZoh6XarwlzJC+1vgME5VzKGpVb0X32JqkgmLKaUNv6EqgBwoQaMUmAwGjVxQoSGEGU/QRAyKZxTlGDS5G9OYWRyGxZHgPusQfBfWSDZMkZo+sYnmV72UTLd8iNPQaWz4GBemkbLBFm2SMEn6HILHFe7hEOy3KgjaRNTb8YOtKkVjjemuI3AgKlTTIU/m1JP8BCphKIIB/IVWyqa3rN6agSEBmA2DNR/t79jc++D1lWZRN55kBJAFlVUGg3gHEIwDIFCAJi9qlP9J9TkGWnT3mN/Cj98hpd9fFszkpBLHu7HwJKrwW57CwndHlgPKrECMGmpxyjFTlOWbMBFVkVfLcFCMZpQ6Osvz0Y+uV5smH5CoJA3v/DBm3mIx/YIYmQNysjbMc9rJBETfwomJx0BQFJgCP2AZAp3MoDGDYKk5Q9yPoj/4IhzWZ3eBoJFyUz5sw44WsEPvBOKZdSSMEIMoDEWFFUZuQZVhzVIguQYBrdgMdRgjGqxEh/uuKx+TYf8SY6pWHmw9wZ3zQBAj8hoGvag2DTfJRxKT9V2uVaARPdIU//73rA1zj4MEp/Vsv3kwQIsWlW8EYPwyfFCgKdYOcaDPJ9BLhs2X4/9me/APyQEIAJsRcyeAGsYdc2VNM3wTJsiOfxk/ZB0H2VBfF2wuApQdh931Z2iFdYvXNvZTxWKOgWPPOd0r8IAuhdnnTxG6XHSv5iT+UkAUJght+Wv7fvQolzHLBmnOwi9pyxH3/gUBxEuFp0c5Jx1ABhAqkxJAaGIMjicSLqlGUAKR6FYxjIsJpY31NAGT7LyzYpU0zOn8Gq8+WY1QSqbVaLKsvECC3XeUWgJWwkF1ng/GzytA9A1kEWHigoSVBqIMDwOQcpOnYfjp5xyiiInjlS2TYZdp97JiBSmwqkbHqUYeKY3bdcgrM5ClDEm0Q4HkBjE59AJJbMlUh4lgAl3kSkpMJGaRvkOCM1uDKhMrE2V2kIPOLgO8AS2VXOHYFBXsjauIzBfOS/J1t7D2Qi9B2ZQkIEPByz/6u7RBinOfl1AvVZMT8ZhwBYsrWhFQnzpgnzKgCkWzZgchbmCxlyHSdiOdnKV5RYsCXzg/RFuQhdiWywgFkgqjSCPblOLSsSryHxAk7BK/1DAOko56Q2VmDKxjhbc06/6ECuX8inPsi+0R+bBo0B+aNzNv4LPNgH3aK3Wmzyo7/s0cqD4KEcv/vRZRjgWcZsI78ghM3SYQ7V/2EB/Aj78G8OPEi0vUTG5G/yZK/6ZXWRvJEJ9un+bBsWIMQSEvQeZkSmLpSS7Vt1hDlwqWtJnvki23y+PFvCI5//cvxKlsjIHCG1gisrmogLgmIu4IfEA91CVpB7TpzzModWR8kTvrJ7/YZtcSo9nLS5koz8xioLjHQNeXu255CjjCa5eDb99BvPImflW4IGJQkRfOTG7Dej8Bj+K61AkugoHba5GUbbCI8YwT7zJaHBhqI+OvRfoET/4S2y5zdkHK94Vkfu98hQ/J7OKtsgYxjPN8VrhQVp7kOubL7rhs1x8GHFgN4UHSVT5Xr8i/lRRiPZRifMM58DvwRn/BUd5C81rzaHIfw+v8hn8b0wAFfAD7zKNN7GRjfZhXmjT/DX/ZUDWhlg6zb82kvDH7NrAQd8UslAF2Ai7IxXfCLmYU/2BCHsbA7vgKs4CDuCD+zB/WN1McTGniUG9QcWuTduJHgxLkk9tmZccNCYYaU+0UfPZxua1QayzPXdvdit54Y8I6HKtnEyCQGyESiQd/772GclOIaj8JSNSJDCPD5E4kT/8rcpTaEWK/qnNUAYc/ooH5BV9094nCLCw9hkp5AgnzEsRAXRAA6yWkpdKC0gBsoUUsZBKQxnyhH4rY1kCAIDBTDuL7tPeQECo6boyBVwYCiIl/txDggQQ9S8tYOjsbzt38g+xwQgAA5Q82zAwwkjIX7L4TMgDh9RaiLRnskJGbvsk34DD046Mm8yGfpNLsgVg5flYORAh3PWH07cd8CFY5f9ICdkgYGL+gESJwz8EAUgC2SAK9Lj9+4FTO1xkDU0Fp8DLmQAoUPKfO8ZQDPedDOmKqz6y7sSAHXsHBagRSjpLfKiFlzWyhyRNznTV/pGDwV3gjZkEEmXDTN/SKLlZve1EhW6KqNO190H8WcTdJsj1egeosVmODU2as4RJ8SYPrFD/6cb/o+M+T9dQsT1mcNhi0gWR0NPOBBjQkL9zXY4kcj4h9Nj33RVEgHxy8cfKxle3ypTTU8F1oIUDpcd0U0BhjHrk+DXdewjziNg33RaxlwfOH8kWdDtXmydTAUg+k6mMIT9GS+iyXELHGKjbigzLBKEG597RDDSpuzuR/6CJ2THfMUrnZF2K4gx/+X4ESeEiIzMheSHQAwmSozAI1hifugVXUFc4IAsvP4jRAg/8uG3gs3AO323H0MAQgYCJ/1FVmCxv80XfBOECgYEtjK68BDRgqfkKttJtt7OQveaMMOcj8JjpCdeaw2vI9DgL2K1BsYJ8CSa6DQ5mQ+6Tf8RFzLWD/ioT7BYQMx/8B30yTjoBaLG3gTWcJn8yAb2eg7choNw1789v0vrig9d7rXSr2GnMAIBt2ptbswb/+itPXw1W2fXgkx4xyfxRz5DZPEKmEGu3vYDZ+iy+7BzuGBOBQw2P9MXf+gm/wdj2TkdlonHPWCELD0dhhtBeiUJ2SfbxWncG6+gP/iKz9ia+/Ddkg1sgR0h8q51zxIfBNPuLSFo3IIkPhiWqWRwveQiH8BWBEL65xlsnG+no+QpiYFT6TN50k8BBXxzH/LkJ9wPfvBBiL8xuJ/flvai7/RekGyc7EfAr2+CDr+1J0SAFyvBK103p+l/DRDGlB4HI8JEZjRKBRA4XWRbi/IeAMBgojFCRs/4ZPvcA/GOFQj/jowdwu0a9/W5KNv/LXvJcsXmovw6z+Vwyqxf/jyEoFwR0G/Lm/rrHjIOiIp/IyLDynBcF2PhgPUzJx3xHnNGSXb6VvYf6EXzzMi8ICshT+SGnDhhSqtx0mQY//eZvpKzbAeC4R5khlzpm+8RFd/HAS1lFmRMlVi1l3clAPRGBihavEc7Nlz6nA5zkuSet3B68Rnn5vf5Zlj/p1PIFX3S6D/ShBjlDWE0v37vWeY2Dt+x2kCfPUOwHPrl9+zGb8KGQ7/0m956tt+7t8/YSBwG6Fp2KHjnrBA/Y8/H4Dd+y7FH80xjiP6yQc8KO3J/9p/rN/vgUOPZspFsK/SbzrNHY/aZoAoJ51gFO8aX379UXv1DHDjaLq+/1Hf9FpRFi/MSRo0/yAGMJF/3YKvs1Dg08xX7q3xGzzyT3JAq2CHR4Jnm2neBSeYCZrhfvhkzf57rYZL5jNKafD5cG6VA9MW/Q7+bDL8LHiMlxgqXPbPsf5M86b/PQ//pEz0jL/NN1iETQRK90RAlY6MLdAV+6mM8N96c5XN7UMxJeVhVG8B1xYdVC5DZwOgc+w9/34ZPfFZuz3RW0oFO0W+4RG/NL502l3F+grl0f/OY8wJzb07pZvhe8xn8IRIa7pmXzcANeggTcv8Yw9KP8JP0gr5ZiYJb+tZWikZH9SnOMXGtMeTBBJyTiGH3mvHT3+AaXeUZL1AgD7IyfvL0/yZ7cQ3ZBH/DRXLbJw/PxjdyfF8LOtw0xhogrNWZr+OuEhghgUoAVr6KCBCaNinnI+PQEQ8O36Y9pS4ygPXNHit//mc5gooPs5RuvXeVwOIlUAOExc9B7UGVwFJKoBKApZyWsTql7EGZoKV4pTjlxjvBgfIoJQqycQIKZQ9dNyeP1Zl68aqSQMWHVTWddTBVAk+RQA0QqlJUCVQJNEqgEoCVrRg2xKr19VYzNe72FKi1zZvygni9oPIHtfzqgEftPVjZkqm970MCFR/6kGK9R5XA8kqgBgjLOze1Z1UCC5VAJQALFf/UD/cWJbXJ0dQOlwcm+s6bQdQ123Ct/ry++ndq0a+JG1R8WBPTXAe5hiVQA4Q1PPl16FUCwyRQCUDVjyqBKoE2CVR8qLpRJbC6JVADhNU9v3V0VQITS6ASgIlFV39YJbDqJVDxYdVPcR3gGpdADRDWuALU4VcJ1Axh1YEqgSqBcSVQA4RxJVavrxJYWRKoAcLKmq/a2yqBuUmgEoC5ibo+qEpgxUmg4sOKm7La4SqBsSSwIgIEB9c42MLhGtEcGmJTncNr8sPIvMbPISVxiIdDafLvhx1yM5bkBoN0KA8Beme4wzWcXunAkbJ5laADUJwo7ARlbxWprbsEvILRqbBOpXTqYVPzNhYnOF511VXpQKh3vvOd3R/Q4Uq6RwfNed4czFJu6tQXm0ND7+iia+is+/i/e+V66Z4OZlmmw1mWmQA0zQfZeftOHLIV80TeMAE2sMX8e9fnh2h1UIWhlziE5/7770+n/r7jHe9YmL5OO45l/r0Dxv7oj/4onUbrBGAHpjVhroPtnCbrpGkn0sapxX2NjQ3nB+u5Lx3jl9h6NDoHD1wLP3xH7/yBAQ7Los90p8QX91rWgxyXAR/ywxjJm2wdrFfia/CFwID8+xwf+tAN8+3QuY9+9KMDp6b7d1Mz9w5ddYKyN4g58bo82byP/qzWe3j7mlc4ezWzk++bGn6IP5x//vnpOgeg9dlKfxL35lPyN8GxazYevIAesmu8IF497f+lPrtf3z5qnPGviADBqXjIt6OzddjJl97pzbCQRkb4xBNPJIE7Wp7SOBZc++M//uN0NLc3dWy99daDe+65Zxz5DL0W0XA0utcJOnXU0eFNAYLT+f7kT/5kcNZZZ6UTCNsAo7eOrbIbOUoeGBx++OGDHXbYoXF0jkd3TcxH3wGCA6ToIEJC17wNhj45pdbx9hoC8MADDwzuvffepKeMPU7KfPGLX5ycP0BwvdNqXffoo4+mUzO9g95x9Ma4LG0ZCECbLMwH0L/rrrsSGXAi87ve9a50AvDBBx88eOSRR5LNeysPuSLr5Iw0HnjggQM65eROr/V0kFhf7bHHHhtcfvnlCQvg1e///u8vRF/7Gs8y3sd8f/KTn0xzzAc0HejmBFjJmLPPPnuw3XbbDa6++ureAwSnU3uNbOjatttuO9hiiy0GF1xwwbq3RTkt9u677x48+OCD606BdrrzJptsMmBfX/3qVwdHHXVUOhH6mmuuSUkO/sL3v/EbvzG49NJL07+XsS0DPuy1114p4Wa+2TpsPeOMMwYXX3xx8gVxejq+4DMY4MRj/77hhhtSsob8TznllPRdH80JxXfeeWfyDfhAm7/nR6699tqUYDzhhBMGZ555Zg0QxpgA2H/99denZMFLX/rSxl+yPTbkb/MgEOuz0S/+hf3SK8kKXJAvevWrX50eJUhh/7inRLETpiWqn//856dTn33/spe9LPkur6F2Wrz7CQx22mmnwd57751eUb2ItiIChCBfn/3sZ5PDJXiALxoUhftc9B3HbQPgaCI2TsSR3oCjKds0jeD/8z//M5EMrS1AiPsDVK0GCNNIvP23yPuuu+6aAra+A4R46mWXXZbA/Pjjj09ZSRk+TfbPoVQf+chHEkHdf//9By95yUtSdgAoyGLIKh9zzDEJ0LTbb789BbT6jBz0DV7TSnkZCMCwMcgkmwtOlhM48cQTU0AmMDj11FPTatKb3/zmgTnLsznf/OY30/fmcPfdd59WTE/5vbnmNBDItgDBj+ahr70PboXdkG2x0VkECETB2R9xxBEpUEXuX/GKV6yTEKKPeCIOyOIrX/nKFJT++Mc/TgRA0kigYyVEsgGG0Gc4kuvzsop8GfCBzPj38847b3DYYYelFaMgZf4t+Ee84OuGG264TpRImWBiv/32SwcDWnXou3Xx93TEoYT77LNPDRD6noCf3s/cw+NZBAgegWPiHILO17zmNSnwDH3ynSDG6fT77rvv4IADDkiBge//6Z/+aXDfffclW6eDzp/Rbr755lQx4TqHWOZ8dkYiar3tigkQYiJkaCzfAPxYJUDSOXsZg8cffzxlZqIBAkCx1VZbDXbbbbfe5VsDhN5FOvEN50G4LFMi/6eddtp6gI6kIgNAgMOKwCEGY3lRtvGOO+5Y58T0V3aTA5sVgZlYmINBsqNlD2a/8IUvDHbZZZeUILCiEOVCZCvzIgv7xS9+cT0xfPnLX06rNzBjFuBbA4RptK7f3846QAj8v+mmm9YjIPyORIGVTYEqG4+y19w3yRojFXvuuWf6WOBqVRweCG6XuS0LPgjMyEqJYV6+aw4cEAiLkcSNN954nTglEWRrBWezCA48qAYIy6G9sw4QjJLPZ8u4gaA/Gv069NBDk36qIClXOyW4YYcEtkQzHQ7/oerg85///EKFuKICBJISiSED6vkPOeSQdfVbsoQys7KIlnijrkv21v/POeecwQYbbLBO2GrHZB8QN9fKMEY9mFIRWR7N5+oCXeczZSKA3md+NyxAiFp0z3KfTTfdNP2mjXTZL+HaaFHLmvfHcyMbmt8/6tn0TR/1NcbmNz73WdTM5vdp0sC8Zk6f3J/y5vWwrgkZukdea+//fqcPjIBs/S2iVg+a38t9YiwhX/eKcUbtnnvkTrZ8/t///d+njHC5gpDX/um/sUeNcHznWXE/fWs7LKopQJBFlKFg2LKFlrrbGrIg46jVAGF67PvBD36QSoasHt52223rykjMpbItpQd//ud/nrK3Gh2EHfTAnOW14n3ZU1uA0FVfQyr6iuRE02dkprRvnxnHMHtswy+rMFrUbgduNs1M1NLHXjC2yFby30QtvWvDznObze3N94GpsX8knpvfx7jjXp6V40XpcEvMRbxdkwfg+Z4A98ufHfcme5+HH8gxI5dNW4BgVftNb3rTYJtttknBaFtzQB1/gMhqNUAYHxOU+sJUCUIBgmRgNKu2F110UQrSkLdoSBmsltTJW/CC2B/Wxgv4B/rhen5bC/uM+7UFCHygP3RNoCKR0baC4BoYELyAvoa9hz/3Wfh4z87vX/rt0t5dH1zBv9172J4X/cn30wQGlHtucm5FVuQYOBH25176HfcL3xzX5XgWNfuuCV8dWGIecozJuZ1+WalTRpavIJT8xj0Dy3wXXCXuq48lRuV60xQg6IfkFR8Ff/LVxfy3yhDtk4IVNUAY3/7X+4UaX2D627/924Mrrrgi1XJR8CgpsBFN1GUpV7M/4Rvf+Eb6PhqjlM1F0CgNgFHeofxDjTtAFw2qcz7ooINSRCjrKEOJhHh2OJy2AAHwKzFwf/2zvGkvxLA9CMoRkBkGhdyoofU7/bHkpPZWnT2np0xCjTMC5A+FJpdjjz02KZkad5Gz/lniUqtpKUumW/31sAwVxQa0ZETe6uyAlA3C+iFLqy/6RR7kSckFNGrolc0wOLW5sUnQEhojAOLuD7hlcBiw5XZ99CzAov9We0TU+s/ZMkDytySsAQfLb+4fG9Vlg8kvDxC+973vDW688cZU68shMFb16G94wxvSGMiQLASbZOgZlqUFlGXGz3ObAgT1psimAFSQ0rXVAKGrpNqvA9xKvc4999ykW+ZVo0tsl+0jATGf9EkZGB1lYxp97NOemgKErvqaj1SNMuJgLBtttFEKhCxjC4bYgz8cEPKjdHKYPTpVGVFS1mZDX+CXzDY7pbtqZNs28rIVtm6PjRrZqKO1xytWZq3ecsZPPvnkgN0hTrDyyCOPTEGz2uywN/byq7/6qwmj2CbsgwnIPBLjOplh8xUb9236tP/Mvh39V4qTB1DKNeCeOfc5H8AXbLbZZuvGS1+Mk6z0EybovxIgJats0n3pgzpi9eGu8bztt9/+KYrYFCDAMDjMb1x44YXrSgq7aHsNELpI6anX2PfDzt/4xjemUl9NsMjv8d1Wc9gOHxV8QQAXGOB6PIDP5Vf4CX6P3fFdasHpjNUdeqmEFUewz0zgwae6V57xbQoQ2IgkJ51iH2wAXrCRpj0IfKvssxI25Bi+XXLJJWmPC3/OJtiueyKYf/u3f5t8IN1mb3wa3FDaAiPIg73ZG8EO+Ew8hfwkU/1eDX1b8z0e4/6y3mTGn1o5R/bZrufDCRihD7/+67+eAjhJGv3xXDbmWv7205/+dOJpZCHRG5UhVn5hG7zDE/xWWRhZwSAYoZzH3kDj0ti3e+mDvkUwIpMfAULwGzKNvQPkAcv0Ub8FkGTrWe6Jd5nzttLlpgBB/3EySUs4NyxxmMu7riBMhgHpVwwSSWWsBKnkiPMLskwxTCZyoPnbyoJNIAEalI5yWPJR++n3nAFSj0za8Pjwww+nZWEKGUtGFFoUKJgYFiBwju6NJDN6QQySq8bMRqi2FQS/0w8GRKFi2Rm5sEmF4TA04ILEGlMQduNWMgFAECCOlvFR3AgQjD9KMoYFCIwf2Ho+wGVQQIDhAQgGr07OZnAEXb8AAfBE0jhpDp9c/c79gOyOO+6YwJnTZJCCFYDNwM0rWf0fe3cC91055gH8WKZkX0pI+FBjyZqQVhRlhAilRbRNoSKVEq2obK1KIYkWKpSistQQRUWTqSyN+WQwWbJMIRHz+d4z1zv3ezrnvz3/5/8//+e578/n/bzvu4YqtAAAIABJREFU+zxnuc91X9fv+l3LfQ5jBB7A3TMDaaRcj6mfR083Ge25557pj8CFo3Y+sMkDBNdYfvnlk5ysB6II4OiIPQL0CAiYn2e95JJLUima3jRVEZoChChhylJED+wgKl4ChEGk1P8Ybw1COGUO2ZnBeco6sYWwVwSQTiIQyGi0F43bnpoChEH1tf609JHuwjm6FS9BMGfBMrxSiu5njwgS25MAQZwDv8LBX3bZZT0DBI6YXUvKmIuggi2qinK+nKrkgDXgaNm1djo99Y5jvwgRnFLlRaA5Tn22bMp8XMdGX205nDM7tnYwC2YjeBIsgnBE2vHx1h8JClgAq1SMBRJkjhwJAOJ52Ts8h0vmqgdYIoJ87FGTGBFwwjkbnMkKIfR7b0erj6YAQaBCHyUObI4MHO+vyaWCMIiMmo5hD8idYJLNsHU+HP7bCGo9rQVshwECWZ0I9CkG4s1XwnMcwBuGtJLy12wPcfZvewb4hyD0jrPe8KZXgKDnnI/mt/lv/ghvMQ+62xQg0Ge25nh67/75Pko+i8+HBdpB/F+yy3wM1VJYyJ/Tc1yC7dD7SAjwufiK45DatgABmeYn2a/EhaRBVGXYFznz0cg122XLkoLuhwPAKkETu3A/QbzEDgwIDGarsMxArrXn4DyCEfIxt9hPAI+sY85v+HZzIC/4EvcX+EWAYC64CrtUvZFghVPWB2ZZF/wA3pAtedIlLwuAJ02jKUBwDThHV8hn0DdUlQBhVBT4v2wfo2JoiKbMD4VCJGV4KCWlEhEzGIaHNEQ22HFIP0OiRNGSg/xSREpDkWWQ9K/6f95TVu9pbaoguBYHxBCQUINz9m+RcK++bvdlfJytuSOpMonRQiXDh1gjOBQpshSM3PxlCAGctzZQUMaZGxCZOKctQBClI1AcueeOjCIj4oQ9AzKv2oJgmUsMWRcOnWEDVFkOAY9gDFAxbnLgeP2Mg0d4gIksKRLg//5NDmTPqOqEy/2tv4ANIERJFDgL6iJAkDkCYAhkbADmHLzxAIkBUoIy8pCB8UwCE0A/TItRBAiuSScHHSVAGFRSvY/jPGTZ6AndQQ7pGEdG95FN+s4ZsCN6G2+L4oDHbU+j6mvTU9JHGT6BDntByA32JbvIvmDaoPZYxy/2yF5gZFsFge0jBl4OERk0REM2FH7CBM4amYpqhznCEhgmuGAjcBeZkmCwJhIm8BAWcPrWRrBPfnDLc8vckgFsQIIE+4bjXTMChFNPPTVhAoITbzRxfwGRcyJAgK3m4d7sXHaSLiANgofAI0GLecggkxE8aGq96BcgeHtJr4xsfc1LBWE0TGDHSB57ZyuqAxJYsuqIqCBbNchaSyIJViWGghfwsfRCIMi3xlAZoIPOl1zie+qbiiMgdM1eAYLz+VYBcPgjSUlEVhDd9hYjesyGzIHdqwYYCLufIbWq+myULSGz0U4tcSdxpaomcJKgbLL3ILjm36avyLHrqSQG2ZXJFyh4HhxEtQ7/kpxkL+wTJ4FP5i0I4XPZr2up1kQLN1ywHki9wS/DHFjh+QRIZOdaRhO/cX/rI+mI6NMLMkPU8S6BI17jOo7hC8xRUsG6wxzXJ1PPwafDWBgQ7U3DBgh8E/wZdJQAYVBJtRyH5FEwisZIkE9RuXJ7lKc4FNGlsl8e9TEex9QJMkNVHeA0HMNxjBIgyGCIihlR3eEOsmlJeZpxiXgZO5LNWcnQUXKOGfC5dv36ntebHAQ+ot9RAgRg6vpIQdtbmbRekT/CVSfEAhPEgXw5Zw6PwnPmMnBNhEQgJbvBAEX2DFxmJ/oa64QLUDA6AUR+//omZc+PCOirrPdWIyIciaCmV8BUV8GmCgLAIQ/rI9AYdJQAYVBJ9T8OsCOoMoaIJMdDpxBS9o6oCgi1vEgARLk39H2c9jSqvrY9JbwT/CMqspxIiufk6BHYYexxlAAhrk9+TW9lYtP0H/7U3+Mva4t4aW+SNIGzdXurywv2cNLwD+ljV6okectfHiC4p/+zw/r968/btC8i5E7OWjr7BUz5OjUFCPHqQySDXg3zpqwSIPS39bYj+G0JQu0uAlZthAi+P/yFqjpfA6NVG2Lfh+upfCPY9DQIqJ9LigkqEVTH8L+jBAiIKh028gThoG8xEnyYLxIuCBbI0hW8B7+RLFPZ40Pz60cLpuBDgIDjjBIgxPVl+POEab4WAmxtw/U9gJJ97FOGXkAxaEAVSQdBnmdly7kvr/MbVSLzq98/36SsjSneONT0zSGJY9eNAAG/yPWhTfeaKggCEz6oVBBGt+mRzhSxqhxEHyrCKMqT7ZZJYuhIrkXmoPKNbDJlSKwKg8XLN9dwXLJVsj5K76MECB7IdQUuowQIzqfoAI1jlXWgpDLj5hPZcy0BgCL2WjgP4UTKZdJkS0YJEOL6gFHWIi/BxmLJyJsfMBDd54MhKzFaGwHbIAGC860hI/NconoZ4HjzR51AxP8FQ/0CBGDAceSvuMvn26+iMkiAABgBINB2r/yVmr0UvAQII5l/40nszdtgZKPovzI8/eDYrI0SNqBXrtcmE2M+7GlUfW2TBkzSoiPod234p+0G5rGVYexxlAAhri8Aa3KWKjhkLClC3jnehn1Jemhz0OfcL0AgB8Sbg4UlsAgJ0ioa7RV5gJC39PQLELR7wsa2NwQNUlHpFyBYL/7J/OEYcjboKAHCoJJqPg7xp4OCA2tNnuwFJqgUCmL5TWsS1ShXkiySLZbck0yIEQEgcquqjuiPEiBERwJ8GiVAMB/cBaZphfN8qiFsk725phZevkcVL//4YxBkFUhBxigBQlwfWdch0bQ/T+AAH3CvXOfDPvEaVYNBAwTPrL1Py5+/JXqskec06vzG/7VrDRIgwCwVhPrbBmPdxxEg4AICMjKPFqxBtLtUEAaRUp9jtJbIqskEMpp4tzxFjq/qcSIIdj5kibQiKS8qK8UrzgQEInBlLoRv1BYjhsMRyjYw3rxvdZAKgrnKagI2wQ5nphzGwSKgKgwMgNHYCJSXA/UV6pmTIVEeGyVAiBYg55JDVGTMC9ACHyBEtv5vw1M+AHF89EfZsV+AoB0EiVASFdEDEWVIhE4GUdBXJ1wRQOl5dEyMegVBgCP7CPxlBgLUyFcmQz+qbOVcKwgqWYJCBEmGClFtG7I4oZ8lQBgDEPzfJZAyDlO7oSoB27a/SMbP+iO3el0RTk4uxnzY06j62ksa7E7ATOeV3ZXSVUokONjgoPY4SoCgL9/1tSdoF8jJB7yNqqneXwkZBCqG7G1sipbZbQrI6/ISRBjWS+uPthF4JIEQe8vyAIE9I/wwF77rH49Rf144Ex8qzJMfEhSwX6VxrhUE94YDepe1NPh3nsjJ11mbGMIUrWMlQJgbJiCpsJjuIPTRbqYtWTuO1x5bF0QyH7FeAgw4HsP64BfWUYCOJ4wSILiOPS8CDEmw+KLvoBUE8+Gb2ZAAyHO5Xrw7nw3EBls+MfZcup9kqPYZLbf87SgBQnzMS0IFz4A/Mfg/g93iI7FRPLiVqiH7tCbWZ9AAIXylIA1G4HKCPuQdFtX5jXUjHzKxVlEhyCsIOjJwJ9xKJTl/4xU+A+vww3EECLFeMFAi1dzyhHTIL+cjWhlLgDA3DEhnyyrJUItIRdbxhonYdAcQELD6xjIKDkCQaxn+2CNgERE7rUlaZ2LPAGBH9DlFpXY9ihxJ2yZlGT3Kh5gylHzTKiLKYOsRfpM4VDFkCQQFsofua8iQmbcqiWeWEYmhPYcxmG+8wcOzMAjE3YiNM217EFxfhkAmgLFr31KWA3CyAq6jVKpdwO/0c2rXiSGgMG9ZDoFEvwDB87kG4NZC4f7x4bvYbFYnENYc4MiIODYIufUWrbu3P7I+gj5BpN5pQZNh3gBGT2RTy0Mv9WxqMTJnGV2Ejdw9T9OGRplrzxlfWCwBwhiAILsEPdF2Zh3YQWSyBdgIpSoSnaq/qnfc9jSqvvaSBtyQJJDh1PIgiBYsGIKcQe3RvgxEgf0jpQJlbTyIftseBJjJ6UrKwBI9zfBQIkAbgD/kCu+0eCBmEUSwNVgcHwIcJECAX+YF8zhMdszeteoI9Iz6HgT7F1RU4B0SEvcXICAKnhdBgf0wE37CMkkM+sEfeEbHjiNA4D8EqVpWon8duclHvMVFu1gEECVAmBsmSCzREwRcwIgQG0gp36Sfv05w/V4LD1x2Pj2P1yJLIOk4sC6Sb2xBIoL/oHMy0Ag/XfKzXnsQHEMPXUsmnG5HgMCH0bs8+G6SBNuAb1oL+en8m0+4BlvWYuxaro98shkJFLbr+o7zMz5LMK0lTtaf3dSTjvkcXFOgwf79W4DNf5IXuzMnMrKHgL2HDPEmtsUWtAANGiBYR8+IU8E/pF/CVDXBveoBgjXEzaw5OUfyNAIEbz/jl3EG/AYeqCL4FpF1xbNwApgxjgCB7NxTW6Y54YX5tzj83roI6Pgq9xVAlABhbhiw5GwRJSWvv2eaA7AYFL6pRQb55iAMGUVlP86F0unxjcEQkD0ZJ5EnRRJA6LG3CQoJtbkJ8RQtI4+uIeKVoZDBZjgARbmJU0dOAAVH1e/LuSokwIxDz8uhCAHn43oCESXRyPKZr8DJsI8CIQViQAX4eSavBNOKhFTk5D6eWzBg/uQoo4Z0q6io0kTGAjAgKrIG5Ax8zQdpp/DkLrMA+DhCxszgkXtk3R4LREKQI7BhFIxVZgIQIAUqIUBOZtI5gNUfThy5NnfXtnbOs2dCBlOGGABrgbK+1jreO03+HIFjOWXgQYaAFUC0fWmbYyF3fdgyme4dr5iLyoQ1tt7m55qAQQUEKaIbMtrOi/c0A2rkQLaHfPy+bXP0mExmqMt05UNIw0yaLPNg0Ln0OV5AEPqbX3Oc9sS2VCxgA12lm7BhUH1t6ouNubJnpN6zsP989LPHwA92xwmqstifRTbmx/EisuQTSZP8+uYPQ9i0wIReI/HsNo5nP/BTdZb92f+B2CMmiAM5sxl4RT4qrfRdAGHNBAQwBEkRyHDi8ErggpDAGQ7efeEnrEE+2A1CDmc5dpUWAYTrsHP30KLkGggTkgK32TR8gwP2sHg+e73gBaIHn1Q/mtopIjCT7DFHGO052Hf+zQRYBjOQJW1Hss+IjIQUYgVnyQ9RkPRyLdlssiFDpLMp6ziMTczXsV3EB7Llp8wNtuZrhzirttGnpsGvI2kSYMi/hALi7Br0EokLrBCs03PrY70FCYJkBFqVCwmWKONv/Nw1+SoJDLpHP+kpHyuQkYSjv+yi14gXhCCcCHe9Rcac2ABuZB5sxLPQ73ila7y5D/Gm83yjuQWp98zBIfK5wB/z16aJXCPjKnyeWXLAYA8y5fiSe8IJeqxyZ05kRR78o4w9sg+b+Ei2xia9Xh2f8NYg92BD5IxnwT82bR08qwACFsIu62Rt42vFcAaewmNdB/gK+4oPa7JXz244V6VIQoMs+HjyhVswC59p+kaM5KAAy7qRnwSJNSUf1+TrYQk7h9ECMxxB0lIghSvRM/IzV9wRzrmeZ4aveGIvvzBf9u26M/ehtFwYDE1puE5yKSmwRYLbwB0Y6ElErCkxRaD8+UIgqYyLo/N7hI8yMnZEgGIDgXwAEort/kpYFIAyMAiZNmQbaaZA/QZny2CifSo/nkNk6IgnJ89JcoJ5ed3xMg4INmUzB0DEoD1LL/lQVGTC9QGJ5/cnl49nZGQcMcMHrhQdkAocAIO5xfB/wUA+OHpGhNjL1hiMDAkiI0ZnDjGQe4GNIToHFgwv3jdMxkrI+hRlS7SYCCK1KQBtBJIcInsgAIuBfNTnF79DAKyHClU+ABOgj0EmHLy1EQzRP32IQFOwxtkL1OhJvK0hzrUmXfp6ahcJQD+b4TDYQp5Zc04E+k0JA78flz1xeLkOAX9JhUH1tdfzIZGcH8IZLQT58b3sMY5DkLUcsAk2w844VcRYpjSqEk3zgLccKJJFl51br5TBO4SHrUmowIwo40sA1O2Nw8/tm7w4U9iDRHPg8BmhglecNwLDhgxYgwjADLYJD+ARnGPn5iIA4CPijWzOtSauTx8ke6J/u47ndfvO5VJ/Hr+TYLD+ge/wm4/RmiWYgUfIHcwgm3gbjOfNCUvcJ/xJP72fxu+7iA98CTJIrnUbkRik/9Hi2SQzhBdBs2bWEpmjE/krKukPG9LO5i15eINkIBLMt7tHju/WO15Lzs5Ukl2f7vNVdJTfzvWhbT2RasRbi1FdVwPHXE+QEAGJRFmOh+xEcAor2bvAlS3gRHhN3nZTnwcyTE/hB/nAo9z/xTdlyMM1HcP3SQ6yVbiA2MfwM7+zL4S/NEJe+JO5Iql4BzmTFxsiX/YUg3wF33V/b35sXJWQf432UngveagtCck3BzjAX5u738UQrMAkHKo+6s9Tn0/8Hx/CccgNTnseAZsgDl+M0bSm5lZviZuUvc90gDApIU3jPjbRUG7EhrMro0hg0hLoIgGYtAy6dD+ZN+SCgx50I3yX5l/msrAkUPBh8uupFRA5ljQcJMk4+RmWOy4kCZQAoUOrKeJXjpIBlQWQLZAtK6NIYBoSKARgGlJf+p4ymrJPqpAy9qpY+YsDpj/DMoPFKoGCD5NZeZV11TvtuDoYVCq0AjZ9l2MyMyp3WSwSKAFCh1ZaT7q+W71uyk9aVdpez9mhaZepLFAJFAIw/YXVUhcbq+1p0OdfRpFAFyRQ8GH+V0GvvZZgex60p+IFkohtr+ec/xmVOywmCZQAoUOrbc+DflrZQj23+hK7ukGtQ2IrU5knCRQCME+CHeKyNuzpidZfq0e231tOhrh0ObRIYE4SKPgwJ/ENdLKefn3rWgvtWfCCk/hK8kAXKAcVCcxBAiVAmIPwyqlFAgtZAoUALOTVLc9WJDA3CRR8mJv8ytlFAl2XQAkQur5CZX5FAlOSQCEAUxJ8uW2RwAxIoODDDCxSmWKRwBwkUAKEOQivnFoksJAlUAjAQl7d8mxFAnOTQMGHucmvnF0k0HUJLIgAwbtvvY/WTn/vPvZ13oU8vBvcx4K84cSGZr2JXnk23/sVfBuBwrhPfMK+Sc7exuT7FN477psRXRxk6MNmhi8uljdC3HmVZpkA+KiPVwJa117v9e6ibg47J33K3q3tuxreG+61yD6+lb+7fdhrDnq8fVOw18d88o851s+3Fv54A1NX8dmz+CAkH9L2vYxB5bIYjus6PvhuiDX9zW9+k+zB93wW8vBuf/rrY4Y+fmgP43zrsW8I8Pewh3zzd/rXZW1+vlHhxSvWY775yihrbVO47yuYo68ed3GOozzXqOfMfIAgOPBlP5t4fHDHR3/iE9ujCqXL5wE9H0FhkD7I4iNoPhJ00EEH3emjReN8DkDrwyq++Eu+PmXeNnxIyQe/fJzIlwO7OBAqH6YBAN4W5YMt8z28ttaGMyDpi5+InI/YdDWI6joBaFsvDtK3AuLLljb5LuTB3mDCfe973/QxHiTBlzh9GGg+v5dAj+Eu/PW1U19nbhu+TOoVreeee25n8Rm2ea00P9LrY1rj1CUfp+Kz4KuPJ/kYnDdXdemL6m3P22V8gK8+jOYr3vykD57WP+o3znWc9rU8Lxv0UcDrr78+fYHXBzp9zK/pA1/jmq8Pv/moGBzg771tqW1IatJtH5+zLtP6OnCvZ5fsMD9+2bcmJpFkEcDCAB/PE3AJsnx8rgvfv5rJAMHnxn0Zb5111kmRsq/1ev2frxv6iuZCDhAYpDcccWScCWLg2RmmrziOc1AOzt+XEGUmfVHaa9aQ/14BAkBW3ZC98Mn1LgxfjgROAZYCLRUnQzZjPioISNFb3vKWJY/va4j01dcqraMv4sqm+pR7F99t32UCUNep+KomOdI/rwLcZpttEtgv5ADBswoGEANfE/XNBDrnS8cHHHDA2B1crtO+qurLysgXPOgVINB3f9haF17RiAjQER+jjOF5EHUOWrA17pHjqWvTWQkU6+btVIJaRGHXXXdNX7ftepDQRXwI/YTviNfuu++ekmmSBQs5QPDVYc9+8cUXJ5LJpxx99NGpku/LxOMcOIGh0gZ/vIaZDm+22WbVoYce2norVXvBCx+88sordyI7X/fR9EbQ7gvQMHXcFQSVbV+TF0wZqi+HH354ejOVL89///vfT4Geb2Hhs/MZ3A2iEzMZIAgCLrjggvRK0ChXW+h3vOMdCfQXcoAgWhd9zzfgARnOS+uAbzPE0NLUL0AYRPEmeYzAhn4gTNqeJjEEsO559tlnp9vJDvhU/Fe+8pUljuprX/ta9dznPjfJF8ki2y6NLhKAJvnQ1b322islDFSFDFn09ddfP8l8IQcIiO4GG2yQsJBdzucQ9LuXVqEYsu3IQb8AYT7nNey16Qv8FLBMqsLZhKeylI94xCMSmQvbR7pkLclT62iXR9fw4bOf/Wyqcuf27tshsunz7S+nuU50S9KQXeI/8zkkCvm1tdZaa0lCIDoGJAh6BQjzOa9Rrt2EZ6NcZ5hzBAcSAZFgFWx5dW3Yu3ZR1Zg3vvGNlVdcS3BNc8xcgCAC22effVLV4MQTT1x0AcIkAI+SXnbZZalycMghh8x0gKCnUJmV49DeMIkAQfsS0oSAAATjyiuvTBmWnXfeubKGecCFKHBg890vOizQdI0ANM2frkoY+LLogQcemNpqFlOAAMCtEwcznwGC/Ufbb799ynrPcoCATMko77DDDonkTILQtOEpu0dSJA2inQA2nHHGGWk9J9XmNCwuxPFdwgdJIPgq87rYAgRJAgGCNqP5DhD4UDp65JFHznSA0IZno9rCIOepUL7kJS+pHv/4xy8JEPiuLbfcsjrmmGPS7wwBrfXEFVSHpzlmKkAQHGil8cVh5R99WgifXrGoICBketsImTOIBQkhx54FpThBhg2MrqOUo0dcNKwsp3/Pv5WbNt9881Qav/rqq5MB6nFWgpKd1HJjMM5rrrkmteQAfqU0/eY27rz0pS9N5//0pz9NmeIcYHfaaafUvnPmmWemrKdeeBnl+oY/81BG1C9v0USWylJveMMb0ofVlO88kzkqW+lr13IULT7m5xqXXnppte666yYnf+2111ZbbbVV2owTgzO7/PLLUwuT+ftAk6wW4qV9IyoIPvmuvcl1yMpxyuRk7tPwnJ41iraDmJ/AQzuP+fm9DHrTQK5l4e+4445qvfXWq84555zqxhtvTJujY72cZz2vuOKKVObUeuaanpu8jaOOOqo64ogjUil0iy22SPoSFRE9qtaa/sQwT9fSX+0cTtoclftz/SBDMtXzTSb0zD39TMuX1gutFOSr3cN6XXTRRSlj6HkM+rDKKqukgFf1az7anOYCLl0iAG3PYe2BKH1jd9oI2KV1jgqCEi67QQyVcelPtG/QV20lzvd7FcmwV/fkfOmC0rPKJPKGzDnGNZzH5qy7jK9rx9fPkT+2pJzuZ3SYbdIdf9iLJEdOuMOe2R59MTyP4+vDc7EJpWj6xTbYarTNuL+WFdeHV3TZ88fwe/JTRue0VLv8nbfdOBbeaVdgS/Y0CMbcS5n8vPPOW1JB0JbDdnJ78G9BOiw1F/YArwwO07ooq1svMmIb8fv8edml9YFfa6+9dpI3/DG07K2++urV3e9+9/R/Abp1cU/D71Q+rC3shG2yzcgkmfi99YSH5J7jxzD6oYXR+fQBpiKsfFEbnrJ3iQPyjpYmZIG8JQxgRZdHV/BBq6jkgGoWrN1kk03SulpzuIoL2F/y61//Otky36rSmLe/0idrZ60M1Uj4Qd/hPj/Bv77yla9MeuKPtXKNXv4HEQ1f4npsOvSD/Wj/EXSbewz66D5GJJPC3uo98eaFD51//vkJo9iXF5bw13DKxmDdFo7jz2AYGwt+YX7uzy7gJ7tga3F+rn+6F/g2mANvrD+8wsskJ/h6lUxBBEzFjYL0hr9DiOFNPJ/5mbv5wUPrx4evueaajarPNlyD3BwLf+xtIB82HPLxDLiG9TQ/mAInzLkXnuGEwV2sV2BKL/0gQy88wQ35IvekK2z61a9+dcI13At3VT0wV/Jzfbiu/Rku4w/wBhfcY489kq+hh9McMxUgcDRInQ25YTyyrpxiBAjIIIVHdAkeYeCYOUiD8JE6xk3RZcgBtFIz5yMy5lApE+WVjbDowF/ZR+YJEbA5mFIwKAapXQRpZhhIP0fpOsgy8mx+lJQBKSG5vuewwZhhc2bIBUBAYOtAQMk5SJEmGQA+ToiyIdHIAofLKSEhQINSn3rqqQnEzM/9KKro1LMzLm032jNiUFDXoZzuofwFTBF6AQmHj7Bw0gg5QzA3AOw5ZXJOOumk6pRTTkk94GRifPKTn0zgCEjMCzECIORfHwze8a4pKHFvwYJ1ZXQ77rhjcvL6+ZEOn6LfbrvtErDYCOnn/nYeMLam1hbJAZqCQcTEzwUYOUEzH/IDlp5NJs+auad7hX74PedBBqoTnJHgz70BGH2iP8cdd1y6n7nUN4wKTJzLgXWRDHSFAPQCSGSQ46LbqjZ0ECG3hgBY8AofrBN9Z2v0AIkwrD0d5TSRVOuFELDXRz7ykcnOHY84c6502LoKAOGK/7MTOm3N2SObkyA4/fTTq9NOOy05A9d0L07LWnMWEg8wSp+0a2o14eQElgLkww47LCUckMU8iA95+B0ARzTMAaawUbpGv2GCrD98ch+Oj4zgCyzxbJ7Ds5GH50Si2UrgpXshFuw69ji5FgdtTvDPNWEox08OuT1w+ogXXGY//k2O5IHMcIywhJ2q9FnLeouo+yNh8ACOwRXJGBgmuJDkEbxwtpItMM36u77n1NMLx2Tk+BCycYx7C3ZC3mybTMmELIfRDzoG39k/nEJ4PLOkRC88zXWbziBZdGPrrbfuXMKgboddwQcEjb+l69bU3i467w8/Yb3hs4BMUoYeWneJGfoJG1SSJOcc53jBMptiW3SWLdI9BFlwgATDCLbc5n9cz7X4BWSWTxfM0El2wo9sh8djAAAgAElEQVRGYpBuIpju517x4gqYRR/23nvvRLbrSST7LAQg/Fb4G/rsfLwAged/dAPACjrOp5ozXgBbJBj4QRhAB8nSz8MGYt1hgCSla+BEZA3v4Bq/zo7xLnP0LJIr7BnJJTO44Fz3iRYb99Z+i0jbPwWT+Fb79erD/GEPW/c8EqrsGXbAHn46kn34g/WhC+TOFumJtTe/JjxzLcEUXaAfntG5vfQDNxLc4WWCff/mzz0LfYHnnjX4G2zjD1yb/Ot+H34JDgx4OImXp/TyrzMVIHgQi4yAG00tRshuBBCUAxlnzCJSD0uJKa/MrcGpCwIsIoO1wMCZU6Ekkd1yTQ4KwTBkrxxHmSIjve222yaDR/IpuMVFxl3fcRYeMDAm1xFNRlsJJQdYFLTXaGoxirIfsgIUDcopWOD8GTwDMn9kBGgCP44MkDRthGl6PtcVIMjOkJf9CQwB4NgY6ZlF3E09ieRA9kDcNTwnJw/4mkb0kJMhh43AWQtgg5wAMJu0gSYgNB+Rer0nGmgCTzICDnmLEQcXJMDfAEwmCkEKw1Q9ABDuBwz9DcxdkyzJFeghHMCVwQ9S8vUssjTm29Ve464QgJ4GkZVkrUO9xYiu0DXkTfCO1NJT+3gEwwJ+SYRwSHSSnkoGWFNOA94Af3rBXjl4mWF2ICChEwb7Z4PwBSFAauGOY5B/m185D3OUJbT2CDmMgluuH5vVzY1zYVcCnbbR1GLkOeMtYgiCwfFwzBy1wEZ7TdgzUsp+OPeohDTdr24vjgm8rNsD+UqgRDWgvkcs7BuRjmymIB/mtu0hg1828AoQ4Cl7F9D5mXXkUG+++eZqtdVWS/K2Hk3+oq1n2vXhK3KBHA2iHwId9yFXZA7eIkzwTvVYH3Gbv8hl7F6uA4/p3SyMLuFD254j64l00gdZa+vF1uAvXw0f6LCkIR2VgBLQWT/2aC3ZfOgvYs2mYAAO4Zq9/E/oH39Mv/grNssP6nYQYCOZ7EAAzv75qtiTwkcjt3xg21t/2vyNQIN9uIbEgeE56bakCD5i7mxXcsDvJCyRZ0mWJnLatOco7Mm1JNXwCUlMeAM7ySxGfQ8jeeBR5Oh3eIe1CZJctwPPCk+RfDIhL/eyXtddd11aL5gK/yKB6RpNe1Sb8Cyub144kgChn34IyNxfQggGxL4B/kFAGEmhwNteezglKPBWAQd+Ne2x4AKEfJNyHfABL4PkvGIAARG6jDPChyQ2bbqTFRSpIyEARCRNgQQSeYCQEwT3UM5CbpEDjofyyk4gJBQcKIjeGZGMemyybFOMeoDAoSMRUZbOyT5yC3hE8SogdXn0Ur5eAUKu4E0G1eSABQJkC+wYtWy6qoBsQdNoA3wyRBBkZRB1z0QGMvzAWOUC4YpNk8MECGSkwvCa17xmyZRcB8H0t+xOE0AiiLFJjFH3CxAEh+aNTMhYdXV0iQD0klH0bDYFCPVNyrmDEtwik4L/aEmT6WFjiBq74YiaEhJ0jU4j1xwhUo6oyoblAQJyzwHH26wQFDgENyQFtLlo9UFwZZToGdsQXCDBMma9Rj1AQDTpqOupZOVvbuG84AsSzg4F88O8dKAtQKjjZd0emhw02dN9WICwkTeSjYwJrppGE35FewO5e14OXZCvemBtYZEkEIIUCaVBA4RR9aPJ/tvwNJ7Teqs2WZdZGV3Ch14BQr5Jue4PkGD6pyVFVjoy9IJN5wnw2HfbS1BkwHv5n7aEJl/IZ6h6CQgMxND1/E6SwsAT2ElUPJt0o0nfcBS2JcsfL8pwrgBatU+FTWuPILYtgdZ0r14BQr5JuW096ngjOGBnuJQqHExC+Nv8YhPfME8Bj+oOLPcGMPwAcYfdqkbWEy/A9yIBMUiAIOgZRD+sJdyDvzHqCaNBAgTYJQCadmtRPMOiChAsGCBGMOtDRo1RtgUIjqdoAgwlb6062nAAeq8AgQOTGRNJRgZb+4BzGCsj1RtJgQd5y049QHAup69ykFdUzFfQIYsQ1ZBpBgjkZX7AChCIshHuto25bQAju4BYc/pARR+4zAESD9xlLaM1CGANEyAADNdu6vcG0CoI4wgQGJ1qgwBuEu9ZHpVwdIkAzEeAAAsEheyz/s5phFLZW/DeFCDEfGQGXUf2HR5oKewVIHAgMpquKYOpmsAJyeBx1KqNghqOAia1BdA5gOeblAWfkh3sQzYtz0LBG9l62TGBCNubVoBg/mxJOyKCAHsRl14bc5vwi33DTUmfeF7VEu1UMEcFiJzZ9bABwqj6MUqAQHcEk9okZ2V0CR9GDRDgL1ukR9oR62+S23jjjZdqYa6/JZHd9/I/bQGCc5Bg948WV7yADkgo8tmSfsgun9nrtbtN+qYtin9VLcg3beMuOhUkJ/kgVbhpBgiSJDgKeUiW8LP+tL0KuS1AEGThd7Atnte1cS5DF4Xkbf4a/EEChKgg99OPcQQI+KUkjsRnV8aiCxBEp0ik3sNBI2TH6SUUncq+MWDXkLWutxjVKwgBEDa/cAKcMlCRLVKqFPVy2ogFMOj38ZCmACHeqSvbmG9u5lD15AED7UbTDBDIkOOW6ZMlYbCqJ+bWNNoAPzKmAioZW88MEJA8LUeuK7M7SgUBYCAqqhRtYxwBggwmUqh3umuvNs2fu0sEYL4CBDqo1a/tdXJtDl4lQIAa5XDZaj2wHFKvAMFzRIZd6Tk247F/ASPHKNNFB9l6P8JYryAIEGQ7nStQrhNu+ia7HpWKaQYIZKHyIlOrFUIAI+HSVkVtCxAQHM+D0MsIxweOEAZ7KWQAtQKOEiCMoh/DBghIG0IHe6bdczwMMekSPsw1QFDBin71Jhm0VRBUGXv5nzb8iPnCjSCx7qu1RDYcRmj58XvtSL1Gk74hwuxI54JKfYxImOEisddomgGChAm7NxdtYDoe8CJBTNN3QNoChKjQqWY6V+JBYgCn0jals8Bz5gHeMAFCP/0YR4AgyakFtq2COoxtjuvYmQ8QRNacXJMBN7UYcUA2uWgjkXGmoJw8JeWsmyoINgJpq3GvII9texDqAYLAQMVCnzkFjcAEWVZGsgB+rwcv3m7Ta3GbWowQYiDFuELpXcNeA0ATey7mEiCQEznXCcWgLUaMVemMwxaVk4VMp5472b36CABVBWDcMfRT2syNEMSmTMFBtI25j7JsW4AQb5jxHHWAkE0mQzog2EAqPLd7+nf0jfdrqagDdsgunkGAaIz7IyzjAoW4TpcIwDABAnmr2DV9ByHX39iTIMDmnATXfo+wAesoczdVEOKNJhxRvKkDpiC79QBBEkDwYAgMkeHYOJ8HAF4soBVJ5cC9tTz2+1hW0x4EVU545Z6CmBg29bI1LZWx52LUACF0epCA2f2b9iBYowjMrIVsrfYqmb+m0YRf1grOadOCgciQ/mX2z4YRNIkcz9kWIMSz1K8/qn4MEiDkmODfMAEedDlhUF+TLuFDPUAI+db9ZVOLEf/Jn8N9VeLAfb6ADtDRJn7hHiqNvfxPBAiOZd/x3SY6LvgUzOb99nRHBdHeHRl+et1vNOmblzcgxiqggoWoJEZLss4G9gBf5hIgeC6tgfEWo3ht8KAtRvy2AD70XlAPF9hvtH3mzx98Q2U1D+j4fThtDVWDzUcFWIBg4BqObwsQQl/qfAZeD6IfowQIs8ALZi5AsPFEe46FlEVWhkO0KYBSlag7NvUxasoP+BFwG4sQTv2GnDlCzlBsQPS2CSVGRmOHuXNE4BQ3Np0JFOxFEFgoBWnhkcUGEPqHlYaUumQA9TMCfZUHx5lf/ZWeNgual/u4b7+v5skOqmB4jZY2hSiJIit6jvXWCYBsxJWRRHwYEpJA0ZUtySjk0YugxpueOFdzlNmyoQsg5hlIAIr4AzvXjrd4MBjrICNikI11Mh9ZUnLUGqBFo+kNPgEwCBSZkq8gjpHTAWtrHWVI3Ns6k7eWLeTLG1xkTwEt+Qvs3BNgOgdQR2CiFUGp17lABcDE5lVvVLL+dE50j9whfOTPcTBya2ddBBKCCOAMoMhGhomTECC6p+DIzwSlgCccRj8nMI3fd4kA9Hp+7STkbWMx/aIXdEEgSQdtHmOznCM9ZusckDW3zvQCOdXuwwZlc2VxBO4CfLpsxEZ4/xa0uh/7oNuOgxsCXkRANhi4Iqmuw/5VD+k1Qmu/EQKft5hJGjiejrhX9CH3enZZc33GdN58PCdMFGjAKD3UEh/0XuUOofdHSyI8Y9O5PHrdi90g5MgUoqUlgBzYZC97sBZsUGY08JkczNe8bfY0Py1G8DY29tbnArcENuQnSEduEC2BlDWAS55RexYbtaY2XAqIBB7mIBMYhMoaw0Zrp1+bP0FwBH1RlR1EP/gScyEL+qNlis5o/xSwWOMmPCVPzws34JO3Wln/WRldwgfrrDWUTvJDdA7BZAfWQKJMa6vKHzvlewXz3lTm3/Cb76JHrgHzJQmsi/WLdl2+CIE3+IZ+/kfSAXHlYwSwqsb8ATzgb+hFJK1cM96yRsfpbexd6qUTcIa+uS7sivYc1XkBCPzxzPDHSxj8jD/zEgR2LOPOpskjTzA23VPyAebxr+wn/DI7yf294ArXos/2a7IP82MfOX/Ak8zdsfYfsF+4BU+aeEEQeL5YW6ZjtOZoqcTlYAd+xB/EWxJhFk7GruEq3gQHmvCM78bTyMaa6+jopx8w1/34dRhO/vgWLiEgwQHxAgMOSWzCJ/4oXhZBNjgNPBDUdqX1eOYCBM5TBUDkiRhaSEbP4ESyCAKlM2QFtbRwtI7nSBklxVLSojicJ5LPIfg5ZXYOJRHZIxmMiOIyWIDC8djl7//IuGsjvwyH80WoEVtE1h/XcP36orsucs1A+pURAQwHxHBsMAJSSCcwdB2tNogJsGG4HC7AizftaGnyfK5DHsipa8R7futgIIqPaN4rAhmUty4AOYZlvjIUSpWuDSjIURaeXMiZbMjEvZABmQxGa476LRlqvGWlfv8IEFRtZGgZFuM1PFNsvjQ3Mge8AgLA4feMUGZCEEA2MgnKtYwWaMXr3VwPuAjyzNfmJr8DcIxVJte5/mbA1oBsyM7cGZDnB8wCBjKjgwDU2vi39XU8IKEP5owUCOzqve9dIgddIgC95EKn6ZfWLX/Tf0EBpyegZcfslbwFeLJpQBixo1OIK8dPJ9kth0qXBX8CT07RgCsIpaATOUBW3QMGAXqEEA7ReY6dM6Nr/vi99XcP5yMi8cax/NnYsJYDZLhfwgDuOJZjR37YHpIN58gCTspCIr/kYD42BbI7+opAsGfyYM9Id68WF/YkGHYfNsERsz33arMHtq9ywXaQEzjIPtkaQqyiKGsLh8iULXqWphFvYYO/sE8gKGnA+bI78hJ4exbBOdxl/2SAOCBGbDFIkawju2ejSIY50iXPRydc13V66QdHjxggBXAfUZIUib1lEURKxuR46t+wCpHV4kb2giU/n5XRJXyAq2RP7ggo3dJuSr/9Tl87Iowgshd64hgBoSSNJIHfaUHmN/l/ti5YwAmCX/i5YCLeltbP/8AdAQK85x90ECCDAgE4wRbrQ9Dv3vSh3xvu2J7En+dGTmEarDE/NoUDwQh+hp3TN76ajGxQho1sg42SB9zLv5VSnxuO5dq4jOP5RX6OTNk0+8ILyB1usgut2XSFH8Zf8Adr4VXzriVAJ2fXhMf8PZxo+i5QBAgCDmvhGZ0Dt8xdskHig//Fd9iywBGW+5lAxL3j5Qg5ngkG4hXt5G7d2C++1aYfOAXccB78og/mBTckHMwNFxGgwRP4DHvpI9lHkjI4hYBSQFH/Dta0MGHmAgQLTaGU3igUZaPcnHOMeIe342JQHMSUgciuc9SMlOEwYgSUEQtAYnDgAgKD8SGtzuHgZd6ADCVGSCgXcJcRAChApn79+iIH0WdI/YiiZ6FEiH8+IgJ1L8/FgMiIDETXZOR3qie5PDyDP21VBBEwGXPA8apCTjTaYzwfGTCEGMg8gsXJxiAXxmiNACM5uqf5kW1bpBwBgioPJ2zu1sg5ufHIGpCLdQCAgAKIIQuOjXeze36ye8ITnpDW2P8RDMM59MMzeW5GQZ8ECHTA2rh+k35YQw4oBh1zLXKT1YqPv8TeEvIDJLIHMlxtAdq0ACG/b5cIQC95WFf65c0d1pxMrW8M62ttrWnoLx0S9NIP6+d8a+I4eoQ00FWkNh8C0SDR7M15/u++7A4ZZS+cMH2hv0gyR+h6+fWbnomzDwfeTwfq+uh4zxSv8mUzoWvIgOuyWcPc/S63Z86u/q2OfA6BQa6F3CIndbys2wO5kGvgM3nDJdlM+CK54xrua35NQVPMQYDAuXP85oros3NzCRzxPNYEDlpDOmzenjde6Rw+AJ6RB/yXMW7yF9Z0GP1g0zn+0UU+CjbmeOpZA3vDn7W9crqfHkzr913Dh7BHGMwG+OfAeDKqr42fhT3TQfpAb+hi4L71h+M5vwiMd34//8MeEE0+GOmn7/n1m/wvQq+rAeFt26wba87P5Prm53wrH+Q+8Vzhz8iFPwt/lO9PqONHk17BWv4WltFr/jK/P+JOPmQZw3zgHs5Vx184TSaStUbOx5ruHwGChIRkh3PZPpyJly44L/wvvx08AzYHXvh5Hc+spbWOwWYjEdmmH54f34j5W0++xf/JKEbwT9VduCthE/zEMfTDdawLPtmVj6bOXIAwLTAc5L5Nr7WqnwdwovdMVYLCx3cbBrnHYjmmrYdxsTx/F56zawSgCzIZZg4ygfXXnDadz2kKRmW+VMFU6uK95cPcb6EfO8weqoUuiy48X8GH/qvQtkm5fmZgACKqTVgGOzoh+t9l8RzRtkl58Uhgsk9aAoQxyVumQtlOT72NykpmTUNJ3iu9ZJCUlpW58x7EMU1n5i+jzUuWQAZWea/fZs2Zf+AOPkAhAHNbFERfW4G2GX+askKy3Vp9EALVR+VwrRJlLC0BmTrESV+wNgUtCF3f5L/Q17DgQ/8V1mIS7cPsvCnwl8nm4yQOVQy8IVFLSr+ugv53X3hHIKxay+Njb21vo1x4Tz6dJyoBwhjkLqrVm670rZSnh06/a2xMyW+hbx1Q+J1NupS9jKUlACD1C+oLV67T76y3Mv8KcpHZ/EugEIDRZezd5YisZIF9N3RYz269pU5CwUsKEAP9qfbvdGWD2uhPP94zlertKbHR0N4gVRn9u7Gxcbx3K1cbVAIFH3pLymZ1AW18CA0vQPzrbXRaTrQhwQI9/VoSm77FM+i6LNTj7A+xR1CSVXuOPUb6/8uYPwmUAGEMstVvFn15cTn9eU2VAX14soZ+R8m70ms2BjGM7RJ6yckzhh5v8tTHWMbkJFAIwOiypr/0OIZAVx9q3d5hh95Y/cJ66WfpPfijS2e4M/Xnkmd9D5W9A6WKMJwsx3l0wYfe0ozv/uRH6Syo27i+fvtT6Lf9S7HnbZxrtRCupX8/3yPgmQZ509tCePZpPUMJEKYl+XLfIoGOS6AQgI4vUJlekcAUJVDwYYrCL7cuEpiABEqAMAEhl1sUCcyiBAoBmMVVK3MuEpiMBAo+TEbO5S5FAtOSQAkQpiX5ct8igY5LoBCAji9QmV6RwBQlUPBhisIvty4SmIAESoAwASGXWxQJzKIECgGYxVUrcy4SmIwECj5MRs7lLkUC05JACRCmJfly3yKBjkugEICOL1CZXpHAFCVQ8GGKwi+3LhKYgARKgDABIZdbFAnMogQKAZjFVStzLhKYjAQKPkxGzuUuRQLTkkAJEKYl+XLfIoGOS6AQgI4vUJlekcAUJVDwYYrCL7cuEpiABEqAMAEhl1sUCcyiBAoBmMVVK3MuEpiMBAo+TEbO5S5FAtOSQAkQpiX5ct8igY5LoBCAji9QmV6RwBQlUPBhisIvty4SmIAESoAwASGXWxQJzKIECgGYxVUrcy4SmIwECj5MRs7lLkUC05JACRCmJfly3yKBjkugEICOL1CZXpHAFCVQ8GGKwi+3LhKYgARKgDABIZdbFAnMogQKAZjFVStzLhKYjAQKPkxGzuUuRQLTkkAJEKYl+XLfIoGOS6AQgI4vUJlekcAUJVDwYYrCL7cuEpiABEqAMAEhl1sUCcyiBAoBmMVVK3MuEpiMBAo+TEbO5S5FAtOSQAkQpiX5ct8igY5LoBCAji9QmV6RwBQlUPBhisIvty4SmIAESoAwASGXWxQJzKIECgGYxVUrcy4SmIwECj5MRs7lLkUC05JACRCmJfly3yKBjkugEICOL1CZXpHAFCVQ8GGKwi+3LhKYgARKgDABIZdbFAnMogQKAZjFVStzLhKYjAQKPkxGzuUuRQLTkkAJEKYl+XLfIoGOS6AQgI4vUJlekcAUJVDwYYrCL7cuEpiABEqAMAEhl1sUCcyiBAoBmMVVK3MuEpiMBAo+TEbO5S5FAtOSQAkQpiX5ct8igY5LoBCAji9QmV6RwBQlUPBhisIvty4SmIAESoAwASGXWxQJzKIECgGYxVUrcy4SmIwECj5MRs7lLkUC05JACRCmJfly3yKBjkugEICOL1CZXpHAFCVQ8GGKwi+3LhKYgARKgDABIZdbFAnMogQKAZjFVStzLhKYjAQKPkxGzuUuRQLTkkAJEKYl+XLfIoGOS6AQgI4vUJlekcAUJVDwYYrCL7cuEpiABEqAMAEhl1sUCcyiBAoBmMVVK3MuEpiMBAo+TEbO5S5FAtOSwIIPEH70ox9VV155ZbXppptWyy233LTkXP30pz+tPvOZz6T7v/zlL68e/vCHjzSXO+64o7r00kvTueuss051t7vdbanr/PnPf67OPffc6rrrrut7/Uc+8pHVa1/72r7H9Trgxz/+cXX22Wen53nZy15W3eMe9+h5vS9/+cvVN77xjSXH7LjjjtXDHvawgefw17/+tfqXf/mXJTJwP/JcddVVB77GfB/4hz/8ofrc5z5X3XDDDUtu9dKXvrR66lOfOt+3Huv1FyIBuP3226sLL7ywetSjHlU96UlPGqu8hrmYeZx33nnV9ddfX6233nrVs5/97Orud7/7MJdYcuzNN99cnX/++RUdu9/97nena/z7v/97dc4551S33HJL3+u/6EUvqtZYY42+x7Ud4Lm++tWvVv/6r/9aPe95z6ue8Yxn9LzWrbfeWn3iE5+ofvnLX6bjzP9Nb3rTUPd3Lnv7r//6r3TeKquskvD+Xve611DXmc+Dv/nNb1YXX3xxBb+MJzzhCdVLXvKSatlll53P287rtWcZH77whS9U973vfZMPneYY1n/2muuHPvShau21127FtSOPPLL6/e9/3/dx52o/dPyyyy6rvva1r1WPf/zjq0022aRaZpllWu9b95cPfehDk/0++MEP7jvX/IDPfvaz1TXXXLPkR7vvvnt1//vff6hrzPfBBx100FK3GJb/zPf86tdf8AHCW97ylgTMH/vYx6qnPOUpk5bvkvt95CMfqfbaa6/0//e+973VDjvsMNJcfve731Xbb799OvejH/3onQyAcf7whz+szjrrrOo973lPxfge+9jHVm9961srgO735HHSSSdV//iP/5jI9lzG4YcfXr3jHe9IYHv11Vf3DXwELvvuu28KYoxvf/vbfUlEPr+//e1vlaDv9a9/fSIiD3jAA6pPfvKT1T/90z/N5THGei6S9IMf/CCtM/Jj0L+5BmNjneQAF5tlAtD2eDfddFO1wQYbJHJ2wAEH9A1oBxDTSIfceOON1Qtf+MKkJ6961auqY489tnrQgx400rUuuOCChCf07dWvfvWdrvHb3/62+u53v1sddthh1Ze+9KX0+6222qradtttq3/4h3+ofvWrX1Uf//jHq4suuqg65phjqn/+538eaR5O+vWvf50CdgSBkz/zzDN7XoutnH766dX+++9f/eQnP0mB23/8x38MdX8YJ0DaeeedK89KruxtxRVXHOo683nwf/7nf1bf+ta3qle+8pXpNtb8xBNPbAzo5nMe47z2rOKDQJK90BWB2zTHsP6zba4SDc9//vOrddddN9lT07j88stTshQPue2229IhuIDxpz/9qfrOd75THXrooSlhMRf7kbB44xvfWH36059O3OOLX/xiJRnZNmAA29hss80SFj396U+vTj755OqJT3ziUEvz/e9/P3ELyQIDjsCTLg1Yffzxx4/Mfyb9LAs6QAAAD3zgA1OWhuNDrO9617tOWsbpfgxg6623Tv9GaJ/1rGeNNA8k/GlPe1rKjgG3Jz/5yY3XESSsv/76FULUZHCU9POf/3wlkzKXgXB4rmc+85nVKaeckgh7rwGYDjnkkOrd7353OmzYACGu/brXvS6BSBcDhJjjCSeckEiLUQKEuWjZ+M6lM3RH9YDuTotEypzTDVXFt7/97ZVExijZZI59v/32q4444ojk2HsF/Lk+vu1tb0uBfVT8zAexZstzCRCQddcWGLPzN7zhDX0X79/+7d9S8HzVVVeNFCC4QY53XQwQQgh3uctd0j9LgNBXLebtAJU7GCCYveKKK+ZUMZvrJIf1n233k5lG7vEdGNBWUf/FL35RbbjhhhWbM/7+978vdUl8QLJiLgGCLob3v//91Tvf+c6ULFDZuPe9791XVAJOhHTUAMENBAgSIUYXA4Rx8Z++whzTAQs6QKCke+65ZxLV5ptvXnGQTSX4MclyIpfZddddkwGHMQTRrt+8LUAQzQOFtdZaK5ESVYhJjnEZSAkQ5n/VZjVD2CYZjuu5z31uym4bqmyyVrM8ohIhg2jIEHKwTaMpQGCPsvwqKqp7cGMuAcIosiwBwihSm/45s4gPAmoB+Qc+8IEkQC0ekmX1Vt3pS3fwGfzsZz9LAbb23Xve857Vu971rmq33XZrTIa2BQiCCz5VkuK0006rXvGKV0w8eVIChMHXfFJHLtgAQZkLmdaGwij0ovm37Hs+OMW8DC77pOx+ySWXJANTil9++eVTW4seNwATQ7n4MY95TMrEX3vttUsAp95Tr0ylcZMAACAASURBVM9QD/B///d/p2sjKXqOlcL06hoymdtss02qNHDyKgz6CfO+5J///OdpPqoIWo2cw7ma36ABgmyj62rJIQ99wm2j3/xkH5XMYkTPHxnJYsgI6idcc801q7/85S/Vaqutllqc6hUE/bjkQ8YG+emn7tWDmAcIAfbK+EDGGubtGrKaZGo++rBVHZ7znOek+YRj0JspsyTroBXNvcnV+sZwvv0fsq3mbB3zHkcZKRkhJMs8lLL32WefdHqpIEwK0trvQ985T38byvH6ZPMhiMjt+T73uU/Kgim/wwo6o09fT+1RRx2V7DBG7Olh01HmpktabfJBH3N9l+2j76qCeY+q/mh4RS/12b/mNa+5k01oJ9CmEDgCQ1TymkZTgKDCqDdZ5o09s8FeexD6zU8GUtBi5HucBDD2fpDvRhttlHqh3cczNwUIdby1Viokbfs08oSI69vHACclQ1RS3Sv6oGGQ/UGwlgNEitiz8/Iqjgrt17/+9ZRU0r5BxnQhhuAK/nk2uA5T6rLTwgE3jBe84AVL2lxLBWE6eKCNTQCs5Q4veMhDHpJ8lXbbfEgiWDt+y2DD9AYJpw/slW+r8wf7HO3Fk8nXtsfftPkzesG2c/8p0w6T6J0BfyQ3tSJqEYIH/FsM+s2u3ve+96W9fXSyVwWtKUCARyp9Bx98cJIHH2hPYdMeHjLoNT+VWefl89fKyB+bGw6FD0liuAc8iIRGU4CQ480geybzCoLqEGwRQMFY9pf7axiE58EaMsCnNt5446XakvBIc9b6pPICH6xxVGjoh3Xx549//GPa04LvhOysD3nSG7LHJxyro8UYtYNiUtazYAOEM844o9KvTkFEx4aMOUPKBxJHCTlPgzJzBEBfORgQIHcW36KGwoqwlfUZFCVTqeBgZCcYdT5+85vfJAfsOhwcgmwu7mEDD4PRb7flllumeVBopf6jjz56qWshNzYQaUvQ32eYk36/+qhXEGRJkBngqAIxSM9+v/khv/7kPX8rr7xyyswiLbIYgMFcH/e4x6VnNuoBAhLFaMgUCAhiOORemwwjQHCM/RUqImQBmLbYYovqwAMPrFZYYYVE5q0Z8k6+iJ7f2Tyl9Kk1ytrar2HeKjLOUVkhV2Bv6M92H8+BIFpLIOEaBgLneM8hiNN7KXD53ve+l35fAoRJQVr7fZCyN7/5zckZIghskbMTrMfgAMOe4QCHQgcQX2tMv5Ww9foj+XvssUeyXzigrYYTQULYO6fO7uobdfXc6pnX8ljvmf/whz9c7bTTTmk67IfeqvpxYBxV3W7Njd24nwB5pZVWSkEq/a6PeoAAC/zMxj57pAZpt+o3PyRckCLwiJYnDhg+kh2CzXEL0gQykeSotxhxzGzQPhGBEVl4prYW0TreOQ8hESjYHwUDolpEjscdd1y13XbbpdYHrVbut8suu6R/G/TCPRFD2ILIuUckRCRB7Juwl4o+eQkFoqat4sUvfnG6hn1e/g8L+BW/1+JmlABhOnggoA6bYleCYv4AtueDLdEFJN9g7/whPuEc7YHO41P5/FNPPTUFE3yAnyOR1pstIN5sre7PJPzoTu4/H/GIRyT78HMBhCSV/Xay+nBGgCDZFYPvgjt0HBaxO1hEz/CR+qgHCObs/nvvvXeyyX49+zhVr/nx5TAv5p8HYGQMM/l/A39iF1GxbAoQPAc746fxhxyrmzQoDxBglYDlgx/8YLJbnMI6GV7cwL5hEo4H/77yla8k+cKGwE+cAUZbVxjhWrAlcBhnsN5wzfyttaqUfR6SBgg2XHE/rZfWy14xPzdKgDAFHBC1U0YLYlFlzDlYkb9ort4nH33JpsphARFZNeBPwTmLiBhlo/xf5h3xRYBdk7OjRG0bDbU7hfOJAMH94nrAgwOj4IIDTsYzRI+wCJazcm+Ox70QDQYg01Xv8csdpvtE7yvCM8ym3n7z43jznj/Pr3IDfBmcwCeidGBpNO1B8CYkYOxPv30MrtHUYgS4AIkAzZoyWgEDosJ4ZVgQjOjBFNwIHhE51wPYiKP7AznBCiDgFPweMKsUyQIgNIJQOhJrBfDoD2BXgQB+kc0tAcIUgCC7pWzxpz71qRQA5vqHfAYJiMM5UetNh6OKiIQjlPDE2ltPQ/UR2TPYpky3wXY5HuSyabT1zHMcdNWAV0gysiKo58jzjXsqHRwMLIj5IiY5vuT3zgOEwAQZrmF69vvNT1Ae9hUBAjw1P44eJkuYwEMOui1AMD/yk5GLlzv00qA2eQZhCLx2DeuFDFlDyYRcH6Inm0xhAPKFJAj6rGlsAA08l3lWqRBkyVAiBZIQyBxcEKRYO3oW+BRzKJuUJ48J9EmCzbryxwI9+s9f1CvWub2wO7ZOFwWL+R4mSSD6Dff5S/7OoDN8Xq99QU098zn+SGLAG76MvchwR/bZPSQTJQ4l5Ohs+Bt2xt7q+5rqAULwgsh09wsQ3LPf/ARMgUcRIMAlmXVDEg0xR9Yl43oFCLgK+Xk+VZl+o0meEibmQ1a5v5bAE7yrFLFNvCtaoCOBG28bgxXun/MCfADf8Fx+71r2hAq6/N/Prb9nEFBKTktACSCia6UECP1WdB5+T+iMFUFDojl2WSMDKFugfOQBAmcRCt60CZZSKCXHq7k4AYGCUhkFa+tlHCRAMG8K2TQ4JkbMWclsUvggs7ILgCMfTRUEmTzZeVHwIBUE18sDhKb51Q1SxkOrkSBG0KMKokri58hWfQ8CAi/roaQLsAd9LVnbHoQAPFG9uQHpeHsQQi/Qkg0SFESAQC6uJ4CQwTFnaywrYzB2Bs7wA+wFFrISsgMyCgGI+VtYyibleTDuES7p1cAqREii1j3rZr3onTYBxF5lLkbuAJEG1QNBZ2z6zwm1DCL9khnzcw6I7SHzMn9t+jxIgEAH4y0j9cemi/CNHgpkBemCdfbFtulsL8KDQHDMzlEZGbSCkAcITfPLCUgECPCRvGXPOEyZTq1ayLSRtxh5FphgvZA57RWDjDZ5yvBpBZVoYfMSGAiVNTUPWVZZP1lDIwIElSZEDMFCzPgMtg3Dcv1wPlmqIEiMwB+BKGxG0Nw3T8iUTcqDrOb8HMPuJQrgtdYgAR3SCPPpv8A21scMcvyW/LG+uEHTJliJB3rE5/HV3pSmekX32EHb6BcgSFY1+fe4nt/RWVVriUIYhYSuvvrqqVWy3u5cDxDwFtfge9ndsAFC0/xy+4gAwd4IcxM4s32VNTLKuy3yCoIErUBC8hWxHvQV9U3yzPGFv2brnpmfN3/rDGfxFnwxEizeiuTV5GSEX9EbFYYIugT9Ak0+xX1dC1bQLfiruguD48UNeYJ4ri9pmR8LufNVF1yLEccJoDlmrSBKYsgqY+Wg9IjKbAf5I5I8QNAywnG2EVB9a1oDZLwBCkWjJPrR6/sbcnH3CxBkKPSpNZX57V1gJAIdBizrwYEBNcMr2wBYbkRtm5Q5M4532AChbX51gwS2nK/5KasxJiApIhddM7Z6Bhcwad3xPAFosozWRYYhH57VMf0ChHhLi5YHRFDPsaBRAAd8AE8ECEBBlg9xcF9rAHBlDGVxA/xD1jnoKqkqRyKJAqgSIEwKuga/j554JI1zkAXKHVhTxr3JwUWwTJfyAIEzhi9aaGCPVjSEm43KTg/SM59fLyfgoZ9NTypRgXgj0gbyi7CbH/1kO0rq+Wjag6DF0l4KdjxIi1G/+TUFCM7RwiBJA4/hgPtxoIKE3IGzN8SNHAX5+TcR2K/WrHzAeU65X4DgnHirCYfteH8QRMQKnucBgoyiAI+cYRiSJ5jS4qDVJOw97h9zEhCoNmkjgGf1im0JEAa323EeCeP5bSQ+KnSR3XWf+lu96gFCVIDb3pKjsoxvqEJrEZRQYpsSkr2+DdQvQGjbI2F+kgE4QVSu/UwFM76TBB/itbohy357EPjvfqMJH/M9HG2/Jw++FIeCNYJ/pDn2UEaAoLqAmONpuiZk7YOvsd38uynm6ndesvDoRz+68S1GOb5ExZg+wGm/E7TABtxPMimvwKrKwFL35dutrfYp98RZ4K4kU33PpHlLJsS3j0K/zHccb3Hst0bj+v2CCxC0AMjkyxTHG4v0HAJ1DhxgK8NFnyhBDhMgcHCyUpRE9pBz5WSaynn5Is0lQODAEG6OKrJuFBrxMR+BiWfIX3naFiDEa00jQAAi0SLRpFRRQRg0QIgABkjJtiDN5gwMkHDPkBsIEqDFgwyVb2UWjHqLVMwtSnKDBggIGmJC/gAcYPoDGHICJgjTUsKQAQZQF9R4hny+slD13s62t7CUCsK4YGr067APOk6vOCIBop/BA44MYUPQtfJwxsYwAYLj6QvCKNAVfCOIsmO93uM9SAWhLUCAY/QSWQ5Sz7HJjiINHBNCXv/OQ7+3GLmWgFyLTFvWc5QAgby1ScnIs0PDXgkZTtiV248WJRU+pAB+q8hE33G9Rcp1IiAfJkBQMYQ71s01ZXwjKxwVBPLk/OlNtBUhQdYYsYg9E8iM9qJ6pagfPpU9CKPb9Chn8qF8UE5kVZfjY5aSPNrOAgOGDRDoC52iLzLkNt66X79E3FwCBH5NgKBaEIMdqGYZ7Kb+nYd+bzHy/Fq04SM+0RTcjBogxPdK4JLsPN9s/rH/MwIE1Vy8A26zR7aJmOMR9fkHlgjgJET6VRAiQJDAkSjE5diy55YYzluMXBufdG24JfkiMMAHJCVivro+8K/6XrO8pbAECKNY7Tyco1WF4QTgu4WsdUSC/k8pg6wOGyA4HjmnVFoTZCRFtP2AYNQAQYYSeVEaz9+gwZHbW8G5Nn3noS1ACAeIGAHIfCPWuAKEuI57kbuSXvRscvo54VZGBBbKt54JIdCSZCAW9RGbFJscsOfRoqBqwKi1Y2kbUh50besmY1zfg5DfQwVB64Y+02iRyANIv4uNhgJEICZ7UFqM5sGYx3BJTkkmF3hHD6zLIomRcUdWw8GMEiA4ByGXyaOzdEEbX6/vGswlQIg9TzLc+Z6nePuZ+TR956HtOwjsNDLb+oKVytvwbJQAIccD/cAyt0i2PlzBVD3AVimArwIIzldQjoibZ/297a4NE9rkKSjhzPUKs2kkA0lwbRVCpK5pD0LMme+AX0gN4sTpm0vYO1mRWbxZxhrE1+7jOxOlxWgMhjzHS1hrWeb6JleV/3iTWehj3KopwdPrPfvhf9rsr+kR5hIgwC++KM/6I9AqcPGdg/p3HtoChBwD+GT2JIgdZ4AQzy8wxwts4jb3+DBi3mIEj8kfjsrUR09/Gy+AX/40ydPzwBMBP1vUHaCSpJrBfrUNCxTrexBy3IrvWOmKiD1oscewzr+0FxqSH4GrJUCYowHP9XRZNVlovaIWvw4EiKcFRepsJhE9ijg5FwQi3hxCIaPFCPGnQCoOQVpjnsplnIZr2ZTUr3c+jne+UjVCYSD5onyOkMF49VfunCib0pcKhRJaPvTQ6ZszAB1HxpgMG+dkujlimUzPyEkayDKChJRz0KoTbaPX/JzjOZTTDdeUAVRVQcZkAeL1YHrxbA6zmZrcojWC8Xp2rQQqCZwv0LO5M6olTXOLAEFEb82AGbLOeAUEZENeMq1ahwzzFKCoDHD2WhwcRye0pclc2oyuvUE5WoDgmjKE/o+4CDBUjzyb4IP8ZV5ljvxxfee7t/7weIOW5wNew34+fq52MZfzZ/E95/nzaimUZbcfIC9TxzFagKy7Ef38ysXabugXe5RZ0p+K+KsKyCLRLw4rzzbqO0Wqlc/pMTLSawiK2Wz9ehxVvBCBXcYrfONaAljYBW/Yb91G2B1nZnCAHLE+X6RVkiLe1uLnNv7pDZaEcF1EKd400lb96De/+FI1ebBhNsKGOGctXnq0EWc2a98GMpPvqRLgc8JsTuDtehw3zISNeY94Lt88QGCbkirWEh5YRz7B/a2tlwyQh/lpDZMsilffwnI/h2uwD4bCCteB0wIAGKvlEybLRMoYIwCIgswrzIHbXjrhfp4X5phjVHndAwa6x6yOWcAHAR775f/5nXobHR8P2/EH7aKwgg9Sacz9K32CCdZRIiD8Xb53yc9UwdmsvwWfvdqLmvyn69F59xIYwxitNrE3EZGX9BCUshtzzNsY6aP7snU6rmrO9/M7uI4AH++JyokMeWz+pefIu/PZJR7U9NawXvPzTPXfk7/r8H8wh40L1iVqVW7YFfnDPV0RKiLwlX7BWr/XssNe2j4AFzaUBwiSxRJDZMX/snt2rkrAnnVT4A/sGOZoFY0ED7/tZziD9YQleA18lHAgX9gGH6yH39Mv94gXFPiZJI42M+tAh1xfZThecgEH+aFBNmBPAycWTIuRMjASgMQROKeiH4/xMCRAYKEYgSG64zDig2GciMEpKQ0ie5wcA3ecRc3JHSVAGDiYfl8LdawNcfHOYw6M0hE+IxTVC0TM2X2ix90clMA4b9UDx8brtwCePQv5Gw1kxgVIwM15wI4xIAEUlNMHMJyWFiDOTLWljcxw4L3mpxWBAQQh8W+kBcFHpAUDWgusiZ8j5MCOQcpsGH4OTDlpBqg3EBgyUFm5th5uAR0DloFA8JWOAYKMKpCIYEhQYv0YMcJPPtZBJiFeRQqEBWHAh7xkXwQ2SIXrcDJIjowGgkCGSsg2fJNrgCIS49o2LXlWMgZI5mhNYx/GNAx9lHvOAgFoey42g8QBfbZlPZA+xJousB+6wU4MRBuho4/sXlDJPukwm+TAXM+5nEC0rOX6CQfYAh3v9UFG1/AKPsRRsO56dEnFC07EhlmEBXkxJ0N/s+PYJSeqnYkzNzyvwITTgwtGvI6RHUkY2C8V30sQwMI5dopUCO6d53kd2/Y2Ns/YNj/35PA8l1cDImPm62+BGrtAHtiQZyZjWMXW2ZZWUHJDaPTuagMVSMEvjtjP2xwp8g3H3APGS1SoJHLisFNPuBYzdu3/sALOWHO2idSzc3uc2LF5u6/qEvyGdfDecWHv5q0tkez4Bpvg6YhzDIQAgYMX8Mox5Kctim1ZP2RjVscs4AP/iIwJ3OmH6lW05LAjOu/3bNLgI9ibYJQehy3BeQE7DA8b4u/oez7sRZChRmaR7F6jyX/CGj93H/5NQE133YvNwgukNd6qQ38ltiJQgQGwjb3RZ0Erks3nsgWBt2Pi+w7xSu7AF3ZB312/6SOSkgmu3Ta/+u8DP1U7dHVIihhahdkiW8PF8DfX1OoLL9gZX20Ose8IvxFg5C1VdflaM4mOaCfFl/APtux3KqvwTvKEHOEfzie5A9PJnh27T7Rywk+8BK57HrxAUsCAYXwInyF5Yc3pSTyn6oigQpCDlwpYcAT3EYjiHPSt/h2OrmDCggkQLBDiGyNAWOTq535fH4gDIIh31QdRiI2Mcbwok6LlrxIVeQMNDqLfazk5b/dnsDE4J040n7PfUaDYkEOp8g+zUe4IUurPG9flGONNG4MoWX6/+vFN98iPV7HhwPPB2M0Z4ZINAGgiac4EWAG9eAew8/xM4OJnZNq0fk3PISOJACEUHDRgsT7WMxy082SRrS8DB7DmYt5AEIgIEgQF5snAZVZkXpB6fyKD4nzBTvR3uodnyt9aZT0RFddj/GTgZ0ipNfWn7T3ug6zVpI+ZBQLQJhPrDoRzm5OtpTPWkm7n+hbX4WgRTEF7bv/+Tcd66aeAnv31+xJx0/05RZnp+isRObB4Var7R2Ad80BaQ8/rzxt4Ruc9Ty6LNrnl92s6hpPMR/14c6DzMfye8+NsrQlMYKfkDDf9XLY9fxmB+ZIHshVExvVi/Zrm5drW1HUlDLR7OBde0uPAbrYNg2CA3wlUHOdc5AYumxfHLkhi86qwfgYr8hdBWA/HCWxc37Xyj1a6HjxwfvgQ5NK1+BjYFR9vm7Rtj+N+s4APdX1kY+Ef2nwo/bOefAR9yW0+911+HvYXxwjiYy9Cv2pxk/+UeIJLdR9JL+kM7MjthY9BViOgrz9vzMv5dDXnE206EB8ObArGZcBdp21+Tb+n+/iYIIxc2Z/nsA588jB4mK9f0/yjXUlAEPxDkKVqEVUU57HJ+MAhks5Hs1VBC9/N95MV7iYJ5HnxA8+R8wu+3j1dz4AB9Cd4AXn4HV2CM55XwgB25LxgHPY4H9dYMAHCfAin1zWVojnrQd7RPem5lfsVCYxDArNAAMbxnOO6hiyZily/hMG47leuUyQwTQkUfLiz9PECGev4KOg016fcu0hgrhIoAcIQEtSioBVFD6KMnH0J+ZuDhrhUObRIoPMSKASg9xJpRZPdV55WqpeRrH+RtfOLXCZYJDCiBAo+/O9+Pv3tWvu0lGod1MbU9j2jEUVdTisSmIoESoAwhNhtSNVXag+BN2HokVXKKqNIYCFKoBCA3qtqv4HN8fEVdP3rStllFAksBgkUfPjftkMBgo2w+ua1sfRrMVwMulGecWFIoAQIQ6yjXtN4rah+VK0EbW/VGOKy5dAigU5KoBCA3suix1xPuT0LelbjS7udXMwyqSKBMUug4MP/vorbvjQ95t4WpkdewqCMIoGFIIESICyEVSzPUCQwDxIoBGAehFouWSSwQCRQ8GGBLGR5jCKBFgmUAKGoRpFAkUCjBAoBKIpRJFAk0CaBgg9FN4oEFrYESoCwsNe3PF2RwMgSKARgZNGVE4sEFrwECj4s+CUuD7jIJVAChEWuAOXxiwRKhrDoQJFAkcCwEigBwrASK8cXCcyWBEqAMFvrVWZbJDAxCRQCMDFRlxsVCcycBAo+zNySlQkXCQwlgRIgDCWu6R3si55vfOMbK59C97U/wxf8vDllHMMXA7fddtvqS1/6Unptm8+S93sbg69Aewe8tzh84AMfSJ9899XBMhaGBAoB6PY6+nLzpz/96SVfgt9nn32qQw89dGyT9hrXTTfdNOHAySefXK2zzjp9r33WWWdVO+20U3oP/Oc+97mlvizc9+RywExJoODD9JeLD/a69R/96EfplasPechD0tfYfb18HMM1d9ttt+ozn/lM+vjbAQcc0PeyvlC8yy67pC/ZH3300dVrX/vavueUA7opgRIgdHNdWmf1+te/vjr++OPHHiAAms022yx9HXq77bar3vOe91T3u9/9ekrHJ9Of97znVb/97W+rd7/73en9zz79XsbCkEAhAN1fR69ZfepTn5omOu4A4Ytf/GIK+pdddtnqYx/7WEoG9BsnnHBCIhIPe9jDElF56EMf2u+U8vsZlUDBh24snATd6173uurLX/7y2AMEvt03n1wb0Q/u0evJr7zyypQkECjsv//+1b777tsNQZVZDC2BEiAMLbLpnsDYDjvssLEHCHfccUf6Euwvf/nL6tGPfnT64NNd73rXng/7l7/8pbr66qurP/3pT+n9zyussEL5LsR01WOsdy8EYKzinJeLAXDrNB8Bwh/+8IeUBVQVXG211ar73ve+fZ/h5ptvThUNyYUnPvGJJWHQV2Kze0DBh26s3S9+8YsUIAjox11BwAtuuOGG6uc//3m1yiqrVCuvvHLfh8YHBAe///3vEwbgBWXMpgRKgDBj6zZfAcKMiaFMdwISKARgAkKe4y3mM0CY49TK6QtcAgUfurHA8xkgdOMJyyymJYESIExL8gPcVyT+iU98ojrxxBOr22+/vVp//fUrrUBnn332nSoI9iXYB3DBBRdUyyyzTPqyq5afDTfcMB0rE3DVVVdVH/7wh9PXoH/2s5+lKkEcc9BBB1Uf/ehHK1WBF77whamlYMUVV6w+//nPp3Yj1YQtt9wyVRie/vSnV0960pOqrbfeuvra176Wrv+2t70t7Vu4xz3ukVqOzNvn543lllsuzX2HHXZIGQhfndQOoUfZ/Z773OdWW2yxRXXkkUem3ylPulbJPAygJPN4SCEA8yjcES+tYqcPWM+x/Uf2Bey3337panmLUdi7fQqyfwYsePOb35zs2mCn7Pzcc8+tZP5l/LQU7bzzztVNN92UsEE1wHCcFgN7ld73vvdVF110UWojsm/JnGJ/lN5j5xqxR8pcrr322ur9739/OtYXZ1UYtt9++4Q18OH0009PNq9t0YA57nXsscem+R5xxBGpxelud7vbiJIrp41bAgUfxi3R/tf7+9//nuzruOOOqz75yU9WK620Umox/PrXv15dc801d6ogIHjvfe97K/uJ2CG/bU/B6quvnny6r8FfcsklldbAn/zkJ4lfOOatb31rtfbaaycs4Mv//Oc/V+utt15qG1RZxCNOO+20ZMeveMUrUvcBDqDFWEvShRdemK7t3x//+MfTg8Ghj3zkI8nvs+N73vOeye87n69XdXjVq16V+Ilhno9//OPT/GHVIYcckjCp7HPsryfjOqIECOOS5Dxch1NkdGussUZy0AzzJS95STLk3AELDhiPQEKPIKer5PiDH/wgGeqqq65aXX755dWOO+6YCL4gghG+4Q1vSIYJXH73u9+l/QSyEREgMGJg8cxnPjPd30ZpgQSiz1l/4xvfWLJxMQKEu9zlLilQQUyQhQMPPDBtpLTxeaONNkpgI4jQO+3/7qcsKki49dZbU0CilcH9Xv7yl8+DVMslB5VAIQCDSmoyxwkKOFCOVjD9ghe8IJHq6AvOAwQ2zf5gxle+8pVE0DlrGxo/9KEPJeetGnnSSSelAGPzzTdP10bQjznmmET8bTDcfffdlwoQEPldd9012bg9SzYlu9e73vWuROT32GOPROZzfPr+97+fHLvjkBEbmAUb5vXBD34w7XmKoAAxMZ71rGelP1/4whdSiwNsgiMPetCDJiPscpe+Eij40FdEYz/gj3/8Y3XwwQcnG2V/7IeN7LnnnullIXmLEbIv2Ya8CwD48qc85SkpoPB/CUJ7C9imFkI/Y2N77bVXCt4lAm655ZaU3PvhD3+4JEDwIhP8wn7IvffeO3EMQYA5wQCtTn7Pt0eAINh3H3OV4IBNzuXn85ei5HuqtC2vueaaKWkoEYk3wDEt+DZOMAAAIABJREFU0GVMRgIlQJiMnIe+C9K/1lprJecoU+8tIgg7w+LgcweMVDNIWUAkW0bO/0855ZRkjJwuR48MPPzhD08kwHHf/e53U7VBAEIRAIHsRAQIiIhMgyBC5pEh//rXv07XX3755dOGZsGDEQECIDDvG2+8MWUFZQgCUADG+eefn4gNwIn7AS3BDfATKBgCDABSNj0PrTpjO6EQgLGJciwXEmwL0Dl4jtXfsobsx4gAgVNH4gXjL33pS9MbSJB0tiWLyAbDeTsPTrB515KpQxY4eoRBBtGICsLb3/726vDDD0/3Fgiwf4kDlUFYUm+BhDeynWxZFlIllE3DJ5iGtAgUYEx+P1jjWQUvqgj3vve9E9EZpAd6LMIuF+krgYIPfUU09gNU/tkc36yCsNVWW6XAIALuPEDwRjN++fnPf36yX9UGVQEkm+15Q5kKpEQfjgBfJBRU+ditPQf8vcqjhGJUEOAF+0XUXV8FAJ7oWoAB4e/NNQKET33qUylAULVw/iabbLLE3u1TML/gIbGnaoMNNqicd+aZZybeY6haqHKWMRkJlABhMnIe+i5KeTIESD/jZ0BG3QEr8yvHyyQa3jhiyBAiAwyUk2XQ3/zmN1NbgtJ/feSEPQKEBz/4wel4FQvByXOe85w0j3DSTQGCDKHzDYYNPHLAUDY86qijlgoQ4n7uUw84ZA3KmI4ECgGYjtzb7vq0pz0tOW8tfhw8x9q0B4Eth72zWyV5rQna+e5///snYiHLh7hrEfD/pjcUNQUI7DuCeETf28sEEQh8Ez7J+LN58zXMw4gAwe+1TEoWNN2v7Lnqlg7msyn4MPm1Uf2v+9e2PQiBF0g5DFDdhwF/+9vfkj2q0Knmscm8RTh/KteuBwhetY6TwBn+WZACB/AFoylA0IKo0pnjTdh7vCUN8c/xTNDihSzmCi8MyVGBRhmTkUAJECYj56HvohSnciCj1ytAkDHQXsRADdkAGfx8iP5dwx4EUb5eP/1/+WgKEGQRv/3tb6fKgbeZyAByCqL6ZzzjGY0VBPMW2LQFCJFRaLpfCRCGVpN5PaEQgHkV79AXtx4Au1+AkNu7NgKtSfVKXBB0Dlv1TntRfTQRdvuitDVoJ1A5QDy0CMGfBz7wgXdKYPgZh37GGWc0BggPeMADlgQoJUAYWiWmekLBh8mLX0vfK1/5yqX8a1uAEHghUJDYi0pjzDpP8PHx2gTrvKApQFAF1B2gkohLGC972csSeRckNAUIWpZ0MPSz96aERwkQJq9ncccSIExP9j3vLErXHqDVqFeAoBQoI68H0VBN0ENo3HbbbcmAGSVCIPsng8jgXNtQZVBW1FpUbzESIMguIPOMX5+hjYzKfcr+TRUEmxqVMWUqmioIWp6UMkuA0FHFy6ZVCEC31kiJXx9/vwBBzy5717fLhm0ofvKTn5weRl8ym2a/eoxlEzlvewp8EC3wwD6gJsLuGoIEbUH2KCAD9ASmmF89448wqGDaM2HUKwhai7Q8STiUAKFb+tZvNgUf+klo/L+/9NJLq3XXXXegACHwQhJREkBbjypCvERAl4E2Iok/rX5alSUUDB9IgwdNLUbxVJKH2gy95EB7sKrkxhtv3Bgg8PlanlwzKpZh75IU0fJUAoTx68xcrlgChLlIbx7PZXB68hB8GXkZAG//sJeAYzaQcW0GnLUgQi8ig/d7JF2mP944pD9Z1g8B0CLgzQCPecxj0tsIlCxlAusBgmDAJiabkfxbuc+fCFiaAgTBhgzHxRdfnHoNt9lmm7QxyTmeyb4Hm6ZLgDCPyjOmSxcCMCZBjukysWnYV1LhgUqhagHnbkT7ngyfjCBSoL1AdYDd6g/2pjL2rFUpWhh9zMyeH7aKgOhv1jbQRNhlMNmvbCTn4aUJ9h2Zj17neoCgYqmFSFJBu6T9EVoK4IF+4he96EVpo7NWhRIgjElRJnSZgg8TEnR2G+Req68XkCDn7Jy9sifBuoQAn40XeGOQF5PAAOeo/AkABOQSBnDEi0A++9nPphZi+wUlCvhp14AbkgH1FiMYIfnoxQFeeqIViK9vSghGx4CXpMAKiUOVBBUH8/dSA4EM/LCnoQQIk9epXncsAUK31mOp2TB4G3oZDgPiXGXj7PQ3OHz7CjhsbyTicGUEkX2gwPiU/mUNLLRAwGZF/4+eRNUGLUqCDfeKkqL7yTYCB19D1CagTxEQCCoEAXk/sk3MSpTmJDBBVgQpSIrAxDyAD0AzvHbNG1VUSNzD/ZAWPzPiejZElzEdCRQCMB25t92VbSPmNhmyWc7f5t94jSAn7+1DggCBuGweBx49yK6rqqD3WCYflsjqGfDA+bBEJtHeIxWGqEaqOCD5zhH8q1o61ksMJCVib5JeY0TEMAebmQUG8AmBUIkU2EQPs5ZIZMaAM57LiPs5zmbr/HrdWpXFO5uCD5NfewSbbUj42XSMeEvA2YMoQci3s2/BgVeEwgtJhPD5Wogcixv4Gb+PuOMH/q8V0b4gryQWIMAPfty+RUEF7gFvJBQQfElFvh4XEDiY03nnnZcqmKoPrn3OOecswRuY4s1KvrYueHA9iQ+45f4qpAIGA2aoPPoTOCSxGd0Sk5f+4rtjCRA6vubaAzhVETtD9GYAvfoMHwD4Y9iroC/QG0s4f5kBvcE5wUb+GRinLGtoc6GgQAaQwWtriuENAtqQgIt2A6CAQDBq0b/+48gKxjkykLKTMpiuhajof3YvACBAEUB4i5L7CVby+ylZCoJiCDhkKcqYjgQKAZiO3HvdFQngvO0nYqNsjp0g6jb4ezWgSqOh+sgG2bskA9vk7POh3C+xIFOosigwd77Eg7aAuj0iDF6M4NWGXqTgvnBJkCATqcUxH7KFMCrHJ/igsgk/ZDENc5BM0O6Q40kEB/GzHKO6tzqLa0YFH6az3tp/+Hk+WfuwFkFv+BI8IPG4QbxIRJJP8K7LAAFnc15IELxA0sHbywT1Eg/sGC+IfYyOl7iLwZcj9+wZD4EfbBiZZ9Nel+4NatocY7hfvETFNxAkGc1LB4N7eZNS4FW88rgNA2ATXKrvs5zOSiz8u5YAYeGvcXnCIoGRJFAIwEhiKycVCSwKCRR8WBTLXB5yEUugBAiLePHLoxcJ9JJAIQBFP4oEigTaJFDwoehGkcDClkAJEBb2+panKxIYWQKFAIwsunJikcCCl0DBhwW/xOUBF7kESoCwyBWgPH6RQMkQFh0oEigSGFYCJUAYVmLl+CKB2ZJACRBma73KbIsEJiaBQgAmJupyoyKBmZNAwYeZW7Iy4SKBoSRQAoShxFUOLhJYPBIoBGDxrHV50iKBYSVQ8GFYiZXjiwRmSwIlQJit9SqzLRKYmAQKAZiYqMuNigRmTgIFH2ZuycqEiwSGkkAJEIYSVzm4SGDxSKAQgMWz1uVJiwSGlUDBh2ElVo4vEpgtCcx8gOALfz6wtdpqqy35IuckluCKK65I991ss82qhzzkIX1v6cMgjvelQh8O6frwIRRfOfTZ9ZivD7T4SrKPLvl0uw+njTpc14eVfLzFV5nJZSEOH6K58cYb08eqfFXSR+cGGWTtS7Q+Jucr1tMYs0gAfCzoyiuvTF8Vpbf3ute9JiK6YfXZPH/4wx9WcMSXQ9lUl0fMl06+6EUvSh9oMnyZ2UcTfQTxgQ984JwewQeZfEnVBxof8YhHzOlaXT6ZrvgAJbnxH4MM8ues4UhX9KWr+HDHHXekj3jRTR8N8zXvSY1h7WFa/GVUefzqV79KH1H0cbT4SCu9/PKXv5z0Mj58OOr1//CHP1Rnn312+mCbL0LPhWOMOodJnNeGp/3uTf54JPydhHw6FyCcfPLJ1ZlnntlPTtW6666bBESZzjjjjMonvF/72tf2PW9cB/iaqXuee+651TOe8Yyel/X1Y18q9NVBXyv1BcQuD186PuaYY9In3J/97GcvmS/Q9WVjBMEXU5dddtmRH4Oib7rpptVd73rX6otf/GJ173vfe+RrdflEMjz++OPTZ+gRoEc+8pF9p+sT9fTaJ+iRCU5kGqMLBMCXOd/2trelIKvf2HfffdNXun092JeBzzvvvGrFFVfsd9pYfj+MPpsbon3wwQdX3/ve96oPf/jDnU4aWAM4d9xxx1W+pHzBBRcsIQJk7ovKvtb62Mc+dk6yPOGEE6r99tuvOuuss1ICYiGOW2+9tTryyCPTl3A5eV+47TeQicsvv7w67LDDEoZ0RV8mhQ/074gjjkiBf79BbzbccMPkvySgyGySCZZh7MEXjH09fBr8pZ8cm37/rW99K/GYL33pS9Xhhx9eveY1r0mH+b8vKZP1y1/+8lEuveScX/ziF4lfvPjFL67e+ta3Vve4xz3mdL0untwLT3vNV0Lh9NNPT7zgda97XcLK+ZZP5wKETTbZJJHSHXfcMZFGRr711ltXnIe///znPyejMvF3vetd1Q033JACBQ5qkgHCX//61/RpcyQZye01ZJGvv/76BFwveMELOh8gtM3373//e3XbbbelqH6ZZZYZyvY4tZe97GXV8ssvn85zLWtpzLeSDzXRMR9MT+jmTjvtlIj+ox71qL53IBvnrbrqqqnCspgDBHb+ile8IgXja6yxRoVgcUY/+MEPUgDw4Ac/OGHBm9/85kQE2Ng73vGO6qtf/WrKak0qQBhWn2U5BY77779/Shx0uaoY+sgpIbQCgsgUwkDPAg/64WCu+Co8n//856tddtllyY8DU11roWYOyVLCaIcddkhBLFkOMrqoL5MKEK699trqgAMOqDbffPNEHg22zm5UnFZfffVKUuWQQw6pbr/99uqkk05KQaZjENlJBgjD2IM1ldAQWE+avwyic/VjzBffetzjHrfUfP2c3Eex27e85S2VZGuMwFH2v1C7CnrhaT8e+fOf/7zaeOONU5WVn5tv7tS5AGHPPfdMhr7ccsslWTF0LSi5Af3mN79J2W1RplYYQNV1A9NOsP76689EgEDu45ovY5AllTU79NBDJ0bYRgHA+TpHVQy5GjRAiHnQa2MxBwgy8yqKr3/965Msfv/736dg67rrrlsqABAQGGuttVbCDxnvSQYIo+iOpIeMY9cDhHi2bbfdtrrooouWChBGeW6BgEykNjpObrENWVJ4oJ1i0ACBjLqmL5MKELRjqV4L/iMxRX5wVbtFVPBvueWWZPuq2/iBhCKfM8kAYVhdRsBmgb/Ec41zvtZO0Dct/zbsWo37+FHwFHawA+3KizJAUEpdc801l6xFU4Dgl3qzZWNvuummmTCwcRHucStp2/XGNV8tMgceeGDK6J5//vklQBigglAChP/XShUrTknWqleAIIOoumA/UAkQ5gclRnFo9ZlIGGivEuQ9//nPTwRusY0SIAy34ny8ke/1awoQHBP8oQQIw8l40KPHFSDYk6lFSUWiBAj/X5Httw6LPkCoC6gtQGiKaJUatR/dfPPNKXuw0UYbVXe/+92XXFLEesoppyTC8eQnP7naddddq4c+9KGNa4KY6PcENE984hPTMXoFtT7pc7RZ7NRTT0390VpBDD1iKhnuo9d85ZVXTpvQ/L5OuJWZ99lnn1QBMV71qldVr371q+9UVkN89OjLjmi1+MQnPlFdfPHFS81fWVPLhaypDMvaa69dfehDH0r7HbRdxcbN0047LT27Dd1K+3k/vOf99Kc/ndo2OHHH6OPU8hV7Jn70ox+le2gl0NIRg9K6dmTDNthgg/Qs2on22GOPJHMlSK1gQF6vonHhhRem3nwl4Rjajsj8c5/7XFpHm8wASfQ3+73n9/uDDjooPY81N3bbbbd0fNvI508/rJV1XGeddVJGT7nOtfzM/Zr0w5oLdjzzM5/5zPScsf5xX/3ynon+POhBD6ruec97poxpXkEgb+0a9JsO2FNjTRwbo1QQ7rySbRWEOJJc8wBBfyz70WqknL3SSistuaiAgh75vfOUbaOvtkmHVDNsQtQ7rHoJD2TBVQHYXZM+qw64viHTScd23nnn9P96Rthx9CSGvRT1FinlfC0X9qgoNWvDZOtIVD5/ctKz7fn0BaviXXbZZWmusM/vtW+yNfZuPxB8yfcVRY904KXruU60GJGfZ3bcdttttwRPcryh856BvdFxNqgaZKMjfBT4wQu6b3568wUi2slye4IveqC1HsBULVn3v//9K+2Qsstkh3R4PnjFtp70pCcl/Om1v8n82bv2R9fW6+6aW221VdpvJRHFltl0k36450c+8pEkA+2A5uVa97vf/ZbMnzxghv5htq49ix7RhbyCQL/gu/n4nTYkGB5jsVYQmmyxLUCIYyNA0IasZRnWw2x+Nu+TZ0/0mn79+Mc/Tptj+ZHA3vq96/6HvgpK3vSmNyU/ULcHukQ33N/16bvNvXSe/60TbnPcfvvtl9xWO6UXANQHu9KipxrHHrQrwgU2RjYrrLBCOoUuSczRZVgFj7bZZpuko4b9RCq07kvXVFtye5Hgo99auei3Y9hvdGyQn5eZkB+5quDGyPXZz/ASiQF+XcsYe2Bz5sYHk7t5+Lnkb/7ykuAnfD8b4ufhRLzcIce7LbfcMrUvB29hR+7dNKyPdXFPa+4ZyNLzkiV5uLffwxTH1PUDR4OlMMj1dIqYQx27++Gp+bmX9aC/OB999SdaiUqAUFvFQQMEhkTxGCmjtRGEQwlCQIH1wSOASruUnHEpmXM09QHA3/nOdyanBfSRXeDNue61116J5Lq+Pxy/awocEHKkUXuDOVlov68HCBRJcOH+rgXE7nOf+yw1DX26nh9pEPx4Jn8oid/5mWCE0zVXxzI2xoUYcUx+h0xQYJta3Hf33XdPRADZ9eYRxvTud787gRujYvSe0f85Sj/zthWKy4EGuTZZ7V4MxpsikASGhWQhQSoHAgMgDaRi02gEFB/4wAfSW33yDAIAYXDKxObh/57Z2iHk1tZ51kUAw/E+7GEPSz9Hrsm5aeTzB0b0RLDCOZCVDaOCLOAoePPswMHzGtYXIOkb9jN/e0bHkm+UuckDQJMHOZufQFCvaQQIIW/kQg+6QMKGLJUzpDBGCRDuvJLDBAjeqiFB4JwgyhGMWs8IPGHBOeeck9bCmvm7PhzPXtgQ58vBcywC3L333jsdXtdna8l2OGU67Dg6pzXCqBM+eCQwl1lnU7EHJZ8Le+fAvE2MjdibQf+1YXAu733ve5Ne0l82zNYQAgEEGzQHzyjAFwTDKNgo2PbcsSeAvXCWcEUgQm6y/d4KhdTCDXjj/jbbIgwSKfCFPXhmeo9wxcZj95ck+M53vpPsGla5r3lfcsklqV+cncDZ2JOBoHPwW2yxRcIdz4X0C2ZgluDGHMkU+WJ3gjXO2DVhbNN6kqn18eww0nqa+8Mf/vD0M2th7WAXMmLu7Dfe1uJ8eCGBIpGgVQLWs2NzhxHkEvKgG3D+CU94QgpgA88iQIDn2gasi31qZE3mgtCQRQkQ/t8SBg0Q6A4bkAhE8qwrLAi8tp564Oken4iMIXZ4QtN+sdz/0C3YYp0ECO4R6x72YFM/m2KDdMyx7s93sZd6gCDopkP8CF3g4/MkJwnQV/OjQ8goPfMzlTk+07209LIz8xFIeC62aIMr/TIPOsn/HXvssSm4wF34Rgm9ePubhCjsgisSoLDDfSJAQNgFEPQUJrFLI/QZJ3BviUXYQwaO4wPjLUju7RlhGNmwE8dGC42AIBKSZC5ogW3Oc3/rFHgnKMG/DL6cnJ7+9KenOTatp2tYC3OCL3r8BR2CPlhrXfEKOuT5BVT8xlFHHZWub508I+wxJ62vkVgMfmUu/fA09nTxRWTvWjDHtSS2PDf9LAHCiAGCvQtInr0LlAyAcFSckEVjdBxqKKU3coQS+l0bqRQRi+gZk8VxfU4eqFBgAJ8HABQ22iEoF4JdDxAQfsQCOImk64FBPpcgRFdddVVSds9DiREIz8nQKWwEILJyjAUxFyCI0M0DEUCYOCzOmyLGbnjX8SwCgJAPYsuwbQrL37okiwBIAIThPI5SFhNpEPV6JiBrjo5va/moE2AgY66CJoGY4bk44agOMUqO0vwBGTA0kBAgBryQrLZhPggYQoFYAZTY+IYwyDoGyQcQSDwS4nfuIVsSpW6EwbzoCHnYMMshATTgTR7WgfNBUiJAkIkAWOSPbDoGCJChtbPGRgkQRg8QyJJeywJxKPBA0B/rySlyAkDcEPDJjlkj+tdWiZKhpnfWm6Pl1B7zmMckXKivl4AyWp6QVjjEYdONPEBAiOkRnXANjqrXENB6HnpHh9wDOeWoVPdk5zlDdsKxwkX46P4SJuTCYSGhsCf0nc0j5EiU63s+JD+Xj0x6vkn5f9i715jbrqpu4PuDGv1g0AQSESWm1iotFylKSylKvZS2qKVAlEIbRGq0gE0lrQqpFLRcjYKIVQxVahUaQDBQa1FRhAoSW6WtRSGSBsErWG+Jnwi++a33/Z93dnattde+72efOZOTc87z7L3WnGOO8R//MeaYc5KH/rAnhAcxgA++z1HrD6yDvZycIJjMfKYuMaoJMLIjE0d2fsdRwzT4qV/mTvZUkO59nitjSobJMsrkW/UYaoIcfclpN/Ay+Mg56zMZBQ/5AvZMp6wiwSPBZZp+cu4IhPknL4EW+zYW8iBj/TUusmT/gh5zZp40qyKSRhIo5GclsgUI/38WpwYIgiv2xj6RO1lpc8NW+Co2BAsQbY0dWU0T+PIxNTn3mewpozs+S+eRdHNV24MAAAfgP/AC74Qf3lcHCJ4F/8295NTYkdjx93wODoAEwy6+UjJDHyUsE4DwXXw1f2fzLz/Ghuhf+AeOwKbIi5z4bc/2mRw7TD78ZbnnM3gkKBEgIP/0WaIP8dbYo8SFZCw9HvJvNQFmIznlCWFOxQc+xP7wHbaJVONoV111VfeHHfkuP8C2I48+HAgewRfvII/YH1KOF5GvZuxWYjwP7ns2f04ekgv4lWQs3cEFJBhgwBQ8JWvPpQPZg+t9qUzRhxYgVDM4dQVhTGFNsCwToE4JB5CWUZBx4lD7GifKkBkKgC9bHSAwBg7VSsMLXvCCDtxlDxg5JY5Bm2SZcErIYKIIQw4sAYLfCxCydA3sOJxHP/rRnYIO7RkQsFBUGa6cXS7bwHBlBsktx0MiKvnM0PPKAEHmL0tuntHX6pKPctmtJFQcPzIn0+nd2YzGEXPSABahQ/77Nv3W8zEkzzrAGZJvHJDgALj5v8BM0FA2QA5UQpA4AaRMkJRW9hdBA2RIBePPOJFXwOC7CMgQgA6NaxM/39YmxEX6vsgKQrlJuZxPAI8UAvNy6ZlNCHZDIPr6VROA8jN1gCBgRTQkIjhuGTx9UvqocZR+By/oGBsNURmTSe2Q81mkJyuOAuc+QilDyXnTZfaWk4eUF8omSqogRTmxqDxDvm8PQi0PmUK6T+eHgqypAYK58H7BdYkv+ql/sBa+DdX0TwmwyUtSAg4KWLQ++dZ4iDQJTvRNIiSNXJFOgRD5k6sMpeeHZNT9tWpCHyUnytIWAZyARUBHT1uA8P+tYmqAUG5Srv251QA2Y/6TMWeHviNzDqf7ytPGDp2o7UFCgr2be32W+MIVPFdgHwIvqIRLEgV0Yd4pgUP+GTnFc/SdXiKXfZugYYDAUyCQxr+yX4GxYMJqH3/mZ2l9exBqe3Fio7F6juBoqPXZZ02A8QLjyEpNWe0BU3E3QY0yrD776LPvuj9DeNTXv3J+4bnVBqsi7DvBJJkbvxUc8rKSOQVP4QZ+Gv+gn8qWcA7PN1ctQKhmbx0BAoO2LI/k1xcnmZCyzrN8/SIBgu9xXFYmOC3lAUAmBhiDZnAyDpwzIjK2euCZQwRWgKPfatNkDoYAg/xEsYinLFTZZLgpuaVtGYIyAJkSIMhIqhXuC6DynqkBQiJ2wFiWHCFx+mWZFqnW120HCIhKQLs+dcTqigAsAMHh1PIo+5uMMtlZ+akdgSxEiNUUgrMIuV70s4caIISgKbFxIkTZEDLLvUMXly0SICh5FHRbGZCJ4kgQgdQHc2gyXZIJWeqeco74UIAgs+750b8+hxk8YfsCVAFz2cgEqZVxrE/dmhIgSMQgtAmY+3RuaoAgk68UqbYnKwbs0WqBBMIuAoSsQEtMlKfkIJjsGjGUbdV/eF/Ko+5v5FHWVEdusr2CJJniFiCsN0AgT0QYN6iPi5SpRubHVhD6TqWr8QHB5TMkC2SSYQtyLuAztyHcCC6ym9LcecdXDvlnyT/vkFQztqFDXBBWqwnlMcORLh+krEgAXFcQTAkQjJd9GnNKjvpwYEqAYMVFIGB1reQnnif5Ihkhiy8Y2XaAILkiYVlWVOiXOfczpYo4ocTfFDwlD9UDfT4AJzAvLUDYUIDAcamPH9qU3KfAiwYInoEQq4+XWaIcnDbSGINGEiz9ceSM2N9jbShAKFck1PaPBQjGjsBabahbFE5t7KIBAsBjuFlC7bs4bWqAYLVF9kTG3r4OxpAWgi3ThrhtO0BQaw5QlSooxyibAEyGQ584DIHYlAABKRBc9O1/yfNbgHB/y1jHCkIIWmpyFwmcFgkQPFeAK6Nk+d3SPTuTQJCoiEOT7aNHVhcs/c+7cGwoQPA85ZIpERgLEKxqlsdJ1wG9+v5lAgQlT8GblO7V8p0aIMhc2kxpuV4WPi3zJ+MqC7qLAIHtIiVwQaInLfpp3gWGEhop+chhF0MBAhwZ2yTfAoT1BwgIJnuyejO1LbKC4Jk5299eAAkBSQi6gQyGcLNZZTNIJeIruTjWhvx9nscO2c3QCgK9tfdNSW5fyyZvZTxlifGUAAH/8XyZ8NI26vdMCRDYjiSrElB4kOSKZ8UelGwJIrYdIEju4j8OL8CvUn2hb1nh0n8J2il4Sh6SAVaBhloLECrJrGMFQQmICZOhQchlzzTRtWyZiL6vLRIgMFhLQakfNpGUVjTJEEuD5uzUBat9VJOsrnVoSTEOR12wMpsQStkGiil69v0hwNCZMDd0AAAgAElEQVQnS+fGr5xBiQ/AQrYFTMoaOCbZecQl2esyoJHJS6tLdKyUIDuyMJYkRdUct7InwZBxlXsQyN77/bwGCH1R/w08RdHepT5X/bIonOEoK9h2gCBLmSCAMylXnJQD0S9zY64FYVlyTDlV+utIN4CiLlJwaO7UaStDM78CECsL2VfRAoTNBAjmU+ZMpptuC9gt+audFZyqfc8+kLoHiwQI9B62ZNVStln5Qm4KLh2aFTzYIWMGkwTLdXY/fUmAIEOXDXkIaS5d83t22ecwOVo1usqp6KGSJo7Oz9m/kscQWCuuNhHn0rKsIFgyTxBTy0P2UDYUwfFdeJULoQQ/nH0ZIHCcuWRJgqK8FwI+I9VsKjW+ZACbYB7ynY3Y8K2+V2CK/SxbYmQM6tvpDUKU0k9ZWVlTMjD3gj96APPJjyzrAMcKg/EIyJS9CiLNA10QNKp1ls1uAcJ6AwT6bg7ZHZsQNEe/ZOKtLvaVAC8SIFhF5FvpJ3vmA+w14OsE80rSUgIkYw/7+QEEnw4NXT4Y/wy76Ewa+zMefeSHho4lpZOIK9/q8/SXnRq3ZyuJgg0SfOWeozyPv5P41OqEhc/QZ6VG9gdY0TAO9qlkRok1vCvt03twAvZTnvOPKzjsRKJVP1Km571Kw5Ra4wyet+0AATbhPPwGeaRvKUm2QqS83R4K8z8PT3Ee2GocOaEOLuN6qj/gYAsQCo9MaRAwCsKRiL7r2/WQXJtvEzFTRI7fsi9nI6PL0dh9r76WUwkJRsYQBUbW1xBSp2dko19IPEOyfMfhywrYe8CoKIG+Wj6kNIAAIRSBh9D7HaWxhCfz7rQNRBE49JUbJUBQz0ZxkFPjYeBxKJxwnk9Jc6KPMfm+TIKxULqsIihR4sD83xjUQcuiAC3Hr5FrjvOyeYaRI/5WYMiEw7YMqoSCoSo1sqkZGSJXxmP8DNzccf6CCGTAGCzdcnpAU3mFsfueZwgCZFz9HsjI8shkkJfnybAg2FZojNd8kAc5Assc4VbPqWwKICn7rxTAvGnGZHwIpCVC8+T3yoKQAKBH1oiVpUVzbkkY2CslMCbEiDz93x4R4/N540ESgD6j93uBo+8ic95p/DIuHBWgDEmMfAbTChv6xT6WGFnN4dCQKoFZnflLjb0AFbkyBjhijgG1Ujby5ag4OIEZ0krnOXMATL/KTFXE6zlwRdBNn534ERLPKdT6jBB6v+/QabWydJlTs3pBZ803u6aTaoARFrWocIRt9t0oHIfs/fRK0K1P7Jz9IDuCVc/3e9iIeHgW+yEbfaKHkgwIKD03fjbq3/BSf9iuYMnKmQSDjCNMhlfGZIwyn2zHYQzBK+TLmJAMz6XD8MS76Ln50G9L7+QKg9PfyIPcJVGQGPYCo5SEepf5My72HOxlQ+wULiHXbNR4YWfffHq+BI2gzTuDV/yEDZvmyt8a+Ur4KAeDMfphpYQPsJKQ005ycITAAAYqT2Tj/BbMgpNk4d9wHFaTCawhXwcnwDo4JInjPfqXwx5KfdmQ2U9+7K7wQQ0//bFybi7oWdn4A36JvvFndExDJMk2/jyb4NkTDqHsV0OSc8xtHaTDCzpjruwby3fy/toerLL7GZsx9/7PZ7BTY+C3S/6ifE6AyZewByS/ryVA4I8E+3QdMcd5lKNJQLKv8CM2ZNwpmeL/4R4dY8tKn+EBH4nIwjL67Xv8KR33PASWH8SbjAP+shf2aYz8HxkpwcuhBJ6vn7gOopuN+N4JN5Freg73BEywwDvpvZ9L1HouvOFDcRE2ZLUN5nk3vDPP7LjEDyszdEB/yLwOuOCDVUjPgKc5QCJ4Tvb6bXUATpEDjIH/sACW5kQ2MpcUhO9wS4JEMMRnTcFTwSN+qhID1rAvPABue6/9i+YN3yzlM9lgl/jg3t2knDEgfwhflpA5cCfscAo5d56hIt9ZlufsfAaIyvAoUwESDINBmTTGxNmZOJNG0H1XenOslMCEmBjGkKO2PJ8jRVI8hwIyUEDAOCgTooxYOinAxPsMRUOEEUd/OBXKz6A5E8uL/l22OFz9sPENgMgWyjJRTMZCSfN8zhEZAGBkxgAAngjc8yk+oJJ5A5aCHo6VY5ORRLAAls+Qf45c5NQZj4wAI+CsBRjGKwtLHsBFf2Q1Eewc8weYAQonLbDi/DwbwANgcgNs+ouUMGaKSVaCObJlMLIc+s2ZAlqyNwYGpG+5UItcyLZsAiTzJguh/3TF3gFyyzIrQgbkzLvlTGVPgipAbekP2AIzJBNYKXcyDwA9J04gV/TS+/wMKAkGLdkCMyQESNJvn8u9ExwBQAU6xgZsEEryARr0qK9EbAmbn/yVXRGAoQ6SF2fEZjh3Oko3yqNhzZfAi56TJSLMqcniIf/0GenlABEDRC7nzsvg0Ie+Eh+Oi+Oky+yFveoPp0J/ZONqfabjyCl8ocv+IIscIHxBdJFEOsRpcqLIjrGxI8FCNqyXMkmAwE7pPsLEqRi74IlNIq55vmCE/iA9+sLRCZ5gAmdGT5EFmOG9Obtd/+k8EkBv6bSx02O6SI6CNM9jpzCEXcAKeJBld44f0Uam9Jc9IN/mTX8RJYTf/9kPeZAtm8jZ7eyRXGAq/Fe+YP4FH+aND/B7MiMHMvId+OdncKe2H0QHnsFx88kfaHRCYkJyh4xhNds0z1YM+AKyQrYQUSSfncIV31FapW9+r09kSJaCBXKig/TYs8yLn/mc1QzkRjDiWXCM3Pxb0EpnSn3puzdnsnGv4YO7wAe2igiycb6RLpA3vOc/EEW4Sd78rmQA/6LRB9/jz+FrEg14ge/ABzri+RI55Z00EZd5N3dIX87ixwvgg7ms7UGiUEKM7+MP2anED93DRWr+QgfZCx/AbmGYsQgcy5YAgW74jPeze8+GebhPyY/4KkQd4U+Do3ACBrBhxNNY2CndxQXooxIecuVrPR9W0U14IkjyHnzLXHi3Z7BxfAMOSGTCEL4Q0c/GbzotCGFj7JO/1T9znEQkTuLdfoYXGCcMIA/Ze7ZPxsaRO7CCpzAux8DjhfqNl5QNv9Rf/ccvyABGwy54ruEeMArPkfhI8kEi0JwGu/zc//kkfkFiBd5OwVPv4B88izz1C6bwB4ID/iqHGVjlJh+YlTt21mDOvY/Y2wABYFJaAF82Rpso0O8ZRRrn448sdRpFz4YfiuZ3nunnyPHQEh6l8/nyOamx97vyvX7uvX5OGTzf5FIO7/BZfU3zTj/3+XJ8goo6Y5EAwecAmfH7jO8nsOl7vjGXY5s3dv3WH3I3Fs8mK3Lyb+/w+1oe+uI7Putv74w88lnPzlxasjWWcm59J/31O331x7+925+MRZbDe8p++Hc5Hz5f74fwvLL/mYNyXjxHP4xFnzXj87NkX9M3v/OzPh3SlzzD7z3Dz/w7GeFynJ4VffTZyKuvD5sCgr7n7oIAjI2vtnefNY+lI+fUyhb7G5rP6LzvxF77+uD7dK+0V3oREtinz3TF96KvPhu99PlSj6MnpY7Slb4ShwQInCKn7R0+69nkEfuqn1+WMfpMjkKu8cT4a/303TyPnHynlkfwy3djA7HhjC+y9e7gS7Cw7G9k6/MlvqSvZOnfnq90Ic3P/M77M1elDZdzK9iJXvh55FP6jz6cIGv9i61Gh4L5kU/eVcra8/zemDKWYL6+eLe+573J+Nb+SF/nnXSzaazYBT7Uvi4YUPq76FbGH8wdsocSr0s76pNfn//x7j58SGlh7CS8gJ2a1z7+4lm1PsOAmqckQFCOIzlmDPQoz9b3vufXeBIMqH1Qxl7aXnxUyoH8v5Z1ybdqTlH6v/hseh3O4F1D+FfjUel7h/AuHCJj6bOZWp9ik+GJ4QC+W/7Mz4N3pd2OYUDJHWo8LXlBMCA+KXjiPTCrbGOXQK7D/vc2QFjH4A7hGUOblA9hbG0M+y2BXRCA/ZbIfvRuaJPyfvSu9eJ4kUDDh93N9NCew931qL35ECXQAoQ9n1VL7tkMZC/AIqcw7fnQWvf2XAKNAOzfBOVm3rpGfv962np06BJo+LC7Ge7bE7O73rQ3H6oEWoCwxzNr74CavrJGXk1g9mDscddb1w5AAo0A7N8kOp2krJG3CdipX601CWxbAg0fti3x//s+ew7tk7C/Sd26OvXsOdxNj9pbD1UCLUDY45lVk1fXnKm37jvZZI+H0bp2RCXQCMD+TZwa3bpGvr78cf963Xp0iBJo+LCbWVXLXu6dy56Yof2Uu+lle+shSKAFCIcwi20MTQIbkEAjABsQantkk8CBSKDhw4FMZBtGk8CABFqA0FSjSaBJoFcCjQA0xWgSaBIYkkDDh6YbTQKHLYEWIBz2/LbRNQksLYFGAJYWXftik8DBS6Dhw8FPcRvgcS6BrQQILpdwyZDbQV36tEpTg+tiGhdEuGjDxWLOPn/JS17SXXrjUiQXDh1CM0ZnHLtoxWUZh9BchuJSJhfRuKCkvq/gqI3RhjEXGLm0yIVKh9Q2SQBcTOMmTJdhuQxnlcb+XYTm4i43bKa50MqFdC4kclvtIbRD1DcXq7l8zZ0O/MRRboc4P5tcQXBBlBt63ekxdNv1VH2wadcmfod4xF867celXi5AdAHWIdTpu0TNjcIua8OFDqUdCl4f0vxsJUBwO64bQRmt2+VWaciEW+5szHFTnpsEbeR1S673uO3Ozv51NZdwuCHPZRV9lxat6z2e4yZiNz9qbg50MRri4ybfVR2njY3uVBgCYe9zE+Rdd93VfU4/3IrqIhH/BuROS3BT4CKtHJPvufnUbZFuT3Sr4bov+9Fft+gu6mwYAv10S3Mu1ps3TrdIuoHTDY50sr6lcd739/33mwwQnNDl5A23gPp72eYSIrJHMDjM8iIzSQOBiADuoQ996LKv6P3eZz/72c4WcpHVWh9ePMylSQ4rcNvxJvStts96HG6WdXuvP2wKFjhq1a2jboRGvupbXsdk0YenbryVPOIbcqTzOuVJfjBNn/sOePB+usKfmFc3nbuVV7MhFA66ydRtqm4vHTokYhPzs045rPtZ68AHp/SZF75HoLBsS2Dm79Jf3nnnnd0tvW7wdaP2OgMEG4UlJ+jHJlsuf3Qpln87xczJZeT/p3/6pyu9eh6/8T63Tr/1rW/tbjGGd24l9j2HI7gxnN9zq/Qih6f04ecm8brmV2NCgxf/8A//0N28jVO6c8LP3NDMX0lAG+v5559/P66w7vlZaXLX8OWtBAhr6OexRzBKAGDyEiCs8/n1s0y4K8Ndf845bKohOq5Cv/nmm4+9Amn/7u/+7rUECB/60IdmH/7wh0cDDX148Ytf3BEqx6kCVYaCfLlu3dXqArSpWX+BlaDC9fPbah//+Me7+XrZy142+ZWM/5d/+Ze7sd9www2zCy64YPJ3/+Zv/qYDjhYgTBbZRj6IvMke1jevb+JlyKYMHnJsBXOTzZi8A7nR1qlvAiqJG7Y+1ATcv/qrv9oFcTJ8yMm9997bERQ4bHVTcgZZmdK2hadlXz75yU92K84XX3xxR0b7mpXpX/mVX+lWNREe9qz9y7/8S0fCrrnmmu7maHfRPOEJTxgc6jrnZ4o8d/mZdQQI6+z/X/zFX8wc+7uOhNqUfr3rXe+aWREX5GyqCcYlrZxeaIVNczcSHcWFVg0QptgjDJCovOSSSzr74WMFzrfccsvshS98YZek5T+/9Vu/tbvNeV6TrLz66qu7hNwiyYV5zx37fR+/6vs8LoB3CVYkQ2D8Ix7xiC4ogAXvfe97u7GeeeaZHSb0JY3XOT+rjHkd320BwhwpiiCRTcR4kwECA2T0st9p6wI8IHDllVd2mQ7gOdZuvPHGrlymJLx/+7d/2/XNEq4SLsHSlIY4CDbuueeeKR9fy2c4+P/4j/845uCnPFQW1cVTApnTTjutIxNDRKJ+HgPiKFuAMEXSm/vMtgIEDpt+cBLmnHPcVPvMZz7TBQavetWrjgUI69I35EJZD4d4/fXXjw6BU2T//rzyla/sPgtT9A1Gwcepq0DbwtMMCLkRzOin4GbMrvVNhrAMEDzHMwRRylSsmtx0002D8lrX/GxKp9b53OM5QPjc5z7XEXa6sskAgS+DbbLVWaFGVP3M6uKqAcJUe8QBJAOsrpf+3OpC7B/pn7L6fscdd3RjEWB93dd93TpVcvBZffyq78OSJk9/+tNnX/M1X9NhfCo68ln4/wd/8AfdyoLgyMpu3dY5P1sRzshLWoAwIhzGefbZZ3dL6jJ5mwoQ1KyJzi2Petc6AwTPU6f4ghe8oAOVOPihYb/jHe/o9giUhDcKjxhxlKLqeU3tN1BTQrStAEFGA2hfdNFFCwUIv/u7v9vVqhsfg7AC8aQnPWneELvfHzIh2DcCMDYh2woQZM/tNbHc/od/+IcbCxA4/2c/+9ndviplk1lBWJe+KRkQINgTNi9AME7ylYEv8cMeEitufiezPq9tC0/LfiA2cOibv/mbu1LAsQBBEPHYxz72fgGC51nZhStK1T760Y+2AGE26xIj28L2ebrl9+tKqM17l9UmdmD1TQJskwGC1Tu+W5C77gBhEXtUZmN1pg4QrG58//d/f7e/AzeYl1hTDv6iF72oK5+2sreNAGGIX/XNs5UDc0reeFDfiojVCPtqrST2lbO3AGGeBRW/p4TAlTERvjIa9WdKhJSuIJvqPS3vujGYEahHzeU/FAppRdp8jwIqIxDBKzFSF6ueGYG37J96PV1QCvC2t72tWwqjjLJmFFmW2ZKRZ7zmNa/pyKF/qyu77LLLOgLgvSJEpFGpDMdgOUkWnfPmNH/nd35npsZRXZ4MtHIaS4Hq7PVHpkl/HvKQh8y+6Zu+qTOM2oD8XlafQiIfan7V+InKrSYwSvWZJ510UmeAZAQw7ElQAy0D+MEPfnB23XXXzThDyqvkRbmMukB7Ci699NLOqVFmS3pI9FAJTl+A8Pd///fdvDAKZJrDRVQYuf6YF89WkoREcBqyCZ6lnXLKKV2plD5bgjdeS3TGBXCMgRx9Xh0v+clWkJf++g4CYvzmWvBivORuzgVxt9566+yqq67qMipqWekU4jJvc7e5pW82y/q3ufeHPqr5nNdC2GQb6KL+6CNZGB99kIE0RwiKOTJec0wO5jWBhu/KTGgPfvCDO1JjTH2NkzIXSr+UgQkErexY9lxXTeymCAA7FAxbYTJOBNVSvSyPf5tPy7r06/bbb7+PvpMF+7PSRV+AsWyPeaDrsIG8/d+csEErQ5Ezm2K373nPe2Yy9Oyc3bJfcmOPdMvvfdazZJSQabXpz33uc7vnwZYTTzyx6z/b/aqv+qpuaVowjqj4PVvwXDb/X//1X51uWIGjZzJTnA98qlv2VDmggO1xojJVxmJ53rysom90jG4qO2S3bEXtPyzu2xPUFyDoB7mREzyFo8vgKbtlG74ve09/NfoME8yV31u2h4Vwi6zNL9nRf6sXKRGCRWzGeOAp3PrIRz7SPROGk/vQCkACBIEP/DY/+iEYdHOtubURfgxTDhkPaj1dBR/MF9JJnnTp2muv7exPGax/2/NHHxFQmeb4vxNOOOE++mFlH97y3TAVLqTEiA3iHq997Ws7+w3B9j62Cj/5G+UywWN+Gj7RO/hiPuF1/BGM1x+Zc5vr+Sf9ppf8qvIbOkmv2S6/wo/pN33in4yPj+SnrEDiJEkAlDLWB37UwQts1OfpId+ZFQQBBELLDm3O5mO+8Ru/sXvMMvaI3/S1oQDB+wRI/uBz5k2/zZvxwzIygBFkAAN9DmnHC8iO/03Gvg+v2Z15NM/Km8n6u77ruzq9wQsTPJERnln6X/Y7xK/gd9n01Xx4Do6hZGqZVgYIy86PcdI74yNPesgP+HnJG+r+2U9lpdQqDf2nM7ggeS2zX27jKwgUyykiHAChCxAojkFQLmSMwpsYgI8kMLDU2yEJlAPon3766Z2DtclVEyAgyRweQ/K71CCnnp5joTScCjBKfT3nBjS8j6Pk/Cmx/1NSmSSNcXOQWUEAJiZNUIHwIggACKE0JuMDIJ4jy+C5xsPIGcJQhN2XAeWwyOYpT3lKJw9jYHjkQZYmnUN88pOf3G0i5vRNqBIhsiJzgZaNmsARSZ+6ggDEGD0ygzC9/OUv7zbqMVaBEqUTeAkakGAlTDbsImnJCqQuOVkmpTwcANIE/G0eEyAgAk6k0m+Ze6AtCEIS6QiS6H2+6x2J6oElggOME8n7XV0iMGbkAhxjBfiCxsc85jHdx8lXudG8FkKgPvvUU0/tSAiD9sc4gGBKtM4555xOT43H5/0u2Vt6BhCAH90lD7IRVPc1BFvmBtjKbCKtCYgW2X8xNr5VCMDYc9kkGzF/yWADNnrGhmp95/zMB91ALHzO+GECHWGf9I+NZdOdoJJ9C5445QQI5GzDLVs0V+SMZCDjAl+kwWde//rXd/aVTLv325sytAdKHzkq82teERXJBsE5QIeBbBeOAGr/lyTxZ6gJiPWlbwVhVX2LwxeMTV1BMH4OniM1Xn2ASeZDcLoMntIFmAKr+ILgk4SG+SU/ZT1sxu85bZhgDuEHH2D1Fd4JovgLvyNneMVJZsVz6gqCJA0sMk/wHpmEoeyNPY4dVnHIeLDOAIHfYqPm1DyxL/MnIQcX+HU4YNUe4eNjBersVeML2JdgDu5JKikBY08JEOABDKaXWRFHGOkMzPd9xJ//4Ose//jHdziAR3gmXXLaHmzljwSifJeWFSX6mhUE5Sme6bv8sfc/9alP7Z4Lp9mKTb1sGtYIJvg13+8LELynb8UwBNTvLr/88o6DeDfs8b6s5i1jj0NYFLyQQODP2EZKhbInC5YbMzshK3PjFCm4AZ/JRgDBNskIL8AVcIwhvGbjAgFcy3fNMTxny4IzQRwZ9uEhHyHJpk1ZYU6CAN7Ql2VXN9Y1P+ZXX+i0oA/nNN84UPxZPV+4oGA5CQ48l/yWHc/GAwQDSAYKOWfkGgdjCR3ZBwgUjqFyWIg8QcjwcTyIu885gYBzohAmv9ykXCuAulngQ5lSGiSiFkzIrMskIcwcAEDQ+hxyHSAANoYnQ+z7CKmJ836Gqj7N5AAaE0nJKAzgYyiLBgiWsRgXQ2GcDCmAihgiQCJ0MgMOfQ5xmQABYCH+nqf/AiZGKsMiG8ogGWzIBQDUR/JO5F0HCGTsWYgXMPF5c4qgUWKyRoqRQPJk/OaLkSP9yhkQCkCrWQLkvGUYc4zlogHCFVdc0a3wRL8QICtHCCS5zztlKQCO2AJAxmmu9RdhFNgBSfIyTnPVtwRpvGRtjII6gOw5CYZrIBCkmg8OUOZoEcI3L+jJ7zcVIHh+X3/NNUCs9Z39AjgOx/yzO3NExvQn+oLMl5uU4Qt7TIDAAbCRBNf6gVj4OZvicAT6yLvPGH/toPsCBM/g5JFKCQLNSoRAQX+UCUkYCBoERrJlfk//EZ9lAoRV9W0RfQl+S+IIoOGyP+Qjm27O4PcyeGrsNT7BZDog6M+eKSs5ZMtHkKN35WABRM4BCnRBMAVHYDBbWyZAcJpSVgmM0/xJIkkYWOEQlA61Q8aDdQYIeZZVIXiX/TzmyyodHwML6ZwVHPhIB/h9tgNr/RyP4Ic0fIBfKTcp1/bruWw1K/rwXUJGokh2ln5LEiCi5p1v6POffQGCPuEnssbJ1noXHKeP9IpO21MkaNVv44FfywQIxs/3eaZnIJF8tpUNbVl77NPt4AVZwTl2geDjPHAWrno3LoW7kYGgvfb3/HsdIOR9NV5LNgoSyS+EWOLAHEke4IGSzX7Xh4f6kANHFgkQBIWr7C+Lf191fvglSUX+CGfSrLrr31CAYCWMDUiKazV/ner787mdBQh9BlbXEMpMcRSAQmSkDWXwagUAErLeFHpoZ71SBQ05NQlILmdfZuxqAVsaszRF0QMCMr+AQQNixgHkLHGL6im57LTPD/VlaAWhPpWhBjwZEePQH1kEhIMDlW1NxmyZAIFxiT4RHI4XeAJT/fezUq7mh4ECEcv5iwQIZA8sRcnl6Ujk6XfGIBjrk08NKOS/SIAA5KzQcP45TcEqh59Z+bFCMu+Umr4MTx0Q0w/yMkdkJHuF4CMa2WRmzgSeyGOWUMl76MQoJCrPJHPBt5UvczYvIzwVJLYdIPTVENfyRbBl8wC4ZELaFP2go4K0ckWhlgWd84eOc74IoWxlMpB9+CNrJlhJZi3PREYkBMy10oRkdwQGygpkosc29Y2tIJR7hJbRt2UCBH2GZzCHndFP/Q+mLYOnfQGCcjPzaXWyxgQBOFyXNOqTj6QCgkI+CNIyAUK5Asl2c9SjgET5gaTEUBnfIePBtgKEesWnTqiwN/oh+x8ipG9T8EOCQdINwRw6lhq2snOBi+SO1S2+QLImK1x9/AUvkLArbRpGwxOkWIKDL1MpAac9D6lnTwlyahmPrSDUm5TrhNyy9jgWILB5fIJdaPFROeIU/yE72MavCt6tpgu4UjEwNUCQZJVYEwAFY8gKrsIi3ADHqg8J6UtILxIgIOP6KDhcpg3tQVh0fshYMltSmhwEm/wIWQ8d15sj+SXDyU/Cy0r5snto9zpAQABlCErCPjVAYIgCDMo0VEue5W1EQLaJYxIkjAUIBCban1eCIrK1MiKipuAAyerJUEZ62QCBQaobVMYE2PTNEiOQWiVAGDuVB3myjC9jy4kLgCyXAoNFVxBkPjgE2TkZmKE6uSkEcJEAAdDIEsowl/phbEBH8EAP5l1+NoUQcBCWY82LPTn0ANkAAOUpFByfVQHZLPtIrGIIEPuaAAoAM3ygIagRYMg4HHKAIGAXkIYALhIgwAR4Ys6tGPQ1hFKg5Y+SMSUBMn9jAUICcGVJYyd8sRlZLw4MJihXsQo2dH74sgHCFH1bJkCoNynX8lsGT/sChDhYZRp0eqhtI0DIu+G5PRKCBckTWdS+dsh4sC8BwtCm+SkBQhJ8Oaa4j2gh1laq4LVKA1ghuSBzOxYgIIA4C9weamyTXwuUDWAAACAASURBVPF+Nsj3CWhz78Y6A4Rl7XEsQKg3KdefzdGrMNqeA5UHMDsVA4usILBvCRq+c+iOjCn2po9TAgRcUVBgHvlV+L9MmxogTJkffl5ZnWoUpF+gJVjoOz1JX5XjSUhZ3VHGhp+FJyxzyM5eBwhq6hGpbGYmgKkBgiyTVQSGiBikMVBLPzLEjJlSINcyyFNKjDgKpEE0nDKbPFudrOUvgYYm+wxoEAdlSMoMhpanlw0QLCf6rj9WLShUnYFZZgVhLEAQ0SKy5oJ8LQMvW2IkqJERRpzJP+VD5Ieo+zmgWXeAIKBRliBzX25WEiCoORWt0wnzN3bvwxSAYqBqVzkCOtcHIGrws3FVdoo86ZHsdR84AjABKGcjk7YI4ZsKevu4gmCFzAqLVRhZuDj4KfrhuwJ1Tt58hJizGXoWPVZiQK85CISizFD14Q9dko20clbfE2Ku6RRiaX69UymV58tKSiCoee9rywYIU/RtEX0ZImRln2WulsHTvgAhm4vNi2xrqf/w2u+Vbm4zQJAJVpbi3chi6VNKORwyHuxLgEC/2RvCU94kPCVAQMpc0slPS6CVREtJi8SMjLdVAxgsQTO1xEgggQBbDS/9WMkHwhHgCtInqQbHlAj3rSYuu4Kwij2uEiBIwOBd+JFkJVJblhQvEiDwbZJ45kkJU/AaBku2IPNWbNa1gmDcVnUkluE4G+9LWErsmRd43pfwnRIgTJkfz8GP6FIOapH8I9/shSnnCjZJquCn2VN50CVGDF5m2dI8Z4CwxUFzHOqv1cD1RYiWgWW2KRFFS/SEpHsG0um5FE9GSGOkojWflzU0+UhYNikrE5J5Bk4URAbQhFiK5OhFuoIaRuIdamYtjSF5NtIBHFmIvlYSHFlMLU58rKZSAJXbpRFFSoL4Aj6GRcFFk9mk7FkIviXyvmVNkb+l0LEAQUmL5wE2JQeUWECmttKcBCjLJTVBmUy3/pWAweiVx/g+eSHRgijy9TwlZvYXTCGAZFaWGCHd9UkFPqO/5KBm0nzVTZmZ95lHeibLO9TmEQI6KED0PnqAZAgyU1Mrwhe0Gqt5s5+AjnEeAidkUl1h2ZDNHIvr/fTPKhLdsoJg74M2tGx9lAMETlyJAPuU4WOTpf2TTU5Aq0vQlAAh8Yg/nbN8C+wFBOxFSQr5IwkCMEkEgb3vsIdk+8uLGjk8TlDggfDbVCtAR2D11RIvZ+kd9tLIQmtsyP4U/RhapSoJsEyc+XSy0phDnKpvdBD5UaZFX4yBffY5RAErfXUy0NAhB7C6xlM2nRXZITy1GbXEJ8+3KgpX1NsaDyen5pbDFFjBHUHVMgECbJW46SNjVpJzw3suSout5Bx131ObXttkPnfIeLAvAUIImNVX9enmTKODsIG98dtaPR/wwf4lpWjIeWxPEKDMmD+wwZZ90nu64rn0n73SaXhNB+wBpK9wm7+XgKDv/s+PwRF2xo/Yp8MvshEBCD3ihySi+Bv61pcIqvvPp1l91J+xEqNV7DH4Wc43+fDbSofGjraFuRImxsNG9JdfsnGZ3GFqWWKEn+EjuF2N106ehJnKsMwJLNAk9XAzfGGZACH8qm8/qAw82UoY6SeyXTa+mU+mg+Zy2QBhyvw4vCUBlz4IKmA2H4L31U1ik5yV3eEOGl+EGxgLv9E3t2N8YOMrCARKWXQY6DNAZSUMTZQuC89JZuIZHYKKVCFwyC/ni/gwOjXijJLiqUNljOq2TSoBUU7OxHu9C+EXgTFOzpUiIhaUxOkCslTIFiX1b46cMXs2wkDY+uo9yKdgwmdElz4vu+zn3kFxkQ5Rnu8BC88wOd4JlIZuDhQRejbijdRyxkp4cnICAuldyiPsL7C5VpRok5OlJMAoWLE5myKRE4MiA/0kHwaHcCNVxlw3RmEVQlYmz+87sSOn8giIADHSTwkRYEuJDJcTJwv7Iiit/vkZAyR3ZTYIALADOMbLQZtzJAVxE4ABlRBqZDrzCxxlKBgR/Qp4MHqZYO9D6OhN2ZAPepJTFWJINQH3e8TJs8yrEp6+JphUemEp1FwJeELuyZxu+wx5IzU2TQFbxJMsBKUAlK6Qq8w4kkqu5Gt+6xIUGWmlF2oM6QIQBVhOyQFYCKx5WPWWyk2uIOQQAYE7Rwy46DM5soUhfbdiwLb8Yacwgf6wc/IkF7okEDNn7IBDBqzkBk+U+XBUkg903vI++SMD5oIeIg4ctmcrN0JCOEgZLX3TV++W0fZuc8f+PI/NmDO6JhtF12WdzS1gF8jTLXOvnGxonws94jSVP5ITWya3deibgIijQXY8jyyzR6LUc1itn7k1mSwTlJWf40iWwVMkH9mCe9kg6rkcP3lLktAN8oTp5jd7y/gKyRtzl5NkzE0utuRbZP7ZuKwuLIH7dKDeCwZP2BxMgqcw2ziNH3Hkc2APTIZnQ3vJDhkP1h0gsBXJH7YKw/ltdoSIS2CZM0ErHYClfgcTkUqJKH6GrzBvMBPhh+30iI3TZ9giCRh8FmSzb35OGalklCBVXwTzDhYw1/Td82G2z8vgehceQAdhOP+if/SBrgriJR/oSfwYvcUVcBg/88f3lYpkc7Jn8h19OsX3IdrGZIypWsihFvwuPWUv3s9v8hlsZll7rPuhn3yNQN1zPZ+c+hpc9IfMcS5EmI+GpfqD1ymB0X8+G2+C9+a0D6/NCfsVUGUlnyz8nDxwJkmZMf+rn338qu+SSz5CfxFr1Rl4IAymp/RFItjffMwQbttIvY75Ma84DjzzPv4tCR0YVTc6SeYCKns0cAz+kv7iQeZvqLR2KEjYeIBAIcpLdAzQ0gzHmRbypZwjzecQREYJdP3hcBkXAgw0OAWO3t8CBo3j49DyTKSJMiHJyDril2VFGQjg4v8UgSFyOhy4//s5ASHLGiKQo6/0NWfec97IAqJmQhgQxfVdBlv/vm8yZBuBmqb/+l0egUgeMg5IchqlAT764fMcPqABnoIUiuUkASRAGRUjB2TGDKjKZjzej/CUDUnpAwMnRqi1VL9v74CNMP7WhzyffGVzKCXlFEDknHPvYOjRA0uG+hcCAsApubku5xdIIMXmO6c1eBZQQoo5cyQMWUPm6gDHSkZ5YgQSzYmkZUOiQCwNEHg+WZbN2ET4acYjKCr1nbOjc7JL5hOII3oyX/qafQYCCVkrjogTQ0gRmqGNUuRlrn1e0MFxmG8BGf0ZO2llCAzqn28qQCCH8vjWIfuv9T36Qket7NAd9qbczWZCzhixpyOeH/0o548t2Ojsu5b+kQA2lJKA2LugV6KCo8tdFHQXJpC559MVupaz2cnePLI/emV+EG9Nf9mDlU0NJrCToUy0zxgnHBLk0Bm4ggCtQ988A0bAZ3YTvKt1IMcE5udky9mSTd2WwVNOj32klfZo/Mi8IABeeadAwtyQf+kv4IUsv5WQEh+RC05d4MDvsMO6fheWlPpSj4ujtopEt4bKwXzn0PFgnfhQ+xtJMf7Dn7KZ/1I/Sv+OfEoysln+kR5LQuII/ByiX+Izf5nn87d+L+hAqJSWwGZ4gmz7nVIm+IAg8ueCeb6WveMTAlV+EH7QTckZmIAn0FlEO34s/lYf8Rh+iO+sf1/LWIDrnZIU7E7iq/Rf5MHHeF9Kmaxc8FfGsSy/GfOHfqff/HKdQWez8DLHm5I5nJF1D3+KfBFxgRgfN4TXfABfDGfJDL+AhZI6U+wteFLzq3n+UTKZn+CnYZTG/5jnoX2BPlPzi1XmB+7zOZJ/ME+1DE4zdOKV9+MUMI49mXsrW/ireRDILlpVsPEAYSoZaZ9rEmgS2C8JbCpA2K9Rtt40CTQJLCOBhg/LSK19p0ng6EigBQhHZ65aT5sEtiqBRgC2Ku72siaBIyWBhg9HarpaZ5sEFpZACxAWFln7QpPA8SGBRgCOj3luo2wSWEYCDR+WkVr7TpPA0ZFACxCOzly1njYJbFUCjQBsVdztZU0CR0oCDR+O1HS1zjYJLCyBFiAsLLL2hSaB40MCjQAcH/PcRtkksIwEGj4sI7X2nSaBoyOBFiAcnblqPW0S2KoEGgHYqrjby5oEjpQEGj4cqelqnW0SWFgCLUBYWGTtC00Cx4cEGgE4Pua5jbJJYBkJNHxYRmrtO00CR0cCLUA4OnPVetoksFUJNAKwVXHf72XOAHe3grO03dMwdDlXvujOFedlu/TL5/tuC17XiFye5M4B73M3gvPGneu+b8155vqpf4veIrpvY9m3/jR82LcZaf1pElivBFqAsF557tXTXDLmsg+Xu7nFsL6Rt+4sp3/33Xd3F6ycccYZ3SV0m2purnQLpPe5ydLlI27EnkeCNtWfoee6LMflJy7e6btBdtv92eb7jjIBoF9uBEeWNbdufsd3fMf9LvaJPF1Gk8vxXLrUd8tmn+xdDOQioDS3wrqQcR3NLar67EZYF0LlJtG+Z7tQyQVuLgRzK7zLlVzGtonmMii3yXvP7bff3l3w51ZZFy3uW5Bgbly25aKlsQuOhuTk8il4OHRBGnwgg7FGJvDDRW2H1I4yPhzSPCw6ln/7t3/rLnhzO/C3fMu3zP26S+FcKOdiutxYPvdLS34AZ3HJmssL4R27wQv2rbk0zx8Xsg7dqLxvfV6mPy1AGJDaP/3TP3U39SEaGsLsttr6BuJ83W17v/iLv9j9162MCMmU5nbhn//5nz/2UVd8IyjraIsQDESHozdmgcQb3/jGjREMAOV2awDFASMzbmz9tV/7te5K9n1qboB2Q+xv/MZvdDduLtpy47MbNPuam6oFcWONTNyi6ZbmbbajTACQWLdyu3lXc4sk/XrAAx7QK8I3v/nN3Y3MGnvM9+bJ23vcAvqmN72pu/XbTZtlwDDv+2O/55g/+MEPdqsBbv50y+tQE9zndu1PfvKTM+PZVIDg1la3nrsZlTxz27GbjHMj9SrjXud3P/WpT80+8YlPdAT/gQ984MKPfsMb3tARAdjeF/z89V//9eyWW26Z+Zx3veMd77jPO9xk6jZWt/GuK3BceBAb+sJRxodSJH/yJ3/SBZBpdMUN3ENYsSFxbu2xdBa+uSVeUD/W3Dzv9nby4Qetam6yufWXn/3P//zPDofdYI4fuDl5n5q+wb93v/vdXR8XbfiioMst2H3Nc2+88cYZ+Y+1dfLFvve0AGFA+pbrP/axj80e+chHdp+YRzA4EFdba65gFyRMaUgAB/vSl760+7gr3dcVpX/hC1+YIf6u1xbYjGXnfZbxu8YcKdkkwXj729/eXQlurED4hhtu6EjV2972tsmZ2ymyXcdnzA+5IAfzVmD63nf11Vd3qyQIZN8KhOcLzDgk+qYcomwIKIB80pOetHWCcdQJAJ3OnM2z3z/7sz/ryL32m7/5m92K29Rm5eE5z3lOt9K0zgBh6vvLzwloEfhN2a9ABL5xboIE9utn/rCRsSBmmfHs8jtICrJiJZU8h4iAZJL5f+973zv73//93/t02f/hKQx+3OMet8vhrP3dRx0fIhC6i/w+85nPnElenXvuuV1CaOoq4roFW65krvvZnofb8Ctf8iVfMvuyL/uy0VfQX/K55JJLZpJZtX6vs38SOgIEATkswRNe+MIXdnNz0kknrfNVKz/Liq3g/8u//MtnX/RFX7Tw8/DF173udbN77rmn97ueD1cuvvji7jM4wBOf+MRjn7Uy/trXvnZmxXpdfLGvIy1AmDO1IdXzCMZdd93VkWvNMr+M39TG+XAw2joDhKnv3ybBoPgyrlY3xjK6y/R9375jjAJMJVQCBOUifUGa8hbzL9PYB8B33nlnN7QEq9sa5yEQgKn2u4pMEXLzpxTl0AMEpFnWSjt0+7UqcO21185k+6yswnekqm7z7HcV3drn7x4CPkS+VpmUoFmF22WAgAzCEhn+qauY29ARfcJTNhkgPP3pT+/K9YZI8zbGuY134AXPfe5zuzKvX//1Xz/G/freLeEjgSpg7VuBhFGSi5tqLUBYU4CwygQdTwHC8UQwrCgJHAGBEqGf+Zmf6c3Y7CvBOAQCUAYIr371q7vslNr500477VjZzqc//enZ+973vi6rJhvkd6k5Vzak9lapWL3c+5CHPGR2wQUXdBn7BAgyPjLsH/jAB2ZWh4C6PUBDzSZfS/6f//znZyeffHK3D0IpzLd927d137/jjju6rPx3fud3do/wuY9+9KPdz62QqM+V4X7sYx/b/b5eQVDCWNbIjzkTmcJbb721K5WxaqYEkCxkybSU07z1rW/t/n/hhRd27yaDvtUxz1NPjGAbC31Sqilg1mwgZh9+p/8CYatoJ5xwQvc5m7Pr/suiKZ3SR3KXxXvYwx52TD61nD3P3JlPewDMsdUPqz1PeMITZu9///u7sZJvueeKTSrBoDOXXXZZtwIgw9lXDz1mv/THHO9bicQq/irfPQR82KcAgX297GUv68rUEMLjLUCgT9ohBwgw2/zCULwA1lkhGdrfOC9AWIcdjz1j6wECMCYgGwjTUmPtlIlk4S3HpuZfxPVbv/VbnQPxWYYD8IE2B/ILv/ALx551zTXXdJk8JSwUrt5AJkvASaqp4xxkhJ7xjGcM1oKVBEO5iLowCuw7yj4QCv3yPI6gfp7PvuUtbzlGAsrJeMpTnjJ71rOe1UXmWUEQEXJ4+o8YIJljm3SQG6QCeeHkLF099KEP7ZYE7Z9QP6jMAkHVOGPlPDbXWcYjR87LWPoIhmVFGxLTLPsNNctexmLzMXJw+umnd3JK7S+HTk6Zk8c85jGzBz3oQR1xfvCDH3y/x3qesiOkRamUz1900UXH5AFQ//iP/7gbH2d+3XXXdeU89nDYNGl8Vits3kyzuoP4vOc97+nmUhuaf+9nxJ7pveRqCZreeKY5s1fDWMnX79OQImSRPJ73vOd1KyZqlU888cT7jXOMYCCz5vjUU0/dNBbc7/mHQABiv2eddVaHHcCYXZCn+TdG8v/Jn/zJbgkdpvzsz/5stx/B/FvyFjwoOTrzzDM7Ao7Ymls6YVWoDBDyHjaJvNJFpThD+g3XLB/rh+enhl1/zLtlaO9AGDS2IHPvufROrSp8SA11HSAYLz1Fhtm4Moq+prQNvsEvGxc5Bs+27+UVr3hFZ8NOVPJ+Oq9Z2iZTpL2vPh8uszP194iyfUfkaLyI+k033dQFU+wGLsOmP//zP5/ZQ2FTtlVb9eE//uM/3uGnemmZN05Vplf2TYbf6tw555xzv2GZF9m32267rcNFwRpc1X/vhxHe5w/5lnu/YKRATIBg/PyKuYIVdRuyX/rjufp73nnnbd1+N/3CdeOD+aLbacpL8AVzoZTN3CupEHSaC3PIBuvMaskXBJl0w9yWOso2cRBlYU774luuuuqqLnlQryB4D3v2eRl0v1c7rk8+D9/T+F22zhfxyerU8RrlIvbo4Cv0nB+AIZICGjuhZ3RRY4MSC/FXZX+RTLZMf4cOLhBAs33Pg2HwQT+UMrNtOMPuos/l8/lTQbj386tavYIQ/uR39Lv2f6Xu8ZnGZ371Hc7pezb38qeed/PNN3dfYyuCeXjUtw9EktHzrLgrl7KyHv/s+Z/5zGe6d5E5HMe/vOOUU06Z/diP/Vgnh7L/3qn/cKycz/Cz2o5UQSgLhiVki0/gJ5JPMM1+Q5yBntLhci+Yz/EvZKBaBA+Di0O4PBQgmF96hCtusm09QGBgDNhEIa8INiEiiZy5ek3OEYjbuMqZczAmDtnixAPyJpSyCCoIW2OQnAuH59mcnCVx72SMDEZGDKgzUJEbJzO0gS0EQ8AiAOAkZfF8nvOidEAMEeUQGVcIhmVL4AUojM9kctScKid5/vnnd8paBgiW2WQfU4uOZAOnviP6jJ3j8wfhoYicoX4KgoxZnZpMXwgGEEI8yIwjtnT+tKc9rZsDrSYYsm9AQr9+6Id+aDBYEZT5jMwnIJel+4mf+IlOvjJvsq1ASOYu7yIvvwcCffX9gFdtOGfAKPUTqJEHxTVvgFi2EcADegEDcu7nQNz/gRtjAvDmkV54Hj1hoHSqnn/yFxjRJ04GGSFf8+a9iBT98znARr4AJe31r399984rr7yy+44x+56gdyrBML/mC3naBcFYNwHYJJANPTv2mzlku5yin9NL2e/gBhvWYr/s8od/+Ic78k6fkFsOgU0lIKW/ZYCgbtln4Q6d8V4OKgF42U9YCIcuvfTSLpOOrMAmNgdX6ClSIyES+9U3eCLTqHFUSDDd6rNfNqjckW2yj6F9SIiwfnNy6pI5XsQMjsDjvG/qCiA8IC9/9M9Y4S3S4g9SAQ/U9fsdh221gj1Jmqg5hmv6wm7ZDbzgC9IET8ibwKFv74PnS2jAtCzR8yfIPiLqcAAJEliFeOaABL7GqgFygagJimCI+SiTDelHGSCUOGJcAk5y3YX9btre1o0P5sucmi/t8ssv72Qn0KK35sj/+U16ggzyB+wY/mvmQjBgzx39QaTYDxvjg/1cYG3fH2ync6985Ss7X4A00/syQODb2RZijZM4LheZwylgO72jl0lswgd2bOXK/Ft9pDd8GVsQ7OqXdwskJKDoOn+EKOagBD4DTsEXfUp/9dP3vJud8N9142MFzXyQPrBfus62kWryksE2BmPX2B89F7CzccES22ArWh0gJPEgScAv04U+Hy6Z4j1INCxha/pu3uCr1UTzIYGQJJgEI5nBztquM3d4omSq59vvaa+Q8fq8MXm2g2Kyb8iKKy4h4Qov/B8mkpW5Ze+CLTolcYRjkkmdnPV+eA8vcQd8UoDiZ+ScfWiCE/zWz+JXyFGiQ//ouUQpXJAIMV99SZa+AAHmCRzp7tAm53XZ/tYDhHScghEMQ2dwjJBzROg4F85Eo4DAnFAZD4CW1WL0FCIZP0RK44gpNSDxO4bIgCmByZUV8n0GiRxwHH3ZvfQzDtXpHLLZWY72e5sZ00+72mOsIRgUkDEikhRWph7ZEwAwFkoGsMoAQWRJWSgGo0Y4gMjQiSRx2BRPppOTA4rehcTqf0kwHMvFoNVKM2jyRk6yqToBAnLjWYzQysTYCRxkAlAYCeNOYyCIFIAgH/2ZSjBkARB/YJ2MJXIgOMimJdG44MJRaGQoCKE7yhXoDdAGGIgOgo605ahDJMv8y1KkPKPPqOqAiRMzv/RVXwSxAjH6kSBOAMZReLaglEzMAV0XvNTBXkkwynnmGLzPHOyCYKybAKwLtBZ5Tr0HgUPJUjb9iIPss1/6xsaRj2z6pQ/AmVMw/+yzbw9C3/P6+j3PHmr7hXMykbCDA0cA4ElOydI/K1XsUYArWC83t/X1gRNAAAQSMLVsCBEyDyc53Hn9zXclKSQUkoXzfySfQy03HbJH9pBNoX3Pl4FE8PTTdwUN2UxKFmP4jfQhmgkQYr+wAK7WG1HJE8Hze7JG0LwLnghUyqCyL0Aoa7ThEyIgyNiF/S5iJ8t8dhP4YL5klzV+hx8SYCdosBpAR/kEfyN1/s4KfPgCrgCnzQH/IBnGTpF09uLzkn2SO3wMXfiBH/iBLrlWBghIJd8HA/hjxBUuINMyxIhv6b8F9pKVnpeVDr/P6Tz4S4kX5Z5DY0oCKSVG9ClJTlhg5YyP03/P8TyBU18zP/SdzQhc+R/BSE4xMq7gn+fpuwAGL5D8RGD1uwwQ+CRyMy8CNSR1rOFf8Mh4rAog4GxIxp5NJ/HgGVNKjIwBptANRJ98zHNpz5GPyg4kHn6ZV7yPPeM7mvnBF+kMzmhuExAi62QydOhC8J1sE4SYP1hjTBIzVhGMWaCThofAUWPX9ElwhK9afa4bvfcu3CYBBBk6/ZHu4HKbbDsLEJB1AYKJFf1RIBEk4iyKtfzEmRMMMo+IU0YTLSLzWdEw4VOQBAiywfVyjWgeQRPNi7q9C3ml6MlmDwl5FYJB4Qm4BJwECPooY6D/fXsQErGLsGXQhxzMPIddEwyKaZXBuBEH7wcYMXQKaZlM/4CPaH/ehmukV38Ziuxq2ayaCPzIHADN62++K0rmXK2g+MPYgD6gLwlGPT5km0NAmARssqYIu0ywAEVGkVORgVD6gQCOtaEAYYhgpMYQ+AgQjB0AyFDIpMoMBRzmEQwrELIZgo9dEIxNEIBNglnfs1exX9kfGTvBABtFyBMguJvAfLLPbQYIVheQf5gW4lne0aF/dBzplnyQKRzbA0FmnCj7FcyHMESWIUPs1+rgVPvN92UtJWXYJXnB70UDBM9CKgQ7nK9khnGRAewfa4sGCGzOvEtQKO0KQeBrBA5kICgps31jJYLGK5sI+w6tbQIfygCBHkuy9fnHvgC85AvII/ynr+yDj1O1wKdIOPq31UQ4zW8NbVKG2zAb3wivELBI+tADfqbsH/7i82kILH33TjYpWcmf8w1WquYFCLiPcRivsiQElD/xDD5xbK/CEOHuCxAkUPlCuo98Sw4i0kmohY/gBvBOSY+gaexkRBwPOUd8ySin/UioeR5ckMyMLU0JENgn+ZMFPDYWQQhcTMAfe7TyRPZpfc83X3ghOUrmCUBgIL4wFHh53lCAAH+H+BouKLiRuAkmCwyUpUVf68st+1YQyNVcWYXpW5leJ87sLEAwCJOHpFIcwC+6t/TDKDXOJctOsr3AwqqAJnMMrLUyQIjBl0IiUEEFcBBJcy6UXOlNagA3ESAgdRxLaohTayuDwcn7g6xuM0BAsGVcZD9l3iis4ClRLoXkjIEZcAXSWb4dklGWG2VgaoJhaTUlOIBiUYIBhGVrZDJl8ADBogEC0s4QgZ/lXXrk35bn5p1NvmiAIGMlAABcMgUhGMhMVr+yEXVegOD3ANEzd3G+/CYIwDrBa8qzVgkQOEgrbhwyJ8SeZTclFjgIegS7thkgIBycGBuQDIElMmD0GSbSV5gjEWKlwyoc5zd2BwD7ggECcc6tbJ6n3hdGCUim2m/KRSQjlErAcRhslXOZAEHCgLzZv6BZcC/bOnSBWcawaIDw8Y9/vMtumufyThbjgdd0AUEprvTf7QAAIABJREFU9zzs6yEDU+xjlc9sAh9WCRBKvtAXICC+EoMSkAks5wUIggs6y3cj6mVNvHJVJSjzDhkRiAgGBOLsVRZbYJAscI6p7FtBgP30PAcDqGIoG9+MhPe1RQIEAbGyGsENXiABYuU/+wQSIPiZ5JrkpnLpsWON+Tv8qqxg0E+r957HjmT5k4CcEiD4vqCJD4VzEpDmx17PZQIEQZbEiAS0ZKgkgHlSjtZ3YlnkvEyAQHaCg3JFV8kj/yHgoRv1aujQHgQ4TI72cG6y7TRAyIYVG3cEB5br1NpFKUV2Kf9ANGXz6vKMLBlnBaEvQCBAhI2xc66iYgEHxZ138+cqBMMSneDAkrh6NgbACXO0lugSoW4zQCALARPDBJQMTbScm1oppJ/5W5ZF4KAMyUrOUDN/ZAkM6ku/PIfRGaNM7FSCIfpnUJYLyY5u+D9jWjRA0G/ZJeVbjIrsGaoSk3kGtmiAQK5WOgBIaezGg2AaD0dQbnbcV4KxCQKwSTDre/Yq9pv9UjLWnI9gnjNDFGFIkhXbDBCQfSuvMIWjFOzTZz/PJmr9ERALSpUSsk24OnRShqQBh82hIzJls4JAP9mvIHWq/Qo6kGxJGX8jEnWJoPdMKTFKfxATGJ4N48ofyuX7vvlfNEAQVKVuuyQ/fia5YOUVmZARzgbRKfYr84pQHdLlW5vAh1UCBPqelZq+AEFCkS/qSygOrSBkxZ/uC3b7/OC8AAEJlAD1OatoSnj4XL5g3gpC7C2BASI/5otLG1gkQPA9gYzVLlwAeRcA0VstAQLCKFDHm8xVefJXbX8+qw9KkYy9xJ88T0lPVhamBAgSZlZfYBYfK4hns/q5TICQY9cFR9nQLcHHVsfaogEC3VQOJNAr5w8X8yzYQhdyhHTefdydYlQLXX24JRbKiZDWNbCUipHIxspmq9EWVSOJMgL1HoShAMF7TZAsNOVGOi2TzbvYZxWC4Z3Is+U7YJA+qM0tj73bZoAAHGUbEx0LvCz15abgkhALpqwKKD8iL/V0fc3YzKF6TUuGOcbQZ4EJGVJ+YDKVYAByS7r2YwjmBJPIjmyBEwnUUnrulBKj9JlxytbIungmIAjJGwKDRQIE9ZmyEErm+i4v8X5j8m5zHnIzj2AAUbqvVI1sgZpAR/nSPP1dhZBvggCs0p9Fv0tu0XN6SJdk7XLpjjpRtfwaAp3NZLJH5pGcOR944++hE0PU2LJpzlXmHgnwPI5My/P6+q++XemMhtSXDtf7lQkhpOwTsbBCJ/Bkl4IEWVMrofRJqQQCKxPl/+pb9YPDF9ToY44srftCDrCBTAS4np1kCiKePupv6r31aYggCFoEVuQoA4ok+b/SDHbABo3NCgcnr7/G5XOej5DX8tBntoMITMVvc2FM+sN+yJQsZJIFUsE0/gfpkDlFfOwtqpux6yO78P6UICmZIFvYJGFV6gkdlJyS7YQ35A/bZC7z/0X1el8+vwl8WCVAIBfZdNlkyR/2x9+YJ5tjBas4BvIraDY3AlkBp5UjmV01/laH6KP9KZJo9EXzfQSOzzDXxg9LxgIEOK2sCLZ4H/2DF/wDLLKXLSWn5QoC3bd6if+wTRyCDxRosE86htDS26E9RosECEpWjFtyVp9hhrGzV1n6EHo+Tv+V5uBiVjGHMIXM2Ll+s7+s9rFx8ue7y4TilABBObP5si9PooS9kQ17hgvZa6G/U0qM9FHiQX/wHBhu/8W8tkiAYI4ksMnJHo+6ZU+MQIm8yyT4vAABZsESpW6CDWP2nnm8Zt748vudriDoBCOhmEDWxNT17hyzpT1C5gA5eoIQ4ckIUGZAQKE15DF1c7UQlAxQLlkGzm2sxsx3LWtH+XNqiQg+feSMKYqmn9lwoz8AysQBJKQQge5rjAcw5Kg0ARKHSmEBGYAQRGUTYv0MCl3eAFtmrckWyMlWAzGg4jnKisjR/5VOCGIs2XPU3i1TQj5WOvSBY0d8ANNQkKA0gfIDNRt+AI0VFI7T3Pi5Zpd/WcupdKivKSMAogiBPiEr9jcIZpAeJ1XQCaBSjo9CyzL6Y25KY5N98D2rCcY3tjk5fQI0pTwYIDlw+AhGyCbgp3vAXhDadyyp+bCUKDAgIzLNySgcjwBLzXIZYNFBwIdkKL1DeOkD52DuxjI4U0Fg6HObIACr9mnq9zkhTimngnBmsjQyTdknAz84aPOBBOfoZXWdZKuUSPYLwbD6k0wX8mxFS4bNvNN7GT66wQnCK/Zgn4oGOzyvth36wNEhAxoSzvGyHXouKORY4A1sQV6ReLonOeJ5PocUCSKUAhlbjmH1LA6VI6U39I39506FUpa+A8PomlJAemr8MEJCQVIFnrFL5QUaXaf/OaihfJ73KtWh01YQfE4gDGesJMAfzhB5p2eeAccQRMkEuF7KI8/mTJ1E4jnz8FtwYB58Rx/Mi5JF9iNjTD7wiT/wc3NEnvAmR2rmvRIf5piM2CgyoR5c6QmZ+J3xIZNlOSC5IpOSU56L0NA1umGFfGo2eKreb/Nzm8AHc5Pz/5FPwa/APplV/pFPLf0tmfO3Amh+jr9iK+zd/jOYLyGndFbtt2yuDD6/53tWG6ya89Hmi14hnXwPPea7+BV2791IKF5AZwW5Zf/oUDDHXKTklB/VBAp+Bpv8LdBHxOEJ/MmmUzbLvj0vp2gpr+PPcpQq/8cW+ppgNkEunS79hJPPcvRxeWRxbJn/YV/kJxGbDbtsk+4bu3lgT+QI/4bu+RAM8+M4Hplasec3BVv+r6JCYzspZcYbhm6x1ncBEQzJ3TNwko1b1Ys/zsmF5hiG8aMqHCQeankoGzRnOAG7lEgYa4IkHA8PEZjgGoI/8wh/lWnBWvqI3ylZyulVEiJ1w4n0lw56LlzB+/SZ3sNkY+OLkrDmd6z4kqN5kEBnL/pjTvo2PC+DDTsPEAAm50f5GVq9BEtxKCoQzpIVgEUyfZYDAvIAX+PMQ1T7BEKxGADSOlZjhnhzXqnJRWQ5awCWCJMTRTAoM2ejL5qIm+OnbNngVG4U9H4AhogwBo4jt+VSIASeAQpGRIJAxLtrgiF69F5GoCEs3gtsECGOXP2/vgNNnwW4siQM1bPJkGOW9U5mDZEWjMlWIgUcPXIkYOBoc6dCKd8cIyoTIastUAFKxmOsjBQwyKaI2DVBnr7qc92QJ9EzpScnYI30AXPv11+ZSOVPxgdYET46hKjn8qi65Ml8Ai0kpO/o2PTD/AMw3488kAAGa5VK/7yPg7GPATDIKpozpR5kWWZ2ZHplgpA4z6MPQISuAGNkCVAB0rJswmdlxACYeRIcAwTzauxjJ7gsAwjldzZBAFbt09TvkyU9AKRpZF0fU8kpcYDmtPxsLu4CynWjy2qPzQOiC5TTOAW2JXOZ5yEWnlc7Pc4W+JeNsxGoy2gKCjkjje2zP4TFip3ncz7IaDJz9XjZHsJTjpneDQXGdM076TB7R3451eAkvKnvlWFD7LuvGUPurlFeqL/IjmfScQS5vHxOfznTPnnkZ+RlnIjWGH77vHkRtKX1Pd/8wyDzz7+kkVGJ2fAAOSn7a56Rxb6jT8sxwFl4SmfMp2w2/NCfoVWpqXq+y8+tGx9yVCSs1MiM7+e3cjoemcFgQXX8LV/Ah/D9fIQggf9jd+bfHPGx8JpeI9bIGHJlzyNO4BnwG54imXyOQBqh9Cz+2PvMpcoDJA6e5N6E9I9u809J+NEX+E5f+UVkmY3zgzCc30IscQl4YMWNz0P4cJSseglg+bacVMgfCg6SdS/1gM3x3bm/CJ/gS9mgxJKAn+3hIZ5DdvgBkk1W8IecfE9wxecKrnAxCQYEFMmW5CIfwbLxGkuduTZ+/cCb6D6/LKHhO57NhtmzZ8EGjf8UlJlnAVjZBFUSs+RBP3AjwZDx+rn+GQt79j7j45/JTYCjv6U88mx+nhz45/qd5fthJF5FV+CFvppTyRQ6CTvpABkL8siZPrB5mOJnJX/KkbLkC2MEX/y8Z+izP3RYwlEQlgABD/BOPkHwYL7olASS4DPHd6+KDzsPEAyA0E0mR923Kz5LJwybAvpcFNHvCDnN93OCR59wEC0KMq/GTH84V04tjeLoa9lMKFJAccvPcoDlmc7ldwCFZ4X8lv33O2MzrrScCZwMZn4uatXHsjF639dPipVj9zgifdJPYyNLDYiSl8/V4xU8eEc5Zrvsh+p+Pdc79d08+Kxnl1Ev4lY2shvKgnuvKDpzro9kFWJfjo9sjLEkBN6T/Sx5p3IPQFneWdCnJ33zr5/AqJ5/MiRXwW4aGZUnEgDKWkeMK3M1Zsjk53nGRwbk4nnmp5UYrQqBw9/PfQPZ31R/EoGoCf7metOeTAKCaisXHHNru5XAugMEAXXtH2Bc7R/hKqyt/W3wls/iB9gv7ORrYXSJlfAdjnonDPYZ/+ZHgsv5fOnXSBz/yJ0iflf7hNpH5px/fQl30T/vC1cpfWR8XriFd5b91S/fG7rXpPY1Ps9/IOM1X4r/9U7fwwv0xWfDxwRd4QvhV/5f+rt6HKVm1vKr58Nz+Mayha/UfJD/Izfy03cyogf677n6Xc6Hsdf6Usoj74TxgrIcsTtkWd6vr+Fn+ke3yKNMHkQeflbqb60b+l7K13vpoD/eUR6b3Ncn7wkfIkdyyFyvAx32IkBYx0CGngEELDPK9lC67EMYixLX1R/vtbpheToBjQmn3KJF2QLZg9Y2J4GAByNWxib7I7NYnlCyubcf7SevmwBsUxqWbQHvKs2Ss6yd1aEy6y6LL3sHU3KD8bz3WOUa2iQ877vH8+85PCuFAn14KstZn+RyPMtnl2M/yviwS7m1d+9eAniBYBRh5ysECJKHm1yR3/2oF+/BTgIEyy3KI7bRLMNYErIMbjleKVPq4Tf9fmUsSKlAIHXlAgQ1kHbN2w8x9Rxby6fzam43PZ6j+HzL/wiecgHLjgIEZSGtzZfAUSYA2VQ3f5Tb+cTYeeXb6cHRfAvnLTiTtbNqAMuH6p2P5giPbq/XjQ/KP+tM8tGVznZ6LlvMp7W2mASUpNujKPEgw69czL6jeWWLi73l6H96JwFCuev/6ItwOyNoBGM5Oau5VEcOSJUmqGdtIDBNlusmANPeup5P1TXl63nq8k+pa9qXf9Lx9U1L9PYlZAP40KEGx5dU9mO068YHzyv3gezHKPe7FxJf9qi1tpgE7LWyX8EqgioPm8/tEWrtvhLYSYCghqxlChZTRQRXmUxri0lAbaBSE3WH6zz+a7FeHM1Pr5sAHE0ptF43CTQJ9Elg3fjgpLpsym8SnyYBdfhjlyBOe8rx9yl6hheo6LDXQvn52K3Qx5+E/u+IdxIgHK/CbuNuEjhKElg3AThKY299bRJoEhiXQMOHpiFNAoctgRYgHPb8ttE1CSwtgUYAlhZd+2KTwMFLoOHDwU9xG+BxLoEWIBznCtCG3yQwJIFGAJpuNAk0CTR8aDrQJHB8SqAFCCvOu+NKHYXoDgEXvg1dO+5sXvceuDRkWyc4rTi0vfm62xxd9OJoyaFLmXTW5S1OwnDxUa6v35tBHMGOtABh8Umz5yW3XLtQqOnr4jKc9w1Hn7oQCC649LC+XDPfdzoJPHBhm4uqWq32PMku9vuGD8PycpmhC6tyY/bQJ30uF7S5GOwoX5y3mPas/mmXsrogDf9yImRfg8cuZnUjtQvFjvLN5atLbPEnHIkAAbl2Y63b5hBxdxi4FRH4uzWPk/AZm0xcL+5YS7/XHAF2yy23dJdrOObUbaDrajZauyXRjYduzaWAfc7K7XpAwG2MjtVqpw4sNgNOMXFGsUvu3IrY19yM6dgyYIA8lNfdL/a2/k87f91xtfSHrgkEnZmsX25z1Gx8MrduY3Tbts/aROa2Vv22GUpA6chaAY+bOd1q7Sxmz3LCktsg96XtMwEwH25Mv+uuu7rL/NiVm8+f8YxndLZPzmze5n63V77qVa/qLuTjkF2SmFsrnWDhd+tqboGFP87u92foSN1N6+u6xrOPz3GiEYfPftyO2ndxI8eGoMGEb//2b585Ba6+yXrVsfFJb3/727tz1F1ShHywmRtuuOHYLcz6etttt3U3v7op1qkpApWzzjqru6nX7cwubTQWegM/BEB01eeM7+EPf/iqXd3I9/cBH9yAzJbJlR64XddRuLDAzczk7yIpmGBeyNU9Jn6PNzi8An7z4UMXdi4qPDcOs/2rr766u29nyN87ScdN0frFX7mZvbxcc9H3Hm+fd1S8y2jJcOh2eHem/NzP/Vxng+ahvB19HfLiT/hzN6NLSJg/pyG9+tWvPnabMf/k3e5f4rfuvvvujgs86lGP6ngjHeT/3bDtKHvP8TzcwW32/NfQZZ3rGMPYM45EgGAAbpUTMYqygSvAdx62CJExutrapWjuPSgvwXJMHuN3H4HrvOubdVcVsMnUJ20oQMg7ci16CxBWlXr/911rjgxsIkDIGxGOK664YnbJJZd0Dp2ha0BAtsgqkvPaXZfuGnRBq7s3OHpBIkeQ4EXAi0D6vKN/rXzsU9sHAjAmj3vvvXfmvgNyd838ZZdd1oEqB03GHIh5uPbaa++TmXMPi9879/qpT33q2kWOmOgX5zV258Y29HXtgztiD4QHCOImAgSikPz5wR/8wdn73ve+2fvf//4Of9IkswSfkgUCUXfesCl+yirTdddd132PvjziEY/ofm7lk59DMCQL3Ki6r20f8EESAN4LGC+++OJOppqAwc8kcM4555xu/iUI0/z+vPPO68gdPB9a+V9F9lP8PaxCDB252QKEVaQ9/N0cq7+JAMFbJQde+tKXdnxA0M/vRJ/wUwlDQcqjH/3omVVlft7v3cWAA9BZOohHOlXJ9/ky+iqg2OWlrkcmQDARANSFFiJ9k/7IRz6y0wrHo/3oj/7o7KabbuqytuUyEgBB3GUaAPS6j7JqAcJmjHqZp26DcCH6rmN/8YtffB9AVzomo3366afP3vCGN9wvGyXAvf7662cyzDJcmv7KfJx77rkbIzDLyDHf2QcCMK//yNXZZ5/dJQiAdDJwyBpyDpR9pmxK0WCFs6+HylPmvXfs9y1AWEV66/3upgOE4L9MZUlAkIbLL7+8IwCCV1nq+v4VAS5i8KxnPasjFpqVRUHFUbj3Zl/wwaqxgFzyz2pzGvlKvlg5whfKC/as/iv/s7KzKQLWAoT12vKyT9t0gKBfEocCPNyAn0+zMiiBIBBl17kwN7+XWBQgSGTjsDAi/kMgoYxyl+1IBQgEZTUAETDpll40Udcv/dIvdeCKuKlVT1PCYXnGcvAmLtlpAcIu1fe+795VgCBAtaIgk+3mZsFoXwMGbtdGTFuAsB69sRSbVQBBWurMZW5OPfXUbqn25ptv7v6tfeELX+gA3FxYQbDisO7WAoR1S3T55+0qQPijP/qj2YUXXthhgRWEL/7iL+4dhCyiMgWr4i1AWG6enWePhAn64e83fMM3HHuQMh/cAF+QpU+T1f3Lv/zLrgxoU60FCJuS7GLP3WWAIPmngkAQkLL3uvfsX9mb5KIEVwsQFpvf+3xa3d5pp53WZWTUnlmqQQYEAEoJ1Jip9UxWgLD9X+lHmlUFWQelSTIOlnFlcGR8TjjhhNm73vWubm+D2lLZHYRCJCdDfMcdd3QrEVmyHgoQZChkkoEWAFMbiUB+xVd8xWBNoo02lp6RFstLH/7wh7vVEP2x/BSCaezq4NXY2vBMAQU/yinUYMtWC5bUx1oxsVfDcioZGYugaSxDJeD69Kc/PZMVk2EhB9kZy16yspwdmaulM8Y777yzG6OaOjKUFVdXZ3Og9wFispM9t5pjblIb7HPKvxgQgue5J554YrcsLCsksraHxGakq666ava0pz3tGNFTKiL4835zqNbfRvCyxMg8eK6x6A/5+47aP/K88cYbu30k9qqo/bUknUBT3+rWt4KADOirvTHeMbW1FYSpkhr+nEyt+ZadEQiwTe1Tn/pUt++DHksYSByYT6UFVnBkdJSAafR9nfbUFyAITKboazlS+yv0UwCqvvp7vud7Ov3WVysmbEt9OnxQ9zpmjzJZyqrYipKG4BcyBQ/gJj0eqtNnl26n9k4YxVbs91K2aVVX00/2BOdk09maJXWYpP9WgGNvz372s7tyG/Zm1ff5z39+l013GST8knlXagMb9M2z4L3VY2Wk+mFJXz/S/NuGRbb+2c9+dnbGGWd0Nf4wPeO1T+j222/vCCMfQB9+5Ed+pMNXz0Yw4Sqsoleygp7l+wkyyznqW0HwDqVCME0yyzOmtraCMFVS9/2cciIb0c0lvdDMA3/Ff5iPl7/85Z1+hS/Qy2CAz/M17IQ/oAf0lf3ZG6Y2nD81PzCF/ZlXtshvsyG8pMz49gUIPmcvoj7RHfuhlJnx3X0lRmyKz9Mv+KW/b3nLWzqb4M/5U1zB31ZLZHztafj93//9rtTS5/htq6zGftJJJ3U/V9vO3vks+Mie+Eo2jqsMNXZNPjDO2PETQRZ/ygeTobIYfWBnbNlKDfsiQzjIJvWXLGCxUkxlNaecckpnv+xV40vhOv6glNcKkIAfprBJ3/EcSWP8JnPu/XiJOcQPySeYRB7hNzAGN1AmyLbxAnhoXHib5yoNxBNgD/0aOvykbwXB+M1XWRI/RbtbgDBFSgOfYVQUBLmy0c+EUiSKgihTDuB//vnnd09A3Hyes9CAA8VCmpEGP2cUNi0BFEEDRfQzDlkEmCUjS5aMR1ZoLEDwOYrNWNWeIcQcO1AZ27SE1FBATtwYOPIoPefJqBFthuX/ngWwGA9n6t+WqQRDwCDZkwQInmVjVmoyZV36mogW+HD8nseogQxQs9mHAVNimXDEXT9tFGNAnC4jB6yIhH6qxbXawwgROsYkCPA59cEA0zs5ZMGb/zvNQQ0uGZoT3yGT1HSbf8DtucAHkHoXYCwDBARE8EL2AhskSb+AnGwzgCA3emK+AQOgQlj6Noz1BQjJUAD5cnlxnpq3AGGehKb9ni5mGZdj0jgeOuFv9d3myEYwGVv6RUey3Ltue+oLEKbqaz1i+inoll2CcdmQK2hnx2xbqeU8e4RjCI/gHY4EvxBkdsyZjQUI7AqBYYf6IrEieeLdcIFTRc7YMyKmdEvfYAL7hCf6DHvhElwR3HDYbIoDl2n3PNiL8MEx+O7d5lUmWKCALPkdrOfsNX5BsJKNn+bac71H8ifj9Wzzr6+wXwIEpvocvNEHY5TRgy/e5z3+3bcRsi9AQMDgiX7Dk7GTrOr5bgHCNJuvP8WukUe+kH5IaCGIVnGUauAIEgh0378FgQ6dKE+2ghH8tb1idJwf5jeQ3je+8Y2dvuIY/B3dD6H3PPPNv4wFCEht9jv4rs8jsrAI/+gLEHASCU46Kliml2WZCtwTZAi4rZZKdMlUS3CyDbosoGAvz3zmMzvC7l10OvbOJ9M74ya7oQABP8GlrNgav4DcqgxMYl9kK4iSqBV0CBrI0JiNA0eRgIABeIOA4nnPe96xw2PYL97mc5qgRsKH7cEOz/EzY2XnAkE2VvIbCVNziNhfcMEFx+bQWFMCiGsIBmANefm5d5ANvLWxXUAAb/EL4xDU8CN4QV/rCxDoDFxRdkQ+sHFKawHCFCkNfIaiIZYMxiRSWM6F8chgi9aUe4hKKRUF5ARyfBjSQME5VwDBSBFgRmTyAYoykAAOhSxJX71k3beCAIgYKAelTxpHjHhaHRjbpCxzRSn1m9LqH8Ok9MBKxM4IgQnnIyOgcf4AAEGXpZM9paAMvzQghFtmY2gFgXw5Y98x7uznYJDAibMVZOkb4DTGNP00RkaNmAFpRm33vjnjkIER0sBgBSvABUCK4oGliB+oeBfHWi65ZdMnYDL/xuLZqe0VMDoRJAHCxz72sQ7AyEn2QpONAIDkRzYCG/IANoiSAEWmRmalr40FCEgQ4JvaWoAwVVLjn2PnHDTnxlkjpyGmiIDgnPNnE/5Pf3OwANtftz3VAcJUfe0bJadFpwTJdF1grRmTsQqAJSSm2mONX1mBgaFDAQLiIXGBIGUlVr9kPuEqTJAtF8gINIJJqav1OfKHzXBVgkUAACORBHhp/uAV+yU/42KTMnD6KKCHzSF0Pu+ZCRDe+c53diVjghWZXM372ToMS4CA2EhUwA92TnZwk1/Qj+ARecMTyRDYzbn3bRieFyDMy8i2AGE9GMCmkWWlQ/TI6oBA1dz7nfnmg5A1GC6gxRNS+oU/CKL5+6xA6BkiLTuNK8B3yaZ6U3ECQsRyLECwF4UfF4TGH/GZ7IJuD21S1n991werY/qpWUmj81YQBbKIPx6DzMZvS34g1MZb6ndt7yG4+j8UICC7eABSnk24EoZsQECl+S7/b5zsBb7K1ONWOI0VNX0yV3wvvsLm4AncgCcwQuOXrZ4IHPAA47J/THCv9fEbtm9lEzf0HXxGwCLBiXcJYvAjWGee+Qp9hGsOBhAUSPSRleABJ/A7GEBXhk6ZGgsQjBX+TG0tQJgqqYHPURIOglIhfJwTgxPpifIoL+dBMWWSGFEa8slBcDiekUZ5LBUim5wcg+JMFw0QKJDo1Ls5xnLJfkpNIoKqlAYwUdIHPehBXYCTZXjKjPww0vr5xoz4ABLguEyA4P2eD7jIqG8TZ+QPCOojIv0M2WcQMm5IOIXXNwSjJiSCCAAHQIAaMBBIAApzoUSqJlyIv7kzP5YB0+o9CPQCmCAVKRcCGAgPB0I+CMJYwFSr4FiAgKTJck5tLUCYKqnxzyGJ9BCp5GQFlsiduVfWw3nRabooMcDZ5sSo6Ps67WlZfR0aJYyDYTJixiHgQK4RFhn2RexxmQDB8zk5K67Z91X2FRHXP0ELMlOW5iEiSL+AgJOGDbW91fIS+MsAKudE7Ni61VKYkGfXAQLcM6/KAcp9JfV4PUPwb4VVg6cIju/BWHgtwTAWMJVjnxcgIIP80tTWVhCmSur+n5Nplz2H7ZJJiL2VLnpEX1Lrfbs1AAAgAElEQVSuizA6vCAbwz1J5thn+O6yjISvQMiVnyGW9GTZAAFH4f8Q++jx1FOM+CkrBbgNvy9g5SuVzwh0BCcy4vTZ83MYSxIMdBoZx3369HtegOD5uBWOlFXaegbIH1+CwxKDaVZyyNYYkP+pAVVIO07heZIF/HeSdzW/Mc+4gyAEB0or9yDgg+QmWBOYRE7GJwjQT7whAYLkTAKSMc0cCxAEf/pQH1Iw9LwWICyPAd03TaZolkOSOaKYslfqcDkJ/zaxMuCWnMpzspFRKwjIL6dWnmrEcaUO0d/LBAj6x1khsXVGbkqA4PuCA4oq8ucgjYWTYXzqBCmslQYntZRHtyGcImirKoxnmQAhz7ciQX4yE3UDUPqn3IehlY2Dk8VIxnBegJAASnbAZ9VdyzCK5h/3uMd1QFoTiPzfsXZlxr4OEIwfGAgK69MD0ud5KypTAgQrIQjU137t13b7RqZufG0BwopAUHwdieXIzTOb4SjZN9CXNZb5Q6plGK2OpW3CnpbV1yFpwCKrh1bYJC+U7MmUCxAE8IvY4zIBQp7PlmTX6qZ0Ah5Y4UBISkcY++K02bfVnnkBgucjE8gaUgYDYHpKmPy+DBDKkp6sKKSP9XjJy+qmVYO+NmVFpfxeX4BgvvQVfsqkLnInSwsQVsMEJJb+CWTZPp3FF/hkq0Kwmp6yndJ3CiiUxfAnAtm0zK/gEWmkX8sECKlIkEAsKwimBgj6w6/iPZIhkiCSHfwmfqN/AghjVwZVrnaFIMM+fV8mQMjzBQhKhPr25yHWkmRKCMk3LfaJr+AVUwME38eh2ITV0hwFm0NAan7j/7C/Pua8DBDMud/DfRxjqFJgHQECLmBfSEocp17S1gKE1TCg+7bgwJKVZSm14zmNgCEyFESBQpUZZt9DJESZMtYi3WSSOAaKZxJlHZYtMUIOKamMpOAky+3ePTVAoLyciiwWJ6NEhoOVCddPYwVolq9TwpTAAnD4HXK0TIAgoyoL672yX+UlPZy/DVoAl4M2ViS3bMBV8GIFQTnEvADBmAAGxy1YQ3z0X6ZAVscKSk24cnSYpUTlIWl1gCDToT/KSuhCiLtxyAzJJvRlNMfUs28FgUxkcfST7mS5te85gpUcz9sChDUAwf97hDkVsObYUzZi0zqyJrvDKbBFKzzwIW0T9rSsvo5Jg03RYWMSOMMpmSk6nQzpFHtcJkDwbgGw5Xn6X972qlyA3bJzmIEElPJVksAe4IHyjr6AvJaXch8NNkgYKPn0Xsmf7/3e7+1+VwYI8NL/JYs8vzzOsh4vv4A8WtEoz75H2hz0gACuuoKgf/oLE+zjQkiHjtJEYgWvkgtaCxBWwwSrA3ynZKHgkr1oSkfsMZGFVmpYnnTo93ythBwCXfoUc+PnVrqVpPB9ywQIfFz6YkUw9zEtEiAg196Ns0gE0qlk6ukRO1P5IKBX6qtJlBi78cIJNrVMgOD5Vl30gf8Nl/EOwbxmtTZ3AOEewQk1/+xTslGJ1NQAIb4SHghK4LiSP7zAXpOa36jagImSCSooUj5WBghsXBBovgWMJT/jQ6y+sNl1BAiZL+OXlBDQ9QVWJR8R2LUAYTUM6L5N6LLqsmkUIxfUUGSBAyO3pFbX0/m8zLOIGPFOqYETQER6lFDGMXsGGIKsg8jfqgAnxwENbVKmuH6HmIqmywDFsxisaHjejYnIuXdxysbD2WgcmEyAZVCEIWfq+x1iLcuY2ll/q+GT5beqoMlCGN/QHgT9Y4wCKIEXULS8ygnb3CdLK5CSjfUsMiyXamUYBGBAyBjnBQgCA85UqQTQ837/t0Ro/oy/JhBICcDxe2PMBnSEGzkR4ABEQR5QBpbqM40nzhuZspJEj1YtMRLMkIM+yWDJpKRWvFR1OiljkU2LLUBYAxAUj+A4lOHY6MYpZPULaTU3yKEN6mWGexP2tKy+jklD6Y5VBAGOMcqM5jjHlBxNsUc4YMmf/fu+jD6H6mdDexAQABlxwTu8EYixbc405ANGwihOkOyDb1ZrkDbvgxtTAgQrqOqT2Scn7z0CFM9W5lQHCP4vUy8ohO2pP/ZzfkFfvB85QCAkboxZjbGfIU50RFIFqVlHgMB/wD7JCX2WNKo3KSKbZAOjc3JLCxBWw4TskxMgIIHZs2KPgcCaHdFVyaWyIdbINzLKv+b3fI0SOb6c7lhRsPokQKdn8Bzhl9k3v2N7EOibCga2m/2FCRDodpmwHJKCMbEzyS1+OnsZfB7XYAN8Xw7lSAmlVT7Yob90Th/4TwSZfStdVFWhGmNoD4JgxEZmuuz5fLf+C4D5XTJRDkRG7C0BUfYN6BfOMjVAIHeJYHI1b+TDt8JwAXUdIBgHjMEZYBBfrCVAkKw4+eSTO25I/sbpmfhBbBEHUamxjgDBu2GZVVdBB6xMIiDza1w+oymPawHCavZ/7NscO9InYwvcEy36uV3w/jCg8pSCEGzkm8PjHDhVTgEhNTmMJ88CCAwSIMhkUz5GxGFxtL7DkTl2VFRvr4D3ARWkHhABBb9HBkW1mu/JSlDkoUZxfJfBlpuOfB4hiEEzOhtpODn9BVoIkqwesBMAeY+6TEERo/N7G6MYejJyZT8YsP4zfEtySL/gSGDhO8i1bAFjV5PJiK1kACBZe8YliAIUsjUMF2AAkZAZQUCAWimB5TjBGRlbQjUeztXyKQMXsKhJNOfmmJMnV4DHAXOwyIs5YPh+BqyAiMwRkMmtx1ZF9NW7EjB5rufPC9zIHYHIZvaUr5kvmRt9AgL6i8jRG/oiiPVzQV/0y/iQEns1yAd47VPbl4uQpsqELnIKMoicaMoHUwIjoVDWxua567QnATRHoA+L6iuSUGbn63HDGs4MWZYBL7NRU+zR8+i779NDK1meKcCNndDH8hz5YCaM9R0yZj/0WqKDbXKqVmqUFXgOWyJnpQ6w089hh/7CT+UesBAOwpIcEOHdkhkCLAQErsFJgYvP+i5bgWvGAJ8kAfTXz7xLdtO7/OF4JTXgOidtbxdsltDweXjgd7ABsYKRdMH+B7YMn8ZWA8kGpno2PyRAzel5fieDCCtgsnp4pIQOIiNIJUzQZ3hFV+GlQBaJhDMwbEwfptrFpj63j/gQ/8If0JkkA/gMeicQRuL7ykD5IPZLn/EAvpzOCB4k3mTss+LIf0iS0R1JLfZB9wSqAmgrD/TMe6z04xpWq5HPHCrg93QfV0GuzX1ZO983b7k9Xt/oVXyaz+obzoAvpB90WCkNnypAyio9HWc3fBS94/P5ID6T/SbpVvYBVuizIB8G4BbkSQ5sIGPlA2GL/uknW/ZemXurdriQsQpe2IFAQ6KCbcMitscXmkM/ww98hn1KVuIb3oWDWSXCG3LohBUK2O/9ZAlzJDlxPj7bewUu+A0O4rlszBwaWw4+0HdVG94JQ8ZOIGLPwT4ytpKpnxpegJPgaf7NpuE8fRDMqjTBYcgsR/brA/9BdjBsamnSJuz8yF2UVgrB8jPjKzch+72IlTLLCAxdUIOAckaiUdmGOM2SIAJ/jgM59ntKyGlRXsqGPNeXYlE6JMX7EVZBCiAN4HMUlrgd7zevcbYyHwksys9zPhwSp4kAcWRWFRK157OUEzgE7LIiYSxj8gmpR3ZkYhkWGZTyMUarFkBIgCRDTz5IM8UCVvqWxuiASdkYDlAQ1dsYrcn6MW5yY/z6kCbAAXgaICQbBBywyzgyQH0AdIwaoRGwMHIALdgxF+QPuMgwjaMu69PLfnIqSI+VlLIhgUAkjUwsXXou0KV/jnwFyEALYCE1MoUyGmUzJ4vUK8/Tn1V/v48EYN6YgCuQru2Sg2d3Wdqvn7Mue+LcSh0H7oLZqfo6Nj5ODPG1OmmMdRuzx3xWZlvWjGNiM8gAQsr2kPFyyb1+Prxlexw8XfbdOtuYs8nZmiQE3MipQn32pmyhtG/ygleCZ6Tb8jt8hgfwCvHyzJx0AmsESzCDbcIDeCQYR+48W7KC/XmO5rsIlOfTBwkEeIFM1npT23cpk3o8fidwgiHBd6QA8UQEZWzhkX1XMMM4UuJqvLDUeMsWfzJP73fx+33EBzbCBpEuWF82dojM9SXF8jmEl8/BC8ylAI7NlbXq9IcN2Q+EpAqcZdZl9bP3ocR3840c6w9/7rueb5WCr6Kj/HapD0PzST8QXuQ+dwKVn0W68QY+iK0IVpD3Uq8FBPRNQMDe2YzgBicy3thrXx+suNNT+EE+Eq2l/yN/pUHkwb7wDrbHbyPjvs8fprFfQYrVSf5Si7zwJzYtEcFOjAX2sSGEPJl334EZnlP7e/1j45IOAoeUP8J7pYvsUp99Fw7w1/rud2ky+54f0l/KpR5Pfpf+5P/4kOCF3ARDxgPDBSpZ6fDZvktW9a0+DGZb9n6kA4RtCWkX72HEDEIAMCWY2EUf2zsPWwL7SAAOW+Ljo0NIZLXqfQDHs0za2HcngYYP25e9wDtHcjdesH35H29vbAHCHs247EbOEVfTbgkq2fI96mbrynEigUYAdj/RMlQya7JqSlRkHMsM3O572HpwvEqg4cN2Zt4qvJUHmWbZbVlypb5D1RHb6VV7y/EggRYg7NEsq0W0ec5StyVymcLyRI496mrrynEggUYAdj/Jatot/6u5tfyvnGbqMbq7733rwSFLoOHD5mdXSRGbt2qgVl4ZU8p7Nv/29objXQItQNgjDbDnwWaVbLBSl9h3LNYedbl15YAl0AjA7ifXpn97pdSh2p8ytllu971tPTieJNDwYfOzrT5eQGDfgj0LSgwFCa01CWxDAi1A2IaU2zuaBI6gBBoBOIKT1rrcJLAlCTR82JKg22uaBHYkgRYg7Ejw7bVNAvsugUYA9n2GWv+aBHYngYYPu5N9e3OTwDYk0AKEbUi5vaNJ4AhKoBGAIzhprctNAluSQMOHLQm6vaZJYEcSaAHCjgTfXtsksO8SaARg32eo9a9JYHcSaPiwO9m3NzcJbEMCBxEgOPbLQBwR6jY8F3gccnMZigtdXFzkhkIXm5x44okbP93EcWt33313t3Ha5SFD7d///d+72yqd01xfab8v8+LWSZenkJ9LktqRcfefmaNMAOiqC3vclOsSrUNuLhr89Kc/Pfurv/qrGdszb24g3caGZpcY3XPPPd0RjGM3fsIqf9ysvq/47Dbq2267rfMhbSPofIvZd3xwu7c5dYEge/jKr/zK+YM6wp9wizQOhAu5lddFXJvWYzdUu2ka9rhpur6otRSn/rm01GWtLljcx9PYcCsXLerjox71qL3s4zZV9MgHCIIDtxK6KfX3fu/3uiu169s9tynQTb8L6Ll1GAFCANyw59Qjf48Z56r9csKS97r1EeFyC+NQc0OwW5dzs/Gq797E98nP7Y6CHadElFfWb+J9numeC++io26IdJTtxRdfPHvYwx62qVeu9Nx9JwBDg3OZkAsG3Wh75plnzq6//vqV5LDvX0YKOF565P4Uwbkbxd2M7MjkTTU307px1E3zjmFE/oea22kd1/jud797b/HZDbRudne89BOf+MRNie0+z3V77Tvf+c7uNni3q7ot+vu+7/vuc2P9VjqyxEv2GR/gq5ur3WCs0dOHP/zhS4zyaHzFeOnu6aef3gXsbgh227Ibk/tuAF7XqCQmHM/+pje9aXbhhRfOXvGKVww+2l0O7nB58pOfPHvNa17THdu6b82dM/rntm03pX/pl37pxruIi7jN3o3rTq2SbHE7dW5+33gHRl5wJAMEV4gjWcAUsRP1OQaMk7r11lv31gGtY6JdnHTRRRd1wCcr4oz0K6+8shv/2WefvY5XHHvG3/3d33XBl/cJTLxbMIL8jwUIgrZ77723c3iuUt+H5rjI88477xhYMsTPfe5zXddkNK0krLu95CUv6eYl7TnPec7sq7/6qzv5yWq5Bl4/3vzmN3fZnn1r+0wAalkBV9msM844o7tI6CMf+UgXoJ577rkHHSAIOtn/SSed1F2kxu6uuOKKzrnQv3U7uFKnZQThgMsc/T0WIPz3f//3zJ8HPvCBGw1aptrQP/7jP3aZQjaZZjxusNfHTZCXEk+9k8668Op1r3tdl912CdZv//Zvz57//OfPrrrqqr2Q05g89xEfop9w9X/+53+6iwWtXMHYQw4QjE8Q9IEPfKCbMok8SUOrqDL762w4gSYYgbUf+tCHZpdeeunMnS0JyPreZ6XzX//1Xzte8IAHPGAjPnfRcdY+Wh/xAn20CrNuXmC1RTJHYlCDN/oguXPOOed0uuo466//+q/vElybDO6myOrIBQhIgOBAechP/dRPdYqmyVD99E//dJc9O+QVBA5EILRpwPv85z/fKerJJ5/cEdk0BjMvQJiieNv8jHIsZ8lfc8013Y20m250VHaVw5cd1CzBAgDgkJUeQPv4xz9+dskll8yuvfbavVvO3EcC0Dd3nBRdPeuss7r7ArRPfOIT3UqXoPmQVxD++Z//uRvni170os4uN9mQDSRAhjJNtt3ljvMChE32a9FnI49WCzhrRHwbrQ9PJSwkDPQl5RYSBUgKeZ7yf9q7e5erji0M4O9trVKJnaVCQFDS+h+k01goWCgRSaFgkTaKWKVKLKys/QAhwSaECNYJBHsr/5J7+e17Hxnnzux9zsn52Mf3DLwkno+9Z9as9axnfcw+X365jamtfI854YM95Rv/+OOPT+ydXeAKm/aXKwtxDV+kLwLdDx8+DPxnk0NyVkJAJSAJgXQM+PdYgLDJea1y7RaerXKdZb6jgqpakASrlkbBlX8LYOmxaowkwYsXL4Yk1y7H3gUIojtZFxltUfJxCxC2AXgA582bNx9LhvscIKguKXv+9ttvQ/vFNgIE2ck7d+4M2VyBgvHu3bujS5cuHd28eXNo/8gQcCFdAGJuPbJzIgA9kASoSIHsuX2+fPnysQoQALh9oj+bDBCUwV1fO9E+BwiCd4Tm22+/HQLKbRCaHp5qI5AxhLWnTp0a9Ba+v3z5ctjPsXNeuyQNufec8MGe+pHRr7766tgFCNpiBAiqiZsOELQuPXz48Ojnn3/e6wChh2ebtCstVtoHz58//zFA+PPPPweeJYGJBxgCWvvJp0mC73LsVYAg46Ocrhde6UWJSwaGcFNBQMgA8qtXr4aWg+vXrw8bkiHbqC1HJCfYcA3A4noM7K+//hr+OA/RnR7R27dvDwdWlISRTH13ytDffPPNx9YQPYA227W15MgY6zcXwOgF9n2O9Ycffvg4lzNnzgx9g74r2BHR+rzX4jDyYfNQQtRCpT1FK8UXX3xx9ODBg4EgOHT7+vXrodynbKVkdeXKlaPTp08Pl3AP10eUZa5k1fUuK8GWPfBkR4ZI7N9//z1UY7QvcKgXL14cSm6IgtYZYOGQomyCzK0zEQgbQ/jll1+GHnuk2Mj8kDnZMpUJB4EYTGuQ8e+//34k86YnUBZYVtiB7OyX79lPoGjtDmi6pn0hb0P5Tj+6+2tJI1fyNd6+fTvsGf3J8NP29pjT1nKgF9GfVo1SP7SuaJngzMmEnp07d254TYuH15UpZbC97n06IUDJwW36QA50gl7P7dDWnAhADySV1PWK2kfnDRzWFyQo0aaC8OjRo6Pnz58PxJBd05+03tBX/d/01fvK8bFX99RWRxfomeup9NAx+6kNRTZIJYh92VekDyYZbMx3zcVrdJht0h1/7MXc2FBG7Bl2yM4b1uPz9bh3795wINEc4IH7qEgloHd/FSzXh1f02PozvM926J25y1xduHDhk4qhzzrTIfhCWh36ZuvaGwW7zn2lguA67LC0B2d82B5MNReBM70yBNL2RfCsJYqM2HreL9frGh6QoC2ITbFN+GNcvXp1WHceNAAf7ad7GvTCeSN7+/79+yHBBIf0aPMNcE3JHx6Se4kfy+gHB+/7fAC7Jh961sNTWOAQrUPROVBur+mSjHf0qKf7u359LvjAbgVWdIPPoQt03Z7Df4SL74S37JFc2a+HaGSQOd/ofbxBtffrr78e2ryC+7/++utgG3iBPwSOfx3zP5JEOh7oK91m03w0n3vjxo3BHzgrwUdl0Ef+1jBPI/bGp5QD7v34448DN+Er2YZ13bp1a7ApCTK+SMulOThbw/+HX5if+/OteIszC3xgvl/eS6VL9UBAAm8cgIZX5IMTqCDAAmcSnFm0D2mlcR3yt0dwIuszP3ZsfmyAr4SvbKI1YJFrkJtefW047kU+cDvygRds3H7ijVqjYfPZs2dH8UzgEO5i/4MpY/pBhmwe9ukacE/8UsJPJYC+4W+qlbgFnCY/18d/8JDwIXijcvD9998PeLnrbpi9ChCQV8qgncCgrDaBoiZA8BrHoPed8lA2BppeLsBrA20AIktR0zsHyF2H42JoHB2niIBwrFpBGIQshUM22kUoK5BwTe8JOhgL4sKQCFjZGLFAtLVB+S5H4L9A1rooPqWR0eLwOOLaQVJ0jlqQYJ4CDIai1xLpZWzffffd8G9RvvVx4GTAWJB+rzEka6SsgLUuswMSpEPwIIK9du3a4FwZMJkICPIUAjKzRuQCAHJ4wJjRc5DJ0AEXQOpejE27D1Liv/VA2J4+fTqAJpkAM5k2AQu5kK/+SvOxBnJHsgGjfQVw5E0+Ag1y5iSePXs2yAt4CCC1aiF5ZUYUubdGemFvBUL0DRlzr+iHJx8BIwRIaVugSVfoHeKFCNkHwSySQH51AAA86Ca5zPHJLnMhAGNEiHMCxvaHDgBdQTaSiNDTU07BPtk/NvT48eNPKg1skvMQXMRu6A8g9/AD2TIBZ5IESD5HCl84V8G9veYMOSL3EYx7n87BF6V5ASxcgEXsgh7DDbrm+8iyrDFSItCkt4IP52fqhAGZWCPbYyP0ErFlE3SNftNdf+wg8yUnOk5Hnzx5MtgZp4xQ+Td74KzL3tecKYJ7BoJz4sSJAXs5MaSA3H2XfZb24HPWiMSQFSJjT+yb75CNe3sPPiFhtVP0WQ7YXpgbPBJskynHS+YwRwKA3KyRrfmvdd69e3cgdv7NtuGnIEHgkAyd6zlEDb/4CCTKQJwW0Q8YT+88ycneu4fEAxI4hqelblsLv2Od/MO6+5/XHVDMBR/gueBOOwZyDdfhsD86xU/DZ/9G5uhsfDn9pEc+h1DTLbYDI+w7m6a/cIHuwRg6gQziGva753/0sCObsASZ5Ze0BPL1evHZLf+pNYjfFqAICNhyzg1YF3s1P6QZjpUDbkgA8FHxN/DD9/1bdlogTP/5OZ8TnMA0wZS5sXv24vowxTzYQJ28IzvBhvWaI1nzp+QBy9gAbPQa0ut1smSX7Cs+V+IyLTYwzrxgiP9KmEhAsNV6OMODPP/0009DkgfPg3fszVpdw/cNfIM9wzf7gOf4Pnnac2up8QxXsK+w2f1xFb57TD9wNThvThIY/t/acRx6R6a4FB3le+gYnCY/+pgOmKwVfuEF9g/XM9ddjr0KEAiKAopujVaLEaOmDMb9+/eHiA6BoLycBWKHkCVL5TOcCwfKKYjwY6Qct4AEefM5JJgiGjY/h4WTsUO8GYHP6iujvJyTOXgNeUXifZ6yINEhhsAH6aVMY6NuMZLtMGdEW/YqWSfEQgaF80RCOW9Gw4iAHMfIsJDq1kHi1vrMi9Oi9AwcwMiwcujWyahE3K2eRCUzBAtxBnJAF+GXOW+N9JAzFEYO4Bm4LIXvAzAABUxkTuwfWdY90QwTuNMBQVLZYhQdSIBgPwAn0I5h0heBnvsJaGSB7S3jZcjk4TvABLkQjC1S8pU1dR3kDzGd45gLAZiSTUqyDnvWLUaINV2WRBAUC+bpKuchW4MAcNT20gDyMl9sH7nWyghv2Ao7QoqBJj0XgMg0JvvH/lW94ItkAAcGd+CKubFF73Fu/p9OqgLSXY6b/SKtBlLOfuEZ3e+NVouRAJvDtrZk6uAWxwRnOHlBfuzZGthzDnb29LG2F98PXtb24N+pDPhcfUYs9i1gSCYXSYITvawZ/IIfIXeqIuZt78iUHXLmAh7/b/0tf9HrmXZ9+MrJ27NF9APp4y9UJgQ39t2eWAcMkaDp+YtyT+2376jgtMjRlA3s4v054UPvzJH9hN18MHvifyX86ABfzb8I4gWoyCoyKclnL9iqfeUzor8+Aw/4DHorsTDmf6J//DG+wp8gonQdLgmmXYPvUCWkPxIMyVwLxvk/JL73RLKWv6G7/DyfBocScPBtdJtc/Pkc/0g+EmL0j/7iEa3HJLfOHMWeJAfgHz7hnhKGZZIweFOeYZQcgEu4FL/OR7sentIa1irgMDf2ilMIPmCC/2qBtF8JzPmGFv54rYVnub7gCPbiTVP6wSdYk8+Zf6of5olzwji8oLX+eo30RIDLP+FXux6fXYBQHlKuAV+WHSgg6hlKhYyP4csqifhbh+44cxke1xDxM2yEHnCUAUJJENyDwlEYmQYKRJlci6MTdQItzkEAgRy0WglKJakDBNkp8xVwcJYIRwYwsj73lZmq5TGmfGMBQmngtUGJuFsOWKmXzBgdYiVyRiBCVOq59AAfuUEGyC4HeZAfxFD2gwyAYg5NLhMgCDjILwddzYm+yPq6nnu0AJID4lDiNKYCBKQimdsQwl0DQev+cyIAY/IZCxDqQ8rlIXvVNAfD2CLCbsj8AGdZN3vN6bUSEsnwCyboDQeP2NHbMkCg6xxwqp70UeAhS8WJymKlkolQypAhAjLmsntsZmzUAYKEgWsIDAQDStcZCACn7T0EGglZ5qEDvQChxsvaHloOmo0I9l3T9wXfcJX9la0f5dpb+IWAIQecsHYLMpT95eAFg2Qg48nZJqG0aICwqn607L+Hp1mf/abH/MC+jDnhw1iAUB5Srv2BpyCyTRllWJEMvaDdnuADqly9h6DwNWP+p5fQFAQILAXD7NJQkcBD+LiQTEkklZGxJ4S19I0vdG0JClXsDOtUVdBKo1KAzPcSaC09HAsQykPKvf2o8QYW6Q6QKOQLU/BkaiMAABAXSURBVBGQtGmNFt/wORVKARF+5FqCfFigWmEuqnlan+xpEhCLBAi6OxbRD/4f7sHfjDph5PUpvMWT6NsmH1m/DL4cqwDBhlHGVnSqBCjzJIPfeyoHAkgJkAalQv9P8cYCBAAhmqWkyWAzTucaZCdkJf1bBE85plpN6gDBdzlDEWpZUQngICNAB5neZYDA+QNTc+C4ERVG1VtvD2C8rmVCyVWwgeSoRCgbuxaZKhOuEiAADNnaVpCGCGo5WUeAIMOrMqWdZBOPU1wGAMY+OycCsIkAAUGX5ZEwqLM1AnkOL/247l/bl9cQP05HS45g0t6OBQgciOCQY+S4BZ2ckCoEnYYDAlR2LrMvyF8mQFDxYO9sIwFrvg+LEHBZO/ZhzlMOq7z3OgME14V98EDmL+d0zK03WviF8CHVyFXWK8iD47BCIoKcBYDLBgir6scqAYKgTjDZaiVblz2v+zpzwodVAwQJLQkAOIyU121d/IGsfi9AYLtj/qcXIMgsS465fyrYknx0QLAsi6wVSaWfbxv70cOWvmlzgS05v5e9V0GBM7omtCadPHlypwGCfbNWmIkX4DKqCSo5ywQIfL6Ay/fz1DpJAwGeSqNEgc+Uj8FfJEAI/k/pxzoCBHogGYmnzmUcuwCBM9cu0Ovt6j22j/NXmhSpOuhjIymFTOBUgACAZBxlAxEPJIHRK7+5pvYHkb1DT1M/bFQHCEDFQWBVCCVuxpDBoQIYgYyodJcBgjkhL7JywJY8ZQzyGNDaIHqAn4yplg3ykhWQ7ZE9cFBJm4aIf9UAQXZBBqc31hEgyGAq7SJFcx5zIgCbChBkDWMfrXv0HLysoaCbPqfioG1AtnIsQHCPZNhlvJPNYscqEfRCAME+2C4HvkyAAAckLXzXXARA5UCAtN6wD33HuwwQzEtLECyFo2xeZUMfeGv0AgQZUK16yvPOBcFjFVsVYXiqQmSdqwQIq+jHsgFCzljImm7jBxvXhTlzwod/GiAgp5Jpvd8N6QUI7GjM//TwI/MVDPBZGa4nA+6/kg+SSDL+Y6Olb8gxsqwal8P6rpEKCnwRREgY7LKCYE70X5JAkG9O7FeLYosL9SoIqdA5r+EPP5AYgIXakAREEgllh8kyAcKUfqwjQLAG5+VSzV6Xnf6T6+x9gMAhyOiLQuvfQagdCifkEIs/5FxULgOgdYjDV45rtRjl4LGoVruOkRKha6YFQeuNFiOZMRtt+K6IWG9f+cNF7sf5CFY4xPRCT21mHSC4jv53zo+DLH8MBtAg4CoXjGHVAEG2E3Eh55pQLNpipOQnGyrLL1DQnkV2si+tXxIOgMqylG0WejJlRpAsB8fTbyhwMzcHPQEEgJURcq8SAAVQAjRrqQFCtoUMBR3J7uSXuu29J56sEiDoUVW6TvlaVsJ1c+h7as939f6cCMCiAYJgmbxlY1q/g1DqrzMJWomQaPihCoc0I+tsmL7SlVaLETvnuJF5bYLsQwbL2RpOyHW193HwWlzS1mhuSLDv6Qcuz8SwEUGGoEEmXQCSXuTe+ltnEGATh+WPnUXvnOFBSNiK1gVj1QCB/rIhRGOVFiP2DXf1HxtaIvRGk7fWw7EAgX064BxyAedUUp0Rs2+SLuwf4UbQYL19hZHsWJ9ynrqCiNgTa/F+eQZhVf2YChBKPLUG95fZjS/blb0ve9854UMZIPAN2VPZ8rEWI/rgM/wkvfKUPPvAb/AFsFolsRUgsIEp/yMxCD/wDDafQ6mSA3QYNsQWyZ8es32+QZeCM1FTo6VvEo6q3gJwOBMfqyVZ4GzuWvHgy6oBAv9qXSoRsac8lGTRFiNcDDYG5wQHujzYr06BeoRvkJ2koIAOZuhOkAhw5kfbFOyDz7DJ+zAvAYL2HZhY+v/gmcPj+TVzfBKuL6IfqwQIdKvsILA3xpySBHsXICgfa88xcaV0CsORU0yZvJwcJ+iQUGV7yiLbxwkh7XlUn+u5FgVH4hkNo3UuAdmgSNpjKACHoRUgh3Hcj/Px52CfeQkwRK1Ot1MuDlQvsdfqvjokQ5TvPu439avDQMu9ZATMQwaeg0eaOR0Kj3yIdrWwOPykXOVQJgMgI0AXeYw9VjMEyDX08SH2sg0yc2TJgCk32TAoIMoIARvwIy8ZUZG7wfDzE+JAUtaF/JCp1uP8AjAIPRlq7yF/Z0Dc11rIQ7SNhDFm/5aNtIfAQwDmfSBkvgDaZxAwTkAwRX4Ig3nLXuj3ND9rVlpGXBgu4oZA2keZDmTC3gEfe6eaIWujJA3krR8h43RkSICS/UUErN09HSyf228flIA8JwIw5iSRcvJOiZleIwgcLRvRvsEBcZbkjZTSB3vu3ID9o6taeuwR4prH6QoWUvJVuk4LCIfEsdMT/4Up8Ai5FWxwng7wSzrkqRUqha5NN+ksnSkdhGBfYIrUIjnmMDUcrnQttscZwiZzyfP06R98pPfsjY4KRBAbus32SnmM3Y/DhpeSJGTMjuAQPR6zByQCprKb4DP7RpzYscDb/OCgtidBfi9AsEZ7hlSwYdjHgSM9bFeQYO2CBPvpbAi8M2CEbGIOoVq75Ar7zBPk4KO12FdjSj9cS4LHXMiVfjmESh/5JiSTnrXwFGmDH9oeYAL97Z3JmtKDXbw/J3zQ2sfW6Shdsx/O3yCN/BF7gsF54h8/bU/4bb34fDd/zW/nUZgwms+AHcgl+5EgShscfzTlf+gYPUd66SY8oDP03hN1tMKVv3TMT/OZKoBpDZ7aW74ef4Fv8CDnENkRe9E9AHPgD/snH6+zD/IQYKtikIfExtggNy3Trmf/+XQ4ksegx9/DQbjjjBX/jchbNx5R8gdzgz/+CwvTbgRXcI1egGAtnvZl/+w9DPB9lcTwD9UTwQ//DbvZJ24gcPIjhC08E5RJPEr64A30aEo/8kN11ulJdeRvH52VZNtam4Ll5sjG4ROszhlEnAGO4pv+O9ZSNqUP63x/7wIEjoQSEqIMISDWd27jAxI216Bk3tPKQfF8ngE54OpgEMWxWZwAkot8ikA5L4rtdZss0qN43ufAZLCBO0dE4V2Ps3RYTiaCwttojj4HZVy/7je3FgEL41ZlGBsqDci/6zNqCscAKL75qRKQC8XUTy0zwtg5HQqMBHF8CD15ACBBUi9DmaCIoQNZj+fSwiQ76frkQrk5NsCCyFu/9ZCTVi7A53OA1TXIntGToTnp9e+1EyRA8Fl7JBPDkbs3si5gMFI9QeYRhPxmg3lYo8+ZMzKG4JuHz5AlABUguJ4gyhqRSQEdkMmPmPmO9iVGDdARAPL3GFj6xWmQE+BBJsmcTgDiPBsaAUWqrMP3VE5ktrbxw22rAsacCMDYGti9PdJniihy2qplyCOw5rzYCj1VduZcfI5OsFG6gPBLINARwSxdhg8wQOBoaC0UIJILR0If6Atd88c5cJz0SZtbHl3MTuAFuxUgwhY23zp/w57N0/ynMkkqZWyKrnJ2riuBwMEhDa7hfQkDuq1kz944R04PcWYb5MFO6etYkkILhvXDUWsnS9dxr549cNzeh88CZrZs3vYI5sEfNg0b2ZvPs8+xAEGlD7awJQPREVyQF10QsMFLr9sz7Qvmam+sX5uFYEHwxF4lQSRd2D3cI0cBl32zz2P6QY8EqAiQoNC+q0rAP7rl2l6TdOCrgqf0y54gpQIu+OM+COq+jDnhA98AV8lSNR4JZZsIrfdgM7Ko9QRO2GfYzAZgsAeHsBe2To8EAfw7Aspvhl9IIgom7N8i/keLYAIEumgu9C+vI9j14BvMlQ5OPeGO3gpe6Bts4VMlDMyPTqq+81lsi53z0xJjklu4CTkhsfScPAQL5W+l1HPjl2EF7kIOgnudEWTKJvOoWcEQWSLYrmkd/K25sHd24/uuJWuPF8Anf2wAX2olMFNBgFNkF0yFf3DbtSUx7LOEBL9trfAFV7BGOCB4rPHMtdigYAlm8uPW5vWefvgcLKFr5ob7SUwi1vinoE3ywX7CE/sCcwVEMDTtRHDL3Oii98ugcZd4sHcBAmHZMCU0zgWB5Jjz+FHv58cyGEyGzzF8GRsK5A84I9LAmzLK+Mk8Zng90TgSLgJGbL3GuCi2QIDRiQY5G4GHIAMA1NevN5oD4VQQgimFsBb3d81y5IfQzIvDjNMkA/M0L+/JhJbysDZ/vWdtk5PvkGvKouabU/pkL8plABkAiTxFwxnuz/jtEWAELO5pfrJt9e895HtlixFjN3d7ZE1ldO2a5OKaZAhgGGrW73X3tBaysy/kkb10P9+xh9bkM6U+Wbs1uU5LP+iFOWSYm2uRm3u6ZvTDZ8iPzMwJQayfa71LMKjvPScCMCaX7BnnSz/IlOwz4ni8H/1lvzAhP+Rlz+2Jz9JLuuzfsmPl4GTTGwtDfI/zydkimEBnODAtRsg0xyTrRlfK67fWJPEhmMgPDI6tu9ZHn2UDOeDHZqJr5mhOOXtl7t4r7ZlzGqsqBoNcy33YWY2XtT2Qi9eCz+zIHrFPspPQcA33zbPre2tOiyTCJhPre8GRJDpiX3Awvz9i3tZrv9OS4N9J4JgPf9LyF+bCvhfVDzZd4l/8g/uWeGqtwV73dX8yrX8Ia054MHd8iE7DYL7ePsD6jHpvvB57pkv0QVLAngX3Sz+Y67h2fidkyv+YU1qMEP60mpZ+pZaroFvFQQJjSh/YValvruU75pdKqnXF/swdPsTvCpTKUeJHS/dgLd8JG/k1/y7v757u4Z4Z5sM+2WSNv2wCBvgzSj7Wun8CBIEQ+aRdGM64T2wq/pe82bf7w2Z2mIcz1HgWO8x9fTYJvJ5+WL85Zf7uT//Ix19G+CcZwF0yMq/Ml566jn2hpz1etm082MsAYdtCWvR+rcda1d+lNJSNwqpqGOVjVxe91+f+uV4P4+e+7jmtb18ChDnJrJyL7F79mNN6rhwcZ4Z0I/wyUDJY5Q+VzXV9257XMmeotj2343i/Az5M73rvkHL5TRgg0EBmEU28QGY+52ym73J8PtE7pHx8JLDdlR4ChDXJm4ErjSn16bPtPcdWT6PsgEy2/nutDQcy8P+boD9T24bSMBn1ni6xpu07XKYhgQMB+GdqIROo3VGrm7a7VjufA356aZWaYYf2RW0ph/GpBCRVZGC1DWilFEiNVTsO8tu8BA74MC1jhFb10NCO22opzQF7HEKFT7uPVrupR55P3/3z+4Q2RdUD7YOwYNe/NPz5SfjTFY0GCMptU32wn7uAFl0f4/a0AOUjPYD6S1tDdhBoGHro5vKDGIuuc1uf07/twLEyXJ4otK17H+7zXwk4W5FfJj7IZHkJIP96c50F0tfeChAAcM44aD/SWjTn38ZYXgrr+YbMqqQBgqAPXM/xIUBYj2xXvcoBH6Ylp8WIHzPwgtYZH+1rzgFot6HTqgfl0win73J8PqFNCM/ysAi9/VOPhT8+ktnMShMDeKiH8a9//69JTJ9ZHru0mVsfrnqQwEECBwkcJHCQwEECBwkcJHCQwEECc5SAIkF+X+s/6cRs7AUxnT8AAAAASUVORK5CYII=\" width=\"776\" height=\"606\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eFor all scenarios, respondents were asked to assume that the patient had a cancer diagnosis that was responsive to first-line ICI therapy without considering medication funding/access. Cancer diagnosis was intentionally omitted to broaden applicability to different tumour types, especially as ICI use expands to new cancer indications. Gender was deliberately omitted to limit differences between scenarios. Overall, the vignette scenarios were designed to minimise clinical factors in the change in treatment selection so that the responses could be as closely attributed to the change in irAE risk proposed. In the first question of each scenario, respondents were asked to select their preferred treatment approach at baseline without irAE risk grading. In the second question, the patient was assigned a hypothetical irAE risk grading for colitis. In the third question, the patient was assigned a hypothetical irAE risk grading for any irAE. In questions 2 and 3, respondents were asked to consider their treatment approach from baseline. By introducing irAE risk while maintaining all other variables, the scenarios explored the influence of proposed risk on treatment selection. This method provided valuable quantitative data on decision-making patterns and qualitative insights into the rationale behind each choice.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eAll results were exported from QualtricsXM platform to a secure network. Basic statistical methods were used to report results. Responses to the vignette study were compared within each scenario to observe the change in treatment preference with change in irAE risk. There were no defined endpoints to this study. The study sought to gain baseline understanding and establish a benchmark for subsequent surveys of a similar nature as the role of risk factors for predicting irAE advances.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eAll data are provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eRespondent demographics\u003c/p\u003e\n\u003cp\u003eA total of 58 participants started the survey, and responses from the 40 participants who completed the survey were included for analysis.\u0026nbsp;Respondents were medical oncologists\u0026nbsp;(57.5%), medical oncology trainees (17.5%), cancer nurse specialists (17.5%), and cancer nurse practitioners (7.5%). Survey respondents were predominantly from Australia (92.5%), mostly working in metropolitan settings (42.5%), mostly treat melanoma/skin (65%) or lung (60%) cancers. Six participants worked in more than one healthcare setting and 28 in more than one cancer sub-group. See Supplementary Table S1 for summary of respondent demographics.\u003c/p\u003e\n\u003cp\u003eCurrent practice\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn assessing the likelihood of irAE prior to ICI initiation, 92.5% of clinicians placed high importance on pre-existing autoimmune diseases. Other significant considerations included previous tolerance of ICI and baseline laboratory markers of organ function. A notable observation was the emphasis on patient/carers\u0026apos; ability to identify and report potential irAE, highlighting the importance of social factors in treatment planning such as health literacy. No respondents reported using a standardized method beyond general risk profiling, underscoring a reliance on clinical judgment. Most clinicians always assessed irAE risk and discussed it with patients before starting ICI treatment. See Supplementary Table S2 for summary of responses.\u003c/p\u003e\n\u003cp\u003eRisk prediction model\u003c/p\u003e\n\u003cp\u003eThe survey revealed a positive inclination among respondents toward using a risk prediction tool, with 55% indicating they were likely to use a tool if it were available and validated. Most respondents recognized the potential of irAE risk prediction in informing treatment discussions (60%) and decision-making (47.5%). Respondents\u0026rsquo; readiness to incorporate additional baseline assessments to inform risk prediction was notable, with 95% \u0026lsquo;certainly\u0026rsquo; or \u0026lsquo;likely\u0026rsquo; to perform simple assessments. However, the likelihood of undertaking more complex assessments, such as genomic testing, was met with divisive responses, reflecting concerns about practicality and access.\u003c/p\u003e\n\u003cp\u003eCost implications, timing of assessments in relation to treatment initiation, and uncertainties regarding the accuracy/specificity were the most cited barriers to integrating an irAE risk prediction tool into clinical practice, each noted by 75% of respondents. Other barriers included additional work for clinicians and patient access to baseline assessments. Clinicians anticipated that the most significant impact of a risk prediction tool would be on patient education and tailored instructions (80%), suggesting strong support for informed patient engagement. Frequency of clinic reviews and post-treatment follow-up were also identified as likely benefits of irAE risk prediction. Avoiding combination ICI therapy and increasing supportive care were among the management strategies clinicians would consider in response to higher predicted risk of irAE.\u003c/p\u003e\n\u003cp\u003ePreferences for the format of risk prediction scores varied, with a slight majority favouring a percentage range (37.5%). Notably, some respondents expressed reservation about the quantification of risk, highlighting the need for tools that accurately reflect clinical realities and do not overstate the risk of common, less severe irAE (such as hypothyroidism). See Supplementary Table S3 for summary of responses.\u003c/p\u003e\n\u003cp\u003eVignette study\u003c/p\u003e\n\u003cp\u003eThirty-eight respondents completed the vignette study, and these were analysed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario 1\u003c/strong\u003e: BRAF mutant disease with brain metastases\u003c/p\u003e\n\u003cp\u003eIn the initial baseline decision, most respondents (89.5%) expressed a preference for combination ICI therapy for a hypothetical patient diagnosed with BRAF mutant disease and presenting with brain metastases. Change in treatment selection from baseline with proposed risk of colitis and risk of any irAE are illustrated in Figure 1 for Scenario 1. Blue sections of the bars depict the number of respondents who would consider combination ICI therapy. When prompted with the introduction of predicted \u0026lsquo;high\u0026rsquo; risk of colitis or \u0026lsquo;very high\u0026rsquo; risk of any irAE, preference for combination ICI therapy was progressively less favoured (65.8% and 52.6%, respectively). A shift occurred towards favouring single-agent ICI therapy (orange sections), not considering ICI therapy (grey sections), or avoiding ICI treatment (yellow sections). See Supplementary Table S4 for full responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario 2\u003c/strong\u003e: BRAF wildtype disease with well-controlled rheumatoid arthritis\u003c/p\u003e\n\u003cp\u003eFor the second scenario, respondents were asked to consider their preferred ICI therapy for a patient with BRAF wildtype disease and well-controlled rheumatoid arthritis. Initially, a majority (60.5%) showed a preference for single-agent ICI therapy with combination ICI therapy being less favoured upfront (36.9%) (See Figure 2). When presented with the introduction of \u0026lsquo;moderate\u0026rsquo; and \u0026lsquo;high\u0026rsquo; predicted irAE risks, the number who chose single-agent ICI therapy remained the same but there was a shift in other treatment preferences. Respondents increasingly tended towards not considering or avoiding ICI therapy, as depicted by the yellow and grey sections of the bars. Overall, combination ICI therapy was less preferred in this vignette scenario compared to scenario 1. See Supplementary Table S5 for full responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario 3\u003c/strong\u003e: Older age with previous medication toxicity\u003c/p\u003e\n\u003cp\u003eIn the third scenario, respondents evaluated their treatment preference for a patient of older age with a history of medication toxicity. Without considering any proposed risk, the preference strongly favoured single-agent ICI therapy (71.1%), as shown in Figure 3, with a greater number of respondents electing to not consider or avoid ICI therapy upfront compared to both previous scenarios. As predicted risks for irAE were introduced, clinicians\u0026apos; preferences shifted towards further caution, with a greater inclination to either continue favouring single-agent ICI therapy (78.9%) for \u0026lsquo;low\u0026rsquo; risk of colitis, or to not consider or avoid ICI therapy (10.5%, respectively) for \u0026lsquo;moderate\u0026rsquo; risk of any irAE. See Supplementary Table S6 for full responses.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eFindings from our survey demonstrate that while oncology practitioners regard irAE risk identification as beneficial to inform treatment selection, they perceive numerous limitations to adoption of risk prediction in real-world practice. The survey has drawn out key insights about current practice; while clinicians reported irAE risk as an important consideration prior to starting ICI, they do not use a standard assessment method. This reflects the lack of robust evidence on irAE risk factors and the fluctuating nature of risk with different treatments, patient factors, cancer type and stage of disease. Nonetheless, participants conveyed an interest in a structured approach to consider potential contributors to irAE risk that may potentiate serious toxicities. While clinicians recognized commonly evaluated risk factors, such as pre-existing autoimmune diseases, there was an emphasis on the role of patient/carers\u0026rsquo; ability to recognize and report early signs of potential irAE. This highlights that the determinants of toxicities are not limited to clinical measures or discrete patient factors, and that social contributors are of high importance.\u003c/p\u003e\u003cp\u003eClinicians reported practical barriers to the use of irAE risk models with costs, timing, and unknown accuracy/specificity being prominent concerns. Complexity of tests and associated availability of results were unfavourably ranked if these caused delays to treatment commencement. Complex tests could be a source of inequitable care as these technologies are not accessible across different healthcare settings. These concerns point to a need for prospective risk prediction tools to be simple, cost-effective, timely, demonstrate reliable accuracy, and be equitably accessed.\u003c/p\u003e\u003cp\u003eValidating an irAE risk prediction model in the cancer setting would be practically and ethically challenging; there is insufficient evidence about strategies to mitigate toxicities based on the presence/absence of proposed risk factors. Additionally, it is unlikely that a single tool would be effective across different cancers, treatment regimens, and irAE. The risk of a specific irAE may be of more clinical relevance than general propensity for toxicity. Patients\u0026rsquo; acceptance of toxicities may influence treatment selection in line with their individual goals, irrespective of predicted risk. Some patient preference studies illustrate that survival outcomes were more important than quality of life (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), while patient preference for immunotherapy may also depend on presence of other co-morbidities (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Therefore, even if a risk prediction tool was developed and validated, the influence on patient care is multifaceted.\u003c/p\u003e\u003cp\u003eBased on participants\u0026rsquo; responses, some clinical uses of a prediction tool could be to identify risk where there is unclear benefit of ICI therapy or to identify individuals with highest risk. Logically, when making treatment decisions, oncologists implicitly consider the risk of toxicity based on patient/social factors, disease/clinical features, and specific treatment. The value of a prediction tool may vary by healthcare setting; utility of a tool may be different in community-based or regional healthcare centres compared to specialist oncology centres, and with different experiences with managing irAE. The extent to which predicted irAE risk would influence treatment selection beyond expert reasoning and experiential evidence needs closer examination.\u003c/p\u003e\u003cp\u003eDespite the various barriers reported, the vignette study illustrates the influence that predicted irAE risk could have on treatment approach. While the scenarios were brief, the change in treatment approach can be attributed to the change in irAE risk. Across the scenarios, a consistent pattern was noted: when confronted with higher predicted irAE risk, clinicians demonstrated a propensity to adjust treatment strategies.\u003c/p\u003e\u003cp\u003eThe marked choice of combination ICI therapy in scenario 1 compared to scenarios 2 and 3 was expected given the patient\u0026rsquo;s younger age, absence of co-morbidities, and presence of brain metastases. In scenario 2, the shift toward upfront single-agent ICI therapy is logical in the presence of auto-immune disease, which is a commonly considered risk factor (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and was reported by respondents as a consideration in current practice. Similarly, in scenario 3, the prominent choice of single-agent ICI therapy at baseline signifies consideration of pre-existing medical history and older age. Thus, at baseline, clinicians were considering risk and adjusting treatment preference to reflect their caution with specific patient factors they deemed to contribute to risk. As predicted irAE risk was introduced and escalated within each scenario, respondents indicated a strategic shift towards minimizing toxicities associated with combination ICI therapy. These findings demonstrate the potential impact of irAE risk prediction tools on clinical decision-making, suggesting that such tools could inform treatment strategies, particularly for the choice of combination ICI therapy.\u003c/p\u003e\u003cp\u003eThis was a small study conducted in a defined geographic area. Collated responses indicate an interest from oncology practitioners in irAE risk prediction prior to ICI treatment. These results support the need for a broader, global assessment of clinicians\u0026rsquo; perspectives into the nature of potential irAE risk models and the characteristics that oncology practitioners perceive are important in developing predictive models. This survey proposes scenarios with a hypothetical predicted risk, which may have impacted participation rate. Level of experience with ICI therapy or certain cancer types, and disease stage (metastatic, adjuvant or neo-adjuvant) may influence treatment selection and acceptability of toxicities. In the absence of a validated tool, it is essential that the usability and practicability of prediction models/tools are considered alongside new research into irAE risk factors.\u003c/p\u003e\u003cp\u003eICI-related toxicities are a contributor to treatment interruptions/delays, hospital admissions, long-term morbidity, and death. Understanding potential risks could support shared patient-cantered care. This is of heightened importance as ICI therapies are used to treat cancers among diverse patient groups, increasingly in early-stage cancers, and in combination with other treatment modalities. As research into the reliability of risk factors expands, this may garner research interest into how irAE can be predicted and prevented. While potential tools to predict risk could lead to personalized treatment approaches, the practical barriers to develop cost-effective, timely, and equitably accessible tests that incorporate numerous variables must be considered in tandem with emerging evidence of irAE risk factors.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study identified variability in how oncology practitioners currently assess irAE risk prior to commencing ICI therapy, underscoring a need for standardized, evidence-driven tools to guide clinical decision-making. Pre-existing autoimmune diseases and patient/carer's ability to recognize and report irAE consistently emerged, pointing to the importance of both clinical and social determinants of toxicity. Findings from this study support the development of irAE risk assessments that are simple, cost-effective, timely in relation to treatment commencement, demonstrate reliable accuracy, and are broadly accessible. The vignette studies illustrate that respondents are likely to adjust their treatment approach based on level of irAE risk, with a preference for less toxic treatment as proposed risk increases. This study demonstrates the potential of irAE risk prediction tools to influence clinical practice and contribute to patient-tailored care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAmerican Society of Clinical Oncology (ASCO); Cytotoxic T-lymphocyte associated protein-4 (CTLA-4); immune checkpoint inhibitor (ICI); immune-related adverse events (irAE); programmed cell death receptor-1 (PD-1); programmed cell death ligand-1 (PD-L1).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDISCLOSURE\u003c/h2\u003e\u003cp\u003eThe author reports no conflicts of interest relating to this work.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBJ contributed to the methodology including survey design \u0026amp; build, survey distribution for data collection, analysis, manuscript writing, preparation of figures/tables, and manuscript writing and finalisation. JC contributed to manuscript review. JD, JL, DM and LS contributed to survey design \u0026amp; testing, and manuscript review. LS also contributed to methodology. FF, MIJ, GA contributed to concept, methodology, manuscript preparation \u0026amp; review, and project administration. All authors reviewed the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors wish to thank the health professionals who participated in this survey for contributing their valuable time and insights, and to the organisations who kindly agreed to distribute this survey, including the Clinical Oncology Society of Australia, Victorian Comprehensive Cancer Centre Alliance, and Cancer Nursing Society of Australia. This study is part of graduate research supported by an Australian Government Research Training Program (RTP) Scholarship and was supported by resources from the Victorian Comprehensive Cancer Centre Alliance as part of the Strategic Program Plan 2021-2024.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJohnson DB, Reynolds KL, Sullivan RJ, Balko JM, Patrinely JR, Cappelli LC, et al. Immune checkpoint inhibitor toxicities: systems-based approaches to improve patient care and research. Lancet Oncology. 2020;21(8):e398-e404. [https://dx.doi.org/10.1016/S1470-2045(20)30107-8].\u003c/li\u003e\n\u003cli\u003ePardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nature Reviews Cancer. 2012;12(4):252-64. [https://dx.doi.org/10.1038/nrc3239].\u003c/li\u003e\n\u003cli\u003eBuchbinder E, Hodi FS. Cytotoxic T lymphocyte antigen-4 and immune checkpoint blockade. Journal of Clinical Investigation. 2015;125(9):3377-83. 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[https://dx.doi.org/10.1007/s00520-021-06359-7].\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Immune-related adverse events, risk prediction, risk factors, oncology, clinicians","lastPublishedDoi":"10.21203/rs.3.rs-6947226/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6947226/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentification of risk factors to predict immune-related adverse events (irAE) is a growing research area. This baseline study explores which factors oncology clinicians consider, perspectives on the value of risk prediction, and how this may impact clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn electronic survey was developed to explore current practice, perceived value of risk prediction, and a vignette study where respondents selected their preferred treatment at baseline and according to proposed risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForty responses were analysed from medical oncologists (57.5%), medical oncology trainees (17.5%), nurse specialists (17.5%), and nurse practitioners (7.5%) who practice in various settings. If validated, a risk prediction tool was reported to be useful to tailor education, inform clinic review frequency and post-treatment follow-up. Perceived barriers included cost, timeliness of pre-treatment assessment, and unknown specificity of predicted risk. When confronted with higher predicted irAE risk, clinicians demonstrated a propensity to adjust treatment strategies in favour of single-agent ICI therapy compared to combination ICI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOncology clinicians perceive value in assessing irAE risk prior to commencing ICI, with 80% anticipating impact on patient education. Respondents perceived cost, complexity of assessments, and reliability/accuracy as important requirements for prediction tools. As predicted irAE risk increased, respondents were more likely to prefer less toxic treatment.\u003c/p\u003e","manuscriptTitle":"Clinician survey on the value of risk prediction for immune-related adverse events induced by immune checkpoint inhibitors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 10:19:12","doi":"10.21203/rs.3.rs-6947226/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T10:39:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T20:09:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91542073378625347900653953799723568172","date":"2026-04-17T19:21:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334627751473845471661910221358829257462","date":"2025-12-04T10:44:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235678827467172784445120099764451043573","date":"2025-12-01T16:13:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T18:41:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328937103345729814163568899952682677662","date":"2025-10-10T13:16:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318386323926838224551569903125342506112","date":"2025-08-29T08:54:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T06:57:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T06:52:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-25T11:32:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-25T03:53:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-22T01:40:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c9bc4d9-3e05-4c21-9f36-ba2460c66a10","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52212836,"name":"Health sciences/Oncology/Cancer"},{"id":52212837,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-21T07:11:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 10:19:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6947226","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6947226","identity":"rs-6947226","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}